[ { "path": ".gitignore", "content": ".vscode\n*.log\n**/__pycache__/\n**/data/\n**/output/\n" }, { "path": "LICENSE", "content": "MIT License\n\nCopyright (c) 2025 Fangfu-0830\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n" }, { "path": "README.md", "content": "
\n\n# ✨LangScene-X: Reconstruct Generalizable 3D Language-Embedded Scenes with TriMap Video Diffusion✨\n\n

\nFangfu Liu1,\nHao Li2,\nJiawei Chi1,\nHanyang Wang1,3,\nMinghui Yang3,\nFudong Wang3, \nYueqi Duan1\n
\n 1Tsinghua University, 2NTU, 3Ant Group \n

\n

ICCV 2025 🔥

\n     \n     \n     \n     \n\n![Teaser Visualization](assets/teaser.png)\n
\n\n**LangScene-X:** We propose LangScene-X, a unified model that generates RGB, segmentation map, and normal map, enabling to reconstruct 3D field from sparse views input.\n\n## 📢 News\n- 🔥 [04/07/2025] We release \"LangScene-X: Reconstruct Generalizable 3D Language-Embedded Scenes with TriMap Video Diffusion\". Check our [project page](https://liuff19.github.io/LangScene-X) and [arXiv paper](https://arxiv.org/abs/2507.02813).\n\n## 🌟 Pipeline\n\n![Pipeline Visualization](assets/pipeline.png)\n\nPipeline of LangScene-X. Our model is composed of a TriMap Video Diffusion model which generates RGB, segmentation map, and normal map videos, an Auto Encoder that compresses the language feature, and a field constructor that reconstructs 3DGS from the generated videos. \n\n\n## 🎨 Video Demos from TriMap Video Diffusion\n\nhttps://github.com/user-attachments/assets/55346d53-eb04-490e-bb70-64555e97e040\n\nhttps://github.com/user-attachments/assets/d6eb28b9-2af8-49a7-bb8b-0d4cba7843a5\n\nhttps://github.com/user-attachments/assets/396f11ef-85dc-41de-882e-e249c25b9961\n\n## ⚙️ Setup\n\n### 1. Clone Repository\n```bash\ngit clone https://github.com/liuff19/LangScene-X.git\ncd LangScene-X\n```\n### 2. Environment Setup\n\n1. **Create conda environment**\n\n```bash\nconda create -n langscenex python=3.10 -y\nconda activate langscenex\n```\n2. **Install dependencies**\n```bash\nconda install pytorch torchvision -c pytorch -y\npip install -e field_construction/submodules/simple-knn\npip install -e field_construction/submodules/diff-langsurf-rasterizer\npip install -e auto-seg/submodules/segment-anything-1\npip install -e auto-seg/submodules/segment-anything-2\npip install -r requirements.txt\n```\n\n### 3. Model Checkpoints\nThe checkpoints of SAM, SAM2 and fine-tuned CogVideoX can be downloaded from our [huggingface repository](https://huggingface.co/chijw/LangScene-X).\n\n## 💻Running\n\n### Quick Start\nYou can start quickly by running the following scripts:\n```bash\nchmod +x quick_start.sh\n./quick_start.sh \n```\n### Render\nRun the following command to render from the reconstructed 3DGS field:\n```bash \npython entry_point.py \\\n pipeline.rgb_video_path=\"does/not/matter\" \\\n pipeline.normal_video_path=\"does/not/matter\" \\\n pipeline.seg_video_path=\"does/not/matter\" \\\n pipeline.data_path=\"does/not/matter\" \\\n gaussian.dataset.source_path=\"does/not/matter\" \\\n gaussian.dataset.model_path=\"output/path\" \\\n pipeline.selection=False \\\n gaussian.opt.max_geo_iter=1500 \\\n gaussian.opt.normal_optim=True \\\n gaussian.opt.optim_pose=True \\\n pipeline.skip_video_process=True \\\n pipeline.skip_lang_feature_extraction=True \\\n pipeline.mode=\"render\"\n```\nYou can also configurate by editting `configs/field_construction.yaml`.\n\n## ✒️ TODO List\n- [x] Per-scene Auto Encoder released\n- [x] Fine-tuned CogVideoX checkpoints released\n- [ ] Generalizable Auto Encoder (LQC)\n- [ ] Improved TriMap Video Diffusion model\n\n## 🔗Acknowledgement\n\nWe are thankful for the following great works when implementing LangScene-X:\n\n- [CogVideoX](https://github.com/THUDM/CogVideo), [CogvideX-Interpolation](https://github.com/feizc/CogvideX-Interpolation), [LangSplat](https://github.com/minghanqin/LangSplat), [LangSurf](https://github.com/lifuguan/LangSurf), [VGGT](https://github.com/facebookresearch/vggt), [3DGS](https://github.com/graphdeco-inria/gaussian-splatting), [SAM2](https://github.com/facebookresearch/sam2)\n\n## 📚Citation\n\n```bibtex\n@misc{liu2025langscenexreconstructgeneralizable3d,\n title={LangScene-X: Reconstruct Generalizable 3D Language-Embedded Scenes with TriMap Video Diffusion}, \n author={Fangfu Liu and Hao Li and Jiawei Chi and Hanyang Wang and Minghui Yang and Fudong Wang and Yueqi Duan},\n year={2025},\n eprint={2507.02813},\n archivePrefix={arXiv},\n primaryClass={cs.CV},\n url={https://arxiv.org/abs/2507.02813}, \n}\n```\n" }, { "path": "auto-seg/auto-mask-align.py", "content": "import argparse\nimport os\nimport random\n\nimport cv2\nimport imageio\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport torch\nfrom loguru import logger\nfrom PIL import Image\nfrom segment_anything import SamAutomaticMaskGenerator, sam_model_registry\nfrom tqdm import tqdm\n\n# use bfloat16 for the entire notebook\ntorch.autocast(device_type=\"cuda\", dtype=torch.bfloat16).__enter__()\n\nif torch.cuda.get_device_properties(0).major >= 8:\n # turn on tfloat32 for Ampere GPUs (https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices)\n torch.backends.cuda.matmul.allow_tf32 = True\n torch.backends.cudnn.allow_tf32 = True\n\nfrom sam2.automatic_mask_generator import SAM2AutomaticMaskGenerator\nfrom sam2.build_sam import build_sam2, build_sam2_video_predictor\n\n\ndef show_anns(anns, borders=True):\n if len(anns) == 0:\n return\n sorted_anns = sorted(anns, key=(lambda x: x['area']), reverse=True)\n ax = plt.gca()\n ax.set_autoscale_on(False)\n\n img = np.ones((sorted_anns[0]['segmentation'].shape[0], sorted_anns[0]['segmentation'].shape[1], 4))\n img[:,:,3] = 0\n for ann in sorted_anns:\n m = ann['segmentation']\n color_mask = np.concatenate([np.random.random(3), [0.5]])\n img[m] = color_mask \n if borders:\n import cv2\n contours, _ = cv2.findContours(m.astype(np.uint8),cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE) \n # Try to smooth contours\n contours = [cv2.approxPolyDP(contour, epsilon=0.01, closed=True) for contour in contours]\n cv2.drawContours(img, contours, -1, (0,0,1,0.4), thickness=1) \n\n ax.imshow(img)\n\n\ndef mask_nms(masks, scores, iou_thr=0.7, score_thr=0.1, inner_thr=0.2, **kwargs):\n \"\"\"\n Perform mask non-maximum suppression (NMS) on a set of masks based on their scores.\n \n Args:\n masks (torch.Tensor): has shape (num_masks, H, W)\n scores (torch.Tensor): The scores of the masks, has shape (num_masks,)\n iou_thr (float, optional): The threshold for IoU.\n score_thr (float, optional): The threshold for the mask scores.\n inner_thr (float, optional): The threshold for the overlap rate.\n **kwargs: Additional keyword arguments.\n Returns:\n selected_idx (torch.Tensor): A tensor representing the selected indices of the masks after NMS.\n \"\"\"\n scores, idx = scores.sort(0, descending=True)\n num_masks = idx.shape[0]\n \n masks_ord = masks[idx.view(-1), :]\n masks_area = torch.sum(masks_ord, dim=(1, 2), dtype=torch.float)\n\n mask_chunk_size = 20\n \n mask_chunks = masks_ord.split(mask_chunk_size, dim=0)\n area_chunks = masks_area.split(mask_chunk_size, dim=0)\n\n iou_matrix = []\n inner_iou_matrix = []\n\n for i_areas, i_chunk in zip(area_chunks, mask_chunks):\n row_iou_matrix = []\n row_inner_iou_matrix = []\n for j_areas, j_chunk in zip(area_chunks, mask_chunks):\n intersection = torch.logical_and(i_chunk.unsqueeze(1), j_chunk.unsqueeze(0)).sum(dim=(-1, -2))\n union = torch.logical_or(i_chunk.unsqueeze(1), j_chunk.unsqueeze(0)).sum(dim=(-1, -2))\n local_iou_mat = intersection / union \n row_iou_matrix.append(local_iou_mat)\n\n row_inter_mat = intersection / i_areas[:, None]\n col_inter_mat = intersection / j_areas[None, :]\n\n inter = torch.logical_and(row_inter_mat < 0.5, col_inter_mat >= 0.85)\n\n local_inner_iou_mat = torch.zeros((len(i_areas), len(j_areas)))\n local_inner_iou_mat[inter] = 1 - row_inter_mat[inter] * col_inter_mat[inter]\n row_inner_iou_matrix.append(local_inner_iou_mat)\n\n row_iou_matrix = torch.cat(row_iou_matrix, dim=1)\n row_inner_iou_matrix = torch.cat(row_inner_iou_matrix, dim=1)\n iou_matrix.append(row_iou_matrix)\n inner_iou_matrix.append(row_inner_iou_matrix)\n iou_matrix = torch.cat(iou_matrix, dim=0)\n inner_iou_matrix = torch.cat(inner_iou_matrix, dim=0)\n\n iou_matrix.triu_(diagonal=1)\n iou_max, _ = iou_matrix.max(dim=0)\n inner_iou_matrix_u = torch.triu(inner_iou_matrix, diagonal=1)\n inner_iou_max_u, _ = inner_iou_matrix_u.max(dim=0)\n inner_iou_matrix_l = torch.tril(inner_iou_matrix, diagonal=1)\n inner_iou_max_l, _ = inner_iou_matrix_l.max(dim=0)\n \n keep = iou_max <= iou_thr\n keep_conf = scores > score_thr\n keep_inner_u = inner_iou_max_u <= 1 - inner_thr\n keep_inner_l = inner_iou_max_l <= 1 - inner_thr\n \n if keep_conf.sum() == 0:\n index = scores.topk(3).indices\n keep_conf[index, 0] = True\n if keep_inner_u.sum() == 0:\n index = scores.topk(3).indices\n keep_inner_u[index, 0] = True\n if keep_inner_l.sum() == 0:\n index = scores.topk(3).indices\n keep_inner_l[index, 0] = True\n keep *= keep_conf\n keep *= keep_inner_u\n keep *= keep_inner_l\n\n selected_idx = idx[keep]\n return selected_idx\n\n\ndef filter(keep: torch.Tensor, masks_result) -> None:\n keep = keep.int().cpu().numpy()\n result_keep = []\n for i, m in enumerate(masks_result):\n if i in keep: result_keep.append(m)\n return result_keep\n\n\ndef masks_update(*args, **kwargs):\n # remove redundant masks based on the scores and overlap rate between masks\n masks_new = ()\n for masks_lvl in (args):\n if isinstance(masks_lvl, tuple):\n masks_lvl = masks_lvl[0] # If it's a tuple, take the first element\n if len(masks_lvl) == 0:\n masks_new += (masks_lvl,)\n continue\n \n # Check if masks_lvl is a list of dictionaries\n if isinstance(masks_lvl[0], dict):\n seg_pred = torch.from_numpy(np.stack([m['segmentation'] for m in masks_lvl], axis=0))\n iou_pred = torch.from_numpy(np.stack([m['predicted_iou'] for m in masks_lvl], axis=0))\n stability = torch.from_numpy(np.stack([m['stability_score'] for m in masks_lvl], axis=0))\n else:\n # If it's a direct list of masks, use them directly\n seg_pred = torch.from_numpy(np.stack(masks_lvl, axis=0))\n # Create default values for cases without iou and stability\n iou_pred = torch.ones(len(masks_lvl))\n stability = torch.ones(len(masks_lvl))\n\n scores = stability * iou_pred\n keep_mask_nms = mask_nms(seg_pred, scores, **kwargs)\n masks_lvl = filter(keep_mask_nms, masks_lvl)\n masks_new += (masks_lvl,)\n return masks_new\n\ndef show_mask(mask, ax, obj_id=None, random_color=False):\n if random_color:\n color = np.concatenate([np.random.random(3), np.array([0.6])], axis=0)\n else:\n cmap = plt.get_cmap(\"tab20\")\n cmap_idx = 0 if obj_id is None else obj_id\n color = np.array([*cmap(cmap_idx)[:3], 0.6])\n h, w = mask.shape[-2:]\n mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)\n ax.imshow(mask_image)\n\ndef save_mask(mask,frame_idx,save_dir):\n image_array = (mask * 255).astype(np.uint8)\n # Create image object\n image = Image.fromarray(image_array[0])\n\n # Save image\n image.save(os.path.join(save_dir,f'{frame_idx:03}.png'))\n\ndef save_masks(mask_list,frame_idx,save_dir):\n os.makedirs(save_dir,exist_ok=True)\n if len(mask_list[0].shape) == 3:\n # Calculate dimensions for concatenated image\n total_width = mask_list[0].shape[2] * len(mask_list)\n max_height = mask_list[0].shape[1]\n # Create large image\n final_image = Image.new('RGB', (total_width, max_height))\n for i, img in enumerate(mask_list):\n img = Image.fromarray((img[0] * 255).astype(np.uint8)).convert(\"RGB\")\n final_image.paste(img, (i * img.width, 0))\n final_image.save(os.path.join(save_dir,f\"mask_{frame_idx:03}.png\"))\n else:\n # Calculate dimensions for concatenated image\n total_width = mask_list[0].shape[1] * len(mask_list)\n max_height = mask_list[0].shape[0]\n # Create large image\n final_image = Image.new('RGB', (total_width, max_height))\n for i, img in enumerate(mask_list):\n img = Image.fromarray((img * 255).astype(np.uint8)).convert(\"RGB\")\n final_image.paste(img, (i * img.width, 0))\n final_image.save(os.path.join(save_dir,f\"mask_{frame_idx:03}.png\"))\n\ndef save_masks_npy(mask_list,frame_idx,save_dir):\n np.save(os.path.join(save_dir,f\"mask_{frame_idx:03}.npy\"),np.array(mask_list))\n \ndef show_points(coords, labels, ax, marker_size=200):\n pos_points = coords[labels==1]\n neg_points = coords[labels==0]\n ax.scatter(pos_points[:, 0], pos_points[:, 1], color='green', marker='*', s=marker_size, edgecolor='white', linewidth=1.25)\n ax.scatter(neg_points[:, 0], neg_points[:, 1], color='red', marker='*', s=marker_size, edgecolor='white', linewidth=1.25) \n\ndef make_enlarge_bbox(origin_bbox, max_width,max_height,ratio):\n width = origin_bbox[2]\n height = origin_bbox[3]\n new_box = [max(origin_bbox[0]-width*(ratio-1)/2,0),max(origin_bbox[1]-height*(ratio-1)/2,0)]\n new_box.append(min(width*ratio,max_width-new_box[0]))\n new_box.append(min(height*ratio,max_height-new_box[1]))\n return new_box\n\ndef sample_points(masks, enlarge_bbox,positive_num=1,negtive_num=40):\n ex, ey, ewidth, eheight = enlarge_bbox\n positive_count = positive_num\n negtive_count = negtive_num\n output_points = []\n while True:\n x = int(np.random.uniform(ex, ex + ewidth))\n y = int(np.random.uniform(ey, ey + eheight))\n if masks[y][x]==True and positive_count>0:\n output_points.append((x,y,1))\n positive_count-=1\n elif masks[y][x]==False and negtive_count>0:\n output_points.append((x,y,0))\n negtive_count-=1\n if positive_count == 0 and negtive_count == 0:\n break\n\n return output_points\n\ndef sample_points_from_mask(mask):\n # Get indices of all True values\n true_indices = np.argwhere(mask)\n\n # Check if there are any True values\n if true_indices.size == 0:\n raise ValueError(\"The mask does not contain any True values.\")\n\n # Randomly select a point from True value indices\n random_index = np.random.choice(len(true_indices))\n sample_point = true_indices[random_index]\n\n return tuple(sample_point)\n\ndef search_new_obj(masks_from_prev, mask_list,other_masks_list=None,mask_ratio_thresh=0,ratio=0.5, area_threash = 5000):\n new_mask_list = []\n\n # Calculate mask_none, representing areas not included in any previous masks\n mask_none = ~masks_from_prev[0].copy()[0]\n for prev_mask in masks_from_prev[1:]:\n mask_none &= ~prev_mask[0]\n\n for mask in mask_list:\n seg = mask['segmentation']\n if (mask_none & seg).sum()/seg.sum() > ratio and seg.sum() > area_threash:\n new_mask_list.append(mask)\n \n for mask in new_mask_list:\n mask_none &= ~mask['segmentation']\n logger.info(len(new_mask_list))\n logger.info(\"now ratio:\",mask_none.sum() / (mask_none.shape[0] * mask_none.shape[1]) )\n logger.info(\"expected ratios:\",mask_ratio_thresh)\n if other_masks_list is not None:\n for mask in other_masks_list:\n if mask_none.sum() / (mask_none.shape[0] * mask_none.shape[1]) > mask_ratio_thresh: # Still a lot of gaps, greater than current thresh\n seg = mask['segmentation']\n if (mask_none & seg).sum()/seg.sum() > ratio and seg.sum() > area_threash:\n new_mask_list.append(mask)\n mask_none &= ~seg\n else:\n break\n logger.info(len(new_mask_list))\n\n return new_mask_list\n\ndef get_bbox_from_mask(mask):\n # Get row and column indices of non-zero elements\n rows = np.any(mask, axis=1)\n cols = np.any(mask, axis=0)\n \n # Find min and max indices of non-zero rows and columns\n ymin, ymax = np.where(rows)[0][[0, -1]]\n xmin, xmax = np.where(cols)[0][[0, -1]]\n \n # Calculate width and height\n width = xmax - xmin + 1\n height = ymax - ymin + 1\n \n return xmin, ymin, width, height\n\ndef cal_no_mask_area_ratio(out_mask_list):\n h = out_mask_list[0].shape[1]\n w = out_mask_list[0].shape[2]\n mask_none = ~out_mask_list[0].copy()\n for prev_mask in out_mask_list[1:]:\n mask_none &= ~prev_mask\n return(mask_none.sum() / (h * w))\n\n\nclass Prompts:\n def __init__(self,bs:int):\n self.batch_size = bs\n self.prompts = {}\n self.obj_list = []\n self.key_frame_list = []\n self.key_frame_obj_begin_list = []\n\n def add(self,obj_id,frame_id,mask):\n if obj_id not in self.obj_list:\n new_obj = True\n self.prompts[obj_id] = []\n self.obj_list.append(obj_id)\n else:\n new_obj = False\n self.prompts[obj_id].append((frame_id,mask))\n if frame_id not in self.key_frame_list and new_obj:\n # import ipdb; ipdb.set_trace()\n self.key_frame_list.append(frame_id)\n self.key_frame_obj_begin_list.append(obj_id)\n logger.info(\"key_frame_obj_begin_list:\",self.key_frame_obj_begin_list)\n \n def get_obj_num(self):\n return len(self.obj_list)\n \n def __len__(self):\n if self.obj_list % self.batch_size == 0:\n return len(self.obj_list) // self.batch_size\n else:\n return len(self.obj_list) // self.batch_size +1\n \n def __iter__(self):\n # self.batch_index = 0\n self.start_idx = 0\n self.iter_frameindex = 0\n return self\n\n def __next__(self):\n if self.start_idx < len(self.obj_list):\n if self.iter_frameindex == len(self.key_frame_list)-1:\n end_idx = min(self.start_idx+self.batch_size, len(self.obj_list))\n else:\n if self.start_idx+self.batch_size < self.key_frame_obj_begin_list[self.iter_frameindex+1]:\n end_idx = self.start_idx+self.batch_size\n else:\n end_idx = self.key_frame_obj_begin_list[self.iter_frameindex+1]\n self.iter_frameindex+=1\n # end_idx = min(self.start_idx+self.batch_size, self.key_frame_obj_begin_list[self.iter_frameindex+1])\n batch_keys = self.obj_list[self.start_idx:end_idx]\n batch_prompts = {key: self.prompts[key] for key in batch_keys}\n self.start_idx = end_idx\n return batch_prompts\n # if self.batch_index * self.batch_size < len(self.obj_list):\n # start_idx = self.batch_index * self.batch_size\n # end_idx = min(start_idx + self.batch_size, len(self.obj_list))\n # batch_keys = self.obj_list[start_idx:end_idx]\n # batch_prompts = {key: self.prompts[key] for key in batch_keys}\n # self.batch_index += 1\n # return batch_prompts\n else:\n raise StopIteration\n \ndef get_video_segments(prompts_loader,predictor,inference_state,final_output=False):\n\n video_segments = {}\n for batch_prompts in tqdm(prompts_loader,desc=\"processing prompts\\n\"):\n predictor.reset_state(inference_state)\n for id, prompt_list in batch_prompts.items():\n for prompt in prompt_list:\n # import ipdb; ipdb.set_trace()\n _, out_obj_ids, out_mask_logits = predictor.add_new_mask(\n inference_state=inference_state,\n frame_idx=prompt[0],\n obj_id=id,\n mask=prompt[1]\n )\n # start_frame_idx = 0 if final_output else None\n for out_frame_idx, out_obj_ids, out_mask_logits in predictor.propagate_in_video(inference_state):\n if out_frame_idx not in video_segments:\n video_segments[out_frame_idx] = { }\n for i, out_obj_id in enumerate(out_obj_ids):\n video_segments[out_frame_idx][out_obj_id]= (out_mask_logits[i] > 0.0).cpu().numpy()\n \n if final_output:\n for out_frame_idx, out_obj_ids, out_mask_logits in predictor.propagate_in_video(inference_state,reverse=True):\n for i, out_obj_id in enumerate(out_obj_ids):\n video_segments[out_frame_idx][out_obj_id]= (out_mask_logits[i] > 0.0).cpu().numpy()\n return video_segments\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--video_path\",type=str,required=True)\n parser.add_argument(\"--output_dir\",type=str,required=True)\n parser.add_argument(\"--level\",choices=['default','small','middle','large'])\n parser.add_argument(\"--batch_size\",type=int,default=20)\n parser.add_argument(\"--sam1_checkpoint\",type=str,default=\"/home/lff/bigdata1/cjw/checkpoints/sam/sam_vit_h_4b8939.pth\")\n parser.add_argument(\"--sam2_checkpoint\",type=str,default=\"/home/lff/bigdata1/cjw/checkpoints/sam2/sam2_hiera_large.pt\")\n parser.add_argument(\"--detect_stride\",type=int,default=10)\n parser.add_argument(\"--use_other_level\",type=int,default=1)\n parser.add_argument(\"--postnms\",type=int,default=1)\n parser.add_argument(\"--pred_iou_thresh\",type=float,default=0.7)\n parser.add_argument(\"--box_nms_thresh\",type=float,default=0.7)\n parser.add_argument(\"--stability_score_thresh\",type=float,default=0.85)\n parser.add_argument(\"--reverse\", action=\"store_true\")\n \n level_dict = {\n \"default\": 0,\n \"small\": 1, \n \"middle\": 2,\n \"large\": 3\n }\n args = parser.parse_args()\n logger.add(os.path.join(args.output_dir,f'{args.level}.log'), rotation=\"500 MB\")\n logger.info(args)\n video_dir = args.video_path\n level = args.level\n base_dir = args.output_dir\n\n ##### load Sam2 and Sam1 Model #####\n sam2_checkpoint = args.sam2_checkpoint\n model_cfg = \"sam2_hiera_l.yaml\"\n predictor = build_sam2_video_predictor(model_cfg, sam2_checkpoint)\n\n sam2 = build_sam2(model_cfg, sam2_checkpoint, device='cuda', apply_postprocessing=False)\n\n sam_ckpt_path = args.sam1_checkpoint\n\n sam = sam_model_registry[\"vit_h\"](checkpoint=sam_ckpt_path).to('cuda')\n mask_generator = SamAutomaticMaskGenerator(\n model=sam,\n points_per_side=32,\n pred_iou_thresh=args.pred_iou_thresh, \n box_nms_thresh=args.box_nms_thresh, \n stability_score_thresh=args.stability_score_thresh, \n crop_n_layers=1,\n crop_n_points_downscale_factor=1,\n min_mask_region_area=100,\n )\n # scan all the JPEG frame names in this directory\n frame_names = [\n p for p in os.listdir(video_dir)\n if os.path.splitext(p)[-1] in [\".jpg\", \".jpeg\", \".JPG\", \".JPEG\", \".png\"]\n ]\n try:\n frame_names.sort(key=lambda p: int(os.path.splitext(p)[0]), reverse=args.reverse)\n except:\n frame_names.sort(key=lambda p: os.path.splitext(p)[0], reverse=args.reverse)\n\n now_frame = 0\n inference_state = predictor.init_state(video_path=video_dir)\n masks_from_prev = []\n sum_id = 0 # Record total number of objects\n\n prompts_loader = Prompts(bs=args.batch_size) # hold all the clicks we add for visualization\n \n while True:\n logger.info(f\"frame: {now_frame}\")\n \n sum_id = prompts_loader.get_obj_num()\n image_path = os.path.join(video_dir,frame_names[now_frame])\n image = cv2.imread(image_path)\n \n image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n # resize if the input is too large:\n orig_h, orig_w = image.shape[:2]\n if orig_h > 1080:\n logger.info(\"Resizing original image to 1080P...\")\n scale = 1080 / orig_h\n h = int(orig_h * scale)\n w = int(orig_w * scale)\n image = cv2.resize(image, (w, h))\n\n # Generate only large masks\n # masks_l = mask_generator.generate_l(image)\n all_masks = mask_generator.generate(image)\n masks = all_masks[level_dict[args.level]]\n # masks_l = mask_generator.generate(image)\n \n if args.postnms:\n # # Pass masks_l directly, no need to wrap in tuple\n # # masks_l = masks_update(masks_l, iou_thr=0.8, score_thr=0.7, inner_thr=0.5)[0]\n masks = masks_update(masks, iou_thr=0.8, score_thr=0.7, inner_thr=0.5)[0]\n\n # Use large level masks\n # masks = masks_l\n other_masks = None\n \n if not args.use_other_level:\n other_masks = None\n\n if now_frame == 0: # first frame\n ann_obj_id_list = range(len(masks))\n for ann_obj_id in tqdm(ann_obj_id_list):\n seg = masks[ann_obj_id]['segmentation']\n prompts_loader.add(ann_obj_id,0,seg)\n else: \n new_mask_list = search_new_obj(masks_from_prev, masks, other_masks,mask_ratio_thresh)\n logger.info(f\"number of new obj: {len(new_mask_list)}\")\n\n for id,mask in enumerate(masks_from_prev):\n if mask.sum() == 0:\n continue\n prompts_loader.add(id,now_frame,mask[0])\n\n for i in range(len(new_mask_list)):\n new_mask = new_mask_list[i]['segmentation']\n prompts_loader.add(sum_id+i,now_frame,new_mask)\n\n logger.info(f\"obj num: {prompts_loader.get_obj_num()}\")\n\n if now_frame==0 or len(new_mask_list)!=0:\n video_segments = get_video_segments(prompts_loader,predictor,inference_state)\n \n vis_frame_stride = args.detect_stride\n max_area_no_mask = (0,-1)\n for out_frame_idx in tqdm(range(0, len(frame_names), vis_frame_stride)):\n if out_frame_idx < now_frame:\n continue\n \n out_mask_list = []\n for out_obj_id, out_mask in video_segments[out_frame_idx].items():\n out_mask_list.append(out_mask)\n \n no_mask_ratio = cal_no_mask_area_ratio(out_mask_list)\n if now_frame == out_frame_idx:\n mask_ratio_thresh = no_mask_ratio\n logger.info(f\"mask_ratio_thresh: {mask_ratio_thresh}\")\n\n if no_mask_ratio > mask_ratio_thresh + 0.01 and out_frame_idx > now_frame:\n masks_from_prev = out_mask_list\n max_area_no_mask = (no_mask_ratio, out_frame_idx)\n logger.info(max_area_no_mask)\n break\n if max_area_no_mask[1] == -1:\n break\n logger.info(\"max_area_no_mask:\", max_area_no_mask)\n now_frame = max_area_no_mask[1]\n\n ###### Final output ######\n save_dir = os.path.join(base_dir,level,\"final-output\")\n os.makedirs(save_dir, exist_ok=True)\n \n video_segments = get_video_segments(prompts_loader,predictor,inference_state,final_output=True)\n for out_frame_idx in tqdm(range(0, len(frame_names), 1)):\n out_mask_list = []\n for out_obj_id, out_mask in video_segments[out_frame_idx].items():\n out_mask_list.append(out_mask)\n\n no_mask_ratio = cal_no_mask_area_ratio(out_mask_list)\n logger.info(no_mask_ratio)\n\n save_masks(out_mask_list, out_frame_idx,save_dir)\n save_masks_npy(out_mask_list, out_frame_idx,save_dir)\n\n ###### Generate Visualization Frames ######\n logger.info(\"Start generating visualization frames...\")\n vis_save_dir = os.path.join(base_dir,level,'visualization','full-mask-npy')\n os.makedirs(vis_save_dir,exist_ok=True)\n frame_save_dir = os.path.join(base_dir,level,'visualization','frames')\n os.makedirs(frame_save_dir, exist_ok=True)\n\n # Read all npy files\n npy_name_list = []\n for name in os.listdir(save_dir):\n if 'npy' in name:\n npy_name_list.append(name)\n npy_name_list.sort()\n logger.info(f\"Found {len(npy_name_list)} npy files\")\n\n npy_list = [np.load(os.path.join(save_dir,name)) for name in npy_name_list]\n image_list = [Image.open(os.path.join(video_dir,name)) for name in frame_names]\n\n assert len(npy_list) == len(image_list), \"Number of npy files does not match number of images\"\n logger.info(f\"Processing {len(npy_list)} frames in total\")\n\n # Generate random colors\n def generate_random_colors(num_colors):\n colors = []\n for _ in range(num_colors):\n reroll = True\n iter_cnt = 0\n while reroll and iter_cnt < 100:\n iter_cnt += 1\n reroll = False\n color = tuple(random.randint(1, 255) for _ in range(3))\n for selected_color in colors:\n if np.linalg.norm(np.array(color) - np.array(selected_color)) < 70:\n reroll = True\n break\n colors.append(color)\n return colors\n\n num_masks = max(len(masks) for masks in npy_list)\n colors = generate_random_colors(num_masks)\n post_colors = [(0, 0, 0)] + colors\n post_colors = np.array(post_colors) # [num_masks, 3]\n np.save(os.path.join(base_dir, \"colors.npy\"), post_colors)\n\n # Only process first and last frames\n # frames_to_process = [0, -1] # Indices for first and last frames\n \n for frame_idx in range(len(frame_names)):\n # for frame_idx in frames_to_process:\n masks = npy_list[frame_idx]\n image = image_list[frame_idx]\n \n image_np = np.array(image)\n mask_combined = np.zeros_like(image_np, dtype=np.uint8)\n\n for mask_id, mask in enumerate(masks):\n mask = mask.squeeze(0)\n mask_area = mask > 0\n mask_combined[mask_area, :] = colors[mask_id]\n\n # Blend original image with colored mask\n mask_combined = np.clip(mask_combined, 0, 255)\n\n # blended_image = cv2.addWeighted(image_np, 0.7, mask_combined, 0.3, 0)\n blended_image = mask_combined\n\n # change the save path\n frame_name = frame_names[frame_idx]\n frame_save_dir = base_dir\n output_path = os.path.join(frame_save_dir, frame_name)\n Image.fromarray(blended_image).save(output_path)\n logger.info(f\"Frame saved to: {output_path}\")" }, { "path": "auto-seg/sam2/__init__.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nfrom hydra import initialize_config_module\n\ninitialize_config_module(\"sam2_configs\", version_base=\"1.2\")\n" }, { "path": "auto-seg/sam2/automatic_mask_generator.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\n# Adapted from https://github.com/facebookresearch/segment-anything/blob/main/segment_anything/automatic_mask_generator.py\nfrom typing import Any, Dict, List, Optional, Tuple\n\nimport numpy as np\nimport torch\nfrom torchvision.ops.boxes import batched_nms, box_area # type: ignore\n\nfrom sam2.modeling.sam2_base import SAM2Base\nfrom sam2.sam2_image_predictor import SAM2ImagePredictor\nfrom sam2.utils.amg import (\n area_from_rle,\n batch_iterator,\n batched_mask_to_box,\n box_xyxy_to_xywh,\n build_all_layer_point_grids,\n calculate_stability_score,\n coco_encode_rle,\n generate_crop_boxes,\n is_box_near_crop_edge,\n mask_to_rle_pytorch,\n MaskData,\n remove_small_regions,\n rle_to_mask,\n uncrop_boxes_xyxy,\n uncrop_masks,\n uncrop_points,\n)\n\n\nclass SAM2AutomaticMaskGenerator:\n def __init__(\n self,\n model: SAM2Base,\n points_per_side: Optional[int] = 32,\n points_per_batch: int = 64,\n pred_iou_thresh: float = 0.8,\n stability_score_thresh: float = 0.95,\n stability_score_offset: float = 1.0,\n mask_threshold: float = 0.0,\n box_nms_thresh: float = 0.7,\n crop_n_layers: int = 0,\n crop_nms_thresh: float = 0.7,\n crop_overlap_ratio: float = 512 / 1500,\n crop_n_points_downscale_factor: int = 1,\n point_grids: Optional[List[np.ndarray]] = None,\n min_mask_region_area: int = 0,\n output_mode: str = \"binary_mask\",\n use_m2m: bool = False,\n multimask_output: bool = True,\n **kwargs,\n ) -> None:\n \"\"\"\n Using a SAM 2 model, generates masks for the entire image.\n Generates a grid of point prompts over the image, then filters\n low quality and duplicate masks. The default settings are chosen\n for SAM 2 with a HieraL backbone.\n\n Arguments:\n model (Sam): The SAM 2 model to use for mask prediction.\n points_per_side (int or None): The number of points to be sampled\n along one side of the image. The total number of points is\n points_per_side**2. If None, 'point_grids' must provide explicit\n point sampling.\n points_per_batch (int): Sets the number of points run simultaneously\n by the model. Higher numbers may be faster but use more GPU memory.\n pred_iou_thresh (float): A filtering threshold in [0,1], using the\n model's predicted mask quality.\n stability_score_thresh (float): A filtering threshold in [0,1], using\n the stability of the mask under changes to the cutoff used to binarize\n the model's mask predictions.\n stability_score_offset (float): The amount to shift the cutoff when\n calculated the stability score.\n mask_threshold (float): Threshold for binarizing the mask logits\n box_nms_thresh (float): The box IoU cutoff used by non-maximal\n suppression to filter duplicate masks.\n crop_n_layers (int): If >0, mask prediction will be run again on\n crops of the image. Sets the number of layers to run, where each\n layer has 2**i_layer number of image crops.\n crop_nms_thresh (float): The box IoU cutoff used by non-maximal\n suppression to filter duplicate masks between different crops.\n crop_overlap_ratio (float): Sets the degree to which crops overlap.\n In the first crop layer, crops will overlap by this fraction of\n the image length. Later layers with more crops scale down this overlap.\n crop_n_points_downscale_factor (int): The number of points-per-side\n sampled in layer n is scaled down by crop_n_points_downscale_factor**n.\n point_grids (list(np.ndarray) or None): A list over explicit grids\n of points used for sampling, normalized to [0,1]. The nth grid in the\n list is used in the nth crop layer. Exclusive with points_per_side.\n min_mask_region_area (int): If >0, postprocessing will be applied\n to remove disconnected regions and holes in masks with area smaller\n than min_mask_region_area. Requires opencv.\n output_mode (str): The form masks are returned in. Can be 'binary_mask',\n 'uncompressed_rle', or 'coco_rle'. 'coco_rle' requires pycocotools.\n For large resolutions, 'binary_mask' may consume large amounts of\n memory.\n use_m2m (bool): Whether to add a one step refinement using previous mask predictions.\n multimask_output (bool): Whether to output multimask at each point of the grid.\n \"\"\"\n\n assert (points_per_side is None) != (\n point_grids is None\n ), \"Exactly one of points_per_side or point_grid must be provided.\"\n if points_per_side is not None:\n self.point_grids = build_all_layer_point_grids(\n points_per_side,\n crop_n_layers,\n crop_n_points_downscale_factor,\n )\n elif point_grids is not None:\n self.point_grids = point_grids\n else:\n raise ValueError(\"Can't have both points_per_side and point_grid be None.\")\n\n assert output_mode in [\n \"binary_mask\",\n \"uncompressed_rle\",\n \"coco_rle\",\n ], f\"Unknown output_mode {output_mode}.\"\n if output_mode == \"coco_rle\":\n try:\n from pycocotools import mask as mask_utils # type: ignore # noqa: F401\n except ImportError as e:\n print(\"Please install pycocotools\")\n raise e\n\n self.predictor = SAM2ImagePredictor(\n model,\n max_hole_area=min_mask_region_area,\n max_sprinkle_area=min_mask_region_area,\n )\n self.points_per_batch = points_per_batch\n self.pred_iou_thresh = pred_iou_thresh\n self.stability_score_thresh = stability_score_thresh\n self.stability_score_offset = stability_score_offset\n self.mask_threshold = mask_threshold\n self.box_nms_thresh = box_nms_thresh\n self.crop_n_layers = crop_n_layers\n self.crop_nms_thresh = crop_nms_thresh\n self.crop_overlap_ratio = crop_overlap_ratio\n self.crop_n_points_downscale_factor = crop_n_points_downscale_factor\n self.min_mask_region_area = min_mask_region_area\n self.output_mode = output_mode\n self.use_m2m = use_m2m\n self.multimask_output = multimask_output\n\n @classmethod\n def from_pretrained(cls, model_id: str, **kwargs) -> \"SAM2AutomaticMaskGenerator\":\n \"\"\"\n Load a pretrained model from the Hugging Face hub.\n\n Arguments:\n model_id (str): The Hugging Face repository ID.\n **kwargs: Additional arguments to pass to the model constructor.\n\n Returns:\n (SAM2AutomaticMaskGenerator): The loaded model.\n \"\"\"\n from sam2.build_sam import build_sam2_hf\n\n sam_model = build_sam2_hf(model_id, **kwargs)\n return cls(sam_model, **kwargs)\n\n @torch.no_grad()\n def generate(self, image: np.ndarray) -> List[Dict[str, Any]]:\n \"\"\"\n Generates masks for the given image.\n\n Arguments:\n image (np.ndarray): The image to generate masks for, in HWC uint8 format.\n\n Returns:\n list(dict(str, any)): A list over records for masks. Each record is\n a dict containing the following keys:\n segmentation (dict(str, any) or np.ndarray): The mask. If\n output_mode='binary_mask', is an array of shape HW. Otherwise,\n is a dictionary containing the RLE.\n bbox (list(float)): The box around the mask, in XYWH format.\n area (int): The area in pixels of the mask.\n predicted_iou (float): The model's own prediction of the mask's\n quality. This is filtered by the pred_iou_thresh parameter.\n point_coords (list(list(float))): The point coordinates input\n to the model to generate this mask.\n stability_score (float): A measure of the mask's quality. This\n is filtered on using the stability_score_thresh parameter.\n crop_box (list(float)): The crop of the image used to generate\n the mask, given in XYWH format.\n \"\"\"\n\n # Generate masks\n mask_data = self._generate_masks(image)\n\n # Encode masks\n if self.output_mode == \"coco_rle\":\n mask_data[\"segmentations\"] = [\n coco_encode_rle(rle) for rle in mask_data[\"rles\"]\n ]\n elif self.output_mode == \"binary_mask\":\n mask_data[\"segmentations\"] = [rle_to_mask(rle) for rle in mask_data[\"rles\"]]\n else:\n mask_data[\"segmentations\"] = mask_data[\"rles\"]\n\n # Write mask records\n curr_anns = []\n for idx in range(len(mask_data[\"segmentations\"])):\n ann = {\n \"segmentation\": mask_data[\"segmentations\"][idx],\n \"area\": area_from_rle(mask_data[\"rles\"][idx]),\n \"bbox\": box_xyxy_to_xywh(mask_data[\"boxes\"][idx]).tolist(),\n \"predicted_iou\": mask_data[\"iou_preds\"][idx].item(),\n \"point_coords\": [mask_data[\"points\"][idx].tolist()],\n \"stability_score\": mask_data[\"stability_score\"][idx].item(),\n \"crop_box\": box_xyxy_to_xywh(mask_data[\"crop_boxes\"][idx]).tolist(),\n }\n curr_anns.append(ann)\n\n return curr_anns\n\n def _generate_masks(self, image: np.ndarray) -> MaskData:\n orig_size = image.shape[:2]\n crop_boxes, layer_idxs = generate_crop_boxes(\n orig_size, self.crop_n_layers, self.crop_overlap_ratio\n )\n\n # Iterate over image crops\n data = MaskData()\n for crop_box, layer_idx in zip(crop_boxes, layer_idxs):\n crop_data = self._process_crop(image, crop_box, layer_idx, orig_size)\n data.cat(crop_data)\n\n # Remove duplicate masks between crops\n if len(crop_boxes) > 1:\n # Prefer masks from smaller crops\n scores = 1 / box_area(data[\"crop_boxes\"])\n scores = scores.to(data[\"boxes\"].device)\n keep_by_nms = batched_nms(\n data[\"boxes\"].float(),\n scores,\n torch.zeros_like(data[\"boxes\"][:, 0]), # categories\n iou_threshold=self.crop_nms_thresh,\n )\n data.filter(keep_by_nms)\n data.to_numpy()\n return data\n\n def _process_crop(\n self,\n image: np.ndarray,\n crop_box: List[int],\n crop_layer_idx: int,\n orig_size: Tuple[int, ...],\n ) -> MaskData:\n # Crop the image and calculate embeddings\n x0, y0, x1, y1 = crop_box\n cropped_im = image[y0:y1, x0:x1, :]\n cropped_im_size = cropped_im.shape[:2]\n self.predictor.set_image(cropped_im)\n\n # Get points for this crop\n points_scale = np.array(cropped_im_size)[None, ::-1]\n points_for_image = self.point_grids[crop_layer_idx] * points_scale\n\n # Generate masks for this crop in batches\n data = MaskData()\n for (points,) in batch_iterator(self.points_per_batch, points_for_image):\n batch_data = self._process_batch(\n points, cropped_im_size, crop_box, orig_size, normalize=True\n )\n data.cat(batch_data)\n del batch_data\n self.predictor.reset_predictor()\n\n # Remove duplicates within this crop.\n keep_by_nms = batched_nms(\n data[\"boxes\"].float(),\n data[\"iou_preds\"],\n torch.zeros_like(data[\"boxes\"][:, 0]), # categories\n iou_threshold=self.box_nms_thresh,\n )\n data.filter(keep_by_nms)\n\n # Return to the original image frame\n data[\"boxes\"] = uncrop_boxes_xyxy(data[\"boxes\"], crop_box)\n data[\"points\"] = uncrop_points(data[\"points\"], crop_box)\n data[\"crop_boxes\"] = torch.tensor([crop_box for _ in range(len(data[\"rles\"]))])\n\n return data\n\n def _process_batch(\n self,\n points: np.ndarray,\n im_size: Tuple[int, ...],\n crop_box: List[int],\n orig_size: Tuple[int, ...],\n normalize=False,\n ) -> MaskData:\n orig_h, orig_w = orig_size\n\n # Run model on this batch\n points = torch.as_tensor(\n points, dtype=torch.float32, device=self.predictor.device\n )\n in_points = self.predictor._transforms.transform_coords(\n points, normalize=normalize, orig_hw=im_size\n )\n in_labels = torch.ones(\n in_points.shape[0], dtype=torch.int, device=in_points.device\n )\n masks, iou_preds, low_res_masks = self.predictor._predict(\n in_points[:, None, :],\n in_labels[:, None],\n multimask_output=self.multimask_output,\n return_logits=True,\n )\n\n # Serialize predictions and store in MaskData\n data = MaskData(\n masks=masks.flatten(0, 1),\n iou_preds=iou_preds.flatten(0, 1),\n points=points.repeat_interleave(masks.shape[1], dim=0),\n low_res_masks=low_res_masks.flatten(0, 1),\n )\n del masks\n\n if not self.use_m2m:\n # Filter by predicted IoU\n if self.pred_iou_thresh > 0.0:\n keep_mask = data[\"iou_preds\"] > self.pred_iou_thresh\n data.filter(keep_mask)\n\n # Calculate and filter by stability score\n data[\"stability_score\"] = calculate_stability_score(\n data[\"masks\"], self.mask_threshold, self.stability_score_offset\n )\n if self.stability_score_thresh > 0.0:\n keep_mask = data[\"stability_score\"] >= self.stability_score_thresh\n data.filter(keep_mask)\n else:\n # One step refinement using previous mask predictions\n in_points = self.predictor._transforms.transform_coords(\n data[\"points\"], normalize=normalize, orig_hw=im_size\n )\n labels = torch.ones(\n in_points.shape[0], dtype=torch.int, device=in_points.device\n )\n masks, ious = self.refine_with_m2m(\n in_points, labels, data[\"low_res_masks\"], self.points_per_batch\n )\n data[\"masks\"] = masks.squeeze(1)\n data[\"iou_preds\"] = ious.squeeze(1)\n\n if self.pred_iou_thresh > 0.0:\n keep_mask = data[\"iou_preds\"] > self.pred_iou_thresh\n data.filter(keep_mask)\n\n data[\"stability_score\"] = calculate_stability_score(\n data[\"masks\"], self.mask_threshold, self.stability_score_offset\n )\n if self.stability_score_thresh > 0.0:\n keep_mask = data[\"stability_score\"] >= self.stability_score_thresh\n data.filter(keep_mask)\n\n # Threshold masks and calculate boxes\n data[\"masks\"] = data[\"masks\"] > self.mask_threshold\n data[\"boxes\"] = batched_mask_to_box(data[\"masks\"])\n\n # Filter boxes that touch crop boundaries\n keep_mask = ~is_box_near_crop_edge(\n data[\"boxes\"], crop_box, [0, 0, orig_w, orig_h]\n )\n if not torch.all(keep_mask):\n data.filter(keep_mask)\n\n # Compress to RLE\n data[\"masks\"] = uncrop_masks(data[\"masks\"], crop_box, orig_h, orig_w)\n data[\"rles\"] = mask_to_rle_pytorch(data[\"masks\"])\n del data[\"masks\"]\n\n return data\n\n @staticmethod\n def postprocess_small_regions(\n mask_data: MaskData, min_area: int, nms_thresh: float\n ) -> MaskData:\n \"\"\"\n Removes small disconnected regions and holes in masks, then reruns\n box NMS to remove any new duplicates.\n\n Edits mask_data in place.\n\n Requires open-cv as a dependency.\n \"\"\"\n if len(mask_data[\"rles\"]) == 0:\n return mask_data\n\n # Filter small disconnected regions and holes\n new_masks = []\n scores = []\n for rle in mask_data[\"rles\"]:\n mask = rle_to_mask(rle)\n\n mask, changed = remove_small_regions(mask, min_area, mode=\"holes\")\n unchanged = not changed\n mask, changed = remove_small_regions(mask, min_area, mode=\"islands\")\n unchanged = unchanged and not changed\n\n new_masks.append(torch.as_tensor(mask).unsqueeze(0))\n # Give score=0 to changed masks and score=1 to unchanged masks\n # so NMS will prefer ones that didn't need postprocessing\n scores.append(float(unchanged))\n\n # Recalculate boxes and remove any new duplicates\n masks = torch.cat(new_masks, dim=0)\n boxes = batched_mask_to_box(masks)\n keep_by_nms = batched_nms(\n boxes.float(),\n torch.as_tensor(scores),\n torch.zeros_like(boxes[:, 0]), # categories\n iou_threshold=nms_thresh,\n )\n\n # Only recalculate RLEs for masks that have changed\n for i_mask in keep_by_nms:\n if scores[i_mask] == 0.0:\n mask_torch = masks[i_mask].unsqueeze(0)\n mask_data[\"rles\"][i_mask] = mask_to_rle_pytorch(mask_torch)[0]\n mask_data[\"boxes\"][i_mask] = boxes[i_mask] # update res directly\n mask_data.filter(keep_by_nms)\n\n return mask_data\n\n def refine_with_m2m(self, points, point_labels, low_res_masks, points_per_batch):\n new_masks = []\n new_iou_preds = []\n\n for cur_points, cur_point_labels, low_res_mask in batch_iterator(\n points_per_batch, points, point_labels, low_res_masks\n ):\n best_masks, best_iou_preds, _ = self.predictor._predict(\n cur_points[:, None, :],\n cur_point_labels[:, None],\n mask_input=low_res_mask[:, None, :],\n multimask_output=False,\n return_logits=True,\n )\n new_masks.append(best_masks)\n new_iou_preds.append(best_iou_preds)\n masks = torch.cat(new_masks, dim=0)\n return masks, torch.cat(new_iou_preds, dim=0)\n" }, { "path": "auto-seg/sam2/build_sam.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport logging\n\nimport torch\nfrom hydra import compose\nfrom hydra.utils import instantiate\nfrom omegaconf import OmegaConf\n\n\ndef build_sam2(\n config_file,\n ckpt_path=None,\n device=\"cuda\",\n mode=\"eval\",\n hydra_overrides_extra=[],\n apply_postprocessing=True,\n **kwargs,\n):\n\n if apply_postprocessing:\n hydra_overrides_extra = hydra_overrides_extra.copy()\n hydra_overrides_extra += [\n # dynamically fall back to multi-mask if the single mask is not stable\n \"++model.sam_mask_decoder_extra_args.dynamic_multimask_via_stability=true\",\n \"++model.sam_mask_decoder_extra_args.dynamic_multimask_stability_delta=0.05\",\n \"++model.sam_mask_decoder_extra_args.dynamic_multimask_stability_thresh=0.98\",\n ]\n # Read config and init model\n cfg = compose(config_name=config_file, overrides=hydra_overrides_extra)\n OmegaConf.resolve(cfg)\n model = instantiate(cfg.model, _recursive_=True)\n _load_checkpoint(model, ckpt_path)\n model = model.to(device)\n if mode == \"eval\":\n model.eval()\n return model\n\n\ndef build_sam2_video_predictor(\n config_file,\n ckpt_path=None,\n device=\"cuda\",\n mode=\"eval\",\n hydra_overrides_extra=[],\n apply_postprocessing=True,\n **kwargs,\n):\n hydra_overrides = [\n \"++model._target_=sam2.sam2_video_predictor.SAM2VideoPredictor\",\n ]\n if apply_postprocessing:\n hydra_overrides_extra = hydra_overrides_extra.copy()\n hydra_overrides_extra += [\n # dynamically fall back to multi-mask if the single mask is not stable\n \"++model.sam_mask_decoder_extra_args.dynamic_multimask_via_stability=true\",\n \"++model.sam_mask_decoder_extra_args.dynamic_multimask_stability_delta=0.05\",\n \"++model.sam_mask_decoder_extra_args.dynamic_multimask_stability_thresh=0.98\",\n # the sigmoid mask logits on interacted frames with clicks in the memory encoder so that the encoded masks are exactly as what users see from clicking\n \"++model.binarize_mask_from_pts_for_mem_enc=true\",\n # fill small holes in the low-res masks up to `fill_hole_area` (before resizing them to the original video resolution)\n \"++model.fill_hole_area=8\",\n ]\n hydra_overrides.extend(hydra_overrides_extra)\n\n # Read config and init model\n cfg = compose(config_name=config_file, overrides=hydra_overrides)\n OmegaConf.resolve(cfg)\n model = instantiate(cfg.model, _recursive_=True)\n _load_checkpoint(model, ckpt_path)\n model = model.to(device)\n if mode == \"eval\":\n model.eval()\n return model\n\n\ndef build_sam2_hf(model_id, **kwargs):\n\n from huggingface_hub import hf_hub_download\n\n model_id_to_filenames = {\n \"facebook/sam2-hiera-tiny\": (\"sam2_hiera_t.yaml\", \"sam2_hiera_tiny.pt\"),\n \"facebook/sam2-hiera-small\": (\"sam2_hiera_s.yaml\", \"sam2_hiera_small.pt\"),\n \"facebook/sam2-hiera-base-plus\": (\n \"sam2_hiera_b+.yaml\",\n \"sam2_hiera_base_plus.pt\",\n ),\n \"facebook/sam2-hiera-large\": (\"sam2_hiera_l.yaml\", \"sam2_hiera_large.pt\"),\n }\n config_name, checkpoint_name = model_id_to_filenames[model_id]\n ckpt_path = hf_hub_download(repo_id=model_id, filename=checkpoint_name)\n return build_sam2(config_file=config_name, ckpt_path=ckpt_path, **kwargs)\n\n\ndef build_sam2_video_predictor_hf(model_id, **kwargs):\n\n from huggingface_hub import hf_hub_download\n\n model_id_to_filenames = {\n \"facebook/sam2-hiera-tiny\": (\"sam2_hiera_t.yaml\", \"sam2_hiera_tiny.pt\"),\n \"facebook/sam2-hiera-small\": (\"sam2_hiera_s.yaml\", \"sam2_hiera_small.pt\"),\n \"facebook/sam2-hiera-base-plus\": (\n \"sam2_hiera_b+.yaml\",\n \"sam2_hiera_base_plus.pt\",\n ),\n \"facebook/sam2-hiera-large\": (\"sam2_hiera_l.yaml\", \"sam2_hiera_large.pt\"),\n }\n config_name, checkpoint_name = model_id_to_filenames[model_id]\n ckpt_path = hf_hub_download(repo_id=model_id, filename=checkpoint_name)\n return build_sam2_video_predictor(\n config_file=config_name, ckpt_path=ckpt_path, **kwargs\n )\n\n\ndef _load_checkpoint(model, ckpt_path):\n if ckpt_path is not None:\n sd = torch.load(ckpt_path, map_location=\"cpu\")[\"model\"]\n missing_keys, unexpected_keys = model.load_state_dict(sd)\n if missing_keys:\n logging.error(missing_keys)\n raise RuntimeError()\n if unexpected_keys:\n logging.error(unexpected_keys)\n raise RuntimeError()\n logging.info(\"Loaded checkpoint sucessfully\")\n" }, { "path": "auto-seg/sam2/csrc/connected_components.cu", "content": "// Copyright (c) Meta Platforms, Inc. and affiliates.\n// All rights reserved.\n\n// This source code is licensed under the license found in the\n// LICENSE file in the root directory of this source tree.\n\n// adapted from https://github.com/zsef123/Connected_components_PyTorch\n// with license found in the LICENSE_cctorch file in the root directory.\n#include \n#include \n#include \n#include \n#include \n#include \n\n// 2d\n#define BLOCK_ROWS 16\n#define BLOCK_COLS 16\n\nnamespace cc2d {\n\ntemplate \n__device__ __forceinline__ unsigned char hasBit(T bitmap, unsigned char pos) {\n return (bitmap >> pos) & 1;\n}\n\n__device__ int32_t find(const int32_t* s_buf, int32_t n) {\n while (s_buf[n] != n)\n n = s_buf[n];\n return n;\n}\n\n__device__ int32_t find_n_compress(int32_t* s_buf, int32_t n) {\n const int32_t id = n;\n while (s_buf[n] != n) {\n n = s_buf[n];\n s_buf[id] = n;\n }\n return n;\n}\n\n__device__ void union_(int32_t* s_buf, int32_t a, int32_t b) {\n bool done;\n do {\n a = find(s_buf, a);\n b = find(s_buf, b);\n\n if (a < b) {\n int32_t old = atomicMin(s_buf + b, a);\n done = (old == b);\n b = old;\n } else if (b < a) {\n int32_t old = atomicMin(s_buf + a, b);\n done = (old == a);\n a = old;\n } else\n done = true;\n\n } while (!done);\n}\n\n__global__ void\ninit_labeling(int32_t* label, const uint32_t W, const uint32_t H) {\n const uint32_t row = (blockIdx.y * blockDim.y + threadIdx.y) * 2;\n const uint32_t col = (blockIdx.x * blockDim.x + threadIdx.x) * 2;\n const uint32_t idx = row * W + col;\n\n if (row < H && col < W)\n label[idx] = idx;\n}\n\n__global__ void\nmerge(uint8_t* img, int32_t* label, const uint32_t W, const uint32_t H) {\n const uint32_t row = (blockIdx.y * blockDim.y + threadIdx.y) * 2;\n const uint32_t col = (blockIdx.x * blockDim.x + threadIdx.x) * 2;\n const uint32_t idx = row * W + col;\n\n if (row >= H || col >= W)\n return;\n\n uint32_t P = 0;\n\n if (img[idx])\n P |= 0x777;\n if (row + 1 < H && img[idx + W])\n P |= 0x777 << 4;\n if (col + 1 < W && img[idx + 1])\n P |= 0x777 << 1;\n\n if (col == 0)\n P &= 0xEEEE;\n if (col + 1 >= W)\n P &= 0x3333;\n else if (col + 2 >= W)\n P &= 0x7777;\n\n if (row == 0)\n P &= 0xFFF0;\n if (row + 1 >= H)\n P &= 0xFF;\n\n if (P > 0) {\n // If need check about top-left pixel(if flag the first bit) and hit the\n // top-left pixel\n if (hasBit(P, 0) && img[idx - W - 1]) {\n union_(label, idx, idx - 2 * W - 2); // top left block\n }\n\n if ((hasBit(P, 1) && img[idx - W]) || (hasBit(P, 2) && img[idx - W + 1]))\n union_(label, idx, idx - 2 * W); // top bottom block\n\n if (hasBit(P, 3) && img[idx + 2 - W])\n union_(label, idx, idx - 2 * W + 2); // top right block\n\n if ((hasBit(P, 4) && img[idx - 1]) || (hasBit(P, 8) && img[idx + W - 1]))\n union_(label, idx, idx - 2); // just left block\n }\n}\n\n__global__ void compression(int32_t* label, const int32_t W, const int32_t H) {\n const uint32_t row = (blockIdx.y * blockDim.y + threadIdx.y) * 2;\n const uint32_t col = (blockIdx.x * blockDim.x + threadIdx.x) * 2;\n const uint32_t idx = row * W + col;\n\n if (row < H && col < W)\n find_n_compress(label, idx);\n}\n\n__global__ void final_labeling(\n const uint8_t* img,\n int32_t* label,\n const int32_t W,\n const int32_t H) {\n const uint32_t row = (blockIdx.y * blockDim.y + threadIdx.y) * 2;\n const uint32_t col = (blockIdx.x * blockDim.x + threadIdx.x) * 2;\n const uint32_t idx = row * W + col;\n\n if (row >= H || col >= W)\n return;\n\n int32_t y = label[idx] + 1;\n\n if (img[idx])\n label[idx] = y;\n else\n label[idx] = 0;\n\n if (col + 1 < W) {\n if (img[idx + 1])\n label[idx + 1] = y;\n else\n label[idx + 1] = 0;\n\n if (row + 1 < H) {\n if (img[idx + W + 1])\n label[idx + W + 1] = y;\n else\n label[idx + W + 1] = 0;\n }\n }\n\n if (row + 1 < H) {\n if (img[idx + W])\n label[idx + W] = y;\n else\n label[idx + W] = 0;\n }\n}\n\n__global__ void init_counting(\n const int32_t* label,\n int32_t* count_init,\n const int32_t W,\n const int32_t H) {\n const uint32_t row = (blockIdx.y * blockDim.y + threadIdx.y);\n const uint32_t col = (blockIdx.x * blockDim.x + threadIdx.x);\n const uint32_t idx = row * W + col;\n\n if (row >= H || col >= W)\n return;\n\n int32_t y = label[idx];\n if (y > 0) {\n int32_t count_idx = y - 1;\n atomicAdd(count_init + count_idx, 1);\n }\n}\n\n__global__ void final_counting(\n const int32_t* label,\n const int32_t* count_init,\n int32_t* count_final,\n const int32_t W,\n const int32_t H) {\n const uint32_t row = (blockIdx.y * blockDim.y + threadIdx.y);\n const uint32_t col = (blockIdx.x * blockDim.x + threadIdx.x);\n const uint32_t idx = row * W + col;\n\n if (row >= H || col >= W)\n return;\n\n int32_t y = label[idx];\n if (y > 0) {\n int32_t count_idx = y - 1;\n count_final[idx] = count_init[count_idx];\n } else {\n count_final[idx] = 0;\n }\n}\n\n} // namespace cc2d\n\nstd::vector get_connected_componnets(\n const torch::Tensor& inputs) {\n AT_ASSERTM(inputs.is_cuda(), \"inputs must be a CUDA tensor\");\n AT_ASSERTM(inputs.ndimension() == 4, \"inputs must be [N, 1, H, W] shape\");\n AT_ASSERTM(\n inputs.scalar_type() == torch::kUInt8, \"inputs must be a uint8 type\");\n\n const uint32_t N = inputs.size(0);\n const uint32_t C = inputs.size(1);\n const uint32_t H = inputs.size(2);\n const uint32_t W = inputs.size(3);\n\n AT_ASSERTM(C == 1, \"inputs must be [N, 1, H, W] shape\");\n AT_ASSERTM((H % 2) == 0, \"height must be an even number\");\n AT_ASSERTM((W % 2) == 0, \"width must be an even number\");\n\n // label must be uint32_t\n auto label_options =\n torch::TensorOptions().dtype(torch::kInt32).device(inputs.device());\n torch::Tensor labels = torch::zeros({N, C, H, W}, label_options);\n torch::Tensor counts_init = torch::zeros({N, C, H, W}, label_options);\n torch::Tensor counts_final = torch::zeros({N, C, H, W}, label_options);\n\n dim3 grid = dim3(\n ((W + 1) / 2 + BLOCK_COLS - 1) / BLOCK_COLS,\n ((H + 1) / 2 + BLOCK_ROWS - 1) / BLOCK_ROWS);\n dim3 block = dim3(BLOCK_COLS, BLOCK_ROWS);\n dim3 grid_count =\n dim3((W + BLOCK_COLS) / BLOCK_COLS, (H + BLOCK_ROWS) / BLOCK_ROWS);\n dim3 block_count = dim3(BLOCK_COLS, BLOCK_ROWS);\n cudaStream_t stream = at::cuda::getCurrentCUDAStream();\n\n for (int n = 0; n < N; n++) {\n uint32_t offset = n * H * W;\n\n cc2d::init_labeling<<>>(\n labels.data_ptr() + offset, W, H);\n cc2d::merge<<>>(\n inputs.data_ptr() + offset,\n labels.data_ptr() + offset,\n W,\n H);\n cc2d::compression<<>>(\n labels.data_ptr() + offset, W, H);\n cc2d::final_labeling<<>>(\n inputs.data_ptr() + offset,\n labels.data_ptr() + offset,\n W,\n H);\n\n // get the counting of each pixel\n cc2d::init_counting<<>>(\n labels.data_ptr() + offset,\n counts_init.data_ptr() + offset,\n W,\n H);\n cc2d::final_counting<<>>(\n labels.data_ptr() + offset,\n counts_init.data_ptr() + offset,\n counts_final.data_ptr() + offset,\n W,\n H);\n }\n\n // returned values are [labels, counts]\n std::vector outputs;\n outputs.push_back(labels);\n outputs.push_back(counts_final);\n return outputs;\n}\n\nPYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {\n m.def(\n \"get_connected_componnets\",\n &get_connected_componnets,\n \"get_connected_componnets\");\n}\n" }, { "path": "auto-seg/sam2/modeling/__init__.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n" }, { "path": "auto-seg/sam2/modeling/backbones/__init__.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n" }, { "path": "auto-seg/sam2/modeling/backbones/hieradet.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nfrom functools import partial\nfrom typing import List, Tuple, Union\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom sam2.modeling.backbones.utils import (\n PatchEmbed,\n window_partition,\n window_unpartition,\n)\n\nfrom sam2.modeling.sam2_utils import DropPath, MLP\n\n\ndef do_pool(x: torch.Tensor, pool: nn.Module, norm: nn.Module = None) -> torch.Tensor:\n if pool is None:\n return x\n # (B, H, W, C) -> (B, C, H, W)\n x = x.permute(0, 3, 1, 2)\n x = pool(x)\n # (B, C, H', W') -> (B, H', W', C)\n x = x.permute(0, 2, 3, 1)\n if norm:\n x = norm(x)\n\n return x\n\n\nclass MultiScaleAttention(nn.Module):\n def __init__(\n self,\n dim: int,\n dim_out: int,\n num_heads: int,\n q_pool: nn.Module = None,\n ):\n super().__init__()\n\n self.dim = dim\n self.dim_out = dim_out\n self.num_heads = num_heads\n self.q_pool = q_pool\n self.qkv = nn.Linear(dim, dim_out * 3)\n self.proj = nn.Linear(dim_out, dim_out)\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n B, H, W, _ = x.shape\n # qkv with shape (B, H * W, 3, nHead, C)\n qkv = self.qkv(x).reshape(B, H * W, 3, self.num_heads, -1)\n # q, k, v with shape (B, H * W, nheads, C)\n q, k, v = torch.unbind(qkv, 2)\n\n # Q pooling (for downsample at stage changes)\n if self.q_pool:\n q = do_pool(q.reshape(B, H, W, -1), self.q_pool)\n H, W = q.shape[1:3] # downsampled shape\n q = q.reshape(B, H * W, self.num_heads, -1)\n\n # Torch's SDPA expects [B, nheads, H*W, C] so we transpose\n x = F.scaled_dot_product_attention(\n q.transpose(1, 2),\n k.transpose(1, 2),\n v.transpose(1, 2),\n )\n # Transpose back\n x = x.transpose(1, 2)\n x = x.reshape(B, H, W, -1)\n\n x = self.proj(x)\n\n return x\n\n\nclass MultiScaleBlock(nn.Module):\n def __init__(\n self,\n dim: int,\n dim_out: int,\n num_heads: int,\n mlp_ratio: float = 4.0,\n drop_path: float = 0.0,\n norm_layer: Union[nn.Module, str] = \"LayerNorm\",\n q_stride: Tuple[int, int] = None,\n act_layer: nn.Module = nn.GELU,\n window_size: int = 0,\n ):\n super().__init__()\n\n if isinstance(norm_layer, str):\n norm_layer = partial(getattr(nn, norm_layer), eps=1e-6)\n\n self.dim = dim\n self.dim_out = dim_out\n self.norm1 = norm_layer(dim)\n\n self.window_size = window_size\n\n self.pool, self.q_stride = None, q_stride\n if self.q_stride:\n self.pool = nn.MaxPool2d(\n kernel_size=q_stride, stride=q_stride, ceil_mode=False\n )\n\n self.attn = MultiScaleAttention(\n dim,\n dim_out,\n num_heads=num_heads,\n q_pool=self.pool,\n )\n self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()\n\n self.norm2 = norm_layer(dim_out)\n self.mlp = MLP(\n dim_out,\n int(dim_out * mlp_ratio),\n dim_out,\n num_layers=2,\n activation=act_layer,\n )\n\n if dim != dim_out:\n self.proj = nn.Linear(dim, dim_out)\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n shortcut = x # B, H, W, C\n x = self.norm1(x)\n\n # Skip connection\n if self.dim != self.dim_out:\n shortcut = do_pool(self.proj(x), self.pool)\n\n # Window partition\n window_size = self.window_size\n if window_size > 0:\n H, W = x.shape[1], x.shape[2]\n x, pad_hw = window_partition(x, window_size)\n\n # Window Attention + Q Pooling (if stage change)\n x = self.attn(x)\n if self.q_stride:\n # Shapes have changed due to Q pooling\n window_size = self.window_size // self.q_stride[0]\n H, W = shortcut.shape[1:3]\n\n pad_h = (window_size - H % window_size) % window_size\n pad_w = (window_size - W % window_size) % window_size\n pad_hw = (H + pad_h, W + pad_w)\n\n # Reverse window partition\n if self.window_size > 0:\n x = window_unpartition(x, window_size, pad_hw, (H, W))\n\n x = shortcut + self.drop_path(x)\n # MLP\n x = x + self.drop_path(self.mlp(self.norm2(x)))\n return x\n\n\nclass Hiera(nn.Module):\n \"\"\"\n Reference: https://arxiv.org/abs/2306.00989\n \"\"\"\n\n def __init__(\n self,\n embed_dim: int = 96, # initial embed dim\n num_heads: int = 1, # initial number of heads\n drop_path_rate: float = 0.0, # stochastic depth\n q_pool: int = 3, # number of q_pool stages\n q_stride: Tuple[int, int] = (2, 2), # downsample stride bet. stages\n stages: Tuple[int, ...] = (2, 3, 16, 3), # blocks per stage\n dim_mul: float = 2.0, # dim_mul factor at stage shift\n head_mul: float = 2.0, # head_mul factor at stage shift\n window_pos_embed_bkg_spatial_size: Tuple[int, int] = (14, 14),\n # window size per stage, when not using global att.\n window_spec: Tuple[int, ...] = (\n 8,\n 4,\n 14,\n 7,\n ),\n # global attn in these blocks\n global_att_blocks: Tuple[int, ...] = (\n 12,\n 16,\n 20,\n ),\n return_interm_layers=True, # return feats from every stage\n ):\n super().__init__()\n\n assert len(stages) == len(window_spec)\n self.window_spec = window_spec\n\n depth = sum(stages)\n self.q_stride = q_stride\n self.stage_ends = [sum(stages[:i]) - 1 for i in range(1, len(stages) + 1)]\n assert 0 <= q_pool <= len(self.stage_ends[:-1])\n self.q_pool_blocks = [x + 1 for x in self.stage_ends[:-1]][:q_pool]\n self.return_interm_layers = return_interm_layers\n\n self.patch_embed = PatchEmbed(\n embed_dim=embed_dim,\n )\n # Which blocks have global att?\n self.global_att_blocks = global_att_blocks\n\n # Windowed positional embedding (https://arxiv.org/abs/2311.05613)\n self.window_pos_embed_bkg_spatial_size = window_pos_embed_bkg_spatial_size\n self.pos_embed = nn.Parameter(\n torch.zeros(1, embed_dim, *self.window_pos_embed_bkg_spatial_size)\n )\n self.pos_embed_window = nn.Parameter(\n torch.zeros(1, embed_dim, self.window_spec[0], self.window_spec[0])\n )\n\n dpr = [\n x.item() for x in torch.linspace(0, drop_path_rate, depth)\n ] # stochastic depth decay rule\n\n cur_stage = 1\n self.blocks = nn.ModuleList()\n\n for i in range(depth):\n dim_out = embed_dim\n # lags by a block, so first block of\n # next stage uses an initial window size\n # of previous stage and final window size of current stage\n window_size = self.window_spec[cur_stage - 1]\n\n if self.global_att_blocks is not None:\n window_size = 0 if i in self.global_att_blocks else window_size\n\n if i - 1 in self.stage_ends:\n dim_out = int(embed_dim * dim_mul)\n num_heads = int(num_heads * head_mul)\n cur_stage += 1\n\n block = MultiScaleBlock(\n dim=embed_dim,\n dim_out=dim_out,\n num_heads=num_heads,\n drop_path=dpr[i],\n q_stride=self.q_stride if i in self.q_pool_blocks else None,\n window_size=window_size,\n )\n\n embed_dim = dim_out\n self.blocks.append(block)\n\n self.channel_list = (\n [self.blocks[i].dim_out for i in self.stage_ends[::-1]]\n if return_interm_layers\n else [self.blocks[-1].dim_out]\n )\n\n def _get_pos_embed(self, hw: Tuple[int, int]) -> torch.Tensor:\n h, w = hw\n window_embed = self.pos_embed_window\n pos_embed = F.interpolate(self.pos_embed, size=(h, w), mode=\"bicubic\")\n pos_embed = pos_embed + window_embed.tile(\n [x // y for x, y in zip(pos_embed.shape, window_embed.shape)]\n )\n pos_embed = pos_embed.permute(0, 2, 3, 1)\n return pos_embed\n\n def forward(self, x: torch.Tensor) -> List[torch.Tensor]:\n x = self.patch_embed(x)\n # x: (B, H, W, C)\n\n # Add pos embed\n x = x + self._get_pos_embed(x.shape[1:3])\n\n outputs = []\n for i, blk in enumerate(self.blocks):\n x = blk(x)\n if (i == self.stage_ends[-1]) or (\n i in self.stage_ends and self.return_interm_layers\n ):\n feats = x.permute(0, 3, 1, 2)\n outputs.append(feats)\n\n return outputs\n" }, { "path": "auto-seg/sam2/modeling/backbones/image_encoder.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nfrom typing import List, Optional\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\nclass ImageEncoder(nn.Module):\n def __init__(\n self,\n trunk: nn.Module,\n neck: nn.Module,\n scalp: int = 0,\n ):\n super().__init__()\n self.trunk = trunk\n self.neck = neck\n self.scalp = scalp\n assert (\n self.trunk.channel_list == self.neck.backbone_channel_list\n ), f\"Channel dims of trunk and neck do not match. Trunk: {self.trunk.channel_list}, neck: {self.neck.backbone_channel_list}\"\n\n def forward(self, sample: torch.Tensor):\n # Forward through backbone\n features, pos = self.neck(self.trunk(sample))\n if self.scalp > 0:\n # Discard the lowest resolution features\n features, pos = features[: -self.scalp], pos[: -self.scalp]\n\n src = features[-1]\n output = {\n \"vision_features\": src,\n \"vision_pos_enc\": pos,\n \"backbone_fpn\": features,\n }\n return output\n\n\nclass FpnNeck(nn.Module):\n \"\"\"\n A modified variant of Feature Pyramid Network (FPN) neck\n (we remove output conv and also do bicubic interpolation similar to ViT\n pos embed interpolation)\n \"\"\"\n\n def __init__(\n self,\n position_encoding: nn.Module,\n d_model: int,\n backbone_channel_list: List[int],\n kernel_size: int = 1,\n stride: int = 1,\n padding: int = 0,\n fpn_interp_model: str = \"bilinear\",\n fuse_type: str = \"sum\",\n fpn_top_down_levels: Optional[List[int]] = None,\n ):\n \"\"\"Initialize the neck\n :param trunk: the backbone\n :param position_encoding: the positional encoding to use\n :param d_model: the dimension of the model\n :param neck_norm: the normalization to use\n \"\"\"\n super().__init__()\n self.position_encoding = position_encoding\n self.convs = nn.ModuleList()\n self.backbone_channel_list = backbone_channel_list\n for dim in backbone_channel_list:\n current = nn.Sequential()\n current.add_module(\n \"conv\",\n nn.Conv2d(\n in_channels=dim,\n out_channels=d_model,\n kernel_size=kernel_size,\n stride=stride,\n padding=padding,\n ),\n )\n\n self.convs.append(current)\n self.fpn_interp_model = fpn_interp_model\n assert fuse_type in [\"sum\", \"avg\"]\n self.fuse_type = fuse_type\n\n # levels to have top-down features in its outputs\n # e.g. if fpn_top_down_levels is [2, 3], then only outputs of level 2 and 3\n # have top-down propagation, while outputs of level 0 and level 1 have only\n # lateral features from the same backbone level.\n if fpn_top_down_levels is None:\n # default is to have top-down features on all levels\n fpn_top_down_levels = range(len(self.convs))\n self.fpn_top_down_levels = list(fpn_top_down_levels)\n\n def forward(self, xs: List[torch.Tensor]):\n\n out = [None] * len(self.convs)\n pos = [None] * len(self.convs)\n assert len(xs) == len(self.convs)\n # fpn forward pass\n # see https://github.com/facebookresearch/detectron2/blob/main/detectron2/modeling/backbone/fpn.py\n prev_features = None\n # forward in top-down order (from low to high resolution)\n n = len(self.convs) - 1\n for i in range(n, -1, -1):\n x = xs[i]\n lateral_features = self.convs[n - i](x)\n if i in self.fpn_top_down_levels and prev_features is not None:\n top_down_features = F.interpolate(\n prev_features.to(dtype=torch.float32),\n scale_factor=2.0,\n mode=self.fpn_interp_model,\n align_corners=(\n None if self.fpn_interp_model == \"nearest\" else False\n ),\n antialias=False,\n )\n prev_features = lateral_features + top_down_features\n if self.fuse_type == \"avg\":\n prev_features /= 2\n else:\n prev_features = lateral_features\n x_out = prev_features\n out[i] = x_out\n pos[i] = self.position_encoding(x_out).to(x_out.dtype)\n\n return out, pos\n" }, { "path": "auto-seg/sam2/modeling/backbones/utils.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\n\"\"\"Some utilities for backbones, in particular for windowing\"\"\"\n\nfrom typing import Tuple\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\ndef window_partition(x, window_size):\n \"\"\"\n Partition into non-overlapping windows with padding if needed.\n Args:\n x (tensor): input tokens with [B, H, W, C].\n window_size (int): window size.\n Returns:\n windows: windows after partition with [B * num_windows, window_size, window_size, C].\n (Hp, Wp): padded height and width before partition\n \"\"\"\n B, H, W, C = x.shape\n\n pad_h = (window_size - H % window_size) % window_size\n pad_w = (window_size - W % window_size) % window_size\n if pad_h > 0 or pad_w > 0:\n x = F.pad(x, (0, 0, 0, pad_w, 0, pad_h))\n Hp, Wp = H + pad_h, W + pad_w\n\n x = x.view(B, Hp // window_size, window_size, Wp // window_size, window_size, C)\n windows = (\n x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)\n )\n return windows, (Hp, Wp)\n\n\ndef window_unpartition(windows, window_size, pad_hw, hw):\n \"\"\"\n Window unpartition into original sequences and removing padding.\n Args:\n x (tensor): input tokens with [B * num_windows, window_size, window_size, C].\n window_size (int): window size.\n pad_hw (Tuple): padded height and width (Hp, Wp).\n hw (Tuple): original height and width (H, W) before padding.\n Returns:\n x: unpartitioned sequences with [B, H, W, C].\n \"\"\"\n Hp, Wp = pad_hw\n H, W = hw\n B = windows.shape[0] // (Hp * Wp // window_size // window_size)\n x = windows.view(\n B, Hp // window_size, Wp // window_size, window_size, window_size, -1\n )\n x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, Hp, Wp, -1)\n\n if Hp > H or Wp > W:\n x = x[:, :H, :W, :].contiguous()\n return x\n\n\nclass PatchEmbed(nn.Module):\n \"\"\"\n Image to Patch Embedding.\n \"\"\"\n\n def __init__(\n self,\n kernel_size: Tuple[int, ...] = (7, 7),\n stride: Tuple[int, ...] = (4, 4),\n padding: Tuple[int, ...] = (3, 3),\n in_chans: int = 3,\n embed_dim: int = 768,\n ):\n \"\"\"\n Args:\n kernel_size (Tuple): kernel size of the projection layer.\n stride (Tuple): stride of the projection layer.\n padding (Tuple): padding size of the projection layer.\n in_chans (int): Number of input image channels.\n embed_dim (int): embed_dim (int): Patch embedding dimension.\n \"\"\"\n super().__init__()\n self.proj = nn.Conv2d(\n in_chans, embed_dim, kernel_size=kernel_size, stride=stride, padding=padding\n )\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n x = self.proj(x)\n # B C H W -> B H W C\n x = x.permute(0, 2, 3, 1)\n return x\n" }, { "path": "auto-seg/sam2/modeling/memory_attention.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nfrom typing import Optional\n\nimport torch\nfrom torch import nn, Tensor\n\nfrom sam2.modeling.sam.transformer import RoPEAttention\n\nfrom sam2.modeling.sam2_utils import get_activation_fn, get_clones\n\n\nclass MemoryAttentionLayer(nn.Module):\n\n def __init__(\n self,\n activation: str,\n cross_attention: nn.Module,\n d_model: int,\n dim_feedforward: int,\n dropout: float,\n pos_enc_at_attn: bool,\n pos_enc_at_cross_attn_keys: bool,\n pos_enc_at_cross_attn_queries: bool,\n self_attention: nn.Module,\n ):\n super().__init__()\n self.d_model = d_model\n self.dim_feedforward = dim_feedforward\n self.dropout_value = dropout\n self.self_attn = self_attention\n self.cross_attn_image = cross_attention\n\n # Implementation of Feedforward model\n self.linear1 = nn.Linear(d_model, dim_feedforward)\n self.dropout = nn.Dropout(dropout)\n self.linear2 = nn.Linear(dim_feedforward, d_model)\n\n self.norm1 = nn.LayerNorm(d_model)\n self.norm2 = nn.LayerNorm(d_model)\n self.norm3 = nn.LayerNorm(d_model)\n self.dropout1 = nn.Dropout(dropout)\n self.dropout2 = nn.Dropout(dropout)\n self.dropout3 = nn.Dropout(dropout)\n\n self.activation_str = activation\n self.activation = get_activation_fn(activation)\n\n # Where to add pos enc\n self.pos_enc_at_attn = pos_enc_at_attn\n self.pos_enc_at_cross_attn_queries = pos_enc_at_cross_attn_queries\n self.pos_enc_at_cross_attn_keys = pos_enc_at_cross_attn_keys\n\n def _forward_sa(self, tgt, query_pos):\n # Self-Attention\n tgt2 = self.norm1(tgt)\n q = k = tgt2 + query_pos if self.pos_enc_at_attn else tgt2\n tgt2 = self.self_attn(q, k, v=tgt2)\n tgt = tgt + self.dropout1(tgt2)\n return tgt\n\n def _forward_ca(self, tgt, memory, query_pos, pos, num_k_exclude_rope=0):\n kwds = {}\n if num_k_exclude_rope > 0:\n assert isinstance(self.cross_attn_image, RoPEAttention)\n kwds = {\"num_k_exclude_rope\": num_k_exclude_rope}\n\n # Cross-Attention\n tgt2 = self.norm2(tgt)\n tgt2 = self.cross_attn_image(\n q=tgt2 + query_pos if self.pos_enc_at_cross_attn_queries else tgt2,\n k=memory + pos if self.pos_enc_at_cross_attn_keys else memory,\n v=memory,\n **kwds,\n )\n tgt = tgt + self.dropout2(tgt2)\n return tgt\n\n def forward(\n self,\n tgt,\n memory,\n pos: Optional[Tensor] = None,\n query_pos: Optional[Tensor] = None,\n num_k_exclude_rope: int = 0,\n ) -> torch.Tensor:\n\n # Self-Attn, Cross-Attn\n tgt = self._forward_sa(tgt, query_pos)\n tgt = self._forward_ca(tgt, memory, query_pos, pos, num_k_exclude_rope)\n # MLP\n tgt2 = self.norm3(tgt)\n tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))\n tgt = tgt + self.dropout3(tgt2)\n return tgt\n\n\nclass MemoryAttention(nn.Module):\n def __init__(\n self,\n d_model: int,\n pos_enc_at_input: bool,\n layer: nn.Module,\n num_layers: int,\n batch_first: bool = True, # Do layers expect batch first input?\n ):\n super().__init__()\n self.d_model = d_model\n self.layers = get_clones(layer, num_layers)\n self.num_layers = num_layers\n self.norm = nn.LayerNorm(d_model)\n self.pos_enc_at_input = pos_enc_at_input\n self.batch_first = batch_first\n\n def forward(\n self,\n curr: torch.Tensor, # self-attention inputs\n memory: torch.Tensor, # cross-attention inputs\n curr_pos: Optional[Tensor] = None, # pos_enc for self-attention inputs\n memory_pos: Optional[Tensor] = None, # pos_enc for cross-attention inputs\n num_obj_ptr_tokens: int = 0, # number of object pointer *tokens*\n ):\n if isinstance(curr, list):\n assert isinstance(curr_pos, list)\n assert len(curr) == len(curr_pos) == 1\n curr, curr_pos = (\n curr[0],\n curr_pos[0],\n )\n\n assert (\n curr.shape[1] == memory.shape[1]\n ), \"Batch size must be the same for curr and memory\"\n\n output = curr\n if self.pos_enc_at_input and curr_pos is not None:\n output = output + 0.1 * curr_pos\n\n if self.batch_first:\n # Convert to batch first\n output = output.transpose(0, 1)\n curr_pos = curr_pos.transpose(0, 1)\n memory = memory.transpose(0, 1)\n memory_pos = memory_pos.transpose(0, 1)\n\n for layer in self.layers:\n kwds = {}\n if isinstance(layer.cross_attn_image, RoPEAttention):\n kwds = {\"num_k_exclude_rope\": num_obj_ptr_tokens}\n\n output = layer(\n tgt=output,\n memory=memory,\n pos=memory_pos,\n query_pos=curr_pos,\n **kwds,\n )\n normed_output = self.norm(output)\n\n if self.batch_first:\n # Convert back to seq first\n normed_output = normed_output.transpose(0, 1)\n curr_pos = curr_pos.transpose(0, 1)\n\n return normed_output\n" }, { "path": "auto-seg/sam2/modeling/memory_encoder.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport math\nfrom typing import Tuple\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom sam2.modeling.sam2_utils import DropPath, get_clones, LayerNorm2d\n\n\nclass MaskDownSampler(nn.Module):\n \"\"\"\n Progressively downsample a mask by total_stride, each time by stride.\n Note that LayerNorm is applied per *token*, like in ViT.\n\n With each downsample (by a factor stride**2), channel capacity increases by the same factor.\n In the end, we linearly project to embed_dim channels.\n \"\"\"\n\n def __init__(\n self,\n embed_dim=256,\n kernel_size=4,\n stride=4,\n padding=0,\n total_stride=16,\n activation=nn.GELU,\n ):\n super().__init__()\n num_layers = int(math.log2(total_stride) // math.log2(stride))\n assert stride**num_layers == total_stride\n self.encoder = nn.Sequential()\n mask_in_chans, mask_out_chans = 1, 1\n for _ in range(num_layers):\n mask_out_chans = mask_in_chans * (stride**2)\n self.encoder.append(\n nn.Conv2d(\n mask_in_chans,\n mask_out_chans,\n kernel_size=kernel_size,\n stride=stride,\n padding=padding,\n )\n )\n self.encoder.append(LayerNorm2d(mask_out_chans))\n self.encoder.append(activation())\n mask_in_chans = mask_out_chans\n\n self.encoder.append(nn.Conv2d(mask_out_chans, embed_dim, kernel_size=1))\n\n def forward(self, x):\n return self.encoder(x)\n\n\n# Lightly adapted from ConvNext (https://github.com/facebookresearch/ConvNeXt)\nclass CXBlock(nn.Module):\n r\"\"\"ConvNeXt Block. There are two equivalent implementations:\n (1) DwConv -> LayerNorm (channels_first) -> 1x1 Conv -> GELU -> 1x1 Conv; all in (N, C, H, W)\n (2) DwConv -> Permute to (N, H, W, C); LayerNorm (channels_last) -> Linear -> GELU -> Linear; Permute back\n We use (2) as we find it slightly faster in PyTorch\n\n Args:\n dim (int): Number of input channels.\n drop_path (float): Stochastic depth rate. Default: 0.0\n layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6.\n \"\"\"\n\n def __init__(\n self,\n dim,\n kernel_size=7,\n padding=3,\n drop_path=0.0,\n layer_scale_init_value=1e-6,\n use_dwconv=True,\n ):\n super().__init__()\n self.dwconv = nn.Conv2d(\n dim,\n dim,\n kernel_size=kernel_size,\n padding=padding,\n groups=dim if use_dwconv else 1,\n ) # depthwise conv\n self.norm = LayerNorm2d(dim, eps=1e-6)\n self.pwconv1 = nn.Linear(\n dim, 4 * dim\n ) # pointwise/1x1 convs, implemented with linear layers\n self.act = nn.GELU()\n self.pwconv2 = nn.Linear(4 * dim, dim)\n self.gamma = (\n nn.Parameter(layer_scale_init_value * torch.ones((dim)), requires_grad=True)\n if layer_scale_init_value > 0\n else None\n )\n self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()\n\n def forward(self, x):\n input = x\n x = self.dwconv(x)\n x = self.norm(x)\n x = x.permute(0, 2, 3, 1) # (N, C, H, W) -> (N, H, W, C)\n x = self.pwconv1(x)\n x = self.act(x)\n x = self.pwconv2(x)\n if self.gamma is not None:\n x = self.gamma * x\n x = x.permute(0, 3, 1, 2) # (N, H, W, C) -> (N, C, H, W)\n\n x = input + self.drop_path(x)\n return x\n\n\nclass Fuser(nn.Module):\n def __init__(self, layer, num_layers, dim=None, input_projection=False):\n super().__init__()\n self.proj = nn.Identity()\n self.layers = get_clones(layer, num_layers)\n\n if input_projection:\n assert dim is not None\n self.proj = nn.Conv2d(dim, dim, kernel_size=1)\n\n def forward(self, x):\n # normally x: (N, C, H, W)\n x = self.proj(x)\n for layer in self.layers:\n x = layer(x)\n return x\n\n\nclass MemoryEncoder(nn.Module):\n def __init__(\n self,\n out_dim,\n mask_downsampler,\n fuser,\n position_encoding,\n in_dim=256, # in_dim of pix_feats\n ):\n super().__init__()\n\n self.mask_downsampler = mask_downsampler\n\n self.pix_feat_proj = nn.Conv2d(in_dim, in_dim, kernel_size=1)\n self.fuser = fuser\n self.position_encoding = position_encoding\n self.out_proj = nn.Identity()\n if out_dim != in_dim:\n self.out_proj = nn.Conv2d(in_dim, out_dim, kernel_size=1)\n\n def forward(\n self,\n pix_feat: torch.Tensor,\n masks: torch.Tensor,\n skip_mask_sigmoid: bool = False,\n ) -> Tuple[torch.Tensor, torch.Tensor]:\n ## Process masks\n # sigmoid, so that less domain shift from gt masks which are bool\n if not skip_mask_sigmoid:\n masks = F.sigmoid(masks)\n masks = self.mask_downsampler(masks)\n\n ## Fuse pix_feats and downsampled masks\n # in case the visual features are on CPU, cast them to CUDA\n pix_feat = pix_feat.to(masks.device)\n\n x = self.pix_feat_proj(pix_feat)\n x = x + masks\n x = self.fuser(x)\n x = self.out_proj(x)\n\n pos = self.position_encoding(x).to(x.dtype)\n\n return {\"vision_features\": x, \"vision_pos_enc\": [pos]}\n" }, { "path": "auto-seg/sam2/modeling/position_encoding.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport math\nfrom typing import Any, Optional, Tuple\n\nimport numpy as np\n\nimport torch\nfrom torch import nn\n\n\nclass PositionEmbeddingSine(nn.Module):\n \"\"\"\n This is a more standard version of the position embedding, very similar to the one\n used by the Attention Is All You Need paper, generalized to work on images.\n \"\"\"\n\n def __init__(\n self,\n num_pos_feats,\n temperature: int = 10000,\n normalize: bool = True,\n scale: Optional[float] = None,\n ):\n super().__init__()\n assert num_pos_feats % 2 == 0, \"Expecting even model width\"\n self.num_pos_feats = num_pos_feats // 2\n self.temperature = temperature\n self.normalize = normalize\n if scale is not None and normalize is False:\n raise ValueError(\"normalize should be True if scale is passed\")\n if scale is None:\n scale = 2 * math.pi\n self.scale = scale\n\n self.cache = {}\n\n def _encode_xy(self, x, y):\n # The positions are expected to be normalized\n assert len(x) == len(y) and x.ndim == y.ndim == 1\n x_embed = x * self.scale\n y_embed = y * self.scale\n\n dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)\n dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)\n\n pos_x = x_embed[:, None] / dim_t\n pos_y = y_embed[:, None] / dim_t\n pos_x = torch.stack(\n (pos_x[:, 0::2].sin(), pos_x[:, 1::2].cos()), dim=2\n ).flatten(1)\n pos_y = torch.stack(\n (pos_y[:, 0::2].sin(), pos_y[:, 1::2].cos()), dim=2\n ).flatten(1)\n return pos_x, pos_y\n\n @torch.no_grad()\n def encode_boxes(self, x, y, w, h):\n pos_x, pos_y = self._encode_xy(x, y)\n pos = torch.cat((pos_y, pos_x, h[:, None], w[:, None]), dim=1)\n return pos\n\n encode = encode_boxes # Backwards compatibility\n\n @torch.no_grad()\n def encode_points(self, x, y, labels):\n (bx, nx), (by, ny), (bl, nl) = x.shape, y.shape, labels.shape\n assert bx == by and nx == ny and bx == bl and nx == nl\n pos_x, pos_y = self._encode_xy(x.flatten(), y.flatten())\n pos_x, pos_y = pos_x.reshape(bx, nx, -1), pos_y.reshape(by, ny, -1)\n pos = torch.cat((pos_y, pos_x, labels[:, :, None]), dim=2)\n return pos\n\n @torch.no_grad()\n def forward(self, x: torch.Tensor):\n cache_key = (x.shape[-2], x.shape[-1])\n if cache_key in self.cache:\n return self.cache[cache_key][None].repeat(x.shape[0], 1, 1, 1)\n y_embed = (\n torch.arange(1, x.shape[-2] + 1, dtype=torch.float32, device=x.device)\n .view(1, -1, 1)\n .repeat(x.shape[0], 1, x.shape[-1])\n )\n x_embed = (\n torch.arange(1, x.shape[-1] + 1, dtype=torch.float32, device=x.device)\n .view(1, 1, -1)\n .repeat(x.shape[0], x.shape[-2], 1)\n )\n\n if self.normalize:\n eps = 1e-6\n y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale\n x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale\n\n dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)\n dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)\n\n pos_x = x_embed[:, :, :, None] / dim_t\n pos_y = y_embed[:, :, :, None] / dim_t\n pos_x = torch.stack(\n (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4\n ).flatten(3)\n pos_y = torch.stack(\n (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4\n ).flatten(3)\n pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)\n self.cache[cache_key] = pos[0]\n return pos\n\n\nclass PositionEmbeddingRandom(nn.Module):\n \"\"\"\n Positional encoding using random spatial frequencies.\n \"\"\"\n\n def __init__(self, num_pos_feats: int = 64, scale: Optional[float] = None) -> None:\n super().__init__()\n if scale is None or scale <= 0.0:\n scale = 1.0\n self.register_buffer(\n \"positional_encoding_gaussian_matrix\",\n scale * torch.randn((2, num_pos_feats)),\n )\n\n def _pe_encoding(self, coords: torch.Tensor) -> torch.Tensor:\n \"\"\"Positionally encode points that are normalized to [0,1].\"\"\"\n # assuming coords are in [0, 1]^2 square and have d_1 x ... x d_n x 2 shape\n coords = 2 * coords - 1\n coords = coords @ self.positional_encoding_gaussian_matrix\n coords = 2 * np.pi * coords\n # outputs d_1 x ... x d_n x C shape\n return torch.cat([torch.sin(coords), torch.cos(coords)], dim=-1)\n\n def forward(self, size: Tuple[int, int]) -> torch.Tensor:\n \"\"\"Generate positional encoding for a grid of the specified size.\"\"\"\n h, w = size\n device: Any = self.positional_encoding_gaussian_matrix.device\n grid = torch.ones((h, w), device=device, dtype=torch.float32)\n y_embed = grid.cumsum(dim=0) - 0.5\n x_embed = grid.cumsum(dim=1) - 0.5\n y_embed = y_embed / h\n x_embed = x_embed / w\n\n pe = self._pe_encoding(torch.stack([x_embed, y_embed], dim=-1))\n return pe.permute(2, 0, 1) # C x H x W\n\n def forward_with_coords(\n self, coords_input: torch.Tensor, image_size: Tuple[int, int]\n ) -> torch.Tensor:\n \"\"\"Positionally encode points that are not normalized to [0,1].\"\"\"\n coords = coords_input.clone()\n coords[:, :, 0] = coords[:, :, 0] / image_size[1]\n coords[:, :, 1] = coords[:, :, 1] / image_size[0]\n return self._pe_encoding(coords.to(torch.float)) # B x N x C\n\n\n# Rotary Positional Encoding, adapted from:\n# 1. https://github.com/meta-llama/codellama/blob/main/llama/model.py\n# 2. https://github.com/naver-ai/rope-vit\n# 3. https://github.com/lucidrains/rotary-embedding-torch\n\n\ndef init_t_xy(end_x: int, end_y: int):\n t = torch.arange(end_x * end_y, dtype=torch.float32)\n t_x = (t % end_x).float()\n t_y = torch.div(t, end_x, rounding_mode=\"floor\").float()\n return t_x, t_y\n\n\ndef compute_axial_cis(dim: int, end_x: int, end_y: int, theta: float = 10000.0):\n freqs_x = 1.0 / (theta ** (torch.arange(0, dim, 4)[: (dim // 4)].float() / dim))\n freqs_y = 1.0 / (theta ** (torch.arange(0, dim, 4)[: (dim // 4)].float() / dim))\n\n t_x, t_y = init_t_xy(end_x, end_y)\n freqs_x = torch.outer(t_x, freqs_x)\n freqs_y = torch.outer(t_y, freqs_y)\n freqs_cis_x = torch.polar(torch.ones_like(freqs_x), freqs_x)\n freqs_cis_y = torch.polar(torch.ones_like(freqs_y), freqs_y)\n return torch.cat([freqs_cis_x, freqs_cis_y], dim=-1)\n\n\ndef reshape_for_broadcast(freqs_cis: torch.Tensor, x: torch.Tensor):\n ndim = x.ndim\n assert 0 <= 1 < ndim\n assert freqs_cis.shape == (x.shape[-2], x.shape[-1])\n shape = [d if i >= ndim - 2 else 1 for i, d in enumerate(x.shape)]\n return freqs_cis.view(*shape)\n\n\ndef apply_rotary_enc(\n xq: torch.Tensor,\n xk: torch.Tensor,\n freqs_cis: torch.Tensor,\n repeat_freqs_k: bool = False,\n):\n xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2))\n xk_ = (\n torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2))\n if xk.shape[-2] != 0\n else None\n )\n freqs_cis = reshape_for_broadcast(freqs_cis, xq_)\n xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(3)\n if xk_ is None:\n # no keys to rotate, due to dropout\n return xq_out.type_as(xq).to(xq.device), xk\n # repeat freqs along seq_len dim to match k seq_len\n if repeat_freqs_k:\n r = xk_.shape[-2] // xq_.shape[-2]\n if freqs_cis.is_cuda:\n freqs_cis = freqs_cis.repeat(*([1] * (freqs_cis.ndim - 2)), r, 1)\n else:\n # torch.repeat on complex numbers may not be supported on non-CUDA devices\n # (freqs_cis has 4 dims and we repeat on dim 2) so we use expand + flatten\n freqs_cis = freqs_cis.unsqueeze(2).expand(-1, -1, r, -1, -1).flatten(2, 3)\n xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(3)\n return xq_out.type_as(xq).to(xq.device), xk_out.type_as(xk).to(xk.device)\n" }, { "path": "auto-seg/sam2/modeling/sam/__init__.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n" }, { "path": "auto-seg/sam2/modeling/sam/mask_decoder.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nfrom typing import List, Optional, Tuple, Type\n\nimport torch\nfrom torch import nn\n\nfrom sam2.modeling.sam2_utils import LayerNorm2d, MLP\n\n\nclass MaskDecoder(nn.Module):\n def __init__(\n self,\n *,\n transformer_dim: int,\n transformer: nn.Module,\n num_multimask_outputs: int = 3,\n activation: Type[nn.Module] = nn.GELU,\n iou_head_depth: int = 3,\n iou_head_hidden_dim: int = 256,\n use_high_res_features: bool = False,\n iou_prediction_use_sigmoid=False,\n dynamic_multimask_via_stability=False,\n dynamic_multimask_stability_delta=0.05,\n dynamic_multimask_stability_thresh=0.98,\n pred_obj_scores: bool = False,\n pred_obj_scores_mlp: bool = False,\n use_multimask_token_for_obj_ptr: bool = False,\n ) -> None:\n \"\"\"\n Predicts masks given an image and prompt embeddings, using a\n transformer architecture.\n\n Arguments:\n transformer_dim (int): the channel dimension of the transformer\n transformer (nn.Module): the transformer used to predict masks\n num_multimask_outputs (int): the number of masks to predict\n when disambiguating masks\n activation (nn.Module): the type of activation to use when\n upscaling masks\n iou_head_depth (int): the depth of the MLP used to predict\n mask quality\n iou_head_hidden_dim (int): the hidden dimension of the MLP\n used to predict mask quality\n \"\"\"\n super().__init__()\n self.transformer_dim = transformer_dim\n self.transformer = transformer\n\n self.num_multimask_outputs = num_multimask_outputs\n\n self.iou_token = nn.Embedding(1, transformer_dim)\n self.num_mask_tokens = num_multimask_outputs + 1\n self.mask_tokens = nn.Embedding(self.num_mask_tokens, transformer_dim)\n\n self.pred_obj_scores = pred_obj_scores\n if self.pred_obj_scores:\n self.obj_score_token = nn.Embedding(1, transformer_dim)\n self.use_multimask_token_for_obj_ptr = use_multimask_token_for_obj_ptr\n\n self.output_upscaling = nn.Sequential(\n nn.ConvTranspose2d(\n transformer_dim, transformer_dim // 4, kernel_size=2, stride=2\n ),\n LayerNorm2d(transformer_dim // 4),\n activation(),\n nn.ConvTranspose2d(\n transformer_dim // 4, transformer_dim // 8, kernel_size=2, stride=2\n ),\n activation(),\n )\n self.use_high_res_features = use_high_res_features\n if use_high_res_features:\n self.conv_s0 = nn.Conv2d(\n transformer_dim, transformer_dim // 8, kernel_size=1, stride=1\n )\n self.conv_s1 = nn.Conv2d(\n transformer_dim, transformer_dim // 4, kernel_size=1, stride=1\n )\n\n self.output_hypernetworks_mlps = nn.ModuleList(\n [\n MLP(transformer_dim, transformer_dim, transformer_dim // 8, 3)\n for i in range(self.num_mask_tokens)\n ]\n )\n\n self.iou_prediction_head = MLP(\n transformer_dim,\n iou_head_hidden_dim,\n self.num_mask_tokens,\n iou_head_depth,\n sigmoid_output=iou_prediction_use_sigmoid,\n )\n if self.pred_obj_scores:\n self.pred_obj_score_head = nn.Linear(transformer_dim, 1)\n if pred_obj_scores_mlp:\n self.pred_obj_score_head = MLP(transformer_dim, transformer_dim, 1, 3)\n\n # When outputting a single mask, optionally we can dynamically fall back to the best\n # multimask output token if the single mask output token gives low stability scores.\n self.dynamic_multimask_via_stability = dynamic_multimask_via_stability\n self.dynamic_multimask_stability_delta = dynamic_multimask_stability_delta\n self.dynamic_multimask_stability_thresh = dynamic_multimask_stability_thresh\n\n def forward(\n self,\n image_embeddings: torch.Tensor,\n image_pe: torch.Tensor,\n sparse_prompt_embeddings: torch.Tensor,\n dense_prompt_embeddings: torch.Tensor,\n multimask_output: bool,\n repeat_image: bool,\n high_res_features: Optional[List[torch.Tensor]] = None,\n ) -> Tuple[torch.Tensor, torch.Tensor]:\n \"\"\"\n Predict masks given image and prompt embeddings.\n\n Arguments:\n image_embeddings (torch.Tensor): the embeddings from the image encoder\n image_pe (torch.Tensor): positional encoding with the shape of image_embeddings\n sparse_prompt_embeddings (torch.Tensor): the embeddings of the points and boxes\n dense_prompt_embeddings (torch.Tensor): the embeddings of the mask inputs\n multimask_output (bool): Whether to return multiple masks or a single\n mask.\n\n Returns:\n torch.Tensor: batched predicted masks\n torch.Tensor: batched predictions of mask quality\n torch.Tensor: batched SAM token for mask output\n \"\"\"\n masks, iou_pred, mask_tokens_out, object_score_logits = self.predict_masks(\n image_embeddings=image_embeddings,\n image_pe=image_pe,\n sparse_prompt_embeddings=sparse_prompt_embeddings,\n dense_prompt_embeddings=dense_prompt_embeddings,\n repeat_image=repeat_image,\n high_res_features=high_res_features,\n )\n\n # Select the correct mask or masks for output\n if multimask_output:\n masks = masks[:, 1:, :, :]\n iou_pred = iou_pred[:, 1:]\n elif self.dynamic_multimask_via_stability and not self.training:\n masks, iou_pred = self._dynamic_multimask_via_stability(masks, iou_pred)\n else:\n masks = masks[:, 0:1, :, :]\n iou_pred = iou_pred[:, 0:1]\n\n if multimask_output and self.use_multimask_token_for_obj_ptr:\n sam_tokens_out = mask_tokens_out[:, 1:] # [b, 3, c] shape\n else:\n # Take the mask output token. Here we *always* use the token for single mask output.\n # At test time, even if we track after 1-click (and using multimask_output=True),\n # we still take the single mask token here. The rationale is that we always track\n # after multiple clicks during training, so the past tokens seen during training\n # are always the single mask token (and we'll let it be the object-memory token).\n sam_tokens_out = mask_tokens_out[:, 0:1] # [b, 1, c] shape\n\n # Prepare output\n return masks, iou_pred, sam_tokens_out, object_score_logits\n\n def predict_masks(\n self,\n image_embeddings: torch.Tensor,\n image_pe: torch.Tensor,\n sparse_prompt_embeddings: torch.Tensor,\n dense_prompt_embeddings: torch.Tensor,\n repeat_image: bool,\n high_res_features: Optional[List[torch.Tensor]] = None,\n ) -> Tuple[torch.Tensor, torch.Tensor]:\n \"\"\"Predicts masks. See 'forward' for more details.\"\"\"\n # Concatenate output tokens\n s = 0\n if self.pred_obj_scores:\n output_tokens = torch.cat(\n [\n self.obj_score_token.weight,\n self.iou_token.weight,\n self.mask_tokens.weight,\n ],\n dim=0,\n )\n s = 1\n else:\n output_tokens = torch.cat(\n [self.iou_token.weight, self.mask_tokens.weight], dim=0\n )\n output_tokens = output_tokens.unsqueeze(0).expand(\n sparse_prompt_embeddings.size(0), -1, -1\n )\n tokens = torch.cat((output_tokens, sparse_prompt_embeddings), dim=1)\n\n # Expand per-image data in batch direction to be per-mask\n if repeat_image:\n src = torch.repeat_interleave(image_embeddings, tokens.shape[0], dim=0)\n else:\n assert image_embeddings.shape[0] == tokens.shape[0]\n src = image_embeddings\n src = src + dense_prompt_embeddings\n assert (\n image_pe.size(0) == 1\n ), \"image_pe should have size 1 in batch dim (from `get_dense_pe()`)\"\n pos_src = torch.repeat_interleave(image_pe, tokens.shape[0], dim=0)\n b, c, h, w = src.shape\n\n # Run the transformer\n hs, src = self.transformer(src, pos_src, tokens)\n iou_token_out = hs[:, s, :]\n mask_tokens_out = hs[:, s + 1 : (s + 1 + self.num_mask_tokens), :]\n\n # Upscale mask embeddings and predict masks using the mask tokens\n src = src.transpose(1, 2).view(b, c, h, w)\n if not self.use_high_res_features:\n upscaled_embedding = self.output_upscaling(src)\n else:\n dc1, ln1, act1, dc2, act2 = self.output_upscaling\n feat_s0, feat_s1 = high_res_features\n upscaled_embedding = act1(ln1(dc1(src) + feat_s1))\n upscaled_embedding = act2(dc2(upscaled_embedding) + feat_s0)\n\n hyper_in_list: List[torch.Tensor] = []\n for i in range(self.num_mask_tokens):\n hyper_in_list.append(\n self.output_hypernetworks_mlps[i](mask_tokens_out[:, i, :])\n )\n hyper_in = torch.stack(hyper_in_list, dim=1)\n b, c, h, w = upscaled_embedding.shape\n masks = (hyper_in @ upscaled_embedding.view(b, c, h * w)).view(b, -1, h, w)\n\n # Generate mask quality predictions\n iou_pred = self.iou_prediction_head(iou_token_out)\n if self.pred_obj_scores:\n assert s == 1\n object_score_logits = self.pred_obj_score_head(hs[:, 0, :])\n else:\n # Obj scores logits - default to 10.0, i.e. assuming the object is present, sigmoid(10)=1\n object_score_logits = 10.0 * iou_pred.new_ones(iou_pred.shape[0], 1)\n\n return masks, iou_pred, mask_tokens_out, object_score_logits\n\n def _get_stability_scores(self, mask_logits):\n \"\"\"\n Compute stability scores of the mask logits based on the IoU between upper and\n lower thresholds, similar to https://github.com/fairinternal/onevision/pull/568.\n \"\"\"\n mask_logits = mask_logits.flatten(-2)\n stability_delta = self.dynamic_multimask_stability_delta\n area_i = torch.sum(mask_logits > stability_delta, dim=-1).float()\n area_u = torch.sum(mask_logits > -stability_delta, dim=-1).float()\n stability_scores = torch.where(area_u > 0, area_i / area_u, 1.0)\n return stability_scores\n\n def _dynamic_multimask_via_stability(self, all_mask_logits, all_iou_scores):\n \"\"\"\n When outputting a single mask, if the stability score from the current single-mask\n output (based on output token 0) falls below a threshold, we instead select from\n multi-mask outputs (based on output token 1~3) the mask with the highest predicted\n IoU score. This is intended to ensure a valid mask for both clicking and tracking.\n \"\"\"\n # The best mask from multimask output tokens (1~3)\n multimask_logits = all_mask_logits[:, 1:, :, :]\n multimask_iou_scores = all_iou_scores[:, 1:]\n best_scores_inds = torch.argmax(multimask_iou_scores, dim=-1)\n batch_inds = torch.arange(\n multimask_iou_scores.size(0), device=all_iou_scores.device\n )\n best_multimask_logits = multimask_logits[batch_inds, best_scores_inds]\n best_multimask_logits = best_multimask_logits.unsqueeze(1)\n best_multimask_iou_scores = multimask_iou_scores[batch_inds, best_scores_inds]\n best_multimask_iou_scores = best_multimask_iou_scores.unsqueeze(1)\n\n # The mask from singlemask output token 0 and its stability score\n singlemask_logits = all_mask_logits[:, 0:1, :, :]\n singlemask_iou_scores = all_iou_scores[:, 0:1]\n stability_scores = self._get_stability_scores(singlemask_logits)\n is_stable = stability_scores >= self.dynamic_multimask_stability_thresh\n\n # Dynamically fall back to best multimask output upon low stability scores.\n mask_logits_out = torch.where(\n is_stable[..., None, None].expand_as(singlemask_logits),\n singlemask_logits,\n best_multimask_logits,\n )\n iou_scores_out = torch.where(\n is_stable.expand_as(singlemask_iou_scores),\n singlemask_iou_scores,\n best_multimask_iou_scores,\n )\n return mask_logits_out, iou_scores_out\n" }, { "path": "auto-seg/sam2/modeling/sam/prompt_encoder.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nfrom typing import Optional, Tuple, Type\n\nimport torch\nfrom torch import nn\n\nfrom sam2.modeling.position_encoding import PositionEmbeddingRandom\n\nfrom sam2.modeling.sam2_utils import LayerNorm2d\n\n\nclass PromptEncoder(nn.Module):\n def __init__(\n self,\n embed_dim: int,\n image_embedding_size: Tuple[int, int],\n input_image_size: Tuple[int, int],\n mask_in_chans: int,\n activation: Type[nn.Module] = nn.GELU,\n ) -> None:\n \"\"\"\n Encodes prompts for input to SAM's mask decoder.\n\n Arguments:\n embed_dim (int): The prompts' embedding dimension\n image_embedding_size (tuple(int, int)): The spatial size of the\n image embedding, as (H, W).\n input_image_size (int): The padded size of the image as input\n to the image encoder, as (H, W).\n mask_in_chans (int): The number of hidden channels used for\n encoding input masks.\n activation (nn.Module): The activation to use when encoding\n input masks.\n \"\"\"\n super().__init__()\n self.embed_dim = embed_dim\n self.input_image_size = input_image_size\n self.image_embedding_size = image_embedding_size\n self.pe_layer = PositionEmbeddingRandom(embed_dim // 2)\n\n self.num_point_embeddings: int = 4 # pos/neg point + 2 box corners\n point_embeddings = [\n nn.Embedding(1, embed_dim) for i in range(self.num_point_embeddings)\n ]\n self.point_embeddings = nn.ModuleList(point_embeddings)\n self.not_a_point_embed = nn.Embedding(1, embed_dim)\n\n self.mask_input_size = (\n 4 * image_embedding_size[0],\n 4 * image_embedding_size[1],\n )\n self.mask_downscaling = nn.Sequential(\n nn.Conv2d(1, mask_in_chans // 4, kernel_size=2, stride=2),\n LayerNorm2d(mask_in_chans // 4),\n activation(),\n nn.Conv2d(mask_in_chans // 4, mask_in_chans, kernel_size=2, stride=2),\n LayerNorm2d(mask_in_chans),\n activation(),\n nn.Conv2d(mask_in_chans, embed_dim, kernel_size=1),\n )\n self.no_mask_embed = nn.Embedding(1, embed_dim)\n\n def get_dense_pe(self) -> torch.Tensor:\n \"\"\"\n Returns the positional encoding used to encode point prompts,\n applied to a dense set of points the shape of the image encoding.\n\n Returns:\n torch.Tensor: Positional encoding with shape\n 1x(embed_dim)x(embedding_h)x(embedding_w)\n \"\"\"\n return self.pe_layer(self.image_embedding_size).unsqueeze(0)\n\n def _embed_points(\n self,\n points: torch.Tensor,\n labels: torch.Tensor,\n pad: bool,\n ) -> torch.Tensor:\n \"\"\"Embeds point prompts.\"\"\"\n points = points + 0.5 # Shift to center of pixel\n if pad:\n padding_point = torch.zeros((points.shape[0], 1, 2), device=points.device)\n padding_label = -torch.ones((labels.shape[0], 1), device=labels.device)\n points = torch.cat([points, padding_point], dim=1)\n labels = torch.cat([labels, padding_label], dim=1)\n point_embedding = self.pe_layer.forward_with_coords(\n points, self.input_image_size\n )\n point_embedding[labels == -1] = 0.0\n point_embedding[labels == -1] += self.not_a_point_embed.weight\n point_embedding[labels == 0] += self.point_embeddings[0].weight\n point_embedding[labels == 1] += self.point_embeddings[1].weight\n point_embedding[labels == 2] += self.point_embeddings[2].weight\n point_embedding[labels == 3] += self.point_embeddings[3].weight\n return point_embedding\n\n def _embed_boxes(self, boxes: torch.Tensor) -> torch.Tensor:\n \"\"\"Embeds box prompts.\"\"\"\n boxes = boxes + 0.5 # Shift to center of pixel\n coords = boxes.reshape(-1, 2, 2)\n corner_embedding = self.pe_layer.forward_with_coords(\n coords, self.input_image_size\n )\n corner_embedding[:, 0, :] += self.point_embeddings[2].weight\n corner_embedding[:, 1, :] += self.point_embeddings[3].weight\n return corner_embedding\n\n def _embed_masks(self, masks: torch.Tensor) -> torch.Tensor:\n \"\"\"Embeds mask inputs.\"\"\"\n mask_embedding = self.mask_downscaling(masks)\n return mask_embedding\n\n def _get_batch_size(\n self,\n points: Optional[Tuple[torch.Tensor, torch.Tensor]],\n boxes: Optional[torch.Tensor],\n masks: Optional[torch.Tensor],\n ) -> int:\n \"\"\"\n Gets the batch size of the output given the batch size of the input prompts.\n \"\"\"\n if points is not None:\n return points[0].shape[0]\n elif boxes is not None:\n return boxes.shape[0]\n elif masks is not None:\n return masks.shape[0]\n else:\n return 1\n\n def _get_device(self) -> torch.device:\n return self.point_embeddings[0].weight.device\n\n def forward(\n self,\n points: Optional[Tuple[torch.Tensor, torch.Tensor]],\n boxes: Optional[torch.Tensor],\n masks: Optional[torch.Tensor],\n ) -> Tuple[torch.Tensor, torch.Tensor]:\n \"\"\"\n Embeds different types of prompts, returning both sparse and dense\n embeddings.\n\n Arguments:\n points (tuple(torch.Tensor, torch.Tensor) or none): point coordinates\n and labels to embed.\n boxes (torch.Tensor or none): boxes to embed\n masks (torch.Tensor or none): masks to embed\n\n Returns:\n torch.Tensor: sparse embeddings for the points and boxes, with shape\n BxNx(embed_dim), where N is determined by the number of input points\n and boxes.\n torch.Tensor: dense embeddings for the masks, in the shape\n Bx(embed_dim)x(embed_H)x(embed_W)\n \"\"\"\n bs = self._get_batch_size(points, boxes, masks)\n sparse_embeddings = torch.empty(\n (bs, 0, self.embed_dim), device=self._get_device()\n )\n if points is not None:\n coords, labels = points\n point_embeddings = self._embed_points(coords, labels, pad=(boxes is None))\n sparse_embeddings = torch.cat([sparse_embeddings, point_embeddings], dim=1)\n if boxes is not None:\n box_embeddings = self._embed_boxes(boxes)\n sparse_embeddings = torch.cat([sparse_embeddings, box_embeddings], dim=1)\n\n if masks is not None:\n dense_embeddings = self._embed_masks(masks)\n else:\n dense_embeddings = self.no_mask_embed.weight.reshape(1, -1, 1, 1).expand(\n bs, -1, self.image_embedding_size[0], self.image_embedding_size[1]\n )\n\n return sparse_embeddings, dense_embeddings\n" }, { "path": "auto-seg/sam2/modeling/sam/transformer.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport contextlib\nimport math\nimport warnings\nfrom functools import partial\nfrom typing import Tuple, Type\n\nimport torch\nimport torch.nn.functional as F\nfrom torch import nn, Tensor\n\nfrom sam2.modeling.position_encoding import apply_rotary_enc, compute_axial_cis\nfrom sam2.modeling.sam2_utils import MLP\nfrom sam2.utils.misc import get_sdpa_settings\n\nwarnings.simplefilter(action=\"ignore\", category=FutureWarning)\n# Check whether Flash Attention is available (and use it by default)\nOLD_GPU, USE_FLASH_ATTN, MATH_KERNEL_ON = get_sdpa_settings()\n# A fallback setting to allow all available kernels if Flash Attention fails\nALLOW_ALL_KERNELS = False\n\n\ndef sdp_kernel_context(dropout_p):\n \"\"\"\n Get the context for the attention scaled dot-product kernel. We use Flash Attention\n by default, but fall back to all available kernels if Flash Attention fails.\n \"\"\"\n if ALLOW_ALL_KERNELS:\n return contextlib.nullcontext()\n\n return torch.backends.cuda.sdp_kernel(\n enable_flash=USE_FLASH_ATTN,\n # if Flash attention kernel is off, then math kernel needs to be enabled\n enable_math=(OLD_GPU and dropout_p > 0.0) or MATH_KERNEL_ON,\n enable_mem_efficient=OLD_GPU,\n )\n\n\nclass TwoWayTransformer(nn.Module):\n def __init__(\n self,\n depth: int,\n embedding_dim: int,\n num_heads: int,\n mlp_dim: int,\n activation: Type[nn.Module] = nn.ReLU,\n attention_downsample_rate: int = 2,\n ) -> None:\n \"\"\"\n A transformer decoder that attends to an input image using\n queries whose positional embedding is supplied.\n\n Args:\n depth (int): number of layers in the transformer\n embedding_dim (int): the channel dimension for the input embeddings\n num_heads (int): the number of heads for multihead attention. Must\n divide embedding_dim\n mlp_dim (int): the channel dimension internal to the MLP block\n activation (nn.Module): the activation to use in the MLP block\n \"\"\"\n super().__init__()\n self.depth = depth\n self.embedding_dim = embedding_dim\n self.num_heads = num_heads\n self.mlp_dim = mlp_dim\n self.layers = nn.ModuleList()\n\n for i in range(depth):\n self.layers.append(\n TwoWayAttentionBlock(\n embedding_dim=embedding_dim,\n num_heads=num_heads,\n mlp_dim=mlp_dim,\n activation=activation,\n attention_downsample_rate=attention_downsample_rate,\n skip_first_layer_pe=(i == 0),\n )\n )\n\n self.final_attn_token_to_image = Attention(\n embedding_dim, num_heads, downsample_rate=attention_downsample_rate\n )\n self.norm_final_attn = nn.LayerNorm(embedding_dim)\n\n def forward(\n self,\n image_embedding: Tensor,\n image_pe: Tensor,\n point_embedding: Tensor,\n ) -> Tuple[Tensor, Tensor]:\n \"\"\"\n Args:\n image_embedding (torch.Tensor): image to attend to. Should be shape\n B x embedding_dim x h x w for any h and w.\n image_pe (torch.Tensor): the positional encoding to add to the image. Must\n have the same shape as image_embedding.\n point_embedding (torch.Tensor): the embedding to add to the query points.\n Must have shape B x N_points x embedding_dim for any N_points.\n\n Returns:\n torch.Tensor: the processed point_embedding\n torch.Tensor: the processed image_embedding\n \"\"\"\n # BxCxHxW -> BxHWxC == B x N_image_tokens x C\n bs, c, h, w = image_embedding.shape\n image_embedding = image_embedding.flatten(2).permute(0, 2, 1)\n image_pe = image_pe.flatten(2).permute(0, 2, 1)\n\n # Prepare queries\n queries = point_embedding\n keys = image_embedding\n\n # Apply transformer blocks and final layernorm\n for layer in self.layers:\n queries, keys = layer(\n queries=queries,\n keys=keys,\n query_pe=point_embedding,\n key_pe=image_pe,\n )\n\n # Apply the final attention layer from the points to the image\n q = queries + point_embedding\n k = keys + image_pe\n attn_out = self.final_attn_token_to_image(q=q, k=k, v=keys)\n queries = queries + attn_out\n queries = self.norm_final_attn(queries)\n\n return queries, keys\n\n\nclass TwoWayAttentionBlock(nn.Module):\n def __init__(\n self,\n embedding_dim: int,\n num_heads: int,\n mlp_dim: int = 2048,\n activation: Type[nn.Module] = nn.ReLU,\n attention_downsample_rate: int = 2,\n skip_first_layer_pe: bool = False,\n ) -> None:\n \"\"\"\n A transformer block with four layers: (1) self-attention of sparse\n inputs, (2) cross attention of sparse inputs to dense inputs, (3) mlp\n block on sparse inputs, and (4) cross attention of dense inputs to sparse\n inputs.\n\n Arguments:\n embedding_dim (int): the channel dimension of the embeddings\n num_heads (int): the number of heads in the attention layers\n mlp_dim (int): the hidden dimension of the mlp block\n activation (nn.Module): the activation of the mlp block\n skip_first_layer_pe (bool): skip the PE on the first layer\n \"\"\"\n super().__init__()\n self.self_attn = Attention(embedding_dim, num_heads)\n self.norm1 = nn.LayerNorm(embedding_dim)\n\n self.cross_attn_token_to_image = Attention(\n embedding_dim, num_heads, downsample_rate=attention_downsample_rate\n )\n self.norm2 = nn.LayerNorm(embedding_dim)\n\n self.mlp = MLP(\n embedding_dim, mlp_dim, embedding_dim, num_layers=2, activation=activation\n )\n self.norm3 = nn.LayerNorm(embedding_dim)\n\n self.norm4 = nn.LayerNorm(embedding_dim)\n self.cross_attn_image_to_token = Attention(\n embedding_dim, num_heads, downsample_rate=attention_downsample_rate\n )\n\n self.skip_first_layer_pe = skip_first_layer_pe\n\n def forward(\n self, queries: Tensor, keys: Tensor, query_pe: Tensor, key_pe: Tensor\n ) -> Tuple[Tensor, Tensor]:\n # Self attention block\n if self.skip_first_layer_pe:\n queries = self.self_attn(q=queries, k=queries, v=queries)\n else:\n q = queries + query_pe\n attn_out = self.self_attn(q=q, k=q, v=queries)\n queries = queries + attn_out\n queries = self.norm1(queries)\n\n # Cross attention block, tokens attending to image embedding\n q = queries + query_pe\n k = keys + key_pe\n attn_out = self.cross_attn_token_to_image(q=q, k=k, v=keys)\n queries = queries + attn_out\n queries = self.norm2(queries)\n\n # MLP block\n mlp_out = self.mlp(queries)\n queries = queries + mlp_out\n queries = self.norm3(queries)\n\n # Cross attention block, image embedding attending to tokens\n q = queries + query_pe\n k = keys + key_pe\n attn_out = self.cross_attn_image_to_token(q=k, k=q, v=queries)\n keys = keys + attn_out\n keys = self.norm4(keys)\n\n return queries, keys\n\n\nclass Attention(nn.Module):\n \"\"\"\n An attention layer that allows for downscaling the size of the embedding\n after projection to queries, keys, and values.\n \"\"\"\n\n def __init__(\n self,\n embedding_dim: int,\n num_heads: int,\n downsample_rate: int = 1,\n dropout: float = 0.0,\n kv_in_dim: int = None,\n ) -> None:\n super().__init__()\n self.embedding_dim = embedding_dim\n self.kv_in_dim = kv_in_dim if kv_in_dim is not None else embedding_dim\n self.internal_dim = embedding_dim // downsample_rate\n self.num_heads = num_heads\n assert (\n self.internal_dim % num_heads == 0\n ), \"num_heads must divide embedding_dim.\"\n\n self.q_proj = nn.Linear(embedding_dim, self.internal_dim)\n self.k_proj = nn.Linear(self.kv_in_dim, self.internal_dim)\n self.v_proj = nn.Linear(self.kv_in_dim, self.internal_dim)\n self.out_proj = nn.Linear(self.internal_dim, embedding_dim)\n\n self.dropout_p = dropout\n\n def _separate_heads(self, x: Tensor, num_heads: int) -> Tensor:\n b, n, c = x.shape\n x = x.reshape(b, n, num_heads, c // num_heads)\n return x.transpose(1, 2) # B x N_heads x N_tokens x C_per_head\n\n def _recombine_heads(self, x: Tensor) -> Tensor:\n b, n_heads, n_tokens, c_per_head = x.shape\n x = x.transpose(1, 2)\n return x.reshape(b, n_tokens, n_heads * c_per_head) # B x N_tokens x C\n\n def forward(self, q: Tensor, k: Tensor, v: Tensor) -> Tensor:\n # Input projections\n q = self.q_proj(q)\n k = self.k_proj(k)\n v = self.v_proj(v)\n\n # Separate into heads\n q = self._separate_heads(q, self.num_heads)\n k = self._separate_heads(k, self.num_heads)\n v = self._separate_heads(v, self.num_heads)\n\n dropout_p = self.dropout_p if self.training else 0.0\n # Attention\n try:\n with sdp_kernel_context(dropout_p):\n out = F.scaled_dot_product_attention(q, k, v, dropout_p=dropout_p)\n except Exception as e:\n # Fall back to all kernels if the Flash attention kernel fails\n warnings.warn(\n f\"Flash Attention kernel failed due to: {e}\\nFalling back to all available \"\n f\"kernels for scaled_dot_product_attention (which may have a slower speed).\",\n category=UserWarning,\n stacklevel=2,\n )\n global ALLOW_ALL_KERNELS\n ALLOW_ALL_KERNELS = True\n out = F.scaled_dot_product_attention(q, k, v, dropout_p=dropout_p)\n\n out = self._recombine_heads(out)\n out = self.out_proj(out)\n\n return out\n\n\nclass RoPEAttention(Attention):\n \"\"\"Attention with rotary position encoding.\"\"\"\n\n def __init__(\n self,\n *args,\n rope_theta=10000.0,\n # whether to repeat q rope to match k length\n # this is needed for cross-attention to memories\n rope_k_repeat=False,\n feat_sizes=(32, 32), # [w, h] for stride 16 feats at 512 resolution\n **kwargs,\n ):\n super().__init__(*args, **kwargs)\n\n self.compute_cis = partial(\n compute_axial_cis, dim=self.internal_dim // self.num_heads, theta=rope_theta\n )\n freqs_cis = self.compute_cis(end_x=feat_sizes[0], end_y=feat_sizes[1])\n self.freqs_cis = freqs_cis\n self.rope_k_repeat = rope_k_repeat\n\n def forward(\n self, q: Tensor, k: Tensor, v: Tensor, num_k_exclude_rope: int = 0\n ) -> Tensor:\n # Input projections\n q = self.q_proj(q)\n k = self.k_proj(k)\n v = self.v_proj(v)\n\n # Separate into heads\n q = self._separate_heads(q, self.num_heads)\n k = self._separate_heads(k, self.num_heads)\n v = self._separate_heads(v, self.num_heads)\n\n # Apply rotary position encoding\n w = h = math.sqrt(q.shape[-2])\n self.freqs_cis = self.freqs_cis.to(q.device)\n if self.freqs_cis.shape[0] != q.shape[-2]:\n self.freqs_cis = self.compute_cis(end_x=w, end_y=h).to(q.device)\n if q.shape[-2] != k.shape[-2]:\n assert self.rope_k_repeat\n\n num_k_rope = k.size(-2) - num_k_exclude_rope\n q, k[:, :, :num_k_rope] = apply_rotary_enc(\n q,\n k[:, :, :num_k_rope],\n freqs_cis=self.freqs_cis,\n repeat_freqs_k=self.rope_k_repeat,\n )\n\n dropout_p = self.dropout_p if self.training else 0.0\n # Attention\n try:\n with sdp_kernel_context(dropout_p):\n out = F.scaled_dot_product_attention(q, k, v, dropout_p=dropout_p)\n except Exception as e:\n # Fall back to all kernels if the Flash attention kernel fails\n warnings.warn(\n f\"Flash Attention kernel failed due to: {e}\\nFalling back to all available \"\n f\"kernels for scaled_dot_product_attention (which may have a slower speed).\",\n category=UserWarning,\n stacklevel=2,\n )\n global ALLOW_ALL_KERNELS\n ALLOW_ALL_KERNELS = True\n out = F.scaled_dot_product_attention(q, k, v, dropout_p=dropout_p)\n\n out = self._recombine_heads(out)\n out = self.out_proj(out)\n\n return out\n" }, { "path": "auto-seg/sam2/modeling/sam2_base.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport torch\nimport torch.distributed\nimport torch.nn.functional as F\n\nfrom torch.nn.init import trunc_normal_\n\nfrom sam2.modeling.sam.mask_decoder import MaskDecoder\nfrom sam2.modeling.sam.prompt_encoder import PromptEncoder\nfrom sam2.modeling.sam.transformer import TwoWayTransformer\nfrom sam2.modeling.sam2_utils import get_1d_sine_pe, MLP, select_closest_cond_frames\n\n# a large negative value as a placeholder score for missing objects\nNO_OBJ_SCORE = -1024.0\n\n\nclass SAM2Base(torch.nn.Module):\n def __init__(\n self,\n image_encoder,\n memory_attention,\n memory_encoder,\n num_maskmem=7, # default 1 input frame + 6 previous frames\n image_size=512,\n backbone_stride=16, # stride of the image backbone output\n sigmoid_scale_for_mem_enc=1.0, # scale factor for mask sigmoid prob\n sigmoid_bias_for_mem_enc=0.0, # bias factor for mask sigmoid prob\n # During evaluation, whether to binarize the sigmoid mask logits on interacted frames with clicks\n binarize_mask_from_pts_for_mem_enc=False,\n use_mask_input_as_output_without_sam=False, # on frames with mask input, whether to directly output the input mask without using a SAM prompt encoder + mask decoder\n # The maximum number of conditioning frames to participate in the memory attention (-1 means no limit; if there are more conditioning frames than this limit,\n # we only cross-attend to the temporally closest `max_cond_frames_in_attn` conditioning frames in the encoder when tracking each frame). This gives the model\n # a temporal locality when handling a large number of annotated frames (since closer frames should be more important) and also avoids GPU OOM.\n max_cond_frames_in_attn=-1,\n # on the first frame, whether to directly add the no-memory embedding to the image feature\n # (instead of using the transformer encoder)\n directly_add_no_mem_embed=False,\n # whether to use high-resolution feature maps in the SAM mask decoder\n use_high_res_features_in_sam=False,\n # whether to output multiple (3) masks for the first click on initial conditioning frames\n multimask_output_in_sam=False,\n # the minimum and maximum number of clicks to use multimask_output_in_sam (only relevant when `multimask_output_in_sam=True`;\n # default is 1 for both, meaning that only the first click gives multimask output; also note that a box counts as two points)\n multimask_min_pt_num=1,\n multimask_max_pt_num=1,\n # whether to also use multimask output for tracking (not just for the first click on initial conditioning frames; only relevant when `multimask_output_in_sam=True`)\n multimask_output_for_tracking=False,\n # Whether to use multimask tokens for obj ptr; Only relevant when both\n # use_obj_ptrs_in_encoder=True and multimask_output_for_tracking=True\n use_multimask_token_for_obj_ptr: bool = False,\n # whether to use sigmoid to restrict ious prediction to [0-1]\n iou_prediction_use_sigmoid=False,\n # The memory bank's temporal stride during evaluation (i.e. the `r` parameter in XMem and Cutie; XMem and Cutie use r=5).\n # For r>1, the (self.num_maskmem - 1) non-conditioning memory frames consist of\n # (self.num_maskmem - 2) nearest frames from every r-th frames, plus the last frame.\n memory_temporal_stride_for_eval=1,\n # if `add_all_frames_to_correct_as_cond` is True, we also append to the conditioning frame list any frame that receives a later correction click\n # if `add_all_frames_to_correct_as_cond` is False, we conditioning frame list to only use those initial conditioning frames\n add_all_frames_to_correct_as_cond=False,\n # whether to apply non-overlapping constraints on the object masks in the memory encoder during evaluation (to avoid/alleviate superposing masks)\n non_overlap_masks_for_mem_enc=False,\n # whether to cross-attend to object pointers from other frames (based on SAM output tokens) in the encoder\n use_obj_ptrs_in_encoder=False,\n # the maximum number of object pointers from other frames in encoder cross attention (only relevant when `use_obj_ptrs_in_encoder=True`)\n max_obj_ptrs_in_encoder=16,\n # whether to add temporal positional encoding to the object pointers in the encoder (only relevant when `use_obj_ptrs_in_encoder=True`)\n add_tpos_enc_to_obj_ptrs=True,\n # whether to add an extra linear projection layer for the temporal positional encoding in the object pointers to avoid potential interference\n # with spatial positional encoding (only relevant when both `use_obj_ptrs_in_encoder=True` and `add_tpos_enc_to_obj_ptrs=True`)\n proj_tpos_enc_in_obj_ptrs=False,\n # whether to only attend to object pointers in the past (before the current frame) in the encoder during evaluation\n # (only relevant when `use_obj_ptrs_in_encoder=True`; this might avoid pointer information too far in the future to distract the initial tracking)\n only_obj_ptrs_in_the_past_for_eval=False,\n # Whether to predict if there is an object in the frame\n pred_obj_scores: bool = False,\n # Whether to use an MLP to predict object scores\n pred_obj_scores_mlp: bool = False,\n # Only relevant if pred_obj_scores=True and use_obj_ptrs_in_encoder=True;\n # Whether to have a fixed no obj pointer when there is no object present\n # or to use it as an additive embedding with obj_ptr produced by decoder\n fixed_no_obj_ptr: bool = False,\n # Soft no object, i.e. mix in no_obj_ptr softly,\n # hope to make recovery easier if there is a mistake and mitigate accumulation of errors\n soft_no_obj_ptr: bool = False,\n use_mlp_for_obj_ptr_proj: bool = False,\n # extra arguments used to construct the SAM mask decoder; if not None, it should be a dict of kwargs to be passed into `MaskDecoder` class.\n sam_mask_decoder_extra_args=None,\n compile_image_encoder: bool = False,\n ):\n super().__init__()\n\n # Part 1: the image backbone\n self.image_encoder = image_encoder\n # Use level 0, 1, 2 for high-res setting, or just level 2 for the default setting\n self.use_high_res_features_in_sam = use_high_res_features_in_sam\n self.num_feature_levels = 3 if use_high_res_features_in_sam else 1\n self.use_obj_ptrs_in_encoder = use_obj_ptrs_in_encoder\n self.max_obj_ptrs_in_encoder = max_obj_ptrs_in_encoder\n if use_obj_ptrs_in_encoder:\n # A conv layer to downsample the mask prompt to stride 4 (the same stride as\n # low-res SAM mask logits) and to change its scales from 0~1 to SAM logit scale,\n # so that it can be fed into the SAM mask decoder to generate a pointer.\n self.mask_downsample = torch.nn.Conv2d(1, 1, kernel_size=4, stride=4)\n self.add_tpos_enc_to_obj_ptrs = add_tpos_enc_to_obj_ptrs\n if proj_tpos_enc_in_obj_ptrs:\n assert add_tpos_enc_to_obj_ptrs # these options need to be used together\n self.proj_tpos_enc_in_obj_ptrs = proj_tpos_enc_in_obj_ptrs\n self.only_obj_ptrs_in_the_past_for_eval = only_obj_ptrs_in_the_past_for_eval\n\n # Part 2: memory attention to condition current frame's visual features\n # with memories (and obj ptrs) from past frames\n self.memory_attention = memory_attention\n self.hidden_dim = memory_attention.d_model\n\n # Part 3: memory encoder for the previous frame's outputs\n self.memory_encoder = memory_encoder\n self.mem_dim = self.hidden_dim\n if hasattr(self.memory_encoder, \"out_proj\") and hasattr(\n self.memory_encoder.out_proj, \"weight\"\n ):\n # if there is compression of memories along channel dim\n self.mem_dim = self.memory_encoder.out_proj.weight.shape[0]\n self.num_maskmem = num_maskmem # Number of memories accessible\n # Temporal encoding of the memories\n self.maskmem_tpos_enc = torch.nn.Parameter(\n torch.zeros(num_maskmem, 1, 1, self.mem_dim)\n )\n trunc_normal_(self.maskmem_tpos_enc, std=0.02)\n # a single token to indicate no memory embedding from previous frames\n self.no_mem_embed = torch.nn.Parameter(torch.zeros(1, 1, self.hidden_dim))\n self.no_mem_pos_enc = torch.nn.Parameter(torch.zeros(1, 1, self.hidden_dim))\n trunc_normal_(self.no_mem_embed, std=0.02)\n trunc_normal_(self.no_mem_pos_enc, std=0.02)\n self.directly_add_no_mem_embed = directly_add_no_mem_embed\n # Apply sigmoid to the output raw mask logits (to turn them from\n # range (-inf, +inf) to range (0, 1)) before feeding them into the memory encoder\n self.sigmoid_scale_for_mem_enc = sigmoid_scale_for_mem_enc\n self.sigmoid_bias_for_mem_enc = sigmoid_bias_for_mem_enc\n self.binarize_mask_from_pts_for_mem_enc = binarize_mask_from_pts_for_mem_enc\n self.non_overlap_masks_for_mem_enc = non_overlap_masks_for_mem_enc\n self.memory_temporal_stride_for_eval = memory_temporal_stride_for_eval\n # On frames with mask input, whether to directly output the input mask without\n # using a SAM prompt encoder + mask decoder\n self.use_mask_input_as_output_without_sam = use_mask_input_as_output_without_sam\n self.multimask_output_in_sam = multimask_output_in_sam\n self.multimask_min_pt_num = multimask_min_pt_num\n self.multimask_max_pt_num = multimask_max_pt_num\n self.multimask_output_for_tracking = multimask_output_for_tracking\n self.use_multimask_token_for_obj_ptr = use_multimask_token_for_obj_ptr\n self.iou_prediction_use_sigmoid = iou_prediction_use_sigmoid\n\n # Part 4: SAM-style prompt encoder (for both mask and point inputs)\n # and SAM-style mask decoder for the final mask output\n self.image_size = image_size\n self.backbone_stride = backbone_stride\n self.sam_mask_decoder_extra_args = sam_mask_decoder_extra_args\n self.pred_obj_scores = pred_obj_scores\n self.pred_obj_scores_mlp = pred_obj_scores_mlp\n self.fixed_no_obj_ptr = fixed_no_obj_ptr\n self.soft_no_obj_ptr = soft_no_obj_ptr\n if self.fixed_no_obj_ptr:\n assert self.pred_obj_scores\n assert self.use_obj_ptrs_in_encoder\n if self.pred_obj_scores and self.use_obj_ptrs_in_encoder:\n self.no_obj_ptr = torch.nn.Parameter(torch.zeros(1, self.hidden_dim))\n trunc_normal_(self.no_obj_ptr, std=0.02)\n self.use_mlp_for_obj_ptr_proj = use_mlp_for_obj_ptr_proj\n\n self._build_sam_heads()\n self.add_all_frames_to_correct_as_cond = add_all_frames_to_correct_as_cond\n self.max_cond_frames_in_attn = max_cond_frames_in_attn\n\n # Model compilation\n if compile_image_encoder:\n # Compile the forward function (not the full module) to allow loading checkpoints.\n print(\n \"Image encoder compilation is enabled. First forward pass will be slow.\"\n )\n self.image_encoder.forward = torch.compile(\n self.image_encoder.forward,\n mode=\"max-autotune\",\n fullgraph=True,\n dynamic=False,\n )\n\n @property\n def device(self):\n return next(self.parameters()).device\n\n def forward(self, *args, **kwargs):\n raise NotImplementedError(\n \"Please use the corresponding methods in SAM2VideoPredictor for inference.\"\n \"See notebooks/video_predictor_example.ipynb for an example.\"\n )\n\n def _build_sam_heads(self):\n \"\"\"Build SAM-style prompt encoder and mask decoder.\"\"\"\n self.sam_prompt_embed_dim = self.hidden_dim\n self.sam_image_embedding_size = self.image_size // self.backbone_stride\n\n # build PromptEncoder and MaskDecoder from SAM\n # (their hyperparameters like `mask_in_chans=16` are from SAM code)\n self.sam_prompt_encoder = PromptEncoder(\n embed_dim=self.sam_prompt_embed_dim,\n image_embedding_size=(\n self.sam_image_embedding_size,\n self.sam_image_embedding_size,\n ),\n input_image_size=(self.image_size, self.image_size),\n mask_in_chans=16,\n )\n self.sam_mask_decoder = MaskDecoder(\n num_multimask_outputs=3,\n transformer=TwoWayTransformer(\n depth=2,\n embedding_dim=self.sam_prompt_embed_dim,\n mlp_dim=2048,\n num_heads=8,\n ),\n transformer_dim=self.sam_prompt_embed_dim,\n iou_head_depth=3,\n iou_head_hidden_dim=256,\n use_high_res_features=self.use_high_res_features_in_sam,\n iou_prediction_use_sigmoid=self.iou_prediction_use_sigmoid,\n pred_obj_scores=self.pred_obj_scores,\n pred_obj_scores_mlp=self.pred_obj_scores_mlp,\n use_multimask_token_for_obj_ptr=self.use_multimask_token_for_obj_ptr,\n **(self.sam_mask_decoder_extra_args or {}),\n )\n if self.use_obj_ptrs_in_encoder:\n # a linear projection on SAM output tokens to turn them into object pointers\n self.obj_ptr_proj = torch.nn.Linear(self.hidden_dim, self.hidden_dim)\n if self.use_mlp_for_obj_ptr_proj:\n self.obj_ptr_proj = MLP(\n self.hidden_dim, self.hidden_dim, self.hidden_dim, 3\n )\n else:\n self.obj_ptr_proj = torch.nn.Identity()\n if self.proj_tpos_enc_in_obj_ptrs:\n # a linear projection on temporal positional encoding in object pointers to\n # avoid potential interference with spatial positional encoding\n self.obj_ptr_tpos_proj = torch.nn.Linear(self.hidden_dim, self.mem_dim)\n else:\n self.obj_ptr_tpos_proj = torch.nn.Identity()\n\n def _forward_sam_heads(\n self,\n backbone_features,\n point_inputs=None,\n mask_inputs=None,\n high_res_features=None,\n multimask_output=False,\n ):\n \"\"\"\n Forward SAM prompt encoders and mask heads.\n\n Inputs:\n - backbone_features: image features of [B, C, H, W] shape\n - point_inputs: a dictionary with \"point_coords\" and \"point_labels\", where\n 1) \"point_coords\" has [B, P, 2] shape and float32 dtype and contains the\n absolute pixel-unit coordinate in (x, y) format of the P input points\n 2) \"point_labels\" has shape [B, P] and int32 dtype, where 1 means\n positive clicks, 0 means negative clicks, and -1 means padding\n - mask_inputs: a mask of [B, 1, H*16, W*16] shape, float or bool, with the\n same spatial size as the image.\n - high_res_features: either 1) None or 2) or a list of length 2 containing\n two feature maps of [B, C, 4*H, 4*W] and [B, C, 2*H, 2*W] shapes respectively,\n which will be used as high-resolution feature maps for SAM decoder.\n - multimask_output: if it's True, we output 3 candidate masks and their 3\n corresponding IoU estimates, and if it's False, we output only 1 mask and\n its corresponding IoU estimate.\n\n Outputs:\n - low_res_multimasks: [B, M, H*4, W*4] shape (where M = 3 if\n `multimask_output=True` and M = 1 if `multimask_output=False`), the SAM\n output mask logits (before sigmoid) for the low-resolution masks, with 4x\n the resolution (1/4 stride) of the input backbone_features.\n - high_res_multimasks: [B, M, H*16, W*16] shape (where M = 3\n if `multimask_output=True` and M = 1 if `multimask_output=False`),\n upsampled from the low-resolution masks, with shape size as the image\n (stride is 1 pixel).\n - ious, [B, M] shape, where (where M = 3 if `multimask_output=True` and M = 1\n if `multimask_output=False`), the estimated IoU of each output mask.\n - low_res_masks: [B, 1, H*4, W*4] shape, the best mask in `low_res_multimasks`.\n If `multimask_output=True`, it's the mask with the highest IoU estimate.\n If `multimask_output=False`, it's the same as `low_res_multimasks`.\n - high_res_masks: [B, 1, H*16, W*16] shape, the best mask in `high_res_multimasks`.\n If `multimask_output=True`, it's the mask with the highest IoU estimate.\n If `multimask_output=False`, it's the same as `high_res_multimasks`.\n - obj_ptr: [B, C] shape, the object pointer vector for the output mask, extracted\n based on the output token from the SAM mask decoder.\n \"\"\"\n B = backbone_features.size(0)\n device = backbone_features.device\n assert backbone_features.size(1) == self.sam_prompt_embed_dim\n assert backbone_features.size(2) == self.sam_image_embedding_size\n assert backbone_features.size(3) == self.sam_image_embedding_size\n\n # a) Handle point prompts\n if point_inputs is not None:\n sam_point_coords = point_inputs[\"point_coords\"]\n sam_point_labels = point_inputs[\"point_labels\"]\n assert sam_point_coords.size(0) == B and sam_point_labels.size(0) == B\n else:\n # If no points are provide, pad with an empty point (with label -1)\n sam_point_coords = torch.zeros(B, 1, 2, device=device)\n sam_point_labels = -torch.ones(B, 1, dtype=torch.int32, device=device)\n\n # b) Handle mask prompts\n if mask_inputs is not None:\n # If mask_inputs is provided, downsize it into low-res mask input if needed\n # and feed it as a dense mask prompt into the SAM mask encoder\n assert len(mask_inputs.shape) == 4 and mask_inputs.shape[:2] == (B, 1)\n if mask_inputs.shape[-2:] != self.sam_prompt_encoder.mask_input_size:\n sam_mask_prompt = F.interpolate(\n mask_inputs.float(),\n size=self.sam_prompt_encoder.mask_input_size,\n align_corners=False,\n mode=\"bilinear\",\n antialias=True, # use antialias for downsampling\n )\n else:\n sam_mask_prompt = mask_inputs\n else:\n # Otherwise, simply feed None (and SAM's prompt encoder will add\n # a learned `no_mask_embed` to indicate no mask input in this case).\n sam_mask_prompt = None\n\n sparse_embeddings, dense_embeddings = self.sam_prompt_encoder(\n points=(sam_point_coords, sam_point_labels),\n boxes=None,\n masks=sam_mask_prompt,\n )\n (\n low_res_multimasks,\n ious,\n sam_output_tokens,\n object_score_logits,\n ) = self.sam_mask_decoder(\n image_embeddings=backbone_features,\n image_pe=self.sam_prompt_encoder.get_dense_pe(),\n sparse_prompt_embeddings=sparse_embeddings,\n dense_prompt_embeddings=dense_embeddings,\n multimask_output=multimask_output,\n repeat_image=False, # the image is already batched\n high_res_features=high_res_features,\n )\n if self.pred_obj_scores:\n is_obj_appearing = object_score_logits > 0\n\n # Mask used for spatial memories is always a *hard* choice between obj and no obj,\n # consistent with the actual mask prediction\n low_res_multimasks = torch.where(\n is_obj_appearing[:, None, None],\n low_res_multimasks,\n NO_OBJ_SCORE,\n )\n\n # convert masks from possibly bfloat16 (or float16) to float32\n # (older PyTorch versions before 2.1 don't support `interpolate` on bf16)\n low_res_multimasks = low_res_multimasks.float()\n high_res_multimasks = F.interpolate(\n low_res_multimasks,\n size=(self.image_size, self.image_size),\n mode=\"bilinear\",\n align_corners=False,\n )\n\n sam_output_token = sam_output_tokens[:, 0]\n if multimask_output:\n # take the best mask prediction (with the highest IoU estimation)\n best_iou_inds = torch.argmax(ious, dim=-1)\n batch_inds = torch.arange(B, device=device)\n low_res_masks = low_res_multimasks[batch_inds, best_iou_inds].unsqueeze(1)\n high_res_masks = high_res_multimasks[batch_inds, best_iou_inds].unsqueeze(1)\n if sam_output_tokens.size(1) > 1:\n sam_output_token = sam_output_tokens[batch_inds, best_iou_inds]\n else:\n low_res_masks, high_res_masks = low_res_multimasks, high_res_multimasks\n\n # Extract object pointer from the SAM output token (with occlusion handling)\n obj_ptr = self.obj_ptr_proj(sam_output_token)\n if self.pred_obj_scores:\n # Allow *soft* no obj ptr, unlike for masks\n if self.soft_no_obj_ptr:\n # Only hard possible with gt\n assert not self.teacher_force_obj_scores_for_mem\n lambda_is_obj_appearing = object_score_logits.sigmoid()\n else:\n lambda_is_obj_appearing = is_obj_appearing.float()\n\n if self.fixed_no_obj_ptr:\n obj_ptr = lambda_is_obj_appearing * obj_ptr\n obj_ptr = obj_ptr + (1 - lambda_is_obj_appearing) * self.no_obj_ptr\n\n return (\n low_res_multimasks,\n high_res_multimasks,\n ious,\n low_res_masks,\n high_res_masks,\n obj_ptr,\n object_score_logits,\n )\n\n def _use_mask_as_output(self, backbone_features, high_res_features, mask_inputs):\n \"\"\"\n Directly turn binary `mask_inputs` into a output mask logits without using SAM.\n (same input and output shapes as in _forward_sam_heads above).\n \"\"\"\n # Use -10/+10 as logits for neg/pos pixels (very close to 0/1 in prob after sigmoid).\n out_scale, out_bias = 20.0, -10.0 # sigmoid(-10.0)=4.5398e-05\n mask_inputs_float = mask_inputs.float()\n high_res_masks = mask_inputs_float * out_scale + out_bias\n low_res_masks = F.interpolate(\n high_res_masks,\n size=(high_res_masks.size(-2) // 4, high_res_masks.size(-1) // 4),\n align_corners=False,\n mode=\"bilinear\",\n antialias=True, # use antialias for downsampling\n )\n # a dummy IoU prediction of all 1's under mask input\n ious = mask_inputs.new_ones(mask_inputs.size(0), 1).float()\n if not self.use_obj_ptrs_in_encoder:\n # all zeros as a dummy object pointer (of shape [B, C])\n obj_ptr = torch.zeros(\n mask_inputs.size(0), self.hidden_dim, device=mask_inputs.device\n )\n else:\n # produce an object pointer using the SAM decoder from the mask input\n _, _, _, _, _, obj_ptr, _ = self._forward_sam_heads(\n backbone_features=backbone_features,\n mask_inputs=self.mask_downsample(mask_inputs_float),\n high_res_features=high_res_features,\n )\n # In this method, we are treating mask_input as output, e.g. using it directly to create spatial mem;\n # Below, we follow the same design axiom to use mask_input to decide if obj appears or not instead of relying\n # on the object_scores from the SAM decoder.\n is_obj_appearing = torch.any(mask_inputs.flatten(1).float() > 0.0, dim=1)\n is_obj_appearing = is_obj_appearing[..., None]\n lambda_is_obj_appearing = is_obj_appearing.float()\n object_score_logits = out_scale * lambda_is_obj_appearing + out_bias\n if self.pred_obj_scores:\n if self.fixed_no_obj_ptr:\n obj_ptr = lambda_is_obj_appearing * obj_ptr\n obj_ptr = obj_ptr + (1 - lambda_is_obj_appearing) * self.no_obj_ptr\n\n return (\n low_res_masks,\n high_res_masks,\n ious,\n low_res_masks,\n high_res_masks,\n obj_ptr,\n object_score_logits,\n )\n\n def forward_image(self, img_batch: torch.Tensor):\n \"\"\"Get the image feature on the input batch.\"\"\"\n backbone_out = self.image_encoder(img_batch)\n if self.use_high_res_features_in_sam:\n # precompute projected level 0 and level 1 features in SAM decoder\n # to avoid running it again on every SAM click\n backbone_out[\"backbone_fpn\"][0] = self.sam_mask_decoder.conv_s0(\n backbone_out[\"backbone_fpn\"][0]\n )\n backbone_out[\"backbone_fpn\"][1] = self.sam_mask_decoder.conv_s1(\n backbone_out[\"backbone_fpn\"][1]\n )\n return backbone_out\n\n def _prepare_backbone_features(self, backbone_out):\n \"\"\"Prepare and flatten visual features.\"\"\"\n backbone_out = backbone_out.copy()\n assert len(backbone_out[\"backbone_fpn\"]) == len(backbone_out[\"vision_pos_enc\"])\n assert len(backbone_out[\"backbone_fpn\"]) >= self.num_feature_levels\n\n feature_maps = backbone_out[\"backbone_fpn\"][-self.num_feature_levels :]\n vision_pos_embeds = backbone_out[\"vision_pos_enc\"][-self.num_feature_levels :]\n\n feat_sizes = [(x.shape[-2], x.shape[-1]) for x in vision_pos_embeds]\n # flatten NxCxHxW to HWxNxC\n vision_feats = [x.flatten(2).permute(2, 0, 1) for x in feature_maps]\n vision_pos_embeds = [x.flatten(2).permute(2, 0, 1) for x in vision_pos_embeds]\n\n return backbone_out, vision_feats, vision_pos_embeds, feat_sizes\n\n def _prepare_memory_conditioned_features(\n self,\n frame_idx,\n is_init_cond_frame,\n current_vision_feats,\n current_vision_pos_embeds,\n feat_sizes,\n output_dict,\n num_frames,\n track_in_reverse=False, # tracking in reverse time order (for demo usage)\n ):\n \"\"\"Fuse the current frame's visual feature map with previous memory.\"\"\"\n B = current_vision_feats[-1].size(1) # batch size on this frame\n C = self.hidden_dim\n H, W = feat_sizes[-1] # top-level (lowest-resolution) feature size\n device = current_vision_feats[-1].device\n # The case of `self.num_maskmem == 0` below is primarily used for reproducing SAM on images.\n # In this case, we skip the fusion with any memory.\n if self.num_maskmem == 0: # Disable memory and skip fusion\n pix_feat = current_vision_feats[-1].permute(1, 2, 0).view(B, C, H, W)\n return pix_feat\n\n num_obj_ptr_tokens = 0\n # Step 1: condition the visual features of the current frame on previous memories\n if not is_init_cond_frame:\n # Retrieve the memories encoded with the maskmem backbone\n to_cat_memory, to_cat_memory_pos_embed = [], []\n # Add conditioning frames's output first (all cond frames have t_pos=0 for\n # when getting temporal positional embedding below)\n assert len(output_dict[\"cond_frame_outputs\"]) > 0\n # Select a maximum number of temporally closest cond frames for cross attention\n cond_outputs = output_dict[\"cond_frame_outputs\"]\n selected_cond_outputs, unselected_cond_outputs = select_closest_cond_frames(\n frame_idx, cond_outputs, self.max_cond_frames_in_attn\n )\n t_pos_and_prevs = [(0, out) for out in selected_cond_outputs.values()]\n # Add last (self.num_maskmem - 1) frames before current frame for non-conditioning memory\n # the earliest one has t_pos=1 and the latest one has t_pos=self.num_maskmem-1\n # We also allow taking the memory frame non-consecutively (with r>1), in which case\n # we take (self.num_maskmem - 2) frames among every r-th frames plus the last frame.\n r = self.memory_temporal_stride_for_eval\n for t_pos in range(1, self.num_maskmem):\n t_rel = self.num_maskmem - t_pos # how many frames before current frame\n if t_rel == 1:\n # for t_rel == 1, we take the last frame (regardless of r)\n if not track_in_reverse:\n # the frame immediately before this frame (i.e. frame_idx - 1)\n prev_frame_idx = frame_idx - t_rel\n else:\n # the frame immediately after this frame (i.e. frame_idx + 1)\n prev_frame_idx = frame_idx + t_rel\n else:\n # for t_rel >= 2, we take the memory frame from every r-th frames\n if not track_in_reverse:\n # first find the nearest frame among every r-th frames before this frame\n # for r=1, this would be (frame_idx - 2)\n prev_frame_idx = ((frame_idx - 2) // r) * r\n # then seek further among every r-th frames\n prev_frame_idx = prev_frame_idx - (t_rel - 2) * r\n else:\n # first find the nearest frame among every r-th frames after this frame\n # for r=1, this would be (frame_idx + 2)\n prev_frame_idx = -(-(frame_idx + 2) // r) * r\n # then seek further among every r-th frames\n prev_frame_idx = prev_frame_idx + (t_rel - 2) * r\n out = output_dict[\"non_cond_frame_outputs\"].get(prev_frame_idx, None)\n if out is None:\n # If an unselected conditioning frame is among the last (self.num_maskmem - 1)\n # frames, we still attend to it as if it's a non-conditioning frame.\n out = unselected_cond_outputs.get(prev_frame_idx, None)\n t_pos_and_prevs.append((t_pos, out))\n\n for t_pos, prev in t_pos_and_prevs:\n if prev is None:\n continue # skip padding frames\n # \"maskmem_features\" might have been offloaded to CPU in demo use cases,\n # so we load it back to GPU (it's a no-op if it's already on GPU).\n feats = prev[\"maskmem_features\"].to(device, non_blocking=True)\n to_cat_memory.append(feats.flatten(2).permute(2, 0, 1))\n # Spatial positional encoding (it might have been offloaded to CPU in eval)\n maskmem_enc = prev[\"maskmem_pos_enc\"][-1].to(device)\n maskmem_enc = maskmem_enc.flatten(2).permute(2, 0, 1)\n # Temporal positional encoding\n maskmem_enc = (\n maskmem_enc + self.maskmem_tpos_enc[self.num_maskmem - t_pos - 1]\n )\n to_cat_memory_pos_embed.append(maskmem_enc)\n\n # Construct the list of past object pointers\n if self.use_obj_ptrs_in_encoder:\n max_obj_ptrs_in_encoder = min(num_frames, self.max_obj_ptrs_in_encoder)\n # First add those object pointers from selected conditioning frames\n # (optionally, only include object pointers in the past during evaluation)\n if not self.training and self.only_obj_ptrs_in_the_past_for_eval:\n ptr_cond_outputs = {\n t: out\n for t, out in selected_cond_outputs.items()\n if (t >= frame_idx if track_in_reverse else t <= frame_idx)\n }\n else:\n ptr_cond_outputs = selected_cond_outputs\n pos_and_ptrs = [\n # Temporal pos encoding contains how far away each pointer is from current frame\n (abs(frame_idx - t), out[\"obj_ptr\"])\n for t, out in ptr_cond_outputs.items()\n ]\n # Add up to (max_obj_ptrs_in_encoder - 1) non-conditioning frames before current frame\n for t_diff in range(1, max_obj_ptrs_in_encoder):\n t = frame_idx + t_diff if track_in_reverse else frame_idx - t_diff\n if t < 0 or (num_frames is not None and t >= num_frames):\n break\n out = output_dict[\"non_cond_frame_outputs\"].get(\n t, unselected_cond_outputs.get(t, None)\n )\n if out is not None:\n pos_and_ptrs.append((t_diff, out[\"obj_ptr\"]))\n # If we have at least one object pointer, add them to the across attention\n if len(pos_and_ptrs) > 0:\n pos_list, ptrs_list = zip(*pos_and_ptrs)\n # stack object pointers along dim=0 into [ptr_seq_len, B, C] shape\n obj_ptrs = torch.stack(ptrs_list, dim=0)\n # a temporal positional embedding based on how far each object pointer is from\n # the current frame (sine embedding normalized by the max pointer num).\n if self.add_tpos_enc_to_obj_ptrs:\n t_diff_max = max_obj_ptrs_in_encoder - 1\n tpos_dim = C if self.proj_tpos_enc_in_obj_ptrs else self.mem_dim\n obj_pos = torch.tensor(pos_list, device=device)\n obj_pos = get_1d_sine_pe(obj_pos / t_diff_max, dim=tpos_dim)\n obj_pos = self.obj_ptr_tpos_proj(obj_pos)\n obj_pos = obj_pos.unsqueeze(1).expand(-1, B, self.mem_dim)\n else:\n obj_pos = obj_ptrs.new_zeros(len(pos_list), B, self.mem_dim)\n if self.mem_dim < C:\n # split a pointer into (C // self.mem_dim) tokens for self.mem_dim < C\n obj_ptrs = obj_ptrs.reshape(\n -1, B, C // self.mem_dim, self.mem_dim\n )\n obj_ptrs = obj_ptrs.permute(0, 2, 1, 3).flatten(0, 1)\n obj_pos = obj_pos.repeat_interleave(C // self.mem_dim, dim=0)\n to_cat_memory.append(obj_ptrs)\n to_cat_memory_pos_embed.append(obj_pos)\n num_obj_ptr_tokens = obj_ptrs.shape[0]\n else:\n num_obj_ptr_tokens = 0\n else:\n # for initial conditioning frames, encode them without using any previous memory\n if self.directly_add_no_mem_embed:\n # directly add no-mem embedding (instead of using the transformer encoder)\n pix_feat_with_mem = current_vision_feats[-1] + self.no_mem_embed\n pix_feat_with_mem = pix_feat_with_mem.permute(1, 2, 0).view(B, C, H, W)\n return pix_feat_with_mem\n\n # Use a dummy token on the first frame (to avoid empty memory input to tranformer encoder)\n to_cat_memory = [self.no_mem_embed.expand(1, B, self.mem_dim)]\n to_cat_memory_pos_embed = [self.no_mem_pos_enc.expand(1, B, self.mem_dim)]\n\n # Step 2: Concatenate the memories and forward through the transformer encoder\n memory = torch.cat(to_cat_memory, dim=0)\n memory_pos_embed = torch.cat(to_cat_memory_pos_embed, dim=0)\n\n pix_feat_with_mem = self.memory_attention(\n curr=current_vision_feats,\n curr_pos=current_vision_pos_embeds,\n memory=memory,\n memory_pos=memory_pos_embed,\n num_obj_ptr_tokens=num_obj_ptr_tokens,\n )\n # reshape the output (HW)BC => BCHW\n pix_feat_with_mem = pix_feat_with_mem.permute(1, 2, 0).view(B, C, H, W)\n return pix_feat_with_mem\n\n def _encode_new_memory(\n self,\n current_vision_feats,\n feat_sizes,\n pred_masks_high_res,\n is_mask_from_pts,\n ):\n \"\"\"Encode the current image and its prediction into a memory feature.\"\"\"\n B = current_vision_feats[-1].size(1) # batch size on this frame\n C = self.hidden_dim\n H, W = feat_sizes[-1] # top-level (lowest-resolution) feature size\n # top-level feature, (HW)BC => BCHW\n pix_feat = current_vision_feats[-1].permute(1, 2, 0).view(B, C, H, W)\n if self.non_overlap_masks_for_mem_enc and not self.training:\n # optionally, apply non-overlapping constraints to the masks (it's applied\n # in the batch dimension and should only be used during eval, where all\n # the objects come from the same video under batch size 1).\n pred_masks_high_res = self._apply_non_overlapping_constraints(\n pred_masks_high_res\n )\n # scale the raw mask logits with a temperature before applying sigmoid\n binarize = self.binarize_mask_from_pts_for_mem_enc and is_mask_from_pts\n if binarize and not self.training:\n mask_for_mem = (pred_masks_high_res > 0).float()\n else:\n # apply sigmoid on the raw mask logits to turn them into range (0, 1)\n mask_for_mem = torch.sigmoid(pred_masks_high_res)\n # apply scale and bias terms to the sigmoid probabilities\n if self.sigmoid_scale_for_mem_enc != 1.0:\n mask_for_mem = mask_for_mem * self.sigmoid_scale_for_mem_enc\n if self.sigmoid_bias_for_mem_enc != 0.0:\n mask_for_mem = mask_for_mem + self.sigmoid_bias_for_mem_enc\n maskmem_out = self.memory_encoder(\n pix_feat, mask_for_mem, skip_mask_sigmoid=True # sigmoid already applied\n )\n maskmem_features = maskmem_out[\"vision_features\"]\n maskmem_pos_enc = maskmem_out[\"vision_pos_enc\"]\n\n return maskmem_features, maskmem_pos_enc\n\n def track_step(\n self,\n frame_idx,\n is_init_cond_frame,\n current_vision_feats,\n current_vision_pos_embeds,\n feat_sizes,\n point_inputs,\n mask_inputs,\n output_dict,\n num_frames,\n track_in_reverse=False, # tracking in reverse time order (for demo usage)\n # Whether to run the memory encoder on the predicted masks. Sometimes we might want\n # to skip the memory encoder with `run_mem_encoder=False`. For example,\n # in demo we might call `track_step` multiple times for each user click,\n # and only encode the memory when the user finalizes their clicks. And in ablation\n # settings like SAM training on static images, we don't need the memory encoder.\n run_mem_encoder=True,\n # The previously predicted SAM mask logits (which can be fed together with new clicks in demo).\n prev_sam_mask_logits=None,\n ):\n current_out = {\"point_inputs\": point_inputs, \"mask_inputs\": mask_inputs}\n # High-resolution feature maps for the SAM head, reshape (HW)BC => BCHW\n if len(current_vision_feats) > 1:\n high_res_features = [\n x.permute(1, 2, 0).view(x.size(1), x.size(2), *s)\n for x, s in zip(current_vision_feats[:-1], feat_sizes[:-1])\n ]\n else:\n high_res_features = None\n if mask_inputs is not None and self.use_mask_input_as_output_without_sam:\n # When use_mask_input_as_output_without_sam=True, we directly output the mask input\n # (see it as a GT mask) without using a SAM prompt encoder + mask decoder.\n pix_feat = current_vision_feats[-1].permute(1, 2, 0)\n pix_feat = pix_feat.view(-1, self.hidden_dim, *feat_sizes[-1])\n sam_outputs = self._use_mask_as_output(\n pix_feat, high_res_features, mask_inputs\n )\n else:\n # fused the visual feature with previous memory features in the memory bank\n pix_feat_with_mem = self._prepare_memory_conditioned_features(\n frame_idx=frame_idx,\n is_init_cond_frame=is_init_cond_frame,\n current_vision_feats=current_vision_feats[-1:],\n current_vision_pos_embeds=current_vision_pos_embeds[-1:],\n feat_sizes=feat_sizes[-1:],\n output_dict=output_dict,\n num_frames=num_frames,\n track_in_reverse=track_in_reverse,\n )\n # apply SAM-style segmentation head\n # here we might feed previously predicted low-res SAM mask logits into the SAM mask decoder,\n # e.g. in demo where such logits come from earlier interaction instead of correction sampling\n # (in this case, any `mask_inputs` shouldn't reach here as they are sent to _use_mask_as_output instead)\n if prev_sam_mask_logits is not None:\n assert point_inputs is not None and mask_inputs is None\n mask_inputs = prev_sam_mask_logits\n multimask_output = self._use_multimask(is_init_cond_frame, point_inputs)\n sam_outputs = self._forward_sam_heads(\n backbone_features=pix_feat_with_mem,\n point_inputs=point_inputs,\n mask_inputs=mask_inputs,\n high_res_features=high_res_features,\n multimask_output=multimask_output,\n )\n (\n _,\n _,\n _,\n low_res_masks,\n high_res_masks,\n obj_ptr,\n _,\n ) = sam_outputs\n\n current_out[\"pred_masks\"] = low_res_masks\n current_out[\"pred_masks_high_res\"] = high_res_masks\n current_out[\"obj_ptr\"] = obj_ptr\n\n # Finally run the memory encoder on the predicted mask to encode\n # it into a new memory feature (that can be used in future frames)\n if run_mem_encoder and self.num_maskmem > 0:\n high_res_masks_for_mem_enc = high_res_masks\n maskmem_features, maskmem_pos_enc = self._encode_new_memory(\n current_vision_feats=current_vision_feats,\n feat_sizes=feat_sizes,\n pred_masks_high_res=high_res_masks_for_mem_enc,\n is_mask_from_pts=(point_inputs is not None),\n )\n current_out[\"maskmem_features\"] = maskmem_features\n current_out[\"maskmem_pos_enc\"] = maskmem_pos_enc\n else:\n current_out[\"maskmem_features\"] = None\n current_out[\"maskmem_pos_enc\"] = None\n\n return current_out\n\n def _use_multimask(self, is_init_cond_frame, point_inputs):\n \"\"\"Whether to use multimask output in the SAM head.\"\"\"\n num_pts = 0 if point_inputs is None else point_inputs[\"point_labels\"].size(1)\n multimask_output = (\n self.multimask_output_in_sam\n and (is_init_cond_frame or self.multimask_output_for_tracking)\n and (self.multimask_min_pt_num <= num_pts <= self.multimask_max_pt_num)\n )\n return multimask_output\n\n def _apply_non_overlapping_constraints(self, pred_masks):\n \"\"\"\n Apply non-overlapping constraints to the object scores in pred_masks. Here we\n keep only the highest scoring object at each spatial location in pred_masks.\n \"\"\"\n batch_size = pred_masks.size(0)\n if batch_size == 1:\n return pred_masks\n\n device = pred_masks.device\n # \"max_obj_inds\": object index of the object with the highest score at each location\n max_obj_inds = torch.argmax(pred_masks, dim=0, keepdim=True)\n # \"batch_obj_inds\": object index of each object slice (along dim 0) in `pred_masks`\n batch_obj_inds = torch.arange(batch_size, device=device)[:, None, None, None]\n keep = max_obj_inds == batch_obj_inds\n # suppress overlapping regions' scores below -10.0 so that the foreground regions\n # don't overlap (here sigmoid(-10.0)=4.5398e-05)\n pred_masks = torch.where(keep, pred_masks, torch.clamp(pred_masks, max=-10.0))\n return pred_masks\n" }, { "path": "auto-seg/sam2/modeling/sam2_utils.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\n\nimport copy\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\ndef select_closest_cond_frames(frame_idx, cond_frame_outputs, max_cond_frame_num):\n \"\"\"\n Select up to `max_cond_frame_num` conditioning frames from `cond_frame_outputs`\n that are temporally closest to the current frame at `frame_idx`. Here, we take\n - a) the closest conditioning frame before `frame_idx` (if any);\n - b) the closest conditioning frame after `frame_idx` (if any);\n - c) any other temporally closest conditioning frames until reaching a total\n of `max_cond_frame_num` conditioning frames.\n\n Outputs:\n - selected_outputs: selected items (keys & values) from `cond_frame_outputs`.\n - unselected_outputs: items (keys & values) not selected in `cond_frame_outputs`.\n \"\"\"\n if max_cond_frame_num == -1 or len(cond_frame_outputs) <= max_cond_frame_num:\n selected_outputs = cond_frame_outputs\n unselected_outputs = {}\n else:\n assert max_cond_frame_num >= 2, \"we should allow using 2+ conditioning frames\"\n selected_outputs = {}\n\n # the closest conditioning frame before `frame_idx` (if any)\n idx_before = max((t for t in cond_frame_outputs if t < frame_idx), default=None)\n if idx_before is not None:\n selected_outputs[idx_before] = cond_frame_outputs[idx_before]\n\n # the closest conditioning frame after `frame_idx` (if any)\n idx_after = min((t for t in cond_frame_outputs if t >= frame_idx), default=None)\n if idx_after is not None:\n selected_outputs[idx_after] = cond_frame_outputs[idx_after]\n\n # add other temporally closest conditioning frames until reaching a total\n # of `max_cond_frame_num` conditioning frames.\n num_remain = max_cond_frame_num - len(selected_outputs)\n inds_remain = sorted(\n (t for t in cond_frame_outputs if t not in selected_outputs),\n key=lambda x: abs(x - frame_idx),\n )[:num_remain]\n selected_outputs.update((t, cond_frame_outputs[t]) for t in inds_remain)\n unselected_outputs = {\n t: v for t, v in cond_frame_outputs.items() if t not in selected_outputs\n }\n\n return selected_outputs, unselected_outputs\n\n\ndef get_1d_sine_pe(pos_inds, dim, temperature=10000):\n \"\"\"\n Get 1D sine positional embedding as in the original Transformer paper.\n \"\"\"\n pe_dim = dim // 2\n dim_t = torch.arange(pe_dim, dtype=torch.float32, device=pos_inds.device)\n dim_t = temperature ** (2 * (dim_t // 2) / pe_dim)\n\n pos_embed = pos_inds.unsqueeze(-1) / dim_t\n pos_embed = torch.cat([pos_embed.sin(), pos_embed.cos()], dim=-1)\n return pos_embed\n\n\ndef get_activation_fn(activation):\n \"\"\"Return an activation function given a string\"\"\"\n if activation == \"relu\":\n return F.relu\n if activation == \"gelu\":\n return F.gelu\n if activation == \"glu\":\n return F.glu\n raise RuntimeError(f\"activation should be relu/gelu, not {activation}.\")\n\n\ndef get_clones(module, N):\n return nn.ModuleList([copy.deepcopy(module) for i in range(N)])\n\n\nclass DropPath(nn.Module):\n # adapted from https://github.com/huggingface/pytorch-image-models/blob/main/timm/layers/drop.py\n def __init__(self, drop_prob=0.0, scale_by_keep=True):\n super(DropPath, self).__init__()\n self.drop_prob = drop_prob\n self.scale_by_keep = scale_by_keep\n\n def forward(self, x):\n if self.drop_prob == 0.0 or not self.training:\n return x\n keep_prob = 1 - self.drop_prob\n shape = (x.shape[0],) + (1,) * (x.ndim - 1)\n random_tensor = x.new_empty(shape).bernoulli_(keep_prob)\n if keep_prob > 0.0 and self.scale_by_keep:\n random_tensor.div_(keep_prob)\n return x * random_tensor\n\n\n# Lightly adapted from\n# https://github.com/facebookresearch/MaskFormer/blob/main/mask_former/modeling/transformer/transformer_predictor.py # noqa\nclass MLP(nn.Module):\n def __init__(\n self,\n input_dim: int,\n hidden_dim: int,\n output_dim: int,\n num_layers: int,\n activation: nn.Module = nn.ReLU,\n sigmoid_output: bool = False,\n ) -> None:\n super().__init__()\n self.num_layers = num_layers\n h = [hidden_dim] * (num_layers - 1)\n self.layers = nn.ModuleList(\n nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim])\n )\n self.sigmoid_output = sigmoid_output\n self.act = activation()\n\n def forward(self, x):\n for i, layer in enumerate(self.layers):\n x = self.act(layer(x)) if i < self.num_layers - 1 else layer(x)\n if self.sigmoid_output:\n x = F.sigmoid(x)\n return x\n\n\n# From https://github.com/facebookresearch/detectron2/blob/main/detectron2/layers/batch_norm.py # noqa\n# Itself from https://github.com/facebookresearch/ConvNeXt/blob/d1fa8f6fef0a165b27399986cc2bdacc92777e40/models/convnext.py#L119 # noqa\nclass LayerNorm2d(nn.Module):\n def __init__(self, num_channels: int, eps: float = 1e-6) -> None:\n super().__init__()\n self.weight = nn.Parameter(torch.ones(num_channels))\n self.bias = nn.Parameter(torch.zeros(num_channels))\n self.eps = eps\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n u = x.mean(1, keepdim=True)\n s = (x - u).pow(2).mean(1, keepdim=True)\n x = (x - u) / torch.sqrt(s + self.eps)\n x = self.weight[:, None, None] * x + self.bias[:, None, None]\n return x\n" }, { "path": "auto-seg/sam2/sam2_image_predictor.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport logging\n\nfrom typing import List, Optional, Tuple, Union\n\nimport numpy as np\nimport torch\nfrom PIL.Image import Image\n\nfrom sam2.modeling.sam2_base import SAM2Base\n\nfrom sam2.utils.transforms import SAM2Transforms\n\n\nclass SAM2ImagePredictor:\n def __init__(\n self,\n sam_model: SAM2Base,\n mask_threshold=0.0,\n max_hole_area=0.0,\n max_sprinkle_area=0.0,\n **kwargs,\n ) -> None:\n \"\"\"\n Uses SAM-2 to calculate the image embedding for an image, and then\n allow repeated, efficient mask prediction given prompts.\n\n Arguments:\n sam_model (Sam-2): The model to use for mask prediction.\n mask_threshold (float): The threshold to use when converting mask logits\n to binary masks. Masks are thresholded at 0 by default.\n max_hole_area (int): If max_hole_area > 0, we fill small holes in up to\n the maximum area of max_hole_area in low_res_masks.\n max_sprinkle_area (int): If max_sprinkle_area > 0, we remove small sprinkles up to\n the maximum area of max_sprinkle_area in low_res_masks.\n \"\"\"\n super().__init__()\n self.model = sam_model\n self._transforms = SAM2Transforms(\n resolution=self.model.image_size,\n mask_threshold=mask_threshold,\n max_hole_area=max_hole_area,\n max_sprinkle_area=max_sprinkle_area,\n )\n\n # Predictor state\n self._is_image_set = False\n self._features = None\n self._orig_hw = None\n # Whether the predictor is set for single image or a batch of images\n self._is_batch = False\n\n # Predictor config\n self.mask_threshold = mask_threshold\n\n # Spatial dim for backbone feature maps\n self._bb_feat_sizes = [\n (256, 256),\n (128, 128),\n (64, 64),\n ]\n\n @classmethod\n def from_pretrained(cls, model_id: str, **kwargs) -> \"SAM2ImagePredictor\":\n \"\"\"\n Load a pretrained model from the Hugging Face hub.\n\n Arguments:\n model_id (str): The Hugging Face repository ID.\n **kwargs: Additional arguments to pass to the model constructor.\n\n Returns:\n (SAM2ImagePredictor): The loaded model.\n \"\"\"\n from sam2.build_sam import build_sam2_hf\n\n sam_model = build_sam2_hf(model_id, **kwargs)\n return cls(sam_model, **kwargs)\n\n @torch.no_grad()\n def set_image(\n self,\n image: Union[np.ndarray, Image],\n ) -> None:\n \"\"\"\n Calculates the image embeddings for the provided image, allowing\n masks to be predicted with the 'predict' method.\n\n Arguments:\n image (np.ndarray or PIL Image): The input image to embed in RGB format. The image should be in HWC format if np.ndarray, or WHC format if PIL Image\n with pixel values in [0, 255].\n image_format (str): The color format of the image, in ['RGB', 'BGR'].\n \"\"\"\n self.reset_predictor()\n # Transform the image to the form expected by the model\n if isinstance(image, np.ndarray):\n logging.info(\"For numpy array image, we assume (HxWxC) format\")\n self._orig_hw = [image.shape[:2]]\n elif isinstance(image, Image):\n w, h = image.size\n self._orig_hw = [(h, w)]\n else:\n raise NotImplementedError(\"Image format not supported\")\n\n input_image = self._transforms(image)\n input_image = input_image[None, ...].to(self.device)\n\n assert (\n len(input_image.shape) == 4 and input_image.shape[1] == 3\n ), f\"input_image must be of size 1x3xHxW, got {input_image.shape}\"\n logging.info(\"Computing image embeddings for the provided image...\")\n backbone_out = self.model.forward_image(input_image)\n _, vision_feats, _, _ = self.model._prepare_backbone_features(backbone_out)\n # Add no_mem_embed, which is added to the lowest rest feat. map during training on videos\n if self.model.directly_add_no_mem_embed:\n vision_feats[-1] = vision_feats[-1] + self.model.no_mem_embed\n\n feats = [\n feat.permute(1, 2, 0).view(1, -1, *feat_size)\n for feat, feat_size in zip(vision_feats[::-1], self._bb_feat_sizes[::-1])\n ][::-1]\n self._features = {\"image_embed\": feats[-1], \"high_res_feats\": feats[:-1]}\n self._is_image_set = True\n logging.info(\"Image embeddings computed.\")\n\n @torch.no_grad()\n def set_image_batch(\n self,\n image_list: List[Union[np.ndarray]],\n ) -> None:\n \"\"\"\n Calculates the image embeddings for the provided image batch, allowing\n masks to be predicted with the 'predict_batch' method.\n\n Arguments:\n image_list (List[np.ndarray]): The input images to embed in RGB format. The image should be in HWC format if np.ndarray\n with pixel values in [0, 255].\n \"\"\"\n self.reset_predictor()\n assert isinstance(image_list, list)\n self._orig_hw = []\n for image in image_list:\n assert isinstance(\n image, np.ndarray\n ), \"Images are expected to be an np.ndarray in RGB format, and of shape HWC\"\n self._orig_hw.append(image.shape[:2])\n # Transform the image to the form expected by the model\n img_batch = self._transforms.forward_batch(image_list)\n img_batch = img_batch.to(self.device)\n batch_size = img_batch.shape[0]\n assert (\n len(img_batch.shape) == 4 and img_batch.shape[1] == 3\n ), f\"img_batch must be of size Bx3xHxW, got {img_batch.shape}\"\n logging.info(\"Computing image embeddings for the provided images...\")\n backbone_out = self.model.forward_image(img_batch)\n _, vision_feats, _, _ = self.model._prepare_backbone_features(backbone_out)\n # Add no_mem_embed, which is added to the lowest rest feat. map during training on videos\n if self.model.directly_add_no_mem_embed:\n vision_feats[-1] = vision_feats[-1] + self.model.no_mem_embed\n\n feats = [\n feat.permute(1, 2, 0).view(batch_size, -1, *feat_size)\n for feat, feat_size in zip(vision_feats[::-1], self._bb_feat_sizes[::-1])\n ][::-1]\n self._features = {\"image_embed\": feats[-1], \"high_res_feats\": feats[:-1]}\n self._is_image_set = True\n self._is_batch = True\n logging.info(\"Image embeddings computed.\")\n\n def predict_batch(\n self,\n point_coords_batch: List[np.ndarray] = None,\n point_labels_batch: List[np.ndarray] = None,\n box_batch: List[np.ndarray] = None,\n mask_input_batch: List[np.ndarray] = None,\n multimask_output: bool = True,\n return_logits: bool = False,\n normalize_coords=True,\n ) -> Tuple[List[np.ndarray], List[np.ndarray], List[np.ndarray]]:\n \"\"\"This function is very similar to predict(...), however it is used for batched mode, when the model is expected to generate predictions on multiple images.\n It returns a tuple of lists of masks, ious, and low_res_masks_logits.\n \"\"\"\n assert self._is_batch, \"This function should only be used when in batched mode\"\n if not self._is_image_set:\n raise RuntimeError(\n \"An image must be set with .set_image_batch(...) before mask prediction.\"\n )\n num_images = len(self._features[\"image_embed\"])\n all_masks = []\n all_ious = []\n all_low_res_masks = []\n for img_idx in range(num_images):\n # Transform input prompts\n point_coords = (\n point_coords_batch[img_idx] if point_coords_batch is not None else None\n )\n point_labels = (\n point_labels_batch[img_idx] if point_labels_batch is not None else None\n )\n box = box_batch[img_idx] if box_batch is not None else None\n mask_input = (\n mask_input_batch[img_idx] if mask_input_batch is not None else None\n )\n mask_input, unnorm_coords, labels, unnorm_box = self._prep_prompts(\n point_coords,\n point_labels,\n box,\n mask_input,\n normalize_coords,\n img_idx=img_idx,\n )\n masks, iou_predictions, low_res_masks = self._predict(\n unnorm_coords,\n labels,\n unnorm_box,\n mask_input,\n multimask_output,\n return_logits=return_logits,\n img_idx=img_idx,\n )\n masks_np = masks.squeeze(0).float().detach().cpu().numpy()\n iou_predictions_np = (\n iou_predictions.squeeze(0).float().detach().cpu().numpy()\n )\n low_res_masks_np = low_res_masks.squeeze(0).float().detach().cpu().numpy()\n all_masks.append(masks_np)\n all_ious.append(iou_predictions_np)\n all_low_res_masks.append(low_res_masks_np)\n\n return all_masks, all_ious, all_low_res_masks\n\n def predict(\n self,\n point_coords: Optional[np.ndarray] = None,\n point_labels: Optional[np.ndarray] = None,\n box: Optional[np.ndarray] = None,\n mask_input: Optional[np.ndarray] = None,\n multimask_output: bool = True,\n return_logits: bool = False,\n normalize_coords=True,\n ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:\n \"\"\"\n Predict masks for the given input prompts, using the currently set image.\n\n Arguments:\n point_coords (np.ndarray or None): A Nx2 array of point prompts to the\n model. Each point is in (X,Y) in pixels.\n point_labels (np.ndarray or None): A length N array of labels for the\n point prompts. 1 indicates a foreground point and 0 indicates a\n background point.\n box (np.ndarray or None): A length 4 array given a box prompt to the\n model, in XYXY format.\n mask_input (np.ndarray): A low resolution mask input to the model, typically\n coming from a previous prediction iteration. Has form 1xHxW, where\n for SAM, H=W=256.\n multimask_output (bool): If true, the model will return three masks.\n For ambiguous input prompts (such as a single click), this will often\n produce better masks than a single prediction. If only a single\n mask is needed, the model's predicted quality score can be used\n to select the best mask. For non-ambiguous prompts, such as multiple\n input prompts, multimask_output=False can give better results.\n return_logits (bool): If true, returns un-thresholded masks logits\n instead of a binary mask.\n normalize_coords (bool): If true, the point coordinates will be normalized to the range [0,1] and point_coords is expected to be wrt. image dimensions.\n\n Returns:\n (np.ndarray): The output masks in CxHxW format, where C is the\n number of masks, and (H, W) is the original image size.\n (np.ndarray): An array of length C containing the model's\n predictions for the quality of each mask.\n (np.ndarray): An array of shape CxHxW, where C is the number\n of masks and H=W=256. These low resolution logits can be passed to\n a subsequent iteration as mask input.\n \"\"\"\n if not self._is_image_set:\n raise RuntimeError(\n \"An image must be set with .set_image(...) before mask prediction.\"\n )\n\n # Transform input prompts\n\n mask_input, unnorm_coords, labels, unnorm_box = self._prep_prompts(\n point_coords, point_labels, box, mask_input, normalize_coords\n )\n\n masks, iou_predictions, low_res_masks = self._predict(\n unnorm_coords,\n labels,\n unnorm_box,\n mask_input,\n multimask_output,\n return_logits=return_logits,\n )\n\n masks_np = masks.squeeze(0).float().detach().cpu().numpy()\n iou_predictions_np = iou_predictions.squeeze(0).float().detach().cpu().numpy()\n low_res_masks_np = low_res_masks.squeeze(0).float().detach().cpu().numpy()\n return masks_np, iou_predictions_np, low_res_masks_np\n\n def _prep_prompts(\n self, point_coords, point_labels, box, mask_logits, normalize_coords, img_idx=-1\n ):\n\n unnorm_coords, labels, unnorm_box, mask_input = None, None, None, None\n if point_coords is not None:\n assert (\n point_labels is not None\n ), \"point_labels must be supplied if point_coords is supplied.\"\n point_coords = torch.as_tensor(\n point_coords, dtype=torch.float, device=self.device\n )\n unnorm_coords = self._transforms.transform_coords(\n point_coords, normalize=normalize_coords, orig_hw=self._orig_hw[img_idx]\n )\n labels = torch.as_tensor(point_labels, dtype=torch.int, device=self.device)\n if len(unnorm_coords.shape) == 2:\n unnorm_coords, labels = unnorm_coords[None, ...], labels[None, ...]\n if box is not None:\n box = torch.as_tensor(box, dtype=torch.float, device=self.device)\n unnorm_box = self._transforms.transform_boxes(\n box, normalize=normalize_coords, orig_hw=self._orig_hw[img_idx]\n ) # Bx2x2\n if mask_logits is not None:\n mask_input = torch.as_tensor(\n mask_logits, dtype=torch.float, device=self.device\n )\n if len(mask_input.shape) == 3:\n mask_input = mask_input[None, :, :, :]\n return mask_input, unnorm_coords, labels, unnorm_box\n\n @torch.no_grad()\n def _predict(\n self,\n point_coords: Optional[torch.Tensor],\n point_labels: Optional[torch.Tensor],\n boxes: Optional[torch.Tensor] = None,\n mask_input: Optional[torch.Tensor] = None,\n multimask_output: bool = True,\n return_logits: bool = False,\n img_idx: int = -1,\n ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:\n \"\"\"\n Predict masks for the given input prompts, using the currently set image.\n Input prompts are batched torch tensors and are expected to already be\n transformed to the input frame using SAM2Transforms.\n\n Arguments:\n point_coords (torch.Tensor or None): A BxNx2 array of point prompts to the\n model. Each point is in (X,Y) in pixels.\n point_labels (torch.Tensor or None): A BxN array of labels for the\n point prompts. 1 indicates a foreground point and 0 indicates a\n background point.\n boxes (np.ndarray or None): A Bx4 array given a box prompt to the\n model, in XYXY format.\n mask_input (np.ndarray): A low resolution mask input to the model, typically\n coming from a previous prediction iteration. Has form Bx1xHxW, where\n for SAM, H=W=256. Masks returned by a previous iteration of the\n predict method do not need further transformation.\n multimask_output (bool): If true, the model will return three masks.\n For ambiguous input prompts (such as a single click), this will often\n produce better masks than a single prediction. If only a single\n mask is needed, the model's predicted quality score can be used\n to select the best mask. For non-ambiguous prompts, such as multiple\n input prompts, multimask_output=False can give better results.\n return_logits (bool): If true, returns un-thresholded masks logits\n instead of a binary mask.\n\n Returns:\n (torch.Tensor): The output masks in BxCxHxW format, where C is the\n number of masks, and (H, W) is the original image size.\n (torch.Tensor): An array of shape BxC containing the model's\n predictions for the quality of each mask.\n (torch.Tensor): An array of shape BxCxHxW, where C is the number\n of masks and H=W=256. These low res logits can be passed to\n a subsequent iteration as mask input.\n \"\"\"\n if not self._is_image_set:\n raise RuntimeError(\n \"An image must be set with .set_image(...) before mask prediction.\"\n )\n\n if point_coords is not None:\n concat_points = (point_coords, point_labels)\n else:\n concat_points = None\n\n # Embed prompts\n if boxes is not None:\n box_coords = boxes.reshape(-1, 2, 2)\n box_labels = torch.tensor([[2, 3]], dtype=torch.int, device=boxes.device)\n box_labels = box_labels.repeat(boxes.size(0), 1)\n # we merge \"boxes\" and \"points\" into a single \"concat_points\" input (where\n # boxes are added at the beginning) to sam_prompt_encoder\n if concat_points is not None:\n concat_coords = torch.cat([box_coords, concat_points[0]], dim=1)\n concat_labels = torch.cat([box_labels, concat_points[1]], dim=1)\n concat_points = (concat_coords, concat_labels)\n else:\n concat_points = (box_coords, box_labels)\n\n sparse_embeddings, dense_embeddings = self.model.sam_prompt_encoder(\n points=concat_points,\n boxes=None,\n masks=mask_input,\n )\n\n # Predict masks\n batched_mode = (\n concat_points is not None and concat_points[0].shape[0] > 1\n ) # multi object prediction\n high_res_features = [\n feat_level[img_idx].unsqueeze(0)\n for feat_level in self._features[\"high_res_feats\"]\n ]\n low_res_masks, iou_predictions, _, _ = self.model.sam_mask_decoder(\n image_embeddings=self._features[\"image_embed\"][img_idx].unsqueeze(0),\n image_pe=self.model.sam_prompt_encoder.get_dense_pe(),\n sparse_prompt_embeddings=sparse_embeddings,\n dense_prompt_embeddings=dense_embeddings,\n multimask_output=multimask_output,\n repeat_image=batched_mode,\n high_res_features=high_res_features,\n )\n\n # Upscale the masks to the original image resolution\n masks = self._transforms.postprocess_masks(\n low_res_masks, self._orig_hw[img_idx]\n )\n low_res_masks = torch.clamp(low_res_masks, -32.0, 32.0)\n if not return_logits:\n masks = masks > self.mask_threshold\n\n return masks, iou_predictions, low_res_masks\n\n def get_image_embedding(self) -> torch.Tensor:\n \"\"\"\n Returns the image embeddings for the currently set image, with\n shape 1xCxHxW, where C is the embedding dimension and (H,W) are\n the embedding spatial dimension of SAM (typically C=256, H=W=64).\n \"\"\"\n if not self._is_image_set:\n raise RuntimeError(\n \"An image must be set with .set_image(...) to generate an embedding.\"\n )\n assert (\n self._features is not None\n ), \"Features must exist if an image has been set.\"\n return self._features[\"image_embed\"]\n\n @property\n def device(self) -> torch.device:\n return self.model.device\n\n def reset_predictor(self) -> None:\n \"\"\"\n Resets the image embeddings and other state variables.\n \"\"\"\n self._is_image_set = False\n self._features = None\n self._orig_hw = None\n self._is_batch = False\n" }, { "path": "auto-seg/sam2/sam2_video_predictor.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport warnings\nfrom collections import OrderedDict\n\nimport torch\n\nfrom tqdm import tqdm\n\nfrom sam2.modeling.sam2_base import NO_OBJ_SCORE, SAM2Base\nfrom sam2.utils.misc import concat_points, fill_holes_in_mask_scores, load_video_frames\n\n\nclass SAM2VideoPredictor(SAM2Base):\n \"\"\"The predictor class to handle user interactions and manage inference states.\"\"\"\n\n def __init__(\n self,\n fill_hole_area=0,\n # whether to apply non-overlapping constraints on the output object masks\n non_overlap_masks=False,\n # whether to clear non-conditioning memory of the surrounding frames (which may contain outdated information) after adding correction clicks;\n # note that this would only apply to *single-object tracking* unless `clear_non_cond_mem_for_multi_obj` is also set to True)\n clear_non_cond_mem_around_input=False,\n # whether to also clear non-conditioning memory of the surrounding frames (only effective when `clear_non_cond_mem_around_input` is True).\n clear_non_cond_mem_for_multi_obj=False,\n **kwargs,\n ):\n super().__init__(**kwargs)\n self.fill_hole_area = fill_hole_area\n self.non_overlap_masks = non_overlap_masks\n self.clear_non_cond_mem_around_input = clear_non_cond_mem_around_input\n self.clear_non_cond_mem_for_multi_obj = clear_non_cond_mem_for_multi_obj\n\n @torch.inference_mode()\n def init_state(\n self,\n video_path,\n offload_video_to_cpu=False,\n offload_state_to_cpu=False,\n async_loading_frames=False,\n ):\n \"\"\"Initialize an inference state.\"\"\"\n compute_device = self.device # device of the model\n images, video_height, video_width = load_video_frames(\n video_path=video_path,\n image_size=self.image_size,\n offload_video_to_cpu=offload_video_to_cpu,\n async_loading_frames=async_loading_frames,\n compute_device=compute_device,\n )\n inference_state = {}\n inference_state[\"images\"] = images\n inference_state[\"num_frames\"] = len(images)\n # whether to offload the video frames to CPU memory\n # turning on this option saves the GPU memory with only a very small overhead\n inference_state[\"offload_video_to_cpu\"] = offload_video_to_cpu\n # whether to offload the inference state to CPU memory\n # turning on this option saves the GPU memory at the cost of a lower tracking fps\n # (e.g. in a test case of 768x768 model, fps dropped from 27 to 24 when tracking one object\n # and from 24 to 21 when tracking two objects)\n inference_state[\"offload_state_to_cpu\"] = offload_state_to_cpu\n # the original video height and width, used for resizing final output scores\n inference_state[\"video_height\"] = video_height\n inference_state[\"video_width\"] = video_width\n inference_state[\"device\"] = compute_device\n if offload_state_to_cpu:\n inference_state[\"storage_device\"] = torch.device(\"cpu\")\n else:\n inference_state[\"storage_device\"] = compute_device\n # inputs on each frame\n inference_state[\"point_inputs_per_obj\"] = {}\n inference_state[\"mask_inputs_per_obj\"] = {}\n # visual features on a small number of recently visited frames for quick interactions\n inference_state[\"cached_features\"] = {}\n # values that don't change across frames (so we only need to hold one copy of them)\n inference_state[\"constants\"] = {}\n # mapping between client-side object id and model-side object index\n inference_state[\"obj_id_to_idx\"] = OrderedDict()\n inference_state[\"obj_idx_to_id\"] = OrderedDict()\n inference_state[\"obj_ids\"] = []\n # A storage to hold the model's tracking results and states on each frame\n inference_state[\"output_dict\"] = {\n \"cond_frame_outputs\": {}, # dict containing {frame_idx: }\n \"non_cond_frame_outputs\": {}, # dict containing {frame_idx: }\n }\n # Slice (view) of each object tracking results, sharing the same memory with \"output_dict\"\n inference_state[\"output_dict_per_obj\"] = {}\n # A temporary storage to hold new outputs when user interact with a frame\n # to add clicks or mask (it's merged into \"output_dict\" before propagation starts)\n inference_state[\"temp_output_dict_per_obj\"] = {}\n # Frames that already holds consolidated outputs from click or mask inputs\n # (we directly use their consolidated outputs during tracking)\n inference_state[\"consolidated_frame_inds\"] = {\n \"cond_frame_outputs\": set(), # set containing frame indices\n \"non_cond_frame_outputs\": set(), # set containing frame indices\n }\n # metadata for each tracking frame (e.g. which direction it's tracked)\n inference_state[\"tracking_has_started\"] = False\n inference_state[\"frames_already_tracked\"] = {}\n # Warm up the visual backbone and cache the image feature on frame 0\n self._get_image_feature(inference_state, frame_idx=0, batch_size=1)\n return inference_state\n\n @classmethod\n def from_pretrained(cls, model_id: str, **kwargs) -> \"SAM2VideoPredictor\":\n \"\"\"\n Load a pretrained model from the Hugging Face hub.\n\n Arguments:\n model_id (str): The Hugging Face repository ID.\n **kwargs: Additional arguments to pass to the model constructor.\n\n Returns:\n (SAM2VideoPredictor): The loaded model.\n \"\"\"\n from sam2.build_sam import build_sam2_video_predictor_hf\n\n sam_model = build_sam2_video_predictor_hf(model_id, **kwargs)\n return sam_model\n\n def _obj_id_to_idx(self, inference_state, obj_id):\n \"\"\"Map client-side object id to model-side object index.\"\"\"\n obj_idx = inference_state[\"obj_id_to_idx\"].get(obj_id, None)\n if obj_idx is not None:\n return obj_idx\n\n # This is a new object id not sent to the server before. We only allow adding\n # new objects *before* the tracking starts.\n allow_new_object = not inference_state[\"tracking_has_started\"]\n if allow_new_object:\n # get the next object slot\n obj_idx = len(inference_state[\"obj_id_to_idx\"])\n inference_state[\"obj_id_to_idx\"][obj_id] = obj_idx\n inference_state[\"obj_idx_to_id\"][obj_idx] = obj_id\n inference_state[\"obj_ids\"] = list(inference_state[\"obj_id_to_idx\"])\n # set up input and output structures for this object\n inference_state[\"point_inputs_per_obj\"][obj_idx] = {}\n inference_state[\"mask_inputs_per_obj\"][obj_idx] = {}\n inference_state[\"output_dict_per_obj\"][obj_idx] = {\n \"cond_frame_outputs\": {}, # dict containing {frame_idx: }\n \"non_cond_frame_outputs\": {}, # dict containing {frame_idx: }\n }\n inference_state[\"temp_output_dict_per_obj\"][obj_idx] = {\n \"cond_frame_outputs\": {}, # dict containing {frame_idx: }\n \"non_cond_frame_outputs\": {}, # dict containing {frame_idx: }\n }\n return obj_idx\n else:\n raise RuntimeError(\n f\"Cannot add new object id {obj_id} after tracking starts. \"\n f\"All existing object ids: {inference_state['obj_ids']}. \"\n f\"Please call 'reset_state' to restart from scratch.\"\n )\n\n def _obj_idx_to_id(self, inference_state, obj_idx):\n \"\"\"Map model-side object index to client-side object id.\"\"\"\n return inference_state[\"obj_idx_to_id\"][obj_idx]\n\n def _get_obj_num(self, inference_state):\n \"\"\"Get the total number of unique object ids received so far in this session.\"\"\"\n return len(inference_state[\"obj_idx_to_id\"])\n\n @torch.inference_mode()\n def add_new_points_or_box(\n self,\n inference_state,\n frame_idx,\n obj_id,\n points=None,\n labels=None,\n clear_old_points=True,\n normalize_coords=True,\n box=None,\n ):\n \"\"\"Add new points to a frame.\"\"\"\n obj_idx = self._obj_id_to_idx(inference_state, obj_id)\n point_inputs_per_frame = inference_state[\"point_inputs_per_obj\"][obj_idx]\n mask_inputs_per_frame = inference_state[\"mask_inputs_per_obj\"][obj_idx]\n\n if (points is not None) != (labels is not None):\n raise ValueError(\"points and labels must be provided together\")\n if points is None and box is None:\n raise ValueError(\"at least one of points or box must be provided as input\")\n\n if points is None:\n points = torch.zeros(0, 2, dtype=torch.float32)\n elif not isinstance(points, torch.Tensor):\n points = torch.tensor(points, dtype=torch.float32)\n if labels is None:\n labels = torch.zeros(0, dtype=torch.int32)\n elif not isinstance(labels, torch.Tensor):\n labels = torch.tensor(labels, dtype=torch.int32)\n if points.dim() == 2:\n points = points.unsqueeze(0) # add batch dimension\n if labels.dim() == 1:\n labels = labels.unsqueeze(0) # add batch dimension\n\n # If `box` is provided, we add it as the first two points with labels 2 and 3\n # along with the user-provided points (consistent with how SAM 2 is trained).\n if box is not None:\n if not clear_old_points:\n raise ValueError(\n \"cannot add box without clearing old points, since \"\n \"box prompt must be provided before any point prompt \"\n \"(please use clear_old_points=True instead)\"\n )\n if inference_state[\"tracking_has_started\"]:\n warnings.warn(\n \"You are adding a box after tracking starts. SAM 2 may not always be \"\n \"able to incorporate a box prompt for *refinement*. If you intend to \"\n \"use box prompt as an *initial* input before tracking, please call \"\n \"'reset_state' on the inference state to restart from scratch.\",\n category=UserWarning,\n stacklevel=2,\n )\n if not isinstance(box, torch.Tensor):\n box = torch.tensor(box, dtype=torch.float32, device=points.device)\n box_coords = box.reshape(1, 2, 2)\n box_labels = torch.tensor([2, 3], dtype=torch.int32, device=labels.device)\n box_labels = box_labels.reshape(1, 2)\n points = torch.cat([box_coords, points], dim=1)\n labels = torch.cat([box_labels, labels], dim=1)\n\n if normalize_coords:\n video_H = inference_state[\"video_height\"]\n video_W = inference_state[\"video_width\"]\n points = points / torch.tensor([video_W, video_H]).to(points.device)\n # scale the (normalized) coordinates by the model's internal image size\n points = points * self.image_size\n points = points.to(inference_state[\"device\"])\n labels = labels.to(inference_state[\"device\"])\n\n if not clear_old_points:\n point_inputs = point_inputs_per_frame.get(frame_idx, None)\n else:\n point_inputs = None\n point_inputs = concat_points(point_inputs, points, labels)\n\n point_inputs_per_frame[frame_idx] = point_inputs\n mask_inputs_per_frame.pop(frame_idx, None)\n # If this frame hasn't been tracked before, we treat it as an initial conditioning\n # frame, meaning that the inputs points are to generate segments on this frame without\n # using any memory from other frames, like in SAM. Otherwise (if it has been tracked),\n # the input points will be used to correct the already tracked masks.\n is_init_cond_frame = frame_idx not in inference_state[\"frames_already_tracked\"]\n # whether to track in reverse time order\n if is_init_cond_frame:\n reverse = False\n else:\n reverse = inference_state[\"frames_already_tracked\"][frame_idx][\"reverse\"]\n obj_output_dict = inference_state[\"output_dict_per_obj\"][obj_idx]\n obj_temp_output_dict = inference_state[\"temp_output_dict_per_obj\"][obj_idx]\n # Add a frame to conditioning output if it's an initial conditioning frame or\n # if the model sees all frames receiving clicks/mask as conditioning frames.\n is_cond = is_init_cond_frame or self.add_all_frames_to_correct_as_cond\n storage_key = \"cond_frame_outputs\" if is_cond else \"non_cond_frame_outputs\"\n\n # Get any previously predicted mask logits on this object and feed it along with\n # the new clicks into the SAM mask decoder.\n prev_sam_mask_logits = None\n # lookup temporary output dict first, which contains the most recent output\n # (if not found, then lookup conditioning and non-conditioning frame output)\n prev_out = obj_temp_output_dict[storage_key].get(frame_idx)\n if prev_out is None:\n prev_out = obj_output_dict[\"cond_frame_outputs\"].get(frame_idx)\n if prev_out is None:\n prev_out = obj_output_dict[\"non_cond_frame_outputs\"].get(frame_idx)\n\n if prev_out is not None and prev_out[\"pred_masks\"] is not None:\n device = inference_state[\"device\"]\n prev_sam_mask_logits = prev_out[\"pred_masks\"].to(device, non_blocking=True)\n # Clamp the scale of prev_sam_mask_logits to avoid rare numerical issues.\n prev_sam_mask_logits = torch.clamp(prev_sam_mask_logits, -32.0, 32.0)\n current_out, _ = self._run_single_frame_inference(\n inference_state=inference_state,\n output_dict=obj_output_dict, # run on the slice of a single object\n frame_idx=frame_idx,\n batch_size=1, # run on the slice of a single object\n is_init_cond_frame=is_init_cond_frame,\n point_inputs=point_inputs,\n mask_inputs=None,\n reverse=reverse,\n # Skip the memory encoder when adding clicks or mask. We execute the memory encoder\n # at the beginning of `propagate_in_video` (after user finalize their clicks). This\n # allows us to enforce non-overlapping constraints on all objects before encoding\n # them into memory.\n run_mem_encoder=False,\n prev_sam_mask_logits=prev_sam_mask_logits,\n )\n # Add the output to the output dict (to be used as future memory)\n obj_temp_output_dict[storage_key][frame_idx] = current_out\n\n # Resize the output mask to the original video resolution\n obj_ids = inference_state[\"obj_ids\"]\n consolidated_out = self._consolidate_temp_output_across_obj(\n inference_state,\n frame_idx,\n is_cond=is_cond,\n run_mem_encoder=False,\n consolidate_at_video_res=True,\n )\n _, video_res_masks = self._get_orig_video_res_output(\n inference_state, consolidated_out[\"pred_masks_video_res\"]\n )\n return frame_idx, obj_ids, video_res_masks\n\n def add_new_points(self, *args, **kwargs):\n \"\"\"Deprecated method. Please use `add_new_points_or_box` instead.\"\"\"\n return self.add_new_points_or_box(*args, **kwargs)\n\n @torch.inference_mode()\n def add_new_mask(\n self,\n inference_state,\n frame_idx,\n obj_id,\n mask,\n ):\n \"\"\"Add new mask to a frame.\"\"\"\n obj_idx = self._obj_id_to_idx(inference_state, obj_id)\n point_inputs_per_frame = inference_state[\"point_inputs_per_obj\"][obj_idx]\n mask_inputs_per_frame = inference_state[\"mask_inputs_per_obj\"][obj_idx]\n\n if not isinstance(mask, torch.Tensor):\n mask = torch.tensor(mask, dtype=torch.bool)\n assert mask.dim() == 2\n mask_H, mask_W = mask.shape\n mask_inputs_orig = mask[None, None] # add batch and channel dimension\n mask_inputs_orig = mask_inputs_orig.float().to(inference_state[\"device\"])\n\n # resize the mask if it doesn't match the model's image size\n if mask_H != self.image_size or mask_W != self.image_size:\n mask_inputs = torch.nn.functional.interpolate(\n mask_inputs_orig,\n size=(self.image_size, self.image_size),\n align_corners=False,\n mode=\"bilinear\",\n antialias=True, # use antialias for downsampling\n )\n mask_inputs = (mask_inputs >= 0.5).float()\n else:\n mask_inputs = mask_inputs_orig\n\n mask_inputs_per_frame[frame_idx] = mask_inputs\n point_inputs_per_frame.pop(frame_idx, None)\n # If this frame hasn't been tracked before, we treat it as an initial conditioning\n # frame, meaning that the inputs points are to generate segments on this frame without\n # using any memory from other frames, like in SAM. Otherwise (if it has been tracked),\n # the input points will be used to correct the already tracked masks.\n is_init_cond_frame = frame_idx not in inference_state[\"frames_already_tracked\"]\n # whether to track in reverse time order\n if is_init_cond_frame:\n reverse = False\n else:\n reverse = inference_state[\"frames_already_tracked\"][frame_idx][\"reverse\"]\n obj_output_dict = inference_state[\"output_dict_per_obj\"][obj_idx]\n obj_temp_output_dict = inference_state[\"temp_output_dict_per_obj\"][obj_idx]\n # Add a frame to conditioning output if it's an initial conditioning frame or\n # if the model sees all frames receiving clicks/mask as conditioning frames.\n is_cond = is_init_cond_frame or self.add_all_frames_to_correct_as_cond\n storage_key = \"cond_frame_outputs\" if is_cond else \"non_cond_frame_outputs\"\n\n current_out, _ = self._run_single_frame_inference(\n inference_state=inference_state,\n output_dict=obj_output_dict, # run on the slice of a single object\n frame_idx=frame_idx,\n batch_size=1, # run on the slice of a single object\n is_init_cond_frame=is_init_cond_frame,\n point_inputs=None,\n mask_inputs=mask_inputs,\n reverse=reverse,\n # Skip the memory encoder when adding clicks or mask. We execute the memory encoder\n # at the beginning of `propagate_in_video` (after user finalize their clicks). This\n # allows us to enforce non-overlapping constraints on all objects before encoding\n # them into memory.\n run_mem_encoder=False,\n )\n # Add the output to the output dict (to be used as future memory)\n obj_temp_output_dict[storage_key][frame_idx] = current_out\n\n # Resize the output mask to the original video resolution\n obj_ids = inference_state[\"obj_ids\"]\n consolidated_out = self._consolidate_temp_output_across_obj(\n inference_state,\n frame_idx,\n is_cond=is_cond,\n run_mem_encoder=False,\n consolidate_at_video_res=True,\n )\n _, video_res_masks = self._get_orig_video_res_output(\n inference_state, consolidated_out[\"pred_masks_video_res\"]\n )\n return frame_idx, obj_ids, video_res_masks\n\n def _get_orig_video_res_output(self, inference_state, any_res_masks):\n \"\"\"\n Resize the object scores to the original video resolution (video_res_masks)\n and apply non-overlapping constraints for final output.\n \"\"\"\n device = inference_state[\"device\"]\n video_H = inference_state[\"video_height\"]\n video_W = inference_state[\"video_width\"]\n any_res_masks = any_res_masks.to(device, non_blocking=True)\n if any_res_masks.shape[-2:] == (video_H, video_W):\n video_res_masks = any_res_masks\n else:\n video_res_masks = torch.nn.functional.interpolate(\n any_res_masks,\n size=(video_H, video_W),\n mode=\"bilinear\",\n align_corners=False,\n )\n if self.non_overlap_masks:\n video_res_masks = self._apply_non_overlapping_constraints(video_res_masks)\n return any_res_masks, video_res_masks\n\n def _consolidate_temp_output_across_obj(\n self,\n inference_state,\n frame_idx,\n is_cond,\n run_mem_encoder,\n consolidate_at_video_res=False,\n ):\n \"\"\"\n Consolidate the per-object temporary outputs in `temp_output_dict_per_obj` on\n a frame into a single output for all objects, including\n 1) fill any missing objects either from `output_dict_per_obj` (if they exist in\n `output_dict_per_obj` for this frame) or leave them as placeholder values\n (if they don't exist in `output_dict_per_obj` for this frame);\n 2) if specified, rerun memory encoder after apply non-overlapping constraints\n on the object scores.\n \"\"\"\n batch_size = self._get_obj_num(inference_state)\n storage_key = \"cond_frame_outputs\" if is_cond else \"non_cond_frame_outputs\"\n # Optionally, we allow consolidating the temporary outputs at the original\n # video resolution (to provide a better editing experience for mask prompts).\n if consolidate_at_video_res:\n assert not run_mem_encoder, \"memory encoder cannot run at video resolution\"\n consolidated_H = inference_state[\"video_height\"]\n consolidated_W = inference_state[\"video_width\"]\n consolidated_mask_key = \"pred_masks_video_res\"\n else:\n consolidated_H = consolidated_W = self.image_size // 4\n consolidated_mask_key = \"pred_masks\"\n\n # Initialize `consolidated_out`. Its \"maskmem_features\" and \"maskmem_pos_enc\"\n # will be added when rerunning the memory encoder after applying non-overlapping\n # constraints to object scores. Its \"pred_masks\" are prefilled with a large\n # negative value (NO_OBJ_SCORE) to represent missing objects.\n consolidated_out = {\n \"maskmem_features\": None,\n \"maskmem_pos_enc\": None,\n consolidated_mask_key: torch.full(\n size=(batch_size, 1, consolidated_H, consolidated_W),\n fill_value=NO_OBJ_SCORE,\n dtype=torch.float32,\n device=inference_state[\"storage_device\"],\n ),\n \"obj_ptr\": torch.full(\n size=(batch_size, self.hidden_dim),\n fill_value=NO_OBJ_SCORE,\n dtype=torch.float32,\n device=inference_state[\"device\"],\n ),\n }\n empty_mask_ptr = None\n for obj_idx in range(batch_size):\n obj_temp_output_dict = inference_state[\"temp_output_dict_per_obj\"][obj_idx]\n obj_output_dict = inference_state[\"output_dict_per_obj\"][obj_idx]\n out = obj_temp_output_dict[storage_key].get(frame_idx, None)\n # If the object doesn't appear in \"temp_output_dict_per_obj\" on this frame,\n # we fall back and look up its previous output in \"output_dict_per_obj\".\n # We look up both \"cond_frame_outputs\" and \"non_cond_frame_outputs\" in\n # \"output_dict_per_obj\" to find a previous output for this object.\n if out is None:\n out = obj_output_dict[\"cond_frame_outputs\"].get(frame_idx, None)\n if out is None:\n out = obj_output_dict[\"non_cond_frame_outputs\"].get(frame_idx, None)\n # If the object doesn't appear in \"output_dict_per_obj\" either, we skip it\n # and leave its mask scores to the default scores (i.e. the NO_OBJ_SCORE\n # placeholder above) and set its object pointer to be a dummy pointer.\n if out is None:\n # Fill in dummy object pointers for those objects without any inputs or\n # tracking outcomes on this frame (only do it under `run_mem_encoder=True`,\n # i.e. when we need to build the memory for tracking).\n if run_mem_encoder:\n if empty_mask_ptr is None:\n empty_mask_ptr = self._get_empty_mask_ptr(\n inference_state, frame_idx\n )\n # fill object pointer with a dummy pointer (based on an empty mask)\n consolidated_out[\"obj_ptr\"][obj_idx : obj_idx + 1] = empty_mask_ptr\n continue\n # Add the temporary object output mask to consolidated output mask\n obj_mask = out[\"pred_masks\"]\n consolidated_pred_masks = consolidated_out[consolidated_mask_key]\n if obj_mask.shape[-2:] == consolidated_pred_masks.shape[-2:]:\n consolidated_pred_masks[obj_idx : obj_idx + 1] = obj_mask\n else:\n # Resize first if temporary object mask has a different resolution\n resized_obj_mask = torch.nn.functional.interpolate(\n obj_mask,\n size=consolidated_pred_masks.shape[-2:],\n mode=\"bilinear\",\n align_corners=False,\n )\n consolidated_pred_masks[obj_idx : obj_idx + 1] = resized_obj_mask\n consolidated_out[\"obj_ptr\"][obj_idx : obj_idx + 1] = out[\"obj_ptr\"]\n\n # Optionally, apply non-overlapping constraints on the consolidated scores\n # and rerun the memory encoder\n if run_mem_encoder:\n device = inference_state[\"device\"]\n high_res_masks = torch.nn.functional.interpolate(\n consolidated_out[\"pred_masks\"].to(device, non_blocking=True),\n size=(self.image_size, self.image_size),\n mode=\"bilinear\",\n align_corners=False,\n )\n if self.non_overlap_masks_for_mem_enc:\n high_res_masks = self._apply_non_overlapping_constraints(high_res_masks)\n maskmem_features, maskmem_pos_enc = self._run_memory_encoder(\n inference_state=inference_state,\n frame_idx=frame_idx,\n batch_size=batch_size,\n high_res_masks=high_res_masks,\n is_mask_from_pts=True, # these frames are what the user interacted with\n )\n consolidated_out[\"maskmem_features\"] = maskmem_features\n consolidated_out[\"maskmem_pos_enc\"] = maskmem_pos_enc\n\n return consolidated_out\n\n def _get_empty_mask_ptr(self, inference_state, frame_idx):\n \"\"\"Get a dummy object pointer based on an empty mask on the current frame.\"\"\"\n # A dummy (empty) mask with a single object\n batch_size = 1\n mask_inputs = torch.zeros(\n (batch_size, 1, self.image_size, self.image_size),\n dtype=torch.float32,\n device=inference_state[\"device\"],\n )\n\n # Retrieve correct image features\n (\n _,\n _,\n current_vision_feats,\n current_vision_pos_embeds,\n feat_sizes,\n ) = self._get_image_feature(inference_state, frame_idx, batch_size)\n\n # Feed the empty mask and image feature above to get a dummy object pointer\n current_out = self.track_step(\n frame_idx=frame_idx,\n is_init_cond_frame=True,\n current_vision_feats=current_vision_feats,\n current_vision_pos_embeds=current_vision_pos_embeds,\n feat_sizes=feat_sizes,\n point_inputs=None,\n mask_inputs=mask_inputs,\n output_dict={},\n num_frames=inference_state[\"num_frames\"],\n track_in_reverse=False,\n run_mem_encoder=False,\n prev_sam_mask_logits=None,\n )\n return current_out[\"obj_ptr\"]\n\n @torch.inference_mode()\n def propagate_in_video_preflight(self, inference_state):\n \"\"\"Prepare inference_state and consolidate temporary outputs before tracking.\"\"\"\n # Tracking has started and we don't allow adding new objects until session is reset.\n inference_state[\"tracking_has_started\"] = True\n batch_size = self._get_obj_num(inference_state)\n\n # Consolidate per-object temporary outputs in \"temp_output_dict_per_obj\" and\n # add them into \"output_dict\".\n temp_output_dict_per_obj = inference_state[\"temp_output_dict_per_obj\"]\n output_dict = inference_state[\"output_dict\"]\n # \"consolidated_frame_inds\" contains indices of those frames where consolidated\n # temporary outputs have been added (either in this call or any previous calls\n # to `propagate_in_video_preflight`).\n consolidated_frame_inds = inference_state[\"consolidated_frame_inds\"]\n for is_cond in [False, True]:\n # Separately consolidate conditioning and non-conditioning temp outputs\n storage_key = \"cond_frame_outputs\" if is_cond else \"non_cond_frame_outputs\"\n # Find all the frames that contain temporary outputs for any objects\n # (these should be the frames that have just received clicks for mask inputs\n # via `add_new_points_or_box` or `add_new_mask`)\n temp_frame_inds = set()\n for obj_temp_output_dict in temp_output_dict_per_obj.values():\n temp_frame_inds.update(obj_temp_output_dict[storage_key].keys())\n consolidated_frame_inds[storage_key].update(temp_frame_inds)\n # consolidate the temporary output across all objects on this frame\n for frame_idx in temp_frame_inds:\n consolidated_out = self._consolidate_temp_output_across_obj(\n inference_state, frame_idx, is_cond=is_cond, run_mem_encoder=True\n )\n # merge them into \"output_dict\" and also create per-object slices\n output_dict[storage_key][frame_idx] = consolidated_out\n self._add_output_per_object(\n inference_state, frame_idx, consolidated_out, storage_key\n )\n clear_non_cond_mem = self.clear_non_cond_mem_around_input and (\n self.clear_non_cond_mem_for_multi_obj or batch_size <= 1\n )\n if clear_non_cond_mem:\n # clear non-conditioning memory of the surrounding frames\n self._clear_non_cond_mem_around_input(inference_state, frame_idx)\n\n # clear temporary outputs in `temp_output_dict_per_obj`\n for obj_temp_output_dict in temp_output_dict_per_obj.values():\n obj_temp_output_dict[storage_key].clear()\n\n # edge case: if an output is added to \"cond_frame_outputs\", we remove any prior\n # output on the same frame in \"non_cond_frame_outputs\"\n for frame_idx in output_dict[\"cond_frame_outputs\"]:\n output_dict[\"non_cond_frame_outputs\"].pop(frame_idx, None)\n for obj_output_dict in inference_state[\"output_dict_per_obj\"].values():\n for frame_idx in obj_output_dict[\"cond_frame_outputs\"]:\n obj_output_dict[\"non_cond_frame_outputs\"].pop(frame_idx, None)\n for frame_idx in consolidated_frame_inds[\"cond_frame_outputs\"]:\n assert frame_idx in output_dict[\"cond_frame_outputs\"]\n consolidated_frame_inds[\"non_cond_frame_outputs\"].discard(frame_idx)\n\n # Make sure that the frame indices in \"consolidated_frame_inds\" are exactly those frames\n # with either points or mask inputs (which should be true under a correct workflow).\n all_consolidated_frame_inds = (\n consolidated_frame_inds[\"cond_frame_outputs\"]\n | consolidated_frame_inds[\"non_cond_frame_outputs\"]\n )\n input_frames_inds = set()\n for point_inputs_per_frame in inference_state[\"point_inputs_per_obj\"].values():\n input_frames_inds.update(point_inputs_per_frame.keys())\n for mask_inputs_per_frame in inference_state[\"mask_inputs_per_obj\"].values():\n input_frames_inds.update(mask_inputs_per_frame.keys())\n assert all_consolidated_frame_inds == input_frames_inds\n\n @torch.inference_mode()\n def propagate_in_video(\n self,\n inference_state,\n start_frame_idx=None,\n max_frame_num_to_track=None,\n reverse=False,\n ):\n \"\"\"Propagate the input points across frames to track in the entire video.\"\"\"\n self.propagate_in_video_preflight(inference_state)\n\n output_dict = inference_state[\"output_dict\"]\n consolidated_frame_inds = inference_state[\"consolidated_frame_inds\"]\n obj_ids = inference_state[\"obj_ids\"]\n num_frames = inference_state[\"num_frames\"]\n batch_size = self._get_obj_num(inference_state)\n if len(output_dict[\"cond_frame_outputs\"]) == 0:\n raise RuntimeError(\"No points are provided; please add points first\")\n clear_non_cond_mem = self.clear_non_cond_mem_around_input and (\n self.clear_non_cond_mem_for_multi_obj or batch_size <= 1\n )\n\n # set start index, end index, and processing order\n if start_frame_idx is None:\n # default: start from the earliest frame with input points\n start_frame_idx = min(output_dict[\"cond_frame_outputs\"])\n if max_frame_num_to_track is None:\n # default: track all the frames in the video\n max_frame_num_to_track = num_frames\n if reverse:\n end_frame_idx = max(start_frame_idx - max_frame_num_to_track, 0)\n if start_frame_idx > 0:\n processing_order = range(start_frame_idx, end_frame_idx - 1, -1)\n else:\n processing_order = [] # skip reverse tracking if starting from frame 0\n else:\n end_frame_idx = min(\n start_frame_idx + max_frame_num_to_track, num_frames - 1\n )\n processing_order = range(start_frame_idx, end_frame_idx + 1)\n\n for frame_idx in tqdm(processing_order, desc=\"propagate in video\"):\n # We skip those frames already in consolidated outputs (these are frames\n # that received input clicks or mask). Note that we cannot directly run\n # batched forward on them via `_run_single_frame_inference` because the\n # number of clicks on each object might be different.\n if frame_idx in consolidated_frame_inds[\"cond_frame_outputs\"]:\n storage_key = \"cond_frame_outputs\"\n current_out = output_dict[storage_key][frame_idx]\n pred_masks = current_out[\"pred_masks\"]\n if clear_non_cond_mem:\n # clear non-conditioning memory of the surrounding frames\n self._clear_non_cond_mem_around_input(inference_state, frame_idx)\n elif frame_idx in consolidated_frame_inds[\"non_cond_frame_outputs\"]:\n storage_key = \"non_cond_frame_outputs\"\n current_out = output_dict[storage_key][frame_idx]\n pred_masks = current_out[\"pred_masks\"]\n else:\n storage_key = \"non_cond_frame_outputs\"\n current_out, pred_masks = self._run_single_frame_inference(\n inference_state=inference_state,\n output_dict=output_dict,\n frame_idx=frame_idx,\n batch_size=batch_size,\n is_init_cond_frame=False,\n point_inputs=None,\n mask_inputs=None,\n reverse=reverse,\n run_mem_encoder=True,\n )\n output_dict[storage_key][frame_idx] = current_out\n # Create slices of per-object outputs for subsequent interaction with each\n # individual object after tracking.\n self._add_output_per_object(\n inference_state, frame_idx, current_out, storage_key\n )\n inference_state[\"frames_already_tracked\"][frame_idx] = {\"reverse\": reverse}\n\n # Resize the output mask to the original video resolution (we directly use\n # the mask scores on GPU for output to avoid any CPU conversion in between)\n _, video_res_masks = self._get_orig_video_res_output(\n inference_state, pred_masks\n )\n yield frame_idx, obj_ids, video_res_masks\n\n def _add_output_per_object(\n self, inference_state, frame_idx, current_out, storage_key\n ):\n \"\"\"\n Split a multi-object output into per-object output slices and add them into\n `output_dict_per_obj`. The resulting slices share the same tensor storage.\n \"\"\"\n maskmem_features = current_out[\"maskmem_features\"]\n assert maskmem_features is None or isinstance(maskmem_features, torch.Tensor)\n\n maskmem_pos_enc = current_out[\"maskmem_pos_enc\"]\n assert maskmem_pos_enc is None or isinstance(maskmem_pos_enc, list)\n\n output_dict_per_obj = inference_state[\"output_dict_per_obj\"]\n for obj_idx, obj_output_dict in output_dict_per_obj.items():\n obj_slice = slice(obj_idx, obj_idx + 1)\n obj_out = {\n \"maskmem_features\": None,\n \"maskmem_pos_enc\": None,\n \"pred_masks\": current_out[\"pred_masks\"][obj_slice],\n \"obj_ptr\": current_out[\"obj_ptr\"][obj_slice],\n }\n if maskmem_features is not None:\n obj_out[\"maskmem_features\"] = maskmem_features[obj_slice]\n if maskmem_pos_enc is not None:\n obj_out[\"maskmem_pos_enc\"] = [x[obj_slice] for x in maskmem_pos_enc]\n obj_output_dict[storage_key][frame_idx] = obj_out\n\n @torch.inference_mode()\n def reset_state(self, inference_state):\n \"\"\"Remove all input points or mask in all frames throughout the video.\"\"\"\n self._reset_tracking_results(inference_state)\n # Remove all object ids\n inference_state[\"obj_id_to_idx\"].clear()\n inference_state[\"obj_idx_to_id\"].clear()\n inference_state[\"obj_ids\"].clear()\n inference_state[\"point_inputs_per_obj\"].clear()\n inference_state[\"mask_inputs_per_obj\"].clear()\n inference_state[\"output_dict_per_obj\"].clear()\n inference_state[\"temp_output_dict_per_obj\"].clear()\n\n def _reset_tracking_results(self, inference_state):\n \"\"\"Reset all tracking inputs and results across the videos.\"\"\"\n for v in inference_state[\"point_inputs_per_obj\"].values():\n v.clear()\n for v in inference_state[\"mask_inputs_per_obj\"].values():\n v.clear()\n for v in inference_state[\"output_dict_per_obj\"].values():\n v[\"cond_frame_outputs\"].clear()\n v[\"non_cond_frame_outputs\"].clear()\n for v in inference_state[\"temp_output_dict_per_obj\"].values():\n v[\"cond_frame_outputs\"].clear()\n v[\"non_cond_frame_outputs\"].clear()\n inference_state[\"output_dict\"][\"cond_frame_outputs\"].clear()\n inference_state[\"output_dict\"][\"non_cond_frame_outputs\"].clear()\n inference_state[\"consolidated_frame_inds\"][\"cond_frame_outputs\"].clear()\n inference_state[\"consolidated_frame_inds\"][\"non_cond_frame_outputs\"].clear()\n inference_state[\"tracking_has_started\"] = False\n inference_state[\"frames_already_tracked\"].clear()\n\n def _get_image_feature(self, inference_state, frame_idx, batch_size):\n \"\"\"Compute the image features on a given frame.\"\"\"\n # Look up in the cache first\n image, backbone_out = inference_state[\"cached_features\"].get(\n frame_idx, (None, None)\n )\n if backbone_out is None:\n # Cache miss -- we will run inference on a single image\n device = inference_state[\"device\"]\n image = inference_state[\"images\"][frame_idx].to(device).float().unsqueeze(0)\n backbone_out = self.forward_image(image)\n # Cache the most recent frame's feature (for repeated interactions with\n # a frame; we can use an LRU cache for more frames in the future).\n inference_state[\"cached_features\"] = {frame_idx: (image, backbone_out)}\n\n # expand the features to have the same dimension as the number of objects\n expanded_image = image.expand(batch_size, -1, -1, -1)\n expanded_backbone_out = {\n \"backbone_fpn\": backbone_out[\"backbone_fpn\"].copy(),\n \"vision_pos_enc\": backbone_out[\"vision_pos_enc\"].copy(),\n }\n for i, feat in enumerate(expanded_backbone_out[\"backbone_fpn\"]):\n expanded_backbone_out[\"backbone_fpn\"][i] = feat.expand(\n batch_size, -1, -1, -1\n )\n for i, pos in enumerate(expanded_backbone_out[\"vision_pos_enc\"]):\n pos = pos.expand(batch_size, -1, -1, -1)\n expanded_backbone_out[\"vision_pos_enc\"][i] = pos\n\n features = self._prepare_backbone_features(expanded_backbone_out)\n features = (expanded_image,) + features\n return features\n\n def _run_single_frame_inference(\n self,\n inference_state,\n output_dict,\n frame_idx,\n batch_size,\n is_init_cond_frame,\n point_inputs,\n mask_inputs,\n reverse,\n run_mem_encoder,\n prev_sam_mask_logits=None,\n ):\n \"\"\"Run tracking on a single frame based on current inputs and previous memory.\"\"\"\n # Retrieve correct image features\n (\n _,\n _,\n current_vision_feats,\n current_vision_pos_embeds,\n feat_sizes,\n ) = self._get_image_feature(inference_state, frame_idx, batch_size)\n\n # point and mask should not appear as input simultaneously on the same frame\n assert point_inputs is None or mask_inputs is None\n current_out = self.track_step(\n frame_idx=frame_idx,\n is_init_cond_frame=is_init_cond_frame,\n current_vision_feats=current_vision_feats,\n current_vision_pos_embeds=current_vision_pos_embeds,\n feat_sizes=feat_sizes,\n point_inputs=point_inputs,\n mask_inputs=mask_inputs,\n output_dict=output_dict,\n num_frames=inference_state[\"num_frames\"],\n track_in_reverse=reverse,\n run_mem_encoder=run_mem_encoder,\n prev_sam_mask_logits=prev_sam_mask_logits,\n )\n\n # optionally offload the output to CPU memory to save GPU space\n storage_device = inference_state[\"storage_device\"]\n maskmem_features = current_out[\"maskmem_features\"]\n if maskmem_features is not None:\n maskmem_features = maskmem_features.to(torch.bfloat16)\n maskmem_features = maskmem_features.to(storage_device, non_blocking=True)\n pred_masks_gpu = current_out[\"pred_masks\"]\n # potentially fill holes in the predicted masks\n if self.fill_hole_area > 0:\n pred_masks_gpu = fill_holes_in_mask_scores(\n pred_masks_gpu, self.fill_hole_area\n )\n pred_masks = pred_masks_gpu.to(storage_device, non_blocking=True)\n # \"maskmem_pos_enc\" is the same across frames, so we only need to store one copy of it\n maskmem_pos_enc = self._get_maskmem_pos_enc(inference_state, current_out)\n # object pointer is a small tensor, so we always keep it on GPU memory for fast access\n obj_ptr = current_out[\"obj_ptr\"]\n # make a compact version of this frame's output to reduce the state size\n compact_current_out = {\n \"maskmem_features\": maskmem_features,\n \"maskmem_pos_enc\": maskmem_pos_enc,\n \"pred_masks\": pred_masks,\n \"obj_ptr\": obj_ptr,\n }\n return compact_current_out, pred_masks_gpu\n\n def _run_memory_encoder(\n self, inference_state, frame_idx, batch_size, high_res_masks, is_mask_from_pts\n ):\n \"\"\"\n Run the memory encoder on `high_res_masks`. This is usually after applying\n non-overlapping constraints to object scores. Since their scores changed, their\n memory also need to be computed again with the memory encoder.\n \"\"\"\n # Retrieve correct image features\n _, _, current_vision_feats, _, feat_sizes = self._get_image_feature(\n inference_state, frame_idx, batch_size\n )\n maskmem_features, maskmem_pos_enc = self._encode_new_memory(\n current_vision_feats=current_vision_feats,\n feat_sizes=feat_sizes,\n pred_masks_high_res=high_res_masks,\n is_mask_from_pts=is_mask_from_pts,\n )\n\n # optionally offload the output to CPU memory to save GPU space\n storage_device = inference_state[\"storage_device\"]\n maskmem_features = maskmem_features.to(torch.bfloat16)\n maskmem_features = maskmem_features.to(storage_device, non_blocking=True)\n # \"maskmem_pos_enc\" is the same across frames, so we only need to store one copy of it\n maskmem_pos_enc = self._get_maskmem_pos_enc(\n inference_state, {\"maskmem_pos_enc\": maskmem_pos_enc}\n )\n return maskmem_features, maskmem_pos_enc\n\n def _get_maskmem_pos_enc(self, inference_state, current_out):\n \"\"\"\n `maskmem_pos_enc` is the same across frames and objects, so we cache it as\n a constant in the inference session to reduce session storage size.\n \"\"\"\n model_constants = inference_state[\"constants\"]\n # \"out_maskmem_pos_enc\" should be either a list of tensors or None\n out_maskmem_pos_enc = current_out[\"maskmem_pos_enc\"]\n if out_maskmem_pos_enc is not None:\n if \"maskmem_pos_enc\" not in model_constants:\n assert isinstance(out_maskmem_pos_enc, list)\n # only take the slice for one object, since it's same across objects\n maskmem_pos_enc = [x[0:1].clone() for x in out_maskmem_pos_enc]\n model_constants[\"maskmem_pos_enc\"] = maskmem_pos_enc\n else:\n maskmem_pos_enc = model_constants[\"maskmem_pos_enc\"]\n # expand the cached maskmem_pos_enc to the actual batch size\n batch_size = out_maskmem_pos_enc[0].size(0)\n expanded_maskmem_pos_enc = [\n x.expand(batch_size, -1, -1, -1) for x in maskmem_pos_enc\n ]\n else:\n expanded_maskmem_pos_enc = None\n return expanded_maskmem_pos_enc\n\n def _clear_non_cond_mem_around_input(self, inference_state, frame_idx):\n \"\"\"\n Remove the non-conditioning memory around the input frame. When users provide\n correction clicks, the surrounding frames' non-conditioning memories can still\n contain outdated object appearance information and could confuse the model.\n\n This method clears those non-conditioning memories surrounding the interacted\n frame to avoid giving the model both old and new information about the object.\n \"\"\"\n r = self.memory_temporal_stride_for_eval\n frame_idx_begin = frame_idx - r * self.num_maskmem\n frame_idx_end = frame_idx + r * self.num_maskmem\n output_dict = inference_state[\"output_dict\"]\n non_cond_frame_outputs = output_dict[\"non_cond_frame_outputs\"]\n for t in range(frame_idx_begin, frame_idx_end + 1):\n non_cond_frame_outputs.pop(t, None)\n for obj_output_dict in inference_state[\"output_dict_per_obj\"].values():\n obj_output_dict[\"non_cond_frame_outputs\"].pop(t, None)\n" }, { "path": "auto-seg/sam2/utils/__init__.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n" }, { "path": "auto-seg/sam2/utils/amg.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport math\nfrom copy import deepcopy\nfrom itertools import product\nfrom typing import Any, Dict, Generator, ItemsView, List, Tuple\n\nimport numpy as np\nimport torch\n\n# Very lightly adapted from https://github.com/facebookresearch/segment-anything/blob/main/segment_anything/utils/amg.py\n\n\nclass MaskData:\n \"\"\"\n A structure for storing masks and their related data in batched format.\n Implements basic filtering and concatenation.\n \"\"\"\n\n def __init__(self, **kwargs) -> None:\n for v in kwargs.values():\n assert isinstance(\n v, (list, np.ndarray, torch.Tensor)\n ), \"MaskData only supports list, numpy arrays, and torch tensors.\"\n self._stats = dict(**kwargs)\n\n def __setitem__(self, key: str, item: Any) -> None:\n assert isinstance(\n item, (list, np.ndarray, torch.Tensor)\n ), \"MaskData only supports list, numpy arrays, and torch tensors.\"\n self._stats[key] = item\n\n def __delitem__(self, key: str) -> None:\n del self._stats[key]\n\n def __getitem__(self, key: str) -> Any:\n return self._stats[key]\n\n def items(self) -> ItemsView[str, Any]:\n return self._stats.items()\n\n def filter(self, keep: torch.Tensor) -> None:\n for k, v in self._stats.items():\n if v is None:\n self._stats[k] = None\n elif isinstance(v, torch.Tensor):\n self._stats[k] = v[torch.as_tensor(keep, device=v.device)]\n elif isinstance(v, np.ndarray):\n self._stats[k] = v[keep.detach().cpu().numpy()]\n elif isinstance(v, list) and keep.dtype == torch.bool:\n self._stats[k] = [a for i, a in enumerate(v) if keep[i]]\n elif isinstance(v, list):\n self._stats[k] = [v[i] for i in keep]\n else:\n raise TypeError(f\"MaskData key {k} has an unsupported type {type(v)}.\")\n\n def cat(self, new_stats: \"MaskData\") -> None:\n for k, v in new_stats.items():\n if k not in self._stats or self._stats[k] is None:\n self._stats[k] = deepcopy(v)\n elif isinstance(v, torch.Tensor):\n self._stats[k] = torch.cat([self._stats[k], v], dim=0)\n elif isinstance(v, np.ndarray):\n self._stats[k] = np.concatenate([self._stats[k], v], axis=0)\n elif isinstance(v, list):\n self._stats[k] = self._stats[k] + deepcopy(v)\n else:\n raise TypeError(f\"MaskData key {k} has an unsupported type {type(v)}.\")\n\n def to_numpy(self) -> None:\n for k, v in self._stats.items():\n if isinstance(v, torch.Tensor):\n self._stats[k] = v.float().detach().cpu().numpy()\n\n\ndef is_box_near_crop_edge(\n boxes: torch.Tensor, crop_box: List[int], orig_box: List[int], atol: float = 20.0\n) -> torch.Tensor:\n \"\"\"Filter masks at the edge of a crop, but not at the edge of the original image.\"\"\"\n crop_box_torch = torch.as_tensor(crop_box, dtype=torch.float, device=boxes.device)\n orig_box_torch = torch.as_tensor(orig_box, dtype=torch.float, device=boxes.device)\n boxes = uncrop_boxes_xyxy(boxes, crop_box).float()\n near_crop_edge = torch.isclose(boxes, crop_box_torch[None, :], atol=atol, rtol=0)\n near_image_edge = torch.isclose(boxes, orig_box_torch[None, :], atol=atol, rtol=0)\n near_crop_edge = torch.logical_and(near_crop_edge, ~near_image_edge)\n return torch.any(near_crop_edge, dim=1)\n\n\ndef box_xyxy_to_xywh(box_xyxy: torch.Tensor) -> torch.Tensor:\n box_xywh = deepcopy(box_xyxy)\n box_xywh[2] = box_xywh[2] - box_xywh[0]\n box_xywh[3] = box_xywh[3] - box_xywh[1]\n return box_xywh\n\n\ndef batch_iterator(batch_size: int, *args) -> Generator[List[Any], None, None]:\n assert len(args) > 0 and all(\n len(a) == len(args[0]) for a in args\n ), \"Batched iteration must have inputs of all the same size.\"\n n_batches = len(args[0]) // batch_size + int(len(args[0]) % batch_size != 0)\n for b in range(n_batches):\n yield [arg[b * batch_size : (b + 1) * batch_size] for arg in args]\n\n\ndef mask_to_rle_pytorch(tensor: torch.Tensor) -> List[Dict[str, Any]]:\n \"\"\"\n Encodes masks to an uncompressed RLE, in the format expected by\n pycoco tools.\n \"\"\"\n # Put in fortran order and flatten h,w\n b, h, w = tensor.shape\n tensor = tensor.permute(0, 2, 1).flatten(1)\n\n # Compute change indices\n diff = tensor[:, 1:] ^ tensor[:, :-1]\n change_indices = diff.nonzero()\n\n # Encode run length\n out = []\n for i in range(b):\n cur_idxs = change_indices[change_indices[:, 0] == i, 1]\n cur_idxs = torch.cat(\n [\n torch.tensor([0], dtype=cur_idxs.dtype, device=cur_idxs.device),\n cur_idxs + 1,\n torch.tensor([h * w], dtype=cur_idxs.dtype, device=cur_idxs.device),\n ]\n )\n btw_idxs = cur_idxs[1:] - cur_idxs[:-1]\n counts = [] if tensor[i, 0] == 0 else [0]\n counts.extend(btw_idxs.detach().cpu().tolist())\n out.append({\"size\": [h, w], \"counts\": counts})\n return out\n\n\ndef rle_to_mask(rle: Dict[str, Any]) -> np.ndarray:\n \"\"\"Compute a binary mask from an uncompressed RLE.\"\"\"\n h, w = rle[\"size\"]\n mask = np.empty(h * w, dtype=bool)\n idx = 0\n parity = False\n for count in rle[\"counts\"]:\n mask[idx : idx + count] = parity\n idx += count\n parity ^= True\n mask = mask.reshape(w, h)\n return mask.transpose() # Put in C order\n\n\ndef area_from_rle(rle: Dict[str, Any]) -> int:\n return sum(rle[\"counts\"][1::2])\n\n\ndef calculate_stability_score(\n masks: torch.Tensor, mask_threshold: float, threshold_offset: float\n) -> torch.Tensor:\n \"\"\"\n Computes the stability score for a batch of masks. The stability\n score is the IoU between the binary masks obtained by thresholding\n the predicted mask logits at high and low values.\n \"\"\"\n # One mask is always contained inside the other.\n # Save memory by preventing unnecessary cast to torch.int64\n intersections = (\n (masks > (mask_threshold + threshold_offset))\n .sum(-1, dtype=torch.int16)\n .sum(-1, dtype=torch.int32)\n )\n unions = (\n (masks > (mask_threshold - threshold_offset))\n .sum(-1, dtype=torch.int16)\n .sum(-1, dtype=torch.int32)\n )\n return intersections / unions\n\n\ndef build_point_grid(n_per_side: int) -> np.ndarray:\n \"\"\"Generates a 2D grid of points evenly spaced in [0,1]x[0,1].\"\"\"\n offset = 1 / (2 * n_per_side)\n points_one_side = np.linspace(offset, 1 - offset, n_per_side)\n points_x = np.tile(points_one_side[None, :], (n_per_side, 1))\n points_y = np.tile(points_one_side[:, None], (1, n_per_side))\n points = np.stack([points_x, points_y], axis=-1).reshape(-1, 2)\n return points\n\n\ndef build_all_layer_point_grids(\n n_per_side: int, n_layers: int, scale_per_layer: int\n) -> List[np.ndarray]:\n \"\"\"Generates point grids for all crop layers.\"\"\"\n points_by_layer = []\n for i in range(n_layers + 1):\n n_points = int(n_per_side / (scale_per_layer**i))\n points_by_layer.append(build_point_grid(n_points))\n return points_by_layer\n\n\ndef generate_crop_boxes(\n im_size: Tuple[int, ...], n_layers: int, overlap_ratio: float\n) -> Tuple[List[List[int]], List[int]]:\n \"\"\"\n Generates a list of crop boxes of different sizes. Each layer\n has (2**i)**2 boxes for the ith layer.\n \"\"\"\n crop_boxes, layer_idxs = [], []\n im_h, im_w = im_size\n short_side = min(im_h, im_w)\n\n # Original image\n crop_boxes.append([0, 0, im_w, im_h])\n layer_idxs.append(0)\n\n def crop_len(orig_len, n_crops, overlap):\n return int(math.ceil((overlap * (n_crops - 1) + orig_len) / n_crops))\n\n for i_layer in range(n_layers):\n n_crops_per_side = 2 ** (i_layer + 1)\n overlap = int(overlap_ratio * short_side * (2 / n_crops_per_side))\n\n crop_w = crop_len(im_w, n_crops_per_side, overlap)\n crop_h = crop_len(im_h, n_crops_per_side, overlap)\n\n crop_box_x0 = [int((crop_w - overlap) * i) for i in range(n_crops_per_side)]\n crop_box_y0 = [int((crop_h - overlap) * i) for i in range(n_crops_per_side)]\n\n # Crops in XYWH format\n for x0, y0 in product(crop_box_x0, crop_box_y0):\n box = [x0, y0, min(x0 + crop_w, im_w), min(y0 + crop_h, im_h)]\n crop_boxes.append(box)\n layer_idxs.append(i_layer + 1)\n\n return crop_boxes, layer_idxs\n\n\ndef uncrop_boxes_xyxy(boxes: torch.Tensor, crop_box: List[int]) -> torch.Tensor:\n x0, y0, _, _ = crop_box\n offset = torch.tensor([[x0, y0, x0, y0]], device=boxes.device)\n # Check if boxes has a channel dimension\n if len(boxes.shape) == 3:\n offset = offset.unsqueeze(1)\n return boxes + offset\n\n\ndef uncrop_points(points: torch.Tensor, crop_box: List[int]) -> torch.Tensor:\n x0, y0, _, _ = crop_box\n offset = torch.tensor([[x0, y0]], device=points.device)\n # Check if points has a channel dimension\n if len(points.shape) == 3:\n offset = offset.unsqueeze(1)\n return points + offset\n\n\ndef uncrop_masks(\n masks: torch.Tensor, crop_box: List[int], orig_h: int, orig_w: int\n) -> torch.Tensor:\n x0, y0, x1, y1 = crop_box\n if x0 == 0 and y0 == 0 and x1 == orig_w and y1 == orig_h:\n return masks\n # Coordinate transform masks\n pad_x, pad_y = orig_w - (x1 - x0), orig_h - (y1 - y0)\n pad = (x0, pad_x - x0, y0, pad_y - y0)\n return torch.nn.functional.pad(masks, pad, value=0)\n\n\ndef remove_small_regions(\n mask: np.ndarray, area_thresh: float, mode: str\n) -> Tuple[np.ndarray, bool]:\n \"\"\"\n Removes small disconnected regions and holes in a mask. Returns the\n mask and an indicator of if the mask has been modified.\n \"\"\"\n import cv2 # type: ignore\n\n assert mode in [\"holes\", \"islands\"]\n correct_holes = mode == \"holes\"\n working_mask = (correct_holes ^ mask).astype(np.uint8)\n n_labels, regions, stats, _ = cv2.connectedComponentsWithStats(working_mask, 8)\n sizes = stats[:, -1][1:] # Row 0 is background label\n small_regions = [i + 1 for i, s in enumerate(sizes) if s < area_thresh]\n if len(small_regions) == 0:\n return mask, False\n fill_labels = [0] + small_regions\n if not correct_holes:\n fill_labels = [i for i in range(n_labels) if i not in fill_labels]\n # If every region is below threshold, keep largest\n if len(fill_labels) == 0:\n fill_labels = [int(np.argmax(sizes)) + 1]\n mask = np.isin(regions, fill_labels)\n return mask, True\n\n\ndef coco_encode_rle(uncompressed_rle: Dict[str, Any]) -> Dict[str, Any]:\n from pycocotools import mask as mask_utils # type: ignore\n\n h, w = uncompressed_rle[\"size\"]\n rle = mask_utils.frPyObjects(uncompressed_rle, h, w)\n rle[\"counts\"] = rle[\"counts\"].decode(\"utf-8\") # Necessary to serialize with json\n return rle\n\n\ndef batched_mask_to_box(masks: torch.Tensor) -> torch.Tensor:\n \"\"\"\n Calculates boxes in XYXY format around masks. Return [0,0,0,0] for\n an empty mask. For input shape C1xC2x...xHxW, the output shape is C1xC2x...x4.\n \"\"\"\n # torch.max below raises an error on empty inputs, just skip in this case\n if torch.numel(masks) == 0:\n return torch.zeros(*masks.shape[:-2], 4, device=masks.device)\n\n # Normalize shape to CxHxW\n shape = masks.shape\n h, w = shape[-2:]\n if len(shape) > 2:\n masks = masks.flatten(0, -3)\n else:\n masks = masks.unsqueeze(0)\n\n # Get top and bottom edges\n in_height, _ = torch.max(masks, dim=-1)\n in_height_coords = in_height * torch.arange(h, device=in_height.device)[None, :]\n bottom_edges, _ = torch.max(in_height_coords, dim=-1)\n in_height_coords = in_height_coords + h * (~in_height)\n top_edges, _ = torch.min(in_height_coords, dim=-1)\n\n # Get left and right edges\n in_width, _ = torch.max(masks, dim=-2)\n in_width_coords = in_width * torch.arange(w, device=in_width.device)[None, :]\n right_edges, _ = torch.max(in_width_coords, dim=-1)\n in_width_coords = in_width_coords + w * (~in_width)\n left_edges, _ = torch.min(in_width_coords, dim=-1)\n\n # If the mask is empty the right edge will be to the left of the left edge.\n # Replace these boxes with [0, 0, 0, 0]\n empty_filter = (right_edges < left_edges) | (bottom_edges < top_edges)\n out = torch.stack([left_edges, top_edges, right_edges, bottom_edges], dim=-1)\n out = out * (~empty_filter).unsqueeze(-1)\n\n # Return to original shape\n if len(shape) > 2:\n out = out.reshape(*shape[:-2], 4)\n else:\n out = out[0]\n\n return out\n" }, { "path": "auto-seg/sam2/utils/misc.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport os\nimport warnings\nfrom threading import Thread\n\nimport numpy as np\nimport torch\nfrom PIL import Image\nfrom tqdm import tqdm\n\n\ndef get_sdpa_settings():\n if torch.cuda.is_available():\n old_gpu = torch.cuda.get_device_properties(0).major < 7\n # only use Flash Attention on Ampere (8.0) or newer GPUs\n use_flash_attn = torch.cuda.get_device_properties(0).major >= 8\n if not use_flash_attn:\n warnings.warn(\n \"Flash Attention is disabled as it requires a GPU with Ampere (8.0) CUDA capability.\",\n category=UserWarning,\n stacklevel=2,\n )\n # keep math kernel for PyTorch versions before 2.2 (Flash Attention v2 is only\n # available on PyTorch 2.2+, while Flash Attention v1 cannot handle all cases)\n pytorch_version = tuple(int(v) for v in torch.__version__.split(\".\")[:2])\n if pytorch_version < (2, 2):\n warnings.warn(\n f\"You are using PyTorch {torch.__version__} without Flash Attention v2 support. \"\n \"Consider upgrading to PyTorch 2.2+ for Flash Attention v2 (which could be faster).\",\n category=UserWarning,\n stacklevel=2,\n )\n math_kernel_on = pytorch_version < (2, 2) or not use_flash_attn\n else:\n old_gpu = True\n use_flash_attn = False\n math_kernel_on = True\n\n return old_gpu, use_flash_attn, math_kernel_on\n\n\ndef get_connected_components(mask):\n \"\"\"\n Get the connected components (8-connectivity) of binary masks of shape (N, 1, H, W).\n\n Inputs:\n - mask: A binary mask tensor of shape (N, 1, H, W), where 1 is foreground and 0 is\n background.\n\n Outputs:\n - labels: A tensor of shape (N, 1, H, W) containing the connected component labels\n for foreground pixels and 0 for background pixels.\n - counts: A tensor of shape (N, 1, H, W) containing the area of the connected\n components for foreground pixels and 0 for background pixels.\n \"\"\"\n from sam2 import _C\n\n return _C.get_connected_componnets(mask.to(torch.uint8).contiguous())\n\n\ndef mask_to_box(masks: torch.Tensor):\n \"\"\"\n compute bounding box given an input mask\n\n Inputs:\n - masks: [B, 1, H, W] masks, dtype=torch.Tensor\n\n Returns:\n - box_coords: [B, 1, 4], contains (x, y) coordinates of top left and bottom right box corners, dtype=torch.Tensor\n \"\"\"\n B, _, h, w = masks.shape\n device = masks.device\n xs = torch.arange(w, device=device, dtype=torch.int32)\n ys = torch.arange(h, device=device, dtype=torch.int32)\n grid_xs, grid_ys = torch.meshgrid(xs, ys, indexing=\"xy\")\n grid_xs = grid_xs[None, None, ...].expand(B, 1, h, w)\n grid_ys = grid_ys[None, None, ...].expand(B, 1, h, w)\n min_xs, _ = torch.min(torch.where(masks, grid_xs, w).flatten(-2), dim=-1)\n max_xs, _ = torch.max(torch.where(masks, grid_xs, -1).flatten(-2), dim=-1)\n min_ys, _ = torch.min(torch.where(masks, grid_ys, h).flatten(-2), dim=-1)\n max_ys, _ = torch.max(torch.where(masks, grid_ys, -1).flatten(-2), dim=-1)\n bbox_coords = torch.stack((min_xs, min_ys, max_xs, max_ys), dim=-1)\n\n return bbox_coords\n\n\ndef _load_img_as_tensor(img_path, image_size):\n img_pil = Image.open(img_path)\n img_np = np.array(img_pil.convert(\"RGB\").resize((image_size, image_size)))\n if img_np.dtype == np.uint8: # np.uint8 is expected for JPEG images\n img_np = img_np / 255.0\n else:\n raise RuntimeError(f\"Unknown image dtype: {img_np.dtype} on {img_path}\")\n img = torch.from_numpy(img_np).permute(2, 0, 1)\n video_width, video_height = img_pil.size # the original video size\n return img, video_height, video_width\n\n\nclass AsyncVideoFrameLoader:\n \"\"\"\n A list of video frames to be load asynchronously without blocking session start.\n \"\"\"\n\n def __init__(\n self,\n img_paths,\n image_size,\n offload_video_to_cpu,\n img_mean,\n img_std,\n compute_device,\n ):\n self.img_paths = img_paths\n self.image_size = image_size\n self.offload_video_to_cpu = offload_video_to_cpu\n self.img_mean = img_mean\n self.img_std = img_std\n # items in `self.images` will be loaded asynchronously\n self.images = [None] * len(img_paths)\n # catch and raise any exceptions in the async loading thread\n self.exception = None\n # video_height and video_width be filled when loading the first image\n self.video_height = None\n self.video_width = None\n self.compute_device = compute_device\n\n # load the first frame to fill video_height and video_width and also\n # to cache it (since it's most likely where the user will click)\n self.__getitem__(0)\n\n # load the rest of frames asynchronously without blocking the session start\n def _load_frames():\n try:\n for n in tqdm(range(len(self.images)), desc=\"frame loading (JPEG)\"):\n self.__getitem__(n)\n except Exception as e:\n self.exception = e\n\n self.thread = Thread(target=_load_frames, daemon=True)\n self.thread.start()\n\n def __getitem__(self, index):\n if self.exception is not None:\n raise RuntimeError(\"Failure in frame loading thread\") from self.exception\n\n img = self.images[index]\n if img is not None:\n return img\n\n img, video_height, video_width = _load_img_as_tensor(\n self.img_paths[index], self.image_size\n )\n self.video_height = video_height\n self.video_width = video_width\n # normalize by mean and std\n img -= self.img_mean\n img /= self.img_std\n if not self.offload_video_to_cpu:\n img = img.to(self.compute_device, non_blocking=True)\n self.images[index] = img\n return img\n\n def __len__(self):\n return len(self.images)\n\n\ndef load_video_frames(\n video_path,\n image_size,\n offload_video_to_cpu,\n img_mean=(0.485, 0.456, 0.406),\n img_std=(0.229, 0.224, 0.225),\n async_loading_frames=False,\n compute_device=torch.device(\"cuda\"),\n):\n \"\"\"\n Load the video frames from a directory of JPEG files (\".jpg\" format).\n\n The frames are resized to image_size x image_size and are loaded to GPU if\n `offload_video_to_cpu` is `False` and to CPU if `offload_video_to_cpu` is `True`.\n\n You can load a frame asynchronously by setting `async_loading_frames` to `True`.\n \"\"\"\n if isinstance(video_path, str) and os.path.isdir(video_path):\n jpg_folder = video_path\n else:\n raise NotImplementedError(\n \"Only JPEG frames are supported at this moment. For video files, you may use \"\n \"ffmpeg (https://ffmpeg.org/) to extract frames into a folder of JPEG files, such as \\n\"\n \"```\\n\"\n \"ffmpeg -i .mp4 -q:v 2 -start_number 0 /'%05d.jpg'\\n\"\n \"```\\n\"\n \"where `-q:v` generates high-quality JPEG frames and `-start_number 0` asks \"\n \"ffmpeg to start the JPEG file from 00000.jpg.\"\n )\n\n frame_names = [\n p\n for p in os.listdir(jpg_folder)\n if os.path.splitext(p)[-1] in [\".jpg\", \".jpeg\", \".JPG\", \".JPEG\", \".png\"]\n ]\n try:\n frame_names.sort(key=lambda p: int(os.path.splitext(p)[0]))\n except:\n frame_names.sort(key=lambda p: os.path.splitext(p)[0])\n num_frames = len(frame_names)\n if num_frames == 0:\n raise RuntimeError(f\"no images found in {jpg_folder}\")\n img_paths = [os.path.join(jpg_folder, frame_name) for frame_name in frame_names]\n img_mean = torch.tensor(img_mean, dtype=torch.float32)[:, None, None]\n img_std = torch.tensor(img_std, dtype=torch.float32)[:, None, None]\n\n if async_loading_frames:\n lazy_images = AsyncVideoFrameLoader(\n img_paths,\n image_size,\n offload_video_to_cpu,\n img_mean,\n img_std,\n compute_device,\n )\n return lazy_images, lazy_images.video_height, lazy_images.video_width\n\n images = torch.zeros(num_frames, 3, image_size, image_size, dtype=torch.float32)\n for n, img_path in enumerate(tqdm(img_paths, desc=\"frame loading (JPEG)\")):\n images[n], video_height, video_width = _load_img_as_tensor(img_path, image_size)\n if not offload_video_to_cpu:\n images = images.to(compute_device)\n img_mean = img_mean.to(compute_device)\n img_std = img_std.to(compute_device)\n # normalize by mean and std\n images -= img_mean\n images /= img_std\n return images, video_height, video_width\n\n\ndef fill_holes_in_mask_scores(mask, max_area):\n \"\"\"\n A post processor to fill small holes in mask scores with area under `max_area`.\n \"\"\"\n # Holes are those connected components in background with area <= self.max_area\n # (background regions are those with mask scores <= 0)\n assert max_area > 0, \"max_area must be positive\"\n\n input_mask = mask\n try:\n labels, areas = get_connected_components(mask <= 0)\n is_hole = (labels > 0) & (areas <= max_area)\n # We fill holes with a small positive mask score (0.1) to change them to foreground.\n mask = torch.where(is_hole, 0.1, mask)\n except Exception as e:\n # Skip the post-processing step on removing small holes if the CUDA kernel fails\n warnings.warn(\n f\"{e}\\n\\nSkipping the post-processing step due to the error above. You can \"\n \"still use SAM 2 and it's OK to ignore the error above, although some post-processing \"\n \"functionality may be limited (which doesn't affect the results in most cases; see \"\n \"https://github.com/facebookresearch/segment-anything-2/blob/main/INSTALL.md).\",\n category=UserWarning,\n stacklevel=2,\n )\n mask = input_mask\n\n return mask\n\n\ndef concat_points(old_point_inputs, new_points, new_labels):\n \"\"\"Add new points and labels to previous point inputs (add at the end).\"\"\"\n if old_point_inputs is None:\n points, labels = new_points, new_labels\n else:\n points = torch.cat([old_point_inputs[\"point_coords\"], new_points], dim=1)\n labels = torch.cat([old_point_inputs[\"point_labels\"], new_labels], dim=1)\n\n return {\"point_coords\": points, \"point_labels\": labels}\n" }, { "path": "auto-seg/sam2/utils/transforms.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport warnings\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torchvision.transforms import Normalize, Resize, ToTensor\n\n\nclass SAM2Transforms(nn.Module):\n def __init__(\n self, resolution, mask_threshold, max_hole_area=0.0, max_sprinkle_area=0.0\n ):\n \"\"\"\n Transforms for SAM2.\n \"\"\"\n super().__init__()\n self.resolution = resolution\n self.mask_threshold = mask_threshold\n self.max_hole_area = max_hole_area\n self.max_sprinkle_area = max_sprinkle_area\n self.mean = [0.485, 0.456, 0.406]\n self.std = [0.229, 0.224, 0.225]\n self.to_tensor = ToTensor()\n self.transforms = torch.jit.script(\n nn.Sequential(\n Resize((self.resolution, self.resolution)),\n Normalize(self.mean, self.std),\n )\n )\n\n def __call__(self, x):\n x = self.to_tensor(x)\n return self.transforms(x)\n\n def forward_batch(self, img_list):\n img_batch = [self.transforms(self.to_tensor(img)) for img in img_list]\n img_batch = torch.stack(img_batch, dim=0)\n return img_batch\n\n def transform_coords(\n self, coords: torch.Tensor, normalize=False, orig_hw=None\n ) -> torch.Tensor:\n \"\"\"\n Expects a torch tensor with length 2 in the last dimension. The coordinates can be in absolute image or normalized coordinates,\n If the coords are in absolute image coordinates, normalize should be set to True and original image size is required.\n\n Returns\n Un-normalized coordinates in the range of [0, 1] which is expected by the SAM2 model.\n \"\"\"\n if normalize:\n assert orig_hw is not None\n h, w = orig_hw\n coords = coords.clone()\n coords[..., 0] = coords[..., 0] / w\n coords[..., 1] = coords[..., 1] / h\n\n coords = coords * self.resolution # unnormalize coords\n return coords\n\n def transform_boxes(\n self, boxes: torch.Tensor, normalize=False, orig_hw=None\n ) -> torch.Tensor:\n \"\"\"\n Expects a tensor of shape Bx4. The coordinates can be in absolute image or normalized coordinates,\n if the coords are in absolute image coordinates, normalize should be set to True and original image size is required.\n \"\"\"\n boxes = self.transform_coords(boxes.reshape(-1, 2, 2), normalize, orig_hw)\n return boxes\n\n def postprocess_masks(self, masks: torch.Tensor, orig_hw) -> torch.Tensor:\n \"\"\"\n Perform PostProcessing on output masks.\n \"\"\"\n from sam2.utils.misc import get_connected_components\n\n masks = masks.float()\n input_masks = masks\n mask_flat = masks.flatten(0, 1).unsqueeze(1) # flatten as 1-channel image\n try:\n if self.max_hole_area > 0:\n # Holes are those connected components in background with area <= self.fill_hole_area\n # (background regions are those with mask scores <= self.mask_threshold)\n labels, areas = get_connected_components(\n mask_flat <= self.mask_threshold\n )\n is_hole = (labels > 0) & (areas <= self.max_hole_area)\n is_hole = is_hole.reshape_as(masks)\n # We fill holes with a small positive mask score (10.0) to change them to foreground.\n masks = torch.where(is_hole, self.mask_threshold + 10.0, masks)\n\n if self.max_sprinkle_area > 0:\n labels, areas = get_connected_components(\n mask_flat > self.mask_threshold\n )\n is_hole = (labels > 0) & (areas <= self.max_sprinkle_area)\n is_hole = is_hole.reshape_as(masks)\n # We fill holes with negative mask score (-10.0) to change them to background.\n masks = torch.where(is_hole, self.mask_threshold - 10.0, masks)\n except Exception as e:\n # Skip the post-processing step if the CUDA kernel fails\n warnings.warn(\n f\"{e}\\n\\nSkipping the post-processing step due to the error above. You can \"\n \"still use SAM 2 and it's OK to ignore the error above, although some post-processing \"\n \"functionality may be limited (which doesn't affect the results in most cases; see \"\n \"https://github.com/facebookresearch/segment-anything-2/blob/main/INSTALL.md).\",\n category=UserWarning,\n stacklevel=2,\n )\n masks = input_masks\n\n masks = F.interpolate(masks, orig_hw, mode=\"bilinear\", align_corners=False)\n return masks\n" }, { "path": "auto-seg/sam2_configs/__init__.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n" }, { "path": "auto-seg/sam2_configs/sam2_hiera_b+.yaml", "content": "# @package _global_\n\n# Model\nmodel:\n _target_: sam2.modeling.sam2_base.SAM2Base\n image_encoder:\n _target_: sam2.modeling.backbones.image_encoder.ImageEncoder\n scalp: 1\n trunk:\n _target_: sam2.modeling.backbones.hieradet.Hiera\n embed_dim: 112\n num_heads: 2\n neck:\n _target_: sam2.modeling.backbones.image_encoder.FpnNeck\n position_encoding:\n _target_: sam2.modeling.position_encoding.PositionEmbeddingSine\n num_pos_feats: 256\n normalize: true\n scale: null\n temperature: 10000\n d_model: 256\n backbone_channel_list: [896, 448, 224, 112]\n fpn_top_down_levels: [2, 3] # output level 0 and 1 directly use the backbone features\n fpn_interp_model: nearest\n\n memory_attention:\n _target_: sam2.modeling.memory_attention.MemoryAttention\n d_model: 256\n pos_enc_at_input: true\n layer:\n _target_: sam2.modeling.memory_attention.MemoryAttentionLayer\n activation: relu\n dim_feedforward: 2048\n dropout: 0.1\n pos_enc_at_attn: false\n self_attention:\n _target_: sam2.modeling.sam.transformer.RoPEAttention\n rope_theta: 10000.0\n feat_sizes: [32, 32]\n embedding_dim: 256\n num_heads: 1\n downsample_rate: 1\n dropout: 0.1\n d_model: 256\n pos_enc_at_cross_attn_keys: true\n pos_enc_at_cross_attn_queries: false\n cross_attention:\n _target_: sam2.modeling.sam.transformer.RoPEAttention\n rope_theta: 10000.0\n feat_sizes: [32, 32]\n rope_k_repeat: True\n embedding_dim: 256\n num_heads: 1\n downsample_rate: 1\n dropout: 0.1\n kv_in_dim: 64\n num_layers: 4\n\n memory_encoder:\n _target_: sam2.modeling.memory_encoder.MemoryEncoder\n out_dim: 64\n position_encoding:\n _target_: sam2.modeling.position_encoding.PositionEmbeddingSine\n num_pos_feats: 64\n normalize: true\n scale: null\n temperature: 10000\n mask_downsampler:\n _target_: sam2.modeling.memory_encoder.MaskDownSampler\n kernel_size: 3\n stride: 2\n padding: 1\n fuser:\n _target_: sam2.modeling.memory_encoder.Fuser\n layer:\n _target_: sam2.modeling.memory_encoder.CXBlock\n dim: 256\n kernel_size: 7\n padding: 3\n layer_scale_init_value: 1e-6\n use_dwconv: True # depth-wise convs\n num_layers: 2\n\n num_maskmem: 7\n image_size: 1024\n # apply scaled sigmoid on mask logits for memory encoder, and directly feed input mask as output mask\n sigmoid_scale_for_mem_enc: 20.0\n sigmoid_bias_for_mem_enc: -10.0\n use_mask_input_as_output_without_sam: true\n # Memory\n directly_add_no_mem_embed: true\n # use high-resolution feature map in the SAM mask decoder\n use_high_res_features_in_sam: true\n # output 3 masks on the first click on initial conditioning frames\n multimask_output_in_sam: true\n # SAM heads\n iou_prediction_use_sigmoid: True\n # cross-attend to object pointers from other frames (based on SAM output tokens) in the encoder\n use_obj_ptrs_in_encoder: true\n add_tpos_enc_to_obj_ptrs: false\n only_obj_ptrs_in_the_past_for_eval: true\n # object occlusion prediction\n pred_obj_scores: true\n pred_obj_scores_mlp: true\n fixed_no_obj_ptr: true\n # multimask tracking settings\n multimask_output_for_tracking: true\n use_multimask_token_for_obj_ptr: true\n multimask_min_pt_num: 0\n multimask_max_pt_num: 1\n use_mlp_for_obj_ptr_proj: true\n # Compilation flag\n compile_image_encoder: False\n" }, { "path": "auto-seg/sam2_configs/sam2_hiera_l.yaml", "content": "# @package _global_\n\n# Model\nmodel:\n _target_: sam2.modeling.sam2_base.SAM2Base\n image_encoder:\n _target_: sam2.modeling.backbones.image_encoder.ImageEncoder\n scalp: 1\n trunk:\n _target_: sam2.modeling.backbones.hieradet.Hiera\n embed_dim: 144\n num_heads: 2\n stages: [2, 6, 36, 4]\n global_att_blocks: [23, 33, 43]\n window_pos_embed_bkg_spatial_size: [7, 7]\n window_spec: [8, 4, 16, 8]\n neck:\n _target_: sam2.modeling.backbones.image_encoder.FpnNeck\n position_encoding:\n _target_: sam2.modeling.position_encoding.PositionEmbeddingSine\n num_pos_feats: 256\n normalize: true\n scale: null\n temperature: 10000\n d_model: 256\n backbone_channel_list: [1152, 576, 288, 144]\n fpn_top_down_levels: [2, 3] # output level 0 and 1 directly use the backbone features\n fpn_interp_model: nearest\n\n memory_attention:\n _target_: sam2.modeling.memory_attention.MemoryAttention\n d_model: 256\n pos_enc_at_input: true\n layer:\n _target_: sam2.modeling.memory_attention.MemoryAttentionLayer\n activation: relu\n dim_feedforward: 2048\n dropout: 0.1\n pos_enc_at_attn: false\n self_attention:\n _target_: sam2.modeling.sam.transformer.RoPEAttention\n rope_theta: 10000.0\n feat_sizes: [32, 32]\n embedding_dim: 256\n num_heads: 1\n downsample_rate: 1\n dropout: 0.1\n d_model: 256\n pos_enc_at_cross_attn_keys: true\n pos_enc_at_cross_attn_queries: false\n cross_attention:\n _target_: sam2.modeling.sam.transformer.RoPEAttention\n rope_theta: 10000.0\n feat_sizes: [32, 32]\n rope_k_repeat: True\n embedding_dim: 256\n num_heads: 1\n downsample_rate: 1\n dropout: 0.1\n kv_in_dim: 64\n num_layers: 4\n\n memory_encoder:\n _target_: sam2.modeling.memory_encoder.MemoryEncoder\n out_dim: 64\n position_encoding:\n _target_: sam2.modeling.position_encoding.PositionEmbeddingSine\n num_pos_feats: 64\n normalize: true\n scale: null\n temperature: 10000\n mask_downsampler:\n _target_: sam2.modeling.memory_encoder.MaskDownSampler\n kernel_size: 3\n stride: 2\n padding: 1\n fuser:\n _target_: sam2.modeling.memory_encoder.Fuser\n layer:\n _target_: sam2.modeling.memory_encoder.CXBlock\n dim: 256\n kernel_size: 7\n padding: 3\n layer_scale_init_value: 1e-6\n use_dwconv: True # depth-wise convs\n num_layers: 2\n\n num_maskmem: 7\n image_size: 1024\n # apply scaled sigmoid on mask logits for memory encoder, and directly feed input mask as output mask\n sigmoid_scale_for_mem_enc: 20.0\n sigmoid_bias_for_mem_enc: -10.0\n use_mask_input_as_output_without_sam: true\n # Memory\n directly_add_no_mem_embed: true\n # use high-resolution feature map in the SAM mask decoder\n use_high_res_features_in_sam: true\n # output 3 masks on the first click on initial conditioning frames\n multimask_output_in_sam: true\n # SAM heads\n iou_prediction_use_sigmoid: True\n # cross-attend to object pointers from other frames (based on SAM output tokens) in the encoder\n use_obj_ptrs_in_encoder: true\n add_tpos_enc_to_obj_ptrs: false\n only_obj_ptrs_in_the_past_for_eval: true\n # object occlusion prediction\n pred_obj_scores: true\n pred_obj_scores_mlp: true\n fixed_no_obj_ptr: true\n # multimask tracking settings\n multimask_output_for_tracking: true\n use_multimask_token_for_obj_ptr: true\n multimask_min_pt_num: 0\n multimask_max_pt_num: 1\n use_mlp_for_obj_ptr_proj: true\n # Compilation flag\n compile_image_encoder: False\n" }, { "path": "auto-seg/sam2_configs/sam2_hiera_s.yaml", "content": "# @package _global_\n\n# Model\nmodel:\n _target_: sam2.modeling.sam2_base.SAM2Base\n image_encoder:\n _target_: sam2.modeling.backbones.image_encoder.ImageEncoder\n scalp: 1\n trunk:\n _target_: sam2.modeling.backbones.hieradet.Hiera\n embed_dim: 96\n num_heads: 1\n stages: [1, 2, 11, 2]\n global_att_blocks: [7, 10, 13]\n window_pos_embed_bkg_spatial_size: [7, 7]\n neck:\n _target_: sam2.modeling.backbones.image_encoder.FpnNeck\n position_encoding:\n _target_: sam2.modeling.position_encoding.PositionEmbeddingSine\n num_pos_feats: 256\n normalize: true\n scale: null\n temperature: 10000\n d_model: 256\n backbone_channel_list: [768, 384, 192, 96]\n fpn_top_down_levels: [2, 3] # output level 0 and 1 directly use the backbone features\n fpn_interp_model: nearest\n\n memory_attention:\n _target_: sam2.modeling.memory_attention.MemoryAttention\n d_model: 256\n pos_enc_at_input: true\n layer:\n _target_: sam2.modeling.memory_attention.MemoryAttentionLayer\n activation: relu\n dim_feedforward: 2048\n dropout: 0.1\n pos_enc_at_attn: false\n self_attention:\n _target_: sam2.modeling.sam.transformer.RoPEAttention\n rope_theta: 10000.0\n feat_sizes: [32, 32]\n embedding_dim: 256\n num_heads: 1\n downsample_rate: 1\n dropout: 0.1\n d_model: 256\n pos_enc_at_cross_attn_keys: true\n pos_enc_at_cross_attn_queries: false\n cross_attention:\n _target_: sam2.modeling.sam.transformer.RoPEAttention\n rope_theta: 10000.0\n feat_sizes: [32, 32]\n rope_k_repeat: True\n embedding_dim: 256\n num_heads: 1\n downsample_rate: 1\n dropout: 0.1\n kv_in_dim: 64\n num_layers: 4\n\n memory_encoder:\n _target_: sam2.modeling.memory_encoder.MemoryEncoder\n out_dim: 64\n position_encoding:\n _target_: sam2.modeling.position_encoding.PositionEmbeddingSine\n num_pos_feats: 64\n normalize: true\n scale: null\n temperature: 10000\n mask_downsampler:\n _target_: sam2.modeling.memory_encoder.MaskDownSampler\n kernel_size: 3\n stride: 2\n padding: 1\n fuser:\n _target_: sam2.modeling.memory_encoder.Fuser\n layer:\n _target_: sam2.modeling.memory_encoder.CXBlock\n dim: 256\n kernel_size: 7\n padding: 3\n layer_scale_init_value: 1e-6\n use_dwconv: True # depth-wise convs\n num_layers: 2\n\n num_maskmem: 7\n image_size: 1024\n # apply scaled sigmoid on mask logits for memory encoder, and directly feed input mask as output mask\n sigmoid_scale_for_mem_enc: 20.0\n sigmoid_bias_for_mem_enc: -10.0\n use_mask_input_as_output_without_sam: true\n # Memory\n directly_add_no_mem_embed: true\n # use high-resolution feature map in the SAM mask decoder\n use_high_res_features_in_sam: true\n # output 3 masks on the first click on initial conditioning frames\n multimask_output_in_sam: true\n # SAM heads\n iou_prediction_use_sigmoid: True\n # cross-attend to object pointers from other frames (based on SAM output tokens) in the encoder\n use_obj_ptrs_in_encoder: true\n add_tpos_enc_to_obj_ptrs: false\n only_obj_ptrs_in_the_past_for_eval: true\n # object occlusion prediction\n pred_obj_scores: true\n pred_obj_scores_mlp: true\n fixed_no_obj_ptr: true\n # multimask tracking settings\n multimask_output_for_tracking: true\n use_multimask_token_for_obj_ptr: true\n multimask_min_pt_num: 0\n multimask_max_pt_num: 1\n use_mlp_for_obj_ptr_proj: true\n # Compilation flag\n compile_image_encoder: False\n" }, { "path": "auto-seg/sam2_configs/sam2_hiera_t.yaml", "content": "# @package _global_\n\n# Model\nmodel:\n _target_: sam2.modeling.sam2_base.SAM2Base\n image_encoder:\n _target_: sam2.modeling.backbones.image_encoder.ImageEncoder\n scalp: 1\n trunk:\n _target_: sam2.modeling.backbones.hieradet.Hiera\n embed_dim: 96\n num_heads: 1\n stages: [1, 2, 7, 2]\n global_att_blocks: [5, 7, 9]\n window_pos_embed_bkg_spatial_size: [7, 7]\n neck:\n _target_: sam2.modeling.backbones.image_encoder.FpnNeck\n position_encoding:\n _target_: sam2.modeling.position_encoding.PositionEmbeddingSine\n num_pos_feats: 256\n normalize: true\n scale: null\n temperature: 10000\n d_model: 256\n backbone_channel_list: [768, 384, 192, 96]\n fpn_top_down_levels: [2, 3] # output level 0 and 1 directly use the backbone features\n fpn_interp_model: nearest\n\n memory_attention:\n _target_: sam2.modeling.memory_attention.MemoryAttention\n d_model: 256\n pos_enc_at_input: true\n layer:\n _target_: sam2.modeling.memory_attention.MemoryAttentionLayer\n activation: relu\n dim_feedforward: 2048\n dropout: 0.1\n pos_enc_at_attn: false\n self_attention:\n _target_: sam2.modeling.sam.transformer.RoPEAttention\n rope_theta: 10000.0\n feat_sizes: [32, 32]\n embedding_dim: 256\n num_heads: 1\n downsample_rate: 1\n dropout: 0.1\n d_model: 256\n pos_enc_at_cross_attn_keys: true\n pos_enc_at_cross_attn_queries: false\n cross_attention:\n _target_: sam2.modeling.sam.transformer.RoPEAttention\n rope_theta: 10000.0\n feat_sizes: [32, 32]\n rope_k_repeat: True\n embedding_dim: 256\n num_heads: 1\n downsample_rate: 1\n dropout: 0.1\n kv_in_dim: 64\n num_layers: 4\n\n memory_encoder:\n _target_: sam2.modeling.memory_encoder.MemoryEncoder\n out_dim: 64\n position_encoding:\n _target_: sam2.modeling.position_encoding.PositionEmbeddingSine\n num_pos_feats: 64\n normalize: true\n scale: null\n temperature: 10000\n mask_downsampler:\n _target_: sam2.modeling.memory_encoder.MaskDownSampler\n kernel_size: 3\n stride: 2\n padding: 1\n fuser:\n _target_: sam2.modeling.memory_encoder.Fuser\n layer:\n _target_: sam2.modeling.memory_encoder.CXBlock\n dim: 256\n kernel_size: 7\n padding: 3\n layer_scale_init_value: 1e-6\n use_dwconv: True # depth-wise convs\n num_layers: 2\n\n num_maskmem: 7\n image_size: 1024\n # apply scaled sigmoid on mask logits for memory encoder, and directly feed input mask as output mask\n # SAM decoder\n sigmoid_scale_for_mem_enc: 20.0\n sigmoid_bias_for_mem_enc: -10.0\n use_mask_input_as_output_without_sam: true\n # Memory\n directly_add_no_mem_embed: true\n # use high-resolution feature map in the SAM mask decoder\n use_high_res_features_in_sam: true\n # output 3 masks on the first click on initial conditioning frames\n multimask_output_in_sam: true\n # SAM heads\n iou_prediction_use_sigmoid: True\n # cross-attend to object pointers from other frames (based on SAM output tokens) in the encoder\n use_obj_ptrs_in_encoder: true\n add_tpos_enc_to_obj_ptrs: false\n only_obj_ptrs_in_the_past_for_eval: true\n # object occlusion prediction\n pred_obj_scores: true\n pred_obj_scores_mlp: true\n fixed_no_obj_ptr: true\n # multimask tracking settings\n multimask_output_for_tracking: true\n use_multimask_token_for_obj_ptr: true\n multimask_min_pt_num: 0\n multimask_max_pt_num: 1\n use_mlp_for_obj_ptr_proj: true\n # Compilation flag\n # HieraT does not currently support compilation, should always be set to False\n compile_image_encoder: False\n" }, { "path": "auto-seg/sam2_hiera_l.yaml", "content": "# @package _global_\n# model\nmodel:\n _target_: sam2.modeling.sam2_base.SAM2Base\n image_encoder:\n _target_: sam2.modeling.backbones.image_encoder.ImageEncoder\n scalp: 1\n trunk:\n _target_: sam2.modeling.backbones.hieradet.Hiera\n embed_dim: 144\n num_heads: 2\n stages: [2, 6, 36, 4]\n global_att_blocks: [23, 33, 43]\n window_pos_embed_bkg_spatial_size: [7, 7]\n window_spec: [8, 4, 16, 8]\n neck:\n _target_: sam2.modeling.backbones.image_encoder.FpnNeck\n position_encoding:\n _target_: sam2.modeling.position_encoding.PositionEmbeddingSine\n num_pos_feats: 256\n normalize: true\n scale: null\n temperature: 10000\n d_model: 256\n backbone_channel_list: [1152, 576, 288, 144]\n fpn_top_down_levels: [2, 3] # output level 0 and 1 directly use the backbone features\n fpn_interp_model: nearest\n\n memory_attention:\n _target_: sam2.modeling.memory_attention.MemoryAttention\n d_model: 256\n pos_enc_at_input: true\n layer:\n _target_: sam2.modeling.memory_attention.MemoryAttentionLayer\n activation: relu\n dim_feedforward: 2048\n dropout: 0.1\n pos_enc_at_attn: false\n self_attention:\n _target_: sam2.modeling.sam.transformer.RoPEAttention\n rope_theta: 10000.0\n feat_sizes: [32, 32]\n embedding_dim: 256\n num_heads: 1\n downsample_rate: 1\n dropout: 0.1\n d_model: 256\n pos_enc_at_cross_attn_keys: true\n pos_enc_at_cross_attn_queries: false\n cross_attention:\n _target_: sam2.modeling.sam.transformer.RoPEAttention\n rope_theta: 10000.0\n feat_sizes: [32, 32]\n rope_k_repeat: True\n embedding_dim: 256\n num_heads: 1\n downsample_rate: 1\n dropout: 0.1\n kv_in_dim: 64\n num_layers: 4\n\n memory_encoder:\n _target_: sam2.modeling.memory_encoder.MemoryEncoder\n out_dim: 64\n position_encoding:\n _target_: sam2.modeling.position_encoding.PositionEmbeddingSine\n num_pos_feats: 64\n normalize: true\n scale: null\n temperature: 10000\n mask_downsampler:\n _target_: sam2.modeling.memory_encoder.MaskDownSampler\n kernel_size: 3\n stride: 2\n padding: 1\n fuser:\n _target_: sam2.modeling.memory_encoder.Fuser\n layer:\n _target_: sam2.modeling.memory_encoder.CXBlock\n dim: 256\n kernel_size: 7\n padding: 3\n layer_scale_init_value: 1e-6\n use_dwconv: True # depth-wise convs\n num_layers: 2\n\n num_maskmem: 7\n image_size: 1024\n # apply scaled sigmoid on mask logits for memory encoder, and directly feed input mask as output mask\n sigmoid_scale_for_mem_enc: 20.0\n sigmoid_bias_for_mem_enc: -10.0\n use_mask_input_as_output_without_sam: true\n # Memory\n directly_add_no_mem_embed: true\n # use high-resolution feature map in the SAM mask decoder\n use_high_res_features_in_sam: true\n # output 3 masks on the first click on initial conditioning frames\n multimask_output_in_sam: true\n # SAM heads\n iou_prediction_use_sigmoid: True\n # cross-attend to object pointers from other frames (based on SAM output tokens) in the encoder\n use_obj_ptrs_in_encoder: true\n add_tpos_enc_to_obj_ptrs: false\n only_obj_ptrs_in_the_past_for_eval: true\n # object occlusion prediction\n pred_obj_scores: true\n pred_obj_scores_mlp: true\n fixed_no_obj_ptr: true\n # multimask tracking settings\n multimask_output_for_tracking: true\n use_multimask_token_for_obj_ptr: true\n multimask_min_pt_num: 0\n multimask_max_pt_num: 1\n use_mlp_for_obj_ptr_proj: true\n # Compilation flag\n compile_image_encoder: False\n" }, { "path": "auto-seg/submodules/segment-anything-1/.gitignore", "content": ".nfs*\n\n# compilation and distribution\n__pycache__\n_ext\n*.pyc\n*.pyd\n*.so\n*.dll\n*.egg-info/\nbuild/\ndist/\nwheels/\n\n# pytorch/python/numpy formats\n*.pth\n*.pkl\n*.npy\n*.ts\nmodel_ts*.txt\n\n# onnx models\n*.onnx\n\n# ipython/jupyter notebooks\n**/.ipynb_checkpoints/\n\n# Editor temporaries\n*.swn\n*.swo\n*.swp\n*~\n\n# editor settings\n.idea\n.vscode\n_darcs\n\n# demo \n**/node_modules\nyarn.lock\npackage-lock.json" }, { "path": "auto-seg/submodules/segment-anything-1/README.md", "content": "# Segment-Anything-LangSplat\nThis is the modidied version of [SAM](https://github.com/facebookresearch/segment-anything) for [LangSplat](https://github.com/minghanqin/LangSplat). Please follow the original repository of [SAM](https://github.com/facebookresearch/segment-anything) to install." }, { "path": "auto-seg/submodules/segment-anything-1/scripts/amg.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport cv2 # type: ignore\n\nfrom segment_anything import SamAutomaticMaskGenerator, sam_model_registry\n\nimport argparse\nimport json\nimport os\nfrom typing import Any, Dict, List\n\nparser = argparse.ArgumentParser(\n description=(\n \"Runs automatic mask generation on an input image or directory of images, \"\n \"and outputs masks as either PNGs or COCO-style RLEs. Requires open-cv, \"\n \"as well as pycocotools if saving in RLE format.\"\n )\n)\n\nparser.add_argument(\n \"--input\",\n type=str,\n required=True,\n help=\"Path to either a single input image or folder of images.\",\n)\n\nparser.add_argument(\n \"--output\",\n type=str,\n required=True,\n help=(\n \"Path to the directory where masks will be output. Output will be either a folder \"\n \"of PNGs per image or a single json with COCO-style masks.\"\n ),\n)\n\nparser.add_argument(\n \"--model-type\",\n type=str,\n required=True,\n help=\"The type of model to load, in ['default', 'vit_h', 'vit_l', 'vit_b']\",\n)\n\nparser.add_argument(\n \"--checkpoint\",\n type=str,\n required=True,\n help=\"The path to the SAM checkpoint to use for mask generation.\",\n)\n\nparser.add_argument(\"--device\", type=str, default=\"cuda\", help=\"The device to run generation on.\")\n\nparser.add_argument(\n \"--convert-to-rle\",\n action=\"store_true\",\n help=(\n \"Save masks as COCO RLEs in a single json instead of as a folder of PNGs. \"\n \"Requires pycocotools.\"\n ),\n)\n\namg_settings = parser.add_argument_group(\"AMG Settings\")\n\namg_settings.add_argument(\n \"--points-per-side\",\n type=int,\n default=None,\n help=\"Generate masks by sampling a grid over the image with this many points to a side.\",\n)\n\namg_settings.add_argument(\n \"--points-per-batch\",\n type=int,\n default=None,\n help=\"How many input points to process simultaneously in one batch.\",\n)\n\namg_settings.add_argument(\n \"--pred-iou-thresh\",\n type=float,\n default=None,\n help=\"Exclude masks with a predicted score from the model that is lower than this threshold.\",\n)\n\namg_settings.add_argument(\n \"--stability-score-thresh\",\n type=float,\n default=None,\n help=\"Exclude masks with a stability score lower than this threshold.\",\n)\n\namg_settings.add_argument(\n \"--stability-score-offset\",\n type=float,\n default=None,\n help=\"Larger values perturb the mask more when measuring stability score.\",\n)\n\namg_settings.add_argument(\n \"--box-nms-thresh\",\n type=float,\n default=None,\n help=\"The overlap threshold for excluding a duplicate mask.\",\n)\n\namg_settings.add_argument(\n \"--crop-n-layers\",\n type=int,\n default=None,\n help=(\n \"If >0, mask generation is run on smaller crops of the image to generate more masks. \"\n \"The value sets how many different scales to crop at.\"\n ),\n)\n\namg_settings.add_argument(\n \"--crop-nms-thresh\",\n type=float,\n default=None,\n help=\"The overlap threshold for excluding duplicate masks across different crops.\",\n)\n\namg_settings.add_argument(\n \"--crop-overlap-ratio\",\n type=int,\n default=None,\n help=\"Larger numbers mean image crops will overlap more.\",\n)\n\namg_settings.add_argument(\n \"--crop-n-points-downscale-factor\",\n type=int,\n default=None,\n help=\"The number of points-per-side in each layer of crop is reduced by this factor.\",\n)\n\namg_settings.add_argument(\n \"--min-mask-region-area\",\n type=int,\n default=None,\n help=(\n \"Disconnected mask regions or holes with area smaller than this value \"\n \"in pixels are removed by postprocessing.\"\n ),\n)\n\n\ndef write_masks_to_folder(masks: List[Dict[str, Any]], path: str) -> None:\n header = \"id,area,bbox_x0,bbox_y0,bbox_w,bbox_h,point_input_x,point_input_y,predicted_iou,stability_score,crop_box_x0,crop_box_y0,crop_box_w,crop_box_h\" # noqa\n metadata = [header]\n for i, mask_data in enumerate(masks):\n mask = mask_data[\"segmentation\"]\n filename = f\"{i}.png\"\n cv2.imwrite(os.path.join(path, filename), mask * 255)\n mask_metadata = [\n str(i),\n str(mask_data[\"area\"]),\n *[str(x) for x in mask_data[\"bbox\"]],\n *[str(x) for x in mask_data[\"point_coords\"][0]],\n str(mask_data[\"predicted_iou\"]),\n str(mask_data[\"stability_score\"]),\n *[str(x) for x in mask_data[\"crop_box\"]],\n ]\n row = \",\".join(mask_metadata)\n metadata.append(row)\n metadata_path = os.path.join(path, \"metadata.csv\")\n with open(metadata_path, \"w\") as f:\n f.write(\"\\n\".join(metadata))\n\n return\n\n\ndef get_amg_kwargs(args):\n amg_kwargs = {\n \"points_per_side\": args.points_per_side,\n \"points_per_batch\": args.points_per_batch,\n \"pred_iou_thresh\": args.pred_iou_thresh,\n \"stability_score_thresh\": args.stability_score_thresh,\n \"stability_score_offset\": args.stability_score_offset,\n \"box_nms_thresh\": args.box_nms_thresh,\n \"crop_n_layers\": args.crop_n_layers,\n \"crop_nms_thresh\": args.crop_nms_thresh,\n \"crop_overlap_ratio\": args.crop_overlap_ratio,\n \"crop_n_points_downscale_factor\": args.crop_n_points_downscale_factor,\n \"min_mask_region_area\": args.min_mask_region_area,\n }\n amg_kwargs = {k: v for k, v in amg_kwargs.items() if v is not None}\n return amg_kwargs\n\n\ndef main(args: argparse.Namespace) -> None:\n print(\"Loading model...\")\n sam = sam_model_registry[args.model_type](checkpoint=args.checkpoint)\n _ = sam.to(device=args.device)\n output_mode = \"coco_rle\" if args.convert_to_rle else \"binary_mask\"\n amg_kwargs = get_amg_kwargs(args)\n generator = SamAutomaticMaskGenerator(sam, output_mode=output_mode, **amg_kwargs)\n\n if not os.path.isdir(args.input):\n targets = [args.input]\n else:\n targets = [\n f for f in os.listdir(args.input) if not os.path.isdir(os.path.join(args.input, f))\n ]\n targets = [os.path.join(args.input, f) for f in targets]\n\n os.makedirs(args.output, exist_ok=True)\n\n for t in targets:\n print(f\"Processing '{t}'...\")\n image = cv2.imread(t)\n if image is None:\n print(f\"Could not load '{t}' as an image, skipping...\")\n continue\n image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n\n masks = generator.generate(image)\n\n base = os.path.basename(t)\n base = os.path.splitext(base)[0]\n save_base = os.path.join(args.output, base)\n if output_mode == \"binary_mask\":\n os.makedirs(save_base, exist_ok=False)\n write_masks_to_folder(masks, save_base)\n else:\n save_file = save_base + \".json\"\n with open(save_file, \"w\") as f:\n json.dump(masks, f)\n print(\"Done!\")\n\n\nif __name__ == \"__main__\":\n args = parser.parse_args()\n main(args)\n" }, { "path": "auto-seg/submodules/segment-anything-1/scripts/export_onnx_model.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport torch\n\nfrom segment_anything import sam_model_registry\nfrom segment_anything.utils.onnx import SamOnnxModel\n\nimport argparse\nimport warnings\n\ntry:\n import onnxruntime # type: ignore\n\n onnxruntime_exists = True\nexcept ImportError:\n onnxruntime_exists = False\n\nparser = argparse.ArgumentParser(\n description=\"Export the SAM prompt encoder and mask decoder to an ONNX model.\"\n)\n\nparser.add_argument(\n \"--checkpoint\", type=str, required=True, help=\"The path to the SAM model checkpoint.\"\n)\n\nparser.add_argument(\n \"--output\", type=str, required=True, help=\"The filename to save the ONNX model to.\"\n)\n\nparser.add_argument(\n \"--model-type\",\n type=str,\n required=True,\n help=\"In ['default', 'vit_h', 'vit_l', 'vit_b']. Which type of SAM model to export.\",\n)\n\nparser.add_argument(\n \"--return-single-mask\",\n action=\"store_true\",\n help=(\n \"If true, the exported ONNX model will only return the best mask, \"\n \"instead of returning multiple masks. For high resolution images \"\n \"this can improve runtime when upscaling masks is expensive.\"\n ),\n)\n\nparser.add_argument(\n \"--opset\",\n type=int,\n default=17,\n help=\"The ONNX opset version to use. Must be >=11\",\n)\n\nparser.add_argument(\n \"--quantize-out\",\n type=str,\n default=None,\n help=(\n \"If set, will quantize the model and save it with this name. \"\n \"Quantization is performed with quantize_dynamic from onnxruntime.quantization.quantize.\"\n ),\n)\n\nparser.add_argument(\n \"--gelu-approximate\",\n action=\"store_true\",\n help=(\n \"Replace GELU operations with approximations using tanh. Useful \"\n \"for some runtimes that have slow or unimplemented erf ops, used in GELU.\"\n ),\n)\n\nparser.add_argument(\n \"--use-stability-score\",\n action=\"store_true\",\n help=(\n \"Replaces the model's predicted mask quality score with the stability \"\n \"score calculated on the low resolution masks using an offset of 1.0. \"\n ),\n)\n\nparser.add_argument(\n \"--return-extra-metrics\",\n action=\"store_true\",\n help=(\n \"The model will return five results: (masks, scores, stability_scores, \"\n \"areas, low_res_logits) instead of the usual three. This can be \"\n \"significantly slower for high resolution outputs.\"\n ),\n)\n\n\ndef run_export(\n model_type: str,\n checkpoint: str,\n output: str,\n opset: int,\n return_single_mask: bool,\n gelu_approximate: bool = False,\n use_stability_score: bool = False,\n return_extra_metrics=False,\n):\n print(\"Loading model...\")\n sam = sam_model_registry[model_type](checkpoint=checkpoint)\n\n onnx_model = SamOnnxModel(\n model=sam,\n return_single_mask=return_single_mask,\n use_stability_score=use_stability_score,\n return_extra_metrics=return_extra_metrics,\n )\n\n if gelu_approximate:\n for n, m in onnx_model.named_modules():\n if isinstance(m, torch.nn.GELU):\n m.approximate = \"tanh\"\n\n dynamic_axes = {\n \"point_coords\": {1: \"num_points\"},\n \"point_labels\": {1: \"num_points\"},\n }\n\n embed_dim = sam.prompt_encoder.embed_dim\n embed_size = sam.prompt_encoder.image_embedding_size\n mask_input_size = [4 * x for x in embed_size]\n dummy_inputs = {\n \"image_embeddings\": torch.randn(1, embed_dim, *embed_size, dtype=torch.float),\n \"point_coords\": torch.randint(low=0, high=1024, size=(1, 5, 2), dtype=torch.float),\n \"point_labels\": torch.randint(low=0, high=4, size=(1, 5), dtype=torch.float),\n \"mask_input\": torch.randn(1, 1, *mask_input_size, dtype=torch.float),\n \"has_mask_input\": torch.tensor([1], dtype=torch.float),\n \"orig_im_size\": torch.tensor([1500, 2250], dtype=torch.float),\n }\n\n _ = onnx_model(**dummy_inputs)\n\n output_names = [\"masks\", \"iou_predictions\", \"low_res_masks\"]\n\n with warnings.catch_warnings():\n warnings.filterwarnings(\"ignore\", category=torch.jit.TracerWarning)\n warnings.filterwarnings(\"ignore\", category=UserWarning)\n with open(output, \"wb\") as f:\n print(f\"Exporting onnx model to {output}...\")\n torch.onnx.export(\n onnx_model,\n tuple(dummy_inputs.values()),\n f,\n export_params=True,\n verbose=False,\n opset_version=opset,\n do_constant_folding=True,\n input_names=list(dummy_inputs.keys()),\n output_names=output_names,\n dynamic_axes=dynamic_axes,\n )\n\n if onnxruntime_exists:\n ort_inputs = {k: to_numpy(v) for k, v in dummy_inputs.items()}\n # set cpu provider default\n providers = [\"CPUExecutionProvider\"]\n ort_session = onnxruntime.InferenceSession(output, providers=providers)\n _ = ort_session.run(None, ort_inputs)\n print(\"Model has successfully been run with ONNXRuntime.\")\n\n\ndef to_numpy(tensor):\n return tensor.cpu().numpy()\n\n\nif __name__ == \"__main__\":\n args = parser.parse_args()\n run_export(\n model_type=args.model_type,\n checkpoint=args.checkpoint,\n output=args.output,\n opset=args.opset,\n return_single_mask=args.return_single_mask,\n gelu_approximate=args.gelu_approximate,\n use_stability_score=args.use_stability_score,\n return_extra_metrics=args.return_extra_metrics,\n )\n\n if args.quantize_out is not None:\n assert onnxruntime_exists, \"onnxruntime is required to quantize the model.\"\n from onnxruntime.quantization import QuantType # type: ignore\n from onnxruntime.quantization.quantize import quantize_dynamic # type: ignore\n\n print(f\"Quantizing model and writing to {args.quantize_out}...\")\n quantize_dynamic(\n model_input=args.output,\n model_output=args.quantize_out,\n optimize_model=True,\n per_channel=False,\n reduce_range=False,\n weight_type=QuantType.QUInt8,\n )\n print(\"Done!\")\n" }, { "path": "auto-seg/submodules/segment-anything-1/segment_anything/__init__.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nfrom .build_sam import (\n build_sam,\n build_sam_vit_h,\n build_sam_vit_l,\n build_sam_vit_b,\n sam_model_registry,\n)\nfrom .predictor import SamPredictor\nfrom .automatic_mask_generator import SamAutomaticMaskGenerator\n" }, { "path": "auto-seg/submodules/segment-anything-1/segment_anything/automatic_mask_generator.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport numpy as np\nimport torch\nfrom torchvision.ops.boxes import batched_nms, box_area # type: ignore\n\nfrom typing import Any, Dict, List, Optional, Tuple\n\nfrom .modeling import Sam\nfrom .predictor import SamPredictor\nfrom .utils.amg import (\n MaskData,\n area_from_rle,\n batch_iterator,\n batched_mask_to_box,\n box_xyxy_to_xywh,\n build_all_layer_point_grids,\n calculate_stability_score,\n coco_encode_rle,\n generate_crop_boxes,\n is_box_near_crop_edge,\n mask_to_rle_pytorch,\n remove_small_regions,\n rle_to_mask,\n uncrop_boxes_xyxy,\n uncrop_masks,\n uncrop_points,\n)\n\n\nclass SamAutomaticMaskGenerator:\n def __init__(\n self,\n model: Sam,\n points_per_side: Optional[int] = 32,\n points_per_batch: int = 64,\n pred_iou_thresh: float = 0.88,\n stability_score_thresh: float = 0.95,\n stability_score_offset: float = 1.0,\n box_nms_thresh: float = 0.7,\n crop_n_layers: int = 0,\n crop_nms_thresh: float = 0.7,\n crop_overlap_ratio: float = 512 / 1500,\n crop_n_points_downscale_factor: int = 1,\n point_grids: Optional[List[np.ndarray]] = None,\n min_mask_region_area: int = 0,\n output_mode: str = \"binary_mask\",\n ) -> None:\n \"\"\"\n Using a SAM model, generates masks for the entire image.\n Generates a grid of point prompts over the image, then filters\n low quality and duplicate masks. The default settings are chosen\n for SAM with a ViT-H backbone.\n\n Arguments:\n model (Sam): The SAM model to use for mask prediction.\n points_per_side (int or None): The number of points to be sampled\n along one side of the image. The total number of points is\n points_per_side**2. If None, 'point_grids' must provide explicit\n point sampling.\n points_per_batch (int): Sets the number of points run simultaneously\n by the model. Higher numbers may be faster but use more GPU memory.\n pred_iou_thresh (float): A filtering threshold in [0,1], using the\n model's predicted mask quality.\n stability_score_thresh (float): A filtering threshold in [0,1], using\n the stability of the mask under changes to the cutoff used to binarize\n the model's mask predictions.\n stability_score_offset (float): The amount to shift the cutoff when\n calculated the stability score.\n box_nms_thresh (float): The box IoU cutoff used by non-maximal\n suppression to filter duplicate masks.\n crop_n_layers (int): If >0, mask prediction will be run again on\n crops of the image. Sets the number of layers to run, where each\n layer has 2**i_layer number of image crops.\n crop_nms_thresh (float): The box IoU cutoff used by non-maximal\n suppression to filter duplicate masks between different crops.\n crop_overlap_ratio (float): Sets the degree to which crops overlap.\n In the first crop layer, crops will overlap by this fraction of\n the image length. Later layers with more crops scale down this overlap.\n crop_n_points_downscale_factor (int): The number of points-per-side\n sampled in layer n is scaled down by crop_n_points_downscale_factor**n.\n point_grids (list(np.ndarray) or None): A list over explicit grids\n of points used for sampling, normalized to [0,1]. The nth grid in the\n list is used in the nth crop layer. Exclusive with points_per_side.\n min_mask_region_area (int): If >0, postprocessing will be applied\n to remove disconnected regions and holes in masks with area smaller\n than min_mask_region_area. Requires opencv.\n output_mode (str): The form masks are returned in. Can be 'binary_mask',\n 'uncompressed_rle', or 'coco_rle'. 'coco_rle' requires pycocotools.\n For large resolutions, 'binary_mask' may consume large amounts of\n memory.\n \"\"\"\n\n assert (points_per_side is None) != (\n point_grids is None\n ), \"Exactly one of points_per_side or point_grid must be provided.\"\n if points_per_side is not None:\n self.point_grids = build_all_layer_point_grids(\n points_per_side,\n crop_n_layers,\n crop_n_points_downscale_factor,\n )\n elif point_grids is not None:\n self.point_grids = point_grids\n else:\n raise ValueError(\"Can't have both points_per_side and point_grid be None.\")\n\n assert output_mode in [\n \"binary_mask\",\n \"uncompressed_rle\",\n \"coco_rle\",\n ], f\"Unknown output_mode {output_mode}.\"\n if output_mode == \"coco_rle\":\n from pycocotools import mask as mask_utils # type: ignore # noqa: F401\n\n # if min_mask_region_area > 0:\n # import cv2 # type: ignore # noqa: F401\n\n self.predictor = SamPredictor(model)\n self.points_per_batch = points_per_batch\n self.pred_iou_thresh = pred_iou_thresh\n self.stability_score_thresh = stability_score_thresh\n self.stability_score_offset = stability_score_offset\n self.box_nms_thresh = box_nms_thresh\n self.crop_n_layers = crop_n_layers\n self.crop_nms_thresh = crop_nms_thresh\n self.crop_overlap_ratio = crop_overlap_ratio\n self.crop_n_points_downscale_factor = crop_n_points_downscale_factor\n self.min_mask_region_area = min_mask_region_area\n self.output_mode = output_mode\n\n @torch.no_grad()\n def generate(self, image: np.ndarray) -> List[Dict[str, Any]]:\n \"\"\"\n Generates masks for the given image.\n\n Arguments:\n image (np.ndarray): The image to generate masks for, in HWC uint8 format.\n\n Returns:\n list(dict(str, any)): A list over records for masks. Each record is\n a dict containing the following keys:\n segmentation (dict(str, any) or np.ndarray): The mask. If\n output_mode='binary_mask', is an array of shape HW. Otherwise,\n is a dictionary containing the RLE.\n bbox (list(float)): The box around the mask, in XYWH format.\n area (int): The area in pixels of the mask.\n predicted_iou (float): The model's own prediction of the mask's\n quality. This is filtered by the pred_iou_thresh parameter.\n point_coords (list(list(float))): The point coordinates input\n to the model to generate this mask.\n stability_score (float): A measure of the mask's quality. This\n is filtered on using the stability_score_thresh parameter.\n crop_box (list(float)): The crop of the image used to generate\n the mask, given in XYWH format.\n \"\"\"\n\n # Generate masks\n mask_data, mask_data_s, mask_data_m, mask_data_l = self._generate_masks(image)\n\n curr_anns = self.generate_curr_anns(mask_data)\n curr_anns_s = self.generate_curr_anns(mask_data_s)\n curr_anns_m = self.generate_curr_anns(mask_data_m)\n curr_anns_l = self.generate_curr_anns(mask_data_l)\n return curr_anns, curr_anns_s, curr_anns_m, curr_anns_l\n\n def generate_curr_anns(\n self,\n mask_data,\n\n ) -> List[Dict[str, Any]]:\n\n # Filter small disconnected regions and holes in masks\n if self.min_mask_region_area > 0:\n mask_data = self.postprocess_small_regions(\n mask_data,\n self.min_mask_region_area,\n max(self.box_nms_thresh, self.crop_nms_thresh),\n )\n\n # Encode masks\n if self.output_mode == \"coco_rle\":\n mask_data[\"segmentations\"] = [coco_encode_rle(rle) for rle in mask_data[\"rles\"]]\n elif self.output_mode == \"binary_mask\":\n mask_data[\"segmentations\"] = [rle_to_mask(rle) for rle in mask_data[\"rles\"]]\n else:\n mask_data[\"segmentations\"] = mask_data[\"rles\"]\n\n # Write mask records\n curr_anns = []\n for idx in range(len(mask_data[\"segmentations\"])):\n ann = {\n \"segmentation\": mask_data[\"segmentations\"][idx],\n \"area\": area_from_rle(mask_data[\"rles\"][idx]),\n \"bbox\": box_xyxy_to_xywh(mask_data[\"boxes\"][idx]).tolist(),\n \"predicted_iou\": mask_data[\"iou_preds\"][idx].item(),\n \"point_coords\": [mask_data[\"points\"][idx].tolist()],\n \"stability_score\": mask_data[\"stability_score\"][idx].item(),\n \"crop_box\": box_xyxy_to_xywh(mask_data[\"crop_boxes\"][idx]).tolist(),\n }\n curr_anns.append(ann)\n\n return curr_anns\n\n def _generate_masks(self, image: np.ndarray) -> MaskData:\n orig_size = image.shape[:2]\n crop_boxes, layer_idxs = generate_crop_boxes(\n orig_size, self.crop_n_layers, self.crop_overlap_ratio\n )\n\n # Iterate over image crops\n data = MaskData()\n data_s, data_m, data_l = MaskData(), MaskData(), MaskData()\n for crop_box, layer_idx in zip(crop_boxes, layer_idxs):\n crop_data, crop_data_s, crop_data_m, crop_data_l = self._process_crop(image, crop_box, layer_idx, orig_size)\n data.cat(crop_data)\n data_s.cat(crop_data_s)\n data_m.cat(crop_data_m)\n data_l.cat(crop_data_l)\n data = self._generate_masks_data(data, crop_boxes)\n data_s = self._generate_masks_data(data_s, crop_boxes)\n data_m = self._generate_masks_data(data_m, crop_boxes)\n data_l = self._generate_masks_data(data_l, crop_boxes)\n return data, data_s, data_m, data_l\n\n def _generate_masks_data(\n self,\n data,\n crop_boxes,\n\n ) -> MaskData:\n\n # Remove duplicate masks between crops\n if len(crop_boxes) > 1:\n # Prefer masks from smaller crops\n scores = 1 / box_area(data[\"crop_boxes\"])\n scores = scores.to(data[\"boxes\"].device)\n keep_by_nms = batched_nms(\n data[\"boxes\"].float(),\n scores,\n torch.zeros_like(data[\"boxes\"][:, 0]), # categories\n iou_threshold=self.crop_nms_thresh,\n )\n data.filter(keep_by_nms)\n\n data.to_numpy()\n return data\n\n def _process_crop(\n self,\n image: np.ndarray,\n crop_box: List[int],\n crop_layer_idx: int,\n orig_size: Tuple[int, ...],\n ) -> MaskData:\n # Crop the image and calculate embeddings\n x0, y0, x1, y1 = crop_box\n cropped_im = image[y0:y1, x0:x1, :]\n cropped_im_size = cropped_im.shape[:2]\n self.predictor.set_image(cropped_im)\n\n # Get points for this crop\n points_scale = np.array(cropped_im_size)[None, ::-1]\n points_for_image = self.point_grids[crop_layer_idx] * points_scale\n\n # Generate masks for this crop in batches\n data = MaskData()\n data_s, data_m, data_l = MaskData(), MaskData(), MaskData()\n for (points,) in batch_iterator(self.points_per_batch, points_for_image):\n batch_data, batch_data_s, batch_data_m, batch_data_l = self._process_batch(points, cropped_im_size, crop_box, orig_size)\n data.cat(batch_data)\n data_s.cat(batch_data_s)\n data_m.cat(batch_data_m)\n data_l.cat(batch_data_l)\n del batch_data, batch_data_s, batch_data_m, batch_data_l\n self.predictor.reset_image()\n data = self._process_crop_data(data, crop_box)\n data_s = self._process_crop_data(data_s, crop_box)\n data_m = self._process_crop_data(data_m, crop_box)\n data_l = self._process_crop_data(data_l, crop_box)\n return data, data_s, data_m, data_l\n\n def _process_crop_data(\n self,\n data,\n crop_box: List[int],\n ) -> MaskData:\n # Remove duplicates within this crop.\n keep_by_nms = batched_nms(\n data[\"boxes\"].float(),\n data[\"iou_preds\"],\n torch.zeros_like(data[\"boxes\"][:, 0]), # categories\n iou_threshold=self.box_nms_thresh,\n )\n data.filter(keep_by_nms)\n\n # Return to the original image frame\n data[\"boxes\"] = uncrop_boxes_xyxy(data[\"boxes\"], crop_box)\n data[\"points\"] = uncrop_points(data[\"points\"], crop_box)\n data[\"crop_boxes\"] = torch.tensor([crop_box for _ in range(len(data[\"rles\"]))])\n\n return data\n\n def _process_batch(\n self,\n points: np.ndarray,\n im_size: Tuple[int, ...],\n crop_box: List[int],\n orig_size: Tuple[int, ...],\n ) -> MaskData:\n orig_h, orig_w = orig_size\n\n # Run model on this batch\n transformed_points = self.predictor.transform.apply_coords(points, im_size)\n in_points = torch.as_tensor(transformed_points, device=self.predictor.device)\n in_labels = torch.ones(in_points.shape[0], dtype=torch.int, device=in_points.device)\n masks, iou_preds, _ = self.predictor.predict_torch(\n in_points[:, None, :],\n in_labels[:, None],\n multimask_output=True,\n return_logits=True,\n )\n # print('..............................................')\n # print(masks.shape)torch.Size([64, 3, 738, 994])\n # print(masks.flatten(0, 1).shape)torch.Size([192, 738, 994])\n # print(masks[:,0,:,:].shape)torch.Size([64, 738, 994]\n # print(points.shape)(64, 2)\n # print(torch.as_tensor(points.repeat(masks.shape[1], axis=0)).shape)torch.Size([192, 2])\n # print(iou_preds.shape)torch.Size([64, 3])\n # Serialize predictions and store in MaskData\n # mask type: s m l\n data_s = MaskData(\n masks=masks[:,0,:,:],\n iou_preds=iou_preds[:,0],\n points=torch.as_tensor(points),\n )\n data_m = MaskData(\n masks=masks[:,1,:,:],\n iou_preds=iou_preds[:,1],\n points=torch.as_tensor(points),\n )\n data_l = MaskData(\n masks=masks[:,2,:,:],\n iou_preds=iou_preds[:,2],\n points=torch.as_tensor(points),\n )\n data = MaskData(\n masks=masks.flatten(0, 1),\n iou_preds=iou_preds.flatten(0, 1),\n points=torch.as_tensor(points.repeat(masks.shape[1], axis=0)),\n )\n del masks\n\n data = self._process_batch_data(data, crop_box, orig_w, orig_h)\n data_s = self._process_batch_data(data_s, crop_box, orig_w, orig_h)\n data_m = self._process_batch_data(data_m, crop_box, orig_w, orig_h)\n data_l = self._process_batch_data(data_l, crop_box, orig_w, orig_h)\n return data, data_s, data_m, data_l\n \n def _process_batch_data(\n self,\n data,\n crop_box,\n orig_w,\n orig_h,\n\n ):\n # Filter by predicted IoU\n if self.pred_iou_thresh > 0.0:\n keep_mask = data[\"iou_preds\"] > self.pred_iou_thresh\n data.filter(keep_mask)\n\n # Calculate stability score\n data[\"stability_score\"] = calculate_stability_score(\n data[\"masks\"], self.predictor.model.mask_threshold, self.stability_score_offset\n )\n if self.stability_score_thresh > 0.0:\n keep_mask = data[\"stability_score\"] >= self.stability_score_thresh\n data.filter(keep_mask)\n\n # Threshold masks and calculate boxes\n data[\"masks\"] = data[\"masks\"] > self.predictor.model.mask_threshold\n data[\"boxes\"] = batched_mask_to_box(data[\"masks\"])\n\n # Filter boxes that touch crop boundaries\n keep_mask = ~is_box_near_crop_edge(data[\"boxes\"], crop_box, [0, 0, orig_w, orig_h])\n if not torch.all(keep_mask):\n data.filter(keep_mask)\n\n # Compress to RLE\n data[\"masks\"] = uncrop_masks(data[\"masks\"], crop_box, orig_h, orig_w)\n data[\"rles\"] = mask_to_rle_pytorch(data[\"masks\"])\n del data[\"masks\"]\n\n return data\n\n @staticmethod\n def postprocess_small_regions(\n mask_data: MaskData, min_area: int, nms_thresh: float\n ) -> MaskData:\n \"\"\"\n Removes small disconnected regions and holes in masks, then reruns\n box NMS to remove any new duplicates.\n\n Edits mask_data in place.\n\n Requires open-cv as a dependency.\n \"\"\"\n if len(mask_data[\"rles\"]) == 0:\n return mask_data\n\n # Filter small disconnected regions and holes\n new_masks = []\n scores = []\n for rle in mask_data[\"rles\"]:\n mask = rle_to_mask(rle)\n\n mask, changed = remove_small_regions(mask, min_area, mode=\"holes\")\n unchanged = not changed\n mask, changed = remove_small_regions(mask, min_area, mode=\"islands\")\n unchanged = unchanged and not changed\n\n new_masks.append(torch.as_tensor(mask).unsqueeze(0))\n # Give score=0 to changed masks and score=1 to unchanged masks\n # so NMS will prefer ones that didn't need postprocessing\n scores.append(float(unchanged))\n\n # Recalculate boxes and remove any new duplicates\n masks = torch.cat(new_masks, dim=0)\n boxes = batched_mask_to_box(masks)\n keep_by_nms = batched_nms(\n boxes.float(),\n torch.as_tensor(scores),\n torch.zeros_like(boxes[:, 0]), # categories\n iou_threshold=nms_thresh,\n )\n\n # Only recalculate RLEs for masks that have changed\n for i_mask in keep_by_nms:\n if scores[i_mask] == 0.0:\n mask_torch = masks[i_mask].unsqueeze(0)\n mask_data[\"rles\"][i_mask] = mask_to_rle_pytorch(mask_torch)[0]\n mask_data[\"boxes\"][i_mask] = boxes[i_mask] # update res directly\n mask_data.filter(keep_by_nms)\n\n return mask_data\n\n @torch.no_grad()\n def generate_l(self, image: np.ndarray) -> List[Dict[str, Any]]:\n \"\"\"\n Generates only large masks for the given image, skipping the generation of small and medium masks.\n\n Arguments:\n image (np.ndarray): The image to generate masks for, in HWC uint8 format.\n\n Returns:\n list(dict(str, any)): A list of records for large masks only.\n \"\"\"\n # Generate only large masks\n mask_data_l = self._generate_masks_l(image)\n curr_anns_l = self.generate_curr_anns(mask_data_l)\n return curr_anns_l\n\n def _generate_masks_l(self, image: np.ndarray) -> MaskData:\n \"\"\"\n Internal method that generates only large masks for the given image.\n Skips the generation of small and medium masks for better performance.\n \n Arguments:\n image (np.ndarray): The image to generate masks for.\n \n Returns:\n MaskData: The mask data for large masks only.\n \"\"\"\n orig_size = image.shape[:2]\n crop_boxes, layer_idxs = generate_crop_boxes(\n orig_size, self.crop_n_layers, self.crop_overlap_ratio\n )\n\n # Iterate over image crops\n data_l = MaskData()\n for crop_box, layer_idx in zip(crop_boxes, layer_idxs):\n crop_data_l = self._process_crop_l(image, crop_box, layer_idx, orig_size)\n data_l.cat(crop_data_l)\n \n data_l = self._generate_masks_data(data_l, crop_boxes)\n return data_l\n\n def _process_crop_l(\n self,\n image: np.ndarray,\n crop_box: List[int],\n crop_layer_idx: int,\n orig_size: Tuple[int, ...],\n ) -> MaskData:\n # Crop the image and calculate embeddings\n x0, y0, x1, y1 = crop_box\n cropped_im = image[y0:y1, x0:x1, :]\n cropped_im_size = cropped_im.shape[:2]\n self.predictor.set_image(cropped_im)\n\n # Get points for this crop\n points_scale = np.array(cropped_im_size)[None, ::-1]\n points_for_image = self.point_grids[crop_layer_idx] * points_scale\n\n # Generate only large masks for this crop in batches\n data_l = MaskData()\n for (points,) in batch_iterator(self.points_per_batch, points_for_image):\n batch_data_l = self._process_batch_l(points, cropped_im_size, crop_box, orig_size)\n data_l.cat(batch_data_l)\n del batch_data_l\n self.predictor.reset_image()\n \n data_l = self._process_crop_data(data_l, crop_box)\n return data_l\n\n def _process_batch_l(\n self,\n points: np.ndarray,\n im_size: Tuple[int, ...],\n crop_box: List[int],\n orig_size: Tuple[int, ...],\n ) -> MaskData:\n orig_h, orig_w = orig_size\n\n # Run model on this batch\n transformed_points = self.predictor.transform.apply_coords(points, im_size)\n in_points = torch.as_tensor(transformed_points, device=self.predictor.device)\n in_labels = torch.ones(in_points.shape[0], dtype=torch.int, device=in_points.device)\n masks, iou_preds, _ = self.predictor.predict_torch(\n in_points[:, None, :],\n in_labels[:, None],\n multimask_output=True,\n return_logits=True,\n )\n\n # Only process the large masks (index 2)\n data_l = MaskData(\n masks=masks[:,2,:,:], # Only take the large mask\n iou_preds=iou_preds[:,2], # Only take the large mask IoU\n points=torch.as_tensor(points),\n )\n del masks\n\n # Filter by predicted IoU and stability score\n if self.pred_iou_thresh > 0.0:\n keep_mask = data_l[\"iou_preds\"] > self.pred_iou_thresh\n data_l.filter(keep_mask)\n\n # Calculate stability score\n data_l[\"stability_score\"] = calculate_stability_score(\n data_l[\"masks\"], self.predictor.model.mask_threshold, self.stability_score_offset\n )\n if self.stability_score_thresh > 0.0:\n keep_mask = data_l[\"stability_score\"] >= self.stability_score_thresh\n data_l.filter(keep_mask)\n\n # Threshold masks and calculate boxes\n data_l[\"masks\"] = data_l[\"masks\"] > self.predictor.model.mask_threshold\n data_l[\"boxes\"] = batched_mask_to_box(data_l[\"masks\"])\n\n # Filter boxes that touch crop boundaries\n keep_mask = ~is_box_near_crop_edge(data_l[\"boxes\"], crop_box, [0, 0, orig_w, orig_h])\n if not torch.all(keep_mask):\n data_l.filter(keep_mask)\n\n # Compress to RLE\n data_l[\"masks\"] = uncrop_masks(data_l[\"masks\"], crop_box, orig_h, orig_w)\n data_l[\"rles\"] = mask_to_rle_pytorch(data_l[\"masks\"])\n del data_l[\"masks\"]\n\n return data_l\n" }, { "path": "auto-seg/submodules/segment-anything-1/segment_anything/build_sam.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport torch\n\nfrom functools import partial\n\nfrom .modeling import ImageEncoderViT, MaskDecoder, PromptEncoder, Sam, TwoWayTransformer\n\n\ndef build_sam_vit_h(checkpoint=None):\n return _build_sam(\n encoder_embed_dim=1280,\n encoder_depth=32,\n encoder_num_heads=16,\n encoder_global_attn_indexes=[7, 15, 23, 31],\n checkpoint=checkpoint,\n )\n\n\nbuild_sam = build_sam_vit_h\n\n\ndef build_sam_vit_l(checkpoint=None):\n return _build_sam(\n encoder_embed_dim=1024,\n encoder_depth=24,\n encoder_num_heads=16,\n encoder_global_attn_indexes=[5, 11, 17, 23],\n checkpoint=checkpoint,\n )\n\n\ndef build_sam_vit_b(checkpoint=None):\n return _build_sam(\n encoder_embed_dim=768,\n encoder_depth=12,\n encoder_num_heads=12,\n encoder_global_attn_indexes=[2, 5, 8, 11],\n checkpoint=checkpoint,\n )\n\n\nsam_model_registry = {\n \"default\": build_sam_vit_h,\n \"vit_h\": build_sam_vit_h,\n \"vit_l\": build_sam_vit_l,\n \"vit_b\": build_sam_vit_b,\n}\n\n\ndef _build_sam(\n encoder_embed_dim,\n encoder_depth,\n encoder_num_heads,\n encoder_global_attn_indexes,\n checkpoint=None,\n):\n prompt_embed_dim = 256\n image_size = 1024\n vit_patch_size = 16\n image_embedding_size = image_size // vit_patch_size\n sam = Sam(\n image_encoder=ImageEncoderViT(\n depth=encoder_depth,\n embed_dim=encoder_embed_dim,\n img_size=image_size,\n mlp_ratio=4,\n norm_layer=partial(torch.nn.LayerNorm, eps=1e-6),\n num_heads=encoder_num_heads,\n patch_size=vit_patch_size,\n qkv_bias=True,\n use_rel_pos=True,\n global_attn_indexes=encoder_global_attn_indexes,\n window_size=14,\n out_chans=prompt_embed_dim,\n ),\n prompt_encoder=PromptEncoder(\n embed_dim=prompt_embed_dim,\n image_embedding_size=(image_embedding_size, image_embedding_size),\n input_image_size=(image_size, image_size),\n mask_in_chans=16,\n ),\n mask_decoder=MaskDecoder(\n num_multimask_outputs=3,\n transformer=TwoWayTransformer(\n depth=2,\n embedding_dim=prompt_embed_dim,\n mlp_dim=2048,\n num_heads=8,\n ),\n transformer_dim=prompt_embed_dim,\n iou_head_depth=3,\n iou_head_hidden_dim=256,\n ),\n pixel_mean=[123.675, 116.28, 103.53],\n pixel_std=[58.395, 57.12, 57.375],\n )\n sam.eval()\n if checkpoint is not None:\n with open(checkpoint, \"rb\") as f:\n state_dict = torch.load(f)\n sam.load_state_dict(state_dict)\n return sam\n" }, { "path": "auto-seg/submodules/segment-anything-1/segment_anything/modeling/__init__.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nfrom .sam import Sam\nfrom .image_encoder import ImageEncoderViT\nfrom .mask_decoder import MaskDecoder\nfrom .prompt_encoder import PromptEncoder\nfrom .transformer import TwoWayTransformer\n" }, { "path": "auto-seg/submodules/segment-anything-1/segment_anything/modeling/common.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport torch\nimport torch.nn as nn\n\nfrom typing import Type\n\n\nclass MLPBlock(nn.Module):\n def __init__(\n self,\n embedding_dim: int,\n mlp_dim: int,\n act: Type[nn.Module] = nn.GELU,\n ) -> None:\n super().__init__()\n self.lin1 = nn.Linear(embedding_dim, mlp_dim)\n self.lin2 = nn.Linear(mlp_dim, embedding_dim)\n self.act = act()\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n return self.lin2(self.act(self.lin1(x)))\n\n\n# From https://github.com/facebookresearch/detectron2/blob/main/detectron2/layers/batch_norm.py # noqa\n# Itself from https://github.com/facebookresearch/ConvNeXt/blob/d1fa8f6fef0a165b27399986cc2bdacc92777e40/models/convnext.py#L119 # noqa\nclass LayerNorm2d(nn.Module):\n def __init__(self, num_channels: int, eps: float = 1e-6) -> None:\n super().__init__()\n self.weight = nn.Parameter(torch.ones(num_channels))\n self.bias = nn.Parameter(torch.zeros(num_channels))\n self.eps = eps\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n u = x.mean(1, keepdim=True)\n s = (x - u).pow(2).mean(1, keepdim=True)\n x = (x - u) / torch.sqrt(s + self.eps)\n x = self.weight[:, None, None] * x + self.bias[:, None, None]\n return x\n" }, { "path": "auto-seg/submodules/segment-anything-1/segment_anything/modeling/image_encoder.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom typing import Optional, Tuple, Type\n\nfrom .common import LayerNorm2d, MLPBlock\n\n\n# This class and its supporting functions below lightly adapted from the ViTDet backbone available at: https://github.com/facebookresearch/detectron2/blob/main/detectron2/modeling/backbone/vit.py # noqa\nclass ImageEncoderViT(nn.Module):\n def __init__(\n self,\n img_size: int = 1024,\n patch_size: int = 16,\n in_chans: int = 3,\n embed_dim: int = 768,\n depth: int = 12,\n num_heads: int = 12,\n mlp_ratio: float = 4.0,\n out_chans: int = 256,\n qkv_bias: bool = True,\n norm_layer: Type[nn.Module] = nn.LayerNorm,\n act_layer: Type[nn.Module] = nn.GELU,\n use_abs_pos: bool = True,\n use_rel_pos: bool = False,\n rel_pos_zero_init: bool = True,\n window_size: int = 0,\n global_attn_indexes: Tuple[int, ...] = (),\n ) -> None:\n \"\"\"\n Args:\n img_size (int): Input image size.\n patch_size (int): Patch size.\n in_chans (int): Number of input image channels.\n embed_dim (int): Patch embedding dimension.\n depth (int): Depth of ViT.\n num_heads (int): Number of attention heads in each ViT block.\n mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.\n qkv_bias (bool): If True, add a learnable bias to query, key, value.\n norm_layer (nn.Module): Normalization layer.\n act_layer (nn.Module): Activation layer.\n use_abs_pos (bool): If True, use absolute positional embeddings.\n use_rel_pos (bool): If True, add relative positional embeddings to the attention map.\n rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.\n window_size (int): Window size for window attention blocks.\n global_attn_indexes (list): Indexes for blocks using global attention.\n \"\"\"\n super().__init__()\n self.img_size = img_size\n\n self.patch_embed = PatchEmbed(\n kernel_size=(patch_size, patch_size),\n stride=(patch_size, patch_size),\n in_chans=in_chans,\n embed_dim=embed_dim,\n )\n\n self.pos_embed: Optional[nn.Parameter] = None\n if use_abs_pos:\n # Initialize absolute positional embedding with pretrain image size.\n self.pos_embed = nn.Parameter(\n torch.zeros(1, img_size // patch_size, img_size // patch_size, embed_dim)\n )\n\n self.blocks = nn.ModuleList()\n for i in range(depth):\n block = Block(\n dim=embed_dim,\n num_heads=num_heads,\n mlp_ratio=mlp_ratio,\n qkv_bias=qkv_bias,\n norm_layer=norm_layer,\n act_layer=act_layer,\n use_rel_pos=use_rel_pos,\n rel_pos_zero_init=rel_pos_zero_init,\n window_size=window_size if i not in global_attn_indexes else 0,\n input_size=(img_size // patch_size, img_size // patch_size),\n )\n self.blocks.append(block)\n\n self.neck = nn.Sequential(\n nn.Conv2d(\n embed_dim,\n out_chans,\n kernel_size=1,\n bias=False,\n ),\n LayerNorm2d(out_chans),\n nn.Conv2d(\n out_chans,\n out_chans,\n kernel_size=3,\n padding=1,\n bias=False,\n ),\n LayerNorm2d(out_chans),\n )\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n x = self.patch_embed(x)\n if self.pos_embed is not None:\n x = x + self.pos_embed\n\n for blk in self.blocks:\n x = blk(x)\n\n x = self.neck(x.permute(0, 3, 1, 2))\n\n return x\n\n\nclass Block(nn.Module):\n \"\"\"Transformer blocks with support of window attention and residual propagation blocks\"\"\"\n\n def __init__(\n self,\n dim: int,\n num_heads: int,\n mlp_ratio: float = 4.0,\n qkv_bias: bool = True,\n norm_layer: Type[nn.Module] = nn.LayerNorm,\n act_layer: Type[nn.Module] = nn.GELU,\n use_rel_pos: bool = False,\n rel_pos_zero_init: bool = True,\n window_size: int = 0,\n input_size: Optional[Tuple[int, int]] = None,\n ) -> None:\n \"\"\"\n Args:\n dim (int): Number of input channels.\n num_heads (int): Number of attention heads in each ViT block.\n mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.\n qkv_bias (bool): If True, add a learnable bias to query, key, value.\n norm_layer (nn.Module): Normalization layer.\n act_layer (nn.Module): Activation layer.\n use_rel_pos (bool): If True, add relative positional embeddings to the attention map.\n rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.\n window_size (int): Window size for window attention blocks. If it equals 0, then\n use global attention.\n input_size (tuple(int, int) or None): Input resolution for calculating the relative\n positional parameter size.\n \"\"\"\n super().__init__()\n self.norm1 = norm_layer(dim)\n self.attn = Attention(\n dim,\n num_heads=num_heads,\n qkv_bias=qkv_bias,\n use_rel_pos=use_rel_pos,\n rel_pos_zero_init=rel_pos_zero_init,\n input_size=input_size if window_size == 0 else (window_size, window_size),\n )\n\n self.norm2 = norm_layer(dim)\n self.mlp = MLPBlock(embedding_dim=dim, mlp_dim=int(dim * mlp_ratio), act=act_layer)\n\n self.window_size = window_size\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n shortcut = x\n x = self.norm1(x)\n # Window partition\n if self.window_size > 0:\n H, W = x.shape[1], x.shape[2]\n x, pad_hw = window_partition(x, self.window_size)\n\n x = self.attn(x)\n # Reverse window partition\n if self.window_size > 0:\n x = window_unpartition(x, self.window_size, pad_hw, (H, W))\n\n x = shortcut + x\n x = x + self.mlp(self.norm2(x))\n\n return x\n\n\nclass Attention(nn.Module):\n \"\"\"Multi-head Attention block with relative position embeddings.\"\"\"\n\n def __init__(\n self,\n dim: int,\n num_heads: int = 8,\n qkv_bias: bool = True,\n use_rel_pos: bool = False,\n rel_pos_zero_init: bool = True,\n input_size: Optional[Tuple[int, int]] = None,\n ) -> None:\n \"\"\"\n Args:\n dim (int): Number of input channels.\n num_heads (int): Number of attention heads.\n qkv_bias (bool): If True, add a learnable bias to query, key, value.\n rel_pos (bool): If True, add relative positional embeddings to the attention map.\n rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.\n input_size (tuple(int, int) or None): Input resolution for calculating the relative\n positional parameter size.\n \"\"\"\n super().__init__()\n self.num_heads = num_heads\n head_dim = dim // num_heads\n self.scale = head_dim**-0.5\n\n self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)\n self.proj = nn.Linear(dim, dim)\n\n self.use_rel_pos = use_rel_pos\n if self.use_rel_pos:\n assert (\n input_size is not None\n ), \"Input size must be provided if using relative positional encoding.\"\n # initialize relative positional embeddings\n self.rel_pos_h = nn.Parameter(torch.zeros(2 * input_size[0] - 1, head_dim))\n self.rel_pos_w = nn.Parameter(torch.zeros(2 * input_size[1] - 1, head_dim))\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n B, H, W, _ = x.shape\n # qkv with shape (3, B, nHead, H * W, C)\n qkv = self.qkv(x).reshape(B, H * W, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)\n # q, k, v with shape (B * nHead, H * W, C)\n q, k, v = qkv.reshape(3, B * self.num_heads, H * W, -1).unbind(0)\n\n attn = (q * self.scale) @ k.transpose(-2, -1)\n\n if self.use_rel_pos:\n attn = add_decomposed_rel_pos(attn, q, self.rel_pos_h, self.rel_pos_w, (H, W), (H, W))\n\n attn = attn.softmax(dim=-1)\n x = (attn @ v).view(B, self.num_heads, H, W, -1).permute(0, 2, 3, 1, 4).reshape(B, H, W, -1)\n x = self.proj(x)\n\n return x\n\n\ndef window_partition(x: torch.Tensor, window_size: int) -> Tuple[torch.Tensor, Tuple[int, int]]:\n \"\"\"\n Partition into non-overlapping windows with padding if needed.\n Args:\n x (tensor): input tokens with [B, H, W, C].\n window_size (int): window size.\n\n Returns:\n windows: windows after partition with [B * num_windows, window_size, window_size, C].\n (Hp, Wp): padded height and width before partition\n \"\"\"\n B, H, W, C = x.shape\n\n pad_h = (window_size - H % window_size) % window_size\n pad_w = (window_size - W % window_size) % window_size\n if pad_h > 0 or pad_w > 0:\n x = F.pad(x, (0, 0, 0, pad_w, 0, pad_h))\n Hp, Wp = H + pad_h, W + pad_w\n\n x = x.view(B, Hp // window_size, window_size, Wp // window_size, window_size, C)\n windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)\n return windows, (Hp, Wp)\n\n\ndef window_unpartition(\n windows: torch.Tensor, window_size: int, pad_hw: Tuple[int, int], hw: Tuple[int, int]\n) -> torch.Tensor:\n \"\"\"\n Window unpartition into original sequences and removing padding.\n Args:\n windows (tensor): input tokens with [B * num_windows, window_size, window_size, C].\n window_size (int): window size.\n pad_hw (Tuple): padded height and width (Hp, Wp).\n hw (Tuple): original height and width (H, W) before padding.\n\n Returns:\n x: unpartitioned sequences with [B, H, W, C].\n \"\"\"\n Hp, Wp = pad_hw\n H, W = hw\n B = windows.shape[0] // (Hp * Wp // window_size // window_size)\n x = windows.view(B, Hp // window_size, Wp // window_size, window_size, window_size, -1)\n x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, Hp, Wp, -1)\n\n if Hp > H or Wp > W:\n x = x[:, :H, :W, :].contiguous()\n return x\n\n\ndef get_rel_pos(q_size: int, k_size: int, rel_pos: torch.Tensor) -> torch.Tensor:\n \"\"\"\n Get relative positional embeddings according to the relative positions of\n query and key sizes.\n Args:\n q_size (int): size of query q.\n k_size (int): size of key k.\n rel_pos (Tensor): relative position embeddings (L, C).\n\n Returns:\n Extracted positional embeddings according to relative positions.\n \"\"\"\n max_rel_dist = int(2 * max(q_size, k_size) - 1)\n # Interpolate rel pos if needed.\n if rel_pos.shape[0] != max_rel_dist:\n # Interpolate rel pos.\n rel_pos_resized = F.interpolate(\n rel_pos.reshape(1, rel_pos.shape[0], -1).permute(0, 2, 1),\n size=max_rel_dist,\n mode=\"linear\",\n )\n rel_pos_resized = rel_pos_resized.reshape(-1, max_rel_dist).permute(1, 0)\n else:\n rel_pos_resized = rel_pos\n\n # Scale the coords with short length if shapes for q and k are different.\n q_coords = torch.arange(q_size)[:, None] * max(k_size / q_size, 1.0)\n k_coords = torch.arange(k_size)[None, :] * max(q_size / k_size, 1.0)\n relative_coords = (q_coords - k_coords) + (k_size - 1) * max(q_size / k_size, 1.0)\n\n return rel_pos_resized[relative_coords.long()]\n\n\ndef add_decomposed_rel_pos(\n attn: torch.Tensor,\n q: torch.Tensor,\n rel_pos_h: torch.Tensor,\n rel_pos_w: torch.Tensor,\n q_size: Tuple[int, int],\n k_size: Tuple[int, int],\n) -> torch.Tensor:\n \"\"\"\n Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`.\n https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py # noqa B950\n Args:\n attn (Tensor): attention map.\n q (Tensor): query q in the attention layer with shape (B, q_h * q_w, C).\n rel_pos_h (Tensor): relative position embeddings (Lh, C) for height axis.\n rel_pos_w (Tensor): relative position embeddings (Lw, C) for width axis.\n q_size (Tuple): spatial sequence size of query q with (q_h, q_w).\n k_size (Tuple): spatial sequence size of key k with (k_h, k_w).\n\n Returns:\n attn (Tensor): attention map with added relative positional embeddings.\n \"\"\"\n q_h, q_w = q_size\n k_h, k_w = k_size\n Rh = get_rel_pos(q_h, k_h, rel_pos_h)\n Rw = get_rel_pos(q_w, k_w, rel_pos_w)\n\n B, _, dim = q.shape\n r_q = q.reshape(B, q_h, q_w, dim)\n rel_h = torch.einsum(\"bhwc,hkc->bhwk\", r_q, Rh)\n rel_w = torch.einsum(\"bhwc,wkc->bhwk\", r_q, Rw)\n\n attn = (\n attn.view(B, q_h, q_w, k_h, k_w) + rel_h[:, :, :, :, None] + rel_w[:, :, :, None, :]\n ).view(B, q_h * q_w, k_h * k_w)\n\n return attn\n\n\nclass PatchEmbed(nn.Module):\n \"\"\"\n Image to Patch Embedding.\n \"\"\"\n\n def __init__(\n self,\n kernel_size: Tuple[int, int] = (16, 16),\n stride: Tuple[int, int] = (16, 16),\n padding: Tuple[int, int] = (0, 0),\n in_chans: int = 3,\n embed_dim: int = 768,\n ) -> None:\n \"\"\"\n Args:\n kernel_size (Tuple): kernel size of the projection layer.\n stride (Tuple): stride of the projection layer.\n padding (Tuple): padding size of the projection layer.\n in_chans (int): Number of input image channels.\n embed_dim (int): Patch embedding dimension.\n \"\"\"\n super().__init__()\n\n self.proj = nn.Conv2d(\n in_chans, embed_dim, kernel_size=kernel_size, stride=stride, padding=padding\n )\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n x = self.proj(x)\n # B C H W -> B H W C\n x = x.permute(0, 2, 3, 1)\n return x\n" }, { "path": "auto-seg/submodules/segment-anything-1/segment_anything/modeling/mask_decoder.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\nfrom typing import List, Tuple, Type\n\nfrom .common import LayerNorm2d\n\n\nclass MaskDecoder(nn.Module):\n def __init__(\n self,\n *,\n transformer_dim: int,\n transformer: nn.Module,\n num_multimask_outputs: int = 3,\n activation: Type[nn.Module] = nn.GELU,\n iou_head_depth: int = 3,\n iou_head_hidden_dim: int = 256,\n ) -> None:\n \"\"\"\n Predicts masks given an image and prompt embeddings, using a\n transformer architecture.\n\n Arguments:\n transformer_dim (int): the channel dimension of the transformer\n transformer (nn.Module): the transformer used to predict masks\n num_multimask_outputs (int): the number of masks to predict\n when disambiguating masks\n activation (nn.Module): the type of activation to use when\n upscaling masks\n iou_head_depth (int): the depth of the MLP used to predict\n mask quality\n iou_head_hidden_dim (int): the hidden dimension of the MLP\n used to predict mask quality\n \"\"\"\n super().__init__()\n self.transformer_dim = transformer_dim\n self.transformer = transformer\n\n self.num_multimask_outputs = num_multimask_outputs\n\n self.iou_token = nn.Embedding(1, transformer_dim)\n self.num_mask_tokens = num_multimask_outputs + 1\n self.mask_tokens = nn.Embedding(self.num_mask_tokens, transformer_dim)\n\n self.output_upscaling = nn.Sequential(\n nn.ConvTranspose2d(transformer_dim, transformer_dim // 4, kernel_size=2, stride=2),\n LayerNorm2d(transformer_dim // 4),\n activation(),\n nn.ConvTranspose2d(transformer_dim // 4, transformer_dim // 8, kernel_size=2, stride=2),\n activation(),\n )\n self.output_hypernetworks_mlps = nn.ModuleList(\n [\n MLP(transformer_dim, transformer_dim, transformer_dim // 8, 3)\n for i in range(self.num_mask_tokens)\n ]\n )\n\n self.iou_prediction_head = MLP(\n transformer_dim, iou_head_hidden_dim, self.num_mask_tokens, iou_head_depth\n )\n\n def forward(\n self,\n image_embeddings: torch.Tensor,\n image_pe: torch.Tensor,\n sparse_prompt_embeddings: torch.Tensor,\n dense_prompt_embeddings: torch.Tensor,\n multimask_output: bool,\n ) -> Tuple[torch.Tensor, torch.Tensor]:\n \"\"\"\n Predict masks given image and prompt embeddings.\n\n Arguments:\n image_embeddings (torch.Tensor): the embeddings from the image encoder\n image_pe (torch.Tensor): positional encoding with the shape of image_embeddings\n sparse_prompt_embeddings (torch.Tensor): the embeddings of the points and boxes\n dense_prompt_embeddings (torch.Tensor): the embeddings of the mask inputs\n multimask_output (bool): Whether to return multiple masks or a single\n mask.\n\n Returns:\n torch.Tensor: batched predicted masks\n torch.Tensor: batched predictions of mask quality\n \"\"\"\n masks, iou_pred = self.predict_masks(\n image_embeddings=image_embeddings,\n image_pe=image_pe,\n sparse_prompt_embeddings=sparse_prompt_embeddings,\n dense_prompt_embeddings=dense_prompt_embeddings,\n )\n\n # Select the correct mask or masks for output\n if multimask_output:\n mask_slice = slice(1, None)\n else:\n mask_slice = slice(0, 1)\n masks = masks[:, mask_slice, :, :]\n iou_pred = iou_pred[:, mask_slice]\n\n # Prepare output\n return masks, iou_pred\n\n def predict_masks(\n self,\n image_embeddings: torch.Tensor,\n image_pe: torch.Tensor,\n sparse_prompt_embeddings: torch.Tensor,\n dense_prompt_embeddings: torch.Tensor,\n ) -> Tuple[torch.Tensor, torch.Tensor]:\n \"\"\"Predicts masks. See 'forward' for more details.\"\"\"\n # Concatenate output tokens\n output_tokens = torch.cat([self.iou_token.weight, self.mask_tokens.weight], dim=0)\n output_tokens = output_tokens.unsqueeze(0).expand(sparse_prompt_embeddings.size(0), -1, -1)\n tokens = torch.cat((output_tokens, sparse_prompt_embeddings), dim=1)\n\n # Expand per-image data in batch direction to be per-mask\n src = torch.repeat_interleave(image_embeddings, tokens.shape[0], dim=0)\n src = src + dense_prompt_embeddings\n pos_src = torch.repeat_interleave(image_pe, tokens.shape[0], dim=0)\n b, c, h, w = src.shape\n\n # Run the transformer\n hs, src = self.transformer(src, pos_src, tokens)\n iou_token_out = hs[:, 0, :]\n mask_tokens_out = hs[:, 1 : (1 + self.num_mask_tokens), :]\n\n # Upscale mask embeddings and predict masks using the mask tokens\n src = src.transpose(1, 2).view(b, c, h, w)\n upscaled_embedding = self.output_upscaling(src)\n hyper_in_list: List[torch.Tensor] = []\n for i in range(self.num_mask_tokens):\n hyper_in_list.append(self.output_hypernetworks_mlps[i](mask_tokens_out[:, i, :]))\n hyper_in = torch.stack(hyper_in_list, dim=1)\n b, c, h, w = upscaled_embedding.shape\n masks = (hyper_in @ upscaled_embedding.view(b, c, h * w)).view(b, -1, h, w)\n\n # Generate mask quality predictions\n iou_pred = self.iou_prediction_head(iou_token_out)\n\n return masks, iou_pred\n\n\n# Lightly adapted from\n# https://github.com/facebookresearch/MaskFormer/blob/main/mask_former/modeling/transformer/transformer_predictor.py # noqa\nclass MLP(nn.Module):\n def __init__(\n self,\n input_dim: int,\n hidden_dim: int,\n output_dim: int,\n num_layers: int,\n sigmoid_output: bool = False,\n ) -> None:\n super().__init__()\n self.num_layers = num_layers\n h = [hidden_dim] * (num_layers - 1)\n self.layers = nn.ModuleList(\n nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim])\n )\n self.sigmoid_output = sigmoid_output\n\n def forward(self, x):\n for i, layer in enumerate(self.layers):\n x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)\n if self.sigmoid_output:\n x = F.sigmoid(x)\n return x\n" }, { "path": "auto-seg/submodules/segment-anything-1/segment_anything/modeling/prompt_encoder.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport numpy as np\nimport torch\nfrom torch import nn\n\nfrom typing import Any, Optional, Tuple, Type\n\nfrom .common import LayerNorm2d\n\n\nclass PromptEncoder(nn.Module):\n def __init__(\n self,\n embed_dim: int,\n image_embedding_size: Tuple[int, int],\n input_image_size: Tuple[int, int],\n mask_in_chans: int,\n activation: Type[nn.Module] = nn.GELU,\n ) -> None:\n \"\"\"\n Encodes prompts for input to SAM's mask decoder.\n\n Arguments:\n embed_dim (int): The prompts' embedding dimension\n image_embedding_size (tuple(int, int)): The spatial size of the\n image embedding, as (H, W).\n input_image_size (int): The padded size of the image as input\n to the image encoder, as (H, W).\n mask_in_chans (int): The number of hidden channels used for\n encoding input masks.\n activation (nn.Module): The activation to use when encoding\n input masks.\n \"\"\"\n super().__init__()\n self.embed_dim = embed_dim\n self.input_image_size = input_image_size\n self.image_embedding_size = image_embedding_size\n self.pe_layer = PositionEmbeddingRandom(embed_dim // 2)\n\n self.num_point_embeddings: int = 4 # pos/neg point + 2 box corners\n point_embeddings = [nn.Embedding(1, embed_dim) for i in range(self.num_point_embeddings)]\n self.point_embeddings = nn.ModuleList(point_embeddings)\n self.not_a_point_embed = nn.Embedding(1, embed_dim)\n\n self.mask_input_size = (4 * image_embedding_size[0], 4 * image_embedding_size[1])\n self.mask_downscaling = nn.Sequential(\n nn.Conv2d(1, mask_in_chans // 4, kernel_size=2, stride=2),\n LayerNorm2d(mask_in_chans // 4),\n activation(),\n nn.Conv2d(mask_in_chans // 4, mask_in_chans, kernel_size=2, stride=2),\n LayerNorm2d(mask_in_chans),\n activation(),\n nn.Conv2d(mask_in_chans, embed_dim, kernel_size=1),\n )\n self.no_mask_embed = nn.Embedding(1, embed_dim)\n\n def get_dense_pe(self) -> torch.Tensor:\n \"\"\"\n Returns the positional encoding used to encode point prompts,\n applied to a dense set of points the shape of the image encoding.\n\n Returns:\n torch.Tensor: Positional encoding with shape\n 1x(embed_dim)x(embedding_h)x(embedding_w)\n \"\"\"\n return self.pe_layer(self.image_embedding_size).unsqueeze(0)\n\n def _embed_points(\n self,\n points: torch.Tensor,\n labels: torch.Tensor,\n pad: bool,\n ) -> torch.Tensor:\n \"\"\"Embeds point prompts.\"\"\"\n points = points + 0.5 # Shift to center of pixel\n if pad:\n padding_point = torch.zeros((points.shape[0], 1, 2), device=points.device)\n padding_label = -torch.ones((labels.shape[0], 1), device=labels.device)\n points = torch.cat([points, padding_point], dim=1)\n labels = torch.cat([labels, padding_label], dim=1)\n point_embedding = self.pe_layer.forward_with_coords(points, self.input_image_size)\n point_embedding[labels == -1] = 0.0\n point_embedding[labels == -1] += self.not_a_point_embed.weight\n point_embedding[labels == 0] += self.point_embeddings[0].weight\n point_embedding[labels == 1] += self.point_embeddings[1].weight\n return point_embedding\n\n def _embed_boxes(self, boxes: torch.Tensor) -> torch.Tensor:\n \"\"\"Embeds box prompts.\"\"\"\n boxes = boxes + 0.5 # Shift to center of pixel\n coords = boxes.reshape(-1, 2, 2)\n corner_embedding = self.pe_layer.forward_with_coords(coords, self.input_image_size)\n corner_embedding[:, 0, :] += self.point_embeddings[2].weight\n corner_embedding[:, 1, :] += self.point_embeddings[3].weight\n return corner_embedding\n\n def _embed_masks(self, masks: torch.Tensor) -> torch.Tensor:\n \"\"\"Embeds mask inputs.\"\"\"\n mask_embedding = self.mask_downscaling(masks)\n return mask_embedding\n\n def _get_batch_size(\n self,\n points: Optional[Tuple[torch.Tensor, torch.Tensor]],\n boxes: Optional[torch.Tensor],\n masks: Optional[torch.Tensor],\n ) -> int:\n \"\"\"\n Gets the batch size of the output given the batch size of the input prompts.\n \"\"\"\n if points is not None:\n return points[0].shape[0]\n elif boxes is not None:\n return boxes.shape[0]\n elif masks is not None:\n return masks.shape[0]\n else:\n return 1\n\n def _get_device(self) -> torch.device:\n return self.point_embeddings[0].weight.device\n\n def forward(\n self,\n points: Optional[Tuple[torch.Tensor, torch.Tensor]],\n boxes: Optional[torch.Tensor],\n masks: Optional[torch.Tensor],\n ) -> Tuple[torch.Tensor, torch.Tensor]:\n \"\"\"\n Embeds different types of prompts, returning both sparse and dense\n embeddings.\n\n Arguments:\n points (tuple(torch.Tensor, torch.Tensor) or none): point coordinates\n and labels to embed.\n boxes (torch.Tensor or none): boxes to embed\n masks (torch.Tensor or none): masks to embed\n\n Returns:\n torch.Tensor: sparse embeddings for the points and boxes, with shape\n BxNx(embed_dim), where N is determined by the number of input points\n and boxes.\n torch.Tensor: dense embeddings for the masks, in the shape\n Bx(embed_dim)x(embed_H)x(embed_W)\n \"\"\"\n bs = self._get_batch_size(points, boxes, masks)\n sparse_embeddings = torch.empty((bs, 0, self.embed_dim), device=self._get_device())\n if points is not None:\n coords, labels = points\n point_embeddings = self._embed_points(coords, labels, pad=(boxes is None))\n sparse_embeddings = torch.cat([sparse_embeddings, point_embeddings], dim=1)\n if boxes is not None:\n box_embeddings = self._embed_boxes(boxes)\n sparse_embeddings = torch.cat([sparse_embeddings, box_embeddings], dim=1)\n\n if masks is not None:\n dense_embeddings = self._embed_masks(masks)\n else:\n dense_embeddings = self.no_mask_embed.weight.reshape(1, -1, 1, 1).expand(\n bs, -1, self.image_embedding_size[0], self.image_embedding_size[1]\n )\n\n return sparse_embeddings, dense_embeddings\n\n\nclass PositionEmbeddingRandom(nn.Module):\n \"\"\"\n Positional encoding using random spatial frequencies.\n \"\"\"\n\n def __init__(self, num_pos_feats: int = 64, scale: Optional[float] = None) -> None:\n super().__init__()\n if scale is None or scale <= 0.0:\n scale = 1.0\n self.register_buffer(\n \"positional_encoding_gaussian_matrix\",\n scale * torch.randn((2, num_pos_feats)),\n )\n\n def _pe_encoding(self, coords: torch.Tensor) -> torch.Tensor:\n \"\"\"Positionally encode points that are normalized to [0,1].\"\"\"\n # assuming coords are in [0, 1]^2 square and have d_1 x ... x d_n x 2 shape\n coords = 2 * coords - 1\n coords = coords @ self.positional_encoding_gaussian_matrix\n coords = 2 * np.pi * coords\n # outputs d_1 x ... x d_n x C shape\n return torch.cat([torch.sin(coords), torch.cos(coords)], dim=-1)\n\n def forward(self, size: Tuple[int, int]) -> torch.Tensor:\n \"\"\"Generate positional encoding for a grid of the specified size.\"\"\"\n h, w = size\n device: Any = self.positional_encoding_gaussian_matrix.device\n grid = torch.ones((h, w), device=device, dtype=torch.float32)\n y_embed = grid.cumsum(dim=0) - 0.5\n x_embed = grid.cumsum(dim=1) - 0.5\n y_embed = y_embed / h\n x_embed = x_embed / w\n\n pe = self._pe_encoding(torch.stack([x_embed, y_embed], dim=-1))\n return pe.permute(2, 0, 1) # C x H x W\n\n def forward_with_coords(\n self, coords_input: torch.Tensor, image_size: Tuple[int, int]\n ) -> torch.Tensor:\n \"\"\"Positionally encode points that are not normalized to [0,1].\"\"\"\n coords = coords_input.clone()\n coords[:, :, 0] = coords[:, :, 0] / image_size[1]\n coords[:, :, 1] = coords[:, :, 1] / image_size[0]\n return self._pe_encoding(coords.to(torch.float)) # B x N x C\n" }, { "path": "auto-seg/submodules/segment-anything-1/segment_anything/modeling/sam.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\nfrom typing import Any, Dict, List, Tuple\n\nfrom .image_encoder import ImageEncoderViT\nfrom .mask_decoder import MaskDecoder\nfrom .prompt_encoder import PromptEncoder\n\n\nclass Sam(nn.Module):\n mask_threshold: float = 0.0\n image_format: str = \"RGB\"\n\n def __init__(\n self,\n image_encoder: ImageEncoderViT,\n prompt_encoder: PromptEncoder,\n mask_decoder: MaskDecoder,\n pixel_mean: List[float] = [123.675, 116.28, 103.53],\n pixel_std: List[float] = [58.395, 57.12, 57.375],\n ) -> None:\n \"\"\"\n SAM predicts object masks from an image and input prompts.\n\n Arguments:\n image_encoder (ImageEncoderViT): The backbone used to encode the\n image into image embeddings that allow for efficient mask prediction.\n prompt_encoder (PromptEncoder): Encodes various types of input prompts.\n mask_decoder (MaskDecoder): Predicts masks from the image embeddings\n and encoded prompts.\n pixel_mean (list(float)): Mean values for normalizing pixels in the input image.\n pixel_std (list(float)): Std values for normalizing pixels in the input image.\n \"\"\"\n super().__init__()\n self.image_encoder = image_encoder\n self.prompt_encoder = prompt_encoder\n self.mask_decoder = mask_decoder\n self.register_buffer(\"pixel_mean\", torch.Tensor(pixel_mean).view(-1, 1, 1), False)\n self.register_buffer(\"pixel_std\", torch.Tensor(pixel_std).view(-1, 1, 1), False)\n\n @property\n def device(self) -> Any:\n return self.pixel_mean.device\n\n @torch.no_grad()\n def forward(\n self,\n batched_input: List[Dict[str, Any]],\n multimask_output: bool,\n ) -> List[Dict[str, torch.Tensor]]:\n \"\"\"\n Predicts masks end-to-end from provided images and prompts.\n If prompts are not known in advance, using SamPredictor is\n recommended over calling the model directly.\n\n Arguments:\n batched_input (list(dict)): A list over input images, each a\n dictionary with the following keys. A prompt key can be\n excluded if it is not present.\n 'image': The image as a torch tensor in 3xHxW format,\n already transformed for input to the model.\n 'original_size': (tuple(int, int)) The original size of\n the image before transformation, as (H, W).\n 'point_coords': (torch.Tensor) Batched point prompts for\n this image, with shape BxNx2. Already transformed to the\n input frame of the model.\n 'point_labels': (torch.Tensor) Batched labels for point prompts,\n with shape BxN.\n 'boxes': (torch.Tensor) Batched box inputs, with shape Bx4.\n Already transformed to the input frame of the model.\n 'mask_inputs': (torch.Tensor) Batched mask inputs to the model,\n in the form Bx1xHxW.\n multimask_output (bool): Whether the model should predict multiple\n disambiguating masks, or return a single mask.\n\n Returns:\n (list(dict)): A list over input images, where each element is\n as dictionary with the following keys.\n 'masks': (torch.Tensor) Batched binary mask predictions,\n with shape BxCxHxW, where B is the number of input prompts,\n C is determined by multimask_output, and (H, W) is the\n original size of the image.\n 'iou_predictions': (torch.Tensor) The model's predictions\n of mask quality, in shape BxC.\n 'low_res_logits': (torch.Tensor) Low resolution logits with\n shape BxCxHxW, where H=W=256. Can be passed as mask input\n to subsequent iterations of prediction.\n \"\"\"\n input_images = torch.stack([self.preprocess(x[\"image\"]) for x in batched_input], dim=0)\n image_embeddings = self.image_encoder(input_images)\n\n outputs = []\n for image_record, curr_embedding in zip(batched_input, image_embeddings):\n if \"point_coords\" in image_record:\n points = (image_record[\"point_coords\"], image_record[\"point_labels\"])\n else:\n points = None\n sparse_embeddings, dense_embeddings = self.prompt_encoder(\n points=points,\n boxes=image_record.get(\"boxes\", None),\n masks=image_record.get(\"mask_inputs\", None),\n )\n low_res_masks, iou_predictions = self.mask_decoder(\n image_embeddings=curr_embedding.unsqueeze(0),\n image_pe=self.prompt_encoder.get_dense_pe(),\n sparse_prompt_embeddings=sparse_embeddings,\n dense_prompt_embeddings=dense_embeddings,\n multimask_output=multimask_output,\n )\n masks = self.postprocess_masks(\n low_res_masks,\n input_size=image_record[\"image\"].shape[-2:],\n original_size=image_record[\"original_size\"],\n )\n masks = masks > self.mask_threshold\n outputs.append(\n {\n \"masks\": masks,\n \"iou_predictions\": iou_predictions,\n \"low_res_logits\": low_res_masks,\n }\n )\n return outputs\n\n def postprocess_masks(\n self,\n masks: torch.Tensor,\n input_size: Tuple[int, ...],\n original_size: Tuple[int, ...],\n ) -> torch.Tensor:\n \"\"\"\n Remove padding and upscale masks to the original image size.\n\n Arguments:\n masks (torch.Tensor): Batched masks from the mask_decoder,\n in BxCxHxW format.\n input_size (tuple(int, int)): The size of the image input to the\n model, in (H, W) format. Used to remove padding.\n original_size (tuple(int, int)): The original size of the image\n before resizing for input to the model, in (H, W) format.\n\n Returns:\n (torch.Tensor): Batched masks in BxCxHxW format, where (H, W)\n is given by original_size.\n \"\"\"\n masks = F.interpolate(\n masks,\n (self.image_encoder.img_size, self.image_encoder.img_size),\n mode=\"bilinear\",\n align_corners=False,\n )\n masks = masks[..., : input_size[0], : input_size[1]]\n masks = F.interpolate(masks, original_size, mode=\"bilinear\", align_corners=False)\n return masks\n\n def preprocess(self, x: torch.Tensor) -> torch.Tensor:\n \"\"\"Normalize pixel values and pad to a square input.\"\"\"\n # Normalize colors\n x = (x - self.pixel_mean) / self.pixel_std\n\n # Pad\n h, w = x.shape[-2:]\n padh = self.image_encoder.img_size - h\n padw = self.image_encoder.img_size - w\n x = F.pad(x, (0, padw, 0, padh))\n return x\n" }, { "path": "auto-seg/submodules/segment-anything-1/segment_anything/modeling/transformer.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport torch\nfrom torch import Tensor, nn\n\nimport math\nfrom typing import Tuple, Type\n\nfrom .common import MLPBlock\n\n\nclass TwoWayTransformer(nn.Module):\n def __init__(\n self,\n depth: int,\n embedding_dim: int,\n num_heads: int,\n mlp_dim: int,\n activation: Type[nn.Module] = nn.ReLU,\n attention_downsample_rate: int = 2,\n ) -> None:\n \"\"\"\n A transformer decoder that attends to an input image using\n queries whose positional embedding is supplied.\n\n Args:\n depth (int): number of layers in the transformer\n embedding_dim (int): the channel dimension for the input embeddings\n num_heads (int): the number of heads for multihead attention. Must\n divide embedding_dim\n mlp_dim (int): the channel dimension internal to the MLP block\n activation (nn.Module): the activation to use in the MLP block\n \"\"\"\n super().__init__()\n self.depth = depth\n self.embedding_dim = embedding_dim\n self.num_heads = num_heads\n self.mlp_dim = mlp_dim\n self.layers = nn.ModuleList()\n\n for i in range(depth):\n self.layers.append(\n TwoWayAttentionBlock(\n embedding_dim=embedding_dim,\n num_heads=num_heads,\n mlp_dim=mlp_dim,\n activation=activation,\n attention_downsample_rate=attention_downsample_rate,\n skip_first_layer_pe=(i == 0),\n )\n )\n\n self.final_attn_token_to_image = Attention(\n embedding_dim, num_heads, downsample_rate=attention_downsample_rate\n )\n self.norm_final_attn = nn.LayerNorm(embedding_dim)\n\n def forward(\n self,\n image_embedding: Tensor,\n image_pe: Tensor,\n point_embedding: Tensor,\n ) -> Tuple[Tensor, Tensor]:\n \"\"\"\n Args:\n image_embedding (torch.Tensor): image to attend to. Should be shape\n B x embedding_dim x h x w for any h and w.\n image_pe (torch.Tensor): the positional encoding to add to the image. Must\n have the same shape as image_embedding.\n point_embedding (torch.Tensor): the embedding to add to the query points.\n Must have shape B x N_points x embedding_dim for any N_points.\n\n Returns:\n torch.Tensor: the processed point_embedding\n torch.Tensor: the processed image_embedding\n \"\"\"\n # BxCxHxW -> BxHWxC == B x N_image_tokens x C\n bs, c, h, w = image_embedding.shape\n image_embedding = image_embedding.flatten(2).permute(0, 2, 1)\n image_pe = image_pe.flatten(2).permute(0, 2, 1)\n\n # Prepare queries\n queries = point_embedding\n keys = image_embedding\n\n # Apply transformer blocks and final layernorm\n for layer in self.layers:\n queries, keys = layer(\n queries=queries,\n keys=keys,\n query_pe=point_embedding,\n key_pe=image_pe,\n )\n\n # Apply the final attention layer from the points to the image\n q = queries + point_embedding\n k = keys + image_pe\n attn_out = self.final_attn_token_to_image(q=q, k=k, v=keys)\n queries = queries + attn_out\n queries = self.norm_final_attn(queries)\n\n return queries, keys\n\n\nclass TwoWayAttentionBlock(nn.Module):\n def __init__(\n self,\n embedding_dim: int,\n num_heads: int,\n mlp_dim: int = 2048,\n activation: Type[nn.Module] = nn.ReLU,\n attention_downsample_rate: int = 2,\n skip_first_layer_pe: bool = False,\n ) -> None:\n \"\"\"\n A transformer block with four layers: (1) self-attention of sparse\n inputs, (2) cross attention of sparse inputs to dense inputs, (3) mlp\n block on sparse inputs, and (4) cross attention of dense inputs to sparse\n inputs.\n\n Arguments:\n embedding_dim (int): the channel dimension of the embeddings\n num_heads (int): the number of heads in the attention layers\n mlp_dim (int): the hidden dimension of the mlp block\n activation (nn.Module): the activation of the mlp block\n skip_first_layer_pe (bool): skip the PE on the first layer\n \"\"\"\n super().__init__()\n self.self_attn = Attention(embedding_dim, num_heads)\n self.norm1 = nn.LayerNorm(embedding_dim)\n\n self.cross_attn_token_to_image = Attention(\n embedding_dim, num_heads, downsample_rate=attention_downsample_rate\n )\n self.norm2 = nn.LayerNorm(embedding_dim)\n\n self.mlp = MLPBlock(embedding_dim, mlp_dim, activation)\n self.norm3 = nn.LayerNorm(embedding_dim)\n\n self.norm4 = nn.LayerNorm(embedding_dim)\n self.cross_attn_image_to_token = Attention(\n embedding_dim, num_heads, downsample_rate=attention_downsample_rate\n )\n\n self.skip_first_layer_pe = skip_first_layer_pe\n\n def forward(\n self, queries: Tensor, keys: Tensor, query_pe: Tensor, key_pe: Tensor\n ) -> Tuple[Tensor, Tensor]:\n # Self attention block\n if self.skip_first_layer_pe:\n queries = self.self_attn(q=queries, k=queries, v=queries)\n else:\n q = queries + query_pe\n attn_out = self.self_attn(q=q, k=q, v=queries)\n queries = queries + attn_out\n queries = self.norm1(queries)\n\n # Cross attention block, tokens attending to image embedding\n q = queries + query_pe\n k = keys + key_pe\n attn_out = self.cross_attn_token_to_image(q=q, k=k, v=keys)\n queries = queries + attn_out\n queries = self.norm2(queries)\n\n # MLP block\n mlp_out = self.mlp(queries)\n queries = queries + mlp_out\n queries = self.norm3(queries)\n\n # Cross attention block, image embedding attending to tokens\n q = queries + query_pe\n k = keys + key_pe\n attn_out = self.cross_attn_image_to_token(q=k, k=q, v=queries)\n keys = keys + attn_out\n keys = self.norm4(keys)\n\n return queries, keys\n\n\nclass Attention(nn.Module):\n \"\"\"\n An attention layer that allows for downscaling the size of the embedding\n after projection to queries, keys, and values.\n \"\"\"\n\n def __init__(\n self,\n embedding_dim: int,\n num_heads: int,\n downsample_rate: int = 1,\n ) -> None:\n super().__init__()\n self.embedding_dim = embedding_dim\n self.internal_dim = embedding_dim // downsample_rate\n self.num_heads = num_heads\n assert self.internal_dim % num_heads == 0, \"num_heads must divide embedding_dim.\"\n\n self.q_proj = nn.Linear(embedding_dim, self.internal_dim)\n self.k_proj = nn.Linear(embedding_dim, self.internal_dim)\n self.v_proj = nn.Linear(embedding_dim, self.internal_dim)\n self.out_proj = nn.Linear(self.internal_dim, embedding_dim)\n\n def _separate_heads(self, x: Tensor, num_heads: int) -> Tensor:\n b, n, c = x.shape\n x = x.reshape(b, n, num_heads, c // num_heads)\n return x.transpose(1, 2) # B x N_heads x N_tokens x C_per_head\n\n def _recombine_heads(self, x: Tensor) -> Tensor:\n b, n_heads, n_tokens, c_per_head = x.shape\n x = x.transpose(1, 2)\n return x.reshape(b, n_tokens, n_heads * c_per_head) # B x N_tokens x C\n\n def forward(self, q: Tensor, k: Tensor, v: Tensor) -> Tensor:\n # Input projections\n q = self.q_proj(q)\n k = self.k_proj(k)\n v = self.v_proj(v)\n\n # Separate into heads\n q = self._separate_heads(q, self.num_heads)\n k = self._separate_heads(k, self.num_heads)\n v = self._separate_heads(v, self.num_heads)\n\n # Attention\n _, _, _, c_per_head = q.shape\n attn = q @ k.permute(0, 1, 3, 2) # B x N_heads x N_tokens x N_tokens\n attn = attn / math.sqrt(c_per_head)\n attn = torch.softmax(attn, dim=-1)\n\n # Get output\n out = attn @ v\n out = self._recombine_heads(out)\n out = self.out_proj(out)\n\n return out\n" }, { "path": "auto-seg/submodules/segment-anything-1/segment_anything/predictor.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport numpy as np\nimport torch\n\nfrom segment_anything.modeling import Sam\n\nfrom typing import Optional, Tuple\n\nfrom .utils.transforms import ResizeLongestSide\n\n\nclass SamPredictor:\n def __init__(\n self,\n sam_model: Sam,\n ) -> None:\n \"\"\"\n Uses SAM to calculate the image embedding for an image, and then\n allow repeated, efficient mask prediction given prompts.\n\n Arguments:\n sam_model (Sam): The model to use for mask prediction.\n \"\"\"\n super().__init__()\n self.model = sam_model\n self.transform = ResizeLongestSide(sam_model.image_encoder.img_size)\n self.reset_image()\n\n def set_image(\n self,\n image: np.ndarray,\n image_format: str = \"RGB\",\n ) -> None:\n \"\"\"\n Calculates the image embeddings for the provided image, allowing\n masks to be predicted with the 'predict' method.\n\n Arguments:\n image (np.ndarray): The image for calculating masks. Expects an\n image in HWC uint8 format, with pixel values in [0, 255].\n image_format (str): The color format of the image, in ['RGB', 'BGR'].\n \"\"\"\n assert image_format in [\n \"RGB\",\n \"BGR\",\n ], f\"image_format must be in ['RGB', 'BGR'], is {image_format}.\"\n if image_format != self.model.image_format:\n image = image[..., ::-1]\n\n # Transform the image to the form expected by the model\n input_image = self.transform.apply_image(image)\n input_image_torch = torch.as_tensor(input_image, device=self.device)\n input_image_torch = input_image_torch.permute(2, 0, 1).contiguous()[None, :, :, :]\n\n self.set_torch_image(input_image_torch, image.shape[:2])\n\n @torch.no_grad()\n def set_torch_image(\n self,\n transformed_image: torch.Tensor,\n original_image_size: Tuple[int, ...],\n ) -> None:\n \"\"\"\n Calculates the image embeddings for the provided image, allowing\n masks to be predicted with the 'predict' method. Expects the input\n image to be already transformed to the format expected by the model.\n\n Arguments:\n transformed_image (torch.Tensor): The input image, with shape\n 1x3xHxW, which has been transformed with ResizeLongestSide.\n original_image_size (tuple(int, int)): The size of the image\n before transformation, in (H, W) format.\n \"\"\"\n assert (\n len(transformed_image.shape) == 4\n and transformed_image.shape[1] == 3\n and max(*transformed_image.shape[2:]) == self.model.image_encoder.img_size\n ), f\"set_torch_image input must be BCHW with long side {self.model.image_encoder.img_size}.\"\n self.reset_image()\n\n self.original_size = original_image_size\n self.input_size = tuple(transformed_image.shape[-2:])\n input_image = self.model.preprocess(transformed_image)\n self.features = self.model.image_encoder(input_image)\n self.is_image_set = True\n\n def predict(\n self,\n point_coords: Optional[np.ndarray] = None,\n point_labels: Optional[np.ndarray] = None,\n box: Optional[np.ndarray] = None,\n mask_input: Optional[np.ndarray] = None,\n multimask_output: bool = True,\n return_logits: bool = False,\n ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:\n \"\"\"\n Predict masks for the given input prompts, using the currently set image.\n\n Arguments:\n point_coords (np.ndarray or None): A Nx2 array of point prompts to the\n model. Each point is in (X,Y) in pixels.\n point_labels (np.ndarray or None): A length N array of labels for the\n point prompts. 1 indicates a foreground point and 0 indicates a\n background point.\n box (np.ndarray or None): A length 4 array given a box prompt to the\n model, in XYXY format.\n mask_input (np.ndarray): A low resolution mask input to the model, typically\n coming from a previous prediction iteration. Has form 1xHxW, where\n for SAM, H=W=256.\n multimask_output (bool): If true, the model will return three masks.\n For ambiguous input prompts (such as a single click), this will often\n produce better masks than a single prediction. If only a single\n mask is needed, the model's predicted quality score can be used\n to select the best mask. For non-ambiguous prompts, such as multiple\n input prompts, multimask_output=False can give better results.\n return_logits (bool): If true, returns un-thresholded masks logits\n instead of a binary mask.\n\n Returns:\n (np.ndarray): The output masks in CxHxW format, where C is the\n number of masks, and (H, W) is the original image size.\n (np.ndarray): An array of length C containing the model's\n predictions for the quality of each mask.\n (np.ndarray): An array of shape CxHxW, where C is the number\n of masks and H=W=256. These low resolution logits can be passed to\n a subsequent iteration as mask input.\n \"\"\"\n if not self.is_image_set:\n raise RuntimeError(\"An image must be set with .set_image(...) before mask prediction.\")\n\n # Transform input prompts\n coords_torch, labels_torch, box_torch, mask_input_torch = None, None, None, None\n if point_coords is not None:\n assert (\n point_labels is not None\n ), \"point_labels must be supplied if point_coords is supplied.\"\n point_coords = self.transform.apply_coords(point_coords, self.original_size)\n coords_torch = torch.as_tensor(point_coords, dtype=torch.float, device=self.device)\n labels_torch = torch.as_tensor(point_labels, dtype=torch.int, device=self.device)\n coords_torch, labels_torch = coords_torch[None, :, :], labels_torch[None, :]\n if box is not None:\n box = self.transform.apply_boxes(box, self.original_size)\n box_torch = torch.as_tensor(box, dtype=torch.float, device=self.device)\n box_torch = box_torch[None, :]\n if mask_input is not None:\n mask_input_torch = torch.as_tensor(mask_input, dtype=torch.float, device=self.device)\n mask_input_torch = mask_input_torch[None, :, :, :]\n\n masks, iou_predictions, low_res_masks = self.predict_torch(\n coords_torch,\n labels_torch,\n box_torch,\n mask_input_torch,\n multimask_output,\n return_logits=return_logits,\n )\n\n masks_np = masks[0].detach().cpu().numpy()\n iou_predictions_np = iou_predictions[0].detach().cpu().numpy()\n low_res_masks_np = low_res_masks[0].detach().cpu().numpy()\n return masks_np, iou_predictions_np, low_res_masks_np\n\n @torch.no_grad()\n def predict_torch(\n self,\n point_coords: Optional[torch.Tensor],\n point_labels: Optional[torch.Tensor],\n boxes: Optional[torch.Tensor] = None,\n mask_input: Optional[torch.Tensor] = None,\n multimask_output: bool = True,\n return_logits: bool = False,\n ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:\n \"\"\"\n Predict masks for the given input prompts, using the currently set image.\n Input prompts are batched torch tensors and are expected to already be\n transformed to the input frame using ResizeLongestSide.\n\n Arguments:\n point_coords (torch.Tensor or None): A BxNx2 array of point prompts to the\n model. Each point is in (X,Y) in pixels.\n point_labels (torch.Tensor or None): A BxN array of labels for the\n point prompts. 1 indicates a foreground point and 0 indicates a\n background point.\n boxes (np.ndarray or None): A Bx4 array given a box prompt to the\n model, in XYXY format.\n mask_input (np.ndarray): A low resolution mask input to the model, typically\n coming from a previous prediction iteration. Has form Bx1xHxW, where\n for SAM, H=W=256. Masks returned by a previous iteration of the\n predict method do not need further transformation.\n multimask_output (bool): If true, the model will return three masks.\n For ambiguous input prompts (such as a single click), this will often\n produce better masks than a single prediction. If only a single\n mask is needed, the model's predicted quality score can be used\n to select the best mask. For non-ambiguous prompts, such as multiple\n input prompts, multimask_output=False can give better results.\n return_logits (bool): If true, returns un-thresholded masks logits\n instead of a binary mask.\n\n Returns:\n (torch.Tensor): The output masks in BxCxHxW format, where C is the\n number of masks, and (H, W) is the original image size.\n (torch.Tensor): An array of shape BxC containing the model's\n predictions for the quality of each mask.\n (torch.Tensor): An array of shape BxCxHxW, where C is the number\n of masks and H=W=256. These low res logits can be passed to\n a subsequent iteration as mask input.\n \"\"\"\n if not self.is_image_set:\n raise RuntimeError(\"An image must be set with .set_image(...) before mask prediction.\")\n\n if point_coords is not None:\n points = (point_coords, point_labels)\n else:\n points = None\n\n # Embed prompts\n sparse_embeddings, dense_embeddings = self.model.prompt_encoder(\n points=points,\n boxes=boxes,\n masks=mask_input,\n )\n\n # Predict masks\n low_res_masks, iou_predictions = self.model.mask_decoder(\n image_embeddings=self.features,\n image_pe=self.model.prompt_encoder.get_dense_pe(),\n sparse_prompt_embeddings=sparse_embeddings,\n dense_prompt_embeddings=dense_embeddings,\n multimask_output=multimask_output,\n )\n\n # Upscale the masks to the original image resolution\n masks = self.model.postprocess_masks(low_res_masks, self.input_size, self.original_size)\n\n if not return_logits:\n masks = masks > self.model.mask_threshold\n\n return masks, iou_predictions, low_res_masks\n\n def get_image_embedding(self) -> torch.Tensor:\n \"\"\"\n Returns the image embeddings for the currently set image, with\n shape 1xCxHxW, where C is the embedding dimension and (H,W) are\n the embedding spatial dimension of SAM (typically C=256, H=W=64).\n \"\"\"\n if not self.is_image_set:\n raise RuntimeError(\n \"An image must be set with .set_image(...) to generate an embedding.\"\n )\n assert self.features is not None, \"Features must exist if an image has been set.\"\n return self.features\n\n @property\n def device(self) -> torch.device:\n return self.model.device\n\n def reset_image(self) -> None:\n \"\"\"Resets the currently set image.\"\"\"\n self.is_image_set = False\n self.features = None\n self.orig_h = None\n self.orig_w = None\n self.input_h = None\n self.input_w = None\n" }, { "path": "auto-seg/submodules/segment-anything-1/segment_anything/utils/__init__.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n" }, { "path": "auto-seg/submodules/segment-anything-1/segment_anything/utils/amg.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport numpy as np\nimport torch\n\nimport math\nfrom copy import deepcopy\nfrom itertools import product\nfrom typing import Any, Dict, Generator, ItemsView, List, Tuple\n\n\nclass MaskData:\n \"\"\"\n A structure for storing masks and their related data in batched format.\n Implements basic filtering and concatenation.\n \"\"\"\n\n def __init__(self, **kwargs) -> None:\n for v in kwargs.values():\n assert isinstance(\n v, (list, np.ndarray, torch.Tensor)\n ), \"MaskData only supports list, numpy arrays, and torch tensors.\"\n self._stats = dict(**kwargs)\n\n def __setitem__(self, key: str, item: Any) -> None:\n assert isinstance(\n item, (list, np.ndarray, torch.Tensor)\n ), \"MaskData only supports list, numpy arrays, and torch tensors.\"\n self._stats[key] = item\n\n def __delitem__(self, key: str) -> None:\n del self._stats[key]\n\n def __getitem__(self, key: str) -> Any:\n return self._stats[key]\n\n def items(self) -> ItemsView[str, Any]:\n return self._stats.items()\n\n def filter(self, keep: torch.Tensor) -> None:\n for k, v in self._stats.items():\n if v is None:\n self._stats[k] = None\n elif isinstance(v, torch.Tensor):\n self._stats[k] = v[torch.as_tensor(keep, device=v.device)]\n elif isinstance(v, np.ndarray):\n self._stats[k] = v[keep.detach().cpu().numpy()]\n elif isinstance(v, list) and keep.dtype == torch.bool:\n self._stats[k] = [a for i, a in enumerate(v) if keep[i]]\n elif isinstance(v, list):\n self._stats[k] = [v[i] for i in keep]\n else:\n raise TypeError(f\"MaskData key {k} has an unsupported type {type(v)}.\")\n\n def cat(self, new_stats: \"MaskData\") -> None:\n for k, v in new_stats.items():\n if k not in self._stats or self._stats[k] is None:\n self._stats[k] = deepcopy(v)\n elif isinstance(v, torch.Tensor):\n self._stats[k] = torch.cat([self._stats[k], v], dim=0)\n elif isinstance(v, np.ndarray):\n self._stats[k] = np.concatenate([self._stats[k], v], axis=0)\n elif isinstance(v, list):\n self._stats[k] = self._stats[k] + deepcopy(v)\n else:\n raise TypeError(f\"MaskData key {k} has an unsupported type {type(v)}.\")\n\n def to_numpy(self) -> None:\n for k, v in self._stats.items():\n if isinstance(v, torch.Tensor):\n if v.dtype == torch.bfloat16:\n v = v.to(torch.float32)\n self._stats[k] = v.detach().cpu().numpy()\n\n\ndef is_box_near_crop_edge(\n boxes: torch.Tensor, crop_box: List[int], orig_box: List[int], atol: float = 20.0\n) -> torch.Tensor:\n \"\"\"Filter masks at the edge of a crop, but not at the edge of the original image.\"\"\"\n crop_box_torch = torch.as_tensor(crop_box, dtype=torch.float, device=boxes.device)\n orig_box_torch = torch.as_tensor(orig_box, dtype=torch.float, device=boxes.device)\n boxes = uncrop_boxes_xyxy(boxes, crop_box).float()\n near_crop_edge = torch.isclose(boxes, crop_box_torch[None, :], atol=atol, rtol=0)\n near_image_edge = torch.isclose(boxes, orig_box_torch[None, :], atol=atol, rtol=0)\n near_crop_edge = torch.logical_and(near_crop_edge, ~near_image_edge)\n return torch.any(near_crop_edge, dim=1)\n\n\ndef box_xyxy_to_xywh(box_xyxy: torch.Tensor) -> torch.Tensor:\n box_xywh = deepcopy(box_xyxy)\n box_xywh[2] = box_xywh[2] - box_xywh[0]\n box_xywh[3] = box_xywh[3] - box_xywh[1]\n return box_xywh\n\n\ndef batch_iterator(batch_size: int, *args) -> Generator[List[Any], None, None]:\n assert len(args) > 0 and all(\n len(a) == len(args[0]) for a in args\n ), \"Batched iteration must have inputs of all the same size.\"\n n_batches = len(args[0]) // batch_size + int(len(args[0]) % batch_size != 0)\n for b in range(n_batches):\n yield [arg[b * batch_size : (b + 1) * batch_size] for arg in args]\n\n\ndef mask_to_rle_pytorch(tensor: torch.Tensor) -> List[Dict[str, Any]]:\n \"\"\"\n Encodes masks to an uncompressed RLE, in the format expected by\n pycoco tools.\n \"\"\"\n # Put in fortran order and flatten h,w\n b, h, w = tensor.shape\n tensor = tensor.permute(0, 2, 1).flatten(1)\n\n # Compute change indices\n diff = tensor[:, 1:] ^ tensor[:, :-1]\n change_indices = diff.nonzero()\n\n # Encode run length\n out = []\n for i in range(b):\n cur_idxs = change_indices[change_indices[:, 0] == i, 1]\n cur_idxs = torch.cat(\n [\n torch.tensor([0], dtype=cur_idxs.dtype, device=cur_idxs.device),\n cur_idxs + 1,\n torch.tensor([h * w], dtype=cur_idxs.dtype, device=cur_idxs.device),\n ]\n )\n btw_idxs = cur_idxs[1:] - cur_idxs[:-1]\n counts = [] if tensor[i, 0] == 0 else [0]\n counts.extend(btw_idxs.detach().cpu().tolist())\n out.append({\"size\": [h, w], \"counts\": counts})\n return out\n\n\ndef rle_to_mask(rle: Dict[str, Any]) -> np.ndarray:\n \"\"\"Compute a binary mask from an uncompressed RLE.\"\"\"\n h, w = rle[\"size\"]\n mask = np.empty(h * w, dtype=bool)\n idx = 0\n parity = False\n for count in rle[\"counts\"]:\n mask[idx : idx + count] = parity\n idx += count\n parity ^= True\n mask = mask.reshape(w, h)\n return mask.transpose() # Put in C order\n\n\ndef area_from_rle(rle: Dict[str, Any]) -> int:\n return sum(rle[\"counts\"][1::2])\n\n\ndef calculate_stability_score(\n masks: torch.Tensor, mask_threshold: float, threshold_offset: float\n) -> torch.Tensor:\n \"\"\"\n Computes the stability score for a batch of masks. The stability\n score is the IoU between the binary masks obtained by thresholding\n the predicted mask logits at high and low values.\n \"\"\"\n # One mask is always contained inside the other.\n # Save memory by preventing unnecessary cast to torch.int64\n intersections = (\n (masks > (mask_threshold + threshold_offset))\n .sum(-1, dtype=torch.int16)\n .sum(-1, dtype=torch.int32)\n )\n unions = (\n (masks > (mask_threshold - threshold_offset))\n .sum(-1, dtype=torch.int16)\n .sum(-1, dtype=torch.int32)\n )\n return intersections / unions\n\n\ndef build_point_grid(n_per_side: int) -> np.ndarray:\n \"\"\"Generates a 2D grid of points evenly spaced in [0,1]x[0,1].\"\"\"\n offset = 1 / (2 * n_per_side)\n points_one_side = np.linspace(offset, 1 - offset, n_per_side)\n points_x = np.tile(points_one_side[None, :], (n_per_side, 1))\n points_y = np.tile(points_one_side[:, None], (1, n_per_side))\n points = np.stack([points_x, points_y], axis=-1).reshape(-1, 2)\n return points\n\n\ndef build_all_layer_point_grids(\n n_per_side: int, n_layers: int, scale_per_layer: int\n) -> List[np.ndarray]:\n \"\"\"Generates point grids for all crop layers.\"\"\"\n points_by_layer = []\n for i in range(n_layers + 1):\n n_points = int(n_per_side / (scale_per_layer**i))\n points_by_layer.append(build_point_grid(n_points))\n return points_by_layer\n\n\ndef generate_crop_boxes(\n im_size: Tuple[int, ...], n_layers: int, overlap_ratio: float\n) -> Tuple[List[List[int]], List[int]]:\n \"\"\"\n Generates a list of crop boxes of different sizes. Each layer\n has (2**i)**2 boxes for the ith layer.\n \"\"\"\n crop_boxes, layer_idxs = [], []\n im_h, im_w = im_size\n short_side = min(im_h, im_w)\n\n # Original image\n crop_boxes.append([0, 0, im_w, im_h])\n layer_idxs.append(0)\n\n def crop_len(orig_len, n_crops, overlap):\n return int(math.ceil((overlap * (n_crops - 1) + orig_len) / n_crops))\n\n for i_layer in range(n_layers):\n n_crops_per_side = 2 ** (i_layer + 1)\n overlap = int(overlap_ratio * short_side * (2 / n_crops_per_side))\n\n crop_w = crop_len(im_w, n_crops_per_side, overlap)\n crop_h = crop_len(im_h, n_crops_per_side, overlap)\n\n crop_box_x0 = [int((crop_w - overlap) * i) for i in range(n_crops_per_side)]\n crop_box_y0 = [int((crop_h - overlap) * i) for i in range(n_crops_per_side)]\n\n # Crops in XYWH format\n for x0, y0 in product(crop_box_x0, crop_box_y0):\n box = [x0, y0, min(x0 + crop_w, im_w), min(y0 + crop_h, im_h)]\n crop_boxes.append(box)\n layer_idxs.append(i_layer + 1)\n\n return crop_boxes, layer_idxs\n\n\ndef uncrop_boxes_xyxy(boxes: torch.Tensor, crop_box: List[int]) -> torch.Tensor:\n x0, y0, _, _ = crop_box\n offset = torch.tensor([[x0, y0, x0, y0]], device=boxes.device)\n # Check if boxes has a channel dimension\n if len(boxes.shape) == 3:\n offset = offset.unsqueeze(1)\n return boxes + offset\n\n\ndef uncrop_points(points: torch.Tensor, crop_box: List[int]) -> torch.Tensor:\n x0, y0, _, _ = crop_box\n offset = torch.tensor([[x0, y0]], device=points.device)\n # Check if points has a channel dimension\n if len(points.shape) == 3:\n offset = offset.unsqueeze(1)\n return points + offset\n\n\ndef uncrop_masks(\n masks: torch.Tensor, crop_box: List[int], orig_h: int, orig_w: int\n) -> torch.Tensor:\n x0, y0, x1, y1 = crop_box\n if x0 == 0 and y0 == 0 and x1 == orig_w and y1 == orig_h:\n return masks\n # Coordinate transform masks\n pad_x, pad_y = orig_w - (x1 - x0), orig_h - (y1 - y0)\n pad = (x0, pad_x - x0, y0, pad_y - y0)\n return torch.nn.functional.pad(masks, pad, value=0)\n\n\ndef remove_small_regions(\n mask: np.ndarray, area_thresh: float, mode: str\n) -> Tuple[np.ndarray, bool]:\n \"\"\"\n Removes small disconnected regions and holes in a mask. Returns the\n mask and an indicator of if the mask has been modified.\n \"\"\"\n import cv2 # type: ignore\n\n assert mode in [\"holes\", \"islands\"]\n correct_holes = mode == \"holes\"\n working_mask = (correct_holes ^ mask).astype(np.uint8)\n n_labels, regions, stats, _ = cv2.connectedComponentsWithStats(working_mask, 8)\n sizes = stats[:, -1][1:] # Row 0 is background label\n small_regions = [i + 1 for i, s in enumerate(sizes) if s < area_thresh]\n if len(small_regions) == 0:\n return mask, False\n fill_labels = [0] + small_regions\n if not correct_holes:\n fill_labels = [i for i in range(n_labels) if i not in fill_labels]\n # If every region is below threshold, keep largest\n if len(fill_labels) == 0:\n fill_labels = [int(np.argmax(sizes)) + 1]\n mask = np.isin(regions, fill_labels)\n return mask, True\n\n\ndef coco_encode_rle(uncompressed_rle: Dict[str, Any]) -> Dict[str, Any]:\n from pycocotools import mask as mask_utils # type: ignore\n\n h, w = uncompressed_rle[\"size\"]\n rle = mask_utils.frPyObjects(uncompressed_rle, h, w)\n rle[\"counts\"] = rle[\"counts\"].decode(\"utf-8\") # Necessary to serialize with json\n return rle\n\n\ndef batched_mask_to_box(masks: torch.Tensor) -> torch.Tensor:\n \"\"\"\n Calculates boxes in XYXY format around masks. Return [0,0,0,0] for\n an empty mask. For input shape C1xC2x...xHxW, the output shape is C1xC2x...x4.\n \"\"\"\n # torch.max below raises an error on empty inputs, just skip in this case\n if torch.numel(masks) == 0:\n return torch.zeros(*masks.shape[:-2], 4, device=masks.device)\n\n # Normalize shape to CxHxW\n shape = masks.shape\n h, w = shape[-2:]\n if len(shape) > 2:\n masks = masks.flatten(0, -3)\n else:\n masks = masks.unsqueeze(0)\n\n # Get top and bottom edges\n in_height, _ = torch.max(masks, dim=-1)\n in_height_coords = in_height * torch.arange(h, device=in_height.device)[None, :]\n bottom_edges, _ = torch.max(in_height_coords, dim=-1)\n in_height_coords = in_height_coords + h * (~in_height)\n top_edges, _ = torch.min(in_height_coords, dim=-1)\n\n # Get left and right edges\n in_width, _ = torch.max(masks, dim=-2)\n in_width_coords = in_width * torch.arange(w, device=in_width.device)[None, :]\n right_edges, _ = torch.max(in_width_coords, dim=-1)\n in_width_coords = in_width_coords + w * (~in_width)\n left_edges, _ = torch.min(in_width_coords, dim=-1)\n\n # If the mask is empty the right edge will be to the left of the left edge.\n # Replace these boxes with [0, 0, 0, 0]\n empty_filter = (right_edges < left_edges) | (bottom_edges < top_edges)\n out = torch.stack([left_edges, top_edges, right_edges, bottom_edges], dim=-1)\n out = out * (~empty_filter).unsqueeze(-1)\n\n # Return to original shape\n if len(shape) > 2:\n out = out.reshape(*shape[:-2], 4)\n else:\n out = out[0]\n\n return out\n" }, { "path": "auto-seg/submodules/segment-anything-1/segment_anything/utils/onnx.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport torch\nimport torch.nn as nn\nfrom torch.nn import functional as F\n\nfrom typing import Tuple\n\nfrom ..modeling import Sam\nfrom .amg import calculate_stability_score\n\n\nclass SamOnnxModel(nn.Module):\n \"\"\"\n This model should not be called directly, but is used in ONNX export.\n It combines the prompt encoder, mask decoder, and mask postprocessing of Sam,\n with some functions modified to enable model tracing. Also supports extra\n options controlling what information. See the ONNX export script for details.\n \"\"\"\n\n def __init__(\n self,\n model: Sam,\n return_single_mask: bool,\n use_stability_score: bool = False,\n return_extra_metrics: bool = False,\n ) -> None:\n super().__init__()\n self.mask_decoder = model.mask_decoder\n self.model = model\n self.img_size = model.image_encoder.img_size\n self.return_single_mask = return_single_mask\n self.use_stability_score = use_stability_score\n self.stability_score_offset = 1.0\n self.return_extra_metrics = return_extra_metrics\n\n @staticmethod\n def resize_longest_image_size(\n input_image_size: torch.Tensor, longest_side: int\n ) -> torch.Tensor:\n input_image_size = input_image_size.to(torch.float32)\n scale = longest_side / torch.max(input_image_size)\n transformed_size = scale * input_image_size\n transformed_size = torch.floor(transformed_size + 0.5).to(torch.int64)\n return transformed_size\n\n def _embed_points(self, point_coords: torch.Tensor, point_labels: torch.Tensor) -> torch.Tensor:\n point_coords = point_coords + 0.5\n point_coords = point_coords / self.img_size\n point_embedding = self.model.prompt_encoder.pe_layer._pe_encoding(point_coords)\n point_labels = point_labels.unsqueeze(-1).expand_as(point_embedding)\n\n point_embedding = point_embedding * (point_labels != -1)\n point_embedding = point_embedding + self.model.prompt_encoder.not_a_point_embed.weight * (\n point_labels == -1\n )\n\n for i in range(self.model.prompt_encoder.num_point_embeddings):\n point_embedding = point_embedding + self.model.prompt_encoder.point_embeddings[\n i\n ].weight * (point_labels == i)\n\n return point_embedding\n\n def _embed_masks(self, input_mask: torch.Tensor, has_mask_input: torch.Tensor) -> torch.Tensor:\n mask_embedding = has_mask_input * self.model.prompt_encoder.mask_downscaling(input_mask)\n mask_embedding = mask_embedding + (\n 1 - has_mask_input\n ) * self.model.prompt_encoder.no_mask_embed.weight.reshape(1, -1, 1, 1)\n return mask_embedding\n\n def mask_postprocessing(self, masks: torch.Tensor, orig_im_size: torch.Tensor) -> torch.Tensor:\n masks = F.interpolate(\n masks,\n size=(self.img_size, self.img_size),\n mode=\"bilinear\",\n align_corners=False,\n )\n\n prepadded_size = self.resize_longest_image_size(orig_im_size, self.img_size).to(torch.int64)\n masks = masks[..., : prepadded_size[0], : prepadded_size[1]] # type: ignore\n\n orig_im_size = orig_im_size.to(torch.int64)\n h, w = orig_im_size[0], orig_im_size[1]\n masks = F.interpolate(masks, size=(h, w), mode=\"bilinear\", align_corners=False)\n return masks\n\n def select_masks(\n self, masks: torch.Tensor, iou_preds: torch.Tensor, num_points: int\n ) -> Tuple[torch.Tensor, torch.Tensor]:\n # Determine if we should return the multiclick mask or not from the number of points.\n # The reweighting is used to avoid control flow.\n score_reweight = torch.tensor(\n [[1000] + [0] * (self.model.mask_decoder.num_mask_tokens - 1)]\n ).to(iou_preds.device)\n score = iou_preds + (num_points - 2.5) * score_reweight\n best_idx = torch.argmax(score, dim=1)\n masks = masks[torch.arange(masks.shape[0]), best_idx, :, :].unsqueeze(1)\n iou_preds = iou_preds[torch.arange(masks.shape[0]), best_idx].unsqueeze(1)\n\n return masks, iou_preds\n\n @torch.no_grad()\n def forward(\n self,\n image_embeddings: torch.Tensor,\n point_coords: torch.Tensor,\n point_labels: torch.Tensor,\n mask_input: torch.Tensor,\n has_mask_input: torch.Tensor,\n orig_im_size: torch.Tensor,\n ):\n sparse_embedding = self._embed_points(point_coords, point_labels)\n dense_embedding = self._embed_masks(mask_input, has_mask_input)\n\n masks, scores = self.model.mask_decoder.predict_masks(\n image_embeddings=image_embeddings,\n image_pe=self.model.prompt_encoder.get_dense_pe(),\n sparse_prompt_embeddings=sparse_embedding,\n dense_prompt_embeddings=dense_embedding,\n )\n\n if self.use_stability_score:\n scores = calculate_stability_score(\n masks, self.model.mask_threshold, self.stability_score_offset\n )\n\n if self.return_single_mask:\n masks, scores = self.select_masks(masks, scores, point_coords.shape[1])\n\n upscaled_masks = self.mask_postprocessing(masks, orig_im_size)\n\n if self.return_extra_metrics:\n stability_scores = calculate_stability_score(\n upscaled_masks, self.model.mask_threshold, self.stability_score_offset\n )\n areas = (upscaled_masks > self.model.mask_threshold).sum(-1).sum(-1)\n return upscaled_masks, scores, stability_scores, areas, masks\n\n return upscaled_masks, scores, masks\n" }, { "path": "auto-seg/submodules/segment-anything-1/segment_anything/utils/transforms.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport numpy as np\nimport torch\nfrom torch.nn import functional as F\nfrom torchvision.transforms.functional import resize, to_pil_image # type: ignore\n\nfrom copy import deepcopy\nfrom typing import Tuple\n\n\nclass ResizeLongestSide:\n \"\"\"\n Resizes images to the longest side 'target_length', as well as provides\n methods for resizing coordinates and boxes. Provides methods for\n transforming both numpy array and batched torch tensors.\n \"\"\"\n\n def __init__(self, target_length: int) -> None:\n self.target_length = target_length\n\n def apply_image(self, image: np.ndarray) -> np.ndarray:\n \"\"\"\n Expects a numpy array with shape HxWxC in uint8 format.\n \"\"\"\n target_size = self.get_preprocess_shape(image.shape[0], image.shape[1], self.target_length)\n return np.array(resize(to_pil_image(image), target_size))\n\n def apply_coords(self, coords: np.ndarray, original_size: Tuple[int, ...]) -> np.ndarray:\n \"\"\"\n Expects a numpy array of length 2 in the final dimension. Requires the\n original image size in (H, W) format.\n \"\"\"\n old_h, old_w = original_size\n new_h, new_w = self.get_preprocess_shape(\n original_size[0], original_size[1], self.target_length\n )\n coords = deepcopy(coords).astype(float)\n coords[..., 0] = coords[..., 0] * (new_w / old_w)\n coords[..., 1] = coords[..., 1] * (new_h / old_h)\n return coords\n\n def apply_boxes(self, boxes: np.ndarray, original_size: Tuple[int, ...]) -> np.ndarray:\n \"\"\"\n Expects a numpy array shape Bx4. Requires the original image size\n in (H, W) format.\n \"\"\"\n boxes = self.apply_coords(boxes.reshape(-1, 2, 2), original_size)\n return boxes.reshape(-1, 4)\n\n def apply_image_torch(self, image: torch.Tensor) -> torch.Tensor:\n \"\"\"\n Expects batched images with shape BxCxHxW and float format. This\n transformation may not exactly match apply_image. apply_image is\n the transformation expected by the model.\n \"\"\"\n # Expects an image in BCHW format. May not exactly match apply_image.\n target_size = self.get_preprocess_shape(image.shape[2], image.shape[3], self.target_length)\n return F.interpolate(\n image, target_size, mode=\"bilinear\", align_corners=False, antialias=True\n )\n\n def apply_coords_torch(\n self, coords: torch.Tensor, original_size: Tuple[int, ...]\n ) -> torch.Tensor:\n \"\"\"\n Expects a torch tensor with length 2 in the last dimension. Requires the\n original image size in (H, W) format.\n \"\"\"\n old_h, old_w = original_size\n new_h, new_w = self.get_preprocess_shape(\n original_size[0], original_size[1], self.target_length\n )\n coords = deepcopy(coords).to(torch.float)\n coords[..., 0] = coords[..., 0] * (new_w / old_w)\n coords[..., 1] = coords[..., 1] * (new_h / old_h)\n return coords\n\n def apply_boxes_torch(\n self, boxes: torch.Tensor, original_size: Tuple[int, ...]\n ) -> torch.Tensor:\n \"\"\"\n Expects a torch tensor with shape Bx4. Requires the original image\n size in (H, W) format.\n \"\"\"\n boxes = self.apply_coords_torch(boxes.reshape(-1, 2, 2), original_size)\n return boxes.reshape(-1, 4)\n\n @staticmethod\n def get_preprocess_shape(oldh: int, oldw: int, long_side_length: int) -> Tuple[int, int]:\n \"\"\"\n Compute the output size given input size and target long side length.\n \"\"\"\n scale = long_side_length * 1.0 / max(oldh, oldw)\n newh, neww = oldh * scale, oldw * scale\n neww = int(neww + 0.5)\n newh = int(newh + 0.5)\n return (newh, neww)\n" }, { "path": "auto-seg/submodules/segment-anything-1/setup.cfg", "content": "[isort]\nline_length=100\nmulti_line_output=3\ninclude_trailing_comma=True\nknown_standard_library=numpy,setuptools\nskip_glob=*/__init__.py\nknown_myself=segment_anything\nknown_third_party=matplotlib,cv2,torch,torchvision,pycocotools,onnx,black,isort\nno_lines_before=STDLIB,THIRDPARTY\nsections=FUTURE,STDLIB,THIRDPARTY,MYSELF,FIRSTPARTY,LOCALFOLDER\ndefault_section=FIRSTPARTY\n" }, { "path": "auto-seg/submodules/segment-anything-1/setup.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nfrom setuptools import find_packages, setup\n\nsetup(\n name=\"segment_anything\",\n version=\"1.0\",\n install_requires=[],\n packages=find_packages(exclude=\"notebooks\"),\n extras_require={\n \"all\": [\"matplotlib\", \"pycocotools\", \"opencv-python\", \"onnx\", \"onnxruntime\"],\n \"dev\": [\"flake8\", \"isort\", \"black\", \"mypy\"],\n },\n)\n" }, { "path": "auto-seg/submodules/segment-anything-2/.clang-format", "content": "AccessModifierOffset: -1\nAlignAfterOpenBracket: AlwaysBreak\nAlignConsecutiveAssignments: false\nAlignConsecutiveDeclarations: false\nAlignEscapedNewlinesLeft: true\nAlignOperands: false\nAlignTrailingComments: false\nAllowAllParametersOfDeclarationOnNextLine: false\nAllowShortBlocksOnASingleLine: false\nAllowShortCaseLabelsOnASingleLine: false\nAllowShortFunctionsOnASingleLine: Empty\nAllowShortIfStatementsOnASingleLine: false\nAllowShortLoopsOnASingleLine: false\nAlwaysBreakAfterReturnType: None\nAlwaysBreakBeforeMultilineStrings: true\nAlwaysBreakTemplateDeclarations: true\nBinPackArguments: false\nBinPackParameters: false\nBraceWrapping:\n AfterClass: false\n AfterControlStatement: false\n AfterEnum: false\n AfterFunction: false\n AfterNamespace: false\n AfterObjCDeclaration: false\n AfterStruct: false\n AfterUnion: false\n BeforeCatch: false\n BeforeElse: false\n IndentBraces: false\nBreakBeforeBinaryOperators: None\nBreakBeforeBraces: Attach\nBreakBeforeTernaryOperators: true\nBreakConstructorInitializersBeforeComma: false\nBreakAfterJavaFieldAnnotations: false\nBreakStringLiterals: false\nColumnLimit: 80\nCommentPragmas: '^ IWYU pragma:'\nConstructorInitializerAllOnOneLineOrOnePerLine: true\nConstructorInitializerIndentWidth: 4\nContinuationIndentWidth: 4\nCpp11BracedListStyle: true\nDerivePointerAlignment: false\nDisableFormat: false\nForEachMacros: [ FOR_EACH, FOR_EACH_R, FOR_EACH_RANGE, ]\nIncludeCategories:\n - Regex: '^<.*\\.h(pp)?>'\n Priority: 1\n - Regex: '^<.*'\n Priority: 2\n - Regex: '.*'\n Priority: 3\nIndentCaseLabels: true\nIndentWidth: 2\nIndentWrappedFunctionNames: false\nKeepEmptyLinesAtTheStartOfBlocks: false\nMacroBlockBegin: ''\nMacroBlockEnd: ''\nMaxEmptyLinesToKeep: 1\nNamespaceIndentation: None\nObjCBlockIndentWidth: 2\nObjCSpaceAfterProperty: false\nObjCSpaceBeforeProtocolList: false\nPenaltyBreakBeforeFirstCallParameter: 1\nPenaltyBreakComment: 300\nPenaltyBreakFirstLessLess: 120\nPenaltyBreakString: 1000\nPenaltyExcessCharacter: 1000000\nPenaltyReturnTypeOnItsOwnLine: 200\nPointerAlignment: Left\nReflowComments: true\nSortIncludes: true\nSpaceAfterCStyleCast: false\nSpaceBeforeAssignmentOperators: true\nSpaceBeforeParens: ControlStatements\nSpaceInEmptyParentheses: false\nSpacesBeforeTrailingComments: 1\nSpacesInAngles: false\nSpacesInContainerLiterals: true\nSpacesInCStyleCastParentheses: false\nSpacesInParentheses: false\nSpacesInSquareBrackets: false\nStandard: Cpp11\nTabWidth: 8\nUseTab: Never\n" }, { "path": "auto-seg/submodules/segment-anything-2/.gitignore", "content": ".vscode/\n.DS_Store\n__pycache__/\n*-checkpoint.ipynb\n.venv\n*.egg*\nbuild/*\n_C.*\noutputs/*\ncheckpoints/*.pt\n" }, { "path": "auto-seg/submodules/segment-anything-2/.watchmanconfig", "content": "{}" }, { "path": "auto-seg/submodules/segment-anything-2/CODE_OF_CONDUCT.md", "content": "# Code of Conduct\n\n## Our Pledge\n\nIn the interest of fostering an open and welcoming environment, we as\ncontributors and maintainers pledge to make participation in our project and\nour community a harassment-free experience for everyone, regardless of age, body\nsize, disability, ethnicity, sex characteristics, gender identity and expression,\nlevel of experience, education, socio-economic status, nationality, personal\nappearance, race, religion, or sexual identity and orientation.\n\n## Our Standards\n\nExamples of behavior that contributes to creating a positive environment\ninclude:\n\n* Using welcoming and inclusive language\n* Being respectful of differing viewpoints and experiences\n* Gracefully accepting constructive criticism\n* Focusing on what is best for the community\n* Showing empathy towards other community members\n\nExamples of unacceptable behavior by participants include:\n\n* The use of sexualized language or imagery and unwelcome sexual attention or\n advances\n* Trolling, insulting/derogatory comments, and personal or political attacks\n* Public or private harassment\n* Publishing others' private information, such as a physical or electronic\n address, without explicit permission\n* Other conduct which could reasonably be considered inappropriate in a\n professional setting\n\n## Our Responsibilities\n\nProject maintainers are responsible for clarifying the standards of acceptable\nbehavior and are expected to take appropriate and fair corrective action in\nresponse to any instances of unacceptable behavior.\n\nProject maintainers have the right and responsibility to remove, edit, or\nreject comments, commits, code, wiki edits, issues, and other contributions\nthat are not aligned to this Code of Conduct, or to ban temporarily or\npermanently any contributor for other behaviors that they deem inappropriate,\nthreatening, offensive, or harmful.\n\n## Scope\n\nThis Code of Conduct applies within all project spaces, and it also applies when\nan individual is representing the project or its community in public spaces.\nExamples of representing a project or community include using an official\nproject e-mail address, posting via an official social media account, or acting\nas an appointed representative at an online or offline event. Representation of\na project may be further defined and clarified by project maintainers.\n\nThis Code of Conduct also applies outside the project spaces when there is a\nreasonable belief that an individual's behavior may have a negative impact on\nthe project or its community.\n\n## Enforcement\n\nInstances of abusive, harassing, or otherwise unacceptable behavior may be\nreported by contacting the project team at . All\ncomplaints will be reviewed and investigated and will result in a response that\nis deemed necessary and appropriate to the circumstances. The project team is\nobligated to maintain confidentiality with regard to the reporter of an incident.\nFurther details of specific enforcement policies may be posted separately.\n\nProject maintainers who do not follow or enforce the Code of Conduct in good\nfaith may face temporary or permanent repercussions as determined by other\nmembers of the project's leadership.\n\n## Attribution\n\nThis Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4,\navailable at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html\n\n[homepage]: https://www.contributor-covenant.org\n\nFor answers to common questions about this code of conduct, see\nhttps://www.contributor-covenant.org/faq\n" }, { "path": "auto-seg/submodules/segment-anything-2/CONTRIBUTING.md", "content": "# Contributing to segment-anything\nWe want to make contributing to this project as easy and transparent as\npossible.\n\n## Pull Requests\nWe actively welcome your pull requests.\n\n1. Fork the repo and create your branch from `main`.\n2. If you've added code that should be tested, add tests.\n3. If you've changed APIs, update the documentation.\n4. Ensure the test suite passes.\n5. Make sure your code lints, using the `ufmt format` command. Linting requires `black==24.2.0`, `usort==1.0.2`, and `ufmt==2.0.0b2`, which can be installed via `pip install -e \".[dev]\"`.\n6. If you haven't already, complete the Contributor License Agreement (\"CLA\").\n\n## Contributor License Agreement (\"CLA\")\nIn order to accept your pull request, we need you to submit a CLA. You only need\nto do this once to work on any of Facebook's open source projects.\n\nComplete your CLA here: \n\n## Issues\nWe use GitHub issues to track public bugs. Please ensure your description is\nclear and has sufficient instructions to be able to reproduce the issue.\n\nFacebook has a [bounty program](https://www.facebook.com/whitehat/) for the safe\ndisclosure of security bugs. In those cases, please go through the process\noutlined on that page and do not file a public issue.\n\n## License\nBy contributing to segment-anything, you agree that your contributions will be licensed\nunder the LICENSE file in the root directory of this source tree.\n" }, { "path": "auto-seg/submodules/segment-anything-2/INSTALL.md", "content": "## Installation\n\n### Requirements\n\n- Linux with Python ≥ 3.10, PyTorch ≥ 2.3.1 and [torchvision](https://github.com/pytorch/vision/) that matches the PyTorch installation. Install them together at https://pytorch.org to ensure this.\n * Note older versions of Python or PyTorch may also work. However, the versions above are strongly recommended to provide all features such as `torch.compile`.\n- [CUDA toolkits](https://developer.nvidia.com/cuda-toolkit-archive) that match the CUDA version for your PyTorch installation. This should typically be CUDA 12.1 if you follow the default installation command.\n- If you are installing on Windows, it's strongly recommended to use [Windows Subsystem for Linux (WSL)](https://learn.microsoft.com/en-us/windows/wsl/install) with Ubuntu.\n\nThen, install SAM 2 from the root of this repository via\n```bash\npip install -e \".[notebooks]\"\n```\n\nNote that you may skip building the SAM 2 CUDA extension during installation via environment variable `SAM2_BUILD_CUDA=0`, as follows:\n```bash\n# skip the SAM 2 CUDA extension\nSAM2_BUILD_CUDA=0 pip install -e \".[notebooks]\"\n```\nThis would also skip the post-processing step at runtime (removing small holes and sprinkles in the output masks, which requires the CUDA extension), but shouldn't affect the results in most cases.\n\n### Building the SAM 2 CUDA extension\n\nBy default, we allow the installation to proceed even if the SAM 2 CUDA extension fails to build. (In this case, the build errors are hidden unless using `-v` for verbose output in `pip install`.)\n\nIf you see a message like `Skipping the post-processing step due to the error above` at runtime or `Failed to build the SAM 2 CUDA extension due to the error above` during installation, it indicates that the SAM 2 CUDA extension failed to build in your environment. In this case, **you can still use SAM 2 for both image and video applications**. The post-processing step (removing small holes and sprinkles in the output masks) will be skipped, but this shouldn't affect the results in most cases.\n\nIf you would like to enable this post-processing step, you can reinstall SAM 2 on a GPU machine with environment variable `SAM2_BUILD_ALLOW_ERRORS=0` to force building the CUDA extension (and raise errors if it fails to build), as follows\n```bash\npip uninstall -y SAM-2 && \\\nrm -f ./sam2/*.so && \\\nSAM2_BUILD_ALLOW_ERRORS=0 pip install -v -e \".[notebooks]\"\n```\n\nNote that PyTorch needs to be installed first before building the SAM 2 CUDA extension. It's also necessary to install [CUDA toolkits](https://developer.nvidia.com/cuda-toolkit-archive) that match the CUDA version for your PyTorch installation. (This should typically be CUDA 12.1 if you follow the default installation command.) After installing the CUDA toolkits, you can check its version via `nvcc --version`.\n\nPlease check the section below on common installation issues if the CUDA extension fails to build during installation or load at runtime.\n\n### Common Installation Issues\n\nClick each issue for its solutions:\n\n
\n\nI got `ImportError: cannot import name '_C' from 'sam2'`\n\n
\n\nThis is usually because you haven't run the `pip install -e \".[notebooks]\"` step above or the installation failed. Please install SAM 2 first, and see the other issues if your installation fails.\n\nIn some systems, you may need to run `python setup.py build_ext --inplace` in the SAM 2 repo root as suggested in https://github.com/facebookresearch/sam2/issues/77.\n
\n\n
\n\nI got `MissingConfigException: Cannot find primary config 'configs/sam2.1/sam2.1_hiera_l.yaml'`\n\n
\n\nThis is usually because you haven't run the `pip install -e .` step above, so `sam2` isn't in your Python's `sys.path`. Please run this installation step. In case it still fails after the installation step, you may try manually adding the root of this repo to `PYTHONPATH` via\n```bash\nexport SAM2_REPO_ROOT=/path/to/sam2 # path to this repo\nexport PYTHONPATH=\"${SAM2_REPO_ROOT}:${PYTHONPATH}\"\n```\nto manually add `sam2_configs` into your Python's `sys.path`.\n\n
\n\n
\n\nI got `RuntimeError: Error(s) in loading state_dict for SAM2Base` when loading the new SAM 2.1 checkpoints\n\n
\n\nThis is likely because you have installed a previous version of this repo, which doesn't have the new modules to support the SAM 2.1 checkpoints yet. Please try the following steps:\n\n1. pull the latest code from the `main` branch of this repo\n2. run `pip uninstall -y SAM-2` to uninstall any previous installations\n3. then install the latest repo again using `pip install -e \".[notebooks]\"`\n\nIn case the steps above still don't resolve the error, please try running in your Python environment the following\n```python\nfrom sam2.modeling import sam2_base\n\nprint(sam2_base.__file__)\n```\nand check whether the content in the printed local path of `sam2/modeling/sam2_base.py` matches the latest one in https://github.com/facebookresearch/sam2/blob/main/sam2/modeling/sam2_base.py (e.g. whether your local file has `no_obj_embed_spatial`) to indentify if you're still using a previous installation.\n\n
\n\n
\n\nMy installation failed with `CUDA_HOME environment variable is not set`\n\n
\n\nThis usually happens because the installation step cannot find the CUDA toolkits (that contain the NVCC compiler) to build a custom CUDA kernel in SAM 2. Please install [CUDA toolkits](https://developer.nvidia.com/cuda-toolkit-archive) or the version that matches the CUDA version for your PyTorch installation. If the error persists after installing CUDA toolkits, you may explicitly specify `CUDA_HOME` via\n```\nexport CUDA_HOME=/usr/local/cuda # change to your CUDA toolkit path\n```\nand rerun the installation.\n\nAlso, you should make sure\n```\npython -c 'import torch; from torch.utils.cpp_extension import CUDA_HOME; print(torch.cuda.is_available(), CUDA_HOME)'\n```\nprint `(True, a directory with cuda)` to verify that the CUDA toolkits are correctly set up.\n\nIf you are still having problems after verifying that the CUDA toolkit is installed and the `CUDA_HOME` environment variable is set properly, you may have to add the `--no-build-isolation` flag to the pip command:\n```\npip install --no-build-isolation -e .\n```\n\n
\n\n
\n\nI got `undefined symbol: _ZN3c1015SmallVectorBaseIjE8grow_podEPKvmm` (or similar errors)\n\n
\n\nThis usually happens because you have multiple versions of dependencies (PyTorch or CUDA) in your environment. During installation, the SAM 2 library is compiled against one version library while at run time it links against another version. This might be due to that you have different versions of PyTorch or CUDA installed separately via `pip` or `conda`. You may delete one of the duplicates to only keep a single PyTorch and CUDA version.\n\nIn particular, if you have a lower PyTorch version than 2.3.1, it's recommended to upgrade to PyTorch 2.3.1 or higher first. Otherwise, the installation script will try to upgrade to the latest PyTorch using `pip`, which could sometimes lead to duplicated PyTorch installation if you have previously installed another PyTorch version using `conda`.\n\nWe have been building SAM 2 against PyTorch 2.3.1 internally. However, a few user comments (e.g. https://github.com/facebookresearch/sam2/issues/22, https://github.com/facebookresearch/sam2/issues/14) suggested that downgrading to PyTorch 2.1.0 might resolve this problem. In case the error persists, you may try changing the restriction from `torch>=2.3.1` to `torch>=2.1.0` in both [`pyproject.toml`](pyproject.toml) and [`setup.py`](setup.py) to allow PyTorch 2.1.0.\n
\n\n
\n\nI got `CUDA error: no kernel image is available for execution on the device`\n\n
\n\nA possible cause could be that the CUDA kernel is somehow not compiled towards your GPU's CUDA [capability](https://developer.nvidia.com/cuda-gpus). This could happen if the installation is done in an environment different from the runtime (e.g. in a slurm system).\n\nYou can try pulling the latest code from the SAM 2 repo and running the following\n```\nexport TORCH_CUDA_ARCH_LIST=9.0 8.0 8.6 8.9 7.0 7.2 7.5 6.0`\n```\nto manually specify the CUDA capability in the compilation target that matches your GPU.\n
\n\n
\n\nI got `RuntimeError: No available kernel. Aborting execution.` (or similar errors)\n\n
\n\nThis is probably because your machine doesn't have a GPU or a compatible PyTorch version for Flash Attention (see also https://discuss.pytorch.org/t/using-f-scaled-dot-product-attention-gives-the-error-runtimeerror-no-available-kernel-aborting-execution/180900 for a discussion in PyTorch forum). You may be able to resolve this error by replacing the line\n```python\nOLD_GPU, USE_FLASH_ATTN, MATH_KERNEL_ON = get_sdpa_settings()\n```\nin [`sam2/modeling/sam/transformer.py`](sam2/modeling/sam/transformer.py) with\n```python\nOLD_GPU, USE_FLASH_ATTN, MATH_KERNEL_ON = True, True, True\n```\nto relax the attention kernel setting and use other kernels than Flash Attention.\n
\n\n
\n\nI got `Error compiling objects for extension`\n\n
\n\nYou may see error log of:\n> unsupported Microsoft Visual Studio version! Only the versions between 2017 and 2022 (inclusive) are supported! The nvcc flag '-allow-unsupported-compiler' can be used to override this version check; however, using an unsupported host compiler may cause compilation failure or incorrect run time execution. Use at your own risk.\n\nThis is probably because your versions of CUDA and Visual Studio are incompatible. (see also https://stackoverflow.com/questions/78515942/cuda-compatibility-with-visual-studio-2022-version-17-10 for a discussion in stackoverflow).
\nYou may be able to fix this by adding the `-allow-unsupported-compiler` argument to `nvcc` after L48 in the [setup.py](https://github.com/facebookresearch/sam2/blob/main/setup.py).
\nAfter adding the argument, `get_extension()` will look like this:\n```python\ndef get_extensions():\n srcs = [\"sam2/csrc/connected_components.cu\"]\n compile_args = {\n \"cxx\": [],\n \"nvcc\": [\n \"-DCUDA_HAS_FP16=1\",\n \"-D__CUDA_NO_HALF_OPERATORS__\",\n \"-D__CUDA_NO_HALF_CONVERSIONS__\",\n \"-D__CUDA_NO_HALF2_OPERATORS__\",\n \"-allow-unsupported-compiler\" # Add this argument\n ],\n }\n ext_modules = [CUDAExtension(\"sam2._C\", srcs, extra_compile_args=compile_args)]\n return ext_modules\n```\n
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We extend SAM to video by considering images as a video with a single frame. The model design is a simple transformer architecture with streaming memory for real-time video processing. We build a model-in-the-loop data engine, which improves model and data via user interaction, to collect [**our SA-V dataset**](https://ai.meta.com/datasets/segment-anything-video), the largest video segmentation dataset to date. SAM 2 trained on our data provides strong performance across a wide range of tasks and visual domains.\n\n![SA-V dataset](assets/sa_v_dataset.jpg?raw=true)\n\n## Latest updates\n\n**09/30/2024 -- SAM 2.1 Developer Suite (new checkpoints, training code, web demo) is released**\n\n- A new suite of improved model checkpoints (denoted as **SAM 2.1**) are released. See [Model Description](#model-description) for details.\n * To use the new SAM 2.1 checkpoints, you need the latest model code from this repo. If you have installed an earlier version of this repo, please first uninstall the previous version via `pip uninstall SAM-2`, pull the latest code from this repo (with `git pull`), and then reinstall the repo following [Installation](#installation) below.\n- The training (and fine-tuning) code has been released. See [`training/README.md`](training/README.md) on how to get started.\n- The frontend + backend code for the SAM 2 web demo has been released. See [`demo/README.md`](demo/README.md) for details.\n\n## Installation\n\nSAM 2 needs to be installed first before use. The code requires `python>=3.10`, as well as `torch>=2.3.1` and `torchvision>=0.18.1`. Please follow the instructions [here](https://pytorch.org/get-started/locally/) to install both PyTorch and TorchVision dependencies. You can install SAM 2 on a GPU machine using:\n\n```bash\ngit clone https://github.com/facebookresearch/sam2.git && cd sam2\n\npip install -e .\n```\nIf you are installing on Windows, it's strongly recommended to use [Windows Subsystem for Linux (WSL)](https://learn.microsoft.com/en-us/windows/wsl/install) with Ubuntu.\n\nTo use the SAM 2 predictor and run the example notebooks, `jupyter` and `matplotlib` are required and can be installed by:\n\n```bash\npip install -e \".[notebooks]\"\n```\n\nNote:\n1. It's recommended to create a new Python environment via [Anaconda](https://www.anaconda.com/) for this installation and install PyTorch 2.3.1 (or higher) via `pip` following https://pytorch.org/. If you have a PyTorch version lower than 2.3.1 in your current environment, the installation command above will try to upgrade it to the latest PyTorch version using `pip`.\n2. The step above requires compiling a custom CUDA kernel with the `nvcc` compiler. If it isn't already available on your machine, please install the [CUDA toolkits](https://developer.nvidia.com/cuda-toolkit-archive) with a version that matches your PyTorch CUDA version.\n3. If you see a message like `Failed to build the SAM 2 CUDA extension` during installation, you can ignore it and still use SAM 2 (some post-processing functionality may be limited, but it doesn't affect the results in most cases).\n\nPlease see [`INSTALL.md`](./INSTALL.md) for FAQs on potential issues and solutions.\n\n## Getting Started\n\n### Download Checkpoints\n\nFirst, we need to download a model checkpoint. All the model checkpoints can be downloaded by running:\n\n```bash\ncd checkpoints && \\\n./download_ckpts.sh && \\\ncd ..\n```\n\nor individually from:\n\n- [sam2.1_hiera_tiny.pt](https://dl.fbaipublicfiles.com/segment_anything_2/092824/sam2.1_hiera_tiny.pt)\n- [sam2.1_hiera_small.pt](https://dl.fbaipublicfiles.com/segment_anything_2/092824/sam2.1_hiera_small.pt)\n- [sam2.1_hiera_base_plus.pt](https://dl.fbaipublicfiles.com/segment_anything_2/092824/sam2.1_hiera_base_plus.pt)\n- [sam2.1_hiera_large.pt](https://dl.fbaipublicfiles.com/segment_anything_2/092824/sam2.1_hiera_large.pt)\n\n(note that these are the improved checkpoints denoted as SAM 2.1; see [Model Description](#model-description) for details.)\n\nThen SAM 2 can be used in a few lines as follows for image and video prediction.\n\n### Image prediction\n\nSAM 2 has all the capabilities of [SAM](https://github.com/facebookresearch/segment-anything) on static images, and we provide image prediction APIs that closely resemble SAM for image use cases. The `SAM2ImagePredictor` class has an easy interface for image prompting.\n\n```python\nimport torch\nfrom sam2.build_sam import build_sam2\nfrom sam2.sam2_image_predictor import SAM2ImagePredictor\n\ncheckpoint = \"./checkpoints/sam2.1_hiera_large.pt\"\nmodel_cfg = \"configs/sam2.1/sam2.1_hiera_l.yaml\"\npredictor = SAM2ImagePredictor(build_sam2(model_cfg, checkpoint))\n\nwith torch.inference_mode(), torch.autocast(\"cuda\", dtype=torch.bfloat16):\n predictor.set_image()\n masks, _, _ = predictor.predict()\n```\n\nPlease refer to the examples in [image_predictor_example.ipynb](./notebooks/image_predictor_example.ipynb) (also in Colab [here](https://colab.research.google.com/github/facebookresearch/sam2/blob/main/notebooks/image_predictor_example.ipynb)) for static image use cases.\n\nSAM 2 also supports automatic mask generation on images just like SAM. Please see [automatic_mask_generator_example.ipynb](./notebooks/automatic_mask_generator_example.ipynb) (also in Colab [here](https://colab.research.google.com/github/facebookresearch/sam2/blob/main/notebooks/automatic_mask_generator_example.ipynb)) for automatic mask generation in images.\n\n### Video prediction\n\nFor promptable segmentation and tracking in videos, we provide a video predictor with APIs for example to add prompts and propagate masklets throughout a video. SAM 2 supports video inference on multiple objects and uses an inference state to keep track of the interactions in each video.\n\n```python\nimport torch\nfrom sam2.build_sam import build_sam2_video_predictor\n\ncheckpoint = \"./checkpoints/sam2.1_hiera_large.pt\"\nmodel_cfg = \"configs/sam2.1/sam2.1_hiera_l.yaml\"\npredictor = build_sam2_video_predictor(model_cfg, checkpoint)\n\nwith torch.inference_mode(), torch.autocast(\"cuda\", dtype=torch.bfloat16):\n state = predictor.init_state()\n\n # add new prompts and instantly get the output on the same frame\n frame_idx, object_ids, masks = predictor.add_new_points_or_box(state, ):\n\n # propagate the prompts to get masklets throughout the video\n for frame_idx, object_ids, masks in predictor.propagate_in_video(state):\n ...\n```\n\nPlease refer to the examples in [video_predictor_example.ipynb](./notebooks/video_predictor_example.ipynb) (also in Colab [here](https://colab.research.google.com/github/facebookresearch/sam2/blob/main/notebooks/video_predictor_example.ipynb)) for details on how to add click or box prompts, make refinements, and track multiple objects in videos.\n\n## Load from 🤗 Hugging Face\n\nAlternatively, models can also be loaded from [Hugging Face](https://huggingface.co/models?search=facebook/sam2) (requires `pip install huggingface_hub`).\n\nFor image prediction:\n\n```python\nimport torch\nfrom sam2.sam2_image_predictor import SAM2ImagePredictor\n\npredictor = SAM2ImagePredictor.from_pretrained(\"facebook/sam2-hiera-large\")\n\nwith torch.inference_mode(), torch.autocast(\"cuda\", dtype=torch.bfloat16):\n predictor.set_image()\n masks, _, _ = predictor.predict()\n```\n\nFor video prediction:\n\n```python\nimport torch\nfrom sam2.sam2_video_predictor import SAM2VideoPredictor\n\npredictor = SAM2VideoPredictor.from_pretrained(\"facebook/sam2-hiera-large\")\n\nwith torch.inference_mode(), torch.autocast(\"cuda\", dtype=torch.bfloat16):\n state = predictor.init_state()\n\n # add new prompts and instantly get the output on the same frame\n frame_idx, object_ids, masks = predictor.add_new_points_or_box(state, ):\n\n # propagate the prompts to get masklets throughout the video\n for frame_idx, object_ids, masks in predictor.propagate_in_video(state):\n ...\n```\n\n## Model Description\n\n### SAM 2.1 checkpoints\n\nThe table below shows the improved SAM 2.1 checkpoints released on September 29, 2024.\n| **Model** | **Size (M)** | **Speed (FPS)** | **SA-V test (J&F)** | **MOSE val (J&F)** | **LVOS v2 (J&F)** |\n| :------------------: | :----------: | :--------------------: | :-----------------: | :----------------: | :---------------: |\n| sam2.1_hiera_tiny
([config](sam2/configs/sam2.1/sam2.1_hiera_t.yaml), [checkpoint](https://dl.fbaipublicfiles.com/segment_anything_2/092824/sam2.1_hiera_tiny.pt)) | 38.9 | 47.2 | 76.5 | 71.8 | 77.3 |\n| sam2.1_hiera_small
([config](sam2/configs/sam2.1/sam2.1_hiera_s.yaml), [checkpoint](https://dl.fbaipublicfiles.com/segment_anything_2/092824/sam2.1_hiera_small.pt)) | 46 | 43.3 (53.0 compiled\\*) | 76.6 | 73.5 | 78.3 |\n| sam2.1_hiera_base_plus
([config](sam2/configs/sam2.1/sam2.1_hiera_b+.yaml), [checkpoint](https://dl.fbaipublicfiles.com/segment_anything_2/092824/sam2.1_hiera_base_plus.pt)) | 80.8 | 34.8 (43.8 compiled\\*) | 78.2 | 73.7 | 78.2 |\n| sam2.1_hiera_large
([config](sam2/configs/sam2.1/sam2.1_hiera_l.yaml), [checkpoint](https://dl.fbaipublicfiles.com/segment_anything_2/092824/sam2.1_hiera_large.pt)) | 224.4 | 24.2 (30.2 compiled\\*) | 79.5 | 74.6 | 80.6 |\n\n### SAM 2 checkpoints\n\nThe previous SAM 2 checkpoints released on July 29, 2024 can be found as follows:\n\n| **Model** | **Size (M)** | **Speed (FPS)** | **SA-V test (J&F)** | **MOSE val (J&F)** | **LVOS v2 (J&F)** |\n| :------------------: | :----------: | :--------------------: | :-----------------: | :----------------: | :---------------: |\n| sam2_hiera_tiny
([config](sam2/configs/sam2/sam2_hiera_t.yaml), [checkpoint](https://dl.fbaipublicfiles.com/segment_anything_2/072824/sam2_hiera_tiny.pt)) | 38.9 | 47.2 | 75.0 | 70.9 | 75.3 |\n| sam2_hiera_small
([config](sam2/configs/sam2/sam2_hiera_s.yaml), [checkpoint](https://dl.fbaipublicfiles.com/segment_anything_2/072824/sam2_hiera_small.pt)) | 46 | 43.3 (53.0 compiled\\*) | 74.9 | 71.5 | 76.4 |\n| sam2_hiera_base_plus
([config](sam2/configs/sam2/sam2_hiera_b+.yaml), [checkpoint](https://dl.fbaipublicfiles.com/segment_anything_2/072824/sam2_hiera_base_plus.pt)) | 80.8 | 34.8 (43.8 compiled\\*) | 74.7 | 72.8 | 75.8 |\n| sam2_hiera_large
([config](sam2/configs/sam2/sam2_hiera_l.yaml), [checkpoint](https://dl.fbaipublicfiles.com/segment_anything_2/072824/sam2_hiera_large.pt)) | 224.4 | 24.2 (30.2 compiled\\*) | 76.0 | 74.6 | 79.8 |\n\n\\* Compile the model by setting `compile_image_encoder: True` in the config.\n\n## Segment Anything Video Dataset\n\nSee [sav_dataset/README.md](sav_dataset/README.md) for details.\n\n## Training SAM 2\n\nYou can train or fine-tune SAM 2 on custom datasets of images, videos, or both. Please check the training [README](training/README.md) on how to get started.\n\n## Web demo for SAM 2\n\nWe have released the frontend + backend code for the SAM 2 web demo (a locally deployable version similar to https://sam2.metademolab.com/demo). Please see the web demo [README](demo/README.md) for details.\n\n## License\n\nThe SAM 2 model checkpoints, SAM 2 demo code (front-end and back-end), and SAM 2 training code are licensed under [Apache 2.0](./LICENSE), however the [Inter Font](https://github.com/rsms/inter?tab=OFL-1.1-1-ov-file) and [Noto Color Emoji](https://github.com/googlefonts/noto-emoji) used in the SAM 2 demo code are made available under the [SIL Open Font License, version 1.1](https://openfontlicense.org/open-font-license-official-text/).\n\n## Contributing\n\nSee [contributing](CONTRIBUTING.md) and the [code of conduct](CODE_OF_CONDUCT.md).\n\n## Contributors\n\nThe SAM 2 project was made possible with the help of many contributors (alphabetical):\n\nKaren Bergan, Daniel Bolya, Alex Bosenberg, Kai Brown, Vispi Cassod, Christopher Chedeau, Ida Cheng, Luc Dahlin, Shoubhik Debnath, Rene Martinez Doehner, Grant Gardner, Sahir Gomez, Rishi Godugu, Baishan Guo, Caleb Ho, Andrew Huang, Somya Jain, Bob Kamma, Amanda Kallet, Jake Kinney, Alexander Kirillov, Shiva Koduvayur, Devansh Kukreja, Robert Kuo, Aohan Lin, Parth Malani, Jitendra Malik, Mallika Malhotra, Miguel Martin, Alexander Miller, Sasha Mitts, William Ngan, George Orlin, Joelle Pineau, Kate Saenko, Rodrick Shepard, Azita Shokrpour, David Soofian, Jonathan Torres, Jenny Truong, Sagar Vaze, Meng Wang, Claudette Ward, Pengchuan Zhang.\n\nThird-party code: we use a GPU-based connected component algorithm adapted from [`cc_torch`](https://github.com/zsef123/Connected_components_PyTorch) (with its license in [`LICENSE_cctorch`](./LICENSE_cctorch)) as an optional post-processing step for the mask predictions.\n\n## Citing SAM 2\n\nIf you use SAM 2 or the SA-V dataset in your research, please use the following BibTeX entry.\n\n```bibtex\n@article{ravi2024sam2,\n title={SAM 2: Segment Anything in Images and Videos},\n author={Ravi, Nikhila and Gabeur, Valentin and Hu, Yuan-Ting and Hu, Ronghang and Ryali, Chaitanya and Ma, Tengyu and Khedr, Haitham and R{\\\"a}dle, Roman and Rolland, Chloe and Gustafson, Laura and Mintun, Eric and Pan, Junting and Alwala, Kalyan Vasudev and Carion, Nicolas and Wu, Chao-Yuan and Girshick, Ross and Doll{\\'a}r, Piotr and Feichtenhofer, Christoph},\n journal={arXiv preprint arXiv:2408.00714},\n url={https://arxiv.org/abs/2408.00714},\n year={2024}\n}\n```\n" }, { "path": "auto-seg/submodules/segment-anything-2/backend.Dockerfile", "content": "ARG BASE_IMAGE=pytorch/pytorch:2.3.1-cuda12.1-cudnn8-runtime\nARG MODEL_SIZE=base_plus\n\nFROM ${BASE_IMAGE}\n\n# Gunicorn environment variables\nENV GUNICORN_WORKERS=1\nENV GUNICORN_THREADS=2\nENV GUNICORN_PORT=5000\n\n# SAM 2 environment variables\nENV APP_ROOT=/opt/sam2\nENV PYTHONUNBUFFERED=1\nENV SAM2_BUILD_CUDA=0\nENV MODEL_SIZE=${MODEL_SIZE}\n\n# Install system requirements\nRUN apt-get update && apt-get install -y --no-install-recommends \\\n ffmpeg \\\n libavutil-dev \\\n libavcodec-dev \\\n libavformat-dev \\\n libswscale-dev \\\n pkg-config \\\n build-essential \\\n libffi-dev\n\nCOPY setup.py .\nCOPY README.md .\n\nRUN pip install --upgrade pip setuptools\nRUN pip install -e \".[interactive-demo]\"\n\n# https://github.com/Kosinkadink/ComfyUI-VideoHelperSuite/issues/69#issuecomment-1826764707\nRUN rm /opt/conda/bin/ffmpeg && ln -s /bin/ffmpeg /opt/conda/bin/ffmpeg\n\n# Make app directory. This directory will host all files required for the\n# backend and SAM 2 inference files.\nRUN mkdir ${APP_ROOT}\n\n# Copy backend server files\nCOPY demo/backend/server ${APP_ROOT}/server\n\n# Copy SAM 2 inference files\nCOPY sam2 ${APP_ROOT}/server/sam2\n\n# Download SAM 2.1 checkpoints\nADD https://dl.fbaipublicfiles.com/segment_anything_2/092824/sam2.1_hiera_tiny.pt ${APP_ROOT}/checkpoints/sam2.1_hiera_tiny.pt\nADD https://dl.fbaipublicfiles.com/segment_anything_2/092824/sam2.1_hiera_small.pt ${APP_ROOT}/checkpoints/sam2.1_hiera_small.pt\nADD https://dl.fbaipublicfiles.com/segment_anything_2/092824/sam2.1_hiera_base_plus.pt ${APP_ROOT}/checkpoints/sam2.1_hiera_base_plus.pt\nADD https://dl.fbaipublicfiles.com/segment_anything_2/092824/sam2.1_hiera_large.pt ${APP_ROOT}/checkpoints/sam2.1_hiera_large.pt\n\nWORKDIR ${APP_ROOT}/server\n\n# https://pythonspeed.com/articles/gunicorn-in-docker/\nCMD gunicorn --worker-tmp-dir /dev/shm \\\n --worker-class gthread app:app \\\n --log-level info \\\n --access-logfile /dev/stdout \\\n --log-file /dev/stderr \\\n --workers ${GUNICORN_WORKERS} \\\n --threads ${GUNICORN_THREADS} \\\n --bind 0.0.0.0:${GUNICORN_PORT} \\\n --timeout 60\n" }, { "path": "auto-seg/submodules/segment-anything-2/checkpoints/download_ckpts.sh", "content": "#!/bin/bash\n\n# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\n# Use either wget or curl to download the checkpoints\nif command -v wget &> /dev/null; then\n CMD=\"wget\"\nelif command -v curl &> /dev/null; then\n CMD=\"curl -L -O\"\nelse\n echo \"Please install wget or curl to download the checkpoints.\"\n exit 1\nfi\n\n# Define the URLs for SAM 2 checkpoints\n# SAM2_BASE_URL=\"https://dl.fbaipublicfiles.com/segment_anything_2/072824\"\n# sam2_hiera_t_url=\"${SAM2_BASE_URL}/sam2_hiera_tiny.pt\"\n# sam2_hiera_s_url=\"${SAM2_BASE_URL}/sam2_hiera_small.pt\"\n# sam2_hiera_b_plus_url=\"${SAM2_BASE_URL}/sam2_hiera_base_plus.pt\"\n# sam2_hiera_l_url=\"${SAM2_BASE_URL}/sam2_hiera_large.pt\"\n\n# Download each of the four checkpoints using wget\n# echo \"Downloading sam2_hiera_tiny.pt checkpoint...\"\n# $CMD $sam2_hiera_t_url || { echo \"Failed to download checkpoint from $sam2_hiera_t_url\"; exit 1; }\n\n# echo \"Downloading sam2_hiera_small.pt checkpoint...\"\n# $CMD $sam2_hiera_s_url || { echo \"Failed to download checkpoint from $sam2_hiera_s_url\"; exit 1; }\n\n# echo \"Downloading sam2_hiera_base_plus.pt checkpoint...\"\n# $CMD $sam2_hiera_b_plus_url || { echo \"Failed to download checkpoint from $sam2_hiera_b_plus_url\"; exit 1; }\n\n# echo \"Downloading sam2_hiera_large.pt checkpoint...\"\n# $CMD $sam2_hiera_l_url || { echo \"Failed to download checkpoint from $sam2_hiera_l_url\"; exit 1; }\n\n# Define the URLs for SAM 2.1 checkpoints\nSAM2p1_BASE_URL=\"https://dl.fbaipublicfiles.com/segment_anything_2/092824\"\nsam2p1_hiera_t_url=\"${SAM2p1_BASE_URL}/sam2.1_hiera_tiny.pt\"\nsam2p1_hiera_s_url=\"${SAM2p1_BASE_URL}/sam2.1_hiera_small.pt\"\nsam2p1_hiera_b_plus_url=\"${SAM2p1_BASE_URL}/sam2.1_hiera_base_plus.pt\"\nsam2p1_hiera_l_url=\"${SAM2p1_BASE_URL}/sam2.1_hiera_large.pt\"\n\n# SAM 2.1 checkpoints\necho \"Downloading sam2.1_hiera_tiny.pt checkpoint...\"\n$CMD $sam2p1_hiera_t_url || { echo \"Failed to download checkpoint from $sam2p1_hiera_t_url\"; exit 1; }\n\necho \"Downloading sam2.1_hiera_small.pt checkpoint...\"\n$CMD $sam2p1_hiera_s_url || { echo \"Failed to download checkpoint from $sam2p1_hiera_s_url\"; exit 1; }\n\necho \"Downloading sam2.1_hiera_base_plus.pt checkpoint...\"\n$CMD $sam2p1_hiera_b_plus_url || { echo \"Failed to download checkpoint from $sam2p1_hiera_b_plus_url\"; exit 1; }\n\necho \"Downloading sam2.1_hiera_large.pt checkpoint...\"\n$CMD $sam2p1_hiera_l_url || { echo \"Failed to download checkpoint from $sam2p1_hiera_l_url\"; exit 1; }\n\necho \"All checkpoints are downloaded successfully.\"\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/.gitignore", "content": "data/uploads\ndata/posters\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/README.md", "content": "# SAM 2 Demo\n\nWelcome to the SAM 2 Demo! This project consists of a frontend built with React TypeScript and Vite and a backend service using Python Flask and Strawberry GraphQL. Both components can be run in Docker containers or locally on MPS (Metal Performance Shaders) or CPU. However, running the backend service on MPS or CPU devices may result in significantly slower performance (FPS).\n\n## Prerequisites\n\nBefore you begin, ensure you have the following installed on your system:\n\n- Docker and Docker Compose\n- [OPTIONAL] Node.js and Yarn for running frontend locally\n- [OPTIONAL] Anaconda for running backend locally\n\n### Installing Docker\n\nTo install Docker, follow these steps:\n\n1. Go to the [Docker website](https://www.docker.com/get-started)\n2. Follow the installation instructions for your operating system.\n\n### [OPTIONAL] Installing Node.js and Yarn\n\nTo install Node.js and Yarn, follow these steps:\n\n1. Go to the [Node.js website](https://nodejs.org/en/download/).\n2. Follow the installation instructions for your operating system.\n3. Once Node.js is installed, open a terminal or command prompt and run the following command to install Yarn:\n\n```\nnpm install -g yarn\n```\n\n### [OPTIONAL] Installing Anaconda\n\nTo install Anaconda, follow these steps:\n\n1. Go to the [Anaconda website](https://www.anaconda.com/products/distribution).\n2. Follow the installation instructions for your operating system.\n\n## Quick Start\n\nTo get both the frontend and backend running quickly using Docker, you can use the following command:\n\n```bash\ndocker compose up --build\n```\n\n> [!WARNING]\n> On macOS, Docker containers only support running on CPU. MPS is not supported through Docker. If you want to run the demo backend service on MPS, you will need to run it locally (see \"Running the Backend Locally\" below).\n\nThis will build and start both services. You can access them at:\n\n- **Frontend:** [http://localhost:7262](http://localhost:7262)\n- **Backend:** [http://localhost:7263/graphql](http://localhost:7263/graphql)\n\n## Running Backend with MPS Support\n\nMPS (Metal Performance Shaders) is not supported with Docker. To use MPS, you need to run the backend on your local machine.\n\n### Setting Up Your Environment\n\n1. **Create Conda environment**\n\n Create a new Conda environment for this project by running the following command or use your existing conda environment for SAM 2:\n\n ```\n conda create --name sam2-demo python=3.10 --yes\n ```\n\n This will create a new environment named `sam2-demo` with Python 3.10 as the interpreter.\n\n2. **Activate the Conda environment:**\n\n ```bash\n conda activate sam2-demo\n ```\n\n3. **Install ffmpeg**\n\n ```bash\n conda install -c conda-forge ffmpeg\n ```\n\n4. **Install SAM 2 demo dependencies:**\n\nInstall project dependencies by running the following command in the SAM 2 checkout root directory:\n\n```bash\npip install -e '.[interactive-demo]'\n```\n\n### Running the Backend Locally\n\nDownload the SAM 2 checkpoints:\n\n```bash\n(cd ./checkpoints && ./download_ckpts.sh)\n```\n\nUse the following command to start the backend with MPS support:\n\n```bash\ncd demo/backend/server/\n```\n\n```bash\nPYTORCH_ENABLE_MPS_FALLBACK=1 \\\nAPP_ROOT=\"$(pwd)/../../../\" \\\nAPP_URL=http://localhost:7263 \\\nMODEL_SIZE=base_plus \\\nDATA_PATH=\"$(pwd)/../../data\" \\\nDEFAULT_VIDEO_PATH=gallery/05_default_juggle.mp4 \\\ngunicorn \\\n --worker-class gthread app:app \\\n --workers 1 \\\n --threads 2 \\\n --bind 0.0.0.0:7263 \\\n --timeout 60\n```\n\nOptions for the `MODEL_SIZE` argument are \"tiny\", \"small\", \"base_plus\" (default), and \"large\".\n\n> [!WARNING]\n> Running the backend service on MPS devices can cause fatal crashes with the Gunicorn worker due to insufficient MPS memory. Try switching to CPU devices by setting the `SAM2_DEMO_FORCE_CPU_DEVICE=1` environment variable.\n\n### Starting the Frontend\n\nIf you wish to run the frontend separately (useful for development), follow these steps:\n\n1. **Navigate to demo frontend directory:**\n\n ```bash\n cd demo/frontend\n ```\n\n2. **Install dependencies:**\n\n ```bash\n yarn install\n ```\n\n3. **Start the development server:**\n\n ```bash\n yarn dev --port 7262\n ```\n\nThis will start the frontend development server on [http://localhost:7262](http://localhost:7262).\n\n## Docker Tips\n\n- To rebuild the Docker containers (useful if you've made changes to the Dockerfile or dependencies):\n\n ```bash\n docker compose up --build\n ```\n\n- To stop the Docker containers:\n\n ```bash\n docker compose down\n ```\n\n## Contributing\n\nContributions are welcome! Please read our contributing guidelines to get started.\n\n## License\n\nSee the LICENSE file for details.\n\n---\n\nBy following these instructions, you should have a fully functional development environment for both the frontend and backend of the SAM 2 Demo. Happy coding!\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/backend/server/app.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport logging\nfrom typing import Any, Generator\n\nfrom app_conf import (\n GALLERY_PATH,\n GALLERY_PREFIX,\n POSTERS_PATH,\n POSTERS_PREFIX,\n UPLOADS_PATH,\n UPLOADS_PREFIX,\n)\nfrom data.loader import preload_data\nfrom data.schema import schema\nfrom data.store import set_videos\nfrom flask import Flask, make_response, Request, request, Response, send_from_directory\nfrom flask_cors import CORS\nfrom inference.data_types import PropagateDataResponse, PropagateInVideoRequest\nfrom inference.multipart import MultipartResponseBuilder\nfrom inference.predictor import InferenceAPI\nfrom strawberry.flask.views import GraphQLView\n\nlogger = logging.getLogger(__name__)\n\napp = Flask(__name__)\ncors = CORS(app, supports_credentials=True)\n\nvideos = preload_data()\nset_videos(videos)\n\ninference_api = InferenceAPI()\n\n\n@app.route(\"/healthy\")\ndef healthy() -> Response:\n return make_response(\"OK\", 200)\n\n\n@app.route(f\"/{GALLERY_PREFIX}/\", methods=[\"GET\"])\ndef send_gallery_video(path: str) -> Response:\n try:\n return send_from_directory(\n GALLERY_PATH,\n path,\n )\n except:\n raise ValueError(\"resource not found\")\n\n\n@app.route(f\"/{POSTERS_PREFIX}/\", methods=[\"GET\"])\ndef send_poster_image(path: str) -> Response:\n try:\n return send_from_directory(\n POSTERS_PATH,\n path,\n )\n except:\n raise ValueError(\"resource not found\")\n\n\n@app.route(f\"/{UPLOADS_PREFIX}/\", methods=[\"GET\"])\ndef send_uploaded_video(path: str):\n try:\n return send_from_directory(\n UPLOADS_PATH,\n path,\n )\n except:\n raise ValueError(\"resource not found\")\n\n\n# TOOD: Protect route with ToS permission check\n@app.route(\"/propagate_in_video\", methods=[\"POST\"])\ndef propagate_in_video() -> Response:\n data = request.json\n args = {\n \"session_id\": data[\"session_id\"],\n \"start_frame_index\": data.get(\"start_frame_index\", 0),\n }\n\n boundary = \"frame\"\n frame = gen_track_with_mask_stream(boundary, **args)\n return Response(frame, mimetype=\"multipart/x-savi-stream; boundary=\" + boundary)\n\n\ndef gen_track_with_mask_stream(\n boundary: str,\n session_id: str,\n start_frame_index: int,\n) -> Generator[bytes, None, None]:\n with inference_api.autocast_context():\n request = PropagateInVideoRequest(\n type=\"propagate_in_video\",\n session_id=session_id,\n start_frame_index=start_frame_index,\n )\n\n for chunk in inference_api.propagate_in_video(request=request):\n yield MultipartResponseBuilder.build(\n boundary=boundary,\n headers={\n \"Content-Type\": \"application/json; charset=utf-8\",\n \"Frame-Current\": \"-1\",\n # Total frames minus the reference frame\n \"Frame-Total\": \"-1\",\n \"Mask-Type\": \"RLE[]\",\n },\n body=chunk.to_json().encode(\"UTF-8\"),\n ).get_message()\n\n\nclass MyGraphQLView(GraphQLView):\n def get_context(self, request: Request, response: Response) -> Any:\n return {\"inference_api\": inference_api}\n\n\n# Add GraphQL route to Flask app.\napp.add_url_rule(\n \"/graphql\",\n view_func=MyGraphQLView.as_view(\n \"graphql_view\",\n schema=schema,\n # Disable GET queries\n # https://strawberry.rocks/docs/operations/deployment\n # https://strawberry.rocks/docs/integrations/flask\n allow_queries_via_get=False,\n # Strawberry recently changed multipart request handling, which now\n # requires enabling support explicitly for views.\n # https://github.com/strawberry-graphql/strawberry/issues/3655\n multipart_uploads_enabled=True,\n ),\n)\n\n\nif __name__ == \"__main__\":\n app.run(host=\"0.0.0.0\", port=5000)\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/backend/server/app_conf.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport logging\nimport os\nfrom pathlib import Path\n\nlogger = logging.getLogger(__name__)\n\nAPP_ROOT = os.getenv(\"APP_ROOT\", \"/opt/sam2\")\n\nAPI_URL = os.getenv(\"API_URL\", \"http://localhost:7263\")\n\nMODEL_SIZE = os.getenv(\"MODEL_SIZE\", \"base_plus\")\n\nlogger.info(f\"using model size {MODEL_SIZE}\")\n\nFFMPEG_NUM_THREADS = int(os.getenv(\"FFMPEG_NUM_THREADS\", \"1\"))\n\n# Path for all data used in API\nDATA_PATH = Path(os.getenv(\"DATA_PATH\", \"/data\"))\n\n# Max duration an uploaded video can have in seconds. The default is 10\n# seconds.\nMAX_UPLOAD_VIDEO_DURATION = float(os.environ.get(\"MAX_UPLOAD_VIDEO_DURATION\", \"10\"))\n\n# If set, it will define which video is returned by the default video query for\n# desktop\nDEFAULT_VIDEO_PATH = os.getenv(\"DEFAULT_VIDEO_PATH\")\n\n# Prefix for gallery videos\nGALLERY_PREFIX = \"gallery\"\n\n# Path where all gallery videos are stored\nGALLERY_PATH = DATA_PATH / GALLERY_PREFIX\n\n# Prefix for uploaded videos\nUPLOADS_PREFIX = \"uploads\"\n\n# Path where all uploaded videos are stored\nUPLOADS_PATH = DATA_PATH / UPLOADS_PREFIX\n\n# Prefix for video posters (1st frame of video)\nPOSTERS_PREFIX = \"posters\"\n\n# Path where all posters are stored\nPOSTERS_PATH = DATA_PATH / POSTERS_PREFIX\n\n# Make sure any of those paths exist\nos.makedirs(DATA_PATH, exist_ok=True)\nos.makedirs(GALLERY_PATH, exist_ok=True)\nos.makedirs(UPLOADS_PATH, exist_ok=True)\nos.makedirs(POSTERS_PATH, exist_ok=True)\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/backend/server/inference/data_types.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nfrom dataclasses import dataclass\nfrom typing import Dict, List, Optional, Union\n\nfrom dataclasses_json import dataclass_json\nfrom torch import Tensor\n\n\n@dataclass_json\n@dataclass\nclass Mask:\n size: List[int]\n counts: str\n\n\n@dataclass_json\n@dataclass\nclass BaseRequest:\n type: str\n\n\n@dataclass_json\n@dataclass\nclass StartSessionRequest(BaseRequest):\n type: str\n path: str\n session_id: Optional[str] = None\n\n\n@dataclass_json\n@dataclass\nclass SaveSessionRequest(BaseRequest):\n type: str\n session_id: str\n\n\n@dataclass_json\n@dataclass\nclass LoadSessionRequest(BaseRequest):\n type: str\n session_id: str\n\n\n@dataclass_json\n@dataclass\nclass RenewSessionRequest(BaseRequest):\n type: str\n session_id: str\n\n\n@dataclass_json\n@dataclass\nclass CloseSessionRequest(BaseRequest):\n type: str\n session_id: str\n\n\n@dataclass_json\n@dataclass\nclass AddPointsRequest(BaseRequest):\n type: str\n session_id: str\n frame_index: int\n clear_old_points: bool\n object_id: int\n labels: List[int]\n points: List[List[float]]\n\n\n@dataclass_json\n@dataclass\nclass AddMaskRequest(BaseRequest):\n type: str\n session_id: str\n frame_index: int\n object_id: int\n mask: Mask\n\n\n@dataclass_json\n@dataclass\nclass ClearPointsInFrameRequest(BaseRequest):\n type: str\n session_id: str\n frame_index: int\n object_id: int\n\n\n@dataclass_json\n@dataclass\nclass ClearPointsInVideoRequest(BaseRequest):\n type: str\n session_id: str\n\n\n@dataclass_json\n@dataclass\nclass RemoveObjectRequest(BaseRequest):\n type: str\n session_id: str\n object_id: int\n\n\n@dataclass_json\n@dataclass\nclass PropagateInVideoRequest(BaseRequest):\n type: str\n session_id: str\n start_frame_index: int\n\n\n@dataclass_json\n@dataclass\nclass CancelPropagateInVideoRequest(BaseRequest):\n type: str\n session_id: str\n\n\n@dataclass_json\n@dataclass\nclass StartSessionResponse:\n session_id: str\n\n\n@dataclass_json\n@dataclass\nclass SaveSessionResponse:\n session_id: str\n\n\n@dataclass_json\n@dataclass\nclass LoadSessionResponse:\n session_id: str\n\n\n@dataclass_json\n@dataclass\nclass RenewSessionResponse:\n session_id: str\n\n\n@dataclass_json\n@dataclass\nclass CloseSessionResponse:\n success: bool\n\n\n@dataclass_json\n@dataclass\nclass ClearPointsInVideoResponse:\n success: bool\n\n\n@dataclass_json\n@dataclass\nclass PropagateDataValue:\n object_id: int\n mask: Mask\n\n\n@dataclass_json\n@dataclass\nclass PropagateDataResponse:\n frame_index: int\n results: List[PropagateDataValue]\n\n\n@dataclass_json\n@dataclass\nclass RemoveObjectResponse:\n results: List[PropagateDataResponse]\n\n\n@dataclass_json\n@dataclass\nclass CancelPorpagateResponse:\n success: bool\n\n\n@dataclass_json\n@dataclass\nclass InferenceSession:\n start_time: float\n last_use_time: float\n session_id: str\n state: Dict[str, Dict[str, Union[Tensor, Dict[int, Tensor]]]]\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/backend/server/inference/multipart.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nfrom typing import Dict, Union\n\n\nclass MultipartResponseBuilder:\n message: bytes\n\n def __init__(self, boundary: str) -> None:\n self.message = b\"--\" + boundary.encode(\"utf-8\") + b\"\\r\\n\"\n\n @classmethod\n def build(\n cls, boundary: str, headers: Dict[str, str], body: Union[str, bytes]\n ) -> \"MultipartResponseBuilder\":\n builder = cls(boundary=boundary)\n for k, v in headers.items():\n builder.__append_header(key=k, value=v)\n if isinstance(body, bytes):\n builder.__append_body(body)\n elif isinstance(body, str):\n builder.__append_body(body.encode(\"utf-8\"))\n else:\n raise ValueError(\n f\"body needs to be of type bytes or str but got {type(body)}\"\n )\n\n return builder\n\n def get_message(self) -> bytes:\n return self.message\n\n def __append_header(self, key: str, value: str) -> \"MultipartResponseBuilder\":\n self.message += key.encode(\"utf-8\") + b\": \" + value.encode(\"utf-8\") + b\"\\r\\n\"\n return self\n\n def __close_header(self) -> \"MultipartResponseBuilder\":\n self.message += b\"\\r\\n\"\n return self\n\n def __append_body(self, body: bytes) -> \"MultipartResponseBuilder\":\n self.__append_header(key=\"Content-Length\", value=str(len(body)))\n self.__close_header()\n self.message += body\n return self\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/backend/server/inference/predictor.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport contextlib\nimport logging\nimport os\nimport uuid\nfrom pathlib import Path\nfrom threading import Lock\nfrom typing import Any, Dict, Generator, List\n\nimport numpy as np\nimport torch\nfrom app_conf import APP_ROOT, MODEL_SIZE\nfrom inference.data_types import (\n AddMaskRequest,\n AddPointsRequest,\n CancelPorpagateResponse,\n CancelPropagateInVideoRequest,\n ClearPointsInFrameRequest,\n ClearPointsInVideoRequest,\n ClearPointsInVideoResponse,\n CloseSessionRequest,\n CloseSessionResponse,\n Mask,\n PropagateDataResponse,\n PropagateDataValue,\n PropagateInVideoRequest,\n RemoveObjectRequest,\n RemoveObjectResponse,\n StartSessionRequest,\n StartSessionResponse,\n)\nfrom pycocotools.mask import decode as decode_masks, encode as encode_masks\nfrom sam2.build_sam import build_sam2_video_predictor\n\n\nlogger = logging.getLogger(__name__)\n\n\nclass InferenceAPI:\n\n def __init__(self) -> None:\n super(InferenceAPI, self).__init__()\n\n self.session_states: Dict[str, Any] = {}\n self.score_thresh = 0\n\n if MODEL_SIZE == \"tiny\":\n checkpoint = Path(APP_ROOT) / \"checkpoints/sam2.1_hiera_tiny.pt\"\n model_cfg = \"configs/sam2.1/sam2.1_hiera_t.yaml\"\n elif MODEL_SIZE == \"small\":\n checkpoint = Path(APP_ROOT) / \"checkpoints/sam2.1_hiera_small.pt\"\n model_cfg = \"configs/sam2.1/sam2.1_hiera_s.yaml\"\n elif MODEL_SIZE == \"large\":\n checkpoint = Path(APP_ROOT) / \"checkpoints/sam2.1_hiera_large.pt\"\n model_cfg = \"configs/sam2.1/sam2.1_hiera_l.yaml\"\n else: # base_plus (default)\n checkpoint = Path(APP_ROOT) / \"checkpoints/sam2.1_hiera_base_plus.pt\"\n model_cfg = \"configs/sam2.1/sam2.1_hiera_b+.yaml\"\n\n # select the device for computation\n force_cpu_device = os.environ.get(\"SAM2_DEMO_FORCE_CPU_DEVICE\", \"0\") == \"1\"\n if force_cpu_device:\n logger.info(\"forcing CPU device for SAM 2 demo\")\n if torch.cuda.is_available() and not force_cpu_device:\n device = torch.device(\"cuda\")\n elif torch.backends.mps.is_available() and not force_cpu_device:\n device = torch.device(\"mps\")\n else:\n device = torch.device(\"cpu\")\n logger.info(f\"using device: {device}\")\n\n if device.type == \"cuda\":\n # turn on tfloat32 for Ampere GPUs (https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices)\n if torch.cuda.get_device_properties(0).major >= 8:\n torch.backends.cuda.matmul.allow_tf32 = True\n torch.backends.cudnn.allow_tf32 = True\n elif device.type == \"mps\":\n logging.warning(\n \"\\nSupport for MPS devices is preliminary. SAM 2 is trained with CUDA and might \"\n \"give numerically different outputs and sometimes degraded performance on MPS. \"\n \"See e.g. https://github.com/pytorch/pytorch/issues/84936 for a discussion.\"\n )\n\n self.device = device\n self.predictor = build_sam2_video_predictor(\n model_cfg, checkpoint, device=device\n )\n self.inference_lock = Lock()\n\n def autocast_context(self):\n if self.device.type == \"cuda\":\n return torch.autocast(\"cuda\", dtype=torch.bfloat16)\n else:\n return contextlib.nullcontext()\n\n def start_session(self, request: StartSessionRequest) -> StartSessionResponse:\n with self.autocast_context(), self.inference_lock:\n session_id = str(uuid.uuid4())\n # for MPS devices, we offload the video frames to CPU by default to avoid\n # memory fragmentation in MPS (which sometimes crashes the entire process)\n offload_video_to_cpu = self.device.type == \"mps\"\n inference_state = self.predictor.init_state(\n request.path,\n offload_video_to_cpu=offload_video_to_cpu,\n )\n self.session_states[session_id] = {\n \"canceled\": False,\n \"state\": inference_state,\n }\n return StartSessionResponse(session_id=session_id)\n\n def close_session(self, request: CloseSessionRequest) -> CloseSessionResponse:\n is_successful = self.__clear_session_state(request.session_id)\n return CloseSessionResponse(success=is_successful)\n\n def add_points(\n self, request: AddPointsRequest, test: str = \"\"\n ) -> PropagateDataResponse:\n with self.autocast_context(), self.inference_lock:\n session = self.__get_session(request.session_id)\n inference_state = session[\"state\"]\n\n frame_idx = request.frame_index\n obj_id = request.object_id\n points = request.points\n labels = request.labels\n clear_old_points = request.clear_old_points\n\n # add new prompts and instantly get the output on the same frame\n frame_idx, object_ids, masks = self.predictor.add_new_points_or_box(\n inference_state=inference_state,\n frame_idx=frame_idx,\n obj_id=obj_id,\n points=points,\n labels=labels,\n clear_old_points=clear_old_points,\n normalize_coords=False,\n )\n\n masks_binary = (masks > self.score_thresh)[:, 0].cpu().numpy()\n\n rle_mask_list = self.__get_rle_mask_list(\n object_ids=object_ids, masks=masks_binary\n )\n\n return PropagateDataResponse(\n frame_index=frame_idx,\n results=rle_mask_list,\n )\n\n def add_mask(self, request: AddMaskRequest) -> PropagateDataResponse:\n \"\"\"\n Add new points on a specific video frame.\n - mask is a numpy array of shape [H_im, W_im] (containing 1 for foreground and 0 for background).\n Note: providing an input mask would overwrite any previous input points on this frame.\n \"\"\"\n with self.autocast_context(), self.inference_lock:\n session_id = request.session_id\n frame_idx = request.frame_index\n obj_id = request.object_id\n rle_mask = {\n \"counts\": request.mask.counts,\n \"size\": request.mask.size,\n }\n\n mask = decode_masks(rle_mask)\n\n logger.info(\n f\"add mask on frame {frame_idx} in session {session_id}: {obj_id=}, {mask.shape=}\"\n )\n session = self.__get_session(session_id)\n inference_state = session[\"state\"]\n\n frame_idx, obj_ids, video_res_masks = self.model.add_new_mask(\n inference_state=inference_state,\n frame_idx=frame_idx,\n obj_id=obj_id,\n mask=torch.tensor(mask > 0),\n )\n masks_binary = (video_res_masks > self.score_thresh)[:, 0].cpu().numpy()\n\n rle_mask_list = self.__get_rle_mask_list(\n object_ids=obj_ids, masks=masks_binary\n )\n\n return PropagateDataResponse(\n frame_index=frame_idx,\n results=rle_mask_list,\n )\n\n def clear_points_in_frame(\n self, request: ClearPointsInFrameRequest\n ) -> PropagateDataResponse:\n \"\"\"\n Remove all input points in a specific frame.\n \"\"\"\n with self.autocast_context(), self.inference_lock:\n session_id = request.session_id\n frame_idx = request.frame_index\n obj_id = request.object_id\n\n logger.info(\n f\"clear inputs on frame {frame_idx} in session {session_id}: {obj_id=}\"\n )\n session = self.__get_session(session_id)\n inference_state = session[\"state\"]\n frame_idx, obj_ids, video_res_masks = (\n self.predictor.clear_all_prompts_in_frame(\n inference_state, frame_idx, obj_id\n )\n )\n masks_binary = (video_res_masks > self.score_thresh)[:, 0].cpu().numpy()\n\n rle_mask_list = self.__get_rle_mask_list(\n object_ids=obj_ids, masks=masks_binary\n )\n\n return PropagateDataResponse(\n frame_index=frame_idx,\n results=rle_mask_list,\n )\n\n def clear_points_in_video(\n self, request: ClearPointsInVideoRequest\n ) -> ClearPointsInVideoResponse:\n \"\"\"\n Remove all input points in all frames throughout the video.\n \"\"\"\n with self.autocast_context(), self.inference_lock:\n session_id = request.session_id\n logger.info(f\"clear all inputs across the video in session {session_id}\")\n session = self.__get_session(session_id)\n inference_state = session[\"state\"]\n self.predictor.reset_state(inference_state)\n return ClearPointsInVideoResponse(success=True)\n\n def remove_object(self, request: RemoveObjectRequest) -> RemoveObjectResponse:\n \"\"\"\n Remove an object id from the tracking state.\n \"\"\"\n with self.autocast_context(), self.inference_lock:\n session_id = request.session_id\n obj_id = request.object_id\n logger.info(f\"remove object in session {session_id}: {obj_id=}\")\n session = self.__get_session(session_id)\n inference_state = session[\"state\"]\n new_obj_ids, updated_frames = self.predictor.remove_object(\n inference_state, obj_id\n )\n\n results = []\n for frame_index, video_res_masks in updated_frames:\n masks = (video_res_masks > self.score_thresh)[:, 0].cpu().numpy()\n rle_mask_list = self.__get_rle_mask_list(\n object_ids=new_obj_ids, masks=masks\n )\n results.append(\n PropagateDataResponse(\n frame_index=frame_index,\n results=rle_mask_list,\n )\n )\n\n return RemoveObjectResponse(results=results)\n\n def propagate_in_video(\n self, request: PropagateInVideoRequest\n ) -> Generator[PropagateDataResponse, None, None]:\n session_id = request.session_id\n start_frame_idx = request.start_frame_index\n propagation_direction = \"both\"\n max_frame_num_to_track = None\n\n \"\"\"\n Propagate existing input points in all frames to track the object across video.\n \"\"\"\n\n # Note that as this method is a generator, we also need to use autocast_context\n # in caller to this method to ensure that it's called under the correct context\n # (we've added `autocast_context` to `gen_track_with_mask_stream` in app.py).\n with self.autocast_context(), self.inference_lock:\n logger.info(\n f\"propagate in video in session {session_id}: \"\n f\"{propagation_direction=}, {start_frame_idx=}, {max_frame_num_to_track=}\"\n )\n\n try:\n session = self.__get_session(session_id)\n session[\"canceled\"] = False\n\n inference_state = session[\"state\"]\n if propagation_direction not in [\"both\", \"forward\", \"backward\"]:\n raise ValueError(\n f\"invalid propagation direction: {propagation_direction}\"\n )\n\n # First doing the forward propagation\n if propagation_direction in [\"both\", \"forward\"]:\n for outputs in self.predictor.propagate_in_video(\n inference_state=inference_state,\n start_frame_idx=start_frame_idx,\n max_frame_num_to_track=max_frame_num_to_track,\n reverse=False,\n ):\n if session[\"canceled\"]:\n return None\n\n frame_idx, obj_ids, video_res_masks = outputs\n masks_binary = (\n (video_res_masks > self.score_thresh)[:, 0].cpu().numpy()\n )\n\n rle_mask_list = self.__get_rle_mask_list(\n object_ids=obj_ids, masks=masks_binary\n )\n\n yield PropagateDataResponse(\n frame_index=frame_idx,\n results=rle_mask_list,\n )\n\n # Then doing the backward propagation (reverse in time)\n if propagation_direction in [\"both\", \"backward\"]:\n for outputs in self.predictor.propagate_in_video(\n inference_state=inference_state,\n start_frame_idx=start_frame_idx,\n max_frame_num_to_track=max_frame_num_to_track,\n reverse=True,\n ):\n if session[\"canceled\"]:\n return None\n\n frame_idx, obj_ids, video_res_masks = outputs\n masks_binary = (\n (video_res_masks > self.score_thresh)[:, 0].cpu().numpy()\n )\n\n rle_mask_list = self.__get_rle_mask_list(\n object_ids=obj_ids, masks=masks_binary\n )\n\n yield PropagateDataResponse(\n frame_index=frame_idx,\n results=rle_mask_list,\n )\n finally:\n # Log upon completion (so that e.g. we can see if two propagations happen in parallel).\n # Using `finally` here to log even when the tracking is aborted with GeneratorExit.\n logger.info(\n f\"propagation ended in session {session_id}; {self.__get_session_stats()}\"\n )\n\n def cancel_propagate_in_video(\n self, request: CancelPropagateInVideoRequest\n ) -> CancelPorpagateResponse:\n session = self.__get_session(request.session_id)\n session[\"canceled\"] = True\n return CancelPorpagateResponse(success=True)\n\n def __get_rle_mask_list(\n self, object_ids: List[int], masks: np.ndarray\n ) -> List[PropagateDataValue]:\n \"\"\"\n Return a list of data values, i.e. list of object/mask combos.\n \"\"\"\n return [\n self.__get_mask_for_object(object_id=object_id, mask=mask)\n for object_id, mask in zip(object_ids, masks)\n ]\n\n def __get_mask_for_object(\n self, object_id: int, mask: np.ndarray\n ) -> PropagateDataValue:\n \"\"\"\n Create a data value for an object/mask combo.\n \"\"\"\n mask_rle = encode_masks(np.array(mask, dtype=np.uint8, order=\"F\"))\n mask_rle[\"counts\"] = mask_rle[\"counts\"].decode()\n return PropagateDataValue(\n object_id=object_id,\n mask=Mask(\n size=mask_rle[\"size\"],\n counts=mask_rle[\"counts\"],\n ),\n )\n\n def __get_session(self, session_id: str):\n session = self.session_states.get(session_id, None)\n if session is None:\n raise RuntimeError(\n f\"Cannot find session {session_id}; it might have expired\"\n )\n return session\n\n def __get_session_stats(self):\n \"\"\"Get a statistics string for live sessions and their GPU usage.\"\"\"\n # print both the session ids and their video frame numbers\n live_session_strs = [\n f\"'{session_id}' ({session['state']['num_frames']} frames, \"\n f\"{len(session['state']['obj_ids'])} objects)\"\n for session_id, session in self.session_states.items()\n ]\n session_stats_str = (\n \"Test String Here - -\"\n f\"live sessions: [{', '.join(live_session_strs)}], GPU memory: \"\n f\"{torch.cuda.memory_allocated() // 1024**2} MiB used and \"\n f\"{torch.cuda.memory_reserved() // 1024**2} MiB reserved\"\n f\" (max over time: {torch.cuda.max_memory_allocated() // 1024**2} MiB used \"\n f\"and {torch.cuda.max_memory_reserved() // 1024**2} MiB reserved)\"\n )\n return session_stats_str\n\n def __clear_session_state(self, session_id: str) -> bool:\n session = self.session_states.pop(session_id, None)\n if session is None:\n logger.warning(\n f\"cannot close session {session_id} as it does not exist (it might have expired); \"\n f\"{self.__get_session_stats()}\"\n )\n return False\n else:\n logger.info(f\"removed session {session_id}; {self.__get_session_stats()}\")\n return True\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/.babelrc", "content": "{\n \"env\": {\n \"production\": {\n \"plugins\": [\"babel-plugin-strip-invariant\"]\n }\n }\n}" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/.dockerignore", "content": "# Logs\nlogs\n*.log\nnpm-debug.log*\nyarn-debug.log*\nyarn-error.log*\npnpm-debug.log*\nlerna-debug.log*\n\nnode_modules\ndist\ndist-ssr\n*.local\nstorybook-static\n.env\n\n# Editor directories and files\n.vscode/*\n!.vscode/extensions.json\n.idea\n.DS_Store\n*.suo\n*.ntvs*\n*.njsproj\n*.sln\n*.sw?\n\n# Test results\n/coverage/\n/test-results/\n/playwright-report/\n/playwright/.cache/\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/.eslintignore", "content": "node_modules/\ndist/\nenv.d.ts" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/.eslintrc.cjs", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nmodule.exports = {\n root: true,\n env: {browser: true, es2020: true},\n extends: [\n 'eslint:recommended',\n 'plugin:@typescript-eslint/recommended',\n 'plugin:react/recommended',\n 'plugin:react-hooks/recommended',\n 'plugin:import/recommended',\n 'plugin:prettier/recommended',\n ],\n ignorePatterns: ['dist', '.eslintrc.cjs'],\n parser: '@typescript-eslint/parser',\n parserOptions: {\n ecmaVersion: 'latest',\n sourceType: 'module',\n project: ['./tsconfig.json', './tsconfig.node.json'],\n tsconfigRootDir: __dirname,\n },\n plugins: ['react-refresh'],\n settings: {\n react: {\n version: 'detect',\n },\n 'import/resolver': {\n typescript: {},\n node: {},\n },\n },\n rules: {\n 'no-console': 'warn',\n curly: 'warn',\n 'react/jsx-no-useless-fragment': 'warn',\n '@typescript-eslint/no-unused-vars': [\n 'warn',\n {\n argsIgnorePattern: '^_',\n },\n ],\n 'react-refresh/only-export-components': [\n 'warn',\n {\n allowConstantExport: true,\n },\n ],\n 'react/react-in-jsx-scope': 'off',\n },\n};\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/.gitignore", "content": "# Logs\nlogs\n*.log\nnpm-debug.log*\nyarn-debug.log*\nyarn-error.log*\npnpm-debug.log*\nlerna-debug.log*\n\nnode_modules\ndist\ndist-ssr\n*.local\nstorybook-static\n.env\n\n# Editor directories and files\n.vscode/*\n!.vscode/extensions.json\n.idea\n.DS_Store\n*.suo\n*.ntvs*\n*.njsproj\n*.sln\n*.sw?\n\n# Test results\n/coverage/\n/test-results/\n/playwright-report/\n/playwright/.cache/\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/.prettierignore", "content": "node_modules/\ndist/" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/.prettierrc.json", "content": "{\n \"arrowParens\": \"avoid\",\n \"bracketSameLine\": true,\n \"bracketSpacing\": false,\n \"singleQuote\": true,\n \"tabWidth\": 2,\n \"trailingComma\": \"all\",\n \"endOfLine\": \"auto\"\n}" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/.watchmanconfig", "content": "{}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/frontend.Dockerfile", "content": "# Stage 1: Build Stage\nFROM node:22.9.0 AS build\n\nWORKDIR /app\n\n# Copy package.json and yarn.lock\nCOPY package.json ./\nCOPY yarn.lock ./\n\n# Install dependencies\nRUN yarn install --frozen-lockfile\n\n# Copy source code\nCOPY . .\n\n# Build the application\nRUN yarn build\n\n# Stage 2: Production Stage\nFROM nginx:latest\n\n# Copy built files from the build stage to the production image\nCOPY --from=build /app/dist /usr/share/nginx/html\n\n# Container startup command for the web server (nginx in this case)\nCMD [\"nginx\", \"-g\", \"daemon off;\"]\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/index.html", "content": "\n\n \n \n \n SAM 2 Demo | By Meta FAIR\n \n \n
\n \n \n\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/package.json", "content": "{\n \"name\": \"frontend-vite\",\n \"private\": true,\n \"version\": \"0.0.0\",\n \"type\": \"module\",\n \"scripts\": {\n \"dev\": \"vite\",\n \"merge-schemas\": \"tsx schemas/merge-schemas\",\n \"relay\": \"yarn merge-schemas && relay-compiler\",\n \"build\": \"tsc && vite build\",\n \"lint\": \"eslint . --ext ts,tsx --report-unused-disable-directives --max-warnings 0\",\n \"preview\": \"vite preview --open\"\n },\n \"dependencies\": {\n \"@carbon/icons-react\": \"^11.34.1\",\n \"@heroicons/react\": \"^2.0.18\",\n \"@monaco-editor/react\": \"^4.6.0\",\n \"@stylexjs/stylex\": \"^0.6.1\",\n \"graphql\": \"^16.8.1\",\n \"immer\": \"^10.0.3\",\n \"immutability-helper\": \"^3.1.1\",\n \"jotai\": \"^2.6.1\",\n \"jotai-immer\": \"^0.3.0\",\n \"localforage\": \"^1.10.0\",\n \"monaco-editor\": \"^0.48.0\",\n \"mp4box\": \"^0.5.2\",\n \"pts\": \"^0.12.8\",\n \"react\": \"^18.2.0\",\n \"react-daisyui\": \"^4.1.0\",\n \"react-device-detect\": \"^2.2.3\",\n \"react-dom\": \"^18.2.0\",\n \"react-dropzone\": \"^14.2.3\",\n \"react-error-boundary\": \"^4.0.11\",\n \"react-photo-album\": \"^2.3.0\",\n \"react-pts-canvas\": \"^0.5.2\",\n \"react-relay\": \"^16.2.0\",\n \"react-router-dom\": \"^6.15.0\",\n \"relay-runtime\": \"^16.2.0\",\n \"serialize-error\": \"^11.0.3\",\n \"use-immer\": \"^0.9.0\",\n \"use-resize-observer\": \"^9.1.0\"\n },\n \"devDependencies\": {\n \"@graphql-tools/load-files\": \"^7.0.0\",\n \"@graphql-tools/merge\": \"^9.0.4\",\n \"@tailwindcss/typography\": \"^0.5.9\",\n \"@types/dom-webcodecs\": \"^0.1.11\",\n \"@types/invariant\": \"^2.2.37\",\n \"@types/node\": \"^20.14.10\",\n \"@types/react\": \"^18.2.47\",\n \"@types/react-dom\": \"^18.2.7\",\n \"@types/react-relay\": \"^16.0.6\",\n \"@types/relay-runtime\": \"^14.1.13\",\n \"@typescript-eslint/eslint-plugin\": \"^6.18.1\",\n \"@typescript-eslint/parser\": \"^6.18.1\",\n \"@vitejs/plugin-react\": \"^4.2.1\",\n \"autoprefixer\": \"^10.4.15\",\n \"babel-plugin-relay\": \"^16.2.0\",\n \"babel-plugin-strip-invariant\": \"^1.0.0\",\n \"daisyui\": \"^3.6.3\",\n \"eslint\": \"^8.48.0\",\n \"eslint-config-prettier\": \"^9.0.0\",\n \"eslint-import-resolver-alias\": \"^1.1.2\",\n \"eslint-import-resolver-typescript\": \"^3.6.3\",\n \"eslint-plugin-import\": \"^2.28.1\",\n \"eslint-plugin-prettier\": \"^5.1.3\",\n \"eslint-plugin-react\": \"^7.33.2\",\n \"eslint-plugin-react-hooks\": \"^4.6.0\",\n \"eslint-plugin-react-refresh\": \"^0.4.3\",\n \"invariant\": \"^2.2.4\",\n \"postcss\": \"^8.4.28\",\n \"postinstall-postinstall\": \"^2.1.0\",\n \"prettier\": \"^3.0.3\",\n \"relay-compiler\": \"^16.2.0\",\n \"sass\": \"^1.66.1\",\n \"strip-ansi\": \"^7.1.0\",\n \"tailwindcss\": \"^3.3.3\",\n \"tsx\": \"^4.16.2\",\n \"typescript\": \">=4.3.5 <5.4.0\",\n \"vite\": \"^5.0.11\",\n \"vite-plugin-babel\": \"^1.2.0\",\n \"vite-plugin-relay\": \"^2.0.0\",\n \"vite-plugin-stylex-dev\": \"^0.5.2\"\n },\n \"resolutions\": {\n \"wrap-ansi\": \"7.0.0\"\n },\n \"relay\": {\n \"src\": \"./src/\",\n \"schema\": \"./schema.graphql\",\n \"language\": \"typescript\",\n \"eagerEsModules\": true,\n \"exclude\": [\n \"**/node_modules/**\",\n \"**/__mocks__/**\",\n \"**/__generated__/**\"\n ]\n }\n}" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/postcss.config.js", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nexport default {\n plugins: {\n 'postcss-import': {},\n 'tailwindcss/nesting': {},\n tailwindcss: {},\n autoprefixer: {},\n },\n};\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/schema.graphql", "content": "input AddPointsInput {\n sessionId: String!\n frameIndex: Int!\n clearOldPoints: Boolean!\n objectId: Int!\n labels: [Int!]!\n points: [[Float!]!]!\n}\n\ntype CancelPropagateInVideo {\n success: Boolean!\n}\n\ninput CancelPropagateInVideoInput {\n sessionId: String!\n}\n\ninput ClearPointsInFrameInput {\n sessionId: String!\n frameIndex: Int!\n objectId: Int!\n}\n\ntype ClearPointsInVideo {\n success: Boolean!\n}\n\ninput ClearPointsInVideoInput {\n sessionId: String!\n}\n\ntype CloseSession {\n success: Boolean!\n}\n\ninput CloseSessionInput {\n sessionId: String!\n}\n\ntype Mutation {\n startSession(input: StartSessionInput!): StartSession!\n closeSession(input: CloseSessionInput!): CloseSession!\n addPoints(input: AddPointsInput!): RLEMaskListOnFrame!\n clearPointsInFrame(input: ClearPointsInFrameInput!): RLEMaskListOnFrame!\n clearPointsInVideo(input: ClearPointsInVideoInput!): ClearPointsInVideo!\n removeObject(input: RemoveObjectInput!): [RLEMaskListOnFrame!]!\n cancelPropagateInVideo(\n input: CancelPropagateInVideoInput!\n ): CancelPropagateInVideo!\n createDeletionId: String!\n acceptTos: Boolean!\n acceptTermsOfService: String!\n uploadVideo(\n file: Upload!\n startTimeSec: Float = null\n durationTimeSec: Float = null\n ): Video!\n uploadSharedVideo(file: Upload!): SharedVideo!\n uploadAnnotations(file: Upload!): Boolean!\n}\n\ninput PingInput {\n sessionId: String!\n}\n\ntype Pong {\n success: Boolean!\n}\n\ntype Query {\n ping(input: PingInput!): Pong!\n defaultVideo: Video!\n videos(\n \"\"\"\n Returns the items in the list that come before the specified cursor.\n \"\"\"\n before: String = null\n \"\"\"\n Returns the items in the list that come after the specified cursor.\n \"\"\"\n after: String = null\n \"\"\"\n Returns the first n items from the list.\n \"\"\"\n first: Int = null\n \"\"\"\n Returns the items in the list that come after the specified cursor.\n \"\"\"\n last: Int = null\n ): VideoConnection!\n sharedVideo(path: String!): SharedVideo!\n}\n\ntype RLEMask {\n size: [Int!]!\n counts: String!\n order: String!\n}\n\ntype RLEMaskForObject {\n objectId: Int!\n rleMask: RLEMask!\n}\n\ntype RLEMaskListOnFrame {\n frameIndex: Int!\n rleMaskList: [RLEMaskForObject!]!\n}\n\ninput RemoveObjectInput {\n sessionId: String!\n objectId: Int!\n}\n\ntype StartSession {\n sessionId: String!\n}\n\ninput StartSessionInput {\n path: String!\n}\n\n\"\"\"\nThe `ID` scalar type represents a unique identifier, often used to refetch an object or as key for a cache. The ID type appears in a JSON response as a String; however, it is not intended to be human-readable. When expected as an input type, any string (such as `\"4\"`) or integer (such as `4`) input value will be accepted as an ID.\n\"\"\"\nscalar GlobalID\n @specifiedBy(url: \"https://relay.dev/graphql/objectidentification.htm\")\n\n\"\"\"\nAn object with a Globally Unique ID\n\"\"\"\ninterface Node {\n \"\"\"\n The Globally Unique ID of this object\n \"\"\"\n id: GlobalID!\n}\n\n\"\"\"\nInformation to aid in pagination.\n\"\"\"\ntype PageInfo {\n \"\"\"\n When paginating forwards, are there more items?\n \"\"\"\n hasNextPage: Boolean!\n \"\"\"\n When paginating backwards, are there more items?\n \"\"\"\n hasPreviousPage: Boolean!\n \"\"\"\n When paginating backwards, the cursor to continue.\n \"\"\"\n startCursor: String\n \"\"\"\n When paginating forwards, the cursor to continue.\n \"\"\"\n endCursor: String\n}\n\ntype SharedVideo {\n path: String!\n url: String!\n}\n\nscalar Upload\n\ntype Video implements Node {\n \"\"\"\n The Globally Unique ID of this object\n \"\"\"\n id: GlobalID!\n path: String!\n posterPath: String\n width: Int!\n height: Int!\n url: String!\n posterUrl: String!\n}\n\n\"\"\"\nA connection to a list of items.\n\"\"\"\ntype VideoConnection {\n \"\"\"\n Pagination data for this connection\n \"\"\"\n pageInfo: PageInfo!\n \"\"\"\n Contains the nodes in this connection\n \"\"\"\n edges: [VideoEdge!]!\n}\n\n\"\"\"\nAn edge in a connection.\n\"\"\"\ntype VideoEdge {\n \"\"\"\n A cursor for use in pagination\n \"\"\"\n cursor: String!\n \"\"\"\n The item at the end of the edge\n \"\"\"\n node: Video!\n}\n\nschema {\n query: Query\n mutation: Mutation\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/schemas/inference-api-schema.graphql", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\ninput AddPointsInput {\n sessionId: String!\n frameIndex: Int!\n clearOldPoints: Boolean!\n objectId: Int!\n labels: [Int!]!\n points: [[Float!]!]!\n}\n\ntype CancelPropagateInVideo {\n success: Boolean!\n}\n\ninput CancelPropagateInVideoInput {\n sessionId: String!\n}\n\ninput ClearPointsInFrameInput {\n sessionId: String!\n frameIndex: Int!\n objectId: Int!\n}\n\ntype ClearPointsInVideo {\n success: Boolean!\n}\n\ninput ClearPointsInVideoInput {\n sessionId: String!\n}\n\ntype CloseSession {\n success: Boolean!\n}\n\ninput CloseSessionInput {\n sessionId: String!\n}\n\ntype Mutation {\n startSession(input: StartSessionInput!): StartSession!\n closeSession(input: CloseSessionInput!): CloseSession!\n addPoints(input: AddPointsInput!): RLEMaskListOnFrame!\n clearPointsInFrame(input: ClearPointsInFrameInput!): RLEMaskListOnFrame!\n clearPointsInVideo(input: ClearPointsInVideoInput!): ClearPointsInVideo!\n removeObject(input: RemoveObjectInput!): [RLEMaskListOnFrame!]!\n cancelPropagateInVideo(\n input: CancelPropagateInVideoInput!\n ): CancelPropagateInVideo!\n}\n\ninput PingInput {\n sessionId: String!\n}\n\ntype Pong {\n success: Boolean!\n}\n\ntype Query {\n ping(input: PingInput!): Pong!\n}\n\ntype RLEMask {\n size: [Int!]!\n counts: String!\n order: String!\n}\n\ntype RLEMaskForObject {\n objectId: Int!\n rleMask: RLEMask!\n}\n\ntype RLEMaskListOnFrame {\n frameIndex: Int!\n rleMaskList: [RLEMaskForObject!]!\n}\n\ninput RemoveObjectInput {\n sessionId: String!\n objectId: Int!\n}\n\ntype StartSession {\n sessionId: String!\n}\n\ninput StartSessionInput {\n path: String!\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/schemas/merge-schemas.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {loadFilesSync} from '@graphql-tools/load-files';\nimport {mergeTypeDefs} from '@graphql-tools/merge';\nimport fs from 'fs';\nimport {print} from 'graphql';\nimport path from 'path';\nimport * as prettier from 'prettier';\nimport {fileURLToPath} from 'url';\n\nconst __filename = fileURLToPath(import.meta.url);\nconst __dirname = path.dirname(__filename);\n\nconst loadedFiles = loadFilesSync(`${__dirname}/*.graphql`);\nconst typeDefs = mergeTypeDefs(loadedFiles);\nconst printedTypeDefs = print(typeDefs);\nconst prettyTypeDefs = await prettier.format(printedTypeDefs, {\n parser: 'graphql',\n});\nfs.writeFileSync('schema.graphql', prettyTypeDefs);\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/schemas/video-api-schema.graphql", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\"\"\"\nThe `ID` scalar type represents a unique identifier, often used to refetch an object or as key for a cache. The ID type appears in a JSON response as a String; however, it is not intended to be human-readable. When expected as an input type, any string (such as `\"4\"`) or integer (such as `4`) input value will be accepted as an ID.\n\"\"\"\nscalar GlobalID\n @specifiedBy(url: \"https://relay.dev/graphql/objectidentification.htm\")\n\ntype Mutation {\n createDeletionId: String!\n acceptTos: Boolean!\n acceptTermsOfService: String!\n uploadVideo(\n file: Upload!\n startTimeSec: Float = null\n durationTimeSec: Float = null\n ): Video!\n uploadSharedVideo(file: Upload!): SharedVideo!\n uploadAnnotations(file: Upload!): Boolean!\n}\n\n\"\"\"\nAn object with a Globally Unique ID\n\"\"\"\ninterface Node {\n \"\"\"\n The Globally Unique ID of this object\n \"\"\"\n id: GlobalID!\n}\n\n\"\"\"\nInformation to aid in pagination.\n\"\"\"\ntype PageInfo {\n \"\"\"\n When paginating forwards, are there more items?\n \"\"\"\n hasNextPage: Boolean!\n\n \"\"\"\n When paginating backwards, are there more items?\n \"\"\"\n hasPreviousPage: Boolean!\n\n \"\"\"\n When paginating backwards, the cursor to continue.\n \"\"\"\n startCursor: String\n\n \"\"\"\n When paginating forwards, the cursor to continue.\n \"\"\"\n endCursor: String\n}\n\ntype Query {\n defaultVideo: Video!\n videos(\n \"\"\"\n Returns the items in the list that come before the specified cursor.\n \"\"\"\n before: String = null\n\n \"\"\"\n Returns the items in the list that come after the specified cursor.\n \"\"\"\n after: String = null\n\n \"\"\"\n Returns the first n items from the list.\n \"\"\"\n first: Int = null\n\n \"\"\"\n Returns the items in the list that come after the specified cursor.\n \"\"\"\n last: Int = null\n ): VideoConnection!\n sharedVideo(path: String!): SharedVideo!\n}\n\ntype SharedVideo {\n path: String!\n url: String!\n}\n\nscalar Upload\n\ntype Video implements Node {\n \"\"\"\n The Globally Unique ID of this object\n \"\"\"\n id: GlobalID!\n path: String!\n posterPath: String\n width: Int!\n height: Int!\n url: String!\n posterUrl: String!\n}\n\n\"\"\"\nA connection to a list of items.\n\"\"\"\ntype VideoConnection {\n \"\"\"\n Pagination data for this connection\n \"\"\"\n pageInfo: PageInfo!\n\n \"\"\"\n Contains the nodes in this connection\n \"\"\"\n edges: [VideoEdge!]!\n}\n\n\"\"\"\nAn edge in a connection.\n\"\"\"\ntype VideoEdge {\n \"\"\"\n A cursor for use in pagination\n \"\"\"\n cursor: String!\n\n \"\"\"\n The item at the end of the edge\n \"\"\"\n node: Video!\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/App.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport SAM2DemoApp from '@/demo/SAM2DemoApp';\nimport SettingsContextProvider from '@/settings/SettingsContextProvider';\nimport {RouterProvider, createBrowserRouter} from 'react-router-dom';\n\nexport default function App() {\n const router = createBrowserRouter([\n {\n path: '*',\n element: (\n \n \n \n ),\n },\n ]);\n\n return ;\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/assets/scss/App.scss", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n * \n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n * \n * http://www.apache.org/licenses/LICENSE-2.0\n * \n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n@tailwind base;\n@tailwind components;\n@tailwind utilities;\n\n.tab {\n display: flex;\n padding: 0px 0px;\n margin-right: 6px;\n align-items: center;\n height: 100%;\n}\n\n@layer base {\n @font-face {\n font-family: 'Inter';\n src: url(/fonts/Inter-VariableFont.ttf) format('truetype-variations');\n }\n}\n\nbody {\n font-family: 'Inter', sans-serif;\n -webkit-font-smoothing: antialiased;\n -moz-osx-font-smoothing: grayscale;\n}\n\nbody,\nhtml,\n#root {\n height: 100%;\n @media screen and (max-width: '768px') {\n overflow: hidden;\n }\n}\n\n:root {\n --segEv-font: 'Inter', system-ui, -apple-system, BlinkMacSystemFont,\n 'Segoe UI', Oxygen, Ubuntu, Cantarell, 'Open Sans', 'Helvetica Neue',\n sans-serif;\n --perspective: 4000px;\n color-scheme: dark;\n}\n\nh1,\nh2,\nh3,\nh4,\nh5,\nh6 {\n font-family: 'Inter', sans-serif;\n}\n\n.prose .display h1 {\n @apply text-4xl text-gray-800 font-medium leading-tight;\n}\n\n.prose .display h2 {\n @apply text-gray-800 font-medium leading-tight;\n font-size: 2.5rem;\n}\n\n.prose h1 {\n @apply text-3xl text-gray-800 font-medium leading-tight mt-2 mb-4;\n letter-spacing: 0.016rem;\n}\n\n.prose h2 {\n @apply text-2xl text-gray-800 font-medium leading-tight my-2;\n letter-spacing: 0.01rem;\n}\n\n.prose h3 {\n @apply text-xl text-gray-800 font-medium leading-tight my-2;\n letter-spacing: 0.005rem;\n}\n\n.prose h4 {\n @apply text-lg text-gray-800 font-medium leading-tight my-2;\n}\n\n.prose h5 {\n @apply text-xl text-gray-700 font-normal leading-normal my-2;\n letter-spacing: 0.005rem;\n}\n\n.prose h6 {\n @apply text-base text-gray-700 font-normal leading-normal my-2;\n}\n\n.prose p {\n @apply text-sm text-gray-700 font-normal leading-normal;\n @apply leading-snug;\n}\n\n.prose ol,\n.prose ul {\n @apply text-sm text-gray-700 font-normal leading-normal;\n padding-right: 2rem;\n}\n\n.dark-mode h1,\n.dark-mode h2,\n.dark-mode h3,\n.dark-mode h4,\n.dark-mode h5,\n.dark-mode p,\n.dark-mode ol,\n.dark-mode ul,\n.dark-mode p *,\n.dark-mode ol *,\n.dark-mode ul *,\n≈ {\n @apply text-white;\n}\n\n.dark-mode h4,\n.dark-mode h6,\n.dark-mode li::marker,\n.dark-mode a {\n @apply text-gray-200;\n}\n\n.flex-grow-2 {\n flex-grow: 2;\n}\n\n.flex-grow-3 {\n flex-grow: 3;\n}\n\n.flex-grow-4 {\n flex-grow: 4;\n}\n\n.flex-grow-5 {\n flex-grow: 5;\n}\n\n.nav-title {\n font-family: var(--segEv-font);\n}\n\n.segment-active {\n animation: segment-highlight 2s linear infinite;\n stroke-dasharray: 5, 10;\n stroke-width: 4px;\n}\n\n@keyframes segment-highlight {\n to {\n stroke-dashoffset: 60;\n }\n}\n\n.segment-select {\n animation: segment-dotted 2s linear infinite;\n stroke-dasharray: 3, 5;\n stroke-width: 3px;\n}\n\n@keyframes segment-dotted {\n to {\n stroke-dashoffset: 24;\n }\n}\n\n/**\n * Daisy UI customizations\n */\n\n.btn {\n @apply normal-case rounded-md;\n}\n\n.comp_summary h1,\n.comp_summary h2,\n.comp_summary h3 {\n @apply mb-4;\n}\n\n.disabled {\n opacity: 0.4;\n pointer-events: none;\n}\n\n.absolute-center {\n top: 50%;\n left: 50%;\n transform: translate(-50%, -50%);\n}\n\n@screen lg {\n .drawer .grid {\n grid-template-columns: max-content 1fr;\n }\n}\n\n.fade-in {\n transition: opacity 0.5s;\n opacity: 1 !important;\n}\n\n.react-photo-gallery--gallery > div {\n gap: 0.25rem;\n}\n\n.sticker {\n filter: drop-shadow(0.25rem 0.25rem 5px #fff)\n drop-shadow(-0.25rem 0.25rem 5px #fff)\n drop-shadow(0.25rem -0.25rem 5px #fff)\n drop-shadow(-0.25rem -0.25rem 5px #fff);\n transition: filter 0.3s ease-out;\n}\n\n.sticker:hover,\n.sticker-select {\n filter: drop-shadow(0.25rem 0.25rem 1px #2962d9)\n drop-shadow(-0.25rem 0.25rem 1px #2962d9)\n drop-shadow(0.25rem -0.25rem 1px #2962d9)\n drop-shadow(-0.25rem -0.25rem 1px #2962d9);\n}\n\n/* keyframe animations */\n.mask-path,\n.reveal {\n opacity: 0;\n animation: reveal 0.4s ease-in forwards;\n}\n\n.slow-reveal {\n animation: reveal 1s ease-in;\n}\n\n.reveal-then-conceal {\n opacity: 0;\n animation: reveal-then-conceal 1.5s ease-in-out forwards;\n}\n\n@keyframes reveal {\n from {\n opacity: 0;\n }\n to {\n opacity: 1;\n }\n}\n\n@keyframes reveal-then-conceal {\n from {\n opacity: 0;\n }\n 50% {\n opacity: 1;\n }\n to {\n opacity: 0;\n }\n}\n\n.background-animate {\n background-size: 400%;\n animation: pulse 3s ease infinite;\n}\n\n@keyframes pulse {\n 0%,\n 100% {\n background-position: 0% 50%;\n }\n 50% {\n background-position: 100% 50%;\n }\n}\n\n/* Fix for Safari and Mobile Safari:\nExtracted Tailwind progress-bar styles and applied\nthem to a
instead of a element */\n\n.loading-bar {\n position: relative;\n width: 100%;\n -webkit-appearance: none;\n -moz-appearance: none;\n appearance: none;\n overflow: hidden;\n height: 0.5rem;\n border-radius: 1rem;\n border-radius: var(--rounded-box, 1rem);\n vertical-align: baseline;\n background-color: hsl(var(--n) / var(--tw-bg-opacity));\n --tw-bg-opacity: 0.2;\n\n &::after {\n --tw-bg-opacity: 1;\n background-color: hsl(var(--n) / var(--tw-bg-opacity));\n content: '';\n position: absolute;\n top: 0px;\n bottom: 0px;\n left: -40%;\n width: 33.333333%;\n border-radius: 1rem;\n border-radius: var(--rounded-box, 1rem);\n animation: loading 5s infinite ease-in-out;\n }\n}\n\n@keyframes loading {\n 50% {\n left: 107%;\n }\n}\n\n@keyframes inAnimation {\n 0% {\n opacity: 0;\n max-height: 0px;\n }\n 50% {\n opacity: 1;\n }\n 100% {\n opacity: 1;\n max-height: 600px;\n }\n}\n\n@keyframes outAnimation {\n 0% {\n opacity: 1;\n max-height: 600px;\n }\n 50% {\n opacity: 0;\n }\n 100% {\n opacity: 0;\n max-height: 0px;\n }\n}\n\n@keyframes ellipsisAnimation {\n 0% {\n content: '';\n }\n 25% {\n content: '.';\n }\n 50% {\n content: '..';\n }\n 75% {\n content: '...';\n }\n}\n\n.ellipsis::after {\n content: '';\n animation: ellipsisAnimation 1.5s infinite;\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/codecs/VideoDecoder.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {cloneFrame} from '@/common/codecs/WebCodecUtils';\nimport {FileStream} from '@/common/utils/FileUtils';\nimport {\n createFile,\n DataStream,\n MP4ArrayBuffer,\n MP4File,\n MP4Sample,\n MP4VideoTrack,\n} from 'mp4box';\nimport {isAndroid, isChrome, isEdge, isWindows} from 'react-device-detect';\n\nexport type ImageFrame = {\n bitmap: VideoFrame;\n timestamp: number;\n duration: number;\n};\n\nexport type DecodedVideo = {\n width: number;\n height: number;\n frames: ImageFrame[];\n numFrames: number;\n fps: number;\n};\n\nfunction decodeInternal(\n identifier: string,\n onReady: (mp4File: MP4File) => Promise,\n onProgress: (decodedVideo: DecodedVideo) => void,\n): Promise {\n return new Promise((resolve, reject) => {\n const imageFrames: ImageFrame[] = [];\n const globalSamples: MP4Sample[] = [];\n\n let decoder: VideoDecoder;\n\n let track: MP4VideoTrack | null = null;\n const mp4File = createFile();\n\n mp4File.onError = reject;\n mp4File.onReady = async info => {\n if (info.videoTracks.length > 0) {\n track = info.videoTracks[0];\n } else {\n // The video does not have a video track, so looking if there is an\n // \"otherTracks\" available. Note, I couldn't find any documentation\n // about \"otherTracks\" in WebCodecs [1], but it was available in the\n // info for MP4V-ES, which isn't supported by Chrome [2].\n // However, we'll still try to get the track and then throw an error\n // further down in the VideoDecoder.isConfigSupported if the codec is\n // not supported by the browser.\n //\n // [1] https://www.w3.org/TR/webcodecs/\n // [2] https://developer.mozilla.org/en-US/docs/Web/Media/Formats/Video_codecs#mp4v-es\n track = info.otherTracks[0];\n }\n\n if (track == null) {\n reject(new Error(`${identifier} does not contain a video track`));\n return;\n }\n\n const timescale = track.timescale;\n const edits = track.edits;\n\n let frame_n = 0;\n decoder = new VideoDecoder({\n // Be careful with any await in this function. The VideoDecoder will\n // not await output and continue calling it with decoded frames.\n async output(inputFrame) {\n if (track == null) {\n reject(new Error(`${identifier} does not contain a video track`));\n return;\n }\n\n const saveTrack = track;\n\n // If the track has edits, we'll need to check that only frames are\n // returned that are within the edit list. This can happen for\n // trimmed videos that have not been transcoded and therefore the\n // video track contains more frames than those visually rendered when\n // playing back the video.\n if (edits != null && edits.length > 0) {\n const cts = Math.round(\n (inputFrame.timestamp * timescale) / 1_000_000,\n );\n if (cts < edits[0].media_time) {\n inputFrame.close();\n return;\n }\n }\n\n // Workaround for Chrome where the decoding stops at ~17 frames unless\n // the VideoFrame is closed. So, the workaround here is to create a\n // new VideoFrame and close the decoded VideoFrame.\n // The frame has to be cloned, or otherwise some frames at the end of the\n // video will be black. Note, the default VideoFrame.clone doesn't work\n // and it is using a frame cloning found here:\n // https://webcodecs-blogpost-demo.glitch.me/\n if (\n (isAndroid && isChrome) ||\n (isWindows && isChrome) ||\n (isWindows && isEdge)\n ) {\n const clonedFrame = await cloneFrame(inputFrame);\n inputFrame.close();\n inputFrame = clonedFrame;\n }\n\n const sample = globalSamples[frame_n];\n if (sample != null) {\n const duration = (sample.duration * 1_000_000) / sample.timescale;\n imageFrames.push({\n bitmap: inputFrame,\n timestamp: inputFrame.timestamp,\n duration,\n });\n // Sort frames in order of timestamp. This is needed because Safari\n // can return decoded frames out of order.\n imageFrames.sort((a, b) => (a.timestamp < b.timestamp ? 1 : -1));\n // Update progress on first frame and then every 40th frame\n if (onProgress != null && frame_n % 100 === 0) {\n onProgress({\n width: saveTrack.track_width,\n height: saveTrack.track_height,\n frames: imageFrames,\n numFrames: saveTrack.nb_samples,\n fps:\n (saveTrack.nb_samples / saveTrack.duration) *\n saveTrack.timescale,\n });\n }\n }\n frame_n++;\n\n if (saveTrack.nb_samples === frame_n) {\n // Sort frames in order of timestamp. This is needed because Safari\n // can return decoded frames out of order.\n imageFrames.sort((a, b) => (a.timestamp > b.timestamp ? 1 : -1));\n resolve({\n width: saveTrack.track_width,\n height: saveTrack.track_height,\n frames: imageFrames,\n numFrames: saveTrack.nb_samples,\n fps:\n (saveTrack.nb_samples / saveTrack.duration) *\n saveTrack.timescale,\n });\n }\n },\n error(error) {\n reject(error);\n },\n });\n\n let description;\n const trak = mp4File.getTrackById(track.id);\n const entries = trak?.mdia?.minf?.stbl?.stsd?.entries;\n if (entries == null) {\n return;\n }\n for (const entry of entries) {\n if (entry.avcC || entry.hvcC) {\n const stream = new DataStream(undefined, 0, DataStream.BIG_ENDIAN);\n if (entry.avcC) {\n entry.avcC.write(stream);\n } else if (entry.hvcC) {\n entry.hvcC.write(stream);\n }\n description = new Uint8Array(stream.buffer, 8); // Remove the box header.\n break;\n }\n }\n\n const configuration: VideoDecoderConfig = {\n codec: track.codec,\n codedWidth: track.track_width,\n codedHeight: track.track_height,\n description,\n };\n const supportedConfig =\n await VideoDecoder.isConfigSupported(configuration);\n if (supportedConfig.supported == true) {\n decoder.configure(configuration);\n\n mp4File.setExtractionOptions(track.id, null, {\n nbSamples: Infinity,\n });\n mp4File.start();\n } else {\n reject(\n new Error(\n `Decoder config faile: config ${JSON.stringify(\n supportedConfig.config,\n )} is not supported`,\n ),\n );\n return;\n }\n };\n\n mp4File.onSamples = async (\n _id: number,\n _user: unknown,\n samples: MP4Sample[],\n ) => {\n for (const sample of samples) {\n globalSamples.push(sample);\n decoder.decode(\n new EncodedVideoChunk({\n type: sample.is_sync ? 'key' : 'delta',\n timestamp: (sample.cts * 1_000_000) / sample.timescale,\n duration: (sample.duration * 1_000_000) / sample.timescale,\n data: sample.data,\n }),\n );\n }\n await decoder.flush();\n decoder.close();\n };\n\n onReady(mp4File);\n });\n}\n\nexport function decode(\n file: File,\n onProgress: (decodedVideo: DecodedVideo) => void,\n): Promise {\n return decodeInternal(\n file.name,\n async (mp4File: MP4File) => {\n const reader = new FileReader();\n reader.onload = function () {\n const result = this.result as MP4ArrayBuffer;\n if (result != null) {\n result.fileStart = 0;\n mp4File.appendBuffer(result);\n }\n mp4File.flush();\n };\n reader.readAsArrayBuffer(file);\n },\n onProgress,\n );\n}\n\nexport function decodeStream(\n fileStream: FileStream,\n onProgress: (decodedVideo: DecodedVideo) => void,\n): Promise {\n return decodeInternal(\n 'stream',\n async (mp4File: MP4File) => {\n let part = await fileStream.next();\n while (part.done === false) {\n const result = part.value.data.buffer as MP4ArrayBuffer;\n if (result != null) {\n result.fileStart = part.value.range.start;\n mp4File.appendBuffer(result);\n }\n mp4File.flush();\n part = await fileStream.next();\n }\n },\n onProgress,\n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/codecs/VideoEncoder.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {ImageFrame} from '@/common/codecs/VideoDecoder';\nimport {MP4ArrayBuffer, createFile} from 'mp4box';\n\n// The selection of timescale and seconds/key-frame value are\n// explained in the following docs: https://github.com/vjeux/mp4-h264-re-encode\nconst TIMESCALE = 90000;\nconst SECONDS_PER_KEY_FRAME = 2;\n\nexport function encode(\n width: number,\n height: number,\n numFrames: number,\n framesGenerator: AsyncGenerator,\n progressCallback?: (progress: number) => void,\n): Promise {\n return new Promise((resolve, reject) => {\n let encodedFrameIndex = 0;\n let nextKeyFrameTimestamp = 0;\n let trackID: number | null = null;\n const durations: number[] = [];\n\n const outputFile = createFile();\n\n const encoder = new VideoEncoder({\n output(chunk, metaData) {\n const uint8 = new Uint8Array(chunk.byteLength);\n chunk.copyTo(uint8);\n\n const description = metaData?.decoderConfig?.description;\n if (trackID === null) {\n trackID = outputFile.addTrack({\n width: width,\n height: height,\n timescale: TIMESCALE,\n avcDecoderConfigRecord: description,\n });\n }\n const shiftedDuration = durations.shift();\n if (shiftedDuration != null) {\n outputFile.addSample(trackID, uint8, {\n duration: getScaledDuration(shiftedDuration),\n is_sync: chunk.type === 'key',\n });\n encodedFrameIndex++;\n progressCallback?.(encodedFrameIndex / numFrames);\n }\n\n if (encodedFrameIndex === numFrames) {\n resolve(outputFile.getBuffer());\n }\n },\n error(error) {\n reject(error);\n return;\n },\n });\n\n const setConfigurationAndEncodeFrames = async () => {\n // The codec value was taken from the following implementation and seems\n // reasonable for our use case for now:\n // https://github.com/vjeux/mp4-h264-re-encode/blob/main/mp4box.html#L103\n\n // Additional details about codecs can be found here:\n // - https://developer.mozilla.org/en-US/docs/Web/Media/Formats/codecs_parameter\n // - https://www.w3.org/TR/webcodecs-codec-registry/#video-codec-registry\n //\n // The following setting is a good compromise between output video file\n // size and quality. The latencyMode \"realtime\" is needed for Safari,\n // which otherwise will produce 20x larger files when in quality\n // latencyMode. Chrome does a really good job with file size even when\n // latencyMode is set to quality.\n const configuration: VideoEncoderConfig = {\n codec: 'avc1.4d0034',\n width: roundToNearestEven(width),\n height: roundToNearestEven(height),\n bitrate: 14_000_000,\n alpha: 'discard',\n bitrateMode: 'variable',\n latencyMode: 'realtime',\n };\n const supportedConfig =\n await VideoEncoder.isConfigSupported(configuration);\n if (supportedConfig.supported === true) {\n encoder.configure(configuration);\n } else {\n throw new Error(\n `Unsupported video encoder config ${JSON.stringify(supportedConfig)}`,\n );\n }\n\n for await (const frame of framesGenerator) {\n const {bitmap, duration, timestamp} = frame;\n durations.push(duration);\n let keyFrame = false;\n if (timestamp >= nextKeyFrameTimestamp) {\n await encoder.flush();\n keyFrame = true;\n nextKeyFrameTimestamp = timestamp + SECONDS_PER_KEY_FRAME * 1e6;\n }\n encoder.encode(bitmap, {keyFrame});\n bitmap.close();\n }\n\n await encoder.flush();\n encoder.close();\n };\n\n setConfigurationAndEncodeFrames();\n });\n}\n\nfunction getScaledDuration(rawDuration: number) {\n return rawDuration / (1_000_000 / TIMESCALE);\n}\n\nfunction roundToNearestEven(dim: number) {\n const rounded = Math.round(dim);\n\n if (rounded % 2 === 0) {\n return rounded;\n } else {\n return rounded + (rounded > dim ? -1 : 1);\n }\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/codecs/WebCodecUtils.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n// https://github.com/w3c/webcodecs/issues/88\n// https://issues.chromium.org/issues/40725065\n// https://webcodecs-blogpost-demo.glitch.me/\nexport async function cloneFrame(frame: VideoFrame): Promise {\n const {\n codedHeight,\n codedWidth,\n colorSpace,\n displayHeight,\n displayWidth,\n format,\n timestamp,\n } = frame;\n const rect = {x: 0, y: 0, width: codedWidth, height: codedHeight};\n const data = new ArrayBuffer(frame.allocationSize({rect}));\n try {\n await frame.copyTo(data, {rect});\n } catch (error) {\n // The VideoFrame#copyTo on x64 builds on macOS fails. The workaround here\n // is to clone the frame.\n // https://stackoverflow.com/questions/77898766/inconsistent-behavior-of-webcodecs-copyto-method-across-different-browsers-an\n return frame.clone();\n }\n return new VideoFrame(data, {\n codedHeight,\n codedWidth,\n colorSpace,\n displayHeight,\n displayWidth,\n duration: frame.duration ?? undefined,\n format: format!,\n timestamp,\n visibleRect: frame.visibleRect ?? undefined,\n });\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/MobileFirstClickBanner.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport ChangeVideoModal from '@/common/components/gallery/ChangeVideoModal';\nimport {DEMO_SHORT_NAME} from '@/demo/DemoConfig';\nimport {spacing} from '@/theme/tokens.stylex';\nimport {ImageCopy} from '@carbon/icons-react';\nimport stylex from '@stylexjs/stylex';\nimport {Button} from 'react-daisyui';\n\nconst styles = stylex.create({\n container: {\n position: 'relative',\n backgroundColor: '#000',\n padding: spacing[5],\n paddingVertical: spacing[6],\n display: 'flex',\n flexDirection: 'column',\n gap: spacing[4],\n },\n});\n\nexport default function MobileFirstClickBanner() {\n return (\n
\n
\n Click an object in the video to start\n
\n
\n

\n You'll be able to use {DEMO_SHORT_NAME} to make fun edits to any\n video by tracking objects and applying visual effects. To start, click\n any object in the video.\n

\n
\n
\n \n
\n
\n );\n}\n\ntype MobileVideoGalleryModalTriggerProps = {\n onClick: () => void;\n};\n\nfunction MobileVideoGalleryModalTrigger({\n onClick,\n}: MobileVideoGalleryModalTriggerProps) {\n return (\n }\n onClick={onClick}\n className=\"text-white p-0\">\n Change video\n \n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/Tooltip.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {PropsWithChildren} from 'react';\n\ntype Props = PropsWithChildren<{\n className?: string;\n message: string;\n position?: 'left' | 'top' | 'right' | 'bottom';\n}>;\n\n/**\n * This is a custom Tooltip component because React Daisy UI does not have an\n * option to *only* show tooltip on large devices.\n */\nexport default function Tooltip({\n children,\n className = '',\n message,\n position = 'top',\n}: Props) {\n return (\n \n {children}\n
\n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/annotations/AddObjectButton.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport useMessagesSnackbar from '@/common/components/snackbar/useDemoMessagesSnackbar';\nimport useVideo from '@/common/components/video/editor/useVideo';\nimport {activeTrackletObjectIdAtom, labelTypeAtom} from '@/demo/atoms';\nimport {Add} from '@carbon/icons-react';\nimport {useSetAtom} from 'jotai';\n\nexport default function AddObjectButton() {\n const video = useVideo();\n const setActiveTrackletId = useSetAtom(activeTrackletObjectIdAtom);\n const setLabelType = useSetAtom(labelTypeAtom);\n const {enqueueMessage} = useMessagesSnackbar();\n\n async function addObject() {\n enqueueMessage('addObjectClick');\n const tracklet = await video?.createTracklet();\n if (tracklet != null) {\n setActiveTrackletId(tracklet.id);\n setLabelType('positive');\n }\n }\n\n return (\n \n
\n
\n \n
\n
Add another object
\n
\n \n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/annotations/ClearAllPointsInVideoButton.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport useRestartSession from '@/common/components/session/useRestartSession';\nimport useMessagesSnackbar from '@/common/components/snackbar/useDemoMessagesSnackbar';\nimport useVideo from '@/common/components/video/editor/useVideo';\nimport {isPlayingAtom, isStreamingAtom, labelTypeAtom} from '@/demo/atoms';\nimport {Reset} from '@carbon/icons-react';\nimport stylex from '@stylexjs/stylex';\nimport {useAtomValue, useSetAtom} from 'jotai';\nimport {useState} from 'react';\nimport {Button, Loading} from 'react-daisyui';\n\nconst styles = stylex.create({\n container: {\n display: 'flex',\n alignItems: 'center',\n },\n});\n\ntype Props = {\n onRestart: () => void;\n};\n\nexport default function ClearAllPointsInVideoButton({onRestart}: Props) {\n const [isLoading, setIsLoading] = useState(false);\n const isPlaying = useAtomValue(isPlayingAtom);\n const isStreaming = useAtomValue(isStreamingAtom);\n const setLabelType = useSetAtom(labelTypeAtom);\n const {clearMessage} = useMessagesSnackbar();\n const {restartSession} = useRestartSession();\n\n const video = useVideo();\n\n async function handleRestart() {\n if (video === null) {\n return;\n }\n\n setIsLoading(true);\n if (isPlaying) {\n video.pause();\n }\n if (isStreaming) {\n await video.abortStreamMasks();\n }\n const isSuccessful = await video.clearPointsInVideo();\n if (!isSuccessful) {\n await restartSession();\n }\n video.frame = 0;\n setLabelType('positive');\n onRestart();\n clearMessage();\n setIsLoading(false);\n }\n\n return (\n
\n : }>\n Start over\n \n
\n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/annotations/CloseSessionButton.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport PrimaryCTAButton from '@/common/components/button/PrimaryCTAButton';\nimport useVideo from '@/common/components/video/editor/useVideo';\nimport {ChevronRight} from '@carbon/icons-react';\n\ntype Props = {\n onSessionClose: () => void;\n};\n\nexport default function CloseSessionButton({onSessionClose}: Props) {\n const video = useVideo();\n\n function handleCloseSession() {\n video?.closeSession();\n video?.logAnnotations();\n onSessionClose();\n }\n\n return (\n }>\n Good to go\n \n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/annotations/FirstClickView.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport ChangeVideo from '@/common/components/gallery/ChangeVideoModal';\nimport useMessagesSnackbar from '@/common/components/snackbar/useDemoMessagesSnackbar';\nimport {DEMO_SHORT_NAME} from '@/demo/DemoConfig';\nimport {useEffect, useRef} from 'react';\n\nexport default function FirstClickView() {\n const isFirstClickMessageShown = useRef(false);\n const {enqueueMessage} = useMessagesSnackbar();\n\n useEffect(() => {\n if (!isFirstClickMessageShown.current) {\n isFirstClickMessageShown.current = true;\n enqueueMessage('firstClick');\n }\n }, [enqueueMessage]);\n\n return (\n
\n
\n

Click an object in the video to start

\n

\n You'll be able to use {DEMO_SHORT_NAME} to make fun edits to any\n video by tracking objects and applying visual effects.\n

\n

\n To start, click any object in the video.\n

\n
\n
\n \n
\n
\n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/annotations/LimitNotice.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {InformationFilled} from '@carbon/icons-react';\n\nexport default function LimitNotice() {\n return (\n
\n
\n \n
\n
\n In this demo, you can track up to 3 objects, even though the SAM 2 model\n does not have a limit.\n
\n
\n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/annotations/MobileObjectsList.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport ClearAllPointsInVideoButton from '@/common/components/annotations/ClearAllPointsInVideoButton';\nimport ObjectThumbnail from '@/common/components/annotations/ObjectThumbnail';\nimport {OBJECT_TOOLBAR_INDEX} from '@/common/components/toolbar/ToolbarConfig';\nimport {BaseTracklet} from '@/common/tracker/Tracker';\nimport {activeTrackletObjectIdAtom, trackletObjectsAtom} from '@/demo/atoms';\nimport {spacing} from '@/theme/tokens.stylex';\nimport stylex from '@stylexjs/stylex';\nimport {useAtomValue, useSetAtom} from 'jotai';\n\nconst styles = stylex.create({\n container: {\n display: 'flex',\n padding: spacing[5],\n borderTop: '1px solid #DEE3E9',\n },\n trackletsContainer: {\n flexGrow: 1,\n display: 'flex',\n alignItems: 'center',\n gap: spacing[5],\n },\n});\n\ntype Props = {\n showActiveObject: () => void;\n onTabChange: (newIndex: number) => void;\n};\n\nexport default function MobileObjectsList({\n showActiveObject,\n onTabChange,\n}: Props) {\n const tracklets = useAtomValue(trackletObjectsAtom);\n const setActiveTrackletId = useSetAtom(activeTrackletObjectIdAtom);\n\n function handleSelectObject(tracklet: BaseTracklet) {\n setActiveTrackletId(tracklet.id);\n showActiveObject();\n }\n\n return (\n
\n
\n {tracklets.map(tracklet => {\n const {id, color, thumbnail} = tracklet;\n return (\n {\n handleSelectObject(tracklet);\n }}\n />\n );\n })}\n
\n onTabChange(OBJECT_TOOLBAR_INDEX)}\n />\n
\n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/annotations/MobileObjectsToolbar.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport MobileObjectsToolbarHeader from '@/common/components/annotations/MobileObjectsToolbarHeader';\nimport ObjectsToolbarBottomActions from '@/common/components/annotations/ObjectsToolbarBottomActions';\nimport {getObjectLabel} from '@/common/components/annotations/ObjectUtils';\nimport ToolbarObject from '@/common/components/annotations/ToolbarObject';\nimport MobileFirstClickBanner from '@/common/components/MobileFirstClickBanner';\nimport {activeTrackletObjectAtom, isFirstClickMadeAtom} from '@/demo/atoms';\nimport {useAtomValue} from 'jotai';\n\ntype Props = {\n onTabChange: (newIndex: number) => void;\n};\n\nexport default function MobileObjectsToolbar({onTabChange}: Props) {\n const activeTracklet = useAtomValue(activeTrackletObjectAtom);\n const isFirstClickMade = useAtomValue(isFirstClickMadeAtom);\n\n if (!isFirstClickMade) {\n return ;\n }\n\n return (\n
\n \n {activeTracklet != null && (\n \n )}\n\n \n
\n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/annotations/MobileObjectsToolbarHeader.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport ToolbarProgressChip from '@/common/components/toolbar/ToolbarProgressChip';\nimport {isStreamingAtom, streamingStateAtom} from '@/demo/atoms';\nimport {useAtomValue} from 'jotai';\n\nexport default function MobileObjectsToolbarHeader() {\n const isStreaming = useAtomValue(isStreamingAtom);\n const streamingState = useAtomValue(streamingStateAtom);\n\n return (\n
\n
\n \n {streamingState === 'full'\n ? 'Review your selected objects across the video, and continue to edit if needed. Once everything looks good, press “Next” to continue.'\n : isStreaming\n ? 'Watch the video closely for any places where your objects aren’t tracked correctly. You can also stop tracking to make additional edits.'\n : 'Edit your object selection with a few more clicks if needed. Press “Track objects” to track your objects throughout the video.'}\n
\n
\n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/annotations/ObjectActions.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport PointsToggle from '@/common/components/annotations/PointsToggle';\nimport useVideo from '@/common/components/video/editor/useVideo';\nimport useReportError from '@/common/error/useReportError';\nimport {\n activeTrackletObjectIdAtom,\n isPlayingAtom,\n isStreamingAtom,\n} from '@/demo/atoms';\nimport {\n AddFilled,\n Select_02,\n SubtractFilled,\n TrashCan,\n} from '@carbon/icons-react';\nimport {useAtom, useAtomValue} from 'jotai';\nimport {useState} from 'react';\nimport type {ButtonProps} from 'react-daisyui';\nimport {Button} from 'react-daisyui';\n\ntype Props = {\n objectId: number;\n active: boolean;\n};\n\nfunction CustomButton({className, ...props}: ButtonProps) {\n return (\n \n {props.children}\n \n );\n}\n\nexport default function ObjectActions({objectId, active}: Props) {\n const [isRemovingObject, setIsRemovingObject] = useState(false);\n const [activeTrackId, setActiveTrackletId] = useAtom(\n activeTrackletObjectIdAtom,\n );\n const isStreaming = useAtomValue(isStreamingAtom);\n const isPlaying = useAtom(isPlayingAtom);\n\n const video = useVideo();\n const reportError = useReportError();\n\n async function handleRemoveObject(\n event: React.MouseEvent,\n ) {\n try {\n event.stopPropagation();\n setIsRemovingObject(true);\n if (isStreaming) {\n await video?.abortStreamMasks();\n }\n if (isPlaying) {\n video?.pause();\n }\n await video?.deleteTracklet(objectId);\n } catch (error) {\n reportError(error);\n } finally {\n setIsRemovingObject(false);\n if (activeTrackId === objectId) {\n setActiveTrackletId(null);\n }\n }\n }\n\n return (\n
\n {active && (\n \n )}\n\n
\n {active ? (\n \n ) : (\n <>\n }>\n Edit selection\n \n }>\n Clear\n \n \n )}\n
\n
\n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/annotations/ObjectPlaceholder.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {BLUE_PINK_FILL_BR} from '@/theme/gradientStyle';\n\ntype Props = {\n showPlus?: boolean;\n onClick?: () => void;\n};\n\nexport default function ObjectPlaceholder({showPlus = true, onClick}: Props) {\n return (\n \n {showPlus && (\n
\n \n \n \n
\n )}\n \n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/annotations/ObjectThumbnail.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\ntype Props = {\n thumbnail: string | null;\n color: string;\n onClick?: () => void;\n};\n\nexport default function ObjectThumbnail({thumbnail, color, onClick}: Props) {\n return (\n \n \n \n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/annotations/ObjectUtils.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {BaseTracklet} from '@/common/tracker/Tracker';\n\nexport function getObjectLabel(tracklet: BaseTracklet) {\n return `Object ${tracklet.id + 1}`;\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/annotations/ObjectsToolbar.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport AddObjectButton from '@/common/components/annotations/AddObjectButton';\nimport FirstClickView from '@/common/components/annotations/FirstClickView';\nimport LimitNotice from '@/common/components/annotations/LimitNotice';\nimport ObjectsToolbarBottomActions from '@/common/components/annotations/ObjectsToolbarBottomActions';\nimport ObjectsToolbarHeader from '@/common/components/annotations/ObjectsToolbarHeader';\nimport {getObjectLabel} from '@/common/components/annotations/ObjectUtils';\nimport ToolbarObject from '@/common/components/annotations/ToolbarObject';\nimport {\n activeTrackletObjectAtom,\n activeTrackletObjectIdAtom,\n isAddObjectEnabledAtom,\n isFirstClickMadeAtom,\n isTrackletObjectLimitReachedAtom,\n trackletObjectsAtom,\n} from '@/demo/atoms';\nimport {useAtomValue, useSetAtom} from 'jotai';\n\ntype Props = {\n onTabChange: (newIndex: number) => void;\n};\n\nexport default function ObjectsToolbar({onTabChange}: Props) {\n const tracklets = useAtomValue(trackletObjectsAtom);\n const activeTracklet = useAtomValue(activeTrackletObjectAtom);\n const setActiveTrackletId = useSetAtom(activeTrackletObjectIdAtom);\n const isFirstClickMade = useAtomValue(isFirstClickMadeAtom);\n const isObjectLimitReached = useAtomValue(isTrackletObjectLimitReachedAtom);\n const isAddObjectEnabled = useAtomValue(isAddObjectEnabledAtom);\n\n if (!isFirstClickMade) {\n return ;\n }\n\n return (\n
\n \n
\n {tracklets.map(tracklet => {\n return (\n {\n setActiveTrackletId(tracklet.id);\n }}\n />\n );\n })}\n {isAddObjectEnabled && }\n {isObjectLimitReached && }\n
\n \n
\n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/annotations/ObjectsToolbarBottomActions.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport ClearAllPointsInVideoButton from '@/common/components/annotations/ClearAllPointsInVideoButton';\nimport CloseSessionButton from '@/common/components/annotations/CloseSessionButton';\nimport TrackAndPlayButton from '@/common/components/button/TrackAndPlayButton';\nimport ToolbarBottomActionsWrapper from '@/common/components/toolbar/ToolbarBottomActionsWrapper';\nimport {\n EFFECT_TOOLBAR_INDEX,\n OBJECT_TOOLBAR_INDEX,\n} from '@/common/components/toolbar/ToolbarConfig';\nimport {streamingStateAtom} from '@/demo/atoms';\nimport {useAtomValue} from 'jotai';\n\ntype Props = {\n onTabChange: (newIndex: number) => void;\n};\n\nexport default function ObjectsToolbarBottomActions({onTabChange}: Props) {\n const streamingState = useAtomValue(streamingStateAtom);\n\n const isTrackingEnabled =\n streamingState !== 'none' && streamingState !== 'full';\n\n function handleSwitchToEffectsTab() {\n onTabChange(EFFECT_TOOLBAR_INDEX);\n }\n\n return (\n \n onTabChange(OBJECT_TOOLBAR_INDEX)}\n />\n {isTrackingEnabled && }\n {streamingState === 'full' && (\n \n )}\n \n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/annotations/ObjectsToolbarHeader.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport ToolbarHeaderWrapper from '@/common/components/toolbar/ToolbarHeaderWrapper';\nimport {isStreamingAtom, streamingStateAtom} from '@/demo/atoms';\nimport {useAtomValue} from 'jotai';\n\nexport default function ObjectsToolbarHeader() {\n const isStreaming = useAtomValue(isStreamingAtom);\n const streamingState = useAtomValue(streamingStateAtom);\n\n return (\n \n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/annotations/PointsToggle.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {labelTypeAtom} from '@/demo/atoms';\nimport {AddFilled, SubtractFilled} from '@carbon/icons-react';\nimport {useAtom} from 'jotai';\n\nexport default function PointsToggle() {\n const [labelType, setLabelType] = useAtom(labelTypeAtom);\n const isPositive = labelType === 'positive';\n\n const buttonStyle = (selected: boolean) =>\n `btn-md bg-graydark-800 !text-white md:px-2 lg:px-4 py-0.5 ${selected ? `border border-white hover:bg-graydark-800` : `border-graydark-700 hover:bg-graydark-700`}`;\n\n return (\n
\n
\n setLabelType('positive')}>\n Add\n \n setLabelType('negative')}>\n \n Remove\n \n
\n
\n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/annotations/PrimaryCTAButton.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport GradientBorder from '@/common/components/button/GradientBorder';\nimport type {ReactNode} from 'react';\n\ntype Props = {\n disabled?: boolean;\n endIcon?: ReactNode;\n} & React.DOMAttributes;\n\nexport default function PrimaryCTAButton({\n children,\n disabled,\n endIcon,\n ...props\n}: Props) {\n return (\n \n \n {children}\n {endIcon != null && endIcon}\n \n \n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/annotations/ToolbarObject.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport ObjectActions from '@/common/components/annotations/ObjectActions';\nimport ObjectPlaceholder from '@/common/components/annotations/ObjectPlaceholder';\nimport ObjectThumbnail from '@/common/components/annotations/ObjectThumbnail';\nimport ToolbarObjectContainer from '@/common/components/annotations/ToolbarObjectContainer';\nimport useVideo from '@/common/components/video/editor/useVideo';\nimport {BaseTracklet} from '@/common/tracker/Tracker';\nimport emptyFunction from '@/common/utils/emptyFunction';\nimport {activeTrackletObjectIdAtom} from '@/demo/atoms';\nimport {useSetAtom} from 'jotai';\n\ntype Props = {\n label: string;\n tracklet: BaseTracklet;\n isActive: boolean;\n isMobile?: boolean;\n onClick?: () => void;\n onThumbnailClick?: () => void;\n};\n\nexport default function ToolbarObject({\n label,\n tracklet,\n isActive,\n isMobile = false,\n onClick,\n onThumbnailClick = emptyFunction,\n}: Props) {\n const video = useVideo();\n const setActiveTrackletId = useSetAtom(activeTrackletObjectIdAtom);\n\n async function handleCancelNewObject() {\n try {\n await video?.deleteTracklet(tracklet.id);\n } catch (error) {\n reportError(error);\n } finally {\n setActiveTrackletId(null);\n }\n }\n\n if (!tracklet.isInitialized) {\n return (\n }\n isMobile={isMobile}\n onClick={onClick}\n onCancel={handleCancelNewObject}\n />\n );\n }\n\n return (\n \n }\n isMobile={isMobile}>\n \n \n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/annotations/ToolbarObjectContainer.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {spacing} from '@/theme/tokens.stylex';\nimport {Close} from '@carbon/icons-react';\nimport stylex from '@stylexjs/stylex';\nimport {PropsWithChildren, ReactNode} from 'react';\n\nconst sharedStyles = stylex.create({\n container: {\n display: 'flex',\n overflow: 'hidden',\n cursor: 'pointer',\n flexShrink: 0,\n borderTop: 'none',\n backgroundColor: {\n '@media screen and (max-width: 768px)': '#000',\n },\n paddingHorizontal: {\n default: spacing[8],\n '@media screen and (max-width: 768px)': spacing[5],\n },\n paddingBottom: {\n default: spacing[8],\n '@media screen and (max-width: 768px)': 10,\n },\n },\n activeContainer: {\n background: '#000',\n borderRadius: 16,\n marginHorizontal: 16,\n padding: {\n default: spacing[4],\n '@media screen and (max-width: 768px)': spacing[5],\n },\n marginBottom: {\n default: spacing[8],\n '@media screen and (max-width: 768px)': 0,\n },\n },\n itemsCenter: {\n alignItems: 'center',\n },\n rightColumn: {\n marginStart: {\n default: spacing[4],\n '@media screen and (max-width: 768px)': 0,\n },\n flexGrow: 1,\n alignItems: 'center',\n },\n});\n\ntype ToolbarObjectContainerProps = PropsWithChildren<{\n alignItems?: 'top' | 'center';\n isActive: boolean;\n title: string;\n subtitle: string;\n thumbnail: ReactNode;\n isMobile: boolean;\n onCancel?: () => void;\n onClick?: () => void;\n}>;\n\nexport default function ToolbarObjectContainer({\n alignItems = 'top',\n children,\n isActive,\n title,\n subtitle,\n thumbnail,\n isMobile,\n onClick,\n onCancel,\n}: ToolbarObjectContainerProps) {\n if (isMobile) {\n return (\n \n
{children}
\n \n );\n }\n\n return (\n \n {thumbnail}\n
\n
{title}
\n {subtitle.length > 0 && (\n
\n {subtitle}\n
\n )}\n {children}\n
\n {onCancel != null && (\n
\n \n
\n )}\n \n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/annotations/TrackletSwimlane.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport useSelectedFrameHelper from '@/common/components/video/filmstrip/useSelectedFrameHelper';\nimport {BaseTracklet, DatalessMask} from '@/common/tracker/Tracker';\nimport {spacing, w} from '@/theme/tokens.stylex';\nimport stylex from '@stylexjs/stylex';\nimport {useMemo} from 'react';\n\nconst styles = stylex.create({\n container: {\n display: 'flex',\n alignItems: 'center',\n gap: spacing[4],\n width: '100%',\n },\n trackletNameContainer: {\n width: w[12],\n textAlign: 'center',\n fontSize: '10px',\n color: 'white',\n },\n swimlaneContainer: {\n flexGrow: 1,\n position: 'relative',\n display: 'flex',\n height: 12,\n marginVertical: '0.25rem' /* 4px */,\n '@media screen and (max-width: 768px)': {\n marginVertical: 0,\n },\n },\n swimlane: {\n position: 'absolute',\n left: 0,\n top: '50%',\n width: '100%',\n height: 1,\n transform: 'translate3d(0, -50%, 0)',\n opacity: 0.4,\n },\n segment: {\n position: 'absolute',\n top: '50%',\n height: 1,\n transform: 'translate3d(0, -50%, 0)',\n },\n segmentationPoint: {\n position: 'absolute',\n top: '50%',\n transform: 'translate3d(0, -50%, 0)',\n borderRadius: '50%',\n cursor: 'pointer',\n width: 12,\n height: 12,\n '@media screen and (max-width: 768px)': {\n width: 8,\n height: 8,\n },\n },\n});\n\ntype SwimlineSegment = {\n left: number;\n width: number;\n};\n\ntype Props = {\n tracklet: BaseTracklet;\n onSelectFrame: (tracklet: BaseTracklet, index: number) => void;\n};\n\nfunction getSwimlaneSegments(masks: DatalessMask[]): SwimlineSegment[] {\n if (masks.length === 0) {\n return [];\n }\n\n const swimlineSegments: SwimlineSegment[] = [];\n let left = -1;\n\n for (let frameIndex = 0; frameIndex < masks.length; ++frameIndex) {\n const isEmpty = masks?.[frameIndex]?.isEmpty ?? true;\n if (left === -1 && !isEmpty) {\n left = frameIndex;\n } else if (left !== -1 && (isEmpty || frameIndex == masks.length - 1)) {\n swimlineSegments.push({\n left,\n width: frameIndex - left + 1,\n });\n left = -1;\n }\n }\n\n return swimlineSegments;\n}\n\nexport default function TrackletSwimlane({tracklet, onSelectFrame}: Props) {\n const selection = useSelectedFrameHelper();\n\n const segments = useMemo(() => {\n return getSwimlaneSegments(tracklet.masks);\n }, [tracklet.masks]);\n\n const framesWithPoints = tracklet.points.reduce(\n (frames, pts, frameIndex) => {\n if (pts != null && pts.length > 0) {\n frames.push(frameIndex);\n }\n return frames;\n },\n [],\n );\n\n if (selection === null) {\n return;\n }\n\n return (\n
\n
\n Object {tracklet.id + 1}\n
\n
\n \n {segments.map(segment => {\n return (\n \n );\n })}\n {framesWithPoints.map(index => {\n return (\n {\n onSelectFrame?.(tracklet, index);\n }}\n {...stylex.props(styles.segmentationPoint)}\n style={{\n left: Math.floor(selection.toPosition(index) - 4),\n backgroundColor: tracklet.color,\n }}\n />\n );\n })}\n
\n
\n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/annotations/TrackletsAnnotation.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport TrackletSwimlane from '@/common/components/annotations/TrackletSwimlane';\nimport useTracklets from '@/common/components/annotations/useTracklets';\nimport useVideo from '@/common/components/video/editor/useVideo';\nimport {BaseTracklet} from '@/common/tracker/Tracker';\nimport {m, spacing} from '@/theme/tokens.stylex';\nimport stylex from '@stylexjs/stylex';\n\nconst styles = stylex.create({\n container: {\n marginTop: m[3],\n height: 75,\n paddingHorizontal: spacing[4],\n '@media screen and (max-width: 768px)': {\n height: 25,\n },\n },\n});\n\nexport default function TrackletsAnnotation() {\n const video = useVideo();\n const tracklets = useTracklets();\n\n function handleSelectFrame(_tracklet: BaseTracklet, index: number) {\n if (video !== null) {\n video.frame = index;\n }\n }\n\n return (\n
\n {tracklets.map(tracklet => (\n \n ))}\n
\n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/annotations/useTracklets.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {trackletObjectsAtom} from '@/demo/atoms';\nimport {useAtomValue} from 'jotai';\n\nexport default function useTracklets() {\n return useAtomValue(trackletObjectsAtom);\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/button/GradientBorder.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport stylex from '@stylexjs/stylex';\n\nimport {gradients} from '@/theme/tokens.stylex';\n\nenum GradientTypes {\n fullGradient = 'fullGradient',\n bluePinkGradient = 'bluePinkGradient',\n}\n\ntype Props = {\n gradientType?: GradientTypes;\n disabled?: boolean;\n rounded?: boolean;\n className?: string;\n} & React.DOMAttributes;\n\nconst styles = stylex.create({\n animationHover: {\n ':hover': {\n backgroundPosition: '300% 100%',\n },\n },\n\n fullGradient: {\n border: '2px solid transparent',\n background: gradients['rainbow'],\n backgroundSize: '100% 400%',\n transition: 'background 0.35s ease-in-out',\n },\n\n bluePinkGradient: {\n border: '2px solid transparent',\n background: gradients['rainbow'],\n },\n});\n\nexport default function GradientBorder({\n gradientType = GradientTypes.fullGradient,\n disabled,\n rounded = true,\n className = '',\n children,\n}: Props) {\n const gradient = (name: GradientTypes) => {\n if (name === GradientTypes.fullGradient) {\n return styles.fullGradient;\n } else if (name === GradientTypes.bluePinkGradient) {\n return styles.bluePinkGradient;\n }\n };\n\n return (\n \n {children}\n \n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/button/PlaybackButton.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {OBJECT_TOOLBAR_INDEX} from '@/common/components/toolbar/ToolbarConfig';\nimport Tooltip from '@/common/components/Tooltip';\nimport useVideo from '@/common/components/video/editor/useVideo';\nimport {isPlayingAtom, streamingStateAtom, toolbarTabIndex} from '@/demo/atoms';\nimport {PauseFilled, PlayFilledAlt} from '@carbon/icons-react';\nimport {useAtomValue} from 'jotai';\nimport {useCallback, useEffect} from 'react';\n\nexport default function PlaybackButton() {\n const tabIndex = useAtomValue(toolbarTabIndex);\n const streamingState = useAtomValue(streamingStateAtom);\n const isPlaying = useAtomValue(isPlayingAtom);\n const video = useVideo();\n\n const isDisabled =\n tabIndex === OBJECT_TOOLBAR_INDEX &&\n streamingState !== 'none' &&\n streamingState !== 'full';\n\n const handlePlay = useCallback(() => {\n video?.play();\n }, [video]);\n\n const handlePause = useCallback(() => {\n video?.pause();\n }, [video]);\n\n const handleClick = useCallback(() => {\n if (isDisabled) {\n return;\n }\n if (isPlaying) {\n handlePause();\n } else {\n handlePlay();\n }\n }, [isDisabled, isPlaying, handlePlay, handlePause]);\n\n useEffect(() => {\n const handleKey = (event: KeyboardEvent) => {\n const callback = {\n KeyK: handleClick,\n }[event.code];\n if (callback != null) {\n event.preventDefault();\n callback();\n }\n };\n document.addEventListener('keydown', handleKey);\n return () => {\n document.removeEventListener('keydown', handleKey);\n };\n }, [handleClick]);\n\n return (\n \n \n {isPlaying ? (\n \n ) : (\n \n )}\n \n \n );\n}\n\nfunction getButtonStyles(isDisabled: boolean): string {\n if (isDisabled) {\n return '!bg-gray-600 !text-graydark-700';\n }\n return `!text-black bg-white`;\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/button/PrimaryCTAButton.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport GradientBorder from '@/common/components/button/GradientBorder';\nimport type {ReactNode} from 'react';\n\ntype Props = {\n disabled?: boolean;\n endIcon?: ReactNode;\n} & React.DOMAttributes;\n\nexport default function PrimaryCTAButton({\n children,\n disabled,\n endIcon,\n ...props\n}: Props) {\n return (\n \n \n {children}\n {endIcon != null && endIcon}\n \n \n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/button/ResponsiveButton.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport useScreenSize from '@/common/screen/useScreenSize';\nimport type {ReactNode} from 'react';\nimport type {ButtonProps} from 'react-daisyui';\nimport {Button} from 'react-daisyui';\n\ntype Props = ButtonProps & {startIcon: ReactNode};\n\nexport default function ResponsiveButton(props: Props) {\n const {isMobile} = useScreenSize();\n\n return ;\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/button/TrackAndPlayButton.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport PrimaryCTAButton from '@/common/components/button/PrimaryCTAButton';\nimport useMessagesSnackbar from '@/common/components/snackbar/useDemoMessagesSnackbar';\nimport useFunctionThrottle from '@/common/components/useFunctionThrottle';\nimport useVideo from '@/common/components/video/editor/useVideo';\nimport {\n areTrackletObjectsInitializedAtom,\n isStreamingAtom,\n sessionAtom,\n streamingStateAtom,\n} from '@/demo/atoms';\nimport {ChevronRight} from '@carbon/icons-react';\nimport {useAtom, useAtomValue, useSetAtom} from 'jotai';\nimport {useCallback, useEffect} from 'react';\n\nexport default function TrackAndPlayButton() {\n const video = useVideo();\n const [isStreaming, setIsStreaming] = useAtom(isStreamingAtom);\n const streamingState = useAtomValue(streamingStateAtom);\n const areObjectsInitialized = useAtomValue(areTrackletObjectsInitializedAtom);\n const setSession = useSetAtom(sessionAtom);\n const {enqueueMessage} = useMessagesSnackbar();\n const {isThrottled, maxThrottles, throttle} = useFunctionThrottle(250, 4);\n\n const isTrackAndPlayDisabled =\n streamingState === 'aborting' || streamingState === 'requesting';\n\n useEffect(() => {\n function onStreamingStarted() {\n setIsStreaming(true);\n }\n video?.addEventListener('streamingStarted', onStreamingStarted);\n\n function onStreamingCompleted() {\n enqueueMessage('trackAndPlayComplete');\n setIsStreaming(false);\n }\n video?.addEventListener('streamingCompleted', onStreamingCompleted);\n\n return () => {\n video?.removeEventListener('streamingStarted', onStreamingStarted);\n video?.removeEventListener('streamingCompleted', onStreamingCompleted);\n };\n }, [video, setIsStreaming, enqueueMessage]);\n\n const handleTrackAndPlay = useCallback(() => {\n if (isTrackAndPlayDisabled) {\n return;\n }\n if (maxThrottles && isThrottled) {\n enqueueMessage('trackAndPlayThrottlingWarning');\n }\n\n // Throttling is only applied while streaming because we should\n // only throttle after a user has aborted inference. This way,\n // a user can still quickly abort a stream if they notice the\n // inferred mask is misaligned.\n throttle(\n () => {\n if (!isStreaming) {\n enqueueMessage('trackAndPlayClick');\n video?.streamMasks();\n setSession(previousSession =>\n previousSession == null\n ? previousSession\n : {...previousSession, ranPropagation: true},\n );\n } else {\n video?.abortStreamMasks();\n }\n },\n {enableThrottling: isStreaming},\n );\n }, [\n isTrackAndPlayDisabled,\n isThrottled,\n isStreaming,\n maxThrottles,\n video,\n setSession,\n enqueueMessage,\n throttle,\n ]);\n\n useEffect(() => {\n const handleKey = (event: KeyboardEvent) => {\n const callback = {\n KeyK: handleTrackAndPlay,\n }[event.code];\n if (callback != null) {\n event.preventDefault();\n callback();\n }\n };\n document.addEventListener('keydown', handleKey);\n return () => {\n document.removeEventListener('keydown', handleKey);\n };\n }, [handleTrackAndPlay]);\n\n return (\n }>\n {isStreaming ? 'Cancel Tracking' : 'Track objects'}\n \n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/code/InitializeLocalMonaco.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {loader} from '@monaco-editor/react';\n\nimport Logger from '@/common/logger/Logger';\nimport * as monaco from 'monaco-editor';\nimport editorWorker from 'monaco-editor/esm/vs/editor/editor.worker?worker';\nimport tsWorker from 'monaco-editor/esm/vs/language/typescript/ts.worker?worker';\n\nself.MonacoEnvironment = {\n getWorker(_, label) {\n if (label === 'typescript' || label === 'javascript') {\n return new tsWorker();\n }\n return new editorWorker();\n },\n};\n\nloader.config({monaco});\n\nloader.init().then(monaco => {\n Logger.debug('initialized monaco', monaco);\n});\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/effects/BackgroundEffects.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {backgroundEffects} from '@/common/components/effects/EffectsUtils';\nimport EffectVariantBadge from '@/common/components/effects/EffectVariantBadge';\nimport ToolbarActionIcon from '@/common/components/toolbar/ToolbarActionIcon';\nimport ToolbarSection from '@/common/components/toolbar/ToolbarSection';\nimport useVideoEffect from '@/common/components/video/editor/useVideoEffect';\nimport {EffectIndex} from '@/common/components/video/effects/Effects';\nimport {activeBackgroundEffectAtom} from '@/demo/atoms';\nimport {useAtomValue} from 'jotai';\n\nexport default function BackgroundEffects() {\n const setEffect = useVideoEffect();\n const activeEffect = useAtomValue(activeBackgroundEffectAtom);\n\n return (\n \n {backgroundEffects.map(backgroundEffect => {\n return (\n \n )\n }\n onClick={() => {\n if (activeEffect.name === backgroundEffect.effectName) {\n setEffect(backgroundEffect.effectName, EffectIndex.BACKGROUND, {\n variant:\n (activeEffect.variant + 1) % activeEffect.numVariants,\n });\n } else {\n setEffect(backgroundEffect.effectName, EffectIndex.BACKGROUND);\n }\n }}\n />\n );\n })}\n \n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/effects/EffectVariantBadge.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {right, top} from '@/theme/tokens.stylex';\nimport stylex from '@stylexjs/stylex';\n\nconst styles = stylex.create({\n variantBadge: {\n position: 'absolute',\n top: top[1],\n right: right[1],\n backgroundColor: '#280578',\n color: '#D2D2FF',\n fontVariantNumeric: 'tabular-nums',\n paddingHorizontal: 4,\n paddingVertical: 1,\n fontSize: 9,\n borderRadius: 6,\n fontWeight: 'bold',\n },\n});\n\ntype Props = {\n label: string;\n};\n\nexport default function VariantBadge({label}: Props) {\n return
{label}
;\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/effects/EffectsCarousel.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {CarouselContainerShadow} from '@/common/components/effects/EffectsCarouselShadow';\nimport {DemoEffect} from '@/common/components/effects/EffectsUtils';\nimport useVideoEffect from '@/common/components/video/editor/useVideoEffect';\nimport type {EffectIndex} from '@/common/components/video/effects/Effects';\nimport {Effects} from '@/common/components/video/effects/Effects';\nimport {color, fontSize, spacing} from '@/theme/tokens.stylex';\nimport stylex from '@stylexjs/stylex';\n\ntype Props = {\n label: string;\n effects: DemoEffect[];\n activeEffect: keyof Effects;\n index: EffectIndex;\n};\n\nconst styles = stylex.create({\n container: {\n display: 'flex',\n flexDirection: 'column',\n gap: spacing[2],\n width: '100%',\n },\n label: {\n fontSize: fontSize['xs'],\n color: '#A6ACB2',\n textAlign: 'center',\n },\n carouselContainer: {\n position: 'relative',\n borderRadius: '8px',\n overflow: 'hidden',\n width: '100%',\n height: '120px',\n backgroundColor: color['gray-700'],\n },\n});\n\nexport default function EffectsCarousel({\n label,\n effects,\n activeEffect,\n index: effectIndex,\n}: Props) {\n const setEffect = useVideoEffect();\n\n return (\n
\n
{label}
\n
\n \n
\n
\n {effects.map(({effectName, Icon, title}, index) => {\n const isActive = activeEffect === effectName;\n return (\n setEffect(effectName, effectIndex)}>\n \n \n {title}\n
\n
\n );\n })}\n
\n
\n \n
\n
\n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/effects/EffectsCarouselShadow.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {spacing} from '@/theme/tokens.stylex';\nimport stylex from '@stylexjs/stylex';\n\nconst styles = stylex.create({\n container: {\n position: 'absolute',\n width: '100%',\n height: spacing[8],\n pointerEvents: 'none',\n },\n});\n\ntype CarouselContainerShadowProps = {\n isTop: boolean;\n};\n\nconst edgeColor = 'rgba(55, 62, 65, 1)';\nconst transitionColor = 'rgba(55, 62, 65, 0.2)';\n\nexport function CarouselContainerShadow({isTop}: CarouselContainerShadowProps) {\n return (\n \n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/effects/EffectsToolbar.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport BackgroundEffects from '@/common/components/effects/BackgroundEffects';\nimport EffectsToolbarBottomActions from '@/common/components/effects/EffectsToolbarBottomActions';\nimport EffectsToolbarHeader from '@/common/components/effects/EffectsToolbarHeader';\nimport HighlightEffects from '@/common/components/effects/HighlightEffects';\nimport useMessagesSnackbar from '@/common/components/snackbar/useDemoMessagesSnackbar';\nimport {useEffect, useRef} from 'react';\n\ntype Props = {\n onTabChange: (newIndex: number) => void;\n};\n\nexport default function EffectsToolbar({onTabChange}: Props) {\n const isEffectsMessageShown = useRef(false);\n const {enqueueMessage} = useMessagesSnackbar();\n\n useEffect(() => {\n if (!isEffectsMessageShown.current) {\n isEffectsMessageShown.current = true;\n enqueueMessage('effectsMessage');\n }\n }, [enqueueMessage]);\n\n return (\n
\n \n
\n \n \n
\n \n
\n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/effects/EffectsToolbarBottomActions.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport PrimaryCTAButton from '@/common/components/button/PrimaryCTAButton';\nimport RestartSessionButton from '@/common/components/session/RestartSessionButton';\nimport ToolbarBottomActionsWrapper from '@/common/components/toolbar/ToolbarBottomActionsWrapper';\nimport {\n MORE_OPTIONS_TOOLBAR_INDEX,\n OBJECT_TOOLBAR_INDEX,\n} from '@/common/components/toolbar/ToolbarConfig';\nimport {ChevronRight} from '@carbon/icons-react';\n\ntype Props = {\n onTabChange: (newIndex: number) => void;\n};\n\nexport default function EffectsToolbarBottomActions({onTabChange}: Props) {\n function handleSwitchToMoreOptionsTab() {\n onTabChange(MORE_OPTIONS_TOOLBAR_INDEX);\n }\n\n return (\n \n onTabChange(OBJECT_TOOLBAR_INDEX)}\n />\n }>\n Next\n \n \n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/effects/EffectsToolbarHeader.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport ToolbarHeaderWrapper from '@/common/components/toolbar/ToolbarHeaderWrapper';\nimport useVideoEffect from '@/common/components/video/editor/useVideoEffect';\nimport {\n EffectIndex,\n effectPresets,\n} from '@/common/components/video/effects/Effects';\nimport {BLUE_PINK_FILL} from '@/theme/gradientStyle';\nimport {MagicWandFilled} from '@carbon/icons-react';\nimport {useCallback, useRef} from 'react';\nimport {Button} from 'react-daisyui';\n\nexport default function EffectsToolbarHeader() {\n const preset = useRef(0);\n const setEffect = useVideoEffect();\n\n const handleTogglePreset = useCallback(() => {\n preset.current++;\n const [background, highlight] =\n effectPresets[preset.current % effectPresets.length];\n setEffect(background.name, EffectIndex.BACKGROUND, {\n variant: background.variant,\n });\n setEffect(highlight.name, EffectIndex.HIGHLIGHT, {\n variant: highlight.variant,\n });\n }, [setEffect]);\n\n return (\n \n }\n onClick={handleTogglePreset}>\n Surprise Me\n \n \n }\n className=\"pb-4\"\n />\n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/effects/EffectsUtils.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {Effects} from '@/common/components/video/effects/Effects';\nimport type {CarbonIconType} from '@carbon/icons-react';\nimport {\n AppleDash,\n Asterisk,\n Barcode,\n CenterCircle,\n ColorPalette,\n ColorSwitch,\n Development,\n Erase,\n FaceWink,\n Humidity,\n Image,\n Overlay,\n TextFont,\n} from '@carbon/icons-react';\n\nexport type DemoEffect = {\n title: string;\n Icon: CarbonIconType;\n effectName: keyof Effects;\n};\n\nexport const backgroundEffects: DemoEffect[] = [\n {title: 'Original', Icon: Image, effectName: 'Original'},\n {title: 'Erase', Icon: Erase, effectName: 'EraseBackground'},\n {\n title: 'Gradient',\n Icon: ColorPalette,\n effectName: 'Gradient',\n },\n {\n title: 'Pixelate',\n Icon: Development,\n effectName: 'Pixelate',\n },\n {title: 'Desaturate', Icon: ColorSwitch, effectName: 'Desaturate'},\n {title: 'Text', Icon: TextFont, effectName: 'BackgroundText'},\n {title: 'Blur', Icon: Humidity, effectName: 'BackgroundBlur'},\n {title: 'Outline', Icon: AppleDash, effectName: 'Sobel'},\n];\n\nexport const highlightEffects: DemoEffect[] = [\n {title: 'Original', Icon: Image, effectName: 'Cutout'},\n {title: 'Erase', Icon: Erase, effectName: 'EraseForeground'},\n {title: 'Gradient', Icon: ColorPalette, effectName: 'VibrantMask'},\n {title: 'Pixelate', Icon: Development, effectName: 'PixelateMask'},\n {\n title: 'Overlay',\n Icon: Overlay,\n effectName: 'Overlay',\n },\n {title: 'Emoji', Icon: FaceWink, effectName: 'Replace'},\n {title: 'Burst', Icon: Asterisk, effectName: 'Burst'},\n {title: 'Spotlight', Icon: CenterCircle, effectName: 'Scope'},\n];\n\nexport const moreEffects: DemoEffect[] = [\n {title: 'Noisy', Icon: Barcode, effectName: 'NoisyMask'},\n];\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/effects/HighlightEffects.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport EffectVariantBadge from '@/common/components/effects/EffectVariantBadge';\nimport ToolbarActionIcon from '@/common/components/toolbar/ToolbarActionIcon';\nimport ToolbarSection from '@/common/components/toolbar/ToolbarSection';\nimport useVideoEffect from '@/common/components/video/editor/useVideoEffect';\nimport {EffectIndex} from '@/common/components/video/effects/Effects';\nimport {\n activeHighlightEffectAtom,\n activeHighlightEffectGroupAtom,\n} from '@/demo/atoms';\nimport {useAtomValue} from 'jotai';\n\nexport default function HighlightEffects() {\n const setEffect = useVideoEffect();\n const activeEffect = useAtomValue(activeHighlightEffectAtom);\n const activeEffectsGroup = useAtomValue(activeHighlightEffectGroupAtom);\n\n return (\n \n {activeEffectsGroup.map(highlightEffect => {\n return (\n \n )\n }\n onClick={() => {\n if (activeEffect.name === highlightEffect.effectName) {\n setEffect(highlightEffect.effectName, EffectIndex.HIGHLIGHT, {\n variant:\n (activeEffect.variant + 1) % activeEffect.numVariants,\n });\n } else {\n setEffect(highlightEffect.effectName, EffectIndex.HIGHLIGHT);\n }\n }}\n />\n );\n })}\n \n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/effects/MobileEffectsToolbar.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport EffectsCarousel from '@/common/components/effects/EffectsCarousel';\nimport {backgroundEffects} from '@/common/components/effects/EffectsUtils';\nimport useVideoEffect from '@/common/components/video/editor/useVideoEffect';\nimport {\n EffectIndex,\n effectPresets,\n} from '@/common/components/video/effects/Effects';\nimport {ListBoxes, MagicWand, MagicWandFilled} from '@carbon/icons-react';\nimport {useCallback, useRef, useState} from 'react';\nimport {Button} from 'react-daisyui';\n\nimport EffectsToolbarBottomActions from '@/common/components/effects/EffectsToolbarBottomActions';\nimport ToolbarProgressChip from '@/common/components/toolbar/ToolbarProgressChip';\nimport {\n activeBackgroundEffectAtom,\n activeHighlightEffectAtom,\n activeHighlightEffectGroupAtom,\n} from '@/demo/atoms';\nimport {BLUE_PINK_FILL} from '@/theme/gradientStyle';\nimport {useAtomValue} from 'jotai';\n\ntype Props = {\n onTabChange: (newIndex: number) => void;\n};\n\nexport default function MobileEffectsToolbar({onTabChange}: Props) {\n const preset = useRef(0);\n const setEffect = useVideoEffect();\n const [showEffectsCarousels, setShowEffectsCarousels] = useState();\n\n const activeBackground = useAtomValue(activeBackgroundEffectAtom);\n const activeHighlight = useAtomValue(activeHighlightEffectAtom);\n const activeHighlightEffectsGroup = useAtomValue(\n activeHighlightEffectGroupAtom,\n );\n\n const handleTogglePreset = useCallback(() => {\n preset.current++;\n const [background, highlight] =\n effectPresets[preset.current % effectPresets.length];\n setEffect(background.name, EffectIndex.BACKGROUND, {\n variant: background.variant,\n });\n setEffect(highlight.name, EffectIndex.HIGHLIGHT, {\n variant: highlight.variant,\n });\n }, [setEffect]);\n\n return (\n
\n {showEffectsCarousels ? (\n
\n }\n onClick={handleTogglePreset}\n />\n \n \n
\n ) : (\n
\n
\n \n Apply visual effects to your selected objects and the background.\n
\n
\n }\n className={`font-bold bg-black !rounded-full !bg-gradient-to-br ${BLUE_PINK_FILL} border-none text-white`}\n onClick={handleTogglePreset}>\n Surprise Me\n \n }\n onClick={() => setShowEffectsCarousels(true)}>\n More effects\n \n
\n
\n )}\n\n \n
\n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/effects/MoreFunEffects.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {moreEffects} from '@/common/components/effects/EffectsUtils';\nimport EffectVariantBadge from '@/common/components/effects/EffectVariantBadge';\nimport ToolbarActionIcon from '@/common/components/toolbar/ToolbarActionIcon';\nimport ToolbarSection from '@/common/components/toolbar/ToolbarSection';\nimport useVideoEffect from '@/common/components/video/editor/useVideoEffect';\nimport {EffectIndex} from '@/common/components/video/effects/Effects';\nimport {activeHighlightEffectAtom} from '@/demo/atoms';\nimport {useAtomValue} from 'jotai';\n\nexport default function MoreFunEffects() {\n const setEffect = useVideoEffect();\n const activeEffect = useAtomValue(activeHighlightEffectAtom);\n\n return (\n \n {moreEffects.map(effect => {\n return (\n \n )\n }\n onClick={() => {\n setEffect(effect.effectName, EffectIndex.HIGHLIGHT);\n }}\n />\n );\n })}\n \n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/gallery/ChangeVideoModal.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport type {VideoGalleryTriggerProps} from '@/common/components/gallery/DemoVideoGalleryModal';\nimport DemoVideoGalleryModal from '@/common/components/gallery/DemoVideoGalleryModal';\nimport useVideo from '@/common/components/video/editor/useVideo';\nimport Logger from '@/common/logger/Logger';\nimport {isStreamingAtom, uploadingStateAtom, VideoData} from '@/demo/atoms';\nimport {useAtomValue, useSetAtom} from 'jotai';\nimport {ComponentType, useCallback} from 'react';\nimport {useNavigate} from 'react-router-dom';\n\ntype Props = {\n videoGalleryModalTrigger?: ComponentType;\n showUploadInGallery?: boolean;\n onChangeVideo?: () => void;\n};\n\nexport default function ChangeVideoModal({\n videoGalleryModalTrigger: VideoGalleryModalTriggerComponent,\n showUploadInGallery = true,\n onChangeVideo,\n}: Props) {\n const isStreaming = useAtomValue(isStreamingAtom);\n const setUploadingState = useSetAtom(uploadingStateAtom);\n const video = useVideo();\n const navigate = useNavigate();\n\n const handlePause = useCallback(() => {\n video?.pause();\n }, [video]);\n\n function handlePauseOrAbortVideo() {\n if (isStreaming) {\n video?.abortStreamMasks();\n } else {\n handlePause();\n }\n }\n\n function handleSwitchVideos(video: VideoData) {\n // Retain any search parameter\n navigate(\n {\n pathname: location.pathname,\n search: location.search,\n },\n {\n state: {\n video,\n },\n },\n );\n onChangeVideo?.();\n }\n\n function handleUploadVideoError(error: Error) {\n setUploadingState('error');\n Logger.error(error);\n }\n\n return (\n \n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/gallery/DefaultVideoGalleryModalTrigger.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport ResponsiveButton from '@/common/components/button/ResponsiveButton';\nimport type {VideoGalleryTriggerProps} from '@/common/components/gallery/DemoVideoGalleryModal';\nimport {ImageCopy} from '@carbon/icons-react';\n\nexport default function DefaultVideoGalleryModalTrigger({\n onClick,\n}: VideoGalleryTriggerProps) {\n return (\n }\n onClick={onClick}>\n Change video\n \n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/gallery/DemoVideoGallery.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {DemoVideoGalleryQuery} from '@/common/components/gallery/__generated__/DemoVideoGalleryQuery.graphql';\nimport VideoGalleryUploadVideo from '@/common/components/gallery/VideoGalleryUploadPhoto';\nimport VideoPhoto from '@/common/components/gallery/VideoPhoto';\nimport useScreenSize from '@/common/screen/useScreenSize';\nimport {VideoData} from '@/demo/atoms';\nimport {DEMO_SHORT_NAME} from '@/demo/DemoConfig';\nimport {fontSize, fontWeight, spacing} from '@/theme/tokens.stylex';\nimport stylex from '@stylexjs/stylex';\nimport {useMemo} from 'react';\nimport PhotoAlbum, {Photo, RenderPhotoProps} from 'react-photo-album';\nimport {graphql, useLazyLoadQuery} from 'react-relay';\nimport {useLocation, useNavigate} from 'react-router-dom';\n\nconst styles = stylex.create({\n container: {\n display: 'flex',\n flexDirection: 'column',\n marginHorizontal: spacing[1],\n height: '100%',\n lineHeight: 1.2,\n paddingTop: spacing[8],\n },\n headerContainer: {\n marginBottom: spacing[8],\n fontWeight: fontWeight['medium'],\n fontSize: fontSize['2xl'],\n '@media screen and (max-width: 768px)': {\n marginTop: spacing[0],\n marginBottom: spacing[8],\n marginHorizontal: spacing[4],\n fontSize: fontSize['xl'],\n },\n },\n albumContainer: {\n flex: '1 1 0%',\n width: '100%',\n overflowY: 'auto',\n },\n});\n\ntype Props = {\n showUploadInGallery?: boolean;\n onSelect?: (video: VideoPhotoData) => void;\n onUpload: (video: VideoData) => void;\n onUploadStart?: () => void;\n onUploadError?: (error: Error) => void;\n};\n\ntype VideoPhotoData = Photo &\n VideoData & {\n poster: string;\n isUploadOption: boolean;\n };\n\nexport default function DemoVideoGallery({\n showUploadInGallery = false,\n onSelect,\n onUpload,\n onUploadStart,\n onUploadError,\n}: Props) {\n const navigate = useNavigate();\n const location = useLocation();\n const {isMobile: isMobileScreenSize} = useScreenSize();\n\n const data = useLazyLoadQuery(\n graphql`\n query DemoVideoGalleryQuery {\n videos {\n edges {\n node {\n id\n path\n posterPath\n url\n posterUrl\n height\n width\n posterUrl\n }\n }\n }\n }\n `,\n {},\n );\n\n const allVideos: VideoPhotoData[] = useMemo(() => {\n return data.videos.edges.map(video => {\n return {\n src: video.node.url,\n path: video.node.path,\n poster: video.node.posterPath,\n posterPath: video.node.posterPath,\n url: video.node.url,\n posterUrl: video.node.posterUrl,\n width: video.node.width,\n height: video.node.height,\n isUploadOption: false,\n } as VideoPhotoData;\n });\n }, [data.videos.edges]);\n\n const shareableVideos: VideoPhotoData[] = useMemo(() => {\n const filteredVideos = [...allVideos];\n\n if (showUploadInGallery) {\n const uploadOption = {\n src: '',\n width: 1280,\n height: 720,\n poster: '',\n isUploadOption: true,\n } as VideoPhotoData;\n filteredVideos.unshift(uploadOption);\n }\n\n return filteredVideos;\n }, [allVideos, showUploadInGallery]);\n\n const renderPhoto = ({\n photo: video,\n imageProps,\n }: RenderPhotoProps) => {\n const {style} = imageProps;\n const {url, posterUrl} = video;\n\n return video.isUploadOption ? (\n \n ) : (\n {\n navigate(location.pathname, {\n state: {\n video,\n },\n });\n onSelect?.(video);\n }}\n />\n );\n };\n\n function handleUploadVideo(video: VideoData) {\n navigate(location.pathname, {\n state: {\n video,\n },\n });\n onUpload?.(video);\n }\n\n const descriptionStyle = 'text-sm md:text-base text-gray-400 leading-snug';\n\n return (\n
\n
\n
\n
\n

\n Select a video to try{' '}\n \n with the {DEMO_SHORT_NAME}\n \n

\n

\n You’ll be able to download what you make.\n

\n
\n\n \n layout=\"rows\"\n photos={shareableVideos}\n targetRowHeight={isMobileScreenSize ? 120 : 200}\n rowConstraints={{\n singleRowMaxHeight: isMobileScreenSize ? 120 : 240,\n maxPhotos: 3,\n }}\n renderPhoto={renderPhoto}\n spacing={4}\n />\n
\n
\n
\n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/gallery/DemoVideoGalleryModal.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport DefaultVideoGalleryModalTrigger from '@/common/components/gallery/DefaultVideoGalleryModalTrigger';\nimport {\n frameIndexAtom,\n sessionAtom,\n uploadingStateAtom,\n VideoData,\n} from '@/demo/atoms';\nimport {spacing} from '@/theme/tokens.stylex';\nimport {Close} from '@carbon/icons-react';\nimport stylex from '@stylexjs/stylex';\nimport {useSetAtom} from 'jotai';\nimport {ComponentType, useCallback, useRef} from 'react';\nimport {Modal} from 'react-daisyui';\nimport DemoVideoGallery from './DemoVideoGallery';\n\nconst styles = stylex.create({\n container: {\n position: 'relative',\n minWidth: '85vw',\n minHeight: '85vh',\n overflow: 'hidden',\n color: '#fff',\n boxShadow: '0 0 100px 50px #000',\n borderRadius: 16,\n border: '2px solid transparent',\n background:\n 'linear-gradient(#1A1C1F, #1A1C1F) padding-box, linear-gradient(to right bottom, #FB73A5,#595FEF,#94EAE2,#FCCB6B) border-box',\n },\n closeButton: {\n position: 'absolute',\n top: 0,\n right: 0,\n padding: spacing[3],\n zIndex: 10,\n cursor: 'pointer',\n ':hover': {\n opacity: 0.7,\n },\n },\n galleryContainer: {\n position: 'absolute',\n top: spacing[4],\n left: 0,\n right: 0,\n bottom: 0,\n overflowY: 'auto',\n },\n});\n\nexport type VideoGalleryTriggerProps = {\n onClick: () => void;\n};\n\ntype Props = {\n trigger?: ComponentType;\n showUploadInGallery?: boolean;\n onOpen?: () => void;\n onSelect?: (video: VideoData, isUpload?: boolean) => void;\n onUploadVideoError?: (error: Error) => void;\n};\n\nexport default function DemoVideoGalleryModal({\n trigger: VideoGalleryModalTrigger = DefaultVideoGalleryModalTrigger,\n showUploadInGallery = false,\n onOpen,\n onSelect,\n onUploadVideoError,\n}: Props) {\n const modalRef = useRef(null);\n\n const setFrameIndex = useSetAtom(frameIndexAtom);\n const setUploadingState = useSetAtom(uploadingStateAtom);\n const setSession = useSetAtom(sessionAtom);\n\n function openModal() {\n const modal = modalRef.current;\n if (modal != null) {\n modal.style.display = 'grid';\n modal.showModal();\n }\n }\n\n function closeModal() {\n const modal = modalRef.current;\n if (modal != null) {\n modal.close();\n modal.style.display = 'none';\n }\n }\n\n const handleSelect = useCallback(\n async (video: VideoData, isUpload?: boolean) => {\n closeModal();\n setFrameIndex(0);\n onSelect?.(video, isUpload);\n setUploadingState('default');\n setSession(null);\n },\n [setFrameIndex, onSelect, setUploadingState, setSession],\n );\n\n function handleUploadVideoStart() {\n setUploadingState('uploading');\n closeModal();\n }\n\n function handleOpenVideoGalleryModal() {\n onOpen?.();\n openModal();\n }\n\n return (\n <>\n \n \n
\n \n
\n \n
\n handleSelect(video)}\n onUpload={video => handleSelect(video, true)}\n onUploadStart={handleUploadVideoStart}\n onUploadError={onUploadVideoError}\n />\n
\n \n \n \n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/gallery/VideoGalleryUploadPhoto.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport useUploadVideo from '@/common/components/gallery/useUploadVideo';\nimport useScreenSize from '@/common/screen/useScreenSize';\nimport {VideoData} from '@/demo/atoms';\nimport {MAX_UPLOAD_FILE_SIZE} from '@/demo/DemoConfig';\nimport {BLUE_PINK_FILL_BR} from '@/theme/gradientStyle';\nimport {RetryFailed, Upload} from '@carbon/icons-react';\nimport {CSSProperties, ReactNode} from 'react';\nimport {Loading} from 'react-daisyui';\n\ntype Props = {\n style: CSSProperties;\n onUpload: (video: VideoData) => void;\n onUploadStart?: () => void;\n onUploadError?: (error: Error) => void;\n};\n\nexport default function VideoGalleryUploadVideo({\n style,\n onUpload,\n onUploadStart,\n onUploadError,\n}: Props) {\n const {getRootProps, getInputProps, isUploading, error} = useUploadVideo({\n onUpload,\n onUploadStart,\n onUploadError,\n });\n const {isMobile} = useScreenSize();\n\n return (\n
\n \n \n
\n
\n {isUploading && (\n \n }\n title=\"Uploading ...\"\n />\n )}\n {error !== null && (\n }\n title={error}\n />\n )}\n {!isUploading && error === null && (\n }\n title={\n <>\n Upload{' '}\n
\n Max {MAX_UPLOAD_FILE_SIZE}\n
\n \n }\n />\n )}\n
\n
\n \n
\n );\n}\n\ntype IconWrapperProps = {\n icon: ReactNode;\n title: ReactNode | string;\n};\n\nfunction IconWrapper({icon, title}: IconWrapperProps) {\n return (\n <>\n
{icon}
\n
\n {title}\n
\n \n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/gallery/VideoPhoto.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport Logger from '@/common/logger/Logger';\nimport stylex from '@stylexjs/stylex';\nimport {\n CSSProperties,\n MouseEventHandler,\n useCallback,\n useEffect,\n useRef,\n} from 'react';\n\nconst styles = stylex.create({\n background: {\n backgroundRepeat: 'no-repeat',\n backgroundSize: 'cover',\n backgroundPosition: 'center',\n cursor: 'pointer',\n },\n video: {\n width: '100%',\n height: '100%',\n },\n});\n\ntype Props = {\n onClick: MouseEventHandler | undefined;\n src: string;\n poster: string;\n style: CSSProperties;\n};\n\nexport default function VideoPhoto({src, poster, style, onClick}: Props) {\n const videoRef = useRef(null);\n const playPromiseRef = useRef | null>(null);\n\n const play = useCallback(() => {\n const video = videoRef.current;\n // Only play video if it is not already playing\n if (video != null && video.paused) {\n // This quirky way of handling video play/pause in the browser is needed\n // due to the async nature of the video play API:\n // https://developer.chrome.com/blog/play-request-was-interrupted/\n const playPromise = video.play();\n playPromise.catch(error => {\n Logger.error('Failed to play video', error);\n });\n playPromiseRef.current = playPromise;\n }\n }, []);\n\n const pause = useCallback(() => {\n // Only pause video if it is playing\n const playPromise = playPromiseRef.current;\n if (playPromise != null) {\n playPromise\n .then(() => {\n videoRef.current?.pause();\n })\n .catch(error => {\n Logger.error('Failed to pause video', error);\n })\n .finally(() => {\n playPromiseRef.current = null;\n });\n }\n }, []);\n\n useEffect(() => {\n return () => {\n pause();\n };\n }, [pause]);\n\n return (\n \n \n \n Sorry, your browser does not support embedded videos.\n \n \n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/gallery/__generated__/DemoVideoGalleryModalQuery.graphql.ts", "content": "/**\n * @generated SignedSource<>\n * @lightSyntaxTransform\n * @nogrep\n */\n\n/* tslint:disable */\n/* eslint-disable */\n// @ts-nocheck\n\nimport { ConcreteRequest, Query } from 'relay-runtime';\nimport { FragmentRefs } from \"relay-runtime\";\nexport type DemoVideoGalleryModalQuery$variables = Record;\nexport type DemoVideoGalleryModalQuery$data = {\n readonly \" $fragmentSpreads\": FragmentRefs<\"DatasetsDropdown_datasets\" | \"VideoGallery_videos\">;\n};\nexport type DemoVideoGalleryModalQuery = {\n response: DemoVideoGalleryModalQuery$data;\n variables: DemoVideoGalleryModalQuery$variables;\n};\n\nconst node: ConcreteRequest = (function(){\nvar v0 = [\n {\n \"alias\": null,\n \"args\": null,\n \"kind\": \"ScalarField\",\n \"name\": \"name\",\n \"storageKey\": null\n }\n],\nv1 = [\n {\n \"kind\": \"Literal\",\n \"name\": \"after\",\n \"value\": \"\"\n },\n {\n \"kind\": \"Literal\",\n \"name\": \"first\",\n \"value\": 20\n }\n],\nv2 = {\n \"alias\": null,\n \"args\": null,\n \"kind\": \"ScalarField\",\n \"name\": \"__typename\",\n \"storageKey\": null\n};\nreturn {\n \"fragment\": {\n \"argumentDefinitions\": [],\n \"kind\": \"Fragment\",\n \"metadata\": null,\n \"name\": \"DemoVideoGalleryModalQuery\",\n \"selections\": [\n {\n \"args\": null,\n \"kind\": \"FragmentSpread\",\n \"name\": \"DatasetsDropdown_datasets\"\n },\n {\n \"args\": null,\n \"kind\": \"FragmentSpread\",\n \"name\": \"VideoGallery_videos\"\n }\n ],\n \"type\": \"Query\",\n \"abstractKey\": null\n },\n \"kind\": \"Request\",\n \"operation\": {\n \"argumentDefinitions\": [],\n \"kind\": \"Operation\",\n \"name\": \"DemoVideoGalleryModalQuery\",\n \"selections\": [\n {\n \"alias\": null,\n \"args\": null,\n \"concreteType\": \"DatasetConnection\",\n \"kind\": \"LinkedField\",\n \"name\": \"datasets\",\n \"plural\": false,\n \"selections\": [\n {\n \"alias\": null,\n \"args\": null,\n \"concreteType\": \"DatasetEdge\",\n \"kind\": \"LinkedField\",\n \"name\": \"edges\",\n \"plural\": true,\n \"selections\": [\n {\n \"alias\": null,\n \"args\": null,\n \"concreteType\": \"Dataset\",\n \"kind\": \"LinkedField\",\n \"name\": \"node\",\n \"plural\": false,\n \"selections\": (v0/*: any*/),\n \"storageKey\": null\n }\n ],\n \"storageKey\": null\n }\n ],\n \"storageKey\": null\n },\n {\n \"alias\": null,\n \"args\": (v1/*: any*/),\n \"concreteType\": \"VideoConnection\",\n \"kind\": \"LinkedField\",\n \"name\": \"videos\",\n \"plural\": false,\n \"selections\": [\n (v2/*: any*/),\n {\n \"alias\": null,\n \"args\": null,\n \"concreteType\": \"PageInfo\",\n \"kind\": \"LinkedField\",\n \"name\": \"pageInfo\",\n \"plural\": false,\n \"selections\": [\n (v2/*: any*/),\n {\n \"alias\": null,\n \"args\": null,\n \"kind\": \"ScalarField\",\n \"name\": \"hasPreviousPage\",\n \"storageKey\": null\n },\n {\n \"alias\": null,\n \"args\": null,\n \"kind\": \"ScalarField\",\n \"name\": \"hasNextPage\",\n \"storageKey\": null\n },\n {\n \"alias\": null,\n \"args\": null,\n \"kind\": \"ScalarField\",\n \"name\": \"startCursor\",\n \"storageKey\": null\n },\n {\n \"alias\": null,\n \"args\": null,\n \"kind\": \"ScalarField\",\n \"name\": \"endCursor\",\n \"storageKey\": null\n }\n ],\n \"storageKey\": null\n },\n {\n \"alias\": null,\n \"args\": null,\n \"concreteType\": \"VideoEdge\",\n \"kind\": \"LinkedField\",\n \"name\": \"edges\",\n \"plural\": true,\n \"selections\": [\n (v2/*: any*/),\n {\n \"alias\": null,\n \"args\": null,\n \"concreteType\": \"Video\",\n \"kind\": \"LinkedField\",\n \"name\": \"node\",\n \"plural\": false,\n \"selections\": [\n (v2/*: any*/),\n {\n \"alias\": null,\n \"args\": null,\n \"kind\": \"ScalarField\",\n \"name\": \"id\",\n \"storageKey\": null\n },\n {\n \"alias\": null,\n \"args\": null,\n \"kind\": \"ScalarField\",\n \"name\": \"path\",\n \"storageKey\": null\n },\n {\n \"alias\": null,\n \"args\": null,\n \"kind\": \"ScalarField\",\n \"name\": \"posterPath\",\n \"storageKey\": null\n },\n {\n \"alias\": null,\n \"args\": null,\n \"kind\": \"ScalarField\",\n \"name\": \"url\",\n \"storageKey\": null\n },\n {\n \"alias\": null,\n \"args\": null,\n \"kind\": \"ScalarField\",\n \"name\": \"posterUrl\",\n \"storageKey\": null\n },\n {\n \"alias\": null,\n \"args\": null,\n \"kind\": \"ScalarField\",\n \"name\": \"width\",\n \"storageKey\": null\n },\n {\n \"alias\": null,\n \"args\": null,\n \"kind\": \"ScalarField\",\n \"name\": \"height\",\n \"storageKey\": null\n },\n {\n \"alias\": null,\n \"args\": null,\n \"concreteType\": \"Dataset\",\n \"kind\": \"LinkedField\",\n \"name\": \"dataset\",\n \"plural\": false,\n \"selections\": (v0/*: any*/),\n \"storageKey\": null\n },\n {\n \"alias\": null,\n \"args\": null,\n \"concreteType\": \"VideoPermissions\",\n \"kind\": \"LinkedField\",\n \"name\": \"permissions\",\n \"plural\": false,\n \"selections\": [\n {\n \"alias\": null,\n \"args\": null,\n \"kind\": \"ScalarField\",\n \"name\": \"canShare\",\n \"storageKey\": null\n },\n {\n \"alias\": null,\n \"args\": null,\n \"kind\": \"ScalarField\",\n \"name\": \"canDownload\",\n \"storageKey\": null\n }\n ],\n \"storageKey\": null\n }\n ],\n \"storageKey\": null\n },\n {\n \"alias\": null,\n \"args\": null,\n \"kind\": \"ScalarField\",\n \"name\": \"cursor\",\n \"storageKey\": null\n }\n ],\n \"storageKey\": null\n }\n ],\n \"storageKey\": \"videos(after:\\\"\\\",first:20)\"\n },\n {\n \"alias\": null,\n \"args\": (v1/*: any*/),\n \"filters\": [\n \"datasetName\"\n ],\n \"handle\": \"connection\",\n \"key\": \"VideoGallery_videos\",\n \"kind\": \"LinkedHandle\",\n \"name\": \"videos\"\n }\n ]\n },\n \"params\": {\n \"cacheID\": \"e0bccf553377682e6bc283c2ce53bee5\",\n \"id\": null,\n \"metadata\": {},\n \"name\": \"DemoVideoGalleryModalQuery\",\n \"operationKind\": \"query\",\n \"text\": \"query DemoVideoGalleryModalQuery {\\n ...DatasetsDropdown_datasets\\n ...VideoGallery_videos\\n}\\n\\nfragment DatasetsDropdown_datasets on Query {\\n datasets {\\n edges {\\n node {\\n name\\n }\\n }\\n }\\n}\\n\\nfragment VideoGallery_videos on Query {\\n videos(first: 20, after: \\\"\\\") {\\n __typename\\n pageInfo {\\n __typename\\n hasPreviousPage\\n hasNextPage\\n startCursor\\n endCursor\\n }\\n edges {\\n __typename\\n node {\\n __typename\\n id\\n path\\n posterPath\\n url\\n posterUrl\\n width\\n height\\n dataset {\\n name\\n }\\n permissions {\\n canShare\\n canDownload\\n }\\n }\\n cursor\\n }\\n }\\n}\\n\"\n }\n};\n})();\n\n(node as any).hash = \"d09e34e2b9f2e25c2d564106de5f9c89\";\n\nexport default node;\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/gallery/__generated__/DemoVideoGalleryQuery.graphql.ts", "content": "/**\n * @generated SignedSource<<20d31a82b5f3b251b0e42b4f0e3522b8>>\n * @lightSyntaxTransform\n * @nogrep\n */\n\n/* tslint:disable */\n/* eslint-disable */\n// @ts-nocheck\n\nimport { ConcreteRequest, Query } from 'relay-runtime';\nexport type DemoVideoGalleryQuery$variables = Record;\nexport type DemoVideoGalleryQuery$data = {\n readonly videos: {\n readonly edges: ReadonlyArray<{\n readonly node: {\n readonly height: number;\n readonly id: any;\n readonly path: string;\n readonly posterPath: string | null | undefined;\n readonly posterUrl: string;\n readonly url: string;\n readonly width: number;\n };\n }>;\n };\n};\nexport type DemoVideoGalleryQuery = {\n response: DemoVideoGalleryQuery$data;\n variables: DemoVideoGalleryQuery$variables;\n};\n\nconst node: ConcreteRequest = (function(){\nvar v0 = [\n {\n \"alias\": null,\n \"args\": null,\n \"concreteType\": \"VideoConnection\",\n \"kind\": \"LinkedField\",\n \"name\": \"videos\",\n \"plural\": false,\n \"selections\": [\n {\n \"alias\": null,\n \"args\": null,\n \"concreteType\": \"VideoEdge\",\n \"kind\": \"LinkedField\",\n \"name\": \"edges\",\n \"plural\": true,\n \"selections\": [\n {\n \"alias\": null,\n \"args\": null,\n \"concreteType\": \"Video\",\n \"kind\": \"LinkedField\",\n \"name\": \"node\",\n \"plural\": false,\n \"selections\": [\n {\n \"alias\": null,\n \"args\": null,\n \"kind\": \"ScalarField\",\n \"name\": \"id\",\n \"storageKey\": null\n },\n {\n \"alias\": null,\n \"args\": null,\n \"kind\": \"ScalarField\",\n \"name\": \"path\",\n \"storageKey\": null\n },\n {\n \"alias\": null,\n \"args\": null,\n \"kind\": \"ScalarField\",\n \"name\": \"posterPath\",\n \"storageKey\": null\n },\n {\n \"alias\": null,\n \"args\": null,\n \"kind\": \"ScalarField\",\n \"name\": \"url\",\n \"storageKey\": null\n },\n {\n \"alias\": null,\n \"args\": null,\n \"kind\": \"ScalarField\",\n \"name\": \"posterUrl\",\n \"storageKey\": null\n },\n {\n \"alias\": null,\n \"args\": null,\n \"kind\": \"ScalarField\",\n \"name\": \"height\",\n \"storageKey\": null\n },\n {\n \"alias\": null,\n \"args\": null,\n \"kind\": \"ScalarField\",\n \"name\": \"width\",\n \"storageKey\": null\n }\n ],\n \"storageKey\": null\n }\n ],\n \"storageKey\": null\n }\n ],\n \"storageKey\": null\n }\n];\nreturn {\n \"fragment\": {\n \"argumentDefinitions\": [],\n \"kind\": \"Fragment\",\n \"metadata\": null,\n \"name\": \"DemoVideoGalleryQuery\",\n \"selections\": (v0/*: any*/),\n \"type\": \"Query\",\n \"abstractKey\": null\n },\n \"kind\": \"Request\",\n \"operation\": {\n \"argumentDefinitions\": [],\n \"kind\": \"Operation\",\n \"name\": \"DemoVideoGalleryQuery\",\n \"selections\": (v0/*: any*/)\n },\n \"params\": {\n \"cacheID\": \"4dae74153a5528f2631b59dfb0adb021\",\n \"id\": null,\n \"metadata\": {},\n \"name\": \"DemoVideoGalleryQuery\",\n \"operationKind\": \"query\",\n \"text\": \"query DemoVideoGalleryQuery {\\n videos {\\n edges {\\n node {\\n id\\n path\\n posterPath\\n url\\n posterUrl\\n height\\n width\\n }\\n }\\n }\\n}\\n\"\n }\n};\n})();\n\n(node as any).hash = \"d22ac5e58f6e4eb696651be49b410e4e\";\n\nexport default node;\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/gallery/__generated__/useUploadVideoMutation.graphql.ts", "content": "/**\n * @generated SignedSource<<76014dced98d6c8989e7322712e38963>>\n * @lightSyntaxTransform\n * @nogrep\n */\n\n/* tslint:disable */\n/* eslint-disable */\n// @ts-nocheck\n\nimport { ConcreteRequest, Mutation } from 'relay-runtime';\nexport type useUploadVideoMutation$variables = {\n file: any;\n};\nexport type useUploadVideoMutation$data = {\n readonly uploadVideo: {\n readonly height: number;\n readonly id: any;\n readonly path: string;\n readonly posterPath: string | null | undefined;\n readonly posterUrl: string;\n readonly url: string;\n readonly width: number;\n };\n};\nexport type useUploadVideoMutation = {\n response: useUploadVideoMutation$data;\n variables: useUploadVideoMutation$variables;\n};\n\nconst node: ConcreteRequest = (function(){\nvar v0 = [\n {\n \"defaultValue\": null,\n \"kind\": \"LocalArgument\",\n \"name\": \"file\"\n }\n],\nv1 = [\n {\n \"alias\": null,\n \"args\": [\n {\n \"kind\": \"Variable\",\n \"name\": \"file\",\n \"variableName\": \"file\"\n }\n ],\n \"concreteType\": \"Video\",\n \"kind\": \"LinkedField\",\n \"name\": \"uploadVideo\",\n \"plural\": false,\n \"selections\": [\n {\n \"alias\": null,\n \"args\": null,\n \"kind\": \"ScalarField\",\n \"name\": \"id\",\n \"storageKey\": null\n },\n {\n \"alias\": null,\n \"args\": null,\n \"kind\": \"ScalarField\",\n \"name\": \"height\",\n \"storageKey\": null\n },\n {\n \"alias\": null,\n \"args\": null,\n \"kind\": \"ScalarField\",\n \"name\": \"width\",\n \"storageKey\": null\n },\n {\n \"alias\": null,\n \"args\": null,\n \"kind\": \"ScalarField\",\n \"name\": \"url\",\n \"storageKey\": null\n },\n {\n \"alias\": null,\n \"args\": null,\n \"kind\": \"ScalarField\",\n \"name\": \"path\",\n \"storageKey\": null\n },\n {\n \"alias\": null,\n \"args\": null,\n \"kind\": \"ScalarField\",\n \"name\": \"posterPath\",\n \"storageKey\": null\n },\n {\n \"alias\": null,\n \"args\": null,\n \"kind\": \"ScalarField\",\n \"name\": \"posterUrl\",\n \"storageKey\": null\n }\n ],\n \"storageKey\": null\n }\n];\nreturn {\n \"fragment\": {\n \"argumentDefinitions\": (v0/*: any*/),\n \"kind\": \"Fragment\",\n \"metadata\": null,\n \"name\": \"useUploadVideoMutation\",\n \"selections\": (v1/*: any*/),\n \"type\": \"Mutation\",\n \"abstractKey\": null\n },\n \"kind\": \"Request\",\n \"operation\": {\n \"argumentDefinitions\": (v0/*: any*/),\n \"kind\": \"Operation\",\n \"name\": \"useUploadVideoMutation\",\n \"selections\": (v1/*: any*/)\n },\n \"params\": {\n \"cacheID\": \"dcbaf1bf411627fdb9dfbb827592cfc0\",\n \"id\": null,\n \"metadata\": {},\n \"name\": \"useUploadVideoMutation\",\n \"operationKind\": \"mutation\",\n \"text\": \"mutation useUploadVideoMutation(\\n $file: Upload!\\n) {\\n uploadVideo(file: $file) {\\n id\\n height\\n width\\n url\\n path\\n posterPath\\n posterUrl\\n }\\n}\\n\"\n }\n};\n})();\n\n(node as any).hash = \"710e462504d76597af8695b7fc70b4cf\";\n\nexport default node;\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/gallery/useUploadVideo.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {useUploadVideoMutation} from '@/common/components/gallery/__generated__/useUploadVideoMutation.graphql';\nimport Logger from '@/common/logger/Logger';\nimport {VideoData} from '@/demo/atoms';\nimport {useState} from 'react';\nimport {FileRejection, FileWithPath, useDropzone} from 'react-dropzone';\nimport {graphql, useMutation} from 'react-relay';\n\nconst ACCEPT_VIDEOS = {\n 'video/mp4': ['.mp4'],\n 'video/quicktime': ['.mov'],\n};\n\n// 70 MB default max video upload size\nconst MAX_FILE_SIZE_IN_MB = 70;\nconst MAX_VIDEO_UPLOAD_SIZE = MAX_FILE_SIZE_IN_MB * 1024 ** 2;\n\ntype Props = {\n onUpload: (video: VideoData) => void;\n onUploadStart?: () => void;\n onUploadError?: (error: Error) => void;\n};\n\nexport default function useUploadVideo({\n onUpload,\n onUploadStart,\n onUploadError,\n}: Props) {\n const [error, setError] = useState(null);\n const [commit, isMutationInFlight] = useMutation(\n graphql`\n mutation useUploadVideoMutation($file: Upload!) {\n uploadVideo(file: $file) {\n id\n height\n width\n url\n path\n posterPath\n posterUrl\n }\n }\n `,\n );\n\n const {getRootProps, getInputProps} = useDropzone({\n accept: ACCEPT_VIDEOS,\n multiple: false,\n maxFiles: 1,\n onDrop: (\n acceptedFiles: FileWithPath[],\n fileRejections: FileRejection[],\n ) => {\n setError(null);\n\n // Check if any of the files (only 1 file allowed) is rejected. The\n // rejected file has an error (e.g., 'file-too-large'). Rendering an\n // appropriate message.\n if (fileRejections.length > 0 && fileRejections[0].errors.length > 0) {\n const code = fileRejections[0].errors[0].code;\n if (code === 'file-too-large') {\n setError(\n `File too large. Try a video under ${MAX_FILE_SIZE_IN_MB} MB`,\n );\n return;\n }\n }\n\n if (acceptedFiles.length === 0) {\n setError('File not accepted. Please try again.');\n return;\n }\n if (acceptedFiles.length > 1) {\n setError('Too many files. Please try again with 1 file.');\n return;\n }\n\n onUploadStart?.();\n const file = acceptedFiles[0];\n\n commit({\n variables: {\n file,\n },\n uploadables: {\n file,\n },\n onCompleted: response => onUpload(response.uploadVideo),\n onError: error => {\n Logger.error(error);\n onUploadError?.(error);\n setError('Upload failed.');\n },\n });\n },\n onError: error => {\n Logger.error(error);\n setError('File not supported.');\n },\n maxSize: MAX_VIDEO_UPLOAD_SIZE,\n });\n\n return {\n getRootProps,\n getInputProps,\n isUploading: isMutationInFlight,\n error,\n setError,\n };\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/icons/GitHubIcon.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\ntype Props = {\n className?: string;\n};\n\nexport function GitHubIcon({className}: Props) {\n return (\n \n \n \n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/options/DownloadOption.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {Package} from '@carbon/icons-react';\nimport OptionButton from './OptionButton';\nimport useDownloadVideo from './useDownloadVideo';\n\nexport default function DownloadOption() {\n const {download, state} = useDownloadVideo();\n\n return (\n \n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/options/GalleryOption.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport ChangeVideoModal from '@/common/components/gallery/ChangeVideoModal';\nimport type {VideoGalleryTriggerProps} from '@/common/components/gallery/DemoVideoGalleryModal';\nimport useScreenSize from '@/common/screen/useScreenSize';\nimport {ImageCopy} from '@carbon/icons-react';\nimport OptionButton from './OptionButton';\n\ntype Props = {\n onChangeVideo: () => void;\n};\nexport default function GalleryOption({onChangeVideo}: Props) {\n return (\n \n );\n}\n\nfunction GalleryTrigger({onClick}: VideoGalleryTriggerProps) {\n const {isMobile} = useScreenSize();\n\n return (\n \n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/options/MoreOptionsToolbar.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport MoreOptionsToolbarBottomActions from '@/common/components/options/MoreOptionsToolbarBottomActions';\nimport ShareSection from '@/common/components/options/ShareSection';\nimport TryAnotherVideoSection from '@/common/components/options/TryAnotherVideoSection';\nimport useMessagesSnackbar from '@/common/components/snackbar/useDemoMessagesSnackbar';\nimport ToolbarHeaderWrapper from '@/common/components/toolbar/ToolbarHeaderWrapper';\nimport useScreenSize from '@/common/screen/useScreenSize';\nimport {useEffect, useRef} from 'react';\n\ntype Props = {\n onTabChange: (newIndex: number) => void;\n};\n\nexport default function MoreOptionsToolbar({onTabChange}: Props) {\n const {isMobile} = useScreenSize();\n const {clearMessage} = useMessagesSnackbar();\n const didClearMessageSnackbar = useRef(false);\n\n useEffect(() => {\n if (!didClearMessageSnackbar.current) {\n didClearMessageSnackbar.current = true;\n clearMessage();\n }\n }, [clearMessage]);\n\n return (\n
\n
\n \n \n {!isMobile &&
}\n \n
\n {!isMobile && (\n \n )}\n
\n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/options/MoreOptionsToolbarBottomActions.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport RestartSessionButton from '@/common/components/session/RestartSessionButton';\nimport {\n EFFECT_TOOLBAR_INDEX,\n OBJECT_TOOLBAR_INDEX,\n} from '@/common/components/toolbar/ToolbarConfig';\nimport {ChevronLeft} from '@carbon/icons-react';\nimport {Button} from 'react-daisyui';\nimport ToolbarBottomActionsWrapper from '../toolbar/ToolbarBottomActionsWrapper';\n\ntype Props = {\n onTabChange: (newIndex: number) => void;\n};\n\nexport default function MoreOptionsToolbarBottomActions({onTabChange}: Props) {\n function handleReturnToEffectsTab() {\n onTabChange(EFFECT_TOOLBAR_INDEX);\n }\n\n return (\n \n }>\n Edit effects\n \n onTabChange(OBJECT_TOOLBAR_INDEX)}\n />\n \n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/options/OptionButton.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport GradientBorder from '@/common/components/button/GradientBorder';\nimport useScreenSize from '@/common/screen/useScreenSize';\nimport {BLUE_PINK_FILL_BR} from '@/theme/gradientStyle';\nimport type {CarbonIconType} from '@carbon/icons-react';\nimport {Loading} from 'react-daisyui';\n\ntype Props = {\n variant?: 'default' | 'flat' | 'gradient';\n title: string | React.ReactNode;\n Icon: CarbonIconType;\n isActive?: boolean;\n isDisabled?: boolean;\n loadingProps?: {\n loading: boolean;\n label?: string;\n };\n onClick: () => void;\n};\n\nexport default function OptionButton({\n variant = 'default',\n title,\n Icon,\n isActive = false,\n isDisabled = false,\n loadingProps,\n onClick,\n}: Props) {\n const {isMobile} = useScreenSize();\n const isLoading = loadingProps?.loading === true;\n\n function handleClick() {\n if (isDisabled) {\n return;\n }\n onClick();\n }\n\n const ButtonBase = (\n \n
\n {isLoading ? (\n \n ) : (\n \n )}\n
\n {isLoading && loadingProps?.label != null\n ? loadingProps.label\n : title}\n
\n
\n \n );\n\n return variant === 'gradient' ? (\n \n {ButtonBase}\n \n ) : (\n ButtonBase\n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/options/ShareSection.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport DownloadOption from './DownloadOption';\n\nexport default function ShareSection() {\n return (\n
\n \n
\n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/options/ShareUtils.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nexport async function handleSaveVideo(\n videoPath: string,\n fileName?: string,\n): Promise {\n const blob = await fetch(videoPath).then(res => res.blob());\n\n return new Promise(resolve => {\n const reader = new FileReader();\n reader.readAsDataURL(blob);\n reader.addEventListener('load', () => {\n const elem = document.createElement('a');\n elem.download = fileName ?? getFileName();\n if (typeof reader.result === 'string') {\n elem.href = reader.result;\n }\n elem.click();\n resolve();\n });\n });\n}\n\nexport function getFileName() {\n const date = new Date();\n const timestamp = date.getTime();\n return `sam2_masked_video_${timestamp}.mp4`;\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/options/TryAnotherVideoSection.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport GalleryOption from '@/common/components/options/GalleryOption';\nimport UploadOption from '@/common/components/options/UploadOption';\nimport {OBJECT_TOOLBAR_INDEX} from '@/common/components/toolbar/ToolbarConfig';\nimport useVideo from '@/common/components/video/editor/useVideo';\nimport useScreenSize from '@/common/screen/useScreenSize';\n\ntype Props = {\n onTabChange: (tabIndex: number) => void;\n};\n\nexport default function TryAnotherVideoSection({onTabChange}: Props) {\n const {isMobile} = useScreenSize();\n const video = useVideo();\n\n function handleVideoChange() {\n if (video != null) {\n video.pause();\n video.frame = 0;\n }\n onTabChange(OBJECT_TOOLBAR_INDEX);\n }\n\n if (isMobile) {\n return (\n
\n
\n Or, try another video\n
\n
\n
\n \n
\n
\n \n
\n
\n
\n );\n }\n\n return (\n
\n
\n Try another video\n
\n
\n \n \n
\n
\n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/options/UploadOption.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport useUploadVideo from '@/common/components/gallery/useUploadVideo';\nimport OptionButton from '@/common/components/options/OptionButton';\nimport Logger from '@/common/logger/Logger';\nimport useScreenSize from '@/common/screen/useScreenSize';\nimport {sessionAtom, uploadingStateAtom} from '@/demo/atoms';\nimport {MAX_UPLOAD_FILE_SIZE} from '@/demo/DemoConfig';\nimport {Close, CloudUpload} from '@carbon/icons-react';\nimport {useSetAtom} from 'jotai';\nimport {useNavigate} from 'react-router-dom';\n\ntype Props = {\n onUpload: () => void;\n};\n\nexport default function UploadOption({onUpload}: Props) {\n const navigate = useNavigate();\n const {isMobile} = useScreenSize();\n const setUploadingState = useSetAtom(uploadingStateAtom);\n const setSession = useSetAtom(sessionAtom);\n\n const {getRootProps, getInputProps, isUploading, error} = useUploadVideo({\n onUpload: videoData => {\n navigate(\n {pathname: location.pathname, search: location.search},\n {state: {video: videoData}},\n );\n onUpload();\n setUploadingState('default');\n setSession(null);\n },\n onUploadError: (error: Error) => {\n setUploadingState('error');\n Logger.error(error);\n },\n onUploadStart: () => {\n setUploadingState('uploading');\n },\n });\n\n return (\n
\n \n\n \n Upload{' '}\n
\n Max {MAX_UPLOAD_FILE_SIZE}\n
\n \n ) : (\n <>\n Upload your own{' '}\n
\n Max {MAX_UPLOAD_FILE_SIZE}\n
\n \n )\n }\n Icon={error !== null ? Close : CloudUpload}\n loadingProps={{loading: isUploading, label: 'Uploading...'}}\n onClick={() => {}}\n />\n
\n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/options/__generated__/GetLinkOptionShareVideoMutation.graphql.ts", "content": "/**\n * @generated SignedSource<<39d7e92a6c15de1583c90ae21a7825e5>>\n * @lightSyntaxTransform\n * @nogrep\n */\n\n/* tslint:disable */\n/* eslint-disable */\n// @ts-nocheck\n\nimport { ConcreteRequest, Mutation } from 'relay-runtime';\nexport type GetLinkOptionShareVideoMutation$variables = {\n file: any;\n};\nexport type GetLinkOptionShareVideoMutation$data = {\n readonly uploadSharedVideo: {\n readonly path: string;\n };\n};\nexport type GetLinkOptionShareVideoMutation = {\n response: GetLinkOptionShareVideoMutation$data;\n variables: GetLinkOptionShareVideoMutation$variables;\n};\n\nconst node: ConcreteRequest = (function(){\nvar v0 = [\n {\n \"defaultValue\": null,\n \"kind\": \"LocalArgument\",\n \"name\": \"file\"\n }\n],\nv1 = [\n {\n \"alias\": null,\n \"args\": [\n {\n \"kind\": \"Variable\",\n \"name\": \"file\",\n \"variableName\": \"file\"\n }\n ],\n \"concreteType\": \"SharedVideo\",\n \"kind\": \"LinkedField\",\n \"name\": \"uploadSharedVideo\",\n \"plural\": false,\n \"selections\": [\n {\n \"alias\": null,\n \"args\": null,\n \"kind\": \"ScalarField\",\n \"name\": \"path\",\n \"storageKey\": null\n }\n ],\n \"storageKey\": null\n }\n];\nreturn {\n \"fragment\": {\n \"argumentDefinitions\": (v0/*: any*/),\n \"kind\": \"Fragment\",\n \"metadata\": null,\n \"name\": \"GetLinkOptionShareVideoMutation\",\n \"selections\": (v1/*: any*/),\n \"type\": \"Mutation\",\n \"abstractKey\": null\n },\n \"kind\": \"Request\",\n \"operation\": {\n \"argumentDefinitions\": (v0/*: any*/),\n \"kind\": \"Operation\",\n \"name\": \"GetLinkOptionShareVideoMutation\",\n \"selections\": (v1/*: any*/)\n },\n \"params\": {\n \"cacheID\": \"f02ec81a41c8d75c3733853e1fb04f58\",\n \"id\": null,\n \"metadata\": {},\n \"name\": \"GetLinkOptionShareVideoMutation\",\n \"operationKind\": \"mutation\",\n \"text\": \"mutation GetLinkOptionShareVideoMutation(\\n $file: Upload!\\n) {\\n uploadSharedVideo(file: $file) {\\n path\\n }\\n}\\n\"\n }\n};\n})();\n\n(node as any).hash = \"c1b085da9afaac5f19eeb99ff561ed55\";\n\nexport default node;\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/options/useDownloadVideo.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {getFileName} from '@/common/components/options/ShareUtils';\nimport {\n EncodingCompletedEvent,\n EncodingStateUpdateEvent,\n} from '@/common/components/video/VideoWorkerBridge';\nimport useVideo from '@/common/components/video/editor/useVideo';\nimport {MP4ArrayBuffer} from 'mp4box';\nimport {useState} from 'react';\n\ntype DownloadingState = 'default' | 'started' | 'encoding' | 'completed';\n\ntype State = {\n state: DownloadingState;\n progress: number;\n download: (shouldSave?: boolean) => Promise;\n};\n\nexport default function useDownloadVideo(): State {\n const [downloadingState, setDownloadingState] =\n useState('default');\n const [progress, setProgress] = useState(0);\n\n const video = useVideo();\n\n async function download(shouldSave = true): Promise {\n return new Promise(resolve => {\n function onEncodingStateUpdate(event: EncodingStateUpdateEvent) {\n setDownloadingState('encoding');\n setProgress(event.progress);\n }\n\n function onEncodingComplete(event: EncodingCompletedEvent) {\n const file = event.file;\n\n if (shouldSave) {\n saveVideo(file, getFileName());\n }\n\n video?.removeEventListener('encodingCompleted', onEncodingComplete);\n video?.removeEventListener(\n 'encodingStateUpdate',\n onEncodingStateUpdate,\n );\n setDownloadingState('completed');\n resolve(file);\n }\n\n video?.addEventListener('encodingStateUpdate', onEncodingStateUpdate);\n video?.addEventListener('encodingCompleted', onEncodingComplete);\n\n if (downloadingState === 'default' || downloadingState === 'completed') {\n setDownloadingState('started');\n video?.pause();\n video?.encode();\n }\n });\n }\n\n function saveVideo(file: MP4ArrayBuffer, fileName: string) {\n const blob = new Blob([file]);\n const url = window.URL.createObjectURL(blob);\n\n const a = document.createElement('a');\n document.body.appendChild(a);\n a.setAttribute('href', url);\n a.setAttribute('download', fileName);\n a.setAttribute('target', '_self');\n a.click();\n window.URL.revokeObjectURL(url);\n }\n\n return {download, progress, state: downloadingState};\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/session/RestartSessionButton.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport useRestartSession from '@/common/components/session/useRestartSession';\nimport {Reset} from '@carbon/icons-react';\nimport {Button, Loading} from 'react-daisyui';\n\ntype Props = {\n onRestartSession: () => void;\n};\n\nexport default function RestartSessionButton({onRestartSession}: Props) {\n const {restartSession, isLoading} = useRestartSession();\n\n function handleRestartSession() {\n restartSession(onRestartSession);\n }\n\n return (\n : }>\n Start over\n \n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/session/__generated__/useCloseSessionBeforeUnloadMutation.graphql.ts", "content": "/**\n * @generated SignedSource<>\n * @lightSyntaxTransform\n * @nogrep\n */\n\n/* tslint:disable */\n/* eslint-disable */\n// @ts-nocheck\n\nimport { ConcreteRequest, Mutation } from 'relay-runtime';\nexport type CloseSessionInput = {\n sessionId: string;\n};\nexport type useCloseSessionBeforeUnloadMutation$variables = {\n input: CloseSessionInput;\n};\nexport type useCloseSessionBeforeUnloadMutation$data = {\n readonly closeSession: {\n readonly success: boolean;\n };\n};\nexport type useCloseSessionBeforeUnloadMutation = {\n response: useCloseSessionBeforeUnloadMutation$data;\n variables: useCloseSessionBeforeUnloadMutation$variables;\n};\n\nconst node: ConcreteRequest = (function(){\nvar v0 = [\n {\n \"defaultValue\": null,\n \"kind\": \"LocalArgument\",\n \"name\": \"input\"\n }\n],\nv1 = [\n {\n \"alias\": null,\n \"args\": [\n {\n \"kind\": \"Variable\",\n \"name\": \"input\",\n \"variableName\": \"input\"\n }\n ],\n \"concreteType\": \"CloseSession\",\n \"kind\": \"LinkedField\",\n \"name\": \"closeSession\",\n \"plural\": false,\n \"selections\": [\n {\n \"alias\": null,\n \"args\": null,\n \"kind\": \"ScalarField\",\n \"name\": \"success\",\n \"storageKey\": null\n }\n ],\n \"storageKey\": null\n }\n];\nreturn {\n \"fragment\": {\n \"argumentDefinitions\": (v0/*: any*/),\n \"kind\": \"Fragment\",\n \"metadata\": null,\n \"name\": \"useCloseSessionBeforeUnloadMutation\",\n \"selections\": (v1/*: any*/),\n \"type\": \"Mutation\",\n \"abstractKey\": null\n },\n \"kind\": \"Request\",\n \"operation\": {\n \"argumentDefinitions\": (v0/*: any*/),\n \"kind\": \"Operation\",\n \"name\": \"useCloseSessionBeforeUnloadMutation\",\n \"selections\": (v1/*: any*/)\n },\n \"params\": {\n \"cacheID\": \"99b73bd43a9f74104d545778cebbd15c\",\n \"id\": null,\n \"metadata\": {},\n \"name\": \"useCloseSessionBeforeUnloadMutation\",\n \"operationKind\": \"mutation\",\n \"text\": \"mutation useCloseSessionBeforeUnloadMutation(\\n $input: CloseSessionInput!\\n) {\\n closeSession(input: $input) {\\n success\\n }\\n}\\n\"\n }\n};\n})();\n\n(node as any).hash = \"55dd870645c9736b797b90819ddb1b92\";\n\nexport default node;\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/session/useCloseSessionBeforeUnload.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {useCloseSessionBeforeUnloadMutation$variables} from '@/common/components/session/__generated__/useCloseSessionBeforeUnloadMutation.graphql';\nimport {sessionAtom} from '@/demo/atoms';\nimport useSettingsContext from '@/settings/useSettingsContext';\nimport {useAtomValue} from 'jotai';\nimport {useEffect, useMemo} from 'react';\nimport {ConcreteRequest, graphql} from 'relay-runtime';\n\n/**\n * The useCloseSessionBeforeUnload is a dirty workaround to send close session\n * requests on window/tab close. Going through Relay does not send the request\n * even if the `keepalive` flag is set for the request. It does work when the\n * fetch is called directly with the close session mutation.\n *\n * Caveat: there is static typing, but there might be other caveats around this\n * quirky hack.\n */\nexport default function useCloseSessionBeforeUnload() {\n const session = useAtomValue(sessionAtom);\n const {settings} = useSettingsContext();\n\n const data = useMemo(() => {\n if (session == null) {\n return null;\n }\n\n const graphQLTaggedNode = graphql`\n mutation useCloseSessionBeforeUnloadMutation($input: CloseSessionInput!) {\n closeSession(input: $input) {\n success\n }\n }\n ` as ConcreteRequest;\n\n const variables: useCloseSessionBeforeUnloadMutation$variables = {\n input: {\n sessionId: session.id,\n },\n };\n\n const query = graphQLTaggedNode.params.text;\n if (query === null) {\n return null;\n }\n\n return {\n query,\n variables,\n };\n }, [session]);\n\n useEffect(() => {\n function onBeforeUpload() {\n if (data == null) {\n return;\n }\n\n fetch(`${settings.inferenceAPIEndpoint}/graphql`, {\n method: 'POST',\n credentials: 'include',\n headers: {\n 'Content-Type': 'application/json',\n },\n keepalive: true,\n body: JSON.stringify(data),\n });\n }\n window.addEventListener('beforeunload', onBeforeUpload);\n return () => {\n window.removeEventListener('beforeunload', onBeforeUpload);\n };\n }, [data, session, settings.inferenceAPIEndpoint]);\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/session/useRestartSession.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport useMessagesSnackbar from '@/common/components/snackbar/useDemoMessagesSnackbar';\nimport useVideo from '@/common/components/video/editor/useVideo';\nimport useInputVideo from '@/common/components/video/useInputVideo';\nimport {\n activeTrackletObjectIdAtom,\n isPlayingAtom,\n isStreamingAtom,\n labelTypeAtom,\n trackletObjectsAtom,\n} from '@/demo/atoms';\nimport {useAtomValue, useSetAtom} from 'jotai';\nimport {useState} from 'react';\n\nexport default function useRestartSession() {\n const [isLoading, setIsLoading] = useState();\n const isPlaying = useAtomValue(isPlayingAtom);\n const isStreaming = useAtomValue(isStreamingAtom);\n const setActiveTrackletObjectId = useSetAtom(activeTrackletObjectIdAtom);\n const setTracklets = useSetAtom(trackletObjectsAtom);\n const setLabelType = useSetAtom(labelTypeAtom);\n const {clearMessage} = useMessagesSnackbar();\n\n const {inputVideo} = useInputVideo();\n const video = useVideo();\n\n async function restartSession(onRestart?: () => void) {\n if (video === null || inputVideo === null) {\n return;\n }\n\n setIsLoading(true);\n if (isPlaying) {\n video.pause();\n }\n if (isStreaming) {\n await video.abortStreamMasks();\n }\n await video?.startSession(inputVideo.path);\n video.frame = 0;\n setActiveTrackletObjectId(0);\n setTracklets([]);\n setLabelType('positive');\n onRestart?.();\n clearMessage();\n setIsLoading(false);\n }\n\n return {isLoading, restartSession};\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/snackbar/DemoMessagesSnackbarUtils.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {EnqueueOption} from '@/common/components/snackbar/useMessagesSnackbar';\n\nexport type MessageOptions = EnqueueOption & {\n repeat?: boolean;\n};\n\ntype MessageEvent = {\n text: string;\n shown: boolean;\n action?: Element;\n options?: MessageOptions;\n};\n\nexport interface MessagesEventMap {\n startSession: MessageEvent;\n firstClick: MessageEvent;\n pointClick: MessageEvent;\n addObjectClick: MessageEvent;\n trackAndPlayClick: MessageEvent;\n trackAndPlayComplete: MessageEvent;\n trackAndPlayThrottlingWarning: MessageEvent;\n effectsMessage: MessageEvent;\n}\n\nexport const defaultMessageMap: MessagesEventMap = {\n startSession: {\n text: 'Starting session',\n shown: false,\n options: {type: 'loading', showClose: false, repeat: true, duration: 2000},\n },\n firstClick: {\n text: 'Tip: Click on any object in the video to get started.',\n shown: false,\n options: {expire: false, repeat: false},\n },\n pointClick: {\n text: 'Tip: Not what you expected? Add a few more clicks until the full object you want is selected.',\n shown: false,\n options: {expire: false, repeat: false},\n },\n addObjectClick: {\n text: 'Tip: Add a new object by clicking on it in the video.',\n shown: false,\n options: {expire: false, repeat: false},\n },\n trackAndPlayClick: {\n text: 'Hang tight while your objects are tracked! You’ll be able to apply visual effects in the next step. Stop tracking at any point to adjust your selections if the tracking doesn’t look right.',\n shown: false,\n options: {expire: false, repeat: false},\n },\n trackAndPlayComplete: {\n text: 'Tip: You can fix tracking issues by going back to the frames where tracking is not quite right and adding or removing clicks.',\n shown: false,\n options: {expire: false, repeat: false},\n },\n trackAndPlayThrottlingWarning: {\n text: 'Looks like you have clicked the tracking button a bit too often! To keep things running smoothly, we have temporarily disabled the button.',\n shown: false,\n options: {repeat: true},\n },\n effectsMessage: {\n text: 'Tip: If you aren’t sure where to get started, click “Surprise Me” to apply a surprise effect to your video.',\n shown: false,\n options: {expire: false, repeat: false},\n },\n};\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/snackbar/MessagesSnackbar.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport useScreenSize from '@/common/screen/useScreenSize';\nimport {color, gradients} from '@/theme/tokens.stylex';\nimport {Close} from '@carbon/icons-react';\nimport stylex from '@stylexjs/stylex';\nimport {useAtomValue} from 'jotai';\nimport {Loading, RadialProgress} from 'react-daisyui';\nimport {messageAtom} from './snackbarAtoms';\nimport useExpireMessage from './useExpireMessage';\nimport useMessagesSnackbar from './useMessagesSnackbar';\n\nconst styles = stylex.create({\n container: {\n position: 'absolute',\n top: '8px',\n right: '8px',\n },\n mobileContainer: {\n position: 'absolute',\n bottom: '8px',\n left: '8px',\n right: '8px',\n },\n messageContainer: {\n padding: '20px 20px',\n color: '#FFF',\n borderRadius: '8px',\n fontSize: '0.9rem',\n maxWidth: 400,\n border: '2px solid transparent',\n background: gradients['yellowTeal'],\n },\n messageWarningContainer: {\n background: '#FFDC32',\n color: color['gray-900'],\n },\n messageContent: {\n display: 'flex',\n alignItems: 'center',\n gap: '8px',\n },\n progress: {\n flexShrink: 0,\n color: 'rgba(255, 255, 255, 0.1)',\n },\n closeColumn: {\n display: 'flex',\n alignSelf: 'stretch',\n alignItems: 'start',\n },\n});\n\nexport default function MessagesSnackbar() {\n const message = useAtomValue(messageAtom);\n const {clearMessage} = useMessagesSnackbar();\n const {isMobile} = useScreenSize();\n\n useExpireMessage();\n\n if (message == null) {\n return null;\n }\n\n const closeIcon = (\n \n );\n\n return (\n \n \n
\n
{message.text}
\n {message.type === 'loading' && }\n {message.showClose && (\n
\n {message.expire ? (\n \n {closeIcon}\n \n ) : (\n closeIcon\n )}\n
\n )}\n
\n \n \n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/snackbar/snackbarAtoms.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {atom} from 'jotai';\n\nexport type MessageType = 'info' | 'loading' | 'warning';\n\nexport type Message = {\n type: MessageType;\n text: string;\n duration: number;\n progress: number;\n startTime: number;\n expire: boolean;\n showClose: boolean;\n showReset: boolean;\n};\n\nexport const messageAtom = atom(null);\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/snackbar/useDemoMessagesSnackbar.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {MessagesEventMap} from '@/common/components/snackbar/DemoMessagesSnackbarUtils';\nimport useMessagesSnackbar from '@/common/components/snackbar/useMessagesSnackbar';\nimport {messageMapAtom} from '@/demo/atoms';\nimport {useAtom} from 'jotai';\nimport {useCallback} from 'react';\n\ntype State = {\n enqueueMessage: (messageType: keyof MessagesEventMap) => void;\n clearMessage: () => void;\n};\n\nexport default function useDemoMessagesSnackbar(): State {\n const [messageMap, setMessageMap] = useAtom(messageMapAtom);\n const {enqueueMessage: enqueue, clearMessage} = useMessagesSnackbar();\n\n const enqueueMessage = useCallback(\n (messageType: keyof MessagesEventMap) => {\n const {text, shown, options} = messageMap[messageType];\n if (!options?.repeat && shown === true) {\n return;\n }\n enqueue(text, options);\n const newState = {...messageMap};\n newState[messageType].shown = true;\n setMessageMap(newState);\n },\n [enqueue, messageMap, setMessageMap],\n );\n\n return {enqueueMessage, clearMessage};\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/snackbar/useExpireMessage.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {useAtom} from 'jotai';\nimport {useEffect, useRef} from 'react';\nimport {Message, messageAtom} from '@/common/components/snackbar/snackbarAtoms';\n\nexport default function useExpireMessage() {\n const [message, setMessage] = useAtom(messageAtom);\n const messageRef = useRef(null);\n const intervalRef = useRef(null);\n\n useEffect(() => {\n messageRef.current = message;\n }, [message]);\n\n useEffect(() => {\n function resetInterval() {\n if (intervalRef.current != null) {\n clearInterval(intervalRef.current);\n intervalRef.current = null;\n }\n }\n if (intervalRef.current == null && message != null && message.expire) {\n intervalRef.current = setInterval(() => {\n const prevMessage = messageRef.current;\n if (prevMessage == null) {\n setMessage(null);\n resetInterval();\n return;\n }\n const messageDuration = Date.now() - prevMessage.startTime;\n if (messageDuration > prevMessage.duration) {\n setMessage(null);\n resetInterval();\n return;\n }\n setMessage({\n ...prevMessage,\n progress: messageDuration / prevMessage.duration,\n });\n }, 20);\n }\n }, [message, setMessage]);\n\n useEffect(() => {\n return () => {\n if (intervalRef.current != null) {\n clearInterval(intervalRef.current);\n }\n };\n }, []);\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/snackbar/useMessagesSnackbar.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {useSetAtom} from 'jotai';\nimport {useCallback} from 'react';\nimport {\n MessageType,\n messageAtom,\n} from '@/common/components/snackbar/snackbarAtoms';\n\nexport type EnqueueOption = {\n duration?: number;\n type?: MessageType;\n expire?: boolean;\n showClose?: boolean;\n showReset?: boolean;\n};\n\ntype State = {\n clearMessage: () => void;\n enqueueMessage: (message: string, options?: EnqueueOption) => void;\n};\n\nexport default function useMessagesSnackbar(): State {\n const setMessage = useSetAtom(messageAtom);\n\n const enqueueMessage = useCallback(\n (message: string, options?: EnqueueOption) => {\n setMessage({\n text: message,\n type: options?.type ?? 'info',\n duration: options?.duration ?? 5000,\n progress: 0,\n startTime: Date.now(),\n expire: options?.expire ?? true,\n showClose: options?.showClose ?? true,\n showReset: options?.showReset ?? false,\n });\n },\n [setMessage],\n );\n\n function clearMessage() {\n setMessage(null);\n }\n\n return {enqueueMessage, clearMessage};\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/toolbar/DesktopToolbar.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport ObjectsToolbar from '@/common/components/annotations/ObjectsToolbar';\nimport EffectsToolbar from '@/common/components/effects/EffectsToolbar';\nimport MoreOptionsToolbar from '@/common/components/options/MoreOptionsToolbar';\nimport type {CSSProperties} from 'react';\n\ntype Props = {\n tabIndex: number;\n onTabChange: (newIndex: number) => void;\n};\n\nexport default function DesktopToolbar({tabIndex, onTabChange}: Props) {\n const toolbarShadow: CSSProperties = {\n boxShadow: '0px 1px 3px 1px rgba(0,0,0,.25)',\n transition: 'box-shadow 0.8s ease-out',\n };\n\n const tabs = [\n ,\n ,\n ,\n ];\n\n return (\n \n {tabs[tabIndex]}\n \n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/toolbar/MobileToolbar.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport MobileObjectsToolbar from '@/common/components/annotations/MobileObjectsToolbar';\nimport MobileEffectsToolbar from '@/common/components/effects/MobileEffectsToolbar';\nimport MoreOptionsToolbar from '@/common/components/options/MoreOptionsToolbar';\n\ntype Props = {\n tabIndex: number;\n onTabChange: (newIndex: number) => void;\n};\n\nexport default function MobileToolbar({tabIndex, onTabChange}: Props) {\n const tabs = [\n ,\n ,\n ,\n ];\n\n return (\n
{tabs[tabIndex]}
\n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/toolbar/Toolbar.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport useListenToStreamingState from '@/common/components/toolbar/useListenToStreamingState';\nimport useToolbarTabs from '@/common/components/toolbar/useToolbarTabs';\nimport useVideo from '@/common/components/video/editor/useVideo';\nimport useVideoEffect from '@/common/components/video/editor/useVideoEffect';\nimport {EffectIndex} from '@/common/components/video/effects/Effects';\nimport useScreenSize from '@/common/screen/useScreenSize';\nimport {\n codeEditorOpenedAtom,\n isPlayingAtom,\n isStreamingAtom,\n} from '@/demo/atoms';\nimport {useAtom, useAtomValue, useSetAtom} from 'jotai';\nimport {useCallback, useEffect} from 'react';\nimport DesktopToolbar from './DesktopToolbar';\nimport MobileToolbar from './MobileToolbar';\nimport {OBJECT_TOOLBAR_INDEX} from './ToolbarConfig';\n\nexport default function Toolbar() {\n const [tabIndex, setTabIndex] = useToolbarTabs();\n const video = useVideo();\n const setIsPlaying = useSetAtom(isPlayingAtom);\n const [isStreaming, setIsStreaming] = useAtom(isStreamingAtom);\n const codeEditorOpened = useAtomValue(codeEditorOpenedAtom);\n const {isMobile} = useScreenSize();\n const setEffect = useVideoEffect();\n\n const resetEffects = useCallback(() => {\n setEffect('Original', EffectIndex.BACKGROUND, {variant: 0});\n setEffect('Overlay', EffectIndex.HIGHLIGHT, {variant: 0});\n }, [setEffect]);\n\n const handleStopVideo = useCallback(() => {\n if (isStreaming) {\n video?.abortStreamMasks();\n } else {\n video?.pause();\n }\n }, [video, isStreaming]);\n\n const handleTabChange = useCallback(\n (newIndex: number) => {\n if (newIndex === OBJECT_TOOLBAR_INDEX) {\n handleStopVideo();\n resetEffects();\n }\n\n setTabIndex(newIndex);\n },\n [handleStopVideo, resetEffects, setTabIndex],\n );\n\n useListenToStreamingState();\n\n useEffect(() => {\n function onPlay() {\n setIsPlaying(true);\n }\n function onPause() {\n setIsPlaying(false);\n }\n\n video?.addEventListener('play', onPlay);\n video?.addEventListener('pause', onPause);\n return () => {\n video?.removeEventListener('play', onPlay);\n video?.removeEventListener('pause', onPause);\n };\n }, [video, resetEffects, setIsStreaming, setIsPlaying]);\n\n if (codeEditorOpened) {\n return null;\n }\n\n return isMobile ? (\n \n ) : (\n \n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/toolbar/ToolbarActionIcon.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport GradientBorder from '@/common/components/button/GradientBorder';\nimport useScreenSize from '@/common/screen/useScreenSize';\nimport {BLUE_PINK_FILL_BR} from '@/theme/gradientStyle';\nimport type {CarbonIconType} from '@carbon/icons-react';\nimport {Loading} from 'react-daisyui';\n\ntype Props = {\n isDisabled?: boolean;\n isActive?: boolean;\n icon: CarbonIconType;\n title: string;\n badge?: React.ReactNode;\n variant: 'toggle' | 'button' | 'gradient' | 'flat';\n span?: 1 | 2;\n loadingProps?: {\n loading: boolean;\n label?: string;\n };\n onClick: () => void;\n};\n\nexport default function ToolbarActionIcon({\n variant,\n isDisabled = false,\n isActive = false,\n title,\n badge,\n loadingProps,\n icon: Icon,\n span = 1,\n onClick,\n}: Props) {\n const {isMobile} = useScreenSize();\n const isLoading = loadingProps?.loading === true;\n\n function handleClick() {\n if (isDisabled) {\n return;\n }\n onClick();\n }\n\n const ButtonBase = (\n \n
\n
\n {isLoading ? (\n \n ) : (\n \n )}\n
\n \n {isLoading && loadingProps?.label != null\n ? loadingProps.label\n : title}\n
\n {isActive && badge}\n \n \n );\n\n return variant == 'gradient' ? (\n \n {ButtonBase}\n \n ) : (\n ButtonBase\n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/toolbar/ToolbarBottomActionsWrapper.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {spacing} from '@/theme/tokens.stylex';\nimport stylex from '@stylexjs/stylex';\nimport {PropsWithChildren} from 'react';\n\nconst styles = stylex.create({\n container: {\n display: 'flex',\n alignItems: 'center',\n justifyContent: 'space-between',\n paddingTop: {\n default: spacing[2],\n '@media screen and (max-width: 768px)': spacing[4],\n },\n paddingBottom: spacing[6],\n paddingHorizontal: spacing[6],\n },\n});\n\nexport default function ToolbarBottomActionsWrapper({\n children,\n}: PropsWithChildren) {\n return
{children}
;\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/toolbar/ToolbarConfig.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nexport const OBJECT_TOOLBAR_INDEX = 0;\nexport const EFFECT_TOOLBAR_INDEX = 1;\nexport const MORE_OPTIONS_TOOLBAR_INDEX = 2;\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/toolbar/ToolbarHeaderWrapper.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {ReactNode} from 'react';\nimport ToolbarProgressChip from './ToolbarProgressChip';\n\ntype Props = {\n title: string;\n description?: string;\n bottomSection?: ReactNode;\n showProgressChip?: boolean;\n className?: string;\n};\n\nexport default function ToolbarHeaderWrapper({\n title,\n description,\n bottomSection,\n showProgressChip = true,\n className,\n}: Props) {\n return (\n \n
\n {showProgressChip && }\n

{title}

\n
\n\n {description != null && (\n
{description}
\n )}\n {bottomSection != null && bottomSection}\n \n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/toolbar/ToolbarProgressChip.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {OBJECT_TOOLBAR_INDEX} from '@/common/components/toolbar/ToolbarConfig';\nimport useToolbarTabs from '@/common/components/toolbar/useToolbarTabs';\nimport {streamingStateAtom} from '@/demo/atoms';\nimport {useAtomValue} from 'jotai';\nimport {useMemo} from 'react';\nimport {Loading} from 'react-daisyui';\n\nconst TOTAL_DEMO_STEPS = 3;\n\nexport default function ToolbarProgressChip() {\n const [toolbarIndex] = useToolbarTabs();\n const streamingState = useAtomValue(streamingStateAtom);\n\n const showLoader = useMemo(() => {\n return streamingState === 'partial' || streamingState === 'requesting';\n }, [streamingState]);\n\n function getStepValue() {\n if (toolbarIndex === OBJECT_TOOLBAR_INDEX) {\n return streamingState !== 'full' ? 1 : 2;\n }\n return 3;\n }\n\n return (\n \n {showLoader ? (\n \n ) : (\n `${getStepValue()}/${TOTAL_DEMO_STEPS}`\n )}\n \n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/toolbar/ToolbarSection.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {PropsWithChildren} from 'react';\n\ntype Props = PropsWithChildren<{\n title: string;\n borderBottom?: boolean;\n}>;\n\nexport default function ToolbarSection({\n children,\n title,\n borderBottom = false,\n}: Props) {\n return (\n
\n
{title}
\n
{children}
\n
\n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/toolbar/useListenToStreamingState.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {StreamingStateUpdateEvent} from '@/common/components/video/VideoWorkerBridge';\nimport useVideo from '@/common/components/video/editor/useVideo';\nimport {StreamingState} from '@/common/tracker/Tracker';\nimport {isStreamingAtom, streamingStateAtom} from '@/demo/atoms';\nimport {useAtom} from 'jotai';\nimport {useEffect} from 'react';\n\nexport default function useListenToStreamingState(): {\n isStreaming: boolean;\n streamingState: StreamingState;\n} {\n const [streamingState, setStreamingState] = useAtom(streamingStateAtom);\n const [isStreaming, setIsStreaming] = useAtom(isStreamingAtom);\n const video = useVideo();\n\n useEffect(() => {\n function onStreamingStateUpdate(event: StreamingStateUpdateEvent) {\n setStreamingState(event.state);\n }\n function onStreamingStarted() {\n setIsStreaming(true);\n }\n function onStreamingCompleted() {\n setIsStreaming(false);\n }\n video?.addEventListener('streamingStateUpdate', onStreamingStateUpdate);\n video?.addEventListener('streamingStarted', onStreamingStarted);\n video?.addEventListener('streamingCompleted', onStreamingCompleted);\n\n return () => {\n video?.removeEventListener(\n 'streamingStateUpdate',\n onStreamingStateUpdate,\n );\n video?.removeEventListener('streamingStarted', onStreamingStarted);\n video?.removeEventListener('streamingCompleted', onStreamingCompleted);\n };\n }, [video, setStreamingState, setIsStreaming]);\n\n return {isStreaming, streamingState};\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/toolbar/useToolbarTabs.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {toolbarTabIndex} from '@/demo/atoms';\nimport {useAtom} from 'jotai';\n\ntype State = [tabIndex: number, setTabIndex: (tabIndex: number) => void];\n\nexport default function useToolbarTabs(): State {\n return useAtom(toolbarTabIndex);\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/useFunctionThrottle.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {useCallback, useState} from 'react';\n\ntype ThrottleOptions = {\n enableThrottling?: boolean;\n};\ntype State = {\n isThrottled: boolean;\n maxThrottles: boolean;\n throttle: (callback: () => void, options?: ThrottleOptions) => void;\n};\n\nexport default function useFunctionThrottle(\n initialDelay: number,\n numThrottles: number,\n): State {\n const [isThrottled, setIsThrottled] = useState(false);\n const [lastClickTime, setLastClickTime] = useState(null);\n const [numTimesThrottled, setNumTimesThrottled] = useState(1);\n\n /**\n * The following function's callback gets throttled when the time between two\n * executions is less than a threshold.\n *\n * The threshold is calculated linearly by multiplying the initial delay\n * and the number of times the button has been throttled. The button can be\n * throttled up to numThrottles times.\n *\n * The function has an optional flag - enableThrottling - which allows a callsite\n * to optionally disable throttling. This is useful in cases where throttling may\n * not be necessary. (e.g. for the Track & Play button, we would only like to\n * throttle after a stream is aborted.)\n */\n const throttle = useCallback(\n (\n callback: () => void,\n options: ThrottleOptions = {\n enableThrottling: true,\n },\n ) => {\n if (isThrottled) {\n return;\n }\n\n const currentTime = Date.now();\n if (lastClickTime == null) {\n callback();\n setLastClickTime(currentTime);\n return;\n }\n\n const timeBetweenClicks = currentTime - lastClickTime;\n const delay = initialDelay * numTimesThrottled;\n const shouldThrottle =\n options.enableThrottling && delay > timeBetweenClicks;\n\n if (shouldThrottle) {\n setIsThrottled(true);\n setTimeout(() => {\n setIsThrottled(false);\n }, delay);\n setNumTimesThrottled(prev => {\n return prev === numThrottles ? numThrottles : prev + 1;\n });\n }\n\n callback();\n setLastClickTime(currentTime);\n },\n [initialDelay, numThrottles, isThrottled, lastClickTime, numTimesThrottled],\n );\n\n return {\n isThrottled,\n maxThrottles: numTimesThrottled === numThrottles,\n throttle,\n };\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/video/ChangeVideoModal.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport type {VideoGalleryTriggerProps} from '@/common/components/gallery/DemoVideoGalleryModal';\nimport DemoVideoGalleryModal from '@/common/components/gallery/DemoVideoGalleryModal';\nimport useVideo from '@/common/components/video/editor/useVideo';\nimport Logger from '@/common/logger/Logger';\nimport {isStreamingAtom, uploadingStateAtom, VideoData} from '@/demo/atoms';\nimport {useAtomValue, useSetAtom} from 'jotai';\nimport {ComponentType, useCallback} from 'react';\nimport {useNavigate} from 'react-router-dom';\n\ntype Props = {\n videoGalleryModalTrigger?: ComponentType;\n showUploadInGallery?: boolean;\n onChangeVideo?: () => void;\n};\n\nexport default function ChangeVideoModal({\n videoGalleryModalTrigger: VideoGalleryModalTriggerComponent,\n showUploadInGallery = true,\n onChangeVideo,\n}: Props) {\n const isStreaming = useAtomValue(isStreamingAtom);\n const setUploadingState = useSetAtom(uploadingStateAtom);\n const video = useVideo();\n const navigate = useNavigate();\n\n const handlePause = useCallback(() => {\n video?.pause();\n }, [video]);\n\n function handlePauseOrAbortVideo() {\n if (isStreaming) {\n video?.abortStreamMasks();\n } else {\n handlePause();\n }\n }\n\n function handleSwitchVideos(video: VideoData) {\n // Retain any search parameter\n navigate(\n {\n pathname: location.pathname,\n search: location.search,\n },\n {\n state: {\n video,\n },\n },\n );\n onChangeVideo?.();\n }\n\n function handleUploadVideoError(error: Error) {\n setUploadingState('error');\n Logger.error(error);\n }\n\n return (\n \n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/video/EventEmitter.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\ntype EventMap = {\n type: keyof WorkerEventMap;\n listener: (ev: WorkerEventMap[keyof WorkerEventMap]) => unknown;\n};\n\nexport class EventEmitter {\n listeners: EventMap[] = [];\n\n trigger(type: K, ev: WorkerEventMap[K]) {\n this.listeners\n .filter(listener => type === listener.type)\n .forEach(({listener}) => {\n setTimeout(() => listener(ev), 0);\n });\n }\n\n addEventListener(\n type: K,\n listener: (ev: WorkerEventMap[K]) => unknown,\n ): void {\n // @ts-expect-error Incorrect typing. Not sure how to correctly type it\n this.listeners.push({type, listener});\n }\n\n removeEventListener(\n type: K,\n listener: (ev: WorkerEventMap[K]) => unknown,\n ): void {\n this.listeners = this.listeners.filter(\n existingListener =>\n !(\n existingListener.type === type &&\n existingListener.listener === listener\n ),\n );\n }\n\n destroy() {\n this.listeners.length = 0;\n }\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/video/Video.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {BaseTracklet, SegmentationPoint} from '@/common/tracker/Tracker';\nimport {TrackerOptions, Trackers} from '@/common/tracker/Trackers';\nimport {PauseFilled, PlayFilledAlt} from '@carbon/icons-react';\nimport stylex, {StyleXStyles} from '@stylexjs/stylex';\nimport {\n CSSProperties,\n forwardRef,\n useEffect,\n useImperativeHandle,\n useMemo,\n useRef,\n} from 'react';\nimport {Button} from 'react-daisyui';\n\nimport {EffectIndex, Effects} from '@/common/components/video/effects/Effects';\nimport useReportError from '@/common/error/useReportError';\nimport Logger from '@/common/logger/Logger';\nimport {isPlayingAtom, isVideoLoadingAtom} from '@/demo/atoms';\nimport {color} from '@/theme/tokens.stylex';\nimport {useAtom} from 'jotai';\nimport useResizeObserver from 'use-resize-observer';\nimport VideoLoadingOverlay from './VideoLoadingOverlay';\nimport {\n StreamingStateUpdateEvent,\n VideoWorkerEventMap,\n} from './VideoWorkerBridge';\nimport {EffectOptions} from './effects/Effect';\nimport useVideoWorker from './useVideoWorker';\n\nconst styles = stylex.create({\n container: {\n position: 'relative',\n width: '100%',\n height: '100%',\n },\n canvasContainer: {\n display: 'flex',\n justifyContent: 'center',\n alignItems: 'center',\n backgroundColor: color['gray-800'],\n width: '100%',\n height: '100%',\n },\n controls: {\n position: 'absolute',\n bottom: 0,\n left: 0,\n width: '100%',\n padding: 8,\n background: 'linear-gradient(#00000000, #000000ff)',\n },\n controlButton: {\n color: 'white',\n },\n});\n\ntype Props = {\n src: string;\n width: number;\n height: number;\n loading?: boolean;\n containerStyle?: StyleXStyles<{\n position: CSSProperties['position'];\n }>;\n canvasStyle?: StyleXStyles<{\n width: CSSProperties['width'];\n }>;\n controls?: boolean;\n createVideoWorker?: () => Worker;\n};\n\nexport type VideoRef = {\n getCanvas(): HTMLCanvasElement | null;\n get width(): number;\n get height(): number;\n get frame(): number;\n set frame(index: number);\n get numberOfFrames(): number;\n play(): void;\n pause(): void;\n stop(): void;\n previousFrame(): void;\n nextFrame(): void;\n setEffect(\n name: keyof Effects,\n index: EffectIndex,\n options?: EffectOptions,\n ): void;\n encode(): void;\n streamMasks(): void;\n abortStreamMasks(): Promise;\n addEventListener(\n type: K,\n listener: (ev: VideoWorkerEventMap[K]) => unknown,\n ): void;\n removeEventListener(\n type: K,\n listener: (ev: VideoWorkerEventMap[K]) => unknown,\n ): void;\n createFilmstrip(width: number, height: number): Promise;\n // Tracker\n initializeTracker(name: keyof Trackers, options?: TrackerOptions): void;\n startSession(videoUrl: string): Promise;\n closeSession(): void;\n logAnnotations(): void;\n createTracklet(): Promise;\n deleteTracklet(trackletId: number): Promise;\n updatePoints(trackletId: number, points: SegmentationPoint[]): void;\n clearPointsInVideo(): Promise;\n getWorker_ONLY_USE_WITH_CAUTION(): Worker;\n};\n\nexport default forwardRef(function Video(\n {\n src,\n width,\n height,\n containerStyle,\n canvasStyle,\n createVideoWorker,\n controls = false,\n loading = false,\n },\n ref,\n) {\n const reportError = useReportError();\n const canvasRef = useRef(null);\n const [isPlaying, setIsPlaying] = useAtom(isPlayingAtom);\n const [isVideoLoading, setIsVideoLoading] = useAtom(isVideoLoadingAtom);\n\n const bridge = useVideoWorker(src, canvasRef, {\n createVideoWorker,\n });\n\n const {\n ref: resizeObserverRef,\n width: resizeWidth = 1,\n height: resizeHeight = 1,\n } = useResizeObserver();\n\n const canvasHeight = useMemo(() => {\n const resizeRatio = resizeWidth / width;\n return Math.min(height * resizeRatio, resizeHeight);\n }, [resizeWidth, height, width, resizeHeight]);\n\n useImperativeHandle(\n ref,\n () => ({\n getCanvas() {\n return canvasRef.current;\n },\n get width() {\n return bridge.width;\n },\n get height() {\n return bridge.width;\n },\n get frame() {\n return bridge.frame;\n },\n set frame(index: number) {\n bridge.frame = index;\n },\n get numberOfFrames() {\n return bridge.numberOfFrames;\n },\n play(): void {\n bridge.play();\n },\n pause(): void {\n bridge.pause();\n },\n stop(): void {\n bridge.stop();\n },\n previousFrame(): void {\n bridge.previousFrame();\n },\n nextFrame(): void {\n bridge.nextFrame();\n },\n setEffect(\n name: keyof Effects,\n index: number,\n options?: EffectOptions,\n ): void {\n bridge.setEffect(name, index, options);\n },\n encode(): void {\n bridge.encode();\n },\n streamMasks(): void {\n bridge.streamMasks();\n },\n abortStreamMasks(): Promise {\n return bridge.abortStreamMasks();\n },\n addEventListener(\n type: K,\n listener: (ev: VideoWorkerEventMap[K]) => unknown,\n ): void {\n bridge.addEventListener(type, listener);\n },\n removeEventListener(\n type: K,\n listener: (ev: VideoWorkerEventMap[K]) => unknown,\n ): void {\n bridge.removeEventListener(type, listener);\n },\n createFilmstrip(width: number, height: number): Promise {\n return bridge.createFilmstrip(width, height);\n },\n // Tracker\n initializeTracker(name: keyof Trackers, options: TrackerOptions): void {\n bridge.initializeTracker(name, options);\n },\n startSession(videoUrl: string): Promise {\n return bridge.startSession(videoUrl);\n },\n closeSession(): void {\n bridge.closeSession();\n },\n logAnnotations(): void {\n bridge.logAnnotations();\n },\n createTracklet(): Promise {\n return bridge.createTracklet();\n },\n deleteTracklet(trackletId: number): Promise {\n return bridge.deleteTracklet(trackletId);\n },\n updatePoints(trackletId: number, points: SegmentationPoint[]): void {\n bridge.updatePoints(trackletId, points);\n },\n clearPointsInVideo(): Promise {\n return bridge.clearPointsInVideo();\n },\n getWorker_ONLY_USE_WITH_CAUTION() {\n return bridge.getWorker_ONLY_USE_WITH_CAUTION();\n },\n }),\n [bridge],\n );\n\n // Handle video playback events (get playback state to main thread)\n useEffect(() => {\n let isPlaying = false;\n\n function onFocus() {\n // Workaround for Safari where the video frame renders black on\n // unknown events. Trigger re-render frame on focus.\n if (!isPlaying) {\n bridge.goToFrame(bridge.frame);\n }\n }\n\n function onVisibilityChange() {\n // Workaround for Safari where the video frame renders black on\n // visibility change hidden. Returning to visible shows a black\n // frame instead of rendering the current frame.\n if (document.visibilityState === 'visible' && !isPlaying) {\n bridge.goToFrame(bridge.frame);\n }\n }\n\n function onError(event: ErrorEvent) {\n const error = event.error;\n Logger.error(error);\n reportError(error);\n }\n\n function onPlay() {\n isPlaying = true;\n setIsPlaying(true);\n }\n function onPause() {\n isPlaying = false;\n setIsPlaying(false);\n }\n\n function onStreamingDone(event: StreamingStateUpdateEvent) {\n // continue to play after streaming is done (state is \"full\")\n if (event.state === 'full') {\n bridge.play();\n }\n }\n\n function onLoadStart() {\n setIsVideoLoading(true);\n }\n\n function onDecodeStart() {\n setIsVideoLoading(false);\n }\n\n window.addEventListener('focus', onFocus);\n window.addEventListener('visibilitychange', onVisibilityChange);\n bridge.addEventListener('error', onError);\n bridge.addEventListener('play', onPlay);\n bridge.addEventListener('pause', onPause);\n bridge.addEventListener('streamingStateUpdate', onStreamingDone);\n bridge.addEventListener('loadstart', onLoadStart);\n bridge.addEventListener('decode', onDecodeStart);\n return () => {\n window.removeEventListener('focus', onFocus);\n window.removeEventListener('visibilitychange', onVisibilityChange);\n bridge.removeEventListener('error', onError);\n bridge.removeEventListener('play', onPlay);\n bridge.removeEventListener('pause', onPause);\n bridge.removeEventListener('streamingStateUpdate', onStreamingDone);\n bridge.removeEventListener('loadstart', onLoadStart);\n bridge.removeEventListener('decode', onDecodeStart);\n };\n }, [bridge, reportError, setIsPlaying, setIsVideoLoading]);\n\n return (\n \n
\n {(isVideoLoading || loading) && }\n \n
\n {controls && (\n
\n \n ) : (\n \n )\n }\n onClick={() => {\n isPlaying ? bridge.pause() : bridge.play();\n }}\n />\n
\n )}\n \n );\n});\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/video/VideoFilmstripWithPlayback.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport PlaybackButton from '@/common/components/button/PlaybackButton';\nimport VideoFilmstrip from '@/common/components/video/filmstrip/VideoFilmstrip';\nimport {spacing, w} from '@/theme/tokens.stylex';\nimport stylex from '@stylexjs/stylex';\n\nconst styles = stylex.create({\n container: {\n display: 'flex',\n alignItems: 'end',\n gap: spacing[4],\n paddingHorizontal: spacing[4],\n width: '100%',\n },\n playbackButtonContainer: {\n display: 'flex',\n justifyContent: 'center',\n alignItems: 'center',\n width: w[12],\n height: w[12],\n },\n filmstripContainer: {\n flexGrow: 1,\n },\n});\n\nexport default function VideoFilmstripWithPlayback() {\n return (\n
\n
\n \n
\n
\n \n
\n
\n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/video/VideoLoadingOverlay.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {fontSize, fontWeight, spacing} from '@/theme/tokens.stylex';\nimport stylex from '@stylexjs/stylex';\nimport {Loading} from 'react-daisyui';\n\nconst styles = stylex.create({\n overlay: {\n position: 'absolute',\n width: '100%',\n height: '100%',\n background: 'rgba(0,0,0,0.5)',\n },\n indicatorContainer: {\n position: 'absolute',\n top: '50%',\n left: '50%',\n transform: 'translate(-50%, -50%)',\n display: 'flex',\n alignItems: 'center',\n gap: spacing[4],\n color: 'white',\n },\n indicatorText: {\n color: 'white',\n fontSize: fontSize['sm'],\n fontWeight: fontWeight['medium'],\n },\n});\n\ntype Props = {\n label?: string;\n};\n\nexport default function VideoLoadingOverlay({label}: Props) {\n return (\n
\n
\n \n
\n {label ?? 'Loading video...'}\n
\n
\n
\n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/video/VideoWorker.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {registerSerializableConstructors} from '@/common/error/ErrorSerializationUtils';\nimport {Tracker} from '@/common/tracker/Tracker';\nimport {TrackerRequestMessageEvent} from '@/common/tracker/TrackerTypes';\nimport {TRACKER_MAPPING} from '@/common/tracker/Trackers';\nimport {serializeError} from 'serialize-error';\nimport VideoWorkerContext from './VideoWorkerContext';\nimport {\n ErrorResponse,\n VideoWorkerRequestMessageEvent,\n} from './VideoWorkerTypes';\n\nregisterSerializableConstructors();\n\nconst context = new VideoWorkerContext();\nlet tracker: Tracker | null = null;\n\nlet statsEnabled = false;\n\nself.addEventListener(\n 'message',\n async (\n event: VideoWorkerRequestMessageEvent | TrackerRequestMessageEvent,\n ) => {\n try {\n switch (event.data.action) {\n // Initialize context\n case 'setCanvas':\n context.setCanvas(event.data.canvas);\n break;\n case 'setSource':\n context.setSource(event.data.source);\n break;\n\n // Playback\n case 'play':\n context.play();\n break;\n case 'pause':\n context.pause();\n break;\n case 'stop':\n context.stop();\n break;\n case 'frameUpdate':\n context.goToFrame(event.data.index);\n break;\n\n // Filmstrip\n case 'filmstrip': {\n const {width, height} = event.data;\n await context.createFilmstrip(width, height);\n break;\n }\n\n // Effects\n case 'setEffect': {\n const {name, index, options} = event.data;\n await context.setEffect(name, index, options);\n break;\n }\n\n // Encode\n case 'encode': {\n await context.encode();\n break;\n }\n\n case 'enableStats': {\n statsEnabled = true;\n context.enableStats();\n tracker?.enableStats();\n break;\n }\n\n // Tracker\n case 'initializeTracker': {\n const {name, options} = event.data;\n const Tracker = TRACKER_MAPPING[name];\n // Update the endpoint for the streaming API\n tracker = new Tracker(context, options);\n if (statsEnabled) {\n tracker.enableStats();\n }\n break;\n }\n case 'startSession': {\n const {videoUrl} = event.data;\n await tracker?.startSession(videoUrl);\n break;\n }\n case 'createTracklet':\n tracker?.createTracklet();\n break;\n case 'deleteTracklet':\n await tracker?.deleteTracklet(event.data.trackletId);\n break;\n case 'closeSession':\n tracker?.closeSession();\n break;\n case 'updatePoints': {\n const {frameIndex, objectId, points} = event.data;\n context.allowEffectAnimation(true, objectId, points);\n await tracker?.updatePoints(frameIndex, objectId, points);\n break;\n }\n case 'clearPointsInFrame': {\n const {frameIndex, objectId} = event.data;\n await tracker?.clearPointsInFrame(frameIndex, objectId);\n break;\n }\n case 'clearPointsInVideo':\n await tracker?.clearPointsInVideo();\n break;\n case 'streamMasks': {\n const {frameIndex} = event.data;\n context.allowEffectAnimation(false);\n await tracker?.streamMasks(frameIndex);\n break;\n }\n case 'abortStreamMasks':\n tracker?.abortStreamMasks();\n break;\n }\n } catch (error) {\n const serializedError = serializeError(error);\n const errorResponse: ErrorResponse = {\n action: 'error',\n error: serializedError,\n };\n self.postMessage(errorResponse);\n }\n },\n);\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/video/VideoWorkerBridge.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {EffectIndex, Effects} from '@/common/components/video/effects/Effects';\nimport {registerSerializableConstructors} from '@/common/error/ErrorSerializationUtils';\nimport {\n BaseTracklet,\n SegmentationPoint,\n StreamingState,\n} from '@/common/tracker/Tracker';\nimport {\n AbortStreamMasksRequest,\n AddPointsResponse,\n ClearPointsInFrameRequest,\n ClearPointsInVideoRequest,\n ClearPointsInVideoResponse,\n CloseSessionRequest,\n CreateTrackletRequest,\n DeleteTrackletRequest,\n InitializeTrackerRequest,\n LogAnnotationsRequest,\n SessionStartFailedResponse,\n SessionStartedResponse,\n StartSessionRequest,\n StreamMasksRequest,\n StreamingStateUpdateResponse,\n TrackerRequest,\n TrackerResponseMessageEvent,\n TrackletCreatedResponse,\n TrackletDeletedResponse,\n UpdatePointsRequest,\n} from '@/common/tracker/TrackerTypes';\nimport {TrackerOptions, Trackers} from '@/common/tracker/Trackers';\nimport {MP4ArrayBuffer} from 'mp4box';\nimport {deserializeError, type ErrorObject} from 'serialize-error';\nimport {EventEmitter} from './EventEmitter';\nimport {\n EncodeVideoRequest,\n FilmstripRequest,\n FilmstripResponse,\n FrameUpdateRequest,\n PauseRequest,\n PlayRequest,\n SetCanvasRequest,\n SetEffectRequest,\n SetSourceRequest,\n StopRequest,\n VideoWorkerRequest,\n VideoWorkerResponseMessageEvent,\n} from './VideoWorkerTypes';\nimport {EffectOptions} from './effects/Effect';\n\nregisterSerializableConstructors();\n\nexport type DecodeEvent = {\n totalFrames: number;\n numFrames: number;\n fps: number;\n width: number;\n height: number;\n done: boolean;\n};\n\nexport type LoadStartEvent = unknown;\n\nexport type EffectUpdateEvent = {\n name: keyof Effects;\n index: EffectIndex;\n variant: number;\n numVariants: number;\n};\n\nexport type EncodingStateUpdateEvent = {\n progress: number;\n};\n\nexport type EncodingCompletedEvent = {\n file: MP4ArrayBuffer;\n};\n\nexport interface PlayEvent {}\n\nexport interface PauseEvent {}\n\nexport interface FilmstripEvent {\n filmstrip: ImageBitmap;\n}\n\nexport interface FrameUpdateEvent {\n index: number;\n}\n\nexport interface SessionStartedEvent {\n sessionId: string;\n}\n\nexport interface SessionStartFailedEvent {}\n\nexport interface TrackletCreatedEvent {\n // Do not send masks between workers and main thread because they are huge,\n // and sending them would eventually slow down the main thread.\n tracklet: BaseTracklet;\n}\n\nexport interface TrackletsEvent {\n // Do not send masks between workers and main thread because they are huge,\n // and sending them would eventually slow down the main thread.\n tracklets: BaseTracklet[];\n}\n\nexport interface TrackletDeletedEvent {\n isSuccessful: boolean;\n}\n\nexport interface AddPointsEvent {\n isSuccessful: boolean;\n}\n\nexport interface ClearPointsInVideoEvent {\n isSuccessful: boolean;\n}\n\nexport interface StreamingStartedEvent {}\n\nexport interface StreamingCompletedEvent {}\n\nexport interface StreamingStateUpdateEvent {\n state: StreamingState;\n}\n\nexport interface RenderingErrorEvent {\n error: ErrorObject;\n}\n\nexport interface VideoWorkerEventMap {\n error: ErrorEvent;\n decode: DecodeEvent;\n encodingStateUpdate: EncodingStateUpdateEvent;\n encodingCompleted: EncodingCompletedEvent;\n play: PlayEvent;\n pause: PauseEvent;\n filmstrip: FilmstripEvent;\n frameUpdate: FrameUpdateEvent;\n sessionStarted: SessionStartedEvent;\n sessionStartFailed: SessionStartFailedEvent;\n trackletCreated: TrackletCreatedEvent;\n trackletsUpdated: TrackletsEvent;\n trackletDeleted: TrackletDeletedEvent;\n addPoints: AddPointsEvent;\n clearPointsInVideo: ClearPointsInVideoEvent;\n streamingStarted: StreamingStartedEvent;\n streamingCompleted: StreamingCompletedEvent;\n streamingStateUpdate: StreamingStateUpdateEvent;\n // HTMLVideoElement events https://developer.mozilla.org/en-US/docs/Web/HTML/Element/video#events\n loadstart: LoadStartEvent;\n effectUpdate: EffectUpdateEvent;\n renderingError: RenderingErrorEvent;\n}\n\ntype Metadata = {\n totalFrames: number;\n fps: number;\n width: number;\n height: number;\n};\n\nexport default class VideoWorkerBridge extends EventEmitter {\n static create(workerFactory: () => Worker) {\n const worker = workerFactory();\n return new VideoWorkerBridge(worker);\n }\n\n protected worker: Worker;\n private metadata: Metadata | null = null;\n private frameIndex: number = 0;\n\n private _sessionId: string | null = null;\n\n public get sessionId() {\n return this._sessionId;\n }\n\n public get width() {\n return this.metadata?.width ?? 0;\n }\n\n public get height() {\n return this.metadata?.height ?? 0;\n }\n\n public get numberOfFrames() {\n return this.metadata?.totalFrames ?? 0;\n }\n\n public get fps() {\n return this.metadata?.fps ?? 0;\n }\n\n public get frame() {\n return this.frameIndex;\n }\n\n constructor(worker: Worker) {\n super();\n this.worker = worker;\n\n worker.addEventListener(\n 'message',\n (\n event: VideoWorkerResponseMessageEvent | TrackerResponseMessageEvent,\n ) => {\n switch (event.data.action) {\n case 'error':\n // Deserialize error before triggering the event\n event.data.error = deserializeError(event.data.error);\n break;\n case 'decode':\n this.metadata = event.data;\n break;\n case 'frameUpdate':\n this.frameIndex = event.data.index;\n break;\n case 'sessionStarted':\n this._sessionId = event.data.sessionId;\n break;\n }\n this.trigger(event.data.action, event.data);\n },\n );\n }\n\n public setCanvas(canvas: HTMLCanvasElement): void {\n const offscreenCanvas = canvas.transferControlToOffscreen();\n this.sendRequest(\n 'setCanvas',\n {\n canvas: offscreenCanvas,\n },\n [offscreenCanvas],\n );\n }\n\n public setSource(source: string): void {\n this.sendRequest('setSource', {\n source,\n });\n }\n\n public terminate(): void {\n super.destroy();\n this.worker.terminate();\n }\n\n public play(): void {\n this.sendRequest('play');\n }\n\n public pause(): void {\n this.sendRequest('pause');\n }\n\n public stop(): void {\n this.sendRequest('stop');\n }\n\n public goToFrame(index: number): void {\n this.sendRequest('frameUpdate', {\n index,\n });\n }\n\n public previousFrame(): void {\n const index = Math.max(0, this.frameIndex - 1);\n this.goToFrame(index);\n }\n\n public nextFrame(): void {\n const index = Math.min(this.frameIndex + 1, this.numberOfFrames - 1);\n this.goToFrame(index);\n }\n\n public set frame(index: number) {\n this.sendRequest('frameUpdate', {index});\n }\n\n createFilmstrip(width: number, height: number): Promise {\n return new Promise((resolve, _reject) => {\n const handleFilmstripResponse = (\n event: MessageEvent,\n ) => {\n if (event.data.action === 'filmstrip') {\n this.worker.removeEventListener('message', handleFilmstripResponse);\n resolve(event.data.filmstrip);\n }\n };\n\n this.worker.addEventListener('message', handleFilmstripResponse);\n\n this.sendRequest('filmstrip', {\n width,\n height,\n });\n });\n }\n\n setEffect(name: keyof Effects, index: EffectIndex, options?: EffectOptions) {\n this.sendRequest('setEffect', {\n name,\n index,\n options,\n });\n }\n\n encode(): void {\n this.sendRequest('encode');\n }\n\n initializeTracker(name: keyof Trackers, options: TrackerOptions): void {\n this.sendRequest('initializeTracker', {\n name,\n options,\n });\n }\n\n startSession(videoUrl: string): Promise {\n return new Promise(resolve => {\n const handleResponse = (\n event: MessageEvent<\n SessionStartedResponse | SessionStartFailedResponse\n >,\n ) => {\n if (event.data.action === 'sessionStarted') {\n this.worker.removeEventListener('message', handleResponse);\n resolve(event.data.sessionId);\n }\n if (event.data.action === 'sessionStartFailed') {\n this.worker.removeEventListener('message', handleResponse);\n resolve(null);\n }\n };\n\n this.worker.addEventListener('message', handleResponse);\n this.sendRequest('startSession', {\n videoUrl,\n });\n });\n }\n\n closeSession(): void {\n this.sendRequest('closeSession');\n }\n\n logAnnotations(): void {\n this.sendRequest('logAnnotations');\n }\n\n createTracklet(): Promise {\n return new Promise(resolve => {\n const handleResponse = (event: MessageEvent) => {\n if (event.data.action === 'trackletCreated') {\n this.worker.removeEventListener('message', handleResponse);\n resolve(event.data.tracklet);\n }\n };\n\n this.worker.addEventListener('message', handleResponse);\n\n this.sendRequest('createTracklet');\n });\n }\n\n deleteTracklet(trackletId: number): Promise {\n return new Promise((resolve, reject) => {\n const handleResponse = (event: MessageEvent) => {\n if (event.data.action === 'trackletDeleted') {\n this.worker.removeEventListener('message', handleResponse);\n if (event.data.isSuccessful) {\n resolve();\n } else {\n reject(`could not delete tracklet ${trackletId}`);\n }\n }\n };\n this.worker.addEventListener('message', handleResponse);\n this.sendRequest('deleteTracklet', {trackletId});\n });\n }\n\n updatePoints(\n objectId: number,\n points: SegmentationPoint[],\n ): Promise {\n return new Promise(resolve => {\n const handleResponse = (event: MessageEvent) => {\n if (event.data.action === 'addPoints') {\n this.worker.removeEventListener('message', handleResponse);\n resolve(event.data.isSuccessful);\n }\n };\n\n this.worker.addEventListener('message', handleResponse);\n\n this.sendRequest('updatePoints', {\n frameIndex: this.frame,\n objectId,\n points,\n });\n });\n }\n\n clearPointsInFrame(objectId: number) {\n this.sendRequest('clearPointsInFrame', {\n frameIndex: this.frame,\n objectId,\n });\n }\n\n clearPointsInVideo(): Promise {\n return new Promise(resolve => {\n const handleResponse = (\n event: MessageEvent,\n ) => {\n if (event.data.action === 'clearPointsInVideo') {\n this.worker.removeEventListener('message', handleResponse);\n resolve(event.data.isSuccessful);\n }\n };\n this.worker.addEventListener('message', handleResponse);\n this.sendRequest('clearPointsInVideo');\n });\n }\n\n streamMasks(): void {\n this.sendRequest('streamMasks', {\n frameIndex: this.frame,\n });\n }\n\n abortStreamMasks(): Promise {\n return new Promise(resolve => {\n const handleAbortResponse = (\n event: MessageEvent,\n ) => {\n if (\n event.data.action === 'streamingStateUpdate' &&\n event.data.state === 'aborted'\n ) {\n this.worker.removeEventListener('message', handleAbortResponse);\n resolve();\n }\n };\n\n this.worker.addEventListener('message', handleAbortResponse);\n this.sendRequest('abortStreamMasks');\n });\n }\n\n getWorker_ONLY_USE_WITH_CAUTION(): Worker {\n return this.worker;\n }\n\n /**\n * Convenient function to have typed postMessage.\n *\n * @param action Video worker action\n * @param message Actual payload\n * @param transfer Any object that should be transferred instead of cloned\n */\n protected sendRequest(\n action: T['action'],\n payload?: Omit,\n transfer?: Transferable[],\n ) {\n this.worker.postMessage(\n {\n action,\n ...payload,\n },\n {\n transfer,\n },\n );\n }\n\n // // Override EventEmitter\n\n // addEventListener(\n // type: K,\n // listener: (ev: WorkerEventMap[K]) => unknown,\n // ): void {\n // switch (type) {\n // case 'frameUpdate':\n // {\n // const event: FrameUpdateEvent = {\n // index: this.frameIndex,\n // };\n // // @ts-expect-error Incorrect typing. Not sure how to correctly type it\n // listener(event);\n // }\n // break;\n // case 'sessionStarted': {\n // if (this.sessionId !== null) {\n // const event: SessionStartedEvent = {\n // sessionId: this.sessionId,\n // };\n // // @ts-expect-error Incorrect typing. Not sure how to correctly type it\n // listener(event);\n // }\n // }\n // }\n // super.addEventListener(type, listener);\n // }\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/video/VideoWorkerContext.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {\n DecodedVideo,\n ImageFrame,\n decodeStream,\n} from '@/common/codecs/VideoDecoder';\nimport {encode as encodeVideo} from '@/common/codecs/VideoEncoder';\nimport {\n Effect,\n EffectActionPoint,\n EffectFrameContext,\n EffectOptions,\n} from '@/common/components/video/effects/Effect';\nimport AllEffects, {\n EffectIndex,\n Effects,\n} from '@/common/components/video/effects/Effects';\nimport Logger from '@/common/logger/Logger';\nimport {Mask, SegmentationPoint, Tracklet} from '@/common/tracker/Tracker';\nimport {streamFile} from '@/common/utils/FileUtils';\nimport {Stats} from '@/debug/stats/Stats';\nimport {VIDEO_WATERMARK_TEXT} from '@/demo/DemoConfig';\nimport CreateFilmstripError from '@/graphql/errors/CreateFilmstripError';\nimport DrawFrameError from '@/graphql/errors/DrawFrameError';\nimport WebGLContextError from '@/graphql/errors/WebGLContextError';\nimport {RLEObject} from '@/jscocotools/mask';\nimport invariant from 'invariant';\nimport {CanvasForm} from 'pts';\nimport {serializeError} from 'serialize-error';\nimport {\n DecodeResponse,\n EffectUpdateResponse,\n EncodingCompletedResponse,\n EncodingStateUpdateResponse,\n FilmstripResponse,\n FrameUpdateResponse,\n PauseRequest,\n PlayRequest,\n RenderingErrorResponse,\n VideoWorkerResponse,\n} from './VideoWorkerTypes';\n\nfunction getEvenlySpacedItems(decodedVideo: DecodedVideo, x: number) {\n const p = Math.floor(decodedVideo.numFrames / Math.max(1, x - 1));\n const middleFrames = decodedVideo.frames\n .slice(p, p * x)\n .filter(function (_, i) {\n return 0 == i % p;\n });\n return [\n decodedVideo.frames[0],\n ...middleFrames,\n decodedVideo.frames[decodedVideo.numFrames - 1],\n ];\n}\n\nexport type FrameInfo = {\n tracklet: Tracklet;\n mask: Mask;\n};\n\nconst WATERMARK_BOX_HORIZONTAL_PADDING = 10;\nconst WATERMARK_BOX_VERTICAL_PADDING = 10;\n\nexport type VideoStats = {\n fps?: Stats;\n videoFps?: Stats;\n total?: Stats;\n effect0?: Stats;\n effect1?: Stats;\n frameBmp?: Stats;\n maskBmp?: Stats;\n memory?: Stats;\n};\n\nexport default class VideoWorkerContext {\n private _canvas: OffscreenCanvas | null = null;\n private _stats: VideoStats = {};\n private _ctx: OffscreenCanvasRenderingContext2D | null = null;\n private _form: CanvasForm | null = null;\n private _decodedVideo: DecodedVideo | null = null;\n private _frameIndex: number = 0;\n private _isPlaying: boolean = false;\n private _playbackRAFHandle: number | null = null;\n private _playbackTimeoutHandle: NodeJS.Timeout | null = null;\n private _isDrawing: boolean = false;\n private _glObjects: WebGL2RenderingContext | null = null;\n private _glBackground: WebGL2RenderingContext | null = null;\n private _canvasHighlights: OffscreenCanvas | null = null;\n private _canvasBackground: OffscreenCanvas | null = null;\n private _allowAnimation: boolean = false;\n private _currentSegmetationPoint: EffectActionPoint | null = null;\n\n private _effects: Effect[];\n private _tracklets: Tracklet[] = [];\n\n public get width(): number {\n return this._decodedVideo?.width ?? 0;\n }\n\n public get height(): number {\n return this._decodedVideo?.height ?? 0;\n }\n\n public get frameIndex(): number {\n return this._frameIndex;\n }\n\n public get currentFrame(): VideoFrame | null {\n return this._decodedVideo?.frames[this._frameIndex].bitmap ?? null;\n }\n\n constructor() {\n this._effects = [\n AllEffects.Original, // Image as background\n AllEffects.Overlay, // Masks on top\n ];\n\n // Loading watermark fonts. This is going to be async, but by the time of\n // video encoding, the fonts should be available.\n this._loadWatermarkFonts();\n }\n\n private initializeWebGLContext(width: number, height: number): void {\n // Given that we use highlight and background effects as layers,\n // we need to create two WebGL contexts, one for each set.\n // To avoid memory leaks and too many active contexts,\n // these contexts must be re-used over the lifecycle of the session.\n\n if (this._canvasHighlights == null && this._glObjects == null) {\n this._canvasHighlights = new OffscreenCanvas(width, height);\n this._glObjects = this._canvasHighlights.getContext('webgl2');\n\n this._canvasHighlights.addEventListener(\n 'webglcontextlost',\n event => {\n event.preventDefault();\n this._sendRenderingError(\n new WebGLContextError('WebGL context lost.'),\n );\n },\n false,\n );\n } else if (\n this._canvasHighlights != null &&\n (this._canvasHighlights.width !== width ||\n this._canvasHighlights.height !== height)\n ) {\n // Resize canvas and webgl viewport\n this._canvasHighlights.width = width;\n this._canvasHighlights.height = height;\n if (this._glObjects != null) {\n this._glObjects.viewport(0, 0, width, height);\n }\n }\n\n if (this._canvasBackground == null && this._glBackground == null) {\n this._canvasBackground = new OffscreenCanvas(width, height);\n this._glBackground = this._canvasBackground.getContext('webgl2');\n\n this._canvasBackground.addEventListener(\n 'webglcontextlost',\n event => {\n event.preventDefault();\n this._sendRenderingError(\n new WebGLContextError('WebGL context lost.'),\n );\n },\n false,\n );\n } else if (\n this._canvasBackground != null &&\n (this._canvasBackground.width != width ||\n this._canvasBackground.height != height)\n ) {\n // Resize canvas and webgl viewport\n this._canvasBackground.width = width;\n this._canvasBackground.height = height;\n if (this._glBackground != null) {\n this._glBackground.viewport(0, 0, width, height);\n }\n }\n }\n\n public setCanvas(canvas: OffscreenCanvas) {\n this._canvas = canvas;\n this._ctx = canvas.getContext('2d');\n if (this._ctx == null) {\n throw new Error('could not initialize drawing context');\n }\n this._form = new CanvasForm(this._ctx);\n }\n\n public setSource(src: string) {\n this.close();\n\n // Clear state of previous source.\n this.updateFrameIndex(0);\n this._tracklets = [];\n\n this._decodeVideo(src);\n }\n\n public goToFrame(index: number): void {\n // Cancel any ongoing render\n this._cancelRender();\n this.updateFrameIndex(index);\n this._playbackRAFHandle = requestAnimationFrame(this._drawFrame.bind(this));\n }\n\n public play(): void {\n // Video already playing\n if (this._isPlaying) {\n return;\n }\n\n // Cannot playback without frames\n if (this._decodedVideo === null) {\n throw new Error('no decoded video');\n }\n\n const {numFrames, fps} = this._decodedVideo;\n const timePerFrame = 1000 / (fps ?? 30);\n let startTime: number | null = null;\n // The offset frame index compensate for cases where the video playback\n // does not start at frame index 0.\n const offsetFrameIndex = this._frameIndex;\n\n const updateFrame = (time: number) => {\n if (startTime === null) {\n startTime = time;\n }\n\n this._stats.fps?.begin();\n\n const diff = time - startTime;\n const expectedFrame =\n (Math.floor(diff / timePerFrame) + offsetFrameIndex) % numFrames;\n\n if (this._frameIndex !== expectedFrame && !this._isDrawing) {\n // Update to the next expected frame\n this.updateFrameIndex(expectedFrame);\n this._drawFrame();\n }\n\n this._playbackRAFHandle = requestAnimationFrame(updateFrame);\n\n this._stats.fps?.end();\n };\n\n this.updatePlayback(true);\n this._playbackRAFHandle = requestAnimationFrame(updateFrame);\n }\n\n public pause(): void {\n this.updatePlayback(false);\n this._cancelRender();\n }\n\n public stop(): void {\n this.pause();\n this.updateFrameIndex(0);\n }\n\n public async createFilmstrip(width: number, height: number): Promise {\n if (width < 1 || height < 1) {\n Logger.warn(\n `Cannot create filmstrip because width ${width} or height ${height} is too small.`,\n );\n return;\n }\n\n try {\n const canvas = new OffscreenCanvas(width, height);\n const ctx = canvas.getContext('2d');\n\n if (this._decodedVideo !== null) {\n const scale = canvas.height / this._decodedVideo.height;\n const resizeWidth = this._decodedVideo.width * scale;\n\n const spacedFrames = getEvenlySpacedItems(\n this._decodedVideo,\n Math.ceil(canvas.width / resizeWidth),\n );\n\n spacedFrames.forEach((frame, idx) => {\n if (frame != null) {\n ctx?.drawImage(\n frame.bitmap,\n resizeWidth * idx,\n 0,\n resizeWidth,\n canvas.height,\n );\n }\n });\n }\n\n const filmstrip = await createImageBitmap(canvas);\n\n this.sendResponse(\n 'filmstrip',\n {\n filmstrip,\n },\n [filmstrip],\n );\n } catch {\n this._sendRenderingError(\n new CreateFilmstripError('Failed to create filmstrip'),\n );\n }\n }\n\n public async setEffect(\n name: keyof Effects,\n index: EffectIndex,\n options?: EffectOptions,\n ): Promise {\n const effect: Effect = AllEffects[name];\n\n // The effect has changed.\n if (this._effects[index] !== effect) {\n // Effect changed. Cleanup old effect first. Effects are responsible for\n // cleaning up their memory.\n await this._effects[index].cleanup();\n\n const offCanvas =\n index === EffectIndex.BACKGROUND\n ? this._canvasBackground\n : this._canvasHighlights;\n invariant(offCanvas != null, 'need OffscreenCanvas to render effects');\n const webglContext =\n index === EffectIndex.BACKGROUND ? this._glBackground : this._glObjects;\n invariant(webglContext != null, 'need WebGL context to render effects');\n\n // Initialize the effect. This can be used by effects to prepare\n // resources needed for rendering. If the video wasn't decoded yet, the\n // effect setup will happen in the _decodeVideo function.\n if (this._decodedVideo != null) {\n await effect.setup({\n width: this._decodedVideo.width,\n height: this._decodedVideo.height,\n canvas: offCanvas,\n gl: webglContext,\n });\n }\n }\n\n // Update effect if already set effect was clicked again. This can happen\n // when there is a new variant of the effect.\n if (options != null) {\n // Update effect if already set effect was clicked again. This can happen\n // when there is a new variant of the effect.\n await effect.update(options);\n }\n\n // Notify the frontend about the effect state including its variant.\n this.sendResponse('effectUpdate', {\n name,\n index,\n variant: effect.variant,\n numVariants: effect.numVariants,\n });\n\n this._effects[index] = effect;\n this._playbackRAFHandle = requestAnimationFrame(this._drawFrame.bind(this));\n }\n\n async encode() {\n const decodedVideo = this._decodedVideo;\n invariant(\n decodedVideo !== null,\n 'cannot encode video because there is no decoded video available',\n );\n\n const canvas = new OffscreenCanvas(this.width, this.height);\n const ctx = canvas.getContext('2d', {willReadFrequently: true});\n invariant(\n ctx !== null,\n 'cannot encode video because failed to construct offscreen canvas context',\n );\n\n const form = new CanvasForm(ctx);\n\n const file = await encodeVideo(\n this.width,\n this.height,\n decodedVideo.frames.length,\n this._framesGenerator(decodedVideo, canvas, form),\n progress => {\n this.sendResponse('encodingStateUpdate', {\n progress,\n });\n },\n );\n this.sendResponse(\n 'encodingCompleted',\n {\n file,\n },\n [file],\n );\n }\n\n private async *_framesGenerator(\n decodedVideo: DecodedVideo,\n canvas: OffscreenCanvas,\n form: CanvasForm,\n ): AsyncGenerator {\n const frames = decodedVideo.frames;\n\n for (let frameIndex = 0; frameIndex < frames.length; ++frameIndex) {\n await this._drawFrameImpl(form, frameIndex, true);\n\n const frame = frames[frameIndex];\n const videoFrame = new VideoFrame(canvas, {\n timestamp: frame.bitmap.timestamp,\n });\n\n yield {\n bitmap: videoFrame,\n timestamp: frame.timestamp,\n duration: frame.duration,\n };\n\n videoFrame.close();\n }\n }\n\n public enableStats() {\n this._stats.fps = new Stats('fps');\n this._stats.videoFps = new Stats('fps', 'V');\n this._stats.total = new Stats('ms', 'T');\n this._stats.effect0 = new Stats('ms', 'B');\n this._stats.effect1 = new Stats('ms', 'H');\n this._stats.frameBmp = new Stats('ms', 'F');\n this._stats.maskBmp = new Stats('ms', 'M');\n this._stats.memory = new Stats('memory');\n }\n\n public allowEffectAnimation(\n allow: boolean = true,\n objectId?: number,\n points?: SegmentationPoint[],\n ) {\n if (objectId != null && points != null && points.length) {\n const last_point_position = points[points.length - 1];\n this._currentSegmetationPoint = {\n objectId,\n position: [last_point_position[0], last_point_position[1]],\n };\n }\n\n if (!allow) {\n this._currentSegmetationPoint = null;\n }\n\n this._allowAnimation = allow;\n }\n\n public close(): void {\n // Clear any frame content\n this._ctx?.reset();\n\n // Close frames of previously decoded video.\n this._decodedVideo?.frames.forEach(f => f.bitmap.close());\n this._decodedVideo = null;\n }\n\n // TRACKER\n\n public updateTracklets(\n frameIndex: number,\n tracklets: Tracklet[],\n shouldGoToFrame: boolean = true,\n ): void {\n this._tracklets = tracklets;\n if (shouldGoToFrame) {\n this.goToFrame(frameIndex);\n }\n }\n\n public clearTrackletMasks(tracklet: Tracklet): void {\n this._tracklets = this._tracklets.filter(t => t.id != tracklet.id);\n }\n\n public clearMasks(): void {\n this._tracklets = [];\n }\n\n // PRIVATE FUNCTIONS\n\n private sendResponse(\n action: T['action'],\n message?: Omit,\n transfer?: Transferable[],\n ): void {\n self.postMessage(\n {\n action,\n ...message,\n },\n {\n transfer,\n },\n );\n }\n\n private async _decodeVideo(src: string): Promise {\n const canvas = this._canvas;\n invariant(canvas != null, 'need canvas to render decoded video');\n\n this.sendResponse('loadstart');\n\n const fileStream = streamFile(src, {\n credentials: 'same-origin',\n cache: 'no-store',\n });\n\n let renderedFirstFrame = false;\n this._decodedVideo = await decodeStream(fileStream, async progress => {\n const {fps, height, width, numFrames, frames} = progress;\n this._decodedVideo = progress;\n if (!renderedFirstFrame) {\n renderedFirstFrame = true;\n canvas.width = width;\n canvas.height = height;\n // Set WebGL contexts right after the first frame decoded\n this.initializeWebGLContext(width, height);\n\n // Initialize effect once first frame was decoded.\n for (const [i, effect] of this._effects.entries()) {\n const offCanvas =\n i === EffectIndex.BACKGROUND\n ? this._canvasBackground\n : this._canvasHighlights;\n invariant(offCanvas != null, 'need canvas to render effects');\n const webglContext =\n i === EffectIndex.BACKGROUND ? this._glBackground : this._glObjects;\n invariant(\n webglContext != null,\n 'need WebGL context to render effects',\n );\n await effect.setup({\n width,\n height,\n canvas: offCanvas,\n gl: webglContext,\n });\n }\n\n // Need to render frame immediately. Cannot go through\n // requestAnimationFrame because then rendering this frame would be\n // delayed until the full video has finished decoding.\n this._drawFrame();\n\n this._stats.videoFps?.updateMaxValue(fps);\n this._stats.total?.updateMaxValue(1000 / fps);\n this._stats.effect0?.updateMaxValue(1000 / fps);\n this._stats.effect1?.updateMaxValue(1000 / fps);\n this._stats.frameBmp?.updateMaxValue(1000 / fps);\n this._stats.maskBmp?.updateMaxValue(1000 / fps);\n }\n this.sendResponse('decode', {\n totalFrames: numFrames,\n numFrames: frames.length,\n fps: fps,\n width: width,\n height: height,\n done: false,\n });\n });\n\n if (!renderedFirstFrame) {\n canvas.width = this._decodedVideo.width;\n canvas.height = this._decodedVideo.height;\n this._drawFrame();\n }\n\n this.sendResponse('decode', {\n totalFrames: this._decodedVideo.numFrames,\n numFrames: this._decodedVideo.frames.length,\n fps: this._decodedVideo.fps,\n width: this._decodedVideo.width,\n height: this._decodedVideo.height,\n done: true,\n });\n }\n\n private _drawFrame(): void {\n if (this._canvas !== null && this._form !== null) {\n this._drawFrameImpl(this._form, this._frameIndex);\n }\n }\n\n private async _drawFrameImpl(\n form: CanvasForm,\n frameIndex: number,\n enableWatermark: boolean = false,\n step: number = 0,\n maxSteps: number = 40,\n ): Promise {\n if (this._decodedVideo === null) {\n return;\n }\n\n {\n this._stats.videoFps?.begin();\n this._stats.total?.begin();\n this._stats.memory?.begin();\n }\n\n try {\n const frame = this._decodedVideo.frames[frameIndex];\n const {bitmap} = frame;\n\n this._stats.frameBmp?.begin();\n\n // Need to convert VideoFrame to ImageBitmap because Safari can only apply\n // globalCompositeOperation on ImageBitmap and fails on VideoFrame. FWIW,\n // Chrome treats VideoFrame similarly to ImageBitmap.\n const frameBitmap = await createImageBitmap(bitmap);\n\n this._stats.frameBmp?.end();\n\n const masks: Mask[] = [];\n const colors: string[] = [];\n const tracklets: Tracklet[] = [];\n this._tracklets.forEach(tracklet => {\n const mask = tracklet.masks[frameIndex];\n if (mask != null) {\n masks.push(mask);\n tracklets.push(tracklet);\n colors.push(tracklet.color);\n }\n });\n const effectActionPoint = this._currentSegmetationPoint;\n\n this._stats.maskBmp?.begin();\n\n const effectMaskPromises = masks.map(async ({data, bounds}) => {\n return {\n bounds,\n bitmap: data as RLEObject,\n };\n });\n const effectMasks = await Promise.all(effectMaskPromises);\n\n this._stats.maskBmp?.end();\n\n form.ctx.fillStyle = 'rgba(0, 0, 0, 0)';\n form.ctx.fillRect(0, 0, this.width, this.height);\n\n const effectParams: EffectFrameContext = {\n frame: frameBitmap,\n masks: effectMasks,\n maskColors: colors,\n frameIndex: frameIndex,\n totalFrames: this._decodedVideo.frames.length,\n fps: this._decodedVideo.fps,\n width: frameBitmap.width,\n height: frameBitmap.height,\n actionPoint: null,\n };\n\n // Allows animation within a single frame.\n if (this._allowAnimation && step < maxSteps) {\n const animationDuration = 2; // Total duration of the animation in seconds\n const progress = step / maxSteps;\n const timeParameter = progress * animationDuration;\n // Pass dynamic effect params\n effectParams.timeParameter = timeParameter;\n effectParams.actionPoint = effectActionPoint;\n\n this._processEffects(form, effectParams, tracklets);\n\n // Use RAF to draw frame, and update the display,\n // this avoids to wait until the javascript call stack is cleared.\n requestAnimationFrame(() =>\n this._drawFrameImpl(form, frameIndex, false, step + 1, maxSteps),\n );\n } else {\n this._processEffects(form, effectParams, tracklets);\n }\n\n if (enableWatermark) {\n this._drawWatermark(form, frameBitmap);\n }\n\n // Do not simply drop the JavaScript reference to the ImageBitmap; doing so\n // will keep its graphics resource alive until the next time the garbage\n // collector runs.\n frameBitmap.close();\n\n {\n this._stats.videoFps?.end();\n this._stats.total?.end();\n this._stats.memory?.end();\n }\n\n this._isDrawing = false;\n } catch {\n this._sendRenderingError(new DrawFrameError('Failed to draw frame'));\n }\n }\n\n private _drawWatermark(form: CanvasForm, frameBitmap: ImageBitmap): void {\n const frameWidth = this._canvas?.width || frameBitmap.width;\n const frameHeight = this._canvas?.height || frameBitmap.height;\n // Font size is either 12 or smaller based on available width\n // since the font is not monospaced, we approximate it'll fit 1.5 more characters than monospaced\n const approximateFontSize = Math.min(\n Math.floor(frameWidth / (VIDEO_WATERMARK_TEXT.length / 1.5)),\n 12,\n );\n\n form.ctx.font = `${approximateFontSize}px \"Inter\", sans-serif`;\n const measureGeneratedBy = form.ctx.measureText(VIDEO_WATERMARK_TEXT);\n\n const textBoxWidth =\n measureGeneratedBy.width + 2 * WATERMARK_BOX_HORIZONTAL_PADDING;\n const textBoxHeight =\n measureGeneratedBy.actualBoundingBoxAscent +\n 2 * WATERMARK_BOX_VERTICAL_PADDING;\n const textBoxX = frameWidth - textBoxWidth;\n const textBoxY = frameHeight - textBoxHeight;\n\n form.ctx.fillStyle = 'rgba(0, 0, 0, 0.4)';\n form.ctx.beginPath();\n form.ctx.roundRect(\n Math.round(textBoxX),\n Math.round(textBoxY),\n Math.round(textBoxWidth),\n Math.round(textBoxHeight),\n [WATERMARK_BOX_HORIZONTAL_PADDING, 0, 0, 0],\n );\n form.ctx.fill();\n\n // Always reset the text style because some effects may change text styling in the same ctx\n form.ctx.fillStyle = 'rgba(255, 255, 255, 0.8)';\n form.ctx.textAlign = 'left';\n\n form.ctx.fillText(\n VIDEO_WATERMARK_TEXT,\n Math.round(textBoxX + WATERMARK_BOX_HORIZONTAL_PADDING),\n Math.round(\n textBoxY +\n WATERMARK_BOX_VERTICAL_PADDING +\n measureGeneratedBy.actualBoundingBoxAscent,\n ),\n );\n }\n\n private updateFrameIndex(index: number): void {\n this._frameIndex = index;\n this.sendResponse('frameUpdate', {\n index,\n });\n }\n\n private _loadWatermarkFonts() {\n const requiredFonts = [\n {\n url: '/fonts/Inter-VariableFont.ttf',\n format: 'truetype-variations',\n },\n ];\n requiredFonts.forEach(requiredFont => {\n const fontFace = new FontFace(\n 'Inter',\n `url(${requiredFont.url}) format('${requiredFont.format}')`,\n );\n fontFace.load().then(font => {\n self.fonts.add(font);\n });\n });\n }\n\n private updatePlayback(playing: boolean): void {\n if (playing) {\n this.sendResponse('play');\n } else {\n this.sendResponse('pause');\n }\n this._isPlaying = playing;\n }\n\n private _cancelRender(): void {\n if (this._playbackTimeoutHandle !== null) {\n clearTimeout(this._playbackTimeoutHandle);\n this._playbackTimeoutHandle = null;\n }\n if (this._playbackRAFHandle !== null) {\n cancelAnimationFrame(this._playbackRAFHandle);\n this._playbackRAFHandle = null;\n }\n }\n\n private _sendRenderingError(error: Error): void {\n this.sendResponse('renderingError', {\n error: serializeError(error),\n });\n }\n\n private _processEffects(\n form: CanvasForm,\n effectParams: EffectFrameContext,\n tracklets: Tracklet[],\n ) {\n for (let i = 0; i < this._effects.length; i++) {\n const effect = this._effects[i];\n\n if (i === 0) {\n this._stats.effect0?.begin();\n } else if (i === 1) {\n this._stats.effect1?.begin();\n }\n\n effect.apply(form, effectParams, tracklets);\n\n if (i === 0) {\n this._stats.effect0?.end();\n } else if (i === 1) {\n this._stats.effect1?.end();\n }\n }\n }\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/video/VideoWorkerTypes.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {\n DecodeEvent,\n EffectUpdateEvent,\n EncodingCompletedEvent,\n EncodingStateUpdateEvent,\n FilmstripEvent,\n FrameUpdateEvent,\n LoadStartEvent,\n RenderingErrorEvent,\n} from './VideoWorkerBridge';\nimport {EffectOptions} from './effects/Effect';\nimport type {Effects} from './effects/Effects';\n\nexport type Request = {\n action: A;\n} & P;\n\n// REQUESTS\n\nexport type SetCanvasRequest = Request<\n 'setCanvas',\n {\n canvas: OffscreenCanvas;\n }\n>;\nexport type SetSourceRequest = Request<\n 'setSource',\n {\n source: string;\n }\n>;\nexport type PlayRequest = Request<'play', unknown>;\nexport type PauseRequest = Request<'pause', unknown>;\nexport type StopRequest = Request<'stop', unknown>;\nexport type FrameUpdateRequest = Request<\n 'frameUpdate',\n {\n index: number;\n }\n>;\nexport type FilmstripRequest = Request<\n 'filmstrip',\n {\n width: number;\n height: number;\n }\n>;\nexport type SetEffectRequest = Request<\n 'setEffect',\n {\n name: keyof Effects;\n index: number;\n options?: EffectOptions;\n }\n>;\n\nexport type EncodeVideoRequest = Request<'encode', unknown>;\n\nexport type EnableStatsRequest = Request<'enableStats', unknown>;\n\nexport type VideoWorkerRequest =\n | SetCanvasRequest\n | SetSourceRequest\n | PlayRequest\n | PauseRequest\n | StopRequest\n | FrameUpdateRequest\n | FilmstripRequest\n | SetEffectRequest\n | EncodeVideoRequest\n | EnableStatsRequest;\n\nexport type VideoWorkerRequestMessageEvent = MessageEvent;\n\n// RESPONSES\n\nexport type ErrorResponse = Request<\n 'error',\n {\n error: unknown;\n }\n>;\n\nexport type DecodeResponse = Request<'decode', DecodeEvent>;\n\nexport type EncodingStateUpdateResponse = Request<\n 'encodingStateUpdate',\n EncodingStateUpdateEvent\n>;\n\nexport type EncodingCompletedResponse = Request<\n 'encodingCompleted',\n EncodingCompletedEvent\n>;\n\nexport type FilmstripResponse = Request<'filmstrip', FilmstripEvent>;\n\nexport type PlayResponse = Request<'play', unknown>;\n\nexport type PauseResponse = Request<'pause', unknown>;\n\nexport type FrameUpdateResponse = Request<'frameUpdate', FrameUpdateEvent>;\n\nexport type RenderingErrorResponse = Request<\n 'renderingError',\n RenderingErrorEvent\n>;\n\n// HTMLVideoElement events https://developer.mozilla.org/en-US/docs/Web/HTML/Element/video#events\n\nexport type LoadStartResponse = Request<'loadstart', LoadStartEvent>;\n\nexport type EffectUpdateResponse = Request<'effectUpdate', EffectUpdateEvent>;\n\nexport type VideoWorkerResponse =\n | ErrorResponse\n | FilmstripResponse\n | DecodeResponse\n | EncodingStateUpdateResponse\n | EncodingCompletedResponse\n | PlayResponse\n | PauseResponse\n | FrameUpdateResponse\n | LoadStartResponse\n | RenderingErrorResponse\n | EffectUpdateResponse;\n\nexport type VideoWorkerResponseMessageEvent = MessageEvent;\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/video/editor/DemoVideoEditor.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport TrackletsAnnotation from '@/common/components/annotations/TrackletsAnnotation';\nimport useCloseSessionBeforeUnload from '@/common/components/session/useCloseSessionBeforeUnload';\nimport MessagesSnackbar from '@/common/components/snackbar/MessagesSnackbar';\nimport useMessagesSnackbar from '@/common/components/snackbar/useDemoMessagesSnackbar';\nimport {OBJECT_TOOLBAR_INDEX} from '@/common/components/toolbar/ToolbarConfig';\nimport useToolbarTabs from '@/common/components/toolbar/useToolbarTabs';\nimport VideoFilmstripWithPlayback from '@/common/components/video/VideoFilmstripWithPlayback';\nimport {\n FrameUpdateEvent,\n RenderingErrorEvent,\n SessionStartedEvent,\n TrackletsEvent,\n} from '@/common/components/video/VideoWorkerBridge';\nimport VideoEditor from '@/common/components/video/editor/VideoEditor';\nimport useResetDemoEditor from '@/common/components/video/editor/useResetEditor';\nimport useVideo from '@/common/components/video/editor/useVideo';\nimport InteractionLayer from '@/common/components/video/layers/InteractionLayer';\nimport {PointsLayer} from '@/common/components/video/layers/PointsLayer';\nimport LoadingStateScreen from '@/common/loading/LoadingStateScreen';\nimport UploadLoadingScreen from '@/common/loading/UploadLoadingScreen';\nimport useScreenSize from '@/common/screen/useScreenSize';\nimport {SegmentationPoint} from '@/common/tracker/Tracker';\nimport {\n activeTrackletObjectIdAtom,\n frameIndexAtom,\n isAddObjectEnabledAtom,\n isPlayingAtom,\n isVideoLoadingAtom,\n pointsAtom,\n sessionAtom,\n streamingStateAtom,\n trackletObjectsAtom,\n uploadingStateAtom,\n VideoData,\n} from '@/demo/atoms';\nimport useSettingsContext from '@/settings/useSettingsContext';\nimport {color, spacing} from '@/theme/tokens.stylex';\nimport stylex from '@stylexjs/stylex';\nimport {useAtom, useAtomValue, useSetAtom} from 'jotai';\nimport {useEffect, useState} from 'react';\nimport type {ErrorObject} from 'serialize-error';\n\nconst styles = stylex.create({\n container: {\n display: 'flex',\n flexDirection: 'column',\n overflow: 'auto',\n width: '100%',\n borderColor: color['gray-800'],\n backgroundColor: color['gray-800'],\n borderWidth: 8,\n borderRadius: 12,\n '@media screen and (max-width: 768px)': {\n // on mobile, we want to grow the editor container so that the editor\n // fills the remaining vertical space between the navbar and bottom\n // of the page\n flexGrow: 1,\n borderWidth: 0,\n borderRadius: 0,\n paddingBottom: spacing[4],\n },\n },\n loadingScreenWrapper: {\n position: 'absolute',\n top: 0,\n left: 0,\n width: '100%',\n height: '100%',\n background: 'white',\n overflow: 'hidden',\n overflowY: 'auto',\n zIndex: 999,\n },\n});\n\ntype Props = {\n video: VideoData;\n};\n\nexport default function DemoVideoEditor({video: inputVideo}: Props) {\n const {settings} = useSettingsContext();\n const video = useVideo();\n\n const [isSessionStartFailed, setIsSessionStartFailed] =\n useState(false);\n\n const [session, setSession] = useAtom(sessionAtom);\n\n const [activeTrackletId, setActiveTrackletObjectId] = useAtom(\n activeTrackletObjectIdAtom,\n );\n const setTrackletObjects = useSetAtom(trackletObjectsAtom);\n const setFrameIndex = useSetAtom(frameIndexAtom);\n const points = useAtomValue(pointsAtom);\n const isAddObjectEnabled = useAtomValue(isAddObjectEnabledAtom);\n const streamingState = useAtomValue(streamingStateAtom);\n const isPlaying = useAtomValue(isPlayingAtom);\n const isVideoLoading = useAtomValue(isVideoLoadingAtom);\n const uploadingState = useAtomValue(uploadingStateAtom);\n\n const [renderingError, setRenderingError] = useState(\n null,\n );\n\n const {isMobile} = useScreenSize();\n\n const [tabIndex] = useToolbarTabs();\n const {enqueueMessage} = useMessagesSnackbar();\n\n useCloseSessionBeforeUnload();\n\n const {resetEditor, resetSession} = useResetDemoEditor();\n useEffect(() => {\n resetEditor();\n }, [inputVideo, resetEditor]);\n\n useEffect(() => {\n function onFrameUpdate(event: FrameUpdateEvent) {\n setFrameIndex(event.index);\n }\n\n // Listen to frame updates to fetch the frame index in the main thread,\n // which is then used downstream to render points per frame.\n video?.addEventListener('frameUpdate', onFrameUpdate);\n\n function onSessionStarted(event: SessionStartedEvent) {\n setSession({id: event.sessionId, ranPropagation: false});\n }\n\n video?.addEventListener('sessionStarted', onSessionStarted);\n\n function onSessionStartFailed() {\n setIsSessionStartFailed(true);\n }\n\n video?.addEventListener('sessionStartFailed', onSessionStartFailed);\n\n function onTrackletsUpdated(event: TrackletsEvent) {\n const tracklets = event.tracklets;\n if (tracklets.length === 0) {\n resetSession();\n }\n setTrackletObjects(tracklets);\n }\n\n video?.addEventListener('trackletsUpdated', onTrackletsUpdated);\n\n function onRenderingError(event: RenderingErrorEvent) {\n setRenderingError(event.error);\n }\n\n video?.addEventListener('renderingError', onRenderingError);\n\n video?.initializeTracker('SAM 2', {\n inferenceEndpoint: settings.inferenceAPIEndpoint,\n });\n\n video?.startSession(inputVideo.path);\n\n return () => {\n video?.closeSession();\n video?.removeEventListener('frameUpdate', onFrameUpdate);\n video?.removeEventListener('sessionStarted', onSessionStarted);\n video?.removeEventListener('sessionStartFailed', onSessionStartFailed);\n video?.removeEventListener('trackletsUpdated', onTrackletsUpdated);\n video?.removeEventListener('renderingError', onRenderingError);\n };\n }, [\n setFrameIndex,\n setSession,\n setTrackletObjects,\n resetSession,\n inputVideo,\n video,\n settings.inferenceAPIEndpoint,\n settings.videoAPIEndpoint,\n ]);\n\n async function handleOptimisticPointUpdate(newPoints: SegmentationPoint[]) {\n if (session == null) {\n return;\n }\n\n async function createActiveTracklet() {\n if (!isAddObjectEnabled || newPoints.length === 0) {\n return;\n }\n const tracklet = await video?.createTracklet();\n if (tracklet != null && newPoints.length > 0) {\n setActiveTrackletObjectId(tracklet.id);\n video?.updatePoints(tracklet.id, [newPoints[newPoints.length - 1]]);\n }\n }\n\n if (activeTrackletId != null) {\n video?.updatePoints(activeTrackletId, newPoints);\n } else {\n await createActiveTracklet();\n }\n enqueueMessage('pointClick');\n }\n\n async function handleAddPoint(point: SegmentationPoint) {\n if (streamingState === 'partial' || streamingState === 'requesting') {\n return;\n }\n if (isPlaying) {\n return video?.pause();\n }\n handleOptimisticPointUpdate([...points, point]);\n }\n\n function handleRemovePoint(point: SegmentationPoint) {\n if (\n isPlaying ||\n streamingState === 'partial' ||\n streamingState === 'requesting'\n ) {\n return;\n }\n handleOptimisticPointUpdate(points.filter(p => p !== point));\n }\n\n // The interaction layer handles clicks onto the video canvas. It is used\n // to get absolute point clicks within the video's coordinate system.\n // The PointsLayer handles rendering of input points and allows removing\n // individual points by clicking on them.\n const layers = (\n <>\n {tabIndex === OBJECT_TOOLBAR_INDEX && (\n <>\n handleAddPoint(point)}\n />\n \n \n )}\n {!isMobile && }\n \n );\n\n return (\n <>\n {(isVideoLoading || session === null) && !isSessionStartFailed && (\n
\n \n
\n )}\n {isSessionStartFailed && (\n
\n Uh oh, it looks like there was an issue starting a session.\n }\n linkProps={{to: '..', label: 'Back to homepage'}}\n />\n
\n )}\n {isMobile && renderingError != null && (\n
\n \n
\n )}\n {uploadingState !== 'default' && (\n
\n \n
\n )}\n
\n \n
\n \n \n
\n \n
\n \n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/video/editor/ImageUtils.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nexport function convertVideoFrameToImageData(\n videoFrame: VideoFrame,\n): ImageData | undefined {\n const canvas = new OffscreenCanvas(\n videoFrame.displayWidth,\n videoFrame.displayHeight,\n );\n const ctx = canvas.getContext('2d');\n ctx?.drawImage(videoFrame, 0, 0);\n return ctx?.getImageData(0, 0, canvas.width, canvas.height);\n}\n\n/**\n * This utility provides two functions:\n * `process`: to find the bounding box of non-empty pixels from an ImageData, when looping through all its pixels\n * `crop` to cut out the subsection found in `process`\n * @returns\n */\nexport function findBoundingBox() {\n let xMin = Number.MAX_VALUE;\n let yMin = Number.MAX_VALUE;\n let xMax = Number.MIN_VALUE;\n let yMax = Number.MIN_VALUE;\n return {\n process: function (x: number, y: number, hasData: boolean) {\n if (hasData) {\n xMin = Math.min(x, xMin);\n xMax = Math.max(x, xMax);\n yMin = Math.min(y, yMin);\n yMax = Math.max(y, yMax);\n }\n return [xMin, xMax, yMin, yMax];\n },\n crop(imageData: ImageData): ImageData | null {\n const canvas = new OffscreenCanvas(imageData.width, imageData.height);\n const ctx = canvas.getContext('2d');\n\n const boundingBoxWidth = xMax - xMin;\n const boundingBoxHeight = yMax - yMin;\n if (ctx && boundingBoxWidth > 0 && boundingBoxHeight > 0) {\n ctx.clearRect(0, 0, canvas.width, canvas.height);\n ctx.putImageData(imageData, 0, 0);\n return ctx.getImageData(\n xMin,\n yMin,\n boundingBoxWidth,\n boundingBoxHeight,\n );\n } else {\n return null;\n }\n },\n getBox(): [[number, number], [number, number]] {\n return [\n [xMin, yMin],\n [xMax, yMax],\n ];\n },\n };\n}\n\nexport function magnifyImageRegion(\n canvas: HTMLCanvasElement | null,\n x: number,\n y: number,\n radius: number = 25,\n scale: number = 2,\n): string {\n if (canvas == null) {\n return '';\n }\n const ctx = canvas.getContext('2d');\n if (ctx) {\n const minX = x - radius < 0 ? radius - x : 0;\n const minY = y - radius < 0 ? radius - y : 0;\n const region = ctx.getImageData(\n Math.max(x - radius, 0),\n Math.max(y - radius, 0),\n radius * 2,\n radius * 2,\n );\n\n // ImageData doesn't scale-transform correctly on canvas\n // So we first draw the original size on an offscreen canvas, and then scale it\n const regionCanvas = new OffscreenCanvas(region.width, region.height);\n const regionCtx = regionCanvas.getContext('2d');\n regionCtx?.putImageData(region, minX > 0 ? minX : 0, minY > 0 ? minY : 0);\n\n const scaleCanvas = document.createElement('canvas');\n scaleCanvas.width = Math.round(region.width * scale);\n scaleCanvas.height = Math.round(region.height * scale);\n const scaleCtx = scaleCanvas.getContext('2d');\n scaleCtx?.scale(scale, scale);\n scaleCtx?.drawImage(regionCanvas, 0, 0);\n\n return scaleCanvas.toDataURL();\n }\n return '';\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/video/editor/VideoEditor.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {VideoData} from '@/demo/atoms';\nimport stylex, {StyleXStyles} from '@stylexjs/stylex';\nimport {useSetAtom} from 'jotai';\nimport {PropsWithChildren, RefObject, useEffect, useRef} from 'react';\nimport Video, {VideoRef} from '../Video';\nimport {videoAtom} from './atoms';\n\nconst MAX_VIDEO_WIDTH = 1280;\n\nconst styles = stylex.create({\n editorContainer: {\n position: 'relative',\n display: 'flex',\n flexDirection: 'column',\n alignItems: 'center',\n width: '100%',\n height: '100%',\n borderRadius: '0.375rem',\n overflow: {\n default: 'clip',\n '@media screen and (max-width: 768px)': 'visible',\n },\n },\n videoContainer: {\n position: 'relative',\n flexGrow: 1,\n overflow: 'hidden',\n width: '100%',\n maxWidth: MAX_VIDEO_WIDTH,\n },\n layers: {\n position: 'absolute',\n left: 0,\n top: 0,\n bottom: 0,\n right: 0,\n },\n loadingMessage: {\n position: 'absolute',\n top: '8px',\n right: '8px',\n padding: '6px 10px',\n backgroundColor: '#6441D2CC',\n color: '#FFF',\n display: 'flex',\n alignItems: 'center',\n gap: '8px',\n borderRadius: '8px',\n fontSize: '0.8rem',\n },\n});\n\nexport type InteractionLayerProps = {\n style: StyleXStyles;\n videoRef: RefObject;\n};\n\nexport type ControlsProps = {\n isPlaying: boolean;\n onPlay: () => void;\n onPause: () => void;\n onPreviousFrame?: () => void;\n onNextFrame?: () => void;\n};\n\ntype Props = PropsWithChildren<{\n video: VideoData;\n layers?: React.ReactNode;\n loading?: boolean;\n}>;\n\nexport default function VideoEditor({\n video: inputVideo,\n layers,\n loading,\n children,\n}: Props) {\n const videoRef = useRef(null);\n const setVideo = useSetAtom(videoAtom);\n\n // Initialize video atom\n useEffect(() => {\n setVideo(videoRef.current);\n return () => {\n setVideo(null);\n };\n }, [setVideo]);\n\n return (\n
\n
\n \n
{layers}
\n
\n {children}\n
\n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/video/editor/VideoEditorUtils.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {Mask, Tracklet} from '@/common/tracker/Tracker';\nimport {\n convertVideoFrameToImageData,\n findBoundingBox,\n} from '@/common/utils/ImageUtils';\nimport {DataArray} from '@/jscocotools/mask';\nimport invariant from 'invariant';\n\nfunction getCanvas(\n width: number,\n height: number,\n isOffscreen: boolean = false,\n): HTMLCanvasElement | OffscreenCanvas {\n if (isOffscreen || typeof document === 'undefined') {\n return new OffscreenCanvas(width, height);\n }\n const canvas = document.createElement('canvas');\n canvas.width = width;\n canvas.height = height;\n return canvas;\n}\n\nexport function drawFrame(\n ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D,\n frame: VideoFrame | HTMLImageElement,\n width: number,\n height: number,\n) {\n ctx?.drawImage(frame, 0, 0, width, height);\n}\n\n/**\n * Given a mask and the image frame, get the masked image cropped to its bounding box.\n */\nexport function getThumbnailImageDataOld(\n mask: DataArray,\n videoFrame: VideoFrame,\n): ImageData | null {\n const data = mask.data;\n if (!ArrayBuffer.isView(data) || !(data instanceof Uint8Array)) {\n return new ImageData(0, 0);\n }\n\n const frame = convertVideoFrameToImageData(videoFrame);\n if (!frame) {\n return new ImageData(0, 0);\n }\n\n const frameData = frame.data;\n const scaleX = frame.width / mask.shape[1];\n const scaleY = frame.height / mask.shape[0];\n const boundingBox = findBoundingBox();\n const transformedData = new Uint8ClampedArray(data.length * 4);\n\n for (let i = 0; i < data.length; i++) {\n // Since the mask is rotated, new width is the mask's height = mask.shape[1];\n // Transform matrix: doing a rotate 90deg and then flip horizontal is the same as flipping x and y\n // [ 0 1 ] [ -1 0 ] = [ 0 1 ]\n // [-1 0 ] x [ 0 1 ] = [ 1 0 ]\n // So, we can find the new index as: newY * newWidth + newX\n const newX = Math.floor(i / mask.shape[0]); // ie, new x is the current y\n const newY = i % mask.shape[0];\n const transformedIndex = (newY * mask.shape[1] + newX) * 4;\n const frameDataIndex = (newY * mask.shape[1] * scaleY + newX * scaleX) * 4;\n\n transformedData[transformedIndex] = frameData[frameDataIndex];\n transformedData[transformedIndex + 1] = frameData[frameDataIndex + 1];\n transformedData[transformedIndex + 2] = frameData[frameDataIndex + 2];\n transformedData[transformedIndex + 3] = (data[i] && 255) || 0; // A value\n\n boundingBox.process(newX, newY, data[i] > 0);\n }\n\n const rotatedData = new ImageData(\n transformedData,\n mask.shape[1],\n mask.shape[0],\n ); // flip w and h of the mask\n\n return boundingBox.crop(rotatedData);\n}\n\n/**\n * Given a mask, the mask rendering context, and the video frame, get the\n * masked image cropped to its bounding box.\n */\nfunction getThumbnailImageData(\n mask: Mask,\n maskCtx: OffscreenCanvasRenderingContext2D,\n frameBitmap: ImageBitmap,\n): ImageData | null {\n const x = mask.bounds[0][0];\n const y = mask.bounds[0][1];\n const w = mask.bounds[1][0] - mask.bounds[0][0];\n const h = mask.bounds[1][1] - mask.bounds[0][1];\n\n if (w <= 0 || h <= 0) {\n return null;\n }\n\n const thumbnailMaskData = maskCtx.getImageData(x, y, w, h);\n\n const canvas = new OffscreenCanvas(w, h);\n const ctx = canvas.getContext('2d');\n invariant(ctx !== null, '2d context cannot be null');\n\n ctx.putImageData(thumbnailMaskData, 0, 0);\n ctx.globalCompositeOperation = 'source-in';\n ctx.drawImage(frameBitmap, x, y, w, h, 0, 0, w, h);\n\n return ctx.getImageData(0, 0, w, h);\n}\n\nexport async function generateThumbnail(\n track: Tracklet,\n frameIndex: number,\n mask: Mask,\n frame: VideoFrame,\n ctx: OffscreenCanvasRenderingContext2D,\n): Promise {\n // If a frame doesn't have points, the points will be undefined.\n const hasPoints = (track.points[frameIndex]?.length ?? 0) > 0;\n if (!hasPoints) {\n return;\n }\n invariant(frame !== null, 'frame must be ready');\n const bitmap = await createImageBitmap(frame);\n const thumbnailImageData = getThumbnailImageData(\n mask,\n ctx as OffscreenCanvasRenderingContext2D,\n bitmap,\n );\n\n bitmap.close();\n if (thumbnailImageData != null) {\n const thumbnailDataURL = await getDataURLFromImageData(thumbnailImageData);\n track.thumbnail = thumbnailDataURL;\n }\n}\n\nexport async function getDataURLFromImageData(\n imageData: ImageData | null,\n): Promise {\n if (!imageData) {\n return '';\n }\n\n const canvas = getCanvas(imageData.width, imageData.height);\n const ctx = canvas.getContext('2d');\n\n if (ctx === null) {\n return '';\n }\n\n ctx?.putImageData(imageData, 0, 0);\n\n if (canvas instanceof OffscreenCanvas) {\n const blob = await canvas.convertToBlob();\n return new Promise(resolve => {\n const reader = new FileReader();\n reader.addEventListener(\n 'load',\n () => {\n const result = reader.result;\n if (typeof result === 'string') {\n resolve(result);\n } else {\n resolve('');\n }\n },\n false,\n );\n reader.readAsDataURL(blob);\n });\n }\n return canvas.toDataURL();\n}\n\nexport function hexToRgb(hex: string): {\n r: number;\n g: number;\n b: number;\n a: number;\n} {\n const result = /^#?([a-f\\d]{2})([a-f\\d]{2})([a-f\\d]{2})([a-f\\d]{2})?$/i.exec(\n hex,\n );\n return result\n ? {\n r: parseInt(result[1], 16),\n g: parseInt(result[2], 16),\n b: parseInt(result[3], 16),\n a: result[4] != null ? parseInt(result[4], 16) : 128,\n }\n : {r: 255, g: 0, b: 0, a: 128};\n}\n\nexport function getPointInImage(\n event: React.MouseEvent,\n canvas: HTMLCanvasElement,\n normalized: boolean = false,\n): [x: number, y: number] {\n const rect = canvas.getBoundingClientRect();\n\n const matrix = new DOMMatrix();\n\n // First, center the image\n const elementCenter = new DOMPoint(\n canvas.clientWidth / 2,\n canvas.clientHeight / 2,\n );\n const imageCenter = new DOMPoint(canvas.width / 2, canvas.height / 2);\n matrix.translateSelf(\n elementCenter.x - imageCenter.x,\n elementCenter.y - imageCenter.y,\n );\n\n // Containing the object take the minimal scale\n const scale = Math.min(\n canvas.clientWidth / canvas.width,\n canvas.clientHeight / canvas.height,\n );\n matrix.scaleSelf(scale, scale, 1, imageCenter.x, imageCenter.y);\n\n const point = new DOMPoint(\n event.clientX - rect.left,\n event.clientY - rect.top,\n );\n const imagePoint = matrix.inverse().transformPoint(point);\n\n const x = Math.max(Math.min(imagePoint.x, canvas.width), 0);\n const y = Math.max(Math.min(imagePoint.y, canvas.height), 0);\n\n if (normalized) {\n return [x / canvas.width, y / canvas.height];\n }\n return [x, y];\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/video/editor/atoms.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {atom} from 'jotai';\nimport {VideoRef} from '../Video';\n\nexport const videoAtom = atom(null);\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/video/editor/useResetEditor.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {OBJECT_TOOLBAR_INDEX} from '@/common/components/toolbar/ToolbarConfig';\nimport useToolbarTabs from '@/common/components/toolbar/useToolbarTabs';\nimport useVideo from '@/common/components/video/editor/useVideo';\nimport {\n activeTrackletObjectIdAtom,\n frameIndexAtom,\n isPlayingAtom,\n isStreamingAtom,\n sessionAtom,\n streamingStateAtom,\n trackletObjectsAtom,\n} from '@/demo/atoms';\nimport {DEFAULT_EFFECT_LAYERS} from '@/demo/DemoConfig';\nimport {useSetAtom} from 'jotai';\nimport {useCallback} from 'react';\n\ntype State = {\n resetEditor: () => void;\n resetEffects: () => void;\n resetSession: () => void;\n};\n\nexport default function useResetEditor(): State {\n const video = useVideo();\n\n const setSession = useSetAtom(sessionAtom);\n const setActiveTrackletObjectId = useSetAtom(activeTrackletObjectIdAtom);\n const setTrackletObjects = useSetAtom(trackletObjectsAtom);\n const setFrameIndex = useSetAtom(frameIndexAtom);\n const setStreamingState = useSetAtom(streamingStateAtom);\n const setIsPlaying = useSetAtom(isPlayingAtom);\n const setIsStreaming = useSetAtom(isStreamingAtom);\n const [, setDemoTabIndex] = useToolbarTabs();\n\n const resetEffects = useCallback(() => {\n video?.setEffect(DEFAULT_EFFECT_LAYERS.background, 0, {variant: 0});\n video?.setEffect(DEFAULT_EFFECT_LAYERS.highlight, 1, {variant: 0});\n }, [video]);\n\n const resetEditor = useCallback(() => {\n setFrameIndex(0);\n setSession(null);\n setActiveTrackletObjectId(0);\n setTrackletObjects([]);\n setStreamingState('none');\n setIsPlaying(false);\n setIsStreaming(false);\n resetEffects();\n setDemoTabIndex(OBJECT_TOOLBAR_INDEX);\n }, [\n setFrameIndex,\n setSession,\n setActiveTrackletObjectId,\n setTrackletObjects,\n setStreamingState,\n setIsPlaying,\n setIsStreaming,\n resetEffects,\n setDemoTabIndex,\n ]);\n\n const resetSession = useCallback(() => {\n setSession(prev => {\n if (prev === null) {\n return prev;\n }\n return {...prev, ranPropagation: false};\n });\n setActiveTrackletObjectId(null);\n resetEffects();\n }, [setSession, setActiveTrackletObjectId, resetEffects]);\n\n return {resetEditor, resetEffects, resetSession};\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/video/editor/useVideo.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {useAtomValue} from 'jotai';\nimport {videoAtom} from './atoms';\n\nexport default function useVideo() {\n return useAtomValue(videoAtom);\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/video/editor/useVideoEffect.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport useVideo from '@/common/components/video/editor/useVideo';\nimport {\n activeBackgroundEffectAtom,\n activeHighlightEffectAtom,\n} from '@/demo/atoms';\nimport {useSetAtom} from 'jotai';\nimport {useCallback, useEffect} from 'react';\nimport {EffectUpdateEvent} from '../VideoWorkerBridge';\nimport {EffectOptions} from '../effects/Effect';\nimport Effects, {EffectIndex, Effects as EffectsType} from '../effects/Effects';\n\nexport default function useVideoEffect() {\n const video = useVideo();\n const setBackgroundEffect = useSetAtom(activeBackgroundEffectAtom);\n const setHighlightEffect = useSetAtom(activeHighlightEffectAtom);\n\n // The useEffect will listen to any effect updates from the worker. The\n // worker is the source of truth, which effect and effect variant is\n // currently applied. The main thread will be notified whenever an effect\n // or effect variant changes.\n useEffect(() => {\n function onEffectUpdate(event: EffectUpdateEvent) {\n if (event.index === EffectIndex.BACKGROUND) {\n setBackgroundEffect(event);\n } else {\n setHighlightEffect(event);\n }\n }\n video?.addEventListener('effectUpdate', onEffectUpdate);\n return () => {\n video?.removeEventListener('effectUpdate', onEffectUpdate);\n };\n }, [video, setBackgroundEffect, setHighlightEffect]);\n\n return useCallback(\n (name: keyof EffectsType, index: EffectIndex, options?: EffectOptions) => {\n video?.setEffect(name, index, options);\n const effect = Effects[name];\n const effectVariant = options?.variant ?? 0;\n\n if (index === EffectIndex.BACKGROUND) {\n setBackgroundEffect({\n name,\n variant: effectVariant,\n numVariants: effect.numVariants,\n });\n } else {\n setHighlightEffect({\n name,\n variant: options?.variant ?? 0,\n numVariants: effect.numVariants,\n });\n }\n },\n [video, setBackgroundEffect, setHighlightEffect],\n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/video/effects/ArrowGLEffect.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport BaseGLEffect from '@/common/components/video/effects/BaseGLEffect';\nimport {\n EffectFrameContext,\n EffectInit,\n} from '@/common/components/video/effects/Effect';\nimport fragmentShaderSource from '@/common/components/video/effects/shaders/Arrow.frag?raw';\nimport vertexShaderSource from '@/common/components/video/effects/shaders/DefaultVert.vert?raw';\nimport {Tracklet} from '@/common/tracker/Tracker';\nimport {normalizeBounds} from '@/common/utils/ShaderUtils';\nimport {RLEObject, decode} from '@/jscocotools/mask';\nimport invariant from 'invariant';\nimport {CanvasForm} from 'pts';\n\nexport default class ArrowGLEffect extends BaseGLEffect {\n private _numMasks: number = 0;\n private _numMasksUniformLocation: WebGLUniformLocation | null = null;\n\n // Must from start 1, main texture takes.\n private _masksTextureUnitStart: number = 1;\n\n constructor() {\n super(4);\n this.vertexShaderSource = vertexShaderSource;\n this.fragmentShaderSource = fragmentShaderSource;\n }\n\n protected setupUniforms(\n gl: WebGL2RenderingContext,\n program: WebGLProgram,\n init: EffectInit,\n ): void {\n super.setupUniforms(gl, program, init);\n\n this._numMasksUniformLocation = gl.getUniformLocation(program, 'uNumMasks');\n gl.uniform1i(this._numMasksUniformLocation, this._numMasks);\n }\n\n apply(form: CanvasForm, context: EffectFrameContext, _tracklets: Tracklet[]) {\n const gl = this._gl;\n const program = this._program;\n\n if (!program) {\n return;\n }\n invariant(gl !== null, 'WebGL2 context is required');\n\n gl.clearColor(0.0, 0.0, 0.0, 1.0);\n gl.clear(gl.COLOR_BUFFER_BIT);\n\n // dynamic uniforms per frame\n const styleIndex = Math.floor(this.variant / 2) % 2;\n gl.uniform1i(this._numMasksUniformLocation, context.masks.length);\n gl.uniform1f(\n gl.getUniformLocation(program, 'uCurrentFrame'),\n context.frameIndex,\n );\n gl.uniform1i(\n gl.getUniformLocation(program, 'uLineColor'),\n this.variant % 2 === 0 ? 0 : 1,\n );\n gl.uniform1i(\n gl.getUniformLocation(program, 'uArrow'),\n styleIndex === 0 ? 1 : 0,\n );\n\n gl.activeTexture(gl.TEXTURE0);\n gl.bindTexture(gl.TEXTURE_2D, this._frameTexture);\n gl.texImage2D(\n gl.TEXTURE_2D,\n 0,\n gl.RGBA,\n context.width,\n context.height,\n 0,\n gl.RGBA,\n gl.UNSIGNED_BYTE,\n context.frame,\n );\n\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n\n // Create and bind 2D textures for each mask\n context.masks.forEach((mask, index) => {\n const maskTexture = gl.createTexture();\n const decodedMask = decode([mask.bitmap as RLEObject]);\n const maskData = decodedMask.data as Uint8Array;\n gl.activeTexture(gl.TEXTURE0 + index + this._masksTextureUnitStart);\n gl.bindTexture(gl.TEXTURE_2D, maskTexture);\n\n const boundaries = normalizeBounds(\n mask.bounds[0],\n mask.bounds[1],\n context.width,\n context.height,\n );\n\n gl.uniform1i(\n gl.getUniformLocation(program, `uMaskTexture${index}`),\n index + this._masksTextureUnitStart,\n );\n gl.uniform4fv(gl.getUniformLocation(program, `bbox${index}`), boundaries);\n\n // dynamic uniforms per mask\n gl.uniform1i(\n gl.getUniformLocation(program, `uMaskTexture${index}`),\n this._masksTextureUnitStart + index,\n );\n\n gl.pixelStorei(gl.UNPACK_ALIGNMENT, 1);\n gl.texImage2D(\n gl.TEXTURE_2D,\n 0,\n gl.LUMINANCE,\n context.height,\n context.width,\n 0,\n gl.LUMINANCE,\n gl.UNSIGNED_BYTE,\n maskData,\n );\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n\n gl.drawArrays(gl.TRIANGLE_STRIP, 0, 4);\n });\n\n const ctx = form.ctx;\n invariant(this._canvas !== null, 'canvas is required');\n ctx.drawImage(this._canvas, 0, 0);\n }\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/video/effects/BackgroundBlurEffect.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport BaseGLEffect from '@/common/components/video/effects/BaseGLEffect';\nimport {\n EffectFrameContext,\n EffectInit,\n} from '@/common/components/video/effects/Effect';\nimport fragmentShaderSource from '@/common/components/video/effects/shaders/BackgroundBlur.frag?raw';\nimport vertexShaderSource from '@/common/components/video/effects/shaders/DefaultVert.vert?raw';\nimport {Tracklet} from '@/common/tracker/Tracker';\nimport invariant from 'invariant';\nimport {CanvasForm} from 'pts';\n\nexport default class BackgroundBlurEffect extends BaseGLEffect {\n private _blurRadius: number = 3;\n\n constructor() {\n super(3);\n\n this.vertexShaderSource = vertexShaderSource;\n this.fragmentShaderSource = fragmentShaderSource;\n }\n\n protected setupUniforms(\n gl: WebGL2RenderingContext,\n program: WebGLProgram,\n init: EffectInit,\n ): void {\n super.setupUniforms(gl, program, init);\n\n gl.uniform1i(\n gl.getUniformLocation(program, 'uBlurRadius'),\n this._blurRadius,\n );\n }\n\n apply(form: CanvasForm, context: EffectFrameContext, _tracklets: Tracklet[]) {\n const gl = this._gl;\n const program = this._program;\n\n if (!program) {\n return;\n }\n invariant(gl !== null, 'WebGL2 context is required');\n\n gl.clearColor(0.0, 0.0, 0.0, 1.0);\n gl.clear(gl.COLOR_BUFFER_BIT);\n\n const blurRadius = [3, 6, 12][this.variant % 3];\n gl.uniform1i(gl.getUniformLocation(program, 'uBlurRadius'), blurRadius);\n\n gl.activeTexture(gl.TEXTURE0);\n gl.bindTexture(gl.TEXTURE_2D, this._frameTexture);\n gl.texImage2D(\n gl.TEXTURE_2D,\n 0,\n gl.RGBA,\n context.width,\n context.height,\n 0,\n gl.RGBA,\n gl.UNSIGNED_BYTE,\n context.frame,\n );\n\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n\n gl.drawArrays(gl.TRIANGLE_STRIP, 0, 4);\n\n const ctx = form.ctx;\n invariant(this._canvas !== null, 'canvas is required');\n ctx.drawImage(this._canvas, 0, 0);\n }\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/video/effects/BackgroundTextEffect.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {Tracklet} from '@/common/tracker/Tracker';\nimport {DEMO_SHORT_NAME} from '@/demo/DemoConfig';\nimport {Bound, CanvasForm, Num, Pt, Shaping} from 'pts';\nimport {AbstractEffect, EffectFrameContext} from './Effect';\n\nexport default class BackgroundTextEffect extends AbstractEffect {\n constructor() {\n super(2);\n }\n\n apply(\n form: CanvasForm,\n context: EffectFrameContext,\n _tracklets: Tracklet[],\n ): void {\n form.image([0, 0], context.frame);\n\n const words = ['SEGMENT', 'ANYTHING', 'WOW'];\n const paragraph = `${DEMO_SHORT_NAME} is designed for efficient video processing with streaming inference to enable real-time, interactive applications.`;\n const progress = context.frameIndex / context.totalFrames;\n\n // Zooming heading\n if (this.variant % 2 === 0) {\n const step = context.totalFrames / words.length;\n const wordIndex = Math.floor(progress * words.length);\n const fontSize = context.width / Math.max(4, words[wordIndex].length - 1);\n const sizeMax = fontSize * 1.2;\n\n const t = Shaping.quadraticInOut(\n Num.cycle((context.frameIndex - wordIndex * step) / step),\n );\n const currentSize = fontSize + Shaping.sineInOut(t, sizeMax - fontSize);\n form.fillOnly('#fff').font(currentSize, 'bold');\n const area = new Pt(\n context.width,\n context.height - (context.height / 4) * (1 - t),\n )\n .toBound()\n .scale(1.5, [context.width / 2, 0]);\n\n form\n .alignText('center', 'middle')\n .textBox(area, words[wordIndex], 'middle');\n\n // Scrolling paragraph\n } else {\n const t = Shaping.quadraticInOut(Num.cycle(progress));\n const offset = t * context.height;\n const area = Bound.fromArray([\n [0, -context.height + offset],\n [context.width, context.height],\n ]);\n form.fillOnly('#00000066').rect(area);\n form.fillOnly('#fff').font(context.width / 8, 'bold');\n form\n .fillOnly('#fff')\n .alignText('start')\n .paragraphBox(area, paragraph, 0.8, 'top', false);\n }\n }\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/video/effects/BaseGLEffect.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport Logger from '@/common/logger/Logger';\nimport {Tracklet} from '@/common/tracker/Tracker';\nimport invariant from 'invariant';\nimport {CanvasForm} from 'pts';\nimport {AbstractEffect, EffectFrameContext, EffectInit} from './Effect';\n\nexport default abstract class BaseGLEffect extends AbstractEffect {\n protected _canvas: OffscreenCanvas | null = null;\n protected _gl: WebGL2RenderingContext | null = null;\n protected _program: WebGLProgram | null = null;\n\n protected _frameTextureUnit: number = 0;\n protected _frameTexture: WebGLTexture | null = null;\n\n protected vertexShaderSource: string = '';\n protected fragmentShaderSource: string = '';\n\n protected _vertexShader: WebGLShader | null = null;\n protected _fragmentShader: WebGLShader | null = null;\n\n async setup(init: EffectInit): Promise {\n const {canvas, gl} = init;\n\n if (canvas != null && gl != null) {\n this._canvas = canvas;\n this._gl = gl;\n }\n\n invariant(this._gl !== null, 'WebGL2 context is required');\n\n const program = this._gl.createProgram();\n this._program = program;\n\n {\n const vertexShader = this._gl.createShader(this._gl.VERTEX_SHADER);\n this._vertexShader = vertexShader;\n invariant(vertexShader !== null, 'vertexShader required');\n this._gl.shaderSource(vertexShader, this.vertexShaderSource);\n this._gl.compileShader(vertexShader);\n invariant(program !== null, 'program required');\n this._gl.attachShader(program, vertexShader);\n\n const fragmentShader = this._gl.createShader(this._gl.FRAGMENT_SHADER);\n this._fragmentShader = fragmentShader;\n invariant(fragmentShader !== null, 'fragmentShader required');\n this._gl.shaderSource(fragmentShader, this.fragmentShaderSource);\n this._gl.compileShader(fragmentShader);\n this._gl.attachShader(program, fragmentShader);\n\n this._gl.linkProgram(program);\n\n if (!this._gl.getProgramParameter(program, this._gl.LINK_STATUS)) {\n Logger.error(this._gl.getShaderInfoLog(vertexShader));\n Logger.error(this._gl.getShaderInfoLog(fragmentShader));\n }\n }\n this._gl.useProgram(program);\n\n this.setupBuffers(this._gl);\n this.setupUniforms(this._gl, program, init);\n }\n\n apply(form: CanvasForm, context: EffectFrameContext, _tracklets: Tracklet[]) {\n const gl = this._gl;\n invariant(gl !== null, 'WebGL2 context is required');\n\n gl.clearColor(0.0, 0.0, 0.0, 1.0);\n gl.clear(gl.COLOR_BUFFER_BIT);\n\n gl.activeTexture(gl.TEXTURE0 + this._frameTextureUnit);\n gl.bindTexture(gl.TEXTURE_2D, this._frameTexture);\n gl.texImage2D(\n gl.TEXTURE_2D,\n 0,\n gl.RGBA,\n context.frame.width,\n context.frame.height,\n 0,\n gl.RGBA,\n gl.UNSIGNED_BYTE,\n context.frame,\n );\n\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n gl.pixelStorei(gl.UNPACK_FLIP_Y_WEBGL, true);\n\n // Apply shader\n gl.drawArrays(gl.TRIANGLE_STRIP, 0, 4);\n\n const ctx = form.ctx;\n invariant(this._canvas !== null, 'canvas is required');\n ctx.drawImage(this._canvas, 0, 0);\n }\n\n async cleanup(): Promise {\n if (this._gl != null) {\n // Dispose of WebGL resources, e.g., textures, buffers, etc.\n if (this._frameTexture != null) {\n this._gl.deleteTexture(this._frameTexture);\n this._frameTexture = null;\n }\n\n if (\n this._program != null &&\n this._vertexShader != null &&\n this._fragmentShader != null\n ) {\n this._gl.detachShader(this._program, this._vertexShader);\n this._gl.deleteShader(this._vertexShader);\n this._gl.detachShader(this._program, this._fragmentShader);\n this._gl.deleteShader(this._fragmentShader);\n }\n }\n }\n\n protected setupBuffers(gl: WebGL2RenderingContext) {\n const vertexBufferData = new Float32Array([\n 1.0, 1.0, -1.0, 1.0, 1.0, -1.0, -1.0, -1.0,\n ]);\n\n const texCoordBufferData = new Float32Array([\n 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0,\n ]);\n\n const vertexBuffer = gl.createBuffer();\n gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer);\n gl.bufferData(gl.ARRAY_BUFFER, vertexBufferData, gl.STATIC_DRAW);\n gl.vertexAttribPointer(0, 2, gl.FLOAT, false, 0, 0);\n gl.enableVertexAttribArray(0);\n\n const texCoordBuffer = gl.createBuffer();\n gl.bindBuffer(gl.ARRAY_BUFFER, texCoordBuffer);\n gl.bufferData(gl.ARRAY_BUFFER, texCoordBufferData, gl.STATIC_DRAW);\n gl.vertexAttribPointer(1, 2, gl.FLOAT, false, 0, 0);\n gl.enableVertexAttribArray(1);\n }\n\n protected setupUniforms(\n gl: WebGL2RenderingContext,\n program: WebGLProgram,\n init: EffectInit,\n ) {\n this._frameTexture = gl.createTexture();\n\n gl.uniform1i(\n gl.getUniformLocation(program, 'uSampler'),\n this._frameTextureUnit,\n );\n\n gl.uniform2f(\n gl.getUniformLocation(program, 'uSize'),\n init.width,\n init.height,\n );\n }\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/video/effects/BurstGLEffect.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {hexToRgb} from '@/common/components/video/editor/VideoEditorUtils';\nimport BaseGLEffect from '@/common/components/video/effects/BaseGLEffect';\nimport {\n EffectFrameContext,\n EffectInit,\n} from '@/common/components/video/effects/Effect';\nimport fragmentShaderSource from '@/common/components/video/effects/shaders/Burst.frag?raw';\nimport vertexShaderSource from '@/common/components/video/effects/shaders/DefaultVert.vert?raw';\nimport {Tracklet} from '@/common/tracker/Tracker';\nimport {normalizeBounds, preAllocateTextures} from '@/common/utils/ShaderUtils';\nimport {RLEObject, decode} from '@/jscocotools/mask';\nimport invariant from 'invariant';\nimport {CanvasForm} from 'pts';\n\nexport default class BurstGLEffect extends BaseGLEffect {\n private _numMasks: number = 0;\n private _numMasksUniformLocation: WebGLUniformLocation | null = null;\n\n // Must from start 1, main texture takes.\n private _masksTextureUnitStart: number = 1;\n private _maskTextures: WebGLTexture[] = [];\n\n constructor() {\n super(4);\n this.vertexShaderSource = vertexShaderSource;\n this.fragmentShaderSource = fragmentShaderSource;\n }\n\n protected setupUniforms(\n gl: WebGL2RenderingContext,\n program: WebGLProgram,\n init: EffectInit,\n ): void {\n super.setupUniforms(gl, program, init);\n\n this._numMasksUniformLocation = gl.getUniformLocation(program, 'uNumMasks');\n gl.uniform1i(this._numMasksUniformLocation, this._numMasks);\n\n // We know the max number of textures, pre-allocate 3.\n this._maskTextures = preAllocateTextures(gl, 3);\n }\n\n apply(form: CanvasForm, context: EffectFrameContext, _tracklets: Tracklet[]) {\n const gl = this._gl;\n const program = this._program;\n\n if (!program) {\n return;\n }\n invariant(gl !== null, 'WebGL2 context is required');\n\n gl.clearColor(0.0, 0.0, 0.0, 1.0);\n gl.clear(gl.COLOR_BUFFER_BIT);\n\n const styleIndex = Math.floor(this.variant / 2) % 2;\n\n // dynamic uniforms per frame\n gl.uniform1i(this._numMasksUniformLocation, context.masks.length);\n gl.uniform1i(\n gl.getUniformLocation(program, 'uLineColor'),\n this.variant % 2 === 0 ? 1 : 0,\n );\n gl.uniform1i(\n gl.getUniformLocation(program, 'uInterleave'),\n styleIndex === 0 ? 0 : 1,\n );\n\n gl.activeTexture(gl.TEXTURE0);\n gl.bindTexture(gl.TEXTURE_2D, this._frameTexture);\n gl.texImage2D(\n gl.TEXTURE_2D,\n 0,\n gl.RGBA,\n context.width,\n context.height,\n 0,\n gl.RGBA,\n gl.UNSIGNED_BYTE,\n context.frame,\n );\n\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n\n // Create and bind 2D textures for each mask\n context.masks.forEach((mask, index) => {\n const decodedMask = decode([mask.bitmap as RLEObject]);\n const maskData = decodedMask.data as Uint8Array;\n gl.activeTexture(gl.TEXTURE0 + index + this._masksTextureUnitStart);\n gl.bindTexture(gl.TEXTURE_2D, this._maskTextures[index]);\n\n const boundaries = normalizeBounds(\n mask.bounds[0],\n mask.bounds[1],\n context.width,\n context.height,\n );\n\n // dynamic uniforms per mask\n gl.uniform1i(\n gl.getUniformLocation(program, `uMaskTexture${index}`),\n this._masksTextureUnitStart + index,\n );\n const color = hexToRgb(context.maskColors[index]);\n gl.uniform4f(\n gl.getUniformLocation(program, `uMaskColor${index}`),\n color.r,\n color.g,\n color.b,\n color.a,\n );\n gl.uniform4fv(gl.getUniformLocation(program, `bbox${index}`), boundaries);\n\n gl.pixelStorei(gl.UNPACK_ALIGNMENT, 1);\n gl.texImage2D(\n gl.TEXTURE_2D,\n 0,\n gl.LUMINANCE,\n context.height,\n context.width,\n 0,\n gl.LUMINANCE,\n gl.UNSIGNED_BYTE,\n maskData,\n );\n });\n\n gl.drawArrays(gl.TRIANGLE_STRIP, 0, 4);\n\n // Unbind textures\n gl.bindTexture(gl.TEXTURE_2D, null);\n context.masks.forEach((_, index) => {\n gl.activeTexture(gl.TEXTURE0 + index + this._masksTextureUnitStart);\n gl.bindTexture(gl.TEXTURE_2D, null);\n });\n\n const ctx = form.ctx;\n invariant(this._canvas !== null, 'canvas is required');\n ctx.drawImage(this._canvas, 0, 0);\n }\n\n async cleanup(): Promise {\n super.cleanup();\n\n if (this._gl != null) {\n // Delete mask textures to prevent memory leaks\n this._maskTextures.forEach(texture => {\n if (texture != null && this._gl != null) {\n this._gl.deleteTexture(texture);\n }\n });\n this._maskTextures = [];\n }\n }\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/video/effects/CutoutGLEffect.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport BaseGLEffect from '@/common/components/video/effects/BaseGLEffect';\nimport {\n EffectFrameContext,\n EffectInit,\n} from '@/common/components/video/effects/Effect';\nimport fragmentShaderSource from '@/common/components/video/effects/shaders/Cutout.frag?raw';\nimport vertexShaderSource from '@/common/components/video/effects/shaders/DefaultVert.vert?raw';\nimport {Tracklet} from '@/common/tracker/Tracker';\nimport {preAllocateTextures} from '@/common/utils/ShaderUtils';\nimport {RLEObject, decode} from '@/jscocotools/mask';\nimport invariant from 'invariant';\nimport {CanvasForm} from 'pts';\n\nexport default class CutoutGLEffect extends BaseGLEffect {\n private _numMasks: number = 0;\n private _numMasksUniformLocation: WebGLUniformLocation | null = null;\n\n // Must from start 1, main texture takes.\n private _masksTextureUnitStart: number = 1;\n private _maskTextures: WebGLTexture[] = [];\n\n constructor() {\n super(4);\n this.vertexShaderSource = vertexShaderSource;\n this.fragmentShaderSource = fragmentShaderSource;\n }\n\n protected setupUniforms(\n gl: WebGL2RenderingContext,\n program: WebGLProgram,\n init: EffectInit,\n ): void {\n super.setupUniforms(gl, program, init);\n\n this._numMasksUniformLocation = gl.getUniformLocation(program, 'uNumMasks');\n gl.uniform1i(this._numMasksUniformLocation, this._numMasks);\n\n // We know the max number of textures, pre-allocate 3.\n this._maskTextures = preAllocateTextures(gl, 3);\n }\n\n apply(form: CanvasForm, context: EffectFrameContext, _tracklets: Tracklet[]) {\n const gl = this._gl;\n const program = this._program;\n\n if (!program) {\n return;\n }\n invariant(gl !== null, 'WebGL2 context is required');\n\n gl.clearColor(0.0, 0.0, 0.0, 1.0);\n gl.clear(gl.COLOR_BUFFER_BIT);\n\n // dynamic uniforms per frame\n const contrastValue = [1.0, 1.6, 0.75, 0.0][this.variant % 4];\n gl.uniform1f(gl.getUniformLocation(program, 'uContrast'), contrastValue);\n gl.uniform1i(this._numMasksUniformLocation, context.masks.length);\n\n gl.activeTexture(gl.TEXTURE0);\n gl.bindTexture(gl.TEXTURE_2D, this._frameTexture);\n gl.texImage2D(\n gl.TEXTURE_2D,\n 0,\n gl.RGBA,\n context.width,\n context.height,\n 0,\n gl.RGBA,\n gl.UNSIGNED_BYTE,\n context.frame,\n );\n\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n\n // Create and bind 2D textures for each mask\n context.masks.forEach((mask, index) => {\n const decodedMask = decode([mask.bitmap as RLEObject]);\n const maskData = decodedMask.data as Uint8Array;\n gl.activeTexture(gl.TEXTURE0 + index + this._masksTextureUnitStart);\n gl.bindTexture(gl.TEXTURE_2D, this._maskTextures[index]);\n\n // dynamic uniforms per mask\n gl.uniform1i(\n gl.getUniformLocation(program, `uMaskTexture${index}`),\n this._masksTextureUnitStart + index,\n );\n\n gl.pixelStorei(gl.UNPACK_ALIGNMENT, 1);\n gl.texImage2D(\n gl.TEXTURE_2D,\n 0,\n gl.LUMINANCE,\n context.height,\n context.width,\n 0,\n gl.LUMINANCE,\n gl.UNSIGNED_BYTE,\n maskData,\n );\n });\n\n gl.drawArrays(gl.TRIANGLE_STRIP, 0, 4);\n\n // Unbind textures\n gl.bindTexture(gl.TEXTURE_2D, null);\n context.masks.forEach((_, index) => {\n gl.activeTexture(gl.TEXTURE0 + index + this._masksTextureUnitStart);\n gl.bindTexture(gl.TEXTURE_2D, null);\n });\n\n const ctx = form.ctx;\n invariant(this._canvas !== null, 'canvas is required');\n ctx.drawImage(this._canvas, 0, 0);\n }\n\n async cleanup(): Promise {\n super.cleanup();\n\n if (this._gl != null) {\n // Delete mask textures to prevent memory leaks\n this._maskTextures.forEach(texture => {\n if (texture != null && this._gl != null) {\n this._gl.deleteTexture(texture);\n }\n });\n this._maskTextures = [];\n }\n }\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/video/effects/DesaturateEffect.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {Tracklet} from '@/common/tracker/Tracker';\nimport {CanvasForm} from 'pts';\nimport {AbstractEffect, EffectFrameContext} from './Effect';\n\nexport default class DesaturateEffect extends AbstractEffect {\n constructor() {\n super(3);\n }\n\n apply(form: CanvasForm, context: EffectFrameContext, _tracklets: Tracklet[]) {\n form.ctx.save();\n form.ctx.filter = ['contrast(100%)', 'contrast(150%)', 'contrast(50%)'][\n this.variant % 3\n ];\n form.image([0, 0], context.frame);\n form.ctx.globalCompositeOperation = 'hue';\n form.fillOnly('#fff').rect([\n [0, 0],\n [context.width, context.height],\n ]);\n form.ctx.restore();\n }\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/video/effects/Effect.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {Effects} from '@/common/components/video/effects/Effects';\nimport {Tracklet} from '@/common/tracker/Tracker';\nimport {RLEObject} from '@/jscocotools/mask';\nimport {CanvasForm} from 'pts';\n\nexport type EffectLayers = {\n background: keyof Effects;\n highlight: keyof Effects;\n};\n\nexport type EffectOptions = {\n variant: number;\n};\n\nexport type EffectInit = {\n width: number;\n height: number;\n gl?: WebGL2RenderingContext;\n canvas?: OffscreenCanvas;\n};\n\nexport type EffectMask = {\n bitmap: ImageBitmap | RLEObject;\n bounds: [[number, number], [number, number]];\n};\n\nexport type EffectActionPoint = {\n objectId: number;\n position: [number, number];\n};\n\nexport type EffectFrameContext = {\n frameIndex: number;\n totalFrames: number;\n fps: number;\n width: number;\n height: number;\n masks: EffectMask[];\n maskColors: string[];\n frame: ImageBitmap;\n timeParameter?: number;\n actionPoint: EffectActionPoint | null;\n};\n\nexport interface Effect {\n variant: number;\n numVariants: number;\n nextVariant(): void;\n setup(init: EffectInit): Promise;\n update(options: EffectOptions): Promise;\n cleanup(): Promise;\n apply(\n form: CanvasForm,\n context: EffectFrameContext,\n tracklets: Tracklet[],\n ): void;\n}\n\nexport abstract class AbstractEffect implements Effect {\n public numVariants: number;\n public variant: number;\n\n constructor(numVariants: number) {\n this.numVariants = numVariants;\n this.variant = 0;\n }\n\n nextVariant() {\n // Cycle through variants\n this.variant = (this.variant + 1) % this.numVariants;\n }\n\n async setup(_init: EffectInit): Promise {\n // noop\n }\n\n async update(options: EffectOptions): Promise {\n this.variant = options.variant;\n }\n\n async cleanup(): Promise {\n // noop\n }\n\n abstract apply(\n form: CanvasForm,\n context: EffectFrameContext,\n tracklets: Tracklet[],\n ): void;\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/video/effects/EffectUtils.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport invariant from 'invariant';\nimport {Group} from 'pts';\nimport {EffectFrameContext} from './Effect';\n\nexport type MaskCanvas = {\n maskCanvas: OffscreenCanvas;\n bounds: number[][];\n scaleX: number;\n scaleY: number;\n};\n\nimport {Effects} from '@/common/components/video/effects/Effects';\nimport type {CarbonIconType} from '@carbon/icons-react';\nimport {\n AppleDash,\n Asterisk,\n Barcode,\n CenterCircle,\n ColorPalette,\n ColorSwitch,\n Development,\n Erase,\n FaceWink,\n Humidity,\n Image,\n Overlay,\n TextFont,\n} from '@carbon/icons-react';\n\nexport type DemoEffect = {\n title: string;\n Icon: CarbonIconType;\n effectName: keyof Effects;\n};\n\nexport const backgroundEffects: DemoEffect[] = [\n {title: 'Original', Icon: Image, effectName: 'Original'},\n {title: 'Erase', Icon: Erase, effectName: 'EraseBackground'},\n {\n title: 'Gradient',\n Icon: ColorPalette,\n effectName: 'Gradient',\n },\n {\n title: 'Pixelate',\n Icon: Development,\n effectName: 'Pixelate',\n },\n {title: 'Desaturate', Icon: ColorSwitch, effectName: 'Desaturate'},\n {title: 'Text', Icon: TextFont, effectName: 'BackgroundText'},\n {title: 'Blur', Icon: Humidity, effectName: 'BackgroundBlur'},\n {title: 'Outline', Icon: AppleDash, effectName: 'Sobel'},\n];\n\nexport const highlightEffects: DemoEffect[] = [\n {title: 'Original', Icon: Image, effectName: 'Cutout'},\n {title: 'Erase', Icon: Erase, effectName: 'EraseForeground'},\n {title: 'Gradient', Icon: ColorPalette, effectName: 'VibrantMask'},\n {title: 'Pixelate', Icon: Development, effectName: 'PixelateMask'},\n {\n title: 'Overlay',\n Icon: Overlay,\n effectName: 'Overlay',\n },\n {title: 'Emoji', Icon: FaceWink, effectName: 'Replace'},\n {title: 'Burst', Icon: Asterisk, effectName: 'Burst'},\n {title: 'Spotlight', Icon: CenterCircle, effectName: 'Scope'},\n];\n\nexport const moreEffects: DemoEffect[] = [\n {title: 'Noisy', Icon: Barcode, effectName: 'NoisyMask'},\n];\n\n// Store existing content in a temporary canvas\n// This can be used in HighlightEffect composite blending, so that the existing background effect can be put back via \"destination-over\"\nexport function copyCanvasContent(\n ctx: CanvasRenderingContext2D,\n effectContext: EffectFrameContext,\n): OffscreenCanvas {\n const {width, height} = effectContext;\n const previousContent = ctx.getImageData(0, 0, width, height);\n const tempCanvas = new OffscreenCanvas(width, height);\n const tempCtx = tempCanvas.getContext('2d');\n tempCtx?.putImageData(previousContent, 0, 0);\n return tempCanvas;\n}\n\nexport function isInvalidMask(bound: number[][] | Group) {\n return (\n bound[0].length < 2 ||\n bound[1].length < 2 ||\n bound[1][0] - bound[0][0] < 1 ||\n bound[1][1] - bound[0][1] < 1\n );\n}\n\nexport type MaskRenderingData = {\n canvas: OffscreenCanvas;\n scale: number[];\n bounds: number[][];\n};\n\nexport class EffectLayer {\n canvas: OffscreenCanvas;\n ctx: OffscreenCanvasRenderingContext2D;\n width: number;\n height: number;\n\n constructor(context: EffectFrameContext) {\n this.canvas = new OffscreenCanvas(context.width, context.height);\n const ctx = this.canvas.getContext('2d');\n invariant(ctx !== null, 'context cannot be null');\n this.ctx = ctx;\n this.width = context.width;\n this.height = context.height;\n }\n\n image(source: CanvasImageSourceWebCodecs) {\n this.ctx.drawImage(source, 0, 0);\n }\n\n filter(filterString: string) {\n this.ctx.filter = filterString;\n }\n\n composite(blend: GlobalCompositeOperation) {\n this.ctx.globalCompositeOperation = blend;\n }\n\n fill(color: string) {\n this.ctx.fillStyle = color;\n this.ctx.fillRect(0, 0, this.width, this.height);\n }\n\n clear() {\n this.ctx.clearRect(0, 0, this.width, this.height);\n }\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/video/effects/Effects.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport BackgroundTextEffect from './BackgroundTextEffect';\nimport DesaturateEffect from './DesaturateEffect';\nimport {Effect} from './Effect';\nimport EraseBackgroundEffect from './EraseBackgroundEffect';\nimport OriginalEffect from './OriginalEffect';\nimport OverlayEffect from './OverlayEffect';\nimport ArrowGLEffect from './ArrowGLEffect';\nimport BackgroundBlurEffect from './BackgroundBlurEffect';\nimport BurstGLEffect from './BurstGLEffect';\nimport CutoutGLEffect from './CutoutGLEffect';\nimport EraseForegroundGLEffect from './EraseForegroundGLEffect';\nimport GradientEffect from './GradientEffect';\nimport NoisyMaskEffect from './NoisyMaskEffect';\nimport PixelateEffect from './PixelateEffect';\nimport PixelateMaskGLEffect from './PixelateMaskGLEffect';\nimport ReplaceGLEffect from './ReplaceGLEffect';\nimport ScopeGLEffect from './ScopeGLEffect';\nimport SobelEffect from './SobelEffect';\nimport VibrantMaskEffect from './VibrantMaskEffect';\n\nexport type Effects = {\n /* Backgrounds */\n Original: Effect;\n EraseBackground: Effect;\n Desaturate: Effect;\n Pixelate: Effect;\n Sobel: Effect;\n BackgroundText: Effect;\n BackgroundBlur: Effect;\n Gradient: Effect;\n\n /* Highlights */\n Overlay: Effect;\n EraseForeground: Effect;\n Cutout: Effect;\n Scope: Effect;\n VibrantMask: Effect;\n Replace: Effect;\n Burst: Effect;\n PixelateMask: Effect;\n Arrow: Effect;\n\n /* More Effects */\n NoisyMask: Effect;\n};\n\nexport default {\n /* Backgrounds */\n Original: new OriginalEffect(),\n EraseBackground: new EraseBackgroundEffect(),\n Desaturate: new DesaturateEffect(),\n Pixelate: new PixelateEffect(),\n Sobel: new SobelEffect(),\n BackgroundText: new BackgroundTextEffect(),\n BackgroundBlur: new BackgroundBlurEffect(),\n Gradient: new GradientEffect(),\n\n /* Highlights */\n Overlay: new OverlayEffect(),\n EraseForeground: new EraseForegroundGLEffect(),\n Cutout: new CutoutGLEffect(),\n Scope: new ScopeGLEffect(),\n VibrantMask: new VibrantMaskEffect(),\n Replace: new ReplaceGLEffect(),\n Burst: new BurstGLEffect(),\n PixelateMask: new PixelateMaskGLEffect(),\n Arrow: new ArrowGLEffect(),\n\n /* More Effects */\n NoisyMask: new NoisyMaskEffect(),\n} as Effects;\n\nexport enum EffectIndex {\n BACKGROUND = 0,\n HIGHLIGHT = 1,\n}\n\ntype EffectComboItem = {name: keyof Effects; variant: number};\n\nexport type EffectsCombo = [EffectComboItem, EffectComboItem];\n\nexport const effectPresets: EffectsCombo[] = [\n [\n {name: 'Original', variant: 0},\n {name: 'Overlay', variant: 0},\n ],\n [\n {name: 'Desaturate', variant: 0},\n {name: 'Burst', variant: 2},\n ],\n [\n {name: 'Desaturate', variant: 1},\n {name: 'VibrantMask', variant: 0},\n ],\n [\n {name: 'BackgroundText', variant: 1},\n {name: 'Cutout', variant: 0},\n ],\n [\n {name: 'Original', variant: 0},\n {name: 'PixelateMask', variant: 1},\n ],\n [\n {name: 'Desaturate', variant: 2},\n {name: 'Cutout', variant: 0},\n ],\n [\n {name: 'Sobel', variant: 3},\n {name: 'Cutout', variant: 1},\n ],\n [\n {name: 'Sobel', variant: 2},\n {name: 'EraseForeground', variant: 2},\n ],\n [\n {name: 'EraseBackground', variant: 0},\n {name: 'EraseForeground', variant: 0},\n ],\n];\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/video/effects/EraseBackgroundEffect.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {Tracklet} from '@/common/tracker/Tracker';\nimport {CanvasForm} from 'pts';\nimport {AbstractEffect, EffectFrameContext} from './Effect';\n\nexport default class EraseBackgroundEffect extends AbstractEffect {\n constructor() {\n super(3);\n }\n\n apply(\n form: CanvasForm,\n context: EffectFrameContext,\n _tracklets: Tracklet[],\n ): void {\n const fillColor = ['#000', '#fff', '#0f0'][this.variant % 3];\n form.fillOnly(fillColor).rect([\n [0, 0],\n [context.width, context.height],\n ]);\n }\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/video/effects/EraseForegroundEffect.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {Tracklet} from '@/common/tracker/Tracker';\nimport {CanvasForm} from 'pts';\nimport {AbstractEffect, EffectFrameContext} from './Effect';\nimport {EffectLayer} from './EffectUtils';\n\nexport default class EraseForegroundEffect extends AbstractEffect {\n constructor() {\n super(3);\n }\n\n apply(\n form: CanvasForm,\n context: EffectFrameContext,\n _tracklets: Tracklet[],\n ): void {\n const effect = new EffectLayer(context);\n const fillColor = ['#fff', '#000', '#0f0'][this.variant % 3];\n for (const mask of context.masks) {\n effect.image(mask.bitmap as ImageBitmap);\n effect.composite('source-in');\n effect.fill(fillColor);\n }\n form.image([0, 0], effect.canvas);\n }\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/video/effects/EraseForegroundGLEffect.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport BaseGLEffect from '@/common/components/video/effects/BaseGLEffect';\nimport {\n EffectFrameContext,\n EffectInit,\n} from '@/common/components/video/effects/Effect';\nimport vertexShaderSource from '@/common/components/video/effects/shaders/DefaultVert.vert?raw';\nimport fragmentShaderSource from '@/common/components/video/effects/shaders/EraseForeground.frag?raw';\nimport {Tracklet} from '@/common/tracker/Tracker';\nimport {preAllocateTextures} from '@/common/utils/ShaderUtils';\nimport {RLEObject, decode} from '@/jscocotools/mask';\nimport invariant from 'invariant';\nimport {CanvasForm} from 'pts';\n\nexport default class EraseForegroundGLEffect extends BaseGLEffect {\n private _numMasks: number = 0;\n private _numMasksUniformLocation: WebGLUniformLocation | null = null;\n\n private _maskTextures: WebGLTexture[] = [];\n\n constructor() {\n super(3);\n this.vertexShaderSource = vertexShaderSource;\n this.fragmentShaderSource = fragmentShaderSource;\n }\n\n protected setupUniforms(\n gl: WebGL2RenderingContext,\n program: WebGLProgram,\n init: EffectInit,\n ): void {\n super.setupUniforms(gl, program, init);\n\n this._numMasksUniformLocation = gl.getUniformLocation(program, 'uNumMasks');\n gl.uniform1i(this._numMasksUniformLocation, this._numMasks);\n\n // We know the max number of textures, pre-allocate 3.\n this._maskTextures = preAllocateTextures(gl, 3);\n }\n\n apply(form: CanvasForm, context: EffectFrameContext, _tracklets: Tracklet[]) {\n const gl = this._gl;\n const program = this._program;\n\n invariant(gl !== null, 'WebGL2 context is required');\n invariant(program !== null, 'Not WebGL program found');\n\n const fillColor = [\n [1, 1, 1],\n [0, 0, 0],\n [0, 1, 0],\n ][this.variant % 3];\n\n gl.clearColor(0.0, 0.0, 0.0, 1.0);\n gl.clear(gl.COLOR_BUFFER_BIT);\n\n gl.uniform1i(this._numMasksUniformLocation, context.masks.length);\n gl.uniform3fv(gl.getUniformLocation(program, 'uBgColor'), fillColor);\n\n context.masks.forEach((mask, index) => {\n const decodedMask = decode([mask.bitmap as RLEObject]);\n const maskData = decodedMask.data as Uint8Array;\n gl.activeTexture(gl.TEXTURE0 + index);\n gl.bindTexture(gl.TEXTURE_2D, this._maskTextures[index]);\n\n gl.uniform1i(\n gl.getUniformLocation(program, `uMaskTexture${index}`),\n index,\n );\n\n gl.pixelStorei(gl.UNPACK_ALIGNMENT, 1);\n gl.texImage2D(\n gl.TEXTURE_2D,\n 0,\n gl.LUMINANCE,\n context.height,\n context.width,\n 0,\n gl.LUMINANCE,\n gl.UNSIGNED_BYTE,\n maskData,\n );\n });\n\n gl.drawArrays(gl.TRIANGLE_STRIP, 0, 4);\n\n // Unbind textures\n gl.bindTexture(gl.TEXTURE_2D, null);\n context.masks.forEach((_, index) => {\n gl.activeTexture(gl.TEXTURE0 + index);\n gl.bindTexture(gl.TEXTURE_2D, null);\n });\n\n const ctx = form.ctx;\n invariant(this._canvas !== null, 'canvas is required');\n\n if (context.masks.length) {\n ctx.drawImage(this._canvas, 0, 0);\n }\n }\n\n async cleanup(): Promise {\n super.cleanup();\n\n if (this._gl != null) {\n // Delete mask textures to prevent memory leaks\n this._maskTextures.forEach(texture => {\n if (texture != null && this._gl != null) {\n this._gl.deleteTexture(texture);\n }\n });\n this._maskTextures = [];\n }\n }\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/video/effects/GradientEffect.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport BaseGLEffect from '@/common/components/video/effects/BaseGLEffect';\nimport {\n EffectFrameContext,\n EffectInit,\n} from '@/common/components/video/effects/Effect';\nimport vertexShaderSource from '@/common/components/video/effects/shaders/DefaultVert.vert?raw';\nimport fragmentShaderSource from '@/common/components/video/effects/shaders/Gradient.frag?raw';\nimport {Tracklet} from '@/common/tracker/Tracker';\nimport {generateLUTDATA, load3DLUT} from '@/common/utils/ShaderUtils';\nimport invariant from 'invariant';\nimport {CanvasForm} from 'pts';\n\nexport default class GradientEffect extends BaseGLEffect {\n private lutSize: number = 2;\n private _lutTextures: WebGLTexture[] = [];\n\n // Must be 1, main background texture takes 0.\n private _extraTextureUnit: number = 1;\n\n constructor() {\n super(3);\n\n this.vertexShaderSource = vertexShaderSource;\n this.fragmentShaderSource = fragmentShaderSource;\n }\n\n protected setupUniforms(\n gl: WebGL2RenderingContext,\n program: WebGLProgram,\n init: EffectInit,\n ): void {\n super.setupUniforms(gl, program, init);\n\n gl.uniform1i(\n gl.getUniformLocation(program, 'uColorGradeLUT'),\n this._extraTextureUnit,\n );\n\n this._lutTextures = []; // clear any previous pool of textures\n\n for (let i = 0; i < this.numVariants; i++) {\n const _lutData = generateLUTDATA(this.lutSize);\n const _extraTexture = load3DLUT(gl, this.lutSize, _lutData);\n this._lutTextures.push(_extraTexture as WebGLTexture);\n }\n }\n\n apply(form: CanvasForm, context: EffectFrameContext, _tracklets: Tracklet[]) {\n const gl = this._gl;\n const program = this._program;\n\n if (!program) {\n return;\n }\n invariant(gl !== null, 'WebGL2 context is required');\n\n gl.clearColor(0.0, 0.0, 0.0, 1.0);\n gl.clear(gl.COLOR_BUFFER_BIT);\n\n // Bind the LUT texture to texture unit 1\n const lutTexture = this._lutTextures[this.variant];\n gl.activeTexture(gl.TEXTURE1);\n gl.bindTexture(gl.TEXTURE_3D, lutTexture);\n\n gl.activeTexture(gl.TEXTURE0);\n gl.bindTexture(gl.TEXTURE_2D, this._frameTexture);\n gl.texImage2D(\n gl.TEXTURE_2D,\n 0,\n gl.RGBA,\n context.width,\n context.height,\n 0,\n gl.RGBA,\n gl.UNSIGNED_BYTE,\n context.frame,\n );\n\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n\n gl.drawArrays(gl.TRIANGLE_STRIP, 0, 4);\n\n const ctx = form.ctx;\n invariant(this._canvas !== null, 'canvas is required');\n ctx.drawImage(this._canvas, 0, 0);\n }\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/video/effects/NoisyMaskEffect.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport BaseGLEffect from '@/common/components/video/effects/BaseGLEffect';\nimport {\n EffectFrameContext,\n EffectInit,\n} from '@/common/components/video/effects/Effect';\nimport vertexShaderSource from '@/common/components/video/effects/shaders/DefaultVert.vert?raw';\nimport fragmentShaderSource from '@/common/components/video/effects/shaders/NoisyMask.frag?raw';\nimport {Tracklet} from '@/common/tracker/Tracker';\nimport {RLEObject, decode} from '@/jscocotools/mask';\nimport invariant from 'invariant';\nimport {CanvasForm} from 'pts';\n\nexport default class NoisyMaskEffect extends BaseGLEffect {\n private _numMasks: number = 0;\n private _numMasksUniformLocation: WebGLUniformLocation | null = null;\n private _currentFrameLocation: WebGLUniformLocation | null = null;\n\n constructor() {\n super(1);\n this.vertexShaderSource = vertexShaderSource;\n this.fragmentShaderSource = fragmentShaderSource;\n }\n\n protected setupUniforms(\n gl: WebGL2RenderingContext,\n program: WebGLProgram,\n init: EffectInit,\n ): void {\n super.setupUniforms(gl, program, init);\n\n this._numMasksUniformLocation = gl.getUniformLocation(program, 'uNumMasks');\n gl.uniform1i(this._numMasksUniformLocation, this._numMasks);\n\n this._currentFrameLocation = gl.getUniformLocation(\n program,\n 'uCurrentFrame',\n );\n gl.uniform1f(this._currentFrameLocation, 0);\n }\n\n apply(form: CanvasForm, context: EffectFrameContext, _tracklets: Tracklet[]) {\n const gl = this._gl;\n const program = this._program;\n\n if (!program) {\n return;\n }\n invariant(gl !== null, 'WebGL2 context is required');\n\n gl.clearColor(0.0, 0.0, 0.0, 1.0);\n gl.clear(gl.COLOR_BUFFER_BIT);\n\n // dynamic uniforms per frame\n gl.uniform1f(this._currentFrameLocation, context.frameIndex);\n gl.uniform1i(this._numMasksUniformLocation, context.masks.length);\n\n // Create and bind 2D textures for each mask\n context.masks.forEach((mask, index) => {\n const maskTexture = gl.createTexture();\n const decodedMask = decode([mask.bitmap as RLEObject]);\n const maskData = decodedMask.data as Uint8Array;\n gl.activeTexture(gl.TEXTURE0 + index);\n gl.bindTexture(gl.TEXTURE_2D, maskTexture);\n\n // dynamic uniforms per mask\n gl.uniform1i(\n gl.getUniformLocation(program, `uMaskTexture${index}`),\n index,\n );\n\n gl.pixelStorei(gl.UNPACK_ALIGNMENT, 1);\n gl.texImage2D(\n gl.TEXTURE_2D,\n 0,\n gl.LUMINANCE,\n context.height,\n context.width,\n 0,\n gl.LUMINANCE,\n gl.UNSIGNED_BYTE,\n maskData,\n );\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n\n gl.drawArrays(gl.TRIANGLE_STRIP, 0, 4);\n });\n\n const ctx = form.ctx;\n invariant(this._canvas !== null, 'canvas is required');\n ctx.drawImage(this._canvas, 0, 0);\n }\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/video/effects/OriginalEffect.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {Tracklet} from '@/common/tracker/Tracker';\nimport {CanvasForm} from 'pts';\nimport {AbstractEffect, EffectFrameContext} from './Effect';\n\nexport default class OriginalEffect extends AbstractEffect {\n constructor() {\n super(3);\n }\n\n apply(\n form: CanvasForm,\n context: EffectFrameContext,\n _tracklets: Tracklet[],\n ): void {\n form.ctx.save();\n if (this.variant % 3 === 1) {\n form.ctx.filter = 'saturate(120%) contrast(120%)';\n } else if (this.variant % 3 === 2) {\n form.ctx.filter = 'brightness(70%) contrast(115%)';\n }\n\n form.image([0, 0], context.frame);\n form.ctx.restore();\n\n if (this.variant % 3 === 2) {\n form.fillOnly('#00000066').rect([\n [0, 0],\n [context.width, context.height],\n ]);\n }\n }\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/video/effects/OverlayEffect.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {hexToRgb} from '@/common/components/video/editor/VideoEditorUtils';\nimport BaseGLEffect from '@/common/components/video/effects/BaseGLEffect';\nimport {\n EffectFrameContext,\n EffectInit,\n} from '@/common/components/video/effects/Effect';\nimport vertexShaderSource from '@/common/components/video/effects/shaders/DefaultVert.vert?raw';\nimport fragmentShaderSource from '@/common/components/video/effects/shaders/Overlay.frag?raw';\nimport {Tracklet} from '@/common/tracker/Tracker';\nimport {\n findIndexByTrackletId,\n preAllocateTextures,\n} from '@/common/utils/ShaderUtils';\nimport {RLEObject, decode} from '@/jscocotools/mask';\nimport invariant from 'invariant';\nimport {CanvasForm} from 'pts';\n\nexport default class OverlayEffect extends BaseGLEffect {\n private _numMasks: number = 0;\n private _numMasksUniformLocation: WebGLUniformLocation | null = null;\n\n // Must start from 1, main texture takes 0.\n private _masksTextureUnitStart: number = 1;\n private _maskTextures: WebGLTexture[] = [];\n private _clickPosition: number[] | null = null;\n private _activeMask: number = 0;\n\n constructor() {\n super(8);\n this.vertexShaderSource = vertexShaderSource;\n this.fragmentShaderSource = fragmentShaderSource;\n }\n\n protected setupUniforms(\n gl: WebGL2RenderingContext,\n program: WebGLProgram,\n init: EffectInit,\n ): void {\n super.setupUniforms(gl, program, init);\n\n this._numMasksUniformLocation = gl.getUniformLocation(program, 'uNumMasks');\n gl.uniform1i(this._numMasksUniformLocation, this._numMasks);\n\n // We know the max number of textures, pre-allocate 3.\n this._maskTextures = preAllocateTextures(gl, 3);\n }\n\n apply(form: CanvasForm, context: EffectFrameContext, _tracklets: Tracklet[]) {\n const gl = this._gl;\n const program = this._program;\n\n invariant(gl !== null, 'WebGL2 context is required');\n invariant(program !== null, 'Not WebGL program found');\n\n gl.clearColor(0.0, 0.0, 0.0, 1.0);\n gl.clear(gl.COLOR_BUFFER_BIT);\n\n const opacity = [0.5, 0.75, 0.35, 0.95][this.variant % 4];\n gl.uniform1f(\n gl.getUniformLocation(program, 'uTime'),\n context.timeParameter ?? 1.5, // Pass a constant value when no time parameter\n );\n gl.uniform1f(gl.getUniformLocation(program, 'uOpacity'), opacity);\n gl.uniform1i(this._numMasksUniformLocation, context.masks.length);\n gl.uniform1i(\n gl.getUniformLocation(program, 'uBorder'),\n this.variant % this.numVariants < 4 ? 1 : 0,\n );\n\n if (context.actionPoint) {\n const clickPos = [\n context.actionPoint.position[0] / context.width,\n context.actionPoint.position[1] / context.height,\n ];\n this._clickPosition = clickPos;\n this._activeMask = findIndexByTrackletId(\n context.actionPoint.objectId,\n _tracklets,\n );\n }\n\n gl.uniform2fv(\n gl.getUniformLocation(program, 'uClickPos'),\n this._clickPosition ?? [0, 0],\n );\n gl.uniform1i(\n gl.getUniformLocation(program, 'uActiveMask'),\n this._activeMask,\n );\n\n // Activate original frame texture\n gl.activeTexture(gl.TEXTURE0);\n gl.bindTexture(gl.TEXTURE_2D, this._frameTexture);\n gl.texImage2D(\n gl.TEXTURE_2D,\n 0,\n gl.RGBA,\n context.width,\n context.height,\n 0,\n gl.RGBA,\n gl.UNSIGNED_BYTE,\n context.frame,\n );\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n\n context.masks.forEach((mask, index) => {\n const decodedMask = decode([mask.bitmap as RLEObject]);\n const maskData = decodedMask.data as Uint8Array;\n gl.activeTexture(gl.TEXTURE0 + index + this._masksTextureUnitStart);\n gl.bindTexture(gl.TEXTURE_2D, this._maskTextures[index]);\n\n gl.uniform1i(\n gl.getUniformLocation(program, `uMaskTexture${index}`),\n this._masksTextureUnitStart + index,\n );\n\n const color = hexToRgb(context.maskColors[index]);\n gl.uniform4f(\n gl.getUniformLocation(program, `uMaskColor${index}`),\n color.r,\n color.g,\n color.b,\n color.a,\n );\n\n // 1 byte aligment\n gl.pixelStorei(gl.UNPACK_ALIGNMENT, 1);\n gl.texImage2D(\n gl.TEXTURE_2D,\n 0,\n gl.LUMINANCE,\n context.height,\n context.width,\n 0,\n gl.LUMINANCE,\n gl.UNSIGNED_BYTE,\n maskData,\n );\n });\n\n gl.drawArrays(gl.TRIANGLE_STRIP, 0, 4);\n\n // Unbind textures\n gl.bindTexture(gl.TEXTURE_2D, null);\n context.masks.forEach((_, index) => {\n gl.activeTexture(gl.TEXTURE0 + index + this._masksTextureUnitStart);\n gl.bindTexture(gl.TEXTURE_2D, null);\n });\n\n const ctx = form.ctx;\n invariant(this._canvas !== null, 'canvas is required');\n\n ctx.drawImage(this._canvas, 0, 0);\n this._clickPosition = null;\n }\n\n async cleanup(): Promise {\n super.cleanup();\n\n if (this._gl != null) {\n // Delete mask textures to prevent memory leaks\n this._maskTextures.forEach(texture => {\n if (texture != null && this._gl != null) {\n this._gl.deleteTexture(texture);\n }\n });\n this._maskTextures = [];\n }\n }\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/video/effects/PixelateEffect.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport BaseGLEffect from '@/common/components/video/effects/BaseGLEffect';\nimport {\n EffectFrameContext,\n EffectInit,\n} from '@/common/components/video/effects/Effect';\nimport vertexShaderSource from '@/common/components/video/effects/shaders/DefaultVert.vert?raw';\nimport fragmentShaderSource from '@/common/components/video/effects/shaders/Pixelate.frag?raw';\nimport {Tracklet} from '@/common/tracker/Tracker';\nimport invariant from 'invariant';\nimport {CanvasForm} from 'pts';\n\nexport default class PixelateEffect extends BaseGLEffect {\n private _blockSize: number = 10.0;\n\n constructor() {\n super(3);\n\n this.vertexShaderSource = vertexShaderSource;\n this.fragmentShaderSource = fragmentShaderSource;\n }\n\n protected setupUniforms(\n gl: WebGL2RenderingContext,\n program: WebGLProgram,\n init: EffectInit,\n ): void {\n super.setupUniforms(gl, program, init);\n\n gl.uniform1f(gl.getUniformLocation(program, 'uBlockSize'), this._blockSize);\n }\n\n apply(form: CanvasForm, context: EffectFrameContext, _tracklets: Tracklet[]) {\n const gl = this._gl;\n const program = this._program;\n\n if (!program) {\n return;\n }\n invariant(gl !== null, 'WebGL2 context is required');\n\n gl.clearColor(0.0, 0.0, 0.0, 1.0);\n gl.clear(gl.COLOR_BUFFER_BIT);\n\n const blockSize = [10, 20, 30][this.variant];\n\n // dynamic uniforms per frame\n gl.uniform1f(gl.getUniformLocation(program, 'uBlockSize'), blockSize);\n\n gl.activeTexture(gl.TEXTURE0);\n gl.bindTexture(gl.TEXTURE_2D, this._frameTexture);\n gl.texImage2D(\n gl.TEXTURE_2D,\n 0,\n gl.RGBA,\n context.width,\n context.height,\n 0,\n gl.RGBA,\n gl.UNSIGNED_BYTE,\n context.frame,\n );\n\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n gl.pixelStorei(gl.UNPACK_FLIP_Y_WEBGL, true);\n\n // Apply shader\n gl.drawArrays(gl.TRIANGLE_STRIP, 0, 4);\n\n const ctx = form.ctx;\n invariant(this._canvas !== null, 'canvas is required');\n ctx.drawImage(this._canvas, 0, 0);\n }\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/video/effects/PixelateMaskGLEffect.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport BaseGLEffect from '@/common/components/video/effects/BaseGLEffect';\nimport {\n EffectFrameContext,\n EffectInit,\n} from '@/common/components/video/effects/Effect';\nimport vertexShaderSource from '@/common/components/video/effects/shaders/DefaultVert.vert?raw';\nimport fragmentShaderSource from '@/common/components/video/effects/shaders/PixelateMask.frag?raw';\nimport {Tracklet} from '@/common/tracker/Tracker';\nimport {preAllocateTextures} from '@/common/utils/ShaderUtils';\nimport {RLEObject, decode} from '@/jscocotools/mask';\nimport invariant from 'invariant';\nimport {CanvasForm} from 'pts';\n\nexport default class PixelateMaskGLEffect extends BaseGLEffect {\n private _numMasks: number = 0;\n private _numMasksUniformLocation: WebGLUniformLocation | null = null;\n\n // Must from start 1, main texture takes.\n private _masksTextureUnitStart: number = 1;\n private _maskTextures: WebGLTexture[] = [];\n\n constructor() {\n super(3);\n this.vertexShaderSource = vertexShaderSource;\n this.fragmentShaderSource = fragmentShaderSource;\n }\n\n protected setupUniforms(\n gl: WebGL2RenderingContext,\n program: WebGLProgram,\n init: EffectInit,\n ): void {\n super.setupUniforms(gl, program, init);\n\n this._numMasksUniformLocation = gl.getUniformLocation(program, 'uNumMasks');\n gl.uniform1i(this._numMasksUniformLocation, this._numMasks);\n\n // We know the max number of textures, pre-allocate 3.\n this._maskTextures = preAllocateTextures(gl, 3);\n }\n\n apply(form: CanvasForm, context: EffectFrameContext, _tracklets: Tracklet[]) {\n const gl = this._gl;\n const program = this._program;\n\n if (!program) {\n return;\n }\n invariant(gl !== null, 'WebGL2 context is required');\n\n gl.clearColor(0.0, 0.0, 0.0, 1.0);\n gl.clear(gl.COLOR_BUFFER_BIT);\n\n const blockSize = [10, 20, 30][this.variant];\n\n // dynamic uniforms per frame\n gl.uniform1i(this._numMasksUniformLocation, context.masks.length);\n gl.uniform1f(gl.getUniformLocation(program, 'uBlockSize'), blockSize);\n\n gl.activeTexture(gl.TEXTURE0);\n gl.bindTexture(gl.TEXTURE_2D, this._frameTexture);\n gl.texImage2D(\n gl.TEXTURE_2D,\n 0,\n gl.RGBA,\n context.width,\n context.height,\n 0,\n gl.RGBA,\n gl.UNSIGNED_BYTE,\n context.frame,\n );\n\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n\n // Create and bind 2D textures for each mask\n context.masks.forEach((mask, index) => {\n const decodedMask = decode([mask.bitmap as RLEObject]);\n const maskData = decodedMask.data as Uint8Array;\n gl.activeTexture(gl.TEXTURE0 + index + this._masksTextureUnitStart);\n gl.bindTexture(gl.TEXTURE_2D, this._maskTextures[index]);\n gl.pixelStorei(gl.UNPACK_ALIGNMENT, 1);\n gl.texImage2D(\n gl.TEXTURE_2D,\n 0,\n gl.LUMINANCE,\n context.height,\n context.width,\n 0,\n gl.LUMINANCE,\n gl.UNSIGNED_BYTE,\n maskData,\n );\n\n // dynamic uniforms per mask\n gl.uniform1i(\n gl.getUniformLocation(program, `uMaskTexture${index}`),\n this._masksTextureUnitStart + index,\n );\n });\n\n gl.drawArrays(gl.TRIANGLE_STRIP, 0, 4);\n\n // Unbind textures\n gl.bindTexture(gl.TEXTURE_2D, null);\n context.masks.forEach((_, index) => {\n gl.activeTexture(gl.TEXTURE0 + index + this._masksTextureUnitStart);\n gl.bindTexture(gl.TEXTURE_2D, null);\n });\n\n const ctx = form.ctx;\n invariant(this._canvas !== null, 'canvas is required');\n ctx.drawImage(this._canvas, 0, 0);\n }\n\n async cleanup(): Promise {\n super.cleanup();\n\n if (this._gl != null) {\n // Delete mask textures to prevent memory leaks\n this._maskTextures.forEach(texture => {\n if (texture != null && this._gl != null) {\n this._gl.deleteTexture(texture);\n }\n });\n this._maskTextures = [];\n }\n }\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/video/effects/ReplaceGLEffect.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport angeryIcon from '@/assets/icons/angery.png';\nimport heartIcon from '@/assets/icons/heart.png';\nimport whistleIcon from '@/assets/icons/whistle.png';\nimport BaseGLEffect from '@/common/components/video/effects/BaseGLEffect';\nimport {\n EffectFrameContext,\n EffectInit,\n} from '@/common/components/video/effects/Effect';\nimport vertexShaderSource from '@/common/components/video/effects/shaders/DefaultVert.vert?raw';\nimport fragmentShaderSource from '@/common/components/video/effects/shaders/Replace.frag?raw';\nimport {Tracklet} from '@/common/tracker/Tracker';\nimport {normalizeBounds, preAllocateTextures} from '@/common/utils/ShaderUtils';\nimport {RLEObject, decode} from '@/jscocotools/mask';\nimport invariant from 'invariant';\nimport {CanvasForm} from 'pts';\n\nexport default class ReplaceGLEffect extends BaseGLEffect {\n private _numMasks: number = 0;\n private _numMasksUniformLocation: WebGLUniformLocation | null = null;\n private _bitmap: ImageBitmap[] = [];\n private _extraTextureUnit: number = 1;\n private _extraTexture: WebGLTexture | null = null;\n private _fillBg: number = 0;\n private _fillBgLocation: WebGLUniformLocation | null = null;\n private _masksTextureUnitStart: number = 2;\n private _maskTextures: WebGLTexture[] = [];\n\n constructor() {\n super(6);\n this.vertexShaderSource = vertexShaderSource;\n this.fragmentShaderSource = fragmentShaderSource;\n }\n\n protected async setupUniforms(\n gl: WebGL2RenderingContext,\n program: WebGLProgram,\n init: EffectInit,\n ) {\n super.setupUniforms(gl, program, init);\n this._extraTexture = gl.createTexture();\n\n this._numMasksUniformLocation = gl.getUniformLocation(program, 'uNumMasks');\n gl.uniform1i(this._numMasksUniformLocation, this._numMasks);\n\n this._fillBgLocation = gl.getUniformLocation(program, 'uFill');\n gl.uniform1i(this._fillBgLocation, this._fillBg);\n\n gl.uniform1i(\n gl.getUniformLocation(program, 'uEmojiTexture'),\n this._extraTextureUnit,\n );\n\n // We know the max number of textures, pre-allocate 3.\n this._maskTextures = preAllocateTextures(gl, 3);\n\n this._bitmap = []; // clear any previous pool of texture\n\n let response = await fetch(angeryIcon);\n let blob = await response.blob();\n const angery = await createImageBitmap(blob);\n\n response = await fetch(heartIcon);\n blob = await response.blob();\n const heart = await createImageBitmap(blob);\n\n response = await fetch(whistleIcon);\n blob = await response.blob();\n const whistle = await createImageBitmap(blob);\n\n this._bitmap = [angery, heart, whistle];\n }\n\n apply(form: CanvasForm, context: EffectFrameContext, _tracklets: Tracklet[]) {\n const gl = this._gl;\n const program = this._program;\n\n invariant(gl !== null, 'WebGL2 context is required');\n invariant(program !== null, 'Not WebGL program found');\n\n const iconIndex = Math.floor(this.variant / 2) % this._bitmap.length;\n\n if (this._bitmap === null) {\n return;\n }\n\n gl.clearColor(0.0, 0.0, 0.0, 1.0);\n gl.clear(gl.COLOR_BUFFER_BIT);\n\n // dynamic uniforms per frame\n gl.uniform1i(this._numMasksUniformLocation, context.masks.length);\n gl.uniform1i(this._fillBgLocation, this.variant % 2 === 0 ? 0 : 1);\n\n // Bind the extra texture/emoji to texture unit 1\n if (this._bitmap.length) {\n gl.activeTexture(gl.TEXTURE0 + this._extraTextureUnit);\n gl.bindTexture(gl.TEXTURE_2D, this._extraTexture);\n\n gl.texImage2D(\n gl.TEXTURE_2D,\n 0,\n gl.RGBA,\n this._bitmap[iconIndex].width,\n this._bitmap[iconIndex].height,\n 0,\n gl.RGBA,\n gl.UNSIGNED_BYTE,\n this._bitmap[iconIndex],\n );\n\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n }\n\n context.masks.forEach((mask, index) => {\n const decodedMask = decode([mask.bitmap as RLEObject]);\n const maskData = decodedMask.data as Uint8Array;\n gl.activeTexture(gl.TEXTURE0 + index + this._masksTextureUnitStart);\n gl.bindTexture(gl.TEXTURE_2D, this._maskTextures[index]);\n\n const boundaries = normalizeBounds(\n mask.bounds[0],\n mask.bounds[1],\n context.width,\n context.height,\n );\n\n gl.uniform1i(\n gl.getUniformLocation(program, `uMaskTexture${index}`),\n index + this._masksTextureUnitStart,\n );\n gl.uniform4fv(gl.getUniformLocation(program, `bbox${index}`), boundaries);\n\n gl.pixelStorei(gl.UNPACK_ALIGNMENT, 1);\n gl.texImage2D(\n gl.TEXTURE_2D,\n 0,\n gl.LUMINANCE,\n context.height,\n context.width,\n 0,\n gl.LUMINANCE,\n gl.UNSIGNED_BYTE,\n maskData,\n );\n });\n\n gl.drawArrays(gl.TRIANGLE_STRIP, 0, 4);\n\n // Unbind textures\n gl.bindTexture(gl.TEXTURE_2D, null);\n context.masks.forEach((_, index) => {\n gl.activeTexture(gl.TEXTURE0 + index + this._masksTextureUnitStart);\n gl.bindTexture(gl.TEXTURE_2D, null);\n });\n\n const ctx = form.ctx;\n invariant(this._canvas !== null, 'canvas is required');\n ctx.drawImage(this._canvas, 0, 0);\n }\n\n async cleanup(): Promise {\n super.cleanup();\n\n if (this._gl != null) {\n // Delete mask textures to prevent memory leaks\n this._maskTextures.forEach(texture => {\n if (texture != null && this._gl != null) {\n this._gl.deleteTexture(texture);\n }\n });\n this._maskTextures = [];\n }\n }\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/video/effects/ScopeGLEffect.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {hexToRgb} from '@/common/components/video/editor/VideoEditorUtils';\nimport BaseGLEffect from '@/common/components/video/effects/BaseGLEffect';\nimport {\n EffectFrameContext,\n EffectInit,\n} from '@/common/components/video/effects/Effect';\nimport vertexShaderSource from '@/common/components/video/effects/shaders/DefaultVert.vert?raw';\nimport fragmentShaderSource from '@/common/components/video/effects/shaders/Scope.frag?raw';\nimport {Tracklet} from '@/common/tracker/Tracker';\nimport {normalizeBounds, preAllocateTextures} from '@/common/utils/ShaderUtils';\nimport {RLEObject, decode} from '@/jscocotools/mask';\nimport invariant from 'invariant';\nimport {CanvasForm} from 'pts';\n\nexport default class ScopeGLEffect extends BaseGLEffect {\n private _numMasks: number = 0;\n private _numMasksUniformLocation: WebGLUniformLocation | null = null;\n\n // Must from start 2, main texture takes 0 and 1.\n private _masksTextureUnitStart: number = 2;\n private _maskTextures: WebGLTexture[] = [];\n\n constructor() {\n super(6);\n this.vertexShaderSource = vertexShaderSource;\n this.fragmentShaderSource = fragmentShaderSource;\n }\n\n protected setupUniforms(\n gl: WebGL2RenderingContext,\n program: WebGLProgram,\n init: EffectInit,\n ): void {\n super.setupUniforms(gl, program, init);\n\n this._numMasksUniformLocation = gl.getUniformLocation(program, 'uNumMasks');\n gl.uniform1i(this._numMasksUniformLocation, this._numMasks);\n\n // We know the max number of textures, pre-allocate 3.\n this._maskTextures = preAllocateTextures(gl, 3);\n }\n\n apply(form: CanvasForm, context: EffectFrameContext, _tracklets: Tracklet[]) {\n const gl = this._gl;\n const program = this._program;\n\n if (!program) {\n return;\n }\n invariant(gl !== null, 'WebGL2 context is required');\n\n gl.clearColor(0.0, 0.0, 0.0, 1.0);\n gl.clear(gl.COLOR_BUFFER_BIT);\n\n // dynamic uniforms per frame\n gl.uniform1i(this._numMasksUniformLocation, context.masks.length);\n\n gl.activeTexture(gl.TEXTURE0);\n gl.bindTexture(gl.TEXTURE_2D, this._frameTexture);\n gl.texImage2D(\n gl.TEXTURE_2D,\n 0,\n gl.RGBA,\n context.width,\n context.height,\n 0,\n gl.RGBA,\n gl.UNSIGNED_BYTE,\n context.frame,\n );\n\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n\n // Create and bind 2D textures for each mask\n context.masks.forEach((mask, index) => {\n const decodedMask = decode([mask.bitmap as RLEObject]);\n const maskData = decodedMask.data as Uint8Array;\n gl.activeTexture(gl.TEXTURE0 + index + this._masksTextureUnitStart);\n gl.bindTexture(gl.TEXTURE_2D, this._maskTextures[index]);\n\n const boundaries = normalizeBounds(\n mask.bounds[0],\n mask.bounds[1],\n context.width,\n context.height,\n );\n\n const styleIndex = Math.floor(this.variant / 2) % 2;\n\n // dynamic uniforms per mask\n gl.uniform1i(\n gl.getUniformLocation(program, `uMaskTexture${index}`),\n this._masksTextureUnitStart + index,\n );\n const color = hexToRgb(context.maskColors[index]);\n gl.uniform4f(\n gl.getUniformLocation(program, `uMaskColor${index}`),\n color.r,\n color.g,\n color.b,\n color.a,\n );\n gl.uniform4fv(gl.getUniformLocation(program, `bbox${index}`), boundaries);\n gl.uniform1i(\n gl.getUniformLocation(program, 'uFillColor'),\n this.variant % 2 === 0 ? 0 : 1,\n );\n gl.uniform1i(\n gl.getUniformLocation(program, 'uLight'),\n styleIndex === 0 ? 0 : 1,\n );\n gl.uniform1i(\n gl.getUniformLocation(program, 'uTransparency'),\n Math.floor(this.variant / 2) % 3 === 2 ? 1 : 0,\n );\n\n gl.pixelStorei(gl.UNPACK_ALIGNMENT, 1);\n gl.texImage2D(\n gl.TEXTURE_2D,\n 0,\n gl.LUMINANCE,\n context.height,\n context.width,\n 0,\n gl.LUMINANCE,\n gl.UNSIGNED_BYTE,\n maskData,\n );\n });\n\n gl.drawArrays(gl.TRIANGLE_STRIP, 0, 4);\n\n // Unbind textures\n gl.bindTexture(gl.TEXTURE_2D, null);\n context.masks.forEach((_, index) => {\n gl.activeTexture(gl.TEXTURE0 + index + this._masksTextureUnitStart);\n gl.bindTexture(gl.TEXTURE_2D, null);\n });\n\n const ctx = form.ctx;\n invariant(this._canvas !== null, 'canvas is required');\n ctx.drawImage(this._canvas, 0, 0);\n }\n\n async cleanup(): Promise {\n super.cleanup();\n\n if (this._gl != null) {\n // Delete mask textures to prevent memory leaks\n this._maskTextures.forEach(texture => {\n if (texture != null && this._gl != null) {\n this._gl.deleteTexture(texture);\n }\n });\n this._maskTextures = [];\n }\n }\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/video/effects/SobelEffect.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport BaseGLEffect from '@/common/components/video/effects/BaseGLEffect';\nimport {\n EffectFrameContext,\n EffectInit,\n} from '@/common/components/video/effects/Effect';\nimport vertexShaderSource from '@/common/components/video/effects/shaders/DefaultVert.vert?raw';\nimport fragmentShaderSource from '@/common/components/video/effects/shaders/Sobel.frag?raw';\nimport {Tracklet} from '@/common/tracker/Tracker';\nimport invariant from 'invariant';\nimport {CanvasForm} from 'pts';\n\nexport default class SobelEffect extends BaseGLEffect {\n constructor() {\n super(4);\n\n this.vertexShaderSource = vertexShaderSource;\n this.fragmentShaderSource = fragmentShaderSource;\n }\n\n protected setupUniforms(\n gl: WebGL2RenderingContext,\n program: WebGLProgram,\n init: EffectInit,\n ): void {\n super.setupUniforms(gl, program, init);\n }\n\n apply(form: CanvasForm, context: EffectFrameContext, _tracklets: Tracklet[]) {\n const gl = this._gl;\n const program = this._program;\n\n if (!program) {\n return;\n }\n invariant(gl !== null, 'WebGL2 context is required');\n\n gl.clearColor(0.0, 0.0, 0.0, 1.0);\n gl.clear(gl.COLOR_BUFFER_BIT);\n\n const pairIndex = Math.floor(this.variant / 2) % 2;\n gl.uniform1i(\n gl.getUniformLocation(program, 'uSwapColor'),\n this.variant % 2 === 0 ? 1 : 0,\n );\n gl.uniform1i(\n gl.getUniformLocation(program, 'uMonocolor'),\n pairIndex === 0 ? 0 : 1,\n );\n\n gl.activeTexture(gl.TEXTURE0);\n gl.bindTexture(gl.TEXTURE_2D, this._frameTexture);\n gl.texImage2D(\n gl.TEXTURE_2D,\n 0,\n gl.RGBA,\n context.width,\n context.height,\n 0,\n gl.RGBA,\n gl.UNSIGNED_BYTE,\n context.frame,\n );\n\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n\n gl.drawArrays(gl.TRIANGLE_STRIP, 0, 4);\n\n const ctx = form.ctx;\n invariant(this._canvas !== null, 'canvas is required');\n ctx.drawImage(this._canvas, 0, 0);\n }\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/video/effects/VibrantMaskEffect.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport BaseGLEffect from '@/common/components/video/effects/BaseGLEffect';\nimport {\n EffectFrameContext,\n EffectInit,\n} from '@/common/components/video/effects/Effect';\nimport vertexShaderSource from '@/common/components/video/effects/shaders/DefaultVert.vert?raw';\nimport fragmentShaderSource from '@/common/components/video/effects/shaders/VibrantMask.frag?raw';\nimport {Tracklet} from '@/common/tracker/Tracker';\nimport {\n generateLUTDATA,\n load3DLUT,\n preAllocateTextures,\n} from '@/common/utils/ShaderUtils';\nimport {RLEObject, decode} from '@/jscocotools/mask';\nimport invariant from 'invariant';\nimport {CanvasForm} from 'pts';\n\nexport default class VibrantMaskEffect extends BaseGLEffect {\n private lutSize: number = 4;\n\n private _numMasks: number = 0;\n private _numMasksUniformLocation: WebGLUniformLocation | null = null;\n private _currentFrameLocation: WebGLUniformLocation | null = null;\n private _lutTextures: WebGLTexture[] = [];\n private _maskTextures: WebGLTexture[] = [];\n\n // Must be 1, main background texture takes 0.\n private _extraTextureUnit: number = 1;\n\n // Must from start 2, main texture takes 0 and 1.\n private _masksTextureUnitStart: number = 2;\n\n constructor() {\n super(3);\n this.vertexShaderSource = vertexShaderSource;\n this.fragmentShaderSource = fragmentShaderSource;\n }\n\n protected setupUniforms(\n gl: WebGL2RenderingContext,\n program: WebGLProgram,\n init: EffectInit,\n ): void {\n super.setupUniforms(gl, program, init);\n\n gl.uniform1i(\n gl.getUniformLocation(program, 'uColorGradeLUT'),\n this._extraTextureUnit,\n );\n\n this._numMasksUniformLocation = gl.getUniformLocation(program, 'uNumMasks');\n gl.uniform1i(this._numMasksUniformLocation, this._numMasks);\n\n this._currentFrameLocation = gl.getUniformLocation(\n program,\n 'uCurrentFrame',\n );\n gl.uniform1f(this._currentFrameLocation, 0);\n\n // We know the max number of textures, pre-allocate 3.\n this._maskTextures = preAllocateTextures(gl, 3);\n\n this._lutTextures = []; // clear any previous pool of textures\n\n for (let i = 0; i < this.numVariants; i++) {\n const _lutData = generateLUTDATA(this.lutSize);\n const _extraTexture = load3DLUT(gl, this.lutSize, _lutData);\n this._lutTextures.push(_extraTexture as WebGLTexture);\n }\n }\n\n apply(form: CanvasForm, context: EffectFrameContext, _tracklets: Tracklet[]) {\n const gl = this._gl;\n const program = this._program;\n\n if (!program) {\n return;\n }\n invariant(gl !== null, 'WebGL2 context is required');\n\n gl.clearColor(0.0, 0.0, 0.0, 1.0);\n gl.clear(gl.COLOR_BUFFER_BIT);\n\n // dynamic uniforms per frame\n gl.uniform1f(this._currentFrameLocation, context.frameIndex);\n gl.uniform1i(this._numMasksUniformLocation, context.masks.length);\n\n // Bind the LUT texture to texture unit 1\n const lutTexture = this._lutTextures[this.variant];\n gl.activeTexture(gl.TEXTURE1);\n gl.bindTexture(gl.TEXTURE_3D, lutTexture);\n\n gl.activeTexture(gl.TEXTURE0);\n gl.bindTexture(gl.TEXTURE_2D, this._frameTexture);\n gl.texImage2D(\n gl.TEXTURE_2D,\n 0,\n gl.RGBA,\n context.width,\n context.height,\n 0,\n gl.RGBA,\n gl.UNSIGNED_BYTE,\n context.frame,\n );\n\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n\n // Create and bind 2D textures for each mask\n context.masks.forEach((mask, index) => {\n const decodedMask = decode([mask.bitmap as RLEObject]);\n const maskData = decodedMask.data as Uint8Array;\n gl.activeTexture(gl.TEXTURE0 + index + this._masksTextureUnitStart);\n gl.bindTexture(gl.TEXTURE_2D, this._maskTextures[index]);\n\n // dynamic uniforms per mask\n gl.uniform1i(\n gl.getUniformLocation(program, `uMaskTexture${index}`),\n this._masksTextureUnitStart + index,\n );\n\n gl.pixelStorei(gl.UNPACK_ALIGNMENT, 1);\n gl.texImage2D(\n gl.TEXTURE_2D,\n 0,\n gl.LUMINANCE,\n context.height,\n context.width,\n 0,\n gl.LUMINANCE,\n gl.UNSIGNED_BYTE,\n maskData,\n );\n });\n\n gl.drawArrays(gl.TRIANGLE_STRIP, 0, 4);\n\n // Unbind textures\n gl.bindTexture(gl.TEXTURE_2D, null);\n context.masks.forEach((_, index) => {\n gl.activeTexture(gl.TEXTURE0 + index + this._masksTextureUnitStart);\n gl.bindTexture(gl.TEXTURE_2D, null);\n });\n\n const ctx = form.ctx;\n invariant(this._canvas !== null, 'canvas is required');\n ctx.drawImage(this._canvas, 0, 0);\n }\n\n async cleanup(): Promise {\n super.cleanup();\n\n if (this._gl != null) {\n // Delete mask textures to prevent memory leaks\n this._maskTextures.forEach(texture => {\n if (texture != null && this._gl != null) {\n this._gl.deleteTexture(texture);\n }\n });\n this._maskTextures = [];\n }\n }\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/video/effects/shaders/Arrow.frag", "content": "#version 300 es\n// Copyright (c) Meta Platforms, Inc. and affiliates.\n// \n// Licensed under the Apache License, Version 2.0 (the \"License\");\n// you may not use this file except in compliance with the License.\n// You may obtain a copy of the License at\n// \n// http://www.apache.org/licenses/LICENSE-2.0\n// \n// Unless required by applicable law or agreed to in writing, software\n// distributed under the License is distributed on an \"AS IS\" BASIS,\n// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n// See the License for the specific language governing permissions and\n// limitations under the License.\n\nprecision mediump float;\n\nin vec2 vTexCoord;\n\nuniform sampler2D uSampler;\nuniform vec2 uSize;\nuniform int uNumMasks;\nuniform float uCurrentFrame;\nuniform bool uLineColor;\nuniform bool uArrow;\nuniform sampler2D uMaskTexture0;\nuniform sampler2D uMaskTexture1;\nuniform sampler2D uMaskTexture2;\n\nuniform vec4 bbox0;\nuniform vec4 bbox1;\nuniform vec4 bbox2;\n\nout vec4 fragColor;\n\nfloat addv(vec2 a) {\n return a.x + a.y;\n}\n\n#define dd(a) dot(a,a)\n\nvec2 solveCubic2(vec3 a) {\n float p = a.y - a.x * a.x / 3.0f;\n float p3 = p * p * p;\n float q = a.x * (2.0f * a.x * a.x - 9.0f * a.y) / 27.0f + a.z;\n float d = q * q + 4.0f * p3 / 27.0f;\n if(d > 0.0f) {\n vec2 x = (vec2(1.0f, -1.0f) * sqrt(d) - q) * 0.5f;\n return vec2(addv(sign(x) * pow(abs(x), vec2(1.0f / 3.0f))) - a.x / 3.0f);\n }\n float v = acos(-sqrt(-27.0f / p3) * q * 0.5f) / 3.0f;\n float m = cos(v);\n float n = sin(v) * 1.732050808f;\n return vec2(m + m, -n - m) * sqrt(-p / 3.0f) - a.x / 3.0f;\n}\n\nfloat calculateDistanceToQuadraticBezier(vec2 p, vec2 a, vec2 b, vec2 c) {\n b += mix(vec2(1e-4f), vec2(0.0f), abs(sign(b * 2.0f - a - c)));\n vec2 A = b - a;\n vec2 B = c - b - A;\n vec2 C = p - a;\n vec2 D = A * 2.0f;\n vec2 T = clamp((solveCubic2(vec3(-3.0f * dot(A, B), dot(C, B) - 2.0f * dd(A), dot(C, A)) / -dd(B))), 0.0f, 1.0f);\n return sqrt(min(dd(C - (D + B * T.x) * T.x), dd(C - (D + B * T.y) * T.y)));\n}\n\nfloat crossProduct(vec2 a, vec2 b) {\n return a.x * b.y - a.y * b.x;\n}\n\nbool pointInTriangle(vec2 pt, vec2 v0, vec2 v1, vec2 v2) {\n vec2 v0v1 = v1 - v0;\n vec2 v1v2 = v2 - v1;\n vec2 v2v0 = v0 - v2;\n float d0 = sign(crossProduct(v0v1, pt - v0));\n float d1 = sign(crossProduct(v1v2, pt - v1));\n float d2 = sign(crossProduct(v2v0, pt - v2));\n bool has_neg = (d0 < 0.0f) || (d1 < 0.0f) || (d2 < 0.0f);\n bool has_pos = (d0 > 0.0f) || (d1 > 0.0f) || (d2 > 0.0f);\n return !(has_neg && has_pos);\n}\n\nvoid main() {\n vec4 color = texture(uSampler, vTexCoord);\n vec2 fragCoord = vTexCoord * uSize;\n float aspectRatio = uSize.y / uSize.x;\n float time = uCurrentFrame * 0.05f;\n vec3 multicolor = vec3(0.5f + 0.5f * sin(time), 0.5f + 0.5f * cos(time), 0.5f - 0.5f * sin(time));\n\n vec4 mask1 = vec4(0.0f);\n vec4 mask2 = vec4(0.0f);\n vec4 mask3 = vec4(0.0f);\n\n bool scoped = false;\n bool intersected = false;\n float threshold = 0.75f;\n float circleRadius = 0.015f;\n\n if(uNumMasks > 0) {\n mask1 = texture(uMaskTexture0, vec2(vTexCoord.y, vTexCoord.x));\n bool visible = bbox0 != vec4(0.0f);\n\n vec2 p0 = vec2((bbox0.x + bbox0.z) * 0.5f, bbox0.y); // Top center\n vec2 p1 = vec2(bbox0.x + 0.5f * (bbox0.z - bbox0.x) * (0.5f + 0.5f * sin(time)), bbox0.y - 0.25f);\n //vec2 p1 = vec2(0.5f, 0.5f);\n vec2 p2 = vec2(bbox0.x + 0.5f * (bbox0.z - bbox0.x) * (0.5f + 0.5f * cos(time)), (bbox0.w + bbox0.y) * 0.5f);\n float d = calculateDistanceToQuadraticBezier(vTexCoord, p0, p1, p2);\n d *= length(uSize.xy) * 0.25f;\n\n vec2 v0 = p0 + vec2(-0.020f, -0.020f); // Left vertex\n vec2 v1 = p0 + vec2(0.020f, -0.020f); // Right vertex\n vec2 v2 = p0 + vec2(0.0f, 0.020f); // Bottom vertex\n // Check if the point is inside the triangle\n bool inside = pointInTriangle(vTexCoord, v0, v1, v2);\n\n // Circle drawing\n vec2 adjustedCoord = vTexCoord - p0;\n adjustedCoord.x /= aspectRatio;\n float circleDistance = length(adjustedCoord);\n\n if(d < threshold && visible) {\n scoped = true;\n }\n\n if(uArrow && inside && visible) {\n intersected = true;\n } else if(!uArrow && circleDistance < circleRadius && visible) {\n intersected = true;\n }\n }\n\n if(uNumMasks > 1) {\n mask2 = texture(uMaskTexture1, vec2(vTexCoord.y, vTexCoord.x));\n bool visible = bbox1 != vec4(0.0f);\n\n vec2 p0 = vec2((bbox1.x + bbox1.z) * 0.5f, bbox1.y);\n vec2 p1 = vec2(bbox1.x + 0.5f * (bbox1.z - bbox1.x) * (0.5f + 0.5f * sin(time)), bbox1.y - 0.25f);\n vec2 p2 = vec2(bbox1.x + 0.5f * (bbox1.z - bbox1.x) * (0.5f + 0.5f * cos(time)), (bbox1.w + bbox1.y) * 0.5f);\n float d = calculateDistanceToQuadraticBezier(vTexCoord, p0, p1, p2);\n d *= length(uSize.xy) * 0.25f;\n\n vec2 v0 = p0 + vec2(-0.020f, -0.020f);\n vec2 v1 = p0 + vec2(0.020f, -0.020f);\n vec2 v2 = p0 + vec2(0.0f, 0.020f);\n bool inside = pointInTriangle(vTexCoord, v0, v1, v2);\n\n // Circle drawing\n vec2 adjustedCoord = vTexCoord - p0;\n adjustedCoord.x /= aspectRatio;\n float circleDistance = length(adjustedCoord);\n\n if(d < threshold && visible) {\n scoped = true;\n }\n\n if(uArrow && inside && visible) {\n intersected = true;\n } else if(!uArrow && circleDistance < circleRadius && visible) {\n intersected = true;\n }\n }\n\n if(uNumMasks > 2) {\n mask3 = texture(uMaskTexture2, vec2(vTexCoord.y, vTexCoord.x));\n bool visible = bbox2 != vec4(0.0f);\n\n vec2 p0 = vec2((bbox2.x + bbox2.z) * 0.5f, bbox2.y);\n vec2 p1 = vec2(bbox2.x + 0.5f * (bbox2.z - bbox2.x) * (0.5f + 0.5f * sin(time)), bbox2.y - 0.25f);\n vec2 p2 = vec2(bbox2.x + 0.5f * (bbox2.z - bbox2.x) * (0.5f + 0.5f * cos(time)), (bbox2.w + bbox2.y) * 0.5f);\n float d = calculateDistanceToQuadraticBezier(vTexCoord, p0, p1, p2);\n d *= length(uSize.xy) * 0.25f;\n\n vec2 v0 = p0 + vec2(-0.020f, -0.020f);\n vec2 v1 = p0 + vec2(0.020f, -0.020f);\n vec2 v2 = p0 + vec2(0.0f, 0.020f);\n bool inside = pointInTriangle(vTexCoord, v0, v1, v2);\n\n vec2 adjustedCoord = vTexCoord - p0;\n adjustedCoord.x /= aspectRatio;\n float circleDistance = length(adjustedCoord);\n\n if(d < threshold && visible) {\n scoped = true;\n }\n\n if(uArrow && inside && visible) {\n intersected = true;\n } else if(!uArrow && circleDistance < circleRadius && visible) {\n intersected = true;\n }\n }\n\n bool overlap = (mask1.r > 0.0f || mask2.r > 0.0f || mask3.r > 0.0f);\n if(overlap) {\n fragColor = color;\n }\n\n if(scoped || intersected) {\n fragColor = uLineColor ? vec4(multicolor, 1.0f) : vec4(1.0f);\n if(intersected) {\n fragColor = vec4(multicolor, 1.0f);\n }\n } else {\n fragColor = overlap ? color : vec4(0.0f, 0.0f, 0.0f, 0.0f);\n }\n}" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/video/effects/shaders/BackgroundBlur.frag", "content": "#version 300 es\n// Copyright (c) Meta Platforms, Inc. and affiliates.\n// \n// Licensed under the Apache License, Version 2.0 (the \"License\");\n// you may not use this file except in compliance with the License.\n// You may obtain a copy of the License at\n// \n// http://www.apache.org/licenses/LICENSE-2.0\n// \n// Unless required by applicable law or agreed to in writing, software\n// distributed under the License is distributed on an \"AS IS\" BASIS,\n// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n// See the License for the specific language governing permissions and\n// limitations under the License.\n\nprecision mediump float;\n\nin vec2 vTexCoord;\n\nuniform sampler2D uSampler;\nuniform vec2 uSize;\nuniform int uBlurRadius;\n\nout vec4 fragColor;\n\nvoid main() {\n vec2 texOffset = 1.0f / uSize;\n // texel color\n vec3 color = texture(uSampler, vTexCoord).rgb;\n float sampleCount = 0.0f;\n\n // sample the surrounding pixels based on the blur radius\n for(int x = -uBlurRadius; x <= uBlurRadius; x++) {\n for(int y = -uBlurRadius; y <= uBlurRadius; y++) {\n vec2 offset = vec2(float(x), float(y)) * texOffset;\n color += texture(uSampler, vTexCoord + offset).rgb;\n sampleCount += 1.0f;\n }\n }\n\n // average the colors of the sampled pixels\n color /= sampleCount;\n fragColor = vec4(color, 1.0f);\n}" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/video/effects/shaders/Burst.frag", "content": "#version 300 es\n// Copyright (c) Meta Platforms, Inc. and affiliates.\n// \n// Licensed under the Apache License, Version 2.0 (the \"License\");\n// you may not use this file except in compliance with the License.\n// You may obtain a copy of the License at\n// \n// http://www.apache.org/licenses/LICENSE-2.0\n// \n// Unless required by applicable law or agreed to in writing, software\n// distributed under the License is distributed on an \"AS IS\" BASIS,\n// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n// See the License for the specific language governing permissions and\n// limitations under the License.\n\nprecision highp float;\n\nin vec2 vTexCoord;\n\nuniform sampler2D uSampler;\nuniform vec2 uSize; // resolution\nuniform int uNumMasks;\nuniform bool uLineColor;\nuniform bool uInterleave;\nuniform sampler2D uMaskTexture0;\nuniform sampler2D uMaskTexture1;\nuniform sampler2D uMaskTexture2;\n\nuniform vec4 uMaskColor0;\nuniform vec4 uMaskColor1;\nuniform vec4 uMaskColor2;\n\nuniform vec4 bbox0;\nuniform vec4 bbox1;\nuniform vec4 bbox2;\n\nout vec4 fragColor;\n\nvoid main() {\n float PI = radians(180.0f);\n float lines = uInterleave ? 12.0f : 80.0f;\n vec4 color = texture(uSampler, vTexCoord);\n vec4 color1 = uMaskColor0 / 255.0;\n vec4 color2 = uMaskColor1 / 255.0;\n vec4 color3 = uMaskColor2 / 255.0;\n\n vec4 mask1 = vec4(0.0f);\n vec4 mask2 = vec4(0.0f);\n vec4 mask3 = vec4(0.0f);\n vec4 scopedColor = vec4(0.0f);\n\n vec2 fragCoord = vTexCoord * uSize; // transform to pixel space\n bool scoped = false;\n vec4 transparent = vec4(0.0);\n float p = PI / lines;\n\n if(uNumMasks > 0) {\n mask1 = texture(uMaskTexture0, vec2(vTexCoord.y, vTexCoord.x));\n\n vec2 center1 = (bbox0.xy + bbox0.zw) * 0.5f * uSize;\n vec2 fragCoordT = (fragCoord - center1) / uSize.y;\n float a = mod(atan(fragCoordT.y, fragCoordT.x) + p, p + p) - p; // angle of fragment\n\n float pattern = sin(a * lines);\n // smoothstep for antialiasing\n float line = smoothstep(2.8 / uSize.y, 0.0, length(fragCoordT) * abs(sin(a)));\n \n vec4 colorToBlend = uLineColor ? vec4(color1.rgb, 0.80f) : vec4(1.0f);\n bool visible = bbox0 != vec4(0.0f);\n\n if (uInterleave && visible) {\n vec4 tempColor = mix(transparent, colorToBlend, step(0.0, pattern));\n scopedColor += tempColor;\n scoped = true;\n } else if (!uInterleave && visible) {\n vec4 tempColor = uLineColor ? vec4(color1.rgb * line, line) : vec4(line);\n scopedColor += tempColor;\n scoped = true;\n }\n }\n\n if(uNumMasks > 1) {\n mask2 = texture(uMaskTexture1, vec2(vTexCoord.y, vTexCoord.x));\n\n vec2 center2 = (bbox1.xy + bbox1.zw) * 0.5f * uSize;\n vec2 fragCoordT = (fragCoord - center2) / uSize.y;\n float a = mod(atan(fragCoordT.y, fragCoordT.x) + p, p + p) - p; // angle of fragment\n\n float pattern = sin(a * lines);\n float line = smoothstep(2.8 / uSize.y, 0.0, length(fragCoordT) * abs(sin(a)));\n \n vec4 colorToBlend = uLineColor ? vec4(color2.rgb, 0.8f) : vec4(1.0f);\n bool visible = bbox1 != vec4(0.0f);\n\n if (uInterleave && visible) {\n vec4 tempColor = mix(transparent, colorToBlend, step(0.0, pattern));\n if (scopedColor == vec4(0.0)) {\n scopedColor += tempColor;\n }\n scoped = true;\n } else if (!uInterleave && visible) {\n vec4 tempColor = uLineColor ? vec4(color2.rgb * line, line) : vec4(line);\n scopedColor += tempColor; \n scoped = true;\n }\n }\n\n if (uNumMasks > 2) {\n mask3 = texture(uMaskTexture2, vec2(vTexCoord.y, vTexCoord.x));\n\n vec2 center3 = (bbox2.xy + bbox2.zw) * 0.5f * uSize;\n vec2 fragCoordT = (fragCoord - center3) / uSize.y;\n\n float a = mod(atan(fragCoordT.y, fragCoordT.x) + p, p + p) - p; // angle of fragment\n\n float pattern = sin(a * lines);\n float line = smoothstep(2.8 / uSize.y, 0.0, length(fragCoordT) * abs(sin(a)));\n\n vec4 colorToBlend = uLineColor ? vec4(color3.rgb, 0.8f) : vec4(1.0f);\n bool visible = bbox2 != vec4(0.0f);\n\n if (uInterleave && visible) {\n vec4 tempColor = mix(transparent, colorToBlend, step(0.0, pattern));\n if (scopedColor == vec4(0.0)) {\n scopedColor += tempColor;\n }\n scoped = true;\n } else if (!uInterleave && visible) {\n vec4 tempColor = uLineColor ? vec4(color3.rgb * line, line) : vec4(line);\n scopedColor += tempColor;\n scoped = true;\n }\n }\n\n bool overlap = (mask1.r > 0.0f || mask2.r > 0.0f || mask3.r > 0.0f);\n if(scoped) {\n fragColor = overlap ? color : scopedColor;\n } else {\n fragColor = overlap ? color : vec4(0.0f, 0.0f, 0.0f, 0.0f);\n }\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/video/effects/shaders/Cutout.frag", "content": "#version 300 es\n// Copyright (c) Meta Platforms, Inc. and affiliates.\n// \n// Licensed under the Apache License, Version 2.0 (the \"License\");\n// you may not use this file except in compliance with the License.\n// You may obtain a copy of the License at\n// \n// http://www.apache.org/licenses/LICENSE-2.0\n// \n// Unless required by applicable law or agreed to in writing, software\n// distributed under the License is distributed on an \"AS IS\" BASIS,\n// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n// See the License for the specific language governing permissions and\n// limitations under the License.\n\nprecision mediump float;\n\nin vec2 vTexCoord;\n\nuniform sampler2D uSampler;\nuniform float uContrast;\nuniform int uNumMasks;\nuniform sampler2D uMaskTexture0;\nuniform sampler2D uMaskTexture1;\nuniform sampler2D uMaskTexture2;\n\nout vec4 fragColor;\n\nvec3 applySepia(vec4 color) {\n float gray = dot(color.rgb, vec3(0.3, 0.59, 0.11));\n vec3 sepia = vec3(gray) * vec3(1.2, 1.0, 0.8);\n sepia.r = min(sepia.r, 1.0);\n sepia.g = min(sepia.g, 1.0);\n sepia.b = min(sepia.b, 1.0);\n\n return sepia;\n}\n\nvoid main() {\n vec4 color = texture(uSampler, vTexCoord);\n\n vec4 color1 = vec4(0.0f);\n vec4 color2 = vec4(0.0f);\n vec4 color3 = vec4(0.0f);\n\n if(uNumMasks > 0) {\n color1 = texture(uMaskTexture0, vec2(vTexCoord.y, vTexCoord.x));\n }\n if(uNumMasks > 1) {\n color2 = texture(uMaskTexture1, vec2(vTexCoord.y, vTexCoord.x));\n }\n if(uNumMasks > 2) {\n color3 = texture(uMaskTexture2, vec2(vTexCoord.y, vTexCoord.x));\n }\n\n bool overlap = (color1.r > 0.0f || color2.r > 0.0f || color3.r > 0.0f);\n if(overlap) { \n if (uContrast == 0.0) {\n color = vec4(applySepia(color), color.a);\n } else {\n color.rgb = ((color.rgb - 0.5) * max(uContrast, 0.0)) + 0.5; \n }\n fragColor = color;\n } else {\n fragColor = vec4(0.0f);\n }\n}" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/video/effects/shaders/DefaultVert.vert", "content": "#version 300 es\n// Copyright (c) Meta Platforms, Inc. and affiliates.\n// \n// Licensed under the Apache License, Version 2.0 (the \"License\");\n// you may not use this file except in compliance with the License.\n// You may obtain a copy of the License at\n// \n// http://www.apache.org/licenses/LICENSE-2.0\n// \n// Unless required by applicable law or agreed to in writing, software\n// distributed under the License is distributed on an \"AS IS\" BASIS,\n// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n// See the License for the specific language governing permissions and\n// limitations under the License.\n\nlayout(location = 0) in vec4 aPosition;\nlayout(location = 1) in vec2 aTexCoord;\n\nout vec2 vTexCoord;\n\nvoid main() {\n vTexCoord = vec2(aTexCoord.s, 1.0f - aTexCoord.t);\n gl_Position = aPosition;\n}" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/video/effects/shaders/EraseForeground.frag", "content": "#version 300 es\n// Copyright (c) Meta Platforms, Inc. and affiliates.\n// \n// Licensed under the Apache License, Version 2.0 (the \"License\");\n// you may not use this file except in compliance with the License.\n// You may obtain a copy of the License at\n// \n// http://www.apache.org/licenses/LICENSE-2.0\n// \n// Unless required by applicable law or agreed to in writing, software\n// distributed under the License is distributed on an \"AS IS\" BASIS,\n// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n// See the License for the specific language governing permissions and\n// limitations under the License.\n\nprecision lowp float;\n\nin vec2 vTexCoord;\nuniform int uNumMasks;\nuniform vec3 uBgColor;\nuniform sampler2D uMaskTexture0;\nuniform sampler2D uMaskTexture1;\nuniform sampler2D uMaskTexture2;\n\nout vec4 fragColor;\n\nvoid main() {\n vec4 finalColor = vec4(0.0f, 0.0f, 0.0f, 0.0f);\n float totalMaskValue = 0.0f;\n\n if(uNumMasks > 0) {\n float maskValue0 = texture(uMaskTexture0, vec2(vTexCoord.y, vTexCoord.x)).r;\n totalMaskValue += maskValue0;\n }\n if(uNumMasks > 1) {\n float maskValue1 = texture(uMaskTexture1, vec2(vTexCoord.y, vTexCoord.x)).r;\n totalMaskValue += maskValue1;\n }\n if(uNumMasks > 2) {\n float maskValue2 = texture(uMaskTexture2, vec2(vTexCoord.y, vTexCoord.x)).r;\n totalMaskValue += maskValue2;\n }\n\n if(totalMaskValue > 0.0f) {\n finalColor = vec4(uBgColor, 1.0f);\n } else {\n finalColor.a = 0.0f;\n }\n fragColor = finalColor;\n}" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/video/effects/shaders/Gradient.frag", "content": "#version 300 es\n// Copyright (c) Meta Platforms, Inc. and affiliates.\n// \n// Licensed under the Apache License, Version 2.0 (the \"License\");\n// you may not use this file except in compliance with the License.\n// You may obtain a copy of the License at\n// \n// http://www.apache.org/licenses/LICENSE-2.0\n// \n// Unless required by applicable law or agreed to in writing, software\n// distributed under the License is distributed on an \"AS IS\" BASIS,\n// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n// See the License for the specific language governing permissions and\n// limitations under the License.\n\nprecision mediump float;\nprecision mediump sampler3D;\n\nin vec2 vTexCoord;\n\nuniform sampler2D uSampler;\nuniform sampler3D uColorGradeLUT;\nuniform mediump vec2 uSize;\n\nout vec4 fragColor;\n\nvoid main() {\n\n // texel color\n vec3 color = texture(uSampler, vTexCoord).rgb;\n vec3 gradedColor = texture(uColorGradeLUT, color).rgb;\n fragColor = vec4(gradedColor, 1);\n}" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/video/effects/shaders/NoisyMask.frag", "content": "#version 300 es\n// Copyright (c) Meta Platforms, Inc. and affiliates.\n// \n// Licensed under the Apache License, Version 2.0 (the \"License\");\n// you may not use this file except in compliance with the License.\n// You may obtain a copy of the License at\n// \n// http://www.apache.org/licenses/LICENSE-2.0\n// \n// Unless required by applicable law or agreed to in writing, software\n// distributed under the License is distributed on an \"AS IS\" BASIS,\n// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n// See the License for the specific language governing permissions and\n// limitations under the License.\n\nprecision mediump float;\n\nin vec2 vTexCoord;\n\nuniform float uCurrentFrame;\nuniform int uNumMasks;\nuniform sampler2D uMaskTexture0;\nuniform sampler2D uMaskTexture1;\nuniform sampler2D uMaskTexture2;\n\nout vec4 fragColor;\n\nvec3 startColor = vec3(0.0f, 0.67f, 1.0f);\nvec3 endColor = vec3(0.05f, 0.06f, 0.05f);\n\nfloat random(vec2 st) {\n return fract(sin(dot(st.xy, vec2(12.9898f, 78.233f))) *\n 43758.5453123f);\n}\n\nvoid main() {\n vec4 finalColor = vec4(0.0f, 0.0f, 0.0f, 0.0f);\n float totalMaskValue = 0.0f;\n\n if(uNumMasks > 0) {\n float maskValue0 = texture(uMaskTexture0, vec2(vTexCoord.y, vTexCoord.x)).r;\n totalMaskValue += maskValue0;\n }\n if(uNumMasks > 1) {\n float maskValue1 = texture(uMaskTexture1, vec2(vTexCoord.y, vTexCoord.x)).r;\n totalMaskValue += maskValue1;\n }\n if(uNumMasks > 2) {\n float maskValue2 = texture(uMaskTexture2, vec2(vTexCoord.y, vTexCoord.x)).r;\n totalMaskValue += maskValue2;\n }\n\n // Dynamic color alteration using sin(time)\n float time = uCurrentFrame * 0.1f;\n vec3 dynamicColor = mix(startColor, endColor, sin(time));\n vec3 colorVariation = mix(vec3(0.0f, 0.0f, 0.0f), vec3(1.0f, 1.0f, 1.0f), vTexCoord.y);\n\n // apply randomness to the final color\n float rnd = random(vTexCoord.xy);\n \n if(totalMaskValue > 0.0f) {\n finalColor = vec4(mix(dynamicColor, colorVariation, rnd), 1.0f);\n } else {\n finalColor.a = 0.0f;\n }\n fragColor = finalColor;\n}" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/video/effects/shaders/Overlay.frag", "content": "#version 300 es\n// Copyright (c) Meta Platforms, Inc. and affiliates.\n// \n// Licensed under the Apache License, Version 2.0 (the \"License\");\n// you may not use this file except in compliance with the License.\n// You may obtain a copy of the License at\n// \n// http://www.apache.org/licenses/LICENSE-2.0\n// \n// Unless required by applicable law or agreed to in writing, software\n// distributed under the License is distributed on an \"AS IS\" BASIS,\n// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n// See the License for the specific language governing permissions and\n// limitations under the License.\n\nprecision highp float;\n\nin vec2 vTexCoord;\n\nuniform sampler2D uSampler;\nuniform vec2 uSize;\nuniform int uNumMasks;\nuniform float uOpacity;\nuniform bool uBorder;\nuniform sampler2D uMaskTexture0;\nuniform sampler2D uMaskTexture1;\nuniform sampler2D uMaskTexture2;\n\nuniform vec4 uMaskColor0;\nuniform vec4 uMaskColor1;\nuniform vec4 uMaskColor2;\n\nuniform float uTime;\nuniform vec2 uClickPos;\nuniform int uActiveMask;\n\nout vec4 fragColor;\n\nvec4 lowerSaturation(vec4 color, float saturationFactor) {\n float luminance = 0.299f * color.r + 0.587f * color.g + 0.114f * color.b; // Calculate luminance\n vec3 gray = vec3(luminance);\n vec3 saturated = mix(gray, color.rgb, saturationFactor); // Mix gray with original color based on saturation factor\n return vec4(saturated, color.a);\n}\n\nvec4 detectEdges(sampler2D textureSampler, float coverage, vec4 edgeColor) {\n vec2 tvTexCoord = vec2(vTexCoord.y, vTexCoord.x);\n vec2 texOffset = 1.0f / uSize;\n vec3 result = vec3(0.0f);\n // neighboring pixels\n vec3 tLeft = texture(textureSampler, tvTexCoord + texOffset * vec2(-coverage, coverage)).rgb;\n vec3 tRight = texture(textureSampler, tvTexCoord + texOffset * vec2(coverage, -coverage)).rgb;\n vec3 bLeft = texture(textureSampler, tvTexCoord + texOffset * vec2(-coverage, -coverage)).rgb;\n vec3 bRight = texture(textureSampler, tvTexCoord + texOffset * vec2(coverage, coverage)).rgb;\n\n // calculate the gradient edge of the current pixel using [3x3] sobel operator.\n vec3 xEdge = tLeft + 2.0f * texture(textureSampler, tvTexCoord + texOffset * vec2(-coverage, 0)).rgb + bLeft - tRight - 2.0f * texture(textureSampler, tvTexCoord + texOffset * vec2(coverage, 0)).rgb - bRight;\n vec3 yEdge = tLeft + 2.0f * texture(textureSampler, tvTexCoord + texOffset * vec2(0, coverage)).rgb + tRight - bLeft - 2.0f * texture(textureSampler, tvTexCoord + texOffset * vec2(0, -coverage)).rgb - bRight;\n\n // magnitude of the gradient at the current pixel.\n result = sqrt(xEdge * xEdge + yEdge * yEdge);\n return result.r > 1e-6f ? edgeColor : vec4(0.0f, 0.0f, 0.0f, 0.0f);\n}\n\nvec2 calculateAdjustedTexCoord(vec2 vTexCoord, vec4 bbox, float aspectRatio) {\n vec2 center = vec2((bbox.x + bbox.z) * 0.5f, bbox.w);\n float radiusX = abs(bbox.z - bbox.x);\n float radiusY = radiusX / aspectRatio;\n float scale = 1.0f;\n radiusX *= scale;\n radiusY *= scale;\n vec2 adjustedTexCoord = (vTexCoord - center) / vec2(radiusX, radiusY) + vec2(0.5f);\n return adjustedTexCoord;\n}\n\nvoid main() {\n vec4 color = texture(uSampler, vTexCoord);\n vec4 color1 = uMaskColor0 / 255.0;\n vec4 color2 = uMaskColor1 / 255.0;\n vec4 color3 = uMaskColor2 / 255.0;\n float saturationFactor = 0.7;\n float aspectRatio = uSize.y / uSize.x;\n vec2 tvTexCoord = vec2(vTexCoord.y, vTexCoord.x);\n\n vec4 finalColor = vec4(0.0f, 0.0f, 0.0f, 0.0f);\n float totalMaskValue = 0.0f;\n vec4 edgeColor = vec4(0.0f, 0.0f, 0.0f, 0.0f);\n float numRipples = 1.75;\n float timeThreshold = 1.1; // can take any value from [0.0, 1.5]\n vec2 adjustedClickCoord = calculateAdjustedTexCoord(vTexCoord, vec4(uClickPos, uClickPos + 0.1), aspectRatio);\n\n if(uNumMasks > 0) {\n float maskValue0 = texture(uMaskTexture0, tvTexCoord).r;\n vec4 saturatedColor = lowerSaturation(color1, saturationFactor);\n vec4 plainColor= vec4(vec3(saturatedColor).rgb, 1.0);\n vec4 rippleColor = vec4(color1.rgb, 0.2);\n \n if (uActiveMask == 0 && uTime < timeThreshold) {\n float dist = length(adjustedClickCoord);\n float colorFactor = abs(sin((dist - uTime) * numRipples));\n plainColor = vec4(mix(rippleColor, plainColor, colorFactor));\n };\n \n if (uTime >= timeThreshold) {\n plainColor= vec4(vec3(saturatedColor).rgb, 1.0);\n }\n finalColor += maskValue0 * plainColor;\n totalMaskValue += maskValue0;\n\n edgeColor = detectEdges(uMaskTexture0, 1.25, color1);\n }\n if(uNumMasks > 1) {\n float maskValue1 = texture(uMaskTexture1, tvTexCoord).r;\n vec4 saturatedColor = lowerSaturation(color2, saturationFactor);\n vec4 plainColor= vec4(vec3(saturatedColor).rgb, 1.0);\n vec4 rippleColor = vec4(color2.rgb, 0.2);\n\n if (uActiveMask == 1 && uTime < timeThreshold) {\n float dist = length(adjustedClickCoord);\n float colorFactor = abs(sin((dist - uTime) * numRipples));\n plainColor = vec4(mix(rippleColor, plainColor, colorFactor));\n }\n\n if (uTime >= timeThreshold) {\n plainColor= vec4(vec3(saturatedColor).rgb, 1.0);\n }\n finalColor += maskValue1 * plainColor;\n totalMaskValue += maskValue1;\n\n if(edgeColor.a <= 0.0f) {\n edgeColor = detectEdges(uMaskTexture1, 1.25, color2);\n }\n }\n if(uNumMasks > 2) {\n float maskValue2 = texture(uMaskTexture2, tvTexCoord).r;\n vec4 saturatedColor = lowerSaturation(color3, saturationFactor);\n vec4 plainColor= vec4(vec3(saturatedColor).rgb, 1.0);\n vec4 rippleColor = vec4(color3.rgb, 0.2);\n\n if (uActiveMask == 2 && uTime < timeThreshold) {\n float dist = length(adjustedClickCoord);\n float colorFactor = abs(sin((dist - uTime) * numRipples));\n plainColor = vec4(mix(rippleColor, plainColor, colorFactor));\n }\n\n if (uTime >= timeThreshold) {\n plainColor= vec4(vec3(saturatedColor).rgb, 1.0);\n }\n\n finalColor += maskValue2 * plainColor;\n totalMaskValue += maskValue2;\n\n if(edgeColor.a <= 0.0f) {\n edgeColor = detectEdges(uMaskTexture2, 1.25, color3);\n }\n }\n\n if(totalMaskValue > 0.0f) {\n finalColor /= totalMaskValue;\n finalColor = mix(color, finalColor, uOpacity);\n } else {\n finalColor.a = 0.0f;\n }\n\n if(edgeColor.a > 0.0f && uBorder) {\n finalColor = vec4(vec3(edgeColor), 1.0f);\n }\n fragColor = finalColor;\n}" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/video/effects/shaders/Overlay.vert", "content": "#version 300 es\n// Copyright (c) Meta Platforms, Inc. and affiliates.\n// \n// Licensed under the Apache License, Version 2.0 (the \"License\");\n// you may not use this file except in compliance with the License.\n// You may obtain a copy of the License at\n// \n// http://www.apache.org/licenses/LICENSE-2.0\n// \n// Unless required by applicable law or agreed to in writing, software\n// distributed under the License is distributed on an \"AS IS\" BASIS,\n// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n// See the License for the specific language governing permissions and\n// limitations under the License.\n\nlayout(location = 0) in vec4 aPosition;\nlayout(location = 1) in vec2 aTexCoord;\n\nout vec2 vTexCoord;\n\nvoid main() {\n // Rotate texture 90 degrees clockwise\n vTexCoord = vec2(1.0f - aTexCoord.t, aTexCoord.s);\n gl_Position = aPosition;\n}" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/video/effects/shaders/Pixelate.frag", "content": "#version 300 es\n// Copyright (c) Meta Platforms, Inc. and affiliates.\n// \n// Licensed under the Apache License, Version 2.0 (the \"License\");\n// you may not use this file except in compliance with the License.\n// You may obtain a copy of the License at\n// \n// http://www.apache.org/licenses/LICENSE-2.0\n// \n// Unless required by applicable law or agreed to in writing, software\n// distributed under the License is distributed on an \"AS IS\" BASIS,\n// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n// See the License for the specific language governing permissions and\n// limitations under the License.\n\nprecision mediump float;\n\nin vec2 vTexCoord;\n\nuniform sampler2D uSampler;\nuniform mediump vec2 uSize;\nuniform lowp float uBlockSize;\n\nout vec4 fragColor;\n\nvoid main() {\n vec2 uv = vTexCoord.xy;\n float dx = uBlockSize / uSize.x;\n float dy = uBlockSize / uSize.y;\n\n // Sample from 2 places to get a better average texel color\n vec2 sampleCoord = (vec2(dx * floor((uv.x / dx)), dy * floor((uv.y / dy))) +\n vec2(dx * ceil((uv.x / dx)), dy * ceil((uv.y / dy)))) / 2.0f;\n\n vec4 frameColor = texture(uSampler, sampleCoord);\n fragColor = frameColor;\n}" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/video/effects/shaders/PixelateMask.frag", "content": "#version 300 es\n// Copyright (c) Meta Platforms, Inc. and affiliates.\n// \n// Licensed under the Apache License, Version 2.0 (the \"License\");\n// you may not use this file except in compliance with the License.\n// You may obtain a copy of the License at\n// \n// http://www.apache.org/licenses/LICENSE-2.0\n// \n// Unless required by applicable law or agreed to in writing, software\n// distributed under the License is distributed on an \"AS IS\" BASIS,\n// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n// See the License for the specific language governing permissions and\n// limitations under the License.\n\nprecision mediump float;\n\nin vec2 vTexCoord;\n\nuniform sampler2D uSampler;\nuniform mediump vec2 uSize;\nuniform lowp float uBlockSize;\nuniform int uNumMasks;\nuniform sampler2D uMaskTexture0;\nuniform sampler2D uMaskTexture1;\nuniform sampler2D uMaskTexture2;\n\nout vec4 fragColor;\n\nvoid main() {\n vec4 color = texture(uSampler, vTexCoord);\n vec2 uv = vTexCoord.xy;\n float dx = uBlockSize / uSize.x;\n float dy = uBlockSize / uSize.y;\n\n vec4 color1 = vec4(0.0f);\n vec4 color2 = vec4(0.0f);\n vec4 color3 = vec4(0.0f);\n\n vec2 sampleCoord = (vec2(dx * floor((uv.x / dx)), dy * floor((uv.y / dy))) +\n vec2(dx * ceil((uv.x / dx)), dy * ceil((uv.y / dy)))) / 2.0f;\n vec4 frameColor = texture(uSampler, sampleCoord);\n color = frameColor;\n\n if(uNumMasks > 0) {\n color1 = texture(uMaskTexture0, vec2(vTexCoord.y, vTexCoord.x));\n }\n if(uNumMasks > 1) {\n color2 = texture(uMaskTexture1, vec2(vTexCoord.y, vTexCoord.x)); \n }\n if(uNumMasks > 2) {\n color3 = texture(uMaskTexture2, vec2(vTexCoord.y, vTexCoord.x));\n }\n\n bool overlap = (color1.r > 0.0f || color2.r > 0.0f || color3.r > 0.0f);\n if(overlap) {\n fragColor = color;\n } else {\n fragColor = vec4(0.0f);\n }\n}" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/video/effects/shaders/Replace.frag", "content": "#version 300 es\n// Copyright (c) Meta Platforms, Inc. and affiliates.\n// \n// Licensed under the Apache License, Version 2.0 (the \"License\");\n// you may not use this file except in compliance with the License.\n// You may obtain a copy of the License at\n// \n// http://www.apache.org/licenses/LICENSE-2.0\n// \n// Unless required by applicable law or agreed to in writing, software\n// distributed under the License is distributed on an \"AS IS\" BASIS,\n// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n// See the License for the specific language governing permissions and\n// limitations under the License.\n\nprecision lowp float;\n\nin vec2 vTexCoord;\nuniform vec2 uSize;\nuniform int uNumMasks;\nuniform sampler2D uEmojiTexture;\nuniform bool uFill; // use all emoji texture\nuniform sampler2D uMaskTexture0;\nuniform sampler2D uMaskTexture1;\nuniform sampler2D uMaskTexture2;\n\nuniform vec4 bbox0;\nuniform vec4 bbox1;\nuniform vec4 bbox2;\n\nout vec4 fragColor;\n\nvec2 calculateAdjustedTexCoord(vec2 vTexCoord, vec4 bbox, float aspectRatio, out float distanceFromCenter) {\n vec2 center = (bbox.xy + bbox.zw) * 0.5f;\n float radiusX = abs(bbox.z - bbox.x);\n float radiusY = radiusX / aspectRatio;\n float scale = 1.25f;\n radiusX *= scale;\n radiusY *= scale;\n vec2 adjustedTexCoord = (vTexCoord - center) / vec2(radiusX, radiusY) + vec2(0.5f);\n distanceFromCenter = length((vTexCoord - center) / vec2(radiusX * 0.5f, radiusY * 0.5f));\n return adjustedTexCoord;\n}\n\nvoid main() {\n vec4 finalColor = vec4(0.0f);\n\n float aspectRatio = uSize.y / uSize.x;\n float totalMaskValue = 0.0f;\n vec4 bgFill = vec4(1.0f, 0.0f, 0.0f, 1.0f);\n\n vec4 emojiColor;\n\n if(uNumMasks > 0) {\n float maskValue0 = texture(uMaskTexture0, vec2(vTexCoord.y, vTexCoord.x)).r;\n float distanceFromCenter;\n vec2 adjustedTexCoord = calculateAdjustedTexCoord(vTexCoord, bbox0, aspectRatio, distanceFromCenter);\n\n if(maskValue0 > 0.0f) {\n emojiColor = texture(uEmojiTexture, adjustedTexCoord);\n if(distanceFromCenter > 0.85f && !uFill) {\n emojiColor = bgFill;\n }\n }\n if(uFill) {\n emojiColor = texture(uEmojiTexture, adjustedTexCoord);\n }\n \n totalMaskValue += maskValue0;\n }\n if(uNumMasks > 1) {\n float maskValue1 = texture(uMaskTexture1, vec2(vTexCoord.y, vTexCoord.x)).r;\n float distanceFromCenter;\n vec2 adjustedTexCoord = calculateAdjustedTexCoord(vTexCoord, bbox1, aspectRatio, distanceFromCenter);\n\n if(maskValue1 > 0.0f) {\n emojiColor = texture(uEmojiTexture, adjustedTexCoord);\n if(distanceFromCenter > 0.85f && !uFill) {\n emojiColor = bgFill;\n }\n }\n if(uFill && emojiColor.a == 0.0f) {\n emojiColor = texture(uEmojiTexture, adjustedTexCoord);\n }\n \n totalMaskValue += maskValue1;\n }\n if(uNumMasks > 2) {\n float maskValue2 = texture(uMaskTexture2, vec2(vTexCoord.y, vTexCoord.x)).r;\n float distanceFromCenter;\n vec2 adjustedTexCoord = calculateAdjustedTexCoord(vTexCoord, bbox2, aspectRatio, distanceFromCenter);\n if(maskValue2 > 0.0f) {\n emojiColor = texture(uEmojiTexture, adjustedTexCoord);\n if(distanceFromCenter > 0.85f && !uFill) {\n emojiColor = bgFill;\n }\n }\n if(uFill && emojiColor.a == 0.0f) {\n emojiColor = texture(uEmojiTexture, adjustedTexCoord);\n }\n\n totalMaskValue += maskValue2;\n }\n\n if(totalMaskValue > 0.0f) {\n finalColor = emojiColor;\n } else {\n finalColor = uFill ? emojiColor : vec4(0.0f);\n }\n fragColor = finalColor;\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/video/effects/shaders/Scope.frag", "content": "#version 300 es\n// Copyright (c) Meta Platforms, Inc. and affiliates.\n// \n// Licensed under the Apache License, Version 2.0 (the \"License\");\n// you may not use this file except in compliance with the License.\n// You may obtain a copy of the License at\n// \n// http://www.apache.org/licenses/LICENSE-2.0\n// \n// Unless required by applicable law or agreed to in writing, software\n// distributed under the License is distributed on an \"AS IS\" BASIS,\n// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n// See the License for the specific language governing permissions and\n// limitations under the License.\n\nprecision mediump float;\n\nin vec2 vTexCoord;\n\nuniform sampler2D uSampler;\nuniform vec2 uSize;\nuniform int uNumMasks;\nuniform bool uFillColor;\nuniform bool uLight;\nuniform bool uTransparency;\nuniform sampler2D uMaskTexture0;\nuniform sampler2D uMaskTexture1;\nuniform sampler2D uMaskTexture2;\n\nuniform vec4 uMaskColor0;\nuniform vec4 uMaskColor1;\nuniform vec4 uMaskColor2;\n\nuniform vec4 bbox0;\nuniform vec4 bbox1;\nuniform vec4 bbox2;\n\nout vec4 fragColor;\n\nvoid main() {\n vec4 color = texture(uSampler, vTexCoord);\n float aspectRatio = uSize.y / uSize.x;\n float radiusThreshold = 0.8f;\n float tickness = 0.085f;\n\n vec4 mask1 = vec4(0.0f);\n vec4 mask2 = vec4(0.0f);\n vec4 mask3 = vec4(0.0f);\n vec4 color1 = uMaskColor0 / 255.0;\n vec4 color2 = uMaskColor1 / 255.0;\n vec4 color3 = uMaskColor2 / 255.0;\n vec4 scopedColor = vec4(0.0f);\n\n bool scoped = false;\n vec4 whiteVariation = uTransparency ? vec4(0.0,0.0,0.0,1.0) : vec4(1.0);\n\n if(uNumMasks > 0) {\n mask1 = texture(uMaskTexture0, vec2(vTexCoord.y, vTexCoord.x));\n\n vec2 center1 = (bbox0.xy + bbox0.zw) * 0.5f;\n float radiusX1 = abs(bbox0.y - bbox0.w) * 0.5f;\n float radiusY1 = radiusX1 / aspectRatio;\n\n float distX1 = (vTexCoord.x - center1.x) / radiusX1;\n float distY1 = (vTexCoord.y - center1.y) / radiusY1;\n float dist1 = sqrt(pow(distX1, 2.0f) + pow(distY1, 2.0f));\n \n if(uFillColor) {\n if(dist1 >= radiusThreshold - tickness && dist1 <= radiusThreshold) {\n scoped = true;\n scopedColor = uLight ? whiteVariation : color1;\n }\n } else if(dist1 <= radiusThreshold) {\n scoped = true;\n scopedColor = uLight ? whiteVariation : color1;\n }\n }\n if(uNumMasks > 1) {\n mask2 = texture(uMaskTexture1, vec2(vTexCoord.y, vTexCoord.x));\n\n vec2 center2 = (bbox1.xy + bbox1.zw) * 0.5f;\n float radiusX2 = abs(bbox1.y - bbox1.w) * 0.5f;\n float radiusY2 = radiusX2 / aspectRatio;\n\n float distX2 = (vTexCoord.x - center2.x) / radiusX2;\n float distY2 = (vTexCoord.y - center2.y) / radiusY2;\n float dist2 = sqrt(pow(distX2, 2.0f) + pow(distY2, 2.0f));\n\n if(uFillColor) {\n if(dist2 >= radiusThreshold - tickness && dist2 <= radiusThreshold) {\n scoped = true;\n scopedColor = uLight ? whiteVariation : color2;\n }\n } else if(dist2 <= radiusThreshold) {\n scoped = true;\n scopedColor = uLight ? whiteVariation : color2;\n }\n }\n if(uNumMasks > 2) {\n mask3 = texture(uMaskTexture2, vec2(vTexCoord.y, vTexCoord.x));\n\n vec2 center3 = (bbox2.xy + bbox2.zw) * 0.5f;\n float radiusX3 = abs(bbox2.y - bbox2.w) * 0.5f;\n float radiusY3 = radiusX3 / aspectRatio;\n\n float distX3 = (vTexCoord.x - center3.x) / radiusX3;\n float distY3 = (vTexCoord.y - center3.y) / radiusY3;\n float dist3 = sqrt(pow(distX3, 2.0f) + pow(distY3, 2.0f));\n\n if(uFillColor) {\n if(dist3 >= radiusThreshold - tickness && dist3 <= radiusThreshold) {\n scoped = true;\n scopedColor = uLight ? whiteVariation : color3;\n }\n } else if(dist3 <= radiusThreshold) {\n scoped = true;\n scopedColor = uLight ? whiteVariation : color3;\n }\n }\n\n bool overlap = (mask1.r > 0.0f || mask2.r > 0.0f || mask3.r > 0.0f);\n\n if(scoped) {\n fragColor = overlap ? color : scopedColor;\n fragColor.a = uTransparency ? fragColor.a : 1.0;\n } else {\n fragColor = overlap ? color : vec4(0.0f, 0.0f, 0.0f, 0.0f);\n }\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/video/effects/shaders/Sobel.frag", "content": "#version 300 es\n// Copyright (c) Meta Platforms, Inc. and affiliates.\n// \n// Licensed under the Apache License, Version 2.0 (the \"License\");\n// you may not use this file except in compliance with the License.\n// You may obtain a copy of the License at\n// \n// http://www.apache.org/licenses/LICENSE-2.0\n// \n// Unless required by applicable law or agreed to in writing, software\n// distributed under the License is distributed on an \"AS IS\" BASIS,\n// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n// See the License for the specific language governing permissions and\n// limitations under the License.\n\nprecision mediump float;\n\nin vec2 vTexCoord;\n\nuniform sampler2D uSampler;\nuniform vec2 uSize;\nuniform bool uSwapColor;\nuniform bool uMonocolor;\n\nout vec4 fragColor;\n\nvoid main() {\n // calculate the offset for one pixel in texture coordinates\n vec2 texOffset = 1.0f / uSize;\n vec3 result = vec3(0.0f);\n // neighboring pixels\n vec3 tLeft = texture(uSampler, vTexCoord + texOffset * vec2(-1, 1)).rgb;\n vec3 tRight = texture(uSampler, vTexCoord + texOffset * vec2(1, -1)).rgb;\n vec3 bLeft = texture(uSampler, vTexCoord + texOffset * vec2(-1, -1)).rgb;\n vec3 bRight = texture(uSampler, vTexCoord + texOffset * vec2(1, 1)).rgb;\n \n // calculate the gradient edge of the current pixel using [3x3] sobel operator.\n vec3 xEdge = tLeft + 2.0f * texture(uSampler, vTexCoord + texOffset * vec2(-1, 0)).rgb + bLeft - tRight - 2.0f * texture(uSampler, vTexCoord + texOffset * vec2(1, 0)).rgb - bRight;\n vec3 yEdge = tLeft + 2.0f * texture(uSampler, vTexCoord + texOffset * vec2(0, 1)).rgb + tRight - bLeft - 2.0f * texture(uSampler, vTexCoord + texOffset * vec2(0, -1)).rgb - bRight;\n\n // magnitude of the gradient at the current pixel.\n result = sqrt(xEdge * xEdge + yEdge * yEdge);\n \n if (uMonocolor) {\n // Convert result to a grayscale intensity\n float intensity = length(result) / sqrt(3.0);\n // Threshold to determine if the color should be white or black\n float threshold = 0.2;\n if (intensity > threshold) {\n fragColor = uSwapColor ? vec4(1.0) : vec4(0.0, 0.0, 0.0, 1.0);\n } else {\n fragColor = uSwapColor ? vec4(0.0, 0.0, 0.0, 1.0) : vec4(1.0);\n }\n } else {\n result = uSwapColor ? result : vec3(0.0, 1.0, 0.0) * result;\n vec4 finalColor = vec4(result, 1.0f);\n fragColor = finalColor;\n }\n}" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/video/effects/shaders/VibrantMask.frag", "content": "#version 300 es\n// Copyright (c) Meta Platforms, Inc. and affiliates.\n// \n// Licensed under the Apache License, Version 2.0 (the \"License\");\n// you may not use this file except in compliance with the License.\n// You may obtain a copy of the License at\n// \n// http://www.apache.org/licenses/LICENSE-2.0\n// \n// Unless required by applicable law or agreed to in writing, software\n// distributed under the License is distributed on an \"AS IS\" BASIS,\n// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n// See the License for the specific language governing permissions and\n// limitations under the License.\n\nprecision mediump float;\nprecision mediump sampler3D;\n\nin vec2 vTexCoord;\n\nuniform sampler2D uSampler;\nuniform float uCurrentFrame;\nuniform sampler3D uColorGradeLUT;\nuniform int uNumMasks;\nuniform sampler2D uMaskTexture0;\nuniform sampler2D uMaskTexture1;\nuniform sampler2D uMaskTexture2;\n\nout vec4 fragColor;\n\nvoid main() {\n vec4 color = texture(uSampler, vTexCoord);\n vec3 gradedColor = texture(uColorGradeLUT, color.rgb).rgb;\n\n vec4 color1 = vec4(0.0f);\n vec4 color2 = vec4(0.0f);\n vec4 color3 = vec4(0.0f);\n\n // Apply edge detection for each mask\n // We can't use dynamic indexing with samplers in GLSL ES 3.0.\n // https://registry.khronos.org/OpenGL/specs/es/3.0/GLSL_ES_Specification_3.00.pdf Ch 12.30\n if(uNumMasks > 0) {\n color1 = texture(uMaskTexture0, vec2(vTexCoord.y, vTexCoord.x));\n }\n if(uNumMasks > 1) {\n color2 = texture(uMaskTexture1, vec2(vTexCoord.y, vTexCoord.x));\n }\n if(uNumMasks > 2) {\n color3 = texture(uMaskTexture2, vec2(vTexCoord.y, vTexCoord.x));\n }\n\n bool overlap = (color1.r > 0.0f || color2.r > 0.0f || color3.r > 0.0f);\n if(overlap) {\n fragColor = vec4(gradedColor, 1);\n } else {\n fragColor = vec4(0.0f);\n }\n}" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/video/filmstrip/FilmstripUtil.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {CanvasForm, CanvasSpace, Font, Group, Pt, Triangle} from 'pts';\nimport SelectedFrameHelper from './SelectedFrameHelper';\nimport {PADDING_BOTTOM, PADDING_TOP} from './VideoFilmstrip';\n\nexport function getPointerPosition(\n event: React.PointerEvent,\n) {\n const rect = event.currentTarget.getBoundingClientRect();\n return new Pt(event.clientX - rect.left, event.clientY - rect.top);\n}\n\nexport function drawFilmstrip(\n filmstrip: ImageBitmap | null,\n space: CanvasSpace | undefined,\n form: CanvasForm | undefined,\n) {\n if (filmstrip == null || space == undefined || form?.ctx == undefined) {\n return;\n }\n\n const ratio =\n filmstrip.width / (filmstrip.height + PADDING_TOP + PADDING_BOTTOM);\n\n form.image(\n [\n [0, PADDING_TOP],\n [space.size.x, space.size.x / ratio],\n ],\n filmstrip,\n );\n}\n\nexport function getTimeFromFrame(frame: number, fps: number): string {\n const seconds = Math.floor(frame / fps);\n const frameRemaining = frame - fps * seconds;\n return `${seconds}:${frameRemaining.toFixed().toString().padStart(2, '0')}`;\n}\n\nexport function drawMarker(\n space: CanvasSpace | undefined,\n form: CanvasForm | undefined,\n selectedFrameHelper: SelectedFrameHelper,\n pointerPosition: Pt | null,\n scanLabel: string | false,\n fps: number,\n) {\n if (space == undefined || form?.ctx == undefined) {\n return;\n }\n\n const marker = Group.fromArray([\n [0, PADDING_TOP],\n [0, space.height - PADDING_BOTTOM],\n ]);\n\n const currentMarker = marker\n .clone()\n .add(Math.max(5, selectedFrameHelper.position), 0);\n\n const getTextPosition = (label: string, marker: Group) => {\n const textWidth = form.ctx.measureText(label).width;\n return marker[0]\n .$subtract(textWidth / 2, 0)\n .$min(space.width - textWidth, PADDING_TOP - 10)\n .$max(textWidth / 2 - 2, 0);\n };\n\n // draw current marker\n form\n .strokeOnly('#00000066', 5)\n .line(currentMarker)\n .strokeOnly('#fff', 1)\n .line(currentMarker)\n .fill('#000')\n .polygon(\n Triangle.fromCenter(currentMarker[0].$add(0, 10), 5).rotate2D(Math.PI),\n );\n\n // draw text\n const frameLabel = getTimeFromFrame(selectedFrameHelper.index, fps);\n form\n .font(new Font(12, 'monospace'))\n .fillOnly('#fff')\n .text(getTextPosition(frameLabel, currentMarker), frameLabel);\n\n // draw scanning ghost marker\n if (\n selectedFrameHelper.isScanning &&\n pointerPosition != null &&\n scanLabel != false\n ) {\n const scanMarker = marker.clone().add(pointerPosition.x, 0);\n form.strokeOnly('#ffffff66', 5).line(scanMarker);\n\n form\n .font(new Font(12, 'monospace'))\n .fillOnly('#8595A4')\n .text(getTextPosition(scanLabel, scanMarker), scanLabel);\n }\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/video/filmstrip/SelectedFrameHelper.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nexport default class SelectedFrameHelper {\n private frames = 0;\n private frameToWidthRatio = 1;\n private selectedIndex = 0;\n private scanning = false;\n\n constructor(totalFrames: number, totalWidth: number, index?: number) {\n this.reset(totalFrames, totalWidth, index);\n }\n\n reset(totalFrames: number, totalWidth: number, index?: number) {\n this.frames = totalFrames;\n this.frameToWidthRatio = totalWidth / this.frames;\n if (index != null) {\n this.select(index);\n }\n }\n\n select(index: number) {\n this.selectedIndex = index >= this.frames ? this.frames - index : index;\n }\n\n toPosition(index: number) {\n return index * this.frameToWidthRatio;\n }\n\n toIndex(position: number) {\n return Math.floor(position / this.frameToWidthRatio);\n }\n\n get index(): number {\n return this.selectedIndex;\n }\n\n get position(): number {\n return this.selectedIndex * this.frameToWidthRatio;\n }\n\n scan(state: boolean) {\n this.scanning = state;\n }\n\n get isScanning(): boolean {\n return this.scanning;\n }\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/video/filmstrip/VideoFilmstrip.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport SelectedFrameHelper from '@/common/components/video/filmstrip/SelectedFrameHelper';\nimport {isPlayingAtom} from '@/demo/atoms';\nimport stylex from '@stylexjs/stylex';\nimport {useAtomValue, useSetAtom} from 'jotai';\nimport {CanvasSpace, Pt} from 'pts';\nimport {useCallback, useEffect, useMemo, useRef} from 'react';\nimport {PtsCanvas, PtsCanvasImperative} from 'react-pts-canvas';\nimport {VideoRef} from '../Video';\nimport {DecodeEvent, FrameUpdateEvent} from '../VideoWorkerBridge';\nimport useVideo from '../editor/useVideo';\nimport {\n drawFilmstrip,\n drawMarker,\n getPointerPosition,\n getTimeFromFrame,\n} from './FilmstripUtil';\nimport {selectedFrameHelperAtom} from './atoms';\nimport useDisableScrolling from './useDisableScrolling';\n\nconst styles = stylex.create({\n container: {\n display: 'flex',\n flexDirection: 'column',\n },\n filmstripWrapper: {\n position: 'relative',\n width: '100%',\n height: '5rem' /* 80px */,\n },\n filmstrip: {\n position: 'absolute',\n top: 0,\n left: 0,\n bottom: 0,\n right: 0,\n cursor: 'col-resize',\n overflow: 'hidden',\n },\n canvas: {\n width: '100%',\n height: '100%',\n },\n});\n\nexport const PADDING_TOP = 30;\nexport const PADDING_BOTTOM = 0;\n\nexport default function VideoFilmstrip() {\n const video = useVideo();\n const ptsCanvasRef = useRef(null);\n const filmstripRef = useRef(null);\n const isPlayingOnPointerDownRef = useRef(false);\n const isPlaying = useAtomValue(isPlayingAtom);\n\n const {enable: enableScrolling, disable: disableScrolling} =\n useDisableScrolling();\n\n const pointerPositionRef = useRef(null);\n const animateRAFHandle = useRef(null);\n\n const selectedFrameHelper = useMemo(() => new SelectedFrameHelper(1, 1), []);\n const setSelectedFrameHelper = useSetAtom(selectedFrameHelperAtom);\n\n const fpsRef = useRef(30);\n\n useEffect(() => {\n function onDecode(event: DecodeEvent) {\n video?.removeEventListener('decode', onDecode);\n fpsRef.current = event.fps;\n }\n video?.addEventListener('decode', onDecode);\n return () => {\n video?.removeEventListener('decode', onDecode);\n };\n }, [video]);\n\n useEffect(() => {\n setSelectedFrameHelper(selectedFrameHelper);\n }, [setSelectedFrameHelper, selectedFrameHelper]);\n\n const computeFrame = useCallback(\n (normalizedPosition: number): {index: number} | null => {\n if (video == null) {\n return null;\n }\n\n const numFrames = video.numberOfFrames;\n const index = Math.min(\n Math.max(0, Math.floor(normalizedPosition * numFrames)),\n numFrames - 1,\n );\n // The frame is needed for the CAE model. Do we still want to support it?\n // return {image: decodedVideo.frames[index], index: index};\n return {index};\n },\n [video],\n );\n\n const createFilmstrip = useCallback(\n async (\n video: VideoRef | null,\n space: CanvasSpace | undefined,\n frameIndex?: number,\n ) => {\n if (video === null || space == undefined) {\n return;\n }\n\n const bitmap: ImageBitmap = await video?.createFilmstrip(\n space.width,\n space.height - (PADDING_TOP - PADDING_BOTTOM),\n );\n\n filmstripRef.current = bitmap;\n\n selectedFrameHelper.reset(video.numberOfFrames, space.width, frameIndex); // also reset index to first frame\n\n return bitmap;\n },\n [selectedFrameHelper],\n );\n\n // Custom animation handler\n const handleRAF = useCallback(() => {\n animateRAFHandle.current = null;\n const space = ptsCanvasRef.current?.getSpace();\n const form = ptsCanvasRef.current?.getForm();\n if (space == undefined || form == undefined) {\n return;\n }\n\n // Clear space, in particular clearing the frame index number of\n // previous renders.\n space.clear();\n\n drawFilmstrip(filmstripRef.current, space, form);\n\n const scanLabel =\n selectedFrameHelper.isScanning &&\n pointerPositionRef.current !== null &&\n fpsRef.current !== null &&\n getTimeFromFrame(\n computeFrame(pointerPositionRef.current.x / space.width)?.index ?? 0,\n fpsRef.current,\n );\n\n drawMarker(\n space,\n form,\n selectedFrameHelper,\n pointerPositionRef.current,\n scanLabel,\n fpsRef.current,\n );\n }, [computeFrame, selectedFrameHelper]);\n\n const handleAnimate = useCallback(() => {\n if (animateRAFHandle.current === null) {\n animateRAFHandle.current = requestAnimationFrame(handleRAF);\n }\n }, [handleRAF]);\n\n const handleFrameUpdate = useCallback(\n (event: FrameUpdateEvent) => {\n if (!selectedFrameHelper.isScanning) {\n selectedFrameHelper.select(event.index);\n }\n handleAnimate();\n },\n [handleAnimate, selectedFrameHelper],\n );\n\n // Register a frame update listener on the video to update the filmstrip\n // indicator when the video changes frames.\n useEffect(() => {\n video?.addEventListener('frameUpdate', handleFrameUpdate);\n return () => {\n video?.removeEventListener('frameUpdate', handleFrameUpdate);\n };\n }, [video, handleFrameUpdate]);\n\n // Initiate filmstrip image\n useEffect(() => {\n const space = ptsCanvasRef.current?.getSpace();\n\n async function onLoadStart() {\n await createFilmstrip(video, space, 0);\n handleAnimate();\n }\n\n async function progress() {\n await createFilmstrip(video, space, 0);\n handleAnimate();\n }\n\n void progress();\n\n video?.addEventListener('loadstart', onLoadStart);\n video?.addEventListener('decode', progress);\n\n return () => {\n video?.removeEventListener('loadstart', onLoadStart);\n video?.removeEventListener('decode', progress);\n };\n }, [createFilmstrip, selectedFrameHelper, handleAnimate, video]);\n\n return (\n
\n
\n
\n {\n if (video != null && space != undefined) {\n selectedFrameHelper.reset(video.numberOfFrames, space.width);\n }\n if (video !== null) {\n await createFilmstrip(video, space);\n }\n handleAnimate();\n }}\n onPointerDown={event => {\n const canvas = ptsCanvasRef.current?.getCanvas();\n canvas?.setPointerCapture(event.pointerId);\n\n // Disable page scrolling while interacting with the filmstrip\n disableScrolling();\n\n pointerPositionRef.current = getPointerPosition(event);\n selectedFrameHelper.scan(true);\n\n // Pause the video when a user initially has their pointer down.\n // Playback will resume once the onPointerUp event is triggered.\n isPlayingOnPointerDownRef.current = isPlaying;\n if (isPlaying) {\n video?.pause();\n }\n }}\n onPointerUp={event => {\n // Enable page scrolling after interaction with filmstrip is done\n enableScrolling();\n\n const space = ptsCanvasRef.current?.getSpace();\n if (space != undefined) {\n pointerPositionRef.current = getPointerPosition(event);\n\n selectedFrameHelper.scan(false);\n const frame = computeFrame(\n pointerPositionRef.current.x / space.size.x,\n );\n if (\n frame != null &&\n selectedFrameHelper.index !== frame.index\n ) {\n selectedFrameHelper.select(frame.index);\n if (video !== null) {\n video.frame = frame.index;\n if (isPlayingOnPointerDownRef.current) {\n video.play();\n }\n }\n }\n handleAnimate();\n }\n\n pointerPositionRef.current = null;\n }}\n onPointerMove={event => {\n if (\n !selectedFrameHelper.isScanning ||\n pointerPositionRef.current === null\n ) {\n return;\n }\n\n const space = ptsCanvasRef.current?.getSpace();\n const form = ptsCanvasRef.current?.getForm();\n if (\n selectedFrameHelper.isScanning &&\n space != null &&\n form != null\n ) {\n pointerPositionRef.current = getPointerPosition(event);\n\n const frame = computeFrame(\n pointerPositionRef.current.x / space.size.x,\n );\n if (frame != null) {\n handleAnimate();\n if (video !== null) {\n video.frame = frame.index;\n }\n }\n }\n }}\n />\n
\n
\n
\n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/video/filmstrip/atoms.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {atom} from 'jotai';\nimport SelectedFrameHelper from './SelectedFrameHelper';\n\nexport const selectedFrameHelperAtom = atom(null);\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/video/filmstrip/useDisableScrolling.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {useCallback, useEffect, useRef} from 'react';\n\nfunction preventDefault(event: TouchEvent) {\n event.preventDefault();\n}\n\nexport default function useDisableScrolling() {\n const isDisabledRef = useRef(false);\n\n const disable = useCallback(() => {\n // Scrolling is already disabled\n if (isDisabledRef.current) {\n return;\n }\n isDisabledRef.current = true;\n document.body.addEventListener('touchmove', preventDefault, {\n passive: false,\n });\n }, []);\n\n const enable = useCallback(() => {\n // Scrolling is not disabled\n if (!isDisabledRef.current) {\n return;\n }\n isDisabledRef.current = false;\n document.body.removeEventListener('touchmove', preventDefault);\n }, []);\n\n useEffect(() => {\n // Enable scrolling again on unmount\n return () => {\n enable();\n };\n }, [enable]);\n\n return {\n disable,\n enable,\n };\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/video/filmstrip/useSelectedFrameHelper.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {useAtomValue} from 'jotai';\nimport {selectedFrameHelperAtom} from './atoms';\n\nexport default function useSelectedFrameHelper() {\n return useAtomValue(selectedFrameHelperAtom);\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/video/layers/InteractionLayer.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport useVideo from '@/common/components/video/editor/useVideo';\nimport {getPointInImage} from '@/common/components/video/editor/VideoEditorUtils';\nimport {SegmentationPoint} from '@/common/tracker/Tracker';\nimport {labelTypeAtom} from '@/demo/atoms';\nimport stylex from '@stylexjs/stylex';\nimport {useAtomValue} from 'jotai';\nimport {MouseEvent} from 'react';\n\nconst styles = stylex.create({\n container: {\n position: 'absolute',\n left: 0,\n top: 0,\n right: 0,\n bottom: 0,\n },\n});\n\ntype Props = {\n onPoint: (point: SegmentationPoint) => void;\n};\n\nexport default function InteractionLayer({onPoint}: Props) {\n const video = useVideo();\n // Use labelType to swap positive and negative points. The most important use\n // case is the switch between positive and negative label for left mouse\n // clicks.\n const labelType = useAtomValue(labelTypeAtom);\n\n return (\n ) => {\n const canvas = video?.getCanvas();\n if (canvas != null) {\n const point = getPointInImage(event, canvas);\n onPoint([...point, labelType === 'positive' ? 1 : 0]);\n }\n }}\n onContextMenu={event => {\n event.preventDefault();\n const canvas = video?.getCanvas();\n if (canvas != null) {\n const point = getPointInImage(event, canvas);\n onPoint([...point, labelType === 'positive' ? 0 : 1]);\n }\n }}\n />\n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/video/layers/PointsLayer.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {SegmentationPoint} from '@/common/tracker/Tracker';\nimport stylex from '@stylexjs/stylex';\nimport {useMemo} from 'react';\nimport useResizeObserver from 'use-resize-observer';\nimport useVideo from '../editor/useVideo';\n\nconst styles = stylex.create({\n container: {\n position: 'absolute',\n width: '100%',\n height: '100%',\n pointerEvents: 'none',\n },\n});\n\ntype Props = {\n points: SegmentationPoint[];\n onRemovePoint: (point: SegmentationPoint) => void;\n};\n\nexport function PointsLayer({points, onRemovePoint}: Props) {\n const video = useVideo();\n const videoCanvas = useMemo(() => video?.getCanvas(), [video]);\n\n const {\n ref,\n width: containerWidth = 1,\n height: containerHeight = 1,\n } = useResizeObserver();\n\n const canvasWidth = videoCanvas?.width ?? 1;\n const canvasHeight = videoCanvas?.height ?? 1;\n\n const sizeMultiplier = useMemo(() => {\n const widthMultiplier = canvasWidth / containerWidth;\n const heightMultiplier = canvasHeight / containerHeight;\n\n return Math.max(widthMultiplier, heightMultiplier);\n }, [canvasWidth, canvasHeight, containerWidth, containerHeight]);\n\n const pointRadius = useMemo(() => 8 * sizeMultiplier, [sizeMultiplier]);\n const pointStroke = useMemo(() => 2 * sizeMultiplier, [sizeMultiplier]);\n\n return (\n \n {/*\n * This is a debug element to verify the SVG element overlays\n * perfectly with the canvas element.\n */}\n {/*\n \n */}\n {/* Render points */}\n {points.map((point, idx) => {\n const isAdd = point[2] === 1;\n return (\n \n {\n event.stopPropagation();\n onRemovePoint(point);\n }}\n />\n \n {isAdd && (\n \n )}\n \n );\n })}\n \n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/video/useInputVideo.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {inputVideoAtom} from '@/demo/atoms';\nimport {useAtom} from 'jotai';\n\nexport default function useInputVideo() {\n const [inputVideo, setInputVideo] = useAtom(inputVideoAtom);\n return {inputVideo, setInputVideo};\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/components/video/useVideoWorker.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {RefObject, useEffect, useMemo, useRef} from 'react';\nimport VideoWorkerBridge from './VideoWorkerBridge';\n\ntype Options = {\n createVideoWorker?: () => Worker;\n createWorkerBridge?: CreateWorkerBridgeFunction;\n};\n\nconst DEFAULT_OPTIONS: Options = {\n createVideoWorker: () =>\n new Worker(new URL('./VideoWorker', import.meta.url), {\n type: 'module',\n }),\n};\n\ntype WorkerFactory = () => Worker;\n\ntype CreateWorkerBridgeFunction = (\n workerFactory: WorkerFactory,\n) => VideoWorkerBridge;\n\nexport default function useVideoWorker(\n src: string,\n canvasRef: RefObject,\n options: Options = {},\n) {\n const isControlTransferredToOffscreenRef = useRef(false);\n\n const mergedOptions = useMemo(() => {\n const definedProps = (o: Options) =>\n Object.fromEntries(\n Object.entries(o).filter(([_k, v]) => v !== undefined),\n );\n return Object.assign(\n DEFAULT_OPTIONS,\n definedProps(options),\n ) as Required;\n }, [options]);\n\n const worker = useMemo(() => {\n if (mergedOptions.createWorkerBridge) {\n return mergedOptions.createWorkerBridge(mergedOptions.createVideoWorker);\n }\n return VideoWorkerBridge.create(mergedOptions.createVideoWorker);\n }, [mergedOptions]);\n\n useEffect(() => {\n const canvas = canvasRef.current;\n\n if (canvas == null) {\n return;\n }\n\n if (isControlTransferredToOffscreenRef.current) {\n return;\n }\n\n isControlTransferredToOffscreenRef.current = true;\n\n worker.setCanvas(canvas);\n\n return () => {\n // Cannot terminate worker in DEV mode\n // workerRef.current?.terminate();\n };\n }, [canvasRef, mergedOptions, worker]);\n\n useEffect(() => {\n worker.setSource(src);\n }, [src, worker]);\n\n return worker;\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/error/ErrorFallback.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport useReportError from '@/common/error/useReportError';\nimport {Button} from 'react-daisyui';\nimport {FallbackProps} from 'react-error-boundary';\n\nexport default function ErrorFallback({\n error,\n resetErrorBoundary,\n}: FallbackProps) {\n const reportError = useReportError();\n\n function handleReportError() {\n reportError(error);\n }\n\n return (\n
\n

Please check your connection and retry or report error.

\n
\n \n \n Report Error\n \n
\n
\n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/error/ErrorReport.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {getErrorTitle} from '@/common/error/ErrorUtils';\nimport errorReportAtom from '@/common/error/errorReportAtom';\nimport emptyFunction from '@/common/utils/emptyFunction';\nimport {BugAntIcon} from '@heroicons/react/24/outline';\nimport {Editor} from '@monaco-editor/react';\nimport {useAtom} from 'jotai';\nimport {useEffect, useRef} from 'react';\nimport {Button, Modal} from 'react-daisyui';\n\ntype Props = {\n onReport?: (error: Error) => void;\n};\n\nexport default function ErrorReport({onReport = emptyFunction}: Props) {\n const [error, setError] = useAtom(errorReportAtom);\n const errorModalRef = useRef(null);\n\n // Clean error state on ESC\n useEffect(() => {\n function onCloseDialog() {\n setError(null);\n }\n const errorModal = errorModalRef.current;\n errorModal?.addEventListener('close', onCloseDialog);\n return () => {\n errorModal?.removeEventListener('close', onCloseDialog);\n };\n }, [setError]);\n\n useEffect(() => {\n if (error != null) {\n errorModalRef.current?.showModal();\n } else {\n errorModalRef.current?.close();\n }\n }, [error, setError]);\n\n function handleCloseModal() {\n errorModalRef.current?.close();\n }\n\n function handleReport() {\n if (error != null) {\n onReport(error);\n }\n }\n\n return (\n \n \n {error != null ? getErrorTitle(error) : 'Unknown error'}\n \n \n \n \n \n }\n onClick={handleReport}>\n Report\n \n \n \n \n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/error/ErrorSerializationUtils.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport CreateFilmstripError from '@/graphql/errors/CreateFilmstripError';\nimport DrawFrameError from '@/graphql/errors/DrawFrameError';\nimport WebGLContextError from '@/graphql/errors/WebGLContextError';\nimport {errorConstructors} from 'serialize-error';\n\nexport function registerSerializableConstructors() {\n // @ts-expect-error Wrong `errorConstructors` types\n errorConstructors.set('DrawFrameError', DrawFrameError);\n // @ts-expect-error Wrong `errorConstructors` types\n errorConstructors.set('CreateFilmstripError', CreateFilmstripError);\n // @ts-expect-error Wrong `errorConstructors` types\n errorConstructors.set('WebGLContextError', WebGLContextError);\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/error/ErrorUtils.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport CreateFilmstripError from '@/graphql/errors/CreateFilmstripError';\nimport DrawFrameError from '@/graphql/errors/DrawFrameError';\nimport WebGLContextError from '@/graphql/errors/WebGLContextError';\nimport {deserializeError, type ErrorObject} from 'serialize-error';\n\nexport type RenderingErrorType =\n | 'webgl_context'\n | 'draw_frame'\n | 'create_filmstrip'\n | 'error';\n\nexport function getRenderErrorType(error?: ErrorObject): RenderingErrorType {\n const deserializedError = deserializeError(error);\n\n if (deserializedError instanceof WebGLContextError) {\n return 'webgl_context';\n }\n if (deserializedError instanceof DrawFrameError) {\n return 'draw_frame';\n }\n if (deserializedError instanceof CreateFilmstripError) {\n return 'create_filmstrip';\n }\n return 'error';\n}\n\n/**\n * This function extracts the title from an error message.\n * The title is defined as the text before the first newline character.\n *\n * @param error The error object from which the title is to be extracted.\n * @returns The title of the error message.\n * @example\n * ```ts\n * const error = new Error('This is the title\\nThis is the body');\n * const title = getErrorTitle(error);\n * console.log(title); // 'This is the title'\n * ```\n */\nexport function getErrorTitle({message}: Error): string {\n const idx = message.indexOf('\\n');\n return idx < 0 ? message : message.substring(0, idx);\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/error/errorReportAtom.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {atom} from 'jotai';\n\nexport default atom(null);\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/error/useReportError.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport errorReportAtom from '@/common/error/errorReportAtom';\nimport {useSetAtom} from 'jotai';\nimport {useCallback} from 'react';\n\nexport default function useReportError() {\n const setError = useSetAtom(errorReportAtom);\n return useCallback(\n (error: unknown) => {\n if (typeof error === 'string') {\n setError(new Error(error));\n } else if (error instanceof Error) {\n setError(error);\n } else {\n setError(new Error('unknown error occurred'));\n }\n },\n [setError],\n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/loading/LoadingMessage.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {Loading} from 'react-daisyui';\n\nexport default function LoadingMessage() {\n return (\n
\n
\n Fetching data\n
\n
\n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/loading/LoadingStateScreen.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport introVideo from '@/assets/videos/sam2_720px_dark.mp4';\nimport introVideoPoster from '@/assets/videos/sam2_video_poster.png';\nimport StaticVideoPlayer from '@/common/loading/StaticVideoPlayer';\nimport {borderRadius, fontSize, spacing} from '@/theme/tokens.stylex';\nimport stylex from '@stylexjs/stylex';\nimport {PropsWithChildren, ReactNode} from 'react';\nimport {Link} from 'react-router-dom';\n\nconst styles = stylex.create({\n container: {\n backgroundColor: '#000',\n minHeight: '100%',\n },\n content: {\n display: 'flex',\n flexDirection: 'column',\n gap: spacing[8],\n maxWidth: '36rem', //* 576px */\n marginHorizontal: 'auto',\n paddingVertical: {\n default: '6rem',\n '@media screen and (max-width: 768px)': '3rem',\n },\n paddingHorizontal: spacing[8],\n color: '#fff',\n },\n animationContainer: {\n display: 'flex',\n justifyContent: 'center',\n },\n animation: {\n border: '2px solid white',\n borderRadius: borderRadius['xl'],\n maxWidth: 450,\n maxHeight: 450,\n height: '100%',\n overflow: 'hidden',\n '@media screen and (max-width: 768px)': {\n height: 300,\n width: 300,\n },\n },\n title: {\n textAlign: 'center',\n lineHeight: '2rem',\n fontSize: fontSize['2xl'],\n fontWeight: 400,\n },\n description: {\n textAlign: 'center',\n color: '#A7B3BF',\n },\n link: {\n textAlign: 'center',\n textDecorationLine: 'underline',\n color: '#A7B3BF',\n },\n});\n\ntype Props = PropsWithChildren<{\n title: string;\n description?: string | ReactNode;\n linkProps?: {\n to: string;\n label: string;\n };\n}>;\n\nexport default function LoadingStateScreen({\n title,\n description,\n children,\n linkProps,\n}: Props) {\n return (\n
\n
\n
\n
\n \n
\n
\n

{title}

\n {description != null && (\n
{description}
\n )}\n {children}\n {linkProps != null && (\n \n {linkProps.label}\n \n )}\n
\n
\n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/loading/StaticVideoPlayer.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport React from 'react';\n\nexport type VideoAspectRatio = 'wide' | 'square' | 'normal' | 'fill';\n\nexport type VideoProps = {\n src: string;\n aspectRatio?: VideoAspectRatio;\n className?: string;\n containerClassName?: string;\n} & React.VideoHTMLAttributes;\n\nexport default function StaticVideoPlayer({\n src,\n aspectRatio,\n className = '',\n containerClassName = '',\n ...props\n}: VideoProps) {\n let aspect =\n aspectRatio === 'wide'\n ? `aspect-video`\n : aspectRatio === 'square'\n ? 'aspect-square'\n : 'aspect-auto';\n\n let videoSize = '';\n\n if (aspectRatio === 'fill') {\n aspect =\n 'absolute object-cover right-0 bottom-0 min-w-full min-h-full h-full';\n videoSize = 'w-full h-full object-cover object-center';\n }\n\n return (\n \n \n \n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/loading/UploadLoadingScreen.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport ChangeVideoModal from '@/common/components/gallery/ChangeVideoModal';\nimport type {VideoGalleryTriggerProps} from '@/common/components/gallery/DemoVideoGalleryModal';\nimport LoadingStateScreen from '@/common/loading/LoadingStateScreen';\nimport {uploadingStateAtom} from '@/demo/atoms';\nimport {ImageCopy} from '@carbon/icons-react';\nimport {useAtomValue} from 'jotai';\nimport OptionButton from '../components/options/OptionButton';\n\nexport default function UploadLoadingScreen() {\n const uploadingState = useAtomValue(uploadingStateAtom);\n\n if (uploadingState === 'error') {\n return (\n \n
\n \n
\n \n );\n }\n\n return (\n \n );\n}\n\nfunction UploadLoadingScreenChangeVideoTrigger({\n onClick,\n}: VideoGalleryTriggerProps) {\n return (\n \n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/logger/DemoLogger.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {RenderingErrorType} from '@/common/error/ErrorUtils';\nimport Logger from './Logger';\n\ntype UploadSourceType = 'gallery' | 'option';\n\n// Maps event names to an optional payload for each event\ntype DemoEventMap = {\n // User events\n user_click_canvas: {\n click_type: 'add_point' | 'remove_point';\n click_action: 'add_object' | 'refine_object';\n click_variant?: 'positive' | 'negative';\n };\n user_click_object: {\n tracklet_id: number;\n };\n user_click_track_and_play: {\n track_and_play_click_type: 'stream' | 'abort';\n };\n user_click_apply_effect: {\n effect_type: 'background' | 'object';\n effect_name: string;\n effect_variant: number;\n };\n user_change_video: {\n gallery_video_url: string;\n };\n user_upload_video: {\n upload_source: UploadSourceType;\n };\n user_click_share: {\n gallery_video_url: string;\n };\n user_click_download: {\n gallery_video_url: string;\n };\n user_click_web_share: undefined;\n // Error events\n client_error_rendering: {\n rendering_error_type: RenderingErrorType;\n };\n client_error_start_session: undefined;\n client_error_upload_video: {\n upload_source: UploadSourceType;\n upload_error_message: string;\n };\n client_error_unsupported_browser: undefined;\n client_error_page_not_found: {\n path: string;\n };\n client_error_general: {\n message: string;\n };\n client_error_fallback: {\n fallback_error_message: string;\n };\n\n // Dataset events\n client_error_fallback_dataset: {\n dataset_fallback_error_message: string;\n };\n dataset_client_impression_event: {\n impression_type: 'grid_view' | 'detailed_view';\n video_id?: string;\n };\n dataset_client_click_events: {\n click_type: 'search' | 'next_page' | 'prev_page';\n video_id?: string;\n };\n};\n\nexport interface LoggerInterface {\n event: (\n eventName: K,\n options?: TEventMap[K],\n ) => void;\n}\n\nexport function initialize(): void {\n // noop\n}\n\nexport class DemoLogger implements LoggerInterface {\n event(eventName: K, options?: DemoEventMap[K]) {\n Logger.info(eventName, options ?? {});\n }\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/logger/LogEnvironment.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {LogLevel} from '@/common/logger/Logger';\n\n// Only enable debug logging in modes that are set in MODES_WITH_LOGGER. The\n// default is always error only.\nexport const LOG_LEVEL: LogLevel =\n import.meta.env.MODE === 'production' ? 'debug' : 'error';\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/logger/Logger.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {LOG_LEVEL} from './LogEnvironment';\n\n/** Signature of a logging function */\nexport type LogFn = {\n (message?: unknown, ...optionalParams: unknown[]): void;\n};\n\n/** Basic logger interface */\nexport interface Logger {\n info: LogFn;\n warn: LogFn;\n error: LogFn;\n debug: LogFn;\n}\n\n/** Log levels */\nexport type LogLevel = 'info' | 'warn' | 'error' | 'debug';\n\nconst NO_OP: LogFn = (_message?: unknown, ..._optionalParams: unknown[]) => {};\n\n/** Logger which outputs to the browser console */\nexport class ConsoleLogger implements Logger {\n readonly info: LogFn;\n readonly warn: LogFn;\n readonly error: LogFn;\n readonly debug: LogFn;\n\n constructor(options?: {level?: LogLevel}) {\n const {level} = options || {};\n\n // eslint-disable-next-line no-console\n this.error = console.error.bind(console);\n\n if (level === 'error') {\n this.debug = NO_OP;\n this.warn = NO_OP;\n this.info = NO_OP;\n\n return;\n }\n\n // eslint-disable-next-line no-console\n this.warn = console.warn.bind(console);\n\n if (level === 'warn') {\n this.debug = NO_OP;\n this.info = NO_OP;\n\n return;\n }\n\n // eslint-disable-next-line no-console\n this.info = console.log.bind(console);\n\n if (level === 'info') {\n this.debug = NO_OP;\n\n return;\n }\n\n // eslint-disable-next-line no-console\n this.debug = console.debug.bind(console);\n }\n}\n\nexport default new ConsoleLogger({level: LOG_LEVEL});\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/screen/useScreenSize.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {screenSizes} from '@/theme/tokens.stylex';\nimport {useLayoutEffect, useState} from 'react';\n\nexport default function useScreenSize(): {\n screenSize: number;\n isMobile: boolean;\n} {\n const [screenSize, setScreenSize] = useState(0);\n\n useLayoutEffect(() => {\n const updateSize = (): void => {\n setScreenSize(window.innerWidth);\n };\n window.addEventListener('resize', updateSize);\n updateSize();\n return (): void => window.removeEventListener('resize', updateSize);\n }, []);\n\n return {isMobile: screenSize < screenSizes['md'], screenSize};\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/tracker/SAM2Model.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {generateThumbnail} from '@/common/components/video/editor/VideoEditorUtils';\nimport VideoWorkerContext from '@/common/components/video/VideoWorkerContext';\nimport Logger from '@/common/logger/Logger';\nimport {\n SAM2ModelAddNewPointsMutation,\n SAM2ModelAddNewPointsMutation$data,\n} from '@/common/tracker/__generated__/SAM2ModelAddNewPointsMutation.graphql';\nimport {SAM2ModelCancelPropagateInVideoMutation} from '@/common/tracker/__generated__/SAM2ModelCancelPropagateInVideoMutation.graphql';\nimport {SAM2ModelClearPointsInFrameMutation} from '@/common/tracker/__generated__/SAM2ModelClearPointsInFrameMutation.graphql';\nimport {SAM2ModelClearPointsInVideoMutation} from '@/common/tracker/__generated__/SAM2ModelClearPointsInVideoMutation.graphql';\nimport {SAM2ModelCloseSessionMutation} from '@/common/tracker/__generated__/SAM2ModelCloseSessionMutation.graphql';\nimport {SAM2ModelRemoveObjectMutation} from '@/common/tracker/__generated__/SAM2ModelRemoveObjectMutation.graphql';\nimport {SAM2ModelStartSessionMutation} from '@/common/tracker/__generated__/SAM2ModelStartSessionMutation.graphql';\nimport {\n BaseTracklet,\n Mask,\n SegmentationPoint,\n StreamingState,\n Tracker,\n Tracklet,\n} from '@/common/tracker/Tracker';\nimport {TrackerOptions} from '@/common/tracker/Trackers';\nimport {\n ClearPointsInVideoResponse,\n SessionStartFailedResponse,\n SessionStartedResponse,\n StreamingCompletedResponse,\n StreamingStartedResponse,\n StreamingStateUpdateResponse,\n TrackletCreatedResponse,\n TrackletDeletedResponse,\n TrackletsUpdatedResponse,\n} from '@/common/tracker/TrackerTypes';\nimport {convertMaskToRGBA} from '@/common/utils/MaskUtils';\nimport multipartStream from '@/common/utils/MultipartStream';\nimport {Stats} from '@/debug/stats/Stats';\nimport {INFERENCE_API_ENDPOINT} from '@/demo/DemoConfig';\nimport {createEnvironment} from '@/graphql/RelayEnvironment';\nimport {\n DataArray,\n Masks,\n RLEObject,\n decode,\n encode,\n toBbox,\n} from '@/jscocotools/mask';\nimport {THEME_COLORS} from '@/theme/colors';\nimport invariant from 'invariant';\nimport {IEnvironment, commitMutation, graphql} from 'relay-runtime';\n\ntype Options = Pick;\n\ntype Session = {\n id: string | null;\n tracklets: {[id: number]: Tracklet};\n};\n\ntype StreamMasksResult = {\n frameIndex: number;\n rleMaskList: Array<{\n objectId: number;\n rleMask: RLEObject;\n }>;\n};\n\ntype StreamMasksAbortResult = {\n aborted: boolean;\n};\n\nexport class SAM2Model extends Tracker {\n private _endpoint: string;\n private _environment: IEnvironment;\n\n private abortController: AbortController | null = null;\n private _session: Session = {\n id: null,\n tracklets: {},\n };\n private _streamingState: StreamingState = 'none';\n\n private _emptyMask: RLEObject | null = null;\n\n private _maskCanvas: OffscreenCanvas;\n private _maskCtx: OffscreenCanvasRenderingContext2D;\n\n private _stats?: Stats;\n\n constructor(\n context: VideoWorkerContext,\n options: Options = {\n inferenceEndpoint: INFERENCE_API_ENDPOINT,\n },\n ) {\n super(context);\n this._endpoint = options.inferenceEndpoint;\n this._environment = createEnvironment(options.inferenceEndpoint);\n\n this._maskCanvas = new OffscreenCanvas(0, 0);\n const maskCtx = this._maskCanvas.getContext('2d');\n invariant(maskCtx != null, 'context cannot be null');\n this._maskCtx = maskCtx;\n }\n\n public startSession(videoPath: string): Promise {\n // Reset streaming state. Force update with the true flag to make sure the\n // UI updates its state.\n this._updateStreamingState('none', true);\n\n return new Promise(resolve => {\n try {\n commitMutation(this._environment, {\n mutation: graphql`\n mutation SAM2ModelStartSessionMutation($input: StartSessionInput!) {\n startSession(input: $input) {\n sessionId\n }\n }\n `,\n variables: {\n input: {\n path: videoPath,\n },\n },\n onCompleted: response => {\n const {sessionId} = response.startSession;\n this._session.id = sessionId;\n\n this._sendResponse('sessionStarted', {\n sessionId,\n });\n\n // Clear any tracklets from the previous session when\n // a new session is started\n this._clearTracklets();\n\n // Make an empty tracklet\n this.createTracklet();\n resolve();\n },\n onError: error => {\n Logger.error(error);\n this._sendResponse(\n 'sessionStartFailed',\n );\n resolve();\n },\n });\n } catch (error) {\n Logger.error(error);\n this._sendResponse('sessionStartFailed');\n resolve();\n }\n });\n }\n\n public closeSession(): Promise {\n const sessionId = this._session.id;\n\n // Do not call cleanup before retrieving the session id because cleanup\n // will reset the session id. If the order would be changed, it would\n // never execute the closeSession mutation.\n this._cleanup();\n\n if (sessionId === null) {\n return Promise.resolve();\n }\n return new Promise((resolve, reject) => {\n commitMutation(this._environment, {\n mutation: graphql`\n mutation SAM2ModelCloseSessionMutation($input: CloseSessionInput!) {\n closeSession(input: $input) {\n success\n }\n }\n `,\n variables: {\n input: {\n sessionId,\n },\n },\n onCompleted: response => {\n const {success} = response.closeSession;\n if (success === false) {\n reject(new Error('Failed to close session'));\n return;\n }\n resolve();\n },\n onError: error => {\n Logger.error(error);\n reject(error);\n },\n });\n });\n }\n\n public createTracklet(): void {\n // This will return 0 for for empty tracklets and otherwise the next\n // largest number.\n const nextId =\n Object.values(this._session.tracklets).reduce(\n (prev, curr) => Math.max(prev, curr.id),\n -1,\n ) + 1;\n\n const newTracklet = {\n id: nextId,\n color: THEME_COLORS[nextId % THEME_COLORS.length],\n thumbnail: null,\n points: [],\n masks: [],\n isInitialized: false,\n };\n\n this._session.tracklets[nextId] = newTracklet;\n\n // Notify the main thread\n this._updateTracklets();\n\n this._sendResponse('trackletCreated', {\n tracklet: newTracklet,\n });\n }\n\n public deleteTracklet(trackletId: number): Promise {\n const sessionId = this._session.id;\n if (sessionId === null) {\n return Promise.reject('No active session');\n }\n\n const tracklet = this._session.tracklets[trackletId];\n invariant(\n tracklet != null,\n 'tracklet for tracklet id %s not initialized',\n trackletId,\n );\n\n return new Promise((resolve, reject) => {\n commitMutation(this._environment, {\n mutation: graphql`\n mutation SAM2ModelRemoveObjectMutation($input: RemoveObjectInput!) {\n removeObject(input: $input) {\n frameIndex\n rleMaskList {\n objectId\n rleMask {\n counts\n size\n }\n }\n }\n }\n `,\n variables: {\n input: {objectId: trackletId, sessionId},\n },\n onCompleted: response => {\n const trackletUpdates = response.removeObject;\n this._sendResponse('trackletDeleted', {\n isSuccessful: true,\n });\n for (const trackletUpdate of trackletUpdates) {\n this._updateTrackletMasks(\n trackletUpdate,\n trackletUpdate.frameIndex === this._context.frameIndex,\n false, // shouldGoToFrame\n );\n }\n this._removeTrackletMasks(tracklet);\n resolve();\n },\n onError: error => {\n this._sendResponse('trackletDeleted', {\n isSuccessful: false,\n });\n Logger.error(error);\n reject(error);\n },\n });\n });\n }\n\n public updatePoints(\n frameIndex: number,\n objectId: number,\n points: SegmentationPoint[],\n ): Promise {\n const sessionId = this._session.id;\n if (sessionId === null) {\n return Promise.reject('No active session');\n }\n\n // TODO: This is not the right place to initialize the empty mask.\n // Move this into the constructor and listen to events on the context.\n // Note, the initial context.width and context.height is 0, so it needs\n // to happen based on an event, so when the video is initialized, it needs\n // to notify the tracker to update the empty mask.\n if (this._emptyMask === null) {\n // We need to round the height/width to the nearest integer since\n // Masks.toTensor() expects an integer value for the height/width.\n const tensor = new Masks(\n Math.trunc(this._context.height),\n Math.trunc(this._context.width),\n 1,\n ).toDataArray();\n this._emptyMask = encode(tensor)[0];\n }\n\n const tracklet = this._session.tracklets[objectId];\n invariant(\n tracklet != null,\n 'tracklet for object id %s not initialized',\n objectId,\n );\n\n // Mark session needing propagation when point is set\n this._updateStreamingState('required');\n\n // Clear all points in frame if no points are provided.\n if (points.length === 0) {\n return this.clearPointsInFrame(frameIndex, objectId);\n }\n return new Promise((resolve, reject) => {\n const normalizedPoints = points.map(p => [\n p[0] / this._context.width,\n p[1] / this._context.height,\n ]);\n const labels = points.map(p => p[2]);\n commitMutation(this._environment, {\n mutation: graphql`\n mutation SAM2ModelAddNewPointsMutation($input: AddPointsInput!) {\n addPoints(input: $input) {\n frameIndex\n rleMaskList {\n objectId\n rleMask {\n counts\n size\n }\n }\n }\n }\n `,\n variables: {\n input: {\n sessionId,\n frameIndex,\n objectId,\n labels: labels,\n points: normalizedPoints,\n clearOldPoints: true,\n },\n },\n onCompleted: response => {\n tracklet.points[frameIndex] = points;\n tracklet.isInitialized = true;\n this._updateTrackletMasks(response.addPoints, true);\n resolve();\n },\n onError: error => {\n Logger.error(error);\n reject(error);\n },\n });\n });\n }\n\n public clearPointsInFrame(\n frameIndex: number,\n objectId: number,\n ): Promise {\n const sessionId = this._session.id;\n if (sessionId === null) {\n return Promise.reject('No active session');\n }\n\n const tracklet = this._session.tracklets[objectId];\n invariant(\n tracklet != null,\n 'tracklet for object id %s not initialized',\n objectId,\n );\n\n // Mark session needing propagation when point is set\n this._updateStreamingState('required');\n\n return new Promise((resolve, reject) => {\n commitMutation(this._environment, {\n mutation: graphql`\n mutation SAM2ModelClearPointsInFrameMutation(\n $input: ClearPointsInFrameInput!\n ) {\n clearPointsInFrame(input: $input) {\n frameIndex\n rleMaskList {\n objectId\n rleMask {\n counts\n size\n }\n }\n }\n }\n `,\n variables: {\n input: {\n sessionId,\n frameIndex,\n objectId,\n },\n },\n onCompleted: response => {\n tracklet.points[frameIndex] = [];\n tracklet.isInitialized = true;\n this._updateTrackletMasks(response.clearPointsInFrame, true);\n resolve();\n },\n onError: error => {\n Logger.error(error);\n reject(error);\n },\n });\n });\n }\n\n public clearPointsInVideo(): Promise {\n const sessionId = this._session.id;\n if (sessionId === null) {\n return Promise.reject('No active session');\n }\n\n // Mark session needing propagation when point is set\n this._updateStreamingState('none');\n\n return new Promise(resolve => {\n commitMutation(this._environment, {\n mutation: graphql`\n mutation SAM2ModelClearPointsInVideoMutation(\n $input: ClearPointsInVideoInput!\n ) {\n clearPointsInVideo(input: $input) {\n success\n }\n }\n `,\n variables: {\n input: {\n sessionId,\n },\n },\n onCompleted: response => {\n const {success} = response.clearPointsInVideo;\n if (!success) {\n this._sendResponse(\n 'clearPointsInVideo',\n {isSuccessful: false},\n );\n return;\n }\n\n // Reset points and masks for each tracklet\n this._clearTracklets();\n\n // Notify the main thread\n this._context.goToFrame(this._context.frameIndex);\n this._updateTracklets();\n this._sendResponse('clearPointsInVideo', {\n isSuccessful: true,\n });\n resolve();\n },\n onError: error => {\n this._sendResponse('clearPointsInVideo', {\n isSuccessful: false,\n });\n Logger.error(error);\n },\n });\n });\n }\n\n public async streamMasks(frameIndex: number): Promise {\n const sessionId = this._session.id;\n if (sessionId === null) {\n return Promise.reject('No active session');\n }\n try {\n this._sendResponse('streamingStarted');\n\n // 1. Clear previous masks\n this._context.clearMasks();\n this._clearTrackletMasks();\n\n // 2. Create abort controller and async generator\n const controller = new AbortController();\n this.abortController = controller;\n\n this._updateStreamingState('requesting');\n const generator = this._streamMasksForSession(\n controller,\n sessionId,\n frameIndex,\n );\n\n // 3. parse stream response and update masks in session objects\n let isAborted = false;\n for await (const result of generator) {\n if ('aborted' in result) {\n this._updateStreamingState('aborting');\n await this._abortRequest();\n this._updateStreamingState('aborted');\n isAborted = true;\n } else {\n await this._updateTrackletMasks(result, false);\n this._updateStreamingState('partial');\n }\n }\n\n if (!isAborted) {\n // Mark session needing propagation when point is set\n this._updateStreamingState('full');\n }\n } catch (error) {\n Logger.error(error);\n throw error;\n }\n\n this._sendResponse('streamingCompleted');\n }\n\n public abortStreamMasks() {\n this.abortController?.abort();\n this._sendResponse('streamingCompleted');\n }\n\n public enableStats(): void {\n this._stats = new Stats('ms', 'D', 1000 / 25);\n }\n\n // PRIVATE\n\n private _cleanup() {\n this._session.id = null;\n // Clear existing tracklets\n this._session.tracklets = [];\n }\n\n private _clearTracklets() {\n this._session.tracklets = [];\n this._context.clearMasks();\n }\n\n private _updateStreamingState(\n state: StreamingState,\n forceUpdate: boolean = false,\n ) {\n if (!forceUpdate && this._streamingState === state) {\n return;\n }\n this._streamingState = state;\n this._sendResponse('streamingStateUpdate', {\n state,\n });\n }\n\n private async _removeTrackletMasks(tracklet: Tracklet) {\n this._context.clearTrackletMasks(tracklet);\n delete this._session.tracklets[tracklet.id];\n\n // Notify the main thread\n this._context.goToFrame(this._context.frameIndex);\n this._updateTracklets();\n }\n\n private async _updateTrackletMasks(\n data: SAM2ModelAddNewPointsMutation$data['addPoints'],\n updateThumbnails: boolean,\n shouldGoToFrame: boolean = true,\n ) {\n const {frameIndex, rleMaskList} = data;\n\n // 1. parse and decode masks for all objects\n for (const {objectId, rleMask} of rleMaskList) {\n const track = this._session.tracklets[objectId];\n const {size, counts} = rleMask;\n const rleObject: RLEObject = {\n size: [size[0], size[1]],\n counts: counts,\n };\n const isEmpty = counts === this._emptyMask?.counts;\n\n this._stats?.begin();\n\n const decodedMask = decode([rleObject]);\n const bbox = toBbox([rleObject]);\n\n const mask: Mask = {\n data: rleObject as RLEObject,\n shape: [...decodedMask.shape],\n bounds: [\n [bbox[0], bbox[1]],\n [bbox[0] + bbox[2], bbox[1] + bbox[3]],\n ],\n isEmpty,\n } as const;\n track.masks[frameIndex] = mask;\n\n if (updateThumbnails && !isEmpty) {\n const {ctx} = await this._compressMaskForCanvas(decodedMask);\n const frame = this._context.currentFrame as VideoFrame;\n await generateThumbnail(track, frameIndex, mask, frame, ctx);\n }\n }\n\n this._context.updateTracklets(\n frameIndex,\n Object.values(this._session.tracklets),\n shouldGoToFrame,\n );\n\n // Notify the main thread\n this._updateTracklets();\n }\n\n private _updateTracklets() {\n const tracklets: BaseTracklet[] = Object.values(\n this._session.tracklets,\n ).map(tracklet => {\n // Notify the main thread\n const {\n id,\n color,\n isInitialized,\n points: trackletPoints,\n thumbnail,\n masks,\n } = tracklet;\n return {\n id,\n color,\n isInitialized,\n points: trackletPoints,\n thumbnail,\n masks: masks.map(mask => ({\n shape: mask.shape,\n bounds: mask.bounds,\n isEmpty: mask.isEmpty,\n })),\n };\n });\n\n this._sendResponse('trackletsUpdated', {\n tracklets,\n });\n }\n\n private _clearTrackletMasks() {\n const keys = Object.keys(this._session.tracklets);\n for (const key of keys) {\n const trackletId = Number(key);\n const tracklet = {...this._session.tracklets[trackletId], masks: []};\n this._session.tracklets[trackletId] = tracklet;\n }\n this._updateTracklets();\n }\n\n private async _compressMaskForCanvas(\n decodedMask: DataArray,\n ): Promise<{compressedData: Blob; ctx: OffscreenCanvasRenderingContext2D}> {\n const data = convertMaskToRGBA(decodedMask.data as Uint8Array);\n\n this._maskCanvas.width = decodedMask.shape[0];\n this._maskCanvas.height = decodedMask.shape[1];\n\n const imageData = new ImageData(\n data,\n decodedMask.shape[0],\n decodedMask.shape[1],\n );\n this._maskCtx.putImageData(imageData, 0, 0);\n\n const canvas = new OffscreenCanvas(\n decodedMask.shape[1],\n decodedMask.shape[0],\n );\n\n const ctx = canvas.getContext('2d');\n invariant(ctx != null, 'context cannot be null');\n ctx.save();\n ctx.rotate(Math.PI / 2);\n // Since the image was previously rotated 90° clockwise, after the image is rotated,\n // we scale the canvas's width using scaleY and height using scaleX.\n ctx.scale(1, -1);\n ctx.drawImage(this._maskCanvas, 0, 0);\n ctx.restore();\n\n const compressedData = await canvas.convertToBlob({type: 'image/png'});\n\n return {compressedData, ctx};\n }\n\n private async *_streamMasksForSession(\n abortController: AbortController,\n sessionId: string,\n startFrameIndex: undefined | number = 0,\n ): AsyncGenerator {\n const url = `${this._endpoint}/propagate_in_video`;\n\n const requestBody = {\n session_id: sessionId,\n start_frame_index: startFrameIndex,\n };\n\n const headers: {[name: string]: string} = Object.assign({\n 'Content-Type': 'application/json',\n });\n\n const response = await fetch(url, {\n method: 'POST',\n body: JSON.stringify(requestBody),\n headers,\n });\n\n const contentType = response.headers.get('Content-Type');\n if (\n contentType == null ||\n !contentType.startsWith('multipart/x-savi-stream;')\n ) {\n throw new Error(\n 'endpoint needs to support Content-Type \"multipart/x-savi-stream\"',\n );\n }\n\n const responseBody = response.body;\n if (responseBody == null) {\n throw new Error('response body is null');\n }\n\n const reader = multipartStream(contentType, responseBody).getReader();\n\n const textDecoder = new TextDecoder();\n\n while (true) {\n if (abortController.signal.aborted) {\n reader.releaseLock();\n yield {aborted: true};\n return;\n }\n\n const {done, value} = await reader.read();\n if (done) {\n return;\n }\n\n const {headers, body} = value;\n\n const contentType = headers.get('Content-Type') as string;\n\n if (contentType.startsWith('application/json')) {\n const jsonResponse = JSON.parse(textDecoder.decode(body));\n const maskResults = jsonResponse.results;\n const rleMaskList = maskResults.map(\n (mask: {object_id: number; mask: RLEObject}) => {\n return {\n objectId: mask.object_id,\n rleMask: mask.mask,\n };\n },\n );\n yield {\n frameIndex: jsonResponse.frame_index,\n rleMaskList,\n };\n }\n }\n }\n\n private async _abortRequest(): Promise {\n const sessionId = this._session.id;\n invariant(sessionId != null, 'session id cannot be empty');\n return new Promise((resolve, reject) => {\n try {\n commitMutation(\n this._environment,\n {\n mutation: graphql`\n mutation SAM2ModelCancelPropagateInVideoMutation(\n $input: CancelPropagateInVideoInput!\n ) {\n cancelPropagateInVideo(input: $input) {\n success\n }\n }\n `,\n variables: {\n input: {\n sessionId,\n },\n },\n onCompleted: response => {\n const {success} = response.cancelPropagateInVideo;\n if (!success) {\n reject(`could not abort session ${sessionId}`);\n return;\n }\n resolve();\n },\n onError: error => {\n Logger.error(error);\n reject(error);\n },\n },\n );\n } catch (error) {\n Logger.error(error);\n reject(error);\n }\n });\n }\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/tracker/Tracker.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport VideoWorkerContext from '@/common/components/video/VideoWorkerContext';\n\nimport {TrackerOptions} from '@/common/tracker/Trackers';\nimport {TrackerResponse} from '@/common/tracker/TrackerTypes';\nimport {RLEObject} from '@/jscocotools/mask';\n\nexport type Point = [x: number, y: number];\n\nexport type SegmentationPoint = [...point: Point, label: 0 | 1];\n\nexport type FramePoints = Array | undefined;\n\nexport type Mask = DatalessMask & {\n data: Blob | RLEObject;\n};\n\nexport type DatalessMask = {\n shape: number[];\n bounds: [[number, number], [number, number]];\n isEmpty: boolean;\n};\n\nexport type Tracklet = {\n id: number;\n color: string;\n thumbnail: string | null;\n points: FramePoints[];\n masks: Mask[];\n isInitialized: boolean;\n};\n\nexport type BaseTracklet = Omit & {\n masks: DatalessMask[];\n};\n\nexport type StreamingState =\n | 'none'\n | 'required'\n | 'requesting'\n | 'aborting'\n | 'aborted'\n | 'partial'\n | 'full';\n\nexport interface ITracker {\n startSession(videoUrl: string): Promise;\n closeSession(): Promise;\n createTracklet(): void;\n deleteTracklet(trackletId: number): Promise;\n updatePoints(\n frameIndex: number,\n objectId: number,\n points: SegmentationPoint[],\n ): Promise;\n clearPointsInFrame(frameIndex: number, objectId: number): Promise;\n clearPointsInVideo(): Promise;\n streamMasks(frameIndex: number): Promise;\n abortStreamMasks(): void;\n enableStats(): void;\n}\n\nexport abstract class Tracker implements ITracker {\n protected _context: VideoWorkerContext;\n constructor(context: VideoWorkerContext, _options?: TrackerOptions) {\n this._context = context;\n }\n abstract startSession(videoUrl: string): Promise;\n abstract closeSession(): Promise;\n abstract createTracklet(): void;\n abstract deleteTracklet(trackletId: number): Promise;\n abstract updatePoints(\n frameIndex: number,\n objectId: number,\n points: SegmentationPoint[],\n ): Promise;\n abstract clearPointsInFrame(\n frameIndex: number,\n objectId: number,\n ): Promise;\n abstract clearPointsInVideo(): Promise;\n abstract streamMasks(frameIndex: number): Promise;\n abstract abortStreamMasks(): void;\n abstract enableStats(): void;\n\n // PRIVATE FUNCTIONS\n\n protected _sendResponse(\n action: T['action'],\n message?: Omit,\n transfer?: Transferable[],\n ): void {\n self.postMessage(\n {\n action,\n ...message,\n },\n {\n transfer,\n },\n );\n }\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/tracker/TrackerTypes.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {SegmentationPoint} from '@/common/tracker/Tracker';\nimport {TrackerOptions, Trackers} from '@/common/tracker/Trackers';\nimport {\n AddPointsEvent,\n ClearPointsInVideoEvent,\n SessionStartFailedEvent,\n SessionStartedEvent,\n StreamingCompletedEvent,\n StreamingStartedEvent,\n StreamingStateUpdateEvent,\n TrackletCreatedEvent,\n TrackletDeletedEvent,\n TrackletsEvent,\n} from '../components/video/VideoWorkerBridge';\n\nexport type Flags = {\n masks: boolean;\n effect: boolean;\n};\n\nexport type Request = {\n action: A;\n} & P;\n\n// REQUESTS\n\nexport type InitializeTrackerRequest = Request<\n 'initializeTracker',\n {\n name: keyof Trackers;\n options: TrackerOptions;\n }\n>;\nexport type StartSessionRequest = Request<\n 'startSession',\n {\n videoUrl: string;\n }\n>;\nexport type CloseSessionRequest = Request<'closeSession', unknown>;\nexport type CreateTrackletRequest = Request<'createTracklet', unknown>;\nexport type DeleteTrackletRequest = Request<\n 'deleteTracklet',\n {\n trackletId: number;\n }\n>;\nexport type UpdatePointsRequest = Request<\n 'updatePoints',\n {\n frameIndex: number;\n objectId: number;\n points: SegmentationPoint[];\n }\n>;\nexport type ClearPointsInFrameRequest = Request<\n 'clearPointsInFrame',\n {\n frameIndex: number;\n objectId: number;\n }\n>;\nexport type ClearPointsInVideoRequest = Request<'clearPointsInVideo', unknown>;\nexport type StreamMasksRequest = Request<\n 'streamMasks',\n {\n frameIndex: number;\n }\n>;\nexport type AbortStreamMasksRequest = Request<'abortStreamMasks', unknown>;\n\nexport type LogAnnotationsRequest = Request<'logAnnotations', unknown>;\n\nexport type TrackerRequest =\n | InitializeTrackerRequest\n | StartSessionRequest\n | CloseSessionRequest\n | CreateTrackletRequest\n | DeleteTrackletRequest\n | UpdatePointsRequest\n | ClearPointsInFrameRequest\n | ClearPointsInVideoRequest\n | StreamMasksRequest\n | AbortStreamMasksRequest\n | LogAnnotationsRequest;\n\nexport type TrackerRequestMessageEvent = MessageEvent;\n\n// RESPONSES\n\nexport type SessionStartedResponse = Request<\n 'sessionStarted',\n SessionStartedEvent\n>;\n\nexport type SessionStartFailedResponse = Request<\n 'sessionStartFailed',\n SessionStartFailedEvent\n>;\n\nexport type TrackletCreatedResponse = Request<\n 'trackletCreated',\n TrackletCreatedEvent\n>;\n\nexport type TrackletsUpdatedResponse = Request<\n 'trackletsUpdated',\n TrackletsEvent\n>;\n\nexport type TrackletDeletedResponse = Request<\n 'trackletDeleted',\n TrackletDeletedEvent\n>;\n\nexport type AddPointsResponse = Request<'addPoints', AddPointsEvent>;\n\nexport type ClearPointsInVideoResponse = Request<\n 'clearPointsInVideo',\n ClearPointsInVideoEvent\n>;\n\nexport type StreamingStartedResponse = Request<\n 'streamingStarted',\n StreamingStartedEvent\n>;\n\nexport type StreamingCompletedResponse = Request<\n 'streamingCompleted',\n StreamingCompletedEvent\n>;\n\nexport type StreamingStateUpdateResponse = Request<\n 'streamingStateUpdate',\n StreamingStateUpdateEvent\n>;\n\nexport type TrackerResponse =\n | SessionStartedResponse\n | SessionStartFailedResponse\n | TrackletCreatedResponse\n | TrackletsUpdatedResponse\n | TrackletDeletedResponse\n | AddPointsResponse\n | ClearPointsInVideoResponse\n | StreamingStartedResponse\n | StreamingCompletedResponse\n | StreamingStateUpdateResponse;\n\nexport type TrackerResponseMessageEvent = MessageEvent;\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/tracker/Trackers.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {SAM2Model} from './SAM2Model';\n\nexport type Headers = {[name: string]: string};\n\nexport type TrackerOptions = {\n inferenceEndpoint: string;\n};\n\nexport type Trackers = {\n 'SAM 2': typeof SAM2Model;\n};\n\nexport const TRACKER_MAPPING: Trackers = {\n 'SAM 2': SAM2Model,\n};\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/tracker/__generated__/SAM2ModelAddNewPointsMutation.graphql.ts", "content": "/**\n * @generated SignedSource<>\n * @lightSyntaxTransform\n * @nogrep\n */\n\n/* tslint:disable */\n/* eslint-disable */\n// @ts-nocheck\n\nimport { ConcreteRequest, Mutation } from 'relay-runtime';\nexport type AddPointsInput = {\n clearOldPoints: boolean;\n frameIndex: number;\n labels: ReadonlyArray;\n objectId: number;\n points: ReadonlyArray>;\n sessionId: string;\n};\nexport type SAM2ModelAddNewPointsMutation$variables = {\n input: AddPointsInput;\n};\nexport type SAM2ModelAddNewPointsMutation$data = {\n readonly addPoints: {\n readonly frameIndex: number;\n readonly rleMaskList: ReadonlyArray<{\n readonly objectId: number;\n readonly rleMask: {\n readonly counts: string;\n readonly size: ReadonlyArray;\n };\n }>;\n };\n};\nexport type SAM2ModelAddNewPointsMutation = {\n response: SAM2ModelAddNewPointsMutation$data;\n variables: SAM2ModelAddNewPointsMutation$variables;\n};\n\nconst node: ConcreteRequest = (function(){\nvar v0 = [\n {\n \"defaultValue\": null,\n \"kind\": \"LocalArgument\",\n \"name\": \"input\"\n }\n],\nv1 = [\n {\n \"alias\": null,\n \"args\": [\n {\n \"kind\": \"Variable\",\n \"name\": \"input\",\n \"variableName\": \"input\"\n }\n ],\n \"concreteType\": \"RLEMaskListOnFrame\",\n \"kind\": \"LinkedField\",\n \"name\": \"addPoints\",\n \"plural\": false,\n \"selections\": [\n {\n \"alias\": null,\n \"args\": null,\n \"kind\": \"ScalarField\",\n \"name\": \"frameIndex\",\n \"storageKey\": null\n },\n {\n \"alias\": null,\n \"args\": null,\n \"concreteType\": \"RLEMaskForObject\",\n \"kind\": \"LinkedField\",\n \"name\": \"rleMaskList\",\n \"plural\": true,\n \"selections\": [\n {\n \"alias\": null,\n \"args\": null,\n \"kind\": \"ScalarField\",\n \"name\": \"objectId\",\n \"storageKey\": null\n },\n {\n \"alias\": null,\n \"args\": null,\n \"concreteType\": \"RLEMask\",\n \"kind\": \"LinkedField\",\n \"name\": \"rleMask\",\n \"plural\": false,\n \"selections\": [\n {\n \"alias\": null,\n \"args\": null,\n \"kind\": \"ScalarField\",\n \"name\": \"counts\",\n \"storageKey\": null\n },\n {\n \"alias\": null,\n \"args\": null,\n \"kind\": \"ScalarField\",\n \"name\": \"size\",\n \"storageKey\": null\n }\n ],\n \"storageKey\": null\n }\n ],\n \"storageKey\": null\n }\n ],\n \"storageKey\": null\n }\n];\nreturn {\n \"fragment\": {\n \"argumentDefinitions\": (v0/*: any*/),\n \"kind\": \"Fragment\",\n \"metadata\": null,\n \"name\": \"SAM2ModelAddNewPointsMutation\",\n \"selections\": (v1/*: any*/),\n \"type\": \"Mutation\",\n \"abstractKey\": null\n },\n \"kind\": \"Request\",\n \"operation\": {\n \"argumentDefinitions\": (v0/*: any*/),\n \"kind\": \"Operation\",\n \"name\": \"SAM2ModelAddNewPointsMutation\",\n \"selections\": (v1/*: any*/)\n },\n \"params\": {\n \"cacheID\": \"dc86527e91907e696683458ed0943d2f\",\n \"id\": null,\n \"metadata\": {},\n \"name\": \"SAM2ModelAddNewPointsMutation\",\n \"operationKind\": \"mutation\",\n \"text\": \"mutation SAM2ModelAddNewPointsMutation(\\n $input: AddPointsInput!\\n) {\\n addPoints(input: $input) {\\n frameIndex\\n rleMaskList {\\n objectId\\n rleMask {\\n counts\\n size\\n }\\n }\\n }\\n}\\n\"\n }\n};\n})();\n\n(node as any).hash = \"3c96f05877dd91668c1f9e8a3f1203a5\";\n\nexport default node;\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/tracker/__generated__/SAM2ModelCancelPropagateInVideoMutation.graphql.ts", "content": "/**\n * @generated SignedSource<<87827cb79ef9276cd5a66026151e937c>>\n * @lightSyntaxTransform\n * @nogrep\n */\n\n/* tslint:disable */\n/* eslint-disable */\n// @ts-nocheck\n\nimport { ConcreteRequest, Mutation } from 'relay-runtime';\nexport type CancelPropagateInVideoInput = {\n sessionId: string;\n};\nexport type SAM2ModelCancelPropagateInVideoMutation$variables = {\n input: CancelPropagateInVideoInput;\n};\nexport type SAM2ModelCancelPropagateInVideoMutation$data = {\n readonly cancelPropagateInVideo: {\n readonly success: boolean;\n };\n};\nexport type SAM2ModelCancelPropagateInVideoMutation = {\n response: SAM2ModelCancelPropagateInVideoMutation$data;\n variables: SAM2ModelCancelPropagateInVideoMutation$variables;\n};\n\nconst node: ConcreteRequest = (function(){\nvar v0 = [\n {\n \"defaultValue\": null,\n \"kind\": \"LocalArgument\",\n \"name\": \"input\"\n }\n],\nv1 = [\n {\n \"alias\": null,\n \"args\": [\n {\n \"kind\": \"Variable\",\n \"name\": \"input\",\n \"variableName\": \"input\"\n }\n ],\n \"concreteType\": \"CancelPropagateInVideo\",\n \"kind\": \"LinkedField\",\n \"name\": \"cancelPropagateInVideo\",\n \"plural\": false,\n \"selections\": [\n {\n \"alias\": null,\n \"args\": null,\n \"kind\": \"ScalarField\",\n \"name\": \"success\",\n \"storageKey\": null\n }\n ],\n \"storageKey\": null\n }\n];\nreturn {\n \"fragment\": {\n \"argumentDefinitions\": (v0/*: any*/),\n \"kind\": \"Fragment\",\n \"metadata\": null,\n \"name\": \"SAM2ModelCancelPropagateInVideoMutation\",\n \"selections\": (v1/*: any*/),\n \"type\": \"Mutation\",\n \"abstractKey\": null\n },\n \"kind\": \"Request\",\n \"operation\": {\n \"argumentDefinitions\": (v0/*: any*/),\n \"kind\": \"Operation\",\n \"name\": \"SAM2ModelCancelPropagateInVideoMutation\",\n \"selections\": (v1/*: any*/)\n },\n \"params\": {\n \"cacheID\": \"f00f78f24741d27828f0bd95b0f373c2\",\n \"id\": null,\n \"metadata\": {},\n \"name\": \"SAM2ModelCancelPropagateInVideoMutation\",\n \"operationKind\": \"mutation\",\n \"text\": \"mutation SAM2ModelCancelPropagateInVideoMutation(\\n $input: CancelPropagateInVideoInput!\\n) {\\n cancelPropagateInVideo(input: $input) {\\n success\\n }\\n}\\n\"\n }\n};\n})();\n\n(node as any).hash = \"1abafecade479ab3c45f9cecf0360285\";\n\nexport default node;\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/tracker/__generated__/SAM2ModelClearPointsInFrameMutation.graphql.ts", "content": "/**\n * @generated SignedSource<<7330d05db0fe66bbd89190cc665dd8d9>>\n * @lightSyntaxTransform\n * @nogrep\n */\n\n/* tslint:disable */\n/* eslint-disable */\n// @ts-nocheck\n\nimport { ConcreteRequest, Mutation } from 'relay-runtime';\nexport type ClearPointsInFrameInput = {\n frameIndex: number;\n objectId: number;\n sessionId: string;\n};\nexport type SAM2ModelClearPointsInFrameMutation$variables = {\n input: ClearPointsInFrameInput;\n};\nexport type SAM2ModelClearPointsInFrameMutation$data = {\n readonly clearPointsInFrame: {\n readonly frameIndex: number;\n readonly rleMaskList: ReadonlyArray<{\n readonly objectId: number;\n readonly rleMask: {\n readonly counts: string;\n readonly size: ReadonlyArray;\n };\n }>;\n };\n};\nexport type SAM2ModelClearPointsInFrameMutation = {\n response: SAM2ModelClearPointsInFrameMutation$data;\n variables: SAM2ModelClearPointsInFrameMutation$variables;\n};\n\nconst node: ConcreteRequest = (function(){\nvar v0 = [\n {\n \"defaultValue\": null,\n \"kind\": \"LocalArgument\",\n \"name\": \"input\"\n }\n],\nv1 = [\n {\n \"alias\": null,\n \"args\": [\n {\n \"kind\": \"Variable\",\n \"name\": \"input\",\n \"variableName\": \"input\"\n }\n ],\n \"concreteType\": \"RLEMaskListOnFrame\",\n \"kind\": \"LinkedField\",\n \"name\": \"clearPointsInFrame\",\n \"plural\": false,\n \"selections\": [\n {\n \"alias\": null,\n \"args\": null,\n \"kind\": \"ScalarField\",\n \"name\": \"frameIndex\",\n \"storageKey\": null\n },\n {\n \"alias\": null,\n \"args\": null,\n \"concreteType\": \"RLEMaskForObject\",\n \"kind\": \"LinkedField\",\n \"name\": \"rleMaskList\",\n \"plural\": true,\n \"selections\": [\n {\n \"alias\": null,\n \"args\": null,\n \"kind\": \"ScalarField\",\n \"name\": \"objectId\",\n \"storageKey\": null\n },\n {\n \"alias\": null,\n \"args\": null,\n \"concreteType\": \"RLEMask\",\n \"kind\": \"LinkedField\",\n \"name\": \"rleMask\",\n \"plural\": false,\n \"selections\": [\n {\n \"alias\": null,\n \"args\": null,\n \"kind\": \"ScalarField\",\n \"name\": \"counts\",\n \"storageKey\": null\n },\n {\n \"alias\": null,\n \"args\": null,\n \"kind\": \"ScalarField\",\n \"name\": \"size\",\n \"storageKey\": null\n }\n ],\n \"storageKey\": null\n }\n ],\n \"storageKey\": null\n }\n ],\n \"storageKey\": null\n }\n];\nreturn {\n \"fragment\": {\n \"argumentDefinitions\": (v0/*: any*/),\n \"kind\": \"Fragment\",\n \"metadata\": null,\n \"name\": \"SAM2ModelClearPointsInFrameMutation\",\n \"selections\": (v1/*: any*/),\n \"type\": \"Mutation\",\n \"abstractKey\": null\n },\n \"kind\": \"Request\",\n \"operation\": {\n \"argumentDefinitions\": (v0/*: any*/),\n \"kind\": \"Operation\",\n \"name\": \"SAM2ModelClearPointsInFrameMutation\",\n \"selections\": (v1/*: any*/)\n },\n \"params\": {\n \"cacheID\": \"b4f20e0205c26d5dc3614935ac73fa3f\",\n \"id\": null,\n \"metadata\": {},\n \"name\": \"SAM2ModelClearPointsInFrameMutation\",\n \"operationKind\": \"mutation\",\n \"text\": \"mutation SAM2ModelClearPointsInFrameMutation(\\n $input: ClearPointsInFrameInput!\\n) {\\n clearPointsInFrame(input: $input) {\\n frameIndex\\n rleMaskList {\\n objectId\\n rleMask {\\n counts\\n size\\n }\\n }\\n }\\n}\\n\"\n }\n};\n})();\n\n(node as any).hash = \"880295870f14839040acf8f191fa1409\";\n\nexport default node;\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/tracker/__generated__/SAM2ModelClearPointsInVideoMutation.graphql.ts", "content": "/**\n * @generated SignedSource<<092c43655450b8af706e546837e0a01c>>\n * @lightSyntaxTransform\n * @nogrep\n */\n\n/* tslint:disable */\n/* eslint-disable */\n// @ts-nocheck\n\nimport { ConcreteRequest, Mutation } from 'relay-runtime';\nexport type ClearPointsInVideoInput = {\n sessionId: string;\n};\nexport type SAM2ModelClearPointsInVideoMutation$variables = {\n input: ClearPointsInVideoInput;\n};\nexport type SAM2ModelClearPointsInVideoMutation$data = {\n readonly clearPointsInVideo: {\n readonly success: boolean;\n };\n};\nexport type SAM2ModelClearPointsInVideoMutation = {\n response: SAM2ModelClearPointsInVideoMutation$data;\n variables: SAM2ModelClearPointsInVideoMutation$variables;\n};\n\nconst node: ConcreteRequest = (function(){\nvar v0 = [\n {\n \"defaultValue\": null,\n \"kind\": \"LocalArgument\",\n \"name\": \"input\"\n }\n],\nv1 = [\n {\n \"alias\": null,\n \"args\": [\n {\n \"kind\": \"Variable\",\n \"name\": \"input\",\n \"variableName\": \"input\"\n }\n ],\n \"concreteType\": \"ClearPointsInVideo\",\n \"kind\": \"LinkedField\",\n \"name\": \"clearPointsInVideo\",\n \"plural\": false,\n \"selections\": [\n {\n \"alias\": null,\n \"args\": null,\n \"kind\": \"ScalarField\",\n \"name\": \"success\",\n \"storageKey\": null\n }\n ],\n \"storageKey\": null\n }\n];\nreturn {\n \"fragment\": {\n \"argumentDefinitions\": (v0/*: any*/),\n \"kind\": \"Fragment\",\n \"metadata\": null,\n \"name\": \"SAM2ModelClearPointsInVideoMutation\",\n \"selections\": (v1/*: any*/),\n \"type\": \"Mutation\",\n \"abstractKey\": null\n },\n \"kind\": \"Request\",\n \"operation\": {\n \"argumentDefinitions\": (v0/*: any*/),\n \"kind\": \"Operation\",\n \"name\": \"SAM2ModelClearPointsInVideoMutation\",\n \"selections\": (v1/*: any*/)\n },\n \"params\": {\n \"cacheID\": \"c23b3d5afca5b235328a562369056527\",\n \"id\": null,\n \"metadata\": {},\n \"name\": \"SAM2ModelClearPointsInVideoMutation\",\n \"operationKind\": \"mutation\",\n \"text\": \"mutation SAM2ModelClearPointsInVideoMutation(\\n $input: ClearPointsInVideoInput!\\n) {\\n clearPointsInVideo(input: $input) {\\n success\\n }\\n}\\n\"\n }\n};\n})();\n\n(node as any).hash = \"020267989385cb8b8f0e5cdde784d17e\";\n\nexport default node;\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/tracker/__generated__/SAM2ModelCloseSessionMutation.graphql.ts", "content": "/**\n * @generated SignedSource<<48ee5db240b8093e9e53bf0329c8bab7>>\n * @lightSyntaxTransform\n * @nogrep\n */\n\n/* tslint:disable */\n/* eslint-disable */\n// @ts-nocheck\n\nimport { ConcreteRequest, Mutation } from 'relay-runtime';\nexport type CloseSessionInput = {\n sessionId: string;\n};\nexport type SAM2ModelCloseSessionMutation$variables = {\n input: CloseSessionInput;\n};\nexport type SAM2ModelCloseSessionMutation$data = {\n readonly closeSession: {\n readonly success: boolean;\n };\n};\nexport type SAM2ModelCloseSessionMutation = {\n response: SAM2ModelCloseSessionMutation$data;\n variables: SAM2ModelCloseSessionMutation$variables;\n};\n\nconst node: ConcreteRequest = (function(){\nvar v0 = [\n {\n \"defaultValue\": null,\n \"kind\": \"LocalArgument\",\n \"name\": \"input\"\n }\n],\nv1 = [\n {\n \"alias\": null,\n \"args\": [\n {\n \"kind\": \"Variable\",\n \"name\": \"input\",\n \"variableName\": \"input\"\n }\n ],\n \"concreteType\": \"CloseSession\",\n \"kind\": \"LinkedField\",\n \"name\": \"closeSession\",\n \"plural\": false,\n \"selections\": [\n {\n \"alias\": null,\n \"args\": null,\n \"kind\": \"ScalarField\",\n \"name\": \"success\",\n \"storageKey\": null\n }\n ],\n \"storageKey\": null\n }\n];\nreturn {\n \"fragment\": {\n \"argumentDefinitions\": (v0/*: any*/),\n \"kind\": \"Fragment\",\n \"metadata\": null,\n \"name\": \"SAM2ModelCloseSessionMutation\",\n \"selections\": (v1/*: any*/),\n \"type\": \"Mutation\",\n \"abstractKey\": null\n },\n \"kind\": \"Request\",\n \"operation\": {\n \"argumentDefinitions\": (v0/*: any*/),\n \"kind\": \"Operation\",\n \"name\": \"SAM2ModelCloseSessionMutation\",\n \"selections\": (v1/*: any*/)\n },\n \"params\": {\n \"cacheID\": \"aa7177838c16536b397bfee2d15a94ee\",\n \"id\": null,\n \"metadata\": {},\n \"name\": \"SAM2ModelCloseSessionMutation\",\n \"operationKind\": \"mutation\",\n \"text\": \"mutation SAM2ModelCloseSessionMutation(\\n $input: CloseSessionInput!\\n) {\\n closeSession(input: $input) {\\n success\\n }\\n}\\n\"\n }\n};\n})();\n\n(node as any).hash = \"6e1008de944562dc1922cd3f9cc40f10\";\n\nexport default node;\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/tracker/__generated__/SAM2ModelRemoveObjectMutation.graphql.ts", "content": "/**\n * @generated SignedSource<<3d0d7bdc0d4304f08ea91b7df9efeb1f>>\n * @lightSyntaxTransform\n * @nogrep\n */\n\n/* tslint:disable */\n/* eslint-disable */\n// @ts-nocheck\n\nimport { ConcreteRequest, Mutation } from 'relay-runtime';\nexport type RemoveObjectInput = {\n objectId: number;\n sessionId: string;\n};\nexport type SAM2ModelRemoveObjectMutation$variables = {\n input: RemoveObjectInput;\n};\nexport type SAM2ModelRemoveObjectMutation$data = {\n readonly removeObject: ReadonlyArray<{\n readonly frameIndex: number;\n readonly rleMaskList: ReadonlyArray<{\n readonly objectId: number;\n readonly rleMask: {\n readonly counts: string;\n readonly size: ReadonlyArray;\n };\n }>;\n }>;\n};\nexport type SAM2ModelRemoveObjectMutation = {\n response: SAM2ModelRemoveObjectMutation$data;\n variables: SAM2ModelRemoveObjectMutation$variables;\n};\n\nconst node: ConcreteRequest = (function(){\nvar v0 = [\n {\n \"defaultValue\": null,\n \"kind\": \"LocalArgument\",\n \"name\": \"input\"\n }\n],\nv1 = [\n {\n \"alias\": null,\n \"args\": [\n {\n \"kind\": \"Variable\",\n \"name\": \"input\",\n \"variableName\": \"input\"\n }\n ],\n \"concreteType\": \"RLEMaskListOnFrame\",\n \"kind\": \"LinkedField\",\n \"name\": \"removeObject\",\n \"plural\": true,\n \"selections\": [\n {\n \"alias\": null,\n \"args\": null,\n \"kind\": \"ScalarField\",\n \"name\": \"frameIndex\",\n \"storageKey\": null\n },\n {\n \"alias\": null,\n \"args\": null,\n \"concreteType\": \"RLEMaskForObject\",\n \"kind\": \"LinkedField\",\n \"name\": \"rleMaskList\",\n \"plural\": true,\n \"selections\": [\n {\n \"alias\": null,\n \"args\": null,\n \"kind\": \"ScalarField\",\n \"name\": \"objectId\",\n \"storageKey\": null\n },\n {\n \"alias\": null,\n \"args\": null,\n \"concreteType\": \"RLEMask\",\n \"kind\": \"LinkedField\",\n \"name\": \"rleMask\",\n \"plural\": false,\n \"selections\": [\n {\n \"alias\": null,\n \"args\": null,\n \"kind\": \"ScalarField\",\n \"name\": \"counts\",\n \"storageKey\": null\n },\n {\n \"alias\": null,\n \"args\": null,\n \"kind\": \"ScalarField\",\n \"name\": \"size\",\n \"storageKey\": null\n }\n ],\n \"storageKey\": null\n }\n ],\n \"storageKey\": null\n }\n ],\n \"storageKey\": null\n }\n];\nreturn {\n \"fragment\": {\n \"argumentDefinitions\": (v0/*: any*/),\n \"kind\": \"Fragment\",\n \"metadata\": null,\n \"name\": \"SAM2ModelRemoveObjectMutation\",\n \"selections\": (v1/*: any*/),\n \"type\": \"Mutation\",\n \"abstractKey\": null\n },\n \"kind\": \"Request\",\n \"operation\": {\n \"argumentDefinitions\": (v0/*: any*/),\n \"kind\": \"Operation\",\n \"name\": \"SAM2ModelRemoveObjectMutation\",\n \"selections\": (v1/*: any*/)\n },\n \"params\": {\n \"cacheID\": \"0accbe68b8deea021539365678e58172\",\n \"id\": null,\n \"metadata\": {},\n \"name\": \"SAM2ModelRemoveObjectMutation\",\n \"operationKind\": \"mutation\",\n \"text\": \"mutation SAM2ModelRemoveObjectMutation(\\n $input: RemoveObjectInput!\\n) {\\n removeObject(input: $input) {\\n frameIndex\\n rleMaskList {\\n objectId\\n rleMask {\\n counts\\n size\\n }\\n }\\n }\\n}\\n\"\n }\n};\n})();\n\n(node as any).hash = \"2dddf010d202332e6e012443cc1d8e55\";\n\nexport default node;\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/tracker/__generated__/SAM2ModelStartSessionMutation.graphql.ts", "content": "/**\n * @generated SignedSource<<90910bae5bb646118174e736434aac56>>\n * @lightSyntaxTransform\n * @nogrep\n */\n\n/* tslint:disable */\n/* eslint-disable */\n// @ts-nocheck\n\nimport { ConcreteRequest, Mutation } from 'relay-runtime';\nexport type StartSessionInput = {\n path: string;\n};\nexport type SAM2ModelStartSessionMutation$variables = {\n input: StartSessionInput;\n};\nexport type SAM2ModelStartSessionMutation$data = {\n readonly startSession: {\n readonly sessionId: string;\n };\n};\nexport type SAM2ModelStartSessionMutation = {\n response: SAM2ModelStartSessionMutation$data;\n variables: SAM2ModelStartSessionMutation$variables;\n};\n\nconst node: ConcreteRequest = (function(){\nvar v0 = [\n {\n \"defaultValue\": null,\n \"kind\": \"LocalArgument\",\n \"name\": \"input\"\n }\n],\nv1 = [\n {\n \"alias\": null,\n \"args\": [\n {\n \"kind\": \"Variable\",\n \"name\": \"input\",\n \"variableName\": \"input\"\n }\n ],\n \"concreteType\": \"StartSession\",\n \"kind\": \"LinkedField\",\n \"name\": \"startSession\",\n \"plural\": false,\n \"selections\": [\n {\n \"alias\": null,\n \"args\": null,\n \"kind\": \"ScalarField\",\n \"name\": \"sessionId\",\n \"storageKey\": null\n }\n ],\n \"storageKey\": null\n }\n];\nreturn {\n \"fragment\": {\n \"argumentDefinitions\": (v0/*: any*/),\n \"kind\": \"Fragment\",\n \"metadata\": null,\n \"name\": \"SAM2ModelStartSessionMutation\",\n \"selections\": (v1/*: any*/),\n \"type\": \"Mutation\",\n \"abstractKey\": null\n },\n \"kind\": \"Request\",\n \"operation\": {\n \"argumentDefinitions\": (v0/*: any*/),\n \"kind\": \"Operation\",\n \"name\": \"SAM2ModelStartSessionMutation\",\n \"selections\": (v1/*: any*/)\n },\n \"params\": {\n \"cacheID\": \"2403f5005f5bb3805109874569f2050e\",\n \"id\": null,\n \"metadata\": {},\n \"name\": \"SAM2ModelStartSessionMutation\",\n \"operationKind\": \"mutation\",\n \"text\": \"mutation SAM2ModelStartSessionMutation(\\n $input: StartSessionInput!\\n) {\\n startSession(input: $input) {\\n sessionId\\n }\\n}\\n\"\n }\n};\n})();\n\n(node as any).hash = \"5cf0005c7a54fc87c539dd4cbd5fef5d\";\n\nexport default node;\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/utils/FileUtils.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport Logger from '@/common/logger/Logger';\n\ntype Range = {\n start: number;\n end: number;\n};\n\ntype FileStreamPart = {\n data: Uint8Array;\n range: Range;\n contentLength: number;\n};\n\nexport type FileStream = AsyncGenerator;\n\n/**\n * Asynchronously generates a SHA-256 hash for a Blob object.\n *\n * DO NOT USE this function casually. Computing the SHA-256 is expensive and can\n * take several 100 milliseconds to complete.\n *\n * @param blob - The Blob object to be hashed.\n * @returns A Promise that resolves to a string representing the SHA-256 hash of\n * the Blob.\n */\nexport async function hashBlob(blob: Blob): Promise {\n const buffer = await blob.arrayBuffer();\n // Crypto subtle is only availabe in secure contexts. For example, this will\n // be the case when running the project locally with http protocol.\n // https://developer.mozilla.org/en-US/docs/Web/API/Crypto/subtle\n if (crypto.subtle != null) {\n const hashBuffer = await crypto.subtle.digest('SHA-256', buffer);\n const hashArray = Array.from(new Uint8Array(hashBuffer));\n return hashArray.map(b => b.toString(16).padStart(2, '0')).join('');\n }\n // If not secure context, return random string\n return (Math.random() + 1).toString(36).substring(7);\n}\n\nexport async function* streamFile(url: string, init?: RequestInit): FileStream {\n try {\n const response = await fetch(url, init);\n\n let blob: Blob;\n\n // Try to download the file with a stream reader. This has the benefit\n // of providing progress during the download. It requires the body and\n // Content-Length. As a fallback, it uses the blob function on the\n // response object.\n const contentLength = response.headers.get('Content-Length');\n if (response.body != null && contentLength != null) {\n const totalLength = parseInt(contentLength);\n const chunks: Uint8Array[] = [];\n let start = 0;\n let end = 0;\n\n const reader = response.body.getReader();\n try {\n while (true) {\n const {done, value} = await reader.read();\n if (done) {\n break;\n }\n\n start = end;\n end += value.length;\n\n yield {\n data: value,\n range: {start, end},\n contentLength: totalLength,\n };\n }\n } finally {\n reader.releaseLock();\n }\n blob = new Blob(chunks);\n } else {\n blob = await response.blob();\n }\n\n const filename = await hashBlob(blob);\n return new File([blob], `${filename}.mp4`);\n } catch (error) {\n Logger.error('aborting download due to component unmount', error);\n }\n return null;\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/utils/ImageUtils.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nexport function convertVideoFrameToImageData(\n videoFrame: VideoFrame,\n): ImageData | undefined {\n const canvas = new OffscreenCanvas(\n videoFrame.displayWidth,\n videoFrame.displayHeight,\n );\n const ctx = canvas.getContext('2d');\n ctx?.drawImage(videoFrame, 0, 0);\n return ctx?.getImageData(0, 0, canvas.width, canvas.height);\n}\n\n/**\n * This utility provides two functions:\n * `process`: to find the bounding box of non-empty pixels from an ImageData, when looping through all its pixels\n * `crop` to cut out the subsection found in `process`\n * @returns\n */\nexport function findBoundingBox() {\n let xMin = Number.MAX_VALUE;\n let yMin = Number.MAX_VALUE;\n let xMax = Number.MIN_VALUE;\n let yMax = Number.MIN_VALUE;\n return {\n process: function (x: number, y: number, hasData: boolean) {\n if (hasData) {\n xMin = Math.min(x, xMin);\n xMax = Math.max(x, xMax);\n yMin = Math.min(y, yMin);\n yMax = Math.max(y, yMax);\n }\n return [xMin, xMax, yMin, yMax];\n },\n crop(imageData: ImageData): ImageData | null {\n const canvas = new OffscreenCanvas(imageData.width, imageData.height);\n const ctx = canvas.getContext('2d');\n\n const boundingBoxWidth = xMax - xMin;\n const boundingBoxHeight = yMax - yMin;\n if (ctx && boundingBoxWidth > 0 && boundingBoxHeight > 0) {\n ctx.clearRect(0, 0, canvas.width, canvas.height);\n ctx.putImageData(imageData, 0, 0);\n return ctx.getImageData(\n xMin,\n yMin,\n boundingBoxWidth,\n boundingBoxHeight,\n );\n } else {\n return null;\n }\n },\n getBox(): [[number, number], [number, number]] {\n return [\n [xMin, yMin],\n [xMax, yMax],\n ];\n },\n };\n}\n\nexport function magnifyImageRegion(\n canvas: HTMLCanvasElement | null,\n x: number,\n y: number,\n radius: number = 25,\n scale: number = 2,\n): string {\n if (canvas == null) {\n return '';\n }\n const ctx = canvas.getContext('2d');\n if (ctx) {\n const minX = x - radius < 0 ? radius - x : 0;\n const minY = y - radius < 0 ? radius - y : 0;\n const region = ctx.getImageData(\n Math.max(x - radius, 0),\n Math.max(y - radius, 0),\n radius * 2,\n radius * 2,\n );\n\n // ImageData doesn't scale-transform correctly on canvas\n // So we first draw the original size on an offscreen canvas, and then scale it\n const regionCanvas = new OffscreenCanvas(region.width, region.height);\n const regionCtx = regionCanvas.getContext('2d');\n regionCtx?.putImageData(region, minX > 0 ? minX : 0, minY > 0 ? minY : 0);\n\n const scaleCanvas = document.createElement('canvas');\n scaleCanvas.width = Math.round(region.width * scale);\n scaleCanvas.height = Math.round(region.height * scale);\n const scaleCtx = scaleCanvas.getContext('2d');\n scaleCtx?.scale(scale, scale);\n scaleCtx?.drawImage(regionCanvas, 0, 0);\n\n return scaleCanvas.toDataURL();\n }\n return '';\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/utils/MaskUtils.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n/**\n * Converts an image mask represented as a binary image (foreground pixels are\n * `>1` and background pixels are `0`) stored in a Uint8Array to an RGBA\n * representation where background pixels have an alpha value of 0 and\n * foreground pixels have an alpha value of 255. This is useful for compositing\n * the mask onto another image.\n *\n * ```typescript\n * const rgba = convertMaskDataToRGBA(mask.data);\n * ```\n *\n * @param data - The image mask represented as a Uint8Array\n * @returns A new Uint8ClampedArray representing the mask in RGBA format\n */\nexport function convertMaskToRGBA(data: Uint8Array): Uint8ClampedArray {\n // Shifting pixels instead of assigning them individually per pixel is\n // much faster. See JSPerf benchamrk: https://jsperf.app/morifo\n const len = data.length;\n const tempData = new Uint32Array(len);\n const RGA = 0x00ffffff;\n const FOREGROUND = 0xff000000;\n const BACKGROUND = 0x00000000;\n for (let i = 0; i < len; i++) {\n const alpha = data[i] > 0 ? FOREGROUND : BACKGROUND; // alpha is the high byte. Bits 24-31\n tempData[i] = alpha + RGA;\n }\n return new Uint8ClampedArray(tempData.buffer);\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/utils/MultipartStream.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nconst decoder = new TextDecoder();\nconst encoder = new TextEncoder();\nconst blankLine = encoder.encode('\\r\\n');\n\nconst STATE_BOUNDARY = 0;\nconst STATE_HEADERS = 1;\nconst STATE_BODY = 2;\n\n/**\n * Compares two Uint8Array objects for equality.\n * @param {Uint8Array} a\n * @param {Uint8Array} b\n * @return {bool}\n */\nfunction compareArrays(a: Uint8Array, b: Uint8Array): boolean {\n if (a.length != b.length) {\n return false;\n }\n for (let i = 0; i < a.length; i++) {\n if (a[i] != b[i]) {\n return false;\n }\n }\n return true;\n}\n\n/**\n * Parses a Content-Type into a multipart boundary.\n * @param {string} contentType\n * @return {Uint8Array} boundary line, including preceding -- and trailing \\r\\n\n */\nfunction getBoundary(contentType: string): Uint8Array | null {\n // Expects the form \"multipart/...; boundary=...\".\n // This is not a full MIME media type parser but should be good enough.\n const MULTIPART_TYPE = 'multipart/';\n const BOUNDARY_PARAM = '; boundary=';\n if (!contentType.startsWith(MULTIPART_TYPE)) {\n return null;\n }\n const i = contentType.indexOf(BOUNDARY_PARAM, MULTIPART_TYPE.length);\n if (i == -1) {\n return null;\n }\n const suffix = contentType.substring(i + BOUNDARY_PARAM.length);\n return encoder.encode('--' + suffix + '\\r\\n');\n}\n\n/**\n * Creates a multipart stream.\n * @param {string} contentType A Content-Type header.\n * @param {ReadableStream} body The body of a HTTP response.\n * @return {ReadableStream} a stream of {headers: Headers, body: Uint8Array}\n * objects.\n */\nexport default function multipartStream(\n contentType: string,\n body: ReadableStream,\n): ReadableStream {\n const reader = body.getReader();\n return new ReadableStream({\n async start(controller) {\n // Define the boundary.\n const boundary = getBoundary(contentType);\n if (boundary === null) {\n controller.error(\n new Error(\n 'Invalid content type for multipart stream: ' + contentType,\n ),\n );\n return;\n }\n let pos = 0;\n let buf = new Uint8Array(); // buf.slice(pos) has unprocessed data.\n let state = STATE_BOUNDARY;\n let headers: Headers | null = null; // non-null in STATE_HEADERS and STATE_BODY.\n let contentLength: number | null = null; // non-null in STATE_BODY.\n\n /**\n * Consumes all complete data in buf or raises an Error.\n * May leave incomplete data at buf.slice(pos).\n */\n function processBuf() {\n // The while(true) condition is reqired\n // eslint-disable-next-line no-constant-condition\n while (true) {\n if (boundary === null) {\n controller.error(\n new Error(\n 'Invalid content type for multipart stream: ' + contentType,\n ),\n );\n return;\n }\n switch (state) {\n case STATE_BOUNDARY:\n // Read blank lines (if any) then boundary.\n while (\n buf.length >= pos + blankLine.length &&\n compareArrays(buf.slice(pos, pos + blankLine.length), blankLine)\n ) {\n pos += blankLine.length;\n }\n\n // Check that it starts with a boundary.\n if (buf.length < pos + boundary.length) {\n return;\n }\n\n if (\n !compareArrays(buf.slice(pos, pos + boundary.length), boundary)\n ) {\n throw new Error('bad part boundary');\n }\n pos += boundary.length;\n state = STATE_HEADERS;\n headers = new Headers();\n break;\n\n case STATE_HEADERS: {\n const cr = buf.indexOf('\\r'.charCodeAt(0), pos);\n if (cr == -1 || buf.length == cr + 1) {\n return;\n }\n if (buf[cr + 1] != '\\n'.charCodeAt(0)) {\n throw new Error('bad part header line (CR without NL)');\n }\n const line = decoder.decode(buf.slice(pos, cr));\n pos = cr + 2;\n if (line == '') {\n const rawContentLength = headers?.get('Content-Length');\n if (rawContentLength == null) {\n throw new Error('missing/invalid part Content-Length');\n }\n contentLength = parseInt(rawContentLength, 10);\n if (isNaN(contentLength)) {\n throw new Error('missing/invalid part Content-Length');\n }\n state = STATE_BODY;\n break;\n }\n const colon = line.indexOf(':');\n const name = line.substring(0, colon);\n if (colon == line.length || line[colon + 1] != ' ') {\n throw new Error('bad part header line (no \": \")');\n }\n const value = line.substring(colon + 2);\n headers?.append(name, value);\n break;\n }\n case STATE_BODY: {\n if (contentLength === null) {\n throw new Error('content length not set');\n }\n if (buf.length < pos + contentLength) {\n return;\n }\n const body = buf.slice(pos, pos + contentLength);\n pos += contentLength;\n controller.enqueue({\n headers: headers,\n body: body,\n });\n headers = null;\n contentLength = null;\n state = STATE_BOUNDARY;\n break;\n }\n }\n }\n }\n\n // The while(true) condition is required\n // eslint-disable-next-line no-constant-condition\n while (true) {\n const {done, value} = await reader.read();\n const buffered = buf.length - pos;\n if (done) {\n if (state != STATE_BOUNDARY || buffered > 0) {\n throw Error('multipart stream ended mid-part');\n }\n controller.close();\n return;\n }\n\n // Update buf.slice(pos) to include the new data from value.\n if (buffered == 0) {\n buf = value;\n } else {\n const newLen = buffered + value.length;\n const newBuf = new Uint8Array(newLen);\n newBuf.set(buf.slice(pos), 0);\n newBuf.set(value, buffered);\n buf = newBuf;\n }\n pos = 0;\n\n processBuf();\n }\n },\n cancel(reason) {\n return body.cancel(reason);\n },\n });\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/utils/ShaderUtils.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {Tracklet} from '@/common/tracker/Tracker';\n\n/**\n * util funtion to generate a WebGL texture using a look up table.\n * @param {WebGL2RenderingContext} gl - The WebGL2 rendering context.\n * @param {number} lutSize - The size of the LUT in each dimension.\n * @param {Uint8Array} lutData - The LUT data as an array of unsigned 8-bit integers.\n * @returns {WebGLTexture} - The WebGL texture object representing the loaded LUT.\n */\nexport function load3DLUT(\n gl: WebGL2RenderingContext,\n lutSize: number,\n lutData: Uint8Array,\n) {\n const texture = gl.createTexture();\n gl.bindTexture(gl.TEXTURE_3D, texture);\n gl.texParameteri(gl.TEXTURE_3D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_3D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_3D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_3D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_3D, gl.TEXTURE_WRAP_R, gl.CLAMP_TO_EDGE);\n // Pixel storage modes must be set to default for 3D textures\n gl.pixelStorei(gl.UNPACK_FLIP_Y_WEBGL, false);\n gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, false);\n\n gl.texImage3D(\n gl.TEXTURE_3D,\n 0,\n gl.RGBA,\n lutSize,\n lutSize,\n lutSize,\n 0,\n gl.RGBA,\n gl.UNSIGNED_BYTE,\n lutData,\n );\n gl.bindTexture(gl.TEXTURE_3D, null);\n return texture;\n}\n\n/**\n * Generates a 3D lookup table (LUT) data with random RGBA values.\n * @param {number} lutSize - The size of the LUT in each dimension.\n * @returns {Uint8Array} - The LUT data as an array of unsigned 8-bit integers.\n */\nexport function generateLUTDATA(lutSize: number) {\n const totalEntries = lutSize * lutSize * lutSize; // 3D LUT nodes\n const lutData = new Uint8Array(totalEntries * 4); // Each entry has an RGBA value\n\n for (let i = 0; i < totalEntries; i++) {\n lutData[i * 4 + 0] = Math.floor(Math.random() * 256); // Random red value\n lutData[i * 4 + 1] = Math.floor(Math.random() * 256); // Random green value\n lutData[i * 4 + 2] = Math.floor(Math.random() * 256); // Random blue value\n lutData[i * 4 + 3] = 1; // alpha value\n }\n\n return lutData;\n}\n\n/**\n * Normalizes the bounds of a rectangle defined by two points (A and B) within a given width and height.\n * @param {number[]} pointA - The coordinates of the first point defining the rectangle.\n * @param {number[]} pointB - The coordinates of the second point defining the rectangle.\n * @param {number} width - The width of the canvas or container where the rectangle is drawn.\n * @param {number} height - The height of the canvas or container where the rectangle is drawn.\n * @returns {number[]} - An array containing the normalized x and y coordinates of the rectangle's corners.\n */\nexport function normalizeBounds(\n pointA: number[],\n pointB: number[],\n width: number,\n height: number,\n) {\n return [\n pointA[0] / width,\n pointA[1] / height,\n pointB[0] / width,\n pointB[1] / height,\n ];\n}\n\n/**\n * Pre-allocates a specified number of 2D textures for use in WebGL2 rendering.\n * @param {WebGL2RenderingContext} gl - The WebGL2 rendering context.\n * @param {number} numTextures - The number of textures to be pre-allocated.\n * @returns {WebGLTexture[]} - An array of WebGL textures, each pre-allocated and ready for use.\n */\nexport function preAllocateTextures(\n gl: WebGL2RenderingContext,\n numTextures: number,\n) {\n const maskTextures = [];\n\n for (let i = 0; i < numTextures; i++) {\n const maskTexture = gl.createTexture();\n\n gl.bindTexture(gl.TEXTURE_2D, maskTexture);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n maskTextures.push(maskTexture);\n }\n\n return maskTextures as WebGLTexture[];\n}\n\n/**\n * Finds the index of a Tracklet object within an array based on its unique identifier.\n * @param objects - The array of Tracklet objects to search within.\n * @param id - The unique identifier of the Tracklet object to find.\n * @returns The index of the `Tracklet` object with the specified `id` in the `objects` array.\n */\nexport function findIndexByTrackletId(id: number, objects: Tracklet[]): number {\n return objects.findIndex(obj => obj.id === id);\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/utils/emptyFunction.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n/**\n * This function accepts and discards inputs; it has no side effects. This is\n * primarily useful idiomatically for overridable function endpoints which\n * always need to be callable, since JS lacks a null-call idiom ala Cocoa.\n */\nexport default function () {}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/common/utils/uuid.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n/**\n * Generates a random UUID (Universally Unique Identifier) following the version\n * 4 standard.\n *\n * The function replaces each 'x' and 'y' in the template\n * 'xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx' with random hexadecimal digits. For\n * 'y', the function ensures the first hexadecimal digit is '8', '9', 'A', or\n * 'B' as per the UUID v4 standard.\n *\n * @returns A string representing a version 4 UUID.\n *\n * @example\n *\n * const id = uuidv4();\n * console.log(id); // Outputs: '3f0d2c77-4f69-4c1e-8a6e-35e866e8a5d1'\n */\nexport function uuidv4() {\n return 'xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx'.replace(/[xy]/g, function (c) {\n const r = (Math.random() * 16) | 0,\n v = c == 'x' ? r : (r & 0x3) | 0x8;\n return v.toString(16);\n });\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/debug/stats/Stats.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n/**\n * Derived from mrdoob / http://mrdoob.com/\n */\n\nimport Logger from '@/common/logger/Logger';\nimport {uuidv4} from '@/common/utils/uuid';\nimport invariant from 'invariant';\n\nexport type Request = {\n action: A;\n} & P;\n\nexport type Response = Request;\n\nexport type GetStatsCanvasRequest = Request<\n 'getStatsCanvas',\n {\n id: string;\n width: number;\n height: number;\n }\n>;\n\nexport type GetMemoryStatsRequest = Request<\n 'getMemoryStats',\n {\n id: string;\n jsHeapSizeLimit: number;\n totalJSHeapSize: number;\n usedJSHeapSize: number;\n }\n>;\n\nexport type SetStatsCanvasResponse = Response<\n 'setStatsCanvas',\n {\n id: string;\n canvas: OffscreenCanvas;\n devicePixelRatio: number;\n }\n>;\n\nexport type MemoryStatsResponse = Response<\n 'memoryStats',\n {\n id: string;\n jsHeapSizeLimit: number;\n totalJSHeapSize: number;\n usedJSHeapSize: number;\n }\n>;\n\nexport type StatsType = 'fps' | 'ms' | 'memory';\n\nexport class Stats {\n private maxValue: number;\n private beginTime: number;\n private prevTime: number;\n private frames: number;\n\n private fpsPanel: Panel | null = null;\n private msPanel: Panel | null = null;\n private memPanel: Panel | null = null;\n\n constructor(type: StatsType, label: string = '', maxValue: number = 100) {\n const id = uuidv4();\n\n this.maxValue = maxValue;\n this.beginTime = (performance || Date).now();\n this.prevTime = this.beginTime;\n this.frames = 0;\n\n const onMessage = (event: MessageEvent) => {\n if (event.data.action === 'setStatsCanvas' && event.data.id === id) {\n const {canvas, devicePixelRatio} = event.data;\n if (type === 'fps') {\n this.fpsPanel = new Panel(\n canvas,\n devicePixelRatio,\n `FPS ${label}`.trim(),\n '#0ff',\n '#002',\n );\n } else if (type === 'ms') {\n this.msPanel = new Panel(\n canvas,\n devicePixelRatio,\n `MS ${label}`.trim(),\n '#0f0',\n '#020',\n );\n } else if (type === 'memory') {\n this.memPanel = new Panel(\n canvas,\n devicePixelRatio,\n `MB ${label}`.trim(),\n '#f08',\n '#201',\n );\n }\n self.removeEventListener('message', onMessage);\n }\n };\n\n self.addEventListener('message', onMessage);\n\n self.postMessage({\n action: 'getStatsCanvas',\n id,\n width: 80,\n height: 48,\n } as GetStatsCanvasRequest);\n }\n\n updateMaxValue(maxValue: number) {\n this.maxValue = maxValue;\n }\n\n begin() {\n this.beginTime = (performance || Date).now();\n }\n\n end() {\n this.frames++;\n\n const time = (performance || Date).now();\n\n this.msPanel?.update(time - this.beginTime, this.maxValue);\n\n if (time >= this.prevTime + 1000) {\n this.fpsPanel?.update(\n (this.frames * 1000) / (time - this.prevTime),\n this.maxValue,\n );\n\n this.prevTime = time;\n this.frames = 0;\n\n const id = uuidv4();\n const onMessage = (event: MessageEvent) => {\n if (event.data.action === 'memoryStats' && event.data.id === id) {\n const {usedJSHeapSize, jsHeapSizeLimit} = event.data;\n this.memPanel?.update(\n usedJSHeapSize / 1048576,\n jsHeapSizeLimit / 1048576,\n );\n }\n };\n\n self.addEventListener('message', onMessage);\n\n self.postMessage({\n action: 'getMemoryStats',\n id,\n } as GetMemoryStatsRequest);\n }\n\n return time;\n }\n\n update() {\n this.beginTime = this.end();\n }\n}\n\nexport class Panel {\n private min = Infinity;\n private max = 0;\n private round = Math.round;\n\n private PR: number;\n private WIDTH: number;\n private HEIGHT: number;\n private TEXT_X: number;\n private TEXT_Y: number;\n private GRAPH_X: number;\n private GRAPH_Y: number;\n private GRAPH_WIDTH: number;\n private GRAPH_HEIGHT: number;\n\n public canvas: HTMLCanvasElement | OffscreenCanvas;\n private context:\n | CanvasRenderingContext2D\n | OffscreenCanvasRenderingContext2D\n | null = null;\n\n private name: string;\n private fg: string;\n private bg: string;\n\n constructor(\n canvas: HTMLCanvasElement | OffscreenCanvas,\n devicePixelRatio: number,\n name: string,\n fg: string,\n bg: string,\n ) {\n this.canvas = canvas;\n this.name = name;\n this.fg = fg;\n this.bg = bg;\n\n this.PR = this.round(devicePixelRatio || 1);\n this.WIDTH = 80 * this.PR;\n this.HEIGHT = 48 * this.PR;\n this.TEXT_X = 3 * this.PR;\n this.TEXT_Y = 2 * this.PR;\n this.GRAPH_X = 3 * this.PR;\n this.GRAPH_Y = 15 * this.PR;\n this.GRAPH_WIDTH = 74 * this.PR;\n this.GRAPH_HEIGHT = 30 * this.PR;\n\n const context: OffscreenCanvasRenderingContext2D | RenderingContext | null =\n canvas.getContext('2d');\n invariant(context !== null, 'context 2d is required');\n\n if (\n !(context instanceof CanvasRenderingContext2D) &&\n !(context instanceof OffscreenCanvasRenderingContext2D)\n ) {\n Logger.warn(\n 'rendering stats requires CanvasRenderingContext2D or OffscreenCanvasRenderingContext2D',\n );\n return;\n }\n\n context.font = 'bold ' + 9 * this.PR + 'px Helvetica,Arial,sans-serif';\n context.textBaseline = 'top';\n\n context.fillStyle = bg;\n context.fillRect(0, 0, this.WIDTH, this.HEIGHT);\n\n context.fillStyle = fg;\n context.fillText(name, this.TEXT_X, this.TEXT_Y);\n context.fillRect(\n this.GRAPH_X,\n this.GRAPH_Y,\n this.GRAPH_WIDTH,\n this.GRAPH_HEIGHT,\n );\n\n context.fillStyle = bg;\n context.globalAlpha = 0.9;\n context.fillRect(\n this.GRAPH_X,\n this.GRAPH_Y,\n this.GRAPH_WIDTH,\n this.GRAPH_HEIGHT,\n );\n\n this.context = context;\n }\n\n update(value: number, maxValue: number) {\n invariant(this.context !== null, 'context 2d is required');\n\n this.min = Math.min(this.min, value);\n this.max = Math.max(this.max, value);\n\n this.context.fillStyle = this.bg;\n this.context.globalAlpha = 1;\n this.context.fillRect(0, 0, this.WIDTH, this.GRAPH_Y);\n this.context.fillStyle = this.fg;\n this.context.fillText(\n this.round(value) +\n ' ' +\n this.name +\n ' (' +\n this.round(this.min) +\n '-' +\n this.round(this.max) +\n ')',\n this.TEXT_X,\n this.TEXT_Y,\n );\n\n this.context.drawImage(\n this.canvas,\n this.GRAPH_X + this.PR,\n this.GRAPH_Y,\n this.GRAPH_WIDTH - this.PR,\n this.GRAPH_HEIGHT,\n this.GRAPH_X,\n this.GRAPH_Y,\n this.GRAPH_WIDTH - this.PR,\n this.GRAPH_HEIGHT,\n );\n\n this.context.fillRect(\n this.GRAPH_X + this.GRAPH_WIDTH - this.PR,\n this.GRAPH_Y,\n this.PR,\n this.GRAPH_HEIGHT,\n );\n\n this.context.fillStyle = this.bg;\n this.context.globalAlpha = 0.9;\n this.context.fillRect(\n this.GRAPH_X + this.GRAPH_WIDTH - this.PR,\n this.GRAPH_Y,\n this.PR,\n this.round((1 - value / maxValue) * this.GRAPH_HEIGHT),\n );\n }\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/debug/stats/StatsView.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {EnableStatsRequest} from '@/common/components/video/VideoWorkerTypes';\nimport stylex from '@stylexjs/stylex';\nimport {useEffect, useMemo, useRef, useState} from 'react';\nimport {useLocation} from 'react-router-dom';\nimport useVideo from '../../common/components/video/editor/useVideo';\nimport {\n GetMemoryStatsRequest,\n GetStatsCanvasRequest,\n MemoryStatsResponse,\n SetStatsCanvasResponse,\n} from './Stats';\n\nconst styles = stylex.create({\n container: {\n position: 'fixed',\n top: 0,\n left: 0,\n width: '100%',\n overflowX: 'auto',\n display: 'flex',\n flexDirection: 'row',\n cursor: 'pointer',\n opacity: 0.9,\n zIndex: 10000,\n },\n});\n\nconst URL_PARAM = 'monitors';\n\nexport default function StatsView() {\n const {search} = useLocation();\n const video = useVideo();\n const containerRef = useRef(null);\n const [isWrapped, setIsWrapped] = useState(false);\n\n const isEnabled = useMemo(() => {\n const urlSearchParams = new URLSearchParams(search);\n return (\n urlSearchParams.has(URL_PARAM) &&\n ['true', ''].includes(urlSearchParams.get('monitors') ?? '')\n );\n }, [search]);\n\n useEffect(() => {\n if (!isEnabled) {\n return;\n }\n\n const worker = video?.getWorker_ONLY_USE_WITH_CAUTION();\n\n // Enable stats for video worker\n worker?.postMessage({\n action: 'enableStats',\n } as EnableStatsRequest);\n\n function onMessage(\n event: MessageEvent,\n ) {\n if (event.data.action === 'getStatsCanvas') {\n // Add stats canvas and hand control over to worker\n const canvas = document.createElement('canvas');\n canvas.width = event.data.width * window.devicePixelRatio;\n canvas.height = event.data.height * window.devicePixelRatio;\n canvas.style.width = `${event.data.width}px`;\n canvas.style.height = `${event.data.height}px`;\n containerRef.current?.appendChild(canvas);\n\n const offscreenCanvas = canvas.transferControlToOffscreen();\n worker?.postMessage(\n {\n action: 'setStatsCanvas',\n id: event.data.id,\n canvas: offscreenCanvas,\n devicePixelRatio: window.devicePixelRatio,\n } as SetStatsCanvasResponse,\n {\n transfer: [offscreenCanvas],\n },\n );\n } else if (event.data.action === 'getMemoryStats') {\n // @ts-expect-error performance.memory might not exist\n const memory = performance.memory ?? {\n jsHeapSizeLimit: 0,\n totalJSHeapSize: 0,\n usedJSHeapSize: 0,\n };\n worker?.postMessage({\n action: 'memoryStats',\n id: event.data.id,\n jsHeapSizeLimit: memory.jsHeapSizeLimit,\n totalJSHeapSize: memory.totalJSHeapSize,\n usedJSHeapSize: memory.usedJSHeapSize,\n } as MemoryStatsResponse);\n }\n }\n\n worker?.addEventListener('message', onMessage);\n return () => {\n worker?.removeEventListener('message', onMessage);\n };\n }, [video, isEnabled]);\n\n function handleClick() {\n setIsWrapped(w => !w);\n }\n\n if (!isEnabled) {\n return null;\n }\n\n return (\n \n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/demo/DemoConfig.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {Effects} from '@/common/components/video/effects/Effects';\n\ntype EffectLayers = {\n background: keyof Effects;\n highlight: keyof Effects;\n};\n\nexport const DEMO_SHORT_NAME = 'SAM 2 Demo';\nexport const RESEARCH_BY_META_AI = 'By Meta FAIR';\nexport const DEMO_FRIENDLY_NAME = 'Segment Anything 2 Demo';\nexport const VIDEO_WATERMARK_TEXT = `Modified with ${DEMO_FRIENDLY_NAME}`;\nexport const PROJECT_GITHUB_URL =\n 'https://github.com/facebookresearch/sam2';\nexport const AIDEMOS_URL = 'https://aidemos.meta.com';\nexport const ABOUT_URL = 'https://ai.meta.com/sam2';\nexport const EMAIL_ADDRESS = 'segment-anything@meta.com';\nexport const BLOG_URL = 'http://ai.meta.com/blog/sam2';\n\nexport const VIDEO_API_ENDPOINT = 'http://localhost:7263';\nexport const INFERENCE_API_ENDPOINT = 'http://localhost:7263';\n\nexport const demoObjectLimit = 3;\n\nexport const DEFAULT_EFFECT_LAYERS: EffectLayers = {\n background: 'Original',\n highlight: 'Overlay',\n};\n\nexport const MAX_UPLOAD_FILE_SIZE = '70MB';\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/demo/DemoErrorFallback.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport LoadingStateScreen from '@/common/loading/LoadingStateScreen';\nimport {FallbackProps} from 'react-error-boundary';\n\nexport default function DemoErrorFallback(_props: FallbackProps) {\n return (\n \n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/demo/DemoSuspenseFallback.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport LoadingStateScreen from '@/common/loading/LoadingStateScreen';\n\nexport default function DemoSuspenseFallback() {\n return ;\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/demo/SAM2DemoApp.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport '@/assets/scss/App.scss';\nimport ErrorReport from '@/common/error/ErrorReport';\nimport DemoErrorFallback from '@/demo/DemoErrorFallback';\nimport DemoSuspenseFallback from '@/demo/DemoSuspenseFallback';\nimport RelayEnvironmentProvider from '@/graphql/RelayEnvironmentProvider';\nimport RootLayout from '@/layouts/RootLayout';\nimport SAM2DemoPage from '@/routes/DemoPageWrapper';\nimport PageNotFoundPage from '@/routes/PageNotFoundPage';\nimport useSettingsContext from '@/settings/useSettingsContext';\nimport {Route, Routes} from 'react-router-dom';\n\nexport default function DemoAppWrapper() {\n const {settings} = useSettingsContext();\n return (\n }\n errorFallback={DemoErrorFallback}>\n \n \n );\n}\n\nfunction DemoApp() {\n return (\n <>\n \n }>\n } />\n } />\n \n \n \n \n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/demo/atoms.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {\n defaultMessageMap,\n MessagesEventMap,\n} from '@/common/components/snackbar/DemoMessagesSnackbarUtils';\nimport {Effects} from '@/common/components/video/effects/Effects';\nimport {\n DemoEffect,\n highlightEffects,\n} from '@/common/components/video/effects/EffectUtils';\nimport {\n BaseTracklet,\n SegmentationPoint,\n StreamingState,\n} from '@/common/tracker/Tracker';\nimport type {DataArray} from '@/jscocotools/mask';\nimport {atom} from 'jotai';\n\nexport type VideoData = {\n path: string;\n posterPath: string | null | undefined;\n url: string;\n posterUrl: string;\n width: number;\n height: number;\n};\n\nexport const frameIndexAtom = atom(0);\n\nexport const inputVideoAtom = atom(null);\n\n// #####################\n// SESSION\n// #####################\n\nexport type Session = {\n id: string;\n ranPropagation: boolean;\n};\n\nexport const sessionAtom = atom(null);\n\n// #####################\n// STREAMING/PLAYBACK\n// #####################\n\nexport const isVideoLoadingAtom = atom(false);\n\nexport const streamingStateAtom = atom('none');\n\nexport const isPlayingAtom = atom(false);\n\nexport const isStreamingAtom = atom(false);\n\n// #####################\n// OBJECTS\n// #####################\n\nexport type TrackletMask = {\n mask: DataArray;\n isEmpty: boolean;\n};\n\nexport type TrackletObject = {\n id: number;\n color: string;\n thumbnail: string | null;\n points: SegmentationPoint[][];\n masks: TrackletMask[];\n isInitialized: boolean;\n};\n\nconst MAX_NUMBER_TRACKLET_OBJECTS = 3;\n\nexport const activeTrackletObjectIdAtom = atom(0);\n\nexport const activeTrackletObjectAtom = atom(get => {\n const objectId = get(activeTrackletObjectIdAtom);\n const tracklets = get(trackletObjectsAtom);\n return tracklets.find(obj => obj.id === objectId) ?? null;\n});\n\nexport const trackletObjectsAtom = atom([]);\n\nexport const maxTrackletObjectIdAtom = atom(get => {\n const tracklets = get(trackletObjectsAtom);\n return tracklets.reduce((prev, curr) => Math.max(prev, curr.id), 0);\n});\n\nexport const isTrackletObjectLimitReachedAtom = atom(\n get => get(trackletObjectsAtom).length >= MAX_NUMBER_TRACKLET_OBJECTS,\n);\n\nexport const areTrackletObjectsInitializedAtom = atom(get =>\n get(trackletObjectsAtom).every(obj => obj.isInitialized),\n);\n\nexport const isFirstClickMadeAtom = atom(get => {\n const tracklets = get(trackletObjectsAtom);\n return tracklets.some(tracklet => tracklet.points.length > 0);\n});\n\nexport const pointsAtom = atom(get => {\n const frameIndex = get(frameIndexAtom);\n const activeTracklet = get(activeTrackletObjectAtom);\n return activeTracklet?.points[frameIndex] ?? [];\n});\n\nexport const labelTypeAtom = atom<'positive' | 'negative'>('positive');\n\nexport const isAddObjectEnabledAtom = atom(get => {\n const session = get(sessionAtom);\n const trackletsInitialized = get(areTrackletObjectsInitializedAtom);\n const isObjectLimitReached = get(isTrackletObjectLimitReachedAtom);\n return (\n session?.ranPropagation === false &&\n trackletsInitialized &&\n !isObjectLimitReached\n );\n});\n\nexport const codeEditorOpenedAtom = atom(false);\n\nexport const tutorialVideoEnabledAtom = atom(true);\n\n// #####################\n// Effects\n// #####################\n\ntype EffectConfig = {\n name: keyof Effects;\n variant: number;\n numVariants: number;\n};\n\nexport const activeBackgroundEffectAtom = atom({\n name: 'Original',\n variant: 0,\n numVariants: 0,\n});\n\nexport const activeHighlightEffectAtom = atom({\n name: 'Overlay',\n variant: 0,\n numVariants: 0,\n});\n\nexport const activeHighlightEffectGroupAtom =\n atom(highlightEffects);\n\n// #####################\n// Toolbar\n// #####################\n\nexport const toolbarTabIndex = atom(0);\n\n// #####################\n// Messages snackbar\n// #####################\n\nexport const messageMapAtom = atom(defaultMessageMap);\n\n// #####################\n// Upload state\n// #####################\n\nexport const uploadingStateAtom = atom<'default' | 'uploading' | 'error'>(\n 'default',\n);\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/graphql/RelayEnvironment.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport Logger from '@/common/logger/Logger';\nimport {\n CacheConfig,\n Environment,\n FetchFunction,\n GraphQLResponse,\n LogEvent,\n Network,\n ObservableFromValue,\n RecordSource,\n RequestParameters,\n Store,\n UploadableMap,\n Variables,\n} from 'relay-runtime';\nimport fetchGraphQL from './fetchGraphQL';\n\nfunction createFetchRelay(endpoint: string): FetchFunction {\n return (\n request: RequestParameters,\n variables: Variables,\n cacheConfig: CacheConfig,\n uploadables?: UploadableMap | null,\n ): ObservableFromValue => {\n Logger.debug(\n `fetching query ${request.name} with ${JSON.stringify(variables)}`,\n );\n return fetchGraphQL(endpoint, request, variables, cacheConfig, uploadables);\n };\n}\n\nexport function createEnvironment(endpoint: string): Environment {\n return new Environment({\n log: (logEvent: LogEvent) => Logger.debug(logEvent.name, logEvent),\n network: Network.create(createFetchRelay(endpoint)),\n store: new Store(new RecordSource()),\n });\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/graphql/RelayEnvironmentProvider.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport ErrorFallback from '@/common/error/ErrorFallback';\nimport LoadingMessage from '@/common/loading/LoadingMessage';\nimport {createEnvironment} from '@/graphql/RelayEnvironment';\nimport {\n ComponentType,\n PropsWithChildren,\n ReactNode,\n Suspense,\n useMemo,\n useState,\n} from 'react';\nimport {ErrorBoundary, FallbackProps} from 'react-error-boundary';\nimport {RelayEnvironmentProvider} from 'react-relay';\n\ntype Props = PropsWithChildren<{\n suspenseFallback?: ReactNode;\n errorFallback?: ComponentType;\n endpoint: string;\n}>;\n\nexport default function OnevisionRelayEnvironmentProvider({\n suspenseFallback,\n errorFallback = ErrorFallback,\n endpoint,\n children,\n}: Props) {\n const [retryKey, setRetryKey] = useState(0);\n\n const environment = useMemo(() => {\n return createEnvironment(endpoint);\n // The retryKey is needed to force a new Relay Environment\n // instance when the user retries after an error occurred.\n // eslint-disable-next-line react-hooks/exhaustive-deps\n }, [endpoint, retryKey]);\n\n // Force re-creating Relay Environment\n function handleReset() {\n setRetryKey(k => k + 1);\n }\n\n return (\n \n \n }>\n {children}\n \n \n \n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/graphql/errors/CreateFilmstripError.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nexport default class CreateFilmstripError extends Error {\n override name = 'CreateFilmstripError';\n constructor(message?: string) {\n super(message);\n }\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/graphql/errors/DrawFrameError.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nexport default class DrawFrameError extends Error {\n override name = 'DrawFrameError';\n constructor(message?: string) {\n super(message);\n }\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/graphql/errors/WebGLContextError.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nexport default class WebGLContextError extends Error {\n override name = 'WebGLContextError';\n constructor(message?: string) {\n super(message);\n }\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/graphql/fetchGraphQL.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport Logger from '@/common/logger/Logger';\nimport {\n CacheConfig,\n GraphQLResponse,\n RequestParameters,\n UploadableMap,\n Variables,\n} from 'relay-runtime';\n\n/**\n * Inspired by https://github.com/facebook/relay/issues/1844\n */\nexport default async function fetchGraphQL(\n endpoint: string,\n request: RequestParameters,\n variables: Variables,\n cacheConfig: CacheConfig,\n uploadables?: UploadableMap | null,\n): Promise {\n const url = `${endpoint}/graphql`;\n\n const headers: {[name: string]: string} = {};\n const requestInit: RequestInit = {\n method: 'POST',\n headers,\n credentials: 'include',\n };\n\n const customHeaders = (cacheConfig?.metadata?.headers ?? {}) as {\n [key: string]: string;\n };\n\n requestInit.headers = Object.assign(customHeaders, requestInit.headers);\n\n if (uploadables != null) {\n const formData = new FormData();\n formData.append(\n 'operations',\n JSON.stringify({\n query: request.text,\n variables,\n }),\n );\n\n const uploadableMap: {\n [key: string]: string[];\n } = {};\n\n Object.keys(uploadables).forEach(key => {\n uploadableMap[key] = [`variables.${key}`];\n });\n\n formData.append('map', JSON.stringify(uploadableMap));\n\n Object.keys(uploadables).forEach(key => {\n formData.append(key, uploadables[key]);\n });\n\n requestInit.body = formData;\n } else {\n requestInit.headers = Object.assign(\n {'Content-Type': 'application/json'},\n requestInit.headers,\n );\n\n requestInit.body = JSON.stringify({\n query: request.text,\n variables,\n });\n }\n\n try {\n const response = await fetch(url, requestInit);\n const result = await response.json();\n\n // Handle any intentional GraphQL errors, which are passed through the\n // errors property in the JSON payload.\n if ('errors' in result) {\n for (const error of result.errors) {\n Logger.error(error);\n }\n }\n\n return result;\n } catch (error) {\n Logger.error(`Could not connect to GraphQL endpoint ${url}`, error);\n throw error;\n }\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/jscocotools/mask.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nexport class DataArray {\n data: Uint8Array;\n readonly shape: number[];\n\n constructor(data: Uint8Array, shape: Array) {\n this.data = data;\n this.shape = shape;\n }\n}\n\nexport type RLEObject = {\n size: [h: number, w: number];\n counts: string;\n};\n\ntype RLE = {\n h: number;\n w: number;\n m: number;\n cnts: number[];\n};\n\ntype BB = number[];\n\nfunction rleInit(R: RLE, h: number, w: number, m: number, cnts: number[]) {\n R.h = h;\n R.w = w;\n R.m = m;\n R.cnts = m === 0 ? [0] : cnts;\n}\n\nfunction rlesInit(R: RLE[], n: number) {\n let i;\n for (i = 0; i < n; i++) {\n R[i] = {h: 0, w: 0, m: 0, cnts: [0]};\n rleInit(R[i], 0, 0, 0, [0]);\n }\n}\n\nclass RLEs {\n _R: RLE[];\n _n: number;\n\n constructor(n: number) {\n this._R = [];\n rlesInit(this._R, n);\n this._n = n;\n }\n}\n\nexport class Masks {\n _mask: Uint8Array;\n _h: number;\n _w: number;\n _n: number;\n\n constructor(h: number, w: number, n: number) {\n this._mask = new Uint8Array(h * w * n);\n this._h = h;\n this._w = w;\n this._n = n;\n }\n\n toDataArray(): DataArray {\n return new DataArray(this._mask, [this._h, this._w, this._n]);\n }\n}\n\n// encode mask to RLEs objects\n// list of RLE string can be generated by RLEs member function\nexport function encode(mask: DataArray): RLEObject[] {\n const h = mask.shape[0];\n const w = mask.shape[1];\n const n = mask.shape[2];\n const Rs = new RLEs(n);\n rleEncode(Rs._R, mask.data, h, w, n);\n const objs = _toString(Rs);\n return objs;\n}\n\n// decode mask from compressed list of RLE string or RLEs object\nexport function decode(rleObjs: RLEObject[]): DataArray {\n const Rs = _frString(rleObjs);\n const h = Rs._R[0].h;\n const w = Rs._R[0].w;\n const n = Rs._n;\n const masks = new Masks(h, w, n);\n rleDecode(Rs._R, masks._mask, n);\n return masks.toDataArray();\n}\n\nexport function toBbox(rleObjs: RLEObject[]): BB {\n const Rs = _frString(rleObjs);\n const n = Rs._n;\n const bb: BB = [];\n rleToBbox(Rs._R, bb, n);\n return bb;\n}\n\nfunction rleEncode(R: RLE[], M: Uint8Array, h: number, w: number, n: number) {\n let i;\n let j;\n let k;\n const a = w * h;\n let c;\n const cnts: number[] = [];\n let p;\n for (i = 0; i < n; i++) {\n const from = a * i;\n const to = a * (i + 1);\n // Slice data for current RLE object\n const T = M.slice(from, to);\n k = 0;\n p = 0;\n c = 0;\n for (j = 0; j < a; j++) {\n if (T[j] !== p) {\n cnts[k++] = c;\n c = 0;\n p = T[j];\n }\n c++;\n }\n cnts[k++] = c;\n rleInit(R[i], h, w, k, [...cnts]);\n }\n}\n\nfunction rleDecode(R: RLE[], M: Uint8Array, n: number): void {\n let i;\n let j;\n let k;\n let p = 0;\n for (i = 0; i < n; i++) {\n let v = false;\n for (j = 0; j < R[i].m; j++) {\n for (k = 0; k < R[i].cnts[j]; k++) {\n M[p++] = v === false ? 0 : 1;\n }\n v = !v;\n }\n }\n}\n\nfunction rleToString(R: RLE): string {\n /* Similar to LEB128 but using 6 bits/char and ascii chars 48-111. */\n let i;\n const m = R.m;\n let p = 0;\n let x: number;\n let more;\n const s: string[] = [];\n for (i = 0; i < m; i++) {\n x = R.cnts[i];\n if (i > 2) {\n x -= R.cnts[i - 2];\n }\n more = true; // 1;\n while (more) {\n let c = x & 0x1f;\n x >>= 5;\n more = c & 0x10 ? x != -1 : x != 0;\n if (more) {\n c |= 0x20;\n }\n c += 48;\n s[p++] = String.fromCharCode(c);\n }\n }\n return s.join('');\n}\n\n// internal conversion from Python RLEs object to compressed RLE format\nfunction _toString(Rs: RLEs): RLEObject[] {\n const n = Rs._n;\n let py_string;\n let c_string;\n const objs: RLEObject[] = [];\n for (let i = 0; i < n; i++) {\n c_string = rleToString(Rs._R[i]);\n py_string = c_string;\n objs.push({\n size: [Rs._R[i].h, Rs._R[i].w],\n counts: py_string,\n });\n }\n return objs;\n}\n\n// internal conversion from compressed RLE format to Python RLEs object\nfunction _frString(rleObjs: RLEObject[]): RLEs {\n const n = rleObjs.length;\n const Rs = new RLEs(n);\n let py_string;\n let c_string;\n for (let i = 0; i < rleObjs.length; i++) {\n const obj = rleObjs[i];\n py_string = obj.counts;\n c_string = py_string;\n rleFrString(Rs._R[i], c_string, obj.size[0], obj.size[1]);\n }\n return Rs;\n}\n\nfunction rleToBbox(R: RLE[], bb: BB, n: number) {\n for (let i = 0; i < n; i++) {\n const h = R[i].h;\n const w = R[i].w;\n let m = R[i].m;\n // The RLE structure likely contains run-length encoded data where each\n // element represents a count of consecutive pixels with the same value in\n // a binary image (black or white). Since the counts represent both black\n // and white pixels, this operation ((siz)(m/2)) * 2 is used to ensure that\n // m is always an even number. By doing so, the code can later check\n // whether the current pixel is black or white based on whether the index j\n // is even or odd.\n m = Math.floor(m / 2) * 2;\n let xs = w;\n let ys = h;\n let xe = 0;\n let ye = 0;\n let cc = 0;\n let t;\n let y;\n let x;\n let xp = 0;\n if (m === 0) {\n bb[4 * i] = bb[4 * i + 1] = bb[4 * i + 2] = bb[4 * i + 3] = 0;\n continue;\n }\n for (let j = 0; j < m; j++) {\n cc += R[i].cnts[j];\n t = cc - (j % 2);\n y = t % h;\n x = Math.floor((t - y) / h);\n if (j % 2 === 0) {\n xp = x;\n } else if (xp < x) {\n ys = 0;\n ye = h - 1;\n }\n xs = Math.min(xs, x);\n xe = Math.max(xe, x);\n ys = Math.min(ys, y);\n ye = Math.max(ye, y);\n }\n bb[4 * i] = xs;\n bb[4 * i + 2] = xe - xs + 1;\n bb[4 * i + 1] = ys;\n bb[4 * i + 3] = ye - ys + 1;\n }\n}\n\nfunction rleFrString(R: RLE, s: string, h: number, w: number): void {\n let m = 0;\n let p = 0;\n let k;\n let x;\n let more;\n let cnts = [];\n while (s[m]) {\n m++;\n }\n cnts = [];\n m = 0;\n while (s[p]) {\n x = 0;\n k = 0;\n more = 1;\n while (more) {\n const c = s.charCodeAt(p) - 48;\n x |= (c & 0x1f) << (5 * k);\n more = c & 0x20;\n p++;\n k++;\n if (!more && c & 0x10) {\n x |= -1 << (5 * k);\n }\n }\n if (m > 2) {\n x += cnts[m - 2];\n }\n cnts[m++] = x;\n }\n rleInit(R, h, w, m, cnts);\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/layouts/DemoPageLayout.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {spacing} from '@/theme/tokens.stylex';\nimport stylex from '@stylexjs/stylex';\nimport {PropsWithChildren} from 'react';\n\ntype Props = PropsWithChildren;\n\nconst styles = stylex.create({\n container: {\n width: '100%',\n height: '100%',\n display: 'flex',\n justifyContent: 'stretch',\n alignItems: 'stretch',\n gap: spacing[12],\n paddingHorizontal: spacing[12],\n paddingVertical: spacing[4],\n '@media screen and (max-width: 768px)': {\n display: 'flex',\n flexDirection: 'column-reverse',\n gap: 0,\n marginTop: spacing[0],\n marginBottom: spacing[0],\n paddingHorizontal: spacing[0],\n paddingBottom: spacing[0],\n },\n },\n});\n\nexport default function DemoPageLayout({children}: Props) {\n return
{children}
;\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/layouts/RootLayout.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport LoadingStateScreen from '@/common/loading/LoadingStateScreen';\nimport useSettingsContext from '@/settings/useSettingsContext';\nimport {Cog6ToothIcon} from '@heroicons/react/24/outline';\nimport stylex from '@stylexjs/stylex';\nimport {Suspense} from 'react';\nimport {Button, Indicator} from 'react-daisyui';\nimport {Outlet} from 'react-router-dom';\n\nconst styles = stylex.create({\n container: {\n display: 'flex',\n flexDirection: 'column',\n height: '100%',\n maxHeight: '100vh',\n backgroundColor: '#000',\n },\n content: {\n position: 'relative',\n flex: '1 1 0%',\n display: 'flex',\n flexDirection: 'column',\n overflowX: 'auto',\n overflowY: {\n default: 'auto',\n '@media screen and (max-width: 768px)': 'auto',\n },\n },\n debugActions: {\n display: 'flex',\n flexDirection: 'column',\n position: 'fixed',\n top: 100,\n right: 0,\n backgroundColor: 'white',\n borderRadius: 3,\n },\n});\n\nexport default function RootLayout() {\n const {openModal, hasChanged} = useSettingsContext();\n\n return (\n
\n
\n \n }>\n \n \n
\n
\n \n {hasChanged && (\n \n )}\n }\n title=\"Bugnub\"\n />\n \n
\n
\n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/routes/DemoPage.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport Toolbar from '@/common/components/toolbar/Toolbar';\nimport DemoVideoEditor from '@/common/components/video/editor/DemoVideoEditor';\nimport useInputVideo from '@/common/components/video/useInputVideo';\nimport StatsView from '@/debug/stats/StatsView';\nimport {VideoData} from '@/demo/atoms';\nimport DemoPageLayout from '@/layouts/DemoPageLayout';\nimport {DemoPageQuery} from '@/routes/__generated__/DemoPageQuery.graphql';\nimport {useEffect, useMemo} from 'react';\nimport {graphql, useLazyLoadQuery} from 'react-relay';\nimport {Location, useLocation} from 'react-router-dom';\n\ntype LocationState = {\n video?: VideoData;\n};\n\nexport default function DemoPage() {\n const {state} = useLocation() as Location;\n const data = useLazyLoadQuery(\n graphql`\n query DemoPageQuery {\n defaultVideo {\n path\n posterPath\n url\n posterUrl\n height\n width\n }\n }\n `,\n {},\n );\n const {setInputVideo} = useInputVideo();\n\n const video = useMemo(() => {\n return state?.video ?? data.defaultVideo;\n }, [state, data]);\n\n useEffect(() => {\n setInputVideo(video);\n }, [video, setInputVideo]);\n\n return (\n \n \n \n \n \n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/routes/DemoPageWrapper.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport LoadingStateScreen from '@/common/loading/LoadingStateScreen';\nimport DemoPage from '@/routes/DemoPage';\nimport stylex from '@stylexjs/stylex';\nimport {isFirefox} from 'react-device-detect';\n\nconst styles = stylex.create({\n link: {\n textDecorationLine: 'underline',\n color: '#A7B3BF',\n },\n});\n\nconst REQUIRED_WINDOW_APIS = ['VideoEncoder', 'VideoDecoder', 'VideoFrame'];\n\nfunction isBrowserSupported() {\n for (const api of REQUIRED_WINDOW_APIS) {\n if (!(api in window)) {\n return false;\n }\n }\n\n // Test if transferControlToOffscreen is supported. For example, this will\n // fail on iOS version < 16.4\n // https://developer.mozilla.org/en-US/docs/Web/API/HTMLCanvasElement/transferControlToOffscreen\n const canvas = document.createElement('canvas');\n if (typeof canvas.transferControlToOffscreen !== 'function') {\n return false;\n }\n\n return true;\n}\n\nexport default function DemoPageWrapper() {\n const isBrowserUnsupported = !isBrowserSupported();\n\n if (isBrowserUnsupported && isFirefox) {\n const nightlyUrl = 'https://wiki.mozilla.org/Nightly';\n return (\n \n This version of Firefox doesn’t support the video features we’ll\n need to run this demo. You can either update Firefox to the latest\n nightly build{' '}\n \n here\n \n , or try again using Chrome or Safari.\n \n }\n linkProps={{to: '..', label: 'Back to homepage'}}\n />\n );\n }\n\n if (isBrowserUnsupported) {\n return (\n \n );\n }\n\n return ;\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/routes/PageNotFoundPage.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport LoadingStateScreen from '@/common/loading/LoadingStateScreen';\n\nexport default function PageNotFoundPage() {\n return (\n \n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/routes/__generated__/DemoPageQuery.graphql.ts", "content": "/**\n * @generated SignedSource<>\n * @lightSyntaxTransform\n * @nogrep\n */\n\n/* tslint:disable */\n/* eslint-disable */\n// @ts-nocheck\n\nimport { ConcreteRequest, Query } from 'relay-runtime';\nexport type DemoPageQuery$variables = Record;\nexport type DemoPageQuery$data = {\n readonly defaultVideo: {\n readonly height: number;\n readonly path: string;\n readonly posterPath: string | null | undefined;\n readonly posterUrl: string;\n readonly url: string;\n readonly width: number;\n };\n};\nexport type DemoPageQuery = {\n response: DemoPageQuery$data;\n variables: DemoPageQuery$variables;\n};\n\nconst node: ConcreteRequest = (function(){\nvar v0 = [\n {\n \"alias\": null,\n \"args\": null,\n \"concreteType\": \"Video\",\n \"kind\": \"LinkedField\",\n \"name\": \"defaultVideo\",\n \"plural\": false,\n \"selections\": [\n {\n \"alias\": null,\n \"args\": null,\n \"kind\": \"ScalarField\",\n \"name\": \"path\",\n \"storageKey\": null\n },\n {\n \"alias\": null,\n \"args\": null,\n \"kind\": \"ScalarField\",\n \"name\": \"posterPath\",\n \"storageKey\": null\n },\n {\n \"alias\": null,\n \"args\": null,\n \"kind\": \"ScalarField\",\n \"name\": \"url\",\n \"storageKey\": null\n },\n {\n \"alias\": null,\n \"args\": null,\n \"kind\": \"ScalarField\",\n \"name\": \"posterUrl\",\n \"storageKey\": null\n },\n {\n \"alias\": null,\n \"args\": null,\n \"kind\": \"ScalarField\",\n \"name\": \"height\",\n \"storageKey\": null\n },\n {\n \"alias\": null,\n \"args\": null,\n \"kind\": \"ScalarField\",\n \"name\": \"width\",\n \"storageKey\": null\n }\n ],\n \"storageKey\": null\n }\n];\nreturn {\n \"fragment\": {\n \"argumentDefinitions\": [],\n \"kind\": \"Fragment\",\n \"metadata\": null,\n \"name\": \"DemoPageQuery\",\n \"selections\": (v0/*: any*/),\n \"type\": \"Query\",\n \"abstractKey\": null\n },\n \"kind\": \"Request\",\n \"operation\": {\n \"argumentDefinitions\": [],\n \"kind\": \"Operation\",\n \"name\": \"DemoPageQuery\",\n \"selections\": (v0/*: any*/)\n },\n \"params\": {\n \"cacheID\": \"71cbafce4d2d047acdc54d86504f2d2e\",\n \"id\": null,\n \"metadata\": {},\n \"name\": \"DemoPageQuery\",\n \"operationKind\": \"query\",\n \"text\": \"query DemoPageQuery {\\n defaultVideo {\\n path\\n posterPath\\n url\\n posterUrl\\n height\\n width\\n }\\n}\\n\"\n }\n};\n})();\n\n(node as any).hash = \"63c9465d78b30d42d6fc11e50a9af142\";\n\nexport default node;\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/settings/ApprovableInput.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport Tooltip from '@/common/components/Tooltip';\nimport {ArrowPathIcon, CheckIcon, XMarkIcon} from '@heroicons/react/24/solid';\nimport {ChangeEvent, KeyboardEvent, useEffect, useMemo, useState} from 'react';\nimport {Button, Form, Input, Join} from 'react-daisyui';\n\ntype Props = Omit<\n React.InputHTMLAttributes,\n 'size' | 'color' | 'onChange'\n> & {\n label: string;\n defaultValue: T;\n initialValue: T;\n onChange: (value: string) => void;\n};\n\nfunction getStep(value: number) {\n const stringValue = String(value);\n const decimals = stringValue.split('.')[1];\n if (decimals != null) {\n // Not using 0.1 ** decimals.length because this will result in rounding\n // errors, e.g., 0.1 ** 2 => 0.010000000000000002.\n return 1 / 10 ** decimals.length;\n }\n return 1;\n}\n\nexport default function ApprovableInput({\n label,\n defaultValue,\n initialValue,\n onChange,\n ...otherProps\n}: Props) {\n const [value, setValue] = useState(`${initialValue}`);\n\n useEffect(() => {\n setValue(`${initialValue}`);\n }, [initialValue]);\n\n const step = useMemo(() => {\n return typeof defaultValue === 'number' && isFinite(defaultValue)\n ? getStep(defaultValue)\n : undefined;\n }, [defaultValue]);\n\n return (\n
\n \n \n ) => {\n setValue(event.target.value);\n }}\n onKeyDown={(event: KeyboardEvent) => {\n if (event.key === 'Enter') {\n event.preventDefault();\n onChange(value);\n }\n }}\n />\n \n {\n event.preventDefault();\n setValue(`${defaultValue}`);\n }}>\n \n \n \n \n {\n event.preventDefault();\n setValue(`${initialValue}`);\n }}>\n \n \n \n \n {\n event.preventDefault();\n onChange(value);\n }}>\n \n \n \n \n \n
\n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/settings/SAM2Settings.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {INFERENCE_API_ENDPOINT, VIDEO_API_ENDPOINT} from '@/demo/DemoConfig';\nimport ApprovableInput from '@/settings/ApprovableInput';\nimport useSettingsContext from '@/settings/useSettingsContext';\n\nexport default function SAMVSettings() {\n const {settings, dispatch} = useSettingsContext();\n\n return (\n
\n dispatch({type: 'change-video-api-endpoint', url})}\n />\n dispatch({type: 'change-inference-api-endpoint', url})}\n />\n
\n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/settings/SettingsContextProvider.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport emptyFunction from '@/common/utils/emptyFunction';\nimport {INFERENCE_API_ENDPOINT, VIDEO_API_ENDPOINT} from '@/demo/DemoConfig';\nimport SettingsModal from '@/settings/SettingsModal';\nimport {\n Action,\n DEFAULT_SETTINGS,\n Settings,\n settingsReducer,\n} from '@/settings/SettingsReducer';\nimport {\n PropsWithChildren,\n createContext,\n useCallback,\n useMemo,\n useRef,\n} from 'react';\nimport {useImmerReducer} from 'use-immer';\n\ntype ContextProps = {\n settings: Settings;\n dispatch: React.Dispatch;\n openModal: () => void;\n closeModal: () => void;\n hasChanged: boolean;\n};\n\nexport const SettingsContext = createContext({\n settings: DEFAULT_SETTINGS,\n dispatch: emptyFunction,\n openModal: emptyFunction,\n closeModal: emptyFunction,\n hasChanged: false,\n});\n\ntype Props = PropsWithChildren;\n\nexport default function SettingsContextProvider({children}: Props) {\n const [state, dispatch] = useImmerReducer(\n settingsReducer,\n DEFAULT_SETTINGS,\n settings => {\n // Load the settings from local storage. Eventually use the reducer init\n // to handle initial loading.\n return settingsReducer(settings, {type: 'load-state'});\n },\n );\n\n const modalRef = useRef(null);\n\n const openModal = useCallback(() => {\n modalRef.current?.showModal();\n }, [modalRef]);\n\n const handleCloseModal = useCallback(() => {\n modalRef.current?.close();\n }, [modalRef]);\n\n const hasChanged = useMemo(() => {\n return (\n VIDEO_API_ENDPOINT !== state.videoAPIEndpoint ||\n INFERENCE_API_ENDPOINT !== state.inferenceAPIEndpoint\n );\n }, [state.videoAPIEndpoint, state.inferenceAPIEndpoint]);\n\n const value = useMemo(\n () => ({\n settings: state,\n dispatch,\n openModal,\n closeModal: handleCloseModal,\n hasChanged,\n }),\n [state, dispatch, openModal, handleCloseModal, hasChanged],\n );\n\n return (\n \n {children}\n \n \n );\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/settings/SettingsModal.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {DEMO_FRIENDLY_NAME} from '@/demo/DemoConfig';\nimport SAM2Settings from '@/settings/SAM2Settings';\nimport {XMarkIcon} from '@heroicons/react/24/solid';\nimport {forwardRef, useState} from 'react';\nimport {Button, Modal} from 'react-daisyui';\nimport useSettingsContext from './useSettingsContext';\n\ntype Props = unknown;\n\ntype Config = {\n key: 'sam2';\n title: string;\n component: React.ElementType;\n};\n\nconst SettingsConfig: Config[] = [\n {\n key: 'sam2',\n title: DEMO_FRIENDLY_NAME,\n component: SAM2Settings,\n },\n];\n\nexport default forwardRef(\n function SettingsModal(_props, ref) {\n const {closeModal} = useSettingsContext();\n const [activeConfig, setActiveConfig] = useState(SettingsConfig[0]);\n\n const SettingsComponent = activeConfig.component;\n\n return (\n \n }\n onClick={closeModal}\n />\n Settings\n \n
\n
\n {SettingsConfig.map(config => (\n setActiveConfig(config)}>\n {config.title}\n
\n ))}\n
\n \n
\n

{activeConfig.title}

\n \n
\n \n \n \n \n \n \n \n );\n },\n);\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/settings/SettingsReducer.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {INFERENCE_API_ENDPOINT, VIDEO_API_ENDPOINT} from '@/demo/DemoConfig';\n\nexport type Settings = {\n videoAPIEndpoint: string;\n inferenceAPIEndpoint: string;\n};\n\n// Key used to store the settings in the browser's local storage.\nexport const SAM2_SETTINGS_KEY = 'SAM2_SETTINGS_KEY';\n\nexport type Action =\n | {type: 'load-state'}\n | {type: 'change-video-api-endpoint'; url: string}\n | {type: 'change-inference-api-endpoint'; url: string};\n\nexport const DEFAULT_SETTINGS: Settings = {\n videoAPIEndpoint: VIDEO_API_ENDPOINT,\n inferenceAPIEndpoint: INFERENCE_API_ENDPOINT,\n};\n\nexport function settingsReducer(state: Settings, action: Action): Settings {\n function storeSettings(newState: Settings): void {\n localStorage.setItem(SAM2_SETTINGS_KEY, JSON.stringify(newState));\n }\n\n switch (action.type) {\n case 'load-state': {\n try {\n const serializedSettings = localStorage.getItem(SAM2_SETTINGS_KEY);\n if (serializedSettings != null) {\n return JSON.parse(serializedSettings) as Settings;\n } else {\n // Store default settings in local storage. This will populate the\n // settings in the local storage on first app load or when user\n // cleared the browser cache.\n storeSettings(state);\n }\n } catch {\n // Could not parse settings. Using default settings instead.\n }\n return state;\n }\n case 'change-video-api-endpoint':\n state.videoAPIEndpoint = action.url;\n break;\n case 'change-inference-api-endpoint':\n state.inferenceAPIEndpoint = action.url;\n break;\n }\n\n // Store the settings state on every change\n storeSettings(state);\n\n return state;\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/settings/useSettingsContext.tsx", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport {useContext} from 'react';\nimport {SettingsContext} from '@/settings/SettingsContextProvider';\n\nexport default function useSettingsContext() {\n return useContext(SettingsContext);\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/theme/colors.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nexport const THEME_COLORS = [\n '#3880F3',\n '#F0AA19',\n '#00D2BE',\n '#28D232',\n '#8773FF',\n '#00C8F0',\n '#FA8719',\n '#E6193B',\n '#FA7DC8',\n];\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/theme/gradientStyle.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nexport const BLUE_PINK_FILL = 'from-[#595FEF] from-40% to-[#FB73A5]';\nexport const BLUE_PINK_FILL_BR =\n 'bg-gradient-to-br from-[#595FEF] from-30% to-[#FB73A5]';\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/theme/tokens.stylex.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport * as stylex from '@stylexjs/stylex';\n\nexport const spacing = stylex.defineVars({\n '0': '0rem',\n '0.5': '0.125rem',\n '1': '0.25rem',\n '1.5': '0.375rem',\n '2': '0.5rem',\n '2.5': '0.625rem',\n '3': '0.75rem',\n '3.5': '0.875rem',\n '4': '1rem',\n '5': '1.25rem',\n '6': '1.5rem',\n '7': '1.75rem',\n '8': '2rem',\n '9': '2.25rem',\n '10': '2.5rem',\n '11': '2.75rem',\n '12': '3rem',\n});\n\nexport const gap = stylex.defineVars({\n 4: '1rem' /* 16px */,\n});\n\nexport const w = stylex.defineVars({\n full: '100%',\n 12: '3rem' /* 48px */,\n 96: '24rem' /* 384px */,\n});\n\nexport const m = stylex.defineVars({\n 3: '0.75rem' /* 12px */,\n});\n\nexport const fontSize = stylex.defineVars({\n xs: '0.75rem',\n sm: '0.875rem',\n base: '1rem',\n lg: '1.125rem',\n xl: '1.25rem',\n '2xl': '1.5rem',\n});\n\nexport const fontWeight = stylex.defineVars({\n thin: 100,\n extralight: 200,\n light: 300,\n normal: 400,\n medium: 500,\n semibold: 600,\n bold: 700,\n extrabold: 800,\n});\n\nexport const color = stylex.defineVars({\n subtitle: 'rgb(107 114 128)',\n 'gray-900': 'rgb(17 24 39)',\n 'gray-800': 'rgb(26 28 31)',\n 'gray-700': 'rgb(55 62 65)',\n 'blue-600': 'rgb(37 99 235)',\n});\n\nexport const screenSizes = {\n sm: 640,\n md: 768,\n lg: 1024,\n xl: 1280,\n '2xl': 1536,\n};\n\nexport const borderRadius = stylex.defineVars({\n sm: '0.125rem',\n md: '0.375rem',\n lg: '0.5rem',\n xl: '0.75rem',\n});\n\nexport const top = stylex.defineVars({\n 0: 0,\n 1: '0.25rem' /* 4px */,\n 2: '0.5rem' /* 8px */,\n});\n\nexport const right = stylex.defineVars({\n 0: 0,\n 1: '0.25rem' /* 4px */,\n 2: '0.5rem' /* 8px */,\n});\n\nexport const gradients = stylex.defineVars({\n rainbow:\n 'linear-gradient(#000, #000) padding-box, linear-gradient(to right bottom, #FB73A5,#595FEF,#94EAE2,#FCCB6B) border-box',\n\n rainbowReverse:\n 'linear-gradient(#000, #000) padding-box, linear-gradient(to left top, #FB73A5,#595FEF,#94EAE2,#FCCB6B) border-box',\n\n yellowTeal:\n 'linear-gradient(#000, #000) padding-box, linear-gradient(to right bottom, #94EAE2,#FCCB6B) border-box',\n});\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/types/mp4box/index.d.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\ndeclare module 'mp4box' {\n export interface MP4MediaTrackEdit {\n media_rate_fraction: number;\n media_rate_integer: number;\n media_time: number;\n segment_duration: number;\n }\n\n export interface MP4MediaTrack {\n id: number;\n created: Date;\n modified: Date;\n movie_duration: number;\n movie_timescale: number;\n layer: number;\n alternate_group: number;\n volume: number;\n track_width: number;\n track_height: number;\n timescale: number;\n duration: number;\n bitrate: number;\n codec: string;\n language: string;\n nb_samples: number;\n samples_duration: number;\n edits: MP4MediaTrackEdit[];\n }\n\n export interface MP4VideoData {\n width: number;\n height: number;\n }\n\n export interface MP4VideoTrack extends MP4MediaTrack {\n video: MP4VideoData;\n }\n\n export interface MP4AudioData {\n sample_rate: number;\n channel_count: number;\n sample_size: number;\n }\n\n export interface MP4AudioTrack extends MP4MediaTrack {\n audio: MP4AudioData;\n }\n\n export type MP4Track = MP4VideoTrack | MP4AudioTrack;\n\n export interface MP4Info {\n duration: number;\n timescale: number;\n fragment_duration: number;\n isFragmented: boolean;\n isProgressive: boolean;\n hasIOD: boolean;\n brands: string[];\n created: Date;\n modified: Date;\n tracks: MP4Track[];\n audioTracks: MP4AudioTrack[];\n videoTracks: MP4VideoTrack[];\n otherTracks: MP4VideoTrack[];\n }\n\n export interface MP4Sample {\n alreadyRead: number;\n chunk_index: number;\n chunk_run_index: number;\n cts: number;\n data: Uint8Array;\n degradation_priority: number;\n depends_on: number;\n description: unknown;\n description_index: number;\n dts: number;\n duration: number;\n has_redundancy: number;\n is_depended_on: number;\n is_leading: number;\n is_sync: boolean;\n number: number;\n offset: number;\n size: number;\n timescale: number;\n track_id: number;\n }\n\n export type MP4ArrayBuffer = ArrayBuffer & {fileStart: number};\n\n export class DataStream {\n static BIG_ENDIAN: boolean;\n static LITTLE_ENDIAN: boolean;\n buffer: ArrayBuffer;\n constructor(\n arrayBuffer?: ArrayBuffer,\n byteOffset: number,\n endianness: boolean,\n ): void;\n }\n\n export interface Trak {\n mdia?: {\n minf?: {\n stbl?: {\n stsd?: {\n entries: {\n avcC?: {\n write: (stream: DataStream) => void;\n };\n hvcC?: {\n write: (stream: DataStream) => void;\n };\n }[];\n };\n };\n };\n };\n }\n\n export namespace BoxParser {\n export class Box {\n size?: number;\n data?: Uint8Array;\n\n constructor(type?: string, size?: number);\n\n add(name: string): Box;\n addBox(box: Box): Box;\n addEntry(value: string, prop?: string): void;\n write(stream: DataStream): void;\n writeHeader(stream: DataStream, msg?: string): void;\n computeSize(): void;\n }\n\n export class ContainerBox extends Box {}\n\n export class avcCBox extends ContainerBox {}\n\n export class hvcCBox extends ContainerBox {}\n\n export class vpcCBox extends ContainerBox {}\n\n export class av1CBox extends ContainerBox {}\n }\n\n export interface TrackOptions {\n id?: number;\n type?: string;\n width?: number;\n height?: number;\n duration?: number;\n layer?: number;\n timescale?: number;\n media_duration?: number;\n language?: string;\n hdlr?: string;\n\n // video\n avcDecoderConfigRecord?: BufferSource;\n\n // audio\n balance?: number;\n channel_count?: number;\n samplesize?: number;\n samplerate?: number;\n\n //captions\n namespace?: string;\n schema_location?: string;\n auxiliary_mime_types?: string;\n\n description?: BoxParser.Box;\n description_boxes?: BoxParser.Box[];\n\n default_sample_description_index_id?: number;\n default_sample_duration?: number;\n default_sample_size?: number;\n default_sample_flags?: number;\n }\n\n export interface SampleOptions {\n sample_description_index?: number;\n duration?: number;\n cts?: number;\n dts?: number;\n is_sync?: boolean;\n is_leading?: number;\n depends_on?: number;\n is_depended_on?: number;\n has_redundancy?: number;\n degradation_priority?: number;\n }\n\n export interface Sample {\n number: number;\n track_id: number;\n timescale: number;\n description_index: number;\n description: {\n avcC?: BoxParser.avcCBox; // h.264\n hvcC?: BoxParser.hvcCBox; // hevc\n vpcC?: BoxParser.vpcCBox; // vp9\n av1C?: BoxParser.av1CBox; // av1\n };\n data: ArrayBuffer;\n size: number;\n alreadyRead?: number;\n duration: number;\n cts: number;\n dts: number;\n is_sync: boolean;\n is_leading?: number;\n depends_on?: number;\n is_depended_on?: number;\n has_redundancy?: number;\n degradation_priority?: number;\n offset?: number;\n }\n\n export interface MP4File {\n getBuffer(): MP4ArrayBuffer;\n addTrack(options?: TrackOptions): number;\n addSample(\n track: number,\n data: ArrayBuffer,\n options?: SampleOptions,\n ): Sample;\n addSample(\n trackID: number,\n uint8: Uint8Array,\n arg2: {duration: number; is_sync: boolean},\n ): void;\n onMoovStart?: () => void;\n onReady?: (info: MP4Info) => void;\n onError?: (e: string) => void;\n onSamples?: (id: number, user: unknown, samples: MP4Sample[]) => unknown;\n appendBuffer(data: MP4ArrayBuffer): number;\n save(fileName: string): void;\n start(): void;\n stop(): void;\n /**\n * Indicates that the next samples to process (for extraction or\n * segmentation) start at the given time (Number, in seconds) or at the\n * time of the previous Random Access Point (if useRap is true, default\n * is false). Returns the offset in the file of the next bytes to be\n * provided via appendBuffer.\n *\n * @param time - Start at the given time (Number, in seconds)\n * @param useRap - Random Access Point (if useRap is true, default is false)\n * @returns Returns the offset in the file of the next bytes to be provided via appendBuffer.\n */\n seek: (time: number, useRap: boolean) => number;\n flush(): void;\n releaseUsedSamples(trackId: number, sampleNumber: number): void;\n setExtractionOptions(\n trackId: number,\n user?: unknown,\n options?: {nbSamples?: number; rapAlignment?: number},\n ): void;\n getTrackById(trackId: number): Trak;\n }\n\n export function createFile(): MP4File;\n\n export {};\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/src/vite-env.d.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n/// \n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/tailwind.config.js", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport tailwindCSSTypography from '@tailwindcss/typography';\nimport daisyui from 'daisyui';\nimport * as daisyColorThemes from 'daisyui/src/theming/themes';\n\n/** @type {import('tailwindcss').Config} */\nexport default {\n content: [\n './index.html',\n './src/**/*.{js,ts,jsx,tsx}',\n 'node_modules/daisyui/dist/**/*.js',\n 'node_modules/react-daisyui/dist/**/*.js',\n ],\n\n daisyui: {\n styled: true,\n themes: [\n {\n light: {\n ...daisyColorThemes['[data-theme=light]'],\n 'base-100': '#FFFFFF',\n 'base-200': '#F1F4F7',\n 'base-300': '#DEE3E9',\n primary: '#0064E0',\n 'primary-content': '#FFFFFF',\n secondary: '#0F191E',\n 'secondary-content': '#FFFFFF',\n accent: '#6441D2',\n 'accent-content': '#FFFFFF',\n info: '#009B9B',\n 'info-content': '#FFFFFF',\n success: '#0F9B14',\n 'success-content': '#FFFFFF',\n warning: '#FA8719',\n 'warning-content': '#FFFFFF',\n error: '#C80A28',\n 'error-content': '#FFFFFF',\n\n '--rounded-box': '0.35rem', // border radius rounded-box utility class, used in card and other large boxes\n '--rounded-btn': '0.35rem', // border radius rounded-btn utility class, used in buttons and similar element\n '--rounded-badge': '1rem', // border radius rounded-badge utility class, used in badges and similar\n },\n },\n 'dark',\n ],\n },\n theme: {\n fontSize: {\n xs: ['0.75rem', {lineHeight: '1.5'}],\n sm: ['0.875rem', {lineHeight: '1.5'}],\n base: ['1rem', {lineHeight: '1.5'}],\n lg: ['1.125rem', {lineHeight: '1.2', fontWeight: 500}],\n xl: ['1.25rem', {lineHeight: '1.2', fontWeight: 500}],\n '2xl': [\n '1.5rem',\n {lineHeight: '1.2', fontWeight: 500, letterSpacing: '0.005rem'},\n ],\n '3xl': [\n '2.25rem',\n {lineHeight: '1.2', fontWeight: 500, letterSpacing: '0.01rem'},\n ],\n '4xl': [\n '3rem',\n {lineHeight: '1.2', fontWeight: 500, letterSpacing: '0.016rem'},\n ],\n '5xl': [\n '4rem',\n {lineHeight: '1.2', fontWeight: 400, letterSpacing: '0.016rem'},\n ],\n '6xl': [\n '5rem',\n {lineHeight: '1.2', fontWeight: 400, letterSpacing: '0.016rem'},\n ],\n },\n extend: {\n colors: {\n graydark: {\n 50: '#f1f4f7',\n 100: '#DEE3E9',\n 200: '#CBD2D9',\n 300: '#A7B3BF',\n 400: '#8595A4',\n 500: '#667788',\n 600: '#465A69',\n 700: '#343845',\n 800: '#1A1C1F',\n 900: '#0F191E',\n },\n },\n lineHeight: {\n tight: 1.2,\n },\n backgroundImage: {\n dot: 'url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAgAAAAICAYAAADED76LAAAAAXNSR0IArs4c6QAAABdJREFUGBljYGBg+A/FQAoTMGEKDUcRAATwAgFGIXEOAAAAAElFTkSuQmCC)',\n },\n keyframes: {\n wiggle: {\n '0%, 100%': {transform: 'rotate(-3deg)'},\n '50%': {transform: 'rotate(3deg)'},\n },\n },\n animation: {\n wiggle: 'wiggle .25s ease-in-out',\n },\n typography: {\n DEFAULT: {\n css: {\n maxWidth: '100%', // add required value here\n a: {\n textDecoration: 'none',\n },\n },\n },\n },\n },\n },\n plugins: [tailwindCSSTypography, daisyui],\n};\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/tsconfig.json", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n{\n \"compilerOptions\": {\n \"target\": \"ES2020\",\n \"useDefineForClassFields\": true,\n \"lib\": [\"ES2020\", \"DOM\", \"DOM.Iterable\", \"webworker\"],\n \"module\": \"ESNext\",\n \"skipLibCheck\": true,\n\n /* Bundler mode */\n \"moduleResolution\": \"bundler\",\n \"allowImportingTsExtensions\": true,\n \"resolveJsonModule\": true,\n \"isolatedModules\": true,\n \"esModuleInterop\": true, // esModuleInterop true required for Jest\n \"noEmit\": true,\n \"jsx\": \"react-jsx\",\n\n /* Linting */\n \"strict\": true,\n \"noUnusedLocals\": true,\n \"noUnusedParameters\": true,\n \"noFallthroughCasesInSwitch\": true,\n\n \"baseUrl\": \"./src\",\n \"paths\": {\n \"mp4box\": [\"types/mp4box\"],\n \"@/*\": [\"*\"]\n }\n },\n \"include\": [\"src\"],\n \"references\": [{\"path\": \"./tsconfig.node.json\"}]\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/tsconfig.node.json", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n{\n \"compilerOptions\": {\n \"composite\": true,\n \"skipLibCheck\": true,\n \"module\": \"ESNext\",\n \"target\": \"ES2017\",\n \"moduleResolution\": \"bundler\",\n \"allowSyntheticDefaultImports\": true,\n \"strictNullChecks\": true\n },\n \"include\": [\"vite.config.ts\", \"schemas\"]\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/demo/frontend/vite.config.ts", "content": "/**\n * Copyright (c) Meta Platforms, Inc. and affiliates.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\nimport react from '@vitejs/plugin-react';\nimport jotaiDebugLabel from 'jotai/babel/plugin-debug-label';\nimport jotaiReactRefresh from 'jotai/babel/plugin-react-refresh';\nimport path from 'path';\nimport {defineConfig} from 'vite';\nimport babel from 'vite-plugin-babel';\nimport relay from 'vite-plugin-relay';\nimport {stylexPlugin} from 'vite-plugin-stylex-dev';\n\nexport default defineConfig({\n resolve: {\n alias: {\n '@': path.resolve(__dirname, './src'),\n },\n },\n plugins: [\n react({\n babel: {\n plugins: [jotaiDebugLabel, jotaiReactRefresh],\n },\n }),\n stylexPlugin(),\n relay,\n babel(),\n ],\n worker: {\n plugins: () => [relay],\n },\n});\n" }, { "path": "auto-seg/submodules/segment-anything-2/docker-compose.yaml", "content": "services:\n frontend:\n image: sam2/frontend\n build:\n context: ./demo/frontend\n dockerfile: frontend.Dockerfile\n ports:\n - 7262:80\n\n backend:\n image: sam2/backend\n build:\n context: .\n dockerfile: backend.Dockerfile\n ports:\n - 7263:5000\n volumes:\n - ./demo/data/:/data/:rw\n environment:\n - SERVER_ENVIRONMENT=DEV\n - GUNICORN_WORKERS=1\n # Inference API needs to have at least 2 threads to handle an incoming\n # parallel cancel propagation request\n - GUNICORN_THREADS=2\n - GUNICORN_PORT=5000\n - API_URL=http://localhost:7263\n - DEFAULT_VIDEO_PATH=gallery/05_default_juggle.mp4\n # # ffmpeg/video encode settings\n - FFMPEG_NUM_THREADS=1\n - VIDEO_ENCODE_CODEC=libx264\n - VIDEO_ENCODE_CRF=23\n - VIDEO_ENCODE_FPS=24\n - VIDEO_ENCODE_MAX_WIDTH=1280\n - VIDEO_ENCODE_MAX_HEIGHT=720\n - VIDEO_ENCODE_VERBOSE=False\n deploy:\n resources:\n reservations:\n devices:\n - driver: nvidia\n count: 1\n capabilities: [gpu]\n" }, { "path": "auto-seg/submodules/segment-anything-2/pyproject.toml", "content": "[build-system]\nrequires = [\n \"setuptools>=61.0\", \n \"torch>=2.3.1\",\n ]\nbuild-backend = \"setuptools.build_meta\"\n" }, { "path": "auto-seg/submodules/segment-anything-2/sam2/__init__.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nfrom hydra import initialize_config_module\nfrom hydra.core.global_hydra import GlobalHydra\n\nif not GlobalHydra.instance().is_initialized():\n initialize_config_module(\"sam2\", version_base=\"1.2\")\n" }, { "path": "auto-seg/submodules/segment-anything-2/sam2/automatic_mask_generator.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\n# Adapted from https://github.com/facebookresearch/segment-anything/blob/main/segment_anything/automatic_mask_generator.py\nfrom typing import Any, Dict, List, Optional, Tuple\n\nimport numpy as np\nimport torch\nfrom torchvision.ops.boxes import batched_nms, box_area # type: ignore\n\nfrom sam2.modeling.sam2_base import SAM2Base\nfrom sam2.sam2_image_predictor import SAM2ImagePredictor\nfrom sam2.utils.amg import (\n area_from_rle,\n batch_iterator,\n batched_mask_to_box,\n box_xyxy_to_xywh,\n build_all_layer_point_grids,\n calculate_stability_score,\n coco_encode_rle,\n generate_crop_boxes,\n is_box_near_crop_edge,\n mask_to_rle_pytorch,\n MaskData,\n remove_small_regions,\n rle_to_mask,\n uncrop_boxes_xyxy,\n uncrop_masks,\n uncrop_points,\n)\n\n\nclass SAM2AutomaticMaskGenerator:\n def __init__(\n self,\n model: SAM2Base,\n points_per_side: Optional[int] = 32,\n points_per_batch: int = 64,\n pred_iou_thresh: float = 0.8,\n stability_score_thresh: float = 0.95,\n stability_score_offset: float = 1.0,\n mask_threshold: float = 0.0,\n box_nms_thresh: float = 0.7,\n crop_n_layers: int = 0,\n crop_nms_thresh: float = 0.7,\n crop_overlap_ratio: float = 512 / 1500,\n crop_n_points_downscale_factor: int = 1,\n point_grids: Optional[List[np.ndarray]] = None,\n min_mask_region_area: int = 0,\n output_mode: str = \"binary_mask\",\n use_m2m: bool = False,\n multimask_output: bool = True,\n **kwargs,\n ) -> None:\n \"\"\"\n Using a SAM 2 model, generates masks for the entire image.\n Generates a grid of point prompts over the image, then filters\n low quality and duplicate masks. The default settings are chosen\n for SAM 2 with a HieraL backbone.\n\n Arguments:\n model (Sam): The SAM 2 model to use for mask prediction.\n points_per_side (int or None): The number of points to be sampled\n along one side of the image. The total number of points is\n points_per_side**2. If None, 'point_grids' must provide explicit\n point sampling.\n points_per_batch (int): Sets the number of points run simultaneously\n by the model. Higher numbers may be faster but use more GPU memory.\n pred_iou_thresh (float): A filtering threshold in [0,1], using the\n model's predicted mask quality.\n stability_score_thresh (float): A filtering threshold in [0,1], using\n the stability of the mask under changes to the cutoff used to binarize\n the model's mask predictions.\n stability_score_offset (float): The amount to shift the cutoff when\n calculated the stability score.\n mask_threshold (float): Threshold for binarizing the mask logits\n box_nms_thresh (float): The box IoU cutoff used by non-maximal\n suppression to filter duplicate masks.\n crop_n_layers (int): If >0, mask prediction will be run again on\n crops of the image. Sets the number of layers to run, where each\n layer has 2**i_layer number of image crops.\n crop_nms_thresh (float): The box IoU cutoff used by non-maximal\n suppression to filter duplicate masks between different crops.\n crop_overlap_ratio (float): Sets the degree to which crops overlap.\n In the first crop layer, crops will overlap by this fraction of\n the image length. Later layers with more crops scale down this overlap.\n crop_n_points_downscale_factor (int): The number of points-per-side\n sampled in layer n is scaled down by crop_n_points_downscale_factor**n.\n point_grids (list(np.ndarray) or None): A list over explicit grids\n of points used for sampling, normalized to [0,1]. The nth grid in the\n list is used in the nth crop layer. Exclusive with points_per_side.\n min_mask_region_area (int): If >0, postprocessing will be applied\n to remove disconnected regions and holes in masks with area smaller\n than min_mask_region_area. Requires opencv.\n output_mode (str): The form masks are returned in. Can be 'binary_mask',\n 'uncompressed_rle', or 'coco_rle'. 'coco_rle' requires pycocotools.\n For large resolutions, 'binary_mask' may consume large amounts of\n memory.\n use_m2m (bool): Whether to add a one step refinement using previous mask predictions.\n multimask_output (bool): Whether to output multimask at each point of the grid.\n \"\"\"\n\n assert (points_per_side is None) != (\n point_grids is None\n ), \"Exactly one of points_per_side or point_grid must be provided.\"\n if points_per_side is not None:\n self.point_grids = build_all_layer_point_grids(\n points_per_side,\n crop_n_layers,\n crop_n_points_downscale_factor,\n )\n elif point_grids is not None:\n self.point_grids = point_grids\n else:\n raise ValueError(\"Can't have both points_per_side and point_grid be None.\")\n\n assert output_mode in [\n \"binary_mask\",\n \"uncompressed_rle\",\n \"coco_rle\",\n ], f\"Unknown output_mode {output_mode}.\"\n if output_mode == \"coco_rle\":\n try:\n from pycocotools import mask as mask_utils # type: ignore # noqa: F401\n except ImportError as e:\n print(\"Please install pycocotools\")\n raise e\n\n self.predictor = SAM2ImagePredictor(\n model,\n max_hole_area=min_mask_region_area,\n max_sprinkle_area=min_mask_region_area,\n )\n self.points_per_batch = points_per_batch\n self.pred_iou_thresh = pred_iou_thresh\n self.stability_score_thresh = stability_score_thresh\n self.stability_score_offset = stability_score_offset\n self.mask_threshold = mask_threshold\n self.box_nms_thresh = box_nms_thresh\n self.crop_n_layers = crop_n_layers\n self.crop_nms_thresh = crop_nms_thresh\n self.crop_overlap_ratio = crop_overlap_ratio\n self.crop_n_points_downscale_factor = crop_n_points_downscale_factor\n self.min_mask_region_area = min_mask_region_area\n self.output_mode = output_mode\n self.use_m2m = use_m2m\n self.multimask_output = multimask_output\n\n @classmethod\n def from_pretrained(cls, model_id: str, **kwargs) -> \"SAM2AutomaticMaskGenerator\":\n \"\"\"\n Load a pretrained model from the Hugging Face hub.\n\n Arguments:\n model_id (str): The Hugging Face repository ID.\n **kwargs: Additional arguments to pass to the model constructor.\n\n Returns:\n (SAM2AutomaticMaskGenerator): The loaded model.\n \"\"\"\n from sam2.build_sam import build_sam2_hf\n\n sam_model = build_sam2_hf(model_id, **kwargs)\n return cls(sam_model, **kwargs)\n\n @torch.no_grad()\n def generate(self, image: np.ndarray) -> List[Dict[str, Any]]:\n \"\"\"\n Generates masks for the given image.\n\n Arguments:\n image (np.ndarray): The image to generate masks for, in HWC uint8 format.\n\n Returns:\n list(dict(str, any)): A list over records for masks. Each record is\n a dict containing the following keys:\n segmentation (dict(str, any) or np.ndarray): The mask. If\n output_mode='binary_mask', is an array of shape HW. Otherwise,\n is a dictionary containing the RLE.\n bbox (list(float)): The box around the mask, in XYWH format.\n area (int): The area in pixels of the mask.\n predicted_iou (float): The model's own prediction of the mask's\n quality. This is filtered by the pred_iou_thresh parameter.\n point_coords (list(list(float))): The point coordinates input\n to the model to generate this mask.\n stability_score (float): A measure of the mask's quality. This\n is filtered on using the stability_score_thresh parameter.\n crop_box (list(float)): The crop of the image used to generate\n the mask, given in XYWH format.\n \"\"\"\n\n # Generate masks\n mask_data = self._generate_masks(image)\n\n # Encode masks\n if self.output_mode == \"coco_rle\":\n mask_data[\"segmentations\"] = [\n coco_encode_rle(rle) for rle in mask_data[\"rles\"]\n ]\n elif self.output_mode == \"binary_mask\":\n mask_data[\"segmentations\"] = [rle_to_mask(rle) for rle in mask_data[\"rles\"]]\n else:\n mask_data[\"segmentations\"] = mask_data[\"rles\"]\n\n # Write mask records\n curr_anns = []\n for idx in range(len(mask_data[\"segmentations\"])):\n ann = {\n \"segmentation\": mask_data[\"segmentations\"][idx],\n \"area\": area_from_rle(mask_data[\"rles\"][idx]),\n \"bbox\": box_xyxy_to_xywh(mask_data[\"boxes\"][idx]).tolist(),\n \"predicted_iou\": mask_data[\"iou_preds\"][idx].item(),\n \"point_coords\": [mask_data[\"points\"][idx].tolist()],\n \"stability_score\": mask_data[\"stability_score\"][idx].item(),\n \"crop_box\": box_xyxy_to_xywh(mask_data[\"crop_boxes\"][idx]).tolist(),\n }\n curr_anns.append(ann)\n\n return curr_anns\n\n def _generate_masks(self, image: np.ndarray) -> MaskData:\n orig_size = image.shape[:2]\n crop_boxes, layer_idxs = generate_crop_boxes(\n orig_size, self.crop_n_layers, self.crop_overlap_ratio\n )\n\n # Iterate over image crops\n data = MaskData()\n for crop_box, layer_idx in zip(crop_boxes, layer_idxs):\n crop_data = self._process_crop(image, crop_box, layer_idx, orig_size)\n data.cat(crop_data)\n\n # Remove duplicate masks between crops\n if len(crop_boxes) > 1:\n # Prefer masks from smaller crops\n scores = 1 / box_area(data[\"crop_boxes\"])\n scores = scores.to(data[\"boxes\"].device)\n keep_by_nms = batched_nms(\n data[\"boxes\"].float(),\n scores,\n torch.zeros_like(data[\"boxes\"][:, 0]), # categories\n iou_threshold=self.crop_nms_thresh,\n )\n data.filter(keep_by_nms)\n data.to_numpy()\n return data\n\n def _process_crop(\n self,\n image: np.ndarray,\n crop_box: List[int],\n crop_layer_idx: int,\n orig_size: Tuple[int, ...],\n ) -> MaskData:\n # Crop the image and calculate embeddings\n x0, y0, x1, y1 = crop_box\n cropped_im = image[y0:y1, x0:x1, :]\n cropped_im_size = cropped_im.shape[:2]\n self.predictor.set_image(cropped_im)\n\n # Get points for this crop\n points_scale = np.array(cropped_im_size)[None, ::-1]\n points_for_image = self.point_grids[crop_layer_idx] * points_scale\n\n # Generate masks for this crop in batches\n data = MaskData()\n for (points,) in batch_iterator(self.points_per_batch, points_for_image):\n batch_data = self._process_batch(\n points, cropped_im_size, crop_box, orig_size, normalize=True\n )\n data.cat(batch_data)\n del batch_data\n self.predictor.reset_predictor()\n\n # Remove duplicates within this crop.\n keep_by_nms = batched_nms(\n data[\"boxes\"].float(),\n data[\"iou_preds\"],\n torch.zeros_like(data[\"boxes\"][:, 0]), # categories\n iou_threshold=self.box_nms_thresh,\n )\n data.filter(keep_by_nms)\n\n # Return to the original image frame\n data[\"boxes\"] = uncrop_boxes_xyxy(data[\"boxes\"], crop_box)\n data[\"points\"] = uncrop_points(data[\"points\"], crop_box)\n data[\"crop_boxes\"] = torch.tensor([crop_box for _ in range(len(data[\"rles\"]))])\n\n return data\n\n def _process_batch(\n self,\n points: np.ndarray,\n im_size: Tuple[int, ...],\n crop_box: List[int],\n orig_size: Tuple[int, ...],\n normalize=False,\n ) -> MaskData:\n orig_h, orig_w = orig_size\n\n # Run model on this batch\n points = torch.as_tensor(\n points, dtype=torch.float32, device=self.predictor.device\n )\n in_points = self.predictor._transforms.transform_coords(\n points, normalize=normalize, orig_hw=im_size\n )\n in_labels = torch.ones(\n in_points.shape[0], dtype=torch.int, device=in_points.device\n )\n masks, iou_preds, low_res_masks = self.predictor._predict(\n in_points[:, None, :],\n in_labels[:, None],\n multimask_output=self.multimask_output,\n return_logits=True,\n )\n\n # Serialize predictions and store in MaskData\n data = MaskData(\n masks=masks.flatten(0, 1),\n iou_preds=iou_preds.flatten(0, 1),\n points=points.repeat_interleave(masks.shape[1], dim=0),\n low_res_masks=low_res_masks.flatten(0, 1),\n )\n del masks\n\n if not self.use_m2m:\n # Filter by predicted IoU\n if self.pred_iou_thresh > 0.0:\n keep_mask = data[\"iou_preds\"] > self.pred_iou_thresh\n data.filter(keep_mask)\n\n # Calculate and filter by stability score\n data[\"stability_score\"] = calculate_stability_score(\n data[\"masks\"], self.mask_threshold, self.stability_score_offset\n )\n if self.stability_score_thresh > 0.0:\n keep_mask = data[\"stability_score\"] >= self.stability_score_thresh\n data.filter(keep_mask)\n else:\n # One step refinement using previous mask predictions\n in_points = self.predictor._transforms.transform_coords(\n data[\"points\"], normalize=normalize, orig_hw=im_size\n )\n labels = torch.ones(\n in_points.shape[0], dtype=torch.int, device=in_points.device\n )\n masks, ious = self.refine_with_m2m(\n in_points, labels, data[\"low_res_masks\"], self.points_per_batch\n )\n data[\"masks\"] = masks.squeeze(1)\n data[\"iou_preds\"] = ious.squeeze(1)\n\n if self.pred_iou_thresh > 0.0:\n keep_mask = data[\"iou_preds\"] > self.pred_iou_thresh\n data.filter(keep_mask)\n\n data[\"stability_score\"] = calculate_stability_score(\n data[\"masks\"], self.mask_threshold, self.stability_score_offset\n )\n if self.stability_score_thresh > 0.0:\n keep_mask = data[\"stability_score\"] >= self.stability_score_thresh\n data.filter(keep_mask)\n\n # Threshold masks and calculate boxes\n data[\"masks\"] = data[\"masks\"] > self.mask_threshold\n data[\"boxes\"] = batched_mask_to_box(data[\"masks\"])\n\n # Filter boxes that touch crop boundaries\n keep_mask = ~is_box_near_crop_edge(\n data[\"boxes\"], crop_box, [0, 0, orig_w, orig_h]\n )\n if not torch.all(keep_mask):\n data.filter(keep_mask)\n\n # Compress to RLE\n data[\"masks\"] = uncrop_masks(data[\"masks\"], crop_box, orig_h, orig_w)\n data[\"rles\"] = mask_to_rle_pytorch(data[\"masks\"])\n del data[\"masks\"]\n\n return data\n\n @staticmethod\n def postprocess_small_regions(\n mask_data: MaskData, min_area: int, nms_thresh: float\n ) -> MaskData:\n \"\"\"\n Removes small disconnected regions and holes in masks, then reruns\n box NMS to remove any new duplicates.\n\n Edits mask_data in place.\n\n Requires open-cv as a dependency.\n \"\"\"\n if len(mask_data[\"rles\"]) == 0:\n return mask_data\n\n # Filter small disconnected regions and holes\n new_masks = []\n scores = []\n for rle in mask_data[\"rles\"]:\n mask = rle_to_mask(rle)\n\n mask, changed = remove_small_regions(mask, min_area, mode=\"holes\")\n unchanged = not changed\n mask, changed = remove_small_regions(mask, min_area, mode=\"islands\")\n unchanged = unchanged and not changed\n\n new_masks.append(torch.as_tensor(mask).unsqueeze(0))\n # Give score=0 to changed masks and score=1 to unchanged masks\n # so NMS will prefer ones that didn't need postprocessing\n scores.append(float(unchanged))\n\n # Recalculate boxes and remove any new duplicates\n masks = torch.cat(new_masks, dim=0)\n boxes = batched_mask_to_box(masks)\n keep_by_nms = batched_nms(\n boxes.float(),\n torch.as_tensor(scores),\n torch.zeros_like(boxes[:, 0]), # categories\n iou_threshold=nms_thresh,\n )\n\n # Only recalculate RLEs for masks that have changed\n for i_mask in keep_by_nms:\n if scores[i_mask] == 0.0:\n mask_torch = masks[i_mask].unsqueeze(0)\n mask_data[\"rles\"][i_mask] = mask_to_rle_pytorch(mask_torch)[0]\n mask_data[\"boxes\"][i_mask] = boxes[i_mask] # update res directly\n mask_data.filter(keep_by_nms)\n\n return mask_data\n\n def refine_with_m2m(self, points, point_labels, low_res_masks, points_per_batch):\n new_masks = []\n new_iou_preds = []\n\n for cur_points, cur_point_labels, low_res_mask in batch_iterator(\n points_per_batch, points, point_labels, low_res_masks\n ):\n best_masks, best_iou_preds, _ = self.predictor._predict(\n cur_points[:, None, :],\n cur_point_labels[:, None],\n mask_input=low_res_mask[:, None, :],\n multimask_output=False,\n return_logits=True,\n )\n new_masks.append(best_masks)\n new_iou_preds.append(best_iou_preds)\n masks = torch.cat(new_masks, dim=0)\n return masks, torch.cat(new_iou_preds, dim=0)\n" }, { "path": "auto-seg/submodules/segment-anything-2/sam2/build_sam.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport logging\nimport os\n\nimport torch\nfrom hydra import compose\nfrom hydra.utils import instantiate\nfrom omegaconf import OmegaConf\n\nimport sam2\n\n# Check if the user is running Python from the parent directory of the sam2 repo\n# (i.e. the directory where this repo is cloned into) -- this is not supported since\n# it could shadow the sam2 package and cause issues.\nif os.path.isdir(os.path.join(sam2.__path__[0], \"sam2\")):\n # If the user has \"sam2/sam2\" in their path, they are likey importing the repo itself\n # as \"sam2\" rather than importing the \"sam2\" python package (i.e. \"sam2/sam2\" directory).\n # This typically happens because the user is running Python from the parent directory\n # that contains the sam2 repo they cloned.\n raise RuntimeError(\n \"You're likely running Python from the parent directory of the sam2 repository \"\n \"(i.e. the directory where https://github.com/facebookresearch/sam2 is cloned into). \"\n \"This is not supported since the `sam2` Python package could be shadowed by the \"\n \"repository name (the repository is also named `sam2` and contains the Python package \"\n \"in `sam2/sam2`). Please run Python from another directory (e.g. from the repo dir \"\n \"rather than its parent dir, or from your home directory) after installing SAM 2.\"\n )\n\n\nHF_MODEL_ID_TO_FILENAMES = {\n \"facebook/sam2-hiera-tiny\": (\n \"configs/sam2/sam2_hiera_t.yaml\",\n \"sam2_hiera_tiny.pt\",\n ),\n \"facebook/sam2-hiera-small\": (\n \"configs/sam2/sam2_hiera_s.yaml\",\n \"sam2_hiera_small.pt\",\n ),\n \"facebook/sam2-hiera-base-plus\": (\n \"configs/sam2/sam2_hiera_b+.yaml\",\n \"sam2_hiera_base_plus.pt\",\n ),\n \"facebook/sam2-hiera-large\": (\n \"configs/sam2/sam2_hiera_l.yaml\",\n \"sam2_hiera_large.pt\",\n ),\n \"facebook/sam2.1-hiera-tiny\": (\n \"configs/sam2.1/sam2.1_hiera_t.yaml\",\n \"sam2.1_hiera_tiny.pt\",\n ),\n \"facebook/sam2.1-hiera-small\": (\n \"configs/sam2.1/sam2.1_hiera_s.yaml\",\n \"sam2.1_hiera_small.pt\",\n ),\n \"facebook/sam2.1-hiera-base-plus\": (\n \"configs/sam2.1/sam2.1_hiera_b+.yaml\",\n \"sam2.1_hiera_base_plus.pt\",\n ),\n \"facebook/sam2.1-hiera-large\": (\n \"configs/sam2.1/sam2.1_hiera_l.yaml\",\n \"sam2.1_hiera_large.pt\",\n ),\n}\n\n\ndef build_sam2(\n config_file,\n ckpt_path=None,\n device=\"cuda\",\n mode=\"eval\",\n hydra_overrides_extra=[],\n apply_postprocessing=True,\n **kwargs,\n):\n\n if apply_postprocessing:\n hydra_overrides_extra = hydra_overrides_extra.copy()\n hydra_overrides_extra += [\n # dynamically fall back to multi-mask if the single mask is not stable\n \"++model.sam_mask_decoder_extra_args.dynamic_multimask_via_stability=true\",\n \"++model.sam_mask_decoder_extra_args.dynamic_multimask_stability_delta=0.05\",\n \"++model.sam_mask_decoder_extra_args.dynamic_multimask_stability_thresh=0.98\",\n ]\n # Read config and init model\n cfg = compose(config_name=config_file, overrides=hydra_overrides_extra)\n OmegaConf.resolve(cfg)\n model = instantiate(cfg.model, _recursive_=True)\n _load_checkpoint(model, ckpt_path)\n model = model.to(device)\n if mode == \"eval\":\n model.eval()\n return model\n\n\ndef build_sam2_video_predictor(\n config_file,\n ckpt_path=None,\n device=\"cuda\",\n mode=\"eval\",\n hydra_overrides_extra=[],\n apply_postprocessing=True,\n **kwargs,\n):\n hydra_overrides = [\n \"++model._target_=sam2.sam2_video_predictor.SAM2VideoPredictor\",\n ]\n if apply_postprocessing:\n hydra_overrides_extra = hydra_overrides_extra.copy()\n hydra_overrides_extra += [\n # dynamically fall back to multi-mask if the single mask is not stable\n \"++model.sam_mask_decoder_extra_args.dynamic_multimask_via_stability=true\",\n \"++model.sam_mask_decoder_extra_args.dynamic_multimask_stability_delta=0.05\",\n \"++model.sam_mask_decoder_extra_args.dynamic_multimask_stability_thresh=0.98\",\n # the sigmoid mask logits on interacted frames with clicks in the memory encoder so that the encoded masks are exactly as what users see from clicking\n \"++model.binarize_mask_from_pts_for_mem_enc=true\",\n # fill small holes in the low-res masks up to `fill_hole_area` (before resizing them to the original video resolution)\n \"++model.fill_hole_area=8\",\n ]\n hydra_overrides.extend(hydra_overrides_extra)\n\n # Read config and init model\n cfg = compose(config_name=config_file, overrides=hydra_overrides)\n OmegaConf.resolve(cfg)\n model = instantiate(cfg.model, _recursive_=True)\n _load_checkpoint(model, ckpt_path)\n model = model.to(device)\n if mode == \"eval\":\n model.eval()\n return model\n\n\ndef _hf_download(model_id):\n from huggingface_hub import hf_hub_download\n\n config_name, checkpoint_name = HF_MODEL_ID_TO_FILENAMES[model_id]\n ckpt_path = hf_hub_download(repo_id=model_id, filename=checkpoint_name)\n return config_name, ckpt_path\n\n\ndef build_sam2_hf(model_id, **kwargs):\n config_name, ckpt_path = _hf_download(model_id)\n return build_sam2(config_file=config_name, ckpt_path=ckpt_path, **kwargs)\n\n\ndef build_sam2_video_predictor_hf(model_id, **kwargs):\n config_name, ckpt_path = _hf_download(model_id)\n return build_sam2_video_predictor(\n config_file=config_name, ckpt_path=ckpt_path, **kwargs\n )\n\n\ndef _load_checkpoint(model, ckpt_path):\n if ckpt_path is not None:\n sd = torch.load(ckpt_path, map_location=\"cpu\", weights_only=True)[\"model\"]\n missing_keys, unexpected_keys = model.load_state_dict(sd)\n if missing_keys:\n logging.error(missing_keys)\n raise RuntimeError()\n if unexpected_keys:\n logging.error(unexpected_keys)\n raise RuntimeError()\n logging.info(\"Loaded checkpoint sucessfully\")\n" }, { "path": "auto-seg/submodules/segment-anything-2/sam2/configs/sam2/sam2_hiera_b+.yaml", "content": "# @package _global_\n\n# Model\nmodel:\n _target_: sam2.modeling.sam2_base.SAM2Base\n image_encoder:\n _target_: sam2.modeling.backbones.image_encoder.ImageEncoder\n scalp: 1\n trunk:\n _target_: sam2.modeling.backbones.hieradet.Hiera\n embed_dim: 112\n num_heads: 2\n neck:\n _target_: sam2.modeling.backbones.image_encoder.FpnNeck\n position_encoding:\n _target_: sam2.modeling.position_encoding.PositionEmbeddingSine\n num_pos_feats: 256\n normalize: true\n scale: null\n temperature: 10000\n d_model: 256\n backbone_channel_list: [896, 448, 224, 112]\n fpn_top_down_levels: [2, 3] # output level 0 and 1 directly use the backbone features\n fpn_interp_model: nearest\n\n memory_attention:\n _target_: sam2.modeling.memory_attention.MemoryAttention\n d_model: 256\n pos_enc_at_input: true\n layer:\n _target_: sam2.modeling.memory_attention.MemoryAttentionLayer\n activation: relu\n dim_feedforward: 2048\n dropout: 0.1\n pos_enc_at_attn: false\n self_attention:\n _target_: sam2.modeling.sam.transformer.RoPEAttention\n rope_theta: 10000.0\n feat_sizes: [32, 32]\n embedding_dim: 256\n num_heads: 1\n downsample_rate: 1\n dropout: 0.1\n d_model: 256\n pos_enc_at_cross_attn_keys: true\n pos_enc_at_cross_attn_queries: false\n cross_attention:\n _target_: sam2.modeling.sam.transformer.RoPEAttention\n rope_theta: 10000.0\n feat_sizes: [32, 32]\n rope_k_repeat: True\n embedding_dim: 256\n num_heads: 1\n downsample_rate: 1\n dropout: 0.1\n kv_in_dim: 64\n num_layers: 4\n\n memory_encoder:\n _target_: sam2.modeling.memory_encoder.MemoryEncoder\n out_dim: 64\n position_encoding:\n _target_: sam2.modeling.position_encoding.PositionEmbeddingSine\n num_pos_feats: 64\n normalize: true\n scale: null\n temperature: 10000\n mask_downsampler:\n _target_: sam2.modeling.memory_encoder.MaskDownSampler\n kernel_size: 3\n stride: 2\n padding: 1\n fuser:\n _target_: sam2.modeling.memory_encoder.Fuser\n layer:\n _target_: sam2.modeling.memory_encoder.CXBlock\n dim: 256\n kernel_size: 7\n padding: 3\n layer_scale_init_value: 1e-6\n use_dwconv: True # depth-wise convs\n num_layers: 2\n\n num_maskmem: 7\n image_size: 1024\n # apply scaled sigmoid on mask logits for memory encoder, and directly feed input mask as output mask\n sigmoid_scale_for_mem_enc: 20.0\n sigmoid_bias_for_mem_enc: -10.0\n use_mask_input_as_output_without_sam: true\n # Memory\n directly_add_no_mem_embed: true\n # use high-resolution feature map in the SAM mask decoder\n use_high_res_features_in_sam: true\n # output 3 masks on the first click on initial conditioning frames\n multimask_output_in_sam: true\n # SAM heads\n iou_prediction_use_sigmoid: True\n # cross-attend to object pointers from other frames (based on SAM output tokens) in the encoder\n use_obj_ptrs_in_encoder: true\n add_tpos_enc_to_obj_ptrs: false\n only_obj_ptrs_in_the_past_for_eval: true\n # object occlusion prediction\n pred_obj_scores: true\n pred_obj_scores_mlp: true\n fixed_no_obj_ptr: true\n # multimask tracking settings\n multimask_output_for_tracking: true\n use_multimask_token_for_obj_ptr: true\n multimask_min_pt_num: 0\n multimask_max_pt_num: 1\n use_mlp_for_obj_ptr_proj: true\n # Compilation flag\n compile_image_encoder: False\n" }, { "path": "auto-seg/submodules/segment-anything-2/sam2/configs/sam2/sam2_hiera_l.yaml", "content": "# @package _global_\n\n# Model\nmodel:\n _target_: sam2.modeling.sam2_base.SAM2Base\n image_encoder:\n _target_: sam2.modeling.backbones.image_encoder.ImageEncoder\n scalp: 1\n trunk:\n _target_: sam2.modeling.backbones.hieradet.Hiera\n embed_dim: 144\n num_heads: 2\n stages: [2, 6, 36, 4]\n global_att_blocks: [23, 33, 43]\n window_pos_embed_bkg_spatial_size: [7, 7]\n window_spec: [8, 4, 16, 8]\n neck:\n _target_: sam2.modeling.backbones.image_encoder.FpnNeck\n position_encoding:\n _target_: sam2.modeling.position_encoding.PositionEmbeddingSine\n num_pos_feats: 256\n normalize: true\n scale: null\n temperature: 10000\n d_model: 256\n backbone_channel_list: [1152, 576, 288, 144]\n fpn_top_down_levels: [2, 3] # output level 0 and 1 directly use the backbone features\n fpn_interp_model: nearest\n\n memory_attention:\n _target_: sam2.modeling.memory_attention.MemoryAttention\n d_model: 256\n pos_enc_at_input: true\n layer:\n _target_: sam2.modeling.memory_attention.MemoryAttentionLayer\n activation: relu\n dim_feedforward: 2048\n dropout: 0.1\n pos_enc_at_attn: false\n self_attention:\n _target_: sam2.modeling.sam.transformer.RoPEAttention\n rope_theta: 10000.0\n feat_sizes: [32, 32]\n embedding_dim: 256\n num_heads: 1\n downsample_rate: 1\n dropout: 0.1\n d_model: 256\n pos_enc_at_cross_attn_keys: true\n pos_enc_at_cross_attn_queries: false\n cross_attention:\n _target_: sam2.modeling.sam.transformer.RoPEAttention\n rope_theta: 10000.0\n feat_sizes: [32, 32]\n rope_k_repeat: True\n embedding_dim: 256\n num_heads: 1\n downsample_rate: 1\n dropout: 0.1\n kv_in_dim: 64\n num_layers: 4\n\n memory_encoder:\n _target_: sam2.modeling.memory_encoder.MemoryEncoder\n out_dim: 64\n position_encoding:\n _target_: sam2.modeling.position_encoding.PositionEmbeddingSine\n num_pos_feats: 64\n normalize: true\n scale: null\n temperature: 10000\n mask_downsampler:\n _target_: sam2.modeling.memory_encoder.MaskDownSampler\n kernel_size: 3\n stride: 2\n padding: 1\n fuser:\n _target_: sam2.modeling.memory_encoder.Fuser\n layer:\n _target_: sam2.modeling.memory_encoder.CXBlock\n dim: 256\n kernel_size: 7\n padding: 3\n layer_scale_init_value: 1e-6\n use_dwconv: True # depth-wise convs\n num_layers: 2\n\n num_maskmem: 7\n image_size: 1024\n # apply scaled sigmoid on mask logits for memory encoder, and directly feed input mask as output mask\n sigmoid_scale_for_mem_enc: 20.0\n sigmoid_bias_for_mem_enc: -10.0\n use_mask_input_as_output_without_sam: true\n # Memory\n directly_add_no_mem_embed: true\n # use high-resolution feature map in the SAM mask decoder\n use_high_res_features_in_sam: true\n # output 3 masks on the first click on initial conditioning frames\n multimask_output_in_sam: true\n # SAM heads\n iou_prediction_use_sigmoid: True\n # cross-attend to object pointers from other frames (based on SAM output tokens) in the encoder\n use_obj_ptrs_in_encoder: true\n add_tpos_enc_to_obj_ptrs: false\n only_obj_ptrs_in_the_past_for_eval: true\n # object occlusion prediction\n pred_obj_scores: true\n pred_obj_scores_mlp: true\n fixed_no_obj_ptr: true\n # multimask tracking settings\n multimask_output_for_tracking: true\n use_multimask_token_for_obj_ptr: true\n multimask_min_pt_num: 0\n multimask_max_pt_num: 1\n use_mlp_for_obj_ptr_proj: true\n # Compilation flag\n compile_image_encoder: False\n" }, { "path": "auto-seg/submodules/segment-anything-2/sam2/configs/sam2/sam2_hiera_s.yaml", "content": "# @package _global_\n\n# Model\nmodel:\n _target_: sam2.modeling.sam2_base.SAM2Base\n image_encoder:\n _target_: sam2.modeling.backbones.image_encoder.ImageEncoder\n scalp: 1\n trunk:\n _target_: sam2.modeling.backbones.hieradet.Hiera\n embed_dim: 96\n num_heads: 1\n stages: [1, 2, 11, 2]\n global_att_blocks: [7, 10, 13]\n window_pos_embed_bkg_spatial_size: [7, 7]\n neck:\n _target_: sam2.modeling.backbones.image_encoder.FpnNeck\n position_encoding:\n _target_: sam2.modeling.position_encoding.PositionEmbeddingSine\n num_pos_feats: 256\n normalize: true\n scale: null\n temperature: 10000\n d_model: 256\n backbone_channel_list: [768, 384, 192, 96]\n fpn_top_down_levels: [2, 3] # output level 0 and 1 directly use the backbone features\n fpn_interp_model: nearest\n\n memory_attention:\n _target_: sam2.modeling.memory_attention.MemoryAttention\n d_model: 256\n pos_enc_at_input: true\n layer:\n _target_: sam2.modeling.memory_attention.MemoryAttentionLayer\n activation: relu\n dim_feedforward: 2048\n dropout: 0.1\n pos_enc_at_attn: false\n self_attention:\n _target_: sam2.modeling.sam.transformer.RoPEAttention\n rope_theta: 10000.0\n feat_sizes: [32, 32]\n embedding_dim: 256\n num_heads: 1\n downsample_rate: 1\n dropout: 0.1\n d_model: 256\n pos_enc_at_cross_attn_keys: true\n pos_enc_at_cross_attn_queries: false\n cross_attention:\n _target_: sam2.modeling.sam.transformer.RoPEAttention\n rope_theta: 10000.0\n feat_sizes: [32, 32]\n rope_k_repeat: True\n embedding_dim: 256\n num_heads: 1\n downsample_rate: 1\n dropout: 0.1\n kv_in_dim: 64\n num_layers: 4\n\n memory_encoder:\n _target_: sam2.modeling.memory_encoder.MemoryEncoder\n out_dim: 64\n position_encoding:\n _target_: sam2.modeling.position_encoding.PositionEmbeddingSine\n num_pos_feats: 64\n normalize: true\n scale: null\n temperature: 10000\n mask_downsampler:\n _target_: sam2.modeling.memory_encoder.MaskDownSampler\n kernel_size: 3\n stride: 2\n padding: 1\n fuser:\n _target_: sam2.modeling.memory_encoder.Fuser\n layer:\n _target_: sam2.modeling.memory_encoder.CXBlock\n dim: 256\n kernel_size: 7\n padding: 3\n layer_scale_init_value: 1e-6\n use_dwconv: True # depth-wise convs\n num_layers: 2\n\n num_maskmem: 7\n image_size: 1024\n # apply scaled sigmoid on mask logits for memory encoder, and directly feed input mask as output mask\n sigmoid_scale_for_mem_enc: 20.0\n sigmoid_bias_for_mem_enc: -10.0\n use_mask_input_as_output_without_sam: true\n # Memory\n directly_add_no_mem_embed: true\n # use high-resolution feature map in the SAM mask decoder\n use_high_res_features_in_sam: true\n # output 3 masks on the first click on initial conditioning frames\n multimask_output_in_sam: true\n # SAM heads\n iou_prediction_use_sigmoid: True\n # cross-attend to object pointers from other frames (based on SAM output tokens) in the encoder\n use_obj_ptrs_in_encoder: true\n add_tpos_enc_to_obj_ptrs: false\n only_obj_ptrs_in_the_past_for_eval: true\n # object occlusion prediction\n pred_obj_scores: true\n pred_obj_scores_mlp: true\n fixed_no_obj_ptr: true\n # multimask tracking settings\n multimask_output_for_tracking: true\n use_multimask_token_for_obj_ptr: true\n multimask_min_pt_num: 0\n multimask_max_pt_num: 1\n use_mlp_for_obj_ptr_proj: true\n # Compilation flag\n compile_image_encoder: False\n" }, { "path": "auto-seg/submodules/segment-anything-2/sam2/configs/sam2/sam2_hiera_t.yaml", "content": "# @package _global_\n\n# Model\nmodel:\n _target_: sam2.modeling.sam2_base.SAM2Base\n image_encoder:\n _target_: sam2.modeling.backbones.image_encoder.ImageEncoder\n scalp: 1\n trunk:\n _target_: sam2.modeling.backbones.hieradet.Hiera\n embed_dim: 96\n num_heads: 1\n stages: [1, 2, 7, 2]\n global_att_blocks: [5, 7, 9]\n window_pos_embed_bkg_spatial_size: [7, 7]\n neck:\n _target_: sam2.modeling.backbones.image_encoder.FpnNeck\n position_encoding:\n _target_: sam2.modeling.position_encoding.PositionEmbeddingSine\n num_pos_feats: 256\n normalize: true\n scale: null\n temperature: 10000\n d_model: 256\n backbone_channel_list: [768, 384, 192, 96]\n fpn_top_down_levels: [2, 3] # output level 0 and 1 directly use the backbone features\n fpn_interp_model: nearest\n\n memory_attention:\n _target_: sam2.modeling.memory_attention.MemoryAttention\n d_model: 256\n pos_enc_at_input: true\n layer:\n _target_: sam2.modeling.memory_attention.MemoryAttentionLayer\n activation: relu\n dim_feedforward: 2048\n dropout: 0.1\n pos_enc_at_attn: false\n self_attention:\n _target_: sam2.modeling.sam.transformer.RoPEAttention\n rope_theta: 10000.0\n feat_sizes: [32, 32]\n embedding_dim: 256\n num_heads: 1\n downsample_rate: 1\n dropout: 0.1\n d_model: 256\n pos_enc_at_cross_attn_keys: true\n pos_enc_at_cross_attn_queries: false\n cross_attention:\n _target_: sam2.modeling.sam.transformer.RoPEAttention\n rope_theta: 10000.0\n feat_sizes: [32, 32]\n rope_k_repeat: True\n embedding_dim: 256\n num_heads: 1\n downsample_rate: 1\n dropout: 0.1\n kv_in_dim: 64\n num_layers: 4\n\n memory_encoder:\n _target_: sam2.modeling.memory_encoder.MemoryEncoder\n out_dim: 64\n position_encoding:\n _target_: sam2.modeling.position_encoding.PositionEmbeddingSine\n num_pos_feats: 64\n normalize: true\n scale: null\n temperature: 10000\n mask_downsampler:\n _target_: sam2.modeling.memory_encoder.MaskDownSampler\n kernel_size: 3\n stride: 2\n padding: 1\n fuser:\n _target_: sam2.modeling.memory_encoder.Fuser\n layer:\n _target_: sam2.modeling.memory_encoder.CXBlock\n dim: 256\n kernel_size: 7\n padding: 3\n layer_scale_init_value: 1e-6\n use_dwconv: True # depth-wise convs\n num_layers: 2\n\n num_maskmem: 7\n image_size: 1024\n # apply scaled sigmoid on mask logits for memory encoder, and directly feed input mask as output mask\n # SAM decoder\n sigmoid_scale_for_mem_enc: 20.0\n sigmoid_bias_for_mem_enc: -10.0\n use_mask_input_as_output_without_sam: true\n # Memory\n directly_add_no_mem_embed: true\n # use high-resolution feature map in the SAM mask decoder\n use_high_res_features_in_sam: true\n # output 3 masks on the first click on initial conditioning frames\n multimask_output_in_sam: true\n # SAM heads\n iou_prediction_use_sigmoid: True\n # cross-attend to object pointers from other frames (based on SAM output tokens) in the encoder\n use_obj_ptrs_in_encoder: true\n add_tpos_enc_to_obj_ptrs: false\n only_obj_ptrs_in_the_past_for_eval: true\n # object occlusion prediction\n pred_obj_scores: true\n pred_obj_scores_mlp: true\n fixed_no_obj_ptr: true\n # multimask tracking settings\n multimask_output_for_tracking: true\n use_multimask_token_for_obj_ptr: true\n multimask_min_pt_num: 0\n multimask_max_pt_num: 1\n use_mlp_for_obj_ptr_proj: true\n # Compilation flag\n # HieraT does not currently support compilation, should always be set to False\n compile_image_encoder: False\n" }, { "path": "auto-seg/submodules/segment-anything-2/sam2/configs/sam2.1/sam2.1_hiera_b+.yaml", "content": "# @package _global_\n\n# Model\nmodel:\n _target_: sam2.modeling.sam2_base.SAM2Base\n image_encoder:\n _target_: sam2.modeling.backbones.image_encoder.ImageEncoder\n scalp: 1\n trunk:\n _target_: sam2.modeling.backbones.hieradet.Hiera\n embed_dim: 112\n num_heads: 2\n neck:\n _target_: sam2.modeling.backbones.image_encoder.FpnNeck\n position_encoding:\n _target_: sam2.modeling.position_encoding.PositionEmbeddingSine\n num_pos_feats: 256\n normalize: true\n scale: null\n temperature: 10000\n d_model: 256\n backbone_channel_list: [896, 448, 224, 112]\n fpn_top_down_levels: [2, 3] # output level 0 and 1 directly use the backbone features\n fpn_interp_model: nearest\n\n memory_attention:\n _target_: sam2.modeling.memory_attention.MemoryAttention\n d_model: 256\n pos_enc_at_input: true\n layer:\n _target_: sam2.modeling.memory_attention.MemoryAttentionLayer\n activation: relu\n dim_feedforward: 2048\n dropout: 0.1\n pos_enc_at_attn: false\n self_attention:\n _target_: sam2.modeling.sam.transformer.RoPEAttention\n rope_theta: 10000.0\n feat_sizes: [32, 32]\n embedding_dim: 256\n num_heads: 1\n downsample_rate: 1\n dropout: 0.1\n d_model: 256\n pos_enc_at_cross_attn_keys: true\n pos_enc_at_cross_attn_queries: false\n cross_attention:\n _target_: sam2.modeling.sam.transformer.RoPEAttention\n rope_theta: 10000.0\n feat_sizes: [32, 32]\n rope_k_repeat: True\n embedding_dim: 256\n num_heads: 1\n downsample_rate: 1\n dropout: 0.1\n kv_in_dim: 64\n num_layers: 4\n\n memory_encoder:\n _target_: sam2.modeling.memory_encoder.MemoryEncoder\n out_dim: 64\n position_encoding:\n _target_: sam2.modeling.position_encoding.PositionEmbeddingSine\n num_pos_feats: 64\n normalize: true\n scale: null\n temperature: 10000\n mask_downsampler:\n _target_: sam2.modeling.memory_encoder.MaskDownSampler\n kernel_size: 3\n stride: 2\n padding: 1\n fuser:\n _target_: sam2.modeling.memory_encoder.Fuser\n layer:\n _target_: sam2.modeling.memory_encoder.CXBlock\n dim: 256\n kernel_size: 7\n padding: 3\n layer_scale_init_value: 1e-6\n use_dwconv: True # depth-wise convs\n num_layers: 2\n\n num_maskmem: 7\n image_size: 1024\n # apply scaled sigmoid on mask logits for memory encoder, and directly feed input mask as output mask\n sigmoid_scale_for_mem_enc: 20.0\n sigmoid_bias_for_mem_enc: -10.0\n use_mask_input_as_output_without_sam: true\n # Memory\n directly_add_no_mem_embed: true\n no_obj_embed_spatial: true\n # use high-resolution feature map in the SAM mask decoder\n use_high_res_features_in_sam: true\n # output 3 masks on the first click on initial conditioning frames\n multimask_output_in_sam: true\n # SAM heads\n iou_prediction_use_sigmoid: True\n # cross-attend to object pointers from other frames (based on SAM output tokens) in the encoder\n use_obj_ptrs_in_encoder: true\n add_tpos_enc_to_obj_ptrs: true\n proj_tpos_enc_in_obj_ptrs: true\n use_signed_tpos_enc_to_obj_ptrs: true\n only_obj_ptrs_in_the_past_for_eval: true\n # object occlusion prediction\n pred_obj_scores: true\n pred_obj_scores_mlp: true\n fixed_no_obj_ptr: true\n # multimask tracking settings\n multimask_output_for_tracking: true\n use_multimask_token_for_obj_ptr: true\n multimask_min_pt_num: 0\n multimask_max_pt_num: 1\n use_mlp_for_obj_ptr_proj: true\n # Compilation flag\n compile_image_encoder: False\n" }, { "path": "auto-seg/submodules/segment-anything-2/sam2/configs/sam2.1/sam2.1_hiera_l.yaml", "content": "# @package _global_\n\n# Model\nmodel:\n _target_: sam2.modeling.sam2_base.SAM2Base\n image_encoder:\n _target_: sam2.modeling.backbones.image_encoder.ImageEncoder\n scalp: 1\n trunk:\n _target_: sam2.modeling.backbones.hieradet.Hiera\n embed_dim: 144\n num_heads: 2\n stages: [2, 6, 36, 4]\n global_att_blocks: [23, 33, 43]\n window_pos_embed_bkg_spatial_size: [7, 7]\n window_spec: [8, 4, 16, 8]\n neck:\n _target_: sam2.modeling.backbones.image_encoder.FpnNeck\n position_encoding:\n _target_: sam2.modeling.position_encoding.PositionEmbeddingSine\n num_pos_feats: 256\n normalize: true\n scale: null\n temperature: 10000\n d_model: 256\n backbone_channel_list: [1152, 576, 288, 144]\n fpn_top_down_levels: [2, 3] # output level 0 and 1 directly use the backbone features\n fpn_interp_model: nearest\n\n memory_attention:\n _target_: sam2.modeling.memory_attention.MemoryAttention\n d_model: 256\n pos_enc_at_input: true\n layer:\n _target_: sam2.modeling.memory_attention.MemoryAttentionLayer\n activation: relu\n dim_feedforward: 2048\n dropout: 0.1\n pos_enc_at_attn: false\n self_attention:\n _target_: sam2.modeling.sam.transformer.RoPEAttention\n rope_theta: 10000.0\n feat_sizes: [32, 32]\n embedding_dim: 256\n num_heads: 1\n downsample_rate: 1\n dropout: 0.1\n d_model: 256\n pos_enc_at_cross_attn_keys: true\n pos_enc_at_cross_attn_queries: false\n cross_attention:\n _target_: sam2.modeling.sam.transformer.RoPEAttention\n rope_theta: 10000.0\n feat_sizes: [32, 32]\n rope_k_repeat: True\n embedding_dim: 256\n num_heads: 1\n downsample_rate: 1\n dropout: 0.1\n kv_in_dim: 64\n num_layers: 4\n\n memory_encoder:\n _target_: sam2.modeling.memory_encoder.MemoryEncoder\n out_dim: 64\n position_encoding:\n _target_: sam2.modeling.position_encoding.PositionEmbeddingSine\n num_pos_feats: 64\n normalize: true\n scale: null\n temperature: 10000\n mask_downsampler:\n _target_: sam2.modeling.memory_encoder.MaskDownSampler\n kernel_size: 3\n stride: 2\n padding: 1\n fuser:\n _target_: sam2.modeling.memory_encoder.Fuser\n layer:\n _target_: sam2.modeling.memory_encoder.CXBlock\n dim: 256\n kernel_size: 7\n padding: 3\n layer_scale_init_value: 1e-6\n use_dwconv: True # depth-wise convs\n num_layers: 2\n\n num_maskmem: 7\n image_size: 1024\n # apply scaled sigmoid on mask logits for memory encoder, and directly feed input mask as output mask\n sigmoid_scale_for_mem_enc: 20.0\n sigmoid_bias_for_mem_enc: -10.0\n use_mask_input_as_output_without_sam: true\n # Memory\n directly_add_no_mem_embed: true\n no_obj_embed_spatial: true\n # use high-resolution feature map in the SAM mask decoder\n use_high_res_features_in_sam: true\n # output 3 masks on the first click on initial conditioning frames\n multimask_output_in_sam: true\n # SAM heads\n iou_prediction_use_sigmoid: True\n # cross-attend to object pointers from other frames (based on SAM output tokens) in the encoder\n use_obj_ptrs_in_encoder: true\n add_tpos_enc_to_obj_ptrs: true\n proj_tpos_enc_in_obj_ptrs: true\n use_signed_tpos_enc_to_obj_ptrs: true\n only_obj_ptrs_in_the_past_for_eval: true\n # object occlusion prediction\n pred_obj_scores: true\n pred_obj_scores_mlp: true\n fixed_no_obj_ptr: true\n # multimask tracking settings\n multimask_output_for_tracking: true\n use_multimask_token_for_obj_ptr: true\n multimask_min_pt_num: 0\n multimask_max_pt_num: 1\n use_mlp_for_obj_ptr_proj: true\n # Compilation flag\n compile_image_encoder: False\n" }, { "path": "auto-seg/submodules/segment-anything-2/sam2/configs/sam2.1/sam2.1_hiera_s.yaml", "content": "# @package _global_\n\n# Model\nmodel:\n _target_: sam2.modeling.sam2_base.SAM2Base\n image_encoder:\n _target_: sam2.modeling.backbones.image_encoder.ImageEncoder\n scalp: 1\n trunk:\n _target_: sam2.modeling.backbones.hieradet.Hiera\n embed_dim: 96\n num_heads: 1\n stages: [1, 2, 11, 2]\n global_att_blocks: [7, 10, 13]\n window_pos_embed_bkg_spatial_size: [7, 7]\n neck:\n _target_: sam2.modeling.backbones.image_encoder.FpnNeck\n position_encoding:\n _target_: sam2.modeling.position_encoding.PositionEmbeddingSine\n num_pos_feats: 256\n normalize: true\n scale: null\n temperature: 10000\n d_model: 256\n backbone_channel_list: [768, 384, 192, 96]\n fpn_top_down_levels: [2, 3] # output level 0 and 1 directly use the backbone features\n fpn_interp_model: nearest\n\n memory_attention:\n _target_: sam2.modeling.memory_attention.MemoryAttention\n d_model: 256\n pos_enc_at_input: true\n layer:\n _target_: sam2.modeling.memory_attention.MemoryAttentionLayer\n activation: relu\n dim_feedforward: 2048\n dropout: 0.1\n pos_enc_at_attn: false\n self_attention:\n _target_: sam2.modeling.sam.transformer.RoPEAttention\n rope_theta: 10000.0\n feat_sizes: [32, 32]\n embedding_dim: 256\n num_heads: 1\n downsample_rate: 1\n dropout: 0.1\n d_model: 256\n pos_enc_at_cross_attn_keys: true\n pos_enc_at_cross_attn_queries: false\n cross_attention:\n _target_: sam2.modeling.sam.transformer.RoPEAttention\n rope_theta: 10000.0\n feat_sizes: [32, 32]\n rope_k_repeat: True\n embedding_dim: 256\n num_heads: 1\n downsample_rate: 1\n dropout: 0.1\n kv_in_dim: 64\n num_layers: 4\n\n memory_encoder:\n _target_: sam2.modeling.memory_encoder.MemoryEncoder\n out_dim: 64\n position_encoding:\n _target_: sam2.modeling.position_encoding.PositionEmbeddingSine\n num_pos_feats: 64\n normalize: true\n scale: null\n temperature: 10000\n mask_downsampler:\n _target_: sam2.modeling.memory_encoder.MaskDownSampler\n kernel_size: 3\n stride: 2\n padding: 1\n fuser:\n _target_: sam2.modeling.memory_encoder.Fuser\n layer:\n _target_: sam2.modeling.memory_encoder.CXBlock\n dim: 256\n kernel_size: 7\n padding: 3\n layer_scale_init_value: 1e-6\n use_dwconv: True # depth-wise convs\n num_layers: 2\n\n num_maskmem: 7\n image_size: 1024\n # apply scaled sigmoid on mask logits for memory encoder, and directly feed input mask as output mask\n sigmoid_scale_for_mem_enc: 20.0\n sigmoid_bias_for_mem_enc: -10.0\n use_mask_input_as_output_without_sam: true\n # Memory\n directly_add_no_mem_embed: true\n no_obj_embed_spatial: true\n # use high-resolution feature map in the SAM mask decoder\n use_high_res_features_in_sam: true\n # output 3 masks on the first click on initial conditioning frames\n multimask_output_in_sam: true\n # SAM heads\n iou_prediction_use_sigmoid: True\n # cross-attend to object pointers from other frames (based on SAM output tokens) in the encoder\n use_obj_ptrs_in_encoder: true\n add_tpos_enc_to_obj_ptrs: true\n proj_tpos_enc_in_obj_ptrs: true\n use_signed_tpos_enc_to_obj_ptrs: true\n only_obj_ptrs_in_the_past_for_eval: true\n # object occlusion prediction\n pred_obj_scores: true\n pred_obj_scores_mlp: true\n fixed_no_obj_ptr: true\n # multimask tracking settings\n multimask_output_for_tracking: true\n use_multimask_token_for_obj_ptr: true\n multimask_min_pt_num: 0\n multimask_max_pt_num: 1\n use_mlp_for_obj_ptr_proj: true\n # Compilation flag\n compile_image_encoder: False\n" }, { "path": "auto-seg/submodules/segment-anything-2/sam2/configs/sam2.1/sam2.1_hiera_t.yaml", "content": "# @package _global_\n\n# Model\nmodel:\n _target_: sam2.modeling.sam2_base.SAM2Base\n image_encoder:\n _target_: sam2.modeling.backbones.image_encoder.ImageEncoder\n scalp: 1\n trunk:\n _target_: sam2.modeling.backbones.hieradet.Hiera\n embed_dim: 96\n num_heads: 1\n stages: [1, 2, 7, 2]\n global_att_blocks: [5, 7, 9]\n window_pos_embed_bkg_spatial_size: [7, 7]\n neck:\n _target_: sam2.modeling.backbones.image_encoder.FpnNeck\n position_encoding:\n _target_: sam2.modeling.position_encoding.PositionEmbeddingSine\n num_pos_feats: 256\n normalize: true\n scale: null\n temperature: 10000\n d_model: 256\n backbone_channel_list: [768, 384, 192, 96]\n fpn_top_down_levels: [2, 3] # output level 0 and 1 directly use the backbone features\n fpn_interp_model: nearest\n\n memory_attention:\n _target_: sam2.modeling.memory_attention.MemoryAttention\n d_model: 256\n pos_enc_at_input: true\n layer:\n _target_: sam2.modeling.memory_attention.MemoryAttentionLayer\n activation: relu\n dim_feedforward: 2048\n dropout: 0.1\n pos_enc_at_attn: false\n self_attention:\n _target_: sam2.modeling.sam.transformer.RoPEAttention\n rope_theta: 10000.0\n feat_sizes: [32, 32]\n embedding_dim: 256\n num_heads: 1\n downsample_rate: 1\n dropout: 0.1\n d_model: 256\n pos_enc_at_cross_attn_keys: true\n pos_enc_at_cross_attn_queries: false\n cross_attention:\n _target_: sam2.modeling.sam.transformer.RoPEAttention\n rope_theta: 10000.0\n feat_sizes: [32, 32]\n rope_k_repeat: True\n embedding_dim: 256\n num_heads: 1\n downsample_rate: 1\n dropout: 0.1\n kv_in_dim: 64\n num_layers: 4\n\n memory_encoder:\n _target_: sam2.modeling.memory_encoder.MemoryEncoder\n out_dim: 64\n position_encoding:\n _target_: sam2.modeling.position_encoding.PositionEmbeddingSine\n num_pos_feats: 64\n normalize: true\n scale: null\n temperature: 10000\n mask_downsampler:\n _target_: sam2.modeling.memory_encoder.MaskDownSampler\n kernel_size: 3\n stride: 2\n padding: 1\n fuser:\n _target_: sam2.modeling.memory_encoder.Fuser\n layer:\n _target_: sam2.modeling.memory_encoder.CXBlock\n dim: 256\n kernel_size: 7\n padding: 3\n layer_scale_init_value: 1e-6\n use_dwconv: True # depth-wise convs\n num_layers: 2\n\n num_maskmem: 7\n image_size: 1024\n # apply scaled sigmoid on mask logits for memory encoder, and directly feed input mask as output mask\n # SAM decoder\n sigmoid_scale_for_mem_enc: 20.0\n sigmoid_bias_for_mem_enc: -10.0\n use_mask_input_as_output_without_sam: true\n # Memory\n directly_add_no_mem_embed: true\n no_obj_embed_spatial: true\n # use high-resolution feature map in the SAM mask decoder\n use_high_res_features_in_sam: true\n # output 3 masks on the first click on initial conditioning frames\n multimask_output_in_sam: true\n # SAM heads\n iou_prediction_use_sigmoid: True\n # cross-attend to object pointers from other frames (based on SAM output tokens) in the encoder\n use_obj_ptrs_in_encoder: true\n add_tpos_enc_to_obj_ptrs: true\n proj_tpos_enc_in_obj_ptrs: true\n use_signed_tpos_enc_to_obj_ptrs: true\n only_obj_ptrs_in_the_past_for_eval: true\n # object occlusion prediction\n pred_obj_scores: true\n pred_obj_scores_mlp: true\n fixed_no_obj_ptr: true\n # multimask tracking settings\n multimask_output_for_tracking: true\n use_multimask_token_for_obj_ptr: true\n multimask_min_pt_num: 0\n multimask_max_pt_num: 1\n use_mlp_for_obj_ptr_proj: true\n # Compilation flag\n # HieraT does not currently support compilation, should always be set to False\n compile_image_encoder: False\n" }, { "path": "auto-seg/submodules/segment-anything-2/sam2/configs/sam2.1_training/sam2.1_hiera_b+_MOSE_finetune.yaml", "content": "# @package _global_\n\nscratch:\n resolution: 1024\n train_batch_size: 1\n num_train_workers: 10\n num_frames: 8\n max_num_objects: 3\n base_lr: 5.0e-6\n vision_lr: 3.0e-06\n phases_per_epoch: 1\n num_epochs: 40\n\ndataset:\n # PATHS to Dataset\n img_folder: /fsx-onevision/shared/data/academic_vos_data/MOSE/train/JPEGImages # PATH to MOSE JPEGImages folder\n gt_folder: /fsx-onevision/shared/data/academic_vos_data/MOSE/train/Annotations/ # PATH to MOSE Annotations folder\n file_list_txt: training/assets/MOSE_sample_train_list.txt # Optional PATH to filelist containing a subset of videos to be used for training\n multiplier: 2\n\n# Video transforms\nvos:\n train_transforms:\n - _target_: training.dataset.transforms.ComposeAPI\n transforms:\n - _target_: training.dataset.transforms.RandomHorizontalFlip\n consistent_transform: True\n - _target_: training.dataset.transforms.RandomAffine\n degrees: 25\n shear: 20\n image_interpolation: bilinear\n consistent_transform: True\n - _target_: training.dataset.transforms.RandomResizeAPI\n sizes: ${scratch.resolution}\n square: true\n consistent_transform: True\n - _target_: training.dataset.transforms.ColorJitter\n consistent_transform: True\n brightness: 0.1\n contrast: 0.03\n saturation: 0.03\n hue: null\n - _target_: training.dataset.transforms.RandomGrayscale\n p: 0.05\n consistent_transform: True\n - _target_: training.dataset.transforms.ColorJitter\n consistent_transform: False\n brightness: 0.1\n contrast: 0.05\n saturation: 0.05\n hue: null\n - _target_: training.dataset.transforms.ToTensorAPI\n - _target_: training.dataset.transforms.NormalizeAPI\n mean: [0.485, 0.456, 0.406]\n std: [0.229, 0.224, 0.225]\n\ntrainer:\n _target_: training.trainer.Trainer\n mode: train_only\n max_epochs: ${times:${scratch.num_epochs},${scratch.phases_per_epoch}}\n accelerator: cuda\n seed_value: 123\n\n model:\n _target_: training.model.sam2.SAM2Train\n image_encoder:\n _target_: sam2.modeling.backbones.image_encoder.ImageEncoder\n scalp: 1\n trunk:\n _target_: sam2.modeling.backbones.hieradet.Hiera\n embed_dim: 112\n num_heads: 2\n drop_path_rate: 0.1\n neck:\n _target_: sam2.modeling.backbones.image_encoder.FpnNeck\n position_encoding:\n _target_: sam2.modeling.position_encoding.PositionEmbeddingSine\n num_pos_feats: 256\n normalize: true\n scale: null\n temperature: 10000\n d_model: 256\n backbone_channel_list: [896, 448, 224, 112]\n fpn_top_down_levels: [2, 3] # output level 0 and 1 directly use the backbone features\n fpn_interp_model: nearest\n\n memory_attention:\n _target_: sam2.modeling.memory_attention.MemoryAttention\n d_model: 256\n pos_enc_at_input: true\n layer:\n _target_: sam2.modeling.memory_attention.MemoryAttentionLayer\n activation: relu\n dim_feedforward: 2048\n dropout: 0.1\n pos_enc_at_attn: false\n self_attention:\n _target_: sam2.modeling.sam.transformer.RoPEAttention\n rope_theta: 10000.0\n feat_sizes: [32, 32]\n embedding_dim: 256\n num_heads: 1\n downsample_rate: 1\n dropout: 0.1\n d_model: 256\n pos_enc_at_cross_attn_keys: true\n pos_enc_at_cross_attn_queries: false\n cross_attention:\n _target_: sam2.modeling.sam.transformer.RoPEAttention\n rope_theta: 10000.0\n feat_sizes: [32, 32]\n rope_k_repeat: True\n embedding_dim: 256\n num_heads: 1\n downsample_rate: 1\n dropout: 0.1\n kv_in_dim: 64\n num_layers: 4\n\n memory_encoder:\n _target_: sam2.modeling.memory_encoder.MemoryEncoder\n out_dim: 64\n position_encoding:\n _target_: sam2.modeling.position_encoding.PositionEmbeddingSine\n num_pos_feats: 64\n normalize: true\n scale: null\n temperature: 10000\n mask_downsampler:\n _target_: sam2.modeling.memory_encoder.MaskDownSampler\n kernel_size: 3\n stride: 2\n padding: 1\n fuser:\n _target_: sam2.modeling.memory_encoder.Fuser\n layer:\n _target_: sam2.modeling.memory_encoder.CXBlock\n dim: 256\n kernel_size: 7\n padding: 3\n layer_scale_init_value: 1e-6\n use_dwconv: True # depth-wise convs\n num_layers: 2\n\n num_maskmem: 7\n image_size: ${scratch.resolution}\n # apply scaled sigmoid on mask logits for memory encoder, and directly feed input mask as output mask\n sigmoid_scale_for_mem_enc: 20.0\n sigmoid_bias_for_mem_enc: -10.0\n use_mask_input_as_output_without_sam: true\n # Memory\n directly_add_no_mem_embed: true\n no_obj_embed_spatial: true\n # use high-resolution feature map in the SAM mask decoder\n use_high_res_features_in_sam: true\n # output 3 masks on the first click on initial conditioning frames\n multimask_output_in_sam: true\n # SAM heads\n iou_prediction_use_sigmoid: True\n # cross-attend to object pointers from other frames (based on SAM output tokens) in the encoder\n use_obj_ptrs_in_encoder: true\n add_tpos_enc_to_obj_ptrs: true\n proj_tpos_enc_in_obj_ptrs: true\n use_signed_tpos_enc_to_obj_ptrs: true\n only_obj_ptrs_in_the_past_for_eval: true\n # object occlusion prediction\n pred_obj_scores: true\n pred_obj_scores_mlp: true\n fixed_no_obj_ptr: true\n # multimask tracking settings\n multimask_output_for_tracking: true\n use_multimask_token_for_obj_ptr: true\n multimask_min_pt_num: 0\n multimask_max_pt_num: 1\n use_mlp_for_obj_ptr_proj: true\n # Compilation flag\n # compile_image_encoder: False\n\n ####### Training specific params #######\n # box/point input and corrections\n prob_to_use_pt_input_for_train: 0.5\n prob_to_use_pt_input_for_eval: 0.0\n prob_to_use_box_input_for_train: 0.5 # 0.5*0.5 = 0.25 prob to use box instead of points\n prob_to_use_box_input_for_eval: 0.0\n prob_to_sample_from_gt_for_train: 0.1 # with a small prob, sampling correction points from GT mask instead of prediction errors\n num_frames_to_correct_for_train: 2 # iteratively sample on random 1~2 frames (always include the first frame)\n num_frames_to_correct_for_eval: 1 # only iteratively sample on first frame\n rand_frames_to_correct_for_train: True # random #init-cond-frame ~ 2\n add_all_frames_to_correct_as_cond: True # when a frame receives a correction click, it becomes a conditioning frame (even if it's not initially a conditioning frame)\n # maximum 2 initial conditioning frames\n num_init_cond_frames_for_train: 2\n rand_init_cond_frames_for_train: True # random 1~2\n num_correction_pt_per_frame: 7\n use_act_ckpt_iterative_pt_sampling: false\n \n\n \n num_init_cond_frames_for_eval: 1 # only mask on the first frame\n forward_backbone_per_frame_for_eval: True\n \n\n data:\n train:\n _target_: training.dataset.sam2_datasets.TorchTrainMixedDataset\n phases_per_epoch: ${scratch.phases_per_epoch}\n batch_sizes:\n - ${scratch.train_batch_size}\n\n datasets:\n - _target_: training.dataset.utils.RepeatFactorWrapper\n dataset:\n _target_: training.dataset.utils.ConcatDataset\n datasets:\n - _target_: training.dataset.vos_dataset.VOSDataset\n transforms: ${vos.train_transforms}\n training: true\n video_dataset:\n _target_: training.dataset.vos_raw_dataset.PNGRawDataset\n img_folder: ${dataset.img_folder}\n gt_folder: ${dataset.gt_folder}\n file_list_txt: ${dataset.file_list_txt}\n sampler:\n _target_: training.dataset.vos_sampler.RandomUniformSampler\n num_frames: ${scratch.num_frames}\n max_num_objects: ${scratch.max_num_objects}\n multiplier: ${dataset.multiplier}\n shuffle: True\n num_workers: ${scratch.num_train_workers}\n pin_memory: True\n drop_last: True\n collate_fn:\n _target_: training.utils.data_utils.collate_fn\n _partial_: true\n dict_key: all\n\n optim:\n amp:\n enabled: True\n amp_dtype: bfloat16\n\n optimizer:\n _target_: torch.optim.AdamW\n\n gradient_clip:\n _target_: training.optimizer.GradientClipper\n max_norm: 0.1\n norm_type: 2\n\n param_group_modifiers:\n - _target_: training.optimizer.layer_decay_param_modifier\n _partial_: True\n layer_decay_value: 0.9\n apply_to: 'image_encoder.trunk'\n overrides:\n - pattern: '*pos_embed*'\n value: 1.0\n\n options:\n lr:\n - scheduler:\n _target_: fvcore.common.param_scheduler.CosineParamScheduler\n start_value: ${scratch.base_lr}\n end_value: ${divide:${scratch.base_lr},10}\n - scheduler:\n _target_: fvcore.common.param_scheduler.CosineParamScheduler\n start_value: ${scratch.vision_lr}\n end_value: ${divide:${scratch.vision_lr},10}\n param_names:\n - 'image_encoder.*'\n weight_decay:\n - scheduler:\n _target_: fvcore.common.param_scheduler.ConstantParamScheduler\n value: 0.1\n - scheduler:\n _target_: fvcore.common.param_scheduler.ConstantParamScheduler\n value: 0.0\n param_names:\n - '*bias*'\n module_cls_names: ['torch.nn.LayerNorm']\n\n loss:\n all:\n _target_: training.loss_fns.MultiStepMultiMasksAndIous\n weight_dict:\n loss_mask: 20\n loss_dice: 1\n loss_iou: 1\n loss_class: 1\n supervise_all_iou: true\n iou_use_l1_loss: true\n pred_obj_scores: true\n focal_gamma_obj_score: 0.0\n focal_alpha_obj_score: -1.0\n\n distributed:\n backend: nccl\n find_unused_parameters: True\n\n logging:\n tensorboard_writer:\n _target_: training.utils.logger.make_tensorboard_logger\n log_dir: ${launcher.experiment_log_dir}/tensorboard\n flush_secs: 120\n should_log: True\n log_dir: ${launcher.experiment_log_dir}/logs\n log_freq: 10\n\n # initialize from a SAM 2 checkpoint\n checkpoint:\n save_dir: ${launcher.experiment_log_dir}/checkpoints\n save_freq: 0 # 0 only last checkpoint is saved.\n model_weight_initializer:\n _partial_: True\n _target_: training.utils.checkpoint_utils.load_state_dict_into_model\n strict: True\n ignore_unexpected_keys: null\n ignore_missing_keys: null\n\n state_dict:\n _target_: training.utils.checkpoint_utils.load_checkpoint_and_apply_kernels\n checkpoint_path: ./checkpoints/sam2.1_hiera_base_plus.pt # PATH to SAM 2.1 checkpoint\n ckpt_state_dict_keys: ['model']\n\nlauncher:\n num_nodes: 1\n gpus_per_node: 8\n experiment_log_dir: null # Path to log directory, defaults to ./sam2_logs/${config_name}\n\n# SLURM args if running on a cluster\nsubmitit:\n partition: null\n account: null\n qos: null\n cpus_per_task: 10\n use_cluster: false\n timeout_hour: 24\n name: null\n port_range: [10000, 65000]\n\n" }, { "path": "auto-seg/submodules/segment-anything-2/sam2/csrc/connected_components.cu", "content": "// Copyright (c) Meta Platforms, Inc. and affiliates.\n// All rights reserved.\n\n// This source code is licensed under the license found in the\n// LICENSE file in the root directory of this source tree.\n\n// adapted from https://github.com/zsef123/Connected_components_PyTorch\n// with license found in the LICENSE_cctorch file in the root directory.\n#include \n#include \n#include \n#include \n#include \n#include \n\n// 2d\n#define BLOCK_ROWS 16\n#define BLOCK_COLS 16\n\nnamespace cc2d {\n\ntemplate \n__device__ __forceinline__ unsigned char hasBit(T bitmap, unsigned char pos) {\n return (bitmap >> pos) & 1;\n}\n\n__device__ int32_t find(const int32_t* s_buf, int32_t n) {\n while (s_buf[n] != n)\n n = s_buf[n];\n return n;\n}\n\n__device__ int32_t find_n_compress(int32_t* s_buf, int32_t n) {\n const int32_t id = n;\n while (s_buf[n] != n) {\n n = s_buf[n];\n s_buf[id] = n;\n }\n return n;\n}\n\n__device__ void union_(int32_t* s_buf, int32_t a, int32_t b) {\n bool done;\n do {\n a = find(s_buf, a);\n b = find(s_buf, b);\n\n if (a < b) {\n int32_t old = atomicMin(s_buf + b, a);\n done = (old == b);\n b = old;\n } else if (b < a) {\n int32_t old = atomicMin(s_buf + a, b);\n done = (old == a);\n a = old;\n } else\n done = true;\n\n } while (!done);\n}\n\n__global__ void\ninit_labeling(int32_t* label, const uint32_t W, const uint32_t H) {\n const uint32_t row = (blockIdx.y * blockDim.y + threadIdx.y) * 2;\n const uint32_t col = (blockIdx.x * blockDim.x + threadIdx.x) * 2;\n const uint32_t idx = row * W + col;\n\n if (row < H && col < W)\n label[idx] = idx;\n}\n\n__global__ void\nmerge(uint8_t* img, int32_t* label, const uint32_t W, const uint32_t H) {\n const uint32_t row = (blockIdx.y * blockDim.y + threadIdx.y) * 2;\n const uint32_t col = (blockIdx.x * blockDim.x + threadIdx.x) * 2;\n const uint32_t idx = row * W + col;\n\n if (row >= H || col >= W)\n return;\n\n uint32_t P = 0;\n\n if (img[idx])\n P |= 0x777;\n if (row + 1 < H && img[idx + W])\n P |= 0x777 << 4;\n if (col + 1 < W && img[idx + 1])\n P |= 0x777 << 1;\n\n if (col == 0)\n P &= 0xEEEE;\n if (col + 1 >= W)\n P &= 0x3333;\n else if (col + 2 >= W)\n P &= 0x7777;\n\n if (row == 0)\n P &= 0xFFF0;\n if (row + 1 >= H)\n P &= 0xFF;\n\n if (P > 0) {\n // If need check about top-left pixel(if flag the first bit) and hit the\n // top-left pixel\n if (hasBit(P, 0) && img[idx - W - 1]) {\n union_(label, idx, idx - 2 * W - 2); // top left block\n }\n\n if ((hasBit(P, 1) && img[idx - W]) || (hasBit(P, 2) && img[idx - W + 1]))\n union_(label, idx, idx - 2 * W); // top bottom block\n\n if (hasBit(P, 3) && img[idx + 2 - W])\n union_(label, idx, idx - 2 * W + 2); // top right block\n\n if ((hasBit(P, 4) && img[idx - 1]) || (hasBit(P, 8) && img[idx + W - 1]))\n union_(label, idx, idx - 2); // just left block\n }\n}\n\n__global__ void compression(int32_t* label, const int32_t W, const int32_t H) {\n const uint32_t row = (blockIdx.y * blockDim.y + threadIdx.y) * 2;\n const uint32_t col = (blockIdx.x * blockDim.x + threadIdx.x) * 2;\n const uint32_t idx = row * W + col;\n\n if (row < H && col < W)\n find_n_compress(label, idx);\n}\n\n__global__ void final_labeling(\n const uint8_t* img,\n int32_t* label,\n const int32_t W,\n const int32_t H) {\n const uint32_t row = (blockIdx.y * blockDim.y + threadIdx.y) * 2;\n const uint32_t col = (blockIdx.x * blockDim.x + threadIdx.x) * 2;\n const uint32_t idx = row * W + col;\n\n if (row >= H || col >= W)\n return;\n\n int32_t y = label[idx] + 1;\n\n if (img[idx])\n label[idx] = y;\n else\n label[idx] = 0;\n\n if (col + 1 < W) {\n if (img[idx + 1])\n label[idx + 1] = y;\n else\n label[idx + 1] = 0;\n\n if (row + 1 < H) {\n if (img[idx + W + 1])\n label[idx + W + 1] = y;\n else\n label[idx + W + 1] = 0;\n }\n }\n\n if (row + 1 < H) {\n if (img[idx + W])\n label[idx + W] = y;\n else\n label[idx + W] = 0;\n }\n}\n\n__global__ void init_counting(\n const int32_t* label,\n int32_t* count_init,\n const int32_t W,\n const int32_t H) {\n const uint32_t row = (blockIdx.y * blockDim.y + threadIdx.y);\n const uint32_t col = (blockIdx.x * blockDim.x + threadIdx.x);\n const uint32_t idx = row * W + col;\n\n if (row >= H || col >= W)\n return;\n\n int32_t y = label[idx];\n if (y > 0) {\n int32_t count_idx = y - 1;\n atomicAdd(count_init + count_idx, 1);\n }\n}\n\n__global__ void final_counting(\n const int32_t* label,\n const int32_t* count_init,\n int32_t* count_final,\n const int32_t W,\n const int32_t H) {\n const uint32_t row = (blockIdx.y * blockDim.y + threadIdx.y);\n const uint32_t col = (blockIdx.x * blockDim.x + threadIdx.x);\n const uint32_t idx = row * W + col;\n\n if (row >= H || col >= W)\n return;\n\n int32_t y = label[idx];\n if (y > 0) {\n int32_t count_idx = y - 1;\n count_final[idx] = count_init[count_idx];\n } else {\n count_final[idx] = 0;\n }\n}\n\n} // namespace cc2d\n\nstd::vector get_connected_componnets(\n const torch::Tensor& inputs) {\n AT_ASSERTM(inputs.is_cuda(), \"inputs must be a CUDA tensor\");\n AT_ASSERTM(inputs.ndimension() == 4, \"inputs must be [N, 1, H, W] shape\");\n AT_ASSERTM(\n inputs.scalar_type() == torch::kUInt8, \"inputs must be a uint8 type\");\n\n const uint32_t N = inputs.size(0);\n const uint32_t C = inputs.size(1);\n const uint32_t H = inputs.size(2);\n const uint32_t W = inputs.size(3);\n\n AT_ASSERTM(C == 1, \"inputs must be [N, 1, H, W] shape\");\n AT_ASSERTM((H % 2) == 0, \"height must be an even number\");\n AT_ASSERTM((W % 2) == 0, \"width must be an even number\");\n\n // label must be uint32_t\n auto label_options =\n torch::TensorOptions().dtype(torch::kInt32).device(inputs.device());\n torch::Tensor labels = torch::zeros({N, C, H, W}, label_options);\n torch::Tensor counts_init = torch::zeros({N, C, H, W}, label_options);\n torch::Tensor counts_final = torch::zeros({N, C, H, W}, label_options);\n\n dim3 grid = dim3(\n ((W + 1) / 2 + BLOCK_COLS - 1) / BLOCK_COLS,\n ((H + 1) / 2 + BLOCK_ROWS - 1) / BLOCK_ROWS);\n dim3 block = dim3(BLOCK_COLS, BLOCK_ROWS);\n dim3 grid_count =\n dim3((W + BLOCK_COLS) / BLOCK_COLS, (H + BLOCK_ROWS) / BLOCK_ROWS);\n dim3 block_count = dim3(BLOCK_COLS, BLOCK_ROWS);\n cudaStream_t stream = at::cuda::getCurrentCUDAStream();\n\n for (int n = 0; n < N; n++) {\n uint32_t offset = n * H * W;\n\n cc2d::init_labeling<<>>(\n labels.data_ptr() + offset, W, H);\n cc2d::merge<<>>(\n inputs.data_ptr() + offset,\n labels.data_ptr() + offset,\n W,\n H);\n cc2d::compression<<>>(\n labels.data_ptr() + offset, W, H);\n cc2d::final_labeling<<>>(\n inputs.data_ptr() + offset,\n labels.data_ptr() + offset,\n W,\n H);\n\n // get the counting of each pixel\n cc2d::init_counting<<>>(\n labels.data_ptr() + offset,\n counts_init.data_ptr() + offset,\n W,\n H);\n cc2d::final_counting<<>>(\n labels.data_ptr() + offset,\n counts_init.data_ptr() + offset,\n counts_final.data_ptr() + offset,\n W,\n H);\n }\n\n // returned values are [labels, counts]\n std::vector outputs;\n outputs.push_back(labels);\n outputs.push_back(counts_final);\n return outputs;\n}\n\nPYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {\n m.def(\n \"get_connected_componnets\",\n &get_connected_componnets,\n \"get_connected_componnets\");\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/sam2/modeling/__init__.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n" }, { "path": "auto-seg/submodules/segment-anything-2/sam2/modeling/backbones/__init__.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n" }, { "path": "auto-seg/submodules/segment-anything-2/sam2/modeling/backbones/hieradet.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport logging\nfrom functools import partial\nfrom typing import List, Tuple, Union\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom iopath.common.file_io import g_pathmgr\n\nfrom sam2.modeling.backbones.utils import (\n PatchEmbed,\n window_partition,\n window_unpartition,\n)\n\nfrom sam2.modeling.sam2_utils import DropPath, MLP\n\n\ndef do_pool(x: torch.Tensor, pool: nn.Module, norm: nn.Module = None) -> torch.Tensor:\n if pool is None:\n return x\n # (B, H, W, C) -> (B, C, H, W)\n x = x.permute(0, 3, 1, 2)\n x = pool(x)\n # (B, C, H', W') -> (B, H', W', C)\n x = x.permute(0, 2, 3, 1)\n if norm:\n x = norm(x)\n\n return x\n\n\nclass MultiScaleAttention(nn.Module):\n def __init__(\n self,\n dim: int,\n dim_out: int,\n num_heads: int,\n q_pool: nn.Module = None,\n ):\n super().__init__()\n\n self.dim = dim\n self.dim_out = dim_out\n self.num_heads = num_heads\n self.q_pool = q_pool\n self.qkv = nn.Linear(dim, dim_out * 3)\n self.proj = nn.Linear(dim_out, dim_out)\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n B, H, W, _ = x.shape\n # qkv with shape (B, H * W, 3, nHead, C)\n qkv = self.qkv(x).reshape(B, H * W, 3, self.num_heads, -1)\n # q, k, v with shape (B, H * W, nheads, C)\n q, k, v = torch.unbind(qkv, 2)\n\n # Q pooling (for downsample at stage changes)\n if self.q_pool:\n q = do_pool(q.reshape(B, H, W, -1), self.q_pool)\n H, W = q.shape[1:3] # downsampled shape\n q = q.reshape(B, H * W, self.num_heads, -1)\n\n # Torch's SDPA expects [B, nheads, H*W, C] so we transpose\n x = F.scaled_dot_product_attention(\n q.transpose(1, 2),\n k.transpose(1, 2),\n v.transpose(1, 2),\n )\n # Transpose back\n x = x.transpose(1, 2)\n x = x.reshape(B, H, W, -1)\n\n x = self.proj(x)\n\n return x\n\n\nclass MultiScaleBlock(nn.Module):\n def __init__(\n self,\n dim: int,\n dim_out: int,\n num_heads: int,\n mlp_ratio: float = 4.0,\n drop_path: float = 0.0,\n norm_layer: Union[nn.Module, str] = \"LayerNorm\",\n q_stride: Tuple[int, int] = None,\n act_layer: nn.Module = nn.GELU,\n window_size: int = 0,\n ):\n super().__init__()\n\n if isinstance(norm_layer, str):\n norm_layer = partial(getattr(nn, norm_layer), eps=1e-6)\n\n self.dim = dim\n self.dim_out = dim_out\n self.norm1 = norm_layer(dim)\n\n self.window_size = window_size\n\n self.pool, self.q_stride = None, q_stride\n if self.q_stride:\n self.pool = nn.MaxPool2d(\n kernel_size=q_stride, stride=q_stride, ceil_mode=False\n )\n\n self.attn = MultiScaleAttention(\n dim,\n dim_out,\n num_heads=num_heads,\n q_pool=self.pool,\n )\n self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()\n\n self.norm2 = norm_layer(dim_out)\n self.mlp = MLP(\n dim_out,\n int(dim_out * mlp_ratio),\n dim_out,\n num_layers=2,\n activation=act_layer,\n )\n\n if dim != dim_out:\n self.proj = nn.Linear(dim, dim_out)\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n shortcut = x # B, H, W, C\n x = self.norm1(x)\n\n # Skip connection\n if self.dim != self.dim_out:\n shortcut = do_pool(self.proj(x), self.pool)\n\n # Window partition\n window_size = self.window_size\n if window_size > 0:\n H, W = x.shape[1], x.shape[2]\n x, pad_hw = window_partition(x, window_size)\n\n # Window Attention + Q Pooling (if stage change)\n x = self.attn(x)\n if self.q_stride:\n # Shapes have changed due to Q pooling\n window_size = self.window_size // self.q_stride[0]\n H, W = shortcut.shape[1:3]\n\n pad_h = (window_size - H % window_size) % window_size\n pad_w = (window_size - W % window_size) % window_size\n pad_hw = (H + pad_h, W + pad_w)\n\n # Reverse window partition\n if self.window_size > 0:\n x = window_unpartition(x, window_size, pad_hw, (H, W))\n\n x = shortcut + self.drop_path(x)\n # MLP\n x = x + self.drop_path(self.mlp(self.norm2(x)))\n return x\n\n\nclass Hiera(nn.Module):\n \"\"\"\n Reference: https://arxiv.org/abs/2306.00989\n \"\"\"\n\n def __init__(\n self,\n embed_dim: int = 96, # initial embed dim\n num_heads: int = 1, # initial number of heads\n drop_path_rate: float = 0.0, # stochastic depth\n q_pool: int = 3, # number of q_pool stages\n q_stride: Tuple[int, int] = (2, 2), # downsample stride bet. stages\n stages: Tuple[int, ...] = (2, 3, 16, 3), # blocks per stage\n dim_mul: float = 2.0, # dim_mul factor at stage shift\n head_mul: float = 2.0, # head_mul factor at stage shift\n window_pos_embed_bkg_spatial_size: Tuple[int, int] = (14, 14),\n # window size per stage, when not using global att.\n window_spec: Tuple[int, ...] = (\n 8,\n 4,\n 14,\n 7,\n ),\n # global attn in these blocks\n global_att_blocks: Tuple[int, ...] = (\n 12,\n 16,\n 20,\n ),\n weights_path=None,\n return_interm_layers=True, # return feats from every stage\n ):\n super().__init__()\n\n assert len(stages) == len(window_spec)\n self.window_spec = window_spec\n\n depth = sum(stages)\n self.q_stride = q_stride\n self.stage_ends = [sum(stages[:i]) - 1 for i in range(1, len(stages) + 1)]\n assert 0 <= q_pool <= len(self.stage_ends[:-1])\n self.q_pool_blocks = [x + 1 for x in self.stage_ends[:-1]][:q_pool]\n self.return_interm_layers = return_interm_layers\n\n self.patch_embed = PatchEmbed(\n embed_dim=embed_dim,\n )\n # Which blocks have global att?\n self.global_att_blocks = global_att_blocks\n\n # Windowed positional embedding (https://arxiv.org/abs/2311.05613)\n self.window_pos_embed_bkg_spatial_size = window_pos_embed_bkg_spatial_size\n self.pos_embed = nn.Parameter(\n torch.zeros(1, embed_dim, *self.window_pos_embed_bkg_spatial_size)\n )\n self.pos_embed_window = nn.Parameter(\n torch.zeros(1, embed_dim, self.window_spec[0], self.window_spec[0])\n )\n\n dpr = [\n x.item() for x in torch.linspace(0, drop_path_rate, depth)\n ] # stochastic depth decay rule\n\n cur_stage = 1\n self.blocks = nn.ModuleList()\n\n for i in range(depth):\n dim_out = embed_dim\n # lags by a block, so first block of\n # next stage uses an initial window size\n # of previous stage and final window size of current stage\n window_size = self.window_spec[cur_stage - 1]\n\n if self.global_att_blocks is not None:\n window_size = 0 if i in self.global_att_blocks else window_size\n\n if i - 1 in self.stage_ends:\n dim_out = int(embed_dim * dim_mul)\n num_heads = int(num_heads * head_mul)\n cur_stage += 1\n\n block = MultiScaleBlock(\n dim=embed_dim,\n dim_out=dim_out,\n num_heads=num_heads,\n drop_path=dpr[i],\n q_stride=self.q_stride if i in self.q_pool_blocks else None,\n window_size=window_size,\n )\n\n embed_dim = dim_out\n self.blocks.append(block)\n\n self.channel_list = (\n [self.blocks[i].dim_out for i in self.stage_ends[::-1]]\n if return_interm_layers\n else [self.blocks[-1].dim_out]\n )\n\n if weights_path is not None:\n with g_pathmgr.open(weights_path, \"rb\") as f:\n chkpt = torch.load(f, map_location=\"cpu\")\n logging.info(\"loading Hiera\", self.load_state_dict(chkpt, strict=False))\n\n def _get_pos_embed(self, hw: Tuple[int, int]) -> torch.Tensor:\n h, w = hw\n window_embed = self.pos_embed_window\n pos_embed = F.interpolate(self.pos_embed, size=(h, w), mode=\"bicubic\")\n pos_embed = pos_embed + window_embed.tile(\n [x // y for x, y in zip(pos_embed.shape, window_embed.shape)]\n )\n pos_embed = pos_embed.permute(0, 2, 3, 1)\n return pos_embed\n\n def forward(self, x: torch.Tensor) -> List[torch.Tensor]:\n x = self.patch_embed(x)\n # x: (B, H, W, C)\n\n # Add pos embed\n x = x + self._get_pos_embed(x.shape[1:3])\n\n outputs = []\n for i, blk in enumerate(self.blocks):\n x = blk(x)\n if (i == self.stage_ends[-1]) or (\n i in self.stage_ends and self.return_interm_layers\n ):\n feats = x.permute(0, 3, 1, 2)\n outputs.append(feats)\n\n return outputs\n\n def get_layer_id(self, layer_name):\n # https://github.com/microsoft/unilm/blob/master/beit/optim_factory.py#L33\n num_layers = self.get_num_layers()\n\n if layer_name.find(\"rel_pos\") != -1:\n return num_layers + 1\n elif layer_name.find(\"pos_embed\") != -1:\n return 0\n elif layer_name.find(\"patch_embed\") != -1:\n return 0\n elif layer_name.find(\"blocks\") != -1:\n return int(layer_name.split(\"blocks\")[1].split(\".\")[1]) + 1\n else:\n return num_layers + 1\n\n def get_num_layers(self) -> int:\n return len(self.blocks)\n" }, { "path": "auto-seg/submodules/segment-anything-2/sam2/modeling/backbones/image_encoder.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nfrom typing import List, Optional\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\nclass ImageEncoder(nn.Module):\n def __init__(\n self,\n trunk: nn.Module,\n neck: nn.Module,\n scalp: int = 0,\n ):\n super().__init__()\n self.trunk = trunk\n self.neck = neck\n self.scalp = scalp\n assert (\n self.trunk.channel_list == self.neck.backbone_channel_list\n ), f\"Channel dims of trunk and neck do not match. Trunk: {self.trunk.channel_list}, neck: {self.neck.backbone_channel_list}\"\n\n def forward(self, sample: torch.Tensor):\n # Forward through backbone\n features, pos = self.neck(self.trunk(sample))\n if self.scalp > 0:\n # Discard the lowest resolution features\n features, pos = features[: -self.scalp], pos[: -self.scalp]\n\n src = features[-1]\n output = {\n \"vision_features\": src,\n \"vision_pos_enc\": pos,\n \"backbone_fpn\": features,\n }\n return output\n\n\nclass FpnNeck(nn.Module):\n \"\"\"\n A modified variant of Feature Pyramid Network (FPN) neck\n (we remove output conv and also do bicubic interpolation similar to ViT\n pos embed interpolation)\n \"\"\"\n\n def __init__(\n self,\n position_encoding: nn.Module,\n d_model: int,\n backbone_channel_list: List[int],\n kernel_size: int = 1,\n stride: int = 1,\n padding: int = 0,\n fpn_interp_model: str = \"bilinear\",\n fuse_type: str = \"sum\",\n fpn_top_down_levels: Optional[List[int]] = None,\n ):\n \"\"\"Initialize the neck\n :param trunk: the backbone\n :param position_encoding: the positional encoding to use\n :param d_model: the dimension of the model\n :param neck_norm: the normalization to use\n \"\"\"\n super().__init__()\n self.position_encoding = position_encoding\n self.convs = nn.ModuleList()\n self.backbone_channel_list = backbone_channel_list\n self.d_model = d_model\n for dim in backbone_channel_list:\n current = nn.Sequential()\n current.add_module(\n \"conv\",\n nn.Conv2d(\n in_channels=dim,\n out_channels=d_model,\n kernel_size=kernel_size,\n stride=stride,\n padding=padding,\n ),\n )\n\n self.convs.append(current)\n self.fpn_interp_model = fpn_interp_model\n assert fuse_type in [\"sum\", \"avg\"]\n self.fuse_type = fuse_type\n\n # levels to have top-down features in its outputs\n # e.g. if fpn_top_down_levels is [2, 3], then only outputs of level 2 and 3\n # have top-down propagation, while outputs of level 0 and level 1 have only\n # lateral features from the same backbone level.\n if fpn_top_down_levels is None:\n # default is to have top-down features on all levels\n fpn_top_down_levels = range(len(self.convs))\n self.fpn_top_down_levels = list(fpn_top_down_levels)\n\n def forward(self, xs: List[torch.Tensor]):\n\n out = [None] * len(self.convs)\n pos = [None] * len(self.convs)\n assert len(xs) == len(self.convs)\n # fpn forward pass\n # see https://github.com/facebookresearch/detectron2/blob/main/detectron2/modeling/backbone/fpn.py\n prev_features = None\n # forward in top-down order (from low to high resolution)\n n = len(self.convs) - 1\n for i in range(n, -1, -1):\n x = xs[i]\n lateral_features = self.convs[n - i](x)\n if i in self.fpn_top_down_levels and prev_features is not None:\n top_down_features = F.interpolate(\n prev_features.to(dtype=torch.float32),\n scale_factor=2.0,\n mode=self.fpn_interp_model,\n align_corners=(\n None if self.fpn_interp_model == \"nearest\" else False\n ),\n antialias=False,\n )\n prev_features = lateral_features + top_down_features\n if self.fuse_type == \"avg\":\n prev_features /= 2\n else:\n prev_features = lateral_features\n x_out = prev_features\n out[i] = x_out\n pos[i] = self.position_encoding(x_out).to(x_out.dtype)\n\n return out, pos\n" }, { "path": "auto-seg/submodules/segment-anything-2/sam2/modeling/backbones/utils.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\n\"\"\"Some utilities for backbones, in particular for windowing\"\"\"\n\nfrom typing import Tuple\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\ndef window_partition(x, window_size):\n \"\"\"\n Partition into non-overlapping windows with padding if needed.\n Args:\n x (tensor): input tokens with [B, H, W, C].\n window_size (int): window size.\n Returns:\n windows: windows after partition with [B * num_windows, window_size, window_size, C].\n (Hp, Wp): padded height and width before partition\n \"\"\"\n B, H, W, C = x.shape\n\n pad_h = (window_size - H % window_size) % window_size\n pad_w = (window_size - W % window_size) % window_size\n if pad_h > 0 or pad_w > 0:\n x = F.pad(x, (0, 0, 0, pad_w, 0, pad_h))\n Hp, Wp = H + pad_h, W + pad_w\n\n x = x.view(B, Hp // window_size, window_size, Wp // window_size, window_size, C)\n windows = (\n x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)\n )\n return windows, (Hp, Wp)\n\n\ndef window_unpartition(windows, window_size, pad_hw, hw):\n \"\"\"\n Window unpartition into original sequences and removing padding.\n Args:\n x (tensor): input tokens with [B * num_windows, window_size, window_size, C].\n window_size (int): window size.\n pad_hw (Tuple): padded height and width (Hp, Wp).\n hw (Tuple): original height and width (H, W) before padding.\n Returns:\n x: unpartitioned sequences with [B, H, W, C].\n \"\"\"\n Hp, Wp = pad_hw\n H, W = hw\n B = windows.shape[0] // (Hp * Wp // window_size // window_size)\n x = windows.view(\n B, Hp // window_size, Wp // window_size, window_size, window_size, -1\n )\n x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, Hp, Wp, -1)\n\n if Hp > H or Wp > W:\n x = x[:, :H, :W, :].contiguous()\n return x\n\n\nclass PatchEmbed(nn.Module):\n \"\"\"\n Image to Patch Embedding.\n \"\"\"\n\n def __init__(\n self,\n kernel_size: Tuple[int, ...] = (7, 7),\n stride: Tuple[int, ...] = (4, 4),\n padding: Tuple[int, ...] = (3, 3),\n in_chans: int = 3,\n embed_dim: int = 768,\n ):\n \"\"\"\n Args:\n kernel_size (Tuple): kernel size of the projection layer.\n stride (Tuple): stride of the projection layer.\n padding (Tuple): padding size of the projection layer.\n in_chans (int): Number of input image channels.\n embed_dim (int): embed_dim (int): Patch embedding dimension.\n \"\"\"\n super().__init__()\n self.proj = nn.Conv2d(\n in_chans, embed_dim, kernel_size=kernel_size, stride=stride, padding=padding\n )\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n x = self.proj(x)\n # B C H W -> B H W C\n x = x.permute(0, 2, 3, 1)\n return x\n" }, { "path": "auto-seg/submodules/segment-anything-2/sam2/modeling/memory_attention.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nfrom typing import Optional\n\nimport torch\nfrom torch import nn, Tensor\n\nfrom sam2.modeling.sam.transformer import RoPEAttention\n\nfrom sam2.modeling.sam2_utils import get_activation_fn, get_clones\n\n\nclass MemoryAttentionLayer(nn.Module):\n\n def __init__(\n self,\n activation: str,\n cross_attention: nn.Module,\n d_model: int,\n dim_feedforward: int,\n dropout: float,\n pos_enc_at_attn: bool,\n pos_enc_at_cross_attn_keys: bool,\n pos_enc_at_cross_attn_queries: bool,\n self_attention: nn.Module,\n ):\n super().__init__()\n self.d_model = d_model\n self.dim_feedforward = dim_feedforward\n self.dropout_value = dropout\n self.self_attn = self_attention\n self.cross_attn_image = cross_attention\n\n # Implementation of Feedforward model\n self.linear1 = nn.Linear(d_model, dim_feedforward)\n self.dropout = nn.Dropout(dropout)\n self.linear2 = nn.Linear(dim_feedforward, d_model)\n\n self.norm1 = nn.LayerNorm(d_model)\n self.norm2 = nn.LayerNorm(d_model)\n self.norm3 = nn.LayerNorm(d_model)\n self.dropout1 = nn.Dropout(dropout)\n self.dropout2 = nn.Dropout(dropout)\n self.dropout3 = nn.Dropout(dropout)\n\n self.activation_str = activation\n self.activation = get_activation_fn(activation)\n\n # Where to add pos enc\n self.pos_enc_at_attn = pos_enc_at_attn\n self.pos_enc_at_cross_attn_queries = pos_enc_at_cross_attn_queries\n self.pos_enc_at_cross_attn_keys = pos_enc_at_cross_attn_keys\n\n def _forward_sa(self, tgt, query_pos):\n # Self-Attention\n tgt2 = self.norm1(tgt)\n q = k = tgt2 + query_pos if self.pos_enc_at_attn else tgt2\n tgt2 = self.self_attn(q, k, v=tgt2)\n tgt = tgt + self.dropout1(tgt2)\n return tgt\n\n def _forward_ca(self, tgt, memory, query_pos, pos, num_k_exclude_rope=0):\n kwds = {}\n if num_k_exclude_rope > 0:\n assert isinstance(self.cross_attn_image, RoPEAttention)\n kwds = {\"num_k_exclude_rope\": num_k_exclude_rope}\n\n # Cross-Attention\n tgt2 = self.norm2(tgt)\n tgt2 = self.cross_attn_image(\n q=tgt2 + query_pos if self.pos_enc_at_cross_attn_queries else tgt2,\n k=memory + pos if self.pos_enc_at_cross_attn_keys else memory,\n v=memory,\n **kwds,\n )\n tgt = tgt + self.dropout2(tgt2)\n return tgt\n\n def forward(\n self,\n tgt,\n memory,\n pos: Optional[Tensor] = None,\n query_pos: Optional[Tensor] = None,\n num_k_exclude_rope: int = 0,\n ) -> torch.Tensor:\n\n # Self-Attn, Cross-Attn\n tgt = self._forward_sa(tgt, query_pos)\n tgt = self._forward_ca(tgt, memory, query_pos, pos, num_k_exclude_rope)\n # MLP\n tgt2 = self.norm3(tgt)\n tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))\n tgt = tgt + self.dropout3(tgt2)\n return tgt\n\n\nclass MemoryAttention(nn.Module):\n def __init__(\n self,\n d_model: int,\n pos_enc_at_input: bool,\n layer: nn.Module,\n num_layers: int,\n batch_first: bool = True, # Do layers expect batch first input?\n ):\n super().__init__()\n self.d_model = d_model\n self.layers = get_clones(layer, num_layers)\n self.num_layers = num_layers\n self.norm = nn.LayerNorm(d_model)\n self.pos_enc_at_input = pos_enc_at_input\n self.batch_first = batch_first\n\n def forward(\n self,\n curr: torch.Tensor, # self-attention inputs\n memory: torch.Tensor, # cross-attention inputs\n curr_pos: Optional[Tensor] = None, # pos_enc for self-attention inputs\n memory_pos: Optional[Tensor] = None, # pos_enc for cross-attention inputs\n num_obj_ptr_tokens: int = 0, # number of object pointer *tokens*\n ):\n if isinstance(curr, list):\n assert isinstance(curr_pos, list)\n assert len(curr) == len(curr_pos) == 1\n curr, curr_pos = (\n curr[0],\n curr_pos[0],\n )\n\n assert (\n curr.shape[1] == memory.shape[1]\n ), \"Batch size must be the same for curr and memory\"\n\n output = curr\n if self.pos_enc_at_input and curr_pos is not None:\n output = output + 0.1 * curr_pos\n\n if self.batch_first:\n # Convert to batch first\n output = output.transpose(0, 1)\n curr_pos = curr_pos.transpose(0, 1)\n memory = memory.transpose(0, 1)\n memory_pos = memory_pos.transpose(0, 1)\n\n for layer in self.layers:\n kwds = {}\n if isinstance(layer.cross_attn_image, RoPEAttention):\n kwds = {\"num_k_exclude_rope\": num_obj_ptr_tokens}\n\n output = layer(\n tgt=output,\n memory=memory,\n pos=memory_pos,\n query_pos=curr_pos,\n **kwds,\n )\n normed_output = self.norm(output)\n\n if self.batch_first:\n # Convert back to seq first\n normed_output = normed_output.transpose(0, 1)\n curr_pos = curr_pos.transpose(0, 1)\n\n return normed_output\n" }, { "path": "auto-seg/submodules/segment-anything-2/sam2/modeling/memory_encoder.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport math\nfrom typing import Tuple\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom sam2.modeling.sam2_utils import DropPath, get_clones, LayerNorm2d\n\n\nclass MaskDownSampler(nn.Module):\n \"\"\"\n Progressively downsample a mask by total_stride, each time by stride.\n Note that LayerNorm is applied per *token*, like in ViT.\n\n With each downsample (by a factor stride**2), channel capacity increases by the same factor.\n In the end, we linearly project to embed_dim channels.\n \"\"\"\n\n def __init__(\n self,\n embed_dim=256,\n kernel_size=4,\n stride=4,\n padding=0,\n total_stride=16,\n activation=nn.GELU,\n ):\n super().__init__()\n num_layers = int(math.log2(total_stride) // math.log2(stride))\n assert stride**num_layers == total_stride\n self.encoder = nn.Sequential()\n mask_in_chans, mask_out_chans = 1, 1\n for _ in range(num_layers):\n mask_out_chans = mask_in_chans * (stride**2)\n self.encoder.append(\n nn.Conv2d(\n mask_in_chans,\n mask_out_chans,\n kernel_size=kernel_size,\n stride=stride,\n padding=padding,\n )\n )\n self.encoder.append(LayerNorm2d(mask_out_chans))\n self.encoder.append(activation())\n mask_in_chans = mask_out_chans\n\n self.encoder.append(nn.Conv2d(mask_out_chans, embed_dim, kernel_size=1))\n\n def forward(self, x):\n return self.encoder(x)\n\n\n# Lightly adapted from ConvNext (https://github.com/facebookresearch/ConvNeXt)\nclass CXBlock(nn.Module):\n r\"\"\"ConvNeXt Block. There are two equivalent implementations:\n (1) DwConv -> LayerNorm (channels_first) -> 1x1 Conv -> GELU -> 1x1 Conv; all in (N, C, H, W)\n (2) DwConv -> Permute to (N, H, W, C); LayerNorm (channels_last) -> Linear -> GELU -> Linear; Permute back\n We use (2) as we find it slightly faster in PyTorch\n\n Args:\n dim (int): Number of input channels.\n drop_path (float): Stochastic depth rate. Default: 0.0\n layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6.\n \"\"\"\n\n def __init__(\n self,\n dim,\n kernel_size=7,\n padding=3,\n drop_path=0.0,\n layer_scale_init_value=1e-6,\n use_dwconv=True,\n ):\n super().__init__()\n self.dwconv = nn.Conv2d(\n dim,\n dim,\n kernel_size=kernel_size,\n padding=padding,\n groups=dim if use_dwconv else 1,\n ) # depthwise conv\n self.norm = LayerNorm2d(dim, eps=1e-6)\n self.pwconv1 = nn.Linear(\n dim, 4 * dim\n ) # pointwise/1x1 convs, implemented with linear layers\n self.act = nn.GELU()\n self.pwconv2 = nn.Linear(4 * dim, dim)\n self.gamma = (\n nn.Parameter(layer_scale_init_value * torch.ones((dim)), requires_grad=True)\n if layer_scale_init_value > 0\n else None\n )\n self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()\n\n def forward(self, x):\n input = x\n x = self.dwconv(x)\n x = self.norm(x)\n x = x.permute(0, 2, 3, 1) # (N, C, H, W) -> (N, H, W, C)\n x = self.pwconv1(x)\n x = self.act(x)\n x = self.pwconv2(x)\n if self.gamma is not None:\n x = self.gamma * x\n x = x.permute(0, 3, 1, 2) # (N, H, W, C) -> (N, C, H, W)\n\n x = input + self.drop_path(x)\n return x\n\n\nclass Fuser(nn.Module):\n def __init__(self, layer, num_layers, dim=None, input_projection=False):\n super().__init__()\n self.proj = nn.Identity()\n self.layers = get_clones(layer, num_layers)\n\n if input_projection:\n assert dim is not None\n self.proj = nn.Conv2d(dim, dim, kernel_size=1)\n\n def forward(self, x):\n # normally x: (N, C, H, W)\n x = self.proj(x)\n for layer in self.layers:\n x = layer(x)\n return x\n\n\nclass MemoryEncoder(nn.Module):\n def __init__(\n self,\n out_dim,\n mask_downsampler,\n fuser,\n position_encoding,\n in_dim=256, # in_dim of pix_feats\n ):\n super().__init__()\n\n self.mask_downsampler = mask_downsampler\n\n self.pix_feat_proj = nn.Conv2d(in_dim, in_dim, kernel_size=1)\n self.fuser = fuser\n self.position_encoding = position_encoding\n self.out_proj = nn.Identity()\n if out_dim != in_dim:\n self.out_proj = nn.Conv2d(in_dim, out_dim, kernel_size=1)\n\n def forward(\n self,\n pix_feat: torch.Tensor,\n masks: torch.Tensor,\n skip_mask_sigmoid: bool = False,\n ) -> Tuple[torch.Tensor, torch.Tensor]:\n ## Process masks\n # sigmoid, so that less domain shift from gt masks which are bool\n if not skip_mask_sigmoid:\n masks = F.sigmoid(masks)\n masks = self.mask_downsampler(masks)\n\n ## Fuse pix_feats and downsampled masks\n # in case the visual features are on CPU, cast them to CUDA\n pix_feat = pix_feat.to(masks.device)\n\n x = self.pix_feat_proj(pix_feat)\n x = x + masks\n x = self.fuser(x)\n x = self.out_proj(x)\n\n pos = self.position_encoding(x).to(x.dtype)\n\n return {\"vision_features\": x, \"vision_pos_enc\": [pos]}\n" }, { "path": "auto-seg/submodules/segment-anything-2/sam2/modeling/position_encoding.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport math\nfrom typing import Any, Optional, Tuple\n\nimport numpy as np\n\nimport torch\nfrom torch import nn\n\n\nclass PositionEmbeddingSine(nn.Module):\n \"\"\"\n This is a more standard version of the position embedding, very similar to the one\n used by the Attention Is All You Need paper, generalized to work on images.\n \"\"\"\n\n def __init__(\n self,\n num_pos_feats,\n temperature: int = 10000,\n normalize: bool = True,\n scale: Optional[float] = None,\n ):\n super().__init__()\n assert num_pos_feats % 2 == 0, \"Expecting even model width\"\n self.num_pos_feats = num_pos_feats // 2\n self.temperature = temperature\n self.normalize = normalize\n if scale is not None and normalize is False:\n raise ValueError(\"normalize should be True if scale is passed\")\n if scale is None:\n scale = 2 * math.pi\n self.scale = scale\n\n self.cache = {}\n\n def _encode_xy(self, x, y):\n # The positions are expected to be normalized\n assert len(x) == len(y) and x.ndim == y.ndim == 1\n x_embed = x * self.scale\n y_embed = y * self.scale\n\n dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)\n dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)\n\n pos_x = x_embed[:, None] / dim_t\n pos_y = y_embed[:, None] / dim_t\n pos_x = torch.stack(\n (pos_x[:, 0::2].sin(), pos_x[:, 1::2].cos()), dim=2\n ).flatten(1)\n pos_y = torch.stack(\n (pos_y[:, 0::2].sin(), pos_y[:, 1::2].cos()), dim=2\n ).flatten(1)\n return pos_x, pos_y\n\n @torch.no_grad()\n def encode_boxes(self, x, y, w, h):\n pos_x, pos_y = self._encode_xy(x, y)\n pos = torch.cat((pos_y, pos_x, h[:, None], w[:, None]), dim=1)\n return pos\n\n encode = encode_boxes # Backwards compatibility\n\n @torch.no_grad()\n def encode_points(self, x, y, labels):\n (bx, nx), (by, ny), (bl, nl) = x.shape, y.shape, labels.shape\n assert bx == by and nx == ny and bx == bl and nx == nl\n pos_x, pos_y = self._encode_xy(x.flatten(), y.flatten())\n pos_x, pos_y = pos_x.reshape(bx, nx, -1), pos_y.reshape(by, ny, -1)\n pos = torch.cat((pos_y, pos_x, labels[:, :, None]), dim=2)\n return pos\n\n @torch.no_grad()\n def forward(self, x: torch.Tensor):\n cache_key = (x.shape[-2], x.shape[-1])\n if cache_key in self.cache:\n return self.cache[cache_key][None].repeat(x.shape[0], 1, 1, 1)\n y_embed = (\n torch.arange(1, x.shape[-2] + 1, dtype=torch.float32, device=x.device)\n .view(1, -1, 1)\n .repeat(x.shape[0], 1, x.shape[-1])\n )\n x_embed = (\n torch.arange(1, x.shape[-1] + 1, dtype=torch.float32, device=x.device)\n .view(1, 1, -1)\n .repeat(x.shape[0], x.shape[-2], 1)\n )\n\n if self.normalize:\n eps = 1e-6\n y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale\n x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale\n\n dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)\n dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)\n\n pos_x = x_embed[:, :, :, None] / dim_t\n pos_y = y_embed[:, :, :, None] / dim_t\n pos_x = torch.stack(\n (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4\n ).flatten(3)\n pos_y = torch.stack(\n (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4\n ).flatten(3)\n pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)\n self.cache[cache_key] = pos[0]\n return pos\n\n\nclass PositionEmbeddingRandom(nn.Module):\n \"\"\"\n Positional encoding using random spatial frequencies.\n \"\"\"\n\n def __init__(self, num_pos_feats: int = 64, scale: Optional[float] = None) -> None:\n super().__init__()\n if scale is None or scale <= 0.0:\n scale = 1.0\n self.register_buffer(\n \"positional_encoding_gaussian_matrix\",\n scale * torch.randn((2, num_pos_feats)),\n )\n\n def _pe_encoding(self, coords: torch.Tensor) -> torch.Tensor:\n \"\"\"Positionally encode points that are normalized to [0,1].\"\"\"\n # assuming coords are in [0, 1]^2 square and have d_1 x ... x d_n x 2 shape\n coords = 2 * coords - 1\n coords = coords @ self.positional_encoding_gaussian_matrix\n coords = 2 * np.pi * coords\n # outputs d_1 x ... x d_n x C shape\n return torch.cat([torch.sin(coords), torch.cos(coords)], dim=-1)\n\n def forward(self, size: Tuple[int, int]) -> torch.Tensor:\n \"\"\"Generate positional encoding for a grid of the specified size.\"\"\"\n h, w = size\n device: Any = self.positional_encoding_gaussian_matrix.device\n grid = torch.ones((h, w), device=device, dtype=torch.float32)\n y_embed = grid.cumsum(dim=0) - 0.5\n x_embed = grid.cumsum(dim=1) - 0.5\n y_embed = y_embed / h\n x_embed = x_embed / w\n\n pe = self._pe_encoding(torch.stack([x_embed, y_embed], dim=-1))\n return pe.permute(2, 0, 1) # C x H x W\n\n def forward_with_coords(\n self, coords_input: torch.Tensor, image_size: Tuple[int, int]\n ) -> torch.Tensor:\n \"\"\"Positionally encode points that are not normalized to [0,1].\"\"\"\n coords = coords_input.clone()\n coords[:, :, 0] = coords[:, :, 0] / image_size[1]\n coords[:, :, 1] = coords[:, :, 1] / image_size[0]\n return self._pe_encoding(coords.to(torch.float)) # B x N x C\n\n\n# Rotary Positional Encoding, adapted from:\n# 1. https://github.com/meta-llama/codellama/blob/main/llama/model.py\n# 2. https://github.com/naver-ai/rope-vit\n# 3. https://github.com/lucidrains/rotary-embedding-torch\n\n\ndef init_t_xy(end_x: int, end_y: int):\n t = torch.arange(end_x * end_y, dtype=torch.float32)\n t_x = (t % end_x).float()\n t_y = torch.div(t, end_x, rounding_mode=\"floor\").float()\n return t_x, t_y\n\n\ndef compute_axial_cis(dim: int, end_x: int, end_y: int, theta: float = 10000.0):\n freqs_x = 1.0 / (theta ** (torch.arange(0, dim, 4)[: (dim // 4)].float() / dim))\n freqs_y = 1.0 / (theta ** (torch.arange(0, dim, 4)[: (dim // 4)].float() / dim))\n\n t_x, t_y = init_t_xy(end_x, end_y)\n freqs_x = torch.outer(t_x, freqs_x)\n freqs_y = torch.outer(t_y, freqs_y)\n freqs_cis_x = torch.polar(torch.ones_like(freqs_x), freqs_x)\n freqs_cis_y = torch.polar(torch.ones_like(freqs_y), freqs_y)\n return torch.cat([freqs_cis_x, freqs_cis_y], dim=-1)\n\n\ndef reshape_for_broadcast(freqs_cis: torch.Tensor, x: torch.Tensor):\n ndim = x.ndim\n assert 0 <= 1 < ndim\n assert freqs_cis.shape == (x.shape[-2], x.shape[-1])\n shape = [d if i >= ndim - 2 else 1 for i, d in enumerate(x.shape)]\n return freqs_cis.view(*shape)\n\n\ndef apply_rotary_enc(\n xq: torch.Tensor,\n xk: torch.Tensor,\n freqs_cis: torch.Tensor,\n repeat_freqs_k: bool = False,\n):\n xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2))\n xk_ = (\n torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2))\n if xk.shape[-2] != 0\n else None\n )\n freqs_cis = reshape_for_broadcast(freqs_cis, xq_)\n xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(3)\n if xk_ is None:\n # no keys to rotate, due to dropout\n return xq_out.type_as(xq).to(xq.device), xk\n # repeat freqs along seq_len dim to match k seq_len\n if repeat_freqs_k:\n r = xk_.shape[-2] // xq_.shape[-2]\n if freqs_cis.is_cuda:\n freqs_cis = freqs_cis.repeat(*([1] * (freqs_cis.ndim - 2)), r, 1)\n else:\n # torch.repeat on complex numbers may not be supported on non-CUDA devices\n # (freqs_cis has 4 dims and we repeat on dim 2) so we use expand + flatten\n freqs_cis = freqs_cis.unsqueeze(2).expand(-1, -1, r, -1, -1).flatten(2, 3)\n xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(3)\n return xq_out.type_as(xq).to(xq.device), xk_out.type_as(xk).to(xk.device)\n" }, { "path": "auto-seg/submodules/segment-anything-2/sam2/modeling/sam/__init__.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n" }, { "path": "auto-seg/submodules/segment-anything-2/sam2/modeling/sam/mask_decoder.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nfrom typing import List, Optional, Tuple, Type\n\nimport torch\nfrom torch import nn\n\nfrom sam2.modeling.sam2_utils import LayerNorm2d, MLP\n\n\nclass MaskDecoder(nn.Module):\n def __init__(\n self,\n *,\n transformer_dim: int,\n transformer: nn.Module,\n num_multimask_outputs: int = 3,\n activation: Type[nn.Module] = nn.GELU,\n iou_head_depth: int = 3,\n iou_head_hidden_dim: int = 256,\n use_high_res_features: bool = False,\n iou_prediction_use_sigmoid=False,\n dynamic_multimask_via_stability=False,\n dynamic_multimask_stability_delta=0.05,\n dynamic_multimask_stability_thresh=0.98,\n pred_obj_scores: bool = False,\n pred_obj_scores_mlp: bool = False,\n use_multimask_token_for_obj_ptr: bool = False,\n ) -> None:\n \"\"\"\n Predicts masks given an image and prompt embeddings, using a\n transformer architecture.\n\n Arguments:\n transformer_dim (int): the channel dimension of the transformer\n transformer (nn.Module): the transformer used to predict masks\n num_multimask_outputs (int): the number of masks to predict\n when disambiguating masks\n activation (nn.Module): the type of activation to use when\n upscaling masks\n iou_head_depth (int): the depth of the MLP used to predict\n mask quality\n iou_head_hidden_dim (int): the hidden dimension of the MLP\n used to predict mask quality\n \"\"\"\n super().__init__()\n self.transformer_dim = transformer_dim\n self.transformer = transformer\n\n self.num_multimask_outputs = num_multimask_outputs\n\n self.iou_token = nn.Embedding(1, transformer_dim)\n self.num_mask_tokens = num_multimask_outputs + 1\n self.mask_tokens = nn.Embedding(self.num_mask_tokens, transformer_dim)\n\n self.pred_obj_scores = pred_obj_scores\n if self.pred_obj_scores:\n self.obj_score_token = nn.Embedding(1, transformer_dim)\n self.use_multimask_token_for_obj_ptr = use_multimask_token_for_obj_ptr\n\n self.output_upscaling = nn.Sequential(\n nn.ConvTranspose2d(\n transformer_dim, transformer_dim // 4, kernel_size=2, stride=2\n ),\n LayerNorm2d(transformer_dim // 4),\n activation(),\n nn.ConvTranspose2d(\n transformer_dim // 4, transformer_dim // 8, kernel_size=2, stride=2\n ),\n activation(),\n )\n self.use_high_res_features = use_high_res_features\n if use_high_res_features:\n self.conv_s0 = nn.Conv2d(\n transformer_dim, transformer_dim // 8, kernel_size=1, stride=1\n )\n self.conv_s1 = nn.Conv2d(\n transformer_dim, transformer_dim // 4, kernel_size=1, stride=1\n )\n\n self.output_hypernetworks_mlps = nn.ModuleList(\n [\n MLP(transformer_dim, transformer_dim, transformer_dim // 8, 3)\n for i in range(self.num_mask_tokens)\n ]\n )\n\n self.iou_prediction_head = MLP(\n transformer_dim,\n iou_head_hidden_dim,\n self.num_mask_tokens,\n iou_head_depth,\n sigmoid_output=iou_prediction_use_sigmoid,\n )\n if self.pred_obj_scores:\n self.pred_obj_score_head = nn.Linear(transformer_dim, 1)\n if pred_obj_scores_mlp:\n self.pred_obj_score_head = MLP(transformer_dim, transformer_dim, 1, 3)\n\n # When outputting a single mask, optionally we can dynamically fall back to the best\n # multimask output token if the single mask output token gives low stability scores.\n self.dynamic_multimask_via_stability = dynamic_multimask_via_stability\n self.dynamic_multimask_stability_delta = dynamic_multimask_stability_delta\n self.dynamic_multimask_stability_thresh = dynamic_multimask_stability_thresh\n\n def forward(\n self,\n image_embeddings: torch.Tensor,\n image_pe: torch.Tensor,\n sparse_prompt_embeddings: torch.Tensor,\n dense_prompt_embeddings: torch.Tensor,\n multimask_output: bool,\n repeat_image: bool,\n high_res_features: Optional[List[torch.Tensor]] = None,\n ) -> Tuple[torch.Tensor, torch.Tensor]:\n \"\"\"\n Predict masks given image and prompt embeddings.\n\n Arguments:\n image_embeddings (torch.Tensor): the embeddings from the image encoder\n image_pe (torch.Tensor): positional encoding with the shape of image_embeddings\n sparse_prompt_embeddings (torch.Tensor): the embeddings of the points and boxes\n dense_prompt_embeddings (torch.Tensor): the embeddings of the mask inputs\n multimask_output (bool): Whether to return multiple masks or a single\n mask.\n\n Returns:\n torch.Tensor: batched predicted masks\n torch.Tensor: batched predictions of mask quality\n torch.Tensor: batched SAM token for mask output\n \"\"\"\n masks, iou_pred, mask_tokens_out, object_score_logits = self.predict_masks(\n image_embeddings=image_embeddings,\n image_pe=image_pe,\n sparse_prompt_embeddings=sparse_prompt_embeddings,\n dense_prompt_embeddings=dense_prompt_embeddings,\n repeat_image=repeat_image,\n high_res_features=high_res_features,\n )\n\n # Select the correct mask or masks for output\n if multimask_output:\n masks = masks[:, 1:, :, :]\n iou_pred = iou_pred[:, 1:]\n elif self.dynamic_multimask_via_stability and not self.training:\n masks, iou_pred = self._dynamic_multimask_via_stability(masks, iou_pred)\n else:\n masks = masks[:, 0:1, :, :]\n iou_pred = iou_pred[:, 0:1]\n\n if multimask_output and self.use_multimask_token_for_obj_ptr:\n sam_tokens_out = mask_tokens_out[:, 1:] # [b, 3, c] shape\n else:\n # Take the mask output token. Here we *always* use the token for single mask output.\n # At test time, even if we track after 1-click (and using multimask_output=True),\n # we still take the single mask token here. The rationale is that we always track\n # after multiple clicks during training, so the past tokens seen during training\n # are always the single mask token (and we'll let it be the object-memory token).\n sam_tokens_out = mask_tokens_out[:, 0:1] # [b, 1, c] shape\n\n # Prepare output\n return masks, iou_pred, sam_tokens_out, object_score_logits\n\n def predict_masks(\n self,\n image_embeddings: torch.Tensor,\n image_pe: torch.Tensor,\n sparse_prompt_embeddings: torch.Tensor,\n dense_prompt_embeddings: torch.Tensor,\n repeat_image: bool,\n high_res_features: Optional[List[torch.Tensor]] = None,\n ) -> Tuple[torch.Tensor, torch.Tensor]:\n \"\"\"Predicts masks. See 'forward' for more details.\"\"\"\n # Concatenate output tokens\n s = 0\n if self.pred_obj_scores:\n output_tokens = torch.cat(\n [\n self.obj_score_token.weight,\n self.iou_token.weight,\n self.mask_tokens.weight,\n ],\n dim=0,\n )\n s = 1\n else:\n output_tokens = torch.cat(\n [self.iou_token.weight, self.mask_tokens.weight], dim=0\n )\n output_tokens = output_tokens.unsqueeze(0).expand(\n sparse_prompt_embeddings.size(0), -1, -1\n )\n tokens = torch.cat((output_tokens, sparse_prompt_embeddings), dim=1)\n\n # Expand per-image data in batch direction to be per-mask\n if repeat_image:\n src = torch.repeat_interleave(image_embeddings, tokens.shape[0], dim=0)\n else:\n assert image_embeddings.shape[0] == tokens.shape[0]\n src = image_embeddings\n src = src + dense_prompt_embeddings\n assert (\n image_pe.size(0) == 1\n ), \"image_pe should have size 1 in batch dim (from `get_dense_pe()`)\"\n pos_src = torch.repeat_interleave(image_pe, tokens.shape[0], dim=0)\n b, c, h, w = src.shape\n\n # Run the transformer\n hs, src = self.transformer(src, pos_src, tokens)\n iou_token_out = hs[:, s, :]\n mask_tokens_out = hs[:, s + 1 : (s + 1 + self.num_mask_tokens), :]\n\n # Upscale mask embeddings and predict masks using the mask tokens\n src = src.transpose(1, 2).view(b, c, h, w)\n if not self.use_high_res_features:\n upscaled_embedding = self.output_upscaling(src)\n else:\n dc1, ln1, act1, dc2, act2 = self.output_upscaling\n feat_s0, feat_s1 = high_res_features\n upscaled_embedding = act1(ln1(dc1(src) + feat_s1))\n upscaled_embedding = act2(dc2(upscaled_embedding) + feat_s0)\n\n hyper_in_list: List[torch.Tensor] = []\n for i in range(self.num_mask_tokens):\n hyper_in_list.append(\n self.output_hypernetworks_mlps[i](mask_tokens_out[:, i, :])\n )\n hyper_in = torch.stack(hyper_in_list, dim=1)\n b, c, h, w = upscaled_embedding.shape\n masks = (hyper_in @ upscaled_embedding.view(b, c, h * w)).view(b, -1, h, w)\n\n # Generate mask quality predictions\n iou_pred = self.iou_prediction_head(iou_token_out)\n if self.pred_obj_scores:\n assert s == 1\n object_score_logits = self.pred_obj_score_head(hs[:, 0, :])\n else:\n # Obj scores logits - default to 10.0, i.e. assuming the object is present, sigmoid(10)=1\n object_score_logits = 10.0 * iou_pred.new_ones(iou_pred.shape[0], 1)\n\n return masks, iou_pred, mask_tokens_out, object_score_logits\n\n def _get_stability_scores(self, mask_logits):\n \"\"\"\n Compute stability scores of the mask logits based on the IoU between upper and\n lower thresholds.\n \"\"\"\n mask_logits = mask_logits.flatten(-2)\n stability_delta = self.dynamic_multimask_stability_delta\n area_i = torch.sum(mask_logits > stability_delta, dim=-1).float()\n area_u = torch.sum(mask_logits > -stability_delta, dim=-1).float()\n stability_scores = torch.where(area_u > 0, area_i / area_u, 1.0)\n return stability_scores\n\n def _dynamic_multimask_via_stability(self, all_mask_logits, all_iou_scores):\n \"\"\"\n When outputting a single mask, if the stability score from the current single-mask\n output (based on output token 0) falls below a threshold, we instead select from\n multi-mask outputs (based on output token 1~3) the mask with the highest predicted\n IoU score. This is intended to ensure a valid mask for both clicking and tracking.\n \"\"\"\n # The best mask from multimask output tokens (1~3)\n multimask_logits = all_mask_logits[:, 1:, :, :]\n multimask_iou_scores = all_iou_scores[:, 1:]\n best_scores_inds = torch.argmax(multimask_iou_scores, dim=-1)\n batch_inds = torch.arange(\n multimask_iou_scores.size(0), device=all_iou_scores.device\n )\n best_multimask_logits = multimask_logits[batch_inds, best_scores_inds]\n best_multimask_logits = best_multimask_logits.unsqueeze(1)\n best_multimask_iou_scores = multimask_iou_scores[batch_inds, best_scores_inds]\n best_multimask_iou_scores = best_multimask_iou_scores.unsqueeze(1)\n\n # The mask from singlemask output token 0 and its stability score\n singlemask_logits = all_mask_logits[:, 0:1, :, :]\n singlemask_iou_scores = all_iou_scores[:, 0:1]\n stability_scores = self._get_stability_scores(singlemask_logits)\n is_stable = stability_scores >= self.dynamic_multimask_stability_thresh\n\n # Dynamically fall back to best multimask output upon low stability scores.\n mask_logits_out = torch.where(\n is_stable[..., None, None].expand_as(singlemask_logits),\n singlemask_logits,\n best_multimask_logits,\n )\n iou_scores_out = torch.where(\n is_stable.expand_as(singlemask_iou_scores),\n singlemask_iou_scores,\n best_multimask_iou_scores,\n )\n return mask_logits_out, iou_scores_out\n" }, { "path": "auto-seg/submodules/segment-anything-2/sam2/modeling/sam/prompt_encoder.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nfrom typing import Optional, Tuple, Type\n\nimport torch\nfrom torch import nn\n\nfrom sam2.modeling.position_encoding import PositionEmbeddingRandom\n\nfrom sam2.modeling.sam2_utils import LayerNorm2d\n\n\nclass PromptEncoder(nn.Module):\n def __init__(\n self,\n embed_dim: int,\n image_embedding_size: Tuple[int, int],\n input_image_size: Tuple[int, int],\n mask_in_chans: int,\n activation: Type[nn.Module] = nn.GELU,\n ) -> None:\n \"\"\"\n Encodes prompts for input to SAM's mask decoder.\n\n Arguments:\n embed_dim (int): The prompts' embedding dimension\n image_embedding_size (tuple(int, int)): The spatial size of the\n image embedding, as (H, W).\n input_image_size (int): The padded size of the image as input\n to the image encoder, as (H, W).\n mask_in_chans (int): The number of hidden channels used for\n encoding input masks.\n activation (nn.Module): The activation to use when encoding\n input masks.\n \"\"\"\n super().__init__()\n self.embed_dim = embed_dim\n self.input_image_size = input_image_size\n self.image_embedding_size = image_embedding_size\n self.pe_layer = PositionEmbeddingRandom(embed_dim // 2)\n\n self.num_point_embeddings: int = 4 # pos/neg point + 2 box corners\n point_embeddings = [\n nn.Embedding(1, embed_dim) for i in range(self.num_point_embeddings)\n ]\n self.point_embeddings = nn.ModuleList(point_embeddings)\n self.not_a_point_embed = nn.Embedding(1, embed_dim)\n\n self.mask_input_size = (\n 4 * image_embedding_size[0],\n 4 * image_embedding_size[1],\n )\n self.mask_downscaling = nn.Sequential(\n nn.Conv2d(1, mask_in_chans // 4, kernel_size=2, stride=2),\n LayerNorm2d(mask_in_chans // 4),\n activation(),\n nn.Conv2d(mask_in_chans // 4, mask_in_chans, kernel_size=2, stride=2),\n LayerNorm2d(mask_in_chans),\n activation(),\n nn.Conv2d(mask_in_chans, embed_dim, kernel_size=1),\n )\n self.no_mask_embed = nn.Embedding(1, embed_dim)\n\n def get_dense_pe(self) -> torch.Tensor:\n \"\"\"\n Returns the positional encoding used to encode point prompts,\n applied to a dense set of points the shape of the image encoding.\n\n Returns:\n torch.Tensor: Positional encoding with shape\n 1x(embed_dim)x(embedding_h)x(embedding_w)\n \"\"\"\n return self.pe_layer(self.image_embedding_size).unsqueeze(0)\n\n def _embed_points(\n self,\n points: torch.Tensor,\n labels: torch.Tensor,\n pad: bool,\n ) -> torch.Tensor:\n \"\"\"Embeds point prompts.\"\"\"\n points = points + 0.5 # Shift to center of pixel\n if pad:\n padding_point = torch.zeros((points.shape[0], 1, 2), device=points.device)\n padding_label = -torch.ones((labels.shape[0], 1), device=labels.device)\n points = torch.cat([points, padding_point], dim=1)\n labels = torch.cat([labels, padding_label], dim=1)\n point_embedding = self.pe_layer.forward_with_coords(\n points, self.input_image_size\n )\n point_embedding[labels == -1] = 0.0\n point_embedding[labels == -1] += self.not_a_point_embed.weight\n point_embedding[labels == 0] += self.point_embeddings[0].weight\n point_embedding[labels == 1] += self.point_embeddings[1].weight\n point_embedding[labels == 2] += self.point_embeddings[2].weight\n point_embedding[labels == 3] += self.point_embeddings[3].weight\n return point_embedding\n\n def _embed_boxes(self, boxes: torch.Tensor) -> torch.Tensor:\n \"\"\"Embeds box prompts.\"\"\"\n boxes = boxes + 0.5 # Shift to center of pixel\n coords = boxes.reshape(-1, 2, 2)\n corner_embedding = self.pe_layer.forward_with_coords(\n coords, self.input_image_size\n )\n corner_embedding[:, 0, :] += self.point_embeddings[2].weight\n corner_embedding[:, 1, :] += self.point_embeddings[3].weight\n return corner_embedding\n\n def _embed_masks(self, masks: torch.Tensor) -> torch.Tensor:\n \"\"\"Embeds mask inputs.\"\"\"\n mask_embedding = self.mask_downscaling(masks)\n return mask_embedding\n\n def _get_batch_size(\n self,\n points: Optional[Tuple[torch.Tensor, torch.Tensor]],\n boxes: Optional[torch.Tensor],\n masks: Optional[torch.Tensor],\n ) -> int:\n \"\"\"\n Gets the batch size of the output given the batch size of the input prompts.\n \"\"\"\n if points is not None:\n return points[0].shape[0]\n elif boxes is not None:\n return boxes.shape[0]\n elif masks is not None:\n return masks.shape[0]\n else:\n return 1\n\n def _get_device(self) -> torch.device:\n return self.point_embeddings[0].weight.device\n\n def forward(\n self,\n points: Optional[Tuple[torch.Tensor, torch.Tensor]],\n boxes: Optional[torch.Tensor],\n masks: Optional[torch.Tensor],\n ) -> Tuple[torch.Tensor, torch.Tensor]:\n \"\"\"\n Embeds different types of prompts, returning both sparse and dense\n embeddings.\n\n Arguments:\n points (tuple(torch.Tensor, torch.Tensor) or none): point coordinates\n and labels to embed.\n boxes (torch.Tensor or none): boxes to embed\n masks (torch.Tensor or none): masks to embed\n\n Returns:\n torch.Tensor: sparse embeddings for the points and boxes, with shape\n BxNx(embed_dim), where N is determined by the number of input points\n and boxes.\n torch.Tensor: dense embeddings for the masks, in the shape\n Bx(embed_dim)x(embed_H)x(embed_W)\n \"\"\"\n bs = self._get_batch_size(points, boxes, masks)\n sparse_embeddings = torch.empty(\n (bs, 0, self.embed_dim), device=self._get_device()\n )\n if points is not None:\n coords, labels = points\n point_embeddings = self._embed_points(coords, labels, pad=(boxes is None))\n sparse_embeddings = torch.cat([sparse_embeddings, point_embeddings], dim=1)\n if boxes is not None:\n box_embeddings = self._embed_boxes(boxes)\n sparse_embeddings = torch.cat([sparse_embeddings, box_embeddings], dim=1)\n\n if masks is not None:\n dense_embeddings = self._embed_masks(masks)\n else:\n dense_embeddings = self.no_mask_embed.weight.reshape(1, -1, 1, 1).expand(\n bs, -1, self.image_embedding_size[0], self.image_embedding_size[1]\n )\n\n return sparse_embeddings, dense_embeddings\n" }, { "path": "auto-seg/submodules/segment-anything-2/sam2/modeling/sam/transformer.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport contextlib\nimport math\nimport warnings\nfrom functools import partial\nfrom typing import Tuple, Type\n\nimport torch\nimport torch.nn.functional as F\nfrom torch import nn, Tensor\n\nfrom sam2.modeling.position_encoding import apply_rotary_enc, compute_axial_cis\nfrom sam2.modeling.sam2_utils import MLP\nfrom sam2.utils.misc import get_sdpa_settings\n\nwarnings.simplefilter(action=\"ignore\", category=FutureWarning)\n# Check whether Flash Attention is available (and use it by default)\nOLD_GPU, USE_FLASH_ATTN, MATH_KERNEL_ON = get_sdpa_settings()\n# A fallback setting to allow all available kernels if Flash Attention fails\nALLOW_ALL_KERNELS = False\n\n\ndef sdp_kernel_context(dropout_p):\n \"\"\"\n Get the context for the attention scaled dot-product kernel. We use Flash Attention\n by default, but fall back to all available kernels if Flash Attention fails.\n \"\"\"\n if ALLOW_ALL_KERNELS:\n return contextlib.nullcontext()\n\n return torch.backends.cuda.sdp_kernel(\n enable_flash=USE_FLASH_ATTN,\n # if Flash attention kernel is off, then math kernel needs to be enabled\n enable_math=(OLD_GPU and dropout_p > 0.0) or MATH_KERNEL_ON,\n enable_mem_efficient=OLD_GPU,\n )\n\n\nclass TwoWayTransformer(nn.Module):\n def __init__(\n self,\n depth: int,\n embedding_dim: int,\n num_heads: int,\n mlp_dim: int,\n activation: Type[nn.Module] = nn.ReLU,\n attention_downsample_rate: int = 2,\n ) -> None:\n \"\"\"\n A transformer decoder that attends to an input image using\n queries whose positional embedding is supplied.\n\n Args:\n depth (int): number of layers in the transformer\n embedding_dim (int): the channel dimension for the input embeddings\n num_heads (int): the number of heads for multihead attention. Must\n divide embedding_dim\n mlp_dim (int): the channel dimension internal to the MLP block\n activation (nn.Module): the activation to use in the MLP block\n \"\"\"\n super().__init__()\n self.depth = depth\n self.embedding_dim = embedding_dim\n self.num_heads = num_heads\n self.mlp_dim = mlp_dim\n self.layers = nn.ModuleList()\n\n for i in range(depth):\n self.layers.append(\n TwoWayAttentionBlock(\n embedding_dim=embedding_dim,\n num_heads=num_heads,\n mlp_dim=mlp_dim,\n activation=activation,\n attention_downsample_rate=attention_downsample_rate,\n skip_first_layer_pe=(i == 0),\n )\n )\n\n self.final_attn_token_to_image = Attention(\n embedding_dim, num_heads, downsample_rate=attention_downsample_rate\n )\n self.norm_final_attn = nn.LayerNorm(embedding_dim)\n\n def forward(\n self,\n image_embedding: Tensor,\n image_pe: Tensor,\n point_embedding: Tensor,\n ) -> Tuple[Tensor, Tensor]:\n \"\"\"\n Args:\n image_embedding (torch.Tensor): image to attend to. Should be shape\n B x embedding_dim x h x w for any h and w.\n image_pe (torch.Tensor): the positional encoding to add to the image. Must\n have the same shape as image_embedding.\n point_embedding (torch.Tensor): the embedding to add to the query points.\n Must have shape B x N_points x embedding_dim for any N_points.\n\n Returns:\n torch.Tensor: the processed point_embedding\n torch.Tensor: the processed image_embedding\n \"\"\"\n # BxCxHxW -> BxHWxC == B x N_image_tokens x C\n bs, c, h, w = image_embedding.shape\n image_embedding = image_embedding.flatten(2).permute(0, 2, 1)\n image_pe = image_pe.flatten(2).permute(0, 2, 1)\n\n # Prepare queries\n queries = point_embedding\n keys = image_embedding\n\n # Apply transformer blocks and final layernorm\n for layer in self.layers:\n queries, keys = layer(\n queries=queries,\n keys=keys,\n query_pe=point_embedding,\n key_pe=image_pe,\n )\n\n # Apply the final attention layer from the points to the image\n q = queries + point_embedding\n k = keys + image_pe\n attn_out = self.final_attn_token_to_image(q=q, k=k, v=keys)\n queries = queries + attn_out\n queries = self.norm_final_attn(queries)\n\n return queries, keys\n\n\nclass TwoWayAttentionBlock(nn.Module):\n def __init__(\n self,\n embedding_dim: int,\n num_heads: int,\n mlp_dim: int = 2048,\n activation: Type[nn.Module] = nn.ReLU,\n attention_downsample_rate: int = 2,\n skip_first_layer_pe: bool = False,\n ) -> None:\n \"\"\"\n A transformer block with four layers: (1) self-attention of sparse\n inputs, (2) cross attention of sparse inputs to dense inputs, (3) mlp\n block on sparse inputs, and (4) cross attention of dense inputs to sparse\n inputs.\n\n Arguments:\n embedding_dim (int): the channel dimension of the embeddings\n num_heads (int): the number of heads in the attention layers\n mlp_dim (int): the hidden dimension of the mlp block\n activation (nn.Module): the activation of the mlp block\n skip_first_layer_pe (bool): skip the PE on the first layer\n \"\"\"\n super().__init__()\n self.self_attn = Attention(embedding_dim, num_heads)\n self.norm1 = nn.LayerNorm(embedding_dim)\n\n self.cross_attn_token_to_image = Attention(\n embedding_dim, num_heads, downsample_rate=attention_downsample_rate\n )\n self.norm2 = nn.LayerNorm(embedding_dim)\n\n self.mlp = MLP(\n embedding_dim, mlp_dim, embedding_dim, num_layers=2, activation=activation\n )\n self.norm3 = nn.LayerNorm(embedding_dim)\n\n self.norm4 = nn.LayerNorm(embedding_dim)\n self.cross_attn_image_to_token = Attention(\n embedding_dim, num_heads, downsample_rate=attention_downsample_rate\n )\n\n self.skip_first_layer_pe = skip_first_layer_pe\n\n def forward(\n self, queries: Tensor, keys: Tensor, query_pe: Tensor, key_pe: Tensor\n ) -> Tuple[Tensor, Tensor]:\n # Self attention block\n if self.skip_first_layer_pe:\n queries = self.self_attn(q=queries, k=queries, v=queries)\n else:\n q = queries + query_pe\n attn_out = self.self_attn(q=q, k=q, v=queries)\n queries = queries + attn_out\n queries = self.norm1(queries)\n\n # Cross attention block, tokens attending to image embedding\n q = queries + query_pe\n k = keys + key_pe\n attn_out = self.cross_attn_token_to_image(q=q, k=k, v=keys)\n queries = queries + attn_out\n queries = self.norm2(queries)\n\n # MLP block\n mlp_out = self.mlp(queries)\n queries = queries + mlp_out\n queries = self.norm3(queries)\n\n # Cross attention block, image embedding attending to tokens\n q = queries + query_pe\n k = keys + key_pe\n attn_out = self.cross_attn_image_to_token(q=k, k=q, v=queries)\n keys = keys + attn_out\n keys = self.norm4(keys)\n\n return queries, keys\n\n\nclass Attention(nn.Module):\n \"\"\"\n An attention layer that allows for downscaling the size of the embedding\n after projection to queries, keys, and values.\n \"\"\"\n\n def __init__(\n self,\n embedding_dim: int,\n num_heads: int,\n downsample_rate: int = 1,\n dropout: float = 0.0,\n kv_in_dim: int = None,\n ) -> None:\n super().__init__()\n self.embedding_dim = embedding_dim\n self.kv_in_dim = kv_in_dim if kv_in_dim is not None else embedding_dim\n self.internal_dim = embedding_dim // downsample_rate\n self.num_heads = num_heads\n assert (\n self.internal_dim % num_heads == 0\n ), \"num_heads must divide embedding_dim.\"\n\n self.q_proj = nn.Linear(embedding_dim, self.internal_dim)\n self.k_proj = nn.Linear(self.kv_in_dim, self.internal_dim)\n self.v_proj = nn.Linear(self.kv_in_dim, self.internal_dim)\n self.out_proj = nn.Linear(self.internal_dim, embedding_dim)\n\n self.dropout_p = dropout\n\n def _separate_heads(self, x: Tensor, num_heads: int) -> Tensor:\n b, n, c = x.shape\n x = x.reshape(b, n, num_heads, c // num_heads)\n return x.transpose(1, 2) # B x N_heads x N_tokens x C_per_head\n\n def _recombine_heads(self, x: Tensor) -> Tensor:\n b, n_heads, n_tokens, c_per_head = x.shape\n x = x.transpose(1, 2)\n return x.reshape(b, n_tokens, n_heads * c_per_head) # B x N_tokens x C\n\n def forward(self, q: Tensor, k: Tensor, v: Tensor) -> Tensor:\n # Input projections\n q = self.q_proj(q)\n k = self.k_proj(k)\n v = self.v_proj(v)\n\n # Separate into heads\n q = self._separate_heads(q, self.num_heads)\n k = self._separate_heads(k, self.num_heads)\n v = self._separate_heads(v, self.num_heads)\n\n dropout_p = self.dropout_p if self.training else 0.0\n # Attention\n try:\n with sdp_kernel_context(dropout_p):\n out = F.scaled_dot_product_attention(q, k, v, dropout_p=dropout_p)\n except Exception as e:\n # Fall back to all kernels if the Flash attention kernel fails\n warnings.warn(\n f\"Flash Attention kernel failed due to: {e}\\nFalling back to all available \"\n f\"kernels for scaled_dot_product_attention (which may have a slower speed).\",\n category=UserWarning,\n stacklevel=2,\n )\n global ALLOW_ALL_KERNELS\n ALLOW_ALL_KERNELS = True\n out = F.scaled_dot_product_attention(q, k, v, dropout_p=dropout_p)\n\n out = self._recombine_heads(out)\n out = self.out_proj(out)\n\n return out\n\n\nclass RoPEAttention(Attention):\n \"\"\"Attention with rotary position encoding.\"\"\"\n\n def __init__(\n self,\n *args,\n rope_theta=10000.0,\n # whether to repeat q rope to match k length\n # this is needed for cross-attention to memories\n rope_k_repeat=False,\n feat_sizes=(32, 32), # [w, h] for stride 16 feats at 512 resolution\n **kwargs,\n ):\n super().__init__(*args, **kwargs)\n\n self.compute_cis = partial(\n compute_axial_cis, dim=self.internal_dim // self.num_heads, theta=rope_theta\n )\n freqs_cis = self.compute_cis(end_x=feat_sizes[0], end_y=feat_sizes[1])\n self.freqs_cis = freqs_cis\n self.rope_k_repeat = rope_k_repeat\n\n def forward(\n self, q: Tensor, k: Tensor, v: Tensor, num_k_exclude_rope: int = 0\n ) -> Tensor:\n # Input projections\n q = self.q_proj(q)\n k = self.k_proj(k)\n v = self.v_proj(v)\n\n # Separate into heads\n q = self._separate_heads(q, self.num_heads)\n k = self._separate_heads(k, self.num_heads)\n v = self._separate_heads(v, self.num_heads)\n\n # Apply rotary position encoding\n w = h = math.sqrt(q.shape[-2])\n self.freqs_cis = self.freqs_cis.to(q.device)\n if self.freqs_cis.shape[0] != q.shape[-2]:\n self.freqs_cis = self.compute_cis(end_x=w, end_y=h).to(q.device)\n if q.shape[-2] != k.shape[-2]:\n assert self.rope_k_repeat\n\n num_k_rope = k.size(-2) - num_k_exclude_rope\n q, k[:, :, :num_k_rope] = apply_rotary_enc(\n q,\n k[:, :, :num_k_rope],\n freqs_cis=self.freqs_cis,\n repeat_freqs_k=self.rope_k_repeat,\n )\n\n dropout_p = self.dropout_p if self.training else 0.0\n # Attention\n try:\n with sdp_kernel_context(dropout_p):\n out = F.scaled_dot_product_attention(q, k, v, dropout_p=dropout_p)\n except Exception as e:\n # Fall back to all kernels if the Flash attention kernel fails\n warnings.warn(\n f\"Flash Attention kernel failed due to: {e}\\nFalling back to all available \"\n f\"kernels for scaled_dot_product_attention (which may have a slower speed).\",\n category=UserWarning,\n stacklevel=2,\n )\n global ALLOW_ALL_KERNELS\n ALLOW_ALL_KERNELS = True\n out = F.scaled_dot_product_attention(q, k, v, dropout_p=dropout_p)\n\n out = self._recombine_heads(out)\n out = self.out_proj(out)\n\n return out\n" }, { "path": "auto-seg/submodules/segment-anything-2/sam2/modeling/sam2_base.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport torch\nimport torch.distributed\nimport torch.nn.functional as F\n\nfrom torch.nn.init import trunc_normal_\n\nfrom sam2.modeling.sam.mask_decoder import MaskDecoder\nfrom sam2.modeling.sam.prompt_encoder import PromptEncoder\nfrom sam2.modeling.sam.transformer import TwoWayTransformer\nfrom sam2.modeling.sam2_utils import get_1d_sine_pe, MLP, select_closest_cond_frames\n\n# a large negative value as a placeholder score for missing objects\nNO_OBJ_SCORE = -1024.0\n\n\nclass SAM2Base(torch.nn.Module):\n def __init__(\n self,\n image_encoder,\n memory_attention,\n memory_encoder,\n num_maskmem=7, # default 1 input frame + 6 previous frames\n image_size=512,\n backbone_stride=16, # stride of the image backbone output\n sigmoid_scale_for_mem_enc=1.0, # scale factor for mask sigmoid prob\n sigmoid_bias_for_mem_enc=0.0, # bias factor for mask sigmoid prob\n # During evaluation, whether to binarize the sigmoid mask logits on interacted frames with clicks\n binarize_mask_from_pts_for_mem_enc=False,\n use_mask_input_as_output_without_sam=False, # on frames with mask input, whether to directly output the input mask without using a SAM prompt encoder + mask decoder\n # The maximum number of conditioning frames to participate in the memory attention (-1 means no limit; if there are more conditioning frames than this limit,\n # we only cross-attend to the temporally closest `max_cond_frames_in_attn` conditioning frames in the encoder when tracking each frame). This gives the model\n # a temporal locality when handling a large number of annotated frames (since closer frames should be more important) and also avoids GPU OOM.\n max_cond_frames_in_attn=-1,\n # on the first frame, whether to directly add the no-memory embedding to the image feature\n # (instead of using the transformer encoder)\n directly_add_no_mem_embed=False,\n # whether to use high-resolution feature maps in the SAM mask decoder\n use_high_res_features_in_sam=False,\n # whether to output multiple (3) masks for the first click on initial conditioning frames\n multimask_output_in_sam=False,\n # the minimum and maximum number of clicks to use multimask_output_in_sam (only relevant when `multimask_output_in_sam=True`;\n # default is 1 for both, meaning that only the first click gives multimask output; also note that a box counts as two points)\n multimask_min_pt_num=1,\n multimask_max_pt_num=1,\n # whether to also use multimask output for tracking (not just for the first click on initial conditioning frames; only relevant when `multimask_output_in_sam=True`)\n multimask_output_for_tracking=False,\n # Whether to use multimask tokens for obj ptr; Only relevant when both\n # use_obj_ptrs_in_encoder=True and multimask_output_for_tracking=True\n use_multimask_token_for_obj_ptr: bool = False,\n # whether to use sigmoid to restrict ious prediction to [0-1]\n iou_prediction_use_sigmoid=False,\n # The memory bank's temporal stride during evaluation (i.e. the `r` parameter in XMem and Cutie; XMem and Cutie use r=5).\n # For r>1, the (self.num_maskmem - 1) non-conditioning memory frames consist of\n # (self.num_maskmem - 2) nearest frames from every r-th frames, plus the last frame.\n memory_temporal_stride_for_eval=1,\n # whether to apply non-overlapping constraints on the object masks in the memory encoder during evaluation (to avoid/alleviate superposing masks)\n non_overlap_masks_for_mem_enc=False,\n # whether to cross-attend to object pointers from other frames (based on SAM output tokens) in the encoder\n use_obj_ptrs_in_encoder=False,\n # the maximum number of object pointers from other frames in encoder cross attention (only relevant when `use_obj_ptrs_in_encoder=True`)\n max_obj_ptrs_in_encoder=16,\n # whether to add temporal positional encoding to the object pointers in the encoder (only relevant when `use_obj_ptrs_in_encoder=True`)\n add_tpos_enc_to_obj_ptrs=True,\n # whether to add an extra linear projection layer for the temporal positional encoding in the object pointers to avoid potential interference\n # with spatial positional encoding (only relevant when both `use_obj_ptrs_in_encoder=True` and `add_tpos_enc_to_obj_ptrs=True`)\n proj_tpos_enc_in_obj_ptrs=False,\n # whether to use signed distance (instead of unsigned absolute distance) in the temporal positional encoding in the object pointers\n # (only relevant when both `use_obj_ptrs_in_encoder=True` and `add_tpos_enc_to_obj_ptrs=True`)\n use_signed_tpos_enc_to_obj_ptrs=False,\n # whether to only attend to object pointers in the past (before the current frame) in the encoder during evaluation\n # (only relevant when `use_obj_ptrs_in_encoder=True`; this might avoid pointer information too far in the future to distract the initial tracking)\n only_obj_ptrs_in_the_past_for_eval=False,\n # Whether to predict if there is an object in the frame\n pred_obj_scores: bool = False,\n # Whether to use an MLP to predict object scores\n pred_obj_scores_mlp: bool = False,\n # Only relevant if pred_obj_scores=True and use_obj_ptrs_in_encoder=True;\n # Whether to have a fixed no obj pointer when there is no object present\n # or to use it as an additive embedding with obj_ptr produced by decoder\n fixed_no_obj_ptr: bool = False,\n # Soft no object, i.e. mix in no_obj_ptr softly,\n # hope to make recovery easier if there is a mistake and mitigate accumulation of errors\n soft_no_obj_ptr: bool = False,\n use_mlp_for_obj_ptr_proj: bool = False,\n # add no obj embedding to spatial frames\n no_obj_embed_spatial: bool = False,\n # extra arguments used to construct the SAM mask decoder; if not None, it should be a dict of kwargs to be passed into `MaskDecoder` class.\n sam_mask_decoder_extra_args=None,\n compile_image_encoder: bool = False,\n ):\n super().__init__()\n\n # Part 1: the image backbone\n self.image_encoder = image_encoder\n # Use level 0, 1, 2 for high-res setting, or just level 2 for the default setting\n self.use_high_res_features_in_sam = use_high_res_features_in_sam\n self.num_feature_levels = 3 if use_high_res_features_in_sam else 1\n self.use_obj_ptrs_in_encoder = use_obj_ptrs_in_encoder\n self.max_obj_ptrs_in_encoder = max_obj_ptrs_in_encoder\n if use_obj_ptrs_in_encoder:\n # A conv layer to downsample the mask prompt to stride 4 (the same stride as\n # low-res SAM mask logits) and to change its scales from 0~1 to SAM logit scale,\n # so that it can be fed into the SAM mask decoder to generate a pointer.\n self.mask_downsample = torch.nn.Conv2d(1, 1, kernel_size=4, stride=4)\n self.add_tpos_enc_to_obj_ptrs = add_tpos_enc_to_obj_ptrs\n if proj_tpos_enc_in_obj_ptrs:\n assert add_tpos_enc_to_obj_ptrs # these options need to be used together\n self.proj_tpos_enc_in_obj_ptrs = proj_tpos_enc_in_obj_ptrs\n self.use_signed_tpos_enc_to_obj_ptrs = use_signed_tpos_enc_to_obj_ptrs\n self.only_obj_ptrs_in_the_past_for_eval = only_obj_ptrs_in_the_past_for_eval\n\n # Part 2: memory attention to condition current frame's visual features\n # with memories (and obj ptrs) from past frames\n self.memory_attention = memory_attention\n self.hidden_dim = image_encoder.neck.d_model\n\n # Part 3: memory encoder for the previous frame's outputs\n self.memory_encoder = memory_encoder\n self.mem_dim = self.hidden_dim\n if hasattr(self.memory_encoder, \"out_proj\") and hasattr(\n self.memory_encoder.out_proj, \"weight\"\n ):\n # if there is compression of memories along channel dim\n self.mem_dim = self.memory_encoder.out_proj.weight.shape[0]\n self.num_maskmem = num_maskmem # Number of memories accessible\n # Temporal encoding of the memories\n self.maskmem_tpos_enc = torch.nn.Parameter(\n torch.zeros(num_maskmem, 1, 1, self.mem_dim)\n )\n trunc_normal_(self.maskmem_tpos_enc, std=0.02)\n # a single token to indicate no memory embedding from previous frames\n self.no_mem_embed = torch.nn.Parameter(torch.zeros(1, 1, self.hidden_dim))\n self.no_mem_pos_enc = torch.nn.Parameter(torch.zeros(1, 1, self.hidden_dim))\n trunc_normal_(self.no_mem_embed, std=0.02)\n trunc_normal_(self.no_mem_pos_enc, std=0.02)\n self.directly_add_no_mem_embed = directly_add_no_mem_embed\n # Apply sigmoid to the output raw mask logits (to turn them from\n # range (-inf, +inf) to range (0, 1)) before feeding them into the memory encoder\n self.sigmoid_scale_for_mem_enc = sigmoid_scale_for_mem_enc\n self.sigmoid_bias_for_mem_enc = sigmoid_bias_for_mem_enc\n self.binarize_mask_from_pts_for_mem_enc = binarize_mask_from_pts_for_mem_enc\n self.non_overlap_masks_for_mem_enc = non_overlap_masks_for_mem_enc\n self.memory_temporal_stride_for_eval = memory_temporal_stride_for_eval\n # On frames with mask input, whether to directly output the input mask without\n # using a SAM prompt encoder + mask decoder\n self.use_mask_input_as_output_without_sam = use_mask_input_as_output_without_sam\n self.multimask_output_in_sam = multimask_output_in_sam\n self.multimask_min_pt_num = multimask_min_pt_num\n self.multimask_max_pt_num = multimask_max_pt_num\n self.multimask_output_for_tracking = multimask_output_for_tracking\n self.use_multimask_token_for_obj_ptr = use_multimask_token_for_obj_ptr\n self.iou_prediction_use_sigmoid = iou_prediction_use_sigmoid\n\n # Part 4: SAM-style prompt encoder (for both mask and point inputs)\n # and SAM-style mask decoder for the final mask output\n self.image_size = image_size\n self.backbone_stride = backbone_stride\n self.sam_mask_decoder_extra_args = sam_mask_decoder_extra_args\n self.pred_obj_scores = pred_obj_scores\n self.pred_obj_scores_mlp = pred_obj_scores_mlp\n self.fixed_no_obj_ptr = fixed_no_obj_ptr\n self.soft_no_obj_ptr = soft_no_obj_ptr\n if self.fixed_no_obj_ptr:\n assert self.pred_obj_scores\n assert self.use_obj_ptrs_in_encoder\n if self.pred_obj_scores and self.use_obj_ptrs_in_encoder:\n self.no_obj_ptr = torch.nn.Parameter(torch.zeros(1, self.hidden_dim))\n trunc_normal_(self.no_obj_ptr, std=0.02)\n self.use_mlp_for_obj_ptr_proj = use_mlp_for_obj_ptr_proj\n self.no_obj_embed_spatial = None\n if no_obj_embed_spatial:\n self.no_obj_embed_spatial = torch.nn.Parameter(torch.zeros(1, self.mem_dim))\n trunc_normal_(self.no_obj_embed_spatial, std=0.02)\n\n self._build_sam_heads()\n self.max_cond_frames_in_attn = max_cond_frames_in_attn\n\n # Model compilation\n if compile_image_encoder:\n # Compile the forward function (not the full module) to allow loading checkpoints.\n print(\n \"Image encoder compilation is enabled. First forward pass will be slow.\"\n )\n self.image_encoder.forward = torch.compile(\n self.image_encoder.forward,\n mode=\"max-autotune\",\n fullgraph=True,\n dynamic=False,\n )\n\n @property\n def device(self):\n return next(self.parameters()).device\n\n def forward(self, *args, **kwargs):\n raise NotImplementedError(\n \"Please use the corresponding methods in SAM2VideoPredictor for inference or SAM2Train for training/fine-tuning\"\n \"See notebooks/video_predictor_example.ipynb for an inference example.\"\n )\n\n def _build_sam_heads(self):\n \"\"\"Build SAM-style prompt encoder and mask decoder.\"\"\"\n self.sam_prompt_embed_dim = self.hidden_dim\n self.sam_image_embedding_size = self.image_size // self.backbone_stride\n\n # build PromptEncoder and MaskDecoder from SAM\n # (their hyperparameters like `mask_in_chans=16` are from SAM code)\n self.sam_prompt_encoder = PromptEncoder(\n embed_dim=self.sam_prompt_embed_dim,\n image_embedding_size=(\n self.sam_image_embedding_size,\n self.sam_image_embedding_size,\n ),\n input_image_size=(self.image_size, self.image_size),\n mask_in_chans=16,\n )\n self.sam_mask_decoder = MaskDecoder(\n num_multimask_outputs=3,\n transformer=TwoWayTransformer(\n depth=2,\n embedding_dim=self.sam_prompt_embed_dim,\n mlp_dim=2048,\n num_heads=8,\n ),\n transformer_dim=self.sam_prompt_embed_dim,\n iou_head_depth=3,\n iou_head_hidden_dim=256,\n use_high_res_features=self.use_high_res_features_in_sam,\n iou_prediction_use_sigmoid=self.iou_prediction_use_sigmoid,\n pred_obj_scores=self.pred_obj_scores,\n pred_obj_scores_mlp=self.pred_obj_scores_mlp,\n use_multimask_token_for_obj_ptr=self.use_multimask_token_for_obj_ptr,\n **(self.sam_mask_decoder_extra_args or {}),\n )\n if self.use_obj_ptrs_in_encoder:\n # a linear projection on SAM output tokens to turn them into object pointers\n self.obj_ptr_proj = torch.nn.Linear(self.hidden_dim, self.hidden_dim)\n if self.use_mlp_for_obj_ptr_proj:\n self.obj_ptr_proj = MLP(\n self.hidden_dim, self.hidden_dim, self.hidden_dim, 3\n )\n else:\n self.obj_ptr_proj = torch.nn.Identity()\n if self.proj_tpos_enc_in_obj_ptrs:\n # a linear projection on temporal positional encoding in object pointers to\n # avoid potential interference with spatial positional encoding\n self.obj_ptr_tpos_proj = torch.nn.Linear(self.hidden_dim, self.mem_dim)\n else:\n self.obj_ptr_tpos_proj = torch.nn.Identity()\n\n def _forward_sam_heads(\n self,\n backbone_features,\n point_inputs=None,\n mask_inputs=None,\n high_res_features=None,\n multimask_output=False,\n ):\n \"\"\"\n Forward SAM prompt encoders and mask heads.\n\n Inputs:\n - backbone_features: image features of [B, C, H, W] shape\n - point_inputs: a dictionary with \"point_coords\" and \"point_labels\", where\n 1) \"point_coords\" has [B, P, 2] shape and float32 dtype and contains the\n absolute pixel-unit coordinate in (x, y) format of the P input points\n 2) \"point_labels\" has shape [B, P] and int32 dtype, where 1 means\n positive clicks, 0 means negative clicks, and -1 means padding\n - mask_inputs: a mask of [B, 1, H*16, W*16] shape, float or bool, with the\n same spatial size as the image.\n - high_res_features: either 1) None or 2) or a list of length 2 containing\n two feature maps of [B, C, 4*H, 4*W] and [B, C, 2*H, 2*W] shapes respectively,\n which will be used as high-resolution feature maps for SAM decoder.\n - multimask_output: if it's True, we output 3 candidate masks and their 3\n corresponding IoU estimates, and if it's False, we output only 1 mask and\n its corresponding IoU estimate.\n\n Outputs:\n - low_res_multimasks: [B, M, H*4, W*4] shape (where M = 3 if\n `multimask_output=True` and M = 1 if `multimask_output=False`), the SAM\n output mask logits (before sigmoid) for the low-resolution masks, with 4x\n the resolution (1/4 stride) of the input backbone_features.\n - high_res_multimasks: [B, M, H*16, W*16] shape (where M = 3\n if `multimask_output=True` and M = 1 if `multimask_output=False`),\n upsampled from the low-resolution masks, with shape size as the image\n (stride is 1 pixel).\n - ious, [B, M] shape, where (where M = 3 if `multimask_output=True` and M = 1\n if `multimask_output=False`), the estimated IoU of each output mask.\n - low_res_masks: [B, 1, H*4, W*4] shape, the best mask in `low_res_multimasks`.\n If `multimask_output=True`, it's the mask with the highest IoU estimate.\n If `multimask_output=False`, it's the same as `low_res_multimasks`.\n - high_res_masks: [B, 1, H*16, W*16] shape, the best mask in `high_res_multimasks`.\n If `multimask_output=True`, it's the mask with the highest IoU estimate.\n If `multimask_output=False`, it's the same as `high_res_multimasks`.\n - obj_ptr: [B, C] shape, the object pointer vector for the output mask, extracted\n based on the output token from the SAM mask decoder.\n \"\"\"\n B = backbone_features.size(0)\n device = backbone_features.device\n assert backbone_features.size(1) == self.sam_prompt_embed_dim\n assert backbone_features.size(2) == self.sam_image_embedding_size\n assert backbone_features.size(3) == self.sam_image_embedding_size\n\n # a) Handle point prompts\n if point_inputs is not None:\n sam_point_coords = point_inputs[\"point_coords\"]\n sam_point_labels = point_inputs[\"point_labels\"]\n assert sam_point_coords.size(0) == B and sam_point_labels.size(0) == B\n else:\n # If no points are provide, pad with an empty point (with label -1)\n sam_point_coords = torch.zeros(B, 1, 2, device=device)\n sam_point_labels = -torch.ones(B, 1, dtype=torch.int32, device=device)\n\n # b) Handle mask prompts\n if mask_inputs is not None:\n # If mask_inputs is provided, downsize it into low-res mask input if needed\n # and feed it as a dense mask prompt into the SAM mask encoder\n assert len(mask_inputs.shape) == 4 and mask_inputs.shape[:2] == (B, 1)\n if mask_inputs.shape[-2:] != self.sam_prompt_encoder.mask_input_size:\n sam_mask_prompt = F.interpolate(\n mask_inputs.float(),\n size=self.sam_prompt_encoder.mask_input_size,\n align_corners=False,\n mode=\"bilinear\",\n antialias=True, # use antialias for downsampling\n )\n else:\n sam_mask_prompt = mask_inputs\n else:\n # Otherwise, simply feed None (and SAM's prompt encoder will add\n # a learned `no_mask_embed` to indicate no mask input in this case).\n sam_mask_prompt = None\n\n sparse_embeddings, dense_embeddings = self.sam_prompt_encoder(\n points=(sam_point_coords, sam_point_labels),\n boxes=None,\n masks=sam_mask_prompt,\n )\n (\n low_res_multimasks,\n ious,\n sam_output_tokens,\n object_score_logits,\n ) = self.sam_mask_decoder(\n image_embeddings=backbone_features,\n image_pe=self.sam_prompt_encoder.get_dense_pe(),\n sparse_prompt_embeddings=sparse_embeddings,\n dense_prompt_embeddings=dense_embeddings,\n multimask_output=multimask_output,\n repeat_image=False, # the image is already batched\n high_res_features=high_res_features,\n )\n if self.pred_obj_scores:\n is_obj_appearing = object_score_logits > 0\n\n # Mask used for spatial memories is always a *hard* choice between obj and no obj,\n # consistent with the actual mask prediction\n low_res_multimasks = torch.where(\n is_obj_appearing[:, None, None],\n low_res_multimasks,\n NO_OBJ_SCORE,\n )\n\n # convert masks from possibly bfloat16 (or float16) to float32\n # (older PyTorch versions before 2.1 don't support `interpolate` on bf16)\n low_res_multimasks = low_res_multimasks.float()\n high_res_multimasks = F.interpolate(\n low_res_multimasks,\n size=(self.image_size, self.image_size),\n mode=\"bilinear\",\n align_corners=False,\n )\n\n sam_output_token = sam_output_tokens[:, 0]\n if multimask_output:\n # take the best mask prediction (with the highest IoU estimation)\n best_iou_inds = torch.argmax(ious, dim=-1)\n batch_inds = torch.arange(B, device=device)\n low_res_masks = low_res_multimasks[batch_inds, best_iou_inds].unsqueeze(1)\n high_res_masks = high_res_multimasks[batch_inds, best_iou_inds].unsqueeze(1)\n if sam_output_tokens.size(1) > 1:\n sam_output_token = sam_output_tokens[batch_inds, best_iou_inds]\n else:\n low_res_masks, high_res_masks = low_res_multimasks, high_res_multimasks\n\n # Extract object pointer from the SAM output token (with occlusion handling)\n obj_ptr = self.obj_ptr_proj(sam_output_token)\n if self.pred_obj_scores:\n # Allow *soft* no obj ptr, unlike for masks\n if self.soft_no_obj_ptr:\n lambda_is_obj_appearing = object_score_logits.sigmoid()\n else:\n lambda_is_obj_appearing = is_obj_appearing.float()\n\n if self.fixed_no_obj_ptr:\n obj_ptr = lambda_is_obj_appearing * obj_ptr\n obj_ptr = obj_ptr + (1 - lambda_is_obj_appearing) * self.no_obj_ptr\n\n return (\n low_res_multimasks,\n high_res_multimasks,\n ious,\n low_res_masks,\n high_res_masks,\n obj_ptr,\n object_score_logits,\n )\n\n def _use_mask_as_output(self, backbone_features, high_res_features, mask_inputs):\n \"\"\"\n Directly turn binary `mask_inputs` into a output mask logits without using SAM.\n (same input and output shapes as in _forward_sam_heads above).\n \"\"\"\n # Use -10/+10 as logits for neg/pos pixels (very close to 0/1 in prob after sigmoid).\n out_scale, out_bias = 20.0, -10.0 # sigmoid(-10.0)=4.5398e-05\n mask_inputs_float = mask_inputs.float()\n high_res_masks = mask_inputs_float * out_scale + out_bias\n low_res_masks = F.interpolate(\n high_res_masks,\n size=(high_res_masks.size(-2) // 4, high_res_masks.size(-1) // 4),\n align_corners=False,\n mode=\"bilinear\",\n antialias=True, # use antialias for downsampling\n )\n # a dummy IoU prediction of all 1's under mask input\n ious = mask_inputs.new_ones(mask_inputs.size(0), 1).float()\n if not self.use_obj_ptrs_in_encoder:\n # all zeros as a dummy object pointer (of shape [B, C])\n obj_ptr = torch.zeros(\n mask_inputs.size(0), self.hidden_dim, device=mask_inputs.device\n )\n else:\n # produce an object pointer using the SAM decoder from the mask input\n _, _, _, _, _, obj_ptr, _ = self._forward_sam_heads(\n backbone_features=backbone_features,\n mask_inputs=self.mask_downsample(mask_inputs_float),\n high_res_features=high_res_features,\n )\n # In this method, we are treating mask_input as output, e.g. using it directly to create spatial mem;\n # Below, we follow the same design axiom to use mask_input to decide if obj appears or not instead of relying\n # on the object_scores from the SAM decoder.\n is_obj_appearing = torch.any(mask_inputs.flatten(1).float() > 0.0, dim=1)\n is_obj_appearing = is_obj_appearing[..., None]\n lambda_is_obj_appearing = is_obj_appearing.float()\n object_score_logits = out_scale * lambda_is_obj_appearing + out_bias\n if self.pred_obj_scores:\n if self.fixed_no_obj_ptr:\n obj_ptr = lambda_is_obj_appearing * obj_ptr\n obj_ptr = obj_ptr + (1 - lambda_is_obj_appearing) * self.no_obj_ptr\n\n return (\n low_res_masks,\n high_res_masks,\n ious,\n low_res_masks,\n high_res_masks,\n obj_ptr,\n object_score_logits,\n )\n\n def forward_image(self, img_batch: torch.Tensor):\n \"\"\"Get the image feature on the input batch.\"\"\"\n backbone_out = self.image_encoder(img_batch)\n if self.use_high_res_features_in_sam:\n # precompute projected level 0 and level 1 features in SAM decoder\n # to avoid running it again on every SAM click\n backbone_out[\"backbone_fpn\"][0] = self.sam_mask_decoder.conv_s0(\n backbone_out[\"backbone_fpn\"][0]\n )\n backbone_out[\"backbone_fpn\"][1] = self.sam_mask_decoder.conv_s1(\n backbone_out[\"backbone_fpn\"][1]\n )\n return backbone_out\n\n def _prepare_backbone_features(self, backbone_out):\n \"\"\"Prepare and flatten visual features.\"\"\"\n backbone_out = backbone_out.copy()\n assert len(backbone_out[\"backbone_fpn\"]) == len(backbone_out[\"vision_pos_enc\"])\n assert len(backbone_out[\"backbone_fpn\"]) >= self.num_feature_levels\n\n feature_maps = backbone_out[\"backbone_fpn\"][-self.num_feature_levels :]\n vision_pos_embeds = backbone_out[\"vision_pos_enc\"][-self.num_feature_levels :]\n\n feat_sizes = [(x.shape[-2], x.shape[-1]) for x in vision_pos_embeds]\n # flatten NxCxHxW to HWxNxC\n vision_feats = [x.flatten(2).permute(2, 0, 1) for x in feature_maps]\n vision_pos_embeds = [x.flatten(2).permute(2, 0, 1) for x in vision_pos_embeds]\n\n return backbone_out, vision_feats, vision_pos_embeds, feat_sizes\n\n def _prepare_memory_conditioned_features(\n self,\n frame_idx,\n is_init_cond_frame,\n current_vision_feats,\n current_vision_pos_embeds,\n feat_sizes,\n output_dict,\n num_frames,\n track_in_reverse=False, # tracking in reverse time order (for demo usage)\n ):\n \"\"\"Fuse the current frame's visual feature map with previous memory.\"\"\"\n B = current_vision_feats[-1].size(1) # batch size on this frame\n C = self.hidden_dim\n H, W = feat_sizes[-1] # top-level (lowest-resolution) feature size\n device = current_vision_feats[-1].device\n # The case of `self.num_maskmem == 0` below is primarily used for reproducing SAM on images.\n # In this case, we skip the fusion with any memory.\n if self.num_maskmem == 0: # Disable memory and skip fusion\n pix_feat = current_vision_feats[-1].permute(1, 2, 0).view(B, C, H, W)\n return pix_feat\n\n num_obj_ptr_tokens = 0\n tpos_sign_mul = -1 if track_in_reverse else 1\n # Step 1: condition the visual features of the current frame on previous memories\n if not is_init_cond_frame:\n # Retrieve the memories encoded with the maskmem backbone\n to_cat_memory, to_cat_memory_pos_embed = [], []\n # Add conditioning frames's output first (all cond frames have t_pos=0 for\n # when getting temporal positional embedding below)\n assert len(output_dict[\"cond_frame_outputs\"]) > 0\n # Select a maximum number of temporally closest cond frames for cross attention\n cond_outputs = output_dict[\"cond_frame_outputs\"]\n selected_cond_outputs, unselected_cond_outputs = select_closest_cond_frames(\n frame_idx, cond_outputs, self.max_cond_frames_in_attn\n )\n t_pos_and_prevs = [(0, out) for out in selected_cond_outputs.values()]\n # Add last (self.num_maskmem - 1) frames before current frame for non-conditioning memory\n # the earliest one has t_pos=1 and the latest one has t_pos=self.num_maskmem-1\n # We also allow taking the memory frame non-consecutively (with stride>1), in which case\n # we take (self.num_maskmem - 2) frames among every stride-th frames plus the last frame.\n stride = 1 if self.training else self.memory_temporal_stride_for_eval\n for t_pos in range(1, self.num_maskmem):\n t_rel = self.num_maskmem - t_pos # how many frames before current frame\n if t_rel == 1:\n # for t_rel == 1, we take the last frame (regardless of r)\n if not track_in_reverse:\n # the frame immediately before this frame (i.e. frame_idx - 1)\n prev_frame_idx = frame_idx - t_rel\n else:\n # the frame immediately after this frame (i.e. frame_idx + 1)\n prev_frame_idx = frame_idx + t_rel\n else:\n # for t_rel >= 2, we take the memory frame from every r-th frames\n if not track_in_reverse:\n # first find the nearest frame among every r-th frames before this frame\n # for r=1, this would be (frame_idx - 2)\n prev_frame_idx = ((frame_idx - 2) // stride) * stride\n # then seek further among every r-th frames\n prev_frame_idx = prev_frame_idx - (t_rel - 2) * stride\n else:\n # first find the nearest frame among every r-th frames after this frame\n # for r=1, this would be (frame_idx + 2)\n prev_frame_idx = -(-(frame_idx + 2) // stride) * stride\n # then seek further among every r-th frames\n prev_frame_idx = prev_frame_idx + (t_rel - 2) * stride\n out = output_dict[\"non_cond_frame_outputs\"].get(prev_frame_idx, None)\n if out is None:\n # If an unselected conditioning frame is among the last (self.num_maskmem - 1)\n # frames, we still attend to it as if it's a non-conditioning frame.\n out = unselected_cond_outputs.get(prev_frame_idx, None)\n t_pos_and_prevs.append((t_pos, out))\n\n for t_pos, prev in t_pos_and_prevs:\n if prev is None:\n continue # skip padding frames\n # \"maskmem_features\" might have been offloaded to CPU in demo use cases,\n # so we load it back to GPU (it's a no-op if it's already on GPU).\n feats = prev[\"maskmem_features\"].to(device, non_blocking=True)\n to_cat_memory.append(feats.flatten(2).permute(2, 0, 1))\n # Spatial positional encoding (it might have been offloaded to CPU in eval)\n maskmem_enc = prev[\"maskmem_pos_enc\"][-1].to(device)\n maskmem_enc = maskmem_enc.flatten(2).permute(2, 0, 1)\n # Temporal positional encoding\n maskmem_enc = (\n maskmem_enc + self.maskmem_tpos_enc[self.num_maskmem - t_pos - 1]\n )\n to_cat_memory_pos_embed.append(maskmem_enc)\n\n # Construct the list of past object pointers\n if self.use_obj_ptrs_in_encoder:\n max_obj_ptrs_in_encoder = min(num_frames, self.max_obj_ptrs_in_encoder)\n # First add those object pointers from selected conditioning frames\n # (optionally, only include object pointers in the past during evaluation)\n if not self.training and self.only_obj_ptrs_in_the_past_for_eval:\n ptr_cond_outputs = {\n t: out\n for t, out in selected_cond_outputs.items()\n if (t >= frame_idx if track_in_reverse else t <= frame_idx)\n }\n else:\n ptr_cond_outputs = selected_cond_outputs\n pos_and_ptrs = [\n # Temporal pos encoding contains how far away each pointer is from current frame\n (\n (\n (frame_idx - t) * tpos_sign_mul\n if self.use_signed_tpos_enc_to_obj_ptrs\n else abs(frame_idx - t)\n ),\n out[\"obj_ptr\"],\n )\n for t, out in ptr_cond_outputs.items()\n ]\n # Add up to (max_obj_ptrs_in_encoder - 1) non-conditioning frames before current frame\n for t_diff in range(1, max_obj_ptrs_in_encoder):\n t = frame_idx + t_diff if track_in_reverse else frame_idx - t_diff\n if t < 0 or (num_frames is not None and t >= num_frames):\n break\n out = output_dict[\"non_cond_frame_outputs\"].get(\n t, unselected_cond_outputs.get(t, None)\n )\n if out is not None:\n pos_and_ptrs.append((t_diff, out[\"obj_ptr\"]))\n # If we have at least one object pointer, add them to the across attention\n if len(pos_and_ptrs) > 0:\n pos_list, ptrs_list = zip(*pos_and_ptrs)\n # stack object pointers along dim=0 into [ptr_seq_len, B, C] shape\n obj_ptrs = torch.stack(ptrs_list, dim=0)\n # a temporal positional embedding based on how far each object pointer is from\n # the current frame (sine embedding normalized by the max pointer num).\n if self.add_tpos_enc_to_obj_ptrs:\n t_diff_max = max_obj_ptrs_in_encoder - 1\n tpos_dim = C if self.proj_tpos_enc_in_obj_ptrs else self.mem_dim\n obj_pos = torch.tensor(pos_list, device=device)\n obj_pos = get_1d_sine_pe(obj_pos / t_diff_max, dim=tpos_dim)\n obj_pos = self.obj_ptr_tpos_proj(obj_pos)\n obj_pos = obj_pos.unsqueeze(1).expand(-1, B, self.mem_dim)\n else:\n obj_pos = obj_ptrs.new_zeros(len(pos_list), B, self.mem_dim)\n if self.mem_dim < C:\n # split a pointer into (C // self.mem_dim) tokens for self.mem_dim < C\n obj_ptrs = obj_ptrs.reshape(\n -1, B, C // self.mem_dim, self.mem_dim\n )\n obj_ptrs = obj_ptrs.permute(0, 2, 1, 3).flatten(0, 1)\n obj_pos = obj_pos.repeat_interleave(C // self.mem_dim, dim=0)\n to_cat_memory.append(obj_ptrs)\n to_cat_memory_pos_embed.append(obj_pos)\n num_obj_ptr_tokens = obj_ptrs.shape[0]\n else:\n num_obj_ptr_tokens = 0\n else:\n # for initial conditioning frames, encode them without using any previous memory\n if self.directly_add_no_mem_embed:\n # directly add no-mem embedding (instead of using the transformer encoder)\n pix_feat_with_mem = current_vision_feats[-1] + self.no_mem_embed\n pix_feat_with_mem = pix_feat_with_mem.permute(1, 2, 0).view(B, C, H, W)\n return pix_feat_with_mem\n\n # Use a dummy token on the first frame (to avoid empty memory input to tranformer encoder)\n to_cat_memory = [self.no_mem_embed.expand(1, B, self.mem_dim)]\n to_cat_memory_pos_embed = [self.no_mem_pos_enc.expand(1, B, self.mem_dim)]\n\n # Step 2: Concatenate the memories and forward through the transformer encoder\n memory = torch.cat(to_cat_memory, dim=0)\n memory_pos_embed = torch.cat(to_cat_memory_pos_embed, dim=0)\n\n pix_feat_with_mem = self.memory_attention(\n curr=current_vision_feats,\n curr_pos=current_vision_pos_embeds,\n memory=memory,\n memory_pos=memory_pos_embed,\n num_obj_ptr_tokens=num_obj_ptr_tokens,\n )\n # reshape the output (HW)BC => BCHW\n pix_feat_with_mem = pix_feat_with_mem.permute(1, 2, 0).view(B, C, H, W)\n return pix_feat_with_mem\n\n def _encode_new_memory(\n self,\n current_vision_feats,\n feat_sizes,\n pred_masks_high_res,\n object_score_logits,\n is_mask_from_pts,\n ):\n \"\"\"Encode the current image and its prediction into a memory feature.\"\"\"\n B = current_vision_feats[-1].size(1) # batch size on this frame\n C = self.hidden_dim\n H, W = feat_sizes[-1] # top-level (lowest-resolution) feature size\n # top-level feature, (HW)BC => BCHW\n pix_feat = current_vision_feats[-1].permute(1, 2, 0).view(B, C, H, W)\n if self.non_overlap_masks_for_mem_enc and not self.training:\n # optionally, apply non-overlapping constraints to the masks (it's applied\n # in the batch dimension and should only be used during eval, where all\n # the objects come from the same video under batch size 1).\n pred_masks_high_res = self._apply_non_overlapping_constraints(\n pred_masks_high_res\n )\n # scale the raw mask logits with a temperature before applying sigmoid\n binarize = self.binarize_mask_from_pts_for_mem_enc and is_mask_from_pts\n if binarize and not self.training:\n mask_for_mem = (pred_masks_high_res > 0).float()\n else:\n # apply sigmoid on the raw mask logits to turn them into range (0, 1)\n mask_for_mem = torch.sigmoid(pred_masks_high_res)\n # apply scale and bias terms to the sigmoid probabilities\n if self.sigmoid_scale_for_mem_enc != 1.0:\n mask_for_mem = mask_for_mem * self.sigmoid_scale_for_mem_enc\n if self.sigmoid_bias_for_mem_enc != 0.0:\n mask_for_mem = mask_for_mem + self.sigmoid_bias_for_mem_enc\n maskmem_out = self.memory_encoder(\n pix_feat, mask_for_mem, skip_mask_sigmoid=True # sigmoid already applied\n )\n maskmem_features = maskmem_out[\"vision_features\"]\n maskmem_pos_enc = maskmem_out[\"vision_pos_enc\"]\n # add a no-object embedding to the spatial memory to indicate that the frame\n # is predicted to be occluded (i.e. no object is appearing in the frame)\n if self.no_obj_embed_spatial is not None:\n is_obj_appearing = (object_score_logits > 0).float()\n maskmem_features += (\n 1 - is_obj_appearing[..., None, None]\n ) * self.no_obj_embed_spatial[..., None, None].expand(\n *maskmem_features.shape\n )\n\n return maskmem_features, maskmem_pos_enc\n\n def _track_step(\n self,\n frame_idx,\n is_init_cond_frame,\n current_vision_feats,\n current_vision_pos_embeds,\n feat_sizes,\n point_inputs,\n mask_inputs,\n output_dict,\n num_frames,\n track_in_reverse,\n prev_sam_mask_logits,\n ):\n current_out = {\"point_inputs\": point_inputs, \"mask_inputs\": mask_inputs}\n # High-resolution feature maps for the SAM head, reshape (HW)BC => BCHW\n if len(current_vision_feats) > 1:\n high_res_features = [\n x.permute(1, 2, 0).view(x.size(1), x.size(2), *s)\n for x, s in zip(current_vision_feats[:-1], feat_sizes[:-1])\n ]\n else:\n high_res_features = None\n if mask_inputs is not None and self.use_mask_input_as_output_without_sam:\n # When use_mask_input_as_output_without_sam=True, we directly output the mask input\n # (see it as a GT mask) without using a SAM prompt encoder + mask decoder.\n pix_feat = current_vision_feats[-1].permute(1, 2, 0)\n pix_feat = pix_feat.view(-1, self.hidden_dim, *feat_sizes[-1])\n sam_outputs = self._use_mask_as_output(\n pix_feat, high_res_features, mask_inputs\n )\n else:\n # fused the visual feature with previous memory features in the memory bank\n pix_feat = self._prepare_memory_conditioned_features(\n frame_idx=frame_idx,\n is_init_cond_frame=is_init_cond_frame,\n current_vision_feats=current_vision_feats[-1:],\n current_vision_pos_embeds=current_vision_pos_embeds[-1:],\n feat_sizes=feat_sizes[-1:],\n output_dict=output_dict,\n num_frames=num_frames,\n track_in_reverse=track_in_reverse,\n )\n # apply SAM-style segmentation head\n # here we might feed previously predicted low-res SAM mask logits into the SAM mask decoder,\n # e.g. in demo where such logits come from earlier interaction instead of correction sampling\n # (in this case, any `mask_inputs` shouldn't reach here as they are sent to _use_mask_as_output instead)\n if prev_sam_mask_logits is not None:\n assert point_inputs is not None and mask_inputs is None\n mask_inputs = prev_sam_mask_logits\n multimask_output = self._use_multimask(is_init_cond_frame, point_inputs)\n sam_outputs = self._forward_sam_heads(\n backbone_features=pix_feat,\n point_inputs=point_inputs,\n mask_inputs=mask_inputs,\n high_res_features=high_res_features,\n multimask_output=multimask_output,\n )\n\n return current_out, sam_outputs, high_res_features, pix_feat\n\n def _encode_memory_in_output(\n self,\n current_vision_feats,\n feat_sizes,\n point_inputs,\n run_mem_encoder,\n high_res_masks,\n object_score_logits,\n current_out,\n ):\n if run_mem_encoder and self.num_maskmem > 0:\n high_res_masks_for_mem_enc = high_res_masks\n maskmem_features, maskmem_pos_enc = self._encode_new_memory(\n current_vision_feats=current_vision_feats,\n feat_sizes=feat_sizes,\n pred_masks_high_res=high_res_masks_for_mem_enc,\n object_score_logits=object_score_logits,\n is_mask_from_pts=(point_inputs is not None),\n )\n current_out[\"maskmem_features\"] = maskmem_features\n current_out[\"maskmem_pos_enc\"] = maskmem_pos_enc\n else:\n current_out[\"maskmem_features\"] = None\n current_out[\"maskmem_pos_enc\"] = None\n\n def track_step(\n self,\n frame_idx,\n is_init_cond_frame,\n current_vision_feats,\n current_vision_pos_embeds,\n feat_sizes,\n point_inputs,\n mask_inputs,\n output_dict,\n num_frames,\n track_in_reverse=False, # tracking in reverse time order (for demo usage)\n # Whether to run the memory encoder on the predicted masks. Sometimes we might want\n # to skip the memory encoder with `run_mem_encoder=False`. For example,\n # in demo we might call `track_step` multiple times for each user click,\n # and only encode the memory when the user finalizes their clicks. And in ablation\n # settings like SAM training on static images, we don't need the memory encoder.\n run_mem_encoder=True,\n # The previously predicted SAM mask logits (which can be fed together with new clicks in demo).\n prev_sam_mask_logits=None,\n ):\n current_out, sam_outputs, _, _ = self._track_step(\n frame_idx,\n is_init_cond_frame,\n current_vision_feats,\n current_vision_pos_embeds,\n feat_sizes,\n point_inputs,\n mask_inputs,\n output_dict,\n num_frames,\n track_in_reverse,\n prev_sam_mask_logits,\n )\n\n (\n _,\n _,\n _,\n low_res_masks,\n high_res_masks,\n obj_ptr,\n object_score_logits,\n ) = sam_outputs\n\n current_out[\"pred_masks\"] = low_res_masks\n current_out[\"pred_masks_high_res\"] = high_res_masks\n current_out[\"obj_ptr\"] = obj_ptr\n if not self.training:\n # Only add this in inference (to avoid unused param in activation checkpointing;\n # it's mainly used in the demo to encode spatial memories w/ consolidated masks)\n current_out[\"object_score_logits\"] = object_score_logits\n\n # Finally run the memory encoder on the predicted mask to encode\n # it into a new memory feature (that can be used in future frames)\n self._encode_memory_in_output(\n current_vision_feats,\n feat_sizes,\n point_inputs,\n run_mem_encoder,\n high_res_masks,\n object_score_logits,\n current_out,\n )\n\n return current_out\n\n def _use_multimask(self, is_init_cond_frame, point_inputs):\n \"\"\"Whether to use multimask output in the SAM head.\"\"\"\n num_pts = 0 if point_inputs is None else point_inputs[\"point_labels\"].size(1)\n multimask_output = (\n self.multimask_output_in_sam\n and (is_init_cond_frame or self.multimask_output_for_tracking)\n and (self.multimask_min_pt_num <= num_pts <= self.multimask_max_pt_num)\n )\n return multimask_output\n\n def _apply_non_overlapping_constraints(self, pred_masks):\n \"\"\"\n Apply non-overlapping constraints to the object scores in pred_masks. Here we\n keep only the highest scoring object at each spatial location in pred_masks.\n \"\"\"\n batch_size = pred_masks.size(0)\n if batch_size == 1:\n return pred_masks\n\n device = pred_masks.device\n # \"max_obj_inds\": object index of the object with the highest score at each location\n max_obj_inds = torch.argmax(pred_masks, dim=0, keepdim=True)\n # \"batch_obj_inds\": object index of each object slice (along dim 0) in `pred_masks`\n batch_obj_inds = torch.arange(batch_size, device=device)[:, None, None, None]\n keep = max_obj_inds == batch_obj_inds\n # suppress overlapping regions' scores below -10.0 so that the foreground regions\n # don't overlap (here sigmoid(-10.0)=4.5398e-05)\n pred_masks = torch.where(keep, pred_masks, torch.clamp(pred_masks, max=-10.0))\n return pred_masks\n" }, { "path": "auto-seg/submodules/segment-anything-2/sam2/modeling/sam2_utils.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\n\nimport copy\nfrom typing import Tuple\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom sam2.utils.misc import mask_to_box\n\n\ndef select_closest_cond_frames(frame_idx, cond_frame_outputs, max_cond_frame_num):\n \"\"\"\n Select up to `max_cond_frame_num` conditioning frames from `cond_frame_outputs`\n that are temporally closest to the current frame at `frame_idx`. Here, we take\n - a) the closest conditioning frame before `frame_idx` (if any);\n - b) the closest conditioning frame after `frame_idx` (if any);\n - c) any other temporally closest conditioning frames until reaching a total\n of `max_cond_frame_num` conditioning frames.\n\n Outputs:\n - selected_outputs: selected items (keys & values) from `cond_frame_outputs`.\n - unselected_outputs: items (keys & values) not selected in `cond_frame_outputs`.\n \"\"\"\n if max_cond_frame_num == -1 or len(cond_frame_outputs) <= max_cond_frame_num:\n selected_outputs = cond_frame_outputs\n unselected_outputs = {}\n else:\n assert max_cond_frame_num >= 2, \"we should allow using 2+ conditioning frames\"\n selected_outputs = {}\n\n # the closest conditioning frame before `frame_idx` (if any)\n idx_before = max((t for t in cond_frame_outputs if t < frame_idx), default=None)\n if idx_before is not None:\n selected_outputs[idx_before] = cond_frame_outputs[idx_before]\n\n # the closest conditioning frame after `frame_idx` (if any)\n idx_after = min((t for t in cond_frame_outputs if t >= frame_idx), default=None)\n if idx_after is not None:\n selected_outputs[idx_after] = cond_frame_outputs[idx_after]\n\n # add other temporally closest conditioning frames until reaching a total\n # of `max_cond_frame_num` conditioning frames.\n num_remain = max_cond_frame_num - len(selected_outputs)\n inds_remain = sorted(\n (t for t in cond_frame_outputs if t not in selected_outputs),\n key=lambda x: abs(x - frame_idx),\n )[:num_remain]\n selected_outputs.update((t, cond_frame_outputs[t]) for t in inds_remain)\n unselected_outputs = {\n t: v for t, v in cond_frame_outputs.items() if t not in selected_outputs\n }\n\n return selected_outputs, unselected_outputs\n\n\ndef get_1d_sine_pe(pos_inds, dim, temperature=10000):\n \"\"\"\n Get 1D sine positional embedding as in the original Transformer paper.\n \"\"\"\n pe_dim = dim // 2\n dim_t = torch.arange(pe_dim, dtype=torch.float32, device=pos_inds.device)\n dim_t = temperature ** (2 * (dim_t // 2) / pe_dim)\n\n pos_embed = pos_inds.unsqueeze(-1) / dim_t\n pos_embed = torch.cat([pos_embed.sin(), pos_embed.cos()], dim=-1)\n return pos_embed\n\n\ndef get_activation_fn(activation):\n \"\"\"Return an activation function given a string\"\"\"\n if activation == \"relu\":\n return F.relu\n if activation == \"gelu\":\n return F.gelu\n if activation == \"glu\":\n return F.glu\n raise RuntimeError(f\"activation should be relu/gelu, not {activation}.\")\n\n\ndef get_clones(module, N):\n return nn.ModuleList([copy.deepcopy(module) for i in range(N)])\n\n\nclass DropPath(nn.Module):\n # adapted from https://github.com/huggingface/pytorch-image-models/blob/main/timm/layers/drop.py\n def __init__(self, drop_prob=0.0, scale_by_keep=True):\n super(DropPath, self).__init__()\n self.drop_prob = drop_prob\n self.scale_by_keep = scale_by_keep\n\n def forward(self, x):\n if self.drop_prob == 0.0 or not self.training:\n return x\n keep_prob = 1 - self.drop_prob\n shape = (x.shape[0],) + (1,) * (x.ndim - 1)\n random_tensor = x.new_empty(shape).bernoulli_(keep_prob)\n if keep_prob > 0.0 and self.scale_by_keep:\n random_tensor.div_(keep_prob)\n return x * random_tensor\n\n\n# Lightly adapted from\n# https://github.com/facebookresearch/MaskFormer/blob/main/mask_former/modeling/transformer/transformer_predictor.py # noqa\nclass MLP(nn.Module):\n def __init__(\n self,\n input_dim: int,\n hidden_dim: int,\n output_dim: int,\n num_layers: int,\n activation: nn.Module = nn.ReLU,\n sigmoid_output: bool = False,\n ) -> None:\n super().__init__()\n self.num_layers = num_layers\n h = [hidden_dim] * (num_layers - 1)\n self.layers = nn.ModuleList(\n nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim])\n )\n self.sigmoid_output = sigmoid_output\n self.act = activation()\n\n def forward(self, x):\n for i, layer in enumerate(self.layers):\n x = self.act(layer(x)) if i < self.num_layers - 1 else layer(x)\n if self.sigmoid_output:\n x = F.sigmoid(x)\n return x\n\n\n# From https://github.com/facebookresearch/detectron2/blob/main/detectron2/layers/batch_norm.py # noqa\n# Itself from https://github.com/facebookresearch/ConvNeXt/blob/d1fa8f6fef0a165b27399986cc2bdacc92777e40/models/convnext.py#L119 # noqa\nclass LayerNorm2d(nn.Module):\n def __init__(self, num_channels: int, eps: float = 1e-6) -> None:\n super().__init__()\n self.weight = nn.Parameter(torch.ones(num_channels))\n self.bias = nn.Parameter(torch.zeros(num_channels))\n self.eps = eps\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n u = x.mean(1, keepdim=True)\n s = (x - u).pow(2).mean(1, keepdim=True)\n x = (x - u) / torch.sqrt(s + self.eps)\n x = self.weight[:, None, None] * x + self.bias[:, None, None]\n return x\n\n\ndef sample_box_points(\n masks: torch.Tensor,\n noise: float = 0.1, # SAM default\n noise_bound: int = 20, # SAM default\n top_left_label: int = 2,\n bottom_right_label: int = 3,\n) -> Tuple[np.array, np.array]:\n \"\"\"\n Sample a noised version of the top left and bottom right corners of a given `bbox`\n\n Inputs:\n - masks: [B, 1, H,W] boxes, dtype=torch.Tensor\n - noise: noise as a fraction of box width and height, dtype=float\n - noise_bound: maximum amount of noise (in pure pixesl), dtype=int\n\n Returns:\n - box_coords: [B, num_pt, 2], contains (x, y) coordinates of top left and bottom right box corners, dtype=torch.float\n - box_labels: [B, num_pt], label 2 is reserverd for top left and 3 for bottom right corners, dtype=torch.int32\n \"\"\"\n device = masks.device\n box_coords = mask_to_box(masks)\n B, _, H, W = masks.shape\n box_labels = torch.tensor(\n [top_left_label, bottom_right_label], dtype=torch.int, device=device\n ).repeat(B)\n if noise > 0.0:\n if not isinstance(noise_bound, torch.Tensor):\n noise_bound = torch.tensor(noise_bound, device=device)\n bbox_w = box_coords[..., 2] - box_coords[..., 0]\n bbox_h = box_coords[..., 3] - box_coords[..., 1]\n max_dx = torch.min(bbox_w * noise, noise_bound)\n max_dy = torch.min(bbox_h * noise, noise_bound)\n box_noise = 2 * torch.rand(B, 1, 4, device=device) - 1\n box_noise = box_noise * torch.stack((max_dx, max_dy, max_dx, max_dy), dim=-1)\n\n box_coords = box_coords + box_noise\n img_bounds = (\n torch.tensor([W, H, W, H], device=device) - 1\n ) # uncentered pixel coords\n box_coords.clamp_(torch.zeros_like(img_bounds), img_bounds) # In place clamping\n\n box_coords = box_coords.reshape(-1, 2, 2) # always 2 points\n box_labels = box_labels.reshape(-1, 2)\n return box_coords, box_labels\n\n\ndef sample_random_points_from_errors(gt_masks, pred_masks, num_pt=1):\n \"\"\"\n Sample `num_pt` random points (along with their labels) independently from the error regions.\n\n Inputs:\n - gt_masks: [B, 1, H_im, W_im] masks, dtype=torch.bool\n - pred_masks: [B, 1, H_im, W_im] masks, dtype=torch.bool or None\n - num_pt: int, number of points to sample independently for each of the B error maps\n\n Outputs:\n - points: [B, num_pt, 2], dtype=torch.float, contains (x, y) coordinates of each sampled point\n - labels: [B, num_pt], dtype=torch.int32, where 1 means positive clicks and 0 means\n negative clicks\n \"\"\"\n if pred_masks is None: # if pred_masks is not provided, treat it as empty\n pred_masks = torch.zeros_like(gt_masks)\n assert gt_masks.dtype == torch.bool and gt_masks.size(1) == 1\n assert pred_masks.dtype == torch.bool and pred_masks.shape == gt_masks.shape\n assert num_pt >= 0\n\n B, _, H_im, W_im = gt_masks.shape\n device = gt_masks.device\n\n # false positive region, a new point sampled in this region should have\n # negative label to correct the FP error\n fp_masks = ~gt_masks & pred_masks\n # false negative region, a new point sampled in this region should have\n # positive label to correct the FN error\n fn_masks = gt_masks & ~pred_masks\n # whether the prediction completely match the ground-truth on each mask\n all_correct = torch.all((gt_masks == pred_masks).flatten(2), dim=2)\n all_correct = all_correct[..., None, None]\n\n # channel 0 is FP map, while channel 1 is FN map\n pts_noise = torch.rand(B, num_pt, H_im, W_im, 2, device=device)\n # sample a negative new click from FP region or a positive new click\n # from FN region, depend on where the maximum falls,\n # and in case the predictions are all correct (no FP or FN), we just\n # sample a negative click from the background region\n pts_noise[..., 0] *= fp_masks | (all_correct & ~gt_masks)\n pts_noise[..., 1] *= fn_masks\n pts_idx = pts_noise.flatten(2).argmax(dim=2)\n labels = (pts_idx % 2).to(torch.int32)\n pts_idx = pts_idx // 2\n pts_x = pts_idx % W_im\n pts_y = pts_idx // W_im\n points = torch.stack([pts_x, pts_y], dim=2).to(torch.float)\n return points, labels\n\n\ndef sample_one_point_from_error_center(gt_masks, pred_masks, padding=True):\n \"\"\"\n Sample 1 random point (along with its label) from the center of each error region,\n that is, the point with the largest distance to the boundary of each error region.\n This is the RITM sampling method from https://github.com/saic-vul/ritm_interactive_segmentation/blob/master/isegm/inference/clicker.py\n\n Inputs:\n - gt_masks: [B, 1, H_im, W_im] masks, dtype=torch.bool\n - pred_masks: [B, 1, H_im, W_im] masks, dtype=torch.bool or None\n - padding: if True, pad with boundary of 1 px for distance transform\n\n Outputs:\n - points: [B, 1, 2], dtype=torch.float, contains (x, y) coordinates of each sampled point\n - labels: [B, 1], dtype=torch.int32, where 1 means positive clicks and 0 means negative clicks\n \"\"\"\n import cv2\n\n if pred_masks is None:\n pred_masks = torch.zeros_like(gt_masks)\n assert gt_masks.dtype == torch.bool and gt_masks.size(1) == 1\n assert pred_masks.dtype == torch.bool and pred_masks.shape == gt_masks.shape\n\n B, _, _, W_im = gt_masks.shape\n device = gt_masks.device\n\n # false positive region, a new point sampled in this region should have\n # negative label to correct the FP error\n fp_masks = ~gt_masks & pred_masks\n # false negative region, a new point sampled in this region should have\n # positive label to correct the FN error\n fn_masks = gt_masks & ~pred_masks\n\n fp_masks = fp_masks.cpu().numpy()\n fn_masks = fn_masks.cpu().numpy()\n points = torch.zeros(B, 1, 2, dtype=torch.float)\n labels = torch.ones(B, 1, dtype=torch.int32)\n for b in range(B):\n fn_mask = fn_masks[b, 0]\n fp_mask = fp_masks[b, 0]\n if padding:\n fn_mask = np.pad(fn_mask, ((1, 1), (1, 1)), \"constant\")\n fp_mask = np.pad(fp_mask, ((1, 1), (1, 1)), \"constant\")\n # compute the distance of each point in FN/FP region to its boundary\n fn_mask_dt = cv2.distanceTransform(fn_mask.astype(np.uint8), cv2.DIST_L2, 0)\n fp_mask_dt = cv2.distanceTransform(fp_mask.astype(np.uint8), cv2.DIST_L2, 0)\n if padding:\n fn_mask_dt = fn_mask_dt[1:-1, 1:-1]\n fp_mask_dt = fp_mask_dt[1:-1, 1:-1]\n\n # take the point in FN/FP region with the largest distance to its boundary\n fn_mask_dt_flat = fn_mask_dt.reshape(-1)\n fp_mask_dt_flat = fp_mask_dt.reshape(-1)\n fn_argmax = np.argmax(fn_mask_dt_flat)\n fp_argmax = np.argmax(fp_mask_dt_flat)\n is_positive = fn_mask_dt_flat[fn_argmax] > fp_mask_dt_flat[fp_argmax]\n pt_idx = fn_argmax if is_positive else fp_argmax\n points[b, 0, 0] = pt_idx % W_im # x\n points[b, 0, 1] = pt_idx // W_im # y\n labels[b, 0] = int(is_positive)\n\n points = points.to(device)\n labels = labels.to(device)\n return points, labels\n\n\ndef get_next_point(gt_masks, pred_masks, method):\n if method == \"uniform\":\n return sample_random_points_from_errors(gt_masks, pred_masks)\n elif method == \"center\":\n return sample_one_point_from_error_center(gt_masks, pred_masks)\n else:\n raise ValueError(f\"unknown sampling method {method}\")\n" }, { "path": "auto-seg/submodules/segment-anything-2/sam2/sam2_image_predictor.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport logging\n\nfrom typing import List, Optional, Tuple, Union\n\nimport numpy as np\nimport torch\nfrom PIL.Image import Image\n\nfrom sam2.modeling.sam2_base import SAM2Base\n\nfrom sam2.utils.transforms import SAM2Transforms\n\n\nclass SAM2ImagePredictor:\n def __init__(\n self,\n sam_model: SAM2Base,\n mask_threshold=0.0,\n max_hole_area=0.0,\n max_sprinkle_area=0.0,\n **kwargs,\n ) -> None:\n \"\"\"\n Uses SAM-2 to calculate the image embedding for an image, and then\n allow repeated, efficient mask prediction given prompts.\n\n Arguments:\n sam_model (Sam-2): The model to use for mask prediction.\n mask_threshold (float): The threshold to use when converting mask logits\n to binary masks. Masks are thresholded at 0 by default.\n max_hole_area (int): If max_hole_area > 0, we fill small holes in up to\n the maximum area of max_hole_area in low_res_masks.\n max_sprinkle_area (int): If max_sprinkle_area > 0, we remove small sprinkles up to\n the maximum area of max_sprinkle_area in low_res_masks.\n \"\"\"\n super().__init__()\n self.model = sam_model\n self._transforms = SAM2Transforms(\n resolution=self.model.image_size,\n mask_threshold=mask_threshold,\n max_hole_area=max_hole_area,\n max_sprinkle_area=max_sprinkle_area,\n )\n\n # Predictor state\n self._is_image_set = False\n self._features = None\n self._orig_hw = None\n # Whether the predictor is set for single image or a batch of images\n self._is_batch = False\n\n # Predictor config\n self.mask_threshold = mask_threshold\n\n # Spatial dim for backbone feature maps\n self._bb_feat_sizes = [\n (256, 256),\n (128, 128),\n (64, 64),\n ]\n\n @classmethod\n def from_pretrained(cls, model_id: str, **kwargs) -> \"SAM2ImagePredictor\":\n \"\"\"\n Load a pretrained model from the Hugging Face hub.\n\n Arguments:\n model_id (str): The Hugging Face repository ID.\n **kwargs: Additional arguments to pass to the model constructor.\n\n Returns:\n (SAM2ImagePredictor): The loaded model.\n \"\"\"\n from sam2.build_sam import build_sam2_hf\n\n sam_model = build_sam2_hf(model_id, **kwargs)\n return cls(sam_model, **kwargs)\n\n @torch.no_grad()\n def set_image(\n self,\n image: Union[np.ndarray, Image],\n ) -> None:\n \"\"\"\n Calculates the image embeddings for the provided image, allowing\n masks to be predicted with the 'predict' method.\n\n Arguments:\n image (np.ndarray or PIL Image): The input image to embed in RGB format. The image should be in HWC format if np.ndarray, or WHC format if PIL Image\n with pixel values in [0, 255].\n image_format (str): The color format of the image, in ['RGB', 'BGR'].\n \"\"\"\n self.reset_predictor()\n # Transform the image to the form expected by the model\n if isinstance(image, np.ndarray):\n logging.info(\"For numpy array image, we assume (HxWxC) format\")\n self._orig_hw = [image.shape[:2]]\n elif isinstance(image, Image):\n w, h = image.size\n self._orig_hw = [(h, w)]\n else:\n raise NotImplementedError(\"Image format not supported\")\n\n input_image = self._transforms(image)\n input_image = input_image[None, ...].to(self.device)\n\n assert (\n len(input_image.shape) == 4 and input_image.shape[1] == 3\n ), f\"input_image must be of size 1x3xHxW, got {input_image.shape}\"\n logging.info(\"Computing image embeddings for the provided image...\")\n backbone_out = self.model.forward_image(input_image)\n _, vision_feats, _, _ = self.model._prepare_backbone_features(backbone_out)\n # Add no_mem_embed, which is added to the lowest rest feat. map during training on videos\n if self.model.directly_add_no_mem_embed:\n vision_feats[-1] = vision_feats[-1] + self.model.no_mem_embed\n\n feats = [\n feat.permute(1, 2, 0).view(1, -1, *feat_size)\n for feat, feat_size in zip(vision_feats[::-1], self._bb_feat_sizes[::-1])\n ][::-1]\n self._features = {\"image_embed\": feats[-1], \"high_res_feats\": feats[:-1]}\n self._is_image_set = True\n logging.info(\"Image embeddings computed.\")\n\n @torch.no_grad()\n def set_image_batch(\n self,\n image_list: List[Union[np.ndarray]],\n ) -> None:\n \"\"\"\n Calculates the image embeddings for the provided image batch, allowing\n masks to be predicted with the 'predict_batch' method.\n\n Arguments:\n image_list (List[np.ndarray]): The input images to embed in RGB format. The image should be in HWC format if np.ndarray\n with pixel values in [0, 255].\n \"\"\"\n self.reset_predictor()\n assert isinstance(image_list, list)\n self._orig_hw = []\n for image in image_list:\n assert isinstance(\n image, np.ndarray\n ), \"Images are expected to be an np.ndarray in RGB format, and of shape HWC\"\n self._orig_hw.append(image.shape[:2])\n # Transform the image to the form expected by the model\n img_batch = self._transforms.forward_batch(image_list)\n img_batch = img_batch.to(self.device)\n batch_size = img_batch.shape[0]\n assert (\n len(img_batch.shape) == 4 and img_batch.shape[1] == 3\n ), f\"img_batch must be of size Bx3xHxW, got {img_batch.shape}\"\n logging.info(\"Computing image embeddings for the provided images...\")\n backbone_out = self.model.forward_image(img_batch)\n _, vision_feats, _, _ = self.model._prepare_backbone_features(backbone_out)\n # Add no_mem_embed, which is added to the lowest rest feat. map during training on videos\n if self.model.directly_add_no_mem_embed:\n vision_feats[-1] = vision_feats[-1] + self.model.no_mem_embed\n\n feats = [\n feat.permute(1, 2, 0).view(batch_size, -1, *feat_size)\n for feat, feat_size in zip(vision_feats[::-1], self._bb_feat_sizes[::-1])\n ][::-1]\n self._features = {\"image_embed\": feats[-1], \"high_res_feats\": feats[:-1]}\n self._is_image_set = True\n self._is_batch = True\n logging.info(\"Image embeddings computed.\")\n\n def predict_batch(\n self,\n point_coords_batch: List[np.ndarray] = None,\n point_labels_batch: List[np.ndarray] = None,\n box_batch: List[np.ndarray] = None,\n mask_input_batch: List[np.ndarray] = None,\n multimask_output: bool = True,\n return_logits: bool = False,\n normalize_coords=True,\n ) -> Tuple[List[np.ndarray], List[np.ndarray], List[np.ndarray]]:\n \"\"\"This function is very similar to predict(...), however it is used for batched mode, when the model is expected to generate predictions on multiple images.\n It returns a tuple of lists of masks, ious, and low_res_masks_logits.\n \"\"\"\n assert self._is_batch, \"This function should only be used when in batched mode\"\n if not self._is_image_set:\n raise RuntimeError(\n \"An image must be set with .set_image_batch(...) before mask prediction.\"\n )\n num_images = len(self._features[\"image_embed\"])\n all_masks = []\n all_ious = []\n all_low_res_masks = []\n for img_idx in range(num_images):\n # Transform input prompts\n point_coords = (\n point_coords_batch[img_idx] if point_coords_batch is not None else None\n )\n point_labels = (\n point_labels_batch[img_idx] if point_labels_batch is not None else None\n )\n box = box_batch[img_idx] if box_batch is not None else None\n mask_input = (\n mask_input_batch[img_idx] if mask_input_batch is not None else None\n )\n mask_input, unnorm_coords, labels, unnorm_box = self._prep_prompts(\n point_coords,\n point_labels,\n box,\n mask_input,\n normalize_coords,\n img_idx=img_idx,\n )\n masks, iou_predictions, low_res_masks = self._predict(\n unnorm_coords,\n labels,\n unnorm_box,\n mask_input,\n multimask_output,\n return_logits=return_logits,\n img_idx=img_idx,\n )\n masks_np = masks.squeeze(0).float().detach().cpu().numpy()\n iou_predictions_np = (\n iou_predictions.squeeze(0).float().detach().cpu().numpy()\n )\n low_res_masks_np = low_res_masks.squeeze(0).float().detach().cpu().numpy()\n all_masks.append(masks_np)\n all_ious.append(iou_predictions_np)\n all_low_res_masks.append(low_res_masks_np)\n\n return all_masks, all_ious, all_low_res_masks\n\n def predict(\n self,\n point_coords: Optional[np.ndarray] = None,\n point_labels: Optional[np.ndarray] = None,\n box: Optional[np.ndarray] = None,\n mask_input: Optional[np.ndarray] = None,\n multimask_output: bool = True,\n return_logits: bool = False,\n normalize_coords=True,\n ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:\n \"\"\"\n Predict masks for the given input prompts, using the currently set image.\n\n Arguments:\n point_coords (np.ndarray or None): A Nx2 array of point prompts to the\n model. Each point is in (X,Y) in pixels.\n point_labels (np.ndarray or None): A length N array of labels for the\n point prompts. 1 indicates a foreground point and 0 indicates a\n background point.\n box (np.ndarray or None): A length 4 array given a box prompt to the\n model, in XYXY format.\n mask_input (np.ndarray): A low resolution mask input to the model, typically\n coming from a previous prediction iteration. Has form 1xHxW, where\n for SAM, H=W=256.\n multimask_output (bool): If true, the model will return three masks.\n For ambiguous input prompts (such as a single click), this will often\n produce better masks than a single prediction. If only a single\n mask is needed, the model's predicted quality score can be used\n to select the best mask. For non-ambiguous prompts, such as multiple\n input prompts, multimask_output=False can give better results.\n return_logits (bool): If true, returns un-thresholded masks logits\n instead of a binary mask.\n normalize_coords (bool): If true, the point coordinates will be normalized to the range [0,1] and point_coords is expected to be wrt. image dimensions.\n\n Returns:\n (np.ndarray): The output masks in CxHxW format, where C is the\n number of masks, and (H, W) is the original image size.\n (np.ndarray): An array of length C containing the model's\n predictions for the quality of each mask.\n (np.ndarray): An array of shape CxHxW, where C is the number\n of masks and H=W=256. These low resolution logits can be passed to\n a subsequent iteration as mask input.\n \"\"\"\n if not self._is_image_set:\n raise RuntimeError(\n \"An image must be set with .set_image(...) before mask prediction.\"\n )\n\n # Transform input prompts\n\n mask_input, unnorm_coords, labels, unnorm_box = self._prep_prompts(\n point_coords, point_labels, box, mask_input, normalize_coords\n )\n\n masks, iou_predictions, low_res_masks = self._predict(\n unnorm_coords,\n labels,\n unnorm_box,\n mask_input,\n multimask_output,\n return_logits=return_logits,\n )\n\n masks_np = masks.squeeze(0).float().detach().cpu().numpy()\n iou_predictions_np = iou_predictions.squeeze(0).float().detach().cpu().numpy()\n low_res_masks_np = low_res_masks.squeeze(0).float().detach().cpu().numpy()\n return masks_np, iou_predictions_np, low_res_masks_np\n\n def _prep_prompts(\n self, point_coords, point_labels, box, mask_logits, normalize_coords, img_idx=-1\n ):\n\n unnorm_coords, labels, unnorm_box, mask_input = None, None, None, None\n if point_coords is not None:\n assert (\n point_labels is not None\n ), \"point_labels must be supplied if point_coords is supplied.\"\n point_coords = torch.as_tensor(\n point_coords, dtype=torch.float, device=self.device\n )\n unnorm_coords = self._transforms.transform_coords(\n point_coords, normalize=normalize_coords, orig_hw=self._orig_hw[img_idx]\n )\n labels = torch.as_tensor(point_labels, dtype=torch.int, device=self.device)\n if len(unnorm_coords.shape) == 2:\n unnorm_coords, labels = unnorm_coords[None, ...], labels[None, ...]\n if box is not None:\n box = torch.as_tensor(box, dtype=torch.float, device=self.device)\n unnorm_box = self._transforms.transform_boxes(\n box, normalize=normalize_coords, orig_hw=self._orig_hw[img_idx]\n ) # Bx2x2\n if mask_logits is not None:\n mask_input = torch.as_tensor(\n mask_logits, dtype=torch.float, device=self.device\n )\n if len(mask_input.shape) == 3:\n mask_input = mask_input[None, :, :, :]\n return mask_input, unnorm_coords, labels, unnorm_box\n\n @torch.no_grad()\n def _predict(\n self,\n point_coords: Optional[torch.Tensor],\n point_labels: Optional[torch.Tensor],\n boxes: Optional[torch.Tensor] = None,\n mask_input: Optional[torch.Tensor] = None,\n multimask_output: bool = True,\n return_logits: bool = False,\n img_idx: int = -1,\n ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:\n \"\"\"\n Predict masks for the given input prompts, using the currently set image.\n Input prompts are batched torch tensors and are expected to already be\n transformed to the input frame using SAM2Transforms.\n\n Arguments:\n point_coords (torch.Tensor or None): A BxNx2 array of point prompts to the\n model. Each point is in (X,Y) in pixels.\n point_labels (torch.Tensor or None): A BxN array of labels for the\n point prompts. 1 indicates a foreground point and 0 indicates a\n background point.\n boxes (np.ndarray or None): A Bx4 array given a box prompt to the\n model, in XYXY format.\n mask_input (np.ndarray): A low resolution mask input to the model, typically\n coming from a previous prediction iteration. Has form Bx1xHxW, where\n for SAM, H=W=256. Masks returned by a previous iteration of the\n predict method do not need further transformation.\n multimask_output (bool): If true, the model will return three masks.\n For ambiguous input prompts (such as a single click), this will often\n produce better masks than a single prediction. If only a single\n mask is needed, the model's predicted quality score can be used\n to select the best mask. For non-ambiguous prompts, such as multiple\n input prompts, multimask_output=False can give better results.\n return_logits (bool): If true, returns un-thresholded masks logits\n instead of a binary mask.\n\n Returns:\n (torch.Tensor): The output masks in BxCxHxW format, where C is the\n number of masks, and (H, W) is the original image size.\n (torch.Tensor): An array of shape BxC containing the model's\n predictions for the quality of each mask.\n (torch.Tensor): An array of shape BxCxHxW, where C is the number\n of masks and H=W=256. These low res logits can be passed to\n a subsequent iteration as mask input.\n \"\"\"\n if not self._is_image_set:\n raise RuntimeError(\n \"An image must be set with .set_image(...) before mask prediction.\"\n )\n\n if point_coords is not None:\n concat_points = (point_coords, point_labels)\n else:\n concat_points = None\n\n # Embed prompts\n if boxes is not None:\n box_coords = boxes.reshape(-1, 2, 2)\n box_labels = torch.tensor([[2, 3]], dtype=torch.int, device=boxes.device)\n box_labels = box_labels.repeat(boxes.size(0), 1)\n # we merge \"boxes\" and \"points\" into a single \"concat_points\" input (where\n # boxes are added at the beginning) to sam_prompt_encoder\n if concat_points is not None:\n concat_coords = torch.cat([box_coords, concat_points[0]], dim=1)\n concat_labels = torch.cat([box_labels, concat_points[1]], dim=1)\n concat_points = (concat_coords, concat_labels)\n else:\n concat_points = (box_coords, box_labels)\n\n sparse_embeddings, dense_embeddings = self.model.sam_prompt_encoder(\n points=concat_points,\n boxes=None,\n masks=mask_input,\n )\n\n # Predict masks\n batched_mode = (\n concat_points is not None and concat_points[0].shape[0] > 1\n ) # multi object prediction\n high_res_features = [\n feat_level[img_idx].unsqueeze(0)\n for feat_level in self._features[\"high_res_feats\"]\n ]\n low_res_masks, iou_predictions, _, _ = self.model.sam_mask_decoder(\n image_embeddings=self._features[\"image_embed\"][img_idx].unsqueeze(0),\n image_pe=self.model.sam_prompt_encoder.get_dense_pe(),\n sparse_prompt_embeddings=sparse_embeddings,\n dense_prompt_embeddings=dense_embeddings,\n multimask_output=multimask_output,\n repeat_image=batched_mode,\n high_res_features=high_res_features,\n )\n\n # Upscale the masks to the original image resolution\n masks = self._transforms.postprocess_masks(\n low_res_masks, self._orig_hw[img_idx]\n )\n low_res_masks = torch.clamp(low_res_masks, -32.0, 32.0)\n if not return_logits:\n masks = masks > self.mask_threshold\n\n return masks, iou_predictions, low_res_masks\n\n def get_image_embedding(self) -> torch.Tensor:\n \"\"\"\n Returns the image embeddings for the currently set image, with\n shape 1xCxHxW, where C is the embedding dimension and (H,W) are\n the embedding spatial dimension of SAM (typically C=256, H=W=64).\n \"\"\"\n if not self._is_image_set:\n raise RuntimeError(\n \"An image must be set with .set_image(...) to generate an embedding.\"\n )\n assert (\n self._features is not None\n ), \"Features must exist if an image has been set.\"\n return self._features[\"image_embed\"]\n\n @property\n def device(self) -> torch.device:\n return self.model.device\n\n def reset_predictor(self) -> None:\n \"\"\"\n Resets the image embeddings and other state variables.\n \"\"\"\n self._is_image_set = False\n self._features = None\n self._orig_hw = None\n self._is_batch = False\n" }, { "path": "auto-seg/submodules/segment-anything-2/sam2/sam2_video_predictor.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport warnings\nfrom collections import OrderedDict\n\nimport torch\nimport torch.nn.functional as F\nfrom sam2.modeling.sam2_base import NO_OBJ_SCORE, SAM2Base\nfrom sam2.utils.misc import (concat_points, fill_holes_in_mask_scores,\n load_video_frames)\nfrom tqdm import tqdm\n\n\nclass SAM2VideoPredictor(SAM2Base):\n \"\"\"The predictor class to handle user interactions and manage inference states.\"\"\"\n\n def __init__(\n self,\n fill_hole_area=0,\n # whether to apply non-overlapping constraints on the output object masks\n non_overlap_masks=False,\n # whether to clear non-conditioning memory of the surrounding frames (which may contain outdated information) after adding correction clicks;\n # note that this would only apply to *single-object tracking* unless `clear_non_cond_mem_for_multi_obj` is also set to True)\n clear_non_cond_mem_around_input=False,\n # if `add_all_frames_to_correct_as_cond` is True, we also append to the conditioning frame list any frame that receives a later correction click\n # if `add_all_frames_to_correct_as_cond` is False, we conditioning frame list to only use those initial conditioning frames\n add_all_frames_to_correct_as_cond=False,\n **kwargs,\n ):\n super().__init__(**kwargs)\n self.fill_hole_area = fill_hole_area\n self.non_overlap_masks = non_overlap_masks\n self.clear_non_cond_mem_around_input = clear_non_cond_mem_around_input\n self.add_all_frames_to_correct_as_cond = add_all_frames_to_correct_as_cond\n\n @torch.inference_mode()\n def init_state(\n self,\n video_path,\n offload_video_to_cpu=False,\n offload_state_to_cpu=False,\n async_loading_frames=False,\n ):\n \"\"\"Initialize an inference state.\"\"\"\n compute_device = self.device # device of the model\n images, video_height, video_width = load_video_frames(\n video_path=video_path,\n image_size=self.image_size,\n offload_video_to_cpu=offload_video_to_cpu,\n async_loading_frames=async_loading_frames,\n compute_device=compute_device,\n )\n inference_state = {}\n inference_state[\"images\"] = images\n inference_state[\"num_frames\"] = len(images)\n # whether to offload the video frames to CPU memory\n # turning on this option saves the GPU memory with only a very small overhead\n inference_state[\"offload_video_to_cpu\"] = offload_video_to_cpu\n # whether to offload the inference state to CPU memory\n # turning on this option saves the GPU memory at the cost of a lower tracking fps\n # (e.g. in a test case of 768x768 model, fps dropped from 27 to 24 when tracking one object\n # and from 24 to 21 when tracking two objects)\n inference_state[\"offload_state_to_cpu\"] = offload_state_to_cpu\n # the original video height and width, used for resizing final output scores\n inference_state[\"video_height\"] = video_height\n inference_state[\"video_width\"] = video_width\n inference_state[\"device\"] = compute_device\n if offload_state_to_cpu:\n inference_state[\"storage_device\"] = torch.device(\"cpu\")\n else:\n inference_state[\"storage_device\"] = compute_device\n # inputs on each frame\n inference_state[\"point_inputs_per_obj\"] = {}\n inference_state[\"mask_inputs_per_obj\"] = {}\n # visual features on a small number of recently visited frames for quick interactions\n inference_state[\"cached_features\"] = {}\n # values that don't change across frames (so we only need to hold one copy of them)\n inference_state[\"constants\"] = {}\n # mapping between client-side object id and model-side object index\n inference_state[\"obj_id_to_idx\"] = OrderedDict()\n inference_state[\"obj_idx_to_id\"] = OrderedDict()\n inference_state[\"obj_ids\"] = []\n # Slice (view) of each object tracking results, sharing the same memory with \"output_dict\"\n inference_state[\"output_dict_per_obj\"] = {}\n # A temporary storage to hold new outputs when user interact with a frame\n # to add clicks or mask (it's merged into \"output_dict\" before propagation starts)\n inference_state[\"temp_output_dict_per_obj\"] = {}\n # Frames that already holds consolidated outputs from click or mask inputs\n # (we directly use their consolidated outputs during tracking)\n # metadata for each tracking frame (e.g. which direction it's tracked)\n inference_state[\"frames_tracked_per_obj\"] = {}\n # Warm up the visual backbone and cache the image feature on frame 0\n self._get_image_feature(inference_state, frame_idx=0, batch_size=1)\n return inference_state\n\n @classmethod\n def from_pretrained(cls, model_id: str, **kwargs) -> \"SAM2VideoPredictor\":\n \"\"\"\n Load a pretrained model from the Hugging Face hub.\n\n Arguments:\n model_id (str): The Hugging Face repository ID.\n **kwargs: Additional arguments to pass to the model constructor.\n\n Returns:\n (SAM2VideoPredictor): The loaded model.\n \"\"\"\n from sam2.build_sam import build_sam2_video_predictor_hf\n\n sam_model = build_sam2_video_predictor_hf(model_id, **kwargs)\n return sam_model\n\n def _obj_id_to_idx(self, inference_state, obj_id):\n \"\"\"Map client-side object id to model-side object index.\"\"\"\n obj_idx = inference_state[\"obj_id_to_idx\"].get(obj_id, None)\n if obj_idx is not None:\n return obj_idx\n\n # We always allow adding new objects (including after tracking starts).\n allow_new_object = True\n if allow_new_object:\n # get the next object slot\n obj_idx = len(inference_state[\"obj_id_to_idx\"])\n inference_state[\"obj_id_to_idx\"][obj_id] = obj_idx\n inference_state[\"obj_idx_to_id\"][obj_idx] = obj_id\n inference_state[\"obj_ids\"] = list(inference_state[\"obj_id_to_idx\"])\n # set up input and output structures for this object\n inference_state[\"point_inputs_per_obj\"][obj_idx] = {}\n inference_state[\"mask_inputs_per_obj\"][obj_idx] = {}\n inference_state[\"output_dict_per_obj\"][obj_idx] = {\n \"cond_frame_outputs\": {}, # dict containing {frame_idx: }\n \"non_cond_frame_outputs\": {}, # dict containing {frame_idx: }\n }\n inference_state[\"temp_output_dict_per_obj\"][obj_idx] = {\n \"cond_frame_outputs\": {}, # dict containing {frame_idx: }\n \"non_cond_frame_outputs\": {}, # dict containing {frame_idx: }\n }\n inference_state[\"frames_tracked_per_obj\"][obj_idx] = {}\n return obj_idx\n else:\n raise RuntimeError(\n f\"Cannot add new object id {obj_id} after tracking starts. \"\n f\"All existing object ids: {inference_state['obj_ids']}. \"\n f\"Please call 'reset_state' to restart from scratch.\"\n )\n\n def _obj_idx_to_id(self, inference_state, obj_idx):\n \"\"\"Map model-side object index to client-side object id.\"\"\"\n return inference_state[\"obj_idx_to_id\"][obj_idx]\n\n def _get_obj_num(self, inference_state):\n \"\"\"Get the total number of unique object ids received so far in this session.\"\"\"\n return len(inference_state[\"obj_idx_to_id\"])\n\n @torch.inference_mode()\n def add_new_points_or_box(\n self,\n inference_state,\n frame_idx,\n obj_id,\n points=None,\n labels=None,\n clear_old_points=True,\n normalize_coords=True,\n box=None,\n ):\n \"\"\"Add new points to a frame.\"\"\"\n obj_idx = self._obj_id_to_idx(inference_state, obj_id)\n point_inputs_per_frame = inference_state[\"point_inputs_per_obj\"][obj_idx]\n mask_inputs_per_frame = inference_state[\"mask_inputs_per_obj\"][obj_idx]\n\n if (points is not None) != (labels is not None):\n raise ValueError(\"points and labels must be provided together\")\n if points is None and box is None:\n raise ValueError(\"at least one of points or box must be provided as input\")\n\n if points is None:\n points = torch.zeros(0, 2, dtype=torch.float32)\n elif not isinstance(points, torch.Tensor):\n points = torch.tensor(points, dtype=torch.float32)\n if labels is None:\n labels = torch.zeros(0, dtype=torch.int32)\n elif not isinstance(labels, torch.Tensor):\n labels = torch.tensor(labels, dtype=torch.int32)\n if points.dim() == 2:\n points = points.unsqueeze(0) # add batch dimension\n if labels.dim() == 1:\n labels = labels.unsqueeze(0) # add batch dimension\n\n # If `box` is provided, we add it as the first two points with labels 2 and 3\n # along with the user-provided points (consistent with how SAM 2 is trained).\n if box is not None:\n if not clear_old_points:\n raise ValueError(\n \"cannot add box without clearing old points, since \"\n \"box prompt must be provided before any point prompt \"\n \"(please use clear_old_points=True instead)\"\n )\n if not isinstance(box, torch.Tensor):\n box = torch.tensor(box, dtype=torch.float32, device=points.device)\n box_coords = box.reshape(1, 2, 2)\n box_labels = torch.tensor([2, 3], dtype=torch.int32, device=labels.device)\n box_labels = box_labels.reshape(1, 2)\n points = torch.cat([box_coords, points], dim=1)\n labels = torch.cat([box_labels, labels], dim=1)\n\n if normalize_coords:\n video_H = inference_state[\"video_height\"]\n video_W = inference_state[\"video_width\"]\n points = points / torch.tensor([video_W, video_H]).to(points.device)\n # scale the (normalized) coordinates by the model's internal image size\n points = points * self.image_size\n points = points.to(inference_state[\"device\"])\n labels = labels.to(inference_state[\"device\"])\n\n if not clear_old_points:\n point_inputs = point_inputs_per_frame.get(frame_idx, None)\n else:\n point_inputs = None\n point_inputs = concat_points(point_inputs, points, labels)\n\n point_inputs_per_frame[frame_idx] = point_inputs\n mask_inputs_per_frame.pop(frame_idx, None)\n # If this frame hasn't been tracked before, we treat it as an initial conditioning\n # frame, meaning that the inputs points are to generate segments on this frame without\n # using any memory from other frames, like in SAM. Otherwise (if it has been tracked),\n # the input points will be used to correct the already tracked masks.\n obj_frames_tracked = inference_state[\"frames_tracked_per_obj\"][obj_idx]\n is_init_cond_frame = frame_idx not in obj_frames_tracked\n # whether to track in reverse time order\n if is_init_cond_frame:\n reverse = False\n else:\n reverse = obj_frames_tracked[frame_idx][\"reverse\"]\n obj_output_dict = inference_state[\"output_dict_per_obj\"][obj_idx]\n obj_temp_output_dict = inference_state[\"temp_output_dict_per_obj\"][obj_idx]\n # Add a frame to conditioning output if it's an initial conditioning frame or\n # if the model sees all frames receiving clicks/mask as conditioning frames.\n is_cond = is_init_cond_frame or self.add_all_frames_to_correct_as_cond\n storage_key = \"cond_frame_outputs\" if is_cond else \"non_cond_frame_outputs\"\n\n # Get any previously predicted mask logits on this object and feed it along with\n # the new clicks into the SAM mask decoder.\n prev_sam_mask_logits = None\n # lookup temporary output dict first, which contains the most recent output\n # (if not found, then lookup conditioning and non-conditioning frame output)\n prev_out = obj_temp_output_dict[storage_key].get(frame_idx)\n if prev_out is None:\n prev_out = obj_output_dict[\"cond_frame_outputs\"].get(frame_idx)\n if prev_out is None:\n prev_out = obj_output_dict[\"non_cond_frame_outputs\"].get(frame_idx)\n\n if prev_out is not None and prev_out[\"pred_masks\"] is not None:\n device = inference_state[\"device\"]\n prev_sam_mask_logits = prev_out[\"pred_masks\"].to(device, non_blocking=True)\n # Clamp the scale of prev_sam_mask_logits to avoid rare numerical issues.\n prev_sam_mask_logits = torch.clamp(prev_sam_mask_logits, -32.0, 32.0)\n current_out, _ = self._run_single_frame_inference(\n inference_state=inference_state,\n output_dict=obj_output_dict, # run on the slice of a single object\n frame_idx=frame_idx,\n batch_size=1, # run on the slice of a single object\n is_init_cond_frame=is_init_cond_frame,\n point_inputs=point_inputs,\n mask_inputs=None,\n reverse=reverse,\n # Skip the memory encoder when adding clicks or mask. We execute the memory encoder\n # at the beginning of `propagate_in_video` (after user finalize their clicks). This\n # allows us to enforce non-overlapping constraints on all objects before encoding\n # them into memory.\n run_mem_encoder=False,\n prev_sam_mask_logits=prev_sam_mask_logits,\n )\n # Add the output to the output dict (to be used as future memory)\n obj_temp_output_dict[storage_key][frame_idx] = current_out\n\n # Resize the output mask to the original video resolution\n obj_ids = inference_state[\"obj_ids\"]\n consolidated_out = self._consolidate_temp_output_across_obj(\n inference_state,\n frame_idx,\n is_cond=is_cond,\n consolidate_at_video_res=True,\n )\n _, video_res_masks = self._get_orig_video_res_output(\n inference_state, consolidated_out[\"pred_masks_video_res\"]\n )\n return frame_idx, obj_ids, video_res_masks\n\n def add_new_points(self, *args, **kwargs):\n \"\"\"Deprecated method. Please use `add_new_points_or_box` instead.\"\"\"\n return self.add_new_points_or_box(*args, **kwargs)\n\n @torch.inference_mode()\n def add_new_mask(\n self,\n inference_state,\n frame_idx,\n obj_id,\n mask,\n ):\n \"\"\"Add new mask to a frame.\"\"\"\n obj_idx = self._obj_id_to_idx(inference_state, obj_id)\n point_inputs_per_frame = inference_state[\"point_inputs_per_obj\"][obj_idx]\n mask_inputs_per_frame = inference_state[\"mask_inputs_per_obj\"][obj_idx]\n\n if not isinstance(mask, torch.Tensor):\n mask = torch.tensor(mask, dtype=torch.bool)\n assert mask.dim() == 2\n mask_H, mask_W = mask.shape\n mask_inputs_orig = mask[None, None] # add batch and channel dimension\n mask_inputs_orig = mask_inputs_orig.float().to(inference_state[\"device\"])\n\n # resize the mask if it doesn't match the model's image size\n if mask_H != self.image_size or mask_W != self.image_size:\n mask_inputs = torch.nn.functional.interpolate(\n mask_inputs_orig,\n size=(self.image_size, self.image_size),\n align_corners=False,\n mode=\"bilinear\",\n antialias=True, # use antialias for downsampling\n )\n mask_inputs = (mask_inputs >= 0.5).float()\n else:\n mask_inputs = mask_inputs_orig\n\n mask_inputs_per_frame[frame_idx] = mask_inputs\n point_inputs_per_frame.pop(frame_idx, None)\n # If this frame hasn't been tracked before, we treat it as an initial conditioning\n # frame, meaning that the inputs points are to generate segments on this frame without\n # using any memory from other frames, like in SAM. Otherwise (if it has been tracked),\n # the input points will be used to correct the already tracked masks.\n obj_frames_tracked = inference_state[\"frames_tracked_per_obj\"][obj_idx]\n is_init_cond_frame = frame_idx not in obj_frames_tracked\n # whether to track in reverse time order\n if is_init_cond_frame:\n reverse = False\n else:\n reverse = obj_frames_tracked[frame_idx][\"reverse\"]\n obj_output_dict = inference_state[\"output_dict_per_obj\"][obj_idx]\n obj_temp_output_dict = inference_state[\"temp_output_dict_per_obj\"][obj_idx]\n # Add a frame to conditioning output if it's an initial conditioning frame or\n # if the model sees all frames receiving clicks/mask as conditioning frames.\n is_cond = is_init_cond_frame or self.add_all_frames_to_correct_as_cond\n storage_key = \"cond_frame_outputs\" if is_cond else \"non_cond_frame_outputs\"\n\n current_out, _ = self._run_single_frame_inference(\n inference_state=inference_state,\n output_dict=obj_output_dict, # run on the slice of a single object\n frame_idx=frame_idx,\n batch_size=1, # run on the slice of a single object\n is_init_cond_frame=is_init_cond_frame,\n point_inputs=None,\n mask_inputs=mask_inputs,\n reverse=reverse,\n # Skip the memory encoder when adding clicks or mask. We execute the memory encoder\n # at the beginning of `propagate_in_video` (after user finalize their clicks). This\n # allows us to enforce non-overlapping constraints on all objects before encoding\n # them into memory.\n run_mem_encoder=False,\n )\n # Add the output to the output dict (to be used as future memory)\n obj_temp_output_dict[storage_key][frame_idx] = current_out\n\n # Resize the output mask to the original video resolution\n obj_ids = inference_state[\"obj_ids\"]\n consolidated_out = self._consolidate_temp_output_across_obj(\n inference_state,\n frame_idx,\n is_cond=is_cond,\n consolidate_at_video_res=True,\n )\n _, video_res_masks = self._get_orig_video_res_output(\n inference_state, consolidated_out[\"pred_masks_video_res\"]\n )\n return frame_idx, obj_ids, video_res_masks\n\n def _get_orig_video_res_output(self, inference_state, any_res_masks):\n \"\"\"\n Resize the object scores to the original video resolution (video_res_masks)\n and apply non-overlapping constraints for final output.\n \"\"\"\n device = inference_state[\"device\"]\n video_H = inference_state[\"video_height\"]\n video_W = inference_state[\"video_width\"]\n any_res_masks = any_res_masks.to(device, non_blocking=True)\n if any_res_masks.shape[-2:] == (video_H, video_W):\n video_res_masks = any_res_masks\n else:\n video_res_masks = torch.nn.functional.interpolate(\n any_res_masks,\n size=(video_H, video_W),\n mode=\"bilinear\",\n align_corners=False,\n )\n if self.non_overlap_masks:\n video_res_masks = self._apply_non_overlapping_constraints(video_res_masks)\n return any_res_masks, video_res_masks\n\n def _consolidate_temp_output_across_obj(\n self,\n inference_state,\n frame_idx,\n is_cond,\n consolidate_at_video_res=False,\n ):\n \"\"\"\n Consolidate the per-object temporary outputs in `temp_output_dict_per_obj` on\n a frame into a single output for all objects, including\n 1) fill any missing objects either from `output_dict_per_obj` (if they exist in\n `output_dict_per_obj` for this frame) or leave them as placeholder values\n (if they don't exist in `output_dict_per_obj` for this frame);\n 2) if specified, rerun memory encoder after apply non-overlapping constraints\n on the object scores.\n \"\"\"\n batch_size = self._get_obj_num(inference_state)\n storage_key = \"cond_frame_outputs\" if is_cond else \"non_cond_frame_outputs\"\n # Optionally, we allow consolidating the temporary outputs at the original\n # video resolution (to provide a better editing experience for mask prompts).\n if consolidate_at_video_res:\n consolidated_H = inference_state[\"video_height\"]\n consolidated_W = inference_state[\"video_width\"]\n consolidated_mask_key = \"pred_masks_video_res\"\n else:\n consolidated_H = consolidated_W = self.image_size // 4\n consolidated_mask_key = \"pred_masks\"\n\n # Initialize `consolidated_out`. Its \"maskmem_features\" and \"maskmem_pos_enc\"\n # will be added when rerunning the memory encoder after applying non-overlapping\n # constraints to object scores. Its \"pred_masks\" are prefilled with a large\n # negative value (NO_OBJ_SCORE) to represent missing objects.\n consolidated_out = {\n consolidated_mask_key: torch.full(\n size=(batch_size, 1, consolidated_H, consolidated_W),\n fill_value=NO_OBJ_SCORE,\n dtype=torch.float32,\n device=inference_state[\"storage_device\"],\n ),\n }\n for obj_idx in range(batch_size):\n obj_temp_output_dict = inference_state[\"temp_output_dict_per_obj\"][obj_idx]\n obj_output_dict = inference_state[\"output_dict_per_obj\"][obj_idx]\n out = obj_temp_output_dict[storage_key].get(frame_idx, None)\n # If the object doesn't appear in \"temp_output_dict_per_obj\" on this frame,\n # we fall back and look up its previous output in \"output_dict_per_obj\".\n # We look up both \"cond_frame_outputs\" and \"non_cond_frame_outputs\" in\n # \"output_dict_per_obj\" to find a previous output for this object.\n if out is None:\n out = obj_output_dict[\"cond_frame_outputs\"].get(frame_idx, None)\n if out is None:\n out = obj_output_dict[\"non_cond_frame_outputs\"].get(frame_idx, None)\n # If the object doesn't appear in \"output_dict_per_obj\" either, we skip it\n # and leave its mask scores to the default scores (i.e. the NO_OBJ_SCORE\n # placeholder above) and set its object pointer to be a dummy pointer.\n if out is None:\n continue\n # Add the temporary object output mask to consolidated output mask\n obj_mask = out[\"pred_masks\"]\n consolidated_pred_masks = consolidated_out[consolidated_mask_key]\n if obj_mask.shape[-2:] == consolidated_pred_masks.shape[-2:]:\n consolidated_pred_masks[obj_idx : obj_idx + 1] = obj_mask\n else:\n # Resize first if temporary object mask has a different resolution\n resized_obj_mask = torch.nn.functional.interpolate(\n obj_mask,\n size=consolidated_pred_masks.shape[-2:],\n mode=\"bilinear\",\n align_corners=False,\n )\n consolidated_pred_masks[obj_idx : obj_idx + 1] = resized_obj_mask\n\n return consolidated_out\n\n @torch.inference_mode()\n def propagate_in_video_preflight(self, inference_state):\n \"\"\"Prepare inference_state and consolidate temporary outputs before tracking.\"\"\"\n # Check and make sure that every object has received input points or masks.\n batch_size = self._get_obj_num(inference_state)\n if batch_size == 0:\n raise RuntimeError(\n \"No input points or masks are provided for any object; please add inputs first.\"\n )\n\n # Consolidate per-object temporary outputs in \"temp_output_dict_per_obj\" and\n # add them into \"output_dict\".\n for obj_idx in range(batch_size):\n obj_output_dict = inference_state[\"output_dict_per_obj\"][obj_idx]\n obj_temp_output_dict = inference_state[\"temp_output_dict_per_obj\"][obj_idx]\n for is_cond in [False, True]:\n # Separately consolidate conditioning and non-conditioning temp outputs\n storage_key = (\n \"cond_frame_outputs\" if is_cond else \"non_cond_frame_outputs\"\n )\n # Find all the frames that contain temporary outputs for any objects\n # (these should be the frames that have just received clicks for mask inputs\n # via `add_new_points_or_box` or `add_new_mask`)\n for frame_idx, out in obj_temp_output_dict[storage_key].items():\n # Run memory encoder on the temporary outputs (if the memory feature is missing)\n if out[\"maskmem_features\"] is None:\n high_res_masks = torch.nn.functional.interpolate(\n out[\"pred_masks\"].to(inference_state[\"device\"]),\n size=(self.image_size, self.image_size),\n mode=\"bilinear\",\n align_corners=False,\n )\n maskmem_features, maskmem_pos_enc = self._run_memory_encoder(\n inference_state=inference_state,\n frame_idx=frame_idx,\n batch_size=1, # run on the slice of a single object\n high_res_masks=high_res_masks,\n object_score_logits=out[\"object_score_logits\"],\n # these frames are what the user interacted with\n is_mask_from_pts=True,\n )\n out[\"maskmem_features\"] = maskmem_features\n out[\"maskmem_pos_enc\"] = maskmem_pos_enc\n\n obj_output_dict[storage_key][frame_idx] = out\n if self.clear_non_cond_mem_around_input:\n # clear non-conditioning memory of the surrounding frames\n self._clear_obj_non_cond_mem_around_input(\n inference_state, frame_idx, obj_idx\n )\n\n # clear temporary outputs in `temp_output_dict_per_obj`\n obj_temp_output_dict[storage_key].clear()\n\n # check and make sure that every object has received input points or masks\n obj_output_dict = inference_state[\"output_dict_per_obj\"][obj_idx]\n if len(obj_output_dict[\"cond_frame_outputs\"]) == 0:\n obj_id = self._obj_idx_to_id(inference_state, obj_idx)\n raise RuntimeError(\n f\"No input points or masks are provided for object id {obj_id}; please add inputs first.\"\n )\n # edge case: if an output is added to \"cond_frame_outputs\", we remove any prior\n # output on the same frame in \"non_cond_frame_outputs\"\n for frame_idx in obj_output_dict[\"cond_frame_outputs\"]:\n obj_output_dict[\"non_cond_frame_outputs\"].pop(frame_idx, None)\n\n @torch.inference_mode()\n def propagate_in_video(\n self,\n inference_state,\n start_frame_idx=None,\n max_frame_num_to_track=None,\n reverse=False,\n ):\n \"\"\"Propagate the input points across frames to track in the entire video.\"\"\"\n self.propagate_in_video_preflight(inference_state)\n\n obj_ids = inference_state[\"obj_ids\"]\n num_frames = inference_state[\"num_frames\"]\n batch_size = self._get_obj_num(inference_state)\n\n # set start index, end index, and processing order\n if start_frame_idx is None:\n # default: start from the earliest frame with input points\n start_frame_idx = min(\n t\n for obj_output_dict in inference_state[\"output_dict_per_obj\"].values()\n for t in obj_output_dict[\"cond_frame_outputs\"]\n )\n if max_frame_num_to_track is None:\n # default: track all the frames in the video\n max_frame_num_to_track = num_frames\n if reverse:\n end_frame_idx = max(start_frame_idx - max_frame_num_to_track, 0)\n if start_frame_idx > 0:\n processing_order = range(start_frame_idx, end_frame_idx - 1, -1)\n else:\n processing_order = [] # skip reverse tracking if starting from frame 0\n else:\n end_frame_idx = min(\n start_frame_idx + max_frame_num_to_track, num_frames - 1\n )\n processing_order = range(start_frame_idx, end_frame_idx + 1)\n\n for frame_idx in tqdm(processing_order, desc=\"propagate in video\"):\n pred_masks_per_obj = [None] * batch_size\n for obj_idx in range(batch_size):\n obj_output_dict = inference_state[\"output_dict_per_obj\"][obj_idx]\n # We skip those frames already in consolidated outputs (these are frames\n # that received input clicks or mask). Note that we cannot directly run\n # batched forward on them via `_run_single_frame_inference` because the\n # number of clicks on each object might be different.\n if frame_idx in obj_output_dict[\"cond_frame_outputs\"]:\n storage_key = \"cond_frame_outputs\"\n current_out = obj_output_dict[storage_key][frame_idx]\n device = inference_state[\"device\"]\n pred_masks = current_out[\"pred_masks\"].to(device, non_blocking=True)\n if self.clear_non_cond_mem_around_input:\n # clear non-conditioning memory of the surrounding frames\n self._clear_obj_non_cond_mem_around_input(\n inference_state, frame_idx, obj_idx\n )\n else:\n storage_key = \"non_cond_frame_outputs\"\n current_out, pred_masks = self._run_single_frame_inference(\n inference_state=inference_state,\n output_dict=obj_output_dict,\n frame_idx=frame_idx,\n batch_size=1, # run on the slice of a single object\n is_init_cond_frame=False,\n point_inputs=None,\n mask_inputs=None,\n reverse=reverse,\n run_mem_encoder=True,\n )\n obj_output_dict[storage_key][frame_idx] = current_out\n\n inference_state[\"frames_tracked_per_obj\"][obj_idx][frame_idx] = {\n \"reverse\": reverse\n }\n pred_masks_per_obj[obj_idx] = pred_masks\n\n # Resize the output mask to the original video resolution (we directly use\n # the mask scores on GPU for output to avoid any CPU conversion in between)\n if len(pred_masks_per_obj) > 1:\n all_pred_masks = torch.cat(pred_masks_per_obj, dim=0)\n else:\n all_pred_masks = pred_masks_per_obj[0]\n _, video_res_masks = self._get_orig_video_res_output(\n inference_state, all_pred_masks\n )\n yield frame_idx, obj_ids, video_res_masks\n\n @torch.inference_mode()\n def clear_all_prompts_in_frame(\n self, inference_state, frame_idx, obj_id, need_output=True\n ):\n \"\"\"Remove all input points or mask in a specific frame for a given object.\"\"\"\n obj_idx = self._obj_id_to_idx(inference_state, obj_id)\n\n # Clear the conditioning information on the given frame\n inference_state[\"point_inputs_per_obj\"][obj_idx].pop(frame_idx, None)\n inference_state[\"mask_inputs_per_obj\"][obj_idx].pop(frame_idx, None)\n\n temp_output_dict_per_obj = inference_state[\"temp_output_dict_per_obj\"]\n temp_output_dict_per_obj[obj_idx][\"cond_frame_outputs\"].pop(frame_idx, None)\n temp_output_dict_per_obj[obj_idx][\"non_cond_frame_outputs\"].pop(frame_idx, None)\n\n # Remove the frame's conditioning output (possibly downgrading it to non-conditioning)\n obj_output_dict = inference_state[\"output_dict_per_obj\"][obj_idx]\n out = obj_output_dict[\"cond_frame_outputs\"].pop(frame_idx, None)\n if out is not None:\n # The frame is not a conditioning frame anymore since it's not receiving inputs,\n # so we \"downgrade\" its output (if exists) to a non-conditioning frame output.\n obj_output_dict[\"non_cond_frame_outputs\"][frame_idx] = out\n inference_state[\"frames_tracked_per_obj\"][obj_idx].pop(frame_idx, None)\n\n if not need_output:\n return\n # Finally, output updated masks per object (after removing the inputs above)\n obj_ids = inference_state[\"obj_ids\"]\n is_cond = any(\n frame_idx in obj_temp_output_dict[\"cond_frame_outputs\"]\n for obj_temp_output_dict in temp_output_dict_per_obj.values()\n )\n consolidated_out = self._consolidate_temp_output_across_obj(\n inference_state,\n frame_idx,\n is_cond=is_cond,\n consolidate_at_video_res=True,\n )\n _, video_res_masks = self._get_orig_video_res_output(\n inference_state, consolidated_out[\"pred_masks_video_res\"]\n )\n return frame_idx, obj_ids, video_res_masks\n\n @torch.inference_mode()\n def reset_state(self, inference_state):\n \"\"\"Remove all input points or mask in all frames throughout the video.\"\"\"\n self._reset_tracking_results(inference_state)\n # Remove all object ids\n inference_state[\"obj_id_to_idx\"].clear()\n inference_state[\"obj_idx_to_id\"].clear()\n inference_state[\"obj_ids\"].clear()\n inference_state[\"point_inputs_per_obj\"].clear()\n inference_state[\"mask_inputs_per_obj\"].clear()\n inference_state[\"output_dict_per_obj\"].clear()\n inference_state[\"temp_output_dict_per_obj\"].clear()\n inference_state[\"frames_tracked_per_obj\"].clear()\n\n def _reset_tracking_results(self, inference_state):\n \"\"\"Reset all tracking inputs and results across the videos.\"\"\"\n for v in inference_state[\"point_inputs_per_obj\"].values():\n v.clear()\n for v in inference_state[\"mask_inputs_per_obj\"].values():\n v.clear()\n for v in inference_state[\"output_dict_per_obj\"].values():\n v[\"cond_frame_outputs\"].clear()\n v[\"non_cond_frame_outputs\"].clear()\n for v in inference_state[\"temp_output_dict_per_obj\"].values():\n v[\"cond_frame_outputs\"].clear()\n v[\"non_cond_frame_outputs\"].clear()\n for v in inference_state[\"frames_tracked_per_obj\"].values():\n v.clear()\n\n def _get_image_feature(self, inference_state, frame_idx, batch_size):\n \"\"\"Compute the image features on a given frame.\"\"\"\n # Look up in the cache first\n image, backbone_out = inference_state[\"cached_features\"].get(\n frame_idx, (None, None)\n )\n if backbone_out is None:\n # Cache miss -- we will run inference on a single image\n device = inference_state[\"device\"]\n image = inference_state[\"images\"][frame_idx].to(device).float().unsqueeze(0)\n backbone_out = self.forward_image(image)\n # Cache the most recent frame's feature (for repeated interactions with\n # a frame; we can use an LRU cache for more frames in the future).\n inference_state[\"cached_features\"] = {frame_idx: (image, backbone_out)}\n\n # expand the features to have the same dimension as the number of objects\n expanded_image = image.expand(batch_size, -1, -1, -1)\n expanded_backbone_out = {\n \"backbone_fpn\": backbone_out[\"backbone_fpn\"].copy(),\n \"vision_pos_enc\": backbone_out[\"vision_pos_enc\"].copy(),\n }\n for i, feat in enumerate(expanded_backbone_out[\"backbone_fpn\"]):\n expanded_backbone_out[\"backbone_fpn\"][i] = feat.expand(\n batch_size, -1, -1, -1\n )\n for i, pos in enumerate(expanded_backbone_out[\"vision_pos_enc\"]):\n pos = pos.expand(batch_size, -1, -1, -1)\n expanded_backbone_out[\"vision_pos_enc\"][i] = pos\n\n features = self._prepare_backbone_features(expanded_backbone_out)\n features = (expanded_image,) + features\n return features\n\n def _run_single_frame_inference(\n self,\n inference_state,\n output_dict,\n frame_idx,\n batch_size,\n is_init_cond_frame,\n point_inputs,\n mask_inputs,\n reverse,\n run_mem_encoder,\n prev_sam_mask_logits=None,\n ):\n \"\"\"Run tracking on a single frame based on current inputs and previous memory.\"\"\"\n # Retrieve correct image features\n (\n _,\n _,\n current_vision_feats,\n current_vision_pos_embeds,\n feat_sizes,\n ) = self._get_image_feature(inference_state, frame_idx, batch_size)\n\n # point and mask should not appear as input simultaneously on the same frame\n assert point_inputs is None or mask_inputs is None\n current_out = self.track_step(\n frame_idx=frame_idx,\n is_init_cond_frame=is_init_cond_frame,\n current_vision_feats=current_vision_feats,\n current_vision_pos_embeds=current_vision_pos_embeds,\n feat_sizes=feat_sizes,\n point_inputs=point_inputs,\n mask_inputs=mask_inputs,\n output_dict=output_dict,\n num_frames=inference_state[\"num_frames\"],\n track_in_reverse=reverse,\n run_mem_encoder=run_mem_encoder,\n prev_sam_mask_logits=prev_sam_mask_logits,\n )\n\n # optionally offload the output to CPU memory to save GPU space\n storage_device = inference_state[\"storage_device\"]\n maskmem_features = current_out[\"maskmem_features\"]\n if maskmem_features is not None:\n maskmem_features = maskmem_features.to(torch.bfloat16)\n maskmem_features = maskmem_features.to(storage_device, non_blocking=True)\n pred_masks_gpu = current_out[\"pred_masks\"]\n # potentially fill holes in the predicted masks\n if self.fill_hole_area > 0:\n pred_masks_gpu = fill_holes_in_mask_scores(\n pred_masks_gpu, self.fill_hole_area\n )\n pred_masks = pred_masks_gpu.to(storage_device, non_blocking=True)\n # \"maskmem_pos_enc\" is the same across frames, so we only need to store one copy of it\n maskmem_pos_enc = self._get_maskmem_pos_enc(inference_state, current_out)\n # object pointer is a small tensor, so we always keep it on GPU memory for fast access\n obj_ptr = current_out[\"obj_ptr\"]\n object_score_logits = current_out[\"object_score_logits\"]\n # make a compact version of this frame's output to reduce the state size\n compact_current_out = {\n \"maskmem_features\": maskmem_features,\n \"maskmem_pos_enc\": maskmem_pos_enc,\n \"pred_masks\": pred_masks,\n \"obj_ptr\": obj_ptr,\n \"object_score_logits\": object_score_logits,\n }\n return compact_current_out, pred_masks_gpu\n\n def _run_memory_encoder(\n self,\n inference_state,\n frame_idx,\n batch_size,\n high_res_masks,\n object_score_logits,\n is_mask_from_pts,\n ):\n \"\"\"\n Run the memory encoder on `high_res_masks`. This is usually after applying\n non-overlapping constraints to object scores. Since their scores changed, their\n memory also need to be computed again with the memory encoder.\n \"\"\"\n # Retrieve correct image features\n _, _, current_vision_feats, _, feat_sizes = self._get_image_feature(\n inference_state, frame_idx, batch_size\n )\n maskmem_features, maskmem_pos_enc = self._encode_new_memory(\n current_vision_feats=current_vision_feats,\n feat_sizes=feat_sizes,\n pred_masks_high_res=high_res_masks,\n object_score_logits=object_score_logits,\n is_mask_from_pts=is_mask_from_pts,\n )\n\n # optionally offload the output to CPU memory to save GPU space\n storage_device = inference_state[\"storage_device\"]\n maskmem_features = maskmem_features.to(torch.bfloat16)\n maskmem_features = maskmem_features.to(storage_device, non_blocking=True)\n # \"maskmem_pos_enc\" is the same across frames, so we only need to store one copy of it\n maskmem_pos_enc = self._get_maskmem_pos_enc(\n inference_state, {\"maskmem_pos_enc\": maskmem_pos_enc}\n )\n return maskmem_features, maskmem_pos_enc\n\n def _get_maskmem_pos_enc(self, inference_state, current_out):\n \"\"\"\n `maskmem_pos_enc` is the same across frames and objects, so we cache it as\n a constant in the inference session to reduce session storage size.\n \"\"\"\n model_constants = inference_state[\"constants\"]\n # \"out_maskmem_pos_enc\" should be either a list of tensors or None\n out_maskmem_pos_enc = current_out[\"maskmem_pos_enc\"]\n if out_maskmem_pos_enc is not None:\n if \"maskmem_pos_enc\" not in model_constants:\n assert isinstance(out_maskmem_pos_enc, list)\n # only take the slice for one object, since it's same across objects\n maskmem_pos_enc = [x[0:1].clone() for x in out_maskmem_pos_enc]\n model_constants[\"maskmem_pos_enc\"] = maskmem_pos_enc\n else:\n maskmem_pos_enc = model_constants[\"maskmem_pos_enc\"]\n # expand the cached maskmem_pos_enc to the actual batch size\n batch_size = out_maskmem_pos_enc[0].size(0)\n expanded_maskmem_pos_enc = [\n x.expand(batch_size, -1, -1, -1) for x in maskmem_pos_enc\n ]\n else:\n expanded_maskmem_pos_enc = None\n return expanded_maskmem_pos_enc\n\n @torch.inference_mode()\n def remove_object(self, inference_state, obj_id, strict=False, need_output=True):\n \"\"\"\n Remove an object id from the tracking state. If strict is True, we check whether\n the object id actually exists and raise an error if it doesn't exist.\n \"\"\"\n old_obj_idx_to_rm = inference_state[\"obj_id_to_idx\"].get(obj_id, None)\n updated_frames = []\n # Check whether this object_id to remove actually exists and possibly raise an error.\n if old_obj_idx_to_rm is None:\n if not strict:\n return inference_state[\"obj_ids\"], updated_frames\n raise RuntimeError(\n f\"Cannot remove object id {obj_id} as it doesn't exist. \"\n f\"All existing object ids: {inference_state['obj_ids']}.\"\n )\n\n # If this is the only remaining object id, we simply reset the state.\n if len(inference_state[\"obj_id_to_idx\"]) == 1:\n self.reset_state(inference_state)\n return inference_state[\"obj_ids\"], updated_frames\n\n # There are still remaining objects after removing this object id. In this case,\n # we need to delete the object storage from inference state tensors.\n # Step 0: clear the input on those frames where this object id has point or mask input\n # (note that this step is required as it might downgrade conditioning frames to\n # non-conditioning ones)\n obj_input_frames_inds = set()\n obj_input_frames_inds.update(\n inference_state[\"point_inputs_per_obj\"][old_obj_idx_to_rm]\n )\n obj_input_frames_inds.update(\n inference_state[\"mask_inputs_per_obj\"][old_obj_idx_to_rm]\n )\n for frame_idx in obj_input_frames_inds:\n self.clear_all_prompts_in_frame(\n inference_state, frame_idx, obj_id, need_output=False\n )\n\n # Step 1: Update the object id mapping (note that it must be done after Step 0,\n # since Step 0 still requires the old object id mappings in inference_state)\n old_obj_ids = inference_state[\"obj_ids\"]\n old_obj_inds = list(range(len(old_obj_ids)))\n remain_old_obj_inds = old_obj_inds.copy()\n remain_old_obj_inds.remove(old_obj_idx_to_rm)\n new_obj_ids = [old_obj_ids[old_idx] for old_idx in remain_old_obj_inds]\n new_obj_inds = list(range(len(new_obj_ids)))\n # build new mappings\n old_idx_to_new_idx = dict(zip(remain_old_obj_inds, new_obj_inds))\n inference_state[\"obj_id_to_idx\"] = dict(zip(new_obj_ids, new_obj_inds))\n inference_state[\"obj_idx_to_id\"] = dict(zip(new_obj_inds, new_obj_ids))\n inference_state[\"obj_ids\"] = new_obj_ids\n\n # Step 2: For per-object tensor storage, we shift their obj_idx in the dict keys.\n def _map_keys(container):\n new_kvs = []\n for k in old_obj_inds:\n v = container.pop(k)\n if k in old_idx_to_new_idx:\n new_kvs.append((old_idx_to_new_idx[k], v))\n container.update(new_kvs)\n\n _map_keys(inference_state[\"point_inputs_per_obj\"])\n _map_keys(inference_state[\"mask_inputs_per_obj\"])\n _map_keys(inference_state[\"output_dict_per_obj\"])\n _map_keys(inference_state[\"temp_output_dict_per_obj\"])\n _map_keys(inference_state[\"frames_tracked_per_obj\"])\n\n # Step 3: Further collect the outputs on those frames in `obj_input_frames_inds`, which\n # could show an updated mask for objects previously occluded by the object being removed\n if need_output:\n temp_output_dict_per_obj = inference_state[\"temp_output_dict_per_obj\"]\n for frame_idx in obj_input_frames_inds:\n is_cond = any(\n frame_idx in obj_temp_output_dict[\"cond_frame_outputs\"]\n for obj_temp_output_dict in temp_output_dict_per_obj.values()\n )\n consolidated_out = self._consolidate_temp_output_across_obj(\n inference_state,\n frame_idx,\n is_cond=is_cond,\n consolidate_at_video_res=True,\n )\n _, video_res_masks = self._get_orig_video_res_output(\n inference_state, consolidated_out[\"pred_masks_video_res\"]\n )\n updated_frames.append((frame_idx, video_res_masks))\n\n return inference_state[\"obj_ids\"], updated_frames\n\n def _clear_non_cond_mem_around_input(self, inference_state, frame_idx):\n \"\"\"\n Remove the non-conditioning memory around the input frame. When users provide\n correction clicks, the surrounding frames' non-conditioning memories can still\n contain outdated object appearance information and could confuse the model.\n\n This method clears those non-conditioning memories surrounding the interacted\n frame to avoid giving the model both old and new information about the object.\n \"\"\"\n r = self.memory_temporal_stride_for_eval\n frame_idx_begin = frame_idx - r * self.num_maskmem\n frame_idx_end = frame_idx + r * self.num_maskmem\n batch_size = self._get_obj_num(inference_state)\n for obj_idx in range(batch_size):\n obj_output_dict = inference_state[\"output_dict_per_obj\"][obj_idx]\n non_cond_frame_outputs = obj_output_dict[\"non_cond_frame_outputs\"]\n for t in range(frame_idx_begin, frame_idx_end + 1):\n non_cond_frame_outputs.pop(t, None)\n\n\nclass SAM2VideoPredictorVOS(SAM2VideoPredictor):\n \"\"\"Optimized for the VOS setting\"\"\"\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self._compile_all_components()\n\n def _compile_all_components(self):\n print(\"Compiling all components for VOS setting. First time may be very slow.\")\n self.memory_encoder.forward = torch.compile(\n self.memory_encoder.forward,\n mode=\"max-autotune\",\n fullgraph=True,\n dynamic=False,\n )\n\n self.memory_attention.forward = torch.compile(\n self.memory_attention.forward,\n mode=\"max-autotune\",\n fullgraph=True,\n dynamic=True, # Num. of memories varies\n )\n\n self.sam_prompt_encoder.forward = torch.compile(\n self.sam_prompt_encoder.forward,\n mode=\"max-autotune\",\n fullgraph=True,\n dynamic=False, # Accuracy regression on True\n )\n\n self.sam_mask_decoder.forward = torch.compile(\n self.sam_mask_decoder.forward,\n mode=\"max-autotune\",\n fullgraph=True,\n dynamic=False, # Accuracy regression on True\n )\n\n def forward_image(self, img_batch: torch.Tensor):\n \"\"\"\n Identical to the corresponding method in the parent (SAM2VideoPredictor), but\n cloning the backbone features and pos encoding to enable compilation.\n \"\"\"\n backbone_out = self.image_encoder(img_batch)\n if self.use_high_res_features_in_sam:\n # precompute projected level 0 and level 1 features in SAM decoder\n # to avoid running it again on every SAM click\n backbone_out[\"backbone_fpn\"][0] = self.sam_mask_decoder.conv_s0(\n backbone_out[\"backbone_fpn\"][0]\n )\n backbone_out[\"backbone_fpn\"][1] = self.sam_mask_decoder.conv_s1(\n backbone_out[\"backbone_fpn\"][1]\n )\n # Clone to help torch.compile\n for i in range(len(backbone_out[\"backbone_fpn\"])):\n backbone_out[\"backbone_fpn\"][i] = backbone_out[\"backbone_fpn\"][i].clone()\n backbone_out[\"vision_pos_enc\"][i] = backbone_out[\"vision_pos_enc\"][\n i\n ].clone()\n return backbone_out\n\n def _forward_sam_heads(\n self,\n backbone_features,\n point_inputs=None,\n mask_inputs=None,\n high_res_features=None,\n multimask_output=False,\n ):\n \"\"\"\n Identical to the corresponding method in the parent (SAM2VideoPredictor), but\n cloning the outputs of prompt_encoder and mask_decoder to enable compilation.\n \"\"\"\n B = backbone_features.size(0)\n device = backbone_features.device\n assert backbone_features.size(1) == self.sam_prompt_embed_dim\n assert backbone_features.size(2) == self.sam_image_embedding_size\n assert backbone_features.size(3) == self.sam_image_embedding_size\n\n # a) Handle point prompts\n if point_inputs is not None:\n sam_point_coords = point_inputs[\"point_coords\"]\n sam_point_labels = point_inputs[\"point_labels\"]\n assert sam_point_coords.size(0) == B and sam_point_labels.size(0) == B\n else:\n # If no points are provide, pad with an empty point (with label -1)\n sam_point_coords = torch.zeros(B, 1, 2, device=device)\n sam_point_labels = -torch.ones(B, 1, dtype=torch.int32, device=device)\n\n # b) Handle mask prompts\n if mask_inputs is not None:\n # If mask_inputs is provided, downsize it into low-res mask input if needed\n # and feed it as a dense mask prompt into the SAM mask encoder\n assert len(mask_inputs.shape) == 4 and mask_inputs.shape[:2] == (B, 1)\n if mask_inputs.shape[-2:] != self.sam_prompt_encoder.mask_input_size:\n sam_mask_prompt = F.interpolate(\n mask_inputs.float(),\n size=self.sam_prompt_encoder.mask_input_size,\n align_corners=False,\n mode=\"bilinear\",\n antialias=True, # use antialias for downsampling\n )\n else:\n sam_mask_prompt = mask_inputs\n else:\n # Otherwise, simply feed None (and SAM's prompt encoder will add\n # a learned `no_mask_embed` to indicate no mask input in this case).\n sam_mask_prompt = None\n\n sparse_embeddings, dense_embeddings = self.sam_prompt_encoder(\n points=(sam_point_coords, sam_point_labels),\n boxes=None,\n masks=sam_mask_prompt,\n )\n # Clone image_pe and the outputs of sam_prompt_encoder\n # to enable compilation\n sparse_embeddings = sparse_embeddings.clone()\n dense_embeddings = dense_embeddings.clone()\n image_pe = self.sam_prompt_encoder.get_dense_pe().clone()\n (\n low_res_multimasks,\n ious,\n sam_output_tokens,\n object_score_logits,\n ) = self.sam_mask_decoder(\n image_embeddings=backbone_features,\n image_pe=image_pe,\n sparse_prompt_embeddings=sparse_embeddings,\n dense_prompt_embeddings=dense_embeddings,\n multimask_output=multimask_output,\n repeat_image=False, # the image is already batched\n high_res_features=high_res_features,\n )\n # Clone the output of sam_mask_decoder\n # to enable compilation\n low_res_multimasks = low_res_multimasks.clone()\n ious = ious.clone()\n sam_output_tokens = sam_output_tokens.clone()\n object_score_logits = object_score_logits.clone()\n\n if self.pred_obj_scores:\n is_obj_appearing = object_score_logits > 0\n\n # Mask used for spatial memories is always a *hard* choice between obj and no obj,\n # consistent with the actual mask prediction\n low_res_multimasks = torch.where(\n is_obj_appearing[:, None, None],\n low_res_multimasks,\n NO_OBJ_SCORE,\n )\n\n # convert masks from possibly bfloat16 (or float16) to float32\n # (older PyTorch versions before 2.1 don't support `interpolate` on bf16)\n low_res_multimasks = low_res_multimasks.float()\n high_res_multimasks = F.interpolate(\n low_res_multimasks,\n size=(self.image_size, self.image_size),\n mode=\"bilinear\",\n align_corners=False,\n )\n\n sam_output_token = sam_output_tokens[:, 0]\n if multimask_output:\n # take the best mask prediction (with the highest IoU estimation)\n best_iou_inds = torch.argmax(ious, dim=-1)\n batch_inds = torch.arange(B, device=device)\n low_res_masks = low_res_multimasks[batch_inds, best_iou_inds].unsqueeze(1)\n high_res_masks = high_res_multimasks[batch_inds, best_iou_inds].unsqueeze(1)\n if sam_output_tokens.size(1) > 1:\n sam_output_token = sam_output_tokens[batch_inds, best_iou_inds]\n else:\n low_res_masks, high_res_masks = low_res_multimasks, high_res_multimasks\n\n # Extract object pointer from the SAM output token (with occlusion handling)\n obj_ptr = self.obj_ptr_proj(sam_output_token)\n if self.pred_obj_scores:\n # Allow *soft* no obj ptr, unlike for masks\n if self.soft_no_obj_ptr:\n lambda_is_obj_appearing = object_score_logits.sigmoid()\n else:\n lambda_is_obj_appearing = is_obj_appearing.float()\n\n if self.fixed_no_obj_ptr:\n obj_ptr = lambda_is_obj_appearing * obj_ptr\n obj_ptr = obj_ptr + (1 - lambda_is_obj_appearing) * self.no_obj_ptr\n\n return (\n low_res_multimasks,\n high_res_multimasks,\n ious,\n low_res_masks,\n high_res_masks,\n obj_ptr,\n object_score_logits,\n )\n\n def _encode_new_memory(\n self,\n current_vision_feats,\n feat_sizes,\n pred_masks_high_res,\n object_score_logits,\n is_mask_from_pts,\n ):\n \"\"\"\n Identical to the corresponding method in the parent (SAM2VideoPredictor), but\n cloning the memories and their pos enc to enable compilation.\n \"\"\"\n B = current_vision_feats[-1].size(1) # batch size on this frame\n C = self.hidden_dim\n H, W = feat_sizes[-1] # top-level (lowest-resolution) feature size\n # top-level feature, (HW)BC => BCHW\n pix_feat = current_vision_feats[-1].permute(1, 2, 0).view(B, C, H, W)\n if self.non_overlap_masks_for_mem_enc and not self.training:\n # optionally, apply non-overlapping constraints to the masks (it's applied\n # in the batch dimension and should only be used during eval, where all\n # the objects come from the same video under batch size 1).\n pred_masks_high_res = self._apply_non_overlapping_constraints(\n pred_masks_high_res\n )\n # scale the raw mask logits with a temperature before applying sigmoid\n binarize = self.binarize_mask_from_pts_for_mem_enc and is_mask_from_pts\n if binarize and not self.training:\n mask_for_mem = (pred_masks_high_res > 0).float()\n else:\n # apply sigmoid on the raw mask logits to turn them into range (0, 1)\n mask_for_mem = torch.sigmoid(pred_masks_high_res)\n # apply scale and bias terms to the sigmoid probabilities\n if self.sigmoid_scale_for_mem_enc != 1.0:\n mask_for_mem = mask_for_mem * self.sigmoid_scale_for_mem_enc\n if self.sigmoid_bias_for_mem_enc != 0.0:\n mask_for_mem = mask_for_mem + self.sigmoid_bias_for_mem_enc\n maskmem_out = self.memory_encoder(\n pix_feat, mask_for_mem, skip_mask_sigmoid=True # sigmoid already applied\n )\n # Clone the feats and pos_enc to enable compilation\n maskmem_features = maskmem_out[\"vision_features\"].clone()\n maskmem_pos_enc = [m.clone() for m in maskmem_out[\"vision_pos_enc\"]]\n # add a no-object embedding to the spatial memory to indicate that the frame\n # is predicted to be occluded (i.e. no object is appearing in the frame)\n if self.no_obj_embed_spatial is not None:\n is_obj_appearing = (object_score_logits > 0).float()\n maskmem_features += (\n 1 - is_obj_appearing[..., None, None]\n ) * self.no_obj_embed_spatial[..., None, None].expand(\n *maskmem_features.shape\n )\n\n return maskmem_features, maskmem_pos_enc" }, { "path": "auto-seg/submodules/segment-anything-2/sam2/sam2_video_predictor_legacy.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport warnings\nfrom collections import OrderedDict\n\nimport torch\nfrom sam2.modeling.sam2_base import NO_OBJ_SCORE, SAM2Base\nfrom sam2.utils.misc import (concat_points, fill_holes_in_mask_scores,\n load_video_frames)\nfrom tqdm import tqdm\n\n\nclass SAM2VideoPredictor(SAM2Base):\n \"\"\"The predictor class to handle user interactions and manage inference states.\"\"\"\n\n def __init__(\n self,\n fill_hole_area=0,\n # whether to apply non-overlapping constraints on the output object masks\n non_overlap_masks=False,\n # whether to clear non-conditioning memory of the surrounding frames (which may contain outdated information) after adding correction clicks;\n # note that this would only apply to *single-object tracking* unless `clear_non_cond_mem_for_multi_obj` is also set to True)\n clear_non_cond_mem_around_input=False,\n # whether to also clear non-conditioning memory of the surrounding frames (only effective when `clear_non_cond_mem_around_input` is True).\n clear_non_cond_mem_for_multi_obj=False,\n # if `add_all_frames_to_correct_as_cond` is True, we also append to the conditioning frame list any frame that receives a later correction click\n # if `add_all_frames_to_correct_as_cond` is False, we conditioning frame list to only use those initial conditioning frames\n add_all_frames_to_correct_as_cond=False,\n **kwargs,\n ):\n super().__init__(**kwargs)\n self.fill_hole_area = fill_hole_area\n self.non_overlap_masks = non_overlap_masks\n self.clear_non_cond_mem_around_input = clear_non_cond_mem_around_input\n self.clear_non_cond_mem_for_multi_obj = clear_non_cond_mem_for_multi_obj\n self.add_all_frames_to_correct_as_cond = add_all_frames_to_correct_as_cond\n\n @torch.inference_mode()\n def init_state(\n self,\n video_path,\n offload_video_to_cpu=False,\n offload_state_to_cpu=False,\n async_loading_frames=False,\n ):\n \"\"\"Initialize an inference state.\"\"\"\n compute_device = self.device # device of the model\n images, video_height, video_width = load_video_frames(\n video_path=video_path,\n image_size=self.image_size,\n offload_video_to_cpu=offload_video_to_cpu,\n async_loading_frames=async_loading_frames,\n compute_device=compute_device,\n )\n inference_state = {}\n inference_state[\"images\"] = images\n inference_state[\"num_frames\"] = len(images)\n # whether to offload the video frames to CPU memory\n # turning on this option saves the GPU memory with only a very small overhead\n inference_state[\"offload_video_to_cpu\"] = offload_video_to_cpu\n # whether to offload the inference state to CPU memory\n # turning on this option saves the GPU memory at the cost of a lower tracking fps\n # (e.g. in a test case of 768x768 model, fps dropped from 27 to 24 when tracking one object\n # and from 24 to 21 when tracking two objects)\n inference_state[\"offload_state_to_cpu\"] = offload_state_to_cpu\n # the original video height and width, used for resizing final output scores\n inference_state[\"video_height\"] = video_height\n inference_state[\"video_width\"] = video_width\n inference_state[\"device\"] = compute_device\n if offload_state_to_cpu:\n inference_state[\"storage_device\"] = torch.device(\"cpu\")\n else:\n inference_state[\"storage_device\"] = compute_device\n # inputs on each frame\n inference_state[\"point_inputs_per_obj\"] = {}\n inference_state[\"mask_inputs_per_obj\"] = {}\n # visual features on a small number of recently visited frames for quick interactions\n inference_state[\"cached_features\"] = {}\n # values that don't change across frames (so we only need to hold one copy of them)\n inference_state[\"constants\"] = {}\n # mapping between client-side object id and model-side object index\n inference_state[\"obj_id_to_idx\"] = OrderedDict()\n inference_state[\"obj_idx_to_id\"] = OrderedDict()\n inference_state[\"obj_ids\"] = []\n # A storage to hold the model's tracking results and states on each frame\n inference_state[\"output_dict\"] = {\n \"cond_frame_outputs\": {}, # dict containing {frame_idx: }\n \"non_cond_frame_outputs\": {}, # dict containing {frame_idx: }\n }\n # Slice (view) of each object tracking results, sharing the same memory with \"output_dict\"\n inference_state[\"output_dict_per_obj\"] = {}\n # A temporary storage to hold new outputs when user interact with a frame\n # to add clicks or mask (it's merged into \"output_dict\" before propagation starts)\n inference_state[\"temp_output_dict_per_obj\"] = {}\n # Frames that already holds consolidated outputs from click or mask inputs\n # (we directly use their consolidated outputs during tracking)\n inference_state[\"consolidated_frame_inds\"] = {\n \"cond_frame_outputs\": set(), # set containing frame indices\n \"non_cond_frame_outputs\": set(), # set containing frame indices\n }\n # metadata for each tracking frame (e.g. which direction it's tracked)\n inference_state[\"tracking_has_started\"] = False\n inference_state[\"frames_already_tracked\"] = {}\n # Warm up the visual backbone and cache the image feature on frame 0\n self._get_image_feature(inference_state, frame_idx=0, batch_size=1)\n return inference_state\n\n @classmethod\n def from_pretrained(cls, model_id: str, **kwargs) -> \"SAM2VideoPredictor\":\n \"\"\"\n Load a pretrained model from the Hugging Face hub.\n\n Arguments:\n model_id (str): The Hugging Face repository ID.\n **kwargs: Additional arguments to pass to the model constructor.\n\n Returns:\n (SAM2VideoPredictor): The loaded model.\n \"\"\"\n from sam2.build_sam import build_sam2_video_predictor_hf\n\n sam_model = build_sam2_video_predictor_hf(model_id, **kwargs)\n return sam_model\n\n def _obj_id_to_idx(self, inference_state, obj_id):\n \"\"\"Map client-side object id to model-side object index.\"\"\"\n obj_idx = inference_state[\"obj_id_to_idx\"].get(obj_id, None)\n if obj_idx is not None:\n return obj_idx\n\n # This is a new object id not sent to the server before. We only allow adding\n # new objects *before* the tracking starts.\n allow_new_object = not inference_state[\"tracking_has_started\"]\n if allow_new_object:\n # get the next object slot\n obj_idx = len(inference_state[\"obj_id_to_idx\"])\n inference_state[\"obj_id_to_idx\"][obj_id] = obj_idx\n inference_state[\"obj_idx_to_id\"][obj_idx] = obj_id\n inference_state[\"obj_ids\"] = list(inference_state[\"obj_id_to_idx\"])\n # set up input and output structures for this object\n inference_state[\"point_inputs_per_obj\"][obj_idx] = {}\n inference_state[\"mask_inputs_per_obj\"][obj_idx] = {}\n inference_state[\"output_dict_per_obj\"][obj_idx] = {\n \"cond_frame_outputs\": {}, # dict containing {frame_idx: }\n \"non_cond_frame_outputs\": {}, # dict containing {frame_idx: }\n }\n inference_state[\"temp_output_dict_per_obj\"][obj_idx] = {\n \"cond_frame_outputs\": {}, # dict containing {frame_idx: }\n \"non_cond_frame_outputs\": {}, # dict containing {frame_idx: }\n }\n return obj_idx\n else:\n raise RuntimeError(\n f\"Cannot add new object id {obj_id} after tracking starts. \"\n f\"All existing object ids: {inference_state['obj_ids']}. \"\n f\"Please call 'reset_state' to restart from scratch.\"\n )\n\n def _obj_idx_to_id(self, inference_state, obj_idx):\n \"\"\"Map model-side object index to client-side object id.\"\"\"\n return inference_state[\"obj_idx_to_id\"][obj_idx]\n\n def _get_obj_num(self, inference_state):\n \"\"\"Get the total number of unique object ids received so far in this session.\"\"\"\n return len(inference_state[\"obj_idx_to_id\"])\n\n @torch.inference_mode()\n def add_new_points_or_box(\n self,\n inference_state,\n frame_idx,\n obj_id,\n points=None,\n labels=None,\n clear_old_points=True,\n normalize_coords=True,\n box=None,\n ):\n \"\"\"Add new points to a frame.\"\"\"\n obj_idx = self._obj_id_to_idx(inference_state, obj_id)\n point_inputs_per_frame = inference_state[\"point_inputs_per_obj\"][obj_idx]\n mask_inputs_per_frame = inference_state[\"mask_inputs_per_obj\"][obj_idx]\n\n if (points is not None) != (labels is not None):\n raise ValueError(\"points and labels must be provided together\")\n if points is None and box is None:\n raise ValueError(\"at least one of points or box must be provided as input\")\n\n if points is None:\n points = torch.zeros(0, 2, dtype=torch.float32)\n elif not isinstance(points, torch.Tensor):\n points = torch.tensor(points, dtype=torch.float32)\n if labels is None:\n labels = torch.zeros(0, dtype=torch.int32)\n elif not isinstance(labels, torch.Tensor):\n labels = torch.tensor(labels, dtype=torch.int32)\n if points.dim() == 2:\n points = points.unsqueeze(0) # add batch dimension\n if labels.dim() == 1:\n labels = labels.unsqueeze(0) # add batch dimension\n\n # If `box` is provided, we add it as the first two points with labels 2 and 3\n # along with the user-provided points (consistent with how SAM 2 is trained).\n if box is not None:\n if not clear_old_points:\n raise ValueError(\n \"cannot add box without clearing old points, since \"\n \"box prompt must be provided before any point prompt \"\n \"(please use clear_old_points=True instead)\"\n )\n if inference_state[\"tracking_has_started\"]:\n warnings.warn(\n \"You are adding a box after tracking starts. SAM 2 may not always be \"\n \"able to incorporate a box prompt for *refinement*. If you intend to \"\n \"use box prompt as an *initial* input before tracking, please call \"\n \"'reset_state' on the inference state to restart from scratch.\",\n category=UserWarning,\n stacklevel=2,\n )\n if not isinstance(box, torch.Tensor):\n box = torch.tensor(box, dtype=torch.float32, device=points.device)\n box_coords = box.reshape(1, 2, 2)\n box_labels = torch.tensor([2, 3], dtype=torch.int32, device=labels.device)\n box_labels = box_labels.reshape(1, 2)\n points = torch.cat([box_coords, points], dim=1)\n labels = torch.cat([box_labels, labels], dim=1)\n\n if normalize_coords:\n video_H = inference_state[\"video_height\"]\n video_W = inference_state[\"video_width\"]\n points = points / torch.tensor([video_W, video_H]).to(points.device)\n # scale the (normalized) coordinates by the model's internal image size\n points = points * self.image_size\n points = points.to(inference_state[\"device\"])\n labels = labels.to(inference_state[\"device\"])\n\n if not clear_old_points:\n point_inputs = point_inputs_per_frame.get(frame_idx, None)\n else:\n point_inputs = None\n point_inputs = concat_points(point_inputs, points, labels)\n\n point_inputs_per_frame[frame_idx] = point_inputs\n mask_inputs_per_frame.pop(frame_idx, None)\n # If this frame hasn't been tracked before, we treat it as an initial conditioning\n # frame, meaning that the inputs points are to generate segments on this frame without\n # using any memory from other frames, like in SAM. Otherwise (if it has been tracked),\n # the input points will be used to correct the already tracked masks.\n is_init_cond_frame = frame_idx not in inference_state[\"frames_already_tracked\"]\n # whether to track in reverse time order\n if is_init_cond_frame:\n reverse = False\n else:\n reverse = inference_state[\"frames_already_tracked\"][frame_idx][\"reverse\"]\n obj_output_dict = inference_state[\"output_dict_per_obj\"][obj_idx]\n obj_temp_output_dict = inference_state[\"temp_output_dict_per_obj\"][obj_idx]\n # Add a frame to conditioning output if it's an initial conditioning frame or\n # if the model sees all frames receiving clicks/mask as conditioning frames.\n is_cond = is_init_cond_frame or self.add_all_frames_to_correct_as_cond\n storage_key = \"cond_frame_outputs\" if is_cond else \"non_cond_frame_outputs\"\n\n # Get any previously predicted mask logits on this object and feed it along with\n # the new clicks into the SAM mask decoder.\n prev_sam_mask_logits = None\n # lookup temporary output dict first, which contains the most recent output\n # (if not found, then lookup conditioning and non-conditioning frame output)\n prev_out = obj_temp_output_dict[storage_key].get(frame_idx)\n if prev_out is None:\n prev_out = obj_output_dict[\"cond_frame_outputs\"].get(frame_idx)\n if prev_out is None:\n prev_out = obj_output_dict[\"non_cond_frame_outputs\"].get(frame_idx)\n\n if prev_out is not None and prev_out[\"pred_masks\"] is not None:\n device = inference_state[\"device\"]\n prev_sam_mask_logits = prev_out[\"pred_masks\"].to(device, non_blocking=True)\n # Clamp the scale of prev_sam_mask_logits to avoid rare numerical issues.\n prev_sam_mask_logits = torch.clamp(prev_sam_mask_logits, -32.0, 32.0)\n current_out, _ = self._run_single_frame_inference(\n inference_state=inference_state,\n output_dict=obj_output_dict, # run on the slice of a single object\n frame_idx=frame_idx,\n batch_size=1, # run on the slice of a single object\n is_init_cond_frame=is_init_cond_frame,\n point_inputs=point_inputs,\n mask_inputs=None,\n reverse=reverse,\n # Skip the memory encoder when adding clicks or mask. We execute the memory encoder\n # at the beginning of `propagate_in_video` (after user finalize their clicks). This\n # allows us to enforce non-overlapping constraints on all objects before encoding\n # them into memory.\n run_mem_encoder=False,\n prev_sam_mask_logits=prev_sam_mask_logits,\n )\n # Add the output to the output dict (to be used as future memory)\n obj_temp_output_dict[storage_key][frame_idx] = current_out\n\n # Resize the output mask to the original video resolution\n obj_ids = inference_state[\"obj_ids\"]\n consolidated_out = self._consolidate_temp_output_across_obj(\n inference_state,\n frame_idx,\n is_cond=is_cond,\n run_mem_encoder=False,\n consolidate_at_video_res=True,\n )\n _, video_res_masks = self._get_orig_video_res_output(\n inference_state, consolidated_out[\"pred_masks_video_res\"]\n )\n return frame_idx, obj_ids, video_res_masks\n\n def add_new_points(self, *args, **kwargs):\n \"\"\"Deprecated method. Please use `add_new_points_or_box` instead.\"\"\"\n return self.add_new_points_or_box(*args, **kwargs)\n\n @torch.inference_mode()\n def add_new_mask(\n self,\n inference_state,\n frame_idx,\n obj_id,\n mask,\n ):\n \"\"\"Add new mask to a frame.\"\"\"\n obj_idx = self._obj_id_to_idx(inference_state, obj_id)\n point_inputs_per_frame = inference_state[\"point_inputs_per_obj\"][obj_idx]\n mask_inputs_per_frame = inference_state[\"mask_inputs_per_obj\"][obj_idx]\n\n if not isinstance(mask, torch.Tensor):\n mask = torch.tensor(mask, dtype=torch.bool)\n assert mask.dim() == 2\n mask_H, mask_W = mask.shape\n mask_inputs_orig = mask[None, None] # add batch and channel dimension\n mask_inputs_orig = mask_inputs_orig.float().to(inference_state[\"device\"])\n\n # resize the mask if it doesn't match the model's image size\n if mask_H != self.image_size or mask_W != self.image_size:\n mask_inputs = torch.nn.functional.interpolate(\n mask_inputs_orig,\n size=(self.image_size, self.image_size),\n align_corners=False,\n mode=\"bilinear\",\n antialias=True, # use antialias for downsampling\n )\n mask_inputs = (mask_inputs >= 0.5).float()\n else:\n mask_inputs = mask_inputs_orig\n\n mask_inputs_per_frame[frame_idx] = mask_inputs\n point_inputs_per_frame.pop(frame_idx, None)\n # If this frame hasn't been tracked before, we treat it as an initial conditioning\n # frame, meaning that the inputs points are to generate segments on this frame without\n # using any memory from other frames, like in SAM. Otherwise (if it has been tracked),\n # the input points will be used to correct the already tracked masks.\n is_init_cond_frame = frame_idx not in inference_state[\"frames_already_tracked\"]\n # whether to track in reverse time order\n if is_init_cond_frame:\n reverse = False\n else:\n reverse = inference_state[\"frames_already_tracked\"][frame_idx][\"reverse\"]\n obj_output_dict = inference_state[\"output_dict_per_obj\"][obj_idx]\n obj_temp_output_dict = inference_state[\"temp_output_dict_per_obj\"][obj_idx]\n # Add a frame to conditioning output if it's an initial conditioning frame or\n # if the model sees all frames receiving clicks/mask as conditioning frames.\n is_cond = is_init_cond_frame or self.add_all_frames_to_correct_as_cond\n storage_key = \"cond_frame_outputs\" if is_cond else \"non_cond_frame_outputs\"\n\n current_out, _ = self._run_single_frame_inference(\n inference_state=inference_state,\n output_dict=obj_output_dict, # run on the slice of a single object\n frame_idx=frame_idx,\n batch_size=1, # run on the slice of a single object\n is_init_cond_frame=is_init_cond_frame,\n point_inputs=None,\n mask_inputs=mask_inputs,\n reverse=reverse,\n # Skip the memory encoder when adding clicks or mask. We execute the memory encoder\n # at the beginning of `propagate_in_video` (after user finalize their clicks). This\n # allows us to enforce non-overlapping constraints on all objects before encoding\n # them into memory.\n run_mem_encoder=False,\n )\n # Add the output to the output dict (to be used as future memory)\n obj_temp_output_dict[storage_key][frame_idx] = current_out\n\n # Resize the output mask to the original video resolution\n obj_ids = inference_state[\"obj_ids\"]\n consolidated_out = self._consolidate_temp_output_across_obj(\n inference_state,\n frame_idx,\n is_cond=is_cond,\n run_mem_encoder=False,\n consolidate_at_video_res=True,\n )\n _, video_res_masks = self._get_orig_video_res_output(\n inference_state, consolidated_out[\"pred_masks_video_res\"]\n )\n return frame_idx, obj_ids, video_res_masks\n\n def _get_orig_video_res_output(self, inference_state, any_res_masks):\n \"\"\"\n Resize the object scores to the original video resolution (video_res_masks)\n and apply non-overlapping constraints for final output.\n \"\"\"\n device = inference_state[\"device\"]\n video_H = inference_state[\"video_height\"]\n video_W = inference_state[\"video_width\"]\n any_res_masks = any_res_masks.to(device, non_blocking=True)\n if any_res_masks.shape[-2:] == (video_H, video_W):\n video_res_masks = any_res_masks\n else:\n video_res_masks = torch.nn.functional.interpolate(\n any_res_masks,\n size=(video_H, video_W),\n mode=\"bilinear\",\n align_corners=False,\n )\n if self.non_overlap_masks:\n video_res_masks = self._apply_non_overlapping_constraints(video_res_masks)\n return any_res_masks, video_res_masks\n\n def _consolidate_temp_output_across_obj(\n self,\n inference_state,\n frame_idx,\n is_cond,\n run_mem_encoder,\n consolidate_at_video_res=False,\n ):\n \"\"\"\n Consolidate the per-object temporary outputs in `temp_output_dict_per_obj` on\n a frame into a single output for all objects, including\n 1) fill any missing objects either from `output_dict_per_obj` (if they exist in\n `output_dict_per_obj` for this frame) or leave them as placeholder values\n (if they don't exist in `output_dict_per_obj` for this frame);\n 2) if specified, rerun memory encoder after apply non-overlapping constraints\n on the object scores.\n \"\"\"\n batch_size = self._get_obj_num(inference_state)\n storage_key = \"cond_frame_outputs\" if is_cond else \"non_cond_frame_outputs\"\n # Optionally, we allow consolidating the temporary outputs at the original\n # video resolution (to provide a better editing experience for mask prompts).\n if consolidate_at_video_res:\n assert not run_mem_encoder, \"memory encoder cannot run at video resolution\"\n consolidated_H = inference_state[\"video_height\"]\n consolidated_W = inference_state[\"video_width\"]\n consolidated_mask_key = \"pred_masks_video_res\"\n else:\n consolidated_H = consolidated_W = self.image_size // 4\n consolidated_mask_key = \"pred_masks\"\n\n # Initialize `consolidated_out`. Its \"maskmem_features\" and \"maskmem_pos_enc\"\n # will be added when rerunning the memory encoder after applying non-overlapping\n # constraints to object scores. Its \"pred_masks\" are prefilled with a large\n # negative value (NO_OBJ_SCORE) to represent missing objects.\n consolidated_out = {\n \"maskmem_features\": None,\n \"maskmem_pos_enc\": None,\n consolidated_mask_key: torch.full(\n size=(batch_size, 1, consolidated_H, consolidated_W),\n fill_value=NO_OBJ_SCORE,\n dtype=torch.float32,\n device=inference_state[\"storage_device\"],\n ),\n \"obj_ptr\": torch.full(\n size=(batch_size, self.hidden_dim),\n fill_value=NO_OBJ_SCORE,\n dtype=torch.float32,\n device=inference_state[\"device\"],\n ),\n \"object_score_logits\": torch.full(\n size=(batch_size, 1),\n # default to 10.0 for object_score_logits, i.e. assuming the object is\n # present as sigmoid(10)=1, same as in `predict_masks` of `MaskDecoder`\n fill_value=10.0,\n dtype=torch.float32,\n device=inference_state[\"device\"],\n ),\n }\n empty_mask_ptr = None\n for obj_idx in range(batch_size):\n obj_temp_output_dict = inference_state[\"temp_output_dict_per_obj\"][obj_idx]\n obj_output_dict = inference_state[\"output_dict_per_obj\"][obj_idx]\n out = obj_temp_output_dict[storage_key].get(frame_idx, None)\n # If the object doesn't appear in \"temp_output_dict_per_obj\" on this frame,\n # we fall back and look up its previous output in \"output_dict_per_obj\".\n # We look up both \"cond_frame_outputs\" and \"non_cond_frame_outputs\" in\n # \"output_dict_per_obj\" to find a previous output for this object.\n if out is None:\n out = obj_output_dict[\"cond_frame_outputs\"].get(frame_idx, None)\n if out is None:\n out = obj_output_dict[\"non_cond_frame_outputs\"].get(frame_idx, None)\n # If the object doesn't appear in \"output_dict_per_obj\" either, we skip it\n # and leave its mask scores to the default scores (i.e. the NO_OBJ_SCORE\n # placeholder above) and set its object pointer to be a dummy pointer.\n if out is None:\n # Fill in dummy object pointers for those objects without any inputs or\n # tracking outcomes on this frame (only do it under `run_mem_encoder=True`,\n # i.e. when we need to build the memory for tracking).\n if run_mem_encoder:\n if empty_mask_ptr is None:\n empty_mask_ptr = self._get_empty_mask_ptr(\n inference_state, frame_idx\n )\n # fill object pointer with a dummy pointer (based on an empty mask)\n consolidated_out[\"obj_ptr\"][obj_idx : obj_idx + 1] = empty_mask_ptr\n continue\n # Add the temporary object output mask to consolidated output mask\n obj_mask = out[\"pred_masks\"]\n consolidated_pred_masks = consolidated_out[consolidated_mask_key]\n if obj_mask.shape[-2:] == consolidated_pred_masks.shape[-2:]:\n consolidated_pred_masks[obj_idx : obj_idx + 1] = obj_mask\n else:\n # Resize first if temporary object mask has a different resolution\n resized_obj_mask = torch.nn.functional.interpolate(\n obj_mask,\n size=consolidated_pred_masks.shape[-2:],\n mode=\"bilinear\",\n align_corners=False,\n )\n consolidated_pred_masks[obj_idx : obj_idx + 1] = resized_obj_mask\n consolidated_out[\"obj_ptr\"][obj_idx : obj_idx + 1] = out[\"obj_ptr\"]\n consolidated_out[\"object_score_logits\"][obj_idx : obj_idx + 1] = out[\n \"object_score_logits\"\n ]\n\n # Optionally, apply non-overlapping constraints on the consolidated scores\n # and rerun the memory encoder\n if run_mem_encoder:\n device = inference_state[\"device\"]\n high_res_masks = torch.nn.functional.interpolate(\n consolidated_out[\"pred_masks\"].to(device, non_blocking=True),\n size=(self.image_size, self.image_size),\n mode=\"bilinear\",\n align_corners=False,\n )\n if self.non_overlap_masks_for_mem_enc:\n high_res_masks = self._apply_non_overlapping_constraints(high_res_masks)\n maskmem_features, maskmem_pos_enc = self._run_memory_encoder(\n inference_state=inference_state,\n frame_idx=frame_idx,\n batch_size=batch_size,\n high_res_masks=high_res_masks,\n object_score_logits=consolidated_out[\"object_score_logits\"],\n is_mask_from_pts=True, # these frames are what the user interacted with\n )\n consolidated_out[\"maskmem_features\"] = maskmem_features\n consolidated_out[\"maskmem_pos_enc\"] = maskmem_pos_enc\n\n return consolidated_out\n\n def _get_empty_mask_ptr(self, inference_state, frame_idx):\n \"\"\"Get a dummy object pointer based on an empty mask on the current frame.\"\"\"\n # A dummy (empty) mask with a single object\n batch_size = 1\n mask_inputs = torch.zeros(\n (batch_size, 1, self.image_size, self.image_size),\n dtype=torch.float32,\n device=inference_state[\"device\"],\n )\n\n # Retrieve correct image features\n (\n _,\n _,\n current_vision_feats,\n current_vision_pos_embeds,\n feat_sizes,\n ) = self._get_image_feature(inference_state, frame_idx, batch_size)\n\n # Feed the empty mask and image feature above to get a dummy object pointer\n current_out = self.track_step(\n frame_idx=frame_idx,\n is_init_cond_frame=True,\n current_vision_feats=current_vision_feats,\n current_vision_pos_embeds=current_vision_pos_embeds,\n feat_sizes=feat_sizes,\n point_inputs=None,\n mask_inputs=mask_inputs,\n output_dict={},\n num_frames=inference_state[\"num_frames\"],\n track_in_reverse=False,\n run_mem_encoder=False,\n prev_sam_mask_logits=None,\n )\n return current_out[\"obj_ptr\"]\n\n @torch.inference_mode()\n def propagate_in_video_preflight(self, inference_state):\n \"\"\"Prepare inference_state and consolidate temporary outputs before tracking.\"\"\"\n # Tracking has started and we don't allow adding new objects until session is reset.\n inference_state[\"tracking_has_started\"] = True\n batch_size = self._get_obj_num(inference_state)\n\n # Consolidate per-object temporary outputs in \"temp_output_dict_per_obj\" and\n # add them into \"output_dict\".\n temp_output_dict_per_obj = inference_state[\"temp_output_dict_per_obj\"]\n output_dict = inference_state[\"output_dict\"]\n # \"consolidated_frame_inds\" contains indices of those frames where consolidated\n # temporary outputs have been added (either in this call or any previous calls\n # to `propagate_in_video_preflight`).\n consolidated_frame_inds = inference_state[\"consolidated_frame_inds\"]\n for is_cond in [False, True]:\n # Separately consolidate conditioning and non-conditioning temp outputs\n storage_key = \"cond_frame_outputs\" if is_cond else \"non_cond_frame_outputs\"\n # Find all the frames that contain temporary outputs for any objects\n # (these should be the frames that have just received clicks for mask inputs\n # via `add_new_points_or_box` or `add_new_mask`)\n temp_frame_inds = set()\n for obj_temp_output_dict in temp_output_dict_per_obj.values():\n temp_frame_inds.update(obj_temp_output_dict[storage_key].keys())\n consolidated_frame_inds[storage_key].update(temp_frame_inds)\n # consolidate the temporary output across all objects on this frame\n for frame_idx in temp_frame_inds:\n consolidated_out = self._consolidate_temp_output_across_obj(\n inference_state, frame_idx, is_cond=is_cond, run_mem_encoder=True\n )\n # merge them into \"output_dict\" and also create per-object slices\n output_dict[storage_key][frame_idx] = consolidated_out\n self._add_output_per_object(\n inference_state, frame_idx, consolidated_out, storage_key\n )\n clear_non_cond_mem = self.clear_non_cond_mem_around_input and (\n self.clear_non_cond_mem_for_multi_obj or batch_size <= 1\n )\n if clear_non_cond_mem:\n # clear non-conditioning memory of the surrounding frames\n self._clear_non_cond_mem_around_input(inference_state, frame_idx)\n\n # clear temporary outputs in `temp_output_dict_per_obj`\n for obj_temp_output_dict in temp_output_dict_per_obj.values():\n obj_temp_output_dict[storage_key].clear()\n\n # edge case: if an output is added to \"cond_frame_outputs\", we remove any prior\n # output on the same frame in \"non_cond_frame_outputs\"\n for frame_idx in output_dict[\"cond_frame_outputs\"]:\n output_dict[\"non_cond_frame_outputs\"].pop(frame_idx, None)\n for obj_output_dict in inference_state[\"output_dict_per_obj\"].values():\n for frame_idx in obj_output_dict[\"cond_frame_outputs\"]:\n obj_output_dict[\"non_cond_frame_outputs\"].pop(frame_idx, None)\n for frame_idx in consolidated_frame_inds[\"cond_frame_outputs\"]:\n assert frame_idx in output_dict[\"cond_frame_outputs\"]\n consolidated_frame_inds[\"non_cond_frame_outputs\"].discard(frame_idx)\n\n # Make sure that the frame indices in \"consolidated_frame_inds\" are exactly those frames\n # with either points or mask inputs (which should be true under a correct workflow).\n all_consolidated_frame_inds = (\n consolidated_frame_inds[\"cond_frame_outputs\"]\n | consolidated_frame_inds[\"non_cond_frame_outputs\"]\n )\n input_frames_inds = set()\n for point_inputs_per_frame in inference_state[\"point_inputs_per_obj\"].values():\n input_frames_inds.update(point_inputs_per_frame.keys())\n for mask_inputs_per_frame in inference_state[\"mask_inputs_per_obj\"].values():\n input_frames_inds.update(mask_inputs_per_frame.keys())\n assert all_consolidated_frame_inds == input_frames_inds\n\n @torch.inference_mode()\n def propagate_in_video(\n self,\n inference_state,\n start_frame_idx=None,\n max_frame_num_to_track=None,\n reverse=False,\n ):\n \"\"\"Propagate the input points across frames to track in the entire video.\"\"\"\n self.propagate_in_video_preflight(inference_state)\n\n output_dict = inference_state[\"output_dict\"]\n consolidated_frame_inds = inference_state[\"consolidated_frame_inds\"]\n obj_ids = inference_state[\"obj_ids\"]\n num_frames = inference_state[\"num_frames\"]\n batch_size = self._get_obj_num(inference_state)\n if len(output_dict[\"cond_frame_outputs\"]) == 0:\n raise RuntimeError(\"No points are provided; please add points first\")\n clear_non_cond_mem = self.clear_non_cond_mem_around_input and (\n self.clear_non_cond_mem_for_multi_obj or batch_size <= 1\n )\n\n # set start index, end index, and processing order\n if start_frame_idx is None:\n # default: start from the earliest frame with input points\n start_frame_idx = min(output_dict[\"cond_frame_outputs\"])\n if max_frame_num_to_track is None:\n # default: track all the frames in the video\n max_frame_num_to_track = num_frames\n if reverse:\n end_frame_idx = max(start_frame_idx - max_frame_num_to_track, 0)\n if start_frame_idx > 0:\n processing_order = range(start_frame_idx, end_frame_idx - 1, -1)\n else:\n processing_order = [] # skip reverse tracking if starting from frame 0\n else:\n end_frame_idx = min(\n start_frame_idx + max_frame_num_to_track, num_frames - 1\n )\n processing_order = range(start_frame_idx, end_frame_idx + 1)\n\n for frame_idx in tqdm(processing_order, desc=\"propagate in video\"):\n # We skip those frames already in consolidated outputs (these are frames\n # that received input clicks or mask). Note that we cannot directly run\n # batched forward on them via `_run_single_frame_inference` because the\n # number of clicks on each object might be different.\n if frame_idx in consolidated_frame_inds[\"cond_frame_outputs\"]:\n storage_key = \"cond_frame_outputs\"\n current_out = output_dict[storage_key][frame_idx]\n pred_masks = current_out[\"pred_masks\"]\n if clear_non_cond_mem:\n # clear non-conditioning memory of the surrounding frames\n self._clear_non_cond_mem_around_input(inference_state, frame_idx)\n elif frame_idx in consolidated_frame_inds[\"non_cond_frame_outputs\"]:\n storage_key = \"non_cond_frame_outputs\"\n current_out = output_dict[storage_key][frame_idx]\n pred_masks = current_out[\"pred_masks\"]\n else:\n storage_key = \"non_cond_frame_outputs\"\n current_out, pred_masks = self._run_single_frame_inference(\n inference_state=inference_state,\n output_dict=output_dict,\n frame_idx=frame_idx,\n batch_size=batch_size,\n is_init_cond_frame=False,\n point_inputs=None,\n mask_inputs=None,\n reverse=reverse,\n run_mem_encoder=True,\n )\n output_dict[storage_key][frame_idx] = current_out\n # Create slices of per-object outputs for subsequent interaction with each\n # individual object after tracking.\n self._add_output_per_object(\n inference_state, frame_idx, current_out, storage_key\n )\n inference_state[\"frames_already_tracked\"][frame_idx] = {\"reverse\": reverse}\n\n # Resize the output mask to the original video resolution (we directly use\n # the mask scores on GPU for output to avoid any CPU conversion in between)\n _, video_res_masks = self._get_orig_video_res_output(\n inference_state, pred_masks\n )\n yield frame_idx, obj_ids, video_res_masks\n\n def _add_output_per_object(\n self, inference_state, frame_idx, current_out, storage_key\n ):\n \"\"\"\n Split a multi-object output into per-object output slices and add them into\n `output_dict_per_obj`. The resulting slices share the same tensor storage.\n \"\"\"\n maskmem_features = current_out[\"maskmem_features\"]\n assert maskmem_features is None or isinstance(maskmem_features, torch.Tensor)\n\n maskmem_pos_enc = current_out[\"maskmem_pos_enc\"]\n assert maskmem_pos_enc is None or isinstance(maskmem_pos_enc, list)\n\n output_dict_per_obj = inference_state[\"output_dict_per_obj\"]\n for obj_idx, obj_output_dict in output_dict_per_obj.items():\n obj_slice = slice(obj_idx, obj_idx + 1)\n obj_out = {\n \"maskmem_features\": None,\n \"maskmem_pos_enc\": None,\n \"pred_masks\": current_out[\"pred_masks\"][obj_slice],\n \"obj_ptr\": current_out[\"obj_ptr\"][obj_slice],\n \"object_score_logits\": current_out[\"object_score_logits\"][obj_slice],\n }\n if maskmem_features is not None:\n obj_out[\"maskmem_features\"] = maskmem_features[obj_slice]\n if maskmem_pos_enc is not None:\n obj_out[\"maskmem_pos_enc\"] = [x[obj_slice] for x in maskmem_pos_enc]\n obj_output_dict[storage_key][frame_idx] = obj_out\n\n @torch.inference_mode()\n def clear_all_prompts_in_frame(\n self, inference_state, frame_idx, obj_id, need_output=True\n ):\n \"\"\"Remove all input points or mask in a specific frame for a given object.\"\"\"\n obj_idx = self._obj_id_to_idx(inference_state, obj_id)\n\n # Clear the conditioning information on the given frame\n inference_state[\"point_inputs_per_obj\"][obj_idx].pop(frame_idx, None)\n inference_state[\"mask_inputs_per_obj\"][obj_idx].pop(frame_idx, None)\n\n temp_output_dict_per_obj = inference_state[\"temp_output_dict_per_obj\"]\n temp_output_dict_per_obj[obj_idx][\"cond_frame_outputs\"].pop(frame_idx, None)\n temp_output_dict_per_obj[obj_idx][\"non_cond_frame_outputs\"].pop(frame_idx, None)\n\n # Check and see if there are still any inputs left on this frame\n batch_size = self._get_obj_num(inference_state)\n frame_has_input = False\n for obj_idx2 in range(batch_size):\n if frame_idx in inference_state[\"point_inputs_per_obj\"][obj_idx2]:\n frame_has_input = True\n break\n if frame_idx in inference_state[\"mask_inputs_per_obj\"][obj_idx2]:\n frame_has_input = True\n break\n\n # If this frame has no remaining inputs for any objects, we further clear its\n # conditioning frame status\n if not frame_has_input:\n output_dict = inference_state[\"output_dict\"]\n consolidated_frame_inds = inference_state[\"consolidated_frame_inds\"]\n consolidated_frame_inds[\"cond_frame_outputs\"].discard(frame_idx)\n consolidated_frame_inds[\"non_cond_frame_outputs\"].discard(frame_idx)\n # Remove the frame's conditioning output (possibly downgrading it to non-conditioning)\n out = output_dict[\"cond_frame_outputs\"].pop(frame_idx, None)\n if out is not None:\n # The frame is not a conditioning frame anymore since it's not receiving inputs,\n # so we \"downgrade\" its output (if exists) to a non-conditioning frame output.\n output_dict[\"non_cond_frame_outputs\"][frame_idx] = out\n inference_state[\"frames_already_tracked\"].pop(frame_idx, None)\n # Similarly, do it for the sliced output on each object.\n for obj_idx2 in range(batch_size):\n obj_output_dict = inference_state[\"output_dict_per_obj\"][obj_idx2]\n obj_out = obj_output_dict[\"cond_frame_outputs\"].pop(frame_idx, None)\n if obj_out is not None:\n obj_output_dict[\"non_cond_frame_outputs\"][frame_idx] = obj_out\n\n # If all the conditioning frames have been removed, we also clear the tracking outputs\n if len(output_dict[\"cond_frame_outputs\"]) == 0:\n self._reset_tracking_results(inference_state)\n\n if not need_output:\n return\n # Finally, output updated masks per object (after removing the inputs above)\n obj_ids = inference_state[\"obj_ids\"]\n is_cond = any(\n frame_idx in obj_temp_output_dict[\"cond_frame_outputs\"]\n for obj_temp_output_dict in temp_output_dict_per_obj.values()\n )\n consolidated_out = self._consolidate_temp_output_across_obj(\n inference_state,\n frame_idx,\n is_cond=is_cond,\n run_mem_encoder=False,\n consolidate_at_video_res=True,\n )\n _, video_res_masks = self._get_orig_video_res_output(\n inference_state, consolidated_out[\"pred_masks_video_res\"]\n )\n return frame_idx, obj_ids, video_res_masks\n\n @torch.inference_mode()\n def reset_state(self, inference_state):\n \"\"\"Remove all input points or mask in all frames throughout the video.\"\"\"\n self._reset_tracking_results(inference_state)\n # Remove all object ids\n inference_state[\"obj_id_to_idx\"].clear()\n inference_state[\"obj_idx_to_id\"].clear()\n inference_state[\"obj_ids\"].clear()\n inference_state[\"point_inputs_per_obj\"].clear()\n inference_state[\"mask_inputs_per_obj\"].clear()\n inference_state[\"output_dict_per_obj\"].clear()\n inference_state[\"temp_output_dict_per_obj\"].clear()\n\n def _reset_tracking_results(self, inference_state):\n \"\"\"Reset all tracking inputs and results across the videos.\"\"\"\n for v in inference_state[\"point_inputs_per_obj\"].values():\n v.clear()\n for v in inference_state[\"mask_inputs_per_obj\"].values():\n v.clear()\n for v in inference_state[\"output_dict_per_obj\"].values():\n v[\"cond_frame_outputs\"].clear()\n v[\"non_cond_frame_outputs\"].clear()\n for v in inference_state[\"temp_output_dict_per_obj\"].values():\n v[\"cond_frame_outputs\"].clear()\n v[\"non_cond_frame_outputs\"].clear()\n inference_state[\"output_dict\"][\"cond_frame_outputs\"].clear()\n inference_state[\"output_dict\"][\"non_cond_frame_outputs\"].clear()\n inference_state[\"consolidated_frame_inds\"][\"cond_frame_outputs\"].clear()\n inference_state[\"consolidated_frame_inds\"][\"non_cond_frame_outputs\"].clear()\n inference_state[\"tracking_has_started\"] = False\n inference_state[\"frames_already_tracked\"].clear()\n\n def _get_image_feature(self, inference_state, frame_idx, batch_size):\n \"\"\"Compute the image features on a given frame.\"\"\"\n # Look up in the cache first\n image, backbone_out = inference_state[\"cached_features\"].get(\n frame_idx, (None, None)\n )\n if backbone_out is None:\n # Cache miss -- we will run inference on a single image\n device = inference_state[\"device\"]\n image = inference_state[\"images\"][frame_idx].to(device).float().unsqueeze(0)\n backbone_out = self.forward_image(image)\n # Cache the most recent frame's feature (for repeated interactions with\n # a frame; we can use an LRU cache for more frames in the future).\n inference_state[\"cached_features\"] = {frame_idx: (image, backbone_out)}\n\n # expand the features to have the same dimension as the number of objects\n expanded_image = image.expand(batch_size, -1, -1, -1)\n expanded_backbone_out = {\n \"backbone_fpn\": backbone_out[\"backbone_fpn\"].copy(),\n \"vision_pos_enc\": backbone_out[\"vision_pos_enc\"].copy(),\n }\n for i, feat in enumerate(expanded_backbone_out[\"backbone_fpn\"]):\n expanded_backbone_out[\"backbone_fpn\"][i] = feat.expand(\n batch_size, -1, -1, -1\n )\n for i, pos in enumerate(expanded_backbone_out[\"vision_pos_enc\"]):\n pos = pos.expand(batch_size, -1, -1, -1)\n expanded_backbone_out[\"vision_pos_enc\"][i] = pos\n\n features = self._prepare_backbone_features(expanded_backbone_out)\n features = (expanded_image,) + features\n return features\n\n def _run_single_frame_inference(\n self,\n inference_state,\n output_dict,\n frame_idx,\n batch_size,\n is_init_cond_frame,\n point_inputs,\n mask_inputs,\n reverse,\n run_mem_encoder,\n prev_sam_mask_logits=None,\n ):\n \"\"\"Run tracking on a single frame based on current inputs and previous memory.\"\"\"\n # Retrieve correct image features\n (\n _,\n _,\n current_vision_feats,\n current_vision_pos_embeds,\n feat_sizes,\n ) = self._get_image_feature(inference_state, frame_idx, batch_size)\n\n # point and mask should not appear as input simultaneously on the same frame\n assert point_inputs is None or mask_inputs is None\n current_out = self.track_step(\n frame_idx=frame_idx,\n is_init_cond_frame=is_init_cond_frame,\n current_vision_feats=current_vision_feats,\n current_vision_pos_embeds=current_vision_pos_embeds,\n feat_sizes=feat_sizes,\n point_inputs=point_inputs,\n mask_inputs=mask_inputs,\n output_dict=output_dict,\n num_frames=inference_state[\"num_frames\"],\n track_in_reverse=reverse,\n run_mem_encoder=run_mem_encoder,\n prev_sam_mask_logits=prev_sam_mask_logits,\n )\n\n # optionally offload the output to CPU memory to save GPU space\n storage_device = inference_state[\"storage_device\"]\n maskmem_features = current_out[\"maskmem_features\"]\n if maskmem_features is not None:\n maskmem_features = maskmem_features.to(torch.bfloat16)\n maskmem_features = maskmem_features.to(storage_device, non_blocking=True)\n pred_masks_gpu = current_out[\"pred_masks\"]\n # potentially fill holes in the predicted masks\n if self.fill_hole_area > 0:\n pred_masks_gpu = fill_holes_in_mask_scores(\n pred_masks_gpu, self.fill_hole_area\n )\n pred_masks = pred_masks_gpu.to(storage_device, non_blocking=True)\n # \"maskmem_pos_enc\" is the same across frames, so we only need to store one copy of it\n maskmem_pos_enc = self._get_maskmem_pos_enc(inference_state, current_out)\n # object pointer is a small tensor, so we always keep it on GPU memory for fast access\n obj_ptr = current_out[\"obj_ptr\"]\n object_score_logits = current_out[\"object_score_logits\"]\n # make a compact version of this frame's output to reduce the state size\n compact_current_out = {\n \"maskmem_features\": maskmem_features,\n \"maskmem_pos_enc\": maskmem_pos_enc,\n \"pred_masks\": pred_masks,\n \"obj_ptr\": obj_ptr,\n \"object_score_logits\": object_score_logits,\n }\n return compact_current_out, pred_masks_gpu\n\n def _run_memory_encoder(\n self,\n inference_state,\n frame_idx,\n batch_size,\n high_res_masks,\n object_score_logits,\n is_mask_from_pts,\n ):\n \"\"\"\n Run the memory encoder on `high_res_masks`. This is usually after applying\n non-overlapping constraints to object scores. Since their scores changed, their\n memory also need to be computed again with the memory encoder.\n \"\"\"\n # Retrieve correct image features\n _, _, current_vision_feats, _, feat_sizes = self._get_image_feature(\n inference_state, frame_idx, batch_size\n )\n maskmem_features, maskmem_pos_enc = self._encode_new_memory(\n current_vision_feats=current_vision_feats,\n feat_sizes=feat_sizes,\n pred_masks_high_res=high_res_masks,\n object_score_logits=object_score_logits,\n is_mask_from_pts=is_mask_from_pts,\n )\n\n # optionally offload the output to CPU memory to save GPU space\n storage_device = inference_state[\"storage_device\"]\n maskmem_features = maskmem_features.to(torch.bfloat16)\n maskmem_features = maskmem_features.to(storage_device, non_blocking=True)\n # \"maskmem_pos_enc\" is the same across frames, so we only need to store one copy of it\n maskmem_pos_enc = self._get_maskmem_pos_enc(\n inference_state, {\"maskmem_pos_enc\": maskmem_pos_enc}\n )\n return maskmem_features, maskmem_pos_enc\n\n def _get_maskmem_pos_enc(self, inference_state, current_out):\n \"\"\"\n `maskmem_pos_enc` is the same across frames and objects, so we cache it as\n a constant in the inference session to reduce session storage size.\n \"\"\"\n model_constants = inference_state[\"constants\"]\n # \"out_maskmem_pos_enc\" should be either a list of tensors or None\n out_maskmem_pos_enc = current_out[\"maskmem_pos_enc\"]\n if out_maskmem_pos_enc is not None:\n if \"maskmem_pos_enc\" not in model_constants:\n assert isinstance(out_maskmem_pos_enc, list)\n # only take the slice for one object, since it's same across objects\n maskmem_pos_enc = [x[0:1].clone() for x in out_maskmem_pos_enc]\n model_constants[\"maskmem_pos_enc\"] = maskmem_pos_enc\n else:\n maskmem_pos_enc = model_constants[\"maskmem_pos_enc\"]\n # expand the cached maskmem_pos_enc to the actual batch size\n batch_size = out_maskmem_pos_enc[0].size(0)\n expanded_maskmem_pos_enc = [\n x.expand(batch_size, -1, -1, -1) for x in maskmem_pos_enc\n ]\n else:\n expanded_maskmem_pos_enc = None\n return expanded_maskmem_pos_enc\n\n @torch.inference_mode()\n def remove_object(self, inference_state, obj_id, strict=False, need_output=True):\n \"\"\"\n Remove an object id from the tracking state. If strict is True, we check whether\n the object id actually exists and raise an error if it doesn't exist.\n \"\"\"\n old_obj_idx_to_rm = inference_state[\"obj_id_to_idx\"].get(obj_id, None)\n updated_frames = []\n # Check whether this object_id to remove actually exists and possibly raise an error.\n if old_obj_idx_to_rm is None:\n if not strict:\n return inference_state[\"obj_ids\"], updated_frames\n raise RuntimeError(\n f\"Cannot remove object id {obj_id} as it doesn't exist. \"\n f\"All existing object ids: {inference_state['obj_ids']}.\"\n )\n\n # If this is the only remaining object id, we simply reset the state.\n if len(inference_state[\"obj_id_to_idx\"]) == 1:\n self.reset_state(inference_state)\n return inference_state[\"obj_ids\"], updated_frames\n\n # There are still remaining objects after removing this object id. In this case,\n # we need to delete the object storage from inference state tensors.\n # Step 0: clear the input on those frames where this object id has point or mask input\n # (note that this step is required as it might downgrade conditioning frames to\n # non-conditioning ones)\n obj_input_frames_inds = set()\n obj_input_frames_inds.update(\n inference_state[\"point_inputs_per_obj\"][old_obj_idx_to_rm]\n )\n obj_input_frames_inds.update(\n inference_state[\"mask_inputs_per_obj\"][old_obj_idx_to_rm]\n )\n for frame_idx in obj_input_frames_inds:\n self.clear_all_prompts_in_frame(\n inference_state, frame_idx, obj_id, need_output=False\n )\n\n # Step 1: Update the object id mapping (note that it must be done after Step 0,\n # since Step 0 still requires the old object id mappings in inference_state)\n old_obj_ids = inference_state[\"obj_ids\"]\n old_obj_inds = list(range(len(old_obj_ids)))\n remain_old_obj_inds = old_obj_inds.copy()\n remain_old_obj_inds.remove(old_obj_idx_to_rm)\n new_obj_ids = [old_obj_ids[old_idx] for old_idx in remain_old_obj_inds]\n new_obj_inds = list(range(len(new_obj_ids)))\n # build new mappings\n old_idx_to_new_idx = dict(zip(remain_old_obj_inds, new_obj_inds))\n inference_state[\"obj_id_to_idx\"] = dict(zip(new_obj_ids, new_obj_inds))\n inference_state[\"obj_idx_to_id\"] = dict(zip(new_obj_inds, new_obj_ids))\n inference_state[\"obj_ids\"] = new_obj_ids\n\n # Step 2: For per-object tensor storage, we shift their obj_idx in the dict keys.\n # (note that \"consolidated_frame_inds\" doesn't need to be updated in this step as\n # it's already handled in Step 0)\n def _map_keys(container):\n new_kvs = []\n for k in old_obj_inds:\n v = container.pop(k)\n if k in old_idx_to_new_idx:\n new_kvs.append((old_idx_to_new_idx[k], v))\n container.update(new_kvs)\n\n _map_keys(inference_state[\"point_inputs_per_obj\"])\n _map_keys(inference_state[\"mask_inputs_per_obj\"])\n _map_keys(inference_state[\"output_dict_per_obj\"])\n _map_keys(inference_state[\"temp_output_dict_per_obj\"])\n\n # Step 3: For packed tensor storage, we index the remaining ids and rebuild the per-object slices.\n def _slice_state(output_dict, storage_key):\n for frame_idx, out in output_dict[storage_key].items():\n out[\"maskmem_features\"] = out[\"maskmem_features\"][remain_old_obj_inds]\n out[\"maskmem_pos_enc\"] = [\n x[remain_old_obj_inds] for x in out[\"maskmem_pos_enc\"]\n ]\n # \"maskmem_pos_enc\" is the same across frames, so we only need to store one copy of it\n out[\"maskmem_pos_enc\"] = self._get_maskmem_pos_enc(inference_state, out)\n out[\"pred_masks\"] = out[\"pred_masks\"][remain_old_obj_inds]\n out[\"obj_ptr\"] = out[\"obj_ptr\"][remain_old_obj_inds]\n out[\"object_score_logits\"] = out[\"object_score_logits\"][\n remain_old_obj_inds\n ]\n # also update the per-object slices\n self._add_output_per_object(\n inference_state, frame_idx, out, storage_key\n )\n\n _slice_state(inference_state[\"output_dict\"], \"cond_frame_outputs\")\n _slice_state(inference_state[\"output_dict\"], \"non_cond_frame_outputs\")\n\n # Step 4: Further collect the outputs on those frames in `obj_input_frames_inds`, which\n # could show an updated mask for objects previously occluded by the object being removed\n if need_output:\n temp_output_dict_per_obj = inference_state[\"temp_output_dict_per_obj\"]\n for frame_idx in obj_input_frames_inds:\n is_cond = any(\n frame_idx in obj_temp_output_dict[\"cond_frame_outputs\"]\n for obj_temp_output_dict in temp_output_dict_per_obj.values()\n )\n consolidated_out = self._consolidate_temp_output_across_obj(\n inference_state,\n frame_idx,\n is_cond=is_cond,\n run_mem_encoder=False,\n consolidate_at_video_res=True,\n )\n _, video_res_masks = self._get_orig_video_res_output(\n inference_state, consolidated_out[\"pred_masks_video_res\"]\n )\n updated_frames.append((frame_idx, video_res_masks))\n\n return inference_state[\"obj_ids\"], updated_frames\n\n def _clear_non_cond_mem_around_input(self, inference_state, frame_idx):\n \"\"\"\n Remove the non-conditioning memory around the input frame. When users provide\n correction clicks, the surrounding frames' non-conditioning memories can still\n contain outdated object appearance information and could confuse the model.\n\n This method clears those non-conditioning memories surrounding the interacted\n frame to avoid giving the model both old and new information about the object.\n \"\"\"\n r = self.memory_temporal_stride_for_eval\n frame_idx_begin = frame_idx - r * self.num_maskmem\n frame_idx_end = frame_idx + r * self.num_maskmem\n output_dict = inference_state[\"output_dict\"]\n non_cond_frame_outputs = output_dict[\"non_cond_frame_outputs\"]\n for t in range(frame_idx_begin, frame_idx_end + 1):\n non_cond_frame_outputs.pop(t, None)\n for obj_output_dict in inference_state[\"output_dict_per_obj\"].values():\n obj_output_dict[\"non_cond_frame_outputs\"].pop(t, None)" }, { "path": "auto-seg/submodules/segment-anything-2/sam2/utils/__init__.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n" }, { "path": "auto-seg/submodules/segment-anything-2/sam2/utils/amg.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport math\nfrom copy import deepcopy\nfrom itertools import product\nfrom typing import Any, Dict, Generator, ItemsView, List, Tuple\n\nimport numpy as np\nimport torch\n\n# Very lightly adapted from https://github.com/facebookresearch/segment-anything/blob/main/segment_anything/utils/amg.py\n\n\nclass MaskData:\n \"\"\"\n A structure for storing masks and their related data in batched format.\n Implements basic filtering and concatenation.\n \"\"\"\n\n def __init__(self, **kwargs) -> None:\n for v in kwargs.values():\n assert isinstance(\n v, (list, np.ndarray, torch.Tensor)\n ), \"MaskData only supports list, numpy arrays, and torch tensors.\"\n self._stats = dict(**kwargs)\n\n def __setitem__(self, key: str, item: Any) -> None:\n assert isinstance(\n item, (list, np.ndarray, torch.Tensor)\n ), \"MaskData only supports list, numpy arrays, and torch tensors.\"\n self._stats[key] = item\n\n def __delitem__(self, key: str) -> None:\n del self._stats[key]\n\n def __getitem__(self, key: str) -> Any:\n return self._stats[key]\n\n def items(self) -> ItemsView[str, Any]:\n return self._stats.items()\n\n def filter(self, keep: torch.Tensor) -> None:\n for k, v in self._stats.items():\n if v is None:\n self._stats[k] = None\n elif isinstance(v, torch.Tensor):\n self._stats[k] = v[torch.as_tensor(keep, device=v.device)]\n elif isinstance(v, np.ndarray):\n self._stats[k] = v[keep.detach().cpu().numpy()]\n elif isinstance(v, list) and keep.dtype == torch.bool:\n self._stats[k] = [a for i, a in enumerate(v) if keep[i]]\n elif isinstance(v, list):\n self._stats[k] = [v[i] for i in keep]\n else:\n raise TypeError(f\"MaskData key {k} has an unsupported type {type(v)}.\")\n\n def cat(self, new_stats: \"MaskData\") -> None:\n for k, v in new_stats.items():\n if k not in self._stats or self._stats[k] is None:\n self._stats[k] = deepcopy(v)\n elif isinstance(v, torch.Tensor):\n self._stats[k] = torch.cat([self._stats[k], v], dim=0)\n elif isinstance(v, np.ndarray):\n self._stats[k] = np.concatenate([self._stats[k], v], axis=0)\n elif isinstance(v, list):\n self._stats[k] = self._stats[k] + deepcopy(v)\n else:\n raise TypeError(f\"MaskData key {k} has an unsupported type {type(v)}.\")\n\n def to_numpy(self) -> None:\n for k, v in self._stats.items():\n if isinstance(v, torch.Tensor):\n self._stats[k] = v.float().detach().cpu().numpy()\n\n\ndef is_box_near_crop_edge(\n boxes: torch.Tensor, crop_box: List[int], orig_box: List[int], atol: float = 20.0\n) -> torch.Tensor:\n \"\"\"Filter masks at the edge of a crop, but not at the edge of the original image.\"\"\"\n crop_box_torch = torch.as_tensor(crop_box, dtype=torch.float, device=boxes.device)\n orig_box_torch = torch.as_tensor(orig_box, dtype=torch.float, device=boxes.device)\n boxes = uncrop_boxes_xyxy(boxes, crop_box).float()\n near_crop_edge = torch.isclose(boxes, crop_box_torch[None, :], atol=atol, rtol=0)\n near_image_edge = torch.isclose(boxes, orig_box_torch[None, :], atol=atol, rtol=0)\n near_crop_edge = torch.logical_and(near_crop_edge, ~near_image_edge)\n return torch.any(near_crop_edge, dim=1)\n\n\ndef box_xyxy_to_xywh(box_xyxy: torch.Tensor) -> torch.Tensor:\n box_xywh = deepcopy(box_xyxy)\n box_xywh[2] = box_xywh[2] - box_xywh[0]\n box_xywh[3] = box_xywh[3] - box_xywh[1]\n return box_xywh\n\n\ndef batch_iterator(batch_size: int, *args) -> Generator[List[Any], None, None]:\n assert len(args) > 0 and all(\n len(a) == len(args[0]) for a in args\n ), \"Batched iteration must have inputs of all the same size.\"\n n_batches = len(args[0]) // batch_size + int(len(args[0]) % batch_size != 0)\n for b in range(n_batches):\n yield [arg[b * batch_size : (b + 1) * batch_size] for arg in args]\n\n\ndef mask_to_rle_pytorch(tensor: torch.Tensor) -> List[Dict[str, Any]]:\n \"\"\"\n Encodes masks to an uncompressed RLE, in the format expected by\n pycoco tools.\n \"\"\"\n # Put in fortran order and flatten h,w\n b, h, w = tensor.shape\n tensor = tensor.permute(0, 2, 1).flatten(1)\n\n # Compute change indices\n diff = tensor[:, 1:] ^ tensor[:, :-1]\n change_indices = diff.nonzero()\n\n # Encode run length\n out = []\n for i in range(b):\n cur_idxs = change_indices[change_indices[:, 0] == i, 1]\n cur_idxs = torch.cat(\n [\n torch.tensor([0], dtype=cur_idxs.dtype, device=cur_idxs.device),\n cur_idxs + 1,\n torch.tensor([h * w], dtype=cur_idxs.dtype, device=cur_idxs.device),\n ]\n )\n btw_idxs = cur_idxs[1:] - cur_idxs[:-1]\n counts = [] if tensor[i, 0] == 0 else [0]\n counts.extend(btw_idxs.detach().cpu().tolist())\n out.append({\"size\": [h, w], \"counts\": counts})\n return out\n\n\ndef rle_to_mask(rle: Dict[str, Any]) -> np.ndarray:\n \"\"\"Compute a binary mask from an uncompressed RLE.\"\"\"\n h, w = rle[\"size\"]\n mask = np.empty(h * w, dtype=bool)\n idx = 0\n parity = False\n for count in rle[\"counts\"]:\n mask[idx : idx + count] = parity\n idx += count\n parity ^= True\n mask = mask.reshape(w, h)\n return mask.transpose() # Put in C order\n\n\ndef area_from_rle(rle: Dict[str, Any]) -> int:\n return sum(rle[\"counts\"][1::2])\n\n\ndef calculate_stability_score(\n masks: torch.Tensor, mask_threshold: float, threshold_offset: float\n) -> torch.Tensor:\n \"\"\"\n Computes the stability score for a batch of masks. The stability\n score is the IoU between the binary masks obtained by thresholding\n the predicted mask logits at high and low values.\n \"\"\"\n # One mask is always contained inside the other.\n # Save memory by preventing unnecessary cast to torch.int64\n intersections = (\n (masks > (mask_threshold + threshold_offset))\n .sum(-1, dtype=torch.int16)\n .sum(-1, dtype=torch.int32)\n )\n unions = (\n (masks > (mask_threshold - threshold_offset))\n .sum(-1, dtype=torch.int16)\n .sum(-1, dtype=torch.int32)\n )\n return intersections / unions\n\n\ndef build_point_grid(n_per_side: int) -> np.ndarray:\n \"\"\"Generates a 2D grid of points evenly spaced in [0,1]x[0,1].\"\"\"\n offset = 1 / (2 * n_per_side)\n points_one_side = np.linspace(offset, 1 - offset, n_per_side)\n points_x = np.tile(points_one_side[None, :], (n_per_side, 1))\n points_y = np.tile(points_one_side[:, None], (1, n_per_side))\n points = np.stack([points_x, points_y], axis=-1).reshape(-1, 2)\n return points\n\n\ndef build_all_layer_point_grids(\n n_per_side: int, n_layers: int, scale_per_layer: int\n) -> List[np.ndarray]:\n \"\"\"Generates point grids for all crop layers.\"\"\"\n points_by_layer = []\n for i in range(n_layers + 1):\n n_points = int(n_per_side / (scale_per_layer**i))\n points_by_layer.append(build_point_grid(n_points))\n return points_by_layer\n\n\ndef generate_crop_boxes(\n im_size: Tuple[int, ...], n_layers: int, overlap_ratio: float\n) -> Tuple[List[List[int]], List[int]]:\n \"\"\"\n Generates a list of crop boxes of different sizes. Each layer\n has (2**i)**2 boxes for the ith layer.\n \"\"\"\n crop_boxes, layer_idxs = [], []\n im_h, im_w = im_size\n short_side = min(im_h, im_w)\n\n # Original image\n crop_boxes.append([0, 0, im_w, im_h])\n layer_idxs.append(0)\n\n def crop_len(orig_len, n_crops, overlap):\n return int(math.ceil((overlap * (n_crops - 1) + orig_len) / n_crops))\n\n for i_layer in range(n_layers):\n n_crops_per_side = 2 ** (i_layer + 1)\n overlap = int(overlap_ratio * short_side * (2 / n_crops_per_side))\n\n crop_w = crop_len(im_w, n_crops_per_side, overlap)\n crop_h = crop_len(im_h, n_crops_per_side, overlap)\n\n crop_box_x0 = [int((crop_w - overlap) * i) for i in range(n_crops_per_side)]\n crop_box_y0 = [int((crop_h - overlap) * i) for i in range(n_crops_per_side)]\n\n # Crops in XYWH format\n for x0, y0 in product(crop_box_x0, crop_box_y0):\n box = [x0, y0, min(x0 + crop_w, im_w), min(y0 + crop_h, im_h)]\n crop_boxes.append(box)\n layer_idxs.append(i_layer + 1)\n\n return crop_boxes, layer_idxs\n\n\ndef uncrop_boxes_xyxy(boxes: torch.Tensor, crop_box: List[int]) -> torch.Tensor:\n x0, y0, _, _ = crop_box\n offset = torch.tensor([[x0, y0, x0, y0]], device=boxes.device)\n # Check if boxes has a channel dimension\n if len(boxes.shape) == 3:\n offset = offset.unsqueeze(1)\n return boxes + offset\n\n\ndef uncrop_points(points: torch.Tensor, crop_box: List[int]) -> torch.Tensor:\n x0, y0, _, _ = crop_box\n offset = torch.tensor([[x0, y0]], device=points.device)\n # Check if points has a channel dimension\n if len(points.shape) == 3:\n offset = offset.unsqueeze(1)\n return points + offset\n\n\ndef uncrop_masks(\n masks: torch.Tensor, crop_box: List[int], orig_h: int, orig_w: int\n) -> torch.Tensor:\n x0, y0, x1, y1 = crop_box\n if x0 == 0 and y0 == 0 and x1 == orig_w and y1 == orig_h:\n return masks\n # Coordinate transform masks\n pad_x, pad_y = orig_w - (x1 - x0), orig_h - (y1 - y0)\n pad = (x0, pad_x - x0, y0, pad_y - y0)\n return torch.nn.functional.pad(masks, pad, value=0)\n\n\ndef remove_small_regions(\n mask: np.ndarray, area_thresh: float, mode: str\n) -> Tuple[np.ndarray, bool]:\n \"\"\"\n Removes small disconnected regions and holes in a mask. Returns the\n mask and an indicator of if the mask has been modified.\n \"\"\"\n import cv2 # type: ignore\n\n assert mode in [\"holes\", \"islands\"]\n correct_holes = mode == \"holes\"\n working_mask = (correct_holes ^ mask).astype(np.uint8)\n n_labels, regions, stats, _ = cv2.connectedComponentsWithStats(working_mask, 8)\n sizes = stats[:, -1][1:] # Row 0 is background label\n small_regions = [i + 1 for i, s in enumerate(sizes) if s < area_thresh]\n if len(small_regions) == 0:\n return mask, False\n fill_labels = [0] + small_regions\n if not correct_holes:\n fill_labels = [i for i in range(n_labels) if i not in fill_labels]\n # If every region is below threshold, keep largest\n if len(fill_labels) == 0:\n fill_labels = [int(np.argmax(sizes)) + 1]\n mask = np.isin(regions, fill_labels)\n return mask, True\n\n\ndef coco_encode_rle(uncompressed_rle: Dict[str, Any]) -> Dict[str, Any]:\n from pycocotools import mask as mask_utils # type: ignore\n\n h, w = uncompressed_rle[\"size\"]\n rle = mask_utils.frPyObjects(uncompressed_rle, h, w)\n rle[\"counts\"] = rle[\"counts\"].decode(\"utf-8\") # Necessary to serialize with json\n return rle\n\n\ndef batched_mask_to_box(masks: torch.Tensor) -> torch.Tensor:\n \"\"\"\n Calculates boxes in XYXY format around masks. Return [0,0,0,0] for\n an empty mask. For input shape C1xC2x...xHxW, the output shape is C1xC2x...x4.\n \"\"\"\n # torch.max below raises an error on empty inputs, just skip in this case\n if torch.numel(masks) == 0:\n return torch.zeros(*masks.shape[:-2], 4, device=masks.device)\n\n # Normalize shape to CxHxW\n shape = masks.shape\n h, w = shape[-2:]\n if len(shape) > 2:\n masks = masks.flatten(0, -3)\n else:\n masks = masks.unsqueeze(0)\n\n # Get top and bottom edges\n in_height, _ = torch.max(masks, dim=-1)\n in_height_coords = in_height * torch.arange(h, device=in_height.device)[None, :]\n bottom_edges, _ = torch.max(in_height_coords, dim=-1)\n in_height_coords = in_height_coords + h * (~in_height)\n top_edges, _ = torch.min(in_height_coords, dim=-1)\n\n # Get left and right edges\n in_width, _ = torch.max(masks, dim=-2)\n in_width_coords = in_width * torch.arange(w, device=in_width.device)[None, :]\n right_edges, _ = torch.max(in_width_coords, dim=-1)\n in_width_coords = in_width_coords + w * (~in_width)\n left_edges, _ = torch.min(in_width_coords, dim=-1)\n\n # If the mask is empty the right edge will be to the left of the left edge.\n # Replace these boxes with [0, 0, 0, 0]\n empty_filter = (right_edges < left_edges) | (bottom_edges < top_edges)\n out = torch.stack([left_edges, top_edges, right_edges, bottom_edges], dim=-1)\n out = out * (~empty_filter).unsqueeze(-1)\n\n # Return to original shape\n if len(shape) > 2:\n out = out.reshape(*shape[:-2], 4)\n else:\n out = out[0]\n\n return out\n" }, { "path": "auto-seg/submodules/segment-anything-2/sam2/utils/misc.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport os\nimport warnings\nfrom threading import Thread\n\nimport numpy as np\nimport torch\nfrom PIL import Image\nfrom tqdm import tqdm\n\n\ndef get_sdpa_settings():\n if torch.cuda.is_available():\n old_gpu = torch.cuda.get_device_properties(0).major < 7\n # only use Flash Attention on Ampere (8.0) or newer GPUs\n use_flash_attn = torch.cuda.get_device_properties(0).major >= 8\n if not use_flash_attn:\n warnings.warn(\n \"Flash Attention is disabled as it requires a GPU with Ampere (8.0) CUDA capability.\",\n category=UserWarning,\n stacklevel=2,\n )\n # keep math kernel for PyTorch versions before 2.2 (Flash Attention v2 is only\n # available on PyTorch 2.2+, while Flash Attention v1 cannot handle all cases)\n pytorch_version = tuple(int(v) for v in torch.__version__.split(\".\")[:2])\n if pytorch_version < (2, 2):\n warnings.warn(\n f\"You are using PyTorch {torch.__version__} without Flash Attention v2 support. \"\n \"Consider upgrading to PyTorch 2.2+ for Flash Attention v2 (which could be faster).\",\n category=UserWarning,\n stacklevel=2,\n )\n math_kernel_on = pytorch_version < (2, 2) or not use_flash_attn\n else:\n old_gpu = True\n use_flash_attn = False\n math_kernel_on = True\n\n return old_gpu, use_flash_attn, math_kernel_on\n\n\ndef get_connected_components(mask):\n \"\"\"\n Get the connected components (8-connectivity) of binary masks of shape (N, 1, H, W).\n\n Inputs:\n - mask: A binary mask tensor of shape (N, 1, H, W), where 1 is foreground and 0 is\n background.\n\n Outputs:\n - labels: A tensor of shape (N, 1, H, W) containing the connected component labels\n for foreground pixels and 0 for background pixels.\n - counts: A tensor of shape (N, 1, H, W) containing the area of the connected\n components for foreground pixels and 0 for background pixels.\n \"\"\"\n from sam2 import _C\n\n return _C.get_connected_componnets(mask.to(torch.uint8).contiguous())\n\n\ndef mask_to_box(masks: torch.Tensor):\n \"\"\"\n compute bounding box given an input mask\n\n Inputs:\n - masks: [B, 1, H, W] masks, dtype=torch.Tensor\n\n Returns:\n - box_coords: [B, 1, 4], contains (x, y) coordinates of top left and bottom right box corners, dtype=torch.Tensor\n \"\"\"\n B, _, h, w = masks.shape\n device = masks.device\n xs = torch.arange(w, device=device, dtype=torch.int32)\n ys = torch.arange(h, device=device, dtype=torch.int32)\n grid_xs, grid_ys = torch.meshgrid(xs, ys, indexing=\"xy\")\n grid_xs = grid_xs[None, None, ...].expand(B, 1, h, w)\n grid_ys = grid_ys[None, None, ...].expand(B, 1, h, w)\n min_xs, _ = torch.min(torch.where(masks, grid_xs, w).flatten(-2), dim=-1)\n max_xs, _ = torch.max(torch.where(masks, grid_xs, -1).flatten(-2), dim=-1)\n min_ys, _ = torch.min(torch.where(masks, grid_ys, h).flatten(-2), dim=-1)\n max_ys, _ = torch.max(torch.where(masks, grid_ys, -1).flatten(-2), dim=-1)\n bbox_coords = torch.stack((min_xs, min_ys, max_xs, max_ys), dim=-1)\n\n return bbox_coords\n\n\ndef _load_img_as_tensor(img_path, image_size):\n img_pil = Image.open(img_path)\n img_np = np.array(img_pil.convert(\"RGB\").resize((image_size, image_size)))\n if img_np.dtype == np.uint8: # np.uint8 is expected for JPEG images\n img_np = img_np / 255.0\n else:\n raise RuntimeError(f\"Unknown image dtype: {img_np.dtype} on {img_path}\")\n img = torch.from_numpy(img_np).permute(2, 0, 1)\n video_width, video_height = img_pil.size # the original video size\n return img, video_height, video_width\n\n\nclass AsyncVideoFrameLoader:\n \"\"\"\n A list of video frames to be load asynchronously without blocking session start.\n \"\"\"\n\n def __init__(\n self,\n img_paths,\n image_size,\n offload_video_to_cpu,\n img_mean,\n img_std,\n compute_device,\n ):\n self.img_paths = img_paths\n self.image_size = image_size\n self.offload_video_to_cpu = offload_video_to_cpu\n self.img_mean = img_mean\n self.img_std = img_std\n # items in `self.images` will be loaded asynchronously\n self.images = [None] * len(img_paths)\n # catch and raise any exceptions in the async loading thread\n self.exception = None\n # video_height and video_width be filled when loading the first image\n self.video_height = None\n self.video_width = None\n self.compute_device = compute_device\n\n # load the first frame to fill video_height and video_width and also\n # to cache it (since it's most likely where the user will click)\n self.__getitem__(0)\n\n # load the rest of frames asynchronously without blocking the session start\n def _load_frames():\n try:\n for n in tqdm(range(len(self.images)), desc=\"frame loading (JPEG)\"):\n self.__getitem__(n)\n except Exception as e:\n self.exception = e\n\n self.thread = Thread(target=_load_frames, daemon=True)\n self.thread.start()\n\n def __getitem__(self, index):\n if self.exception is not None:\n raise RuntimeError(\"Failure in frame loading thread\") from self.exception\n\n img = self.images[index]\n if img is not None:\n return img\n\n img, video_height, video_width = _load_img_as_tensor(\n self.img_paths[index], self.image_size\n )\n self.video_height = video_height\n self.video_width = video_width\n # normalize by mean and std\n img -= self.img_mean\n img /= self.img_std\n if not self.offload_video_to_cpu:\n img = img.to(self.compute_device, non_blocking=True)\n self.images[index] = img\n return img\n\n def __len__(self):\n return len(self.images)\n\n\ndef load_video_frames(\n video_path,\n image_size,\n offload_video_to_cpu,\n img_mean=(0.485, 0.456, 0.406),\n img_std=(0.229, 0.224, 0.225),\n async_loading_frames=False,\n compute_device=torch.device(\"cuda\"),\n):\n \"\"\"\n Load the video frames from video_path. The frames are resized to image_size as in\n the model and are loaded to GPU if offload_video_to_cpu=False. This is used by the demo.\n \"\"\"\n is_bytes = isinstance(video_path, bytes)\n is_str = isinstance(video_path, str)\n is_mp4_path = is_str and os.path.splitext(video_path)[-1] in [\".mp4\", \".MP4\"]\n if is_bytes or is_mp4_path:\n return load_video_frames_from_video_file(\n video_path=video_path,\n image_size=image_size,\n offload_video_to_cpu=offload_video_to_cpu,\n img_mean=img_mean,\n img_std=img_std,\n compute_device=compute_device,\n )\n elif is_str and os.path.isdir(video_path):\n return load_video_frames_from_jpg_images(\n video_path=video_path,\n image_size=image_size,\n offload_video_to_cpu=offload_video_to_cpu,\n img_mean=img_mean,\n img_std=img_std,\n async_loading_frames=async_loading_frames,\n compute_device=compute_device,\n )\n else:\n raise NotImplementedError(\n \"Only MP4 video and JPEG folder are supported at this moment\"\n )\n\n\ndef load_video_frames_from_jpg_images(\n video_path,\n image_size,\n offload_video_to_cpu,\n img_mean=(0.485, 0.456, 0.406),\n img_std=(0.229, 0.224, 0.225),\n async_loading_frames=False,\n compute_device=torch.device(\"cuda\"),\n):\n \"\"\"\n Load the video frames from a directory of JPEG files (\".jpg\" format).\n\n The frames are resized to image_size x image_size and are loaded to GPU if\n `offload_video_to_cpu` is `False` and to CPU if `offload_video_to_cpu` is `True`.\n\n You can load a frame asynchronously by setting `async_loading_frames` to `True`.\n \"\"\"\n if isinstance(video_path, str) and os.path.isdir(video_path):\n jpg_folder = video_path\n else:\n raise NotImplementedError(\n \"Only JPEG frames are supported at this moment. For video files, you may use \"\n \"ffmpeg (https://ffmpeg.org/) to extract frames into a folder of JPEG files, such as \\n\"\n \"```\\n\"\n \"ffmpeg -i .mp4 -q:v 2 -start_number 0 /'%05d.jpg'\\n\"\n \"```\\n\"\n \"where `-q:v` generates high-quality JPEG frames and `-start_number 0` asks \"\n \"ffmpeg to start the JPEG file from 00000.jpg.\"\n )\n\n frame_names = [\n p\n for p in os.listdir(jpg_folder)\n if os.path.splitext(p)[-1] in [\".jpg\", \".jpeg\", \".JPG\", \".JPEG\"]\n ]\n frame_names.sort(key=lambda p: int(os.path.splitext(p)[0]))\n num_frames = len(frame_names)\n if num_frames == 0:\n raise RuntimeError(f\"no images found in {jpg_folder}\")\n img_paths = [os.path.join(jpg_folder, frame_name) for frame_name in frame_names]\n img_mean = torch.tensor(img_mean, dtype=torch.float32)[:, None, None]\n img_std = torch.tensor(img_std, dtype=torch.float32)[:, None, None]\n\n if async_loading_frames:\n lazy_images = AsyncVideoFrameLoader(\n img_paths,\n image_size,\n offload_video_to_cpu,\n img_mean,\n img_std,\n compute_device,\n )\n return lazy_images, lazy_images.video_height, lazy_images.video_width\n\n images = torch.zeros(num_frames, 3, image_size, image_size, dtype=torch.float32)\n for n, img_path in enumerate(tqdm(img_paths, desc=\"frame loading (JPEG)\")):\n images[n], video_height, video_width = _load_img_as_tensor(img_path, image_size)\n if not offload_video_to_cpu:\n images = images.to(compute_device)\n img_mean = img_mean.to(compute_device)\n img_std = img_std.to(compute_device)\n # normalize by mean and std\n images -= img_mean\n images /= img_std\n return images, video_height, video_width\n\n\ndef load_video_frames_from_video_file(\n video_path,\n image_size,\n offload_video_to_cpu,\n img_mean=(0.485, 0.456, 0.406),\n img_std=(0.229, 0.224, 0.225),\n compute_device=torch.device(\"cuda\"),\n):\n \"\"\"Load the video frames from a video file.\"\"\"\n import decord\n\n img_mean = torch.tensor(img_mean, dtype=torch.float32)[:, None, None]\n img_std = torch.tensor(img_std, dtype=torch.float32)[:, None, None]\n # Get the original video height and width\n decord.bridge.set_bridge(\"torch\")\n video_height, video_width, _ = decord.VideoReader(video_path).next().shape\n # Iterate over all frames in the video\n images = []\n for frame in decord.VideoReader(video_path, width=image_size, height=image_size):\n images.append(frame.permute(2, 0, 1))\n\n images = torch.stack(images, dim=0).float() / 255.0\n if not offload_video_to_cpu:\n images = images.to(compute_device)\n img_mean = img_mean.to(compute_device)\n img_std = img_std.to(compute_device)\n # normalize by mean and std\n images -= img_mean\n images /= img_std\n return images, video_height, video_width\n\n\ndef fill_holes_in_mask_scores(mask, max_area):\n \"\"\"\n A post processor to fill small holes in mask scores with area under `max_area`.\n \"\"\"\n # Holes are those connected components in background with area <= self.max_area\n # (background regions are those with mask scores <= 0)\n assert max_area > 0, \"max_area must be positive\"\n\n input_mask = mask\n try:\n labels, areas = get_connected_components(mask <= 0)\n is_hole = (labels > 0) & (areas <= max_area)\n # We fill holes with a small positive mask score (0.1) to change them to foreground.\n mask = torch.where(is_hole, 0.1, mask)\n except Exception as e:\n # Skip the post-processing step on removing small holes if the CUDA kernel fails\n warnings.warn(\n f\"{e}\\n\\nSkipping the post-processing step due to the error above. You can \"\n \"still use SAM 2 and it's OK to ignore the error above, although some post-processing \"\n \"functionality may be limited (which doesn't affect the results in most cases; see \"\n \"https://github.com/facebookresearch/sam2/blob/main/INSTALL.md).\",\n category=UserWarning,\n stacklevel=2,\n )\n mask = input_mask\n\n return mask\n\n\ndef concat_points(old_point_inputs, new_points, new_labels):\n \"\"\"Add new points and labels to previous point inputs (add at the end).\"\"\"\n if old_point_inputs is None:\n points, labels = new_points, new_labels\n else:\n points = torch.cat([old_point_inputs[\"point_coords\"], new_points], dim=1)\n labels = torch.cat([old_point_inputs[\"point_labels\"], new_labels], dim=1)\n\n return {\"point_coords\": points, \"point_labels\": labels}\n" }, { "path": "auto-seg/submodules/segment-anything-2/sam2/utils/transforms.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport warnings\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torchvision.transforms import Normalize, Resize, ToTensor\n\n\nclass SAM2Transforms(nn.Module):\n def __init__(\n self, resolution, mask_threshold, max_hole_area=0.0, max_sprinkle_area=0.0\n ):\n \"\"\"\n Transforms for SAM2.\n \"\"\"\n super().__init__()\n self.resolution = resolution\n self.mask_threshold = mask_threshold\n self.max_hole_area = max_hole_area\n self.max_sprinkle_area = max_sprinkle_area\n self.mean = [0.485, 0.456, 0.406]\n self.std = [0.229, 0.224, 0.225]\n self.to_tensor = ToTensor()\n self.transforms = torch.jit.script(\n nn.Sequential(\n Resize((self.resolution, self.resolution)),\n Normalize(self.mean, self.std),\n )\n )\n\n def __call__(self, x):\n x = self.to_tensor(x)\n return self.transforms(x)\n\n def forward_batch(self, img_list):\n img_batch = [self.transforms(self.to_tensor(img)) for img in img_list]\n img_batch = torch.stack(img_batch, dim=0)\n return img_batch\n\n def transform_coords(\n self, coords: torch.Tensor, normalize=False, orig_hw=None\n ) -> torch.Tensor:\n \"\"\"\n Expects a torch tensor with length 2 in the last dimension. The coordinates can be in absolute image or normalized coordinates,\n If the coords are in absolute image coordinates, normalize should be set to True and original image size is required.\n\n Returns\n Un-normalized coordinates in the range of [0, 1] which is expected by the SAM2 model.\n \"\"\"\n if normalize:\n assert orig_hw is not None\n h, w = orig_hw\n coords = coords.clone()\n coords[..., 0] = coords[..., 0] / w\n coords[..., 1] = coords[..., 1] / h\n\n coords = coords * self.resolution # unnormalize coords\n return coords\n\n def transform_boxes(\n self, boxes: torch.Tensor, normalize=False, orig_hw=None\n ) -> torch.Tensor:\n \"\"\"\n Expects a tensor of shape Bx4. The coordinates can be in absolute image or normalized coordinates,\n if the coords are in absolute image coordinates, normalize should be set to True and original image size is required.\n \"\"\"\n boxes = self.transform_coords(boxes.reshape(-1, 2, 2), normalize, orig_hw)\n return boxes\n\n def postprocess_masks(self, masks: torch.Tensor, orig_hw) -> torch.Tensor:\n \"\"\"\n Perform PostProcessing on output masks.\n \"\"\"\n from sam2.utils.misc import get_connected_components\n\n masks = masks.float()\n input_masks = masks\n mask_flat = masks.flatten(0, 1).unsqueeze(1) # flatten as 1-channel image\n try:\n if self.max_hole_area > 0:\n # Holes are those connected components in background with area <= self.fill_hole_area\n # (background regions are those with mask scores <= self.mask_threshold)\n labels, areas = get_connected_components(\n mask_flat <= self.mask_threshold\n )\n is_hole = (labels > 0) & (areas <= self.max_hole_area)\n is_hole = is_hole.reshape_as(masks)\n # We fill holes with a small positive mask score (10.0) to change them to foreground.\n masks = torch.where(is_hole, self.mask_threshold + 10.0, masks)\n\n if self.max_sprinkle_area > 0:\n labels, areas = get_connected_components(\n mask_flat > self.mask_threshold\n )\n is_hole = (labels > 0) & (areas <= self.max_sprinkle_area)\n is_hole = is_hole.reshape_as(masks)\n # We fill holes with negative mask score (-10.0) to change them to background.\n masks = torch.where(is_hole, self.mask_threshold - 10.0, masks)\n except Exception as e:\n # Skip the post-processing step if the CUDA kernel fails\n warnings.warn(\n f\"{e}\\n\\nSkipping the post-processing step due to the error above. You can \"\n \"still use SAM 2 and it's OK to ignore the error above, although some post-processing \"\n \"functionality may be limited (which doesn't affect the results in most cases; see \"\n \"https://github.com/facebookresearch/sam2/blob/main/INSTALL.md).\",\n category=UserWarning,\n stacklevel=2,\n )\n masks = input_masks\n\n masks = F.interpolate(masks, orig_hw, mode=\"bilinear\", align_corners=False)\n return masks\n" }, { "path": "auto-seg/submodules/segment-anything-2/sav_dataset/LICENSE", "content": "BSD License\n\nFor SAM 2 Eval software\n\nCopyright (c) Meta Platforms, Inc. and affiliates.\n\nRedistribution and use in source and binary forms, with or without modification,\nare permitted provided that the following conditions are met:\n\n * Redistributions of source code must retain the above copyright notice, this\n list of conditions and the following disclaimer.\n\n * Redistributions in binary form must reproduce the above copyright notice,\n this list of conditions and the following disclaimer in the documentation\n and/or other materials provided with the distribution.\n\n * Neither the name Meta nor the names of its contributors may be used to\n endorse or promote products derived from this software without specific\n prior written permission.\n\nTHIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND\nANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED\nWARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\nDISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR\nANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES\n(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;\nLOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON\nANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT\n(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS\nSOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n" }, { "path": "auto-seg/submodules/segment-anything-2/sav_dataset/LICENSE_DAVIS", "content": "BSD 3-Clause License\n\nCopyright (c) 2020, DAVIS: Densely Annotated VIdeo Segmentation\nAll rights reserved.\n\nRedistribution and use in source and binary forms, with or without\nmodification, are permitted provided that the following conditions are met:\n\n1. Redistributions of source code must retain the above copyright notice, this\n list of conditions and the following disclaimer.\n\n2. Redistributions in binary form must reproduce the above copyright notice,\n this list of conditions and the following disclaimer in the documentation\n and/or other materials provided with the distribution.\n\n3. Neither the name of the copyright holder nor the names of its\n contributors may be used to endorse or promote products derived from\n this software without specific prior written permission.\n\nTHIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\nAND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\nIMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\nDISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE\nFOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL\nDAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR\nSERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER\nCAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,\nOR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\nOF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE." }, { "path": "auto-seg/submodules/segment-anything-2/sav_dataset/LICENSE_VOS_BENCHMARK", "content": "Copyright 2023 Rex Cheng\n\nPermission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE." }, { "path": "auto-seg/submodules/segment-anything-2/sav_dataset/README.md", "content": "# Segment Anything Video (SA-V) Dataset\n\n## Overview\n\n[Segment Anything Video (SA-V)](https://ai.meta.com/datasets/segment-anything-video/), consists of 51K diverse videos and 643K high-quality spatio-temporal segmentation masks (i.e., masklets). The dataset is released under the CC by 4.0 license. Browse the dataset [here](https://sam2.metademolab.com/dataset).\n\n![SA-V dataset](../assets/sa_v_dataset.jpg?raw=true)\n\n## Getting Started\n\n### Download the dataset\n\nVisit [here](https://ai.meta.com/datasets/segment-anything-video-downloads/) to download SA-V including the training, val and test sets.\n\n### Dataset Stats\n\n| | Num Videos | Num Masklets |\n| ---------- | ---------- | ----------------------------------------- |\n| SA-V train | 50,583 | 642,036 (auto 451,720 and manual 190,316) |\n| SA-V val | 155 | 293 |\n| SA-V test | 150 | 278 |\n\n### Notebooks\n\nTo load and visualize the SA-V training set annotations, refer to the example [sav_visualization_example.ipynb](./sav_visualization_example.ipynb) notebook.\n\n### SA-V train\n\nFor SA-V training set we release the mp4 videos and store the masklet annotations per video as json files . Automatic masklets and manual masklets are stored separately as two json files: `{video_id}_auto.json` and `{video_id}_manual.json`. They can be loaded as dictionaries in python in the format below.\n\n```\n{\n \"video_id\" : str; video id\n \"video_duration\" : float64; the duration in seconds of this video\n \"video_frame_count\" : float64; the number of frames in the video\n \"video_height\" : float64; the height of the video\n \"video_width\" : float64; the width of the video\n \"video_resolution\" : float64; video_height $\\times$ video_width\n \"video_environment\" : List[str]; \"Indoor\" or \"Outdoor\"\n \"video_split\" : str; \"train\" for training set\n \"masklet\" : List[List[Dict]]; masklet annotations in list of list of RLEs.\n The outer list is over frames in the video and the inner list\n is over objects in the video.\n \"masklet_id\" : List[int]; the masklet ids\n \"masklet_size_rel\" : List[float]; the average mask area normalized by resolution\n across all the frames where the object is visible\n \"masklet_size_abs\" : List[float]; the average mask area (in pixels)\n across all the frames where the object is visible\n \"masklet_size_bucket\" : List[str]; \"small\": $1$ <= masklet_size_abs < $32^2$,\n \"medium\": $32^2$ <= masklet_size_abs < $96^2$,\n and \"large\": masklet_size_abs > $96^2$\n \"masklet_visibility_changes\" : List[int]; the number of times where the visibility changes\n after the first appearance (e.g., invisible -> visible\n or visible -> invisible)\n \"masklet_first_appeared_frame\" : List[int]; the index of the frame where the object appears\n the first time in the video. Always 0 for auto masklets.\n \"masklet_frame_count\" : List[int]; the number of frames being annotated. Note that\n videos are annotated at 6 fps (annotated every 4 frames)\n while the videos are at 24 fps.\n \"masklet_edited_frame_count\" : List[int]; the number of frames being edited by human annotators.\n Always 0 for auto masklets.\n \"masklet_type\" : List[str]; \"auto\" or \"manual\"\n \"masklet_stability_score\" : Optional[List[List[float]]]; per-mask stability scores. Auto annotation only.\n \"masklet_num\" : int; the number of manual/auto masklets in the video\n\n}\n```\n\nNote that in SA-V train, there are in total 50,583 videos where all of them have manual annotations. Among the 50,583 videos there are 48,436 videos that also have automatic annotations.\n\n### SA-V val and test\n\nFor SA-V val and test sets, we release the extracted frames as jpeg files, and the masks as png files with the following directory structure:\n\n```\nsav_val(sav_test)\n├── sav_val.txt (sav_test.txt): a list of video ids in the split\n├── JPEGImages_24fps # videos are extracted at 24 fps\n│ ├── {video_id}\n│ │ ├── 00000.jpg # video frame\n│ │ ├── 00001.jpg # video frame\n│ │ ├── 00002.jpg # video frame\n│ │ ├── 00003.jpg # video frame\n│ │ └── ...\n│ ├── {video_id}\n│ ├── {video_id}\n│ └── ...\n└── Annotations_6fps # videos are annotated at 6 fps\n ├── {video_id}\n │ ├── 000 # obj 000\n │ │ ├── 00000.png # mask for object 000 in 00000.jpg\n │ │ ├── 00004.png # mask for object 000 in 00004.jpg\n │ │ ├── 00008.png # mask for object 000 in 00008.jpg\n │ │ ├── 00012.png # mask for object 000 in 00012.jpg\n │ │ └── ...\n │ ├── 001 # obj 001\n │ ├── 002 # obj 002\n │ └── ...\n ├── {video_id}\n ├── {video_id}\n └── ...\n```\n\nAll masklets in val and test sets are manually annotated in every frame by annotators. For each annotated object in a video, we store the annotated masks in a single png. This is because the annotated objects may overlap, e.g., it is possible in our SA-V dataset for there to be a mask for the whole person as well as a separate mask for their hands.\n\n## SA-V Val and Test Evaluation\n\nWe provide an evaluator to compute the common J and F metrics on SA-V val and test sets. To run the evaluation, we need to first install a few dependencies as follows:\n\n```\npip install -r requirements.txt\n```\n\nThen we can evaluate the predictions as follows:\n\n```\npython sav_evaluator.py --gt_root {GT_ROOT} --pred_root {PRED_ROOT}\n```\n\nor run\n\n```\npython sav_evaluator.py --help\n```\n\nto print a complete help message.\n\nThe evaluator expects the `GT_ROOT` to be one of the following folder structures, and `GT_ROOT` and `PRED_ROOT` to have the same structure.\n\n- Same as SA-V val and test directory structure\n\n```\n{GT_ROOT} # gt root folder\n├── {video_id}\n│ ├── 000 # all masks associated with obj 000\n│ │ ├── 00000.png # mask for object 000 in frame 00000 (binary mask)\n│ │ └── ...\n│ ├── 001 # all masks associated with obj 001\n│ ├── 002 # all masks associated with obj 002\n│ └── ...\n├── {video_id}\n├── {video_id}\n└── ...\n```\n\nIn the paper for the experiments on SA-V val and test, we run inference on the 24 fps videos, and evaluate on the subset of frames where we have ground truth annotations (first and last annotated frames dropped). The evaluator will ignore the masks in frames where we don't have ground truth annotations.\n\n- Same as [DAVIS](https://github.com/davisvideochallenge/davis2017-evaluation) directory structure\n\n```\n{GT_ROOT} # gt root folder\n├── {video_id}\n│ ├── 00000.png # annotations in frame 00000 (may contain multiple objects)\n│ └── ...\n├── {video_id}\n├── {video_id}\n└── ...\n```\n\n## License\n\nThe evaluation code is licensed under the [BSD 3 license](./LICENSE). Please refer to the paper for more details on the models. The videos and annotations in SA-V Dataset are released under CC BY 4.0.\n\nThird-party code: the evaluation software is heavily adapted from [`VOS-Benchmark`](https://github.com/hkchengrex/vos-benchmark) and [`DAVIS`](https://github.com/davisvideochallenge/davis2017-evaluation) (with their licenses in [`LICENSE_DAVIS`](./LICENSE_DAVIS) and [`LICENSE_VOS_BENCHMARK`](./LICENSE_VOS_BENCHMARK)).\n" }, { "path": "auto-seg/submodules/segment-anything-2/sav_dataset/example/sav_000001_auto.json", "content": "{\"video_id\": \"sav_000001\", \"video_duration\": 20.125, \"video_frame_count\": 483.0, \"video_height\": 848.0, \"video_width\": 480.0, \"video_resolution\": 407040.0, \"video_environment\": \"Indoor\", \"video_split\": \"train\", \"masklet\": [[{\"size\": [848, 480], \"counts\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"sav_dataset.visualize_annotation(\\n\",\n \" frames, manual_annot, auto_annot, \\n\",\n \" annotated_frame_id=30,\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Show the SA-V annotations in frame 0 - manual only\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"sav_dataset.visualize_annotation(\\n\",\n \" frames, manual_annot, auto_annot,\\n\",\n \" annotated_frame_id=0,\\n\",\n \" show_auto=False,\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Show the SA-V annotations in frame 0 - auto only\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"sav_dataset.visualize_annotation(\\n\",\n \" frames, manual_annot, auto_annot,\\n\",\n \" annotated_frame_id=0,\\n\",\n \" show_manual=False,\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Masklet annotations and Metadata\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Manual annotations and metadata\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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video_idvideo_durationvideo_frame_countvideo_heightvideo_widthvideo_resolutionvideo_environmentvideo_splitmaskletmasklet_idmasklet_size_relmasklet_size_absmasklet_size_bucketmasklet_visibility_changesmasklet_first_appeared_framemasklet_frame_countmasklet_edited_frame_countmasklet_typemasklet_stability_scoremasklet_num
0sav_00000120.125483.0848.0480.0407040.0Indoortrain[[{'size': [848, 480], 'counts': 'i\\\\Y4<Qj05K4L...[0, 1, 2, 3, 4][0.0035249812, 0.0946159778, 0.011285757, 0.00...[1434.8083333333, 38512.4876033058, 4593.75454...[medium, large, medium, medium, medium][2, 0, 10, 0, 0][0.0, 0.0, 0.0, 113.0, 0.0][121, 121, 121, 121, 121][41, 11, 22, 4, 115][manual, manual, manual, manual, manual][None, None, None, None, None]5
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" video_id video_duration video_frame_count video_height video_width \\\\\\n\",\n \"0 sav_000001 20.125 483.0 848.0 480.0 \\n\",\n \"\\n\",\n \" video_resolution video_environment video_split \\\\\\n\",\n \"0 407040.0 Indoor train \\n\",\n \"\\n\",\n \" masklet masklet_id \\\\\\n\",\n \"0 [[{'size': [848, 480], 'counts': 'i\\\\Y4\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
video_idvideo_durationvideo_frame_countvideo_heightvideo_widthvideo_resolutionvideo_environmentvideo_splitmaskletmasklet_idmasklet_size_relmasklet_size_absmasklet_size_bucketmasklet_visibility_changesmasklet_first_appeared_framemasklet_frame_countmasklet_edited_frame_countmasklet_typemasklet_stability_scoremasklet_num
0sav_00000120.125483.0848.0480.0407040.0Indoortrain[[{'size': [848, 480], 'counts': 'ka0e8ka001O1...[0, 1, 2, 3, 4, 5, 6, 7, 8][0.010841089678796047, 0.038489445267425544, 0...[4412.757142857143, 15666.743801652892, 7663.1...[medium, large, medium, large, medium, medium,...[5, 0, 0, 0, 0, 0, 10, 3, 12][0, 0, 0, 0, 0, 0, 0, 0, 0][121, 121, 121, 121, 121, 121, 121, 121, 121][0, 0, 0, 0, 0, 0, 0, 0, 0][auto, auto, auto, auto, auto, auto, auto, aut...[[1.0, 0.999616265296936, 1.0, 1.0, 1.0, 1.0, ...9
\\n\",\n \"\"\n ],\n \"text/plain\": [\n \" video_id video_duration video_frame_count video_height video_width \\\\\\n\",\n \"0 sav_000001 20.125 483.0 848.0 480.0 \\n\",\n \"\\n\",\n \" video_resolution video_environment video_split \\\\\\n\",\n \"0 407040.0 Indoor train \\n\",\n \"\\n\",\n \" masklet \\\\\\n\",\n \"0 [[{'size': [848, 480], 'counts': 'ka0e8ka001O1... \\n\",\n \"\\n\",\n \" masklet_id \\\\\\n\",\n \"0 [0, 1, 2, 3, 4, 5, 6, 7, 8] \\n\",\n \"\\n\",\n \" masklet_size_rel \\\\\\n\",\n \"0 [0.010841089678796047, 0.038489445267425544, 0... \\n\",\n \"\\n\",\n \" masklet_size_abs \\\\\\n\",\n \"0 [4412.757142857143, 15666.743801652892, 7663.1... \\n\",\n \"\\n\",\n \" masklet_size_bucket \\\\\\n\",\n \"0 [medium, large, medium, large, medium, medium,... \\n\",\n \"\\n\",\n \" masklet_visibility_changes masklet_first_appeared_frame \\\\\\n\",\n \"0 [5, 0, 0, 0, 0, 0, 10, 3, 12] [0, 0, 0, 0, 0, 0, 0, 0, 0] \\n\",\n \"\\n\",\n \" masklet_frame_count masklet_edited_frame_count \\\\\\n\",\n \"0 [121, 121, 121, 121, 121, 121, 121, 121, 121] [0, 0, 0, 0, 0, 0, 0, 0, 0] \\n\",\n \"\\n\",\n \" masklet_type \\\\\\n\",\n \"0 [auto, auto, auto, auto, auto, auto, auto, aut... \\n\",\n \"\\n\",\n \" masklet_stability_score masklet_num \\n\",\n \"0 [[1.0, 0.999616265296936, 1.0, 1.0, 1.0, 1.0, ... 9 \"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"pd.DataFrame([auto_annot])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Video info\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"sav_000001 is 20.125 seconds long with 483.0 frames. The video resolution is 848.0 x 480.0.\\n\",\n \"This video is captured in Indoor environment.\\n\"\n ]\n }\n ],\n \"source\": [\n \"video_id = manual_annot[\\\"video_id\\\"]\\n\",\n \"video_duration = manual_annot[\\\"video_duration\\\"]\\n\",\n \"video_frame_count = manual_annot[\\\"video_frame_count\\\"]\\n\",\n \"H = manual_annot[\\\"video_height\\\"]\\n\",\n \"W = manual_annot[\\\"video_width\\\"]\\n\",\n \"environment = manual_annot[\\\"video_environment\\\"]\\n\",\n \"print(\\n\",\n \" f\\\"{video_id} is {video_duration} seconds long with {video_frame_count} frames. The video resolution is {H} x {W}.\\\"\\n\",\n \")\\n\",\n \"print(f\\\"This video is captured in {environment} environment.\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Masklet info\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"There are 5 manually labeled masklets and 9 automatically generated masklets.\\n\",\n \"In SA-V, videos are annotated every 4 frames. Therefore, there are 121 frames being annotated.\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\n\",\n \" f\\\"There are {manual_annot['masklet_num']} manually labeled masklets and {auto_annot['masklet_num']} automatically generated masklets.\\\"\\n\",\n \")\\n\",\n \"print(\\n\",\n \" f\\\"In SA-V, videos are annotated every 4 frames. Therefore, there are {manual_annot['masklet_frame_count'][0]} frames being annotated.\\\"\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'size': [848, 480],\\n\",\n \" 'counts': 'i\\\\\\\\Y40` to get the binary segmentation mask\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'size': [848, 480],\\n\",\n \" 'counts': 'Q_T6S1Xh0X1eNY1[Od0E;M4N10000O101O00000000000000O0100000000001M2O1O1N3N1M4H8B?@e0POc1jMfZ[5'}\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# Get the rle of the masklet with masklet_id=5 in frame 100\\n\",\n \"masklet_id = 5\\n\",\n \"annotated_frame_id = 100\\n\",\n \"auto_annot[\\\"masklet\\\"][annotated_frame_id][masklet_id]\\n\",\n \"# decode the rle using `mask_util.decode(rle)>0` to get the binary segmentation mask\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"onevision_ta_2_pseudo_labeling\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.10.12\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "auto-seg/submodules/segment-anything-2/sav_dataset/utils/sav_utils.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the sav_dataset directory of this source tree.\nimport json\nimport os\nfrom typing import Dict, List, Optional, Tuple\n\nimport cv2\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pycocotools.mask as mask_util\n\n\ndef decode_video(video_path: str) -> List[np.ndarray]:\n \"\"\"\n Decode the video and return the RGB frames\n \"\"\"\n video = cv2.VideoCapture(video_path)\n video_frames = []\n while video.isOpened():\n ret, frame = video.read()\n if ret:\n frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n video_frames.append(frame)\n else:\n break\n return video_frames\n\n\ndef show_anns(masks, colors: List, borders=True) -> None:\n \"\"\"\n show the annotations\n \"\"\"\n # return if no masks\n if len(masks) == 0:\n return\n\n # sort masks by size\n sorted_annot_and_color = sorted(\n zip(masks, colors), key=(lambda x: x[0].sum()), reverse=True\n )\n H, W = sorted_annot_and_color[0][0].shape[0], sorted_annot_and_color[0][0].shape[1]\n\n canvas = np.ones((H, W, 4))\n canvas[:, :, 3] = 0 # set the alpha channel\n contour_thickness = max(1, int(min(5, 0.01 * min(H, W))))\n for mask, color in sorted_annot_and_color:\n canvas[mask] = np.concatenate([color, [0.55]])\n if borders:\n contours, _ = cv2.findContours(\n np.array(mask, dtype=np.uint8), cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE\n )\n cv2.drawContours(\n canvas, contours, -1, (0.05, 0.05, 0.05, 1), thickness=contour_thickness\n )\n\n ax = plt.gca()\n ax.imshow(canvas)\n\n\nclass SAVDataset:\n \"\"\"\n SAVDataset is a class to load the SAV dataset and visualize the annotations.\n \"\"\"\n\n def __init__(self, sav_dir, annot_sample_rate=4):\n \"\"\"\n Args:\n sav_dir: the directory of the SAV dataset\n annot_sample_rate: the sampling rate of the annotations.\n The annotations are aligned with the videos at 6 fps.\n \"\"\"\n self.sav_dir = sav_dir\n self.annot_sample_rate = annot_sample_rate\n self.manual_mask_colors = np.random.random((256, 3))\n self.auto_mask_colors = np.random.random((256, 3))\n\n def read_frames(self, mp4_path: str) -> None:\n \"\"\"\n Read the frames and downsample them to align with the annotations.\n \"\"\"\n if not os.path.exists(mp4_path):\n print(f\"{mp4_path} doesn't exist.\")\n return None\n else:\n # decode the video\n frames = decode_video(mp4_path)\n print(f\"There are {len(frames)} frames decoded from {mp4_path} (24fps).\")\n\n # downsample the frames to align with the annotations\n frames = frames[:: self.annot_sample_rate]\n print(\n f\"Videos are annotated every {self.annot_sample_rate} frames. \"\n \"To align with the annotations, \"\n f\"downsample the video to {len(frames)} frames.\"\n )\n return frames\n\n def get_frames_and_annotations(\n self, video_id: str\n ) -> Tuple[List | None, Dict | None, Dict | None]:\n \"\"\"\n Get the frames and annotations for video.\n \"\"\"\n # load the video\n mp4_path = os.path.join(self.sav_dir, video_id + \".mp4\")\n frames = self.read_frames(mp4_path)\n if frames is None:\n return None, None, None\n\n # load the manual annotations\n manual_annot_path = os.path.join(self.sav_dir, video_id + \"_manual.json\")\n if not os.path.exists(manual_annot_path):\n print(f\"{manual_annot_path} doesn't exist. Something might be wrong.\")\n manual_annot = None\n else:\n manual_annot = json.load(open(manual_annot_path))\n\n # load the manual annotations\n auto_annot_path = os.path.join(self.sav_dir, video_id + \"_auto.json\")\n if not os.path.exists(auto_annot_path):\n print(f\"{auto_annot_path} doesn't exist.\")\n auto_annot = None\n else:\n auto_annot = json.load(open(auto_annot_path))\n\n return frames, manual_annot, auto_annot\n\n def visualize_annotation(\n self,\n frames: List[np.ndarray],\n auto_annot: Optional[Dict],\n manual_annot: Optional[Dict],\n annotated_frame_id: int,\n show_auto=True,\n show_manual=True,\n ) -> None:\n \"\"\"\n Visualize the annotations on the annotated_frame_id.\n If show_manual is True, show the manual annotations.\n If show_auto is True, show the auto annotations.\n By default, show both auto and manual annotations.\n \"\"\"\n\n if annotated_frame_id >= len(frames):\n print(\"invalid annotated_frame_id\")\n return\n\n rles = []\n colors = []\n if show_manual and manual_annot is not None:\n rles.extend(manual_annot[\"masklet\"][annotated_frame_id])\n colors.extend(\n self.manual_mask_colors[\n : len(manual_annot[\"masklet\"][annotated_frame_id])\n ]\n )\n if show_auto and auto_annot is not None:\n rles.extend(auto_annot[\"masklet\"][annotated_frame_id])\n colors.extend(\n self.auto_mask_colors[: len(auto_annot[\"masklet\"][annotated_frame_id])]\n )\n\n plt.imshow(frames[annotated_frame_id])\n\n if len(rles) > 0:\n masks = [mask_util.decode(rle) > 0 for rle in rles]\n show_anns(masks, colors)\n else:\n print(\"No annotation will be shown\")\n\n plt.axis(\"off\")\n plt.show()\n" }, { "path": "auto-seg/submodules/segment-anything-2/setup.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\nimport os\n\nfrom setuptools import find_packages, setup\n\n# Package metadata\nNAME = \"SAM-2\"\nVERSION = \"1.0\"\nDESCRIPTION = \"SAM 2: Segment Anything in Images and Videos\"\nURL = \"https://github.com/facebookresearch/sam2\"\nAUTHOR = \"Meta AI\"\nAUTHOR_EMAIL = \"segment-anything@meta.com\"\nLICENSE = \"Apache 2.0\"\n\n# Read the contents of README file\nwith open(\"README.md\", \"r\", encoding=\"utf-8\") as f:\n LONG_DESCRIPTION = f.read()\n\n# Required dependencies\nREQUIRED_PACKAGES = [\n \"torch>=2.3.1\",\n \"torchvision>=0.18.1\",\n \"numpy>=1.24.4\",\n \"tqdm>=4.66.1\",\n \"hydra-core>=1.3.2\",\n \"iopath>=0.1.10\",\n \"pillow>=9.4.0\",\n]\n\nEXTRA_PACKAGES = {\n \"notebooks\": [\n \"matplotlib>=3.9.1\",\n \"jupyter>=1.0.0\",\n \"opencv-python>=4.7.0\",\n \"eva-decord>=0.6.1\",\n ],\n \"interactive-demo\": [\n \"Flask>=3.0.3\",\n \"Flask-Cors>=5.0.0\",\n \"av>=13.0.0\",\n \"dataclasses-json>=0.6.7\",\n \"eva-decord>=0.6.1\",\n \"gunicorn>=23.0.0\",\n \"imagesize>=1.4.1\",\n \"pycocotools>=2.0.8\",\n \"strawberry-graphql>=0.243.0\",\n ],\n \"dev\": [\n \"black==24.2.0\",\n \"usort==1.0.2\",\n \"ufmt==2.0.0b2\",\n \"fvcore>=0.1.5.post20221221\",\n \"pandas>=2.2.2\",\n \"scikit-image>=0.24.0\",\n \"tensorboard>=2.17.0\",\n \"pycocotools>=2.0.8\",\n \"tensordict>=0.5.0\",\n \"opencv-python>=4.7.0\",\n \"submitit>=1.5.1\",\n ],\n}\n\n# By default, we also build the SAM 2 CUDA extension.\n# You may turn off CUDA build with `export SAM2_BUILD_CUDA=0`.\nBUILD_CUDA = os.getenv(\"SAM2_BUILD_CUDA\", \"1\") == \"1\"\n# By default, we allow SAM 2 installation to proceed even with build errors.\n# You may force stopping on errors with `export SAM2_BUILD_ALLOW_ERRORS=0`.\nBUILD_ALLOW_ERRORS = os.getenv(\"SAM2_BUILD_ALLOW_ERRORS\", \"1\") == \"1\"\n\n# Catch and skip errors during extension building and print a warning message\n# (note that this message only shows up under verbose build mode\n# \"pip install -v -e .\" or \"python setup.py build_ext -v\")\nCUDA_ERROR_MSG = (\n \"{}\\n\\n\"\n \"Failed to build the SAM 2 CUDA extension due to the error above. \"\n \"You can still use SAM 2 and it's OK to ignore the error above, although some \"\n \"post-processing functionality may be limited (which doesn't affect the results in most cases; \"\n \"(see https://github.com/facebookresearch/sam2/blob/main/INSTALL.md).\\n\"\n)\n\n\ndef get_extensions():\n if not BUILD_CUDA:\n return []\n\n try:\n from torch.utils.cpp_extension import CUDAExtension\n\n srcs = [\"sam2/csrc/connected_components.cu\"]\n compile_args = {\n \"cxx\": [],\n \"nvcc\": [\n \"-DCUDA_HAS_FP16=1\",\n \"-D__CUDA_NO_HALF_OPERATORS__\",\n \"-D__CUDA_NO_HALF_CONVERSIONS__\",\n \"-D__CUDA_NO_HALF2_OPERATORS__\",\n ],\n }\n ext_modules = [CUDAExtension(\"sam2._C\", srcs, extra_compile_args=compile_args)]\n except Exception as e:\n if BUILD_ALLOW_ERRORS:\n print(CUDA_ERROR_MSG.format(e))\n ext_modules = []\n else:\n raise e\n\n return ext_modules\n\n\ntry:\n from torch.utils.cpp_extension import BuildExtension\n\n class BuildExtensionIgnoreErrors(BuildExtension):\n\n def finalize_options(self):\n try:\n super().finalize_options()\n except Exception as e:\n print(CUDA_ERROR_MSG.format(e))\n self.extensions = []\n\n def build_extensions(self):\n try:\n super().build_extensions()\n except Exception as e:\n print(CUDA_ERROR_MSG.format(e))\n self.extensions = []\n\n def get_ext_filename(self, ext_name):\n try:\n return super().get_ext_filename(ext_name)\n except Exception as e:\n print(CUDA_ERROR_MSG.format(e))\n self.extensions = []\n return \"_C.so\"\n\n cmdclass = {\n \"build_ext\": (\n BuildExtensionIgnoreErrors.with_options(no_python_abi_suffix=True)\n if BUILD_ALLOW_ERRORS\n else BuildExtension.with_options(no_python_abi_suffix=True)\n )\n }\nexcept Exception as e:\n cmdclass = {}\n if BUILD_ALLOW_ERRORS:\n print(CUDA_ERROR_MSG.format(e))\n else:\n raise e\n\n\n# Setup configuration\nsetup(\n name=NAME,\n version=VERSION,\n description=DESCRIPTION,\n long_description=LONG_DESCRIPTION,\n long_description_content_type=\"text/markdown\",\n url=URL,\n author=AUTHOR,\n author_email=AUTHOR_EMAIL,\n license=LICENSE,\n packages=find_packages(exclude=\"notebooks\"),\n include_package_data=True,\n install_requires=REQUIRED_PACKAGES,\n extras_require=EXTRA_PACKAGES,\n python_requires=\">=3.10.0\",\n ext_modules=get_extensions(),\n cmdclass=cmdclass,\n)\n" }, { "path": "auto-seg/submodules/segment-anything-2/tools/README.md", "content": "## SAM 2 toolkits\n\nThis directory provides toolkits for additional SAM 2 use cases.\n\n### Semi-supervised VOS inference\n\nThe `vos_inference.py` script can be used to generate predictions for semi-supervised video object segmentation (VOS) evaluation on datasets such as [DAVIS](https://davischallenge.org/index.html), [MOSE](https://henghuiding.github.io/MOSE/) or the SA-V dataset.\n\nAfter installing SAM 2 and its dependencies, it can be used as follows ([DAVIS 2017 dataset](https://davischallenge.org/davis2017/code.html) as an example). This script saves the prediction PNG files to the `--output_mask_dir`.\n```bash\npython ./tools/vos_inference.py \\\n --sam2_cfg configs/sam2.1/sam2.1_hiera_b+.yaml \\\n --sam2_checkpoint ./checkpoints/sam2.1_hiera_base_plus.pt \\\n --base_video_dir /path-to-davis-2017/JPEGImages/480p \\\n --input_mask_dir /path-to-davis-2017/Annotations/480p \\\n --video_list_file /path-to-davis-2017/ImageSets/2017/val.txt \\\n --output_mask_dir ./outputs/davis_2017_pred_pngs\n```\n(replace `/path-to-davis-2017` with the path to DAVIS 2017 dataset)\n\nTo evaluate on the SA-V dataset with per-object PNG files for the object masks, we need to **add the `--per_obj_png_file` flag** as follows (using SA-V val as an example). This script will also save per-object PNG files for the output masks under the `--per_obj_png_file` flag.\n```bash\npython ./tools/vos_inference.py \\\n --sam2_cfg configs/sam2.1/sam2.1_hiera_b+.yaml \\\n --sam2_checkpoint ./checkpoints/sam2.1_hiera_base_plus.pt \\\n --base_video_dir /path-to-sav-val/JPEGImages_24fps \\\n --input_mask_dir /path-to-sav-val/Annotations_6fps \\\n --video_list_file /path-to-sav-val/sav_val.txt \\\n --per_obj_png_file \\\n --output_mask_dir ./outputs/sav_val_pred_pngs\n```\n(replace `/path-to-sav-val` with the path to SA-V val)\n\nThen, we can use the evaluation tools or servers for each dataset to get the performance of the prediction PNG files above.\n\nNote: by default, the `vos_inference.py` script above assumes that all objects to track already appear on frame 0 in each video (as is the case in DAVIS, MOSE or SA-V). **For VOS datasets that don't have all objects to track appearing in the first frame (such as LVOS or YouTube-VOS), please add the `--track_object_appearing_later_in_video` flag when using `vos_inference.py`**.\n" }, { "path": "auto-seg/submodules/segment-anything-2/tools/vos_inference.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport argparse\nimport os\nfrom collections import defaultdict\n\nimport numpy as np\nimport torch\nfrom PIL import Image\nfrom sam2.build_sam import build_sam2_video_predictor\n\n\n# the PNG palette for DAVIS 2017 dataset\nDAVIS_PALETTE = b\"\\x00\\x00\\x00\\x80\\x00\\x00\\x00\\x80\\x00\\x80\\x80\\x00\\x00\\x00\\x80\\x80\\x00\\x80\\x00\\x80\\x80\\x80\\x80\\x80@\\x00\\x00\\xc0\\x00\\x00@\\x80\\x00\\xc0\\x80\\x00@\\x00\\x80\\xc0\\x00\\x80@\\x80\\x80\\xc0\\x80\\x80\\x00@\\x00\\x80@\\x00\\x00\\xc0\\x00\\x80\\xc0\\x00\\x00@\\x80\\x80@\\x80\\x00\\xc0\\x80\\x80\\xc0\\x80@@\\x00\\xc0@\\x00@\\xc0\\x00\\xc0\\xc0\\x00@@\\x80\\xc0@\\x80@\\xc0\\x80\\xc0\\xc0\\x80\\x00\\x00@\\x80\\x00@\\x00\\x80@\\x80\\x80@\\x00\\x00\\xc0\\x80\\x00\\xc0\\x00\\x80\\xc0\\x80\\x80\\xc0@\\x00@\\xc0\\x00@@\\x80@\\xc0\\x80@@\\x00\\xc0\\xc0\\x00\\xc0@\\x80\\xc0\\xc0\\x80\\xc0\\x00@@\\x80@@\\x00\\xc0@\\x80\\xc0@\\x00@\\xc0\\x80@\\xc0\\x00\\xc0\\xc0\\x80\\xc0\\xc0@@@\\xc0@@@\\xc0@\\xc0\\xc0@@@\\xc0\\xc0@\\xc0@\\xc0\\xc0\\xc0\\xc0\\xc0 \\x00\\x00\\xa0\\x00\\x00 \\x80\\x00\\xa0\\x80\\x00 \\x00\\x80\\xa0\\x00\\x80 \\x80\\x80\\xa0\\x80\\x80`\\x00\\x00\\xe0\\x00\\x00`\\x80\\x00\\xe0\\x80\\x00`\\x00\\x80\\xe0\\x00\\x80`\\x80\\x80\\xe0\\x80\\x80 @\\x00\\xa0@\\x00 \\xc0\\x00\\xa0\\xc0\\x00 @\\x80\\xa0@\\x80 \\xc0\\x80\\xa0\\xc0\\x80`@\\x00\\xe0@\\x00`\\xc0\\x00\\xe0\\xc0\\x00`@\\x80\\xe0@\\x80`\\xc0\\x80\\xe0\\xc0\\x80 \\x00@\\xa0\\x00@ \\x80@\\xa0\\x80@ \\x00\\xc0\\xa0\\x00\\xc0 \\x80\\xc0\\xa0\\x80\\xc0`\\x00@\\xe0\\x00@`\\x80@\\xe0\\x80@`\\x00\\xc0\\xe0\\x00\\xc0`\\x80\\xc0\\xe0\\x80\\xc0 @@\\xa0@@ \\xc0@\\xa0\\xc0@ @\\xc0\\xa0@\\xc0 \\xc0\\xc0\\xa0\\xc0\\xc0`@@\\xe0@@`\\xc0@\\xe0\\xc0@`@\\xc0\\xe0@\\xc0`\\xc0\\xc0\\xe0\\xc0\\xc0\\x00 \\x00\\x80 \\x00\\x00\\xa0\\x00\\x80\\xa0\\x00\\x00 \\x80\\x80 \\x80\\x00\\xa0\\x80\\x80\\xa0\\x80@ \\x00\\xc0 \\x00@\\xa0\\x00\\xc0\\xa0\\x00@ \\x80\\xc0 \\x80@\\xa0\\x80\\xc0\\xa0\\x80\\x00`\\x00\\x80`\\x00\\x00\\xe0\\x00\\x80\\xe0\\x00\\x00`\\x80\\x80`\\x80\\x00\\xe0\\x80\\x80\\xe0\\x80@`\\x00\\xc0`\\x00@\\xe0\\x00\\xc0\\xe0\\x00@`\\x80\\xc0`\\x80@\\xe0\\x80\\xc0\\xe0\\x80\\x00 @\\x80 @\\x00\\xa0@\\x80\\xa0@\\x00 \\xc0\\x80 \\xc0\\x00\\xa0\\xc0\\x80\\xa0\\xc0@ @\\xc0 @@\\xa0@\\xc0\\xa0@@ \\xc0\\xc0 \\xc0@\\xa0\\xc0\\xc0\\xa0\\xc0\\x00`@\\x80`@\\x00\\xe0@\\x80\\xe0@\\x00`\\xc0\\x80`\\xc0\\x00\\xe0\\xc0\\x80\\xe0\\xc0@`@\\xc0`@@\\xe0@\\xc0\\xe0@@`\\xc0\\xc0`\\xc0@\\xe0\\xc0\\xc0\\xe0\\xc0 \\x00\\xa0 \\x00 \\xa0\\x00\\xa0\\xa0\\x00 \\x80\\xa0 \\x80 \\xa0\\x80\\xa0\\xa0\\x80` \\x00\\xe0 \\x00`\\xa0\\x00\\xe0\\xa0\\x00` \\x80\\xe0 \\x80`\\xa0\\x80\\xe0\\xa0\\x80 `\\x00\\xa0`\\x00 \\xe0\\x00\\xa0\\xe0\\x00 `\\x80\\xa0`\\x80 \\xe0\\x80\\xa0\\xe0\\x80``\\x00\\xe0`\\x00`\\xe0\\x00\\xe0\\xe0\\x00``\\x80\\xe0`\\x80`\\xe0\\x80\\xe0\\xe0\\x80 @\\xa0 @ \\xa0@\\xa0\\xa0@ \\xc0\\xa0 \\xc0 \\xa0\\xc0\\xa0\\xa0\\xc0` @\\xe0 @`\\xa0@\\xe0\\xa0@` \\xc0\\xe0 \\xc0`\\xa0\\xc0\\xe0\\xa0\\xc0 `@\\xa0`@ \\xe0@\\xa0\\xe0@ `\\xc0\\xa0`\\xc0 \\xe0\\xc0\\xa0\\xe0\\xc0``@\\xe0`@`\\xe0@\\xe0\\xe0@``\\xc0\\xe0`\\xc0`\\xe0\\xc0\\xe0\\xe0\\xc0\"\n\n\ndef load_ann_png(path):\n \"\"\"Load a PNG file as a mask and its palette.\"\"\"\n mask = Image.open(path)\n palette = mask.getpalette()\n mask = np.array(mask).astype(np.uint8)\n return mask, palette\n\n\ndef save_ann_png(path, mask, palette):\n \"\"\"Save a mask as a PNG file with the given palette.\"\"\"\n assert mask.dtype == np.uint8\n assert mask.ndim == 2\n output_mask = Image.fromarray(mask)\n output_mask.putpalette(palette)\n output_mask.save(path)\n\n\ndef get_per_obj_mask(mask):\n \"\"\"Split a mask into per-object masks.\"\"\"\n object_ids = np.unique(mask)\n object_ids = object_ids[object_ids > 0].tolist()\n per_obj_mask = {object_id: (mask == object_id) for object_id in object_ids}\n return per_obj_mask\n\n\ndef put_per_obj_mask(per_obj_mask, height, width):\n \"\"\"Combine per-object masks into a single mask.\"\"\"\n mask = np.zeros((height, width), dtype=np.uint8)\n object_ids = sorted(per_obj_mask)[::-1]\n for object_id in object_ids:\n object_mask = per_obj_mask[object_id]\n object_mask = object_mask.reshape(height, width)\n mask[object_mask] = object_id\n return mask\n\n\ndef load_masks_from_dir(\n input_mask_dir, video_name, frame_name, per_obj_png_file, allow_missing=False\n):\n \"\"\"Load masks from a directory as a dict of per-object masks.\"\"\"\n if not per_obj_png_file:\n input_mask_path = os.path.join(input_mask_dir, video_name, f\"{frame_name}.png\")\n if allow_missing and not os.path.exists(input_mask_path):\n return {}, None\n input_mask, input_palette = load_ann_png(input_mask_path)\n per_obj_input_mask = get_per_obj_mask(input_mask)\n else:\n per_obj_input_mask = {}\n input_palette = None\n # each object is a directory in \"{object_id:%03d}\" format\n for object_name in os.listdir(os.path.join(input_mask_dir, video_name)):\n object_id = int(object_name)\n input_mask_path = os.path.join(\n input_mask_dir, video_name, object_name, f\"{frame_name}.png\"\n )\n if allow_missing and not os.path.exists(input_mask_path):\n continue\n input_mask, input_palette = load_ann_png(input_mask_path)\n per_obj_input_mask[object_id] = input_mask > 0\n\n return per_obj_input_mask, input_palette\n\n\ndef save_masks_to_dir(\n output_mask_dir,\n video_name,\n frame_name,\n per_obj_output_mask,\n height,\n width,\n per_obj_png_file,\n output_palette,\n):\n \"\"\"Save masks to a directory as PNG files.\"\"\"\n os.makedirs(os.path.join(output_mask_dir, video_name), exist_ok=True)\n if not per_obj_png_file:\n output_mask = put_per_obj_mask(per_obj_output_mask, height, width)\n output_mask_path = os.path.join(\n output_mask_dir, video_name, f\"{frame_name}.png\"\n )\n save_ann_png(output_mask_path, output_mask, output_palette)\n else:\n for object_id, object_mask in per_obj_output_mask.items():\n object_name = f\"{object_id:03d}\"\n os.makedirs(\n os.path.join(output_mask_dir, video_name, object_name),\n exist_ok=True,\n )\n output_mask = object_mask.reshape(height, width).astype(np.uint8)\n output_mask_path = os.path.join(\n output_mask_dir, video_name, object_name, f\"{frame_name}.png\"\n )\n save_ann_png(output_mask_path, output_mask, output_palette)\n\n\n@torch.inference_mode()\n@torch.autocast(device_type=\"cuda\", dtype=torch.bfloat16)\ndef vos_inference(\n predictor,\n base_video_dir,\n input_mask_dir,\n output_mask_dir,\n video_name,\n score_thresh=0.0,\n use_all_masks=False,\n per_obj_png_file=False,\n):\n \"\"\"Run VOS inference on a single video with the given predictor.\"\"\"\n # load the video frames and initialize the inference state on this video\n video_dir = os.path.join(base_video_dir, video_name)\n frame_names = [\n os.path.splitext(p)[0]\n for p in os.listdir(video_dir)\n if os.path.splitext(p)[-1] in [\".jpg\", \".jpeg\", \".JPG\", \".JPEG\"]\n ]\n frame_names.sort(key=lambda p: int(os.path.splitext(p)[0]))\n inference_state = predictor.init_state(\n video_path=video_dir, async_loading_frames=False\n )\n height = inference_state[\"video_height\"]\n width = inference_state[\"video_width\"]\n input_palette = None\n\n # fetch mask inputs from input_mask_dir (either only mask for the first frame, or all available masks)\n if not use_all_masks:\n # use only the first video's ground-truth mask as the input mask\n input_frame_inds = [0]\n else:\n # use all mask files available in the input_mask_dir as the input masks\n if not per_obj_png_file:\n input_frame_inds = [\n idx\n for idx, name in enumerate(frame_names)\n if os.path.exists(\n os.path.join(input_mask_dir, video_name, f\"{name}.png\")\n )\n ]\n else:\n input_frame_inds = [\n idx\n for object_name in os.listdir(os.path.join(input_mask_dir, video_name))\n for idx, name in enumerate(frame_names)\n if os.path.exists(\n os.path.join(input_mask_dir, video_name, object_name, f\"{name}.png\")\n )\n ]\n # check and make sure we got at least one input frame\n if len(input_frame_inds) == 0:\n raise RuntimeError(\n f\"In {video_name=}, got no input masks in {input_mask_dir=}. \"\n \"Please make sure the input masks are available in the correct format.\"\n )\n input_frame_inds = sorted(set(input_frame_inds))\n\n # add those input masks to SAM 2 inference state before propagation\n object_ids_set = None\n for input_frame_idx in input_frame_inds:\n try:\n per_obj_input_mask, input_palette = load_masks_from_dir(\n input_mask_dir=input_mask_dir,\n video_name=video_name,\n frame_name=frame_names[input_frame_idx],\n per_obj_png_file=per_obj_png_file,\n )\n except FileNotFoundError as e:\n raise RuntimeError(\n f\"In {video_name=}, failed to load input mask for frame {input_frame_idx=}. \"\n \"Please add the `--track_object_appearing_later_in_video` flag \"\n \"for VOS datasets that don't have all objects to track appearing \"\n \"in the first frame (such as LVOS or YouTube-VOS).\"\n ) from e\n # get the list of object ids to track from the first input frame\n if object_ids_set is None:\n object_ids_set = set(per_obj_input_mask)\n for object_id, object_mask in per_obj_input_mask.items():\n # check and make sure no new object ids appear only in later frames\n if object_id not in object_ids_set:\n raise RuntimeError(\n f\"In {video_name=}, got a new {object_id=} appearing only in a \"\n f\"later {input_frame_idx=} (but not appearing in the first frame). \"\n \"Please add the `--track_object_appearing_later_in_video` flag \"\n \"for VOS datasets that don't have all objects to track appearing \"\n \"in the first frame (such as LVOS or YouTube-VOS).\"\n )\n predictor.add_new_mask(\n inference_state=inference_state,\n frame_idx=input_frame_idx,\n obj_id=object_id,\n mask=object_mask,\n )\n\n # check and make sure we have at least one object to track\n if object_ids_set is None or len(object_ids_set) == 0:\n raise RuntimeError(\n f\"In {video_name=}, got no object ids on {input_frame_inds=}. \"\n \"Please add the `--track_object_appearing_later_in_video` flag \"\n \"for VOS datasets that don't have all objects to track appearing \"\n \"in the first frame (such as LVOS or YouTube-VOS).\"\n )\n # run propagation throughout the video and collect the results in a dict\n os.makedirs(os.path.join(output_mask_dir, video_name), exist_ok=True)\n output_palette = input_palette or DAVIS_PALETTE\n video_segments = {} # video_segments contains the per-frame segmentation results\n for out_frame_idx, out_obj_ids, out_mask_logits in predictor.propagate_in_video(\n inference_state\n ):\n per_obj_output_mask = {\n out_obj_id: (out_mask_logits[i] > score_thresh).cpu().numpy()\n for i, out_obj_id in enumerate(out_obj_ids)\n }\n video_segments[out_frame_idx] = per_obj_output_mask\n\n # write the output masks as palette PNG files to output_mask_dir\n for out_frame_idx, per_obj_output_mask in video_segments.items():\n save_masks_to_dir(\n output_mask_dir=output_mask_dir,\n video_name=video_name,\n frame_name=frame_names[out_frame_idx],\n per_obj_output_mask=per_obj_output_mask,\n height=height,\n width=width,\n per_obj_png_file=per_obj_png_file,\n output_palette=output_palette,\n )\n\n\n@torch.inference_mode()\n@torch.autocast(device_type=\"cuda\", dtype=torch.bfloat16)\ndef vos_separate_inference_per_object(\n predictor,\n base_video_dir,\n input_mask_dir,\n output_mask_dir,\n video_name,\n score_thresh=0.0,\n use_all_masks=False,\n per_obj_png_file=False,\n):\n \"\"\"\n Run VOS inference on a single video with the given predictor.\n\n Unlike `vos_inference`, this function run inference separately for each object\n in a video, which could be applied to datasets like LVOS or YouTube-VOS that\n don't have all objects to track appearing in the first frame (i.e. some objects\n might appear only later in the video).\n \"\"\"\n # load the video frames and initialize the inference state on this video\n video_dir = os.path.join(base_video_dir, video_name)\n frame_names = [\n os.path.splitext(p)[0]\n for p in os.listdir(video_dir)\n if os.path.splitext(p)[-1] in [\".jpg\", \".jpeg\", \".JPG\", \".JPEG\"]\n ]\n frame_names.sort(key=lambda p: int(os.path.splitext(p)[0]))\n inference_state = predictor.init_state(\n video_path=video_dir, async_loading_frames=False\n )\n height = inference_state[\"video_height\"]\n width = inference_state[\"video_width\"]\n input_palette = None\n\n # collect all the object ids and their input masks\n inputs_per_object = defaultdict(dict)\n for idx, name in enumerate(frame_names):\n if per_obj_png_file or os.path.exists(\n os.path.join(input_mask_dir, video_name, f\"{name}.png\")\n ):\n per_obj_input_mask, input_palette = load_masks_from_dir(\n input_mask_dir=input_mask_dir,\n video_name=video_name,\n frame_name=frame_names[idx],\n per_obj_png_file=per_obj_png_file,\n allow_missing=True,\n )\n for object_id, object_mask in per_obj_input_mask.items():\n # skip empty masks\n if not np.any(object_mask):\n continue\n # if `use_all_masks=False`, we only use the first mask for each object\n if len(inputs_per_object[object_id]) > 0 and not use_all_masks:\n continue\n print(f\"adding mask from frame {idx} as input for {object_id=}\")\n inputs_per_object[object_id][idx] = object_mask\n\n # run inference separately for each object in the video\n object_ids = sorted(inputs_per_object)\n output_scores_per_object = defaultdict(dict)\n for object_id in object_ids:\n # add those input masks to SAM 2 inference state before propagation\n input_frame_inds = sorted(inputs_per_object[object_id])\n predictor.reset_state(inference_state)\n for input_frame_idx in input_frame_inds:\n predictor.add_new_mask(\n inference_state=inference_state,\n frame_idx=input_frame_idx,\n obj_id=object_id,\n mask=inputs_per_object[object_id][input_frame_idx],\n )\n\n # run propagation throughout the video and collect the results in a dict\n for out_frame_idx, _, out_mask_logits in predictor.propagate_in_video(\n inference_state,\n start_frame_idx=min(input_frame_inds),\n reverse=False,\n ):\n obj_scores = out_mask_logits.cpu().numpy()\n output_scores_per_object[object_id][out_frame_idx] = obj_scores\n\n # post-processing: consolidate the per-object scores into per-frame masks\n os.makedirs(os.path.join(output_mask_dir, video_name), exist_ok=True)\n output_palette = input_palette or DAVIS_PALETTE\n video_segments = {} # video_segments contains the per-frame segmentation results\n for frame_idx in range(len(frame_names)):\n scores = torch.full(\n size=(len(object_ids), 1, height, width),\n fill_value=-1024.0,\n dtype=torch.float32,\n )\n for i, object_id in enumerate(object_ids):\n if frame_idx in output_scores_per_object[object_id]:\n scores[i] = torch.from_numpy(\n output_scores_per_object[object_id][frame_idx]\n )\n\n if not per_obj_png_file:\n scores = predictor._apply_non_overlapping_constraints(scores)\n per_obj_output_mask = {\n object_id: (scores[i] > score_thresh).cpu().numpy()\n for i, object_id in enumerate(object_ids)\n }\n video_segments[frame_idx] = per_obj_output_mask\n\n # write the output masks as palette PNG files to output_mask_dir\n for frame_idx, per_obj_output_mask in video_segments.items():\n save_masks_to_dir(\n output_mask_dir=output_mask_dir,\n video_name=video_name,\n frame_name=frame_names[frame_idx],\n per_obj_output_mask=per_obj_output_mask,\n height=height,\n width=width,\n per_obj_png_file=per_obj_png_file,\n output_palette=output_palette,\n )\n\n\ndef main():\n parser = argparse.ArgumentParser()\n parser.add_argument(\n \"--sam2_cfg\",\n type=str,\n default=\"configs/sam2.1/sam2.1_hiera_b+.yaml\",\n help=\"SAM 2 model configuration file\",\n )\n parser.add_argument(\n \"--sam2_checkpoint\",\n type=str,\n default=\"./checkpoints/sam2.1_hiera_b+.pt\",\n help=\"path to the SAM 2 model checkpoint\",\n )\n parser.add_argument(\n \"--base_video_dir\",\n type=str,\n required=True,\n help=\"directory containing videos (as JPEG files) to run VOS prediction on\",\n )\n parser.add_argument(\n \"--input_mask_dir\",\n type=str,\n required=True,\n help=\"directory containing input masks (as PNG files) of each video\",\n )\n parser.add_argument(\n \"--video_list_file\",\n type=str,\n default=None,\n help=\"text file containing the list of video names to run VOS prediction on\",\n )\n parser.add_argument(\n \"--output_mask_dir\",\n type=str,\n required=True,\n help=\"directory to save the output masks (as PNG files)\",\n )\n parser.add_argument(\n \"--score_thresh\",\n type=float,\n default=0.0,\n help=\"threshold for the output mask logits (default: 0.0)\",\n )\n parser.add_argument(\n \"--use_all_masks\",\n action=\"store_true\",\n help=\"whether to use all available PNG files in input_mask_dir \"\n \"(default without this flag: just the first PNG file as input to the SAM 2 model; \"\n \"usually we don't need this flag, since semi-supervised VOS evaluation usually takes input from the first frame only)\",\n )\n parser.add_argument(\n \"--per_obj_png_file\",\n action=\"store_true\",\n help=\"whether use separate per-object PNG files for input and output masks \"\n \"(default without this flag: all object masks are packed into a single PNG file on each frame following DAVIS format; \"\n \"note that the SA-V dataset stores each object mask as an individual PNG file and requires this flag)\",\n )\n parser.add_argument(\n \"--apply_postprocessing\",\n action=\"store_true\",\n help=\"whether to apply postprocessing (e.g. hole-filling) to the output masks \"\n \"(we don't apply such post-processing in the SAM 2 model evaluation)\",\n )\n parser.add_argument(\n \"--track_object_appearing_later_in_video\",\n action=\"store_true\",\n help=\"whether to track objects that appear later in the video (i.e. not on the first frame; \"\n \"some VOS datasets like LVOS or YouTube-VOS don't have all objects appearing in the first frame)\",\n )\n args = parser.parse_args()\n\n # if we use per-object PNG files, they could possibly overlap in inputs and outputs\n hydra_overrides_extra = [\n \"++model.non_overlap_masks=\" + (\"false\" if args.per_obj_png_file else \"true\")\n ]\n predictor = build_sam2_video_predictor(\n config_file=args.sam2_cfg,\n ckpt_path=args.sam2_checkpoint,\n apply_postprocessing=args.apply_postprocessing,\n hydra_overrides_extra=hydra_overrides_extra,\n )\n\n if args.use_all_masks:\n print(\"using all available masks in input_mask_dir as input to the SAM 2 model\")\n else:\n print(\n \"using only the first frame's mask in input_mask_dir as input to the SAM 2 model\"\n )\n # if a video list file is provided, read the video names from the file\n # (otherwise, we use all subdirectories in base_video_dir)\n if args.video_list_file is not None:\n with open(args.video_list_file, \"r\") as f:\n video_names = [v.strip() for v in f.readlines()]\n else:\n video_names = [\n p\n for p in os.listdir(args.base_video_dir)\n if os.path.isdir(os.path.join(args.base_video_dir, p))\n ]\n print(f\"running VOS prediction on {len(video_names)} videos:\\n{video_names}\")\n\n for n_video, video_name in enumerate(video_names):\n print(f\"\\n{n_video + 1}/{len(video_names)} - running on {video_name}\")\n if not args.track_object_appearing_later_in_video:\n vos_inference(\n predictor=predictor,\n base_video_dir=args.base_video_dir,\n input_mask_dir=args.input_mask_dir,\n output_mask_dir=args.output_mask_dir,\n video_name=video_name,\n score_thresh=args.score_thresh,\n use_all_masks=args.use_all_masks,\n per_obj_png_file=args.per_obj_png_file,\n )\n else:\n vos_separate_inference_per_object(\n predictor=predictor,\n base_video_dir=args.base_video_dir,\n input_mask_dir=args.input_mask_dir,\n output_mask_dir=args.output_mask_dir,\n video_name=video_name,\n score_thresh=args.score_thresh,\n use_all_masks=args.use_all_masks,\n per_obj_png_file=args.per_obj_png_file,\n )\n\n print(\n f\"completed VOS prediction on {len(video_names)} videos -- \"\n f\"output masks saved to {args.output_mask_dir}\"\n )\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "auto-seg/submodules/segment-anything-2/training/README.md", "content": "# Training Code for SAM 2\n\nThis folder contains the training code for SAM 2, a foundation model for promptable visual segmentation in images and videos. \nThe code allows users to train and fine-tune SAM 2 on their own datasets (image, video, or both).\n\n## Structure\n\nThe training code is organized into the following subfolders:\n\n* `dataset`: This folder contains image and video dataset and dataloader classes as well as their transforms.\n* `model`: This folder contains the main model class (`SAM2Train`) for training/fine-tuning. `SAM2Train` inherits from `SAM2Base` model and provides functions to enable training or fine-tuning SAM 2. It also accepts all training-time parameters used for simulating user prompts (e.g. iterative point sampling).\n* `utils`: This folder contains training utils such as loggers and distributed training utils.\n* `scripts`: This folder contains the script to extract the frames of SA-V dataset to be used in training.\n* `loss_fns.py`: This file has the main loss class (`MultiStepMultiMasksAndIous`) used for training.\n* `optimizer.py`: This file contains all optimizer utils that support arbitrary schedulers.\n* `trainer.py`: This file contains the `Trainer` class that accepts all the `Hydra` configurable modules (model, optimizer, datasets, etc..) and implements the main train/eval loop.\n* `train.py`: This script is used to launch training jobs. It supports single and multi-node jobs. For usage, please check the [Getting Started](README.md#getting-started) section or run `python training/train.py -h`\n\n## Getting Started\n\nTo get started with the training code, we provide a simple example to fine-tune our checkpoints on [MOSE](https://henghuiding.github.io/MOSE/) dataset, which can be extended to your custom datasets.\n\n#### Requirements:\n- We assume training on A100 GPUs with **80 GB** of memory.\n- Download the MOSE dataset using one of the provided links from [here](https://github.com/henghuiding/MOSE-api?tab=readme-ov-file#download).\n\n#### Steps to fine-tune on MOSE:\n- Install the packages required for training by running `pip install -e \".[dev]\"`.\n- Set the paths for MOSE dataset in `configs/sam2.1_training/sam2.1_hiera_b+_MOSE_finetune.yaml`.\n ```yaml\n dataset:\n # PATHS to Dataset\n img_folder: null # PATH to MOSE JPEGImages folder\n gt_folder: null # PATH to MOSE Annotations folder\n file_list_txt: null # Optional PATH to filelist containing a subset of videos to be used for training\n ```\n- To fine-tune the base model on MOSE using 8 GPUs, run \n\n ```python\n python training/train.py \\\n -c configs/sam2.1_training/sam2.1_hiera_b+_MOSE_finetune.yaml \\\n --use-cluster 0 \\\n --num-gpus 8\n ```\n\n We also support multi-node training on a cluster using [SLURM](https://slurm.schedmd.com/documentation.html), for example, you can train on 2 nodes by running\n\n ```python\n python training/train.py \\\n -c configs/sam2.1_training/sam2.1_hiera_b+_MOSE_finetune.yaml \\\n --use-cluster 1 \\\n --num-gpus 8 \\\n --num-nodes 2\n --partition $PARTITION \\\n --qos $QOS \\\n --account $ACCOUNT\n ```\n where partition, qos, and account are optional and depend on your SLURM configuration.\n By default, the checkpoint and logs will be saved under `sam2_logs` directory in the root of the repo. Alternatively, you can set the experiment log directory in the config file as follows:\n \n ```yaml\n experiment_log_dir: null # Path to log directory, defaults to ./sam2_logs/${config_name}\n ```\n The training losses can be monitored using `tensorboard` logs stored under `tensorboard/` in the experiment log directory. We also provide a sample validation [split]( ../training/assets/MOSE_sample_val_list.txt) for evaluation purposes. To generate predictions, follow this [guide](../tools/README.md) on how to use our `vos_inference.py` script. After generating the predictions, you can run the `sav_evaluator.py` as detailed [here](../sav_dataset/README.md#sa-v-val-and-test-evaluation). The expected MOSE J&F after fine-tuning the Base plus model is 79.4.\n \n \n After training/fine-tuning, you can then use the new checkpoint (saved in `checkpoints/` in the experiment log directory) similar to SAM 2 released checkpoints (as illustrated [here](../README.md#image-prediction)).\n## Training on images and videos\nThe code supports training on images and videos (similar to how SAM 2 is trained). We provide classes for loading SA-1B as a sample image dataset, SA-V as a sample video dataset, as well as any DAVIS-style video dataset (e.g. MOSE). Note that to train on SA-V, you must first extract all videos to JPEG frames using the provided extraction [script](./scripts/sav_frame_extraction_submitit.py). Below is an example of how to setup the datasets in your config to train on a mix of image and video datasets:\n\n```yaml\ndata:\n train:\n _target_: training.dataset.sam2_datasets.TorchTrainMixedDataset \n phases_per_epoch: ${phases_per_epoch} # Chunks a single epoch into smaller phases\n batch_sizes: # List of batch sizes corresponding to each dataset\n - ${bs1} # Batch size of dataset 1\n - ${bs2} # Batch size of dataset 2\n datasets:\n # SA1B as an example of an image dataset\n - _target_: training.dataset.vos_dataset.VOSDataset\n training: true\n video_dataset:\n _target_: training.dataset.vos_raw_dataset.SA1BRawDataset\n img_folder: ${path_to_img_folder}\n gt_folder: ${path_to_gt_folder}\n file_list_txt: ${path_to_train_filelist} # Optional\n sampler:\n _target_: training.dataset.vos_sampler.RandomUniformSampler\n num_frames: 1\n max_num_objects: ${max_num_objects_per_image}\n transforms: ${image_transforms}\n # SA-V as an example of a video dataset\n - _target_: training.dataset.vos_dataset.VOSDataset\n training: true\n video_dataset:\n _target_: training.dataset.vos_raw_dataset.JSONRawDataset\n img_folder: ${path_to_img_folder}\n gt_folder: ${path_to_gt_folder}\n file_list_txt: ${path_to_train_filelist} # Optional\n ann_every: 4\n sampler:\n _target_: training.dataset.vos_sampler.RandomUniformSampler\n num_frames: 8 # Number of frames per video\n max_num_objects: ${max_num_objects_per_video}\n reverse_time_prob: ${reverse_time_prob} # probability to reverse video\n transforms: ${video_transforms}\n shuffle: True\n num_workers: ${num_train_workers}\n pin_memory: True\n drop_last: True\n collate_fn:\n _target_: training.utils.data_utils.collate_fn\n _partial_: true\n dict_key: all\n```\n" }, { "path": "auto-seg/submodules/segment-anything-2/training/__init__.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n" }, { "path": "auto-seg/submodules/segment-anything-2/training/assets/MOSE_sample_train_list.txt", "content": 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}, { "path": "auto-seg/submodules/segment-anything-2/training/assets/MOSE_sample_val_list.txt", "content": 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}, { "path": "auto-seg/submodules/segment-anything-2/training/dataset/__init__.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n" }, { "path": "auto-seg/submodules/segment-anything-2/training/dataset/sam2_datasets.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport logging\nimport math\nfrom typing import Callable, Iterable, List, Optional, Sequence\n\nimport torch\n\nfrom torch.utils.data import BatchSampler, DataLoader, Dataset, IterableDataset, Subset\n\nfrom torch.utils.data.distributed import DistributedSampler\n\n\nclass MixedDataLoader:\n def __init__(self, dataloaders: List[DataLoader], mixing_prob: torch.FloatTensor):\n \"\"\"\n Args:\n dataloaders (List[DataLoader]): List of DataLoaders to be mixed.\n mixing_prob (torch.FloatTensor): Probability of each dataloader to be sampled from\n\n \"\"\"\n assert len(dataloaders) == mixing_prob.shape[0]\n self.dataloaders = dataloaders\n self.mixing_prob = mixing_prob\n # Iterator state\n self._iter_dls = None\n self._iter_mixing_prob = None\n self.random_generator = torch.Generator()\n\n def __len__(self):\n return sum([len(d) for d in self.dataloaders])\n\n def __iter__(self):\n # Synchronize dataloader seeds\n self.random_generator.manual_seed(42)\n self._iter_dls = [iter(loader) for loader in self.dataloaders]\n self._iter_mixing_prob = self.mixing_prob.clone()\n return self\n\n def __next__(self):\n \"\"\"\n Sample a dataloader to sample from based on mixing probabilities. If one of the dataloaders is exhausted, we continue sampling from the other loaders until all are exhausted.\n \"\"\"\n if self._iter_dls is None:\n raise TypeError(f\"{type(self).__name__} object is not an iterator\")\n\n while self._iter_mixing_prob.any(): # at least one D-Loader with non-zero prob.\n dataset_idx = self._iter_mixing_prob.multinomial(\n 1, generator=self.random_generator\n ).item()\n try:\n item = next(self._iter_dls[dataset_idx])\n return item\n except StopIteration:\n # No more iterations for this dataset, set it's mixing probability to zero and try again.\n self._iter_mixing_prob[dataset_idx] = 0\n except Exception as e:\n # log and raise any other unexpected error.\n logging.error(e)\n raise e\n\n # Exhausted all iterators\n raise StopIteration\n\n\nclass TorchTrainMixedDataset:\n def __init__(\n self,\n datasets: List[Dataset],\n batch_sizes: List[int],\n num_workers: int,\n shuffle: bool,\n pin_memory: bool,\n drop_last: bool,\n collate_fn: Optional[Callable] = None,\n worker_init_fn: Optional[Callable] = None,\n phases_per_epoch: int = 1,\n dataset_prob: Optional[List[float]] = None,\n ) -> None:\n \"\"\"\n Args:\n datasets (List[Dataset]): List of Datasets to be mixed.\n batch_sizes (List[int]): Batch sizes for each dataset in the list.\n num_workers (int): Number of workers per dataloader.\n shuffle (bool): Whether or not to shuffle data.\n pin_memory (bool): If True, use pinned memory when loading tensors from disk.\n drop_last (bool): Whether or not to drop the last batch of data.\n collate_fn (Callable): Function to merge a list of samples into a mini-batch.\n worker_init_fn (Callable): Function to init each dataloader worker.\n phases_per_epoch (int): Number of phases per epoch.\n dataset_prob (List[float]): Probability of choosing the dataloader to sample from. Should sum to 1.0\n \"\"\"\n\n self.datasets = datasets\n self.batch_sizes = batch_sizes\n self.num_workers = num_workers\n self.shuffle = shuffle\n self.pin_memory = pin_memory\n self.drop_last = drop_last\n self.collate_fn = collate_fn\n self.worker_init_fn = worker_init_fn\n assert len(self.datasets) > 0\n for dataset in self.datasets:\n assert not isinstance(dataset, IterableDataset), \"Not supported\"\n # `RepeatFactorWrapper` requires calling set_epoch first to get its length\n self._set_dataset_epoch(dataset, 0)\n self.phases_per_epoch = phases_per_epoch\n self.chunks = [None] * len(datasets)\n if dataset_prob is None:\n # If not provided, assign each dataset a probability proportional to its length.\n dataset_lens = [\n (math.floor(len(d) / bs) if drop_last else math.ceil(len(d) / bs))\n for d, bs in zip(datasets, batch_sizes)\n ]\n total_len = sum(dataset_lens)\n dataset_prob = torch.tensor([d_len / total_len for d_len in dataset_lens])\n else:\n assert len(dataset_prob) == len(datasets)\n dataset_prob = torch.tensor(dataset_prob)\n\n logging.info(f\"Dataset mixing probabilities: {dataset_prob.tolist()}\")\n assert dataset_prob.sum().item() == 1.0, \"Probabilities should sum to 1.0\"\n self.dataset_prob = dataset_prob\n\n def _set_dataset_epoch(self, dataset, epoch: int) -> None:\n if hasattr(dataset, \"epoch\"):\n dataset.epoch = epoch\n if hasattr(dataset, \"set_epoch\"):\n dataset.set_epoch(epoch)\n\n def get_loader(self, epoch) -> Iterable:\n dataloaders = []\n for d_idx, (dataset, batch_size) in enumerate(\n zip(self.datasets, self.batch_sizes)\n ):\n if self.phases_per_epoch > 1:\n # Major epoch that looops over entire dataset\n # len(main_epoch) == phases_per_epoch * len(epoch)\n main_epoch = epoch // self.phases_per_epoch\n\n # Phase with in the main epoch\n local_phase = epoch % self.phases_per_epoch\n\n # Start of new data-epoch or job is resumed after preemtion.\n if local_phase == 0 or self.chunks[d_idx] is None:\n # set seed for dataset epoch\n # If using RepeatFactorWrapper, this step currectly re-samples indices before chunking.\n self._set_dataset_epoch(dataset, main_epoch)\n\n # Separate random generator for subset sampling\n g = torch.Generator()\n g.manual_seed(main_epoch)\n self.chunks[d_idx] = torch.chunk(\n torch.randperm(len(dataset), generator=g),\n self.phases_per_epoch,\n )\n\n dataset = Subset(dataset, self.chunks[d_idx][local_phase])\n else:\n self._set_dataset_epoch(dataset, epoch)\n\n sampler = DistributedSampler(dataset, shuffle=self.shuffle)\n sampler.set_epoch(epoch)\n\n batch_sampler = BatchSampler(sampler, batch_size, drop_last=self.drop_last)\n dataloaders.append(\n DataLoader(\n dataset,\n num_workers=self.num_workers,\n pin_memory=self.pin_memory,\n batch_sampler=batch_sampler,\n collate_fn=self.collate_fn,\n worker_init_fn=self.worker_init_fn,\n )\n )\n return MixedDataLoader(dataloaders, self.dataset_prob)\n" }, { "path": "auto-seg/submodules/segment-anything-2/training/dataset/transforms.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\n\"\"\"\nTransforms and data augmentation for both image + bbox.\n\"\"\"\n\nimport logging\n\nimport random\nfrom typing import Iterable\n\nimport torch\nimport torchvision.transforms as T\nimport torchvision.transforms.functional as F\nimport torchvision.transforms.v2.functional as Fv2\nfrom PIL import Image as PILImage\n\nfrom torchvision.transforms import InterpolationMode\n\nfrom training.utils.data_utils import VideoDatapoint\n\n\ndef hflip(datapoint, index):\n\n datapoint.frames[index].data = F.hflip(datapoint.frames[index].data)\n for obj in datapoint.frames[index].objects:\n if obj.segment is not None:\n obj.segment = F.hflip(obj.segment)\n\n return datapoint\n\n\ndef get_size_with_aspect_ratio(image_size, size, max_size=None):\n w, h = image_size\n if max_size is not None:\n min_original_size = float(min((w, h)))\n max_original_size = float(max((w, h)))\n if max_original_size / min_original_size * size > max_size:\n size = max_size * min_original_size / max_original_size\n\n if (w <= h and w == size) or (h <= w and h == size):\n return (h, w)\n\n if w < h:\n ow = int(round(size))\n oh = int(round(size * h / w))\n else:\n oh = int(round(size))\n ow = int(round(size * w / h))\n\n return (oh, ow)\n\n\ndef resize(datapoint, index, size, max_size=None, square=False, v2=False):\n # size can be min_size (scalar) or (w, h) tuple\n\n def get_size(image_size, size, max_size=None):\n if isinstance(size, (list, tuple)):\n return size[::-1]\n else:\n return get_size_with_aspect_ratio(image_size, size, max_size)\n\n if square:\n size = size, size\n else:\n cur_size = (\n datapoint.frames[index].data.size()[-2:][::-1]\n if v2\n else datapoint.frames[index].data.size\n )\n size = get_size(cur_size, size, max_size)\n\n old_size = (\n datapoint.frames[index].data.size()[-2:][::-1]\n if v2\n else datapoint.frames[index].data.size\n )\n if v2:\n datapoint.frames[index].data = Fv2.resize(\n datapoint.frames[index].data, size, antialias=True\n )\n else:\n datapoint.frames[index].data = F.resize(datapoint.frames[index].data, size)\n\n new_size = (\n datapoint.frames[index].data.size()[-2:][::-1]\n if v2\n else datapoint.frames[index].data.size\n )\n\n for obj in datapoint.frames[index].objects:\n if obj.segment is not None:\n obj.segment = F.resize(obj.segment[None, None], size).squeeze()\n\n h, w = size\n datapoint.frames[index].size = (h, w)\n return datapoint\n\n\ndef pad(datapoint, index, padding, v2=False):\n old_h, old_w = datapoint.frames[index].size\n h, w = old_h, old_w\n if len(padding) == 2:\n # assumes that we only pad on the bottom right corners\n datapoint.frames[index].data = F.pad(\n datapoint.frames[index].data, (0, 0, padding[0], padding[1])\n )\n h += padding[1]\n w += padding[0]\n else:\n # left, top, right, bottom\n datapoint.frames[index].data = F.pad(\n datapoint.frames[index].data,\n (padding[0], padding[1], padding[2], padding[3]),\n )\n h += padding[1] + padding[3]\n w += padding[0] + padding[2]\n\n datapoint.frames[index].size = (h, w)\n\n for obj in datapoint.frames[index].objects:\n if obj.segment is not None:\n if v2:\n if len(padding) == 2:\n obj.segment = Fv2.pad(obj.segment, (0, 0, padding[0], padding[1]))\n else:\n obj.segment = Fv2.pad(obj.segment, tuple(padding))\n else:\n if len(padding) == 2:\n obj.segment = F.pad(obj.segment, (0, 0, padding[0], padding[1]))\n else:\n obj.segment = F.pad(obj.segment, tuple(padding))\n return datapoint\n\n\nclass RandomHorizontalFlip:\n def __init__(self, consistent_transform, p=0.5):\n self.p = p\n self.consistent_transform = consistent_transform\n\n def __call__(self, datapoint, **kwargs):\n if self.consistent_transform:\n if random.random() < self.p:\n for i in range(len(datapoint.frames)):\n datapoint = hflip(datapoint, i)\n return datapoint\n for i in range(len(datapoint.frames)):\n if random.random() < self.p:\n datapoint = hflip(datapoint, i)\n return datapoint\n\n\nclass RandomResizeAPI:\n def __init__(\n self, sizes, consistent_transform, max_size=None, square=False, v2=False\n ):\n if isinstance(sizes, int):\n sizes = (sizes,)\n assert isinstance(sizes, Iterable)\n self.sizes = list(sizes)\n self.max_size = max_size\n self.square = square\n self.consistent_transform = consistent_transform\n self.v2 = v2\n\n def __call__(self, datapoint, **kwargs):\n if self.consistent_transform:\n size = random.choice(self.sizes)\n for i in range(len(datapoint.frames)):\n datapoint = resize(\n datapoint, i, size, self.max_size, square=self.square, v2=self.v2\n )\n return datapoint\n for i in range(len(datapoint.frames)):\n size = random.choice(self.sizes)\n datapoint = resize(\n datapoint, i, size, self.max_size, square=self.square, v2=self.v2\n )\n return datapoint\n\n\nclass ToTensorAPI:\n def __init__(self, v2=False):\n self.v2 = v2\n\n def __call__(self, datapoint: VideoDatapoint, **kwargs):\n for img in datapoint.frames:\n if self.v2:\n img.data = Fv2.to_image_tensor(img.data)\n else:\n img.data = F.to_tensor(img.data)\n return datapoint\n\n\nclass NormalizeAPI:\n def __init__(self, mean, std, v2=False):\n self.mean = mean\n self.std = std\n self.v2 = v2\n\n def __call__(self, datapoint: VideoDatapoint, **kwargs):\n for img in datapoint.frames:\n if self.v2:\n img.data = Fv2.convert_image_dtype(img.data, torch.float32)\n img.data = Fv2.normalize(img.data, mean=self.mean, std=self.std)\n else:\n img.data = F.normalize(img.data, mean=self.mean, std=self.std)\n\n return datapoint\n\n\nclass ComposeAPI:\n def __init__(self, transforms):\n self.transforms = transforms\n\n def __call__(self, datapoint, **kwargs):\n for t in self.transforms:\n datapoint = t(datapoint, **kwargs)\n return datapoint\n\n def __repr__(self):\n format_string = self.__class__.__name__ + \"(\"\n for t in self.transforms:\n format_string += \"\\n\"\n format_string += \" {0}\".format(t)\n format_string += \"\\n)\"\n return format_string\n\n\nclass RandomGrayscale:\n def __init__(self, consistent_transform, p=0.5):\n self.p = p\n self.consistent_transform = consistent_transform\n self.Grayscale = T.Grayscale(num_output_channels=3)\n\n def __call__(self, datapoint: VideoDatapoint, **kwargs):\n if self.consistent_transform:\n if random.random() < self.p:\n for img in datapoint.frames:\n img.data = self.Grayscale(img.data)\n return datapoint\n for img in datapoint.frames:\n if random.random() < self.p:\n img.data = self.Grayscale(img.data)\n return datapoint\n\n\nclass ColorJitter:\n def __init__(self, consistent_transform, brightness, contrast, saturation, hue):\n self.consistent_transform = consistent_transform\n self.brightness = (\n brightness\n if isinstance(brightness, list)\n else [max(0, 1 - brightness), 1 + brightness]\n )\n self.contrast = (\n contrast\n if isinstance(contrast, list)\n else [max(0, 1 - contrast), 1 + contrast]\n )\n self.saturation = (\n saturation\n if isinstance(saturation, list)\n else [max(0, 1 - saturation), 1 + saturation]\n )\n self.hue = hue if isinstance(hue, list) or hue is None else ([-hue, hue])\n\n def __call__(self, datapoint: VideoDatapoint, **kwargs):\n if self.consistent_transform:\n # Create a color jitter transformation params\n (\n fn_idx,\n brightness_factor,\n contrast_factor,\n saturation_factor,\n hue_factor,\n ) = T.ColorJitter.get_params(\n self.brightness, self.contrast, self.saturation, self.hue\n )\n for img in datapoint.frames:\n if not self.consistent_transform:\n (\n fn_idx,\n brightness_factor,\n contrast_factor,\n saturation_factor,\n hue_factor,\n ) = T.ColorJitter.get_params(\n self.brightness, self.contrast, self.saturation, self.hue\n )\n for fn_id in fn_idx:\n if fn_id == 0 and brightness_factor is not None:\n img.data = F.adjust_brightness(img.data, brightness_factor)\n elif fn_id == 1 and contrast_factor is not None:\n img.data = F.adjust_contrast(img.data, contrast_factor)\n elif fn_id == 2 and saturation_factor is not None:\n img.data = F.adjust_saturation(img.data, saturation_factor)\n elif fn_id == 3 and hue_factor is not None:\n img.data = F.adjust_hue(img.data, hue_factor)\n return datapoint\n\n\nclass RandomAffine:\n def __init__(\n self,\n degrees,\n consistent_transform,\n scale=None,\n translate=None,\n shear=None,\n image_mean=(123, 116, 103),\n log_warning=True,\n num_tentatives=1,\n image_interpolation=\"bicubic\",\n ):\n \"\"\"\n The mask is required for this transform.\n if consistent_transform if True, then the same random affine is applied to all frames and masks.\n \"\"\"\n self.degrees = degrees if isinstance(degrees, list) else ([-degrees, degrees])\n self.scale = scale\n self.shear = (\n shear if isinstance(shear, list) else ([-shear, shear] if shear else None)\n )\n self.translate = translate\n self.fill_img = image_mean\n self.consistent_transform = consistent_transform\n self.log_warning = log_warning\n self.num_tentatives = num_tentatives\n\n if image_interpolation == \"bicubic\":\n self.image_interpolation = InterpolationMode.BICUBIC\n elif image_interpolation == \"bilinear\":\n self.image_interpolation = InterpolationMode.BILINEAR\n else:\n raise NotImplementedError\n\n def __call__(self, datapoint: VideoDatapoint, **kwargs):\n for _tentative in range(self.num_tentatives):\n res = self.transform_datapoint(datapoint)\n if res is not None:\n return res\n\n if self.log_warning:\n logging.warning(\n f\"Skip RandomAffine for zero-area mask in first frame after {self.num_tentatives} tentatives\"\n )\n return datapoint\n\n def transform_datapoint(self, datapoint: VideoDatapoint):\n _, height, width = F.get_dimensions(datapoint.frames[0].data)\n img_size = [width, height]\n\n if self.consistent_transform:\n # Create a random affine transformation\n affine_params = T.RandomAffine.get_params(\n degrees=self.degrees,\n translate=self.translate,\n scale_ranges=self.scale,\n shears=self.shear,\n img_size=img_size,\n )\n\n for img_idx, img in enumerate(datapoint.frames):\n this_masks = [\n obj.segment.unsqueeze(0) if obj.segment is not None else None\n for obj in img.objects\n ]\n if not self.consistent_transform:\n # if not consistent we create a new affine params for every frame&mask pair Create a random affine transformation\n affine_params = T.RandomAffine.get_params(\n degrees=self.degrees,\n translate=self.translate,\n scale_ranges=self.scale,\n shears=self.shear,\n img_size=img_size,\n )\n\n transformed_bboxes, transformed_masks = [], []\n for i in range(len(img.objects)):\n if this_masks[i] is None:\n transformed_masks.append(None)\n # Dummy bbox for a dummy target\n transformed_bboxes.append(torch.tensor([[0, 0, 1, 1]]))\n else:\n transformed_mask = F.affine(\n this_masks[i],\n *affine_params,\n interpolation=InterpolationMode.NEAREST,\n fill=0.0,\n )\n if img_idx == 0 and transformed_mask.max() == 0:\n # We are dealing with a video and the object is not visible in the first frame\n # Return the datapoint without transformation\n return None\n transformed_masks.append(transformed_mask.squeeze())\n\n for i in range(len(img.objects)):\n img.objects[i].segment = transformed_masks[i]\n\n img.data = F.affine(\n img.data,\n *affine_params,\n interpolation=self.image_interpolation,\n fill=self.fill_img,\n )\n return datapoint\n\n\ndef random_mosaic_frame(\n datapoint,\n index,\n grid_h,\n grid_w,\n target_grid_y,\n target_grid_x,\n should_hflip,\n):\n # Step 1: downsize the images and paste them into a mosaic\n image_data = datapoint.frames[index].data\n is_pil = isinstance(image_data, PILImage.Image)\n if is_pil:\n H_im = image_data.height\n W_im = image_data.width\n image_data_output = PILImage.new(\"RGB\", (W_im, H_im))\n else:\n H_im = image_data.size(-2)\n W_im = image_data.size(-1)\n image_data_output = torch.zeros_like(image_data)\n\n downsize_cache = {}\n for grid_y in range(grid_h):\n for grid_x in range(grid_w):\n y_offset_b = grid_y * H_im // grid_h\n x_offset_b = grid_x * W_im // grid_w\n y_offset_e = (grid_y + 1) * H_im // grid_h\n x_offset_e = (grid_x + 1) * W_im // grid_w\n H_im_downsize = y_offset_e - y_offset_b\n W_im_downsize = x_offset_e - x_offset_b\n\n if (H_im_downsize, W_im_downsize) in downsize_cache:\n image_data_downsize = downsize_cache[(H_im_downsize, W_im_downsize)]\n else:\n image_data_downsize = F.resize(\n image_data,\n size=(H_im_downsize, W_im_downsize),\n interpolation=InterpolationMode.BILINEAR,\n antialias=True, # antialiasing for downsizing\n )\n downsize_cache[(H_im_downsize, W_im_downsize)] = image_data_downsize\n if should_hflip[grid_y, grid_x].item():\n image_data_downsize = F.hflip(image_data_downsize)\n\n if is_pil:\n image_data_output.paste(image_data_downsize, (x_offset_b, y_offset_b))\n else:\n image_data_output[:, y_offset_b:y_offset_e, x_offset_b:x_offset_e] = (\n image_data_downsize\n )\n\n datapoint.frames[index].data = image_data_output\n\n # Step 2: downsize the masks and paste them into the target grid of the mosaic\n for obj in datapoint.frames[index].objects:\n if obj.segment is None:\n continue\n assert obj.segment.shape == (H_im, W_im) and obj.segment.dtype == torch.uint8\n segment_output = torch.zeros_like(obj.segment)\n\n target_y_offset_b = target_grid_y * H_im // grid_h\n target_x_offset_b = target_grid_x * W_im // grid_w\n target_y_offset_e = (target_grid_y + 1) * H_im // grid_h\n target_x_offset_e = (target_grid_x + 1) * W_im // grid_w\n target_H_im_downsize = target_y_offset_e - target_y_offset_b\n target_W_im_downsize = target_x_offset_e - target_x_offset_b\n\n segment_downsize = F.resize(\n obj.segment[None, None],\n size=(target_H_im_downsize, target_W_im_downsize),\n interpolation=InterpolationMode.BILINEAR,\n antialias=True, # antialiasing for downsizing\n )[0, 0]\n if should_hflip[target_grid_y, target_grid_x].item():\n segment_downsize = F.hflip(segment_downsize[None, None])[0, 0]\n\n segment_output[\n target_y_offset_b:target_y_offset_e, target_x_offset_b:target_x_offset_e\n ] = segment_downsize\n obj.segment = segment_output\n\n return datapoint\n\n\nclass RandomMosaicVideoAPI:\n def __init__(self, prob=0.15, grid_h=2, grid_w=2, use_random_hflip=False):\n self.prob = prob\n self.grid_h = grid_h\n self.grid_w = grid_w\n self.use_random_hflip = use_random_hflip\n\n def __call__(self, datapoint, **kwargs):\n if random.random() > self.prob:\n return datapoint\n\n # select a random location to place the target mask in the mosaic\n target_grid_y = random.randint(0, self.grid_h - 1)\n target_grid_x = random.randint(0, self.grid_w - 1)\n # whether to flip each grid in the mosaic horizontally\n if self.use_random_hflip:\n should_hflip = torch.rand(self.grid_h, self.grid_w) < 0.5\n else:\n should_hflip = torch.zeros(self.grid_h, self.grid_w, dtype=torch.bool)\n for i in range(len(datapoint.frames)):\n datapoint = random_mosaic_frame(\n datapoint,\n i,\n grid_h=self.grid_h,\n grid_w=self.grid_w,\n target_grid_y=target_grid_y,\n target_grid_x=target_grid_x,\n should_hflip=should_hflip,\n )\n\n return datapoint\n" }, { "path": "auto-seg/submodules/segment-anything-2/training/dataset/utils.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\n\"\"\"Some wrapping utilities extended from pytorch's to support repeat factor sampling in particular\"\"\"\n\nfrom typing import Iterable\n\nimport torch\nfrom torch.utils.data import (\n ConcatDataset as TorchConcatDataset,\n Dataset,\n Subset as TorchSubset,\n)\n\n\nclass ConcatDataset(TorchConcatDataset):\n def __init__(self, datasets: Iterable[Dataset]) -> None:\n super(ConcatDataset, self).__init__(datasets)\n\n self.repeat_factors = torch.cat([d.repeat_factors for d in datasets])\n\n def set_epoch(self, epoch: int):\n for dataset in self.datasets:\n if hasattr(dataset, \"epoch\"):\n dataset.epoch = epoch\n if hasattr(dataset, \"set_epoch\"):\n dataset.set_epoch(epoch)\n\n\nclass Subset(TorchSubset):\n def __init__(self, dataset, indices) -> None:\n super(Subset, self).__init__(dataset, indices)\n\n self.repeat_factors = dataset.repeat_factors[indices]\n assert len(indices) == len(self.repeat_factors)\n\n\n# Adapted from Detectron2\nclass RepeatFactorWrapper(Dataset):\n \"\"\"\n Thin wrapper around a dataset to implement repeat factor sampling.\n The underlying dataset must have a repeat_factors member to indicate the per-image factor.\n Set it to uniformly ones to disable repeat factor sampling\n \"\"\"\n\n def __init__(self, dataset, seed: int = 0):\n self.dataset = dataset\n self.epoch_ids = None\n self._seed = seed\n\n # Split into whole number (_int_part) and fractional (_frac_part) parts.\n self._int_part = torch.trunc(dataset.repeat_factors)\n self._frac_part = dataset.repeat_factors - self._int_part\n\n def _get_epoch_indices(self, generator):\n \"\"\"\n Create a list of dataset indices (with repeats) to use for one epoch.\n\n Args:\n generator (torch.Generator): pseudo random number generator used for\n stochastic rounding.\n\n Returns:\n torch.Tensor: list of dataset indices to use in one epoch. Each index\n is repeated based on its calculated repeat factor.\n \"\"\"\n # Since repeat factors are fractional, we use stochastic rounding so\n # that the target repeat factor is achieved in expectation over the\n # course of training\n rands = torch.rand(len(self._frac_part), generator=generator)\n rep_factors = self._int_part + (rands < self._frac_part).float()\n # Construct a list of indices in which we repeat images as specified\n indices = []\n for dataset_index, rep_factor in enumerate(rep_factors):\n indices.extend([dataset_index] * int(rep_factor.item()))\n return torch.tensor(indices, dtype=torch.int64)\n\n def __len__(self):\n if self.epoch_ids is None:\n # Here we raise an error instead of returning original len(self.dataset) avoid\n # accidentally using unwrapped length. Otherwise it's error-prone since the\n # length changes to `len(self.epoch_ids)`changes after set_epoch is called.\n raise RuntimeError(\"please call set_epoch first to get wrapped length\")\n # return len(self.dataset)\n\n return len(self.epoch_ids)\n\n def set_epoch(self, epoch: int):\n g = torch.Generator()\n g.manual_seed(self._seed + epoch)\n self.epoch_ids = self._get_epoch_indices(g)\n if hasattr(self.dataset, \"set_epoch\"):\n self.dataset.set_epoch(epoch)\n\n def __getitem__(self, idx):\n if self.epoch_ids is None:\n raise RuntimeError(\n \"Repeat ids haven't been computed. Did you forget to call set_epoch?\"\n )\n\n return self.dataset[self.epoch_ids[idx]]\n" }, { "path": "auto-seg/submodules/segment-anything-2/training/dataset/vos_dataset.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport logging\nimport random\nfrom copy import deepcopy\n\nimport numpy as np\n\nimport torch\nfrom iopath.common.file_io import g_pathmgr\nfrom PIL import Image as PILImage\nfrom torchvision.datasets.vision import VisionDataset\n\nfrom training.dataset.vos_raw_dataset import VOSRawDataset\nfrom training.dataset.vos_sampler import VOSSampler\nfrom training.dataset.vos_segment_loader import JSONSegmentLoader\n\nfrom training.utils.data_utils import Frame, Object, VideoDatapoint\n\nMAX_RETRIES = 100\n\n\nclass VOSDataset(VisionDataset):\n def __init__(\n self,\n transforms,\n training: bool,\n video_dataset: VOSRawDataset,\n sampler: VOSSampler,\n multiplier: int,\n always_target=True,\n target_segments_available=True,\n ):\n self._transforms = transforms\n self.training = training\n self.video_dataset = video_dataset\n self.sampler = sampler\n\n self.repeat_factors = torch.ones(len(self.video_dataset), dtype=torch.float32)\n self.repeat_factors *= multiplier\n print(f\"Raw dataset length = {len(self.video_dataset)}\")\n\n self.curr_epoch = 0 # Used in case data loader behavior changes across epochs\n self.always_target = always_target\n self.target_segments_available = target_segments_available\n\n def _get_datapoint(self, idx):\n\n for retry in range(MAX_RETRIES):\n try:\n if isinstance(idx, torch.Tensor):\n idx = idx.item()\n # sample a video\n video, segment_loader = self.video_dataset.get_video(idx)\n # sample frames and object indices to be used in a datapoint\n sampled_frms_and_objs = self.sampler.sample(\n video, segment_loader, epoch=self.curr_epoch\n )\n break # Succesfully loaded video\n except Exception as e:\n if self.training:\n logging.warning(\n f\"Loading failed (id={idx}); Retry {retry} with exception: {e}\"\n )\n idx = random.randrange(0, len(self.video_dataset))\n else:\n # Shouldn't fail to load a val video\n raise e\n\n datapoint = self.construct(video, sampled_frms_and_objs, segment_loader)\n for transform in self._transforms:\n datapoint = transform(datapoint, epoch=self.curr_epoch)\n return datapoint\n\n def construct(self, video, sampled_frms_and_objs, segment_loader):\n \"\"\"\n Constructs a VideoDatapoint sample to pass to transforms\n \"\"\"\n sampled_frames = sampled_frms_and_objs.frames\n sampled_object_ids = sampled_frms_and_objs.object_ids\n\n images = []\n rgb_images = load_images(sampled_frames)\n # Iterate over the sampled frames and store their rgb data and object data (bbox, segment)\n for frame_idx, frame in enumerate(sampled_frames):\n w, h = rgb_images[frame_idx].size\n images.append(\n Frame(\n data=rgb_images[frame_idx],\n objects=[],\n )\n )\n # We load the gt segments associated with the current frame\n if isinstance(segment_loader, JSONSegmentLoader):\n segments = segment_loader.load(\n frame.frame_idx, obj_ids=sampled_object_ids\n )\n else:\n segments = segment_loader.load(frame.frame_idx)\n for obj_id in sampled_object_ids:\n # Extract the segment\n if obj_id in segments:\n assert (\n segments[obj_id] is not None\n ), \"None targets are not supported\"\n # segment is uint8 and remains uint8 throughout the transforms\n segment = segments[obj_id].to(torch.uint8)\n else:\n # There is no target, we either use a zero mask target or drop this object\n if not self.always_target:\n continue\n segment = torch.zeros(h, w, dtype=torch.uint8)\n\n images[frame_idx].objects.append(\n Object(\n object_id=obj_id,\n frame_index=frame.frame_idx,\n segment=segment,\n )\n )\n return VideoDatapoint(\n frames=images,\n video_id=video.video_id,\n size=(h, w),\n )\n\n def __getitem__(self, idx):\n return self._get_datapoint(idx)\n\n def __len__(self):\n return len(self.video_dataset)\n\n\ndef load_images(frames):\n all_images = []\n cache = {}\n for frame in frames:\n if frame.data is None:\n # Load the frame rgb data from file\n path = frame.image_path\n if path in cache:\n all_images.append(deepcopy(all_images[cache[path]]))\n continue\n with g_pathmgr.open(path, \"rb\") as fopen:\n all_images.append(PILImage.open(fopen).convert(\"RGB\"))\n cache[path] = len(all_images) - 1\n else:\n # The frame rgb data has already been loaded\n # Convert it to a PILImage\n all_images.append(tensor_2_PIL(frame.data))\n\n return all_images\n\n\ndef tensor_2_PIL(data: torch.Tensor) -> PILImage.Image:\n data = data.cpu().numpy().transpose((1, 2, 0)) * 255.0\n data = data.astype(np.uint8)\n return PILImage.fromarray(data)\n" }, { "path": "auto-seg/submodules/segment-anything-2/training/dataset/vos_raw_dataset.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport glob\nimport logging\nimport os\nfrom dataclasses import dataclass\n\nfrom typing import List, Optional\n\nimport pandas as pd\n\nimport torch\n\nfrom iopath.common.file_io import g_pathmgr\n\nfrom omegaconf.listconfig import ListConfig\n\nfrom training.dataset.vos_segment_loader import (\n JSONSegmentLoader,\n MultiplePNGSegmentLoader,\n PalettisedPNGSegmentLoader,\n SA1BSegmentLoader,\n)\n\n\n@dataclass\nclass VOSFrame:\n frame_idx: int\n image_path: str\n data: Optional[torch.Tensor] = None\n is_conditioning_only: Optional[bool] = False\n\n\n@dataclass\nclass VOSVideo:\n video_name: str\n video_id: int\n frames: List[VOSFrame]\n\n def __len__(self):\n return len(self.frames)\n\n\nclass VOSRawDataset:\n def __init__(self):\n pass\n\n def get_video(self, idx):\n raise NotImplementedError()\n\n\nclass PNGRawDataset(VOSRawDataset):\n def __init__(\n self,\n img_folder,\n gt_folder,\n file_list_txt=None,\n excluded_videos_list_txt=None,\n sample_rate=1,\n is_palette=True,\n single_object_mode=False,\n truncate_video=-1,\n frames_sampling_mult=False,\n ):\n self.img_folder = img_folder\n self.gt_folder = gt_folder\n self.sample_rate = sample_rate\n self.is_palette = is_palette\n self.single_object_mode = single_object_mode\n self.truncate_video = truncate_video\n\n # Read the subset defined in file_list_txt\n if file_list_txt is not None:\n with g_pathmgr.open(file_list_txt, \"r\") as f:\n subset = [os.path.splitext(line.strip())[0] for line in f]\n else:\n subset = os.listdir(self.img_folder)\n\n # Read and process excluded files if provided\n if excluded_videos_list_txt is not None:\n with g_pathmgr.open(excluded_videos_list_txt, \"r\") as f:\n excluded_files = [os.path.splitext(line.strip())[0] for line in f]\n else:\n excluded_files = []\n\n # Check if it's not in excluded_files\n self.video_names = sorted(\n [video_name for video_name in subset if video_name not in excluded_files]\n )\n\n if self.single_object_mode:\n # single object mode\n self.video_names = sorted(\n [\n os.path.join(video_name, obj)\n for video_name in self.video_names\n for obj in os.listdir(os.path.join(self.gt_folder, video_name))\n ]\n )\n\n if frames_sampling_mult:\n video_names_mult = []\n for video_name in self.video_names:\n num_frames = len(os.listdir(os.path.join(self.img_folder, video_name)))\n video_names_mult.extend([video_name] * num_frames)\n self.video_names = video_names_mult\n\n def get_video(self, idx):\n \"\"\"\n Given a VOSVideo object, return the mask tensors.\n \"\"\"\n video_name = self.video_names[idx]\n\n if self.single_object_mode:\n video_frame_root = os.path.join(\n self.img_folder, os.path.dirname(video_name)\n )\n else:\n video_frame_root = os.path.join(self.img_folder, video_name)\n\n video_mask_root = os.path.join(self.gt_folder, video_name)\n\n if self.is_palette:\n segment_loader = PalettisedPNGSegmentLoader(video_mask_root)\n else:\n segment_loader = MultiplePNGSegmentLoader(\n video_mask_root, self.single_object_mode\n )\n\n all_frames = sorted(glob.glob(os.path.join(video_frame_root, \"*.jpg\")))\n if self.truncate_video > 0:\n all_frames = all_frames[: self.truncate_video]\n frames = []\n for _, fpath in enumerate(all_frames[:: self.sample_rate]):\n fid = int(os.path.basename(fpath).split(\".\")[0])\n frames.append(VOSFrame(fid, image_path=fpath))\n video = VOSVideo(video_name, idx, frames)\n return video, segment_loader\n\n def __len__(self):\n return len(self.video_names)\n\n\nclass SA1BRawDataset(VOSRawDataset):\n def __init__(\n self,\n img_folder,\n gt_folder,\n file_list_txt=None,\n excluded_videos_list_txt=None,\n num_frames=1,\n mask_area_frac_thresh=1.1, # no filtering by default\n uncertain_iou=-1, # no filtering by default\n ):\n self.img_folder = img_folder\n self.gt_folder = gt_folder\n self.num_frames = num_frames\n self.mask_area_frac_thresh = mask_area_frac_thresh\n self.uncertain_iou = uncertain_iou # stability score\n\n # Read the subset defined in file_list_txt\n if file_list_txt is not None:\n with g_pathmgr.open(file_list_txt, \"r\") as f:\n subset = [os.path.splitext(line.strip())[0] for line in f]\n else:\n subset = os.listdir(self.img_folder)\n subset = [\n path.split(\".\")[0] for path in subset if path.endswith(\".jpg\")\n ] # remove extension\n\n # Read and process excluded files if provided\n if excluded_videos_list_txt is not None:\n with g_pathmgr.open(excluded_videos_list_txt, \"r\") as f:\n excluded_files = [os.path.splitext(line.strip())[0] for line in f]\n else:\n excluded_files = []\n\n # Check if it's not in excluded_files and it exists\n self.video_names = [\n video_name for video_name in subset if video_name not in excluded_files\n ]\n\n def get_video(self, idx):\n \"\"\"\n Given a VOSVideo object, return the mask tensors.\n \"\"\"\n video_name = self.video_names[idx]\n\n video_frame_path = os.path.join(self.img_folder, video_name + \".jpg\")\n video_mask_path = os.path.join(self.gt_folder, video_name + \".json\")\n\n segment_loader = SA1BSegmentLoader(\n video_mask_path,\n mask_area_frac_thresh=self.mask_area_frac_thresh,\n video_frame_path=video_frame_path,\n uncertain_iou=self.uncertain_iou,\n )\n\n frames = []\n for frame_idx in range(self.num_frames):\n frames.append(VOSFrame(frame_idx, image_path=video_frame_path))\n video_name = video_name.split(\"_\")[-1] # filename is sa_{int}\n # video id needs to be image_id to be able to load correct annotation file during eval\n video = VOSVideo(video_name, int(video_name), frames)\n return video, segment_loader\n\n def __len__(self):\n return len(self.video_names)\n\n\nclass JSONRawDataset(VOSRawDataset):\n \"\"\"\n Dataset where the annotation in the format of SA-V json files\n \"\"\"\n\n def __init__(\n self,\n img_folder,\n gt_folder,\n file_list_txt=None,\n excluded_videos_list_txt=None,\n sample_rate=1,\n rm_unannotated=True,\n ann_every=1,\n frames_fps=24,\n ):\n self.gt_folder = gt_folder\n self.img_folder = img_folder\n self.sample_rate = sample_rate\n self.rm_unannotated = rm_unannotated\n self.ann_every = ann_every\n self.frames_fps = frames_fps\n\n # Read and process excluded files if provided\n excluded_files = []\n if excluded_videos_list_txt is not None:\n if isinstance(excluded_videos_list_txt, str):\n excluded_videos_lists = [excluded_videos_list_txt]\n elif isinstance(excluded_videos_list_txt, ListConfig):\n excluded_videos_lists = list(excluded_videos_list_txt)\n else:\n raise NotImplementedError\n\n for excluded_videos_list_txt in excluded_videos_lists:\n with open(excluded_videos_list_txt, \"r\") as f:\n excluded_files.extend(\n [os.path.splitext(line.strip())[0] for line in f]\n )\n excluded_files = set(excluded_files)\n\n # Read the subset defined in file_list_txt\n if file_list_txt is not None:\n with g_pathmgr.open(file_list_txt, \"r\") as f:\n subset = [os.path.splitext(line.strip())[0] for line in f]\n else:\n subset = os.listdir(self.img_folder)\n\n self.video_names = sorted(\n [video_name for video_name in subset if video_name not in excluded_files]\n )\n\n def get_video(self, video_idx):\n \"\"\"\n Given a VOSVideo object, return the mask tensors.\n \"\"\"\n video_name = self.video_names[video_idx]\n video_json_path = os.path.join(self.gt_folder, video_name + \"_manual.json\")\n segment_loader = JSONSegmentLoader(\n video_json_path=video_json_path,\n ann_every=self.ann_every,\n frames_fps=self.frames_fps,\n )\n\n frame_ids = [\n int(os.path.splitext(frame_name)[0])\n for frame_name in sorted(\n os.listdir(os.path.join(self.img_folder, video_name))\n )\n ]\n\n frames = [\n VOSFrame(\n frame_id,\n image_path=os.path.join(\n self.img_folder, f\"{video_name}/%05d.jpg\" % (frame_id)\n ),\n )\n for frame_id in frame_ids[:: self.sample_rate]\n ]\n\n if self.rm_unannotated:\n # Eliminate the frames that have not been annotated\n valid_frame_ids = [\n i * segment_loader.ann_every\n for i, annot in enumerate(segment_loader.frame_annots)\n if annot is not None and None not in annot\n ]\n frames = [f for f in frames if f.frame_idx in valid_frame_ids]\n\n video = VOSVideo(video_name, video_idx, frames)\n return video, segment_loader\n\n def __len__(self):\n return len(self.video_names)\n" }, { "path": "auto-seg/submodules/segment-anything-2/training/dataset/vos_sampler.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport random\nfrom dataclasses import dataclass\nfrom typing import List\n\nfrom training.dataset.vos_segment_loader import LazySegments\n\nMAX_RETRIES = 1000\n\n\n@dataclass\nclass SampledFramesAndObjects:\n frames: List[int]\n object_ids: List[int]\n\n\nclass VOSSampler:\n def __init__(self, sort_frames=True):\n # frames are ordered by frame id when sort_frames is True\n self.sort_frames = sort_frames\n\n def sample(self, video):\n raise NotImplementedError()\n\n\nclass RandomUniformSampler(VOSSampler):\n def __init__(\n self,\n num_frames,\n max_num_objects,\n reverse_time_prob=0.0,\n ):\n self.num_frames = num_frames\n self.max_num_objects = max_num_objects\n self.reverse_time_prob = reverse_time_prob\n\n def sample(self, video, segment_loader, epoch=None):\n\n for retry in range(MAX_RETRIES):\n if len(video.frames) < self.num_frames:\n raise Exception(\n f\"Cannot sample {self.num_frames} frames from video {video.video_name} as it only has {len(video.frames)} annotated frames.\"\n )\n start = random.randrange(0, len(video.frames) - self.num_frames + 1)\n frames = [video.frames[start + step] for step in range(self.num_frames)]\n if random.uniform(0, 1) < self.reverse_time_prob:\n # Reverse time\n frames = frames[::-1]\n\n # Get first frame object ids\n visible_object_ids = []\n loaded_segms = segment_loader.load(frames[0].frame_idx)\n if isinstance(loaded_segms, LazySegments):\n # LazySegments for SA1BRawDataset\n visible_object_ids = list(loaded_segms.keys())\n else:\n for object_id, segment in segment_loader.load(\n frames[0].frame_idx\n ).items():\n if segment.sum():\n visible_object_ids.append(object_id)\n\n # First frame needs to have at least a target to track\n if len(visible_object_ids) > 0:\n break\n if retry >= MAX_RETRIES - 1:\n raise Exception(\"No visible objects\")\n\n object_ids = random.sample(\n visible_object_ids,\n min(len(visible_object_ids), self.max_num_objects),\n )\n return SampledFramesAndObjects(frames=frames, object_ids=object_ids)\n\n\nclass EvalSampler(VOSSampler):\n \"\"\"\n VOS Sampler for evaluation: sampling all the frames and all the objects in a video\n \"\"\"\n\n def __init__(\n self,\n ):\n super().__init__()\n\n def sample(self, video, segment_loader, epoch=None):\n \"\"\"\n Sampling all the frames and all the objects\n \"\"\"\n if self.sort_frames:\n # ordered by frame id\n frames = sorted(video.frames, key=lambda x: x.frame_idx)\n else:\n # use the original order\n frames = video.frames\n object_ids = segment_loader.load(frames[0].frame_idx).keys()\n if len(object_ids) == 0:\n raise Exception(\"First frame of the video has no objects\")\n\n return SampledFramesAndObjects(frames=frames, object_ids=object_ids)\n" }, { "path": "auto-seg/submodules/segment-anything-2/training/dataset/vos_segment_loader.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport glob\nimport json\nimport os\n\nimport numpy as np\nimport pandas as pd\nimport torch\n\nfrom PIL import Image as PILImage\n\ntry:\n from pycocotools import mask as mask_utils\nexcept:\n pass\n\n\nclass JSONSegmentLoader:\n def __init__(self, video_json_path, ann_every=1, frames_fps=24, valid_obj_ids=None):\n # Annotations in the json are provided every ann_every th frame\n self.ann_every = ann_every\n # Ids of the objects to consider when sampling this video\n self.valid_obj_ids = valid_obj_ids\n with open(video_json_path, \"r\") as f:\n data = json.load(f)\n if isinstance(data, list):\n self.frame_annots = data\n elif isinstance(data, dict):\n masklet_field_name = \"masklet\" if \"masklet\" in data else \"masks\"\n self.frame_annots = data[masklet_field_name]\n if \"fps\" in data:\n if isinstance(data[\"fps\"], list):\n annotations_fps = int(data[\"fps\"][0])\n else:\n annotations_fps = int(data[\"fps\"])\n assert frames_fps % annotations_fps == 0\n self.ann_every = frames_fps // annotations_fps\n else:\n raise NotImplementedError\n\n def load(self, frame_id, obj_ids=None):\n assert frame_id % self.ann_every == 0\n rle_mask = self.frame_annots[frame_id // self.ann_every]\n\n valid_objs_ids = set(range(len(rle_mask)))\n if self.valid_obj_ids is not None:\n # Remove the masklets that have been filtered out for this video\n valid_objs_ids &= set(self.valid_obj_ids)\n if obj_ids is not None:\n # Only keep the objects that have been sampled\n valid_objs_ids &= set(obj_ids)\n valid_objs_ids = sorted(list(valid_objs_ids))\n\n # Construct rle_masks_filtered that only contains the rle masks we are interested in\n id_2_idx = {}\n rle_mask_filtered = []\n for obj_id in valid_objs_ids:\n if rle_mask[obj_id] is not None:\n id_2_idx[obj_id] = len(rle_mask_filtered)\n rle_mask_filtered.append(rle_mask[obj_id])\n else:\n id_2_idx[obj_id] = None\n\n # Decode the masks\n raw_segments = torch.from_numpy(mask_utils.decode(rle_mask_filtered)).permute(\n 2, 0, 1\n ) # （num_obj, h, w）\n segments = {}\n for obj_id in valid_objs_ids:\n if id_2_idx[obj_id] is None:\n segments[obj_id] = None\n else:\n idx = id_2_idx[obj_id]\n segments[obj_id] = raw_segments[idx]\n return segments\n\n def get_valid_obj_frames_ids(self, num_frames_min=None):\n # For each object, find all the frames with a valid (not None) mask\n num_objects = len(self.frame_annots[0])\n\n # The result dict associates each obj_id with the id of its valid frames\n res = {obj_id: [] for obj_id in range(num_objects)}\n\n for annot_idx, annot in enumerate(self.frame_annots):\n for obj_id in range(num_objects):\n if annot[obj_id] is not None:\n res[obj_id].append(int(annot_idx * self.ann_every))\n\n if num_frames_min is not None:\n # Remove masklets that have less than num_frames_min valid masks\n for obj_id, valid_frames in list(res.items()):\n if len(valid_frames) < num_frames_min:\n res.pop(obj_id)\n\n return res\n\n\nclass PalettisedPNGSegmentLoader:\n def __init__(self, video_png_root):\n \"\"\"\n SegmentLoader for datasets with masks stored as palettised PNGs.\n video_png_root: the folder contains all the masks stored in png\n \"\"\"\n self.video_png_root = video_png_root\n # build a mapping from frame id to their PNG mask path\n # note that in some datasets, the PNG paths could have more\n # than 5 digits, e.g. \"00000000.png\" instead of \"00000.png\"\n png_filenames = os.listdir(self.video_png_root)\n self.frame_id_to_png_filename = {}\n for filename in png_filenames:\n frame_id, _ = os.path.splitext(filename)\n self.frame_id_to_png_filename[int(frame_id)] = filename\n\n def load(self, frame_id):\n \"\"\"\n load the single palettised mask from the disk (path: f'{self.video_png_root}/{frame_id:05d}.png')\n Args:\n frame_id: int, define the mask path\n Return:\n binary_segments: dict\n \"\"\"\n # check the path\n mask_path = os.path.join(\n self.video_png_root, self.frame_id_to_png_filename[frame_id]\n )\n\n # load the mask\n masks = PILImage.open(mask_path).convert(\"P\")\n masks = np.array(masks)\n\n object_id = pd.unique(masks.flatten())\n object_id = object_id[object_id != 0] # remove background (0)\n\n # convert into N binary segmentation masks\n binary_segments = {}\n for i in object_id:\n bs = masks == i\n binary_segments[i] = torch.from_numpy(bs)\n\n return binary_segments\n\n def __len__(self):\n return\n\n\nclass MultiplePNGSegmentLoader:\n def __init__(self, video_png_root, single_object_mode=False):\n \"\"\"\n video_png_root: the folder contains all the masks stored in png\n single_object_mode: whether to load only a single object at a time\n \"\"\"\n self.video_png_root = video_png_root\n self.single_object_mode = single_object_mode\n # read a mask to know the resolution of the video\n if self.single_object_mode:\n tmp_mask_path = glob.glob(os.path.join(video_png_root, \"*.png\"))[0]\n else:\n tmp_mask_path = glob.glob(os.path.join(video_png_root, \"*\", \"*.png\"))[0]\n tmp_mask = np.array(PILImage.open(tmp_mask_path))\n self.H = tmp_mask.shape[0]\n self.W = tmp_mask.shape[1]\n if self.single_object_mode:\n self.obj_id = (\n int(video_png_root.split(\"/\")[-1]) + 1\n ) # offset by 1 as bg is 0\n else:\n self.obj_id = None\n\n def load(self, frame_id):\n if self.single_object_mode:\n return self._load_single_png(frame_id)\n else:\n return self._load_multiple_pngs(frame_id)\n\n def _load_single_png(self, frame_id):\n \"\"\"\n load single png from the disk (path: f'{self.obj_id}/{frame_id:05d}.png')\n Args:\n frame_id: int, define the mask path\n Return:\n binary_segments: dict\n \"\"\"\n mask_path = os.path.join(self.video_png_root, f\"{frame_id:05d}.png\")\n binary_segments = {}\n\n if os.path.exists(mask_path):\n mask = np.array(PILImage.open(mask_path))\n else:\n # if png doesn't exist, empty mask\n mask = np.zeros((self.H, self.W), dtype=bool)\n binary_segments[self.obj_id] = torch.from_numpy(mask > 0)\n return binary_segments\n\n def _load_multiple_pngs(self, frame_id):\n \"\"\"\n load multiple png masks from the disk (path: f'{obj_id}/{frame_id:05d}.png')\n Args:\n frame_id: int, define the mask path\n Return:\n binary_segments: dict\n \"\"\"\n # get the path\n all_objects = sorted(glob.glob(os.path.join(self.video_png_root, \"*\")))\n num_objects = len(all_objects)\n assert num_objects > 0\n\n # load the masks\n binary_segments = {}\n for obj_folder in all_objects:\n # obj_folder is {video_name}/{obj_id}, obj_id is specified by the name of the folder\n obj_id = int(obj_folder.split(\"/\")[-1])\n obj_id = obj_id + 1 # offset 1 as bg is 0\n mask_path = os.path.join(obj_folder, f\"{frame_id:05d}.png\")\n if os.path.exists(mask_path):\n mask = np.array(PILImage.open(mask_path))\n else:\n mask = np.zeros((self.H, self.W), dtype=bool)\n binary_segments[obj_id] = torch.from_numpy(mask > 0)\n\n return binary_segments\n\n def __len__(self):\n return\n\n\nclass LazySegments:\n \"\"\"\n Only decodes segments that are actually used.\n \"\"\"\n\n def __init__(self):\n self.segments = {}\n self.cache = {}\n\n def __setitem__(self, key, item):\n self.segments[key] = item\n\n def __getitem__(self, key):\n if key in self.cache:\n return self.cache[key]\n rle = self.segments[key]\n mask = torch.from_numpy(mask_utils.decode([rle])).permute(2, 0, 1)[0]\n self.cache[key] = mask\n return mask\n\n def __contains__(self, key):\n return key in self.segments\n\n def __len__(self):\n return len(self.segments)\n\n def keys(self):\n return self.segments.keys()\n\n\nclass SA1BSegmentLoader:\n def __init__(\n self,\n video_mask_path,\n mask_area_frac_thresh=1.1,\n video_frame_path=None,\n uncertain_iou=-1,\n ):\n with open(video_mask_path, \"r\") as f:\n self.frame_annots = json.load(f)\n\n if mask_area_frac_thresh <= 1.0:\n # Lazily read frame\n orig_w, orig_h = PILImage.open(video_frame_path).size\n area = orig_w * orig_h\n\n self.frame_annots = self.frame_annots[\"annotations\"]\n\n rle_masks = []\n for frame_annot in self.frame_annots:\n if not frame_annot[\"area\"] > 0:\n continue\n if (\"uncertain_iou\" in frame_annot) and (\n frame_annot[\"uncertain_iou\"] < uncertain_iou\n ):\n # uncertain_iou is stability score\n continue\n if (\n mask_area_frac_thresh <= 1.0\n and (frame_annot[\"area\"] / area) >= mask_area_frac_thresh\n ):\n continue\n rle_masks.append(frame_annot[\"segmentation\"])\n\n self.segments = LazySegments()\n for i, rle in enumerate(rle_masks):\n self.segments[i] = rle\n\n def load(self, frame_idx):\n return self.segments\n" }, { "path": "auto-seg/submodules/segment-anything-2/training/loss_fns.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nfrom collections import defaultdict\nfrom typing import Dict, List\n\nimport torch\nimport torch.distributed\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom training.trainer import CORE_LOSS_KEY\n\nfrom training.utils.distributed import get_world_size, is_dist_avail_and_initialized\n\n\ndef dice_loss(inputs, targets, num_objects, loss_on_multimask=False):\n \"\"\"\n Compute the DICE loss, similar to generalized IOU for masks\n Args:\n inputs: A float tensor of arbitrary shape.\n The predictions for each example.\n targets: A float tensor with the same shape as inputs. Stores the binary\n classification label for each element in inputs\n (0 for the negative class and 1 for the positive class).\n num_objects: Number of objects in the batch\n loss_on_multimask: True if multimask prediction is enabled\n Returns:\n Dice loss tensor\n \"\"\"\n inputs = inputs.sigmoid()\n if loss_on_multimask:\n # inputs and targets are [N, M, H, W] where M corresponds to multiple predicted masks\n assert inputs.dim() == 4 and targets.dim() == 4\n # flatten spatial dimension while keeping multimask channel dimension\n inputs = inputs.flatten(2)\n targets = targets.flatten(2)\n numerator = 2 * (inputs * targets).sum(-1)\n else:\n inputs = inputs.flatten(1)\n numerator = 2 * (inputs * targets).sum(1)\n denominator = inputs.sum(-1) + targets.sum(-1)\n loss = 1 - (numerator + 1) / (denominator + 1)\n if loss_on_multimask:\n return loss / num_objects\n return loss.sum() / num_objects\n\n\ndef sigmoid_focal_loss(\n inputs,\n targets,\n num_objects,\n alpha: float = 0.25,\n gamma: float = 2,\n loss_on_multimask=False,\n):\n \"\"\"\n Loss used in RetinaNet for dense detection: https://arxiv.org/abs/1708.02002.\n Args:\n inputs: A float tensor of arbitrary shape.\n The predictions for each example.\n targets: A float tensor with the same shape as inputs. Stores the binary\n classification label for each element in inputs\n (0 for the negative class and 1 for the positive class).\n num_objects: Number of objects in the batch\n alpha: (optional) Weighting factor in range (0,1) to balance\n positive vs negative examples. Default = -1 (no weighting).\n gamma: Exponent of the modulating factor (1 - p_t) to\n balance easy vs hard examples.\n loss_on_multimask: True if multimask prediction is enabled\n Returns:\n focal loss tensor\n \"\"\"\n prob = inputs.sigmoid()\n ce_loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction=\"none\")\n p_t = prob * targets + (1 - prob) * (1 - targets)\n loss = ce_loss * ((1 - p_t) ** gamma)\n\n if alpha >= 0:\n alpha_t = alpha * targets + (1 - alpha) * (1 - targets)\n loss = alpha_t * loss\n\n if loss_on_multimask:\n # loss is [N, M, H, W] where M corresponds to multiple predicted masks\n assert loss.dim() == 4\n return loss.flatten(2).mean(-1) / num_objects # average over spatial dims\n return loss.mean(1).sum() / num_objects\n\n\ndef iou_loss(\n inputs, targets, pred_ious, num_objects, loss_on_multimask=False, use_l1_loss=False\n):\n \"\"\"\n Args:\n inputs: A float tensor of arbitrary shape.\n The predictions for each example.\n targets: A float tensor with the same shape as inputs. Stores the binary\n classification label for each element in inputs\n (0 for the negative class and 1 for the positive class).\n pred_ious: A float tensor containing the predicted IoUs scores per mask\n num_objects: Number of objects in the batch\n loss_on_multimask: True if multimask prediction is enabled\n use_l1_loss: Whether to use L1 loss is used instead of MSE loss\n Returns:\n IoU loss tensor\n \"\"\"\n assert inputs.dim() == 4 and targets.dim() == 4\n pred_mask = inputs.flatten(2) > 0\n gt_mask = targets.flatten(2) > 0\n area_i = torch.sum(pred_mask & gt_mask, dim=-1).float()\n area_u = torch.sum(pred_mask | gt_mask, dim=-1).float()\n actual_ious = area_i / torch.clamp(area_u, min=1.0)\n\n if use_l1_loss:\n loss = F.l1_loss(pred_ious, actual_ious, reduction=\"none\")\n else:\n loss = F.mse_loss(pred_ious, actual_ious, reduction=\"none\")\n if loss_on_multimask:\n return loss / num_objects\n return loss.sum() / num_objects\n\n\nclass MultiStepMultiMasksAndIous(nn.Module):\n def __init__(\n self,\n weight_dict,\n focal_alpha=0.25,\n focal_gamma=2,\n supervise_all_iou=False,\n iou_use_l1_loss=False,\n pred_obj_scores=False,\n focal_gamma_obj_score=0.0,\n focal_alpha_obj_score=-1,\n ):\n \"\"\"\n This class computes the multi-step multi-mask and IoU losses.\n Args:\n weight_dict: dict containing weights for focal, dice, iou losses\n focal_alpha: alpha for sigmoid focal loss\n focal_gamma: gamma for sigmoid focal loss\n supervise_all_iou: if True, back-prop iou losses for all predicted masks\n iou_use_l1_loss: use L1 loss instead of MSE loss for iou\n pred_obj_scores: if True, compute loss for object scores\n focal_gamma_obj_score: gamma for sigmoid focal loss on object scores\n focal_alpha_obj_score: alpha for sigmoid focal loss on object scores\n \"\"\"\n\n super().__init__()\n self.weight_dict = weight_dict\n self.focal_alpha = focal_alpha\n self.focal_gamma = focal_gamma\n assert \"loss_mask\" in self.weight_dict\n assert \"loss_dice\" in self.weight_dict\n assert \"loss_iou\" in self.weight_dict\n if \"loss_class\" not in self.weight_dict:\n self.weight_dict[\"loss_class\"] = 0.0\n\n self.focal_alpha_obj_score = focal_alpha_obj_score\n self.focal_gamma_obj_score = focal_gamma_obj_score\n self.supervise_all_iou = supervise_all_iou\n self.iou_use_l1_loss = iou_use_l1_loss\n self.pred_obj_scores = pred_obj_scores\n\n def forward(self, outs_batch: List[Dict], targets_batch: torch.Tensor):\n assert len(outs_batch) == len(targets_batch)\n num_objects = torch.tensor(\n (targets_batch.shape[1]), device=targets_batch.device, dtype=torch.float\n ) # Number of objects is fixed within a batch\n if is_dist_avail_and_initialized():\n torch.distributed.all_reduce(num_objects)\n num_objects = torch.clamp(num_objects / get_world_size(), min=1).item()\n\n losses = defaultdict(int)\n for outs, targets in zip(outs_batch, targets_batch):\n cur_losses = self._forward(outs, targets, num_objects)\n for k, v in cur_losses.items():\n losses[k] += v\n\n return losses\n\n def _forward(self, outputs: Dict, targets: torch.Tensor, num_objects):\n \"\"\"\n Compute the losses related to the masks: the focal loss and the dice loss.\n and also the MAE or MSE loss between predicted IoUs and actual IoUs.\n\n Here \"multistep_pred_multimasks_high_res\" is a list of multimasks (tensors\n of shape [N, M, H, W], where M could be 1 or larger, corresponding to\n one or multiple predicted masks from a click.\n\n We back-propagate focal, dice losses only on the prediction channel\n with the lowest focal+dice loss between predicted mask and ground-truth.\n If `supervise_all_iou` is True, we backpropagate ious losses for all predicted masks.\n \"\"\"\n\n target_masks = targets.unsqueeze(1).float()\n assert target_masks.dim() == 4 # [N, 1, H, W]\n src_masks_list = outputs[\"multistep_pred_multimasks_high_res\"]\n ious_list = outputs[\"multistep_pred_ious\"]\n object_score_logits_list = outputs[\"multistep_object_score_logits\"]\n\n assert len(src_masks_list) == len(ious_list)\n assert len(object_score_logits_list) == len(ious_list)\n\n # accumulate the loss over prediction steps\n losses = {\"loss_mask\": 0, \"loss_dice\": 0, \"loss_iou\": 0, \"loss_class\": 0}\n for src_masks, ious, object_score_logits in zip(\n src_masks_list, ious_list, object_score_logits_list\n ):\n self._update_losses(\n losses, src_masks, target_masks, ious, num_objects, object_score_logits\n )\n losses[CORE_LOSS_KEY] = self.reduce_loss(losses)\n return losses\n\n def _update_losses(\n self, losses, src_masks, target_masks, ious, num_objects, object_score_logits\n ):\n target_masks = target_masks.expand_as(src_masks)\n # get focal, dice and iou loss on all output masks in a prediction step\n loss_multimask = sigmoid_focal_loss(\n src_masks,\n target_masks,\n num_objects,\n alpha=self.focal_alpha,\n gamma=self.focal_gamma,\n loss_on_multimask=True,\n )\n loss_multidice = dice_loss(\n src_masks, target_masks, num_objects, loss_on_multimask=True\n )\n if not self.pred_obj_scores:\n loss_class = torch.tensor(\n 0.0, dtype=loss_multimask.dtype, device=loss_multimask.device\n )\n target_obj = torch.ones(\n loss_multimask.shape[0],\n 1,\n dtype=loss_multimask.dtype,\n device=loss_multimask.device,\n )\n else:\n target_obj = torch.any((target_masks[:, 0] > 0).flatten(1), dim=-1)[\n ..., None\n ].float()\n loss_class = sigmoid_focal_loss(\n object_score_logits,\n target_obj,\n num_objects,\n alpha=self.focal_alpha_obj_score,\n gamma=self.focal_gamma_obj_score,\n )\n\n loss_multiiou = iou_loss(\n src_masks,\n target_masks,\n ious,\n num_objects,\n loss_on_multimask=True,\n use_l1_loss=self.iou_use_l1_loss,\n )\n assert loss_multimask.dim() == 2\n assert loss_multidice.dim() == 2\n assert loss_multiiou.dim() == 2\n if loss_multimask.size(1) > 1:\n # take the mask indices with the smallest focal + dice loss for back propagation\n loss_combo = (\n loss_multimask * self.weight_dict[\"loss_mask\"]\n + loss_multidice * self.weight_dict[\"loss_dice\"]\n )\n best_loss_inds = torch.argmin(loss_combo, dim=-1)\n batch_inds = torch.arange(loss_combo.size(0), device=loss_combo.device)\n loss_mask = loss_multimask[batch_inds, best_loss_inds].unsqueeze(1)\n loss_dice = loss_multidice[batch_inds, best_loss_inds].unsqueeze(1)\n # calculate the iou prediction and slot losses only in the index\n # with the minimum loss for each mask (to be consistent w/ SAM)\n if self.supervise_all_iou:\n loss_iou = loss_multiiou.mean(dim=-1).unsqueeze(1)\n else:\n loss_iou = loss_multiiou[batch_inds, best_loss_inds].unsqueeze(1)\n else:\n loss_mask = loss_multimask\n loss_dice = loss_multidice\n loss_iou = loss_multiiou\n\n # backprop focal, dice and iou loss only if obj present\n loss_mask = loss_mask * target_obj\n loss_dice = loss_dice * target_obj\n loss_iou = loss_iou * target_obj\n\n # sum over batch dimension (note that the losses are already divided by num_objects)\n losses[\"loss_mask\"] += loss_mask.sum()\n losses[\"loss_dice\"] += loss_dice.sum()\n losses[\"loss_iou\"] += loss_iou.sum()\n losses[\"loss_class\"] += loss_class\n\n def reduce_loss(self, losses):\n reduced_loss = 0.0\n for loss_key, weight in self.weight_dict.items():\n if loss_key not in losses:\n raise ValueError(f\"{type(self)} doesn't compute {loss_key}\")\n if weight != 0:\n reduced_loss += losses[loss_key] * weight\n\n return reduced_loss\n" }, { "path": "auto-seg/submodules/segment-anything-2/training/model/__init__.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n" }, { "path": "auto-seg/submodules/segment-anything-2/training/optimizer.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport fnmatch\nimport inspect\nimport itertools\nimport logging\nimport types\nfrom typing import (\n Any,\n Callable,\n Dict,\n Iterable,\n List,\n Mapping,\n Optional,\n Set,\n Tuple,\n Type,\n Union,\n)\n\nimport hydra\n\nimport torch\nimport torch.nn as nn\nfrom omegaconf import DictConfig\nfrom torch import Tensor\n\n\nclass Optimizer:\n def __init__(self, optimizer, schedulers=None) -> None:\n self.optimizer = optimizer\n self.schedulers = schedulers\n self._validate_optimizer_schedulers()\n self.step_schedulers(0.0, 0)\n\n def _validate_optimizer_schedulers(self):\n if self.schedulers is None:\n return\n for _, set_of_schedulers in enumerate(self.schedulers):\n for option, _ in set_of_schedulers.items():\n assert option in self.optimizer.defaults, (\n \"Optimizer option \"\n f\"{option} not found in {self.optimizer}. Valid options are \"\n f\"{self.optimizer.defaults.keys()}\"\n )\n\n def step_schedulers(self, where: float, step: int) -> None:\n if self.schedulers is None:\n return\n for i, param_group in enumerate(self.optimizer.param_groups):\n for option, scheduler in self.schedulers[i].items():\n if \"step\" in inspect.signature(scheduler.__call__).parameters:\n new_value = scheduler(step=step, where=where)\n elif (\n hasattr(scheduler, \"scheduler\")\n and \"step\"\n in inspect.signature(scheduler.scheduler.__call__).parameters\n ):\n # To handle ValueScaler wrappers\n new_value = scheduler(step=step, where=where)\n else:\n new_value = scheduler(where)\n param_group[option] = new_value\n\n def step(self, where, step, closure=None):\n self.step_schedulers(where, step)\n return self.optimizer.step(closure)\n\n def zero_grad(self, *args, **kwargs):\n return self.optimizer.zero_grad(*args, **kwargs)\n\n\ndef set_default_parameters(\n scheduler_cfgs: List[DictConfig], all_parameter_names: Set[str]\n) -> None:\n \"\"\"Set up the \"default\" scheduler with the right parameters.\n\n Args:\n scheduler_cgfs: A list of scheduler configs, where each scheduler also\n specifies which parameters it applies to, based on the names of parameters\n or the class of the modules. At most one scheduler is allowed to skip this\n specification, which is used as a \"default\" specification for any remaining\n parameters.\n all_parameter_names: Names of all the parameters to consider.\n \"\"\"\n constraints = [\n scheduler_cfg.parameter_names\n for scheduler_cfg in scheduler_cfgs\n if scheduler_cfg.parameter_names is not None\n ]\n if len(constraints) == 0:\n default_params = set(all_parameter_names)\n else:\n default_params = all_parameter_names - set.union(*constraints)\n default_count = 0\n for scheduler_cfg in scheduler_cfgs:\n if scheduler_cfg.parameter_names is None:\n scheduler_cfg.parameter_names = default_params\n default_count += 1\n assert default_count <= 1, \"Only one scheduler per option can be default\"\n if default_count == 0:\n # No default scheduler specified, add a default, but without any scheduler\n # for that option\n scheduler_cfgs.append({\"parameter_names\": default_params})\n\n\ndef name_constraints_to_parameters(\n param_constraints: List[Set[str]], named_parameters: Dict[str, Tensor]\n) -> List[torch.nn.Parameter]:\n \"\"\"Return parameters which match the intersection of parameter constraints.\n\n Note that this returns the parameters themselves, not their names.\n\n Args:\n param_constraints: A list, with each element being a set of allowed parameters.\n named_parameters: Mapping from a parameter name to the parameter itself.\n\n Returns:\n A list containing the parameters which overlap with _each_ constraint set from\n param_constraints.\n \"\"\"\n matching_names = set.intersection(*param_constraints)\n return [value for name, value in named_parameters.items() if name in matching_names]\n\n\ndef map_scheduler_cfgs_to_param_groups(\n all_scheduler_cfgs: Iterable[List[Dict]],\n named_parameters: Dict[str, Tensor],\n) -> Tuple[List[Dict[Any, Any]], List[Dict[str, List[torch.nn.Parameter]]]]:\n \"\"\"Produce parameter groups corresponding to all the scheduler configs.\n\n Takes all the scheduler configs, each of which applies to a specific optimizer\n option (like \"lr\" or \"weight_decay\") and has a set of parameter names which it\n applies to, and produces a final set of param groups where each param group\n covers all the options which apply to a particular set of parameters.\n\n Args:\n all_scheduler_cfgs: All the scheduler configs covering every option.\n named_parameters: Mapping from a parameter name to the parameter itself.\n Returns:\n Tuple of lists of schedulers and param_groups, where schedulers[i]\n applies to param_groups[i].\n \"\"\"\n\n scheduler_cfgs_per_param_group = itertools.product(*all_scheduler_cfgs)\n schedulers = []\n param_groups = []\n for scheduler_cfgs in scheduler_cfgs_per_param_group:\n param_constraints = [\n scheduler_cfg[\"parameter_names\"] for scheduler_cfg in scheduler_cfgs\n ]\n matching_parameters = name_constraints_to_parameters(\n param_constraints, named_parameters\n )\n if len(matching_parameters) == 0: # If no overlap of parameters, skip\n continue\n schedulers_for_group = {\n scheduler_cfg[\"option\"]: scheduler_cfg[\"scheduler\"]\n for scheduler_cfg in scheduler_cfgs\n if \"option\" in scheduler_cfg\n }\n schedulers.append(schedulers_for_group)\n param_groups.append({\"params\": matching_parameters})\n return schedulers, param_groups\n\n\ndef validate_param_group_params(param_groups: List[Dict], model: nn.Module):\n \"\"\"Check that the param groups are non-overlapping and cover all the parameters.\n\n Args:\n param_groups: List of all param groups\n model: Model to validate against. The check ensures that all the model\n parameters are part of param_groups\n \"\"\"\n for pg in param_groups:\n # no param should be repeated within a group\n assert len(pg[\"params\"]) == len(set(pg[\"params\"]))\n parameters = [set(param_group[\"params\"]) for param_group in param_groups]\n model_parameters = {parameter for _, parameter in model.named_parameters()}\n for p1, p2 in itertools.permutations(parameters, 2):\n assert p1.isdisjoint(p2), \"Scheduler generated param_groups should be disjoint\"\n assert set.union(*parameters) == model_parameters, (\n \"Scheduler generated param_groups must include all parameters of the model.\"\n f\" Found {len(set.union(*parameters))} params whereas model has\"\n f\" {len(model_parameters)} params\"\n )\n\n\ndef unix_module_cls_pattern_to_parameter_names(\n filter_module_cls_names: List[str],\n module_cls_to_param_names: Dict[Type, str],\n) -> Union[None, Set[str]]:\n \"\"\"Returns param names which pass the filters specified in filter_module_cls_names.\n\n Args:\n filter_module_cls_names: A list of filter strings containing class names, like\n [\"torch.nn.LayerNorm\", \"torch.nn.BatchNorm2d\"]\n module_cls_to_param_names: Mapping from module classes to the parameter names\n they contain. See `get_module_cls_to_param_names`.\n \"\"\"\n if filter_module_cls_names is None:\n return set()\n allowed_parameter_names = []\n for module_cls_name in filter_module_cls_names:\n module_cls = hydra.utils.get_class(module_cls_name)\n if module_cls not in module_cls_to_param_names:\n raise AssertionError(\n f\"module_cls_name {module_cls_name} does not \"\n \"match any classes in the model\"\n )\n matching_parameters = module_cls_to_param_names[module_cls]\n assert (\n len(matching_parameters) > 0\n ), f\"module_cls_name {module_cls_name} does not contain any parameters in the model\"\n logging.info(\n f\"Matches for module_cls_name [{module_cls_name}]: {matching_parameters} \"\n )\n allowed_parameter_names.append(matching_parameters)\n return set.union(*allowed_parameter_names)\n\n\ndef unix_param_pattern_to_parameter_names(\n filter_param_names: Optional[List[str]],\n parameter_names: Dict[str, torch.Tensor],\n) -> Union[None, Set[str]]:\n \"\"\"Returns param names which pass the filters specified in filter_param_names.\n\n Args:\n filter_param_names: A list of unix-style filter strings with optional\n wildcards, like [\"block.2.*\", \"block.2.linear.weight\"]\n module_cls_to_param_names: Mapping from module classes to the parameter names\n they contain. See `get_module_cls_to_param_names`.\n \"\"\"\n\n if filter_param_names is None:\n return set()\n allowed_parameter_names = []\n for param_name in filter_param_names:\n matching_parameters = set(fnmatch.filter(parameter_names, param_name))\n assert (\n len(matching_parameters) >= 1\n ), f\"param_name {param_name} does not match any parameters in the model\"\n logging.info(f\"Matches for param_name [{param_name}]: {matching_parameters}\")\n allowed_parameter_names.append(matching_parameters)\n return set.union(*allowed_parameter_names)\n\n\ndef _unix_pattern_to_parameter_names(\n scheduler_cfg: DictConfig,\n parameter_names: Set[str],\n module_cls_to_param_names: Dict[Type, str],\n) -> Union[None, Set[str]]:\n \"\"\"Returns param names which pass the filters specified in scheduler_cfg.\n\n Args:\n scheduler_cfg: The config for the scheduler\n parameter_names: The set of all parameter names which will be filtered\n \"\"\"\n if \"param_names\" not in scheduler_cfg and \"module_cls_names\" not in scheduler_cfg:\n return None\n return unix_param_pattern_to_parameter_names(\n scheduler_cfg.get(\"param_names\"), parameter_names\n ).union(\n unix_module_cls_pattern_to_parameter_names(\n scheduler_cfg.get(\"module_cls_names\"), module_cls_to_param_names\n )\n )\n\n\ndef get_module_cls_to_param_names(\n model: nn.Module, param_allowlist: Set[str] = None\n) -> Dict[Type, str]:\n \"\"\"Produce a mapping from all the modules classes to the names of parames they own.\n\n Only counts a parameter as part of the immediate parent module, i.e. recursive\n parents do not count.\n\n Args:\n model: Model to iterate over\n param_allowlist: If specified, only these param names will be processed\n \"\"\"\n\n module_cls_to_params = {}\n for module_name, module in model.named_modules():\n module_cls = type(module)\n module_cls_to_params.setdefault(module_cls, set())\n for param_name, _ in module.named_parameters(recurse=False):\n full_param_name = get_full_parameter_name(module_name, param_name)\n if param_allowlist is None or full_param_name in param_allowlist:\n module_cls_to_params[module_cls].add(full_param_name)\n return module_cls_to_params\n\n\ndef construct_optimizer(\n model: torch.nn.Module,\n optimizer_conf: Any,\n options_conf: Mapping[str, List] = None,\n param_group_modifiers_conf: List[Callable] = None,\n param_allowlist: Optional[Set[str]] = None,\n validate_param_groups=True,\n) -> Optimizer:\n \"\"\"\n Constructs a stochastic gradient descent or ADAM (or ADAMw) optimizer\n with momentum. i.e, constructs a torch.optim.Optimizer with zero-weight decay\n Batchnorm and/or no-update 1-D parameters support, based on the config.\n\n Supports wrapping the optimizer with Layer-wise Adaptive Rate Scaling\n (LARS): https://arxiv.org/abs/1708.03888\n\n Args:\n model: model to perform stochastic gradient descent\n optimization or ADAM optimization.\n optimizer_conf: Hydra config consisting a partial torch optimizer like SGD or\n ADAM, still missing the params argument which this function provides to\n produce the final optimizer\n param_group_modifiers_conf: Optional user specified functions which can modify\n the final scheduler configs before the optimizer's param groups are built\n param_allowlist: The parameters to optimize. Parameters which are not part of\n this allowlist will be skipped.\n validate_param_groups: If enabled, valides that the produced param_groups don't\n overlap and cover all the model parameters.\n \"\"\"\n if param_allowlist is None:\n param_allowlist = {name for name, _ in model.named_parameters()}\n\n named_parameters = {\n name: param\n for name, param in model.named_parameters()\n if name in param_allowlist\n }\n\n if not options_conf:\n optimizer = hydra.utils.instantiate(optimizer_conf, named_parameters.values())\n return Optimizer(optimizer)\n\n all_parameter_names = {\n name for name, _ in model.named_parameters() if name in param_allowlist\n }\n module_cls_to_all_param_names = get_module_cls_to_param_names(\n model, param_allowlist\n )\n\n scheduler_cfgs_per_option = hydra.utils.instantiate(options_conf)\n all_scheduler_cfgs = []\n for option, scheduler_cfgs in scheduler_cfgs_per_option.items():\n for config in scheduler_cfgs:\n config.option = option\n config.parameter_names = _unix_pattern_to_parameter_names(\n config, all_parameter_names, module_cls_to_all_param_names\n )\n set_default_parameters(scheduler_cfgs, all_parameter_names)\n all_scheduler_cfgs.append(scheduler_cfgs)\n\n if param_group_modifiers_conf:\n for custom_param_modifier in param_group_modifiers_conf:\n custom_param_modifier = hydra.utils.instantiate(custom_param_modifier)\n all_scheduler_cfgs = custom_param_modifier(\n scheduler_cfgs=all_scheduler_cfgs, model=model\n )\n schedulers, param_groups = map_scheduler_cfgs_to_param_groups(\n all_scheduler_cfgs, named_parameters\n )\n if validate_param_groups:\n validate_param_group_params(param_groups, model)\n optimizer = hydra.utils.instantiate(optimizer_conf, param_groups)\n return Optimizer(optimizer, schedulers)\n\n\ndef get_full_parameter_name(module_name, param_name):\n if module_name == \"\":\n return param_name\n return f\"{module_name}.{param_name}\"\n\n\nclass GradientClipper:\n \"\"\"\n Gradient clipping utils that works for DDP\n \"\"\"\n\n def __init__(self, max_norm: float = 1.0, norm_type: int = 2):\n assert isinstance(max_norm, (int, float)) or max_norm is None\n self.max_norm = max_norm if max_norm is None else float(max_norm)\n self.norm_type = norm_type\n\n def __call__(self, model: nn.Module):\n if self.max_norm is None:\n return # no-op\n\n nn.utils.clip_grad_norm_(\n model.parameters(), max_norm=self.max_norm, norm_type=self.norm_type\n )\n\n\nclass ValueScaler:\n def __init__(self, scheduler, mult_val: float):\n self.scheduler = scheduler\n self.mult_val = mult_val\n\n def __call__(self, *args, **kwargs):\n val = self.scheduler(*args, **kwargs)\n return val * self.mult_val\n\n\ndef rgetattr(obj, rattrs: str = None):\n \"\"\"\n Like getattr(), but supports dotted notation for nested objects.\n rattrs is a str of form 'attr1.attr2', returns obj.attr1.attr2\n \"\"\"\n if rattrs is None:\n return obj\n attrs = rattrs.split(\".\")\n for attr in attrs:\n obj = getattr(obj, attr)\n return obj\n\n\ndef layer_decay_param_modifier(\n scheduler_cfgs: List[List[Dict]],\n model,\n layer_decay_value: float,\n layer_decay_min: Optional[float] = None,\n apply_to: Optional[str] = None,\n overrides: List[Dict] = (),\n) -> List[List[Dict]]:\n \"\"\"\n Args\n - scheduler_cfgs: a list of omegaconf.ListConfigs.\n Each element in the list is a omegaconfg.DictConfig with the following structure\n {\n \"scheduler\": \n \"option\": possible options are \"lr\", \"weight_decay\" etc.\n \"parameter_names\": Set of str indicating param names that this scheduler applies to\n }\n - model: a model that implements a method `get_layer_id` that maps layer_name to an integer and\n and a method get_num_layers.\n Alternatively, use apply_to argument to select a specific component of the model.\n - layer_decay_value: float\n - layer_decay_min: min val for layer decay\n - apply_to: optional arg to select which component of the model to apply the the layer decay modifier to\n - overrides: to manually override lr for specific patterns. Is a list of dicts. Each dict, has keys \"pattern\", \"value\".\n Returns\n - scheduler_configs: same structure as the input, elements can be modified\n \"\"\"\n model = rgetattr(model, apply_to)\n num_layers = model.get_num_layers() + 1\n layer_decays = [\n layer_decay_value ** (num_layers - i) for i in range(num_layers + 1)\n ]\n if layer_decay_min is not None:\n layer_decays = [max(val, layer_decay_min) for val in layer_decays]\n final_scheduler_cfgs = []\n # scheduler_cfgs is a list of lists\n for scheduler_cfg_group in scheduler_cfgs:\n curr_cfg_group = []\n # scheduler_cfg_group is a list of dictionaries\n for scheduler_cfg in scheduler_cfg_group:\n if scheduler_cfg[\"option\"] != \"lr\":\n curr_cfg_group.append(scheduler_cfg)\n continue\n # Need sorted so that the list of parameter names is deterministic and consistent\n # across re-runs of this job. Else it was causing issues with loading the optimizer\n # state during a job restart (D38591759)\n parameter_names = sorted(scheduler_cfg[\"parameter_names\"])\n\n # Only want one cfg group per layer\n layer_cfg_groups = {}\n for param_name in parameter_names:\n layer_id = num_layers\n this_scale = layer_decays[layer_id]\n if param_name.startswith(apply_to):\n layer_id = model.get_layer_id(param_name)\n this_scale = layer_decays[layer_id]\n # Overrides\n for override in overrides:\n if fnmatch.fnmatchcase(param_name, override[\"pattern\"]):\n this_scale = float(override[\"value\"])\n layer_id = override[\"pattern\"]\n break\n\n if layer_id not in layer_cfg_groups:\n curr_param = {\n \"option\": scheduler_cfg[\"option\"],\n \"scheduler\": ValueScaler(\n scheduler_cfg[\"scheduler\"], this_scale\n ),\n \"parameter_names\": {param_name},\n }\n else:\n curr_param = layer_cfg_groups[layer_id]\n curr_param[\"parameter_names\"].add(param_name)\n layer_cfg_groups[layer_id] = curr_param\n\n for layer_cfg in layer_cfg_groups.values():\n curr_cfg_group.append(layer_cfg)\n\n final_scheduler_cfgs.append(curr_cfg_group)\n return final_scheduler_cfgs\n" }, { "path": "auto-seg/submodules/segment-anything-2/training/scripts/sav_frame_extraction_submitit.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\nimport argparse\nimport os\nfrom pathlib import Path\n\nimport cv2\n\nimport numpy as np\nimport submitit\nimport tqdm\n\n\ndef get_args_parser():\n parser = argparse.ArgumentParser(\n description=\"[SA-V Preprocessing] Extracting JPEG frames\",\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n )\n\n # ------------\n # DATA\n # ------------\n data_parser = parser.add_argument_group(\n title=\"SA-V dataset data root\",\n description=\"What data to load and how to process it.\",\n )\n data_parser.add_argument(\n \"--sav-vid-dir\",\n type=str,\n required=True,\n help=(\"Where to find the SAV videos\"),\n )\n data_parser.add_argument(\n \"--sav-frame-sample-rate\",\n type=int,\n default=4,\n help=\"Rate at which to sub-sample frames\",\n )\n\n # ------------\n # LAUNCH\n # ------------\n launch_parser = parser.add_argument_group(\n title=\"Cluster launch settings\",\n description=\"Number of jobs and retry settings.\",\n )\n launch_parser.add_argument(\n \"--n-jobs\",\n type=int,\n required=True,\n help=\"Shard the run over this many jobs.\",\n )\n launch_parser.add_argument(\n \"--timeout\", type=int, required=True, help=\"SLURM timeout parameter in minutes.\"\n )\n launch_parser.add_argument(\n \"--partition\", type=str, required=True, help=\"Partition to launch on.\"\n )\n launch_parser.add_argument(\n \"--account\", type=str, required=True, help=\"Partition to launch on.\"\n )\n launch_parser.add_argument(\"--qos\", type=str, required=True, help=\"QOS.\")\n\n # ------------\n # OUTPUT\n # ------------\n output_parser = parser.add_argument_group(\n title=\"Setting for results output\", description=\"Where and how to save results.\"\n )\n output_parser.add_argument(\n \"--output-dir\",\n type=str,\n required=True,\n help=(\"Where to dump the extracted jpeg frames\"),\n )\n output_parser.add_argument(\n \"--slurm-output-root-dir\",\n type=str,\n required=True,\n help=(\"Where to save slurm outputs\"),\n )\n return parser\n\n\ndef decode_video(video_path: str):\n assert os.path.exists(video_path)\n video = cv2.VideoCapture(video_path)\n video_frames = []\n while video.isOpened():\n ret, frame = video.read()\n if ret:\n video_frames.append(frame)\n else:\n break\n return video_frames\n\n\ndef extract_frames(video_path, sample_rate):\n frames = decode_video(video_path)\n return frames[::sample_rate]\n\n\ndef submitit_launch(video_paths, sample_rate, save_root):\n for path in tqdm.tqdm(video_paths):\n frames = extract_frames(path, sample_rate)\n output_folder = os.path.join(save_root, Path(path).stem)\n if not os.path.exists(output_folder):\n os.makedirs(output_folder)\n for fid, frame in enumerate(frames):\n frame_path = os.path.join(output_folder, f\"{fid*sample_rate:05d}.jpg\")\n cv2.imwrite(frame_path, frame)\n print(f\"Saved output to {save_root}\")\n\n\nif __name__ == \"__main__\":\n parser = get_args_parser()\n args = parser.parse_args()\n\n sav_vid_dir = args.sav_vid_dir\n save_root = args.output_dir\n sample_rate = args.sav_frame_sample_rate\n\n # List all SA-V videos\n mp4_files = sorted([str(p) for p in Path(sav_vid_dir).glob(\"*/*.mp4\")])\n mp4_files = np.array(mp4_files)\n chunked_mp4_files = [x.tolist() for x in np.array_split(mp4_files, args.n_jobs)]\n\n print(f\"Processing videos in: {sav_vid_dir}\")\n print(f\"Processing {len(mp4_files)} files\")\n print(f\"Beginning processing in {args.n_jobs} processes\")\n\n # Submitit params\n jobs_dir = os.path.join(args.slurm_output_root_dir, \"%j\")\n cpus_per_task = 4\n executor = submitit.AutoExecutor(folder=jobs_dir)\n executor.update_parameters(\n timeout_min=args.timeout,\n gpus_per_node=0,\n tasks_per_node=1,\n slurm_array_parallelism=args.n_jobs,\n cpus_per_task=cpus_per_task,\n slurm_partition=args.partition,\n slurm_account=args.account,\n slurm_qos=args.qos,\n )\n executor.update_parameters(slurm_srun_args=[\"-vv\", \"--cpu-bind\", \"none\"])\n\n # Launch\n jobs = []\n with executor.batch():\n for _, mp4_chunk in tqdm.tqdm(enumerate(chunked_mp4_files)):\n job = executor.submit(\n submitit_launch,\n video_paths=mp4_chunk,\n sample_rate=sample_rate,\n save_root=save_root,\n )\n jobs.append(job)\n\n for j in jobs:\n print(f\"Slurm JobID: {j.job_id}\")\n print(f\"Saving outputs to {save_root}\")\n print(f\"Slurm outputs at {args.slurm_output_root_dir}\")\n" }, { "path": "auto-seg/submodules/segment-anything-2/training/train.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport logging\nimport os\nimport random\nimport sys\nimport traceback\nfrom argparse import ArgumentParser\n\nimport submitit\nimport torch\n\nfrom hydra import compose, initialize_config_module\nfrom hydra.utils import instantiate\n\nfrom iopath.common.file_io import g_pathmgr\nfrom omegaconf import OmegaConf\n\nfrom training.utils.train_utils import makedir, register_omegaconf_resolvers\n\nos.environ[\"HYDRA_FULL_ERROR\"] = \"1\"\n\n\ndef single_proc_run(local_rank, main_port, cfg, world_size):\n \"\"\"Single GPU process\"\"\"\n os.environ[\"MASTER_ADDR\"] = \"localhost\"\n os.environ[\"MASTER_PORT\"] = str(main_port)\n os.environ[\"RANK\"] = str(local_rank)\n os.environ[\"LOCAL_RANK\"] = str(local_rank)\n os.environ[\"WORLD_SIZE\"] = str(world_size)\n try:\n register_omegaconf_resolvers()\n except Exception as e:\n logging.info(e)\n\n trainer = instantiate(cfg.trainer, _recursive_=False)\n trainer.run()\n\n\ndef single_node_runner(cfg, main_port: int):\n assert cfg.launcher.num_nodes == 1\n num_proc = cfg.launcher.gpus_per_node\n torch.multiprocessing.set_start_method(\n \"spawn\"\n ) # CUDA runtime does not support `fork`\n if num_proc == 1:\n # directly call single_proc so we can easily set breakpoints\n # mp.spawn does not let us set breakpoints\n single_proc_run(local_rank=0, main_port=main_port, cfg=cfg, world_size=num_proc)\n else:\n mp_runner = torch.multiprocessing.start_processes\n args = (main_port, cfg, num_proc)\n # Note: using \"fork\" below, \"spawn\" causes time and error regressions. Using\n # spawn changes the default multiprocessing context to spawn, which doesn't\n # interact well with the dataloaders (likely due to the use of OpenCV).\n mp_runner(single_proc_run, args=args, nprocs=num_proc, start_method=\"spawn\")\n\n\ndef format_exception(e: Exception, limit=20):\n traceback_str = \"\".join(traceback.format_tb(e.__traceback__, limit=limit))\n return f\"{type(e).__name__}: {e}\\nTraceback:\\n{traceback_str}\"\n\n\nclass SubmititRunner(submitit.helpers.Checkpointable):\n \"\"\"A callable which is passed to submitit to launch the jobs.\"\"\"\n\n def __init__(self, port, cfg):\n self.cfg = cfg\n self.port = port\n self.has_setup = False\n\n def run_trainer(self):\n job_env = submitit.JobEnvironment()\n # Need to add this again so the hydra.job.set_env PYTHONPATH\n # is also set when launching jobs.\n add_pythonpath_to_sys_path()\n os.environ[\"MASTER_ADDR\"] = job_env.hostnames[0]\n os.environ[\"MASTER_PORT\"] = str(self.port)\n os.environ[\"RANK\"] = str(job_env.global_rank)\n os.environ[\"LOCAL_RANK\"] = str(job_env.local_rank)\n os.environ[\"WORLD_SIZE\"] = str(job_env.num_tasks)\n\n register_omegaconf_resolvers()\n cfg_resolved = OmegaConf.to_container(self.cfg, resolve=False)\n cfg_resolved = OmegaConf.create(cfg_resolved)\n\n trainer = instantiate(cfg_resolved.trainer, _recursive_=False)\n trainer.run()\n\n def __call__(self):\n job_env = submitit.JobEnvironment()\n self.setup_job_info(job_env.job_id, job_env.global_rank)\n try:\n self.run_trainer()\n except Exception as e:\n # Log the exception. Then raise it again (as what SubmititRunner currently does).\n message = format_exception(e)\n logging.error(message)\n raise e\n\n def setup_job_info(self, job_id, rank):\n \"\"\"Set up slurm job info\"\"\"\n self.job_info = {\n \"job_id\": job_id,\n \"rank\": rank,\n \"cluster\": self.cfg.get(\"cluster\", None),\n \"experiment_log_dir\": self.cfg.launcher.experiment_log_dir,\n }\n\n self.has_setup = True\n\n\ndef add_pythonpath_to_sys_path():\n if \"PYTHONPATH\" not in os.environ or not os.environ[\"PYTHONPATH\"]:\n return\n sys.path = os.environ[\"PYTHONPATH\"].split(\":\") + sys.path\n\n\ndef main(args) -> None:\n cfg = compose(config_name=args.config)\n if cfg.launcher.experiment_log_dir is None:\n cfg.launcher.experiment_log_dir = os.path.join(\n os.getcwd(), \"sam2_logs\", args.config\n )\n print(\"###################### Train App Config ####################\")\n print(OmegaConf.to_yaml(cfg))\n print(\"############################################################\")\n\n add_pythonpath_to_sys_path()\n makedir(cfg.launcher.experiment_log_dir)\n with g_pathmgr.open(\n os.path.join(cfg.launcher.experiment_log_dir, \"config.yaml\"), \"w\"\n ) as f:\n f.write(OmegaConf.to_yaml(cfg))\n\n cfg_resolved = OmegaConf.to_container(cfg, resolve=False)\n cfg_resolved = OmegaConf.create(cfg_resolved)\n\n with g_pathmgr.open(\n os.path.join(cfg.launcher.experiment_log_dir, \"config_resolved.yaml\"), \"w\"\n ) as f:\n f.write(OmegaConf.to_yaml(cfg_resolved, resolve=True))\n\n submitit_conf = cfg.get(\"submitit\", None)\n assert submitit_conf is not None, \"Missing submitit config\"\n\n submitit_dir = cfg.launcher.experiment_log_dir\n submitit_dir = os.path.join(submitit_dir, \"submitit_logs\")\n # Priotrize cmd line args\n cfg.launcher.gpus_per_node = (\n args.num_gpus if args.num_gpus is not None else cfg.launcher.gpus_per_node\n )\n cfg.launcher.num_nodes = (\n args.num_nodes if args.num_nodes is not None else cfg.launcher.num_nodes\n )\n submitit_conf.use_cluster = (\n args.use_cluster if args.use_cluster is not None else submitit_conf.use_cluster\n )\n if submitit_conf.use_cluster:\n executor = submitit.AutoExecutor(folder=submitit_dir)\n submitit_conf.partition = (\n args.partition\n if args.partition is not None\n else submitit_conf.get(\"partition\", None)\n )\n submitit_conf.account = (\n args.account\n if args.account is not None\n else submitit_conf.get(\"account\", None)\n )\n submitit_conf.qos = (\n args.qos if args.qos is not None else submitit_conf.get(\"qos\", None)\n )\n job_kwargs = {\n \"timeout_min\": 60 * submitit_conf.timeout_hour,\n \"name\": (\n submitit_conf.name if hasattr(submitit_conf, \"name\") else args.config\n ),\n \"slurm_partition\": submitit_conf.partition,\n \"gpus_per_node\": cfg.launcher.gpus_per_node,\n \"tasks_per_node\": cfg.launcher.gpus_per_node, # one task per GPU\n \"cpus_per_task\": submitit_conf.cpus_per_task,\n \"nodes\": cfg.launcher.num_nodes,\n \"slurm_additional_parameters\": {\n \"exclude\": \" \".join(submitit_conf.get(\"exclude_nodes\", [])),\n },\n }\n if \"include_nodes\" in submitit_conf:\n assert (\n len(submitit_conf[\"include_nodes\"]) >= cfg.launcher.num_nodes\n ), \"Not enough nodes\"\n job_kwargs[\"slurm_additional_parameters\"][\"nodelist\"] = \" \".join(\n submitit_conf[\"include_nodes\"]\n )\n if submitit_conf.account is not None:\n job_kwargs[\"slurm_additional_parameters\"][\"account\"] = submitit_conf.account\n if submitit_conf.qos is not None:\n job_kwargs[\"slurm_additional_parameters\"][\"qos\"] = submitit_conf.qos\n\n if submitit_conf.get(\"mem_gb\", None) is not None:\n job_kwargs[\"mem_gb\"] = submitit_conf.mem_gb\n elif submitit_conf.get(\"mem\", None) is not None:\n job_kwargs[\"slurm_mem\"] = submitit_conf.mem\n\n if submitit_conf.get(\"constraints\", None) is not None:\n job_kwargs[\"slurm_constraint\"] = submitit_conf.constraints\n\n if submitit_conf.get(\"comment\", None) is not None:\n job_kwargs[\"slurm_comment\"] = submitit_conf.comment\n\n # Supports only cpu-bind option within srun_args. New options can be added here\n if submitit_conf.get(\"srun_args\", None) is not None:\n job_kwargs[\"slurm_srun_args\"] = []\n if submitit_conf.srun_args.get(\"cpu_bind\", None) is not None:\n job_kwargs[\"slurm_srun_args\"].extend(\n [\"--cpu-bind\", submitit_conf.srun_args.cpu_bind]\n )\n\n print(\"###################### SLURM Config ####################\")\n print(job_kwargs)\n print(\"##########################################\")\n executor.update_parameters(**job_kwargs)\n\n main_port = random.randint(\n submitit_conf.port_range[0], submitit_conf.port_range[1]\n )\n runner = SubmititRunner(main_port, cfg)\n job = executor.submit(runner)\n print(f\"Submitit Job ID: {job.job_id}\")\n runner.setup_job_info(job.job_id, rank=0)\n else:\n cfg.launcher.num_nodes = 1\n main_port = random.randint(\n submitit_conf.port_range[0], submitit_conf.port_range[1]\n )\n single_node_runner(cfg, main_port)\n\n\nif __name__ == \"__main__\":\n\n initialize_config_module(\"sam2\", version_base=\"1.2\")\n parser = ArgumentParser()\n parser.add_argument(\n \"-c\",\n \"--config\",\n required=True,\n type=str,\n help=\"path to config file (e.g. configs/sam2.1_training/sam2.1_hiera_b+_MOSE_finetune.yaml)\",\n )\n parser.add_argument(\n \"--use-cluster\",\n type=int,\n default=None,\n help=\"whether to launch on a cluster, 0: run locally, 1: run on a cluster\",\n )\n parser.add_argument(\"--partition\", type=str, default=None, help=\"SLURM partition\")\n parser.add_argument(\"--account\", type=str, default=None, help=\"SLURM account\")\n parser.add_argument(\"--qos\", type=str, default=None, help=\"SLURM qos\")\n parser.add_argument(\n \"--num-gpus\", type=int, default=None, help=\"number of GPUS per node\"\n )\n parser.add_argument(\"--num-nodes\", type=int, default=None, help=\"Number of nodes\")\n args = parser.parse_args()\n args.use_cluster = bool(args.use_cluster) if args.use_cluster is not None else None\n register_omegaconf_resolvers()\n main(args)\n" }, { "path": "auto-seg/submodules/segment-anything-2/training/trainer.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport gc\nimport json\nimport logging\nimport math\nimport os\nimport time\nfrom collections import OrderedDict\nfrom dataclasses import dataclass, field\nfrom typing import Any, Dict, List, Mapping, Optional\n\nimport numpy as np\n\nimport torch\nimport torch.distributed as dist\nimport torch.nn as nn\nfrom hydra.utils import instantiate\nfrom iopath.common.file_io import g_pathmgr\n\nfrom training.optimizer import construct_optimizer\n\nfrom training.utils.checkpoint_utils import (\n assert_skipped_parameters_are_frozen,\n exclude_params_matching_unix_pattern,\n load_state_dict_into_model,\n with_check_parameter_frozen,\n)\nfrom training.utils.data_utils import BatchedVideoDatapoint\nfrom training.utils.distributed import all_reduce_max, barrier, get_rank\n\nfrom training.utils.logger import Logger, setup_logging\n\nfrom training.utils.train_utils import (\n AverageMeter,\n collect_dict_keys,\n DurationMeter,\n get_amp_type,\n get_machine_local_and_dist_rank,\n get_resume_checkpoint,\n human_readable_time,\n is_dist_avail_and_initialized,\n log_env_variables,\n makedir,\n MemMeter,\n Phase,\n ProgressMeter,\n set_seeds,\n setup_distributed_backend,\n)\n\n\nCORE_LOSS_KEY = \"core_loss\"\n\n\ndef unwrap_ddp_if_wrapped(model):\n if isinstance(model, torch.nn.parallel.DistributedDataParallel):\n return model.module\n return model\n\n\n@dataclass\nclass OptimAMPConf:\n enabled: bool = False\n amp_dtype: str = \"float16\"\n\n\n@dataclass\nclass OptimConf:\n optimizer: torch.optim.Optimizer = None\n options: Optional[Dict[str, Any]] = None\n param_group_modifiers: Optional[List] = None\n amp: Optional[Dict[str, Any]] = None\n gradient_clip: Any = None\n gradient_logger: Any = None\n\n def __post_init__(self):\n # amp\n if not isinstance(self.amp, OptimAMPConf):\n if self.amp is None:\n self.amp = {}\n assert isinstance(self.amp, Mapping)\n self.amp = OptimAMPConf(**self.amp)\n\n\n@dataclass\nclass DistributedConf:\n backend: Optional[str] = None # inferred from accelerator type\n comms_dtype: Optional[str] = None\n find_unused_parameters: bool = False\n timeout_mins: int = 30\n\n\n@dataclass\nclass CudaConf:\n cudnn_deterministic: bool = False\n cudnn_benchmark: bool = True\n allow_tf32: bool = False\n # if not None, `matmul_allow_tf32` key will override `allow_tf32` for matmul\n matmul_allow_tf32: Optional[bool] = None\n # if not None, `cudnn_allow_tf32` key will override `allow_tf32` for cudnn\n cudnn_allow_tf32: Optional[bool] = None\n\n\n@dataclass\nclass CheckpointConf:\n save_dir: str\n save_freq: int\n save_list: List[int] = field(default_factory=list)\n model_weight_initializer: Any = None\n save_best_meters: List[str] = None\n skip_saving_parameters: List[str] = field(default_factory=list)\n initialize_after_preemption: Optional[bool] = None\n # if not None, training will be resumed from this checkpoint\n resume_from: Optional[str] = None\n\n def infer_missing(self):\n if self.initialize_after_preemption is None:\n with_skip_saving = len(self.skip_saving_parameters) > 0\n self.initialize_after_preemption = with_skip_saving\n return self\n\n\n@dataclass\nclass LoggingConf:\n log_dir: str\n log_freq: int # In iterations\n tensorboard_writer: Any\n log_level_primary: str = \"INFO\"\n log_level_secondary: str = \"ERROR\"\n log_scalar_frequency: int = 100\n log_visual_frequency: int = 100\n scalar_keys_to_log: Optional[Dict[str, Any]] = None\n log_batch_stats: bool = False\n\n\nclass Trainer:\n \"\"\"\n Trainer supporting the DDP training strategies.\n \"\"\"\n\n EPSILON = 1e-8\n\n def __init__(\n self,\n *, # the order of these args can change at any time, so they are keyword-only\n data: Dict[str, Any],\n model: Dict[str, Any],\n logging: Dict[str, Any],\n checkpoint: Dict[str, Any],\n max_epochs: int,\n mode: str = \"train\",\n accelerator: str = \"cuda\",\n seed_value: int = 123,\n val_epoch_freq: int = 1,\n distributed: Dict[str, bool] = None,\n cuda: Dict[str, bool] = None,\n env_variables: Optional[Dict[str, Any]] = None,\n optim: Optional[Dict[str, Any]] = None,\n optim_overrides: Optional[List[Dict[str, Any]]] = None,\n meters: Optional[Dict[str, Any]] = None,\n loss: Optional[Dict[str, Any]] = None,\n ):\n\n self._setup_env_variables(env_variables)\n self._setup_timers()\n\n self.data_conf = data\n self.model_conf = model\n self.logging_conf = LoggingConf(**logging)\n self.checkpoint_conf = CheckpointConf(**checkpoint).infer_missing()\n self.max_epochs = max_epochs\n self.mode = mode\n self.val_epoch_freq = val_epoch_freq\n self.optim_conf = OptimConf(**optim) if optim is not None else None\n self.meters_conf = meters\n self.loss_conf = loss\n distributed = DistributedConf(**distributed or {})\n cuda = CudaConf(**cuda or {})\n self.where = 0.0\n\n self._infer_distributed_backend_if_none(distributed, accelerator)\n\n self._setup_device(accelerator)\n\n self._setup_torch_dist_and_backend(cuda, distributed)\n\n makedir(self.logging_conf.log_dir)\n setup_logging(\n __name__,\n output_dir=self.logging_conf.log_dir,\n rank=self.rank,\n log_level_primary=self.logging_conf.log_level_primary,\n log_level_secondary=self.logging_conf.log_level_secondary,\n )\n\n set_seeds(seed_value, self.max_epochs, self.distributed_rank)\n log_env_variables()\n\n assert (\n is_dist_avail_and_initialized()\n ), \"Torch distributed needs to be initialized before calling the trainer.\"\n\n self._setup_components() # Except Optimizer everything is setup here.\n self._move_to_device()\n self._construct_optimizers()\n self._setup_dataloaders()\n\n self.time_elapsed_meter = DurationMeter(\"Time Elapsed\", self.device, \":.2f\")\n\n if self.checkpoint_conf.resume_from is not None:\n assert os.path.exists(\n self.checkpoint_conf.resume_from\n ), f\"The 'resume_from' checkpoint {self.checkpoint_conf.resume_from} does not exist!\"\n dst = os.path.join(self.checkpoint_conf.save_dir, \"checkpoint.pt\")\n if self.distributed_rank == 0 and not os.path.exists(dst):\n # Copy the \"resume_from\" checkpoint to the checkpoint folder\n # if there is not a checkpoint to resume from already there\n makedir(self.checkpoint_conf.save_dir)\n g_pathmgr.copy(self.checkpoint_conf.resume_from, dst)\n barrier()\n\n self.load_checkpoint()\n self._setup_ddp_distributed_training(distributed, accelerator)\n barrier()\n\n def _setup_timers(self):\n \"\"\"\n Initializes counters for elapsed time and eta.\n \"\"\"\n self.start_time = time.time()\n self.ckpt_time_elapsed = 0\n self.est_epoch_time = dict.fromkeys([Phase.TRAIN, Phase.VAL], 0)\n\n def _get_meters(self, phase_filters=None):\n if self.meters is None:\n return {}\n meters = {}\n for phase, phase_meters in self.meters.items():\n if phase_filters is not None and phase not in phase_filters:\n continue\n for key, key_meters in phase_meters.items():\n if key_meters is None:\n continue\n for name, meter in key_meters.items():\n meters[f\"{phase}_{key}/{name}\"] = meter\n return meters\n\n def _infer_distributed_backend_if_none(self, distributed_conf, accelerator):\n if distributed_conf.backend is None:\n distributed_conf.backend = \"nccl\" if accelerator == \"cuda\" else \"gloo\"\n\n def _setup_env_variables(self, env_variables_conf) -> None:\n if env_variables_conf is not None:\n for variable_name, value in env_variables_conf.items():\n os.environ[variable_name] = value\n\n def _setup_torch_dist_and_backend(self, cuda_conf, distributed_conf) -> None:\n if torch.cuda.is_available():\n torch.backends.cudnn.deterministic = cuda_conf.cudnn_deterministic\n torch.backends.cudnn.benchmark = cuda_conf.cudnn_benchmark\n torch.backends.cuda.matmul.allow_tf32 = (\n cuda_conf.matmul_allow_tf32\n if cuda_conf.matmul_allow_tf32 is not None\n else cuda_conf.allow_tf32\n )\n torch.backends.cudnn.allow_tf32 = (\n cuda_conf.cudnn_allow_tf32\n if cuda_conf.cudnn_allow_tf32 is not None\n else cuda_conf.allow_tf32\n )\n\n self.rank = setup_distributed_backend(\n distributed_conf.backend, distributed_conf.timeout_mins\n )\n\n def _setup_device(self, accelerator):\n self.local_rank, self.distributed_rank = get_machine_local_and_dist_rank()\n if accelerator == \"cuda\":\n self.device = torch.device(\"cuda\", self.local_rank)\n torch.cuda.set_device(self.local_rank)\n elif accelerator == \"cpu\":\n self.device = torch.device(\"cpu\")\n else:\n raise ValueError(f\"Unsupported accelerator: {accelerator}\")\n\n def _setup_ddp_distributed_training(self, distributed_conf, accelerator):\n\n assert isinstance(self.model, torch.nn.Module)\n\n self.model = nn.parallel.DistributedDataParallel(\n self.model,\n device_ids=[self.local_rank] if accelerator == \"cuda\" else [],\n find_unused_parameters=distributed_conf.find_unused_parameters,\n )\n if distributed_conf.comms_dtype is not None: # noqa\n from torch.distributed.algorithms import ddp_comm_hooks\n\n amp_type = get_amp_type(distributed_conf.comms_dtype)\n if amp_type == torch.bfloat16:\n hook = ddp_comm_hooks.default_hooks.bf16_compress_hook\n logging.info(\"Enabling bfloat16 grad communication\")\n else:\n hook = ddp_comm_hooks.default_hooks.fp16_compress_hook\n logging.info(\"Enabling fp16 grad communication\")\n process_group = None\n self.model.register_comm_hook(process_group, hook)\n\n def _move_to_device(self):\n logging.info(\n f\"Moving components to device {self.device} and local rank {self.local_rank}.\"\n )\n\n self.model.to(self.device)\n\n logging.info(\n f\"Done moving components to device {self.device} and local rank {self.local_rank}.\"\n )\n\n def save_checkpoint(self, epoch, checkpoint_names=None):\n checkpoint_folder = self.checkpoint_conf.save_dir\n makedir(checkpoint_folder)\n if checkpoint_names is None:\n checkpoint_names = [\"checkpoint\"]\n if (\n self.checkpoint_conf.save_freq > 0\n and (int(epoch) % self.checkpoint_conf.save_freq == 0)\n ) or int(epoch) in self.checkpoint_conf.save_list:\n checkpoint_names.append(f\"checkpoint_{int(epoch)}\")\n\n checkpoint_paths = []\n for ckpt_name in checkpoint_names:\n checkpoint_paths.append(os.path.join(checkpoint_folder, f\"{ckpt_name}.pt\"))\n\n state_dict = unwrap_ddp_if_wrapped(self.model).state_dict()\n state_dict = exclude_params_matching_unix_pattern(\n patterns=self.checkpoint_conf.skip_saving_parameters, state_dict=state_dict\n )\n\n checkpoint = {\n \"model\": state_dict,\n \"optimizer\": self.optim.optimizer.state_dict(),\n \"epoch\": epoch,\n \"loss\": self.loss.state_dict(),\n \"steps\": self.steps,\n \"time_elapsed\": self.time_elapsed_meter.val,\n \"best_meter_values\": self.best_meter_values,\n }\n if self.optim_conf.amp.enabled:\n checkpoint[\"scaler\"] = self.scaler.state_dict()\n\n # DDP checkpoints are only saved on rank 0 (all workers are identical)\n if self.distributed_rank != 0:\n return\n\n for checkpoint_path in checkpoint_paths:\n self._save_checkpoint(checkpoint, checkpoint_path)\n\n def _save_checkpoint(self, checkpoint, checkpoint_path):\n \"\"\"\n Save a checkpoint while guarding against the job being killed in the middle\n of checkpoint saving (which corrupts the checkpoint file and ruins the\n entire training since usually only the last checkpoint is kept per run).\n\n We first save the new checkpoint to a temp file (with a '.tmp' suffix), and\n and move it to overwrite the old checkpoint_path.\n \"\"\"\n checkpoint_path_tmp = f\"{checkpoint_path}.tmp\"\n with g_pathmgr.open(checkpoint_path_tmp, \"wb\") as f:\n torch.save(checkpoint, f)\n # after torch.save is completed, replace the old checkpoint with the new one\n if g_pathmgr.exists(checkpoint_path):\n # remove the old checkpoint_path file first (otherwise g_pathmgr.mv fails)\n g_pathmgr.rm(checkpoint_path)\n success = g_pathmgr.mv(checkpoint_path_tmp, checkpoint_path)\n assert success\n\n def load_checkpoint(self):\n ckpt_path = get_resume_checkpoint(self.checkpoint_conf.save_dir)\n if ckpt_path is None:\n self._init_model_state()\n else:\n if self.checkpoint_conf.initialize_after_preemption:\n self._call_model_initializer()\n self._load_resuming_checkpoint(ckpt_path)\n\n def _init_model_state(self):\n # Checking that parameters that won't be saved are indeed frozen\n # We do this check here before even saving the model to catch errors\n # are early as possible and not at the end of the first epoch\n assert_skipped_parameters_are_frozen(\n patterns=self.checkpoint_conf.skip_saving_parameters,\n model=self.model,\n )\n\n # Checking that parameters that won't be saved are initialized from\n # within the model definition, unless `initialize_after_preemption`\n # is explicitly set to `True`. If not, this is a bug, and after\n # preemption, the `skip_saving_parameters` will have random values\n allow_init_skip_parameters = self.checkpoint_conf.initialize_after_preemption\n with with_check_parameter_frozen(\n patterns=self.checkpoint_conf.skip_saving_parameters,\n model=self.model,\n disabled=allow_init_skip_parameters,\n ):\n self._call_model_initializer()\n\n def _call_model_initializer(self):\n model_weight_initializer = instantiate(\n self.checkpoint_conf.model_weight_initializer\n )\n if model_weight_initializer is not None:\n logging.info(\n f\"Loading pretrained checkpoint from {self.checkpoint_conf.model_weight_initializer}\"\n )\n self.model = model_weight_initializer(model=self.model)\n\n def _load_resuming_checkpoint(self, ckpt_path: str):\n logging.info(f\"Resuming training from {ckpt_path}\")\n\n with g_pathmgr.open(ckpt_path, \"rb\") as f:\n checkpoint = torch.load(f, map_location=\"cpu\")\n load_state_dict_into_model(\n model=self.model,\n state_dict=checkpoint[\"model\"],\n ignore_missing_keys=self.checkpoint_conf.skip_saving_parameters,\n )\n\n self.optim.optimizer.load_state_dict(checkpoint[\"optimizer\"])\n self.loss.load_state_dict(checkpoint[\"loss\"], strict=True)\n self.epoch = checkpoint[\"epoch\"]\n self.steps = checkpoint[\"steps\"]\n self.ckpt_time_elapsed = checkpoint.get(\"time_elapsed\")\n\n if self.optim_conf.amp.enabled and \"scaler\" in checkpoint:\n self.scaler.load_state_dict(checkpoint[\"scaler\"])\n\n self.best_meter_values = checkpoint.get(\"best_meter_values\", {})\n\n if \"train_dataset\" in checkpoint and self.train_dataset is not None:\n self.train_dataset.load_checkpoint_state(checkpoint[\"train_dataset\"])\n\n def is_intermediate_val_epoch(self, epoch):\n return epoch % self.val_epoch_freq == 0 and epoch < self.max_epochs - 1\n\n def _step(\n self,\n batch: BatchedVideoDatapoint,\n model: nn.Module,\n phase: str,\n ):\n\n outputs = model(batch)\n targets = batch.masks\n batch_size = len(batch.img_batch)\n\n key = batch.dict_key # key for dataset\n loss = self.loss[key](outputs, targets)\n loss_str = f\"Losses/{phase}_{key}_loss\"\n\n loss_log_str = os.path.join(\"Step_Losses\", loss_str)\n\n # loss contains multiple sub-components we wish to log\n step_losses = {}\n if isinstance(loss, dict):\n step_losses.update(\n {f\"Losses/{phase}_{key}_{k}\": v for k, v in loss.items()}\n )\n loss = self._log_loss_detailed_and_return_core_loss(\n loss, loss_log_str, self.steps[phase]\n )\n\n if self.steps[phase] % self.logging_conf.log_scalar_frequency == 0:\n self.logger.log(\n loss_log_str,\n loss,\n self.steps[phase],\n )\n\n self.steps[phase] += 1\n\n ret_tuple = {loss_str: loss}, batch_size, step_losses\n\n if phase in self.meters and key in self.meters[phase]:\n meters_dict = self.meters[phase][key]\n if meters_dict is not None:\n for _, meter in meters_dict.items():\n meter.update(\n find_stages=outputs,\n find_metadatas=batch.metadata,\n )\n\n return ret_tuple\n\n def run(self):\n assert self.mode in [\"train\", \"train_only\", \"val\"]\n if self.mode == \"train\":\n if self.epoch > 0:\n logging.info(f\"Resuming training from epoch: {self.epoch}\")\n # resuming from a checkpoint\n if self.is_intermediate_val_epoch(self.epoch - 1):\n logging.info(\"Running previous val epoch\")\n self.epoch -= 1\n self.run_val()\n self.epoch += 1\n self.run_train()\n self.run_val()\n elif self.mode == \"val\":\n self.run_val()\n elif self.mode == \"train_only\":\n self.run_train()\n\n def _setup_dataloaders(self):\n self.train_dataset = None\n self.val_dataset = None\n\n if self.mode in [\"train\", \"val\"]:\n self.val_dataset = instantiate(self.data_conf.get(Phase.VAL, None))\n\n if self.mode in [\"train\", \"train_only\"]:\n self.train_dataset = instantiate(self.data_conf.train)\n\n def run_train(self):\n\n while self.epoch < self.max_epochs:\n dataloader = self.train_dataset.get_loader(epoch=int(self.epoch))\n barrier()\n outs = self.train_epoch(dataloader)\n self.logger.log_dict(outs, self.epoch) # Logged only on rank 0\n\n # log train to text file.\n if self.distributed_rank == 0:\n with g_pathmgr.open(\n os.path.join(self.logging_conf.log_dir, \"train_stats.json\"),\n \"a\",\n ) as f:\n f.write(json.dumps(outs) + \"\\n\")\n\n # Save checkpoint before validating\n self.save_checkpoint(self.epoch + 1)\n\n del dataloader\n gc.collect()\n\n # Run val, not running on last epoch since will run after the\n # loop anyway\n if self.is_intermediate_val_epoch(self.epoch):\n self.run_val()\n\n if self.distributed_rank == 0:\n self.best_meter_values.update(self._get_trainer_state(\"train\"))\n with g_pathmgr.open(\n os.path.join(self.logging_conf.log_dir, \"best_stats.json\"),\n \"a\",\n ) as f:\n f.write(json.dumps(self.best_meter_values) + \"\\n\")\n\n self.epoch += 1\n # epoch was incremented in the loop but the val step runs out of the loop\n self.epoch -= 1\n\n def run_val(self):\n if not self.val_dataset:\n return\n\n dataloader = self.val_dataset.get_loader(epoch=int(self.epoch))\n outs = self.val_epoch(dataloader, phase=Phase.VAL)\n del dataloader\n gc.collect()\n self.logger.log_dict(outs, self.epoch) # Logged only on rank 0\n\n if self.distributed_rank == 0:\n with g_pathmgr.open(\n os.path.join(self.logging_conf.log_dir, \"val_stats.json\"),\n \"a\",\n ) as f:\n f.write(json.dumps(outs) + \"\\n\")\n\n def val_epoch(self, val_loader, phase):\n batch_time = AverageMeter(\"Batch Time\", self.device, \":.2f\")\n data_time = AverageMeter(\"Data Time\", self.device, \":.2f\")\n mem = MemMeter(\"Mem (GB)\", self.device, \":.2f\")\n\n iters_per_epoch = len(val_loader)\n\n curr_phases = [phase]\n curr_models = [self.model]\n\n loss_names = []\n for p in curr_phases:\n for key in self.loss.keys():\n loss_names.append(f\"Losses/{p}_{key}_loss\")\n\n loss_mts = OrderedDict(\n [(name, AverageMeter(name, self.device, \":.2e\")) for name in loss_names]\n )\n extra_loss_mts = {}\n\n for model in curr_models:\n model.eval()\n if hasattr(unwrap_ddp_if_wrapped(model), \"on_validation_epoch_start\"):\n unwrap_ddp_if_wrapped(model).on_validation_epoch_start()\n\n progress = ProgressMeter(\n iters_per_epoch,\n [batch_time, data_time, mem, self.time_elapsed_meter, *loss_mts.values()],\n self._get_meters(curr_phases),\n prefix=\"Val Epoch: [{}]\".format(self.epoch),\n )\n\n end = time.time()\n\n for data_iter, batch in enumerate(val_loader):\n\n # measure data loading time\n data_time.update(time.time() - end)\n\n batch = batch.to(self.device, non_blocking=True)\n\n # compute output\n with torch.no_grad():\n with torch.cuda.amp.autocast(\n enabled=(self.optim_conf.amp.enabled if self.optim_conf else False),\n dtype=(\n get_amp_type(self.optim_conf.amp.amp_dtype)\n if self.optim_conf\n else None\n ),\n ):\n for phase, model in zip(curr_phases, curr_models):\n loss_dict, batch_size, extra_losses = self._step(\n batch,\n model,\n phase,\n )\n\n assert len(loss_dict) == 1\n loss_key, loss = loss_dict.popitem()\n\n loss_mts[loss_key].update(loss.item(), batch_size)\n\n for k, v in extra_losses.items():\n if k not in extra_loss_mts:\n extra_loss_mts[k] = AverageMeter(k, self.device, \":.2e\")\n extra_loss_mts[k].update(v.item(), batch_size)\n\n # measure elapsed time\n batch_time.update(time.time() - end)\n end = time.time()\n\n self.time_elapsed_meter.update(\n time.time() - self.start_time + self.ckpt_time_elapsed\n )\n\n if torch.cuda.is_available():\n mem.update(reset_peak_usage=True)\n\n if data_iter % self.logging_conf.log_freq == 0:\n progress.display(data_iter)\n\n if data_iter % self.logging_conf.log_scalar_frequency == 0:\n # Log progress meters.\n for progress_meter in progress.meters:\n self.logger.log(\n os.path.join(\"Step_Stats\", phase, progress_meter.name),\n progress_meter.val,\n self.steps[Phase.VAL],\n )\n\n if data_iter % 10 == 0:\n dist.barrier()\n\n self.est_epoch_time[phase] = batch_time.avg * iters_per_epoch\n self._log_timers(phase)\n for model in curr_models:\n if hasattr(unwrap_ddp_if_wrapped(model), \"on_validation_epoch_end\"):\n unwrap_ddp_if_wrapped(model).on_validation_epoch_end()\n\n out_dict = self._log_meters_and_save_best_ckpts(curr_phases)\n\n for k, v in loss_mts.items():\n out_dict[k] = v.avg\n for k, v in extra_loss_mts.items():\n out_dict[k] = v.avg\n\n for phase in curr_phases:\n out_dict.update(self._get_trainer_state(phase))\n self._reset_meters(curr_phases)\n logging.info(f\"Meters: {out_dict}\")\n return out_dict\n\n def _get_trainer_state(self, phase):\n return {\n \"Trainer/where\": self.where,\n \"Trainer/epoch\": self.epoch,\n f\"Trainer/steps_{phase}\": self.steps[phase],\n }\n\n def train_epoch(self, train_loader):\n\n # Init stat meters\n batch_time_meter = AverageMeter(\"Batch Time\", self.device, \":.2f\")\n data_time_meter = AverageMeter(\"Data Time\", self.device, \":.2f\")\n mem_meter = MemMeter(\"Mem (GB)\", self.device, \":.2f\")\n data_times = []\n phase = Phase.TRAIN\n\n iters_per_epoch = len(train_loader)\n\n loss_names = []\n for batch_key in self.loss.keys():\n loss_names.append(f\"Losses/{phase}_{batch_key}_loss\")\n\n loss_mts = OrderedDict(\n [(name, AverageMeter(name, self.device, \":.2e\")) for name in loss_names]\n )\n extra_loss_mts = {}\n\n progress = ProgressMeter(\n iters_per_epoch,\n [\n batch_time_meter,\n data_time_meter,\n mem_meter,\n self.time_elapsed_meter,\n *loss_mts.values(),\n ],\n self._get_meters([phase]),\n prefix=\"Train Epoch: [{}]\".format(self.epoch),\n )\n\n # Model training loop\n self.model.train()\n end = time.time()\n\n for data_iter, batch in enumerate(train_loader):\n # measure data loading time\n data_time_meter.update(time.time() - end)\n data_times.append(data_time_meter.val)\n batch = batch.to(\n self.device, non_blocking=True\n ) # move tensors in a tensorclass\n\n try:\n self._run_step(batch, phase, loss_mts, extra_loss_mts)\n\n # compute gradient and do optim step\n exact_epoch = self.epoch + float(data_iter) / iters_per_epoch\n self.where = float(exact_epoch) / self.max_epochs\n assert self.where <= 1 + self.EPSILON\n if self.where < 1.0:\n self.optim.step_schedulers(\n self.where, step=int(exact_epoch * iters_per_epoch)\n )\n else:\n logging.warning(\n f\"Skipping scheduler update since the training is at the end, i.e, {self.where} of [0,1].\"\n )\n\n # Log schedulers\n if data_iter % self.logging_conf.log_scalar_frequency == 0:\n for j, param_group in enumerate(self.optim.optimizer.param_groups):\n for option in self.optim.schedulers[j]:\n optim_prefix = (\n \"\" + f\"{j}_\"\n if len(self.optim.optimizer.param_groups) > 1\n else \"\"\n )\n self.logger.log(\n os.path.join(\"Optim\", f\"{optim_prefix}\", option),\n param_group[option],\n self.steps[phase],\n )\n\n # Clipping gradients and detecting diverging gradients\n if self.gradient_clipper is not None:\n self.scaler.unscale_(self.optim.optimizer)\n self.gradient_clipper(model=self.model)\n\n if self.gradient_logger is not None:\n self.gradient_logger(\n self.model, rank=self.distributed_rank, where=self.where\n )\n\n # Optimizer step: the scaler will make sure gradients are not\n # applied if the gradients are infinite\n self.scaler.step(self.optim.optimizer)\n self.scaler.update()\n\n # measure elapsed time\n batch_time_meter.update(time.time() - end)\n end = time.time()\n\n self.time_elapsed_meter.update(\n time.time() - self.start_time + self.ckpt_time_elapsed\n )\n\n mem_meter.update(reset_peak_usage=True)\n if data_iter % self.logging_conf.log_freq == 0:\n progress.display(data_iter)\n\n if data_iter % self.logging_conf.log_scalar_frequency == 0:\n # Log progress meters.\n for progress_meter in progress.meters:\n self.logger.log(\n os.path.join(\"Step_Stats\", phase, progress_meter.name),\n progress_meter.val,\n self.steps[phase],\n )\n\n # Catching NaN/Inf errors in the loss\n except FloatingPointError as e:\n raise e\n\n self.est_epoch_time[Phase.TRAIN] = batch_time_meter.avg * iters_per_epoch\n self._log_timers(Phase.TRAIN)\n self._log_sync_data_times(Phase.TRAIN, data_times)\n\n out_dict = self._log_meters_and_save_best_ckpts([Phase.TRAIN])\n\n for k, v in loss_mts.items():\n out_dict[k] = v.avg\n for k, v in extra_loss_mts.items():\n out_dict[k] = v.avg\n out_dict.update(self._get_trainer_state(phase))\n logging.info(f\"Losses and meters: {out_dict}\")\n self._reset_meters([phase])\n return out_dict\n\n def _log_sync_data_times(self, phase, data_times):\n data_times = all_reduce_max(torch.tensor(data_times)).tolist()\n steps = range(self.steps[phase] - len(data_times), self.steps[phase])\n for step, data_time in zip(steps, data_times):\n if step % self.logging_conf.log_scalar_frequency == 0:\n self.logger.log(\n os.path.join(\"Step_Stats\", phase, \"Data Time Synced\"),\n data_time,\n step,\n )\n\n def _run_step(\n self,\n batch: BatchedVideoDatapoint,\n phase: str,\n loss_mts: Dict[str, AverageMeter],\n extra_loss_mts: Dict[str, AverageMeter],\n raise_on_error: bool = True,\n ):\n \"\"\"\n Run the forward / backward\n \"\"\"\n\n # it's important to set grads to None, especially with Adam since 0\n # grads will also update a model even if the step doesn't produce\n # gradients\n self.optim.zero_grad(set_to_none=True)\n with torch.cuda.amp.autocast(\n enabled=self.optim_conf.amp.enabled,\n dtype=get_amp_type(self.optim_conf.amp.amp_dtype),\n ):\n loss_dict, batch_size, extra_losses = self._step(\n batch,\n self.model,\n phase,\n )\n\n assert len(loss_dict) == 1\n loss_key, loss = loss_dict.popitem()\n\n if not math.isfinite(loss.item()):\n error_msg = f\"Loss is {loss.item()}, attempting to stop training\"\n logging.error(error_msg)\n if raise_on_error:\n raise FloatingPointError(error_msg)\n else:\n return\n\n self.scaler.scale(loss).backward()\n loss_mts[loss_key].update(loss.item(), batch_size)\n for extra_loss_key, extra_loss in extra_losses.items():\n if extra_loss_key not in extra_loss_mts:\n extra_loss_mts[extra_loss_key] = AverageMeter(\n extra_loss_key, self.device, \":.2e\"\n )\n extra_loss_mts[extra_loss_key].update(extra_loss.item(), batch_size)\n\n def _log_meters_and_save_best_ckpts(self, phases: List[str]):\n logging.info(\"Synchronizing meters\")\n out_dict = {}\n checkpoint_save_keys = []\n for key, meter in self._get_meters(phases).items():\n meter_output = meter.compute_synced()\n is_better_check = getattr(meter, \"is_better\", None)\n\n for meter_subkey, meter_value in meter_output.items():\n out_dict[os.path.join(\"Meters_train\", key, meter_subkey)] = meter_value\n\n if is_better_check is None:\n continue\n\n tracked_meter_key = os.path.join(key, meter_subkey)\n if tracked_meter_key not in self.best_meter_values or is_better_check(\n meter_value,\n self.best_meter_values[tracked_meter_key],\n ):\n self.best_meter_values[tracked_meter_key] = meter_value\n\n if (\n self.checkpoint_conf.save_best_meters is not None\n and key in self.checkpoint_conf.save_best_meters\n ):\n checkpoint_save_keys.append(tracked_meter_key.replace(\"/\", \"_\"))\n\n if len(checkpoint_save_keys) > 0:\n self.save_checkpoint(self.epoch + 1, checkpoint_save_keys)\n\n return out_dict\n\n def _log_timers(self, phase):\n time_remaining = 0\n epochs_remaining = self.max_epochs - self.epoch - 1\n val_epochs_remaining = sum(\n n % self.val_epoch_freq == 0 for n in range(self.epoch, self.max_epochs)\n )\n\n # Adding the guaranteed val run at the end if val_epoch_freq doesn't coincide with\n # the end epoch.\n if (self.max_epochs - 1) % self.val_epoch_freq != 0:\n val_epochs_remaining += 1\n\n # Remove the current val run from estimate\n if phase == Phase.VAL:\n val_epochs_remaining -= 1\n\n time_remaining += (\n epochs_remaining * self.est_epoch_time[Phase.TRAIN]\n + val_epochs_remaining * self.est_epoch_time[Phase.VAL]\n )\n\n self.logger.log(\n os.path.join(\"Step_Stats\", phase, self.time_elapsed_meter.name),\n self.time_elapsed_meter.val,\n self.steps[phase],\n )\n\n logging.info(f\"Estimated time remaining: {human_readable_time(time_remaining)}\")\n\n def _reset_meters(self, phases: str) -> None:\n for meter in self._get_meters(phases).values():\n meter.reset()\n\n def _check_val_key_match(self, val_keys, phase):\n if val_keys is not None:\n # Check if there are any duplicates\n assert len(val_keys) == len(\n set(val_keys)\n ), f\"Duplicate keys in val datasets, keys: {val_keys}\"\n\n # Check that the keys match the meter keys\n if self.meters_conf is not None and phase in self.meters_conf:\n assert set(val_keys) == set(self.meters_conf[phase].keys()), (\n f\"Keys in val datasets do not match the keys in meters.\"\n f\"\\nMissing in meters: {set(val_keys) - set(self.meters_conf[phase].keys())}\"\n f\"\\nMissing in val datasets: {set(self.meters_conf[phase].keys()) - set(val_keys)}\"\n )\n\n if self.loss_conf is not None:\n loss_keys = set(self.loss_conf.keys()) - set([\"all\"])\n assert all([k in loss_keys for k in val_keys]), (\n f\"Keys in val datasets do not match the keys in losses.\"\n f\"\\nMissing in losses: {set(val_keys) - loss_keys}\"\n f\"\\nMissing in val datasets: {loss_keys - set(val_keys)}\"\n )\n\n def _setup_components(self):\n\n # Get the keys for all the val datasets, if any\n val_phase = Phase.VAL\n val_keys = None\n if self.data_conf.get(val_phase, None) is not None:\n val_keys = collect_dict_keys(self.data_conf[val_phase])\n # Additional checks on the sanity of the config for val datasets\n self._check_val_key_match(val_keys, phase=val_phase)\n\n logging.info(\"Setting up components: Model, loss, optim, meters etc.\")\n self.epoch = 0\n self.steps = {Phase.TRAIN: 0, Phase.VAL: 0}\n\n self.logger = Logger(self.logging_conf)\n\n self.model = instantiate(self.model_conf, _convert_=\"all\")\n print_model_summary(self.model)\n\n self.loss = None\n if self.loss_conf:\n self.loss = {\n key: el # wrap_base_loss(el)\n for (key, el) in instantiate(self.loss_conf, _convert_=\"all\").items()\n }\n self.loss = nn.ModuleDict(self.loss)\n\n self.meters = {}\n self.best_meter_values = {}\n if self.meters_conf:\n self.meters = instantiate(self.meters_conf, _convert_=\"all\")\n\n self.scaler = torch.amp.GradScaler(\n self.device,\n enabled=self.optim_conf.amp.enabled if self.optim_conf else False,\n )\n\n self.gradient_clipper = (\n instantiate(self.optim_conf.gradient_clip) if self.optim_conf else None\n )\n self.gradient_logger = (\n instantiate(self.optim_conf.gradient_logger) if self.optim_conf else None\n )\n\n logging.info(\"Finished setting up components: Model, loss, optim, meters etc.\")\n\n def _construct_optimizers(self):\n self.optim = construct_optimizer(\n self.model,\n self.optim_conf.optimizer,\n self.optim_conf.options,\n self.optim_conf.param_group_modifiers,\n )\n\n def _log_loss_detailed_and_return_core_loss(self, loss, loss_str, step):\n core_loss = loss.pop(CORE_LOSS_KEY)\n if step % self.logging_conf.log_scalar_frequency == 0:\n for k in loss:\n log_str = os.path.join(loss_str, k)\n self.logger.log(log_str, loss[k], step)\n return core_loss\n\n\ndef print_model_summary(model: torch.nn.Module, log_dir: str = \"\"):\n \"\"\"\n Prints the model and the number of parameters in the model.\n # Multiple packages provide this info in a nice table format\n # However, they need us to provide an `input` (as they also write down the output sizes)\n # Our models are complex, and a single input is restrictive.\n # https://github.com/sksq96/pytorch-summary\n # https://github.com/nmhkahn/torchsummaryX\n \"\"\"\n if get_rank() != 0:\n return\n param_kwargs = {}\n trainable_parameters = sum(\n p.numel() for p in model.parameters(**param_kwargs) if p.requires_grad\n )\n total_parameters = sum(p.numel() for p in model.parameters(**param_kwargs))\n non_trainable_parameters = total_parameters - trainable_parameters\n logging.info(\"==\" * 10)\n logging.info(f\"Summary for model {type(model)}\")\n logging.info(f\"Model is {model}\")\n logging.info(f\"\\tTotal parameters {get_human_readable_count(total_parameters)}\")\n logging.info(\n f\"\\tTrainable parameters {get_human_readable_count(trainable_parameters)}\"\n )\n logging.info(\n f\"\\tNon-Trainable parameters {get_human_readable_count(non_trainable_parameters)}\"\n )\n logging.info(\"==\" * 10)\n\n if log_dir:\n output_fpath = os.path.join(log_dir, \"model.txt\")\n with g_pathmgr.open(output_fpath, \"w\") as f:\n print(model, file=f)\n\n\nPARAMETER_NUM_UNITS = [\" \", \"K\", \"M\", \"B\", \"T\"]\n\n\ndef get_human_readable_count(number: int) -> str:\n \"\"\"\n Abbreviates an integer number with K, M, B, T for thousands, millions,\n billions and trillions, respectively.\n Examples:\n >>> get_human_readable_count(123)\n '123 '\n >>> get_human_readable_count(1234) # (one thousand)\n '1.2 K'\n >>> get_human_readable_count(2e6) # (two million)\n '2.0 M'\n >>> get_human_readable_count(3e9) # (three billion)\n '3.0 B'\n >>> get_human_readable_count(4e14) # (four hundred trillion)\n '400 T'\n >>> get_human_readable_count(5e15) # (more than trillion)\n '5,000 T'\n Args:\n number: a positive integer number\n Return:\n A string formatted according to the pattern described above.\n \"\"\"\n assert number >= 0\n labels = PARAMETER_NUM_UNITS\n num_digits = int(np.floor(np.log10(number)) + 1 if number > 0 else 1)\n num_groups = int(np.ceil(num_digits / 3))\n num_groups = min(num_groups, len(labels)) # don't abbreviate beyond trillions\n shift = -3 * (num_groups - 1)\n number = number * (10**shift)\n index = num_groups - 1\n if index < 1 or number >= 100:\n return f\"{int(number):,d} {labels[index]}\"\n else:\n return f\"{number:,.1f} {labels[index]}\"\n" }, { "path": "auto-seg/submodules/segment-anything-2/training/utils/__init__.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n" }, { "path": "auto-seg/submodules/segment-anything-2/training/utils/checkpoint_utils.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport contextlib\nimport fnmatch\nimport logging\nfrom typing import (\n Any,\n Callable,\n Dict,\n List,\n Mapping,\n Optional,\n Sequence,\n Set,\n Tuple,\n Union,\n)\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nfrom iopath.common.file_io import g_pathmgr\nfrom torch.jit._script import RecursiveScriptModule\n\n\ndef unix_pattern_to_parameter_names(\n constraints: List[str], all_parameter_names: Sequence[str]\n) -> Union[None, Set[str]]:\n \"\"\"\n Go through the list of parameter names and select those that match\n any of the provided constraints\n \"\"\"\n parameter_names = []\n for param_name in constraints:\n matching_parameters = set(fnmatch.filter(all_parameter_names, param_name))\n assert (\n len(matching_parameters) > 0\n ), f\"param_names {param_name} don't match any param in the given names.\"\n parameter_names.append(matching_parameters)\n return set.union(*parameter_names)\n\n\ndef filter_params_matching_unix_pattern(\n patterns: List[str], state_dict: Dict[str, torch.Tensor]\n) -> Dict[str, torch.Tensor]:\n \"\"\"\n Remove from the state dictionary the parameters matching the provided unix patterns\n\n Args:\n patterns: the list of unix patterns to exclude\n state_dict: the dictionary to filter\n\n Returns:\n A new state dictionary\n \"\"\"\n if len(patterns) == 0:\n return {}\n\n all_keys = list(state_dict.keys())\n included_keys = unix_pattern_to_parameter_names(patterns, all_keys)\n return {k: state_dict[k] for k in included_keys}\n\n\ndef exclude_params_matching_unix_pattern(\n patterns: List[str], state_dict: Dict[str, torch.Tensor]\n) -> Dict[str, torch.Tensor]:\n \"\"\"\n Remove from the state dictionary the parameters matching the provided unix patterns\n\n Args:\n patterns: the list of unix patterns to exclude\n state_dict: the dictionary to filter\n\n Returns:\n A new state dictionary\n \"\"\"\n if len(patterns) == 0:\n return state_dict\n\n all_keys = list(state_dict.keys())\n excluded_keys = unix_pattern_to_parameter_names(patterns, all_keys)\n return {k: v for k, v in state_dict.items() if k not in excluded_keys}\n\n\ndef _get_state_dict_summary(state_dict: Dict[str, torch.Tensor]):\n keys = []\n trace = []\n for k, v in state_dict.items():\n keys.append(k)\n trace.append(v.sum().item())\n trace = np.array(trace)[np.argsort(keys)]\n return trace\n\n\ndef assert_skipped_parameters_are_frozen(model: nn.Module, patterns: List[str]):\n \"\"\"\n Verifies that all the parameters matching the provided patterns\n are frozen - this acts as a safeguard when ignoring parameter\n when saving checkpoints - if the parameters are in fact trainable\n \"\"\"\n if not patterns:\n return\n\n frozen_state_dict = filter_params_matching_unix_pattern(\n patterns=patterns, state_dict=model.state_dict()\n )\n non_frozen_keys = {\n n\n for n, p in model.named_parameters()\n if n in frozen_state_dict and p.requires_grad\n }\n if non_frozen_keys:\n raise ValueError(\n f\"Parameters excluded with `skip_saving_parameters` should be frozen: {non_frozen_keys}\"\n )\n\n\n@contextlib.contextmanager\ndef with_check_parameter_frozen(\n model: nn.Module, patterns: List[str], disabled: bool = True\n):\n \"\"\"\n Context manager that inspects a model surrounding a piece of code\n and verifies if the model has been updated by this piece of code\n\n The function will raise an exception if the model has been updated\n on at least one of the parameter that matches one of the pattern\n\n Args:\n model: the model that might have been updated\n patterns: for the parameters we want to observe\n allowed:\n \"\"\"\n if not patterns or disabled:\n yield\n return\n\n frozen_state_dict = filter_params_matching_unix_pattern(\n patterns=patterns, state_dict=model.state_dict()\n )\n summary_before = _get_state_dict_summary(frozen_state_dict)\n\n yield\n\n frozen_state_dict = filter_params_matching_unix_pattern(\n patterns=patterns, state_dict=model.state_dict()\n )\n summary_after = _get_state_dict_summary(frozen_state_dict)\n\n if not np.allclose(summary_before, summary_after, atol=1e-6):\n raise ValueError(\n f\"\"\"\n The `model_weight_initializer` has initialized parameters frozen with `skip_saving_parameters`.\n You can resolve this error by either initializing those parameters from within the model definition\n or using the flag `trainer.checkpoint.initialize_after_preemption` to True.\n \"\"\"\n )\n\n\nclass CkptExcludeKernel:\n \"\"\"\n Removes the keys from the given model state_dict that match the key_pattern.\n\n Args:\n key_pattern: Patterns used to select the keys in the state_dict\n that are eligible for this kernel.\n \"\"\"\n\n def __init__(self, key_pattern: List[str]):\n self.key_pattern = key_pattern\n\n def __call__(self, state_dict: Dict):\n \"\"\"\n Args:\n state_dict: A dictionary representing the given checkpoint's state dict.\n \"\"\"\n if len(self.key_pattern) == 0:\n return state_dict\n exclude_keys = unix_pattern_to_parameter_names(\n self.key_pattern, state_dict.keys()\n )\n return {k: v for k, v in state_dict.items() if k not in exclude_keys}\n\n\ndef load_checkpoint(\n path_list: List[str],\n pick_recursive_keys: Optional[List[str]] = None,\n map_location: str = \"cpu\",\n) -> Any:\n \"\"\"\n Loads a checkpoint from the specified path.\n\n Args:\n path_list: A list of paths which contain the checkpoint. Each element\n is tried (in order) until a file that exists is found. That file is then\n used to read the checkpoint.\n pick_recursive_keys: Picks sub dicts from the loaded checkpoint if not None.\n For pick_recursive_keys = [\"a\", \"b\"], will return checkpoint_dict[\"a\"][\"b\"]\n map_location (str): a function, torch.device, string or a dict specifying how to\n remap storage locations\n\n Returns: Model with the matchin pre-trained weights loaded.\n \"\"\"\n path_exists = False\n for path in path_list:\n if g_pathmgr.exists(path):\n path_exists = True\n break\n\n if not path_exists:\n raise ValueError(f\"No path exists in {path_list}\")\n\n with g_pathmgr.open(path, \"rb\") as f:\n checkpoint = torch.load(f, map_location=map_location)\n\n logging.info(f\"Loaded checkpoint from {path}\")\n if pick_recursive_keys is not None:\n for key in pick_recursive_keys:\n checkpoint = checkpoint[key]\n return checkpoint\n\n\ndef get_state_dict(checkpoint, ckpt_state_dict_keys):\n if isinstance(checkpoint, RecursiveScriptModule):\n # This is a torchscript JIT model\n return checkpoint.state_dict()\n pre_train_dict = checkpoint\n for i, key in enumerate(ckpt_state_dict_keys):\n if (isinstance(pre_train_dict, Mapping) and key not in pre_train_dict) or (\n isinstance(pre_train_dict, Sequence) and key >= len(pre_train_dict)\n ):\n key_str = (\n '[\"' + '\"][\"'.join(list(map(ckpt_state_dict_keys[:i], str))) + '\"]'\n )\n raise KeyError(\n f\"'{key}' not found in checkpoint{key_str} \"\n f\"with keys: {pre_train_dict.keys()}\"\n )\n pre_train_dict = pre_train_dict[key]\n return pre_train_dict\n\n\ndef load_checkpoint_and_apply_kernels(\n checkpoint_path: str,\n checkpoint_kernels: List[Callable] = None,\n ckpt_state_dict_keys: Tuple[str] = (\"state_dict\",),\n map_location: str = \"cpu\",\n) -> nn.Module:\n \"\"\"\n Performs checkpoint loading with a variety of pre-processing kernel applied in\n sequence.\n\n Args:\n checkpoint_path (str): Path to the checkpoint.\n checkpoint_kernels List(Callable): A list of checkpoint processing kernels\n to apply in the specified order. Supported kernels include `CkptIncludeKernel`,\n `CkptExcludeKernel`, etc. These kernels are applied in the\n given order.\n ckpt_state_dict_keys (str): Keys containing the model state dict.\n map_location (str): a function, torch.device, string or a dict specifying how to\n remap storage locations\n\n Returns: Model with the matchin pre-trained weights loaded.\n \"\"\"\n assert g_pathmgr.exists(checkpoint_path), \"Checkpoint '{}' not found\".format(\n checkpoint_path\n )\n\n # Load the checkpoint on CPU to avoid GPU mem spike.\n with g_pathmgr.open(checkpoint_path, \"rb\") as f:\n checkpoint = torch.load(f, map_location=map_location)\n\n pre_train_dict = get_state_dict(checkpoint, ckpt_state_dict_keys)\n\n # Not logging into info etc since it's a huge log\n logging.debug(\n \"Loaded Checkpoint State Dict pre-kernel application: %s\"\n % str(\", \".join(list(pre_train_dict.keys())))\n )\n # Apply kernels\n if checkpoint_kernels is not None:\n for f in checkpoint_kernels:\n pre_train_dict = f(state_dict=pre_train_dict)\n\n logging.debug(\n \"Loaded Checkpoint State Dict Post-kernel application %s\"\n % str(\", \".join(list(pre_train_dict.keys())))\n )\n\n return pre_train_dict\n\n\ndef check_load_state_dict_errors(\n missing_keys,\n unexpected_keys,\n strict: bool,\n ignore_missing_keys: List[str] = None,\n ignore_unexpected_keys: List[str] = None,\n):\n if ignore_missing_keys is not None and len(ignore_missing_keys) > 0:\n ignored_keys = unix_pattern_to_parameter_names(\n ignore_missing_keys, missing_keys\n )\n missing_keys = [key for key in missing_keys if key not in ignored_keys]\n\n if ignore_unexpected_keys is not None and len(ignore_unexpected_keys) > 0:\n ignored_unexpected_keys = unix_pattern_to_parameter_names(\n ignore_unexpected_keys, unexpected_keys\n )\n unexpected_keys = [\n key for key in unexpected_keys if key not in ignored_unexpected_keys\n ]\n\n err = \"State key mismatch.\"\n if unexpected_keys:\n err += f\" Unexpected keys: {unexpected_keys}.\"\n if missing_keys:\n err += f\" Missing keys: {missing_keys}.\"\n\n if unexpected_keys or missing_keys:\n logging.warning(err)\n if unexpected_keys or strict:\n raise KeyError(err)\n\n\ndef load_state_dict_into_model(\n state_dict: Dict,\n model: nn.Module,\n strict: bool = True,\n ignore_missing_keys: List[str] = None,\n ignore_unexpected_keys: List[str] = None,\n checkpoint_kernels: List[Callable] = None,\n):\n \"\"\"\n Loads a state dict into the given model.\n\n Args:\n state_dict: A dictionary containing the model's\n state dict, or a subset if strict is False\n model: Model to load the checkpoint weights into\n strict: raise if the state_dict has missing state keys\n ignore_missing_keys: unix pattern of keys to ignore\n \"\"\"\n # Apply kernels\n if checkpoint_kernels is not None:\n for f in checkpoint_kernels:\n state_dict = f(state_dict=state_dict)\n missing_keys, unexpected_keys = model.load_state_dict(state_dict, strict=False)\n\n check_load_state_dict_errors(\n missing_keys,\n unexpected_keys,\n strict=strict,\n ignore_missing_keys=ignore_missing_keys,\n ignore_unexpected_keys=ignore_unexpected_keys,\n )\n return model\n" }, { "path": "auto-seg/submodules/segment-anything-2/training/utils/data_utils.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\n\"\"\"\nMisc functions, including distributed helpers.\n\nMostly copy-paste from torchvision references.\n\"\"\"\n\nfrom dataclasses import dataclass\nfrom typing import List, Optional, Tuple, Union\n\nimport torch\n\nfrom PIL import Image as PILImage\nfrom tensordict import tensorclass\n\n\n@tensorclass\nclass BatchedVideoMetaData:\n \"\"\"\n This class represents metadata about a batch of videos.\n Attributes:\n unique_objects_identifier: A tensor of shape Bx3 containing unique identifiers for each object in the batch. Index consists of (video_id, obj_id, frame_id)\n frame_orig_size: A tensor of shape Bx2 containing the original size of each frame in the batch.\n \"\"\"\n\n unique_objects_identifier: torch.LongTensor\n frame_orig_size: torch.LongTensor\n\n\n@tensorclass\nclass BatchedVideoDatapoint:\n \"\"\"\n This class represents a batch of videos with associated annotations and metadata.\n Attributes:\n img_batch: A [TxBxCxHxW] tensor containing the image data for each frame in the batch, where T is the number of frames per video, and B is the number of videos in the batch.\n obj_to_frame_idx: A [TxOx2] tensor containing the image_batch index which the object belongs to. O is the number of objects in the batch.\n masks: A [TxOxHxW] tensor containing binary masks for each object in the batch.\n metadata: An instance of BatchedVideoMetaData containing metadata about the batch.\n dict_key: A string key used to identify the batch.\n \"\"\"\n\n img_batch: torch.FloatTensor\n obj_to_frame_idx: torch.IntTensor\n masks: torch.BoolTensor\n metadata: BatchedVideoMetaData\n\n dict_key: str\n\n def pin_memory(self, device=None):\n return self.apply(torch.Tensor.pin_memory, device=device)\n\n @property\n def num_frames(self) -> int:\n \"\"\"\n Returns the number of frames per video.\n \"\"\"\n return self.batch_size[0]\n\n @property\n def num_videos(self) -> int:\n \"\"\"\n Returns the number of videos in the batch.\n \"\"\"\n return self.img_batch.shape[1]\n\n @property\n def flat_obj_to_img_idx(self) -> torch.IntTensor:\n \"\"\"\n Returns a flattened tensor containing the object to img index.\n The flat index can be used to access a flattened img_batch of shape [(T*B)xCxHxW]\n \"\"\"\n frame_idx, video_idx = self.obj_to_frame_idx.unbind(dim=-1)\n flat_idx = video_idx * self.num_frames + frame_idx\n return flat_idx\n\n @property\n def flat_img_batch(self) -> torch.FloatTensor:\n \"\"\"\n Returns a flattened img_batch_tensor of shape [(B*T)xCxHxW]\n \"\"\"\n\n return self.img_batch.transpose(0, 1).flatten(0, 1)\n\n\n@dataclass\nclass Object:\n # Id of the object in the media\n object_id: int\n # Index of the frame in the media (0 if single image)\n frame_index: int\n segment: Union[torch.Tensor, dict] # RLE dict or binary mask\n\n\n@dataclass\nclass Frame:\n data: Union[torch.Tensor, PILImage.Image]\n objects: List[Object]\n\n\n@dataclass\nclass VideoDatapoint:\n \"\"\"Refers to an image/video and all its annotations\"\"\"\n\n frames: List[Frame]\n video_id: int\n size: Tuple[int, int]\n\n\ndef collate_fn(\n batch: List[VideoDatapoint],\n dict_key,\n) -> BatchedVideoDatapoint:\n \"\"\"\n Args:\n batch: A list of VideoDatapoint instances.\n dict_key (str): A string key used to identify the batch.\n \"\"\"\n img_batch = []\n for video in batch:\n img_batch += [torch.stack([frame.data for frame in video.frames], dim=0)]\n\n img_batch = torch.stack(img_batch, dim=0).permute((1, 0, 2, 3, 4))\n T = img_batch.shape[0]\n # Prepare data structures for sequential processing. Per-frame processing but batched across videos.\n step_t_objects_identifier = [[] for _ in range(T)]\n step_t_frame_orig_size = [[] for _ in range(T)]\n\n step_t_masks = [[] for _ in range(T)]\n step_t_obj_to_frame_idx = [\n [] for _ in range(T)\n ] # List to store frame indices for each time step\n\n for video_idx, video in enumerate(batch):\n orig_video_id = video.video_id\n orig_frame_size = video.size\n for t, frame in enumerate(video.frames):\n objects = frame.objects\n for obj in objects:\n orig_obj_id = obj.object_id\n orig_frame_idx = obj.frame_index\n step_t_obj_to_frame_idx[t].append(\n torch.tensor([t, video_idx], dtype=torch.int)\n )\n step_t_masks[t].append(obj.segment.to(torch.bool))\n step_t_objects_identifier[t].append(\n torch.tensor([orig_video_id, orig_obj_id, orig_frame_idx])\n )\n step_t_frame_orig_size[t].append(torch.tensor(orig_frame_size))\n\n obj_to_frame_idx = torch.stack(\n [\n torch.stack(obj_to_frame_idx, dim=0)\n for obj_to_frame_idx in step_t_obj_to_frame_idx\n ],\n dim=0,\n )\n masks = torch.stack([torch.stack(masks, dim=0) for masks in step_t_masks], dim=0)\n objects_identifier = torch.stack(\n [torch.stack(id, dim=0) for id in step_t_objects_identifier], dim=0\n )\n frame_orig_size = torch.stack(\n [torch.stack(id, dim=0) for id in step_t_frame_orig_size], dim=0\n )\n return BatchedVideoDatapoint(\n img_batch=img_batch,\n obj_to_frame_idx=obj_to_frame_idx,\n masks=masks,\n metadata=BatchedVideoMetaData(\n unique_objects_identifier=objects_identifier,\n frame_orig_size=frame_orig_size,\n ),\n dict_key=dict_key,\n batch_size=[T],\n )\n" }, { "path": "auto-seg/submodules/segment-anything-2/training/utils/distributed.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport datetime\nimport functools\nimport io\nimport logging\nimport os\nimport random\nimport tempfile\nimport time\nfrom typing import Any, Callable, List, Tuple\n\nimport torch\nimport torch.autograd as autograd\nimport torch.distributed as dist\n\n\n# Default to GPU 0\n_cuda_device_index: int = 0\n\n# Setting _cuda_device_index to -1 internally implies that we should use CPU\n_CPU_DEVICE_INDEX = -1\n_PRIMARY_RANK = 0\n\n\n@functools.lru_cache()\ndef _get_global_gloo_group():\n \"\"\"\n Return a process group based on gloo backend, containing all the ranks\n The result is cached.\n \"\"\"\n\n if dist.get_backend() == \"nccl\":\n # Increase timeout from 1800 sec to 43200 sec (12 hr) to avoid some processes\n # being much slower than others causing a timeout (which can happen in relation\n # or LVIS class mAP evaluation).\n timeout = 43200\n return dist.new_group(\n backend=\"gloo\",\n timeout=datetime.timedelta(seconds=timeout),\n )\n\n return dist.group.WORLD\n\n\ndef is_main_process():\n \"\"\"Return true if the current process is the main one\"\"\"\n return get_rank() == 0\n\n\ndef all_gather_via_filesys(data, filesys_save_dir=None, gather_to_rank_0_only=False):\n \"\"\"\n Run all_gather on arbitrary picklable data (not necessarily tensors), similar to\n `all_gather` above, but using filesystem instead of collective ops.\n\n If gather_to_rank_0_only is True, only rank 0 will load the gathered object list\n (and other ranks will have an empty list).\n \"\"\"\n world_size = get_world_size()\n if world_size == 1:\n return [data]\n\n print(\"gathering via files\")\n cpu_group = _get_global_gloo_group()\n\n # if unspecified, we will save to the current python file dir\n if filesys_save_dir is not None:\n save_dir = filesys_save_dir\n elif \"EXP_DIR\" in os.environ:\n save_dir = os.environ[\"EXP_DIR\"]\n else:\n # try the same directory where the code is stored\n save_dir = filesys_save_dir or os.path.dirname(__file__)\n save_dir = os.path.join(save_dir, \"all_gather_via_filesys\")\n if is_main_process():\n os.makedirs(save_dir, exist_ok=True)\n\n # use a timestamp and salt to distinguish different all_gather\n timestamp = int(time.time()) if is_main_process() else 0\n salt = random.randint(0, 2**31 - 1) if is_main_process() else 0\n # broadcast the timestamp and salt across ranks\n # (all-reduce will do the broadcasting since only rank 0 is non-zero)\n timestamp_and_salt = torch.tensor([timestamp, salt], dtype=torch.long)\n dist.all_reduce(timestamp_and_salt, group=cpu_group)\n timestamp, salt = timestamp_and_salt.tolist()\n\n # save the data to a file on the disk\n rank_save = get_rank()\n save_data_filename = f\"data_to_gather_{timestamp}_{salt}_{rank_save}.pkl\"\n save_data_path = os.path.join(save_dir, save_data_filename)\n assert not os.path.exists(save_data_path), f\"{save_data_path} already exists\"\n torch.save(data, save_data_path)\n dist.barrier(group=cpu_group)\n\n # read the data from the files\n data_list = []\n if rank_save == 0 or not gather_to_rank_0_only:\n for rank_load in range(world_size):\n load_data_filename = f\"data_to_gather_{timestamp}_{salt}_{rank_load}.pkl\"\n load_data_path = os.path.join(save_dir, load_data_filename)\n assert os.path.exists(load_data_path), f\"cannot read {save_data_path}\"\n data_list.append(torch.load(load_data_path))\n dist.barrier(group=cpu_group)\n\n # delete the saved file\n os.remove(save_data_path)\n return data_list\n\n\ndef all_gather(data, force_cpu=False, force_filesys=False, filesys_save_dir=None):\n \"\"\"\n Run all_gather on arbitrary picklable data (not necessarily tensors)\n Args:\n data: any picklable object\n Returns:\n list[data]: list of data gathered from each rank\n \"\"\"\n\n world_size = get_world_size()\n if world_size == 1:\n return [data]\n\n if os.getenv(\"MDETR_FILESYS_REDUCE_RANK_0_ONLY\") == \"1\":\n return all_gather_via_filesys(\n data, filesys_save_dir, gather_to_rank_0_only=True\n )\n\n if os.getenv(\"MDETR_FILESYS_REDUCE\") == \"1\" or force_filesys:\n return all_gather_via_filesys(data, filesys_save_dir)\n\n cpu_group = None\n if os.getenv(\"MDETR_CPU_REDUCE\") == \"1\" or force_cpu:\n cpu_group = _get_global_gloo_group()\n\n buffer = io.BytesIO()\n torch.save(data, buffer)\n data_view = buffer.getbuffer()\n device = \"cuda\" if cpu_group is None else \"cpu\"\n tensor = torch.ByteTensor(data_view).to(device)\n\n # obtain Tensor size of each rank\n local_size = torch.tensor([tensor.numel()], device=device, dtype=torch.long)\n size_list = [\n torch.tensor([0], device=device, dtype=torch.long) for _ in range(world_size)\n ]\n if cpu_group is None:\n dist.all_gather(size_list, local_size)\n else:\n print(\"gathering on cpu\")\n dist.all_gather(size_list, local_size, group=cpu_group)\n size_list = [int(size.item()) for size in size_list]\n max_size = max(size_list)\n assert isinstance(local_size.item(), int)\n local_size = int(local_size.item())\n\n # receiving Tensor from all ranks\n # we pad the tensor because torch all_gather does not support\n # gathering tensors of different shapes\n tensor_list = []\n for _ in size_list:\n tensor_list.append(torch.empty((max_size,), dtype=torch.uint8, device=device))\n if local_size != max_size:\n padding = torch.empty(\n size=(max_size - local_size,), dtype=torch.uint8, device=device\n )\n tensor = torch.cat((tensor, padding), dim=0)\n if cpu_group is None:\n dist.all_gather(tensor_list, tensor)\n else:\n dist.all_gather(tensor_list, tensor, group=cpu_group)\n\n data_list = []\n for size, tensor in zip(size_list, tensor_list):\n tensor = torch.split(tensor, [size, max_size - size], dim=0)[0]\n buffer = io.BytesIO(tensor.cpu().numpy())\n obj = torch.load(buffer)\n data_list.append(obj)\n\n return data_list\n\n\ndef convert_to_distributed_tensor(tensor: torch.Tensor) -> Tuple[torch.Tensor, str]:\n \"\"\"\n For some backends, such as NCCL, communication only works if the\n tensor is on the GPU. This helper function converts to the correct\n device and returns the tensor + original device.\n \"\"\"\n orig_device = \"cpu\" if not tensor.is_cuda else \"gpu\"\n if (\n torch.distributed.is_available()\n and torch.distributed.get_backend() == torch.distributed.Backend.NCCL\n and not tensor.is_cuda\n ):\n tensor = tensor.cuda()\n return (tensor, orig_device)\n\n\ndef convert_to_normal_tensor(tensor: torch.Tensor, orig_device: str) -> torch.Tensor:\n \"\"\"\n For some backends, such as NCCL, communication only works if the\n tensor is on the GPU. This converts the tensor back to original device.\n \"\"\"\n if tensor.is_cuda and orig_device == \"cpu\":\n tensor = tensor.cpu()\n return tensor\n\n\ndef is_distributed_training_run() -> bool:\n return (\n torch.distributed.is_available()\n and torch.distributed.is_initialized()\n and (torch.distributed.get_world_size() > 1)\n )\n\n\ndef is_primary() -> bool:\n \"\"\"\n Returns True if this is rank 0 of a distributed training job OR if it is\n a single trainer job. Otherwise False.\n \"\"\"\n return get_rank() == _PRIMARY_RANK\n\n\ndef all_reduce_mean(tensor: torch.Tensor) -> torch.Tensor:\n \"\"\"\n Wrapper over torch.distributed.all_reduce for performing mean reduction\n of tensor over all processes.\n \"\"\"\n return all_reduce_op(\n tensor,\n torch.distributed.ReduceOp.SUM,\n lambda t: t / torch.distributed.get_world_size(),\n )\n\n\ndef all_reduce_sum(tensor: torch.Tensor) -> torch.Tensor:\n \"\"\"\n Wrapper over torch.distributed.all_reduce for performing sum\n reduction of tensor over all processes in both distributed /\n non-distributed scenarios.\n \"\"\"\n return all_reduce_op(tensor, torch.distributed.ReduceOp.SUM)\n\n\ndef all_reduce_min(tensor: torch.Tensor) -> torch.Tensor:\n \"\"\"\n Wrapper over torch.distributed.all_reduce for performing min\n reduction of tensor over all processes in both distributed /\n non-distributed scenarios.\n \"\"\"\n return all_reduce_op(tensor, torch.distributed.ReduceOp.MIN)\n\n\ndef all_reduce_max(tensor: torch.Tensor) -> torch.Tensor:\n \"\"\"\n Wrapper over torch.distributed.all_reduce for performing min\n reduction of tensor over all processes in both distributed /\n non-distributed scenarios.\n \"\"\"\n return all_reduce_op(tensor, torch.distributed.ReduceOp.MAX)\n\n\ndef all_reduce_op(\n tensor: torch.Tensor,\n op: torch.distributed.ReduceOp,\n after_op_func: Callable[[torch.Tensor], torch.Tensor] = None,\n) -> torch.Tensor:\n \"\"\"\n Wrapper over torch.distributed.all_reduce for performing\n reduction of tensor over all processes in both distributed /\n non-distributed scenarios.\n \"\"\"\n if is_distributed_training_run():\n tensor, orig_device = convert_to_distributed_tensor(tensor)\n torch.distributed.all_reduce(tensor, op)\n if after_op_func is not None:\n tensor = after_op_func(tensor)\n tensor = convert_to_normal_tensor(tensor, orig_device)\n return tensor\n\n\ndef gather_tensors_from_all(tensor: torch.Tensor) -> List[torch.Tensor]:\n \"\"\"\n Wrapper over torch.distributed.all_gather for performing\n 'gather' of 'tensor' over all processes in both distributed /\n non-distributed scenarios.\n \"\"\"\n if tensor.ndim == 0:\n # 0 dim tensors cannot be gathered. so unsqueeze\n tensor = tensor.unsqueeze(0)\n\n if is_distributed_training_run():\n tensor, orig_device = convert_to_distributed_tensor(tensor)\n gathered_tensors = [\n torch.zeros_like(tensor) for _ in range(torch.distributed.get_world_size())\n ]\n torch.distributed.all_gather(gathered_tensors, tensor)\n gathered_tensors = [\n convert_to_normal_tensor(_tensor, orig_device)\n for _tensor in gathered_tensors\n ]\n else:\n gathered_tensors = [tensor]\n\n return gathered_tensors\n\n\ndef gather_from_all(tensor: torch.Tensor) -> torch.Tensor:\n gathered_tensors = gather_tensors_from_all(tensor)\n gathered_tensor = torch.cat(gathered_tensors, 0)\n return gathered_tensor\n\n\ndef broadcast(tensor: torch.Tensor, src: int = 0) -> torch.Tensor:\n \"\"\"\n Wrapper over torch.distributed.broadcast for broadcasting a tensor from the source\n to all processes in both distributed / non-distributed scenarios.\n \"\"\"\n if is_distributed_training_run():\n tensor, orig_device = convert_to_distributed_tensor(tensor)\n torch.distributed.broadcast(tensor, src)\n tensor = convert_to_normal_tensor(tensor, orig_device)\n return tensor\n\n\ndef barrier() -> None:\n \"\"\"\n Wrapper over torch.distributed.barrier, returns without waiting\n if the distributed process group is not initialized instead of throwing error.\n \"\"\"\n if not torch.distributed.is_available() or not torch.distributed.is_initialized():\n return\n torch.distributed.barrier()\n\n\ndef get_world_size() -> int:\n \"\"\"\n Simple wrapper for correctly getting worldsize in both distributed\n / non-distributed settings\n \"\"\"\n return (\n torch.distributed.get_world_size()\n if torch.distributed.is_available() and torch.distributed.is_initialized()\n else 1\n )\n\n\ndef get_rank() -> int:\n \"\"\"\n Simple wrapper for correctly getting rank in both distributed\n / non-distributed settings\n \"\"\"\n return (\n torch.distributed.get_rank()\n if torch.distributed.is_available() and torch.distributed.is_initialized()\n else 0\n )\n\n\ndef get_primary_rank() -> int:\n return _PRIMARY_RANK\n\n\ndef set_cuda_device_index(idx: int) -> None:\n global _cuda_device_index\n _cuda_device_index = idx\n torch.cuda.set_device(_cuda_device_index)\n\n\ndef set_cpu_device() -> None:\n global _cuda_device_index\n _cuda_device_index = _CPU_DEVICE_INDEX\n\n\ndef get_cuda_device_index() -> int:\n return _cuda_device_index\n\n\ndef init_distributed_data_parallel_model(\n model: torch.nn.Module,\n broadcast_buffers: bool = False,\n find_unused_parameters: bool = True,\n bucket_cap_mb: int = 25,\n) -> torch.nn.parallel.DistributedDataParallel:\n global _cuda_device_index\n\n if _cuda_device_index == _CPU_DEVICE_INDEX:\n # CPU-only model, don't specify device\n return torch.nn.parallel.DistributedDataParallel(\n model,\n broadcast_buffers=broadcast_buffers,\n find_unused_parameters=find_unused_parameters,\n bucket_cap_mb=bucket_cap_mb,\n )\n else:\n # GPU model\n return torch.nn.parallel.DistributedDataParallel(\n model,\n device_ids=[_cuda_device_index],\n output_device=_cuda_device_index,\n broadcast_buffers=broadcast_buffers,\n find_unused_parameters=find_unused_parameters,\n bucket_cap_mb=bucket_cap_mb,\n )\n\n\ndef broadcast_object(obj: Any, src: int = _PRIMARY_RANK, use_disk: bool = True) -> Any:\n \"\"\"Broadcast an object from a source to all workers.\n\n Args:\n obj: Object to broadcast, must be serializable\n src: Source rank for broadcast (default is primary)\n use_disk: If enabled, removes redundant CPU memory copies by writing to\n disk\n \"\"\"\n # Either broadcast from primary to the fleet (default),\n # or use the src setting as the original rank\n if get_rank() == src:\n # Emit data\n buffer = io.BytesIO()\n torch.save(obj, buffer)\n data_view = buffer.getbuffer()\n length_tensor = torch.LongTensor([len(data_view)])\n length_tensor = broadcast(length_tensor, src=src)\n data_tensor = torch.ByteTensor(data_view)\n data_tensor = broadcast(data_tensor, src=src)\n else:\n # Fetch from the source\n length_tensor = torch.LongTensor([0])\n length_tensor = broadcast(length_tensor, src=src)\n data_tensor = torch.empty([length_tensor.item()], dtype=torch.uint8)\n data_tensor = broadcast(data_tensor, src=src)\n if use_disk:\n with tempfile.TemporaryFile(\"r+b\") as f:\n f.write(data_tensor.numpy())\n # remove reference to the data tensor and hope that Python garbage\n # collects it\n del data_tensor\n f.seek(0)\n obj = torch.load(f)\n else:\n buffer = io.BytesIO(data_tensor.numpy())\n obj = torch.load(buffer)\n return obj\n\n\ndef all_gather_tensor(tensor: torch.Tensor, world_size=None):\n if world_size is None:\n world_size = get_world_size()\n # make contiguous because NCCL won't gather the tensor otherwise\n assert tensor.is_contiguous(), f\"{tensor.shape} is not contiguous!\"\n tensor, orig_device = convert_to_distributed_tensor(tensor)\n tensor_all = [torch.ones_like(tensor) for _ in range(world_size)]\n dist.all_gather(tensor_all, tensor, async_op=False) # performance opt\n tensor_all = [\n convert_to_normal_tensor(tensor, orig_device) for tensor in tensor_all\n ]\n return tensor_all\n\n\ndef all_gather_batch(tensors: List[torch.Tensor]):\n \"\"\"\n Performs all_gather operation on the provided tensors.\n \"\"\"\n # Queue the gathered tensors\n world_size = get_world_size()\n # There is no need for reduction in the single-proc case\n if world_size == 1:\n return tensors\n tensor_list = []\n output_tensor = []\n for tensor in tensors:\n tensor_all = all_gather_tensor(tensor, world_size)\n tensor_list.append(tensor_all)\n\n for tensor_all in tensor_list:\n output_tensor.append(torch.cat(tensor_all, dim=0))\n return output_tensor\n\n\nclass GatherLayer(autograd.Function):\n \"\"\"\n Gather tensors from all workers with support for backward propagation:\n This implementation does not cut the gradients as torch.distributed.all_gather does.\n \"\"\"\n\n @staticmethod\n def forward(ctx, x):\n output = [torch.zeros_like(x) for _ in range(dist.get_world_size())]\n dist.all_gather(output, x)\n return tuple(output)\n\n @staticmethod\n def backward(ctx, *grads):\n all_gradients = torch.stack(grads)\n dist.all_reduce(all_gradients)\n return all_gradients[dist.get_rank()]\n\n\ndef all_gather_batch_with_grad(tensors):\n \"\"\"\n Performs all_gather operation on the provided tensors.\n Graph remains connected for backward grad computation.\n \"\"\"\n # Queue the gathered tensors\n world_size = get_world_size()\n # There is no need for reduction in the single-proc case\n if world_size == 1:\n return tensors\n tensor_list = []\n output_tensor = []\n\n for tensor in tensors:\n tensor_all = GatherLayer.apply(tensor)\n tensor_list.append(tensor_all)\n\n for tensor_all in tensor_list:\n output_tensor.append(torch.cat(tensor_all, dim=0))\n return output_tensor\n\n\ndef unwrap_ddp_if_wrapped(model):\n if isinstance(model, torch.nn.parallel.DistributedDataParallel):\n return model.module\n return model\n\n\ndef create_new_process_group(group_size):\n \"\"\"\n Creates process groups of a gives `group_size` and returns\n process group that current GPU participates in.\n\n `group_size` must divide the total number of GPUs (world_size).\n\n Modified from\n https://github.com/NVIDIA/apex/blob/4e1ae43f7f7ac69113ef426dd15f37123f0a2ed3/apex/parallel/__init__.py#L60\n\n Args:\n group_size (int): number of GPU's to collaborate for sync bn\n \"\"\"\n\n assert group_size > 0\n\n world_size = torch.distributed.get_world_size()\n if world_size <= 8:\n if group_size > world_size:\n logging.warning(\n f\"Requested group size [{group_size}] > world size [{world_size}]. \"\n \"Assuming local debug run and capping it to world size.\"\n )\n group_size = world_size\n assert world_size >= group_size\n assert world_size % group_size == 0\n\n group = None\n for group_num in range(world_size // group_size):\n group_ids = range(group_num * group_size, (group_num + 1) * group_size)\n cur_group = torch.distributed.new_group(ranks=group_ids)\n if torch.distributed.get_rank() // group_size == group_num:\n group = cur_group\n # can not drop out and return here, every process must go through creation of all subgroups\n\n assert group is not None\n return group\n\n\ndef is_dist_avail_and_initialized():\n if not dist.is_available():\n return False\n if not dist.is_initialized():\n return False\n return True\n" }, { "path": "auto-seg/submodules/segment-anything-2/training/utils/logger.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\n# Code borrowed from TLC - https://www.internalfb.com/code/fbsource/fbcode/pytorch/tlc/torchtlc/loggers/tensorboard.py\nimport atexit\nimport functools\nimport logging\nimport sys\nimport uuid\nfrom typing import Any, Dict, Optional, Union\n\nfrom hydra.utils import instantiate\n\nfrom iopath.common.file_io import g_pathmgr\nfrom numpy import ndarray\nfrom torch import Tensor\nfrom torch.utils.tensorboard import SummaryWriter\n\nfrom training.utils.train_utils import get_machine_local_and_dist_rank, makedir\n\nScalar = Union[Tensor, ndarray, int, float]\n\n\ndef make_tensorboard_logger(log_dir: str, **writer_kwargs: Any):\n makedir(log_dir)\n summary_writer_method = SummaryWriter\n return TensorBoardLogger(\n path=log_dir, summary_writer_method=summary_writer_method, **writer_kwargs\n )\n\n\nclass TensorBoardWriterWrapper:\n \"\"\"\n A wrapper around a SummaryWriter object.\n \"\"\"\n\n def __init__(\n self,\n path: str,\n *args: Any,\n filename_suffix: str = None,\n summary_writer_method: Any = SummaryWriter,\n **kwargs: Any,\n ) -> None:\n \"\"\"Create a new TensorBoard logger.\n On construction, the logger creates a new events file that logs\n will be written to. If the environment variable `RANK` is defined,\n logger will only log if RANK = 0.\n\n NOTE: If using the logger with distributed training:\n - This logger can call collective operations\n - Logs will be written on rank 0 only\n - Logger must be constructed synchronously *after* initializing distributed process group.\n\n Args:\n path (str): path to write logs to\n *args, **kwargs: Extra arguments to pass to SummaryWriter\n \"\"\"\n self._writer: Optional[SummaryWriter] = None\n _, self._rank = get_machine_local_and_dist_rank()\n self._path: str = path\n if self._rank == 0:\n logging.info(\n f\"TensorBoard SummaryWriter instantiated. Files will be stored in: {path}\"\n )\n self._writer = summary_writer_method(\n log_dir=path,\n *args,\n filename_suffix=filename_suffix or str(uuid.uuid4()),\n **kwargs,\n )\n else:\n logging.debug(\n f\"Not logging meters on this host because env RANK: {self._rank} != 0\"\n )\n atexit.register(self.close)\n\n @property\n def writer(self) -> Optional[SummaryWriter]:\n return self._writer\n\n @property\n def path(self) -> str:\n return self._path\n\n def flush(self) -> None:\n \"\"\"Writes pending logs to disk.\"\"\"\n\n if not self._writer:\n return\n\n self._writer.flush()\n\n def close(self) -> None:\n \"\"\"Close writer, flushing pending logs to disk.\n Logs cannot be written after `close` is called.\n \"\"\"\n\n if not self._writer:\n return\n\n self._writer.close()\n self._writer = None\n\n\nclass TensorBoardLogger(TensorBoardWriterWrapper):\n \"\"\"\n A simple logger for TensorBoard.\n \"\"\"\n\n def log_dict(self, payload: Dict[str, Scalar], step: int) -> None:\n \"\"\"Add multiple scalar values to TensorBoard.\n\n Args:\n payload (dict): dictionary of tag name and scalar value\n step (int, Optional): step value to record\n \"\"\"\n if not self._writer:\n return\n for k, v in payload.items():\n self.log(k, v, step)\n\n def log(self, name: str, data: Scalar, step: int) -> None:\n \"\"\"Add scalar data to TensorBoard.\n\n Args:\n name (string): tag name used to group scalars\n data (float/int/Tensor): scalar data to log\n step (int, optional): step value to record\n \"\"\"\n if not self._writer:\n return\n self._writer.add_scalar(name, data, global_step=step, new_style=True)\n\n def log_hparams(\n self, hparams: Dict[str, Scalar], meters: Dict[str, Scalar]\n ) -> None:\n \"\"\"Add hyperparameter data to TensorBoard.\n\n Args:\n hparams (dict): dictionary of hyperparameter names and corresponding values\n meters (dict): dictionary of name of meter and corersponding values\n \"\"\"\n if not self._writer:\n return\n self._writer.add_hparams(hparams, meters)\n\n\nclass Logger:\n \"\"\"\n A logger class that can interface with multiple loggers. It now supports tensorboard only for simplicity, but you can extend it with your own logger.\n \"\"\"\n\n def __init__(self, logging_conf):\n # allow turning off TensorBoard with \"should_log: false\" in config\n tb_config = logging_conf.tensorboard_writer\n tb_should_log = tb_config and tb_config.pop(\"should_log\", True)\n self.tb_logger = instantiate(tb_config) if tb_should_log else None\n\n def log_dict(self, payload: Dict[str, Scalar], step: int) -> None:\n if self.tb_logger:\n self.tb_logger.log_dict(payload, step)\n\n def log(self, name: str, data: Scalar, step: int) -> None:\n if self.tb_logger:\n self.tb_logger.log(name, data, step)\n\n def log_hparams(\n self, hparams: Dict[str, Scalar], meters: Dict[str, Scalar]\n ) -> None:\n if self.tb_logger:\n self.tb_logger.log_hparams(hparams, meters)\n\n\n# cache the opened file object, so that different calls to `setup_logger`\n# with the same file name can safely write to the same file.\n@functools.lru_cache(maxsize=None)\ndef _cached_log_stream(filename):\n # we tune the buffering value so that the logs are updated\n # frequently.\n log_buffer_kb = 10 * 1024 # 10KB\n io = g_pathmgr.open(filename, mode=\"a\", buffering=log_buffer_kb)\n atexit.register(io.close)\n return io\n\n\ndef setup_logging(\n name,\n output_dir=None,\n rank=0,\n log_level_primary=\"INFO\",\n log_level_secondary=\"ERROR\",\n):\n \"\"\"\n Setup various logging streams: stdout and file handlers.\n For file handlers, we only setup for the master gpu.\n \"\"\"\n # get the filename if we want to log to the file as well\n log_filename = None\n if output_dir:\n makedir(output_dir)\n if rank == 0:\n log_filename = f\"{output_dir}/log.txt\"\n\n logger = logging.getLogger(name)\n logger.setLevel(log_level_primary)\n\n # create formatter\n FORMAT = \"%(levelname)s %(asctime)s %(filename)s:%(lineno)4d: %(message)s\"\n formatter = logging.Formatter(FORMAT)\n\n # Cleanup any existing handlers\n for h in logger.handlers:\n logger.removeHandler(h)\n logger.root.handlers = []\n\n # setup the console handler\n console_handler = logging.StreamHandler(sys.stdout)\n console_handler.setFormatter(formatter)\n logger.addHandler(console_handler)\n if rank == 0:\n console_handler.setLevel(log_level_primary)\n else:\n console_handler.setLevel(log_level_secondary)\n\n # we log to file as well if user wants\n if log_filename and rank == 0:\n file_handler = logging.StreamHandler(_cached_log_stream(log_filename))\n file_handler.setLevel(log_level_primary)\n file_handler.setFormatter(formatter)\n logger.addHandler(file_handler)\n\n logging.root = logger\n\n\ndef shutdown_logging():\n \"\"\"\n After training is done, we ensure to shut down all the logger streams.\n \"\"\"\n logging.info(\"Shutting down loggers...\")\n handlers = logging.root.handlers\n for handler in handlers:\n handler.close()\n" }, { "path": "auto-seg/submodules/segment-anything-2/training/utils/train_utils.py", "content": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport logging\nimport math\nimport os\nimport random\nimport re\nfrom datetime import timedelta\nfrom typing import Optional\n\nimport hydra\n\nimport numpy as np\nimport omegaconf\nimport torch\nimport torch.distributed as dist\nfrom iopath.common.file_io import g_pathmgr\nfrom omegaconf import OmegaConf\n\n\ndef multiply_all(*args):\n return np.prod(np.array(args)).item()\n\n\ndef collect_dict_keys(config):\n \"\"\"This function recursively iterates through a dataset configuration, and collect all the dict_key that are defined\"\"\"\n val_keys = []\n # If the this config points to the collate function, then it has a key\n if \"_target_\" in config and re.match(r\".*collate_fn.*\", config[\"_target_\"]):\n val_keys.append(config[\"dict_key\"])\n else:\n # Recursively proceed\n for v in config.values():\n if isinstance(v, type(config)):\n val_keys.extend(collect_dict_keys(v))\n elif isinstance(v, omegaconf.listconfig.ListConfig):\n for item in v:\n if isinstance(item, type(config)):\n val_keys.extend(collect_dict_keys(item))\n return val_keys\n\n\nclass Phase:\n TRAIN = \"train\"\n VAL = \"val\"\n\n\ndef register_omegaconf_resolvers():\n OmegaConf.register_new_resolver(\"get_method\", hydra.utils.get_method)\n OmegaConf.register_new_resolver(\"get_class\", hydra.utils.get_class)\n OmegaConf.register_new_resolver(\"add\", lambda x, y: x + y)\n OmegaConf.register_new_resolver(\"times\", multiply_all)\n OmegaConf.register_new_resolver(\"divide\", lambda x, y: x / y)\n OmegaConf.register_new_resolver(\"pow\", lambda x, y: x**y)\n OmegaConf.register_new_resolver(\"subtract\", lambda x, y: x - y)\n OmegaConf.register_new_resolver(\"range\", lambda x: list(range(x)))\n OmegaConf.register_new_resolver(\"int\", lambda x: int(x))\n OmegaConf.register_new_resolver(\"ceil_int\", lambda x: int(math.ceil(x)))\n OmegaConf.register_new_resolver(\"merge\", lambda *x: OmegaConf.merge(*x))\n\n\ndef setup_distributed_backend(backend, timeout_mins):\n \"\"\"\n Initialize torch.distributed and set the CUDA device.\n Expects environment variables to be set as per\n https://pytorch.org/docs/stable/distributed.html#environment-variable-initialization\n along with the environ variable \"LOCAL_RANK\" which is used to set the CUDA device.\n \"\"\"\n # enable TORCH_NCCL_ASYNC_ERROR_HANDLING to ensure dist nccl ops time out after timeout_mins\n # of waiting\n os.environ[\"TORCH_NCCL_ASYNC_ERROR_HANDLING\"] = \"1\"\n logging.info(f\"Setting up torch.distributed with a timeout of {timeout_mins} mins\")\n dist.init_process_group(backend=backend, timeout=timedelta(minutes=timeout_mins))\n return dist.get_rank()\n\n\ndef get_machine_local_and_dist_rank():\n \"\"\"\n Get the distributed and local rank of the current gpu.\n \"\"\"\n local_rank = int(os.environ.get(\"LOCAL_RANK\", None))\n distributed_rank = int(os.environ.get(\"RANK\", None))\n assert (\n local_rank is not None and distributed_rank is not None\n ), \"Please the set the RANK and LOCAL_RANK environment variables.\"\n return local_rank, distributed_rank\n\n\ndef print_cfg(cfg):\n \"\"\"\n Supports printing both Hydra DictConfig and also the AttrDict config\n \"\"\"\n logging.info(\"Training with config:\")\n logging.info(OmegaConf.to_yaml(cfg))\n\n\ndef set_seeds(seed_value, max_epochs, dist_rank):\n \"\"\"\n Set the python random, numpy and torch seed for each gpu. Also set the CUDA\n seeds if the CUDA is available. This ensures deterministic nature of the training.\n \"\"\"\n # Since in the pytorch sampler, we increment the seed by 1 for every epoch.\n seed_value = (seed_value + dist_rank) * max_epochs\n logging.info(f\"MACHINE SEED: {seed_value}\")\n random.seed(seed_value)\n np.random.seed(seed_value)\n torch.manual_seed(seed_value)\n if torch.cuda.is_available():\n torch.cuda.manual_seed_all(seed_value)\n\n\ndef makedir(dir_path):\n \"\"\"\n Create the directory if it does not exist.\n \"\"\"\n is_success = False\n try:\n if not g_pathmgr.exists(dir_path):\n g_pathmgr.mkdirs(dir_path)\n is_success = True\n except BaseException:\n logging.info(f\"Error creating directory: {dir_path}\")\n return is_success\n\n\ndef is_dist_avail_and_initialized():\n if not dist.is_available():\n return False\n if not dist.is_initialized():\n return False\n return True\n\n\ndef get_amp_type(amp_type: Optional[str] = None):\n if amp_type is None:\n return None\n assert amp_type in [\"bfloat16\", \"float16\"], \"Invalid Amp type.\"\n if amp_type == \"bfloat16\":\n return torch.bfloat16\n else:\n return torch.float16\n\n\ndef log_env_variables():\n env_keys = sorted(list(os.environ.keys()))\n st = \"\"\n for k in env_keys:\n v = os.environ[k]\n st += f\"{k}={v}\\n\"\n logging.info(\"Logging ENV_VARIABLES\")\n logging.info(st)\n\n\nclass AverageMeter:\n \"\"\"Computes and stores the average and current value\"\"\"\n\n def __init__(self, name, device, fmt=\":f\"):\n self.name = name\n self.fmt = fmt\n self.device = device\n self.reset()\n\n def reset(self):\n self.val = 0\n self.avg = 0\n self.sum = 0\n self.count = 0\n self._allow_updates = True\n\n def update(self, val, n=1):\n self.val = val\n self.sum += val * n\n self.count += n\n self.avg = self.sum / self.count\n\n def __str__(self):\n fmtstr = \"{name}: {val\" + self.fmt + \"} ({avg\" + self.fmt + \"})\"\n return fmtstr.format(**self.__dict__)\n\n\nclass MemMeter:\n \"\"\"Computes and stores the current, avg, and max of peak Mem usage per iteration\"\"\"\n\n def __init__(self, name, device, fmt=\":f\"):\n self.name = name\n self.fmt = fmt\n self.device = device\n self.reset()\n\n def reset(self):\n self.val = 0 # Per iteration max usage\n self.avg = 0 # Avg per iteration max usage\n self.peak = 0 # Peak usage for lifetime of program\n self.sum = 0\n self.count = 0\n self._allow_updates = True\n\n def update(self, n=1, reset_peak_usage=True):\n self.val = torch.cuda.max_memory_allocated() // 1e9\n self.sum += self.val * n\n self.count += n\n self.avg = self.sum / self.count\n self.peak = max(self.peak, self.val)\n if reset_peak_usage:\n torch.cuda.reset_peak_memory_stats()\n\n def __str__(self):\n fmtstr = (\n \"{name}: {val\"\n + self.fmt\n + \"} ({avg\"\n + self.fmt\n + \"}/{peak\"\n + self.fmt\n + \"})\"\n )\n return fmtstr.format(**self.__dict__)\n\n\ndef human_readable_time(time_seconds):\n time = int(time_seconds)\n minutes, seconds = divmod(time, 60)\n hours, minutes = divmod(minutes, 60)\n days, hours = divmod(hours, 24)\n return f\"{days:02}d {hours:02}h {minutes:02}m\"\n\n\nclass DurationMeter:\n def __init__(self, name, device, fmt=\":f\"):\n self.name = name\n self.device = device\n self.fmt = fmt\n self.val = 0\n\n def reset(self):\n self.val = 0\n\n def update(self, val):\n self.val = val\n\n def add(self, val):\n self.val += val\n\n def __str__(self):\n return f\"{self.name}: {human_readable_time(self.val)}\"\n\n\nclass ProgressMeter:\n def __init__(self, num_batches, meters, real_meters, prefix=\"\"):\n self.batch_fmtstr = self._get_batch_fmtstr(num_batches)\n self.meters = meters\n self.real_meters = real_meters\n self.prefix = prefix\n\n def display(self, batch, enable_print=False):\n entries = [self.prefix + self.batch_fmtstr.format(batch)]\n entries += [str(meter) for meter in self.meters]\n entries += [\n \" | \".join(\n [\n f\"{os.path.join(name, subname)}: {val:.4f}\"\n for subname, val in meter.compute().items()\n ]\n )\n for name, meter in self.real_meters.items()\n ]\n logging.info(\" | \".join(entries))\n if enable_print:\n print(\" | \".join(entries))\n\n def _get_batch_fmtstr(self, num_batches):\n num_digits = len(str(num_batches // 1))\n fmt = \"{:\" + str(num_digits) + \"d}\"\n return \"[\" + fmt + \"/\" + fmt.format(num_batches) + \"]\"\n\n\ndef get_resume_checkpoint(checkpoint_save_dir):\n if not g_pathmgr.isdir(checkpoint_save_dir):\n return None\n ckpt_file = os.path.join(checkpoint_save_dir, \"checkpoint.pt\")\n if not g_pathmgr.isfile(ckpt_file):\n return None\n\n return ckpt_file\n" }, { "path": "cogvideox_interpolation/datasets.py", "content": "import json \nimport torch \nimport cv2\nfrom typing import Any, Dict, List, Optional, Tuple\nfrom torch.utils.data import DataLoader, Dataset \nimport torchvision.transforms as TT \nfrom torchvision import transforms\nfrom torchvision.transforms.functional import center_crop, resize \nfrom torchvision.transforms import InterpolationMode \nimport numpy as np\nimport random, os \ntry:\n import decord\nexcept ImportError:\n raise ImportError(\n \"The `decord` package is required for loading the video dataset. Install with `pip install decord`\"\n )\n\ndecord.bridge.set_bridge(\"torch\")\n\nclass ImageVideoDataset(Dataset): \n def __init__(\n self,\n root_path,\n annotation_json,\n tokenizer,\n max_sequence_length: int = 226,\n height: int = 480,\n width: int = 640,\n video_reshape_mode: str = \"center\",\n fps: int = 8,\n stripe: int = 2,\n max_num_frames: int = 49,\n skip_frames_start: int = 0,\n skip_frames_end: int = 0,\n random_flip: Optional[float] = None,\n ) -> None:\n super().__init__() \n\n self.root_path = root_path\n with open(annotation_json, 'r') as f: \n self.data_list = json.load(f)\n \n self.tokenizer = tokenizer \n self.max_sequence_length = max_sequence_length \n self.height = height\n self.width = width\n self.video_reshape_mode = video_reshape_mode\n self.fps = fps\n self.max_num_frames = max_num_frames\n self.skip_frames_start = skip_frames_start\n self.skip_frames_end = skip_frames_end \n self.stripe = stripe \n self.video_transforms = transforms.Compose(\n [\n transforms.RandomHorizontalFlip(random_flip) if random_flip else transforms.Lambda(lambda x: x),\n transforms.Lambda(lambda x: x / 255.0),\n transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True),\n ]\n )\n\n \n def __len__(self):\n return len(self.data_list) \n \n def _resize_for_rectangle_crop(self, arr):\n image_size = self.height, self.width\n reshape_mode = self.video_reshape_mode\n if arr.shape[3] / arr.shape[2] > image_size[1] / image_size[0]:\n arr = resize(\n arr,\n size=[image_size[0], int(arr.shape[3] * image_size[0] / arr.shape[2])],\n interpolation=InterpolationMode.BICUBIC,\n )\n else:\n arr = resize(\n arr,\n size=[int(arr.shape[2] * image_size[1] / arr.shape[3]), image_size[1]],\n interpolation=InterpolationMode.BICUBIC,\n )\n\n h, w = arr.shape[2], arr.shape[3]\n arr = arr.squeeze(0)\n\n delta_h = h - image_size[0]\n delta_w = w - image_size[1]\n\n if reshape_mode == \"random\" or reshape_mode == \"none\":\n top = np.random.randint(0, delta_h + 1)\n left = np.random.randint(0, delta_w + 1)\n elif reshape_mode == \"center\":\n top, left = delta_h // 2, delta_w // 2\n else:\n raise NotImplementedError\n arr = TT.functional.crop(arr, top=top, left=left, height=image_size[0], width=image_size[1])\n return arr\n \n def __getitem__(self, index): \n while True: \n try:\n video_path = os.path.join(self.root_path, self.data_list[index]['clip_path'])\n video_reader = decord.VideoReader(video_path, width=self.width, height=self.height)\n video_num_frames = len(video_reader) \n # print(video_num_frames, video_reader.get_avg_fps()) \n if self.stripe * self.max_num_frames > video_num_frames: \n stripe = 1\n else:\n stripe = self.stripe \n\n random_range = video_num_frames - stripe * self.max_num_frames - 1\n random_range = max(1, random_range)\n start_frame = random.randint(1, random_range) if random_range > 0 else 1\n \n indices = list(range(start_frame, start_frame + stripe * self.max_num_frames, stripe)) # (end_frame - start_frame) // self.max_num_frames))\n frames = video_reader.get_batch(indices)\n\n # Ensure that we don't go over the limit\n frames = frames[: self.max_num_frames]\n selected_num_frames = frames.shape[0]\n\n # Choose first (4k + 1) frames as this is how many is required by the VAE\n remainder = (3 + (selected_num_frames % 4)) % 4\n if remainder != 0:\n frames = frames[:-remainder]\n selected_num_frames = frames.shape[0]\n\n assert (selected_num_frames - 1) % 4 == 0 \n if selected_num_frames == self.max_num_frames: \n break \n else:\n index = (index + 1) % len(self.data_list) \n continue \n \n except Exception as e:\n index = (index + 1) % len(self.data_list) \n print(video_num_frames, start_frame, indices)\n print(\n \"Error encounter during audio feature extraction: \", e, \n )\n continue\n\n # Training transforms\n # frames = (frames - 127.5) / 127.5\n frames = frames.permute(0, 3, 1, 2).contiguous() # [F, C, H, W]\n frames = self._resize_for_rectangle_crop(frames) \n frames = torch.stack([self.video_transforms(frame) for frame in frames], dim=0) \n\n text_inputs = self.tokenizer(\n [self.data_list[index]['caption']],\n padding=\"max_length\",\n max_length=self.max_sequence_length,\n truncation=True,\n add_special_tokens=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids[0]\n\n return frames.contiguous(), text_input_ids\n\n\nclass AutoEncoderDataset(ImageVideoDataset):\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n def __getitem__(self, index): \n while True: \n try:\n video_path = os.path.join(self.root_path, self.data_list[index]['clip_path'])\n video_reader = decord.VideoReader(video_path, width=self.width, height=self.height)\n video_num_frames = len(video_reader) \n # print(video_num_frames, video_reader.get_avg_fps()) \n if self.stripe * self.max_num_frames > video_num_frames: \n stripe = 1\n else:\n stripe = self.stripe \n \n random_indice = [random.randint(1, video_num_frames - 1)] # random selects a frame from the video\n frames = video_reader.get_batch(random_indice)\n break \n \n except Exception as e:\n print(\"[WARN] Get problem when loading video: \", self.data_list[index]['clip_path'])\n print(\n \"Error encounter during audio feature extraction: \", e, \n )\n index = random.randint(0, len(self.data_list) - 1)\n continue\n return frames\n\n\nclass LvisDataset(Dataset): \n def __init__(\n self,\n root_path,\n annotation_json,\n height: int = 480,\n width: int = 640,\n random_flip: Optional[float] = None,\n ) -> None:\n super().__init__() \n\n self.root_path = root_path\n with open(annotation_json, 'r') as f: \n self.data_list = json.load(f)['images']\n \n self.height = height\n self.width = width\n self.width = width\n self.video_transforms = transforms.Compose(\n [\n transforms.Lambda(lambda x: x / 255.0),\n transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True),\n ]\n )\n\n\n def __len__(self):\n return len(self.data_list) \n \n def __getitem__(self, index): \n\n image_path = os.path.join(self.root_path, \"unlabeled2017\", self.data_list[index]['file_name'])\n image = cv2.imread(image_path)\n image = cv2.resize(image, (self.width, self.height))\n image = self.video_transforms(torch.from_numpy(image).permute(2, 0, 1))\n\n return image.contiguous()\n" }, { "path": "cogvideox_interpolation/losses.py", "content": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom einops import rearrange, repeat\n\nfrom .lpips import LPIPS\n\ndef l2_loss(network_output, gt):\n return ((network_output - gt) ** 2).mean()\n\ndef cos_loss(network_output, gt):\n return 1 - F.cosine_similarity(network_output, gt, dim=1).mean()\n\ndef hinge_d_loss(logits_real, logits_fake):\n loss_real = torch.mean(F.relu(1.0 - logits_real))\n loss_fake = torch.mean(F.relu(1.0 + logits_fake))\n d_loss = 0.5 * (loss_real + loss_fake)\n return d_loss\n\n\ndef vanilla_d_loss(logits_real, logits_fake):\n d_loss = 0.5 * (\n torch.mean(torch.nn.functional.softplus(-logits_real)) + torch.mean(torch.nn.functional.softplus(logits_fake))\n )\n return d_loss\n\n\n# from MAGVIT, used in place hof hinge_d_loss\ndef sigmoid_cross_entropy_with_logits(labels, logits):\n # The final formulation is: max(x, 0) - x * z + log(1 + exp(-abs(x)))\n zeros = torch.zeros_like(logits, dtype=logits.dtype)\n condition = logits >= zeros\n relu_logits = torch.where(condition, logits, zeros)\n neg_abs_logits = torch.where(condition, -logits, logits)\n return relu_logits - logits * labels + torch.log1p(torch.exp(neg_abs_logits))\n\n\ndef lecam_reg(real_pred, fake_pred, ema_real_pred, ema_fake_pred):\n assert real_pred.ndim == 0 and ema_fake_pred.ndim == 0\n lecam_loss = torch.mean(torch.pow(nn.ReLU()(real_pred - ema_fake_pred), 2))\n lecam_loss += torch.mean(torch.pow(nn.ReLU()(ema_real_pred - fake_pred), 2))\n return lecam_loss\n\n\ndef gradient_penalty_fn(images, output):\n gradients = torch.autograd.grad(\n outputs=output,\n inputs=images,\n grad_outputs=torch.ones(output.size(), device=images.device),\n create_graph=True,\n retain_graph=True,\n only_inputs=True,\n )[0]\n\n gradients = rearrange(gradients, \"b ... -> b (...)\")\n return ((gradients.norm(2, dim=1) - 1) ** 2).mean()\n\n\nclass VAELoss(nn.Module):\n def __init__(\n self,\n logvar_init=0.0,\n perceptual_loss_weight=0.1,\n kl_loss_weight=0.000001,\n device=\"cpu\",\n dtype=\"bf16\",\n ):\n super().__init__()\n\n if type(dtype) == str:\n if dtype == \"bf16\":\n dtype = torch.bfloat16\n elif dtype == \"fp16\":\n dtype = torch.float16\n else:\n raise NotImplementedError(f\"dtype: {dtype}\")\n\n # KL Loss\n self.kl_loss_weight = kl_loss_weight\n # Perceptual Loss\n self.perceptual_loss_fn = LPIPS().eval().to(device, dtype)\n self.perceptual_loss_weight = perceptual_loss_weight\n self.logvar = nn.Parameter(torch.ones(size=()) * logvar_init)\n\n def forward(\n self,\n video,\n recon_video,\n posterior,\n nll_weights=None,\n no_perceptual=False,\n ):\n video = rearrange(video, \"b c t h w -> (b t) c h w\").contiguous()\n recon_video = rearrange(recon_video, \"b c t h w -> (b t) c h w\").contiguous()\n\n # reconstruction loss\n recon_loss = torch.abs(video - recon_video)\n\n # perceptual loss\n if self.perceptual_loss_weight is not None and self.perceptual_loss_weight > 0.0 and not no_perceptual:\n # handle channels\n channels = video.shape[1]\n assert channels in {1, 3}\n if channels == 1:\n input_vgg_input = repeat(video, \"b 1 h w -> b c h w\", c=3)\n recon_vgg_input = repeat(recon_video, \"b 1 h w -> b c h w\", c=3)\n else:\n input_vgg_input = video\n recon_vgg_input = recon_video\n\n perceptual_loss = self.perceptual_loss_fn(input_vgg_input, recon_vgg_input)\n recon_loss = recon_loss + self.perceptual_loss_weight * perceptual_loss\n\n nll_loss = recon_loss / torch.exp(self.logvar) + self.logvar\n\n weighted_nll_loss = nll_loss\n if nll_weights is not None:\n weighted_nll_loss = nll_weights * nll_loss\n weighted_nll_loss = torch.sum(weighted_nll_loss) / weighted_nll_loss.shape[0]\n nll_loss = torch.sum(nll_loss) / nll_loss.shape[0]\n\n # KL Loss\n weighted_kl_loss = 0\n if self.kl_loss_weight is not None and self.kl_loss_weight > 0.0:\n kl_loss = posterior.kl()\n kl_loss = torch.sum(kl_loss) / kl_loss.shape[0]\n weighted_kl_loss = kl_loss * self.kl_loss_weight\n\n return nll_loss, weighted_nll_loss, weighted_kl_loss\n\n\ndef adopt_weight(weight, global_step, threshold=0, value=0.0):\n if global_step < threshold:\n weight = value\n return weight\n\n\nclass AdversarialLoss(nn.Module):\n def __init__(\n self,\n discriminator_factor=1.0,\n discriminator_start=50001,\n generator_factor=0.5,\n generator_loss_type=\"non-saturating\",\n ):\n super().__init__()\n self.discriminator_factor = discriminator_factor\n self.discriminator_start = discriminator_start\n self.generator_factor = generator_factor\n self.generator_loss_type = generator_loss_type\n\n def calculate_adaptive_weight(self, nll_loss, g_loss, last_layer):\n nll_grads = torch.autograd.grad(nll_loss, last_layer, retain_graph=True)[0]\n g_grads = torch.autograd.grad(g_loss, last_layer, retain_graph=True)[0]\n d_weight = torch.norm(nll_grads) / (torch.norm(g_grads) + 1e-4)\n d_weight = torch.clamp(d_weight, 0.0, 1e4).detach()\n d_weight = d_weight * self.generator_factor\n return d_weight\n\n def forward(\n self,\n fake_logits,\n nll_loss,\n last_layer,\n global_step,\n is_training=True,\n ):\n # NOTE: following MAGVIT to allow non_saturating\n assert self.generator_loss_type in [\"hinge\", \"vanilla\", \"non-saturating\"]\n\n if self.generator_loss_type == \"hinge\":\n gen_loss = -torch.mean(fake_logits)\n elif self.generator_loss_type == \"non-saturating\":\n gen_loss = torch.mean(\n sigmoid_cross_entropy_with_logits(labels=torch.ones_like(fake_logits), logits=fake_logits)\n )\n else:\n raise ValueError(\"Generator loss {} not supported\".format(self.generator_loss_type))\n\n if self.discriminator_factor is not None and self.discriminator_factor > 0.0:\n try:\n d_weight = self.calculate_adaptive_weight(nll_loss, gen_loss, last_layer)\n except RuntimeError:\n assert not is_training\n d_weight = torch.tensor(0.0)\n else:\n d_weight = torch.tensor(0.0)\n\n disc_factor = adopt_weight(self.discriminator_factor, global_step, threshold=self.discriminator_start)\n weighted_gen_loss = d_weight * disc_factor * gen_loss\n\n return weighted_gen_loss\n\n\nclass LeCamEMA:\n def __init__(self, ema_real=0.0, ema_fake=0.0, decay=0.999, dtype=torch.bfloat16, device=\"cpu\"):\n self.decay = decay\n self.ema_real = torch.tensor(ema_real).to(device, dtype)\n self.ema_fake = torch.tensor(ema_fake).to(device, dtype)\n\n def update(self, ema_real, ema_fake):\n self.ema_real = self.ema_real * self.decay + ema_real * (1 - self.decay)\n self.ema_fake = self.ema_fake * self.decay + ema_fake * (1 - self.decay)\n\n def get(self):\n return self.ema_real, self.ema_fake\n\n\nclass DiscriminatorLoss(nn.Module):\n def __init__(\n self,\n discriminator_factor=1.0,\n discriminator_start=50001,\n discriminator_loss_type=\"non-saturating\",\n lecam_loss_weight=None,\n gradient_penalty_loss_weight=None, # SCH: following MAGVIT config.vqgan.grad_penalty_cost\n ):\n super().__init__()\n\n assert discriminator_loss_type in [\"hinge\", \"vanilla\", \"non-saturating\"]\n self.discriminator_factor = discriminator_factor\n self.discriminator_start = discriminator_start\n self.lecam_loss_weight = lecam_loss_weight\n self.gradient_penalty_loss_weight = gradient_penalty_loss_weight\n self.discriminator_loss_type = discriminator_loss_type\n\n def forward(\n self,\n real_logits,\n fake_logits,\n global_step,\n lecam_ema_real=None,\n lecam_ema_fake=None,\n real_video=None,\n split=\"train\",\n ):\n if self.discriminator_factor is not None and self.discriminator_factor > 0.0:\n disc_factor = adopt_weight(self.discriminator_factor, global_step, threshold=self.discriminator_start)\n\n if self.discriminator_loss_type == \"hinge\":\n disc_loss = hinge_d_loss(real_logits, fake_logits)\n elif self.discriminator_loss_type == \"non-saturating\":\n if real_logits is not None:\n real_loss = sigmoid_cross_entropy_with_logits(\n labels=torch.ones_like(real_logits), logits=real_logits\n )\n else:\n real_loss = 0.0\n if fake_logits is not None:\n fake_loss = sigmoid_cross_entropy_with_logits(\n labels=torch.zeros_like(fake_logits), logits=fake_logits\n )\n else:\n fake_loss = 0.0\n disc_loss = 0.5 * (torch.mean(real_loss) + torch.mean(fake_loss))\n elif self.discriminator_loss_type == \"vanilla\":\n disc_loss = vanilla_d_loss(real_logits, fake_logits)\n else:\n raise ValueError(f\"Unknown GAN loss '{self.discriminator_loss_type}'.\")\n\n weighted_d_adversarial_loss = disc_factor * disc_loss\n\n else:\n weighted_d_adversarial_loss = 0\n\n lecam_loss = torch.tensor(0.0)\n if self.lecam_loss_weight is not None and self.lecam_loss_weight > 0.0:\n real_pred = torch.mean(real_logits)\n fake_pred = torch.mean(fake_logits)\n lecam_loss = lecam_reg(real_pred, fake_pred, lecam_ema_real, lecam_ema_fake)\n lecam_loss = lecam_loss * self.lecam_loss_weight\n\n gradient_penalty = torch.tensor(0.0)\n if self.gradient_penalty_loss_weight is not None and self.gradient_penalty_loss_weight > 0.0:\n assert real_video is not None\n gradient_penalty = gradient_penalty_fn(real_video, real_logits)\n gradient_penalty *= self.gradient_penalty_loss_weight\n\n return (weighted_d_adversarial_loss, lecam_loss, gradient_penalty)\n" }, { "path": "cogvideox_interpolation/lpips.py", "content": "import hashlib\nimport os\nfrom collections import namedtuple\n\nimport requests\nimport torch\nimport torch.nn as nn\nfrom torchvision import models\nfrom tqdm import tqdm\n\nURL_MAP = {\"vgg_lpips\": \"https://heibox.uni-heidelberg.de/f/607503859c864bc1b30b/?dl=1\"}\n\nCKPT_MAP = {\"vgg_lpips\": \"vgg.pth\"}\n\nMD5_MAP = {\"vgg_lpips\": \"d507d7349b931f0638a25a48a722f98a\"}\n\n\ndef md5_hash(path):\n with open(path, \"rb\") as f:\n content = f.read()\n return hashlib.md5(content).hexdigest()\n\n\ndef download(url, local_path, chunk_size=1024):\n os.makedirs(os.path.split(local_path)[0], exist_ok=True)\n with requests.get(url, stream=True) as r:\n total_size = int(r.headers.get(\"content-length\", 0))\n with tqdm(total=total_size, unit=\"B\", unit_scale=True) as pbar:\n with open(local_path, \"wb\") as f:\n for data in r.iter_content(chunk_size=chunk_size):\n if data:\n f.write(data)\n pbar.update(chunk_size)\n\n\ndef get_ckpt_path(name, root, check=False):\n assert name in URL_MAP\n path = os.path.join(root, CKPT_MAP[name])\n if not os.path.exists(path) or (check and not md5_hash(path) == MD5_MAP[name]):\n print(\"Downloading {} model from {} to {}\".format(name, URL_MAP[name], path))\n download(URL_MAP[name], path)\n md5 = md5_hash(path)\n assert md5 == MD5_MAP[name], md5\n return path\n\n\nclass LPIPS(nn.Module):\n # Learned perceptual metric\n def __init__(self, use_dropout=True):\n super().__init__()\n self.scaling_layer = ScalingLayer()\n self.chns = [64, 128, 256, 512, 512] # vg16 features\n self.net = vgg16(pretrained=True, requires_grad=False)\n self.lin0 = NetLinLayer(self.chns[0], use_dropout=use_dropout)\n self.lin1 = NetLinLayer(self.chns[1], use_dropout=use_dropout)\n self.lin2 = NetLinLayer(self.chns[2], use_dropout=use_dropout)\n self.lin3 = NetLinLayer(self.chns[3], use_dropout=use_dropout)\n self.lin4 = NetLinLayer(self.chns[4], use_dropout=use_dropout)\n self.load_from_pretrained()\n for param in self.parameters():\n param.requires_grad = False\n\n def load_from_pretrained(self, name=\"vgg_lpips\"):\n ckpt = get_ckpt_path(name, \"model_zoo/taming/modules/autoencoder/lpips\")\n self.load_state_dict(torch.load(ckpt, map_location=torch.device(\"cpu\")), strict=False)\n # print(\"loaded pretrained LPIPS loss from {}\".format(ckpt))\n\n @classmethod\n def from_pretrained(cls, name=\"vgg_lpips\"):\n if name != \"vgg_lpips\":\n raise NotImplementedError\n model = cls()\n ckpt = get_ckpt_path(name)\n model.load_state_dict(torch.load(ckpt, map_location=torch.device(\"cpu\")), strict=False)\n return model\n\n def forward(self, input, target):\n in0_input, in1_input = (self.scaling_layer(input), self.scaling_layer(target))\n outs0, outs1 = self.net(in0_input), self.net(in1_input)\n feats0, feats1, diffs = {}, {}, {}\n lins = [self.lin0, self.lin1, self.lin2, self.lin3, self.lin4]\n for kk in range(len(self.chns)):\n feats0[kk], feats1[kk] = normalize_tensor(outs0[kk]), normalize_tensor(outs1[kk])\n diffs[kk] = (feats0[kk] - feats1[kk]) ** 2\n\n res = [spatial_average(lins[kk].model(diffs[kk]), keepdim=True) for kk in range(len(self.chns))]\n val = res[0]\n for l in range(1, len(self.chns)):\n val += res[l]\n return val\n\n\nclass ScalingLayer(nn.Module):\n def __init__(self):\n super(ScalingLayer, self).__init__()\n self.register_buffer(\"shift\", torch.Tensor([-0.030, -0.088, -0.188])[None, :, None, None])\n self.register_buffer(\"scale\", torch.Tensor([0.458, 0.448, 0.450])[None, :, None, None])\n\n def forward(self, inp):\n return (inp - self.shift) / self.scale\n\n\nclass NetLinLayer(nn.Module):\n \"\"\"A single linear layer which does a 1x1 conv\"\"\"\n\n def __init__(self, chn_in, chn_out=1, use_dropout=False):\n super(NetLinLayer, self).__init__()\n layers = (\n [\n nn.Dropout(),\n ]\n if (use_dropout)\n else []\n )\n layers += [\n nn.Conv2d(chn_in, chn_out, 1, stride=1, padding=0, bias=False),\n ]\n self.model = nn.Sequential(*layers)\n\n\nclass vgg16(torch.nn.Module):\n def __init__(self, requires_grad=False, pretrained=True):\n super(vgg16, self).__init__()\n vgg_pretrained_features = models.vgg16(pretrained=pretrained).features\n self.slice1 = torch.nn.Sequential()\n self.slice2 = torch.nn.Sequential()\n self.slice3 = torch.nn.Sequential()\n self.slice4 = torch.nn.Sequential()\n self.slice5 = torch.nn.Sequential()\n self.N_slices = 5\n for x in range(4):\n self.slice1.add_module(str(x), vgg_pretrained_features[x])\n for x in range(4, 9):\n self.slice2.add_module(str(x), vgg_pretrained_features[x])\n for x in range(9, 16):\n self.slice3.add_module(str(x), vgg_pretrained_features[x])\n for x in range(16, 23):\n self.slice4.add_module(str(x), vgg_pretrained_features[x])\n for x in range(23, 30):\n self.slice5.add_module(str(x), vgg_pretrained_features[x])\n if not requires_grad:\n for param in self.parameters():\n param.requires_grad = False\n\n def forward(self, X):\n h = self.slice1(X)\n h_relu1_2 = h\n h = self.slice2(h)\n h_relu2_2 = h\n h = self.slice3(h)\n h_relu3_3 = h\n h = self.slice4(h)\n h_relu4_3 = h\n h = self.slice5(h)\n h_relu5_3 = h\n vgg_outputs = namedtuple(\"VggOutputs\", [\"relu1_2\", \"relu2_2\", \"relu3_3\", \"relu4_3\", \"relu5_3\"])\n out = vgg_outputs(h_relu1_2, h_relu2_2, h_relu3_3, h_relu4_3, h_relu5_3)\n return out\n\n\ndef normalize_tensor(x, eps=1e-10):\n norm_factor = torch.sqrt(torch.sum(x**2, dim=1, keepdim=True))\n return x / (norm_factor + eps)\n\n\ndef spatial_average(x, keepdim=True):\n return x.mean([2, 3], keepdim=keepdim)\n" }, { "path": "cogvideox_interpolation/pipeline.py", "content": "import inspect\nimport math\nfrom typing import Callable, Dict, List, Optional, Tuple, Union\n\nimport PIL\nimport torch\nfrom diffusers.callbacks import MultiPipelineCallbacks, PipelineCallback\nfrom diffusers.image_processor import PipelineImageInput\nfrom diffusers.models import (AutoencoderKLCogVideoX,\n CogVideoXTransformer3DModel)\nfrom diffusers.models.embeddings import get_3d_rotary_pos_embed\nfrom diffusers.pipelines.pipeline_utils import DiffusionPipeline\nfrom diffusers.schedulers import CogVideoXDDIMScheduler, CogVideoXDPMScheduler\nfrom diffusers.utils import logging, replace_example_docstring\nfrom diffusers.utils.torch_utils import randn_tensor\nfrom diffusers.video_processor import VideoProcessor\nfrom transformers import T5EncoderModel, T5Tokenizer\n\n\ndef get_resize_crop_region_for_grid(src, tgt_width, tgt_height):\n tw = tgt_width\n th = tgt_height\n h, w = src\n r = h / w\n if r > (th / tw):\n resize_height = th\n resize_width = int(round(th / h * w))\n else:\n resize_width = tw\n resize_height = int(round(tw / w * h))\n\n crop_top = int(round((th - resize_height) / 2.0))\n crop_left = int(round((tw - resize_width) / 2.0))\n\n return (crop_top, crop_left), (crop_top + resize_height, crop_left + resize_width)\n\n\ndef retrieve_timesteps(\n scheduler,\n num_inference_steps: Optional[int] = None,\n device: Optional[Union[str, torch.device]] = None,\n timesteps: Optional[List[int]] = None,\n sigmas: Optional[List[float]] = None,\n **kwargs,\n):\n \"\"\"\n Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles\n custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.\n\n Args:\n scheduler (`SchedulerMixin`):\n The scheduler to get timesteps from.\n num_inference_steps (`int`):\n The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`\n must be `None`.\n device (`str` or `torch.device`, *optional*):\n The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.\n timesteps (`List[int]`, *optional*):\n Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,\n `num_inference_steps` and `sigmas` must be `None`.\n sigmas (`List[float]`, *optional*):\n Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,\n `num_inference_steps` and `timesteps` must be `None`.\n\n Returns:\n `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the\n second element is the number of inference steps.\n \"\"\"\n if timesteps is not None and sigmas is not None:\n raise ValueError(\"Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values\")\n if timesteps is not None:\n accepts_timesteps = \"timesteps\" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())\n if not accepts_timesteps:\n raise ValueError(\n f\"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom\"\n f\" timestep schedules. Please check whether you are using the correct scheduler.\"\n )\n scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)\n timesteps = scheduler.timesteps\n num_inference_steps = len(timesteps)\n elif sigmas is not None:\n accept_sigmas = \"sigmas\" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())\n if not accept_sigmas:\n raise ValueError(\n f\"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom\"\n f\" sigmas schedules. Please check whether you are using the correct scheduler.\"\n )\n scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)\n timesteps = scheduler.timesteps\n num_inference_steps = len(timesteps)\n else:\n scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)\n timesteps = scheduler.timesteps\n return timesteps, num_inference_steps\n\n# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents\ndef retrieve_latents(\n encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = \"sample\"\n):\n if hasattr(encoder_output, \"latent_dist\") and sample_mode == \"sample\":\n return encoder_output.latent_dist.sample(generator)\n elif hasattr(encoder_output, \"latent_dist\") and sample_mode == \"argmax\":\n return encoder_output.latent_dist.mode()\n elif hasattr(encoder_output, \"latents\"):\n return encoder_output.latents\n else:\n raise AttributeError(\"Could not access latents of provided encoder_output\")\n\nclass CogVideoXInterpolationPipeline(DiffusionPipeline): \n _optional_components = []\n model_cpu_offload_seq = \"text_encoder->transformer->vae\"\n\n _callback_tensor_inputs = [\n \"latents\",\n \"prompt_embeds\",\n \"negative_prompt_embeds\",\n ]\n def __init__(\n self,\n tokenizer: T5Tokenizer,\n text_encoder: T5EncoderModel,\n vae: AutoencoderKLCogVideoX,\n transformer: CogVideoXTransformer3DModel,\n scheduler: Union[CogVideoXDDIMScheduler, CogVideoXDPMScheduler],\n ):\n super().__init__()\n\n self.register_modules(\n tokenizer=tokenizer,\n text_encoder=text_encoder,\n vae=vae,\n transformer=transformer,\n scheduler=scheduler,\n )\n self.vae_scale_factor_spatial = (\n 2 ** (len(self.vae.config.block_out_channels) - 1) if hasattr(self, \"vae\") and self.vae is not None else 8\n )\n self.vae_scale_factor_temporal = (\n self.vae.config.temporal_compression_ratio if hasattr(self, \"vae\") and self.vae is not None else 4\n )\n\n self.video_processor = VideoProcessor(vae_scale_factor=self.vae_scale_factor_spatial)\n\n\n # Copied from diffusers.pipelines.cogvideo.pipeline_cogvideox.CogVideoXPipeline._get_t5_prompt_embeds\n def _get_t5_prompt_embeds(\n self,\n prompt: Union[str, List[str]] = None,\n num_videos_per_prompt: int = 1,\n max_sequence_length: int = 226,\n device: Optional[torch.device] = None,\n dtype: Optional[torch.dtype] = None,\n ):\n device = device or self._execution_device\n dtype = dtype or self.text_encoder.dtype\n\n prompt = [prompt] if isinstance(prompt, str) else prompt\n batch_size = len(prompt)\n\n text_inputs = self.tokenizer(\n prompt,\n padding=\"max_length\",\n max_length=max_sequence_length,\n truncation=True,\n add_special_tokens=True,\n return_tensors=\"pt\",\n )\n text_input_ids = text_inputs.input_ids\n untruncated_ids = self.tokenizer(prompt, padding=\"longest\", return_tensors=\"pt\").input_ids\n\n if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):\n removed_text = self.tokenizer.batch_decode(untruncated_ids[:, max_sequence_length - 1 : -1])\n logger.warning(\n \"The following part of your input was truncated because `max_sequence_length` is set to \"\n f\" {max_sequence_length} tokens: {removed_text}\"\n )\n\n prompt_embeds = self.text_encoder(text_input_ids.to(device))[0]\n prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)\n\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n _, seq_len, _ = prompt_embeds.shape\n prompt_embeds = prompt_embeds.repeat(1, num_videos_per_prompt, 1)\n prompt_embeds = prompt_embeds.view(batch_size * num_videos_per_prompt, seq_len, -1)\n\n return prompt_embeds\n\n # Copied from diffusers.pipelines.cogvideo.pipeline_cogvideox.CogVideoXPipeline.encode_prompt\n def encode_prompt(\n self,\n prompt: Union[str, List[str]],\n negative_prompt: Optional[Union[str, List[str]]] = None,\n do_classifier_free_guidance: bool = True,\n num_videos_per_prompt: int = 1,\n prompt_embeds: Optional[torch.Tensor] = None,\n negative_prompt_embeds: Optional[torch.Tensor] = None,\n max_sequence_length: int = 226,\n device: Optional[torch.device] = None,\n dtype: Optional[torch.dtype] = None,\n ):\n r\"\"\"\n Encodes the prompt into text encoder hidden states.\n\n Args:\n prompt (`str` or `List[str]`, *optional*):\n prompt to be encoded\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n do_classifier_free_guidance (`bool`, *optional*, defaults to `True`):\n Whether to use classifier free guidance or not.\n num_videos_per_prompt (`int`, *optional*, defaults to 1):\n Number of videos that should be generated per prompt. torch device to place the resulting embeddings on\n prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.Tensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n device: (`torch.device`, *optional*):\n torch device\n dtype: (`torch.dtype`, *optional*):\n torch dtype\n \"\"\"\n device = device or self._execution_device\n\n prompt = [prompt] if isinstance(prompt, str) else prompt\n if prompt is not None:\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n if prompt_embeds is None:\n prompt_embeds = self._get_t5_prompt_embeds(\n prompt=prompt,\n num_videos_per_prompt=num_videos_per_prompt,\n max_sequence_length=max_sequence_length,\n device=device,\n dtype=dtype,\n )\n\n if do_classifier_free_guidance and negative_prompt_embeds is None:\n negative_prompt = negative_prompt or \"\"\n negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt\n\n if prompt is not None and type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n\n negative_prompt_embeds = self._get_t5_prompt_embeds(\n prompt=negative_prompt,\n num_videos_per_prompt=num_videos_per_prompt,\n max_sequence_length=max_sequence_length,\n device=device,\n dtype=dtype,\n )\n\n return prompt_embeds, negative_prompt_embeds\n\n def prepare_latents(\n self,\n first_image: torch.Tensor,\n last_image: torch.Tensor,\n batch_size: int = 1,\n num_channels_latents: int = 16,\n num_frames: int = 13,\n height: int = 60,\n width: int = 90,\n dtype: Optional[torch.dtype] = None,\n device: Optional[torch.device] = None,\n generator: Optional[torch.Generator] = None,\n latents: Optional[torch.Tensor] = None,\n ):\n num_frames = (num_frames - 1) // self.vae_scale_factor_temporal + 1\n shape = (\n batch_size,\n num_frames,\n num_channels_latents,\n height // self.vae_scale_factor_spatial,\n width // self.vae_scale_factor_spatial,\n )\n\n if isinstance(generator, list) and len(generator) != batch_size:\n raise ValueError(\n f\"You have passed a list of generators of length {len(generator)}, but requested an effective batch\"\n f\" size of {batch_size}. Make sure the batch size matches the length of the generators.\"\n )\n\n first_image = first_image.unsqueeze(2) # [B, C, F, H, W]\n last_image = last_image.unsqueeze(2) # [B, C, F, H, W] \n\n if isinstance(generator, list):\n first_image_latents = [\n retrieve_latents(self.vae.encode(first_image[i].unsqueeze(0)), generator[i]) for i in range(batch_size)\n ]\n else:\n first_image_latents = [retrieve_latents(self.vae.encode(first_img.unsqueeze(0)), generator) for first_img in first_image]\n \n if isinstance(generator, list):\n last_image_latents = [\n retrieve_latents(self.vae.encode(last_image[i].unsqueeze(0)), generator[i]) for i in range(batch_size)\n ]\n else:\n last_image_latents = [retrieve_latents(self.vae.encode(last_img.unsqueeze(0)), generator) for last_img in last_image]\n\n\n first_image_latents = torch.cat(first_image_latents, dim=0).to(dtype).permute(0, 2, 1, 3, 4) # [B, F, C, H, W]\n first_image_latents = self.vae.config.scaling_factor * first_image_latents \n last_image_latents = torch.cat(last_image_latents, dim=0).to(dtype).permute(0, 2, 1, 3, 4) # [B, F, C, H, W]\n last_image_latents = self.vae.config.scaling_factor * last_image_latents \n\n\n padding_shape = (\n batch_size,\n num_frames - 2,\n num_channels_latents,\n height // self.vae_scale_factor_spatial,\n width // self.vae_scale_factor_spatial,\n )\n latent_padding = torch.zeros(padding_shape, device=device, dtype=dtype)\n image_latents = torch.cat([first_image_latents, latent_padding, last_image_latents], dim=1)\n\n if latents is None:\n latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)\n else:\n latents = latents.to(device)\n\n # scale the initial noise by the standard deviation required by the scheduler\n latents = latents * self.scheduler.init_noise_sigma\n return latents, image_latents\n\n # Copied from diffusers.pipelines.cogvideo.pipeline_cogvideox.CogVideoXPipeline.decode_latents\n def decode_latents(self, latents: torch.Tensor) -> torch.Tensor:\n latents = latents.permute(0, 2, 1, 3, 4) # [batch_size, num_channels, num_frames, height, width]\n latents = 1 / self.vae.config.scaling_factor * latents\n\n frames = self.vae.decode(latents).sample\n return frames\n\n # Copied from diffusers.pipelines.animatediff.pipeline_animatediff_video2video.AnimateDiffVideoToVideoPipeline.get_timesteps\n def get_timesteps(self, num_inference_steps, timesteps, strength, device):\n # get the original timestep using init_timestep\n init_timestep = min(int(num_inference_steps * strength), num_inference_steps)\n\n t_start = max(num_inference_steps - init_timestep, 0)\n timesteps = timesteps[t_start * self.scheduler.order :]\n\n return timesteps, num_inference_steps - t_start\n\n # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs\n def prepare_extra_step_kwargs(self, generator, eta):\n # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature\n # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.\n # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502\n # and should be between [0, 1]\n\n accepts_eta = \"eta\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(inspect.signature(self.scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n def check_inputs(\n self,\n first_image,\n last_image,\n prompt,\n height,\n width,\n negative_prompt,\n callback_on_step_end_tensor_inputs,\n video=None,\n latents=None,\n prompt_embeds=None,\n negative_prompt_embeds=None,\n ):\n if (\n not isinstance(first_image, torch.Tensor)\n and not isinstance(first_image, PIL.Image.Image)\n and not isinstance(first_image, list)\n ):\n raise ValueError(\n \"`image` has to be of type `torch.Tensor` or `PIL.Image.Image` or `List[PIL.Image.Image]` but is\"\n f\" {type(first_image)}\"\n )\n \n if (\n not isinstance(last_image, torch.Tensor)\n and not isinstance(last_image, PIL.Image.Image)\n and not isinstance(last_image, list)\n ):\n raise ValueError(\n \"`image` has to be of type `torch.Tensor` or `PIL.Image.Image` or `List[PIL.Image.Image]` but is\"\n f\" {type(last_image)}\"\n )\n\n\n if height % 8 != 0 or width % 8 != 0:\n raise ValueError(f\"`height` and `width` have to be divisible by 8 but are {height} and {width}.\")\n\n if callback_on_step_end_tensor_inputs is not None and not all(\n k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs\n ):\n raise ValueError(\n f\"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}\"\n )\n if prompt is not None and prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to\"\n \" only forward one of the two.\"\n )\n elif prompt is None and prompt_embeds is None:\n raise ValueError(\n \"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.\"\n )\n elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):\n raise ValueError(f\"`prompt` has to be of type `str` or `list` but is {type(prompt)}\")\n\n if prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `prompt`: {prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if negative_prompt is not None and negative_prompt_embeds is not None:\n raise ValueError(\n f\"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:\"\n f\" {negative_prompt_embeds}. Please make sure to only forward one of the two.\"\n )\n\n if prompt_embeds is not None and negative_prompt_embeds is not None:\n if prompt_embeds.shape != negative_prompt_embeds.shape:\n raise ValueError(\n \"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but\"\n f\" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`\"\n f\" {negative_prompt_embeds.shape}.\"\n )\n\n if video is not None and latents is not None:\n raise ValueError(\"Only one of `video` or `latents` should be provided\")\n\n # Copied from diffusers.pipelines.cogvideo.pipeline_cogvideox.CogVideoXPipeline.fuse_qkv_projections\n def fuse_qkv_projections(self) -> None:\n r\"\"\"Enables fused QKV projections.\"\"\"\n self.fusing_transformer = True\n self.transformer.fuse_qkv_projections()\n\n # Copied from diffusers.pipelines.cogvideo.pipeline_cogvideox.CogVideoXPipeline.unfuse_qkv_projections\n def unfuse_qkv_projections(self) -> None:\n r\"\"\"Disable QKV projection fusion if enabled.\"\"\"\n if not self.fusing_transformer:\n logger.warning(\"The Transformer was not initially fused for QKV projections. Doing nothing.\")\n else:\n self.transformer.unfuse_qkv_projections()\n self.fusing_transformer = False\n\n # Copied from diffusers.pipelines.cogvideo.pipeline_cogvideox.CogVideoXPipeline._prepare_rotary_positional_embeddings\n def _prepare_rotary_positional_embeddings(\n self,\n height: int,\n width: int,\n num_frames: int,\n device: torch.device,\n ) -> Tuple[torch.Tensor, torch.Tensor]:\n grid_height = height // (self.vae_scale_factor_spatial * self.transformer.config.patch_size)\n grid_width = width // (self.vae_scale_factor_spatial * self.transformer.config.patch_size)\n base_size_width = 720 // (self.vae_scale_factor_spatial * self.transformer.config.patch_size)\n base_size_height = 480 // (self.vae_scale_factor_spatial * self.transformer.config.patch_size)\n\n grid_crops_coords = get_resize_crop_region_for_grid(\n (grid_height, grid_width), base_size_width, base_size_height\n )\n freqs_cos, freqs_sin = get_3d_rotary_pos_embed(\n embed_dim=self.transformer.config.attention_head_dim,\n crops_coords=grid_crops_coords,\n grid_size=(grid_height, grid_width),\n temporal_size=num_frames,\n )\n\n freqs_cos = freqs_cos.to(device=device)\n freqs_sin = freqs_sin.to(device=device)\n return freqs_cos, freqs_sin\n\n @property\n def guidance_scale(self):\n return self._guidance_scale\n\n @property\n def num_timesteps(self):\n return self._num_timesteps\n\n @property\n def interrupt(self):\n return self._interrupt\n\n @torch.no_grad()\n def __call__(\n self,\n first_image: PipelineImageInput,\n last_image: PipelineImageInput,\n prompt: Optional[Union[str, List[str]]] = None,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n height: int = 480,\n width: int = 720,\n num_frames: int = 49,\n num_inference_steps: int = 50,\n timesteps: Optional[List[int]] = None,\n guidance_scale: float = 6,\n use_dynamic_cfg: bool = False,\n num_videos_per_prompt: int = 1,\n eta: float = 0.0,\n generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,\n latents: Optional[torch.FloatTensor] = None,\n prompt_embeds: Optional[torch.FloatTensor] = None,\n negative_prompt_embeds: Optional[torch.FloatTensor] = None,\n output_type: str = \"pil\",\n return_dict: bool = True,\n callback_on_step_end: Optional[\n Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]\n ] = None,\n callback_on_step_end_tensor_inputs: List[str] = [\"latents\"],\n max_sequence_length: int = 226,\n ):\n \"\"\"\n Function invoked when calling the pipeline for generation.\n\n Args:\n image (`PipelineImageInput`):\n The input video to condition the generation on. Must be an image, a list of images or a `torch.Tensor`.\n prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.\n instead.\n negative_prompt (`str` or `List[str]`, *optional*):\n The prompt or prompts not to guide the image generation. If not defined, one has to pass\n `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is\n less than `1`).\n height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\n The height in pixels of the generated image. This is set to 1024 by default for the best results.\n width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):\n The width in pixels of the generated image. This is set to 1024 by default for the best results.\n num_frames (`int`, defaults to `48`):\n Number of frames to generate. Must be divisible by self.vae_scale_factor_temporal. Generated video will\n contain 1 extra frame because CogVideoX is conditioned with (num_seconds * fps + 1) frames where\n num_seconds is 6 and fps is 4. However, since videos can be saved at any fps, the only condition that\n needs to be satisfied is that of divisibility mentioned above.\n num_inference_steps (`int`, *optional*, defaults to 50):\n The number of denoising steps. More denoising steps usually lead to a higher quality image at the\n expense of slower inference.\n timesteps (`List[int]`, *optional*):\n Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument\n in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is\n passed will be used. Must be in descending order.\n guidance_scale (`float`, *optional*, defaults to 7.0):\n Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).\n `guidance_scale` is defined as `w` of equation 2. of [Imagen\n Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >\n 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,\n usually at the expense of lower image quality.\n num_videos_per_prompt (`int`, *optional*, defaults to 1):\n The number of videos to generate per prompt.\n generator (`torch.Generator` or `List[torch.Generator]`, *optional*):\n One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)\n to make generation deterministic.\n latents (`torch.FloatTensor`, *optional*):\n Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image\n generation. Can be used to tweak the same generation with different prompts. If not provided, a latents\n tensor will ge generated by sampling using the supplied random `generator`.\n prompt_embeds (`torch.FloatTensor`, *optional*):\n Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not\n provided, text embeddings will be generated from `prompt` input argument.\n negative_prompt_embeds (`torch.FloatTensor`, *optional*):\n Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt\n weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input\n argument.\n output_type (`str`, *optional*, defaults to `\"pil\"`):\n The output format of the generate image. Choose between\n [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.\n return_dict (`bool`, *optional*, defaults to `True`):\n Whether or not to return a [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] instead\n of a plain tuple.\n callback_on_step_end (`Callable`, *optional*):\n A function that calls at the end of each denoising steps during the inference. The function is called\n with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,\n callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by\n `callback_on_step_end_tensor_inputs`.\n callback_on_step_end_tensor_inputs (`List`, *optional*):\n The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list\n will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the\n `._callback_tensor_inputs` attribute of your pipeline class.\n max_sequence_length (`int`, defaults to `226`):\n Maximum sequence length in encoded prompt. Must be consistent with\n `self.transformer.config.max_text_seq_length` otherwise may lead to poor results.\n\n Examples:\n\n Returns:\n [`~pipelines.cogvideo.pipeline_output.CogVideoXPipelineOutput`] or `tuple`:\n [`~pipelines.cogvideo.pipeline_output.CogVideoXPipelineOutput`] if `return_dict` is True, otherwise a\n `tuple`. When returning a tuple, the first element is a list with the generated images.\n \"\"\"\n\n if num_frames > 49:\n raise ValueError(\n \"The number of frames must be less than 49 for now due to static positional embeddings. This will be updated in the future to remove this limitation.\"\n )\n\n if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):\n callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs\n\n height = height or self.transformer.config.sample_size * self.vae_scale_factor_spatial\n width = width or self.transformer.config.sample_size * self.vae_scale_factor_spatial\n num_videos_per_prompt = 1\n\n # 1. Check inputs. Raise error if not correct\n self.check_inputs(\n first_image,\n last_image,\n prompt,\n height,\n width,\n negative_prompt,\n callback_on_step_end_tensor_inputs,\n prompt_embeds,\n negative_prompt_embeds,\n )\n self._guidance_scale = guidance_scale\n self._interrupt = False\n\n # 2. Default call parameters\n if prompt is not None and isinstance(prompt, str):\n batch_size = 1\n elif prompt is not None and isinstance(prompt, list):\n batch_size = len(prompt)\n else:\n batch_size = prompt_embeds.shape[0]\n\n device = self._execution_device\n\n # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)\n # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`\n # corresponds to doing no classifier free guidance.\n do_classifier_free_guidance = guidance_scale > 1.0\n\n # 3. Encode input prompt\n prompt_embeds, negative_prompt_embeds = self.encode_prompt(\n prompt=prompt,\n negative_prompt=negative_prompt,\n do_classifier_free_guidance=do_classifier_free_guidance,\n num_videos_per_prompt=num_videos_per_prompt,\n prompt_embeds=prompt_embeds,\n negative_prompt_embeds=negative_prompt_embeds,\n max_sequence_length=max_sequence_length,\n device=device,\n )\n if do_classifier_free_guidance:\n prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)\n\n # 4. Prepare timesteps\n timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps)\n self._num_timesteps = len(timesteps)\n\n # 5. Prepare latents\n first_image = self.video_processor.preprocess(first_image, height=height, width=width).to(\n device, dtype=prompt_embeds.dtype\n )\n last_image = self.video_processor.preprocess(last_image, height=height, width=width).to(\n device, dtype=prompt_embeds.dtype\n )\n\n latent_channels = self.transformer.config.in_channels // 2\n latents, image_latents = self.prepare_latents(\n first_image,\n last_image,\n batch_size * num_videos_per_prompt,\n latent_channels,\n num_frames,\n height,\n width,\n prompt_embeds.dtype,\n device,\n generator,\n latents,\n )\n\n # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n # 7. Create rotary embeds if required\n image_rotary_emb = (\n self._prepare_rotary_positional_embeddings(height, width, latents.size(1), device)\n if self.transformer.config.use_rotary_positional_embeddings\n else None\n )\n\n # 8. Denoising loop\n num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)\n\n with self.progress_bar(total=num_inference_steps) as progress_bar:\n # for DPM-solver++\n old_pred_original_sample = None\n for i, t in enumerate(timesteps):\n if self.interrupt:\n continue\n\n latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)\n\n latent_image_input = torch.cat([image_latents] * 2) if do_classifier_free_guidance else image_latents\n latent_model_input = torch.cat([latent_model_input, latent_image_input], dim=2)\n\n # broadcast to batch dimension in a way that's compatible with ONNX/Core ML\n timestep = t.expand(latent_model_input.shape[0])\n\n # predict noise model_output\n noise_pred = self.transformer(\n hidden_states=latent_model_input,\n encoder_hidden_states=prompt_embeds,\n timestep=timestep,\n image_rotary_emb=image_rotary_emb,\n return_dict=False,\n )[0]\n noise_pred = noise_pred.float()\n\n # perform guidance\n if use_dynamic_cfg:\n self._guidance_scale = 1 + guidance_scale * (\n (1 - math.cos(math.pi * ((num_inference_steps - t.item()) / num_inference_steps) ** 5.0)) / 2\n )\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)\n\n # compute the previous noisy sample x_t -> x_t-1\n if not isinstance(self.scheduler, CogVideoXDPMScheduler):\n latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]\n else:\n latents, old_pred_original_sample = self.scheduler.step(\n noise_pred,\n old_pred_original_sample,\n t,\n timesteps[i - 1] if i > 0 else None,\n latents,\n **extra_step_kwargs,\n return_dict=False,\n )\n latents = latents.to(prompt_embeds.dtype)\n\n # call the callback, if provided\n if callback_on_step_end is not None:\n callback_kwargs = {}\n for k in callback_on_step_end_tensor_inputs:\n callback_kwargs[k] = locals()[k]\n callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)\n\n latents = callback_outputs.pop(\"latents\", latents)\n prompt_embeds = callback_outputs.pop(\"prompt_embeds\", prompt_embeds)\n negative_prompt_embeds = callback_outputs.pop(\"negative_prompt_embeds\", negative_prompt_embeds)\n\n if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):\n progress_bar.update()\n\n if not output_type == \"latent\":\n video = self.decode_latents(latents)\n video = self.video_processor.postprocess_video(video=video, output_type=output_type)\n else:\n video = latents\n\n # Offload all models\n self.maybe_free_model_hooks()\n\n if not return_dict:\n return (video,)\n\n return (video,)\n" }, { "path": "cogvideox_interpolation/utils/colormaps.py", "content": "# Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\"\"\" Helper functions for visualizing outputs \"\"\"\n\nfrom dataclasses import dataclass\nfrom typing import Optional\n\nimport matplotlib\nimport mediapy as media\nimport torch\nfrom jaxtyping import Bool, Float\nfrom torch import Tensor\n\nfrom . import colors\n\n# Colormaps = Literal[\"default\", \"turbo\", \"viridis\", \"magma\", \"inferno\", \"cividis\", \"gray\", \"pca\"]\nColormaps = \"turbo\"\n\n@dataclass(frozen=True)\nclass ColormapOptions:\n \"\"\"Options for colormap\"\"\"\n\n colormap: Colormaps = \"default\"\n \"\"\" The colormap to use \"\"\"\n normalize: bool = False\n \"\"\" Whether to normalize the input tensor image \"\"\"\n colormap_min: float = 0\n \"\"\" Minimum value for the output colormap \"\"\"\n colormap_max: float = 1\n \"\"\" Maximum value for the output colormap \"\"\"\n invert: bool = False\n \"\"\" Whether to invert the output colormap \"\"\"\n\n\ndef apply_colormap(\n image: Float[Tensor, \"*bs channels\"],\n colormap_options: ColormapOptions = ColormapOptions(),\n eps: float = 1e-9,\n mask_thresh=None,\n):\n \"\"\"\n Applies a colormap to a tensor image.\n If single channel, applies a colormap to the image.\n If 3 channel, treats the channels as RGB.\n If more than 3 channel, applies a PCA reduction on the dimensions to 3 channels\n\n Args:\n image: Input tensor image.\n eps: Epsilon value for numerical stability.\n\n Returns:\n Tensor with the colormap applied.\n \"\"\"\n\n # default for rgb images\n if image.shape[-1] == 3:\n return image\n\n # rendering depth outputs\n if image.shape[-1] == 1 and torch.is_floating_point(image):\n output = image\n if mask_thresh is not None:\n mask = output < mask_thresh\n if colormap_options.normalize:\n output = output - torch.min(output)\n output = output / (torch.max(output) + eps)\n output = (\n output * (colormap_options.colormap_max - colormap_options.colormap_min) + colormap_options.colormap_min\n )\n output = torch.clip(output, 0, 1)\n if mask_thresh is not None:\n output[mask] = 0\n if colormap_options.invert:\n output = 1 - output\n return apply_float_colormap(output, colormap=colormap_options.colormap)\n\n # rendering boolean outputs\n if image.dtype == torch.bool:\n return apply_boolean_colormap(image)\n\n if image.shape[-1] > 3:\n return apply_pca_colormap(image)\n\n raise NotImplementedError\n\n\ndef apply_float_colormap(image: Float[Tensor, \"*bs 1\"], colormap: Colormaps = \"viridis\"):\n \"\"\"Convert single channel to a color image.\n\n Args:\n image: Single channel image.\n colormap: Colormap for image.\n\n Returns:\n Tensor: Colored image with colors in [0, 1]\n \"\"\"\n if colormap == \"default\":\n colormap = \"turbo\"\n\n image = torch.nan_to_num(image, 0)\n if colormap == \"gray\":\n return image.repeat(1, 1, 3)\n image_long = (image * 255).long()\n image_long_min = torch.min(image_long)\n image_long_max = torch.max(image_long)\n assert image_long_min >= 0, f\"the min value is {image_long_min}\"\n assert image_long_max <= 255, f\"the max value is {image_long_max}\"\n return torch.tensor(matplotlib.colormaps[colormap].colors, device=image.device)[image_long[..., 0]]\n\n\ndef apply_depth_colormap(\n depth: Float[Tensor, \"*bs 1\"],\n accumulation: Optional[Float[Tensor, \"*bs 1\"]] = None,\n near_plane: Optional[float] = None,\n far_plane: Optional[float] = None,\n colormap_options: ColormapOptions = ColormapOptions(),\n):\n \"\"\"Converts a depth image to color for easier analysis.\n\n Args:\n depth: Depth image.\n accumulation: Ray accumulation used for masking vis.\n near_plane: Closest depth to consider. If None, use min image value.\n far_plane: Furthest depth to consider. If None, use max image value.\n colormap: Colormap to apply.\n\n Returns:\n Colored depth image with colors in [0, 1]\n \"\"\"\n\n near_plane = near_plane or float(torch.min(depth))\n far_plane = far_plane or float(torch.max(depth))\n\n depth = (depth - near_plane) / (far_plane - near_plane + 1e-10)\n depth = torch.clip(depth, 0, 1)\n # depth = torch.nan_to_num(depth, nan=0.0) # TODO(ethan): remove this\n\n colored_image = apply_colormap(depth, colormap_options=colormap_options)\n\n if accumulation is not None:\n colored_image = colored_image * accumulation + (1 - accumulation)\n\n return colored_image\n\n\ndef apply_boolean_colormap(\n image: Bool[Tensor, \"*bs 1\"],\n true_color = colors.WHITE,\n false_color = colors.BLACK,\n):\n \"\"\"Converts a depth image to color for easier analysis.\n\n Args:\n image: Boolean image.\n true_color: Color to use for True.\n false_color: Color to use for False.\n\n Returns:\n Colored boolean image\n \"\"\"\n\n colored_image = torch.ones(image.shape[:-1] + (3,))\n colored_image[image[..., 0], :] = true_color\n colored_image[~image[..., 0], :] = false_color\n return colored_image\n\n\ndef apply_pca_colormap(image: Float[Tensor, \"*bs dim\"]):\n \"\"\"Convert feature image to 3-channel RGB via PCA. The first three principle\n components are used for the color channels, with outlier rejection per-channel\n\n Args:\n image: image of arbitrary vectors\n\n Returns:\n Tensor: Colored image\n \"\"\"\n original_shape = image.shape\n image = image.contiguous().view(-1, image.shape[-1])\n _, _, v = torch.pca_lowrank(image)\n image = torch.matmul(image, v[..., :3])\n d = torch.abs(image - torch.median(image, dim=0).values)\n mdev = torch.median(d, dim=0).values\n s = d / mdev\n m = 3.0 # this is a hyperparam controlling how many std dev outside for outliers\n rins = image[s[:, 0] < m, 0]\n gins = image[s[:, 1] < m, 1]\n bins = image[s[:, 2] < m, 2]\n\n if len(rins) == 0 or len(gins) == 0 or len(bins) == 0:\n return image.new_zeros(*original_shape[:-1], 3)\n\n image[:, 0] -= rins.min()\n image[:, 1] -= gins.min()\n image[:, 2] -= bins.min()\n\n image[:, 0] /= rins.max() - rins.min()\n image[:, 1] /= gins.max() - gins.min()\n image[:, 2] /= bins.max() - bins.min()\n\n image = torch.clamp(image, 0, 1)\n image_long = (image * 255).long()\n image_long_min = torch.min(image_long)\n image_long_max = torch.max(image_long)\n assert image_long_min >= 0, f\"the min value is {image_long_min}\"\n assert image_long_max <= 255, f\"the max value is {image_long_max}\"\n return image.view(*original_shape[:-1], 3)\n\n\ndef colormap_saving(image: torch.Tensor, colormap_options, mask_thresh=None, save_path=None):\n \"\"\"\n if image's shape is (h, w, 1): draw colored relevance map;\n if image's shape is (h, w, 3): return directively;\n if image's shape is (h, w, c): execute PCA and transform it into (h, w, 3).\n \"\"\"\n output_image = (\n apply_colormap(\n image=image,\n mask_thresh=mask_thresh,\n colormap_options=colormap_options,\n ).cpu().numpy()\n )\n if save_path is not None:\n media.write_image(save_path.with_suffix(\".png\"), output_image, fmt=\"png\")\n return output_image\n" }, { "path": "cogvideox_interpolation/utils/colors.py", "content": "# Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\"\"\"Common Colors\"\"\"\nfrom typing import Union\n\nimport torch\nfrom jaxtyping import Float\nfrom torch import Tensor\n\nWHITE = torch.tensor([1.0, 1.0, 1.0])\nBLACK = torch.tensor([0.0, 0.0, 0.0])\nRED = torch.tensor([1.0, 0.0, 0.0])\nGREEN = torch.tensor([0.0, 1.0, 0.0])\nBLUE = torch.tensor([0.0, 0.0, 1.0])\n\nCOLORS_DICT = {\n \"white\": WHITE,\n \"black\": BLACK,\n \"red\": RED,\n \"green\": GREEN,\n \"blue\": BLUE,\n}\n\n\ndef get_color(color: Union[str, list]) -> Float[Tensor, \"3\"]:\n \"\"\"\n Args:\n Color as a string or a rgb list\n\n Returns:\n Parsed color\n \"\"\"\n if isinstance(color, str):\n color = color.lower()\n if color not in COLORS_DICT:\n raise ValueError(f\"{color} is not a valid preset color\")\n return COLORS_DICT[color]\n if isinstance(color, list):\n if len(color) != 3:\n raise ValueError(f\"Color should be 3 values (RGB) instead got {color}\")\n return torch.tensor(color)\n\n raise ValueError(f\"Color should be an RGB list or string, instead got {type(color)}\")\n" }, { "path": "cogvideox_interpolation/utils/config_utils.py", "content": "import argparse\nimport os\n\nfrom mmengine.config import Config\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(description=\"Simple example of a training script.\")\n \n parser.add_argument(\"--config\", help=\"model config file path\")\n parser.add_argument(\n \"--pretrained_model_name_or_path\",\n type=str,\n default=None,\n required=True,\n help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--pretrained_model_ae\",\n type=str,\n default=None,\n help=\"Path to pretrained model or model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--root_path\",\n type=str,\n help=\"The output directory where the model predictions and checkpoints will be written.\",\n )\n parser.add_argument(\n \"--annotation_json\",\n type=str,\n help=\"The output directory where the model predictions and checkpoints will be written.\",\n )\n parser.add_argument(\n \"--output_dir\",\n type=str,\n default=\"results\",\n help=\"The output directory where the model predictions and checkpoints will be written.\",\n )\n parser.add_argument(\n \"--logging_dir\",\n type=str,\n default=\"logs\",\n )\n parser.add_argument(\n \"--mixed_precision\",\n type=str,\n default=None,\n choices=[\"no\", \"fp16\", \"bf16\"],\n help=(\n \"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=\"\n \" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the\"\n \" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config.\"\n ),\n )\n parser.add_argument(\n \"--report_to\",\n type=str,\n default=\"tensorboard\",\n help=(\n 'The integration to report the results and logs to. Supported platforms are `\"tensorboard\"`'\n ' (default), `\"wandb\"` and `\"comet_ml\"`. Use `\"all\"` to report to all integrations.'\n ),\n )\n parser.add_argument(\n \"--gradient_accumulation_steps\",\n type=int,\n default=8,\n help=\"Number of updates steps to accumulate before performing a backward/update pass.\",\n )\n parser.add_argument(\n \"--revision\",\n type=str,\n default=None,\n required=False,\n help=\"Revision of pretrained model identifier from huggingface.co/models.\",\n )\n parser.add_argument(\n \"--variant\",\n type=str,\n default=None,\n help=\"Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16\",\n ) \n parser.add_argument(\n \"--gradient_checkpointing\",\n type=bool,\n default=True,\n help=\"Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.\",\n )\n parser.add_argument(\n \"--learning_rate\",\n type=float,\n default=1e-4,\n help=\"Initial learning rate (after the potential warmup period) to use.\",\n )\n parser.add_argument(\n \"--use_8bit_adam\", \n type=bool,\n default=True,\n help=\"Whether or not to use 8-bit Adam from bitsandbytes.\"\n )\n parser.add_argument(\n \"--use_came\",\n type=bool,\n default=False,\n help=\"whether to use came\",\n )\n parser.add_argument(\n \"--allow_tf32\",\n type=bool,\n default=True,\n help=(\n \"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see\"\n \" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices\"\n ),\n )\n parser.add_argument(\n \"--lr_scheduler\",\n type=str,\n default=\"constant\",\n help=(\n 'The scheduler type to use. Choose between [\"linear\", \"cosine\", \"cosine_with_restarts\", \"polynomial\",'\n ' \"constant\", \"constant_with_warmup\"]'\n ),\n )\n parser.add_argument(\n \"--lr_warmup_steps\", type=int, default=500, help=\"Number of steps for the warmup in the lr scheduler.\"\n )\n parser.add_argument(\n \"--max_train_steps\",\n type=int,\n default=None,\n help=\"Total number of training steps to perform. If provided, overrides num_train_epochs.\",\n ) \n parser.add_argument(\n \"--dataloader_num_workers\",\n type=int,\n default=0,\n help=(\n \"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.\"\n ),\n )\n parser.add_argument(\n \"--train_batch_size\", type=int, default=1, help=\"Batch size (per device) for the training dataloader.\"\n )\n parser.add_argument(\n \"--checkpointing_steps\",\n type=int,\n default=4000,\n help=(\n \"Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming\"\n \" training using `--resume_from_checkpoint`.\"\n ),\n )\n parser.add_argument(\"--seed\", type=int, default=42, help=\"A seed for reproducible training.\")\n parser.add_argument(\"--num_train_epochs\", type=int, default=100)\n parser.add_argument(\"--adam_beta1\", type=float, default=0.9, help=\"The beta1 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_beta2\", type=float, default=0.999, help=\"The beta2 parameter for the Adam optimizer.\")\n parser.add_argument(\"--adam_weight_decay\", type=float, default=1e-2, help=\"Weight decay to use.\")\n parser.add_argument(\"--adam_epsilon\", type=float, default=1e-08, help=\"Epsilon value for the Adam optimizer\")\n parser.add_argument(\"--max_grad_norm\", default=1.0, type=float, help=\"Max gradient norm.\")\n parser.add_argument(\"--local_rank\", type=int, default=-1, help=\"For distributed training: local_rank\")\n\n parser.add_argument(\"--classes\", type=str, nargs=\"+\")\n parser.add_argument(\"--img_path\", type=str)\n parser.add_argument(\"--save_path\", type=str)\n\n args = parser.parse_args()\n env_local_rank = int(os.environ.get(\"LOCAL_RANK\", -1))\n if env_local_rank != -1 and env_local_rank != args.local_rank:\n args.local_rank = env_local_rank\n return args \n\n\ndef merge_args(cfg, args):\n # if args.ckpt_path is not None:\n # cfg.model[\"from_pretrained\"] = args.ckpt_path\n # if cfg.get(\"discriminator\") is not None:\n # cfg.discriminator[\"from_pretrained\"] = args.ckpt_path\n # args.ckpt_path = None\n for k, v in vars(args).items():\n if v is not None:\n cfg[k] = v\n\n return cfg\n\n\ndef read_config(config_path):\n cfg = Config.fromfile(config_path)\n return cfg\n\n\ndef parse_configs():\n args = parse_args()\n cfg = read_config(args.config)\n cfg = merge_args(cfg, args)\n return cfg\n\ndef str2bool(v):\n if isinstance(v, bool):\n return v\n if v.lower() in (\"yes\", \"true\", \"t\", \"y\", \"1\"):\n return True\n elif v.lower() in (\"no\", \"false\", \"f\", \"n\", \"0\"):\n return False\n else:\n raise argparse.ArgumentTypeError(\"Boolean value expected.\")\n" }, { "path": "cogvideox_interpolation/utils/misc.py", "content": "import torch, time\nclass Timer:\n def __init__(self, name, log=False):\n self.name = name\n self.start_time = None\n self.end_time = None\n\n @property\n def elapsed_time(self):\n return self.end_time - self.start_time\n\n def __enter__(self):\n torch.cuda.synchronize()\n self.start_time = time.time()\n return self\n\n def __exit__(self, exc_type, exc_val, exc_tb):\n torch.cuda.synchronize()\n self.end_time = time.time()" }, { "path": "configs/field_construction.yaml", "content": "wandb:\n enable_wandb: False \n project_name: \"proj\"\npipeline:\n rgb_video_path: outputs/kitchen/rgb/video_ckpt_800.mp4\n seg_video_path: outputs/kitchen/seg/video_ckpt_800.mp4\n normal_video_path: outputs/kitchen/normal/video_ckpt_800.mp4\n data_path: \"field_construction/data/kitchen\"\n skip_video_process: False\n skip_pose_estimate: True\n skip_lang_feature_extraction: False\n load_iteration: 5_000\n selection: False\n selected_idxs: []\n chunk_num: 8 \n keep_num_per_chunk: 3\n mode: \"train\"\n\nvideo_processor:\n img_format: \"png\"\n\nfeature_extractor:\n type: \"open-seg\"\n model_path: \"/home/lff/data1/cjw/langscene/model_zoo/openseg_exported_clip\"\n\npose_estimator:\n type: \"vggt\"\n device: \"cuda\"\n\nlseg:\n model_path: \"model_zoo/lseg/demo_e200.ckpt\"\n device: \"cuda\"\n\nae:\n model_path: \"model_zoo/ae/model.safetensors\"\n device: \"cuda\"\n\ngaussian:\n debug_from: -100\n detect_anomaly: False\n test_iterations: [100, 500, 1000, 2000, 5000, 10_000, 12_000]\n save_iterations: [100, 500, 1000, 2000, 5000, 10_000, 12_000]\n quiet: False\n checkpoint_iterations: [100, 500, 1000, 5000, 10_000, 12_000]\n start_checkpoint: None\n\n dataset:\n sh_degree: 3\n source_path: \"/home/lff/data1/cjw/langscene/field_construction/data/kitchen\"\n model_path: \"/home/lff/data1/cjw/langscene/field_construction/outputs/kitchen\"\n images: \"input\"\n normal: \"normal\"\n resolution: -1\n white_background: False\n data_device: \"cuda\" # \"cuda\" or \"cpu\"\n eval: False\n preload_img: True\n ncc_scale: 1.0\n multi_view_num: 8\n multi_view_max_angle: 30\n multi_view_min_dis: 0.01\n multi_view_max_dis: 1.5\n language_features_name: \"lang_features_dim3\"\n \n opt:\n pp_optimizer: False\n optim_pose: True\n pose_until_iter: 2000\n iterations: 12_000\n max_geo_iter: 1500 \n normal_optim: False\n\n position_lr_init: 0.00016\n position_lr_final: 0.0000016\n position_lr_delay_mult: 0.01\n position_lr_max_steps: 1000\n feature_lr: 0.0025\n opacity_lr: 0.05\n language_feature_lr: 0.0050\n instance_feature_lr: 0.0050\n scaling_lr: 0.005\n rotation_lr: 0.001\n percent_dense: 0.001\n lambda_dssim: 0.2\n densification_interval: 100\n opacity_reset_interval: 999_999\n densify_from_iter: 500\n densify_until_iter: 1200\n # densify_from_iter: 999_999\n # densify_until_iter: -1\n # densify_grad_threshold: 0.0002\n densify_grad_threshold: 0.004\n scale_loss_weight: 100.0\n wo_image_weight: False\n single_view_weight: 0.10\n single_view_weight_from_iter: 500\n single_view_weight_end_iter: 2000\n instance_supervision_from_iter: 12_001\n use_virtul_cam: False\n virtul_cam_prob: 0.5\n use_multi_view_trim: True\n multi_view_ncc_weight: 0.15\n multi_view_geo_weight: 0.03\n multi_view_weight_from_iter: 500\n multi_view_weight_end_iter: 2000\n multi_view_patch_size: 3\n multi_view_sample_num: 102400\n multi_view_pixel_noise_th: 1.0\n wo_use_geo_occ_aware: False\n opacity_cull_threshold: 0.05\n # densify_abs_grad_threshold: 0.0008\n densify_abs_grad_threshold: 0.016\n abs_split_radii2D_threshold: 20\n max_abs_split_points: 0\n max_all_points: 12_000_000\n exposure_compensation: False\n random_background: False\n reg3d_start: 2\n reg3d_k: 5\n reg3d_lambda_val: 4\n lang_loss_start_iter: 1200\n grouping_loss: True\n loss_obj_3d: True\n\n pipe:\n convert_SHs_python: False\n compute_cov3D_python: False\n debug: False\n \n render:\n load_iteration: 5_000\n pose_optim_iter: 100\n voxel_size: 0.01\n normalized: True\n include_features: True\n \n eval:\n eval_data_path: \"\"\n pose_optim_iter: 100" }, { "path": "configs/test_config.py", "content": "# validation \nval_steps=10\nwandb=False\nrecord_time = True\n\n# autoencoder config\n# exp_name = \"AE-gpu8-bs1-channel-16\"\n# autoencoder_name = \"ae-channel-16\"\n\nexp_name = \"test\"\nautoencoder_name = \"ae-channel-3\"\n\ndataset_name = \"lvis\"\n\nlseg_weights=\"model_zoo/lseg/demo_e200.ckpt\"\n\n# loss weights\nperceptual_loss_weight = 0.1 # use vgg is not None and more than 0\nkl_loss_weight = 1e-6\n\nmixed_strategy = \"mixed_video_image\"\nmixed_image_ratio = 0.2\nuse_real_rec_loss = False\nuse_z_rec_loss = True\nuse_image_identity_loss = True" }, { "path": "configs/unet_config_c16.py", "content": "# == datasets == \ndataset_path = \"/mnt/juicefs/datasets/lvis\"\njson_path = \"/mnt/juicefs/datasets/lvis/annotations/image_info_unlabeled2017.json\"\n\n#== train ==\nmixed_precision = \"no\"\nnum_train_epochs = 5\ntrain_batch_size = 4\nwandb=True\nexp_name = \"train-acc-unet-c16\"\nrecord_time = False\n\npretrained_model_ae = None\n\n# validation \nval_steps=100\ncheckpointing_steps = 50000\n\n#== model ==\nin_channels = 512\nout_channels = 512\nlatent_channels = 16\nencoder_block_out_channels=[256, 128, 64, 32, 16]\ndecoder_block_out_channels=[16, 32, 64, 128, 256]\nnum_encoder_blocks=(1, 1, 1, 1, 1)\nnum_decoder_blocks=(1, 1, 1, 1, 1)" }, { "path": "configs/unet_config_c32.py", "content": "# == datasets == \ndataset_path = \"/mnt/juicefs/datasets/lvis\"\njson_path = \"/mnt/juicefs/datasets/lvis/annotations/image_info_unlabeled2017.json\"\n\n#== train ==\nmixed_precision = \"no\"\nnum_train_epochs = 5\ntrain_batch_size = 4\nwandb=True\nexp_name = \"train-acc-unet-c16\"\nrecord_time = False\n\npretrained_model_ae = None\n\n# validation \nval_steps=100\ncheckpointing_steps = 50000\n\n#== model ==\nin_channels = 512\nout_channels = 512\nlatent_channels = 32\nencoder_block_out_channels=[256, 64, 16]\ndecoder_block_out_channels=[16, 64, 256]\nnum_encoder_blocks=(1, 1, 1)\nnum_decoder_blocks=(1, 1, 1)" }, { "path": "entry_point.py", "content": "import logging\nimport os\nimport random\nimport warnings\nfrom random import randint\n\nimport hydra\nimport numpy as np\nimport torch\nfrom omegaconf import DictConfig\n\nfrom field_construction.pipeline import FieldConstructionPipeline\n\ndef setup_seed(seed):\n torch.manual_seed(seed)\n torch.cuda.manual_seed_all(seed)\n np.random.seed(seed)\n random.seed(seed)\n\n\n@hydra.main(config_path=\"configs\", config_name=\"field_construction\", version_base=None)\ndef main(cfg: DictConfig):\n logging.basicConfig(\n level=logging.INFO,\n format=\"%(asctime)s - %(name)s - %(levelname)s - %(message)s\",\n handlers=[logging.StreamHandler]\n )\n # ignore pil debug message.\n pil_logger = logging.getLogger(\"PIL\")\n pil_logger.setLevel(logging.WARNING)\n os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' \n warnings.filterwarnings(\"ignore\", category=FutureWarning)\n\n setup_seed(42)\n \n pipeline = FieldConstructionPipeline(cfg)\n if cfg.pipeline.mode == \"train\":\n pipeline.construct_field()\n elif cfg.pipeline.mode == \"render\":\n pipeline.render_result()\n elif cfg.pipeline.mode == \"eval\":\n pipeline.eval()\n else:\n raise NotImplementedError\n\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "field_construction/auto_encoder.py", "content": "import glob\nimport os\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nfrom torch.utils.data import Dataset\n\n\nclass Autoencoder_dataset(Dataset):\n def __init__(self, data_dir):\n data_names = sorted(glob.glob(os.path.join(data_dir, '*.npy')))\n data = []\n for i in range(len(data_names)):\n features = torch.from_numpy(np.load(data_names[i]))\n data.append(features)\n\n self.data = torch.cat(data, dim=0).float()\n\n def __getitem__(self, index):\n data = self.data[index]\n return data\n\n def __len__(self):\n return self.data.shape[0] \n\n\nclass Autoencoder(nn.Module):\n def __init__(self, encoder_hidden_dims=None, decoder_hidden_dims=None):\n super(Autoencoder, self).__init__()\n encoder_layers = []\n if not encoder_hidden_dims:\n encoder_hidden_dims = [512, 256, 128, 64, 32, 16, 3]\n if not decoder_hidden_dims:\n decoder_hidden_dims = [16, 32, 64, 128, 256, 512, 768]\n for i in range(len(encoder_hidden_dims)):\n if i == 0:\n encoder_layers.append(nn.Linear(768, encoder_hidden_dims[i]))\n else:\n encoder_layers.append(torch.nn.BatchNorm1d(encoder_hidden_dims[i-1]))\n encoder_layers.append(nn.ReLU())\n encoder_layers.append(nn.Linear(encoder_hidden_dims[i-1], encoder_hidden_dims[i]))\n self.encoder = nn.ModuleList(encoder_layers)\n\n decoder_layers = []\n for i in range(len(decoder_hidden_dims)):\n if i == 0:\n decoder_layers.append(nn.Linear(encoder_hidden_dims[-1], decoder_hidden_dims[i]))\n else:\n decoder_layers.append(nn.ReLU())\n decoder_layers.append(nn.Linear(decoder_hidden_dims[i-1], decoder_hidden_dims[i]))\n self.decoder = nn.ModuleList(decoder_layers)\n print(self.encoder, self.decoder)\n def forward(self, x):\n for m in self.encoder:\n x = m(x)\n x = x / x.norm(dim=-1, keepdim=True)\n for m in self.decoder:\n x = m(x)\n x = x / x.norm(dim=-1, keepdim=True)\n return x\n\n def encode(self, x):\n for m in self.encoder:\n x = m(x)\n x = x / x.norm(dim=-1, keepdim=True)\n return x\n\n def decode(self, x):\n for m in self.decoder:\n x = m(x)\n x = x / x.norm(dim=-1, keepdim=True)\n return x" }, { "path": "field_construction/extract_with_openseg.py", "content": "import os\nfrom argparse import ArgumentParser\n\nimport cv2\nimport numpy as np\nimport torch\nfrom tqdm import tqdm\n\n\ndef extract_with_openseg(cfg):\n import tensorflow as tf2\n import tensorflow._api.v2.compat.v1 as tf\n gpus = tf.config.experimental.list_physical_devices('GPU')\n if gpus:\n try:\n for gpu in gpus:\n tf.config.experimental.set_memory_growth(gpu, True)\n except RuntimeError as e:\n print(e)\n\n openseg = tf2.saved_model.load(\n cfg.feature_extractor.model_path, \n tags=[tf.saved_model.tag_constants.SERVING]\n )\n imgs_path = os.path.join(cfg.pipeline.data_path, \"input\")\n img_names = list(\n filter(\n lambda x: x.endswith(\"png\") or x.endswith(\"jpg\"), os.listdir(imgs_path)\n )\n )\n img_list = []\n np_image_string_list = []\n for img_name in img_names:\n img_path = os.path.join(imgs_path, img_name)\n image = cv2.imread(img_path)\n with tf.gfile.GFile(img_path, 'rb') as f:\n np_image_string = np.array([f.read()])\n\n image = torch.from_numpy(image)\n img_list.append(image)\n np_image_string_list.append(np_image_string)\n\n images = [img_list[i].permute(2, 0, 1)[None, ...] for i in range(len(img_list))]\n imgs = torch.cat(images)\n save_path = os.path.join(cfg.pipeline.data_path, \"lang_features\")\n os.makedirs(save_path, exist_ok=True)\n embed_size = 768\n for i, (img, np_image_string) in enumerate(tqdm((zip(imgs, np_image_string_list)), desc=\"Extracting lang features\")):\n text_emb = tf.zeros([1, 1, embed_size])\n results = openseg.signatures[\"serving_default\"](\n inp_image_bytes=tf.convert_to_tensor(np_image_string[0]),\n inp_text_emb=text_emb\n )\n img_info = results['image_info']\n crop_sz = [\n int(img_info[0, 0] * img_info[2, 0]),\n int(img_info[0, 1] * img_info[2, 1])\n ]\n image_embedding_feat = results['image_embedding_feat'][:, :crop_sz[0], :crop_sz[1]]\n img_size = (img.shape[1], img.shape[2])\n feat_2d = tf.cast(\n tf.image.resize_nearest_neighbor(\n image_embedding_feat, img_size, align_corners=True\n )[0], dtype=tf.float16\n ).numpy()\n # save feat_2d\n np.save(os.path.join(save_path, str(i+1).zfill(4)+\".npy\"), feat_2d)\n \nif __name__ == \"__main__\":\n arg_parser = ArgumentParser()\n arg_parser.add_argument(\"--cfg\")\n args = arg_parser.parse_args()\n extract_with_openseg(args.cfg)" }, { "path": "field_construction/gaussian_field.py", "content": "#\n# Copyright (C) 2023, Inria\n# GRAPHDECO research group, https://team.inria.fr/graphdeco\n# All rights reserved.\n#\n# This software is free for non-commercial, research and evaluation use\n# under the terms of the LICENSE.md file.\n#\n# For inquiries contact george.drettakis@inria.fr\n#\n\nimport copy\nimport logging\nimport os\nimport random\nfrom random import randint\n\nimport cv2\nimport numpy as np\nimport open3d as o3d\nimport torch\nimport torch.nn.functional as F\nimport torchvision\nfrom tqdm import tqdm\n\nfrom cogvideox_interpolation.utils.colormaps import apply_pca_colormap\n\nfrom .gaussian_renderer import render\nfrom .scene import GaussianModel, Scene\nfrom .scene.app_model import AppModel\nfrom .scene.cameras import Camera\nfrom .utils.camera_utils import gen_virtul_cam\nfrom .utils.general_utils import safe_state\nfrom .utils.graphics_utils import patch_offsets, patch_warp\nfrom .utils.image_utils import psnr\nfrom .utils.loss_utils import (get_img_grad_weight, get_loss_instance_group,\n get_loss_semantic_group, l1_loss, lncc,\n loss_cls_3d, ranking_loss, ssim)\nfrom .utils.pose_utils import (get_camera_from_tensor, get_tensor_from_camera,\n post_pose_process, quad2rotation)\n\n\ndef post_process_mesh(mesh, cluster_to_keep=3):\n \"\"\"\n Post-process a mesh to filter out floaters and disconnected parts\n \"\"\"\n print(\"post processing the mesh to have {} clusterscluster_to_kep\".format(cluster_to_keep))\n mesh_0 = copy.deepcopy(mesh)\n with o3d.utility.VerbosityContextManager(o3d.utility.VerbosityLevel.Debug) as cm:\n triangle_clusters, cluster_n_triangles, cluster_area = (mesh_0.cluster_connected_triangles())\n\n triangle_clusters = np.asarray(triangle_clusters)\n cluster_n_triangles = np.asarray(cluster_n_triangles)\n cluster_area = np.asarray(cluster_area)\n n_cluster = np.sort(cluster_n_triangles.copy())[-cluster_to_keep]\n n_cluster = max(n_cluster, 50) # filter meshes smaller than 50\n triangles_to_remove = cluster_n_triangles[triangle_clusters] < n_cluster\n mesh_0.remove_triangles_by_mask(triangles_to_remove)\n mesh_0.remove_unreferenced_vertices()\n mesh_0.remove_degenerate_triangles()\n print(\"num vertices raw {}\".format(len(mesh.vertices)))\n print(\"num vertices post {}\".format(len(mesh_0.vertices)))\n return mesh_0\n\ndef permuted_pca(image):\n return apply_pca_colormap(image.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)\n\ndef save_pose(path, quat_pose, train_cams):\n # Get camera IDs and convert quaternion poses to camera matrices\n camera_ids = [cam.colmap_id for cam in train_cams]\n world_to_camera = [get_camera_from_tensor(quat) for quat in quat_pose]\n \n # Reorder poses according to colmap IDs\n colmap_poses = []\n for i in range(len(camera_ids)):\n idx = camera_ids.index(i + 1) # Find position of camera i+1\n pose = world_to_camera[idx]\n colmap_poses.append(pose)\n \n # Convert to numpy array and save\n colmap_poses = torch.stack(colmap_poses).detach().cpu().numpy()\n np.save(path, colmap_poses)\n\n\ndef load_and_prepare_confidence(confidence_path, device='cuda', scale=(0.1, 1.0)):\n \"\"\"\n Loads, normalizes, inverts, and scales confidence values to obtain learning rate modifiers.\n \n Args:\n confidence_path (str): Path to the .npy confidence file.\n device (str): Device to load the tensor onto.\n scale (tuple): Desired range for the learning rate modifiers.\n \n Returns:\n torch.Tensor: Learning rate modifiers.\n \"\"\"\n # Load and normalize\n confidence_np = np.load(confidence_path)\n confidence_tensor = torch.from_numpy(confidence_np).float().to(device)\n normalized_confidence = torch.sigmoid(confidence_tensor)\n\n # Invert confidence and scale to desired range\n inverted_confidence = 1.0 - normalized_confidence\n min_scale, max_scale = scale\n lr_modifiers = inverted_confidence * (max_scale - min_scale) + min_scale\n \n return lr_modifiers\n\nclass GaussianField():\n def __init__(self, cfg):\n self.cfg = cfg\n\n def train(self):\n cfg = self.cfg\n dataset = cfg.gaussian.dataset\n opt = cfg.gaussian.opt\n pipe = cfg.gaussian.pipe\n device = cfg.gaussian.dataset.data_device\n\n self.gaussians = GaussianModel(cfg.gaussian.dataset.sh_degree)\n self.scene = Scene(cfg.gaussian.dataset, self.gaussians)\n self.app_model = AppModel()\n self.app_model.train().cuda()\n\n logging.info(\"Optimizing \" + dataset.model_path)\n safe_state(cfg.gaussian.quiet)\n \n if opt.pp_optimizer:\n confidence_path = os.path.join(dataset.source_path, f\"sparse/0\", \"confidence_dsp.npy\")\n try:\n confidence_lr = load_and_prepare_confidence(confidence_path, device='cuda', scale=(2, 100))\n self.gaussians.training_setup_pp(opt, confidence_lr, device) \n except:\n logging.warning(\"can not load confidence. \")\n cfg.opt.pp_optimizer = False\n self.gaussians.training_setup(opt, device)\n else:\n self.gaussians.training_setup(opt, device)\n \n train_cams_init = self.scene.getTrainCameras().copy()\n for save_iter in cfg.gaussian.save_iterations:\n os.makedirs(self.scene.model_path + f'/pose/iter_{save_iter}', exist_ok=True)\n save_pose(self.scene.model_path + f'/pose/iter_{save_iter}/pose_org.npy', self.gaussians.P, train_cams_init)\n \n first_iter = 0\n if cfg.gaussian.start_checkpoint != \"None\":\n model_params, first_iter = torch.load(cfg.gaussian.start_checkpoint)\n self.gaussians.restore(model_params, opt)\n self.app_model.load_weights(self.scene.model_path)\n \n bg_color = [1, 1, 1] if dataset.white_background else [0, 0, 0]\n background = torch.tensor(bg_color, dtype=torch.float32, device=device)\n\n iter_start = torch.cuda.Event(enable_timing=True)\n iter_end = torch.cuda.Event(enable_timing=True)\n\n viewpoint_stack = None\n ema_loss_for_log = 0.0\n ema_single_view_for_log = 0.0\n ema_multi_view_geo_for_log = 0.0\n ema_multi_view_pho_for_log = 0.0\n ema_language_loss_for_log = 0.0\n ema_grouping_loss = 0.0\n ema_loss_obj_3d = 0.0\n ema_ins_grouping_loss = 0.0\n ema_ins_obj_3d_loss = 0.0\n normal_loss, geo_loss, ncc_loss = None, None, None\n language_loss = None\n grouping_loss = None\n include_feature = True\n progress_bar = tqdm(range(first_iter, opt.iterations), desc=\"Training progress\")\n first_iter += 1\n debug_path = os.path.join(self.scene.model_path, \"debug\")\n os.makedirs(debug_path, exist_ok=True)\n\n camera_list = self.scene.getTrainCameras().copy()\n last_cam_id = -1\n\n self.gaussians.change_reqiures_grad(\"semantic\", iteration=first_iter, quiet=False)\n\n if not opt.optim_pose:\n self.gaussians.P.requires_grad_(False)\n\n for iteration in range(first_iter, opt.iterations + 1):\n iter_start.record()\n\n self.gaussians.update_learning_rate(iteration)\n\n if iteration % 100 == 0:\n self.gaussians.oneupSHdegree()\n \n if not viewpoint_stack:\n viewpoint_stack = camera_list.copy()\n\n # update camera lists: \n for cam_idx, cam in enumerate(camera_list):\n if cam.uid == last_cam_id:\n updated_pose = self.gaussians.get_RT(self.gaussians.index_mapping[last_cam_id]).clone().detach()\n extrinsics = get_camera_from_tensor(updated_pose)\n camera_list[cam_idx].R = extrinsics[:3, :3].T\n camera_list[cam_idx].T = extrinsics[:3, 3]\n break\n \n viewpoint_cam: Camera = viewpoint_stack.pop(randint(0, len(viewpoint_stack) - 1))\n last_cam_id = viewpoint_cam.uid\n pose = self.gaussians.get_RT(self.gaussians.index_mapping[last_cam_id]) # quad t\n\n if (iteration - 1) == cfg.gaussian.debug_from:\n pipe.debug = True\n \n bg = torch.rand((3), device=\"cuda\") if opt.random_background else background\n\n if not opt.optim_pose:\n render_pkg = render(viewpoint_cam, self.gaussians, pipe, bg, app_model=self.app_model,\n return_depth_normal=iteration > opt.single_view_weight_from_iter,\n include_feature=include_feature)\n else:\n render_pkg = render(viewpoint_cam, self.gaussians, pipe, bg, app_model=self.app_model,\n return_depth_normal=iteration > opt.single_view_weight_from_iter,\n include_feature=include_feature, camera_pose=pose)\n \n image, viewspace_point_tensor, visibility_filter, radii, language_feature, instance_feature = \\\n render_pkg[\"render\"], render_pkg[\"viewspace_points\"], render_pkg[\"visibility_filter\"], render_pkg[\"radii\"], \\\n render_pkg[\"language_feature\"], render_pkg[\"instance_feature\"]\n \n\n overall_loss = 0\n image_loss = None\n obj_3d_loss = None\n grouping_loss = None\n ins_obj_3d_loss = None\n ins_grouping_loss = None\n\n if iteration == opt.max_geo_iter:\n self.gaussians.change_reqiures_grad(\"semantic_only\", iteration=iteration, quiet=False)\n\n if iteration < opt.max_geo_iter:\n gt_image, gt_image_gray = viewpoint_cam.get_image()\n ssim_loss = (1.0 - ssim(image, gt_image))\n if 'app_image' in render_pkg and ssim_loss < 0.5:\n app_image = render_pkg['app_image']\n Ll1 = l1_loss(app_image, gt_image)\n else:\n Ll1 = l1_loss(image, gt_image)\n \n image_loss = (1.0 - opt.lambda_dssim) * Ll1 + opt.lambda_dssim * ssim_loss\n\n overall_loss = overall_loss + image_loss\n\n # scale loss\n if visibility_filter.sum() > 0:\n scale = self.gaussians.get_scaling[visibility_filter]\n sorted_scale, _ = torch.sort(scale, dim=-1)\n min_scale_loss = sorted_scale[..., 0]\n overall_loss = overall_loss + opt.scale_loss_weight * min_scale_loss.mean()\n \n # single view loss:\n if opt.single_view_weight_from_iter < iteration < opt.single_view_weight_end_iter:\n weight = opt.single_view_weight\n normal = render_pkg[\"rendered_normal\"]\n depth_normal = render_pkg[\"depth_normal\"]\n\n image_weight = (1.0 - get_img_grad_weight(gt_image))\n image_weight = (image_weight).clamp(0, 1).detach() ** 2\n\n if opt.normal_optim:\n render_normal = (normal.permute(1, 2, 0) @ (viewpoint_cam.world_view_transform[:3, :3].T)).permute(2, 0, 1)\n rendered_depth_normal = (depth_normal.permute(1, 2, 0) @ (viewpoint_cam.world_view_transform[:3, :3].T)).permute(2, 0, 1)\n normal_gt, normal_mask = viewpoint_cam.get_normal()\n prior_normal = normal_gt\n prior_normal_mask = normal_mask[0]\n normal_prior_error = (1 - F.cosine_similarity(prior_normal, render_normal, dim=0)) + \\\n (1 - F.cosine_similarity(prior_normal, rendered_depth_normal, dim=0))\n normal_prior_error = ranking_loss(normal_prior_error[prior_normal_mask], \n penalize_ratio=1.0, type=\"mean\")\n normal_loss = weight * normal_prior_error\n else:\n if not opt.wo_image_weight:\n normal_loss = weight * (image_weight * (((depth_normal - normal)).abs().sum(0))).mean()\n else:\n normal_loss = weight * (((depth_normal - normal)).abs().sum(0)).mean()\n overall_loss = overall_loss + normal_loss\n \n # multi-view loss\n if opt.multi_view_weight_from_iter < iteration < opt.multi_view_weight_end_iter:\n nearest_cam = None if len(viewpoint_cam.nearest_id) == 0 else camera_list[\n random.sample(viewpoint_cam.nearest_id, 1)[0]]\n use_virtul_cam = False\n if opt.use_virtul_cam and (np.random.random() < opt.virtul_cam_prob or nearest_cam is None):\n nearest_cam = gen_virtul_cam(viewpoint_cam, trans_noise=dataset.multi_view_max_dis,\n deg_noise=dataset.multi_view_max_angle, device=device)\n use_virtul_cam = True\n if nearest_cam is not None:\n patch_size = opt.multi_view_patch_size\n sample_num = opt.multi_view_sample_num\n pixel_noise_th = opt.multi_view_pixel_noise_th\n total_patch_size = (patch_size * 2 + 1) ** 2\n ncc_weight = opt.multi_view_ncc_weight\n geo_weight = opt.multi_view_geo_weight\n H, W = render_pkg['plane_depth'].squeeze().shape\n ix, iy = torch.meshgrid(\n torch.arange(W), torch.arange(H), indexing='xy')\n pixels = torch.stack([ix, iy], dim=-1).float().to(render_pkg['plane_depth'].device)\n if not use_virtul_cam:\n nearest_pose = self.gaussians.get_RT(self.gaussians.index_mapping[nearest_cam.uid]) # quad t\n if not opt.optim_pose:\n nearest_render_pkg = render(nearest_cam, self.gaussians, pipe, bg, app_model=self.app_model,\n return_plane=True, return_depth_normal=False)\n else:\n nearest_render_pkg = render(nearest_cam, self.gaussians, pipe, bg, app_model=self.app_model,\n return_plane=True, return_depth_normal=False, camera_pose=nearest_pose.clone().detach())\n else:\n nearest_render_pkg = render(nearest_cam, self.gaussians, pipe, bg, app_model=self.app_model,\n return_plane=True, return_depth_normal=False)\n pts = self.gaussians.get_points_from_depth(viewpoint_cam, render_pkg['plane_depth'])\n pts_in_nearest_cam = pts @ nearest_cam.world_view_transform[:3,\n :3] + nearest_cam.world_view_transform[3, :3]\n map_z, d_mask = self.gaussians.get_points_depth_in_depth_map(nearest_cam,\n nearest_render_pkg['plane_depth'],\n pts_in_nearest_cam)\n\n pts_in_nearest_cam = pts_in_nearest_cam / (pts_in_nearest_cam[:, 2:3])\n pts_in_nearest_cam = pts_in_nearest_cam * map_z.squeeze()[..., None]\n R = torch.tensor(nearest_cam.R).float().cuda()\n T = torch.tensor(nearest_cam.T).float().cuda()\n pts_ = (pts_in_nearest_cam - T) @ R.transpose(-1, -2)\n pts_in_view_cam = pts_ @ viewpoint_cam.world_view_transform[:3,\n :3] + viewpoint_cam.world_view_transform[3, :3]\n pts_projections = torch.stack(\n [pts_in_view_cam[:, 0] * viewpoint_cam.Fx / pts_in_view_cam[:, 2] + viewpoint_cam.Cx,\n pts_in_view_cam[:, 1] * viewpoint_cam.Fy / pts_in_view_cam[:, 2] + viewpoint_cam.Cy],\n -1).float()\n pixel_noise = torch.norm(pts_projections - pixels.reshape(*pts_projections.shape), dim=-1)\n if not opt.wo_use_geo_occ_aware:\n d_mask = d_mask & (pixel_noise < pixel_noise_th)\n weights = (1.0 / torch.exp(pixel_noise)).detach()\n weights[~d_mask] = 0\n else:\n d_mask = d_mask\n weights = torch.ones_like(pixel_noise)\n weights[~d_mask] = 0\n if iteration % 200 == 0:\n gt_img_show = ((gt_image).permute(1, 2, 0).clamp(0, 1)[:, :,\n [2, 1, 0]] * 255).detach().cpu().numpy().astype(np.uint8)\n if 'app_image' in render_pkg:\n img_show = ((render_pkg['app_image']).permute(1, 2, 0).clamp(0, 1)[:, :,\n [2, 1, 0]] * 255).detach().cpu().numpy().astype(np.uint8)\n else:\n img_show = ((image).permute(1, 2, 0).clamp(0, 1)[:, :,\n [2, 1, 0]] * 255).detach().cpu().numpy().astype(np.uint8)\n normal_show = (((normal + 1.0) * 0.5).permute(1, 2, 0).clamp(0,1) * 255).detach().cpu().numpy().astype(np.uint8)\n depth_normal_show = (((depth_normal + 1.0) * 0.5).permute(1, 2, 0).clamp(0,1) * 255).detach().cpu().numpy().astype(np.uint8)\n\n if not opt.normal_optim:\n normal_gt = torch.zeros_like(normal)\n \n normal_gt_show = (normal_gt.permute(1, 2, 0) @ (viewpoint_cam.world_view_transform[:3, :3])).permute(2, 0, 1)\n normal_gt_show = (((normal_gt_show + 1.0) * 0.5).permute(1, 2, 0).clamp(0, 1) * 255).detach().cpu().numpy().astype(np.uint8)\n d_mask_show = (weights.float() * 255).detach().cpu().numpy().astype(np.uint8).reshape(H, W)\n d_mask_show_color = cv2.applyColorMap(d_mask_show, cv2.COLORMAP_JET)\n depth = render_pkg['plane_depth'].squeeze().detach().cpu().numpy()\n depth_i = (depth - depth.min()) / (depth.max() - depth.min() + 1e-20)\n depth_i = (depth_i * 255).clip(0, 255).astype(np.uint8)\n depth_color = cv2.applyColorMap(depth_i, cv2.COLORMAP_JET)\n distance = render_pkg['rendered_distance'].squeeze().detach().cpu().numpy()\n distance_i = (distance - distance.min()) / (distance.max() - distance.min() + 1e-20)\n distance_i = (distance_i * 255).clip(0, 255).astype(np.uint8)\n distance_color = cv2.applyColorMap(distance_i, cv2.COLORMAP_JET)\n image_weight = image_weight.detach().cpu().numpy()\n image_weight = (image_weight * 255).clip(0, 255).astype(np.uint8)\n image_weight_color = cv2.applyColorMap(image_weight, cv2.COLORMAP_JET)\n row0 = np.concatenate([gt_img_show, img_show, normal_show, distance_color], axis=1)\n row1 = np.concatenate([d_mask_show_color, depth_color, depth_normal_show, normal_gt_show],\n axis=1)\n image_to_show = np.concatenate([row0, row1], axis=0)\n cv2.imwrite(\n os.path.join(debug_path, \"%05d\" % iteration + \"_\" + viewpoint_cam.image_name + \".jpg\"),\n image_to_show)\n\n if d_mask.sum() > 0:\n geo_loss = geo_weight * ((weights * pixel_noise)[d_mask]).mean()\n overall_loss += geo_loss\n if use_virtul_cam is False:\n with torch.no_grad():\n # sample mask\n d_mask = d_mask.reshape(-1)\n valid_indices = torch.arange(d_mask.shape[0], device=d_mask.device)[d_mask]\n if d_mask.sum() > sample_num:\n index = np.random.choice(d_mask.sum().cpu().numpy(), sample_num, replace=False)\n valid_indices = valid_indices[index]\n\n weights = weights.reshape(-1)[valid_indices]\n # sample ref frame patch\n pixels = pixels.reshape(-1, 2)[valid_indices]\n offsets = patch_offsets(patch_size, pixels.device)\n ori_pixels_patch = pixels.reshape(-1, 1, 2) / viewpoint_cam.ncc_scale + offsets.float()\n\n H, W = gt_image_gray.squeeze().shape\n pixels_patch = ori_pixels_patch.clone()\n pixels_patch[:, :, 0] = 2 * pixels_patch[:, :, 0] / (W - 1) - 1.0\n pixels_patch[:, :, 1] = 2 * pixels_patch[:, :, 1] / (H - 1) - 1.0\n ref_gray_val = F.grid_sample(gt_image_gray.unsqueeze(1), pixels_patch.view(1, -1, 1, 2),\n align_corners=True)\n ref_gray_val = ref_gray_val.reshape(-1, total_patch_size)\n\n ref_to_neareast_r = nearest_cam.world_view_transform[:3, :3].transpose(-1,\n -2) @ viewpoint_cam.world_view_transform[\n :3, :3]\n ref_to_neareast_t = -ref_to_neareast_r @ viewpoint_cam.world_view_transform[3,\n :3] + nearest_cam.world_view_transform[3, :3]\n\n # compute Homography\n ref_local_n = render_pkg[\"rendered_normal\"].permute(1, 2, 0)\n ref_local_n = ref_local_n.reshape(-1, 3)[valid_indices]\n ref_local_d = render_pkg['rendered_distance'].squeeze()\n ref_local_d = ref_local_d.reshape(-1)[valid_indices]\n H_ref_to_neareast = ref_to_neareast_r[None] - \\\n torch.matmul(\n ref_to_neareast_t[None, :, None].expand(ref_local_d.shape[0], 3, 1),\n ref_local_n[:, :, None].expand(ref_local_d.shape[0], 3, 1).permute(\n 0, 2, 1)) / ref_local_d[..., None, None]\n H_ref_to_neareast = torch.matmul(\n nearest_cam.get_k(nearest_cam.ncc_scale)[None].expand(ref_local_d.shape[0], 3, 3),\n H_ref_to_neareast)\n H_ref_to_neareast = H_ref_to_neareast @ viewpoint_cam.get_inv_k(viewpoint_cam.ncc_scale)\n\n # compute neareast frame patch\n grid = patch_warp(H_ref_to_neareast.reshape(-1, 3, 3), ori_pixels_patch)\n grid[:, :, 0] = 2 * grid[:, :, 0] / (W - 1) - 1.0\n grid[:, :, 1] = 2 * grid[:, :, 1] / (H - 1) - 1.0\n _, nearest_image_gray = nearest_cam.get_image()\n sampled_gray_val = F.grid_sample(nearest_image_gray[None], grid.reshape(1, -1, 1, 2),\n align_corners=True)\n sampled_gray_val = sampled_gray_val.reshape(-1, total_patch_size)\n\n # compute loss\n ncc, ncc_mask = lncc(ref_gray_val, sampled_gray_val)\n mask = ncc_mask.reshape(-1)\n ncc = ncc.reshape(-1) * weights\n ncc = ncc[mask].squeeze()\n\n if mask.sum() > 0:\n ncc_loss = ncc_weight * ncc.mean()\n overall_loss = overall_loss + ncc_loss\n\n if opt.lang_loss_start_iter <= iteration < opt.instance_supervision_from_iter:\n # language feature loss\n lf_path = os.path.join(dataset.source_path, dataset.language_features_name)\n gt_language_feature, language_feature_mask, gt_seg = viewpoint_cam.get_language_feature(lf_path)\n language_loss = l1_loss(language_feature * language_feature_mask,\n gt_language_feature * language_feature_mask)\n \n overall_loss = overall_loss + language_loss\n\n language_feature_mask = language_feature_mask.reshape(-1)\n if opt.grouping_loss:\n grouping_loss = get_loss_semantic_group(gt_seg.reshape(-1)[language_feature_mask],\n language_feature.permute(1, 2, 0).reshape(-1, 3)[\n language_feature_mask])\n overall_loss = overall_loss + grouping_loss\n if opt.loss_obj_3d:\n obj_3d_loss = loss_cls_3d(self.gaussians._xyz.detach().squeeze(),\n self.gaussians._language_feature.squeeze(), opt.reg3d_k,\n opt.reg3d_lambda_val, 2000000, 800)\n overall_loss += obj_3d_loss\n\n elif iteration >= opt.instance_supervision_from_iter:\n # change the grad mode and copy the semantic featuers into instance-level\n if iteration == opt.instance_supervision_from_iter:\n self.gaussians._instance_feature.data.copy_(self.gaussians._language_feature.detach().clone())\n self.gaussians.change_reqiures_grad(\"instance\", iteration=iteration, quiet=False)\n _, language_feature_mask, gt_seg = viewpoint_cam.get_language_feature(lf_path)\n language_feature_mask = language_feature_mask.reshape(-1)\n # supervise the instance features\n if opt.grouping_loss:\n ins_grouping_loss = get_loss_instance_group(gt_seg.reshape(-1)[language_feature_mask],\n instance_feature.permute(1, 2, 0).reshape(-1, 3)[\n language_feature_mask],\n language_feature.permute(1, 2, 0).reshape(-1, 3)[\n language_feature_mask])\n overall_loss = overall_loss + ins_grouping_loss\n if opt.loss_obj_3d:\n ins_obj_3d_loss = loss_cls_3d(self.gaussians._xyz.detach().squeeze(), self.gaussians._instance_feature.squeeze(),\n opt.reg3d_k, opt.reg3d_lambda_val, 2000000, 800)\n overall_loss += ins_obj_3d_loss\n\n overall_loss.backward()\n iter_end.record()\n\n with torch.no_grad():\n ema_loss_for_log = 0.4 * image_loss.item() + 0.6 * ema_loss_for_log if image_loss is not None else 0.0 + 0.6 * ema_loss_for_log\n ema_single_view_for_log = 0.4 * normal_loss.item() if normal_loss is not None else 0.0 + 0.6 * ema_single_view_for_log\n ema_multi_view_geo_for_log = 0.4 * geo_loss.item() if geo_loss is not None else 0.0 + 0.6 * ema_multi_view_geo_for_log\n ema_multi_view_pho_for_log = 0.4 * ncc_loss.item() if ncc_loss is not None else 0.0 + 0.6 * ema_multi_view_pho_for_log\n ema_language_loss_for_log = 0.4 * language_loss.item() if language_loss is not None else 0.0 + 0.6 * ema_language_loss_for_log\n ema_grouping_loss = 0.4 * grouping_loss.item() if grouping_loss is not None else 0.0 + 0.6 * ema_grouping_loss\n ema_loss_obj_3d = 0.4 * obj_3d_loss.item() if obj_3d_loss is not None else 0.0 + 0.6 * ema_loss_obj_3d\n\n ema_ins_obj_3d_loss = 0.4 * ins_obj_3d_loss.item() if ins_obj_3d_loss is not None else 0.0 + 0.6 * ema_ins_obj_3d_loss\n ema_ins_grouping_loss = 0.4 * ins_grouping_loss.item() if ins_grouping_loss is not None else 0.0 + 0.6 * ema_ins_grouping_loss\n if iteration % 10 == 0:\n loss_dict = {\n \"Loss\": f\"{ema_loss_for_log:.{5}f}\",\n \"Lang\": f\"{ema_language_loss_for_log:.{5}f}\",\n \"Points\": f\"{len(self.gaussians.get_xyz)}\",\n \"gp\": f\"{ema_grouping_loss:.{5}f}\",\n \"3d\": f\"{ema_loss_obj_3d:.{5}f}\",\n \"Ins\": f\"{ema_ins_grouping_loss:.{5}f}\",\n }\n progress_bar.set_postfix(loss_dict)\n progress_bar.update(10)\n if iteration == opt.iterations:\n progress_bar.close()\n self.training_report(iteration, camera_list, l1_loss, render, (pipe, background))\n if (iteration in cfg.gaussian.save_iterations):\n print(\"\\n[ITER {}] Saving Gaussians\".format(iteration))\n self.scene.save(iteration, include_feature=include_feature)\n save_pose(self.scene.model_path + f'/pose/iter_{iteration}/pose_optimized.npy', self.gaussians.P, train_cams_init)\n\n # Densification\n if iteration < min(opt.max_geo_iter, opt.densify_until_iter):\n # Keep track of max radii in image-space for pruning\n mask = (render_pkg[\"out_observe\"] > 0) & visibility_filter\n self.gaussians.max_radii2D[mask] = torch.max(self.gaussians.max_radii2D[mask], radii[mask])\n viewspace_point_tensor_abs = render_pkg[\"viewspace_points_abs\"]\n self.gaussians.add_densification_stats(viewspace_point_tensor, viewspace_point_tensor_abs, visibility_filter)\n\n if opt.densify_from_iter < iteration < min(opt.max_geo_iter, opt.densify_until_iter) and iteration % opt.densification_interval == 0:\n logging.info(\"densifying and pruning...\")\n size_threshold = 20 if iteration > opt.opacity_reset_interval else None\n self.gaussians.densify_and_prune(opt.densify_grad_threshold, opt.densify_abs_grad_threshold,\n opt.opacity_cull_threshold, self.scene.cameras_extent, size_threshold)\n \n if iteration % opt.opacity_reset_interval == 0 or (dataset.white_background and iteration == opt.densify_from_iter):\n self.gaussians.reset_opacity() \n\n if iteration < opt.iterations:\n self.gaussians.optimizer.step()\n self.gaussians.cam_optimizer.step()\n self.app_model.optimizer.step()\n self.gaussians.optimizer.zero_grad(set_to_none=True)\n self.gaussians.cam_optimizer.zero_grad(set_to_none=True)\n self.app_model.optimizer.zero_grad(set_to_none=True)\n \n if (iteration in cfg.gaussian.checkpoint_iterations):\n print(\"\\n[ITER {}] Saving Checkpoint\".format(iteration))\n torch.save((self.gaussians.capture(include_feature=include_feature), iteration),\n self.scene.model_path + \"/chkpnt\" + str(iteration) + \".pth\")\n self.app_model.save_weights(self.scene.model_path, iteration)\n\n self.app_model.save_weights(self.scene.model_path, opt.iterations)\n torch.cuda.empty_cache()\n # move camera poses to target path. \n max_save_iter = max(cfg.gaussian.save_iterations)\n orig_path = self.scene.model_path + f'/pose/iter_{max_save_iter}/pose_optimized.npy'\n camera_path = os.path.join(cfg.pipeline.data_path, \"camera\")\n eg_file = os.listdir(camera_path)[0]\n logging.info(\"Post processing pose & move to data path...\")\n post_pose_process(orig_path, os.path.join(camera_path, eg_file), os.path.join(cfg.pipeline.data_path, \"render_camera\"))\n\n\n def training_report(self, iteration, camera_list, l1_loss, renderFunc, renderArgs):\n # Report test and samples of training set\n # do not use the optimized poses. \n if iteration in self.cfg.gaussian.test_iterations:\n torch.cuda.empty_cache()\n validation_configs = ({'name': 'test', 'cameras': camera_list},\n {'name': 'train',\n 'cameras': [self.scene.getTrainCameras()[idx % len(self.scene.getTrainCameras())] for idx in\n range(5, 30, 5)]})\n\n for config in validation_configs:\n if config['cameras'] and len(config['cameras']) > 0:\n l1_test = 0.0\n psnr_test = 0.0\n for idx, viewpoint in enumerate(config['cameras']):\n if self.cfg.gaussian.opt.optim_pose:\n camera_pose = get_tensor_from_camera(viewpoint.world_view_transform.transpose(0, 1))\n out = renderFunc(viewpoint, self.scene.gaussians, *renderArgs, app_model=self.app_model, camera_pose=camera_pose)\n else:\n out = renderFunc(viewpoint, self.scene.gaussians, *renderArgs, app_model=self.app_model)\n image = out[\"render\"]\n if 'app_image' in out:\n image = out['app_image']\n image = torch.clamp(image, 0.0, 1.0)\n gt_image, _ = viewpoint.get_image()\n gt_image = torch.clamp(gt_image.to(\"cuda\"), 0.0, 1.0)\n l1_test += l1_loss(image, gt_image).mean().double()\n psnr_test += psnr(image, gt_image).mean().double()\n img_show = ((image).permute(1, 2, 0).clamp(0, 1)[:, :,\n [2, 1, 0]] * 255).detach().cpu().numpy().astype(np.uint8)\n img_gt_show = ((gt_image).permute(1, 2, 0).clamp(0, 1)[:, :,\n [2, 1, 0]] * 255).detach().cpu().numpy().astype(np.uint8)\n img_tosave = np.concatenate([img_show, img_gt_show], axis=1)\n valid_path = os.path.join(self.cfg.gaussian.dataset.model_path, \"valid\")\n os.makedirs(valid_path, exist_ok=True)\n cv2.imwrite(os.path.join(valid_path, f\"{iteration}_{viewpoint.uid}.png\"), img_tosave)\n\n psnr_test /= len(config['cameras'])\n l1_test /= len(config['cameras'])\n logging.info(\"\\n[ITER {}] Evaluating {}: L1 {} PSNR {}\".format(iteration, config['name'], l1_test, psnr_test))\n torch.cuda.empty_cache()\n \n \n def render(self):\n cfg = self.cfg\n dataset = cfg.gaussian.dataset\n pipe = cfg.gaussian.pipe\n device = cfg.gaussian.dataset.data_device\n render_cfg = cfg.gaussian.render\n opt = cfg.gaussian.opt\n\n logging.info(\"Rendering \" + dataset.model_path)\n safe_state(cfg.gaussian.quiet)\n\n voxel_size = 0.01\n volume = o3d.pipelines.integration.ScalableTSDFVolume(\n voxel_length=voxel_size,\n sdf_trunc=4.0 * voxel_size,\n color_type=o3d.pipelines.integration.TSDFVolumeColorType.RGB8\n )\n volume_feature = o3d.pipelines.integration.ScalableTSDFVolume(\n voxel_length=voxel_size,\n sdf_trunc=4.0 * voxel_size,\n color_type=o3d.pipelines.integration.TSDFVolumeColorType.RGB8\n )\n\n with torch.no_grad():\n self.gaussians = GaussianModel(cfg.gaussian.dataset.sh_degree)\n self.scene = Scene(cfg.gaussian.dataset, self.gaussians, load_iteration=cfg.pipeline.load_iteration, shuffle=False)\n self.app_model = AppModel()\n\n self.scene.loaded_iter = None\n bg_color = [1, 1, 1] if dataset.white_background else [0, 0, 0]\n background = torch.tensor(bg_color, dtype=torch.float32, device=device)\n \n render_path = os.path.join(dataset.model_path, \"test\", \"renders_rgb\")\n render_depth_path = os.path.join(dataset.model_path, \"test\", \"renders_depth\")\n render_depth_npy_path = os.path.join(dataset.model_path, \"test\", \"renders_depth_npy\")\n render_normal_path = os.path.join(dataset.model_path, \"test\", \"renders_normal\")\n\n os.makedirs(render_path, exist_ok=True)\n os.makedirs(render_depth_path, exist_ok=True)\n os.makedirs(render_depth_npy_path, exist_ok=True)\n os.makedirs(render_normal_path, exist_ok=True)\n depths_tsdf_fusion = []\n\n all_language_feature = []\n all_gt_language_feature = []\n all_instance_feature = []\n for idx, view in enumerate(tqdm(self.scene.getTrainCameras(), desc=\"Rendering progress\")):\n camera_pose = get_tensor_from_camera(view.world_view_transform.transpose(0, 1))\n gt, _ = view.get_image()\n if not opt.optim_pose:\n out = render(view, self.gaussians, pipe, background, app_model=None)\n else:\n out = render(view, self.gaussians, pipe, background, app_model=None, camera_pose=camera_pose)\n\n rendering = out[\"render\"].clamp(0.0, 1.0)\n _, H, W = rendering.shape\n\n depth = out[\"plane_depth\"].squeeze()\n depth_tsdf = depth.clone()\n\n depth = depth.detach().cpu().numpy()\n depth_i = (depth - depth.min()) / (depth.max() - depth.min() + 1e-20)\n depth_i = (depth_i * 255).clip(0, 255).astype(np.uint8)\n depth_color = cv2.applyColorMap(depth_i, cv2.COLORMAP_JET)\n\n normal = out[\"rendered_normal\"].permute(1, 2, 0)\n normal = normal @ view.world_view_transform[:3, :3]\n normal = normal / (normal.norm(dim=-1, keepdim=True) + 1.0e-8)\n\n # normal = normal.detach().cpu().numpy()\n # normal = ((normal + 1) * 127.5).astype(np.uint8).clip(0, 255)\n normal = normal.detach().cpu().numpy()[:, :, ::-1]\n normal = ((1-normal) * 127.5).astype(np.uint8).clip(0, 255)\n\n language_feature = out[\"language_feature\"]\n instance_feature = out[\"instance_feature\"]\n all_language_feature.append(language_feature)\n all_instance_feature.append(instance_feature)\n\n lf_path = os.path.join(dataset.source_path, dataset.language_features_name)\n if os.path.exists(lf_path):\n gt_language, _, _ = view.get_language_feature(lf_path)\n all_gt_language_feature.append(gt_language)\n\n gts_path = os.path.join(dataset.model_path, \"test\", \"gt_rgb\")\n os.makedirs(gts_path, exist_ok=True)\n torchvision.utils.save_image(gt.clamp(0.0, 1.0), os.path.join(gts_path, view.image_name + \".png\"))\n torchvision.utils.save_image(rendering, os.path.join(render_path, view.image_name + \".png\"))\n\n cv2.imwrite(os.path.join(render_depth_path, view.image_name + \".jpg\"), depth_color)\n np.save(os.path.join(render_depth_npy_path, view.image_name + \".npy\"), depth)\n cv2.imwrite(os.path.join(render_normal_path, view.image_name + \".jpg\"), normal)\n\n view_dir = torch.nn.functional.normalize(view.get_rays(), p=2, dim=-1)\n depth_normal = out[\"depth_normal\"].permute(1, 2, 0)\n depth_normal = torch.nn.functional.normalize(depth_normal, p=2, dim=-1)\n dot = torch.sum(view_dir * depth_normal, dim=-1).abs()\n angle = torch.acos(dot)\n mask = angle > (80.0 / 180 * 3.14159)\n depth_tsdf[mask] = 0\n depths_tsdf_fusion.append(depth_tsdf.squeeze().cpu())\n\n depths_tsdf_fusion = torch.stack(depths_tsdf_fusion, dim=0)\n max_depth = 5.0\n for idx, view in enumerate(tqdm(self.scene.getTrainCameras(), desc=\"TSDF Fusion progress\")):\n ref_depth = depths_tsdf_fusion[idx].cuda()\n\n if view.mask is not None:\n ref_depth[view.mask.squeeze() < 0.5] = 0\n ref_depth[ref_depth > max_depth] = 0\n ref_depth = ref_depth.detach().cpu().numpy()\n pose = np.identity(4)\n pose[:3, :3] = view.R.transpose(-1, -2)\n pose[:3, 3] = view.T\n color = o3d.io.read_image(os.path.join(render_path, view.image_name + \".png\"))\n\n depth = o3d.geometry.Image((ref_depth * 1000).astype(np.uint16))\n rgbd = o3d.geometry.RGBDImage.create_from_color_and_depth(\n color, depth, depth_scale=1000.0, depth_trunc=max_depth, convert_rgb_to_intensity=False)\n \n volume.integrate(\n rgbd,\n o3d.camera.PinholeCameraIntrinsic(W, H, view.Fx, view.Fy, view.Cx, view.Cy),\n pose\n )\n num_cluster = 3\n path = os.path.join(dataset.model_path, \"mesh\")\n os.makedirs(path, exist_ok=True)\n \n mesh = volume.extract_triangle_mesh()\n o3d.io.write_triangle_mesh(os.path.join(path, \"tsdf_fusion.ply\"), mesh,\n write_triangle_uvs=True, write_vertex_colors=True, write_vertex_normals=True)\n\n mesh = post_process_mesh(mesh, num_cluster)\n o3d.io.write_triangle_mesh(os.path.join(path, \"tsdf_fusion_post.ply\"), mesh,\n write_triangle_uvs=True, write_vertex_colors=True, write_vertex_normals=True)\n\n \n # perform pca among all lang/instance features\n render_language_path = os.path.join(dataset.model_path, \"test\", \"renders_language\")\n render_instance_path = os.path.join(dataset.model_path, \"test\", \"renders_instance\")\n gts_language_path = os.path.join(dataset.model_path, \"test\", \"gt_language\")\n render_language_npy_path = os.path.join(dataset.model_path, \"test\", \"renders_language_npy\")\n render_instance_npy_path = os.path.join(dataset.model_path, \"test\", \"renders_instance_npy\")\n gts_language_npy_path = os.path.join(dataset.model_path, \"test\", \"gt_language_npy\")\n os.makedirs(render_language_path, exist_ok=True)\n os.makedirs(gts_language_path, exist_ok=True)\n os.makedirs(render_language_npy_path, exist_ok=True)\n os.makedirs(gts_language_npy_path, exist_ok=True)\n os.makedirs(render_instance_path, exist_ok=True)\n os.makedirs(render_instance_npy_path, exist_ok=True)\n\n all_language_feature = torch.stack(all_language_feature)\n all_instance_feature = torch.stack(all_instance_feature)\n if len(all_gt_language_feature):\n all_gt_language_feature = torch.stack(all_gt_language_feature)\n if render_cfg.normalized:\n all_language_feature = torch.clamp(all_language_feature, min=-1, max=2)\n min_value = torch.min(all_language_feature)\n max_value = torch.max(all_language_feature)\n normalized_language_feature = (all_language_feature - min_value) / (max_value - min_value)\n pca_language_feature = permuted_pca(normalized_language_feature)\n for idx, view in enumerate(self.scene.getTrainCameras()):\n torchvision.utils.save_image(normalized_language_feature[idx], os.path.join(render_language_path, view.image_name + \".png\"))\n\n all_instance_feature = torch.clamp(all_instance_feature, min=-1, max=2)\n min_value = torch.min(all_instance_feature)\n max_value = torch.max(all_instance_feature)\n normalized_instance_feature = (all_instance_feature - min_value) / (max_value - min_value)\n pca_instance_feature = permuted_pca(normalized_instance_feature)\n for idx, view in enumerate(self.scene.getTrainCameras()):\n torchvision.utils.save_image(\n # pca_instance_feature[idx],\n normalized_instance_feature[idx],\n os.path.join(render_instance_path, view.image_name + \".png\")\n )\n\n if os.path.exists(lf_path):\n all_gt_language_feature = torch.clamp(all_gt_language_feature, min=-1, max=2)\n min_value = torch.min(all_gt_language_feature)\n max_value = torch.max(all_gt_language_feature)\n normalized_gt_language = (all_gt_language_feature - min_value) / (max_value - min_value)\n pca_gt_language = permuted_pca(normalized_gt_language)\n for idx, view in enumerate(self.scene.getTrainCameras()):\n torchvision.utils.save_image(\n pca_gt_language[idx],\n os.path.join(gts_language_path, view.image_name + \".png\")\n )\n else:\n breakpoint()\n all_language_feature = torch.clamp(all_language_feature, min=-1, max=2)\n pca_language_feature = permuted_pca(all_language_feature)\n for idx, view in enumerate(self.scene.getTrainCameras()):\n torchvision.utils.save_image(\n pca_language_feature[idx],\n os.path.join(render_language_path, view.image_name + \".png\")\n )\n\n all_instance_feature = torch.clamp(all_instance_feature, min=-1, max=2)\n pca_instance_feature = permuted_pca(all_instance_feature)\n for idx, view in enumerate(self.scene.getTrainCameras()):\n torchvision.utils.save_image(\n pca_instance_feature[idx],\n os.path.join(render_instance_path, view.image_name + \".png\")\n )\n\n if os.path.exists(lf_path):\n all_gt_language_feature = torch.clamp(all_gt_language_feature, min=-1, max=2)\n pca_gt_language = permuted_pca(all_gt_language_feature)\n for idx, view in enumerate(self.scene.getTrainCameras()):\n torchvision.utils.save_image(\n pca_gt_language[idx],\n os.path.join(gts_language_path, view.image_name + \".png\")\n )\n\n\n for idx, view in enumerate(self.scene.getTrainCameras()):\n np.save(\n os.path.join(render_language_npy_path, view.image_name + \".npy\"),\n all_language_feature[idx].permute(1, 2, 0).cpu().numpy()\n )\n np.save(\n os.path.join(render_instance_npy_path, view.image_name + \".npy\"),\n all_instance_feature[idx].permute(1, 2, 0).cpu().numpy()\n )\n if os.path.exists(lf_path):\n np.save(\n os.path.join(gts_language_npy_path, view.image_name + \".npy\"),\n all_gt_language_feature[idx].permute(1, 2, 0).cpu().numpy()\n )\n\n for idx, view in enumerate(tqdm(self.scene.getTrainCameras(), desc=\"TSDF Fusion progress\")):\n ref_depth = depths_tsdf_fusion[idx].cuda()\n\n if view.mask is not None:\n ref_depth[view.mask.squeeze() < 0.5] = 0\n ref_depth[ref_depth > max_depth] = 0\n ref_depth = ref_depth.detach().cpu().numpy()\n pose = np.identity(4)\n pose[:3, :3] = view.R.transpose(-1, -2)\n pose[:3, 3] = view.T\n color_feature = o3d.io.read_image(os.path.join(render_language_path, view.image_name + \".png\"))\n depth = o3d.geometry.Image((ref_depth * 1000).astype(np.uint16))\n rgbd_feature = o3d.geometry.RGBDImage.create_from_color_and_depth(\n color_feature, depth, depth_scale=1000.0, depth_trunc=max_depth, convert_rgb_to_intensity=False\n )\n volume_feature.integrate(\n rgbd_feature,\n o3d.camera.PinholeCameraIntrinsic(W, H, view.Fx, view.Fy, view.Cx, view.Cy),\n pose\n )\n \n num_cluster = 3\n mesh_feature = volume_feature.extract_triangle_mesh()\n o3d.io.write_triangle_mesh(os.path.join(path, \"feature_tsdf_fusion.ply\"), mesh_feature,\n write_triangle_uvs=True, write_vertex_colors=True,\n write_vertex_normals=True)\n mesh_feature = post_process_mesh(mesh_feature, num_cluster)\n o3d.io.write_triangle_mesh(os.path.join(path, \"feature_tsdf_fusion_post.ply\"), mesh_feature,\n write_triangle_uvs=True, write_vertex_colors=True,\n write_vertex_normals=True)\n\n\n \n \n def eval(self):\n cfg = self.cfg\n dataset = cfg.gaussian.dataset\n opt = cfg.gaussian.opt\n pipe = cfg.gaussian.pipe\n device = cfg.gaussian.dataset.data_device\n \n dataset.source_path = cfg.gaussian.eval.eval_data_path\n\n logging.info(\"Evaling \" + dataset.model_path)\n safe_state(cfg.gaussian.quiet)\n # optimizing poses:\n self.gaussians = GaussianModel(cfg.gaussian.dataset.sh_degree)\n self.scene = Scene(cfg.gaussian.dataset, self.gaussians, load_iteration=cfg.pipeline.load_iteration, shuffle=False)\n\n self.gaussians.training_setup(opt, device)\n\n self.scene.loaded_iter = None\n bg_color = [1, 1, 1] if dataset.white_background else [0, 0, 0]\n background = torch.tensor(bg_color, dtype=torch.float32, device=device)\n\n render_path = os.path.join(dataset.model_path, \"eval\", \"renders_rgb\")\n render_depth_path = os.path.join(dataset.model_path, \"eval\", \"renders_depth\")\n render_depth_npy_path = os.path.join(dataset.model_path, \"eval\", \"renders_depth_npy\")\n render_normal_path = os.path.join(dataset.model_path, \"eval\", \"renders_normal\")\n\n render_lang_path = os.path.join(dataset.model_path, \"eval\", \"renders_lang\")\n render_instance_path = os.path.join(dataset.model_path, \"eval\", \"renders_instance\")\n render_lang_npy_path = os.path.join(dataset.model_path, \"eval\", \"renders_lang_npy\")\n render_instance_npy_path = os.path.join(dataset.model_path, \"eval\", \"renders_instance_npy\")\n\n os.makedirs(render_path, exist_ok=True)\n os.makedirs(render_depth_path, exist_ok=True)\n os.makedirs(render_depth_npy_path, exist_ok=True)\n os.makedirs(render_normal_path, exist_ok=True)\n os.makedirs(render_lang_path, exist_ok=True)\n os.makedirs(render_instance_path, exist_ok=True) \n os.makedirs(render_lang_npy_path, exist_ok=True)\n os.makedirs(render_instance_npy_path, exist_ok=True)\n\n self.gaussians.change_reqiures_grad(\"pose_only\", iteration=0, quiet=False)\n\n for cam_idx, cam in enumerate(self.scene.getTrainCameras().copy()):\n # optim pose iter:\n first_iter = 1\n ema_loss_for_log = 0.0\n include_feature = True\n progress_bar = tqdm(range(first_iter, cfg.gaussian.eval.pose_optim_iter + 1))\n\n logging.info(f\"Optimizing camera {cam_idx}\")\n iter_start = torch.cuda.Event(enable_timing=True)\n iter_end = torch.cuda.Event(enable_timing=True)\n for iteration in progress_bar:\n iter_start.record()\n self.gaussians.update_learning_rate(iteration)\n pose = self.gaussians.get_RT(self.gaussians.index_mapping[cam.uid])\n bg = torch.rand((3), device=\"cuda\") if opt.random_background else background\n render_pkg = render(cam, self.gaussians, pipe, bg, app_model=None, \n return_plane=False, return_depth_normal=False,\n include_feature=include_feature, camera_pose=pose)\n \n image = render_pkg[\"render\"]\n gt_image, _ = cam.get_image()\n ssim_loss = (1.0 - ssim(image, gt_image))\n Ll1 = l1_loss(image, gt_image)\n image_loss = (1.0 - opt.lambda_dssim) * Ll1 + opt.lambda_dssim * ssim_loss\n image_loss.backward()\n iter_end.record()\n with torch.no_grad():\n ema_loss_for_log = 0.4 * image_loss + 0.6 * ema_loss_for_log\n if iteration % 10 == 0:\n loss_dict = {\n \"Loss\": f\"{ema_loss_for_log:.5f}\"\n }\n progress_bar.set_postfix(loss_dict)\n progress_bar.update(10)\n if iteration < cfg.gaussian.eval.pose_optim_iter:\n self.gaussians.cam_optimizer.step()\n self.gaussians.cam_optimizer.zero_grad(set_to_none=True)\n\n if iteration == cfg.gaussian.eval.pose_optim_iter:\n # saving results:\n progress_bar.close()\n logging.info(\"Saving results...\")\n language_feature, instance_feature = render_pkg[\"language_feature\"], render_pkg[\"instance_feature\"]\n image_tosave = torch.cat([image, gt_image], dim=2).clamp(0, 1)\n torchvision.utils.save_image(image_tosave, os.path.join(render_path, cam.image_name + \".png\"))\n min_value = torch.min(language_feature)\n max_value = torch.max(language_feature)\n normalized_language_feature = (language_feature - min_value) / (max_value - min_value)\n torchvision.utils.save_image(permuted_pca(normalized_language_feature), \n os.path.join(render_lang_path, cam.image_name + \".png\"))\n np.save(os.path.join(render_lang_npy_path, cam.image_name + \".npy\"),\n language_feature.permute(1, 2, 0).cpu().numpy())\n\n min_value = torch.min(instance_feature)\n max_value = torch.max(instance_feature)\n normalized_instance_feature = (instance_feature - min_value) / (max_value - min_value)\n torchvision.utils.save_image(permuted_pca(normalized_instance_feature), \n os.path.join(render_instance_path, cam.image_name + \".png\"))\n np.save(os.path.join(render_instance_npy_path, cam.image_name + \".npy\"),\n instance_feature.permute(1, 2, 0).cpu().numpy())\n\n torch.cuda.empty_cache()\n\n \n" }, { "path": "field_construction/gaussian_renderer/__init__.py", "content": "#\n# Copyright (C) 2023, Inria\n# GRAPHDECO research group, https://team.inria.fr/graphdeco\n# All rights reserved.\n#\n# This software is free for non-commercial, research and evaluation use\n# under the terms of the LICENSE.md file.\n#\n# For inquiries contact george.drettakis@inria.fr\n#\n\nimport math\n\nimport torch\nfrom diff_LangSurf_rasterization import \\\n GaussianRasterizationSettings as PlaneGaussianRasterizationSettings\nfrom diff_LangSurf_rasterization import \\\n GaussianRasterizer as PlaneGaussianRasterizer\n\nfrom field_construction.scene.app_model import AppModel\nfrom field_construction.scene.gaussian_model import GaussianModel\nfrom field_construction.utils.graphics_utils import normal_from_depth_image\nfrom field_construction.utils.pose_utils import (get_camera_from_tensor,\n quadmultiply)\nfrom field_construction.utils.sh_utils import eval_sh\n\n\ndef render_normal(viewpoint_cam, depth, offset=None, normal=None, scale=1):\n # depth: (H, W), bg_color: (3), alpha: (H, W)\n # normal_ref: (3, H, W)\n intrinsic_matrix, extrinsic_matrix = viewpoint_cam.get_calib_matrix_nerf(scale=scale)\n st = max(int(scale/2)-1,0)\n if offset is not None:\n offset = offset[st::scale,st::scale]\n normal_ref = normal_from_depth_image(depth[st::scale,st::scale], \n intrinsic_matrix.to(depth.device), \n extrinsic_matrix.to(depth.device), offset)\n\n normal_ref = normal_ref.permute(2,0,1)\n return normal_ref\n\ndef render(\n viewpoint_camera,\n pc : GaussianModel,\n pipe, \n bg_color : torch.Tensor, \n scaling_modifier=1.0, \n override_color=None, \n app_model: AppModel=None,\n return_plane=True,\n return_depth_normal=True, \n include_feature=True, \n camera_pose=None\n):\n \"\"\"\n Render the scene. \n \n Background tensor (bg_color) must be on GPU!\n \"\"\"\n # Create zero tensor. We will use it to make pytorch return gradients of the 2D (screen-space) means\n screenspace_points = torch.zeros_like(pc.get_xyz, dtype=pc.get_xyz.dtype, requires_grad=True, device=\"cuda\") + 0\n screenspace_points_abs = torch.zeros_like(pc.get_xyz, dtype=pc.get_xyz.dtype, requires_grad=True, device=\"cuda\") + 0\n try:\n screenspace_points.retain_grad()\n screenspace_points_abs.retain_grad()\n except:\n pass\n\n # Set up rasterization configuration\n tanfovx = math.tan(viewpoint_camera.FoVx * 0.5)\n tanfovy = math.tan(viewpoint_camera.FoVy * 0.5)\n\n w2c = torch.eye(4).cuda()\n projmatrix = (\n w2c.unsqueeze(0).bmm(viewpoint_camera.projection_matrix.unsqueeze(0))\n ).squeeze(0)\n camera_pos = w2c.inverse()[3, :3]\n \n if camera_pose is not None:\n rel_w2c = get_camera_from_tensor(camera_pose)\n gaussians_xyz = pc._xyz.clone()\n gaussians_rot = pc._rotation.clone()\n xyz_ones = torch.ones(gaussians_xyz.shape[0], 1).cuda().float()\n xyz_homo = torch.cat((gaussians_xyz, xyz_ones), dim=1)\n gaussians_xyz_trans = (rel_w2c @ xyz_homo.T).T[:, :3]\n gaussians_rot_trans = quadmultiply(camera_pose[:4], gaussians_rot)\n means3D = gaussians_xyz_trans \n \n else:\n means3D = pc.get_xyz\n\n means2D = screenspace_points\n means2D_abs = screenspace_points_abs\n opacity = pc.get_opacity\n \n # If precomputed 3d covariance is provided, use it. If not, then it will be computed from\n # scaling / rotation by the rasterizer.\n scales = None\n rotations = None\n cov3D_precomp = None\n if pipe.compute_cov3D_python:\n cov3D_precomp = pc.get_covariance(scaling_modifier)\n else:\n scales = pc.get_scaling\n rotations = gaussians_rot_trans if camera_pose is not None else pc.get_rotation\n # rotations = pc.get_rotation\n \n # If precomputed colors are provided, use them. Otherwise, if it is desired to precompute colors\n # from SHs in Python, do it. If not, then SH -> RGB conversion will be done by rasterizer.\n shs = None\n colors_precomp = None\n\n if override_color is None:\n if pipe.convert_SHs_python:\n shs_view = pc.get_features.transpose(1, 2).view(-1, 3, (pc.max_sh_degree+1)**2)\n dir_pp = (pc.get_xyz - viewpoint_camera.camera_center.repeat(pc.get_features.shape[0], 1))\n dir_pp_normalized = dir_pp/dir_pp.norm(dim=1, keepdim=True)\n sh2rgb = eval_sh(pc.active_sh_degree, shs_view, dir_pp_normalized)\n colors_precomp = torch.clamp_min(sh2rgb + 0.5, 0.0)\n else:\n shs = pc.get_features\n else:\n colors_precomp = override_color\n\n if include_feature:\n language_feature_precomp = pc.get_language_feature\n instance_feature_precomp = pc.get_instance_feature\n # language_feature_precomp = language_feature_precomp / (language_feature_precomp.norm(dim=-1, keepdim=True) + 1e-9)\n # instance_feature_precomp = instance_feature_precomp / (instance_feature_precomp.norm(dim=-1, keepdim=True) + 1e-9)\n # language_feature_precomp = torch.sigmoid(language_feature_precomp)\n else:\n language_feature_precomp = torch.zeros((1,), dtype=opacity.dtype, device=opacity.device)\n instance_feature_precomp = torch.zeros((1,), dtype=opacity.dtype, device=opacity.device)\n\n return_dict = None\n raster_settings = PlaneGaussianRasterizationSettings(\n image_height=int(viewpoint_camera.image_height),\n image_width=int(viewpoint_camera.image_width),\n tanfovx=tanfovx,\n tanfovy=tanfovy,\n bg=bg_color,\n scale_modifier=scaling_modifier,\n # viewmatrix=viewpoint_camera.world_view_transform,\n # projmatrix=viewpoint_camera.full_proj_transform,\n viewmatrix=w2c if camera_pose is not None else viewpoint_camera.world_view_transform,\n projmatrix=projmatrix if camera_pose is not None else viewpoint_camera.full_proj_transform,\n sh_degree=pc.active_sh_degree,\n # campos=viewpoint_camera.camera_center,\n campos=camera_pos if camera_pose is not None else viewpoint_camera.camera_center,\n prefiltered=False,\n render_geo=return_plane,\n debug=pipe.debug,\n include_feature=include_feature,\n )\n\n rasterizer = PlaneGaussianRasterizer(raster_settings=raster_settings)\n\n\n if not return_plane:\n rendered_image, language_feature, instance_feature, radii, out_observe, _, _ = rasterizer(\n means3D = means3D,\n means2D = means2D,\n means2D_abs = means2D_abs,\n shs = shs,\n colors_precomp = colors_precomp,\n language_feature_precomp = language_feature_precomp,\n language_feature_instance_precomp = instance_feature_precomp,\n opacities = opacity,\n scales = scales,\n rotations = rotations,\n cov3D_precomp = cov3D_precomp)\n \n return_dict = {\"render\": rendered_image,\n \"viewspace_points\": screenspace_points,\n \"viewspace_points_abs\": screenspace_points_abs,\n \"visibility_filter\" : radii > 0,\n \"radii\": radii,\n \"out_observe\": out_observe,\n \"language_feature\": language_feature,\n \"instance_feature\": instance_feature,\n }\n if app_model is not None and pc.use_app:\n appear_ab = app_model.appear_ab[torch.tensor(viewpoint_camera.uid).cuda()]\n app_image = torch.exp(appear_ab[0]) * rendered_image + appear_ab[1]\n return_dict.update({\"app_image\": app_image})\n return return_dict\n\n global_normal = pc.get_normal(viewpoint_camera)\n local_normal = global_normal @ viewpoint_camera.world_view_transform[:3,:3]\n pts_in_cam = means3D @ viewpoint_camera.world_view_transform[:3,:3] + viewpoint_camera.world_view_transform[3,:3]\n depth_z = pts_in_cam[:, 2]\n local_distance = (local_normal * pts_in_cam).sum(-1).abs()\n input_all_map = torch.zeros((means3D.shape[0], 5)).cuda().float()\n input_all_map[:, :3] = local_normal\n input_all_map[:, 3] = 1.0\n input_all_map[:, 4] = local_distance\n\n rendered_image, language_feature, instance_feature, radii, out_observe, out_all_map, plane_depth = rasterizer(\n means3D = means3D,\n means2D = means2D,\n means2D_abs = means2D_abs,\n shs = shs,\n colors_precomp = colors_precomp,\n language_feature_precomp = language_feature_precomp,\n language_feature_instance_precomp = instance_feature_precomp,\n opacities = opacity,\n scales = scales,\n rotations = rotations,\n all_map = input_all_map,\n cov3D_precomp = cov3D_precomp)\n\n rendered_normal = out_all_map[0:3]\n rendered_alpha = out_all_map[3:4, ]\n rendered_distance = out_all_map[4:5, ]\n \n return_dict = {\"render\": rendered_image,\n \"viewspace_points\": screenspace_points,\n \"viewspace_points_abs\": screenspace_points_abs,\n \"visibility_filter\" : radii > 0,\n \"radii\": radii,\n \"out_observe\": out_observe,\n \"rendered_normal\": rendered_normal,\n \"plane_depth\": plane_depth,\n \"rendered_distance\": rendered_distance,\n \"language_feature\": language_feature,\n \"instance_feature\": instance_feature,\n }\n if app_model is not None:\n appear_ab = app_model.appear_ab[torch.tensor(viewpoint_camera.uid).cuda()]\n app_image = torch.exp(appear_ab[0]) * rendered_image + appear_ab[1]\n return_dict.update({\"app_image\": app_image}) \n\n if return_depth_normal:\n depth_normal = render_normal(viewpoint_camera, plane_depth.squeeze()) * (rendered_alpha).detach()\n return_dict.update({\"depth_normal\": depth_normal})\n \n # Those Gaussians that were frustum culled or had a radius of 0 were not visible.\n # They will be excluded from value updates used in the splitting criteria.\n return return_dict" }, { "path": "field_construction/gaussian_renderer/network_gui.py", "content": "#\n# Copyright (C) 2023, Inria\n# GRAPHDECO research group, https://team.inria.fr/graphdeco\n# All rights reserved.\n#\n# This software is free for non-commercial, research and evaluation use\n# under the terms of the LICENSE.md file.\n#\n# For inquiries contact george.drettakis@inria.fr\n#\n\nimport json\nimport socket\nimport traceback\n\nimport torch\n\nfrom field_construction.scene.cameras import MiniCam\n\nhost = \"127.0.0.1\"\nport = 6009\n\nconn = None\naddr = None\n\nlistener = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n\ndef init(wish_host, wish_port):\n global host, port, listener\n host = wish_host\n port = wish_port\n listener.bind((host, port))\n listener.listen()\n listener.settimeout(0)\n\ndef try_connect():\n global conn, addr, listener\n try:\n conn, addr = listener.accept()\n print(f\"\\nConnected by {addr}\")\n conn.settimeout(None)\n except Exception as inst:\n pass\n \ndef read():\n global conn\n messageLength = conn.recv(4)\n messageLength = int.from_bytes(messageLength, 'little')\n message = conn.recv(messageLength)\n return json.loads(message.decode(\"utf-8\"))\n\ndef send(message_bytes, verify):\n global conn\n if message_bytes != None:\n conn.sendall(message_bytes)\n conn.sendall(len(verify).to_bytes(4, 'little'))\n conn.sendall(bytes(verify, 'ascii'))\n\ndef receive():\n message = read()\n\n width = message[\"resolution_x\"]\n height = message[\"resolution_y\"]\n\n if width != 0 and height != 0:\n try:\n do_training = bool(message[\"train\"])\n fovy = message[\"fov_y\"]\n fovx = message[\"fov_x\"]\n znear = message[\"z_near\"]\n zfar = message[\"z_far\"]\n do_shs_python = bool(message[\"shs_python\"])\n do_rot_scale_python = bool(message[\"rot_scale_python\"])\n keep_alive = bool(message[\"keep_alive\"])\n scaling_modifier = message[\"scaling_modifier\"]\n world_view_transform = torch.reshape(torch.tensor(message[\"view_matrix\"]), (4, 4)).cuda()\n world_view_transform[:,1] = -world_view_transform[:,1]\n world_view_transform[:,2] = -world_view_transform[:,2]\n full_proj_transform = torch.reshape(torch.tensor(message[\"view_projection_matrix\"]), (4, 4)).cuda()\n full_proj_transform[:,1] = -full_proj_transform[:,1]\n custom_cam = MiniCam(width, height, fovy, fovx, znear, zfar, world_view_transform, full_proj_transform)\n except Exception as e:\n print(\"\")\n traceback.print_exc()\n raise e\n return custom_cam, do_training, do_shs_python, do_rot_scale_python, keep_alive, scaling_modifier\n else:\n return None, None, None, None, None, None" }, { "path": "field_construction/lpipsPyTorch/__init__.py", "content": "import torch\n\nfrom .modules.lpips import LPIPS\n\n\ndef lpips(x: torch.Tensor,\n y: torch.Tensor,\n net_type: str = 'alex',\n version: str = '0.1'):\n r\"\"\"Function that measures\n Learned Perceptual Image Patch Similarity (LPIPS).\n\n Arguments:\n x, y (torch.Tensor): the input tensors to compare.\n net_type (str): the network type to compare the features: \n 'alex' | 'squeeze' | 'vgg'. Default: 'alex'.\n version (str): the version of LPIPS. Default: 0.1.\n \"\"\"\n device = x.device\n criterion = LPIPS(net_type, version).to(device)\n return criterion(x, y)\n" }, { "path": "field_construction/lpipsPyTorch/modules/lpips.py", "content": "import torch\nimport torch.nn as nn\n\nfrom .networks import get_network, LinLayers\nfrom .utils import get_state_dict\n\n\nclass LPIPS(nn.Module):\n r\"\"\"Creates a criterion that measures\n Learned Perceptual Image Patch Similarity (LPIPS).\n\n Arguments:\n net_type (str): the network type to compare the features: \n 'alex' | 'squeeze' | 'vgg'. Default: 'alex'.\n version (str): the version of LPIPS. Default: 0.1.\n \"\"\"\n def __init__(self, net_type: str = 'alex', version: str = '0.1'):\n\n assert version in ['0.1'], 'v0.1 is only supported now'\n\n super(LPIPS, self).__init__()\n\n # pretrained network\n self.net = get_network(net_type)\n\n # linear layers\n self.lin = LinLayers(self.net.n_channels_list)\n self.lin.load_state_dict(get_state_dict(net_type, version))\n\n def forward(self, x: torch.Tensor, y: torch.Tensor):\n feat_x, feat_y = self.net(x), self.net(y)\n\n diff = [(fx - fy) ** 2 for fx, fy in zip(feat_x, feat_y)]\n res = [l(d).mean((2, 3), True) for d, l in zip(diff, self.lin)]\n\n return torch.sum(torch.cat(res, 0), 0, True)\n" }, { "path": "field_construction/lpipsPyTorch/modules/networks.py", "content": "from typing import Sequence\n\nfrom itertools import chain\n\nimport torch\nimport torch.nn as nn\nfrom torchvision import models\n\nfrom .utils import normalize_activation\n\n\ndef get_network(net_type: str):\n if net_type == 'alex':\n return AlexNet()\n elif net_type == 'squeeze':\n return SqueezeNet()\n elif net_type == 'vgg':\n return VGG16()\n else:\n raise NotImplementedError('choose net_type from [alex, squeeze, vgg].')\n\n\nclass LinLayers(nn.ModuleList):\n def __init__(self, n_channels_list: Sequence[int]):\n super(LinLayers, self).__init__([\n nn.Sequential(\n nn.Identity(),\n nn.Conv2d(nc, 1, 1, 1, 0, bias=False)\n ) for nc in n_channels_list\n ])\n\n for param in self.parameters():\n param.requires_grad = False\n\n\nclass BaseNet(nn.Module):\n def __init__(self):\n super(BaseNet, self).__init__()\n\n # register buffer\n self.register_buffer(\n 'mean', torch.Tensor([-.030, -.088, -.188])[None, :, None, None])\n self.register_buffer(\n 'std', torch.Tensor([.458, .448, .450])[None, :, None, None])\n\n def set_requires_grad(self, state: bool):\n for param in chain(self.parameters(), self.buffers()):\n param.requires_grad = state\n\n def z_score(self, x: torch.Tensor):\n return (x - self.mean) / self.std\n\n def forward(self, x: torch.Tensor):\n x = self.z_score(x)\n\n output = []\n for i, (_, layer) in enumerate(self.layers._modules.items(), 1):\n x = layer(x)\n if i in self.target_layers:\n output.append(normalize_activation(x))\n if len(output) == len(self.target_layers):\n break\n return output\n\n\nclass SqueezeNet(BaseNet):\n def __init__(self):\n super(SqueezeNet, self).__init__()\n\n self.layers = models.squeezenet1_1(True).features\n self.target_layers = [2, 5, 8, 10, 11, 12, 13]\n self.n_channels_list = [64, 128, 256, 384, 384, 512, 512]\n\n self.set_requires_grad(False)\n\n\nclass AlexNet(BaseNet):\n def __init__(self):\n super(AlexNet, self).__init__()\n\n self.layers = models.alexnet(True).features\n self.target_layers = [2, 5, 8, 10, 12]\n self.n_channels_list = [64, 192, 384, 256, 256]\n\n self.set_requires_grad(False)\n\n\nclass VGG16(BaseNet):\n def __init__(self):\n super(VGG16, self).__init__()\n\n self.layers = models.vgg16(weights=models.VGG16_Weights.IMAGENET1K_V1).features\n self.target_layers = [4, 9, 16, 23, 30]\n self.n_channels_list = [64, 128, 256, 512, 512]\n\n self.set_requires_grad(False)\n" }, { "path": "field_construction/lpipsPyTorch/modules/utils.py", "content": "from collections import OrderedDict\n\nimport torch\n\n\ndef normalize_activation(x, eps=1e-10):\n norm_factor = torch.sqrt(torch.sum(x ** 2, dim=1, keepdim=True))\n return x / (norm_factor + eps)\n\n\ndef get_state_dict(net_type: str = 'alex', version: str = '0.1'):\n # build url\n url = 'https://raw.githubusercontent.com/richzhang/PerceptualSimilarity/' \\\n + f'master/lpips/weights/v{version}/{net_type}.pth'\n\n # download\n old_state_dict = torch.hub.load_state_dict_from_url(\n url, progress=True,\n map_location=None if torch.cuda.is_available() else torch.device('cpu')\n )\n\n # rename keys\n new_state_dict = OrderedDict()\n for key, val in old_state_dict.items():\n new_key = key\n new_key = new_key.replace('lin', '')\n new_key = new_key.replace('model.', '')\n new_state_dict[new_key] = val\n\n return new_state_dict\n" }, { "path": "field_construction/pipeline.py", "content": "\nfrom random import randint\n\nfrom .gaussian_field import GaussianField\nfrom .preprocessor import Preprocessor\n\n\nclass FieldConstructionPipeline:\n def __init__(self, cfg):\n self.cfg = cfg\n if cfg.pipeline.mode == \"train\":\n self.preprocessor = Preprocessor(cfg)\n else:\n self.preprocessor = None\n \n def construct_field(self):\n self.preprocessor.preprocess()\n del self.preprocessor\n self.preprocessor = None\n\n self.gaussian_field = GaussianField(self.cfg)\n self.gaussian_field.train()\n\n \n def render_result(self):\n self.gaussian_field = GaussianField(self.cfg)\n self.gaussian_field.render()\n \n def eval(self):\n self.gaussian_field = GaussianField(self.cfg)\n self.gaussian_field.eval()\n\n" }, { "path": "field_construction/pose_estimator/__init__.py", "content": "import logging\nimport os\nimport shutil\nimport time\nfrom abc import ABC, abstractmethod\nfrom pathlib import Path\n\nimport numpy as np\nimport open3d as o3d\nimport torch\n\nfrom utils.sfm_utils import (compute_co_vis_masks, get_sorted_image_files,\n load_images, save_extrinsic, save_intrinsics,\n save_points3D)\n\nfrom .utils import prepare_input, prepare_output, storePly\n\n\nclass BaseEstimator(ABC):\n @abstractmethod\n def get_poses():\n pass\n\n\nclass ColmapEstimator(BaseEstimator):\n def __init__(self, cfg):\n self.cfg = cfg\n \n def get_poses(self, camera_model=\"OPENCV\", use_gpu=True):\n save_path = self.cfg.pipeline.data_path\n database_path = os.path.join(save_path, \"distorted\", \"database.db\")\n raw_img_path = os.path.join(save_path, \"input\")\n sparse_path = os.path.join(save_path, \"distorted\", \"sparse\")\n os.makedirs(os.path.join(save_path, \"distorted\"), exist_ok=True)\n os.makedirs(sparse_path, exist_ok=True)\n\n feat_extraction_cmd = [\n \"colmap\", \"feature_extractor\", \n \"--database_path\", database_path,\n \"--image_path\", raw_img_path,\n \"--ImageReader.single_camera\", \"1\",\n \"--ImageReader.camera_model\", camera_model,\n \"--SiftExtraction.use_gpu\", str(int(use_gpu))\n ]\n feat_extraction_cmd = \" \".join(feat_extraction_cmd)\n exit_code = os.system(feat_extraction_cmd)\n if exit_code != 0:\n logging.error(f\"Feature extraction failed with code {exit_code}. Exiting.\")\n exit(exit_code)\n \n feat_matching_cmd = [\n \"colmap\", \"exhaustive_matcher\",\n \"--database_path\", database_path,\n \"--SiftMatching.use_gpu\", str(int(use_gpu))\n ]\n feat_matching_cmd = \" \".join(feat_matching_cmd)\n exit_code = os.system(feat_matching_cmd)\n if exit_code != 0:\n logging.error(f\"Feature matching failed with code {exit_code}. Exiting.\")\n exit(exit_code)\n \n mapper_cmd = [\n \"colmap\", \"mapper\",\n \"--database_path\", database_path,\n \"--image_path\", raw_img_path,\n \"--output_path\", sparse_path,\n \"--Mapper.ba_global_function_tolerance=0.000001\"\n ]\n mapper_cmd = \" \".join(mapper_cmd)\n exit_code = os.system(mapper_cmd)\n if exit_code != 0:\n logging.error(f\"Mapper failed with code {exit_code}. Exiting.\")\n exit(exit_code)\n\n img_undist_cmd = [\n \"colmap\", \"image_undistorter\",\n \"--image_path\", raw_img_path,\n \"--input_path\", os.path.join(sparse_path, \"0\"),\n \"--output_path\", save_path,\n \"--output_type\", \"COLMAP\"\n ]\n img_undist_cmd = \" \".join(img_undist_cmd)\n exit_code = os.system(img_undist_cmd)\n if exit_code != 0:\n logging.error(f\"Mapper failed with code {exit_code}. Exiting.\")\n exit(exit_code)\n \n # move data:\n curr_path = os.path.join(save_path, \"sparse\")\n dest_path = os.path.join(curr_path, \"0\") \n os.makedirs(dest_path, exist_ok=True)\n files = list(filter(lambda x: x != \"0\", os.listdir(curr_path)))\n for file in files:\n src_file = os.path.join(curr_path, file)\n dest_file = os.path.join(dest_path, file)\n shutil.move(src_file, dest_file)\n\n\nclass MASt3REstimator(BaseEstimator):\n def __init__(self, cfg):\n from mast3r.model import AsymmetricMASt3R\n self.cfg = cfg\n self.device = cfg.pose_estimator.device\n self.model = AsymmetricMASt3R.from_pretrained(cfg.pose_estimator.model_path).to(self.device)\n\n def get_poses(self):\n from dust3r.cloud_opt import GlobalAlignerMode, global_aligner\n from dust3r.image_pairs import make_pairs\n from dust3r.inference import inference\n from dust3r.utils.device import to_numpy\n from dust3r.utils.geometry import inv\n\n save_path = self.cfg.pipeline.data_path\n co_vis_dsp = self.cfg.pose_estimator.co_vis_dsp\n sparse_path = os.path.join(save_path, \"sparse\", \"0\")\n os.makedirs(sparse_path, exist_ok=True)\n image_dir = Path(save_path) / \"input\"\n image_files, image_suffix = get_sorted_image_files(image_dir)\n n_views = len(image_files)\n images, org_imgs_shape = load_images(image_files, size=512)\n\n logging.info(\">> Making pairs...\")\n pairs = make_pairs(images)\n logging.info(\">> Inference...\")\n output = inference(pairs, self.model, self.device, batch_size=1, verbose=True)\n logging.info(f'>> Global alignment...')\n scene = global_aligner(output, device=self.device, mode=GlobalAlignerMode.PointCloudOptimizer)\n\n extrinsics_w2c = inv(to_numpy(scene.get_im_poses()))\n intrinsics = to_numpy(scene.get_intrinsics())\n focals = to_numpy(scene.get_focals())\n imgs = np.array(scene.imgs)\n pts3d = to_numpy(scene.get_pts3d())\n pts3d = np.array(pts3d)\n depthmaps = to_numpy(scene.im_depthmaps.detach().cpu().numpy())\n values = [param.detach().cpu().numpy() for param in scene.im_conf]\n confs = np.array(values)\n \n logging.info(f'>> Confiden-aware Ranking...')\n avg_conf_scores = confs.mean(axis=(1, 2))\n sorted_conf_indices = np.argsort(avg_conf_scores)[::-1]\n sorted_conf_avg_conf_scores = avg_conf_scores[sorted_conf_indices]\n logging.info(\"Sorted indices:\", str(sorted_conf_indices))\n logging.info(\"Sorted average confidence scores:\", str(sorted_conf_avg_conf_scores))\n\n logging.info(f'>> Calculate the co-visibility mask...')\n depth_thre = self.cfg.pose_estimator.depth_thre\n if depth_thre > 0:\n overlapping_masks = compute_co_vis_masks(sorted_conf_indices, depthmaps, pts3d, intrinsics, extrinsics_w2c, imgs.shape, depth_threshold=depth_thre)\n overlapping_masks = ~overlapping_masks\n else:\n co_vis_dsp = False\n overlapping_masks = None\n\n focals = np.repeat(focals[0], n_views)\n logging.info(f'>> Saving results...')\n save_extrinsic(Path(sparse_path), extrinsics_w2c, image_files, image_suffix)\n save_intrinsics(Path(sparse_path), focals, org_imgs_shape, imgs.shape, save_focals=True)\n pts_num = save_points3D(Path(sparse_path), imgs, pts3d, confs.reshape(pts3d.shape[0], -1), overlapping_masks, use_masks=co_vis_dsp, save_all_pts=True, save_txt_path=save_path, depth_threshold=depth_thre)\n # save_images_and_masks(Path(sparse_path), n_views, imgs, overlapping_masks, image_files, image_suffix)\n logging.info(f'MASt3R Reconstruction is successfully converted to COLMAP files in: {sparse_path}')\n logging.info(f'Number of points: {pts3d.reshape(-1, 3).shape[0]}') \n logging.info(f'Number of points after downsampling: {pts_num}')\n \n \nclass CUT3REstimator(BaseEstimator):\n def __init__(self, cfg):\n self.cfg = cfg \n self.device = cfg.pose_estimator.device\n\n def get_poses(self):\n cfg = self.cfg\n if self.device == \"cuda\" and not torch.cuda.is_available():\n print(\"cuda not available. switching to cpu.\")\n self.device = \"cpu\"\n \n from cut3r.dust3r.inference import inference\n from cut3r.dust3r.model import ARCroco3DStereo\n \n save_path = self.cfg.pipeline.data_path\n img_folder_path = os.path.join(save_path, \"input\")\n img_paths = [os.path.join(img_folder_path, img_name) for img_name in os.listdir(img_folder_path)]\n img_mask = [True] * len(img_paths)\n views, orig_shape = prepare_input(\n img_paths=img_paths,\n img_mask=img_mask,\n size=512,\n revisit=1,\n update=True,\n )\n model = ARCroco3DStereo.from_pretrained(cfg.pose_estimator.model_path).to(self.device)\n model.eval()\n\n logging.info(\"Running inference...\")\n start_time = time.time()\n outputs, state_args = inference(views, model, self.device)\n total_time = time.time() - start_time\n per_frame_time = total_time / len(views)\n print(\n f\"Inference completed in {total_time:.2f} seconds (average {per_frame_time:.2f} s per frame).\"\n )\n\n pts3ds_other, colors, conf, cam_dict = prepare_output(\n outputs, orig_shape, save_path, 1, True\n )\n conf = torch.cat(conf, dim=0)\n if self.cfg.pipeline.selection:\n conf_score = conf.mean(dim=(1, 2))\n chunk_num = self.cfg.pipeline.chunk_num\n keep_num_per_chunk = self.cfg.pipeline.keep_num_per_chunk\n conf_scores_tuple = conf_score.chunk(chunk_num)\n selected_idxs = []\n total_conf_len = 0\n for conf_scores_chunk in conf_scores_tuple:\n _, idxs = conf_scores_chunk.sort(descending=True)\n idxs = idxs[:keep_num_per_chunk]\n selected_idxs += [(idx + total_conf_len).item() for idx in idxs]\n total_conf_len += len(conf_scores_chunk)\n self.cfg.pipeline.selected_idxs = sorted(selected_idxs)\n \n pts3ds_to_save = [pts3ds_other[idx].cpu().numpy() for idx in self.cfg.pipeline.selected_idxs]\n colors_to_save = [colors[idx].cpu().numpy() for idx in self.cfg.pipeline.selected_idxs]\n all_pts3ds = np.stack(pts3ds_to_save).reshape(-1, 3)\n all_colors = np.stack(colors_to_save).reshape(-1, 3)\n storePly(os.path.join(save_path, \"points3D.ply\"), all_pts3ds, all_colors)\n\nclass VGGTEstimator(BaseEstimator):\n def __init__(self, cfg):\n self.cfg = cfg\n self.device = cfg.pose_estimator.device\n\n def get_poses(self):\n from vggt.models.vggt import VGGT\n from vggt.utils.geometry import unproject_depth_map_to_point_map\n from vggt.utils.load_fn import load_and_preprocess_images\n from vggt.utils.pose_enc import pose_encoding_to_extri_intri\n\n cfg = self.cfg\n if self.device == \"cuda\" and not torch.cuda.is_available():\n print(\"cuda not available. switching to cpu.\")\n self.device = \"cpu\"\n \n dtype = torch.bfloat16 if torch.cuda.get_device_capability()[0] >= 8 else torch.float16\n logging.info(\"Loading vggt...\")\n model = VGGT.from_pretrained(\"facebook/VGGT-1B\").to(self.device)\n save_path = self.cfg.pipeline.data_path\n img_folder_path = os.path.join(save_path, \"input\")\n img_paths = [os.path.join(img_folder_path, img_name) for img_name in os.listdir(img_folder_path)]\n images = load_and_preprocess_images(img_paths).to(self.device)\n\n with torch.no_grad(), torch.amp.autocast(\"cuda\", dtype=dtype):\n images = images[None]\n aggregated_tokens_list, ps_idx = model.aggregator(images)\n pose_enc = model.camera_head(aggregated_tokens_list)[-1]\n extrinsic, intrinsic = pose_encoding_to_extri_intri(pose_enc, images.shape[-2:])\n depth_map, depth_conf = model.depth_head(aggregated_tokens_list, images, ps_idx)\n point_map = unproject_depth_map_to_point_map(\n depth_map.squeeze(0), \n extrinsic.squeeze(0), \n intrinsic.squeeze(0)\n )\n extrinsic, intrinsic = extrinsic.squeeze(), intrinsic.squeeze()\n extrinsics_w2c = torch.eye(4)[None].repeat(len(extrinsic), 1, 1)\n extrinsics_w2c[:, :3, :4] = extrinsic.cpu()\n extrinsics_w2c = extrinsics_w2c.cpu().numpy()\n intrinsics = intrinsic.cpu().numpy()\n\n scaled_y, scaled_x = images.shape[-2:]\n intrinsics[:, 0, 0] *= 720 / scaled_x\n intrinsics[:, 1, 1] *= 480 / scaled_y\n intrinsics[:, 0, 2] *= 720 / scaled_x\n intrinsics[:, 1, 2] *= 480 / scaled_y\n\n images = torch.stack([images[:, 0], images[:, -1]], dim=1)\n point_map = np.stack([point_map[0], point_map[-1]], axis=0)\n colors = images.permute(0, 1, 3, 4, 2).detach().cpu().numpy()\n colors = colors.reshape(-1, 3)\n point_map = point_map.reshape(-1, 3).astype(np.float32)\n \n pcd = o3d.geometry.PointCloud()\n pcd.points = o3d.utility.Vector3dVector(point_map)\n pcd.colors = o3d.utility.Vector3dVector(colors)\n o3d.io.write_point_cloud(os.path.join(save_path, \"points3D.ply\"), pcd)\n camera_dir = os.path.join(save_path, \"camera\")\n os.makedirs(camera_dir, exist_ok=True)\n for i, (w2c, intrinsic) in enumerate(zip(extrinsics_w2c, intrinsics)):\n c2w = np.eye(4)\n c2w[:3, :3] = w2c[:3, :3].T\n c2w[:3, 3] = - w2c[:3, :3].T @ w2c[:3, 3]\n np.savez(\n os.path.join(camera_dir, f\"{i+1:04d}.npz\"), \n pose=c2w,\n intrinsics=intrinsic\n )\n\ndef get_pose_estimator(cfg):\n POSE_ESTIMATOR = {\n \"colmap\": ColmapEstimator, \n \"mast3r\": MASt3REstimator,\n \"cut3r\": CUT3REstimator,\n \"vggt\": VGGTEstimator,\n }\n return POSE_ESTIMATOR[cfg.pose_estimator.type](cfg)\n" }, { "path": "field_construction/pose_estimator/utils.py", "content": "import os\nfrom copy import deepcopy\n\nimport numpy as np\nimport open3d as o3d\nimport torch\n\n\ndef storePly(path, xyz, rgb):\n pcd = o3d.geometry.PointCloud()\n pcd.points = o3d.utility.Vector3dVector(xyz)\n pcd.colors = o3d.utility.Vector3dVector(rgb)\n o3d.io.write_point_cloud(path, pcd)\n \n \ndef prepare_input(\n img_paths, img_mask, size, raymaps=None, raymap_mask=None, revisit=1, update=True\n):\n \"\"\"\n Prepare input views for inference from a list of image paths.\n\n Args:\n img_paths (list): List of image file paths.\n img_mask (list of bool): Flags indicating valid images.\n size (int): Target image size.\n raymaps (list, optional): List of ray maps.\n raymap_mask (list, optional): Flags indicating valid ray maps.\n revisit (int): How many times to revisit each view.\n update (bool): Whether to update the state on revisits.\n\n Returns:\n list: A list of view dictionaries.\n \"\"\"\n # Import image loader (delayed import needed after adding ckpt path).\n from cut3r.dust3r.utils.image import load_images\n\n images, orig_shape = load_images(img_paths, size=size)\n views = []\n\n if raymaps is None and raymap_mask is None:\n # Only images are provided.\n for i in range(len(images)):\n view = {\n \"img\": images[i][\"img\"],\n \"ray_map\": torch.full(\n (\n images[i][\"img\"].shape[0],\n 6,\n images[i][\"img\"].shape[-2],\n images[i][\"img\"].shape[-1],\n ),\n torch.nan,\n ),\n \"true_shape\": torch.from_numpy(images[i][\"true_shape\"]),\n \"idx\": i,\n \"instance\": str(i),\n \"camera_pose\": torch.from_numpy(np.eye(4, dtype=np.float32)).unsqueeze(\n 0\n ),\n \"img_mask\": torch.tensor(True).unsqueeze(0),\n \"ray_mask\": torch.tensor(False).unsqueeze(0),\n \"update\": torch.tensor(True).unsqueeze(0),\n \"reset\": torch.tensor(False).unsqueeze(0),\n }\n views.append(view)\n else:\n # Combine images and raymaps.\n num_views = len(images) + len(raymaps)\n assert len(img_mask) == len(raymap_mask) == num_views\n assert sum(img_mask) == len(images) and sum(raymap_mask) == len(raymaps)\n\n j = 0\n k = 0\n for i in range(num_views):\n view = {\n \"img\": (\n images[j][\"img\"]\n if img_mask[i]\n else torch.full_like(images[0][\"img\"], torch.nan)\n ),\n \"ray_map\": (\n raymaps[k]\n if raymap_mask[i]\n else torch.full_like(raymaps[0], torch.nan)\n ),\n \"true_shape\": (\n torch.from_numpy(images[j][\"true_shape\"])\n if img_mask[i]\n else torch.from_numpy(np.int32([raymaps[k].shape[1:-1][::-1]]))\n ),\n \"idx\": i,\n \"instance\": str(i),\n \"camera_pose\": torch.from_numpy(np.eye(4, dtype=np.float32)).unsqueeze(\n 0\n ),\n \"img_mask\": torch.tensor(img_mask[i]).unsqueeze(0),\n \"ray_mask\": torch.tensor(raymap_mask[i]).unsqueeze(0),\n \"update\": torch.tensor(img_mask[i]).unsqueeze(0),\n \"reset\": torch.tensor(False).unsqueeze(0),\n }\n if img_mask[i]:\n j += 1\n if raymap_mask[i]:\n k += 1\n views.append(view)\n assert j == len(images) and k == len(raymaps)\n\n if revisit > 1:\n new_views = []\n for r in range(revisit):\n for i, view in enumerate(views):\n new_view = deepcopy(view)\n new_view[\"idx\"] = r * len(views) + i\n new_view[\"instance\"] = str(r * len(views) + i)\n if r > 0 and not update:\n new_view[\"update\"] = torch.tensor(False).unsqueeze(0)\n new_views.append(new_view)\n return new_views\n\n return views, orig_shape\n \n \n\ndef prepare_output(outputs, orig_shape, outdir, revisit=1, use_pose=True):\n \"\"\"\n Process inference outputs to generate point clouds and camera parameters for visualization.\n\n Args:\n outputs (dict): Inference outputs.\n revisit (int): Number of revisits per view.\n use_pose (bool): Whether to transform points using camera pose.\n\n Returns:\n tuple: (points, colors, confidence, camera parameters dictionary)\n \"\"\"\n from cut3r.dust3r.post_process import estimate_focal_knowing_depth\n from cut3r.dust3r.utils.camera import pose_encoding_to_camera\n from cut3r.dust3r.utils.geometry import geotrf\n\n # Only keep the outputs corresponding to one full pass.\n valid_length = len(outputs[\"pred\"]) // revisit\n outputs[\"pred\"] = outputs[\"pred\"][-valid_length:]\n outputs[\"views\"] = outputs[\"views\"][-valid_length:]\n\n pts3ds_self_ls = [output[\"pts3d_in_self_view\"].cpu() for output in outputs[\"pred\"]]\n pts3ds_other = [output[\"pts3d_in_other_view\"].cpu() for output in outputs[\"pred\"]]\n conf_self = [output[\"conf_self\"].cpu() for output in outputs[\"pred\"]]\n conf_other = [output[\"conf\"].cpu() for output in outputs[\"pred\"]]\n pts3ds_self = torch.cat(pts3ds_self_ls, 0)\n\n # Recover camera poses.\n pr_poses = [\n pose_encoding_to_camera(pred[\"camera_pose\"].clone()).cpu()\n for pred in outputs[\"pred\"]\n ]\n R_c2w = torch.cat([pr_pose[:, :3, :3] for pr_pose in pr_poses], 0)\n t_c2w = torch.cat([pr_pose[:, :3, 3] for pr_pose in pr_poses], 0)\n\n if use_pose:\n transformed_pts3ds_other = []\n for pose, pself in zip(pr_poses, pts3ds_self):\n transformed_pts3ds_other.append(geotrf(pose, pself.unsqueeze(0)))\n pts3ds_other = transformed_pts3ds_other\n conf_other = conf_self\n\n # Estimate focal length based on depth.\n B, H, W, _ = pts3ds_self.shape\n orig_H, orig_W = orig_shape\n pp = torch.tensor([orig_W // 2, orig_H // 2], device=pts3ds_self.device).float().repeat(B, 1)\n focal = estimate_focal_knowing_depth(pts3ds_self, pp, focal_mode=\"weiszfeld\")\n # focal = focal.mean().repeat(len(focal))\n focal_x = focal * orig_W / W\n focal_y = focal * orig_H / H\n\n colors = [\n 0.5 * (output[\"img\"].permute(0, 2, 3, 1) + 1.0) for output in outputs[\"views\"]\n ]\n\n cam_dict = {\n \"focal\": focal.cpu().numpy(),\n \"pp\": pp.cpu().numpy(),\n \"R\": R_c2w.cpu().numpy(),\n \"t\": t_c2w.cpu().numpy(),\n }\n\n cam2world_tosave = torch.cat(pr_poses) # B, 4, 4\n intrinsics_tosave = (\n torch.eye(3).unsqueeze(0).repeat(cam2world_tosave.shape[0], 1, 1)\n ) # B, 3, 3\n intrinsics_tosave[:, 0, 0] = focal_x.detach().cpu()\n intrinsics_tosave[:, 1, 1] = focal_y.detach().cpu() \n intrinsics_tosave[:, 0, 2] = pp[:, 0]\n intrinsics_tosave[:, 1, 2] = pp[:, 1]\n\n os.makedirs(os.path.join(outdir, \"camera\"), exist_ok=True)\n\n for f_id in range(len(cam2world_tosave)):\n c2w = cam2world_tosave[f_id].cpu().numpy()\n intrins = intrinsics_tosave[f_id].cpu().numpy()\n np.savez(\n os.path.join(outdir, \"camera\", f\"{f_id+1:04d}.npz\"),\n pose=c2w,\n intrinsics=intrins,\n )\n\n return pts3ds_other, colors, conf_other, cam_dict\n" }, { "path": "field_construction/preprocessor.py", "content": "import glob\nimport logging\nimport os\nimport shutil\nimport subprocess\n\nimport cv2\nimport numpy as np\nimport torch\nfrom diffusers.models.autoencoders.vq_model import VQModel\nfrom safetensors.torch import load_file\nfrom torch.utils.data import DataLoader\nfrom torchvision import transforms\nfrom tqdm import tqdm\n\nfrom .auto_encoder import Autoencoder, Autoencoder_dataset\nfrom .pose_estimator import get_pose_estimator\nfrom .utils.loss_utils import cos_loss, l2_loss\nfrom .video_preprocessor import VideoPreprocessor\n\n\ndef extract_with_openseg(cfg):\n import tensorflow as tf2\n import tensorflow._api.v2.compat.v1 as tf\n gpus = tf.config.experimental.list_physical_devices('GPU')\n if gpus:\n try:\n for gpu in gpus:\n tf.config.experimental.set_memory_growth(gpu, True)\n except RuntimeError as e:\n print(e)\n\n openseg = tf2.saved_model.load(\n cfg.feature_extractor.model_path, \n tags=[tf.saved_model.tag_constants.SERVING]\n )\n imgs_path = os.path.join(cfg.pipeline.data_path, \"input\")\n img_names = list(\n filter(\n lambda x: x.endswith(\"png\") or x.endswith(\"jpg\"), sorted(os.listdir(imgs_path))\n )\n )\n img_list = []\n np_image_string_list = []\n for img_name in img_names:\n img_path = os.path.join(imgs_path, img_name)\n image = cv2.imread(img_path)\n with tf.gfile.GFile(img_path, 'rb') as f:\n np_image_string = np.array([f.read()])\n\n image = torch.from_numpy(image)\n img_list.append(image)\n np_image_string_list.append(np_image_string)\n\n images = [img_list[i].permute(2, 0, 1)[None, ...] for i in range(len(img_list))]\n imgs = torch.cat(images)\n save_path = os.path.join(cfg.pipeline.data_path, \"lang_features\")\n os.makedirs(save_path, exist_ok=True)\n embed_size = 768\n for i, (img, np_image_string) in enumerate(tqdm((zip(imgs, np_image_string_list)), desc=\"Extracting lang features\", total=(len(imgs)))):\n text_emb = tf.zeros([1, 1, embed_size])\n results = openseg.signatures[\"serving_default\"](\n inp_image_bytes=tf.convert_to_tensor(np_image_string[0]),\n inp_text_emb=text_emb\n )\n img_info = results['image_info']\n crop_sz = [\n int(img_info[0, 0] * img_info[2, 0]),\n int(img_info[0, 1] * img_info[2, 1])\n ]\n image_embedding_feat = results['image_embedding_feat'][:, :crop_sz[0], :crop_sz[1]]\n img_size = (img.shape[1], img.shape[2])\n feat_2d = tf.cast(\n tf.image.resize_nearest_neighbor(\n image_embedding_feat, img_size, align_corners=True\n )[0], dtype=tf.float32\n ).numpy()\n # perform mask-pooling over feat2d\n feat_2d = np.transpose(feat_2d, axes=(2, 0, 1))\n pooled_feats2d = []\n curr_mask = np.load(os.path.join(cfg.pipeline.data_path, \"lang_features_dim3\", str(i+1).zfill(4)+\"_s.npy\"))\n for color_id in range(-1, curr_mask.max() + 1):\n if not feat_2d[:, curr_mask == color_id].shape[-1]:\n continue\n pooled = feat_2d[:, curr_mask == color_id].mean(axis=-1)\n pooled /= np.linalg.norm(pooled)\n pooled_feats2d.append(pooled)\n\n pooled_feats2d = np.stack(pooled_feats2d)\n np.save(os.path.join(save_path, str(i+1).zfill(4)+\".npy\"), pooled_feats2d)\n\nclass Preprocessor:\n def __init__(self, cfg):\n self.cfg = cfg\n if not cfg.pipeline.skip_video_process:\n self.video_processor = VideoPreprocessor(cfg)\n else:\n self.video_processor = None\n\n if not cfg.pipeline.skip_pose_estimate:\n self.pose_estimator = get_pose_estimator(cfg)\n else:\n self.pose_estimator = None\n\n if not cfg.pipeline.skip_lang_feature_extraction:\n # load feature extractor\n if cfg.feature_extractor.type == \"open-seg\":\n self.lseg = None\n self.sem_ae = Autoencoder()\n self.sem_ae.cuda()\n\n elif cfg.feature_extractor.type == \"lseg\":\n self.lseg = LSegFeatureExtractor.from_pretrained(cfg.lseg.model_path)\n self.lseg.to(cfg.lseg.device, dtype=torch.float32).eval()\n\n self.sem_ae = VQModel(\n in_channels=512,\n out_channels=512,\n latent_channels=4,\n norm_num_groups=2,\n block_out_channels=[256, 64, 16],\n down_block_types=[\"DownEncoderBlock2D\"] * 3,\n up_block_types=[\"UpDecoderBlock2D\"] * 3,\n layers_per_block=1,\n norm_type=\"spatial\",\n num_vq_embeddings=1024,\n )\n\n self.sem_ae.load_state_dict(load_file(cfg.ae.model_path))\n self.sem_ae.to(cfg.ae.device, dtype=torch.float32).eval()\n self.img_transform = transforms.Compose(\n [\n transforms.Lambda(lambda x: x / 255),\n transforms.Normalize(\n mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True\n ),\n ]\n )\n\n else:\n self.lseg = None\n self.sem_ae = None\n self.img_transform = None\n\n \n def generate_lang_features_with_openseg(self):\n extract_with_openseg(self.cfg)\n logging.info(\"Done feature extraction.\")\n\n num_epochs = 400\n os.makedirs(os.path.join(self.cfg.pipeline.data_path, \"ckpt\"), exist_ok=True)\n save_path = os.path.join(self.cfg.pipeline.data_path, \"lang_features\")\n train_dataset = Autoencoder_dataset(save_path)\n train_loader = DataLoader(\n dataset=train_dataset,\n batch_size=512,\n shuffle=True,\n num_workers=32,\n drop_last=False\n )\n test_loader = DataLoader(\n dataset=train_dataset,\n batch_size=512,\n shuffle=False,\n num_workers=32,\n drop_last=False\n )\n optimizer = torch.optim.Adam(self.sem_ae.parameters(), lr=1e-4)\n pbar = tqdm(range(num_epochs))\n best_eval_loss = 100.0\n best_epoch = 0\n\n for epoch in pbar:\n self.sem_ae.train()\n for idx, feature in enumerate(train_loader):\n data = feature.to(\"cuda\")\n outputs_dim3 = self.sem_ae.encode(data)\n outputs = self.sem_ae.decode(outputs_dim3)\n\n l2loss = l2_loss(outputs, data)\n cosloss = cos_loss(outputs, data)\n loss = l2loss + cosloss * 0.001\n\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n if epoch > 300:\n eval_loss = 0.0\n self.sem_ae.eval()\n for idx, feature in enumerate(test_loader):\n data = feature.to(\"cuda\")\n with torch.no_grad():\n outputs = self.sem_ae(data)\n loss = l2_loss(outputs, data) + cos_loss(outputs, data)\n eval_loss += loss * len(feature)\n eval_loss = eval_loss / len(train_dataset)\n print(\"eval_loss:{:.8f}\".format(eval_loss))\n if eval_loss < best_eval_loss:\n best_eval_loss = eval_loss\n best_epoch = epoch\n torch.save(self.sem_ae.state_dict(), os.path.join(self.cfg.pipeline.data_path, \"ckpt\", \"best_ckpt.pth\"))\n pbar.set_postfix({\"Loss\": f\"{loss.item():.{7}f}\"})\n pbar.update(1)\n\n print(f\"best_epoch: {best_epoch}\")\n print(\"best_loss: {:.8f}\".format(best_eval_loss))\n # compress lang_feats with ae\n logging.info(\"Compresing language features with best ckpt...\")\n best_state_dict = torch.load(os.path.join(self.cfg.pipeline.data_path, \"ckpt\", \"best_ckpt.pth\"), weights_only=False)\n self.sem_ae.load_state_dict(best_state_dict)\n # check device\n orig_lang_feat_names = sorted(glob.glob(os.path.join(save_path, \"*.npy\")))\n dim3_save_path = os.path.join(self.cfg.pipeline.data_path, \"lang_features_dim3\")\n with torch.no_grad():\n for idx, orig_lang_feat_name in enumerate(orig_lang_feat_names):\n orig_lang_feat = torch.from_numpy(np.load(orig_lang_feat_name)).cuda()\n mask = np.load(os.path.join(dim3_save_path, str(idx+1).zfill(4)+\"_s.npy\"))\n # check dtype\n lang_feat = self.sem_ae.encode(orig_lang_feat).detach().cpu().numpy()\n full_lang_feat = np.zeros((3, mask.shape[0], mask.shape[1]))\n curr_id = 0\n for color_id in range(-1, mask.max() + 1):\n if not mask[mask == color_id].shape[-1]:\n continue\n full_lang_feat[:, mask == color_id] = lang_feat[curr_id][:, None]\n curr_id += 1\n np.save(os.path.join(dim3_save_path, str(idx+1).zfill(4)+\"_f.npy\"), full_lang_feat)\n\n def generate_lang_features_with_lseg(self):\n from cogvideox_interpolation.lseg import LSegFeatureExtractor\n imgs_path = os.path.join(self.cfg.pipeline.data_path, \"input\")\n img_names = list(\n filter(\n lambda x: x.endswith(\"png\") or x.endswith(\"jpg\"), os.listdir(imgs_path)\n )\n )\n save_path = os.path.join(self.cfg.pipeline.data_path, \"lang_features_dim4\")\n os.makedirs(save_path, exist_ok=True)\n for img_name in tqdm(img_names):\n img_path = os.path.join(imgs_path, img_name)\n img = cv2.imread(img_path)\n resolution = (640, 480)\n img = cv2.resize(img, resolution)\n frame_embed = self.img_transform(torch.from_numpy(img).permute(2, 0, 1)).to(\n self.cfg.lseg.device, dtype=torch.float32\n )[None, ...]\n lseg_features = self.lseg.extract_features(frame_embed)\n if lseg_features.device != self.sem_ae.device:\n lseg_features = lseg_features.to(\"cpu\").to(self.sem_ae.device)\n\n z = self.sem_ae.encode(lseg_features).latents # [1, 4, 240, 320]\n np.save(\n os.path.join(save_path, f\"{img_name.split('.')[0]}_f.npy\"),\n z.detach().cpu().numpy(),\n )\n\n def select_valid_data(self):\n cfg = self.cfg\n curr_data_path = cfg.pipeline.data_path\n raw_data_path = os.path.join(curr_data_path, \"raw\")\n os.makedirs(raw_data_path, exist_ok=True)\n dirs_to_move = [\"camera\", \"input\", \"lang_features_dim3\", \"normal\"]\n \n orig_view_nums = len(os.listdir(os.path.join(curr_data_path, \"camera\")))\n indexs = np.linspace(0, orig_view_nums-1, cfg.pipeline.chunk_num * cfg.pipeline.keep_num_per_chunk)\n indexs = indexs.astype(np.int32).tolist()\n cfg.pipeline.selected_idxs = indexs\n\n for dir_to_move in dirs_to_move:\n shutil.move(os.path.join(curr_data_path, dir_to_move), raw_data_path)\n src_dir = os.path.join(raw_data_path, dir_to_move)\n tar_dir = os.path.join(curr_data_path, dir_to_move)\n os.makedirs(tar_dir, exist_ok=True)\n file_lst = sorted(os.listdir(src_dir))\n file_suffix = file_lst[0].split(\".\")[-1]\n if dir_to_move == \"lang_features_dim3\":\n f_file_lst = [file_lst[2 * idx] for idx in cfg.pipeline.selected_idxs]\n s_file_lst = [file_lst[2 * idx + 1] for idx in cfg.pipeline.selected_idxs]\n for file_idx in range(len(f_file_lst)):\n shutil.copy(\n os.path.join(src_dir, f_file_lst[file_idx]),\n os.path.join(tar_dir, f\"{file_idx+1:04d}_f.{file_suffix}\"),\n )\n shutil.copy(\n os.path.join(src_dir, s_file_lst[file_idx]),\n os.path.join(tar_dir, f\"{file_idx+1:04d}_s.{file_suffix}\"),\n )\n else:\n file_lst = [file_lst[idx] for idx in cfg.pipeline.selected_idxs]\n for file_idx, file_name in enumerate(file_lst):\n shutil.copy(\n os.path.join(src_dir, file_name),\n os.path.join(tar_dir, f\"{file_idx+1:04d}.{file_suffix}\"),\n )\n\n def preprocess(self):\n if not self.cfg.pipeline.skip_video_process:\n logging.info(\"Processing input videos...\")\n self.video_processor.video_process()\n\n if not self.cfg.pipeline.skip_pose_estimate:\n logging.info(\"Estimating poses...\")\n self.pose_estimator.get_poses()\n\n if not self.cfg.pipeline.skip_lang_feature_extraction:\n logging.info(\"Generating language features...\")\n if self.cfg.feature_extractor.type == \"lseg\":\n self.generate_lang_features_with_lseg()\n elif self.cfg.feature_extractor.type == \"open-seg\":\n self.generate_lang_features_with_openseg()\n\n if self.cfg.pipeline.selection:\n logging.info(\"Selecting views with higher confidence...\")\n self.select_valid_data()\n\n logging.info(\"Done all preprocessing!\")\n" }, { "path": "field_construction/scene/__init__.py", "content": "#\n# Copyright (C) 2023, Inria\n# GRAPHDECO research group, https://team.inria.fr/graphdeco\n# All rights reserved.\n#\n# This software is free for non-commercial, research and evaluation use\n# under the terms of the LICENSE.md file.\n#\n# For inquiries contact george.drettakis@inria.fr\n#\n\nimport json\nimport os\nimport random\n\nimport numpy as np\nimport torch\n\nfrom field_construction.scene.dataset_readers import sceneLoadTypeCallbacks\nfrom field_construction.scene.gaussian_model import GaussianModel\nfrom field_construction.utils.camera_utils import (camera_to_JSON,\n cameraList_from_camInfos)\nfrom field_construction.utils.system_utils import searchForMaxIteration\n\n\nclass Scene:\n gaussians: GaussianModel\n\n def __init__(self, args, gaussians: GaussianModel, load_iteration=None, shuffle=True,\n resolution_scales=[1.0]):\n \"\"\"b\n :param path: Path to colmap scene main folder.\n \"\"\"\n self.model_path = args.model_path\n os.makedirs(self.model_path, exist_ok=True)\n self.loaded_iter = None\n self.gaussians = gaussians\n self.source_path = args.source_path\n\n if load_iteration:\n if load_iteration == -1:\n self.loaded_iter = searchForMaxIteration(os.path.join(self.model_path, \"point_cloud\"))\n else:\n self.loaded_iter = load_iteration\n print(\"Loading trained model at iteration {}\".format(self.loaded_iter))\n\n self.train_cameras = {}\n self.test_cameras = {}\n\n if os.path.exists(os.path.join(args.source_path, \"sparse\")):\n scene_info = sceneLoadTypeCallbacks[\"Colmap\"](args.source_path, \"images\", args.eval, loaded_iter=self.loaded_iter)\n elif os.path.exists(os.path.join(args.source_path, \"transforms_train.json\")):\n print(\"Found transforms_train.json file, assuming Blender data set!\")\n scene_info = sceneLoadTypeCallbacks[\"Blender\"](args.source_path, args.white_background, args.eval)\n else:\n print(\"Assuming CUT3R data set...\")\n scene_info = sceneLoadTypeCallbacks[\"CUT3R\"](args.source_path, args.white_background, args.eval, loaded_iter=self.loaded_iter)\n\n if not self.loaded_iter:\n with open(scene_info.ply_path, 'rb') as src_file, open(os.path.join(self.model_path, \"input.ply\"), 'wb') as dest_file:\n dest_file.write(src_file.read())\n json_cams = []\n camlist = []\n if scene_info.test_cameras:\n camlist.extend(scene_info.test_cameras)\n if scene_info.train_cameras:\n camlist.extend(scene_info.train_cameras)\n for id, cam in enumerate(camlist):\n json_cams.append(camera_to_JSON(id, cam))\n with open(os.path.join(self.model_path, \"cameras.json\"), 'w') as file:\n json.dump(json_cams, file)\n\n if shuffle:\n random.shuffle(scene_info.train_cameras) # Multi-res consistent random shuffling\n random.shuffle(scene_info.test_cameras) # Multi-res consistent random shuffling\n\n self.cameras_extent = scene_info.nerf_normalization[\"radius\"]\n print(f\"cameras_extent {self.cameras_extent}\")\n\n self.multi_view_num = args.multi_view_num\n for resolution_scale in resolution_scales:\n print(\"Loading Training Cameras\")\n self.train_cameras[resolution_scale] = cameraList_from_camInfos(scene_info.train_cameras, resolution_scale,\n args)\n print(\"Loading Test Cameras\")\n self.test_cameras[resolution_scale] = cameraList_from_camInfos(scene_info.test_cameras, resolution_scale,\n args)\n\n print(\"computing nearest_id\")\n self.world_view_transforms = []\n camera_centers = []\n center_rays = []\n for id, cur_cam in enumerate(self.train_cameras[resolution_scale]):\n self.world_view_transforms.append(cur_cam.world_view_transform)\n camera_centers.append(cur_cam.camera_center)\n R = torch.tensor(cur_cam.R).float().cuda()\n T = torch.tensor(cur_cam.T).float().cuda()\n center_ray = torch.tensor([0.0, 0.0, 1.0]).float().cuda()\n center_ray = center_ray @ R.transpose(-1, -2)\n center_rays.append(center_ray)\n self.world_view_transforms = torch.stack(self.world_view_transforms)\n camera_centers = torch.stack(camera_centers, dim=0)\n center_rays = torch.stack(center_rays, dim=0)\n center_rays = torch.nn.functional.normalize(center_rays, dim=-1)\n diss = torch.norm(camera_centers[:, None] - camera_centers[None], dim=-1).detach().cpu().numpy()\n tmp = torch.sum(center_rays[:, None] * center_rays[None], dim=-1)\n angles = torch.arccos(tmp) * 180 / 3.14159\n angles = angles.detach().cpu().numpy()\n with open(os.path.join(self.model_path, \"multi_view.json\"), 'w') as file:\n for id, cur_cam in enumerate(self.train_cameras[resolution_scale]):\n sorted_indices = np.lexsort((angles[id], diss[id]))\n # sorted_indices = np.lexsort((diss[id], angles[id]))\n mask = (angles[id][sorted_indices] < args.multi_view_max_angle) & \\\n (diss[id][sorted_indices] > args.multi_view_min_dis) & \\\n (diss[id][sorted_indices] < args.multi_view_max_dis)\n sorted_indices = sorted_indices[mask]\n multi_view_num = min(self.multi_view_num, len(sorted_indices))\n json_d = {'ref_name': cur_cam.image_name, 'nearest_name': []}\n for index in sorted_indices[:multi_view_num]:\n cur_cam.nearest_id.append(index)\n cur_cam.nearest_names.append(self.train_cameras[resolution_scale][index].image_name)\n json_d[\"nearest_name\"].append(self.train_cameras[resolution_scale][index].image_name)\n json_str = json.dumps(json_d, separators=(',', ':'))\n file.write(json_str)\n file.write('\\n')\n # print(f\"frame {cur_cam.image_name}, neareast {cur_cam.nearest_names}, \\\n # angle {angles[id][cur_cam.nearest_id]}, diss {diss[id][cur_cam.nearest_id]}\")\n\n if self.loaded_iter:\n self.gaussians.load_ply(os.path.join(self.model_path,\n \"point_cloud\",\n \"iteration_\" + str(self.loaded_iter),\n \"point_cloud.ply\"))\n else:\n self.gaussians.create_from_pcd(scene_info.point_cloud, self.cameras_extent)\n self.gaussians.init_RT_seq(self.train_cameras)\n\n def save(self, iteration, mask=None, include_feature=False, finetune=False):\n if include_feature:\n point_cloud_path = os.path.join(self.model_path, \"point_cloud/iteration_{}\".format(iteration))\n else:\n point_cloud_path = os.path.join(self.model_path, \"point_cloud/iteration_{}\".format(iteration))\n if finetune:\n self.gaussians.save_ply(os.path.join(point_cloud_path, \"finetune.ply\"), mask, include_feature)\n else:\n self.gaussians.save_ply(os.path.join(point_cloud_path, \"point_cloud.ply\"), mask, include_feature)\n\n def getTrainCameras(self, scale=1.0):\n return self.train_cameras[scale]\n\n def getTestCameras(self, scale=1.0):\n return self.test_cameras[scale]\n" }, { "path": "field_construction/scene/app_model.py", "content": "import os\n\nimport torch\nimport torch.nn as nn\n\n\ndef searchForMaxIteration(folder):\n saved_iters = [int(fname.split(\"_\")[-1]) for fname in os.listdir(folder)]\n return max(saved_iters)\n\nclass AppModel(nn.Module):\n def __init__(self, num_images=1600): \n super().__init__() \n self.appear_ab = nn.Parameter(torch.zeros(num_images, 2).cuda())\n self.optimizer = torch.optim.Adam([\n {'params': self.appear_ab, 'lr': 0.001, \"name\": \"appear_ab\"},\n ], betas=(0.9, 0.99))\n \n def save_weights(self, model_path, iteration):\n out_weights_path = os.path.join(model_path, \"app_model/iteration_{}\".format(iteration))\n os.makedirs(out_weights_path, exist_ok=True)\n print(f\"save app model. path: {out_weights_path}\")\n torch.save(self.state_dict(), os.path.join(out_weights_path, 'app.pth'))\n\n def load_weights(self, model_path, iteration=-1):\n if iteration == -1:\n loaded_iter = searchForMaxIteration(os.path.join(model_path, \"app_model\"))\n else:\n loaded_iter = iteration\n weights_path = os.path.join(model_path, \"app_model/iteration_{}/app.pth\".format(loaded_iter))\n state_dict = torch.load(weights_path)\n self.load_state_dict(state_dict)\n\n def freeze(self):\n self.appear_ab.requires_grad_(False)\n" }, { "path": "field_construction/scene/cameras.py", "content": "#\n# Copyright (C) 2023, Inria\n# GRAPHDECO research group, https://team.inria.fr/graphdeco\n# All rights reserved.\n#\n# This software is free for non-commercial, research and evaluation use\n# under the terms of the LICENSE.md file.\n#\n# For inquiries contact george.drettakis@inria.fr\n#\n\nimport copy\nimport os\n\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nfrom PIL import Image\nfrom torch import nn\n\nfrom field_construction.utils.general_utils import PILtoTorch\nfrom field_construction.utils.graphics_utils import (\n fov2focal, getProjectionMatrix, getProjectionMatrixCenterShift,\n getWorld2View2)\n\n\ndef dilate(bin_img, ksize=6):\n pad = (ksize - 1) // 2\n bin_img = F.pad(bin_img, pad=[pad, pad, pad, pad], mode='reflect')\n out = F.max_pool2d(bin_img, kernel_size=ksize, stride=1, padding=0)\n return out\n\ndef erode(bin_img, ksize=12):\n out = 1 - dilate(1 - bin_img, ksize)\n return out\n\ndef process_image(image_path, resolution, ncc_scale):\n image = Image.open(image_path)\n if len(image.split()) > 3:\n resized_image_rgb = torch.cat([PILtoTorch(im, resolution) for im in image.split()[:3]], dim=0)\n loaded_mask = PILtoTorch(image.split()[3], resolution)\n gt_image = resized_image_rgb\n if ncc_scale != 1.0:\n ncc_resolution = (int(resolution[0]/ncc_scale), int(resolution[1]/ncc_scale))\n resized_image_rgb = torch.cat([PILtoTorch(im, ncc_resolution) for im in image.split()[:3]], dim=0)\n else:\n resized_image_rgb = PILtoTorch(image, resolution)\n loaded_mask = None\n gt_image = resized_image_rgb\n if ncc_scale != 1.0:\n ncc_resolution = (int(resolution[0]/ncc_scale), int(resolution[1]/ncc_scale))\n resized_image_rgb = PILtoTorch(image, ncc_resolution)\n gray_image = (0.299 * resized_image_rgb[0] + 0.587 * resized_image_rgb[1] + 0.114 * resized_image_rgb[2])[None]\n return gt_image, gray_image, loaded_mask\n\nclass Camera(nn.Module):\n def __init__(self, colmap_id, R, T, FoVx, FoVy,\n image_width, image_height,\n image_path, image_name, uid,\n trans=np.array([0.0, 0.0, 0.0]), scale=1.0, \n ncc_scale=1.0,\n preload_img=True, data_device = \"cuda\"\n ):\n super(Camera, self).__init__()\n self.uid = uid\n self.nearest_id = []\n self.nearest_names = []\n self.colmap_id = colmap_id\n self.R = R\n self.T = T\n self.FoVx = FoVx\n self.FoVy = FoVy\n self.image_name = image_name\n self.image_path = image_path\n self.image_width = image_width\n self.image_height = image_height\n self.resolution = (image_width, image_height)\n self.Fx = fov2focal(FoVx, self.image_width)\n self.Fy = fov2focal(FoVy, self.image_height)\n self.Cx = 0.5 * self.image_width\n self.Cy = 0.5 * self.image_height\n\n base_image_path = \"/\".join(self.image_path.split(\"/\")[:-2])\n self.normal_path = os.path.join(base_image_path, \"normal\", self.image_path.split(\"/\")[-1])\n \n try:\n self.data_device = torch.device(data_device)\n except Exception as e:\n print(e)\n print(f\"[Warning] Custom device {data_device} failed, fallback to default cuda device\" )\n self.data_device = torch.device(\"cuda\")\n\n self.original_image, self.image_gray, self.mask = None, None, None\n self.preload_img = preload_img\n self.ncc_scale = ncc_scale\n if self.preload_img:\n gt_image, gray_image, loaded_mask = process_image(self.image_path, self.resolution, ncc_scale)\n self.original_image = gt_image.to(self.data_device)\n self.original_image_gray = gray_image.to(self.data_device)\n self.mask = loaded_mask\n\n\n self.zfar = 100.0\n self.znear = 0.01\n\n self.trans = trans\n self.scale = scale\n\n self.world_view_transform = torch.tensor(getWorld2View2(R, T, trans, scale)).transpose(0, 1).cuda()\n self.projection_matrix = getProjectionMatrix(znear=self.znear, zfar=self.zfar, fovX=self.FoVx, fovY=self.FoVy).transpose(0,1).cuda()\n self.full_proj_transform = (self.world_view_transform.unsqueeze(0).bmm(self.projection_matrix.unsqueeze(0))).squeeze(0)\n self.camera_center = self.world_view_transform.inverse()[3, :3]\n self.plane_mask, self.non_plane_mask = None, None\n\n def get_image(self):\n if self.preload_img:\n return self.original_image.cuda(), self.original_image_gray.cuda()\n else:\n gt_image, gray_image, _ = process_image(self.image_path, self.resolution, self.ncc_scale)\n return gt_image.cuda(), gray_image.cuda()\n \n def get_normal(self):\n _normal = Image.open(self.normal_path)\n resized_normal = PILtoTorch(_normal, self.resolution)\n resized_normal = resized_normal[:3]\n _normal = - (resized_normal * 2 - 1).cuda()\n # normalize normal\n _normal = _normal.permute(1, 2, 0) @ (torch.linalg.inv(torch.as_tensor(self.R).float()).cuda())\n normal_gt = _normal.permute(2, 0, 1)\n normal_norm = torch.norm(normal_gt, dim=0, keepdim=True)\n normal_mask = ~((normal_norm > 1.1) | (normal_norm < 0.9))\n normal_gt /= normal_norm\n\n return normal_gt, normal_mask\n\n \n def get_language_feature(self, language_feature_dir):\n language_feature_name = os.path.join(language_feature_dir, self.image_name)\n feature_map = torch.from_numpy(np.load(language_feature_name + '_f.npy')).to(self.data_device)\n if len(feature_map.shape) < 4:\n feature_map = feature_map[None]\n point_feature = F.interpolate(feature_map, (self.image_height, self.image_width), mode=\"bilinear\", align_corners=False)\n seg_map = torch.from_numpy(np.load(language_feature_name + \"_s.npy\")).to(self.data_device) # (h, w)\n seg_map = seg_map.long()\n mask = seg_map != -1\n # perform mask_pooling:\n point_feature = point_feature.squeeze(0) # (feat_dim, h, w)\n # for color_id in range(seg_map.max() + 1):\n # point_feature[:, seg_map == color_id] = point_feature[:, seg_map == color_id].mean(dim=-1, keepdim=True)\n\n return point_feature, mask, seg_map\n\n def get_calib_matrix_nerf(self, scale=1.0):\n intrinsic_matrix = torch.tensor([[self.Fx/scale, 0, self.Cx/scale], [0, self.Fy/scale, self.Cy/scale], [0, 0, 1]]).float()\n extrinsic_matrix = self.world_view_transform.transpose(0,1).contiguous() # cam2world\n return intrinsic_matrix, extrinsic_matrix\n \n def get_rays(self, scale=1.0):\n W, H = int(self.image_width/scale), int(self.image_height/scale)\n ix, iy = torch.meshgrid(\n torch.arange(W), torch.arange(H), indexing='xy')\n rays_d = torch.stack(\n [(ix-self.Cx/scale) / self.Fx * scale,\n (iy-self.Cy/scale) / self.Fy * scale,\n torch.ones_like(ix)], -1).float().cuda()\n return rays_d\n \n def get_k(self, scale=1.0):\n K = torch.tensor([[self.Fx / scale, 0, self.Cx / scale],\n [0, self.Fy / scale, self.Cy / scale],\n [0, 0, 1]]).cuda()\n return K\n \n def get_inv_k(self, scale=1.0):\n K_T = torch.tensor([[scale/self.Fx, 0, -self.Cx/self.Fx],\n [0, scale/self.Fy, -self.Cy/self.Fy],\n [0, 0, 1]]).cuda()\n return K_T\n\nclass MiniCam:\n def __init__(self, width, height, fovy, fovx, znear, zfar, world_view_transform, full_proj_transform):\n self.image_width = width\n self.image_height = height \n self.FoVy = fovy\n self.FoVx = fovx\n self.znear = znear\n self.zfar = zfar\n self.world_view_transform = world_view_transform\n self.full_proj_transform = full_proj_transform\n view_inv = torch.inverse(self.world_view_transform)\n self.camera_center = view_inv[3][:3]\n\ndef sample_cam(cam_l: Camera, cam_r: Camera):\n cam = copy.copy(cam_l)\n\n Rt = np.zeros((4, 4))\n Rt[:3, :3] = cam_l.R.transpose()\n Rt[:3, 3] = cam_l.T\n Rt[3, 3] = 1.0\n\n Rt2 = np.zeros((4, 4))\n Rt2[:3, :3] = cam_r.R.transpose()\n Rt2[:3, 3] = cam_r.T\n Rt2[3, 3] = 1.0\n\n C2W = np.linalg.inv(Rt)\n C2W2 = np.linalg.inv(Rt2)\n w = np.random.rand()\n pose_c2w_at_unseen = w * C2W + (1 - w) * C2W2\n Rt = np.linalg.inv(pose_c2w_at_unseen)\n cam.R = Rt[:3, :3]\n cam.T = Rt[:3, 3]\n\n cam.world_view_transform = torch.tensor(getWorld2View2(cam.R, cam.T, cam.trans, cam.scale)).transpose(0, 1).cuda()\n cam.projection_matrix = getProjectionMatrix(znear=cam.znear, zfar=cam.zfar, fovX=cam.FoVx, fovY=cam.FoVy).transpose(0,1).cuda()\n cam.full_proj_transform = (cam.world_view_transform.unsqueeze(0).bmm(cam.projection_matrix.unsqueeze(0))).squeeze(0)\n cam.camera_center = cam.world_view_transform.inverse()[3, :3]\n return cam\n" }, { "path": "field_construction/scene/colmap_loader.py", "content": "#\n# Copyright (C) 2023, Inria\n# GRAPHDECO research group, https://team.inria.fr/graphdeco\n# All rights reserved.\n#\n# This software is free for non-commercial, research and evaluation use\n# under the terms of the LICENSE.md file.\n#\n# For inquiries contact george.drettakis@inria.fr\n#\n\nimport collections\nimport struct\n\nimport numpy as np\n\nCameraModel = collections.namedtuple(\n \"CameraModel\", [\"model_id\", \"model_name\", \"num_params\"])\nCamera = collections.namedtuple(\n \"Camera\", [\"id\", \"model\", \"width\", \"height\", \"params\"])\nBaseImage = collections.namedtuple(\n \"Image\", [\"id\", \"qvec\", \"tvec\", \"camera_id\", \"name\", \"xys\", \"point3D_ids\"])\nPoint3D = collections.namedtuple(\n \"Point3D\", [\"id\", \"xyz\", \"rgb\", \"error\", \"image_ids\", \"point2D_idxs\"])\nCAMERA_MODELS = {\n CameraModel(model_id=0, model_name=\"SIMPLE_PINHOLE\", num_params=3),\n CameraModel(model_id=1, model_name=\"PINHOLE\", num_params=4),\n CameraModel(model_id=2, model_name=\"SIMPLE_RADIAL\", num_params=4),\n CameraModel(model_id=3, model_name=\"RADIAL\", num_params=5),\n CameraModel(model_id=4, model_name=\"OPENCV\", num_params=8),\n CameraModel(model_id=5, model_name=\"OPENCV_FISHEYE\", num_params=8),\n CameraModel(model_id=6, model_name=\"FULL_OPENCV\", num_params=12),\n CameraModel(model_id=7, model_name=\"FOV\", num_params=5),\n CameraModel(model_id=8, model_name=\"SIMPLE_RADIAL_FISHEYE\", num_params=4),\n CameraModel(model_id=9, model_name=\"RADIAL_FISHEYE\", num_params=5),\n CameraModel(model_id=10, model_name=\"THIN_PRISM_FISHEYE\", num_params=12)\n}\nCAMERA_MODEL_IDS = dict([(camera_model.model_id, camera_model)\n for camera_model in CAMERA_MODELS])\nCAMERA_MODEL_NAMES = dict([(camera_model.model_name, camera_model)\n for camera_model in CAMERA_MODELS])\n\n\ndef qvec2rotmat(qvec):\n return np.array([\n [1 - 2 * qvec[2]**2 - 2 * qvec[3]**2,\n 2 * qvec[1] * qvec[2] - 2 * qvec[0] * qvec[3],\n 2 * qvec[3] * qvec[1] + 2 * qvec[0] * qvec[2]],\n [2 * qvec[1] * qvec[2] + 2 * qvec[0] * qvec[3],\n 1 - 2 * qvec[1]**2 - 2 * qvec[3]**2,\n 2 * qvec[2] * qvec[3] - 2 * qvec[0] * qvec[1]],\n [2 * qvec[3] * qvec[1] - 2 * qvec[0] * qvec[2],\n 2 * qvec[2] * qvec[3] + 2 * qvec[0] * qvec[1],\n 1 - 2 * qvec[1]**2 - 2 * qvec[2]**2]])\n\ndef rotmat2qvec(R):\n Rxx, Ryx, Rzx, Rxy, Ryy, Rzy, Rxz, Ryz, Rzz = R.flat\n K = np.array([\n [Rxx - Ryy - Rzz, 0, 0, 0],\n [Ryx + Rxy, Ryy - Rxx - Rzz, 0, 0],\n [Rzx + Rxz, Rzy + Ryz, Rzz - Rxx - Ryy, 0],\n [Ryz - Rzy, Rzx - Rxz, Rxy - Ryx, Rxx + Ryy + Rzz]]) / 3.0\n eigvals, eigvecs = np.linalg.eigh(K)\n qvec = eigvecs[[3, 0, 1, 2], np.argmax(eigvals)]\n if qvec[0] < 0:\n qvec *= -1\n return qvec\n\nclass Image(BaseImage):\n def qvec2rotmat(self):\n return qvec2rotmat(self.qvec)\n\ndef read_next_bytes(fid, num_bytes, format_char_sequence, endian_character=\"<\"):\n \"\"\"Read and unpack the next bytes from a binary file.\n :param fid:\n :param num_bytes: Sum of combination of {2, 4, 8}, e.g. 2, 6, 16, 30, etc.\n :param format_char_sequence: List of {c, e, f, d, h, H, i, I, l, L, q, Q}.\n :param endian_character: Any of {@, =, <, >, !}\n :return: Tuple of read and unpacked values.\n \"\"\"\n data = fid.read(num_bytes)\n return struct.unpack(endian_character + format_char_sequence, data)\n\ndef read_points3D_text(path):\n \"\"\"\n see: src/base/reconstruction.cc\n void Reconstruction::ReadPoints3DText(const std::string& path)\n void Reconstruction::WritePoints3DText(const std::string& path)\n \"\"\"\n xyzs = None\n rgbs = None\n errors = None\n num_points = 0\n with open(path, \"r\") as fid:\n while True:\n line = fid.readline()\n if not line:\n break\n line = line.strip()\n if len(line) > 0 and line[0] != \"#\":\n num_points += 1\n\n\n xyzs = np.empty((num_points, 3))\n rgbs = np.empty((num_points, 3))\n errors = np.empty((num_points, 1))\n count = 0\n with open(path, \"r\") as fid:\n while True:\n line = fid.readline()\n if not line:\n break\n line = line.strip()\n if len(line) > 0 and line[0] != \"#\":\n elems = line.split()\n xyz = np.array(tuple(map(float, elems[1:4])))\n rgb = np.array(tuple(map(int, elems[4:7])))\n error = np.array(float(elems[7]))\n if error > 2.0:\n continue\n xyzs[count] = xyz\n rgbs[count] = rgb\n errors[count] = error\n count += 1\n xyzs = np.delete(xyzs, np.arange(count,num_points),axis=0)\n rgbs = np.delete(rgbs, np.arange(count,num_points),axis=0)\n errors = np.delete(errors, np.arange(count,num_points),axis=0)\n return xyzs, rgbs, errors\n\ndef read_points3D_binary(path_to_model_file):\n \"\"\"\n see: src/base/reconstruction.cc\n void Reconstruction::ReadPoints3DBinary(const std::string& path)\n void Reconstruction::WritePoints3DBinary(const std::string& path)\n \"\"\"\n\n\n with open(path_to_model_file, \"rb\") as fid:\n num_points = read_next_bytes(fid, 8, \"Q\")[0]\n\n xyzs = np.empty((num_points, 3))\n rgbs = np.empty((num_points, 3))\n errors = np.empty((num_points, 1))\n count = 0\n for p_id in range(num_points):\n binary_point_line_properties = read_next_bytes(\n fid, num_bytes=43, format_char_sequence=\"QdddBBBd\")\n xyz = np.array(binary_point_line_properties[1:4])\n rgb = np.array(binary_point_line_properties[4:7])\n error = np.array(binary_point_line_properties[7])\n track_length = read_next_bytes(\n fid, num_bytes=8, format_char_sequence=\"Q\")[0]\n track_elems = read_next_bytes(\n fid, num_bytes=8*track_length,\n format_char_sequence=\"ii\"*track_length)\n if error > 2.0 or track_length < 3:\n continue\n xyzs[count] = xyz\n rgbs[count] = rgb\n errors[count] = error\n count += 1\n xyzs = np.delete(xyzs, np.arange(count,num_points),axis=0)\n rgbs = np.delete(rgbs, np.arange(count,num_points),axis=0)\n errors = np.delete(errors, np.arange(count,num_points),axis=0)\n return xyzs, rgbs, errors\n\ndef read_intrinsics_text(path):\n \"\"\"\n Taken from https://github.com/colmap/colmap/blob/dev/scripts/python/read_write_model.py\n \"\"\"\n cameras = {}\n with open(path, \"r\") as fid:\n while True:\n line = fid.readline()\n if not line:\n break\n line = line.strip()\n if len(line) > 0 and line[0] != \"#\":\n elems = line.split()\n camera_id = int(elems[0])\n model = elems[1]\n assert model == \"PINHOLE\", \"While the loader support other types, the rest of the code assumes PINHOLE\"\n width = int(elems[2])\n height = int(elems[3])\n params = np.array(tuple(map(float, elems[4:])))\n cameras[camera_id] = Camera(id=camera_id, model=model,\n width=width, height=height,\n params=params)\n return cameras\n\ndef read_extrinsics_binary(path_to_model_file):\n \"\"\"\n see: src/base/reconstruction.cc\n void Reconstruction::ReadImagesBinary(const std::string& path)\n void Reconstruction::WriteImagesBinary(const std::string& path)\n \"\"\"\n images = {}\n with open(path_to_model_file, \"rb\") as fid:\n num_reg_images = read_next_bytes(fid, 8, \"Q\")[0]\n for _ in range(num_reg_images):\n binary_image_properties = read_next_bytes(\n fid, num_bytes=64, format_char_sequence=\"idddddddi\")\n image_id = binary_image_properties[0]\n qvec = np.array(binary_image_properties[1:5])\n tvec = np.array(binary_image_properties[5:8])\n camera_id = binary_image_properties[8]\n image_name = \"\"\n current_char = read_next_bytes(fid, 1, \"c\")[0]\n while current_char != b\"\\x00\": # look for the ASCII 0 entry\n image_name += current_char.decode(\"utf-8\")\n current_char = read_next_bytes(fid, 1, \"c\")[0]\n num_points2D = read_next_bytes(fid, num_bytes=8,\n format_char_sequence=\"Q\")[0]\n x_y_id_s = read_next_bytes(fid, num_bytes=24*num_points2D,\n format_char_sequence=\"ddq\"*num_points2D)\n xys = np.column_stack([tuple(map(float, x_y_id_s[0::3])),\n tuple(map(float, x_y_id_s[1::3]))])\n point3D_ids = np.array(tuple(map(int, x_y_id_s[2::3])))\n images[image_id] = Image(\n id=image_id, qvec=qvec, tvec=tvec,\n camera_id=camera_id, name=image_name,\n xys=xys, point3D_ids=point3D_ids)\n return images\n\n\ndef read_intrinsics_binary(path_to_model_file):\n \"\"\"\n see: src/base/reconstruction.cc\n void Reconstruction::WriteCamerasBinary(const std::string& path)\n void Reconstruction::ReadCamerasBinary(const std::string& path)\n \"\"\"\n cameras = {}\n with open(path_to_model_file, \"rb\") as fid:\n num_cameras = read_next_bytes(fid, 8, \"Q\")[0]\n for _ in range(num_cameras):\n camera_properties = read_next_bytes(\n fid, num_bytes=24, format_char_sequence=\"iiQQ\")\n camera_id = camera_properties[0]\n model_id = camera_properties[1]\n model_name = CAMERA_MODEL_IDS[camera_properties[1]].model_name\n width = camera_properties[2]\n height = camera_properties[3]\n num_params = CAMERA_MODEL_IDS[model_id].num_params\n params = read_next_bytes(fid, num_bytes=8*num_params,\n format_char_sequence=\"d\"*num_params)\n cameras[camera_id] = Camera(id=camera_id,\n model=model_name,\n width=width,\n height=height,\n params=np.array(params))\n assert len(cameras) == num_cameras\n return cameras\n\n\ndef read_extrinsics_text(path):\n \"\"\"\n Taken from https://github.com/colmap/colmap/blob/dev/scripts/python/read_write_model.py\n \"\"\"\n images = {}\n with open(path, \"r\") as fid:\n while True:\n line = fid.readline()\n if not line:\n break\n line = line.strip()\n if len(line) > 0 and line[0] != \"#\":\n elems = line.split()\n image_id = int(elems[0])\n qvec = np.array(tuple(map(float, elems[1:5])))\n tvec = np.array(tuple(map(float, elems[5:8])))\n camera_id = int(elems[8])\n image_name = elems[9]\n elems = fid.readline().split()\n xys = np.column_stack([tuple(map(float, elems[0::3])),\n tuple(map(float, elems[1::3]))])\n point3D_ids = np.array(tuple(map(int, elems[2::3])))\n images[image_id] = Image(\n id=image_id, qvec=qvec, tvec=tvec,\n camera_id=camera_id, name=image_name,\n xys=xys, point3D_ids=point3D_ids)\n return images\n\n\ndef read_colmap_bin_array(path):\n \"\"\"\n Taken from https://github.com/colmap/colmap/blob/dev/scripts/python/read_dense.py\n\n :param path: path to the colmap binary file.\n :return: nd array with the floating point values in the value\n \"\"\"\n with open(path, \"rb\") as fid:\n width, height, channels = np.genfromtxt(fid, delimiter=\"&\", max_rows=1,\n usecols=(0, 1, 2), dtype=int)\n fid.seek(0)\n num_delimiter = 0\n byte = fid.read(1)\n while True:\n if byte == b\"&\":\n num_delimiter += 1\n if num_delimiter >= 3:\n break\n byte = fid.read(1)\n array = np.fromfile(fid, np.float32)\n array = array.reshape((width, height, channels), order=\"F\")\n return np.transpose(array, (1, 0, 2)).squeeze()\n\ndef write_cameras_text(cameras, path):\n \"\"\"\n see: src/colmap/scene/reconstruction.cc\n void Reconstruction::WriteCamerasText(const std::string& path)\n void Reconstruction::ReadCamerasText(const std::string& path)\n \"\"\"\n HEADER = (\n \"# Camera list with one line of data per camera:\\n\"\n + \"# CAMERA_ID, MODEL, WIDTH, HEIGHT, PARAMS[]\\n\"\n + \"# Number of cameras: {}\\n\".format(len(cameras))\n )\n with open(path, \"w\") as fid:\n fid.write(HEADER)\n for _, cam in cameras.items():\n to_write = [cam.id, cam.model, cam.width, cam.height, *cam.params]\n line = \" \".join([str(elem) for elem in to_write])\n fid.write(line + \"\\n\")\n\n\ndef write_next_bytes(fid, data, format_char_sequence, endian_character=\"<\"):\n \"\"\"pack and write to a binary file.\n :param fid:\n :param data: data to send, if multiple elements are sent at the same time,\n they should be encapsuled either in a list or a tuple\n :param format_char_sequence: List of {c, e, f, d, h, H, i, I, l, L, q, Q}.\n should be the same length as the data list or tuple\n :param endian_character: Any of {@, =, <, >, !}\n \"\"\"\n if isinstance(data, (list, tuple)):\n bytes = struct.pack(endian_character + format_char_sequence, *data)\n else:\n bytes = struct.pack(endian_character + format_char_sequence, data)\n fid.write(bytes)\n\n\ndef write_cameras_binary(cameras, path_to_model_file):\n \"\"\"\n see: src/colmap/scene/reconstruction.cc\n void Reconstruction::WriteCamerasBinary(const std::string& path)\n void Reconstruction::ReadCamerasBinary(const std::string& path)\n \"\"\"\n with open(path_to_model_file, \"wb\") as fid:\n write_next_bytes(fid, len(cameras), \"Q\")\n for _, cam in cameras.items():\n model_id = CAMERA_MODEL_NAMES[cam.model].model_id\n camera_properties = [cam.id, model_id, cam.width, cam.height]\n write_next_bytes(fid, camera_properties, \"iiQQ\")\n for p in cam.params:\n write_next_bytes(fid, float(p), \"d\")\n return cameras\n\n\ndef write_images_text(images, path):\n \"\"\"\n see: src/colmap/scene/reconstruction.cc\n void Reconstruction::ReadImagesText(const std::string& path)\n void Reconstruction::WriteImagesText(const std::string& path)\n \"\"\"\n if len(images) == 0:\n mean_observations = 0\n else:\n mean_observations = sum(\n (len(img.point3D_ids) for _, img in images.items())\n ) / len(images)\n HEADER = (\n \"# Image list with two lines of data per image:\\n\"\n + \"# IMAGE_ID, QW, QX, QY, QZ, TX, TY, TZ, CAMERA_ID, NAME\\n\"\n + \"# POINTS2D[] as (X, Y, POINT3D_ID)\\n\"\n + \"# Number of images: {}, mean observations per image: {}\\n\".format(\n len(images), mean_observations\n )\n )\n\n with open(path, \"w\") as fid:\n fid.write(HEADER)\n for _, img in images.items():\n image_header = [\n img.id,\n *img.qvec,\n *img.tvec,\n img.camera_id,\n img.name,\n ]\n first_line = \" \".join(map(str, image_header))\n fid.write(first_line + \"\\n\")\n\n points_strings = []\n for xy, point3D_id in zip(img.xys, img.point3D_ids):\n points_strings.append(\" \".join(map(str, [*xy, point3D_id])))\n fid.write(\" \".join(points_strings) + \"\\n\")\n\n\ndef write_images_binary(images, path_to_model_file):\n \"\"\"\n see: src/colmap/scene/reconstruction.cc\n void Reconstruction::ReadImagesBinary(const std::string& path)\n void Reconstruction::WriteImagesBinary(const std::string& path)\n \"\"\"\n with open(path_to_model_file, \"wb\") as fid:\n write_next_bytes(fid, len(images), \"Q\")\n for _, img in images.items():\n write_next_bytes(fid, img.id, \"i\")\n write_next_bytes(fid, img.qvec.tolist(), \"dddd\")\n write_next_bytes(fid, img.tvec.tolist(), \"ddd\")\n write_next_bytes(fid, img.camera_id, \"i\")\n for char in img.name:\n write_next_bytes(fid, char.encode(\"utf-8\"), \"c\")\n write_next_bytes(fid, b\"\\x00\", \"c\")\n write_next_bytes(fid, len(img.point3D_ids), \"Q\")\n for xy, p3d_id in zip(img.xys, img.point3D_ids):\n write_next_bytes(fid, [*xy, p3d_id], \"ddq\")\n\n\ndef write_points3D_text(points3D, path):\n \"\"\"\n see: src/colmap/scene/reconstruction.cc\n void Reconstruction::ReadPoints3DText(const std::string& path)\n void Reconstruction::WritePoints3DText(const std::string& path)\n \"\"\"\n if len(points3D) == 0:\n mean_track_length = 0\n else:\n mean_track_length = sum(\n (len(pt.image_ids) for _, pt in points3D.items())\n ) / len(points3D)\n HEADER = (\n \"# 3D point list with one line of data per point:\\n\"\n + \"# POINT3D_ID, X, Y, Z, R, G, B, ERROR, TRACK[] as (IMAGE_ID, POINT2D_IDX)\\n\"\n + \"# Number of points: {}, mean track length: {}\\n\".format(\n len(points3D), mean_track_length\n )\n )\n\n with open(path, \"w\") as fid:\n fid.write(HEADER)\n for _, pt in points3D.items():\n point_header = [pt.id, *pt.xyz, *pt.rgb, pt.error]\n fid.write(\" \".join(map(str, point_header)) + \" \")\n track_strings = []\n for image_id, point2D in zip(pt.image_ids, pt.point2D_idxs):\n track_strings.append(\" \".join(map(str, [image_id, point2D])))\n fid.write(\" \".join(track_strings) + \"\\n\")\n\n\ndef write_points3D_binary(points3D, path_to_model_file):\n \"\"\"\n see: src/colmap/scene/reconstruction.cc\n void Reconstruction::ReadPoints3DBinary(const std::string& path)\n void Reconstruction::WritePoints3DBinary(const std::string& path)\n \"\"\"\n with open(path_to_model_file, \"wb\") as fid:\n write_next_bytes(fid, len(points3D), \"Q\")\n for _, pt in points3D.items():\n write_next_bytes(fid, pt.id, \"Q\")\n write_next_bytes(fid, pt.xyz.tolist(), \"ddd\")\n write_next_bytes(fid, pt.rgb.tolist(), \"BBB\")\n write_next_bytes(fid, pt.error, \"d\")\n track_length = pt.image_ids.shape[0]\n write_next_bytes(fid, track_length, \"Q\")\n for image_id, point2D_id in zip(pt.image_ids, pt.point2D_idxs):\n write_next_bytes(fid, [image_id, point2D_id], \"ii\")" }, { "path": "field_construction/scene/dataset_readers.py", "content": "#\n# Copyright (C) 2023, Inria\n# GRAPHDECO research group, https://team.inria.fr/graphdeco\n# All rights reserved.\n#\n# This software is free for non-commercial, research and evaluation use\n# under the terms of the LICENSE.md file.\n#\n# For inquiries contact george.drettakis@inria.fr\n#\n\nimport json\nimport os\nimport sys\nfrom pathlib import Path\nfrom typing import NamedTuple\n\nimport numpy as np\nimport open3d as o3d\nfrom PIL import Image\nfrom plyfile import PlyData, PlyElement\nfrom scipy.spatial.transform import Rotation as R\n\nfrom field_construction.scene.colmap_loader import (Camera, Image, qvec2rotmat,\n read_extrinsics_binary,\n read_extrinsics_text,\n read_intrinsics_binary,\n read_intrinsics_text,\n read_points3D_binary,\n read_points3D_text)\nfrom field_construction.scene.gaussian_model import BasicPointCloud\nfrom field_construction.utils.graphics_utils import (focal2fov, fov2focal,\n getWorld2View2)\nfrom field_construction.utils.sh_utils import SH2RGB\n\n\nclass CameraInfo(NamedTuple):\n uid: int\n global_id: int\n R: np.array\n T: np.array\n FovY: np.array\n FovX: np.array\n image_path: str\n image_name: str\n width: int\n height: int\n fx: float\n fy: float\n\nclass SceneInfo(NamedTuple):\n point_cloud: BasicPointCloud\n train_cameras: list\n test_cameras: list\n nerf_normalization: dict\n ply_path: str\n\ndef getNerfppNorm(cam_info):\n def get_center_and_diag(cam_centers):\n cam_centers = np.hstack(cam_centers)\n avg_cam_center = np.mean(cam_centers, axis=1, keepdims=True)\n center = avg_cam_center\n dist = np.linalg.norm(cam_centers - center, axis=0, keepdims=True)\n diagonal = np.max(dist)\n return center.flatten(), diagonal\n\n cam_centers = []\n\n for cam in cam_info:\n W2C = getWorld2View2(cam.R, cam.T)\n C2W = np.linalg.inv(W2C)\n cam_centers.append(C2W[:3, 3:4])\n\n center, diagonal = get_center_and_diag(cam_centers)\n radius = diagonal * 1.1\n\n translate = -center\n\n return {\"translate\": translate, \"radius\": radius}\n\ndef load_poses(pose_path, num):\n poses = []\n with open(pose_path, \"r\") as f:\n lines = f.readlines()\n for i in range(num):\n line = lines[i]\n c2w = np.array(list(map(float, line.split()))).reshape(4, 4)\n c2w[:3,3] = c2w[:3,3] * 10.0\n w2c = np.linalg.inv(c2w)\n w2c = w2c\n poses.append(w2c)\n poses = np.stack(poses, axis=0)\n return poses\n\ndef readColmapCameras(cam_extrinsics, cam_intrinsics, images_folder):\n cam_infos = []\n for idx, key in enumerate(cam_extrinsics):\n sys.stdout.write('\\r')\n # the exact output you're looking for:\n sys.stdout.write(\"Reading camera {}/{}\".format(idx+1, len(cam_extrinsics)))\n sys.stdout.flush()\n\n extr = cam_extrinsics[key]\n intr = cam_intrinsics[extr.camera_id]\n height = intr.height\n width = intr.width\n\n uid = intr.id\n R = np.transpose(qvec2rotmat(extr.qvec))\n T = np.array(extr.tvec)\n\n if intr.model==\"SIMPLE_PINHOLE\":\n focal_length_x = intr.params[0]\n FovY = focal2fov(focal_length_x, height)\n FovX = focal2fov(focal_length_x, width)\n elif intr.model==\"PINHOLE\":\n focal_length_x = intr.params[0]\n focal_length_y = intr.params[1]\n FovY = focal2fov(focal_length_y, height)\n FovX = focal2fov(focal_length_x, width)\n else:\n assert False, \"Colmap camera model not handled: only undistorted datasets (PINHOLE or SIMPLE_PINHOLE cameras) supported!\"\n\n image_path = os.path.join(images_folder, os.path.basename(extr.name))\n image_name = os.path.basename(image_path).split(\".\")[0]\n\n cam_info = CameraInfo(uid=uid, global_id=idx, R=R, T=T, FovY=FovY, FovX=FovX,\n image_path=image_path, image_name=image_name, \n width=width, height=height, fx=focal_length_x, fy=focal_length_y)\n cam_infos.append(cam_info)\n sys.stdout.write('\\n')\n return cam_infos\n\ndef fetchPly_o3d(path):\n pcd = o3d.io.read_point_cloud(path)\n positions = np.asarray(pcd.points)\n colors = np.asarray(pcd.colors)\n normals = np.zeros_like(positions)\n return BasicPointCloud(points=positions, colors=colors, normals=normals)\n\ndef fetchPly(path):\n plydata = PlyData.read(path)\n vertices = plydata['vertex']\n positions = np.vstack([vertices['x'], vertices['y'], vertices['z']]).T\n colors = np.vstack([vertices['red'], vertices['green'], vertices['blue']]).T / 255.0\n normals = np.vstack([vertices['nx'], vertices['ny'], vertices['nz']]).T\n return BasicPointCloud(points=positions, colors=colors, normals=normals)\n\ndef storePly(path, xyz, rgb):\n # Define the dtype for the structured array\n dtype = [('x', 'f4'), ('y', 'f4'), ('z', 'f4'),\n ('nx', 'f4'), ('ny', 'f4'), ('nz', 'f4'),\n ('red', 'u1'), ('green', 'u1'), ('blue', 'u1')]\n \n normals = np.zeros_like(xyz)\n\n elements = np.empty(xyz.shape[0], dtype=dtype)\n attributes = np.concatenate((xyz, normals, rgb), axis=1)\n elements[:] = list(map(tuple, attributes))\n\n # Create the PlyData object and write to file\n vertex_element = PlyElement.describe(elements, 'vertex')\n ply_data = PlyData([vertex_element])\n ply_data.write(path)\n\ndef readColmapSceneInfo(path, images, eval, llffhold=10, loaded_iter=None):\n try:\n cameras_extrinsic_file = os.path.join(path, \"sparse/0\", \"images.txt\")\n cameras_intrinsic_file = os.path.join(path, \"sparse/0\", \"cameras.txt\")\n cam_extrinsics = read_extrinsics_text(cameras_extrinsic_file)\n cam_intrinsics = read_intrinsics_text(cameras_intrinsic_file)\n except:\n cameras_extrinsic_file = os.path.join(path, \"sparse/0\", \"images.bin\")\n cameras_intrinsic_file = os.path.join(path, \"sparse/0\", \"cameras.bin\")\n cam_extrinsics = read_extrinsics_binary(cameras_extrinsic_file)\n cam_intrinsics = read_intrinsics_binary(cameras_intrinsic_file)\n \n reading_dir = \"input\" if images == None else images\n cam_infos_unsorted = readColmapCameras(cam_extrinsics=cam_extrinsics, cam_intrinsics=cam_intrinsics, images_folder=os.path.join(path, reading_dir))\n # cam_infos = sorted(cam_infos_unsorted.copy(), key = lambda x : int(x.image_name.split('_')[-1]))\n cam_infos = sorted(cam_infos_unsorted.copy(), key = lambda x : x.image_name)\n \n js_file = f\"{path}/split.json\"\n train_list = None\n test_list = None\n if os.path.exists(js_file):\n with open(js_file) as file:\n meta = json.load(file)\n train_list = meta[\"train\"]\n test_list = meta[\"test\"]\n print(f\"train_list {len(train_list)}, test_list {len(test_list)}\")\n\n if train_list is not None:\n train_cam_infos = [c for idx, c in enumerate(cam_infos) if c.image_name in train_list]\n test_cam_infos = [c for idx, c in enumerate(cam_infos) if c.image_name in test_list]\n print(f\"train_cam_infos {len(train_cam_infos)}, test_cam_infos {len(test_cam_infos)}\")\n elif eval:\n train_cam_infos = [c for idx, c in enumerate(cam_infos) if idx % llffhold != 0]\n test_cam_infos = [c for idx, c in enumerate(cam_infos) if idx % llffhold == 0]\n print(\"train_cam_infos: \", len(train_cam_infos))\n print(\"test_cam_infos: \", len(test_cam_infos))\n else:\n train_cam_infos = cam_infos\n test_cam_infos = []\n print(\"only train_cam_infos: \", len(train_cam_infos))\n nerf_normalization = getNerfppNorm(train_cam_infos)\n\n ply_path = os.path.join(path, \"sparse/0/points3D.ply\")\n bin_path = os.path.join(path, \"sparse/0/points3D.bin\")\n txt_path = os.path.join(path, \"sparse/0/points3D.txt\")\n if not loaded_iter:\n if not os.path.exists(ply_path):\n print(\"Converting point3d.bin to .ply, will happen only the first time you open the scene.\")\n try:\n xyz, rgb, _ = read_points3D_binary(bin_path)\n print(f\"xyz {xyz.shape}\")\n except:\n xyz, rgb, _ = read_points3D_text(txt_path)\n storePly(ply_path, xyz, rgb)\n try:\n pcd = fetchPly(ply_path)\n except:\n pcd = None\n else:\n pcd = None\n\n scene_info = SceneInfo(point_cloud=pcd,\n train_cameras=train_cam_infos,\n test_cameras=test_cam_infos,\n nerf_normalization=nerf_normalization,\n ply_path=ply_path)\n return scene_info\n\ndef read_camera_npz(camera_dir):\n images = {}\n cameras = {}\n for file_name in sorted(os.listdir(camera_dir)):\n if not file_name.endswith(\".npz\"):\n continue\n \n file_path = os.path.join(camera_dir, file_name)\n data = np.load(file_path)\n pose = data[\"pose\"]\n intrinsics = data[\"intrinsics\"]\n \n R_c2w = pose[:3, :3]\n t_c2w = pose[:3, 3]\n R_w2c = R_c2w.T\n t_w2c = - R_w2c @ t_c2w\n \n rotation = R.from_matrix(R_w2c)\n quat = rotation.as_quat() \n qvec = np.array([quat[3], quat[0], quat[1], quat[2]])\n tvec = t_w2c\n \n fx = intrinsics[0, 0]\n fy = intrinsics[1, 1]\n cx = intrinsics[0, 2]\n cy = intrinsics[1, 2]\n \n model_name = 'PINHOLE'\n params = np.array([fx, fy, cx, cy], dtype=np.float64)\n \n width = int(cx * 2)\n height = int(cy * 2)\n \n try:\n image_id = int(os.path.splitext(file_name)[0])\n except:\n image_id = int(os.path.splitext(file_name.split(\"_\")[1])[0])\n\n camera_id = image_id\n \n cameras[camera_id] = Camera(\n id=camera_id,\n model=model_name,\n width=width,\n height=height,\n params=params\n )\n \n image_name = os.path.splitext(file_name)[0] + \".png\"\n images[image_id] = Image(\n id=image_id,\n qvec=qvec,\n tvec=tvec,\n camera_id=camera_id,\n name=image_name,\n xys=np.zeros((0, 2)), \n point3D_ids=np.zeros(0, dtype=int)\n )\n \n return images, cameras\n \n\ndef readCUT3RInfo(path, images, eval, llffhold=10, loaded_iter=None):\n cameras_file = os.path.join(path, \"camera\")\n extrinsics, intrinsics = read_camera_npz(cameras_file)\n reading_dir = \"input\"\n cam_infos_unsorted = readColmapCameras(cam_extrinsics=extrinsics, cam_intrinsics=intrinsics, images_folder=os.path.join(path, reading_dir))\n # cam_infos = sorted(cam_infos_unsorted.copy(), key = lambda x : int(x.image_name.split('_')[-1]))\n cam_infos = sorted(cam_infos_unsorted.copy(), key = lambda x : x.image_name)\n \n js_file = f\"{path}/split.json\"\n train_list = None\n test_list = None\n if os.path.exists(js_file):\n with open(js_file) as file:\n meta = json.load(file)\n train_list = meta[\"train\"]\n test_list = meta[\"test\"]\n print(f\"train_list {len(train_list)}, test_list {len(test_list)}\")\n\n if train_list is not None:\n train_cam_infos = [c for idx, c in enumerate(cam_infos) if c.image_name in train_list]\n test_cam_infos = [c for idx, c in enumerate(cam_infos) if c.image_name in test_list]\n print(f\"train_cam_infos {len(train_cam_infos)}, test_cam_infos {len(test_cam_infos)}\")\n elif eval:\n train_cam_infos = [c for idx, c in enumerate(cam_infos) if idx % llffhold != 0]\n test_cam_infos = [c for idx, c in enumerate(cam_infos) if idx % llffhold == 0]\n print(\"train_cam_infos: \", len(train_cam_infos))\n print(\"test_cam_infos: \", len(test_cam_infos))\n else:\n train_cam_infos = cam_infos\n test_cam_infos = []\n print(\"only train_cam_infos: \", len(train_cam_infos))\n nerf_normalization = getNerfppNorm(train_cam_infos)\n\n ply_path = os.path.join(path, \"points3D.ply\")\n bin_path = os.path.join(path, \"points3D.bin\")\n txt_path = os.path.join(path, \"points3D.txt\")\n if not loaded_iter:\n if not os.path.exists(ply_path):\n print(\"Converting point3d.bin to .ply, will happen only the first time you open the scene.\")\n try:\n xyz, rgb, _ = read_points3D_binary(bin_path)\n print(f\"xyz {xyz.shape}\")\n except:\n xyz, rgb, _ = read_points3D_text(txt_path)\n storePly(ply_path, xyz, rgb)\n try:\n pcd = fetchPly_o3d(ply_path)\n except:\n pcd = None\n else:\n pcd = None\n\n scene_info = SceneInfo(point_cloud=pcd,\n train_cameras=train_cam_infos,\n test_cameras=test_cam_infos,\n nerf_normalization=nerf_normalization,\n ply_path=ply_path)\n return scene_info\n\n\n\ndef readCamerasFromTransforms(path, transformsfile, white_background, extension=\".png\"):\n cam_infos = []\n\n with open(os.path.join(path, transformsfile)) as json_file:\n contents = json.load(json_file)\n fovx = contents[\"camera_angle_x\"]\n\n frames = contents[\"frames\"]\n for idx, frame in enumerate(frames):\n cam_name = os.path.join(path, frame[\"file_path\"] + extension)\n\n # NeRF 'transform_matrix' is a camera-to-world transform\n c2w = np.array(frame[\"transform_matrix\"])\n # change from OpenGL/Blender camera axes (Y up, Z back) to COLMAP (Y down, Z forward)\n c2w[:3, 1:3] *= -1\n\n # get the world-to-camera transform and set R, T\n w2c = np.linalg.inv(c2w)\n R = np.transpose(w2c[:3,:3]) # R is stored transposed due to 'glm' in CUDA code\n T = w2c[:3, 3]\n\n image_path = os.path.join(path, cam_name)\n image_name = Path(cam_name).stem\n image = Image.open(image_path)\n\n im_data = np.array(image.convert(\"RGBA\"))\n\n bg = np.array([1,1,1]) if white_background else np.array([0, 0, 0])\n\n norm_data = im_data / 255.0\n arr = norm_data[:,:,:3] * norm_data[:, :, 3:4] + bg * (1 - norm_data[:, :, 3:4])\n image = Image.fromarray(np.array(arr*255.0, dtype=np.byte), \"RGB\")\n\n fovy = focal2fov(fov2focal(fovx, image.size[0]), image.size[1])\n FovY = fovy \n FovX = fovx\n\n cam_infos.append(CameraInfo(uid=idx, global_id=idx, R=R, T=T, FovY=FovY, FovX=FovX, image=image,\n image_path=image_path, image_name=image_name, width=image.size[0], height=image.size[1]))\n \n return cam_infos\n\ndef readNerfSyntheticInfo(path, white_background, eval, extension=\".png\"):\n print(\"Reading Training Transforms\")\n train_cam_infos = readCamerasFromTransforms(path, \"transforms_train.json\", white_background, extension)\n print(\"Reading Test Transforms\")\n test_cam_infos = readCamerasFromTransforms(path, \"transforms_test.json\", white_background, extension)\n if not eval:\n train_cam_infos.extend(test_cam_infos)\n test_cam_infos = []\n\n nerf_normalization = getNerfppNorm(train_cam_infos)\n\n ply_path = os.path.join(path, \"points3d.ply\")\n if not os.path.exists(ply_path):\n # Since this data set has no colmap data, we start with random points\n num_pts = 100_000\n print(f\"Generating random point cloud ({num_pts})...\")\n \n # We create random points inside the bounds of the synthetic Blender scenes\n xyz = np.random.random((num_pts, 3)) * 2.6 - 1.3\n shs = np.random.random((num_pts, 3)) / 255.0\n pcd = BasicPointCloud(points=xyz, colors=SH2RGB(shs), normals=np.zeros((num_pts, 3)))\n\n storePly(ply_path, xyz, SH2RGB(shs) * 255)\n try:\n pcd = fetchPly(ply_path)\n except:\n pcd = None\n\n scene_info = SceneInfo(point_cloud=pcd,\n train_cameras=train_cam_infos,\n test_cameras=test_cam_infos,\n nerf_normalization=nerf_normalization,\n ply_path=ply_path)\n return scene_info\n\nsceneLoadTypeCallbacks = {\n \"Colmap\": readColmapSceneInfo,\n \"Blender\" : readNerfSyntheticInfo,\n \"CUT3R\": readCUT3RInfo\n}" }, { "path": "field_construction/scene/gaussian_model.py", "content": "#\n# Copyright (C) 2023, Inria\n# GRAPHDECO research group, https://team.inria.fr/graphdeco\n# All rights reserved.\n#\n# This software is free for non-commercial, research and evaluation use\n# under the terms of the LICENSE.md file.\n#\n# For inquiries contact george.drettakis@inria.fr\n#\n\nimport os\n\nimport numpy as np\nimport torch\nfrom plyfile import PlyData, PlyElement\nfrom pytorch3d.transforms import quaternion_to_matrix\nfrom simple_knn._C import distCUDA2\nfrom torch import nn\n\nfrom field_construction.scene.per_point_adam import PerPointAdam\nfrom field_construction.utils.general_utils import (build_rotation,\n build_scaling,\n build_scaling_rotation,\n get_expon_lr_func,\n inverse_sigmoid,\n strip_symmetric)\nfrom field_construction.utils.graphics_utils import BasicPointCloud\nfrom field_construction.utils.pose_utils import get_tensor_from_camera\nfrom field_construction.utils.sh_utils import RGB2SH\nfrom field_construction.utils.system_utils import mkdir_p\n\n\ndef dilate(bin_img, ksize=5):\n pad = (ksize - 1) // 2\n bin_img = torch.nn.functional.pad(bin_img, pad=[pad, pad, pad, pad], mode='reflect')\n out = torch.nn.functional.max_pool2d(bin_img, kernel_size=ksize, stride=1, padding=0)\n return out\n\ndef erode(bin_img, ksize=5):\n out = 1 - dilate(1 - bin_img, ksize)\n return out\n\nclass GaussianModel:\n\n def setup_functions(self):\n def build_covariance_from_scaling_rotation(scaling, scaling_modifier, rotation):\n L = build_scaling_rotation(scaling_modifier * scaling, rotation)\n actual_covariance = L @ L.transpose(1, 2)\n symm = strip_symmetric(actual_covariance)\n return symm\n \n self.scaling_activation = torch.exp\n self.scaling_inverse_activation = torch.log\n\n self.covariance_activation = build_covariance_from_scaling_rotation\n\n self.opacity_activation = torch.sigmoid\n self.inverse_opacity_activation = inverse_sigmoid\n\n self.rotation_activation = torch.nn.functional.normalize\n\n def __init__(self, sh_degree : int):\n self.active_sh_degree = 0\n self.max_sh_degree = sh_degree \n self._xyz = torch.empty(0)\n self._knn_f = torch.empty(0)\n self._features_dc = torch.empty(0)\n self._features_rest = torch.empty(0)\n self._scaling = torch.empty(0)\n self._rotation = torch.empty(0)\n self._opacity = torch.empty(0)\n self._language_feature = torch.empty(0)\n self._instance_feature=torch.empty(0)\n self.max_radii2D = torch.empty(0)\n self.max_weight = torch.empty(0)\n self.xyz_gradient_accum = torch.empty(0)\n self.xyz_gradient_accum_abs = torch.empty(0)\n self.denom = torch.empty(0)\n self.denom_abs = torch.empty(0)\n self.optimizer = None\n self.cam_optimizer = None\n self.percent_dense = 0\n self.spatial_lr_scale = 0\n self.knn_dists = None\n self.knn_idx = None\n self.setup_functions()\n self.use_app = False\n\n def capture(self, include_feature=False):\n if include_feature:\n return (\n self.active_sh_degree,\n self._xyz,\n self._knn_f,\n self._features_dc,\n self._features_rest,\n self._scaling,\n self._rotation,\n self._opacity,\n self._language_feature,\n self._instance_feature,\n self.max_radii2D,\n self.max_weight,\n self.xyz_gradient_accum,\n self.xyz_gradient_accum_abs,\n self.denom,\n self.denom_abs,\n self.optimizer.state_dict(),\n self.cam_optimizer.state_dict(),\n self.spatial_lr_scale,\n self.P\n )\n else:\n return (\n self.active_sh_degree,\n self._xyz,\n self._knn_f,\n self._features_dc,\n self._features_rest,\n self._scaling,\n self._rotation,\n self._opacity,\n self.max_radii2D,\n self.max_weight,\n self.xyz_gradient_accum,\n self.xyz_gradient_accum_abs,\n self.denom,\n self.denom_abs,\n self.optimizer.state_dict(),\n self.cam_optimizer.state_dict(),\n self.spatial_lr_scale,\n self.P\n )\n \n def restore(self, model_args, training_args, mode='train'):\n # Ckpt with training feature (20 arguments)\n if len(model_args) == 20:\n (self.active_sh_degree, \n self._xyz, \n self._knn_f,\n self._features_dc, \n self._features_rest,\n self._scaling, \n self._rotation, \n self._opacity,\n self._language_feature, # Added training feature: language feature\n self._instance_feature, # Added training feature: instance feature\n self.max_radii2D, \n self.max_weight,\n xyz_gradient_accum, \n xyz_gradient_accum_abs,\n denom,\n denom_abs,\n opt_dict, \n cam_opt_dict,\n self.spatial_lr_scale,\n self.P\n ) = model_args\n # Ckpt without training feature (18 arguments)\n elif len(model_args) == 18:\n (self.active_sh_degree, \n self._xyz, \n self._knn_f,\n self._features_dc, \n self._features_rest,\n self._scaling, \n self._rotation, \n self._opacity,\n self.max_radii2D, \n self.max_weight,\n xyz_gradient_accum, \n xyz_gradient_accum_abs,\n denom,\n denom_abs,\n opt_dict, \n cam_opt_dict,\n self.spatial_lr_scale,\n self.P\n ) = model_args\n if mode == 'train':\n if isinstance(self.optimizer, PerPointAdam):\n self.training_setup_pp(training_args)\n else:\n self.training_setup(training_args)\n self.xyz_gradient_accum = xyz_gradient_accum\n self.xyz_gradient_accum_abs = xyz_gradient_accum_abs\n self.denom = denom\n self.denom_abs = denom_abs\n self.optimizer.load_state_dict(opt_dict)\n self.cam_optimizer.load_state_dict(cam_opt_dict)\n\n @property\n def get_scaling(self):\n return self.scaling_activation(self._scaling)\n \n @property\n def get_rotation(self):\n return self.rotation_activation(self._rotation)\n \n @property\n def get_xyz(self):\n return self._xyz\n \n @property\n def get_features(self):\n features_dc = self._features_dc\n features_rest = self._features_rest\n return torch.cat((features_dc, features_rest), dim=1)\n \n @property\n def get_opacity(self):\n return self.opacity_activation(self._opacity)\n \n @property\n def get_language_feature(self):\n return self._language_feature\n \n @property\n def get_instance_feature(self):\n return self._instance_feature\n \n def get_smallest_axis(self, return_idx=False):\n rotation_matrices = self.get_rotation_matrix()\n smallest_axis_idx = self.get_scaling.min(dim=-1)[1][..., None, None].expand(-1, 3, -1)\n smallest_axis = rotation_matrices.gather(2, smallest_axis_idx)\n if return_idx:\n return smallest_axis.squeeze(dim=2), smallest_axis_idx[..., 0, 0]\n return smallest_axis.squeeze(dim=2)\n \n def get_normal(self, view_cam):\n normal_global = self.get_smallest_axis()\n gaussian_to_cam_global = view_cam.camera_center - self._xyz\n neg_mask = (normal_global * gaussian_to_cam_global).sum(-1) < 0.0\n normal_global[neg_mask] = -normal_global[neg_mask]\n return normal_global\n \n def init_RT_seq(self, cam_list):\n poses =[]\n index_mapping = {}\n for cam_idx, cam in enumerate(cam_list[1.0]):\n p = get_tensor_from_camera(cam.world_view_transform.transpose(0, 1)) # R T -> quat t\n poses.append(p)\n index_mapping[cam.uid] = cam_idx\n poses = torch.stack(poses)\n self.index_mapping = index_mapping\n self.P = poses.cuda().requires_grad_(True)\n\n def get_RT(self, idx):\n pose = self.P[idx]\n return pose\n \n def get_RT_test(self, idx):\n pose = self.test_P[idx]\n return pose\n \n def get_rotation_matrix(self):\n return quaternion_to_matrix(self.get_rotation)\n\n def get_covariance(self, scaling_modifier = 1):\n return self.covariance_activation(self.get_scaling, scaling_modifier, self._rotation)\n\n def oneupSHdegree(self):\n if self.active_sh_degree < self.max_sh_degree:\n self.active_sh_degree += 1\n\n def create_from_pcd(self, pcd : BasicPointCloud, spatial_lr_scale : float):\n self.spatial_lr_scale = spatial_lr_scale\n fused_point_cloud = torch.tensor(np.asarray(pcd.points)).float().cuda()\n fused_color = RGB2SH(torch.tensor(np.asarray(pcd.colors)).float().cuda())\n features = torch.zeros((fused_color.shape[0], 3, (self.max_sh_degree + 1) ** 2)).float().cuda()\n features[:, :3, 0 ] = fused_color\n features[:, 3:, 1:] = 0.0\n\n print(\"Number of points at initialisation : \", fused_point_cloud.shape[0])\n\n dist = torch.sqrt(torch.clamp_min(distCUDA2(torch.from_numpy(np.asarray(pcd.points)).float().cuda()), 0.0000001))\n # print(f\"new scale {torch.quantile(dist, 0.1)}\")\n scales = torch.log(dist)[...,None].repeat(1, 3)\n rots = torch.zeros((fused_point_cloud.shape[0], 4), device=\"cuda\")\n rots[:, 0] = 1\n\n opacities = inverse_sigmoid(0.1 * torch.ones((fused_point_cloud.shape[0], 1), dtype=torch.float, device=\"cuda\"))\n\n knn_f = torch.randn((fused_point_cloud.shape[0], 6)).float().cuda()\n self._xyz = nn.Parameter(fused_point_cloud.requires_grad_(True))\n self._knn_f = nn.Parameter(knn_f.requires_grad_(True))\n self._features_dc = nn.Parameter(features[:,:,0:1].transpose(1, 2).contiguous().requires_grad_(True))\n self._features_rest = nn.Parameter(features[:,:,1:].transpose(1, 2).contiguous().requires_grad_(True))\n self._scaling = nn.Parameter(scales.requires_grad_(True))\n self._rotation = nn.Parameter(rots.requires_grad_(True))\n self._opacity = nn.Parameter(opacities.requires_grad_(True))\n self.max_radii2D = torch.zeros((self.get_xyz.shape[0]), device=\"cuda\")\n self.max_weight = torch.zeros((self.get_xyz.shape[0]), device=\"cuda\")\n\n language_feature = torch.zeros((fused_point_cloud.shape[0], 3), device=\"cuda\")\n self._language_feature = nn.Parameter(language_feature.requires_grad_(True)).requires_grad_(True) # dont train feature at first\n\n # NOTE for instance distinguish\n instance_feature = torch.zeros((fused_point_cloud.shape[0], 3), device=\"cuda\")\n self._instance_feature = nn.Parameter(instance_feature.requires_grad_(False)).requires_grad_(False) # just train feature at last\n\n def training_setup(self, training_args, device):\n self.percent_dense = training_args.percent_dense\n self.xyz_gradient_accum = torch.zeros((self.get_xyz.shape[0], 1), device=device)\n self.xyz_gradient_accum_abs = torch.zeros((self.get_xyz.shape[0], 1), device=device)\n self.denom = torch.zeros((self.get_xyz.shape[0], 1), device=device)\n self.denom_abs = torch.zeros((self.get_xyz.shape[0], 1), device=device)\n self.abs_split_radii2D_threshold = training_args.abs_split_radii2D_threshold\n self.max_abs_split_points = training_args.max_abs_split_points\n self.max_all_points = training_args.max_all_points\n\n l = [\n {'params': [self._xyz], 'lr': training_args.position_lr_init * self.spatial_lr_scale, \"name\": \"xyz\"},\n {'params': [self._knn_f], 'lr': 0.01, \"name\": \"knn_f\"},\n {'params': [self._features_dc], 'lr': training_args.feature_lr, \"name\": \"f_dc\"},\n {'params': [self._features_rest], 'lr': training_args.feature_lr / 20.0, \"name\": \"f_rest\"},\n {'params': [self._opacity], 'lr': training_args.opacity_lr, \"name\": \"opacity\"},\n {'params': [self._scaling], 'lr': training_args.scaling_lr, \"name\": \"scaling\"},\n {'params': [self._rotation], 'lr': training_args.rotation_lr, \"name\": \"rotation\"},\n {'params': [self._language_feature], 'lr': training_args.language_feature_lr, \"name\": \"language_feature\"}, # semantic\n {'params': [self._instance_feature], 'lr': training_args.language_feature_lr, \"name\": \"instance_feature\"}, # instance \n ]\n\n l_cam = [{'params': [self.P],'lr': training_args.rotation_lr*0.1, \"name\": \"pose\"},]\n # l += l_cam\n\n self.optimizer = torch.optim.Adam(l, lr=0.0, eps=1e-15)\n self.cam_optimizer = torch.optim.Adam(l_cam, lr=0.0, eps=1e-15)\n \n self.xyz_scheduler_args = get_expon_lr_func(lr_init=training_args.position_lr_init*self.spatial_lr_scale,\n lr_final=training_args.position_lr_final*self.spatial_lr_scale,\n lr_delay_mult=training_args.position_lr_delay_mult,\n max_steps=training_args.position_lr_max_steps)\n \n self.cam_scheduler_args = get_expon_lr_func(\n lr_init=training_args.rotation_lr*0.1,\n lr_final=training_args.rotation_lr*0.001,\n lr_delay_mult=training_args.position_lr_delay_mult,\n max_steps=training_args.iterations)\n\n \n # per-point optimizer\n def training_setup_pp(self, training_args, confidence_lr=None, device=\"cuda\"):\n self.percent_dense = training_args.percent_dense\n self.xyz_gradient_accum = torch.zeros((self.get_xyz.shape[0], 1), device=device)\n self.xyz_gradient_accum_abs = torch.zeros((self.get_xyz.shape[0], 1), device=device)\n self.denom = torch.zeros((self.get_xyz.shape[0], 1), device=device)\n self.denom_abs = torch.zeros((self.get_xyz.shape[0], 1), device=device)\n self.abs_split_radii2D_threshold = training_args.abs_split_radii2D_threshold\n self.max_abs_split_points = training_args.max_abs_split_points\n self.max_all_points = training_args.max_all_points\n self.per_point_lr = confidence_lr\n\n l = [\n {'params': [self._xyz], 'per_point_lr': self.per_point_lr, 'lr': training_args.position_lr_init * self.spatial_lr_scale, \"name\": \"xyz\"},\n {'params': [self._knn_f], 'lr': 0.01, \"name\": \"knn_f\"},\n {'params': [self._features_dc], 'lr': training_args.feature_lr, \"name\": \"f_dc\"},\n {'params': [self._features_rest], 'lr': training_args.feature_lr / 20.0, \"name\": \"f_rest\"},\n {'params': [self._opacity], 'lr': training_args.opacity_lr, \"name\": \"opacity\"},\n {'params': [self._scaling], 'lr': training_args.scaling_lr, \"name\": \"scaling\"},\n {'params': [self._rotation], 'lr': training_args.rotation_lr, \"name\": \"rotation\"},\n {'params': [self._language_feature], 'lr': training_args.language_feature_lr, \"name\": \"language_feature\"}, # semantic\n {'params': [self._instance_feature], 'lr': training_args.language_feature_lr, \"name\": \"instance_feature\"}, # instance \n ]\n\n l_cam = [{'params': [self.P],'lr': training_args.rotation_lr*0.1, \"name\": \"pose\"},]\n # l += l_cam\n\n self.optimizer = PerPointAdam(l, lr=0, betas=(0.9, 0.999), eps=1e-15, weight_decay=0.0)\n self.cam_optimizer = torch.optim.Adam(l_cam, lr=0.0, eps=1e-15)\n\n self.xyz_scheduler_args = get_expon_lr_func(lr_init=training_args.position_lr_init*self.spatial_lr_scale,\n lr_final=training_args.position_lr_final*self.spatial_lr_scale,\n lr_delay_mult=training_args.position_lr_delay_mult,\n max_steps=training_args.position_lr_max_steps)\n\n self.cam_scheduler_args = get_expon_lr_func(\n lr_init=training_args.rotation_lr*0.1,\n lr_final=training_args.rotation_lr*0.001,\n lr_delay_mult=training_args.position_lr_delay_mult,\n max_steps=training_args.iterations)\n \n def clip_grad(self, norm=1.0):\n for group in self.optimizer.param_groups:\n torch.nn.utils.clip_grad_norm_(group[\"params\"][0], norm)\n\n def update_learning_rate(self, iteration):\n ''' Learning rate scheduling per step '''\n for param_group in self.cam_optimizer.param_groups:\n if param_group[\"name\"] == \"pose\":\n lr = self.cam_scheduler_args(iteration)\n param_group['lr'] = lr\n\n for param_group in self.optimizer.param_groups:\n if param_group[\"name\"] == \"xyz\":\n lr = self.xyz_scheduler_args(iteration)\n param_group['lr'] = lr\n\n def construct_list_of_attributes(self, include_feature=False):\n l = ['x', 'y', 'z', 'nx', 'ny', 'nz']\n # All channels except the 3 DC\n for i in range(self._features_dc.shape[1]*self._features_dc.shape[2]):\n l.append('f_dc_{}'.format(i))\n for i in range(self._features_rest.shape[1]*self._features_rest.shape[2]):\n l.append('f_rest_{}'.format(i))\n l.append('opacity')\n for i in range(self._scaling.shape[1]):\n l.append('scale_{}'.format(i))\n for i in range(self._rotation.shape[1]):\n l.append('rot_{}'.format(i))\n if include_feature:\n for i in range(self._language_feature.shape[1]):\n l.append('language_feature_{}'.format(i))\n for i in range(self._instance_feature.shape[1]):\n l.append('instance_feature_{}'.format(i))\n return l\n\n def save_ply(self, path, mask=None, include_feature=False):\n mkdir_p(os.path.dirname(path))\n\n xyz = self._xyz.detach().cpu().numpy()\n normals = np.zeros_like(xyz)\n f_dc = self._features_dc.detach().transpose(1, 2).flatten(start_dim=1).contiguous().cpu().numpy()\n f_rest = self._features_rest.detach().transpose(1, 2).flatten(start_dim=1).contiguous().cpu().numpy()\n opacities = self._opacity.detach().cpu().numpy()\n scale = self._scaling.detach().cpu().numpy()\n rotation = self._rotation.detach().cpu().numpy()\n language_feature = self._language_feature.detach().cpu().numpy()\n instance_feature = self._instance_feature.detach().cpu().numpy()\n\n dtype_full = [(attribute, 'f4') for attribute in self.construct_list_of_attributes(include_feature)]\n elements = np.empty(xyz.shape[0], dtype=dtype_full)\n if include_feature:\n attributes = np.concatenate((xyz, normals, f_dc, f_rest, opacities, scale, rotation, language_feature, instance_feature), axis=1)\n else: \n attributes = np.concatenate((xyz, normals, f_dc, f_rest, opacities, scale, rotation), axis=1)\n\n elements[:] = list(map(tuple, attributes))\n el = PlyElement.describe(elements, 'vertex')\n PlyData([el]).write(path)\n\n def reset_opacity(self):\n opacities_new = inverse_sigmoid(torch.min(self.get_opacity, torch.ones_like(self.get_opacity)*0.01))\n optimizable_tensors = self.replace_tensor_to_optimizer(opacities_new, \"opacity\")\n self._opacity = optimizable_tensors[\"opacity\"]\n\n def load_ply(self, path):\n plydata = PlyData.read(path)\n\n xyz = np.stack((np.asarray(plydata.elements[0][\"x\"]),\n np.asarray(plydata.elements[0][\"y\"]),\n np.asarray(plydata.elements[0][\"z\"])), axis=1)\n opacities = np.asarray(plydata.elements[0][\"opacity\"])[..., np.newaxis]\n\n features_dc = np.zeros((xyz.shape[0], 3, 1))\n features_dc[:, 0, 0] = np.asarray(plydata.elements[0][\"f_dc_0\"])\n features_dc[:, 1, 0] = np.asarray(plydata.elements[0][\"f_dc_1\"])\n features_dc[:, 2, 0] = np.asarray(plydata.elements[0][\"f_dc_2\"])\n\n extra_f_names = [p.name for p in plydata.elements[0].properties if p.name.startswith(\"f_rest_\")]\n extra_f_names = sorted(extra_f_names, key = lambda x: int(x.split('_')[-1]))\n assert len(extra_f_names)==3*(self.max_sh_degree + 1) ** 2 - 3\n features_extra = np.zeros((xyz.shape[0], len(extra_f_names)))\n for idx, attr_name in enumerate(extra_f_names):\n features_extra[:, idx] = np.asarray(plydata.elements[0][attr_name])\n # Reshape (P,F*SH_coeffs) to (P, F, SH_coeffs except DC)\n features_extra = features_extra.reshape((features_extra.shape[0], 3, (self.max_sh_degree + 1) ** 2 - 1))\n\n scale_names = [p.name for p in plydata.elements[0].properties if p.name.startswith(\"scale_\")]\n scale_names = sorted(scale_names, key = lambda x: int(x.split('_')[-1]))\n scales = np.zeros((xyz.shape[0], len(scale_names)))\n for idx, attr_name in enumerate(scale_names):\n scales[:, idx] = np.asarray(plydata.elements[0][attr_name])\n\n rot_names = [p.name for p in plydata.elements[0].properties if p.name.startswith(\"rot\")]\n rot_names = sorted(rot_names, key = lambda x: int(x.split('_')[-1]))\n rots = np.zeros((xyz.shape[0], len(rot_names)))\n for idx, attr_name in enumerate(rot_names):\n rots[:, idx] = np.asarray(plydata.elements[0][attr_name])\n\n language_feature_names = [p.name for p in plydata.elements[0].properties if p.name.startswith(\"language_feature\")]\n language_feature_names = sorted(language_feature_names, key = lambda x: int(x.split('_')[-1]))\n language_feature = np.zeros((xyz.shape[0], len(language_feature_names)))\n for idx, attr_name in enumerate(language_feature_names):\n language_feature[:, idx] = np.asarray(plydata.elements[0][attr_name])\n\n # NOTE instance\n instance_feature_names = [p.name for p in plydata.elements[0].properties if p.name.startswith(\"instance_feature\")]\n instance_feature_names = sorted(instance_feature_names, key = lambda x: int(x.split('_')[-1]))\n instance_feature = np.zeros((xyz.shape[0], len(instance_feature_names)))\n for idx, attr_name in enumerate(instance_feature_names):\n instance_feature[:, idx] = np.asarray(plydata.elements[0][attr_name])\n\n self._xyz = nn.Parameter(torch.tensor(xyz, dtype=torch.float, device=\"cuda\").requires_grad_(True))\n self._features_dc = nn.Parameter(torch.tensor(features_dc, dtype=torch.float, device=\"cuda\").transpose(1, 2).contiguous().requires_grad_(True))\n self._features_rest = nn.Parameter(torch.tensor(features_extra, dtype=torch.float, device=\"cuda\").transpose(1, 2).contiguous().requires_grad_(True))\n self._opacity = nn.Parameter(torch.tensor(opacities, dtype=torch.float, device=\"cuda\").requires_grad_(True))\n self._scaling = nn.Parameter(torch.tensor(scales, dtype=torch.float, device=\"cuda\").requires_grad_(True))\n self._rotation = nn.Parameter(torch.tensor(rots, dtype=torch.float, device=\"cuda\").requires_grad_(True))\n self._language_feature = nn.Parameter(torch.tensor(language_feature, dtype=torch.float, device=\"cuda\").requires_grad_(False))\n self._instance_feature = nn.Parameter(torch.tensor(instance_feature, dtype=torch.float, device=\"cuda\").requires_grad_(False))\n\n self.active_sh_degree = self.max_sh_degree\n\n def replace_tensor_to_optimizer(self, tensor, name):\n optimizable_tensors = {}\n for group in self.optimizer.param_groups:\n if group[\"name\"] == name:\n stored_state = self.optimizer.state.get(group['params'][0], None)\n stored_state[\"exp_avg\"] = torch.zeros_like(tensor)\n stored_state[\"exp_avg_sq\"] = torch.zeros_like(tensor)\n\n del self.optimizer.state[group['params'][0]]\n group[\"params\"][0] = nn.Parameter(tensor.requires_grad_(True))\n self.optimizer.state[group['params'][0]] = stored_state\n\n optimizable_tensors[group[\"name\"]] = group[\"params\"][0]\n return optimizable_tensors\n\n def _prune_optimizer(self, mask):\n optimizable_tensors = {}\n for group in self.optimizer.param_groups:\n stored_state = self.optimizer.state.get(group['params'][0], None)\n if stored_state is not None:\n stored_state[\"exp_avg\"] = stored_state[\"exp_avg\"][mask]\n stored_state[\"exp_avg_sq\"] = stored_state[\"exp_avg_sq\"][mask]\n\n del self.optimizer.state[group['params'][0]]\n group[\"params\"][0] = nn.Parameter((group[\"params\"][0][mask].requires_grad_(True)))\n self.optimizer.state[group['params'][0]] = stored_state\n\n optimizable_tensors[group[\"name\"]] = group[\"params\"][0]\n else:\n group[\"params\"][0] = nn.Parameter(group[\"params\"][0][mask].requires_grad_(True))\n optimizable_tensors[group[\"name\"]] = group[\"params\"][0]\n return optimizable_tensors\n\n def prune_points(self, mask):\n valid_points_mask = ~mask\n optimizable_tensors = self._prune_optimizer(valid_points_mask)\n\n self._xyz = optimizable_tensors[\"xyz\"]\n self._knn_f = optimizable_tensors[\"knn_f\"]\n self._features_dc = optimizable_tensors[\"f_dc\"]\n self._features_rest = optimizable_tensors[\"f_rest\"]\n self._opacity = optimizable_tensors[\"opacity\"]\n self._scaling = optimizable_tensors[\"scaling\"]\n self._rotation = optimizable_tensors[\"rotation\"]\n self._language_feature = optimizable_tensors[\"language_feature\"]\n self._instance_feature = optimizable_tensors[\"instance_feature\"]\n\n self.xyz_gradient_accum = self.xyz_gradient_accum[valid_points_mask]\n self.xyz_gradient_accum_abs = self.xyz_gradient_accum_abs[valid_points_mask]\n\n self.denom = self.denom[valid_points_mask]\n self.denom_abs = self.denom_abs[valid_points_mask]\n self.max_radii2D = self.max_radii2D[valid_points_mask]\n self.max_weight = self.max_weight[valid_points_mask]\n\n def cat_tensors_to_optimizer(self, tensors_dict):\n optimizable_tensors = {}\n for group in self.optimizer.param_groups:\n assert len(group[\"params\"]) == 1\n extension_tensor = tensors_dict[group[\"name\"]]\n stored_state = self.optimizer.state.get(group['params'][0], None)\n if stored_state is not None:\n\n stored_state[\"exp_avg\"] = torch.cat((stored_state[\"exp_avg\"], torch.zeros_like(extension_tensor)), dim=0)\n stored_state[\"exp_avg_sq\"] = torch.cat((stored_state[\"exp_avg_sq\"], torch.zeros_like(extension_tensor)), dim=0)\n\n del self.optimizer.state[group['params'][0]]\n group[\"params\"][0] = nn.Parameter(torch.cat((group[\"params\"][0], extension_tensor), dim=0).requires_grad_(True))\n self.optimizer.state[group['params'][0]] = stored_state\n\n optimizable_tensors[group[\"name\"]] = group[\"params\"][0]\n else:\n group[\"params\"][0] = nn.Parameter(torch.cat((group[\"params\"][0], extension_tensor), dim=0).requires_grad_(True))\n optimizable_tensors[group[\"name\"]] = group[\"params\"][0]\n\n return optimizable_tensors\n\n def densification_postfix(self, new_xyz, new_knn_f, new_features_dc, new_features_rest, new_opacities, new_scaling, new_rotation, new_language_feature, new_instance_feature):\n d = {\"xyz\": new_xyz,\n \"knn_f\": new_knn_f,\n \"f_dc\": new_features_dc,\n \"f_rest\": new_features_rest,\n \"opacity\": new_opacities,\n \"scaling\" : new_scaling,\n \"rotation\" : new_rotation,\n \"language_feature\": new_language_feature,\n \"instance_feature\": new_instance_feature,\n }\n optimizable_tensors = self.cat_tensors_to_optimizer(d)\n self._xyz = optimizable_tensors[\"xyz\"]\n self._knn_f = optimizable_tensors[\"knn_f\"]\n self._features_dc = optimizable_tensors[\"f_dc\"]\n self._features_rest = optimizable_tensors[\"f_rest\"]\n self._opacity = optimizable_tensors[\"opacity\"]\n self._scaling = optimizable_tensors[\"scaling\"]\n self._rotation = optimizable_tensors[\"rotation\"]\n self._language_feature = optimizable_tensors[\"language_feature\"]\n self._instance_feature = optimizable_tensors[\"instance_feature\"]\n\n self.xyz_gradient_accum = torch.zeros((self.get_xyz.shape[0], 1), device=\"cuda\")\n self.xyz_gradient_accum_abs = torch.zeros((self.get_xyz.shape[0], 1), device=\"cuda\")\n self.denom = torch.zeros((self.get_xyz.shape[0], 1), device=\"cuda\")\n self.denom_abs = torch.zeros((self.get_xyz.shape[0], 1), device=\"cuda\")\n self.max_radii2D = torch.zeros((self.get_xyz.shape[0]), device=\"cuda\")\n self.max_weight = torch.zeros((self.get_xyz.shape[0]), device=\"cuda\")\n\n def densify_and_split(self, grads, grad_threshold, grads_abs, grad_abs_threshold, scene_extent, max_radii2D, N=2):\n n_init_points = self.get_xyz.shape[0]\n # Extract points that satisfy the gradient condition\n padded_grad = torch.zeros((n_init_points), device=\"cuda\")\n padded_grad[:grads.shape[0]] = grads.squeeze()\n padded_grads_abs = torch.zeros((n_init_points), device=\"cuda\")\n padded_grads_abs[:grads_abs.shape[0]] = grads_abs.squeeze()\n padded_max_radii2D = torch.zeros((n_init_points), device=\"cuda\")\n padded_max_radii2D[:max_radii2D.shape[0]] = max_radii2D.squeeze()\n\n selected_pts_mask = torch.where(padded_grad >= grad_threshold, True, False)\n selected_pts_mask = torch.logical_and(selected_pts_mask,\n torch.max(self.get_scaling, dim=1).values > self.percent_dense*scene_extent)\n if selected_pts_mask.sum() + n_init_points > self.max_all_points:\n limited_num = self.max_all_points - n_init_points\n padded_grad[~selected_pts_mask] = 0\n ratio = limited_num / float(n_init_points)\n threshold = torch.quantile(padded_grad, (1.0-ratio))\n selected_pts_mask = torch.where(padded_grad > threshold, True, False)\n # print(f\"split {selected_pts_mask.sum()}, raddi2D {padded_max_radii2D.max()} ,{padded_max_radii2D.median()}\")\n else:\n padded_grads_abs[selected_pts_mask] = 0\n mask = (torch.max(self.get_scaling, dim=1).values > self.percent_dense*scene_extent) & (padded_max_radii2D > self.abs_split_radii2D_threshold)\n padded_grads_abs[~mask] = 0\n selected_pts_mask_abs = torch.where(padded_grads_abs >= grad_abs_threshold, True, False)\n limited_num = min(self.max_all_points - n_init_points - selected_pts_mask.sum(), self.max_abs_split_points)\n if selected_pts_mask_abs.sum() > limited_num:\n ratio = limited_num / float(n_init_points)\n threshold = torch.quantile(padded_grads_abs, (1.0-ratio))\n selected_pts_mask_abs = torch.where(padded_grads_abs > threshold, True, False)\n selected_pts_mask = torch.logical_or(selected_pts_mask, selected_pts_mask_abs)\n # print(f\"split {selected_pts_mask.sum()}, abs {selected_pts_mask_abs.sum()}, raddi2D {padded_max_radii2D.max()} ,{padded_max_radii2D.median()}\")\n\n stds = self.get_scaling[selected_pts_mask].repeat(N,1)\n means =torch.zeros((stds.size(0), 3),device=\"cuda\")\n samples = torch.normal(mean=means, std=stds)\n rots = build_rotation(self._rotation[selected_pts_mask]).repeat(N,1,1)\n new_xyz = torch.bmm(rots, samples.unsqueeze(-1)).squeeze(-1) + self.get_xyz[selected_pts_mask].repeat(N, 1)\n new_scaling = self.scaling_inverse_activation(self.get_scaling[selected_pts_mask].repeat(N,1) / (0.8*N))\n new_rotation = self._rotation[selected_pts_mask].repeat(N,1)\n new_features_dc = self._features_dc[selected_pts_mask].repeat(N,1,1)\n new_features_rest = self._features_rest[selected_pts_mask].repeat(N,1,1)\n new_opacity = self._opacity[selected_pts_mask].repeat(N,1)\n new_knn_f = self._knn_f[selected_pts_mask].repeat(N,1)\n new_language_feature = self._language_feature[selected_pts_mask].repeat(N,1)\n new_instance_feature = self._instance_feature[selected_pts_mask].repeat(N,1)\n\n self.densification_postfix(new_xyz, new_knn_f, new_features_dc, new_features_rest, new_opacity, new_scaling, new_rotation, new_language_feature, new_instance_feature)\n\n prune_filter = torch.cat((selected_pts_mask, torch.zeros(N * selected_pts_mask.sum(), device=\"cuda\", dtype=bool)))\n self.prune_points(prune_filter)\n\n def densify_and_clone(self, grads, grad_threshold, scene_extent):\n n_init_points = self.get_xyz.shape[0]\n # Extract points that satisfy the gradient condition\n selected_pts_mask = torch.where(torch.norm(grads, dim=-1) >= grad_threshold, True, False)\n selected_pts_mask = torch.logical_and(selected_pts_mask,\n torch.max(self.get_scaling, dim=1).values <= self.percent_dense*scene_extent)\n \n if selected_pts_mask.sum() + n_init_points > self.max_all_points:\n limited_num = self.max_all_points - n_init_points\n grads_tmp = grads.squeeze().clone()\n grads_tmp[~selected_pts_mask] = 0\n ratio = min(limited_num / float(n_init_points), 1)\n threshold = torch.quantile(grads_tmp, (1.0-ratio))\n selected_pts_mask = torch.where(grads_tmp > threshold, True, False)\n\n if selected_pts_mask.sum() > 0:\n # print(f\"clone {selected_pts_mask.sum()}\")\n new_xyz = self._xyz[selected_pts_mask]\n\n stds = self.get_scaling[selected_pts_mask]\n means =torch.zeros((stds.size(0), 3),device=\"cuda\")\n samples = torch.normal(mean=means, std=stds)\n rots = build_rotation(self._rotation[selected_pts_mask])\n new_xyz = torch.bmm(rots, samples.unsqueeze(-1)).squeeze(-1) + self.get_xyz[selected_pts_mask]\n \n new_features_dc = self._features_dc[selected_pts_mask]\n new_features_rest = self._features_rest[selected_pts_mask]\n new_opacities = self._opacity[selected_pts_mask]\n new_scaling = self._scaling[selected_pts_mask]\n new_rotation = self._rotation[selected_pts_mask]\n new_knn_f = self._knn_f[selected_pts_mask]\n new_language_feature = self._language_feature[selected_pts_mask]\n new_instance_feature = self._instance_feature[selected_pts_mask]\n\n self.densification_postfix(new_xyz, new_knn_f, new_features_dc, new_features_rest, new_opacities, new_scaling, new_rotation, new_language_feature, new_instance_feature)\n\n def densify_and_prune(self, max_grad, abs_max_grad, min_opacity, extent, max_screen_size):\n grads = self.xyz_gradient_accum / self.denom\n grads_abs = self.xyz_gradient_accum_abs / self.denom_abs\n grads[grads.isnan()] = 0.0\n grads_abs[grads_abs.isnan()] = 0.0\n max_radii2D = self.max_radii2D.clone()\n\n self.densify_and_clone(grads, max_grad, extent)\n self.densify_and_split(grads, max_grad, grads_abs, abs_max_grad, extent, max_radii2D)\n\n prune_mask = (self.get_opacity < min_opacity).squeeze()\n\n if max_screen_size:\n big_points_vs = self.max_radii2D > max_screen_size\n big_points_ws = self.get_scaling.max(dim=1).values > 0.1 * extent\n prune_mask = torch.logical_or(torch.logical_or(prune_mask, big_points_vs), big_points_ws)\n self.prune_points(prune_mask)\n # print(f\"all points {self._xyz.shape[0]}\")\n torch.cuda.empty_cache()\n\n def add_densification_stats(self, viewspace_point_tensor, viewspace_point_tensor_abs, update_filter):\n self.xyz_gradient_accum[update_filter] += torch.norm(viewspace_point_tensor.grad[update_filter,:2], dim=-1, keepdim=True)\n self.xyz_gradient_accum_abs[update_filter] += torch.norm(viewspace_point_tensor_abs.grad[update_filter,:2], dim=-1, keepdim=True)\n self.denom[update_filter] += 1\n self.denom_abs[update_filter] += 1\n\n def get_points_depth_in_depth_map(self, fov_camera, depth, points_in_camera_space, scale=1):\n st = max(int(scale/2)-1,0)\n depth_view = depth[None,:,st::scale,st::scale]\n W, H = int(fov_camera.image_width/scale), int(fov_camera.image_height/scale)\n depth_view = depth_view[:H, :W]\n pts_projections = torch.stack(\n [points_in_camera_space[:,0] * fov_camera.Fx / points_in_camera_space[:,2] + fov_camera.Cx,\n points_in_camera_space[:,1] * fov_camera.Fy / points_in_camera_space[:,2] + fov_camera.Cy], -1).float()/scale\n mask = (pts_projections[:, 0] > 0) & (pts_projections[:, 0] < W) &\\\n (pts_projections[:, 1] > 0) & (pts_projections[:, 1] < H) & (points_in_camera_space[:,2] > 0.1)\n\n pts_projections[..., 0] /= ((W - 1) / 2)\n pts_projections[..., 1] /= ((H - 1) / 2)\n pts_projections -= 1\n pts_projections = pts_projections.view(1, -1, 1, 2)\n map_z = torch.nn.functional.grid_sample(input=depth_view,\n grid=pts_projections,\n mode='bilinear',\n padding_mode='border',\n align_corners=True\n )[0, :, :, 0]\n return map_z, mask\n \n def get_points_from_depth(self, fov_camera, depth, scale=1):\n st = int(max(int(scale/2)-1,0))\n depth_view = depth.squeeze()[st::scale,st::scale]\n rays_d = fov_camera.get_rays(scale=scale)\n depth_view = depth_view[:rays_d.shape[0], :rays_d.shape[1]]\n pts = (rays_d * depth_view[..., None]).reshape(-1,3)\n R = torch.tensor(fov_camera.R).float().cuda()\n T = torch.tensor(fov_camera.T).float().cuda()\n pts = (pts-T)@R.transpose(-1,-2)\n return pts\n \n def change_reqiures_grad(self, change, iteration, quiet=True):\n if change == \"geometry\":\n self._xyz.requires_grad_(True)\n self._knn_f.requires_grad_(True)\n self._features_dc.requires_grad_(True)\n self._features_rest.requires_grad_(True)\n self._scaling.requires_grad_(True)\n self._rotation.requires_grad_(True)\n self._opacity.requires_grad_(True)\n self.P.requires_grad_(True)\n self._language_feature.requires_grad_(False)\n self._instance_feature.requires_grad_(False)\n if not quiet:\n print(f'\\n[ITER {iteration}] Training gaussian params')\n elif change == 'semantic': \n self._xyz.requires_grad_(True)\n self._knn_f.requires_grad_(True)\n self._features_dc.requires_grad_(True)\n self._features_rest.requires_grad_(True)\n self._scaling.requires_grad_(True)\n self._rotation.requires_grad_(True)\n self._opacity.requires_grad_(True)\n self.P.requires_grad_(True)\n self._language_feature.requires_grad_(True)\n self._instance_feature.requires_grad_(False)\n if not quiet:\n print(f'\\n[ITER {iteration}] Training gaussian params and language feature')\n elif change == 'semantic_only': \n self._xyz.requires_grad_(False)\n self._knn_f.requires_grad_(False)\n self._features_dc.requires_grad_(False)\n self._features_rest.requires_grad_(False)\n self._scaling.requires_grad_(False)\n self._rotation.requires_grad_(False)\n self._opacity.requires_grad_(False)\n self.P.requires_grad_(False)\n self._language_feature.requires_grad_(True)\n self._instance_feature.requires_grad_(False)\n if not quiet:\n print(f'\\n[ITER {iteration}] Training language feature')\n elif change == 'instance': \n self._xyz.requires_grad_(False)\n self._knn_f.requires_grad_(False)\n self._features_dc.requires_grad_(False)\n self._features_rest.requires_grad_(False)\n self._scaling.requires_grad_(False)\n self._rotation.requires_grad_(False)\n self._opacity.requires_grad_(False)\n self.P.requires_grad_(False)\n self._language_feature.requires_grad_(False)\n self._instance_feature.requires_grad_(True)\n if not quiet:\n print(f'\\n[ITER {iteration}] Training instance feature')\n elif change == \"pose_only\":\n self._xyz.requires_grad_(False)\n self._knn_f.requires_grad_(False)\n self._features_dc.requires_grad_(False)\n self._features_rest.requires_grad_(False)\n self._scaling.requires_grad_(False)\n self._rotation.requires_grad_(False)\n self._opacity.requires_grad_(False)\n self.P.requires_grad_(True)\n self._language_feature.requires_grad_(False)\n self._instance_feature.requires_grad_(False)\n if not quiet:\n print(f'\\n[ITER {iteration}] Training instance feature')\n elif change == 'finetune':\n self._xyz.requires_grad_(False)\n self._knn_f.requires_grad_(False)\n self._features_dc.requires_grad_(True)\n self._features_rest.requires_grad_(True)\n self._scaling.requires_grad_(False)\n self._rotation.requires_grad_(False)\n self._opacity.requires_grad_(False)\n self.P.requires_grad_(False)\n self._language_feature.requires_grad_(False)\n self._instance_feature.requires_grad_(False)\n if not quiet:\n print(f'\\n[ITER {iteration}] finetune')\n else:\n raise ValueError('Unknown type!')\n " }, { "path": "field_construction/scene/per_point_adam.py", "content": "import torch\nfrom torch.optim import Optimizer\n\n\nclass PerPointAdam(Optimizer):\n \"\"\"Implements Adam optimizer with per-point learning rates.\n \n Allows unique learning rates for each point in specified parameter tensors,\n useful for point cloud optimization.\n\n Args:\n params: Iterable of parameters to optimize or parameter groups\n lr (float, optional): Default learning rate (default: 1e-3)\n betas (tuple, optional): Coefficients for moving averages (default: (0.9, 0.999))\n eps (float, optional): Term for numerical stability (default: 1e-8)\n weight_decay (float, optional): Weight decay (L2 penalty) (default: 0)\n \"\"\"\n def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0):\n if not all(0.0 <= x for x in [lr, eps, weight_decay]):\n raise ValueError(f\"Invalid learning parameters: lr={lr}, eps={eps}, weight_decay={weight_decay}\")\n if not all(0.0 <= beta < 1.0 for beta in betas):\n raise ValueError(f\"Invalid beta parameters: {betas}\")\n\n defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay, per_point_lr=None)\n super().__init__(params, defaults)\n\n def _adjust_per_point_lr(self, per_point_lr, grad, mask):\n \"\"\"Adjusts per-point learning rates based on gradient magnitudes.\"\"\"\n grad_magnitude = grad.norm(dim=-1)\n scaling_factor = torch.ones_like(grad_magnitude)\n grad_sigmoid = torch.sigmoid(grad_magnitude[mask])\n scaling_factor[mask] = 0.99 + (grad_sigmoid * 0.02)\n return per_point_lr * scaling_factor.unsqueeze(1)\n\n def step(self, closure=None):\n \"\"\"Performs a single optimization step.\"\"\"\n loss = closure() if closure is not None else None\n\n for group in self.param_groups:\n per_point_lr = group.get('per_point_lr')\n \n for p in group['params']:\n if p.grad is None:\n continue\n \n grad = p.grad.data\n if grad.is_sparse:\n raise RuntimeError('PerPointAdam does not support sparse gradients')\n\n # Initialize state if needed\n state = self.state[p]\n if len(state) == 0:\n state['step'] = 0\n state['exp_avg'] = torch.zeros_like(p.data)\n state['exp_avg_sq'] = torch.zeros_like(p.data)\n\n # Get state values\n exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']\n beta1, beta2 = group['betas']\n state['step'] += 1\n\n # Apply weight decay if specified\n if group['weight_decay'] != 0:\n grad = grad.add(p.data, alpha=group['weight_decay'])\n\n # Compute mask for non-zero gradients\n grad_norm = grad.norm()\n mask = grad_norm > 0\n\n # Update momentum terms\n exp_avg.masked_scatter_(mask, \n exp_avg[mask].mul_(beta1).add_(grad[mask], alpha=1 - beta1))\n exp_avg_sq.masked_scatter_(mask,\n exp_avg_sq[mask].mul_(beta2).addcmul_(grad[mask], grad[mask], value=1 - beta2))\n\n # Compute bias corrections\n bias_correction1 = 1 - beta1 ** state['step']\n bias_correction2 = 1 - beta2 ** state['step']\n\n # Compute step size\n denom = exp_avg_sq.sqrt().add_(group['eps'])\n step_size = group['lr'] * (bias_correction2 ** 0.5 / bias_correction1)\n\n # Apply updates\n if per_point_lr is not None:\n if not isinstance(per_point_lr, torch.Tensor):\n raise TypeError(\"per_point_lr must be a torch.Tensor\")\n if per_point_lr.device != p.data.device:\n raise ValueError(\"per_point_lr must be on the same device as parameter\")\n \n expected_shape = p.data.shape[:1] + (1,) * (p.data.dim() - 1)\n if per_point_lr.shape != expected_shape:\n raise ValueError(f\"{group['name']}: Invalid per_point_lr shape. Expected {expected_shape}, got {per_point_lr.shape}\")\n\n scaled_step_size = step_size * per_point_lr\n p.data.add_(-scaled_step_size * (exp_avg / denom))\n per_point_lr = self._adjust_per_point_lr(per_point_lr, grad, mask)\n else:\n p.data.addcdiv_(exp_avg, denom, value=-step_size)\n\n return loss" }, { "path": "field_construction/submodules/diff-langsurf-rasterizer/CMakeLists.txt", "content": "#\n# Copyright (C) 2023, Inria\n# GRAPHDECO research group, https://team.inria.fr/graphdeco\n# All rights reserved.\n#\n# This software is free for non-commercial, research and evaluation use \n# under the terms of the LICENSE.md file.\n#\n# For inquiries contact george.drettakis@inria.fr\n#\n\ncmake_minimum_required(VERSION 3.20)\n\nproject(DiffRast LANGUAGES CUDA CXX)\n\nset(CMAKE_CXX_STANDARD 17)\nset(CMAKE_CXX_EXTENSIONS OFF)\nset(CMAKE_CUDA_STANDARD 17)\n\nset(CMAKE_CXX_FLAGS \"${CMAKE_CXX_FLAGS}\")\n\nadd_library(CudaRasterizer\n\tcuda_rasterizer/backward.h\n\tcuda_rasterizer/backward.cu\n\tcuda_rasterizer/forward.h\n\tcuda_rasterizer/forward.cu\n\tcuda_rasterizer/auxiliary.h\n\tcuda_rasterizer/rasterizer_impl.cu\n\tcuda_rasterizer/rasterizer_impl.h\n\tcuda_rasterizer/rasterizer.h\n)\n\nset_target_properties(CudaRasterizer PROPERTIES CUDA_ARCHITECTURES \"70;75;86\")\n\ntarget_include_directories(CudaRasterizer PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}/cuda_rasterizer)\ntarget_include_directories(CudaRasterizer PRIVATE third_party/glm ${CMAKE_CUDA_TOOLKIT_INCLUDE_DIRECTORIES})\n" }, { "path": "field_construction/submodules/diff-langsurf-rasterizer/LICENSE.md", "content": "Gaussian-Splatting License \n=========================== \n\n**Inria** and **the Max Planck Institut for Informatik (MPII)** hold all the ownership rights on the *Software* named **gaussian-splatting**. \nThe *Software* is in the process of being registered with the Agence pour la Protection des \nProgrammes (APP). \n\nThe *Software* is still being developed by the *Licensor*. \n\n*Licensor*'s goal is to allow the research community to use, test and evaluate \nthe *Software*. \n\n## 1. Definitions \n\n*Licensee* means any person or entity that uses the *Software* and distributes \nits *Work*. \n\n*Licensor* means the owners of the *Software*, i.e Inria and MPII \n\n*Software* means the original work of authorship made available under this \nLicense ie gaussian-splatting. \n\n*Work* means the *Software* and any additions to or derivative works of the \n*Software* that are made available under this License. \n\n\n## 2. Purpose \nThis license is intended to define the rights granted to the *Licensee* by \nLicensors under the *Software*. \n\n## 3. Rights granted \n\nFor the above reasons Licensors have decided to distribute the *Software*. \nLicensors grant non-exclusive rights to use the *Software* for research purposes \nto research users (both academic and industrial), free of charge, without right \nto sublicense.. The *Software* may be used \"non-commercially\", i.e., for research \nand/or evaluation purposes only. \n\nSubject to the terms and conditions of this License, you are granted a \nnon-exclusive, royalty-free, license to reproduce, prepare derivative works of, \npublicly display, publicly perform and distribute its *Work* and any resulting \nderivative works in any form. \n\n## 4. Limitations \n\n**4.1 Redistribution.** You may reproduce or distribute the *Work* only if (a) you do \nso under this License, (b) you include a complete copy of this License with \nyour distribution, and (c) you retain without modification any copyright, \npatent, trademark, or attribution notices that are present in the *Work*. \n\n**4.2 Derivative Works.** You may specify that additional or different terms apply \nto the use, reproduction, and distribution of your derivative works of the *Work* \n(\"Your Terms\") only if (a) Your Terms provide that the use limitation in \nSection 2 applies to your derivative works, and (b) you identify the specific \nderivative works that are subject to Your Terms. Notwithstanding Your Terms, \nthis License (including the redistribution requirements in Section 3.1) will \ncontinue to apply to the *Work* itself. \n\n**4.3** Any other use without of prior consent of Licensors is prohibited. Research \nusers explicitly acknowledge having received from Licensors all information \nallowing to appreciate the adequacy between of the *Software* and their needs and \nto undertake all necessary precautions for its execution and use. \n\n**4.4** The *Software* is provided both as a compiled library file and as source \ncode. In case of using the *Software* for a publication or other results obtained \nthrough the use of the *Software*, users are strongly encouraged to cite the \ncorresponding publications as explained in the documentation of the *Software*. \n\n## 5. Disclaimer \n\nTHE USER CANNOT USE, EXPLOIT OR DISTRIBUTE THE *SOFTWARE* FOR COMMERCIAL PURPOSES \nWITHOUT PRIOR AND EXPLICIT CONSENT OF LICENSORS. YOU MUST CONTACT INRIA FOR ANY \nUNAUTHORIZED USE: stip-sophia.transfert@inria.fr . ANY SUCH ACTION WILL \nCONSTITUTE A FORGERY. THIS *SOFTWARE* IS PROVIDED \"AS IS\" WITHOUT ANY WARRANTIES \nOF ANY NATURE AND ANY EXPRESS OR IMPLIED WARRANTIES, WITH REGARDS TO COMMERCIAL \nUSE, PROFESSIONNAL USE, LEGAL OR NOT, OR OTHER, OR COMMERCIALISATION OR \nADAPTATION. UNLESS EXPLICITLY PROVIDED BY LAW, IN NO EVENT, SHALL INRIA OR THE \nAUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR \nCONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE \nGOODS OR SERVICES, LOSS OF USE, DATA, OR PROFITS OR BUSINESS INTERRUPTION) \nHOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT \nLIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING FROM, OUT OF OR \nIN CONNECTION WITH THE *SOFTWARE* OR THE USE OR OTHER DEALINGS IN THE *SOFTWARE*. \n" }, { "path": "field_construction/submodules/diff-langsurf-rasterizer/README.md", "content": "# Differential Gaussian Rasterization for LangSurf\n\nUsed as the rasterization engine for LangSurf which can render rgb, depth, normal, language features and instance-level language features. If you can make use of it in your own research, please be so kind to cite us.\n\n" }, { "path": "field_construction/submodules/diff-langsurf-rasterizer/build/temp.linux-x86_64-cpython-310/.ninja_log", "content": "# ninja log v5\n1\t3269\t1740114322642951852\t/home/lff/data1/cjw/langscene/field_construction/submodules/diff-LangSurf-rasterizer/build/temp.linux-x86_64-cpython-310/cuda_rasterizer/forward.o\taa1daee3d707d538\n1\t3499\t1740114322874950457\t/home/lff/data1/cjw/langscene/field_construction/submodules/diff-LangSurf-rasterizer/build/temp.linux-x86_64-cpython-310/cuda_rasterizer/backward.o\tccc62c27cc08f3d2\n1\t10560\t1740114329930908314\t/home/lff/data1/cjw/langscene/field_construction/submodules/diff-LangSurf-rasterizer/build/temp.linux-x86_64-cpython-310/cuda_rasterizer/rasterizer_impl.o\tb10d15ada471cc56\n2\t16378\t1740114335754873938\t/home/lff/data1/cjw/langscene/field_construction/submodules/diff-LangSurf-rasterizer/build/temp.linux-x86_64-cpython-310/ext.o\t5065cf0abad3b1dc\n2\t51547\t1740114370898674085\t/home/lff/data1/cjw/langscene/field_construction/submodules/diff-LangSurf-rasterizer/build/temp.linux-x86_64-cpython-310/rasterize_points.o\t69fcc9f1b32d75\n17\t3419\t1740115149196330150\t/home/lff/data1/cjw/langscene/field_construction/submodules/diff-LangSurf-rasterizer/build/temp.linux-x86_64-cpython-310/cuda_rasterizer/forward.o\taa1daee3d707d538\n16\t3568\t1740115149352329917\t/home/lff/data1/cjw/langscene/field_construction/submodules/diff-LangSurf-rasterizer/build/temp.linux-x86_64-cpython-310/cuda_rasterizer/backward.o\tccc62c27cc08f3d2\n17\t10358\t1740115156128319828\t/home/lff/data1/cjw/langscene/field_construction/submodules/diff-LangSurf-rasterizer/build/temp.linux-x86_64-cpython-310/cuda_rasterizer/rasterizer_impl.o\tb10d15ada471cc56\n18\t50342\t1740115196108262280\t/home/lff/data1/cjw/langscene/field_construction/submodules/diff-LangSurf-rasterizer/build/temp.linux-x86_64-cpython-310/rasterize_points.o\t69fcc9f1b32d75\n18\t3188\t1740115705015741260\t/home/lff/data1/cjw/langscene/field_construction/submodules/diff-LangSurf-rasterizer/build/temp.linux-x86_64-cpython-310/cuda_rasterizer/forward.o\taa1daee3d707d538\n18\t3450\t1740115705271741067\t/home/lff/data1/cjw/langscene/field_construction/submodules/diff-LangSurf-rasterizer/build/temp.linux-x86_64-cpython-310/cuda_rasterizer/backward.o\tccc62c27cc08f3d2\n18\t10867\t1740115712691735492\t/home/lff/data1/cjw/langscene/field_construction/submodules/diff-LangSurf-rasterizer/build/temp.linux-x86_64-cpython-310/cuda_rasterizer/rasterizer_impl.o\tb10d15ada471cc56\n19\t51652\t1740115753455705563\t/home/lff/data1/cjw/langscene/field_construction/submodules/diff-LangSurf-rasterizer/build/temp.linux-x86_64-cpython-310/rasterize_points.o\t69fcc9f1b32d75\n16\t3577\t1740119157035453892\t/home/lff/data1/cjw/langscene/field_construction/submodules/diff-LangSurf-rasterizer/build/temp.linux-x86_64-cpython-310/cuda_rasterizer/forward.o\taa1daee3d707d538\n15\t3811\t1740119157275453274\t/home/lff/data1/cjw/langscene/field_construction/submodules/diff-LangSurf-rasterizer/build/temp.linux-x86_64-cpython-310/cuda_rasterizer/backward.o\tccc62c27cc08f3d2\n16\t11339\t1740119164799433919\t/home/lff/data1/cjw/langscene/field_construction/submodules/diff-LangSurf-rasterizer/build/temp.linux-x86_64-cpython-310/cuda_rasterizer/rasterizer_impl.o\tb10d15ada471cc56\n17\t56708\t1740119210139319436\t/home/lff/data1/cjw/langscene/field_construction/submodules/diff-LangSurf-rasterizer/build/temp.linux-x86_64-cpython-310/rasterize_points.o\t69fcc9f1b32d75\n21\t3532\t1740331667538673799\t/home/lff/data1/cjw/langscene/field_construction/submodules/diff-LangSurf-rasterizer/build/temp.linux-x86_64-cpython-310/cuda_rasterizer/forward.o\taa1daee3d707d538\n20\t3750\t1740331667758673391\t/home/lff/data1/cjw/langscene/field_construction/submodules/diff-LangSurf-rasterizer/build/temp.linux-x86_64-cpython-310/cuda_rasterizer/backward.o\tccc62c27cc08f3d2\n21\t11715\t1740331675714658604\t/home/lff/data1/cjw/langscene/field_construction/submodules/diff-LangSurf-rasterizer/build/temp.linux-x86_64-cpython-310/cuda_rasterizer/rasterizer_impl.o\tb10d15ada471cc56\n21\t77173\t1740331741158535523\t/home/lff/data1/cjw/langscene/field_construction/submodules/diff-LangSurf-rasterizer/build/temp.linux-x86_64-cpython-310/rasterize_points.o\t69fcc9f1b32d75\n56\t3946\t1743475571713662010\t/home/lff/data1/cjw/langscene/field_construction/submodules/diff-langsurf-rasterizer/build/temp.linux-x86_64-cpython-310/cuda_rasterizer/backward.o\t87ff917ebb3c08f0\n57\t4118\t1743475571889660174\t/home/lff/data1/cjw/langscene/field_construction/submodules/diff-langsurf-rasterizer/build/temp.linux-x86_64-cpython-310/cuda_rasterizer/forward.o\t874625155179daf1\n57\t11768\t1743475579533580803\t/home/lff/data1/cjw/langscene/field_construction/submodules/diff-langsurf-rasterizer/build/temp.linux-x86_64-cpython-310/cuda_rasterizer/rasterizer_impl.o\t355dd834fa004c2c\n58\t45380\t1743475613145239337\t/home/lff/data1/cjw/langscene/field_construction/submodules/diff-langsurf-rasterizer/build/temp.linux-x86_64-cpython-310/ext.o\t6f5822467011ce30\n58\t78044\t1743475645788918929\t/home/lff/data1/cjw/langscene/field_construction/submodules/diff-langsurf-rasterizer/build/temp.linux-x86_64-cpython-310/rasterize_points.o\tbad4edeb9a9f8d44\n" }, { "path": "field_construction/submodules/diff-langsurf-rasterizer/build/temp.linux-x86_64-cpython-310/build.ninja", "content": "ninja_required_version = 1.3\ncxx = c++\nnvcc = /usr/local/cuda-12.2/bin/nvcc\n\ncflags = -pthread -B /home/lff/miniconda3/envs/langscene/compiler_compat -Wno-unused-result -Wsign-compare -DNDEBUG -fwrapv -O2 -Wall -fPIC -O2 -isystem /home/lff/miniconda3/envs/langscene/include -fPIC -O2 -isystem /home/lff/miniconda3/envs/langscene/include -fPIC -I/home/lff/miniconda3/envs/langscene/lib/python3.10/site-packages/torch/include -I/home/lff/miniconda3/envs/langscene/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -I/home/lff/miniconda3/envs/langscene/lib/python3.10/site-packages/torch/include/TH -I/home/lff/miniconda3/envs/langscene/lib/python3.10/site-packages/torch/include/THC -I/usr/local/cuda-12.2/include -I/home/lff/miniconda3/envs/langscene/include/python3.10 -c\npost_cflags = -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE=\"_gcc\"' '-DPYBIND11_STDLIB=\"_libstdcpp\"' '-DPYBIND11_BUILD_ABI=\"_cxxabi1011\"' -DTORCH_EXTENSION_NAME=_C -D_GLIBCXX_USE_CXX11_ABI=0 -std=c++17\ncuda_cflags = -I/home/lff/miniconda3/envs/langscene/lib/python3.10/site-packages/torch/include -I/home/lff/miniconda3/envs/langscene/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -I/home/lff/miniconda3/envs/langscene/lib/python3.10/site-packages/torch/include/TH -I/home/lff/miniconda3/envs/langscene/lib/python3.10/site-packages/torch/include/THC -I/usr/local/cuda-12.2/include -I/home/lff/miniconda3/envs/langscene/include/python3.10 -c\ncuda_post_cflags = -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr --compiler-options ''\"'\"'-fPIC'\"'\"'' -I/home/lff/data1/cjw/langscene/field_construction/submodules/diff-langsurf-rasterizer/third_party/glm/ -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE=\"_gcc\"' '-DPYBIND11_STDLIB=\"_libstdcpp\"' '-DPYBIND11_BUILD_ABI=\"_cxxabi1011\"' -DTORCH_EXTENSION_NAME=_C -D_GLIBCXX_USE_CXX11_ABI=0 -gencode=arch=compute_86,code=compute_86 -gencode=arch=compute_86,code=sm_86 -std=c++17\ncuda_dlink_post_cflags = \nldflags = \n\nrule compile\n command = $cxx -MMD -MF $out.d $cflags -c $in -o $out $post_cflags\n depfile = $out.d\n deps = gcc\n\nrule cuda_compile\n depfile = $out.d\n deps = gcc\n command = $nvcc --generate-dependencies-with-compile --dependency-output $out.d $cuda_cflags -c $in -o $out $cuda_post_cflags\n\n\n\n\n\nbuild /home/lff/data1/cjw/langscene/field_construction/submodules/diff-langsurf-rasterizer/build/temp.linux-x86_64-cpython-310/cuda_rasterizer/backward.o: cuda_compile /home/lff/data1/cjw/langscene/field_construction/submodules/diff-langsurf-rasterizer/cuda_rasterizer/backward.cu\nbuild /home/lff/data1/cjw/langscene/field_construction/submodules/diff-langsurf-rasterizer/build/temp.linux-x86_64-cpython-310/cuda_rasterizer/forward.o: cuda_compile /home/lff/data1/cjw/langscene/field_construction/submodules/diff-langsurf-rasterizer/cuda_rasterizer/forward.cu\nbuild /home/lff/data1/cjw/langscene/field_construction/submodules/diff-langsurf-rasterizer/build/temp.linux-x86_64-cpython-310/cuda_rasterizer/rasterizer_impl.o: cuda_compile /home/lff/data1/cjw/langscene/field_construction/submodules/diff-langsurf-rasterizer/cuda_rasterizer/rasterizer_impl.cu\nbuild /home/lff/data1/cjw/langscene/field_construction/submodules/diff-langsurf-rasterizer/build/temp.linux-x86_64-cpython-310/ext.o: compile /home/lff/data1/cjw/langscene/field_construction/submodules/diff-langsurf-rasterizer/ext.cpp\nbuild /home/lff/data1/cjw/langscene/field_construction/submodules/diff-langsurf-rasterizer/build/temp.linux-x86_64-cpython-310/rasterize_points.o: cuda_compile /home/lff/data1/cjw/langscene/field_construction/submodules/diff-langsurf-rasterizer/rasterize_points.cu\n\n\n\n\n\n\n" }, { "path": "field_construction/submodules/diff-langsurf-rasterizer/cuda_rasterizer/auxiliary.h", "content": "/*\n * Copyright (C) 2023, Inria\n * GRAPHDECO research group, https://team.inria.fr/graphdeco\n * All rights reserved.\n *\n * This software is free for non-commercial, research and evaluation use \n * under the terms of the LICENSE.md file.\n *\n * For inquiries contact george.drettakis@inria.fr\n */\n\n#ifndef CUDA_RASTERIZER_AUXILIARY_H_INCLUDED\n#define CUDA_RASTERIZER_AUXILIARY_H_INCLUDED\n\n#include \"config.h\"\n#include \"stdio.h\"\n\n#define BLOCK_SIZE (BLOCK_X * BLOCK_Y)\n#define NUM_WARPS (BLOCK_SIZE/32)\n\n// Spherical harmonics coefficients\n__device__ const float SH_C0 = 0.28209479177387814f;\n__device__ const float SH_C1 = 0.4886025119029199f;\n__device__ const float SH_C2[] = {\n\t1.0925484305920792f,\n\t-1.0925484305920792f,\n\t0.31539156525252005f,\n\t-1.0925484305920792f,\n\t0.5462742152960396f\n};\n__device__ const float SH_C3[] = {\n\t-0.5900435899266435f,\n\t2.890611442640554f,\n\t-0.4570457994644658f,\n\t0.3731763325901154f,\n\t-0.4570457994644658f,\n\t1.445305721320277f,\n\t-0.5900435899266435f\n};\n\n__forceinline__ __device__ float ndc2Pix(float v, int S)\n{\n\treturn ((v + 1.0) * S - 1.0) * 0.5;\n}\n\n__forceinline__ __device__ void getRect(const float2 p, int max_radius, uint2& rect_min, uint2& rect_max, dim3 grid)\n{\n\trect_min = {\n\t\tmin(grid.x, max((int)0, (int)((p.x - max_radius) / BLOCK_X))),\n\t\tmin(grid.y, max((int)0, (int)((p.y - max_radius) / BLOCK_Y)))\n\t};\n\trect_max = {\n\t\tmin(grid.x, max((int)0, (int)((p.x + max_radius + BLOCK_X - 1) / BLOCK_X))),\n\t\tmin(grid.y, max((int)0, (int)((p.y + max_radius + BLOCK_Y - 1) / BLOCK_Y)))\n\t};\n}\n\n__forceinline__ __device__ float3 transformPoint4x3(const float3& p, const float* matrix)\n{\n\tfloat3 transformed = {\n\t\tmatrix[0] * p.x + matrix[4] * p.y + matrix[8] * p.z + matrix[12],\n\t\tmatrix[1] * p.x + matrix[5] * p.y + matrix[9] * p.z + matrix[13],\n\t\tmatrix[2] * p.x + matrix[6] * p.y + matrix[10] * p.z + matrix[14],\n\t};\n\treturn transformed;\n}\n\n__forceinline__ __device__ float4 transformPoint4x4(const float3& p, const float* matrix)\n{\n\tfloat4 transformed = {\n\t\tmatrix[0] * p.x + matrix[4] * p.y + matrix[8] * p.z + matrix[12],\n\t\tmatrix[1] * p.x + matrix[5] * p.y + matrix[9] * p.z + matrix[13],\n\t\tmatrix[2] * p.x + matrix[6] * p.y + matrix[10] * p.z + matrix[14],\n\t\tmatrix[3] * p.x + matrix[7] * p.y + matrix[11] * p.z + matrix[15]\n\t};\n\treturn transformed;\n}\n\n__forceinline__ __device__ float3 transformVec4x3(const float3& p, const float* matrix)\n{\n\tfloat3 transformed = {\n\t\tmatrix[0] * p.x + matrix[4] * p.y + matrix[8] * p.z,\n\t\tmatrix[1] * p.x + matrix[5] * p.y + matrix[9] * p.z,\n\t\tmatrix[2] * p.x + matrix[6] * p.y + matrix[10] * p.z,\n\t};\n\treturn transformed;\n}\n\n__forceinline__ __device__ float3 transformVec4x3Transpose(const float3& p, const float* matrix)\n{\n\tfloat3 transformed = {\n\t\tmatrix[0] * p.x + matrix[1] * p.y + matrix[2] * p.z,\n\t\tmatrix[4] * p.x + matrix[5] * p.y + matrix[6] * p.z,\n\t\tmatrix[8] * p.x + matrix[9] * p.y + matrix[10] * p.z,\n\t};\n\treturn transformed;\n}\n\n__forceinline__ __device__ float dnormvdz(float3 v, float3 dv)\n{\n\tfloat sum2 = v.x * v.x + v.y * v.y + v.z * v.z;\n\tfloat invsum32 = 1.0f / sqrt(sum2 * sum2 * sum2);\n\tfloat dnormvdz = (-v.x * v.z * dv.x - v.y * v.z * dv.y + (sum2 - v.z * v.z) * dv.z) * invsum32;\n\treturn dnormvdz;\n}\n\n__forceinline__ __device__ float3 dnormvdv(float3 v, float3 dv)\n{\n\tfloat sum2 = v.x * v.x + v.y * v.y + v.z * v.z;\n\tfloat invsum32 = 1.0f / sqrt(sum2 * sum2 * sum2);\n\n\tfloat3 dnormvdv;\n\tdnormvdv.x = ((+sum2 - v.x * v.x) * dv.x - v.y * v.x * dv.y - v.z * v.x * dv.z) * invsum32;\n\tdnormvdv.y = (-v.x * v.y * dv.x + (sum2 - v.y * v.y) * dv.y - v.z * v.y * dv.z) * invsum32;\n\tdnormvdv.z = (-v.x * v.z * dv.x - v.y * v.z * dv.y + (sum2 - v.z * v.z) * dv.z) * invsum32;\n\treturn dnormvdv;\n}\n\n__forceinline__ __device__ float4 dnormvdv(float4 v, float4 dv)\n{\n\tfloat sum2 = v.x * v.x + v.y * v.y + v.z * v.z + v.w * v.w;\n\tfloat invsum32 = 1.0f / sqrt(sum2 * sum2 * sum2);\n\n\tfloat4 vdv = { v.x * dv.x, v.y * dv.y, v.z * dv.z, v.w * dv.w };\n\tfloat vdv_sum = vdv.x + vdv.y + vdv.z + vdv.w;\n\tfloat4 dnormvdv;\n\tdnormvdv.x = ((sum2 - v.x * v.x) * dv.x - v.x * (vdv_sum - vdv.x)) * invsum32;\n\tdnormvdv.y = ((sum2 - v.y * v.y) * dv.y - v.y * (vdv_sum - vdv.y)) * invsum32;\n\tdnormvdv.z = ((sum2 - v.z * v.z) * dv.z - v.z * (vdv_sum - vdv.z)) * invsum32;\n\tdnormvdv.w = ((sum2 - v.w * v.w) * dv.w - v.w * (vdv_sum - vdv.w)) * invsum32;\n\treturn dnormvdv;\n}\n\n__forceinline__ __device__ float sigmoid(float x)\n{\n\treturn 1.0f / (1.0f + expf(-x));\n}\n\n__forceinline__ __device__ bool in_frustum(int idx,\n\tconst float* orig_points,\n\tconst float* viewmatrix,\n\tconst float* projmatrix,\n\tbool prefiltered,\n\tfloat3& p_view)\n{\n\tfloat3 p_orig = { orig_points[3 * idx], orig_points[3 * idx + 1], orig_points[3 * idx + 2] };\n\n\t// Bring points to screen space\n\tfloat4 p_hom = transformPoint4x4(p_orig, projmatrix);\n\tfloat p_w = 1.0f / (p_hom.w + 0.0000001f);\n\tfloat3 p_proj = { p_hom.x * p_w, p_hom.y * p_w, p_hom.z * p_w };\n\tp_view = transformPoint4x3(p_orig, viewmatrix);\n\n\tif (p_view.z <= 0.2f)// || ((p_proj.x < -1.3 || p_proj.x > 1.3 || p_proj.y < -1.3 || p_proj.y > 1.3)))\n\t{\n\t\tif (prefiltered)\n\t\t{\n\t\t\tprintf(\"Point is filtered although prefiltered is set. This shouldn't happen!\");\n\t\t\t__trap();\n\t\t}\n\t\treturn false;\n\t}\n\treturn true;\n}\n\n#define CHECK_CUDA(A, debug) \\\nA; if(debug) { \\\nauto ret = cudaDeviceSynchronize(); \\\nif (ret != cudaSuccess) { \\\nstd::cerr << \"\\n[CUDA ERROR] in \" << __FILE__ << \"\\nLine \" << __LINE__ << \": \" << cudaGetErrorString(ret); \\\nthrow std::runtime_error(cudaGetErrorString(ret)); \\\n} \\\n}\n\n#endif" }, { "path": "field_construction/submodules/diff-langsurf-rasterizer/cuda_rasterizer/backward.cu", "content": "/*\n * Copyright (C) 2023, Inria\n * GRAPHDECO research group, https://team.inria.fr/graphdeco\n * All rights reserved.\n *\n * This software is free for non-commercial, research and evaluation use \n * under the terms of the LICENSE.md file.\n *\n * For inquiries contact george.drettakis@inria.fr\n */\n\n#include \"backward.h\"\n#include \"auxiliary.h\"\n#include \n#include \nnamespace cg = cooperative_groups;\n\n// Backward pass for conversion of spherical harmonics to RGB for\n// each Gaussian.\n__device__ void computeColorFromSH(int idx, int deg, int max_coeffs, const glm::vec3* means, glm::vec3 campos, const float* shs, const bool* clamped, const glm::vec3* dL_dcolor, glm::vec3* dL_dmeans, glm::vec3* dL_dshs)\n{\n\t// Compute intermediate values, as it is done during forward\n\tglm::vec3 pos = means[idx];\n\tglm::vec3 dir_orig = pos - campos;\n\tglm::vec3 dir = dir_orig / glm::length(dir_orig);\n\n\tglm::vec3* sh = ((glm::vec3*)shs) + idx * max_coeffs;\n\n\t// Use PyTorch rule for clamping: if clamping was applied,\n\t// gradient becomes 0.\n\tglm::vec3 dL_dRGB = dL_dcolor[idx];\n\tdL_dRGB.x *= clamped[3 * idx + 0] ? 0 : 1;\n\tdL_dRGB.y *= clamped[3 * idx + 1] ? 0 : 1;\n\tdL_dRGB.z *= clamped[3 * idx + 2] ? 0 : 1;\n\n\tglm::vec3 dRGBdx(0, 0, 0);\n\tglm::vec3 dRGBdy(0, 0, 0);\n\tglm::vec3 dRGBdz(0, 0, 0);\n\tfloat x = dir.x;\n\tfloat y = dir.y;\n\tfloat z = dir.z;\n\n\t// Target location for this Gaussian to write SH gradients to\n\tglm::vec3* dL_dsh = dL_dshs + idx * max_coeffs;\n\n\t// No tricks here, just high school-level calculus.\n\tfloat dRGBdsh0 = SH_C0;\n\tdL_dsh[0] = dRGBdsh0 * dL_dRGB;\n\tif (deg > 0)\n\t{\n\t\tfloat dRGBdsh1 = -SH_C1 * y;\n\t\tfloat dRGBdsh2 = SH_C1 * z;\n\t\tfloat dRGBdsh3 = -SH_C1 * x;\n\t\tdL_dsh[1] = dRGBdsh1 * dL_dRGB;\n\t\tdL_dsh[2] = dRGBdsh2 * dL_dRGB;\n\t\tdL_dsh[3] = dRGBdsh3 * dL_dRGB;\n\n\t\tdRGBdx = -SH_C1 * sh[3];\n\t\tdRGBdy = -SH_C1 * sh[1];\n\t\tdRGBdz = SH_C1 * sh[2];\n\n\t\tif (deg > 1)\n\t\t{\n\t\t\tfloat xx = x * x, yy = y * y, zz = z * z;\n\t\t\tfloat xy = x * y, yz = y * z, xz = x * z;\n\n\t\t\tfloat dRGBdsh4 = SH_C2[0] * xy;\n\t\t\tfloat dRGBdsh5 = SH_C2[1] * yz;\n\t\t\tfloat dRGBdsh6 = SH_C2[2] * (2.f * zz - xx - yy);\n\t\t\tfloat dRGBdsh7 = SH_C2[3] * xz;\n\t\t\tfloat dRGBdsh8 = SH_C2[4] * (xx - yy);\n\t\t\tdL_dsh[4] = dRGBdsh4 * dL_dRGB;\n\t\t\tdL_dsh[5] = dRGBdsh5 * dL_dRGB;\n\t\t\tdL_dsh[6] = dRGBdsh6 * dL_dRGB;\n\t\t\tdL_dsh[7] = dRGBdsh7 * dL_dRGB;\n\t\t\tdL_dsh[8] = dRGBdsh8 * dL_dRGB;\n\n\t\t\tdRGBdx += SH_C2[0] * y * sh[4] + SH_C2[2] * 2.f * -x * sh[6] + SH_C2[3] * z * sh[7] + SH_C2[4] * 2.f * x * sh[8];\n\t\t\tdRGBdy += SH_C2[0] * x * sh[4] + SH_C2[1] * z * sh[5] + SH_C2[2] * 2.f * -y * sh[6] + SH_C2[4] * 2.f * -y * sh[8];\n\t\t\tdRGBdz += SH_C2[1] * y * sh[5] + SH_C2[2] * 2.f * 2.f * z * sh[6] + SH_C2[3] * x * sh[7];\n\n\t\t\tif (deg > 2)\n\t\t\t{\n\t\t\t\tfloat dRGBdsh9 = SH_C3[0] * y * (3.f * xx - yy);\n\t\t\t\tfloat dRGBdsh10 = SH_C3[1] * xy * z;\n\t\t\t\tfloat dRGBdsh11 = SH_C3[2] * y * (4.f * zz - xx - yy);\n\t\t\t\tfloat dRGBdsh12 = SH_C3[3] * z * (2.f * zz - 3.f * xx - 3.f * yy);\n\t\t\t\tfloat dRGBdsh13 = SH_C3[4] * x * (4.f * zz - xx - yy);\n\t\t\t\tfloat dRGBdsh14 = SH_C3[5] * z * (xx - yy);\n\t\t\t\tfloat dRGBdsh15 = SH_C3[6] * x * (xx - 3.f * yy);\n\t\t\t\tdL_dsh[9] = dRGBdsh9 * dL_dRGB;\n\t\t\t\tdL_dsh[10] = dRGBdsh10 * dL_dRGB;\n\t\t\t\tdL_dsh[11] = dRGBdsh11 * dL_dRGB;\n\t\t\t\tdL_dsh[12] = dRGBdsh12 * dL_dRGB;\n\t\t\t\tdL_dsh[13] = dRGBdsh13 * dL_dRGB;\n\t\t\t\tdL_dsh[14] = dRGBdsh14 * dL_dRGB;\n\t\t\t\tdL_dsh[15] = dRGBdsh15 * dL_dRGB;\n\n\t\t\t\tdRGBdx += (\n\t\t\t\t\tSH_C3[0] * sh[9] * 3.f * 2.f * xy +\n\t\t\t\t\tSH_C3[1] * sh[10] * yz +\n\t\t\t\t\tSH_C3[2] * sh[11] * -2.f * xy +\n\t\t\t\t\tSH_C3[3] * sh[12] * -3.f * 2.f * xz +\n\t\t\t\t\tSH_C3[4] * sh[13] * (-3.f * xx + 4.f * zz - yy) +\n\t\t\t\t\tSH_C3[5] * sh[14] * 2.f * xz +\n\t\t\t\t\tSH_C3[6] * sh[15] * 3.f * (xx - yy));\n\n\t\t\t\tdRGBdy += (\n\t\t\t\t\tSH_C3[0] * sh[9] * 3.f * (xx - yy) +\n\t\t\t\t\tSH_C3[1] * sh[10] * xz +\n\t\t\t\t\tSH_C3[2] * sh[11] * (-3.f * yy + 4.f * zz - xx) +\n\t\t\t\t\tSH_C3[3] * sh[12] * -3.f * 2.f * yz +\n\t\t\t\t\tSH_C3[4] * sh[13] * -2.f * xy +\n\t\t\t\t\tSH_C3[5] * sh[14] * -2.f * yz +\n\t\t\t\t\tSH_C3[6] * sh[15] * -3.f * 2.f * xy);\n\n\t\t\t\tdRGBdz += (\n\t\t\t\t\tSH_C3[1] * sh[10] * xy +\n\t\t\t\t\tSH_C3[2] * sh[11] * 4.f * 2.f * yz +\n\t\t\t\t\tSH_C3[3] * sh[12] * 3.f * (2.f * zz - xx - yy) +\n\t\t\t\t\tSH_C3[4] * sh[13] * 4.f * 2.f * xz +\n\t\t\t\t\tSH_C3[5] * sh[14] * (xx - yy));\n\t\t\t}\n\t\t}\n\t}\n\n\t// The view direction is an input to the computation. View direction\n\t// is influenced by the Gaussian's mean, so SHs gradients\n\t// must propagate back into 3D position.\n\tglm::vec3 dL_ddir(glm::dot(dRGBdx, dL_dRGB), glm::dot(dRGBdy, dL_dRGB), glm::dot(dRGBdz, dL_dRGB));\n\n\t// Account for normalization of direction\n\tfloat3 dL_dmean = dnormvdv(float3{ dir_orig.x, dir_orig.y, dir_orig.z }, float3{ dL_ddir.x, dL_ddir.y, dL_ddir.z });\n\n\t// Gradients of loss w.r.t. Gaussian means, but only the portion \n\t// that is caused because the mean affects the view-dependent color.\n\t// Additional mean gradient is accumulated in below methods.\n\tdL_dmeans[idx] += glm::vec3(dL_dmean.x, dL_dmean.y, dL_dmean.z);\n}\n\n// Backward version of INVERSE 2D covariance matrix computation\n// (due to length launched as separate kernel before other \n// backward steps contained in preprocess)\n__global__ void computeCov2DCUDA(int P,\n\tconst float3* means,\n\tconst int* radii,\n\tconst float* cov3Ds,\n\tconst float h_x, float h_y,\n\tconst float tan_fovx, float tan_fovy,\n\tconst float* view_matrix,\n\tconst float* dL_dconics,\n\tfloat3* dL_dmeans,\n\tfloat* dL_dcov)\n{\n\tauto idx = cg::this_grid().thread_rank();\n\tif (idx >= P || !(radii[idx] > 0))\n\t\treturn;\n\n\t// Reading location of 3D covariance for this Gaussian\n\tconst float* cov3D = cov3Ds + 6 * idx;\n\n\t// Fetch gradients, recompute 2D covariance and relevant \n\t// intermediate forward results needed in the backward.\n\tfloat3 mean = means[idx];\n\tfloat3 dL_dconic = { dL_dconics[4 * idx], dL_dconics[4 * idx + 1], dL_dconics[4 * idx + 3] };\n\tfloat3 t = transformPoint4x3(mean, view_matrix);\n\t\n\tconst float limx = 1.3f * tan_fovx;\n\tconst float limy = 1.3f * tan_fovy;\n\tconst float txtz = t.x / t.z;\n\tconst float tytz = t.y / t.z;\n\tt.x = min(limx, max(-limx, txtz)) * t.z;\n\tt.y = min(limy, max(-limy, tytz)) * t.z;\n\t\n\tconst float x_grad_mul = txtz < -limx || txtz > limx ? 0 : 1;\n\tconst float y_grad_mul = tytz < -limy || tytz > limy ? 0 : 1;\n\n\tglm::mat3 J = glm::mat3(h_x / t.z, 0.0f, -(h_x * t.x) / (t.z * t.z),\n\t\t0.0f, h_y / t.z, -(h_y * t.y) / (t.z * t.z),\n\t\t0, 0, 0);\n\n\tglm::mat3 W = glm::mat3(\n\t\tview_matrix[0], view_matrix[4], view_matrix[8],\n\t\tview_matrix[1], view_matrix[5], view_matrix[9],\n\t\tview_matrix[2], view_matrix[6], view_matrix[10]);\n\n\tglm::mat3 Vrk = glm::mat3(\n\t\tcov3D[0], cov3D[1], cov3D[2],\n\t\tcov3D[1], cov3D[3], cov3D[4],\n\t\tcov3D[2], cov3D[4], cov3D[5]);\n\n\tglm::mat3 T = W * J;\n\n\tglm::mat3 cov2D = glm::transpose(T) * glm::transpose(Vrk) * T;\n\n\t// Use helper variables for 2D covariance entries. More compact.\n\tfloat a = cov2D[0][0] += 0.3f;\n\tfloat b = cov2D[0][1];\n\tfloat c = cov2D[1][1] += 0.3f;\n\n\tfloat denom = a * c - b * b;\n\tfloat dL_da = 0, dL_db = 0, dL_dc = 0;\n\tfloat denom2inv = 1.0f / ((denom * denom) + 0.0000001f);\n\n\tif (denom2inv != 0)\n\t{\n\t\t// Gradients of loss w.r.t. entries of 2D covariance matrix,\n\t\t// given gradients of loss w.r.t. conic matrix (inverse covariance matrix).\n\t\t// e.g., dL / da = dL / d_conic_a * d_conic_a / d_a\n\t\tdL_da = denom2inv * (-c * c * dL_dconic.x + 2 * b * c * dL_dconic.y + (denom - a * c) * dL_dconic.z);\n\t\tdL_dc = denom2inv * (-a * a * dL_dconic.z + 2 * a * b * dL_dconic.y + (denom - a * c) * dL_dconic.x);\n\t\tdL_db = denom2inv * 2 * (b * c * dL_dconic.x - (denom + 2 * b * b) * dL_dconic.y + a * b * dL_dconic.z);\n\n\t\t// Gradients of loss L w.r.t. each 3D covariance matrix (Vrk) entry, \n\t\t// given gradients w.r.t. 2D covariance matrix (diagonal).\n\t\t// cov2D = transpose(T) * transpose(Vrk) * T;\n\t\tdL_dcov[6 * idx + 0] = (T[0][0] * T[0][0] * dL_da + T[0][0] * T[1][0] * dL_db + T[1][0] * T[1][0] * dL_dc);\n\t\tdL_dcov[6 * idx + 3] = (T[0][1] * T[0][1] * dL_da + T[0][1] * T[1][1] * dL_db + T[1][1] * T[1][1] * dL_dc);\n\t\tdL_dcov[6 * idx + 5] = (T[0][2] * T[0][2] * dL_da + T[0][2] * T[1][2] * dL_db + T[1][2] * T[1][2] * dL_dc);\n\n\t\t// Gradients of loss L w.r.t. each 3D covariance matrix (Vrk) entry, \n\t\t// given gradients w.r.t. 2D covariance matrix (off-diagonal).\n\t\t// Off-diagonal elements appear twice --> double the gradient.\n\t\t// cov2D = transpose(T) * transpose(Vrk) * T;\n\t\tdL_dcov[6 * idx + 1] = 2 * T[0][0] * T[0][1] * dL_da + (T[0][0] * T[1][1] + T[0][1] * T[1][0]) * dL_db + 2 * T[1][0] * T[1][1] * dL_dc;\n\t\tdL_dcov[6 * idx + 2] = 2 * T[0][0] * T[0][2] * dL_da + (T[0][0] * T[1][2] + T[0][2] * T[1][0]) * dL_db + 2 * T[1][0] * T[1][2] * dL_dc;\n\t\tdL_dcov[6 * idx + 4] = 2 * T[0][2] * T[0][1] * dL_da + (T[0][1] * T[1][2] + T[0][2] * T[1][1]) * dL_db + 2 * T[1][1] * T[1][2] * dL_dc;\n\t}\n\telse\n\t{\n\t\tfor (int i = 0; i < 6; i++)\n\t\t\tdL_dcov[6 * idx + i] = 0;\n\t}\n\n\t// Gradients of loss w.r.t. upper 2x3 portion of intermediate matrix T\n\t// cov2D = transpose(T) * transpose(Vrk) * T;\n\tfloat dL_dT00 = 2 * (T[0][0] * Vrk[0][0] + T[0][1] * Vrk[0][1] + T[0][2] * Vrk[0][2]) * dL_da +\n\t\t(T[1][0] * Vrk[0][0] + T[1][1] * Vrk[0][1] + T[1][2] * Vrk[0][2]) * dL_db;\n\tfloat dL_dT01 = 2 * (T[0][0] * Vrk[1][0] + T[0][1] * Vrk[1][1] + T[0][2] * Vrk[1][2]) * dL_da +\n\t\t(T[1][0] * Vrk[1][0] + T[1][1] * Vrk[1][1] + T[1][2] * Vrk[1][2]) * dL_db;\n\tfloat dL_dT02 = 2 * (T[0][0] * Vrk[2][0] + T[0][1] * Vrk[2][1] + T[0][2] * Vrk[2][2]) * dL_da +\n\t\t(T[1][0] * Vrk[2][0] + T[1][1] * Vrk[2][1] + T[1][2] * Vrk[2][2]) * dL_db;\n\tfloat dL_dT10 = 2 * (T[1][0] * Vrk[0][0] + T[1][1] * Vrk[0][1] + T[1][2] * Vrk[0][2]) * dL_dc +\n\t\t(T[0][0] * Vrk[0][0] + T[0][1] * Vrk[0][1] + T[0][2] * Vrk[0][2]) * dL_db;\n\tfloat dL_dT11 = 2 * (T[1][0] * Vrk[1][0] + T[1][1] * Vrk[1][1] + T[1][2] * Vrk[1][2]) * dL_dc +\n\t\t(T[0][0] * Vrk[1][0] + T[0][1] * Vrk[1][1] + T[0][2] * Vrk[1][2]) * dL_db;\n\tfloat dL_dT12 = 2 * (T[1][0] * Vrk[2][0] + T[1][1] * Vrk[2][1] + T[1][2] * Vrk[2][2]) * dL_dc +\n\t\t(T[0][0] * Vrk[2][0] + T[0][1] * Vrk[2][1] + T[0][2] * Vrk[2][2]) * dL_db;\n\n\t// Gradients of loss w.r.t. upper 3x2 non-zero entries of Jacobian matrix\n\t// T = W * J\n\tfloat dL_dJ00 = W[0][0] * dL_dT00 + W[0][1] * dL_dT01 + W[0][2] * dL_dT02;\n\tfloat dL_dJ02 = W[2][0] * dL_dT00 + W[2][1] * dL_dT01 + W[2][2] * dL_dT02;\n\tfloat dL_dJ11 = W[1][0] * dL_dT10 + W[1][1] * dL_dT11 + W[1][2] * dL_dT12;\n\tfloat dL_dJ12 = W[2][0] * dL_dT10 + W[2][1] * dL_dT11 + W[2][2] * dL_dT12;\n\n\tfloat tz = 1.f / t.z;\n\tfloat tz2 = tz * tz;\n\tfloat tz3 = tz2 * tz;\n\n\t// Gradients of loss w.r.t. transformed Gaussian mean t\n\tfloat dL_dtx = x_grad_mul * -h_x * tz2 * dL_dJ02;\n\tfloat dL_dty = y_grad_mul * -h_y * tz2 * dL_dJ12;\n\tfloat dL_dtz = -h_x * tz2 * dL_dJ00 - h_y * tz2 * dL_dJ11 + (2 * h_x * t.x) * tz3 * dL_dJ02 + (2 * h_y * t.y) * tz3 * dL_dJ12;\n\n\t// Account for transformation of mean to t\n\t// t = transformPoint4x3(mean, view_matrix);\n\tfloat3 dL_dmean = transformVec4x3Transpose({ dL_dtx, dL_dty, dL_dtz }, view_matrix);\n\n\t// Gradients of loss w.r.t. Gaussian means, but only the portion \n\t// that is caused because the mean affects the covariance matrix.\n\t// Additional mean gradient is accumulated in BACKWARD::preprocess.\n\tdL_dmeans[idx] = dL_dmean;\n}\n\n// Backward pass for the conversion of scale and rotation to a \n// 3D covariance matrix for each Gaussian. \n__device__ void computeCov3D(int idx, const glm::vec3 scale, float mod, const glm::vec4 rot, const float* dL_dcov3Ds, glm::vec3* dL_dscales, glm::vec4* dL_drots)\n{\n\t// Recompute (intermediate) results for the 3D covariance computation.\n\tglm::vec4 q = rot;// / glm::length(rot);\n\tfloat r = q.x;\n\tfloat x = q.y;\n\tfloat y = q.z;\n\tfloat z = q.w;\n\n\tglm::mat3 R = glm::mat3(\n\t\t1.f - 2.f * (y * y + z * z), 2.f * (x * y - r * z), 2.f * (x * z + r * y),\n\t\t2.f * (x * y + r * z), 1.f - 2.f * (x * x + z * z), 2.f * (y * z - r * x),\n\t\t2.f * (x * z - r * y), 2.f * (y * z + r * x), 1.f - 2.f * (x * x + y * y)\n\t);\n\n\tglm::mat3 S = glm::mat3(1.0f);\n\n\tglm::vec3 s = mod * scale;\n\tS[0][0] = s.x;\n\tS[1][1] = s.y;\n\tS[2][2] = s.z;\n\n\tglm::mat3 M = S * R;\n\n\tconst float* dL_dcov3D = dL_dcov3Ds + 6 * idx;\n\n\tglm::vec3 dunc(dL_dcov3D[0], dL_dcov3D[3], dL_dcov3D[5]);\n\tglm::vec3 ounc = 0.5f * glm::vec3(dL_dcov3D[1], dL_dcov3D[2], dL_dcov3D[4]);\n\n\t// Convert per-element covariance loss gradients to matrix form\n\tglm::mat3 dL_dSigma = glm::mat3(\n\t\tdL_dcov3D[0], 0.5f * dL_dcov3D[1], 0.5f * dL_dcov3D[2],\n\t\t0.5f * dL_dcov3D[1], dL_dcov3D[3], 0.5f * dL_dcov3D[4],\n\t\t0.5f * dL_dcov3D[2], 0.5f * dL_dcov3D[4], dL_dcov3D[5]\n\t);\n\n\t// Compute loss gradient w.r.t. matrix M\n\t// dSigma_dM = 2 * M\n\tglm::mat3 dL_dM = 2.0f * M * dL_dSigma;\n\n\tglm::mat3 Rt = glm::transpose(R);\n\tglm::mat3 dL_dMt = glm::transpose(dL_dM);\n\n\t// Gradients of loss w.r.t. scale\n\tglm::vec3* dL_dscale = dL_dscales + idx;\n\tdL_dscale->x = glm::dot(Rt[0], dL_dMt[0]);\n\tdL_dscale->y = glm::dot(Rt[1], dL_dMt[1]);\n\tdL_dscale->z = glm::dot(Rt[2], dL_dMt[2]);\n\n\tdL_dMt[0] *= s.x;\n\tdL_dMt[1] *= s.y;\n\tdL_dMt[2] *= s.z;\n\n\t// Gradients of loss w.r.t. normalized quaternion\n\tglm::vec4 dL_dq;\n\tdL_dq.x = 2 * z * (dL_dMt[0][1] - dL_dMt[1][0]) + 2 * y * (dL_dMt[2][0] - dL_dMt[0][2]) + 2 * x * (dL_dMt[1][2] - dL_dMt[2][1]);\n\tdL_dq.y = 2 * y * (dL_dMt[1][0] + dL_dMt[0][1]) + 2 * z * (dL_dMt[2][0] + dL_dMt[0][2]) + 2 * r * (dL_dMt[1][2] - dL_dMt[2][1]) - 4 * x * (dL_dMt[2][2] + dL_dMt[1][1]);\n\tdL_dq.z = 2 * x * (dL_dMt[1][0] + dL_dMt[0][1]) + 2 * r * (dL_dMt[2][0] - dL_dMt[0][2]) + 2 * z * (dL_dMt[1][2] + dL_dMt[2][1]) - 4 * y * (dL_dMt[2][2] + dL_dMt[0][0]);\n\tdL_dq.w = 2 * r * (dL_dMt[0][1] - dL_dMt[1][0]) + 2 * x * (dL_dMt[2][0] + dL_dMt[0][2]) + 2 * y * (dL_dMt[1][2] + dL_dMt[2][1]) - 4 * z * (dL_dMt[1][1] + dL_dMt[0][0]);\n\n\t// Gradients of loss w.r.t. unnormalized quaternion\n\tfloat4* dL_drot = (float4*)(dL_drots + idx);\n\t*dL_drot = float4{ dL_dq.x, dL_dq.y, dL_dq.z, dL_dq.w };//dnormvdv(float4{ rot.x, rot.y, rot.z, rot.w }, float4{ dL_dq.x, dL_dq.y, dL_dq.z, dL_dq.w });\n}\n\n// Backward pass of the preprocessing steps, except\n// for the covariance computation and inversion\n// (those are handled by a previous kernel call)\ntemplate\n__global__ void preprocessCUDA(\n\tint P, int D, int M,\n\tconst float3* means,\n\tconst int* radii,\n\tconst float* shs,\n\tconst bool* clamped,\n\tconst glm::vec3* scales,\n\tconst glm::vec4* rotations,\n\tconst float scale_modifier,\n\tconst float* proj,\n\tconst glm::vec3* campos,\n\tconst float3* dL_dmean2D,\n\tglm::vec3* dL_dmeans,\n\tfloat* dL_dcolor,\n\tfloat* dL_dcov3D,\n\tfloat* dL_dsh,\n\tglm::vec3* dL_dscale,\n\tglm::vec4* dL_drot)\n{\n\tauto idx = cg::this_grid().thread_rank();\n\tif (idx >= P || !(radii[idx] > 0))\n\t\treturn;\n\n\tfloat3 m = means[idx];\n\n\t// Taking care of gradients from the screenspace points\n\tfloat4 m_hom = transformPoint4x4(m, proj);\n\tfloat m_w = 1.0f / (m_hom.w + 0.0000001f);\n\n\t// Compute loss gradient w.r.t. 3D means due to gradients of 2D means\n\t// from rendering procedure\n\tglm::vec3 dL_dmean;\n\tfloat mul1 = (proj[0] * m.x + proj[4] * m.y + proj[8] * m.z + proj[12]) * m_w * m_w;\n\tfloat mul2 = (proj[1] * m.x + proj[5] * m.y + proj[9] * m.z + proj[13]) * m_w * m_w;\n\tdL_dmean.x = (proj[0] * m_w - proj[3] * mul1) * dL_dmean2D[idx].x + (proj[1] * m_w - proj[3] * mul2) * dL_dmean2D[idx].y;\n\tdL_dmean.y = (proj[4] * m_w - proj[7] * mul1) * dL_dmean2D[idx].x + (proj[5] * m_w - proj[7] * mul2) * dL_dmean2D[idx].y;\n\tdL_dmean.z = (proj[8] * m_w - proj[11] * mul1) * dL_dmean2D[idx].x + (proj[9] * m_w - proj[11] * mul2) * dL_dmean2D[idx].y;\n\n\t// That's the second part of the mean gradient. Previous computation\n\t// of cov2D and following SH conversion also affects it.\n\tdL_dmeans[idx] += dL_dmean;\n\n\t// Compute gradient updates due to computing colors from SHs\n\tif (shs)\n\t\tcomputeColorFromSH(idx, D, M, (glm::vec3*)means, *campos, shs, clamped, (glm::vec3*)dL_dcolor, (glm::vec3*)dL_dmeans, (glm::vec3*)dL_dsh);\n\n\t// Compute gradient updates due to computing covariance from scale/rotation\n\tif (scales)\n\t\tcomputeCov3D(idx, scales[idx], scale_modifier, rotations[idx], dL_dcov3D, dL_dscale, dL_drot);\n}\n\n// Backward version of the rendering procedure.\ntemplate \n__global__ void __launch_bounds__(BLOCK_X * BLOCK_Y)\nrenderCUDA(\n\tconst uint2* __restrict__ ranges,\n\tconst uint32_t* __restrict__ point_list,\n\tint W, int H,\n\tfloat fx, float fy,\n\tconst float* __restrict__ bg_color,\n\tconst float2* __restrict__ points_xy_image,\n\tconst float4* __restrict__ conic_opacity,\n\tconst float* __restrict__ colors,\n\tconst float* __restrict__ language_feature,\n\tconst float* __restrict__ language_feature_instance,\n\tconst float* __restrict__ all_maps,\n\tconst float* __restrict__ all_map_pixels,\n\tconst float* __restrict__ final_Ts,\n\tconst uint32_t* __restrict__ n_contrib,\n\tconst float* __restrict__ dL_dpixels,\n\tconst float* __restrict__ dL_dpixels_F,\n\tconst float* __restrict__ dL_dpixels_F_instance,\n\tconst float* __restrict__ dL_dout_all_maps,\n\tconst float* __restrict__ dL_dout_plane_depths,\n\tfloat3* __restrict__ dL_dmean2D,\n\tfloat3* __restrict__ dL_dmean2D_abs,\n\tfloat4* __restrict__ dL_dconic2D,\n\tfloat* __restrict__ dL_dopacity,\n\tfloat* __restrict__ dL_dcolors,\n\tfloat* __restrict__ dL_dlanguage_feature,\n\tfloat* __restrict__ dL_dlanguage_feature_instance,\n\tfloat* __restrict__ dL_dall_map,\n\tconst bool render_geo,\n\tbool include_feature)\n{\n\t// We rasterize again. Compute necessary block info.\n\tauto block = cg::this_thread_block();\n\tconst uint32_t horizontal_blocks = (W + BLOCK_X - 1) / BLOCK_X;\n\tconst uint2 pix_min = { block.group_index().x * BLOCK_X, block.group_index().y * BLOCK_Y };\n\tconst uint2 pix_max = { min(pix_min.x + BLOCK_X, W), min(pix_min.y + BLOCK_Y , H) };\n\tconst uint2 pix = { pix_min.x + block.thread_index().x, pix_min.y + block.thread_index().y };\n\tconst uint32_t pix_id = W * pix.y + pix.x;\n\tconst float2 pixf = { (float)pix.x, (float)pix.y };\n\tconst float2 ray = { (pixf.x - W * 0.5) / fx, (pixf.y - H * 0.5) / fy };\n\n\tconst bool inside = pix.x < W&& pix.y < H;\n\tconst uint2 range = ranges[block.group_index().y * horizontal_blocks + block.group_index().x];\n\n\tconst int rounds = ((range.y - range.x + BLOCK_SIZE - 1) / BLOCK_SIZE);\n\n\tbool done = !inside;\n\tint toDo = range.y - range.x;\n\n\t__shared__ int collected_id[BLOCK_SIZE];\n\t__shared__ float2 collected_xy[BLOCK_SIZE];\n\t__shared__ float4 collected_conic_opacity[BLOCK_SIZE];\n\t__shared__ float collected_colors[C * BLOCK_SIZE];\n\t__shared__ float collected_feature[F * BLOCK_SIZE];\n\t__shared__ float collected_feature_instance[F_ins * BLOCK_SIZE];\n\t__shared__ float collected_all_maps[MAP_N * BLOCK_SIZE];\n\n\t// In the forward, we stored the final value for T, the\n\t// product of all (1 - alpha) factors. \n\tconst float T_final = inside ? final_Ts[pix_id] : 0;\n\tfloat T = T_final;\n\n\t// We start from the back. The ID of the last contributing\n\t// Gaussian is known from each pixel from the forward.\n\tuint32_t contributor = toDo;\n\tconst int last_contributor = inside ? n_contrib[pix_id] : 0;\n\n\tfloat accum_rec[C] = { 0 };\n\tfloat accum_all_map[MAP_N] = { 0 };\n\tfloat accum_rec_F[F] = { 0 };\n\tfloat accum_rec_F_instance[F_ins] = { 0 };\n\n\tfloat dL_dpixel[C];\n\tfloat dL_dout_all_map[MAP_N];\n\tfloat dL_dpixel_F[F] = { 0 };\n\tfloat dL_dpixel_F_instance[F_ins] = { 0 };\n\t// float grad_sum = 0;\n\n\tif (inside) {\n\t\tfor (int i = 0; i < C; i++) {\n\t\t\tdL_dpixel[i] = dL_dpixels[i * H * W + pix_id];\n\t\t\t// grad_sum += fabs(dL_dpixel[i]);\n\t\t}\n\t\tif (include_feature) {\n\t\t\tfor (int i = 0; i < F; i++) {\n\t\t\t\tdL_dpixel_F[i] = dL_dpixels_F[i * H * W + pix_id];\n\t\t\t}\n\t\t\tfor (int i = 0; i < F_ins; i++) {\n\t\t\t\tdL_dpixel_F_instance[i] = dL_dpixels_F_instance[i * H * W + pix_id];\n\t\t\t}\n\t\t}\n\t\tif(render_geo) {\n\t\t\tfor (int i = 0; i < MAP_N; i++) {\n\t\t\t\tdL_dout_all_map[i] = dL_dout_all_maps[i * H * W + pix_id];\n\t\t\t\t// grad_sum += fabs(dL_dout_all_map[i]);\n\t\t\t}\n\t\t\tconst float3 normal = {all_map_pixels[pix_id], all_map_pixels[H * W + pix_id], all_map_pixels[2 * H * W + pix_id]};\n\t\t\tconst float distance = all_map_pixels[4 * H * W + pix_id];\n\t\t\tconst float tmp = (normal.x * ray.x + normal.y * ray.y + normal.z + 1.0e-8);\n\t\t\tdL_dout_all_map[MAP_N-1] += (-dL_dout_plane_depths[pix_id] / tmp);\n\t\t\tdL_dout_all_map[0] += dL_dout_plane_depths[pix_id] * (distance / (tmp * tmp) * ray.x);\n\t\t\tdL_dout_all_map[1] += dL_dout_plane_depths[pix_id] * (distance / (tmp * tmp) * ray.y);\n\t\t\tdL_dout_all_map[2] += dL_dout_plane_depths[pix_id] * (distance / (tmp * tmp));\n\t\t}\n\t}\n\t// If grad is too small, skip\n\t// if (grad_sum < 0.000001f) {\n\t// \tdone = true;\n\t// }\n\n\tfloat last_alpha = 0;\n\tfloat last_color[C] = { 0 };\n\tfloat last_language_feature[F] = { 0 };\n\tfloat last_language_feature_instance[F_ins] = { 0 };\n\tfloat last_all_map[MAP_N] = { 0 };\n\n\n\t// Gradient of pixel coordinate w.r.t. normalized \n\t// screen-space viewport corrdinates (-1 to 1)\n\tconst float ddelx_dx = 0.5 * W;\n\tconst float ddely_dy = 0.5 * H;\n\n\t// Traverse all Gaussians\n\tfor (int i = 0; i < rounds; i++, toDo -= BLOCK_SIZE)\n\t{\n\t\t// Load auxiliary data into shared memory, start in the BACK\n\t\t// and load them in revers order.\n\t\tblock.sync();\n\t\tconst int progress = i * BLOCK_SIZE + block.thread_rank();\n\t\tif (range.x + progress < range.y)\n\t\t{\n\t\t\tconst int coll_id = point_list[range.y - progress - 1];\n\t\t\tcollected_id[block.thread_rank()] = coll_id;\n\t\t\tcollected_xy[block.thread_rank()] = points_xy_image[coll_id];\n\t\t\tcollected_conic_opacity[block.thread_rank()] = conic_opacity[coll_id];\n\t\t\tfor (int i = 0; i < C; i++)\n\t\t\t\tcollected_colors[i * BLOCK_SIZE + block.thread_rank()] = colors[coll_id * C + i];\n\t\t\tif (include_feature) {\n\t\t\t\tfor (int i = 0; i < F; i++)\n\t\t\t\t\tcollected_feature[i * BLOCK_SIZE + block.thread_rank()] = language_feature[coll_id * F + i];\n\t\t\t\tfor (int i = 0; i < F_ins; i++)\n\t\t\t\t\tcollected_feature_instance[i * BLOCK_SIZE + block.thread_rank()] = language_feature_instance[coll_id * F_ins + i];\n\t\t\t}\n\t\t\tif (render_geo) {\n\t\t\t\tfor (int i = 0; i < MAP_N; i++)\n\t\t\t\t\tcollected_all_maps[i * BLOCK_SIZE + block.thread_rank()] = all_maps[coll_id * MAP_N + i];\n\t\t\t}\n\t\t}\n\t\tblock.sync();\n\n\t\t// Iterate over Gaussians\n\t\tfor (int j = 0; !done && j < min(BLOCK_SIZE, toDo); j++)\n\t\t{\n\t\t\t// Keep track of current Gaussian ID. Skip, if this one\n\t\t\t// is behind the last contributor for this pixel.\n\t\t\tcontributor--;\n\t\t\tif (contributor >= last_contributor)\n\t\t\t\tcontinue;\n\n\t\t\t// Compute blending values, as before.\n\t\t\tconst float2 xy = collected_xy[j];\n\t\t\tconst float2 d = { xy.x - pixf.x, xy.y - pixf.y };\n\t\t\tconst float4 con_o = collected_conic_opacity[j];\n\t\t\tconst float power = -0.5f * (con_o.x * d.x * d.x + con_o.z * d.y * d.y) - con_o.y * d.x * d.y;\n\t\t\tif (power > 0.0f)\n\t\t\t\tcontinue;\n\n\t\t\tconst float G = exp(power);\n\t\t\tconst float alpha = min(0.99f, con_o.w * G);\n\t\t\tif (alpha < 1.0f / 255.0f)\n\t\t\t\tcontinue;\n\n\t\t\tT = T / (1.f - alpha);\n\t\t\tconst float dchannel_dcolor = alpha * T;\n\n\t\t\t// Propagate gradients to per-Gaussian colors and keep\n\t\t\t// gradients w.r.t. alpha (blending factor for a Gaussian/pixel\n\t\t\t// pair).\n\t\t\tfloat dL_dalpha = 0.0f;\n\t\t\tconst int global_id = collected_id[j];\n\t\t\tfor (int ch = 0; ch < C; ch++)\n\t\t\t{\n\t\t\t\tconst float c = collected_colors[ch * BLOCK_SIZE + j];\n\t\t\t\t// Update last color (to be used in the next iteration)\n\t\t\t\taccum_rec[ch] = last_alpha * last_color[ch] + (1.f - last_alpha) * accum_rec[ch];\n\t\t\t\tlast_color[ch] = c;\n\n\t\t\t\tconst float dL_dchannel = dL_dpixel[ch];\n\t\t\t\tdL_dalpha += (c - accum_rec[ch]) * dL_dchannel;\n\t\t\t\t// Update the gradients w.r.t. color of the Gaussian. \n\t\t\t\t// Atomic, since this pixel is just one of potentially\n\t\t\t\t// many that were affected by this Gaussian.\n\t\t\t\tatomicAdd(&(dL_dcolors[global_id * C + ch]), dchannel_dcolor * dL_dchannel);\n\t\t\t}\n\t\t\tif (include_feature) {\n\t\t\t\tfor (int ch = 0; ch < F; ch++)\n\t\t\t\t{\n\t\t\t\t\tconst float f = collected_feature[ch * BLOCK_SIZE + j];\n\t\t\t\t\t// Update last color (to be used in the next iteration)\n\t\t\t\t\taccum_rec_F[ch] = last_alpha * last_language_feature[ch] + (1.f - last_alpha) * accum_rec_F[ch];\n\t\t\t\t\tlast_language_feature[ch] = f;\n\n\t\t\t\t\tconst float dL_dchannel_F = dL_dpixel_F[ch];\n\t\t\t\t\tdL_dalpha += (f - accum_rec_F[ch]) * dL_dchannel_F;\n\t\t\t\t\t// Update the gradients w.r.t. color of the Gaussian. \n\t\t\t\t\t// Atomic, since this pixel is just one of potentially\n\t\t\t\t\t// many that were affected by this Gaussian.\n\t\t\t\t\tatomicAdd(&(dL_dlanguage_feature[global_id * F + ch]), dchannel_dcolor * dL_dchannel_F);\n\t\t\t\t}\n\t\t\t\tfor (int ch = 0; ch < F_ins; ch++)\n\t\t\t\t{\n\t\t\t\t\t// instance\n\t\t\t\t\tconst float f_ins = collected_feature_instance[ch * BLOCK_SIZE + j];\n\t\t\t\t\t// Update last color (to be used in the next iteration)\n\t\t\t\t\taccum_rec_F_instance[ch] = last_alpha * last_language_feature_instance[ch] + (1.f - last_alpha) * accum_rec_F_instance[ch];\n\t\t\t\t\tlast_language_feature_instance[ch] = f_ins;\n\n\t\t\t\t\tconst float dL_dchannel_F_instance = dL_dpixel_F_instance[ch];\n\t\t\t\t\tdL_dalpha += (f_ins - accum_rec_F_instance[ch]) * dL_dchannel_F_instance;\n\t\t\t\t\t// Update the gradients w.r.t. color of the Gaussian. \n\t\t\t\t\t// Atomic, since this pixel is just one of potentially\n\t\t\t\t\t// many that were affected by this Gaussian.\n\t\t\t\t\tatomicAdd(&(dL_dlanguage_feature_instance[global_id * F_ins + ch]), dchannel_dcolor * dL_dchannel_F_instance);\n\t\t\t\t}\n\t\t\t}\n\t\t\tif (render_geo) {\n\t\t\t\tfor (int ch = 0; ch < MAP_N; ch++)\n\t\t\t\t{\n\t\t\t\t\tconst float c = collected_all_maps[ch * BLOCK_SIZE + j];\n\t\t\t\t\t// Update last color (to be used in the next iteration)\n\t\t\t\t\taccum_all_map[ch] = last_alpha * last_all_map[ch] + (1.f - last_alpha) * accum_all_map[ch];\n\t\t\t\t\tlast_all_map[ch] = c;\n\n\t\t\t\t\tconst float dL_dchannel = dL_dout_all_map[ch];\n\t\t\t\t\tdL_dalpha += (c - accum_all_map[ch]) * dL_dchannel;\n\t\t\t\t\t// Update the gradients w.r.t. color of the Gaussian. \n\t\t\t\t\t// Atomic, since this pixel is just one of potentially\n\t\t\t\t\t// many that were affected by this Gaussian.\n\t\t\t\t\tatomicAdd(&(dL_dall_map[global_id * MAP_N + ch]), dchannel_dcolor * dL_dchannel);\n\t\t\t\t}\n\t\t\t}\n\t\t\t\n\t\t\tdL_dalpha *= T;\n\t\t\t// Update last alpha (to be used in the next iteration)\n\t\t\tlast_alpha = alpha;\n\n\t\t\t// Account for fact that alpha also influences how much of\n\t\t\t// the background color is added if nothing left to blend\n\t\t\tfloat bg_dot_dpixel = 0;\n\t\t\tfor (int i = 0; i < C; i++)\n\t\t\t\tbg_dot_dpixel += bg_color[i] * dL_dpixel[i];\n\t\t\tdL_dalpha += (-T_final / (1.f - alpha)) * bg_dot_dpixel;\n\n\n\t\t\t// Helpful reusable temporary variables\n\t\t\tconst float dL_dG = con_o.w * dL_dalpha;\n\t\t\tconst float gdx = G * d.x;\n\t\t\tconst float gdy = G * d.y;\n\t\t\tconst float dG_ddelx = -gdx * con_o.x - gdy * con_o.y;\n\t\t\tconst float dG_ddely = -gdy * con_o.z - gdx * con_o.y;\n\n\t\t\t// Update gradients w.r.t. 2D mean position of the Gaussian\n\t\t\tatomicAdd(&dL_dmean2D[global_id].x, dL_dG * dG_ddelx * ddelx_dx);\n\t\t\tatomicAdd(&dL_dmean2D[global_id].y, dL_dG * dG_ddely * ddely_dy);\n\t\t\tatomicAdd(&dL_dmean2D_abs[global_id].x, fabs(dL_dG * dG_ddelx * ddelx_dx));\n\t\t\tatomicAdd(&dL_dmean2D_abs[global_id].y, fabs(dL_dG * dG_ddely * ddely_dy));\n\n\t\t\t// Update gradients w.r.t. 2D covariance (2x2 matrix, symmetric)\n\t\t\tatomicAdd(&dL_dconic2D[global_id].x, -0.5f * gdx * d.x * dL_dG);\n\t\t\tatomicAdd(&dL_dconic2D[global_id].y, -0.5f * gdx * d.y * dL_dG);\n\t\t\tatomicAdd(&dL_dconic2D[global_id].w, -0.5f * gdy * d.y * dL_dG);\n\n\t\t\t// Update gradients w.r.t. opacity of the Gaussian\n\t\t\tatomicAdd(&(dL_dopacity[global_id]), G * dL_dalpha);\n\t\t}\n\t}\n}\n\nvoid BACKWARD::preprocess(\n\tint P, int D, int M,\n\tconst float3* means3D,\n\tconst int* radii,\n\tconst float* shs,\n\tconst bool* clamped,\n\tconst glm::vec3* scales,\n\tconst glm::vec4* rotations,\n\tconst float scale_modifier,\n\tconst float* cov3Ds,\n\tconst float* viewmatrix,\n\tconst float* projmatrix,\n\tconst float focal_x, float focal_y,\n\tconst float tan_fovx, float tan_fovy,\n\tconst glm::vec3* campos,\n\tconst float3* dL_dmean2D,\n\tconst float* dL_dconic,\n\tglm::vec3* dL_dmean3D,\n\tfloat* dL_dcolor,\n\tfloat* dL_dcov3D,\n\tfloat* dL_dsh,\n\tglm::vec3* dL_dscale,\n\tglm::vec4* dL_drot)\n{\n\t// Propagate gradients for the path of 2D conic matrix computation. \n\t// Somewhat long, thus it is its own kernel rather than being part of \n\t// \"preprocess\". When done, loss gradient w.r.t. 3D means has been\n\t// modified and gradient w.r.t. 3D covariance matrix has been computed.\t\n\tcomputeCov2DCUDA << <(P + 255) / 256, 256 >> > (\n\t\tP,\n\t\tmeans3D,\n\t\tradii,\n\t\tcov3Ds,\n\t\tfocal_x,\n\t\tfocal_y,\n\t\ttan_fovx,\n\t\ttan_fovy,\n\t\tviewmatrix,\n\t\tdL_dconic,\n\t\t(float3*)dL_dmean3D,\n\t\tdL_dcov3D);\n\n\t// Propagate gradients for remaining steps: finish 3D mean gradients,\n\t// propagate color gradients to SH (if desireD), propagate 3D covariance\n\t// matrix gradients to scale and rotation.\n\tpreprocessCUDA << < (P + 255) / 256, 256 >> > (\n\t\tP, D, M,\n\t\t(float3*)means3D,\n\t\tradii,\n\t\tshs,\n\t\tclamped,\n\t\t(glm::vec3*)scales,\n\t\t(glm::vec4*)rotations,\n\t\tscale_modifier,\n\t\tprojmatrix,\n\t\tcampos,\n\t\t(float3*)dL_dmean2D,\n\t\t(glm::vec3*)dL_dmean3D,\n\t\tdL_dcolor,\n\t\tdL_dcov3D,\n\t\tdL_dsh,\n\t\tdL_dscale,\n\t\tdL_drot);\n}\n\nvoid BACKWARD::render(\n\tconst dim3 grid, const dim3 block,\n\tconst uint2* ranges,\n\tconst uint32_t* point_list,\n\tint W, int H,\n\tfloat fx, float fy,\n\tconst float* bg_color,\n\tconst float2* means2D,\n\tconst float4* conic_opacity,\n\tconst float* colors,\n\tconst float* language_feature,\n\tconst float* language_feature_instance,\n\tconst float* all_maps,\n\tconst float* all_map_pixels,\n\tconst float* final_Ts,\n\tconst uint32_t* n_contrib,\n\tconst float* dL_dpixels,\n\tconst float* dL_dpixels_F,\n\tconst float* dL_dpixels_F_instance,\n\tconst float* dL_dout_all_map,\n\tconst float* dL_dout_plane_depth,\n\tfloat3* dL_dmean2D,\n\tfloat3* dL_dmean2D_abs,\n\tfloat4* dL_dconic2D,\n\tfloat* dL_dopacity,\n\tfloat* dL_dcolors,\n\tfloat* dL_dlanguage_feature,\n\tfloat* dL_dlanguage_feature_instance,\n\tfloat* dL_dall_map,\n\tconst bool render_geo,\n\tbool include_feature)\n{\n\trenderCUDA << > >(\n\t\tranges,\n\t\tpoint_list,\n\t\tW, H,\n\t\tfx, fy,\n\t\tbg_color,\n\t\tmeans2D,\n\t\tconic_opacity,\n\t\tcolors,\n\t\tlanguage_feature,\n\t\tlanguage_feature_instance,\n\t\tall_maps,\n\t\tall_map_pixels,\n\t\tfinal_Ts,\n\t\tn_contrib,\n\t\tdL_dpixels,\n\t\tdL_dpixels_F,\n\t\tdL_dpixels_F_instance,\n\t\tdL_dout_all_map,\n\t\tdL_dout_plane_depth,\n\t\tdL_dmean2D,\n\t\tdL_dmean2D_abs,\n\t\tdL_dconic2D,\n\t\tdL_dopacity,\n\t\tdL_dcolors,\n\t\tdL_dlanguage_feature,\n\t\tdL_dlanguage_feature_instance,\n\t\tdL_dall_map,\n\t\trender_geo,\n\t\tinclude_feature\n\t\t);\n}" }, { "path": "field_construction/submodules/diff-langsurf-rasterizer/cuda_rasterizer/backward.h", "content": "/*\n * Copyright (C) 2023, Inria\n * GRAPHDECO research group, https://team.inria.fr/graphdeco\n * All rights reserved.\n *\n * This software is free for non-commercial, research and evaluation use \n * under the terms of the LICENSE.md file.\n *\n * For inquiries contact george.drettakis@inria.fr\n */\n\n#ifndef CUDA_RASTERIZER_BACKWARD_H_INCLUDED\n#define CUDA_RASTERIZER_BACKWARD_H_INCLUDED\n\n#include \n#include \"cuda_runtime.h\"\n#include \"device_launch_parameters.h\"\n#define GLM_FORCE_CUDA\n#include \n\nnamespace BACKWARD\n{\n\tvoid render(\n\t\tconst dim3 grid, dim3 block,\n\t\tconst uint2* ranges,\n\t\tconst uint32_t* point_list,\n\t\tint W, int H,\n\t\tfloat fx, float fy,\n\t\tconst float* bg_color,\n\t\tconst float2* means2D,\n\t\tconst float4* conic_opacity,\n\t\tconst float* colors,\n\t\tconst float* language_feature,\n\t\tconst float* language_feature_instance,\n\t\tconst float* all_maps,\n\t\tconst float* all_map_pixels,\n\t\tconst float* final_Ts,\n\t\tconst uint32_t* n_contrib,\n\t\tconst float* dL_dpixels,\n\t\tconst float* dL_dpixels_F,\n\t\tconst float* dL_dpixels_F_instance,\n\t\tconst float* dL_dout_all_map,\n\t\tconst float* dL_dout_plane_depth,\n\t\tfloat3* dL_dmean2D,\n\t\tfloat3* dL_dmean2D_abs,\n\t\tfloat4* dL_dconic2D,\n\t\tfloat* dL_dopacity,\n\t\tfloat* dL_dcolors,\n\t\tfloat* dL_dlanguage_feature,\n\t\tfloat* dL_dlanguage_feature_instance,\n\t\tfloat* dL_dall_map,\n\t\tconst bool render_geo,\n\t\tbool include_feature);\n\n\tvoid preprocess(\n\t\tint P, int D, int M,\n\t\tconst float3* means,\n\t\tconst int* radii,\n\t\tconst float* shs,\n\t\tconst bool* clamped,\n\t\tconst glm::vec3* scales,\n\t\tconst glm::vec4* rotations,\n\t\tconst float scale_modifier,\n\t\tconst float* cov3Ds,\n\t\tconst float* view,\n\t\tconst float* proj,\n\t\tconst float focal_x, float focal_y,\n\t\tconst float tan_fovx, float tan_fovy,\n\t\tconst glm::vec3* campos,\n\t\tconst float3* dL_dmean2D,\n\t\tconst float* dL_dconics,\n\t\tglm::vec3* dL_dmeans,\n\t\tfloat* dL_dcolor,\n\t\tfloat* dL_dcov3D,\n\t\tfloat* dL_dsh,\n\t\tglm::vec3* dL_dscale,\n\t\tglm::vec4* dL_drot);\n}\n\n#endif" }, { "path": "field_construction/submodules/diff-langsurf-rasterizer/cuda_rasterizer/config.h", "content": "/*\n * Copyright (C) 2023, Inria\n * GRAPHDECO research group, https://team.inria.fr/graphdeco\n * All rights reserved.\n *\n * This software is free for non-commercial, research and evaluation use\n * under the terms of the LICENSE.md file.\n *\n * For inquiries contact george.drettakis@inria.fr\n */\n\n#ifndef CUDA_RASTERIZER_CONFIG_H_INCLUDED\n#define CUDA_RASTERIZER_CONFIG_H_INCLUDED\n\n#define NUM_CHANNELS 3 // Default 3, RGB\n#define NUM_CHANNELS_language_feature 3\n#define NUM_CHANNELS_instance_feature 3\n#define NUM_ALL_MAP 5\n#define BLOCK_X 16\n#define BLOCK_Y 16\n\n#endif" }, { "path": "field_construction/submodules/diff-langsurf-rasterizer/cuda_rasterizer/forward.cu", "content": "/*\n * Copyright (C) 2023, Inria\n * GRAPHDECO research group, https://team.inria.fr/graphdeco\n * All rights reserved.\n *\n * This software is free for non-commercial, research and evaluation use\n * under the terms of the LICENSE.md file.\n *\n * For inquiries contact george.drettakis@inria.fr\n */\n\n#include \"forward.h\"\n#include \"auxiliary.h\"\n#include \n#include \n#include \nnamespace cg = cooperative_groups;\n\n// Forward method for converting the input spherical harmonics\n// coefficients of each Gaussian to a simple RGB color.\n__device__ glm::vec3 computeColorFromSH(int idx, int deg, int max_coeffs, const glm::vec3 *means, glm::vec3 campos, const float *shs, bool *clamped)\n{\n\t// The implementation is loosely based on code for\n\t// \"Differentiable Point-Based Radiance Fields for\n\t// Efficient View Synthesis\" by Zhang et al. (2022)\n\tglm::vec3 pos = means[idx];\n\tglm::vec3 dir = pos - campos;\n\tdir = dir / glm::length(dir);\n\n\tglm::vec3 *sh = ((glm::vec3 *)shs) + idx * max_coeffs;\n\tglm::vec3 result = SH_C0 * sh[0];\n\n\tif (deg > 0)\n\t{\n\t\tfloat x = dir.x;\n\t\tfloat y = dir.y;\n\t\tfloat z = dir.z;\n\t\tresult = result - SH_C1 * y * sh[1] + SH_C1 * z * sh[2] - SH_C1 * x * sh[3];\n\n\t\tif (deg > 1)\n\t\t{\n\t\t\tfloat xx = x * x, yy = y * y, zz = z * z;\n\t\t\tfloat xy = x * y, yz = y * z, xz = x * z;\n\t\t\tresult = result +\n\t\t\t\t\t SH_C2[0] * xy * sh[4] +\n\t\t\t\t\t SH_C2[1] * yz * sh[5] +\n\t\t\t\t\t SH_C2[2] * (2.0f * zz - xx - yy) * sh[6] +\n\t\t\t\t\t SH_C2[3] * xz * sh[7] +\n\t\t\t\t\t SH_C2[4] * (xx - yy) * sh[8];\n\n\t\t\tif (deg > 2)\n\t\t\t{\n\t\t\t\tresult = result +\n\t\t\t\t\t\t SH_C3[0] * y * (3.0f * xx - yy) * sh[9] +\n\t\t\t\t\t\t SH_C3[1] * xy * z * sh[10] +\n\t\t\t\t\t\t SH_C3[2] * y * (4.0f * zz - xx - yy) * sh[11] +\n\t\t\t\t\t\t SH_C3[3] * z * (2.0f * zz - 3.0f * xx - 3.0f * yy) * sh[12] +\n\t\t\t\t\t\t SH_C3[4] * x * (4.0f * zz - xx - yy) * sh[13] +\n\t\t\t\t\t\t SH_C3[5] * z * (xx - yy) * sh[14] +\n\t\t\t\t\t\t SH_C3[6] * x * (xx - 3.0f * yy) * sh[15];\n\t\t\t}\n\t\t}\n\t}\n\tresult += 0.5f;\n\n\t// RGB colors are clamped to positive values. If values are\n\t// clamped, we need to keep track of this for the backward pass.\n\tclamped[3 * idx + 0] = (result.x < 0);\n\tclamped[3 * idx + 1] = (result.y < 0);\n\tclamped[3 * idx + 2] = (result.z < 0);\n\treturn glm::max(result, 0.0f);\n}\n\n// Forward version of 2D covariance matrix computation\n__device__ float3 computeCov2D(const float3 &mean, float focal_x, float focal_y, float tan_fovx, float tan_fovy, const float *cov3D, const float *viewmatrix)\n{\n\t// The following models the steps outlined by equations 29\n\t// and 31 in \"EWA Splatting\" (Zwicker et al., 2002).\n\t// Additionally considers aspect / scaling of viewport.\n\t// Transposes used to account for row-/column-major conventions.\n\tfloat3 t = transformPoint4x3(mean, viewmatrix);\n\n\tconst float limx = 1.3f * tan_fovx;\n\tconst float limy = 1.3f * tan_fovy;\n\tconst float txtz = t.x / t.z;\n\tconst float tytz = t.y / t.z;\n\tt.x = min(limx, max(-limx, txtz)) * t.z;\n\tt.y = min(limy, max(-limy, tytz)) * t.z;\n\n\tglm::mat3 J = glm::mat3(\n\t\tfocal_x / t.z, 0.0f, -(focal_x * t.x) / (t.z * t.z),\n\t\t0.0f, focal_y / t.z, -(focal_y * t.y) / (t.z * t.z),\n\t\t0, 0, 0);\n\n\tglm::mat3 W = glm::mat3(\n\t\tviewmatrix[0], viewmatrix[4], viewmatrix[8],\n\t\tviewmatrix[1], viewmatrix[5], viewmatrix[9],\n\t\tviewmatrix[2], viewmatrix[6], viewmatrix[10]);\n\n\tglm::mat3 T = W * J;\n\n\tglm::mat3 Vrk = glm::mat3(\n\t\tcov3D[0], cov3D[1], cov3D[2],\n\t\tcov3D[1], cov3D[3], cov3D[4],\n\t\tcov3D[2], cov3D[4], cov3D[5]);\n\n\tglm::mat3 cov = glm::transpose(T) * glm::transpose(Vrk) * T;\n\n\t// Apply low-pass filter: every Gaussian should be at least\n\t// one pixel wide/high. Discard 3rd row and column.\n\tcov[0][0] += 0.3f;\n\tcov[1][1] += 0.3f;\n\treturn {float(cov[0][0]), float(cov[0][1]), float(cov[1][1])};\n}\n\n// Forward method for converting scale and rotation properties of each\n// Gaussian to a 3D covariance matrix in world space. Also takes care\n// of quaternion normalization.\n__device__ void computeCov3D(const glm::vec3 scale, float mod, const glm::vec4 rot, float *cov3D)\n{\n\t// Create scaling matrix\n\tglm::mat3 S = glm::mat3(1.0f);\n\tS[0][0] = mod * scale.x;\n\tS[1][1] = mod * scale.y;\n\tS[2][2] = mod * scale.z;\n\n\t// Normalize quaternion to get valid rotation\n\tglm::vec4 q = rot; // / glm::length(rot);\n\tfloat r = q.x;\n\tfloat x = q.y;\n\tfloat y = q.z;\n\tfloat z = q.w;\n\n\t// Compute rotation matrix from quaternion\n\tglm::mat3 R = glm::mat3(\n\t\t1.f - 2.f * (y * y + z * z), 2.f * (x * y - r * z), 2.f * (x * z + r * y),\n\t\t2.f * (x * y + r * z), 1.f - 2.f * (x * x + z * z), 2.f * (y * z - r * x),\n\t\t2.f * (x * z - r * y), 2.f * (y * z + r * x), 1.f - 2.f * (x * x + y * y));\n\n\tglm::mat3 M = S * R;\n\n\t// Compute 3D world covariance matrix Sigma\n\tglm::mat3 Sigma = glm::transpose(M) * M;\n\n\t// Covariance is symmetric, only store upper right\n\tcov3D[0] = Sigma[0][0];\n\tcov3D[1] = Sigma[0][1];\n\tcov3D[2] = Sigma[0][2];\n\tcov3D[3] = Sigma[1][1];\n\tcov3D[4] = Sigma[1][2];\n\tcov3D[5] = Sigma[2][2];\n}\n\n// Perform initial steps for each Gaussian prior to rasterization.\ntemplate \n__global__ void preprocessCUDA(int P, int D, int M,\n\t\t\t\t\t\t\t const float *orig_points,\n\t\t\t\t\t\t\t const glm::vec3 *scales,\n\t\t\t\t\t\t\t const float scale_modifier,\n\t\t\t\t\t\t\t const glm::vec4 *rotations,\n\t\t\t\t\t\t\t const float *opacities,\n\t\t\t\t\t\t\t const float *shs,\n\t\t\t\t\t\t\t bool *clamped,\n\t\t\t\t\t\t\t const float *cov3D_precomp,\n\t\t\t\t\t\t\t const float *colors_precomp,\n\t\t\t\t\t\t\t const float *viewmatrix,\n\t\t\t\t\t\t\t const float *projmatrix,\n\t\t\t\t\t\t\t const glm::vec3 *cam_pos,\n\t\t\t\t\t\t\t const int W, int H,\n\t\t\t\t\t\t\t const float tan_fovx, float tan_fovy,\n\t\t\t\t\t\t\t const float focal_x, float focal_y,\n\t\t\t\t\t\t\t int *radii,\n\t\t\t\t\t\t\t float2 *points_xy_image,\n\t\t\t\t\t\t\t float *depths,\n\t\t\t\t\t\t\t float *cov3Ds,\n\t\t\t\t\t\t\t float *rgb,\n\t\t\t\t\t\t\t float4 *conic_opacity,\n\t\t\t\t\t\t\t const dim3 grid,\n\t\t\t\t\t\t\t uint32_t *tiles_touched,\n\t\t\t\t\t\t\t bool prefiltered)\n{\n\tauto idx = cg::this_grid().thread_rank();\n\tif (idx >= P)\n\t\treturn;\n\n\t// Initialize radius and touched tiles to 0. If this isn't changed,\n\t// this Gaussian will not be processed further.\n\tradii[idx] = 0;\n\ttiles_touched[idx] = 0;\n\n\t// Perform near culling, quit if outside.\n\tfloat3 p_view;\n\tif (!in_frustum(idx, orig_points, viewmatrix, projmatrix, prefiltered, p_view))\n\t\treturn;\n\n\t// Transform point by projecting\n\tfloat3 p_orig = {orig_points[3 * idx], orig_points[3 * idx + 1], orig_points[3 * idx + 2]};\n\tfloat4 p_hom = transformPoint4x4(p_orig, projmatrix);\n\tfloat p_w = 1.0f / (p_hom.w + 0.0000001f);\n\tfloat3 p_proj = {p_hom.x * p_w, p_hom.y * p_w, p_hom.z * p_w};\n\n\t// If 3D covariance matrix is precomputed, use it, otherwise compute\n\t// from scaling and rotation parameters.\n\tconst float *cov3D;\n\tif (cov3D_precomp != nullptr)\n\t{\n\t\tcov3D = cov3D_precomp + idx * 6;\n\t}\n\telse\n\t{\n\t\tcomputeCov3D(scales[idx], scale_modifier, rotations[idx], cov3Ds + idx * 6);\n\t\tcov3D = cov3Ds + idx * 6;\n\t}\n\n\t// Compute 2D screen-space covariance matrix\n\tfloat3 cov = computeCov2D(p_orig, focal_x, focal_y, tan_fovx, tan_fovy, cov3D, viewmatrix);\n\n\t// Invert covariance (EWA algorithm)\n\tfloat det = (cov.x * cov.z - cov.y * cov.y);\n\tif (det == 0.0f)\n\t\treturn;\n\tfloat det_inv = 1.f / det;\n\tfloat3 conic = {cov.z * det_inv, -cov.y * det_inv, cov.x * det_inv};\n\n\t// Compute extent in screen space (by finding eigenvalues of\n\t// 2D covariance matrix). Use extent to compute a bounding rectangle\n\t// of screen-space tiles that this Gaussian overlaps with. Quit if\n\t// rectangle covers 0 tiles.\n\tfloat mid = 0.5f * (cov.x + cov.z);\n\tfloat lambda1 = mid + sqrt(max(0.1f, mid * mid - det));\n\tfloat lambda2 = mid - sqrt(max(0.1f, mid * mid - det));\n\tfloat my_radius = ceil(3.f * sqrt(max(lambda1, lambda2)));\n\tfloat2 point_image = {ndc2Pix(p_proj.x, W), ndc2Pix(p_proj.y, H)};\n\tuint2 rect_min, rect_max;\n\tgetRect(point_image, my_radius, rect_min, rect_max, grid);\n\tif ((rect_max.x - rect_min.x) * (rect_max.y - rect_min.y) == 0)\n\t\treturn;\n\n\t// If colors have been precomputed, use them, otherwise convert\n\t// spherical harmonics coefficients to RGB color.\n\tif (colors_precomp == nullptr)\n\t{\n\t\tglm::vec3 result = computeColorFromSH(idx, D, M, (glm::vec3 *)orig_points, *cam_pos, shs, clamped);\n\t\trgb[idx * C + 0] = result.x;\n\t\trgb[idx * C + 1] = result.y;\n\t\trgb[idx * C + 2] = result.z;\n\t}\n\n\t// if (opacities[idx] > 0.9 && point_image.x > 0 && point_image.x < 1264 && point_image.y > 0 && point_image.y < 832) {\n\t// \tglm::vec4 q = rotations[idx];\n\t// \tglm::vec3 cp = *cam_pos;\n\t// \tprintf(\"q(wxyz) %lf %lf %lf %lf, scale %lf %lf %lf, mean3d %lf %lf %lf, c %lf %lf %lf\\n viewmatrix %lf %lf %lf %lf, %lf %lf %lf %lf, %lf %lf %lf %lf, %lf %lf %lf %lf\\n\",\n\t// \t\tq.x, q.y, q.z, q.w, scales[idx].x, scales[idx].y, scales[idx].z,\n\t// \t\tp_orig.x, p_orig.y, p_orig.z, cp.x, cp.y, cp.z,\n\t// \t\tviewmatrix[0],viewmatrix[4],viewmatrix[8],viewmatrix[12],\n\t// \t\tviewmatrix[1],viewmatrix[5],viewmatrix[9],viewmatrix[13],\n\t// \t\tviewmatrix[2],viewmatrix[6],viewmatrix[10],viewmatrix[14],\n\t// \t\tviewmatrix[3],viewmatrix[7],viewmatrix[11],viewmatrix[15]);\n\t// }\n\n\t// Store some useful helper data for the next steps.\n\tdepths[idx] = p_view.z;\n\tradii[idx] = my_radius;\n\tpoints_xy_image[idx] = point_image;\n\t// Inverse 2D covariance and opacity neatly pack into one float4\n\tconic_opacity[idx] = {conic.x, conic.y, conic.z, opacities[idx]};\n\ttiles_touched[idx] = (rect_max.y - rect_min.y) * (rect_max.x - rect_min.x);\n}\n\n// Main rasterization method. Collaboratively works on one tile per\n// block, each thread treats one pixel. Alternates between fetching\n// and rasterizing data.\ntemplate \n__global__ void __launch_bounds__(BLOCK_X *BLOCK_Y)\n\trenderCUDA(\n\t\tconst uint2 *__restrict__ ranges,\n\t\tconst uint32_t *__restrict__ point_list,\n\t\tint W, int H,\n\t\tconst float focal_x, const float focal_y,\n\t\tconst float cx, const float cy,\n\t\tconst float *__restrict__ viewmatrix,\n\t\tconst float *__restrict__ cam_pos,\n\t\tconst float2 *__restrict__ points_xy_image,\n\t\tconst float *__restrict__ features,\n\t\tconst float *__restrict__ language_feature,\n\t\tconst float *__restrict__ language_feature_instance,\n\t\tconst float *__restrict__ all_map,\n\t\tconst float4 *__restrict__ conic_opacity,\n\t\tfloat *__restrict__ final_T,\n\t\tuint32_t *__restrict__ n_contrib,\n\t\tconst float *__restrict__ bg_color,\n\t\tfloat *__restrict__ out_color,\n\t\tfloat *__restrict__ out_language_feature,\n\t\tfloat *__restrict__ out_language_feature_instance,\n\t\tint *__restrict__ out_observe,\n\t\tfloat *__restrict__ out_all_map,\n\t\tfloat *__restrict__ out_plane_depth,\n\t\tconst bool render_geo,\n\t\tbool include_feature)\n{\n\t// Identify current tile and associated min/max pixel range.\n\tauto block = cg::this_thread_block();\n\tconst uint32_t horizontal_blocks = (W + BLOCK_X - 1) / BLOCK_X;\n\tconst uint2 pix_min = {block.group_index().x * BLOCK_X, block.group_index().y * BLOCK_Y};\n\tconst uint2 pix_max = {min(pix_min.x + BLOCK_X, W), min(pix_min.y + BLOCK_Y, H)};\n\tconst uint2 pix = {pix_min.x + block.thread_index().x, pix_min.y + block.thread_index().y};\n\tconst uint32_t pix_id = W * pix.y + pix.x;\n\tconst float2 pixf = {(float)pix.x, (float)pix.y};\n\tconst float2 ray = {(pixf.x - cx) / focal_x, (pixf.y - cy) / focal_y};\n\t// Check if this thread is associated with a valid pixel or outside.\n\tbool inside = pix.x < W && pix.y < H;\n\t// Done threads can help with fetching, but don't rasterize\n\tbool done = !inside;\n\n\t// Load start/end range of IDs to process in bit sorted list.\n\tuint2 range = ranges[block.group_index().y * horizontal_blocks + block.group_index().x];\n\tconst int rounds = ((range.y - range.x + BLOCK_SIZE - 1) / BLOCK_SIZE);\n\tint toDo = range.y - range.x;\n\n\t// Allocate storage for batches of collectively fetched data.\n\t__shared__ int collected_id[BLOCK_SIZE];\n\t__shared__ float2 collected_xy[BLOCK_SIZE];\n\t__shared__ float4 collected_conic_opacity[BLOCK_SIZE];\n\n\t// Initialize helper variables\n\tfloat T = 1.0f;\n\tuint32_t contributor = 0;\n\tuint32_t last_contributor = 0;\n\tfloat C[CHANNELS] = {0};\n\tfloat F[CHANNELS_language_feature] = {0};\n\tfloat F_ins[CHANNELS_instance_feature] = {0};\n\tfloat All_map[ALL_MAP] = {0};\n\n\t// Iterate over batches until all done or range is complete\n\tfor (int i = 0; i < rounds; i++, toDo -= BLOCK_SIZE)\n\t{\n\t\t// End if entire block votes that it is done rasterizing\n\t\tint num_done = __syncthreads_count(done);\n\t\tif (num_done == BLOCK_SIZE)\n\t\t\tbreak;\n\n\t\t// Collectively fetch per-Gaussian data from global to shared\n\t\tint progress = i * BLOCK_SIZE + block.thread_rank();\n\t\tif (range.x + progress < range.y)\n\t\t{\n\t\t\tint coll_id = point_list[range.x + progress];\n\t\t\tcollected_id[block.thread_rank()] = coll_id;\n\t\t\tcollected_xy[block.thread_rank()] = points_xy_image[coll_id];\n\t\t\tcollected_conic_opacity[block.thread_rank()] = conic_opacity[coll_id];\n\t\t}\n\t\tblock.sync();\n\n\t\t// Iterate over current batch\n\t\tfor (int j = 0; !done && j < min(BLOCK_SIZE, toDo); j++)\n\t\t{\n\t\t\t// Keep track of current position in range\n\t\t\tcontributor++;\n\t\t\t// Resample using conic matrix (cf. \"Surface\n\t\t\t// Splatting\" by Zwicker et al., 2001)\n\t\t\tfloat2 xy = collected_xy[j];\n\t\t\tfloat2 d = {xy.x - pixf.x, xy.y - pixf.y};\n\t\t\tfloat4 con_o = collected_conic_opacity[j];\n\t\t\tfloat power = -0.5f * (con_o.x * d.x * d.x + con_o.z * d.y * d.y) - con_o.y * d.x * d.y;\n\t\t\tif (power > 0.0f)\n\t\t\t\tcontinue;\n\n\t\t\t// Eq. (2) from 3D Gaussian splatting paper.\n\t\t\t// Obtain alpha by multiplying with Gaussian opacity\n\t\t\t// and its exponential falloff from mean.\n\t\t\t// Avoid numerical instabilities (see paper appendix).\n\t\t\tfloat alpha = min(0.99f, con_o.w * exp(power));\n\t\t\tif (alpha < 1.0f / 255.0f)\n\t\t\t\tcontinue;\n\t\t\tfloat test_T = T * (1 - alpha);\n\t\t\tif (test_T < 0.0001f)\n\t\t\t{\n\t\t\t\tdone = true;\n\t\t\t\tcontinue;\n\t\t\t}\n\n\t\t\t// Eq. (3) from 3D Gaussian splatting paper.\n\t\t\tfor (int ch = 0; ch < CHANNELS; ch++)\n\t\t\t\tC[ch] += features[collected_id[j] * CHANNELS + ch] * alpha * T;\n\t\t\tif (include_feature)\n\t\t\t{\n\t\t\t\tfor (int ch = 0; ch < CHANNELS_language_feature; ch++)\n\t\t\t\t\tF[ch] += language_feature[collected_id[j] * CHANNELS_language_feature + ch] * alpha * T;\n\t\t\t\tfor (int ch = 0; ch < CHANNELS_instance_feature; ch++)\n\t\t\t\t\tF_ins[ch] += language_feature_instance[collected_id[j] * CHANNELS_instance_feature + ch] * alpha * T;\n\t\t\t}\n\t\t\tif (render_geo)\n\t\t\t{\n\t\t\t\tfor (int ch = 0; ch < ALL_MAP; ch++)\n\t\t\t\t\tAll_map[ch] += all_map[collected_id[j] * ALL_MAP + ch] * alpha * T;\n\t\t\t}\n\n\t\t\tif (T > 0.5)\n\t\t\t{\n\t\t\t\tatomicAdd(&(out_observe[collected_id[j]]), 1);\n\t\t\t}\n\t\t\tT = test_T;\n\n\t\t\t// Keep track of last range entry to update this\n\t\t\t// pixel.\n\t\t\tlast_contributor = contributor;\n\t\t}\n\t}\n\n\t// All threads that treat valid pixel write out their final\n\t// rendering data to the frame and auxiliary buffers.\n\tif (inside)\n\t{\n\t\tfinal_T[pix_id] = T;\n\t\tn_contrib[pix_id] = last_contributor;\n\t\tfor (int ch = 0; ch < CHANNELS; ch++)\n\t\t\tout_color[ch * H * W + pix_id] = C[ch] + T * bg_color[ch];\n\t\tif (include_feature)\n\t\t{\n\t\t\tfor (int ch = 0; ch < CHANNELS_language_feature; ch++)\n\t\t\t\tout_language_feature[ch * H * W + pix_id] = F[ch]; // bg_color ???\n\t\t\tfor (int ch = 0; ch < CHANNELS_instance_feature; ch++)\n\t\t\t\tout_language_feature_instance[ch * H * W + pix_id] = F_ins[ch];\n\t\t}\n\t\tif (render_geo)\n\t\t{\n\t\t\tfor (int ch = 0; ch < ALL_MAP; ch++)\n\t\t\t\tout_all_map[ch * H * W + pix_id] = All_map[ch];\n\t\t\tout_plane_depth[pix_id] = All_map[4] / -(All_map[0] * ray.x + All_map[1] * ray.y + All_map[2] + 1.0e-8);\n\t\t}\n\t}\n}\n\nvoid FORWARD::render(\n\tconst dim3 grid, dim3 block,\n\tconst uint2 *ranges,\n\tconst uint32_t *point_list,\n\tint W, int H,\n\tconst float focal_x, const float focal_y,\n\tconst float cx, const float cy,\n\tconst float *viewmatrix,\n\tconst float *cam_pos,\n\tconst float2 *means2D,\n\tconst float *colors,\n\tconst float *language_feature,\n\tconst float *language_feature_instance,\n\tconst float *all_map,\n\tconst float4 *conic_opacity,\n\tfloat *final_T,\n\tuint32_t *n_contrib,\n\tconst float *bg_color,\n\tfloat *out_color,\n\tfloat *out_language_feature,\n\tfloat *out_language_feature_instance,\n\tint *out_observe,\n\tfloat *out_all_map,\n\tfloat *out_plane_depth,\n\tconst bool render_geo,\n\tbool include_feature)\n{\n\trenderCUDA<<>>(\n\t\tranges,\n\t\tpoint_list,\n\t\tW, H,\n\t\tfocal_x, focal_y,\n\t\tcx, cy,\n\t\tviewmatrix,\n\t\tcam_pos,\n\t\tmeans2D,\n\t\tcolors,\n\t\tlanguage_feature,\n\t\tlanguage_feature_instance,\n\t\tall_map,\n\t\tconic_opacity,\n\t\tfinal_T,\n\t\tn_contrib,\n\t\tbg_color,\n\t\tout_color,\n\t\tout_language_feature,\n\t\tout_language_feature_instance,\n\t\tout_observe,\n\t\tout_all_map,\n\t\tout_plane_depth,\n\t\trender_geo,\n\t\tinclude_feature);\n}\n\nvoid FORWARD::preprocess(int P, int D, int M,\n\t\t\t\t\t\t const float *means3D,\n\t\t\t\t\t\t const glm::vec3 *scales,\n\t\t\t\t\t\t const float scale_modifier,\n\t\t\t\t\t\t const glm::vec4 *rotations,\n\t\t\t\t\t\t const float *opacities,\n\t\t\t\t\t\t const float *shs,\n\t\t\t\t\t\t bool *clamped,\n\t\t\t\t\t\t const float *cov3D_precomp,\n\t\t\t\t\t\t const float *colors_precomp,\n\t\t\t\t\t\t const float *viewmatrix,\n\t\t\t\t\t\t const float *projmatrix,\n\t\t\t\t\t\t const glm::vec3 *cam_pos,\n\t\t\t\t\t\t const int W, int H,\n\t\t\t\t\t\t const float focal_x, float focal_y,\n\t\t\t\t\t\t const float tan_fovx, float tan_fovy,\n\t\t\t\t\t\t int *radii,\n\t\t\t\t\t\t float2 *means2D,\n\t\t\t\t\t\t float *depths,\n\t\t\t\t\t\t float *cov3Ds,\n\t\t\t\t\t\t float *rgb,\n\t\t\t\t\t\t float4 *conic_opacity,\n\t\t\t\t\t\t const dim3 grid,\n\t\t\t\t\t\t uint32_t *tiles_touched,\n\t\t\t\t\t\t bool prefiltered)\n{\n\tpreprocessCUDA<<<(P + 255) / 256, 256>>>(\n\t\tP, D, M,\n\t\tmeans3D,\n\t\tscales,\n\t\tscale_modifier,\n\t\trotations,\n\t\topacities,\n\t\tshs,\n\t\tclamped,\n\t\tcov3D_precomp,\n\t\tcolors_precomp,\n\t\tviewmatrix,\n\t\tprojmatrix,\n\t\tcam_pos,\n\t\tW, H,\n\t\ttan_fovx, tan_fovy,\n\t\tfocal_x, focal_y,\n\t\tradii,\n\t\tmeans2D,\n\t\tdepths,\n\t\tcov3Ds,\n\t\trgb,\n\t\tconic_opacity,\n\t\tgrid,\n\t\ttiles_touched,\n\t\tprefiltered);\n}" }, { "path": "field_construction/submodules/diff-langsurf-rasterizer/cuda_rasterizer/forward.h", "content": "/*\n * Copyright (C) 2023, Inria\n * GRAPHDECO research group, https://team.inria.fr/graphdeco\n * All rights reserved.\n *\n * This software is free for non-commercial, research and evaluation use \n * under the terms of the LICENSE.md file.\n *\n * For inquiries contact george.drettakis@inria.fr\n */\n\n#ifndef CUDA_RASTERIZER_FORWARD_H_INCLUDED\n#define CUDA_RASTERIZER_FORWARD_H_INCLUDED\n\n#include \n#include \"cuda_runtime.h\"\n#include \"device_launch_parameters.h\"\n#define GLM_FORCE_CUDA\n#include \n\nnamespace FORWARD\n{\n\t// Perform initial steps for each Gaussian prior to rasterization.\n\tvoid preprocess(int P, int D, int M,\n\t\tconst float* orig_points,\n\t\tconst glm::vec3* scales,\n\t\tconst float scale_modifier,\n\t\tconst glm::vec4* rotations,\n\t\tconst float* opacities,\n\t\tconst float* shs,\n\t\tbool* clamped,\n\t\tconst float* cov3D_precomp,\n\t\tconst float* colors_precomp,\n\t\tconst float* viewmatrix,\n\t\tconst float* projmatrix,\n\t\tconst glm::vec3* cam_pos,\n\t\tconst int W, int H,\n\t\tconst float focal_x, float focal_y,\n\t\tconst float tan_fovx, float tan_fovy,\n\t\tint* radii,\n\t\tfloat2* points_xy_image,\n\t\tfloat* depths,\n\t\tfloat* cov3Ds,\n\t\tfloat* colors,\n\t\tfloat4* conic_opacity,\n\t\tconst dim3 grid,\n\t\tuint32_t* tiles_touched,\n\t\tbool prefiltered);\n\n\t// Main rasterization method.\n\tvoid render(\n\t\tconst dim3 grid, dim3 block,\n\t\tconst uint2* ranges,\n\t\tconst uint32_t* point_list,\n\t\tint W, int H,\n\t\tconst float focal_x, const float focal_y,\n\t\tconst float cx, const float cy,\n\t\tconst float* viewmatrix,\n\t\tconst float* cam_pos,\n\t\tconst float2* points_xy_image,\n\t\tconst float* features,\n\t\tconst float* language_feature,\n\t\tconst float* language_feature_instance,\n\t\tconst float* all_map,\n\t\tconst float4* conic_opacity,\n\t\tfloat* final_T,\n\t\tuint32_t* n_contrib,\n\t\tconst float* bg_color,\n\t\tfloat* out_color,\n\t\tfloat* out_language_feature,\n\t\tfloat* out_language_feature_instance,\n\t\tint* out_observe,\n\t\tfloat* out_all_map,\n\t\tfloat* out_plane_depth,\n\t\tconst bool render_geo,\n\t\tbool include_feature);\n}\n\n\n#endif" }, { "path": "field_construction/submodules/diff-langsurf-rasterizer/cuda_rasterizer/rasterizer.h", "content": "/*\n * Copyright (C) 2023, Inria\n * GRAPHDECO research group, https://team.inria.fr/graphdeco\n * All rights reserved.\n *\n * This software is free for non-commercial, research and evaluation use \n * under the terms of the LICENSE.md file.\n *\n * For inquiries contact george.drettakis@inria.fr\n */\n\n#ifndef CUDA_RASTERIZER_H_INCLUDED\n#define CUDA_RASTERIZER_H_INCLUDED\n\n#include \n#include \n\nnamespace CudaRasterizer\n{\n\tclass Rasterizer\n\t{\n\tpublic:\n\n\t\tstatic void markVisible(\n\t\t\tint P,\n\t\t\tfloat* means3D,\n\t\t\tfloat* viewmatrix,\n\t\t\tfloat* projmatrix,\n\t\t\tbool* present);\n\n\t\tstatic int forward(\n\t\t\tstd::function geometryBuffer,\n\t\t\tstd::function binningBuffer,\n\t\t\tstd::function imageBuffer,\n\t\t\tconst int P, int D, int M,\n\t\t\tconst float* background,\n\t\t\tconst int width, int height,\n\t\t\tconst float* means3D,\n\t\t\tconst float* shs,\n\t\t\tconst float* colors_precomp,\n\t\t\tconst float* language_feature_precomp,\n\t\t\tconst float* language_feature_instance_precomp,\n\t\t\tconst float* opacities,\n\t\t\tconst float* scales,\n\t\t\tconst float scale_modifier,\n\t\t\tconst float* rotations,\n\t\t\tconst float* cov3D_precomp,\n\t\t\tconst float* all_map,\n\t\t\tconst float* viewmatrix,\n\t\t\tconst float* projmatrix,\n\t\t\tconst float* cam_pos,\n\t\t\tconst float tan_fovx, float tan_fovy,\n\t\t\tconst bool prefiltered,\n\t\t\tfloat* out_color,\n\t\t\tfloat* out_language_feature,\n\t\t\tfloat* out_language_feature_instance,\n\t\t\tint* radii,\n\t\t\tint* out_observe,\n\t\t\tfloat* out_all_map,\n\t\t\tfloat* out_plane_depth,\n\t\t\tconst bool render_geo,\n\t\t\tbool debug = false,\n\t\t\tbool include_feature = false);\n\n\t\tstatic void backward(\n\t\t\tconst int P, int D, int M, int R,\n\t\t\tconst float* background,\n\t\t\tconst float* all_map_pixels,\n\t\t\tconst int width, int height,\n\t\t\tconst float* means3D,\n\t\t\tconst float* shs,\n\t\t\tconst float* colors_precomp,\n\t\t\tconst float* language_feature_precomp,\n\t\t\tconst float* language_feature_instance_precomp,\n\t\t\tconst float* all_maps,\n\t\t\tconst float* scales,\n\t\t\tconst float scale_modifier,\n\t\t\tconst float* rotations,\n\t\t\tconst float* cov3D_precomp,\n\t\t\tconst float* viewmatrix,\n\t\t\tconst float* projmatrix,\n\t\t\tconst float* campos,\n\t\t\tconst float tan_fovx, float tan_fovy,\n\t\t\tconst int* radii,\n\t\t\tchar* geom_buffer,\n\t\t\tchar* binning_buffer,\n\t\t\tchar* image_buffer,\n\t\t\tconst float* dL_dpix,\n\t\t\tconst float* dL_dpix_F,\n\t\t\tconst float* dL_dpix_F_instance,\n\t\t\tconst float* dL_dout_all_map,\n\t\t\tconst float* dL_dout_plane_depth,\n\t\t\tfloat* dL_dmean2D,\n\t\t\tfloat* dL_dmean2D_abs,\n\t\t\tfloat* dL_dconic,\n\t\t\tfloat* dL_dopacity,\n\t\t\tfloat* dL_dcolor,\n\t\t\tfloat* dL_dlanguage_feature,\n\t\t\tfloat* dL_dlanguage_feature_instance,\n\t\t\tfloat* dL_dmean3D,\n\t\t\tfloat* dL_dcov3D,\n\t\t\tfloat* dL_dsh,\n\t\t\tfloat* dL_dscale,\n\t\t\tfloat* dL_drot,\n\t\t\tfloat* dL_dall_map,\n\t\t\tconst bool render_geo,\n\t\t\tbool debug,\n\t\t\tbool include_feature);\n\t};\n};\n\n#endif" }, { "path": "field_construction/submodules/diff-langsurf-rasterizer/cuda_rasterizer/rasterizer_impl.cu", "content": "/*\n * Copyright (C) 2023, Inria\n * GRAPHDECO research group, https://team.inria.fr/graphdeco\n * All rights reserved.\n *\n * This software is free for non-commercial, research and evaluation use \n * under the terms of the LICENSE.md file.\n *\n * For inquiries contact george.drettakis@inria.fr\n */\n\n#include \"rasterizer_impl.h\"\n#include \n#include \n#include \n#include \n#include \n#include \"cuda_runtime.h\"\n#include \"device_launch_parameters.h\"\n#include \n#include \n#define GLM_FORCE_CUDA\n#include \n\n#include \n#include \nnamespace cg = cooperative_groups;\n\n#include \"auxiliary.h\"\n#include \"forward.h\"\n#include \"backward.h\"\n\n// Helper function to find the next-highest bit of the MSB\n// on the CPU.\nuint32_t getHigherMsb(uint32_t n)\n{\n\tuint32_t msb = sizeof(n) * 4;\n\tuint32_t step = msb;\n\twhile (step > 1)\n\t{\n\t\tstep /= 2;\n\t\tif (n >> msb)\n\t\t\tmsb += step;\n\t\telse\n\t\t\tmsb -= step;\n\t}\n\tif (n >> msb)\n\t\tmsb++;\n\treturn msb;\n}\n\n// Wrapper method to call auxiliary coarse frustum containment test.\n// Mark all Gaussians that pass it.\n__global__ void checkFrustum(int P,\n\tconst float* orig_points,\n\tconst float* viewmatrix,\n\tconst float* projmatrix,\n\tbool* present)\n{\n\tauto idx = cg::this_grid().thread_rank();\n\tif (idx >= P)\n\t\treturn;\n\n\tfloat3 p_view;\n\tpresent[idx] = in_frustum(idx, orig_points, viewmatrix, projmatrix, false, p_view);\n}\n\n// Generates one key/value pair for all Gaussian / tile overlaps. \n// Run once per Gaussian (1:N mapping).\n__global__ void duplicateWithKeys(\n\tint P,\n\tconst float2* points_xy,\n\tconst float* depths,\n\tconst uint32_t* offsets,\n\tuint64_t* gaussian_keys_unsorted,\n\tuint32_t* gaussian_values_unsorted,\n\tint* radii,\n\tdim3 grid)\n{\n\tauto idx = cg::this_grid().thread_rank();\n\tif (idx >= P)\n\t\treturn;\n\n\t// Generate no key/value pair for invisible Gaussians\n\tif (radii[idx] > 0)\n\t{\n\t\t// Find this Gaussian's offset in buffer for writing keys/values.\n\t\tuint32_t off = (idx == 0) ? 0 : offsets[idx - 1];\n\t\tuint2 rect_min, rect_max;\n\n\t\tgetRect(points_xy[idx], radii[idx], rect_min, rect_max, grid);\n\n\t\t// For each tile that the bounding rect overlaps, emit a \n\t\t// key/value pair. The key is | tile ID | depth |,\n\t\t// and the value is the ID of the Gaussian. Sorting the values \n\t\t// with this key yields Gaussian IDs in a list, such that they\n\t\t// are first sorted by tile and then by depth. \n\t\tfor (int y = rect_min.y; y < rect_max.y; y++)\n\t\t{\n\t\t\tfor (int x = rect_min.x; x < rect_max.x; x++)\n\t\t\t{\n\t\t\t\tuint64_t key = y * grid.x + x;\n\t\t\t\tkey <<= 32;\n\t\t\t\tkey |= *((uint32_t*)&depths[idx]);\n\t\t\t\tgaussian_keys_unsorted[off] = key;\n\t\t\t\tgaussian_values_unsorted[off] = idx;\n\t\t\t\toff++;\n\t\t\t}\n\t\t}\n\t}\n}\n\n// Check keys to see if it is at the start/end of one tile's range in \n// the full sorted list. If yes, write start/end of this tile. \n// Run once per instanced (duplicated) Gaussian ID.\n__global__ void identifyTileRanges(int L, uint64_t* point_list_keys, uint2* ranges)\n{\n\tauto idx = cg::this_grid().thread_rank();\n\tif (idx >= L)\n\t\treturn;\n\n\t// Read tile ID from key. Update start/end of tile range if at limit.\n\tuint64_t key = point_list_keys[idx];\n\tuint32_t currtile = key >> 32;\n\tif (idx == 0)\n\t\tranges[currtile].x = 0;\n\telse\n\t{\n\t\tuint32_t prevtile = point_list_keys[idx - 1] >> 32;\n\t\tif (currtile != prevtile)\n\t\t{\n\t\t\tranges[prevtile].y = idx;\n\t\t\tranges[currtile].x = idx;\n\t\t}\n\t}\n\tif (idx == L - 1)\n\t\tranges[currtile].y = L;\n}\n\n// Mark Gaussians as visible/invisible, based on view frustum testing\nvoid CudaRasterizer::Rasterizer::markVisible(\n\tint P,\n\tfloat* means3D,\n\tfloat* viewmatrix,\n\tfloat* projmatrix,\n\tbool* present)\n{\n\tcheckFrustum << <(P + 255) / 256, 256 >> > (\n\t\tP,\n\t\tmeans3D,\n\t\tviewmatrix, projmatrix,\n\t\tpresent);\n}\n\nCudaRasterizer::GeometryState CudaRasterizer::GeometryState::fromChunk(char*& chunk, size_t P)\n{\n\tGeometryState geom;\n\tobtain(chunk, geom.depths, P, 128);\n\tobtain(chunk, geom.clamped, P * 3, 128);\n\tobtain(chunk, geom.internal_radii, P, 128);\n\tobtain(chunk, geom.means2D, P, 128);\n\tobtain(chunk, geom.cov3D, P * 6, 128);\n\tobtain(chunk, geom.conic_opacity, P, 128);\n\tobtain(chunk, geom.rgb, P * 3, 128);\n\tobtain(chunk, geom.tiles_touched, P, 128);\n\tcub::DeviceScan::InclusiveSum(nullptr, geom.scan_size, geom.tiles_touched, geom.tiles_touched, P);\n\tobtain(chunk, geom.scanning_space, geom.scan_size, 128);\n\tobtain(chunk, geom.point_offsets, P, 128);\n\treturn geom;\n}\n\nCudaRasterizer::ImageState CudaRasterizer::ImageState::fromChunk(char*& chunk, size_t N)\n{\n\tImageState img;\n\tobtain(chunk, img.accum_alpha, N, 128);\n\tobtain(chunk, img.n_contrib, N, 128);\n\tobtain(chunk, img.ranges, N, 128);\n\treturn img;\n}\n\nCudaRasterizer::BinningState CudaRasterizer::BinningState::fromChunk(char*& chunk, size_t P)\n{\n\tBinningState binning;\n\tobtain(chunk, binning.point_list, P, 128);\n\tobtain(chunk, binning.point_list_unsorted, P, 128);\n\tobtain(chunk, binning.point_list_keys, P, 128);\n\tobtain(chunk, binning.point_list_keys_unsorted, P, 128);\n\tcub::DeviceRadixSort::SortPairs(\n\t\tnullptr, binning.sorting_size,\n\t\tbinning.point_list_keys_unsorted, binning.point_list_keys,\n\t\tbinning.point_list_unsorted, binning.point_list, P);\n\tobtain(chunk, binning.list_sorting_space, binning.sorting_size, 128);\n\treturn binning;\n}\n\n// Forward rendering procedure for differentiable rasterization\n// of Gaussians.\nint CudaRasterizer::Rasterizer::forward(\n\tstd::function geometryBuffer,\n\tstd::function binningBuffer,\n\tstd::function imageBuffer,\n\tconst int P, int D, int M,\n\tconst float* background,\n\tconst int width, int height,\n\tconst float* means3D,\n\tconst float* shs,\n\tconst float* colors_precomp,\n\tconst float* language_feature_precomp,\n\tconst float* language_feature_instance_precomp,\n\tconst float* opacities,\n\tconst float* scales,\n\tconst float scale_modifier,\n\tconst float* rotations,\n\tconst float* cov3D_precomp,\n\tconst float* all_map,\n\tconst float* viewmatrix,\n\tconst float* projmatrix,\n\tconst float* cam_pos,\n\tconst float tan_fovx, float tan_fovy,\n\tconst bool prefiltered,\n\tfloat* out_color,\n\tfloat* out_language_feature,\n\tfloat* out_language_feature_instance,\n\tint* radii,\n\tint* out_observe,\n\tfloat* out_all_map,\n\tfloat* out_plane_depth,\n\tconst bool render_geo,\n\tbool debug,\n\tbool include_feature)\n{\n\tconst float focal_y = height / (2.0f * tan_fovy);\n\tconst float focal_x = width / (2.0f * tan_fovx);\n\n\tsize_t chunk_size = required(P);\n\tchar* chunkptr = geometryBuffer(chunk_size);\n\tGeometryState geomState = GeometryState::fromChunk(chunkptr, P);\n\n\tif (radii == nullptr)\n\t{\n\t\tradii = geomState.internal_radii;\n\t}\n\n\tdim3 tile_grid((width + BLOCK_X - 1) / BLOCK_X, (height + BLOCK_Y - 1) / BLOCK_Y, 1);\n\tdim3 block(BLOCK_X, BLOCK_Y, 1);\n\n\t// Dynamically resize image-based auxiliary buffers during training\n\tsize_t img_chunk_size = required(width * height);\n\tchar* img_chunkptr = imageBuffer(img_chunk_size);\n\tImageState imgState = ImageState::fromChunk(img_chunkptr, width * height);\n\n\tif (NUM_CHANNELS != 3 && colors_precomp == nullptr)\n\t{\n\t\tthrow std::runtime_error(\"For non-RGB, provide precomputed Gaussian colors!\");\n\t}\n\n\t// Run preprocessing per-Gaussian (transformation, bounding, conversion of SHs to RGB)\n\tCHECK_CUDA(FORWARD::preprocess(\n\t\tP, D, M,\n\t\tmeans3D,\n\t\t(glm::vec3*)scales,\n\t\tscale_modifier,\n\t\t(glm::vec4*)rotations,\n\t\topacities,\n\t\tshs,\n\t\tgeomState.clamped,\n\t\tcov3D_precomp,\n\t\tcolors_precomp,\n\t\tviewmatrix, projmatrix,\n\t\t(glm::vec3*)cam_pos,\n\t\twidth, height,\n\t\tfocal_x, focal_y,\n\t\ttan_fovx, tan_fovy,\n\t\tradii,\n\t\tgeomState.means2D,\n\t\tgeomState.depths,\n\t\tgeomState.cov3D,\n\t\tgeomState.rgb,\n\t\tgeomState.conic_opacity,\n\t\ttile_grid,\n\t\tgeomState.tiles_touched,\n\t\tprefiltered\n\t), debug)\n\n\t// Compute prefix sum over full list of touched tile counts by Gaussians\n\t// E.g., [2, 3, 0, 2, 1] -> [2, 5, 5, 7, 8]\n\tCHECK_CUDA(cub::DeviceScan::InclusiveSum(geomState.scanning_space, geomState.scan_size, geomState.tiles_touched, geomState.point_offsets, P), debug)\n\n\t// Retrieve total number of Gaussian instances to launch and resize aux buffers\n\tint num_rendered;\n\tCHECK_CUDA(cudaMemcpy(&num_rendered, geomState.point_offsets + P - 1, sizeof(int), cudaMemcpyDeviceToHost), debug);\n\n\tsize_t binning_chunk_size = required(num_rendered);\n\tchar* binning_chunkptr = binningBuffer(binning_chunk_size);\n\tBinningState binningState = BinningState::fromChunk(binning_chunkptr, num_rendered);\n\n\t// For each instance to be rendered, produce adequate [ tile | depth ] key \n\t// and corresponding dublicated Gaussian indices to be sorted\n\tduplicateWithKeys << <(P + 255) / 256, 256 >> > (\n\t\tP,\n\t\tgeomState.means2D,\n\t\tgeomState.depths,\n\t\tgeomState.point_offsets,\n\t\tbinningState.point_list_keys_unsorted,\n\t\tbinningState.point_list_unsorted,\n\t\tradii,\n\t\ttile_grid)\n\tCHECK_CUDA(, debug)\n\n\tint bit = getHigherMsb(tile_grid.x * tile_grid.y);\n\n\t// Sort complete list of (duplicated) Gaussian indices by keys\n\tCHECK_CUDA(cub::DeviceRadixSort::SortPairs(\n\t\tbinningState.list_sorting_space,\n\t\tbinningState.sorting_size,\n\t\tbinningState.point_list_keys_unsorted, binningState.point_list_keys,\n\t\tbinningState.point_list_unsorted, binningState.point_list,\n\t\tnum_rendered, 0, 32 + bit), debug)\n\n\tCHECK_CUDA(cudaMemset(imgState.ranges, 0, tile_grid.x * tile_grid.y * sizeof(uint2)), debug);\n\n\t// Identify start and end of per-tile workloads in sorted list\n\tif (num_rendered > 0)\n\t\tidentifyTileRanges << <(num_rendered + 255) / 256, 256 >> > (\n\t\t\tnum_rendered,\n\t\t\tbinningState.point_list_keys,\n\t\t\timgState.ranges);\n\tCHECK_CUDA(, debug)\n\n\t// Let each tile blend its range of Gaussians independently in parallel\n\tconst float* feature_ptr = colors_precomp != nullptr ? colors_precomp : geomState.rgb;\n\tconst float* language_feature_ptr = language_feature_precomp;\n\tconst float* language_feature_instance_ptr = language_feature_instance_precomp;\n\tCHECK_CUDA(FORWARD::render(\n\t\ttile_grid, block,\n\t\timgState.ranges,\n\t\tbinningState.point_list,\n\t\twidth, height,\n\t\tfocal_x, focal_y,\n\t\tfloat(width*0.5f), float(height*0.5f),\n\t\tviewmatrix,\n\t\tcam_pos,\n\t\tgeomState.means2D,\n\t\tfeature_ptr,\n\t\tlanguage_feature_ptr,\n\t\tlanguage_feature_instance_ptr,\n\t\tall_map,\n\t\tgeomState.conic_opacity,\n\t\timgState.accum_alpha,\n\t\timgState.n_contrib,\n\t\tbackground,\n\t\tout_color,\n\t\tout_language_feature,\n\t\tout_language_feature_instance,\n\t\tout_observe,\n\t\tout_all_map,\n\t\tout_plane_depth,\n\t\trender_geo,\n\t\tinclude_feature), debug)\n\n\treturn num_rendered;\n}\n\n// Produce necessary gradients for optimization, corresponding\n// to forward render pass\nvoid CudaRasterizer::Rasterizer::backward(\n\tconst int P, int D, int M, int R,\n\tconst float* background,\n\tconst float* all_map_pixels,\n\tconst int width, int height,\n\tconst float* means3D,\n\tconst float* shs,\n\tconst float* colors_precomp,\n\tconst float* language_feature_precomp,\n\tconst float* language_feature_instance_precomp,\n\tconst float* all_maps,\n\tconst float* scales,\n\tconst float scale_modifier,\n\tconst float* rotations,\n\tconst float* cov3D_precomp,\n\tconst float* viewmatrix,\n\tconst float* projmatrix,\n\tconst float* campos,\n\tconst float tan_fovx, float tan_fovy,\n\tconst int* radii,\n\tchar* geom_buffer,\n\tchar* binning_buffer,\n\tchar* img_buffer,\n\tconst float* dL_dpix,\n\tconst float* dL_dpix_F,\n\tconst float* dL_dpix_F_instance,\n\tconst float* dL_dout_all_map,\n\tconst float* dL_dout_plane_depth,\n\tfloat* dL_dmean2D,\n\tfloat* dL_dmean2D_abs,\n\tfloat* dL_dconic,\n\tfloat* dL_dopacity,\n\tfloat* dL_dcolor,\n\tfloat* dL_dlanguage_feature,\n\tfloat* dL_dlanguage_feature_instance,\n\tfloat* dL_dmean3D,\n\tfloat* dL_dcov3D,\n\tfloat* dL_dsh,\n\tfloat* dL_dscale,\n\tfloat* dL_drot,\n\tfloat* dL_dall_map,\n\tconst bool render_geo,\n\tbool debug,\n\tbool include_feature)\n{\n\tGeometryState geomState = GeometryState::fromChunk(geom_buffer, P);\n\tBinningState binningState = BinningState::fromChunk(binning_buffer, R);\n\tImageState imgState = ImageState::fromChunk(img_buffer, width * height);\n\n\tif (radii == nullptr)\n\t{\n\t\tradii = geomState.internal_radii;\n\t}\n\n\tconst float focal_y = height / (2.0f * tan_fovy);\n\tconst float focal_x = width / (2.0f * tan_fovx);\n\n\tconst dim3 tile_grid((width + BLOCK_X - 1) / BLOCK_X, (height + BLOCK_Y - 1) / BLOCK_Y, 1);\n\tconst dim3 block(BLOCK_X, BLOCK_Y, 1);\n\n\t// Compute loss gradients w.r.t. 2D mean position, conic matrix,\n\t// opacity and RGB of Gaussians from per-pixel loss gradients.\n\t// If we were given precomputed colors and not SHs, use them.\n\tconst float* color_ptr = (colors_precomp != nullptr) ? colors_precomp : geomState.rgb;\n\tconst float* language_feature_ptr = language_feature_precomp;\n\tconst float* language_feature_instance_ptr = language_feature_instance_precomp;\n\tCHECK_CUDA(BACKWARD::render(\n\t\ttile_grid,\n\t\tblock,\n\t\timgState.ranges,\n\t\tbinningState.point_list,\n\t\twidth, height,\n\t\tfocal_x, focal_y,\n\t\tbackground,\n\t\tgeomState.means2D,\n\t\tgeomState.conic_opacity,\n\t\tcolor_ptr,\n\t\tlanguage_feature_ptr,\n\t\tlanguage_feature_instance_ptr,\n\t\tall_maps,\n\t\tall_map_pixels,\n\t\timgState.accum_alpha,\n\t\timgState.n_contrib,\n\t\tdL_dpix,\n\t\tdL_dpix_F,\n\t\tdL_dpix_F_instance,\n\t\tdL_dout_all_map,\n\t\tdL_dout_plane_depth,\n\t\t(float3*)dL_dmean2D,\n\t\t(float3*)dL_dmean2D_abs,\n\t\t(float4*)dL_dconic,\n\t\tdL_dopacity,\n\t\tdL_dcolor,\n\t\tdL_dlanguage_feature,\n\t\tdL_dlanguage_feature_instance,\n\t\tdL_dall_map,\n\t\trender_geo,\n\t\tinclude_feature), debug)\n\n\t// Take care of the rest of preprocessing. Was the precomputed covariance\n\t// given to us or a scales/rot pair? If precomputed, pass that. If not,\n\t// use the one we computed ourselves.\n\tconst float* cov3D_ptr = (cov3D_precomp != nullptr) ? cov3D_precomp : geomState.cov3D;\n\tCHECK_CUDA(BACKWARD::preprocess(P, D, M,\n\t\t(float3*)means3D,\n\t\tradii,\n\t\tshs,\n\t\tgeomState.clamped,\n\t\t(glm::vec3*)scales,\n\t\t(glm::vec4*)rotations,\n\t\tscale_modifier,\n\t\tcov3D_ptr,\n\t\tviewmatrix,\n\t\tprojmatrix,\n\t\tfocal_x, focal_y,\n\t\ttan_fovx, tan_fovy,\n\t\t(glm::vec3*)campos,\n\t\t(float3*)dL_dmean2D,\n\t\tdL_dconic,\n\t\t(glm::vec3*)dL_dmean3D,\n\t\tdL_dcolor,\n\t\tdL_dcov3D,\n\t\tdL_dsh,\n\t\t(glm::vec3*)dL_dscale,\n\t\t(glm::vec4*)dL_drot), debug)\n}" }, { "path": "field_construction/submodules/diff-langsurf-rasterizer/cuda_rasterizer/rasterizer_impl.h", "content": "/*\n * Copyright (C) 2023, Inria\n * GRAPHDECO research group, https://team.inria.fr/graphdeco\n * All rights reserved.\n *\n * This software is free for non-commercial, research and evaluation use \n * under the terms of the LICENSE.md file.\n *\n * For inquiries contact george.drettakis@inria.fr\n */\n\n#pragma once\n\n#include \n#include \n#include \"rasterizer.h\"\n#include \n\nnamespace CudaRasterizer\n{\n\ttemplate \n\tstatic void obtain(char*& chunk, T*& ptr, std::size_t count, std::size_t alignment)\n\t{\n\t\tstd::size_t offset = (reinterpret_cast(chunk) + alignment - 1) & ~(alignment - 1);\n\t\tptr = reinterpret_cast(offset);\n\t\tchunk = reinterpret_cast(ptr + count);\n\t}\n\n\tstruct GeometryState\n\t{\n\t\tsize_t scan_size;\n\t\tfloat* depths;\n\t\tchar* scanning_space;\n\t\tbool* clamped;\n\t\tint* internal_radii;\n\t\tfloat2* means2D;\n\t\tfloat* cov3D;\n\t\tfloat4* conic_opacity;\n\t\tfloat* rgb;\n\t\tuint32_t* point_offsets;\n\t\tuint32_t* tiles_touched;\n\n\t\tstatic GeometryState fromChunk(char*& chunk, size_t P);\n\t};\n\n\tstruct ImageState\n\t{\n\t\tuint2* ranges;\n\t\tuint32_t* n_contrib;\n\t\tfloat* accum_alpha;\n\n\t\tstatic ImageState fromChunk(char*& chunk, size_t N);\n\t};\n\n\tstruct BinningState\n\t{\n\t\tsize_t sorting_size;\n\t\tuint64_t* point_list_keys_unsorted;\n\t\tuint64_t* point_list_keys;\n\t\tuint32_t* point_list_unsorted;\n\t\tuint32_t* point_list;\n\t\tchar* list_sorting_space;\n\n\t\tstatic BinningState fromChunk(char*& chunk, size_t P);\n\t};\n\n\ttemplate \n\tsize_t required(size_t P)\n\t{\n\t\tchar* size = nullptr;\n\t\tT::fromChunk(size, P);\n\t\treturn ((size_t)size) + 128;\n\t}\n};" }, { "path": "field_construction/submodules/diff-langsurf-rasterizer/diff_LangSurf_rasterization/__init__.py", "content": "#\n# Copyright (C) 2023, Inria\n# GRAPHDECO research group, https://team.inria.fr/graphdeco\n# All rights reserved.\n#\n# This software is free for non-commercial, research and evaluation use \n# under the terms of the LICENSE.md file.\n#\n# For inquiries contact george.drettakis@inria.fr\n#\n\nfrom typing import NamedTuple\nimport torch.nn as nn\nimport torch\nfrom . import _C\n\ndef cpu_deep_copy_tuple(input_tuple):\n copied_tensors = [item.cpu().clone() if isinstance(item, torch.Tensor) else item for item in input_tuple]\n return tuple(copied_tensors)\n\ndef rasterize_gaussians(\n means3D,\n means2D,\n means2D_abs,\n sh,\n colors_precomp,\n language_feature_precomp,\n language_feature_instance_precomp,\n opacities,\n scales,\n rotations,\n cov3Ds_precomp,\n all_map,\n raster_settings,\n):\n return _RasterizeGaussians.apply(\n means3D,\n means2D,\n means2D_abs,\n sh,\n colors_precomp,\n language_feature_precomp,\n language_feature_instance_precomp,\n opacities,\n scales,\n rotations,\n cov3Ds_precomp,\n all_map,\n raster_settings,\n )\n\nclass _RasterizeGaussians(torch.autograd.Function):\n @staticmethod\n def forward(\n ctx,\n means3D,\n means2D,\n means2D_abs,\n sh,\n colors_precomp,\n language_feature_precomp,\n language_feature_instance_precomp,\n opacities,\n scales,\n rotations,\n cov3Ds_precomp,\n all_maps,\n raster_settings,\n ):\n\n # Restructure arguments the way that the C++ lib expects them\n args = (\n raster_settings.bg, \n means3D,\n colors_precomp,\n language_feature_precomp,\n language_feature_instance_precomp,\n opacities,\n scales,\n rotations,\n raster_settings.scale_modifier,\n cov3Ds_precomp,\n all_maps,\n raster_settings.viewmatrix,\n raster_settings.projmatrix,\n raster_settings.tanfovx,\n raster_settings.tanfovy,\n raster_settings.image_height,\n raster_settings.image_width,\n sh,\n raster_settings.sh_degree,\n raster_settings.campos,\n raster_settings.prefiltered,\n raster_settings.render_geo,\n raster_settings.debug,\n raster_settings.include_feature\n )\n\n # Invoke C++/CUDA rasterizer\n if raster_settings.debug:\n cpu_args = cpu_deep_copy_tuple(args) # Copy them before they can be corrupted\n try:\n num_rendered, color, language_feature, language_feature_instance, radii, out_observe, out_all_map, geomBuffer, binningBuffer, imgBuffer = _C.rasterize_gaussians(*args)\n except Exception as ex:\n torch.save(cpu_args, \"snapshot_fw.dump\")\n print(\"\\nAn error occured in forward. Please forward snapshot_fw.dump for debugging.\")\n raise ex\n else:\n num_rendered, color, language_feature, language_feature_instance, radii, out_observe, out_all_map, out_plane_depth, geomBuffer, binningBuffer, imgBuffer = _C.rasterize_gaussians(*args)\n\n # Keep relevant tensors for backward\n ctx.raster_settings = raster_settings\n ctx.num_rendered = num_rendered\n ctx.save_for_backward(out_all_map, colors_precomp, language_feature_precomp, language_feature_instance_precomp, all_maps, means3D, scales, rotations, cov3Ds_precomp, radii, sh, geomBuffer, binningBuffer, imgBuffer)\n return color, language_feature, language_feature_instance, radii, out_observe, out_all_map, out_plane_depth\n\n @staticmethod\n def backward(ctx, grad_out_color, grad_out_language_feature, grad_out_language_feature_instance, grad_radii, grad_out_observe, grad_out_all_map, grad_out_plane_depth):\n\n # Restore necessary values from context\n num_rendered = ctx.num_rendered\n raster_settings = ctx.raster_settings\n all_map_pixels, colors_precomp, language_feature_precomp, language_feature_instance_precomp, all_maps, means3D, scales, rotations, cov3Ds_precomp, radii, sh, geomBuffer, binningBuffer, imgBuffer = ctx.saved_tensors\n\n # Restructure args as C++ method expects them\n args = (raster_settings.bg,\n all_map_pixels,\n means3D, \n radii, \n colors_precomp, \n language_feature_precomp,\n language_feature_instance_precomp,\n all_maps,\n scales, \n rotations, \n raster_settings.scale_modifier, \n cov3Ds_precomp, \n raster_settings.viewmatrix, \n raster_settings.projmatrix, \n raster_settings.tanfovx, \n raster_settings.tanfovy, \n grad_out_color, \n grad_out_language_feature,\n grad_out_language_feature_instance,\n grad_out_all_map,\n grad_out_plane_depth,\n sh, \n raster_settings.sh_degree, \n raster_settings.campos,\n geomBuffer,\n num_rendered,\n binningBuffer,\n imgBuffer,\n raster_settings.render_geo,\n raster_settings.debug,\n raster_settings.include_feature)\n\n # Compute gradients for relevant tensors by invoking backward method\n if raster_settings.debug:\n cpu_args = cpu_deep_copy_tuple(args) # Copy them before they can be corrupted\n try:\n grad_means2D, grad_means2D_abs, grad_colors_precomp, grad_language_feature_precomp, grad_language_feature_instance_precomp, grad_opacities, grad_means3D, grad_cov3Ds_precomp, grad_sh, grad_scales, grad_rotations, gard_all_map = _C.rasterize_gaussians_backward(*args)\n except Exception as ex:\n torch.save(cpu_args, \"snapshot_bw.dump\")\n print(\"\\nAn error occured in backward. Writing snapshot_bw.dump for debugging.\\n\")\n raise ex\n else:\n grad_means2D, grad_means2D_abs, grad_colors_precomp, grad_language_feature_precomp, grad_language_feature_instance_precomp, grad_opacities, grad_means3D, grad_cov3Ds_precomp, grad_sh, grad_scales, grad_rotations, gard_all_map = _C.rasterize_gaussians_backward(*args)\n # print(f\"grad_means2D {grad_means2D.sum()}, grad_means2D_abs {grad_means2D_abs.sum()}\")\n\n grads = (\n grad_means3D,\n grad_means2D,\n grad_means2D_abs,\n grad_sh,\n grad_colors_precomp,\n grad_language_feature_precomp,\n grad_language_feature_instance_precomp,\n grad_opacities,\n grad_scales,\n grad_rotations,\n grad_cov3Ds_precomp,\n gard_all_map,\n None,\n )\n\n return grads\n\nclass GaussianRasterizationSettings(NamedTuple):\n image_height: int\n image_width: int \n tanfovx : float\n tanfovy : float\n bg : torch.Tensor\n scale_modifier : float\n viewmatrix : torch.Tensor\n projmatrix : torch.Tensor\n sh_degree : int\n campos : torch.Tensor\n prefiltered : bool\n render_geo : bool\n debug : bool\n include_feature: bool\n\nclass GaussianRasterizer(nn.Module):\n def __init__(self, raster_settings):\n super().__init__()\n self.raster_settings = raster_settings\n\n def markVisible(self, positions):\n # Mark visible points (based on frustum culling for camera) with a boolean \n with torch.no_grad():\n raster_settings = self.raster_settings\n visible = _C.mark_visible(\n positions,\n raster_settings.viewmatrix,\n raster_settings.projmatrix)\n \n return visible\n\n def forward(self, means3D, means2D, means2D_abs, opacities, shs = None, colors_precomp = None, language_feature_precomp = None, language_feature_instance_precomp = None, scales = None, rotations = None, cov3D_precomp = None, all_map=None):\n \n raster_settings = self.raster_settings\n\n if (shs is None and colors_precomp is None) or (shs is not None and colors_precomp is not None):\n raise Exception('Please provide excatly one of either SHs or precomputed colors!')\n \n if ((scales is None or rotations is None) and cov3D_precomp is None) or ((scales is not None or rotations is not None) and cov3D_precomp is not None):\n raise Exception('Please provide exactly one of either scale/rotation pair or precomputed 3D covariance!')\n \n if shs is None:\n shs = torch.Tensor([])\n if colors_precomp is None:\n colors_precomp = torch.Tensor([])\n if language_feature_precomp is None:\n language_feature_precomp = torch.Tensor([])\n if language_feature_instance_precomp is None:\n language_feature_instance_precomp = torch.Tensor([])\n\n if scales is None:\n scales = torch.Tensor([])\n if rotations is None:\n rotations = torch.Tensor([])\n if cov3D_precomp is None:\n cov3D_precomp = torch.Tensor([])\n if all_map is None:\n all_map = torch.Tensor([])\n\n # Invoke C++/CUDA rasterization routine\n return rasterize_gaussians(\n means3D,\n means2D,\n means2D_abs,\n shs,\n colors_precomp,\n language_feature_precomp,\n language_feature_instance_precomp,\n opacities,\n scales, \n rotations,\n cov3D_precomp,\n all_map,\n raster_settings, \n )\n\n" }, { "path": "field_construction/submodules/diff-langsurf-rasterizer/diff_LangSurf_rasterization.egg-info/PKG-INFO", "content": "Metadata-Version: 2.4\nName: diff_LangSurf_rasterization\nVersion: 0.0.0\nLicense-File: LICENSE.md\nDynamic: license-file\n" }, { "path": "field_construction/submodules/diff-langsurf-rasterizer/diff_LangSurf_rasterization.egg-info/SOURCES.txt", "content": "LICENSE.md\nREADME.md\next.cpp\nrasterize_points.cu\nsetup.py\ncuda_rasterizer/backward.cu\ncuda_rasterizer/forward.cu\ncuda_rasterizer/rasterizer_impl.cu\ndiff_LangSurf_rasterization/__init__.py\ndiff_LangSurf_rasterization.egg-info/PKG-INFO\ndiff_LangSurf_rasterization.egg-info/SOURCES.txt\ndiff_LangSurf_rasterization.egg-info/dependency_links.txt\ndiff_LangSurf_rasterization.egg-info/top_level.txt" }, { "path": "field_construction/submodules/diff-langsurf-rasterizer/diff_LangSurf_rasterization.egg-info/dependency_links.txt", "content": "\n" }, { "path": "field_construction/submodules/diff-langsurf-rasterizer/diff_LangSurf_rasterization.egg-info/top_level.txt", "content": "diff_LangSurf_rasterization\n" }, { "path": "field_construction/submodules/diff-langsurf-rasterizer/ext.cpp", "content": "/*\n * Copyright (C) 2023, Inria\n * GRAPHDECO research group, https://team.inria.fr/graphdeco\n * All rights reserved.\n *\n * This software is free for non-commercial, research and evaluation use \n * under the terms of the LICENSE.md file.\n *\n * For inquiries contact george.drettakis@inria.fr\n */\n\n#include \n#include \"rasterize_points.h\"\n\nPYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {\n m.def(\"rasterize_gaussians\", &RasterizeGaussiansCUDA);\n m.def(\"rasterize_gaussians_backward\", &RasterizeGaussiansBackwardCUDA);\n m.def(\"mark_visible\", &markVisible);\n}" }, { "path": "field_construction/submodules/diff-langsurf-rasterizer/rasterize_points.cu", "content": "/*\n * Copyright (C) 2023, Inria\n * GRAPHDECO research group, https://team.inria.fr/graphdeco\n * All rights reserved.\n *\n * This software is free for non-commercial, research and evaluation use \n * under the terms of the LICENSE.md file.\n *\n * For inquiries contact george.drettakis@inria.fr\n */\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \"cuda_rasterizer/config.h\"\n#include \"cuda_rasterizer/rasterizer.h\"\n#include \n#include \n#include \n\nstd::function resizeFunctional(torch::Tensor& t) {\n auto lambda = [&t](size_t N) {\n t.resize_({(long long)N});\n\t\treturn reinterpret_cast(t.contiguous().data_ptr());\n };\n return lambda;\n}\n\nstd::tuple\nRasterizeGaussiansCUDA(\n\tconst torch::Tensor& background,\n\tconst torch::Tensor& means3D,\n const torch::Tensor& colors,\n\tconst torch::Tensor& language_feature,\n\tconst torch::Tensor& language_feature_instance,\n const torch::Tensor& opacity,\n\tconst torch::Tensor& scales,\n\tconst torch::Tensor& rotations,\n\tconst float scale_modifier,\n\tconst torch::Tensor& cov3D_precomp,\n\tconst torch::Tensor& all_map,\n\tconst torch::Tensor& viewmatrix,\n\tconst torch::Tensor& projmatrix,\n\tconst float tan_fovx, \n\tconst float tan_fovy,\n const int image_height,\n const int image_width,\n\tconst torch::Tensor& sh,\n\tconst int degree,\n\tconst torch::Tensor& campos,\n\tconst bool prefiltered,\n\tconst bool render_geo,\n\tconst bool debug,\n\tconst bool include_feature)\n{\n if (means3D.ndimension() != 2 || means3D.size(1) != 3) {\n AT_ERROR(\"means3D must have dimensions (num_points, 3)\");\n }\n \n const int P = means3D.size(0);\n const int H = image_height;\n const int W = image_width;\n\n auto int_opts = means3D.options().dtype(torch::kInt32);\n auto float_opts = means3D.options().dtype(torch::kFloat32);\n\n torch::Tensor out_color = torch::full({NUM_CHANNELS, H, W}, 0.0, float_opts);\n torch::Tensor out_language_feature;\n torch::Tensor out_language_feature_instance;\n if (include_feature) {\n\tout_language_feature = torch::full({NUM_CHANNELS_language_feature, H, W}, 0.0, float_opts);\n\tout_language_feature_instance = torch::full({NUM_CHANNELS_instance_feature, H, W}, 0.0, float_opts);\n }\n else {\n\tout_language_feature = torch::full({1}, 0.0, float_opts);\n\tout_language_feature_instance = torch::full({1}, 0.0, float_opts);\n }\n torch::Tensor radii = torch::full({P}, 0, means3D.options().dtype(torch::kInt32));\n torch::Tensor out_observe = torch::full({P}, 0, means3D.options().dtype(torch::kInt32));\n torch::Tensor out_all_map = torch::full({NUM_ALL_MAP, H, W}, 0, float_opts);\n torch::Tensor out_plane_depth = torch::full({1, H, W}, 0, float_opts);\n \n torch::Device device(torch::kCUDA);\n torch::TensorOptions options(torch::kByte);\n torch::Tensor geomBuffer = torch::empty({0}, options.device(device));\n torch::Tensor binningBuffer = torch::empty({0}, options.device(device));\n torch::Tensor imgBuffer = torch::empty({0}, options.device(device));\n std::function geomFunc = resizeFunctional(geomBuffer);\n std::function binningFunc = resizeFunctional(binningBuffer);\n std::function imgFunc = resizeFunctional(imgBuffer);\n \n int rendered = 0;\n if(P != 0)\n {\n\t int M = 0;\n\t if(sh.size(0) != 0)\n\t {\n\t\tM = sh.size(1);\n }\n\n\t rendered = CudaRasterizer::Rasterizer::forward(\n\t geomFunc,\n\t\tbinningFunc,\n\t\timgFunc,\n\t P, degree, M,\n\t\tbackground.contiguous().data(),\n\t\tW, H,\n\t\tmeans3D.contiguous().data(),\n\t\tsh.contiguous().data_ptr(),\n\t\tcolors.contiguous().data(), \n\t\tlanguage_feature.contiguous().data(),\n\t\tlanguage_feature_instance.contiguous().data(),\n\t\topacity.contiguous().data(), \n\t\tscales.contiguous().data_ptr(),\n\t\tscale_modifier,\n\t\trotations.contiguous().data_ptr(),\n\t\tcov3D_precomp.contiguous().data(), \n\t\tall_map.contiguous().data(), \n\t\tviewmatrix.contiguous().data(), \n\t\tprojmatrix.contiguous().data(),\n\t\tcampos.contiguous().data(),\n\t\ttan_fovx,\n\t\ttan_fovy,\n\t\tprefiltered,\n\t\tout_color.contiguous().data(),\n\t\tout_language_feature.contiguous().data(),\n\t\tout_language_feature_instance.contiguous().data(),\n\t\tradii.contiguous().data(),\n\t\tout_observe.contiguous().data(),\n\t\tout_all_map.contiguous().data(),\n\t\tout_plane_depth.contiguous().data(),\n\t\trender_geo,\n\t\tdebug,\n\t\tinclude_feature);\n }\n return std::make_tuple(rendered, out_color, out_language_feature, out_language_feature_instance, radii, out_observe, out_all_map, out_plane_depth, geomBuffer, binningBuffer, imgBuffer);\n}\n\nstd::tuple\nRasterizeGaussiansBackwardCUDA(\n \tconst torch::Tensor& background,\n\tconst torch::Tensor& all_map_pixels,\n\tconst torch::Tensor& means3D,\n\tconst torch::Tensor& radii,\n const torch::Tensor& colors,\n\tconst torch::Tensor& language_feature,\n\tconst torch::Tensor& language_feature_instance,\n\tconst torch::Tensor& all_maps,\n\tconst torch::Tensor& scales,\n\tconst torch::Tensor& rotations,\n\tconst float scale_modifier,\n\tconst torch::Tensor& cov3D_precomp,\n\tconst torch::Tensor& viewmatrix,\n const torch::Tensor& projmatrix,\n\tconst float tan_fovx,\n\tconst float tan_fovy,\n const torch::Tensor& dL_dout_color,\n\tconst torch::Tensor& dL_dout_language_feature,\n\tconst torch::Tensor& dL_dout_language_feature_instance,\n\tconst torch::Tensor& dL_dout_all_map,\n\tconst torch::Tensor& dL_dout_plane_depth,\n\tconst torch::Tensor& sh,\n\tconst int degree,\n\tconst torch::Tensor& campos,\n\tconst torch::Tensor& geomBuffer,\n\tconst int R,\n\tconst torch::Tensor& binningBuffer,\n\tconst torch::Tensor& imageBuffer,\n\tconst bool render_geo,\n\tconst bool debug,\n\tconst bool include_feature) \n{\n const int P = means3D.size(0);\n const int H = dL_dout_color.size(1);\n const int W = dL_dout_color.size(2);\n \n int M = 0;\n if(sh.size(0) != 0)\n {\t\n\tM = sh.size(1);\n }\n\n torch::Tensor dL_dmeans3D = torch::zeros({P, 3}, means3D.options());\n torch::Tensor dL_dmeans2D = torch::zeros({P, 3}, means3D.options());\n torch::Tensor dL_dmeans2D_abs = torch::zeros({P, 3}, means3D.options());\n torch::Tensor dL_dcolors = torch::zeros({P, NUM_CHANNELS}, means3D.options());\n torch::Tensor dL_dlanguage_feature;\n torch::Tensor dL_dlanguage_feature_instance;\n if (include_feature) {\n\tdL_dlanguage_feature = torch::zeros({P, NUM_CHANNELS_language_feature}, means3D.options());\n\tdL_dlanguage_feature_instance = torch::zeros({P, NUM_CHANNELS_instance_feature}, means3D.options());\n } else {\n\tdL_dlanguage_feature = torch::zeros({1}, means3D.options());\n\tdL_dlanguage_feature_instance = torch::zeros({1}, means3D.options());\n }\n torch::Tensor dL_dall_map = torch::zeros({P, NUM_ALL_MAP}, means3D.options());\n torch::Tensor dL_dconic = torch::zeros({P, 2, 2}, means3D.options());\n torch::Tensor dL_dopacity = torch::zeros({P, 1}, means3D.options());\n torch::Tensor dL_dcov3D = torch::zeros({P, 6}, means3D.options());\n torch::Tensor dL_dsh = torch::zeros({P, M, 3}, means3D.options());\n torch::Tensor dL_dscales = torch::zeros({P, 3}, means3D.options());\n torch::Tensor dL_drotations = torch::zeros({P, 4}, means3D.options());\n \n if(P != 0)\n { \n\t CudaRasterizer::Rasterizer::backward(P, degree, M, R,\n\t background.contiguous().data(),\n\t all_map_pixels.contiguous().data(),\n\t W, H, \n\t means3D.contiguous().data(),\n\t sh.contiguous().data(),\n\t colors.contiguous().data(),\n\t language_feature.contiguous().data(),\n\t language_feature_instance.contiguous().data(),\n\t all_maps.contiguous().data(),\n\t scales.data_ptr(),\n\t scale_modifier,\n\t rotations.data_ptr(),\n\t cov3D_precomp.contiguous().data(),\n\t viewmatrix.contiguous().data(),\n\t projmatrix.contiguous().data(),\n\t campos.contiguous().data(),\n\t tan_fovx,\n\t tan_fovy,\n\t radii.contiguous().data(),\n\t reinterpret_cast(geomBuffer.contiguous().data_ptr()),\n\t reinterpret_cast(binningBuffer.contiguous().data_ptr()),\n\t reinterpret_cast(imageBuffer.contiguous().data_ptr()),\n\t dL_dout_color.contiguous().data(),\n\t dL_dout_language_feature.contiguous().data(),\n\t dL_dout_language_feature_instance.contiguous().data(),\n\t dL_dout_all_map.contiguous().data(),\n\t dL_dout_plane_depth.contiguous().data(),\n\t dL_dmeans2D.contiguous().data(),\n\t dL_dmeans2D_abs.contiguous().data(),\n\t dL_dconic.contiguous().data(), \n\t dL_dopacity.contiguous().data(),\n\t dL_dcolors.contiguous().data(),\n\t dL_dlanguage_feature.contiguous().data(),\n\t dL_dlanguage_feature_instance.contiguous().data(),\n\t dL_dmeans3D.contiguous().data(),\n\t dL_dcov3D.contiguous().data(),\n\t dL_dsh.contiguous().data(),\n\t dL_dscales.contiguous().data(),\n\t dL_drotations.contiguous().data(),\n\t dL_dall_map.contiguous().data(),\n\t render_geo,\n\t debug,\n\t include_feature);\n }\n\n return std::make_tuple(dL_dmeans2D, dL_dmeans2D_abs, dL_dcolors, dL_dlanguage_feature, dL_dlanguage_feature_instance, dL_dopacity, dL_dmeans3D, dL_dcov3D, dL_dsh, dL_dscales, dL_drotations, dL_dall_map);\n}\n\ntorch::Tensor markVisible(\n\t\ttorch::Tensor& means3D,\n\t\ttorch::Tensor& viewmatrix,\n\t\ttorch::Tensor& projmatrix)\n{ \n const int P = means3D.size(0);\n \n torch::Tensor present = torch::full({P}, false, means3D.options().dtype(at::kBool));\n \n if(P != 0)\n {\n\tCudaRasterizer::Rasterizer::markVisible(P,\n\t\tmeans3D.contiguous().data(),\n\t\tviewmatrix.contiguous().data(),\n\t\tprojmatrix.contiguous().data(),\n\t\tpresent.contiguous().data());\n }\n \n return present;\n}" }, { "path": "field_construction/submodules/diff-langsurf-rasterizer/rasterize_points.h", "content": "/*\n * Copyright (C) 2023, Inria\n * GRAPHDECO research group, https://team.inria.fr/graphdeco\n * All rights reserved.\n *\n * This software is free for non-commercial, research and evaluation use \n * under the terms of the LICENSE.md file.\n *\n * For inquiries contact george.drettakis@inria.fr\n */\n\n#pragma once\n#include \n#include \n#include \n#include \n\t\nstd::tuple\nRasterizeGaussiansCUDA(\n\tconst torch::Tensor& background,\n\tconst torch::Tensor& means3D,\n const torch::Tensor& colors,\n\tconst torch::Tensor& language_feature,\n\tconst torch::Tensor& language_feature_instance,\n const torch::Tensor& opacity,\n\tconst torch::Tensor& scales,\n\tconst torch::Tensor& rotations,\n\tconst float scale_modifier,\n\tconst torch::Tensor& cov3D_precomp,\n\tconst torch::Tensor& all_map,\n\tconst torch::Tensor& viewmatrix,\n\tconst torch::Tensor& projmatrix,\n\tconst float tan_fovx, \n\tconst float tan_fovy,\n const int image_height,\n const int image_width,\n\tconst torch::Tensor& sh,\n\tconst int degree,\n\tconst torch::Tensor& campos,\n\tconst bool prefiltered,\n\tconst bool render_geo,\n\tconst bool debug,\n\tconst bool include_feature);\n\nstd::tuple\nRasterizeGaussiansBackwardCUDA(\n \tconst torch::Tensor& background,\n\tconst torch::Tensor& all_map_pixels,\n\tconst torch::Tensor& means3D,\n\tconst torch::Tensor& radii,\n const torch::Tensor& colors,\n\tconst torch::Tensor& language_feature,\n\tconst torch::Tensor& language_feature_instance,\n\tconst torch::Tensor& all_maps,\n\tconst torch::Tensor& scales,\n\tconst torch::Tensor& rotations,\n\tconst float scale_modifier,\n\tconst torch::Tensor& cov3D_precomp,\n\tconst torch::Tensor& viewmatrix,\n const torch::Tensor& projmatrix,\n\tconst float tan_fovx,\n\tconst float tan_fovy,\n const torch::Tensor& dL_dout_color,\n\tconst torch::Tensor& dL_dout_language_feature,\n\tconst torch::Tensor& dL_dout_language_feature_instance,\n\tconst torch::Tensor& dL_dout_all_map,\n\tconst torch::Tensor& dL_dout_plane_depth,\n\tconst torch::Tensor& sh,\n\tconst int degree,\n\tconst torch::Tensor& campos,\n\tconst torch::Tensor& geomBuffer,\n\tconst int R,\n\tconst torch::Tensor& binningBuffer,\n\tconst torch::Tensor& imageBuffer,\n\tconst bool render_geo,\n\tconst bool debug,\n\tconst bool include_feature);\n\t\t\ntorch::Tensor markVisible(\n\t\ttorch::Tensor& means3D,\n\t\ttorch::Tensor& viewmatrix,\n\t\ttorch::Tensor& projmatrix);" }, { "path": "field_construction/submodules/diff-langsurf-rasterizer/setup.py", "content": "#\n# Copyright (C) 2023, Inria\n# GRAPHDECO research group, https://team.inria.fr/graphdeco\n# All rights reserved.\n#\n# This software is free for non-commercial, research and evaluation use \n# under the terms of the LICENSE.md file.\n#\n# For inquiries contact george.drettakis@inria.fr\n#\n\nimport os\n\nfrom setuptools import setup\nfrom torch.utils.cpp_extension import BuildExtension, CUDAExtension\n\nos.path.dirname(os.path.abspath(__file__))\n\nsetup(\n name=\"diff_LangSurf_rasterization\",\n packages=['diff_LangSurf_rasterization'],\n ext_modules=[\n CUDAExtension(\n name=\"diff_LangSurf_rasterization._C\",\n sources=[\n \"cuda_rasterizer/rasterizer_impl.cu\",\n \"cuda_rasterizer/forward.cu\",\n \"cuda_rasterizer/backward.cu\",\n \"rasterize_points.cu\",\n \"ext.cpp\"],\n extra_compile_args={\"nvcc\": [\"-I\" + os.path.join(os.path.dirname(os.path.abspath(__file__)), \"third_party/glm/\")]})\n ],\n cmdclass={\n 'build_ext': BuildExtension\n }\n)\n" }, { "path": "field_construction/submodules/diff-langsurf-rasterizer/third_party/glm/.appveyor.yml", "content": "shallow_clone: true\n\nplatform:\n - x86\n - x64\n\nconfiguration:\n - Debug\n - Release\n\nimage:\n - Visual Studio 2013\n - Visual Studio 2015\n - Visual Studio 2017\n - Visual Studio 2019\n\nenvironment:\n matrix:\n - GLM_ARGUMENTS: -DGLM_TEST_FORCE_PURE=ON\n - GLM_ARGUMENTS: -DGLM_TEST_ENABLE_SIMD_SSE2=ON -DGLM_TEST_ENABLE_LANG_EXTENSIONS=ON\n - GLM_ARGUMENTS: -DGLM_TEST_ENABLE_SIMD_AVX=ON -DGLM_TEST_ENABLE_LANG_EXTENSIONS=ON\n - GLM_ARGUMENTS: -DGLM_TEST_ENABLE_SIMD_AVX=ON -DGLM_TEST_ENABLE_LANG_EXTENSIONS=ON -DGLM_TEST_ENABLE_CXX_14=ON\n - GLM_ARGUMENTS: -DGLM_TEST_ENABLE_SIMD_AVX=ON -DGLM_TEST_ENABLE_LANG_EXTENSIONS=ON -DGLM_TEST_ENABLE_CXX_17=ON\n\nmatrix:\n exclude:\n - image: Visual Studio 2013\n GLM_ARGUMENTS: -DGLM_TEST_ENABLE_SIMD_AVX=ON -DGLM_TEST_ENABLE_LANG_EXTENSIONS=ON\n - image: Visual Studio 2013\n GLM_ARGUMENTS: -DGLM_TEST_ENABLE_SIMD_AVX=ON -DGLM_TEST_ENABLE_LANG_EXTENSIONS=ON -DGLM_TEST_ENABLE_CXX_14=ON\n - image: Visual Studio 2013\n GLM_ARGUMENTS: -DGLM_TEST_ENABLE_SIMD_AVX=ON -DGLM_TEST_ENABLE_LANG_EXTENSIONS=ON -DGLM_TEST_ENABLE_CXX_17=ON\n - image: Visual Studio 2013\n configuration: Debug\n - image: Visual Studio 2015\n GLM_ARGUMENTS: -DGLM_TEST_ENABLE_SIMD_SSE2=ON -DGLM_TEST_ENABLE_LANG_EXTENSIONS=ON\n - image: Visual Studio 2015\n GLM_ARGUMENTS: -DGLM_TEST_ENABLE_SIMD_AVX=ON -DGLM_TEST_ENABLE_LANG_EXTENSIONS=ON -DGLM_TEST_ENABLE_CXX_14=ON\n - image: Visual Studio 2015\n GLM_ARGUMENTS: -DGLM_TEST_ENABLE_SIMD_AVX=ON -DGLM_TEST_ENABLE_LANG_EXTENSIONS=ON -DGLM_TEST_ENABLE_CXX_17=ON\n - image: Visual Studio 2015\n platform: x86\n - image: Visual Studio 2015\n configuration: Debug\n - image: Visual Studio 2017\n platform: x86\n - image: Visual Studio 2017\n configuration: Debug\n - image: Visual Studio 2019\n platform: x64\n\nbranches:\n only:\n - master\n\nbefore_build:\n - ps: |\n mkdir build\n cd build\n\n if (\"$env:APPVEYOR_JOB_NAME\" -match \"Image: Visual Studio 2013\") {\n $env:generator=\"Visual Studio 12 2013\"\n } \n if (\"$env:APPVEYOR_JOB_NAME\" -match \"Image: Visual Studio 2015\") {\n $env:generator=\"Visual Studio 14 2015\"\n } \n if (\"$env:APPVEYOR_JOB_NAME\" -match \"Image: Visual Studio 2017\") {\n $env:generator=\"Visual Studio 15 2017\"\n }\n if (\"$env:APPVEYOR_JOB_NAME\" -match \"Image: Visual Studio 2019\") {\n $env:generator=\"Visual Studio 16 2019\"\n }\n if ($env:PLATFORM -eq \"x64\") {\n $env:generator=\"$env:generator Win64\"\n }\n echo generator=\"$env:generator\"\n cmake .. -G \"$env:generator\" -DCMAKE_INSTALL_PREFIX=\"$env:APPVEYOR_BUILD_FOLDER/install\" -DGLM_QUIET=ON -DGLM_TEST_ENABLE=ON \"$env:GLM_ARGUMENTS\"\n\nbuild_script:\n - cmake --build . --parallel --config %CONFIGURATION% -- /m /v:minimal\n - cmake --build . --target install --parallel --config %CONFIGURATION% -- /m /v:minimal\n\ntest_script:\n - ctest --parallel 4 --verbose -C %CONFIGURATION%\n - cd ..\n - ps: |\n mkdir build_test_cmake\n cd build_test_cmake\n cmake ..\\test\\cmake\\ -G \"$env:generator\" -DCMAKE_PREFIX_PATH=\"$env:APPVEYOR_BUILD_FOLDER/install\"\n - cmake --build . --parallel --config %CONFIGURATION% -- /m /v:minimal\n\ndeploy: off\n" }, { "path": "field_construction/submodules/diff-langsurf-rasterizer/third_party/glm/.gitignore", "content": "# Compiled Object files\n*.slo\n*.lo\n*.o\n*.obj\n\n# Precompiled Headers\n*.gch\n*.pch\n\n# Compiled Dynamic libraries\n*.so\n*.dylib\n*.dll\n\n# Fortran module files\n*.mod\n\n# Compiled Static libraries\n*.lai\n*.la\n*.a\n*.lib\n\n# Executables\n*.exe\n*.out\n*.app\n\n# CMake\nCMakeCache.txt\nCMakeFiles\ncmake_install.cmake\ninstall_manifest.txt\n*.cmake\n!glmConfig.cmake\n!glmConfig-version.cmake\n# ^ May need to add future .cmake files as exceptions\n\n# Test logs\nTesting/*\n\n# Test input\ntest/gtc/*.dds\n\n# Project Files\nMakefile\n*.cbp\n*.user\n\n# Misc.\n*.log\n\n# local build(s)\nbuild*\n\n/.vs\n/.vscode\n/CMakeSettings.json\n.DS_Store\n*.swp\n" }, { "path": "field_construction/submodules/diff-langsurf-rasterizer/third_party/glm/.travis.yml", "content": "language: cpp\n\nbranches:\n only:\n - master\n - stable\n\njobs:\n include:\n - name: \"Xcode 7.3 C++98 pure release\"\n os: osx\n osx_image: xcode7.3\n env:\n - MATRIX_EVAL=\"\"\n - CMAKE_BUILD_ENV=\"-DCMAKE_BUILD_TYPE=Release -DGLM_TEST_ENABLE=ON -DGLM_TEST_ENABLE_CXX_98=ON -DGLM_TEST_FORCE_PURE=ON\"\n\n - name: \"Xcode 7.3 C++98 sse2 release\"\n os: osx\n osx_image: xcode7.3\n env:\n - MATRIX_EVAL=\"\"\n - CMAKE_BUILD_ENV=\"-DCMAKE_BUILD_TYPE=Release -DGLM_TEST_ENABLE=ON -DGLM_TEST_ENABLE_CXX_98=ON -DGLM_TEST_ENABLE_LANG_EXTENSIONS=ON -DGLM_TEST_ENABLE_SIMD_SSE2=ON\"\n\n - name: \"Xcode 7.3 C++98 ms release\"\n os: osx\n osx_image: xcode7.3\n env:\n - MATRIX_EVAL=\"\"\n - CMAKE_BUILD_ENV=\"-DCMAKE_BUILD_TYPE=Release -DGLM_TEST_ENABLE=ON -DGLM_TEST_ENABLE_CXX_98=ON -DGLM_TEST_ENABLE_LANG_EXTENSIONS=ON\"\n\n - name: \"XCode 7.3 C++11 pure release\"\n os: osx\n osx_image: xcode7.3\n env:\n - MATRIX_EVAL=\"\"\n - CMAKE_BUILD_ENV=\"-DCMAKE_BUILD_TYPE=Release -DGLM_TEST_ENABLE=ON -DGLM_TEST_ENABLE_CXX_11=ON -DGLM_TEST_FORCE_PURE=ON\"\n\n - name: \"XCode 7.3 C++11 sse2 release\"\n os: osx\n osx_image: xcode7.3\n env:\n - MATRIX_EVAL=\"\"\n - CMAKE_BUILD_ENV=\"-DCMAKE_BUILD_TYPE=Release -DGLM_TEST_ENABLE=ON -DGLM_TEST_ENABLE_CXX_11=ON -DGLM_TEST_ENABLE_LANG_EXTENSIONS=ON -DGLM_TEST_ENABLE_SIMD_SSE3=ON\"\n\n - name: \"XCode 10.3 C++11 sse2 release\"\n os: osx\n osx_image: xcode10.3\n env:\n - MATRIX_EVAL=\"\"\n - CMAKE_BUILD_ENV=\"-DCMAKE_BUILD_TYPE=Release -DGLM_TEST_ENABLE=ON -DGLM_TEST_ENABLE_CXX_11=ON -DGLM_TEST_ENABLE_LANG_EXTENSIONS=ON -DGLM_TEST_ENABLE_SIMD_SSE3=ON\"\n\n - name: \"XCode 12.2 C++11 sse2 release\"\n os: osx\n osx_image: xcode12.2\n env:\n - MATRIX_EVAL=\"\"\n - CMAKE_BUILD_ENV=\"-DCMAKE_BUILD_TYPE=Release -DGLM_TEST_ENABLE=ON -DGLM_TEST_ENABLE_CXX_11=ON -DGLM_TEST_ENABLE_LANG_EXTENSIONS=ON -DGLM_TEST_ENABLE_SIMD_SSE3=ON\"\n - CTEST_ENV=\"--parallel 4 --output-on-failure\"\n - CMAKE_ENV=\"--parallel\"\n\n - name: \"XCode 12.2 C++11 sse2 debug\"\n os: osx\n osx_image: xcode12.2\n env:\n - MATRIX_EVAL=\"\"\n - CMAKE_BUILD_ENV=\"-DCMAKE_BUILD_TYPE=Debug -DGLM_TEST_ENABLE=ON -DGLM_TEST_ENABLE_CXX_11=ON -DGLM_TEST_ENABLE_LANG_EXTENSIONS=ON -DGLM_TEST_ENABLE_SIMD_SSE3=ON\"\n - CTEST_ENV=\"--parallel 4 --output-on-failure\"\n - CMAKE_ENV=\"--parallel\"\n\n - name: \"XCode 12.2 C++11 avx debug\"\n os: osx\n osx_image: xcode12.2\n env:\n - MATRIX_EVAL=\"\"\n - CMAKE_BUILD_ENV=\"-DCMAKE_BUILD_TYPE=Debug -DGLM_TEST_ENABLE=ON -DGLM_TEST_ENABLE_CXX_11=ON -DGLM_TEST_ENABLE_LANG_EXTENSIONS=ON -DGLM_TEST_ENABLE_SIMD_AVX=ON\"\n - CTEST_ENV=\"--parallel 4 --output-on-failure\"\n - CMAKE_ENV=\"--parallel\"\n\n - name: \"XCode 12.2 C++14 avx debug\"\n os: osx\n osx_image: xcode12.2\n env:\n - MATRIX_EVAL=\"\"\n - CMAKE_BUILD_ENV=\"-DCMAKE_BUILD_TYPE=Debug -DGLM_TEST_ENABLE=ON -DGLM_TEST_ENABLE_CXX_14=ON -DGLM_TEST_ENABLE_LANG_EXTENSIONS=ON -DGLM_TEST_ENABLE_SIMD_AVX=ON\"\n - CTEST_ENV=\"--parallel 4 --output-on-failure\"\n - CMAKE_ENV=\"--parallel\"\n\n - name: \"XCode 12.2 C++14 pure debug\"\n os: osx\n osx_image: xcode12.2\n env:\n - MATRIX_EVAL=\"\"\n - CMAKE_BUILD_ENV=\"-DCMAKE_BUILD_TYPE=Debug -DGLM_TEST_ENABLE=ON -DGLM_TEST_ENABLE_CXX_14=ON -DGLM_TEST_ENABLE_LANG_EXTENSIONS=ON -DGLM_TEST_FORCE_PURE=ON\"\n - CTEST_ENV=\"--parallel 4 --output-on-failure\"\n - CMAKE_ENV=\"--parallel\"\n\n - name: \"XCode 12.2 C++17 pure debug\"\n os: osx\n osx_image: xcode12.2\n env:\n - MATRIX_EVAL=\"\"\n - CMAKE_BUILD_ENV=\"-DCMAKE_BUILD_TYPE=Debug -DGLM_TEST_ENABLE=ON -DGLM_TEST_ENABLE_CXX_17=ON -DGLM_TEST_ENABLE_LANG_EXTENSIONS=ON -DGLM_TEST_FORCE_PURE=ON\"\n - CTEST_ENV=\"--parallel 4 --output-on-failure\"\n - CMAKE_ENV=\"--parallel\"\n\n - name: \"XCode 12.2 C++17 sse2 debug\"\n os: osx\n osx_image: xcode12.2\n env:\n - MATRIX_EVAL=\"\"\n - CMAKE_BUILD_ENV=\"-DCMAKE_BUILD_TYPE=Debug -DGLM_TEST_ENABLE=ON -DGLM_TEST_ENABLE_CXX_17=ON -DGLM_TEST_ENABLE_LANG_EXTENSIONS=ON -DGLM_TEST_ENABLE_SIMD_SSE2=ON\"\n - CTEST_ENV=\"--parallel 4 --output-on-failure\"\n - CMAKE_ENV=\"--parallel\"\n\n - name: \"XCode 12.2 C++17 sse2 release\"\n os: osx\n osx_image: xcode12.2\n env:\n - MATRIX_EVAL=\"\"\n - CMAKE_BUILD_ENV=\"-DCMAKE_BUILD_TYPE=Release -DGLM_TEST_ENABLE=ON -DGLM_TEST_ENABLE_CXX_17=ON -DGLM_TEST_ENABLE_LANG_EXTENSIONS=ON -DGLM_TEST_ENABLE_SIMD_SSE2=ON\"\n - CTEST_ENV=\"--parallel 4 --output-on-failure\"\n - CMAKE_ENV=\"--parallel\"\n\n - name: \"XCode 12.2 C++17 avx release\"\n os: osx\n osx_image: xcode12.2\n env:\n - MATRIX_EVAL=\"\"\n - CMAKE_BUILD_ENV=\"-DCMAKE_BUILD_TYPE=Release -DGLM_TEST_ENABLE=ON -DGLM_TEST_ENABLE_CXX_17=ON -DGLM_TEST_ENABLE_LANG_EXTENSIONS=ON -DGLM_TEST_ENABLE_SIMD_AVX=ON\"\n - CTEST_ENV=\"--parallel 4 --output-on-failure\"\n - CMAKE_ENV=\"--parallel\"\n\n - name: \"GCC 4.9 C++98 pure release\"\n os: linux\n dist: Xenial\n addons:\n apt:\n sources:\n - ubuntu-toolchain-r-test\n packages:\n - g++-4.9\n env:\n - MATRIX_EVAL=\"CC=gcc-4.9 && CXX=g++-4.9\"\n - CMAKE_BUILD_ENV=\"-DCMAKE_BUILD_TYPE=Release -DGLM_TEST_ENABLE=ON -DGLM_TEST_ENABLE_CXX_98=ON -DGLM_TEST_FORCE_PURE=ON\"\n - CTEST_ENV=\"--parallel 4 --output-on-failure\"\n - CMAKE_ENV=\"--parallel\"\n\n - name: \"GCC 4.9 C++98 pure debug\"\n os: linux\n dist: Xenial\n addons:\n apt:\n sources:\n - ubuntu-toolchain-r-test\n packages:\n - g++-4.9\n env:\n - MATRIX_EVAL=\"CC=gcc-4.9 && CXX=g++-4.9\"\n - CMAKE_BUILD_ENV=\"-DCMAKE_BUILD_TYPE=Debug -DGLM_TEST_ENABLE=ON -DGLM_TEST_ENABLE_CXX_98=ON -DGLM_TEST_FORCE_PURE=ON\"\n - CTEST_ENV=\"--parallel 4 --output-on-failure\"\n - CMAKE_ENV=\"--parallel\"\n\n - name: \"GCC 4.9 C++98 ms debug\"\n os: linux\n dist: Xenial\n addons:\n apt:\n sources:\n - ubuntu-toolchain-r-test\n packages:\n - g++-4.9\n env:\n - MATRIX_EVAL=\"CC=gcc-4.9 && CXX=g++-4.9\"\n - CMAKE_BUILD_ENV=\"-DCMAKE_BUILD_TYPE=Debug -DGLM_TEST_ENABLE=ON -DGLM_TEST_ENABLE_CXX_98=ON -DGLM_TEST_ENABLE_LANG_EXTENSIONS=ON\"\n - CTEST_ENV=\"--parallel 4 --output-on-failure\"\n - CMAKE_ENV=\"--parallel\"\n\n - name: \"GCC 4.9 C++11 ms debug\"\n os: linux\n dist: Xenial\n addons:\n apt:\n sources:\n - ubuntu-toolchain-r-test\n packages:\n - g++-4.9\n env:\n - MATRIX_EVAL=\"CC=gcc-4.9 && CXX=g++-4.9\"\n - CMAKE_BUILD_ENV=\"-DCMAKE_BUILD_TYPE=Debug -DGLM_TEST_ENABLE=ON -DGLM_TEST_ENABLE_CXX_11=ON -DGLM_TEST_ENABLE_LANG_EXTENSIONS=ON\"\n - CTEST_ENV=\"--parallel 4 --output-on-failure\"\n - CMAKE_ENV=\"--parallel\"\n\n - name: \"GCC 4.9 C++11 pure debug\"\n os: linux\n dist: Xenial\n addons:\n apt:\n sources:\n - ubuntu-toolchain-r-test\n packages:\n - g++-4.9\n env:\n - MATRIX_EVAL=\"CC=gcc-4.9 && CXX=g++-4.9\"\n - CMAKE_BUILD_ENV=\"-DCMAKE_BUILD_TYPE=Debug -DGLM_TEST_ENABLE=ON -DGLM_TEST_ENABLE_CXX_11=ON -DGLM_TEST_FORCE_PURE=ON\"\n - CTEST_ENV=\"--parallel 4 --output-on-failure\"\n - CMAKE_ENV=\"--parallel\"\n\n - name: \"GCC 6 C++14 pure debug\"\n os: linux\n dist: bionic\n addons:\n apt:\n sources:\n - ubuntu-toolchain-r-test\n packages:\n - g++-6\n env:\n - MATRIX_EVAL=\"CC=gcc-6 && CXX=g++-6\"\n - CMAKE_BUILD_ENV=\"-DCMAKE_BUILD_TYPE=Debug -DGLM_TEST_ENABLE=ON -DGLM_TEST_ENABLE_CXX_14=ON -DGLM_TEST_FORCE_PURE=ON\"\n - CTEST_ENV=\"--parallel 4 --output-on-failure\"\n - CMAKE_ENV=\"--parallel\"\n\n - name: \"GCC 6 C++14 ms debug\"\n os: linux\n dist: bionic\n addons:\n apt:\n sources:\n - ubuntu-toolchain-r-test\n packages:\n - g++-6\n env:\n - MATRIX_EVAL=\"CC=gcc-6 && CXX=g++-6\"\n - CMAKE_BUILD_ENV=\"-DCMAKE_BUILD_TYPE=Debug -DGLM_TEST_ENABLE=ON -DGLM_TEST_ENABLE_CXX_14=ON -DGLM_TEST_ENABLE_LANG_EXTENSIONS=ON\"\n - CTEST_ENV=\"--parallel 4 --output-on-failure\"\n - CMAKE_ENV=\"--parallel\"\n\n - name: \"GCC 7 C++17 ms debug\"\n os: linux\n dist: bionic\n addons:\n apt:\n sources:\n - ubuntu-toolchain-r-test\n packages:\n - g++-7\n env:\n - MATRIX_EVAL=\"CC=gcc-7 && CXX=g++-7\"\n - CMAKE_BUILD_ENV=\"-DCMAKE_BUILD_TYPE=Debug -DGLM_TEST_ENABLE=ON -DGLM_TEST_ENABLE_CXX_17=ON -DGLM_TEST_ENABLE_LANG_EXTENSIONS=ON\"\n - CTEST_ENV=\"--parallel 4 --output-on-failure\"\n - CMAKE_ENV=\"--parallel\"\n\n - name: \"GCC 7 C++17 pure debug\"\n os: linux\n dist: bionic\n addons:\n apt:\n sources:\n - ubuntu-toolchain-r-test\n packages:\n - g++-7\n env:\n - MATRIX_EVAL=\"CC=gcc-7 && CXX=g++-7\"\n - CMAKE_BUILD_ENV=\"-DCMAKE_BUILD_TYPE=Debug -DGLM_TEST_ENABLE=ON -DGLM_TEST_ENABLE_CXX_17=ON -DGLM_TEST_FORCE_PURE=ON\"\n - CTEST_ENV=\"--parallel 4 --output-on-failure\"\n - CMAKE_ENV=\"--parallel\"\n\n - name: \"GCC 10 C++17 pure debug\"\n os: linux\n dist: bionic\n addons:\n apt:\n sources:\n - ubuntu-toolchain-r-test\n packages:\n - g++-10\n env:\n - MATRIX_EVAL=\"CC=gcc-10 && CXX=g++-10\"\n - CMAKE_BUILD_ENV=\"-DCMAKE_BUILD_TYPE=Debug -DGLM_TEST_ENABLE=ON -DGLM_TEST_ENABLE_CXX_17=ON -DGLM_TEST_FORCE_PURE=ON\"\n - CTEST_ENV=\"--parallel 4 --output-on-failure\"\n - CMAKE_ENV=\"--parallel\"\n\n - name: \"GCC 10 C++17 pure release\"\n os: linux\n dist: bionic\n addons:\n apt:\n sources:\n - ubuntu-toolchain-r-test\n packages:\n - g++-10\n env:\n - MATRIX_EVAL=\"CC=gcc-10 && CXX=g++-10\"\n - CMAKE_BUILD_ENV=\"-DCMAKE_BUILD_TYPE=Release -DGLM_TEST_ENABLE=ON -DGLM_TEST_ENABLE_CXX_17=ON -DGLM_TEST_FORCE_PURE=ON\"\n - CTEST_ENV=\"--parallel 4 --output-on-failure\"\n - CMAKE_ENV=\"--parallel\"\n\n - name: \"Clang C++14 pure release\"\n os: linux\n dist: Xenial\n env:\n - MATRIX_EVAL=\"CC=clang && CXX=clang++\"\n - CMAKE_BUILD_ENV=\"-DCMAKE_BUILD_TYPE=Release -DGLM_TEST_ENABLE=ON -DGLM_TEST_ENABLE_CXX_14=ON -DGLM_TEST_FORCE_PURE=ON\"\n - CTEST_ENV=\"--parallel 4 --output-on-failure\"\n - CMAKE_ENV=\"--parallel\"\n\n - name: \"Clang C++14 pure debug\"\n os: linux\n dist: Xenial\n env:\n - MATRIX_EVAL=\"CC=clang && CXX=clang++\"\n - CMAKE_BUILD_ENV=\"-DCMAKE_BUILD_TYPE=Debug -DGLM_TEST_ENABLE=ON -DGLM_TEST_ENABLE_CXX_14=ON -DGLM_TEST_FORCE_PURE=ON\"\n - CTEST_ENV=\"--parallel 4 --output-on-failure\"\n - CMAKE_ENV=\"--parallel\"\n\n - name: \"Clang C++14 sse2 debug\"\n os: linux\n dist: Xenial\n env:\n - MATRIX_EVAL=\"CC=clang && CXX=clang++\"\n - CMAKE_BUILD_ENV=\"-DCMAKE_BUILD_TYPE=Debug -DGLM_TEST_ENABLE=ON -DGLM_TEST_ENABLE_CXX_14=ON -DGLM_TEST_ENABLE_LANG_EXTENSIONS=ON -DGLM_TEST_ENABLE_SIMD_SSE2=ON\"\n - CTEST_ENV=\"--parallel 4 --output-on-failure\"\n - CMAKE_ENV=\"--parallel\"\n\n - name: \"Clang C++14 sse2 debug\"\n os: linux\n dist: focal\n env:\n - MATRIX_EVAL=\"CC=clang && CXX=clang++\"\n - CMAKE_BUILD_ENV=\"-DCMAKE_BUILD_TYPE=Debug -DGLM_TEST_ENABLE=ON -DGLM_TEST_ENABLE_CXX_14=ON -DGLM_TEST_ENABLE_LANG_EXTENSIONS=ON -DGLM_TEST_ENABLE_SIMD_SSE2=ON\"\n - CTEST_ENV=\"--parallel 4 --output-on-failure\"\n - CMAKE_ENV=\"--parallel\"\n\n - name: \"Clang C++17 sse2 debug\"\n os: linux\n dist: focal\n env:\n - MATRIX_EVAL=\"CC=clang && CXX=clang++\"\n - CMAKE_BUILD_ENV=\"-DCMAKE_BUILD_TYPE=Debug -DGLM_TEST_ENABLE=ON -DGLM_TEST_ENABLE_CXX_17=ON -DGLM_TEST_ENABLE_LANG_EXTENSIONS=ON -DGLM_TEST_ENABLE_SIMD_SSE2=ON\"\n - CTEST_ENV=\"--parallel 4 --output-on-failure\"\n - CMAKE_ENV=\"--parallel\"\n\n - name: \"Clang C++17 avx2 debug\"\n os: linux\n dist: focal\n env:\n - MATRIX_EVAL=\"CC=clang && CXX=clang++\"\n - CMAKE_BUILD_ENV=\"-DCMAKE_BUILD_TYPE=Debug -DGLM_TEST_ENABLE=ON -DGLM_TEST_ENABLE_CXX_17=ON -DGLM_TEST_ENABLE_LANG_EXTENSIONS=ON -DGLM_TEST_ENABLE_SIMD_AVX2=ON\"\n - CTEST_ENV=\"--parallel 4 --output-on-failure\"\n - CMAKE_ENV=\"--parallel\"\n\n - name: \"Clang C++17 pure debug\"\n os: linux\n dist: focal\n env:\n - MATRIX_EVAL=\"CC=clang && CXX=clang++\"\n - CMAKE_BUILD_ENV=\"-DCMAKE_BUILD_TYPE=Debug -DGLM_TEST_ENABLE=ON -DGLM_TEST_ENABLE_CXX_17=ON -DGLM_TEST_FORCE_PURE=ON\"\n - CTEST_ENV=\"--parallel 4 --output-on-failure\"\n - CMAKE_ENV=\"--parallel\"\n\n - name: \"Clang C++17 pure release\"\n os: linux\n dist: focal\n env:\n - MATRIX_EVAL=\"CC=clang && CXX=clang++\"\n - CMAKE_BUILD_ENV=\"-DCMAKE_BUILD_TYPE=Release -DGLM_TEST_ENABLE=ON -DGLM_TEST_ENABLE_CXX_17=ON -DGLM_TEST_FORCE_PURE=ON\"\n - CTEST_ENV=\"--parallel 4 --output-on-failure\"\n - CMAKE_ENV=\"--parallel\"\n\nbefore_script:\n - cmake --version\n - eval \"${MATRIX_EVAL}\"\n\nscript:\n - ${CC} --version\n - mkdir ./build\n - cd ./build\n - cmake -DCMAKE_INSTALL_PREFIX=$TRAVIS_BUILD_DIR/install -DCMAKE_CXX_COMPILER=$COMPILER ${CMAKE_BUILD_ENV} ..\n - cmake --build . ${CMAKE_ENV}\n - ctest ${CTEST_ENV}\n - cmake --build . --target install ${CMAKE_ENV}\n - cd $TRAVIS_BUILD_DIR\n - mkdir ./build_test_cmake\n - cd ./build_test_cmake\n - cmake -DCMAKE_CXX_COMPILER=$COMPILER $TRAVIS_BUILD_DIR/test/cmake/ -DCMAKE_PREFIX_PATH=$TRAVIS_BUILD_DIR/install\n - cmake --build .\n\n\n" }, { "path": "field_construction/submodules/diff-langsurf-rasterizer/third_party/glm/CMakeLists.txt", "content": "cmake_minimum_required(VERSION 3.2 FATAL_ERROR)\ncmake_policy(VERSION 3.2)\n\n\nfile(READ \"glm/detail/setup.hpp\" GLM_SETUP_FILE)\nstring(REGEX MATCH \"#define[ ]+GLM_VERSION_MAJOR[ ]+([0-9]+)\" _ ${GLM_SETUP_FILE})\nset(GLM_VERSION_MAJOR \"${CMAKE_MATCH_1}\")\nstring(REGEX MATCH \"#define[ ]+GLM_VERSION_MINOR[ ]+([0-9]+)\" _ ${GLM_SETUP_FILE})\nset(GLM_VERSION_MINOR \"${CMAKE_MATCH_1}\")\nstring(REGEX MATCH \"#define[ ]+GLM_VERSION_PATCH[ ]+([0-9]+)\" _ ${GLM_SETUP_FILE})\nset(GLM_VERSION_PATCH \"${CMAKE_MATCH_1}\")\nstring(REGEX MATCH \"#define[ ]+GLM_VERSION_REVISION[ ]+([0-9]+)\" _ ${GLM_SETUP_FILE})\nset(GLM_VERSION_REVISION \"${CMAKE_MATCH_1}\")\n\nset(GLM_VERSION ${GLM_VERSION_MAJOR}.${GLM_VERSION_MINOR}.${GLM_VERSION_PATCH}.${GLM_VERSION_REVISION})\nproject(glm VERSION ${GLM_VERSION} LANGUAGES CXX)\nmessage(STATUS \"GLM: Version \" ${GLM_VERSION})\n\nadd_subdirectory(glm)\nadd_library(glm::glm ALIAS glm)\n\nif(${CMAKE_SOURCE_DIR} STREQUAL ${CMAKE_CURRENT_SOURCE_DIR})\n\n\tinclude(CPack)\n\tinstall(DIRECTORY glm DESTINATION ${CMAKE_INSTALL_INCLUDEDIR} PATTERN \"CMakeLists.txt\" EXCLUDE)\n\tinstall(EXPORT glm FILE glmConfig.cmake DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/glm NAMESPACE glm::)\n\tinclude(CMakePackageConfigHelpers)\n\twrite_basic_package_version_file(\"glmConfigVersion.cmake\" COMPATIBILITY AnyNewerVersion)\n\tinstall(FILES ${CMAKE_CURRENT_BINARY_DIR}/glmConfigVersion.cmake DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/glm)\n\n\tinclude(CTest)\n\tif(BUILD_TESTING)\n\t\tadd_subdirectory(test)\n\tendif()\n\nendif(${CMAKE_SOURCE_DIR} STREQUAL ${CMAKE_CURRENT_SOURCE_DIR})\n\nif (NOT TARGET uninstall)\nconfigure_file(cmake/cmake_uninstall.cmake.in\n cmake_uninstall.cmake IMMEDIATE @ONLY)\n\nadd_custom_target(uninstall\n \"${CMAKE_COMMAND}\" -P\n \"${CMAKE_BINARY_DIR}/cmake_uninstall.cmake\")\nendif()\n" }, { "path": "field_construction/submodules/diff-langsurf-rasterizer/third_party/glm/cmake/cmake_uninstall.cmake.in", "content": "if(NOT EXISTS \"@CMAKE_BINARY_DIR@/install_manifest.txt\")\n message(FATAL_ERROR \"Cannot find install manifest: @CMAKE_BINARY_DIR@/install_manifest.txt\")\nendif()\n\nfile(READ \"@CMAKE_BINARY_DIR@/install_manifest.txt\" files)\nstring(REGEX REPLACE \"\\n\" \";\" files \"${files}\")\nforeach(file ${files})\n message(STATUS \"Uninstalling $ENV{DESTDIR}${file}\")\n if(IS_SYMLINK \"$ENV{DESTDIR}${file}\" OR EXISTS \"$ENV{DESTDIR}${file}\")\n exec_program(\n \"@CMAKE_COMMAND@\" ARGS \"-E remove \\\"$ENV{DESTDIR}${file}\\\"\"\n OUTPUT_VARIABLE rm_out\n RETURN_VALUE rm_retval\n )\n if(NOT \"${rm_retval}\" STREQUAL 0)\n message(FATAL_ERROR \"Problem when removing $ENV{DESTDIR}${file}\")\n endif()\n else(IS_SYMLINK \"$ENV{DESTDIR}${file}\" OR EXISTS \"$ENV{DESTDIR}${file}\")\n message(STATUS \"File $ENV{DESTDIR}${file} does not exist.\")\n endif()\nendforeach()\n" }, { "path": "field_construction/submodules/diff-langsurf-rasterizer/third_party/glm/copying.txt", "content": "================================================================================\nOpenGL Mathematics (GLM)\n--------------------------------------------------------------------------------\nGLM is licensed under The Happy Bunny License or MIT License\n\n================================================================================\nThe Happy Bunny License (Modified MIT License)\n--------------------------------------------------------------------------------\nCopyright (c) 2005 - G-Truc Creation\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in\nall copies or substantial portions of the Software.\n\nRestrictions:\n By making use of the Software for military purposes, you choose to make a\n Bunny unhappy.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\nTHE SOFTWARE.\n\n================================================================================\nThe MIT License\n--------------------------------------------------------------------------------\nCopyright (c) 2005 - G-Truc Creation\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in\nall copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\nTHE SOFTWARE.\n" }, { "path": "field_construction/submodules/diff-langsurf-rasterizer/third_party/glm/doc/api/a00001_source.html", "content": "\n\n\n\n\n\n0.9.9 API documentation: _features.hpp Source File\n\n\n\n\n\n\n\n\n\n\n
\n
\n\n \n \n \n \n \n \n
\"Logo\"\n
0.9.9 API documentation\n
\n
\n
\n\n\n\n
\n \n
\n
\n \n
\n\n
\n
\n\n\n
\n\n
\n\n\n
\n
\n
\n
_features.hpp
\n
\n
\n
1 #pragma once
\n
2 
\n
3 // #define GLM_CXX98_EXCEPTIONS
\n
4 // #define GLM_CXX98_RTTI
\n
5 
\n
6 // #define GLM_CXX11_RVALUE_REFERENCES
\n
7 // Rvalue references - GCC 4.3
\n
8 // http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2006/n2118.html
\n
9 
\n
10 // GLM_CXX11_TRAILING_RETURN
\n
11 // Rvalue references for *this - GCC not supported
\n
12 // http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2007/n2439.htm
\n
13 
\n
14 // GLM_CXX11_NONSTATIC_MEMBER_INIT
\n
15 // Initialization of class objects by rvalues - GCC any
\n
16 // http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2004/n1610.html
\n
17 
\n
18 // GLM_CXX11_NONSTATIC_MEMBER_INIT
\n
19 // Non-static data member initializers - GCC 4.7
\n
20 // http://www.open-std.org/JTC1/SC22/WG21/docs/papers/2008/n2756.htm
\n
21 
\n
22 // #define GLM_CXX11_VARIADIC_TEMPLATE
\n
23 // Variadic templates - GCC 4.3
\n
24 // http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2007/n2242.pdf
\n
25 
\n
26 //
\n
27 // Extending variadic template template parameters - GCC 4.4
\n
28 // http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2008/n2555.pdf
\n
29 
\n
30 // #define GLM_CXX11_GENERALIZED_INITIALIZERS
\n
31 // Initializer lists - GCC 4.4
\n
32 // http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2008/n2672.htm
\n
33 
\n
34 // #define GLM_CXX11_STATIC_ASSERT
\n
35 // Static assertions - GCC 4.3
\n
36 // http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2004/n1720.html
\n
37 
\n
38 // #define GLM_CXX11_AUTO_TYPE
\n
39 // auto-typed variables - GCC 4.4
\n
40 // http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2006/n1984.pdf
\n
41 
\n
42 // #define GLM_CXX11_AUTO_TYPE
\n
43 // Multi-declarator auto - GCC 4.4
\n
44 // http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2004/n1737.pdf
\n
45 
\n
46 // #define GLM_CXX11_AUTO_TYPE
\n
47 // Removal of auto as a storage-class specifier - GCC 4.4
\n
48 // http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2008/n2546.htm
\n
49 
\n
50 // #define GLM_CXX11_AUTO_TYPE
\n
51 // New function declarator syntax - GCC 4.4
\n
52 // http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2008/n2541.htm
\n
53 
\n
54 // #define GLM_CXX11_LAMBDAS
\n
55 // New wording for C++0x lambdas - GCC 4.5
\n
56 // http://www.open-std.org/JTC1/SC22/WG21/docs/papers/2009/n2927.pdf
\n
57 
\n
58 // #define GLM_CXX11_DECLTYPE
\n
59 // Declared type of an expression - GCC 4.3
\n
60 // http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2007/n2343.pdf
\n
61 
\n
62 //
\n
63 // Right angle brackets - GCC 4.3
\n
64 // http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2005/n1757.html
\n
65 
\n
66 //
\n
67 // Default template arguments for function templates DR226 GCC 4.3
\n
68 // http://www.open-std.org/jtc1/sc22/wg21/docs/cwg_defects.html#226
\n
69 
\n
70 //
\n
71 // Solving the SFINAE problem for expressions DR339 GCC 4.4
\n
72 // http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2008/n2634.html
\n
73 
\n
74 // #define GLM_CXX11_ALIAS_TEMPLATE
\n
75 // Template aliases N2258 GCC 4.7
\n
76 // http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2007/n2258.pdf
\n
77 
\n
78 //
\n
79 // Extern templates N1987 Yes
\n
80 // http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2006/n1987.htm
\n
81 
\n
82 // #define GLM_CXX11_NULLPTR
\n
83 // Null pointer constant N2431 GCC 4.6
\n
84 // http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2007/n2431.pdf
\n
85 
\n
86 // #define GLM_CXX11_STRONG_ENUMS
\n
87 // Strongly-typed enums N2347 GCC 4.4
\n
88 // http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2007/n2347.pdf
\n
89 
\n
90 //
\n
91 // Forward declarations for enums N2764 GCC 4.6
\n
92 // http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2008/n2764.pdf
\n
93 
\n
94 //
\n
95 // Generalized attributes N2761 GCC 4.8
\n
96 // http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2008/n2761.pdf
\n
97 
\n
98 //
\n
99 // Generalized constant expressions N2235 GCC 4.6
\n
100 // http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2007/n2235.pdf
\n
101 
\n
102 //
\n
103 // Alignment support N2341 GCC 4.8
\n
104 // http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2007/n2341.pdf
\n
105 
\n
106 // #define GLM_CXX11_DELEGATING_CONSTRUCTORS
\n
107 // Delegating constructors N1986 GCC 4.7
\n
108 // http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2006/n1986.pdf
\n
109 
\n
110 //
\n
111 // Inheriting constructors N2540 GCC 4.8
\n
112 // http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2008/n2540.htm
\n
113 
\n
114 // #define GLM_CXX11_EXPLICIT_CONVERSIONS
\n
115 // Explicit conversion operators N2437 GCC 4.5
\n
116 // http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2007/n2437.pdf
\n
117 
\n
118 //
\n
119 // New character types N2249 GCC 4.4
\n
120 // http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2007/n2249.html
\n
121 
\n
122 //
\n
123 // Unicode string literals N2442 GCC 4.5
\n
124 // http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2007/n2442.htm
\n
125 
\n
126 //
\n
127 // Raw string literals N2442 GCC 4.5
\n
128 // http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2007/n2442.htm
\n
129 
\n
130 //
\n
131 // Universal character name literals N2170 GCC 4.5
\n
132 // http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2007/n2170.html
\n
133 
\n
134 // #define GLM_CXX11_USER_LITERALS
\n
135 // User-defined literals N2765 GCC 4.7
\n
136 // http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2008/n2765.pdf
\n
137 
\n
138 //
\n
139 // Standard Layout Types N2342 GCC 4.5
\n
140 // http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2007/n2342.htm
\n
141 
\n
142 // #define GLM_CXX11_DEFAULTED_FUNCTIONS
\n
143 // #define GLM_CXX11_DELETED_FUNCTIONS
\n
144 // Defaulted and deleted functions N2346 GCC 4.4
\n
145 // http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2007/n2346.htm
\n
146 
\n
147 //
\n
148 // Extended friend declarations N1791 GCC 4.7
\n
149 // http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2005/n1791.pdf
\n
150 
\n
151 //
\n
152 // Extending sizeof N2253 GCC 4.4
\n
153 // http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2007/n2253.html
\n
154 
\n
155 // #define GLM_CXX11_INLINE_NAMESPACES
\n
156 // Inline namespaces N2535 GCC 4.4
\n
157 // http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2008/n2535.htm
\n
158 
\n
159 // #define GLM_CXX11_UNRESTRICTED_UNIONS
\n
160 // Unrestricted unions N2544 GCC 4.6
\n
161 // http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2008/n2544.pdf
\n
162 
\n
163 // #define GLM_CXX11_LOCAL_TYPE_TEMPLATE_ARGS
\n
164 // Local and unnamed types as template arguments N2657 GCC 4.5
\n
165 // http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2008/n2657.htm
\n
166 
\n
167 // #define GLM_CXX11_RANGE_FOR
\n
168 // Range-based for N2930 GCC 4.6
\n
169 // http://www.open-std.org/JTC1/SC22/WG21/docs/papers/2009/n2930.html
\n
170 
\n
171 // #define GLM_CXX11_OVERRIDE_CONTROL
\n
172 // Explicit virtual overrides N2928 N3206 N3272 GCC 4.7
\n
173 // http://www.open-std.org/JTC1/SC22/WG21/docs/papers/2009/n2928.htm
\n
174 // http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2010/n3206.htm
\n
175 // http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2011/n3272.htm
\n
176 
\n
177 //
\n
178 // Minimal support for garbage collection and reachability-based leak detection N2670 No
\n
179 // http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2008/n2670.htm
\n
180 
\n
181 // #define GLM_CXX11_NOEXCEPT
\n
182 // Allowing move constructors to throw [noexcept] N3050 GCC 4.6 (core language only)
\n
183 // http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2010/n3050.html
\n
184 
\n
185 //
\n
186 // Defining move special member functions N3053 GCC 4.6
\n
187 // http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2010/n3053.html
\n
188 
\n
189 //
\n
190 // Sequence points N2239 Yes
\n
191 // http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2007/n2239.html
\n
192 
\n
193 //
\n
194 // Atomic operations N2427 GCC 4.4
\n
195 // http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2007/n2239.html
\n
196 
\n
197 //
\n
198 // Strong Compare and Exchange N2748 GCC 4.5
\n
199 // http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2007/n2427.html
\n
200 
\n
201 //
\n
202 // Bidirectional Fences N2752 GCC 4.8
\n
203 // http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2008/n2752.htm
\n
204 
\n
205 //
\n
206 // Memory model N2429 GCC 4.8
\n
207 // http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2007/n2429.htm
\n
208 
\n
209 //
\n
210 // Data-dependency ordering: atomics and memory model N2664 GCC 4.4
\n
211 // http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2008/n2664.htm
\n
212 
\n
213 //
\n
214 // Propagating exceptions N2179 GCC 4.4
\n
215 // http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2007/n2179.html
\n
216 
\n
217 //
\n
218 // Abandoning a process and at_quick_exit N2440 GCC 4.8
\n
219 // http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2007/n2440.htm
\n
220 
\n
221 //
\n
222 // Allow atomics use in signal handlers N2547 Yes
\n
223 // http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2008/n2547.htm
\n
224 
\n
225 //
\n
226 // Thread-local storage N2659 GCC 4.8
\n
227 // http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2008/n2659.htm
\n
228 
\n
229 //
\n
230 // Dynamic initialization and destruction with concurrency N2660 GCC 4.3
\n
231 // http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2008/n2660.htm
\n
232 
\n
233 //
\n
234 // __func__ predefined identifier N2340 GCC 4.3
\n
235 // http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2007/n2340.htm
\n
236 
\n
237 //
\n
238 // C99 preprocessor N1653 GCC 4.3
\n
239 // http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2004/n1653.htm
\n
240 
\n
241 //
\n
242 // long long N1811 GCC 4.3
\n
243 // http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2005/n1811.pdf
\n
244 
\n
245 //
\n
246 // Extended integral types N1988 Yes
\n
247 // http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2006/n1988.pdf
\n
248 
\n
249 #if(GLM_COMPILER & GLM_COMPILER_GCC)
\n
250 
\n
251 # define GLM_CXX11_STATIC_ASSERT
\n
252 
\n
253 #elif(GLM_COMPILER & GLM_COMPILER_CLANG)
\n
254 # if(__has_feature(cxx_exceptions))
\n
255 # define GLM_CXX98_EXCEPTIONS
\n
256 # endif
\n
257 
\n
258 # if(__has_feature(cxx_rtti))
\n
259 # define GLM_CXX98_RTTI
\n
260 # endif
\n
261 
\n
262 # if(__has_feature(cxx_access_control_sfinae))
\n
263 # define GLM_CXX11_ACCESS_CONTROL_SFINAE
\n
264 # endif
\n
265 
\n
266 # if(__has_feature(cxx_alias_templates))
\n
267 # define GLM_CXX11_ALIAS_TEMPLATE
\n
268 # endif
\n
269 
\n
270 # if(__has_feature(cxx_alignas))
\n
271 # define GLM_CXX11_ALIGNAS
\n
272 # endif
\n
273 
\n
274 # if(__has_feature(cxx_attributes))
\n
275 # define GLM_CXX11_ATTRIBUTES
\n
276 # endif
\n
277 
\n
278 # if(__has_feature(cxx_constexpr))
\n
279 # define GLM_CXX11_CONSTEXPR
\n
280 # endif
\n
281 
\n
282 # if(__has_feature(cxx_decltype))
\n
283 # define GLM_CXX11_DECLTYPE
\n
284 # endif
\n
285 
\n
286 # if(__has_feature(cxx_default_function_template_args))
\n
287 # define GLM_CXX11_DEFAULT_FUNCTION_TEMPLATE_ARGS
\n
288 # endif
\n
289 
\n
290 # if(__has_feature(cxx_defaulted_functions))
\n
291 # define GLM_CXX11_DEFAULTED_FUNCTIONS
\n
292 # endif
\n
293 
\n
294 # if(__has_feature(cxx_delegating_constructors))
\n
295 # define GLM_CXX11_DELEGATING_CONSTRUCTORS
\n
296 # endif
\n
297 
\n
298 # if(__has_feature(cxx_deleted_functions))
\n
299 # define GLM_CXX11_DELETED_FUNCTIONS
\n
300 # endif
\n
301 
\n
302 # if(__has_feature(cxx_explicit_conversions))
\n
303 # define GLM_CXX11_EXPLICIT_CONVERSIONS
\n
304 # endif
\n
305 
\n
306 # if(__has_feature(cxx_generalized_initializers))
\n
307 # define GLM_CXX11_GENERALIZED_INITIALIZERS
\n
308 # endif
\n
309 
\n
310 # if(__has_feature(cxx_implicit_moves))
\n
311 # define GLM_CXX11_IMPLICIT_MOVES
\n
312 # endif
\n
313 
\n
314 # if(__has_feature(cxx_inheriting_constructors))
\n
315 # define GLM_CXX11_INHERITING_CONSTRUCTORS
\n
316 # endif
\n
317 
\n
318 # if(__has_feature(cxx_inline_namespaces))
\n
319 # define GLM_CXX11_INLINE_NAMESPACES
\n
320 # endif
\n
321 
\n
322 # if(__has_feature(cxx_lambdas))
\n
323 # define GLM_CXX11_LAMBDAS
\n
324 # endif
\n
325 
\n
326 # if(__has_feature(cxx_local_type_template_args))
\n
327 # define GLM_CXX11_LOCAL_TYPE_TEMPLATE_ARGS
\n
328 # endif
\n
329 
\n
330 # if(__has_feature(cxx_noexcept))
\n
331 # define GLM_CXX11_NOEXCEPT
\n
332 # endif
\n
333 
\n
334 # if(__has_feature(cxx_nonstatic_member_init))
\n
335 # define GLM_CXX11_NONSTATIC_MEMBER_INIT
\n
336 # endif
\n
337 
\n
338 # if(__has_feature(cxx_nullptr))
\n
339 # define GLM_CXX11_NULLPTR
\n
340 # endif
\n
341 
\n
342 # if(__has_feature(cxx_override_control))
\n
343 # define GLM_CXX11_OVERRIDE_CONTROL
\n
344 # endif
\n
345 
\n
346 # if(__has_feature(cxx_reference_qualified_functions))
\n
347 # define GLM_CXX11_REFERENCE_QUALIFIED_FUNCTIONS
\n
348 # endif
\n
349 
\n
350 # if(__has_feature(cxx_range_for))
\n
351 # define GLM_CXX11_RANGE_FOR
\n
352 # endif
\n
353 
\n
354 # if(__has_feature(cxx_raw_string_literals))
\n
355 # define GLM_CXX11_RAW_STRING_LITERALS
\n
356 # endif
\n
357 
\n
358 # if(__has_feature(cxx_rvalue_references))
\n
359 # define GLM_CXX11_RVALUE_REFERENCES
\n
360 # endif
\n
361 
\n
362 # if(__has_feature(cxx_static_assert))
\n
363 # define GLM_CXX11_STATIC_ASSERT
\n
364 # endif
\n
365 
\n
366 # if(__has_feature(cxx_auto_type))
\n
367 # define GLM_CXX11_AUTO_TYPE
\n
368 # endif
\n
369 
\n
370 # if(__has_feature(cxx_strong_enums))
\n
371 # define GLM_CXX11_STRONG_ENUMS
\n
372 # endif
\n
373 
\n
374 # if(__has_feature(cxx_trailing_return))
\n
375 # define GLM_CXX11_TRAILING_RETURN
\n
376 # endif
\n
377 
\n
378 # if(__has_feature(cxx_unicode_literals))
\n
379 # define GLM_CXX11_UNICODE_LITERALS
\n
380 # endif
\n
381 
\n
382 # if(__has_feature(cxx_unrestricted_unions))
\n
383 # define GLM_CXX11_UNRESTRICTED_UNIONS
\n
384 # endif
\n
385 
\n
386 # if(__has_feature(cxx_user_literals))
\n
387 # define GLM_CXX11_USER_LITERALS
\n
388 # endif
\n
389 
\n
390 # if(__has_feature(cxx_variadic_templates))
\n
391 # define GLM_CXX11_VARIADIC_TEMPLATES
\n
392 # endif
\n
393 
\n
394 #endif//(GLM_COMPILER & GLM_COMPILER_CLANG)
\n
\n\n
\nGenerated by  \n\"doxygen\"/\n 1.8.10\n
\n\n\n" }, { "path": "field_construction/submodules/diff-langsurf-rasterizer/third_party/glm/doc/api/a00002_source.html", "content": "\n\n\n\n\n\n0.9.9 API documentation: _fixes.hpp Source File\n\n\n\n\n\n\n\n\n\n\n
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_fixes.hpp
\n
\n
\n
1 #include <cmath>
\n
2 
\n
4 #ifdef max
\n
5 #undef max
\n
6 #endif
\n
7 
\n
9 #ifdef min
\n
10 #undef min
\n
11 #endif
\n
12 
\n
14 #ifdef isnan
\n
15 #undef isnan
\n
16 #endif
\n
17 
\n
19 #ifdef isinf
\n
20 #undef isinf
\n
21 #endif
\n
22 
\n
24 #ifdef log2
\n
25 #undef log2
\n
26 #endif
\n
27 
\n
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_noise.hpp
\n
\n
\n
1 #pragma once
\n
2 
\n
3 #include "../common.hpp"
\n
4 
\n
5 namespace glm{
\n
6 namespace detail
\n
7 {
\n
8  template<typename T>
\n
9  GLM_FUNC_QUALIFIER T mod289(T const& x)
\n
10  {
\n
11  return x - floor(x * (static_cast<T>(1.0) / static_cast<T>(289.0))) * static_cast<T>(289.0);
\n
12  }
\n
13 
\n
14  template<typename T>
\n
15  GLM_FUNC_QUALIFIER T permute(T const& x)
\n
16  {
\n
17  return mod289(((x * static_cast<T>(34)) + static_cast<T>(1)) * x);
\n
18  }
\n
19 
\n
20  template<typename T, qualifier Q>
\n
21  GLM_FUNC_QUALIFIER vec<2, T, Q> permute(vec<2, T, Q> const& x)
\n
22  {
\n
23  return mod289(((x * static_cast<T>(34)) + static_cast<T>(1)) * x);
\n
24  }
\n
25 
\n
26  template<typename T, qualifier Q>
\n
27  GLM_FUNC_QUALIFIER vec<3, T, Q> permute(vec<3, T, Q> const& x)
\n
28  {
\n
29  return mod289(((x * static_cast<T>(34)) + static_cast<T>(1)) * x);
\n
30  }
\n
31 
\n
32  template<typename T, qualifier Q>
\n
33  GLM_FUNC_QUALIFIER vec<4, T, Q> permute(vec<4, T, Q> const& x)
\n
34  {
\n
35  return mod289(((x * static_cast<T>(34)) + static_cast<T>(1)) * x);
\n
36  }
\n
37 
\n
38  template<typename T>
\n
39  GLM_FUNC_QUALIFIER T taylorInvSqrt(T const& r)
\n
40  {
\n
41  return static_cast<T>(1.79284291400159) - static_cast<T>(0.85373472095314) * r;
\n
42  }
\n
43 
\n
44  template<typename T, qualifier Q>
\n
45  GLM_FUNC_QUALIFIER vec<2, T, Q> taylorInvSqrt(vec<2, T, Q> const& r)
\n
46  {
\n
47  return static_cast<T>(1.79284291400159) - static_cast<T>(0.85373472095314) * r;
\n
48  }
\n
49 
\n
50  template<typename T, qualifier Q>
\n
51  GLM_FUNC_QUALIFIER vec<3, T, Q> taylorInvSqrt(vec<3, T, Q> const& r)
\n
52  {
\n
53  return static_cast<T>(1.79284291400159) - static_cast<T>(0.85373472095314) * r;
\n
54  }
\n
55 
\n
56  template<typename T, qualifier Q>
\n
57  GLM_FUNC_QUALIFIER vec<4, T, Q> taylorInvSqrt(vec<4, T, Q> const& r)
\n
58  {
\n
59  return static_cast<T>(1.79284291400159) - static_cast<T>(0.85373472095314) * r;
\n
60  }
\n
61 
\n
62  template<typename T, qualifier Q>
\n
63  GLM_FUNC_QUALIFIER vec<2, T, Q> fade(vec<2, T, Q> const& t)
\n
64  {
\n
65  return (t * t * t) * (t * (t * static_cast<T>(6) - static_cast<T>(15)) + static_cast<T>(10));
\n
66  }
\n
67 
\n
68  template<typename T, qualifier Q>
\n
69  GLM_FUNC_QUALIFIER vec<3, T, Q> fade(vec<3, T, Q> const& t)
\n
70  {
\n
71  return (t * t * t) * (t * (t * static_cast<T>(6) - static_cast<T>(15)) + static_cast<T>(10));
\n
72  }
\n
73 
\n
74  template<typename T, qualifier Q>
\n
75  GLM_FUNC_QUALIFIER vec<4, T, Q> fade(vec<4, T, Q> const& t)
\n
76  {
\n
77  return (t * t * t) * (t * (t * static_cast<T>(6) - static_cast<T>(15)) + static_cast<T>(10));
\n
78  }
\n
79 }//namespace detail
\n
80 }//namespace glm
\n
81 
\n
glm::floor
GLM_FUNC_DECL vec< L, T, Q > floor(vec< L, T, Q > const &x)
Returns a value equal to the nearest integer that is less then or equal to x.
\n
glm
Definition: common.hpp:20
\n
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\nGenerated by  \n\"doxygen\"/\n 1.8.10\n
\n\n\n" }, { "path": "field_construction/submodules/diff-langsurf-rasterizer/third_party/glm/doc/api/a00004_source.html", "content": "\n\n\n\n\n\n0.9.9 API documentation: _swizzle.hpp Source File\n\n\n\n\n\n\n\n\n\n\n
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_swizzle.hpp
\n
\n
\n
1 #pragma once
\n
2 
\n
3 namespace glm{
\n
4 namespace detail
\n
5 {
\n
6  // Internal class for implementing swizzle operators
\n
7  template<typename T, int N>
\n
8  struct _swizzle_base0
\n
9  {
\n
10  protected:
\n
11  GLM_FUNC_QUALIFIER T& elem(size_t i){ return (reinterpret_cast<T*>(_buffer))[i]; }
\n
12  GLM_FUNC_QUALIFIER T const& elem(size_t i) const{ return (reinterpret_cast<const T*>(_buffer))[i]; }
\n
13 
\n
14  // Use an opaque buffer to *ensure* the compiler doesn't call a constructor.
\n
15  // The size 1 buffer is assumed to aligned to the actual members so that the
\n
16  // elem()
\n
17  char _buffer[1];
\n
18  };
\n
19 
\n
20  template<int N, typename T, qualifier Q, int E0, int E1, int E2, int E3, bool Aligned>
\n
21  struct _swizzle_base1 : public _swizzle_base0<T, N>
\n
22  {
\n
23  };
\n
24 
\n
25  template<typename T, qualifier Q, int E0, int E1, bool Aligned>
\n
26  struct _swizzle_base1<2, T, Q, E0,E1,-1,-2, Aligned> : public _swizzle_base0<T, 2>
\n
27  {
\n
28  GLM_FUNC_QUALIFIER vec<2, T, Q> operator ()() const { return vec<2, T, Q>(this->elem(E0), this->elem(E1)); }
\n
29  };
\n
30 
\n
31  template<typename T, qualifier Q, int E0, int E1, int E2, bool Aligned>
\n
32  struct _swizzle_base1<3, T, Q, E0,E1,E2,-1, Aligned> : public _swizzle_base0<T, 3>
\n
33  {
\n
34  GLM_FUNC_QUALIFIER vec<3, T, Q> operator ()() const { return vec<3, T, Q>(this->elem(E0), this->elem(E1), this->elem(E2)); }
\n
35  };
\n
36 
\n
37  template<typename T, qualifier Q, int E0, int E1, int E2, int E3, bool Aligned>
\n
38  struct _swizzle_base1<4, T, Q, E0,E1,E2,E3, Aligned> : public _swizzle_base0<T, 4>
\n
39  {
\n
40  GLM_FUNC_QUALIFIER vec<4, T, Q> operator ()() const { return vec<4, T, Q>(this->elem(E0), this->elem(E1), this->elem(E2), this->elem(E3)); }
\n
41  };
\n
42 
\n
43  // Internal class for implementing swizzle operators
\n
44  /*
\n
45  Template parameters:
\n
46 
\n
47  T = type of scalar values (e.g. float, double)
\n
48  N = number of components in the vector (e.g. 3)
\n
49  E0...3 = what index the n-th element of this swizzle refers to in the unswizzled vec
\n
50 
\n
51  DUPLICATE_ELEMENTS = 1 if there is a repeated element, 0 otherwise (used to specialize swizzles
\n
52  containing duplicate elements so that they cannot be used as r-values).
\n
53  */
\n
54  template<int N, typename T, qualifier Q, int E0, int E1, int E2, int E3, int DUPLICATE_ELEMENTS>
\n
55  struct _swizzle_base2 : public _swizzle_base1<N, T, Q, E0,E1,E2,E3, detail::is_aligned<Q>::value>
\n
56  {
\n
57  struct op_equal
\n
58  {
\n
59  GLM_FUNC_QUALIFIER void operator() (T& e, T& t) const{ e = t; }
\n
60  };
\n
61 
\n
62  struct op_minus
\n
63  {
\n
64  GLM_FUNC_QUALIFIER void operator() (T& e, T& t) const{ e -= t; }
\n
65  };
\n
66 
\n
67  struct op_plus
\n
68  {
\n
69  GLM_FUNC_QUALIFIER void operator() (T& e, T& t) const{ e += t; }
\n
70  };
\n
71 
\n
72  struct op_mul
\n
73  {
\n
74  GLM_FUNC_QUALIFIER void operator() (T& e, T& t) const{ e *= t; }
\n
75  };
\n
76 
\n
77  struct op_div
\n
78  {
\n
79  GLM_FUNC_QUALIFIER void operator() (T& e, T& t) const{ e /= t; }
\n
80  };
\n
81 
\n
82  public:
\n
83  GLM_FUNC_QUALIFIER _swizzle_base2& operator= (const T& t)
\n
84  {
\n
85  for (int i = 0; i < N; ++i)
\n
86  (*this)[i] = t;
\n
87  return *this;
\n
88  }
\n
89 
\n
90  GLM_FUNC_QUALIFIER _swizzle_base2& operator= (vec<N, T, Q> const& that)
\n
91  {
\n
92  _apply_op(that, op_equal());
\n
93  return *this;
\n
94  }
\n
95 
\n
96  GLM_FUNC_QUALIFIER void operator -= (vec<N, T, Q> const& that)
\n
97  {
\n
98  _apply_op(that, op_minus());
\n
99  }
\n
100 
\n
101  GLM_FUNC_QUALIFIER void operator += (vec<N, T, Q> const& that)
\n
102  {
\n
103  _apply_op(that, op_plus());
\n
104  }
\n
105 
\n
106  GLM_FUNC_QUALIFIER void operator *= (vec<N, T, Q> const& that)
\n
107  {
\n
108  _apply_op(that, op_mul());
\n
109  }
\n
110 
\n
111  GLM_FUNC_QUALIFIER void operator /= (vec<N, T, Q> const& that)
\n
112  {
\n
113  _apply_op(that, op_div());
\n
114  }
\n
115 
\n
116  GLM_FUNC_QUALIFIER T& operator[](size_t i)
\n
117  {
\n
118  const int offset_dst[4] = { E0, E1, E2, E3 };
\n
119  return this->elem(offset_dst[i]);
\n
120  }
\n
121  GLM_FUNC_QUALIFIER T operator[](size_t i) const
\n
122  {
\n
123  const int offset_dst[4] = { E0, E1, E2, E3 };
\n
124  return this->elem(offset_dst[i]);
\n
125  }
\n
126 
\n
127  protected:
\n
128  template<typename U>
\n
129  GLM_FUNC_QUALIFIER void _apply_op(vec<N, T, Q> const& that, const U& op)
\n
130  {
\n
131  // Make a copy of the data in this == &that.
\n
132  // The copier should optimize out the copy in cases where the function is
\n
133  // properly inlined and the copy is not necessary.
\n
134  T t[N];
\n
135  for (int i = 0; i < N; ++i)
\n
136  t[i] = that[i];
\n
137  for (int i = 0; i < N; ++i)
\n
138  op( (*this)[i], t[i] );
\n
139  }
\n
140  };
\n
141 
\n
142  // Specialization for swizzles containing duplicate elements. These cannot be modified.
\n
143  template<int N, typename T, qualifier Q, int E0, int E1, int E2, int E3>
\n
144  struct _swizzle_base2<N, T, Q, E0,E1,E2,E3, 1> : public _swizzle_base1<N, T, Q, E0,E1,E2,E3, detail::is_aligned<Q>::value>
\n
145  {
\n
146  struct Stub {};
\n
147 
\n
148  GLM_FUNC_QUALIFIER _swizzle_base2& operator= (Stub const&) { return *this; }
\n
149 
\n
150  GLM_FUNC_QUALIFIER T operator[] (size_t i) const
\n
151  {
\n
152  const int offset_dst[4] = { E0, E1, E2, E3 };
\n
153  return this->elem(offset_dst[i]);
\n
154  }
\n
155  };
\n
156 
\n
157  template<int N, typename T, qualifier Q, int E0, int E1, int E2, int E3>
\n
158  struct _swizzle : public _swizzle_base2<N, T, Q, E0, E1, E2, E3, (E0 == E1 || E0 == E2 || E0 == E3 || E1 == E2 || E1 == E3 || E2 == E3)>
\n
159  {
\n
160  typedef _swizzle_base2<N, T, Q, E0, E1, E2, E3, (E0 == E1 || E0 == E2 || E0 == E3 || E1 == E2 || E1 == E3 || E2 == E3)> base_type;
\n
161 
\n
162  using base_type::operator=;
\n
163 
\n
164  GLM_FUNC_QUALIFIER operator vec<N, T, Q> () const { return (*this)(); }
\n
165  };
\n
166 
\n
167 //
\n
168 // To prevent the C++ syntax from getting entirely overwhelming, define some alias macros
\n
169 //
\n
170 #define GLM_SWIZZLE_TEMPLATE1 template<int N, typename T, qualifier Q, int E0, int E1, int E2, int E3>
\n
171 #define GLM_SWIZZLE_TEMPLATE2 template<int N, typename T, qualifier Q, int E0, int E1, int E2, int E3, int F0, int F1, int F2, int F3>
\n
172 #define GLM_SWIZZLE_TYPE1 _swizzle<N, T, Q, E0, E1, E2, E3>
\n
173 #define GLM_SWIZZLE_TYPE2 _swizzle<N, T, Q, F0, F1, F2, F3>
\n
174 
\n
175 //
\n
176 // Wrapper for a binary operator (e.g. u.yy + v.zy)
\n
177 //
\n
178 #define GLM_SWIZZLE_VECTOR_BINARY_OPERATOR_IMPLEMENTATION(OPERAND) \\
\n
179  GLM_SWIZZLE_TEMPLATE2 \\
\n
180  GLM_FUNC_QUALIFIER vec<N, T, Q> operator OPERAND ( const GLM_SWIZZLE_TYPE1& a, const GLM_SWIZZLE_TYPE2& b) \\
\n
181  { \\
\n
182  return a() OPERAND b(); \\
\n
183  } \\
\n
184  GLM_SWIZZLE_TEMPLATE1 \\
\n
185  GLM_FUNC_QUALIFIER vec<N, T, Q> operator OPERAND ( const GLM_SWIZZLE_TYPE1& a, const vec<N, T, Q>& b) \\
\n
186  { \\
\n
187  return a() OPERAND b; \\
\n
188  } \\
\n
189  GLM_SWIZZLE_TEMPLATE1 \\
\n
190  GLM_FUNC_QUALIFIER vec<N, T, Q> operator OPERAND ( const vec<N, T, Q>& a, const GLM_SWIZZLE_TYPE1& b) \\
\n
191  { \\
\n
192  return a OPERAND b(); \\
\n
193  }
\n
194 
\n
195 //
\n
196 // Wrapper for a operand between a swizzle and a binary (e.g. 1.0f - u.xyz)
\n
197 //
\n
198 #define GLM_SWIZZLE_SCALAR_BINARY_OPERATOR_IMPLEMENTATION(OPERAND) \\
\n
199  GLM_SWIZZLE_TEMPLATE1 \\
\n
200  GLM_FUNC_QUALIFIER vec<N, T, Q> operator OPERAND ( const GLM_SWIZZLE_TYPE1& a, const T& b) \\
\n
201  { \\
\n
202  return a() OPERAND b; \\
\n
203  } \\
\n
204  GLM_SWIZZLE_TEMPLATE1 \\
\n
205  GLM_FUNC_QUALIFIER vec<N, T, Q> operator OPERAND ( const T& a, const GLM_SWIZZLE_TYPE1& b) \\
\n
206  { \\
\n
207  return a OPERAND b(); \\
\n
208  }
\n
209 
\n
210 //
\n
211 // Macro for wrapping a function taking one argument (e.g. abs())
\n
212 //
\n
213 #define GLM_SWIZZLE_FUNCTION_1_ARGS(RETURN_TYPE,FUNCTION) \\
\n
214  GLM_SWIZZLE_TEMPLATE1 \\
\n
215  GLM_FUNC_QUALIFIER typename GLM_SWIZZLE_TYPE1::RETURN_TYPE FUNCTION(const GLM_SWIZZLE_TYPE1& a) \\
\n
216  { \\
\n
217  return FUNCTION(a()); \\
\n
218  }
\n
219 
\n
220 //
\n
221 // Macro for wrapping a function taking two vector arguments (e.g. dot()).
\n
222 //
\n
223 #define GLM_SWIZZLE_FUNCTION_2_ARGS(RETURN_TYPE,FUNCTION) \\
\n
224  GLM_SWIZZLE_TEMPLATE2 \\
\n
225  GLM_FUNC_QUALIFIER typename GLM_SWIZZLE_TYPE1::RETURN_TYPE FUNCTION(const GLM_SWIZZLE_TYPE1& a, const GLM_SWIZZLE_TYPE2& b) \\
\n
226  { \\
\n
227  return FUNCTION(a(), b()); \\
\n
228  } \\
\n
229  GLM_SWIZZLE_TEMPLATE1 \\
\n
230  GLM_FUNC_QUALIFIER typename GLM_SWIZZLE_TYPE1::RETURN_TYPE FUNCTION(const GLM_SWIZZLE_TYPE1& a, const GLM_SWIZZLE_TYPE1& b) \\
\n
231  { \\
\n
232  return FUNCTION(a(), b()); \\
\n
233  } \\
\n
234  GLM_SWIZZLE_TEMPLATE1 \\
\n
235  GLM_FUNC_QUALIFIER typename GLM_SWIZZLE_TYPE1::RETURN_TYPE FUNCTION(const GLM_SWIZZLE_TYPE1& a, const typename V& b) \\
\n
236  { \\
\n
237  return FUNCTION(a(), b); \\
\n
238  } \\
\n
239  GLM_SWIZZLE_TEMPLATE1 \\
\n
240  GLM_FUNC_QUALIFIER typename GLM_SWIZZLE_TYPE1::RETURN_TYPE FUNCTION(const V& a, const GLM_SWIZZLE_TYPE1& b) \\
\n
241  { \\
\n
242  return FUNCTION(a, b()); \\
\n
243  }
\n
244 
\n
245 //
\n
246 // Macro for wrapping a function take 2 vec arguments followed by a scalar (e.g. mix()).
\n
247 //
\n
248 #define GLM_SWIZZLE_FUNCTION_2_ARGS_SCALAR(RETURN_TYPE,FUNCTION) \\
\n
249  GLM_SWIZZLE_TEMPLATE2 \\
\n
250  GLM_FUNC_QUALIFIER typename GLM_SWIZZLE_TYPE1::RETURN_TYPE FUNCTION(const GLM_SWIZZLE_TYPE1& a, const GLM_SWIZZLE_TYPE2& b, const T& c) \\
\n
251  { \\
\n
252  return FUNCTION(a(), b(), c); \\
\n
253  } \\
\n
254  GLM_SWIZZLE_TEMPLATE1 \\
\n
255  GLM_FUNC_QUALIFIER typename GLM_SWIZZLE_TYPE1::RETURN_TYPE FUNCTION(const GLM_SWIZZLE_TYPE1& a, const GLM_SWIZZLE_TYPE1& b, const T& c) \\
\n
256  { \\
\n
257  return FUNCTION(a(), b(), c); \\
\n
258  } \\
\n
259  GLM_SWIZZLE_TEMPLATE1 \\
\n
260  GLM_FUNC_QUALIFIER typename GLM_SWIZZLE_TYPE1::RETURN_TYPE FUNCTION(const GLM_SWIZZLE_TYPE1& a, const typename S0::vec_type& b, const T& c)\\
\n
261  { \\
\n
262  return FUNCTION(a(), b, c); \\
\n
263  } \\
\n
264  GLM_SWIZZLE_TEMPLATE1 \\
\n
265  GLM_FUNC_QUALIFIER typename GLM_SWIZZLE_TYPE1::RETURN_TYPE FUNCTION(const typename V& a, const GLM_SWIZZLE_TYPE1& b, const T& c) \\
\n
266  { \\
\n
267  return FUNCTION(a, b(), c); \\
\n
268  }
\n
269 
\n
270 }//namespace detail
\n
271 }//namespace glm
\n
272 
\n
273 namespace glm
\n
274 {
\n
275  namespace detail
\n
276  {
\n
277  GLM_SWIZZLE_SCALAR_BINARY_OPERATOR_IMPLEMENTATION(-)
\n
278  GLM_SWIZZLE_SCALAR_BINARY_OPERATOR_IMPLEMENTATION(*)
\n
279  GLM_SWIZZLE_VECTOR_BINARY_OPERATOR_IMPLEMENTATION(+)
\n
280  GLM_SWIZZLE_VECTOR_BINARY_OPERATOR_IMPLEMENTATION(-)
\n
281  GLM_SWIZZLE_VECTOR_BINARY_OPERATOR_IMPLEMENTATION(*)
\n
282  GLM_SWIZZLE_VECTOR_BINARY_OPERATOR_IMPLEMENTATION(/)
\n
283  }
\n
284 
\n
285  //
\n
286  // Swizzles are distinct types from the unswizzled type. The below macros will
\n
287  // provide template specializations for the swizzle types for the given functions
\n
288  // so that the compiler does not have any ambiguity to choosing how to handle
\n
289  // the function.
\n
290  //
\n
291  // The alternative is to use the operator()() when calling the function in order
\n
292  // to explicitly convert the swizzled type to the unswizzled type.
\n
293  //
\n
294 
\n
295  //GLM_SWIZZLE_FUNCTION_1_ARGS(vec_type, abs);
\n
296  //GLM_SWIZZLE_FUNCTION_1_ARGS(vec_type, acos);
\n
297  //GLM_SWIZZLE_FUNCTION_1_ARGS(vec_type, acosh);
\n
298  //GLM_SWIZZLE_FUNCTION_1_ARGS(vec_type, all);
\n
299  //GLM_SWIZZLE_FUNCTION_1_ARGS(vec_type, any);
\n
300 
\n
301  //GLM_SWIZZLE_FUNCTION_2_ARGS(value_type, dot);
\n
302  //GLM_SWIZZLE_FUNCTION_2_ARGS(vec_type, cross);
\n
303  //GLM_SWIZZLE_FUNCTION_2_ARGS(vec_type, step);
\n
304  //GLM_SWIZZLE_FUNCTION_2_ARGS_SCALAR(vec_type, mix);
\n
305 }
\n
306 
\n
307 #define GLM_SWIZZLE2_2_MEMBERS(T, Q, E0,E1) \\
\n
308  struct { detail::_swizzle<2, T, Q, 0,0,-1,-2> E0 ## E0; }; \\
\n
309  struct { detail::_swizzle<2, T, Q, 0,1,-1,-2> E0 ## E1; }; \\
\n
310  struct { detail::_swizzle<2, T, Q, 1,0,-1,-2> E1 ## E0; }; \\
\n
311  struct { detail::_swizzle<2, T, Q, 1,1,-1,-2> E1 ## E1; };
\n
312 
\n
313 #define GLM_SWIZZLE2_3_MEMBERS(T, Q, E0,E1) \\
\n
314  struct { detail::_swizzle<3,T, Q, 0,0,0,-1> E0 ## E0 ## E0; }; \\
\n
315  struct { detail::_swizzle<3,T, Q, 0,0,1,-1> E0 ## E0 ## E1; }; \\
\n
316  struct { detail::_swizzle<3,T, Q, 0,1,0,-1> E0 ## E1 ## E0; }; \\
\n
317  struct { detail::_swizzle<3,T, Q, 0,1,1,-1> E0 ## E1 ## E1; }; \\
\n
318  struct { detail::_swizzle<3,T, Q, 1,0,0,-1> E1 ## E0 ## E0; }; \\
\n
319  struct { detail::_swizzle<3,T, Q, 1,0,1,-1> E1 ## E0 ## E1; }; \\
\n
320  struct { detail::_swizzle<3,T, Q, 1,1,0,-1> E1 ## E1 ## E0; }; \\
\n
321  struct { detail::_swizzle<3,T, Q, 1,1,1,-1> E1 ## E1 ## E1; };
\n
322 
\n
323 #define GLM_SWIZZLE2_4_MEMBERS(T, Q, E0,E1) \\
\n
324  struct { detail::_swizzle<4,T, Q, 0,0,0,0> E0 ## E0 ## E0 ## E0; }; \\
\n
325  struct { detail::_swizzle<4,T, Q, 0,0,0,1> E0 ## E0 ## E0 ## E1; }; \\
\n
326  struct { detail::_swizzle<4,T, Q, 0,0,1,0> E0 ## E0 ## E1 ## E0; }; \\
\n
327  struct { detail::_swizzle<4,T, Q, 0,0,1,1> E0 ## E0 ## E1 ## E1; }; \\
\n
328  struct { detail::_swizzle<4,T, Q, 0,1,0,0> E0 ## E1 ## E0 ## E0; }; \\
\n
329  struct { detail::_swizzle<4,T, Q, 0,1,0,1> E0 ## E1 ## E0 ## E1; }; \\
\n
330  struct { detail::_swizzle<4,T, Q, 0,1,1,0> E0 ## E1 ## E1 ## E0; }; \\
\n
331  struct { detail::_swizzle<4,T, Q, 0,1,1,1> E0 ## E1 ## E1 ## E1; }; \\
\n
332  struct { detail::_swizzle<4,T, Q, 1,0,0,0> E1 ## E0 ## E0 ## E0; }; \\
\n
333  struct { detail::_swizzle<4,T, Q, 1,0,0,1> E1 ## E0 ## E0 ## E1; }; \\
\n
334  struct { detail::_swizzle<4,T, Q, 1,0,1,0> E1 ## E0 ## E1 ## E0; }; \\
\n
335  struct { detail::_swizzle<4,T, Q, 1,0,1,1> E1 ## E0 ## E1 ## E1; }; \\
\n
336  struct { detail::_swizzle<4,T, Q, 1,1,0,0> E1 ## E1 ## E0 ## E0; }; \\
\n
337  struct { detail::_swizzle<4,T, Q, 1,1,0,1> E1 ## E1 ## E0 ## E1; }; \\
\n
338  struct { detail::_swizzle<4,T, Q, 1,1,1,0> E1 ## E1 ## E1 ## E0; }; \\
\n
339  struct { detail::_swizzle<4,T, Q, 1,1,1,1> E1 ## E1 ## E1 ## E1; };
\n
340 
\n
341 #define GLM_SWIZZLE3_2_MEMBERS(T, Q, E0,E1,E2) \\
\n
342  struct { detail::_swizzle<2,T, Q, 0,0,-1,-2> E0 ## E0; }; \\
\n
343  struct { detail::_swizzle<2,T, Q, 0,1,-1,-2> E0 ## E1; }; \\
\n
344  struct { detail::_swizzle<2,T, Q, 0,2,-1,-2> E0 ## E2; }; \\
\n
345  struct { detail::_swizzle<2,T, Q, 1,0,-1,-2> E1 ## E0; }; \\
\n
346  struct { detail::_swizzle<2,T, Q, 1,1,-1,-2> E1 ## E1; }; \\
\n
347  struct { detail::_swizzle<2,T, Q, 1,2,-1,-2> E1 ## E2; }; \\
\n
348  struct { detail::_swizzle<2,T, Q, 2,0,-1,-2> E2 ## E0; }; \\
\n
349  struct { detail::_swizzle<2,T, Q, 2,1,-1,-2> E2 ## E1; }; \\
\n
350  struct { detail::_swizzle<2,T, Q, 2,2,-1,-2> E2 ## E2; };
\n
351 
\n
352 #define GLM_SWIZZLE3_3_MEMBERS(T, Q ,E0,E1,E2) \\
\n
353  struct { detail::_swizzle<3, T, Q, 0,0,0,-1> E0 ## E0 ## E0; }; \\
\n
354  struct { detail::_swizzle<3, T, Q, 0,0,1,-1> E0 ## E0 ## E1; }; \\
\n
355  struct { detail::_swizzle<3, T, Q, 0,0,2,-1> E0 ## E0 ## E2; }; \\
\n
356  struct { detail::_swizzle<3, T, Q, 0,1,0,-1> E0 ## E1 ## E0; }; \\
\n
357  struct { detail::_swizzle<3, T, Q, 0,1,1,-1> E0 ## E1 ## E1; }; \\
\n
358  struct { detail::_swizzle<3, T, Q, 0,1,2,-1> E0 ## E1 ## E2; }; \\
\n
359  struct { detail::_swizzle<3, T, Q, 0,2,0,-1> E0 ## E2 ## E0; }; \\
\n
360  struct { detail::_swizzle<3, T, Q, 0,2,1,-1> E0 ## E2 ## E1; }; \\
\n
361  struct { detail::_swizzle<3, T, Q, 0,2,2,-1> E0 ## E2 ## E2; }; \\
\n
362  struct { detail::_swizzle<3, T, Q, 1,0,0,-1> E1 ## E0 ## E0; }; \\
\n
363  struct { detail::_swizzle<3, T, Q, 1,0,1,-1> E1 ## E0 ## E1; }; \\
\n
364  struct { detail::_swizzle<3, T, Q, 1,0,2,-1> E1 ## E0 ## E2; }; \\
\n
365  struct { detail::_swizzle<3, T, Q, 1,1,0,-1> E1 ## E1 ## E0; }; \\
\n
366  struct { detail::_swizzle<3, T, Q, 1,1,1,-1> E1 ## E1 ## E1; }; \\
\n
367  struct { detail::_swizzle<3, T, Q, 1,1,2,-1> E1 ## E1 ## E2; }; \\
\n
368  struct { detail::_swizzle<3, T, Q, 1,2,0,-1> E1 ## E2 ## E0; }; \\
\n
369  struct { detail::_swizzle<3, T, Q, 1,2,1,-1> E1 ## E2 ## E1; }; \\
\n
370  struct { detail::_swizzle<3, T, Q, 1,2,2,-1> E1 ## E2 ## E2; }; \\
\n
371  struct { detail::_swizzle<3, T, Q, 2,0,0,-1> E2 ## E0 ## E0; }; \\
\n
372  struct { detail::_swizzle<3, T, Q, 2,0,1,-1> E2 ## E0 ## E1; }; \\
\n
373  struct { detail::_swizzle<3, T, Q, 2,0,2,-1> E2 ## E0 ## E2; }; \\
\n
374  struct { detail::_swizzle<3, T, Q, 2,1,0,-1> E2 ## E1 ## E0; }; \\
\n
375  struct { detail::_swizzle<3, T, Q, 2,1,1,-1> E2 ## E1 ## E1; }; \\
\n
376  struct { detail::_swizzle<3, T, Q, 2,1,2,-1> E2 ## E1 ## E2; }; \\
\n
377  struct { detail::_swizzle<3, T, Q, 2,2,0,-1> E2 ## E2 ## E0; }; \\
\n
378  struct { detail::_swizzle<3, T, Q, 2,2,1,-1> E2 ## E2 ## E1; }; \\
\n
379  struct { detail::_swizzle<3, T, Q, 2,2,2,-1> E2 ## E2 ## E2; };
\n
380 
\n
381 #define GLM_SWIZZLE3_4_MEMBERS(T, Q, E0,E1,E2) \\
\n
382  struct { detail::_swizzle<4,T, Q, 0,0,0,0> E0 ## E0 ## E0 ## E0; }; \\
\n
383  struct { detail::_swizzle<4,T, Q, 0,0,0,1> E0 ## E0 ## E0 ## E1; }; \\
\n
384  struct { detail::_swizzle<4,T, Q, 0,0,0,2> E0 ## E0 ## E0 ## E2; }; \\
\n
385  struct { detail::_swizzle<4,T, Q, 0,0,1,0> E0 ## E0 ## E1 ## E0; }; \\
\n
386  struct { detail::_swizzle<4,T, Q, 0,0,1,1> E0 ## E0 ## E1 ## E1; }; \\
\n
387  struct { detail::_swizzle<4,T, Q, 0,0,1,2> E0 ## E0 ## E1 ## E2; }; \\
\n
388  struct { detail::_swizzle<4,T, Q, 0,0,2,0> E0 ## E0 ## E2 ## E0; }; \\
\n
389  struct { detail::_swizzle<4,T, Q, 0,0,2,1> E0 ## E0 ## E2 ## E1; }; \\
\n
390  struct { detail::_swizzle<4,T, Q, 0,0,2,2> E0 ## E0 ## E2 ## E2; }; \\
\n
391  struct { detail::_swizzle<4,T, Q, 0,1,0,0> E0 ## E1 ## E0 ## E0; }; \\
\n
392  struct { detail::_swizzle<4,T, Q, 0,1,0,1> E0 ## E1 ## E0 ## E1; }; \\
\n
393  struct { detail::_swizzle<4,T, Q, 0,1,0,2> E0 ## E1 ## E0 ## E2; }; \\
\n
394  struct { detail::_swizzle<4,T, Q, 0,1,1,0> E0 ## E1 ## E1 ## E0; }; \\
\n
395  struct { detail::_swizzle<4,T, Q, 0,1,1,1> E0 ## E1 ## E1 ## E1; }; \\
\n
396  struct { detail::_swizzle<4,T, Q, 0,1,1,2> E0 ## E1 ## E1 ## E2; }; \\
\n
397  struct { detail::_swizzle<4,T, Q, 0,1,2,0> E0 ## E1 ## E2 ## E0; }; \\
\n
398  struct { detail::_swizzle<4,T, Q, 0,1,2,1> E0 ## E1 ## E2 ## E1; }; \\
\n
399  struct { detail::_swizzle<4,T, Q, 0,1,2,2> E0 ## E1 ## E2 ## E2; }; \\
\n
400  struct { detail::_swizzle<4,T, Q, 0,2,0,0> E0 ## E2 ## E0 ## E0; }; \\
\n
401  struct { detail::_swizzle<4,T, Q, 0,2,0,1> E0 ## E2 ## E0 ## E1; }; \\
\n
402  struct { detail::_swizzle<4,T, Q, 0,2,0,2> E0 ## E2 ## E0 ## E2; }; \\
\n
403  struct { detail::_swizzle<4,T, Q, 0,2,1,0> E0 ## E2 ## E1 ## E0; }; \\
\n
404  struct { detail::_swizzle<4,T, Q, 0,2,1,1> E0 ## E2 ## E1 ## E1; }; \\
\n
405  struct { detail::_swizzle<4,T, Q, 0,2,1,2> E0 ## E2 ## E1 ## E2; }; \\
\n
406  struct { detail::_swizzle<4,T, Q, 0,2,2,0> E0 ## E2 ## E2 ## E0; }; \\
\n
407  struct { detail::_swizzle<4,T, Q, 0,2,2,1> E0 ## E2 ## E2 ## E1; }; \\
\n
408  struct { detail::_swizzle<4,T, Q, 0,2,2,2> E0 ## E2 ## E2 ## E2; }; \\
\n
409  struct { detail::_swizzle<4,T, Q, 1,0,0,0> E1 ## E0 ## E0 ## E0; }; \\
\n
410  struct { detail::_swizzle<4,T, Q, 1,0,0,1> E1 ## E0 ## E0 ## E1; }; \\
\n
411  struct { detail::_swizzle<4,T, Q, 1,0,0,2> E1 ## E0 ## E0 ## E2; }; \\
\n
412  struct { detail::_swizzle<4,T, Q, 1,0,1,0> E1 ## E0 ## E1 ## E0; }; \\
\n
413  struct { detail::_swizzle<4,T, Q, 1,0,1,1> E1 ## E0 ## E1 ## E1; }; \\
\n
414  struct { detail::_swizzle<4,T, Q, 1,0,1,2> E1 ## E0 ## E1 ## E2; }; \\
\n
415  struct { detail::_swizzle<4,T, Q, 1,0,2,0> E1 ## E0 ## E2 ## E0; }; \\
\n
416  struct { detail::_swizzle<4,T, Q, 1,0,2,1> E1 ## E0 ## E2 ## E1; }; \\
\n
417  struct { detail::_swizzle<4,T, Q, 1,0,2,2> E1 ## E0 ## E2 ## E2; }; \\
\n
418  struct { detail::_swizzle<4,T, Q, 1,1,0,0> E1 ## E1 ## E0 ## E0; }; \\
\n
419  struct { detail::_swizzle<4,T, Q, 1,1,0,1> E1 ## E1 ## E0 ## E1; }; \\
\n
420  struct { detail::_swizzle<4,T, Q, 1,1,0,2> E1 ## E1 ## E0 ## E2; }; \\
\n
421  struct { detail::_swizzle<4,T, Q, 1,1,1,0> E1 ## E1 ## E1 ## E0; }; \\
\n
422  struct { detail::_swizzle<4,T, Q, 1,1,1,1> E1 ## E1 ## E1 ## E1; }; \\
\n
423  struct { detail::_swizzle<4,T, Q, 1,1,1,2> E1 ## E1 ## E1 ## E2; }; \\
\n
424  struct { detail::_swizzle<4,T, Q, 1,1,2,0> E1 ## E1 ## E2 ## E0; }; \\
\n
425  struct { detail::_swizzle<4,T, Q, 1,1,2,1> E1 ## E1 ## E2 ## E1; }; \\
\n
426  struct { detail::_swizzle<4,T, Q, 1,1,2,2> E1 ## E1 ## E2 ## E2; }; \\
\n
427  struct { detail::_swizzle<4,T, Q, 1,2,0,0> E1 ## E2 ## E0 ## E0; }; \\
\n
428  struct { detail::_swizzle<4,T, Q, 1,2,0,1> E1 ## E2 ## E0 ## E1; }; \\
\n
429  struct { detail::_swizzle<4,T, Q, 1,2,0,2> E1 ## E2 ## E0 ## E2; }; \\
\n
430  struct { detail::_swizzle<4,T, Q, 1,2,1,0> E1 ## E2 ## E1 ## E0; }; \\
\n
431  struct { detail::_swizzle<4,T, Q, 1,2,1,1> E1 ## E2 ## E1 ## E1; }; \\
\n
432  struct { detail::_swizzle<4,T, Q, 1,2,1,2> E1 ## E2 ## E1 ## E2; }; \\
\n
433  struct { detail::_swizzle<4,T, Q, 1,2,2,0> E1 ## E2 ## E2 ## E0; }; \\
\n
434  struct { detail::_swizzle<4,T, Q, 1,2,2,1> E1 ## E2 ## E2 ## E1; }; \\
\n
435  struct { detail::_swizzle<4,T, Q, 1,2,2,2> E1 ## E2 ## E2 ## E2; }; \\
\n
436  struct { detail::_swizzle<4,T, Q, 2,0,0,0> E2 ## E0 ## E0 ## E0; }; \\
\n
437  struct { detail::_swizzle<4,T, Q, 2,0,0,1> E2 ## E0 ## E0 ## E1; }; \\
\n
438  struct { detail::_swizzle<4,T, Q, 2,0,0,2> E2 ## E0 ## E0 ## E2; }; \\
\n
439  struct { detail::_swizzle<4,T, Q, 2,0,1,0> E2 ## E0 ## E1 ## E0; }; \\
\n
440  struct { detail::_swizzle<4,T, Q, 2,0,1,1> E2 ## E0 ## E1 ## E1; }; \\
\n
441  struct { detail::_swizzle<4,T, Q, 2,0,1,2> E2 ## E0 ## E1 ## E2; }; \\
\n
442  struct { detail::_swizzle<4,T, Q, 2,0,2,0> E2 ## E0 ## E2 ## E0; }; \\
\n
443  struct { detail::_swizzle<4,T, Q, 2,0,2,1> E2 ## E0 ## E2 ## E1; }; \\
\n
444  struct { detail::_swizzle<4,T, Q, 2,0,2,2> E2 ## E0 ## E2 ## E2; }; \\
\n
445  struct { detail::_swizzle<4,T, Q, 2,1,0,0> E2 ## E1 ## E0 ## E0; }; \\
\n
446  struct { detail::_swizzle<4,T, Q, 2,1,0,1> E2 ## E1 ## E0 ## E1; }; \\
\n
447  struct { detail::_swizzle<4,T, Q, 2,1,0,2> E2 ## E1 ## E0 ## E2; }; \\
\n
448  struct { detail::_swizzle<4,T, Q, 2,1,1,0> E2 ## E1 ## E1 ## E0; }; \\
\n
449  struct { detail::_swizzle<4,T, Q, 2,1,1,1> E2 ## E1 ## E1 ## E1; }; \\
\n
450  struct { detail::_swizzle<4,T, Q, 2,1,1,2> E2 ## E1 ## E1 ## E2; }; \\
\n
451  struct { detail::_swizzle<4,T, Q, 2,1,2,0> E2 ## E1 ## E2 ## E0; }; \\
\n
452  struct { detail::_swizzle<4,T, Q, 2,1,2,1> E2 ## E1 ## E2 ## E1; }; \\
\n
453  struct { detail::_swizzle<4,T, Q, 2,1,2,2> E2 ## E1 ## E2 ## E2; }; \\
\n
454  struct { detail::_swizzle<4,T, Q, 2,2,0,0> E2 ## E2 ## E0 ## E0; }; \\
\n
455  struct { detail::_swizzle<4,T, Q, 2,2,0,1> E2 ## E2 ## E0 ## E1; }; \\
\n
456  struct { detail::_swizzle<4,T, Q, 2,2,0,2> E2 ## E2 ## E0 ## E2; }; \\
\n
457  struct { detail::_swizzle<4,T, Q, 2,2,1,0> E2 ## E2 ## E1 ## E0; }; \\
\n
458  struct { detail::_swizzle<4,T, Q, 2,2,1,1> E2 ## E2 ## E1 ## E1; }; \\
\n
459  struct { detail::_swizzle<4,T, Q, 2,2,1,2> E2 ## E2 ## E1 ## E2; }; \\
\n
460  struct { detail::_swizzle<4,T, Q, 2,2,2,0> E2 ## E2 ## E2 ## E0; }; \\
\n
461  struct { detail::_swizzle<4,T, Q, 2,2,2,1> E2 ## E2 ## E2 ## E1; }; \\
\n
462  struct { detail::_swizzle<4,T, Q, 2,2,2,2> E2 ## E2 ## E2 ## E2; };
\n
463 
\n
464 #define GLM_SWIZZLE4_2_MEMBERS(T, Q, E0,E1,E2,E3) \\
\n
465  struct { detail::_swizzle<2,T, Q, 0,0,-1,-2> E0 ## E0; }; \\
\n
466  struct { detail::_swizzle<2,T, Q, 0,1,-1,-2> E0 ## E1; }; \\
\n
467  struct { detail::_swizzle<2,T, Q, 0,2,-1,-2> E0 ## E2; }; \\
\n
468  struct { detail::_swizzle<2,T, Q, 0,3,-1,-2> E0 ## E3; }; \\
\n
469  struct { detail::_swizzle<2,T, Q, 1,0,-1,-2> E1 ## E0; }; \\
\n
470  struct { detail::_swizzle<2,T, Q, 1,1,-1,-2> E1 ## E1; }; \\
\n
471  struct { detail::_swizzle<2,T, Q, 1,2,-1,-2> E1 ## E2; }; \\
\n
472  struct { detail::_swizzle<2,T, Q, 1,3,-1,-2> E1 ## E3; }; \\
\n
473  struct { detail::_swizzle<2,T, Q, 2,0,-1,-2> E2 ## E0; }; \\
\n
474  struct { detail::_swizzle<2,T, Q, 2,1,-1,-2> E2 ## E1; }; \\
\n
475  struct { detail::_swizzle<2,T, Q, 2,2,-1,-2> E2 ## E2; }; \\
\n
476  struct { detail::_swizzle<2,T, Q, 2,3,-1,-2> E2 ## E3; }; \\
\n
477  struct { detail::_swizzle<2,T, Q, 3,0,-1,-2> E3 ## E0; }; \\
\n
478  struct { detail::_swizzle<2,T, Q, 3,1,-1,-2> E3 ## E1; }; \\
\n
479  struct { detail::_swizzle<2,T, Q, 3,2,-1,-2> E3 ## E2; }; \\
\n
480  struct { detail::_swizzle<2,T, Q, 3,3,-1,-2> E3 ## E3; };
\n
481 
\n
482 #define GLM_SWIZZLE4_3_MEMBERS(T, Q, E0,E1,E2,E3) \\
\n
483  struct { detail::_swizzle<3, T, Q, 0,0,0,-1> E0 ## E0 ## E0; }; \\
\n
484  struct { detail::_swizzle<3, T, Q, 0,0,1,-1> E0 ## E0 ## E1; }; \\
\n
485  struct { detail::_swizzle<3, T, Q, 0,0,2,-1> E0 ## E0 ## E2; }; \\
\n
486  struct { detail::_swizzle<3, T, Q, 0,0,3,-1> E0 ## E0 ## E3; }; \\
\n
487  struct { detail::_swizzle<3, T, Q, 0,1,0,-1> E0 ## E1 ## E0; }; \\
\n
488  struct { detail::_swizzle<3, T, Q, 0,1,1,-1> E0 ## E1 ## E1; }; \\
\n
489  struct { detail::_swizzle<3, T, Q, 0,1,2,-1> E0 ## E1 ## E2; }; \\
\n
490  struct { detail::_swizzle<3, T, Q, 0,1,3,-1> E0 ## E1 ## E3; }; \\
\n
491  struct { detail::_swizzle<3, T, Q, 0,2,0,-1> E0 ## E2 ## E0; }; \\
\n
492  struct { detail::_swizzle<3, T, Q, 0,2,1,-1> E0 ## E2 ## E1; }; \\
\n
493  struct { detail::_swizzle<3, T, Q, 0,2,2,-1> E0 ## E2 ## E2; }; \\
\n
494  struct { detail::_swizzle<3, T, Q, 0,2,3,-1> E0 ## E2 ## E3; }; \\
\n
495  struct { detail::_swizzle<3, T, Q, 0,3,0,-1> E0 ## E3 ## E0; }; \\
\n
496  struct { detail::_swizzle<3, T, Q, 0,3,1,-1> E0 ## E3 ## E1; }; \\
\n
497  struct { detail::_swizzle<3, T, Q, 0,3,2,-1> E0 ## E3 ## E2; }; \\
\n
498  struct { detail::_swizzle<3, T, Q, 0,3,3,-1> E0 ## E3 ## E3; }; \\
\n
499  struct { detail::_swizzle<3, T, Q, 1,0,0,-1> E1 ## E0 ## E0; }; \\
\n
500  struct { detail::_swizzle<3, T, Q, 1,0,1,-1> E1 ## E0 ## E1; }; \\
\n
501  struct { detail::_swizzle<3, T, Q, 1,0,2,-1> E1 ## E0 ## E2; }; \\
\n
502  struct { detail::_swizzle<3, T, Q, 1,0,3,-1> E1 ## E0 ## E3; }; \\
\n
503  struct { detail::_swizzle<3, T, Q, 1,1,0,-1> E1 ## E1 ## E0; }; \\
\n
504  struct { detail::_swizzle<3, T, Q, 1,1,1,-1> E1 ## E1 ## E1; }; \\
\n
505  struct { detail::_swizzle<3, T, Q, 1,1,2,-1> E1 ## E1 ## E2; }; \\
\n
506  struct { detail::_swizzle<3, T, Q, 1,1,3,-1> E1 ## E1 ## E3; }; \\
\n
507  struct { detail::_swizzle<3, T, Q, 1,2,0,-1> E1 ## E2 ## E0; }; \\
\n
508  struct { detail::_swizzle<3, T, Q, 1,2,1,-1> E1 ## E2 ## E1; }; \\
\n
509  struct { detail::_swizzle<3, T, Q, 1,2,2,-1> E1 ## E2 ## E2; }; \\
\n
510  struct { detail::_swizzle<3, T, Q, 1,2,3,-1> E1 ## E2 ## E3; }; \\
\n
511  struct { detail::_swizzle<3, T, Q, 1,3,0,-1> E1 ## E3 ## E0; }; \\
\n
512  struct { detail::_swizzle<3, T, Q, 1,3,1,-1> E1 ## E3 ## E1; }; \\
\n
513  struct { detail::_swizzle<3, T, Q, 1,3,2,-1> E1 ## E3 ## E2; }; \\
\n
514  struct { detail::_swizzle<3, T, Q, 1,3,3,-1> E1 ## E3 ## E3; }; \\
\n
515  struct { detail::_swizzle<3, T, Q, 2,0,0,-1> E2 ## E0 ## E0; }; \\
\n
516  struct { detail::_swizzle<3, T, Q, 2,0,1,-1> E2 ## E0 ## E1; }; \\
\n
517  struct { detail::_swizzle<3, T, Q, 2,0,2,-1> E2 ## E0 ## E2; }; \\
\n
518  struct { detail::_swizzle<3, T, Q, 2,0,3,-1> E2 ## E0 ## E3; }; \\
\n
519  struct { detail::_swizzle<3, T, Q, 2,1,0,-1> E2 ## E1 ## E0; }; \\
\n
520  struct { detail::_swizzle<3, T, Q, 2,1,1,-1> E2 ## E1 ## E1; }; \\
\n
521  struct { detail::_swizzle<3, T, Q, 2,1,2,-1> E2 ## E1 ## E2; }; \\
\n
522  struct { detail::_swizzle<3, T, Q, 2,1,3,-1> E2 ## E1 ## E3; }; \\
\n
523  struct { detail::_swizzle<3, T, Q, 2,2,0,-1> E2 ## E2 ## E0; }; \\
\n
524  struct { detail::_swizzle<3, T, Q, 2,2,1,-1> E2 ## E2 ## E1; }; \\
\n
525  struct { detail::_swizzle<3, T, Q, 2,2,2,-1> E2 ## E2 ## E2; }; \\
\n
526  struct { detail::_swizzle<3, T, Q, 2,2,3,-1> E2 ## E2 ## E3; }; \\
\n
527  struct { detail::_swizzle<3, T, Q, 2,3,0,-1> E2 ## E3 ## E0; }; \\
\n
528  struct { detail::_swizzle<3, T, Q, 2,3,1,-1> E2 ## E3 ## E1; }; \\
\n
529  struct { detail::_swizzle<3, T, Q, 2,3,2,-1> E2 ## E3 ## E2; }; \\
\n
530  struct { detail::_swizzle<3, T, Q, 2,3,3,-1> E2 ## E3 ## E3; }; \\
\n
531  struct { detail::_swizzle<3, T, Q, 3,0,0,-1> E3 ## E0 ## E0; }; \\
\n
532  struct { detail::_swizzle<3, T, Q, 3,0,1,-1> E3 ## E0 ## E1; }; \\
\n
533  struct { detail::_swizzle<3, T, Q, 3,0,2,-1> E3 ## E0 ## E2; }; \\
\n
534  struct { detail::_swizzle<3, T, Q, 3,0,3,-1> E3 ## E0 ## E3; }; \\
\n
535  struct { detail::_swizzle<3, T, Q, 3,1,0,-1> E3 ## E1 ## E0; }; \\
\n
536  struct { detail::_swizzle<3, T, Q, 3,1,1,-1> E3 ## E1 ## E1; }; \\
\n
537  struct { detail::_swizzle<3, T, Q, 3,1,2,-1> E3 ## E1 ## E2; }; \\
\n
538  struct { detail::_swizzle<3, T, Q, 3,1,3,-1> E3 ## E1 ## E3; }; \\
\n
539  struct { detail::_swizzle<3, T, Q, 3,2,0,-1> E3 ## E2 ## E0; }; \\
\n
540  struct { detail::_swizzle<3, T, Q, 3,2,1,-1> E3 ## E2 ## E1; }; \\
\n
541  struct { detail::_swizzle<3, T, Q, 3,2,2,-1> E3 ## E2 ## E2; }; \\
\n
542  struct { detail::_swizzle<3, T, Q, 3,2,3,-1> E3 ## E2 ## E3; }; \\
\n
543  struct { detail::_swizzle<3, T, Q, 3,3,0,-1> E3 ## E3 ## E0; }; \\
\n
544  struct { detail::_swizzle<3, T, Q, 3,3,1,-1> E3 ## E3 ## E1; }; \\
\n
545  struct { detail::_swizzle<3, T, Q, 3,3,2,-1> E3 ## E3 ## E2; }; \\
\n
546  struct { detail::_swizzle<3, T, Q, 3,3,3,-1> E3 ## E3 ## E3; };
\n
547 
\n
548 #define GLM_SWIZZLE4_4_MEMBERS(T, Q, E0,E1,E2,E3) \\
\n
549  struct { detail::_swizzle<4, T, Q, 0,0,0,0> E0 ## E0 ## E0 ## E0; }; \\
\n
550  struct { detail::_swizzle<4, T, Q, 0,0,0,1> E0 ## E0 ## E0 ## E1; }; \\
\n
551  struct { detail::_swizzle<4, T, Q, 0,0,0,2> E0 ## E0 ## E0 ## E2; }; \\
\n
552  struct { detail::_swizzle<4, T, Q, 0,0,0,3> E0 ## E0 ## E0 ## E3; }; \\
\n
553  struct { detail::_swizzle<4, T, Q, 0,0,1,0> E0 ## E0 ## E1 ## E0; }; \\
\n
554  struct { detail::_swizzle<4, T, Q, 0,0,1,1> E0 ## E0 ## E1 ## E1; }; \\
\n
555  struct { detail::_swizzle<4, T, Q, 0,0,1,2> E0 ## E0 ## E1 ## E2; }; \\
\n
556  struct { detail::_swizzle<4, T, Q, 0,0,1,3> E0 ## E0 ## E1 ## E3; }; \\
\n
557  struct { detail::_swizzle<4, T, Q, 0,0,2,0> E0 ## E0 ## E2 ## E0; }; \\
\n
558  struct { detail::_swizzle<4, T, Q, 0,0,2,1> E0 ## E0 ## E2 ## E1; }; \\
\n
559  struct { detail::_swizzle<4, T, Q, 0,0,2,2> E0 ## E0 ## E2 ## E2; }; \\
\n
560  struct { detail::_swizzle<4, T, Q, 0,0,2,3> E0 ## E0 ## E2 ## E3; }; \\
\n
561  struct { detail::_swizzle<4, T, Q, 0,0,3,0> E0 ## E0 ## E3 ## E0; }; \\
\n
562  struct { detail::_swizzle<4, T, Q, 0,0,3,1> E0 ## E0 ## E3 ## E1; }; \\
\n
563  struct { detail::_swizzle<4, T, Q, 0,0,3,2> E0 ## E0 ## E3 ## E2; }; \\
\n
564  struct { detail::_swizzle<4, T, Q, 0,0,3,3> E0 ## E0 ## E3 ## E3; }; \\
\n
565  struct { detail::_swizzle<4, T, Q, 0,1,0,0> E0 ## E1 ## E0 ## E0; }; \\
\n
566  struct { detail::_swizzle<4, T, Q, 0,1,0,1> E0 ## E1 ## E0 ## E1; }; \\
\n
567  struct { detail::_swizzle<4, T, Q, 0,1,0,2> E0 ## E1 ## E0 ## E2; }; \\
\n
568  struct { detail::_swizzle<4, T, Q, 0,1,0,3> E0 ## E1 ## E0 ## E3; }; \\
\n
569  struct { detail::_swizzle<4, T, Q, 0,1,1,0> E0 ## E1 ## E1 ## E0; }; \\
\n
570  struct { detail::_swizzle<4, T, Q, 0,1,1,1> E0 ## E1 ## E1 ## E1; }; \\
\n
571  struct { detail::_swizzle<4, T, Q, 0,1,1,2> E0 ## E1 ## E1 ## E2; }; \\
\n
572  struct { detail::_swizzle<4, T, Q, 0,1,1,3> E0 ## E1 ## E1 ## E3; }; \\
\n
573  struct { detail::_swizzle<4, T, Q, 0,1,2,0> E0 ## E1 ## E2 ## E0; }; \\
\n
574  struct { detail::_swizzle<4, T, Q, 0,1,2,1> E0 ## E1 ## E2 ## E1; }; \\
\n
575  struct { detail::_swizzle<4, T, Q, 0,1,2,2> E0 ## E1 ## E2 ## E2; }; \\
\n
576  struct { detail::_swizzle<4, T, Q, 0,1,2,3> E0 ## E1 ## E2 ## E3; }; \\
\n
577  struct { detail::_swizzle<4, T, Q, 0,1,3,0> E0 ## E1 ## E3 ## E0; }; \\
\n
578  struct { detail::_swizzle<4, T, Q, 0,1,3,1> E0 ## E1 ## E3 ## E1; }; \\
\n
579  struct { detail::_swizzle<4, T, Q, 0,1,3,2> E0 ## E1 ## E3 ## E2; }; \\
\n
580  struct { detail::_swizzle<4, T, Q, 0,1,3,3> E0 ## E1 ## E3 ## E3; }; \\
\n
581  struct { detail::_swizzle<4, T, Q, 0,2,0,0> E0 ## E2 ## E0 ## E0; }; \\
\n
582  struct { detail::_swizzle<4, T, Q, 0,2,0,1> E0 ## E2 ## E0 ## E1; }; \\
\n
583  struct { detail::_swizzle<4, T, Q, 0,2,0,2> E0 ## E2 ## E0 ## E2; }; \\
\n
584  struct { detail::_swizzle<4, T, Q, 0,2,0,3> E0 ## E2 ## E0 ## E3; }; \\
\n
585  struct { detail::_swizzle<4, T, Q, 0,2,1,0> E0 ## E2 ## E1 ## E0; }; \\
\n
586  struct { detail::_swizzle<4, T, Q, 0,2,1,1> E0 ## E2 ## E1 ## E1; }; \\
\n
587  struct { detail::_swizzle<4, T, Q, 0,2,1,2> E0 ## E2 ## E1 ## E2; }; \\
\n
588  struct { detail::_swizzle<4, T, Q, 0,2,1,3> E0 ## E2 ## E1 ## E3; }; \\
\n
589  struct { detail::_swizzle<4, T, Q, 0,2,2,0> E0 ## E2 ## E2 ## E0; }; \\
\n
590  struct { detail::_swizzle<4, T, Q, 0,2,2,1> E0 ## E2 ## E2 ## E1; }; \\
\n
591  struct { detail::_swizzle<4, T, Q, 0,2,2,2> E0 ## E2 ## E2 ## E2; }; \\
\n
592  struct { detail::_swizzle<4, T, Q, 0,2,2,3> E0 ## E2 ## E2 ## E3; }; \\
\n
593  struct { detail::_swizzle<4, T, Q, 0,2,3,0> E0 ## E2 ## E3 ## E0; }; \\
\n
594  struct { detail::_swizzle<4, T, Q, 0,2,3,1> E0 ## E2 ## E3 ## E1; }; \\
\n
595  struct { detail::_swizzle<4, T, Q, 0,2,3,2> E0 ## E2 ## E3 ## E2; }; \\
\n
596  struct { detail::_swizzle<4, T, Q, 0,2,3,3> E0 ## E2 ## E3 ## E3; }; \\
\n
597  struct { detail::_swizzle<4, T, Q, 0,3,0,0> E0 ## E3 ## E0 ## E0; }; \\
\n
598  struct { detail::_swizzle<4, T, Q, 0,3,0,1> E0 ## E3 ## E0 ## E1; }; \\
\n
599  struct { detail::_swizzle<4, T, Q, 0,3,0,2> E0 ## E3 ## E0 ## E2; }; \\
\n
600  struct { detail::_swizzle<4, T, Q, 0,3,0,3> E0 ## E3 ## E0 ## E3; }; \\
\n
601  struct { detail::_swizzle<4, T, Q, 0,3,1,0> E0 ## E3 ## E1 ## E0; }; \\
\n
602  struct { detail::_swizzle<4, T, Q, 0,3,1,1> E0 ## E3 ## E1 ## E1; }; \\
\n
603  struct { detail::_swizzle<4, T, Q, 0,3,1,2> E0 ## E3 ## E1 ## E2; }; \\
\n
604  struct { detail::_swizzle<4, T, Q, 0,3,1,3> E0 ## E3 ## E1 ## E3; }; \\
\n
605  struct { detail::_swizzle<4, T, Q, 0,3,2,0> E0 ## E3 ## E2 ## E0; }; \\
\n
606  struct { detail::_swizzle<4, T, Q, 0,3,2,1> E0 ## E3 ## E2 ## E1; }; \\
\n
607  struct { detail::_swizzle<4, T, Q, 0,3,2,2> E0 ## E3 ## E2 ## E2; }; \\
\n
608  struct { detail::_swizzle<4, T, Q, 0,3,2,3> E0 ## E3 ## E2 ## E3; }; \\
\n
609  struct { detail::_swizzle<4, T, Q, 0,3,3,0> E0 ## E3 ## E3 ## E0; }; \\
\n
610  struct { detail::_swizzle<4, T, Q, 0,3,3,1> E0 ## E3 ## E3 ## E1; }; \\
\n
611  struct { detail::_swizzle<4, T, Q, 0,3,3,2> E0 ## E3 ## E3 ## E2; }; \\
\n
612  struct { detail::_swizzle<4, T, Q, 0,3,3,3> E0 ## E3 ## E3 ## E3; }; \\
\n
613  struct { detail::_swizzle<4, T, Q, 1,0,0,0> E1 ## E0 ## E0 ## E0; }; \\
\n
614  struct { detail::_swizzle<4, T, Q, 1,0,0,1> E1 ## E0 ## E0 ## E1; }; \\
\n
615  struct { detail::_swizzle<4, T, Q, 1,0,0,2> E1 ## E0 ## E0 ## E2; }; \\
\n
616  struct { detail::_swizzle<4, T, Q, 1,0,0,3> E1 ## E0 ## E0 ## E3; }; \\
\n
617  struct { detail::_swizzle<4, T, Q, 1,0,1,0> E1 ## E0 ## E1 ## E0; }; \\
\n
618  struct { detail::_swizzle<4, T, Q, 1,0,1,1> E1 ## E0 ## E1 ## E1; }; \\
\n
619  struct { detail::_swizzle<4, T, Q, 1,0,1,2> E1 ## E0 ## E1 ## E2; }; \\
\n
620  struct { detail::_swizzle<4, T, Q, 1,0,1,3> E1 ## E0 ## E1 ## E3; }; \\
\n
621  struct { detail::_swizzle<4, T, Q, 1,0,2,0> E1 ## E0 ## E2 ## E0; }; \\
\n
622  struct { detail::_swizzle<4, T, Q, 1,0,2,1> E1 ## E0 ## E2 ## E1; }; \\
\n
623  struct { detail::_swizzle<4, T, Q, 1,0,2,2> E1 ## E0 ## E2 ## E2; }; \\
\n
624  struct { detail::_swizzle<4, T, Q, 1,0,2,3> E1 ## E0 ## E2 ## E3; }; \\
\n
625  struct { detail::_swizzle<4, T, Q, 1,0,3,0> E1 ## E0 ## E3 ## E0; }; \\
\n
626  struct { detail::_swizzle<4, T, Q, 1,0,3,1> E1 ## E0 ## E3 ## E1; }; \\
\n
627  struct { detail::_swizzle<4, T, Q, 1,0,3,2> E1 ## E0 ## E3 ## E2; }; \\
\n
628  struct { detail::_swizzle<4, T, Q, 1,0,3,3> E1 ## E0 ## E3 ## E3; }; \\
\n
629  struct { detail::_swizzle<4, T, Q, 1,1,0,0> E1 ## E1 ## E0 ## E0; }; \\
\n
630  struct { detail::_swizzle<4, T, Q, 1,1,0,1> E1 ## E1 ## E0 ## E1; }; \\
\n
631  struct { detail::_swizzle<4, T, Q, 1,1,0,2> E1 ## E1 ## E0 ## E2; }; \\
\n
632  struct { detail::_swizzle<4, T, Q, 1,1,0,3> E1 ## E1 ## E0 ## E3; }; \\
\n
633  struct { detail::_swizzle<4, T, Q, 1,1,1,0> E1 ## E1 ## E1 ## E0; }; \\
\n
634  struct { detail::_swizzle<4, T, Q, 1,1,1,1> E1 ## E1 ## E1 ## E1; }; \\
\n
635  struct { detail::_swizzle<4, T, Q, 1,1,1,2> E1 ## E1 ## E1 ## E2; }; \\
\n
636  struct { detail::_swizzle<4, T, Q, 1,1,1,3> E1 ## E1 ## E1 ## E3; }; \\
\n
637  struct { detail::_swizzle<4, T, Q, 1,1,2,0> E1 ## E1 ## E2 ## E0; }; \\
\n
638  struct { detail::_swizzle<4, T, Q, 1,1,2,1> E1 ## E1 ## E2 ## E1; }; \\
\n
639  struct { detail::_swizzle<4, T, Q, 1,1,2,2> E1 ## E1 ## E2 ## E2; }; \\
\n
640  struct { detail::_swizzle<4, T, Q, 1,1,2,3> E1 ## E1 ## E2 ## E3; }; \\
\n
641  struct { detail::_swizzle<4, T, Q, 1,1,3,0> E1 ## E1 ## E3 ## E0; }; \\
\n
642  struct { detail::_swizzle<4, T, Q, 1,1,3,1> E1 ## E1 ## E3 ## E1; }; \\
\n
643  struct { detail::_swizzle<4, T, Q, 1,1,3,2> E1 ## E1 ## E3 ## E2; }; \\
\n
644  struct { detail::_swizzle<4, T, Q, 1,1,3,3> E1 ## E1 ## E3 ## E3; }; \\
\n
645  struct { detail::_swizzle<4, T, Q, 1,2,0,0> E1 ## E2 ## E0 ## E0; }; \\
\n
646  struct { detail::_swizzle<4, T, Q, 1,2,0,1> E1 ## E2 ## E0 ## E1; }; \\
\n
647  struct { detail::_swizzle<4, T, Q, 1,2,0,2> E1 ## E2 ## E0 ## E2; }; \\
\n
648  struct { detail::_swizzle<4, T, Q, 1,2,0,3> E1 ## E2 ## E0 ## E3; }; \\
\n
649  struct { detail::_swizzle<4, T, Q, 1,2,1,0> E1 ## E2 ## E1 ## E0; }; \\
\n
650  struct { detail::_swizzle<4, T, Q, 1,2,1,1> E1 ## E2 ## E1 ## E1; }; \\
\n
651  struct { detail::_swizzle<4, T, Q, 1,2,1,2> E1 ## E2 ## E1 ## E2; }; \\
\n
652  struct { detail::_swizzle<4, T, Q, 1,2,1,3> E1 ## E2 ## E1 ## E3; }; \\
\n
653  struct { detail::_swizzle<4, T, Q, 1,2,2,0> E1 ## E2 ## E2 ## E0; }; \\
\n
654  struct { detail::_swizzle<4, T, Q, 1,2,2,1> E1 ## E2 ## E2 ## E1; }; \\
\n
655  struct { detail::_swizzle<4, T, Q, 1,2,2,2> E1 ## E2 ## E2 ## E2; }; \\
\n
656  struct { detail::_swizzle<4, T, Q, 1,2,2,3> E1 ## E2 ## E2 ## E3; }; \\
\n
657  struct { detail::_swizzle<4, T, Q, 1,2,3,0> E1 ## E2 ## E3 ## E0; }; \\
\n
658  struct { detail::_swizzle<4, T, Q, 1,2,3,1> E1 ## E2 ## E3 ## E1; }; \\
\n
659  struct { detail::_swizzle<4, T, Q, 1,2,3,2> E1 ## E2 ## E3 ## E2; }; \\
\n
660  struct { detail::_swizzle<4, T, Q, 1,2,3,3> E1 ## E2 ## E3 ## E3; }; \\
\n
661  struct { detail::_swizzle<4, T, Q, 1,3,0,0> E1 ## E3 ## E0 ## E0; }; \\
\n
662  struct { detail::_swizzle<4, T, Q, 1,3,0,1> E1 ## E3 ## E0 ## E1; }; \\
\n
663  struct { detail::_swizzle<4, T, Q, 1,3,0,2> E1 ## E3 ## E0 ## E2; }; \\
\n
664  struct { detail::_swizzle<4, T, Q, 1,3,0,3> E1 ## E3 ## E0 ## E3; }; \\
\n
665  struct { detail::_swizzle<4, T, Q, 1,3,1,0> E1 ## E3 ## E1 ## E0; }; \\
\n
666  struct { detail::_swizzle<4, T, Q, 1,3,1,1> E1 ## E3 ## E1 ## E1; }; \\
\n
667  struct { detail::_swizzle<4, T, Q, 1,3,1,2> E1 ## E3 ## E1 ## E2; }; \\
\n
668  struct { detail::_swizzle<4, T, Q, 1,3,1,3> E1 ## E3 ## E1 ## E3; }; \\
\n
669  struct { detail::_swizzle<4, T, Q, 1,3,2,0> E1 ## E3 ## E2 ## E0; }; \\
\n
670  struct { detail::_swizzle<4, T, Q, 1,3,2,1> E1 ## E3 ## E2 ## E1; }; \\
\n
671  struct { detail::_swizzle<4, T, Q, 1,3,2,2> E1 ## E3 ## E2 ## E2; }; \\
\n
672  struct { detail::_swizzle<4, T, Q, 1,3,2,3> E1 ## E3 ## E2 ## E3; }; \\
\n
673  struct { detail::_swizzle<4, T, Q, 1,3,3,0> E1 ## E3 ## E3 ## E0; }; \\
\n
674  struct { detail::_swizzle<4, T, Q, 1,3,3,1> E1 ## E3 ## E3 ## E1; }; \\
\n
675  struct { detail::_swizzle<4, T, Q, 1,3,3,2> E1 ## E3 ## E3 ## E2; }; \\
\n
676  struct { detail::_swizzle<4, T, Q, 1,3,3,3> E1 ## E3 ## E3 ## E3; }; \\
\n
677  struct { detail::_swizzle<4, T, Q, 2,0,0,0> E2 ## E0 ## E0 ## E0; }; \\
\n
678  struct { detail::_swizzle<4, T, Q, 2,0,0,1> E2 ## E0 ## E0 ## E1; }; \\
\n
679  struct { detail::_swizzle<4, T, Q, 2,0,0,2> E2 ## E0 ## E0 ## E2; }; \\
\n
680  struct { detail::_swizzle<4, T, Q, 2,0,0,3> E2 ## E0 ## E0 ## E3; }; \\
\n
681  struct { detail::_swizzle<4, T, Q, 2,0,1,0> E2 ## E0 ## E1 ## E0; }; \\
\n
682  struct { detail::_swizzle<4, T, Q, 2,0,1,1> E2 ## E0 ## E1 ## E1; }; \\
\n
683  struct { detail::_swizzle<4, T, Q, 2,0,1,2> E2 ## E0 ## E1 ## E2; }; \\
\n
684  struct { detail::_swizzle<4, T, Q, 2,0,1,3> E2 ## E0 ## E1 ## E3; }; \\
\n
685  struct { detail::_swizzle<4, T, Q, 2,0,2,0> E2 ## E0 ## E2 ## E0; }; \\
\n
686  struct { detail::_swizzle<4, T, Q, 2,0,2,1> E2 ## E0 ## E2 ## E1; }; \\
\n
687  struct { detail::_swizzle<4, T, Q, 2,0,2,2> E2 ## E0 ## E2 ## E2; }; \\
\n
688  struct { detail::_swizzle<4, T, Q, 2,0,2,3> E2 ## E0 ## E2 ## E3; }; \\
\n
689  struct { detail::_swizzle<4, T, Q, 2,0,3,0> E2 ## E0 ## E3 ## E0; }; \\
\n
690  struct { detail::_swizzle<4, T, Q, 2,0,3,1> E2 ## E0 ## E3 ## E1; }; \\
\n
691  struct { detail::_swizzle<4, T, Q, 2,0,3,2> E2 ## E0 ## E3 ## E2; }; \\
\n
692  struct { detail::_swizzle<4, T, Q, 2,0,3,3> E2 ## E0 ## E3 ## E3; }; \\
\n
693  struct { detail::_swizzle<4, T, Q, 2,1,0,0> E2 ## E1 ## E0 ## E0; }; \\
\n
694  struct { detail::_swizzle<4, T, Q, 2,1,0,1> E2 ## E1 ## E0 ## E1; }; \\
\n
695  struct { detail::_swizzle<4, T, Q, 2,1,0,2> E2 ## E1 ## E0 ## E2; }; \\
\n
696  struct { detail::_swizzle<4, T, Q, 2,1,0,3> E2 ## E1 ## E0 ## E3; }; \\
\n
697  struct { detail::_swizzle<4, T, Q, 2,1,1,0> E2 ## E1 ## E1 ## E0; }; \\
\n
698  struct { detail::_swizzle<4, T, Q, 2,1,1,1> E2 ## E1 ## E1 ## E1; }; \\
\n
699  struct { detail::_swizzle<4, T, Q, 2,1,1,2> E2 ## E1 ## E1 ## E2; }; \\
\n
700  struct { detail::_swizzle<4, T, Q, 2,1,1,3> E2 ## E1 ## E1 ## E3; }; \\
\n
701  struct { detail::_swizzle<4, T, Q, 2,1,2,0> E2 ## E1 ## E2 ## E0; }; \\
\n
702  struct { detail::_swizzle<4, T, Q, 2,1,2,1> E2 ## E1 ## E2 ## E1; }; \\
\n
703  struct { detail::_swizzle<4, T, Q, 2,1,2,2> E2 ## E1 ## E2 ## E2; }; \\
\n
704  struct { detail::_swizzle<4, T, Q, 2,1,2,3> E2 ## E1 ## E2 ## E3; }; \\
\n
705  struct { detail::_swizzle<4, T, Q, 2,1,3,0> E2 ## E1 ## E3 ## E0; }; \\
\n
706  struct { detail::_swizzle<4, T, Q, 2,1,3,1> E2 ## E1 ## E3 ## E1; }; \\
\n
707  struct { detail::_swizzle<4, T, Q, 2,1,3,2> E2 ## E1 ## E3 ## E2; }; \\
\n
708  struct { detail::_swizzle<4, T, Q, 2,1,3,3> E2 ## E1 ## E3 ## E3; }; \\
\n
709  struct { detail::_swizzle<4, T, Q, 2,2,0,0> E2 ## E2 ## E0 ## E0; }; \\
\n
710  struct { detail::_swizzle<4, T, Q, 2,2,0,1> E2 ## E2 ## E0 ## E1; }; \\
\n
711  struct { detail::_swizzle<4, T, Q, 2,2,0,2> E2 ## E2 ## E0 ## E2; }; \\
\n
712  struct { detail::_swizzle<4, T, Q, 2,2,0,3> E2 ## E2 ## E0 ## E3; }; \\
\n
713  struct { detail::_swizzle<4, T, Q, 2,2,1,0> E2 ## E2 ## E1 ## E0; }; \\
\n
714  struct { detail::_swizzle<4, T, Q, 2,2,1,1> E2 ## E2 ## E1 ## E1; }; \\
\n
715  struct { detail::_swizzle<4, T, Q, 2,2,1,2> E2 ## E2 ## E1 ## E2; }; \\
\n
716  struct { detail::_swizzle<4, T, Q, 2,2,1,3> E2 ## E2 ## E1 ## E3; }; \\
\n
717  struct { detail::_swizzle<4, T, Q, 2,2,2,0> E2 ## E2 ## E2 ## E0; }; \\
\n
718  struct { detail::_swizzle<4, T, Q, 2,2,2,1> E2 ## E2 ## E2 ## E1; }; \\
\n
719  struct { detail::_swizzle<4, T, Q, 2,2,2,2> E2 ## E2 ## E2 ## E2; }; \\
\n
720  struct { detail::_swizzle<4, T, Q, 2,2,2,3> E2 ## E2 ## E2 ## E3; }; \\
\n
721  struct { detail::_swizzle<4, T, Q, 2,2,3,0> E2 ## E2 ## E3 ## E0; }; \\
\n
722  struct { detail::_swizzle<4, T, Q, 2,2,3,1> E2 ## E2 ## E3 ## E1; }; \\
\n
723  struct { detail::_swizzle<4, T, Q, 2,2,3,2> E2 ## E2 ## E3 ## E2; }; \\
\n
724  struct { detail::_swizzle<4, T, Q, 2,2,3,3> E2 ## E2 ## E3 ## E3; }; \\
\n
725  struct { detail::_swizzle<4, T, Q, 2,3,0,0> E2 ## E3 ## E0 ## E0; }; \\
\n
726  struct { detail::_swizzle<4, T, Q, 2,3,0,1> E2 ## E3 ## E0 ## E1; }; \\
\n
727  struct { detail::_swizzle<4, T, Q, 2,3,0,2> E2 ## E3 ## E0 ## E2; }; \\
\n
728  struct { detail::_swizzle<4, T, Q, 2,3,0,3> E2 ## E3 ## E0 ## E3; }; \\
\n
729  struct { detail::_swizzle<4, T, Q, 2,3,1,0> E2 ## E3 ## E1 ## E0; }; \\
\n
730  struct { detail::_swizzle<4, T, Q, 2,3,1,1> E2 ## E3 ## E1 ## E1; }; \\
\n
731  struct { detail::_swizzle<4, T, Q, 2,3,1,2> E2 ## E3 ## E1 ## E2; }; \\
\n
732  struct { detail::_swizzle<4, T, Q, 2,3,1,3> E2 ## E3 ## E1 ## E3; }; \\
\n
733  struct { detail::_swizzle<4, T, Q, 2,3,2,0> E2 ## E3 ## E2 ## E0; }; \\
\n
734  struct { detail::_swizzle<4, T, Q, 2,3,2,1> E2 ## E3 ## E2 ## E1; }; \\
\n
735  struct { detail::_swizzle<4, T, Q, 2,3,2,2> E2 ## E3 ## E2 ## E2; }; \\
\n
736  struct { detail::_swizzle<4, T, Q, 2,3,2,3> E2 ## E3 ## E2 ## E3; }; \\
\n
737  struct { detail::_swizzle<4, T, Q, 2,3,3,0> E2 ## E3 ## E3 ## E0; }; \\
\n
738  struct { detail::_swizzle<4, T, Q, 2,3,3,1> E2 ## E3 ## E3 ## E1; }; \\
\n
739  struct { detail::_swizzle<4, T, Q, 2,3,3,2> E2 ## E3 ## E3 ## E2; }; \\
\n
740  struct { detail::_swizzle<4, T, Q, 2,3,3,3> E2 ## E3 ## E3 ## E3; }; \\
\n
741  struct { detail::_swizzle<4, T, Q, 3,0,0,0> E3 ## E0 ## E0 ## E0; }; \\
\n
742  struct { detail::_swizzle<4, T, Q, 3,0,0,1> E3 ## E0 ## E0 ## E1; }; \\
\n
743  struct { detail::_swizzle<4, T, Q, 3,0,0,2> E3 ## E0 ## E0 ## E2; }; \\
\n
744  struct { detail::_swizzle<4, T, Q, 3,0,0,3> E3 ## E0 ## E0 ## E3; }; \\
\n
745  struct { detail::_swizzle<4, T, Q, 3,0,1,0> E3 ## E0 ## E1 ## E0; }; \\
\n
746  struct { detail::_swizzle<4, T, Q, 3,0,1,1> E3 ## E0 ## E1 ## E1; }; \\
\n
747  struct { detail::_swizzle<4, T, Q, 3,0,1,2> E3 ## E0 ## E1 ## E2; }; \\
\n
748  struct { detail::_swizzle<4, T, Q, 3,0,1,3> E3 ## E0 ## E1 ## E3; }; \\
\n
749  struct { detail::_swizzle<4, T, Q, 3,0,2,0> E3 ## E0 ## E2 ## E0; }; \\
\n
750  struct { detail::_swizzle<4, T, Q, 3,0,2,1> E3 ## E0 ## E2 ## E1; }; \\
\n
751  struct { detail::_swizzle<4, T, Q, 3,0,2,2> E3 ## E0 ## E2 ## E2; }; \\
\n
752  struct { detail::_swizzle<4, T, Q, 3,0,2,3> E3 ## E0 ## E2 ## E3; }; \\
\n
753  struct { detail::_swizzle<4, T, Q, 3,0,3,0> E3 ## E0 ## E3 ## E0; }; \\
\n
754  struct { detail::_swizzle<4, T, Q, 3,0,3,1> E3 ## E0 ## E3 ## E1; }; \\
\n
755  struct { detail::_swizzle<4, T, Q, 3,0,3,2> E3 ## E0 ## E3 ## E2; }; \\
\n
756  struct { detail::_swizzle<4, T, Q, 3,0,3,3> E3 ## E0 ## E3 ## E3; }; \\
\n
757  struct { detail::_swizzle<4, T, Q, 3,1,0,0> E3 ## E1 ## E0 ## E0; }; \\
\n
758  struct { detail::_swizzle<4, T, Q, 3,1,0,1> E3 ## E1 ## E0 ## E1; }; \\
\n
759  struct { detail::_swizzle<4, T, Q, 3,1,0,2> E3 ## E1 ## E0 ## E2; }; \\
\n
760  struct { detail::_swizzle<4, T, Q, 3,1,0,3> E3 ## E1 ## E0 ## E3; }; \\
\n
761  struct { detail::_swizzle<4, T, Q, 3,1,1,0> E3 ## E1 ## E1 ## E0; }; \\
\n
762  struct { detail::_swizzle<4, T, Q, 3,1,1,1> E3 ## E1 ## E1 ## E1; }; \\
\n
763  struct { detail::_swizzle<4, T, Q, 3,1,1,2> E3 ## E1 ## E1 ## E2; }; \\
\n
764  struct { detail::_swizzle<4, T, Q, 3,1,1,3> E3 ## E1 ## E1 ## E3; }; \\
\n
765  struct { detail::_swizzle<4, T, Q, 3,1,2,0> E3 ## E1 ## E2 ## E0; }; \\
\n
766  struct { detail::_swizzle<4, T, Q, 3,1,2,1> E3 ## E1 ## E2 ## E1; }; \\
\n
767  struct { detail::_swizzle<4, T, Q, 3,1,2,2> E3 ## E1 ## E2 ## E2; }; \\
\n
768  struct { detail::_swizzle<4, T, Q, 3,1,2,3> E3 ## E1 ## E2 ## E3; }; \\
\n
769  struct { detail::_swizzle<4, T, Q, 3,1,3,0> E3 ## E1 ## E3 ## E0; }; \\
\n
770  struct { detail::_swizzle<4, T, Q, 3,1,3,1> E3 ## E1 ## E3 ## E1; }; \\
\n
771  struct { detail::_swizzle<4, T, Q, 3,1,3,2> E3 ## E1 ## E3 ## E2; }; \\
\n
772  struct { detail::_swizzle<4, T, Q, 3,1,3,3> E3 ## E1 ## E3 ## E3; }; \\
\n
773  struct { detail::_swizzle<4, T, Q, 3,2,0,0> E3 ## E2 ## E0 ## E0; }; \\
\n
774  struct { detail::_swizzle<4, T, Q, 3,2,0,1> E3 ## E2 ## E0 ## E1; }; \\
\n
775  struct { detail::_swizzle<4, T, Q, 3,2,0,2> E3 ## E2 ## E0 ## E2; }; \\
\n
776  struct { detail::_swizzle<4, T, Q, 3,2,0,3> E3 ## E2 ## E0 ## E3; }; \\
\n
777  struct { detail::_swizzle<4, T, Q, 3,2,1,0> E3 ## E2 ## E1 ## E0; }; \\
\n
778  struct { detail::_swizzle<4, T, Q, 3,2,1,1> E3 ## E2 ## E1 ## E1; }; \\
\n
779  struct { detail::_swizzle<4, T, Q, 3,2,1,2> E3 ## E2 ## E1 ## E2; }; \\
\n
780  struct { detail::_swizzle<4, T, Q, 3,2,1,3> E3 ## E2 ## E1 ## E3; }; \\
\n
781  struct { detail::_swizzle<4, T, Q, 3,2,2,0> E3 ## E2 ## E2 ## E0; }; \\
\n
782  struct { detail::_swizzle<4, T, Q, 3,2,2,1> E3 ## E2 ## E2 ## E1; }; \\
\n
783  struct { detail::_swizzle<4, T, Q, 3,2,2,2> E3 ## E2 ## E2 ## E2; }; \\
\n
784  struct { detail::_swizzle<4, T, Q, 3,2,2,3> E3 ## E2 ## E2 ## E3; }; \\
\n
785  struct { detail::_swizzle<4, T, Q, 3,2,3,0> E3 ## E2 ## E3 ## E0; }; \\
\n
786  struct { detail::_swizzle<4, T, Q, 3,2,3,1> E3 ## E2 ## E3 ## E1; }; \\
\n
787  struct { detail::_swizzle<4, T, Q, 3,2,3,2> E3 ## E2 ## E3 ## E2; }; \\
\n
788  struct { detail::_swizzle<4, T, Q, 3,2,3,3> E3 ## E2 ## E3 ## E3; }; \\
\n
789  struct { detail::_swizzle<4, T, Q, 3,3,0,0> E3 ## E3 ## E0 ## E0; }; \\
\n
790  struct { detail::_swizzle<4, T, Q, 3,3,0,1> E3 ## E3 ## E0 ## E1; }; \\
\n
791  struct { detail::_swizzle<4, T, Q, 3,3,0,2> E3 ## E3 ## E0 ## E2; }; \\
\n
792  struct { detail::_swizzle<4, T, Q, 3,3,0,3> E3 ## E3 ## E0 ## E3; }; \\
\n
793  struct { detail::_swizzle<4, T, Q, 3,3,1,0> E3 ## E3 ## E1 ## E0; }; \\
\n
794  struct { detail::_swizzle<4, T, Q, 3,3,1,1> E3 ## E3 ## E1 ## E1; }; \\
\n
795  struct { detail::_swizzle<4, T, Q, 3,3,1,2> E3 ## E3 ## E1 ## E2; }; \\
\n
796  struct { detail::_swizzle<4, T, Q, 3,3,1,3> E3 ## E3 ## E1 ## E3; }; \\
\n
797  struct { detail::_swizzle<4, T, Q, 3,3,2,0> E3 ## E3 ## E2 ## E0; }; \\
\n
798  struct { detail::_swizzle<4, T, Q, 3,3,2,1> E3 ## E3 ## E2 ## E1; }; \\
\n
799  struct { detail::_swizzle<4, T, Q, 3,3,2,2> E3 ## E3 ## E2 ## E2; }; \\
\n
800  struct { detail::_swizzle<4, T, Q, 3,3,2,3> E3 ## E3 ## E2 ## E3; }; \\
\n
801  struct { detail::_swizzle<4, T, Q, 3,3,3,0> E3 ## E3 ## E3 ## E0; }; \\
\n
802  struct { detail::_swizzle<4, T, Q, 3,3,3,1> E3 ## E3 ## E3 ## E1; }; \\
\n
803  struct { detail::_swizzle<4, T, Q, 3,3,3,2> E3 ## E3 ## E3 ## E2; }; \\
\n
804  struct { detail::_swizzle<4, T, Q, 3,3,3,3> E3 ## E3 ## E3 ## E3; };
\n
glm::e
GLM_FUNC_DECL GLM_CONSTEXPR genType e()
Return e constant.
\n
glm
Definition: common.hpp:20
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_swizzle_func.hpp
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1 #pragma once
\n
2 
\n
3 #define GLM_SWIZZLE_GEN_VEC2_ENTRY(T, P, CONST, A, B) \\
\n
4  vec<2, T, Q> A ## B() CONST \\
\n
5  { \\
\n
6  return vec<2, T, Q>(this->A, this->B); \\
\n
7  }
\n
8 
\n
9 #define GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, CONST, A, B, C) \\
\n
10  vec<3, T, Q> A ## B ## C() CONST \\
\n
11  { \\
\n
12  return vec<3, T, Q>(this->A, this->B, this->C); \\
\n
13  }
\n
14 
\n
15 #define GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, CONST, A, B, C, D) \\
\n
16  vec<4, T, Q> A ## B ## C ## D() CONST \\
\n
17  { \\
\n
18  return vec<4, T, Q>(this->A, this->B, this->C, this->D); \\
\n
19  }
\n
20 
\n
21 #define GLM_SWIZZLE_GEN_VEC2_ENTRY_DEF(T, P, L, CONST, A, B) \\
\n
22  template<typename T> \\
\n
23  vec<L, T, Q> vec<L, T, Q>::A ## B() CONST \\
\n
24  { \\
\n
25  return vec<2, T, Q>(this->A, this->B); \\
\n
26  }
\n
27 
\n
28 #define GLM_SWIZZLE_GEN_VEC3_ENTRY_DEF(T, P, L, CONST, A, B, C) \\
\n
29  template<typename T> \\
\n
30  vec<3, T, Q> vec<L, T, Q>::A ## B ## C() CONST \\
\n
31  { \\
\n
32  return vec<3, T, Q>(this->A, this->B, this->C); \\
\n
33  }
\n
34 
\n
35 #define GLM_SWIZZLE_GEN_VEC4_ENTRY_DEF(T, P, L, CONST, A, B, C, D) \\
\n
36  template<typename T> \\
\n
37  vec<4, T, Q> vec<L, T, Q>::A ## B ## C ## D() CONST \\
\n
38  { \\
\n
39  return vec<4, T, Q>(this->A, this->B, this->C, this->D); \\
\n
40  }
\n
41 
\n
42 #define GLM_MUTABLE
\n
43 
\n
44 #define GLM_SWIZZLE_GEN_REF2_FROM_VEC2_SWIZZLE(T, P, A, B) \\
\n
45  GLM_SWIZZLE_GEN_VEC2_ENTRY(T, P, 2, GLM_MUTABLE, A, B) \\
\n
46  GLM_SWIZZLE_GEN_VEC2_ENTRY(T, P, 2, GLM_MUTABLE, B, A)
\n
47 
\n
48 #define GLM_SWIZZLE_GEN_REF_FROM_VEC2(T, P) \\
\n
49  GLM_SWIZZLE_GEN_REF2_FROM_VEC2_SWIZZLE(T, P, x, y) \\
\n
50  GLM_SWIZZLE_GEN_REF2_FROM_VEC2_SWIZZLE(T, P, r, g) \\
\n
51  GLM_SWIZZLE_GEN_REF2_FROM_VEC2_SWIZZLE(T, P, s, t)
\n
52 
\n
53 #define GLM_SWIZZLE_GEN_REF2_FROM_VEC3_SWIZZLE(T, P, A, B, C) \\
\n
54  GLM_SWIZZLE_GEN_VEC2_ENTRY(T, P, GLM_MUTABLE, A, B) \\
\n
55  GLM_SWIZZLE_GEN_VEC2_ENTRY(T, P, GLM_MUTABLE, A, C) \\
\n
56  GLM_SWIZZLE_GEN_VEC2_ENTRY(T, P, GLM_MUTABLE, B, A) \\
\n
57  GLM_SWIZZLE_GEN_VEC2_ENTRY(T, P, GLM_MUTABLE, B, C) \\
\n
58  GLM_SWIZZLE_GEN_VEC2_ENTRY(T, P, GLM_MUTABLE, C, A) \\
\n
59  GLM_SWIZZLE_GEN_VEC2_ENTRY(T, P, GLM_MUTABLE, C, B)
\n
60 
\n
61 #define GLM_SWIZZLE_GEN_REF3_FROM_VEC3_SWIZZLE(T, P, A, B, C) \\
\n
62  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, GLM_MUTABLE, A, B, C) \\
\n
63  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, GLM_MUTABLE, A, C, B) \\
\n
64  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, GLM_MUTABLE, B, A, C) \\
\n
65  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, GLM_MUTABLE, B, C, A) \\
\n
66  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, GLM_MUTABLE, C, A, B) \\
\n
67  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, GLM_MUTABLE, C, B, A)
\n
68 
\n
69 #define GLM_SWIZZLE_GEN_REF_FROM_VEC3_COMP(T, P, A, B, C) \\
\n
70  GLM_SWIZZLE_GEN_REF3_FROM_VEC3_SWIZZLE(T, P, A, B, C) \\
\n
71  GLM_SWIZZLE_GEN_REF2_FROM_VEC3_SWIZZLE(T, P, A, B, C)
\n
72 
\n
73 #define GLM_SWIZZLE_GEN_REF_FROM_VEC3(T, P) \\
\n
74  GLM_SWIZZLE_GEN_REF_FROM_VEC3_COMP(T, P, x, y, z) \\
\n
75  GLM_SWIZZLE_GEN_REF_FROM_VEC3_COMP(T, P, r, g, b) \\
\n
76  GLM_SWIZZLE_GEN_REF_FROM_VEC3_COMP(T, P, s, t, p)
\n
77 
\n
78 #define GLM_SWIZZLE_GEN_REF2_FROM_VEC4_SWIZZLE(T, P, A, B, C, D) \\
\n
79  GLM_SWIZZLE_GEN_VEC2_ENTRY(T, P, GLM_MUTABLE, A, B) \\
\n
80  GLM_SWIZZLE_GEN_VEC2_ENTRY(T, P, GLM_MUTABLE, A, C) \\
\n
81  GLM_SWIZZLE_GEN_VEC2_ENTRY(T, P, GLM_MUTABLE, A, D) \\
\n
82  GLM_SWIZZLE_GEN_VEC2_ENTRY(T, P, GLM_MUTABLE, B, A) \\
\n
83  GLM_SWIZZLE_GEN_VEC2_ENTRY(T, P, GLM_MUTABLE, B, C) \\
\n
84  GLM_SWIZZLE_GEN_VEC2_ENTRY(T, P, GLM_MUTABLE, B, D) \\
\n
85  GLM_SWIZZLE_GEN_VEC2_ENTRY(T, P, GLM_MUTABLE, C, A) \\
\n
86  GLM_SWIZZLE_GEN_VEC2_ENTRY(T, P, GLM_MUTABLE, C, B) \\
\n
87  GLM_SWIZZLE_GEN_VEC2_ENTRY(T, P, GLM_MUTABLE, C, D) \\
\n
88  GLM_SWIZZLE_GEN_VEC2_ENTRY(T, P, GLM_MUTABLE, D, A) \\
\n
89  GLM_SWIZZLE_GEN_VEC2_ENTRY(T, P, GLM_MUTABLE, D, B) \\
\n
90  GLM_SWIZZLE_GEN_VEC2_ENTRY(T, P, GLM_MUTABLE, D, C)
\n
91 
\n
92 #define GLM_SWIZZLE_GEN_REF3_FROM_VEC4_SWIZZLE(T, P, A, B, C, D) \\
\n
93  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, , A, B, C) \\
\n
94  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, , A, B, D) \\
\n
95  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, , A, C, B) \\
\n
96  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, , A, C, D) \\
\n
97  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, , A, D, B) \\
\n
98  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, , A, D, C) \\
\n
99  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, , B, A, C) \\
\n
100  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, , B, A, D) \\
\n
101  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, , B, C, A) \\
\n
102  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, , B, C, D) \\
\n
103  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, , B, D, A) \\
\n
104  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, , B, D, C) \\
\n
105  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, , C, A, B) \\
\n
106  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, , C, A, D) \\
\n
107  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, , C, B, A) \\
\n
108  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, , C, B, D) \\
\n
109  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, , C, D, A) \\
\n
110  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, , C, D, B) \\
\n
111  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, , D, A, B) \\
\n
112  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, , D, A, C) \\
\n
113  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, , D, B, A) \\
\n
114  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, , D, B, C) \\
\n
115  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, , D, C, A) \\
\n
116  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, , D, C, B)
\n
117 
\n
118 #define GLM_SWIZZLE_GEN_REF4_FROM_VEC4_SWIZZLE(T, P, A, B, C, D) \\
\n
119  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, , A, C, B, D) \\
\n
120  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, , A, C, D, B) \\
\n
121  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, , A, D, B, C) \\
\n
122  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, , A, D, C, B) \\
\n
123  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, , A, B, D, C) \\
\n
124  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, , A, B, C, D) \\
\n
125  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, , B, C, A, D) \\
\n
126  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, , B, C, D, A) \\
\n
127  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, , B, D, A, C) \\
\n
128  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, , B, D, C, A) \\
\n
129  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, , B, A, D, C) \\
\n
130  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, , B, A, C, D) \\
\n
131  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, , C, B, A, D) \\
\n
132  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, , C, B, D, A) \\
\n
133  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, , C, D, A, B) \\
\n
134  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, , C, D, B, A) \\
\n
135  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, , C, A, D, B) \\
\n
136  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, , C, A, B, D) \\
\n
137  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, , D, C, B, A) \\
\n
138  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, , D, C, A, B) \\
\n
139  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, , D, A, B, C) \\
\n
140  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, , D, A, C, B) \\
\n
141  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, , D, B, A, C) \\
\n
142  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, , D, B, C, A)
\n
143 
\n
144 #define GLM_SWIZZLE_GEN_REF_FROM_VEC4_COMP(T, P, A, B, C, D) \\
\n
145  GLM_SWIZZLE_GEN_REF2_FROM_VEC4_SWIZZLE(T, P, A, B, C, D) \\
\n
146  GLM_SWIZZLE_GEN_REF3_FROM_VEC4_SWIZZLE(T, P, A, B, C, D) \\
\n
147  GLM_SWIZZLE_GEN_REF4_FROM_VEC4_SWIZZLE(T, P, A, B, C, D)
\n
148 
\n
149 #define GLM_SWIZZLE_GEN_REF_FROM_VEC4(T, P) \\
\n
150  GLM_SWIZZLE_GEN_REF_FROM_VEC4_COMP(T, P, x, y, z, w) \\
\n
151  GLM_SWIZZLE_GEN_REF_FROM_VEC4_COMP(T, P, r, g, b, a) \\
\n
152  GLM_SWIZZLE_GEN_REF_FROM_VEC4_COMP(T, P, s, t, p, q)
\n
153 
\n
154 #define GLM_SWIZZLE_GEN_VEC2_FROM_VEC2_SWIZZLE(T, P, A, B) \\
\n
155  GLM_SWIZZLE_GEN_VEC2_ENTRY(T, P, const, A, A) \\
\n
156  GLM_SWIZZLE_GEN_VEC2_ENTRY(T, P, const, A, B) \\
\n
157  GLM_SWIZZLE_GEN_VEC2_ENTRY(T, P, const, B, A) \\
\n
158  GLM_SWIZZLE_GEN_VEC2_ENTRY(T, P, const, B, B)
\n
159 
\n
160 #define GLM_SWIZZLE_GEN_VEC3_FROM_VEC2_SWIZZLE(T, P, A, B) \\
\n
161  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, A, A, A) \\
\n
162  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, A, A, B) \\
\n
163  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, A, B, A) \\
\n
164  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, A, B, B) \\
\n
165  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, B, A, A) \\
\n
166  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, B, A, B) \\
\n
167  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, B, B, A) \\
\n
168  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, B, B, B)
\n
169 
\n
170 #define GLM_SWIZZLE_GEN_VEC4_FROM_VEC2_SWIZZLE(T, P, A, B) \\
\n
171  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, A, A, A) \\
\n
172  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, A, A, B) \\
\n
173  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, A, B, A) \\
\n
174  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, A, B, B) \\
\n
175  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, B, A, A) \\
\n
176  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, B, A, B) \\
\n
177  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, B, B, A) \\
\n
178  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, B, B, B) \\
\n
179  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, A, A, A) \\
\n
180  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, A, A, B) \\
\n
181  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, A, B, A) \\
\n
182  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, A, B, B) \\
\n
183  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, B, A, A) \\
\n
184  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, B, A, B) \\
\n
185  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, B, B, A) \\
\n
186  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, B, B, B)
\n
187 
\n
188 #define GLM_SWIZZLE_GEN_VEC_FROM_VEC2_COMP(T, P, A, B) \\
\n
189  GLM_SWIZZLE_GEN_VEC2_FROM_VEC2_SWIZZLE(T, P, A, B) \\
\n
190  GLM_SWIZZLE_GEN_VEC3_FROM_VEC2_SWIZZLE(T, P, A, B) \\
\n
191  GLM_SWIZZLE_GEN_VEC4_FROM_VEC2_SWIZZLE(T, P, A, B)
\n
192 
\n
193 #define GLM_SWIZZLE_GEN_VEC_FROM_VEC2(T, P) \\
\n
194  GLM_SWIZZLE_GEN_VEC_FROM_VEC2_COMP(T, P, x, y) \\
\n
195  GLM_SWIZZLE_GEN_VEC_FROM_VEC2_COMP(T, P, r, g) \\
\n
196  GLM_SWIZZLE_GEN_VEC_FROM_VEC2_COMP(T, P, s, t)
\n
197 
\n
198 #define GLM_SWIZZLE_GEN_VEC2_FROM_VEC3_SWIZZLE(T, P, A, B, C) \\
\n
199  GLM_SWIZZLE_GEN_VEC2_ENTRY(T, P, const, A, A) \\
\n
200  GLM_SWIZZLE_GEN_VEC2_ENTRY(T, P, const, A, B) \\
\n
201  GLM_SWIZZLE_GEN_VEC2_ENTRY(T, P, const, A, C) \\
\n
202  GLM_SWIZZLE_GEN_VEC2_ENTRY(T, P, const, B, A) \\
\n
203  GLM_SWIZZLE_GEN_VEC2_ENTRY(T, P, const, B, B) \\
\n
204  GLM_SWIZZLE_GEN_VEC2_ENTRY(T, P, const, B, C) \\
\n
205  GLM_SWIZZLE_GEN_VEC2_ENTRY(T, P, const, C, A) \\
\n
206  GLM_SWIZZLE_GEN_VEC2_ENTRY(T, P, const, C, B) \\
\n
207  GLM_SWIZZLE_GEN_VEC2_ENTRY(T, P, const, C, C)
\n
208 
\n
209 #define GLM_SWIZZLE_GEN_VEC3_FROM_VEC3_SWIZZLE(T, P, A, B, C) \\
\n
210  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, A, A, A) \\
\n
211  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, A, A, B) \\
\n
212  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, A, A, C) \\
\n
213  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, A, B, A) \\
\n
214  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, A, B, B) \\
\n
215  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, A, B, C) \\
\n
216  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, A, C, A) \\
\n
217  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, A, C, B) \\
\n
218  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, A, C, C) \\
\n
219  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, B, A, A) \\
\n
220  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, B, A, B) \\
\n
221  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, B, A, C) \\
\n
222  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, B, B, A) \\
\n
223  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, B, B, B) \\
\n
224  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, B, B, C) \\
\n
225  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, B, C, A) \\
\n
226  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, B, C, B) \\
\n
227  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, B, C, C) \\
\n
228  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, C, A, A) \\
\n
229  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, C, A, B) \\
\n
230  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, C, A, C) \\
\n
231  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, C, B, A) \\
\n
232  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, C, B, B) \\
\n
233  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, C, B, C) \\
\n
234  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, C, C, A) \\
\n
235  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, C, C, B) \\
\n
236  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, C, C, C)
\n
237 
\n
238 #define GLM_SWIZZLE_GEN_VEC4_FROM_VEC3_SWIZZLE(T, P, A, B, C) \\
\n
239  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, A, A, A) \\
\n
240  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, A, A, B) \\
\n
241  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, A, A, C) \\
\n
242  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, A, B, A) \\
\n
243  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, A, B, B) \\
\n
244  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, A, B, C) \\
\n
245  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, A, C, A) \\
\n
246  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, A, C, B) \\
\n
247  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, A, C, C) \\
\n
248  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, B, A, A) \\
\n
249  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, B, A, B) \\
\n
250  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, B, A, C) \\
\n
251  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, B, B, A) \\
\n
252  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, B, B, B) \\
\n
253  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, B, B, C) \\
\n
254  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, B, C, A) \\
\n
255  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, B, C, B) \\
\n
256  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, B, C, C) \\
\n
257  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, C, A, A) \\
\n
258  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, C, A, B) \\
\n
259  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, C, A, C) \\
\n
260  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, C, B, A) \\
\n
261  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, C, B, B) \\
\n
262  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, C, B, C) \\
\n
263  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, C, C, A) \\
\n
264  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, C, C, B) \\
\n
265  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, C, C, C) \\
\n
266  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, A, A, A) \\
\n
267  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, A, A, B) \\
\n
268  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, A, A, C) \\
\n
269  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, A, B, A) \\
\n
270  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, A, B, B) \\
\n
271  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, A, B, C) \\
\n
272  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, A, C, A) \\
\n
273  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, A, C, B) \\
\n
274  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, A, C, C) \\
\n
275  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, B, A, A) \\
\n
276  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, B, A, B) \\
\n
277  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, B, A, C) \\
\n
278  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, B, B, A) \\
\n
279  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, B, B, B) \\
\n
280  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, B, B, C) \\
\n
281  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, B, C, A) \\
\n
282  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, B, C, B) \\
\n
283  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, B, C, C) \\
\n
284  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, C, A, A) \\
\n
285  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, C, A, B) \\
\n
286  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, C, A, C) \\
\n
287  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, C, B, A) \\
\n
288  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, C, B, B) \\
\n
289  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, C, B, C) \\
\n
290  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, C, C, A) \\
\n
291  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, C, C, B) \\
\n
292  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, C, C, C) \\
\n
293  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, A, A, A) \\
\n
294  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, A, A, B) \\
\n
295  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, A, A, C) \\
\n
296  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, A, B, A) \\
\n
297  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, A, B, B) \\
\n
298  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, A, B, C) \\
\n
299  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, A, C, A) \\
\n
300  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, A, C, B) \\
\n
301  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, A, C, C) \\
\n
302  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, B, A, A) \\
\n
303  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, B, A, B) \\
\n
304  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, B, A, C) \\
\n
305  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, B, B, A) \\
\n
306  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, B, B, B) \\
\n
307  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, B, B, C) \\
\n
308  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, B, C, A) \\
\n
309  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, B, C, B) \\
\n
310  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, B, C, C) \\
\n
311  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, C, A, A) \\
\n
312  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, C, A, B) \\
\n
313  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, C, A, C) \\
\n
314  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, C, B, A) \\
\n
315  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, C, B, B) \\
\n
316  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, C, B, C) \\
\n
317  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, C, C, A) \\
\n
318  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, C, C, B) \\
\n
319  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, C, C, C)
\n
320 
\n
321 #define GLM_SWIZZLE_GEN_VEC_FROM_VEC3_COMP(T, P, A, B, C) \\
\n
322  GLM_SWIZZLE_GEN_VEC2_FROM_VEC3_SWIZZLE(T, P, A, B, C) \\
\n
323  GLM_SWIZZLE_GEN_VEC3_FROM_VEC3_SWIZZLE(T, P, A, B, C) \\
\n
324  GLM_SWIZZLE_GEN_VEC4_FROM_VEC3_SWIZZLE(T, P, A, B, C)
\n
325 
\n
326 #define GLM_SWIZZLE_GEN_VEC_FROM_VEC3(T, P) \\
\n
327  GLM_SWIZZLE_GEN_VEC_FROM_VEC3_COMP(T, P, x, y, z) \\
\n
328  GLM_SWIZZLE_GEN_VEC_FROM_VEC3_COMP(T, P, r, g, b) \\
\n
329  GLM_SWIZZLE_GEN_VEC_FROM_VEC3_COMP(T, P, s, t, p)
\n
330 
\n
331 #define GLM_SWIZZLE_GEN_VEC2_FROM_VEC4_SWIZZLE(T, P, A, B, C, D) \\
\n
332  GLM_SWIZZLE_GEN_VEC2_ENTRY(T, P, const, A, A) \\
\n
333  GLM_SWIZZLE_GEN_VEC2_ENTRY(T, P, const, A, B) \\
\n
334  GLM_SWIZZLE_GEN_VEC2_ENTRY(T, P, const, A, C) \\
\n
335  GLM_SWIZZLE_GEN_VEC2_ENTRY(T, P, const, A, D) \\
\n
336  GLM_SWIZZLE_GEN_VEC2_ENTRY(T, P, const, B, A) \\
\n
337  GLM_SWIZZLE_GEN_VEC2_ENTRY(T, P, const, B, B) \\
\n
338  GLM_SWIZZLE_GEN_VEC2_ENTRY(T, P, const, B, C) \\
\n
339  GLM_SWIZZLE_GEN_VEC2_ENTRY(T, P, const, B, D) \\
\n
340  GLM_SWIZZLE_GEN_VEC2_ENTRY(T, P, const, C, A) \\
\n
341  GLM_SWIZZLE_GEN_VEC2_ENTRY(T, P, const, C, B) \\
\n
342  GLM_SWIZZLE_GEN_VEC2_ENTRY(T, P, const, C, C) \\
\n
343  GLM_SWIZZLE_GEN_VEC2_ENTRY(T, P, const, C, D) \\
\n
344  GLM_SWIZZLE_GEN_VEC2_ENTRY(T, P, const, D, A) \\
\n
345  GLM_SWIZZLE_GEN_VEC2_ENTRY(T, P, const, D, B) \\
\n
346  GLM_SWIZZLE_GEN_VEC2_ENTRY(T, P, const, D, C) \\
\n
347  GLM_SWIZZLE_GEN_VEC2_ENTRY(T, P, const, D, D)
\n
348 
\n
349 #define GLM_SWIZZLE_GEN_VEC3_FROM_VEC4_SWIZZLE(T, P, A, B, C, D) \\
\n
350  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, A, A, A) \\
\n
351  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, A, A, B) \\
\n
352  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, A, A, C) \\
\n
353  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, A, A, D) \\
\n
354  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, A, B, A) \\
\n
355  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, A, B, B) \\
\n
356  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, A, B, C) \\
\n
357  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, A, B, D) \\
\n
358  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, A, C, A) \\
\n
359  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, A, C, B) \\
\n
360  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, A, C, C) \\
\n
361  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, A, C, D) \\
\n
362  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, A, D, A) \\
\n
363  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, A, D, B) \\
\n
364  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, A, D, C) \\
\n
365  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, A, D, D) \\
\n
366  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, B, A, A) \\
\n
367  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, B, A, B) \\
\n
368  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, B, A, C) \\
\n
369  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, B, A, D) \\
\n
370  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, B, B, A) \\
\n
371  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, B, B, B) \\
\n
372  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, B, B, C) \\
\n
373  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, B, B, D) \\
\n
374  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, B, C, A) \\
\n
375  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, B, C, B) \\
\n
376  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, B, C, C) \\
\n
377  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, B, C, D) \\
\n
378  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, B, D, A) \\
\n
379  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, B, D, B) \\
\n
380  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, B, D, C) \\
\n
381  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, B, D, D) \\
\n
382  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, C, A, A) \\
\n
383  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, C, A, B) \\
\n
384  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, C, A, C) \\
\n
385  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, C, A, D) \\
\n
386  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, C, B, A) \\
\n
387  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, C, B, B) \\
\n
388  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, C, B, C) \\
\n
389  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, C, B, D) \\
\n
390  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, C, C, A) \\
\n
391  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, C, C, B) \\
\n
392  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, C, C, C) \\
\n
393  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, C, C, D) \\
\n
394  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, C, D, A) \\
\n
395  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, C, D, B) \\
\n
396  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, C, D, C) \\
\n
397  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, C, D, D) \\
\n
398  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, D, A, A) \\
\n
399  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, D, A, B) \\
\n
400  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, D, A, C) \\
\n
401  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, D, A, D) \\
\n
402  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, D, B, A) \\
\n
403  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, D, B, B) \\
\n
404  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, D, B, C) \\
\n
405  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, D, B, D) \\
\n
406  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, D, C, A) \\
\n
407  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, D, C, B) \\
\n
408  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, D, C, C) \\
\n
409  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, D, C, D) \\
\n
410  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, D, D, A) \\
\n
411  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, D, D, B) \\
\n
412  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, D, D, C) \\
\n
413  GLM_SWIZZLE_GEN_VEC3_ENTRY(T, P, const, D, D, D)
\n
414 
\n
415 #define GLM_SWIZZLE_GEN_VEC4_FROM_VEC4_SWIZZLE(T, P, A, B, C, D) \\
\n
416  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, A, A, A) \\
\n
417  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, A, A, B) \\
\n
418  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, A, A, C) \\
\n
419  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, A, A, D) \\
\n
420  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, A, B, A) \\
\n
421  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, A, B, B) \\
\n
422  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, A, B, C) \\
\n
423  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, A, B, D) \\
\n
424  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, A, C, A) \\
\n
425  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, A, C, B) \\
\n
426  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, A, C, C) \\
\n
427  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, A, C, D) \\
\n
428  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, A, D, A) \\
\n
429  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, A, D, B) \\
\n
430  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, A, D, C) \\
\n
431  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, A, D, D) \\
\n
432  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, B, A, A) \\
\n
433  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, B, A, B) \\
\n
434  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, B, A, C) \\
\n
435  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, B, A, D) \\
\n
436  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, B, B, A) \\
\n
437  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, B, B, B) \\
\n
438  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, B, B, C) \\
\n
439  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, B, B, D) \\
\n
440  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, B, C, A) \\
\n
441  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, B, C, B) \\
\n
442  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, B, C, C) \\
\n
443  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, B, C, D) \\
\n
444  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, B, D, A) \\
\n
445  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, B, D, B) \\
\n
446  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, B, D, C) \\
\n
447  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, B, D, D) \\
\n
448  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, C, A, A) \\
\n
449  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, C, A, B) \\
\n
450  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, C, A, C) \\
\n
451  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, C, A, D) \\
\n
452  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, C, B, A) \\
\n
453  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, C, B, B) \\
\n
454  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, C, B, C) \\
\n
455  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, C, B, D) \\
\n
456  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, C, C, A) \\
\n
457  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, C, C, B) \\
\n
458  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, C, C, C) \\
\n
459  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, C, C, D) \\
\n
460  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, C, D, A) \\
\n
461  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, C, D, B) \\
\n
462  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, C, D, C) \\
\n
463  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, C, D, D) \\
\n
464  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, D, A, A) \\
\n
465  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, D, A, B) \\
\n
466  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, D, A, C) \\
\n
467  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, D, A, D) \\
\n
468  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, D, B, A) \\
\n
469  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, D, B, B) \\
\n
470  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, D, B, C) \\
\n
471  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, D, B, D) \\
\n
472  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, D, C, A) \\
\n
473  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, D, C, B) \\
\n
474  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, D, C, C) \\
\n
475  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, D, C, D) \\
\n
476  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, D, D, A) \\
\n
477  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, D, D, B) \\
\n
478  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, D, D, C) \\
\n
479  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, A, D, D, D) \\
\n
480  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, A, A, A) \\
\n
481  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, A, A, B) \\
\n
482  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, A, A, C) \\
\n
483  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, A, A, D) \\
\n
484  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, A, B, A) \\
\n
485  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, A, B, B) \\
\n
486  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, A, B, C) \\
\n
487  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, A, B, D) \\
\n
488  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, A, C, A) \\
\n
489  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, A, C, B) \\
\n
490  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, A, C, C) \\
\n
491  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, A, C, D) \\
\n
492  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, A, D, A) \\
\n
493  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, A, D, B) \\
\n
494  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, A, D, C) \\
\n
495  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, A, D, D) \\
\n
496  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, B, A, A) \\
\n
497  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, B, A, B) \\
\n
498  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, B, A, C) \\
\n
499  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, B, A, D) \\
\n
500  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, B, B, A) \\
\n
501  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, B, B, B) \\
\n
502  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, B, B, C) \\
\n
503  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, B, B, D) \\
\n
504  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, B, C, A) \\
\n
505  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, B, C, B) \\
\n
506  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, B, C, C) \\
\n
507  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, B, C, D) \\
\n
508  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, B, D, A) \\
\n
509  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, B, D, B) \\
\n
510  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, B, D, C) \\
\n
511  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, B, D, D) \\
\n
512  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, C, A, A) \\
\n
513  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, C, A, B) \\
\n
514  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, C, A, C) \\
\n
515  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, C, A, D) \\
\n
516  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, C, B, A) \\
\n
517  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, C, B, B) \\
\n
518  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, C, B, C) \\
\n
519  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, C, B, D) \\
\n
520  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, C, C, A) \\
\n
521  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, C, C, B) \\
\n
522  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, C, C, C) \\
\n
523  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, C, C, D) \\
\n
524  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, C, D, A) \\
\n
525  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, C, D, B) \\
\n
526  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, C, D, C) \\
\n
527  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, C, D, D) \\
\n
528  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, D, A, A) \\
\n
529  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, D, A, B) \\
\n
530  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, D, A, C) \\
\n
531  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, D, A, D) \\
\n
532  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, D, B, A) \\
\n
533  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, D, B, B) \\
\n
534  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, D, B, C) \\
\n
535  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, D, B, D) \\
\n
536  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, D, C, A) \\
\n
537  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, D, C, B) \\
\n
538  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, D, C, C) \\
\n
539  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, D, C, D) \\
\n
540  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, D, D, A) \\
\n
541  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, D, D, B) \\
\n
542  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, D, D, C) \\
\n
543  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, B, D, D, D) \\
\n
544  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, A, A, A) \\
\n
545  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, A, A, B) \\
\n
546  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, A, A, C) \\
\n
547  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, A, A, D) \\
\n
548  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, A, B, A) \\
\n
549  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, A, B, B) \\
\n
550  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, A, B, C) \\
\n
551  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, A, B, D) \\
\n
552  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, A, C, A) \\
\n
553  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, A, C, B) \\
\n
554  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, A, C, C) \\
\n
555  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, A, C, D) \\
\n
556  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, A, D, A) \\
\n
557  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, A, D, B) \\
\n
558  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, A, D, C) \\
\n
559  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, A, D, D) \\
\n
560  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, B, A, A) \\
\n
561  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, B, A, B) \\
\n
562  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, B, A, C) \\
\n
563  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, B, A, D) \\
\n
564  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, B, B, A) \\
\n
565  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, B, B, B) \\
\n
566  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, B, B, C) \\
\n
567  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, B, B, D) \\
\n
568  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, B, C, A) \\
\n
569  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, B, C, B) \\
\n
570  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, B, C, C) \\
\n
571  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, B, C, D) \\
\n
572  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, B, D, A) \\
\n
573  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, B, D, B) \\
\n
574  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, B, D, C) \\
\n
575  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, B, D, D) \\
\n
576  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, C, A, A) \\
\n
577  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, C, A, B) \\
\n
578  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, C, A, C) \\
\n
579  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, C, A, D) \\
\n
580  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, C, B, A) \\
\n
581  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, C, B, B) \\
\n
582  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, C, B, C) \\
\n
583  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, C, B, D) \\
\n
584  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, C, C, A) \\
\n
585  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, C, C, B) \\
\n
586  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, C, C, C) \\
\n
587  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, C, C, D) \\
\n
588  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, C, D, A) \\
\n
589  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, C, D, B) \\
\n
590  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, C, D, C) \\
\n
591  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, C, D, D) \\
\n
592  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, D, A, A) \\
\n
593  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, D, A, B) \\
\n
594  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, D, A, C) \\
\n
595  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, D, A, D) \\
\n
596  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, D, B, A) \\
\n
597  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, D, B, B) \\
\n
598  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, D, B, C) \\
\n
599  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, D, B, D) \\
\n
600  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, D, C, A) \\
\n
601  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, D, C, B) \\
\n
602  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, D, C, C) \\
\n
603  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, D, C, D) \\
\n
604  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, D, D, A) \\
\n
605  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, D, D, B) \\
\n
606  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, D, D, C) \\
\n
607  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, C, D, D, D) \\
\n
608  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, D, A, A, A) \\
\n
609  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, D, A, A, B) \\
\n
610  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, D, A, A, C) \\
\n
611  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, D, A, A, D) \\
\n
612  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, D, A, B, A) \\
\n
613  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, D, A, B, B) \\
\n
614  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, D, A, B, C) \\
\n
615  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, D, A, B, D) \\
\n
616  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, D, A, C, A) \\
\n
617  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, D, A, C, B) \\
\n
618  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, D, A, C, C) \\
\n
619  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, D, A, C, D) \\
\n
620  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, D, A, D, A) \\
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621  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, D, A, D, B) \\
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622  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, D, A, D, C) \\
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623  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, D, A, D, D) \\
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624  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, D, B, A, A) \\
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625  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, D, B, A, B) \\
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626  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, D, B, A, C) \\
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627  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, D, B, A, D) \\
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628  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, D, B, B, A) \\
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629  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, D, B, B, B) \\
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630  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, D, B, B, C) \\
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631  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, D, B, B, D) \\
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632  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, D, B, C, A) \\
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633  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, D, B, C, B) \\
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634  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, D, B, C, C) \\
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635  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, D, B, C, D) \\
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636  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, D, B, D, A) \\
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637  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, D, B, D, B) \\
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638  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, D, B, D, C) \\
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639  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, D, B, D, D) \\
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640  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, D, C, A, A) \\
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641  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, D, C, A, B) \\
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642  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, D, C, A, C) \\
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643  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, D, C, A, D) \\
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644  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, D, C, B, A) \\
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645  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, D, C, B, B) \\
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646  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, D, C, B, C) \\
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647  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, D, C, B, D) \\
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648  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, D, C, C, A) \\
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649  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, D, C, C, B) \\
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650  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, D, C, C, C) \\
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651  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, D, C, C, D) \\
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652  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, D, C, D, A) \\
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653  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, D, C, D, B) \\
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654  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, D, C, D, C) \\
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655  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, D, C, D, D) \\
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656  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, D, D, A, A) \\
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657  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, D, D, A, B) \\
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658  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, D, D, A, C) \\
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659  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, D, D, A, D) \\
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660  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, D, D, B, A) \\
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661  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, D, D, B, B) \\
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662  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, D, D, B, C) \\
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663  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, D, D, B, D) \\
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664  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, D, D, C, A) \\
\n
665  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, D, D, C, B) \\
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666  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, D, D, C, C) \\
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667  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, D, D, C, D) \\
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668  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, D, D, D, A) \\
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669  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, D, D, D, B) \\
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670  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, D, D, D, C) \\
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671  GLM_SWIZZLE_GEN_VEC4_ENTRY(T, P, const, D, D, D, D)
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672 
\n
673 #define GLM_SWIZZLE_GEN_VEC_FROM_VEC4_COMP(T, P, A, B, C, D) \\
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674  GLM_SWIZZLE_GEN_VEC2_FROM_VEC4_SWIZZLE(T, P, A, B, C, D) \\
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675  GLM_SWIZZLE_GEN_VEC3_FROM_VEC4_SWIZZLE(T, P, A, B, C, D) \\
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676  GLM_SWIZZLE_GEN_VEC4_FROM_VEC4_SWIZZLE(T, P, A, B, C, D)
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677 
\n
678 #define GLM_SWIZZLE_GEN_VEC_FROM_VEC4(T, P) \\
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679  GLM_SWIZZLE_GEN_VEC_FROM_VEC4_COMP(T, P, x, y, z, w) \\
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680  GLM_SWIZZLE_GEN_VEC_FROM_VEC4_COMP(T, P, r, g, b, a) \\
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681  GLM_SWIZZLE_GEN_VEC_FROM_VEC4_COMP(T, P, s, t, p, q)
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682 
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_vectorize.hpp
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1 #pragma once
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2 
\n
3 namespace glm{
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4 namespace detail
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5 {
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6  template<template<length_t L, typename T, qualifier Q> class vec, length_t L, typename R, typename T, qualifier Q>
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7  struct functor1{};
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8 
\n
9  template<template<length_t L, typename T, qualifier Q> class vec, typename R, typename T, qualifier Q>
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10  struct functor1<vec, 1, R, T, Q>
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11  {
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12  GLM_FUNC_QUALIFIER GLM_CONSTEXPR static vec<1, R, Q> call(R (*Func) (T x), vec<1, T, Q> const& v)
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13  {
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14  return vec<1, R, Q>(Func(v.x));
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15  }
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16  };
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17 
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18  template<template<length_t L, typename T, qualifier Q> class vec, typename R, typename T, qualifier Q>
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19  struct functor1<vec, 2, R, T, Q>
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20  {
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21  GLM_FUNC_QUALIFIER GLM_CONSTEXPR static vec<2, R, Q> call(R (*Func) (T x), vec<2, T, Q> const& v)
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22  {
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23  return vec<2, R, Q>(Func(v.x), Func(v.y));
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24  }
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25  };
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26 
\n
27  template<template<length_t L, typename T, qualifier Q> class vec, typename R, typename T, qualifier Q>
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28  struct functor1<vec, 3, R, T, Q>
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29  {
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30  GLM_FUNC_QUALIFIER GLM_CONSTEXPR static vec<3, R, Q> call(R (*Func) (T x), vec<3, T, Q> const& v)
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31  {
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32  return vec<3, R, Q>(Func(v.x), Func(v.y), Func(v.z));
\n
33  }
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34  };
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35 
\n
36  template<template<length_t L, typename T, qualifier Q> class vec, typename R, typename T, qualifier Q>
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37  struct functor1<vec, 4, R, T, Q>
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38  {
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39  GLM_FUNC_QUALIFIER GLM_CONSTEXPR static vec<4, R, Q> call(R (*Func) (T x), vec<4, T, Q> const& v)
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40  {
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41  return vec<4, R, Q>(Func(v.x), Func(v.y), Func(v.z), Func(v.w));
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42  }
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43  };
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44 
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45  template<template<length_t L, typename T, qualifier Q> class vec, length_t L, typename T, qualifier Q>
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46  struct functor2{};
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47 
\n
48  template<template<length_t L, typename T, qualifier Q> class vec, typename T, qualifier Q>
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49  struct functor2<vec, 1, T, Q>
\n
50  {
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51  GLM_FUNC_QUALIFIER static vec<1, T, Q> call(T (*Func) (T x, T y), vec<1, T, Q> const& a, vec<1, T, Q> const& b)
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52  {
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53  return vec<1, T, Q>(Func(a.x, b.x));
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54  }
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55  };
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56 
\n
57  template<template<length_t L, typename T, qualifier Q> class vec, typename T, qualifier Q>
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58  struct functor2<vec, 2, T, Q>
\n
59  {
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60  GLM_FUNC_QUALIFIER static vec<2, T, Q> call(T (*Func) (T x, T y), vec<2, T, Q> const& a, vec<2, T, Q> const& b)
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61  {
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62  return vec<2, T, Q>(Func(a.x, b.x), Func(a.y, b.y));
\n
63  }
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64  };
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65 
\n
66  template<template<length_t L, typename T, qualifier Q> class vec, typename T, qualifier Q>
\n
67  struct functor2<vec, 3, T, Q>
\n
68  {
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69  GLM_FUNC_QUALIFIER static vec<3, T, Q> call(T (*Func) (T x, T y), vec<3, T, Q> const& a, vec<3, T, Q> const& b)
\n
70  {
\n
71  return vec<3, T, Q>(Func(a.x, b.x), Func(a.y, b.y), Func(a.z, b.z));
\n
72  }
\n
73  };
\n
74 
\n
75  template<template<length_t L, typename T, qualifier Q> class vec, typename T, qualifier Q>
\n
76  struct functor2<vec, 4, T, Q>
\n
77  {
\n
78  GLM_FUNC_QUALIFIER static vec<4, T, Q> call(T (*Func) (T x, T y), vec<4, T, Q> const& a, vec<4, T, Q> const& b)
\n
79  {
\n
80  return vec<4, T, Q>(Func(a.x, b.x), Func(a.y, b.y), Func(a.z, b.z), Func(a.w, b.w));
\n
81  }
\n
82  };
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83 
\n
84  template<template<length_t L, typename T, qualifier Q> class vec, length_t L, typename T, qualifier Q>
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85  struct functor2_vec_sca{};
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86 
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87  template<template<length_t L, typename T, qualifier Q> class vec, typename T, qualifier Q>
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88  struct functor2_vec_sca<vec, 1, T, Q>
\n
89  {
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90  GLM_FUNC_QUALIFIER static vec<1, T, Q> call(T (*Func) (T x, T y), vec<1, T, Q> const& a, T b)
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91  {
\n
92  return vec<1, T, Q>(Func(a.x, b));
\n
93  }
\n
94  };
\n
95 
\n
96  template<template<length_t L, typename T, qualifier Q> class vec, typename T, qualifier Q>
\n
97  struct functor2_vec_sca<vec, 2, T, Q>
\n
98  {
\n
99  GLM_FUNC_QUALIFIER static vec<2, T, Q> call(T (*Func) (T x, T y), vec<2, T, Q> const& a, T b)
\n
100  {
\n
101  return vec<2, T, Q>(Func(a.x, b), Func(a.y, b));
\n
102  }
\n
103  };
\n
104 
\n
105  template<template<length_t L, typename T, qualifier Q> class vec, typename T, qualifier Q>
\n
106  struct functor2_vec_sca<vec, 3, T, Q>
\n
107  {
\n
108  GLM_FUNC_QUALIFIER static vec<3, T, Q> call(T (*Func) (T x, T y), vec<3, T, Q> const& a, T b)
\n
109  {
\n
110  return vec<3, T, Q>(Func(a.x, b), Func(a.y, b), Func(a.z, b));
\n
111  }
\n
112  };
\n
113 
\n
114  template<template<length_t L, typename T, qualifier Q> class vec, typename T, qualifier Q>
\n
115  struct functor2_vec_sca<vec, 4, T, Q>
\n
116  {
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117  GLM_FUNC_QUALIFIER static vec<4, T, Q> call(T (*Func) (T x, T y), vec<4, T, Q> const& a, T b)
\n
118  {
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119  return vec<4, T, Q>(Func(a.x, b), Func(a.y, b), Func(a.z, b), Func(a.w, b));
\n
120  }
\n
121  };
\n
122 
\n
123  template<length_t L, typename T, qualifier Q>
\n
124  struct functor2_vec_int {};
\n
125 
\n
126  template<typename T, qualifier Q>
\n
127  struct functor2_vec_int<1, T, Q>
\n
128  {
\n
129  GLM_FUNC_QUALIFIER static vec<1, int, Q> call(int (*Func) (T x, int y), vec<1, T, Q> const& a, vec<1, int, Q> const& b)
\n
130  {
\n
131  return vec<1, int, Q>(Func(a.x, b.x));
\n
132  }
\n
133  };
\n
134 
\n
135  template<typename T, qualifier Q>
\n
136  struct functor2_vec_int<2, T, Q>
\n
137  {
\n
138  GLM_FUNC_QUALIFIER static vec<2, int, Q> call(int (*Func) (T x, int y), vec<2, T, Q> const& a, vec<2, int, Q> const& b)
\n
139  {
\n
140  return vec<2, int, Q>(Func(a.x, b.x), Func(a.y, b.y));
\n
141  }
\n
142  };
\n
143 
\n
144  template<typename T, qualifier Q>
\n
145  struct functor2_vec_int<3, T, Q>
\n
146  {
\n
147  GLM_FUNC_QUALIFIER static vec<3, int, Q> call(int (*Func) (T x, int y), vec<3, T, Q> const& a, vec<3, int, Q> const& b)
\n
148  {
\n
149  return vec<3, int, Q>(Func(a.x, b.x), Func(a.y, b.y), Func(a.z, b.z));
\n
150  }
\n
151  };
\n
152 
\n
153  template<typename T, qualifier Q>
\n
154  struct functor2_vec_int<4, T, Q>
\n
155  {
\n
156  GLM_FUNC_QUALIFIER static vec<4, int, Q> call(int (*Func) (T x, int y), vec<4, T, Q> const& a, vec<4, int, Q> const& b)
\n
157  {
\n
158  return vec<4, int, Q>(Func(a.x, b.x), Func(a.y, b.y), Func(a.z, b.z), Func(a.w, b.w));
\n
159  }
\n
160  };
\n
161 }//namespace detail
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162 }//namespace glm
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glm
Definition: common.hpp:20
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\nFunctions
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associated_min_max.hpp File Reference
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GLM_GTX_associated_min_max \nMore...

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Go to the source code of this file.

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\nFunctions

template<typename T , typename U >
GLM_FUNC_DECL U associatedMax (T x, U a, T y, U b)
 Maximum comparison between 2 variables and returns 2 associated variable values. More...
 
template<length_t L, typename T , typename U , qualifier Q>
GLM_FUNC_DECL vec< 2, U, Q > associatedMax (vec< L, T, Q > const &x, vec< L, U, Q > const &a, vec< L, T, Q > const &y, vec< L, U, Q > const &b)
 Maximum comparison between 2 variables and returns 2 associated variable values. More...
 
template<length_t L, typename T , typename U , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > associatedMax (T x, vec< L, U, Q > const &a, T y, vec< L, U, Q > const &b)
 Maximum comparison between 2 variables and returns 2 associated variable values. More...
 
template<length_t L, typename T , typename U , qualifier Q>
GLM_FUNC_DECL vec< L, U, Q > associatedMax (vec< L, T, Q > const &x, U a, vec< L, T, Q > const &y, U b)
 Maximum comparison between 2 variables and returns 2 associated variable values. More...
 
template<typename T , typename U >
GLM_FUNC_DECL U associatedMax (T x, U a, T y, U b, T z, U c)
 Maximum comparison between 3 variables and returns 3 associated variable values. More...
 
template<length_t L, typename T , typename U , qualifier Q>
GLM_FUNC_DECL vec< L, U, Q > associatedMax (vec< L, T, Q > const &x, vec< L, U, Q > const &a, vec< L, T, Q > const &y, vec< L, U, Q > const &b, vec< L, T, Q > const &z, vec< L, U, Q > const &c)
 Maximum comparison between 3 variables and returns 3 associated variable values. More...
 
template<length_t L, typename T , typename U , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > associatedMax (T x, vec< L, U, Q > const &a, T y, vec< L, U, Q > const &b, T z, vec< L, U, Q > const &c)
 Maximum comparison between 3 variables and returns 3 associated variable values. More...
 
template<length_t L, typename T , typename U , qualifier Q>
GLM_FUNC_DECL vec< L, U, Q > associatedMax (vec< L, T, Q > const &x, U a, vec< L, T, Q > const &y, U b, vec< L, T, Q > const &z, U c)
 Maximum comparison between 3 variables and returns 3 associated variable values. More...
 
template<typename T , typename U >
GLM_FUNC_DECL U associatedMax (T x, U a, T y, U b, T z, U c, T w, U d)
 Maximum comparison between 4 variables and returns 4 associated variable values. More...
 
template<length_t L, typename T , typename U , qualifier Q>
GLM_FUNC_DECL vec< L, U, Q > associatedMax (vec< L, T, Q > const &x, vec< L, U, Q > const &a, vec< L, T, Q > const &y, vec< L, U, Q > const &b, vec< L, T, Q > const &z, vec< L, U, Q > const &c, vec< L, T, Q > const &w, vec< L, U, Q > const &d)
 Maximum comparison between 4 variables and returns 4 associated variable values. More...
 
template<length_t L, typename T , typename U , qualifier Q>
GLM_FUNC_DECL vec< L, U, Q > associatedMax (T x, vec< L, U, Q > const &a, T y, vec< L, U, Q > const &b, T z, vec< L, U, Q > const &c, T w, vec< L, U, Q > const &d)
 Maximum comparison between 4 variables and returns 4 associated variable values. More...
 
template<length_t L, typename T , typename U , qualifier Q>
GLM_FUNC_DECL vec< L, U, Q > associatedMax (vec< L, T, Q > const &x, U a, vec< L, T, Q > const &y, U b, vec< L, T, Q > const &z, U c, vec< L, T, Q > const &w, U d)
 Maximum comparison between 4 variables and returns 4 associated variable values. More...
 
template<typename T , typename U , qualifier Q>
GLM_FUNC_DECL U associatedMin (T x, U a, T y, U b)
 Minimum comparison between 2 variables and returns 2 associated variable values. More...
 
template<length_t L, typename T , typename U , qualifier Q>
GLM_FUNC_DECL vec< 2, U, Q > associatedMin (vec< L, T, Q > const &x, vec< L, U, Q > const &a, vec< L, T, Q > const &y, vec< L, U, Q > const &b)
 Minimum comparison between 2 variables and returns 2 associated variable values. More...
 
template<length_t L, typename T , typename U , qualifier Q>
GLM_FUNC_DECL vec< L, U, Q > associatedMin (T x, const vec< L, U, Q > &a, T y, const vec< L, U, Q > &b)
 Minimum comparison between 2 variables and returns 2 associated variable values. More...
 
template<length_t L, typename T , typename U , qualifier Q>
GLM_FUNC_DECL vec< L, U, Q > associatedMin (vec< L, T, Q > const &x, U a, vec< L, T, Q > const &y, U b)
 Minimum comparison between 2 variables and returns 2 associated variable values. More...
 
template<typename T , typename U >
GLM_FUNC_DECL U associatedMin (T x, U a, T y, U b, T z, U c)
 Minimum comparison between 3 variables and returns 3 associated variable values. More...
 
template<length_t L, typename T , typename U , qualifier Q>
GLM_FUNC_DECL vec< L, U, Q > associatedMin (vec< L, T, Q > const &x, vec< L, U, Q > const &a, vec< L, T, Q > const &y, vec< L, U, Q > const &b, vec< L, T, Q > const &z, vec< L, U, Q > const &c)
 Minimum comparison between 3 variables and returns 3 associated variable values. More...
 
template<typename T , typename U >
GLM_FUNC_DECL U associatedMin (T x, U a, T y, U b, T z, U c, T w, U d)
 Minimum comparison between 4 variables and returns 4 associated variable values. More...
 
template<length_t L, typename T , typename U , qualifier Q>
GLM_FUNC_DECL vec< L, U, Q > associatedMin (vec< L, T, Q > const &x, vec< L, U, Q > const &a, vec< L, T, Q > const &y, vec< L, U, Q > const &b, vec< L, T, Q > const &z, vec< L, U, Q > const &c, vec< L, T, Q > const &w, vec< L, U, Q > const &d)
 Minimum comparison between 4 variables and returns 4 associated variable values. More...
 
template<length_t L, typename T , typename U , qualifier Q>
GLM_FUNC_DECL vec< L, U, Q > associatedMin (T x, vec< L, U, Q > const &a, T y, vec< L, U, Q > const &b, T z, vec< L, U, Q > const &c, T w, vec< L, U, Q > const &d)
 Minimum comparison between 4 variables and returns 4 associated variable values. More...
 
template<length_t L, typename T , typename U , qualifier Q>
GLM_FUNC_DECL vec< L, U, Q > associatedMin (vec< L, T, Q > const &x, U a, vec< L, T, Q > const &y, U b, vec< L, T, Q > const &z, U c, vec< L, T, Q > const &w, U d)
 Minimum comparison between 4 variables and returns 4 associated variable values. More...
 
\n

Detailed Description

\n

GLM_GTX_associated_min_max

\n
See also
Core features (dependence)
\n
\ngtx_extented_min_max (dependence)
\n\n

Definition in file associated_min_max.hpp.

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associated_min_max.hpp
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1 
\n
14 #pragma once
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15 
\n
16 // Dependency:
\n
17 #include "../glm.hpp"
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18 
\n
19 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
20 # ifndef GLM_ENABLE_EXPERIMENTAL
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21 # pragma message("GLM: GLM_GTX_associated_min_max is an experimental extension and may change in the future. Use #define GLM_ENABLE_EXPERIMENTAL before including it, if you really want to use it.")
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22 # else
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23 # pragma message("GLM: GLM_GTX_associated_min_max extension included")
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24 # endif
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25 #endif
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26 
\n
27 namespace glm
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28 {
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31 
\n
34  template<typename T, typename U, qualifier Q>
\n
35  GLM_FUNC_DECL U associatedMin(T x, U a, T y, U b);
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36 
\n
39  template<length_t L, typename T, typename U, qualifier Q>
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40  GLM_FUNC_DECL vec<2, U, Q> associatedMin(
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41  vec<L, T, Q> const& x, vec<L, U, Q> const& a,
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42  vec<L, T, Q> const& y, vec<L, U, Q> const& b);
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43 
\n
46  template<length_t L, typename T, typename U, qualifier Q>
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47  GLM_FUNC_DECL vec<L, U, Q> associatedMin(
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48  T x, const vec<L, U, Q>& a,
\n
49  T y, const vec<L, U, Q>& b);
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50 
\n
53  template<length_t L, typename T, typename U, qualifier Q>
\n
54  GLM_FUNC_DECL vec<L, U, Q> associatedMin(
\n
55  vec<L, T, Q> const& x, U a,
\n
56  vec<L, T, Q> const& y, U b);
\n
57 
\n
60  template<typename T, typename U>
\n
61  GLM_FUNC_DECL U associatedMin(
\n
62  T x, U a,
\n
63  T y, U b,
\n
64  T z, U c);
\n
65 
\n
68  template<length_t L, typename T, typename U, qualifier Q>
\n
69  GLM_FUNC_DECL vec<L, U, Q> associatedMin(
\n
70  vec<L, T, Q> const& x, vec<L, U, Q> const& a,
\n
71  vec<L, T, Q> const& y, vec<L, U, Q> const& b,
\n
72  vec<L, T, Q> const& z, vec<L, U, Q> const& c);
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73 
\n
76  template<typename T, typename U>
\n
77  GLM_FUNC_DECL U associatedMin(
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78  T x, U a,
\n
79  T y, U b,
\n
80  T z, U c,
\n
81  T w, U d);
\n
82 
\n
85  template<length_t L, typename T, typename U, qualifier Q>
\n
86  GLM_FUNC_DECL vec<L, U, Q> associatedMin(
\n
87  vec<L, T, Q> const& x, vec<L, U, Q> const& a,
\n
88  vec<L, T, Q> const& y, vec<L, U, Q> const& b,
\n
89  vec<L, T, Q> const& z, vec<L, U, Q> const& c,
\n
90  vec<L, T, Q> const& w, vec<L, U, Q> const& d);
\n
91 
\n
94  template<length_t L, typename T, typename U, qualifier Q>
\n
95  GLM_FUNC_DECL vec<L, U, Q> associatedMin(
\n
96  T x, vec<L, U, Q> const& a,
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97  T y, vec<L, U, Q> const& b,
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98  T z, vec<L, U, Q> const& c,
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99  T w, vec<L, U, Q> const& d);
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100 
\n
103  template<length_t L, typename T, typename U, qualifier Q>
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104  GLM_FUNC_DECL vec<L, U, Q> associatedMin(
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105  vec<L, T, Q> const& x, U a,
\n
106  vec<L, T, Q> const& y, U b,
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107  vec<L, T, Q> const& z, U c,
\n
108  vec<L, T, Q> const& w, U d);
\n
109 
\n
112  template<typename T, typename U>
\n
113  GLM_FUNC_DECL U associatedMax(T x, U a, T y, U b);
\n
114 
\n
117  template<length_t L, typename T, typename U, qualifier Q>
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118  GLM_FUNC_DECL vec<2, U, Q> associatedMax(
\n
119  vec<L, T, Q> const& x, vec<L, U, Q> const& a,
\n
120  vec<L, T, Q> const& y, vec<L, U, Q> const& b);
\n
121 
\n
124  template<length_t L, typename T, typename U, qualifier Q>
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125  GLM_FUNC_DECL vec<L, T, Q> associatedMax(
\n
126  T x, vec<L, U, Q> const& a,
\n
127  T y, vec<L, U, Q> const& b);
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128 
\n
131  template<length_t L, typename T, typename U, qualifier Q>
\n
132  GLM_FUNC_DECL vec<L, U, Q> associatedMax(
\n
133  vec<L, T, Q> const& x, U a,
\n
134  vec<L, T, Q> const& y, U b);
\n
135 
\n
138  template<typename T, typename U>
\n
139  GLM_FUNC_DECL U associatedMax(
\n
140  T x, U a,
\n
141  T y, U b,
\n
142  T z, U c);
\n
143 
\n
146  template<length_t L, typename T, typename U, qualifier Q>
\n
147  GLM_FUNC_DECL vec<L, U, Q> associatedMax(
\n
148  vec<L, T, Q> const& x, vec<L, U, Q> const& a,
\n
149  vec<L, T, Q> const& y, vec<L, U, Q> const& b,
\n
150  vec<L, T, Q> const& z, vec<L, U, Q> const& c);
\n
151 
\n
154  template<length_t L, typename T, typename U, qualifier Q>
\n
155  GLM_FUNC_DECL vec<L, T, Q> associatedMax(
\n
156  T x, vec<L, U, Q> const& a,
\n
157  T y, vec<L, U, Q> const& b,
\n
158  T z, vec<L, U, Q> const& c);
\n
159 
\n
162  template<length_t L, typename T, typename U, qualifier Q>
\n
163  GLM_FUNC_DECL vec<L, U, Q> associatedMax(
\n
164  vec<L, T, Q> const& x, U a,
\n
165  vec<L, T, Q> const& y, U b,
\n
166  vec<L, T, Q> const& z, U c);
\n
167 
\n
170  template<typename T, typename U>
\n
171  GLM_FUNC_DECL U associatedMax(
\n
172  T x, U a,
\n
173  T y, U b,
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174  T z, U c,
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175  T w, U d);
\n
176 
\n
179  template<length_t L, typename T, typename U, qualifier Q>
\n
180  GLM_FUNC_DECL vec<L, U, Q> associatedMax(
\n
181  vec<L, T, Q> const& x, vec<L, U, Q> const& a,
\n
182  vec<L, T, Q> const& y, vec<L, U, Q> const& b,
\n
183  vec<L, T, Q> const& z, vec<L, U, Q> const& c,
\n
184  vec<L, T, Q> const& w, vec<L, U, Q> const& d);
\n
185 
\n
188  template<length_t L, typename T, typename U, qualifier Q>
\n
189  GLM_FUNC_DECL vec<L, U, Q> associatedMax(
\n
190  T x, vec<L, U, Q> const& a,
\n
191  T y, vec<L, U, Q> const& b,
\n
192  T z, vec<L, U, Q> const& c,
\n
193  T w, vec<L, U, Q> const& d);
\n
194 
\n
197  template<length_t L, typename T, typename U, qualifier Q>
\n
198  GLM_FUNC_DECL vec<L, U, Q> associatedMax(
\n
199  vec<L, T, Q> const& x, U a,
\n
200  vec<L, T, Q> const& y, U b,
\n
201  vec<L, T, Q> const& z, U c,
\n
202  vec<L, T, Q> const& w, U d);
\n
203 
\n
205 } //namespace glm
\n
206 
\n
207 #include "associated_min_max.inl"
\n
glm::associatedMax
GLM_FUNC_DECL vec< L, U, Q > associatedMax(vec< L, T, Q > const &x, U a, vec< L, T, Q > const &y, U b, vec< L, T, Q > const &z, U c, vec< L, T, Q > const &w, U d)
Maximum comparison between 4 variables and returns 4 associated variable values.
\n
glm::associatedMin
GLM_FUNC_DECL vec< L, U, Q > associatedMin(vec< L, T, Q > const &x, U a, vec< L, T, Q > const &y, U b, vec< L, T, Q > const &z, U c, vec< L, T, Q > const &w, U d)
Minimum comparison between 4 variables and returns 4 associated variable values.
\n
glm
Definition: common.hpp:20
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bit.hpp File Reference
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GLM_GTX_bit \nMore...

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\nFunctions

template<typename genIUType >
GLM_FUNC_DECL genIUType highestBitValue (genIUType Value)
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > highestBitValue (vec< L, T, Q > const &value)
 Find the highest bit set to 1 in a integer variable and return its value. More...
 
template<typename genIUType >
GLM_FUNC_DECL genIUType lowestBitValue (genIUType Value)
 
template<typename genIUType >
GLM_DEPRECATED GLM_FUNC_DECL genIUType powerOfTwoAbove (genIUType Value)
 Return the power of two number which value is just higher the input value. More...
 
template<length_t L, typename T , qualifier Q>
GLM_DEPRECATED GLM_FUNC_DECL vec< L, T, Q > powerOfTwoAbove (vec< L, T, Q > const &value)
 Return the power of two number which value is just higher the input value. More...
 
template<typename genIUType >
GLM_DEPRECATED GLM_FUNC_DECL genIUType powerOfTwoBelow (genIUType Value)
 Return the power of two number which value is just lower the input value. More...
 
template<length_t L, typename T , qualifier Q>
GLM_DEPRECATED GLM_FUNC_DECL vec< L, T, Q > powerOfTwoBelow (vec< L, T, Q > const &value)
 Return the power of two number which value is just lower the input value. More...
 
template<typename genIUType >
GLM_DEPRECATED GLM_FUNC_DECL genIUType powerOfTwoNearest (genIUType Value)
 Return the power of two number which value is the closet to the input value. More...
 
template<length_t L, typename T , qualifier Q>
GLM_DEPRECATED GLM_FUNC_DECL vec< L, T, Q > powerOfTwoNearest (vec< L, T, Q > const &value)
 Return the power of two number which value is the closet to the input value. More...
 
\n

Detailed Description

\n

GLM_GTX_bit

\n
See also
Core features (dependence)
\n\n

Definition in file bit.hpp.

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bit.hpp
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1 
\n
13 #pragma once
\n
14 
\n
15 // Dependencies
\n
16 #include "../gtc/bitfield.hpp"
\n
17 
\n
18 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
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19 # ifndef GLM_ENABLE_EXPERIMENTAL
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20 # pragma message("GLM: GLM_GTX_bit is an experimental extension and may change in the future. Use #define GLM_ENABLE_EXPERIMENTAL before including it, if you really want to use it.")
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21 # else
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22 # pragma message("GLM: GLM_GTX_bit extension included")
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23 # endif
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24 #endif
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25 
\n
26 namespace glm
\n
27 {
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30 
\n
32  template<typename genIUType>
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33  GLM_FUNC_DECL genIUType highestBitValue(genIUType Value);
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34 
\n
36  template<typename genIUType>
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37  GLM_FUNC_DECL genIUType lowestBitValue(genIUType Value);
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38 
\n
42  template<length_t L, typename T, qualifier Q>
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43  GLM_FUNC_DECL vec<L, T, Q> highestBitValue(vec<L, T, Q> const& value);
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44 
\n
50  template<typename genIUType>
\n
51  GLM_DEPRECATED GLM_FUNC_DECL genIUType powerOfTwoAbove(genIUType Value);
\n
52 
\n
58  template<length_t L, typename T, qualifier Q>
\n
59  GLM_DEPRECATED GLM_FUNC_DECL vec<L, T, Q> powerOfTwoAbove(vec<L, T, Q> const& value);
\n
60 
\n
66  template<typename genIUType>
\n
67  GLM_DEPRECATED GLM_FUNC_DECL genIUType powerOfTwoBelow(genIUType Value);
\n
68 
\n
74  template<length_t L, typename T, qualifier Q>
\n
75  GLM_DEPRECATED GLM_FUNC_DECL vec<L, T, Q> powerOfTwoBelow(vec<L, T, Q> const& value);
\n
76 
\n
82  template<typename genIUType>
\n
83  GLM_DEPRECATED GLM_FUNC_DECL genIUType powerOfTwoNearest(genIUType Value);
\n
84 
\n
90  template<length_t L, typename T, qualifier Q>
\n
91  GLM_DEPRECATED GLM_FUNC_DECL vec<L, T, Q> powerOfTwoNearest(vec<L, T, Q> const& value);
\n
92 
\n
94 } //namespace glm
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95 
\n
96 
\n
97 #include "bit.inl"
\n
98 
\n
glm::highestBitValue
GLM_FUNC_DECL vec< L, T, Q > highestBitValue(vec< L, T, Q > const &value)
Find the highest bit set to 1 in a integer variable and return its value.
\n
glm::powerOfTwoBelow
GLM_DEPRECATED GLM_FUNC_DECL vec< L, T, Q > powerOfTwoBelow(vec< L, T, Q > const &value)
Return the power of two number which value is just lower the input value.
\n
glm::powerOfTwoAbove
GLM_DEPRECATED GLM_FUNC_DECL vec< L, T, Q > powerOfTwoAbove(vec< L, T, Q > const &value)
Return the power of two number which value is just higher the input value.
\n
glm::powerOfTwoNearest
GLM_DEPRECATED GLM_FUNC_DECL vec< L, T, Q > powerOfTwoNearest(vec< L, T, Q > const &value)
Return the power of two number which value is the closet to the input value.
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glm::lowestBitValue
GLM_FUNC_DECL genIUType lowestBitValue(genIUType Value)
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glm
Definition: common.hpp:20
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GLM_GTC_bitfield \nMore...

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\nFunctions

GLM_FUNC_DECL glm::u8vec2 bitfieldDeinterleave (glm::uint16 x)
 Deinterleaves the bits of x. More...
 
GLM_FUNC_DECL glm::u16vec2 bitfieldDeinterleave (glm::uint32 x)
 Deinterleaves the bits of x. More...
 
GLM_FUNC_DECL glm::u32vec2 bitfieldDeinterleave (glm::uint64 x)
 Deinterleaves the bits of x. More...
 
template<typename genIUType >
GLM_FUNC_DECL genIUType bitfieldFillOne (genIUType Value, int FirstBit, int BitCount)
 Set to 1 a range of bits. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > bitfieldFillOne (vec< L, T, Q > const &Value, int FirstBit, int BitCount)
 Set to 1 a range of bits. More...
 
template<typename genIUType >
GLM_FUNC_DECL genIUType bitfieldFillZero (genIUType Value, int FirstBit, int BitCount)
 Set to 0 a range of bits. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > bitfieldFillZero (vec< L, T, Q > const &Value, int FirstBit, int BitCount)
 Set to 0 a range of bits. More...
 
GLM_FUNC_DECL int16 bitfieldInterleave (int8 x, int8 y)
 Interleaves the bits of x and y. More...
 
GLM_FUNC_DECL uint16 bitfieldInterleave (uint8 x, uint8 y)
 Interleaves the bits of x and y. More...
 
GLM_FUNC_DECL uint16 bitfieldInterleave (u8vec2 const &v)
 Interleaves the bits of x and y. More...
 
GLM_FUNC_DECL int32 bitfieldInterleave (int16 x, int16 y)
 Interleaves the bits of x and y. More...
 
GLM_FUNC_DECL uint32 bitfieldInterleave (uint16 x, uint16 y)
 Interleaves the bits of x and y. More...
 
GLM_FUNC_DECL uint32 bitfieldInterleave (u16vec2 const &v)
 Interleaves the bits of x and y. More...
 
GLM_FUNC_DECL int64 bitfieldInterleave (int32 x, int32 y)
 Interleaves the bits of x and y. More...
 
GLM_FUNC_DECL uint64 bitfieldInterleave (uint32 x, uint32 y)
 Interleaves the bits of x and y. More...
 
GLM_FUNC_DECL uint64 bitfieldInterleave (u32vec2 const &v)
 Interleaves the bits of x and y. More...
 
GLM_FUNC_DECL int32 bitfieldInterleave (int8 x, int8 y, int8 z)
 Interleaves the bits of x, y and z. More...
 
GLM_FUNC_DECL uint32 bitfieldInterleave (uint8 x, uint8 y, uint8 z)
 Interleaves the bits of x, y and z. More...
 
GLM_FUNC_DECL int64 bitfieldInterleave (int16 x, int16 y, int16 z)
 Interleaves the bits of x, y and z. More...
 
GLM_FUNC_DECL uint64 bitfieldInterleave (uint16 x, uint16 y, uint16 z)
 Interleaves the bits of x, y and z. More...
 
GLM_FUNC_DECL int64 bitfieldInterleave (int32 x, int32 y, int32 z)
 Interleaves the bits of x, y and z. More...
 
GLM_FUNC_DECL uint64 bitfieldInterleave (uint32 x, uint32 y, uint32 z)
 Interleaves the bits of x, y and z. More...
 
GLM_FUNC_DECL int32 bitfieldInterleave (int8 x, int8 y, int8 z, int8 w)
 Interleaves the bits of x, y, z and w. More...
 
GLM_FUNC_DECL uint32 bitfieldInterleave (uint8 x, uint8 y, uint8 z, uint8 w)
 Interleaves the bits of x, y, z and w. More...
 
GLM_FUNC_DECL int64 bitfieldInterleave (int16 x, int16 y, int16 z, int16 w)
 Interleaves the bits of x, y, z and w. More...
 
GLM_FUNC_DECL uint64 bitfieldInterleave (uint16 x, uint16 y, uint16 z, uint16 w)
 Interleaves the bits of x, y, z and w. More...
 
template<typename genIUType >
GLM_FUNC_DECL genIUType bitfieldRotateLeft (genIUType In, int Shift)
 Rotate all bits to the left. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > bitfieldRotateLeft (vec< L, T, Q > const &In, int Shift)
 Rotate all bits to the left. More...
 
template<typename genIUType >
GLM_FUNC_DECL genIUType bitfieldRotateRight (genIUType In, int Shift)
 Rotate all bits to the right. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > bitfieldRotateRight (vec< L, T, Q > const &In, int Shift)
 Rotate all bits to the right. More...
 
template<typename genIUType >
GLM_FUNC_DECL genIUType mask (genIUType Bits)
 Build a mask of 'count' bits. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > mask (vec< L, T, Q > const &v)
 Build a mask of 'count' bits. More...
 
\n

Detailed Description

\n

GLM_GTC_bitfield

\n
See also
Core features (dependence)
\n
\n to use the features of this extension. \">GLM_GTC_bitfield (dependence)
\n\n

Definition in file bitfield.hpp.

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bitfield.hpp
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1 
\n
14 #include "../detail/setup.hpp"
\n
15 
\n
16 #pragma once
\n
17 
\n
18 // Dependencies
\n
19 #include "../ext/scalar_int_sized.hpp"
\n
20 #include "../ext/scalar_uint_sized.hpp"
\n
21 #include "../detail/qualifier.hpp"
\n
22 #include "../detail/_vectorize.hpp"
\n
23 #include "type_precision.hpp"
\n
24 #include <limits>
\n
25 
\n
26 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
27 # pragma message("GLM: GLM_GTC_bitfield extension included")
\n
28 #endif
\n
29 
\n
30 namespace glm
\n
31 {
\n
34 
\n
38  template<typename genIUType>
\n
39  GLM_FUNC_DECL genIUType mask(genIUType Bits);
\n
40 
\n
48  template<length_t L, typename T, qualifier Q>
\n
49  GLM_FUNC_DECL vec<L, T, Q> mask(vec<L, T, Q> const& v);
\n
50 
\n
54  template<typename genIUType>
\n
55  GLM_FUNC_DECL genIUType bitfieldRotateRight(genIUType In, int Shift);
\n
56 
\n
64  template<length_t L, typename T, qualifier Q>
\n
65  GLM_FUNC_DECL vec<L, T, Q> bitfieldRotateRight(vec<L, T, Q> const& In, int Shift);
\n
66 
\n
70  template<typename genIUType>
\n
71  GLM_FUNC_DECL genIUType bitfieldRotateLeft(genIUType In, int Shift);
\n
72 
\n
80  template<length_t L, typename T, qualifier Q>
\n
81  GLM_FUNC_DECL vec<L, T, Q> bitfieldRotateLeft(vec<L, T, Q> const& In, int Shift);
\n
82 
\n
86  template<typename genIUType>
\n
87  GLM_FUNC_DECL genIUType bitfieldFillOne(genIUType Value, int FirstBit, int BitCount);
\n
88 
\n
96  template<length_t L, typename T, qualifier Q>
\n
97  GLM_FUNC_DECL vec<L, T, Q> bitfieldFillOne(vec<L, T, Q> const& Value, int FirstBit, int BitCount);
\n
98 
\n
102  template<typename genIUType>
\n
103  GLM_FUNC_DECL genIUType bitfieldFillZero(genIUType Value, int FirstBit, int BitCount);
\n
104 
\n
112  template<length_t L, typename T, qualifier Q>
\n
113  GLM_FUNC_DECL vec<L, T, Q> bitfieldFillZero(vec<L, T, Q> const& Value, int FirstBit, int BitCount);
\n
114 
\n
120  GLM_FUNC_DECL int16 bitfieldInterleave(int8 x, int8 y);
\n
121 
\n
127  GLM_FUNC_DECL uint16 bitfieldInterleave(uint8 x, uint8 y);
\n
128 
\n
134  GLM_FUNC_DECL uint16 bitfieldInterleave(u8vec2 const& v);
\n
135 
\n
139  GLM_FUNC_DECL glm::u8vec2 bitfieldDeinterleave(glm::uint16 x);
\n
140 
\n
146  GLM_FUNC_DECL int32 bitfieldInterleave(int16 x, int16 y);
\n
147 
\n
153  GLM_FUNC_DECL uint32 bitfieldInterleave(uint16 x, uint16 y);
\n
154 
\n
160  GLM_FUNC_DECL uint32 bitfieldInterleave(u16vec2 const& v);
\n
161 
\n
165  GLM_FUNC_DECL glm::u16vec2 bitfieldDeinterleave(glm::uint32 x);
\n
166 
\n
172  GLM_FUNC_DECL int64 bitfieldInterleave(int32 x, int32 y);
\n
173 
\n
179  GLM_FUNC_DECL uint64 bitfieldInterleave(uint32 x, uint32 y);
\n
180 
\n
186  GLM_FUNC_DECL uint64 bitfieldInterleave(u32vec2 const& v);
\n
187 
\n
191  GLM_FUNC_DECL glm::u32vec2 bitfieldDeinterleave(glm::uint64 x);
\n
192 
\n
198  GLM_FUNC_DECL int32 bitfieldInterleave(int8 x, int8 y, int8 z);
\n
199 
\n
205  GLM_FUNC_DECL uint32 bitfieldInterleave(uint8 x, uint8 y, uint8 z);
\n
206 
\n
212  GLM_FUNC_DECL int64 bitfieldInterleave(int16 x, int16 y, int16 z);
\n
213 
\n
219  GLM_FUNC_DECL uint64 bitfieldInterleave(uint16 x, uint16 y, uint16 z);
\n
220 
\n
226  GLM_FUNC_DECL int64 bitfieldInterleave(int32 x, int32 y, int32 z);
\n
227 
\n
233  GLM_FUNC_DECL uint64 bitfieldInterleave(uint32 x, uint32 y, uint32 z);
\n
234 
\n
240  GLM_FUNC_DECL int32 bitfieldInterleave(int8 x, int8 y, int8 z, int8 w);
\n
241 
\n
247  GLM_FUNC_DECL uint32 bitfieldInterleave(uint8 x, uint8 y, uint8 z, uint8 w);
\n
248 
\n
254  GLM_FUNC_DECL int64 bitfieldInterleave(int16 x, int16 y, int16 z, int16 w);
\n
255 
\n
261  GLM_FUNC_DECL uint64 bitfieldInterleave(uint16 x, uint16 y, uint16 z, uint16 w);
\n
262 
\n
264 } //namespace glm
\n
265 
\n
266 #include "bitfield.inl"
\n
glm::uint32
detail::uint32 uint32
32 bit unsigned integer type.
Definition: scalar_uint_sized.hpp:64
\n
glm::bitfieldInterleave
GLM_FUNC_DECL uint64 bitfieldInterleave(uint16 x, uint16 y, uint16 z, uint16 w)
Interleaves the bits of x, y, z and w.
\n
glm::bitfieldDeinterleave
GLM_FUNC_DECL glm::u32vec2 bitfieldDeinterleave(glm::uint64 x)
Deinterleaves the bits of x.
\n
glm::bitfieldFillZero
GLM_FUNC_DECL vec< L, T, Q > bitfieldFillZero(vec< L, T, Q > const &Value, int FirstBit, int BitCount)
Set to 0 a range of bits.
\n
glm::uint16
detail::uint16 uint16
16 bit unsigned integer type.
Definition: scalar_uint_sized.hpp:61
\n
glm::u8vec2
vec< 2, u8, defaultp > u8vec2
Default qualifier 8 bit unsigned integer vector of 2 components type.
Definition: fwd.hpp:340
\n
glm::bitfieldRotateLeft
GLM_FUNC_DECL vec< L, T, Q > bitfieldRotateLeft(vec< L, T, Q > const &In, int Shift)
Rotate all bits to the left.
\n
glm::mask
GLM_FUNC_DECL vec< L, T, Q > mask(vec< L, T, Q > const &v)
Build a mask of 'count' bits.
\n
glm::uint64
detail::uint64 uint64
64 bit unsigned integer type.
Definition: scalar_uint_sized.hpp:67
\n
glm::bitfieldFillOne
GLM_FUNC_DECL vec< L, T, Q > bitfieldFillOne(vec< L, T, Q > const &Value, int FirstBit, int BitCount)
Set to 1 a range of bits.
\n
type_precision.hpp
GLM_GTC_type_precision
\n
glm::int64
detail::int64 int64
64 bit signed integer type.
Definition: scalar_int_sized.hpp:67
\n
glm::u32vec2
vec< 2, u32, defaultp > u32vec2
Default qualifier 32 bit unsigned integer vector of 2 components type.
Definition: fwd.hpp:380
\n
glm::bitfieldRotateRight
GLM_FUNC_DECL vec< L, T, Q > bitfieldRotateRight(vec< L, T, Q > const &In, int Shift)
Rotate all bits to the right.
\n
glm::u16vec2
vec< 2, u16, defaultp > u16vec2
Default qualifier 16 bit unsigned integer vector of 2 components type.
Definition: fwd.hpp:360
\n
glm
Definition: common.hpp:20
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closest_point.hpp File Reference
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GLM_GTX_closest_point \nMore...

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\n\n\n\n\n\n\n\n\n\n\n

\nFunctions

template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > closestPointOnLine (vec< 3, T, Q > const &point, vec< 3, T, Q > const &a, vec< 3, T, Q > const &b)
 Find the point on a straight line which is the closet of a point. More...
 
\ntemplate<typename T , qualifier Q>
GLM_FUNC_DECL vec< 2, T, Q > closestPointOnLine (vec< 2, T, Q > const &point, vec< 2, T, Q > const &a, vec< 2, T, Q > const &b)
 2d lines work as well
 
\n

Detailed Description

\n

GLM_GTX_closest_point

\n
See also
Core features (dependence)
\n\n

Definition in file closest_point.hpp.

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closest_point.hpp
\n
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1 
\n
13 #pragma once
\n
14 
\n
15 // Dependency:
\n
16 #include "../glm.hpp"
\n
17 
\n
18 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
19 # ifndef GLM_ENABLE_EXPERIMENTAL
\n
20 # pragma message("GLM: GLM_GTX_closest_point is an experimental extension and may change in the future. Use #define GLM_ENABLE_EXPERIMENTAL before including it, if you really want to use it.")
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21 # else
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22 # pragma message("GLM: GLM_GTX_closest_point extension included")
\n
23 # endif
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24 #endif
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25 
\n
26 namespace glm
\n
27 {
\n
30 
\n
33  template<typename T, qualifier Q>
\n
34  GLM_FUNC_DECL vec<3, T, Q> closestPointOnLine(
\n
35  vec<3, T, Q> const& point,
\n
36  vec<3, T, Q> const& a,
\n
37  vec<3, T, Q> const& b);
\n
38 
\n
40  template<typename T, qualifier Q>
\n
41  GLM_FUNC_DECL vec<2, T, Q> closestPointOnLine(
\n
42  vec<2, T, Q> const& point,
\n
43  vec<2, T, Q> const& a,
\n
44  vec<2, T, Q> const& b);
\n
45 
\n
47 }// namespace glm
\n
48 
\n
49 #include "closest_point.inl"
\n
glm::closestPointOnLine
GLM_FUNC_DECL vec< 2, T, Q > closestPointOnLine(vec< 2, T, Q > const &point, vec< 2, T, Q > const &a, vec< 2, T, Q > const &b)
2d lines work as well
\n
glm
Definition: common.hpp:20
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GLM_GTX_color_encoding \nMore...

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\nFunctions

\ntemplate<typename T , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > convertD65XYZToD50XYZ (vec< 3, T, Q > const &ColorD65XYZ)
 Convert a D65 YUV color to D50 YUV.
 
\ntemplate<typename T , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > convertD65XYZToLinearSRGB (vec< 3, T, Q > const &ColorD65XYZ)
 Convert a D65 YUV color to linear sRGB.
 
\ntemplate<typename T , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > convertLinearSRGBToD50XYZ (vec< 3, T, Q > const &ColorLinearSRGB)
 Convert a linear sRGB color to D50 YUV.
 
\ntemplate<typename T , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > convertLinearSRGBToD65XYZ (vec< 3, T, Q > const &ColorLinearSRGB)
 Convert a linear sRGB color to D65 YUV.
 
\n

Detailed Description

\n

GLM_GTX_color_encoding

\n
See also
Core features (dependence)
\n
\n to use the features of this extension. \">GLM_GTX_color_encoding (dependence)
\n\n

Definition in file color_encoding.hpp.

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color_encoding.hpp
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1 
\n
14 #pragma once
\n
15 
\n
16 // Dependencies
\n
17 #include "../detail/setup.hpp"
\n
18 #include "../detail/qualifier.hpp"
\n
19 #include "../vec3.hpp"
\n
20 #include <limits>
\n
21 
\n
22 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
23 # ifndef GLM_ENABLE_EXPERIMENTAL
\n
24 # pragma message("GLM: GLM_GTC_color_encoding is an experimental extension and may change in the future. Use #define GLM_ENABLE_EXPERIMENTAL before including it, if you really want to use it.")
\n
25 # else
\n
26 # pragma message("GLM: GLM_GTC_color_encoding extension included")
\n
27 # endif
\n
28 #endif
\n
29 
\n
30 namespace glm
\n
31 {
\n
34 
\n
36  template<typename T, qualifier Q>
\n
37  GLM_FUNC_DECL vec<3, T, Q> convertLinearSRGBToD65XYZ(vec<3, T, Q> const& ColorLinearSRGB);
\n
38 
\n
40  template<typename T, qualifier Q>
\n
41  GLM_FUNC_DECL vec<3, T, Q> convertLinearSRGBToD50XYZ(vec<3, T, Q> const& ColorLinearSRGB);
\n
42 
\n
44  template<typename T, qualifier Q>
\n
45  GLM_FUNC_DECL vec<3, T, Q> convertD65XYZToLinearSRGB(vec<3, T, Q> const& ColorD65XYZ);
\n
46 
\n
48  template<typename T, qualifier Q>
\n
49  GLM_FUNC_DECL vec<3, T, Q> convertD65XYZToD50XYZ(vec<3, T, Q> const& ColorD65XYZ);
\n
50 
\n
52 } //namespace glm
\n
53 
\n
54 #include "color_encoding.inl"
\n
glm::convertD65XYZToLinearSRGB
GLM_FUNC_DECL vec< 3, T, Q > convertD65XYZToLinearSRGB(vec< 3, T, Q > const &ColorD65XYZ)
Convert a D65 YUV color to linear sRGB.
\n
glm::convertLinearSRGBToD50XYZ
GLM_FUNC_DECL vec< 3, T, Q > convertLinearSRGBToD50XYZ(vec< 3, T, Q > const &ColorLinearSRGB)
Convert a linear sRGB color to D50 YUV.
\n
glm::convertLinearSRGBToD65XYZ
GLM_FUNC_DECL vec< 3, T, Q > convertLinearSRGBToD65XYZ(vec< 3, T, Q > const &ColorLinearSRGB)
Convert a linear sRGB color to D65 YUV.
\n
glm::convertD65XYZToD50XYZ
GLM_FUNC_DECL vec< 3, T, Q > convertD65XYZToD50XYZ(vec< 3, T, Q > const &ColorD65XYZ)
Convert a D65 YUV color to D50 YUV.
\n
glm
Definition: common.hpp:20
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gtc/color_space.hpp File Reference
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GLM_GTC_color_space \nMore...

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\nFunctions

template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > convertLinearToSRGB (vec< L, T, Q > const &ColorLinear)
 Convert a linear color to sRGB color using a standard gamma correction. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > convertLinearToSRGB (vec< L, T, Q > const &ColorLinear, T Gamma)
 Convert a linear color to sRGB color using a custom gamma correction. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > convertSRGBToLinear (vec< L, T, Q > const &ColorSRGB)
 Convert a sRGB color to linear color using a standard gamma correction. More...
 
\ntemplate<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > convertSRGBToLinear (vec< L, T, Q > const &ColorSRGB, T Gamma)
 Convert a sRGB color to linear color using a custom gamma correction.
 
\n

Detailed Description

\n

GLM_GTC_color_space

\n
See also
Core features (dependence)
\n
\n to use the features of this extension. \">GLM_GTC_color_space (dependence)
\n\n

Definition in file gtc/color_space.hpp.

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1 
\n
14 #pragma once
\n
15 
\n
16 // Dependencies
\n
17 #include "../detail/setup.hpp"
\n
18 #include "../detail/qualifier.hpp"
\n
19 #include "../exponential.hpp"
\n
20 #include "../vec3.hpp"
\n
21 #include "../vec4.hpp"
\n
22 #include <limits>
\n
23 
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24 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
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25 # pragma message("GLM: GLM_GTC_color_space extension included")
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26 #endif
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27 
\n
28 namespace glm
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29 {
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32 
\n
35  template<length_t L, typename T, qualifier Q>
\n
36  GLM_FUNC_DECL vec<L, T, Q> convertLinearToSRGB(vec<L, T, Q> const& ColorLinear);
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37 
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40  template<length_t L, typename T, qualifier Q>
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41  GLM_FUNC_DECL vec<L, T, Q> convertLinearToSRGB(vec<L, T, Q> const& ColorLinear, T Gamma);
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42 
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45  template<length_t L, typename T, qualifier Q>
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46  GLM_FUNC_DECL vec<L, T, Q> convertSRGBToLinear(vec<L, T, Q> const& ColorSRGB);
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47 
\n
49  // IEC 61966-2-1:1999 / Rec. 709 specification https://www.w3.org/Graphics/Color/srgb
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50  template<length_t L, typename T, qualifier Q>
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51  GLM_FUNC_DECL vec<L, T, Q> convertSRGBToLinear(vec<L, T, Q> const& ColorSRGB, T Gamma);
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52 
\n
54 } //namespace glm
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55 
\n
56 #include "color_space.inl"
\n
glm::convertLinearToSRGB
GLM_FUNC_DECL vec< L, T, Q > convertLinearToSRGB(vec< L, T, Q > const &ColorLinear, T Gamma)
Convert a linear color to sRGB color using a custom gamma correction.
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glm::convertSRGBToLinear
GLM_FUNC_DECL vec< L, T, Q > convertSRGBToLinear(vec< L, T, Q > const &ColorSRGB, T Gamma)
Convert a sRGB color to linear color using a custom gamma correction.
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glm
Definition: common.hpp:20
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gtx/color_space.hpp File Reference
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GLM_GTX_color_space \nMore...

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template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > hsvColor (vec< 3, T, Q > const &rgbValue)
 Converts a color from RGB color space to its color in HSV color space. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL T luminosity (vec< 3, T, Q > const &color)
 Compute color luminosity associating ratios (0.33, 0.59, 0.11) to RGB canals. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > rgbColor (vec< 3, T, Q > const &hsvValue)
 Converts a color from HSV color space to its color in RGB color space. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > saturation (T const s)
 Build a saturation matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > saturation (T const s, vec< 3, T, Q > const &color)
 Modify the saturation of a color. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 4, T, Q > saturation (T const s, vec< 4, T, Q > const &color)
 Modify the saturation of a color. More...
 
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Detailed Description

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GLM_GTX_color_space

\n
See also
Core features (dependence)
\n\n

Definition in file gtx/color_space.hpp.

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gtx/color_space.hpp
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1 
\n
13 #pragma once
\n
14 
\n
15 // Dependency:
\n
16 #include "../glm.hpp"
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17 
\n
18 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
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19 # ifndef GLM_ENABLE_EXPERIMENTAL
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20 # pragma message("GLM: GLM_GTX_color_space is an experimental extension and may change in the future. Use #define GLM_ENABLE_EXPERIMENTAL before including it, if you really want to use it.")
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21 # else
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22 # pragma message("GLM: GLM_GTX_color_space extension included")
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23 # endif
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24 #endif
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25 
\n
26 namespace glm
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27 {
\n
30 
\n
33  template<typename T, qualifier Q>
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34  GLM_FUNC_DECL vec<3, T, Q> rgbColor(
\n
35  vec<3, T, Q> const& hsvValue);
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36 
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39  template<typename T, qualifier Q>
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40  GLM_FUNC_DECL vec<3, T, Q> hsvColor(
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41  vec<3, T, Q> const& rgbValue);
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42 
\n
45  template<typename T>
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46  GLM_FUNC_DECL mat<4, 4, T, defaultp> saturation(
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47  T const s);
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48 
\n
51  template<typename T, qualifier Q>
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52  GLM_FUNC_DECL vec<3, T, Q> saturation(
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53  T const s,
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54  vec<3, T, Q> const& color);
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55 
\n
58  template<typename T, qualifier Q>
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59  GLM_FUNC_DECL vec<4, T, Q> saturation(
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60  T const s,
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61  vec<4, T, Q> const& color);
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62 
\n
65  template<typename T, qualifier Q>
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66  GLM_FUNC_DECL T luminosity(
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67  vec<3, T, Q> const& color);
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68 
\n
70 }//namespace glm
\n
71 
\n
72 #include "color_space.inl"
\n
glm::luminosity
GLM_FUNC_DECL T luminosity(vec< 3, T, Q > const &color)
Compute color luminosity associating ratios (0.33, 0.59, 0.11) to RGB canals.
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glm::saturation
GLM_FUNC_DECL vec< 4, T, Q > saturation(T const s, vec< 4, T, Q > const &color)
Modify the saturation of a color.
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glm::rgbColor
GLM_FUNC_DECL vec< 3, T, Q > rgbColor(vec< 3, T, Q > const &hsvValue)
Converts a color from HSV color space to its color in RGB color space.
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glm::hsvColor
GLM_FUNC_DECL vec< 3, T, Q > hsvColor(vec< 3, T, Q > const &rgbValue)
Converts a color from RGB color space to its color in HSV color space.
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glm
Definition: common.hpp:20
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GLM_GTX_color_space_YCoCg \nMore...

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\nFunctions

template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > rgb2YCoCg (vec< 3, T, Q > const &rgbColor)
 Convert a color from RGB color space to YCoCg color space. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > rgb2YCoCgR (vec< 3, T, Q > const &rgbColor)
 Convert a color from RGB color space to YCoCgR color space. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > YCoCg2rgb (vec< 3, T, Q > const &YCoCgColor)
 Convert a color from YCoCg color space to RGB color space. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > YCoCgR2rgb (vec< 3, T, Q > const &YCoCgColor)
 Convert a color from YCoCgR color space to RGB color space. More...
 
\n

Detailed Description

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GLM_GTX_color_space_YCoCg

\n
See also
Core features (dependence)
\n\n

Definition in file color_space_YCoCg.hpp.

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13 #pragma once
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14 
\n
15 // Dependency:
\n
16 #include "../glm.hpp"
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17 
\n
18 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
19 # ifndef GLM_ENABLE_EXPERIMENTAL
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20 # pragma message("GLM: GLM_GTX_color_space_YCoCg is an experimental extension and may change in the future. Use #define GLM_ENABLE_EXPERIMENTAL before including it, if you really want to use it.")
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21 # else
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22 # pragma message("GLM: GLM_GTX_color_space_YCoCg extension included")
\n
23 # endif
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24 #endif
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25 
\n
26 namespace glm
\n
27 {
\n
30 
\n
33  template<typename T, qualifier Q>
\n
34  GLM_FUNC_DECL vec<3, T, Q> rgb2YCoCg(
\n
35  vec<3, T, Q> const& rgbColor);
\n
36 
\n
39  template<typename T, qualifier Q>
\n
40  GLM_FUNC_DECL vec<3, T, Q> YCoCg2rgb(
\n
41  vec<3, T, Q> const& YCoCgColor);
\n
42 
\n
46  template<typename T, qualifier Q>
\n
47  GLM_FUNC_DECL vec<3, T, Q> rgb2YCoCgR(
\n
48  vec<3, T, Q> const& rgbColor);
\n
49 
\n
53  template<typename T, qualifier Q>
\n
54  GLM_FUNC_DECL vec<3, T, Q> YCoCgR2rgb(
\n
55  vec<3, T, Q> const& YCoCgColor);
\n
56 
\n
58 }//namespace glm
\n
59 
\n
60 #include "color_space_YCoCg.inl"
\n
glm::YCoCgR2rgb
GLM_FUNC_DECL vec< 3, T, Q > YCoCgR2rgb(vec< 3, T, Q > const &YCoCgColor)
Convert a color from YCoCgR color space to RGB color space.
\n
glm::YCoCg2rgb
GLM_FUNC_DECL vec< 3, T, Q > YCoCg2rgb(vec< 3, T, Q > const &YCoCgColor)
Convert a color from YCoCg color space to RGB color space.
\n
glm::rgbColor
GLM_FUNC_DECL vec< 3, T, Q > rgbColor(vec< 3, T, Q > const &hsvValue)
Converts a color from HSV color space to its color in RGB color space.
\n
glm::rgb2YCoCg
GLM_FUNC_DECL vec< 3, T, Q > rgb2YCoCg(vec< 3, T, Q > const &rgbColor)
Convert a color from RGB color space to YCoCg color space.
\n
glm::rgb2YCoCgR
GLM_FUNC_DECL vec< 3, T, Q > rgb2YCoCgR(vec< 3, T, Q > const &rgbColor)
Convert a color from RGB color space to YCoCgR color space.
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glm
Definition: common.hpp:20
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common.hpp File Reference
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Core features \nMore...

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\nFunctions

template<typename genType >
GLM_FUNC_DECL GLM_CONSTEXPR genType abs (genType x)
 Returns x if x >= 0; otherwise, it returns -x. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< L, T, Q > abs (vec< L, T, Q > const &x)
 Returns x if x >= 0; otherwise, it returns -x. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > ceil (vec< L, T, Q > const &x)
 Returns a value equal to the nearest integer that is greater than or equal to x. More...
 
template<typename genType >
GLM_FUNC_DECL GLM_CONSTEXPR genType clamp (genType x, genType minVal, genType maxVal)
 Returns min(max(x, minVal), maxVal) for each component in x using the floating-point values minVal and maxVal. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< L, T, Q > clamp (vec< L, T, Q > const &x, T minVal, T maxVal)
 Returns min(max(x, minVal), maxVal) for each component in x using the floating-point values minVal and maxVal. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< L, T, Q > clamp (vec< L, T, Q > const &x, vec< L, T, Q > const &minVal, vec< L, T, Q > const &maxVal)
 Returns min(max(x, minVal), maxVal) for each component in x using the floating-point values minVal and maxVal. More...
 
GLM_FUNC_DECL int floatBitsToInt (float const &v)
 Returns a signed integer value representing the encoding of a floating-point value. More...
 
template<length_t L, qualifier Q>
GLM_FUNC_DECL vec< L, int, Q > floatBitsToInt (vec< L, float, Q > const &v)
 Returns a signed integer value representing the encoding of a floating-point value. More...
 
GLM_FUNC_DECL uint floatBitsToUint (float const &v)
 Returns a unsigned integer value representing the encoding of a floating-point value. More...
 
template<length_t L, qualifier Q>
GLM_FUNC_DECL vec< L, uint, Q > floatBitsToUint (vec< L, float, Q > const &v)
 Returns a unsigned integer value representing the encoding of a floating-point value. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > floor (vec< L, T, Q > const &x)
 Returns a value equal to the nearest integer that is less then or equal to x. More...
 
template<typename genType >
GLM_FUNC_DECL genType fma (genType const &a, genType const &b, genType const &c)
 Computes and returns a * b + c. More...
 
template<typename genType >
GLM_FUNC_DECL genType fract (genType x)
 Return x - floor(x). More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > fract (vec< L, T, Q > const &x)
 Return x - floor(x). More...
 
template<typename genType >
GLM_FUNC_DECL genType frexp (genType x, int &exp)
 Splits x into a floating-point significand in the range [0.5, 1.0) and an integral exponent of two, such that: x = significand * exp(2, exponent) More...
 
GLM_FUNC_DECL float intBitsToFloat (int const &v)
 Returns a floating-point value corresponding to a signed integer encoding of a floating-point value. More...
 
template<length_t L, qualifier Q>
GLM_FUNC_DECL vec< L, float, Q > intBitsToFloat (vec< L, int, Q > const &v)
 Returns a floating-point value corresponding to a signed integer encoding of a floating-point value. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, bool, Q > isinf (vec< L, T, Q > const &x)
 Returns true if x holds a positive infinity or negative infinity representation in the underlying implementation's set of floating point representations. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, bool, Q > isnan (vec< L, T, Q > const &x)
 Returns true if x holds a NaN (not a number) representation in the underlying implementation's set of floating point representations. More...
 
template<typename genType >
GLM_FUNC_DECL genType ldexp (genType const &x, int const &exp)
 Builds a floating-point number from x and the corresponding integral exponent of two in exp, returning: significand * exp(2, exponent) More...
 
template<typename genType >
GLM_FUNC_DECL GLM_CONSTEXPR genType max (genType x, genType y)
 Returns y if x < y; otherwise, it returns x. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< L, T, Q > max (vec< L, T, Q > const &x, T y)
 Returns y if x < y; otherwise, it returns x. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< L, T, Q > max (vec< L, T, Q > const &x, vec< L, T, Q > const &y)
 Returns y if x < y; otherwise, it returns x. More...
 
template<typename genType >
GLM_FUNC_DECL GLM_CONSTEXPR genType min (genType x, genType y)
 Returns y if y < x; otherwise, it returns x. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< L, T, Q > min (vec< L, T, Q > const &x, T y)
 Returns y if y < x; otherwise, it returns x. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< L, T, Q > min (vec< L, T, Q > const &x, vec< L, T, Q > const &y)
 Returns y if y < x; otherwise, it returns x. More...
 
template<typename genTypeT , typename genTypeU >
GLM_FUNC_DECL genTypeT mix (genTypeT x, genTypeT y, genTypeU a)
 If genTypeU is a floating scalar or vector: Returns x * (1.0 - a) + y * a, i.e., the linear blend of x and y using the floating-point value a. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > mod (vec< L, T, Q > const &x, vec< L, T, Q > const &y)
 Modulus. More...
 
template<typename genType >
GLM_FUNC_DECL genType modf (genType x, genType &i)
 Returns the fractional part of x and sets i to the integer part (as a whole number floating point value). More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > round (vec< L, T, Q > const &x)
 Returns a value equal to the nearest integer to x. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > roundEven (vec< L, T, Q > const &x)
 Returns a value equal to the nearest integer to x. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > sign (vec< L, T, Q > const &x)
 Returns 1.0 if x > 0, 0.0 if x == 0, or -1.0 if x < 0. More...
 
template<typename genType >
GLM_FUNC_DECL genType smoothstep (genType edge0, genType edge1, genType x)
 Returns 0.0 if x <= edge0 and 1.0 if x >= edge1 and performs smooth Hermite interpolation between 0 and 1 when edge0 < x < edge1. More...
 
template<typename genType >
GLM_FUNC_DECL genType step (genType edge, genType x)
 Returns 0.0 if x < edge, otherwise it returns 1.0 for each component of a genType. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > step (T edge, vec< L, T, Q > const &x)
 Returns 0.0 if x < edge, otherwise it returns 1.0. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > step (vec< L, T, Q > const &edge, vec< L, T, Q > const &x)
 Returns 0.0 if x < edge, otherwise it returns 1.0. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > trunc (vec< L, T, Q > const &x)
 Returns a value equal to the nearest integer to x whose absolute value is not larger than the absolute value of x. More...
 
GLM_FUNC_DECL float uintBitsToFloat (uint const &v)
 Returns a floating-point value corresponding to a unsigned integer encoding of a floating-point value. More...
 
template<length_t L, qualifier Q>
GLM_FUNC_DECL vec< L, float, Q > uintBitsToFloat (vec< L, uint, Q > const &v)
 Returns a floating-point value corresponding to a unsigned integer encoding of a floating-point value. More...
 
\n

Detailed Description

\n

Core features

\n
See also
GLSL 4.20.8 specification, section 8.3 Common Functions
\n\n

Definition in file common.hpp.

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common.hpp
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1 
\n
15 #pragma once
\n
16 
\n
17 #include "detail/qualifier.hpp"
\n
18 #include "detail/_fixes.hpp"
\n
19 
\n
20 namespace glm
\n
21 {
\n
24 
\n
31  template<typename genType>
\n
32  GLM_FUNC_DECL GLM_CONSTEXPR genType abs(genType x);
\n
33 
\n
42  template<length_t L, typename T, qualifier Q>
\n
43  GLM_FUNC_DECL GLM_CONSTEXPR vec<L, T, Q> abs(vec<L, T, Q> const& x);
\n
44 
\n
53  template<length_t L, typename T, qualifier Q>
\n
54  GLM_FUNC_DECL vec<L, T, Q> sign(vec<L, T, Q> const& x);
\n
55 
\n
64  template<length_t L, typename T, qualifier Q>
\n
65  GLM_FUNC_DECL vec<L, T, Q> floor(vec<L, T, Q> const& x);
\n
66 
\n
76  template<length_t L, typename T, qualifier Q>
\n
77  GLM_FUNC_DECL vec<L, T, Q> trunc(vec<L, T, Q> const& x);
\n
78 
\n
91  template<length_t L, typename T, qualifier Q>
\n
92  GLM_FUNC_DECL vec<L, T, Q> round(vec<L, T, Q> const& x);
\n
93 
\n
105  template<length_t L, typename T, qualifier Q>
\n
106  GLM_FUNC_DECL vec<L, T, Q> roundEven(vec<L, T, Q> const& x);
\n
107 
\n
117  template<length_t L, typename T, qualifier Q>
\n
118  GLM_FUNC_DECL vec<L, T, Q> ceil(vec<L, T, Q> const& x);
\n
119 
\n
126  template<typename genType>
\n
127  GLM_FUNC_DECL genType fract(genType x);
\n
128 
\n
137  template<length_t L, typename T, qualifier Q>
\n
138  GLM_FUNC_DECL vec<L, T, Q> fract(vec<L, T, Q> const& x);
\n
139 
\n
140  template<typename genType>
\n
141  GLM_FUNC_DECL genType mod(genType x, genType y);
\n
142 
\n
143  template<length_t L, typename T, qualifier Q>
\n
144  GLM_FUNC_DECL vec<L, T, Q> mod(vec<L, T, Q> const& x, T y);
\n
145 
\n
155  template<length_t L, typename T, qualifier Q>
\n
156  GLM_FUNC_DECL vec<L, T, Q> mod(vec<L, T, Q> const& x, vec<L, T, Q> const& y);
\n
157 
\n
167  template<typename genType>
\n
168  GLM_FUNC_DECL genType modf(genType x, genType& i);
\n
169 
\n
176  template<typename genType>
\n
177  GLM_FUNC_DECL GLM_CONSTEXPR genType min(genType x, genType y);
\n
178 
\n
187  template<length_t L, typename T, qualifier Q>
\n
188  GLM_FUNC_DECL GLM_CONSTEXPR vec<L, T, Q> min(vec<L, T, Q> const& x, T y);
\n
189 
\n
198  template<length_t L, typename T, qualifier Q>
\n
199  GLM_FUNC_DECL GLM_CONSTEXPR vec<L, T, Q> min(vec<L, T, Q> const& x, vec<L, T, Q> const& y);
\n
200 
\n
207  template<typename genType>
\n
208  GLM_FUNC_DECL GLM_CONSTEXPR genType max(genType x, genType y);
\n
209 
\n
218  template<length_t L, typename T, qualifier Q>
\n
219  GLM_FUNC_DECL GLM_CONSTEXPR vec<L, T, Q> max(vec<L, T, Q> const& x, T y);
\n
220 
\n
229  template<length_t L, typename T, qualifier Q>
\n
230  GLM_FUNC_DECL GLM_CONSTEXPR vec<L, T, Q> max(vec<L, T, Q> const& x, vec<L, T, Q> const& y);
\n
231 
\n
239  template<typename genType>
\n
240  GLM_FUNC_DECL GLM_CONSTEXPR genType clamp(genType x, genType minVal, genType maxVal);
\n
241 
\n
251  template<length_t L, typename T, qualifier Q>
\n
252  GLM_FUNC_DECL GLM_CONSTEXPR vec<L, T, Q> clamp(vec<L, T, Q> const& x, T minVal, T maxVal);
\n
253 
\n
263  template<length_t L, typename T, qualifier Q>
\n
264  GLM_FUNC_DECL GLM_CONSTEXPR vec<L, T, Q> clamp(vec<L, T, Q> const& x, vec<L, T, Q> const& minVal, vec<L, T, Q> const& maxVal);
\n
265 
\n
308  template<typename genTypeT, typename genTypeU>
\n
309  GLM_FUNC_DECL genTypeT mix(genTypeT x, genTypeT y, genTypeU a);
\n
310 
\n
311  template<length_t L, typename T, typename U, qualifier Q>
\n
312  GLM_FUNC_DECL vec<L, T, Q> mix(vec<L, T, Q> const& x, vec<L, T, Q> const& y, vec<L, U, Q> const& a);
\n
313 
\n
314  template<length_t L, typename T, typename U, qualifier Q>
\n
315  GLM_FUNC_DECL vec<L, T, Q> mix(vec<L, T, Q> const& x, vec<L, T, Q> const& y, U a);
\n
316 
\n
321  template<typename genType>
\n
322  GLM_FUNC_DECL genType step(genType edge, genType x);
\n
323 
\n
332  template<length_t L, typename T, qualifier Q>
\n
333  GLM_FUNC_DECL vec<L, T, Q> step(T edge, vec<L, T, Q> const& x);
\n
334 
\n
343  template<length_t L, typename T, qualifier Q>
\n
344  GLM_FUNC_DECL vec<L, T, Q> step(vec<L, T, Q> const& edge, vec<L, T, Q> const& x);
\n
345 
\n
360  template<typename genType>
\n
361  GLM_FUNC_DECL genType smoothstep(genType edge0, genType edge1, genType x);
\n
362 
\n
363  template<length_t L, typename T, qualifier Q>
\n
364  GLM_FUNC_DECL vec<L, T, Q> smoothstep(T edge0, T edge1, vec<L, T, Q> const& x);
\n
365 
\n
366  template<length_t L, typename T, qualifier Q>
\n
367  GLM_FUNC_DECL vec<L, T, Q> smoothstep(vec<L, T, Q> const& edge0, vec<L, T, Q> const& edge1, vec<L, T, Q> const& x);
\n
368 
\n
383  template<length_t L, typename T, qualifier Q>
\n
384  GLM_FUNC_DECL vec<L, bool, Q> isnan(vec<L, T, Q> const& x);
\n
385 
\n
398  template<length_t L, typename T, qualifier Q>
\n
399  GLM_FUNC_DECL vec<L, bool, Q> isinf(vec<L, T, Q> const& x);
\n
400 
\n
407  GLM_FUNC_DECL int floatBitsToInt(float const& v);
\n
408 
\n
418  template<length_t L, qualifier Q>
\n
419  GLM_FUNC_DECL vec<L, int, Q> floatBitsToInt(vec<L, float, Q> const& v);
\n
420 
\n
427  GLM_FUNC_DECL uint floatBitsToUint(float const& v);
\n
428 
\n
438  template<length_t L, qualifier Q>
\n
439  GLM_FUNC_DECL vec<L, uint, Q> floatBitsToUint(vec<L, float, Q> const& v);
\n
440 
\n
449  GLM_FUNC_DECL float intBitsToFloat(int const& v);
\n
450 
\n
462  template<length_t L, qualifier Q>
\n
463  GLM_FUNC_DECL vec<L, float, Q> intBitsToFloat(vec<L, int, Q> const& v);
\n
464 
\n
473  GLM_FUNC_DECL float uintBitsToFloat(uint const& v);
\n
474 
\n
486  template<length_t L, qualifier Q>
\n
487  GLM_FUNC_DECL vec<L, float, Q> uintBitsToFloat(vec<L, uint, Q> const& v);
\n
488 
\n
495  template<typename genType>
\n
496  GLM_FUNC_DECL genType fma(genType const& a, genType const& b, genType const& c);
\n
497 
\n
512  template<typename genType>
\n
513  GLM_FUNC_DECL genType frexp(genType x, int& exp);
\n
514 
\n
515  template<length_t L, typename T, qualifier Q>
\n
516  GLM_FUNC_DECL vec<L, T, Q> frexp(vec<L, T, Q> const& v, vec<L, int, Q>& exp);
\n
517 
\n
529  template<typename genType>
\n
530  GLM_FUNC_DECL genType ldexp(genType const& x, int const& exp);
\n
531 
\n
532  template<length_t L, typename T, qualifier Q>
\n
533  GLM_FUNC_DECL vec<L, T, Q> ldexp(vec<L, T, Q> const& v, vec<L, int, Q> const& exp);
\n
534 
\n
536 }//namespace glm
\n
537 
\n
538 #include "detail/func_common.inl"
\n
539 
\n
glm::floor
GLM_FUNC_DECL vec< L, T, Q > floor(vec< L, T, Q > const &x)
Returns a value equal to the nearest integer that is less then or equal to x.
\n
glm::fma
GLM_FUNC_DECL genType fma(genType const &a, genType const &b, genType const &c)
Computes and returns a * b + c.
\n
glm::trunc
GLM_FUNC_DECL vec< L, T, Q > trunc(vec< L, T, Q > const &x)
Returns a value equal to the nearest integer to x whose absolute value is not larger than the absolut...
\n
glm::mod
GLM_FUNC_DECL vec< L, T, Q > mod(vec< L, T, Q > const &x, vec< L, T, Q > const &y)
Modulus.
\n
glm::clamp
GLM_FUNC_DECL GLM_CONSTEXPR vec< L, T, Q > clamp(vec< L, T, Q > const &x, vec< L, T, Q > const &minVal, vec< L, T, Q > const &maxVal)
Returns min(max(x, minVal), maxVal) for each component in x using the floating-point values minVal an...
\n
glm::round
GLM_FUNC_DECL vec< L, T, Q > round(vec< L, T, Q > const &x)
Returns a value equal to the nearest integer to x.
\n
glm::uintBitsToFloat
GLM_FUNC_DECL vec< L, float, Q > uintBitsToFloat(vec< L, uint, Q > const &v)
Returns a floating-point value corresponding to a unsigned integer encoding of a floating-point value...
\n
glm::sign
GLM_FUNC_DECL vec< L, T, Q > sign(vec< L, T, Q > const &x)
Returns 1.0 if x > 0, 0.0 if x == 0, or -1.0 if x < 0.
\n
glm::isinf
GLM_FUNC_DECL vec< L, bool, Q > isinf(vec< L, T, Q > const &x)
Returns true if x holds a positive infinity or negative infinity representation in the underlying imp...
\n
glm::roundEven
GLM_FUNC_DECL vec< L, T, Q > roundEven(vec< L, T, Q > const &x)
Returns a value equal to the nearest integer to x.
\n
glm::modf
GLM_FUNC_DECL genType modf(genType x, genType &i)
Returns the fractional part of x and sets i to the integer part (as a whole number floating point val...
\n
glm::ceil
GLM_FUNC_DECL vec< L, T, Q > ceil(vec< L, T, Q > const &x)
Returns a value equal to the nearest integer that is greater than or equal to x.
\n
glm::min
GLM_FUNC_DECL GLM_CONSTEXPR vec< L, T, Q > min(vec< L, T, Q > const &x, vec< L, T, Q > const &y)
Returns y if y < x; otherwise, it returns x.
\n
glm::intBitsToFloat
GLM_FUNC_DECL vec< L, float, Q > intBitsToFloat(vec< L, int, Q > const &v)
Returns a floating-point value corresponding to a signed integer encoding of a floating-point value...
\n
glm::isnan
GLM_FUNC_DECL vec< L, bool, Q > isnan(vec< L, T, Q > const &x)
Returns true if x holds a NaN (not a number) representation in the underlying implementation's set of...
\n
glm::exp
GLM_FUNC_DECL vec< L, T, Q > exp(vec< L, T, Q > const &v)
Returns the natural exponentiation of x, i.e., e^x.
\n
glm::floatBitsToUint
GLM_FUNC_DECL vec< L, uint, Q > floatBitsToUint(vec< L, float, Q > const &v)
Returns a unsigned integer value representing the encoding of a floating-point value.
\n
glm::smoothstep
GLM_FUNC_DECL genType smoothstep(genType edge0, genType edge1, genType x)
Returns 0.0 if x <= edge0 and 1.0 if x >= edge1 and performs smooth Hermite interpolation between 0 a...
\n
glm::abs
GLM_FUNC_DECL GLM_CONSTEXPR vec< L, T, Q > abs(vec< L, T, Q > const &x)
Returns x if x >= 0; otherwise, it returns -x.
\n
glm::max
GLM_FUNC_DECL GLM_CONSTEXPR vec< L, T, Q > max(vec< L, T, Q > const &x, vec< L, T, Q > const &y)
Returns y if x < y; otherwise, it returns x.
\n
glm::step
GLM_FUNC_DECL vec< L, T, Q > step(vec< L, T, Q > const &edge, vec< L, T, Q > const &x)
Returns 0.0 if x < edge, otherwise it returns 1.0.
\n
glm::fract
GLM_FUNC_DECL vec< L, T, Q > fract(vec< L, T, Q > const &x)
Return x - floor(x).
\n
glm::ldexp
GLM_FUNC_DECL genType ldexp(genType const &x, int const &exp)
Builds a floating-point number from x and the corresponding integral exponent of two in exp...
\n
glm::floatBitsToInt
GLM_FUNC_DECL vec< L, int, Q > floatBitsToInt(vec< L, float, Q > const &v)
Returns a signed integer value representing the encoding of a floating-point value.
\n
glm::mix
GLM_FUNC_DECL genTypeT mix(genTypeT x, genTypeT y, genTypeU a)
If genTypeU is a floating scalar or vector: Returns x * (1.0 - a) + y * a, i.e., the linear blend of ...
\n
glm::frexp
GLM_FUNC_DECL genType frexp(genType x, int &exp)
Splits x into a floating-point significand in the range [0.5, 1.0) and an integral exponent of two...
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glm
Definition: common.hpp:20
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\nFunctions

template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, bool, Q > closeBounded (vec< L, T, Q > const &Value, vec< L, T, Q > const &Min, vec< L, T, Q > const &Max)
 Returns whether vector components values are within an interval. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > fmod (vec< L, T, Q > const &v)
 Similar to 'mod' but with a different rounding and integer support. More...
 
template<typename genType >
GLM_FUNC_DECL genType::bool_type isdenormal (genType const &x)
 Returns true if x is a denormalized number Numbers whose absolute value is too small to be represented in the normal format are represented in an alternate, denormalized format. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, bool, Q > openBounded (vec< L, T, Q > const &Value, vec< L, T, Q > const &Min, vec< L, T, Q > const &Max)
 Returns whether vector components values are within an interval. More...
 
\n

Detailed Description

\n

GLM_GTX_common

\n
See also
Core features (dependence)
\n\n

Definition in file gtx/common.hpp.

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1 
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13 #pragma once
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14 
\n
15 // Dependencies:
\n
16 #include "../vec2.hpp"
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17 #include "../vec3.hpp"
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18 #include "../vec4.hpp"
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19 #include "../gtc/vec1.hpp"
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20 
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21 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
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22 # ifndef GLM_ENABLE_EXPERIMENTAL
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23 # pragma message("GLM: GLM_GTX_common is an experimental extension and may change in the future. Use #define GLM_ENABLE_EXPERIMENTAL before including it, if you really want to use it.")
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24 # else
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25 # pragma message("GLM: GLM_GTX_common extension included")
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26 # endif
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27 #endif
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28 
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29 namespace glm
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30 {
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33 
\n
42  template<typename genType>
\n
43  GLM_FUNC_DECL typename genType::bool_type isdenormal(genType const& x);
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44 
\n
50  template<length_t L, typename T, qualifier Q>
\n
51  GLM_FUNC_DECL vec<L, T, Q> fmod(vec<L, T, Q> const& v);
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52 
\n
60  template <length_t L, typename T, qualifier Q>
\n
61  GLM_FUNC_DECL vec<L, bool, Q> openBounded(vec<L, T, Q> const& Value, vec<L, T, Q> const& Min, vec<L, T, Q> const& Max);
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62 
\n
70  template <length_t L, typename T, qualifier Q>
\n
71  GLM_FUNC_DECL vec<L, bool, Q> closeBounded(vec<L, T, Q> const& Value, vec<L, T, Q> const& Min, vec<L, T, Q> const& Max);
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72 
\n
74 }//namespace glm
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75 
\n
76 #include "common.inl"
\n
glm::fmod
GLM_FUNC_DECL vec< L, T, Q > fmod(vec< L, T, Q > const &v)
Similar to 'mod' but with a different rounding and integer support.
\n
glm::openBounded
GLM_FUNC_DECL vec< L, bool, Q > openBounded(vec< L, T, Q > const &Value, vec< L, T, Q > const &Min, vec< L, T, Q > const &Max)
Returns whether vector components values are within an interval.
\n
glm::isdenormal
GLM_FUNC_DECL genType::bool_type isdenormal(genType const &x)
Returns true if x is a denormalized number Numbers whose absolute value is too small to be represente...
\n
glm
Definition: common.hpp:20
\n
glm::closeBounded
GLM_FUNC_DECL vec< L, bool, Q > closeBounded(vec< L, T, Q > const &Value, vec< L, T, Q > const &Min, vec< L, T, Q > const &Max)
Returns whether vector components values are within an interval.
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\nTypedefs

\ntypedef bool bool1
 boolean type with 1 component. (From GLM_GTX_compatibility extension)
 
\ntypedef bool bool1x1
 boolean matrix with 1 x 1 component. (From GLM_GTX_compatibility extension)
 
\ntypedef vec< 2, bool, highp > bool2
 boolean type with 2 components. (From GLM_GTX_compatibility extension)
 
\ntypedef mat< 2, 2, bool, highp > bool2x2
 boolean matrix with 2 x 2 components. (From GLM_GTX_compatibility extension)
 
\ntypedef mat< 2, 3, bool, highp > bool2x3
 boolean matrix with 2 x 3 components. (From GLM_GTX_compatibility extension)
 
\ntypedef mat< 2, 4, bool, highp > bool2x4
 boolean matrix with 2 x 4 components. (From GLM_GTX_compatibility extension)
 
\ntypedef vec< 3, bool, highp > bool3
 boolean type with 3 components. (From GLM_GTX_compatibility extension)
 
\ntypedef mat< 3, 2, bool, highp > bool3x2
 boolean matrix with 3 x 2 components. (From GLM_GTX_compatibility extension)
 
\ntypedef mat< 3, 3, bool, highp > bool3x3
 boolean matrix with 3 x 3 components. (From GLM_GTX_compatibility extension)
 
\ntypedef mat< 3, 4, bool, highp > bool3x4
 boolean matrix with 3 x 4 components. (From GLM_GTX_compatibility extension)
 
\ntypedef vec< 4, bool, highp > bool4
 boolean type with 4 components. (From GLM_GTX_compatibility extension)
 
\ntypedef mat< 4, 2, bool, highp > bool4x2
 boolean matrix with 4 x 2 components. (From GLM_GTX_compatibility extension)
 
\ntypedef mat< 4, 3, bool, highp > bool4x3
 boolean matrix with 4 x 3 components. (From GLM_GTX_compatibility extension)
 
\ntypedef mat< 4, 4, bool, highp > bool4x4
 boolean matrix with 4 x 4 components. (From GLM_GTX_compatibility extension)
 
\ntypedef double double1
 double-qualifier floating-point vector with 1 component. (From GLM_GTX_compatibility extension)
 
\ntypedef double double1x1
 double-qualifier floating-point matrix with 1 component. (From GLM_GTX_compatibility extension)
 
\ntypedef vec< 2, double, highp > double2
 double-qualifier floating-point vector with 2 components. (From GLM_GTX_compatibility extension)
 
\ntypedef mat< 2, 2, double, highp > double2x2
 double-qualifier floating-point matrix with 2 x 2 components. (From GLM_GTX_compatibility extension)
 
\ntypedef mat< 2, 3, double, highp > double2x3
 double-qualifier floating-point matrix with 2 x 3 components. (From GLM_GTX_compatibility extension)
 
\ntypedef mat< 2, 4, double, highp > double2x4
 double-qualifier floating-point matrix with 2 x 4 components. (From GLM_GTX_compatibility extension)
 
\ntypedef vec< 3, double, highp > double3
 double-qualifier floating-point vector with 3 components. (From GLM_GTX_compatibility extension)
 
\ntypedef mat< 3, 2, double, highp > double3x2
 double-qualifier floating-point matrix with 3 x 2 components. (From GLM_GTX_compatibility extension)
 
\ntypedef mat< 3, 3, double, highp > double3x3
 double-qualifier floating-point matrix with 3 x 3 components. (From GLM_GTX_compatibility extension)
 
\ntypedef mat< 3, 4, double, highp > double3x4
 double-qualifier floating-point matrix with 3 x 4 components. (From GLM_GTX_compatibility extension)
 
\ntypedef vec< 4, double, highp > double4
 double-qualifier floating-point vector with 4 components. (From GLM_GTX_compatibility extension)
 
\ntypedef mat< 4, 2, double, highp > double4x2
 double-qualifier floating-point matrix with 4 x 2 components. (From GLM_GTX_compatibility extension)
 
\ntypedef mat< 4, 3, double, highp > double4x3
 double-qualifier floating-point matrix with 4 x 3 components. (From GLM_GTX_compatibility extension)
 
\ntypedef mat< 4, 4, double, highp > double4x4
 double-qualifier floating-point matrix with 4 x 4 components. (From GLM_GTX_compatibility extension)
 
\ntypedef float float1
 single-qualifier floating-point vector with 1 component. (From GLM_GTX_compatibility extension)
 
\ntypedef float float1x1
 single-qualifier floating-point matrix with 1 component. (From GLM_GTX_compatibility extension)
 
\ntypedef vec< 2, float, highp > float2
 single-qualifier floating-point vector with 2 components. (From GLM_GTX_compatibility extension)
 
\ntypedef mat< 2, 2, float, highp > float2x2
 single-qualifier floating-point matrix with 2 x 2 components. (From GLM_GTX_compatibility extension)
 
\ntypedef mat< 2, 3, float, highp > float2x3
 single-qualifier floating-point matrix with 2 x 3 components. (From GLM_GTX_compatibility extension)
 
\ntypedef mat< 2, 4, float, highp > float2x4
 single-qualifier floating-point matrix with 2 x 4 components. (From GLM_GTX_compatibility extension)
 
\ntypedef vec< 3, float, highp > float3
 single-qualifier floating-point vector with 3 components. (From GLM_GTX_compatibility extension)
 
\ntypedef mat< 3, 2, float, highp > float3x2
 single-qualifier floating-point matrix with 3 x 2 components. (From GLM_GTX_compatibility extension)
 
\ntypedef mat< 3, 3, float, highp > float3x3
 single-qualifier floating-point matrix with 3 x 3 components. (From GLM_GTX_compatibility extension)
 
\ntypedef mat< 3, 4, float, highp > float3x4
 single-qualifier floating-point matrix with 3 x 4 components. (From GLM_GTX_compatibility extension)
 
\ntypedef vec< 4, float, highp > float4
 single-qualifier floating-point vector with 4 components. (From GLM_GTX_compatibility extension)
 
\ntypedef mat< 4, 2, float, highp > float4x2
 single-qualifier floating-point matrix with 4 x 2 components. (From GLM_GTX_compatibility extension)
 
\ntypedef mat< 4, 3, float, highp > float4x3
 single-qualifier floating-point matrix with 4 x 3 components. (From GLM_GTX_compatibility extension)
 
\ntypedef mat< 4, 4, float, highp > float4x4
 single-qualifier floating-point matrix with 4 x 4 components. (From GLM_GTX_compatibility extension)
 
\ntypedef int int1
 integer vector with 1 component. (From GLM_GTX_compatibility extension)
 
\ntypedef int int1x1
 integer matrix with 1 component. (From GLM_GTX_compatibility extension)
 
\ntypedef vec< 2, int, highp > int2
 integer vector with 2 components. (From GLM_GTX_compatibility extension)
 
\ntypedef mat< 2, 2, int, highp > int2x2
 integer matrix with 2 x 2 components. (From GLM_GTX_compatibility extension)
 
\ntypedef mat< 2, 3, int, highp > int2x3
 integer matrix with 2 x 3 components. (From GLM_GTX_compatibility extension)
 
\ntypedef mat< 2, 4, int, highp > int2x4
 integer matrix with 2 x 4 components. (From GLM_GTX_compatibility extension)
 
\ntypedef vec< 3, int, highp > int3
 integer vector with 3 components. (From GLM_GTX_compatibility extension)
 
\ntypedef mat< 3, 2, int, highp > int3x2
 integer matrix with 3 x 2 components. (From GLM_GTX_compatibility extension)
 
\ntypedef mat< 3, 3, int, highp > int3x3
 integer matrix with 3 x 3 components. (From GLM_GTX_compatibility extension)
 
\ntypedef mat< 3, 4, int, highp > int3x4
 integer matrix with 3 x 4 components. (From GLM_GTX_compatibility extension)
 
\ntypedef vec< 4, int, highp > int4
 integer vector with 4 components. (From GLM_GTX_compatibility extension)
 
\ntypedef mat< 4, 2, int, highp > int4x2
 integer matrix with 4 x 2 components. (From GLM_GTX_compatibility extension)
 
\ntypedef mat< 4, 3, int, highp > int4x3
 integer matrix with 4 x 3 components. (From GLM_GTX_compatibility extension)
 
\ntypedef mat< 4, 4, int, highp > int4x4
 integer matrix with 4 x 4 components. (From GLM_GTX_compatibility extension)
 
\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

\nFunctions

\ntemplate<typename T , qualifier Q>
GLM_FUNC_QUALIFIER T atan2 (T x, T y)
 Arc tangent. Returns an angle whose tangent is y/x. The signs of x and y are used to determine what quadrant the angle is in. The range of values returned by this function is [-PI, PI]. Results are undefined if x and y are both 0. (From GLM_GTX_compatibility)
 
\ntemplate<typename T , qualifier Q>
GLM_FUNC_QUALIFIER vec< 2, T, Q > atan2 (const vec< 2, T, Q > &x, const vec< 2, T, Q > &y)
 Arc tangent. Returns an angle whose tangent is y/x. The signs of x and y are used to determine what quadrant the angle is in. The range of values returned by this function is [-PI, PI]. Results are undefined if x and y are both 0. (From GLM_GTX_compatibility)
 
\ntemplate<typename T , qualifier Q>
GLM_FUNC_QUALIFIER vec< 3, T, Q > atan2 (const vec< 3, T, Q > &x, const vec< 3, T, Q > &y)
 Arc tangent. Returns an angle whose tangent is y/x. The signs of x and y are used to determine what quadrant the angle is in. The range of values returned by this function is [-PI, PI]. Results are undefined if x and y are both 0. (From GLM_GTX_compatibility)
 
\ntemplate<typename T , qualifier Q>
GLM_FUNC_QUALIFIER vec< 4, T, Q > atan2 (const vec< 4, T, Q > &x, const vec< 4, T, Q > &y)
 Arc tangent. Returns an angle whose tangent is y/x. The signs of x and y are used to determine what quadrant the angle is in. The range of values returned by this function is [-PI, PI]. Results are undefined if x and y are both 0. (From GLM_GTX_compatibility)
 
\ntemplate<typename genType >
GLM_FUNC_DECL bool isfinite (genType const &x)
 Test whether or not a scalar or each vector component is a finite value. (From GLM_GTX_compatibility)
 
\ntemplate<typename T , qualifier Q>
GLM_FUNC_DECL vec< 1, bool, Q > isfinite (const vec< 1, T, Q > &x)
 Test whether or not a scalar or each vector component is a finite value. (From GLM_GTX_compatibility)
 
\ntemplate<typename T , qualifier Q>
GLM_FUNC_DECL vec< 2, bool, Q > isfinite (const vec< 2, T, Q > &x)
 Test whether or not a scalar or each vector component is a finite value. (From GLM_GTX_compatibility)
 
\ntemplate<typename T , qualifier Q>
GLM_FUNC_DECL vec< 3, bool, Q > isfinite (const vec< 3, T, Q > &x)
 Test whether or not a scalar or each vector component is a finite value. (From GLM_GTX_compatibility)
 
\ntemplate<typename T , qualifier Q>
GLM_FUNC_DECL vec< 4, bool, Q > isfinite (const vec< 4, T, Q > &x)
 Test whether or not a scalar or each vector component is a finite value. (From GLM_GTX_compatibility)
 
\ntemplate<typename T >
GLM_FUNC_QUALIFIER T lerp (T x, T y, T a)
 Returns x * (1.0 - a) + y * a, i.e., the linear blend of x and y using the floating-point value a. The value for a is not restricted to the range [0, 1]. (From GLM_GTX_compatibility)
 
\ntemplate<typename T , qualifier Q>
GLM_FUNC_QUALIFIER vec< 2, T, Q > lerp (const vec< 2, T, Q > &x, const vec< 2, T, Q > &y, T a)
 Returns x * (1.0 - a) + y * a, i.e., the linear blend of x and y using the floating-point value a. The value for a is not restricted to the range [0, 1]. (From GLM_GTX_compatibility)
 
\ntemplate<typename T , qualifier Q>
GLM_FUNC_QUALIFIER vec< 3, T, Q > lerp (const vec< 3, T, Q > &x, const vec< 3, T, Q > &y, T a)
 Returns x * (1.0 - a) + y * a, i.e., the linear blend of x and y using the floating-point value a. The value for a is not restricted to the range [0, 1]. (From GLM_GTX_compatibility)
 
\ntemplate<typename T , qualifier Q>
GLM_FUNC_QUALIFIER vec< 4, T, Q > lerp (const vec< 4, T, Q > &x, const vec< 4, T, Q > &y, T a)
 Returns x * (1.0 - a) + y * a, i.e., the linear blend of x and y using the floating-point value a. The value for a is not restricted to the range [0, 1]. (From GLM_GTX_compatibility)
 
\ntemplate<typename T , qualifier Q>
GLM_FUNC_QUALIFIER vec< 2, T, Q > lerp (const vec< 2, T, Q > &x, const vec< 2, T, Q > &y, const vec< 2, T, Q > &a)
 Returns the component-wise result of x * (1.0 - a) + y * a, i.e., the linear blend of x and y using vector a. The value for a is not restricted to the range [0, 1]. (From GLM_GTX_compatibility)
 
\ntemplate<typename T , qualifier Q>
GLM_FUNC_QUALIFIER vec< 3, T, Q > lerp (const vec< 3, T, Q > &x, const vec< 3, T, Q > &y, const vec< 3, T, Q > &a)
 Returns the component-wise result of x * (1.0 - a) + y * a, i.e., the linear blend of x and y using vector a. The value for a is not restricted to the range [0, 1]. (From GLM_GTX_compatibility)
 
\ntemplate<typename T , qualifier Q>
GLM_FUNC_QUALIFIER vec< 4, T, Q > lerp (const vec< 4, T, Q > &x, const vec< 4, T, Q > &y, const vec< 4, T, Q > &a)
 Returns the component-wise result of x * (1.0 - a) + y * a, i.e., the linear blend of x and y using vector a. The value for a is not restricted to the range [0, 1]. (From GLM_GTX_compatibility)
 
\ntemplate<typename T , qualifier Q>
GLM_FUNC_QUALIFIER T saturate (T x)
 Returns clamp(x, 0, 1) for each component in x. (From GLM_GTX_compatibility)
 
\ntemplate<typename T , qualifier Q>
GLM_FUNC_QUALIFIER vec< 2, T, Q > saturate (const vec< 2, T, Q > &x)
 Returns clamp(x, 0, 1) for each component in x. (From GLM_GTX_compatibility)
 
\ntemplate<typename T , qualifier Q>
GLM_FUNC_QUALIFIER vec< 3, T, Q > saturate (const vec< 3, T, Q > &x)
 Returns clamp(x, 0, 1) for each component in x. (From GLM_GTX_compatibility)
 
\ntemplate<typename T , qualifier Q>
GLM_FUNC_QUALIFIER vec< 4, T, Q > saturate (const vec< 4, T, Q > &x)
 Returns clamp(x, 0, 1) for each component in x. (From GLM_GTX_compatibility)
 
\n

Detailed Description

\n

GLM_GTX_compatibility

\n
See also
Core features (dependence)
\n\n

Definition in file compatibility.hpp.

\n
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compatibility.hpp
\n
\n
\nGo to the documentation of this file.
1 
\n
13 #pragma once
\n
14 
\n
15 // Dependency:
\n
16 #include "../glm.hpp"
\n
17 #include "../gtc/quaternion.hpp"
\n
18 
\n
19 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
20 # ifndef GLM_ENABLE_EXPERIMENTAL
\n
21 # pragma message("GLM: GLM_GTX_compatibility is an experimental extension and may change in the future. Use #define GLM_ENABLE_EXPERIMENTAL before including it, if you really want to use it.")
\n
22 # else
\n
23 # pragma message("GLM: GLM_GTX_compatibility extension included")
\n
24 # endif
\n
25 #endif
\n
26 
\n
27 #if GLM_COMPILER & GLM_COMPILER_VC
\n
28 # include <cfloat>
\n
29 #elif GLM_COMPILER & GLM_COMPILER_GCC
\n
30 # include <cmath>
\n
31 # if(GLM_PLATFORM & GLM_PLATFORM_ANDROID)
\n
32 # undef isfinite
\n
33 # endif
\n
34 #endif//GLM_COMPILER
\n
35 
\n
36 namespace glm
\n
37 {
\n
40 
\n
41  template<typename T> GLM_FUNC_QUALIFIER T lerp(T x, T y, T a){return mix(x, y, a);}
\n
42  template<typename T, qualifier Q> GLM_FUNC_QUALIFIER vec<2, T, Q> lerp(const vec<2, T, Q>& x, const vec<2, T, Q>& y, T a){return mix(x, y, a);}
\n
43 
\n
44  template<typename T, qualifier Q> GLM_FUNC_QUALIFIER vec<3, T, Q> lerp(const vec<3, T, Q>& x, const vec<3, T, Q>& y, T a){return mix(x, y, a);}
\n
45  template<typename T, qualifier Q> GLM_FUNC_QUALIFIER vec<4, T, Q> lerp(const vec<4, T, Q>& x, const vec<4, T, Q>& y, T a){return mix(x, y, a);}
\n
46  template<typename T, qualifier Q> GLM_FUNC_QUALIFIER vec<2, T, Q> lerp(const vec<2, T, Q>& x, const vec<2, T, Q>& y, const vec<2, T, Q>& a){return mix(x, y, a);}
\n
47  template<typename T, qualifier Q> GLM_FUNC_QUALIFIER vec<3, T, Q> lerp(const vec<3, T, Q>& x, const vec<3, T, Q>& y, const vec<3, T, Q>& a){return mix(x, y, a);}
\n
48  template<typename T, qualifier Q> GLM_FUNC_QUALIFIER vec<4, T, Q> lerp(const vec<4, T, Q>& x, const vec<4, T, Q>& y, const vec<4, T, Q>& a){return mix(x, y, a);}
\n
49 
\n
50  template<typename T, qualifier Q> GLM_FUNC_QUALIFIER T saturate(T x){return clamp(x, T(0), T(1));}
\n
51  template<typename T, qualifier Q> GLM_FUNC_QUALIFIER vec<2, T, Q> saturate(const vec<2, T, Q>& x){return clamp(x, T(0), T(1));}
\n
52  template<typename T, qualifier Q> GLM_FUNC_QUALIFIER vec<3, T, Q> saturate(const vec<3, T, Q>& x){return clamp(x, T(0), T(1));}
\n
53  template<typename T, qualifier Q> GLM_FUNC_QUALIFIER vec<4, T, Q> saturate(const vec<4, T, Q>& x){return clamp(x, T(0), T(1));}
\n
54 
\n
55  template<typename T, qualifier Q> GLM_FUNC_QUALIFIER T atan2(T x, T y){return atan(x, y);}
\n
56  template<typename T, qualifier Q> GLM_FUNC_QUALIFIER vec<2, T, Q> atan2(const vec<2, T, Q>& x, const vec<2, T, Q>& y){return atan(x, y);}
\n
57  template<typename T, qualifier Q> GLM_FUNC_QUALIFIER vec<3, T, Q> atan2(const vec<3, T, Q>& x, const vec<3, T, Q>& y){return atan(x, y);}
\n
58  template<typename T, qualifier Q> GLM_FUNC_QUALIFIER vec<4, T, Q> atan2(const vec<4, T, Q>& x, const vec<4, T, Q>& y){return atan(x, y);}
\n
59 
\n
60  template<typename genType> GLM_FUNC_DECL bool isfinite(genType const& x);
\n
61  template<typename T, qualifier Q> GLM_FUNC_DECL vec<1, bool, Q> isfinite(const vec<1, T, Q>& x);
\n
62  template<typename T, qualifier Q> GLM_FUNC_DECL vec<2, bool, Q> isfinite(const vec<2, T, Q>& x);
\n
63  template<typename T, qualifier Q> GLM_FUNC_DECL vec<3, bool, Q> isfinite(const vec<3, T, Q>& x);
\n
64  template<typename T, qualifier Q> GLM_FUNC_DECL vec<4, bool, Q> isfinite(const vec<4, T, Q>& x);
\n
65 
\n
66  typedef bool bool1;
\n
67  typedef vec<2, bool, highp> bool2;
\n
68  typedef vec<3, bool, highp> bool3;
\n
69  typedef vec<4, bool, highp> bool4;
\n
70 
\n
71  typedef bool bool1x1;
\n
72  typedef mat<2, 2, bool, highp> bool2x2;
\n
73  typedef mat<2, 3, bool, highp> bool2x3;
\n
74  typedef mat<2, 4, bool, highp> bool2x4;
\n
75  typedef mat<3, 2, bool, highp> bool3x2;
\n
76  typedef mat<3, 3, bool, highp> bool3x3;
\n
77  typedef mat<3, 4, bool, highp> bool3x4;
\n
78  typedef mat<4, 2, bool, highp> bool4x2;
\n
79  typedef mat<4, 3, bool, highp> bool4x3;
\n
80  typedef mat<4, 4, bool, highp> bool4x4;
\n
81 
\n
82  typedef int int1;
\n
83  typedef vec<2, int, highp> int2;
\n
84  typedef vec<3, int, highp> int3;
\n
85  typedef vec<4, int, highp> int4;
\n
86 
\n
87  typedef int int1x1;
\n
88  typedef mat<2, 2, int, highp> int2x2;
\n
89  typedef mat<2, 3, int, highp> int2x3;
\n
90  typedef mat<2, 4, int, highp> int2x4;
\n
91  typedef mat<3, 2, int, highp> int3x2;
\n
92  typedef mat<3, 3, int, highp> int3x3;
\n
93  typedef mat<3, 4, int, highp> int3x4;
\n
94  typedef mat<4, 2, int, highp> int4x2;
\n
95  typedef mat<4, 3, int, highp> int4x3;
\n
96  typedef mat<4, 4, int, highp> int4x4;
\n
97 
\n
98  typedef float float1;
\n
99  typedef vec<2, float, highp> float2;
\n
100  typedef vec<3, float, highp> float3;
\n
101  typedef vec<4, float, highp> float4;
\n
102 
\n
103  typedef float float1x1;
\n
104  typedef mat<2, 2, float, highp> float2x2;
\n
105  typedef mat<2, 3, float, highp> float2x3;
\n
106  typedef mat<2, 4, float, highp> float2x4;
\n
107  typedef mat<3, 2, float, highp> float3x2;
\n
108  typedef mat<3, 3, float, highp> float3x3;
\n
109  typedef mat<3, 4, float, highp> float3x4;
\n
110  typedef mat<4, 2, float, highp> float4x2;
\n
111  typedef mat<4, 3, float, highp> float4x3;
\n
112  typedef mat<4, 4, float, highp> float4x4;
\n
113 
\n
114  typedef double double1;
\n
115  typedef vec<2, double, highp> double2;
\n
116  typedef vec<3, double, highp> double3;
\n
117  typedef vec<4, double, highp> double4;
\n
118 
\n
119  typedef double double1x1;
\n
120  typedef mat<2, 2, double, highp> double2x2;
\n
121  typedef mat<2, 3, double, highp> double2x3;
\n
122  typedef mat<2, 4, double, highp> double2x4;
\n
123  typedef mat<3, 2, double, highp> double3x2;
\n
124  typedef mat<3, 3, double, highp> double3x3;
\n
125  typedef mat<3, 4, double, highp> double3x4;
\n
126  typedef mat<4, 2, double, highp> double4x2;
\n
127  typedef mat<4, 3, double, highp> double4x3;
\n
128  typedef mat<4, 4, double, highp> double4x4;
\n
129 
\n
131 }//namespace glm
\n
132 
\n
133 #include "compatibility.inl"
\n
glm::double4x4
mat< 4, 4, double, highp > double4x4
double-qualifier floating-point matrix with 4 x 4 components. (From GLM_GTX_compatibility extension) ...
Definition: compatibility.hpp:128
\n
glm::int3x4
mat< 3, 4, int, highp > int3x4
integer matrix with 3 x 4 components. (From GLM_GTX_compatibility extension)
Definition: compatibility.hpp:93
\n
glm::atan
GLM_FUNC_DECL vec< L, T, Q > atan(vec< L, T, Q > const &y, vec< L, T, Q > const &x)
Arc tangent.
\n
glm::bool1
bool bool1
boolean type with 1 component. (From GLM_GTX_compatibility extension)
Definition: compatibility.hpp:66
\n
glm::float4x3
mat< 4, 3, float, highp > float4x3
single-qualifier floating-point matrix with 4 x 3 components. (From GLM_GTX_compatibility extension) ...
Definition: compatibility.hpp:111
\n
glm::float4x4
mat< 4, 4, float, highp > float4x4
single-qualifier floating-point matrix with 4 x 4 components. (From GLM_GTX_compatibility extension) ...
Definition: compatibility.hpp:112
\n
glm::double2x4
mat< 2, 4, double, highp > double2x4
double-qualifier floating-point matrix with 2 x 4 components. (From GLM_GTX_compatibility extension) ...
Definition: compatibility.hpp:122
\n
glm::double2x2
mat< 2, 2, double, highp > double2x2
double-qualifier floating-point matrix with 2 x 2 components. (From GLM_GTX_compatibility extension) ...
Definition: compatibility.hpp:120
\n
glm::double3x2
mat< 3, 2, double, highp > double3x2
double-qualifier floating-point matrix with 3 x 2 components. (From GLM_GTX_compatibility extension) ...
Definition: compatibility.hpp:123
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glm::atan2
GLM_FUNC_QUALIFIER vec< 4, T, Q > atan2(const vec< 4, T, Q > &x, const vec< 4, T, Q > &y)
Arc tangent. Returns an angle whose tangent is y/x. The signs of x and y are used to determine what q...
Definition: compatibility.hpp:58
\n
glm::double1x1
double double1x1
double-qualifier floating-point matrix with 1 component. (From GLM_GTX_compatibility extension) ...
Definition: compatibility.hpp:119
\n
glm::lerp
GLM_FUNC_QUALIFIER vec< 4, T, Q > lerp(const vec< 4, T, Q > &x, const vec< 4, T, Q > &y, const vec< 4, T, Q > &a)
Returns the component-wise result of x * (1.0 - a) + y * a, i.e., the linear blend of x and y using v...
Definition: compatibility.hpp:48
\n
glm::double3x3
mat< 3, 3, double, highp > double3x3
double-qualifier floating-point matrix with 3 x 3 components. (From GLM_GTX_compatibility extension) ...
Definition: compatibility.hpp:124
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glm::float4
vec< 4, float, highp > float4
single-qualifier floating-point vector with 4 components. (From GLM_GTX_compatibility extension) ...
Definition: compatibility.hpp:101
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glm::int1x1
int int1x1
integer matrix with 1 component. (From GLM_GTX_compatibility extension)
Definition: compatibility.hpp:87
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glm::float2
vec< 2, float, highp > float2
single-qualifier floating-point vector with 2 components. (From GLM_GTX_compatibility extension) ...
Definition: compatibility.hpp:99
\n
glm::isfinite
GLM_FUNC_DECL vec< 4, bool, Q > isfinite(const vec< 4, T, Q > &x)
Test whether or not a scalar or each vector component is a finite value. (From GLM_GTX_compatibility)...
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glm::bool2x3
mat< 2, 3, bool, highp > bool2x3
boolean matrix with 2 x 3 components. (From GLM_GTX_compatibility extension)
Definition: compatibility.hpp:73
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glm::int2x3
mat< 2, 3, int, highp > int2x3
integer matrix with 2 x 3 components. (From GLM_GTX_compatibility extension)
Definition: compatibility.hpp:89
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glm::int1
int int1
integer vector with 1 component. (From GLM_GTX_compatibility extension)
Definition: compatibility.hpp:82
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glm::float3
vec< 3, float, highp > float3
single-qualifier floating-point vector with 3 components. (From GLM_GTX_compatibility extension) ...
Definition: compatibility.hpp:100
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glm::float2x4
mat< 2, 4, float, highp > float2x4
single-qualifier floating-point matrix with 2 x 4 components. (From GLM_GTX_compatibility extension) ...
Definition: compatibility.hpp:106
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glm::bool2x2
mat< 2, 2, bool, highp > bool2x2
boolean matrix with 2 x 2 components. (From GLM_GTX_compatibility extension)
Definition: compatibility.hpp:72
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glm::bool4x4
mat< 4, 4, bool, highp > bool4x4
boolean matrix with 4 x 4 components. (From GLM_GTX_compatibility extension)
Definition: compatibility.hpp:80
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glm::float1
float float1
single-qualifier floating-point vector with 1 component. (From GLM_GTX_compatibility extension) ...
Definition: compatibility.hpp:98
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glm::float1x1
float float1x1
single-qualifier floating-point matrix with 1 component. (From GLM_GTX_compatibility extension) ...
Definition: compatibility.hpp:103
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glm::double4x2
mat< 4, 2, double, highp > double4x2
double-qualifier floating-point matrix with 4 x 2 components. (From GLM_GTX_compatibility extension) ...
Definition: compatibility.hpp:126
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glm::int4x3
mat< 4, 3, int, highp > int4x3
integer matrix with 4 x 3 components. (From GLM_GTX_compatibility extension)
Definition: compatibility.hpp:95
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glm::bool4x2
mat< 4, 2, bool, highp > bool4x2
boolean matrix with 4 x 2 components. (From GLM_GTX_compatibility extension)
Definition: compatibility.hpp:78
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glm::float2x2
mat< 2, 2, float, highp > float2x2
single-qualifier floating-point matrix with 2 x 2 components. (From GLM_GTX_compatibility extension) ...
Definition: compatibility.hpp:104
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glm::int3
vec< 3, int, highp > int3
integer vector with 3 components. (From GLM_GTX_compatibility extension)
Definition: compatibility.hpp:84
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glm::float4x2
mat< 4, 2, float, highp > float4x2
single-qualifier floating-point matrix with 4 x 2 components. (From GLM_GTX_compatibility extension) ...
Definition: compatibility.hpp:110
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glm::double2x3
mat< 2, 3, double, highp > double2x3
double-qualifier floating-point matrix with 2 x 3 components. (From GLM_GTX_compatibility extension) ...
Definition: compatibility.hpp:121
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glm::float2x3
mat< 2, 3, float, highp > float2x3
single-qualifier floating-point matrix with 2 x 3 components. (From GLM_GTX_compatibility extension) ...
Definition: compatibility.hpp:105
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glm::int3x2
mat< 3, 2, int, highp > int3x2
integer matrix with 3 x 2 components. (From GLM_GTX_compatibility extension)
Definition: compatibility.hpp:91
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glm::bool4
vec< 4, bool, highp > bool4
boolean type with 4 components. (From GLM_GTX_compatibility extension)
Definition: compatibility.hpp:69
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glm::int4x2
mat< 4, 2, int, highp > int4x2
integer matrix with 4 x 2 components. (From GLM_GTX_compatibility extension)
Definition: compatibility.hpp:94
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glm::bool1x1
bool bool1x1
boolean matrix with 1 x 1 component. (From GLM_GTX_compatibility extension)
Definition: compatibility.hpp:71
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glm::saturate
GLM_FUNC_QUALIFIER vec< 4, T, Q > saturate(const vec< 4, T, Q > &x)
Returns clamp(x, 0, 1) for each component in x. (From GLM_GTX_compatibility)
Definition: compatibility.hpp:53
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glm::bool3
vec< 3, bool, highp > bool3
boolean type with 3 components. (From GLM_GTX_compatibility extension)
Definition: compatibility.hpp:68
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glm::clamp
GLM_FUNC_DECL GLM_CONSTEXPR genType clamp(genType x, genType minVal, genType maxVal)
Returns min(max(x, minVal), maxVal) for each component in x using the floating-point values minVal an...
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glm::int2x2
mat< 2, 2, int, highp > int2x2
integer matrix with 2 x 2 components. (From GLM_GTX_compatibility extension)
Definition: compatibility.hpp:88
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glm::int2
vec< 2, int, highp > int2
integer vector with 2 components. (From GLM_GTX_compatibility extension)
Definition: compatibility.hpp:83
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glm::int4x4
mat< 4, 4, int, highp > int4x4
integer matrix with 4 x 4 components. (From GLM_GTX_compatibility extension)
Definition: compatibility.hpp:96
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glm::bool3x2
mat< 3, 2, bool, highp > bool3x2
boolean matrix with 3 x 2 components. (From GLM_GTX_compatibility extension)
Definition: compatibility.hpp:75
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glm::double4x3
mat< 4, 3, double, highp > double4x3
double-qualifier floating-point matrix with 4 x 3 components. (From GLM_GTX_compatibility extension) ...
Definition: compatibility.hpp:127
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glm::bool4x3
mat< 4, 3, bool, highp > bool4x3
boolean matrix with 4 x 3 components. (From GLM_GTX_compatibility extension)
Definition: compatibility.hpp:79
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glm::double1
double double1
double-qualifier floating-point vector with 1 component. (From GLM_GTX_compatibility extension) ...
Definition: compatibility.hpp:114
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glm::double3
vec< 3, double, highp > double3
double-qualifier floating-point vector with 3 components. (From GLM_GTX_compatibility extension) ...
Definition: compatibility.hpp:116
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glm::double4
vec< 4, double, highp > double4
double-qualifier floating-point vector with 4 components. (From GLM_GTX_compatibility extension) ...
Definition: compatibility.hpp:117
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glm::int3x3
mat< 3, 3, int, highp > int3x3
integer matrix with 3 x 3 components. (From GLM_GTX_compatibility extension)
Definition: compatibility.hpp:92
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glm::bool3x3
mat< 3, 3, bool, highp > bool3x3
boolean matrix with 3 x 3 components. (From GLM_GTX_compatibility extension)
Definition: compatibility.hpp:76
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glm::float3x2
mat< 3, 2, float, highp > float3x2
single-qualifier floating-point matrix with 3 x 2 components. (From GLM_GTX_compatibility extension) ...
Definition: compatibility.hpp:107
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glm::int4
vec< 4, int, highp > int4
integer vector with 4 components. (From GLM_GTX_compatibility extension)
Definition: compatibility.hpp:85
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glm::double2
vec< 2, double, highp > double2
double-qualifier floating-point vector with 2 components. (From GLM_GTX_compatibility extension) ...
Definition: compatibility.hpp:115
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glm::float3x3
mat< 3, 3, float, highp > float3x3
single-qualifier floating-point matrix with 3 x 3 components. (From GLM_GTX_compatibility extension) ...
Definition: compatibility.hpp:108
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glm::mix
GLM_FUNC_DECL genTypeT mix(genTypeT x, genTypeT y, genTypeU a)
If genTypeU is a floating scalar or vector: Returns x * (1.0 - a) + y * a, i.e., the linear blend of ...
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glm::bool2
vec< 2, bool, highp > bool2
boolean type with 2 components. (From GLM_GTX_compatibility extension)
Definition: compatibility.hpp:67
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glm::bool3x4
mat< 3, 4, bool, highp > bool3x4
boolean matrix with 3 x 4 components. (From GLM_GTX_compatibility extension)
Definition: compatibility.hpp:77
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glm::int2x4
mat< 2, 4, int, highp > int2x4
integer matrix with 2 x 4 components. (From GLM_GTX_compatibility extension)
Definition: compatibility.hpp:90
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glm::bool2x4
mat< 2, 4, bool, highp > bool2x4
boolean matrix with 2 x 4 components. (From GLM_GTX_compatibility extension)
Definition: compatibility.hpp:74
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glm::double3x4
mat< 3, 4, double, highp > double3x4
double-qualifier floating-point matrix with 3 x 4 components. (From GLM_GTX_compatibility extension) ...
Definition: compatibility.hpp:125
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glm
Definition: common.hpp:20
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glm::float3x4
mat< 3, 4, float, highp > float3x4
single-qualifier floating-point matrix with 3 x 4 components. (From GLM_GTX_compatibility extension) ...
Definition: compatibility.hpp:109
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component_wise.hpp File Reference
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GLM_GTX_component_wise \nMore...

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\nFunctions

template<typename genType >
GLM_FUNC_DECL genType::value_type compAdd (genType const &v)
 Add all vector components together. More...
 
template<typename genType >
GLM_FUNC_DECL genType::value_type compMax (genType const &v)
 Find the maximum value between single vector components. More...
 
template<typename genType >
GLM_FUNC_DECL genType::value_type compMin (genType const &v)
 Find the minimum value between single vector components. More...
 
template<typename genType >
GLM_FUNC_DECL genType::value_type compMul (genType const &v)
 Multiply all vector components together. More...
 
template<typename floatType , length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, floatType, Q > compNormalize (vec< L, T, Q > const &v)
 Convert an integer vector to a normalized float vector. More...
 
template<length_t L, typename T , typename floatType , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > compScale (vec< L, floatType, Q > const &v)
 Convert a normalized float vector to an integer vector. More...
 
\n

Detailed Description

\n

GLM_GTX_component_wise

\n
Date
2007-05-21 / 2011-06-07
\n
Author
Christophe Riccio
\n
See also
Core features (dependence)
\n\n

Definition in file component_wise.hpp.

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component_wise.hpp
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\nGo to the documentation of this file.
1 
\n
15 #pragma once
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16 
\n
17 // Dependencies
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18 #include "../detail/setup.hpp"
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19 #include "../detail/qualifier.hpp"
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20 
\n
21 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
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22 # ifndef GLM_ENABLE_EXPERIMENTAL
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23 # pragma message("GLM: GLM_GTX_component_wise is an experimental extension and may change in the future. Use #define GLM_ENABLE_EXPERIMENTAL before including it, if you really want to use it.")
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24 # else
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25 # pragma message("GLM: GLM_GTX_component_wise extension included")
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26 # endif
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27 #endif
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28 
\n
29 namespace glm
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30 {
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33 
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37  template<typename floatType, length_t L, typename T, qualifier Q>
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38  GLM_FUNC_DECL vec<L, floatType, Q> compNormalize(vec<L, T, Q> const& v);
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39 
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43  template<length_t L, typename T, typename floatType, qualifier Q>
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44  GLM_FUNC_DECL vec<L, T, Q> compScale(vec<L, floatType, Q> const& v);
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45 
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48  template<typename genType>
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49  GLM_FUNC_DECL typename genType::value_type compAdd(genType const& v);
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50 
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53  template<typename genType>
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54  GLM_FUNC_DECL typename genType::value_type compMul(genType const& v);
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55 
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58  template<typename genType>
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59  GLM_FUNC_DECL typename genType::value_type compMin(genType const& v);
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60 
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63  template<typename genType>
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64  GLM_FUNC_DECL typename genType::value_type compMax(genType const& v);
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65 
\n
67 }//namespace glm
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68 
\n
69 #include "component_wise.inl"
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glm::compMax
GLM_FUNC_DECL genType::value_type compMax(genType const &v)
Find the maximum value between single vector components.
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glm::compMul
GLM_FUNC_DECL genType::value_type compMul(genType const &v)
Multiply all vector components together.
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glm::compScale
GLM_FUNC_DECL vec< L, T, Q > compScale(vec< L, floatType, Q > const &v)
Convert a normalized float vector to an integer vector.
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glm::compNormalize
GLM_FUNC_DECL vec< L, floatType, Q > compNormalize(vec< L, T, Q > const &v)
Convert an integer vector to a normalized float vector.
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glm::compMin
GLM_FUNC_DECL genType::value_type compMin(genType const &v)
Find the minimum value between single vector components.
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glm::compAdd
GLM_FUNC_DECL genType::value_type compAdd(genType const &v)
Add all vector components together.
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glm
Definition: common.hpp:20
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compute_common.hpp
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1 #pragma once
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2 
\n
3 #include "setup.hpp"
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4 #include <limits>
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5 
\n
6 namespace glm{
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7 namespace detail
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8 {
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9  template<typename genFIType, bool /*signed*/>
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10  struct compute_abs
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11  {};
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12 
\n
13  template<typename genFIType>
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14  struct compute_abs<genFIType, true>
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15  {
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16  GLM_FUNC_QUALIFIER GLM_CONSTEXPR static genFIType call(genFIType x)
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17  {
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18  GLM_STATIC_ASSERT(
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19  std::numeric_limits<genFIType>::is_iec559 || std::numeric_limits<genFIType>::is_signed,
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20  "'abs' only accept floating-point and integer scalar or vector inputs");
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21 
\n
22  return x >= genFIType(0) ? x : -x;
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23  // TODO, perf comp with: *(((int *) &x) + 1) &= 0x7fffffff;
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24  }
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25  };
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26 
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27 #if GLM_COMPILER & GLM_COMPILER_CUDA
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28  template<>
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29  struct compute_abs<float, true>
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30  {
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31  GLM_FUNC_QUALIFIER GLM_CONSTEXPR static float call(float x)
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32  {
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33  return fabsf(x);
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34  }
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35  };
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36 #endif
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37 
\n
38  template<typename genFIType>
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39  struct compute_abs<genFIType, false>
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40  {
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41  GLM_FUNC_QUALIFIER GLM_CONSTEXPR static genFIType call(genFIType x)
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42  {
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43  GLM_STATIC_ASSERT(
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44  (!std::numeric_limits<genFIType>::is_signed && std::numeric_limits<genFIType>::is_integer),
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45  "'abs' only accept floating-point and integer scalar or vector inputs");
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46  return x;
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47  }
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48  };
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49 }//namespace detail
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50 }//namespace glm
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glm
Definition: common.hpp:20
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compute_vector_relational.hpp
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1 #pragma once
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2 
\n
3 //#include "compute_common.hpp"
\n
4 #include "setup.hpp"
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5 #include <limits>
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6 
\n
7 namespace glm{
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8 namespace detail
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9 {
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10  template <typename T, bool isFloat>
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11  struct compute_equal
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12  {
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13  GLM_FUNC_QUALIFIER GLM_CONSTEXPR static bool call(T a, T b)
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14  {
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15  return a == b;
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16  }
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17  };
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18 /*
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19  template <typename T>
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20  struct compute_equal<T, true>
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21  {
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22  GLM_FUNC_QUALIFIER GLM_CONSTEXPR static bool call(T a, T b)
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23  {
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24  return detail::compute_abs<T, std::numeric_limits<T>::is_signed>::call(b - a) <= static_cast<T>(0);
\n
25  //return std::memcmp(&a, &b, sizeof(T)) == 0;
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26  }
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27  };
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28 */
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29 }//namespace detail
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30 }//namespace glm
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glm
Definition: common.hpp:20
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constants.hpp File Reference
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GLM_GTC_constants \nMore...

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\nFunctions

template<typename genType >
GLM_FUNC_DECL GLM_CONSTEXPR genType e ()
 Return e constant. More...
 
template<typename genType >
GLM_FUNC_DECL GLM_CONSTEXPR genType euler ()
 Return Euler's constant. More...
 
template<typename genType >
GLM_FUNC_DECL GLM_CONSTEXPR genType four_over_pi ()
 Return 4 / pi. More...
 
template<typename genType >
GLM_FUNC_DECL GLM_CONSTEXPR genType golden_ratio ()
 Return the golden ratio constant. More...
 
template<typename genType >
GLM_FUNC_DECL GLM_CONSTEXPR genType half_pi ()
 Return pi / 2. More...
 
template<typename genType >
GLM_FUNC_DECL GLM_CONSTEXPR genType ln_ln_two ()
 Return ln(ln(2)). More...
 
template<typename genType >
GLM_FUNC_DECL GLM_CONSTEXPR genType ln_ten ()
 Return ln(10). More...
 
template<typename genType >
GLM_FUNC_DECL GLM_CONSTEXPR genType ln_two ()
 Return ln(2). More...
 
template<typename genType >
GLM_FUNC_DECL GLM_CONSTEXPR genType one ()
 Return 1. More...
 
template<typename genType >
GLM_FUNC_DECL GLM_CONSTEXPR genType one_over_pi ()
 Return 1 / pi. More...
 
template<typename genType >
GLM_FUNC_DECL GLM_CONSTEXPR genType one_over_root_two ()
 Return 1 / sqrt(2). More...
 
template<typename genType >
GLM_FUNC_DECL GLM_CONSTEXPR genType one_over_two_pi ()
 Return 1 / (pi * 2). More...
 
template<typename genType >
GLM_FUNC_DECL GLM_CONSTEXPR genType quarter_pi ()
 Return pi / 4. More...
 
template<typename genType >
GLM_FUNC_DECL GLM_CONSTEXPR genType root_five ()
 Return sqrt(5). More...
 
template<typename genType >
GLM_FUNC_DECL GLM_CONSTEXPR genType root_half_pi ()
 Return sqrt(pi / 2). More...
 
template<typename genType >
GLM_FUNC_DECL GLM_CONSTEXPR genType root_ln_four ()
 Return sqrt(ln(4)). More...
 
template<typename genType >
GLM_FUNC_DECL GLM_CONSTEXPR genType root_pi ()
 Return square root of pi. More...
 
template<typename genType >
GLM_FUNC_DECL GLM_CONSTEXPR genType root_three ()
 Return sqrt(3). More...
 
template<typename genType >
GLM_FUNC_DECL GLM_CONSTEXPR genType root_two ()
 Return sqrt(2). More...
 
template<typename genType >
GLM_FUNC_DECL GLM_CONSTEXPR genType root_two_pi ()
 Return sqrt(2 * pi). More...
 
template<typename genType >
GLM_FUNC_DECL GLM_CONSTEXPR genType third ()
 Return 1 / 3. More...
 
template<typename genType >
GLM_FUNC_DECL GLM_CONSTEXPR genType three_over_two_pi ()
 Return pi / 2 * 3. More...
 
template<typename genType >
GLM_FUNC_DECL GLM_CONSTEXPR genType two_over_pi ()
 Return 2 / pi. More...
 
template<typename genType >
GLM_FUNC_DECL GLM_CONSTEXPR genType two_over_root_pi ()
 Return 2 / sqrt(pi). More...
 
template<typename genType >
GLM_FUNC_DECL GLM_CONSTEXPR genType two_pi ()
 Return pi * 2. More...
 
template<typename genType >
GLM_FUNC_DECL GLM_CONSTEXPR genType two_thirds ()
 Return 2 / 3. More...
 
template<typename genType >
GLM_FUNC_DECL GLM_CONSTEXPR genType zero ()
 Return 0. More...
 
\n

Detailed Description

\n

GLM_GTC_constants

\n
See also
Core features (dependence)
\n\n

Definition in file constants.hpp.

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13 #pragma once
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14 
\n
15 // Dependencies
\n
16 #include "../ext/scalar_constants.hpp"
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17 
\n
18 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
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19 # pragma message("GLM: GLM_GTC_constants extension included")
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20 #endif
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21 
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22 namespace glm
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23 {
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26 
\n
29  template<typename genType>
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30  GLM_FUNC_DECL GLM_CONSTEXPR genType zero();
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31 
\n
34  template<typename genType>
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35  GLM_FUNC_DECL GLM_CONSTEXPR genType one();
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36 
\n
39  template<typename genType>
\n
40  GLM_FUNC_DECL GLM_CONSTEXPR genType two_pi();
\n
41 
\n
44  template<typename genType>
\n
45  GLM_FUNC_DECL GLM_CONSTEXPR genType root_pi();
\n
46 
\n
49  template<typename genType>
\n
50  GLM_FUNC_DECL GLM_CONSTEXPR genType half_pi();
\n
51 
\n
54  template<typename genType>
\n
55  GLM_FUNC_DECL GLM_CONSTEXPR genType three_over_two_pi();
\n
56 
\n
59  template<typename genType>
\n
60  GLM_FUNC_DECL GLM_CONSTEXPR genType quarter_pi();
\n
61 
\n
64  template<typename genType>
\n
65  GLM_FUNC_DECL GLM_CONSTEXPR genType one_over_pi();
\n
66 
\n
69  template<typename genType>
\n
70  GLM_FUNC_DECL GLM_CONSTEXPR genType one_over_two_pi();
\n
71 
\n
74  template<typename genType>
\n
75  GLM_FUNC_DECL GLM_CONSTEXPR genType two_over_pi();
\n
76 
\n
79  template<typename genType>
\n
80  GLM_FUNC_DECL GLM_CONSTEXPR genType four_over_pi();
\n
81 
\n
84  template<typename genType>
\n
85  GLM_FUNC_DECL GLM_CONSTEXPR genType two_over_root_pi();
\n
86 
\n
89  template<typename genType>
\n
90  GLM_FUNC_DECL GLM_CONSTEXPR genType one_over_root_two();
\n
91 
\n
94  template<typename genType>
\n
95  GLM_FUNC_DECL GLM_CONSTEXPR genType root_half_pi();
\n
96 
\n
99  template<typename genType>
\n
100  GLM_FUNC_DECL GLM_CONSTEXPR genType root_two_pi();
\n
101 
\n
104  template<typename genType>
\n
105  GLM_FUNC_DECL GLM_CONSTEXPR genType root_ln_four();
\n
106 
\n
109  template<typename genType>
\n
110  GLM_FUNC_DECL GLM_CONSTEXPR genType e();
\n
111 
\n
114  template<typename genType>
\n
115  GLM_FUNC_DECL GLM_CONSTEXPR genType euler();
\n
116 
\n
119  template<typename genType>
\n
120  GLM_FUNC_DECL GLM_CONSTEXPR genType root_two();
\n
121 
\n
124  template<typename genType>
\n
125  GLM_FUNC_DECL GLM_CONSTEXPR genType root_three();
\n
126 
\n
129  template<typename genType>
\n
130  GLM_FUNC_DECL GLM_CONSTEXPR genType root_five();
\n
131 
\n
134  template<typename genType>
\n
135  GLM_FUNC_DECL GLM_CONSTEXPR genType ln_two();
\n
136 
\n
139  template<typename genType>
\n
140  GLM_FUNC_DECL GLM_CONSTEXPR genType ln_ten();
\n
141 
\n
144  template<typename genType>
\n
145  GLM_FUNC_DECL GLM_CONSTEXPR genType ln_ln_two();
\n
146 
\n
149  template<typename genType>
\n
150  GLM_FUNC_DECL GLM_CONSTEXPR genType third();
\n
151 
\n
154  template<typename genType>
\n
155  GLM_FUNC_DECL GLM_CONSTEXPR genType two_thirds();
\n
156 
\n
159  template<typename genType>
\n
160  GLM_FUNC_DECL GLM_CONSTEXPR genType golden_ratio();
\n
161 
\n
163 } //namespace glm
\n
164 
\n
165 #include "constants.inl"
\n
glm::third
GLM_FUNC_DECL GLM_CONSTEXPR genType third()
Return 1 / 3.
\n
glm::root_two
GLM_FUNC_DECL GLM_CONSTEXPR genType root_two()
Return sqrt(2).
\n
glm::one_over_root_two
GLM_FUNC_DECL GLM_CONSTEXPR genType one_over_root_two()
Return 1 / sqrt(2).
\n
glm::euler
GLM_FUNC_DECL GLM_CONSTEXPR genType euler()
Return Euler's constant.
\n
glm::two_thirds
GLM_FUNC_DECL GLM_CONSTEXPR genType two_thirds()
Return 2 / 3.
\n
glm::two_pi
GLM_FUNC_DECL GLM_CONSTEXPR genType two_pi()
Return pi * 2.
\n
glm::golden_ratio
GLM_FUNC_DECL GLM_CONSTEXPR genType golden_ratio()
Return the golden ratio constant.
\n
glm::quarter_pi
GLM_FUNC_DECL GLM_CONSTEXPR genType quarter_pi()
Return pi / 4.
\n
glm::one
GLM_FUNC_DECL GLM_CONSTEXPR genType one()
Return 1.
\n
glm::root_five
GLM_FUNC_DECL GLM_CONSTEXPR genType root_five()
Return sqrt(5).
\n
glm::three_over_two_pi
GLM_FUNC_DECL GLM_CONSTEXPR genType three_over_two_pi()
Return pi / 2 * 3.
\n
glm::zero
GLM_FUNC_DECL GLM_CONSTEXPR genType zero()
Return 0.
\n
glm::ln_ten
GLM_FUNC_DECL GLM_CONSTEXPR genType ln_ten()
Return ln(10).
\n
glm::root_three
GLM_FUNC_DECL GLM_CONSTEXPR genType root_three()
Return sqrt(3).
\n
glm::root_pi
GLM_FUNC_DECL GLM_CONSTEXPR genType root_pi()
Return square root of pi.
\n
glm::e
GLM_FUNC_DECL GLM_CONSTEXPR genType e()
Return e constant.
\n
glm::one_over_pi
GLM_FUNC_DECL GLM_CONSTEXPR genType one_over_pi()
Return 1 / pi.
\n
glm::two_over_pi
GLM_FUNC_DECL GLM_CONSTEXPR genType two_over_pi()
Return 2 / pi.
\n
glm::four_over_pi
GLM_FUNC_DECL GLM_CONSTEXPR genType four_over_pi()
Return 4 / pi.
\n
glm::root_two_pi
GLM_FUNC_DECL GLM_CONSTEXPR genType root_two_pi()
Return sqrt(2 * pi).
\n
glm::ln_two
GLM_FUNC_DECL GLM_CONSTEXPR genType ln_two()
Return ln(2).
\n
glm::root_ln_four
GLM_FUNC_DECL GLM_CONSTEXPR genType root_ln_four()
Return sqrt(ln(4)).
\n
glm::two_over_root_pi
GLM_FUNC_DECL GLM_CONSTEXPR genType two_over_root_pi()
Return 2 / sqrt(pi).
\n
glm::ln_ln_two
GLM_FUNC_DECL GLM_CONSTEXPR genType ln_ln_two()
Return ln(ln(2)).
\n
glm::root_half_pi
GLM_FUNC_DECL GLM_CONSTEXPR genType root_half_pi()
Return sqrt(pi / 2).
\n
glm::half_pi
GLM_FUNC_DECL GLM_CONSTEXPR genType half_pi()
Return pi / 2.
\n
glm::one_over_two_pi
GLM_FUNC_DECL GLM_CONSTEXPR genType one_over_two_pi()
Return 1 / (pi * 2).
\n
glm
Definition: common.hpp:20
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\nTypedefs |\nFunctions
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dual_quaternion.hpp File Reference
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GLM_GTX_dual_quaternion \nMore...

\n\n

Go to the source code of this file.

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\nTypedefs

typedef highp_ddualquat ddualquat
 Dual-quaternion of default double-qualifier floating-point numbers. More...
 
typedef highp_fdualquat dualquat
 Dual-quaternion of floating-point numbers. More...
 
typedef highp_fdualquat fdualquat
 Dual-quaternion of single-qualifier floating-point numbers. More...
 
typedef tdualquat< double, highp > highp_ddualquat
 Dual-quaternion of high double-qualifier floating-point numbers. More...
 
typedef tdualquat< float, highp > highp_dualquat
 Dual-quaternion of high single-qualifier floating-point numbers. More...
 
typedef tdualquat< float, highp > highp_fdualquat
 Dual-quaternion of high single-qualifier floating-point numbers. More...
 
typedef tdualquat< double, lowp > lowp_ddualquat
 Dual-quaternion of low double-qualifier floating-point numbers. More...
 
typedef tdualquat< float, lowp > lowp_dualquat
 Dual-quaternion of low single-qualifier floating-point numbers. More...
 
typedef tdualquat< float, lowp > lowp_fdualquat
 Dual-quaternion of low single-qualifier floating-point numbers. More...
 
typedef tdualquat< double, mediump > mediump_ddualquat
 Dual-quaternion of medium double-qualifier floating-point numbers. More...
 
typedef tdualquat< float, mediump > mediump_dualquat
 Dual-quaternion of medium single-qualifier floating-point numbers. More...
 
typedef tdualquat< float, mediump > mediump_fdualquat
 Dual-quaternion of medium single-qualifier floating-point numbers. More...
 
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\nFunctions

template<typename T , qualifier Q>
GLM_FUNC_DECL tdualquat< T, Q > dual_quat_identity ()
 Creates an identity dual quaternion. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL tdualquat< T, Q > dualquat_cast (mat< 2, 4, T, Q > const &x)
 Converts a 2 * 4 matrix (matrix which holds real and dual parts) to a quaternion. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL tdualquat< T, Q > dualquat_cast (mat< 3, 4, T, Q > const &x)
 Converts a 3 * 4 matrix (augmented matrix rotation + translation) to a quaternion. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL tdualquat< T, Q > inverse (tdualquat< T, Q > const &q)
 Returns the q inverse. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL tdualquat< T, Q > lerp (tdualquat< T, Q > const &x, tdualquat< T, Q > const &y, T const &a)
 Returns the linear interpolation of two dual quaternion. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 2, 4, T, Q > mat2x4_cast (tdualquat< T, Q > const &x)
 Converts a quaternion to a 2 * 4 matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 3, 4, T, Q > mat3x4_cast (tdualquat< T, Q > const &x)
 Converts a quaternion to a 3 * 4 matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL tdualquat< T, Q > normalize (tdualquat< T, Q > const &q)
 Returns the normalized quaternion. More...
 
\n

Detailed Description

\n

GLM_GTX_dual_quaternion

\n
Author
Maksim Vorobiev (msome.nosp@m.one@.nosp@m.gmail.nosp@m..com)
\n
See also
Core features (dependence)
\n
\n to use the features of this extension. \">GLM_GTC_constants (dependence)
\n
\n to use the features of this extension. \">GLM_GTC_quaternion (dependence)
\n\n

Definition in file dual_quaternion.hpp.

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dual_quaternion.hpp
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1 
\n
16 #pragma once
\n
17 
\n
18 // Dependency:
\n
19 #include "../glm.hpp"
\n
20 #include "../gtc/constants.hpp"
\n
21 #include "../gtc/quaternion.hpp"
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22 
\n
23 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
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24 # ifndef GLM_ENABLE_EXPERIMENTAL
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25 # pragma message("GLM: GLM_GTX_dual_quaternion is an experimental extension and may change in the future. Use #define GLM_ENABLE_EXPERIMENTAL before including it, if you really want to use it.")
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26 # else
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27 # pragma message("GLM: GLM_GTX_dual_quaternion extension included")
\n
28 # endif
\n
29 #endif
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30 
\n
31 namespace glm
\n
32 {
\n
35 
\n
36  template<typename T, qualifier Q = defaultp>
\n
37  struct tdualquat
\n
38  {
\n
39  // -- Implementation detail --
\n
40 
\n
41  typedef T value_type;
\n
42  typedef qua<T, Q> part_type;
\n
43 
\n
44  // -- Data --
\n
45 
\n
46  qua<T, Q> real, dual;
\n
47 
\n
48  // -- Component accesses --
\n
49 
\n
50  typedef length_t length_type;
\n
52  GLM_FUNC_DECL static GLM_CONSTEXPR length_type length(){return 2;}
\n
53 
\n
54  GLM_FUNC_DECL part_type & operator[](length_type i);
\n
55  GLM_FUNC_DECL part_type const& operator[](length_type i) const;
\n
56 
\n
57  // -- Implicit basic constructors --
\n
58 
\n
59  GLM_FUNC_DECL GLM_CONSTEXPR tdualquat() GLM_DEFAULT;
\n
60  GLM_FUNC_DECL GLM_CONSTEXPR tdualquat(tdualquat<T, Q> const& d) GLM_DEFAULT;
\n
61  template<qualifier P>
\n
62  GLM_FUNC_DECL GLM_CONSTEXPR tdualquat(tdualquat<T, P> const& d);
\n
63 
\n
64  // -- Explicit basic constructors --
\n
65 
\n
66  GLM_FUNC_DECL GLM_CONSTEXPR tdualquat(qua<T, Q> const& real);
\n
67  GLM_FUNC_DECL GLM_CONSTEXPR tdualquat(qua<T, Q> const& orientation, vec<3, T, Q> const& translation);
\n
68  GLM_FUNC_DECL GLM_CONSTEXPR tdualquat(qua<T, Q> const& real, qua<T, Q> const& dual);
\n
69 
\n
70  // -- Conversion constructors --
\n
71 
\n
72  template<typename U, qualifier P>
\n
73  GLM_FUNC_DECL GLM_CONSTEXPR GLM_EXPLICIT tdualquat(tdualquat<U, P> const& q);
\n
74 
\n
75  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR tdualquat(mat<2, 4, T, Q> const& holder_mat);
\n
76  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR tdualquat(mat<3, 4, T, Q> const& aug_mat);
\n
77 
\n
78  // -- Unary arithmetic operators --
\n
79 
\n
80  GLM_FUNC_DECL tdualquat<T, Q> & operator=(tdualquat<T, Q> const& m) GLM_DEFAULT;
\n
81 
\n
82  template<typename U>
\n
83  GLM_FUNC_DECL tdualquat<T, Q> & operator=(tdualquat<U, Q> const& m);
\n
84  template<typename U>
\n
85  GLM_FUNC_DECL tdualquat<T, Q> & operator*=(U s);
\n
86  template<typename U>
\n
87  GLM_FUNC_DECL tdualquat<T, Q> & operator/=(U s);
\n
88  };
\n
89 
\n
90  // -- Unary bit operators --
\n
91 
\n
92  template<typename T, qualifier Q>
\n
93  GLM_FUNC_DECL tdualquat<T, Q> operator+(tdualquat<T, Q> const& q);
\n
94 
\n
95  template<typename T, qualifier Q>
\n
96  GLM_FUNC_DECL tdualquat<T, Q> operator-(tdualquat<T, Q> const& q);
\n
97 
\n
98  // -- Binary operators --
\n
99 
\n
100  template<typename T, qualifier Q>
\n
101  GLM_FUNC_DECL tdualquat<T, Q> operator+(tdualquat<T, Q> const& q, tdualquat<T, Q> const& p);
\n
102 
\n
103  template<typename T, qualifier Q>
\n
104  GLM_FUNC_DECL tdualquat<T, Q> operator*(tdualquat<T, Q> const& q, tdualquat<T, Q> const& p);
\n
105 
\n
106  template<typename T, qualifier Q>
\n
107  GLM_FUNC_DECL vec<3, T, Q> operator*(tdualquat<T, Q> const& q, vec<3, T, Q> const& v);
\n
108 
\n
109  template<typename T, qualifier Q>
\n
110  GLM_FUNC_DECL vec<3, T, Q> operator*(vec<3, T, Q> const& v, tdualquat<T, Q> const& q);
\n
111 
\n
112  template<typename T, qualifier Q>
\n
113  GLM_FUNC_DECL vec<4, T, Q> operator*(tdualquat<T, Q> const& q, vec<4, T, Q> const& v);
\n
114 
\n
115  template<typename T, qualifier Q>
\n
116  GLM_FUNC_DECL vec<4, T, Q> operator*(vec<4, T, Q> const& v, tdualquat<T, Q> const& q);
\n
117 
\n
118  template<typename T, qualifier Q>
\n
119  GLM_FUNC_DECL tdualquat<T, Q> operator*(tdualquat<T, Q> const& q, T const& s);
\n
120 
\n
121  template<typename T, qualifier Q>
\n
122  GLM_FUNC_DECL tdualquat<T, Q> operator*(T const& s, tdualquat<T, Q> const& q);
\n
123 
\n
124  template<typename T, qualifier Q>
\n
125  GLM_FUNC_DECL tdualquat<T, Q> operator/(tdualquat<T, Q> const& q, T const& s);
\n
126 
\n
127  // -- Boolean operators --
\n
128 
\n
129  template<typename T, qualifier Q>
\n
130  GLM_FUNC_DECL bool operator==(tdualquat<T, Q> const& q1, tdualquat<T, Q> const& q2);
\n
131 
\n
132  template<typename T, qualifier Q>
\n
133  GLM_FUNC_DECL bool operator!=(tdualquat<T, Q> const& q1, tdualquat<T, Q> const& q2);
\n
134 
\n
138  template <typename T, qualifier Q>
\n
139  GLM_FUNC_DECL tdualquat<T, Q> dual_quat_identity();
\n
140 
\n
144  template<typename T, qualifier Q>
\n
145  GLM_FUNC_DECL tdualquat<T, Q> normalize(tdualquat<T, Q> const& q);
\n
146 
\n
150  template<typename T, qualifier Q>
\n
151  GLM_FUNC_DECL tdualquat<T, Q> lerp(tdualquat<T, Q> const& x, tdualquat<T, Q> const& y, T const& a);
\n
152 
\n
156  template<typename T, qualifier Q>
\n
157  GLM_FUNC_DECL tdualquat<T, Q> inverse(tdualquat<T, Q> const& q);
\n
158 
\n
162  template<typename T, qualifier Q>
\n
163  GLM_FUNC_DECL mat<2, 4, T, Q> mat2x4_cast(tdualquat<T, Q> const& x);
\n
164 
\n
168  template<typename T, qualifier Q>
\n
169  GLM_FUNC_DECL mat<3, 4, T, Q> mat3x4_cast(tdualquat<T, Q> const& x);
\n
170 
\n
174  template<typename T, qualifier Q>
\n
175  GLM_FUNC_DECL tdualquat<T, Q> dualquat_cast(mat<2, 4, T, Q> const& x);
\n
176 
\n
180  template<typename T, qualifier Q>
\n
181  GLM_FUNC_DECL tdualquat<T, Q> dualquat_cast(mat<3, 4, T, Q> const& x);
\n
182 
\n
183 
\n
187  typedef tdualquat<float, lowp> lowp_dualquat;
\n
188 
\n
192  typedef tdualquat<float, mediump> mediump_dualquat;
\n
193 
\n
197  typedef tdualquat<float, highp> highp_dualquat;
\n
198 
\n
199 
\n
203  typedef tdualquat<float, lowp> lowp_fdualquat;
\n
204 
\n
208  typedef tdualquat<float, mediump> mediump_fdualquat;
\n
209 
\n
213  typedef tdualquat<float, highp> highp_fdualquat;
\n
214 
\n
215 
\n
219  typedef tdualquat<double, lowp> lowp_ddualquat;
\n
220 
\n
224  typedef tdualquat<double, mediump> mediump_ddualquat;
\n
225 
\n
229  typedef tdualquat<double, highp> highp_ddualquat;
\n
230 
\n
231 
\n
232 #if(!defined(GLM_PRECISION_HIGHP_FLOAT) && !defined(GLM_PRECISION_MEDIUMP_FLOAT) && !defined(GLM_PRECISION_LOWP_FLOAT))
\n
233  typedef highp_fdualquat dualquat;
\n
237 
\n
241  typedef highp_fdualquat fdualquat;
\n
242 #elif(defined(GLM_PRECISION_HIGHP_FLOAT) && !defined(GLM_PRECISION_MEDIUMP_FLOAT) && !defined(GLM_PRECISION_LOWP_FLOAT))
\n
243  typedef highp_fdualquat dualquat;
\n
244  typedef highp_fdualquat fdualquat;
\n
245 #elif(!defined(GLM_PRECISION_HIGHP_FLOAT) && defined(GLM_PRECISION_MEDIUMP_FLOAT) && !defined(GLM_PRECISION_LOWP_FLOAT))
\n
246  typedef mediump_fdualquat dualquat;
\n
247  typedef mediump_fdualquat fdualquat;
\n
248 #elif(!defined(GLM_PRECISION_HIGHP_FLOAT) && !defined(GLM_PRECISION_MEDIUMP_FLOAT) && defined(GLM_PRECISION_LOWP_FLOAT))
\n
249  typedef lowp_fdualquat dualquat;
\n
250  typedef lowp_fdualquat fdualquat;
\n
251 #else
\n
252 # error "GLM error: multiple default precision requested for single-precision floating-point types"
\n
253 #endif
\n
254 
\n
255 
\n
256 #if(!defined(GLM_PRECISION_HIGHP_DOUBLE) && !defined(GLM_PRECISION_MEDIUMP_DOUBLE) && !defined(GLM_PRECISION_LOWP_DOUBLE))
\n
257  typedef highp_ddualquat ddualquat;
\n
261 #elif(defined(GLM_PRECISION_HIGHP_DOUBLE) && !defined(GLM_PRECISION_MEDIUMP_DOUBLE) && !defined(GLM_PRECISION_LOWP_DOUBLE))
\n
262  typedef highp_ddualquat ddualquat;
\n
263 #elif(!defined(GLM_PRECISION_HIGHP_DOUBLE) && defined(GLM_PRECISION_MEDIUMP_DOUBLE) && !defined(GLM_PRECISION_LOWP_DOUBLE))
\n
264  typedef mediump_ddualquat ddualquat;
\n
265 #elif(!defined(GLM_PRECISION_HIGHP_DOUBLE) && !defined(GLM_PRECISION_MEDIUMP_DOUBLE) && defined(GLM_PRECISION_LOWP_DOUBLE))
\n
266  typedef lowp_ddualquat ddualquat;
\n
267 #else
\n
268 # error "GLM error: Multiple default precision requested for double-precision floating-point types"
\n
269 #endif
\n
270 
\n
272 } //namespace glm
\n
273 
\n
274 #include "dual_quaternion.inl"
\n
glm::ddualquat
highp_ddualquat ddualquat
Dual-quaternion of default double-qualifier floating-point numbers.
Definition: dual_quaternion.hpp:260
\n
glm::fdualquat
highp_fdualquat fdualquat
Dual-quaternion of single-qualifier floating-point numbers.
Definition: dual_quaternion.hpp:241
\n
glm::mat2x4_cast
GLM_FUNC_DECL mat< 2, 4, T, Q > mat2x4_cast(tdualquat< T, Q > const &x)
Converts a quaternion to a 2 * 4 matrix.
\n
glm::highp_ddualquat
tdualquat< double, highp > highp_ddualquat
Dual-quaternion of high double-qualifier floating-point numbers.
Definition: dual_quaternion.hpp:229
\n
glm::normalize
GLM_FUNC_DECL tdualquat< T, Q > normalize(tdualquat< T, Q > const &q)
Returns the normalized quaternion.
\n
glm::dual_quat_identity
GLM_FUNC_DECL tdualquat< T, Q > dual_quat_identity()
Creates an identity dual quaternion.
\n
glm::inverse
GLM_FUNC_DECL tdualquat< T, Q > inverse(tdualquat< T, Q > const &q)
Returns the q inverse.
\n
glm::lerp
GLM_FUNC_DECL tdualquat< T, Q > lerp(tdualquat< T, Q > const &x, tdualquat< T, Q > const &y, T const &a)
Returns the linear interpolation of two dual quaternion.
\n
glm::lowp_dualquat
tdualquat< float, lowp > lowp_dualquat
Dual-quaternion of low single-qualifier floating-point numbers.
Definition: dual_quaternion.hpp:187
\n
glm::lowp_fdualquat
tdualquat< float, lowp > lowp_fdualquat
Dual-quaternion of low single-qualifier floating-point numbers.
Definition: dual_quaternion.hpp:203
\n
glm::length
GLM_FUNC_DECL T length(qua< T, Q > const &q)
Returns the norm of a quaternions.
\n
glm::lowp_ddualquat
tdualquat< double, lowp > lowp_ddualquat
Dual-quaternion of low double-qualifier floating-point numbers.
Definition: dual_quaternion.hpp:219
\n
glm::mat3x4_cast
GLM_FUNC_DECL mat< 3, 4, T, Q > mat3x4_cast(tdualquat< T, Q > const &x)
Converts a quaternion to a 3 * 4 matrix.
\n
glm::dualquat
highp_fdualquat dualquat
Dual-quaternion of floating-point numbers.
Definition: dual_quaternion.hpp:236
\n
glm::highp_fdualquat
tdualquat< float, highp > highp_fdualquat
Dual-quaternion of high single-qualifier floating-point numbers.
Definition: dual_quaternion.hpp:213
\n
glm::orientation
GLM_FUNC_DECL mat< 4, 4, T, Q > orientation(vec< 3, T, Q > const &Normal, vec< 3, T, Q > const &Up)
Build a rotation matrix from a normal and a up vector.
\n
glm::mediump_dualquat
tdualquat< float, mediump > mediump_dualquat
Dual-quaternion of medium single-qualifier floating-point numbers.
Definition: dual_quaternion.hpp:192
\n
glm::mediump_fdualquat
tdualquat< float, mediump > mediump_fdualquat
Dual-quaternion of medium single-qualifier floating-point numbers.
Definition: dual_quaternion.hpp:208
\n
glm::mediump_ddualquat
tdualquat< double, mediump > mediump_ddualquat
Dual-quaternion of medium double-qualifier floating-point numbers.
Definition: dual_quaternion.hpp:224
\n
glm::dualquat_cast
GLM_FUNC_DECL tdualquat< T, Q > dualquat_cast(mat< 3, 4, T, Q > const &x)
Converts a 3 * 4 matrix (augmented matrix rotation + translation) to a quaternion.
\n
glm::highp_dualquat
tdualquat< float, highp > highp_dualquat
Dual-quaternion of high single-qualifier floating-point numbers.
Definition: dual_quaternion.hpp:197
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glm
Definition: common.hpp:20
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\nFunctions
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easing.hpp File Reference
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\n\n

GLM_GTX_easing \nMore...

\n\n

Go to the source code of this file.

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\nFunctions

template<typename genType >
GLM_FUNC_DECL genType backEaseIn (genType const &a)
 
template<typename genType >
GLM_FUNC_DECL genType backEaseIn (genType const &a, genType const &o)
 
template<typename genType >
GLM_FUNC_DECL genType backEaseInOut (genType const &a)
 
template<typename genType >
GLM_FUNC_DECL genType backEaseInOut (genType const &a, genType const &o)
 
template<typename genType >
GLM_FUNC_DECL genType backEaseOut (genType const &a)
 
template<typename genType >
GLM_FUNC_DECL genType backEaseOut (genType const &a, genType const &o)
 
template<typename genType >
GLM_FUNC_DECL genType bounceEaseIn (genType const &a)
 
template<typename genType >
GLM_FUNC_DECL genType bounceEaseInOut (genType const &a)
 
template<typename genType >
GLM_FUNC_DECL genType bounceEaseOut (genType const &a)
 
template<typename genType >
GLM_FUNC_DECL genType circularEaseIn (genType const &a)
 Modelled after shifted quadrant IV of unit circle. More...
 
template<typename genType >
GLM_FUNC_DECL genType circularEaseInOut (genType const &a)
 Modelled after the piecewise circular function y = (1/2)(1 - sqrt(1 - 4x^2)) ; [0, 0.5) y = (1/2)(sqrt(-(2x - 3)*(2x - 1)) + 1) ; [0.5, 1]. More...
 
template<typename genType >
GLM_FUNC_DECL genType circularEaseOut (genType const &a)
 Modelled after shifted quadrant II of unit circle. More...
 
\ntemplate<typename genType >
GLM_FUNC_DECL genType cubicEaseIn (genType const &a)
 Modelled after the cubic y = x^3.
 
template<typename genType >
GLM_FUNC_DECL genType cubicEaseInOut (genType const &a)
 Modelled after the piecewise cubic y = (1/2)((2x)^3) ; [0, 0.5) y = (1/2)((2x-2)^3 + 2) ; [0.5, 1]. More...
 
template<typename genType >
GLM_FUNC_DECL genType cubicEaseOut (genType const &a)
 Modelled after the cubic y = (x - 1)^3 + 1. More...
 
template<typename genType >
GLM_FUNC_DECL genType elasticEaseIn (genType const &a)
 Modelled after the damped sine wave y = sin(13pi/2*x)*pow(2, 10 * (x - 1)) More...
 
template<typename genType >
GLM_FUNC_DECL genType elasticEaseInOut (genType const &a)
 Modelled after the piecewise exponentially-damped sine wave: y = (1/2)*sin(13pi/2*(2*x))*pow(2, 10 * ((2*x) - 1)) ; [0,0.5) y = (1/2)*(sin(-13pi/2*((2x-1)+1))*pow(2,-10(2*x-1)) + 2) ; [0.5, 1]. More...
 
template<typename genType >
GLM_FUNC_DECL genType elasticEaseOut (genType const &a)
 Modelled after the damped sine wave y = sin(-13pi/2*(x + 1))*pow(2, -10x) + 1. More...
 
template<typename genType >
GLM_FUNC_DECL genType exponentialEaseIn (genType const &a)
 Modelled after the exponential function y = 2^(10(x - 1)) More...
 
template<typename genType >
GLM_FUNC_DECL genType exponentialEaseInOut (genType const &a)
 Modelled after the piecewise exponential y = (1/2)2^(10(2x - 1)) ; [0,0.5) y = -(1/2)*2^(-10(2x - 1))) + 1 ; [0.5,1]. More...
 
template<typename genType >
GLM_FUNC_DECL genType exponentialEaseOut (genType const &a)
 Modelled after the exponential function y = -2^(-10x) + 1. More...
 
template<typename genType >
GLM_FUNC_DECL genType linearInterpolation (genType const &a)
 Modelled after the line y = x. More...
 
template<typename genType >
GLM_FUNC_DECL genType quadraticEaseIn (genType const &a)
 Modelled after the parabola y = x^2. More...
 
template<typename genType >
GLM_FUNC_DECL genType quadraticEaseInOut (genType const &a)
 Modelled after the piecewise quadratic y = (1/2)((2x)^2) ; [0, 0.5) y = -(1/2)((2x-1)*(2x-3) - 1) ; [0.5, 1]. More...
 
template<typename genType >
GLM_FUNC_DECL genType quadraticEaseOut (genType const &a)
 Modelled after the parabola y = -x^2 + 2x. More...
 
template<typename genType >
GLM_FUNC_DECL genType quarticEaseIn (genType const &a)
 Modelled after the quartic x^4. More...
 
template<typename genType >
GLM_FUNC_DECL genType quarticEaseInOut (genType const &a)
 Modelled after the piecewise quartic y = (1/2)((2x)^4) ; [0, 0.5) y = -(1/2)((2x-2)^4 - 2) ; [0.5, 1]. More...
 
template<typename genType >
GLM_FUNC_DECL genType quarticEaseOut (genType const &a)
 Modelled after the quartic y = 1 - (x - 1)^4. More...
 
template<typename genType >
GLM_FUNC_DECL genType quinticEaseIn (genType const &a)
 Modelled after the quintic y = x^5. More...
 
template<typename genType >
GLM_FUNC_DECL genType quinticEaseInOut (genType const &a)
 Modelled after the piecewise quintic y = (1/2)((2x)^5) ; [0, 0.5) y = (1/2)((2x-2)^5 + 2) ; [0.5, 1]. More...
 
template<typename genType >
GLM_FUNC_DECL genType quinticEaseOut (genType const &a)
 Modelled after the quintic y = (x - 1)^5 + 1. More...
 
template<typename genType >
GLM_FUNC_DECL genType sineEaseIn (genType const &a)
 Modelled after quarter-cycle of sine wave. More...
 
template<typename genType >
GLM_FUNC_DECL genType sineEaseInOut (genType const &a)
 Modelled after half sine wave. More...
 
template<typename genType >
GLM_FUNC_DECL genType sineEaseOut (genType const &a)
 Modelled after quarter-cycle of sine wave (different phase) More...
 
\n

Detailed Description

\n

GLM_GTX_easing

\n
Author
Robert Chisholm
\n
See also
Core features (dependence)
\n\n

Definition in file easing.hpp.

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easing.hpp
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1 
\n
17 #pragma once
\n
18 
\n
19 // Dependency:
\n
20 #include "../glm.hpp"
\n
21 #include "../gtc/constants.hpp"
\n
22 #include "../detail/qualifier.hpp"
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23 
\n
24 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
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25 # ifndef GLM_ENABLE_EXPERIMENTAL
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26 # pragma message("GLM: GLM_GTX_easing is an experimental extension and may change in the future. Use #define GLM_ENABLE_EXPERIMENTAL before including it, if you really want to use it.")
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27 # else
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28 # pragma message("GLM: GLM_GTX_easing extension included")
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29 # endif
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30 #endif
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31 
\n
32 namespace glm{
\n
35 
\n
38  template <typename genType>
\n
39  GLM_FUNC_DECL genType linearInterpolation(genType const & a);
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40 
\n
43  template <typename genType>
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44  GLM_FUNC_DECL genType quadraticEaseIn(genType const & a);
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45 
\n
48  template <typename genType>
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49  GLM_FUNC_DECL genType quadraticEaseOut(genType const & a);
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50 
\n
55  template <typename genType>
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56  GLM_FUNC_DECL genType quadraticEaseInOut(genType const & a);
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57 
\n
59  template <typename genType>
\n
60  GLM_FUNC_DECL genType cubicEaseIn(genType const & a);
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61 
\n
64  template <typename genType>
\n
65  GLM_FUNC_DECL genType cubicEaseOut(genType const & a);
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66 
\n
71  template <typename genType>
\n
72  GLM_FUNC_DECL genType cubicEaseInOut(genType const & a);
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73 
\n
76  template <typename genType>
\n
77  GLM_FUNC_DECL genType quarticEaseIn(genType const & a);
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78 
\n
81  template <typename genType>
\n
82  GLM_FUNC_DECL genType quarticEaseOut(genType const & a);
\n
83 
\n
88  template <typename genType>
\n
89  GLM_FUNC_DECL genType quarticEaseInOut(genType const & a);
\n
90 
\n
93  template <typename genType>
\n
94  GLM_FUNC_DECL genType quinticEaseIn(genType const & a);
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95 
\n
98  template <typename genType>
\n
99  GLM_FUNC_DECL genType quinticEaseOut(genType const & a);
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100 
\n
105  template <typename genType>
\n
106  GLM_FUNC_DECL genType quinticEaseInOut(genType const & a);
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107 
\n
110  template <typename genType>
\n
111  GLM_FUNC_DECL genType sineEaseIn(genType const & a);
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112 
\n
115  template <typename genType>
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116  GLM_FUNC_DECL genType sineEaseOut(genType const & a);
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117 
\n
120  template <typename genType>
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121  GLM_FUNC_DECL genType sineEaseInOut(genType const & a);
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122 
\n
125  template <typename genType>
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126  GLM_FUNC_DECL genType circularEaseIn(genType const & a);
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127 
\n
130  template <typename genType>
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131  GLM_FUNC_DECL genType circularEaseOut(genType const & a);
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132 
\n
137  template <typename genType>
\n
138  GLM_FUNC_DECL genType circularEaseInOut(genType const & a);
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139 
\n
142  template <typename genType>
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143  GLM_FUNC_DECL genType exponentialEaseIn(genType const & a);
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144 
\n
147  template <typename genType>
\n
148  GLM_FUNC_DECL genType exponentialEaseOut(genType const & a);
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149 
\n
154  template <typename genType>
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155  GLM_FUNC_DECL genType exponentialEaseInOut(genType const & a);
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156 
\n
159  template <typename genType>
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160  GLM_FUNC_DECL genType elasticEaseIn(genType const & a);
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161 
\n
164  template <typename genType>
\n
165  GLM_FUNC_DECL genType elasticEaseOut(genType const & a);
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166 
\n
171  template <typename genType>
\n
172  GLM_FUNC_DECL genType elasticEaseInOut(genType const & a);
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173 
\n
175  template <typename genType>
\n
176  GLM_FUNC_DECL genType backEaseIn(genType const& a);
\n
177 
\n
179  template <typename genType>
\n
180  GLM_FUNC_DECL genType backEaseOut(genType const& a);
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181 
\n
183  template <typename genType>
\n
184  GLM_FUNC_DECL genType backEaseInOut(genType const& a);
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185 
\n
189  template <typename genType>
\n
190  GLM_FUNC_DECL genType backEaseIn(genType const& a, genType const& o);
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191 
\n
195  template <typename genType>
\n
196  GLM_FUNC_DECL genType backEaseOut(genType const& a, genType const& o);
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197 
\n
201  template <typename genType>
\n
202  GLM_FUNC_DECL genType backEaseInOut(genType const& a, genType const& o);
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203 
\n
205  template <typename genType>
\n
206  GLM_FUNC_DECL genType bounceEaseIn(genType const& a);
\n
207 
\n
209  template <typename genType>
\n
210  GLM_FUNC_DECL genType bounceEaseOut(genType const& a);
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211 
\n
213  template <typename genType>
\n
214  GLM_FUNC_DECL genType bounceEaseInOut(genType const& a);
\n
215 
\n
217 }//namespace glm
\n
218 
\n
219 #include "easing.inl"
\n
glm::bounceEaseIn
GLM_FUNC_DECL genType bounceEaseIn(genType const &a)
\n
glm::circularEaseInOut
GLM_FUNC_DECL genType circularEaseInOut(genType const &a)
Modelled after the piecewise circular function y = (1/2)(1 - sqrt(1 - 4x^2)) ; [0, 0.5) y = (1/2)(sqrt(-(2x - 3)*(2x - 1)) + 1) ; [0.5, 1].
\n
glm::cubicEaseIn
GLM_FUNC_DECL genType cubicEaseIn(genType const &a)
Modelled after the cubic y = x^3.
\n
glm::elasticEaseIn
GLM_FUNC_DECL genType elasticEaseIn(genType const &a)
Modelled after the damped sine wave y = sin(13pi/2*x)*pow(2, 10 * (x - 1))
\n
glm::quinticEaseIn
GLM_FUNC_DECL genType quinticEaseIn(genType const &a)
Modelled after the quintic y = x^5.
\n
glm::sineEaseInOut
GLM_FUNC_DECL genType sineEaseInOut(genType const &a)
Modelled after half sine wave.
\n
glm::circularEaseOut
GLM_FUNC_DECL genType circularEaseOut(genType const &a)
Modelled after shifted quadrant II of unit circle.
\n
glm::elasticEaseOut
GLM_FUNC_DECL genType elasticEaseOut(genType const &a)
Modelled after the damped sine wave y = sin(-13pi/2*(x + 1))*pow(2, -10x) + 1.
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glm::elasticEaseInOut
GLM_FUNC_DECL genType elasticEaseInOut(genType const &a)
Modelled after the piecewise exponentially-damped sine wave: y = (1/2)*sin(13pi/2*(2*x))*pow(2, 10 * ((2*x) - 1)) ; [0,0.5) y = (1/2)*(sin(-13pi/2*((2x-1)+1))*pow(2,-10(2*x-1)) + 2) ; [0.5, 1].
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glm::sineEaseIn
GLM_FUNC_DECL genType sineEaseIn(genType const &a)
Modelled after quarter-cycle of sine wave.
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glm::linearInterpolation
GLM_FUNC_DECL genType linearInterpolation(genType const &a)
Modelled after the line y = x.
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glm::quarticEaseIn
GLM_FUNC_DECL genType quarticEaseIn(genType const &a)
Modelled after the quartic x^4.
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glm::quarticEaseOut
GLM_FUNC_DECL genType quarticEaseOut(genType const &a)
Modelled after the quartic y = 1 - (x - 1)^4.
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glm::sineEaseOut
GLM_FUNC_DECL genType sineEaseOut(genType const &a)
Modelled after quarter-cycle of sine wave (different phase)
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glm::quadraticEaseInOut
GLM_FUNC_DECL genType quadraticEaseInOut(genType const &a)
Modelled after the piecewise quadratic y = (1/2)((2x)^2) ; [0, 0.5) y = -(1/2)((2x-1)*(2x-3) - 1) ; [...
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glm::circularEaseIn
GLM_FUNC_DECL genType circularEaseIn(genType const &a)
Modelled after shifted quadrant IV of unit circle.
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glm::quadraticEaseOut
GLM_FUNC_DECL genType quadraticEaseOut(genType const &a)
Modelled after the parabola y = -x^2 + 2x.
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glm::exponentialEaseOut
GLM_FUNC_DECL genType exponentialEaseOut(genType const &a)
Modelled after the exponential function y = -2^(-10x) + 1.
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glm::quinticEaseOut
GLM_FUNC_DECL genType quinticEaseOut(genType const &a)
Modelled after the quintic y = (x - 1)^5 + 1.
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glm::cubicEaseOut
GLM_FUNC_DECL genType cubicEaseOut(genType const &a)
Modelled after the cubic y = (x - 1)^3 + 1.
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glm::exponentialEaseInOut
GLM_FUNC_DECL genType exponentialEaseInOut(genType const &a)
Modelled after the piecewise exponential y = (1/2)2^(10(2x - 1)) ; [0,0.5) y = -(1/2)*2^(-10(2x - 1))...
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glm::bounceEaseOut
GLM_FUNC_DECL genType bounceEaseOut(genType const &a)
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glm::quinticEaseInOut
GLM_FUNC_DECL genType quinticEaseInOut(genType const &a)
Modelled after the piecewise quintic y = (1/2)((2x)^5) ; [0, 0.5) y = (1/2)((2x-2)^5 + 2) ; [0...
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glm::backEaseIn
GLM_FUNC_DECL genType backEaseIn(genType const &a, genType const &o)
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glm::exponentialEaseIn
GLM_FUNC_DECL genType exponentialEaseIn(genType const &a)
Modelled after the exponential function y = 2^(10(x - 1))
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glm::quadraticEaseIn
GLM_FUNC_DECL genType quadraticEaseIn(genType const &a)
Modelled after the parabola y = x^2.
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glm::quarticEaseInOut
GLM_FUNC_DECL genType quarticEaseInOut(genType const &a)
Modelled after the piecewise quartic y = (1/2)((2x)^4) ; [0, 0.5) y = -(1/2)((2x-2)^4 - 2) ; [0...
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glm::cubicEaseInOut
GLM_FUNC_DECL genType cubicEaseInOut(genType const &a)
Modelled after the piecewise cubic y = (1/2)((2x)^3) ; [0, 0.5) y = (1/2)((2x-2)^3 + 2) ; [0...
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glm::bounceEaseInOut
GLM_FUNC_DECL genType bounceEaseInOut(genType const &a)
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glm::backEaseInOut
GLM_FUNC_DECL genType backEaseInOut(genType const &a, genType const &o)
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glm::backEaseOut
GLM_FUNC_DECL genType backEaseOut(genType const &a, genType const &o)
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glm
Definition: common.hpp:20
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epsilon.hpp File Reference
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GLM_GTC_epsilon \nMore...

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\nFunctions

template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, bool, Q > epsilonEqual (vec< L, T, Q > const &x, vec< L, T, Q > const &y, T const &epsilon)
 Returns the component-wise comparison of |x - y| < epsilon. More...
 
template<typename genType >
GLM_FUNC_DECL bool epsilonEqual (genType const &x, genType const &y, genType const &epsilon)
 Returns the component-wise comparison of |x - y| < epsilon. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, bool, Q > epsilonNotEqual (vec< L, T, Q > const &x, vec< L, T, Q > const &y, T const &epsilon)
 Returns the component-wise comparison of |x - y| < epsilon. More...
 
template<typename genType >
GLM_FUNC_DECL bool epsilonNotEqual (genType const &x, genType const &y, genType const &epsilon)
 Returns the component-wise comparison of |x - y| >= epsilon. More...
 
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Detailed Description

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GLM_GTC_epsilon

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See also
Core features (dependence)
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\n to use the features of this extension. \">GLM_GTC_quaternion (dependence)
\n\n

Definition in file epsilon.hpp.

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14 #pragma once
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15 
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16 // Dependencies
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17 #include "../detail/setup.hpp"
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18 #include "../detail/qualifier.hpp"
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19 
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20 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
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21 # pragma message("GLM: GLM_GTC_epsilon extension included")
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22 #endif
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24 namespace glm
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25 {
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33  template<length_t L, typename T, qualifier Q>
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34  GLM_FUNC_DECL vec<L, bool, Q> epsilonEqual(vec<L, T, Q> const& x, vec<L, T, Q> const& y, T const& epsilon);
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35 
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40  template<typename genType>
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41  GLM_FUNC_DECL bool epsilonEqual(genType const& x, genType const& y, genType const& epsilon);
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42 
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47  template<length_t L, typename T, qualifier Q>
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48  GLM_FUNC_DECL vec<L, bool, Q> epsilonNotEqual(vec<L, T, Q> const& x, vec<L, T, Q> const& y, T const& epsilon);
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49 
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54  template<typename genType>
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55  GLM_FUNC_DECL bool epsilonNotEqual(genType const& x, genType const& y, genType const& epsilon);
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56 
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58 }//namespace glm
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59 
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60 #include "epsilon.inl"
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glm::epsilonEqual
GLM_FUNC_DECL bool epsilonEqual(genType const &x, genType const &y, genType const &epsilon)
Returns the component-wise comparison of |x - y| < epsilon.
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glm::epsilonNotEqual
GLM_FUNC_DECL bool epsilonNotEqual(genType const &x, genType const &y, genType const &epsilon)
Returns the component-wise comparison of |x - y| >= epsilon.
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glm::epsilon
GLM_FUNC_DECL GLM_CONSTEXPR genType epsilon()
Return the epsilon constant for floating point types.
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glm
Definition: common.hpp:20
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euler_angles.hpp File Reference
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GLM_GTX_euler_angles \nMore...

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\nFunctions

template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > derivedEulerAngleX (T const &angleX, T const &angularVelocityX)
 Creates a 3D 4 * 4 homogeneous derived matrix from the rotation matrix about X-axis. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > derivedEulerAngleY (T const &angleY, T const &angularVelocityY)
 Creates a 3D 4 * 4 homogeneous derived matrix from the rotation matrix about Y-axis. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > derivedEulerAngleZ (T const &angleZ, T const &angularVelocityZ)
 Creates a 3D 4 * 4 homogeneous derived matrix from the rotation matrix about Z-axis. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > eulerAngleX (T const &angleX)
 Creates a 3D 4 * 4 homogeneous rotation matrix from an euler angle X. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > eulerAngleXY (T const &angleX, T const &angleY)
 Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (X * Y). More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > eulerAngleXYX (T const &t1, T const &t2, T const &t3)
 Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (X * Y * X). More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > eulerAngleXYZ (T const &t1, T const &t2, T const &t3)
 Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (X * Y * Z). More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > eulerAngleXZ (T const &angleX, T const &angleZ)
 Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (X * Z). More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > eulerAngleXZX (T const &t1, T const &t2, T const &t3)
 Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (X * Z * X). More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > eulerAngleXZY (T const &t1, T const &t2, T const &t3)
 Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (X * Z * Y). More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > eulerAngleY (T const &angleY)
 Creates a 3D 4 * 4 homogeneous rotation matrix from an euler angle Y. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > eulerAngleYX (T const &angleY, T const &angleX)
 Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (Y * X). More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > eulerAngleYXY (T const &t1, T const &t2, T const &t3)
 Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (Y * X * Y). More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > eulerAngleYXZ (T const &yaw, T const &pitch, T const &roll)
 Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (Y * X * Z). More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > eulerAngleYZ (T const &angleY, T const &angleZ)
 Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (Y * Z). More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > eulerAngleYZX (T const &t1, T const &t2, T const &t3)
 Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (Y * Z * X). More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > eulerAngleYZY (T const &t1, T const &t2, T const &t3)
 Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (Y * Z * Y). More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > eulerAngleZ (T const &angleZ)
 Creates a 3D 4 * 4 homogeneous rotation matrix from an euler angle Z. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > eulerAngleZX (T const &angle, T const &angleX)
 Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (Z * X). More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > eulerAngleZXY (T const &t1, T const &t2, T const &t3)
 Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (Z * X * Y). More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > eulerAngleZXZ (T const &t1, T const &t2, T const &t3)
 Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (Z * X * Z). More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > eulerAngleZY (T const &angleZ, T const &angleY)
 Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (Z * Y). More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > eulerAngleZYX (T const &t1, T const &t2, T const &t3)
 Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (Z * Y * X). More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > eulerAngleZYZ (T const &t1, T const &t2, T const &t3)
 Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (Z * Y * Z). More...
 
template<typename T >
GLM_FUNC_DECL void extractEulerAngleXYX (mat< 4, 4, T, defaultp > const &M, T &t1, T &t2, T &t3)
 Extracts the (X * Y * X) Euler angles from the rotation matrix M. More...
 
template<typename T >
GLM_FUNC_DECL void extractEulerAngleXYZ (mat< 4, 4, T, defaultp > const &M, T &t1, T &t2, T &t3)
 Extracts the (X * Y * Z) Euler angles from the rotation matrix M. More...
 
template<typename T >
GLM_FUNC_DECL void extractEulerAngleXZX (mat< 4, 4, T, defaultp > const &M, T &t1, T &t2, T &t3)
 Extracts the (X * Z * X) Euler angles from the rotation matrix M. More...
 
template<typename T >
GLM_FUNC_DECL void extractEulerAngleXZY (mat< 4, 4, T, defaultp > const &M, T &t1, T &t2, T &t3)
 Extracts the (X * Z * Y) Euler angles from the rotation matrix M. More...
 
template<typename T >
GLM_FUNC_DECL void extractEulerAngleYXY (mat< 4, 4, T, defaultp > const &M, T &t1, T &t2, T &t3)
 Extracts the (Y * X * Y) Euler angles from the rotation matrix M. More...
 
template<typename T >
GLM_FUNC_DECL void extractEulerAngleYXZ (mat< 4, 4, T, defaultp > const &M, T &t1, T &t2, T &t3)
 Extracts the (Y * X * Z) Euler angles from the rotation matrix M. More...
 
template<typename T >
GLM_FUNC_DECL void extractEulerAngleYZX (mat< 4, 4, T, defaultp > const &M, T &t1, T &t2, T &t3)
 Extracts the (Y * Z * X) Euler angles from the rotation matrix M. More...
 
template<typename T >
GLM_FUNC_DECL void extractEulerAngleYZY (mat< 4, 4, T, defaultp > const &M, T &t1, T &t2, T &t3)
 Extracts the (Y * Z * Y) Euler angles from the rotation matrix M. More...
 
template<typename T >
GLM_FUNC_DECL void extractEulerAngleZXY (mat< 4, 4, T, defaultp > const &M, T &t1, T &t2, T &t3)
 Extracts the (Z * X * Y) Euler angles from the rotation matrix M. More...
 
template<typename T >
GLM_FUNC_DECL void extractEulerAngleZXZ (mat< 4, 4, T, defaultp > const &M, T &t1, T &t2, T &t3)
 Extracts the (Z * X * Z) Euler angles from the rotation matrix M. More...
 
template<typename T >
GLM_FUNC_DECL void extractEulerAngleZYX (mat< 4, 4, T, defaultp > const &M, T &t1, T &t2, T &t3)
 Extracts the (Z * Y * X) Euler angles from the rotation matrix M. More...
 
template<typename T >
GLM_FUNC_DECL void extractEulerAngleZYZ (mat< 4, 4, T, defaultp > const &M, T &t1, T &t2, T &t3)
 Extracts the (Z * Y * Z) Euler angles from the rotation matrix M. More...
 
template<typename T >
GLM_FUNC_DECL mat< 2, 2, T, defaultp > orientate2 (T const &angle)
 Creates a 2D 2 * 2 rotation matrix from an euler angle. More...
 
template<typename T >
GLM_FUNC_DECL mat< 3, 3, T, defaultp > orientate3 (T const &angle)
 Creates a 2D 4 * 4 homogeneous rotation matrix from an euler angle. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 3, 3, T, Q > orientate3 (vec< 3, T, Q > const &angles)
 Creates a 3D 3 * 3 rotation matrix from euler angles (Y * X * Z). More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 4, 4, T, Q > orientate4 (vec< 3, T, Q > const &angles)
 Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (Y * X * Z). More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > yawPitchRoll (T const &yaw, T const &pitch, T const &roll)
 Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (Y * X * Z). More...
 
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Detailed Description

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GLM_GTX_euler_angles

\n
See also
Core features (dependence)
\n\n

Definition in file euler_angles.hpp.

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16 #pragma once
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17 
\n
18 // Dependency:
\n
19 #include "../glm.hpp"
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20 
\n
21 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
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22 # ifndef GLM_ENABLE_EXPERIMENTAL
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23 # pragma message("GLM: GLM_GTX_euler_angles is an experimental extension and may change in the future. Use #define GLM_ENABLE_EXPERIMENTAL before including it, if you really want to use it.")
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24 # else
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25 # pragma message("GLM: GLM_GTX_euler_angles extension included")
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26 # endif
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27 #endif
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28 
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29 namespace glm
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30 {
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33 
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36  template<typename T>
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37  GLM_FUNC_DECL mat<4, 4, T, defaultp> eulerAngleX(
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38  T const& angleX);
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39 
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42  template<typename T>
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43  GLM_FUNC_DECL mat<4, 4, T, defaultp> eulerAngleY(
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44  T const& angleY);
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45 
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48  template<typename T>
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49  GLM_FUNC_DECL mat<4, 4, T, defaultp> eulerAngleZ(
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50  T const& angleZ);
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51 
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54  template <typename T>
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55  GLM_FUNC_DECL mat<4, 4, T, defaultp> derivedEulerAngleX(
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56  T const & angleX, T const & angularVelocityX);
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57 
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60  template <typename T>
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61  GLM_FUNC_DECL mat<4, 4, T, defaultp> derivedEulerAngleY(
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62  T const & angleY, T const & angularVelocityY);
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63 
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66  template <typename T>
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67  GLM_FUNC_DECL mat<4, 4, T, defaultp> derivedEulerAngleZ(
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68  T const & angleZ, T const & angularVelocityZ);
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69 
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72  template<typename T>
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73  GLM_FUNC_DECL mat<4, 4, T, defaultp> eulerAngleXY(
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74  T const& angleX,
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75  T const& angleY);
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76 
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79  template<typename T>
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80  GLM_FUNC_DECL mat<4, 4, T, defaultp> eulerAngleYX(
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81  T const& angleY,
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82  T const& angleX);
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83 
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86  template<typename T>
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87  GLM_FUNC_DECL mat<4, 4, T, defaultp> eulerAngleXZ(
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88  T const& angleX,
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89  T const& angleZ);
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90 
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93  template<typename T>
\n
94  GLM_FUNC_DECL mat<4, 4, T, defaultp> eulerAngleZX(
\n
95  T const& angle,
\n
96  T const& angleX);
\n
97 
\n
100  template<typename T>
\n
101  GLM_FUNC_DECL mat<4, 4, T, defaultp> eulerAngleYZ(
\n
102  T const& angleY,
\n
103  T const& angleZ);
\n
104 
\n
107  template<typename T>
\n
108  GLM_FUNC_DECL mat<4, 4, T, defaultp> eulerAngleZY(
\n
109  T const& angleZ,
\n
110  T const& angleY);
\n
111 
\n
114  template<typename T>
\n
115  GLM_FUNC_DECL mat<4, 4, T, defaultp> eulerAngleXYZ(
\n
116  T const& t1,
\n
117  T const& t2,
\n
118  T const& t3);
\n
119 
\n
122  template<typename T>
\n
123  GLM_FUNC_DECL mat<4, 4, T, defaultp> eulerAngleYXZ(
\n
124  T const& yaw,
\n
125  T const& pitch,
\n
126  T const& roll);
\n
127 
\n
130  template <typename T>
\n
131  GLM_FUNC_DECL mat<4, 4, T, defaultp> eulerAngleXZX(
\n
132  T const & t1,
\n
133  T const & t2,
\n
134  T const & t3);
\n
135 
\n
138  template <typename T>
\n
139  GLM_FUNC_DECL mat<4, 4, T, defaultp> eulerAngleXYX(
\n
140  T const & t1,
\n
141  T const & t2,
\n
142  T const & t3);
\n
143 
\n
146  template <typename T>
\n
147  GLM_FUNC_DECL mat<4, 4, T, defaultp> eulerAngleYXY(
\n
148  T const & t1,
\n
149  T const & t2,
\n
150  T const & t3);
\n
151 
\n
154  template <typename T>
\n
155  GLM_FUNC_DECL mat<4, 4, T, defaultp> eulerAngleYZY(
\n
156  T const & t1,
\n
157  T const & t2,
\n
158  T const & t3);
\n
159 
\n
162  template <typename T>
\n
163  GLM_FUNC_DECL mat<4, 4, T, defaultp> eulerAngleZYZ(
\n
164  T const & t1,
\n
165  T const & t2,
\n
166  T const & t3);
\n
167 
\n
170  template <typename T>
\n
171  GLM_FUNC_DECL mat<4, 4, T, defaultp> eulerAngleZXZ(
\n
172  T const & t1,
\n
173  T const & t2,
\n
174  T const & t3);
\n
175 
\n
178  template <typename T>
\n
179  GLM_FUNC_DECL mat<4, 4, T, defaultp> eulerAngleXZY(
\n
180  T const & t1,
\n
181  T const & t2,
\n
182  T const & t3);
\n
183 
\n
186  template <typename T>
\n
187  GLM_FUNC_DECL mat<4, 4, T, defaultp> eulerAngleYZX(
\n
188  T const & t1,
\n
189  T const & t2,
\n
190  T const & t3);
\n
191 
\n
194  template <typename T>
\n
195  GLM_FUNC_DECL mat<4, 4, T, defaultp> eulerAngleZYX(
\n
196  T const & t1,
\n
197  T const & t2,
\n
198  T const & t3);
\n
199 
\n
202  template <typename T>
\n
203  GLM_FUNC_DECL mat<4, 4, T, defaultp> eulerAngleZXY(
\n
204  T const & t1,
\n
205  T const & t2,
\n
206  T const & t3);
\n
207 
\n
210  template<typename T>
\n
211  GLM_FUNC_DECL mat<4, 4, T, defaultp> yawPitchRoll(
\n
212  T const& yaw,
\n
213  T const& pitch,
\n
214  T const& roll);
\n
215 
\n
218  template<typename T>
\n
219  GLM_FUNC_DECL mat<2, 2, T, defaultp> orientate2(T const& angle);
\n
220 
\n
223  template<typename T>
\n
224  GLM_FUNC_DECL mat<3, 3, T, defaultp> orientate3(T const& angle);
\n
225 
\n
228  template<typename T, qualifier Q>
\n
229  GLM_FUNC_DECL mat<3, 3, T, Q> orientate3(vec<3, T, Q> const& angles);
\n
230 
\n
233  template<typename T, qualifier Q>
\n
234  GLM_FUNC_DECL mat<4, 4, T, Q> orientate4(vec<3, T, Q> const& angles);
\n
235 
\n
238  template<typename T>
\n
239  GLM_FUNC_DECL void extractEulerAngleXYZ(mat<4, 4, T, defaultp> const& M,
\n
240  T & t1,
\n
241  T & t2,
\n
242  T & t3);
\n
243 
\n
246  template <typename T>
\n
247  GLM_FUNC_DECL void extractEulerAngleYXZ(mat<4, 4, T, defaultp> const & M,
\n
248  T & t1,
\n
249  T & t2,
\n
250  T & t3);
\n
251 
\n
254  template <typename T>
\n
255  GLM_FUNC_DECL void extractEulerAngleXZX(mat<4, 4, T, defaultp> const & M,
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256  T & t1,
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257  T & t2,
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258  T & t3);
\n
259 
\n
262  template <typename T>
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263  GLM_FUNC_DECL void extractEulerAngleXYX(mat<4, 4, T, defaultp> const & M,
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264  T & t1,
\n
265  T & t2,
\n
266  T & t3);
\n
267 
\n
270  template <typename T>
\n
271  GLM_FUNC_DECL void extractEulerAngleYXY(mat<4, 4, T, defaultp> const & M,
\n
272  T & t1,
\n
273  T & t2,
\n
274  T & t3);
\n
275 
\n
278  template <typename T>
\n
279  GLM_FUNC_DECL void extractEulerAngleYZY(mat<4, 4, T, defaultp> const & M,
\n
280  T & t1,
\n
281  T & t2,
\n
282  T & t3);
\n
283 
\n
286  template <typename T>
\n
287  GLM_FUNC_DECL void extractEulerAngleZYZ(mat<4, 4, T, defaultp> const & M,
\n
288  T & t1,
\n
289  T & t2,
\n
290  T & t3);
\n
291 
\n
294  template <typename T>
\n
295  GLM_FUNC_DECL void extractEulerAngleZXZ(mat<4, 4, T, defaultp> const & M,
\n
296  T & t1,
\n
297  T & t2,
\n
298  T & t3);
\n
299 
\n
302  template <typename T>
\n
303  GLM_FUNC_DECL void extractEulerAngleXZY(mat<4, 4, T, defaultp> const & M,
\n
304  T & t1,
\n
305  T & t2,
\n
306  T & t3);
\n
307 
\n
310  template <typename T>
\n
311  GLM_FUNC_DECL void extractEulerAngleYZX(mat<4, 4, T, defaultp> const & M,
\n
312  T & t1,
\n
313  T & t2,
\n
314  T & t3);
\n
315 
\n
318  template <typename T>
\n
319  GLM_FUNC_DECL void extractEulerAngleZYX(mat<4, 4, T, defaultp> const & M,
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320  T & t1,
\n
321  T & t2,
\n
322  T & t3);
\n
323 
\n
326  template <typename T>
\n
327  GLM_FUNC_DECL void extractEulerAngleZXY(mat<4, 4, T, defaultp> const & M,
\n
328  T & t1,
\n
329  T & t2,
\n
330  T & t3);
\n
331 
\n
333 }//namespace glm
\n
334 
\n
335 #include "euler_angles.inl"
\n
glm::eulerAngleXY
GLM_FUNC_DECL mat< 4, 4, T, defaultp > eulerAngleXY(T const &angleX, T const &angleY)
Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (X * Y).
\n
glm::eulerAngleYZY
GLM_FUNC_DECL mat< 4, 4, T, defaultp > eulerAngleYZY(T const &t1, T const &t2, T const &t3)
Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (Y * Z * Y).
\n
glm::extractEulerAngleYXZ
GLM_FUNC_DECL void extractEulerAngleYXZ(mat< 4, 4, T, defaultp > const &M, T &t1, T &t2, T &t3)
Extracts the (Y * X * Z) Euler angles from the rotation matrix M.
\n
glm::eulerAngleXYZ
GLM_FUNC_DECL mat< 4, 4, T, defaultp > eulerAngleXYZ(T const &t1, T const &t2, T const &t3)
Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (X * Y * Z).
\n
glm::eulerAngleXZY
GLM_FUNC_DECL mat< 4, 4, T, defaultp > eulerAngleXZY(T const &t1, T const &t2, T const &t3)
Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (X * Z * Y).
\n
glm::derivedEulerAngleZ
GLM_FUNC_DECL mat< 4, 4, T, defaultp > derivedEulerAngleZ(T const &angleZ, T const &angularVelocityZ)
Creates a 3D 4 * 4 homogeneous derived matrix from the rotation matrix about Z-axis.
\n
glm::eulerAngleYX
GLM_FUNC_DECL mat< 4, 4, T, defaultp > eulerAngleYX(T const &angleY, T const &angleX)
Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (Y * X).
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glm::eulerAngleY
GLM_FUNC_DECL mat< 4, 4, T, defaultp > eulerAngleY(T const &angleY)
Creates a 3D 4 * 4 homogeneous rotation matrix from an euler angle Y.
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glm::angle
GLM_FUNC_DECL T angle(qua< T, Q > const &x)
Returns the quaternion rotation angle.
\n
glm::extractEulerAngleZYZ
GLM_FUNC_DECL void extractEulerAngleZYZ(mat< 4, 4, T, defaultp > const &M, T &t1, T &t2, T &t3)
Extracts the (Z * Y * Z) Euler angles from the rotation matrix M.
\n
glm::derivedEulerAngleX
GLM_FUNC_DECL mat< 4, 4, T, defaultp > derivedEulerAngleX(T const &angleX, T const &angularVelocityX)
Creates a 3D 4 * 4 homogeneous derived matrix from the rotation matrix about X-axis.
\n
glm::extractEulerAngleXYX
GLM_FUNC_DECL void extractEulerAngleXYX(mat< 4, 4, T, defaultp > const &M, T &t1, T &t2, T &t3)
Extracts the (X * Y * X) Euler angles from the rotation matrix M.
\n
glm::eulerAngleZXY
GLM_FUNC_DECL mat< 4, 4, T, defaultp > eulerAngleZXY(T const &t1, T const &t2, T const &t3)
Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (Z * X * Y).
\n
glm::roll
GLM_FUNC_DECL T roll(qua< T, Q > const &x)
Returns roll value of euler angles expressed in radians.
\n
glm::eulerAngleX
GLM_FUNC_DECL mat< 4, 4, T, defaultp > eulerAngleX(T const &angleX)
Creates a 3D 4 * 4 homogeneous rotation matrix from an euler angle X.
\n
glm::orientate2
GLM_FUNC_DECL mat< 2, 2, T, defaultp > orientate2(T const &angle)
Creates a 2D 2 * 2 rotation matrix from an euler angle.
\n
glm::eulerAngleXYX
GLM_FUNC_DECL mat< 4, 4, T, defaultp > eulerAngleXYX(T const &t1, T const &t2, T const &t3)
Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (X * Y * X).
\n
glm::eulerAngleYXZ
GLM_FUNC_DECL mat< 4, 4, T, defaultp > eulerAngleYXZ(T const &yaw, T const &pitch, T const &roll)
Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (Y * X * Z).
\n
glm::extractEulerAngleXZX
GLM_FUNC_DECL void extractEulerAngleXZX(mat< 4, 4, T, defaultp > const &M, T &t1, T &t2, T &t3)
Extracts the (X * Z * X) Euler angles from the rotation matrix M.
\n
glm::yaw
GLM_FUNC_DECL T yaw(qua< T, Q > const &x)
Returns yaw value of euler angles expressed in radians.
\n
glm::extractEulerAngleYXY
GLM_FUNC_DECL void extractEulerAngleYXY(mat< 4, 4, T, defaultp > const &M, T &t1, T &t2, T &t3)
Extracts the (Y * X * Y) Euler angles from the rotation matrix M.
\n
glm::extractEulerAngleZXY
GLM_FUNC_DECL void extractEulerAngleZXY(mat< 4, 4, T, defaultp > const &M, T &t1, T &t2, T &t3)
Extracts the (Z * X * Y) Euler angles from the rotation matrix M.
\n
glm::extractEulerAngleXZY
GLM_FUNC_DECL void extractEulerAngleXZY(mat< 4, 4, T, defaultp > const &M, T &t1, T &t2, T &t3)
Extracts the (X * Z * Y) Euler angles from the rotation matrix M.
\n
glm::extractEulerAngleYZX
GLM_FUNC_DECL void extractEulerAngleYZX(mat< 4, 4, T, defaultp > const &M, T &t1, T &t2, T &t3)
Extracts the (Y * Z * X) Euler angles from the rotation matrix M.
\n
glm::eulerAngleXZX
GLM_FUNC_DECL mat< 4, 4, T, defaultp > eulerAngleXZX(T const &t1, T const &t2, T const &t3)
Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (X * Z * X).
\n
glm::eulerAngleZYX
GLM_FUNC_DECL mat< 4, 4, T, defaultp > eulerAngleZYX(T const &t1, T const &t2, T const &t3)
Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (Z * Y * X).
\n
glm::orientate4
GLM_FUNC_DECL mat< 4, 4, T, Q > orientate4(vec< 3, T, Q > const &angles)
Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (Y * X * Z).
\n
glm::extractEulerAngleZYX
GLM_FUNC_DECL void extractEulerAngleZYX(mat< 4, 4, T, defaultp > const &M, T &t1, T &t2, T &t3)
Extracts the (Z * Y * X) Euler angles from the rotation matrix M.
\n
glm::eulerAngleZ
GLM_FUNC_DECL mat< 4, 4, T, defaultp > eulerAngleZ(T const &angleZ)
Creates a 3D 4 * 4 homogeneous rotation matrix from an euler angle Z.
\n
glm::eulerAngleYXY
GLM_FUNC_DECL mat< 4, 4, T, defaultp > eulerAngleYXY(T const &t1, T const &t2, T const &t3)
Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (Y * X * Y).
\n
glm::extractEulerAngleYZY
GLM_FUNC_DECL void extractEulerAngleYZY(mat< 4, 4, T, defaultp > const &M, T &t1, T &t2, T &t3)
Extracts the (Y * Z * Y) Euler angles from the rotation matrix M.
\n
glm::yawPitchRoll
GLM_FUNC_DECL mat< 4, 4, T, defaultp > yawPitchRoll(T const &yaw, T const &pitch, T const &roll)
Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (Y * X * Z).
\n
glm::eulerAngleXZ
GLM_FUNC_DECL mat< 4, 4, T, defaultp > eulerAngleXZ(T const &angleX, T const &angleZ)
Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (X * Z).
\n
glm::extractEulerAngleXYZ
GLM_FUNC_DECL void extractEulerAngleXYZ(mat< 4, 4, T, defaultp > const &M, T &t1, T &t2, T &t3)
Extracts the (X * Y * Z) Euler angles from the rotation matrix M.
\n
glm::eulerAngleZXZ
GLM_FUNC_DECL mat< 4, 4, T, defaultp > eulerAngleZXZ(T const &t1, T const &t2, T const &t3)
Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (Z * X * Z).
\n
glm::eulerAngleYZX
GLM_FUNC_DECL mat< 4, 4, T, defaultp > eulerAngleYZX(T const &t1, T const &t2, T const &t3)
Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (Y * Z * X).
\n
glm::eulerAngleZY
GLM_FUNC_DECL mat< 4, 4, T, defaultp > eulerAngleZY(T const &angleZ, T const &angleY)
Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (Z * Y).
\n
glm::eulerAngleZYZ
GLM_FUNC_DECL mat< 4, 4, T, defaultp > eulerAngleZYZ(T const &t1, T const &t2, T const &t3)
Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (Z * Y * Z).
\n
glm::eulerAngleYZ
GLM_FUNC_DECL mat< 4, 4, T, defaultp > eulerAngleYZ(T const &angleY, T const &angleZ)
Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (Y * Z).
\n
glm::orientate3
GLM_FUNC_DECL mat< 3, 3, T, Q > orientate3(vec< 3, T, Q > const &angles)
Creates a 3D 3 * 3 rotation matrix from euler angles (Y * X * Z).
\n
glm::extractEulerAngleZXZ
GLM_FUNC_DECL void extractEulerAngleZXZ(mat< 4, 4, T, defaultp > const &M, T &t1, T &t2, T &t3)
Extracts the (Z * X * Z) Euler angles from the rotation matrix M.
\n
glm::derivedEulerAngleY
GLM_FUNC_DECL mat< 4, 4, T, defaultp > derivedEulerAngleY(T const &angleY, T const &angularVelocityY)
Creates a 3D 4 * 4 homogeneous derived matrix from the rotation matrix about Y-axis.
\n
glm::pitch
GLM_FUNC_DECL T pitch(qua< T, Q > const &x)
Returns pitch value of euler angles expressed in radians.
\n
glm::eulerAngleZX
GLM_FUNC_DECL mat< 4, 4, T, defaultp > eulerAngleZX(T const &angle, T const &angleX)
Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (Z * X).
\n
glm
Definition: common.hpp:20
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\nFunctions
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exponential.hpp File Reference
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Core features \nMore...

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Go to the source code of this file.

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\nFunctions

template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > exp (vec< L, T, Q > const &v)
 Returns the natural exponentiation of x, i.e., e^x. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > exp2 (vec< L, T, Q > const &v)
 Returns 2 raised to the v power. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > inversesqrt (vec< L, T, Q > const &v)
 Returns the reciprocal of the positive square root of v. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > log (vec< L, T, Q > const &v)
 Returns the natural logarithm of v, i.e., returns the value y which satisfies the equation x = e^y. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > log2 (vec< L, T, Q > const &v)
 Returns the base 2 log of x, i.e., returns the value y, which satisfies the equation x = 2 ^ y. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > pow (vec< L, T, Q > const &base, vec< L, T, Q > const &exponent)
 Returns 'base' raised to the power 'exponent'. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > sqrt (vec< L, T, Q > const &v)
 Returns the positive square root of v. More...
 
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Detailed Description

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Core features

\n
See also
GLSL 4.20.8 specification, section 8.2 Exponential Functions
\n\n

Definition in file exponential.hpp.

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exponential.hpp
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1 
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15 #pragma once
\n
16 
\n
17 #include "detail/type_vec1.hpp"
\n
18 #include "detail/type_vec2.hpp"
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19 #include "detail/type_vec3.hpp"
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20 #include "detail/type_vec4.hpp"
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21 #include <cmath>
\n
22 
\n
23 namespace glm
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24 {
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27 
\n
35  template<length_t L, typename T, qualifier Q>
\n
36  GLM_FUNC_DECL vec<L, T, Q> pow(vec<L, T, Q> const& base, vec<L, T, Q> const& exponent);
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37 
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46  template<length_t L, typename T, qualifier Q>
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47  GLM_FUNC_DECL vec<L, T, Q> exp(vec<L, T, Q> const& v);
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48 
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59  template<length_t L, typename T, qualifier Q>
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60  GLM_FUNC_DECL vec<L, T, Q> log(vec<L, T, Q> const& v);
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61 
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70  template<length_t L, typename T, qualifier Q>
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71  GLM_FUNC_DECL vec<L, T, Q> exp2(vec<L, T, Q> const& v);
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72 
\n
82  template<length_t L, typename T, qualifier Q>
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83  GLM_FUNC_DECL vec<L, T, Q> log2(vec<L, T, Q> const& v);
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84 
\n
93  template<length_t L, typename T, qualifier Q>
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94  GLM_FUNC_DECL vec<L, T, Q> sqrt(vec<L, T, Q> const& v);
\n
95 
\n
104  template<length_t L, typename T, qualifier Q>
\n
105  GLM_FUNC_DECL vec<L, T, Q> inversesqrt(vec<L, T, Q> const& v);
\n
106 
\n
108 }//namespace glm
\n
109 
\n
110 #include "detail/func_exponential.inl"
\n
type_vec2.hpp
Core features
\n
glm::sqrt
GLM_FUNC_DECL vec< L, T, Q > sqrt(vec< L, T, Q > const &v)
Returns the positive square root of v.
\n
glm::exp2
GLM_FUNC_DECL vec< L, T, Q > exp2(vec< L, T, Q > const &v)
Returns 2 raised to the v power.
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glm::inversesqrt
GLM_FUNC_DECL vec< L, T, Q > inversesqrt(vec< L, T, Q > const &v)
Returns the reciprocal of the positive square root of v.
\n
type_vec4.hpp
Core features
\n
type_vec1.hpp
Core features
\n
glm::pow
GLM_FUNC_DECL vec< L, T, Q > pow(vec< L, T, Q > const &base, vec< L, T, Q > const &exponent)
Returns 'base' raised to the power 'exponent'.
\n
glm::exp
GLM_FUNC_DECL vec< L, T, Q > exp(vec< L, T, Q > const &v)
Returns the natural exponentiation of x, i.e., e^x.
\n
glm::log
GLM_FUNC_DECL vec< L, T, Q > log(vec< L, T, Q > const &v)
Returns the natural logarithm of v, i.e., returns the value y which satisfies the equation x = e^y...
\n
type_vec3.hpp
Core features
\n
glm::log2
GLM_FUNC_DECL vec< L, T, Q > log2(vec< L, T, Q > const &v)
Returns the base 2 log of x, i.e., returns the value y, which satisfies the equation x = 2 ^ y...
\n
glm
Definition: common.hpp:20
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ext.hpp File Reference
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Core features (Dependence) \nMore...

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Core features (Dependence)

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Definition in file ext.hpp.

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ext.hpp
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\nGo to the documentation of this file.
1 
\n
5 #include "detail/setup.hpp"
\n
6 
\n
7 #pragma once
\n
8 
\n
9 #include "glm.hpp"
\n
10 
\n
11 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_MESSAGE_EXT_INCLUDED_DISPLAYED)
\n
12 # define GLM_MESSAGE_EXT_INCLUDED_DISPLAYED
\n
13 # pragma message("GLM: All extensions included (not recommended)")
\n
14 #endif//GLM_MESSAGES
\n
15 
\n
16 #include "./ext/matrix_double2x2.hpp"
\n
17 #include "./ext/matrix_double2x2_precision.hpp"
\n
18 #include "./ext/matrix_double2x3.hpp"
\n
19 #include "./ext/matrix_double2x3_precision.hpp"
\n
20 #include "./ext/matrix_double2x4.hpp"
\n
21 #include "./ext/matrix_double2x4_precision.hpp"
\n
22 #include "./ext/matrix_double3x2.hpp"
\n
23 #include "./ext/matrix_double3x2_precision.hpp"
\n
24 #include "./ext/matrix_double3x3.hpp"
\n
25 #include "./ext/matrix_double3x3_precision.hpp"
\n
26 #include "./ext/matrix_double3x4.hpp"
\n
27 #include "./ext/matrix_double3x4_precision.hpp"
\n
28 #include "./ext/matrix_double4x2.hpp"
\n
29 #include "./ext/matrix_double4x2_precision.hpp"
\n
30 #include "./ext/matrix_double4x3.hpp"
\n
31 #include "./ext/matrix_double4x3_precision.hpp"
\n
32 #include "./ext/matrix_double4x4.hpp"
\n
33 #include "./ext/matrix_double4x4_precision.hpp"
\n
34 
\n
35 #include "./ext/matrix_float2x2.hpp"
\n
36 #include "./ext/matrix_float2x2_precision.hpp"
\n
37 #include "./ext/matrix_float2x3.hpp"
\n
38 #include "./ext/matrix_float2x3_precision.hpp"
\n
39 #include "./ext/matrix_float2x4.hpp"
\n
40 #include "./ext/matrix_float2x4_precision.hpp"
\n
41 #include "./ext/matrix_float3x2.hpp"
\n
42 #include "./ext/matrix_float3x2_precision.hpp"
\n
43 #include "./ext/matrix_float3x3.hpp"
\n
44 #include "./ext/matrix_float3x3_precision.hpp"
\n
45 #include "./ext/matrix_float3x4.hpp"
\n
46 #include "./ext/matrix_float3x4_precision.hpp"
\n
47 #include "./ext/matrix_float4x2.hpp"
\n
48 #include "./ext/matrix_float4x2_precision.hpp"
\n
49 #include "./ext/matrix_float4x3.hpp"
\n
50 #include "./ext/matrix_float4x3_precision.hpp"
\n
51 #include "./ext/matrix_float4x4.hpp"
\n
52 #include "./ext/matrix_float4x4_precision.hpp"
\n
53 
\n
54 #include "./ext/matrix_relational.hpp"
\n
55 
\n
56 #include "./ext/quaternion_double.hpp"
\n
57 #include "./ext/quaternion_double_precision.hpp"
\n
58 #include "./ext/quaternion_float.hpp"
\n
59 #include "./ext/quaternion_float_precision.hpp"
\n
60 #include "./ext/quaternion_geometric.hpp"
\n
61 #include "./ext/quaternion_relational.hpp"
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62 
\n
63 #include "./ext/scalar_constants.hpp"
\n
64 #include "./ext/scalar_int_sized.hpp"
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65 #include "./ext/scalar_relational.hpp"
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66 
\n
67 #include "./ext/vector_bool1.hpp"
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68 #include "./ext/vector_bool1_precision.hpp"
\n
69 #include "./ext/vector_bool2.hpp"
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70 #include "./ext/vector_bool2_precision.hpp"
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71 #include "./ext/vector_bool3.hpp"
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72 #include "./ext/vector_bool3_precision.hpp"
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73 #include "./ext/vector_bool4.hpp"
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74 #include "./ext/vector_bool4_precision.hpp"
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75 
\n
76 #include "./ext/vector_double1.hpp"
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77 #include "./ext/vector_double1_precision.hpp"
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78 #include "./ext/vector_double2.hpp"
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79 #include "./ext/vector_double2_precision.hpp"
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80 #include "./ext/vector_double3.hpp"
\n
81 #include "./ext/vector_double3_precision.hpp"
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82 #include "./ext/vector_double4.hpp"
\n
83 #include "./ext/vector_double4_precision.hpp"
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84 
\n
85 #include "./ext/vector_float1.hpp"
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86 #include "./ext/vector_float1_precision.hpp"
\n
87 #include "./ext/vector_float2.hpp"
\n
88 #include "./ext/vector_float2_precision.hpp"
\n
89 #include "./ext/vector_float3.hpp"
\n
90 #include "./ext/vector_float3_precision.hpp"
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91 #include "./ext/vector_float4.hpp"
\n
92 #include "./ext/vector_float4_precision.hpp"
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93 
\n
94 #include "./ext/vector_int1.hpp"
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95 #include "./ext/vector_int1_precision.hpp"
\n
96 #include "./ext/vector_int2.hpp"
\n
97 #include "./ext/vector_int2_precision.hpp"
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98 #include "./ext/vector_int3.hpp"
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99 #include "./ext/vector_int3_precision.hpp"
\n
100 #include "./ext/vector_int4.hpp"
\n
101 #include "./ext/vector_int4_precision.hpp"
\n
102 
\n
103 #include "./ext/vector_relational.hpp"
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104 
\n
105 #include "./ext/vector_uint1.hpp"
\n
106 #include "./ext/vector_uint1_precision.hpp"
\n
107 #include "./ext/vector_uint2.hpp"
\n
108 #include "./ext/vector_uint2_precision.hpp"
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109 #include "./ext/vector_uint3.hpp"
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110 #include "./ext/vector_uint3_precision.hpp"
\n
111 #include "./ext/vector_uint4.hpp"
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112 #include "./ext/vector_uint4_precision.hpp"
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113 
\n
114 #include "./gtc/bitfield.hpp"
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115 #include "./gtc/color_space.hpp"
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116 #include "./gtc/constants.hpp"
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117 #include "./gtc/epsilon.hpp"
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118 #include "./gtc/integer.hpp"
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119 #include "./gtc/matrix_access.hpp"
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120 #include "./gtc/matrix_integer.hpp"
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121 #include "./gtc/matrix_inverse.hpp"
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122 #include "./gtc/matrix_transform.hpp"
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123 #include "./gtc/noise.hpp"
\n
124 #include "./gtc/packing.hpp"
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125 #include "./gtc/quaternion.hpp"
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126 #include "./gtc/random.hpp"
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127 #include "./gtc/reciprocal.hpp"
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128 #include "./gtc/round.hpp"
\n
129 #include "./gtc/type_precision.hpp"
\n
130 #include "./gtc/type_ptr.hpp"
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131 #include "./gtc/ulp.hpp"
\n
132 #include "./gtc/vec1.hpp"
\n
133 #if GLM_CONFIG_ALIGNED_GENTYPES == GLM_ENABLE
\n
134 # include "./gtc/type_aligned.hpp"
\n
135 #endif
\n
136 
\n
137 #ifdef GLM_ENABLE_EXPERIMENTAL
\n
138 #include "./gtx/associated_min_max.hpp"
\n
139 #include "./gtx/bit.hpp"
\n
140 #include "./gtx/closest_point.hpp"
\n
141 #include "./gtx/color_encoding.hpp"
\n
142 #include "./gtx/color_space.hpp"
\n
143 #include "./gtx/color_space_YCoCg.hpp"
\n
144 #include "./gtx/compatibility.hpp"
\n
145 #include "./gtx/component_wise.hpp"
\n
146 #include "./gtx/dual_quaternion.hpp"
\n
147 #include "./gtx/euler_angles.hpp"
\n
148 #include "./gtx/extend.hpp"
\n
149 #include "./gtx/extended_min_max.hpp"
\n
150 #include "./gtx/fast_exponential.hpp"
\n
151 #include "./gtx/fast_square_root.hpp"
\n
152 #include "./gtx/fast_trigonometry.hpp"
\n
153 #include "./gtx/functions.hpp"
\n
154 #include "./gtx/gradient_paint.hpp"
\n
155 #include "./gtx/handed_coordinate_space.hpp"
\n
156 #include "./gtx/integer.hpp"
\n
157 #include "./gtx/intersect.hpp"
\n
158 #include "./gtx/log_base.hpp"
\n
159 #include "./gtx/matrix_cross_product.hpp"
\n
160 #include "./gtx/matrix_interpolation.hpp"
\n
161 #include "./gtx/matrix_major_storage.hpp"
\n
162 #include "./gtx/matrix_operation.hpp"
\n
163 #include "./gtx/matrix_query.hpp"
\n
164 #include "./gtx/mixed_product.hpp"
\n
165 #include "./gtx/norm.hpp"
\n
166 #include "./gtx/normal.hpp"
\n
167 #include "./gtx/normalize_dot.hpp"
\n
168 #include "./gtx/number_precision.hpp"
\n
169 #include "./gtx/optimum_pow.hpp"
\n
170 #include "./gtx/orthonormalize.hpp"
\n
171 #include "./gtx/perpendicular.hpp"
\n
172 #include "./gtx/polar_coordinates.hpp"
\n
173 #include "./gtx/projection.hpp"
\n
174 #include "./gtx/quaternion.hpp"
\n
175 #include "./gtx/raw_data.hpp"
\n
176 #include "./gtx/rotate_vector.hpp"
\n
177 #include "./gtx/spline.hpp"
\n
178 #include "./gtx/std_based_type.hpp"
\n
179 #if !(GLM_COMPILER & GLM_COMPILER_CUDA)
\n
180 # include "./gtx/string_cast.hpp"
\n
181 #endif
\n
182 #include "./gtx/transform.hpp"
\n
183 #include "./gtx/transform2.hpp"
\n
184 #include "./gtx/vec_swizzle.hpp"
\n
185 #include "./gtx/vector_angle.hpp"
\n
186 #include "./gtx/vector_query.hpp"
\n
187 #include "./gtx/wrap.hpp"
\n
188 
\n
189 #if GLM_HAS_TEMPLATE_ALIASES
\n
190 # include "./gtx/scalar_multiplication.hpp"
\n
191 #endif
\n
192 
\n
193 #if GLM_HAS_RANGE_FOR
\n
194 # include "./gtx/range.hpp"
\n
195 #endif
\n
196 #endif//GLM_ENABLE_EXPERIMENTAL
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epsilon.hpp
GLM_GTC_epsilon
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vector_relational.hpp
GLM_EXT_vector_relational
\n
dual_quaternion.hpp
GLM_GTX_dual_quaternion
\n
polar_coordinates.hpp
GLM_GTX_polar_coordinates
\n
closest_point.hpp
GLM_GTX_closest_point
\n
vector_float3.hpp
Core features
\n\n\n
handed_coordinate_space.hpp
GLM_GTX_handed_coordinate_space
\n
vector_bool3.hpp
Core features
\n
raw_data.hpp
GLM_GTX_raw_data
\n\n
vector_bool2.hpp
Core features
\n
string_cast.hpp
GLM_GTX_string_cast
\n
vector_uint1_precision.hpp
GLM_EXT_vector_uint1_precision
\n
intersect.hpp
GLM_GTX_intersect
\n
vector_int1_precision.hpp
GLM_EXT_vector_int1_precision
\n
normalize_dot.hpp
GLM_GTX_normalize_dot
\n
integer.hpp
GLM_GTX_integer
\n
rotate_vector.hpp
GLM_GTX_rotate_vector
\n\n
matrix_major_storage.hpp
GLM_GTX_matrix_major_storage
\n
vector_uint3.hpp
Core features
\n\n
matrix_float3x2.hpp
Core features
\n
matrix_interpolation.hpp
GLM_GTX_matrix_interpolation
\n
vector_angle.hpp
GLM_GTX_vector_angle
\n
transform2.hpp
GLM_GTX_transform2
\n\n\n
wrap.hpp
GLM_GTX_wrap
\n
vector_query.hpp
GLM_GTX_vector_query
\n
projection.hpp
GLM_GTX_projection
\n
constants.hpp
GLM_GTC_constants
\n\n
perpendicular.hpp
GLM_GTX_perpendicular
\n
matrix_double4x4.hpp
Core features
\n
matrix_float4x3.hpp
Core features
\n
matrix_double2x4.hpp
Core features
\n\n
matrix_float2x3.hpp
Core features
\n
std_based_type.hpp
GLM_GTX_std_based_type
\n
matrix_float2x2.hpp
Core features
\n
component_wise.hpp
GLM_GTX_component_wise
\n
ulp.hpp
GLM_GTC_ulp
\n
round.hpp
GLM_GTC_round
\n
vector_int3.hpp
Core features
\n
orthonormalize.hpp
GLM_GTX_orthonormalize
\n\n
integer.hpp
GLM_GTC_integer
\n
vector_float1.hpp
GLM_EXT_vector_float1
\n\n
matrix_query.hpp
GLM_GTX_matrix_query
\n
vector_double1_precision.hpp
GLM_EXT_vector_double1_precision
\n\n
vec_swizzle.hpp
GLM_GTX_vec_swizzle
\n
matrix_double4x2.hpp
Core features
\n
type_ptr.hpp
GLM_GTC_type_ptr
\n
matrix_double2x2.hpp
Core features
\n
gradient_paint.hpp
GLM_GTX_gradient_paint
\n
bitfield.hpp
GLM_GTC_bitfield
\n
range.hpp
GLM_GTX_range
\n\n
matrix_float3x3.hpp
Core features
\n
matrix_transform.hpp
GLM_GTC_matrix_transform
\n
matrix_cross_product.hpp
GLM_GTX_matrix_cross_product
\n
vector_bool1_precision.hpp
GLM_EXT_vector_bool1_precision
\n
type_aligned.hpp
GLM_GTC_type_aligned
\n
vector_uint1.hpp
GLM_EXT_vector_uint1
\n
quaternion.hpp
GLM_GTX_quaternion
\n
color_space_YCoCg.hpp
GLM_GTX_color_space_YCoCg
\n
vector_int1.hpp
GLM_EXT_vector_int1
\n
normal.hpp
GLM_GTX_normal
\n
color_space.hpp
GLM_GTC_color_space
\n
vector_float4.hpp
Core features
\n
noise.hpp
GLM_GTC_noise
\n
vector_bool4.hpp
Core features
\n
vector_double2.hpp
Core features
\n\n
matrix_integer.hpp
GLM_GTC_matrix_integer
\n
matrix_access.hpp
GLM_GTC_matrix_access
\n
extended_min_max.hpp
GLM_GTX_extented_min_max
\n
vec1.hpp
GLM_GTC_vec1
\n
transform.hpp
GLM_GTX_transform
\n\n
quaternion_double_precision.hpp
GLM_EXT_quaternion_double_precision
\n
log_base.hpp
GLM_GTX_log_base
\n
compatibility.hpp
GLM_GTX_compatibility
\n
scalar_int_sized.hpp
GLM_EXT_scalar_int_sized
\n\n
optimum_pow.hpp
GLM_GTX_optimum_pow
\n
functions.hpp
GLM_GTX_functions
\n
quaternion_relational.hpp
GLM_EXT_quaternion_relational
\n\n
fast_square_root.hpp
GLM_GTX_fast_square_root
\n
vector_uint4.hpp
Core features
\n
quaternion_float_precision.hpp
GLM_EXT_quaternion_float_precision
\n
vector_int4.hpp
Core features
\n\n
scalar_relational.hpp
GLM_EXT_scalar_relational
\n\n
glm.hpp
Core features
\n
random.hpp
GLM_GTC_random
\n
euler_angles.hpp
GLM_GTX_euler_angles
\n
spline.hpp
GLM_GTX_spline
\n
quaternion.hpp
GLM_GTC_quaternion
\n
color_space.hpp
GLM_GTX_color_space
\n\n
norm.hpp
GLM_GTX_norm
\n
color_encoding.hpp
GLM_GTX_color_encoding
\n
reciprocal.hpp
GLM_GTC_reciprocal
\n\n
matrix_float4x4.hpp
Core features
\n
mixed_product.hpp
GLM_GTX_mixed_producte
\n
matrix_double3x2.hpp
Core features
\n
vector_double1.hpp
GLM_EXT_vector_double1
\n
matrix_float2x4.hpp
Core features
\n\n
type_precision.hpp
GLM_GTC_type_precision
\n
scalar_constants.hpp
GLM_EXT_scalar_constants
\n\n
fast_trigonometry.hpp
GLM_GTX_fast_trigonometry
\n
bit.hpp
GLM_GTX_bit
\n\n
quaternion_geometric.hpp
GLM_EXT_quaternion_geometric
\n
vector_float2.hpp
Core features
\n
fast_exponential.hpp
GLM_GTX_fast_exponential
\n\n
quaternion_float.hpp
GLM_EXT_quaternion_float
\n\n
vector_bool1.hpp
GLM_EXT_vector_bool1
\n
matrix_double4x3.hpp
Core features
\n
matrix_float4x2.hpp
Core features
\n
matrix_double2x3.hpp
Core features
\n\n
matrix_float3x4.hpp
Core features
\n
extend.hpp
GLM_GTX_extend
\n\n
vector_uint2.hpp
Core features
\n
quaternion_double.hpp
GLM_EXT_quaternion_double
\n\n
vector_int2.hpp
Core features
\n
number_precision.hpp
GLM_GTX_number_precision
\n
vector_double4.hpp
Core features
\n\n
matrix_operation.hpp
GLM_GTX_matrix_operation
\n
vector_double3.hpp
Core features
\n\n
matrix_inverse.hpp
GLM_GTC_matrix_inverse
\n
matrix_double3x4.hpp
Core features
\n
scalar_multiplication.hpp
Experimental extensions
\n\n
packing.hpp
GLM_GTC_packing
\n
matrix_double3x3.hpp
Core features
\n
associated_min_max.hpp
GLM_GTX_associated_min_max
\n
vector_float1_precision.hpp
GLM_EXT_vector_float1_precision
\n
matrix_relational.hpp
GLM_EXT_matrix_relational
\n
\n\n
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extend.hpp File Reference
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GLM_GTX_extend \nMore...

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Go to the source code of this file.

\n\n\n\n\n\n\n

\nFunctions

template<typename genType >
GLM_FUNC_DECL genType extend (genType const &Origin, genType const &Source, typename genType::value_type const Length)
 Extends of Length the Origin position using the (Source - Origin) direction. More...
 
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Detailed Description

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GLM_GTX_extend

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See also
Core features (dependence)
\n\n

Definition in file extend.hpp.

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extend.hpp
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1 
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13 #pragma once
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14 
\n
15 // Dependency:
\n
16 #include "../glm.hpp"
\n
17 
\n
18 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
19 # ifndef GLM_ENABLE_EXPERIMENTAL
\n
20 # pragma message("GLM: GLM_GTX_extend is an experimental extension and may change in the future. Use #define GLM_ENABLE_EXPERIMENTAL before including it, if you really want to use it.")
\n
21 # else
\n
22 # pragma message("GLM: GLM_GTX_extend extension included")
\n
23 # endif
\n
24 #endif
\n
25 
\n
26 namespace glm
\n
27 {
\n
30 
\n
33  template<typename genType>
\n
34  GLM_FUNC_DECL genType extend(
\n
35  genType const& Origin,
\n
36  genType const& Source,
\n
37  typename genType::value_type const Length);
\n
38 
\n
40 }//namespace glm
\n
41 
\n
42 #include "extend.inl"
\n
glm::extend
GLM_FUNC_DECL genType extend(genType const &Origin, genType const &Source, typename genType::value_type const Length)
Extends of Length the Origin position using the (Source - Origin) direction.
\n
glm
Definition: common.hpp:20
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\n
extended_min_max.hpp File Reference
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GLM_GTX_extented_min_max \nMore...

\n\n

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\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

\nFunctions

template<typename genType >
GLM_FUNC_DECL genType fclamp (genType x, genType minVal, genType maxVal)
 Returns min(max(x, minVal), maxVal) for each component in x. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > fclamp (vec< L, T, Q > const &x, T minVal, T maxVal)
 Returns min(max(x, minVal), maxVal) for each component in x. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > fclamp (vec< L, T, Q > const &x, vec< L, T, Q > const &minVal, vec< L, T, Q > const &maxVal)
 Returns min(max(x, minVal), maxVal) for each component in x. More...
 
template<typename genType >
GLM_FUNC_DECL genType fmax (genType x, genType y)
 Returns y if x < y; otherwise, it returns x. More...
 
template<typename genType >
GLM_FUNC_DECL genType fmin (genType x, genType y)
 Returns y if y < x; otherwise, it returns x. More...
 
template<typename T >
GLM_FUNC_DECL T max (T const &x, T const &y, T const &z)
 Return the maximum component-wise values of 3 inputs. More...
 
template<typename T , template< typename > class C>
GLM_FUNC_DECL C< T > max (C< T > const &x, typename C< T >::T const &y, typename C< T >::T const &z)
 Return the maximum component-wise values of 3 inputs. More...
 
template<typename T , template< typename > class C>
GLM_FUNC_DECL C< T > max (C< T > const &x, C< T > const &y, C< T > const &z)
 Return the maximum component-wise values of 3 inputs. More...
 
template<typename T >
GLM_FUNC_DECL T max (T const &x, T const &y, T const &z, T const &w)
 Return the maximum component-wise values of 4 inputs. More...
 
template<typename T , template< typename > class C>
GLM_FUNC_DECL C< T > max (C< T > const &x, typename C< T >::T const &y, typename C< T >::T const &z, typename C< T >::T const &w)
 Return the maximum component-wise values of 4 inputs. More...
 
template<typename T , template< typename > class C>
GLM_FUNC_DECL C< T > max (C< T > const &x, C< T > const &y, C< T > const &z, C< T > const &w)
 Return the maximum component-wise values of 4 inputs. More...
 
template<typename T >
GLM_FUNC_DECL T min (T const &x, T const &y, T const &z)
 Return the minimum component-wise values of 3 inputs. More...
 
template<typename T , template< typename > class C>
GLM_FUNC_DECL C< T > min (C< T > const &x, typename C< T >::T const &y, typename C< T >::T const &z)
 Return the minimum component-wise values of 3 inputs. More...
 
template<typename T , template< typename > class C>
GLM_FUNC_DECL C< T > min (C< T > const &x, C< T > const &y, C< T > const &z)
 Return the minimum component-wise values of 3 inputs. More...
 
template<typename T >
GLM_FUNC_DECL T min (T const &x, T const &y, T const &z, T const &w)
 Return the minimum component-wise values of 4 inputs. More...
 
template<typename T , template< typename > class C>
GLM_FUNC_DECL C< T > min (C< T > const &x, typename C< T >::T const &y, typename C< T >::T const &z, typename C< T >::T const &w)
 Return the minimum component-wise values of 4 inputs. More...
 
template<typename T , template< typename > class C>
GLM_FUNC_DECL C< T > min (C< T > const &x, C< T > const &y, C< T > const &z, C< T > const &w)
 Return the minimum component-wise values of 4 inputs. More...
 
\n

Detailed Description

\n

GLM_GTX_extented_min_max

\n
See also
Core features (dependence)
\n\n

Definition in file extended_min_max.hpp.

\n
\n\n
\nGenerated by  \n\"doxygen\"/\n 1.8.10\n
\n\n\n" }, { "path": "field_construction/submodules/diff-langsurf-rasterizer/third_party/glm/doc/api/a00029_source.html", "content": "\n\n\n\n\n\n0.9.9 API documentation: extended_min_max.hpp Source File\n\n\n\n\n\n\n\n\n\n\n
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extended_min_max.hpp
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1 
\n
13 #pragma once
\n
14 
\n
15 // Dependency:
\n
16 #include "../glm.hpp"
\n
17 
\n
18 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
19 # ifndef GLM_ENABLE_EXPERIMENTAL
\n
20 # pragma message("GLM: GLM_GTX_extented_min_max is an experimental extension and may change in the future. Use #define GLM_ENABLE_EXPERIMENTAL before including it, if you really want to use it.")
\n
21 # else
\n
22 # pragma message("GLM: GLM_GTX_extented_min_max extension included")
\n
23 # endif
\n
24 #endif
\n
25 
\n
26 namespace glm
\n
27 {
\n
30 
\n
33  template<typename T>
\n
34  GLM_FUNC_DECL T min(
\n
35  T const& x,
\n
36  T const& y,
\n
37  T const& z);
\n
38 
\n
41  template<typename T, template<typename> class C>
\n
42  GLM_FUNC_DECL C<T> min(
\n
43  C<T> const& x,
\n
44  typename C<T>::T const& y,
\n
45  typename C<T>::T const& z);
\n
46 
\n
49  template<typename T, template<typename> class C>
\n
50  GLM_FUNC_DECL C<T> min(
\n
51  C<T> const& x,
\n
52  C<T> const& y,
\n
53  C<T> const& z);
\n
54 
\n
57  template<typename T>
\n
58  GLM_FUNC_DECL T min(
\n
59  T const& x,
\n
60  T const& y,
\n
61  T const& z,
\n
62  T const& w);
\n
63 
\n
66  template<typename T, template<typename> class C>
\n
67  GLM_FUNC_DECL C<T> min(
\n
68  C<T> const& x,
\n
69  typename C<T>::T const& y,
\n
70  typename C<T>::T const& z,
\n
71  typename C<T>::T const& w);
\n
72 
\n
75  template<typename T, template<typename> class C>
\n
76  GLM_FUNC_DECL C<T> min(
\n
77  C<T> const& x,
\n
78  C<T> const& y,
\n
79  C<T> const& z,
\n
80  C<T> const& w);
\n
81 
\n
84  template<typename T>
\n
85  GLM_FUNC_DECL T max(
\n
86  T const& x,
\n
87  T const& y,
\n
88  T const& z);
\n
89 
\n
92  template<typename T, template<typename> class C>
\n
93  GLM_FUNC_DECL C<T> max(
\n
94  C<T> const& x,
\n
95  typename C<T>::T const& y,
\n
96  typename C<T>::T const& z);
\n
97 
\n
100  template<typename T, template<typename> class C>
\n
101  GLM_FUNC_DECL C<T> max(
\n
102  C<T> const& x,
\n
103  C<T> const& y,
\n
104  C<T> const& z);
\n
105 
\n
108  template<typename T>
\n
109  GLM_FUNC_DECL T max(
\n
110  T const& x,
\n
111  T const& y,
\n
112  T const& z,
\n
113  T const& w);
\n
114 
\n
117  template<typename T, template<typename> class C>
\n
118  GLM_FUNC_DECL C<T> max(
\n
119  C<T> const& x,
\n
120  typename C<T>::T const& y,
\n
121  typename C<T>::T const& z,
\n
122  typename C<T>::T const& w);
\n
123 
\n
126  template<typename T, template<typename> class C>
\n
127  GLM_FUNC_DECL C<T> max(
\n
128  C<T> const& x,
\n
129  C<T> const& y,
\n
130  C<T> const& z,
\n
131  C<T> const& w);
\n
132 
\n
138  template<typename genType>
\n
139  GLM_FUNC_DECL genType fmin(genType x, genType y);
\n
140 
\n
147  template<typename genType>
\n
148  GLM_FUNC_DECL genType fmax(genType x, genType y);
\n
149 
\n
155  template<typename genType>
\n
156  GLM_FUNC_DECL genType fclamp(genType x, genType minVal, genType maxVal);
\n
157 
\n
165  template<length_t L, typename T, qualifier Q>
\n
166  GLM_FUNC_DECL vec<L, T, Q> fclamp(vec<L, T, Q> const& x, T minVal, T maxVal);
\n
167 
\n
175  template<length_t L, typename T, qualifier Q>
\n
176  GLM_FUNC_DECL vec<L, T, Q> fclamp(vec<L, T, Q> const& x, vec<L, T, Q> const& minVal, vec<L, T, Q> const& maxVal);
\n
177 
\n
178 
\n
180 }//namespace glm
\n
181 
\n
182 #include "extended_min_max.inl"
\n
glm::fclamp
GLM_FUNC_DECL vec< L, T, Q > fclamp(vec< L, T, Q > const &x, vec< L, T, Q > const &minVal, vec< L, T, Q > const &maxVal)
Returns min(max(x, minVal), maxVal) for each component in x.
\n
glm::fmin
GLM_FUNC_DECL genType fmin(genType x, genType y)
Returns y if y < x; otherwise, it returns x.
\n
glm::fmax
GLM_FUNC_DECL genType fmax(genType x, genType y)
Returns y if x < y; otherwise, it returns x.
\n
glm::max
GLM_FUNC_DECL C< T > max(C< T > const &x, C< T > const &y, C< T > const &z, C< T > const &w)
Return the maximum component-wise values of 4 inputs.
\n
glm::min
GLM_FUNC_DECL C< T > min(C< T > const &x, C< T > const &y, C< T > const &z, C< T > const &w)
Return the minimum component-wise values of 4 inputs.
\n
glm
Definition: common.hpp:20
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\nGenerated by  \n\"doxygen\"/\n 1.8.10\n
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exterior_product.hpp File Reference
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GLM_GTX_exterior_product \nMore...

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\nFunctions

template<typename T , qualifier Q>
GLM_FUNC_DECL T cross (vec< 2, T, Q > const &v, vec< 2, T, Q > const &u)
 Returns the cross product of x and y. More...
 
\n

Detailed Description

\n

GLM_GTX_exterior_product

\n
See also
Core features (dependence)
\n
\n to use the features of this extension. \">GLM_GTX_exterior_product (dependence)
\n\n

Definition in file exterior_product.hpp.

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exterior_product.hpp
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1 
\n
14 #pragma once
\n
15 
\n
16 // Dependencies
\n
17 #include "../detail/setup.hpp"
\n
18 #include "../detail/qualifier.hpp"
\n
19 
\n
20 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
21 # ifndef GLM_ENABLE_EXPERIMENTAL
\n
22 # pragma message("GLM: GLM_GTX_exterior_product is an experimental extension and may change in the future. Use #define GLM_ENABLE_EXPERIMENTAL before including it, if you really want to use it.")
\n
23 # else
\n
24 # pragma message("GLM: GLM_GTX_exterior_product extension included")
\n
25 # endif
\n
26 #endif
\n
27 
\n
28 namespace glm
\n
29 {
\n
32 
\n
39  template<typename T, qualifier Q>
\n
40  GLM_FUNC_DECL T cross(vec<2, T, Q> const& v, vec<2, T, Q> const& u);
\n
41 
\n
43 } //namespace glm
\n
44 
\n
45 #include "exterior_product.inl"
\n
glm::cross
GLM_FUNC_DECL T cross(vec< 2, T, Q > const &v, vec< 2, T, Q > const &u)
Returns the cross product of x and y.
\n
glm
Definition: common.hpp:20
\n
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GLM_GTX_fast_exponential \nMore...

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\nFunctions

template<typename T >
GLM_FUNC_DECL T fastExp (T x)
 Faster than the common exp function but less accurate. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > fastExp (vec< L, T, Q > const &x)
 Faster than the common exp function but less accurate. More...
 
template<typename T >
GLM_FUNC_DECL T fastExp2 (T x)
 Faster than the common exp2 function but less accurate. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > fastExp2 (vec< L, T, Q > const &x)
 Faster than the common exp2 function but less accurate. More...
 
template<typename T >
GLM_FUNC_DECL T fastLog (T x)
 Faster than the common log function but less accurate. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > fastLog (vec< L, T, Q > const &x)
 Faster than the common exp2 function but less accurate. More...
 
template<typename T >
GLM_FUNC_DECL T fastLog2 (T x)
 Faster than the common log2 function but less accurate. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > fastLog2 (vec< L, T, Q > const &x)
 Faster than the common log2 function but less accurate. More...
 
template<typename genType >
GLM_FUNC_DECL genType fastPow (genType x, genType y)
 Faster than the common pow function but less accurate. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > fastPow (vec< L, T, Q > const &x, vec< L, T, Q > const &y)
 Faster than the common pow function but less accurate. More...
 
template<typename genTypeT , typename genTypeU >
GLM_FUNC_DECL genTypeT fastPow (genTypeT x, genTypeU y)
 Faster than the common pow function but less accurate. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > fastPow (vec< L, T, Q > const &x)
 Faster than the common pow function but less accurate. More...
 
\n

Detailed Description

\n

GLM_GTX_fast_exponential

\n
See also
Core features (dependence)
\n
\ngtx_half_float (dependence)
\n\n

Definition in file fast_exponential.hpp.

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fast_exponential.hpp
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1 
\n
14 #pragma once
\n
15 
\n
16 // Dependency:
\n
17 #include "../glm.hpp"
\n
18 
\n
19 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
20 # ifndef GLM_ENABLE_EXPERIMENTAL
\n
21 # pragma message("GLM: GLM_GTX_fast_exponential is an experimental extension and may change in the future. Use #define GLM_ENABLE_EXPERIMENTAL before including it, if you really want to use it.")
\n
22 # else
\n
23 # pragma message("GLM: GLM_GTX_fast_exponential extension included")
\n
24 # endif
\n
25 #endif
\n
26 
\n
27 namespace glm
\n
28 {
\n
31 
\n
34  template<typename genType>
\n
35  GLM_FUNC_DECL genType fastPow(genType x, genType y);
\n
36 
\n
39  template<length_t L, typename T, qualifier Q>
\n
40  GLM_FUNC_DECL vec<L, T, Q> fastPow(vec<L, T, Q> const& x, vec<L, T, Q> const& y);
\n
41 
\n
44  template<typename genTypeT, typename genTypeU>
\n
45  GLM_FUNC_DECL genTypeT fastPow(genTypeT x, genTypeU y);
\n
46 
\n
49  template<length_t L, typename T, qualifier Q>
\n
50  GLM_FUNC_DECL vec<L, T, Q> fastPow(vec<L, T, Q> const& x);
\n
51 
\n
54  template<typename T>
\n
55  GLM_FUNC_DECL T fastExp(T x);
\n
56 
\n
59  template<length_t L, typename T, qualifier Q>
\n
60  GLM_FUNC_DECL vec<L, T, Q> fastExp(vec<L, T, Q> const& x);
\n
61 
\n
64  template<typename T>
\n
65  GLM_FUNC_DECL T fastLog(T x);
\n
66 
\n
69  template<length_t L, typename T, qualifier Q>
\n
70  GLM_FUNC_DECL vec<L, T, Q> fastLog(vec<L, T, Q> const& x);
\n
71 
\n
74  template<typename T>
\n
75  GLM_FUNC_DECL T fastExp2(T x);
\n
76 
\n
79  template<length_t L, typename T, qualifier Q>
\n
80  GLM_FUNC_DECL vec<L, T, Q> fastExp2(vec<L, T, Q> const& x);
\n
81 
\n
84  template<typename T>
\n
85  GLM_FUNC_DECL T fastLog2(T x);
\n
86 
\n
89  template<length_t L, typename T, qualifier Q>
\n
90  GLM_FUNC_DECL vec<L, T, Q> fastLog2(vec<L, T, Q> const& x);
\n
91 
\n
93 }//namespace glm
\n
94 
\n
95 #include "fast_exponential.inl"
\n
glm::fastLog
GLM_FUNC_DECL vec< L, T, Q > fastLog(vec< L, T, Q > const &x)
Faster than the common exp2 function but less accurate.
\n
glm::fastPow
GLM_FUNC_DECL vec< L, T, Q > fastPow(vec< L, T, Q > const &x)
Faster than the common pow function but less accurate.
\n
glm::fastLog2
GLM_FUNC_DECL vec< L, T, Q > fastLog2(vec< L, T, Q > const &x)
Faster than the common log2 function but less accurate.
\n
glm::fastExp2
GLM_FUNC_DECL vec< L, T, Q > fastExp2(vec< L, T, Q > const &x)
Faster than the common exp2 function but less accurate.
\n
glm::fastExp
GLM_FUNC_DECL vec< L, T, Q > fastExp(vec< L, T, Q > const &x)
Faster than the common exp function but less accurate.
\n
glm
Definition: common.hpp:20
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\nFunctions
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fast_square_root.hpp File Reference
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GLM_GTX_fast_square_root \nMore...

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\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

\nFunctions

template<typename genType >
GLM_FUNC_DECL genType fastDistance (genType x, genType y)
 Faster than the common distance function but less accurate. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL T fastDistance (vec< L, T, Q > const &x, vec< L, T, Q > const &y)
 Faster than the common distance function but less accurate. More...
 
template<typename genType >
GLM_FUNC_DECL genType fastInverseSqrt (genType x)
 Faster than the common inversesqrt function but less accurate. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > fastInverseSqrt (vec< L, T, Q > const &x)
 Faster than the common inversesqrt function but less accurate. More...
 
template<typename genType >
GLM_FUNC_DECL genType fastLength (genType x)
 Faster than the common length function but less accurate. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL T fastLength (vec< L, T, Q > const &x)
 Faster than the common length function but less accurate. More...
 
template<typename genType >
GLM_FUNC_DECL genType fastNormalize (genType const &x)
 Faster than the common normalize function but less accurate. More...
 
template<typename genType >
GLM_FUNC_DECL genType fastSqrt (genType x)
 Faster than the common sqrt function but less accurate. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > fastSqrt (vec< L, T, Q > const &x)
 Faster than the common sqrt function but less accurate. More...
 
\n

Detailed Description

\n

GLM_GTX_fast_square_root

\n
See also
Core features (dependence)
\n\n

Definition in file fast_square_root.hpp.

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fast_square_root.hpp
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1 
\n
15 #pragma once
\n
16 
\n
17 // Dependency:
\n
18 #include "../common.hpp"
\n
19 #include "../exponential.hpp"
\n
20 #include "../geometric.hpp"
\n
21 
\n
22 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
23 # ifndef GLM_ENABLE_EXPERIMENTAL
\n
24 # pragma message("GLM: GLM_GTX_fast_square_root is an experimental extension and may change in the future. Use #define GLM_ENABLE_EXPERIMENTAL before including it, if you really want to use it.")
\n
25 # else
\n
26 # pragma message("GLM: GLM_GTX_fast_square_root extension included")
\n
27 # endif
\n
28 #endif
\n
29 
\n
30 namespace glm
\n
31 {
\n
34 
\n
38  template<typename genType>
\n
39  GLM_FUNC_DECL genType fastSqrt(genType x);
\n
40 
\n
44  template<length_t L, typename T, qualifier Q>
\n
45  GLM_FUNC_DECL vec<L, T, Q> fastSqrt(vec<L, T, Q> const& x);
\n
46 
\n
50  template<typename genType>
\n
51  GLM_FUNC_DECL genType fastInverseSqrt(genType x);
\n
52 
\n
56  template<length_t L, typename T, qualifier Q>
\n
57  GLM_FUNC_DECL vec<L, T, Q> fastInverseSqrt(vec<L, T, Q> const& x);
\n
58 
\n
62  template<typename genType>
\n
63  GLM_FUNC_DECL genType fastLength(genType x);
\n
64 
\n
68  template<length_t L, typename T, qualifier Q>
\n
69  GLM_FUNC_DECL T fastLength(vec<L, T, Q> const& x);
\n
70 
\n
74  template<typename genType>
\n
75  GLM_FUNC_DECL genType fastDistance(genType x, genType y);
\n
76 
\n
80  template<length_t L, typename T, qualifier Q>
\n
81  GLM_FUNC_DECL T fastDistance(vec<L, T, Q> const& x, vec<L, T, Q> const& y);
\n
82 
\n
86  template<typename genType>
\n
87  GLM_FUNC_DECL genType fastNormalize(genType const& x);
\n
88 
\n
90 }// namespace glm
\n
91 
\n
92 #include "fast_square_root.inl"
\n
glm::fastLength
GLM_FUNC_DECL T fastLength(vec< L, T, Q > const &x)
Faster than the common length function but less accurate.
\n
glm::fastDistance
GLM_FUNC_DECL T fastDistance(vec< L, T, Q > const &x, vec< L, T, Q > const &y)
Faster than the common distance function but less accurate.
\n
glm::fastSqrt
GLM_FUNC_DECL vec< L, T, Q > fastSqrt(vec< L, T, Q > const &x)
Faster than the common sqrt function but less accurate.
\n
glm::fastNormalize
GLM_FUNC_DECL genType fastNormalize(genType const &x)
Faster than the common normalize function but less accurate.
\n
glm::fastInverseSqrt
GLM_FUNC_DECL vec< L, T, Q > fastInverseSqrt(vec< L, T, Q > const &x)
Faster than the common inversesqrt function but less accurate.
\n
glm
Definition: common.hpp:20
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fast_trigonometry.hpp File Reference
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GLM_GTX_fast_trigonometry \nMore...

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\nFunctions

template<typename T >
GLM_FUNC_DECL T fastAcos (T angle)
 Faster than the common acos function but less accurate. More...
 
template<typename T >
GLM_FUNC_DECL T fastAsin (T angle)
 Faster than the common asin function but less accurate. More...
 
template<typename T >
GLM_FUNC_DECL T fastAtan (T y, T x)
 Faster than the common atan function but less accurate. More...
 
template<typename T >
GLM_FUNC_DECL T fastAtan (T angle)
 Faster than the common atan function but less accurate. More...
 
template<typename T >
GLM_FUNC_DECL T fastCos (T angle)
 Faster than the common cos function but less accurate. More...
 
template<typename T >
GLM_FUNC_DECL T fastSin (T angle)
 Faster than the common sin function but less accurate. More...
 
template<typename T >
GLM_FUNC_DECL T fastTan (T angle)
 Faster than the common tan function but less accurate. More...
 
template<typename T >
GLM_FUNC_DECL T wrapAngle (T angle)
 Wrap an angle to [0 2pi[ From GLM_GTX_fast_trigonometry extension. More...
 
\n

Detailed Description

\n

GLM_GTX_fast_trigonometry

\n
See also
Core features (dependence)
\n\n

Definition in file fast_trigonometry.hpp.

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fast_trigonometry.hpp
\n
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\nGo to the documentation of this file.
1 
\n
13 #pragma once
\n
14 
\n
15 // Dependency:
\n
16 #include "../gtc/constants.hpp"
\n
17 
\n
18 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
19 # ifndef GLM_ENABLE_EXPERIMENTAL
\n
20 # pragma message("GLM: GLM_GTX_fast_trigonometry is an experimental extension and may change in the future. Use #define GLM_ENABLE_EXPERIMENTAL before including it, if you really want to use it.")
\n
21 # else
\n
22 # pragma message("GLM: GLM_GTX_fast_trigonometry extension included")
\n
23 # endif
\n
24 #endif
\n
25 
\n
26 namespace glm
\n
27 {
\n
30 
\n
33  template<typename T>
\n
34  GLM_FUNC_DECL T wrapAngle(T angle);
\n
35 
\n
38  template<typename T>
\n
39  GLM_FUNC_DECL T fastSin(T angle);
\n
40 
\n
43  template<typename T>
\n
44  GLM_FUNC_DECL T fastCos(T angle);
\n
45 
\n
49  template<typename T>
\n
50  GLM_FUNC_DECL T fastTan(T angle);
\n
51 
\n
55  template<typename T>
\n
56  GLM_FUNC_DECL T fastAsin(T angle);
\n
57 
\n
61  template<typename T>
\n
62  GLM_FUNC_DECL T fastAcos(T angle);
\n
63 
\n
67  template<typename T>
\n
68  GLM_FUNC_DECL T fastAtan(T y, T x);
\n
69 
\n
73  template<typename T>
\n
74  GLM_FUNC_DECL T fastAtan(T angle);
\n
75 
\n
77 }//namespace glm
\n
78 
\n
79 #include "fast_trigonometry.inl"
\n
glm::fastAsin
GLM_FUNC_DECL T fastAsin(T angle)
Faster than the common asin function but less accurate.
\n
glm::angle
GLM_FUNC_DECL T angle(qua< T, Q > const &x)
Returns the quaternion rotation angle.
\n
glm::fastAcos
GLM_FUNC_DECL T fastAcos(T angle)
Faster than the common acos function but less accurate.
\n
glm::fastTan
GLM_FUNC_DECL T fastTan(T angle)
Faster than the common tan function but less accurate.
\n
glm::fastCos
GLM_FUNC_DECL T fastCos(T angle)
Faster than the common cos function but less accurate.
\n
glm::fastAtan
GLM_FUNC_DECL T fastAtan(T angle)
Faster than the common atan function but less accurate.
\n
glm::fastSin
GLM_FUNC_DECL T fastSin(T angle)
Faster than the common sin function but less accurate.
\n
glm::wrapAngle
GLM_FUNC_DECL T wrapAngle(T angle)
Wrap an angle to [0 2pi[ From GLM_GTX_fast_trigonometry extension.
\n
glm
Definition: common.hpp:20
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GLM_GTX_functions \nMore...

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\nFunctions

template<typename T >
GLM_FUNC_DECL T gauss (T x, T ExpectedValue, T StandardDeviation)
 1D gauss function More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL T gauss (vec< 2, T, Q > const &Coord, vec< 2, T, Q > const &ExpectedValue, vec< 2, T, Q > const &StandardDeviation)
 2D gauss function More...
 
\n

Detailed Description

\n

GLM_GTX_functions

\n
See also
Core features (dependence)
\n
\n to use the features of this extension. \">GLM_GTC_quaternion (dependence)
\n\n

Definition in file functions.hpp.

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functions.hpp
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1 
\n
14 #pragma once
\n
15 
\n
16 // Dependencies
\n
17 #include "../detail/setup.hpp"
\n
18 #include "../detail/qualifier.hpp"
\n
19 #include "../detail/type_vec2.hpp"
\n
20 
\n
21 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
22 # ifndef GLM_ENABLE_EXPERIMENTAL
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23 # pragma message("GLM: GLM_GTX_functions is an experimental extension and may change in the future. Use #define GLM_ENABLE_EXPERIMENTAL before including it, if you really want to use it.")
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24 # else
\n
25 # pragma message("GLM: GLM_GTX_functions extension included")
\n
26 # endif
\n
27 #endif
\n
28 
\n
29 namespace glm
\n
30 {
\n
33 
\n
37  template<typename T>
\n
38  GLM_FUNC_DECL T gauss(
\n
39  T x,
\n
40  T ExpectedValue,
\n
41  T StandardDeviation);
\n
42 
\n
46  template<typename T, qualifier Q>
\n
47  GLM_FUNC_DECL T gauss(
\n
48  vec<2, T, Q> const& Coord,
\n
49  vec<2, T, Q> const& ExpectedValue,
\n
50  vec<2, T, Q> const& StandardDeviation);
\n
51 
\n
53 }//namespace glm
\n
54 
\n
55 #include "functions.inl"
\n
56 
\n
glm::gauss
GLM_FUNC_DECL T gauss(vec< 2, T, Q > const &Coord, vec< 2, T, Q > const &ExpectedValue, vec< 2, T, Q > const &StandardDeviation)
2D gauss function
\n
glm
Definition: common.hpp:20
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1 #pragma once
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2 
\n
3 #include "detail/qualifier.hpp"
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4 
\n
5 namespace glm
\n
6 {
\n
7 #if GLM_HAS_EXTENDED_INTEGER_TYPE
\n
8  typedef std::int8_t int8;
\n
9  typedef std::int16_t int16;
\n
10  typedef std::int32_t int32;
\n
11  typedef std::int64_t int64;
\n
12 
\n
13  typedef std::uint8_t uint8;
\n
14  typedef std::uint16_t uint16;
\n
15  typedef std::uint32_t uint32;
\n
16  typedef std::uint64_t uint64;
\n
17 #else
\n
18  typedef signed char int8;
\n
19  typedef signed short int16;
\n
20  typedef signed int int32;
\n
21  typedef detail::int64 int64;
\n
22 
\n
23  typedef unsigned char uint8;
\n
24  typedef unsigned short uint16;
\n
25  typedef unsigned int uint32;
\n
26  typedef detail::uint64 uint64;
\n
27 #endif
\n
28 
\n
29  // Scalar int
\n
30 
\n
31  typedef int8 lowp_i8;
\n
32  typedef int8 mediump_i8;
\n
33  typedef int8 highp_i8;
\n
34  typedef int8 i8;
\n
35 
\n
36  typedef int8 lowp_int8;
\n
37  typedef int8 mediump_int8;
\n
38  typedef int8 highp_int8;
\n
39 
\n
40  typedef int8 lowp_int8_t;
\n
41  typedef int8 mediump_int8_t;
\n
42  typedef int8 highp_int8_t;
\n
43  typedef int8 int8_t;
\n
44 
\n
45  typedef int16 lowp_i16;
\n
46  typedef int16 mediump_i16;
\n
47  typedef int16 highp_i16;
\n
48  typedef int16 i16;
\n
49 
\n
50  typedef int16 lowp_int16;
\n
51  typedef int16 mediump_int16;
\n
52  typedef int16 highp_int16;
\n
53 
\n
54  typedef int16 lowp_int16_t;
\n
55  typedef int16 mediump_int16_t;
\n
56  typedef int16 highp_int16_t;
\n
57  typedef int16 int16_t;
\n
58 
\n
59  typedef int32 lowp_i32;
\n
60  typedef int32 mediump_i32;
\n
61  typedef int32 highp_i32;
\n
62  typedef int32 i32;
\n
63 
\n
64  typedef int32 lowp_int32;
\n
65  typedef int32 mediump_int32;
\n
66  typedef int32 highp_int32;
\n
67 
\n
68  typedef int32 lowp_int32_t;
\n
69  typedef int32 mediump_int32_t;
\n
70  typedef int32 highp_int32_t;
\n
71  typedef int32 int32_t;
\n
72 
\n
73  typedef int64 lowp_i64;
\n
74  typedef int64 mediump_i64;
\n
75  typedef int64 highp_i64;
\n
76  typedef int64 i64;
\n
77 
\n
78  typedef int64 lowp_int64;
\n
79  typedef int64 mediump_int64;
\n
80  typedef int64 highp_int64;
\n
81 
\n
82  typedef int64 lowp_int64_t;
\n
83  typedef int64 mediump_int64_t;
\n
84  typedef int64 highp_int64_t;
\n
85  typedef int64 int64_t;
\n
86 
\n
87  // Scalar uint
\n
88 
\n
89  typedef uint8 lowp_u8;
\n
90  typedef uint8 mediump_u8;
\n
91  typedef uint8 highp_u8;
\n
92  typedef uint8 u8;
\n
93 
\n
94  typedef uint8 lowp_uint8;
\n
95  typedef uint8 mediump_uint8;
\n
96  typedef uint8 highp_uint8;
\n
97 
\n
98  typedef uint8 lowp_uint8_t;
\n
99  typedef uint8 mediump_uint8_t;
\n
100  typedef uint8 highp_uint8_t;
\n
101  typedef uint8 uint8_t;
\n
102 
\n
103  typedef uint16 lowp_u16;
\n
104  typedef uint16 mediump_u16;
\n
105  typedef uint16 highp_u16;
\n
106  typedef uint16 u16;
\n
107 
\n
108  typedef uint16 lowp_uint16;
\n
109  typedef uint16 mediump_uint16;
\n
110  typedef uint16 highp_uint16;
\n
111 
\n
112  typedef uint16 lowp_uint16_t;
\n
113  typedef uint16 mediump_uint16_t;
\n
114  typedef uint16 highp_uint16_t;
\n
115  typedef uint16 uint16_t;
\n
116 
\n
117  typedef uint32 lowp_u32;
\n
118  typedef uint32 mediump_u32;
\n
119  typedef uint32 highp_u32;
\n
120  typedef uint32 u32;
\n
121 
\n
122  typedef uint32 lowp_uint32;
\n
123  typedef uint32 mediump_uint32;
\n
124  typedef uint32 highp_uint32;
\n
125 
\n
126  typedef uint32 lowp_uint32_t;
\n
127  typedef uint32 mediump_uint32_t;
\n
128  typedef uint32 highp_uint32_t;
\n
129  typedef uint32 uint32_t;
\n
130 
\n
131  typedef uint64 lowp_u64;
\n
132  typedef uint64 mediump_u64;
\n
133  typedef uint64 highp_u64;
\n
134  typedef uint64 u64;
\n
135 
\n
136  typedef uint64 lowp_uint64;
\n
137  typedef uint64 mediump_uint64;
\n
138  typedef uint64 highp_uint64;
\n
139 
\n
140  typedef uint64 lowp_uint64_t;
\n
141  typedef uint64 mediump_uint64_t;
\n
142  typedef uint64 highp_uint64_t;
\n
143  typedef uint64 uint64_t;
\n
144 
\n
145  // Scalar float
\n
146 
\n
147  typedef float lowp_f32;
\n
148  typedef float mediump_f32;
\n
149  typedef float highp_f32;
\n
150  typedef float f32;
\n
151 
\n
152  typedef float lowp_float32;
\n
153  typedef float mediump_float32;
\n
154  typedef float highp_float32;
\n
155  typedef float float32;
\n
156 
\n
157  typedef float lowp_float32_t;
\n
158  typedef float mediump_float32_t;
\n
159  typedef float highp_float32_t;
\n
160  typedef float float32_t;
\n
161 
\n
162 
\n
163  typedef double lowp_f64;
\n
164  typedef double mediump_f64;
\n
165  typedef double highp_f64;
\n
166  typedef double f64;
\n
167 
\n
168  typedef double lowp_float64;
\n
169  typedef double mediump_float64;
\n
170  typedef double highp_float64;
\n
171  typedef double float64;
\n
172 
\n
173  typedef double lowp_float64_t;
\n
174  typedef double mediump_float64_t;
\n
175  typedef double highp_float64_t;
\n
176  typedef double float64_t;
\n
177 
\n
178  // Vector bool
\n
179 
\n
180  typedef vec<1, bool, lowp> lowp_bvec1;
\n
181  typedef vec<2, bool, lowp> lowp_bvec2;
\n
182  typedef vec<3, bool, lowp> lowp_bvec3;
\n
183  typedef vec<4, bool, lowp> lowp_bvec4;
\n
184 
\n
185  typedef vec<1, bool, mediump> mediump_bvec1;
\n
186  typedef vec<2, bool, mediump> mediump_bvec2;
\n
187  typedef vec<3, bool, mediump> mediump_bvec3;
\n
188  typedef vec<4, bool, mediump> mediump_bvec4;
\n
189 
\n
190  typedef vec<1, bool, highp> highp_bvec1;
\n
191  typedef vec<2, bool, highp> highp_bvec2;
\n
192  typedef vec<3, bool, highp> highp_bvec3;
\n
193  typedef vec<4, bool, highp> highp_bvec4;
\n
194 
\n
195  typedef vec<1, bool, defaultp> bvec1;
\n
196  typedef vec<2, bool, defaultp> bvec2;
\n
197  typedef vec<3, bool, defaultp> bvec3;
\n
198  typedef vec<4, bool, defaultp> bvec4;
\n
199 
\n
200  // Vector int
\n
201 
\n
202  typedef vec<1, i32, lowp> lowp_ivec1;
\n
203  typedef vec<2, i32, lowp> lowp_ivec2;
\n
204  typedef vec<3, i32, lowp> lowp_ivec3;
\n
205  typedef vec<4, i32, lowp> lowp_ivec4;
\n
206 
\n
207  typedef vec<1, i32, mediump> mediump_ivec1;
\n
208  typedef vec<2, i32, mediump> mediump_ivec2;
\n
209  typedef vec<3, i32, mediump> mediump_ivec3;
\n
210  typedef vec<4, i32, mediump> mediump_ivec4;
\n
211 
\n
212  typedef vec<1, i32, highp> highp_ivec1;
\n
213  typedef vec<2, i32, highp> highp_ivec2;
\n
214  typedef vec<3, i32, highp> highp_ivec3;
\n
215  typedef vec<4, i32, highp> highp_ivec4;
\n
216 
\n
217  typedef vec<1, i32, defaultp> ivec1;
\n
218  typedef vec<2, i32, defaultp> ivec2;
\n
219  typedef vec<3, i32, defaultp> ivec3;
\n
220  typedef vec<4, i32, defaultp> ivec4;
\n
221 
\n
222  typedef vec<1, i8, lowp> lowp_i8vec1;
\n
223  typedef vec<2, i8, lowp> lowp_i8vec2;
\n
224  typedef vec<3, i8, lowp> lowp_i8vec3;
\n
225  typedef vec<4, i8, lowp> lowp_i8vec4;
\n
226 
\n
227  typedef vec<1, i8, mediump> mediump_i8vec1;
\n
228  typedef vec<2, i8, mediump> mediump_i8vec2;
\n
229  typedef vec<3, i8, mediump> mediump_i8vec3;
\n
230  typedef vec<4, i8, mediump> mediump_i8vec4;
\n
231 
\n
232  typedef vec<1, i8, highp> highp_i8vec1;
\n
233  typedef vec<2, i8, highp> highp_i8vec2;
\n
234  typedef vec<3, i8, highp> highp_i8vec3;
\n
235  typedef vec<4, i8, highp> highp_i8vec4;
\n
236 
\n
237  typedef vec<1, i8, defaultp> i8vec1;
\n
238  typedef vec<2, i8, defaultp> i8vec2;
\n
239  typedef vec<3, i8, defaultp> i8vec3;
\n
240  typedef vec<4, i8, defaultp> i8vec4;
\n
241 
\n
242  typedef vec<1, i16, lowp> lowp_i16vec1;
\n
243  typedef vec<2, i16, lowp> lowp_i16vec2;
\n
244  typedef vec<3, i16, lowp> lowp_i16vec3;
\n
245  typedef vec<4, i16, lowp> lowp_i16vec4;
\n
246 
\n
247  typedef vec<1, i16, mediump> mediump_i16vec1;
\n
248  typedef vec<2, i16, mediump> mediump_i16vec2;
\n
249  typedef vec<3, i16, mediump> mediump_i16vec3;
\n
250  typedef vec<4, i16, mediump> mediump_i16vec4;
\n
251 
\n
252  typedef vec<1, i16, highp> highp_i16vec1;
\n
253  typedef vec<2, i16, highp> highp_i16vec2;
\n
254  typedef vec<3, i16, highp> highp_i16vec3;
\n
255  typedef vec<4, i16, highp> highp_i16vec4;
\n
256 
\n
257  typedef vec<1, i16, defaultp> i16vec1;
\n
258  typedef vec<2, i16, defaultp> i16vec2;
\n
259  typedef vec<3, i16, defaultp> i16vec3;
\n
260  typedef vec<4, i16, defaultp> i16vec4;
\n
261 
\n
262  typedef vec<1, i32, lowp> lowp_i32vec1;
\n
263  typedef vec<2, i32, lowp> lowp_i32vec2;
\n
264  typedef vec<3, i32, lowp> lowp_i32vec3;
\n
265  typedef vec<4, i32, lowp> lowp_i32vec4;
\n
266 
\n
267  typedef vec<1, i32, mediump> mediump_i32vec1;
\n
268  typedef vec<2, i32, mediump> mediump_i32vec2;
\n
269  typedef vec<3, i32, mediump> mediump_i32vec3;
\n
270  typedef vec<4, i32, mediump> mediump_i32vec4;
\n
271 
\n
272  typedef vec<1, i32, highp> highp_i32vec1;
\n
273  typedef vec<2, i32, highp> highp_i32vec2;
\n
274  typedef vec<3, i32, highp> highp_i32vec3;
\n
275  typedef vec<4, i32, highp> highp_i32vec4;
\n
276 
\n
277  typedef vec<1, i32, defaultp> i32vec1;
\n
278  typedef vec<2, i32, defaultp> i32vec2;
\n
279  typedef vec<3, i32, defaultp> i32vec3;
\n
280  typedef vec<4, i32, defaultp> i32vec4;
\n
281 
\n
282  typedef vec<1, i64, lowp> lowp_i64vec1;
\n
283  typedef vec<2, i64, lowp> lowp_i64vec2;
\n
284  typedef vec<3, i64, lowp> lowp_i64vec3;
\n
285  typedef vec<4, i64, lowp> lowp_i64vec4;
\n
286 
\n
287  typedef vec<1, i64, mediump> mediump_i64vec1;
\n
288  typedef vec<2, i64, mediump> mediump_i64vec2;
\n
289  typedef vec<3, i64, mediump> mediump_i64vec3;
\n
290  typedef vec<4, i64, mediump> mediump_i64vec4;
\n
291 
\n
292  typedef vec<1, i64, highp> highp_i64vec1;
\n
293  typedef vec<2, i64, highp> highp_i64vec2;
\n
294  typedef vec<3, i64, highp> highp_i64vec3;
\n
295  typedef vec<4, i64, highp> highp_i64vec4;
\n
296 
\n
297  typedef vec<1, i64, defaultp> i64vec1;
\n
298  typedef vec<2, i64, defaultp> i64vec2;
\n
299  typedef vec<3, i64, defaultp> i64vec3;
\n
300  typedef vec<4, i64, defaultp> i64vec4;
\n
301 
\n
302  // Vector uint
\n
303 
\n
304  typedef vec<1, u32, lowp> lowp_uvec1;
\n
305  typedef vec<2, u32, lowp> lowp_uvec2;
\n
306  typedef vec<3, u32, lowp> lowp_uvec3;
\n
307  typedef vec<4, u32, lowp> lowp_uvec4;
\n
308 
\n
309  typedef vec<1, u32, mediump> mediump_uvec1;
\n
310  typedef vec<2, u32, mediump> mediump_uvec2;
\n
311  typedef vec<3, u32, mediump> mediump_uvec3;
\n
312  typedef vec<4, u32, mediump> mediump_uvec4;
\n
313 
\n
314  typedef vec<1, u32, highp> highp_uvec1;
\n
315  typedef vec<2, u32, highp> highp_uvec2;
\n
316  typedef vec<3, u32, highp> highp_uvec3;
\n
317  typedef vec<4, u32, highp> highp_uvec4;
\n
318 
\n
319  typedef vec<1, u32, defaultp> uvec1;
\n
320  typedef vec<2, u32, defaultp> uvec2;
\n
321  typedef vec<3, u32, defaultp> uvec3;
\n
322  typedef vec<4, u32, defaultp> uvec4;
\n
323 
\n
324  typedef vec<1, u8, lowp> lowp_u8vec1;
\n
325  typedef vec<2, u8, lowp> lowp_u8vec2;
\n
326  typedef vec<3, u8, lowp> lowp_u8vec3;
\n
327  typedef vec<4, u8, lowp> lowp_u8vec4;
\n
328 
\n
329  typedef vec<1, u8, mediump> mediump_u8vec1;
\n
330  typedef vec<2, u8, mediump> mediump_u8vec2;
\n
331  typedef vec<3, u8, mediump> mediump_u8vec3;
\n
332  typedef vec<4, u8, mediump> mediump_u8vec4;
\n
333 
\n
334  typedef vec<1, u8, highp> highp_u8vec1;
\n
335  typedef vec<2, u8, highp> highp_u8vec2;
\n
336  typedef vec<3, u8, highp> highp_u8vec3;
\n
337  typedef vec<4, u8, highp> highp_u8vec4;
\n
338 
\n
339  typedef vec<1, u8, defaultp> u8vec1;
\n
340  typedef vec<2, u8, defaultp> u8vec2;
\n
341  typedef vec<3, u8, defaultp> u8vec3;
\n
342  typedef vec<4, u8, defaultp> u8vec4;
\n
343 
\n
344  typedef vec<1, u16, lowp> lowp_u16vec1;
\n
345  typedef vec<2, u16, lowp> lowp_u16vec2;
\n
346  typedef vec<3, u16, lowp> lowp_u16vec3;
\n
347  typedef vec<4, u16, lowp> lowp_u16vec4;
\n
348 
\n
349  typedef vec<1, u16, mediump> mediump_u16vec1;
\n
350  typedef vec<2, u16, mediump> mediump_u16vec2;
\n
351  typedef vec<3, u16, mediump> mediump_u16vec3;
\n
352  typedef vec<4, u16, mediump> mediump_u16vec4;
\n
353 
\n
354  typedef vec<1, u16, highp> highp_u16vec1;
\n
355  typedef vec<2, u16, highp> highp_u16vec2;
\n
356  typedef vec<3, u16, highp> highp_u16vec3;
\n
357  typedef vec<4, u16, highp> highp_u16vec4;
\n
358 
\n
359  typedef vec<1, u16, defaultp> u16vec1;
\n
360  typedef vec<2, u16, defaultp> u16vec2;
\n
361  typedef vec<3, u16, defaultp> u16vec3;
\n
362  typedef vec<4, u16, defaultp> u16vec4;
\n
363 
\n
364  typedef vec<1, u32, lowp> lowp_u32vec1;
\n
365  typedef vec<2, u32, lowp> lowp_u32vec2;
\n
366  typedef vec<3, u32, lowp> lowp_u32vec3;
\n
367  typedef vec<4, u32, lowp> lowp_u32vec4;
\n
368 
\n
369  typedef vec<1, u32, mediump> mediump_u32vec1;
\n
370  typedef vec<2, u32, mediump> mediump_u32vec2;
\n
371  typedef vec<3, u32, mediump> mediump_u32vec3;
\n
372  typedef vec<4, u32, mediump> mediump_u32vec4;
\n
373 
\n
374  typedef vec<1, u32, highp> highp_u32vec1;
\n
375  typedef vec<2, u32, highp> highp_u32vec2;
\n
376  typedef vec<3, u32, highp> highp_u32vec3;
\n
377  typedef vec<4, u32, highp> highp_u32vec4;
\n
378 
\n
379  typedef vec<1, u32, defaultp> u32vec1;
\n
380  typedef vec<2, u32, defaultp> u32vec2;
\n
381  typedef vec<3, u32, defaultp> u32vec3;
\n
382  typedef vec<4, u32, defaultp> u32vec4;
\n
383 
\n
384  typedef vec<1, u64, lowp> lowp_u64vec1;
\n
385  typedef vec<2, u64, lowp> lowp_u64vec2;
\n
386  typedef vec<3, u64, lowp> lowp_u64vec3;
\n
387  typedef vec<4, u64, lowp> lowp_u64vec4;
\n
388 
\n
389  typedef vec<1, u64, mediump> mediump_u64vec1;
\n
390  typedef vec<2, u64, mediump> mediump_u64vec2;
\n
391  typedef vec<3, u64, mediump> mediump_u64vec3;
\n
392  typedef vec<4, u64, mediump> mediump_u64vec4;
\n
393 
\n
394  typedef vec<1, u64, highp> highp_u64vec1;
\n
395  typedef vec<2, u64, highp> highp_u64vec2;
\n
396  typedef vec<3, u64, highp> highp_u64vec3;
\n
397  typedef vec<4, u64, highp> highp_u64vec4;
\n
398 
\n
399  typedef vec<1, u64, defaultp> u64vec1;
\n
400  typedef vec<2, u64, defaultp> u64vec2;
\n
401  typedef vec<3, u64, defaultp> u64vec3;
\n
402  typedef vec<4, u64, defaultp> u64vec4;
\n
403 
\n
404  // Vector float
\n
405 
\n
406  typedef vec<1, float, lowp> lowp_vec1;
\n
407  typedef vec<2, float, lowp> lowp_vec2;
\n
408  typedef vec<3, float, lowp> lowp_vec3;
\n
409  typedef vec<4, float, lowp> lowp_vec4;
\n
410 
\n
411  typedef vec<1, float, mediump> mediump_vec1;
\n
412  typedef vec<2, float, mediump> mediump_vec2;
\n
413  typedef vec<3, float, mediump> mediump_vec3;
\n
414  typedef vec<4, float, mediump> mediump_vec4;
\n
415 
\n
416  typedef vec<1, float, highp> highp_vec1;
\n
417  typedef vec<2, float, highp> highp_vec2;
\n
418  typedef vec<3, float, highp> highp_vec3;
\n
419  typedef vec<4, float, highp> highp_vec4;
\n
420 
\n
421  typedef vec<1, float, defaultp> vec1;
\n
422  typedef vec<2, float, defaultp> vec2;
\n
423  typedef vec<3, float, defaultp> vec3;
\n
424  typedef vec<4, float, defaultp> vec4;
\n
425 
\n
426  typedef vec<1, float, lowp> lowp_fvec1;
\n
427  typedef vec<2, float, lowp> lowp_fvec2;
\n
428  typedef vec<3, float, lowp> lowp_fvec3;
\n
429  typedef vec<4, float, lowp> lowp_fvec4;
\n
430 
\n
431  typedef vec<1, float, mediump> mediump_fvec1;
\n
432  typedef vec<2, float, mediump> mediump_fvec2;
\n
433  typedef vec<3, float, mediump> mediump_fvec3;
\n
434  typedef vec<4, float, mediump> mediump_fvec4;
\n
435 
\n
436  typedef vec<1, float, highp> highp_fvec1;
\n
437  typedef vec<2, float, highp> highp_fvec2;
\n
438  typedef vec<3, float, highp> highp_fvec3;
\n
439  typedef vec<4, float, highp> highp_fvec4;
\n
440 
\n
441  typedef vec<1, f32, defaultp> fvec1;
\n
442  typedef vec<2, f32, defaultp> fvec2;
\n
443  typedef vec<3, f32, defaultp> fvec3;
\n
444  typedef vec<4, f32, defaultp> fvec4;
\n
445 
\n
446  typedef vec<1, f32, lowp> lowp_f32vec1;
\n
447  typedef vec<2, f32, lowp> lowp_f32vec2;
\n
448  typedef vec<3, f32, lowp> lowp_f32vec3;
\n
449  typedef vec<4, f32, lowp> lowp_f32vec4;
\n
450 
\n
451  typedef vec<1, f32, mediump> mediump_f32vec1;
\n
452  typedef vec<2, f32, mediump> mediump_f32vec2;
\n
453  typedef vec<3, f32, mediump> mediump_f32vec3;
\n
454  typedef vec<4, f32, mediump> mediump_f32vec4;
\n
455 
\n
456  typedef vec<1, f32, highp> highp_f32vec1;
\n
457  typedef vec<2, f32, highp> highp_f32vec2;
\n
458  typedef vec<3, f32, highp> highp_f32vec3;
\n
459  typedef vec<4, f32, highp> highp_f32vec4;
\n
460 
\n
461  typedef vec<1, f32, defaultp> f32vec1;
\n
462  typedef vec<2, f32, defaultp> f32vec2;
\n
463  typedef vec<3, f32, defaultp> f32vec3;
\n
464  typedef vec<4, f32, defaultp> f32vec4;
\n
465 
\n
466  typedef vec<1, f64, lowp> lowp_dvec1;
\n
467  typedef vec<2, f64, lowp> lowp_dvec2;
\n
468  typedef vec<3, f64, lowp> lowp_dvec3;
\n
469  typedef vec<4, f64, lowp> lowp_dvec4;
\n
470 
\n
471  typedef vec<1, f64, mediump> mediump_dvec1;
\n
472  typedef vec<2, f64, mediump> mediump_dvec2;
\n
473  typedef vec<3, f64, mediump> mediump_dvec3;
\n
474  typedef vec<4, f64, mediump> mediump_dvec4;
\n
475 
\n
476  typedef vec<1, f64, highp> highp_dvec1;
\n
477  typedef vec<2, f64, highp> highp_dvec2;
\n
478  typedef vec<3, f64, highp> highp_dvec3;
\n
479  typedef vec<4, f64, highp> highp_dvec4;
\n
480 
\n
481  typedef vec<1, f64, defaultp> dvec1;
\n
482  typedef vec<2, f64, defaultp> dvec2;
\n
483  typedef vec<3, f64, defaultp> dvec3;
\n
484  typedef vec<4, f64, defaultp> dvec4;
\n
485 
\n
486  typedef vec<1, f64, lowp> lowp_f64vec1;
\n
487  typedef vec<2, f64, lowp> lowp_f64vec2;
\n
488  typedef vec<3, f64, lowp> lowp_f64vec3;
\n
489  typedef vec<4, f64, lowp> lowp_f64vec4;
\n
490 
\n
491  typedef vec<1, f64, mediump> mediump_f64vec1;
\n
492  typedef vec<2, f64, mediump> mediump_f64vec2;
\n
493  typedef vec<3, f64, mediump> mediump_f64vec3;
\n
494  typedef vec<4, f64, mediump> mediump_f64vec4;
\n
495 
\n
496  typedef vec<1, f64, highp> highp_f64vec1;
\n
497  typedef vec<2, f64, highp> highp_f64vec2;
\n
498  typedef vec<3, f64, highp> highp_f64vec3;
\n
499  typedef vec<4, f64, highp> highp_f64vec4;
\n
500 
\n
501  typedef vec<1, f64, defaultp> f64vec1;
\n
502  typedef vec<2, f64, defaultp> f64vec2;
\n
503  typedef vec<3, f64, defaultp> f64vec3;
\n
504  typedef vec<4, f64, defaultp> f64vec4;
\n
505 
\n
506  // Matrix NxN
\n
507 
\n
508  typedef mat<2, 2, f32, lowp> lowp_mat2;
\n
509  typedef mat<3, 3, f32, lowp> lowp_mat3;
\n
510  typedef mat<4, 4, f32, lowp> lowp_mat4;
\n
511 
\n
512  typedef mat<2, 2, f32, mediump> mediump_mat2;
\n
513  typedef mat<3, 3, f32, mediump> mediump_mat3;
\n
514  typedef mat<4, 4, f32, mediump> mediump_mat4;
\n
515 
\n
516  typedef mat<2, 2, f32, highp> highp_mat2;
\n
517  typedef mat<3, 3, f32, highp> highp_mat3;
\n
518  typedef mat<4, 4, f32, highp> highp_mat4;
\n
519 
\n
520  typedef mat<2, 2, f32, defaultp> mat2;
\n
521  typedef mat<3, 3, f32, defaultp> mat3;
\n
522  typedef mat<4, 4, f32, defaultp> mat4;
\n
523 
\n
524  typedef mat<2, 2, f32, lowp> lowp_fmat2;
\n
525  typedef mat<3, 3, f32, lowp> lowp_fmat3;
\n
526  typedef mat<4, 4, f32, lowp> lowp_fmat4;
\n
527 
\n
528  typedef mat<2, 2, f32, mediump> mediump_fmat2;
\n
529  typedef mat<3, 3, f32, mediump> mediump_fmat3;
\n
530  typedef mat<4, 4, f32, mediump> mediump_fmat4;
\n
531 
\n
532  typedef mat<2, 2, f32, highp> highp_fmat2;
\n
533  typedef mat<3, 3, f32, highp> highp_fmat3;
\n
534  typedef mat<4, 4, f32, highp> highp_fmat4;
\n
535 
\n
536  typedef mat<2, 2, f32, defaultp> fmat2;
\n
537  typedef mat<3, 3, f32, defaultp> fmat3;
\n
538  typedef mat<4, 4, f32, defaultp> fmat4;
\n
539 
\n
540  typedef mat<2, 2, f32, lowp> lowp_f32mat2;
\n
541  typedef mat<3, 3, f32, lowp> lowp_f32mat3;
\n
542  typedef mat<4, 4, f32, lowp> lowp_f32mat4;
\n
543 
\n
544  typedef mat<2, 2, f32, mediump> mediump_f32mat2;
\n
545  typedef mat<3, 3, f32, mediump> mediump_f32mat3;
\n
546  typedef mat<4, 4, f32, mediump> mediump_f32mat4;
\n
547 
\n
548  typedef mat<2, 2, f32, highp> highp_f32mat2;
\n
549  typedef mat<3, 3, f32, highp> highp_f32mat3;
\n
550  typedef mat<4, 4, f32, highp> highp_f32mat4;
\n
551 
\n
552  typedef mat<2, 2, f32, defaultp> f32mat2;
\n
553  typedef mat<3, 3, f32, defaultp> f32mat3;
\n
554  typedef mat<4, 4, f32, defaultp> f32mat4;
\n
555 
\n
556  typedef mat<2, 2, f64, lowp> lowp_dmat2;
\n
557  typedef mat<3, 3, f64, lowp> lowp_dmat3;
\n
558  typedef mat<4, 4, f64, lowp> lowp_dmat4;
\n
559 
\n
560  typedef mat<2, 2, f64, mediump> mediump_dmat2;
\n
561  typedef mat<3, 3, f64, mediump> mediump_dmat3;
\n
562  typedef mat<4, 4, f64, mediump> mediump_dmat4;
\n
563 
\n
564  typedef mat<2, 2, f64, highp> highp_dmat2;
\n
565  typedef mat<3, 3, f64, highp> highp_dmat3;
\n
566  typedef mat<4, 4, f64, highp> highp_dmat4;
\n
567 
\n
568  typedef mat<2, 2, f64, defaultp> dmat2;
\n
569  typedef mat<3, 3, f64, defaultp> dmat3;
\n
570  typedef mat<4, 4, f64, defaultp> dmat4;
\n
571 
\n
572  typedef mat<2, 2, f64, lowp> lowp_f64mat2;
\n
573  typedef mat<3, 3, f64, lowp> lowp_f64mat3;
\n
574  typedef mat<4, 4, f64, lowp> lowp_f64mat4;
\n
575 
\n
576  typedef mat<2, 2, f64, mediump> mediump_f64mat2;
\n
577  typedef mat<3, 3, f64, mediump> mediump_f64mat3;
\n
578  typedef mat<4, 4, f64, mediump> mediump_f64mat4;
\n
579 
\n
580  typedef mat<2, 2, f64, highp> highp_f64mat2;
\n
581  typedef mat<3, 3, f64, highp> highp_f64mat3;
\n
582  typedef mat<4, 4, f64, highp> highp_f64mat4;
\n
583 
\n
584  typedef mat<2, 2, f64, defaultp> f64mat2;
\n
585  typedef mat<3, 3, f64, defaultp> f64mat3;
\n
586  typedef mat<4, 4, f64, defaultp> f64mat4;
\n
587 
\n
588  // Matrix MxN
\n
589 
\n
590  typedef mat<2, 2, f32, lowp> lowp_mat2x2;
\n
591  typedef mat<2, 3, f32, lowp> lowp_mat2x3;
\n
592  typedef mat<2, 4, f32, lowp> lowp_mat2x4;
\n
593  typedef mat<3, 2, f32, lowp> lowp_mat3x2;
\n
594  typedef mat<3, 3, f32, lowp> lowp_mat3x3;
\n
595  typedef mat<3, 4, f32, lowp> lowp_mat3x4;
\n
596  typedef mat<4, 2, f32, lowp> lowp_mat4x2;
\n
597  typedef mat<4, 3, f32, lowp> lowp_mat4x3;
\n
598  typedef mat<4, 4, f32, lowp> lowp_mat4x4;
\n
599 
\n
600  typedef mat<2, 2, f32, mediump> mediump_mat2x2;
\n
601  typedef mat<2, 3, f32, mediump> mediump_mat2x3;
\n
602  typedef mat<2, 4, f32, mediump> mediump_mat2x4;
\n
603  typedef mat<3, 2, f32, mediump> mediump_mat3x2;
\n
604  typedef mat<3, 3, f32, mediump> mediump_mat3x3;
\n
605  typedef mat<3, 4, f32, mediump> mediump_mat3x4;
\n
606  typedef mat<4, 2, f32, mediump> mediump_mat4x2;
\n
607  typedef mat<4, 3, f32, mediump> mediump_mat4x3;
\n
608  typedef mat<4, 4, f32, mediump> mediump_mat4x4;
\n
609 
\n
610  typedef mat<2, 2, f32, highp> highp_mat2x2;
\n
611  typedef mat<2, 3, f32, highp> highp_mat2x3;
\n
612  typedef mat<2, 4, f32, highp> highp_mat2x4;
\n
613  typedef mat<3, 2, f32, highp> highp_mat3x2;
\n
614  typedef mat<3, 3, f32, highp> highp_mat3x3;
\n
615  typedef mat<3, 4, f32, highp> highp_mat3x4;
\n
616  typedef mat<4, 2, f32, highp> highp_mat4x2;
\n
617  typedef mat<4, 3, f32, highp> highp_mat4x3;
\n
618  typedef mat<4, 4, f32, highp> highp_mat4x4;
\n
619 
\n
620  typedef mat<2, 2, f32, defaultp> mat2x2;
\n
621  typedef mat<3, 2, f32, defaultp> mat3x2;
\n
622  typedef mat<4, 2, f32, defaultp> mat4x2;
\n
623  typedef mat<2, 3, f32, defaultp> mat2x3;
\n
624  typedef mat<3, 3, f32, defaultp> mat3x3;
\n
625  typedef mat<4, 3, f32, defaultp> mat4x3;
\n
626  typedef mat<2, 4, f32, defaultp> mat2x4;
\n
627  typedef mat<3, 4, f32, defaultp> mat3x4;
\n
628  typedef mat<4, 4, f32, defaultp> mat4x4;
\n
629 
\n
630  typedef mat<2, 2, f32, lowp> lowp_fmat2x2;
\n
631  typedef mat<2, 3, f32, lowp> lowp_fmat2x3;
\n
632  typedef mat<2, 4, f32, lowp> lowp_fmat2x4;
\n
633  typedef mat<3, 2, f32, lowp> lowp_fmat3x2;
\n
634  typedef mat<3, 3, f32, lowp> lowp_fmat3x3;
\n
635  typedef mat<3, 4, f32, lowp> lowp_fmat3x4;
\n
636  typedef mat<4, 2, f32, lowp> lowp_fmat4x2;
\n
637  typedef mat<4, 3, f32, lowp> lowp_fmat4x3;
\n
638  typedef mat<4, 4, f32, lowp> lowp_fmat4x4;
\n
639 
\n
640  typedef mat<2, 2, f32, mediump> mediump_fmat2x2;
\n
641  typedef mat<2, 3, f32, mediump> mediump_fmat2x3;
\n
642  typedef mat<2, 4, f32, mediump> mediump_fmat2x4;
\n
643  typedef mat<3, 2, f32, mediump> mediump_fmat3x2;
\n
644  typedef mat<3, 3, f32, mediump> mediump_fmat3x3;
\n
645  typedef mat<3, 4, f32, mediump> mediump_fmat3x4;
\n
646  typedef mat<4, 2, f32, mediump> mediump_fmat4x2;
\n
647  typedef mat<4, 3, f32, mediump> mediump_fmat4x3;
\n
648  typedef mat<4, 4, f32, mediump> mediump_fmat4x4;
\n
649 
\n
650  typedef mat<2, 2, f32, highp> highp_fmat2x2;
\n
651  typedef mat<2, 3, f32, highp> highp_fmat2x3;
\n
652  typedef mat<2, 4, f32, highp> highp_fmat2x4;
\n
653  typedef mat<3, 2, f32, highp> highp_fmat3x2;
\n
654  typedef mat<3, 3, f32, highp> highp_fmat3x3;
\n
655  typedef mat<3, 4, f32, highp> highp_fmat3x4;
\n
656  typedef mat<4, 2, f32, highp> highp_fmat4x2;
\n
657  typedef mat<4, 3, f32, highp> highp_fmat4x3;
\n
658  typedef mat<4, 4, f32, highp> highp_fmat4x4;
\n
659 
\n
660  typedef mat<2, 2, f32, defaultp> fmat2x2;
\n
661  typedef mat<3, 2, f32, defaultp> fmat3x2;
\n
662  typedef mat<4, 2, f32, defaultp> fmat4x2;
\n
663  typedef mat<2, 3, f32, defaultp> fmat2x3;
\n
664  typedef mat<3, 3, f32, defaultp> fmat3x3;
\n
665  typedef mat<4, 3, f32, defaultp> fmat4x3;
\n
666  typedef mat<2, 4, f32, defaultp> fmat2x4;
\n
667  typedef mat<3, 4, f32, defaultp> fmat3x4;
\n
668  typedef mat<4, 4, f32, defaultp> fmat4x4;
\n
669 
\n
670  typedef mat<2, 2, f32, lowp> lowp_f32mat2x2;
\n
671  typedef mat<2, 3, f32, lowp> lowp_f32mat2x3;
\n
672  typedef mat<2, 4, f32, lowp> lowp_f32mat2x4;
\n
673  typedef mat<3, 2, f32, lowp> lowp_f32mat3x2;
\n
674  typedef mat<3, 3, f32, lowp> lowp_f32mat3x3;
\n
675  typedef mat<3, 4, f32, lowp> lowp_f32mat3x4;
\n
676  typedef mat<4, 2, f32, lowp> lowp_f32mat4x2;
\n
677  typedef mat<4, 3, f32, lowp> lowp_f32mat4x3;
\n
678  typedef mat<4, 4, f32, lowp> lowp_f32mat4x4;
\n
679 
\n
680  typedef mat<2, 2, f32, mediump> mediump_f32mat2x2;
\n
681  typedef mat<2, 3, f32, mediump> mediump_f32mat2x3;
\n
682  typedef mat<2, 4, f32, mediump> mediump_f32mat2x4;
\n
683  typedef mat<3, 2, f32, mediump> mediump_f32mat3x2;
\n
684  typedef mat<3, 3, f32, mediump> mediump_f32mat3x3;
\n
685  typedef mat<3, 4, f32, mediump> mediump_f32mat3x4;
\n
686  typedef mat<4, 2, f32, mediump> mediump_f32mat4x2;
\n
687  typedef mat<4, 3, f32, mediump> mediump_f32mat4x3;
\n
688  typedef mat<4, 4, f32, mediump> mediump_f32mat4x4;
\n
689 
\n
690  typedef mat<2, 2, f32, highp> highp_f32mat2x2;
\n
691  typedef mat<2, 3, f32, highp> highp_f32mat2x3;
\n
692  typedef mat<2, 4, f32, highp> highp_f32mat2x4;
\n
693  typedef mat<3, 2, f32, highp> highp_f32mat3x2;
\n
694  typedef mat<3, 3, f32, highp> highp_f32mat3x3;
\n
695  typedef mat<3, 4, f32, highp> highp_f32mat3x4;
\n
696  typedef mat<4, 2, f32, highp> highp_f32mat4x2;
\n
697  typedef mat<4, 3, f32, highp> highp_f32mat4x3;
\n
698  typedef mat<4, 4, f32, highp> highp_f32mat4x4;
\n
699 
\n
700  typedef mat<2, 2, f32, defaultp> f32mat2x2;
\n
701  typedef mat<3, 2, f32, defaultp> f32mat3x2;
\n
702  typedef mat<4, 2, f32, defaultp> f32mat4x2;
\n
703  typedef mat<2, 3, f32, defaultp> f32mat2x3;
\n
704  typedef mat<3, 3, f32, defaultp> f32mat3x3;
\n
705  typedef mat<4, 3, f32, defaultp> f32mat4x3;
\n
706  typedef mat<2, 4, f32, defaultp> f32mat2x4;
\n
707  typedef mat<3, 4, f32, defaultp> f32mat3x4;
\n
708  typedef mat<4, 4, f32, defaultp> f32mat4x4;
\n
709 
\n
710  typedef mat<2, 2, double, lowp> lowp_dmat2x2;
\n
711  typedef mat<2, 3, double, lowp> lowp_dmat2x3;
\n
712  typedef mat<2, 4, double, lowp> lowp_dmat2x4;
\n
713  typedef mat<3, 2, double, lowp> lowp_dmat3x2;
\n
714  typedef mat<3, 3, double, lowp> lowp_dmat3x3;
\n
715  typedef mat<3, 4, double, lowp> lowp_dmat3x4;
\n
716  typedef mat<4, 2, double, lowp> lowp_dmat4x2;
\n
717  typedef mat<4, 3, double, lowp> lowp_dmat4x3;
\n
718  typedef mat<4, 4, double, lowp> lowp_dmat4x4;
\n
719 
\n
720  typedef mat<2, 2, double, mediump> mediump_dmat2x2;
\n
721  typedef mat<2, 3, double, mediump> mediump_dmat2x3;
\n
722  typedef mat<2, 4, double, mediump> mediump_dmat2x4;
\n
723  typedef mat<3, 2, double, mediump> mediump_dmat3x2;
\n
724  typedef mat<3, 3, double, mediump> mediump_dmat3x3;
\n
725  typedef mat<3, 4, double, mediump> mediump_dmat3x4;
\n
726  typedef mat<4, 2, double, mediump> mediump_dmat4x2;
\n
727  typedef mat<4, 3, double, mediump> mediump_dmat4x3;
\n
728  typedef mat<4, 4, double, mediump> mediump_dmat4x4;
\n
729 
\n
730  typedef mat<2, 2, double, highp> highp_dmat2x2;
\n
731  typedef mat<2, 3, double, highp> highp_dmat2x3;
\n
732  typedef mat<2, 4, double, highp> highp_dmat2x4;
\n
733  typedef mat<3, 2, double, highp> highp_dmat3x2;
\n
734  typedef mat<3, 3, double, highp> highp_dmat3x3;
\n
735  typedef mat<3, 4, double, highp> highp_dmat3x4;
\n
736  typedef mat<4, 2, double, highp> highp_dmat4x2;
\n
737  typedef mat<4, 3, double, highp> highp_dmat4x3;
\n
738  typedef mat<4, 4, double, highp> highp_dmat4x4;
\n
739 
\n
740  typedef mat<2, 2, double, defaultp> dmat2x2;
\n
741  typedef mat<3, 2, double, defaultp> dmat3x2;
\n
742  typedef mat<4, 2, double, defaultp> dmat4x2;
\n
743  typedef mat<2, 3, double, defaultp> dmat2x3;
\n
744  typedef mat<3, 3, double, defaultp> dmat3x3;
\n
745  typedef mat<4, 3, double, defaultp> dmat4x3;
\n
746  typedef mat<2, 4, double, defaultp> dmat2x4;
\n
747  typedef mat<3, 4, double, defaultp> dmat3x4;
\n
748  typedef mat<4, 4, double, defaultp> dmat4x4;
\n
749 
\n
750  typedef mat<2, 2, f64, lowp> lowp_f64mat2x2;
\n
751  typedef mat<2, 3, f64, lowp> lowp_f64mat2x3;
\n
752  typedef mat<2, 4, f64, lowp> lowp_f64mat2x4;
\n
753  typedef mat<3, 2, f64, lowp> lowp_f64mat3x2;
\n
754  typedef mat<3, 3, f64, lowp> lowp_f64mat3x3;
\n
755  typedef mat<3, 4, f64, lowp> lowp_f64mat3x4;
\n
756  typedef mat<4, 2, f64, lowp> lowp_f64mat4x2;
\n
757  typedef mat<4, 3, f64, lowp> lowp_f64mat4x3;
\n
758  typedef mat<4, 4, f64, lowp> lowp_f64mat4x4;
\n
759 
\n
760  typedef mat<2, 2, f64, mediump> mediump_f64mat2x2;
\n
761  typedef mat<2, 3, f64, mediump> mediump_f64mat2x3;
\n
762  typedef mat<2, 4, f64, mediump> mediump_f64mat2x4;
\n
763  typedef mat<3, 2, f64, mediump> mediump_f64mat3x2;
\n
764  typedef mat<3, 3, f64, mediump> mediump_f64mat3x3;
\n
765  typedef mat<3, 4, f64, mediump> mediump_f64mat3x4;
\n
766  typedef mat<4, 2, f64, mediump> mediump_f64mat4x2;
\n
767  typedef mat<4, 3, f64, mediump> mediump_f64mat4x3;
\n
768  typedef mat<4, 4, f64, mediump> mediump_f64mat4x4;
\n
769 
\n
770  typedef mat<2, 2, f64, highp> highp_f64mat2x2;
\n
771  typedef mat<2, 3, f64, highp> highp_f64mat2x3;
\n
772  typedef mat<2, 4, f64, highp> highp_f64mat2x4;
\n
773  typedef mat<3, 2, f64, highp> highp_f64mat3x2;
\n
774  typedef mat<3, 3, f64, highp> highp_f64mat3x3;
\n
775  typedef mat<3, 4, f64, highp> highp_f64mat3x4;
\n
776  typedef mat<4, 2, f64, highp> highp_f64mat4x2;
\n
777  typedef mat<4, 3, f64, highp> highp_f64mat4x3;
\n
778  typedef mat<4, 4, f64, highp> highp_f64mat4x4;
\n
779 
\n
780  typedef mat<2, 2, f64, defaultp> f64mat2x2;
\n
781  typedef mat<3, 2, f64, defaultp> f64mat3x2;
\n
782  typedef mat<4, 2, f64, defaultp> f64mat4x2;
\n
783  typedef mat<2, 3, f64, defaultp> f64mat2x3;
\n
784  typedef mat<3, 3, f64, defaultp> f64mat3x3;
\n
785  typedef mat<4, 3, f64, defaultp> f64mat4x3;
\n
786  typedef mat<2, 4, f64, defaultp> f64mat2x4;
\n
787  typedef mat<3, 4, f64, defaultp> f64mat3x4;
\n
788  typedef mat<4, 4, f64, defaultp> f64mat4x4;
\n
789 
\n
790  // Quaternion
\n
791 
\n
792  typedef qua<float, lowp> lowp_quat;
\n
793  typedef qua<float, mediump> mediump_quat;
\n
794  typedef qua<float, highp> highp_quat;
\n
795  typedef qua<float, defaultp> quat;
\n
796 
\n
797  typedef qua<float, lowp> lowp_fquat;
\n
798  typedef qua<float, mediump> mediump_fquat;
\n
799  typedef qua<float, highp> highp_fquat;
\n
800  typedef qua<float, defaultp> fquat;
\n
801 
\n
802  typedef qua<f32, lowp> lowp_f32quat;
\n
803  typedef qua<f32, mediump> mediump_f32quat;
\n
804  typedef qua<f32, highp> highp_f32quat;
\n
805  typedef qua<f32, defaultp> f32quat;
\n
806 
\n
807  typedef qua<double, lowp> lowp_dquat;
\n
808  typedef qua<double, mediump> mediump_dquat;
\n
809  typedef qua<double, highp> highp_dquat;
\n
810  typedef qua<double, defaultp> dquat;
\n
811 
\n
812  typedef qua<f64, lowp> lowp_f64quat;
\n
813  typedef qua<f64, mediump> mediump_f64quat;
\n
814  typedef qua<f64, highp> highp_f64quat;
\n
815  typedef qua<f64, defaultp> f64quat;
\n
816 }//namespace glm
\n
817 
\n
818 
\n
glm::highp_u16vec1
vec< 1, u16, highp > highp_u16vec1
High qualifier 16 bit unsigned integer scalar type.
Definition: fwd.hpp:354
\n
glm::mediump_mat4x2
mat< 4, 2, float, mediump > mediump_mat4x2
4 columns of 2 components matrix of single-precision floating-point numbers using medium precision ar...
Definition: matrix_float4x2_precision.hpp:22
\n
glm::highp_f32mat4x2
mat< 4, 2, f32, highp > highp_f32mat4x2
High single-qualifier floating-point 4x2 matrix.
Definition: fwd.hpp:696
\n
glm::highp_mat4x3
mat< 4, 3, float, highp > highp_mat4x3
4 columns of 3 components matrix of single-precision floating-point numbers using high precision arit...
Definition: matrix_float4x3_precision.hpp:28
\n
glm::mat4x4
mat< 4, 4, float, defaultp > mat4x4
4 columns of 4 components matrix of single-precision floating-point numbers.
Definition: matrix_float4x4.hpp:15
\n
glm::mediump_uvec4
vec< 4, unsigned int, mediump > mediump_uvec4
4 components vector of medium qualifier unsigned integer numbers.
Definition: vector_uint4_precision.hpp:22
\n
glm::highp_u64
uint64 highp_u64
High qualifier 64 bit unsigned integer type.
Definition: fwd.hpp:133
\n
glm::mediump_f64vec1
vec< 1, f64, mediump > mediump_f64vec1
Medium double-qualifier floating-point vector of 1 component.
Definition: fwd.hpp:491
\n
glm::f32vec3
vec< 3, f32, defaultp > f32vec3
Single-qualifier floating-point vector of 3 components.
Definition: fwd.hpp:463
\n
glm::mediump_fmat2
mat< 2, 2, f32, mediump > mediump_fmat2
Medium single-qualifier floating-point 1x1 matrix.
Definition: fwd.hpp:528
\n
glm::highp_float64_t
double highp_float64_t
High 64 bit double-qualifier floating-point scalar.
Definition: fwd.hpp:175
\n
glm::f64mat4
mat< 4, 4, f64, defaultp > f64mat4
Double-qualifier floating-point 4x4 matrix.
Definition: fwd.hpp:586
\n
glm::mediump_ivec1
vec< 1, int, mediump > mediump_ivec1
1 component vector of signed integer values.
Definition: vector_int1_precision.hpp:28
\n
glm::mediump_dvec4
vec< 4, double, mediump > mediump_dvec4
4 components vector of medium double-qualifier floating-point numbers.
Definition: vector_double4_precision.hpp:25
\n
glm::highp_vec3
vec< 3, float, highp > highp_vec3
3 components vector of high single-qualifier floating-point numbers.
Definition: vector_float3_precision.hpp:16
\n
glm::lowp_dmat4x2
mat< 4, 2, double, lowp > lowp_dmat4x2
4 columns of 2 components matrix of double-precision floating-point numbers using low precision arith...
Definition: matrix_double4x2_precision.hpp:16
\n
glm::mat2x2
mat< 2, 2, float, defaultp > mat2x2
2 columns of 2 components matrix of single-precision floating-point numbers.
Definition: matrix_float2x2.hpp:15
\n
glm::f64mat2
mat< 2, 2, f64, defaultp > f64mat2
Double-qualifier floating-point 1x1 matrix.
Definition: fwd.hpp:584
\n
glm::mediump_fmat4x3
mat< 4, 3, f32, mediump > mediump_fmat4x3
Medium single-qualifier floating-point 4x3 matrix.
Definition: fwd.hpp:647
\n
glm::mediump_f32mat3
mat< 3, 3, f32, mediump > mediump_f32mat3
Medium single-qualifier floating-point 3x3 matrix.
Definition: fwd.hpp:545
\n
glm::mediump_uint32_t
uint32 mediump_uint32_t
Medium qualifier 32 bit unsigned integer type.
Definition: fwd.hpp:127
\n
glm::lowp_uint64
uint64 lowp_uint64
Low qualifier 64 bit unsigned integer type.
Definition: fwd.hpp:136
\n
glm::mediump_mat3x3
mat< 3, 3, float, mediump > mediump_mat3x3
3 columns of 3 components matrix of single-precision floating-point numbers using medium precision ar...
Definition: matrix_float3x3_precision.hpp:40
\n
glm::mediump_fmat2x2
mat< 2, 2, f32, mediump > mediump_fmat2x2
Medium single-qualifier floating-point 1x1 matrix.
Definition: fwd.hpp:640
\n
glm::f32vec1
vec< 1, f32, defaultp > f32vec1
Single-qualifier floating-point vector of 1 component.
Definition: fwd.hpp:461
\n
glm::highp_f32mat4
mat< 4, 4, f32, highp > highp_f32mat4
High single-qualifier floating-point 4x4 matrix.
Definition: fwd.hpp:550
\n
glm::highp_quat
qua< float, highp > highp_quat
Quaternion of single-precision floating-point numbers using high precision arithmetic in term of ULPs...
Definition: quaternion_float_precision.hpp:32
\n
glm::highp_float64
double highp_float64
High 64 bit double-qualifier floating-point scalar.
Definition: fwd.hpp:170
\n
glm::mediump_dmat3x2
mat< 3, 2, double, mediump > mediump_dmat3x2
3 columns of 2 components matrix of double-precision floating-point numbers using medium precision ar...
Definition: matrix_double3x2_precision.hpp:22
\n
glm::lowp_u8
uint8 lowp_u8
Low qualifier 8 bit unsigned integer type.
Definition: fwd.hpp:89
\n
glm::lowp_dmat3x2
mat< 3, 2, double, lowp > lowp_dmat3x2
3 columns of 2 components matrix of double-precision floating-point numbers using low precision arith...
Definition: matrix_double3x2_precision.hpp:16
\n
glm::u32
uint32 u32
Default qualifier 32 bit unsigned integer type.
Definition: fwd.hpp:120
\n
glm::f64mat3
mat< 3, 3, f64, defaultp > f64mat3
Double-qualifier floating-point 3x3 matrix.
Definition: fwd.hpp:585
\n
glm::highp_ivec2
vec< 2, int, highp > highp_ivec2
2 components vector of high qualifier signed integer numbers.
Definition: vector_int2_precision.hpp:16
\n
glm::highp_dmat4x3
mat< 4, 3, double, highp > highp_dmat4x3
4 columns of 3 components matrix of double-precision floating-point numbers using medium precision ar...
Definition: matrix_double4x3_precision.hpp:28
\n
glm::mediump_mat2x3
mat< 2, 3, float, mediump > mediump_mat2x3
2 columns of 3 components matrix of single-precision floating-point numbers using medium precision ar...
Definition: matrix_float2x3_precision.hpp:22
\n
glm::lowp_float64
double lowp_float64
Low 64 bit double-qualifier floating-point scalar.
Definition: fwd.hpp:168
\n
glm::i32vec1
vec< 1, i32, defaultp > i32vec1
32 bit signed integer scalar type.
Definition: fwd.hpp:277
\n
glm::highp_uint16
uint16 highp_uint16
High qualifier 16 bit unsigned integer type.
Definition: fwd.hpp:110
\n
glm::mediump_f64mat2x4
mat< 2, 4, f64, mediump > mediump_f64mat2x4
Medium double-qualifier floating-point 2x4 matrix.
Definition: fwd.hpp:762
\n
glm::highp_i64vec4
vec< 4, i64, highp > highp_i64vec4
High qualifier 64 bit signed integer vector of 4 components type.
Definition: fwd.hpp:295
\n
glm::mediump_dmat4x4
mat< 4, 4, double, mediump > mediump_dmat4x4
4 columns of 4 components matrix of double-precision floating-point numbers using medium precision ar...
Definition: matrix_double4x4_precision.hpp:40
\n
glm::f64mat3x4
mat< 3, 4, f64, defaultp > f64mat3x4
Double-qualifier floating-point 3x4 matrix.
Definition: fwd.hpp:787
\n
glm::highp_dvec4
vec< 4, double, highp > highp_dvec4
4 components vector of high double-qualifier floating-point numbers.
Definition: vector_double4_precision.hpp:18
\n
glm::fmat2
mat< 2, 2, f32, defaultp > fmat2
Single-qualifier floating-point 1x1 matrix.
Definition: fwd.hpp:536
\n
glm::lowp_dmat3x4
mat< 3, 4, double, lowp > lowp_dmat3x4
3 columns of 4 components matrix of double-precision floating-point numbers using low precision arith...
Definition: matrix_double3x4_precision.hpp:16
\n
glm::i16vec3
vec< 3, i16, defaultp > i16vec3
16 bit signed integer vector of 3 components type.
Definition: fwd.hpp:259
\n
glm::lowp_uint32_t
uint32 lowp_uint32_t
Low qualifier 32 bit unsigned integer type.
Definition: fwd.hpp:126
\n
glm::lowp_fvec2
vec< 2, float, lowp > lowp_fvec2
Low single-qualifier floating-point vector of 2 components.
Definition: fwd.hpp:427
\n
glm::mediump_uint32
uint32 mediump_uint32
Medium qualifier 32 bit unsigned integer type.
Definition: fwd.hpp:123
\n
glm::mediump_fmat4
mat< 4, 4, f32, mediump > mediump_fmat4
Medium single-qualifier floating-point 4x4 matrix.
Definition: fwd.hpp:530
\n
glm::highp_uint64
uint64 highp_uint64
High qualifier 64 bit unsigned integer type.
Definition: fwd.hpp:138
\n
glm::lowp_fmat2
mat< 2, 2, f32, lowp > lowp_fmat2
Low single-qualifier floating-point 1x1 matrix.
Definition: fwd.hpp:524
\n
glm::lowp_uint32
uint32 lowp_uint32
Low qualifier 32 bit unsigned integer type.
Definition: fwd.hpp:122
\n
glm::lowp_fvec3
vec< 3, float, lowp > lowp_fvec3
Low single-qualifier floating-point vector of 3 components.
Definition: fwd.hpp:428
\n
glm::mediump_fvec2
vec< 2, float, mediump > mediump_fvec2
Medium Single-qualifier floating-point vector of 2 components.
Definition: fwd.hpp:432
\n
glm::highp_mat2x3
mat< 2, 3, float, highp > highp_mat2x3
2 columns of 3 components matrix of single-precision floating-point numbers using high precision arit...
Definition: matrix_float2x3_precision.hpp:28
\n
glm::lowp_fmat3x4
mat< 3, 4, f32, lowp > lowp_fmat3x4
Low single-qualifier floating-point 3x4 matrix.
Definition: fwd.hpp:635
\n
glm::vec2
vec< 2, float, defaultp > vec2
2 components vector of single-precision floating-point numbers.
Definition: vector_float2.hpp:15
\n
glm::lowp_f64mat2x2
mat< 2, 2, f64, lowp > lowp_f64mat2x2
Low double-qualifier floating-point 1x1 matrix.
Definition: fwd.hpp:750
\n
glm::i64vec4
vec< 4, i64, defaultp > i64vec4
64 bit signed integer vector of 4 components type.
Definition: fwd.hpp:300
\n
glm::u16vec3
vec< 3, u16, defaultp > u16vec3
Default qualifier 16 bit unsigned integer vector of 3 components type.
Definition: fwd.hpp:361
\n
glm::lowp_u64vec1
vec< 1, u64, lowp > lowp_u64vec1
Low qualifier 64 bit unsigned integer scalar type.
Definition: fwd.hpp:384
\n
glm::mediump_dmat2
mat< 2, 2, double, mediump > mediump_dmat2
2 columns of 2 components matrix of double-precision floating-point numbers using medium precision ar...
Definition: matrix_double2x2_precision.hpp:22
\n
glm::mediump_u16vec1
vec< 1, u16, mediump > mediump_u16vec1
Medium qualifier 16 bit unsigned integer scalar type.
Definition: fwd.hpp:349
\n
glm::highp_vec2
vec< 2, float, highp > highp_vec2
2 components vector of high single-qualifier floating-point numbers.
Definition: vector_float2_precision.hpp:16
\n
glm::i8vec2
vec< 2, i8, defaultp > i8vec2
8 bit signed integer vector of 2 components type.
Definition: fwd.hpp:238
\n
glm::mediump_f64mat2x3
mat< 2, 3, f64, mediump > mediump_f64mat2x3
Medium double-qualifier floating-point 2x3 matrix.
Definition: fwd.hpp:761
\n
glm::lowp_u32vec4
vec< 4, u32, lowp > lowp_u32vec4
Low qualifier 32 bit unsigned integer vector of 4 components type.
Definition: fwd.hpp:367
\n
glm::highp_f32vec4
vec< 4, f32, highp > highp_f32vec4
High single-qualifier floating-point vector of 4 components.
Definition: fwd.hpp:459
\n
glm::uvec3
vec< 3, unsigned int, defaultp > uvec3
3 components vector of unsigned integer numbers.
Definition: vector_uint3.hpp:15
\n
glm::lowp_f32vec1
vec< 1, f32, lowp > lowp_f32vec1
Low single-qualifier floating-point vector of 1 component.
Definition: fwd.hpp:446
\n
glm::highp_f32mat2x3
mat< 2, 3, f32, highp > highp_f32mat2x3
High single-qualifier floating-point 2x3 matrix.
Definition: fwd.hpp:691
\n
glm::highp_int64
int64 highp_int64
High qualifier 64 bit signed integer type.
Definition: fwd.hpp:80
\n
glm::mediump_i32vec2
vec< 2, i32, mediump > mediump_i32vec2
Medium qualifier 32 bit signed integer vector of 2 components type.
Definition: fwd.hpp:268
\n
glm::lowp_dvec1
vec< 1, double, lowp > lowp_dvec1
1 component vector of double-precision floating-point numbers using low precision arithmetic in term ...
Definition: vector_double1_precision.hpp:33
\n
glm::lowp_f64mat4
mat< 4, 4, f64, lowp > lowp_f64mat4
Low double-qualifier floating-point 4x4 matrix.
Definition: fwd.hpp:574
\n
glm::fmat4
mat< 4, 4, f32, defaultp > fmat4
Single-qualifier floating-point 4x4 matrix.
Definition: fwd.hpp:538
\n
glm::mediump_fmat3x4
mat< 3, 4, f32, mediump > mediump_fmat3x4
Medium single-qualifier floating-point 3x4 matrix.
Definition: fwd.hpp:645
\n
glm::lowp_dmat3
mat< 3, 3, double, lowp > lowp_dmat3
3 columns of 3 components matrix of double-precision floating-point numbers using low precision arith...
Definition: matrix_double3x3_precision.hpp:16
\n
glm::lowp_int16_t
int16 lowp_int16_t
Low qualifier 16 bit signed integer type.
Definition: fwd.hpp:54
\n
glm::highp_i32vec4
vec< 4, i32, highp > highp_i32vec4
High qualifier 32 bit signed integer vector of 4 components type.
Definition: fwd.hpp:275
\n
glm::f32mat4x2
mat< 4, 2, f32, defaultp > f32mat4x2
Single-qualifier floating-point 4x2 matrix.
Definition: fwd.hpp:702
\n
glm::highp_fmat3x2
mat< 3, 2, f32, highp > highp_fmat3x2
High single-qualifier floating-point 3x2 matrix.
Definition: fwd.hpp:653
\n
glm::mat2x4
mat< 2, 4, float, defaultp > mat2x4
2 columns of 4 components matrix of single-precision floating-point numbers.
Definition: matrix_float2x4.hpp:15
\n
glm::mediump_fmat2x3
mat< 2, 3, f32, mediump > mediump_fmat2x3
Medium single-qualifier floating-point 2x3 matrix.
Definition: fwd.hpp:641
\n
glm::mediump_u32
uint32 mediump_u32
Medium qualifier 32 bit unsigned integer type.
Definition: fwd.hpp:118
\n
glm::lowp_fmat3x2
mat< 3, 2, f32, lowp > lowp_fmat3x2
Low single-qualifier floating-point 3x2 matrix.
Definition: fwd.hpp:633
\n
glm::lowp_mat2x3
mat< 2, 3, float, lowp > lowp_mat2x3
2 columns of 3 components matrix of single-precision floating-point numbers using low precision arith...
Definition: matrix_float2x3_precision.hpp:16
\n
glm::lowp_mat2
mat< 2, 2, float, lowp > lowp_mat2
2 columns of 2 components matrix of single-precision floating-point numbers using low precision arith...
Definition: matrix_float2x2_precision.hpp:16
\n
glm::mediump_f64mat4x2
mat< 4, 2, f64, mediump > mediump_f64mat4x2
Medium double-qualifier floating-point 4x2 matrix.
Definition: fwd.hpp:766
\n
glm::lowp_bvec4
vec< 4, bool, lowp > lowp_bvec4
4 components vector of low qualifier bool numbers.
Definition: vector_bool4_precision.hpp:28
\n
glm::highp_u16vec2
vec< 2, u16, highp > highp_u16vec2
High qualifier 16 bit unsigned integer vector of 2 components type.
Definition: fwd.hpp:355
\n
glm::highp_f64vec1
vec< 1, f64, highp > highp_f64vec1
High double-qualifier floating-point vector of 1 component.
Definition: fwd.hpp:496
\n
glm::ivec3
vec< 3, int, defaultp > ivec3
3 components vector of signed integer numbers.
Definition: vector_int3.hpp:15
\n
glm::mediump_i16vec2
vec< 2, i16, mediump > mediump_i16vec2
Medium qualifier 16 bit signed integer vector of 2 components type.
Definition: fwd.hpp:248
\n
glm::highp_fmat2x4
mat< 2, 4, f32, highp > highp_fmat2x4
High single-qualifier floating-point 2x4 matrix.
Definition: fwd.hpp:652
\n
glm::u64vec3
vec< 3, u64, defaultp > u64vec3
Default qualifier 64 bit unsigned integer vector of 3 components type.
Definition: fwd.hpp:401
\n
glm::lowp_uint8
uint8 lowp_uint8
Low qualifier 8 bit unsigned integer type.
Definition: fwd.hpp:94
\n
glm::lowp_f32mat3x2
mat< 3, 2, f32, lowp > lowp_f32mat3x2
Low single-qualifier floating-point 3x2 matrix.
Definition: fwd.hpp:673
\n
glm::mediump_bvec4
vec< 4, bool, mediump > mediump_bvec4
4 components vector of medium qualifier bool numbers.
Definition: vector_bool4_precision.hpp:22
\n
glm::mat3x2
mat< 3, 2, float, defaultp > mat3x2
3 columns of 2 components matrix of single-precision floating-point numbers.
Definition: matrix_float3x2.hpp:15
\n
glm::lowp_u64
uint64 lowp_u64
Low qualifier 64 bit unsigned integer type.
Definition: fwd.hpp:131
\n
glm::mediump_uvec1
vec< 1, unsigned int, mediump > mediump_uvec1
1 component vector of unsigned integer values.
Definition: vector_uint1_precision.hpp:32
\n
glm::highp_i64vec3
vec< 3, i64, highp > highp_i64vec3
High qualifier 64 bit signed integer vector of 3 components type.
Definition: fwd.hpp:294
\n
glm::mediump_int8
int8 mediump_int8
Medium qualifier 8 bit signed integer type.
Definition: fwd.hpp:37
\n
glm::lowp_int64
int64 lowp_int64
Low qualifier 64 bit signed integer type.
Definition: fwd.hpp:78
\n
glm::lowp_vec1
vec< 1, float, lowp > lowp_vec1
1 component vector of single-precision floating-point numbers using low precision arithmetic in term ...
Definition: vector_float1_precision.hpp:33
\n
glm::mediump_f32mat4x2
mat< 4, 2, f32, mediump > mediump_f32mat4x2
Medium single-qualifier floating-point 4x2 matrix.
Definition: fwd.hpp:686
\n
glm::highp_mat3x3
mat< 3, 3, float, highp > highp_mat3x3
3 columns of 3 components matrix of single-precision floating-point numbers using high precision arit...
Definition: matrix_float3x3_precision.hpp:46
\n
glm::lowp_f64vec3
vec< 3, f64, lowp > lowp_f64vec3
Low double-qualifier floating-point vector of 3 components.
Definition: fwd.hpp:488
\n
glm::mat3x4
mat< 3, 4, float, defaultp > mat3x4
3 columns of 4 components matrix of single-precision floating-point numbers.
Definition: matrix_float3x4.hpp:15
\n
glm::lowp_mat3x3
mat< 3, 3, float, lowp > lowp_mat3x3
3 columns of 3 components matrix of single-precision floating-point numbers using low precision arith...
Definition: matrix_float3x3_precision.hpp:34
\n
glm::u64vec2
vec< 2, u64, defaultp > u64vec2
Default qualifier 64 bit unsigned integer vector of 2 components type.
Definition: fwd.hpp:400
\n
glm::lowp_i64vec3
vec< 3, i64, lowp > lowp_i64vec3
Low qualifier 64 bit signed integer vector of 3 components type.
Definition: fwd.hpp:284
\n
glm::mediump_i8vec2
vec< 2, i8, mediump > mediump_i8vec2
Medium qualifier 8 bit signed integer vector of 2 components type.
Definition: fwd.hpp:228
\n
glm::lowp_vec4
vec< 4, float, lowp > lowp_vec4
4 components vector of low single-qualifier floating-point numbers.
Definition: vector_float4_precision.hpp:28
\n
glm::mat4x3
mat< 4, 3, float, defaultp > mat4x3
4 columns of 3 components matrix of single-precision floating-point numbers.
Definition: matrix_float4x3.hpp:15
\n
glm::f32mat3x4
mat< 3, 4, f32, defaultp > f32mat3x4
Single-qualifier floating-point 3x4 matrix.
Definition: fwd.hpp:707
\n
glm::mediump_dmat4x2
mat< 4, 2, double, mediump > mediump_dmat4x2
4 columns of 2 components matrix of double-precision floating-point numbers using medium precision ar...
Definition: matrix_double4x2_precision.hpp:22
\n
glm::lowp_vec2
vec< 2, float, lowp > lowp_vec2
2 components vector of low single-qualifier floating-point numbers.
Definition: vector_float2_precision.hpp:28
\n
glm::highp_i16vec3
vec< 3, i16, highp > highp_i16vec3
High qualifier 16 bit signed integer vector of 3 components type.
Definition: fwd.hpp:254
\n
glm::mediump_dmat2x3
mat< 2, 3, double, mediump > mediump_dmat2x3
2 columns of 3 components matrix of double-precision floating-point numbers using medium precision ar...
Definition: matrix_double2x3_precision.hpp:22
\n
glm::mediump_i16vec3
vec< 3, i16, mediump > mediump_i16vec3
Medium qualifier 16 bit signed integer vector of 3 components type.
Definition: fwd.hpp:249
\n
glm::u64
uint64 u64
Default qualifier 64 bit unsigned integer type.
Definition: fwd.hpp:134
\n
glm::mediump_ivec2
vec< 2, int, mediump > mediump_ivec2
2 components vector of medium qualifier signed integer numbers.
Definition: vector_int2_precision.hpp:22
\n
glm::mediump_fmat3x2
mat< 3, 2, f32, mediump > mediump_fmat3x2
Medium single-qualifier floating-point 3x2 matrix.
Definition: fwd.hpp:643
\n
glm::f64vec1
vec< 1, f64, defaultp > f64vec1
Double-qualifier floating-point vector of 1 component.
Definition: fwd.hpp:501
\n
glm::mediump_i64vec1
vec< 1, i64, mediump > mediump_i64vec1
Medium qualifier 64 bit signed integer scalar type.
Definition: fwd.hpp:287
\n
glm::i16vec1
vec< 1, i16, defaultp > i16vec1
16 bit signed integer scalar type.
Definition: fwd.hpp:257
\n
glm::lowp_dmat2
mat< 2, 2, double, lowp > lowp_dmat2
2 columns of 2 components matrix of double-precision floating-point numbers using low precision arith...
Definition: matrix_double2x2_precision.hpp:16
\n
glm::highp_dmat2x4
mat< 2, 4, double, highp > highp_dmat2x4
2 columns of 4 components matrix of double-precision floating-point numbers using medium precision ar...
Definition: matrix_double2x4_precision.hpp:28
\n
glm::lowp_f64mat3x3
mat< 3, 3, f64, lowp > lowp_f64mat3x3
Low double-qualifier floating-point 3x3 matrix.
Definition: fwd.hpp:754
\n
glm::lowp_f64vec2
vec< 2, f64, lowp > lowp_f64vec2
Low double-qualifier floating-point vector of 2 components.
Definition: fwd.hpp:487
\n
glm::highp_fmat2x3
mat< 2, 3, f32, highp > highp_fmat2x3
High single-qualifier floating-point 2x3 matrix.
Definition: fwd.hpp:651
\n
glm::lowp_f32mat4x3
mat< 4, 3, f32, lowp > lowp_f32mat4x3
Low single-qualifier floating-point 4x3 matrix.
Definition: fwd.hpp:677
\n
glm::lowp_f64mat3
mat< 3, 3, f64, lowp > lowp_f64mat3
Low double-qualifier floating-point 3x3 matrix.
Definition: fwd.hpp:573
\n
glm::mediump_u64vec3
vec< 3, u64, mediump > mediump_u64vec3
Medium qualifier 64 bit unsigned integer vector of 3 components type.
Definition: fwd.hpp:391
\n
glm::mediump_float64
double mediump_float64
Medium 64 bit double-qualifier floating-point scalar.
Definition: fwd.hpp:169
\n
glm::float64
double float64
Double-qualifier floating-point scalar.
Definition: fwd.hpp:171
\n
glm::highp_bvec2
vec< 2, bool, highp > highp_bvec2
2 components vector of high qualifier bool numbers.
Definition: vector_bool2_precision.hpp:16
\n
glm::highp_i16vec2
vec< 2, i16, highp > highp_i16vec2
High qualifier 16 bit signed integer vector of 2 components type.
Definition: fwd.hpp:253
\n
glm::fmat4x2
mat< 4, 2, f32, defaultp > fmat4x2
Single-qualifier floating-point 4x2 matrix.
Definition: fwd.hpp:662
\n
glm::lowp_f64mat2x3
mat< 2, 3, f64, lowp > lowp_f64mat2x3
Low double-qualifier floating-point 2x3 matrix.
Definition: fwd.hpp:751
\n
glm::fmat3x4
mat< 3, 4, f32, defaultp > fmat3x4
Single-qualifier floating-point 3x4 matrix.
Definition: fwd.hpp:667
\n
glm::lowp_dmat3x3
mat< 3, 3, double, lowp > lowp_dmat3x3
3 columns of 3 components matrix of double-precision floating-point numbers using low precision arith...
Definition: matrix_double3x3_precision.hpp:34
\n
glm::lowp_u32vec3
vec< 3, u32, lowp > lowp_u32vec3
Low qualifier 32 bit unsigned integer vector of 3 components type.
Definition: fwd.hpp:366
\n
glm::f32mat2x4
mat< 2, 4, f32, defaultp > f32mat2x4
Single-qualifier floating-point 2x4 matrix.
Definition: fwd.hpp:706
\n
glm::lowp_fvec4
vec< 4, float, lowp > lowp_fvec4
Low single-qualifier floating-point vector of 4 components.
Definition: fwd.hpp:429
\n
glm::mediump_f32vec4
vec< 4, f32, mediump > mediump_f32vec4
Medium single-qualifier floating-point vector of 4 components.
Definition: fwd.hpp:454
\n
glm::i16vec4
vec< 4, i16, defaultp > i16vec4
16 bit signed integer vector of 4 components type.
Definition: fwd.hpp:260
\n
glm::lowp_uint8_t
uint8 lowp_uint8_t
Low qualifier 8 bit unsigned integer type.
Definition: fwd.hpp:98
\n
glm::highp_uint32_t
uint32 highp_uint32_t
High qualifier 32 bit unsigned integer type.
Definition: fwd.hpp:128
\n
glm::fmat3x3
mat< 3, 3, f32, defaultp > fmat3x3
Single-qualifier floating-point 3x3 matrix.
Definition: fwd.hpp:664
\n
glm::mediump_f64mat3x4
mat< 3, 4, f64, mediump > mediump_f64mat3x4
Medium double-qualifier floating-point 3x4 matrix.
Definition: fwd.hpp:765
\n
glm::lowp_fmat2x3
mat< 2, 3, f32, lowp > lowp_fmat2x3
Low single-qualifier floating-point 2x3 matrix.
Definition: fwd.hpp:631
\n
glm::lowp_u32vec1
vec< 1, u32, lowp > lowp_u32vec1
Low qualifier 32 bit unsigned integer scalar type.
Definition: fwd.hpp:364
\n
glm::lowp_mat3x2
mat< 3, 2, float, lowp > lowp_mat3x2
3 columns of 2 components matrix of single-precision floating-point numbers using low precision arith...
Definition: matrix_float3x2_precision.hpp:16
\n
glm::f32mat2x3
mat< 2, 3, f32, defaultp > f32mat2x3
Single-qualifier floating-point 2x3 matrix.
Definition: fwd.hpp:703
\n
glm::mediump_i32vec1
vec< 1, i32, mediump > mediump_i32vec1
Medium qualifier 32 bit signed integer scalar type.
Definition: fwd.hpp:267
\n
glm::highp_u16vec4
vec< 4, u16, highp > highp_u16vec4
High qualifier 16 bit unsigned integer vector of 4 components type.
Definition: fwd.hpp:357
\n
glm::lowp_i32vec1
vec< 1, i32, lowp > lowp_i32vec1
Low qualifier 32 bit signed integer scalar type.
Definition: fwd.hpp:262
\n
glm::lowp_i64vec1
vec< 1, i64, lowp > lowp_i64vec1
Low qualifier 64 bit signed integer scalar type.
Definition: fwd.hpp:282
\n
glm::highp_u32vec1
vec< 1, u32, highp > highp_u32vec1
High qualifier 32 bit unsigned integer scalar type.
Definition: fwd.hpp:374
\n
glm::highp_bvec1
vec< 1, bool, highp > highp_bvec1
1 component vector of bool values.
Definition: vector_bool1_precision.hpp:25
\n
glm::mediump_int16
int16 mediump_int16
Medium qualifier 16 bit signed integer type.
Definition: fwd.hpp:51
\n
glm::mediump_u16
uint16 mediump_u16
Medium qualifier 16 bit unsigned integer type.
Definition: fwd.hpp:104
\n
glm::f64quat
qua< f64, defaultp > f64quat
Double-qualifier floating-point quaternion.
Definition: fwd.hpp:815
\n
glm::mediump_vec4
vec< 4, float, mediump > mediump_vec4
4 components vector of medium single-qualifier floating-point numbers.
Definition: vector_float4_precision.hpp:22
\n
glm::mediump_f64vec3
vec< 3, f64, mediump > mediump_f64vec3
Medium double-qualifier floating-point vector of 3 components.
Definition: fwd.hpp:493
\n
glm::dquat
qua< double, defaultp > dquat
Quaternion of double-precision floating-point numbers.
Definition: quaternion_double.hpp:35
\n
glm::u64vec1
vec< 1, u64, defaultp > u64vec1
Default qualifier 64 bit unsigned integer scalar type.
Definition: fwd.hpp:399
\n
glm::int64_t
int64 int64_t
64 bit signed integer type.
Definition: fwd.hpp:85
\n
glm::u8vec1
vec< 1, u8, defaultp > u8vec1
Default qualifier 8 bit unsigned integer scalar type.
Definition: fwd.hpp:339
\n
glm::highp_i8vec1
vec< 1, i8, highp > highp_i8vec1
High qualifier 8 bit signed integer scalar type.
Definition: fwd.hpp:232
\n
glm::u8vec4
vec< 4, u8, defaultp > u8vec4
Default qualifier 8 bit unsigned integer vector of 4 components type.
Definition: fwd.hpp:342
\n
glm::int8_t
int8 int8_t
8 bit signed integer type.
Definition: fwd.hpp:43
\n
glm::i32
int32 i32
32 bit signed integer type.
Definition: fwd.hpp:62
\n
glm::mediump_u32vec1
vec< 1, u32, mediump > mediump_u32vec1
Medium qualifier 32 bit unsigned integer scalar type.
Definition: fwd.hpp:369
\n
glm::f64mat2x2
mat< 2, 2, f64, defaultp > f64mat2x2
Double-qualifier floating-point 1x1 matrix.
Definition: fwd.hpp:780
\n
glm::lowp_f32mat2x2
mat< 2, 2, f32, lowp > lowp_f32mat2x2
Low single-qualifier floating-point 1x1 matrix.
Definition: fwd.hpp:670
\n
glm::lowp_f32vec4
vec< 4, f32, lowp > lowp_f32vec4
Low single-qualifier floating-point vector of 4 components.
Definition: fwd.hpp:449
\n
glm::highp_fvec3
vec< 3, float, highp > highp_fvec3
High Single-qualifier floating-point vector of 3 components.
Definition: fwd.hpp:438
\n
glm::lowp_f64mat4x2
mat< 4, 2, f64, lowp > lowp_f64mat4x2
Low double-qualifier floating-point 4x2 matrix.
Definition: fwd.hpp:756
\n
glm::mediump_fmat3x3
mat< 3, 3, f32, mediump > mediump_fmat3x3
Medium single-qualifier floating-point 3x3 matrix.
Definition: fwd.hpp:644
\n
glm::highp_i64vec1
vec< 1, i64, highp > highp_i64vec1
High qualifier 64 bit signed integer scalar type.
Definition: fwd.hpp:292
\n
glm::i8vec4
vec< 4, i8, defaultp > i8vec4
8 bit signed integer vector of 4 components type.
Definition: fwd.hpp:240
\n
glm::highp_ivec1
vec< 1, int, highp > highp_ivec1
1 component vector of signed integer values.
Definition: vector_int1_precision.hpp:25
\n
glm::mediump_bvec3
vec< 3, bool, mediump > mediump_bvec3
3 components vector of medium qualifier bool numbers.
Definition: vector_bool3_precision.hpp:22
\n
glm::highp_int32
int32 highp_int32
High qualifier 32 bit signed integer type.
Definition: fwd.hpp:66
\n
glm::mediump_f32mat2x3
mat< 2, 3, f32, mediump > mediump_f32mat2x3
Medium single-qualifier floating-point 2x3 matrix.
Definition: fwd.hpp:681
\n
glm::mediump_dmat3x4
mat< 3, 4, double, mediump > mediump_dmat3x4
3 columns of 4 components matrix of double-precision floating-point numbers using medium precision ar...
Definition: matrix_double3x4_precision.hpp:22
\n
glm::lowp_f64mat3x2
mat< 3, 2, f64, lowp > lowp_f64mat3x2
Low double-qualifier floating-point 3x2 matrix.
Definition: fwd.hpp:753
\n
glm::mat4x2
mat< 4, 2, float, defaultp > mat4x2
4 columns of 2 components matrix of single-precision floating-point numbers.
Definition: matrix_float4x2.hpp:15
\n
glm::mediump_vec1
vec< 1, float, mediump > mediump_vec1
1 component vector of single-precision floating-point numbers using medium precision arithmetic in te...
Definition: vector_float1_precision.hpp:30
\n
glm::highp_u32
uint32 highp_u32
High qualifier 32 bit unsigned integer type.
Definition: fwd.hpp:119
\n
glm::highp_i32
int32 highp_i32
High qualifier 32 bit signed integer type.
Definition: fwd.hpp:61
\n
glm::ivec4
vec< 4, int, defaultp > ivec4
4 components vector of signed integer numbers.
Definition: vector_int4.hpp:15
\n
glm::mediump_mat4x4
mat< 4, 4, float, mediump > mediump_mat4x4
4 columns of 4 components matrix of single-precision floating-point numbers using medium precision ar...
Definition: matrix_float4x4_precision.hpp:40
\n
glm::u64vec4
vec< 4, u64, defaultp > u64vec4
Default qualifier 64 bit unsigned integer vector of 4 components type.
Definition: fwd.hpp:402
\n
glm::lowp_ivec2
vec< 2, int, lowp > lowp_ivec2
2 components vector of low qualifier signed integer numbers.
Definition: vector_int2_precision.hpp:28
\n
glm::f32vec4
vec< 4, f32, defaultp > f32vec4
Single-qualifier floating-point vector of 4 components.
Definition: fwd.hpp:464
\n
glm::f64mat2x3
mat< 2, 3, f64, defaultp > f64mat2x3
Double-qualifier floating-point 2x3 matrix.
Definition: fwd.hpp:783
\n
glm::mediump_f64mat4x4
mat< 4, 4, f64, mediump > mediump_f64mat4x4
Medium double-qualifier floating-point 4x4 matrix.
Definition: fwd.hpp:768
\n
glm::mediump_dmat2x2
mat< 2, 2, double, mediump > mediump_dmat2x2
2 columns of 2 components matrix of double-precision floating-point numbers using medium precision ar...
Definition: matrix_double2x2_precision.hpp:40
\n
glm::lowp_u16vec4
vec< 4, u16, lowp > lowp_u16vec4
Low qualifier 16 bit unsigned integer vector of 4 components type.
Definition: fwd.hpp:347
\n
glm::highp_uvec4
vec< 4, unsigned int, highp > highp_uvec4
4 components vector of high qualifier unsigned integer numbers.
Definition: vector_uint4_precision.hpp:16
\n
glm::highp_uint32
uint32 highp_uint32
High qualifier 32 bit unsigned integer type.
Definition: fwd.hpp:124
\n
glm::lowp_f32mat4
mat< 4, 4, f32, lowp > lowp_f32mat4
Low single-qualifier floating-point 4x4 matrix.
Definition: fwd.hpp:542
\n
glm::f64mat3x2
mat< 3, 2, f64, defaultp > f64mat3x2
Double-qualifier floating-point 3x2 matrix.
Definition: fwd.hpp:781
\n
glm::mediump_float32
float mediump_float32
Medium 32 bit single-qualifier floating-point scalar.
Definition: fwd.hpp:153
\n
glm::u32vec1
vec< 1, u32, defaultp > u32vec1
Default qualifier 32 bit unsigned integer scalar type.
Definition: fwd.hpp:379
\n
glm::lowp_mat4x2
mat< 4, 2, float, lowp > lowp_mat4x2
4 columns of 2 components matrix of single-precision floating-point numbers using low precision arith...
Definition: matrix_float4x2_precision.hpp:16
\n
glm::mediump_f64vec4
vec< 4, f64, mediump > mediump_f64vec4
Medium double-qualifier floating-point vector of 4 components.
Definition: fwd.hpp:494
\n
glm::f64mat3x3
mat< 3, 3, f64, defaultp > f64mat3x3
Double-qualifier floating-point 3x3 matrix.
Definition: fwd.hpp:784
\n
glm::highp_float32
float highp_float32
High 32 bit single-qualifier floating-point scalar.
Definition: fwd.hpp:154
\n
glm::highp_uint8
uint8 highp_uint8
High qualifier 8 bit unsigned integer type.
Definition: fwd.hpp:96
\n
glm::highp_i8
int8 highp_i8
High qualifier 8 bit signed integer type.
Definition: fwd.hpp:33
\n
glm::lowp_f64mat2x4
mat< 2, 4, f64, lowp > lowp_f64mat2x4
Low double-qualifier floating-point 2x4 matrix.
Definition: fwd.hpp:752
\n
glm::lowp_f64mat3x4
mat< 3, 4, f64, lowp > lowp_f64mat3x4
Low double-qualifier floating-point 3x4 matrix.
Definition: fwd.hpp:755
\n
glm::lowp_vec3
vec< 3, float, lowp > lowp_vec3
3 components vector of low single-qualifier floating-point numbers.
Definition: vector_float3_precision.hpp:28
\n
glm::highp_mat3x4
mat< 3, 4, float, highp > highp_mat3x4
3 columns of 4 components matrix of single-precision floating-point numbers using high precision arit...
Definition: matrix_float3x4_precision.hpp:28
\n
glm::lowp_mat4
mat< 4, 4, float, lowp > lowp_mat4
4 columns of 4 components matrix of single-precision floating-point numbers using low precision arith...
Definition: matrix_float4x4_precision.hpp:16
\n
glm::mediump_i8
int8 mediump_i8
Medium qualifier 8 bit signed integer type.
Definition: fwd.hpp:32
\n
glm::highp_int64_t
int64 highp_int64_t
High qualifier 64 bit signed integer type.
Definition: fwd.hpp:84
\n
glm::f32mat4x4
mat< 4, 4, f32, defaultp > f32mat4x4
Single-qualifier floating-point 4x4 matrix.
Definition: fwd.hpp:708
\n
glm::float32_t
float float32_t
Default 32 bit single-qualifier floating-point scalar.
Definition: fwd.hpp:160
\n
glm::f32mat2x2
mat< 2, 2, f32, defaultp > f32mat2x2
Single-qualifier floating-point 1x1 matrix.
Definition: fwd.hpp:700
\n
glm::lowp_i64vec2
vec< 2, i64, lowp > lowp_i64vec2
Low qualifier 64 bit signed integer vector of 2 components type.
Definition: fwd.hpp:283
\n
glm::lowp_f32mat2x4
mat< 2, 4, f32, lowp > lowp_f32mat2x4
Low single-qualifier floating-point 2x4 matrix.
Definition: fwd.hpp:672
\n
glm::highp_bvec4
vec< 4, bool, highp > highp_bvec4
4 components vector of high qualifier bool numbers.
Definition: vector_bool4_precision.hpp:16
\n
glm::uint32_t
uint32 uint32_t
Default qualifier 32 bit unsigned integer type.
Definition: fwd.hpp:129
\n
glm::highp_f32mat3
mat< 3, 3, f32, highp > highp_f32mat3
High single-qualifier floating-point 3x3 matrix.
Definition: fwd.hpp:549
\n
glm::mediump_f64mat3x3
mat< 3, 3, f64, mediump > mediump_f64mat3x3
Medium double-qualifier floating-point 3x3 matrix.
Definition: fwd.hpp:764
\n
glm::bvec2
vec< 2, bool, defaultp > bvec2
2 components vector of boolean.
Definition: vector_bool2.hpp:15
\n
glm::vec4
vec< 4, float, defaultp > vec4
4 components vector of single-precision floating-point numbers.
Definition: vector_float4.hpp:15
\n
glm::u8
uint8 u8
Default qualifier 8 bit unsigned integer type.
Definition: fwd.hpp:92
\n
glm::highp_i32vec3
vec< 3, i32, highp > highp_i32vec3
High qualifier 32 bit signed integer vector of 3 components type.
Definition: fwd.hpp:274
\n
glm::float32
float float32
Single-qualifier floating-point scalar.
Definition: fwd.hpp:155
\n
glm::fvec4
vec< 4, f32, defaultp > fvec4
Single-qualifier floating-point vector of 4 components.
Definition: fwd.hpp:444
\n
glm::highp_i32vec1
vec< 1, i32, highp > highp_i32vec1
High qualifier 32 bit signed integer scalar type.
Definition: fwd.hpp:272
\n
glm::highp_dmat3
mat< 3, 3, double, highp > highp_dmat3
3 columns of 3 components matrix of double-precision floating-point numbers using medium precision ar...
Definition: matrix_double3x3_precision.hpp:28
\n
glm::lowp_f32mat3
mat< 3, 3, f32, lowp > lowp_f32mat3
Low single-qualifier floating-point 3x3 matrix.
Definition: fwd.hpp:541
\n
glm::u16vec1
vec< 1, u16, defaultp > u16vec1
Default qualifier 16 bit unsigned integer scalar type.
Definition: fwd.hpp:359
\n
glm::lowp_mat2x4
mat< 2, 4, float, lowp > lowp_mat2x4
2 columns of 4 components matrix of single-precision floating-point numbers using low precision arith...
Definition: matrix_float2x4_precision.hpp:16
\n
glm::dvec1
vec< 1, double, defaultp > dvec1
1 components vector of double-precision floating-point numbers.
Definition: vector_double1.hpp:28
\n
glm::i8vec1
vec< 1, i8, defaultp > i8vec1
8 bit signed integer scalar type.
Definition: fwd.hpp:237
\n
glm::mediump_i32vec3
vec< 3, i32, mediump > mediump_i32vec3
Medium qualifier 32 bit signed integer vector of 3 components type.
Definition: fwd.hpp:269
\n
glm::i32vec2
vec< 2, i32, defaultp > i32vec2
32 bit signed integer vector of 2 components type.
Definition: fwd.hpp:278
\n
glm::mediump_bvec2
vec< 2, bool, mediump > mediump_bvec2
2 components vector of medium qualifier bool numbers.
Definition: vector_bool2_precision.hpp:22
\n
glm::lowp_i16vec2
vec< 2, i16, lowp > lowp_i16vec2
Low qualifier 16 bit signed integer vector of 2 components type.
Definition: fwd.hpp:243
\n
glm::mediump_vec2
vec< 2, float, mediump > mediump_vec2
2 components vector of medium single-qualifier floating-point numbers.
Definition: vector_float2_precision.hpp:22
\n
glm::mediump_u64vec2
vec< 2, u64, mediump > mediump_u64vec2
Medium qualifier 64 bit unsigned integer vector of 2 components type.
Definition: fwd.hpp:390
\n
glm::lowp_u8vec4
vec< 4, u8, lowp > lowp_u8vec4
Low qualifier 8 bit unsigned integer vector of 4 components type.
Definition: fwd.hpp:327
\n
glm::highp_f32mat3x3
mat< 3, 3, f32, highp > highp_f32mat3x3
High single-qualifier floating-point 3x3 matrix.
Definition: fwd.hpp:694
\n
glm::highp_u8vec1
vec< 1, u8, highp > highp_u8vec1
High qualifier 8 bit unsigned integer scalar type.
Definition: fwd.hpp:334
\n
glm::highp_uint8_t
uint8 highp_uint8_t
High qualifier 8 bit unsigned integer type.
Definition: fwd.hpp:100
\n
glm::mediump_u32vec4
vec< 4, u32, mediump > mediump_u32vec4
Medium qualifier 32 bit unsigned integer vector of 4 components type.
Definition: fwd.hpp:372
\n
glm::highp_f32mat2x2
mat< 2, 2, f32, highp > highp_f32mat2x2
High single-qualifier floating-point 1x1 matrix.
Definition: fwd.hpp:690
\n
glm::highp_f64vec4
vec< 4, f64, highp > highp_f64vec4
High double-qualifier floating-point vector of 4 components.
Definition: fwd.hpp:499
\n
glm::highp_dmat3x3
mat< 3, 3, double, highp > highp_dmat3x3
3 columns of 3 components matrix of double-precision floating-point numbers using medium precision ar...
Definition: matrix_double3x3_precision.hpp:46
\n
glm::lowp_u8vec3
vec< 3, u8, lowp > lowp_u8vec3
Low qualifier 8 bit unsigned integer vector of 3 components type.
Definition: fwd.hpp:326
\n
glm::highp_f32
float highp_f32
High 32 bit single-qualifier floating-point scalar.
Definition: fwd.hpp:149
\n
glm::mediump_uint64
uint64 mediump_uint64
Medium qualifier 64 bit unsigned integer type.
Definition: fwd.hpp:137
\n
glm::highp_int32_t
int32 highp_int32_t
32 bit signed integer type.
Definition: fwd.hpp:70
\n
glm::lowp_f32mat2x3
mat< 2, 3, f32, lowp > lowp_f32mat2x3
Low single-qualifier floating-point 2x3 matrix.
Definition: fwd.hpp:671
\n
glm::f64vec3
vec< 3, f64, defaultp > f64vec3
Double-qualifier floating-point vector of 3 components.
Definition: fwd.hpp:503
\n
glm::mediump_u16vec3
vec< 3, u16, mediump > mediump_u16vec3
Medium qualifier 16 bit unsigned integer vector of 3 components type.
Definition: fwd.hpp:351
\n
glm::f64mat2x4
mat< 2, 4, f64, defaultp > f64mat2x4
Double-qualifier floating-point 2x4 matrix.
Definition: fwd.hpp:786
\n
glm::mediump_dquat
qua< double, mediump > mediump_dquat
Quaternion of medium double-qualifier floating-point numbers using high precision arithmetic in term ...
Definition: quaternion_double_precision.hpp:33
\n
glm::f32mat3
mat< 3, 3, f32, defaultp > f32mat3
Single-qualifier floating-point 3x3 matrix.
Definition: fwd.hpp:553
\n
glm::mediump_f64mat2x2
mat< 2, 2, f64, mediump > mediump_f64mat2x2
Medium double-qualifier floating-point 1x1 matrix.
Definition: fwd.hpp:760
\n
glm::highp_dvec1
vec< 1, double, highp > highp_dvec1
1 component vector of double-precision floating-point numbers using high precision arithmetic in term...
Definition: vector_double1_precision.hpp:27
\n
glm::mat3x3
mat< 3, 3, float, defaultp > mat3x3
3 columns of 3 components matrix of single-precision floating-point numbers.
Definition: matrix_float3x3.hpp:15
\n
glm::mediump_u64
uint64 mediump_u64
Medium qualifier 64 bit unsigned integer type.
Definition: fwd.hpp:132
\n
glm::mediump_mat4
mat< 4, 4, float, mediump > mediump_mat4
4 columns of 4 components matrix of single-precision floating-point numbers using medium precision ar...
Definition: matrix_float4x4_precision.hpp:22
\n
glm::highp_i16vec4
vec< 4, i16, highp > highp_i16vec4
High qualifier 16 bit signed integer vector of 4 components type.
Definition: fwd.hpp:255
\n
glm::lowp_fmat4
mat< 4, 4, f32, lowp > lowp_fmat4
Low single-qualifier floating-point 4x4 matrix.
Definition: fwd.hpp:526
\n
glm::mediump_u32vec2
vec< 2, u32, mediump > mediump_u32vec2
Medium qualifier 32 bit unsigned integer vector of 2 components type.
Definition: fwd.hpp:370
\n
glm::highp_u64vec3
vec< 3, u64, highp > highp_u64vec3
High qualifier 64 bit unsigned integer vector of 3 components type.
Definition: fwd.hpp:396
\n
glm::uvec2
vec< 2, unsigned int, defaultp > uvec2
2 components vector of unsigned integer numbers.
Definition: vector_uint2.hpp:15
\n
glm::lowp_u16
uint16 lowp_u16
Low qualifier 16 bit unsigned integer type.
Definition: fwd.hpp:103
\n
glm::lowp_i16vec3
vec< 3, i16, lowp > lowp_i16vec3
Low qualifier 16 bit signed integer vector of 3 components type.
Definition: fwd.hpp:244
\n
glm::lowp_u16vec3
vec< 3, u16, lowp > lowp_u16vec3
Low qualifier 16 bit unsigned integer vector of 3 components type.
Definition: fwd.hpp:346
\n
glm::uvec1
vec< 1, unsigned int, defaultp > uvec1
1 component vector of unsigned integer numbers.
Definition: vector_uint1.hpp:28
\n
glm::lowp_f32vec3
vec< 3, f32, lowp > lowp_f32vec3
Low single-qualifier floating-point vector of 3 components.
Definition: fwd.hpp:448
\n
glm::highp_fmat4
mat< 4, 4, f32, highp > highp_fmat4
High single-qualifier floating-point 4x4 matrix.
Definition: fwd.hpp:534
\n
glm::lowp_fmat3
mat< 3, 3, f32, lowp > lowp_fmat3
Low single-qualifier floating-point 3x3 matrix.
Definition: fwd.hpp:525
\n
glm::highp_i16
int16 highp_i16
High qualifier 16 bit signed integer type.
Definition: fwd.hpp:47
\n
glm::mediump_f32quat
qua< f32, mediump > mediump_f32quat
Medium single-qualifier floating-point quaternion.
Definition: fwd.hpp:803
\n
glm::highp_int8
int8 highp_int8
High qualifier 8 bit signed integer type.
Definition: fwd.hpp:38
\n
glm::f64mat4x4
mat< 4, 4, f64, defaultp > f64mat4x4
Double-qualifier floating-point 4x4 matrix.
Definition: fwd.hpp:788
\n
glm::fmat4x3
mat< 4, 3, f32, defaultp > fmat4x3
Single-qualifier floating-point 4x3 matrix.
Definition: fwd.hpp:665
\n
glm::lowp_fmat2x4
mat< 2, 4, f32, lowp > lowp_fmat2x4
Low single-qualifier floating-point 2x4 matrix.
Definition: fwd.hpp:632
\n
glm::highp_f64mat3
mat< 3, 3, f64, highp > highp_f64mat3
High double-qualifier floating-point 3x3 matrix.
Definition: fwd.hpp:581
\n
glm::mediump_i8vec3
vec< 3, i8, mediump > mediump_i8vec3
Medium qualifier 8 bit signed integer vector of 3 components type.
Definition: fwd.hpp:229
\n
glm::highp_f32vec1
vec< 1, f32, highp > highp_f32vec1
High single-qualifier floating-point vector of 1 component.
Definition: fwd.hpp:456
\n
glm::lowp_i8vec3
vec< 3, i8, lowp > lowp_i8vec3
Low qualifier 8 bit signed integer vector of 3 components type.
Definition: fwd.hpp:224
\n
glm::mediump_dmat3x3
mat< 3, 3, double, mediump > mediump_dmat3x3
3 columns of 3 components matrix of double-precision floating-point numbers using medium precision ar...
Definition: matrix_double3x3_precision.hpp:40
\n
glm::lowp_f64mat4x3
mat< 4, 3, f64, lowp > lowp_f64mat4x3
Low double-qualifier floating-point 4x3 matrix.
Definition: fwd.hpp:757
\n
glm::highp_u64vec4
vec< 4, u64, highp > highp_u64vec4
High qualifier 64 bit unsigned integer vector of 4 components type.
Definition: fwd.hpp:397
\n
glm::mediump_mat3
mat< 3, 3, float, mediump > mediump_mat3
3 columns of 3 components matrix of single-precision floating-point numbers using medium precision ar...
Definition: matrix_float3x3_precision.hpp:22
\n
glm::fvec3
vec< 3, f32, defaultp > fvec3
Single-qualifier floating-point vector of 3 components.
Definition: fwd.hpp:443
\n
glm::i16vec2
vec< 2, i16, defaultp > i16vec2
16 bit signed integer vector of 2 components type.
Definition: fwd.hpp:258
\n
glm::mediump_bvec1
vec< 1, bool, mediump > mediump_bvec1
1 component vector of bool values.
Definition: vector_bool1_precision.hpp:28
\n
glm::lowp_dmat4
mat< 4, 4, double, lowp > lowp_dmat4
4 columns of 4 components matrix of double-precision floating-point numbers using low precision arith...
Definition: matrix_double4x4_precision.hpp:16
\n
glm::highp_dmat3x4
mat< 3, 4, double, highp > highp_dmat3x4
3 columns of 4 components matrix of double-precision floating-point numbers using medium precision ar...
Definition: matrix_double3x4_precision.hpp:28
\n
glm::f32mat4x3
mat< 4, 3, f32, defaultp > f32mat4x3
Single-qualifier floating-point 4x3 matrix.
Definition: fwd.hpp:705
\n
glm::f32mat2
mat< 2, 2, f32, defaultp > f32mat2
Single-qualifier floating-point 1x1 matrix.
Definition: fwd.hpp:552
\n
glm::mediump_fmat2x4
mat< 2, 4, f32, mediump > mediump_fmat2x4
Medium single-qualifier floating-point 2x4 matrix.
Definition: fwd.hpp:642
\n
glm::mediump_u16vec2
vec< 2, u16, mediump > mediump_u16vec2
Medium qualifier 16 bit unsigned integer vector of 2 components type.
Definition: fwd.hpp:350
\n
glm::lowp_f32mat4x4
mat< 4, 4, f32, lowp > lowp_f32mat4x4
Low single-qualifier floating-point 4x4 matrix.
Definition: fwd.hpp:678
\n
glm::lowp_uvec2
vec< 2, unsigned int, lowp > lowp_uvec2
2 components vector of low qualifier unsigned integer numbers.
Definition: vector_uint2_precision.hpp:28
\n
glm::lowp_mat3
mat< 3, 3, float, lowp > lowp_mat3
3 columns of 3 components matrix of single-precision floating-point numbers using low precision arith...
Definition: matrix_float3x3_precision.hpp:16
\n
glm::lowp_u8vec2
vec< 2, u8, lowp > lowp_u8vec2
Low qualifier 8 bit unsigned integer vector of 2 components type.
Definition: fwd.hpp:325
\n
glm::lowp_dvec2
vec< 2, double, lowp > lowp_dvec2
2 components vector of low double-qualifier floating-point numbers.
Definition: vector_double2_precision.hpp:28
\n
glm::mediump_f64mat3
mat< 3, 3, f64, mediump > mediump_f64mat3
Medium double-qualifier floating-point 3x3 matrix.
Definition: fwd.hpp:577
\n
glm::lowp_i16
int16 lowp_i16
Low qualifier 16 bit signed integer type.
Definition: fwd.hpp:45
\n
glm::vec1
vec< 1, float, defaultp > vec1
1 components vector of single-precision floating-point numbers.
Definition: vector_float1.hpp:28
\n
glm::mediump_uvec3
vec< 3, unsigned int, mediump > mediump_uvec3
3 components vector of medium qualifier unsigned integer numbers.
Definition: vector_uint3_precision.hpp:22
\n
glm::highp_fmat3x4
mat< 3, 4, f32, highp > highp_fmat3x4
High single-qualifier floating-point 3x4 matrix.
Definition: fwd.hpp:655
\n
glm::float64_t
double float64_t
Default 64 bit double-qualifier floating-point scalar.
Definition: fwd.hpp:176
\n
glm::highp_f64mat4x4
mat< 4, 4, f64, highp > highp_f64mat4x4
High double-qualifier floating-point 4x4 matrix.
Definition: fwd.hpp:778
\n
glm::highp_mat2
mat< 2, 2, float, highp > highp_mat2
2 columns of 2 components matrix of single-precision floating-point numbers using high precision arit...
Definition: matrix_float2x2_precision.hpp:28
\n
glm::mediump_f32mat4x3
mat< 4, 3, f32, mediump > mediump_f32mat4x3
Medium single-qualifier floating-point 4x3 matrix.
Definition: fwd.hpp:687
\n
glm::lowp_int16
int16 lowp_int16
Low qualifier 16 bit signed integer type.
Definition: fwd.hpp:50
\n
glm::lowp_ivec3
vec< 3, int, lowp > lowp_ivec3
3 components vector of low qualifier signed integer numbers.
Definition: vector_int3_precision.hpp:28
\n
glm::mediump_fmat3
mat< 3, 3, f32, mediump > mediump_fmat3
Medium single-qualifier floating-point 3x3 matrix.
Definition: fwd.hpp:529
\n
glm::mediump_dmat4
mat< 4, 4, double, mediump > mediump_dmat4
4 columns of 4 components matrix of double-precision floating-point numbers using medium precision ar...
Definition: matrix_double4x4_precision.hpp:22
\n
glm::highp_f32mat4x4
mat< 4, 4, f32, highp > highp_f32mat4x4
High single-qualifier floating-point 4x4 matrix.
Definition: fwd.hpp:698
\n
glm::lowp_int64_t
int64 lowp_int64_t
Low qualifier 64 bit signed integer type.
Definition: fwd.hpp:82
\n
glm::lowp_ivec4
vec< 4, int, lowp > lowp_ivec4
4 components vector of low qualifier signed integer numbers.
Definition: vector_int4_precision.hpp:28
\n
glm::uint16_t
uint16 uint16_t
Default qualifier 16 bit unsigned integer type.
Definition: fwd.hpp:115
\n
glm::lowp_uvec4
vec< 4, unsigned int, lowp > lowp_uvec4
4 components vector of low qualifier unsigned integer numbers.
Definition: vector_uint4_precision.hpp:28
\n
glm::highp_f64vec2
vec< 2, f64, highp > highp_f64vec2
High double-qualifier floating-point vector of 2 components.
Definition: fwd.hpp:497
\n
glm::lowp_u64vec2
vec< 2, u64, lowp > lowp_u64vec2
Low qualifier 64 bit unsigned integer vector of 2 components type.
Definition: fwd.hpp:385
\n
glm::fmat3
mat< 3, 3, f32, defaultp > fmat3
Single-qualifier floating-point 3x3 matrix.
Definition: fwd.hpp:537
\n
glm::mediump_f32mat3x2
mat< 3, 2, f32, mediump > mediump_f32mat3x2
Medium single-qualifier floating-point 3x2 matrix.
Definition: fwd.hpp:683
\n
glm::dmat3x3
mat< 3, 3, double, defaultp > dmat3x3
3 columns of 3 components matrix of double-precision floating-point numbers.
Definition: matrix_double3x3.hpp:15
\n
glm::dmat3
mat< 3, 3, double, defaultp > dmat3
3 columns of 3 components matrix of double-precision floating-point numbers.
Definition: matrix_double3x3.hpp:20
\n
glm::lowp_f32mat4x2
mat< 4, 2, f32, lowp > lowp_f32mat4x2
Low single-qualifier floating-point 4x2 matrix.
Definition: fwd.hpp:676
\n
glm::lowp_int32
int32 lowp_int32
Low qualifier 32 bit signed integer type.
Definition: fwd.hpp:64
\n
glm::mediump_i64vec4
vec< 4, i64, mediump > mediump_i64vec4
Medium qualifier 64 bit signed integer vector of 4 components type.
Definition: fwd.hpp:290
\n
glm::bvec4
vec< 4, bool, defaultp > bvec4
4 components vector of boolean.
Definition: vector_bool4.hpp:15
\n
glm::uint8_t
uint8 uint8_t
Default qualifier 8 bit unsigned integer type.
Definition: fwd.hpp:101
\n
glm::mediump_i8vec1
vec< 1, i8, mediump > mediump_i8vec1
Medium qualifier 8 bit signed integer scalar type.
Definition: fwd.hpp:227
\n
glm::mediump_int32_t
int32 mediump_int32_t
Medium qualifier 32 bit signed integer type.
Definition: fwd.hpp:69
\n
glm::mediump_dmat4x3
mat< 4, 3, double, mediump > mediump_dmat4x3
4 columns of 3 components matrix of double-precision floating-point numbers using medium precision ar...
Definition: matrix_double4x3_precision.hpp:22
\n
glm::highp_float32_t
float highp_float32_t
High 32 bit single-qualifier floating-point scalar.
Definition: fwd.hpp:159
\n
glm::f32mat3x3
mat< 3, 3, f32, defaultp > f32mat3x3
Single-qualifier floating-point 3x3 matrix.
Definition: fwd.hpp:704
\n
glm::highp_dmat4x4
mat< 4, 4, double, highp > highp_dmat4x4
4 columns of 4 components matrix of double-precision floating-point numbers using medium precision ar...
Definition: matrix_double4x4_precision.hpp:46
\n
glm::highp_u8
uint8 highp_u8
High qualifier 8 bit unsigned integer type.
Definition: fwd.hpp:91
\n
glm::highp_dmat2x3
mat< 2, 3, double, highp > highp_dmat2x3
2 columns of 3 components matrix of double-precision floating-point numbers using medium precision ar...
Definition: matrix_double2x3_precision.hpp:28
\n
glm::mediump_uint8
uint8 mediump_uint8
Medium qualifier 8 bit unsigned integer type.
Definition: fwd.hpp:95
\n
glm::highp_fmat4x2
mat< 4, 2, f32, highp > highp_fmat4x2
High single-qualifier floating-point 4x2 matrix.
Definition: fwd.hpp:656
\n
glm::highp_f32vec2
vec< 2, f32, highp > highp_f32vec2
High single-qualifier floating-point vector of 2 components.
Definition: fwd.hpp:457
\n
glm::mediump_dmat2x4
mat< 2, 4, double, mediump > mediump_dmat2x4
2 columns of 4 components matrix of double-precision floating-point numbers using medium precision ar...
Definition: matrix_double2x4_precision.hpp:22
\n
glm::dmat2
mat< 2, 2, double, defaultp > dmat2
2 columns of 2 components matrix of double-precision floating-point numbers.
Definition: matrix_double2x2.hpp:20
\n
glm::highp_vec4
vec< 4, float, highp > highp_vec4
4 components vector of high single-qualifier floating-point numbers.
Definition: vector_float4_precision.hpp:16
\n
glm::mediump_int64_t
int64 mediump_int64_t
Medium qualifier 64 bit signed integer type.
Definition: fwd.hpp:83
\n
glm::lowp_u64vec3
vec< 3, u64, lowp > lowp_u64vec3
Low qualifier 64 bit unsigned integer vector of 3 components type.
Definition: fwd.hpp:386
\n
glm::dmat4x4
mat< 4, 4, double, defaultp > dmat4x4
4 columns of 4 components matrix of double-precision floating-point numbers.
Definition: matrix_double4x4.hpp:15
\n
glm::lowp_bvec1
vec< 1, bool, lowp > lowp_bvec1
1 component vector of bool values.
Definition: vector_bool1_precision.hpp:31
\n
glm::highp_f64mat2x2
mat< 2, 2, f64, highp > highp_f64mat2x2
High double-qualifier floating-point 1x1 matrix.
Definition: fwd.hpp:770
\n
glm::highp_u32vec3
vec< 3, u32, highp > highp_u32vec3
High qualifier 32 bit unsigned integer vector of 3 components type.
Definition: fwd.hpp:376
\n
glm::highp_bvec3
vec< 3, bool, highp > highp_bvec3
3 components vector of high qualifier bool numbers.
Definition: vector_bool3_precision.hpp:16
\n
glm::highp_int8_t
int8 highp_int8_t
High qualifier 8 bit signed integer type.
Definition: fwd.hpp:42
\n
glm::lowp_f32quat
qua< f32, lowp > lowp_f32quat
Low single-qualifier floating-point quaternion.
Definition: fwd.hpp:802
\n
glm::lowp_i32vec4
vec< 4, i32, lowp > lowp_i32vec4
Low qualifier 32 bit signed integer vector of 4 components type.
Definition: fwd.hpp:265
\n
glm::highp_i16vec1
vec< 1, i16, highp > highp_i16vec1
High qualifier 16 bit signed integer scalar type.
Definition: fwd.hpp:252
\n
glm::lowp_fmat4x4
mat< 4, 4, f32, lowp > lowp_fmat4x4
Low single-qualifier floating-point 4x4 matrix.
Definition: fwd.hpp:638
\n
glm::lowp_dmat4x3
mat< 4, 3, double, lowp > lowp_dmat4x3
4 columns of 3 components matrix of double-precision floating-point numbers using low precision arith...
Definition: matrix_double4x3_precision.hpp:16
\n
glm::f32mat3x2
mat< 3, 2, f32, defaultp > f32mat3x2
Single-qualifier floating-point 3x2 matrix.
Definition: fwd.hpp:701
\n
glm::lowp_f32mat3x3
mat< 3, 3, f32, lowp > lowp_f32mat3x3
Low single-qualifier floating-point 3x3 matrix.
Definition: fwd.hpp:674
\n
glm::lowp_i8vec2
vec< 2, i8, lowp > lowp_i8vec2
Low qualifier 8 bit signed integer vector of 2 components type.
Definition: fwd.hpp:223
\n
glm::i32vec4
vec< 4, i32, defaultp > i32vec4
32 bit signed integer vector of 4 components type.
Definition: fwd.hpp:280
\n
glm::highp_f32mat2
mat< 2, 2, f32, highp > highp_f32mat2
High single-qualifier floating-point 1x1 matrix.
Definition: fwd.hpp:548
\n
glm::lowp_f32
float lowp_f32
Low 32 bit single-qualifier floating-point scalar.
Definition: fwd.hpp:147
\n
glm::highp_uvec1
vec< 1, unsigned int, highp > highp_uvec1
1 component vector of unsigned integer values.
Definition: vector_uint1_precision.hpp:27
\n
glm::mediump_u16vec4
vec< 4, u16, mediump > mediump_u16vec4
Medium qualifier 16 bit unsigned integer vector of 4 components type.
Definition: fwd.hpp:352
\n
glm::highp_uvec3
vec< 3, unsigned int, highp > highp_uvec3
3 components vector of high qualifier unsigned integer numbers.
Definition: vector_uint3_precision.hpp:16
\n
glm::u32vec3
vec< 3, u32, defaultp > u32vec3
Default qualifier 32 bit unsigned integer vector of 3 components type.
Definition: fwd.hpp:381
\n
glm::u8vec2
vec< 2, u8, defaultp > u8vec2
Default qualifier 8 bit unsigned integer vector of 2 components type.
Definition: fwd.hpp:340
\n
glm::mediump_dvec3
vec< 3, double, mediump > mediump_dvec3
3 components vector of medium double-qualifier floating-point numbers.
Definition: vector_double3_precision.hpp:24
\n
glm::mediump_i16
int16 mediump_i16
Medium qualifier 16 bit signed integer type.
Definition: fwd.hpp:46
\n
glm::highp_u64vec2
vec< 2, u64, highp > highp_u64vec2
High qualifier 64 bit unsigned integer vector of 2 components type.
Definition: fwd.hpp:395
\n
glm::lowp_ivec1
vec< 1, int, lowp > lowp_ivec1
1 component vector of signed integer values.
Definition: vector_int1_precision.hpp:31
\n
glm::i8vec3
vec< 3, i8, defaultp > i8vec3
8 bit signed integer vector of 3 components type.
Definition: fwd.hpp:239
\n
glm::mediump_f32mat2x2
mat< 2, 2, f32, mediump > mediump_f32mat2x2
High single-qualifier floating-point 1x1 matrix.
Definition: fwd.hpp:680
\n
glm::mat4
mat< 4, 4, float, defaultp > mat4
4 columns of 4 components matrix of single-precision floating-point numbers.
Definition: matrix_float4x4.hpp:20
\n
glm::mediump_uint16_t
uint16 mediump_uint16_t
Medium qualifier 16 bit unsigned integer type.
Definition: fwd.hpp:113
\n
glm::mediump_f64mat4x3
mat< 4, 3, f64, mediump > mediump_f64mat4x3
Medium double-qualifier floating-point 4x3 matrix.
Definition: fwd.hpp:767
\n
glm::u8vec3
vec< 3, u8, defaultp > u8vec3
Default qualifier 8 bit unsigned integer vector of 3 components type.
Definition: fwd.hpp:341
\n
glm::highp_f64
double highp_f64
High 64 bit double-qualifier floating-point scalar.
Definition: fwd.hpp:165
\n
glm::mediump_fvec3
vec< 3, float, mediump > mediump_fvec3
Medium Single-qualifier floating-point vector of 3 components.
Definition: fwd.hpp:433
\n
glm::mediump_int64
int64 mediump_int64
Medium qualifier 64 bit signed integer type.
Definition: fwd.hpp:79
\n
glm::mediump_u64vec4
vec< 4, u64, mediump > mediump_u64vec4
Medium qualifier 64 bit unsigned integer vector of 4 components type.
Definition: fwd.hpp:392
\n
glm::highp_dmat2x2
mat< 2, 2, double, highp > highp_dmat2x2
2 columns of 2 components matrix of double-precision floating-point numbers using medium precision ar...
Definition: matrix_double2x2_precision.hpp:46
\n
glm::uint64_t
uint64 uint64_t
Default qualifier 64 bit unsigned integer type.
Definition: fwd.hpp:143
\n
glm::highp_u32vec2
vec< 2, u32, highp > highp_u32vec2
High qualifier 32 bit unsigned integer vector of 2 components type.
Definition: fwd.hpp:375
\n
glm::mediump_dvec1
vec< 1, double, mediump > mediump_dvec1
1 component vector of double-precision floating-point numbers using medium precision arithmetic in te...
Definition: vector_double1_precision.hpp:30
\n
glm::highp_fvec1
vec< 1, float, highp > highp_fvec1
High single-qualifier floating-point vector of 1 component.
Definition: fwd.hpp:436
\n
glm::lowp_i64vec4
vec< 4, i64, lowp > lowp_i64vec4
Low qualifier 64 bit signed integer vector of 4 components type.
Definition: fwd.hpp:285
\n
glm::highp_ivec4
vec< 4, int, highp > highp_ivec4
4 components vector of high qualifier signed integer numbers.
Definition: vector_int4_precision.hpp:16
\n
glm::i32vec3
vec< 3, i32, defaultp > i32vec3
32 bit signed integer vector of 3 components type.
Definition: fwd.hpp:279
\n
glm::highp_f32mat2x4
mat< 2, 4, f32, highp > highp_f32mat2x4
High single-qualifier floating-point 2x4 matrix.
Definition: fwd.hpp:692
\n
glm::lowp_i8vec1
vec< 1, i8, lowp > lowp_i8vec1
Low qualifier 8 bit signed integer scalar type.
Definition: fwd.hpp:222
\n
glm::highp_f64mat2
mat< 2, 2, f64, highp > highp_f64mat2
High double-qualifier floating-point 1x1 matrix.
Definition: fwd.hpp:580
\n
glm::lowp_dvec3
vec< 3, double, lowp > lowp_dvec3
3 components vector of low double-qualifier floating-point numbers.
Definition: vector_double3_precision.hpp:31
\n
glm::lowp_uint16_t
uint16 lowp_uint16_t
Low qualifier 16 bit unsigned integer type.
Definition: fwd.hpp:112
\n
glm::dvec2
vec< 2, double, defaultp > dvec2
2 components vector of double-precision floating-point numbers.
Definition: vector_double2.hpp:15
\n
glm::highp_f64mat3x2
mat< 3, 2, f64, highp > highp_f64mat3x2
High double-qualifier floating-point 3x2 matrix.
Definition: fwd.hpp:773
\n
glm::mediump_u32vec3
vec< 3, u32, mediump > mediump_u32vec3
Medium qualifier 32 bit unsigned integer vector of 3 components type.
Definition: fwd.hpp:371
\n
glm::lowp_uint16
uint16 lowp_uint16
Low qualifier 16 bit unsigned integer type.
Definition: fwd.hpp:108
\n
glm::highp_mat3
mat< 3, 3, float, highp > highp_mat3
3 columns of 3 components matrix of single-precision floating-point numbers using high precision arit...
Definition: matrix_float3x3_precision.hpp:28
\n
glm::highp_u8vec3
vec< 3, u8, highp > highp_u8vec3
High qualifier 8 bit unsigned integer vector of 3 components type.
Definition: fwd.hpp:336
\n
glm::f64vec4
vec< 4, f64, defaultp > f64vec4
Double-qualifier floating-point vector of 4 components.
Definition: fwd.hpp:504
\n
glm::highp_i8vec2
vec< 2, i8, highp > highp_i8vec2
High qualifier 8 bit signed integer vector of 2 components type.
Definition: fwd.hpp:233
\n
glm::highp_dmat2
mat< 2, 2, double, highp > highp_dmat2
2 columns of 2 components matrix of double-precision floating-point numbers using medium precision ar...
Definition: matrix_double2x2_precision.hpp:28
\n
glm::lowp_i32vec3
vec< 3, i32, lowp > lowp_i32vec3
Low qualifier 32 bit signed integer vector of 3 components type.
Definition: fwd.hpp:264
\n
glm::lowp_i32
int32 lowp_i32
Low qualifier 32 bit signed integer type.
Definition: fwd.hpp:59
\n
glm::mediump_fmat4x4
mat< 4, 4, f32, mediump > mediump_fmat4x4
Medium single-qualifier floating-point 4x4 matrix.
Definition: fwd.hpp:648
\n
glm::vec3
vec< 3, float, defaultp > vec3
3 components vector of single-precision floating-point numbers.
Definition: vector_float3.hpp:15
\n
glm::lowp_dmat4x4
mat< 4, 4, double, lowp > lowp_dmat4x4
4 columns of 4 components matrix of double-precision floating-point numbers using low precision arith...
Definition: matrix_double4x4_precision.hpp:34
\n
glm::mediump_i64
int64 mediump_i64
Medium qualifier 64 bit signed integer type.
Definition: fwd.hpp:74
\n
glm::highp_dmat4
mat< 4, 4, double, highp > highp_dmat4
4 columns of 4 components matrix of double-precision floating-point numbers using medium precision ar...
Definition: matrix_double4x4_precision.hpp:28
\n
glm::lowp_i16vec4
vec< 4, i16, lowp > lowp_i16vec4
Low qualifier 16 bit signed integer vector of 4 components type.
Definition: fwd.hpp:245
\n
glm::bvec1
vec< 1, bool, defaultp > bvec1
1 components vector of boolean.
Definition: vector_bool1.hpp:27
\n
glm::highp_f64mat4x3
mat< 4, 3, f64, highp > highp_f64mat4x3
High double-qualifier floating-point 4x3 matrix.
Definition: fwd.hpp:777
\n
glm::highp_u8vec2
vec< 2, u8, highp > highp_u8vec2
High qualifier 8 bit unsigned integer vector of 2 components type.
Definition: fwd.hpp:335
\n
glm::mediump_ivec3
vec< 3, int, mediump > mediump_ivec3
3 components vector of medium qualifier signed integer numbers.
Definition: vector_int3_precision.hpp:22
\n
glm::highp_i8vec3
vec< 3, i8, highp > highp_i8vec3
High qualifier 8 bit signed integer vector of 3 components type.
Definition: fwd.hpp:234
\n
glm::highp_f64vec3
vec< 3, f64, highp > highp_f64vec3
High double-qualifier floating-point vector of 3 components.
Definition: fwd.hpp:498
\n
glm::fvec2
vec< 2, f32, defaultp > fvec2
Single-qualifier floating-point vector of 2 components.
Definition: fwd.hpp:442
\n
glm::lowp_f64vec4
vec< 4, f64, lowp > lowp_f64vec4
Low double-qualifier floating-point vector of 4 components.
Definition: fwd.hpp:489
\n
glm::highp_dquat
qua< double, highp > highp_dquat
Quaternion of high double-qualifier floating-point numbers using high precision arithmetic in term of...
Definition: quaternion_double_precision.hpp:38
\n
glm::mediump_f32vec3
vec< 3, f32, mediump > mediump_f32vec3
Medium single-qualifier floating-point vector of 3 components.
Definition: fwd.hpp:453
\n
glm::lowp_f64
double lowp_f64
Low 64 bit double-qualifier floating-point scalar.
Definition: fwd.hpp:163
\n
glm::lowp_fmat4x2
mat< 4, 2, f32, lowp > lowp_fmat4x2
Low single-qualifier floating-point 4x2 matrix.
Definition: fwd.hpp:636
\n
glm::highp_ivec3
vec< 3, int, highp > highp_ivec3
3 components vector of high qualifier signed integer numbers.
Definition: vector_int3_precision.hpp:16
\n
glm::highp_f64mat2x4
mat< 2, 4, f64, highp > highp_f64mat2x4
High double-qualifier floating-point 2x4 matrix.
Definition: fwd.hpp:772
\n
glm::highp_f64mat4
mat< 4, 4, f64, highp > highp_f64mat4
High double-qualifier floating-point 4x4 matrix.
Definition: fwd.hpp:582
\n
glm::mediump_i32vec4
vec< 4, i32, mediump > mediump_i32vec4
Medium qualifier 32 bit signed integer vector of 4 components type.
Definition: fwd.hpp:270
\n
glm::lowp_f32mat2
mat< 2, 2, f32, lowp > lowp_f32mat2
Low single-qualifier floating-point 1x1 matrix.
Definition: fwd.hpp:540
\n
glm::int16_t
int16 int16_t
16 bit signed integer type.
Definition: fwd.hpp:57
\n
glm::dmat3x4
mat< 3, 4, double, defaultp > dmat3x4
3 columns of 4 components matrix of double-precision floating-point numbers.
Definition: matrix_double3x4.hpp:15
\n
glm::lowp_dmat2x3
mat< 2, 3, double, lowp > lowp_dmat2x3
2 columns of 3 components matrix of double-precision floating-point numbers using low precision arith...
Definition: matrix_double2x3_precision.hpp:16
\n
glm::highp_i64
int64 highp_i64
High qualifier 64 bit signed integer type.
Definition: fwd.hpp:75
\n
glm::mediump_mat2x4
mat< 2, 4, float, mediump > mediump_mat2x4
2 columns of 4 components matrix of single-precision floating-point numbers using medium precision ar...
Definition: matrix_float2x4_precision.hpp:22
\n
glm::highp_f64mat3x4
mat< 3, 4, f64, highp > highp_f64mat3x4
High double-qualifier floating-point 3x4 matrix.
Definition: fwd.hpp:775
\n
glm::highp_fmat3
mat< 3, 3, f32, highp > highp_fmat3
High single-qualifier floating-point 3x3 matrix.
Definition: fwd.hpp:533
\n
glm::mediump_f32mat3x3
mat< 3, 3, f32, mediump > mediump_f32mat3x3
Medium single-qualifier floating-point 3x3 matrix.
Definition: fwd.hpp:684
\n
glm::mediump_f64quat
qua< f64, mediump > mediump_f64quat
Medium double-qualifier floating-point quaternion.
Definition: fwd.hpp:813
\n
glm::int32_t
int32 int32_t
32 bit signed integer type.
Definition: fwd.hpp:71
\n
glm::f64vec2
vec< 2, f64, defaultp > f64vec2
Double-qualifier floating-point vector of 2 components.
Definition: fwd.hpp:502
\n
glm::uvec4
vec< 4, unsigned int, defaultp > uvec4
4 components vector of unsigned integer numbers.
Definition: vector_uint4.hpp:15
\n
glm::lowp_uint64_t
uint64 lowp_uint64_t
Low qualifier 64 bit unsigned integer type.
Definition: fwd.hpp:140
\n
glm::uint64
detail::uint64 uint64
64 bit unsigned integer type.
Definition: scalar_uint_sized.hpp:67
\n
glm::highp_int16
int16 highp_int16
High qualifier 16 bit signed integer type.
Definition: fwd.hpp:52
\n
glm::dmat2x2
mat< 2, 2, double, defaultp > dmat2x2
2 columns of 2 components matrix of double-precision floating-point numbers.
Definition: matrix_double2x2.hpp:15
\n
glm::mediump_i16vec1
vec< 1, i16, mediump > mediump_i16vec1
Medium qualifier 16 bit signed integer scalar type.
Definition: fwd.hpp:247
\n
glm::dmat2x4
mat< 2, 4, double, defaultp > dmat2x4
2 columns of 4 components matrix of double-precision floating-point numbers.
Definition: matrix_double2x4.hpp:15
\n
glm::highp_dmat3x2
mat< 3, 2, double, highp > highp_dmat3x2
3 columns of 2 components matrix of double-precision floating-point numbers using medium precision ar...
Definition: matrix_double3x2_precision.hpp:28
\n
glm::fmat2x4
mat< 2, 4, f32, defaultp > fmat2x4
Single-qualifier floating-point 2x4 matrix.
Definition: fwd.hpp:666
\n
glm::highp_fmat2x2
mat< 2, 2, f32, highp > highp_fmat2x2
High single-qualifier floating-point 1x1 matrix.
Definition: fwd.hpp:650
\n
glm::highp_fvec4
vec< 4, float, highp > highp_fvec4
High Single-qualifier floating-point vector of 4 components.
Definition: fwd.hpp:439
\n
glm::highp_f64mat3x3
mat< 3, 3, f64, highp > highp_f64mat3x3
High double-qualifier floating-point 3x3 matrix.
Definition: fwd.hpp:774
\n
glm::mediump_i32
int32 mediump_i32
Medium qualifier 32 bit signed integer type.
Definition: fwd.hpp:60
\n
glm::mediump_vec3
vec< 3, float, mediump > mediump_vec3
3 components vector of medium single-qualifier floating-point numbers.
Definition: vector_float3_precision.hpp:22
\n
glm::lowp_u16vec2
vec< 2, u16, lowp > lowp_u16vec2
Low qualifier 16 bit unsigned integer vector of 2 components type.
Definition: fwd.hpp:345
\n
glm::highp_u32vec4
vec< 4, u32, highp > highp_u32vec4
High qualifier 32 bit unsigned integer vector of 4 components type.
Definition: fwd.hpp:377
\n
glm::dmat4x2
mat< 4, 2, double, defaultp > dmat4x2
4 columns of 2 components matrix of double-precision floating-point numbers.
Definition: matrix_double4x2.hpp:15
\n
glm::lowp_dvec4
vec< 4, double, lowp > lowp_dvec4
4 components vector of low double-qualifier floating-point numbers.
Definition: vector_double4_precision.hpp:32
\n
glm::lowp_float32_t
float lowp_float32_t
Low 32 bit single-qualifier floating-point scalar.
Definition: fwd.hpp:157
\n
glm::highp_uint64_t
uint64 highp_uint64_t
High qualifier 64 bit unsigned integer type.
Definition: fwd.hpp:142
\n
glm::lowp_f32vec2
vec< 2, f32, lowp > lowp_f32vec2
Low single-qualifier floating-point vector of 2 components.
Definition: fwd.hpp:447
\n
glm::u32vec4
vec< 4, u32, defaultp > u32vec4
Default qualifier 32 bit unsigned integer vector of 4 components type.
Definition: fwd.hpp:382
\n
glm::mediump_f64mat2
mat< 2, 2, f64, mediump > mediump_f64mat2
Medium double-qualifier floating-point 1x1 matrix.
Definition: fwd.hpp:576
\n
glm::mediump_quat
qua< float, mediump > mediump_quat
Quaternion of single-precision floating-point numbers using high precision arithmetic in term of ULPs...
Definition: quaternion_float_precision.hpp:29
\n
glm::highp_f32mat4x3
mat< 4, 3, f32, highp > highp_f32mat4x3
High single-qualifier floating-point 4x3 matrix.
Definition: fwd.hpp:697
\n
glm::lowp_uvec3
vec< 3, unsigned int, lowp > lowp_uvec3
3 components vector of low qualifier unsigned integer numbers.
Definition: vector_uint3_precision.hpp:28
\n
glm::lowp_mat2x2
mat< 2, 2, float, lowp > lowp_mat2x2
2 columns of 2 components matrix of single-precision floating-point numbers using low precision arith...
Definition: matrix_float2x2_precision.hpp:34
\n
glm::f32quat
qua< f32, defaultp > f32quat
Single-qualifier floating-point quaternion.
Definition: fwd.hpp:805
\n
glm::int64
detail::int64 int64
64 bit signed integer type.
Definition: scalar_int_sized.hpp:67
\n
glm::lowp_dquat
qua< double, lowp > lowp_dquat
Quaternion of double-precision floating-point numbers using high precision arithmetic in term of ULPs...
Definition: quaternion_double_precision.hpp:28
\n
glm::highp_u64vec1
vec< 1, u64, highp > highp_u64vec1
High qualifier 64 bit unsigned integer scalar type.
Definition: fwd.hpp:394
\n
glm::mediump_mat3x4
mat< 3, 4, float, mediump > mediump_mat3x4
3 columns of 4 components matrix of single-precision floating-point numbers using medium precision ar...
Definition: matrix_float3x4_precision.hpp:22
\n
glm::highp_f64mat2x3
mat< 2, 3, f64, highp > highp_f64mat2x3
High double-qualifier floating-point 2x3 matrix.
Definition: fwd.hpp:771
\n
glm::lowp_i8vec4
vec< 4, i8, lowp > lowp_i8vec4
Low qualifier 8 bit signed integer vector of 4 components type.
Definition: fwd.hpp:225
\n
glm::lowp_fmat4x3
mat< 4, 3, f32, lowp > lowp_fmat4x3
Low single-qualifier floating-point 4x3 matrix.
Definition: fwd.hpp:637
\n
glm::f32
float f32
Default 32 bit single-qualifier floating-point scalar.
Definition: fwd.hpp:150
\n
glm::highp_i32vec2
vec< 2, i32, highp > highp_i32vec2
High qualifier 32 bit signed integer vector of 2 components type.
Definition: fwd.hpp:273
\n
glm::mediump_u8vec1
vec< 1, u8, mediump > mediump_u8vec1
Medium qualifier 8 bit unsigned integer scalar type.
Definition: fwd.hpp:329
\n
glm::highp_fmat4x3
mat< 4, 3, f32, highp > highp_fmat4x3
High single-qualifier floating-point 4x3 matrix.
Definition: fwd.hpp:657
\n
glm::dmat3x2
mat< 3, 2, double, defaultp > dmat3x2
3 columns of 2 components matrix of double-precision floating-point numbers.
Definition: matrix_double3x2.hpp:15
\n
glm::mediump_i16vec4
vec< 4, i16, mediump > mediump_i16vec4
Medium qualifier 16 bit signed integer vector of 4 components type.
Definition: fwd.hpp:250
\n
glm::f64mat4x2
mat< 4, 2, f64, defaultp > f64mat4x2
Double-qualifier floating-point 4x2 matrix.
Definition: fwd.hpp:782
\n
glm::fmat2x3
mat< 2, 3, f32, defaultp > fmat2x3
Single-qualifier floating-point 2x3 matrix.
Definition: fwd.hpp:663
\n
glm::mediump_f64mat4
mat< 4, 4, f64, mediump > mediump_f64mat4
Medium double-qualifier floating-point 4x4 matrix.
Definition: fwd.hpp:578
\n
glm::mediump_u8vec4
vec< 4, u8, mediump > mediump_u8vec4
Medium qualifier 8 bit unsigned integer vector of 4 components type.
Definition: fwd.hpp:332
\n
glm::highp_dvec3
vec< 3, double, highp > highp_dvec3
3 components vector of high double-qualifier floating-point numbers.
Definition: vector_double3_precision.hpp:17
\n
glm::lowp_f32mat3x4
mat< 3, 4, f32, lowp > lowp_f32mat3x4
Low single-qualifier floating-point 3x4 matrix.
Definition: fwd.hpp:675
\n
glm::mediump_float64_t
double mediump_float64_t
Medium 64 bit double-qualifier floating-point scalar.
Definition: fwd.hpp:174
\n
glm::highp_mat2x2
mat< 2, 2, float, highp > highp_mat2x2
2 columns of 2 components matrix of single-precision floating-point numbers using high precision arit...
Definition: matrix_float2x2_precision.hpp:46
\n
glm::lowp_mat4x3
mat< 4, 3, float, lowp > lowp_mat4x3
4 columns of 3 components matrix of single-precision floating-point numbers using low precision arith...
Definition: matrix_float4x3_precision.hpp:16
\n
glm::highp_fvec2
vec< 2, float, highp > highp_fvec2
High Single-qualifier floating-point vector of 2 components.
Definition: fwd.hpp:437
\n
glm::u16
uint16 u16
Default qualifier 16 bit unsigned integer type.
Definition: fwd.hpp:106
\n
glm::lowp_i64
int64 lowp_i64
Low qualifier 64 bit signed integer type.
Definition: fwd.hpp:73
\n
glm::lowp_uvec1
vec< 1, unsigned int, lowp > lowp_uvec1
1 component vector of unsigned integer values.
Definition: vector_uint1_precision.hpp:37
\n
glm::ivec2
vec< 2, int, defaultp > ivec2
2 components vector of signed integer numbers.
Definition: vector_int2.hpp:15
\n
glm::f32mat4
mat< 4, 4, f32, defaultp > f32mat4
Single-qualifier floating-point 4x4 matrix.
Definition: fwd.hpp:554
\n
glm::mediump_fmat4x2
mat< 4, 2, f32, mediump > mediump_fmat4x2
Medium single-qualifier floating-point 4x2 matrix.
Definition: fwd.hpp:646
\n
glm::lowp_f64mat2
mat< 2, 2, f64, lowp > lowp_f64mat2
Low double-qualifier floating-point 1x1 matrix.
Definition: fwd.hpp:572
\n
glm::mediump_int8_t
int8 mediump_int8_t
Medium qualifier 8 bit signed integer type.
Definition: fwd.hpp:41
\n
glm::lowp_fmat3x3
mat< 3, 3, f32, lowp > lowp_fmat3x3
Low single-qualifier floating-point 3x3 matrix.
Definition: fwd.hpp:634
\n
glm::lowp_float64_t
double lowp_float64_t
Low 64 bit double-qualifier floating-point scalar.
Definition: fwd.hpp:173
\n
glm::highp_int16_t
int16 highp_int16_t
High qualifier 16 bit signed integer type.
Definition: fwd.hpp:56
\n
glm::highp_fmat3x3
mat< 3, 3, f32, highp > highp_fmat3x3
High single-qualifier floating-point 3x3 matrix.
Definition: fwd.hpp:654
\n
glm::dmat4
mat< 4, 4, double, defaultp > dmat4
4 columns of 4 components matrix of double-precision floating-point numbers.
Definition: matrix_double4x4.hpp:20
\n
glm::i64vec1
vec< 1, i64, defaultp > i64vec1
64 bit signed integer scalar type.
Definition: fwd.hpp:297
\n
glm::lowp_u32
uint32 lowp_u32
Low qualifier 32 bit unsigned integer type.
Definition: fwd.hpp:117
\n
glm::mediump_mat4x3
mat< 4, 3, float, mediump > mediump_mat4x3
4 columns of 3 components matrix of single-precision floating-point numbers using medium precision ar...
Definition: matrix_float4x3_precision.hpp:22
\n
glm::lowp_u8vec1
vec< 1, u8, lowp > lowp_u8vec1
Low qualifier 8 bit unsigned integer scalar type.
Definition: fwd.hpp:324
\n
glm::mediump_i64vec3
vec< 3, i64, mediump > mediump_i64vec3
Medium qualifier 64 bit signed integer vector of 3 components type.
Definition: fwd.hpp:289
\n
glm::ivec1
vec< 1, int, defaultp > ivec1
1 component vector of signed integer numbers.
Definition: vector_int1.hpp:28
\n
glm::highp_f32quat
qua< f32, highp > highp_f32quat
High single-qualifier floating-point quaternion.
Definition: fwd.hpp:804
\n
glm::highp_u16
uint16 highp_u16
High qualifier 16 bit unsigned integer type.
Definition: fwd.hpp:105
\n
glm::fvec1
vec< 1, f32, defaultp > fvec1
Single-qualifier floating-point vector of 1 component.
Definition: fwd.hpp:441
\n
glm::mediump_mat3x2
mat< 3, 2, float, mediump > mediump_mat3x2
3 columns of 2 components matrix of single-precision floating-point numbers using medium precision ar...
Definition: matrix_float3x2_precision.hpp:22
\n
glm::lowp_bvec2
vec< 2, bool, lowp > lowp_bvec2
2 components vector of low qualifier bool numbers.
Definition: vector_bool2_precision.hpp:28
\n
glm::mediump_u8vec2
vec< 2, u8, mediump > mediump_u8vec2
Medium qualifier 8 bit unsigned integer vector of 2 components type.
Definition: fwd.hpp:330
\n
glm::lowp_int32_t
int32 lowp_int32_t
Low qualifier 32 bit signed integer type.
Definition: fwd.hpp:68
\n
glm::lowp_u16vec1
vec< 1, u16, lowp > lowp_u16vec1
Low qualifier 16 bit unsigned integer scalar type.
Definition: fwd.hpp:344
\n
glm::highp_fmat4x4
mat< 4, 4, f32, highp > highp_fmat4x4
High single-qualifier floating-point 4x4 matrix.
Definition: fwd.hpp:658
\n
glm::highp_f32mat3x4
mat< 3, 4, f32, highp > highp_f32mat3x4
High single-qualifier floating-point 3x4 matrix.
Definition: fwd.hpp:695
\n
glm::bvec3
vec< 3, bool, defaultp > bvec3
3 components vector of boolean.
Definition: vector_bool3.hpp:15
\n
glm::f32vec2
vec< 2, f32, defaultp > f32vec2
Single-qualifier floating-point vector of 2 components.
Definition: fwd.hpp:462
\n
glm::highp_u16vec3
vec< 3, u16, highp > highp_u16vec3
High qualifier 16 bit unsigned integer vector of 3 components type.
Definition: fwd.hpp:356
\n
glm::mediump_float32_t
float mediump_float32_t
Medium 32 bit single-qualifier floating-point scalar.
Definition: fwd.hpp:158
\n
glm::fmat2x2
mat< 2, 2, f32, defaultp > fmat2x2
Single-qualifier floating-point 1x1 matrix.
Definition: fwd.hpp:660
\n
glm::mediump_f32
float mediump_f32
Medium 32 bit single-qualifier floating-point scalar.
Definition: fwd.hpp:148
\n
glm::mediump_f32mat4x4
mat< 4, 4, f32, mediump > mediump_f32mat4x4
Medium single-qualifier floating-point 4x4 matrix.
Definition: fwd.hpp:688
\n
glm::lowp_quat
qua< float, lowp > lowp_quat
Quaternion of single-precision floating-point numbers using high precision arithmetic in term of ULPs...
Definition: quaternion_float_precision.hpp:26
\n
glm::mediump_f32vec2
vec< 2, f32, mediump > mediump_f32vec2
Medium single-qualifier floating-point vector of 2 components.
Definition: fwd.hpp:452
\n
glm::lowp_int8
int8 lowp_int8
Low qualifier 8 bit signed integer type.
Definition: fwd.hpp:36
\n
glm::mat2x3
mat< 2, 3, float, defaultp > mat2x3
2 columns of 3 components matrix of single-precision floating-point numbers.
Definition: matrix_float2x3.hpp:15
\n
glm::lowp_f64vec1
vec< 1, f64, lowp > lowp_f64vec1
Low double-qualifier floating-point vector of 1 component.
Definition: fwd.hpp:486
\n
glm::highp_f32mat3x2
mat< 3, 2, f32, highp > highp_f32mat3x2
High single-qualifier floating-point 3x2 matrix.
Definition: fwd.hpp:693
\n
glm::mediump_f64mat3x2
mat< 3, 2, f64, mediump > mediump_f64mat3x2
Medium double-qualifier floating-point 3x2 matrix.
Definition: fwd.hpp:763
\n
glm::mediump_dmat3
mat< 3, 3, double, mediump > mediump_dmat3
3 columns of 3 components matrix of double-precision floating-point numbers using medium precision ar...
Definition: matrix_double3x3_precision.hpp:22
\n
glm::mediump_u8vec3
vec< 3, u8, mediump > mediump_u8vec3
Medium qualifier 8 bit unsigned integer vector of 3 components type.
Definition: fwd.hpp:331
\n
glm::dmat2x3
mat< 2, 3, double, defaultp > dmat2x3
2 columns of 3 components matrix of double-precision floating-point numbers.
Definition: matrix_double2x3.hpp:15
\n
glm::lowp_f64mat4x4
mat< 4, 4, f64, lowp > lowp_f64mat4x4
Low double-qualifier floating-point 4x4 matrix.
Definition: fwd.hpp:758
\n
glm::lowp_i16vec1
vec< 1, i16, lowp > lowp_i16vec1
Low qualifier 16 bit signed integer scalar type.
Definition: fwd.hpp:242
\n
glm::dvec3
vec< 3, double, defaultp > dvec3
3 components vector of double-precision floating-point numbers.
Definition: vector_double3.hpp:15
\n
glm::lowp_dmat2x4
mat< 2, 4, double, lowp > lowp_dmat2x4
2 columns of 4 components matrix of double-precision floating-point numbers using low precision arith...
Definition: matrix_double2x4_precision.hpp:16
\n
glm::lowp_int8_t
int8 lowp_int8_t
Low qualifier 8 bit signed integer type.
Definition: fwd.hpp:40
\n
glm::lowp_u32vec2
vec< 2, u32, lowp > lowp_u32vec2
Low qualifier 32 bit unsigned integer vector of 2 components type.
Definition: fwd.hpp:365
\n
glm::mediump_f32mat2x4
mat< 2, 4, f32, mediump > mediump_f32mat2x4
Medium single-qualifier floating-point 2x4 matrix.
Definition: fwd.hpp:682
\n
glm::f64mat4x3
mat< 4, 3, f64, defaultp > f64mat4x3
Double-qualifier floating-point 4x3 matrix.
Definition: fwd.hpp:785
\n
glm::highp_i64vec2
vec< 2, i64, highp > highp_i64vec2
High qualifier 64 bit signed integer vector of 2 components type.
Definition: fwd.hpp:293
\n
glm::mediump_f32mat4
mat< 4, 4, f32, mediump > mediump_f32mat4
Medium single-qualifier floating-point 4x4 matrix.
Definition: fwd.hpp:546
\n
glm::highp_mat3x2
mat< 3, 2, float, highp > highp_mat3x2
3 columns of 2 components matrix of single-precision floating-point numbers using high precision arit...
Definition: matrix_float3x2_precision.hpp:28
\n
glm::highp_mat4x4
mat< 4, 4, float, highp > highp_mat4x4
4 columns of 4 components matrix of single-precision floating-point numbers using high precision arit...
Definition: matrix_float4x4_precision.hpp:46
\n
glm::mediump_dvec2
vec< 2, double, mediump > mediump_dvec2
2 components vector of medium double-qualifier floating-point numbers.
Definition: vector_double2_precision.hpp:22
\n
glm::lowp_dmat2x2
mat< 2, 2, double, lowp > lowp_dmat2x2
2 columns of 2 components matrix of double-precision floating-point numbers using low precision arith...
Definition: matrix_double2x2_precision.hpp:34
\n
glm::i64
int64 i64
64 bit signed integer type.
Definition: fwd.hpp:76
\n
glm::f64
double f64
Default 64 bit double-qualifier floating-point scalar.
Definition: fwd.hpp:166
\n
glm::lowp_bvec3
vec< 3, bool, lowp > lowp_bvec3
3 components vector of low qualifier bool numbers.
Definition: vector_bool3_precision.hpp:28
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glm::lowp_mat3x4
mat< 3, 4, float, lowp > lowp_mat3x4
3 columns of 4 components matrix of single-precision floating-point numbers using low precision arith...
Definition: matrix_float3x4_precision.hpp:16
\n
glm::lowp_mat4x4
mat< 4, 4, float, lowp > lowp_mat4x4
4 columns of 4 components matrix of single-precision floating-point numbers using low precision arith...
Definition: matrix_float4x4_precision.hpp:34
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glm::highp_vec1
vec< 1, float, highp > highp_vec1
1 component vector of single-precision floating-point numbers using high precision arithmetic in term...
Definition: vector_float1_precision.hpp:27
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glm::mediump_f32vec1
vec< 1, f32, mediump > mediump_f32vec1
Medium single-qualifier floating-point vector of 1 component.
Definition: fwd.hpp:451
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glm::mediump_f32mat3x4
mat< 3, 4, f32, mediump > mediump_f32mat3x4
Medium single-qualifier floating-point 3x4 matrix.
Definition: fwd.hpp:685
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glm::highp_fmat2
mat< 2, 2, f32, highp > highp_fmat2
High single-qualifier floating-point 1x1 matrix.
Definition: fwd.hpp:532
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glm::highp_uvec2
vec< 2, unsigned int, highp > highp_uvec2
2 components vector of high qualifier unsigned integer numbers.
Definition: vector_uint2_precision.hpp:16
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glm::highp_f32vec3
vec< 3, f32, highp > highp_f32vec3
High single-qualifier floating-point vector of 3 components.
Definition: fwd.hpp:458
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glm::mediump_mat2x2
mat< 2, 2, float, mediump > mediump_mat2x2
2 columns of 2 components matrix of single-precision floating-point numbers using medium precision ar...
Definition: matrix_float2x2_precision.hpp:40
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glm::mediump_i8vec4
vec< 4, i8, mediump > mediump_i8vec4
Medium qualifier 8 bit signed integer vector of 4 components type.
Definition: fwd.hpp:230
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glm::lowp_float32
float lowp_float32
Low 32 bit single-qualifier floating-point scalar.
Definition: fwd.hpp:152
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glm::u32vec2
vec< 2, u32, defaultp > u32vec2
Default qualifier 32 bit unsigned integer vector of 2 components type.
Definition: fwd.hpp:380
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glm::mediump_uvec2
vec< 2, unsigned int, mediump > mediump_uvec2
2 components vector of medium qualifier unsigned integer numbers.
Definition: vector_uint2_precision.hpp:22
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glm::quat
qua< float, defaultp > quat
Quaternion of single-precision floating-point numbers.
Definition: quaternion_float.hpp:35
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glm::highp_dvec2
vec< 2, double, highp > highp_dvec2
2 components vector of high double-qualifier floating-point numbers.
Definition: vector_double2_precision.hpp:16
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glm::mediump_fvec4
vec< 4, float, mediump > mediump_fvec4
Medium Single-qualifier floating-point vector of 4 components.
Definition: fwd.hpp:434
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glm::mediump_int32
int32 mediump_int32
Medium qualifier 32 bit signed integer type.
Definition: fwd.hpp:65
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glm::i64vec2
vec< 2, i64, defaultp > i64vec2
64 bit signed integer vector of 2 components type.
Definition: fwd.hpp:298
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glm::i16
int16 i16
16 bit signed integer type.
Definition: fwd.hpp:48
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glm::dvec4
vec< 4, double, defaultp > dvec4
4 components vector of double-precision floating-point numbers.
Definition: vector_double4.hpp:15
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glm::fmat4x4
mat< 4, 4, f32, defaultp > fmat4x4
Single-qualifier floating-point 4x4 matrix.
Definition: fwd.hpp:668
\n
glm::mediump_mat2
mat< 2, 2, float, mediump > mediump_mat2
2 columns of 2 components matrix of single-precision floating-point numbers using medium precision ar...
Definition: matrix_float2x2_precision.hpp:22
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glm::lowp_f64quat
qua< f64, lowp > lowp_f64quat
Low double-qualifier floating-point quaternion.
Definition: fwd.hpp:812
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glm::mat2
mat< 2, 2, float, defaultp > mat2
2 columns of 2 components matrix of single-precision floating-point numbers.
Definition: matrix_float2x2.hpp:20
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glm::fmat3x2
mat< 3, 2, f32, defaultp > fmat3x2
Single-qualifier floating-point 3x2 matrix.
Definition: fwd.hpp:661
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glm::dmat4x3
mat< 4, 3, double, defaultp > dmat4x3
4 columns of 3 components matrix of double-precision floating-point numbers.
Definition: matrix_double4x3.hpp:15
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glm::highp_dmat4x2
mat< 4, 2, double, highp > highp_dmat4x2
4 columns of 2 components matrix of double-precision floating-point numbers using medium precision ar...
Definition: matrix_double4x2_precision.hpp:28
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glm::u16vec4
vec< 4, u16, defaultp > u16vec4
Default qualifier 16 bit unsigned integer vector of 4 components type.
Definition: fwd.hpp:362
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glm::u16vec2
vec< 2, u16, defaultp > u16vec2
Default qualifier 16 bit unsigned integer vector of 2 components type.
Definition: fwd.hpp:360
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glm::mediump_u8
uint8 mediump_u8
Medium qualifier 8 bit unsigned integer type.
Definition: fwd.hpp:90
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glm::lowp_fmat2x2
mat< 2, 2, f32, lowp > lowp_fmat2x2
Low single-qualifier floating-point 1x1 matrix.
Definition: fwd.hpp:630
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glm::highp_i8vec4
vec< 4, i8, highp > highp_i8vec4
High qualifier 8 bit signed integer vector of 4 components type.
Definition: fwd.hpp:235
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glm::lowp_u64vec4
vec< 4, u64, lowp > lowp_u64vec4
Low qualifier 64 bit unsigned integer vector of 4 components type.
Definition: fwd.hpp:387
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glm::mediump_i64vec2
vec< 2, i64, mediump > mediump_i64vec2
Medium qualifier 64 bit signed integer vector of 2 components type.
Definition: fwd.hpp:288
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glm::highp_f64mat4x2
mat< 4, 2, f64, highp > highp_f64mat4x2
High double-qualifier floating-point 4x2 matrix.
Definition: fwd.hpp:776
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glm::highp_mat4
mat< 4, 4, float, highp > highp_mat4
4 columns of 4 components matrix of single-precision floating-point numbers using high precision arit...
Definition: matrix_float4x4_precision.hpp:28
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glm::mediump_int16_t
int16 mediump_int16_t
Medium qualifier 16 bit signed integer type.
Definition: fwd.hpp:55
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glm::lowp_i8
int8 lowp_i8
Low qualifier 8 bit signed integer type.
Definition: fwd.hpp:31
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glm::highp_mat4x2
mat< 4, 2, float, highp > highp_mat4x2
4 columns of 2 components matrix of single-precision floating-point numbers using high precision arit...
Definition: matrix_float4x2_precision.hpp:28
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glm::i64vec3
vec< 3, i64, defaultp > i64vec3
64 bit signed integer vector of 3 components type.
Definition: fwd.hpp:299
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glm::lowp_i32vec2
vec< 2, i32, lowp > lowp_i32vec2
Low qualifier 32 bit signed integer vector of 2 components type.
Definition: fwd.hpp:263
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glm::highp_f64quat
qua< f64, highp > highp_f64quat
High double-qualifier floating-point quaternion.
Definition: fwd.hpp:814
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glm::mat3
mat< 3, 3, float, defaultp > mat3
3 columns of 3 components matrix of single-precision floating-point numbers.
Definition: matrix_float3x3.hpp:20
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glm::mediump_f64vec2
vec< 2, f64, mediump > mediump_f64vec2
Medium double-qualifier floating-point vector of 2 components.
Definition: fwd.hpp:492
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glm::highp_uint16_t
uint16 highp_uint16_t
High qualifier 16 bit unsigned integer type.
Definition: fwd.hpp:114
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glm::lowp_fvec1
vec< 1, float, lowp > lowp_fvec1
Low single-qualifier floating-point vector of 1 component.
Definition: fwd.hpp:426
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glm::i8
int8 i8
8 bit signed integer type.
Definition: fwd.hpp:34
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glm::mediump_uint64_t
uint64 mediump_uint64_t
Medium qualifier 64 bit unsigned integer type.
Definition: fwd.hpp:141
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glm::mediump_u64vec1
vec< 1, u64, mediump > mediump_u64vec1
Medium qualifier 64 bit unsigned integer scalar type.
Definition: fwd.hpp:389
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glm::mediump_f32mat2
mat< 2, 2, f32, mediump > mediump_f32mat2
Medium single-qualifier floating-point 1x1 matrix.
Definition: fwd.hpp:544
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glm::mediump_ivec4
vec< 4, int, mediump > mediump_ivec4
4 components vector of medium qualifier signed integer numbers.
Definition: vector_int4_precision.hpp:22
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glm::highp_mat2x4
mat< 2, 4, float, highp > highp_mat2x4
2 columns of 4 components matrix of single-precision floating-point numbers using high precision arit...
Definition: matrix_float2x4_precision.hpp:28
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glm::mediump_uint8_t
uint8 mediump_uint8_t
Medium qualifier 8 bit unsigned integer type.
Definition: fwd.hpp:99
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glm
Definition: common.hpp:20
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glm::mediump_f64
double mediump_f64
Medium 64 bit double-qualifier floating-point scalar.
Definition: fwd.hpp:164
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glm::mediump_fvec1
vec< 1, float, mediump > mediump_fvec1
Medium single-qualifier floating-point vector of 1 component.
Definition: fwd.hpp:431
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glm::mediump_uint16
uint16 mediump_uint16
Medium qualifier 16 bit unsigned integer type.
Definition: fwd.hpp:109
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glm::highp_u8vec4
vec< 4, u8, highp > highp_u8vec4
High qualifier 8 bit unsigned integer vector of 4 components type.
Definition: fwd.hpp:337
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\nFunctions

template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > cross (vec< 3, T, Q > const &x, vec< 3, T, Q > const &y)
 Returns the cross product of x and y. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL T distance (vec< L, T, Q > const &p0, vec< L, T, Q > const &p1)
 Returns the distance betwwen p0 and p1, i.e., length(p0 - p1). More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL T dot (vec< L, T, Q > const &x, vec< L, T, Q > const &y)
 Returns the dot product of x and y, i.e., result = x * y. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > faceforward (vec< L, T, Q > const &N, vec< L, T, Q > const &I, vec< L, T, Q > const &Nref)
 If dot(Nref, I) < 0.0, return N, otherwise, return -N. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL T length (vec< L, T, Q > const &x)
 Returns the length of x, i.e., sqrt(x * x). More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > normalize (vec< L, T, Q > const &x)
 Returns a vector in the same direction as x but with length of 1. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > reflect (vec< L, T, Q > const &I, vec< L, T, Q > const &N)
 For the incident vector I and surface orientation N, returns the reflection direction : result = I - 2.0 * dot(N, I) * N. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > refract (vec< L, T, Q > const &I, vec< L, T, Q > const &N, T eta)
 For the incident vector I and surface normal N, and the ratio of indices of refraction eta, return the refraction vector. More...
 
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Detailed Description

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Core features

\n
See also
GLSL 4.20.8 specification, section 8.5 Geometric Functions
\n\n

Definition in file geometric.hpp.

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1 
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13 #pragma once
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14 
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15 #include "detail/type_vec3.hpp"
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16 
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17 namespace glm
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18 {
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21 
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29  template<length_t L, typename T, qualifier Q>
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30  GLM_FUNC_DECL T length(vec<L, T, Q> const& x);
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31 
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39  template<length_t L, typename T, qualifier Q>
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40  GLM_FUNC_DECL T distance(vec<L, T, Q> const& p0, vec<L, T, Q> const& p1);
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41 
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49  template<length_t L, typename T, qualifier Q>
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50  GLM_FUNC_DECL T dot(vec<L, T, Q> const& x, vec<L, T, Q> const& y);
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51 
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58  template<typename T, qualifier Q>
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59  GLM_FUNC_DECL vec<3, T, Q> cross(vec<3, T, Q> const& x, vec<3, T, Q> const& y);
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60 
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69  template<length_t L, typename T, qualifier Q>
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70  GLM_FUNC_DECL vec<L, T, Q> normalize(vec<L, T, Q> const& x);
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71 
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79  template<length_t L, typename T, qualifier Q>
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80  GLM_FUNC_DECL vec<L, T, Q> faceforward(
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81  vec<L, T, Q> const& N,
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82  vec<L, T, Q> const& I,
\n
83  vec<L, T, Q> const& Nref);
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84 
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93  template<length_t L, typename T, qualifier Q>
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94  GLM_FUNC_DECL vec<L, T, Q> reflect(
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95  vec<L, T, Q> const& I,
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96  vec<L, T, Q> const& N);
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97 
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107  template<length_t L, typename T, qualifier Q>
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108  GLM_FUNC_DECL vec<L, T, Q> refract(
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109  vec<L, T, Q> const& I,
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110  vec<L, T, Q> const& N,
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111  T eta);
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112 
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114 }//namespace glm
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115 
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116 #include "detail/func_geometric.inl"
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glm::reflect
GLM_FUNC_DECL vec< L, T, Q > reflect(vec< L, T, Q > const &I, vec< L, T, Q > const &N)
For the incident vector I and surface orientation N, returns the reflection direction : result = I - ...
\n
glm::faceforward
GLM_FUNC_DECL vec< L, T, Q > faceforward(vec< L, T, Q > const &N, vec< L, T, Q > const &I, vec< L, T, Q > const &Nref)
If dot(Nref, I) < 0.0, return N, otherwise, return -N.
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glm::length
GLM_FUNC_DECL T length(vec< L, T, Q > const &x)
Returns the length of x, i.e., sqrt(x * x).
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glm::cross
GLM_FUNC_DECL vec< 3, T, Q > cross(vec< 3, T, Q > const &x, vec< 3, T, Q > const &y)
Returns the cross product of x and y.
\n
glm::refract
GLM_FUNC_DECL vec< L, T, Q > refract(vec< L, T, Q > const &I, vec< L, T, Q > const &N, T eta)
For the incident vector I and surface normal N, and the ratio of indices of refraction eta...
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glm::normalize
GLM_FUNC_DECL vec< L, T, Q > normalize(vec< L, T, Q > const &x)
Returns a vector in the same direction as x but with length of 1.
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type_vec3.hpp
Core features
\n
glm::distance
GLM_FUNC_DECL T distance(vec< L, T, Q > const &p0, vec< L, T, Q > const &p1)
Returns the distance betwwen p0 and p1, i.e., length(p0 - p1).
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glm::dot
GLM_FUNC_DECL T dot(vec< L, T, Q > const &x, vec< L, T, Q > const &y)
Returns the dot product of x and y, i.e., result = x * y.
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glm
Definition: common.hpp:20
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Definition in file glm.hpp.

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103 #include "detail/_fixes.hpp"
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104 
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105 #include "detail/setup.hpp"
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106 
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107 #pragma once
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108 
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109 #include <cmath>
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110 #include <climits>
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111 #include <cfloat>
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112 #include <limits>
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113 #include <cassert>
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114 #include "fwd.hpp"
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115 
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116 #include "vec2.hpp"
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117 #include "vec3.hpp"
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118 #include "vec4.hpp"
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119 #include "mat2x2.hpp"
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120 #include "mat2x3.hpp"
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121 #include "mat2x4.hpp"
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122 #include "mat3x2.hpp"
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123 #include "mat3x3.hpp"
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124 #include "mat3x4.hpp"
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125 #include "mat4x2.hpp"
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126 #include "mat4x3.hpp"
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127 #include "mat4x4.hpp"
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128 
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129 #include "trigonometric.hpp"
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130 #include "exponential.hpp"
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131 #include "common.hpp"
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132 #include "packing.hpp"
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133 #include "geometric.hpp"
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134 #include "matrix.hpp"
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135 #include "vector_relational.hpp"
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136 #include "integer.hpp"
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mat4x4.hpp
Core features
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Core features
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mat2x4.hpp
Core features
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mat4x3.hpp
Core features
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vec2.hpp
Core features
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integer.hpp
Core features
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exponential.hpp
Core features
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packing.hpp
Core features
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mat2x3.hpp
Core features
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mat4x2.hpp
Core features
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vector_relational.hpp
Core features
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mat3x3.hpp
Core features
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matrix.hpp
Core features
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vec4.hpp
Core features
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trigonometric.hpp
Core features
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mat3x4.hpp
Core features
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mat3x2.hpp
Core features
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vec3.hpp
Core features
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mat2x2.hpp
Core features
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geometric.hpp
Core features
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GLM_GTX_gradient_paint \nMore...

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template<typename T , qualifier Q>
GLM_FUNC_DECL T linearGradient (vec< 2, T, Q > const &Point0, vec< 2, T, Q > const &Point1, vec< 2, T, Q > const &Position)
 Return a color from a linear gradient. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL T radialGradient (vec< 2, T, Q > const &Center, T const &Radius, vec< 2, T, Q > const &Focal, vec< 2, T, Q > const &Position)
 Return a color from a radial gradient. More...
 
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Detailed Description

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GLM_GTX_gradient_paint

\n
See also
Core features (dependence)
\n
\n to use the features of this extension. \">GLM_GTX_optimum_pow (dependence)
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Definition in file gradient_paint.hpp.

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\n\n\n" }, { "path": "field_construction/submodules/diff-langsurf-rasterizer/third_party/glm/doc/api/a00038_source.html", "content": "\n\n\n\n\n\n0.9.9 API documentation: gradient_paint.hpp Source File\n\n\n\n\n\n\n\n\n\n\n
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gradient_paint.hpp
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1 
\n
14 #pragma once
\n
15 
\n
16 // Dependency:
\n
17 #include "../glm.hpp"
\n
18 #include "../gtx/optimum_pow.hpp"
\n
19 
\n
20 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
21 # ifndef GLM_ENABLE_EXPERIMENTAL
\n
22 # pragma message("GLM: GLM_GTX_gradient_paint is an experimental extension and may change in the future. Use #define GLM_ENABLE_EXPERIMENTAL before including it, if you really want to use it.")
\n
23 # else
\n
24 # pragma message("GLM: GLM_GTX_gradient_paint extension included")
\n
25 # endif
\n
26 #endif
\n
27 
\n
28 namespace glm
\n
29 {
\n
32 
\n
35  template<typename T, qualifier Q>
\n
36  GLM_FUNC_DECL T radialGradient(
\n
37  vec<2, T, Q> const& Center,
\n
38  T const& Radius,
\n
39  vec<2, T, Q> const& Focal,
\n
40  vec<2, T, Q> const& Position);
\n
41 
\n
44  template<typename T, qualifier Q>
\n
45  GLM_FUNC_DECL T linearGradient(
\n
46  vec<2, T, Q> const& Point0,
\n
47  vec<2, T, Q> const& Point1,
\n
48  vec<2, T, Q> const& Position);
\n
49 
\n
51 }// namespace glm
\n
52 
\n
53 #include "gradient_paint.inl"
\n
glm::radialGradient
GLM_FUNC_DECL T radialGradient(vec< 2, T, Q > const &Center, T const &Radius, vec< 2, T, Q > const &Focal, vec< 2, T, Q > const &Position)
Return a color from a radial gradient.
\n
glm::linearGradient
GLM_FUNC_DECL T linearGradient(vec< 2, T, Q > const &Point0, vec< 2, T, Q > const &Point1, vec< 2, T, Q > const &Position)
Return a color from a linear gradient.
\n
glm
Definition: common.hpp:20
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handed_coordinate_space.hpp File Reference
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GLM_GTX_handed_coordinate_space \nMore...

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\nFunctions

template<typename T , qualifier Q>
GLM_FUNC_DECL bool leftHanded (vec< 3, T, Q > const &tangent, vec< 3, T, Q > const &binormal, vec< 3, T, Q > const &normal)
 Return if a trihedron left handed or not. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL bool rightHanded (vec< 3, T, Q > const &tangent, vec< 3, T, Q > const &binormal, vec< 3, T, Q > const &normal)
 Return if a trihedron right handed or not. More...
 
\n

Detailed Description

\n

GLM_GTX_handed_coordinate_space

\n
See also
Core features (dependence)
\n\n

Definition in file handed_coordinate_space.hpp.

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handed_coordinate_space.hpp
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1 
\n
13 #pragma once
\n
14 
\n
15 // Dependency:
\n
16 #include "../glm.hpp"
\n
17 
\n
18 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
19 # ifndef GLM_ENABLE_EXPERIMENTAL
\n
20 # pragma message("GLM: GLM_GTX_handed_coordinate_space is an experimental extension and may change in the future. Use #define GLM_ENABLE_EXPERIMENTAL before including it, if you really want to use it.")
\n
21 # else
\n
22 # pragma message("GLM: GLM_GTX_handed_coordinate_space extension included")
\n
23 # endif
\n
24 #endif
\n
25 
\n
26 namespace glm
\n
27 {
\n
30 
\n
33  template<typename T, qualifier Q>
\n
34  GLM_FUNC_DECL bool rightHanded(
\n
35  vec<3, T, Q> const& tangent,
\n
36  vec<3, T, Q> const& binormal,
\n
37  vec<3, T, Q> const& normal);
\n
38 
\n
41  template<typename T, qualifier Q>
\n
42  GLM_FUNC_DECL bool leftHanded(
\n
43  vec<3, T, Q> const& tangent,
\n
44  vec<3, T, Q> const& binormal,
\n
45  vec<3, T, Q> const& normal);
\n
46 
\n
48 }// namespace glm
\n
49 
\n
50 #include "handed_coordinate_space.inl"
\n
glm::leftHanded
GLM_FUNC_DECL bool leftHanded(vec< 3, T, Q > const &tangent, vec< 3, T, Q > const &binormal, vec< 3, T, Q > const &normal)
Return if a trihedron left handed or not.
\n
glm::rightHanded
GLM_FUNC_DECL bool rightHanded(vec< 3, T, Q > const &tangent, vec< 3, T, Q > const &binormal, vec< 3, T, Q > const &normal)
Return if a trihedron right handed or not.
\n
glm
Definition: common.hpp:20
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hash.hpp File Reference
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GLM_GTX_hash \nMore...

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\n

Detailed Description

\n

GLM_GTX_hash

\n
See also
Core features (dependence)
\n\n

Definition in file hash.hpp.

\n
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hash.hpp
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1 
\n
13 #pragma once
\n
14 
\n
15 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
16 # ifndef GLM_ENABLE_EXPERIMENTAL
\n
17 # pragma message("GLM: GLM_GTX_hash is an experimental extension and may change in the future. Use #define GLM_ENABLE_EXPERIMENTAL before including it, if you really want to use it.")
\n
18 # else
\n
19 # pragma message("GLM: GLM_GTX_hash extension included")
\n
20 # endif
\n
21 #endif
\n
22 
\n
23 #include <functional>
\n
24 
\n
25 #include "../vec2.hpp"
\n
26 #include "../vec3.hpp"
\n
27 #include "../vec4.hpp"
\n
28 #include "../gtc/vec1.hpp"
\n
29 
\n
30 #include "../gtc/quaternion.hpp"
\n
31 #include "../gtx/dual_quaternion.hpp"
\n
32 
\n
33 #include "../mat2x2.hpp"
\n
34 #include "../mat2x3.hpp"
\n
35 #include "../mat2x4.hpp"
\n
36 
\n
37 #include "../mat3x2.hpp"
\n
38 #include "../mat3x3.hpp"
\n
39 #include "../mat3x4.hpp"
\n
40 
\n
41 #include "../mat4x2.hpp"
\n
42 #include "../mat4x3.hpp"
\n
43 #include "../mat4x4.hpp"
\n
44 
\n
45 #if !GLM_HAS_CXX11_STL
\n
46 # error "GLM_GTX_hash requires C++11 standard library support"
\n
47 #endif
\n
48 
\n
49 namespace std
\n
50 {
\n
51  template<typename T, glm::qualifier Q>
\n
52  struct hash<glm::vec<1, T,Q> >
\n
53  {
\n
54  GLM_FUNC_DECL size_t operator()(glm::vec<1, T, Q> const& v) const;
\n
55  };
\n
56 
\n
57  template<typename T, glm::qualifier Q>
\n
58  struct hash<glm::vec<2, T,Q> >
\n
59  {
\n
60  GLM_FUNC_DECL size_t operator()(glm::vec<2, T, Q> const& v) const;
\n
61  };
\n
62 
\n
63  template<typename T, glm::qualifier Q>
\n
64  struct hash<glm::vec<3, T,Q> >
\n
65  {
\n
66  GLM_FUNC_DECL size_t operator()(glm::vec<3, T, Q> const& v) const;
\n
67  };
\n
68 
\n
69  template<typename T, glm::qualifier Q>
\n
70  struct hash<glm::vec<4, T,Q> >
\n
71  {
\n
72  GLM_FUNC_DECL size_t operator()(glm::vec<4, T, Q> const& v) const;
\n
73  };
\n
74 
\n
75  template<typename T, glm::qualifier Q>
\n
76  struct hash<glm::qua<T,Q>>
\n
77  {
\n
78  GLM_FUNC_DECL size_t operator()(glm::qua<T, Q> const& q) const;
\n
79  };
\n
80 
\n
81  template<typename T, glm::qualifier Q>
\n
82  struct hash<glm::tdualquat<T,Q> >
\n
83  {
\n
84  GLM_FUNC_DECL size_t operator()(glm::tdualquat<T,Q> const& q) const;
\n
85  };
\n
86 
\n
87  template<typename T, glm::qualifier Q>
\n
88  struct hash<glm::mat<2, 2, T,Q> >
\n
89  {
\n
90  GLM_FUNC_DECL size_t operator()(glm::mat<2, 2, T,Q> const& m) const;
\n
91  };
\n
92 
\n
93  template<typename T, glm::qualifier Q>
\n
94  struct hash<glm::mat<2, 3, T,Q> >
\n
95  {
\n
96  GLM_FUNC_DECL size_t operator()(glm::mat<2, 3, T,Q> const& m) const;
\n
97  };
\n
98 
\n
99  template<typename T, glm::qualifier Q>
\n
100  struct hash<glm::mat<2, 4, T,Q> >
\n
101  {
\n
102  GLM_FUNC_DECL size_t operator()(glm::mat<2, 4, T,Q> const& m) const;
\n
103  };
\n
104 
\n
105  template<typename T, glm::qualifier Q>
\n
106  struct hash<glm::mat<3, 2, T,Q> >
\n
107  {
\n
108  GLM_FUNC_DECL size_t operator()(glm::mat<3, 2, T,Q> const& m) const;
\n
109  };
\n
110 
\n
111  template<typename T, glm::qualifier Q>
\n
112  struct hash<glm::mat<3, 3, T,Q> >
\n
113  {
\n
114  GLM_FUNC_DECL size_t operator()(glm::mat<3, 3, T,Q> const& m) const;
\n
115  };
\n
116 
\n
117  template<typename T, glm::qualifier Q>
\n
118  struct hash<glm::mat<3, 4, T,Q> >
\n
119  {
\n
120  GLM_FUNC_DECL size_t operator()(glm::mat<3, 4, T,Q> const& m) const;
\n
121  };
\n
122 
\n
123  template<typename T, glm::qualifier Q>
\n
124  struct hash<glm::mat<4, 2, T,Q> >
\n
125  {
\n
126  GLM_FUNC_DECL size_t operator()(glm::mat<4, 2, T,Q> const& m) const;
\n
127  };
\n
128 
\n
129  template<typename T, glm::qualifier Q>
\n
130  struct hash<glm::mat<4, 3, T,Q> >
\n
131  {
\n
132  GLM_FUNC_DECL size_t operator()(glm::mat<4, 3, T,Q> const& m) const;
\n
133  };
\n
134 
\n
135  template<typename T, glm::qualifier Q>
\n
136  struct hash<glm::mat<4, 4, T,Q> >
\n
137  {
\n
138  GLM_FUNC_DECL size_t operator()(glm::mat<4, 4, T,Q> const& m) const;
\n
139  };
\n
140 } // namespace std
\n
141 
\n
142 #include "hash.inl"
\n
std
Definition: hash.hpp:49
\n
glm
Definition: common.hpp:20
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gtc/integer.hpp File Reference
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GLM_GTC_integer \nMore...

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\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

\nFunctions

template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, int, Q > iround (vec< L, T, Q > const &x)
 Returns a value equal to the nearest integer to x. More...
 
template<typename genIUType >
GLM_FUNC_DECL genIUType log2 (genIUType x)
 Returns the log2 of x for integer values. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, uint, Q > uround (vec< L, T, Q > const &x)
 Returns a value equal to the nearest integer to x. More...
 
\n

Detailed Description

\n

GLM_GTC_integer

\n
See also
Core features (dependence)
\n
\n to use the features of this extension. \">GLM_GTC_integer (dependence)
\n\n

Definition in file gtc/integer.hpp.

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gtc/integer.hpp
\n
\n
\nGo to the documentation of this file.
1 
\n
14 #pragma once
\n
15 
\n
16 // Dependencies
\n
17 #include "../detail/setup.hpp"
\n
18 #include "../detail/qualifier.hpp"
\n
19 #include "../common.hpp"
\n
20 #include "../integer.hpp"
\n
21 #include "../exponential.hpp"
\n
22 #include <limits>
\n
23 
\n
24 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
25 # pragma message("GLM: GLM_GTC_integer extension included")
\n
26 #endif
\n
27 
\n
28 namespace glm
\n
29 {
\n
32 
\n
35  template<typename genIUType>
\n
36  GLM_FUNC_DECL genIUType log2(genIUType x);
\n
37 
\n
47  template<length_t L, typename T, qualifier Q>
\n
48  GLM_FUNC_DECL vec<L, int, Q> iround(vec<L, T, Q> const& x);
\n
49 
\n
59  template<length_t L, typename T, qualifier Q>
\n
60  GLM_FUNC_DECL vec<L, uint, Q> uround(vec<L, T, Q> const& x);
\n
61 
\n
63 } //namespace glm
\n
64 
\n
65 #include "integer.inl"
\n
glm::uround
GLM_FUNC_DECL vec< L, uint, Q > uround(vec< L, T, Q > const &x)
Returns a value equal to the nearest integer to x.
\n
glm::log2
GLM_FUNC_DECL genIUType log2(genIUType x)
Returns the log2 of x for integer values.
\n
glm::iround
GLM_FUNC_DECL vec< L, int, Q > iround(vec< L, T, Q > const &x)
Returns a value equal to the nearest integer to x.
\n
glm
Definition: common.hpp:20
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gtx/integer.hpp File Reference
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GLM_GTX_integer \nMore...

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Go to the source code of this file.

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\nTypedefs

typedef signed int sint
 32bit signed integer. More...
 
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\nFunctions

template<typename genType >
GLM_FUNC_DECL genType factorial (genType const &x)
 Return the factorial value of a number (!12 max, integer only) From GLM_GTX_integer extension. More...
 
GLM_FUNC_DECL unsigned int floor_log2 (unsigned int x)
 Returns the floor log2 of x. More...
 
GLM_FUNC_DECL int mod (int x, int y)
 Modulus. More...
 
GLM_FUNC_DECL uint mod (uint x, uint y)
 Modulus. More...
 
GLM_FUNC_DECL uint nlz (uint x)
 Returns the number of leading zeros. More...
 
GLM_FUNC_DECL int pow (int x, uint y)
 Returns x raised to the y power. More...
 
GLM_FUNC_DECL uint pow (uint x, uint y)
 Returns x raised to the y power. More...
 
GLM_FUNC_DECL int sqrt (int x)
 Returns the positive square root of x. More...
 
GLM_FUNC_DECL uint sqrt (uint x)
 Returns the positive square root of x. More...
 
\n

Detailed Description

\n

GLM_GTX_integer

\n
See also
Core features (dependence)
\n\n

Definition in file gtx/integer.hpp.

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gtx/integer.hpp
\n
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1 
\n
13 #pragma once
\n
14 
\n
15 // Dependency:
\n
16 #include "../glm.hpp"
\n
17 #include "../gtc/integer.hpp"
\n
18 
\n
19 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
20 # ifndef GLM_ENABLE_EXPERIMENTAL
\n
21 # pragma message("GLM: GLM_GTX_integer is an experimental extension and may change in the future. Use #define GLM_ENABLE_EXPERIMENTAL before including it, if you really want to use it.")
\n
22 # else
\n
23 # pragma message("GLM: GLM_GTX_integer extension included")
\n
24 # endif
\n
25 #endif
\n
26 
\n
27 namespace glm
\n
28 {
\n
31 
\n
34  GLM_FUNC_DECL int pow(int x, uint y);
\n
35 
\n
38  GLM_FUNC_DECL int sqrt(int x);
\n
39 
\n
42  GLM_FUNC_DECL unsigned int floor_log2(unsigned int x);
\n
43 
\n
46  GLM_FUNC_DECL int mod(int x, int y);
\n
47 
\n
50  template<typename genType>
\n
51  GLM_FUNC_DECL genType factorial(genType const& x);
\n
52 
\n
55  typedef signed int sint;
\n
56 
\n
59  GLM_FUNC_DECL uint pow(uint x, uint y);
\n
60 
\n
63  GLM_FUNC_DECL uint sqrt(uint x);
\n
64 
\n
67  GLM_FUNC_DECL uint mod(uint x, uint y);
\n
68 
\n
71  GLM_FUNC_DECL uint nlz(uint x);
\n
72 
\n
74 }//namespace glm
\n
75 
\n
76 #include "integer.inl"
\n
glm::nlz
GLM_FUNC_DECL uint nlz(uint x)
Returns the number of leading zeros.
\n
glm::mod
GLM_FUNC_DECL uint mod(uint x, uint y)
Modulus.
\n
glm::floor_log2
GLM_FUNC_DECL unsigned int floor_log2(unsigned int x)
Returns the floor log2 of x.
\n
glm::sint
signed int sint
32bit signed integer.
Definition: gtx/integer.hpp:55
\n
glm::factorial
GLM_FUNC_DECL genType factorial(genType const &x)
Return the factorial value of a number (!12 max, integer only) From GLM_GTX_integer extension...
\n
glm::pow
GLM_FUNC_DECL uint pow(uint x, uint y)
Returns x raised to the y power.
\n
glm::sqrt
GLM_FUNC_DECL uint sqrt(uint x)
Returns the positive square root of x.
\n
glm
Definition: common.hpp:20
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integer.hpp File Reference
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Core features \nMore...

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\nFunctions

template<typename genType >
GLM_FUNC_DECL int bitCount (genType v)
 Returns the number of bits set to 1 in the binary representation of value. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, int, Q > bitCount (vec< L, T, Q > const &v)
 Returns the number of bits set to 1 in the binary representation of value. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > bitfieldExtract (vec< L, T, Q > const &Value, int Offset, int Bits)
 Extracts bits [offset, offset + bits - 1] from value, returning them in the least significant bits of the result. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > bitfieldInsert (vec< L, T, Q > const &Base, vec< L, T, Q > const &Insert, int Offset, int Bits)
 Returns the insertion the bits least-significant bits of insert into base. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > bitfieldReverse (vec< L, T, Q > const &v)
 Returns the reversal of the bits of value. More...
 
template<typename genIUType >
GLM_FUNC_DECL int findLSB (genIUType x)
 Returns the bit number of the least significant bit set to 1 in the binary representation of value. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, int, Q > findLSB (vec< L, T, Q > const &v)
 Returns the bit number of the least significant bit set to 1 in the binary representation of value. More...
 
template<typename genIUType >
GLM_FUNC_DECL int findMSB (genIUType x)
 Returns the bit number of the most significant bit in the binary representation of value. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, int, Q > findMSB (vec< L, T, Q > const &v)
 Returns the bit number of the most significant bit in the binary representation of value. More...
 
template<length_t L, qualifier Q>
GLM_FUNC_DECL void imulExtended (vec< L, int, Q > const &x, vec< L, int, Q > const &y, vec< L, int, Q > &msb, vec< L, int, Q > &lsb)
 Multiplies 32-bit integers x and y, producing a 64-bit result. More...
 
template<length_t L, qualifier Q>
GLM_FUNC_DECL vec< L, uint, Q > uaddCarry (vec< L, uint, Q > const &x, vec< L, uint, Q > const &y, vec< L, uint, Q > &carry)
 Adds 32-bit unsigned integer x and y, returning the sum modulo pow(2, 32). More...
 
template<length_t L, qualifier Q>
GLM_FUNC_DECL void umulExtended (vec< L, uint, Q > const &x, vec< L, uint, Q > const &y, vec< L, uint, Q > &msb, vec< L, uint, Q > &lsb)
 Multiplies 32-bit integers x and y, producing a 64-bit result. More...
 
template<length_t L, qualifier Q>
GLM_FUNC_DECL vec< L, uint, Q > usubBorrow (vec< L, uint, Q > const &x, vec< L, uint, Q > const &y, vec< L, uint, Q > &borrow)
 Subtracts the 32-bit unsigned integer y from x, returning the difference if non-negative, or pow(2, 32) plus the difference otherwise. More...
 
\n

Detailed Description

\n

Core features

\n
See also
GLSL 4.20.8 specification, section 8.8 Integer Functions
\n\n

Definition in file integer.hpp.

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integer.hpp
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1 
\n
17 #pragma once
\n
18 
\n
19 #include "detail/qualifier.hpp"
\n
20 #include "common.hpp"
\n
21 #include "vector_relational.hpp"
\n
22 
\n
23 namespace glm
\n
24 {
\n
27 
\n
36  template<length_t L, qualifier Q>
\n
37  GLM_FUNC_DECL vec<L, uint, Q> uaddCarry(
\n
38  vec<L, uint, Q> const& x,
\n
39  vec<L, uint, Q> const& y,
\n
40  vec<L, uint, Q> & carry);
\n
41 
\n
50  template<length_t L, qualifier Q>
\n
51  GLM_FUNC_DECL vec<L, uint, Q> usubBorrow(
\n
52  vec<L, uint, Q> const& x,
\n
53  vec<L, uint, Q> const& y,
\n
54  vec<L, uint, Q> & borrow);
\n
55 
\n
64  template<length_t L, qualifier Q>
\n
65  GLM_FUNC_DECL void umulExtended(
\n
66  vec<L, uint, Q> const& x,
\n
67  vec<L, uint, Q> const& y,
\n
68  vec<L, uint, Q> & msb,
\n
69  vec<L, uint, Q> & lsb);
\n
70 
\n
79  template<length_t L, qualifier Q>
\n
80  GLM_FUNC_DECL void imulExtended(
\n
81  vec<L, int, Q> const& x,
\n
82  vec<L, int, Q> const& y,
\n
83  vec<L, int, Q> & msb,
\n
84  vec<L, int, Q> & lsb);
\n
85 
\n
102  template<length_t L, typename T, qualifier Q>
\n
103  GLM_FUNC_DECL vec<L, T, Q> bitfieldExtract(
\n
104  vec<L, T, Q> const& Value,
\n
105  int Offset,
\n
106  int Bits);
\n
107 
\n
123  template<length_t L, typename T, qualifier Q>
\n
124  GLM_FUNC_DECL vec<L, T, Q> bitfieldInsert(
\n
125  vec<L, T, Q> const& Base,
\n
126  vec<L, T, Q> const& Insert,
\n
127  int Offset,
\n
128  int Bits);
\n
129 
\n
139  template<length_t L, typename T, qualifier Q>
\n
140  GLM_FUNC_DECL vec<L, T, Q> bitfieldReverse(vec<L, T, Q> const& v);
\n
141 
\n
148  template<typename genType>
\n
149  GLM_FUNC_DECL int bitCount(genType v);
\n
150 
\n
158  template<length_t L, typename T, qualifier Q>
\n
159  GLM_FUNC_DECL vec<L, int, Q> bitCount(vec<L, T, Q> const& v);
\n
160 
\n
169  template<typename genIUType>
\n
170  GLM_FUNC_DECL int findLSB(genIUType x);
\n
171 
\n
181  template<length_t L, typename T, qualifier Q>
\n
182  GLM_FUNC_DECL vec<L, int, Q> findLSB(vec<L, T, Q> const& v);
\n
183 
\n
193  template<typename genIUType>
\n
194  GLM_FUNC_DECL int findMSB(genIUType x);
\n
195 
\n
206  template<length_t L, typename T, qualifier Q>
\n
207  GLM_FUNC_DECL vec<L, int, Q> findMSB(vec<L, T, Q> const& v);
\n
208 
\n
210 }//namespace glm
\n
211 
\n
212 #include "detail/func_integer.inl"
\n
common.hpp
Core features
\n
glm::findMSB
GLM_FUNC_DECL vec< L, int, Q > findMSB(vec< L, T, Q > const &v)
Returns the bit number of the most significant bit in the binary representation of value...
\n
glm::umulExtended
GLM_FUNC_DECL void umulExtended(vec< L, uint, Q > const &x, vec< L, uint, Q > const &y, vec< L, uint, Q > &msb, vec< L, uint, Q > &lsb)
Multiplies 32-bit integers x and y, producing a 64-bit result.
\n
glm::imulExtended
GLM_FUNC_DECL void imulExtended(vec< L, int, Q > const &x, vec< L, int, Q > const &y, vec< L, int, Q > &msb, vec< L, int, Q > &lsb)
Multiplies 32-bit integers x and y, producing a 64-bit result.
\n
glm::bitCount
GLM_FUNC_DECL vec< L, int, Q > bitCount(vec< L, T, Q > const &v)
Returns the number of bits set to 1 in the binary representation of value.
\n
glm::uaddCarry
GLM_FUNC_DECL vec< L, uint, Q > uaddCarry(vec< L, uint, Q > const &x, vec< L, uint, Q > const &y, vec< L, uint, Q > &carry)
Adds 32-bit unsigned integer x and y, returning the sum modulo pow(2, 32).
\n
glm::bitfieldExtract
GLM_FUNC_DECL vec< L, T, Q > bitfieldExtract(vec< L, T, Q > const &Value, int Offset, int Bits)
Extracts bits [offset, offset + bits - 1] from value, returning them in the least significant bits of...
\n
glm::bitfieldInsert
GLM_FUNC_DECL vec< L, T, Q > bitfieldInsert(vec< L, T, Q > const &Base, vec< L, T, Q > const &Insert, int Offset, int Bits)
Returns the insertion the bits least-significant bits of insert into base.
\n
vector_relational.hpp
Core features
\n
glm::bitfieldReverse
GLM_FUNC_DECL vec< L, T, Q > bitfieldReverse(vec< L, T, Q > const &v)
Returns the reversal of the bits of value.
\n
glm::usubBorrow
GLM_FUNC_DECL vec< L, uint, Q > usubBorrow(vec< L, uint, Q > const &x, vec< L, uint, Q > const &y, vec< L, uint, Q > &borrow)
Subtracts the 32-bit unsigned integer y from x, returning the difference if non-negative, or pow(2, 32) plus the difference otherwise.
\n
glm::findLSB
GLM_FUNC_DECL vec< L, int, Q > findLSB(vec< L, T, Q > const &v)
Returns the bit number of the least significant bit set to 1 in the binary representation of value...
\n
glm
Definition: common.hpp:20
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GLM_GTX_intersect \nMore...

\n\n

Go to the source code of this file.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

\nFunctions

template<typename genType >
GLM_FUNC_DECL bool intersectLineSphere (genType const &point0, genType const &point1, genType const &sphereCenter, typename genType::value_type sphereRadius, genType &intersectionPosition1, genType &intersectionNormal1, genType &intersectionPosition2=genType(), genType &intersectionNormal2=genType())
 Compute the intersection of a line and a sphere. More...
 
template<typename genType >
GLM_FUNC_DECL bool intersectLineTriangle (genType const &orig, genType const &dir, genType const &vert0, genType const &vert1, genType const &vert2, genType &position)
 Compute the intersection of a line and a triangle. More...
 
template<typename genType >
GLM_FUNC_DECL bool intersectRayPlane (genType const &orig, genType const &dir, genType const &planeOrig, genType const &planeNormal, typename genType::value_type &intersectionDistance)
 Compute the intersection of a ray and a plane. More...
 
template<typename genType >
GLM_FUNC_DECL bool intersectRaySphere (genType const &rayStarting, genType const &rayNormalizedDirection, genType const &sphereCenter, typename genType::value_type const sphereRadiusSquared, typename genType::value_type &intersectionDistance)
 Compute the intersection distance of a ray and a sphere. More...
 
template<typename genType >
GLM_FUNC_DECL bool intersectRaySphere (genType const &rayStarting, genType const &rayNormalizedDirection, genType const &sphereCenter, const typename genType::value_type sphereRadius, genType &intersectionPosition, genType &intersectionNormal)
 Compute the intersection of a ray and a sphere. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL bool intersectRayTriangle (vec< 3, T, Q > const &orig, vec< 3, T, Q > const &dir, vec< 3, T, Q > const &v0, vec< 3, T, Q > const &v1, vec< 3, T, Q > const &v2, vec< 2, T, Q > &baryPosition, T &distance)
 Compute the intersection of a ray and a triangle. More...
 
\n

Detailed Description

\n

GLM_GTX_intersect

\n
See also
Core features (dependence)
\n
\n to use the features of this extension. \">GLM_GTX_closest_point (dependence)
\n\n

Definition in file intersect.hpp.

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intersect.hpp
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1 
\n
14 #pragma once
\n
15 
\n
16 // Dependency:
\n
17 #include <cfloat>
\n
18 #include <limits>
\n
19 #include "../glm.hpp"
\n
20 #include "../geometric.hpp"
\n
21 #include "../gtx/closest_point.hpp"
\n
22 #include "../gtx/vector_query.hpp"
\n
23 
\n
24 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
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25 # ifndef GLM_ENABLE_EXPERIMENTAL
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26 # pragma message("GLM: GLM_GTX_closest_point is an experimental extension and may change in the future. Use #define GLM_ENABLE_EXPERIMENTAL before including it, if you really want to use it.")
\n
27 # else
\n
28 # pragma message("GLM: GLM_GTX_closest_point extension included")
\n
29 # endif
\n
30 #endif
\n
31 
\n
32 namespace glm
\n
33 {
\n
36 
\n
40  template<typename genType>
\n
41  GLM_FUNC_DECL bool intersectRayPlane(
\n
42  genType const& orig, genType const& dir,
\n
43  genType const& planeOrig, genType const& planeNormal,
\n
44  typename genType::value_type & intersectionDistance);
\n
45 
\n
49  template<typename T, qualifier Q>
\n
50  GLM_FUNC_DECL bool intersectRayTriangle(
\n
51  vec<3, T, Q> const& orig, vec<3, T, Q> const& dir,
\n
52  vec<3, T, Q> const& v0, vec<3, T, Q> const& v1, vec<3, T, Q> const& v2,
\n
53  vec<2, T, Q>& baryPosition, T& distance);
\n
54 
\n
57  template<typename genType>
\n
58  GLM_FUNC_DECL bool intersectLineTriangle(
\n
59  genType const& orig, genType const& dir,
\n
60  genType const& vert0, genType const& vert1, genType const& vert2,
\n
61  genType & position);
\n
62 
\n
66  template<typename genType>
\n
67  GLM_FUNC_DECL bool intersectRaySphere(
\n
68  genType const& rayStarting, genType const& rayNormalizedDirection,
\n
69  genType const& sphereCenter, typename genType::value_type const sphereRadiusSquared,
\n
70  typename genType::value_type & intersectionDistance);
\n
71 
\n
74  template<typename genType>
\n
75  GLM_FUNC_DECL bool intersectRaySphere(
\n
76  genType const& rayStarting, genType const& rayNormalizedDirection,
\n
77  genType const& sphereCenter, const typename genType::value_type sphereRadius,
\n
78  genType & intersectionPosition, genType & intersectionNormal);
\n
79 
\n
82  template<typename genType>
\n
83  GLM_FUNC_DECL bool intersectLineSphere(
\n
84  genType const& point0, genType const& point1,
\n
85  genType const& sphereCenter, typename genType::value_type sphereRadius,
\n
86  genType & intersectionPosition1, genType & intersectionNormal1,
\n
87  genType & intersectionPosition2 = genType(), genType & intersectionNormal2 = genType());
\n
88 
\n
90 }//namespace glm
\n
91 
\n
92 #include "intersect.inl"
\n
glm::intersectRayTriangle
GLM_FUNC_DECL bool intersectRayTriangle(vec< 3, T, Q > const &orig, vec< 3, T, Q > const &dir, vec< 3, T, Q > const &v0, vec< 3, T, Q > const &v1, vec< 3, T, Q > const &v2, vec< 2, T, Q > &baryPosition, T &distance)
Compute the intersection of a ray and a triangle.
\n
glm::intersectRaySphere
GLM_FUNC_DECL bool intersectRaySphere(genType const &rayStarting, genType const &rayNormalizedDirection, genType const &sphereCenter, const typename genType::value_type sphereRadius, genType &intersectionPosition, genType &intersectionNormal)
Compute the intersection of a ray and a sphere.
\n
glm::intersectRayPlane
GLM_FUNC_DECL bool intersectRayPlane(genType const &orig, genType const &dir, genType const &planeOrig, genType const &planeNormal, typename genType::value_type &intersectionDistance)
Compute the intersection of a ray and a plane.
\n
glm::intersectLineTriangle
GLM_FUNC_DECL bool intersectLineTriangle(genType const &orig, genType const &dir, genType const &vert0, genType const &vert1, genType const &vert2, genType &position)
Compute the intersection of a line and a triangle.
\n
glm::intersectLineSphere
GLM_FUNC_DECL bool intersectLineSphere(genType const &point0, genType const &point1, genType const &sphereCenter, typename genType::value_type sphereRadius, genType &intersectionPosition1, genType &intersectionNormal1, genType &intersectionPosition2=genType(), genType &intersectionNormal2=genType())
Compute the intersection of a line and a sphere.
\n
glm::distance
GLM_FUNC_DECL T distance(vec< L, T, Q > const &p0, vec< L, T, Q > const &p1)
Returns the distance betwwen p0 and p1, i.e., length(p0 - p1).
\n
glm
Definition: common.hpp:20
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io.hpp File Reference
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GLM_GTX_io \nMore...

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\n

Detailed Description

\n

GLM_GTX_io

\n
Author
Jan P Springer (regni.nosp@m.rpsj.nosp@m.@gmai.nosp@m.l.co.nosp@m.m)
\n
See also
Core features (dependence)
\n
\n to use the features of this extension. \">GLM_GTC_matrix_access (dependence)
\n
\n to use the features of this extension. \">GLM_GTC_quaternion (dependence)
\n\n

Definition in file io.hpp.

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io.hpp
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1 
\n
20 #pragma once
\n
21 
\n
22 // Dependency:
\n
23 #include "../glm.hpp"
\n
24 #include "../gtx/quaternion.hpp"
\n
25 
\n
26 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
27 # ifndef GLM_ENABLE_EXPERIMENTAL
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28 # pragma message("GLM: GLM_GTX_io is an experimental extension and may change in the future. Use #define GLM_ENABLE_EXPERIMENTAL before including it, if you really want to use it.")
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29 # else
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30 # pragma message("GLM: GLM_GTX_io extension included")
\n
31 # endif
\n
32 #endif
\n
33 
\n
34 #include <iosfwd> // std::basic_ostream<> (fwd)
\n
35 #include <locale> // std::locale, std::locale::facet, std::locale::id
\n
36 #include <utility> // std::pair<>
\n
37 
\n
38 namespace glm
\n
39 {
\n
42 
\n
43  namespace io
\n
44  {
\n
45  enum order_type { column_major, row_major};
\n
46 
\n
47  template<typename CTy>
\n
48  class format_punct : public std::locale::facet
\n
49  {
\n
50  typedef CTy char_type;
\n
51 
\n
52  public:
\n
53 
\n
54  static std::locale::id id;
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55 
\n
56  bool formatted;
\n
57  unsigned precision;
\n
58  unsigned width;
\n
59  char_type separator;
\n
60  char_type delim_left;
\n
61  char_type delim_right;
\n
62  char_type space;
\n
63  char_type newline;
\n
64  order_type order;
\n
65 
\n
66  GLM_FUNC_DECL explicit format_punct(size_t a = 0);
\n
67  GLM_FUNC_DECL explicit format_punct(format_punct const&);
\n
68  };
\n
69 
\n
70  template<typename CTy, typename CTr = std::char_traits<CTy> >
\n
71  class basic_state_saver {
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72 
\n
73  public:
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74 
\n
75  GLM_FUNC_DECL explicit basic_state_saver(std::basic_ios<CTy,CTr>&);
\n
76  GLM_FUNC_DECL ~basic_state_saver();
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77 
\n
78  private:
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79 
\n
80  typedef ::std::basic_ios<CTy,CTr> state_type;
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81  typedef typename state_type::char_type char_type;
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82  typedef ::std::ios_base::fmtflags flags_type;
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83  typedef ::std::streamsize streamsize_type;
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84  typedef ::std::locale const locale_type;
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85 
\n
86  state_type& state_;
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87  flags_type flags_;
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88  streamsize_type precision_;
\n
89  streamsize_type width_;
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90  char_type fill_;
\n
91  locale_type locale_;
\n
92 
\n
93  GLM_FUNC_DECL basic_state_saver& operator=(basic_state_saver const&);
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94  };
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95 
\n
96  typedef basic_state_saver<char> state_saver;
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97  typedef basic_state_saver<wchar_t> wstate_saver;
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98 
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99  template<typename CTy, typename CTr = std::char_traits<CTy> >
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100  class basic_format_saver
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101  {
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102  public:
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103 
\n
104  GLM_FUNC_DECL explicit basic_format_saver(std::basic_ios<CTy,CTr>&);
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105  GLM_FUNC_DECL ~basic_format_saver();
\n
106 
\n
107  private:
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108 
\n
109  basic_state_saver<CTy> const bss_;
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110 
\n
111  GLM_FUNC_DECL basic_format_saver& operator=(basic_format_saver const&);
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112  };
\n
113 
\n
114  typedef basic_format_saver<char> format_saver;
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115  typedef basic_format_saver<wchar_t> wformat_saver;
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116 
\n
117  struct precision
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118  {
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119  unsigned value;
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120 
\n
121  GLM_FUNC_DECL explicit precision(unsigned);
\n
122  };
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123 
\n
124  struct width
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125  {
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126  unsigned value;
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127 
\n
128  GLM_FUNC_DECL explicit width(unsigned);
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129  };
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130 
\n
131  template<typename CTy>
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132  struct delimeter
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133  {
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134  CTy value[3];
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135 
\n
136  GLM_FUNC_DECL explicit delimeter(CTy /* left */, CTy /* right */, CTy /* separator */ = ',');
\n
137  };
\n
138 
\n
139  struct order
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140  {
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141  order_type value;
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142 
\n
143  GLM_FUNC_DECL explicit order(order_type);
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144  };
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145 
\n
146  // functions, inlined (inline)
\n
147 
\n
148  template<typename FTy, typename CTy, typename CTr>
\n
149  FTy const& get_facet(std::basic_ios<CTy,CTr>&);
\n
150  template<typename FTy, typename CTy, typename CTr>
\n
151  std::basic_ios<CTy,CTr>& formatted(std::basic_ios<CTy,CTr>&);
\n
152  template<typename FTy, typename CTy, typename CTr>
\n
153  std::basic_ios<CTy,CTr>& unformattet(std::basic_ios<CTy,CTr>&);
\n
154 
\n
155  template<typename CTy, typename CTr>
\n
156  std::basic_ostream<CTy, CTr>& operator<<(std::basic_ostream<CTy, CTr>&, precision const&);
\n
157  template<typename CTy, typename CTr>
\n
158  std::basic_ostream<CTy, CTr>& operator<<(std::basic_ostream<CTy, CTr>&, width const&);
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159  template<typename CTy, typename CTr>
\n
160  std::basic_ostream<CTy, CTr>& operator<<(std::basic_ostream<CTy, CTr>&, delimeter<CTy> const&);
\n
161  template<typename CTy, typename CTr>
\n
162  std::basic_ostream<CTy, CTr>& operator<<(std::basic_ostream<CTy, CTr>&, order const&);
\n
163  }//namespace io
\n
164 
\n
165  template<typename CTy, typename CTr, typename T, qualifier Q>
\n
166  GLM_FUNC_DECL std::basic_ostream<CTy,CTr>& operator<<(std::basic_ostream<CTy,CTr>&, qua<T, Q> const&);
\n
167  template<typename CTy, typename CTr, typename T, qualifier Q>
\n
168  GLM_FUNC_DECL std::basic_ostream<CTy,CTr>& operator<<(std::basic_ostream<CTy,CTr>&, vec<1, T, Q> const&);
\n
169  template<typename CTy, typename CTr, typename T, qualifier Q>
\n
170  GLM_FUNC_DECL std::basic_ostream<CTy,CTr>& operator<<(std::basic_ostream<CTy,CTr>&, vec<2, T, Q> const&);
\n
171  template<typename CTy, typename CTr, typename T, qualifier Q>
\n
172  GLM_FUNC_DECL std::basic_ostream<CTy,CTr>& operator<<(std::basic_ostream<CTy,CTr>&, vec<3, T, Q> const&);
\n
173  template<typename CTy, typename CTr, typename T, qualifier Q>
\n
174  GLM_FUNC_DECL std::basic_ostream<CTy,CTr>& operator<<(std::basic_ostream<CTy,CTr>&, vec<4, T, Q> const&);
\n
175  template<typename CTy, typename CTr, typename T, qualifier Q>
\n
176  GLM_FUNC_DECL std::basic_ostream<CTy,CTr>& operator<<(std::basic_ostream<CTy,CTr>&, mat<2, 2, T, Q> const&);
\n
177  template<typename CTy, typename CTr, typename T, qualifier Q>
\n
178  GLM_FUNC_DECL std::basic_ostream<CTy,CTr>& operator<<(std::basic_ostream<CTy,CTr>&, mat<2, 3, T, Q> const&);
\n
179  template<typename CTy, typename CTr, typename T, qualifier Q>
\n
180  GLM_FUNC_DECL std::basic_ostream<CTy,CTr>& operator<<(std::basic_ostream<CTy,CTr>&, mat<2, 4, T, Q> const&);
\n
181  template<typename CTy, typename CTr, typename T, qualifier Q>
\n
182  GLM_FUNC_DECL std::basic_ostream<CTy,CTr>& operator<<(std::basic_ostream<CTy,CTr>&, mat<3, 2, T, Q> const&);
\n
183  template<typename CTy, typename CTr, typename T, qualifier Q>
\n
184  GLM_FUNC_DECL std::basic_ostream<CTy,CTr>& operator<<(std::basic_ostream<CTy,CTr>&, mat<3, 3, T, Q> const&);
\n
185  template<typename CTy, typename CTr, typename T, qualifier Q>
\n
186  GLM_FUNC_DECL std::basic_ostream<CTy,CTr>& operator<<(std::basic_ostream<CTy,CTr>&, mat<3, 4, T, Q> const&);
\n
187  template<typename CTy, typename CTr, typename T, qualifier Q>
\n
188  GLM_FUNC_DECL std::basic_ostream<CTy,CTr>& operator<<(std::basic_ostream<CTy,CTr>&, mat<4, 2, T, Q> const&);
\n
189  template<typename CTy, typename CTr, typename T, qualifier Q>
\n
190  GLM_FUNC_DECL std::basic_ostream<CTy,CTr>& operator<<(std::basic_ostream<CTy,CTr>&, mat<4, 3, T, Q> const&);
\n
191  template<typename CTy, typename CTr, typename T, qualifier Q>
\n
192  GLM_FUNC_DECL std::basic_ostream<CTy,CTr>& operator<<(std::basic_ostream<CTy,CTr>&, mat<4, 4, T, Q> const&);
\n
193 
\n
194  template<typename CTy, typename CTr, typename T, qualifier Q>
\n
195  GLM_FUNC_DECL std::basic_ostream<CTy,CTr> & operator<<(std::basic_ostream<CTy,CTr> &,
\n
196  std::pair<mat<4, 4, T, Q> const, mat<4, 4, T, Q> const> const&);
\n
197 
\n
199 }//namespace glm
\n
200 
\n
201 #include "io.inl"
\n
glm
Definition: common.hpp:20
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\nFunctions
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log_base.hpp File Reference
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GLM_GTX_log_base \nMore...

\n\n

Go to the source code of this file.

\n\n\n\n\n\n\n\n\n\n\n

\nFunctions

template<typename genType >
GLM_FUNC_DECL genType log (genType const &x, genType const &base)
 Logarithm for any base. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > sign (vec< L, T, Q > const &x, vec< L, T, Q > const &base)
 Logarithm for any base. More...
 
\n

Detailed Description

\n

GLM_GTX_log_base

\n
See also
Core features (dependence)
\n\n

Definition in file log_base.hpp.

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1 
\n
13 #pragma once
\n
14 
\n
15 // Dependency:
\n
16 #include "../glm.hpp"
\n
17 
\n
18 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
19 # ifndef GLM_ENABLE_EXPERIMENTAL
\n
20 # pragma message("GLM: GLM_GTX_log_base is an experimental extension and may change in the future. Use #define GLM_ENABLE_EXPERIMENTAL before including it, if you really want to use it.")
\n
21 # else
\n
22 # pragma message("GLM: GLM_GTX_log_base extension included")
\n
23 # endif
\n
24 #endif
\n
25 
\n
26 namespace glm
\n
27 {
\n
30 
\n
33  template<typename genType>
\n
34  GLM_FUNC_DECL genType log(
\n
35  genType const& x,
\n
36  genType const& base);
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37 
\n
40  template<length_t L, typename T, qualifier Q>
\n
41  GLM_FUNC_DECL vec<L, T, Q> sign(
\n
42  vec<L, T, Q> const& x,
\n
43  vec<L, T, Q> const& base);
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44 
\n
46 }//namespace glm
\n
47 
\n
48 #include "log_base.inl"
\n
glm::sign
GLM_FUNC_DECL vec< L, T, Q > sign(vec< L, T, Q > const &x, vec< L, T, Q > const &base)
Logarithm for any base.
\n
glm::log
GLM_FUNC_DECL genType log(genType const &x, genType const &base)
Logarithm for any base.
\n
glm
Definition: common.hpp:20
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2 
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3 # This file describes the settings to be used by the documentation system
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4 # doxygen (www.doxygen.org) for a project.
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26 
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154 
\n
155 # The STRIP_FROM_PATH tag can be used to strip a user-defined part of the path.
\n
156 # Stripping is only done if one of the specified strings matches the left-hand
\n
157 # part of the path. The tag can be used to show relative paths in the file list.
\n
158 # If left blank the directory from which doxygen is run is used as the path to
\n
159 # strip.
\n
160 #
\n
161 # Note that you can specify absolute paths here, but also relative paths, which
\n
162 # will be relative from the directory where doxygen is started.
\n
163 # This tag requires that the tag FULL_PATH_NAMES is set to YES.
\n
164 
\n
165 STRIP_FROM_PATH = "C:/Documents and Settings/Groove/ "
\n
166 
\n
167 # The STRIP_FROM_INC_PATH tag can be used to strip a user-defined part of the
\n
168 # path mentioned in the documentation of a class, which tells the reader which
\n
169 # header file to include in order to use a class. If left blank only the name of
\n
170 # the header file containing the class definition is used. Otherwise one should
\n
171 # specify the list of include paths that are normally passed to the compiler
\n
172 # using the -I flag.
\n
173 
\n
174 STRIP_FROM_INC_PATH =
\n
175 
\n
176 # If the SHORT_NAMES tag is set to YES, doxygen will generate much shorter (but
\n
177 # less readable) file names. This can be useful is your file systems doesn't
\n
178 # support long names like on DOS, Mac, or CD-ROM.
\n
179 # The default value is: NO.
\n
180 
\n
181 SHORT_NAMES = YES
\n
182 
\n
183 # If the JAVADOC_AUTOBRIEF tag is set to YES then doxygen will interpret the
\n
184 # first line (until the first dot) of a Javadoc-style comment as the brief
\n
185 # description. If set to NO, the Javadoc-style will behave just like regular Qt-
\n
186 # style comments (thus requiring an explicit @brief command for a brief
\n
187 # description.)
\n
188 # The default value is: NO.
\n
189 
\n
190 JAVADOC_AUTOBRIEF = YES
\n
191 
\n
192 # If the QT_AUTOBRIEF tag is set to YES then doxygen will interpret the first
\n
193 # line (until the first dot) of a Qt-style comment as the brief description. If
\n
194 # set to NO, the Qt-style will behave just like regular Qt-style comments (thus
\n
195 # requiring an explicit \\brief command for a brief description.)
\n
196 # The default value is: NO.
\n
197 
\n
198 QT_AUTOBRIEF = NO
\n
199 
\n
200 # The MULTILINE_CPP_IS_BRIEF tag can be set to YES to make doxygen treat a
\n
201 # multi-line C++ special comment block (i.e. a block of
\n
202 # a brief description. This used to be the default behavior. The new default is
\n
203 # to treat a multi-line C++ comment block as a detailed description. Set this
\n
204 # tag to YES if you prefer the old behavior instead.
\n
205 #
\n
206 # Note that setting this tag to YES also means that rational rose comments are
\n
207 # not recognized any more.
\n
208 # The default value is: NO.
\n
209 
\n
210 MULTILINE_CPP_IS_BRIEF = NO
\n
211 
\n
212 # If the INHERIT_DOCS tag is set to YES then an undocumented member inherits the
\n
213 # documentation from any documented member that it re-implements.
\n
214 # The default value is: YES.
\n
215 
\n
216 INHERIT_DOCS = YES
\n
217 
\n
218 # If the SEPARATE_MEMBER_PAGES tag is set to YES then doxygen will produce a new
\n
219 # page for each member. If set to NO, the documentation of a member will be part
\n
220 # of the file/class/namespace that contains it.
\n
221 # The default value is: NO.
\n
222 
\n
223 SEPARATE_MEMBER_PAGES = NO
\n
224 
\n
225 # The TAB_SIZE tag can be used to set the number of spaces in a tab. Doxygen
\n
226 # uses this value to replace tabs by spaces in code fragments.
\n
227 # Minimum value: 1, maximum value: 16, default value: 4.
\n
228 
\n
229 TAB_SIZE = 8
\n
230 
\n
231 # This tag can be used to specify a number of aliases that act as commands in
\n
232 # the documentation. An alias has the form:
\n
233 # name=value
\n
234 # For example adding
\n
235 # "sideeffect=@par Side Effects:\\n"
\n
236 # will allow you to put the command \\sideeffect (or @sideeffect) in the
\n
237 # documentation, which will result in a user-defined paragraph with heading
\n
238 # "Side Effects:". You can put \\n's in the value part of an alias to insert
\n
239 # newlines.
\n
240 
\n
241 ALIASES =
\n
242 
\n
243 # This tag can be used to specify a number of word-keyword mappings (TCL only).
\n
244 # A mapping has the form "name=value". For example adding "class=itcl::class"
\n
245 # will allow you to use the command class in the itcl::class meaning.
\n
246 
\n
247 TCL_SUBST =
\n
248 
\n
249 # Set the OPTIMIZE_OUTPUT_FOR_C tag to YES if your project consists of C sources
\n
250 # only. Doxygen will then generate output that is more tailored for C. For
\n
251 # instance, some of the names that are used will be different. The list of all
\n
252 # members will be omitted, etc.
\n
253 # The default value is: NO.
\n
254 
\n
255 OPTIMIZE_OUTPUT_FOR_C = NO
\n
256 
\n
257 # Set the OPTIMIZE_OUTPUT_JAVA tag to YES if your project consists of Java or
\n
258 # Python sources only. Doxygen will then generate output that is more tailored
\n
259 # for that language. For instance, namespaces will be presented as packages,
\n
260 # qualified scopes will look different, etc.
\n
261 # The default value is: NO.
\n
262 
\n
263 OPTIMIZE_OUTPUT_JAVA = NO
\n
264 
\n
265 # Set the OPTIMIZE_FOR_FORTRAN tag to YES if your project consists of Fortran
\n
266 # sources. Doxygen will then generate output that is tailored for Fortran.
\n
267 # The default value is: NO.
\n
268 
\n
269 OPTIMIZE_FOR_FORTRAN = NO
\n
270 
\n
271 # Set the OPTIMIZE_OUTPUT_VHDL tag to YES if your project consists of VHDL
\n
272 # sources. Doxygen will then generate output that is tailored for VHDL.
\n
273 # The default value is: NO.
\n
274 
\n
275 OPTIMIZE_OUTPUT_VHDL = NO
\n
276 
\n
277 # Doxygen selects the parser to use depending on the extension of the files it
\n
278 # parses. With this tag you can assign which parser to use for a given
\n
279 # extension. Doxygen has a built-in mapping, but you can override or extend it
\n
280 # using this tag. The format is ext=language, where ext is a file extension, and
\n
281 # language is one of the parsers supported by doxygen: IDL, Java, Javascript,
\n
282 # C#, C, C++, D, PHP, Objective-C, Python, Fortran (fixed format Fortran:
\n
283 # FortranFixed, free formatted Fortran: FortranFree, unknown formatted Fortran:
\n
284 # Fortran. In the later case the parser tries to guess whether the code is fixed
\n
285 # or free formatted code, this is the default for Fortran type files), VHDL. For
\n
286 # instance to make doxygen treat .inc files as Fortran files (default is PHP),
\n
287 # and .f files as C (default is Fortran), use: inc=Fortran f=C.
\n
288 #
\n
289 # Note: For files without extension you can use no_extension as a placeholder.
\n
290 #
\n
291 # Note that for custom extensions you also need to set FILE_PATTERNS otherwise
\n
292 # the files are not read by doxygen.
\n
293 
\n
294 EXTENSION_MAPPING =
\n
295 
\n
296 # If the MARKDOWN_SUPPORT tag is enabled then doxygen pre-processes all comments
\n
297 # according to the Markdown format, which allows for more readable
\n
298 # documentation. See http://daringfireball.net/projects/markdown/ for details.
\n
299 # The output of markdown processing is further processed by doxygen, so you can
\n
300 # mix doxygen, HTML, and XML commands with Markdown formatting. Disable only in
\n
301 # case of backward compatibilities issues.
\n
302 # The default value is: YES.
\n
303 
\n
304 MARKDOWN_SUPPORT = YES
\n
305 
\n
306 # When enabled doxygen tries to link words that correspond to documented
\n
307 # classes, or namespaces to their corresponding documentation. Such a link can
\n
308 # be prevented in individual cases by putting a % sign in front of the word or
\n
309 # globally by setting AUTOLINK_SUPPORT to NO.
\n
310 # The default value is: YES.
\n
311 
\n
312 AUTOLINK_SUPPORT = YES
\n
313 
\n
314 # If you use STL classes (i.e. std::string, std::vector, etc.) but do not want
\n
315 # to include (a tag file for) the STL sources as input, then you should set this
\n
316 # tag to YES in order to let doxygen match functions declarations and
\n
317 # definitions whose arguments contain STL classes (e.g. func(std::string);
\n
318 # versus func(std::string) {}). This also make the inheritance and collaboration
\n
319 # diagrams that involve STL classes more complete and accurate.
\n
320 # The default value is: NO.
\n
321 
\n
322 BUILTIN_STL_SUPPORT = NO
\n
323 
\n
324 # If you use Microsoft's C++/CLI language, you should set this option to YES to
\n
325 # enable parsing support.
\n
326 # The default value is: NO.
\n
327 
\n
328 CPP_CLI_SUPPORT = NO
\n
329 
\n
330 # Set the SIP_SUPPORT tag to YES if your project consists of sip (see:
\n
331 # http://www.riverbankcomputing.co.uk/software/sip/intro) sources only. Doxygen
\n
332 # will parse them like normal C++ but will assume all classes use public instead
\n
333 # of private inheritance when no explicit protection keyword is present.
\n
334 # The default value is: NO.
\n
335 
\n
336 SIP_SUPPORT = NO
\n
337 
\n
338 # For Microsoft's IDL there are propget and propput attributes to indicate
\n
339 # getter and setter methods for a property. Setting this option to YES will make
\n
340 # doxygen to replace the get and set methods by a property in the documentation.
\n
341 # This will only work if the methods are indeed getting or setting a simple
\n
342 # type. If this is not the case, or you want to show the methods anyway, you
\n
343 # should set this option to NO.
\n
344 # The default value is: YES.
\n
345 
\n
346 IDL_PROPERTY_SUPPORT = YES
\n
347 
\n
348 # If member grouping is used in the documentation and the DISTRIBUTE_GROUP_DOC
\n
349 # tag is set to YES then doxygen will reuse the documentation of the first
\n
350 # member in the group (if any) for the other members of the group. By default
\n
351 # all members of a group must be documented explicitly.
\n
352 # The default value is: NO.
\n
353 
\n
354 DISTRIBUTE_GROUP_DOC = NO
\n
355 
\n
356 # If one adds a struct or class to a group and this option is enabled, then also
\n
357 # any nested class or struct is added to the same group. By default this option
\n
358 # is disabled and one has to add nested compounds explicitly via \\ingroup.
\n
359 # The default value is: NO.
\n
360 
\n
361 GROUP_NESTED_COMPOUNDS = NO
\n
362 
\n
363 # Set the SUBGROUPING tag to YES to allow class member groups of the same type
\n
364 # (for instance a group of public functions) to be put as a subgroup of that
\n
365 # type (e.g. under the Public Functions section). Set it to NO to prevent
\n
366 # subgrouping. Alternatively, this can be done per class using the
\n
367 # \\nosubgrouping command.
\n
368 # The default value is: YES.
\n
369 
\n
370 SUBGROUPING = NO
\n
371 
\n
372 # When the INLINE_GROUPED_CLASSES tag is set to YES, classes, structs and unions
\n
373 # are shown inside the group in which they are included (e.g. using \\ingroup)
\n
374 # instead of on a separate page (for HTML and Man pages) or section (for LaTeX
\n
375 # and RTF).
\n
376 #
\n
377 # Note that this feature does not work in combination with
\n
378 # SEPARATE_MEMBER_PAGES.
\n
379 # The default value is: NO.
\n
380 
\n
381 INLINE_GROUPED_CLASSES = NO
\n
382 
\n
383 # When the INLINE_SIMPLE_STRUCTS tag is set to YES, structs, classes, and unions
\n
384 # with only public data fields or simple typedef fields will be shown inline in
\n
385 # the documentation of the scope in which they are defined (i.e. file,
\n
386 # namespace, or group documentation), provided this scope is documented. If set
\n
387 # to NO, structs, classes, and unions are shown on a separate page (for HTML and
\n
388 # Man pages) or section (for LaTeX and RTF).
\n
389 # The default value is: NO.
\n
390 
\n
391 INLINE_SIMPLE_STRUCTS = NO
\n
392 
\n
393 # When TYPEDEF_HIDES_STRUCT tag is enabled, a typedef of a struct, union, or
\n
394 # enum is documented as struct, union, or enum with the name of the typedef. So
\n
395 # typedef struct TypeS {} TypeT, will appear in the documentation as a struct
\n
396 # with name TypeT. When disabled the typedef will appear as a member of a file,
\n
397 # namespace, or class. And the struct will be named TypeS. This can typically be
\n
398 # useful for C code in case the coding convention dictates that all compound
\n
399 # types are typedef'ed and only the typedef is referenced, never the tag name.
\n
400 # The default value is: NO.
\n
401 
\n
402 TYPEDEF_HIDES_STRUCT = NO
\n
403 
\n
404 # The size of the symbol lookup cache can be set using LOOKUP_CACHE_SIZE. This
\n
405 # cache is used to resolve symbols given their name and scope. Since this can be
\n
406 # an expensive process and often the same symbol appears multiple times in the
\n
407 # code, doxygen keeps a cache of pre-resolved symbols. If the cache is too small
\n
408 # doxygen will become slower. If the cache is too large, memory is wasted. The
\n
409 # cache size is given by this formula: 2^(16+LOOKUP_CACHE_SIZE). The valid range
\n
410 # is 0..9, the default is 0, corresponding to a cache size of 2^16=65536
\n
411 # symbols. At the end of a run doxygen will report the cache usage and suggest
\n
412 # the optimal cache size from a speed point of view.
\n
413 # Minimum value: 0, maximum value: 9, default value: 0.
\n
414 
\n
415 LOOKUP_CACHE_SIZE = 0
\n
416 
\n
417 #---------------------------------------------------------------------------
\n
418 # Build related configuration options
\n
419 #---------------------------------------------------------------------------
\n
420 
\n
421 # If the EXTRACT_ALL tag is set to YES, doxygen will assume all entities in
\n
422 # documentation are documented, even if no documentation was available. Private
\n
423 # class members and static file members will be hidden unless the
\n
424 # EXTRACT_PRIVATE respectively EXTRACT_STATIC tags are set to YES.
\n
425 # Note: This will also disable the warnings about undocumented members that are
\n
426 # normally produced when WARNINGS is set to YES.
\n
427 # The default value is: NO.
\n
428 
\n
429 EXTRACT_ALL = NO
\n
430 
\n
431 # If the EXTRACT_PRIVATE tag is set to YES, all private members of a class will
\n
432 # be included in the documentation.
\n
433 # The default value is: NO.
\n
434 
\n
435 EXTRACT_PRIVATE = NO
\n
436 
\n
437 # If the EXTRACT_PACKAGE tag is set to YES, all members with package or internal
\n
438 # scope will be included in the documentation.
\n
439 # The default value is: NO.
\n
440 
\n
441 EXTRACT_PACKAGE = NO
\n
442 
\n
443 # If the EXTRACT_STATIC tag is set to YES, all static members of a file will be
\n
444 # included in the documentation.
\n
445 # The default value is: NO.
\n
446 
\n
447 EXTRACT_STATIC = YES
\n
448 
\n
449 # If the EXTRACT_LOCAL_CLASSES tag is set to YES, classes (and structs) defined
\n
450 # locally in source files will be included in the documentation. If set to NO,
\n
451 # only classes defined in header files are included. Does not have any effect
\n
452 # for Java sources.
\n
453 # The default value is: YES.
\n
454 
\n
455 EXTRACT_LOCAL_CLASSES = NO
\n
456 
\n
457 # This flag is only useful for Objective-C code. If set to YES, local methods,
\n
458 # which are defined in the implementation section but not in the interface are
\n
459 # included in the documentation. If set to NO, only methods in the interface are
\n
460 # included.
\n
461 # The default value is: NO.
\n
462 
\n
463 EXTRACT_LOCAL_METHODS = NO
\n
464 
\n
465 # If this flag is set to YES, the members of anonymous namespaces will be
\n
466 # extracted and appear in the documentation as a namespace called
\n
467 # 'anonymous_namespace{file}', where file will be replaced with the base name of
\n
468 # the file that contains the anonymous namespace. By default anonymous namespace
\n
469 # are hidden.
\n
470 # The default value is: NO.
\n
471 
\n
472 EXTRACT_ANON_NSPACES = NO
\n
473 
\n
474 # If the HIDE_UNDOC_MEMBERS tag is set to YES, doxygen will hide all
\n
475 # undocumented members inside documented classes or files. If set to NO these
\n
476 # members will be included in the various overviews, but no documentation
\n
477 # section is generated. This option has no effect if EXTRACT_ALL is enabled.
\n
478 # The default value is: NO.
\n
479 
\n
480 HIDE_UNDOC_MEMBERS = YES
\n
481 
\n
482 # If the HIDE_UNDOC_CLASSES tag is set to YES, doxygen will hide all
\n
483 # undocumented classes that are normally visible in the class hierarchy. If set
\n
484 # to NO, these classes will be included in the various overviews. This option
\n
485 # has no effect if EXTRACT_ALL is enabled.
\n
486 # The default value is: NO.
\n
487 
\n
488 HIDE_UNDOC_CLASSES = YES
\n
489 
\n
490 # If the HIDE_FRIEND_COMPOUNDS tag is set to YES, doxygen will hide all friend
\n
491 # (class|struct|union) declarations. If set to NO, these declarations will be
\n
492 # included in the documentation.
\n
493 # The default value is: NO.
\n
494 
\n
495 HIDE_FRIEND_COMPOUNDS = YES
\n
496 
\n
497 # If the HIDE_IN_BODY_DOCS tag is set to YES, doxygen will hide any
\n
498 # documentation blocks found inside the body of a function. If set to NO, these
\n
499 # blocks will be appended to the function's detailed documentation block.
\n
500 # The default value is: NO.
\n
501 
\n
502 HIDE_IN_BODY_DOCS = YES
\n
503 
\n
504 # The INTERNAL_DOCS tag determines if documentation that is typed after a
\n
505 # \\internal command is included. If the tag is set to NO then the documentation
\n
506 # will be excluded. Set it to YES to include the internal documentation.
\n
507 # The default value is: NO.
\n
508 
\n
509 INTERNAL_DOCS = NO
\n
510 
\n
511 # If the CASE_SENSE_NAMES tag is set to NO then doxygen will only generate file
\n
512 # names in lower-case letters. If set to YES, upper-case letters are also
\n
513 # allowed. This is useful if you have classes or files whose names only differ
\n
514 # in case and if your file system supports case sensitive file names. Windows
\n
515 # and Mac users are advised to set this option to NO.
\n
516 # The default value is: system dependent.
\n
517 
\n
518 CASE_SENSE_NAMES = YES
\n
519 
\n
520 # If the HIDE_SCOPE_NAMES tag is set to NO then doxygen will show members with
\n
521 # their full class and namespace scopes in the documentation. If set to YES, the
\n
522 # scope will be hidden.
\n
523 # The default value is: NO.
\n
524 
\n
525 HIDE_SCOPE_NAMES = YES
\n
526 
\n
527 # If the HIDE_COMPOUND_REFERENCE tag is set to NO (default) then doxygen will
\n
528 # append additional text to a page's title, such as Class Reference. If set to
\n
529 # YES the compound reference will be hidden.
\n
530 # The default value is: NO.
\n
531 
\n
532 HIDE_COMPOUND_REFERENCE= NO
\n
533 
\n
534 # If the SHOW_INCLUDE_FILES tag is set to YES then doxygen will put a list of
\n
535 # the files that are included by a file in the documentation of that file.
\n
536 # The default value is: YES.
\n
537 
\n
538 SHOW_INCLUDE_FILES = NO
\n
539 
\n
540 # If the SHOW_GROUPED_MEMB_INC tag is set to YES then Doxygen will add for each
\n
541 # grouped member an include statement to the documentation, telling the reader
\n
542 # which file to include in order to use the member.
\n
543 # The default value is: NO.
\n
544 
\n
545 SHOW_GROUPED_MEMB_INC = NO
\n
546 
\n
547 # If the FORCE_LOCAL_INCLUDES tag is set to YES then doxygen will list include
\n
548 # files with double quotes in the documentation rather than with sharp brackets.
\n
549 # The default value is: NO.
\n
550 
\n
551 FORCE_LOCAL_INCLUDES = NO
\n
552 
\n
553 # If the INLINE_INFO tag is set to YES then a tag [inline] is inserted in the
\n
554 # documentation for inline members.
\n
555 # The default value is: YES.
\n
556 
\n
557 INLINE_INFO = NO
\n
558 
\n
559 # If the SORT_MEMBER_DOCS tag is set to YES then doxygen will sort the
\n
560 # (detailed) documentation of file and class members alphabetically by member
\n
561 # name. If set to NO, the members will appear in declaration order.
\n
562 # The default value is: YES.
\n
563 
\n
564 SORT_MEMBER_DOCS = YES
\n
565 
\n
566 # If the SORT_BRIEF_DOCS tag is set to YES then doxygen will sort the brief
\n
567 # descriptions of file, namespace and class members alphabetically by member
\n
568 # name. If set to NO, the members will appear in declaration order. Note that
\n
569 # this will also influence the order of the classes in the class list.
\n
570 # The default value is: NO.
\n
571 
\n
572 SORT_BRIEF_DOCS = YES
\n
573 
\n
574 # If the SORT_MEMBERS_CTORS_1ST tag is set to YES then doxygen will sort the
\n
575 # (brief and detailed) documentation of class members so that constructors and
\n
576 # destructors are listed first. If set to NO the constructors will appear in the
\n
577 # respective orders defined by SORT_BRIEF_DOCS and SORT_MEMBER_DOCS.
\n
578 # Note: If SORT_BRIEF_DOCS is set to NO this option is ignored for sorting brief
\n
579 # member documentation.
\n
580 # Note: If SORT_MEMBER_DOCS is set to NO this option is ignored for sorting
\n
581 # detailed member documentation.
\n
582 # The default value is: NO.
\n
583 
\n
584 SORT_MEMBERS_CTORS_1ST = NO
\n
585 
\n
586 # If the SORT_GROUP_NAMES tag is set to YES then doxygen will sort the hierarchy
\n
587 # of group names into alphabetical order. If set to NO the group names will
\n
588 # appear in their defined order.
\n
589 # The default value is: NO.
\n
590 
\n
591 SORT_GROUP_NAMES = NO
\n
592 
\n
593 # If the SORT_BY_SCOPE_NAME tag is set to YES, the class list will be sorted by
\n
594 # fully-qualified names, including namespaces. If set to NO, the class list will
\n
595 # be sorted only by class name, not including the namespace part.
\n
596 # Note: This option is not very useful if HIDE_SCOPE_NAMES is set to YES.
\n
597 # Note: This option applies only to the class list, not to the alphabetical
\n
598 # list.
\n
599 # The default value is: NO.
\n
600 
\n
601 SORT_BY_SCOPE_NAME = YES
\n
602 
\n
603 # If the STRICT_PROTO_MATCHING option is enabled and doxygen fails to do proper
\n
604 # type resolution of all parameters of a function it will reject a match between
\n
605 # the prototype and the implementation of a member function even if there is
\n
606 # only one candidate or it is obvious which candidate to choose by doing a
\n
607 # simple string match. By disabling STRICT_PROTO_MATCHING doxygen will still
\n
608 # accept a match between prototype and implementation in such cases.
\n
609 # The default value is: NO.
\n
610 
\n
611 STRICT_PROTO_MATCHING = NO
\n
612 
\n
613 # The GENERATE_TODOLIST tag can be used to enable (YES) or disable (NO) the todo
\n
614 # list. This list is created by putting \\todo commands in the documentation.
\n
615 # The default value is: YES.
\n
616 
\n
617 GENERATE_TODOLIST = YES
\n
618 
\n
619 # The GENERATE_TESTLIST tag can be used to enable (YES) or disable (NO) the test
\n
620 # list. This list is created by putting \\test commands in the documentation.
\n
621 # The default value is: YES.
\n
622 
\n
623 GENERATE_TESTLIST = YES
\n
624 
\n
625 # The GENERATE_BUGLIST tag can be used to enable (YES) or disable (NO) the bug
\n
626 # list. This list is created by putting \\bug commands in the documentation.
\n
627 # The default value is: YES.
\n
628 
\n
629 GENERATE_BUGLIST = YES
\n
630 
\n
631 # The GENERATE_DEPRECATEDLIST tag can be used to enable (YES) or disable (NO)
\n
632 # the deprecated list. This list is created by putting \\deprecated commands in
\n
633 # the documentation.
\n
634 # The default value is: YES.
\n
635 
\n
636 GENERATE_DEPRECATEDLIST= YES
\n
637 
\n
638 # The ENABLED_SECTIONS tag can be used to enable conditional documentation
\n
639 # sections, marked by \\if <section_label> ... \\endif and \\cond <section_label>
\n
640 # ... \\endcond blocks.
\n
641 
\n
642 ENABLED_SECTIONS =
\n
643 
\n
644 # The MAX_INITIALIZER_LINES tag determines the maximum number of lines that the
\n
645 # initial value of a variable or macro / define can have for it to appear in the
\n
646 # documentation. If the initializer consists of more lines than specified here
\n
647 # it will be hidden. Use a value of 0 to hide initializers completely. The
\n
648 # appearance of the value of individual variables and macros / defines can be
\n
649 # controlled using \\showinitializer or \\hideinitializer command in the
\n
650 # documentation regardless of this setting.
\n
651 # Minimum value: 0, maximum value: 10000, default value: 30.
\n
652 
\n
653 MAX_INITIALIZER_LINES = 30
\n
654 
\n
655 # Set the SHOW_USED_FILES tag to NO to disable the list of files generated at
\n
656 # the bottom of the documentation of classes and structs. If set to YES, the
\n
657 # list will mention the files that were used to generate the documentation.
\n
658 # The default value is: YES.
\n
659 
\n
660 SHOW_USED_FILES = NO
\n
661 
\n
662 # Set the SHOW_FILES tag to NO to disable the generation of the Files page. This
\n
663 # will remove the Files entry from the Quick Index and from the Folder Tree View
\n
664 # (if specified).
\n
665 # The default value is: YES.
\n
666 
\n
667 SHOW_FILES = YES
\n
668 
\n
669 # Set the SHOW_NAMESPACES tag to NO to disable the generation of the Namespaces
\n
670 # page. This will remove the Namespaces entry from the Quick Index and from the
\n
671 # Folder Tree View (if specified).
\n
672 # The default value is: YES.
\n
673 
\n
674 SHOW_NAMESPACES = YES
\n
675 
\n
676 # The FILE_VERSION_FILTER tag can be used to specify a program or script that
\n
677 # doxygen should invoke to get the current version for each file (typically from
\n
678 # the version control system). Doxygen will invoke the program by executing (via
\n
679 # popen()) the command command input-file, where command is the value of the
\n
680 # FILE_VERSION_FILTER tag, and input-file is the name of an input file provided
\n
681 # by doxygen. Whatever the program writes to standard output is used as the file
\n
682 # version. For an example see the documentation.
\n
683 
\n
684 FILE_VERSION_FILTER =
\n
685 
\n
686 # The LAYOUT_FILE tag can be used to specify a layout file which will be parsed
\n
687 # by doxygen. The layout file controls the global structure of the generated
\n
688 # output files in an output format independent way. To create the layout file
\n
689 # that represents doxygen's defaults, run doxygen with the -l option. You can
\n
690 # optionally specify a file name after the option, if omitted DoxygenLayout.xml
\n
691 # will be used as the name of the layout file.
\n
692 #
\n
693 # Note that if you run doxygen from a directory containing a file called
\n
694 # DoxygenLayout.xml, doxygen will parse it automatically even if the LAYOUT_FILE
\n
695 # tag is left empty.
\n
696 
\n
697 LAYOUT_FILE =
\n
698 
\n
699 # The CITE_BIB_FILES tag can be used to specify one or more bib files containing
\n
700 # the reference definitions. This must be a list of .bib files. The .bib
\n
701 # extension is automatically appended if omitted. This requires the bibtex tool
\n
702 # to be installed. See also http://en.wikipedia.org/wiki/BibTeX for more info.
\n
703 # For LaTeX the style of the bibliography can be controlled using
\n
704 # LATEX_BIB_STYLE. To use this feature you need bibtex and perl available in the
\n
705 # search path. See also \\cite for info how to create references.
\n
706 
\n
707 CITE_BIB_FILES =
\n
708 
\n
709 #---------------------------------------------------------------------------
\n
710 # Configuration options related to warning and progress messages
\n
711 #---------------------------------------------------------------------------
\n
712 
\n
713 # The QUIET tag can be used to turn on/off the messages that are generated to
\n
714 # standard output by doxygen. If QUIET is set to YES this implies that the
\n
715 # messages are off.
\n
716 # The default value is: NO.
\n
717 
\n
718 QUIET = NO
\n
719 
\n
720 # The WARNINGS tag can be used to turn on/off the warning messages that are
\n
721 # generated to standard error (stderr) by doxygen. If WARNINGS is set to YES
\n
722 # this implies that the warnings are on.
\n
723 #
\n
724 # Tip: Turn warnings on while writing the documentation.
\n
725 # The default value is: YES.
\n
726 
\n
727 WARNINGS = YES
\n
728 
\n
729 # If the WARN_IF_UNDOCUMENTED tag is set to YES then doxygen will generate
\n
730 # warnings for undocumented members. If EXTRACT_ALL is set to YES then this flag
\n
731 # will automatically be disabled.
\n
732 # The default value is: YES.
\n
733 
\n
734 WARN_IF_UNDOCUMENTED = YES
\n
735 
\n
736 # If the WARN_IF_DOC_ERROR tag is set to YES, doxygen will generate warnings for
\n
737 # potential errors in the documentation, such as not documenting some parameters
\n
738 # in a documented function, or documenting parameters that don't exist or using
\n
739 # markup commands wrongly.
\n
740 # The default value is: YES.
\n
741 
\n
742 WARN_IF_DOC_ERROR = YES
\n
743 
\n
744 # This WARN_NO_PARAMDOC option can be enabled to get warnings for functions that
\n
745 # are documented, but have no documentation for their parameters or return
\n
746 # value. If set to NO, doxygen will only warn about wrong or incomplete
\n
747 # parameter documentation, but not about the absence of documentation.
\n
748 # The default value is: NO.
\n
749 
\n
750 WARN_NO_PARAMDOC = NO
\n
751 
\n
752 # The WARN_FORMAT tag determines the format of the warning messages that doxygen
\n
753 # can produce. The string should contain the $file, $line, and $text tags, which
\n
754 # will be replaced by the file and line number from which the warning originated
\n
755 # and the warning text. Optionally the format may contain $version, which will
\n
756 # be replaced by the version of the file (if it could be obtained via
\n
757 # FILE_VERSION_FILTER)
\n
758 # The default value is: $file:$line: $text.
\n
759 
\n
760 WARN_FORMAT = "$file:$line: $text"
\n
761 
\n
762 # The WARN_LOGFILE tag can be used to specify a file to which warning and error
\n
763 # messages should be written. If left blank the output is written to standard
\n
764 # error (stderr).
\n
765 
\n
766 WARN_LOGFILE =
\n
767 
\n
768 #---------------------------------------------------------------------------
\n
769 # Configuration options related to the input files
\n
770 #---------------------------------------------------------------------------
\n
771 
\n
772 # The INPUT tag is used to specify the files and/or directories that contain
\n
773 # documented source files. You may enter file names like myfile.cpp or
\n
774 # directories like /usr/src/myproject. Separate the files or directories with
\n
775 # spaces. See also FILE_PATTERNS and EXTENSION_MAPPING
\n
776 # Note: If this tag is empty the current directory is searched.
\n
777 
\n
778 INPUT = ../glm \\
\n
779  .
\n
780 
\n
781 # This tag can be used to specify the character encoding of the source files
\n
782 # that doxygen parses. Internally doxygen uses the UTF-8 encoding. Doxygen uses
\n
783 # libiconv (or the iconv built into libc) for the transcoding. See the libiconv
\n
784 # documentation (see: http://www.gnu.org/software/libiconv) for the list of
\n
785 # possible encodings.
\n
786 # The default value is: UTF-8.
\n
787 
\n
788 INPUT_ENCODING = UTF-8
\n
789 
\n
790 # If the value of the INPUT tag contains directories, you can use the
\n
791 # FILE_PATTERNS tag to specify one or more wildcard patterns (like *.cpp and
\n
792 # *.h) to filter out the source-files in the directories.
\n
793 #
\n
794 # Note that for custom extensions or not directly supported extensions you also
\n
795 # need to set EXTENSION_MAPPING for the extension otherwise the files are not
\n
796 # read by doxygen.
\n
797 #
\n
798 # If left blank the following patterns are tested:*.c, *.cc, *.cxx, *.cpp,
\n
799 # *.c++, *.java, *.ii, *.ixx, *.ipp, *.i++, *.inl, *.idl, *.ddl, *.odl, *.h,
\n
800 # *.hh, *.hxx, *.hpp, *.h++, *.cs, *.d, *.php, *.php4, *.php5, *.phtml, *.inc,
\n
801 # *.m, *.markdown, *.md, *.mm, *.dox, *.py, *.f90, *.f, *.for, *.tcl, *.vhd,
\n
802 # *.vhdl, *.ucf, *.qsf, *.as and *.js.
\n
803 
\n
804 FILE_PATTERNS = *.hpp \\
\n
805  *.doxy
\n
806 
\n
807 # The RECURSIVE tag can be used to specify whether or not subdirectories should
\n
808 # be searched for input files as well.
\n
809 # The default value is: NO.
\n
810 
\n
811 RECURSIVE = YES
\n
812 
\n
813 # The EXCLUDE tag can be used to specify files and/or directories that should be
\n
814 # excluded from the INPUT source files. This way you can easily exclude a
\n
815 # subdirectory from a directory tree whose root is specified with the INPUT tag.
\n
816 #
\n
817 # Note that relative paths are relative to the directory from which doxygen is
\n
818 # run.
\n
819 
\n
820 EXCLUDE =
\n
821 
\n
822 # The EXCLUDE_SYMLINKS tag can be used to select whether or not files or
\n
823 # directories that are symbolic links (a Unix file system feature) are excluded
\n
824 # from the input.
\n
825 # The default value is: NO.
\n
826 
\n
827 EXCLUDE_SYMLINKS = NO
\n
828 
\n
829 # If the value of the INPUT tag contains directories, you can use the
\n
830 # EXCLUDE_PATTERNS tag to specify one or more wildcard patterns to exclude
\n
831 # certain files from those directories.
\n
832 #
\n
833 # Note that the wildcards are matched against the file with absolute path, so to
\n
834 # exclude all test directories for example use the pattern */test/*
\n
835 
\n
836 EXCLUDE_PATTERNS =
\n
837 
\n
838 # The EXCLUDE_SYMBOLS tag can be used to specify one or more symbol names
\n
839 # (namespaces, classes, functions, etc.) that should be excluded from the
\n
840 # output. The symbol name can be a fully qualified name, a word, or if the
\n
841 # wildcard * is used, a substring. Examples: ANamespace, AClass,
\n
842 # AClass::ANamespace, ANamespace::*Test
\n
843 #
\n
844 # Note that the wildcards are matched against the file with absolute path, so to
\n
845 # exclude all test directories use the pattern */test/*
\n
846 
\n
847 EXCLUDE_SYMBOLS =
\n
848 
\n
849 # The EXAMPLE_PATH tag can be used to specify one or more files or directories
\n
850 # that contain example code fragments that are included (see the \\include
\n
851 # command).
\n
852 
\n
853 EXAMPLE_PATH =
\n
854 
\n
855 # If the value of the EXAMPLE_PATH tag contains directories, you can use the
\n
856 # EXAMPLE_PATTERNS tag to specify one or more wildcard pattern (like *.cpp and
\n
857 # *.h) to filter out the source-files in the directories. If left blank all
\n
858 # files are included.
\n
859 
\n
860 EXAMPLE_PATTERNS = *
\n
861 
\n
862 # If the EXAMPLE_RECURSIVE tag is set to YES then subdirectories will be
\n
863 # searched for input files to be used with the \\include or \\dontinclude commands
\n
864 # irrespective of the value of the RECURSIVE tag.
\n
865 # The default value is: NO.
\n
866 
\n
867 EXAMPLE_RECURSIVE = NO
\n
868 
\n
869 # The IMAGE_PATH tag can be used to specify one or more files or directories
\n
870 # that contain images that are to be included in the documentation (see the
\n
871 # \\image command).
\n
872 
\n
873 IMAGE_PATH =
\n
874 
\n
875 # The INPUT_FILTER tag can be used to specify a program that doxygen should
\n
876 # invoke to filter for each input file. Doxygen will invoke the filter program
\n
877 # by executing (via popen()) the command:
\n
878 #
\n
879 # <filter> <input-file>
\n
880 #
\n
881 # where <filter> is the value of the INPUT_FILTER tag, and <input-file> is the
\n
882 # name of an input file. Doxygen will then use the output that the filter
\n
883 # program writes to standard output. If FILTER_PATTERNS is specified, this tag
\n
884 # will be ignored.
\n
885 #
\n
886 # Note that the filter must not add or remove lines; it is applied before the
\n
887 # code is scanned, but not when the output code is generated. If lines are added
\n
888 # or removed, the anchors will not be placed correctly.
\n
889 
\n
890 INPUT_FILTER =
\n
891 
\n
892 # The FILTER_PATTERNS tag can be used to specify filters on a per file pattern
\n
893 # basis. Doxygen will compare the file name with each pattern and apply the
\n
894 # filter if there is a match. The filters are a list of the form: pattern=filter
\n
895 # (like *.cpp=my_cpp_filter). See INPUT_FILTER for further information on how
\n
896 # filters are used. If the FILTER_PATTERNS tag is empty or if none of the
\n
897 # patterns match the file name, INPUT_FILTER is applied.
\n
898 
\n
899 FILTER_PATTERNS =
\n
900 
\n
901 # If the FILTER_SOURCE_FILES tag is set to YES, the input filter (if set using
\n
902 # INPUT_FILTER) will also be used to filter the input files that are used for
\n
903 # producing the source files to browse (i.e. when SOURCE_BROWSER is set to YES).
\n
904 # The default value is: NO.
\n
905 
\n
906 FILTER_SOURCE_FILES = NO
\n
907 
\n
908 # The FILTER_SOURCE_PATTERNS tag can be used to specify source filters per file
\n
909 # pattern. A pattern will override the setting for FILTER_PATTERN (if any) and
\n
910 # it is also possible to disable source filtering for a specific pattern using
\n
911 # *.ext= (so without naming a filter).
\n
912 # This tag requires that the tag FILTER_SOURCE_FILES is set to YES.
\n
913 
\n
914 FILTER_SOURCE_PATTERNS =
\n
915 
\n
916 # If the USE_MDFILE_AS_MAINPAGE tag refers to the name of a markdown file that
\n
917 # is part of the input, its contents will be placed on the main page
\n
918 # (index.html). This can be useful if you have a project on for instance GitHub
\n
919 # and want to reuse the introduction page also for the doxygen output.
\n
920 
\n
921 USE_MDFILE_AS_MAINPAGE =
\n
922 
\n
923 #---------------------------------------------------------------------------
\n
924 # Configuration options related to source browsing
\n
925 #---------------------------------------------------------------------------
\n
926 
\n
927 # If the SOURCE_BROWSER tag is set to YES then a list of source files will be
\n
928 # generated. Documented entities will be cross-referenced with these sources.
\n
929 #
\n
930 # Note: To get rid of all source code in the generated output, make sure that
\n
931 # also VERBATIM_HEADERS is set to NO.
\n
932 # The default value is: NO.
\n
933 
\n
934 SOURCE_BROWSER = YES
\n
935 
\n
936 # Setting the INLINE_SOURCES tag to YES will include the body of functions,
\n
937 # classes and enums directly into the documentation.
\n
938 # The default value is: NO.
\n
939 
\n
940 INLINE_SOURCES = NO
\n
941 
\n
942 # Setting the STRIP_CODE_COMMENTS tag to YES will instruct doxygen to hide any
\n
943 # special comment blocks from generated source code fragments. Normal C, C++ and
\n
944 # Fortran comments will always remain visible.
\n
945 # The default value is: YES.
\n
946 
\n
947 STRIP_CODE_COMMENTS = YES
\n
948 
\n
949 # If the REFERENCED_BY_RELATION tag is set to YES then for each documented
\n
950 # function all documented functions referencing it will be listed.
\n
951 # The default value is: NO.
\n
952 
\n
953 REFERENCED_BY_RELATION = YES
\n
954 
\n
955 # If the REFERENCES_RELATION tag is set to YES then for each documented function
\n
956 # all documented entities called/used by that function will be listed.
\n
957 # The default value is: NO.
\n
958 
\n
959 REFERENCES_RELATION = YES
\n
960 
\n
961 # If the REFERENCES_LINK_SOURCE tag is set to YES and SOURCE_BROWSER tag is set
\n
962 # to YES then the hyperlinks from functions in REFERENCES_RELATION and
\n
963 # REFERENCED_BY_RELATION lists will link to the source code. Otherwise they will
\n
964 # link to the documentation.
\n
965 # The default value is: YES.
\n
966 
\n
967 REFERENCES_LINK_SOURCE = YES
\n
968 
\n
969 # If SOURCE_TOOLTIPS is enabled (the default) then hovering a hyperlink in the
\n
970 # source code will show a tooltip with additional information such as prototype,
\n
971 # brief description and links to the definition and documentation. Since this
\n
972 # will make the HTML file larger and loading of large files a bit slower, you
\n
973 # can opt to disable this feature.
\n
974 # The default value is: YES.
\n
975 # This tag requires that the tag SOURCE_BROWSER is set to YES.
\n
976 
\n
977 SOURCE_TOOLTIPS = YES
\n
978 
\n
979 # If the USE_HTAGS tag is set to YES then the references to source code will
\n
980 # point to the HTML generated by the htags(1) tool instead of doxygen built-in
\n
981 # source browser. The htags tool is part of GNU's global source tagging system
\n
982 # (see http://www.gnu.org/software/global/global.html). You will need version
\n
983 # 4.8.6 or higher.
\n
984 #
\n
985 # To use it do the following:
\n
986 # - Install the latest version of global
\n
987 # - Enable SOURCE_BROWSER and USE_HTAGS in the config file
\n
988 # - Make sure the INPUT points to the root of the source tree
\n
989 # - Run doxygen as normal
\n
990 #
\n
991 # Doxygen will invoke htags (and that will in turn invoke gtags), so these
\n
992 # tools must be available from the command line (i.e. in the search path).
\n
993 #
\n
994 # The result: instead of the source browser generated by doxygen, the links to
\n
995 # source code will now point to the output of htags.
\n
996 # The default value is: NO.
\n
997 # This tag requires that the tag SOURCE_BROWSER is set to YES.
\n
998 
\n
999 USE_HTAGS = NO
\n
1000 
\n
1001 # If the VERBATIM_HEADERS tag is set the YES then doxygen will generate a
\n
1002 # verbatim copy of the header file for each class for which an include is
\n
1003 # specified. Set to NO to disable this.
\n
1004 # See also: Section \\class.
\n
1005 # The default value is: YES.
\n
1006 
\n
1007 VERBATIM_HEADERS = YES
\n
1008 
\n
1009 # If the CLANG_ASSISTED_PARSING tag is set to YES then doxygen will use the
\n
1010 # clang parser (see: http://clang.llvm.org/) for more accurate parsing at the
\n
1011 # cost of reduced performance. This can be particularly helpful with template
\n
1012 # rich C++ code for which doxygen's built-in parser lacks the necessary type
\n
1013 # information.
\n
1014 # Note: The availability of this option depends on whether or not doxygen was
\n
1015 # compiled with the --with-libclang option.
\n
1016 # The default value is: NO.
\n
1017 
\n
1018 CLANG_ASSISTED_PARSING = NO
\n
1019 
\n
1020 # If clang assisted parsing is enabled you can provide the compiler with command
\n
1021 # line options that you would normally use when invoking the compiler. Note that
\n
1022 # the include paths will already be set by doxygen for the files and directories
\n
1023 # specified with INPUT and INCLUDE_PATH.
\n
1024 # This tag requires that the tag CLANG_ASSISTED_PARSING is set to YES.
\n
1025 
\n
1026 CLANG_OPTIONS =
\n
1027 
\n
1028 #---------------------------------------------------------------------------
\n
1029 # Configuration options related to the alphabetical class index
\n
1030 #---------------------------------------------------------------------------
\n
1031 
\n
1032 # If the ALPHABETICAL_INDEX tag is set to YES, an alphabetical index of all
\n
1033 # compounds will be generated. Enable this if the project contains a lot of
\n
1034 # classes, structs, unions or interfaces.
\n
1035 # The default value is: YES.
\n
1036 
\n
1037 ALPHABETICAL_INDEX = NO
\n
1038 
\n
1039 # The COLS_IN_ALPHA_INDEX tag can be used to specify the number of columns in
\n
1040 # which the alphabetical index list will be split.
\n
1041 # Minimum value: 1, maximum value: 20, default value: 5.
\n
1042 # This tag requires that the tag ALPHABETICAL_INDEX is set to YES.
\n
1043 
\n
1044 COLS_IN_ALPHA_INDEX = 5
\n
1045 
\n
1046 # In case all classes in a project start with a common prefix, all classes will
\n
1047 # be put under the same header in the alphabetical index. The IGNORE_PREFIX tag
\n
1048 # can be used to specify a prefix (or a list of prefixes) that should be ignored
\n
1049 # while generating the index headers.
\n
1050 # This tag requires that the tag ALPHABETICAL_INDEX is set to YES.
\n
1051 
\n
1052 IGNORE_PREFIX =
\n
1053 
\n
1054 #---------------------------------------------------------------------------
\n
1055 # Configuration options related to the HTML output
\n
1056 #---------------------------------------------------------------------------
\n
1057 
\n
1058 # If the GENERATE_HTML tag is set to YES, doxygen will generate HTML output
\n
1059 # The default value is: YES.
\n
1060 
\n
1061 GENERATE_HTML = YES
\n
1062 
\n
1063 # The HTML_OUTPUT tag is used to specify where the HTML docs will be put. If a
\n
1064 # relative path is entered the value of OUTPUT_DIRECTORY will be put in front of
\n
1065 # it.
\n
1066 # The default directory is: html.
\n
1067 # This tag requires that the tag GENERATE_HTML is set to YES.
\n
1068 
\n
1069 HTML_OUTPUT = html
\n
1070 
\n
1071 # The HTML_FILE_EXTENSION tag can be used to specify the file extension for each
\n
1072 # generated HTML page (for example: .htm, .php, .asp).
\n
1073 # The default value is: .html.
\n
1074 # This tag requires that the tag GENERATE_HTML is set to YES.
\n
1075 
\n
1076 HTML_FILE_EXTENSION = .html
\n
1077 
\n
1078 # The HTML_HEADER tag can be used to specify a user-defined HTML header file for
\n
1079 # each generated HTML page. If the tag is left blank doxygen will generate a
\n
1080 # standard header.
\n
1081 #
\n
1082 # To get valid HTML the header file that includes any scripts and style sheets
\n
1083 # that doxygen needs, which is dependent on the configuration options used (e.g.
\n
1084 # the setting GENERATE_TREEVIEW). It is highly recommended to start with a
\n
1085 # default header using
\n
1086 # doxygen -w html new_header.html new_footer.html new_stylesheet.css
\n
1087 # YourConfigFile
\n
1088 # and then modify the file new_header.html. See also section "Doxygen usage"
\n
1089 # for information on how to generate the default header that doxygen normally
\n
1090 # uses.
\n
1091 # Note: The header is subject to change so you typically have to regenerate the
\n
1092 # default header when upgrading to a newer version of doxygen. For a description
\n
1093 # of the possible markers and block names see the documentation.
\n
1094 # This tag requires that the tag GENERATE_HTML is set to YES.
\n
1095 
\n
1096 HTML_HEADER =
\n
1097 
\n
1098 # The HTML_FOOTER tag can be used to specify a user-defined HTML footer for each
\n
1099 # generated HTML page. If the tag is left blank doxygen will generate a standard
\n
1100 # footer. See HTML_HEADER for more information on how to generate a default
\n
1101 # footer and what special commands can be used inside the footer. See also
\n
1102 # section "Doxygen usage" for information on how to generate the default footer
\n
1103 # that doxygen normally uses.
\n
1104 # This tag requires that the tag GENERATE_HTML is set to YES.
\n
1105 
\n
1106 HTML_FOOTER =
\n
1107 
\n
1108 # The HTML_STYLESHEET tag can be used to specify a user-defined cascading style
\n
1109 # sheet that is used by each HTML page. It can be used to fine-tune the look of
\n
1110 # the HTML output. If left blank doxygen will generate a default style sheet.
\n
1111 # See also section "Doxygen usage" for information on how to generate the style
\n
1112 # sheet that doxygen normally uses.
\n
1113 # Note: It is recommended to use HTML_EXTRA_STYLESHEET instead of this tag, as
\n
1114 # it is more robust and this tag (HTML_STYLESHEET) will in the future become
\n
1115 # obsolete.
\n
1116 # This tag requires that the tag GENERATE_HTML is set to YES.
\n
1117 
\n
1118 HTML_STYLESHEET =
\n
1119 
\n
1120 # The HTML_EXTRA_STYLESHEET tag can be used to specify additional user-defined
\n
1121 # cascading style sheets that are included after the standard style sheets
\n
1122 # created by doxygen. Using this option one can overrule certain style aspects.
\n
1123 # This is preferred over using HTML_STYLESHEET since it does not replace the
\n
1124 # standard style sheet and is therefore more robust against future updates.
\n
1125 # Doxygen will copy the style sheet files to the output directory.
\n
1126 # Note: The order of the extra style sheet files is of importance (e.g. the last
\n
1127 # style sheet in the list overrules the setting of the previous ones in the
\n
1128 # list). For an example see the documentation.
\n
1129 # This tag requires that the tag GENERATE_HTML is set to YES.
\n
1130 
\n
1131 HTML_EXTRA_STYLESHEET =
\n
1132 
\n
1133 # The HTML_EXTRA_FILES tag can be used to specify one or more extra images or
\n
1134 # other source files which should be copied to the HTML output directory. Note
\n
1135 # that these files will be copied to the base HTML output directory. Use the
\n
1136 # $relpath^ marker in the HTML_HEADER and/or HTML_FOOTER files to load these
\n
1137 # files. In the HTML_STYLESHEET file, use the file name only. Also note that the
\n
1138 # files will be copied as-is; there are no commands or markers available.
\n
1139 # This tag requires that the tag GENERATE_HTML is set to YES.
\n
1140 
\n
1141 HTML_EXTRA_FILES =
\n
1142 
\n
1143 # The HTML_COLORSTYLE_HUE tag controls the color of the HTML output. Doxygen
\n
1144 # will adjust the colors in the style sheet and background images according to
\n
1145 # this color. Hue is specified as an angle on a colorwheel, see
\n
1146 # http://en.wikipedia.org/wiki/Hue for more information. For instance the value
\n
1147 # 0 represents red, 60 is yellow, 120 is green, 180 is cyan, 240 is blue, 300
\n
1148 # purple, and 360 is red again.
\n
1149 # Minimum value: 0, maximum value: 359, default value: 220.
\n
1150 # This tag requires that the tag GENERATE_HTML is set to YES.
\n
1151 
\n
1152 HTML_COLORSTYLE_HUE = 220
\n
1153 
\n
1154 # The HTML_COLORSTYLE_SAT tag controls the purity (or saturation) of the colors
\n
1155 # in the HTML output. For a value of 0 the output will use grayscales only. A
\n
1156 # value of 255 will produce the most vivid colors.
\n
1157 # Minimum value: 0, maximum value: 255, default value: 100.
\n
1158 # This tag requires that the tag GENERATE_HTML is set to YES.
\n
1159 
\n
1160 HTML_COLORSTYLE_SAT = 100
\n
1161 
\n
1162 # The HTML_COLORSTYLE_GAMMA tag controls the gamma correction applied to the
\n
1163 # luminance component of the colors in the HTML output. Values below 100
\n
1164 # gradually make the output lighter, whereas values above 100 make the output
\n
1165 # darker. The value divided by 100 is the actual gamma applied, so 80 represents
\n
1166 # a gamma of 0.8, The value 220 represents a gamma of 2.2, and 100 does not
\n
1167 # change the gamma.
\n
1168 # Minimum value: 40, maximum value: 240, default value: 80.
\n
1169 # This tag requires that the tag GENERATE_HTML is set to YES.
\n
1170 
\n
1171 HTML_COLORSTYLE_GAMMA = 80
\n
1172 
\n
1173 # If the HTML_TIMESTAMP tag is set to YES then the footer of each generated HTML
\n
1174 # page will contain the date and time when the page was generated. Setting this
\n
1175 # to YES can help to show when doxygen was last run and thus if the
\n
1176 # documentation is up to date.
\n
1177 # The default value is: NO.
\n
1178 # This tag requires that the tag GENERATE_HTML is set to YES.
\n
1179 
\n
1180 HTML_TIMESTAMP = NO
\n
1181 
\n
1182 # If the HTML_DYNAMIC_SECTIONS tag is set to YES then the generated HTML
\n
1183 # documentation will contain sections that can be hidden and shown after the
\n
1184 # page has loaded.
\n
1185 # The default value is: NO.
\n
1186 # This tag requires that the tag GENERATE_HTML is set to YES.
\n
1187 
\n
1188 HTML_DYNAMIC_SECTIONS = NO
\n
1189 
\n
1190 # With HTML_INDEX_NUM_ENTRIES one can control the preferred number of entries
\n
1191 # shown in the various tree structured indices initially; the user can expand
\n
1192 # and collapse entries dynamically later on. Doxygen will expand the tree to
\n
1193 # such a level that at most the specified number of entries are visible (unless
\n
1194 # a fully collapsed tree already exceeds this amount). So setting the number of
\n
1195 # entries 1 will produce a full collapsed tree by default. 0 is a special value
\n
1196 # representing an infinite number of entries and will result in a full expanded
\n
1197 # tree by default.
\n
1198 # Minimum value: 0, maximum value: 9999, default value: 100.
\n
1199 # This tag requires that the tag GENERATE_HTML is set to YES.
\n
1200 
\n
1201 HTML_INDEX_NUM_ENTRIES = 100
\n
1202 
\n
1203 # If the GENERATE_DOCSET tag is set to YES, additional index files will be
\n
1204 # generated that can be used as input for Apple's Xcode 3 integrated development
\n
1205 # environment (see: http://developer.apple.com/tools/xcode/), introduced with
\n
1206 # OSX 10.5 (Leopard). To create a documentation set, doxygen will generate a
\n
1207 # Makefile in the HTML output directory. Running make will produce the docset in
\n
1208 # that directory and running make install will install the docset in
\n
1209 # ~/Library/Developer/Shared/Documentation/DocSets so that Xcode will find it at
\n
1210 # startup. See http://developer.apple.com/tools/creatingdocsetswithdoxygen.html
\n
1211 # for more information.
\n
1212 # The default value is: NO.
\n
1213 # This tag requires that the tag GENERATE_HTML is set to YES.
\n
1214 
\n
1215 GENERATE_DOCSET = NO
\n
1216 
\n
1217 # This tag determines the name of the docset feed. A documentation feed provides
\n
1218 # an umbrella under which multiple documentation sets from a single provider
\n
1219 # (such as a company or product suite) can be grouped.
\n
1220 # The default value is: Doxygen generated docs.
\n
1221 # This tag requires that the tag GENERATE_DOCSET is set to YES.
\n
1222 
\n
1223 DOCSET_FEEDNAME = "Doxygen generated docs"
\n
1224 
\n
1225 # This tag specifies a string that should uniquely identify the documentation
\n
1226 # set bundle. This should be a reverse domain-name style string, e.g.
\n
1227 # com.mycompany.MyDocSet. Doxygen will append .docset to the name.
\n
1228 # The default value is: org.doxygen.Project.
\n
1229 # This tag requires that the tag GENERATE_DOCSET is set to YES.
\n
1230 
\n
1231 DOCSET_BUNDLE_ID = org.doxygen.Project
\n
1232 
\n
1233 # The DOCSET_PUBLISHER_ID tag specifies a string that should uniquely identify
\n
1234 # the documentation publisher. This should be a reverse domain-name style
\n
1235 # string, e.g. com.mycompany.MyDocSet.documentation.
\n
1236 # The default value is: org.doxygen.Publisher.
\n
1237 # This tag requires that the tag GENERATE_DOCSET is set to YES.
\n
1238 
\n
1239 DOCSET_PUBLISHER_ID = org.doxygen.Publisher
\n
1240 
\n
1241 # The DOCSET_PUBLISHER_NAME tag identifies the documentation publisher.
\n
1242 # The default value is: Publisher.
\n
1243 # This tag requires that the tag GENERATE_DOCSET is set to YES.
\n
1244 
\n
1245 DOCSET_PUBLISHER_NAME = Publisher
\n
1246 
\n
1247 # If the GENERATE_HTMLHELP tag is set to YES then doxygen generates three
\n
1248 # additional HTML index files: index.hhp, index.hhc, and index.hhk. The
\n
1249 # index.hhp is a project file that can be read by Microsoft's HTML Help Workshop
\n
1250 # (see: http://www.microsoft.com/en-us/download/details.aspx?id=21138) on
\n
1251 # Windows.
\n
1252 #
\n
1253 # The HTML Help Workshop contains a compiler that can convert all HTML output
\n
1254 # generated by doxygen into a single compiled HTML file (.chm). Compiled HTML
\n
1255 # files are now used as the Windows 98 help format, and will replace the old
\n
1256 # Windows help format (.hlp) on all Windows platforms in the future. Compressed
\n
1257 # HTML files also contain an index, a table of contents, and you can search for
\n
1258 # words in the documentation. The HTML workshop also contains a viewer for
\n
1259 # compressed HTML files.
\n
1260 # The default value is: NO.
\n
1261 # This tag requires that the tag GENERATE_HTML is set to YES.
\n
1262 
\n
1263 GENERATE_HTMLHELP = NO
\n
1264 
\n
1265 # The CHM_FILE tag can be used to specify the file name of the resulting .chm
\n
1266 # file. You can add a path in front of the file if the result should not be
\n
1267 # written to the html output directory.
\n
1268 # This tag requires that the tag GENERATE_HTMLHELP is set to YES.
\n
1269 
\n
1270 CHM_FILE =
\n
1271 
\n
1272 # The HHC_LOCATION tag can be used to specify the location (absolute path
\n
1273 # including file name) of the HTML help compiler (hhc.exe). If non-empty,
\n
1274 # doxygen will try to run the HTML help compiler on the generated index.hhp.
\n
1275 # The file has to be specified with full path.
\n
1276 # This tag requires that the tag GENERATE_HTMLHELP is set to YES.
\n
1277 
\n
1278 HHC_LOCATION =
\n
1279 
\n
1280 # The GENERATE_CHI flag controls if a separate .chi index file is generated
\n
1281 # (YES) or that it should be included in the master .chm file (NO).
\n
1282 # The default value is: NO.
\n
1283 # This tag requires that the tag GENERATE_HTMLHELP is set to YES.
\n
1284 
\n
1285 GENERATE_CHI = NO
\n
1286 
\n
1287 # The CHM_INDEX_ENCODING is used to encode HtmlHelp index (hhk), content (hhc)
\n
1288 # and project file content.
\n
1289 # This tag requires that the tag GENERATE_HTMLHELP is set to YES.
\n
1290 
\n
1291 CHM_INDEX_ENCODING =
\n
1292 
\n
1293 # The BINARY_TOC flag controls whether a binary table of contents is generated
\n
1294 # (YES) or a normal table of contents (NO) in the .chm file. Furthermore it
\n
1295 # enables the Previous and Next buttons.
\n
1296 # The default value is: NO.
\n
1297 # This tag requires that the tag GENERATE_HTMLHELP is set to YES.
\n
1298 
\n
1299 BINARY_TOC = NO
\n
1300 
\n
1301 # The TOC_EXPAND flag can be set to YES to add extra items for group members to
\n
1302 # the table of contents of the HTML help documentation and to the tree view.
\n
1303 # The default value is: NO.
\n
1304 # This tag requires that the tag GENERATE_HTMLHELP is set to YES.
\n
1305 
\n
1306 TOC_EXPAND = NO
\n
1307 
\n
1308 # If the GENERATE_QHP tag is set to YES and both QHP_NAMESPACE and
\n
1309 # QHP_VIRTUAL_FOLDER are set, an additional index file will be generated that
\n
1310 # can be used as input for Qt's qhelpgenerator to generate a Qt Compressed Help
\n
1311 # (.qch) of the generated HTML documentation.
\n
1312 # The default value is: NO.
\n
1313 # This tag requires that the tag GENERATE_HTML is set to YES.
\n
1314 
\n
1315 GENERATE_QHP = NO
\n
1316 
\n
1317 # If the QHG_LOCATION tag is specified, the QCH_FILE tag can be used to specify
\n
1318 # the file name of the resulting .qch file. The path specified is relative to
\n
1319 # the HTML output folder.
\n
1320 # This tag requires that the tag GENERATE_QHP is set to YES.
\n
1321 
\n
1322 QCH_FILE =
\n
1323 
\n
1324 # The QHP_NAMESPACE tag specifies the namespace to use when generating Qt Help
\n
1325 # Project output. For more information please see Qt Help Project / Namespace
\n
1326 # (see: http://qt-project.org/doc/qt-4.8/qthelpproject.html#namespace).
\n
1327 # The default value is: org.doxygen.Project.
\n
1328 # This tag requires that the tag GENERATE_QHP is set to YES.
\n
1329 
\n
1330 QHP_NAMESPACE = org.doxygen.Project
\n
1331 
\n
1332 # The QHP_VIRTUAL_FOLDER tag specifies the namespace to use when generating Qt
\n
1333 # Help Project output. For more information please see Qt Help Project / Virtual
\n
1334 # Folders (see: http://qt-project.org/doc/qt-4.8/qthelpproject.html#virtual-
\n
1335 # folders).
\n
1336 # The default value is: doc.
\n
1337 # This tag requires that the tag GENERATE_QHP is set to YES.
\n
1338 
\n
1339 QHP_VIRTUAL_FOLDER = doc
\n
1340 
\n
1341 # If the QHP_CUST_FILTER_NAME tag is set, it specifies the name of a custom
\n
1342 # filter to add. For more information please see Qt Help Project / Custom
\n
1343 # Filters (see: http://qt-project.org/doc/qt-4.8/qthelpproject.html#custom-
\n
1344 # filters).
\n
1345 # This tag requires that the tag GENERATE_QHP is set to YES.
\n
1346 
\n
1347 QHP_CUST_FILTER_NAME =
\n
1348 
\n
1349 # The QHP_CUST_FILTER_ATTRS tag specifies the list of the attributes of the
\n
1350 # custom filter to add. For more information please see Qt Help Project / Custom
\n
1351 # Filters (see: http://qt-project.org/doc/qt-4.8/qthelpproject.html#custom-
\n
1352 # filters).
\n
1353 # This tag requires that the tag GENERATE_QHP is set to YES.
\n
1354 
\n
1355 QHP_CUST_FILTER_ATTRS =
\n
1356 
\n
1357 # The QHP_SECT_FILTER_ATTRS tag specifies the list of the attributes this
\n
1358 # project's filter section matches. Qt Help Project / Filter Attributes (see:
\n
1359 # http://qt-project.org/doc/qt-4.8/qthelpproject.html#filter-attributes).
\n
1360 # This tag requires that the tag GENERATE_QHP is set to YES.
\n
1361 
\n
1362 QHP_SECT_FILTER_ATTRS =
\n
1363 
\n
1364 # The QHG_LOCATION tag can be used to specify the location of Qt's
\n
1365 # qhelpgenerator. If non-empty doxygen will try to run qhelpgenerator on the
\n
1366 # generated .qhp file.
\n
1367 # This tag requires that the tag GENERATE_QHP is set to YES.
\n
1368 
\n
1369 QHG_LOCATION =
\n
1370 
\n
1371 # If the GENERATE_ECLIPSEHELP tag is set to YES, additional index files will be
\n
1372 # generated, together with the HTML files, they form an Eclipse help plugin. To
\n
1373 # install this plugin and make it available under the help contents menu in
\n
1374 # Eclipse, the contents of the directory containing the HTML and XML files needs
\n
1375 # to be copied into the plugins directory of eclipse. The name of the directory
\n
1376 # within the plugins directory should be the same as the ECLIPSE_DOC_ID value.
\n
1377 # After copying Eclipse needs to be restarted before the help appears.
\n
1378 # The default value is: NO.
\n
1379 # This tag requires that the tag GENERATE_HTML is set to YES.
\n
1380 
\n
1381 GENERATE_ECLIPSEHELP = NO
\n
1382 
\n
1383 # A unique identifier for the Eclipse help plugin. When installing the plugin
\n
1384 # the directory name containing the HTML and XML files should also have this
\n
1385 # name. Each documentation set should have its own identifier.
\n
1386 # The default value is: org.doxygen.Project.
\n
1387 # This tag requires that the tag GENERATE_ECLIPSEHELP is set to YES.
\n
1388 
\n
1389 ECLIPSE_DOC_ID = org.doxygen.Project
\n
1390 
\n
1391 # If you want full control over the layout of the generated HTML pages it might
\n
1392 # be necessary to disable the index and replace it with your own. The
\n
1393 # DISABLE_INDEX tag can be used to turn on/off the condensed index (tabs) at top
\n
1394 # of each HTML page. A value of NO enables the index and the value YES disables
\n
1395 # it. Since the tabs in the index contain the same information as the navigation
\n
1396 # tree, you can set this option to YES if you also set GENERATE_TREEVIEW to YES.
\n
1397 # The default value is: NO.
\n
1398 # This tag requires that the tag GENERATE_HTML is set to YES.
\n
1399 
\n
1400 DISABLE_INDEX = NO
\n
1401 
\n
1402 # The GENERATE_TREEVIEW tag is used to specify whether a tree-like index
\n
1403 # structure should be generated to display hierarchical information. If the tag
\n
1404 # value is set to YES, a side panel will be generated containing a tree-like
\n
1405 # index structure (just like the one that is generated for HTML Help). For this
\n
1406 # to work a browser that supports JavaScript, DHTML, CSS and frames is required
\n
1407 # (i.e. any modern browser). Windows users are probably better off using the
\n
1408 # HTML help feature. Via custom style sheets (see HTML_EXTRA_STYLESHEET) one can
\n
1409 # further fine-tune the look of the index. As an example, the default style
\n
1410 # sheet generated by doxygen has an example that shows how to put an image at
\n
1411 # the root of the tree instead of the PROJECT_NAME. Since the tree basically has
\n
1412 # the same information as the tab index, you could consider setting
\n
1413 # DISABLE_INDEX to YES when enabling this option.
\n
1414 # The default value is: NO.
\n
1415 # This tag requires that the tag GENERATE_HTML is set to YES.
\n
1416 
\n
1417 GENERATE_TREEVIEW = NO
\n
1418 
\n
1419 # The ENUM_VALUES_PER_LINE tag can be used to set the number of enum values that
\n
1420 # doxygen will group on one line in the generated HTML documentation.
\n
1421 #
\n
1422 # Note that a value of 0 will completely suppress the enum values from appearing
\n
1423 # in the overview section.
\n
1424 # Minimum value: 0, maximum value: 20, default value: 4.
\n
1425 # This tag requires that the tag GENERATE_HTML is set to YES.
\n
1426 
\n
1427 ENUM_VALUES_PER_LINE = 4
\n
1428 
\n
1429 # If the treeview is enabled (see GENERATE_TREEVIEW) then this tag can be used
\n
1430 # to set the initial width (in pixels) of the frame in which the tree is shown.
\n
1431 # Minimum value: 0, maximum value: 1500, default value: 250.
\n
1432 # This tag requires that the tag GENERATE_HTML is set to YES.
\n
1433 
\n
1434 TREEVIEW_WIDTH = 250
\n
1435 
\n
1436 # If the EXT_LINKS_IN_WINDOW option is set to YES, doxygen will open links to
\n
1437 # external symbols imported via tag files in a separate window.
\n
1438 # The default value is: NO.
\n
1439 # This tag requires that the tag GENERATE_HTML is set to YES.
\n
1440 
\n
1441 EXT_LINKS_IN_WINDOW = NO
\n
1442 
\n
1443 # Use this tag to change the font size of LaTeX formulas included as images in
\n
1444 # the HTML documentation. When you change the font size after a successful
\n
1445 # doxygen run you need to manually remove any form_*.png images from the HTML
\n
1446 # output directory to force them to be regenerated.
\n
1447 # Minimum value: 8, maximum value: 50, default value: 10.
\n
1448 # This tag requires that the tag GENERATE_HTML is set to YES.
\n
1449 
\n
1450 FORMULA_FONTSIZE = 10
\n
1451 
\n
1452 # Use the FORMULA_TRANPARENT tag to determine whether or not the images
\n
1453 # generated for formulas are transparent PNGs. Transparent PNGs are not
\n
1454 # supported properly for IE 6.0, but are supported on all modern browsers.
\n
1455 #
\n
1456 # Note that when changing this option you need to delete any form_*.png files in
\n
1457 # the HTML output directory before the changes have effect.
\n
1458 # The default value is: YES.
\n
1459 # This tag requires that the tag GENERATE_HTML is set to YES.
\n
1460 
\n
1461 FORMULA_TRANSPARENT = YES
\n
1462 
\n
1463 # Enable the USE_MATHJAX option to render LaTeX formulas using MathJax (see
\n
1464 # http://www.mathjax.org) which uses client side Javascript for the rendering
\n
1465 # instead of using pre-rendered bitmaps. Use this if you do not have LaTeX
\n
1466 # installed or if you want to formulas look prettier in the HTML output. When
\n
1467 # enabled you may also need to install MathJax separately and configure the path
\n
1468 # to it using the MATHJAX_RELPATH option.
\n
1469 # The default value is: NO.
\n
1470 # This tag requires that the tag GENERATE_HTML is set to YES.
\n
1471 
\n
1472 USE_MATHJAX = NO
\n
1473 
\n
1474 # When MathJax is enabled you can set the default output format to be used for
\n
1475 # the MathJax output. See the MathJax site (see:
\n
1476 # http://docs.mathjax.org/en/latest/output.html) for more details.
\n
1477 # Possible values are: HTML-CSS (which is slower, but has the best
\n
1478 # compatibility), NativeMML (i.e. MathML) and SVG.
\n
1479 # The default value is: HTML-CSS.
\n
1480 # This tag requires that the tag USE_MATHJAX is set to YES.
\n
1481 
\n
1482 MATHJAX_FORMAT = HTML-CSS
\n
1483 
\n
1484 # When MathJax is enabled you need to specify the location relative to the HTML
\n
1485 # output directory using the MATHJAX_RELPATH option. The destination directory
\n
1486 # should contain the MathJax.js script. For instance, if the mathjax directory
\n
1487 # is located at the same level as the HTML output directory, then
\n
1488 # MATHJAX_RELPATH should be ../mathjax. The default value points to the MathJax
\n
1489 # Content Delivery Network so you can quickly see the result without installing
\n
1490 # MathJax. However, it is strongly recommended to install a local copy of
\n
1491 # MathJax from http://www.mathjax.org before deployment.
\n
1492 # The default value is: http://cdn.mathjax.org/mathjax/latest.
\n
1493 # This tag requires that the tag USE_MATHJAX is set to YES.
\n
1494 
\n
1495 MATHJAX_RELPATH = http://www.mathjax.org/mathjax
\n
1496 
\n
1497 # The MATHJAX_EXTENSIONS tag can be used to specify one or more MathJax
\n
1498 # extension names that should be enabled during MathJax rendering. For example
\n
1499 # MATHJAX_EXTENSIONS = TeX/AMSmath TeX/AMSsymbols
\n
1500 # This tag requires that the tag USE_MATHJAX is set to YES.
\n
1501 
\n
1502 MATHJAX_EXTENSIONS =
\n
1503 
\n
1504 # The MATHJAX_CODEFILE tag can be used to specify a file with javascript pieces
\n
1505 # of code that will be used on startup of the MathJax code. See the MathJax site
\n
1506 # (see: http://docs.mathjax.org/en/latest/output.html) for more details. For an
\n
1507 # example see the documentation.
\n
1508 # This tag requires that the tag USE_MATHJAX is set to YES.
\n
1509 
\n
1510 MATHJAX_CODEFILE =
\n
1511 
\n
1512 # When the SEARCHENGINE tag is enabled doxygen will generate a search box for
\n
1513 # the HTML output. The underlying search engine uses javascript and DHTML and
\n
1514 # should work on any modern browser. Note that when using HTML help
\n
1515 # (GENERATE_HTMLHELP), Qt help (GENERATE_QHP), or docsets (GENERATE_DOCSET)
\n
1516 # there is already a search function so this one should typically be disabled.
\n
1517 # For large projects the javascript based search engine can be slow, then
\n
1518 # enabling SERVER_BASED_SEARCH may provide a better solution. It is possible to
\n
1519 # search using the keyboard; to jump to the search box use <access key> + S
\n
1520 # (what the <access key> is depends on the OS and browser, but it is typically
\n
1521 # <CTRL>, <ALT>/<option>, or both). Inside the search box use the <cursor down
\n
1522 # key> to jump into the search results window, the results can be navigated
\n
1523 # using the <cursor keys>. Press <Enter> to select an item or <escape> to cancel
\n
1524 # the search. The filter options can be selected when the cursor is inside the
\n
1525 # search box by pressing <Shift>+<cursor down>. Also here use the <cursor keys>
\n
1526 # to select a filter and <Enter> or <escape> to activate or cancel the filter
\n
1527 # option.
\n
1528 # The default value is: YES.
\n
1529 # This tag requires that the tag GENERATE_HTML is set to YES.
\n
1530 
\n
1531 SEARCHENGINE = YES
\n
1532 
\n
1533 # When the SERVER_BASED_SEARCH tag is enabled the search engine will be
\n
1534 # implemented using a web server instead of a web client using Javascript. There
\n
1535 # are two flavors of web server based searching depending on the EXTERNAL_SEARCH
\n
1536 # setting. When disabled, doxygen will generate a PHP script for searching and
\n
1537 # an index file used by the script. When EXTERNAL_SEARCH is enabled the indexing
\n
1538 # and searching needs to be provided by external tools. See the section
\n
1539 # "External Indexing and Searching" for details.
\n
1540 # The default value is: NO.
\n
1541 # This tag requires that the tag SEARCHENGINE is set to YES.
\n
1542 
\n
1543 SERVER_BASED_SEARCH = NO
\n
1544 
\n
1545 # When EXTERNAL_SEARCH tag is enabled doxygen will no longer generate the PHP
\n
1546 # script for searching. Instead the search results are written to an XML file
\n
1547 # which needs to be processed by an external indexer. Doxygen will invoke an
\n
1548 # external search engine pointed to by the SEARCHENGINE_URL option to obtain the
\n
1549 # search results.
\n
1550 #
\n
1551 # Doxygen ships with an example indexer (doxyindexer) and search engine
\n
1552 # (doxysearch.cgi) which are based on the open source search engine library
\n
1553 # Xapian (see: http://xapian.org/).
\n
1554 #
\n
1555 # See the section "External Indexing and Searching" for details.
\n
1556 # The default value is: NO.
\n
1557 # This tag requires that the tag SEARCHENGINE is set to YES.
\n
1558 
\n
1559 EXTERNAL_SEARCH = NO
\n
1560 
\n
1561 # The SEARCHENGINE_URL should point to a search engine hosted by a web server
\n
1562 # which will return the search results when EXTERNAL_SEARCH is enabled.
\n
1563 #
\n
1564 # Doxygen ships with an example indexer (doxyindexer) and search engine
\n
1565 # (doxysearch.cgi) which are based on the open source search engine library
\n
1566 # Xapian (see: http://xapian.org/). See the section "External Indexing and
\n
1567 # Searching" for details.
\n
1568 # This tag requires that the tag SEARCHENGINE is set to YES.
\n
1569 
\n
1570 SEARCHENGINE_URL =
\n
1571 
\n
1572 # When SERVER_BASED_SEARCH and EXTERNAL_SEARCH are both enabled the unindexed
\n
1573 # search data is written to a file for indexing by an external tool. With the
\n
1574 # SEARCHDATA_FILE tag the name of this file can be specified.
\n
1575 # The default file is: searchdata.xml.
\n
1576 # This tag requires that the tag SEARCHENGINE is set to YES.
\n
1577 
\n
1578 SEARCHDATA_FILE = searchdata.xml
\n
1579 
\n
1580 # When SERVER_BASED_SEARCH and EXTERNAL_SEARCH are both enabled the
\n
1581 # EXTERNAL_SEARCH_ID tag can be used as an identifier for the project. This is
\n
1582 # useful in combination with EXTRA_SEARCH_MAPPINGS to search through multiple
\n
1583 # projects and redirect the results back to the right project.
\n
1584 # This tag requires that the tag SEARCHENGINE is set to YES.
\n
1585 
\n
1586 EXTERNAL_SEARCH_ID =
\n
1587 
\n
1588 # The EXTRA_SEARCH_MAPPINGS tag can be used to enable searching through doxygen
\n
1589 # projects other than the one defined by this configuration file, but that are
\n
1590 # all added to the same external search index. Each project needs to have a
\n
1591 # unique id set via EXTERNAL_SEARCH_ID. The search mapping then maps the id of
\n
1592 # to a relative location where the documentation can be found. The format is:
\n
1593 # EXTRA_SEARCH_MAPPINGS = tagname1=loc1 tagname2=loc2 ...
\n
1594 # This tag requires that the tag SEARCHENGINE is set to YES.
\n
1595 
\n
1596 EXTRA_SEARCH_MAPPINGS =
\n
1597 
\n
1598 #---------------------------------------------------------------------------
\n
1599 # Configuration options related to the LaTeX output
\n
1600 #---------------------------------------------------------------------------
\n
1601 
\n
1602 # If the GENERATE_LATEX tag is set to YES, doxygen will generate LaTeX output.
\n
1603 # The default value is: YES.
\n
1604 
\n
1605 GENERATE_LATEX = NO
\n
1606 
\n
1607 # The LATEX_OUTPUT tag is used to specify where the LaTeX docs will be put. If a
\n
1608 # relative path is entered the value of OUTPUT_DIRECTORY will be put in front of
\n
1609 # it.
\n
1610 # The default directory is: latex.
\n
1611 # This tag requires that the tag GENERATE_LATEX is set to YES.
\n
1612 
\n
1613 LATEX_OUTPUT = latex
\n
1614 
\n
1615 # The LATEX_CMD_NAME tag can be used to specify the LaTeX command name to be
\n
1616 # invoked.
\n
1617 #
\n
1618 # Note that when enabling USE_PDFLATEX this option is only used for generating
\n
1619 # bitmaps for formulas in the HTML output, but not in the Makefile that is
\n
1620 # written to the output directory.
\n
1621 # The default file is: latex.
\n
1622 # This tag requires that the tag GENERATE_LATEX is set to YES.
\n
1623 
\n
1624 LATEX_CMD_NAME = latex
\n
1625 
\n
1626 # The MAKEINDEX_CMD_NAME tag can be used to specify the command name to generate
\n
1627 # index for LaTeX.
\n
1628 # The default file is: makeindex.
\n
1629 # This tag requires that the tag GENERATE_LATEX is set to YES.
\n
1630 
\n
1631 MAKEINDEX_CMD_NAME = makeindex
\n
1632 
\n
1633 # If the COMPACT_LATEX tag is set to YES, doxygen generates more compact LaTeX
\n
1634 # documents. This may be useful for small projects and may help to save some
\n
1635 # trees in general.
\n
1636 # The default value is: NO.
\n
1637 # This tag requires that the tag GENERATE_LATEX is set to YES.
\n
1638 
\n
1639 COMPACT_LATEX = NO
\n
1640 
\n
1641 # The PAPER_TYPE tag can be used to set the paper type that is used by the
\n
1642 # printer.
\n
1643 # Possible values are: a4 (210 x 297 mm), letter (8.5 x 11 inches), legal (8.5 x
\n
1644 # 14 inches) and executive (7.25 x 10.5 inches).
\n
1645 # The default value is: a4.
\n
1646 # This tag requires that the tag GENERATE_LATEX is set to YES.
\n
1647 
\n
1648 PAPER_TYPE = a4wide
\n
1649 
\n
1650 # The EXTRA_PACKAGES tag can be used to specify one or more LaTeX package names
\n
1651 # that should be included in the LaTeX output. The package can be specified just
\n
1652 # by its name or with the correct syntax as to be used with the LaTeX
\n
1653 # \\usepackage command. To get the times font for instance you can specify :
\n
1654 # EXTRA_PACKAGES=times or EXTRA_PACKAGES={times}
\n
1655 # To use the option intlimits with the amsmath package you can specify:
\n
1656 # EXTRA_PACKAGES=[intlimits]{amsmath}
\n
1657 # If left blank no extra packages will be included.
\n
1658 # This tag requires that the tag GENERATE_LATEX is set to YES.
\n
1659 
\n
1660 EXTRA_PACKAGES =
\n
1661 
\n
1662 # The LATEX_HEADER tag can be used to specify a personal LaTeX header for the
\n
1663 # generated LaTeX document. The header should contain everything until the first
\n
1664 # chapter. If it is left blank doxygen will generate a standard header. See
\n
1665 # section "Doxygen usage" for information on how to let doxygen write the
\n
1666 # default header to a separate file.
\n
1667 #
\n
1668 # Note: Only use a user-defined header if you know what you are doing! The
\n
1669 # following commands have a special meaning inside the header: $title,
\n
1670 # $datetime, $date, $doxygenversion, $projectname, $projectnumber,
\n
1671 # $projectbrief, $projectlogo. Doxygen will replace $title with the empty
\n
1672 # string, for the replacement values of the other commands the user is referred
\n
1673 # to HTML_HEADER.
\n
1674 # This tag requires that the tag GENERATE_LATEX is set to YES.
\n
1675 
\n
1676 LATEX_HEADER =
\n
1677 
\n
1678 # The LATEX_FOOTER tag can be used to specify a personal LaTeX footer for the
\n
1679 # generated LaTeX document. The footer should contain everything after the last
\n
1680 # chapter. If it is left blank doxygen will generate a standard footer. See
\n
1681 # LATEX_HEADER for more information on how to generate a default footer and what
\n
1682 # special commands can be used inside the footer.
\n
1683 #
\n
1684 # Note: Only use a user-defined footer if you know what you are doing!
\n
1685 # This tag requires that the tag GENERATE_LATEX is set to YES.
\n
1686 
\n
1687 LATEX_FOOTER =
\n
1688 
\n
1689 # The LATEX_EXTRA_STYLESHEET tag can be used to specify additional user-defined
\n
1690 # LaTeX style sheets that are included after the standard style sheets created
\n
1691 # by doxygen. Using this option one can overrule certain style aspects. Doxygen
\n
1692 # will copy the style sheet files to the output directory.
\n
1693 # Note: The order of the extra style sheet files is of importance (e.g. the last
\n
1694 # style sheet in the list overrules the setting of the previous ones in the
\n
1695 # list).
\n
1696 # This tag requires that the tag GENERATE_LATEX is set to YES.
\n
1697 
\n
1698 LATEX_EXTRA_STYLESHEET =
\n
1699 
\n
1700 # The LATEX_EXTRA_FILES tag can be used to specify one or more extra images or
\n
1701 # other source files which should be copied to the LATEX_OUTPUT output
\n
1702 # directory. Note that the files will be copied as-is; there are no commands or
\n
1703 # markers available.
\n
1704 # This tag requires that the tag GENERATE_LATEX is set to YES.
\n
1705 
\n
1706 LATEX_EXTRA_FILES =
\n
1707 
\n
1708 # If the PDF_HYPERLINKS tag is set to YES, the LaTeX that is generated is
\n
1709 # prepared for conversion to PDF (using ps2pdf or pdflatex). The PDF file will
\n
1710 # contain links (just like the HTML output) instead of page references. This
\n
1711 # makes the output suitable for online browsing using a PDF viewer.
\n
1712 # The default value is: YES.
\n
1713 # This tag requires that the tag GENERATE_LATEX is set to YES.
\n
1714 
\n
1715 PDF_HYPERLINKS = NO
\n
1716 
\n
1717 # If the USE_PDFLATEX tag is set to YES, doxygen will use pdflatex to generate
\n
1718 # the PDF file directly from the LaTeX files. Set this option to YES, to get a
\n
1719 # higher quality PDF documentation.
\n
1720 # The default value is: YES.
\n
1721 # This tag requires that the tag GENERATE_LATEX is set to YES.
\n
1722 
\n
1723 USE_PDFLATEX = YES
\n
1724 
\n
1725 # If the LATEX_BATCHMODE tag is set to YES, doxygen will add the \\batchmode
\n
1726 # command to the generated LaTeX files. This will instruct LaTeX to keep running
\n
1727 # if errors occur, instead of asking the user for help. This option is also used
\n
1728 # when generating formulas in HTML.
\n
1729 # The default value is: NO.
\n
1730 # This tag requires that the tag GENERATE_LATEX is set to YES.
\n
1731 
\n
1732 LATEX_BATCHMODE = NO
\n
1733 
\n
1734 # If the LATEX_HIDE_INDICES tag is set to YES then doxygen will not include the
\n
1735 # index chapters (such as File Index, Compound Index, etc.) in the output.
\n
1736 # The default value is: NO.
\n
1737 # This tag requires that the tag GENERATE_LATEX is set to YES.
\n
1738 
\n
1739 LATEX_HIDE_INDICES = NO
\n
1740 
\n
1741 # If the LATEX_SOURCE_CODE tag is set to YES then doxygen will include source
\n
1742 # code with syntax highlighting in the LaTeX output.
\n
1743 #
\n
1744 # Note that which sources are shown also depends on other settings such as
\n
1745 # SOURCE_BROWSER.
\n
1746 # The default value is: NO.
\n
1747 # This tag requires that the tag GENERATE_LATEX is set to YES.
\n
1748 
\n
1749 LATEX_SOURCE_CODE = NO
\n
1750 
\n
1751 # The LATEX_BIB_STYLE tag can be used to specify the style to use for the
\n
1752 # bibliography, e.g. plainnat, or ieeetr. See
\n
1753 # http://en.wikipedia.org/wiki/BibTeX and \\cite for more info.
\n
1754 # The default value is: plain.
\n
1755 # This tag requires that the tag GENERATE_LATEX is set to YES.
\n
1756 
\n
1757 LATEX_BIB_STYLE = plain
\n
1758 
\n
1759 #---------------------------------------------------------------------------
\n
1760 # Configuration options related to the RTF output
\n
1761 #---------------------------------------------------------------------------
\n
1762 
\n
1763 # If the GENERATE_RTF tag is set to YES, doxygen will generate RTF output. The
\n
1764 # RTF output is optimized for Word 97 and may not look too pretty with other RTF
\n
1765 # readers/editors.
\n
1766 # The default value is: NO.
\n
1767 
\n
1768 GENERATE_RTF = NO
\n
1769 
\n
1770 # The RTF_OUTPUT tag is used to specify where the RTF docs will be put. If a
\n
1771 # relative path is entered the value of OUTPUT_DIRECTORY will be put in front of
\n
1772 # it.
\n
1773 # The default directory is: rtf.
\n
1774 # This tag requires that the tag GENERATE_RTF is set to YES.
\n
1775 
\n
1776 RTF_OUTPUT = glm.rtf
\n
1777 
\n
1778 # If the COMPACT_RTF tag is set to YES, doxygen generates more compact RTF
\n
1779 # documents. This may be useful for small projects and may help to save some
\n
1780 # trees in general.
\n
1781 # The default value is: NO.
\n
1782 # This tag requires that the tag GENERATE_RTF is set to YES.
\n
1783 
\n
1784 COMPACT_RTF = NO
\n
1785 
\n
1786 # If the RTF_HYPERLINKS tag is set to YES, the RTF that is generated will
\n
1787 # contain hyperlink fields. The RTF file will contain links (just like the HTML
\n
1788 # output) instead of page references. This makes the output suitable for online
\n
1789 # browsing using Word or some other Word compatible readers that support those
\n
1790 # fields.
\n
1791 #
\n
1792 # Note: WordPad (write) and others do not support links.
\n
1793 # The default value is: NO.
\n
1794 # This tag requires that the tag GENERATE_RTF is set to YES.
\n
1795 
\n
1796 RTF_HYPERLINKS = YES
\n
1797 
\n
1798 # Load stylesheet definitions from file. Syntax is similar to doxygen's config
\n
1799 # file, i.e. a series of assignments. You only have to provide replacements,
\n
1800 # missing definitions are set to their default value.
\n
1801 #
\n
1802 # See also section "Doxygen usage" for information on how to generate the
\n
1803 # default style sheet that doxygen normally uses.
\n
1804 # This tag requires that the tag GENERATE_RTF is set to YES.
\n
1805 
\n
1806 RTF_STYLESHEET_FILE =
\n
1807 
\n
1808 # Set optional variables used in the generation of an RTF document. Syntax is
\n
1809 # similar to doxygen's config file. A template extensions file can be generated
\n
1810 # using doxygen -e rtf extensionFile.
\n
1811 # This tag requires that the tag GENERATE_RTF is set to YES.
\n
1812 
\n
1813 RTF_EXTENSIONS_FILE =
\n
1814 
\n
1815 # If the RTF_SOURCE_CODE tag is set to YES then doxygen will include source code
\n
1816 # with syntax highlighting in the RTF output.
\n
1817 #
\n
1818 # Note that which sources are shown also depends on other settings such as
\n
1819 # SOURCE_BROWSER.
\n
1820 # The default value is: NO.
\n
1821 # This tag requires that the tag GENERATE_RTF is set to YES.
\n
1822 
\n
1823 RTF_SOURCE_CODE = NO
\n
1824 
\n
1825 #---------------------------------------------------------------------------
\n
1826 # Configuration options related to the man page output
\n
1827 #---------------------------------------------------------------------------
\n
1828 
\n
1829 # If the GENERATE_MAN tag is set to YES, doxygen will generate man pages for
\n
1830 # classes and files.
\n
1831 # The default value is: NO.
\n
1832 
\n
1833 GENERATE_MAN = NO
\n
1834 
\n
1835 # The MAN_OUTPUT tag is used to specify where the man pages will be put. If a
\n
1836 # relative path is entered the value of OUTPUT_DIRECTORY will be put in front of
\n
1837 # it. A directory man3 will be created inside the directory specified by
\n
1838 # MAN_OUTPUT.
\n
1839 # The default directory is: man.
\n
1840 # This tag requires that the tag GENERATE_MAN is set to YES.
\n
1841 
\n
1842 MAN_OUTPUT = man
\n
1843 
\n
1844 # The MAN_EXTENSION tag determines the extension that is added to the generated
\n
1845 # man pages. In case the manual section does not start with a number, the number
\n
1846 # 3 is prepended. The dot (.) at the beginning of the MAN_EXTENSION tag is
\n
1847 # optional.
\n
1848 # The default value is: .3.
\n
1849 # This tag requires that the tag GENERATE_MAN is set to YES.
\n
1850 
\n
1851 MAN_EXTENSION = .3
\n
1852 
\n
1853 # The MAN_SUBDIR tag determines the name of the directory created within
\n
1854 # MAN_OUTPUT in which the man pages are placed. If defaults to man followed by
\n
1855 # MAN_EXTENSION with the initial . removed.
\n
1856 # This tag requires that the tag GENERATE_MAN is set to YES.
\n
1857 
\n
1858 MAN_SUBDIR =
\n
1859 
\n
1860 # If the MAN_LINKS tag is set to YES and doxygen generates man output, then it
\n
1861 # will generate one additional man file for each entity documented in the real
\n
1862 # man page(s). These additional files only source the real man page, but without
\n
1863 # them the man command would be unable to find the correct page.
\n
1864 # The default value is: NO.
\n
1865 # This tag requires that the tag GENERATE_MAN is set to YES.
\n
1866 
\n
1867 MAN_LINKS = NO
\n
1868 
\n
1869 #---------------------------------------------------------------------------
\n
1870 # Configuration options related to the XML output
\n
1871 #---------------------------------------------------------------------------
\n
1872 
\n
1873 # If the GENERATE_XML tag is set to YES, doxygen will generate an XML file that
\n
1874 # captures the structure of the code including all documentation.
\n
1875 # The default value is: NO.
\n
1876 
\n
1877 GENERATE_XML = NO
\n
1878 
\n
1879 # The XML_OUTPUT tag is used to specify where the XML pages will be put. If a
\n
1880 # relative path is entered the value of OUTPUT_DIRECTORY will be put in front of
\n
1881 # it.
\n
1882 # The default directory is: xml.
\n
1883 # This tag requires that the tag GENERATE_XML is set to YES.
\n
1884 
\n
1885 XML_OUTPUT = xml
\n
1886 
\n
1887 # If the XML_PROGRAMLISTING tag is set to YES, doxygen will dump the program
\n
1888 # listings (including syntax highlighting and cross-referencing information) to
\n
1889 # the XML output. Note that enabling this will significantly increase the size
\n
1890 # of the XML output.
\n
1891 # The default value is: YES.
\n
1892 # This tag requires that the tag GENERATE_XML is set to YES.
\n
1893 
\n
1894 XML_PROGRAMLISTING = YES
\n
1895 
\n
1896 #---------------------------------------------------------------------------
\n
1897 # Configuration options related to the DOCBOOK output
\n
1898 #---------------------------------------------------------------------------
\n
1899 
\n
1900 # If the GENERATE_DOCBOOK tag is set to YES, doxygen will generate Docbook files
\n
1901 # that can be used to generate PDF.
\n
1902 # The default value is: NO.
\n
1903 
\n
1904 GENERATE_DOCBOOK = NO
\n
1905 
\n
1906 # The DOCBOOK_OUTPUT tag is used to specify where the Docbook pages will be put.
\n
1907 # If a relative path is entered the value of OUTPUT_DIRECTORY will be put in
\n
1908 # front of it.
\n
1909 # The default directory is: docbook.
\n
1910 # This tag requires that the tag GENERATE_DOCBOOK is set to YES.
\n
1911 
\n
1912 DOCBOOK_OUTPUT = docbook
\n
1913 
\n
1914 # If the DOCBOOK_PROGRAMLISTING tag is set to YES, doxygen will include the
\n
1915 # program listings (including syntax highlighting and cross-referencing
\n
1916 # information) to the DOCBOOK output. Note that enabling this will significantly
\n
1917 # increase the size of the DOCBOOK output.
\n
1918 # The default value is: NO.
\n
1919 # This tag requires that the tag GENERATE_DOCBOOK is set to YES.
\n
1920 
\n
1921 DOCBOOK_PROGRAMLISTING = NO
\n
1922 
\n
1923 #---------------------------------------------------------------------------
\n
1924 # Configuration options for the AutoGen Definitions output
\n
1925 #---------------------------------------------------------------------------
\n
1926 
\n
1927 # If the GENERATE_AUTOGEN_DEF tag is set to YES, doxygen will generate an
\n
1928 # AutoGen Definitions (see http://autogen.sf.net) file that captures the
\n
1929 # structure of the code including all documentation. Note that this feature is
\n
1930 # still experimental and incomplete at the moment.
\n
1931 # The default value is: NO.
\n
1932 
\n
1933 GENERATE_AUTOGEN_DEF = NO
\n
1934 
\n
1935 #---------------------------------------------------------------------------
\n
1936 # Configuration options related to the Perl module output
\n
1937 #---------------------------------------------------------------------------
\n
1938 
\n
1939 # If the GENERATE_PERLMOD tag is set to YES, doxygen will generate a Perl module
\n
1940 # file that captures the structure of the code including all documentation.
\n
1941 #
\n
1942 # Note that this feature is still experimental and incomplete at the moment.
\n
1943 # The default value is: NO.
\n
1944 
\n
1945 GENERATE_PERLMOD = NO
\n
1946 
\n
1947 # If the PERLMOD_LATEX tag is set to YES, doxygen will generate the necessary
\n
1948 # Makefile rules, Perl scripts and LaTeX code to be able to generate PDF and DVI
\n
1949 # output from the Perl module output.
\n
1950 # The default value is: NO.
\n
1951 # This tag requires that the tag GENERATE_PERLMOD is set to YES.
\n
1952 
\n
1953 PERLMOD_LATEX = NO
\n
1954 
\n
1955 # If the PERLMOD_PRETTY tag is set to YES, the Perl module output will be nicely
\n
1956 # formatted so it can be parsed by a human reader. This is useful if you want to
\n
1957 # understand what is going on. On the other hand, if this tag is set to NO, the
\n
1958 # size of the Perl module output will be much smaller and Perl will parse it
\n
1959 # just the same.
\n
1960 # The default value is: YES.
\n
1961 # This tag requires that the tag GENERATE_PERLMOD is set to YES.
\n
1962 
\n
1963 PERLMOD_PRETTY = YES
\n
1964 
\n
1965 # The names of the make variables in the generated doxyrules.make file are
\n
1966 # prefixed with the string contained in PERLMOD_MAKEVAR_PREFIX. This is useful
\n
1967 # so different doxyrules.make files included by the same Makefile don't
\n
1968 # overwrite each other's variables.
\n
1969 # This tag requires that the tag GENERATE_PERLMOD is set to YES.
\n
1970 
\n
1971 PERLMOD_MAKEVAR_PREFIX =
\n
1972 
\n
1973 #---------------------------------------------------------------------------
\n
1974 # Configuration options related to the preprocessor
\n
1975 #---------------------------------------------------------------------------
\n
1976 
\n
1977 # If the ENABLE_PREPROCESSING tag is set to YES, doxygen will evaluate all
\n
1978 # C-preprocessor directives found in the sources and include files.
\n
1979 # The default value is: YES.
\n
1980 
\n
1981 ENABLE_PREPROCESSING = YES
\n
1982 
\n
1983 # If the MACRO_EXPANSION tag is set to YES, doxygen will expand all macro names
\n
1984 # in the source code. If set to NO, only conditional compilation will be
\n
1985 # performed. Macro expansion can be done in a controlled way by setting
\n
1986 # EXPAND_ONLY_PREDEF to YES.
\n
1987 # The default value is: NO.
\n
1988 # This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
\n
1989 
\n
1990 MACRO_EXPANSION = NO
\n
1991 
\n
1992 # If the EXPAND_ONLY_PREDEF and MACRO_EXPANSION tags are both set to YES then
\n
1993 # the macro expansion is limited to the macros specified with the PREDEFINED and
\n
1994 # EXPAND_AS_DEFINED tags.
\n
1995 # The default value is: NO.
\n
1996 # This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
\n
1997 
\n
1998 EXPAND_ONLY_PREDEF = NO
\n
1999 
\n
2000 # If the SEARCH_INCLUDES tag is set to YES, the include files in the
\n
2001 # INCLUDE_PATH will be searched if a #include is found.
\n
2002 # The default value is: YES.
\n
2003 # This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
\n
2004 
\n
2005 SEARCH_INCLUDES = YES
\n
2006 
\n
2007 # The INCLUDE_PATH tag can be used to specify one or more directories that
\n
2008 # contain include files that are not input files but should be processed by the
\n
2009 # preprocessor.
\n
2010 # This tag requires that the tag SEARCH_INCLUDES is set to YES.
\n
2011 
\n
2012 INCLUDE_PATH =
\n
2013 
\n
2014 # You can use the INCLUDE_FILE_PATTERNS tag to specify one or more wildcard
\n
2015 # patterns (like *.h and *.hpp) to filter out the header-files in the
\n
2016 # directories. If left blank, the patterns specified with FILE_PATTERNS will be
\n
2017 # used.
\n
2018 # This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
\n
2019 
\n
2020 INCLUDE_FILE_PATTERNS =
\n
2021 
\n
2022 # The PREDEFINED tag can be used to specify one or more macro names that are
\n
2023 # defined before the preprocessor is started (similar to the -D option of e.g.
\n
2024 # gcc). The argument of the tag is a list of macros of the form: name or
\n
2025 # name=definition (no spaces). If the definition and the "=" are omitted, "=1"
\n
2026 # is assumed. To prevent a macro definition from being undefined via #undef or
\n
2027 # recursively expanded use the := operator instead of the = operator.
\n
2028 # This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
\n
2029 
\n
2030 PREDEFINED =
\n
2031 
\n
2032 # If the MACRO_EXPANSION and EXPAND_ONLY_PREDEF tags are set to YES then this
\n
2033 # tag can be used to specify a list of macro names that should be expanded. The
\n
2034 # macro definition that is found in the sources will be used. Use the PREDEFINED
\n
2035 # tag if you want to use a different macro definition that overrules the
\n
2036 # definition found in the source code.
\n
2037 # This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
\n
2038 
\n
2039 EXPAND_AS_DEFINED =
\n
2040 
\n
2041 # If the SKIP_FUNCTION_MACROS tag is set to YES then doxygen's preprocessor will
\n
2042 # remove all references to function-like macros that are alone on a line, have
\n
2043 # an all uppercase name, and do not end with a semicolon. Such function macros
\n
2044 # are typically used for boiler-plate code, and will confuse the parser if not
\n
2045 # removed.
\n
2046 # The default value is: YES.
\n
2047 # This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
\n
2048 
\n
2049 SKIP_FUNCTION_MACROS = YES
\n
2050 
\n
2051 #---------------------------------------------------------------------------
\n
2052 # Configuration options related to external references
\n
2053 #---------------------------------------------------------------------------
\n
2054 
\n
2055 # The TAGFILES tag can be used to specify one or more tag files. For each tag
\n
2056 # file the location of the external documentation should be added. The format of
\n
2057 # a tag file without this location is as follows:
\n
2058 # TAGFILES = file1 file2 ...
\n
2059 # Adding location for the tag files is done as follows:
\n
2060 # TAGFILES = file1=loc1 "file2 = loc2" ...
\n
2061 # where loc1 and loc2 can be relative or absolute paths or URLs. See the
\n
2062 # section "Linking to external documentation" for more information about the use
\n
2063 # of tag files.
\n
2064 # Note: Each tag file must have a unique name (where the name does NOT include
\n
2065 # the path). If a tag file is not located in the directory in which doxygen is
\n
2066 # run, you must also specify the path to the tagfile here.
\n
2067 
\n
2068 TAGFILES =
\n
2069 
\n
2070 # When a file name is specified after GENERATE_TAGFILE, doxygen will create a
\n
2071 # tag file that is based on the input files it reads. See section "Linking to
\n
2072 # external documentation" for more information about the usage of tag files.
\n
2073 
\n
2074 GENERATE_TAGFILE =
\n
2075 
\n
2076 # If the ALLEXTERNALS tag is set to YES, all external class will be listed in
\n
2077 # the class index. If set to NO, only the inherited external classes will be
\n
2078 # listed.
\n
2079 # The default value is: NO.
\n
2080 
\n
2081 ALLEXTERNALS = NO
\n
2082 
\n
2083 # If the EXTERNAL_GROUPS tag is set to YES, all external groups will be listed
\n
2084 # in the modules index. If set to NO, only the current project's groups will be
\n
2085 # listed.
\n
2086 # The default value is: YES.
\n
2087 
\n
2088 EXTERNAL_GROUPS = YES
\n
2089 
\n
2090 # If the EXTERNAL_PAGES tag is set to YES, all external pages will be listed in
\n
2091 # the related pages index. If set to NO, only the current project's pages will
\n
2092 # be listed.
\n
2093 # The default value is: YES.
\n
2094 
\n
2095 EXTERNAL_PAGES = YES
\n
2096 
\n
2097 # The PERL_PATH should be the absolute path and name of the perl script
\n
2098 # interpreter (i.e. the result of 'which perl').
\n
2099 # The default file (with absolute path) is: /usr/bin/perl.
\n
2100 
\n
2101 PERL_PATH = /usr/bin/perl
\n
2102 
\n
2103 #---------------------------------------------------------------------------
\n
2104 # Configuration options related to the dot tool
\n
2105 #---------------------------------------------------------------------------
\n
2106 
\n
2107 # If the CLASS_DIAGRAMS tag is set to YES, doxygen will generate a class diagram
\n
2108 # (in HTML and LaTeX) for classes with base or super classes. Setting the tag to
\n
2109 # NO turns the diagrams off. Note that this option also works with HAVE_DOT
\n
2110 # disabled, but it is recommended to install and use dot, since it yields more
\n
2111 # powerful graphs.
\n
2112 # The default value is: YES.
\n
2113 
\n
2114 CLASS_DIAGRAMS = YES
\n
2115 
\n
2116 # You can define message sequence charts within doxygen comments using the \\msc
\n
2117 # command. Doxygen will then run the mscgen tool (see:
\n
2118 # http://www.mcternan.me.uk/mscgen/)) to produce the chart and insert it in the
\n
2119 # documentation. The MSCGEN_PATH tag allows you to specify the directory where
\n
2120 # the mscgen tool resides. If left empty the tool is assumed to be found in the
\n
2121 # default search path.
\n
2122 
\n
2123 MSCGEN_PATH =
\n
2124 
\n
2125 # You can include diagrams made with dia in doxygen documentation. Doxygen will
\n
2126 # then run dia to produce the diagram and insert it in the documentation. The
\n
2127 # DIA_PATH tag allows you to specify the directory where the dia binary resides.
\n
2128 # If left empty dia is assumed to be found in the default search path.
\n
2129 
\n
2130 DIA_PATH =
\n
2131 
\n
2132 # If set to YES the inheritance and collaboration graphs will hide inheritance
\n
2133 # and usage relations if the target is undocumented or is not a class.
\n
2134 # The default value is: YES.
\n
2135 
\n
2136 HIDE_UNDOC_RELATIONS = YES
\n
2137 
\n
2138 # If you set the HAVE_DOT tag to YES then doxygen will assume the dot tool is
\n
2139 # available from the path. This tool is part of Graphviz (see:
\n
2140 # http://www.graphviz.org/), a graph visualization toolkit from AT&T and Lucent
\n
2141 # Bell Labs. The other options in this section have no effect if this option is
\n
2142 # set to NO
\n
2143 # The default value is: NO.
\n
2144 
\n
2145 HAVE_DOT = NO
\n
2146 
\n
2147 # The DOT_NUM_THREADS specifies the number of dot invocations doxygen is allowed
\n
2148 # to run in parallel. When set to 0 doxygen will base this on the number of
\n
2149 # processors available in the system. You can set it explicitly to a value
\n
2150 # larger than 0 to get control over the balance between CPU load and processing
\n
2151 # speed.
\n
2152 # Minimum value: 0, maximum value: 32, default value: 0.
\n
2153 # This tag requires that the tag HAVE_DOT is set to YES.
\n
2154 
\n
2155 DOT_NUM_THREADS = 0
\n
2156 
\n
2157 # When you want a differently looking font in the dot files that doxygen
\n
2158 # generates you can specify the font name using DOT_FONTNAME. You need to make
\n
2159 # sure dot is able to find the font, which can be done by putting it in a
\n
2160 # standard location or by setting the DOTFONTPATH environment variable or by
\n
2161 # setting DOT_FONTPATH to the directory containing the font.
\n
2162 # The default value is: Helvetica.
\n
2163 # This tag requires that the tag HAVE_DOT is set to YES.
\n
2164 
\n
2165 DOT_FONTNAME = Helvetica
\n
2166 
\n
2167 # The DOT_FONTSIZE tag can be used to set the size (in points) of the font of
\n
2168 # dot graphs.
\n
2169 # Minimum value: 4, maximum value: 24, default value: 10.
\n
2170 # This tag requires that the tag HAVE_DOT is set to YES.
\n
2171 
\n
2172 DOT_FONTSIZE = 10
\n
2173 
\n
2174 # By default doxygen will tell dot to use the default font as specified with
\n
2175 # DOT_FONTNAME. If you specify a different font using DOT_FONTNAME you can set
\n
2176 # the path where dot can find it using this tag.
\n
2177 # This tag requires that the tag HAVE_DOT is set to YES.
\n
2178 
\n
2179 DOT_FONTPATH =
\n
2180 
\n
2181 # If the CLASS_GRAPH tag is set to YES then doxygen will generate a graph for
\n
2182 # each documented class showing the direct and indirect inheritance relations.
\n
2183 # Setting this tag to YES will force the CLASS_DIAGRAMS tag to NO.
\n
2184 # The default value is: YES.
\n
2185 # This tag requires that the tag HAVE_DOT is set to YES.
\n
2186 
\n
2187 CLASS_GRAPH = YES
\n
2188 
\n
2189 # If the COLLABORATION_GRAPH tag is set to YES then doxygen will generate a
\n
2190 # graph for each documented class showing the direct and indirect implementation
\n
2191 # dependencies (inheritance, containment, and class references variables) of the
\n
2192 # class with other documented classes.
\n
2193 # The default value is: YES.
\n
2194 # This tag requires that the tag HAVE_DOT is set to YES.
\n
2195 
\n
2196 COLLABORATION_GRAPH = YES
\n
2197 
\n
2198 # If the GROUP_GRAPHS tag is set to YES then doxygen will generate a graph for
\n
2199 # groups, showing the direct groups dependencies.
\n
2200 # The default value is: YES.
\n
2201 # This tag requires that the tag HAVE_DOT is set to YES.
\n
2202 
\n
2203 GROUP_GRAPHS = YES
\n
2204 
\n
2205 # If the UML_LOOK tag is set to YES, doxygen will generate inheritance and
\n
2206 # collaboration diagrams in a style similar to the OMG's Unified Modeling
\n
2207 # Language.
\n
2208 # The default value is: NO.
\n
2209 # This tag requires that the tag HAVE_DOT is set to YES.
\n
2210 
\n
2211 UML_LOOK = NO
\n
2212 
\n
2213 # If the UML_LOOK tag is enabled, the fields and methods are shown inside the
\n
2214 # class node. If there are many fields or methods and many nodes the graph may
\n
2215 # become too big to be useful. The UML_LIMIT_NUM_FIELDS threshold limits the
\n
2216 # number of items for each type to make the size more manageable. Set this to 0
\n
2217 # for no limit. Note that the threshold may be exceeded by 50% before the limit
\n
2218 # is enforced. So when you set the threshold to 10, up to 15 fields may appear,
\n
2219 # but if the number exceeds 15, the total amount of fields shown is limited to
\n
2220 # 10.
\n
2221 # Minimum value: 0, maximum value: 100, default value: 10.
\n
2222 # This tag requires that the tag HAVE_DOT is set to YES.
\n
2223 
\n
2224 UML_LIMIT_NUM_FIELDS = 10
\n
2225 
\n
2226 # If the TEMPLATE_RELATIONS tag is set to YES then the inheritance and
\n
2227 # collaboration graphs will show the relations between templates and their
\n
2228 # instances.
\n
2229 # The default value is: NO.
\n
2230 # This tag requires that the tag HAVE_DOT is set to YES.
\n
2231 
\n
2232 TEMPLATE_RELATIONS = NO
\n
2233 
\n
2234 # If the INCLUDE_GRAPH, ENABLE_PREPROCESSING and SEARCH_INCLUDES tags are set to
\n
2235 # YES then doxygen will generate a graph for each documented file showing the
\n
2236 # direct and indirect include dependencies of the file with other documented
\n
2237 # files.
\n
2238 # The default value is: YES.
\n
2239 # This tag requires that the tag HAVE_DOT is set to YES.
\n
2240 
\n
2241 INCLUDE_GRAPH = YES
\n
2242 
\n
2243 # If the INCLUDED_BY_GRAPH, ENABLE_PREPROCESSING and SEARCH_INCLUDES tags are
\n
2244 # set to YES then doxygen will generate a graph for each documented file showing
\n
2245 # the direct and indirect include dependencies of the file with other documented
\n
2246 # files.
\n
2247 # The default value is: YES.
\n
2248 # This tag requires that the tag HAVE_DOT is set to YES.
\n
2249 
\n
2250 INCLUDED_BY_GRAPH = YES
\n
2251 
\n
2252 # If the CALL_GRAPH tag is set to YES then doxygen will generate a call
\n
2253 # dependency graph for every global function or class method.
\n
2254 #
\n
2255 # Note that enabling this option will significantly increase the time of a run.
\n
2256 # So in most cases it will be better to enable call graphs for selected
\n
2257 # functions only using the \\callgraph command. Disabling a call graph can be
\n
2258 # accomplished by means of the command \\hidecallgraph.
\n
2259 # The default value is: NO.
\n
2260 # This tag requires that the tag HAVE_DOT is set to YES.
\n
2261 
\n
2262 CALL_GRAPH = YES
\n
2263 
\n
2264 # If the CALLER_GRAPH tag is set to YES then doxygen will generate a caller
\n
2265 # dependency graph for every global function or class method.
\n
2266 #
\n
2267 # Note that enabling this option will significantly increase the time of a run.
\n
2268 # So in most cases it will be better to enable caller graphs for selected
\n
2269 # functions only using the \\callergraph command. Disabling a caller graph can be
\n
2270 # accomplished by means of the command \\hidecallergraph.
\n
2271 # The default value is: NO.
\n
2272 # This tag requires that the tag HAVE_DOT is set to YES.
\n
2273 
\n
2274 CALLER_GRAPH = YES
\n
2275 
\n
2276 # If the GRAPHICAL_HIERARCHY tag is set to YES then doxygen will graphical
\n
2277 # hierarchy of all classes instead of a textual one.
\n
2278 # The default value is: YES.
\n
2279 # This tag requires that the tag HAVE_DOT is set to YES.
\n
2280 
\n
2281 GRAPHICAL_HIERARCHY = YES
\n
2282 
\n
2283 # If the DIRECTORY_GRAPH tag is set to YES then doxygen will show the
\n
2284 # dependencies a directory has on other directories in a graphical way. The
\n
2285 # dependency relations are determined by the #include relations between the
\n
2286 # files in the directories.
\n
2287 # The default value is: YES.
\n
2288 # This tag requires that the tag HAVE_DOT is set to YES.
\n
2289 
\n
2290 DIRECTORY_GRAPH = YES
\n
2291 
\n
2292 # The DOT_IMAGE_FORMAT tag can be used to set the image format of the images
\n
2293 # generated by dot. For an explanation of the image formats see the section
\n
2294 # output formats in the documentation of the dot tool (Graphviz (see:
\n
2295 # http://www.graphviz.org/)).
\n
2296 # Note: If you choose svg you need to set HTML_FILE_EXTENSION to xhtml in order
\n
2297 # to make the SVG files visible in IE 9+ (other browsers do not have this
\n
2298 # requirement).
\n
2299 # Possible values are: png, jpg, gif, svg, png:gd, png:gd:gd, png:cairo,
\n
2300 # png:cairo:gd, png:cairo:cairo, png:cairo:gdiplus, png:gdiplus and
\n
2301 # png:gdiplus:gdiplus.
\n
2302 # The default value is: png.
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glm
Definition: common.hpp:20
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4 #pragma once
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5 #include "./ext/matrix_double2x2.hpp"
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6 #include "./ext/matrix_double2x2_precision.hpp"
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4 #pragma once
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5 #include "./ext/matrix_double2x3.hpp"
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4 #pragma once
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5 #include "./ext/matrix_double2x4.hpp"
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6 #include "./ext/matrix_double2x4_precision.hpp"
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4 #pragma once
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5 #include "./ext/matrix_double3x2.hpp"
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4 #pragma once
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5 #include "./ext/matrix_double3x3.hpp"
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6 #include "./ext/matrix_double3x3_precision.hpp"
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4 #pragma once
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4 #pragma once
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5 #include "./ext/matrix_double4x2.hpp"
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6 #include "./ext/matrix_double4x2_precision.hpp"
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mat4x3.hpp
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4 #pragma once
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5 #include "./ext/matrix_double4x3.hpp"
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6 #include "./ext/matrix_double4x3_precision.hpp"
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7 #include "./ext/matrix_float4x3.hpp"
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8 #include "./ext/matrix_float4x3_precision.hpp"
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matrix_float4x3.hpp
Core features
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matrix_double4x3.hpp
Core features
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Definition in file mat4x4.hpp.

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mat4x4.hpp
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\n
4 #pragma once
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5 #include "./ext/matrix_double4x4.hpp"
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6 #include "./ext/matrix_double4x4_precision.hpp"
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7 #include "./ext/matrix_float4x4.hpp"
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8 #include "./ext/matrix_float4x4_precision.hpp"
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Core features
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matrix_float4x4.hpp
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matrix.hpp File Reference
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Core features \nMore...

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\nFunctions

template<length_t C, length_t R, typename T , qualifier Q>
GLM_FUNC_DECL T determinant (mat< C, R, T, Q > const &m)
 Return the determinant of a squared matrix. More...
 
template<length_t C, length_t R, typename T , qualifier Q>
GLM_FUNC_DECL mat< C, R, T, Q > inverse (mat< C, R, T, Q > const &m)
 Return the inverse of a squared matrix. More...
 
template<length_t C, length_t R, typename T , qualifier Q>
GLM_FUNC_DECL mat< C, R, T, Q > matrixCompMult (mat< C, R, T, Q > const &x, mat< C, R, T, Q > const &y)
 Multiply matrix x by matrix y component-wise, i.e., result[i][j] is the scalar product of x[i][j] and y[i][j]. More...
 
template<length_t C, length_t R, typename T , qualifier Q>
GLM_FUNC_DECL detail::outerProduct_trait< C, R, T, Q >::type outerProduct (vec< C, T, Q > const &c, vec< R, T, Q > const &r)
 Treats the first parameter c as a column vector and the second parameter r as a row vector and does a linear algebraic matrix multiply c * r. More...
 
template<length_t C, length_t R, typename T , qualifier Q>
GLM_FUNC_DECL mat< C, R, T, Q >::transpose_type transpose (mat< C, R, T, Q > const &x)
 Returns the transposed matrix of x. More...
 
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Detailed Description

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Core features

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See also
GLSL 4.20.8 specification, section 8.6 Matrix Functions
\n\n

Definition in file matrix.hpp.

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matrix.hpp
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1 
\n
13 #pragma once
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14 
\n
15 // Dependencies
\n
16 #include "detail/qualifier.hpp"
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17 #include "detail/setup.hpp"
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18 #include "vec2.hpp"
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19 #include "vec3.hpp"
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20 #include "vec4.hpp"
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21 #include "mat2x2.hpp"
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22 #include "mat2x3.hpp"
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23 #include "mat2x4.hpp"
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24 #include "mat3x2.hpp"
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25 #include "mat3x3.hpp"
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26 #include "mat3x4.hpp"
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27 #include "mat4x2.hpp"
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28 #include "mat4x3.hpp"
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29 #include "mat4x4.hpp"
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30 
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31 namespace glm {
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32 namespace detail
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33 {
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34  template<length_t C, length_t R, typename T, qualifier Q>
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35  struct outerProduct_trait{};
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36 
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37  template<typename T, qualifier Q>
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38  struct outerProduct_trait<2, 2, T, Q>
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39  {
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40  typedef mat<2, 2, T, Q> type;
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41  };
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42 
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43  template<typename T, qualifier Q>
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44  struct outerProduct_trait<2, 3, T, Q>
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45  {
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46  typedef mat<3, 2, T, Q> type;
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47  };
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48 
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49  template<typename T, qualifier Q>
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50  struct outerProduct_trait<2, 4, T, Q>
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51  {
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52  typedef mat<4, 2, T, Q> type;
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53  };
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54 
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55  template<typename T, qualifier Q>
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56  struct outerProduct_trait<3, 2, T, Q>
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57  {
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58  typedef mat<2, 3, T, Q> type;
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59  };
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60 
\n
61  template<typename T, qualifier Q>
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62  struct outerProduct_trait<3, 3, T, Q>
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63  {
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64  typedef mat<3, 3, T, Q> type;
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65  };
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66 
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67  template<typename T, qualifier Q>
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68  struct outerProduct_trait<3, 4, T, Q>
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69  {
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70  typedef mat<4, 3, T, Q> type;
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71  };
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72 
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73  template<typename T, qualifier Q>
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74  struct outerProduct_trait<4, 2, T, Q>
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75  {
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76  typedef mat<2, 4, T, Q> type;
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77  };
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78 
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79  template<typename T, qualifier Q>
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80  struct outerProduct_trait<4, 3, T, Q>
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81  {
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82  typedef mat<3, 4, T, Q> type;
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83  };
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84 
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85  template<typename T, qualifier Q>
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86  struct outerProduct_trait<4, 4, T, Q>
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87  {
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88  typedef mat<4, 4, T, Q> type;
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89  };
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90 }//namespace detail
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91 
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94 
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105  template<length_t C, length_t R, typename T, qualifier Q>
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106  GLM_FUNC_DECL mat<C, R, T, Q> matrixCompMult(mat<C, R, T, Q> const& x, mat<C, R, T, Q> const& y);
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107 
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119  template<length_t C, length_t R, typename T, qualifier Q>
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120  GLM_FUNC_DECL typename detail::outerProduct_trait<C, R, T, Q>::type outerProduct(vec<C, T, Q> const& c, vec<R, T, Q> const& r);
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121 
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131  template<length_t C, length_t R, typename T, qualifier Q>
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132  GLM_FUNC_DECL typename mat<C, R, T, Q>::transpose_type transpose(mat<C, R, T, Q> const& x);
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133 
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143  template<length_t C, length_t R, typename T, qualifier Q>
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144  GLM_FUNC_DECL T determinant(mat<C, R, T, Q> const& m);
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145 
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155  template<length_t C, length_t R, typename T, qualifier Q>
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156  GLM_FUNC_DECL mat<C, R, T, Q> inverse(mat<C, R, T, Q> const& m);
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157 
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159 }//namespace glm
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160 
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161 #include "detail/func_matrix.inl"
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glm::matrixCompMult
GLM_FUNC_DECL mat< C, R, T, Q > matrixCompMult(mat< C, R, T, Q > const &x, mat< C, R, T, Q > const &y)
Multiply matrix x by matrix y component-wise, i.e., result[i][j] is the scalar product of x[i][j] and...
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mat4x4.hpp
Core features
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glm::determinant
GLM_FUNC_DECL T determinant(mat< C, R, T, Q > const &m)
Return the determinant of a squared matrix.
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mat2x4.hpp
Core features
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glm::outerProduct
GLM_FUNC_DECL detail::outerProduct_trait< C, R, T, Q >::type outerProduct(vec< C, T, Q > const &c, vec< R, T, Q > const &r)
Treats the first parameter c as a column vector and the second parameter r as a row vector and does a...
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mat4x3.hpp
Core features
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vec2.hpp
Core features
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glm::transpose
GLM_FUNC_DECL mat< C, R, T, Q >::transpose_type transpose(mat< C, R, T, Q > const &x)
Returns the transposed matrix of x.
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mat2x3.hpp
Core features
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mat4x2.hpp
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mat3x3.hpp
Core features
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vec4.hpp
Core features
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glm::inverse
GLM_FUNC_DECL mat< C, R, T, Q > inverse(mat< C, R, T, Q > const &m)
Return the inverse of a squared matrix.
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mat3x4.hpp
Core features
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mat3x2.hpp
Core features
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vec3.hpp
Core features
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mat2x2.hpp
Core features
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glm
Definition: common.hpp:20
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matrix_access.hpp File Reference
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GLM_GTC_matrix_access \nMore...

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\nFunctions

template<typename genType >
GLM_FUNC_DECL genType::col_type column (genType const &m, length_t index)
 Get a specific column of a matrix. More...
 
template<typename genType >
GLM_FUNC_DECL genType column (genType const &m, length_t index, typename genType::col_type const &x)
 Set a specific column to a matrix. More...
 
template<typename genType >
GLM_FUNC_DECL genType::row_type row (genType const &m, length_t index)
 Get a specific row of a matrix. More...
 
template<typename genType >
GLM_FUNC_DECL genType row (genType const &m, length_t index, typename genType::row_type const &x)
 Set a specific row to a matrix. More...
 
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GLM_GTC_matrix_access

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See also
Core features (dependence)
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Definition in file matrix_access.hpp.

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matrix_access.hpp
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13 #pragma once
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14 
\n
15 // Dependency:
\n
16 #include "../detail/setup.hpp"
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17 
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18 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
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19 # pragma message("GLM: GLM_GTC_matrix_access extension included")
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20 #endif
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21 
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22 namespace glm
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23 {
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26 
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29  template<typename genType>
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30  GLM_FUNC_DECL typename genType::row_type row(
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31  genType const& m,
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32  length_t index);
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33 
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36  template<typename genType>
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37  GLM_FUNC_DECL genType row(
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38  genType const& m,
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39  length_t index,
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40  typename genType::row_type const& x);
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41 
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44  template<typename genType>
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45  GLM_FUNC_DECL typename genType::col_type column(
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46  genType const& m,
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47  length_t index);
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48 
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51  template<typename genType>
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52  GLM_FUNC_DECL genType column(
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53  genType const& m,
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54  length_t index,
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55  typename genType::col_type const& x);
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56 
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58 }//namespace glm
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59 
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60 #include "matrix_access.inl"
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glm::row
GLM_FUNC_DECL genType row(genType const &m, length_t index, typename genType::row_type const &x)
Set a specific row to a matrix.
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glm::column
GLM_FUNC_DECL genType column(genType const &m, length_t index, typename genType::col_type const &x)
Set a specific column to a matrix.
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glm
Definition: common.hpp:20
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matrix_clip_space.hpp File Reference
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GLM_EXT_matrix_clip_space \nMore...

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\nFunctions

template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > frustum (T left, T right, T bottom, T top, T near, T far)
 Creates a frustum matrix with default handedness, using the default handedness and default near and far clip planes definition. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > frustumLH (T left, T right, T bottom, T top, T near, T far)
 Creates a left handed frustum matrix. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > frustumLH_NO (T left, T right, T bottom, T top, T near, T far)
 Creates a left handed frustum matrix. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > frustumLH_ZO (T left, T right, T bottom, T top, T near, T far)
 Creates a left handed frustum matrix. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > frustumNO (T left, T right, T bottom, T top, T near, T far)
 Creates a frustum matrix using left-handed coordinates if GLM_FORCE_LEFT_HANDED if defined or right-handed coordinates otherwise. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > frustumRH (T left, T right, T bottom, T top, T near, T far)
 Creates a right handed frustum matrix. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > frustumRH_NO (T left, T right, T bottom, T top, T near, T far)
 Creates a right handed frustum matrix. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > frustumRH_ZO (T left, T right, T bottom, T top, T near, T far)
 Creates a right handed frustum matrix. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > frustumZO (T left, T right, T bottom, T top, T near, T far)
 Creates a frustum matrix using left-handed coordinates if GLM_FORCE_LEFT_HANDED if defined or right-handed coordinates otherwise. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > infinitePerspective (T fovy, T aspect, T near)
 Creates a matrix for a symmetric perspective-view frustum with far plane at infinite with default handedness. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > infinitePerspectiveLH (T fovy, T aspect, T near)
 Creates a matrix for a left handed, symmetric perspective-view frustum with far plane at infinite. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > infinitePerspectiveRH (T fovy, T aspect, T near)
 Creates a matrix for a right handed, symmetric perspective-view frustum with far plane at infinite. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > ortho (T left, T right, T bottom, T top)
 Creates a matrix for projecting two-dimensional coordinates onto the screen. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > ortho (T left, T right, T bottom, T top, T zNear, T zFar)
 Creates a matrix for an orthographic parallel viewing volume, using the default handedness and default near and far clip planes definition. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > orthoLH (T left, T right, T bottom, T top, T zNear, T zFar)
 Creates a matrix for an orthographic parallel viewing volume, using left-handed coordinates. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > orthoLH_NO (T left, T right, T bottom, T top, T zNear, T zFar)
 Creates a matrix for an orthographic parallel viewing volume using right-handed coordinates. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > orthoLH_ZO (T left, T right, T bottom, T top, T zNear, T zFar)
 Creates a matrix for an orthographic parallel viewing volume, using left-handed coordinates. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > orthoNO (T left, T right, T bottom, T top, T zNear, T zFar)
 Creates a matrix for an orthographic parallel viewing volume, using left-handed coordinates if GLM_FORCE_LEFT_HANDED if defined or right-handed coordinates otherwise. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > orthoRH (T left, T right, T bottom, T top, T zNear, T zFar)
 Creates a matrix for an orthographic parallel viewing volume, using right-handed coordinates. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > orthoRH_NO (T left, T right, T bottom, T top, T zNear, T zFar)
 Creates a matrix for an orthographic parallel viewing volume, using right-handed coordinates. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > orthoRH_ZO (T left, T right, T bottom, T top, T zNear, T zFar)
 Creates a matrix for an orthographic parallel viewing volume, using left-handed coordinates. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > orthoZO (T left, T right, T bottom, T top, T zNear, T zFar)
 Creates a matrix for an orthographic parallel viewing volume, using left-handed coordinates. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > perspective (T fovy, T aspect, T near, T far)
 Creates a matrix for a symetric perspective-view frustum based on the default handedness and default near and far clip planes definition. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > perspectiveFov (T fov, T width, T height, T near, T far)
 Builds a perspective projection matrix based on a field of view and the default handedness and default near and far clip planes definition. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > perspectiveFovLH (T fov, T width, T height, T near, T far)
 Builds a left handed perspective projection matrix based on a field of view. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > perspectiveFovLH_NO (T fov, T width, T height, T near, T far)
 Builds a perspective projection matrix based on a field of view using left-handed coordinates. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > perspectiveFovLH_ZO (T fov, T width, T height, T near, T far)
 Builds a perspective projection matrix based on a field of view using left-handed coordinates. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > perspectiveFovNO (T fov, T width, T height, T near, T far)
 Builds a perspective projection matrix based on a field of view using left-handed coordinates if GLM_FORCE_LEFT_HANDED if defined or right-handed coordinates otherwise. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > perspectiveFovRH (T fov, T width, T height, T near, T far)
 Builds a right handed perspective projection matrix based on a field of view. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > perspectiveFovRH_NO (T fov, T width, T height, T near, T far)
 Builds a perspective projection matrix based on a field of view using right-handed coordinates. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > perspectiveFovRH_ZO (T fov, T width, T height, T near, T far)
 Builds a perspective projection matrix based on a field of view using right-handed coordinates. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > perspectiveFovZO (T fov, T width, T height, T near, T far)
 Builds a perspective projection matrix based on a field of view using left-handed coordinates if GLM_FORCE_LEFT_HANDED if defined or right-handed coordinates otherwise. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > perspectiveLH (T fovy, T aspect, T near, T far)
 Creates a matrix for a left handed, symetric perspective-view frustum. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > perspectiveLH_NO (T fovy, T aspect, T near, T far)
 Creates a matrix for a left handed, symetric perspective-view frustum. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > perspectiveLH_ZO (T fovy, T aspect, T near, T far)
 Creates a matrix for a left handed, symetric perspective-view frustum. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > perspectiveNO (T fovy, T aspect, T near, T far)
 Creates a matrix for a symetric perspective-view frustum using left-handed coordinates if GLM_FORCE_LEFT_HANDED if defined or right-handed coordinates otherwise. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > perspectiveRH (T fovy, T aspect, T near, T far)
 Creates a matrix for a right handed, symetric perspective-view frustum. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > perspectiveRH_NO (T fovy, T aspect, T near, T far)
 Creates a matrix for a right handed, symetric perspective-view frustum. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > perspectiveRH_ZO (T fovy, T aspect, T near, T far)
 Creates a matrix for a right handed, symetric perspective-view frustum. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > perspectiveZO (T fovy, T aspect, T near, T far)
 Creates a matrix for a symetric perspective-view frustum using left-handed coordinates if GLM_FORCE_LEFT_HANDED if defined or right-handed coordinates otherwise. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > tweakedInfinitePerspective (T fovy, T aspect, T near)
 Creates a matrix for a symmetric perspective-view frustum with far plane at infinite for graphics hardware that doesn't support depth clamping. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > tweakedInfinitePerspective (T fovy, T aspect, T near, T ep)
 Creates a matrix for a symmetric perspective-view frustum with far plane at infinite for graphics hardware that doesn't support depth clamping. More...
 
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Detailed Description

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GLM_EXT_matrix_clip_space

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Definition in file matrix_clip_space.hpp.

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matrix_clip_space.hpp
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1 
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20 #pragma once
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21 
\n
22 // Dependencies
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23 #include "../ext/scalar_constants.hpp"
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24 #include "../geometric.hpp"
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25 #include "../trigonometric.hpp"
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26 
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27 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
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28 # pragma message("GLM: GLM_EXT_matrix_clip_space extension included")
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29 #endif
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30 
\n
31 namespace glm
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32 {
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35 
\n
42  template<typename T>
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43  GLM_FUNC_DECL mat<4, 4, T, defaultp> ortho(
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44  T left, T right, T bottom, T top);
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45 
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52  template<typename T>
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53  GLM_FUNC_DECL mat<4, 4, T, defaultp> orthoLH_ZO(
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54  T left, T right, T bottom, T top, T zNear, T zFar);
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55 
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62  template<typename T>
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63  GLM_FUNC_DECL mat<4, 4, T, defaultp> orthoLH_NO(
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64  T left, T right, T bottom, T top, T zNear, T zFar);
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65 
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72  template<typename T>
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73  GLM_FUNC_DECL mat<4, 4, T, defaultp> orthoRH_ZO(
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74  T left, T right, T bottom, T top, T zNear, T zFar);
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75 
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82  template<typename T>
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83  GLM_FUNC_DECL mat<4, 4, T, defaultp> orthoRH_NO(
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84  T left, T right, T bottom, T top, T zNear, T zFar);
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85 
\n
92  template<typename T>
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93  GLM_FUNC_DECL mat<4, 4, T, defaultp> orthoZO(
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94  T left, T right, T bottom, T top, T zNear, T zFar);
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95 
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102  template<typename T>
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103  GLM_FUNC_DECL mat<4, 4, T, defaultp> orthoNO(
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104  T left, T right, T bottom, T top, T zNear, T zFar);
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105 
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113  template<typename T>
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114  GLM_FUNC_DECL mat<4, 4, T, defaultp> orthoLH(
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115  T left, T right, T bottom, T top, T zNear, T zFar);
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116 
\n
124  template<typename T>
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125  GLM_FUNC_DECL mat<4, 4, T, defaultp> orthoRH(
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126  T left, T right, T bottom, T top, T zNear, T zFar);
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127 
\n
135  template<typename T>
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136  GLM_FUNC_DECL mat<4, 4, T, defaultp> ortho(
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137  T left, T right, T bottom, T top, T zNear, T zFar);
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138 
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143  template<typename T>
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144  GLM_FUNC_DECL mat<4, 4, T, defaultp> frustumLH_ZO(
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145  T left, T right, T bottom, T top, T near, T far);
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146 
\n
151  template<typename T>
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152  GLM_FUNC_DECL mat<4, 4, T, defaultp> frustumLH_NO(
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153  T left, T right, T bottom, T top, T near, T far);
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154 
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159  template<typename T>
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160  GLM_FUNC_DECL mat<4, 4, T, defaultp> frustumRH_ZO(
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161  T left, T right, T bottom, T top, T near, T far);
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162 
\n
167  template<typename T>
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168  GLM_FUNC_DECL mat<4, 4, T, defaultp> frustumRH_NO(
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169  T left, T right, T bottom, T top, T near, T far);
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170 
\n
175  template<typename T>
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176  GLM_FUNC_DECL mat<4, 4, T, defaultp> frustumZO(
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177  T left, T right, T bottom, T top, T near, T far);
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178 
\n
183  template<typename T>
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184  GLM_FUNC_DECL mat<4, 4, T, defaultp> frustumNO(
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185  T left, T right, T bottom, T top, T near, T far);
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186 
\n
192  template<typename T>
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193  GLM_FUNC_DECL mat<4, 4, T, defaultp> frustumLH(
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194  T left, T right, T bottom, T top, T near, T far);
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195 
\n
201  template<typename T>
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202  GLM_FUNC_DECL mat<4, 4, T, defaultp> frustumRH(
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203  T left, T right, T bottom, T top, T near, T far);
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204 
\n
210  template<typename T>
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211  GLM_FUNC_DECL mat<4, 4, T, defaultp> frustum(
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212  T left, T right, T bottom, T top, T near, T far);
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213 
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214 
\n
224  template<typename T>
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225  GLM_FUNC_DECL mat<4, 4, T, defaultp> perspectiveRH_ZO(
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226  T fovy, T aspect, T near, T far);
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227 
\n
237  template<typename T>
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238  GLM_FUNC_DECL mat<4, 4, T, defaultp> perspectiveRH_NO(
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239  T fovy, T aspect, T near, T far);
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240 
\n
250  template<typename T>
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251  GLM_FUNC_DECL mat<4, 4, T, defaultp> perspectiveLH_ZO(
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252  T fovy, T aspect, T near, T far);
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253 
\n
263  template<typename T>
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264  GLM_FUNC_DECL mat<4, 4, T, defaultp> perspectiveLH_NO(
\n
265  T fovy, T aspect, T near, T far);
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266 
\n
276  template<typename T>
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277  GLM_FUNC_DECL mat<4, 4, T, defaultp> perspectiveZO(
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278  T fovy, T aspect, T near, T far);
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279 
\n
289  template<typename T>
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290  GLM_FUNC_DECL mat<4, 4, T, defaultp> perspectiveNO(
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291  T fovy, T aspect, T near, T far);
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292 
\n
303  template<typename T>
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304  GLM_FUNC_DECL mat<4, 4, T, defaultp> perspectiveRH(
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305  T fovy, T aspect, T near, T far);
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306 
\n
317  template<typename T>
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318  GLM_FUNC_DECL mat<4, 4, T, defaultp> perspectiveLH(
\n
319  T fovy, T aspect, T near, T far);
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320 
\n
331  template<typename T>
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332  GLM_FUNC_DECL mat<4, 4, T, defaultp> perspective(
\n
333  T fovy, T aspect, T near, T far);
\n
334 
\n
345  template<typename T>
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346  GLM_FUNC_DECL mat<4, 4, T, defaultp> perspectiveFovRH_ZO(
\n
347  T fov, T width, T height, T near, T far);
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348 
\n
359  template<typename T>
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360  GLM_FUNC_DECL mat<4, 4, T, defaultp> perspectiveFovRH_NO(
\n
361  T fov, T width, T height, T near, T far);
\n
362 
\n
373  template<typename T>
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374  GLM_FUNC_DECL mat<4, 4, T, defaultp> perspectiveFovLH_ZO(
\n
375  T fov, T width, T height, T near, T far);
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376 
\n
387  template<typename T>
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388  GLM_FUNC_DECL mat<4, 4, T, defaultp> perspectiveFovLH_NO(
\n
389  T fov, T width, T height, T near, T far);
\n
390 
\n
401  template<typename T>
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402  GLM_FUNC_DECL mat<4, 4, T, defaultp> perspectiveFovZO(
\n
403  T fov, T width, T height, T near, T far);
\n
404 
\n
415  template<typename T>
\n
416  GLM_FUNC_DECL mat<4, 4, T, defaultp> perspectiveFovNO(
\n
417  T fov, T width, T height, T near, T far);
\n
418 
\n
430  template<typename T>
\n
431  GLM_FUNC_DECL mat<4, 4, T, defaultp> perspectiveFovRH(
\n
432  T fov, T width, T height, T near, T far);
\n
433 
\n
445  template<typename T>
\n
446  GLM_FUNC_DECL mat<4, 4, T, defaultp> perspectiveFovLH(
\n
447  T fov, T width, T height, T near, T far);
\n
448 
\n
459  template<typename T>
\n
460  GLM_FUNC_DECL mat<4, 4, T, defaultp> perspectiveFov(
\n
461  T fov, T width, T height, T near, T far);
\n
462 
\n
470  template<typename T>
\n
471  GLM_FUNC_DECL mat<4, 4, T, defaultp> infinitePerspectiveLH(
\n
472  T fovy, T aspect, T near);
\n
473 
\n
481  template<typename T>
\n
482  GLM_FUNC_DECL mat<4, 4, T, defaultp> infinitePerspectiveRH(
\n
483  T fovy, T aspect, T near);
\n
484 
\n
492  template<typename T>
\n
493  GLM_FUNC_DECL mat<4, 4, T, defaultp> infinitePerspective(
\n
494  T fovy, T aspect, T near);
\n
495 
\n
503  template<typename T>
\n
504  GLM_FUNC_DECL mat<4, 4, T, defaultp> tweakedInfinitePerspective(
\n
505  T fovy, T aspect, T near);
\n
506 
\n
515  template<typename T>
\n
516  GLM_FUNC_DECL mat<4, 4, T, defaultp> tweakedInfinitePerspective(
\n
517  T fovy, T aspect, T near, T ep);
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518 
\n
520 }//namespace glm
\n
521 
\n
522 #include "matrix_clip_space.inl"
\n
glm::frustumRH_NO
GLM_FUNC_DECL mat< 4, 4, T, defaultp > frustumRH_NO(T left, T right, T bottom, T top, T near, T far)
Creates a right handed frustum matrix.
\n
glm::infinitePerspective
GLM_FUNC_DECL mat< 4, 4, T, defaultp > infinitePerspective(T fovy, T aspect, T near)
Creates a matrix for a symmetric perspective-view frustum with far plane at infinite with default han...
\n
glm::orthoZO
GLM_FUNC_DECL mat< 4, 4, T, defaultp > orthoZO(T left, T right, T bottom, T top, T zNear, T zFar)
Creates a matrix for an orthographic parallel viewing volume, using left-handed coordinates.
\n
glm::tweakedInfinitePerspective
GLM_FUNC_DECL mat< 4, 4, T, defaultp > tweakedInfinitePerspective(T fovy, T aspect, T near, T ep)
Creates a matrix for a symmetric perspective-view frustum with far plane at infinite for graphics har...
\n
glm::orthoRH
GLM_FUNC_DECL mat< 4, 4, T, defaultp > orthoRH(T left, T right, T bottom, T top, T zNear, T zFar)
Creates a matrix for an orthographic parallel viewing volume, using right-handed coordinates.
\n
glm::perspectiveFovLH
GLM_FUNC_DECL mat< 4, 4, T, defaultp > perspectiveFovLH(T fov, T width, T height, T near, T far)
Builds a left handed perspective projection matrix based on a field of view.
\n
glm::frustumLH_ZO
GLM_FUNC_DECL mat< 4, 4, T, defaultp > frustumLH_ZO(T left, T right, T bottom, T top, T near, T far)
Creates a left handed frustum matrix.
\n
glm::frustumLH_NO
GLM_FUNC_DECL mat< 4, 4, T, defaultp > frustumLH_NO(T left, T right, T bottom, T top, T near, T far)
Creates a left handed frustum matrix.
\n
glm::frustumNO
GLM_FUNC_DECL mat< 4, 4, T, defaultp > frustumNO(T left, T right, T bottom, T top, T near, T far)
Creates a frustum matrix using left-handed coordinates if GLM_FORCE_LEFT_HANDED if defined or right-h...
\n
glm::frustumRH
GLM_FUNC_DECL mat< 4, 4, T, defaultp > frustumRH(T left, T right, T bottom, T top, T near, T far)
Creates a right handed frustum matrix.
\n
glm::frustumLH
GLM_FUNC_DECL mat< 4, 4, T, defaultp > frustumLH(T left, T right, T bottom, T top, T near, T far)
Creates a left handed frustum matrix.
\n
glm::perspectiveFovRH_NO
GLM_FUNC_DECL mat< 4, 4, T, defaultp > perspectiveFovRH_NO(T fov, T width, T height, T near, T far)
Builds a perspective projection matrix based on a field of view using right-handed coordinates...
\n
glm::perspectiveFov
GLM_FUNC_DECL mat< 4, 4, T, defaultp > perspectiveFov(T fov, T width, T height, T near, T far)
Builds a perspective projection matrix based on a field of view and the default handedness and defaul...
\n
glm::perspectiveFovRH
GLM_FUNC_DECL mat< 4, 4, T, defaultp > perspectiveFovRH(T fov, T width, T height, T near, T far)
Builds a right handed perspective projection matrix based on a field of view.
\n
glm::frustumRH_ZO
GLM_FUNC_DECL mat< 4, 4, T, defaultp > frustumRH_ZO(T left, T right, T bottom, T top, T near, T far)
Creates a right handed frustum matrix.
\n
glm::orthoLH_ZO
GLM_FUNC_DECL mat< 4, 4, T, defaultp > orthoLH_ZO(T left, T right, T bottom, T top, T zNear, T zFar)
Creates a matrix for an orthographic parallel viewing volume, using left-handed coordinates.
\n
glm::perspectiveLH_ZO
GLM_FUNC_DECL mat< 4, 4, T, defaultp > perspectiveLH_ZO(T fovy, T aspect, T near, T far)
Creates a matrix for a left handed, symetric perspective-view frustum.
\n
glm::perspectiveRH_NO
GLM_FUNC_DECL mat< 4, 4, T, defaultp > perspectiveRH_NO(T fovy, T aspect, T near, T far)
Creates a matrix for a right handed, symetric perspective-view frustum.
\n
glm::orthoRH_NO
GLM_FUNC_DECL mat< 4, 4, T, defaultp > orthoRH_NO(T left, T right, T bottom, T top, T zNear, T zFar)
Creates a matrix for an orthographic parallel viewing volume, using right-handed coordinates.
\n
glm::perspectiveLH_NO
GLM_FUNC_DECL mat< 4, 4, T, defaultp > perspectiveLH_NO(T fovy, T aspect, T near, T far)
Creates a matrix for a left handed, symetric perspective-view frustum.
\n
glm::ortho
GLM_FUNC_DECL mat< 4, 4, T, defaultp > ortho(T left, T right, T bottom, T top, T zNear, T zFar)
Creates a matrix for an orthographic parallel viewing volume, using the default handedness and defaul...
\n
glm::perspectiveFovLH_ZO
GLM_FUNC_DECL mat< 4, 4, T, defaultp > perspectiveFovLH_ZO(T fov, T width, T height, T near, T far)
Builds a perspective projection matrix based on a field of view using left-handed coordinates...
\n
glm::frustumZO
GLM_FUNC_DECL mat< 4, 4, T, defaultp > frustumZO(T left, T right, T bottom, T top, T near, T far)
Creates a frustum matrix using left-handed coordinates if GLM_FORCE_LEFT_HANDED if defined or right-h...
\n
glm::orthoLH
GLM_FUNC_DECL mat< 4, 4, T, defaultp > orthoLH(T left, T right, T bottom, T top, T zNear, T zFar)
Creates a matrix for an orthographic parallel viewing volume, using left-handed coordinates.
\n
glm::orthoNO
GLM_FUNC_DECL mat< 4, 4, T, defaultp > orthoNO(T left, T right, T bottom, T top, T zNear, T zFar)
Creates a matrix for an orthographic parallel viewing volume, using left-handed coordinates if GLM_FO...
\n
glm::orthoLH_NO
GLM_FUNC_DECL mat< 4, 4, T, defaultp > orthoLH_NO(T left, T right, T bottom, T top, T zNear, T zFar)
Creates a matrix for an orthographic parallel viewing volume using right-handed coordinates.
\n
glm::perspectiveFovNO
GLM_FUNC_DECL mat< 4, 4, T, defaultp > perspectiveFovNO(T fov, T width, T height, T near, T far)
Builds a perspective projection matrix based on a field of view using left-handed coordinates if GLM_...
\n
glm::perspective
GLM_FUNC_DECL mat< 4, 4, T, defaultp > perspective(T fovy, T aspect, T near, T far)
Creates a matrix for a symetric perspective-view frustum based on the default handedness and default ...
\n
glm::orthoRH_ZO
GLM_FUNC_DECL mat< 4, 4, T, defaultp > orthoRH_ZO(T left, T right, T bottom, T top, T zNear, T zFar)
Creates a matrix for an orthographic parallel viewing volume, using left-handed coordinates.
\n
glm::perspectiveFovZO
GLM_FUNC_DECL mat< 4, 4, T, defaultp > perspectiveFovZO(T fov, T width, T height, T near, T far)
Builds a perspective projection matrix based on a field of view using left-handed coordinates if GLM_...
\n
glm::infinitePerspectiveRH
GLM_FUNC_DECL mat< 4, 4, T, defaultp > infinitePerspectiveRH(T fovy, T aspect, T near)
Creates a matrix for a right handed, symmetric perspective-view frustum with far plane at infinite...
\n
glm::perspectiveNO
GLM_FUNC_DECL mat< 4, 4, T, defaultp > perspectiveNO(T fovy, T aspect, T near, T far)
Creates a matrix for a symetric perspective-view frustum using left-handed coordinates if GLM_FORCE_L...
\n
glm::perspectiveFovLH_NO
GLM_FUNC_DECL mat< 4, 4, T, defaultp > perspectiveFovLH_NO(T fov, T width, T height, T near, T far)
Builds a perspective projection matrix based on a field of view using left-handed coordinates...
\n
glm::perspectiveRH_ZO
GLM_FUNC_DECL mat< 4, 4, T, defaultp > perspectiveRH_ZO(T fovy, T aspect, T near, T far)
Creates a matrix for a right handed, symetric perspective-view frustum.
\n
glm::perspectiveZO
GLM_FUNC_DECL mat< 4, 4, T, defaultp > perspectiveZO(T fovy, T aspect, T near, T far)
Creates a matrix for a symetric perspective-view frustum using left-handed coordinates if GLM_FORCE_L...
\n
glm::infinitePerspectiveLH
GLM_FUNC_DECL mat< 4, 4, T, defaultp > infinitePerspectiveLH(T fovy, T aspect, T near)
Creates a matrix for a left handed, symmetric perspective-view frustum with far plane at infinite...
\n
glm::perspectiveLH
GLM_FUNC_DECL mat< 4, 4, T, defaultp > perspectiveLH(T fovy, T aspect, T near, T far)
Creates a matrix for a left handed, symetric perspective-view frustum.
\n
glm::perspectiveFovRH_ZO
GLM_FUNC_DECL mat< 4, 4, T, defaultp > perspectiveFovRH_ZO(T fov, T width, T height, T near, T far)
Builds a perspective projection matrix based on a field of view using right-handed coordinates...
\n
glm::frustum
GLM_FUNC_DECL mat< 4, 4, T, defaultp > frustum(T left, T right, T bottom, T top, T near, T far)
Creates a frustum matrix with default handedness, using the default handedness and default near and f...
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glm::perspectiveRH
GLM_FUNC_DECL mat< 4, 4, T, defaultp > perspectiveRH(T fovy, T aspect, T near, T far)
Creates a matrix for a right handed, symetric perspective-view frustum.
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glm
Definition: common.hpp:20
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GLM_EXT_matrix_common

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Definition in file matrix_common.hpp.

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matrix_common.hpp
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1 
\n
13 #pragma once
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14 
\n
15 #include "../detail/qualifier.hpp"
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16 #include "../detail/_fixes.hpp"
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17 
\n
18 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
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19 # pragma message("GLM: GLM_EXT_matrix_transform extension included")
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20 #endif
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21 
\n
22 namespace glm
\n
23 {
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26 
\n
27  template<length_t C, length_t R, typename T, typename U, qualifier Q>
\n
28  GLM_FUNC_DECL mat<C, R, T, Q> mix(mat<C, R, T, Q> const& x, mat<C, R, T, Q> const& y, mat<C, R, U, Q> const& a);
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29 
\n
30  template<length_t C, length_t R, typename T, typename U, qualifier Q>
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31  GLM_FUNC_DECL mat<C, R, T, Q> mix(mat<C, R, T, Q> const& x, mat<C, R, T, Q> const& y, U a);
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32 
\n
34 }//namespace glm
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35 
\n
36 #include "matrix_common.inl"
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glm::mix
GLM_FUNC_DECL genTypeT mix(genTypeT x, genTypeT y, genTypeU a)
If genTypeU is a floating scalar or vector: Returns x * (1.0 - a) + y * a, i.e., the linear blend of ...
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glm
Definition: common.hpp:20
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GLM_GTX_matrix_cross_product \nMore...

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template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 3, 3, T, Q > matrixCross3 (vec< 3, T, Q > const &x)
 Build a cross product matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 4, 4, T, Q > matrixCross4 (vec< 3, T, Q > const &x)
 Build a cross product matrix. More...
 
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GLM_GTX_matrix_cross_product

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See also
Core features (dependence)
\n
\ngtx_extented_min_max (dependence)
\n\n

Definition in file matrix_cross_product.hpp.

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matrix_cross_product.hpp
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1 
\n
14 #pragma once
\n
15 
\n
16 // Dependency:
\n
17 #include "../glm.hpp"
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18 
\n
19 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
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20 # ifndef GLM_ENABLE_EXPERIMENTAL
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21 # pragma message("GLM: GLM_GTX_matrix_cross_product is an experimental extension and may change in the future. Use #define GLM_ENABLE_EXPERIMENTAL before including it, if you really want to use it.")
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22 # else
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23 # pragma message("GLM: GLM_GTX_matrix_cross_product extension included")
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24 # endif
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25 #endif
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26 
\n
27 namespace glm
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28 {
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31 
\n
34  template<typename T, qualifier Q>
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35  GLM_FUNC_DECL mat<3, 3, T, Q> matrixCross3(
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36  vec<3, T, Q> const& x);
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37 
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40  template<typename T, qualifier Q>
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41  GLM_FUNC_DECL mat<4, 4, T, Q> matrixCross4(
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42  vec<3, T, Q> const& x);
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43 
\n
45 }//namespace glm
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46 
\n
47 #include "matrix_cross_product.inl"
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glm::matrixCross4
GLM_FUNC_DECL mat< 4, 4, T, Q > matrixCross4(vec< 3, T, Q > const &x)
Build a cross product matrix.
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glm::matrixCross3
GLM_FUNC_DECL mat< 3, 3, T, Q > matrixCross3(vec< 3, T, Q > const &x)
Build a cross product matrix.
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glm
Definition: common.hpp:20
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template<typename T , qualifier Q>
GLM_FUNC_DECL bool decompose (mat< 4, 4, T, Q > const &modelMatrix, vec< 3, T, Q > &scale, qua< T, Q > &orientation, vec< 3, T, Q > &translation, vec< 3, T, Q > &skew, vec< 4, T, Q > &perspective)
 Decomposes a model matrix to translations, rotation and scale components. More...
 
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GLM_GTX_matrix_decompose

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See also
Core features (dependence)
\n\n

Definition in file matrix_decompose.hpp.

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matrix_decompose.hpp
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1 
\n
13 #pragma once
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14 
\n
15 // Dependencies
\n
16 #include "../mat4x4.hpp"
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17 #include "../vec3.hpp"
\n
18 #include "../vec4.hpp"
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19 #include "../geometric.hpp"
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20 #include "../gtc/quaternion.hpp"
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21 #include "../gtc/matrix_transform.hpp"
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22 
\n
23 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
24 # ifndef GLM_ENABLE_EXPERIMENTAL
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25 # pragma message("GLM: GLM_GTX_matrix_decompose is an experimental extension and may change in the future. Use #define GLM_ENABLE_EXPERIMENTAL before including it, if you really want to use it.")
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26 # else
\n
27 # pragma message("GLM: GLM_GTX_matrix_decompose extension included")
\n
28 # endif
\n
29 #endif
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30 
\n
31 namespace glm
\n
32 {
\n
35 
\n
38  template<typename T, qualifier Q>
\n
39  GLM_FUNC_DECL bool decompose(
\n
40  mat<4, 4, T, Q> const& modelMatrix,
\n
41  vec<3, T, Q> & scale, qua<T, Q> & orientation, vec<3, T, Q> & translation, vec<3, T, Q> & skew, vec<4, T, Q> & perspective);
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42 
\n
44 }//namespace glm
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45 
\n
46 #include "matrix_decompose.inl"
\n
glm::decompose
GLM_FUNC_DECL bool decompose(mat< 4, 4, T, Q > const &modelMatrix, vec< 3, T, Q > &scale, qua< T, Q > &orientation, vec< 3, T, Q > &translation, vec< 3, T, Q > &skew, vec< 4, T, Q > &perspective)
Decomposes a model matrix to translations, rotation and scale components.
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glm::scale
GLM_FUNC_DECL mat< 4, 4, T, Q > scale(mat< 4, 4, T, Q > const &m, vec< 3, T, Q > const &v)
Builds a scale 4 * 4 matrix created from 3 scalars.
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glm::perspective
GLM_FUNC_DECL mat< 4, 4, T, defaultp > perspective(T fovy, T aspect, T near, T far)
Creates a matrix for a symetric perspective-view frustum based on the default handedness and default ...
\n
glm::orientation
GLM_FUNC_DECL mat< 4, 4, T, Q > orientation(vec< 3, T, Q > const &Normal, vec< 3, T, Q > const &Up)
Build a rotation matrix from a normal and a up vector.
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glm
Definition: common.hpp:20
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typedef mat< 2, 2, double, defaultp > dmat2
 2 columns of 2 components matrix of double-precision floating-point numbers. More...
 
typedef mat< 2, 2, double, defaultp > dmat2x2
 2 columns of 2 components matrix of double-precision floating-point numbers. More...
 
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Core features

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Definition in file matrix_double2x2.hpp.

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matrix_double2x2.hpp
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1 
\n
4 #pragma once
\n
5 #include "../detail/type_mat2x2.hpp"
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6 
\n
7 namespace glm
\n
8 {
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11 
\n
15  typedef mat<2, 2, double, defaultp> dmat2x2;
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16 
\n
20  typedef mat<2, 2, double, defaultp> dmat2;
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21 
\n
23 }//namespace glm
\n
glm::dmat2
mat< 2, 2, double, defaultp > dmat2
2 columns of 2 components matrix of double-precision floating-point numbers.
Definition: matrix_double2x2.hpp:20
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glm::dmat2x2
mat< 2, 2, double, defaultp > dmat2x2
2 columns of 2 components matrix of double-precision floating-point numbers.
Definition: matrix_double2x2.hpp:15
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glm
Definition: common.hpp:20
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typedef mat< 2, 2, double, highp > highp_dmat2
 2 columns of 2 components matrix of double-precision floating-point numbers using medium precision arithmetic in term of ULPs. More...
 
typedef mat< 2, 2, double, highp > highp_dmat2x2
 2 columns of 2 components matrix of double-precision floating-point numbers using medium precision arithmetic in term of ULPs. More...
 
typedef mat< 2, 2, double, lowp > lowp_dmat2
 2 columns of 2 components matrix of double-precision floating-point numbers using low precision arithmetic in term of ULPs. More...
 
typedef mat< 2, 2, double, lowp > lowp_dmat2x2
 2 columns of 2 components matrix of double-precision floating-point numbers using low precision arithmetic in term of ULPs. More...
 
typedef mat< 2, 2, double, mediump > mediump_dmat2
 2 columns of 2 components matrix of double-precision floating-point numbers using medium precision arithmetic in term of ULPs. More...
 
typedef mat< 2, 2, double, mediump > mediump_dmat2x2
 2 columns of 2 components matrix of double-precision floating-point numbers using medium precision arithmetic in term of ULPs. More...
 
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Detailed Description

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Core features

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Definition in file matrix_double2x2_precision.hpp.

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1 
\n
4 #pragma once
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5 #include "../detail/type_mat2x2.hpp"
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6 
\n
7 namespace glm
\n
8 {
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11 
\n
16  typedef mat<2, 2, double, lowp> lowp_dmat2;
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17 
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22  typedef mat<2, 2, double, mediump> mediump_dmat2;
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23 
\n
28  typedef mat<2, 2, double, highp> highp_dmat2;
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29 
\n
34  typedef mat<2, 2, double, lowp> lowp_dmat2x2;
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35 
\n
40  typedef mat<2, 2, double, mediump> mediump_dmat2x2;
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41 
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46  typedef mat<2, 2, double, highp> highp_dmat2x2;
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47 
\n
49 }//namespace glm
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glm::mediump_dmat2
mat< 2, 2, double, mediump > mediump_dmat2
2 columns of 2 components matrix of double-precision floating-point numbers using medium precision ar...
Definition: matrix_double2x2_precision.hpp:22
\n
glm::lowp_dmat2
mat< 2, 2, double, lowp > lowp_dmat2
2 columns of 2 components matrix of double-precision floating-point numbers using low precision arith...
Definition: matrix_double2x2_precision.hpp:16
\n
glm::mediump_dmat2x2
mat< 2, 2, double, mediump > mediump_dmat2x2
2 columns of 2 components matrix of double-precision floating-point numbers using medium precision ar...
Definition: matrix_double2x2_precision.hpp:40
\n
glm::highp_dmat2x2
mat< 2, 2, double, highp > highp_dmat2x2
2 columns of 2 components matrix of double-precision floating-point numbers using medium precision ar...
Definition: matrix_double2x2_precision.hpp:46
\n
glm::highp_dmat2
mat< 2, 2, double, highp > highp_dmat2
2 columns of 2 components matrix of double-precision floating-point numbers using medium precision ar...
Definition: matrix_double2x2_precision.hpp:28
\n
glm::lowp_dmat2x2
mat< 2, 2, double, lowp > lowp_dmat2x2
2 columns of 2 components matrix of double-precision floating-point numbers using low precision arith...
Definition: matrix_double2x2_precision.hpp:34
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Definition: common.hpp:20
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typedef mat< 2, 3, double, defaultp > dmat2x3
 2 columns of 3 components matrix of double-precision floating-point numbers. More...
 
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Definition in file matrix_double2x3.hpp.

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1 
\n
4 #pragma once
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5 #include "../detail/type_mat2x3.hpp"
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6 
\n
7 namespace glm
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8 {
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11 
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15  typedef mat<2, 3, double, defaultp> dmat2x3;
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16 
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18 }//namespace glm
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glm::dmat2x3
mat< 2, 3, double, defaultp > dmat2x3
2 columns of 3 components matrix of double-precision floating-point numbers.
Definition: matrix_double2x3.hpp:15
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glm
Definition: common.hpp:20
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matrix_double2x3_precision.hpp File Reference
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typedef mat< 2, 3, double, highp > highp_dmat2x3
 2 columns of 3 components matrix of double-precision floating-point numbers using medium precision arithmetic in term of ULPs. More...
 
typedef mat< 2, 3, double, lowp > lowp_dmat2x3
 2 columns of 3 components matrix of double-precision floating-point numbers using low precision arithmetic in term of ULPs. More...
 
typedef mat< 2, 3, double, mediump > mediump_dmat2x3
 2 columns of 3 components matrix of double-precision floating-point numbers using medium precision arithmetic in term of ULPs. More...
 
\n

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Definition in file matrix_double2x3_precision.hpp.

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matrix_double2x3_precision.hpp
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\nGo to the documentation of this file.
1 
\n
4 #pragma once
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5 #include "../detail/type_mat2x3.hpp"
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6 
\n
7 namespace glm
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8 {
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16  typedef mat<2, 3, double, lowp> lowp_dmat2x3;
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22  typedef mat<2, 3, double, mediump> mediump_dmat2x3;
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28  typedef mat<2, 3, double, highp> highp_dmat2x3;
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31 }//namespace glm
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glm::mediump_dmat2x3
mat< 2, 3, double, mediump > mediump_dmat2x3
2 columns of 3 components matrix of double-precision floating-point numbers using medium precision ar...
Definition: matrix_double2x3_precision.hpp:22
\n
glm::highp_dmat2x3
mat< 2, 3, double, highp > highp_dmat2x3
2 columns of 3 components matrix of double-precision floating-point numbers using medium precision ar...
Definition: matrix_double2x3_precision.hpp:28
\n
glm::lowp_dmat2x3
mat< 2, 3, double, lowp > lowp_dmat2x3
2 columns of 3 components matrix of double-precision floating-point numbers using low precision arith...
Definition: matrix_double2x3_precision.hpp:16
\n
glm
Definition: common.hpp:20
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typedef mat< 2, 4, double, defaultp > dmat2x4
 2 columns of 4 components matrix of double-precision floating-point numbers. More...
 
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Definition in file matrix_double2x4.hpp.

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matrix_double2x4.hpp
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\nGo to the documentation of this file.
1 
\n
4 #pragma once
\n
5 #include "../detail/type_mat2x4.hpp"
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6 
\n
7 namespace glm
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8 {
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15  typedef mat<2, 4, double, defaultp> dmat2x4;
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18 }//namespace glm
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glm::dmat2x4
mat< 2, 4, double, defaultp > dmat2x4
2 columns of 4 components matrix of double-precision floating-point numbers.
Definition: matrix_double2x4.hpp:15
\n
glm
Definition: common.hpp:20
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matrix_double2x4_precision.hpp File Reference
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typedef mat< 2, 4, double, highp > highp_dmat2x4
 2 columns of 4 components matrix of double-precision floating-point numbers using medium precision arithmetic in term of ULPs. More...
 
typedef mat< 2, 4, double, lowp > lowp_dmat2x4
 2 columns of 4 components matrix of double-precision floating-point numbers using low precision arithmetic in term of ULPs. More...
 
typedef mat< 2, 4, double, mediump > mediump_dmat2x4
 2 columns of 4 components matrix of double-precision floating-point numbers using medium precision arithmetic in term of ULPs. More...
 
\n

Detailed Description

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Definition in file matrix_double2x4_precision.hpp.

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matrix_double2x4_precision.hpp
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1 
\n
4 #pragma once
\n
5 #include "../detail/type_mat2x4.hpp"
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6 
\n
7 namespace glm
\n
8 {
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16  typedef mat<2, 4, double, lowp> lowp_dmat2x4;
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22  typedef mat<2, 4, double, mediump> mediump_dmat2x4;
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28  typedef mat<2, 4, double, highp> highp_dmat2x4;
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31 }//namespace glm
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glm::highp_dmat2x4
mat< 2, 4, double, highp > highp_dmat2x4
2 columns of 4 components matrix of double-precision floating-point numbers using medium precision ar...
Definition: matrix_double2x4_precision.hpp:28
\n
glm::mediump_dmat2x4
mat< 2, 4, double, mediump > mediump_dmat2x4
2 columns of 4 components matrix of double-precision floating-point numbers using medium precision ar...
Definition: matrix_double2x4_precision.hpp:22
\n
glm::lowp_dmat2x4
mat< 2, 4, double, lowp > lowp_dmat2x4
2 columns of 4 components matrix of double-precision floating-point numbers using low precision arith...
Definition: matrix_double2x4_precision.hpp:16
\n
glm
Definition: common.hpp:20
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matrix_double3x2.hpp File Reference
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typedef mat< 3, 2, double, defaultp > dmat3x2
 3 columns of 2 components matrix of double-precision floating-point numbers. More...
 
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Definition in file matrix_double3x2.hpp.

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matrix_double3x2.hpp
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\nGo to the documentation of this file.
1 
\n
4 #pragma once
\n
5 #include "../detail/type_mat3x2.hpp"
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6 
\n
7 namespace glm
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8 {
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11 
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15  typedef mat<3, 2, double, defaultp> dmat3x2;
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16 
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18 }//namespace glm
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glm::dmat3x2
mat< 3, 2, double, defaultp > dmat3x2
3 columns of 2 components matrix of double-precision floating-point numbers.
Definition: matrix_double3x2.hpp:15
\n
glm
Definition: common.hpp:20
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matrix_double3x2_precision.hpp File Reference
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typedef mat< 3, 2, double, highp > highp_dmat3x2
 3 columns of 2 components matrix of double-precision floating-point numbers using medium precision arithmetic in term of ULPs. More...
 
typedef mat< 3, 2, double, lowp > lowp_dmat3x2
 3 columns of 2 components matrix of double-precision floating-point numbers using low precision arithmetic in term of ULPs. More...
 
typedef mat< 3, 2, double, mediump > mediump_dmat3x2
 3 columns of 2 components matrix of double-precision floating-point numbers using medium precision arithmetic in term of ULPs. More...
 
\n

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Definition in file matrix_double3x2_precision.hpp.

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matrix_double3x2_precision.hpp
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\nGo to the documentation of this file.
1 
\n
4 #pragma once
\n
5 #include "../detail/type_mat3x2.hpp"
\n
6 
\n
7 namespace glm
\n
8 {
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11 
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16  typedef mat<3, 2, double, lowp> lowp_dmat3x2;
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17 
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22  typedef mat<3, 2, double, mediump> mediump_dmat3x2;
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28  typedef mat<3, 2, double, highp> highp_dmat3x2;
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29 
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31 }//namespace glm
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glm::mediump_dmat3x2
mat< 3, 2, double, mediump > mediump_dmat3x2
3 columns of 2 components matrix of double-precision floating-point numbers using medium precision ar...
Definition: matrix_double3x2_precision.hpp:22
\n
glm::lowp_dmat3x2
mat< 3, 2, double, lowp > lowp_dmat3x2
3 columns of 2 components matrix of double-precision floating-point numbers using low precision arith...
Definition: matrix_double3x2_precision.hpp:16
\n
glm::highp_dmat3x2
mat< 3, 2, double, highp > highp_dmat3x2
3 columns of 2 components matrix of double-precision floating-point numbers using medium precision ar...
Definition: matrix_double3x2_precision.hpp:28
\n
glm
Definition: common.hpp:20
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typedef mat< 3, 3, double, defaultp > dmat3
 3 columns of 3 components matrix of double-precision floating-point numbers. More...
 
typedef mat< 3, 3, double, defaultp > dmat3x3
 3 columns of 3 components matrix of double-precision floating-point numbers. More...
 
\n

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Definition in file matrix_double3x3.hpp.

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matrix_double3x3.hpp
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\nGo to the documentation of this file.
1 
\n
4 #pragma once
\n
5 #include "../detail/type_mat3x3.hpp"
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6 
\n
7 namespace glm
\n
8 {
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11 
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15  typedef mat<3, 3, double, defaultp> dmat3x3;
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16 
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20  typedef mat<3, 3, double, defaultp> dmat3;
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21 
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23 }//namespace glm
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glm::dmat3x3
mat< 3, 3, double, defaultp > dmat3x3
3 columns of 3 components matrix of double-precision floating-point numbers.
Definition: matrix_double3x3.hpp:15
\n
glm::dmat3
mat< 3, 3, double, defaultp > dmat3
3 columns of 3 components matrix of double-precision floating-point numbers.
Definition: matrix_double3x3.hpp:20
\n
glm
Definition: common.hpp:20
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matrix_double3x3_precision.hpp File Reference
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typedef mat< 3, 3, double, highp > highp_dmat3
 3 columns of 3 components matrix of double-precision floating-point numbers using medium precision arithmetic in term of ULPs. More...
 
typedef mat< 3, 3, double, highp > highp_dmat3x3
 3 columns of 3 components matrix of double-precision floating-point numbers using medium precision arithmetic in term of ULPs. More...
 
typedef mat< 3, 3, double, lowp > lowp_dmat3
 3 columns of 3 components matrix of double-precision floating-point numbers using low precision arithmetic in term of ULPs. More...
 
typedef mat< 3, 3, double, lowp > lowp_dmat3x3
 3 columns of 3 components matrix of double-precision floating-point numbers using low precision arithmetic in term of ULPs. More...
 
typedef mat< 3, 3, double, mediump > mediump_dmat3
 3 columns of 3 components matrix of double-precision floating-point numbers using medium precision arithmetic in term of ULPs. More...
 
typedef mat< 3, 3, double, mediump > mediump_dmat3x3
 3 columns of 3 components matrix of double-precision floating-point numbers using medium precision arithmetic in term of ULPs. More...
 
\n

Detailed Description

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Definition in file matrix_double3x3_precision.hpp.

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matrix_double3x3_precision.hpp
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\nGo to the documentation of this file.
1 
\n
4 #pragma once
\n
5 #include "../detail/type_mat3x3.hpp"
\n
6 
\n
7 namespace glm
\n
8 {
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11 
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16  typedef mat<3, 3, double, lowp> lowp_dmat3;
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17 
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22  typedef mat<3, 3, double, mediump> mediump_dmat3;
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23 
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28  typedef mat<3, 3, double, highp> highp_dmat3;
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29 
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34  typedef mat<3, 3, double, lowp> lowp_dmat3x3;
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35 
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40  typedef mat<3, 3, double, mediump> mediump_dmat3x3;
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41 
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46  typedef mat<3, 3, double, highp> highp_dmat3x3;
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47 
\n
49 }//namespace glm
\n
glm::lowp_dmat3
mat< 3, 3, double, lowp > lowp_dmat3
3 columns of 3 components matrix of double-precision floating-point numbers using low precision arith...
Definition: matrix_double3x3_precision.hpp:16
\n
glm::lowp_dmat3x3
mat< 3, 3, double, lowp > lowp_dmat3x3
3 columns of 3 components matrix of double-precision floating-point numbers using low precision arith...
Definition: matrix_double3x3_precision.hpp:34
\n
glm::highp_dmat3
mat< 3, 3, double, highp > highp_dmat3
3 columns of 3 components matrix of double-precision floating-point numbers using medium precision ar...
Definition: matrix_double3x3_precision.hpp:28
\n
glm::highp_dmat3x3
mat< 3, 3, double, highp > highp_dmat3x3
3 columns of 3 components matrix of double-precision floating-point numbers using medium precision ar...
Definition: matrix_double3x3_precision.hpp:46
\n
glm::mediump_dmat3x3
mat< 3, 3, double, mediump > mediump_dmat3x3
3 columns of 3 components matrix of double-precision floating-point numbers using medium precision ar...
Definition: matrix_double3x3_precision.hpp:40
\n
glm::mediump_dmat3
mat< 3, 3, double, mediump > mediump_dmat3
3 columns of 3 components matrix of double-precision floating-point numbers using medium precision ar...
Definition: matrix_double3x3_precision.hpp:22
\n
glm
Definition: common.hpp:20
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typedef mat< 3, 4, double, defaultp > dmat3x4
 3 columns of 4 components matrix of double-precision floating-point numbers. More...
 
\n

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Definition in file matrix_double3x4.hpp.

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matrix_double3x4.hpp
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\nGo to the documentation of this file.
1 
\n
4 #pragma once
\n
5 #include "../detail/type_mat3x4.hpp"
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6 
\n
7 namespace glm
\n
8 {
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11 
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15  typedef mat<3, 4, double, defaultp> dmat3x4;
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16 
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18 }//namespace glm
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glm::dmat3x4
mat< 3, 4, double, defaultp > dmat3x4
3 columns of 4 components matrix of double-precision floating-point numbers.
Definition: matrix_double3x4.hpp:15
\n
glm
Definition: common.hpp:20
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matrix_double3x4_precision.hpp File Reference
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typedef mat< 3, 4, double, highp > highp_dmat3x4
 3 columns of 4 components matrix of double-precision floating-point numbers using medium precision arithmetic in term of ULPs. More...
 
typedef mat< 3, 4, double, lowp > lowp_dmat3x4
 3 columns of 4 components matrix of double-precision floating-point numbers using low precision arithmetic in term of ULPs. More...
 
typedef mat< 3, 4, double, mediump > mediump_dmat3x4
 3 columns of 4 components matrix of double-precision floating-point numbers using medium precision arithmetic in term of ULPs. More...
 
\n

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Definition in file matrix_double3x4_precision.hpp.

\n
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matrix_double3x4_precision.hpp
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\nGo to the documentation of this file.
1 
\n
4 #pragma once
\n
5 #include "../detail/type_mat3x4.hpp"
\n
6 
\n
7 namespace glm
\n
8 {
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11 
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16  typedef mat<3, 4, double, lowp> lowp_dmat3x4;
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22  typedef mat<3, 4, double, mediump> mediump_dmat3x4;
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28  typedef mat<3, 4, double, highp> highp_dmat3x4;
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29 
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31 }//namespace glm
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glm::lowp_dmat3x4
mat< 3, 4, double, lowp > lowp_dmat3x4
3 columns of 4 components matrix of double-precision floating-point numbers using low precision arith...
Definition: matrix_double3x4_precision.hpp:16
\n
glm::mediump_dmat3x4
mat< 3, 4, double, mediump > mediump_dmat3x4
3 columns of 4 components matrix of double-precision floating-point numbers using medium precision ar...
Definition: matrix_double3x4_precision.hpp:22
\n
glm::highp_dmat3x4
mat< 3, 4, double, highp > highp_dmat3x4
3 columns of 4 components matrix of double-precision floating-point numbers using medium precision ar...
Definition: matrix_double3x4_precision.hpp:28
\n
glm
Definition: common.hpp:20
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typedef mat< 4, 2, double, defaultp > dmat4x2
 4 columns of 2 components matrix of double-precision floating-point numbers. More...
 
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Definition in file matrix_double4x2.hpp.

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matrix_double4x2.hpp
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\nGo to the documentation of this file.
1 
\n
4 #pragma once
\n
5 #include "../detail/type_mat4x2.hpp"
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6 
\n
7 namespace glm
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8 {
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15  typedef mat<4, 2, double, defaultp> dmat4x2;
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16 
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18 }//namespace glm
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glm::dmat4x2
mat< 4, 2, double, defaultp > dmat4x2
4 columns of 2 components matrix of double-precision floating-point numbers.
Definition: matrix_double4x2.hpp:15
\n
glm
Definition: common.hpp:20
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matrix_double4x2_precision.hpp File Reference
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typedef mat< 4, 2, double, highp > highp_dmat4x2
 4 columns of 2 components matrix of double-precision floating-point numbers using medium precision arithmetic in term of ULPs. More...
 
typedef mat< 4, 2, double, lowp > lowp_dmat4x2
 4 columns of 2 components matrix of double-precision floating-point numbers using low precision arithmetic in term of ULPs. More...
 
typedef mat< 4, 2, double, mediump > mediump_dmat4x2
 4 columns of 2 components matrix of double-precision floating-point numbers using medium precision arithmetic in term of ULPs. More...
 
\n

Detailed Description

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Definition in file matrix_double4x2_precision.hpp.

\n
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matrix_double4x2_precision.hpp
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\nGo to the documentation of this file.
1 
\n
4 #pragma once
\n
5 #include "../detail/type_mat4x2.hpp"
\n
6 
\n
7 namespace glm
\n
8 {
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11 
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16  typedef mat<4, 2, double, lowp> lowp_dmat4x2;
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22  typedef mat<4, 2, double, mediump> mediump_dmat4x2;
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28  typedef mat<4, 2, double, highp> highp_dmat4x2;
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29 
\n
31 }//namespace glm
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glm::lowp_dmat4x2
mat< 4, 2, double, lowp > lowp_dmat4x2
4 columns of 2 components matrix of double-precision floating-point numbers using low precision arith...
Definition: matrix_double4x2_precision.hpp:16
\n
glm::mediump_dmat4x2
mat< 4, 2, double, mediump > mediump_dmat4x2
4 columns of 2 components matrix of double-precision floating-point numbers using medium precision ar...
Definition: matrix_double4x2_precision.hpp:22
\n
glm::highp_dmat4x2
mat< 4, 2, double, highp > highp_dmat4x2
4 columns of 2 components matrix of double-precision floating-point numbers using medium precision ar...
Definition: matrix_double4x2_precision.hpp:28
\n
glm
Definition: common.hpp:20
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typedef mat< 4, 3, double, defaultp > dmat4x3
 4 columns of 3 components matrix of double-precision floating-point numbers. More...
 
\n

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Definition in file matrix_double4x3.hpp.

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matrix_double4x3.hpp
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\nGo to the documentation of this file.
1 
\n
4 #pragma once
\n
5 #include "../detail/type_mat4x3.hpp"
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6 
\n
7 namespace glm
\n
8 {
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11 
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15  typedef mat<4, 3, double, defaultp> dmat4x3;
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16 
\n
18 }//namespace glm
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glm::dmat4x3
mat< 4, 3, double, defaultp > dmat4x3
4 columns of 3 components matrix of double-precision floating-point numbers.
Definition: matrix_double4x3.hpp:15
\n
glm
Definition: common.hpp:20
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matrix_double4x3_precision.hpp File Reference
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typedef mat< 4, 3, double, highp > highp_dmat4x3
 4 columns of 3 components matrix of double-precision floating-point numbers using medium precision arithmetic in term of ULPs. More...
 
typedef mat< 4, 3, double, lowp > lowp_dmat4x3
 4 columns of 3 components matrix of double-precision floating-point numbers using low precision arithmetic in term of ULPs. More...
 
typedef mat< 4, 3, double, mediump > mediump_dmat4x3
 4 columns of 3 components matrix of double-precision floating-point numbers using medium precision arithmetic in term of ULPs. More...
 
\n

Detailed Description

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Definition in file matrix_double4x3_precision.hpp.

\n
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matrix_double4x3_precision.hpp
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\nGo to the documentation of this file.
1 
\n
4 #pragma once
\n
5 #include "../detail/type_mat4x3.hpp"
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6 
\n
7 namespace glm
\n
8 {
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11 
\n
16  typedef mat<4, 3, double, lowp> lowp_dmat4x3;
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17 
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22  typedef mat<4, 3, double, mediump> mediump_dmat4x3;
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23 
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28  typedef mat<4, 3, double, highp> highp_dmat4x3;
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29 
\n
31 }//namespace glm
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glm::highp_dmat4x3
mat< 4, 3, double, highp > highp_dmat4x3
4 columns of 3 components matrix of double-precision floating-point numbers using medium precision ar...
Definition: matrix_double4x3_precision.hpp:28
\n
glm::mediump_dmat4x3
mat< 4, 3, double, mediump > mediump_dmat4x3
4 columns of 3 components matrix of double-precision floating-point numbers using medium precision ar...
Definition: matrix_double4x3_precision.hpp:22
\n
glm::lowp_dmat4x3
mat< 4, 3, double, lowp > lowp_dmat4x3
4 columns of 3 components matrix of double-precision floating-point numbers using low precision arith...
Definition: matrix_double4x3_precision.hpp:16
\n
glm
Definition: common.hpp:20
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typedef mat< 4, 4, double, defaultp > dmat4
 4 columns of 4 components matrix of double-precision floating-point numbers. More...
 
typedef mat< 4, 4, double, defaultp > dmat4x4
 4 columns of 4 components matrix of double-precision floating-point numbers. More...
 
\n

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Definition in file matrix_double4x4.hpp.

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matrix_double4x4.hpp
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\nGo to the documentation of this file.
1 
\n
4 #pragma once
\n
5 #include "../detail/type_mat4x4.hpp"
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6 
\n
7 namespace glm
\n
8 {
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11 
\n
15  typedef mat<4, 4, double, defaultp> dmat4x4;
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16 
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20  typedef mat<4, 4, double, defaultp> dmat4;
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21 
\n
23 }//namespace glm
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glm::dmat4x4
mat< 4, 4, double, defaultp > dmat4x4
4 columns of 4 components matrix of double-precision floating-point numbers.
Definition: matrix_double4x4.hpp:15
\n
glm::dmat4
mat< 4, 4, double, defaultp > dmat4
4 columns of 4 components matrix of double-precision floating-point numbers.
Definition: matrix_double4x4.hpp:20
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glm
Definition: common.hpp:20
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matrix_double4x4_precision.hpp File Reference
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typedef mat< 4, 4, double, highp > highp_dmat4
 4 columns of 4 components matrix of double-precision floating-point numbers using medium precision arithmetic in term of ULPs. More...
 
typedef mat< 4, 4, double, highp > highp_dmat4x4
 4 columns of 4 components matrix of double-precision floating-point numbers using medium precision arithmetic in term of ULPs. More...
 
typedef mat< 4, 4, double, lowp > lowp_dmat4
 4 columns of 4 components matrix of double-precision floating-point numbers using low precision arithmetic in term of ULPs. More...
 
typedef mat< 4, 4, double, lowp > lowp_dmat4x4
 4 columns of 4 components matrix of double-precision floating-point numbers using low precision arithmetic in term of ULPs. More...
 
typedef mat< 4, 4, double, mediump > mediump_dmat4
 4 columns of 4 components matrix of double-precision floating-point numbers using medium precision arithmetic in term of ULPs. More...
 
typedef mat< 4, 4, double, mediump > mediump_dmat4x4
 4 columns of 4 components matrix of double-precision floating-point numbers using medium precision arithmetic in term of ULPs. More...
 
\n

Detailed Description

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Core features

\n\n

Definition in file matrix_double4x4_precision.hpp.

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matrix_double4x4_precision.hpp
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1 
\n
4 #pragma once
\n
5 #include "../detail/type_mat4x4.hpp"
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6 
\n
7 namespace glm
\n
8 {
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11 
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16  typedef mat<4, 4, double, lowp> lowp_dmat4;
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17 
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22  typedef mat<4, 4, double, mediump> mediump_dmat4;
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23 
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28  typedef mat<4, 4, double, highp> highp_dmat4;
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29 
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34  typedef mat<4, 4, double, lowp> lowp_dmat4x4;
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35 
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40  typedef mat<4, 4, double, mediump> mediump_dmat4x4;
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41 
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46  typedef mat<4, 4, double, highp> highp_dmat4x4;
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47 
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49 }//namespace glm
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glm::mediump_dmat4x4
mat< 4, 4, double, mediump > mediump_dmat4x4
4 columns of 4 components matrix of double-precision floating-point numbers using medium precision ar...
Definition: matrix_double4x4_precision.hpp:40
\n
glm::lowp_dmat4
mat< 4, 4, double, lowp > lowp_dmat4
4 columns of 4 components matrix of double-precision floating-point numbers using low precision arith...
Definition: matrix_double4x4_precision.hpp:16
\n
glm::mediump_dmat4
mat< 4, 4, double, mediump > mediump_dmat4
4 columns of 4 components matrix of double-precision floating-point numbers using medium precision ar...
Definition: matrix_double4x4_precision.hpp:22
\n
glm::highp_dmat4x4
mat< 4, 4, double, highp > highp_dmat4x4
4 columns of 4 components matrix of double-precision floating-point numbers using medium precision ar...
Definition: matrix_double4x4_precision.hpp:46
\n
glm::lowp_dmat4x4
mat< 4, 4, double, lowp > lowp_dmat4x4
4 columns of 4 components matrix of double-precision floating-point numbers using low precision arith...
Definition: matrix_double4x4_precision.hpp:34
\n
glm::highp_dmat4
mat< 4, 4, double, highp > highp_dmat4
4 columns of 4 components matrix of double-precision floating-point numbers using medium precision ar...
Definition: matrix_double4x4_precision.hpp:28
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glm
Definition: common.hpp:20
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matrix_factorisation.hpp File Reference
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template<length_t C, length_t R, typename T , qualifier Q>
GLM_FUNC_DECL mat< C, R, T, Q > fliplr (mat< C, R, T, Q > const &in)
 Flips the matrix columns right and left. More...
 
template<length_t C, length_t R, typename T , qualifier Q>
GLM_FUNC_DECL mat< C, R, T, Q > flipud (mat< C, R, T, Q > const &in)
 Flips the matrix rows up and down. More...
 
template<length_t C, length_t R, typename T , qualifier Q>
GLM_FUNC_DECL void qr_decompose (mat< C, R, T, Q > const &in, mat<(C< R?C:R), R, T, Q > &q, mat< C,(C< R?C:R), T, Q > &r)
 Performs QR factorisation of a matrix. More...
 
template<length_t C, length_t R, typename T , qualifier Q>
GLM_FUNC_DECL void rq_decompose (mat< C, R, T, Q > const &in, mat<(C< R?C:R), R, T, Q > &r, mat< C,(C< R?C:R), T, Q > &q)
 Performs RQ factorisation of a matrix. More...
 
\n

Detailed Description

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GLM_GTX_matrix_factorisation

\n
See also
Core features (dependence)
\n\n

Definition in file matrix_factorisation.hpp.

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matrix_factorisation.hpp
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1 
\n
13 #pragma once
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14 
\n
15 // Dependency:
\n
16 #include "../glm.hpp"
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17 
\n
18 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
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19 # ifndef GLM_ENABLE_EXPERIMENTAL
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20 # pragma message("GLM: GLM_GTX_matrix_factorisation is an experimental extension and may change in the future. Use #define GLM_ENABLE_EXPERIMENTAL before including it, if you really want to use it.")
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21 # else
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22 # pragma message("GLM: GLM_GTX_matrix_factorisation extension included")
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23 # endif
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24 #endif
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25 
\n
26 /*
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27 Suggestions:
\n
28  - Move helper functions flipud and fliplr to another file: They may be helpful in more general circumstances.
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29  - Implement other types of matrix factorisation, such as: QL and LQ, L(D)U, eigendecompositions, etc...
\n
30 */
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31 
\n
32 namespace glm
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33 {
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36 
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40  template <length_t C, length_t R, typename T, qualifier Q>
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41  GLM_FUNC_DECL mat<C, R, T, Q> flipud(mat<C, R, T, Q> const& in);
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42 
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46  template <length_t C, length_t R, typename T, qualifier Q>
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47  GLM_FUNC_DECL mat<C, R, T, Q> fliplr(mat<C, R, T, Q> const& in);
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48 
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54  template <length_t C, length_t R, typename T, qualifier Q>
\n
55  GLM_FUNC_DECL void qr_decompose(mat<C, R, T, Q> const& in, mat<(C < R ? C : R), R, T, Q>& q, mat<C, (C < R ? C : R), T, Q>& r);
\n
56 
\n
63  template <length_t C, length_t R, typename T, qualifier Q>
\n
64  GLM_FUNC_DECL void rq_decompose(mat<C, R, T, Q> const& in, mat<(C < R ? C : R), R, T, Q>& r, mat<C, (C < R ? C : R), T, Q>& q);
\n
65 
\n
67 }
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68 
\n
69 #include "matrix_factorisation.inl"
\n
glm::rq_decompose
GLM_FUNC_DECL void rq_decompose(mat< C, R, T, Q > const &in, mat<(C< R?C:R), R, T, Q > &r, mat< C,(C< R?C:R), T, Q > &q)
Performs RQ factorisation of a matrix.
\n
glm::qr_decompose
GLM_FUNC_DECL void qr_decompose(mat< C, R, T, Q > const &in, mat<(C< R?C:R), R, T, Q > &q, mat< C,(C< R?C:R), T, Q > &r)
Performs QR factorisation of a matrix.
\n
glm::flipud
GLM_FUNC_DECL mat< C, R, T, Q > flipud(mat< C, R, T, Q > const &in)
Flips the matrix rows up and down.
\n
glm::fliplr
GLM_FUNC_DECL mat< C, R, T, Q > fliplr(mat< C, R, T, Q > const &in)
Flips the matrix columns right and left.
\n
glm
Definition: common.hpp:20
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typedef mat< 2, 2, float, defaultp > mat2
 2 columns of 2 components matrix of single-precision floating-point numbers. More...
 
typedef mat< 2, 2, float, defaultp > mat2x2
 2 columns of 2 components matrix of single-precision floating-point numbers. More...
 
\n

Detailed Description

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Core features

\n\n

Definition in file matrix_float2x2.hpp.

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matrix_float2x2.hpp
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1 
\n
4 #pragma once
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5 #include "../detail/type_mat2x2.hpp"
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6 
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7 namespace glm
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8 {
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15  typedef mat<2, 2, float, defaultp> mat2x2;
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16 
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20  typedef mat<2, 2, float, defaultp> mat2;
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21 
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23 }//namespace glm
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glm::mat2x2
mat< 2, 2, float, defaultp > mat2x2
2 columns of 2 components matrix of single-precision floating-point numbers.
Definition: matrix_float2x2.hpp:15
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glm::mat2
mat< 2, 2, float, defaultp > mat2
2 columns of 2 components matrix of single-precision floating-point numbers.
Definition: matrix_float2x2.hpp:20
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glm
Definition: common.hpp:20
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typedef mat< 2, 2, float, highp > highp_mat2
 2 columns of 2 components matrix of single-precision floating-point numbers using high precision arithmetic in term of ULPs. More...
 
typedef mat< 2, 2, float, highp > highp_mat2x2
 2 columns of 2 components matrix of single-precision floating-point numbers using high precision arithmetic in term of ULPs. More...
 
typedef mat< 2, 2, float, lowp > lowp_mat2
 2 columns of 2 components matrix of single-precision floating-point numbers using low precision arithmetic in term of ULPs. More...
 
typedef mat< 2, 2, float, lowp > lowp_mat2x2
 2 columns of 2 components matrix of single-precision floating-point numbers using low precision arithmetic in term of ULPs. More...
 
typedef mat< 2, 2, float, mediump > mediump_mat2
 2 columns of 2 components matrix of single-precision floating-point numbers using medium precision arithmetic in term of ULPs. More...
 
typedef mat< 2, 2, float, mediump > mediump_mat2x2
 2 columns of 2 components matrix of single-precision floating-point numbers using medium precision arithmetic in term of ULPs. More...
 
\n

Detailed Description

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Core features

\n\n

Definition in file matrix_float2x2_precision.hpp.

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matrix_float2x2_precision.hpp
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1 
\n
4 #pragma once
\n
5 #include "../detail/type_mat2x2.hpp"
\n
6 
\n
7 namespace glm
\n
8 {
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11 
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16  typedef mat<2, 2, float, lowp> lowp_mat2;
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17 
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22  typedef mat<2, 2, float, mediump> mediump_mat2;
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23 
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28  typedef mat<2, 2, float, highp> highp_mat2;
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29 
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34  typedef mat<2, 2, float, lowp> lowp_mat2x2;
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35 
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40  typedef mat<2, 2, float, mediump> mediump_mat2x2;
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41 
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46  typedef mat<2, 2, float, highp> highp_mat2x2;
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47 
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49 }//namespace glm
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glm::lowp_mat2
mat< 2, 2, float, lowp > lowp_mat2
2 columns of 2 components matrix of single-precision floating-point numbers using low precision arith...
Definition: matrix_float2x2_precision.hpp:16
\n
glm::highp_mat2
mat< 2, 2, float, highp > highp_mat2
2 columns of 2 components matrix of single-precision floating-point numbers using high precision arit...
Definition: matrix_float2x2_precision.hpp:28
\n
glm::lowp_mat2x2
mat< 2, 2, float, lowp > lowp_mat2x2
2 columns of 2 components matrix of single-precision floating-point numbers using low precision arith...
Definition: matrix_float2x2_precision.hpp:34
\n
glm::highp_mat2x2
mat< 2, 2, float, highp > highp_mat2x2
2 columns of 2 components matrix of single-precision floating-point numbers using high precision arit...
Definition: matrix_float2x2_precision.hpp:46
\n
glm::mediump_mat2x2
mat< 2, 2, float, mediump > mediump_mat2x2
2 columns of 2 components matrix of single-precision floating-point numbers using medium precision ar...
Definition: matrix_float2x2_precision.hpp:40
\n
glm::mediump_mat2
mat< 2, 2, float, mediump > mediump_mat2
2 columns of 2 components matrix of single-precision floating-point numbers using medium precision ar...
Definition: matrix_float2x2_precision.hpp:22
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glm
Definition: common.hpp:20
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typedef mat< 2, 3, float, defaultp > mat2x3
 2 columns of 3 components matrix of single-precision floating-point numbers. More...
 
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Definition in file matrix_float2x3.hpp.

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matrix_float2x3.hpp
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1 
\n
4 #pragma once
\n
5 #include "../detail/type_mat2x3.hpp"
\n
6 
\n
7 namespace glm
\n
8 {
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11 
\n
15  typedef mat<2, 3, float, defaultp> mat2x3;
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16 
\n
18 }//namespace glm
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glm::mat2x3
mat< 2, 3, float, defaultp > mat2x3
2 columns of 3 components matrix of single-precision floating-point numbers.
Definition: matrix_float2x3.hpp:15
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glm
Definition: common.hpp:20
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typedef mat< 2, 3, float, highp > highp_mat2x3
 2 columns of 3 components matrix of single-precision floating-point numbers using high precision arithmetic in term of ULPs. More...
 
typedef mat< 2, 3, float, lowp > lowp_mat2x3
 2 columns of 3 components matrix of single-precision floating-point numbers using low precision arithmetic in term of ULPs. More...
 
typedef mat< 2, 3, float, mediump > mediump_mat2x3
 2 columns of 3 components matrix of single-precision floating-point numbers using medium precision arithmetic in term of ULPs. More...
 
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Definition in file matrix_float2x3_precision.hpp.

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1 
\n
4 #pragma once
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5 #include "../detail/type_mat2x3.hpp"
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6 
\n
7 namespace glm
\n
8 {
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11 
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16  typedef mat<2, 3, float, lowp> lowp_mat2x3;
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17 
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22  typedef mat<2, 3, float, mediump> mediump_mat2x3;
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23 
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28  typedef mat<2, 3, float, highp> highp_mat2x3;
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29 
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31 }//namespace glm
\n
glm::mediump_mat2x3
mat< 2, 3, float, mediump > mediump_mat2x3
2 columns of 3 components matrix of single-precision floating-point numbers using medium precision ar...
Definition: matrix_float2x3_precision.hpp:22
\n
glm::highp_mat2x3
mat< 2, 3, float, highp > highp_mat2x3
2 columns of 3 components matrix of single-precision floating-point numbers using high precision arit...
Definition: matrix_float2x3_precision.hpp:28
\n
glm::lowp_mat2x3
mat< 2, 3, float, lowp > lowp_mat2x3
2 columns of 3 components matrix of single-precision floating-point numbers using low precision arith...
Definition: matrix_float2x3_precision.hpp:16
\n
glm
Definition: common.hpp:20
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typedef mat< 2, 4, float, defaultp > mat2x4
 2 columns of 4 components matrix of single-precision floating-point numbers. More...
 
\n

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Definition in file matrix_float2x4.hpp.

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matrix_float2x4.hpp
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\nGo to the documentation of this file.
1 
\n
4 #pragma once
\n
5 #include "../detail/type_mat2x4.hpp"
\n
6 
\n
7 namespace glm
\n
8 {
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11 
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15  typedef mat<2, 4, float, defaultp> mat2x4;
\n
16 
\n
18 }//namespace glm
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glm::mat2x4
mat< 2, 4, float, defaultp > mat2x4
2 columns of 4 components matrix of single-precision floating-point numbers.
Definition: matrix_float2x4.hpp:15
\n
glm
Definition: common.hpp:20
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typedef mat< 2, 4, float, highp > highp_mat2x4
 2 columns of 4 components matrix of single-precision floating-point numbers using high precision arithmetic in term of ULPs. More...
 
typedef mat< 2, 4, float, lowp > lowp_mat2x4
 2 columns of 4 components matrix of single-precision floating-point numbers using low precision arithmetic in term of ULPs. More...
 
typedef mat< 2, 4, float, mediump > mediump_mat2x4
 2 columns of 4 components matrix of single-precision floating-point numbers using medium precision arithmetic in term of ULPs. More...
 
\n

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Definition in file matrix_float2x4_precision.hpp.

\n
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matrix_float2x4_precision.hpp
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\nGo to the documentation of this file.
1 
\n
4 #pragma once
\n
5 #include "../detail/type_mat2x4.hpp"
\n
6 
\n
7 namespace glm
\n
8 {
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11 
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16  typedef mat<2, 4, float, lowp> lowp_mat2x4;
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22  typedef mat<2, 4, float, mediump> mediump_mat2x4;
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28  typedef mat<2, 4, float, highp> highp_mat2x4;
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29 
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31 }//namespace glm
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glm::lowp_mat2x4
mat< 2, 4, float, lowp > lowp_mat2x4
2 columns of 4 components matrix of single-precision floating-point numbers using low precision arith...
Definition: matrix_float2x4_precision.hpp:16
\n
glm::mediump_mat2x4
mat< 2, 4, float, mediump > mediump_mat2x4
2 columns of 4 components matrix of single-precision floating-point numbers using medium precision ar...
Definition: matrix_float2x4_precision.hpp:22
\n
glm::highp_mat2x4
mat< 2, 4, float, highp > highp_mat2x4
2 columns of 4 components matrix of single-precision floating-point numbers using high precision arit...
Definition: matrix_float2x4_precision.hpp:28
\n
glm
Definition: common.hpp:20
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typedef mat< 3, 2, float, defaultp > mat3x2
 3 columns of 2 components matrix of single-precision floating-point numbers. More...
 
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Definition in file matrix_float3x2.hpp.

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matrix_float3x2.hpp
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\nGo to the documentation of this file.
1 
\n
4 #pragma once
\n
5 #include "../detail/type_mat3x2.hpp"
\n
6 
\n
7 namespace glm
\n
8 {
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11 
\n
15  typedef mat<3, 2, float, defaultp> mat3x2;
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16 
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18 }//namespace glm
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glm::mat3x2
mat< 3, 2, float, defaultp > mat3x2
3 columns of 2 components matrix of single-precision floating-point numbers.
Definition: matrix_float3x2.hpp:15
\n
glm
Definition: common.hpp:20
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matrix_float3x2_precision.hpp File Reference
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typedef mat< 3, 2, float, highp > highp_mat3x2
 3 columns of 2 components matrix of single-precision floating-point numbers using high precision arithmetic in term of ULPs. More...
 
typedef mat< 3, 2, float, lowp > lowp_mat3x2
 3 columns of 2 components matrix of single-precision floating-point numbers using low precision arithmetic in term of ULPs. More...
 
typedef mat< 3, 2, float, mediump > mediump_mat3x2
 3 columns of 2 components matrix of single-precision floating-point numbers using medium precision arithmetic in term of ULPs. More...
 
\n

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Definition in file matrix_float3x2_precision.hpp.

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matrix_float3x2_precision.hpp
\n
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\nGo to the documentation of this file.
1 
\n
4 #pragma once
\n
5 #include "../detail/type_mat3x2.hpp"
\n
6 
\n
7 namespace glm
\n
8 {
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11 
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16  typedef mat<3, 2, float, lowp> lowp_mat3x2;
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17 
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22  typedef mat<3, 2, float, mediump> mediump_mat3x2;
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23 
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28  typedef mat<3, 2, float, highp> highp_mat3x2;
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29 
\n
31 }//namespace glm
\n
glm::lowp_mat3x2
mat< 3, 2, float, lowp > lowp_mat3x2
3 columns of 2 components matrix of single-precision floating-point numbers using low precision arith...
Definition: matrix_float3x2_precision.hpp:16
\n
glm::mediump_mat3x2
mat< 3, 2, float, mediump > mediump_mat3x2
3 columns of 2 components matrix of single-precision floating-point numbers using medium precision ar...
Definition: matrix_float3x2_precision.hpp:22
\n
glm::highp_mat3x2
mat< 3, 2, float, highp > highp_mat3x2
3 columns of 2 components matrix of single-precision floating-point numbers using high precision arit...
Definition: matrix_float3x2_precision.hpp:28
\n
glm
Definition: common.hpp:20
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typedef mat< 3, 3, float, defaultp > mat3
 3 columns of 3 components matrix of single-precision floating-point numbers. More...
 
typedef mat< 3, 3, float, defaultp > mat3x3
 3 columns of 3 components matrix of single-precision floating-point numbers. More...
 
\n

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Definition in file matrix_float3x3.hpp.

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matrix_float3x3.hpp
\n
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\nGo to the documentation of this file.
1 
\n
4 #pragma once
\n
5 #include "../detail/type_mat3x3.hpp"
\n
6 
\n
7 namespace glm
\n
8 {
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11 
\n
15  typedef mat<3, 3, float, defaultp> mat3x3;
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16 
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20  typedef mat<3, 3, float, defaultp> mat3;
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21 
\n
23 }//namespace glm
\n
glm::mat3x3
mat< 3, 3, float, defaultp > mat3x3
3 columns of 3 components matrix of single-precision floating-point numbers.
Definition: matrix_float3x3.hpp:15
\n
glm::mat3
mat< 3, 3, float, defaultp > mat3
3 columns of 3 components matrix of single-precision floating-point numbers.
Definition: matrix_float3x3.hpp:20
\n
glm
Definition: common.hpp:20
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matrix_float3x3_precision.hpp File Reference
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typedef mat< 3, 3, float, highp > highp_mat3
 3 columns of 3 components matrix of single-precision floating-point numbers using high precision arithmetic in term of ULPs. More...
 
typedef mat< 3, 3, float, highp > highp_mat3x3
 3 columns of 3 components matrix of single-precision floating-point numbers using high precision arithmetic in term of ULPs. More...
 
typedef mat< 3, 3, float, lowp > lowp_mat3
 3 columns of 3 components matrix of single-precision floating-point numbers using low precision arithmetic in term of ULPs. More...
 
typedef mat< 3, 3, float, lowp > lowp_mat3x3
 3 columns of 3 components matrix of single-precision floating-point numbers using low precision arithmetic in term of ULPs. More...
 
typedef mat< 3, 3, float, mediump > mediump_mat3
 3 columns of 3 components matrix of single-precision floating-point numbers using medium precision arithmetic in term of ULPs. More...
 
typedef mat< 3, 3, float, mediump > mediump_mat3x3
 3 columns of 3 components matrix of single-precision floating-point numbers using medium precision arithmetic in term of ULPs. More...
 
\n

Detailed Description

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Definition in file matrix_float3x3_precision.hpp.

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matrix_float3x3_precision.hpp
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\nGo to the documentation of this file.
1 
\n
4 #pragma once
\n
5 #include "../detail/type_mat3x3.hpp"
\n
6 
\n
7 namespace glm
\n
8 {
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11 
\n
16  typedef mat<3, 3, float, lowp> lowp_mat3;
\n
17 
\n
22  typedef mat<3, 3, float, mediump> mediump_mat3;
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23 
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28  typedef mat<3, 3, float, highp> highp_mat3;
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29 
\n
34  typedef mat<3, 3, float, lowp> lowp_mat3x3;
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35 
\n
40  typedef mat<3, 3, float, mediump> mediump_mat3x3;
\n
41 
\n
46  typedef mat<3, 3, float, highp> highp_mat3x3;
\n
47 
\n
49 }//namespace glm
\n
glm::mediump_mat3x3
mat< 3, 3, float, mediump > mediump_mat3x3
3 columns of 3 components matrix of single-precision floating-point numbers using medium precision ar...
Definition: matrix_float3x3_precision.hpp:40
\n
glm::highp_mat3x3
mat< 3, 3, float, highp > highp_mat3x3
3 columns of 3 components matrix of single-precision floating-point numbers using high precision arit...
Definition: matrix_float3x3_precision.hpp:46
\n
glm::lowp_mat3x3
mat< 3, 3, float, lowp > lowp_mat3x3
3 columns of 3 components matrix of single-precision floating-point numbers using low precision arith...
Definition: matrix_float3x3_precision.hpp:34
\n
glm::mediump_mat3
mat< 3, 3, float, mediump > mediump_mat3
3 columns of 3 components matrix of single-precision floating-point numbers using medium precision ar...
Definition: matrix_float3x3_precision.hpp:22
\n
glm::lowp_mat3
mat< 3, 3, float, lowp > lowp_mat3
3 columns of 3 components matrix of single-precision floating-point numbers using low precision arith...
Definition: matrix_float3x3_precision.hpp:16
\n
glm::highp_mat3
mat< 3, 3, float, highp > highp_mat3
3 columns of 3 components matrix of single-precision floating-point numbers using high precision arit...
Definition: matrix_float3x3_precision.hpp:28
\n
glm
Definition: common.hpp:20
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typedef mat< 3, 4, float, defaultp > mat3x4
 3 columns of 4 components matrix of single-precision floating-point numbers. More...
 
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Definition in file matrix_float3x4.hpp.

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matrix_float3x4.hpp
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\nGo to the documentation of this file.
1 
\n
4 #pragma once
\n
5 #include "../detail/type_mat3x4.hpp"
\n
6 
\n
7 namespace glm
\n
8 {
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11 
\n
15  typedef mat<3, 4, float, defaultp> mat3x4;
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16 
\n
18 }//namespace glm
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glm::mat3x4
mat< 3, 4, float, defaultp > mat3x4
3 columns of 4 components matrix of single-precision floating-point numbers.
Definition: matrix_float3x4.hpp:15
\n
glm
Definition: common.hpp:20
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typedef mat< 3, 4, float, highp > highp_mat3x4
 3 columns of 4 components matrix of single-precision floating-point numbers using high precision arithmetic in term of ULPs. More...
 
typedef mat< 3, 4, float, lowp > lowp_mat3x4
 3 columns of 4 components matrix of single-precision floating-point numbers using low precision arithmetic in term of ULPs. More...
 
typedef mat< 3, 4, float, mediump > mediump_mat3x4
 3 columns of 4 components matrix of single-precision floating-point numbers using medium precision arithmetic in term of ULPs. More...
 
\n

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Definition in file matrix_float3x4_precision.hpp.

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matrix_float3x4_precision.hpp
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\nGo to the documentation of this file.
1 
\n
4 #pragma once
\n
5 #include "../detail/type_mat3x4.hpp"
\n
6 
\n
7 namespace glm
\n
8 {
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11 
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16  typedef mat<3, 4, float, lowp> lowp_mat3x4;
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22  typedef mat<3, 4, float, mediump> mediump_mat3x4;
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23 
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28  typedef mat<3, 4, float, highp> highp_mat3x4;
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29 
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31 }//namespace glm
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glm::highp_mat3x4
mat< 3, 4, float, highp > highp_mat3x4
3 columns of 4 components matrix of single-precision floating-point numbers using high precision arit...
Definition: matrix_float3x4_precision.hpp:28
\n
glm::mediump_mat3x4
mat< 3, 4, float, mediump > mediump_mat3x4
3 columns of 4 components matrix of single-precision floating-point numbers using medium precision ar...
Definition: matrix_float3x4_precision.hpp:22
\n
glm::lowp_mat3x4
mat< 3, 4, float, lowp > lowp_mat3x4
3 columns of 4 components matrix of single-precision floating-point numbers using low precision arith...
Definition: matrix_float3x4_precision.hpp:16
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glm
Definition: common.hpp:20
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typedef mat< 4, 2, float, defaultp > mat4x2
 4 columns of 2 components matrix of single-precision floating-point numbers. More...
 
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Definition in file matrix_float4x2.hpp.

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matrix_float4x2.hpp
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1 
\n
4 #pragma once
\n
5 #include "../detail/type_mat4x2.hpp"
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6 
\n
7 namespace glm
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8 {
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11 
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15  typedef mat<4, 2, float, defaultp> mat4x2;
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16 
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18 }//namespace glm
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glm::mat4x2
mat< 4, 2, float, defaultp > mat4x2
4 columns of 2 components matrix of single-precision floating-point numbers.
Definition: matrix_float4x2.hpp:15
\n
glm
Definition: common.hpp:20
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matrix_float4x2_precision.hpp
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1 
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4 #pragma once
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5 #include "../detail/type_mat2x2.hpp"
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6 
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7 namespace glm
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8 {
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11 
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16  typedef mat<4, 2, float, lowp> lowp_mat4x2;
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17 
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22  typedef mat<4, 2, float, mediump> mediump_mat4x2;
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23 
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28  typedef mat<4, 2, float, highp> highp_mat4x2;
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29 
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31 }//namespace glm
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glm::mediump_mat4x2
mat< 4, 2, float, mediump > mediump_mat4x2
4 columns of 2 components matrix of single-precision floating-point numbers using medium precision ar...
Definition: matrix_float4x2_precision.hpp:22
\n
glm::lowp_mat4x2
mat< 4, 2, float, lowp > lowp_mat4x2
4 columns of 2 components matrix of single-precision floating-point numbers using low precision arith...
Definition: matrix_float4x2_precision.hpp:16
\n
glm::highp_mat4x2
mat< 4, 2, float, highp > highp_mat4x2
4 columns of 2 components matrix of single-precision floating-point numbers using high precision arit...
Definition: matrix_float4x2_precision.hpp:28
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Definition: common.hpp:20
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typedef mat< 4, 3, float, defaultp > mat4x3
 4 columns of 3 components matrix of single-precision floating-point numbers. More...
 
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Definition in file matrix_float4x3.hpp.

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matrix_float4x3.hpp
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\nGo to the documentation of this file.
1 
\n
4 #pragma once
\n
5 #include "../detail/type_mat4x3.hpp"
\n
6 
\n
7 namespace glm
\n
8 {
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11 
\n
15  typedef mat<4, 3, float, defaultp> mat4x3;
\n
16 
\n
18 }//namespace glm
\n
glm::mat4x3
mat< 4, 3, float, defaultp > mat4x3
4 columns of 3 components matrix of single-precision floating-point numbers.
Definition: matrix_float4x3.hpp:15
\n
glm
Definition: common.hpp:20
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typedef mat< 4, 3, float, highp > highp_mat4x3
 4 columns of 3 components matrix of single-precision floating-point numbers using high precision arithmetic in term of ULPs. More...
 
typedef mat< 4, 3, float, lowp > lowp_mat4x3
 4 columns of 3 components matrix of single-precision floating-point numbers using low precision arithmetic in term of ULPs. More...
 
typedef mat< 4, 3, float, mediump > mediump_mat4x3
 4 columns of 3 components matrix of single-precision floating-point numbers using medium precision arithmetic in term of ULPs. More...
 
\n

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Definition in file matrix_float4x3_precision.hpp.

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matrix_float4x3_precision.hpp
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\nGo to the documentation of this file.
1 
\n
4 #pragma once
\n
5 #include "../detail/type_mat4x3.hpp"
\n
6 
\n
7 namespace glm
\n
8 {
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11 
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16  typedef mat<4, 3, float, lowp> lowp_mat4x3;
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17 
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22  typedef mat<4, 3, float, mediump> mediump_mat4x3;
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23 
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28  typedef mat<4, 3, float, highp> highp_mat4x3;
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29 
\n
31 }//namespace glm
\n
glm::highp_mat4x3
mat< 4, 3, float, highp > highp_mat4x3
4 columns of 3 components matrix of single-precision floating-point numbers using high precision arit...
Definition: matrix_float4x3_precision.hpp:28
\n
glm::lowp_mat4x3
mat< 4, 3, float, lowp > lowp_mat4x3
4 columns of 3 components matrix of single-precision floating-point numbers using low precision arith...
Definition: matrix_float4x3_precision.hpp:16
\n
glm::mediump_mat4x3
mat< 4, 3, float, mediump > mediump_mat4x3
4 columns of 3 components matrix of single-precision floating-point numbers using medium precision ar...
Definition: matrix_float4x3_precision.hpp:22
\n
glm
Definition: common.hpp:20
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typedef mat< 4, 4, float, defaultp > mat4x4
 4 columns of 4 components matrix of single-precision floating-point numbers. More...
 
typedef mat< 4, 4, float, defaultp > mat4
 4 columns of 4 components matrix of single-precision floating-point numbers. More...
 
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Definition in file matrix_float4x4.hpp.

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matrix_float4x4.hpp
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1 
\n
4 #pragma once
\n
5 #include "../detail/type_mat4x4.hpp"
\n
6 
\n
7 namespace glm
\n
8 {
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11 
\n
15  typedef mat<4, 4, float, defaultp> mat4x4;
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16 
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20  typedef mat<4, 4, float, defaultp> mat4;
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21 
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23 }//namespace glm
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glm::mat4x4
mat< 4, 4, float, defaultp > mat4x4
4 columns of 4 components matrix of single-precision floating-point numbers.
Definition: matrix_float4x4.hpp:15
\n
glm::mat4
mat< 4, 4, float, defaultp > mat4
4 columns of 4 components matrix of single-precision floating-point numbers.
Definition: matrix_float4x4.hpp:20
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glm
Definition: common.hpp:20
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typedef mat< 4, 4, float, highp > highp_mat4
 4 columns of 4 components matrix of single-precision floating-point numbers using high precision arithmetic in term of ULPs. More...
 
typedef mat< 4, 4, float, highp > highp_mat4x4
 4 columns of 4 components matrix of single-precision floating-point numbers using high precision arithmetic in term of ULPs. More...
 
typedef mat< 4, 4, float, lowp > lowp_mat4
 4 columns of 4 components matrix of single-precision floating-point numbers using low precision arithmetic in term of ULPs. More...
 
typedef mat< 4, 4, float, lowp > lowp_mat4x4
 4 columns of 4 components matrix of single-precision floating-point numbers using low precision arithmetic in term of ULPs. More...
 
typedef mat< 4, 4, float, mediump > mediump_mat4
 4 columns of 4 components matrix of single-precision floating-point numbers using medium precision arithmetic in term of ULPs. More...
 
typedef mat< 4, 4, float, mediump > mediump_mat4x4
 4 columns of 4 components matrix of single-precision floating-point numbers using medium precision arithmetic in term of ULPs. More...
 
\n

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Definition in file matrix_float4x4_precision.hpp.

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matrix_float4x4_precision.hpp
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\nGo to the documentation of this file.
1 
\n
4 #pragma once
\n
5 #include "../detail/type_mat4x4.hpp"
\n
6 
\n
7 namespace glm
\n
8 {
\n
11 
\n
16  typedef mat<4, 4, float, lowp> lowp_mat4;
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17 
\n
22  typedef mat<4, 4, float, mediump> mediump_mat4;
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23 
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28  typedef mat<4, 4, float, highp> highp_mat4;
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29 
\n
34  typedef mat<4, 4, float, lowp> lowp_mat4x4;
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35 
\n
40  typedef mat<4, 4, float, mediump> mediump_mat4x4;
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41 
\n
46  typedef mat<4, 4, float, highp> highp_mat4x4;
\n
47 
\n
49 }//namespace glm
\n
glm::mediump_mat4x4
mat< 4, 4, float, mediump > mediump_mat4x4
4 columns of 4 components matrix of single-precision floating-point numbers using medium precision ar...
Definition: matrix_float4x4_precision.hpp:40
\n
glm::lowp_mat4
mat< 4, 4, float, lowp > lowp_mat4
4 columns of 4 components matrix of single-precision floating-point numbers using low precision arith...
Definition: matrix_float4x4_precision.hpp:16
\n
glm::mediump_mat4
mat< 4, 4, float, mediump > mediump_mat4
4 columns of 4 components matrix of single-precision floating-point numbers using medium precision ar...
Definition: matrix_float4x4_precision.hpp:22
\n
glm::highp_mat4x4
mat< 4, 4, float, highp > highp_mat4x4
4 columns of 4 components matrix of single-precision floating-point numbers using high precision arit...
Definition: matrix_float4x4_precision.hpp:46
\n
glm::lowp_mat4x4
mat< 4, 4, float, lowp > lowp_mat4x4
4 columns of 4 components matrix of single-precision floating-point numbers using low precision arith...
Definition: matrix_float4x4_precision.hpp:34
\n
glm::highp_mat4
mat< 4, 4, float, highp > highp_mat4
4 columns of 4 components matrix of single-precision floating-point numbers using high precision arit...
Definition: matrix_float4x4_precision.hpp:28
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glm
Definition: common.hpp:20
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\nTypedefs

typedef mat< 2, 2, int, highp > highp_imat2
 High-qualifier signed integer 2x2 matrix. More...
 
typedef mat< 2, 2, int, highp > highp_imat2x2
 High-qualifier signed integer 2x2 matrix. More...
 
typedef mat< 2, 3, int, highp > highp_imat2x3
 High-qualifier signed integer 2x3 matrix. More...
 
typedef mat< 2, 4, int, highp > highp_imat2x4
 High-qualifier signed integer 2x4 matrix. More...
 
typedef mat< 3, 3, int, highp > highp_imat3
 High-qualifier signed integer 3x3 matrix. More...
 
typedef mat< 3, 2, int, highp > highp_imat3x2
 High-qualifier signed integer 3x2 matrix. More...
 
typedef mat< 3, 3, int, highp > highp_imat3x3
 High-qualifier signed integer 3x3 matrix. More...
 
typedef mat< 3, 4, int, highp > highp_imat3x4
 High-qualifier signed integer 3x4 matrix. More...
 
typedef mat< 4, 4, int, highp > highp_imat4
 High-qualifier signed integer 4x4 matrix. More...
 
typedef mat< 4, 2, int, highp > highp_imat4x2
 High-qualifier signed integer 4x2 matrix. More...
 
typedef mat< 4, 3, int, highp > highp_imat4x3
 High-qualifier signed integer 4x3 matrix. More...
 
typedef mat< 4, 4, int, highp > highp_imat4x4
 High-qualifier signed integer 4x4 matrix. More...
 
typedef mat< 2, 2, uint, highp > highp_umat2
 High-qualifier unsigned integer 2x2 matrix. More...
 
typedef mat< 2, 2, uint, highp > highp_umat2x2
 High-qualifier unsigned integer 2x2 matrix. More...
 
typedef mat< 2, 3, uint, highp > highp_umat2x3
 High-qualifier unsigned integer 2x3 matrix. More...
 
typedef mat< 2, 4, uint, highp > highp_umat2x4
 High-qualifier unsigned integer 2x4 matrix. More...
 
typedef mat< 3, 3, uint, highp > highp_umat3
 High-qualifier unsigned integer 3x3 matrix. More...
 
typedef mat< 3, 2, uint, highp > highp_umat3x2
 High-qualifier unsigned integer 3x2 matrix. More...
 
typedef mat< 3, 3, uint, highp > highp_umat3x3
 High-qualifier unsigned integer 3x3 matrix. More...
 
typedef mat< 3, 4, uint, highp > highp_umat3x4
 High-qualifier unsigned integer 3x4 matrix. More...
 
typedef mat< 4, 4, uint, highp > highp_umat4
 High-qualifier unsigned integer 4x4 matrix. More...
 
typedef mat< 4, 2, uint, highp > highp_umat4x2
 High-qualifier unsigned integer 4x2 matrix. More...
 
typedef mat< 4, 3, uint, highp > highp_umat4x3
 High-qualifier unsigned integer 4x3 matrix. More...
 
typedef mat< 4, 4, uint, highp > highp_umat4x4
 High-qualifier unsigned integer 4x4 matrix. More...
 
typedef mediump_imat2 imat2
 Signed integer 2x2 matrix. More...
 
typedef mediump_imat2x2 imat2x2
 Signed integer 2x2 matrix. More...
 
typedef mediump_imat2x3 imat2x3
 Signed integer 2x3 matrix. More...
 
typedef mediump_imat2x4 imat2x4
 Signed integer 2x4 matrix. More...
 
typedef mediump_imat3 imat3
 Signed integer 3x3 matrix. More...
 
typedef mediump_imat3x2 imat3x2
 Signed integer 3x2 matrix. More...
 
typedef mediump_imat3x3 imat3x3
 Signed integer 3x3 matrix. More...
 
typedef mediump_imat3x4 imat3x4
 Signed integer 3x4 matrix. More...
 
typedef mediump_imat4 imat4
 Signed integer 4x4 matrix. More...
 
typedef mediump_imat4x2 imat4x2
 Signed integer 4x2 matrix. More...
 
typedef mediump_imat4x3 imat4x3
 Signed integer 4x3 matrix. More...
 
typedef mediump_imat4x4 imat4x4
 Signed integer 4x4 matrix. More...
 
typedef mat< 2, 2, int, lowp > lowp_imat2
 Low-qualifier signed integer 2x2 matrix. More...
 
typedef mat< 2, 2, int, lowp > lowp_imat2x2
 Low-qualifier signed integer 2x2 matrix. More...
 
typedef mat< 2, 3, int, lowp > lowp_imat2x3
 Low-qualifier signed integer 2x3 matrix. More...
 
typedef mat< 2, 4, int, lowp > lowp_imat2x4
 Low-qualifier signed integer 2x4 matrix. More...
 
typedef mat< 3, 3, int, lowp > lowp_imat3
 Low-qualifier signed integer 3x3 matrix. More...
 
typedef mat< 3, 2, int, lowp > lowp_imat3x2
 Low-qualifier signed integer 3x2 matrix. More...
 
typedef mat< 3, 3, int, lowp > lowp_imat3x3
 Low-qualifier signed integer 3x3 matrix. More...
 
typedef mat< 3, 4, int, lowp > lowp_imat3x4
 Low-qualifier signed integer 3x4 matrix. More...
 
typedef mat< 4, 4, int, lowp > lowp_imat4
 Low-qualifier signed integer 4x4 matrix. More...
 
typedef mat< 4, 2, int, lowp > lowp_imat4x2
 Low-qualifier signed integer 4x2 matrix. More...
 
typedef mat< 4, 3, int, lowp > lowp_imat4x3
 Low-qualifier signed integer 4x3 matrix. More...
 
typedef mat< 4, 4, int, lowp > lowp_imat4x4
 Low-qualifier signed integer 4x4 matrix. More...
 
typedef mat< 2, 2, uint, lowp > lowp_umat2
 Low-qualifier unsigned integer 2x2 matrix. More...
 
typedef mat< 2, 2, uint, lowp > lowp_umat2x2
 Low-qualifier unsigned integer 2x2 matrix. More...
 
typedef mat< 2, 3, uint, lowp > lowp_umat2x3
 Low-qualifier unsigned integer 2x3 matrix. More...
 
typedef mat< 2, 4, uint, lowp > lowp_umat2x4
 Low-qualifier unsigned integer 2x4 matrix. More...
 
typedef mat< 3, 3, uint, lowp > lowp_umat3
 Low-qualifier unsigned integer 3x3 matrix. More...
 
typedef mat< 3, 2, uint, lowp > lowp_umat3x2
 Low-qualifier unsigned integer 3x2 matrix. More...
 
typedef mat< 3, 3, uint, lowp > lowp_umat3x3
 Low-qualifier unsigned integer 3x3 matrix. More...
 
typedef mat< 3, 4, uint, lowp > lowp_umat3x4
 Low-qualifier unsigned integer 3x4 matrix. More...
 
typedef mat< 4, 4, uint, lowp > lowp_umat4
 Low-qualifier unsigned integer 4x4 matrix. More...
 
typedef mat< 4, 2, uint, lowp > lowp_umat4x2
 Low-qualifier unsigned integer 4x2 matrix. More...
 
typedef mat< 4, 3, uint, lowp > lowp_umat4x3
 Low-qualifier unsigned integer 4x3 matrix. More...
 
typedef mat< 4, 4, uint, lowp > lowp_umat4x4
 Low-qualifier unsigned integer 4x4 matrix. More...
 
typedef mat< 2, 2, int, mediump > mediump_imat2
 Medium-qualifier signed integer 2x2 matrix. More...
 
typedef mat< 2, 2, int, mediump > mediump_imat2x2
 Medium-qualifier signed integer 2x2 matrix. More...
 
typedef mat< 2, 3, int, mediump > mediump_imat2x3
 Medium-qualifier signed integer 2x3 matrix. More...
 
typedef mat< 2, 4, int, mediump > mediump_imat2x4
 Medium-qualifier signed integer 2x4 matrix. More...
 
typedef mat< 3, 3, int, mediump > mediump_imat3
 Medium-qualifier signed integer 3x3 matrix. More...
 
typedef mat< 3, 2, int, mediump > mediump_imat3x2
 Medium-qualifier signed integer 3x2 matrix. More...
 
typedef mat< 3, 3, int, mediump > mediump_imat3x3
 Medium-qualifier signed integer 3x3 matrix. More...
 
typedef mat< 3, 4, int, mediump > mediump_imat3x4
 Medium-qualifier signed integer 3x4 matrix. More...
 
typedef mat< 4, 4, int, mediump > mediump_imat4
 Medium-qualifier signed integer 4x4 matrix. More...
 
typedef mat< 4, 2, int, mediump > mediump_imat4x2
 Medium-qualifier signed integer 4x2 matrix. More...
 
typedef mat< 4, 3, int, mediump > mediump_imat4x3
 Medium-qualifier signed integer 4x3 matrix. More...
 
typedef mat< 4, 4, int, mediump > mediump_imat4x4
 Medium-qualifier signed integer 4x4 matrix. More...
 
typedef mat< 2, 2, uint, mediump > mediump_umat2
 Medium-qualifier unsigned integer 2x2 matrix. More...
 
typedef mat< 2, 2, uint, mediump > mediump_umat2x2
 Medium-qualifier unsigned integer 2x2 matrix. More...
 
typedef mat< 2, 3, uint, mediump > mediump_umat2x3
 Medium-qualifier unsigned integer 2x3 matrix. More...
 
typedef mat< 2, 4, uint, mediump > mediump_umat2x4
 Medium-qualifier unsigned integer 2x4 matrix. More...
 
typedef mat< 3, 3, uint, mediump > mediump_umat3
 Medium-qualifier unsigned integer 3x3 matrix. More...
 
typedef mat< 3, 2, uint, mediump > mediump_umat3x2
 Medium-qualifier unsigned integer 3x2 matrix. More...
 
typedef mat< 3, 3, uint, mediump > mediump_umat3x3
 Medium-qualifier unsigned integer 3x3 matrix. More...
 
typedef mat< 3, 4, uint, mediump > mediump_umat3x4
 Medium-qualifier unsigned integer 3x4 matrix. More...
 
typedef mat< 4, 4, uint, mediump > mediump_umat4
 Medium-qualifier unsigned integer 4x4 matrix. More...
 
typedef mat< 4, 2, uint, mediump > mediump_umat4x2
 Medium-qualifier unsigned integer 4x2 matrix. More...
 
typedef mat< 4, 3, uint, mediump > mediump_umat4x3
 Medium-qualifier unsigned integer 4x3 matrix. More...
 
typedef mat< 4, 4, uint, mediump > mediump_umat4x4
 Medium-qualifier unsigned integer 4x4 matrix. More...
 
typedef mediump_umat2 umat2
 Unsigned integer 2x2 matrix. More...
 
typedef mediump_umat2x2 umat2x2
 Unsigned integer 2x2 matrix. More...
 
typedef mediump_umat2x3 umat2x3
 Unsigned integer 2x3 matrix. More...
 
typedef mediump_umat2x4 umat2x4
 Unsigned integer 2x4 matrix. More...
 
typedef mediump_umat3 umat3
 Unsigned integer 3x3 matrix. More...
 
typedef mediump_umat3x2 umat3x2
 Unsigned integer 3x2 matrix. More...
 
typedef mediump_umat3x3 umat3x3
 Unsigned integer 3x3 matrix. More...
 
typedef mediump_umat3x4 umat3x4
 Unsigned integer 3x4 matrix. More...
 
typedef mediump_umat4 umat4
 Unsigned integer 4x4 matrix. More...
 
typedef mediump_umat4x2 umat4x2
 Unsigned integer 4x2 matrix. More...
 
typedef mediump_umat4x3 umat4x3
 Unsigned integer 4x3 matrix. More...
 
typedef mediump_umat4x4 umat4x4
 Unsigned integer 4x4 matrix. More...
 
\n

Detailed Description

\n

GLM_GTC_matrix_integer

\n
See also
Core features (dependence)
\n\n

Definition in file matrix_integer.hpp.

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matrix_integer.hpp
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\nGo to the documentation of this file.
1 
\n
13 #pragma once
\n
14 
\n
15 // Dependency:
\n
16 #include "../mat2x2.hpp"
\n
17 #include "../mat2x3.hpp"
\n
18 #include "../mat2x4.hpp"
\n
19 #include "../mat3x2.hpp"
\n
20 #include "../mat3x3.hpp"
\n
21 #include "../mat3x4.hpp"
\n
22 #include "../mat4x2.hpp"
\n
23 #include "../mat4x3.hpp"
\n
24 #include "../mat4x4.hpp"
\n
25 
\n
26 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
27 # pragma message("GLM: GLM_GTC_matrix_integer extension included")
\n
28 #endif
\n
29 
\n
30 namespace glm
\n
31 {
\n
34 
\n
37  typedef mat<2, 2, int, highp> highp_imat2;
\n
38 
\n
41  typedef mat<3, 3, int, highp> highp_imat3;
\n
42 
\n
45  typedef mat<4, 4, int, highp> highp_imat4;
\n
46 
\n
49  typedef mat<2, 2, int, highp> highp_imat2x2;
\n
50 
\n
53  typedef mat<2, 3, int, highp> highp_imat2x3;
\n
54 
\n
57  typedef mat<2, 4, int, highp> highp_imat2x4;
\n
58 
\n
61  typedef mat<3, 2, int, highp> highp_imat3x2;
\n
62 
\n
65  typedef mat<3, 3, int, highp> highp_imat3x3;
\n
66 
\n
69  typedef mat<3, 4, int, highp> highp_imat3x4;
\n
70 
\n
73  typedef mat<4, 2, int, highp> highp_imat4x2;
\n
74 
\n
77  typedef mat<4, 3, int, highp> highp_imat4x3;
\n
78 
\n
81  typedef mat<4, 4, int, highp> highp_imat4x4;
\n
82 
\n
83 
\n
86  typedef mat<2, 2, int, mediump> mediump_imat2;
\n
87 
\n
90  typedef mat<3, 3, int, mediump> mediump_imat3;
\n
91 
\n
94  typedef mat<4, 4, int, mediump> mediump_imat4;
\n
95 
\n
96 
\n
99  typedef mat<2, 2, int, mediump> mediump_imat2x2;
\n
100 
\n
103  typedef mat<2, 3, int, mediump> mediump_imat2x3;
\n
104 
\n
107  typedef mat<2, 4, int, mediump> mediump_imat2x4;
\n
108 
\n
111  typedef mat<3, 2, int, mediump> mediump_imat3x2;
\n
112 
\n
115  typedef mat<3, 3, int, mediump> mediump_imat3x3;
\n
116 
\n
119  typedef mat<3, 4, int, mediump> mediump_imat3x4;
\n
120 
\n
123  typedef mat<4, 2, int, mediump> mediump_imat4x2;
\n
124 
\n
127  typedef mat<4, 3, int, mediump> mediump_imat4x3;
\n
128 
\n
131  typedef mat<4, 4, int, mediump> mediump_imat4x4;
\n
132 
\n
133 
\n
136  typedef mat<2, 2, int, lowp> lowp_imat2;
\n
137 
\n
140  typedef mat<3, 3, int, lowp> lowp_imat3;
\n
141 
\n
144  typedef mat<4, 4, int, lowp> lowp_imat4;
\n
145 
\n
146 
\n
149  typedef mat<2, 2, int, lowp> lowp_imat2x2;
\n
150 
\n
153  typedef mat<2, 3, int, lowp> lowp_imat2x3;
\n
154 
\n
157  typedef mat<2, 4, int, lowp> lowp_imat2x4;
\n
158 
\n
161  typedef mat<3, 2, int, lowp> lowp_imat3x2;
\n
162 
\n
165  typedef mat<3, 3, int, lowp> lowp_imat3x3;
\n
166 
\n
169  typedef mat<3, 4, int, lowp> lowp_imat3x4;
\n
170 
\n
173  typedef mat<4, 2, int, lowp> lowp_imat4x2;
\n
174 
\n
177  typedef mat<4, 3, int, lowp> lowp_imat4x3;
\n
178 
\n
181  typedef mat<4, 4, int, lowp> lowp_imat4x4;
\n
182 
\n
183 
\n
186  typedef mat<2, 2, uint, highp> highp_umat2;
\n
187 
\n
190  typedef mat<3, 3, uint, highp> highp_umat3;
\n
191 
\n
194  typedef mat<4, 4, uint, highp> highp_umat4;
\n
195 
\n
198  typedef mat<2, 2, uint, highp> highp_umat2x2;
\n
199 
\n
202  typedef mat<2, 3, uint, highp> highp_umat2x3;
\n
203 
\n
206  typedef mat<2, 4, uint, highp> highp_umat2x4;
\n
207 
\n
210  typedef mat<3, 2, uint, highp> highp_umat3x2;
\n
211 
\n
214  typedef mat<3, 3, uint, highp> highp_umat3x3;
\n
215 
\n
218  typedef mat<3, 4, uint, highp> highp_umat3x4;
\n
219 
\n
222  typedef mat<4, 2, uint, highp> highp_umat4x2;
\n
223 
\n
226  typedef mat<4, 3, uint, highp> highp_umat4x3;
\n
227 
\n
230  typedef mat<4, 4, uint, highp> highp_umat4x4;
\n
231 
\n
232 
\n
235  typedef mat<2, 2, uint, mediump> mediump_umat2;
\n
236 
\n
239  typedef mat<3, 3, uint, mediump> mediump_umat3;
\n
240 
\n
243  typedef mat<4, 4, uint, mediump> mediump_umat4;
\n
244 
\n
245 
\n
248  typedef mat<2, 2, uint, mediump> mediump_umat2x2;
\n
249 
\n
252  typedef mat<2, 3, uint, mediump> mediump_umat2x3;
\n
253 
\n
256  typedef mat<2, 4, uint, mediump> mediump_umat2x4;
\n
257 
\n
260  typedef mat<3, 2, uint, mediump> mediump_umat3x2;
\n
261 
\n
264  typedef mat<3, 3, uint, mediump> mediump_umat3x3;
\n
265 
\n
268  typedef mat<3, 4, uint, mediump> mediump_umat3x4;
\n
269 
\n
272  typedef mat<4, 2, uint, mediump> mediump_umat4x2;
\n
273 
\n
276  typedef mat<4, 3, uint, mediump> mediump_umat4x3;
\n
277 
\n
280  typedef mat<4, 4, uint, mediump> mediump_umat4x4;
\n
281 
\n
282 
\n
285  typedef mat<2, 2, uint, lowp> lowp_umat2;
\n
286 
\n
289  typedef mat<3, 3, uint, lowp> lowp_umat3;
\n
290 
\n
293  typedef mat<4, 4, uint, lowp> lowp_umat4;
\n
294 
\n
295 
\n
298  typedef mat<2, 2, uint, lowp> lowp_umat2x2;
\n
299 
\n
302  typedef mat<2, 3, uint, lowp> lowp_umat2x3;
\n
303 
\n
306  typedef mat<2, 4, uint, lowp> lowp_umat2x4;
\n
307 
\n
310  typedef mat<3, 2, uint, lowp> lowp_umat3x2;
\n
311 
\n
314  typedef mat<3, 3, uint, lowp> lowp_umat3x3;
\n
315 
\n
318  typedef mat<3, 4, uint, lowp> lowp_umat3x4;
\n
319 
\n
322  typedef mat<4, 2, uint, lowp> lowp_umat4x2;
\n
323 
\n
326  typedef mat<4, 3, uint, lowp> lowp_umat4x3;
\n
327 
\n
330  typedef mat<4, 4, uint, lowp> lowp_umat4x4;
\n
331 
\n
332 #if(defined(GLM_PRECISION_HIGHP_INT))
\n
333  typedef highp_imat2 imat2;
\n
334  typedef highp_imat3 imat3;
\n
335  typedef highp_imat4 imat4;
\n
336  typedef highp_imat2x2 imat2x2;
\n
337  typedef highp_imat2x3 imat2x3;
\n
338  typedef highp_imat2x4 imat2x4;
\n
339  typedef highp_imat3x2 imat3x2;
\n
340  typedef highp_imat3x3 imat3x3;
\n
341  typedef highp_imat3x4 imat3x4;
\n
342  typedef highp_imat4x2 imat4x2;
\n
343  typedef highp_imat4x3 imat4x3;
\n
344  typedef highp_imat4x4 imat4x4;
\n
345 #elif(defined(GLM_PRECISION_LOWP_INT))
\n
346  typedef lowp_imat2 imat2;
\n
347  typedef lowp_imat3 imat3;
\n
348  typedef lowp_imat4 imat4;
\n
349  typedef lowp_imat2x2 imat2x2;
\n
350  typedef lowp_imat2x3 imat2x3;
\n
351  typedef lowp_imat2x4 imat2x4;
\n
352  typedef lowp_imat3x2 imat3x2;
\n
353  typedef lowp_imat3x3 imat3x3;
\n
354  typedef lowp_imat3x4 imat3x4;
\n
355  typedef lowp_imat4x2 imat4x2;
\n
356  typedef lowp_imat4x3 imat4x3;
\n
357  typedef lowp_imat4x4 imat4x4;
\n
358 #else //if(defined(GLM_PRECISION_MEDIUMP_INT))
\n
359 
\n
362  typedef mediump_imat2 imat2;
\n
363 
\n
366  typedef mediump_imat3 imat3;
\n
367 
\n
370  typedef mediump_imat4 imat4;
\n
371 
\n
374  typedef mediump_imat2x2 imat2x2;
\n
375 
\n
378  typedef mediump_imat2x3 imat2x3;
\n
379 
\n
382  typedef mediump_imat2x4 imat2x4;
\n
383 
\n
386  typedef mediump_imat3x2 imat3x2;
\n
387 
\n
390  typedef mediump_imat3x3 imat3x3;
\n
391 
\n
394  typedef mediump_imat3x4 imat3x4;
\n
395 
\n
398  typedef mediump_imat4x2 imat4x2;
\n
399 
\n
402  typedef mediump_imat4x3 imat4x3;
\n
403 
\n
406  typedef mediump_imat4x4 imat4x4;
\n
407 #endif//GLM_PRECISION
\n
408 
\n
409 #if(defined(GLM_PRECISION_HIGHP_UINT))
\n
410  typedef highp_umat2 umat2;
\n
411  typedef highp_umat3 umat3;
\n
412  typedef highp_umat4 umat4;
\n
413  typedef highp_umat2x2 umat2x2;
\n
414  typedef highp_umat2x3 umat2x3;
\n
415  typedef highp_umat2x4 umat2x4;
\n
416  typedef highp_umat3x2 umat3x2;
\n
417  typedef highp_umat3x3 umat3x3;
\n
418  typedef highp_umat3x4 umat3x4;
\n
419  typedef highp_umat4x2 umat4x2;
\n
420  typedef highp_umat4x3 umat4x3;
\n
421  typedef highp_umat4x4 umat4x4;
\n
422 #elif(defined(GLM_PRECISION_LOWP_UINT))
\n
423  typedef lowp_umat2 umat2;
\n
424  typedef lowp_umat3 umat3;
\n
425  typedef lowp_umat4 umat4;
\n
426  typedef lowp_umat2x2 umat2x2;
\n
427  typedef lowp_umat2x3 umat2x3;
\n
428  typedef lowp_umat2x4 umat2x4;
\n
429  typedef lowp_umat3x2 umat3x2;
\n
430  typedef lowp_umat3x3 umat3x3;
\n
431  typedef lowp_umat3x4 umat3x4;
\n
432  typedef lowp_umat4x2 umat4x2;
\n
433  typedef lowp_umat4x3 umat4x3;
\n
434  typedef lowp_umat4x4 umat4x4;
\n
435 #else //if(defined(GLM_PRECISION_MEDIUMP_UINT))
\n
436 
\n
439  typedef mediump_umat2 umat2;
\n
440 
\n
443  typedef mediump_umat3 umat3;
\n
444 
\n
447  typedef mediump_umat4 umat4;
\n
448 
\n
451  typedef mediump_umat2x2 umat2x2;
\n
452 
\n
455  typedef mediump_umat2x3 umat2x3;
\n
456 
\n
459  typedef mediump_umat2x4 umat2x4;
\n
460 
\n
463  typedef mediump_umat3x2 umat3x2;
\n
464 
\n
467  typedef mediump_umat3x3 umat3x3;
\n
468 
\n
471  typedef mediump_umat3x4 umat3x4;
\n
472 
\n
475  typedef mediump_umat4x2 umat4x2;
\n
476 
\n
479  typedef mediump_umat4x3 umat4x3;
\n
480 
\n
483  typedef mediump_umat4x4 umat4x4;
\n
484 #endif//GLM_PRECISION
\n
485 
\n
487 }//namespace glm
\n
glm::imat4x4
mediump_imat4x4 imat4x4
Signed integer 4x4 matrix.
Definition: matrix_integer.hpp:406
\n
glm::imat2x2
mediump_imat2x2 imat2x2
Signed integer 2x2 matrix.
Definition: matrix_integer.hpp:374
\n
glm::umat4
mediump_umat4 umat4
Unsigned integer 4x4 matrix.
Definition: matrix_integer.hpp:447
\n
glm::umat4x2
mediump_umat4x2 umat4x2
Unsigned integer 4x2 matrix.
Definition: matrix_integer.hpp:475
\n
glm::lowp_umat4x4
mat< 4, 4, uint, lowp > lowp_umat4x4
Low-qualifier unsigned integer 4x4 matrix.
Definition: matrix_integer.hpp:330
\n
glm::mediump_imat4x2
mat< 4, 2, int, mediump > mediump_imat4x2
Medium-qualifier signed integer 4x2 matrix.
Definition: matrix_integer.hpp:123
\n
glm::lowp_umat4
mat< 4, 4, uint, lowp > lowp_umat4
Low-qualifier unsigned integer 4x4 matrix.
Definition: matrix_integer.hpp:293
\n
glm::highp_imat3x2
mat< 3, 2, int, highp > highp_imat3x2
High-qualifier signed integer 3x2 matrix.
Definition: matrix_integer.hpp:61
\n
glm::highp_umat3x3
mat< 3, 3, uint, highp > highp_umat3x3
High-qualifier unsigned integer 3x3 matrix.
Definition: matrix_integer.hpp:214
\n
glm::lowp_umat2x2
mat< 2, 2, uint, lowp > lowp_umat2x2
Low-qualifier unsigned integer 2x2 matrix.
Definition: matrix_integer.hpp:298
\n
glm::umat3x3
mediump_umat3x3 umat3x3
Unsigned integer 3x3 matrix.
Definition: matrix_integer.hpp:467
\n
glm::highp_umat2x4
mat< 2, 4, uint, highp > highp_umat2x4
High-qualifier unsigned integer 2x4 matrix.
Definition: matrix_integer.hpp:206
\n
glm::umat3x2
mediump_umat3x2 umat3x2
Unsigned integer 3x2 matrix.
Definition: matrix_integer.hpp:463
\n
glm::lowp_imat3x2
mat< 3, 2, int, lowp > lowp_imat3x2
Low-qualifier signed integer 3x2 matrix.
Definition: matrix_integer.hpp:161
\n
glm::highp_umat3
mat< 3, 3, uint, highp > highp_umat3
High-qualifier unsigned integer 3x3 matrix.
Definition: matrix_integer.hpp:190
\n
glm::mediump_imat4x3
mat< 4, 3, int, mediump > mediump_imat4x3
Medium-qualifier signed integer 4x3 matrix.
Definition: matrix_integer.hpp:127
\n
glm::imat3
mediump_imat3 imat3
Signed integer 3x3 matrix.
Definition: matrix_integer.hpp:366
\n
glm::mediump_imat2
mat< 2, 2, int, mediump > mediump_imat2
Medium-qualifier signed integer 2x2 matrix.
Definition: matrix_integer.hpp:86
\n
glm::mediump_umat3x4
mat< 3, 4, uint, mediump > mediump_umat3x4
Medium-qualifier unsigned integer 3x4 matrix.
Definition: matrix_integer.hpp:268
\n
glm::lowp_imat4x4
mat< 4, 4, int, lowp > lowp_imat4x4
Low-qualifier signed integer 4x4 matrix.
Definition: matrix_integer.hpp:181
\n
glm::highp_imat2x4
mat< 2, 4, int, highp > highp_imat2x4
High-qualifier signed integer 2x4 matrix.
Definition: matrix_integer.hpp:57
\n
glm::umat2x3
mediump_umat2x3 umat2x3
Unsigned integer 2x3 matrix.
Definition: matrix_integer.hpp:455
\n
glm::lowp_imat4x3
mat< 4, 3, int, lowp > lowp_imat4x3
Low-qualifier signed integer 4x3 matrix.
Definition: matrix_integer.hpp:177
\n
glm::lowp_umat3
mat< 3, 3, uint, lowp > lowp_umat3
Low-qualifier unsigned integer 3x3 matrix.
Definition: matrix_integer.hpp:289
\n
glm::mediump_umat4x4
mat< 4, 4, uint, mediump > mediump_umat4x4
Medium-qualifier unsigned integer 4x4 matrix.
Definition: matrix_integer.hpp:280
\n
glm::mediump_umat3x2
mat< 3, 2, uint, mediump > mediump_umat3x2
Medium-qualifier unsigned integer 3x2 matrix.
Definition: matrix_integer.hpp:260
\n
glm::mediump_umat2x4
mat< 2, 4, uint, mediump > mediump_umat2x4
Medium-qualifier unsigned integer 2x4 matrix.
Definition: matrix_integer.hpp:256
\n
glm::highp_imat4x4
mat< 4, 4, int, highp > highp_imat4x4
High-qualifier signed integer 4x4 matrix.
Definition: matrix_integer.hpp:81
\n
glm::lowp_umat2x4
mat< 2, 4, uint, lowp > lowp_umat2x4
Low-qualifier unsigned integer 2x4 matrix.
Definition: matrix_integer.hpp:306
\n
glm::imat4x3
mediump_imat4x3 imat4x3
Signed integer 4x3 matrix.
Definition: matrix_integer.hpp:402
\n
glm::mediump_umat3x3
mat< 3, 3, uint, mediump > mediump_umat3x3
Medium-qualifier unsigned integer 3x3 matrix.
Definition: matrix_integer.hpp:264
\n
glm::highp_imat2
mat< 2, 2, int, highp > highp_imat2
High-qualifier signed integer 2x2 matrix.
Definition: matrix_integer.hpp:37
\n
glm::umat2
mediump_umat2 umat2
Unsigned integer 2x2 matrix.
Definition: matrix_integer.hpp:439
\n
glm::lowp_umat3x4
mat< 3, 4, uint, lowp > lowp_umat3x4
Low-qualifier unsigned integer 3x4 matrix.
Definition: matrix_integer.hpp:318
\n
glm::mediump_umat4x2
mat< 4, 2, uint, mediump > mediump_umat4x2
Medium-qualifier unsigned integer 4x2 matrix.
Definition: matrix_integer.hpp:272
\n
glm::imat4x2
mediump_imat4x2 imat4x2
Signed integer 4x2 matrix.
Definition: matrix_integer.hpp:398
\n
glm::mediump_imat2x3
mat< 2, 3, int, mediump > mediump_imat2x3
Medium-qualifier signed integer 2x3 matrix.
Definition: matrix_integer.hpp:103
\n
glm::mediump_umat2
mat< 2, 2, uint, mediump > mediump_umat2
Medium-qualifier unsigned integer 2x2 matrix.
Definition: matrix_integer.hpp:235
\n
glm::imat2
mediump_imat2 imat2
Signed integer 2x2 matrix.
Definition: matrix_integer.hpp:362
\n
glm::mediump_umat4x3
mat< 4, 3, uint, mediump > mediump_umat4x3
Medium-qualifier unsigned integer 4x3 matrix.
Definition: matrix_integer.hpp:276
\n
glm::mediump_imat3
mat< 3, 3, int, mediump > mediump_imat3
Medium-qualifier signed integer 3x3 matrix.
Definition: matrix_integer.hpp:90
\n
glm::highp_umat2
mat< 2, 2, uint, highp > highp_umat2
High-qualifier unsigned integer 2x2 matrix.
Definition: matrix_integer.hpp:186
\n
glm::imat3x4
mediump_imat3x4 imat3x4
Signed integer 3x4 matrix.
Definition: matrix_integer.hpp:394
\n
glm::highp_umat3x2
mat< 3, 2, uint, highp > highp_umat3x2
High-qualifier unsigned integer 3x2 matrix.
Definition: matrix_integer.hpp:210
\n
glm::highp_imat2x2
mat< 2, 2, int, highp > highp_imat2x2
High-qualifier signed integer 2x2 matrix.
Definition: matrix_integer.hpp:49
\n
glm::highp_umat3x4
mat< 3, 4, uint, highp > highp_umat3x4
High-qualifier unsigned integer 3x4 matrix.
Definition: matrix_integer.hpp:218
\n
glm::mediump_imat3x3
mat< 3, 3, int, mediump > mediump_imat3x3
Medium-qualifier signed integer 3x3 matrix.
Definition: matrix_integer.hpp:115
\n
glm::highp_umat4x4
mat< 4, 4, uint, highp > highp_umat4x4
High-qualifier unsigned integer 4x4 matrix.
Definition: matrix_integer.hpp:230
\n
glm::imat2x4
mediump_imat2x4 imat2x4
Signed integer 2x4 matrix.
Definition: matrix_integer.hpp:382
\n
glm::umat2x4
mediump_umat2x4 umat2x4
Unsigned integer 2x4 matrix.
Definition: matrix_integer.hpp:459
\n
glm::mediump_imat2x4
mat< 2, 4, int, mediump > mediump_imat2x4
Medium-qualifier signed integer 2x4 matrix.
Definition: matrix_integer.hpp:107
\n
glm::lowp_imat2
mat< 2, 2, int, lowp > lowp_imat2
Low-qualifier signed integer 2x2 matrix.
Definition: matrix_integer.hpp:136
\n
glm::lowp_imat4x2
mat< 4, 2, int, lowp > lowp_imat4x2
Low-qualifier signed integer 4x2 matrix.
Definition: matrix_integer.hpp:173
\n
glm::lowp_umat4x3
mat< 4, 3, uint, lowp > lowp_umat4x3
Low-qualifier unsigned integer 4x3 matrix.
Definition: matrix_integer.hpp:326
\n
glm::imat4
mediump_imat4 imat4
Signed integer 4x4 matrix.
Definition: matrix_integer.hpp:370
\n
glm::imat3x2
mediump_imat3x2 imat3x2
Signed integer 3x2 matrix.
Definition: matrix_integer.hpp:386
\n
glm::lowp_umat2x3
mat< 2, 3, uint, lowp > lowp_umat2x3
Low-qualifier unsigned integer 2x3 matrix.
Definition: matrix_integer.hpp:302
\n
glm::mediump_imat3x2
mat< 3, 2, int, mediump > mediump_imat3x2
Medium-qualifier signed integer 3x2 matrix.
Definition: matrix_integer.hpp:111
\n
glm::umat4x4
mediump_umat4x4 umat4x4
Unsigned integer 4x4 matrix.
Definition: matrix_integer.hpp:483
\n
glm::highp_imat4x3
mat< 4, 3, int, highp > highp_imat4x3
High-qualifier signed integer 4x3 matrix.
Definition: matrix_integer.hpp:77
\n
glm::umat4x3
mediump_umat4x3 umat4x3
Unsigned integer 4x3 matrix.
Definition: matrix_integer.hpp:479
\n
glm::lowp_umat4x2
mat< 4, 2, uint, lowp > lowp_umat4x2
Low-qualifier unsigned integer 4x2 matrix.
Definition: matrix_integer.hpp:322
\n
glm::lowp_umat3x2
mat< 3, 2, uint, lowp > lowp_umat3x2
Low-qualifier unsigned integer 3x2 matrix.
Definition: matrix_integer.hpp:310
\n
glm::highp_umat2x2
mat< 2, 2, uint, highp > highp_umat2x2
High-qualifier unsigned integer 2x2 matrix.
Definition: matrix_integer.hpp:198
\n
glm::lowp_imat3x3
mat< 3, 3, int, lowp > lowp_imat3x3
Low-qualifier signed integer 3x3 matrix.
Definition: matrix_integer.hpp:165
\n
glm::highp_imat3x3
mat< 3, 3, int, highp > highp_imat3x3
High-qualifier signed integer 3x3 matrix.
Definition: matrix_integer.hpp:65
\n
glm::mediump_umat2x3
mat< 2, 3, uint, mediump > mediump_umat2x3
Medium-qualifier unsigned integer 2x3 matrix.
Definition: matrix_integer.hpp:252
\n
glm::highp_umat4x2
mat< 4, 2, uint, highp > highp_umat4x2
High-qualifier unsigned integer 4x2 matrix.
Definition: matrix_integer.hpp:222
\n
glm::lowp_umat3x3
mat< 3, 3, uint, lowp > lowp_umat3x3
Low-qualifier unsigned integer 3x3 matrix.
Definition: matrix_integer.hpp:314
\n
glm::imat2x3
mediump_imat2x3 imat2x3
Signed integer 2x3 matrix.
Definition: matrix_integer.hpp:378
\n
glm::lowp_imat2x3
mat< 2, 3, int, lowp > lowp_imat2x3
Low-qualifier signed integer 2x3 matrix.
Definition: matrix_integer.hpp:153
\n
glm::highp_umat4
mat< 4, 4, uint, highp > highp_umat4
High-qualifier unsigned integer 4x4 matrix.
Definition: matrix_integer.hpp:194
\n
glm::highp_imat3
mat< 3, 3, int, highp > highp_imat3
High-qualifier signed integer 3x3 matrix.
Definition: matrix_integer.hpp:41
\n
glm::mediump_umat3
mat< 3, 3, uint, mediump > mediump_umat3
Medium-qualifier unsigned integer 3x3 matrix.
Definition: matrix_integer.hpp:239
\n
glm::mediump_imat2x2
mat< 2, 2, int, mediump > mediump_imat2x2
Medium-qualifier signed integer 2x2 matrix.
Definition: matrix_integer.hpp:99
\n
glm::highp_imat2x3
mat< 2, 3, int, highp > highp_imat2x3
High-qualifier signed integer 2x3 matrix.
Definition: matrix_integer.hpp:53
\n
glm::highp_imat4x2
mat< 4, 2, int, highp > highp_imat4x2
High-qualifier signed integer 4x2 matrix.
Definition: matrix_integer.hpp:73
\n
glm::lowp_imat3x4
mat< 3, 4, int, lowp > lowp_imat3x4
Low-qualifier signed integer 3x4 matrix.
Definition: matrix_integer.hpp:169
\n
glm::umat3
mediump_umat3 umat3
Unsigned integer 3x3 matrix.
Definition: matrix_integer.hpp:443
\n
glm::lowp_imat2x2
mat< 2, 2, int, lowp > lowp_imat2x2
Low-qualifier signed integer 2x2 matrix.
Definition: matrix_integer.hpp:149
\n
glm::highp_umat2x3
mat< 2, 3, uint, highp > highp_umat2x3
High-qualifier unsigned integer 2x3 matrix.
Definition: matrix_integer.hpp:202
\n
glm::highp_imat4
mat< 4, 4, int, highp > highp_imat4
High-qualifier signed integer 4x4 matrix.
Definition: matrix_integer.hpp:45
\n
glm::lowp_imat2x4
mat< 2, 4, int, lowp > lowp_imat2x4
Low-qualifier signed integer 2x4 matrix.
Definition: matrix_integer.hpp:157
\n
glm::mediump_imat3x4
mat< 3, 4, int, mediump > mediump_imat3x4
Medium-qualifier signed integer 3x4 matrix.
Definition: matrix_integer.hpp:119
\n
glm::mediump_imat4x4
mat< 4, 4, int, mediump > mediump_imat4x4
Medium-qualifier signed integer 4x4 matrix.
Definition: matrix_integer.hpp:131
\n
glm::mediump_imat4
mat< 4, 4, int, mediump > mediump_imat4
Medium-qualifier signed integer 4x4 matrix.
Definition: matrix_integer.hpp:94
\n
glm::imat3x3
mediump_imat3x3 imat3x3
Signed integer 3x3 matrix.
Definition: matrix_integer.hpp:390
\n
glm::lowp_imat3
mat< 3, 3, int, lowp > lowp_imat3
Low-qualifier signed integer 3x3 matrix.
Definition: matrix_integer.hpp:140
\n
glm::lowp_umat2
mat< 2, 2, uint, lowp > lowp_umat2
Low-qualifier unsigned integer 2x2 matrix.
Definition: matrix_integer.hpp:285
\n
glm::highp_umat4x3
mat< 4, 3, uint, highp > highp_umat4x3
High-qualifier unsigned integer 4x3 matrix.
Definition: matrix_integer.hpp:226
\n
glm::umat2x2
mediump_umat2x2 umat2x2
Unsigned integer 2x2 matrix.
Definition: matrix_integer.hpp:451
\n
glm::mediump_umat4
mat< 4, 4, uint, mediump > mediump_umat4
Medium-qualifier unsigned integer 4x4 matrix.
Definition: matrix_integer.hpp:243
\n
glm::lowp_imat4
mat< 4, 4, int, lowp > lowp_imat4
Low-qualifier signed integer 4x4 matrix.
Definition: matrix_integer.hpp:144
\n
glm::umat3x4
mediump_umat3x4 umat3x4
Unsigned integer 3x4 matrix.
Definition: matrix_integer.hpp:471
\n
glm::highp_imat3x4
mat< 3, 4, int, highp > highp_imat3x4
High-qualifier signed integer 3x4 matrix.
Definition: matrix_integer.hpp:69
\n
glm::mediump_umat2x2
mat< 2, 2, uint, mediump > mediump_umat2x2
Medium-qualifier unsigned integer 2x2 matrix.
Definition: matrix_integer.hpp:248
\n
glm
Definition: common.hpp:20
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matrix_interpolation.hpp File Reference
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GLM_GTX_matrix_interpolation \nMore...

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\nFunctions

template<typename T , qualifier Q>
GLM_FUNC_DECL void axisAngle (mat< 4, 4, T, Q > const &Mat, vec< 3, T, Q > &Axis, T &Angle)
 Get the axis and angle of the rotation from a matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 4, 4, T, Q > axisAngleMatrix (vec< 3, T, Q > const &Axis, T const Angle)
 Build a matrix from axis and angle. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 4, 4, T, Q > extractMatrixRotation (mat< 4, 4, T, Q > const &Mat)
 Extracts the rotation part of a matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 4, 4, T, Q > interpolate (mat< 4, 4, T, Q > const &m1, mat< 4, 4, T, Q > const &m2, T const Delta)
 Build a interpolation of 4 * 4 matrixes. More...
 
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Detailed Description

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GLM_GTX_matrix_interpolation

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Author
Ghenadii Ursachi (the.a.nosp@m.ster.nosp@m.oth@g.nosp@m.mail.nosp@m..com)
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See also
Core features (dependence)
\n\n

Definition in file matrix_interpolation.hpp.

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matrix_interpolation.hpp
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1 
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14 #pragma once
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15 
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16 // Dependency:
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17 #include "../glm.hpp"
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18 
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19 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
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20 # ifndef GLM_ENABLE_EXPERIMENTAL
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21 # pragma message("GLM: GLM_GTX_matrix_interpolation is an experimental extension and may change in the future. Use #define GLM_ENABLE_EXPERIMENTAL before including it, if you really want to use it.")
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22 # else
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23 # pragma message("GLM: GLM_GTX_matrix_interpolation extension included")
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24 # endif
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25 #endif
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26 
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27 namespace glm
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28 {
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31 
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34  template<typename T, qualifier Q>
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35  GLM_FUNC_DECL void axisAngle(
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36  mat<4, 4, T, Q> const& Mat, vec<3, T, Q> & Axis, T & Angle);
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37 
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40  template<typename T, qualifier Q>
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41  GLM_FUNC_DECL mat<4, 4, T, Q> axisAngleMatrix(
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42  vec<3, T, Q> const& Axis, T const Angle);
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43 
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46  template<typename T, qualifier Q>
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47  GLM_FUNC_DECL mat<4, 4, T, Q> extractMatrixRotation(
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48  mat<4, 4, T, Q> const& Mat);
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49 
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53  template<typename T, qualifier Q>
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54  GLM_FUNC_DECL mat<4, 4, T, Q> interpolate(
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55  mat<4, 4, T, Q> const& m1, mat<4, 4, T, Q> const& m2, T const Delta);
\n
56 
\n
58 }//namespace glm
\n
59 
\n
60 #include "matrix_interpolation.inl"
\n
glm::extractMatrixRotation
GLM_FUNC_DECL mat< 4, 4, T, Q > extractMatrixRotation(mat< 4, 4, T, Q > const &Mat)
Extracts the rotation part of a matrix.
\n
glm::interpolate
GLM_FUNC_DECL mat< 4, 4, T, Q > interpolate(mat< 4, 4, T, Q > const &m1, mat< 4, 4, T, Q > const &m2, T const Delta)
Build a interpolation of 4 * 4 matrixes.
\n
glm::axisAngle
GLM_FUNC_DECL void axisAngle(mat< 4, 4, T, Q > const &Mat, vec< 3, T, Q > &Axis, T &Angle)
Get the axis and angle of the rotation from a matrix.
\n
glm::axisAngleMatrix
GLM_FUNC_DECL mat< 4, 4, T, Q > axisAngleMatrix(vec< 3, T, Q > const &Axis, T const Angle)
Build a matrix from axis and angle.
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glm
Definition: common.hpp:20
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matrix_inverse.hpp File Reference
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GLM_GTC_matrix_inverse \nMore...

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\nFunctions

template<typename genType >
GLM_FUNC_DECL genType affineInverse (genType const &m)
 Fast matrix inverse for affine matrix. More...
 
template<typename genType >
GLM_FUNC_DECL genType inverseTranspose (genType const &m)
 Compute the inverse transpose of a matrix. More...
 
\n

Detailed Description

\n

GLM_GTC_matrix_inverse

\n
See also
Core features (dependence)
\n\n

Definition in file matrix_inverse.hpp.

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1 
\n
13 #pragma once
\n
14 
\n
15 // Dependencies
\n
16 #include "../detail/setup.hpp"
\n
17 #include "../matrix.hpp"
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18 #include "../mat2x2.hpp"
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19 #include "../mat3x3.hpp"
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20 #include "../mat4x4.hpp"
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21 
\n
22 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
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23 # pragma message("GLM: GLM_GTC_matrix_inverse extension included")
\n
24 #endif
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25 
\n
26 namespace glm
\n
27 {
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30 
\n
36  template<typename genType>
\n
37  GLM_FUNC_DECL genType affineInverse(genType const& m);
\n
38 
\n
44  template<typename genType>
\n
45  GLM_FUNC_DECL genType inverseTranspose(genType const& m);
\n
46 
\n
48 }//namespace glm
\n
49 
\n
50 #include "matrix_inverse.inl"
\n
glm::inverseTranspose
GLM_FUNC_DECL genType inverseTranspose(genType const &m)
Compute the inverse transpose of a matrix.
\n
glm::affineInverse
GLM_FUNC_DECL genType affineInverse(genType const &m)
Fast matrix inverse for affine matrix.
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glm
Definition: common.hpp:20
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matrix_major_storage.hpp File Reference
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GLM_GTX_matrix_major_storage \nMore...

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\nFunctions

template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 2, 2, T, Q > colMajor2 (vec< 2, T, Q > const &v1, vec< 2, T, Q > const &v2)
 Build a column major matrix from column vectors. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 2, 2, T, Q > colMajor2 (mat< 2, 2, T, Q > const &m)
 Build a column major matrix from other matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 3, 3, T, Q > colMajor3 (vec< 3, T, Q > const &v1, vec< 3, T, Q > const &v2, vec< 3, T, Q > const &v3)
 Build a column major matrix from column vectors. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 3, 3, T, Q > colMajor3 (mat< 3, 3, T, Q > const &m)
 Build a column major matrix from other matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 4, 4, T, Q > colMajor4 (vec< 4, T, Q > const &v1, vec< 4, T, Q > const &v2, vec< 4, T, Q > const &v3, vec< 4, T, Q > const &v4)
 Build a column major matrix from column vectors. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 4, 4, T, Q > colMajor4 (mat< 4, 4, T, Q > const &m)
 Build a column major matrix from other matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 2, 2, T, Q > rowMajor2 (vec< 2, T, Q > const &v1, vec< 2, T, Q > const &v2)
 Build a row major matrix from row vectors. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 2, 2, T, Q > rowMajor2 (mat< 2, 2, T, Q > const &m)
 Build a row major matrix from other matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 3, 3, T, Q > rowMajor3 (vec< 3, T, Q > const &v1, vec< 3, T, Q > const &v2, vec< 3, T, Q > const &v3)
 Build a row major matrix from row vectors. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 3, 3, T, Q > rowMajor3 (mat< 3, 3, T, Q > const &m)
 Build a row major matrix from other matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 4, 4, T, Q > rowMajor4 (vec< 4, T, Q > const &v1, vec< 4, T, Q > const &v2, vec< 4, T, Q > const &v3, vec< 4, T, Q > const &v4)
 Build a row major matrix from row vectors. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 4, 4, T, Q > rowMajor4 (mat< 4, 4, T, Q > const &m)
 Build a row major matrix from other matrix. More...
 
\n

Detailed Description

\n

GLM_GTX_matrix_major_storage

\n
See also
Core features (dependence)
\n
\ngtx_extented_min_max (dependence)
\n\n

Definition in file matrix_major_storage.hpp.

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matrix_major_storage.hpp
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1 
\n
14 #pragma once
\n
15 
\n
16 // Dependency:
\n
17 #include "../glm.hpp"
\n
18 
\n
19 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
20 # ifndef GLM_ENABLE_EXPERIMENTAL
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21 # pragma message("GLM: GLM_GTX_matrix_major_storage is an experimental extension and may change in the future. Use #define GLM_ENABLE_EXPERIMENTAL before including it, if you really want to use it.")
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22 # else
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23 # pragma message("GLM: GLM_GTX_matrix_major_storage extension included")
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24 # endif
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25 #endif
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26 
\n
27 namespace glm
\n
28 {
\n
31 
\n
34  template<typename T, qualifier Q>
\n
35  GLM_FUNC_DECL mat<2, 2, T, Q> rowMajor2(
\n
36  vec<2, T, Q> const& v1,
\n
37  vec<2, T, Q> const& v2);
\n
38 
\n
41  template<typename T, qualifier Q>
\n
42  GLM_FUNC_DECL mat<2, 2, T, Q> rowMajor2(
\n
43  mat<2, 2, T, Q> const& m);
\n
44 
\n
47  template<typename T, qualifier Q>
\n
48  GLM_FUNC_DECL mat<3, 3, T, Q> rowMajor3(
\n
49  vec<3, T, Q> const& v1,
\n
50  vec<3, T, Q> const& v2,
\n
51  vec<3, T, Q> const& v3);
\n
52 
\n
55  template<typename T, qualifier Q>
\n
56  GLM_FUNC_DECL mat<3, 3, T, Q> rowMajor3(
\n
57  mat<3, 3, T, Q> const& m);
\n
58 
\n
61  template<typename T, qualifier Q>
\n
62  GLM_FUNC_DECL mat<4, 4, T, Q> rowMajor4(
\n
63  vec<4, T, Q> const& v1,
\n
64  vec<4, T, Q> const& v2,
\n
65  vec<4, T, Q> const& v3,
\n
66  vec<4, T, Q> const& v4);
\n
67 
\n
70  template<typename T, qualifier Q>
\n
71  GLM_FUNC_DECL mat<4, 4, T, Q> rowMajor4(
\n
72  mat<4, 4, T, Q> const& m);
\n
73 
\n
76  template<typename T, qualifier Q>
\n
77  GLM_FUNC_DECL mat<2, 2, T, Q> colMajor2(
\n
78  vec<2, T, Q> const& v1,
\n
79  vec<2, T, Q> const& v2);
\n
80 
\n
83  template<typename T, qualifier Q>
\n
84  GLM_FUNC_DECL mat<2, 2, T, Q> colMajor2(
\n
85  mat<2, 2, T, Q> const& m);
\n
86 
\n
89  template<typename T, qualifier Q>
\n
90  GLM_FUNC_DECL mat<3, 3, T, Q> colMajor3(
\n
91  vec<3, T, Q> const& v1,
\n
92  vec<3, T, Q> const& v2,
\n
93  vec<3, T, Q> const& v3);
\n
94 
\n
97  template<typename T, qualifier Q>
\n
98  GLM_FUNC_DECL mat<3, 3, T, Q> colMajor3(
\n
99  mat<3, 3, T, Q> const& m);
\n
100 
\n
103  template<typename T, qualifier Q>
\n
104  GLM_FUNC_DECL mat<4, 4, T, Q> colMajor4(
\n
105  vec<4, T, Q> const& v1,
\n
106  vec<4, T, Q> const& v2,
\n
107  vec<4, T, Q> const& v3,
\n
108  vec<4, T, Q> const& v4);
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109 
\n
112  template<typename T, qualifier Q>
\n
113  GLM_FUNC_DECL mat<4, 4, T, Q> colMajor4(
\n
114  mat<4, 4, T, Q> const& m);
\n
115 
\n
117 }//namespace glm
\n
118 
\n
119 #include "matrix_major_storage.inl"
\n
glm::rowMajor4
GLM_FUNC_DECL mat< 4, 4, T, Q > rowMajor4(mat< 4, 4, T, Q > const &m)
Build a row major matrix from other matrix.
\n
glm::rowMajor2
GLM_FUNC_DECL mat< 2, 2, T, Q > rowMajor2(mat< 2, 2, T, Q > const &m)
Build a row major matrix from other matrix.
\n
glm::colMajor4
GLM_FUNC_DECL mat< 4, 4, T, Q > colMajor4(mat< 4, 4, T, Q > const &m)
Build a column major matrix from other matrix.
\n
glm::colMajor3
GLM_FUNC_DECL mat< 3, 3, T, Q > colMajor3(mat< 3, 3, T, Q > const &m)
Build a column major matrix from other matrix.
\n
glm::colMajor2
GLM_FUNC_DECL mat< 2, 2, T, Q > colMajor2(mat< 2, 2, T, Q > const &m)
Build a column major matrix from other matrix.
\n
glm::rowMajor3
GLM_FUNC_DECL mat< 3, 3, T, Q > rowMajor3(mat< 3, 3, T, Q > const &m)
Build a row major matrix from other matrix.
\n
glm
Definition: common.hpp:20
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matrix_operation.hpp File Reference
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GLM_GTX_matrix_operation \nMore...

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\nFunctions

template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 2, 2, T, Q > adjugate (mat< 2, 2, T, Q > const &m)
 Build an adjugate matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 3, 3, T, Q > adjugate (mat< 3, 3, T, Q > const &m)
 Build an adjugate matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 4, 4, T, Q > adjugate (mat< 4, 4, T, Q > const &m)
 Build an adjugate matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 2, 2, T, Q > diagonal2x2 (vec< 2, T, Q > const &v)
 Build a diagonal matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 2, 3, T, Q > diagonal2x3 (vec< 2, T, Q > const &v)
 Build a diagonal matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 2, 4, T, Q > diagonal2x4 (vec< 2, T, Q > const &v)
 Build a diagonal matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 3, 2, T, Q > diagonal3x2 (vec< 2, T, Q > const &v)
 Build a diagonal matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 3, 3, T, Q > diagonal3x3 (vec< 3, T, Q > const &v)
 Build a diagonal matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 3, 4, T, Q > diagonal3x4 (vec< 3, T, Q > const &v)
 Build a diagonal matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 4, 2, T, Q > diagonal4x2 (vec< 2, T, Q > const &v)
 Build a diagonal matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 4, 3, T, Q > diagonal4x3 (vec< 3, T, Q > const &v)
 Build a diagonal matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 4, 4, T, Q > diagonal4x4 (vec< 4, T, Q > const &v)
 Build a diagonal matrix. More...
 
\n

Detailed Description

\n

GLM_GTX_matrix_operation

\n
See also
Core features (dependence)
\n\n

Definition in file matrix_operation.hpp.

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1 
\n
13 #pragma once
\n
14 
\n
15 // Dependency:
\n
16 #include "../glm.hpp"
\n
17 
\n
18 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
19 # ifndef GLM_ENABLE_EXPERIMENTAL
\n
20 # pragma message("GLM: GLM_GTX_matrix_operation is an experimental extension and may change in the future. Use #define GLM_ENABLE_EXPERIMENTAL before including it, if you really want to use it.")
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21 # else
\n
22 # pragma message("GLM: GLM_GTX_matrix_operation extension included")
\n
23 # endif
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24 #endif
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25 
\n
26 namespace glm
\n
27 {
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30 
\n
33  template<typename T, qualifier Q>
\n
34  GLM_FUNC_DECL mat<2, 2, T, Q> diagonal2x2(
\n
35  vec<2, T, Q> const& v);
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36 
\n
39  template<typename T, qualifier Q>
\n
40  GLM_FUNC_DECL mat<2, 3, T, Q> diagonal2x3(
\n
41  vec<2, T, Q> const& v);
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42 
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45  template<typename T, qualifier Q>
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46  GLM_FUNC_DECL mat<2, 4, T, Q> diagonal2x4(
\n
47  vec<2, T, Q> const& v);
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48 
\n
51  template<typename T, qualifier Q>
\n
52  GLM_FUNC_DECL mat<3, 2, T, Q> diagonal3x2(
\n
53  vec<2, T, Q> const& v);
\n
54 
\n
57  template<typename T, qualifier Q>
\n
58  GLM_FUNC_DECL mat<3, 3, T, Q> diagonal3x3(
\n
59  vec<3, T, Q> const& v);
\n
60 
\n
63  template<typename T, qualifier Q>
\n
64  GLM_FUNC_DECL mat<3, 4, T, Q> diagonal3x4(
\n
65  vec<3, T, Q> const& v);
\n
66 
\n
69  template<typename T, qualifier Q>
\n
70  GLM_FUNC_DECL mat<4, 2, T, Q> diagonal4x2(
\n
71  vec<2, T, Q> const& v);
\n
72 
\n
75  template<typename T, qualifier Q>
\n
76  GLM_FUNC_DECL mat<4, 3, T, Q> diagonal4x3(
\n
77  vec<3, T, Q> const& v);
\n
78 
\n
81  template<typename T, qualifier Q>
\n
82  GLM_FUNC_DECL mat<4, 4, T, Q> diagonal4x4(
\n
83  vec<4, T, Q> const& v);
\n
84 
\n
87  template<typename T, qualifier Q>
\n
88  GLM_FUNC_DECL mat<2, 2, T, Q> adjugate(mat<2, 2, T, Q> const& m);
\n
89 
\n
92  template<typename T, qualifier Q>
\n
93  GLM_FUNC_DECL mat<3, 3, T, Q> adjugate(mat<3, 3, T, Q> const& m);
\n
94 
\n
97  template<typename T, qualifier Q>
\n
98  GLM_FUNC_DECL mat<4, 4, T, Q> adjugate(mat<4, 4, T, Q> const& m);
\n
99 
\n
101 }//namespace glm
\n
102 
\n
103 #include "matrix_operation.inl"
\n
glm::diagonal4x3
GLM_FUNC_DECL mat< 4, 3, T, Q > diagonal4x3(vec< 3, T, Q > const &v)
Build a diagonal matrix.
\n
glm::diagonal2x2
GLM_FUNC_DECL mat< 2, 2, T, Q > diagonal2x2(vec< 2, T, Q > const &v)
Build a diagonal matrix.
\n
glm::diagonal3x4
GLM_FUNC_DECL mat< 3, 4, T, Q > diagonal3x4(vec< 3, T, Q > const &v)
Build a diagonal matrix.
\n
glm::diagonal3x2
GLM_FUNC_DECL mat< 3, 2, T, Q > diagonal3x2(vec< 2, T, Q > const &v)
Build a diagonal matrix.
\n
glm::diagonal2x3
GLM_FUNC_DECL mat< 2, 3, T, Q > diagonal2x3(vec< 2, T, Q > const &v)
Build a diagonal matrix.
\n
glm::diagonal3x3
GLM_FUNC_DECL mat< 3, 3, T, Q > diagonal3x3(vec< 3, T, Q > const &v)
Build a diagonal matrix.
\n
glm::adjugate
GLM_FUNC_DECL mat< 4, 4, T, Q > adjugate(mat< 4, 4, T, Q > const &m)
Build an adjugate matrix.
\n
glm::diagonal2x4
GLM_FUNC_DECL mat< 2, 4, T, Q > diagonal2x4(vec< 2, T, Q > const &v)
Build a diagonal matrix.
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glm::diagonal4x2
GLM_FUNC_DECL mat< 4, 2, T, Q > diagonal4x2(vec< 2, T, Q > const &v)
Build a diagonal matrix.
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glm::diagonal4x4
GLM_FUNC_DECL mat< 4, 4, T, Q > diagonal4x4(vec< 4, T, Q > const &v)
Build a diagonal matrix.
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glm
Definition: common.hpp:20
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matrix_projection.hpp File Reference
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GLM_EXT_matrix_projection \nMore...

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\nFunctions

template<typename T , qualifier Q, typename U >
GLM_FUNC_DECL mat< 4, 4, T, Q > pickMatrix (vec< 2, T, Q > const &center, vec< 2, T, Q > const &delta, vec< 4, U, Q > const &viewport)
 Define a picking region. More...
 
template<typename T , typename U , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > project (vec< 3, T, Q > const &obj, mat< 4, 4, T, Q > const &model, mat< 4, 4, T, Q > const &proj, vec< 4, U, Q > const &viewport)
 Map the specified object coordinates (obj.x, obj.y, obj.z) into window coordinates using default near and far clip planes definition. More...
 
template<typename T , typename U , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > projectNO (vec< 3, T, Q > const &obj, mat< 4, 4, T, Q > const &model, mat< 4, 4, T, Q > const &proj, vec< 4, U, Q > const &viewport)
 Map the specified object coordinates (obj.x, obj.y, obj.z) into window coordinates. More...
 
template<typename T , typename U , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > projectZO (vec< 3, T, Q > const &obj, mat< 4, 4, T, Q > const &model, mat< 4, 4, T, Q > const &proj, vec< 4, U, Q > const &viewport)
 Map the specified object coordinates (obj.x, obj.y, obj.z) into window coordinates. More...
 
template<typename T , typename U , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > unProject (vec< 3, T, Q > const &win, mat< 4, 4, T, Q > const &model, mat< 4, 4, T, Q > const &proj, vec< 4, U, Q > const &viewport)
 Map the specified window coordinates (win.x, win.y, win.z) into object coordinates using default near and far clip planes definition. More...
 
template<typename T , typename U , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > unProjectNO (vec< 3, T, Q > const &win, mat< 4, 4, T, Q > const &model, mat< 4, 4, T, Q > const &proj, vec< 4, U, Q > const &viewport)
 Map the specified window coordinates (win.x, win.y, win.z) into object coordinates. More...
 
template<typename T , typename U , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > unProjectZO (vec< 3, T, Q > const &win, mat< 4, 4, T, Q > const &model, mat< 4, 4, T, Q > const &proj, vec< 4, U, Q > const &viewport)
 Map the specified window coordinates (win.x, win.y, win.z) into object coordinates. More...
 
\n

Detailed Description

\n

GLM_EXT_matrix_projection

\n\n

Definition in file matrix_projection.hpp.

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matrix_projection.hpp
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\n
20 #pragma once
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21 
\n
22 // Dependencies
\n
23 #include "../gtc/constants.hpp"
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24 #include "../geometric.hpp"
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25 #include "../trigonometric.hpp"
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26 #include "../matrix.hpp"
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27 
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28 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
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29 # pragma message("GLM: GLM_EXT_matrix_projection extension included")
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30 #endif
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31 
\n
32 namespace glm
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33 {
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36 
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49  template<typename T, typename U, qualifier Q>
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50  GLM_FUNC_DECL vec<3, T, Q> projectZO(
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51  vec<3, T, Q> const& obj, mat<4, 4, T, Q> const& model, mat<4, 4, T, Q> const& proj, vec<4, U, Q> const& viewport);
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52 
\n
65  template<typename T, typename U, qualifier Q>
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66  GLM_FUNC_DECL vec<3, T, Q> projectNO(
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67  vec<3, T, Q> const& obj, mat<4, 4, T, Q> const& model, mat<4, 4, T, Q> const& proj, vec<4, U, Q> const& viewport);
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68 
\n
81  template<typename T, typename U, qualifier Q>
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82  GLM_FUNC_DECL vec<3, T, Q> project(
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83  vec<3, T, Q> const& obj, mat<4, 4, T, Q> const& model, mat<4, 4, T, Q> const& proj, vec<4, U, Q> const& viewport);
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84 
\n
97  template<typename T, typename U, qualifier Q>
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98  GLM_FUNC_DECL vec<3, T, Q> unProjectZO(
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99  vec<3, T, Q> const& win, mat<4, 4, T, Q> const& model, mat<4, 4, T, Q> const& proj, vec<4, U, Q> const& viewport);
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100 
\n
113  template<typename T, typename U, qualifier Q>
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114  GLM_FUNC_DECL vec<3, T, Q> unProjectNO(
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115  vec<3, T, Q> const& win, mat<4, 4, T, Q> const& model, mat<4, 4, T, Q> const& proj, vec<4, U, Q> const& viewport);
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116 
\n
129  template<typename T, typename U, qualifier Q>
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130  GLM_FUNC_DECL vec<3, T, Q> unProject(
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131  vec<3, T, Q> const& win, mat<4, 4, T, Q> const& model, mat<4, 4, T, Q> const& proj, vec<4, U, Q> const& viewport);
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132 
\n
142  template<typename T, qualifier Q, typename U>
\n
143  GLM_FUNC_DECL mat<4, 4, T, Q> pickMatrix(
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144  vec<2, T, Q> const& center, vec<2, T, Q> const& delta, vec<4, U, Q> const& viewport);
\n
145 
\n
147 }//namespace glm
\n
148 
\n
149 #include "matrix_projection.inl"
\n
glm::unProjectZO
GLM_FUNC_DECL vec< 3, T, Q > unProjectZO(vec< 3, T, Q > const &win, mat< 4, 4, T, Q > const &model, mat< 4, 4, T, Q > const &proj, vec< 4, U, Q > const &viewport)
Map the specified window coordinates (win.x, win.y, win.z) into object coordinates.
\n
glm::proj
GLM_FUNC_DECL genType proj(genType const &x, genType const &Normal)
Projects x on Normal.
\n
glm::projectZO
GLM_FUNC_DECL vec< 3, T, Q > projectZO(vec< 3, T, Q > const &obj, mat< 4, 4, T, Q > const &model, mat< 4, 4, T, Q > const &proj, vec< 4, U, Q > const &viewport)
Map the specified object coordinates (obj.x, obj.y, obj.z) into window coordinates.
\n
glm::projectNO
GLM_FUNC_DECL vec< 3, T, Q > projectNO(vec< 3, T, Q > const &obj, mat< 4, 4, T, Q > const &model, mat< 4, 4, T, Q > const &proj, vec< 4, U, Q > const &viewport)
Map the specified object coordinates (obj.x, obj.y, obj.z) into window coordinates.
\n
glm::project
GLM_FUNC_DECL vec< 3, T, Q > project(vec< 3, T, Q > const &obj, mat< 4, 4, T, Q > const &model, mat< 4, 4, T, Q > const &proj, vec< 4, U, Q > const &viewport)
Map the specified object coordinates (obj.x, obj.y, obj.z) into window coordinates using default near...
\n
glm::unProjectNO
GLM_FUNC_DECL vec< 3, T, Q > unProjectNO(vec< 3, T, Q > const &win, mat< 4, 4, T, Q > const &model, mat< 4, 4, T, Q > const &proj, vec< 4, U, Q > const &viewport)
Map the specified window coordinates (win.x, win.y, win.z) into object coordinates.
\n
glm::unProject
GLM_FUNC_DECL vec< 3, T, Q > unProject(vec< 3, T, Q > const &win, mat< 4, 4, T, Q > const &model, mat< 4, 4, T, Q > const &proj, vec< 4, U, Q > const &viewport)
Map the specified window coordinates (win.x, win.y, win.z) into object coordinates using default near...
\n
glm::pickMatrix
GLM_FUNC_DECL mat< 4, 4, T, Q > pickMatrix(vec< 2, T, Q > const &center, vec< 2, T, Q > const &delta, vec< 4, U, Q > const &viewport)
Define a picking region.
\n
glm
Definition: common.hpp:20
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matrix_query.hpp File Reference
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GLM_GTX_matrix_query \nMore...

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template<length_t C, length_t R, typename T , qualifier Q, template< length_t, length_t, typename, qualifier > class matType>
GLM_FUNC_DECL bool isIdentity (matType< C, R, T, Q > const &m, T const &epsilon)
 Return whether a matrix is an identity matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL bool isNormalized (mat< 2, 2, T, Q > const &m, T const &epsilon)
 Return whether a matrix is a normalized matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL bool isNormalized (mat< 3, 3, T, Q > const &m, T const &epsilon)
 Return whether a matrix is a normalized matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL bool isNormalized (mat< 4, 4, T, Q > const &m, T const &epsilon)
 Return whether a matrix is a normalized matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL bool isNull (mat< 2, 2, T, Q > const &m, T const &epsilon)
 Return whether a matrix a null matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL bool isNull (mat< 3, 3, T, Q > const &m, T const &epsilon)
 Return whether a matrix a null matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL bool isNull (mat< 4, 4, T, Q > const &m, T const &epsilon)
 Return whether a matrix is a null matrix. More...
 
template<length_t C, length_t R, typename T , qualifier Q, template< length_t, length_t, typename, qualifier > class matType>
GLM_FUNC_DECL bool isOrthogonal (matType< C, R, T, Q > const &m, T const &epsilon)
 Return whether a matrix is an orthonormalized matrix. More...
 
\n

Detailed Description

\n

GLM_GTX_matrix_query

\n
See also
Core features (dependence)
\n
\n to use the features of this extension. \">GLM_GTX_vector_query (dependence)
\n\n

Definition in file matrix_query.hpp.

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14 #pragma once
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15 
\n
16 // Dependency:
\n
17 #include "../glm.hpp"
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18 #include "../gtx/vector_query.hpp"
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19 #include <limits>
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20 
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21 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
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22 # ifndef GLM_ENABLE_EXPERIMENTAL
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23 # pragma message("GLM: GLM_GTX_matrix_query is an experimental extension and may change in the future. Use #define GLM_ENABLE_EXPERIMENTAL before including it, if you really want to use it.")
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24 # else
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25 # pragma message("GLM: GLM_GTX_matrix_query extension included")
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26 # endif
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27 #endif
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28 
\n
29 namespace glm
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30 {
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33 
\n
36  template<typename T, qualifier Q>
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37  GLM_FUNC_DECL bool isNull(mat<2, 2, T, Q> const& m, T const& epsilon);
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38 
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41  template<typename T, qualifier Q>
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42  GLM_FUNC_DECL bool isNull(mat<3, 3, T, Q> const& m, T const& epsilon);
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43 
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46  template<typename T, qualifier Q>
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47  GLM_FUNC_DECL bool isNull(mat<4, 4, T, Q> const& m, T const& epsilon);
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48 
\n
51  template<length_t C, length_t R, typename T, qualifier Q, template<length_t, length_t, typename, qualifier> class matType>
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52  GLM_FUNC_DECL bool isIdentity(matType<C, R, T, Q> const& m, T const& epsilon);
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53 
\n
56  template<typename T, qualifier Q>
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57  GLM_FUNC_DECL bool isNormalized(mat<2, 2, T, Q> const& m, T const& epsilon);
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58 
\n
61  template<typename T, qualifier Q>
\n
62  GLM_FUNC_DECL bool isNormalized(mat<3, 3, T, Q> const& m, T const& epsilon);
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63 
\n
66  template<typename T, qualifier Q>
\n
67  GLM_FUNC_DECL bool isNormalized(mat<4, 4, T, Q> const& m, T const& epsilon);
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68 
\n
71  template<length_t C, length_t R, typename T, qualifier Q, template<length_t, length_t, typename, qualifier> class matType>
\n
72  GLM_FUNC_DECL bool isOrthogonal(matType<C, R, T, Q> const& m, T const& epsilon);
\n
73 
\n
75 }//namespace glm
\n
76 
\n
77 #include "matrix_query.inl"
\n
glm::isNormalized
GLM_FUNC_DECL bool isNormalized(mat< 4, 4, T, Q > const &m, T const &epsilon)
Return whether a matrix is a normalized matrix.
\n
glm::isIdentity
GLM_FUNC_DECL bool isIdentity(matType< C, R, T, Q > const &m, T const &epsilon)
Return whether a matrix is an identity matrix.
\n
glm::isNull
GLM_FUNC_DECL bool isNull(mat< 4, 4, T, Q > const &m, T const &epsilon)
Return whether a matrix is a null matrix.
\n
glm::epsilon
GLM_FUNC_DECL GLM_CONSTEXPR genType epsilon()
Return the epsilon constant for floating point types.
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glm::isOrthogonal
GLM_FUNC_DECL bool isOrthogonal(matType< C, R, T, Q > const &m, T const &epsilon)
Return whether a matrix is an orthonormalized matrix.
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glm
Definition: common.hpp:20
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GLM_EXT_matrix_relational \nMore...

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template<length_t C, length_t R, typename T , qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< C, bool, Q > equal (mat< C, R, T, Q > const &x, mat< C, R, T, Q > const &y)
 Perform a component-wise equal-to comparison of two matrices. More...
 
template<length_t C, length_t R, typename T , qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< C, bool, Q > equal (mat< C, R, T, Q > const &x, mat< C, R, T, Q > const &y, T epsilon)
 Returns the component-wise comparison of |x - y| < epsilon. More...
 
template<length_t C, length_t R, typename T , qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< C, bool, Q > equal (mat< C, R, T, Q > const &x, mat< C, R, T, Q > const &y, vec< C, T, Q > const &epsilon)
 Returns the component-wise comparison of |x - y| < epsilon. More...
 
template<length_t C, length_t R, typename T , qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< C, bool, Q > equal (mat< C, R, T, Q > const &x, mat< C, R, T, Q > const &y, int ULPs)
 Returns the component-wise comparison between two vectors in term of ULPs. More...
 
template<length_t C, length_t R, typename T , qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< C, bool, Q > equal (mat< C, R, T, Q > const &x, mat< C, R, T, Q > const &y, vec< C, int, Q > const &ULPs)
 Returns the component-wise comparison between two vectors in term of ULPs. More...
 
template<length_t C, length_t R, typename T , qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< C, bool, Q > notEqual (mat< C, R, T, Q > const &x, mat< C, R, T, Q > const &y)
 Perform a component-wise not-equal-to comparison of two matrices. More...
 
template<length_t C, length_t R, typename T , qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< C, bool, Q > notEqual (mat< C, R, T, Q > const &x, mat< C, R, T, Q > const &y, T epsilon)
 Returns the component-wise comparison of |x - y| < epsilon. More...
 
template<length_t C, length_t R, typename T , qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< C, bool, Q > notEqual (mat< C, R, T, Q > const &x, mat< C, R, T, Q > const &y, vec< C, T, Q > const &epsilon)
 Returns the component-wise comparison of |x - y| >= epsilon. More...
 
template<length_t C, length_t R, typename T , qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< C, bool, Q > notEqual (mat< C, R, T, Q > const &x, mat< C, R, T, Q > const &y, int ULPs)
 Returns the component-wise comparison between two vectors in term of ULPs. More...
 
template<length_t C, length_t R, typename T , qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< C, bool, Q > notEqual (mat< C, R, T, Q > const &x, mat< C, R, T, Q > const &y, vec< C, int, Q > const &ULPs)
 Returns the component-wise comparison between two vectors in term of ULPs. More...
 
\n

Detailed Description

\n

GLM_EXT_matrix_relational

\n\n

Definition in file matrix_relational.hpp.

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15 #pragma once
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16 
\n
17 // Dependencies
\n
18 #include "../detail/qualifier.hpp"
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19 
\n
20 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
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21 # pragma message("GLM: GLM_EXT_matrix_relational extension included")
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22 #endif
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23 
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24 namespace glm
\n
25 {
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28 
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36  template<length_t C, length_t R, typename T, qualifier Q>
\n
37  GLM_FUNC_DECL GLM_CONSTEXPR vec<C, bool, Q> equal(mat<C, R, T, Q> const& x, mat<C, R, T, Q> const& y);
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38 
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46  template<length_t C, length_t R, typename T, qualifier Q>
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47  GLM_FUNC_DECL GLM_CONSTEXPR vec<C, bool, Q> notEqual(mat<C, R, T, Q> const& x, mat<C, R, T, Q> const& y);
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48 
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56  template<length_t C, length_t R, typename T, qualifier Q>
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57  GLM_FUNC_DECL GLM_CONSTEXPR vec<C, bool, Q> equal(mat<C, R, T, Q> const& x, mat<C, R, T, Q> const& y, T epsilon);
\n
58 
\n
66  template<length_t C, length_t R, typename T, qualifier Q>
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67  GLM_FUNC_DECL GLM_CONSTEXPR vec<C, bool, Q> equal(mat<C, R, T, Q> const& x, mat<C, R, T, Q> const& y, vec<C, T, Q> const& epsilon);
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68 
\n
76  template<length_t C, length_t R, typename T, qualifier Q>
\n
77  GLM_FUNC_DECL GLM_CONSTEXPR vec<C, bool, Q> notEqual(mat<C, R, T, Q> const& x, mat<C, R, T, Q> const& y, T epsilon);
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78 
\n
86  template<length_t C, length_t R, typename T, qualifier Q>
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87  GLM_FUNC_DECL GLM_CONSTEXPR vec<C, bool, Q> notEqual(mat<C, R, T, Q> const& x, mat<C, R, T, Q> const& y, vec<C, T, Q> const& epsilon);
\n
88 
\n
96  template<length_t C, length_t R, typename T, qualifier Q>
\n
97  GLM_FUNC_DECL GLM_CONSTEXPR vec<C, bool, Q> equal(mat<C, R, T, Q> const& x, mat<C, R, T, Q> const& y, int ULPs);
\n
98 
\n
106  template<length_t C, length_t R, typename T, qualifier Q>
\n
107  GLM_FUNC_DECL GLM_CONSTEXPR vec<C, bool, Q> equal(mat<C, R, T, Q> const& x, mat<C, R, T, Q> const& y, vec<C, int, Q> const& ULPs);
\n
108 
\n
116  template<length_t C, length_t R, typename T, qualifier Q>
\n
117  GLM_FUNC_DECL GLM_CONSTEXPR vec<C, bool, Q> notEqual(mat<C, R, T, Q> const& x, mat<C, R, T, Q> const& y, int ULPs);
\n
118 
\n
126  template<length_t C, length_t R, typename T, qualifier Q>
\n
127  GLM_FUNC_DECL GLM_CONSTEXPR vec<C, bool, Q> notEqual(mat<C, R, T, Q> const& x, mat<C, R, T, Q> const& y, vec<C, int, Q> const& ULPs);
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128 
\n
130 }//namespace glm
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131 
\n
132 #include "matrix_relational.inl"
\n
glm::equal
GLM_FUNC_DECL GLM_CONSTEXPR vec< C, bool, Q > equal(mat< C, R, T, Q > const &x, mat< C, R, T, Q > const &y, vec< C, int, Q > const &ULPs)
Returns the component-wise comparison between two vectors in term of ULPs.
\n
glm::epsilon
GLM_FUNC_DECL GLM_CONSTEXPR genType epsilon()
Return the epsilon constant for floating point types.
\n
glm::notEqual
GLM_FUNC_DECL GLM_CONSTEXPR vec< C, bool, Q > notEqual(mat< C, R, T, Q > const &x, mat< C, R, T, Q > const &y, vec< C, int, Q > const &ULPs)
Returns the component-wise comparison between two vectors in term of ULPs.
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glm
Definition: common.hpp:20
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GLM_EXT_matrix_transform \nMore...

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\nFunctions

\ntemplate<typename genType >
GLM_FUNC_DECL GLM_CONSTEXPR genType identity ()
 Builds an identity matrix.
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 4, 4, T, Q > lookAt (vec< 3, T, Q > const &eye, vec< 3, T, Q > const &center, vec< 3, T, Q > const &up)
 Build a look at view matrix based on the default handedness. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 4, 4, T, Q > lookAtLH (vec< 3, T, Q > const &eye, vec< 3, T, Q > const &center, vec< 3, T, Q > const &up)
 Build a left handed look at view matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 4, 4, T, Q > lookAtRH (vec< 3, T, Q > const &eye, vec< 3, T, Q > const &center, vec< 3, T, Q > const &up)
 Build a right handed look at view matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 4, 4, T, Q > rotate (mat< 4, 4, T, Q > const &m, T angle, vec< 3, T, Q > const &axis)
 Builds a rotation 4 * 4 matrix created from an axis vector and an angle. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 4, 4, T, Q > scale (mat< 4, 4, T, Q > const &m, vec< 3, T, Q > const &v)
 Builds a scale 4 * 4 matrix created from 3 scalars. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 4, 4, T, Q > translate (mat< 4, 4, T, Q > const &m, vec< 3, T, Q > const &v)
 Builds a translation 4 * 4 matrix created from a vector of 3 components. More...
 
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Detailed Description

\n

GLM_EXT_matrix_transform

\n\n

Definition in file ext/matrix_transform.hpp.

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ext/matrix_transform.hpp
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1 
\n
20 #pragma once
\n
21 
\n
22 // Dependencies
\n
23 #include "../gtc/constants.hpp"
\n
24 #include "../geometric.hpp"
\n
25 #include "../trigonometric.hpp"
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26 #include "../matrix.hpp"
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27 
\n
28 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
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29 # pragma message("GLM: GLM_EXT_matrix_transform extension included")
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30 #endif
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31 
\n
32 namespace glm
\n
33 {
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36 
\n
38  template<typename genType>
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39  GLM_FUNC_DECL GLM_CONSTEXPR genType identity();
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40 
\n
63  template<typename T, qualifier Q>
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64  GLM_FUNC_DECL mat<4, 4, T, Q> translate(
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65  mat<4, 4, T, Q> const& m, vec<3, T, Q> const& v);
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66 
\n
79  template<typename T, qualifier Q>
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80  GLM_FUNC_DECL mat<4, 4, T, Q> rotate(
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81  mat<4, 4, T, Q> const& m, T angle, vec<3, T, Q> const& axis);
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82 
\n
94  template<typename T, qualifier Q>
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95  GLM_FUNC_DECL mat<4, 4, T, Q> scale(
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96  mat<4, 4, T, Q> const& m, vec<3, T, Q> const& v);
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97 
\n
108  template<typename T, qualifier Q>
\n
109  GLM_FUNC_DECL mat<4, 4, T, Q> lookAtRH(
\n
110  vec<3, T, Q> const& eye, vec<3, T, Q> const& center, vec<3, T, Q> const& up);
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111 
\n
122  template<typename T, qualifier Q>
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123  GLM_FUNC_DECL mat<4, 4, T, Q> lookAtLH(
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124  vec<3, T, Q> const& eye, vec<3, T, Q> const& center, vec<3, T, Q> const& up);
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125 
\n
137  template<typename T, qualifier Q>
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138  GLM_FUNC_DECL mat<4, 4, T, Q> lookAt(
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139  vec<3, T, Q> const& eye, vec<3, T, Q> const& center, vec<3, T, Q> const& up);
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140 
\n
142 }//namespace glm
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143 
\n
144 #include "matrix_transform.inl"
\n
glm::lookAtLH
GLM_FUNC_DECL mat< 4, 4, T, Q > lookAtLH(vec< 3, T, Q > const &eye, vec< 3, T, Q > const &center, vec< 3, T, Q > const &up)
Build a left handed look at view matrix.
\n
glm::lookAtRH
GLM_FUNC_DECL mat< 4, 4, T, Q > lookAtRH(vec< 3, T, Q > const &eye, vec< 3, T, Q > const &center, vec< 3, T, Q > const &up)
Build a right handed look at view matrix.
\n
glm::angle
GLM_FUNC_DECL T angle(qua< T, Q > const &x)
Returns the quaternion rotation angle.
\n
glm::translate
GLM_FUNC_DECL mat< 4, 4, T, Q > translate(mat< 4, 4, T, Q > const &m, vec< 3, T, Q > const &v)
Builds a translation 4 * 4 matrix created from a vector of 3 components.
\n
glm::rotate
GLM_FUNC_DECL mat< 4, 4, T, Q > rotate(mat< 4, 4, T, Q > const &m, T angle, vec< 3, T, Q > const &axis)
Builds a rotation 4 * 4 matrix created from an axis vector and an angle.
\n
glm::identity
GLM_FUNC_DECL GLM_CONSTEXPR genType identity()
Builds an identity matrix.
\n
glm::scale
GLM_FUNC_DECL mat< 4, 4, T, Q > scale(mat< 4, 4, T, Q > const &m, vec< 3, T, Q > const &v)
Builds a scale 4 * 4 matrix created from 3 scalars.
\n
glm::axis
GLM_FUNC_DECL vec< 3, T, Q > axis(qua< T, Q > const &x)
Returns the q rotation axis.
\n
glm::lookAt
GLM_FUNC_DECL mat< 4, 4, T, Q > lookAt(vec< 3, T, Q > const &eye, vec< 3, T, Q > const &center, vec< 3, T, Q > const &up)
Build a look at view matrix based on the default handedness.
\n
glm
Definition: common.hpp:20
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GLM_GTC_matrix_transform \nMore...

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\n

Detailed Description

\n

GLM_GTC_matrix_transform

\n
See also
Core features (dependence)
\n
\n to use the features of this extension. \">GLM_GTX_transform
\n
\n to use the features of this extension. \">GLM_GTX_transform2
\n\n

Definition in file gtc/matrix_transform.hpp.

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1 
\n
21 #pragma once
\n
22 
\n
23 // Dependencies
\n
24 #include "../mat4x4.hpp"
\n
25 #include "../vec2.hpp"
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26 #include "../vec3.hpp"
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27 #include "../vec4.hpp"
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28 #include "../ext/matrix_projection.hpp"
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29 #include "../ext/matrix_clip_space.hpp"
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30 #include "../ext/matrix_transform.hpp"
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31 
\n
32 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
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33 # pragma message("GLM: GLM_GTC_matrix_transform extension included")
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34 #endif
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35 
\n
36 #include "matrix_transform.inl"
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GLM_GTX_matrix_transform_2d \nMore...

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\nFunctions

template<typename T , qualifier Q>
GLM_FUNC_QUALIFIER mat< 3, 3, T, Q > rotate (mat< 3, 3, T, Q > const &m, T angle)
 Builds a rotation 3 * 3 matrix created from an angle. More...
 
template<typename T , qualifier Q>
GLM_FUNC_QUALIFIER mat< 3, 3, T, Q > scale (mat< 3, 3, T, Q > const &m, vec< 2, T, Q > const &v)
 Builds a scale 3 * 3 matrix created from a vector of 2 components. More...
 
template<typename T , qualifier Q>
GLM_FUNC_QUALIFIER mat< 3, 3, T, Q > shearX (mat< 3, 3, T, Q > const &m, T y)
 Builds an horizontal (parallel to the x axis) shear 3 * 3 matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_QUALIFIER mat< 3, 3, T, Q > shearY (mat< 3, 3, T, Q > const &m, T x)
 Builds a vertical (parallel to the y axis) shear 3 * 3 matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_QUALIFIER mat< 3, 3, T, Q > translate (mat< 3, 3, T, Q > const &m, vec< 2, T, Q > const &v)
 Builds a translation 3 * 3 matrix created from a vector of 2 components. More...
 
\n

Detailed Description

\n

GLM_GTX_matrix_transform_2d

\n
Author
Miguel Ángel Pérez Martínez
\n
See also
Core features (dependence)
\n\n

Definition in file matrix_transform_2d.hpp.

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matrix_transform_2d.hpp
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1 
\n
14 #pragma once
\n
15 
\n
16 // Dependency:
\n
17 #include "../mat3x3.hpp"
\n
18 #include "../vec2.hpp"
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19 
\n
20 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
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21 # ifndef GLM_ENABLE_EXPERIMENTAL
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22 # pragma message("GLM: GLM_GTX_matrix_transform_2d is an experimental extension and may change in the future. Use #define GLM_ENABLE_EXPERIMENTAL before including it, if you really want to use it.")
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23 # else
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24 # pragma message("GLM: GLM_GTX_matrix_transform_2d extension included")
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25 # endif
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26 #endif
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27 
\n
28 namespace glm
\n
29 {
\n
32 
\n
37  template<typename T, qualifier Q>
\n
38  GLM_FUNC_QUALIFIER mat<3, 3, T, Q> translate(
\n
39  mat<3, 3, T, Q> const& m,
\n
40  vec<2, T, Q> const& v);
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41 
\n
46  template<typename T, qualifier Q>
\n
47  GLM_FUNC_QUALIFIER mat<3, 3, T, Q> rotate(
\n
48  mat<3, 3, T, Q> const& m,
\n
49  T angle);
\n
50 
\n
55  template<typename T, qualifier Q>
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56  GLM_FUNC_QUALIFIER mat<3, 3, T, Q> scale(
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57  mat<3, 3, T, Q> const& m,
\n
58  vec<2, T, Q> const& v);
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59 
\n
64  template<typename T, qualifier Q>
\n
65  GLM_FUNC_QUALIFIER mat<3, 3, T, Q> shearX(
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66  mat<3, 3, T, Q> const& m,
\n
67  T y);
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68 
\n
73  template<typename T, qualifier Q>
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74  GLM_FUNC_QUALIFIER mat<3, 3, T, Q> shearY(
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75  mat<3, 3, T, Q> const& m,
\n
76  T x);
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77 
\n
79 }//namespace glm
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80 
\n
81 #include "matrix_transform_2d.inl"
\n
glm::angle
GLM_FUNC_DECL T angle(qua< T, Q > const &x)
Returns the quaternion rotation angle.
\n
glm::translate
GLM_FUNC_QUALIFIER mat< 3, 3, T, Q > translate(mat< 3, 3, T, Q > const &m, vec< 2, T, Q > const &v)
Builds a translation 3 * 3 matrix created from a vector of 2 components.
\n
glm::rotate
GLM_FUNC_QUALIFIER mat< 3, 3, T, Q > rotate(mat< 3, 3, T, Q > const &m, T angle)
Builds a rotation 3 * 3 matrix created from an angle.
\n
glm::shearY
GLM_FUNC_QUALIFIER mat< 3, 3, T, Q > shearY(mat< 3, 3, T, Q > const &m, T x)
Builds a vertical (parallel to the y axis) shear 3 * 3 matrix.
\n
glm::scale
GLM_FUNC_QUALIFIER mat< 3, 3, T, Q > scale(mat< 3, 3, T, Q > const &m, vec< 2, T, Q > const &v)
Builds a scale 3 * 3 matrix created from a vector of 2 components.
\n
glm::shearX
GLM_FUNC_QUALIFIER mat< 3, 3, T, Q > shearX(mat< 3, 3, T, Q > const &m, T y)
Builds an horizontal (parallel to the x axis) shear 3 * 3 matrix.
\n
glm
Definition: common.hpp:20
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\ntemplate<typename T , qualifier Q>
GLM_FUNC_DECL T mixedProduct (vec< 3, T, Q > const &v1, vec< 3, T, Q > const &v2, vec< 3, T, Q > const &v3)
 Mixed product of 3 vectors (from GLM_GTX_mixed_product extension)
 
\n

Detailed Description

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GLM_GTX_mixed_producte

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See also
Core features (dependence)
\n\n

Definition in file mixed_product.hpp.

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mixed_product.hpp
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1 
\n
13 #pragma once
\n
14 
\n
15 // Dependency:
\n
16 #include "../glm.hpp"
\n
17 
\n
18 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
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19 # ifndef GLM_ENABLE_EXPERIMENTAL
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20 # pragma message("GLM: GLM_GTX_mixed_product is an experimental extension and may change in the future. Use #define GLM_ENABLE_EXPERIMENTAL before including it, if you really want to use it.")
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21 # else
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22 # pragma message("GLM: GLM_GTX_mixed_product extension included")
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23 # endif
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24 #endif
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25 
\n
26 namespace glm
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27 {
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30 
\n
32  template<typename T, qualifier Q>
\n
33  GLM_FUNC_DECL T mixedProduct(
\n
34  vec<3, T, Q> const& v1,
\n
35  vec<3, T, Q> const& v2,
\n
36  vec<3, T, Q> const& v3);
\n
37 
\n
39 }// namespace glm
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40 
\n
41 #include "mixed_product.inl"
\n
glm::mixedProduct
GLM_FUNC_DECL T mixedProduct(vec< 3, T, Q > const &v1, vec< 3, T, Q > const &v2, vec< 3, T, Q > const &v3)
Mixed product of 3 vectors (from GLM_GTX_mixed_product extension)
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glm
Definition: common.hpp:20
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noise.hpp File Reference
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\nFunctions

template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL T perlin (vec< L, T, Q > const &p)
 Classic perlin noise. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL T perlin (vec< L, T, Q > const &p, vec< L, T, Q > const &rep)
 Periodic perlin noise. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL T simplex (vec< L, T, Q > const &p)
 Simplex noise. More...
 
\n

Detailed Description

\n

GLM_GTC_noise

\n
See also
Core features (dependence)
\n\n

Definition in file noise.hpp.

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noise.hpp
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1 
\n
17 #pragma once
\n
18 
\n
19 // Dependencies
\n
20 #include "../detail/setup.hpp"
\n
21 #include "../detail/qualifier.hpp"
\n
22 #include "../detail/_noise.hpp"
\n
23 #include "../geometric.hpp"
\n
24 #include "../common.hpp"
\n
25 #include "../vector_relational.hpp"
\n
26 #include "../vec2.hpp"
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27 #include "../vec3.hpp"
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28 #include "../vec4.hpp"
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29 
\n
30 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
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31 # pragma message("GLM: GLM_GTC_noise extension included")
\n
32 #endif
\n
33 
\n
34 namespace glm
\n
35 {
\n
38 
\n
41  template<length_t L, typename T, qualifier Q>
\n
42  GLM_FUNC_DECL T perlin(
\n
43  vec<L, T, Q> const& p);
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44 
\n
47  template<length_t L, typename T, qualifier Q>
\n
48  GLM_FUNC_DECL T perlin(
\n
49  vec<L, T, Q> const& p,
\n
50  vec<L, T, Q> const& rep);
\n
51 
\n
54  template<length_t L, typename T, qualifier Q>
\n
55  GLM_FUNC_DECL T simplex(
\n
56  vec<L, T, Q> const& p);
\n
57 
\n
59 }//namespace glm
\n
60 
\n
61 #include "noise.inl"
\n
glm::simplex
GLM_FUNC_DECL T simplex(vec< L, T, Q > const &p)
Simplex noise.
\n
glm::perlin
GLM_FUNC_DECL T perlin(vec< L, T, Q > const &p, vec< L, T, Q > const &rep)
Periodic perlin noise.
\n
glm
Definition: common.hpp:20
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\nFunctions

template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL T distance2 (vec< L, T, Q > const &p0, vec< L, T, Q > const &p1)
 Returns the squared distance between p0 and p1, i.e., length2(p0 - p1). More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL T l1Norm (vec< 3, T, Q > const &x, vec< 3, T, Q > const &y)
 Returns the L1 norm between x and y. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL T l1Norm (vec< 3, T, Q > const &v)
 Returns the L1 norm of v. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL T l2Norm (vec< 3, T, Q > const &x, vec< 3, T, Q > const &y)
 Returns the L2 norm between x and y. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL T l2Norm (vec< 3, T, Q > const &x)
 Returns the L2 norm of v. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL T length2 (vec< L, T, Q > const &x)
 Returns the squared length of x. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL T lMaxNorm (vec< 3, T, Q > const &x, vec< 3, T, Q > const &y)
 Returns the LMax norm between x and y. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL T lMaxNorm (vec< 3, T, Q > const &x)
 Returns the LMax norm of v. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL T lxNorm (vec< 3, T, Q > const &x, vec< 3, T, Q > const &y, unsigned int Depth)
 Returns the L norm between x and y. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL T lxNorm (vec< 3, T, Q > const &x, unsigned int Depth)
 Returns the L norm of v. More...
 
\n

Detailed Description

\n

GLM_GTX_norm

\n
See also
Core features (dependence)
\n
\n to use the features of this extension. \">GLM_GTX_quaternion (dependence)
\n
\n to use the features of this extension. \">GLM_GTX_component_wise (dependence)
\n\n

Definition in file norm.hpp.

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norm.hpp
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1 
\n
15 #pragma once
\n
16 
\n
17 // Dependency:
\n
18 #include "../geometric.hpp"
\n
19 #include "../gtx/quaternion.hpp"
\n
20 #include "../gtx/component_wise.hpp"
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21 
\n
22 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
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23 # ifndef GLM_ENABLE_EXPERIMENTAL
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24 # pragma message("GLM: GLM_GTX_norm is an experimental extension and may change in the future. Use #define GLM_ENABLE_EXPERIMENTAL before including it, if you really want to use it.")
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25 # else
\n
26 # pragma message("GLM: GLM_GTX_norm extension included")
\n
27 # endif
\n
28 #endif
\n
29 
\n
30 namespace glm
\n
31 {
\n
34 
\n
37  template<length_t L, typename T, qualifier Q>
\n
38  GLM_FUNC_DECL T length2(vec<L, T, Q> const& x);
\n
39 
\n
42  template<length_t L, typename T, qualifier Q>
\n
43  GLM_FUNC_DECL T distance2(vec<L, T, Q> const& p0, vec<L, T, Q> const& p1);
\n
44 
\n
47  template<typename T, qualifier Q>
\n
48  GLM_FUNC_DECL T l1Norm(vec<3, T, Q> const& x, vec<3, T, Q> const& y);
\n
49 
\n
52  template<typename T, qualifier Q>
\n
53  GLM_FUNC_DECL T l1Norm(vec<3, T, Q> const& v);
\n
54 
\n
57  template<typename T, qualifier Q>
\n
58  GLM_FUNC_DECL T l2Norm(vec<3, T, Q> const& x, vec<3, T, Q> const& y);
\n
59 
\n
62  template<typename T, qualifier Q>
\n
63  GLM_FUNC_DECL T l2Norm(vec<3, T, Q> const& x);
\n
64 
\n
67  template<typename T, qualifier Q>
\n
68  GLM_FUNC_DECL T lxNorm(vec<3, T, Q> const& x, vec<3, T, Q> const& y, unsigned int Depth);
\n
69 
\n
72  template<typename T, qualifier Q>
\n
73  GLM_FUNC_DECL T lxNorm(vec<3, T, Q> const& x, unsigned int Depth);
\n
74 
\n
77  template<typename T, qualifier Q>
\n
78  GLM_FUNC_DECL T lMaxNorm(vec<3, T, Q> const& x, vec<3, T, Q> const& y);
\n
79 
\n
82  template<typename T, qualifier Q>
\n
83  GLM_FUNC_DECL T lMaxNorm(vec<3, T, Q> const& x);
\n
84 
\n
86 }//namespace glm
\n
87 
\n
88 #include "norm.inl"
\n
glm::length2
GLM_FUNC_DECL T length2(vec< L, T, Q > const &x)
Returns the squared length of x.
\n
glm::l1Norm
GLM_FUNC_DECL T l1Norm(vec< 3, T, Q > const &v)
Returns the L1 norm of v.
\n
glm::distance2
GLM_FUNC_DECL T distance2(vec< L, T, Q > const &p0, vec< L, T, Q > const &p1)
Returns the squared distance between p0 and p1, i.e., length2(p0 - p1).
\n
glm::lMaxNorm
GLM_FUNC_DECL T lMaxNorm(vec< 3, T, Q > const &x)
Returns the LMax norm of v.
\n
glm::lxNorm
GLM_FUNC_DECL T lxNorm(vec< 3, T, Q > const &x, unsigned int Depth)
Returns the L norm of v.
\n
glm::l2Norm
GLM_FUNC_DECL T l2Norm(vec< 3, T, Q > const &x)
Returns the L2 norm of v.
\n
glm
Definition: common.hpp:20
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GLM_GTX_normal \nMore...

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\nFunctions

template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > triangleNormal (vec< 3, T, Q > const &p1, vec< 3, T, Q > const &p2, vec< 3, T, Q > const &p3)
 Computes triangle normal from triangle points. More...
 
\n

Detailed Description

\n

GLM_GTX_normal

\n
See also
Core features (dependence)
\n
\ngtx_extented_min_max (dependence)
\n\n

Definition in file normal.hpp.

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normal.hpp
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1 
\n
14 #pragma once
\n
15 
\n
16 // Dependency:
\n
17 #include "../glm.hpp"
\n
18 
\n
19 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
20 # ifndef GLM_ENABLE_EXPERIMENTAL
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21 # pragma message("GLM: GLM_GTX_normal is an experimental extension and may change in the future. Use #define GLM_ENABLE_EXPERIMENTAL before including it, if you really want to use it.")
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22 # else
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23 # pragma message("GLM: GLM_GTX_normal extension included")
\n
24 # endif
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25 #endif
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26 
\n
27 namespace glm
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28 {
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31 
\n
35  template<typename T, qualifier Q>
\n
36  GLM_FUNC_DECL vec<3, T, Q> triangleNormal(vec<3, T, Q> const& p1, vec<3, T, Q> const& p2, vec<3, T, Q> const& p3);
\n
37 
\n
39 }//namespace glm
\n
40 
\n
41 #include "normal.inl"
\n
glm::triangleNormal
GLM_FUNC_DECL vec< 3, T, Q > triangleNormal(vec< 3, T, Q > const &p1, vec< 3, T, Q > const &p2, vec< 3, T, Q > const &p3)
Computes triangle normal from triangle points.
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glm
Definition: common.hpp:20
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normalize_dot.hpp File Reference
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\nFunctions

template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL T fastNormalizeDot (vec< L, T, Q > const &x, vec< L, T, Q > const &y)
 Normalize parameters and returns the dot product of x and y. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL T normalizeDot (vec< L, T, Q > const &x, vec< L, T, Q > const &y)
 Normalize parameters and returns the dot product of x and y. More...
 
\n

Detailed Description

\n

GLM_GTX_normalize_dot

\n
See also
Core features (dependence)
\n
\n to use the features of this extension. \">GLM_GTX_fast_square_root (dependence)
\n\n

Definition in file normalize_dot.hpp.

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normalize_dot.hpp
\n
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1 
\n
14 #pragma once
\n
15 
\n
16 // Dependency:
\n
17 #include "../gtx/fast_square_root.hpp"
\n
18 
\n
19 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
20 # ifndef GLM_ENABLE_EXPERIMENTAL
\n
21 # pragma message("GLM: GLM_GTX_normalize_dot is an experimental extension and may change in the future. Use #define GLM_ENABLE_EXPERIMENTAL before including it, if you really want to use it.")
\n
22 # else
\n
23 # pragma message("GLM: GLM_GTX_normalize_dot extension included")
\n
24 # endif
\n
25 #endif
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26 
\n
27 namespace glm
\n
28 {
\n
31 
\n
36  template<length_t L, typename T, qualifier Q>
\n
37  GLM_FUNC_DECL T normalizeDot(vec<L, T, Q> const& x, vec<L, T, Q> const& y);
\n
38 
\n
43  template<length_t L, typename T, qualifier Q>
\n
44  GLM_FUNC_DECL T fastNormalizeDot(vec<L, T, Q> const& x, vec<L, T, Q> const& y);
\n
45 
\n
47 }//namespace glm
\n
48 
\n
49 #include "normalize_dot.inl"
\n
glm::normalizeDot
GLM_FUNC_DECL T normalizeDot(vec< L, T, Q > const &x, vec< L, T, Q > const &y)
Normalize parameters and returns the dot product of x and y.
\n
glm::fastNormalizeDot
GLM_FUNC_DECL T fastNormalizeDot(vec< L, T, Q > const &x, vec< L, T, Q > const &y)
Normalize parameters and returns the dot product of x and y.
\n
glm
Definition: common.hpp:20
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number_precision.hpp File Reference
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GLM_GTX_number_precision \nMore...

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\nTypedefs

\ntypedef f32 f32mat1
 Single-qualifier floating-point scalar. (from GLM_GTX_number_precision extension)
 
\ntypedef f32 f32mat1x1
 Single-qualifier floating-point scalar. (from GLM_GTX_number_precision extension)
 
\ntypedef f32 f32vec1
 Single-qualifier floating-point scalar. (from GLM_GTX_number_precision extension)
 
\ntypedef f64 f64mat1
 Double-qualifier floating-point scalar. (from GLM_GTX_number_precision extension)
 
\ntypedef f64 f64mat1x1
 Double-qualifier floating-point scalar. (from GLM_GTX_number_precision extension)
 
\ntypedef f64 f64vec1
 Single-qualifier floating-point scalar. (from GLM_GTX_number_precision extension)
 
\ntypedef u16 u16vec1
 16bit unsigned integer scalar. (from GLM_GTX_number_precision extension)
 
\ntypedef u32 u32vec1
 32bit unsigned integer scalar. (from GLM_GTX_number_precision extension)
 
\ntypedef u64 u64vec1
 64bit unsigned integer scalar. (from GLM_GTX_number_precision extension)
 
\ntypedef u8 u8vec1
 8bit unsigned integer scalar. (from GLM_GTX_number_precision extension)
 
\n

Detailed Description

\n

GLM_GTX_number_precision

\n
See also
Core features (dependence)
\n
\n to use the features of this extension. \">GLM_GTC_type_precision (dependence)
\n
\n to use the features of this extension. \">GLM_GTC_quaternion (dependence)
\n\n

Definition in file number_precision.hpp.

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number_precision.hpp
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1 
\n
15 #pragma once
\n
16 
\n
17 // Dependency:
\n
18 #include "../glm.hpp"
\n
19 #include "../gtc/type_precision.hpp"
\n
20 
\n
21 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
22 # ifndef GLM_ENABLE_EXPERIMENTAL
\n
23 # pragma message("GLM: GLM_GTX_number_precision is an experimental extension and may change in the future. Use #define GLM_ENABLE_EXPERIMENTAL before including it, if you really want to use it.")
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24 # else
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25 # pragma message("GLM: GLM_GTX_number_precision extension included")
\n
26 # endif
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27 #endif
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28 
\n
29 namespace glm{
\n
30 namespace gtx
\n
31 {
\n
33  // Unsigned int vector types
\n
34 
\n
37 
\n
38  typedef u8 u8vec1;
\n
39  typedef u16 u16vec1;
\n
40  typedef u32 u32vec1;
\n
41  typedef u64 u64vec1;
\n
42 
\n
44  // Float vector types
\n
45 
\n
46  typedef f32 f32vec1;
\n
47  typedef f64 f64vec1;
\n
48 
\n
50  // Float matrix types
\n
51 
\n
52  typedef f32 f32mat1;
\n
53  typedef f32 f32mat1x1;
\n
54  typedef f64 f64mat1;
\n
55  typedef f64 f64mat1x1;
\n
56 
\n
58 }//namespace gtx
\n
59 }//namespace glm
\n
60 
\n
61 #include "number_precision.inl"
\n
glm::u32
uint32 u32
Default qualifier 32 bit unsigned integer type.
Definition: fwd.hpp:120
\n
glm::u64
uint64 u64
Default qualifier 64 bit unsigned integer type.
Definition: fwd.hpp:134
\n
glm::gtx::f32mat1x1
f32 f32mat1x1
Single-qualifier floating-point scalar. (from GLM_GTX_number_precision extension) ...
Definition: number_precision.hpp:53
\n
glm::gtx::f64mat1
f64 f64mat1
Double-qualifier floating-point scalar. (from GLM_GTX_number_precision extension) ...
Definition: number_precision.hpp:54
\n
glm::gtx::u16vec1
u16 u16vec1
16bit unsigned integer scalar. (from GLM_GTX_number_precision extension)
Definition: number_precision.hpp:39
\n
glm::u8
uint8 u8
Default qualifier 8 bit unsigned integer type.
Definition: fwd.hpp:92
\n
glm::gtx::f32mat1
f32 f32mat1
Single-qualifier floating-point scalar. (from GLM_GTX_number_precision extension) ...
Definition: number_precision.hpp:52
\n
glm::gtx::f32vec1
f32 f32vec1
Single-qualifier floating-point scalar. (from GLM_GTX_number_precision extension) ...
Definition: number_precision.hpp:46
\n
glm::gtx::f64vec1
f64 f64vec1
Single-qualifier floating-point scalar. (from GLM_GTX_number_precision extension) ...
Definition: number_precision.hpp:47
\n
glm::gtx::f64mat1x1
f64 f64mat1x1
Double-qualifier floating-point scalar. (from GLM_GTX_number_precision extension) ...
Definition: number_precision.hpp:55
\n
glm::gtx::u64vec1
u64 u64vec1
64bit unsigned integer scalar. (from GLM_GTX_number_precision extension)
Definition: number_precision.hpp:41
\n
glm::gtx::u32vec1
u32 u32vec1
32bit unsigned integer scalar. (from GLM_GTX_number_precision extension)
Definition: number_precision.hpp:40
\n
glm::f32
float f32
Default 32 bit single-qualifier floating-point scalar.
Definition: fwd.hpp:150
\n
glm::u16
uint16 u16
Default qualifier 16 bit unsigned integer type.
Definition: fwd.hpp:106
\n
glm::gtx::u8vec1
u8 u8vec1
8bit unsigned integer scalar. (from GLM_GTX_number_precision extension)
Definition: number_precision.hpp:38
\n
glm::f64
double f64
Default 64 bit double-qualifier floating-point scalar.
Definition: fwd.hpp:166
\n
glm
Definition: common.hpp:20
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optimum_pow.hpp File Reference
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GLM_GTX_optimum_pow \nMore...

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\nFunctions

template<typename genType >
GLM_FUNC_DECL genType pow2 (genType const &x)
 Returns x raised to the power of 2. More...
 
template<typename genType >
GLM_FUNC_DECL genType pow3 (genType const &x)
 Returns x raised to the power of 3. More...
 
template<typename genType >
GLM_FUNC_DECL genType pow4 (genType const &x)
 Returns x raised to the power of 4. More...
 
\n

Detailed Description

\n

GLM_GTX_optimum_pow

\n
See also
Core features (dependence)
\n\n

Definition in file optimum_pow.hpp.

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optimum_pow.hpp
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1 
\n
13 #pragma once
\n
14 
\n
15 // Dependency:
\n
16 #include "../glm.hpp"
\n
17 
\n
18 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
19 # ifndef GLM_ENABLE_EXPERIMENTAL
\n
20 # pragma message("GLM: GLM_GTX_optimum_pow is an experimental extension and may change in the future. Use #define GLM_ENABLE_EXPERIMENTAL before including it, if you really want to use it.")
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21 # else
\n
22 # pragma message("GLM: GLM_GTX_optimum_pow extension included")
\n
23 # endif
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24 #endif
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25 
\n
26 namespace glm{
\n
27 namespace gtx
\n
28 {
\n
31 
\n
35  template<typename genType>
\n
36  GLM_FUNC_DECL genType pow2(genType const& x);
\n
37 
\n
41  template<typename genType>
\n
42  GLM_FUNC_DECL genType pow3(genType const& x);
\n
43 
\n
47  template<typename genType>
\n
48  GLM_FUNC_DECL genType pow4(genType const& x);
\n
49 
\n
51 }//namespace gtx
\n
52 }//namespace glm
\n
53 
\n
54 #include "optimum_pow.inl"
\n
glm::gtx::pow3
GLM_FUNC_DECL genType pow3(genType const &x)
Returns x raised to the power of 3.
\n
glm::gtx::pow4
GLM_FUNC_DECL genType pow4(genType const &x)
Returns x raised to the power of 4.
\n
glm::gtx::pow2
GLM_FUNC_DECL genType pow2(genType const &x)
Returns x raised to the power of 2.
\n
glm
Definition: common.hpp:20
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orthonormalize.hpp File Reference
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GLM_GTX_orthonormalize \nMore...

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\n\n\n\n\n\n\n\n\n\n\n

\nFunctions

template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 3, 3, T, Q > orthonormalize (mat< 3, 3, T, Q > const &m)
 Returns the orthonormalized matrix of m. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > orthonormalize (vec< 3, T, Q > const &x, vec< 3, T, Q > const &y)
 Orthonormalizes x according y. More...
 
\n

Detailed Description

\n

GLM_GTX_orthonormalize

\n
See also
Core features (dependence)
\n
\ngtx_extented_min_max (dependence)
\n\n

Definition in file orthonormalize.hpp.

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orthonormalize.hpp
\n
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1 
\n
14 #pragma once
\n
15 
\n
16 // Dependency:
\n
17 #include "../vec3.hpp"
\n
18 #include "../mat3x3.hpp"
\n
19 #include "../geometric.hpp"
\n
20 
\n
21 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
22 # ifndef GLM_ENABLE_EXPERIMENTAL
\n
23 # pragma message("GLM: GLM_GTX_orthonormalize is an experimental extension and may change in the future. Use #define GLM_ENABLE_EXPERIMENTAL before including it, if you really want to use it.")
\n
24 # else
\n
25 # pragma message("GLM: GLM_GTX_orthonormalize extension included")
\n
26 # endif
\n
27 #endif
\n
28 
\n
29 namespace glm
\n
30 {
\n
33 
\n
37  template<typename T, qualifier Q>
\n
38  GLM_FUNC_DECL mat<3, 3, T, Q> orthonormalize(mat<3, 3, T, Q> const& m);
\n
39 
\n
43  template<typename T, qualifier Q>
\n
44  GLM_FUNC_DECL vec<3, T, Q> orthonormalize(vec<3, T, Q> const& x, vec<3, T, Q> const& y);
\n
45 
\n
47 }//namespace glm
\n
48 
\n
49 #include "orthonormalize.inl"
\n
glm::orthonormalize
GLM_FUNC_DECL vec< 3, T, Q > orthonormalize(vec< 3, T, Q > const &x, vec< 3, T, Q > const &y)
Orthonormalizes x according y.
\n
glm
Definition: common.hpp:20
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GLM_GTC_packing \nMore...

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\nFunctions

GLM_FUNC_DECL uint32 packF2x11_1x10 (vec3 const &v)
 First, converts the first two components of the normalized floating-point value v into 11-bit signless floating-point values. More...
 
GLM_FUNC_DECL uint32 packF3x9_E1x5 (vec3 const &v)
 First, converts the first two components of the normalized floating-point value v into 11-bit signless floating-point values. More...
 
template<length_t L, qualifier Q>
GLM_FUNC_DECL vec< L, uint16, Q > packHalf (vec< L, float, Q > const &v)
 Returns an unsigned integer vector obtained by converting the components of a floating-point vector to the 16-bit floating-point representation found in the OpenGL Specification. More...
 
GLM_FUNC_DECL uint16 packHalf1x16 (float v)
 Returns an unsigned integer obtained by converting the components of a floating-point scalar to the 16-bit floating-point representation found in the OpenGL Specification, and then packing this 16-bit value into a 16-bit unsigned integer. More...
 
GLM_FUNC_DECL uint64 packHalf4x16 (vec4 const &v)
 Returns an unsigned integer obtained by converting the components of a four-component floating-point vector to the 16-bit floating-point representation found in the OpenGL Specification, and then packing these four 16-bit values into a 64-bit unsigned integer. More...
 
GLM_FUNC_DECL uint32 packI3x10_1x2 (ivec4 const &v)
 Returns an unsigned integer obtained by converting the components of a four-component signed integer vector to the 10-10-10-2-bit signed integer representation found in the OpenGL Specification, and then packing these four values into a 32-bit unsigned integer. More...
 
GLM_FUNC_DECL int packInt2x16 (i16vec2 const &v)
 Convert each component from an integer vector into a packed integer. More...
 
GLM_FUNC_DECL int64 packInt2x32 (i32vec2 const &v)
 Convert each component from an integer vector into a packed integer. More...
 
GLM_FUNC_DECL int16 packInt2x8 (i8vec2 const &v)
 Convert each component from an integer vector into a packed integer. More...
 
GLM_FUNC_DECL int64 packInt4x16 (i16vec4 const &v)
 Convert each component from an integer vector into a packed integer. More...
 
GLM_FUNC_DECL int32 packInt4x8 (i8vec4 const &v)
 Convert each component from an integer vector into a packed integer. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< 4, T, Q > packRGBM (vec< 3, T, Q > const &rgb)
 Returns an unsigned integer vector obtained by converting the components of a floating-point vector to the 16-bit floating-point representation found in the OpenGL Specification. More...
 
template<typename intType , length_t L, typename floatType , qualifier Q>
GLM_FUNC_DECL vec< L, intType, Q > packSnorm (vec< L, floatType, Q > const &v)
 Convert each component of the normalized floating-point vector into signed integer values. More...
 
GLM_FUNC_DECL uint16 packSnorm1x16 (float v)
 First, converts the normalized floating-point value v into 16-bit integer value. More...
 
GLM_FUNC_DECL uint8 packSnorm1x8 (float s)
 First, converts the normalized floating-point value v into 8-bit integer value. More...
 
GLM_FUNC_DECL uint16 packSnorm2x8 (vec2 const &v)
 First, converts each component of the normalized floating-point value v into 8-bit integer values. More...
 
GLM_FUNC_DECL uint32 packSnorm3x10_1x2 (vec4 const &v)
 First, converts the first three components of the normalized floating-point value v into 10-bit signed integer values. More...
 
GLM_FUNC_DECL uint64 packSnorm4x16 (vec4 const &v)
 First, converts each component of the normalized floating-point value v into 16-bit integer values. More...
 
GLM_FUNC_DECL uint32 packU3x10_1x2 (uvec4 const &v)
 Returns an unsigned integer obtained by converting the components of a four-component unsigned integer vector to the 10-10-10-2-bit unsigned integer representation found in the OpenGL Specification, and then packing these four values into a 32-bit unsigned integer. More...
 
GLM_FUNC_DECL uint packUint2x16 (u16vec2 const &v)
 Convert each component from an integer vector into a packed unsigned integer. More...
 
GLM_FUNC_DECL uint64 packUint2x32 (u32vec2 const &v)
 Convert each component from an integer vector into a packed unsigned integer. More...
 
GLM_FUNC_DECL uint16 packUint2x8 (u8vec2 const &v)
 Convert each component from an integer vector into a packed unsigned integer. More...
 
GLM_FUNC_DECL uint64 packUint4x16 (u16vec4 const &v)
 Convert each component from an integer vector into a packed unsigned integer. More...
 
GLM_FUNC_DECL uint32 packUint4x8 (u8vec4 const &v)
 Convert each component from an integer vector into a packed unsigned integer. More...
 
template<typename uintType , length_t L, typename floatType , qualifier Q>
GLM_FUNC_DECL vec< L, uintType, Q > packUnorm (vec< L, floatType, Q > const &v)
 Convert each component of the normalized floating-point vector into unsigned integer values. More...
 
GLM_FUNC_DECL uint16 packUnorm1x16 (float v)
 First, converts the normalized floating-point value v into a 16-bit integer value. More...
 
GLM_FUNC_DECL uint16 packUnorm1x5_1x6_1x5 (vec3 const &v)
 Convert each component of the normalized floating-point vector into unsigned integer values. More...
 
GLM_FUNC_DECL uint8 packUnorm1x8 (float v)
 First, converts the normalized floating-point value v into a 8-bit integer value. More...
 
GLM_FUNC_DECL uint8 packUnorm2x3_1x2 (vec3 const &v)
 Convert each component of the normalized floating-point vector into unsigned integer values. More...
 
GLM_FUNC_DECL uint8 packUnorm2x4 (vec2 const &v)
 Convert each component of the normalized floating-point vector into unsigned integer values. More...
 
GLM_FUNC_DECL uint16 packUnorm2x8 (vec2 const &v)
 First, converts each component of the normalized floating-point value v into 8-bit integer values. More...
 
GLM_FUNC_DECL uint32 packUnorm3x10_1x2 (vec4 const &v)
 First, converts the first three components of the normalized floating-point value v into 10-bit unsigned integer values. More...
 
GLM_FUNC_DECL uint16 packUnorm3x5_1x1 (vec4 const &v)
 Convert each component of the normalized floating-point vector into unsigned integer values. More...
 
GLM_FUNC_DECL uint64 packUnorm4x16 (vec4 const &v)
 First, converts each component of the normalized floating-point value v into 16-bit integer values. More...
 
GLM_FUNC_DECL uint16 packUnorm4x4 (vec4 const &v)
 Convert each component of the normalized floating-point vector into unsigned integer values. More...
 
GLM_FUNC_DECL vec3 unpackF2x11_1x10 (uint32 p)
 First, unpacks a single 32-bit unsigned integer p into two 11-bit signless floating-point values and one 10-bit signless floating-point value . More...
 
GLM_FUNC_DECL vec3 unpackF3x9_E1x5 (uint32 p)
 First, unpacks a single 32-bit unsigned integer p into two 11-bit signless floating-point values and one 10-bit signless floating-point value . More...
 
template<length_t L, qualifier Q>
GLM_FUNC_DECL vec< L, float, Q > unpackHalf (vec< L, uint16, Q > const &p)
 Returns a floating-point vector with components obtained by reinterpreting an integer vector as 16-bit floating-point numbers and converting them to 32-bit floating-point values. More...
 
GLM_FUNC_DECL float unpackHalf1x16 (uint16 v)
 Returns a floating-point scalar with components obtained by unpacking a 16-bit unsigned integer into a 16-bit value, interpreted as a 16-bit floating-point number according to the OpenGL Specification, and converting it to 32-bit floating-point values. More...
 
GLM_FUNC_DECL vec4 unpackHalf4x16 (uint64 p)
 Returns a four-component floating-point vector with components obtained by unpacking a 64-bit unsigned integer into four 16-bit values, interpreting those values as 16-bit floating-point numbers according to the OpenGL Specification, and converting them to 32-bit floating-point values. More...
 
GLM_FUNC_DECL ivec4 unpackI3x10_1x2 (uint32 p)
 Unpacks a single 32-bit unsigned integer p into three 10-bit and one 2-bit signed integers. More...
 
GLM_FUNC_DECL i16vec2 unpackInt2x16 (int p)
 Convert a packed integer into an integer vector. More...
 
GLM_FUNC_DECL i32vec2 unpackInt2x32 (int64 p)
 Convert a packed integer into an integer vector. More...
 
GLM_FUNC_DECL i8vec2 unpackInt2x8 (int16 p)
 Convert a packed integer into an integer vector. More...
 
GLM_FUNC_DECL i16vec4 unpackInt4x16 (int64 p)
 Convert a packed integer into an integer vector. More...
 
GLM_FUNC_DECL i8vec4 unpackInt4x8 (int32 p)
 Convert a packed integer into an integer vector. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > unpackRGBM (vec< 4, T, Q > const &rgbm)
 Returns a floating-point vector with components obtained by reinterpreting an integer vector as 16-bit floating-point numbers and converting them to 32-bit floating-point values. More...
 
template<typename floatType , length_t L, typename intType , qualifier Q>
GLM_FUNC_DECL vec< L, floatType, Q > unpackSnorm (vec< L, intType, Q > const &v)
 Convert a packed integer to a normalized floating-point vector. More...
 
GLM_FUNC_DECL float unpackSnorm1x16 (uint16 p)
 First, unpacks a single 16-bit unsigned integer p into a single 16-bit signed integers. More...
 
GLM_FUNC_DECL float unpackSnorm1x8 (uint8 p)
 First, unpacks a single 8-bit unsigned integer p into a single 8-bit signed integers. More...
 
GLM_FUNC_DECL vec2 unpackSnorm2x8 (uint16 p)
 First, unpacks a single 16-bit unsigned integer p into a pair of 8-bit signed integers. More...
 
GLM_FUNC_DECL vec4 unpackSnorm3x10_1x2 (uint32 p)
 First, unpacks a single 32-bit unsigned integer p into four 16-bit signed integers. More...
 
GLM_FUNC_DECL vec4 unpackSnorm4x16 (uint64 p)
 First, unpacks a single 64-bit unsigned integer p into four 16-bit signed integers. More...
 
GLM_FUNC_DECL uvec4 unpackU3x10_1x2 (uint32 p)
 Unpacks a single 32-bit unsigned integer p into three 10-bit and one 2-bit unsigned integers. More...
 
GLM_FUNC_DECL u16vec2 unpackUint2x16 (uint p)
 Convert a packed integer into an integer vector. More...
 
GLM_FUNC_DECL u32vec2 unpackUint2x32 (uint64 p)
 Convert a packed integer into an integer vector. More...
 
GLM_FUNC_DECL u8vec2 unpackUint2x8 (uint16 p)
 Convert a packed integer into an integer vector. More...
 
GLM_FUNC_DECL u16vec4 unpackUint4x16 (uint64 p)
 Convert a packed integer into an integer vector. More...
 
GLM_FUNC_DECL u8vec4 unpackUint4x8 (uint32 p)
 Convert a packed integer into an integer vector. More...
 
template<typename floatType , length_t L, typename uintType , qualifier Q>
GLM_FUNC_DECL vec< L, floatType, Q > unpackUnorm (vec< L, uintType, Q > const &v)
 Convert a packed integer to a normalized floating-point vector. More...
 
GLM_FUNC_DECL float unpackUnorm1x16 (uint16 p)
 First, unpacks a single 16-bit unsigned integer p into a of 16-bit unsigned integers. More...
 
GLM_FUNC_DECL vec3 unpackUnorm1x5_1x6_1x5 (uint16 p)
 Convert a packed integer to a normalized floating-point vector. More...
 
GLM_FUNC_DECL float unpackUnorm1x8 (uint8 p)
 Convert a single 8-bit integer to a normalized floating-point value. More...
 
GLM_FUNC_DECL vec3 unpackUnorm2x3_1x2 (uint8 p)
 Convert a packed integer to a normalized floating-point vector. More...
 
GLM_FUNC_DECL vec2 unpackUnorm2x4 (uint8 p)
 Convert a packed integer to a normalized floating-point vector. More...
 
GLM_FUNC_DECL vec2 unpackUnorm2x8 (uint16 p)
 First, unpacks a single 16-bit unsigned integer p into a pair of 8-bit unsigned integers. More...
 
GLM_FUNC_DECL vec4 unpackUnorm3x10_1x2 (uint32 p)
 First, unpacks a single 32-bit unsigned integer p into four 16-bit signed integers. More...
 
GLM_FUNC_DECL vec4 unpackUnorm3x5_1x1 (uint16 p)
 Convert a packed integer to a normalized floating-point vector. More...
 
GLM_FUNC_DECL vec4 unpackUnorm4x16 (uint64 p)
 First, unpacks a single 64-bit unsigned integer p into four 16-bit unsigned integers. More...
 
GLM_FUNC_DECL vec4 unpackUnorm4x4 (uint16 p)
 Convert a packed integer to a normalized floating-point vector. More...
 
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Detailed Description

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GLM_GTC_packing

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See also
Core features (dependence)
\n\n

Definition in file gtc/packing.hpp.

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gtc/packing.hpp
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1 
\n
14 #pragma once
\n
15 
\n
16 // Dependency:
\n
17 #include "type_precision.hpp"
\n
18 
\n
19 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
20 # pragma message("GLM: GLM_GTC_packing extension included")
\n
21 #endif
\n
22 
\n
23 namespace glm
\n
24 {
\n
27 
\n
39  GLM_FUNC_DECL uint8 packUnorm1x8(float v);
\n
40 
\n
51  GLM_FUNC_DECL float unpackUnorm1x8(uint8 p);
\n
52 
\n
67  GLM_FUNC_DECL uint16 packUnorm2x8(vec2 const& v);
\n
68 
\n
83  GLM_FUNC_DECL vec2 unpackUnorm2x8(uint16 p);
\n
84 
\n
96  GLM_FUNC_DECL uint8 packSnorm1x8(float s);
\n
97 
\n
109  GLM_FUNC_DECL float unpackSnorm1x8(uint8 p);
\n
110 
\n
125  GLM_FUNC_DECL uint16 packSnorm2x8(vec2 const& v);
\n
126 
\n
141  GLM_FUNC_DECL vec2 unpackSnorm2x8(uint16 p);
\n
142 
\n
154  GLM_FUNC_DECL uint16 packUnorm1x16(float v);
\n
155 
\n
167  GLM_FUNC_DECL float unpackUnorm1x16(uint16 p);
\n
168 
\n
183  GLM_FUNC_DECL uint64 packUnorm4x16(vec4 const& v);
\n
184 
\n
199  GLM_FUNC_DECL vec4 unpackUnorm4x16(uint64 p);
\n
200 
\n
212  GLM_FUNC_DECL uint16 packSnorm1x16(float v);
\n
213 
\n
225  GLM_FUNC_DECL float unpackSnorm1x16(uint16 p);
\n
226 
\n
241  GLM_FUNC_DECL uint64 packSnorm4x16(vec4 const& v);
\n
242 
\n
257  GLM_FUNC_DECL vec4 unpackSnorm4x16(uint64 p);
\n
258 
\n
268  GLM_FUNC_DECL uint16 packHalf1x16(float v);
\n
269 
\n
279  GLM_FUNC_DECL float unpackHalf1x16(uint16 v);
\n
280 
\n
292  GLM_FUNC_DECL uint64 packHalf4x16(vec4 const& v);
\n
293 
\n
305  GLM_FUNC_DECL vec4 unpackHalf4x16(uint64 p);
\n
306 
\n
318  GLM_FUNC_DECL uint32 packI3x10_1x2(ivec4 const& v);
\n
319 
\n
329  GLM_FUNC_DECL ivec4 unpackI3x10_1x2(uint32 p);
\n
330 
\n
342  GLM_FUNC_DECL uint32 packU3x10_1x2(uvec4 const& v);
\n
343 
\n
353  GLM_FUNC_DECL uvec4 unpackU3x10_1x2(uint32 p);
\n
354 
\n
371  GLM_FUNC_DECL uint32 packSnorm3x10_1x2(vec4 const& v);
\n
372 
\n
388  GLM_FUNC_DECL vec4 unpackSnorm3x10_1x2(uint32 p);
\n
389 
\n
406  GLM_FUNC_DECL uint32 packUnorm3x10_1x2(vec4 const& v);
\n
407 
\n
423  GLM_FUNC_DECL vec4 unpackUnorm3x10_1x2(uint32 p);
\n
424 
\n
434  GLM_FUNC_DECL uint32 packF2x11_1x10(vec3 const& v);
\n
435 
\n
444  GLM_FUNC_DECL vec3 unpackF2x11_1x10(uint32 p);
\n
445 
\n
446 
\n
458  GLM_FUNC_DECL uint32 packF3x9_E1x5(vec3 const& v);
\n
459 
\n
470  GLM_FUNC_DECL vec3 unpackF3x9_E1x5(uint32 p);
\n
471 
\n
480  template<length_t L, typename T, qualifier Q>
\n
481  GLM_FUNC_DECL vec<4, T, Q> packRGBM(vec<3, T, Q> const& rgb);
\n
482 
\n
490  template<length_t L, typename T, qualifier Q>
\n
491  GLM_FUNC_DECL vec<3, T, Q> unpackRGBM(vec<4, T, Q> const& rgbm);
\n
492 
\n
501  template<length_t L, qualifier Q>
\n
502  GLM_FUNC_DECL vec<L, uint16, Q> packHalf(vec<L, float, Q> const& v);
\n
503 
\n
511  template<length_t L, qualifier Q>
\n
512  GLM_FUNC_DECL vec<L, float, Q> unpackHalf(vec<L, uint16, Q> const& p);
\n
513 
\n
518  template<typename uintType, length_t L, typename floatType, qualifier Q>
\n
519  GLM_FUNC_DECL vec<L, uintType, Q> packUnorm(vec<L, floatType, Q> const& v);
\n
520 
\n
525  template<typename floatType, length_t L, typename uintType, qualifier Q>
\n
526  GLM_FUNC_DECL vec<L, floatType, Q> unpackUnorm(vec<L, uintType, Q> const& v);
\n
527 
\n
532  template<typename intType, length_t L, typename floatType, qualifier Q>
\n
533  GLM_FUNC_DECL vec<L, intType, Q> packSnorm(vec<L, floatType, Q> const& v);
\n
534 
\n
539  template<typename floatType, length_t L, typename intType, qualifier Q>
\n
540  GLM_FUNC_DECL vec<L, floatType, Q> unpackSnorm(vec<L, intType, Q> const& v);
\n
541 
\n
546  GLM_FUNC_DECL uint8 packUnorm2x4(vec2 const& v);
\n
547 
\n
552  GLM_FUNC_DECL vec2 unpackUnorm2x4(uint8 p);
\n
553 
\n
558  GLM_FUNC_DECL uint16 packUnorm4x4(vec4 const& v);
\n
559 
\n
564  GLM_FUNC_DECL vec4 unpackUnorm4x4(uint16 p);
\n
565 
\n
570  GLM_FUNC_DECL uint16 packUnorm1x5_1x6_1x5(vec3 const& v);
\n
571 
\n
576  GLM_FUNC_DECL vec3 unpackUnorm1x5_1x6_1x5(uint16 p);
\n
577 
\n
582  GLM_FUNC_DECL uint16 packUnorm3x5_1x1(vec4 const& v);
\n
583 
\n
588  GLM_FUNC_DECL vec4 unpackUnorm3x5_1x1(uint16 p);
\n
589 
\n
594  GLM_FUNC_DECL uint8 packUnorm2x3_1x2(vec3 const& v);
\n
595 
\n
600  GLM_FUNC_DECL vec3 unpackUnorm2x3_1x2(uint8 p);
\n
601 
\n
602 
\n
603 
\n
608  GLM_FUNC_DECL int16 packInt2x8(i8vec2 const& v);
\n
609 
\n
614  GLM_FUNC_DECL i8vec2 unpackInt2x8(int16 p);
\n
615 
\n
620  GLM_FUNC_DECL uint16 packUint2x8(u8vec2 const& v);
\n
621 
\n
626  GLM_FUNC_DECL u8vec2 unpackUint2x8(uint16 p);
\n
627 
\n
632  GLM_FUNC_DECL int32 packInt4x8(i8vec4 const& v);
\n
633 
\n
638  GLM_FUNC_DECL i8vec4 unpackInt4x8(int32 p);
\n
639 
\n
644  GLM_FUNC_DECL uint32 packUint4x8(u8vec4 const& v);
\n
645 
\n
650  GLM_FUNC_DECL u8vec4 unpackUint4x8(uint32 p);
\n
651 
\n
656  GLM_FUNC_DECL int packInt2x16(i16vec2 const& v);
\n
657 
\n
662  GLM_FUNC_DECL i16vec2 unpackInt2x16(int p);
\n
663 
\n
668  GLM_FUNC_DECL int64 packInt4x16(i16vec4 const& v);
\n
669 
\n
674  GLM_FUNC_DECL i16vec4 unpackInt4x16(int64 p);
\n
675 
\n
680  GLM_FUNC_DECL uint packUint2x16(u16vec2 const& v);
\n
681 
\n
686  GLM_FUNC_DECL u16vec2 unpackUint2x16(uint p);
\n
687 
\n
692  GLM_FUNC_DECL uint64 packUint4x16(u16vec4 const& v);
\n
693 
\n
698  GLM_FUNC_DECL u16vec4 unpackUint4x16(uint64 p);
\n
699 
\n
704  GLM_FUNC_DECL int64 packInt2x32(i32vec2 const& v);
\n
705 
\n
710  GLM_FUNC_DECL i32vec2 unpackInt2x32(int64 p);
\n
711 
\n
716  GLM_FUNC_DECL uint64 packUint2x32(u32vec2 const& v);
\n
717 
\n
722  GLM_FUNC_DECL u32vec2 unpackUint2x32(uint64 p);
\n
723 
\n
724 
\n
726 }// namespace glm
\n
727 
\n
728 #include "packing.inl"
\n
glm::packUnorm4x4
GLM_FUNC_DECL uint16 packUnorm4x4(vec4 const &v)
Convert each component of the normalized floating-point vector into unsigned integer values...
\n
glm::packUint2x8
GLM_FUNC_DECL uint16 packUint2x8(u8vec2 const &v)
Convert each component from an integer vector into a packed unsigned integer.
\n
glm::packUnorm2x4
GLM_FUNC_DECL uint8 packUnorm2x4(vec2 const &v)
Convert each component of the normalized floating-point vector into unsigned integer values...
\n
glm::unpackUnorm4x16
GLM_FUNC_DECL vec4 unpackUnorm4x16(uint64 p)
First, unpacks a single 64-bit unsigned integer p into four 16-bit unsigned integers.
\n
glm::unpackInt2x16
GLM_FUNC_DECL i16vec2 unpackInt2x16(int p)
Convert a packed integer into an integer vector.
\n
glm::unpackInt4x8
GLM_FUNC_DECL i8vec4 unpackInt4x8(int32 p)
Convert a packed integer into an integer vector.
\n
glm::vec2
vec< 2, float, defaultp > vec2
2 components vector of single-precision floating-point numbers.
Definition: vector_float2.hpp:15
\n
glm::unpackUint4x16
GLM_FUNC_DECL u16vec4 unpackUint4x16(uint64 p)
Convert a packed integer into an integer vector.
\n
glm::i8vec2
vec< 2, i8, defaultp > i8vec2
8 bit signed integer vector of 2 components type.
Definition: fwd.hpp:238
\n
glm::unpackUint2x32
GLM_FUNC_DECL u32vec2 unpackUint2x32(uint64 p)
Convert a packed integer into an integer vector.
\n
glm::unpackHalf
GLM_FUNC_DECL vec< L, float, Q > unpackHalf(vec< L, uint16, Q > const &p)
Returns a floating-point vector with components obtained by reinterpreting an integer vector as 16-bi...
\n
glm::packUnorm4x16
GLM_FUNC_DECL uint64 packUnorm4x16(vec4 const &v)
First, converts each component of the normalized floating-point value v into 16-bit integer values...
\n
glm::packUnorm
GLM_FUNC_DECL vec< L, uintType, Q > packUnorm(vec< L, floatType, Q > const &v)
Convert each component of the normalized floating-point vector into unsigned integer values...
\n
glm::unpackF2x11_1x10
GLM_FUNC_DECL vec3 unpackF2x11_1x10(uint32 p)
First, unpacks a single 32-bit unsigned integer p into two 11-bit signless floating-point values and ...
\n
glm::packUnorm1x8
GLM_FUNC_DECL uint8 packUnorm1x8(float v)
First, converts the normalized floating-point value v into a 8-bit integer value. ...
\n
glm::unpackUint2x8
GLM_FUNC_DECL u8vec2 unpackUint2x8(uint16 p)
Convert a packed integer into an integer vector.
\n
glm::unpackUnorm3x10_1x2
GLM_FUNC_DECL vec4 unpackUnorm3x10_1x2(uint32 p)
First, unpacks a single 32-bit unsigned integer p into four 16-bit signed integers.
\n
glm::unpackUnorm2x4
GLM_FUNC_DECL vec2 unpackUnorm2x4(uint8 p)
Convert a packed integer to a normalized floating-point vector.
\n
glm::packRGBM
GLM_FUNC_DECL vec< 4, T, Q > packRGBM(vec< 3, T, Q > const &rgb)
Returns an unsigned integer vector obtained by converting the components of a floating-point vector t...
\n
glm::i16vec4
vec< 4, i16, defaultp > i16vec4
16 bit signed integer vector of 4 components type.
Definition: fwd.hpp:260
\n
glm::unpackSnorm4x16
GLM_FUNC_DECL vec4 unpackSnorm4x16(uint64 p)
First, unpacks a single 64-bit unsigned integer p into four 16-bit signed integers.
\n
glm::packUnorm3x10_1x2
GLM_FUNC_DECL uint32 packUnorm3x10_1x2(vec4 const &v)
First, converts the first three components of the normalized floating-point value v into 10-bit unsig...
\n
glm::u8vec4
vec< 4, u8, defaultp > u8vec4
Default qualifier 8 bit unsigned integer vector of 4 components type.
Definition: fwd.hpp:342
\n
glm::i8vec4
vec< 4, i8, defaultp > i8vec4
8 bit signed integer vector of 4 components type.
Definition: fwd.hpp:240
\n
glm::ivec4
vec< 4, int, defaultp > ivec4
4 components vector of signed integer numbers.
Definition: vector_int4.hpp:15
\n
glm::unpackInt2x32
GLM_FUNC_DECL i32vec2 unpackInt2x32(int64 p)
Convert a packed integer into an integer vector.
\n
glm::packUnorm2x3_1x2
GLM_FUNC_DECL uint8 packUnorm2x3_1x2(vec3 const &v)
Convert each component of the normalized floating-point vector into unsigned integer values...
\n
glm::packUnorm1x16
GLM_FUNC_DECL uint16 packUnorm1x16(float v)
First, converts the normalized floating-point value v into a 16-bit integer value.
\n
glm::vec4
vec< 4, float, defaultp > vec4
4 components vector of single-precision floating-point numbers.
Definition: vector_float4.hpp:15
\n
glm::i32vec2
vec< 2, i32, defaultp > i32vec2
32 bit signed integer vector of 2 components type.
Definition: fwd.hpp:278
\n
glm::unpackUnorm1x8
GLM_FUNC_DECL float unpackUnorm1x8(uint8 p)
Convert a single 8-bit integer to a normalized floating-point value.
\n
glm::unpackSnorm1x8
GLM_FUNC_DECL float unpackSnorm1x8(uint8 p)
First, unpacks a single 8-bit unsigned integer p into a single 8-bit signed integers.
\n
glm::unpackHalf1x16
GLM_FUNC_DECL float unpackHalf1x16(uint16 v)
Returns a floating-point scalar with components obtained by unpacking a 16-bit unsigned integer into ...
\n
glm::unpackInt4x16
GLM_FUNC_DECL i16vec4 unpackInt4x16(int64 p)
Convert a packed integer into an integer vector.
\n
glm::unpackUnorm1x16
GLM_FUNC_DECL float unpackUnorm1x16(uint16 p)
First, unpacks a single 16-bit unsigned integer p into a of 16-bit unsigned integers.
\n
glm::unpackSnorm2x8
GLM_FUNC_DECL vec2 unpackSnorm2x8(uint16 p)
First, unpacks a single 16-bit unsigned integer p into a pair of 8-bit signed integers.
\n
glm::i16vec2
vec< 2, i16, defaultp > i16vec2
16 bit signed integer vector of 2 components type.
Definition: fwd.hpp:258
\n
glm::unpackI3x10_1x2
GLM_FUNC_DECL ivec4 unpackI3x10_1x2(uint32 p)
Unpacks a single 32-bit unsigned integer p into three 10-bit and one 2-bit signed integers...
\n
glm::packSnorm1x16
GLM_FUNC_DECL uint16 packSnorm1x16(float v)
First, converts the normalized floating-point value v into 16-bit integer value.
\n
glm::unpackF3x9_E1x5
GLM_FUNC_DECL vec3 unpackF3x9_E1x5(uint32 p)
First, unpacks a single 32-bit unsigned integer p into two 11-bit signless floating-point values and ...
\n
glm::unpackUnorm
GLM_FUNC_DECL vec< L, floatType, Q > unpackUnorm(vec< L, uintType, Q > const &v)
Convert a packed integer to a normalized floating-point vector.
\n
glm::packSnorm
GLM_FUNC_DECL vec< L, intType, Q > packSnorm(vec< L, floatType, Q > const &v)
Convert each component of the normalized floating-point vector into signed integer values...
\n
glm::packUnorm1x5_1x6_1x5
GLM_FUNC_DECL uint16 packUnorm1x5_1x6_1x5(vec3 const &v)
Convert each component of the normalized floating-point vector into unsigned integer values...
\n
glm::unpackUnorm3x5_1x1
GLM_FUNC_DECL vec4 unpackUnorm3x5_1x1(uint16 p)
Convert a packed integer to a normalized floating-point vector.
\n
glm::u8vec2
vec< 2, u8, defaultp > u8vec2
Default qualifier 8 bit unsigned integer vector of 2 components type.
Definition: fwd.hpp:340
\n
glm::unpackSnorm3x10_1x2
GLM_FUNC_DECL vec4 unpackSnorm3x10_1x2(uint32 p)
First, unpacks a single 32-bit unsigned integer p into four 16-bit signed integers.
\n
glm::packSnorm3x10_1x2
GLM_FUNC_DECL uint32 packSnorm3x10_1x2(vec4 const &v)
First, converts the first three components of the normalized floating-point value v into 10-bit signe...
\n
glm::packInt4x16
GLM_FUNC_DECL int64 packInt4x16(i16vec4 const &v)
Convert each component from an integer vector into a packed integer.
\n
glm::unpackUnorm1x5_1x6_1x5
GLM_FUNC_DECL vec3 unpackUnorm1x5_1x6_1x5(uint16 p)
Convert a packed integer to a normalized floating-point vector.
\n
glm::packF3x9_E1x5
GLM_FUNC_DECL uint32 packF3x9_E1x5(vec3 const &v)
First, converts the first two components of the normalized floating-point value v into 11-bit signles...
\n
glm::packUnorm2x8
GLM_FUNC_DECL uint16 packUnorm2x8(vec2 const &v)
First, converts each component of the normalized floating-point value v into 8-bit integer values...
\n
glm::packUint4x16
GLM_FUNC_DECL uint64 packUint4x16(u16vec4 const &v)
Convert each component from an integer vector into a packed unsigned integer.
\n
glm::vec3
vec< 3, float, defaultp > vec3
3 components vector of single-precision floating-point numbers.
Definition: vector_float3.hpp:15
\n
glm::packUnorm3x5_1x1
GLM_FUNC_DECL uint16 packUnorm3x5_1x1(vec4 const &v)
Convert each component of the normalized floating-point vector into unsigned integer values...
\n
glm::unpackUnorm2x8
GLM_FUNC_DECL vec2 unpackUnorm2x8(uint16 p)
First, unpacks a single 16-bit unsigned integer p into a pair of 8-bit unsigned integers.
\n
glm::unpackUint2x16
GLM_FUNC_DECL u16vec2 unpackUint2x16(uint p)
Convert a packed integer into an integer vector.
\n
glm::packUint2x16
GLM_FUNC_DECL uint packUint2x16(u16vec2 const &v)
Convert each component from an integer vector into a packed unsigned integer.
\n
glm::packSnorm4x16
GLM_FUNC_DECL uint64 packSnorm4x16(vec4 const &v)
First, converts each component of the normalized floating-point value v into 16-bit integer values...
\n
glm::packUint2x32
GLM_FUNC_DECL uint64 packUint2x32(u32vec2 const &v)
Convert each component from an integer vector into a packed unsigned integer.
\n
glm::uvec4
vec< 4, unsigned int, defaultp > uvec4
4 components vector of unsigned integer numbers.
Definition: vector_uint4.hpp:15
\n
glm::unpackHalf4x16
GLM_FUNC_DECL vec4 unpackHalf4x16(uint64 p)
Returns a four-component floating-point vector with components obtained by unpacking a 64-bit unsigne...
\n
glm::uint64
detail::uint64 uint64
64 bit unsigned integer type.
Definition: scalar_uint_sized.hpp:67
\n
glm::unpackUnorm4x4
GLM_FUNC_DECL vec4 unpackUnorm4x4(uint16 p)
Convert a packed integer to a normalized floating-point vector.
\n
glm::packHalf4x16
GLM_FUNC_DECL uint64 packHalf4x16(vec4 const &v)
Returns an unsigned integer obtained by converting the components of a four-component floating-point ...
\n
type_precision.hpp
GLM_GTC_type_precision
\n
glm::unpackInt2x8
GLM_FUNC_DECL i8vec2 unpackInt2x8(int16 p)
Convert a packed integer into an integer vector.
\n
glm::int64
detail::int64 int64
64 bit signed integer type.
Definition: scalar_int_sized.hpp:67
\n
glm::unpackUnorm2x3_1x2
GLM_FUNC_DECL vec3 unpackUnorm2x3_1x2(uint8 p)
Convert a packed integer to a normalized floating-point vector.
\n
glm::packF2x11_1x10
GLM_FUNC_DECL uint32 packF2x11_1x10(vec3 const &v)
First, converts the first two components of the normalized floating-point value v into 11-bit signles...
\n
glm::unpackU3x10_1x2
GLM_FUNC_DECL uvec4 unpackU3x10_1x2(uint32 p)
Unpacks a single 32-bit unsigned integer p into three 10-bit and one 2-bit unsigned integers...
\n
glm::packHalf1x16
GLM_FUNC_DECL uint16 packHalf1x16(float v)
Returns an unsigned integer obtained by converting the components of a floating-point scalar to the 1...
\n
glm::unpackSnorm
GLM_FUNC_DECL vec< L, floatType, Q > unpackSnorm(vec< L, intType, Q > const &v)
Convert a packed integer to a normalized floating-point vector.
\n
glm::unpackRGBM
GLM_FUNC_DECL vec< 3, T, Q > unpackRGBM(vec< 4, T, Q > const &rgbm)
Returns a floating-point vector with components obtained by reinterpreting an integer vector as 16-bi...
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glm::packSnorm2x8
GLM_FUNC_DECL uint16 packSnorm2x8(vec2 const &v)
First, converts each component of the normalized floating-point value v into 8-bit integer values...
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glm::packSnorm1x8
GLM_FUNC_DECL uint8 packSnorm1x8(float s)
First, converts the normalized floating-point value v into 8-bit integer value.
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glm::unpackUint4x8
GLM_FUNC_DECL u8vec4 unpackUint4x8(uint32 p)
Convert a packed integer into an integer vector.
\n
glm::packI3x10_1x2
GLM_FUNC_DECL uint32 packI3x10_1x2(ivec4 const &v)
Returns an unsigned integer obtained by converting the components of a four-component signed integer ...
\n
glm::packInt2x8
GLM_FUNC_DECL int16 packInt2x8(i8vec2 const &v)
Convert each component from an integer vector into a packed integer.
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glm::packInt2x32
GLM_FUNC_DECL int64 packInt2x32(i32vec2 const &v)
Convert each component from an integer vector into a packed integer.
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glm::packUint4x8
GLM_FUNC_DECL uint32 packUint4x8(u8vec4 const &v)
Convert each component from an integer vector into a packed unsigned integer.
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glm::packU3x10_1x2
GLM_FUNC_DECL uint32 packU3x10_1x2(uvec4 const &v)
Returns an unsigned integer obtained by converting the components of a four-component unsigned intege...
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glm::u32vec2
vec< 2, u32, defaultp > u32vec2
Default qualifier 32 bit unsigned integer vector of 2 components type.
Definition: fwd.hpp:380
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glm::packInt2x16
GLM_FUNC_DECL int packInt2x16(i16vec2 const &v)
Convert each component from an integer vector into a packed integer.
\n
glm::packHalf
GLM_FUNC_DECL vec< L, uint16, Q > packHalf(vec< L, float, Q > const &v)
Returns an unsigned integer vector obtained by converting the components of a floating-point vector t...
\n
glm::u16vec4
vec< 4, u16, defaultp > u16vec4
Default qualifier 16 bit unsigned integer vector of 4 components type.
Definition: fwd.hpp:362
\n
glm::packInt4x8
GLM_FUNC_DECL int32 packInt4x8(i8vec4 const &v)
Convert each component from an integer vector into a packed integer.
\n
glm::u16vec2
vec< 2, u16, defaultp > u16vec2
Default qualifier 16 bit unsigned integer vector of 2 components type.
Definition: fwd.hpp:360
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glm::unpackSnorm1x16
GLM_FUNC_DECL float unpackSnorm1x16(uint16 p)
First, unpacks a single 16-bit unsigned integer p into a single 16-bit signed integers.
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Definition: common.hpp:20
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packing.hpp File Reference
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\nFunctions

GLM_FUNC_DECL double packDouble2x32 (uvec2 const &v)
 Returns a double-qualifier value obtained by packing the components of v into a 64-bit value. More...
 
GLM_FUNC_DECL uint packHalf2x16 (vec2 const &v)
 Returns an unsigned integer obtained by converting the components of a two-component floating-point vector to the 16-bit floating-point representation found in the OpenGL Specification, and then packing these two 16- bit integers into a 32-bit unsigned integer. More...
 
GLM_FUNC_DECL uint packSnorm2x16 (vec2 const &v)
 First, converts each component of the normalized floating-point value v into 8- or 16-bit integer values. More...
 
GLM_FUNC_DECL uint packSnorm4x8 (vec4 const &v)
 First, converts each component of the normalized floating-point value v into 8- or 16-bit integer values. More...
 
GLM_FUNC_DECL uint packUnorm2x16 (vec2 const &v)
 First, converts each component of the normalized floating-point value v into 8- or 16-bit integer values. More...
 
GLM_FUNC_DECL uint packUnorm4x8 (vec4 const &v)
 First, converts each component of the normalized floating-point value v into 8- or 16-bit integer values. More...
 
GLM_FUNC_DECL uvec2 unpackDouble2x32 (double v)
 Returns a two-component unsigned integer vector representation of v. More...
 
GLM_FUNC_DECL vec2 unpackHalf2x16 (uint v)
 Returns a two-component floating-point vector with components obtained by unpacking a 32-bit unsigned integer into a pair of 16-bit values, interpreting those values as 16-bit floating-point numbers according to the OpenGL Specification, and converting them to 32-bit floating-point values. More...
 
GLM_FUNC_DECL vec2 unpackSnorm2x16 (uint p)
 First, unpacks a single 32-bit unsigned integer p into a pair of 16-bit unsigned integers, four 8-bit unsigned integers, or four 8-bit signed integers. More...
 
GLM_FUNC_DECL vec4 unpackSnorm4x8 (uint p)
 First, unpacks a single 32-bit unsigned integer p into a pair of 16-bit unsigned integers, four 8-bit unsigned integers, or four 8-bit signed integers. More...
 
GLM_FUNC_DECL vec2 unpackUnorm2x16 (uint p)
 First, unpacks a single 32-bit unsigned integer p into a pair of 16-bit unsigned integers, four 8-bit unsigned integers, or four 8-bit signed integers. More...
 
GLM_FUNC_DECL vec4 unpackUnorm4x8 (uint p)
 First, unpacks a single 32-bit unsigned integer p into a pair of 16-bit unsigned integers, four 8-bit unsigned integers, or four 8-bit signed integers. More...
 
\n

Detailed Description

\n

Core features

\n
See also
GLSL 4.20.8 specification, section 8.4 Floating-Point Pack and Unpack Functions
\n
\n to use the features of this extension. \">GLM_GTC_packing
\n\n

Definition in file packing.hpp.

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packing.hpp
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1 
\n
16 #pragma once
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17 
\n
18 #include "./ext/vector_uint2.hpp"
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19 #include "./ext/vector_float2.hpp"
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20 #include "./ext/vector_float4.hpp"
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21 
\n
22 namespace glm
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23 {
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26 
\n
38  GLM_FUNC_DECL uint packUnorm2x16(vec2 const& v);
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39 
\n
51  GLM_FUNC_DECL uint packSnorm2x16(vec2 const& v);
\n
52 
\n
64  GLM_FUNC_DECL uint packUnorm4x8(vec4 const& v);
\n
65 
\n
77  GLM_FUNC_DECL uint packSnorm4x8(vec4 const& v);
\n
78 
\n
90  GLM_FUNC_DECL vec2 unpackUnorm2x16(uint p);
\n
91 
\n
103  GLM_FUNC_DECL vec2 unpackSnorm2x16(uint p);
\n
104 
\n
116  GLM_FUNC_DECL vec4 unpackUnorm4x8(uint p);
\n
117 
\n
129  GLM_FUNC_DECL vec4 unpackSnorm4x8(uint p);
\n
130 
\n
139  GLM_FUNC_DECL double packDouble2x32(uvec2 const& v);
\n
140 
\n
148  GLM_FUNC_DECL uvec2 unpackDouble2x32(double v);
\n
149 
\n
158  GLM_FUNC_DECL uint packHalf2x16(vec2 const& v);
\n
159 
\n
168  GLM_FUNC_DECL vec2 unpackHalf2x16(uint v);
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169 
\n
171 }//namespace glm
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172 
\n
173 #include "detail/func_packing.inl"
\n
glm::unpackUnorm2x16
GLM_FUNC_DECL vec2 unpackUnorm2x16(uint p)
First, unpacks a single 32-bit unsigned integer p into a pair of 16-bit unsigned integers, four 8-bit unsigned integers, or four 8-bit signed integers.
\n
glm::vec2
vec< 2, float, defaultp > vec2
2 components vector of single-precision floating-point numbers.
Definition: vector_float2.hpp:15
\n
glm::packSnorm2x16
GLM_FUNC_DECL uint packSnorm2x16(vec2 const &v)
First, converts each component of the normalized floating-point value v into 8- or 16-bit integer val...
\n
glm::packSnorm4x8
GLM_FUNC_DECL uint packSnorm4x8(vec4 const &v)
First, converts each component of the normalized floating-point value v into 8- or 16-bit integer val...
\n
glm::packUnorm2x16
GLM_FUNC_DECL uint packUnorm2x16(vec2 const &v)
First, converts each component of the normalized floating-point value v into 8- or 16-bit integer val...
\n
glm::unpackDouble2x32
GLM_FUNC_DECL uvec2 unpackDouble2x32(double v)
Returns a two-component unsigned integer vector representation of v.
\n
glm::vec4
vec< 4, float, defaultp > vec4
4 components vector of single-precision floating-point numbers.
Definition: vector_float4.hpp:15
\n
glm::unpackSnorm2x16
GLM_FUNC_DECL vec2 unpackSnorm2x16(uint p)
First, unpacks a single 32-bit unsigned integer p into a pair of 16-bit unsigned integers, four 8-bit unsigned integers, or four 8-bit signed integers.
\n
glm::uvec2
vec< 2, unsigned int, defaultp > uvec2
2 components vector of unsigned integer numbers.
Definition: vector_uint2.hpp:15
\n
glm::unpackHalf2x16
GLM_FUNC_DECL vec2 unpackHalf2x16(uint v)
Returns a two-component floating-point vector with components obtained by unpacking a 32-bit unsigned...
\n
vector_float4.hpp
Core features
\n
glm::packUnorm4x8
GLM_FUNC_DECL uint packUnorm4x8(vec4 const &v)
First, converts each component of the normalized floating-point value v into 8- or 16-bit integer val...
\n
glm::unpackSnorm4x8
GLM_FUNC_DECL vec4 unpackSnorm4x8(uint p)
First, unpacks a single 32-bit unsigned integer p into a pair of 16-bit unsigned integers, four 8-bit unsigned integers, or four 8-bit signed integers.
\n
glm::packDouble2x32
GLM_FUNC_DECL double packDouble2x32(uvec2 const &v)
Returns a double-qualifier value obtained by packing the components of v into a 64-bit value...
\n
glm::packHalf2x16
GLM_FUNC_DECL uint packHalf2x16(vec2 const &v)
Returns an unsigned integer obtained by converting the components of a two-component floating-point v...
\n
vector_float2.hpp
Core features
\n
glm::unpackUnorm4x8
GLM_FUNC_DECL vec4 unpackUnorm4x8(uint p)
First, unpacks a single 32-bit unsigned integer p into a pair of 16-bit unsigned integers, four 8-bit unsigned integers, or four 8-bit signed integers.
\n
vector_uint2.hpp
Core features
\n
glm
Definition: common.hpp:20
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perpendicular.hpp File Reference
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GLM_GTX_perpendicular \nMore...

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\nFunctions

template<typename genType >
GLM_FUNC_DECL genType perp (genType const &x, genType const &Normal)
 Projects x a perpendicular axis of Normal. More...
 
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Detailed Description

\n

GLM_GTX_perpendicular

\n
See also
Core features (dependence)
\n
\n to use the features of this extension. \">GLM_GTX_projection (dependence)
\n\n

Definition in file perpendicular.hpp.

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perpendicular.hpp
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1 
\n
14 #pragma once
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15 
\n
16 // Dependency:
\n
17 #include "../glm.hpp"
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18 #include "../gtx/projection.hpp"
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19 
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20 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
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21 # ifndef GLM_ENABLE_EXPERIMENTAL
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22 # pragma message("GLM: GLM_GTX_perpendicular is an experimental extension and may change in the future. Use #define GLM_ENABLE_EXPERIMENTAL before including it, if you really want to use it.")
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23 # else
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24 # pragma message("GLM: GLM_GTX_perpendicular extension included")
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25 # endif
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26 #endif
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27 
\n
28 namespace glm
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29 {
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32 
\n
35  template<typename genType>
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36  GLM_FUNC_DECL genType perp(genType const& x, genType const& Normal);
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37 
\n
39 }//namespace glm
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40 
\n
41 #include "perpendicular.inl"
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glm::perp
GLM_FUNC_DECL genType perp(genType const &x, genType const &Normal)
Projects x a perpendicular axis of Normal.
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glm
Definition: common.hpp:20
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GLM_GTX_polar_coordinates \nMore...

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\nFunctions

template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > euclidean (vec< 2, T, Q > const &polar)
 Convert Polar to Euclidean coordinates. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > polar (vec< 3, T, Q > const &euclidean)
 Convert Euclidean to Polar coordinates, x is the xz distance, y, the latitude and z the longitude. More...
 
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Detailed Description

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GLM_GTX_polar_coordinates

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See also
Core features (dependence)
\n\n

Definition in file polar_coordinates.hpp.

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polar_coordinates.hpp
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1 
\n
13 #pragma once
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14 
\n
15 // Dependency:
\n
16 #include "../glm.hpp"
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17 
\n
18 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
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19 # ifndef GLM_ENABLE_EXPERIMENTAL
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20 # pragma message("GLM: GLM_GTX_polar_coordinates is an experimental extension and may change in the future. Use #define GLM_ENABLE_EXPERIMENTAL before including it, if you really want to use it.")
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21 # else
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22 # pragma message("GLM: GLM_GTX_polar_coordinates extension included")
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23 # endif
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24 #endif
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25 
\n
26 namespace glm
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27 {
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30 
\n
34  template<typename T, qualifier Q>
\n
35  GLM_FUNC_DECL vec<3, T, Q> polar(
\n
36  vec<3, T, Q> const& euclidean);
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37 
\n
41  template<typename T, qualifier Q>
\n
42  GLM_FUNC_DECL vec<3, T, Q> euclidean(
\n
43  vec<2, T, Q> const& polar);
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44 
\n
46 }//namespace glm
\n
47 
\n
48 #include "polar_coordinates.inl"
\n
glm::polar
GLM_FUNC_DECL vec< 3, T, Q > polar(vec< 3, T, Q > const &euclidean)
Convert Euclidean to Polar coordinates, x is the xz distance, y, the latitude and z the longitude...
\n
glm::euclidean
GLM_FUNC_DECL vec< 3, T, Q > euclidean(vec< 2, T, Q > const &polar)
Convert Polar to Euclidean coordinates.
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glm
Definition: common.hpp:20
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projection.hpp File Reference
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GLM_GTX_projection \nMore...

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template<typename genType >
GLM_FUNC_DECL genType proj (genType const &x, genType const &Normal)
 Projects x on Normal. More...
 
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Detailed Description

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GLM_GTX_projection

\n
See also
Core features (dependence)
\n\n

Definition in file projection.hpp.

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1 
\n
13 #pragma once
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14 
\n
15 // Dependency:
\n
16 #include "../geometric.hpp"
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17 
\n
18 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
19 # ifndef GLM_ENABLE_EXPERIMENTAL
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20 # pragma message("GLM: GLM_GTX_projection is an experimental extension and may change in the future. Use #define GLM_ENABLE_EXPERIMENTAL before including it, if you really want to use it.")
\n
21 # else
\n
22 # pragma message("GLM: GLM_GTX_projection extension included")
\n
23 # endif
\n
24 #endif
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25 
\n
26 namespace glm
\n
27 {
\n
30 
\n
37  template<typename genType>
\n
38  GLM_FUNC_DECL genType proj(genType const& x, genType const& Normal);
\n
39 
\n
41 }//namespace glm
\n
42 
\n
43 #include "projection.inl"
\n
glm::proj
GLM_FUNC_DECL genType proj(genType const &x, genType const &Normal)
Projects x on Normal.
\n
glm
Definition: common.hpp:20
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qualifier.hpp
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1 #pragma once
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2 
\n
3 #include "setup.hpp"
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4 
\n
5 namespace glm
\n
6 {
\n
8  enum qualifier
\n
9  {
\n
10  packed_highp,
\n
11  packed_mediump,
\n
12  packed_lowp,
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13 
\n
14 # if GLM_CONFIG_ALIGNED_GENTYPES == GLM_ENABLE
\n
15  aligned_highp,
\n
16  aligned_mediump,
\n
17  aligned_lowp, // ///< Typed data is aligned in memory allowing SIMD optimizations and operations are executed with high precision in term of ULPs to maximize performance
\n
18  aligned = aligned_highp,
\n
19 # endif
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20 
\n
21  highp = packed_highp,
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22  mediump = packed_mediump,
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23  lowp = packed_lowp,
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24  packed = packed_highp,
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25 
\n
26 # if GLM_CONFIG_ALIGNED_GENTYPES == GLM_ENABLE && defined(GLM_FORCE_DEFAULT_ALIGNED_GENTYPES)
\n
27  defaultp = aligned_highp
\n
28 # else
\n
29  defaultp = highp
\n
30 # endif
\n
31  };
\n
32 
\n
33  typedef qualifier precision;
\n
34 
\n
35  template<length_t L, typename T, qualifier Q = defaultp> struct vec;
\n
36  template<length_t C, length_t R, typename T, qualifier Q = defaultp> struct mat;
\n
37  template<typename T, qualifier Q = defaultp> struct qua;
\n
38 
\n
39 # if GLM_HAS_TEMPLATE_ALIASES
\n
40  template <typename T, qualifier Q = defaultp> using tvec1 = vec<1, T, Q>;
\n
41  template <typename T, qualifier Q = defaultp> using tvec2 = vec<2, T, Q>;
\n
42  template <typename T, qualifier Q = defaultp> using tvec3 = vec<3, T, Q>;
\n
43  template <typename T, qualifier Q = defaultp> using tvec4 = vec<4, T, Q>;
\n
44  template <typename T, qualifier Q = defaultp> using tmat2x2 = mat<2, 2, T, Q>;
\n
45  template <typename T, qualifier Q = defaultp> using tmat2x3 = mat<2, 3, T, Q>;
\n
46  template <typename T, qualifier Q = defaultp> using tmat2x4 = mat<2, 4, T, Q>;
\n
47  template <typename T, qualifier Q = defaultp> using tmat3x2 = mat<3, 2, T, Q>;
\n
48  template <typename T, qualifier Q = defaultp> using tmat3x3 = mat<3, 3, T, Q>;
\n
49  template <typename T, qualifier Q = defaultp> using tmat3x4 = mat<3, 4, T, Q>;
\n
50  template <typename T, qualifier Q = defaultp> using tmat4x2 = mat<4, 2, T, Q>;
\n
51  template <typename T, qualifier Q = defaultp> using tmat4x3 = mat<4, 3, T, Q>;
\n
52  template <typename T, qualifier Q = defaultp> using tmat4x4 = mat<4, 4, T, Q>;
\n
53  template <typename T, qualifier Q = defaultp> using tquat = qua<T, Q>;
\n
54 # endif
\n
55 
\n
56 namespace detail
\n
57 {
\n
58  template<glm::qualifier P>
\n
59  struct is_aligned
\n
60  {
\n
61  static const bool value = false;
\n
62  };
\n
63 
\n
64 # if GLM_CONFIG_ALIGNED_GENTYPES == GLM_ENABLE
\n
65  template<>
\n
66  struct is_aligned<glm::aligned_lowp>
\n
67  {
\n
68  static const bool value = true;
\n
69  };
\n
70 
\n
71  template<>
\n
72  struct is_aligned<glm::aligned_mediump>
\n
73  {
\n
74  static const bool value = true;
\n
75  };
\n
76 
\n
77  template<>
\n
78  struct is_aligned<glm::aligned_highp>
\n
79  {
\n
80  static const bool value = true;
\n
81  };
\n
82 # endif
\n
83 
\n
84  template<length_t L, typename T, bool is_aligned>
\n
85  struct storage
\n
86  {
\n
87  typedef struct type {
\n
88  T data[L];
\n
89  } type;
\n
90  };
\n
91 
\n
92 # if GLM_HAS_ALIGNOF
\n
93  template<length_t L, typename T>
\n
94  struct storage<L, T, true>
\n
95  {
\n
96  typedef struct alignas(L * sizeof(T)) type {
\n
97  T data[L];
\n
98  } type;
\n
99  };
\n
100 
\n
101  template<typename T>
\n
102  struct storage<3, T, true>
\n
103  {
\n
104  typedef struct alignas(4 * sizeof(T)) type {
\n
105  T data[4];
\n
106  } type;
\n
107  };
\n
108 # endif
\n
109 
\n
110 # if GLM_ARCH & GLM_ARCH_SSE2_BIT
\n
111  template<>
\n
112  struct storage<4, float, true>
\n
113  {
\n
114  typedef glm_f32vec4 type;
\n
115  };
\n
116 
\n
117  template<>
\n
118  struct storage<4, int, true>
\n
119  {
\n
120  typedef glm_i32vec4 type;
\n
121  };
\n
122 
\n
123  template<>
\n
124  struct storage<4, unsigned int, true>
\n
125  {
\n
126  typedef glm_u32vec4 type;
\n
127  };
\n
128 
\n
129  template<>
\n
130  struct storage<2, double, true>
\n
131  {
\n
132  typedef glm_f64vec2 type;
\n
133  };
\n
134 
\n
135  template<>
\n
136  struct storage<2, detail::int64, true>
\n
137  {
\n
138  typedef glm_i64vec2 type;
\n
139  };
\n
140 
\n
141  template<>
\n
142  struct storage<2, detail::uint64, true>
\n
143  {
\n
144  typedef glm_u64vec2 type;
\n
145  };
\n
146 # endif
\n
147 
\n
148 # if (GLM_ARCH & GLM_ARCH_AVX_BIT)
\n
149  template<>
\n
150  struct storage<4, double, true>
\n
151  {
\n
152  typedef glm_f64vec4 type;
\n
153  };
\n
154 # endif
\n
155 
\n
156 # if (GLM_ARCH & GLM_ARCH_AVX2_BIT)
\n
157  template<>
\n
158  struct storage<4, detail::int64, true>
\n
159  {
\n
160  typedef glm_i64vec4 type;
\n
161  };
\n
162 
\n
163  template<>
\n
164  struct storage<4, detail::uint64, true>
\n
165  {
\n
166  typedef glm_u64vec4 type;
\n
167  };
\n
168 # endif
\n
169 
\n
170 # if GLM_ARCH & GLM_ARCH_NEON_BIT
\n
171  template<>
\n
172  struct storage<4, float, true>
\n
173  {
\n
174  typedef glm_f32vec4 type;
\n
175  };
\n
176 
\n
177  template<>
\n
178  struct storage<4, int, true>
\n
179  {
\n
180  typedef glm_i32vec4 type;
\n
181  };
\n
182 
\n
183  template<>
\n
184  struct storage<4, unsigned int, true>
\n
185  {
\n
186  typedef glm_u32vec4 type;
\n
187  };
\n
188 # endif
\n
189 
\n
190  enum genTypeEnum
\n
191  {
\n
192  GENTYPE_VEC,
\n
193  GENTYPE_MAT,
\n
194  GENTYPE_QUAT
\n
195  };
\n
196 
\n
197  template <typename genType>
\n
198  struct genTypeTrait
\n
199  {};
\n
200 
\n
201  template <length_t C, length_t R, typename T>
\n
202  struct genTypeTrait<mat<C, R, T> >
\n
203  {
\n
204  static const genTypeEnum GENTYPE = GENTYPE_MAT;
\n
205  };
\n
206 
\n
207  template<typename genType, genTypeEnum type>
\n
208  struct init_gentype
\n
209  {
\n
210  };
\n
211 
\n
212  template<typename genType>
\n
213  struct init_gentype<genType, GENTYPE_QUAT>
\n
214  {
\n
215  GLM_FUNC_QUALIFIER GLM_CONSTEXPR static genType identity()
\n
216  {
\n
217  return genType(1, 0, 0, 0);
\n
218  }
\n
219  };
\n
220 
\n
221  template<typename genType>
\n
222  struct init_gentype<genType, GENTYPE_MAT>
\n
223  {
\n
224  GLM_FUNC_QUALIFIER GLM_CONSTEXPR static genType identity()
\n
225  {
\n
226  return genType(1);
\n
227  }
\n
228  };
\n
229 }//namespace detail
\n
230 }//namespace glm
\n
glm::identity
GLM_FUNC_DECL GLM_CONSTEXPR genType identity()
Builds an identity matrix.
\n
glm::uint64
detail::uint64 uint64
64 bit unsigned integer type.
Definition: scalar_uint_sized.hpp:67
\n
glm::int64
detail::int64 int64
64 bit signed integer type.
Definition: scalar_int_sized.hpp:67
\n
glm
Definition: common.hpp:20
\n
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\nFunctions
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gtc/quaternion.hpp File Reference
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GLM_GTC_quaternion \nMore...

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\nFunctions

template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > eulerAngles (qua< T, Q > const &x)
 Returns euler angles, pitch as x, yaw as y, roll as z. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 4, bool, Q > greaterThan (qua< T, Q > const &x, qua< T, Q > const &y)
 Returns the component-wise comparison of result x > y. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 4, bool, Q > greaterThanEqual (qua< T, Q > const &x, qua< T, Q > const &y)
 Returns the component-wise comparison of result x >= y. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 4, bool, Q > lessThan (qua< T, Q > const &x, qua< T, Q > const &y)
 Returns the component-wise comparison result of x < y. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 4, bool, Q > lessThanEqual (qua< T, Q > const &x, qua< T, Q > const &y)
 Returns the component-wise comparison of result x <= y. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 3, 3, T, Q > mat3_cast (qua< T, Q > const &x)
 Converts a quaternion to a 3 * 3 matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 4, 4, T, Q > mat4_cast (qua< T, Q > const &x)
 Converts a quaternion to a 4 * 4 matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL T pitch (qua< T, Q > const &x)
 Returns pitch value of euler angles expressed in radians. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL qua< T, Q > quat_cast (mat< 3, 3, T, Q > const &x)
 Converts a pure rotation 3 * 3 matrix to a quaternion. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL qua< T, Q > quat_cast (mat< 4, 4, T, Q > const &x)
 Converts a pure rotation 4 * 4 matrix to a quaternion. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL qua< T, Q > quatLookAt (vec< 3, T, Q > const &direction, vec< 3, T, Q > const &up)
 Build a look at quaternion based on the default handedness. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL qua< T, Q > quatLookAtLH (vec< 3, T, Q > const &direction, vec< 3, T, Q > const &up)
 Build a left-handed look at quaternion. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL qua< T, Q > quatLookAtRH (vec< 3, T, Q > const &direction, vec< 3, T, Q > const &up)
 Build a right-handed look at quaternion. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL T roll (qua< T, Q > const &x)
 Returns roll value of euler angles expressed in radians. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL T yaw (qua< T, Q > const &x)
 Returns yaw value of euler angles expressed in radians. More...
 
\n

Detailed Description

\n

GLM_GTC_quaternion

\n
See also
Core features (dependence)
\n
\n to use the features of this extension. \">GLM_GTC_constants (dependence)
\n\n

Definition in file gtc/quaternion.hpp.

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\n
gtc/quaternion.hpp
\n
\n
\nGo to the documentation of this file.
1 
\n
14 #pragma once
\n
15 
\n
16 // Dependency:
\n
17 #include "../gtc/constants.hpp"
\n
18 #include "../gtc/matrix_transform.hpp"
\n
19 #include "../ext/vector_relational.hpp"
\n
20 #include "../ext/quaternion_common.hpp"
\n
21 #include "../ext/quaternion_float.hpp"
\n
22 #include "../ext/quaternion_float_precision.hpp"
\n
23 #include "../ext/quaternion_double.hpp"
\n
24 #include "../ext/quaternion_double_precision.hpp"
\n
25 #include "../ext/quaternion_relational.hpp"
\n
26 #include "../ext/quaternion_geometric.hpp"
\n
27 #include "../ext/quaternion_trigonometric.hpp"
\n
28 #include "../ext/quaternion_transform.hpp"
\n
29 #include "../detail/type_mat3x3.hpp"
\n
30 #include "../detail/type_mat4x4.hpp"
\n
31 #include "../detail/type_vec3.hpp"
\n
32 #include "../detail/type_vec4.hpp"
\n
33 
\n
34 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
35 # pragma message("GLM: GLM_GTC_quaternion extension included")
\n
36 #endif
\n
37 
\n
38 namespace glm
\n
39 {
\n
42 
\n
49  template<typename T, qualifier Q>
\n
50  GLM_FUNC_DECL vec<3, T, Q> eulerAngles(qua<T, Q> const& x);
\n
51 
\n
57  template<typename T, qualifier Q>
\n
58  GLM_FUNC_DECL T roll(qua<T, Q> const& x);
\n
59 
\n
65  template<typename T, qualifier Q>
\n
66  GLM_FUNC_DECL T pitch(qua<T, Q> const& x);
\n
67 
\n
73  template<typename T, qualifier Q>
\n
74  GLM_FUNC_DECL T yaw(qua<T, Q> const& x);
\n
75 
\n
81  template<typename T, qualifier Q>
\n
82  GLM_FUNC_DECL mat<3, 3, T, Q> mat3_cast(qua<T, Q> const& x);
\n
83 
\n
89  template<typename T, qualifier Q>
\n
90  GLM_FUNC_DECL mat<4, 4, T, Q> mat4_cast(qua<T, Q> const& x);
\n
91 
\n
97  template<typename T, qualifier Q>
\n
98  GLM_FUNC_DECL qua<T, Q> quat_cast(mat<3, 3, T, Q> const& x);
\n
99 
\n
105  template<typename T, qualifier Q>
\n
106  GLM_FUNC_DECL qua<T, Q> quat_cast(mat<4, 4, T, Q> const& x);
\n
107 
\n
114  template<typename T, qualifier Q>
\n
115  GLM_FUNC_DECL vec<4, bool, Q> lessThan(qua<T, Q> const& x, qua<T, Q> const& y);
\n
116 
\n
123  template<typename T, qualifier Q>
\n
124  GLM_FUNC_DECL vec<4, bool, Q> lessThanEqual(qua<T, Q> const& x, qua<T, Q> const& y);
\n
125 
\n
132  template<typename T, qualifier Q>
\n
133  GLM_FUNC_DECL vec<4, bool, Q> greaterThan(qua<T, Q> const& x, qua<T, Q> const& y);
\n
134 
\n
141  template<typename T, qualifier Q>
\n
142  GLM_FUNC_DECL vec<4, bool, Q> greaterThanEqual(qua<T, Q> const& x, qua<T, Q> const& y);
\n
143 
\n
148  template<typename T, qualifier Q>
\n
149  GLM_FUNC_DECL qua<T, Q> quatLookAt(
\n
150  vec<3, T, Q> const& direction,
\n
151  vec<3, T, Q> const& up);
\n
152 
\n
157  template<typename T, qualifier Q>
\n
158  GLM_FUNC_DECL qua<T, Q> quatLookAtRH(
\n
159  vec<3, T, Q> const& direction,
\n
160  vec<3, T, Q> const& up);
\n
161 
\n
166  template<typename T, qualifier Q>
\n
167  GLM_FUNC_DECL qua<T, Q> quatLookAtLH(
\n
168  vec<3, T, Q> const& direction,
\n
169  vec<3, T, Q> const& up);
\n
171 } //namespace glm
\n
172 
\n
173 #include "quaternion.inl"
\n
glm::mat4_cast
GLM_FUNC_DECL mat< 4, 4, T, Q > mat4_cast(qua< T, Q > const &x)
Converts a quaternion to a 4 * 4 matrix.
\n
glm::greaterThan
GLM_FUNC_DECL vec< 4, bool, Q > greaterThan(qua< T, Q > const &x, qua< T, Q > const &y)
Returns the component-wise comparison of result x > y.
\n
glm::greaterThanEqual
GLM_FUNC_DECL vec< 4, bool, Q > greaterThanEqual(qua< T, Q > const &x, qua< T, Q > const &y)
Returns the component-wise comparison of result x >= y.
\n
glm::lessThanEqual
GLM_FUNC_DECL vec< 4, bool, Q > lessThanEqual(qua< T, Q > const &x, qua< T, Q > const &y)
Returns the component-wise comparison of result x <= y.
\n
glm::roll
GLM_FUNC_DECL T roll(qua< T, Q > const &x)
Returns roll value of euler angles expressed in radians.
\n
glm::quatLookAt
GLM_FUNC_DECL qua< T, Q > quatLookAt(vec< 3, T, Q > const &direction, vec< 3, T, Q > const &up)
Build a look at quaternion based on the default handedness.
\n
glm::quat_cast
GLM_FUNC_DECL qua< T, Q > quat_cast(mat< 4, 4, T, Q > const &x)
Converts a pure rotation 4 * 4 matrix to a quaternion.
\n
glm::mat3_cast
GLM_FUNC_DECL mat< 3, 3, T, Q > mat3_cast(qua< T, Q > const &x)
Converts a quaternion to a 3 * 3 matrix.
\n
glm::eulerAngles
GLM_FUNC_DECL vec< 3, T, Q > eulerAngles(qua< T, Q > const &x)
Returns euler angles, pitch as x, yaw as y, roll as z.
\n
glm::lessThan
GLM_FUNC_DECL vec< 4, bool, Q > lessThan(qua< T, Q > const &x, qua< T, Q > const &y)
Returns the component-wise comparison result of x < y.
\n
glm::yaw
GLM_FUNC_DECL T yaw(qua< T, Q > const &x)
Returns yaw value of euler angles expressed in radians.
\n
glm::quatLookAtLH
GLM_FUNC_DECL qua< T, Q > quatLookAtLH(vec< 3, T, Q > const &direction, vec< 3, T, Q > const &up)
Build a left-handed look at quaternion.
\n
glm::quatLookAtRH
GLM_FUNC_DECL qua< T, Q > quatLookAtRH(vec< 3, T, Q > const &direction, vec< 3, T, Q > const &up)
Build a right-handed look at quaternion.
\n
glm::pitch
GLM_FUNC_DECL T pitch(qua< T, Q > const &x)
Returns pitch value of euler angles expressed in radians.
\n
glm
Definition: common.hpp:20
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\nFunctions
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\nFunctions

template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > cross (qua< T, Q > const &q, vec< 3, T, Q > const &v)
 Compute a cross product between a quaternion and a vector. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > cross (vec< 3, T, Q > const &v, qua< T, Q > const &q)
 Compute a cross product between a vector and a quaternion. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL T extractRealComponent (qua< T, Q > const &q)
 Extract the real component of a quaternion. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL qua< T, Q > fastMix (qua< T, Q > const &x, qua< T, Q > const &y, T const &a)
 Quaternion normalized linear interpolation. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL qua< T, Q > intermediate (qua< T, Q > const &prev, qua< T, Q > const &curr, qua< T, Q > const &next)
 Returns an intermediate control point for squad interpolation. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL T length2 (qua< T, Q > const &q)
 Returns the squared length of x. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL qua< T, Q > quat_identity ()
 Create an identity quaternion. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > rotate (qua< T, Q > const &q, vec< 3, T, Q > const &v)
 Returns quarternion square root. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 4, T, Q > rotate (qua< T, Q > const &q, vec< 4, T, Q > const &v)
 Rotates a 4 components vector by a quaternion. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL qua< T, Q > rotation (vec< 3, T, Q > const &orig, vec< 3, T, Q > const &dest)
 Compute the rotation between two vectors. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL qua< T, Q > shortMix (qua< T, Q > const &x, qua< T, Q > const &y, T const &a)
 Quaternion interpolation using the rotation short path. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL qua< T, Q > squad (qua< T, Q > const &q1, qua< T, Q > const &q2, qua< T, Q > const &s1, qua< T, Q > const &s2, T const &h)
 Compute a point on a path according squad equation. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 3, 3, T, Q > toMat3 (qua< T, Q > const &x)
 Converts a quaternion to a 3 * 3 matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 4, 4, T, Q > toMat4 (qua< T, Q > const &x)
 Converts a quaternion to a 4 * 4 matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL qua< T, Q > toQuat (mat< 3, 3, T, Q > const &x)
 Converts a 3 * 3 matrix to a quaternion. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL qua< T, Q > toQuat (mat< 4, 4, T, Q > const &x)
 Converts a 4 * 4 matrix to a quaternion. More...
 
\n

Detailed Description

\n

GLM_GTX_quaternion

\n
See also
Core features (dependence)
\n
\ngtx_extented_min_max (dependence)
\n\n

Definition in file gtx/quaternion.hpp.

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1 
\n
14 #pragma once
\n
15 
\n
16 // Dependency:
\n
17 #include "../glm.hpp"
\n
18 #include "../gtc/constants.hpp"
\n
19 #include "../gtc/quaternion.hpp"
\n
20 #include "../ext/quaternion_exponential.hpp"
\n
21 #include "../gtx/norm.hpp"
\n
22 
\n
23 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
24 # ifndef GLM_ENABLE_EXPERIMENTAL
\n
25 # pragma message("GLM: GLM_GTX_quaternion is an experimental extension and may change in the future. Use #define GLM_ENABLE_EXPERIMENTAL before including it, if you really want to use it.")
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26 # else
\n
27 # pragma message("GLM: GLM_GTX_quaternion extension included")
\n
28 # endif
\n
29 #endif
\n
30 
\n
31 namespace glm
\n
32 {
\n
35 
\n
39  template<typename T, qualifier Q>
\n
40  GLM_FUNC_DECL qua<T, Q> quat_identity();
\n
41 
\n
45  template<typename T, qualifier Q>
\n
46  GLM_FUNC_DECL vec<3, T, Q> cross(
\n
47  qua<T, Q> const& q,
\n
48  vec<3, T, Q> const& v);
\n
49 
\n
53  template<typename T, qualifier Q>
\n
54  GLM_FUNC_DECL vec<3, T, Q> cross(
\n
55  vec<3, T, Q> const& v,
\n
56  qua<T, Q> const& q);
\n
57 
\n
62  template<typename T, qualifier Q>
\n
63  GLM_FUNC_DECL qua<T, Q> squad(
\n
64  qua<T, Q> const& q1,
\n
65  qua<T, Q> const& q2,
\n
66  qua<T, Q> const& s1,
\n
67  qua<T, Q> const& s2,
\n
68  T const& h);
\n
69 
\n
73  template<typename T, qualifier Q>
\n
74  GLM_FUNC_DECL qua<T, Q> intermediate(
\n
75  qua<T, Q> const& prev,
\n
76  qua<T, Q> const& curr,
\n
77  qua<T, Q> const& next);
\n
78 
\n
82  //template<typename T, qualifier Q>
\n
83  //qua<T, Q> sqrt(
\n
84  // qua<T, Q> const& q);
\n
85 
\n
89  template<typename T, qualifier Q>
\n
90  GLM_FUNC_DECL vec<3, T, Q> rotate(
\n
91  qua<T, Q> const& q,
\n
92  vec<3, T, Q> const& v);
\n
93 
\n
97  template<typename T, qualifier Q>
\n
98  GLM_FUNC_DECL vec<4, T, Q> rotate(
\n
99  qua<T, Q> const& q,
\n
100  vec<4, T, Q> const& v);
\n
101 
\n
105  template<typename T, qualifier Q>
\n
106  GLM_FUNC_DECL T extractRealComponent(
\n
107  qua<T, Q> const& q);
\n
108 
\n
112  template<typename T, qualifier Q>
\n
113  GLM_FUNC_DECL mat<3, 3, T, Q> toMat3(
\n
114  qua<T, Q> const& x){return mat3_cast(x);}
\n
115 
\n
119  template<typename T, qualifier Q>
\n
120  GLM_FUNC_DECL mat<4, 4, T, Q> toMat4(
\n
121  qua<T, Q> const& x){return mat4_cast(x);}
\n
122 
\n
126  template<typename T, qualifier Q>
\n
127  GLM_FUNC_DECL qua<T, Q> toQuat(
\n
128  mat<3, 3, T, Q> const& x){return quat_cast(x);}
\n
129 
\n
133  template<typename T, qualifier Q>
\n
134  GLM_FUNC_DECL qua<T, Q> toQuat(
\n
135  mat<4, 4, T, Q> const& x){return quat_cast(x);}
\n
136 
\n
140  template<typename T, qualifier Q>
\n
141  GLM_FUNC_DECL qua<T, Q> shortMix(
\n
142  qua<T, Q> const& x,
\n
143  qua<T, Q> const& y,
\n
144  T const& a);
\n
145 
\n
149  template<typename T, qualifier Q>
\n
150  GLM_FUNC_DECL qua<T, Q> fastMix(
\n
151  qua<T, Q> const& x,
\n
152  qua<T, Q> const& y,
\n
153  T const& a);
\n
154 
\n
160  template<typename T, qualifier Q>
\n
161  GLM_FUNC_DECL qua<T, Q> rotation(
\n
162  vec<3, T, Q> const& orig,
\n
163  vec<3, T, Q> const& dest);
\n
164 
\n
168  template<typename T, qualifier Q>
\n
169  GLM_FUNC_DECL T length2(qua<T, Q> const& q);
\n
170 
\n
172 }//namespace glm
\n
173 
\n
174 #include "quaternion.inl"
\n
glm::mat4_cast
GLM_FUNC_DECL mat< 4, 4, T, Q > mat4_cast(qua< T, Q > const &x)
Converts a quaternion to a 4 * 4 matrix.
\n
glm::shortMix
GLM_FUNC_DECL qua< T, Q > shortMix(qua< T, Q > const &x, qua< T, Q > const &y, T const &a)
Quaternion interpolation using the rotation short path.
\n
glm::quat_identity
GLM_FUNC_DECL qua< T, Q > quat_identity()
Create an identity quaternion.
\n
glm::quat_cast
GLM_FUNC_DECL qua< T, Q > quat_cast(mat< 3, 3, T, Q > const &x)
Converts a pure rotation 3 * 3 matrix to a quaternion.
\n
glm::intermediate
GLM_FUNC_DECL qua< T, Q > intermediate(qua< T, Q > const &prev, qua< T, Q > const &curr, qua< T, Q > const &next)
Returns an intermediate control point for squad interpolation.
\n
glm::mat3_cast
GLM_FUNC_DECL mat< 3, 3, T, Q > mat3_cast(qua< T, Q > const &x)
Converts a quaternion to a 3 * 3 matrix.
\n
glm::toMat4
GLM_FUNC_DECL mat< 4, 4, T, Q > toMat4(qua< T, Q > const &x)
Converts a quaternion to a 4 * 4 matrix.
Definition: gtx/quaternion.hpp:120
\n
glm::extractRealComponent
GLM_FUNC_DECL T extractRealComponent(qua< T, Q > const &q)
Extract the real component of a quaternion.
\n
glm::toMat3
GLM_FUNC_DECL mat< 3, 3, T, Q > toMat3(qua< T, Q > const &x)
Converts a quaternion to a 3 * 3 matrix.
Definition: gtx/quaternion.hpp:113
\n
glm::squad
GLM_FUNC_DECL qua< T, Q > squad(qua< T, Q > const &q1, qua< T, Q > const &q2, qua< T, Q > const &s1, qua< T, Q > const &s2, T const &h)
Compute a point on a path according squad equation.
\n
glm::cross
GLM_FUNC_DECL vec< 3, T, Q > cross(vec< 3, T, Q > const &v, qua< T, Q > const &q)
Compute a cross product between a vector and a quaternion.
\n
glm::toQuat
GLM_FUNC_DECL qua< T, Q > toQuat(mat< 4, 4, T, Q > const &x)
Converts a 4 * 4 matrix to a quaternion.
Definition: gtx/quaternion.hpp:134
\n
glm::rotation
GLM_FUNC_DECL qua< T, Q > rotation(vec< 3, T, Q > const &orig, vec< 3, T, Q > const &dest)
Compute the rotation between two vectors.
\n
glm::rotate
GLM_FUNC_DECL vec< 4, T, Q > rotate(qua< T, Q > const &q, vec< 4, T, Q > const &v)
Rotates a 4 components vector by a quaternion.
\n
glm::fastMix
GLM_FUNC_DECL qua< T, Q > fastMix(qua< T, Q > const &x, qua< T, Q > const &y, T const &a)
Quaternion normalized linear interpolation.
\n
glm::length2
GLM_FUNC_DECL T length2(qua< T, Q > const &q)
Returns the squared length of x.
\n
glm
Definition: common.hpp:20
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\nFunctions
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quaternion_common.hpp File Reference
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GLM_EXT_quaternion_common \nMore...

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\nFunctions

template<typename T , qualifier Q>
GLM_FUNC_DECL qua< T, Q > conjugate (qua< T, Q > const &q)
 Returns the q conjugate. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL qua< T, Q > inverse (qua< T, Q > const &q)
 Returns the q inverse. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 4, bool, Q > isinf (qua< T, Q > const &x)
 Returns true if x holds a positive infinity or negative infinity representation in the underlying implementation's set of floating point representations. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 4, bool, Q > isnan (qua< T, Q > const &x)
 Returns true if x holds a NaN (not a number) representation in the underlying implementation's set of floating point representations. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL qua< T, Q > lerp (qua< T, Q > const &x, qua< T, Q > const &y, T a)
 Linear interpolation of two quaternions. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL qua< T, Q > mix (qua< T, Q > const &x, qua< T, Q > const &y, T a)
 Spherical linear interpolation of two quaternions. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL qua< T, Q > slerp (qua< T, Q > const &x, qua< T, Q > const &y, T a)
 Spherical linear interpolation of two quaternions. More...
 
\n

Detailed Description

\n

GLM_EXT_quaternion_common

\n\n

Definition in file quaternion_common.hpp.

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quaternion_common.hpp
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1 
\n
21 #pragma once
\n
22 
\n
23 // Dependency:
\n
24 #include "../ext/scalar_constants.hpp"
\n
25 #include "../ext/quaternion_geometric.hpp"
\n
26 #include "../common.hpp"
\n
27 #include "../trigonometric.hpp"
\n
28 #include "../exponential.hpp"
\n
29 #include <limits>
\n
30 
\n
31 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
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32 # pragma message("GLM: GLM_EXT_quaternion_common extension included")
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33 #endif
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34 
\n
35 namespace glm
\n
36 {
\n
39 
\n
52  template<typename T, qualifier Q>
\n
53  GLM_FUNC_DECL qua<T, Q> mix(qua<T, Q> const& x, qua<T, Q> const& y, T a);
\n
54 
\n
64  template<typename T, qualifier Q>
\n
65  GLM_FUNC_DECL qua<T, Q> lerp(qua<T, Q> const& x, qua<T, Q> const& y, T a);
\n
66 
\n
76  template<typename T, qualifier Q>
\n
77  GLM_FUNC_DECL qua<T, Q> slerp(qua<T, Q> const& x, qua<T, Q> const& y, T a);
\n
78 
\n
83  template<typename T, qualifier Q>
\n
84  GLM_FUNC_DECL qua<T, Q> conjugate(qua<T, Q> const& q);
\n
85 
\n
90  template<typename T, qualifier Q>
\n
91  GLM_FUNC_DECL qua<T, Q> inverse(qua<T, Q> const& q);
\n
92 
\n
103  template<typename T, qualifier Q>
\n
104  GLM_FUNC_DECL vec<4, bool, Q> isnan(qua<T, Q> const& x);
\n
105 
\n
114  template<typename T, qualifier Q>
\n
115  GLM_FUNC_DECL vec<4, bool, Q> isinf(qua<T, Q> const& x);
\n
116 
\n
118 } //namespace glm
\n
119 
\n
120 #include "quaternion_common.inl"
\n
glm::isinf
GLM_FUNC_DECL vec< 4, bool, Q > isinf(qua< T, Q > const &x)
Returns true if x holds a positive infinity or negative infinity representation in the underlying imp...
\n
glm::isnan
GLM_FUNC_DECL vec< 4, bool, Q > isnan(qua< T, Q > const &x)
Returns true if x holds a NaN (not a number) representation in the underlying implementation's set of...
\n
glm::conjugate
GLM_FUNC_DECL qua< T, Q > conjugate(qua< T, Q > const &q)
Returns the q conjugate.
\n
glm::slerp
GLM_FUNC_DECL qua< T, Q > slerp(qua< T, Q > const &x, qua< T, Q > const &y, T a)
Spherical linear interpolation of two quaternions.
\n
glm::inverse
GLM_FUNC_DECL qua< T, Q > inverse(qua< T, Q > const &q)
Returns the q inverse.
\n
glm::lerp
GLM_FUNC_DECL qua< T, Q > lerp(qua< T, Q > const &x, qua< T, Q > const &y, T a)
Linear interpolation of two quaternions.
\n
glm::mix
GLM_FUNC_DECL qua< T, Q > mix(qua< T, Q > const &x, qua< T, Q > const &y, T a)
Spherical linear interpolation of two quaternions.
\n
glm
Definition: common.hpp:20
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quaternion_double.hpp File Reference
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GLM_EXT_quaternion_double \nMore...

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\nTypedefs

\ntypedef qua< double, defaultp > dquat
 Quaternion of double-precision floating-point numbers.
 
\n

Detailed Description

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GLM_EXT_quaternion_double

\n\n

Definition in file quaternion_double.hpp.

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20 #pragma once
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21 
\n
22 // Dependency:
\n
23 #include "../detail/type_quat.hpp"
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24 
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25 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
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26 # pragma message("GLM: GLM_EXT_quaternion_double extension included")
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27 #endif
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28 
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29 namespace glm
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30 {
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33 
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35  typedef qua<double, defaultp> dquat;
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36 
\n
38 } //namespace glm
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39 
\n
glm::dquat
qua< double, defaultp > dquat
Quaternion of double-precision floating-point numbers.
Definition: quaternion_double.hpp:35
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glm
Definition: common.hpp:20
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quaternion_double_precision.hpp File Reference
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GLM_EXT_quaternion_double_precision \nMore...

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\nTypedefs

typedef qua< double, highp > highp_dquat
 Quaternion of high double-qualifier floating-point numbers using high precision arithmetic in term of ULPs. More...
 
typedef qua< double, lowp > lowp_dquat
 Quaternion of double-precision floating-point numbers using high precision arithmetic in term of ULPs. More...
 
typedef qua< double, mediump > mediump_dquat
 Quaternion of medium double-qualifier floating-point numbers using high precision arithmetic in term of ULPs. More...
 
\n

Detailed Description

\n

GLM_EXT_quaternion_double_precision

\n\n

Definition in file quaternion_double_precision.hpp.

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quaternion_double_precision.hpp
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1 
\n
11 #pragma once
\n
12 
\n
13 // Dependency:
\n
14 #include "../detail/type_quat.hpp"
\n
15 
\n
16 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
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17 # pragma message("GLM: GLM_EXT_quaternion_double_precision extension included")
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18 #endif
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19 
\n
20 namespace glm
\n
21 {
\n
24 
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28  typedef qua<double, lowp> lowp_dquat;
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29 
\n
33  typedef qua<double, mediump> mediump_dquat;
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34 
\n
38  typedef qua<double, highp> highp_dquat;
\n
39 
\n
41 } //namespace glm
\n
42 
\n
glm::mediump_dquat
qua< double, mediump > mediump_dquat
Quaternion of medium double-qualifier floating-point numbers using high precision arithmetic in term ...
Definition: quaternion_double_precision.hpp:33
\n
glm::highp_dquat
qua< double, highp > highp_dquat
Quaternion of high double-qualifier floating-point numbers using high precision arithmetic in term of...
Definition: quaternion_double_precision.hpp:38
\n
glm::lowp_dquat
qua< double, lowp > lowp_dquat
Quaternion of double-precision floating-point numbers using high precision arithmetic in term of ULPs...
Definition: quaternion_double_precision.hpp:28
\n
glm
Definition: common.hpp:20
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quaternion_exponential.hpp File Reference
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GLM_EXT_quaternion_exponential \nMore...

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\nFunctions

template<typename T , qualifier Q>
GLM_FUNC_DECL qua< T, Q > exp (qua< T, Q > const &q)
 Returns a exponential of a quaternion. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL qua< T, Q > log (qua< T, Q > const &q)
 Returns a logarithm of a quaternion. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL qua< T, Q > pow (qua< T, Q > const &q, T y)
 Returns a quaternion raised to a power. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL qua< T, Q > sqrt (qua< T, Q > const &q)
 Returns the square root of a quaternion. More...
 
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Detailed Description

\n

GLM_EXT_quaternion_exponential

\n\n

Definition in file quaternion_exponential.hpp.

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quaternion_exponential.hpp
\n
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\nGo to the documentation of this file.
1 
\n
15 #pragma once
\n
16 
\n
17 // Dependency:
\n
18 #include "../common.hpp"
\n
19 #include "../trigonometric.hpp"
\n
20 #include "../geometric.hpp"
\n
21 #include "../ext/scalar_constants.hpp"
\n
22 
\n
23 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
24 # pragma message("GLM: GLM_EXT_quaternion_exponential extension included")
\n
25 #endif
\n
26 
\n
27 namespace glm
\n
28 {
\n
31 
\n
36  template<typename T, qualifier Q>
\n
37  GLM_FUNC_DECL qua<T, Q> exp(qua<T, Q> const& q);
\n
38 
\n
43  template<typename T, qualifier Q>
\n
44  GLM_FUNC_DECL qua<T, Q> log(qua<T, Q> const& q);
\n
45 
\n
50  template<typename T, qualifier Q>
\n
51  GLM_FUNC_DECL qua<T, Q> pow(qua<T, Q> const& q, T y);
\n
52 
\n
57  template<typename T, qualifier Q>
\n
58  GLM_FUNC_DECL qua<T, Q> sqrt(qua<T, Q> const& q);
\n
59 
\n
61 } //namespace glm
\n
62 
\n
63 #include "quaternion_exponential.inl"
\n
glm::log
GLM_FUNC_DECL qua< T, Q > log(qua< T, Q > const &q)
Returns a logarithm of a quaternion.
\n
glm::pow
GLM_FUNC_DECL qua< T, Q > pow(qua< T, Q > const &q, T y)
Returns a quaternion raised to a power.
\n
glm::sqrt
GLM_FUNC_DECL qua< T, Q > sqrt(qua< T, Q > const &q)
Returns the square root of a quaternion.
\n
glm::exp
GLM_FUNC_DECL qua< T, Q > exp(qua< T, Q > const &q)
Returns a exponential of a quaternion.
\n
glm
Definition: common.hpp:20
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quaternion_float.hpp File Reference
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GLM_EXT_quaternion_float \nMore...

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\nTypedefs

\ntypedef qua< float, defaultp > quat
 Quaternion of single-precision floating-point numbers.
 
\n

Detailed Description

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GLM_EXT_quaternion_float

\n\n

Definition in file quaternion_float.hpp.

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quaternion_float.hpp
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1 
\n
20 #pragma once
\n
21 
\n
22 // Dependency:
\n
23 #include "../detail/type_quat.hpp"
\n
24 
\n
25 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
26 # pragma message("GLM: GLM_EXT_quaternion_float extension included")
\n
27 #endif
\n
28 
\n
29 namespace glm
\n
30 {
\n
33 
\n
35  typedef qua<float, defaultp> quat;
\n
36 
\n
38 } //namespace glm
\n
39 
\n
glm::quat
qua< float, defaultp > quat
Quaternion of single-precision floating-point numbers.
Definition: quaternion_float.hpp:35
\n
glm
Definition: common.hpp:20
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quaternion_float_precision.hpp File Reference
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GLM_EXT_quaternion_float_precision \nMore...

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\nTypedefs

\ntypedef qua< float, highp > highp_quat
 Quaternion of single-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef qua< float, lowp > lowp_quat
 Quaternion of single-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef qua< float, mediump > mediump_quat
 Quaternion of single-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\n

Detailed Description

\n

GLM_EXT_quaternion_float_precision

\n\n

Definition in file quaternion_float_precision.hpp.

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quaternion_float_precision.hpp
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1 
\n
11 #pragma once
\n
12 
\n
13 // Dependency:
\n
14 #include "../detail/type_quat.hpp"
\n
15 
\n
16 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
17 # pragma message("GLM: GLM_EXT_quaternion_float_precision extension included")
\n
18 #endif
\n
19 
\n
20 namespace glm
\n
21 {
\n
24 
\n
26  typedef qua<float, lowp> lowp_quat;
\n
27 
\n
29  typedef qua<float, mediump> mediump_quat;
\n
30 
\n
32  typedef qua<float, highp> highp_quat;
\n
33 
\n
35 } //namespace glm
\n
36 
\n
glm::highp_quat
qua< float, highp > highp_quat
Quaternion of single-precision floating-point numbers using high precision arithmetic in term of ULPs...
Definition: quaternion_float_precision.hpp:32
\n
glm::mediump_quat
qua< float, mediump > mediump_quat
Quaternion of single-precision floating-point numbers using high precision arithmetic in term of ULPs...
Definition: quaternion_float_precision.hpp:29
\n
glm::lowp_quat
qua< float, lowp > lowp_quat
Quaternion of single-precision floating-point numbers using high precision arithmetic in term of ULPs...
Definition: quaternion_float_precision.hpp:26
\n
glm
Definition: common.hpp:20
\n
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quaternion_geometric.hpp File Reference
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GLM_EXT_quaternion_geometric \nMore...

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Go to the source code of this file.

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\nFunctions

template<typename T , qualifier Q>
GLM_FUNC_QUALIFIER qua< T, Q > cross (qua< T, Q > const &q1, qua< T, Q > const &q2)
 Compute a cross product. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL T dot (qua< T, Q > const &x, qua< T, Q > const &y)
 Returns dot product of q1 and q2, i.e., q1[0] * q2[0] + q1[1] * q2[1] + ... More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL T length (qua< T, Q > const &q)
 Returns the norm of a quaternions. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL qua< T, Q > normalize (qua< T, Q > const &q)
 Returns the normalized quaternion. More...
 
\n

Detailed Description

\n

GLM_EXT_quaternion_geometric

\n\n

Definition in file quaternion_geometric.hpp.

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quaternion_geometric.hpp
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1 
\n
15 #pragma once
\n
16 
\n
17 // Dependency:
\n
18 #include "../geometric.hpp"
\n
19 #include "../exponential.hpp"
\n
20 #include "../ext/vector_relational.hpp"
\n
21 
\n
22 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
23 # pragma message("GLM: GLM_EXT_quaternion_geometric extension included")
\n
24 #endif
\n
25 
\n
26 namespace glm
\n
27 {
\n
30 
\n
37  template<typename T, qualifier Q>
\n
38  GLM_FUNC_DECL T length(qua<T, Q> const& q);
\n
39 
\n
46  template<typename T, qualifier Q>
\n
47  GLM_FUNC_DECL qua<T, Q> normalize(qua<T, Q> const& q);
\n
48 
\n
55  template<typename T, qualifier Q>
\n
56  GLM_FUNC_DECL T dot(qua<T, Q> const& x, qua<T, Q> const& y);
\n
57 
\n
64  template<typename T, qualifier Q>
\n
65  GLM_FUNC_QUALIFIER qua<T, Q> cross(qua<T, Q> const& q1, qua<T, Q> const& q2);
\n
66 
\n
68 } //namespace glm
\n
69 
\n
70 #include "quaternion_geometric.inl"
\n
glm::length
GLM_FUNC_DECL T length(qua< T, Q > const &q)
Returns the norm of a quaternions.
\n
glm::dot
GLM_FUNC_DECL T dot(qua< T, Q > const &x, qua< T, Q > const &y)
Returns dot product of q1 and q2, i.e., q1[0] * q2[0] + q1[1] * q2[1] + ...
\n
glm::cross
GLM_FUNC_QUALIFIER qua< T, Q > cross(qua< T, Q > const &q1, qua< T, Q > const &q2)
Compute a cross product.
\n
glm::normalize
GLM_FUNC_DECL qua< T, Q > normalize(qua< T, Q > const &q)
Returns the normalized quaternion.
\n
glm
Definition: common.hpp:20
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quaternion_relational.hpp File Reference
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GLM_EXT_quaternion_relational \nMore...

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template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 4, bool, Q > equal (qua< T, Q > const &x, qua< T, Q > const &y)
 Returns the component-wise comparison of result x == y. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 4, bool, Q > equal (qua< T, Q > const &x, qua< T, Q > const &y, T epsilon)
 Returns the component-wise comparison of |x - y| < epsilon. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 4, bool, Q > notEqual (qua< T, Q > const &x, qua< T, Q > const &y)
 Returns the component-wise comparison of result x != y. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 4, bool, Q > notEqual (qua< T, Q > const &x, qua< T, Q > const &y, T epsilon)
 Returns the component-wise comparison of |x - y| >= epsilon. More...
 
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Detailed Description

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GLM_EXT_quaternion_relational

\n\n

Definition in file quaternion_relational.hpp.

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quaternion_relational.hpp
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1 
\n
17 #pragma once
\n
18 
\n
19 // Dependency:
\n
20 #include "../vector_relational.hpp"
\n
21 
\n
22 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
23 # pragma message("GLM: GLM_EXT_quaternion_relational extension included")
\n
24 #endif
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25 
\n
26 namespace glm
\n
27 {
\n
30 
\n
35  template<typename T, qualifier Q>
\n
36  GLM_FUNC_DECL vec<4, bool, Q> equal(qua<T, Q> const& x, qua<T, Q> const& y);
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37 
\n
42  template<typename T, qualifier Q>
\n
43  GLM_FUNC_DECL vec<4, bool, Q> equal(qua<T, Q> const& x, qua<T, Q> const& y, T epsilon);
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44 
\n
49  template<typename T, qualifier Q>
\n
50  GLM_FUNC_DECL vec<4, bool, Q> notEqual(qua<T, Q> const& x, qua<T, Q> const& y);
\n
51 
\n
56  template<typename T, qualifier Q>
\n
57  GLM_FUNC_DECL vec<4, bool, Q> notEqual(qua<T, Q> const& x, qua<T, Q> const& y, T epsilon);
\n
58 
\n
60 } //namespace glm
\n
61 
\n
62 #include "quaternion_relational.inl"
\n
glm::epsilon
GLM_FUNC_DECL GLM_CONSTEXPR genType epsilon()
Return the epsilon constant for floating point types.
\n
glm::notEqual
GLM_FUNC_DECL vec< 4, bool, Q > notEqual(qua< T, Q > const &x, qua< T, Q > const &y, T epsilon)
Returns the component-wise comparison of |x - y| >= epsilon.
\n
glm::equal
GLM_FUNC_DECL vec< 4, bool, Q > equal(qua< T, Q > const &x, qua< T, Q > const &y, T epsilon)
Returns the component-wise comparison of |x - y| < epsilon.
\n
glm
Definition: common.hpp:20
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template<typename T , qualifier Q>
GLM_FUNC_DECL qua< T, Q > rotate (qua< T, Q > const &q, T const &angle, vec< 3, T, Q > const &axis)
 Rotates a quaternion from a vector of 3 components axis and an angle. More...
 
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Detailed Description

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GLM_EXT_quaternion_transform

\n\n

Definition in file quaternion_transform.hpp.

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quaternion_transform.hpp
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\n
18 #pragma once
\n
19 
\n
20 // Dependency:
\n
21 #include "../common.hpp"
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22 #include "../trigonometric.hpp"
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23 #include "../geometric.hpp"
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24 
\n
25 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
26 # pragma message("GLM: GLM_EXT_quaternion_transform extension included")
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27 #endif
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28 
\n
29 namespace glm
\n
30 {
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33 
\n
42  template<typename T, qualifier Q>
\n
43  GLM_FUNC_DECL qua<T, Q> rotate(qua<T, Q> const& q, T const& angle, vec<3, T, Q> const& axis);
\n
45 } //namespace glm
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46 
\n
47 #include "quaternion_transform.inl"
\n
glm::angle
GLM_FUNC_DECL T angle(qua< T, Q > const &x)
Returns the quaternion rotation angle.
\n
glm::rotate
GLM_FUNC_DECL qua< T, Q > rotate(qua< T, Q > const &q, T const &angle, vec< 3, T, Q > const &axis)
Rotates a quaternion from a vector of 3 components axis and an angle.
\n
glm::axis
GLM_FUNC_DECL vec< 3, T, Q > axis(qua< T, Q > const &x)
Returns the q rotation axis.
\n
glm
Definition: common.hpp:20
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quaternion_trigonometric.hpp File Reference
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GLM_EXT_quaternion_trigonometric \nMore...

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\nFunctions

template<typename T , qualifier Q>
GLM_FUNC_DECL T angle (qua< T, Q > const &x)
 Returns the quaternion rotation angle. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL qua< T, Q > angleAxis (T const &angle, vec< 3, T, Q > const &axis)
 Build a quaternion from an angle and a normalized axis. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > axis (qua< T, Q > const &x)
 Returns the q rotation axis. More...
 
\n

Detailed Description

\n

GLM_EXT_quaternion_trigonometric

\n\n

Definition in file quaternion_trigonometric.hpp.

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quaternion_trigonometric.hpp
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1 
\n
18 #pragma once
\n
19 
\n
20 // Dependency:
\n
21 #include "../trigonometric.hpp"
\n
22 #include "../exponential.hpp"
\n
23 #include "scalar_constants.hpp"
\n
24 #include "vector_relational.hpp"
\n
25 #include <limits>
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26 
\n
27 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
28 # pragma message("GLM: GLM_EXT_quaternion_trigonometric extension included")
\n
29 #endif
\n
30 
\n
31 namespace glm
\n
32 {
\n
35 
\n
40  template<typename T, qualifier Q>
\n
41  GLM_FUNC_DECL T angle(qua<T, Q> const& x);
\n
42 
\n
47  template<typename T, qualifier Q>
\n
48  GLM_FUNC_DECL vec<3, T, Q> axis(qua<T, Q> const& x);
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49 
\n
57  template<typename T, qualifier Q>
\n
58  GLM_FUNC_DECL qua<T, Q> angleAxis(T const& angle, vec<3, T, Q> const& axis);
\n
59 
\n
61 } //namespace glm
\n
62 
\n
63 #include "quaternion_trigonometric.inl"
\n
vector_relational.hpp
GLM_EXT_vector_relational
\n
glm::angle
GLM_FUNC_DECL T angle(qua< T, Q > const &x)
Returns the quaternion rotation angle.
\n
glm::angleAxis
GLM_FUNC_DECL qua< T, Q > angleAxis(T const &angle, vec< 3, T, Q > const &axis)
Build a quaternion from an angle and a normalized axis.
\n
scalar_constants.hpp
GLM_EXT_scalar_constants
\n
glm::axis
GLM_FUNC_DECL vec< 3, T, Q > axis(qua< T, Q > const &x)
Returns the q rotation axis.
\n
glm
Definition: common.hpp:20
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random.hpp File Reference
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GLM_GTC_random \nMore...

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\nFunctions

template<typename T >
GLM_FUNC_DECL vec< 3, T, defaultp > ballRand (T Radius)
 Generate a random 3D vector which coordinates are regulary distributed within the volume of a ball of a given radius. More...
 
template<typename T >
GLM_FUNC_DECL vec< 2, T, defaultp > circularRand (T Radius)
 Generate a random 2D vector which coordinates are regulary distributed on a circle of a given radius. More...
 
template<typename T >
GLM_FUNC_DECL vec< 2, T, defaultp > diskRand (T Radius)
 Generate a random 2D vector which coordinates are regulary distributed within the area of a disk of a given radius. More...
 
template<typename genType >
GLM_FUNC_DECL genType gaussRand (genType Mean, genType Deviation)
 Generate random numbers in the interval [Min, Max], according a gaussian distribution. More...
 
template<typename genType >
GLM_FUNC_DECL genType linearRand (genType Min, genType Max)
 Generate random numbers in the interval [Min, Max], according a linear distribution. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > linearRand (vec< L, T, Q > const &Min, vec< L, T, Q > const &Max)
 Generate random numbers in the interval [Min, Max], according a linear distribution. More...
 
template<typename T >
GLM_FUNC_DECL vec< 3, T, defaultp > sphericalRand (T Radius)
 Generate a random 3D vector which coordinates are regulary distributed on a sphere of a given radius. More...
 
\n

Detailed Description

\n

GLM_GTC_random

\n
See also
Core features (dependence)
\n
\ngtx_random (extended)
\n\n

Definition in file random.hpp.

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random.hpp
\n
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1 
\n
14 #pragma once
\n
15 
\n
16 // Dependency:
\n
17 #include "../ext/scalar_int_sized.hpp"
\n
18 #include "../ext/scalar_uint_sized.hpp"
\n
19 #include "../detail/qualifier.hpp"
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20 
\n
21 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
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22 # pragma message("GLM: GLM_GTC_random extension included")
\n
23 #endif
\n
24 
\n
25 namespace glm
\n
26 {
\n
29 
\n
36  template<typename genType>
\n
37  GLM_FUNC_DECL genType linearRand(genType Min, genType Max);
\n
38 
\n
46  template<length_t L, typename T, qualifier Q>
\n
47  GLM_FUNC_DECL vec<L, T, Q> linearRand(vec<L, T, Q> const& Min, vec<L, T, Q> const& Max);
\n
48 
\n
52  template<typename genType>
\n
53  GLM_FUNC_DECL genType gaussRand(genType Mean, genType Deviation);
\n
54 
\n
58  template<typename T>
\n
59  GLM_FUNC_DECL vec<2, T, defaultp> circularRand(T Radius);
\n
60 
\n
64  template<typename T>
\n
65  GLM_FUNC_DECL vec<3, T, defaultp> sphericalRand(T Radius);
\n
66 
\n
70  template<typename T>
\n
71  GLM_FUNC_DECL vec<2, T, defaultp> diskRand(T Radius);
\n
72 
\n
76  template<typename T>
\n
77  GLM_FUNC_DECL vec<3, T, defaultp> ballRand(T Radius);
\n
78 
\n
80 }//namespace glm
\n
81 
\n
82 #include "random.inl"
\n
glm::circularRand
GLM_FUNC_DECL vec< 2, T, defaultp > circularRand(T Radius)
Generate a random 2D vector which coordinates are regulary distributed on a circle of a given radius...
\n
glm::diskRand
GLM_FUNC_DECL vec< 2, T, defaultp > diskRand(T Radius)
Generate a random 2D vector which coordinates are regulary distributed within the area of a disk of a...
\n
glm::gaussRand
GLM_FUNC_DECL genType gaussRand(genType Mean, genType Deviation)
Generate random numbers in the interval [Min, Max], according a gaussian distribution.
\n
glm::sphericalRand
GLM_FUNC_DECL vec< 3, T, defaultp > sphericalRand(T Radius)
Generate a random 3D vector which coordinates are regulary distributed on a sphere of a given radius...
\n
glm::ballRand
GLM_FUNC_DECL vec< 3, T, defaultp > ballRand(T Radius)
Generate a random 3D vector which coordinates are regulary distributed within the volume of a ball of...
\n
glm::linearRand
GLM_FUNC_DECL vec< L, T, Q > linearRand(vec< L, T, Q > const &Min, vec< L, T, Q > const &Max)
Generate random numbers in the interval [Min, Max], according a linear distribution.
\n
glm
Definition: common.hpp:20
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range.hpp File Reference
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GLM_GTX_range \nMore...

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\n

Detailed Description

\n

GLM_GTX_range

\n
Author
Joshua Moerman
\n\n

Definition in file range.hpp.

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range.hpp
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1 
\n
13 #pragma once
\n
14 
\n
15 // Dependencies
\n
16 #include "../detail/setup.hpp"
\n
17 
\n
18 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
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19 # ifndef GLM_ENABLE_EXPERIMENTAL
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20 # pragma message("GLM: GLM_GTX_range is an experimental extension and may change in the future. Use #define GLM_ENABLE_EXPERIMENTAL before including it, if you really want to use it.")
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21 # else
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22 # pragma message("GLM: GLM_GTX_range extension included")
\n
23 # endif
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24 #endif
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25 
\n
26 #include "../gtc/type_ptr.hpp"
\n
27 #include "../gtc/vec1.hpp"
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28 
\n
29 namespace glm
\n
30 {
\n
33 
\n
34 # if GLM_COMPILER & GLM_COMPILER_VC
\n
35 # pragma warning(push)
\n
36 # pragma warning(disable : 4100) // unreferenced formal parameter
\n
37 # endif
\n
38 
\n
39  template<typename T, qualifier Q>
\n
40  inline length_t components(vec<1, T, Q> const& v)
\n
41  {
\n
42  return v.length();
\n
43  }
\n
44 
\n
45  template<typename T, qualifier Q>
\n
46  inline length_t components(vec<2, T, Q> const& v)
\n
47  {
\n
48  return v.length();
\n
49  }
\n
50 
\n
51  template<typename T, qualifier Q>
\n
52  inline length_t components(vec<3, T, Q> const& v)
\n
53  {
\n
54  return v.length();
\n
55  }
\n
56 
\n
57  template<typename T, qualifier Q>
\n
58  inline length_t components(vec<4, T, Q> const& v)
\n
59  {
\n
60  return v.length();
\n
61  }
\n
62 
\n
63  template<typename genType>
\n
64  inline length_t components(genType const& m)
\n
65  {
\n
66  return m.length() * m[0].length();
\n
67  }
\n
68 
\n
69  template<typename genType>
\n
70  inline typename genType::value_type const * begin(genType const& v)
\n
71  {
\n
72  return value_ptr(v);
\n
73  }
\n
74 
\n
75  template<typename genType>
\n
76  inline typename genType::value_type const * end(genType const& v)
\n
77  {
\n
78  return begin(v) + components(v);
\n
79  }
\n
80 
\n
81  template<typename genType>
\n
82  inline typename genType::value_type * begin(genType& v)
\n
83  {
\n
84  return value_ptr(v);
\n
85  }
\n
86 
\n
87  template<typename genType>
\n
88  inline typename genType::value_type * end(genType& v)
\n
89  {
\n
90  return begin(v) + components(v);
\n
91  }
\n
92 
\n
93 # if GLM_COMPILER & GLM_COMPILER_VC
\n
94 # pragma warning(pop)
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95 # endif
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96 
\n
98 }//namespace glm
\n
glm::value_ptr
GLM_FUNC_DECL genType::value_type const * value_ptr(genType const &v)
Return the constant address to the data of the input parameter.
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glm
Definition: common.hpp:20
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GLM_GTX_raw_data \nMore...

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\nTypedefs

typedef detail::uint8 byte
 Type for byte numbers. More...
 
typedef detail::uint32 dword
 Type for dword numbers. More...
 
typedef detail::uint64 qword
 Type for qword numbers. More...
 
typedef detail::uint16 word
 Type for word numbers. More...
 
\n

Detailed Description

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GLM_GTX_raw_data

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See also
Core features (dependence)
\n\n

Definition in file raw_data.hpp.

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raw_data.hpp
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1 
\n
13 #pragma once
\n
14 
\n
15 // Dependencies
\n
16 #include "../ext/scalar_uint_sized.hpp"
\n
17 #include "../detail/setup.hpp"
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18 
\n
19 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
20 # ifndef GLM_ENABLE_EXPERIMENTAL
\n
21 # pragma message("GLM: GLM_GTX_raw_data is an experimental extension and may change in the future. Use #define GLM_ENABLE_EXPERIMENTAL before including it, if you really want to use it.")
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22 # else
\n
23 # pragma message("GLM: GLM_GTX_raw_data extension included")
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24 # endif
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25 #endif
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26 
\n
27 namespace glm
\n
28 {
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31 
\n
34  typedef detail::uint8 byte;
\n
35 
\n
38  typedef detail::uint16 word;
\n
39 
\n
42  typedef detail::uint32 dword;
\n
43 
\n
46  typedef detail::uint64 qword;
\n
47 
\n
49 }// namespace glm
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50 
\n
51 #include "raw_data.inl"
\n
glm::dword
detail::uint32 dword
Type for dword numbers.
Definition: raw_data.hpp:42
\n
glm::byte
detail::uint8 byte
Type for byte numbers.
Definition: raw_data.hpp:34
\n
glm::qword
detail::uint64 qword
Type for qword numbers.
Definition: raw_data.hpp:46
\n
glm::word
detail::uint16 word
Type for word numbers.
Definition: raw_data.hpp:38
\n
glm
Definition: common.hpp:20
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reciprocal.hpp File Reference
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GLM_GTC_reciprocal \nMore...

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\nFunctions

template<typename genType >
GLM_FUNC_DECL genType acot (genType x)
 Inverse cotangent function. More...
 
template<typename genType >
GLM_FUNC_DECL genType acoth (genType x)
 Inverse cotangent hyperbolic function. More...
 
template<typename genType >
GLM_FUNC_DECL genType acsc (genType x)
 Inverse cosecant function. More...
 
template<typename genType >
GLM_FUNC_DECL genType acsch (genType x)
 Inverse cosecant hyperbolic function. More...
 
template<typename genType >
GLM_FUNC_DECL genType asec (genType x)
 Inverse secant function. More...
 
template<typename genType >
GLM_FUNC_DECL genType asech (genType x)
 Inverse secant hyperbolic function. More...
 
template<typename genType >
GLM_FUNC_DECL genType cot (genType angle)
 Cotangent function. More...
 
template<typename genType >
GLM_FUNC_DECL genType coth (genType angle)
 Cotangent hyperbolic function. More...
 
template<typename genType >
GLM_FUNC_DECL genType csc (genType angle)
 Cosecant function. More...
 
template<typename genType >
GLM_FUNC_DECL genType csch (genType angle)
 Cosecant hyperbolic function. More...
 
template<typename genType >
GLM_FUNC_DECL genType sec (genType angle)
 Secant function. More...
 
template<typename genType >
GLM_FUNC_DECL genType sech (genType angle)
 Secant hyperbolic function. More...
 
\n

Detailed Description

\n

GLM_GTC_reciprocal

\n
See also
Core features (dependence)
\n\n

Definition in file reciprocal.hpp.

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reciprocal.hpp
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1 
\n
13 #pragma once
\n
14 
\n
15 // Dependencies
\n
16 #include "../detail/setup.hpp"
\n
17 
\n
18 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
19 # pragma message("GLM: GLM_GTC_reciprocal extension included")
\n
20 #endif
\n
21 
\n
22 namespace glm
\n
23 {
\n
26 
\n
33  template<typename genType>
\n
34  GLM_FUNC_DECL genType sec(genType angle);
\n
35 
\n
42  template<typename genType>
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43  GLM_FUNC_DECL genType csc(genType angle);
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44 
\n
51  template<typename genType>
\n
52  GLM_FUNC_DECL genType cot(genType angle);
\n
53 
\n
60  template<typename genType>
\n
61  GLM_FUNC_DECL genType asec(genType x);
\n
62 
\n
69  template<typename genType>
\n
70  GLM_FUNC_DECL genType acsc(genType x);
\n
71 
\n
78  template<typename genType>
\n
79  GLM_FUNC_DECL genType acot(genType x);
\n
80 
\n
86  template<typename genType>
\n
87  GLM_FUNC_DECL genType sech(genType angle);
\n
88 
\n
94  template<typename genType>
\n
95  GLM_FUNC_DECL genType csch(genType angle);
\n
96 
\n
102  template<typename genType>
\n
103  GLM_FUNC_DECL genType coth(genType angle);
\n
104 
\n
111  template<typename genType>
\n
112  GLM_FUNC_DECL genType asech(genType x);
\n
113 
\n
120  template<typename genType>
\n
121  GLM_FUNC_DECL genType acsch(genType x);
\n
122 
\n
129  template<typename genType>
\n
130  GLM_FUNC_DECL genType acoth(genType x);
\n
131 
\n
133 }//namespace glm
\n
134 
\n
135 #include "reciprocal.inl"
\n
glm::sec
GLM_FUNC_DECL genType sec(genType angle)
Secant function.
\n
glm::csc
GLM_FUNC_DECL genType csc(genType angle)
Cosecant function.
\n
glm::coth
GLM_FUNC_DECL genType coth(genType angle)
Cotangent hyperbolic function.
\n
glm::asec
GLM_FUNC_DECL genType asec(genType x)
Inverse secant function.
\n
glm::angle
GLM_FUNC_DECL T angle(qua< T, Q > const &x)
Returns the quaternion rotation angle.
\n
glm::cot
GLM_FUNC_DECL genType cot(genType angle)
Cotangent function.
\n
glm::acsc
GLM_FUNC_DECL genType acsc(genType x)
Inverse cosecant function.
\n
glm::sech
GLM_FUNC_DECL genType sech(genType angle)
Secant hyperbolic function.
\n
glm::csch
GLM_FUNC_DECL genType csch(genType angle)
Cosecant hyperbolic function.
\n
glm::acoth
GLM_FUNC_DECL genType acoth(genType x)
Inverse cotangent hyperbolic function.
\n
glm::acot
GLM_FUNC_DECL genType acot(genType x)
Inverse cotangent function.
\n
glm::asech
GLM_FUNC_DECL genType asech(genType x)
Inverse secant hyperbolic function.
\n
glm::acsch
GLM_FUNC_DECL genType acsch(genType x)
Inverse cosecant hyperbolic function.
\n
glm
Definition: common.hpp:20
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rotate_normalized_axis.hpp File Reference
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GLM_GTX_rotate_normalized_axis \nMore...

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Go to the source code of this file.

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\nFunctions

template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 4, 4, T, Q > rotateNormalizedAxis (mat< 4, 4, T, Q > const &m, T const &angle, vec< 3, T, Q > const &axis)
 Builds a rotation 4 * 4 matrix created from a normalized axis and an angle. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL qua< T, Q > rotateNormalizedAxis (qua< T, Q > const &q, T const &angle, vec< 3, T, Q > const &axis)
 Rotates a quaternion from a vector of 3 components normalized axis and an angle. More...
 
\n

Detailed Description

\n

GLM_GTX_rotate_normalized_axis

\n
See also
Core features (dependence)
\n
\n to use the features of this extension. \">GLM_GTC_matrix_transform
\n
\n to use the features of this extension. \">GLM_GTC_quaternion
\n\n

Definition in file rotate_normalized_axis.hpp.

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rotate_normalized_axis.hpp
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1 
\n
15 #pragma once
\n
16 
\n
17 // Dependency:
\n
18 #include "../glm.hpp"
\n
19 #include "../gtc/epsilon.hpp"
\n
20 #include "../gtc/quaternion.hpp"
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21 
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22 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
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23 # ifndef GLM_ENABLE_EXPERIMENTAL
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24 # pragma message("GLM: GLM_GTX_rotate_normalized_axis is an experimental extension and may change in the future. Use #define GLM_ENABLE_EXPERIMENTAL before including it, if you really want to use it.")
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25 # else
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26 # pragma message("GLM: GLM_GTX_rotate_normalized_axis extension included")
\n
27 # endif
\n
28 #endif
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29 
\n
30 namespace glm
\n
31 {
\n
34 
\n
46  template<typename T, qualifier Q>
\n
47  GLM_FUNC_DECL mat<4, 4, T, Q> rotateNormalizedAxis(
\n
48  mat<4, 4, T, Q> const& m,
\n
49  T const& angle,
\n
50  vec<3, T, Q> const& axis);
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51 
\n
59  template<typename T, qualifier Q>
\n
60  GLM_FUNC_DECL qua<T, Q> rotateNormalizedAxis(
\n
61  qua<T, Q> const& q,
\n
62  T const& angle,
\n
63  vec<3, T, Q> const& axis);
\n
64 
\n
66 }//namespace glm
\n
67 
\n
68 #include "rotate_normalized_axis.inl"
\n
glm::angle
GLM_FUNC_DECL T angle(qua< T, Q > const &x)
Returns the quaternion rotation angle.
\n
glm::rotateNormalizedAxis
GLM_FUNC_DECL qua< T, Q > rotateNormalizedAxis(qua< T, Q > const &q, T const &angle, vec< 3, T, Q > const &axis)
Rotates a quaternion from a vector of 3 components normalized axis and an angle.
\n
glm::axis
GLM_FUNC_DECL vec< 3, T, Q > axis(qua< T, Q > const &x)
Returns the q rotation axis.
\n
glm
Definition: common.hpp:20
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rotate_vector.hpp File Reference
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GLM_GTX_rotate_vector \nMore...

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\nFunctions

template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 4, 4, T, Q > orientation (vec< 3, T, Q > const &Normal, vec< 3, T, Q > const &Up)
 Build a rotation matrix from a normal and a up vector. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 2, T, Q > rotate (vec< 2, T, Q > const &v, T const &angle)
 Rotate a two dimensional vector. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > rotate (vec< 3, T, Q > const &v, T const &angle, vec< 3, T, Q > const &normal)
 Rotate a three dimensional vector around an axis. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 4, T, Q > rotate (vec< 4, T, Q > const &v, T const &angle, vec< 3, T, Q > const &normal)
 Rotate a four dimensional vector around an axis. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > rotateX (vec< 3, T, Q > const &v, T const &angle)
 Rotate a three dimensional vector around the X axis. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 4, T, Q > rotateX (vec< 4, T, Q > const &v, T const &angle)
 Rotate a four dimensional vector around the X axis. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > rotateY (vec< 3, T, Q > const &v, T const &angle)
 Rotate a three dimensional vector around the Y axis. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 4, T, Q > rotateY (vec< 4, T, Q > const &v, T const &angle)
 Rotate a four dimensional vector around the Y axis. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > rotateZ (vec< 3, T, Q > const &v, T const &angle)
 Rotate a three dimensional vector around the Z axis. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 4, T, Q > rotateZ (vec< 4, T, Q > const &v, T const &angle)
 Rotate a four dimensional vector around the Z axis. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > slerp (vec< 3, T, Q > const &x, vec< 3, T, Q > const &y, T const &a)
 Returns Spherical interpolation between two vectors. More...
 
\n

Detailed Description

\n

GLM_GTX_rotate_vector

\n
See also
Core features (dependence)
\n
\n to use the features of this extension. \">GLM_GTX_transform (dependence)
\n\n

Definition in file rotate_vector.hpp.

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rotate_vector.hpp
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1 
\n
14 #pragma once
\n
15 
\n
16 // Dependency:
\n
17 #include "../gtx/transform.hpp"
\n
18 #include "../gtc/epsilon.hpp"
\n
19 #include "../ext/vector_relational.hpp"
\n
20 #include "../glm.hpp"
\n
21 
\n
22 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
23 # ifndef GLM_ENABLE_EXPERIMENTAL
\n
24 # pragma message("GLM: GLM_GTX_rotate_vector is an experimental extension and may change in the future. Use #define GLM_ENABLE_EXPERIMENTAL before including it, if you really want to use it.")
\n
25 # else
\n
26 # pragma message("GLM: GLM_GTX_rotate_vector extension included")
\n
27 # endif
\n
28 #endif
\n
29 
\n
30 namespace glm
\n
31 {
\n
34 
\n
42  template<typename T, qualifier Q>
\n
43  GLM_FUNC_DECL vec<3, T, Q> slerp(
\n
44  vec<3, T, Q> const& x,
\n
45  vec<3, T, Q> const& y,
\n
46  T const& a);
\n
47 
\n
50  template<typename T, qualifier Q>
\n
51  GLM_FUNC_DECL vec<2, T, Q> rotate(
\n
52  vec<2, T, Q> const& v,
\n
53  T const& angle);
\n
54 
\n
57  template<typename T, qualifier Q>
\n
58  GLM_FUNC_DECL vec<3, T, Q> rotate(
\n
59  vec<3, T, Q> const& v,
\n
60  T const& angle,
\n
61  vec<3, T, Q> const& normal);
\n
62 
\n
65  template<typename T, qualifier Q>
\n
66  GLM_FUNC_DECL vec<4, T, Q> rotate(
\n
67  vec<4, T, Q> const& v,
\n
68  T const& angle,
\n
69  vec<3, T, Q> const& normal);
\n
70 
\n
73  template<typename T, qualifier Q>
\n
74  GLM_FUNC_DECL vec<3, T, Q> rotateX(
\n
75  vec<3, T, Q> const& v,
\n
76  T const& angle);
\n
77 
\n
80  template<typename T, qualifier Q>
\n
81  GLM_FUNC_DECL vec<3, T, Q> rotateY(
\n
82  vec<3, T, Q> const& v,
\n
83  T const& angle);
\n
84 
\n
87  template<typename T, qualifier Q>
\n
88  GLM_FUNC_DECL vec<3, T, Q> rotateZ(
\n
89  vec<3, T, Q> const& v,
\n
90  T const& angle);
\n
91 
\n
94  template<typename T, qualifier Q>
\n
95  GLM_FUNC_DECL vec<4, T, Q> rotateX(
\n
96  vec<4, T, Q> const& v,
\n
97  T const& angle);
\n
98 
\n
101  template<typename T, qualifier Q>
\n
102  GLM_FUNC_DECL vec<4, T, Q> rotateY(
\n
103  vec<4, T, Q> const& v,
\n
104  T const& angle);
\n
105 
\n
108  template<typename T, qualifier Q>
\n
109  GLM_FUNC_DECL vec<4, T, Q> rotateZ(
\n
110  vec<4, T, Q> const& v,
\n
111  T const& angle);
\n
112 
\n
115  template<typename T, qualifier Q>
\n
116  GLM_FUNC_DECL mat<4, 4, T, Q> orientation(
\n
117  vec<3, T, Q> const& Normal,
\n
118  vec<3, T, Q> const& Up);
\n
119 
\n
121 }//namespace glm
\n
122 
\n
123 #include "rotate_vector.inl"
\n
glm::angle
GLM_FUNC_DECL T angle(qua< T, Q > const &x)
Returns the quaternion rotation angle.
\n
glm::rotateZ
GLM_FUNC_DECL vec< 4, T, Q > rotateZ(vec< 4, T, Q > const &v, T const &angle)
Rotate a four dimensional vector around the Z axis.
\n
glm::rotateY
GLM_FUNC_DECL vec< 4, T, Q > rotateY(vec< 4, T, Q > const &v, T const &angle)
Rotate a four dimensional vector around the Y axis.
\n
glm::rotateX
GLM_FUNC_DECL vec< 4, T, Q > rotateX(vec< 4, T, Q > const &v, T const &angle)
Rotate a four dimensional vector around the X axis.
\n
glm::slerp
GLM_FUNC_DECL vec< 3, T, Q > slerp(vec< 3, T, Q > const &x, vec< 3, T, Q > const &y, T const &a)
Returns Spherical interpolation between two vectors.
\n
glm::orientation
GLM_FUNC_DECL mat< 4, 4, T, Q > orientation(vec< 3, T, Q > const &Normal, vec< 3, T, Q > const &Up)
Build a rotation matrix from a normal and a up vector.
\n
glm::rotate
GLM_FUNC_DECL vec< 4, T, Q > rotate(vec< 4, T, Q > const &v, T const &angle, vec< 3, T, Q > const &normal)
Rotate a four dimensional vector around an axis.
\n
glm
Definition: common.hpp:20
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GLM_GTC_round \nMore...

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\nFunctions

template<typename genType >
GLM_FUNC_DECL genType ceilMultiple (genType v, genType Multiple)
 Higher multiple number of Source. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > ceilMultiple (vec< L, T, Q > const &v, vec< L, T, Q > const &Multiple)
 Higher multiple number of Source. More...
 
template<typename genIUType >
GLM_FUNC_DECL genIUType ceilPowerOfTwo (genIUType v)
 Return the power of two number which value is just higher the input value, round up to a power of two. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > ceilPowerOfTwo (vec< L, T, Q > const &v)
 Return the power of two number which value is just higher the input value, round up to a power of two. More...
 
template<typename genType >
GLM_FUNC_DECL genType floorMultiple (genType v, genType Multiple)
 Lower multiple number of Source. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > floorMultiple (vec< L, T, Q > const &v, vec< L, T, Q > const &Multiple)
 Lower multiple number of Source. More...
 
template<typename genIUType >
GLM_FUNC_DECL genIUType floorPowerOfTwo (genIUType v)
 Return the power of two number which value is just lower the input value, round down to a power of two. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > floorPowerOfTwo (vec< L, T, Q > const &v)
 Return the power of two number which value is just lower the input value, round down to a power of two. More...
 
template<typename genType >
GLM_FUNC_DECL genType roundMultiple (genType v, genType Multiple)
 Lower multiple number of Source. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > roundMultiple (vec< L, T, Q > const &v, vec< L, T, Q > const &Multiple)
 Lower multiple number of Source. More...
 
template<typename genIUType >
GLM_FUNC_DECL genIUType roundPowerOfTwo (genIUType v)
 Return the power of two number which value is the closet to the input value. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > roundPowerOfTwo (vec< L, T, Q > const &v)
 Return the power of two number which value is the closet to the input value. More...
 
\n

Detailed Description

\n

GLM_GTC_round

\n
See also
Core features (dependence)
\n
\n to use the features of this extension. \">GLM_GTC_round (dependence)
\n\n

Definition in file round.hpp.

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round.hpp
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1 
\n
14 #pragma once
\n
15 
\n
16 // Dependencies
\n
17 #include "../detail/setup.hpp"
\n
18 #include "../detail/qualifier.hpp"
\n
19 #include "../detail/_vectorize.hpp"
\n
20 #include "../vector_relational.hpp"
\n
21 #include "../common.hpp"
\n
22 #include <limits>
\n
23 
\n
24 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
25 # pragma message("GLM: GLM_GTC_round extension included")
\n
26 #endif
\n
27 
\n
28 namespace glm
\n
29 {
\n
32 
\n
37  template<typename genIUType>
\n
38  GLM_FUNC_DECL genIUType ceilPowerOfTwo(genIUType v);
\n
39 
\n
48  template<length_t L, typename T, qualifier Q>
\n
49  GLM_FUNC_DECL vec<L, T, Q> ceilPowerOfTwo(vec<L, T, Q> const& v);
\n
50 
\n
55  template<typename genIUType>
\n
56  GLM_FUNC_DECL genIUType floorPowerOfTwo(genIUType v);
\n
57 
\n
66  template<length_t L, typename T, qualifier Q>
\n
67  GLM_FUNC_DECL vec<L, T, Q> floorPowerOfTwo(vec<L, T, Q> const& v);
\n
68 
\n
72  template<typename genIUType>
\n
73  GLM_FUNC_DECL genIUType roundPowerOfTwo(genIUType v);
\n
74 
\n
82  template<length_t L, typename T, qualifier Q>
\n
83  GLM_FUNC_DECL vec<L, T, Q> roundPowerOfTwo(vec<L, T, Q> const& v);
\n
84 
\n
93  template<typename genType>
\n
94  GLM_FUNC_DECL genType ceilMultiple(genType v, genType Multiple);
\n
95 
\n
106  template<length_t L, typename T, qualifier Q>
\n
107  GLM_FUNC_DECL vec<L, T, Q> ceilMultiple(vec<L, T, Q> const& v, vec<L, T, Q> const& Multiple);
\n
108 
\n
117  template<typename genType>
\n
118  GLM_FUNC_DECL genType floorMultiple(genType v, genType Multiple);
\n
119 
\n
130  template<length_t L, typename T, qualifier Q>
\n
131  GLM_FUNC_DECL vec<L, T, Q> floorMultiple(vec<L, T, Q> const& v, vec<L, T, Q> const& Multiple);
\n
132 
\n
141  template<typename genType>
\n
142  GLM_FUNC_DECL genType roundMultiple(genType v, genType Multiple);
\n
143 
\n
154  template<length_t L, typename T, qualifier Q>
\n
155  GLM_FUNC_DECL vec<L, T, Q> roundMultiple(vec<L, T, Q> const& v, vec<L, T, Q> const& Multiple);
\n
156 
\n
158 } //namespace glm
\n
159 
\n
160 #include "round.inl"
\n
glm::roundPowerOfTwo
GLM_FUNC_DECL vec< L, T, Q > roundPowerOfTwo(vec< L, T, Q > const &v)
Return the power of two number which value is the closet to the input value.
\n
glm::ceilMultiple
GLM_FUNC_DECL vec< L, T, Q > ceilMultiple(vec< L, T, Q > const &v, vec< L, T, Q > const &Multiple)
Higher multiple number of Source.
\n
glm::floorPowerOfTwo
GLM_FUNC_DECL vec< L, T, Q > floorPowerOfTwo(vec< L, T, Q > const &v)
Return the power of two number which value is just lower the input value, round down to a power of tw...
\n
glm::roundMultiple
GLM_FUNC_DECL vec< L, T, Q > roundMultiple(vec< L, T, Q > const &v, vec< L, T, Q > const &Multiple)
Lower multiple number of Source.
\n
glm::ceilPowerOfTwo
GLM_FUNC_DECL vec< L, T, Q > ceilPowerOfTwo(vec< L, T, Q > const &v)
Return the power of two number which value is just higher the input value, round up to a power of two...
\n
glm::floorMultiple
GLM_FUNC_DECL vec< L, T, Q > floorMultiple(vec< L, T, Q > const &v, vec< L, T, Q > const &Multiple)
Lower multiple number of Source.
\n
glm
Definition: common.hpp:20
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\nFunctions
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\n
scalar_common.hpp File Reference
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GLM_EXT_scalar_common \nMore...

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\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

\nFunctions

template<typename T >
GLM_FUNC_DECL T fmax (T a, T b)
 Returns the maximum component-wise values of 2 inputs. More...
 
template<typename T >
GLM_FUNC_DECL T fmax (T a, T b, T C)
 Returns the maximum component-wise values of 3 inputs. More...
 
template<typename T >
GLM_FUNC_DECL T fmax (T a, T b, T C, T D)
 Returns the maximum component-wise values of 4 inputs. More...
 
template<typename T >
GLM_FUNC_DECL T fmin (T a, T b)
 Returns the minimum component-wise values of 2 inputs. More...
 
template<typename T >
GLM_FUNC_DECL T fmin (T a, T b, T c)
 Returns the minimum component-wise values of 3 inputs. More...
 
template<typename T >
GLM_FUNC_DECL T fmin (T a, T b, T c, T d)
 Returns the minimum component-wise values of 4 inputs. More...
 
template<typename T >
GLM_FUNC_DECL T max (T a, T b, T c)
 Returns the maximum component-wise values of 3 inputs. More...
 
template<typename T >
GLM_FUNC_DECL T max (T a, T b, T c, T d)
 Returns the maximum component-wise values of 4 inputs. More...
 
template<typename T >
GLM_FUNC_DECL T min (T a, T b, T c)
 Returns the minimum component-wise values of 3 inputs. More...
 
template<typename T >
GLM_FUNC_DECL T min (T a, T b, T c, T d)
 Returns the minimum component-wise values of 4 inputs. More...
 
\n

Detailed Description

\n

GLM_EXT_scalar_common

\n\n

Definition in file scalar_common.hpp.

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scalar_common.hpp
\n
\n
\nGo to the documentation of this file.
1 
\n
14 #pragma once
\n
15 
\n
16 // Dependency:
\n
17 #include "../common.hpp"
\n
18 
\n
19 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
20 # pragma message("GLM: GLM_EXT_scalar_common extension included")
\n
21 #endif
\n
22 
\n
23 namespace glm
\n
24 {
\n
27 
\n
31  template<typename T>
\n
32  GLM_FUNC_DECL T min(T a, T b, T c);
\n
33 
\n
37  template<typename T>
\n
38  GLM_FUNC_DECL T min(T a, T b, T c, T d);
\n
39 
\n
43  template<typename T>
\n
44  GLM_FUNC_DECL T max(T a, T b, T c);
\n
45 
\n
49  template<typename T>
\n
50  GLM_FUNC_DECL T max(T a, T b, T c, T d);
\n
51 
\n
57  template<typename T>
\n
58  GLM_FUNC_DECL T fmin(T a, T b);
\n
59 
\n
65  template<typename T>
\n
66  GLM_FUNC_DECL T fmin(T a, T b, T c);
\n
67 
\n
73  template<typename T>
\n
74  GLM_FUNC_DECL T fmin(T a, T b, T c, T d);
\n
75 
\n
81  template<typename T>
\n
82  GLM_FUNC_DECL T fmax(T a, T b);
\n
83 
\n
89  template<typename T>
\n
90  GLM_FUNC_DECL T fmax(T a, T b, T C);
\n
91 
\n
97  template<typename T>
\n
98  GLM_FUNC_DECL T fmax(T a, T b, T C, T D);
\n
99 
\n
101 }//namespace glm
\n
102 
\n
103 #include "scalar_common.inl"
\n
glm::min
GLM_FUNC_DECL T min(T a, T b, T c, T d)
Returns the minimum component-wise values of 4 inputs.
\n
glm::max
GLM_FUNC_DECL T max(T a, T b, T c, T d)
Returns the maximum component-wise values of 4 inputs.
\n
glm::fmax
GLM_FUNC_DECL T fmax(T a, T b, T C, T D)
Returns the maximum component-wise values of 4 inputs.
\n
glm::fmin
GLM_FUNC_DECL T fmin(T a, T b, T c, T d)
Returns the minimum component-wise values of 4 inputs.
\n
glm
Definition: common.hpp:20
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scalar_constants.hpp File Reference
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GLM_EXT_scalar_constants \nMore...

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\nFunctions

\ntemplate<typename genType >
GLM_FUNC_DECL GLM_CONSTEXPR genType epsilon ()
 Return the epsilon constant for floating point types.
 
\ntemplate<typename genType >
GLM_FUNC_DECL GLM_CONSTEXPR genType pi ()
 Return the pi constant for floating point types.
 
\n

Detailed Description

\n

GLM_EXT_scalar_constants

\n\n

Definition in file scalar_constants.hpp.

\n
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scalar_constants.hpp
\n
\n
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1 
\n
11 #pragma once
\n
12 
\n
13 // Dependencies
\n
14 #include "../detail/setup.hpp"
\n
15 
\n
16 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
17 # pragma message("GLM: GLM_EXT_scalar_constants extension included")
\n
18 #endif
\n
19 
\n
20 namespace glm
\n
21 {
\n
24 
\n
26  template<typename genType>
\n
27  GLM_FUNC_DECL GLM_CONSTEXPR genType epsilon();
\n
28 
\n
30  template<typename genType>
\n
31  GLM_FUNC_DECL GLM_CONSTEXPR genType pi();
\n
32 
\n
34 } //namespace glm
\n
35 
\n
36 #include "scalar_constants.inl"
\n
glm::pi
GLM_FUNC_DECL GLM_CONSTEXPR genType pi()
Return the pi constant for floating point types.
\n
glm::epsilon
GLM_FUNC_DECL GLM_CONSTEXPR genType epsilon()
Return the epsilon constant for floating point types.
\n
glm
Definition: common.hpp:20
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scalar_int_sized.hpp File Reference
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GLM_EXT_scalar_int_sized \nMore...

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Go to the source code of this file.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

\nTypedefs

\ntypedef detail::int16 int16
 16 bit signed integer type.
 
\ntypedef detail::int32 int32
 32 bit signed integer type.
 
\ntypedef detail::int64 int64
 64 bit signed integer type.
 
\ntypedef detail::int8 int8
 8 bit signed integer type.
 
\n

Detailed Description

\n

GLM_EXT_scalar_int_sized

\n\n

Definition in file scalar_int_sized.hpp.

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scalar_int_sized.hpp
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1 
\n
13 #pragma once
\n
14 
\n
15 #include "../detail/setup.hpp"
\n
16 
\n
17 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
18 # pragma message("GLM: GLM_EXT_scalar_int_sized extension included")
\n
19 #endif
\n
20 
\n
21 namespace glm{
\n
22 namespace detail
\n
23 {
\n
24 # if GLM_HAS_EXTENDED_INTEGER_TYPE
\n
25  typedef std::int8_t int8;
\n
26  typedef std::int16_t int16;
\n
27  typedef std::int32_t int32;
\n
28 # else
\n
29  typedef signed char int8;
\n
30  typedef signed short int16;
\n
31  typedef signed int int32;
\n
32 #endif//
\n
33 
\n
34  template<>
\n
35  struct is_int<int8>
\n
36  {
\n
37  enum test {value = ~0};
\n
38  };
\n
39 
\n
40  template<>
\n
41  struct is_int<int16>
\n
42  {
\n
43  enum test {value = ~0};
\n
44  };
\n
45 
\n
46  template<>
\n
47  struct is_int<int64>
\n
48  {
\n
49  enum test {value = ~0};
\n
50  };
\n
51 }//namespace detail
\n
52 
\n
53 
\n
56 
\n
58  typedef detail::int8 int8;
\n
59 
\n
61  typedef detail::int16 int16;
\n
62 
\n
64  typedef detail::int32 int32;
\n
65 
\n
67  typedef detail::int64 int64;
\n
68 
\n
70 }//namespace glm
\n
glm::int8_t
int8 int8_t
8 bit signed integer type.
Definition: fwd.hpp:43
\n
glm::int8
detail::int8 int8
8 bit signed integer type.
Definition: scalar_int_sized.hpp:58
\n
glm::int16_t
int16 int16_t
16 bit signed integer type.
Definition: fwd.hpp:57
\n
glm::int32_t
int32 int32_t
32 bit signed integer type.
Definition: fwd.hpp:71
\n
glm::int64
detail::int64 int64
64 bit signed integer type.
Definition: scalar_int_sized.hpp:67
\n
glm::int32
detail::int32 int32
32 bit signed integer type.
Definition: scalar_int_sized.hpp:64
\n
glm::int16
detail::int16 int16
16 bit signed integer type.
Definition: scalar_int_sized.hpp:61
\n
glm
Definition: common.hpp:20
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GLM_EXT_scalar_integer \nMore...

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\nFunctions

template<typename genIUType >
GLM_FUNC_DECL int findNSB (genIUType x, int significantBitCount)
 Returns the bit number of the Nth significant bit set to 1 in the binary representation of value. More...
 
template<typename genIUType >
GLM_FUNC_DECL bool isMultiple (genIUType v, genIUType Multiple)
 Return true if the 'Value' is a multiple of 'Multiple'. More...
 
template<typename genIUType >
GLM_FUNC_DECL bool isPowerOfTwo (genIUType v)
 Return true if the value is a power of two number. More...
 
template<typename genIUType >
GLM_FUNC_DECL genIUType nextMultiple (genIUType v, genIUType Multiple)
 Higher multiple number of Source. More...
 
template<typename genIUType >
GLM_FUNC_DECL genIUType nextPowerOfTwo (genIUType v)
 Return the power of two number which value is just higher the input value, round up to a power of two. More...
 
template<typename genIUType >
GLM_FUNC_DECL genIUType prevMultiple (genIUType v, genIUType Multiple)
 Lower multiple number of Source. More...
 
template<typename genIUType >
GLM_FUNC_DECL genIUType prevPowerOfTwo (genIUType v)
 Return the power of two number which value is just lower the input value, round down to a power of two. More...
 
\n

Detailed Description

\n

GLM_EXT_scalar_integer

\n
See also
Core features (dependence)
\n\n

Definition in file scalar_integer.hpp.

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scalar_integer.hpp
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1 
\n
11 #pragma once
\n
12 
\n
13 // Dependencies
\n
14 #include "../detail/setup.hpp"
\n
15 #include "../detail/qualifier.hpp"
\n
16 #include "../detail/_vectorize.hpp"
\n
17 #include "../detail/type_float.hpp"
\n
18 #include "../vector_relational.hpp"
\n
19 #include "../common.hpp"
\n
20 #include <limits>
\n
21 
\n
22 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
23 # pragma message("GLM: GLM_EXT_scalar_integer extension included")
\n
24 #endif
\n
25 
\n
26 namespace glm
\n
27 {
\n
30 
\n
34  template<typename genIUType>
\n
35  GLM_FUNC_DECL bool isPowerOfTwo(genIUType v);
\n
36 
\n
41  template<typename genIUType>
\n
42  GLM_FUNC_DECL genIUType nextPowerOfTwo(genIUType v);
\n
43 
\n
48  template<typename genIUType>
\n
49  GLM_FUNC_DECL genIUType prevPowerOfTwo(genIUType v);
\n
50 
\n
54  template<typename genIUType>
\n
55  GLM_FUNC_DECL bool isMultiple(genIUType v, genIUType Multiple);
\n
56 
\n
65  template<typename genIUType>
\n
66  GLM_FUNC_DECL genIUType nextMultiple(genIUType v, genIUType Multiple);
\n
67 
\n
76  template<typename genIUType>
\n
77  GLM_FUNC_DECL genIUType prevMultiple(genIUType v, genIUType Multiple);
\n
78 
\n
86  template<typename genIUType>
\n
87  GLM_FUNC_DECL int findNSB(genIUType x, int significantBitCount);
\n
88 
\n
90 } //namespace glm
\n
91 
\n
92 #include "scalar_integer.inl"
\n
glm::prevPowerOfTwo
GLM_FUNC_DECL genIUType prevPowerOfTwo(genIUType v)
Return the power of two number which value is just lower the input value, round down to a power of tw...
\n
glm::prevMultiple
GLM_FUNC_DECL genIUType prevMultiple(genIUType v, genIUType Multiple)
Lower multiple number of Source.
\n
glm::isMultiple
GLM_FUNC_DECL bool isMultiple(genIUType v, genIUType Multiple)
Return true if the 'Value' is a multiple of 'Multiple'.
\n
glm::findNSB
GLM_FUNC_DECL int findNSB(genIUType x, int significantBitCount)
Returns the bit number of the Nth significant bit set to 1 in the binary representation of value...
\n
glm::nextMultiple
GLM_FUNC_DECL genIUType nextMultiple(genIUType v, genIUType Multiple)
Higher multiple number of Source.
\n
glm::isPowerOfTwo
GLM_FUNC_DECL bool isPowerOfTwo(genIUType v)
Return true if the value is a power of two number.
\n
glm::nextPowerOfTwo
GLM_FUNC_DECL genIUType nextPowerOfTwo(genIUType v)
Return the power of two number which value is just higher the input value, round up to a power of two...
\n
glm
Definition: common.hpp:20
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scalar_multiplication.hpp File Reference
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Experimental extensions \nMore...

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Detailed Description

\n

Experimental extensions

\n
Author
Joshua Moerman
\n

Include <glm/gtx/scalar_multiplication.hpp> to use the features of this extension.

\n

Enables scalar multiplication for all types

\n

Since GLSL is very strict about types, the following (often used) combinations do not work: double * vec4 int * vec4 vec4 / int So we'll fix that! Of course \"float * vec4\" should remain the same (hence the enable_if magic)

\n\n

Definition in file scalar_multiplication.hpp.

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scalar_multiplication.hpp
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1 
\n
15 #pragma once
\n
16 
\n
17 #include "../detail/setup.hpp"
\n
18 
\n
19 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
20 # ifndef GLM_ENABLE_EXPERIMENTAL
\n
21 # pragma message("GLM: GLM_GTX_scalar_multiplication is an experimental extension and may change in the future. Use #define GLM_ENABLE_EXPERIMENTAL before including it, if you really want to use it.")
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22 # else
\n
23 # pragma message("GLM: GLM_GTX_scalar_multiplication extension included")
\n
24 # endif
\n
25 #endif
\n
26 
\n
27 #include "../vec2.hpp"
\n
28 #include "../vec3.hpp"
\n
29 #include "../vec4.hpp"
\n
30 #include "../mat2x2.hpp"
\n
31 #include <type_traits>
\n
32 
\n
33 namespace glm
\n
34 {
\n
35  template<typename T, typename Vec>
\n
36  using return_type_scalar_multiplication = typename std::enable_if<
\n
37  !std::is_same<T, float>::value // T may not be a float
\n
38  && std::is_arithmetic<T>::value, Vec // But it may be an int or double (no vec3 or mat3, ...)
\n
39  >::type;
\n
40 
\n
41 #define GLM_IMPLEMENT_SCAL_MULT(Vec) \\
\n
42  template<typename T> \\
\n
43  return_type_scalar_multiplication<T, Vec> \\
\n
44  operator*(T const& s, Vec rh){ \\
\n
45  return rh *= static_cast<float>(s); \\
\n
46  } \\
\n
47  \\
\n
48  template<typename T> \\
\n
49  return_type_scalar_multiplication<T, Vec> \\
\n
50  operator*(Vec lh, T const& s){ \\
\n
51  return lh *= static_cast<float>(s); \\
\n
52  } \\
\n
53  \\
\n
54  template<typename T> \\
\n
55  return_type_scalar_multiplication<T, Vec> \\
\n
56  operator/(Vec lh, T const& s){ \\
\n
57  return lh *= 1.0f / s; \\
\n
58  }
\n
59 
\n
60 GLM_IMPLEMENT_SCAL_MULT(vec2)
\n
61 GLM_IMPLEMENT_SCAL_MULT(vec3)
\n
62 GLM_IMPLEMENT_SCAL_MULT(vec4)
\n
63 
\n
64 GLM_IMPLEMENT_SCAL_MULT(mat2)
\n
65 GLM_IMPLEMENT_SCAL_MULT(mat2x3)
\n
66 GLM_IMPLEMENT_SCAL_MULT(mat2x4)
\n
67 GLM_IMPLEMENT_SCAL_MULT(mat3x2)
\n
68 GLM_IMPLEMENT_SCAL_MULT(mat3)
\n
69 GLM_IMPLEMENT_SCAL_MULT(mat3x4)
\n
70 GLM_IMPLEMENT_SCAL_MULT(mat4x2)
\n
71 GLM_IMPLEMENT_SCAL_MULT(mat4x3)
\n
72 GLM_IMPLEMENT_SCAL_MULT(mat4)
\n
73 
\n
74 #undef GLM_IMPLEMENT_SCAL_MULT
\n
75 } // namespace glm
\n
glm::vec2
vec< 2, float, defaultp > vec2
2 components vector of single-precision floating-point numbers.
Definition: vector_float2.hpp:15
\n
glm::mat2x4
mat< 2, 4, float, defaultp > mat2x4
2 columns of 4 components matrix of single-precision floating-point numbers.
Definition: matrix_float2x4.hpp:15
\n
glm::mat3x2
mat< 3, 2, float, defaultp > mat3x2
3 columns of 2 components matrix of single-precision floating-point numbers.
Definition: matrix_float3x2.hpp:15
\n
glm::mat3x4
mat< 3, 4, float, defaultp > mat3x4
3 columns of 4 components matrix of single-precision floating-point numbers.
Definition: matrix_float3x4.hpp:15
\n
glm::mat4x3
mat< 4, 3, float, defaultp > mat4x3
4 columns of 3 components matrix of single-precision floating-point numbers.
Definition: matrix_float4x3.hpp:15
\n
glm::mat4x2
mat< 4, 2, float, defaultp > mat4x2
4 columns of 2 components matrix of single-precision floating-point numbers.
Definition: matrix_float4x2.hpp:15
\n
glm::vec4
vec< 4, float, defaultp > vec4
4 components vector of single-precision floating-point numbers.
Definition: vector_float4.hpp:15
\n
glm::mat4
mat< 4, 4, float, defaultp > mat4
4 columns of 4 components matrix of single-precision floating-point numbers.
Definition: matrix_float4x4.hpp:20
\n
glm::vec3
vec< 3, float, defaultp > vec3
3 components vector of single-precision floating-point numbers.
Definition: vector_float3.hpp:15
\n
glm::mat2x3
mat< 2, 3, float, defaultp > mat2x3
2 columns of 3 components matrix of single-precision floating-point numbers.
Definition: matrix_float2x3.hpp:15
\n
glm::mat2
mat< 2, 2, float, defaultp > mat2
2 columns of 2 components matrix of single-precision floating-point numbers.
Definition: matrix_float2x2.hpp:20
\n
glm::mat3
mat< 3, 3, float, defaultp > mat3
3 columns of 3 components matrix of single-precision floating-point numbers.
Definition: matrix_float3x3.hpp:20
\n
glm
Definition: common.hpp:20
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ext/scalar_relational.hpp File Reference
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GLM_EXT_scalar_relational \nMore...

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\nFunctions

template<typename genType >
GLM_FUNC_DECL GLM_CONSTEXPR bool equal (genType const &x, genType const &y, genType const &epsilon)
 Returns the component-wise comparison of |x - y| < epsilon. More...
 
template<typename genType >
GLM_FUNC_DECL GLM_CONSTEXPR bool equal (genType const &x, genType const &y, int ULPs)
 Returns the component-wise comparison between two scalars in term of ULPs. More...
 
template<typename genType >
GLM_FUNC_DECL GLM_CONSTEXPR bool notEqual (genType const &x, genType const &y, genType const &epsilon)
 Returns the component-wise comparison of |x - y| >= epsilon. More...
 
template<typename genType >
GLM_FUNC_DECL GLM_CONSTEXPR bool notEqual (genType const &x, genType const &y, int ULPs)
 Returns the component-wise comparison between two scalars in term of ULPs. More...
 
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Detailed Description

\n

GLM_EXT_scalar_relational

\n\n

Definition in file ext/scalar_relational.hpp.

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ext/scalar_relational.hpp
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1 
\n
15 #pragma once
\n
16 
\n
17 // Dependencies
\n
18 #include "../detail/qualifier.hpp"
\n
19 
\n
20 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
21 # pragma message("GLM: GLM_EXT_scalar_relational extension included")
\n
22 #endif
\n
23 
\n
24 namespace glm
\n
25 {
\n
30  template<typename genType>
\n
31  GLM_FUNC_DECL GLM_CONSTEXPR bool equal(genType const& x, genType const& y, genType const& epsilon);
\n
32 
\n
37  template<typename genType>
\n
38  GLM_FUNC_DECL GLM_CONSTEXPR bool notEqual(genType const& x, genType const& y, genType const& epsilon);
\n
39 
\n
48  template<typename genType>
\n
49  GLM_FUNC_DECL GLM_CONSTEXPR bool equal(genType const& x, genType const& y, int ULPs);
\n
50 
\n
59  template<typename genType>
\n
60  GLM_FUNC_DECL GLM_CONSTEXPR bool notEqual(genType const& x, genType const& y, int ULPs);
\n
61 
\n
63 }//namespace glm
\n
64 
\n
65 #include "scalar_relational.inl"
\n
glm::notEqual
GLM_FUNC_DECL GLM_CONSTEXPR vec< C, bool, Q > notEqual(mat< C, R, T, Q > const &x, mat< C, R, T, Q > const &y)
Perform a component-wise not-equal-to comparison of two matrices.
\n
glm::equal
GLM_FUNC_DECL GLM_CONSTEXPR vec< C, bool, Q > equal(mat< C, R, T, Q > const &x, mat< C, R, T, Q > const &y)
Perform a component-wise equal-to comparison of two matrices.
\n
glm::epsilon
GLM_FUNC_DECL GLM_CONSTEXPR genType epsilon()
Return the epsilon constant for floating point types.
\n
glm
Definition: common.hpp:20
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GLM_GTX_scalar_relational \nMore...

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GLM_GTX_scalar_relational

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See also
Core features (dependence)
\n\n

Definition in file gtx/scalar_relational.hpp.

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1 
\n
13 #pragma once
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14 
\n
15 // Dependency:
\n
16 #include "../glm.hpp"
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17 
\n
18 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
19 # ifndef GLM_ENABLE_EXPERIMENTAL
\n
20 # pragma message("GLM: GLM_GTX_extend is an experimental extension and may change in the future. Use #define GLM_ENABLE_EXPERIMENTAL before including it, if you really want to use it.")
\n
21 # else
\n
22 # pragma message("GLM: GLM_GTX_extend extension included")
\n
23 # endif
\n
24 #endif
\n
25 
\n
26 namespace glm
\n
27 {
\n
30 
\n
31 
\n
32 
\n
34 }//namespace glm
\n
35 
\n
36 #include "scalar_relational.inl"
\n
glm
Definition: common.hpp:20
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scalar_uint_sized.hpp File Reference
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\nTypedefs

\ntypedef detail::uint16 uint16
 16 bit unsigned integer type.
 
\ntypedef detail::uint32 uint32
 32 bit unsigned integer type.
 
\ntypedef detail::uint64 uint64
 64 bit unsigned integer type.
 
\ntypedef detail::uint8 uint8
 8 bit unsigned integer type.
 
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Detailed Description

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GLM_EXT_scalar_uint_sized

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Definition in file scalar_uint_sized.hpp.

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1 
\n
13 #pragma once
\n
14 
\n
15 #include "../detail/setup.hpp"
\n
16 
\n
17 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
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18 # pragma message("GLM: GLM_EXT_scalar_uint_sized extension included")
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19 #endif
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20 
\n
21 namespace glm{
\n
22 namespace detail
\n
23 {
\n
24 # if GLM_HAS_EXTENDED_INTEGER_TYPE
\n
25  typedef std::uint8_t uint8;
\n
26  typedef std::uint16_t uint16;
\n
27  typedef std::uint32_t uint32;
\n
28 # else
\n
29  typedef unsigned char uint8;
\n
30  typedef unsigned short uint16;
\n
31  typedef unsigned int uint32;
\n
32 #endif
\n
33 
\n
34  template<>
\n
35  struct is_int<uint8>
\n
36  {
\n
37  enum test {value = ~0};
\n
38  };
\n
39 
\n
40  template<>
\n
41  struct is_int<uint16>
\n
42  {
\n
43  enum test {value = ~0};
\n
44  };
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45 
\n
46  template<>
\n
47  struct is_int<uint64>
\n
48  {
\n
49  enum test {value = ~0};
\n
50  };
\n
51 }//namespace detail
\n
52 
\n
53 
\n
56 
\n
58  typedef detail::uint8 uint8;
\n
59 
\n
61  typedef detail::uint16 uint16;
\n
62 
\n
64  typedef detail::uint32 uint32;
\n
65 
\n
67  typedef detail::uint64 uint64;
\n
68 
\n
70 }//namespace glm
\n
glm::uint32
detail::uint32 uint32
32 bit unsigned integer type.
Definition: scalar_uint_sized.hpp:64
\n
glm::uint32_t
uint32 uint32_t
Default qualifier 32 bit unsigned integer type.
Definition: fwd.hpp:129
\n
glm::uint16
detail::uint16 uint16
16 bit unsigned integer type.
Definition: scalar_uint_sized.hpp:61
\n
glm::uint16_t
uint16 uint16_t
Default qualifier 16 bit unsigned integer type.
Definition: fwd.hpp:115
\n
glm::uint8_t
uint8 uint8_t
Default qualifier 8 bit unsigned integer type.
Definition: fwd.hpp:101
\n
glm::uint64
detail::uint64 uint64
64 bit unsigned integer type.
Definition: scalar_uint_sized.hpp:67
\n
glm::uint8
detail::uint8 uint8
8 bit unsigned integer type.
Definition: scalar_uint_sized.hpp:58
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glm
Definition: common.hpp:20
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GLM_EXT_scalar_ulp \nMore...

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\nFunctions

GLM_FUNC_DECL int floatDistance (float x, float y)
 Return the distance in the number of ULP between 2 single-precision floating-point scalars. More...
 
GLM_FUNC_DECL int64 floatDistance (double x, double y)
 Return the distance in the number of ULP between 2 double-precision floating-point scalars. More...
 
template<typename genType >
GLM_FUNC_DECL genType nextFloat (genType x)
 Return the next ULP value(s) after the input value(s). More...
 
template<typename genType >
GLM_FUNC_DECL genType nextFloat (genType x, int ULPs)
 Return the value(s) ULP distance after the input value(s). More...
 
template<typename genType >
GLM_FUNC_DECL genType prevFloat (genType x)
 Return the previous ULP value(s) before the input value(s). More...
 
template<typename genType >
GLM_FUNC_DECL genType prevFloat (genType x, int ULPs)
 Return the value(s) ULP distance before the input value(s). More...
 
\n

Detailed Description

\n

GLM_EXT_scalar_ulp

\n\n

Definition in file scalar_ulp.hpp.

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scalar_ulp.hpp
\n
\n
\nGo to the documentation of this file.
1 
\n
16 #pragma once
\n
17 
\n
18 // Dependencies
\n
19 #include "../ext/scalar_int_sized.hpp"
\n
20 #include "../common.hpp"
\n
21 #include "../detail/qualifier.hpp"
\n
22 
\n
23 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
24 # pragma message("GLM: GLM_EXT_scalar_ulp extension included")
\n
25 #endif
\n
26 
\n
27 namespace glm
\n
28 {
\n
34  template<typename genType>
\n
35  GLM_FUNC_DECL genType nextFloat(genType x);
\n
36 
\n
42  template<typename genType>
\n
43  GLM_FUNC_DECL genType prevFloat(genType x);
\n
44 
\n
50  template<typename genType>
\n
51  GLM_FUNC_DECL genType nextFloat(genType x, int ULPs);
\n
52 
\n
58  template<typename genType>
\n
59  GLM_FUNC_DECL genType prevFloat(genType x, int ULPs);
\n
60 
\n
64  GLM_FUNC_DECL int floatDistance(float x, float y);
\n
65 
\n
69  GLM_FUNC_DECL int64 floatDistance(double x, double y);
\n
70 
\n
72 }//namespace glm
\n
73 
\n
74 #include "scalar_ulp.inl"
\n
glm::int64
detail::int64 int64
64 bit signed integer type.
Definition: scalar_int_sized.hpp:67
\n
glm
Definition: common.hpp:20
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setup.hpp
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\n
\n
1 #ifndef GLM_SETUP_INCLUDED
\n
2 
\n
3 #include <cassert>
\n
4 #include <cstddef>
\n
5 
\n
6 #define GLM_VERSION_MAJOR 0
\n
7 #define GLM_VERSION_MINOR 9
\n
8 #define GLM_VERSION_PATCH 9
\n
9 #define GLM_VERSION_REVISION 6
\n
10 #define GLM_VERSION 996
\n
11 #define GLM_VERSION_MESSAGE "GLM: version 0.9.9.6"
\n
12 
\n
13 #define GLM_SETUP_INCLUDED GLM_VERSION
\n
14 
\n
16 // Active states
\n
17 
\n
18 #define GLM_DISABLE 0
\n
19 #define GLM_ENABLE 1
\n
20 
\n
22 // Messages
\n
23 
\n
24 #if defined(GLM_FORCE_MESSAGES)
\n
25 # define GLM_MESSAGES GLM_ENABLE
\n
26 #else
\n
27 # define GLM_MESSAGES GLM_DISABLE
\n
28 #endif
\n
29 
\n
31 // Detect the platform
\n
32 
\n
33 #include "../simd/platform.h"
\n
34 
\n
36 // Build model
\n
37 
\n
38 #if defined(__arch64__) || defined(__LP64__) || defined(_M_X64) || defined(__ppc64__) || defined(__x86_64__)
\n
39 # define GLM_MODEL GLM_MODEL_64
\n
40 #elif defined(__i386__) || defined(__ppc__)
\n
41 # define GLM_MODEL GLM_MODEL_32
\n
42 #else
\n
43 # define GLM_MODEL GLM_MODEL_32
\n
44 #endif//
\n
45 
\n
46 #if !defined(GLM_MODEL) && GLM_COMPILER != 0
\n
47 # error "GLM_MODEL undefined, your compiler may not be supported by GLM. Add #define GLM_MODEL 0 to ignore this message."
\n
48 #endif//GLM_MODEL
\n
49 
\n
51 // C++ Version
\n
52 
\n
53 // User defines: GLM_FORCE_CXX98, GLM_FORCE_CXX03, GLM_FORCE_CXX11, GLM_FORCE_CXX14, GLM_FORCE_CXX17, GLM_FORCE_CXX2A
\n
54 
\n
55 #define GLM_LANG_CXX98_FLAG (1 << 1)
\n
56 #define GLM_LANG_CXX03_FLAG (1 << 2)
\n
57 #define GLM_LANG_CXX0X_FLAG (1 << 3)
\n
58 #define GLM_LANG_CXX11_FLAG (1 << 4)
\n
59 #define GLM_LANG_CXX14_FLAG (1 << 5)
\n
60 #define GLM_LANG_CXX17_FLAG (1 << 6)
\n
61 #define GLM_LANG_CXX2A_FLAG (1 << 7)
\n
62 #define GLM_LANG_CXXMS_FLAG (1 << 8)
\n
63 #define GLM_LANG_CXXGNU_FLAG (1 << 9)
\n
64 
\n
65 #define GLM_LANG_CXX98 GLM_LANG_CXX98_FLAG
\n
66 #define GLM_LANG_CXX03 (GLM_LANG_CXX98 | GLM_LANG_CXX03_FLAG)
\n
67 #define GLM_LANG_CXX0X (GLM_LANG_CXX03 | GLM_LANG_CXX0X_FLAG)
\n
68 #define GLM_LANG_CXX11 (GLM_LANG_CXX0X | GLM_LANG_CXX11_FLAG)
\n
69 #define GLM_LANG_CXX14 (GLM_LANG_CXX11 | GLM_LANG_CXX14_FLAG)
\n
70 #define GLM_LANG_CXX17 (GLM_LANG_CXX14 | GLM_LANG_CXX17_FLAG)
\n
71 #define GLM_LANG_CXX2A (GLM_LANG_CXX17 | GLM_LANG_CXX2A_FLAG)
\n
72 #define GLM_LANG_CXXMS GLM_LANG_CXXMS_FLAG
\n
73 #define GLM_LANG_CXXGNU GLM_LANG_CXXGNU_FLAG
\n
74 
\n
75 #if (defined(_MSC_EXTENSIONS))
\n
76 # define GLM_LANG_EXT GLM_LANG_CXXMS_FLAG
\n
77 #elif ((GLM_COMPILER & (GLM_COMPILER_CLANG | GLM_COMPILER_GCC)) && (GLM_ARCH & GLM_ARCH_SIMD_BIT))
\n
78 # define GLM_LANG_EXT GLM_LANG_CXXMS_FLAG
\n
79 #else
\n
80 # define GLM_LANG_EXT 0
\n
81 #endif
\n
82 
\n
83 #if (defined(GLM_FORCE_CXX_UNKNOWN))
\n
84 # define GLM_LANG 0
\n
85 #elif defined(GLM_FORCE_CXX2A)
\n
86 # define GLM_LANG (GLM_LANG_CXX2A | GLM_LANG_EXT)
\n
87 # define GLM_LANG_STL11_FORCED
\n
88 #elif defined(GLM_FORCE_CXX17)
\n
89 # define GLM_LANG (GLM_LANG_CXX17 | GLM_LANG_EXT)
\n
90 # define GLM_LANG_STL11_FORCED
\n
91 #elif defined(GLM_FORCE_CXX14)
\n
92 # define GLM_LANG (GLM_LANG_CXX14 | GLM_LANG_EXT)
\n
93 # define GLM_LANG_STL11_FORCED
\n
94 #elif defined(GLM_FORCE_CXX11)
\n
95 # define GLM_LANG (GLM_LANG_CXX11 | GLM_LANG_EXT)
\n
96 # define GLM_LANG_STL11_FORCED
\n
97 #elif defined(GLM_FORCE_CXX03)
\n
98 # define GLM_LANG (GLM_LANG_CXX03 | GLM_LANG_EXT)
\n
99 #elif defined(GLM_FORCE_CXX98)
\n
100 # define GLM_LANG (GLM_LANG_CXX98 | GLM_LANG_EXT)
\n
101 #else
\n
102 # if GLM_COMPILER & GLM_COMPILER_VC && defined(_MSVC_LANG)
\n
103 # if GLM_COMPILER >= GLM_COMPILER_VC15_7
\n
104 # define GLM_LANG_PLATFORM _MSVC_LANG
\n
105 # elif GLM_COMPILER >= GLM_COMPILER_VC15
\n
106 # if _MSVC_LANG > 201402L
\n
107 # define GLM_LANG_PLATFORM 201402L
\n
108 # else
\n
109 # define GLM_LANG_PLATFORM _MSVC_LANG
\n
110 # endif
\n
111 # else
\n
112 # define GLM_LANG_PLATFORM 0
\n
113 # endif
\n
114 # else
\n
115 # define GLM_LANG_PLATFORM 0
\n
116 # endif
\n
117 
\n
118 # if __cplusplus > 201703L || GLM_LANG_PLATFORM > 201703L
\n
119 # define GLM_LANG (GLM_LANG_CXX2A | GLM_LANG_EXT)
\n
120 # elif __cplusplus == 201703L || GLM_LANG_PLATFORM == 201703L
\n
121 # define GLM_LANG (GLM_LANG_CXX17 | GLM_LANG_EXT)
\n
122 # elif __cplusplus == 201402L || __cplusplus == 201500L || GLM_LANG_PLATFORM == 201402L
\n
123 # define GLM_LANG (GLM_LANG_CXX14 | GLM_LANG_EXT)
\n
124 # elif __cplusplus == 201103L || GLM_LANG_PLATFORM == 201103L
\n
125 # define GLM_LANG (GLM_LANG_CXX11 | GLM_LANG_EXT)
\n
126 # elif defined(__INTEL_CXX11_MODE__) || defined(_MSC_VER) || defined(__GXX_EXPERIMENTAL_CXX0X__)
\n
127 # define GLM_LANG (GLM_LANG_CXX0X | GLM_LANG_EXT)
\n
128 # elif __cplusplus == 199711L
\n
129 # define GLM_LANG (GLM_LANG_CXX98 | GLM_LANG_EXT)
\n
130 # else
\n
131 # define GLM_LANG (0 | GLM_LANG_EXT)
\n
132 # endif
\n
133 #endif
\n
134 
\n
136 // Has of C++ features
\n
137 
\n
138 // http://clang.llvm.org/cxx_status.html
\n
139 // http://gcc.gnu.org/projects/cxx0x.html
\n
140 // http://msdn.microsoft.com/en-us/library/vstudio/hh567368(v=vs.120).aspx
\n
141 
\n
142 // Android has multiple STLs but C++11 STL detection doesn't always work #284 #564
\n
143 #if GLM_PLATFORM == GLM_PLATFORM_ANDROID && !defined(GLM_LANG_STL11_FORCED)
\n
144 # define GLM_HAS_CXX11_STL 0
\n
145 #elif GLM_COMPILER & GLM_COMPILER_CLANG
\n
146 # if (defined(_LIBCPP_VERSION) || (GLM_LANG & GLM_LANG_CXX11_FLAG) || defined(GLM_LANG_STL11_FORCED))
\n
147 # define GLM_HAS_CXX11_STL 1
\n
148 # else
\n
149 # define GLM_HAS_CXX11_STL 0
\n
150 # endif
\n
151 #elif GLM_LANG & GLM_LANG_CXX11_FLAG
\n
152 # define GLM_HAS_CXX11_STL 1
\n
153 #else
\n
154 # define GLM_HAS_CXX11_STL ((GLM_LANG & GLM_LANG_CXX0X_FLAG) && (\\
\n
155  ((GLM_COMPILER & GLM_COMPILER_GCC) && (GLM_COMPILER >= GLM_COMPILER_GCC48)) || \\
\n
156  ((GLM_COMPILER & GLM_COMPILER_VC) && (GLM_COMPILER >= GLM_COMPILER_VC12)) || \\
\n
157  ((GLM_PLATFORM != GLM_PLATFORM_WINDOWS) && (GLM_COMPILER & GLM_COMPILER_INTEL) && (GLM_COMPILER >= GLM_COMPILER_INTEL15))))
\n
158 #endif
\n
159 
\n
160 // N1720
\n
161 #if GLM_COMPILER & GLM_COMPILER_CLANG
\n
162 # define GLM_HAS_STATIC_ASSERT __has_feature(cxx_static_assert)
\n
163 #elif GLM_LANG & GLM_LANG_CXX11_FLAG
\n
164 # define GLM_HAS_STATIC_ASSERT 1
\n
165 #else
\n
166 # define GLM_HAS_STATIC_ASSERT ((GLM_LANG & GLM_LANG_CXX0X_FLAG) && (\\
\n
167  ((GLM_COMPILER & GLM_COMPILER_CUDA)) || \\
\n
168  ((GLM_COMPILER & GLM_COMPILER_VC))))
\n
169 #endif
\n
170 
\n
171 // N1988
\n
172 #if GLM_LANG & GLM_LANG_CXX11_FLAG
\n
173 # define GLM_HAS_EXTENDED_INTEGER_TYPE 1
\n
174 #else
\n
175 # define GLM_HAS_EXTENDED_INTEGER_TYPE (\\
\n
176  ((GLM_LANG & GLM_LANG_CXX0X_FLAG) && (GLM_COMPILER & GLM_COMPILER_VC)) || \\
\n
177  ((GLM_LANG & GLM_LANG_CXX0X_FLAG) && (GLM_COMPILER & GLM_COMPILER_CUDA)) || \\
\n
178  ((GLM_LANG & GLM_LANG_CXX0X_FLAG) && (GLM_COMPILER & GLM_COMPILER_CLANG)))
\n
179 #endif
\n
180 
\n
181 // N2672 Initializer lists http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2008/n2672.htm
\n
182 #if GLM_COMPILER & GLM_COMPILER_CLANG
\n
183 # define GLM_HAS_INITIALIZER_LISTS __has_feature(cxx_generalized_initializers)
\n
184 #elif GLM_LANG & GLM_LANG_CXX11_FLAG
\n
185 # define GLM_HAS_INITIALIZER_LISTS 1
\n
186 #else
\n
187 # define GLM_HAS_INITIALIZER_LISTS ((GLM_LANG & GLM_LANG_CXX0X_FLAG) && (\\
\n
188  ((GLM_COMPILER & GLM_COMPILER_VC) && (GLM_COMPILER >= GLM_COMPILER_VC15)) || \\
\n
189  ((GLM_COMPILER & GLM_COMPILER_INTEL) && (GLM_COMPILER >= GLM_COMPILER_INTEL14)) || \\
\n
190  ((GLM_COMPILER & GLM_COMPILER_CUDA))))
\n
191 #endif
\n
192 
\n
193 // N2544 Unrestricted unions http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2008/n2544.pdf
\n
194 #if GLM_COMPILER & GLM_COMPILER_CLANG
\n
195 # define GLM_HAS_UNRESTRICTED_UNIONS __has_feature(cxx_unrestricted_unions)
\n
196 #elif GLM_LANG & GLM_LANG_CXX11_FLAG
\n
197 # define GLM_HAS_UNRESTRICTED_UNIONS 1
\n
198 #else
\n
199 # define GLM_HAS_UNRESTRICTED_UNIONS (GLM_LANG & GLM_LANG_CXX0X_FLAG) && (\\
\n
200  (GLM_COMPILER & GLM_COMPILER_VC) || \\
\n
201  ((GLM_COMPILER & GLM_COMPILER_CUDA)))
\n
202 #endif
\n
203 
\n
204 // N2346
\n
205 #if GLM_COMPILER & GLM_COMPILER_CLANG
\n
206 # define GLM_HAS_DEFAULTED_FUNCTIONS __has_feature(cxx_defaulted_functions)
\n
207 #elif GLM_LANG & GLM_LANG_CXX11_FLAG
\n
208 # define GLM_HAS_DEFAULTED_FUNCTIONS 1
\n
209 #else
\n
210 # define GLM_HAS_DEFAULTED_FUNCTIONS ((GLM_LANG & GLM_LANG_CXX0X_FLAG) && (\\
\n
211  ((GLM_COMPILER & GLM_COMPILER_VC) && (GLM_COMPILER >= GLM_COMPILER_VC12)) || \\
\n
212  ((GLM_COMPILER & GLM_COMPILER_INTEL)) || \\
\n
213  (GLM_COMPILER & GLM_COMPILER_CUDA)))
\n
214 #endif
\n
215 
\n
216 // N2118
\n
217 #if GLM_COMPILER & GLM_COMPILER_CLANG
\n
218 # define GLM_HAS_RVALUE_REFERENCES __has_feature(cxx_rvalue_references)
\n
219 #elif GLM_LANG & GLM_LANG_CXX11_FLAG
\n
220 # define GLM_HAS_RVALUE_REFERENCES 1
\n
221 #else
\n
222 # define GLM_HAS_RVALUE_REFERENCES ((GLM_LANG & GLM_LANG_CXX0X_FLAG) && (\\
\n
223  ((GLM_COMPILER & GLM_COMPILER_VC)) || \\
\n
224  ((GLM_COMPILER & GLM_COMPILER_CUDA))))
\n
225 #endif
\n
226 
\n
227 // N2437 http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2007/n2437.pdf
\n
228 #if GLM_COMPILER & GLM_COMPILER_CLANG
\n
229 # define GLM_HAS_EXPLICIT_CONVERSION_OPERATORS __has_feature(cxx_explicit_conversions)
\n
230 #elif GLM_LANG & GLM_LANG_CXX11_FLAG
\n
231 # define GLM_HAS_EXPLICIT_CONVERSION_OPERATORS 1
\n
232 #else
\n
233 # define GLM_HAS_EXPLICIT_CONVERSION_OPERATORS ((GLM_LANG & GLM_LANG_CXX0X_FLAG) && (\\
\n
234  ((GLM_COMPILER & GLM_COMPILER_INTEL) && (GLM_COMPILER >= GLM_COMPILER_INTEL14)) || \\
\n
235  ((GLM_COMPILER & GLM_COMPILER_VC) && (GLM_COMPILER >= GLM_COMPILER_VC12)) || \\
\n
236  ((GLM_COMPILER & GLM_COMPILER_CUDA))))
\n
237 #endif
\n
238 
\n
239 // N2258 http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2007/n2258.pdf
\n
240 #if GLM_COMPILER & GLM_COMPILER_CLANG
\n
241 # define GLM_HAS_TEMPLATE_ALIASES __has_feature(cxx_alias_templates)
\n
242 #elif GLM_LANG & GLM_LANG_CXX11_FLAG
\n
243 # define GLM_HAS_TEMPLATE_ALIASES 1
\n
244 #else
\n
245 # define GLM_HAS_TEMPLATE_ALIASES ((GLM_LANG & GLM_LANG_CXX0X_FLAG) && (\\
\n
246  ((GLM_COMPILER & GLM_COMPILER_INTEL)) || \\
\n
247  ((GLM_COMPILER & GLM_COMPILER_VC) && (GLM_COMPILER >= GLM_COMPILER_VC12)) || \\
\n
248  ((GLM_COMPILER & GLM_COMPILER_CUDA))))
\n
249 #endif
\n
250 
\n
251 // N2930 http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2009/n2930.html
\n
252 #if GLM_COMPILER & GLM_COMPILER_CLANG
\n
253 # define GLM_HAS_RANGE_FOR __has_feature(cxx_range_for)
\n
254 #elif GLM_LANG & GLM_LANG_CXX11_FLAG
\n
255 # define GLM_HAS_RANGE_FOR 1
\n
256 #else
\n
257 # define GLM_HAS_RANGE_FOR ((GLM_LANG & GLM_LANG_CXX0X_FLAG) && (\\
\n
258  ((GLM_COMPILER & GLM_COMPILER_INTEL)) || \\
\n
259  ((GLM_COMPILER & GLM_COMPILER_VC)) || \\
\n
260  ((GLM_COMPILER & GLM_COMPILER_CUDA))))
\n
261 #endif
\n
262 
\n
263 // N2341 http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2007/n2341.pdf
\n
264 #if GLM_COMPILER & GLM_COMPILER_CLANG
\n
265 # define GLM_HAS_ALIGNOF __has_feature(cxx_alignas)
\n
266 #elif GLM_LANG & GLM_LANG_CXX11_FLAG
\n
267 # define GLM_HAS_ALIGNOF 1
\n
268 #else
\n
269 # define GLM_HAS_ALIGNOF ((GLM_LANG & GLM_LANG_CXX0X_FLAG) && (\\
\n
270  ((GLM_COMPILER & GLM_COMPILER_INTEL) && (GLM_COMPILER >= GLM_COMPILER_INTEL15)) || \\
\n
271  ((GLM_COMPILER & GLM_COMPILER_VC) && (GLM_COMPILER >= GLM_COMPILER_VC14)) || \\
\n
272  ((GLM_COMPILER & GLM_COMPILER_CUDA))))
\n
273 #endif
\n
274 
\n
275 // N2235 Generalized Constant Expressions http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2007/n2235.pdf
\n
276 // N3652 Extended Constant Expressions http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2013/n3652.html
\n
277 #if (GLM_ARCH & GLM_ARCH_SIMD_BIT) // Compiler SIMD intrinsics don't support constexpr...
\n
278 # define GLM_HAS_CONSTEXPR 0
\n
279 #elif (GLM_COMPILER & GLM_COMPILER_CLANG)
\n
280 # define GLM_HAS_CONSTEXPR __has_feature(cxx_relaxed_constexpr)
\n
281 #elif (GLM_LANG & GLM_LANG_CXX14_FLAG)
\n
282 # define GLM_HAS_CONSTEXPR 1
\n
283 #else
\n
284 # define GLM_HAS_CONSTEXPR ((GLM_LANG & GLM_LANG_CXX0X_FLAG) && GLM_HAS_INITIALIZER_LISTS && (\\
\n
285  ((GLM_COMPILER & GLM_COMPILER_INTEL) && (GLM_COMPILER >= GLM_COMPILER_INTEL17)) || \\
\n
286  ((GLM_COMPILER & GLM_COMPILER_VC) && (GLM_COMPILER >= GLM_COMPILER_VC15))))
\n
287 #endif
\n
288 
\n
289 #if GLM_HAS_CONSTEXPR
\n
290 # define GLM_CONSTEXPR constexpr
\n
291 #else
\n
292 # define GLM_CONSTEXPR
\n
293 #endif
\n
294 
\n
295 //
\n
296 #if GLM_HAS_CONSTEXPR
\n
297 # if (GLM_COMPILER & GLM_COMPILER_CLANG)
\n
298 # if __has_feature(cxx_if_constexpr)
\n
299 # define GLM_HAS_IF_CONSTEXPR 1
\n
300 # else
\n
301 # define GLM_HAS_IF_CONSTEXPR 0
\n
302 # endif
\n
303 # elif (GLM_LANG & GLM_LANG_CXX17_FLAG)
\n
304 # define GLM_HAS_IF_CONSTEXPR 1
\n
305 # else
\n
306 # define GLM_HAS_IF_CONSTEXPR 0
\n
307 # endif
\n
308 #else
\n
309 # define GLM_HAS_IF_CONSTEXPR 0
\n
310 #endif
\n
311 
\n
312 #if GLM_HAS_IF_CONSTEXPR
\n
313 # define GLM_IF_CONSTEXPR if constexpr
\n
314 #else
\n
315 # define GLM_IF_CONSTEXPR if
\n
316 #endif
\n
317 
\n
318 //
\n
319 #if GLM_LANG & GLM_LANG_CXX11_FLAG
\n
320 # define GLM_HAS_ASSIGNABLE 1
\n
321 #else
\n
322 # define GLM_HAS_ASSIGNABLE ((GLM_LANG & GLM_LANG_CXX0X_FLAG) && (\\
\n
323  ((GLM_COMPILER & GLM_COMPILER_VC) && (GLM_COMPILER >= GLM_COMPILER_VC15)) || \\
\n
324  ((GLM_COMPILER & GLM_COMPILER_GCC) && (GLM_COMPILER >= GLM_COMPILER_GCC49))))
\n
325 #endif
\n
326 
\n
327 //
\n
328 #define GLM_HAS_TRIVIAL_QUERIES 0
\n
329 
\n
330 //
\n
331 #if GLM_LANG & GLM_LANG_CXX11_FLAG
\n
332 # define GLM_HAS_MAKE_SIGNED 1
\n
333 #else
\n
334 # define GLM_HAS_MAKE_SIGNED ((GLM_LANG & GLM_LANG_CXX0X_FLAG) && (\\
\n
335  ((GLM_COMPILER & GLM_COMPILER_VC) && (GLM_COMPILER >= GLM_COMPILER_VC12)) || \\
\n
336  ((GLM_COMPILER & GLM_COMPILER_CUDA))))
\n
337 #endif
\n
338 
\n
339 //
\n
340 #if defined(GLM_FORCE_INTRINSICS)
\n
341 # define GLM_HAS_BITSCAN_WINDOWS ((GLM_PLATFORM & GLM_PLATFORM_WINDOWS) && (\\
\n
342  ((GLM_COMPILER & GLM_COMPILER_INTEL)) || \\
\n
343  ((GLM_COMPILER & GLM_COMPILER_VC) && (GLM_COMPILER >= GLM_COMPILER_VC14) && (GLM_ARCH & GLM_ARCH_X86_BIT))))
\n
344 #else
\n
345 # define GLM_HAS_BITSCAN_WINDOWS 0
\n
346 #endif
\n
347 
\n
349 // OpenMP
\n
350 #ifdef _OPENMP
\n
351 # if GLM_COMPILER & GLM_COMPILER_GCC
\n
352 # if GLM_COMPILER >= GLM_COMPILER_GCC61
\n
353 # define GLM_HAS_OPENMP 45
\n
354 # elif GLM_COMPILER >= GLM_COMPILER_GCC49
\n
355 # define GLM_HAS_OPENMP 40
\n
356 # elif GLM_COMPILER >= GLM_COMPILER_GCC47
\n
357 # define GLM_HAS_OPENMP 31
\n
358 # else
\n
359 # define GLM_HAS_OPENMP 0
\n
360 # endif
\n
361 # elif GLM_COMPILER & GLM_COMPILER_CLANG
\n
362 # if GLM_COMPILER >= GLM_COMPILER_CLANG38
\n
363 # define GLM_HAS_OPENMP 31
\n
364 # else
\n
365 # define GLM_HAS_OPENMP 0
\n
366 # endif
\n
367 # elif GLM_COMPILER & GLM_COMPILER_VC
\n
368 # define GLM_HAS_OPENMP 20
\n
369 # elif GLM_COMPILER & GLM_COMPILER_INTEL
\n
370 # if GLM_COMPILER >= GLM_COMPILER_INTEL16
\n
371 # define GLM_HAS_OPENMP 40
\n
372 # else
\n
373 # define GLM_HAS_OPENMP 0
\n
374 # endif
\n
375 # else
\n
376 # define GLM_HAS_OPENMP 0
\n
377 # endif
\n
378 #else
\n
379 # define GLM_HAS_OPENMP 0
\n
380 #endif
\n
381 
\n
383 // nullptr
\n
384 
\n
385 #if GLM_LANG & GLM_LANG_CXX0X_FLAG
\n
386 # define GLM_CONFIG_NULLPTR GLM_ENABLE
\n
387 #else
\n
388 # define GLM_CONFIG_NULLPTR GLM_DISABLE
\n
389 #endif
\n
390 
\n
391 #if GLM_CONFIG_NULLPTR == GLM_ENABLE
\n
392 # define GLM_NULLPTR nullptr
\n
393 #else
\n
394 # define GLM_NULLPTR 0
\n
395 #endif
\n
396 
\n
398 // Static assert
\n
399 
\n
400 #if GLM_HAS_STATIC_ASSERT
\n
401 # define GLM_STATIC_ASSERT(x, message) static_assert(x, message)
\n
402 #elif GLM_COMPILER & GLM_COMPILER_VC
\n
403 # define GLM_STATIC_ASSERT(x, message) typedef char __CASSERT__##__LINE__[(x) ? 1 : -1]
\n
404 #else
\n
405 # define GLM_STATIC_ASSERT(x, message) assert(x)
\n
406 #endif//GLM_LANG
\n
407 
\n
409 // Qualifiers
\n
410 
\n
411 #if GLM_COMPILER & GLM_COMPILER_CUDA
\n
412 # define GLM_CUDA_FUNC_DEF __device__ __host__
\n
413 # define GLM_CUDA_FUNC_DECL __device__ __host__
\n
414 #else
\n
415 # define GLM_CUDA_FUNC_DEF
\n
416 # define GLM_CUDA_FUNC_DECL
\n
417 #endif
\n
418 
\n
419 #if defined(GLM_FORCE_INLINE)
\n
420 # if GLM_COMPILER & GLM_COMPILER_VC
\n
421 # define GLM_INLINE __forceinline
\n
422 # define GLM_NEVER_INLINE __declspec((noinline))
\n
423 # elif GLM_COMPILER & (GLM_COMPILER_GCC | GLM_COMPILER_CLANG)
\n
424 # define GLM_INLINE inline __attribute__((__always_inline__))
\n
425 # define GLM_NEVER_INLINE __attribute__((__noinline__))
\n
426 # elif GLM_COMPILER & GLM_COMPILER_CUDA
\n
427 # define GLM_INLINE __forceinline__
\n
428 # define GLM_NEVER_INLINE __noinline__
\n
429 # else
\n
430 # define GLM_INLINE inline
\n
431 # define GLM_NEVER_INLINE
\n
432 # endif//GLM_COMPILER
\n
433 #else
\n
434 # define GLM_INLINE inline
\n
435 # define GLM_NEVER_INLINE
\n
436 #endif//defined(GLM_FORCE_INLINE)
\n
437 
\n
438 #define GLM_FUNC_DECL GLM_CUDA_FUNC_DECL
\n
439 #define GLM_FUNC_QUALIFIER GLM_CUDA_FUNC_DEF GLM_INLINE
\n
440 
\n
442 // Swizzle operators
\n
443 
\n
444 // User defines: GLM_FORCE_SWIZZLE
\n
445 
\n
446 #define GLM_SWIZZLE_DISABLED 0
\n
447 #define GLM_SWIZZLE_OPERATOR 1
\n
448 #define GLM_SWIZZLE_FUNCTION 2
\n
449 
\n
450 #if defined(GLM_FORCE_XYZW_ONLY)
\n
451 # undef GLM_FORCE_SWIZZLE
\n
452 #endif
\n
453 
\n
454 #if defined(GLM_SWIZZLE)
\n
455 # pragma message("GLM: GLM_SWIZZLE is deprecated, use GLM_FORCE_SWIZZLE instead.")
\n
456 # define GLM_FORCE_SWIZZLE
\n
457 #endif
\n
458 
\n
459 #if defined(GLM_FORCE_SWIZZLE) && (GLM_LANG & GLM_LANG_CXXMS_FLAG)
\n
460 # define GLM_CONFIG_SWIZZLE GLM_SWIZZLE_OPERATOR
\n
461 #elif defined(GLM_FORCE_SWIZZLE)
\n
462 # define GLM_CONFIG_SWIZZLE GLM_SWIZZLE_FUNCTION
\n
463 #else
\n
464 # define GLM_CONFIG_SWIZZLE GLM_SWIZZLE_DISABLED
\n
465 #endif
\n
466 
\n
468 // Allows using not basic types as genType
\n
469 
\n
470 // #define GLM_FORCE_UNRESTRICTED_GENTYPE
\n
471 
\n
472 #ifdef GLM_FORCE_UNRESTRICTED_GENTYPE
\n
473 # define GLM_CONFIG_UNRESTRICTED_GENTYPE GLM_ENABLE
\n
474 #else
\n
475 # define GLM_CONFIG_UNRESTRICTED_GENTYPE GLM_DISABLE
\n
476 #endif
\n
477 
\n
479 // Clip control, define GLM_FORCE_DEPTH_ZERO_TO_ONE before including GLM
\n
480 // to use a clip space between 0 to 1.
\n
481 // Coordinate system, define GLM_FORCE_LEFT_HANDED before including GLM
\n
482 // to use left handed coordinate system by default.
\n
483 
\n
484 #define GLM_CLIP_CONTROL_ZO_BIT (1 << 0) // ZERO_TO_ONE
\n
485 #define GLM_CLIP_CONTROL_NO_BIT (1 << 1) // NEGATIVE_ONE_TO_ONE
\n
486 #define GLM_CLIP_CONTROL_LH_BIT (1 << 2) // LEFT_HANDED, For DirectX, Metal, Vulkan
\n
487 #define GLM_CLIP_CONTROL_RH_BIT (1 << 3) // RIGHT_HANDED, For OpenGL, default in GLM
\n
488 
\n
489 #define GLM_CLIP_CONTROL_LH_ZO (GLM_CLIP_CONTROL_LH_BIT | GLM_CLIP_CONTROL_ZO_BIT)
\n
490 #define GLM_CLIP_CONTROL_LH_NO (GLM_CLIP_CONTROL_LH_BIT | GLM_CLIP_CONTROL_NO_BIT)
\n
491 #define GLM_CLIP_CONTROL_RH_ZO (GLM_CLIP_CONTROL_RH_BIT | GLM_CLIP_CONTROL_ZO_BIT)
\n
492 #define GLM_CLIP_CONTROL_RH_NO (GLM_CLIP_CONTROL_RH_BIT | GLM_CLIP_CONTROL_NO_BIT)
\n
493 
\n
494 #ifdef GLM_FORCE_DEPTH_ZERO_TO_ONE
\n
495 # ifdef GLM_FORCE_LEFT_HANDED
\n
496 # define GLM_CONFIG_CLIP_CONTROL GLM_CLIP_CONTROL_LH_ZO
\n
497 # else
\n
498 # define GLM_CONFIG_CLIP_CONTROL GLM_CLIP_CONTROL_RH_ZO
\n
499 # endif
\n
500 #else
\n
501 # ifdef GLM_FORCE_LEFT_HANDED
\n
502 # define GLM_CONFIG_CLIP_CONTROL GLM_CLIP_CONTROL_LH_NO
\n
503 # else
\n
504 # define GLM_CONFIG_CLIP_CONTROL GLM_CLIP_CONTROL_RH_NO
\n
505 # endif
\n
506 #endif
\n
507 
\n
509 // Qualifiers
\n
510 
\n
511 #if (GLM_COMPILER & GLM_COMPILER_VC) || ((GLM_COMPILER & GLM_COMPILER_INTEL) && (GLM_PLATFORM & GLM_PLATFORM_WINDOWS))
\n
512 # define GLM_DEPRECATED __declspec(deprecated)
\n
513 # define GLM_ALIGNED_TYPEDEF(type, name, alignment) typedef __declspec(align(alignment)) type name
\n
514 #elif GLM_COMPILER & (GLM_COMPILER_GCC | GLM_COMPILER_CLANG | GLM_COMPILER_INTEL)
\n
515 # define GLM_DEPRECATED __attribute__((__deprecated__))
\n
516 # define GLM_ALIGNED_TYPEDEF(type, name, alignment) typedef type name __attribute__((aligned(alignment)))
\n
517 #elif GLM_COMPILER & GLM_COMPILER_CUDA
\n
518 # define GLM_DEPRECATED
\n
519 # define GLM_ALIGNED_TYPEDEF(type, name, alignment) typedef type name __align__(x)
\n
520 #else
\n
521 # define GLM_DEPRECATED
\n
522 # define GLM_ALIGNED_TYPEDEF(type, name, alignment) typedef type name
\n
523 #endif
\n
524 
\n
526 
\n
527 #ifdef GLM_FORCE_EXPLICIT_CTOR
\n
528 # define GLM_EXPLICIT explicit
\n
529 #else
\n
530 # define GLM_EXPLICIT
\n
531 #endif
\n
532 
\n
534 // SYCL
\n
535 
\n
536 #if GLM_COMPILER==GLM_COMPILER_SYCL
\n
537 
\n
538 #include <CL/sycl.hpp>
\n
539 #include <limits>
\n
540 
\n
541 namespace glm {
\n
542 namespace std {
\n
543  // Import SYCL's functions into the namespace glm::std to force their usages.
\n
544  // It's important to use the math built-in function (sin, exp, ...)
\n
545  // of SYCL instead the std ones.
\n
546  using namespace cl::sycl;
\n
547 
\n
549  // Import some "harmless" std's stuffs used by glm into
\n
550  // the new glm::std namespace.
\n
551  template<typename T>
\n
552  using numeric_limits = ::std::numeric_limits<T>;
\n
553 
\n
554  using ::std::size_t;
\n
555 
\n
556  using ::std::uint8_t;
\n
557  using ::std::uint16_t;
\n
558  using ::std::uint32_t;
\n
559  using ::std::uint64_t;
\n
560 
\n
561  using ::std::int8_t;
\n
562  using ::std::int16_t;
\n
563  using ::std::int32_t;
\n
564  using ::std::int64_t;
\n
565 
\n
566  using ::std::make_unsigned;
\n
568 } //namespace std
\n
569 } //namespace glm
\n
570 
\n
571 #endif
\n
572 
\n
574 
\n
576 // Length type: all length functions returns a length_t type.
\n
577 // When GLM_FORCE_SIZE_T_LENGTH is defined, length_t is a typedef of size_t otherwise
\n
578 // length_t is a typedef of int like GLSL defines it.
\n
579 
\n
580 #define GLM_LENGTH_INT 1
\n
581 #define GLM_LENGTH_SIZE_T 2
\n
582 
\n
583 #ifdef GLM_FORCE_SIZE_T_LENGTH
\n
584 # define GLM_CONFIG_LENGTH_TYPE GLM_LENGTH_SIZE_T
\n
585 #else
\n
586 # define GLM_CONFIG_LENGTH_TYPE GLM_LENGTH_INT
\n
587 #endif
\n
588 
\n
589 namespace glm
\n
590 {
\n
591  using std::size_t;
\n
592 # if GLM_CONFIG_LENGTH_TYPE == GLM_LENGTH_SIZE_T
\n
593  typedef size_t length_t;
\n
594 # else
\n
595  typedef int length_t;
\n
596 # endif
\n
597 }//namespace glm
\n
598 
\n
600 // constexpr
\n
601 
\n
602 #if GLM_HAS_CONSTEXPR
\n
603 # define GLM_CONFIG_CONSTEXP GLM_ENABLE
\n
604 
\n
605  namespace glm
\n
606  {
\n
607  template<typename T, std::size_t N>
\n
608  constexpr std::size_t countof(T const (&)[N])
\n
609  {
\n
610  return N;
\n
611  }
\n
612  }//namespace glm
\n
613 # define GLM_COUNTOF(arr) glm::countof(arr)
\n
614 #elif defined(_MSC_VER)
\n
615 # define GLM_CONFIG_CONSTEXP GLM_DISABLE
\n
616 
\n
617 # define GLM_COUNTOF(arr) _countof(arr)
\n
618 #else
\n
619 # define GLM_CONFIG_CONSTEXP GLM_DISABLE
\n
620 
\n
621 # define GLM_COUNTOF(arr) sizeof(arr) / sizeof(arr[0])
\n
622 #endif
\n
623 
\n
625 // uint
\n
626 
\n
627 namespace glm{
\n
628 namespace detail
\n
629 {
\n
630  template<typename T>
\n
631  struct is_int
\n
632  {
\n
633  enum test {value = 0};
\n
634  };
\n
635 
\n
636  template<>
\n
637  struct is_int<unsigned int>
\n
638  {
\n
639  enum test {value = ~0};
\n
640  };
\n
641 
\n
642  template<>
\n
643  struct is_int<signed int>
\n
644  {
\n
645  enum test {value = ~0};
\n
646  };
\n
647 }//namespace detail
\n
648 
\n
649  typedef unsigned int uint;
\n
650 }//namespace glm
\n
651 
\n
653 // 64-bit int
\n
654 
\n
655 #if GLM_HAS_EXTENDED_INTEGER_TYPE
\n
656 # include <cstdint>
\n
657 #endif
\n
658 
\n
659 namespace glm{
\n
660 namespace detail
\n
661 {
\n
662 # if GLM_HAS_EXTENDED_INTEGER_TYPE
\n
663  typedef std::uint64_t uint64;
\n
664  typedef std::int64_t int64;
\n
665 # elif (defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L)) // C99 detected, 64 bit types available
\n
666  typedef uint64_t uint64;
\n
667  typedef int64_t int64;
\n
668 # elif GLM_COMPILER & GLM_COMPILER_VC
\n
669  typedef unsigned __int64 uint64;
\n
670  typedef signed __int64 int64;
\n
671 # elif GLM_COMPILER & GLM_COMPILER_GCC
\n
672 # pragma GCC diagnostic ignored "-Wlong-long"
\n
673  __extension__ typedef unsigned long long uint64;
\n
674  __extension__ typedef signed long long int64;
\n
675 # elif (GLM_COMPILER & GLM_COMPILER_CLANG)
\n
676 # pragma clang diagnostic ignored "-Wc++11-long-long"
\n
677  typedef unsigned long long uint64;
\n
678  typedef signed long long int64;
\n
679 # else//unknown compiler
\n
680  typedef unsigned long long uint64;
\n
681  typedef signed long long int64;
\n
682 # endif
\n
683 }//namespace detail
\n
684 }//namespace glm
\n
685 
\n
687 // make_unsigned
\n
688 
\n
689 #if GLM_HAS_MAKE_SIGNED
\n
690 # include <type_traits>
\n
691 
\n
692 namespace glm{
\n
693 namespace detail
\n
694 {
\n
695  using std::make_unsigned;
\n
696 }//namespace detail
\n
697 }//namespace glm
\n
698 
\n
699 #else
\n
700 
\n
701 namespace glm{
\n
702 namespace detail
\n
703 {
\n
704  template<typename genType>
\n
705  struct make_unsigned
\n
706  {};
\n
707 
\n
708  template<>
\n
709  struct make_unsigned<char>
\n
710  {
\n
711  typedef unsigned char type;
\n
712  };
\n
713 
\n
714  template<>
\n
715  struct make_unsigned<signed char>
\n
716  {
\n
717  typedef unsigned char type;
\n
718  };
\n
719 
\n
720  template<>
\n
721  struct make_unsigned<short>
\n
722  {
\n
723  typedef unsigned short type;
\n
724  };
\n
725 
\n
726  template<>
\n
727  struct make_unsigned<int>
\n
728  {
\n
729  typedef unsigned int type;
\n
730  };
\n
731 
\n
732  template<>
\n
733  struct make_unsigned<long>
\n
734  {
\n
735  typedef unsigned long type;
\n
736  };
\n
737 
\n
738  template<>
\n
739  struct make_unsigned<int64>
\n
740  {
\n
741  typedef uint64 type;
\n
742  };
\n
743 
\n
744  template<>
\n
745  struct make_unsigned<unsigned char>
\n
746  {
\n
747  typedef unsigned char type;
\n
748  };
\n
749 
\n
750  template<>
\n
751  struct make_unsigned<unsigned short>
\n
752  {
\n
753  typedef unsigned short type;
\n
754  };
\n
755 
\n
756  template<>
\n
757  struct make_unsigned<unsigned int>
\n
758  {
\n
759  typedef unsigned int type;
\n
760  };
\n
761 
\n
762  template<>
\n
763  struct make_unsigned<unsigned long>
\n
764  {
\n
765  typedef unsigned long type;
\n
766  };
\n
767 
\n
768  template<>
\n
769  struct make_unsigned<uint64>
\n
770  {
\n
771  typedef uint64 type;
\n
772  };
\n
773 }//namespace detail
\n
774 }//namespace glm
\n
775 #endif
\n
776 
\n
778 // Only use x, y, z, w as vector type components
\n
779 
\n
780 #ifdef GLM_FORCE_XYZW_ONLY
\n
781 # define GLM_CONFIG_XYZW_ONLY GLM_ENABLE
\n
782 #else
\n
783 # define GLM_CONFIG_XYZW_ONLY GLM_DISABLE
\n
784 #endif
\n
785 
\n
787 // Configure the use of defaulted initialized types
\n
788 
\n
789 #define GLM_CTOR_INIT_DISABLE 0
\n
790 #define GLM_CTOR_INITIALIZER_LIST 1
\n
791 #define GLM_CTOR_INITIALISATION 2
\n
792 
\n
793 #if defined(GLM_FORCE_CTOR_INIT) && GLM_HAS_INITIALIZER_LISTS
\n
794 # define GLM_CONFIG_CTOR_INIT GLM_CTOR_INITIALIZER_LIST
\n
795 #elif defined(GLM_FORCE_CTOR_INIT) && !GLM_HAS_INITIALIZER_LISTS
\n
796 # define GLM_CONFIG_CTOR_INIT GLM_CTOR_INITIALISATION
\n
797 #else
\n
798 # define GLM_CONFIG_CTOR_INIT GLM_CTOR_INIT_DISABLE
\n
799 #endif
\n
800 
\n
802 // Use SIMD instruction sets
\n
803 
\n
804 #if GLM_HAS_ALIGNOF && (GLM_LANG & GLM_LANG_CXXMS_FLAG) && (GLM_ARCH & GLM_ARCH_SIMD_BIT)
\n
805 # define GLM_CONFIG_SIMD GLM_ENABLE
\n
806 #else
\n
807 # define GLM_CONFIG_SIMD GLM_DISABLE
\n
808 #endif
\n
809 
\n
811 // Configure the use of defaulted function
\n
812 
\n
813 #if GLM_HAS_DEFAULTED_FUNCTIONS && GLM_CONFIG_CTOR_INIT == GLM_CTOR_INIT_DISABLE
\n
814 # define GLM_CONFIG_DEFAULTED_FUNCTIONS GLM_ENABLE
\n
815 # define GLM_DEFAULT = default
\n
816 #else
\n
817 # define GLM_CONFIG_DEFAULTED_FUNCTIONS GLM_DISABLE
\n
818 # define GLM_DEFAULT
\n
819 #endif
\n
820 
\n
822 // Configure the use of aligned gentypes
\n
823 
\n
824 #ifdef GLM_FORCE_ALIGNED // Legacy define
\n
825 # define GLM_FORCE_DEFAULT_ALIGNED_GENTYPES
\n
826 #endif
\n
827 
\n
828 #ifdef GLM_FORCE_DEFAULT_ALIGNED_GENTYPES
\n
829 # define GLM_FORCE_ALIGNED_GENTYPES
\n
830 #endif
\n
831 
\n
832 #if GLM_HAS_ALIGNOF && (GLM_LANG & GLM_LANG_CXXMS_FLAG) && (defined(GLM_FORCE_ALIGNED_GENTYPES) || (GLM_CONFIG_SIMD == GLM_ENABLE))
\n
833 # define GLM_CONFIG_ALIGNED_GENTYPES GLM_ENABLE
\n
834 #else
\n
835 # define GLM_CONFIG_ALIGNED_GENTYPES GLM_DISABLE
\n
836 #endif
\n
837 
\n
839 // Configure the use of anonymous structure as implementation detail
\n
840 
\n
841 #if ((GLM_CONFIG_SIMD == GLM_ENABLE) || (GLM_CONFIG_SWIZZLE == GLM_SWIZZLE_OPERATOR) || (GLM_CONFIG_ALIGNED_GENTYPES == GLM_ENABLE))
\n
842 # define GLM_CONFIG_ANONYMOUS_STRUCT GLM_ENABLE
\n
843 #else
\n
844 # define GLM_CONFIG_ANONYMOUS_STRUCT GLM_DISABLE
\n
845 #endif
\n
846 
\n
848 // Silent warnings
\n
849 
\n
850 #ifdef GLM_FORCE_SILENT_WARNINGS
\n
851 # define GLM_SILENT_WARNINGS GLM_ENABLE
\n
852 #else
\n
853 # define GLM_SILENT_WARNINGS GLM_DISABLE
\n
854 #endif
\n
855 
\n
857 // Precision
\n
858 
\n
859 #define GLM_HIGHP 1
\n
860 #define GLM_MEDIUMP 2
\n
861 #define GLM_LOWP 3
\n
862 
\n
863 #if defined(GLM_FORCE_PRECISION_HIGHP_BOOL) || defined(GLM_PRECISION_HIGHP_BOOL)
\n
864 # define GLM_CONFIG_PRECISION_BOOL GLM_HIGHP
\n
865 #elif defined(GLM_FORCE_PRECISION_MEDIUMP_BOOL) || defined(GLM_PRECISION_MEDIUMP_BOOL)
\n
866 # define GLM_CONFIG_PRECISION_BOOL GLM_MEDIUMP
\n
867 #elif defined(GLM_FORCE_PRECISION_LOWP_BOOL) || defined(GLM_PRECISION_LOWP_BOOL)
\n
868 # define GLM_CONFIG_PRECISION_BOOL GLM_LOWP
\n
869 #else
\n
870 # define GLM_CONFIG_PRECISION_BOOL GLM_HIGHP
\n
871 #endif
\n
872 
\n
873 #if defined(GLM_FORCE_PRECISION_HIGHP_INT) || defined(GLM_PRECISION_HIGHP_INT)
\n
874 # define GLM_CONFIG_PRECISION_INT GLM_HIGHP
\n
875 #elif defined(GLM_FORCE_PRECISION_MEDIUMP_INT) || defined(GLM_PRECISION_MEDIUMP_INT)
\n
876 # define GLM_CONFIG_PRECISION_INT GLM_MEDIUMP
\n
877 #elif defined(GLM_FORCE_PRECISION_LOWP_INT) || defined(GLM_PRECISION_LOWP_INT)
\n
878 # define GLM_CONFIG_PRECISION_INT GLM_LOWP
\n
879 #else
\n
880 # define GLM_CONFIG_PRECISION_INT GLM_HIGHP
\n
881 #endif
\n
882 
\n
883 #if defined(GLM_FORCE_PRECISION_HIGHP_UINT) || defined(GLM_PRECISION_HIGHP_UINT)
\n
884 # define GLM_CONFIG_PRECISION_UINT GLM_HIGHP
\n
885 #elif defined(GLM_FORCE_PRECISION_MEDIUMP_UINT) || defined(GLM_PRECISION_MEDIUMP_UINT)
\n
886 # define GLM_CONFIG_PRECISION_UINT GLM_MEDIUMP
\n
887 #elif defined(GLM_FORCE_PRECISION_LOWP_UINT) || defined(GLM_PRECISION_LOWP_UINT)
\n
888 # define GLM_CONFIG_PRECISION_UINT GLM_LOWP
\n
889 #else
\n
890 # define GLM_CONFIG_PRECISION_UINT GLM_HIGHP
\n
891 #endif
\n
892 
\n
893 #if defined(GLM_FORCE_PRECISION_HIGHP_FLOAT) || defined(GLM_PRECISION_HIGHP_FLOAT)
\n
894 # define GLM_CONFIG_PRECISION_FLOAT GLM_HIGHP
\n
895 #elif defined(GLM_FORCE_PRECISION_MEDIUMP_FLOAT) || defined(GLM_PRECISION_MEDIUMP_FLOAT)
\n
896 # define GLM_CONFIG_PRECISION_FLOAT GLM_MEDIUMP
\n
897 #elif defined(GLM_FORCE_PRECISION_LOWP_FLOAT) || defined(GLM_PRECISION_LOWP_FLOAT)
\n
898 # define GLM_CONFIG_PRECISION_FLOAT GLM_LOWP
\n
899 #else
\n
900 # define GLM_CONFIG_PRECISION_FLOAT GLM_HIGHP
\n
901 #endif
\n
902 
\n
903 #if defined(GLM_FORCE_PRECISION_HIGHP_DOUBLE) || defined(GLM_PRECISION_HIGHP_DOUBLE)
\n
904 # define GLM_CONFIG_PRECISION_DOUBLE GLM_HIGHP
\n
905 #elif defined(GLM_FORCE_PRECISION_MEDIUMP_DOUBLE) || defined(GLM_PRECISION_MEDIUMP_DOUBLE)
\n
906 # define GLM_CONFIG_PRECISION_DOUBLE GLM_MEDIUMP
\n
907 #elif defined(GLM_FORCE_PRECISION_LOWP_DOUBLE) || defined(GLM_PRECISION_LOWP_DOUBLE)
\n
908 # define GLM_CONFIG_PRECISION_DOUBLE GLM_LOWP
\n
909 #else
\n
910 # define GLM_CONFIG_PRECISION_DOUBLE GLM_HIGHP
\n
911 #endif
\n
912 
\n
914 // Check inclusions of different versions of GLM
\n
915 
\n
916 #elif ((GLM_SETUP_INCLUDED != GLM_VERSION) && !defined(GLM_FORCE_IGNORE_VERSION))
\n
917 # error "GLM error: A different version of GLM is already included. Define GLM_FORCE_IGNORE_VERSION before including GLM headers to ignore this error."
\n
918 #elif GLM_SETUP_INCLUDED == GLM_VERSION
\n
919 
\n
921 // Messages
\n
922 
\n
923 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_MESSAGE_DISPLAYED)
\n
924 # define GLM_MESSAGE_DISPLAYED
\n
925 # define GLM_STR_HELPER(x) #x
\n
926 # define GLM_STR(x) GLM_STR_HELPER(x)
\n
927 
\n
928  // Report GLM version
\n
929 # pragma message (GLM_STR(GLM_VERSION_MESSAGE))
\n
930 
\n
931  // Report C++ language
\n
932 # if (GLM_LANG & GLM_LANG_CXX2A_FLAG) && (GLM_LANG & GLM_LANG_EXT)
\n
933 # pragma message("GLM: C++ 2A with extensions")
\n
934 # elif (GLM_LANG & GLM_LANG_CXX2A_FLAG)
\n
935 # pragma message("GLM: C++ 2A")
\n
936 # elif (GLM_LANG & GLM_LANG_CXX17_FLAG) && (GLM_LANG & GLM_LANG_EXT)
\n
937 # pragma message("GLM: C++ 17 with extensions")
\n
938 # elif (GLM_LANG & GLM_LANG_CXX17_FLAG)
\n
939 # pragma message("GLM: C++ 17")
\n
940 # elif (GLM_LANG & GLM_LANG_CXX14_FLAG) && (GLM_LANG & GLM_LANG_EXT)
\n
941 # pragma message("GLM: C++ 14 with extensions")
\n
942 # elif (GLM_LANG & GLM_LANG_CXX14_FLAG)
\n
943 # pragma message("GLM: C++ 14")
\n
944 # elif (GLM_LANG & GLM_LANG_CXX11_FLAG) && (GLM_LANG & GLM_LANG_EXT)
\n
945 # pragma message("GLM: C++ 11 with extensions")
\n
946 # elif (GLM_LANG & GLM_LANG_CXX11_FLAG)
\n
947 # pragma message("GLM: C++ 11")
\n
948 # elif (GLM_LANG & GLM_LANG_CXX0X_FLAG) && (GLM_LANG & GLM_LANG_EXT)
\n
949 # pragma message("GLM: C++ 0x with extensions")
\n
950 # elif (GLM_LANG & GLM_LANG_CXX0X_FLAG)
\n
951 # pragma message("GLM: C++ 0x")
\n
952 # elif (GLM_LANG & GLM_LANG_CXX03_FLAG) && (GLM_LANG & GLM_LANG_EXT)
\n
953 # pragma message("GLM: C++ 03 with extensions")
\n
954 # elif (GLM_LANG & GLM_LANG_CXX03_FLAG)
\n
955 # pragma message("GLM: C++ 03")
\n
956 # elif (GLM_LANG & GLM_LANG_CXX98_FLAG) && (GLM_LANG & GLM_LANG_EXT)
\n
957 # pragma message("GLM: C++ 98 with extensions")
\n
958 # elif (GLM_LANG & GLM_LANG_CXX98_FLAG)
\n
959 # pragma message("GLM: C++ 98")
\n
960 # else
\n
961 # pragma message("GLM: C++ language undetected")
\n
962 # endif//GLM_LANG
\n
963 
\n
964  // Report compiler detection
\n
965 # if GLM_COMPILER & GLM_COMPILER_CUDA
\n
966 # pragma message("GLM: CUDA compiler detected")
\n
967 # elif GLM_COMPILER & GLM_COMPILER_VC
\n
968 # pragma message("GLM: Visual C++ compiler detected")
\n
969 # elif GLM_COMPILER & GLM_COMPILER_CLANG
\n
970 # pragma message("GLM: Clang compiler detected")
\n
971 # elif GLM_COMPILER & GLM_COMPILER_INTEL
\n
972 # pragma message("GLM: Intel Compiler detected")
\n
973 # elif GLM_COMPILER & GLM_COMPILER_GCC
\n
974 # pragma message("GLM: GCC compiler detected")
\n
975 # else
\n
976 # pragma message("GLM: Compiler not detected")
\n
977 # endif
\n
978 
\n
979  // Report build target
\n
980 # if (GLM_ARCH & GLM_ARCH_AVX2_BIT) && (GLM_MODEL == GLM_MODEL_64)
\n
981 # pragma message("GLM: x86 64 bits with AVX2 instruction set build target")
\n
982 # elif (GLM_ARCH & GLM_ARCH_AVX2_BIT) && (GLM_MODEL == GLM_MODEL_32)
\n
983 # pragma message("GLM: x86 32 bits with AVX2 instruction set build target")
\n
984 
\n
985 # elif (GLM_ARCH & GLM_ARCH_AVX_BIT) && (GLM_MODEL == GLM_MODEL_64)
\n
986 # pragma message("GLM: x86 64 bits with AVX instruction set build target")
\n
987 # elif (GLM_ARCH & GLM_ARCH_AVX_BIT) && (GLM_MODEL == GLM_MODEL_32)
\n
988 # pragma message("GLM: x86 32 bits with AVX instruction set build target")
\n
989 
\n
990 # elif (GLM_ARCH & GLM_ARCH_SSE42_BIT) && (GLM_MODEL == GLM_MODEL_64)
\n
991 # pragma message("GLM: x86 64 bits with SSE4.2 instruction set build target")
\n
992 # elif (GLM_ARCH & GLM_ARCH_SSE42_BIT) && (GLM_MODEL == GLM_MODEL_32)
\n
993 # pragma message("GLM: x86 32 bits with SSE4.2 instruction set build target")
\n
994 
\n
995 # elif (GLM_ARCH & GLM_ARCH_SSE41_BIT) && (GLM_MODEL == GLM_MODEL_64)
\n
996 # pragma message("GLM: x86 64 bits with SSE4.1 instruction set build target")
\n
997 # elif (GLM_ARCH & GLM_ARCH_SSE41_BIT) && (GLM_MODEL == GLM_MODEL_32)
\n
998 # pragma message("GLM: x86 32 bits with SSE4.1 instruction set build target")
\n
999 
\n
1000 # elif (GLM_ARCH & GLM_ARCH_SSSE3_BIT) && (GLM_MODEL == GLM_MODEL_64)
\n
1001 # pragma message("GLM: x86 64 bits with SSSE3 instruction set build target")
\n
1002 # elif (GLM_ARCH & GLM_ARCH_SSSE3_BIT) && (GLM_MODEL == GLM_MODEL_32)
\n
1003 # pragma message("GLM: x86 32 bits with SSSE3 instruction set build target")
\n
1004 
\n
1005 # elif (GLM_ARCH & GLM_ARCH_SSE3_BIT) && (GLM_MODEL == GLM_MODEL_64)
\n
1006 # pragma message("GLM: x86 64 bits with SSE3 instruction set build target")
\n
1007 # elif (GLM_ARCH & GLM_ARCH_SSE3_BIT) && (GLM_MODEL == GLM_MODEL_32)
\n
1008 # pragma message("GLM: x86 32 bits with SSE3 instruction set build target")
\n
1009 
\n
1010 # elif (GLM_ARCH & GLM_ARCH_SSE2_BIT) && (GLM_MODEL == GLM_MODEL_64)
\n
1011 # pragma message("GLM: x86 64 bits with SSE2 instruction set build target")
\n
1012 # elif (GLM_ARCH & GLM_ARCH_SSE2_BIT) && (GLM_MODEL == GLM_MODEL_32)
\n
1013 # pragma message("GLM: x86 32 bits with SSE2 instruction set build target")
\n
1014 
\n
1015 # elif (GLM_ARCH & GLM_ARCH_X86_BIT) && (GLM_MODEL == GLM_MODEL_64)
\n
1016 # pragma message("GLM: x86 64 bits build target")
\n
1017 # elif (GLM_ARCH & GLM_ARCH_X86_BIT) && (GLM_MODEL == GLM_MODEL_32)
\n
1018 # pragma message("GLM: x86 32 bits build target")
\n
1019 
\n
1020 # elif (GLM_ARCH & GLM_ARCH_NEON_BIT) && (GLM_MODEL == GLM_MODEL_64)
\n
1021 # pragma message("GLM: ARM 64 bits with Neon instruction set build target")
\n
1022 # elif (GLM_ARCH & GLM_ARCH_NEON_BIT) && (GLM_MODEL == GLM_MODEL_32)
\n
1023 # pragma message("GLM: ARM 32 bits with Neon instruction set build target")
\n
1024 
\n
1025 # elif (GLM_ARCH & GLM_ARCH_ARM_BIT) && (GLM_MODEL == GLM_MODEL_64)
\n
1026 # pragma message("GLM: ARM 64 bits build target")
\n
1027 # elif (GLM_ARCH & GLM_ARCH_ARM_BIT) && (GLM_MODEL == GLM_MODEL_32)
\n
1028 # pragma message("GLM: ARM 32 bits build target")
\n
1029 
\n
1030 # elif (GLM_ARCH & GLM_ARCH_MIPS_BIT) && (GLM_MODEL == GLM_MODEL_64)
\n
1031 # pragma message("GLM: MIPS 64 bits build target")
\n
1032 # elif (GLM_ARCH & GLM_ARCH_MIPS_BIT) && (GLM_MODEL == GLM_MODEL_32)
\n
1033 # pragma message("GLM: MIPS 32 bits build target")
\n
1034 
\n
1035 # elif (GLM_ARCH & GLM_ARCH_PPC_BIT) && (GLM_MODEL == GLM_MODEL_64)
\n
1036 # pragma message("GLM: PowerPC 64 bits build target")
\n
1037 # elif (GLM_ARCH & GLM_ARCH_PPC_BIT) && (GLM_MODEL == GLM_MODEL_32)
\n
1038 # pragma message("GLM: PowerPC 32 bits build target")
\n
1039 # else
\n
1040 # pragma message("GLM: Unknown build target")
\n
1041 # endif//GLM_ARCH
\n
1042 
\n
1043  // Report platform name
\n
1044 # if(GLM_PLATFORM & GLM_PLATFORM_QNXNTO)
\n
1045 # pragma message("GLM: QNX platform detected")
\n
1046 //# elif(GLM_PLATFORM & GLM_PLATFORM_IOS)
\n
1047 //# pragma message("GLM: iOS platform detected")
\n
1048 # elif(GLM_PLATFORM & GLM_PLATFORM_APPLE)
\n
1049 # pragma message("GLM: Apple platform detected")
\n
1050 # elif(GLM_PLATFORM & GLM_PLATFORM_WINCE)
\n
1051 # pragma message("GLM: WinCE platform detected")
\n
1052 # elif(GLM_PLATFORM & GLM_PLATFORM_WINDOWS)
\n
1053 # pragma message("GLM: Windows platform detected")
\n
1054 # elif(GLM_PLATFORM & GLM_PLATFORM_CHROME_NACL)
\n
1055 # pragma message("GLM: Native Client detected")
\n
1056 # elif(GLM_PLATFORM & GLM_PLATFORM_ANDROID)
\n
1057 # pragma message("GLM: Android platform detected")
\n
1058 # elif(GLM_PLATFORM & GLM_PLATFORM_LINUX)
\n
1059 # pragma message("GLM: Linux platform detected")
\n
1060 # elif(GLM_PLATFORM & GLM_PLATFORM_UNIX)
\n
1061 # pragma message("GLM: UNIX platform detected")
\n
1062 # elif(GLM_PLATFORM & GLM_PLATFORM_UNKNOWN)
\n
1063 # pragma message("GLM: platform unknown")
\n
1064 # else
\n
1065 # pragma message("GLM: platform not detected")
\n
1066 # endif
\n
1067 
\n
1068  // Report whether only xyzw component are used
\n
1069 # if defined GLM_FORCE_XYZW_ONLY
\n
1070 # pragma message("GLM: GLM_FORCE_XYZW_ONLY is defined. Only x, y, z and w component are available in vector type. This define disables swizzle operators and SIMD instruction sets.")
\n
1071 # endif
\n
1072 
\n
1073  // Report swizzle operator support
\n
1074 # if GLM_CONFIG_SWIZZLE == GLM_SWIZZLE_OPERATOR
\n
1075 # pragma message("GLM: GLM_FORCE_SWIZZLE is defined, swizzling operators enabled.")
\n
1076 # elif GLM_CONFIG_SWIZZLE == GLM_SWIZZLE_FUNCTION
\n
1077 # pragma message("GLM: GLM_FORCE_SWIZZLE is defined, swizzling functions enabled. Enable compiler C++ language extensions to enable swizzle operators.")
\n
1078 # else
\n
1079 # pragma message("GLM: GLM_FORCE_SWIZZLE is undefined. swizzling functions or operators are disabled.")
\n
1080 # endif
\n
1081 
\n
1082  // Report .length() type
\n
1083 # if GLM_CONFIG_LENGTH_TYPE == GLM_LENGTH_SIZE_T
\n
1084 # pragma message("GLM: GLM_FORCE_SIZE_T_LENGTH is defined. .length() returns a glm::length_t, a typedef of std::size_t.")
\n
1085 # else
\n
1086 # pragma message("GLM: GLM_FORCE_SIZE_T_LENGTH is undefined. .length() returns a glm::length_t, a typedef of int following GLSL.")
\n
1087 # endif
\n
1088 
\n
1089 # if GLM_CONFIG_UNRESTRICTED_GENTYPE == GLM_ENABLE
\n
1090 # pragma message("GLM: GLM_FORCE_UNRESTRICTED_GENTYPE is defined. Removes GLSL restrictions on valid function genTypes.")
\n
1091 # else
\n
1092 # pragma message("GLM: GLM_FORCE_UNRESTRICTED_GENTYPE is undefined. Follows strictly GLSL on valid function genTypes.")
\n
1093 # endif
\n
1094 
\n
1095 # if GLM_SILENT_WARNINGS == GLM_ENABLE
\n
1096 # pragma message("GLM: GLM_FORCE_SILENT_WARNINGS is defined. Ignores C++ warnings from using C++ language extensions.")
\n
1097 # else
\n
1098 # pragma message("GLM: GLM_FORCE_SILENT_WARNINGS is undefined. Shows C++ warnings from using C++ language extensions.")
\n
1099 # endif
\n
1100 
\n
1101 # ifdef GLM_FORCE_SINGLE_ONLY
\n
1102 # pragma message("GLM: GLM_FORCE_SINGLE_ONLY is defined. Using only single precision floating-point types.")
\n
1103 # endif
\n
1104 
\n
1105 # if defined(GLM_FORCE_ALIGNED_GENTYPES) && (GLM_CONFIG_ALIGNED_GENTYPES == GLM_ENABLE)
\n
1106 # undef GLM_FORCE_ALIGNED_GENTYPES
\n
1107 # pragma message("GLM: GLM_FORCE_ALIGNED_GENTYPES is defined, allowing aligned types. This prevents the use of C++ constexpr.")
\n
1108 # elif defined(GLM_FORCE_ALIGNED_GENTYPES) && (GLM_CONFIG_ALIGNED_GENTYPES == GLM_DISABLE)
\n
1109 # undef GLM_FORCE_ALIGNED_GENTYPES
\n
1110 # pragma message("GLM: GLM_FORCE_ALIGNED_GENTYPES is defined but is disabled. It requires C++11 and language extensions.")
\n
1111 # endif
\n
1112 
\n
1113 # if defined(GLM_FORCE_DEFAULT_ALIGNED_GENTYPES)
\n
1114 # if GLM_CONFIG_ALIGNED_GENTYPES == GLM_DISABLE
\n
1115 # undef GLM_FORCE_DEFAULT_ALIGNED_GENTYPES
\n
1116 # pragma message("GLM: GLM_FORCE_DEFAULT_ALIGNED_GENTYPES is defined but is disabled. It requires C++11 and language extensions.")
\n
1117 # elif GLM_CONFIG_ALIGNED_GENTYPES == GLM_ENABLE
\n
1118 # pragma message("GLM: GLM_FORCE_DEFAULT_ALIGNED_GENTYPES is defined. All gentypes (e.g. vec3) will be aligned and padded by default.")
\n
1119 # endif
\n
1120 # endif
\n
1121 
\n
1122 # if GLM_CONFIG_CLIP_CONTROL & GLM_CLIP_CONTROL_ZO_BIT
\n
1123 # pragma message("GLM: GLM_FORCE_DEPTH_ZERO_TO_ONE is defined. Using zero to one depth clip space.")
\n
1124 # else
\n
1125 # pragma message("GLM: GLM_FORCE_DEPTH_ZERO_TO_ONE is undefined. Using negative one to one depth clip space.")
\n
1126 # endif
\n
1127 
\n
1128 # if GLM_CONFIG_CLIP_CONTROL & GLM_CLIP_CONTROL_LH_BIT
\n
1129 # pragma message("GLM: GLM_FORCE_LEFT_HANDED is defined. Using left handed coordinate system.")
\n
1130 # else
\n
1131 # pragma message("GLM: GLM_FORCE_LEFT_HANDED is undefined. Using right handed coordinate system.")
\n
1132 # endif
\n
1133 #endif//GLM_MESSAGES
\n
1134 
\n
1135 #endif//GLM_SETUP_INCLUDED
\n
glm::int64_t
int64 int64_t
64 bit signed integer type.
Definition: fwd.hpp:85
\n
glm::int8_t
int8 int8_t
8 bit signed integer type.
Definition: fwd.hpp:43
\n
glm::uint32_t
uint32 uint32_t
Default qualifier 32 bit unsigned integer type.
Definition: fwd.hpp:129
\n
glm::uint16_t
uint16 uint16_t
Default qualifier 16 bit unsigned integer type.
Definition: fwd.hpp:115
\n
glm::uint8_t
uint8 uint8_t
Default qualifier 8 bit unsigned integer type.
Definition: fwd.hpp:101
\n
glm::uint64_t
uint64 uint64_t
Default qualifier 64 bit unsigned integer type.
Definition: fwd.hpp:143
\n
glm::int16_t
int16 int16_t
16 bit signed integer type.
Definition: fwd.hpp:57
\n
std
Definition: hash.hpp:49
\n
glm::int32_t
int32 int32_t
32 bit signed integer type.
Definition: fwd.hpp:71
\n
glm::uint64
detail::uint64 uint64
64 bit unsigned integer type.
Definition: scalar_uint_sized.hpp:67
\n
glm::int64
detail::int64 int64
64 bit signed integer type.
Definition: scalar_int_sized.hpp:67
\n
glm
Definition: common.hpp:20
\n
\n\n
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\nFunctions
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spline.hpp File Reference
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GLM_GTX_spline \nMore...

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\nFunctions

template<typename genType >
GLM_FUNC_DECL genType catmullRom (genType const &v1, genType const &v2, genType const &v3, genType const &v4, typename genType::value_type const &s)
 Return a point from a catmull rom curve. More...
 
template<typename genType >
GLM_FUNC_DECL genType cubic (genType const &v1, genType const &v2, genType const &v3, genType const &v4, typename genType::value_type const &s)
 Return a point from a cubic curve. More...
 
template<typename genType >
GLM_FUNC_DECL genType hermite (genType const &v1, genType const &t1, genType const &v2, genType const &t2, typename genType::value_type const &s)
 Return a point from a hermite curve. More...
 
\n

Detailed Description

\n

GLM_GTX_spline

\n
See also
Core features (dependence)
\n\n

Definition in file spline.hpp.

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spline.hpp
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\n
13 #pragma once
\n
14 
\n
15 // Dependency:
\n
16 #include "../glm.hpp"
\n
17 #include "../gtx/optimum_pow.hpp"
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18 
\n
19 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
20 # ifndef GLM_ENABLE_EXPERIMENTAL
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21 # pragma message("GLM: GLM_GTX_spline is an experimental extension and may change in the future. Use #define GLM_ENABLE_EXPERIMENTAL before including it, if you really want to use it.")
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22 # else
\n
23 # pragma message("GLM: GLM_GTX_spline extension included")
\n
24 # endif
\n
25 #endif
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26 
\n
27 namespace glm
\n
28 {
\n
31 
\n
34  template<typename genType>
\n
35  GLM_FUNC_DECL genType catmullRom(
\n
36  genType const& v1,
\n
37  genType const& v2,
\n
38  genType const& v3,
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39  genType const& v4,
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40  typename genType::value_type const& s);
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41 
\n
44  template<typename genType>
\n
45  GLM_FUNC_DECL genType hermite(
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46  genType const& v1,
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47  genType const& t1,
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48  genType const& v2,
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49  genType const& t2,
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50  typename genType::value_type const& s);
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51 
\n
54  template<typename genType>
\n
55  GLM_FUNC_DECL genType cubic(
\n
56  genType const& v1,
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57  genType const& v2,
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58  genType const& v3,
\n
59  genType const& v4,
\n
60  typename genType::value_type const& s);
\n
61 
\n
63 }//namespace glm
\n
64 
\n
65 #include "spline.inl"
\n
glm::hermite
GLM_FUNC_DECL genType hermite(genType const &v1, genType const &t1, genType const &v2, genType const &t2, typename genType::value_type const &s)
Return a point from a hermite curve.
\n
glm::cubic
GLM_FUNC_DECL genType cubic(genType const &v1, genType const &v2, genType const &v3, genType const &v4, typename genType::value_type const &s)
Return a point from a cubic curve.
\n
glm::catmullRom
GLM_FUNC_DECL genType catmullRom(genType const &v1, genType const &v2, genType const &v3, genType const &v4, typename genType::value_type const &s)
Return a point from a catmull rom curve.
\n
glm
Definition: common.hpp:20
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GLM_GTX_std_based_type \nMore...

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\nTypedefs

typedef vec< 1, std::size_t, defaultp > size1
 Vector type based of one std::size_t component. More...
 
typedef vec< 1, std::size_t, defaultp > size1_t
 Vector type based of one std::size_t component. More...
 
typedef vec< 2, std::size_t, defaultp > size2
 Vector type based of two std::size_t components. More...
 
typedef vec< 2, std::size_t, defaultp > size2_t
 Vector type based of two std::size_t components. More...
 
typedef vec< 3, std::size_t, defaultp > size3
 Vector type based of three std::size_t components. More...
 
typedef vec< 3, std::size_t, defaultp > size3_t
 Vector type based of three std::size_t components. More...
 
typedef vec< 4, std::size_t, defaultp > size4
 Vector type based of four std::size_t components. More...
 
typedef vec< 4, std::size_t, defaultp > size4_t
 Vector type based of four std::size_t components. More...
 
\n

Detailed Description

\n

GLM_GTX_std_based_type

\n
See also
Core features (dependence)
\n
\ngtx_extented_min_max (dependence)
\n\n

Definition in file std_based_type.hpp.

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std_based_type.hpp
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1 
\n
14 #pragma once
\n
15 
\n
16 // Dependency:
\n
17 #include "../glm.hpp"
\n
18 #include <cstdlib>
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19 
\n
20 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
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21 # ifndef GLM_ENABLE_EXPERIMENTAL
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22 # pragma message("GLM: GLM_GTX_std_based_type is an experimental extension and may change in the future. Use #define GLM_ENABLE_EXPERIMENTAL before including it, if you really want to use it.")
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23 # else
\n
24 # pragma message("GLM: GLM_GTX_std_based_type extension included")
\n
25 # endif
\n
26 #endif
\n
27 
\n
28 namespace glm
\n
29 {
\n
32 
\n
35  typedef vec<1, std::size_t, defaultp> size1;
\n
36 
\n
39  typedef vec<2, std::size_t, defaultp> size2;
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40 
\n
43  typedef vec<3, std::size_t, defaultp> size3;
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44 
\n
47  typedef vec<4, std::size_t, defaultp> size4;
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48 
\n
51  typedef vec<1, std::size_t, defaultp> size1_t;
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52 
\n
55  typedef vec<2, std::size_t, defaultp> size2_t;
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56 
\n
59  typedef vec<3, std::size_t, defaultp> size3_t;
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60 
\n
63  typedef vec<4, std::size_t, defaultp> size4_t;
\n
64 
\n
66 }//namespace glm
\n
67 
\n
68 #include "std_based_type.inl"
\n
glm::size1
vec< 1, std::size_t, defaultp > size1
Vector type based of one std::size_t component.
Definition: std_based_type.hpp:35
\n
glm::size3_t
vec< 3, std::size_t, defaultp > size3_t
Vector type based of three std::size_t components.
Definition: std_based_type.hpp:59
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glm::size2_t
vec< 2, std::size_t, defaultp > size2_t
Vector type based of two std::size_t components.
Definition: std_based_type.hpp:55
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glm::size4
vec< 4, std::size_t, defaultp > size4
Vector type based of four std::size_t components.
Definition: std_based_type.hpp:47
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glm::size1_t
vec< 1, std::size_t, defaultp > size1_t
Vector type based of one std::size_t component.
Definition: std_based_type.hpp:51
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glm::size3
vec< 3, std::size_t, defaultp > size3
Vector type based of three std::size_t components.
Definition: std_based_type.hpp:43
\n
glm::size2
vec< 2, std::size_t, defaultp > size2
Vector type based of two std::size_t components.
Definition: std_based_type.hpp:39
\n
glm::size4_t
vec< 4, std::size_t, defaultp > size4_t
Vector type based of four std::size_t components.
Definition: std_based_type.hpp:63
\n
glm
Definition: common.hpp:20
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\nFunctions

template<typename genType >
GLM_FUNC_DECL std::string to_string (genType const &x)
 Create a string from a GLM vector or matrix typed variable. More...
 
\n

Detailed Description

\n

GLM_GTX_string_cast

\n
See also
Core features (dependence)
\n
\n to use the features of this extension. \">GLM_GTX_integer (dependence)
\n
\n to use the features of this extension. \">GLM_GTX_quaternion (dependence)
\n\n

Definition in file string_cast.hpp.

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string_cast.hpp
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1 
\n
17 #pragma once
\n
18 
\n
19 // Dependency:
\n
20 #include "../glm.hpp"
\n
21 #include "../gtc/type_precision.hpp"
\n
22 #include "../gtc/quaternion.hpp"
\n
23 #include "../gtx/dual_quaternion.hpp"
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24 #include <string>
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25 #include <cmath>
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26 
\n
27 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
28 # ifndef GLM_ENABLE_EXPERIMENTAL
\n
29 # pragma message("GLM: GLM_GTX_string_cast is an experimental extension and may change in the future. Use #define GLM_ENABLE_EXPERIMENTAL before including it, if you really want to use it.")
\n
30 # else
\n
31 # pragma message("GLM: GLM_GTX_string_cast extension included")
\n
32 # endif
\n
33 #endif
\n
34 
\n
35 #if(GLM_COMPILER & GLM_COMPILER_CUDA)
\n
36 # error "GLM_GTX_string_cast is not supported on CUDA compiler"
\n
37 #endif
\n
38 
\n
39 namespace glm
\n
40 {
\n
43 
\n
46  template<typename genType>
\n
47  GLM_FUNC_DECL std::string to_string(genType const& x);
\n
48 
\n
50 }//namespace glm
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51 
\n
52 #include "string_cast.inl"
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glm::to_string
GLM_FUNC_DECL std::string to_string(genType const &x)
Create a string from a GLM vector or matrix typed variable.
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glm
Definition: common.hpp:20
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template<length_t L, typename T , qualifier Q>
T levels (vec< L, T, Q > const &Extent)
 Compute the number of mipmaps levels necessary to create a mipmap complete texture. More...
 
\n

Detailed Description

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GLM_GTX_texture

\n
See also
Core features (dependence)
\n\n

Definition in file texture.hpp.

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texture.hpp
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\n
13 #pragma once
\n
14 
\n
15 // Dependency:
\n
16 #include "../glm.hpp"
\n
17 #include "../gtc/integer.hpp"
\n
18 #include "../gtx/component_wise.hpp"
\n
19 
\n
20 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
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21 # ifndef GLM_ENABLE_EXPERIMENTAL
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22 # pragma message("GLM: GLM_GTX_texture is an experimental extension and may change in the future. Use #define GLM_ENABLE_EXPERIMENTAL before including it, if you really want to use it.")
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23 # else
\n
24 # pragma message("GLM: GLM_GTX_texture extension included")
\n
25 # endif
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26 #endif
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27 
\n
28 namespace glm
\n
29 {
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32 
\n
39  template <length_t L, typename T, qualifier Q>
\n
40  T levels(vec<L, T, Q> const& Extent);
\n
41 
\n
43 }// namespace glm
\n
44 
\n
45 #include "texture.inl"
\n
46 
\n
glm::levels
T levels(vec< L, T, Q > const &Extent)
Compute the number of mipmaps levels necessary to create a mipmap complete texture.
\n
glm
Definition: common.hpp:20
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\nFunctions

template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 4, 4, T, Q > rotate (T angle, vec< 3, T, Q > const &v)
 Builds a rotation 4 * 4 matrix created from an axis of 3 scalars and an angle expressed in radians. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 4, 4, T, Q > scale (vec< 3, T, Q > const &v)
 Transforms a matrix with a scale 4 * 4 matrix created from a vector of 3 components. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 4, 4, T, Q > translate (vec< 3, T, Q > const &v)
 Transforms a matrix with a translation 4 * 4 matrix created from 3 scalars. More...
 
\n

Detailed Description

\n

GLM_GTX_transform

\n
See also
Core features (dependence)
\n
\n to use the features of this extension. \">GLM_GTC_matrix_transform (dependence)
\n
\n to use the features of this extension. \">GLM_GTX_transform
\n
\n to use the features of this extension. \">GLM_GTX_transform2
\n\n

Definition in file transform.hpp.

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transform.hpp
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1 
\n
16 #pragma once
\n
17 
\n
18 // Dependency:
\n
19 #include "../glm.hpp"
\n
20 #include "../gtc/matrix_transform.hpp"
\n
21 
\n
22 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
23 # ifndef GLM_ENABLE_EXPERIMENTAL
\n
24 # pragma message("GLM: GLM_GTX_transform is an experimental extension and may change in the future. Use #define GLM_ENABLE_EXPERIMENTAL before including it, if you really want to use it.")
\n
25 # else
\n
26 # pragma message("GLM: GLM_GTX_transform extension included")
\n
27 # endif
\n
28 #endif
\n
29 
\n
30 namespace glm
\n
31 {
\n
34 
\n
38  template<typename T, qualifier Q>
\n
39  GLM_FUNC_DECL mat<4, 4, T, Q> translate(
\n
40  vec<3, T, Q> const& v);
\n
41 
\n
45  template<typename T, qualifier Q>
\n
46  GLM_FUNC_DECL mat<4, 4, T, Q> rotate(
\n
47  T angle,
\n
48  vec<3, T, Q> const& v);
\n
49 
\n
53  template<typename T, qualifier Q>
\n
54  GLM_FUNC_DECL mat<4, 4, T, Q> scale(
\n
55  vec<3, T, Q> const& v);
\n
56 
\n
58 }// namespace glm
\n
59 
\n
60 #include "transform.inl"
\n
glm::translate
GLM_FUNC_DECL mat< 4, 4, T, Q > translate(vec< 3, T, Q > const &v)
Transforms a matrix with a translation 4 * 4 matrix created from 3 scalars.
\n
glm::angle
GLM_FUNC_DECL T angle(qua< T, Q > const &x)
Returns the quaternion rotation angle.
\n
glm::scale
GLM_FUNC_DECL mat< 4, 4, T, Q > scale(vec< 3, T, Q > const &v)
Transforms a matrix with a scale 4 * 4 matrix created from a vector of 3 components.
\n
glm::rotate
GLM_FUNC_DECL mat< 4, 4, T, Q > rotate(T angle, vec< 3, T, Q > const &v)
Builds a rotation 4 * 4 matrix created from an axis of 3 scalars and an angle expressed in radians...
\n
glm
Definition: common.hpp:20
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\nFunctions

template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 3, 3, T, Q > proj2D (mat< 3, 3, T, Q > const &m, vec< 3, T, Q > const &normal)
 Build planar projection matrix along normal axis. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 4, 4, T, Q > proj3D (mat< 4, 4, T, Q > const &m, vec< 3, T, Q > const &normal)
 Build planar projection matrix along normal axis. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 4, 4, T, Q > scaleBias (T scale, T bias)
 Build a scale bias matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 4, 4, T, Q > scaleBias (mat< 4, 4, T, Q > const &m, T scale, T bias)
 Build a scale bias matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 3, 3, T, Q > shearX2D (mat< 3, 3, T, Q > const &m, T y)
 Transforms a matrix with a shearing on X axis. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 4, 4, T, Q > shearX3D (mat< 4, 4, T, Q > const &m, T y, T z)
 Transforms a matrix with a shearing on X axis From GLM_GTX_transform2 extension. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 3, 3, T, Q > shearY2D (mat< 3, 3, T, Q > const &m, T x)
 Transforms a matrix with a shearing on Y axis. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 4, 4, T, Q > shearY3D (mat< 4, 4, T, Q > const &m, T x, T z)
 Transforms a matrix with a shearing on Y axis. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 4, 4, T, Q > shearZ3D (mat< 4, 4, T, Q > const &m, T x, T y)
 Transforms a matrix with a shearing on Z axis. More...
 
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Detailed Description

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GLM_GTX_transform2

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See also
Core features (dependence)
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\n to use the features of this extension. \">GLM_GTX_transform (dependence)
\n\n

Definition in file transform2.hpp.

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transform2.hpp
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14 #pragma once
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15 
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16 // Dependency:
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17 #include "../glm.hpp"
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18 #include "../gtx/transform.hpp"
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19 
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20 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
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21 # ifndef GLM_ENABLE_EXPERIMENTAL
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22 # pragma message("GLM: GLM_GTX_transform2 is an experimental extension and may change in the future. Use #define GLM_ENABLE_EXPERIMENTAL before including it, if you really want to use it.")
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23 # else
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24 # pragma message("GLM: GLM_GTX_transform2 extension included")
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25 # endif
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26 #endif
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27 
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28 namespace glm
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29 {
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32 
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35  template<typename T, qualifier Q>
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36  GLM_FUNC_DECL mat<3, 3, T, Q> shearX2D(mat<3, 3, T, Q> const& m, T y);
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37 
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40  template<typename T, qualifier Q>
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41  GLM_FUNC_DECL mat<3, 3, T, Q> shearY2D(mat<3, 3, T, Q> const& m, T x);
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42 
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45  template<typename T, qualifier Q>
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46  GLM_FUNC_DECL mat<4, 4, T, Q> shearX3D(mat<4, 4, T, Q> const& m, T y, T z);
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47 
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50  template<typename T, qualifier Q>
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51  GLM_FUNC_DECL mat<4, 4, T, Q> shearY3D(mat<4, 4, T, Q> const& m, T x, T z);
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52 
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55  template<typename T, qualifier Q>
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56  GLM_FUNC_DECL mat<4, 4, T, Q> shearZ3D(mat<4, 4, T, Q> const& m, T x, T y);
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57 
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58  //template<typename T> GLM_FUNC_QUALIFIER mat<4, 4, T, Q> shear(const mat<4, 4, T, Q> & m, shearPlane, planePoint, angle)
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59  // Identity + tan(angle) * cross(Normal, OnPlaneVector) 0
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60  // - dot(PointOnPlane, normal) * OnPlaneVector 1
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61 
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62  // Reflect functions seem to don't work
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63  //template<typename T> mat<3, 3, T, Q> reflect2D(const mat<3, 3, T, Q> & m, const vec<3, T, Q>& normal){return reflect2DGTX(m, normal);} //!< \\brief Build a reflection matrix (from GLM_GTX_transform2 extension)
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64  //template<typename T> mat<4, 4, T, Q> reflect3D(const mat<4, 4, T, Q> & m, const vec<3, T, Q>& normal){return reflect3DGTX(m, normal);} //!< \\brief Build a reflection matrix (from GLM_GTX_transform2 extension)
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65 
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68  template<typename T, qualifier Q>
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69  GLM_FUNC_DECL mat<3, 3, T, Q> proj2D(mat<3, 3, T, Q> const& m, vec<3, T, Q> const& normal);
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70 
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73  template<typename T, qualifier Q>
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74  GLM_FUNC_DECL mat<4, 4, T, Q> proj3D(mat<4, 4, T, Q> const & m, vec<3, T, Q> const& normal);
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75 
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78  template<typename T, qualifier Q>
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79  GLM_FUNC_DECL mat<4, 4, T, Q> scaleBias(T scale, T bias);
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80 
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83  template<typename T, qualifier Q>
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84  GLM_FUNC_DECL mat<4, 4, T, Q> scaleBias(mat<4, 4, T, Q> const& m, T scale, T bias);
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85 
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87 }// namespace glm
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88 
\n
89 #include "transform2.inl"
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glm::shearX2D
GLM_FUNC_DECL mat< 3, 3, T, Q > shearX2D(mat< 3, 3, T, Q > const &m, T y)
Transforms a matrix with a shearing on X axis.
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glm::shearY2D
GLM_FUNC_DECL mat< 3, 3, T, Q > shearY2D(mat< 3, 3, T, Q > const &m, T x)
Transforms a matrix with a shearing on Y axis.
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glm::proj3D
GLM_FUNC_DECL mat< 4, 4, T, Q > proj3D(mat< 4, 4, T, Q > const &m, vec< 3, T, Q > const &normal)
Build planar projection matrix along normal axis.
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glm::proj2D
GLM_FUNC_DECL mat< 3, 3, T, Q > proj2D(mat< 3, 3, T, Q > const &m, vec< 3, T, Q > const &normal)
Build planar projection matrix along normal axis.
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glm::shearZ3D
GLM_FUNC_DECL mat< 4, 4, T, Q > shearZ3D(mat< 4, 4, T, Q > const &m, T x, T y)
Transforms a matrix with a shearing on Z axis.
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glm::scale
GLM_FUNC_DECL mat< 4, 4, T, Q > scale(mat< 4, 4, T, Q > const &m, vec< 3, T, Q > const &v)
Builds a scale 4 * 4 matrix created from 3 scalars.
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glm::shearY3D
GLM_FUNC_DECL mat< 4, 4, T, Q > shearY3D(mat< 4, 4, T, Q > const &m, T x, T z)
Transforms a matrix with a shearing on Y axis.
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glm::scaleBias
GLM_FUNC_DECL mat< 4, 4, T, Q > scaleBias(mat< 4, 4, T, Q > const &m, T scale, T bias)
Build a scale bias matrix.
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glm::shearX3D
GLM_FUNC_DECL mat< 4, 4, T, Q > shearX3D(mat< 4, 4, T, Q > const &m, T y, T z)
Transforms a matrix with a shearing on X axis From GLM_GTX_transform2 extension.
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glm
Definition: common.hpp:20
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\nFunctions
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trigonometric.hpp File Reference
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Core features \nMore...

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\nFunctions

template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > acos (vec< L, T, Q > const &x)
 Arc cosine. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > acosh (vec< L, T, Q > const &x)
 Arc hyperbolic cosine; returns the non-negative inverse of cosh. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > asin (vec< L, T, Q > const &x)
 Arc sine. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > asinh (vec< L, T, Q > const &x)
 Arc hyperbolic sine; returns the inverse of sinh. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > atan (vec< L, T, Q > const &y, vec< L, T, Q > const &x)
 Arc tangent. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > atan (vec< L, T, Q > const &y_over_x)
 Arc tangent. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > atanh (vec< L, T, Q > const &x)
 Arc hyperbolic tangent; returns the inverse of tanh. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > cos (vec< L, T, Q > const &angle)
 The standard trigonometric cosine function. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > cosh (vec< L, T, Q > const &angle)
 Returns the hyperbolic cosine function, (exp(x) + exp(-x)) / 2. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< L, T, Q > degrees (vec< L, T, Q > const &radians)
 Converts radians to degrees and returns the result. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< L, T, Q > radians (vec< L, T, Q > const &degrees)
 Converts degrees to radians and returns the result. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > sin (vec< L, T, Q > const &angle)
 The standard trigonometric sine function. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > sinh (vec< L, T, Q > const &angle)
 Returns the hyperbolic sine function, (exp(x) - exp(-x)) / 2. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > tan (vec< L, T, Q > const &angle)
 The standard trigonometric tangent function. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > tanh (vec< L, T, Q > const &angle)
 Returns the hyperbolic tangent function, sinh(angle) / cosh(angle) More...
 
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Detailed Description

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Core features

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See also
GLSL 4.20.8 specification, section 8.1 Angle and Trigonometry Functions
\n\n

Definition in file trigonometric.hpp.

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trigonometric.hpp
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19 #pragma once
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20 
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21 #include "detail/setup.hpp"
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22 #include "detail/qualifier.hpp"
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23 
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24 namespace glm
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25 {
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28 
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37  template<length_t L, typename T, qualifier Q>
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38  GLM_FUNC_DECL GLM_CONSTEXPR vec<L, T, Q> radians(vec<L, T, Q> const& degrees);
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39 
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48  template<length_t L, typename T, qualifier Q>
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49  GLM_FUNC_DECL GLM_CONSTEXPR vec<L, T, Q> degrees(vec<L, T, Q> const& radians);
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50 
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60  template<length_t L, typename T, qualifier Q>
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61  GLM_FUNC_DECL vec<L, T, Q> sin(vec<L, T, Q> const& angle);
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62 
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72  template<length_t L, typename T, qualifier Q>
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73  GLM_FUNC_DECL vec<L, T, Q> cos(vec<L, T, Q> const& angle);
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74 
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83  template<length_t L, typename T, qualifier Q>
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84  GLM_FUNC_DECL vec<L, T, Q> tan(vec<L, T, Q> const& angle);
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85 
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96  template<length_t L, typename T, qualifier Q>
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97  GLM_FUNC_DECL vec<L, T, Q> asin(vec<L, T, Q> const& x);
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98 
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109  template<length_t L, typename T, qualifier Q>
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110  GLM_FUNC_DECL vec<L, T, Q> acos(vec<L, T, Q> const& x);
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111 
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124  template<length_t L, typename T, qualifier Q>
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125  GLM_FUNC_DECL vec<L, T, Q> atan(vec<L, T, Q> const& y, vec<L, T, Q> const& x);
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126 
\n
136  template<length_t L, typename T, qualifier Q>
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137  GLM_FUNC_DECL vec<L, T, Q> atan(vec<L, T, Q> const& y_over_x);
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138 
\n
147  template<length_t L, typename T, qualifier Q>
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148  GLM_FUNC_DECL vec<L, T, Q> sinh(vec<L, T, Q> const& angle);
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149 
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158  template<length_t L, typename T, qualifier Q>
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159  GLM_FUNC_DECL vec<L, T, Q> cosh(vec<L, T, Q> const& angle);
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160 
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169  template<length_t L, typename T, qualifier Q>
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170  GLM_FUNC_DECL vec<L, T, Q> tanh(vec<L, T, Q> const& angle);
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171 
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180  template<length_t L, typename T, qualifier Q>
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181  GLM_FUNC_DECL vec<L, T, Q> asinh(vec<L, T, Q> const& x);
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182 
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192  template<length_t L, typename T, qualifier Q>
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193  GLM_FUNC_DECL vec<L, T, Q> acosh(vec<L, T, Q> const& x);
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194 
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204  template<length_t L, typename T, qualifier Q>
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205  GLM_FUNC_DECL vec<L, T, Q> atanh(vec<L, T, Q> const& x);
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206 
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208 }//namespace glm
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209 
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210 #include "detail/func_trigonometric.inl"
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glm::degrees
GLM_FUNC_DECL GLM_CONSTEXPR vec< L, T, Q > degrees(vec< L, T, Q > const &radians)
Converts radians to degrees and returns the result.
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glm::cosh
GLM_FUNC_DECL vec< L, T, Q > cosh(vec< L, T, Q > const &angle)
Returns the hyperbolic cosine function, (exp(x) + exp(-x)) / 2.
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glm::acos
GLM_FUNC_DECL vec< L, T, Q > acos(vec< L, T, Q > const &x)
Arc cosine.
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glm::sin
GLM_FUNC_DECL vec< L, T, Q > sin(vec< L, T, Q > const &angle)
The standard trigonometric sine function.
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glm::radians
GLM_FUNC_DECL GLM_CONSTEXPR vec< L, T, Q > radians(vec< L, T, Q > const &degrees)
Converts degrees to radians and returns the result.
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glm::angle
GLM_FUNC_DECL T angle(qua< T, Q > const &x)
Returns the quaternion rotation angle.
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glm::asin
GLM_FUNC_DECL vec< L, T, Q > asin(vec< L, T, Q > const &x)
Arc sine.
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glm::tanh
GLM_FUNC_DECL vec< L, T, Q > tanh(vec< L, T, Q > const &angle)
Returns the hyperbolic tangent function, sinh(angle) / cosh(angle)
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glm::sinh
GLM_FUNC_DECL vec< L, T, Q > sinh(vec< L, T, Q > const &angle)
Returns the hyperbolic sine function, (exp(x) - exp(-x)) / 2.
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glm::asinh
GLM_FUNC_DECL vec< L, T, Q > asinh(vec< L, T, Q > const &x)
Arc hyperbolic sine; returns the inverse of sinh.
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glm::atanh
GLM_FUNC_DECL vec< L, T, Q > atanh(vec< L, T, Q > const &x)
Arc hyperbolic tangent; returns the inverse of tanh.
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glm::cos
GLM_FUNC_DECL vec< L, T, Q > cos(vec< L, T, Q > const &angle)
The standard trigonometric cosine function.
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glm::atan
GLM_FUNC_DECL vec< L, T, Q > atan(vec< L, T, Q > const &y_over_x)
Arc tangent.
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glm::acosh
GLM_FUNC_DECL vec< L, T, Q > acosh(vec< L, T, Q > const &x)
Arc hyperbolic cosine; returns the non-negative inverse of cosh.
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glm::tan
GLM_FUNC_DECL vec< L, T, Q > tan(vec< L, T, Q > const &angle)
The standard trigonometric tangent function.
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glm
Definition: common.hpp:20
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\nTypedefs

\ntypedef aligned_highp_bvec1 aligned_bvec1
 1 component vector aligned in memory of bool values.
 
\ntypedef aligned_highp_bvec2 aligned_bvec2
 2 components vector aligned in memory of bool values.
 
\ntypedef aligned_highp_bvec3 aligned_bvec3
 3 components vector aligned in memory of bool values.
 
\ntypedef aligned_highp_bvec4 aligned_bvec4
 4 components vector aligned in memory of bool values.
 
\ntypedef aligned_highp_dmat2 aligned_dmat2
 2 by 2 matrix tightly aligned in memory of double-precision floating-point numbers.
 
\ntypedef aligned_highp_dmat2x2 aligned_dmat2x2
 2 by 2 matrix tightly aligned in memory of double-precision floating-point numbers.
 
\ntypedef aligned_highp_dmat2x3 aligned_dmat2x3
 2 by 3 matrix tightly aligned in memory of double-precision floating-point numbers.
 
\ntypedef aligned_highp_dmat2x4 aligned_dmat2x4
 2 by 4 matrix tightly aligned in memory of double-precision floating-point numbers.
 
\ntypedef aligned_highp_dmat3 aligned_dmat3
 3 by 3 matrix tightly aligned in memory of double-precision floating-point numbers.
 
\ntypedef aligned_highp_dmat3x2 aligned_dmat3x2
 3 by 2 matrix tightly aligned in memory of double-precision floating-point numbers.
 
\ntypedef aligned_highp_dmat3x3 aligned_dmat3x3
 3 by 3 matrix tightly aligned in memory of double-precision floating-point numbers.
 
\ntypedef aligned_highp_dmat3x4 aligned_dmat3x4
 3 by 4 matrix tightly aligned in memory of double-precision floating-point numbers.
 
\ntypedef aligned_highp_dmat4 aligned_dmat4
 4 by 4 matrix tightly aligned in memory of double-precision floating-point numbers.
 
\ntypedef aligned_highp_dmat4x2 aligned_dmat4x2
 4 by 2 matrix tightly aligned in memory of double-precision floating-point numbers.
 
\ntypedef aligned_highp_dmat4x3 aligned_dmat4x3
 4 by 3 matrix tightly aligned in memory of double-precision floating-point numbers.
 
\ntypedef aligned_highp_dmat4x4 aligned_dmat4x4
 4 by 4 matrix tightly aligned in memory of double-precision floating-point numbers.
 
\ntypedef aligned_highp_dvec1 aligned_dvec1
 1 component vector aligned in memory of double-precision floating-point numbers.
 
\ntypedef aligned_highp_dvec2 aligned_dvec2
 2 components vector aligned in memory of double-precision floating-point numbers.
 
\ntypedef aligned_highp_dvec3 aligned_dvec3
 3 components vector aligned in memory of double-precision floating-point numbers.
 
\ntypedef aligned_highp_dvec4 aligned_dvec4
 4 components vector aligned in memory of double-precision floating-point numbers.
 
\ntypedef vec< 1, bool, aligned_highp > aligned_highp_bvec1
 1 component vector aligned in memory of bool values.
 
\ntypedef vec< 2, bool, aligned_highp > aligned_highp_bvec2
 2 components vector aligned in memory of bool values.
 
\ntypedef vec< 3, bool, aligned_highp > aligned_highp_bvec3
 3 components vector aligned in memory of bool values.
 
\ntypedef vec< 4, bool, aligned_highp > aligned_highp_bvec4
 4 components vector aligned in memory of bool values.
 
\ntypedef mat< 2, 2, double, aligned_highp > aligned_highp_dmat2
 2 by 2 matrix aligned in memory of double-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 2, 2, double, aligned_highp > aligned_highp_dmat2x2
 2 by 2 matrix aligned in memory of double-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 2, 3, double, aligned_highp > aligned_highp_dmat2x3
 2 by 3 matrix aligned in memory of double-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 2, 4, double, aligned_highp > aligned_highp_dmat2x4
 2 by 4 matrix aligned in memory of double-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 3, double, aligned_highp > aligned_highp_dmat3
 3 by 3 matrix aligned in memory of double-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 2, double, aligned_highp > aligned_highp_dmat3x2
 3 by 2 matrix aligned in memory of double-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 3, double, aligned_highp > aligned_highp_dmat3x3
 3 by 3 matrix aligned in memory of double-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 4, double, aligned_highp > aligned_highp_dmat3x4
 3 by 4 matrix aligned in memory of double-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 4, double, aligned_highp > aligned_highp_dmat4
 4 by 4 matrix aligned in memory of double-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 2, double, aligned_highp > aligned_highp_dmat4x2
 4 by 2 matrix aligned in memory of double-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 3, double, aligned_highp > aligned_highp_dmat4x3
 4 by 3 matrix aligned in memory of double-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 4, double, aligned_highp > aligned_highp_dmat4x4
 4 by 4 matrix aligned in memory of double-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef vec< 1, double, aligned_highp > aligned_highp_dvec1
 1 component vector aligned in memory of double-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef vec< 2, double, aligned_highp > aligned_highp_dvec2
 2 components vector aligned in memory of double-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef vec< 3, double, aligned_highp > aligned_highp_dvec3
 3 components vector aligned in memory of double-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef vec< 4, double, aligned_highp > aligned_highp_dvec4
 4 components vector aligned in memory of double-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef vec< 1, int, aligned_highp > aligned_highp_ivec1
 1 component vector aligned in memory of signed integer numbers.
 
\ntypedef vec< 2, int, aligned_highp > aligned_highp_ivec2
 2 components vector aligned in memory of signed integer numbers.
 
\ntypedef vec< 3, int, aligned_highp > aligned_highp_ivec3
 3 components vector aligned in memory of signed integer numbers.
 
\ntypedef vec< 4, int, aligned_highp > aligned_highp_ivec4
 4 components vector aligned in memory of signed integer numbers.
 
\ntypedef mat< 2, 2, float, aligned_highp > aligned_highp_mat2
 2 by 2 matrix aligned in memory of single-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 2, 2, float, aligned_highp > aligned_highp_mat2x2
 2 by 2 matrix aligned in memory of single-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 2, 3, float, aligned_highp > aligned_highp_mat2x3
 2 by 3 matrix aligned in memory of single-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 2, 4, float, aligned_highp > aligned_highp_mat2x4
 2 by 4 matrix aligned in memory of single-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 3, float, aligned_highp > aligned_highp_mat3
 3 by 3 matrix aligned in memory of single-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 2, float, aligned_highp > aligned_highp_mat3x2
 3 by 2 matrix aligned in memory of single-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 3, float, aligned_highp > aligned_highp_mat3x3
 3 by 3 matrix aligned in memory of single-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 4, float, aligned_highp > aligned_highp_mat3x4
 3 by 4 matrix aligned in memory of single-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 4, float, aligned_highp > aligned_highp_mat4
 4 by 4 matrix aligned in memory of single-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 2, float, aligned_highp > aligned_highp_mat4x2
 4 by 2 matrix aligned in memory of single-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 3, float, aligned_highp > aligned_highp_mat4x3
 4 by 3 matrix aligned in memory of single-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 4, float, aligned_highp > aligned_highp_mat4x4
 4 by 4 matrix aligned in memory of single-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef vec< 1, uint, aligned_highp > aligned_highp_uvec1
 1 component vector aligned in memory of unsigned integer numbers.
 
\ntypedef vec< 2, uint, aligned_highp > aligned_highp_uvec2
 2 components vector aligned in memory of unsigned integer numbers.
 
\ntypedef vec< 3, uint, aligned_highp > aligned_highp_uvec3
 3 components vector aligned in memory of unsigned integer numbers.
 
\ntypedef vec< 4, uint, aligned_highp > aligned_highp_uvec4
 4 components vector aligned in memory of unsigned integer numbers.
 
\ntypedef vec< 1, float, aligned_highp > aligned_highp_vec1
 1 component vector aligned in memory of single-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef vec< 2, float, aligned_highp > aligned_highp_vec2
 2 components vector aligned in memory of single-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef vec< 3, float, aligned_highp > aligned_highp_vec3
 3 components vector aligned in memory of single-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef vec< 4, float, aligned_highp > aligned_highp_vec4
 4 components vector aligned in memory of single-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef aligned_highp_ivec1 aligned_ivec1
 1 component vector aligned in memory of signed integer numbers.
 
\ntypedef aligned_highp_ivec2 aligned_ivec2
 2 components vector aligned in memory of signed integer numbers.
 
\ntypedef aligned_highp_ivec3 aligned_ivec3
 3 components vector aligned in memory of signed integer numbers.
 
\ntypedef aligned_highp_ivec4 aligned_ivec4
 4 components vector aligned in memory of signed integer numbers.
 
\ntypedef vec< 1, bool, aligned_lowp > aligned_lowp_bvec1
 1 component vector aligned in memory of bool values.
 
\ntypedef vec< 2, bool, aligned_lowp > aligned_lowp_bvec2
 2 components vector aligned in memory of bool values.
 
\ntypedef vec< 3, bool, aligned_lowp > aligned_lowp_bvec3
 3 components vector aligned in memory of bool values.
 
\ntypedef vec< 4, bool, aligned_lowp > aligned_lowp_bvec4
 4 components vector aligned in memory of bool values.
 
\ntypedef mat< 2, 2, double, aligned_lowp > aligned_lowp_dmat2
 2 by 2 matrix aligned in memory of double-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 2, 2, double, aligned_lowp > aligned_lowp_dmat2x2
 2 by 2 matrix aligned in memory of double-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 2, 3, double, aligned_lowp > aligned_lowp_dmat2x3
 2 by 3 matrix aligned in memory of double-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 2, 4, double, aligned_lowp > aligned_lowp_dmat2x4
 2 by 4 matrix aligned in memory of double-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 3, double, aligned_lowp > aligned_lowp_dmat3
 3 by 3 matrix aligned in memory of double-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 2, double, aligned_lowp > aligned_lowp_dmat3x2
 3 by 2 matrix aligned in memory of double-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 3, double, aligned_lowp > aligned_lowp_dmat3x3
 3 by 3 matrix aligned in memory of double-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 4, double, aligned_lowp > aligned_lowp_dmat3x4
 3 by 4 matrix aligned in memory of double-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 4, double, aligned_lowp > aligned_lowp_dmat4
 4 by 4 matrix aligned in memory of double-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 2, double, aligned_lowp > aligned_lowp_dmat4x2
 4 by 2 matrix aligned in memory of double-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 3, double, aligned_lowp > aligned_lowp_dmat4x3
 4 by 3 matrix aligned in memory of double-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 4, double, aligned_lowp > aligned_lowp_dmat4x4
 4 by 4 matrix aligned in memory of double-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef vec< 1, double, aligned_lowp > aligned_lowp_dvec1
 1 component vector aligned in memory of double-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef vec< 2, double, aligned_lowp > aligned_lowp_dvec2
 2 components vector aligned in memory of double-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef vec< 3, double, aligned_lowp > aligned_lowp_dvec3
 3 components vector aligned in memory of double-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef vec< 4, double, aligned_lowp > aligned_lowp_dvec4
 4 components vector aligned in memory of double-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef vec< 1, int, aligned_lowp > aligned_lowp_ivec1
 1 component vector aligned in memory of signed integer numbers.
 
\ntypedef vec< 2, int, aligned_lowp > aligned_lowp_ivec2
 2 components vector aligned in memory of signed integer numbers.
 
\ntypedef vec< 3, int, aligned_lowp > aligned_lowp_ivec3
 3 components vector aligned in memory of signed integer numbers.
 
\ntypedef vec< 4, int, aligned_lowp > aligned_lowp_ivec4
 4 components vector aligned in memory of signed integer numbers.
 
\ntypedef mat< 2, 2, float, aligned_lowp > aligned_lowp_mat2
 2 by 2 matrix aligned in memory of single-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 2, 2, float, aligned_lowp > aligned_lowp_mat2x2
 2 by 2 matrix aligned in memory of single-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 2, 3, float, aligned_lowp > aligned_lowp_mat2x3
 2 by 3 matrix aligned in memory of single-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 2, 4, float, aligned_lowp > aligned_lowp_mat2x4
 2 by 4 matrix aligned in memory of single-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 3, float, aligned_lowp > aligned_lowp_mat3
 3 by 3 matrix aligned in memory of single-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 2, float, aligned_lowp > aligned_lowp_mat3x2
 3 by 2 matrix aligned in memory of single-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 3, float, aligned_lowp > aligned_lowp_mat3x3
 3 by 3 matrix aligned in memory of single-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 4, float, aligned_lowp > aligned_lowp_mat3x4
 3 by 4 matrix aligned in memory of single-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 4, float, aligned_lowp > aligned_lowp_mat4
 4 by 4 matrix aligned in memory of single-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 2, float, aligned_lowp > aligned_lowp_mat4x2
 4 by 2 matrix aligned in memory of single-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 3, float, aligned_lowp > aligned_lowp_mat4x3
 4 by 3 matrix aligned in memory of single-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 4, float, aligned_lowp > aligned_lowp_mat4x4
 4 by 4 matrix aligned in memory of single-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef vec< 1, uint, aligned_lowp > aligned_lowp_uvec1
 1 component vector aligned in memory of unsigned integer numbers.
 
\ntypedef vec< 2, uint, aligned_lowp > aligned_lowp_uvec2
 2 components vector aligned in memory of unsigned integer numbers.
 
\ntypedef vec< 3, uint, aligned_lowp > aligned_lowp_uvec3
 3 components vector aligned in memory of unsigned integer numbers.
 
\ntypedef vec< 4, uint, aligned_lowp > aligned_lowp_uvec4
 4 components vector aligned in memory of unsigned integer numbers.
 
\ntypedef vec< 1, float, aligned_lowp > aligned_lowp_vec1
 1 component vector aligned in memory of single-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef vec< 2, float, aligned_lowp > aligned_lowp_vec2
 2 components vector aligned in memory of single-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef vec< 3, float, aligned_lowp > aligned_lowp_vec3
 3 components vector aligned in memory of single-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef vec< 4, float, aligned_lowp > aligned_lowp_vec4
 4 components vector aligned in memory of single-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef aligned_highp_mat2 aligned_mat2
 2 by 2 matrix tightly aligned in memory of single-precision floating-point numbers.
 
\ntypedef aligned_highp_mat2x2 aligned_mat2x2
 2 by 2 matrix tightly aligned in memory of single-precision floating-point numbers.
 
\ntypedef aligned_highp_mat2x3 aligned_mat2x3
 2 by 3 matrix tightly aligned in memory of single-precision floating-point numbers.
 
\ntypedef aligned_highp_mat2x4 aligned_mat2x4
 2 by 4 matrix tightly aligned in memory of single-precision floating-point numbers.
 
\ntypedef aligned_highp_mat3 aligned_mat3
 3 by 3 matrix tightly aligned in memory of single-precision floating-point numbers.
 
\ntypedef aligned_highp_mat3x2 aligned_mat3x2
 3 by 2 matrix tightly aligned in memory of single-precision floating-point numbers.
 
\ntypedef aligned_highp_mat3x3 aligned_mat3x3
 3 by 3 matrix tightly aligned in memory of single-precision floating-point numbers.
 
\ntypedef aligned_highp_mat3x4 aligned_mat3x4
 3 by 4 matrix tightly aligned in memory of single-precision floating-point numbers.
 
\ntypedef aligned_highp_mat4 aligned_mat4
 4 by 4 matrix tightly aligned in memory of single-precision floating-point numbers.
 
\ntypedef aligned_highp_mat4x2 aligned_mat4x2
 4 by 2 matrix tightly aligned in memory of single-precision floating-point numbers.
 
\ntypedef aligned_highp_mat4x3 aligned_mat4x3
 4 by 3 matrix tightly aligned in memory of single-precision floating-point numbers.
 
\ntypedef aligned_highp_mat4x4 aligned_mat4x4
 4 by 4 matrix tightly aligned in memory of single-precision floating-point numbers.
 
\ntypedef vec< 1, bool, aligned_mediump > aligned_mediump_bvec1
 1 component vector aligned in memory of bool values.
 
\ntypedef vec< 2, bool, aligned_mediump > aligned_mediump_bvec2
 2 components vector aligned in memory of bool values.
 
\ntypedef vec< 3, bool, aligned_mediump > aligned_mediump_bvec3
 3 components vector aligned in memory of bool values.
 
\ntypedef vec< 4, bool, aligned_mediump > aligned_mediump_bvec4
 4 components vector aligned in memory of bool values.
 
\ntypedef mat< 2, 2, double, aligned_mediump > aligned_mediump_dmat2
 2 by 2 matrix aligned in memory of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 2, 2, double, aligned_mediump > aligned_mediump_dmat2x2
 2 by 2 matrix aligned in memory of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 2, 3, double, aligned_mediump > aligned_mediump_dmat2x3
 2 by 3 matrix aligned in memory of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 2, 4, double, aligned_mediump > aligned_mediump_dmat2x4
 2 by 4 matrix aligned in memory of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 3, double, aligned_mediump > aligned_mediump_dmat3
 3 by 3 matrix aligned in memory of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 2, double, aligned_mediump > aligned_mediump_dmat3x2
 3 by 2 matrix aligned in memory of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 3, double, aligned_mediump > aligned_mediump_dmat3x3
 3 by 3 matrix aligned in memory of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 4, double, aligned_mediump > aligned_mediump_dmat3x4
 3 by 4 matrix aligned in memory of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 4, double, aligned_mediump > aligned_mediump_dmat4
 4 by 4 matrix aligned in memory of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 2, double, aligned_mediump > aligned_mediump_dmat4x2
 4 by 2 matrix aligned in memory of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 3, double, aligned_mediump > aligned_mediump_dmat4x3
 4 by 3 matrix aligned in memory of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 4, double, aligned_mediump > aligned_mediump_dmat4x4
 4 by 4 matrix aligned in memory of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef vec< 1, double, aligned_mediump > aligned_mediump_dvec1
 1 component vector aligned in memory of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef vec< 2, double, aligned_mediump > aligned_mediump_dvec2
 2 components vector aligned in memory of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef vec< 3, double, aligned_mediump > aligned_mediump_dvec3
 3 components vector aligned in memory of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef vec< 4, double, aligned_mediump > aligned_mediump_dvec4
 4 components vector aligned in memory of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef vec< 1, int, aligned_mediump > aligned_mediump_ivec1
 1 component vector aligned in memory of signed integer numbers.
 
\ntypedef vec< 2, int, aligned_mediump > aligned_mediump_ivec2
 2 components vector aligned in memory of signed integer numbers.
 
\ntypedef vec< 3, int, aligned_mediump > aligned_mediump_ivec3
 3 components vector aligned in memory of signed integer numbers.
 
\ntypedef vec< 4, int, aligned_mediump > aligned_mediump_ivec4
 4 components vector aligned in memory of signed integer numbers.
 
\ntypedef mat< 2, 2, float, aligned_mediump > aligned_mediump_mat2
 2 by 2 matrix aligned in memory of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 2, 2, float, aligned_mediump > aligned_mediump_mat2x2
 2 by 2 matrix aligned in memory of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 2, 3, float, aligned_mediump > aligned_mediump_mat2x3
 2 by 3 matrix aligned in memory of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 2, 4, float, aligned_mediump > aligned_mediump_mat2x4
 2 by 4 matrix aligned in memory of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 3, float, aligned_mediump > aligned_mediump_mat3
 3 by 3 matrix aligned in memory of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 2, float, aligned_mediump > aligned_mediump_mat3x2
 3 by 2 matrix aligned in memory of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 3, float, aligned_mediump > aligned_mediump_mat3x3
 3 by 3 matrix aligned in memory of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 4, float, aligned_mediump > aligned_mediump_mat3x4
 3 by 4 matrix aligned in memory of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 4, float, aligned_mediump > aligned_mediump_mat4
 4 by 4 matrix aligned in memory of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 2, float, aligned_mediump > aligned_mediump_mat4x2
 4 by 2 matrix aligned in memory of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 3, float, aligned_mediump > aligned_mediump_mat4x3
 4 by 3 matrix aligned in memory of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 4, float, aligned_mediump > aligned_mediump_mat4x4
 4 by 4 matrix aligned in memory of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef vec< 1, uint, aligned_mediump > aligned_mediump_uvec1
 1 component vector aligned in memory of unsigned integer numbers.
 
\ntypedef vec< 2, uint, aligned_mediump > aligned_mediump_uvec2
 2 components vector aligned in memory of unsigned integer numbers.
 
\ntypedef vec< 3, uint, aligned_mediump > aligned_mediump_uvec3
 3 components vector aligned in memory of unsigned integer numbers.
 
\ntypedef vec< 4, uint, aligned_mediump > aligned_mediump_uvec4
 4 components vector aligned in memory of unsigned integer numbers.
 
\ntypedef vec< 1, float, aligned_mediump > aligned_mediump_vec1
 1 component vector aligned in memory of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef vec< 2, float, aligned_mediump > aligned_mediump_vec2
 2 components vector aligned in memory of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef vec< 3, float, aligned_mediump > aligned_mediump_vec3
 3 components vector aligned in memory of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef vec< 4, float, aligned_mediump > aligned_mediump_vec4
 4 components vector aligned in memory of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef aligned_highp_uvec1 aligned_uvec1
 1 component vector aligned in memory of unsigned integer numbers.
 
\ntypedef aligned_highp_uvec2 aligned_uvec2
 2 components vector aligned in memory of unsigned integer numbers.
 
\ntypedef aligned_highp_uvec3 aligned_uvec3
 3 components vector aligned in memory of unsigned integer numbers.
 
\ntypedef aligned_highp_uvec4 aligned_uvec4
 4 components vector aligned in memory of unsigned integer numbers.
 
\ntypedef aligned_highp_vec1 aligned_vec1
 1 component vector aligned in memory of single-precision floating-point numbers.
 
\ntypedef aligned_highp_vec2 aligned_vec2
 2 components vector aligned in memory of single-precision floating-point numbers.
 
\ntypedef aligned_highp_vec3 aligned_vec3
 3 components vector aligned in memory of single-precision floating-point numbers.
 
\ntypedef aligned_highp_vec4 aligned_vec4
 4 components vector aligned in memory of single-precision floating-point numbers.
 
\ntypedef packed_highp_bvec1 packed_bvec1
 1 components vector tightly packed in memory of bool values.
 
\ntypedef packed_highp_bvec2 packed_bvec2
 2 components vector tightly packed in memory of bool values.
 
\ntypedef packed_highp_bvec3 packed_bvec3
 3 components vector tightly packed in memory of bool values.
 
\ntypedef packed_highp_bvec4 packed_bvec4
 4 components vector tightly packed in memory of bool values.
 
\ntypedef packed_highp_dmat2 packed_dmat2
 2 by 2 matrix tightly packed in memory of double-precision floating-point numbers.
 
\ntypedef packed_highp_dmat2x2 packed_dmat2x2
 2 by 2 matrix tightly packed in memory of double-precision floating-point numbers.
 
\ntypedef packed_highp_dmat2x3 packed_dmat2x3
 2 by 3 matrix tightly packed in memory of double-precision floating-point numbers.
 
\ntypedef packed_highp_dmat2x4 packed_dmat2x4
 2 by 4 matrix tightly packed in memory of double-precision floating-point numbers.
 
\ntypedef packed_highp_dmat3 packed_dmat3
 3 by 3 matrix tightly packed in memory of double-precision floating-point numbers.
 
\ntypedef packed_highp_dmat3x2 packed_dmat3x2
 3 by 2 matrix tightly packed in memory of double-precision floating-point numbers.
 
\ntypedef packed_highp_dmat3x3 packed_dmat3x3
 3 by 3 matrix tightly packed in memory of double-precision floating-point numbers.
 
\ntypedef packed_highp_dmat3x4 packed_dmat3x4
 3 by 4 matrix tightly packed in memory of double-precision floating-point numbers.
 
\ntypedef packed_highp_dmat4 packed_dmat4
 4 by 4 matrix tightly packed in memory of double-precision floating-point numbers.
 
\ntypedef packed_highp_dmat4x2 packed_dmat4x2
 4 by 2 matrix tightly packed in memory of double-precision floating-point numbers.
 
\ntypedef packed_highp_dmat4x3 packed_dmat4x3
 4 by 3 matrix tightly packed in memory of double-precision floating-point numbers.
 
\ntypedef packed_highp_dmat4x4 packed_dmat4x4
 4 by 4 matrix tightly packed in memory of double-precision floating-point numbers.
 
\ntypedef packed_highp_dvec1 packed_dvec1
 1 component vector tightly packed in memory of double-precision floating-point numbers.
 
\ntypedef packed_highp_dvec2 packed_dvec2
 2 components vector tightly packed in memory of double-precision floating-point numbers.
 
\ntypedef packed_highp_dvec3 packed_dvec3
 3 components vector tightly packed in memory of double-precision floating-point numbers.
 
\ntypedef packed_highp_dvec4 packed_dvec4
 4 components vector tightly packed in memory of double-precision floating-point numbers.
 
\ntypedef vec< 1, bool, packed_highp > packed_highp_bvec1
 1 component vector tightly packed in memory of bool values.
 
\ntypedef vec< 2, bool, packed_highp > packed_highp_bvec2
 2 components vector tightly packed in memory of bool values.
 
\ntypedef vec< 3, bool, packed_highp > packed_highp_bvec3
 3 components vector tightly packed in memory of bool values.
 
\ntypedef vec< 4, bool, packed_highp > packed_highp_bvec4
 4 components vector tightly packed in memory of bool values.
 
\ntypedef mat< 2, 2, double, packed_highp > packed_highp_dmat2
 2 by 2 matrix tightly packed in memory of double-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 2, 2, double, packed_highp > packed_highp_dmat2x2
 2 by 2 matrix tightly packed in memory of double-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 2, 3, double, packed_highp > packed_highp_dmat2x3
 2 by 3 matrix tightly packed in memory of double-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 2, 4, double, packed_highp > packed_highp_dmat2x4
 2 by 4 matrix tightly packed in memory of double-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 3, double, packed_highp > packed_highp_dmat3
 3 by 3 matrix tightly packed in memory of double-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 2, double, packed_highp > packed_highp_dmat3x2
 3 by 2 matrix tightly packed in memory of double-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 3, double, packed_highp > packed_highp_dmat3x3
 3 by 3 matrix tightly packed in memory of double-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 4, double, packed_highp > packed_highp_dmat3x4
 3 by 4 matrix tightly packed in memory of double-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 4, double, packed_highp > packed_highp_dmat4
 4 by 4 matrix tightly packed in memory of double-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 2, double, packed_highp > packed_highp_dmat4x2
 4 by 2 matrix tightly packed in memory of double-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 3, double, packed_highp > packed_highp_dmat4x3
 4 by 3 matrix tightly packed in memory of double-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 4, double, packed_highp > packed_highp_dmat4x4
 4 by 4 matrix tightly packed in memory of double-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef vec< 1, double, packed_highp > packed_highp_dvec1
 1 component vector tightly packed in memory of double-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef vec< 2, double, packed_highp > packed_highp_dvec2
 2 components vector tightly packed in memory of double-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef vec< 3, double, packed_highp > packed_highp_dvec3
 3 components vector tightly packed in memory of double-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef vec< 4, double, packed_highp > packed_highp_dvec4
 4 components vector tightly packed in memory of double-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef vec< 1, int, packed_highp > packed_highp_ivec1
 1 component vector tightly packed in memory of signed integer numbers.
 
\ntypedef vec< 2, int, packed_highp > packed_highp_ivec2
 2 components vector tightly packed in memory of signed integer numbers.
 
\ntypedef vec< 3, int, packed_highp > packed_highp_ivec3
 3 components vector tightly packed in memory of signed integer numbers.
 
\ntypedef vec< 4, int, packed_highp > packed_highp_ivec4
 4 components vector tightly packed in memory of signed integer numbers.
 
\ntypedef mat< 2, 2, float, packed_highp > packed_highp_mat2
 2 by 2 matrix tightly packed in memory of single-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 2, 2, float, packed_highp > packed_highp_mat2x2
 2 by 2 matrix tightly packed in memory of single-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 2, 3, float, packed_highp > packed_highp_mat2x3
 2 by 3 matrix tightly packed in memory of single-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 2, 4, float, packed_highp > packed_highp_mat2x4
 2 by 4 matrix tightly packed in memory of single-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 3, float, packed_highp > packed_highp_mat3
 3 by 3 matrix tightly packed in memory of single-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 2, float, packed_highp > packed_highp_mat3x2
 3 by 2 matrix tightly packed in memory of single-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 3, float, packed_highp > packed_highp_mat3x3
 3 by 3 matrix tightly packed in memory of single-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 4, float, packed_highp > packed_highp_mat3x4
 3 by 4 matrix tightly packed in memory of single-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 4, float, packed_highp > packed_highp_mat4
 4 by 4 matrix tightly packed in memory of single-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 2, float, packed_highp > packed_highp_mat4x2
 4 by 2 matrix tightly packed in memory of single-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 3, float, packed_highp > packed_highp_mat4x3
 4 by 3 matrix tightly packed in memory of single-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 4, float, packed_highp > packed_highp_mat4x4
 4 by 4 matrix tightly packed in memory of single-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef vec< 1, uint, packed_highp > packed_highp_uvec1
 1 component vector tightly packed in memory of unsigned integer numbers.
 
\ntypedef vec< 2, uint, packed_highp > packed_highp_uvec2
 2 components vector tightly packed in memory of unsigned integer numbers.
 
\ntypedef vec< 3, uint, packed_highp > packed_highp_uvec3
 3 components vector tightly packed in memory of unsigned integer numbers.
 
\ntypedef vec< 4, uint, packed_highp > packed_highp_uvec4
 4 components vector tightly packed in memory of unsigned integer numbers.
 
\ntypedef vec< 1, float, packed_highp > packed_highp_vec1
 1 component vector tightly packed in memory of single-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef vec< 2, float, packed_highp > packed_highp_vec2
 2 components vector tightly packed in memory of single-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef vec< 3, float, packed_highp > packed_highp_vec3
 3 components vector tightly packed in memory of single-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef vec< 4, float, packed_highp > packed_highp_vec4
 4 components vector tightly packed in memory of single-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef packed_highp_ivec1 packed_ivec1
 1 component vector tightly packed in memory of signed integer numbers.
 
\ntypedef packed_highp_ivec2 packed_ivec2
 2 components vector tightly packed in memory of signed integer numbers.
 
\ntypedef packed_highp_ivec3 packed_ivec3
 3 components vector tightly packed in memory of signed integer numbers.
 
\ntypedef packed_highp_ivec4 packed_ivec4
 4 components vector tightly packed in memory of signed integer numbers.
 
\ntypedef vec< 1, bool, packed_lowp > packed_lowp_bvec1
 1 component vector tightly packed in memory of bool values.
 
\ntypedef vec< 2, bool, packed_lowp > packed_lowp_bvec2
 2 components vector tightly packed in memory of bool values.
 
\ntypedef vec< 3, bool, packed_lowp > packed_lowp_bvec3
 3 components vector tightly packed in memory of bool values.
 
\ntypedef vec< 4, bool, packed_lowp > packed_lowp_bvec4
 4 components vector tightly packed in memory of bool values.
 
\ntypedef mat< 2, 2, double, packed_lowp > packed_lowp_dmat2
 2 by 2 matrix tightly packed in memory of double-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 2, 2, double, packed_lowp > packed_lowp_dmat2x2
 2 by 2 matrix tightly packed in memory of double-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 2, 3, double, packed_lowp > packed_lowp_dmat2x3
 2 by 3 matrix tightly packed in memory of double-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 2, 4, double, packed_lowp > packed_lowp_dmat2x4
 2 by 4 matrix tightly packed in memory of double-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 3, double, packed_lowp > packed_lowp_dmat3
 3 by 3 matrix tightly packed in memory of double-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 2, double, packed_lowp > packed_lowp_dmat3x2
 3 by 2 matrix tightly packed in memory of double-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 3, double, packed_lowp > packed_lowp_dmat3x3
 3 by 3 matrix tightly packed in memory of double-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 4, double, packed_lowp > packed_lowp_dmat3x4
 3 by 4 matrix tightly packed in memory of double-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 4, double, packed_lowp > packed_lowp_dmat4
 4 by 4 matrix tightly packed in memory of double-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 2, double, packed_lowp > packed_lowp_dmat4x2
 4 by 2 matrix tightly packed in memory of double-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 3, double, packed_lowp > packed_lowp_dmat4x3
 4 by 3 matrix tightly packed in memory of double-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 4, double, packed_lowp > packed_lowp_dmat4x4
 4 by 4 matrix tightly packed in memory of double-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef vec< 1, double, packed_lowp > packed_lowp_dvec1
 1 component vector tightly packed in memory of double-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef vec< 2, double, packed_lowp > packed_lowp_dvec2
 2 components vector tightly packed in memory of double-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef vec< 3, double, packed_lowp > packed_lowp_dvec3
 3 components vector tightly packed in memory of double-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef vec< 4, double, packed_lowp > packed_lowp_dvec4
 4 components vector tightly packed in memory of double-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef vec< 1, int, packed_lowp > packed_lowp_ivec1
 1 component vector tightly packed in memory of signed integer numbers.
 
\ntypedef vec< 2, int, packed_lowp > packed_lowp_ivec2
 2 components vector tightly packed in memory of signed integer numbers.
 
\ntypedef vec< 3, int, packed_lowp > packed_lowp_ivec3
 3 components vector tightly packed in memory of signed integer numbers.
 
\ntypedef vec< 4, int, packed_lowp > packed_lowp_ivec4
 4 components vector tightly packed in memory of signed integer numbers.
 
\ntypedef mat< 2, 2, float, packed_lowp > packed_lowp_mat2
 2 by 2 matrix tightly packed in memory of single-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 2, 2, float, packed_lowp > packed_lowp_mat2x2
 2 by 2 matrix tightly packed in memory of single-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 2, 3, float, packed_lowp > packed_lowp_mat2x3
 2 by 3 matrix tightly packed in memory of single-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 2, 4, float, packed_lowp > packed_lowp_mat2x4
 2 by 4 matrix tightly packed in memory of single-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 3, float, packed_lowp > packed_lowp_mat3
 3 by 3 matrix tightly packed in memory of single-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 2, float, packed_lowp > packed_lowp_mat3x2
 3 by 2 matrix tightly packed in memory of single-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 3, float, packed_lowp > packed_lowp_mat3x3
 3 by 3 matrix tightly packed in memory of single-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 4, float, packed_lowp > packed_lowp_mat3x4
 3 by 4 matrix tightly packed in memory of single-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 4, float, packed_lowp > packed_lowp_mat4
 4 by 4 matrix tightly packed in memory of single-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 2, float, packed_lowp > packed_lowp_mat4x2
 4 by 2 matrix tightly packed in memory of single-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 3, float, packed_lowp > packed_lowp_mat4x3
 4 by 3 matrix tightly packed in memory of single-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 4, float, packed_lowp > packed_lowp_mat4x4
 4 by 4 matrix tightly packed in memory of single-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef vec< 1, uint, packed_lowp > packed_lowp_uvec1
 1 component vector tightly packed in memory of unsigned integer numbers.
 
\ntypedef vec< 2, uint, packed_lowp > packed_lowp_uvec2
 2 components vector tightly packed in memory of unsigned integer numbers.
 
\ntypedef vec< 3, uint, packed_lowp > packed_lowp_uvec3
 3 components vector tightly packed in memory of unsigned integer numbers.
 
\ntypedef vec< 4, uint, packed_lowp > packed_lowp_uvec4
 4 components vector tightly packed in memory of unsigned integer numbers.
 
\ntypedef vec< 1, float, packed_lowp > packed_lowp_vec1
 1 component vector tightly packed in memory of single-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef vec< 2, float, packed_lowp > packed_lowp_vec2
 2 components vector tightly packed in memory of single-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef vec< 3, float, packed_lowp > packed_lowp_vec3
 3 components vector tightly packed in memory of single-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef vec< 4, float, packed_lowp > packed_lowp_vec4
 4 components vector tightly packed in memory of single-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef packed_highp_mat2 packed_mat2
 2 by 2 matrix tightly packed in memory of single-precision floating-point numbers.
 
\ntypedef packed_highp_mat2x2 packed_mat2x2
 2 by 2 matrix tightly packed in memory of single-precision floating-point numbers.
 
\ntypedef packed_highp_mat2x3 packed_mat2x3
 2 by 3 matrix tightly packed in memory of single-precision floating-point numbers.
 
\ntypedef packed_highp_mat2x4 packed_mat2x4
 2 by 4 matrix tightly packed in memory of single-precision floating-point numbers.
 
\ntypedef packed_highp_mat3 packed_mat3
 3 by 3 matrix tightly packed in memory of single-precision floating-point numbers.
 
\ntypedef packed_highp_mat3x2 packed_mat3x2
 3 by 2 matrix tightly packed in memory of single-precision floating-point numbers.
 
\ntypedef packed_highp_mat3x3 packed_mat3x3
 3 by 3 matrix tightly packed in memory of single-precision floating-point numbers.
 
\ntypedef packed_highp_mat3x4 packed_mat3x4
 3 by 4 matrix tightly packed in memory of single-precision floating-point numbers.
 
\ntypedef packed_highp_mat4 packed_mat4
 4 by 4 matrix tightly packed in memory of single-precision floating-point numbers.
 
\ntypedef packed_highp_mat4x2 packed_mat4x2
 4 by 2 matrix tightly packed in memory of single-precision floating-point numbers.
 
\ntypedef packed_highp_mat4x3 packed_mat4x3
 4 by 3 matrix tightly packed in memory of single-precision floating-point numbers.
 
\ntypedef packed_highp_mat4x4 packed_mat4x4
 4 by 4 matrix tightly packed in memory of single-precision floating-point numbers.
 
\ntypedef vec< 1, bool, packed_mediump > packed_mediump_bvec1
 1 component vector tightly packed in memory of bool values.
 
\ntypedef vec< 2, bool, packed_mediump > packed_mediump_bvec2
 2 components vector tightly packed in memory of bool values.
 
\ntypedef vec< 3, bool, packed_mediump > packed_mediump_bvec3
 3 components vector tightly packed in memory of bool values.
 
\ntypedef vec< 4, bool, packed_mediump > packed_mediump_bvec4
 4 components vector tightly packed in memory of bool values.
 
\ntypedef mat< 2, 2, double, packed_mediump > packed_mediump_dmat2
 2 by 2 matrix tightly packed in memory of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 2, 2, double, packed_mediump > packed_mediump_dmat2x2
 2 by 2 matrix tightly packed in memory of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 2, 3, double, packed_mediump > packed_mediump_dmat2x3
 2 by 3 matrix tightly packed in memory of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 2, 4, double, packed_mediump > packed_mediump_dmat2x4
 2 by 4 matrix tightly packed in memory of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 3, double, packed_mediump > packed_mediump_dmat3
 3 by 3 matrix tightly packed in memory of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 2, double, packed_mediump > packed_mediump_dmat3x2
 3 by 2 matrix tightly packed in memory of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 3, double, packed_mediump > packed_mediump_dmat3x3
 3 by 3 matrix tightly packed in memory of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 4, double, packed_mediump > packed_mediump_dmat3x4
 3 by 4 matrix tightly packed in memory of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 4, double, packed_mediump > packed_mediump_dmat4
 4 by 4 matrix tightly packed in memory of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 2, double, packed_mediump > packed_mediump_dmat4x2
 4 by 2 matrix tightly packed in memory of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 3, double, packed_mediump > packed_mediump_dmat4x3
 4 by 3 matrix tightly packed in memory of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 4, double, packed_mediump > packed_mediump_dmat4x4
 4 by 4 matrix tightly packed in memory of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef vec< 1, double, packed_mediump > packed_mediump_dvec1
 1 component vector tightly packed in memory of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef vec< 2, double, packed_mediump > packed_mediump_dvec2
 2 components vector tightly packed in memory of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef vec< 3, double, packed_mediump > packed_mediump_dvec3
 3 components vector tightly packed in memory of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef vec< 4, double, packed_mediump > packed_mediump_dvec4
 4 components vector tightly packed in memory of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef vec< 1, int, packed_mediump > packed_mediump_ivec1
 1 component vector tightly packed in memory of signed integer numbers.
 
\ntypedef vec< 2, int, packed_mediump > packed_mediump_ivec2
 2 components vector tightly packed in memory of signed integer numbers.
 
\ntypedef vec< 3, int, packed_mediump > packed_mediump_ivec3
 3 components vector tightly packed in memory of signed integer numbers.
 
\ntypedef vec< 4, int, packed_mediump > packed_mediump_ivec4
 4 components vector tightly packed in memory of signed integer numbers.
 
\ntypedef mat< 2, 2, float, packed_mediump > packed_mediump_mat2
 2 by 2 matrix tightly packed in memory of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 2, 2, float, packed_mediump > packed_mediump_mat2x2
 2 by 2 matrix tightly packed in memory of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 2, 3, float, packed_mediump > packed_mediump_mat2x3
 2 by 3 matrix tightly packed in memory of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 2, 4, float, packed_mediump > packed_mediump_mat2x4
 2 by 4 matrix tightly packed in memory of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 3, float, packed_mediump > packed_mediump_mat3
 3 by 3 matrix tightly packed in memory of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 2, float, packed_mediump > packed_mediump_mat3x2
 3 by 2 matrix tightly packed in memory of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 3, float, packed_mediump > packed_mediump_mat3x3
 3 by 3 matrix tightly packed in memory of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 4, float, packed_mediump > packed_mediump_mat3x4
 3 by 4 matrix tightly packed in memory of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 4, float, packed_mediump > packed_mediump_mat4
 4 by 4 matrix tightly packed in memory of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 2, float, packed_mediump > packed_mediump_mat4x2
 4 by 2 matrix tightly packed in memory of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 3, float, packed_mediump > packed_mediump_mat4x3
 4 by 3 matrix tightly packed in memory of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 4, float, packed_mediump > packed_mediump_mat4x4
 4 by 4 matrix tightly packed in memory of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef vec< 1, uint, packed_mediump > packed_mediump_uvec1
 1 component vector tightly packed in memory of unsigned integer numbers.
 
\ntypedef vec< 2, uint, packed_mediump > packed_mediump_uvec2
 2 components vector tightly packed in memory of unsigned integer numbers.
 
\ntypedef vec< 3, uint, packed_mediump > packed_mediump_uvec3
 3 components vector tightly packed in memory of unsigned integer numbers.
 
\ntypedef vec< 4, uint, packed_mediump > packed_mediump_uvec4
 4 components vector tightly packed in memory of unsigned integer numbers.
 
\ntypedef vec< 1, float, packed_mediump > packed_mediump_vec1
 1 component vector tightly packed in memory of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef vec< 2, float, packed_mediump > packed_mediump_vec2
 2 components vector tightly packed in memory of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef vec< 3, float, packed_mediump > packed_mediump_vec3
 3 components vector tightly packed in memory of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef vec< 4, float, packed_mediump > packed_mediump_vec4
 4 components vector tightly packed in memory of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef packed_highp_uvec1 packed_uvec1
 1 component vector tightly packed in memory of unsigned integer numbers.
 
\ntypedef packed_highp_uvec2 packed_uvec2
 2 components vector tightly packed in memory of unsigned integer numbers.
 
\ntypedef packed_highp_uvec3 packed_uvec3
 3 components vector tightly packed in memory of unsigned integer numbers.
 
\ntypedef packed_highp_uvec4 packed_uvec4
 4 components vector tightly packed in memory of unsigned integer numbers.
 
\ntypedef packed_highp_vec1 packed_vec1
 1 component vector tightly packed in memory of single-precision floating-point numbers.
 
\ntypedef packed_highp_vec2 packed_vec2
 2 components vector tightly packed in memory of single-precision floating-point numbers.
 
\ntypedef packed_highp_vec3 packed_vec3
 3 components vector tightly packed in memory of single-precision floating-point numbers.
 
\ntypedef packed_highp_vec4 packed_vec4
 4 components vector tightly packed in memory of single-precision floating-point numbers.
 
\n

Detailed Description

\n

GLM_GTC_type_aligned

\n
See also
Core features (dependence)
\n\n

Definition in file gtc/type_aligned.hpp.

\n
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0.9.9 API documentation\n
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gtc/type_aligned.hpp
\n
\n
\nGo to the documentation of this file.
1 
\n
13 #pragma once
\n
14 
\n
15 #if (GLM_CONFIG_ALIGNED_GENTYPES == GLM_DISABLE)
\n
16 # error "GLM: Aligned gentypes require to enable C++ language extensions. Define GLM_FORCE_ALIGNED_GENTYPES before including GLM headers to use aligned types."
\n
17 #endif
\n
18 
\n
19 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
20 # pragma message("GLM: GLM_GTC_type_aligned extension included")
\n
21 #endif
\n
22 
\n
23 #include "../mat4x4.hpp"
\n
24 #include "../mat4x3.hpp"
\n
25 #include "../mat4x2.hpp"
\n
26 #include "../mat3x4.hpp"
\n
27 #include "../mat3x3.hpp"
\n
28 #include "../mat3x2.hpp"
\n
29 #include "../mat2x4.hpp"
\n
30 #include "../mat2x3.hpp"
\n
31 #include "../mat2x2.hpp"
\n
32 #include "../gtc/vec1.hpp"
\n
33 #include "../vec2.hpp"
\n
34 #include "../vec3.hpp"
\n
35 #include "../vec4.hpp"
\n
36 
\n
37 namespace glm
\n
38 {
\n
41 
\n
42  // -- *vec1 --
\n
43 
\n
45  typedef vec<1, float, aligned_highp> aligned_highp_vec1;
\n
46 
\n
48  typedef vec<1, float, aligned_mediump> aligned_mediump_vec1;
\n
49 
\n
51  typedef vec<1, float, aligned_lowp> aligned_lowp_vec1;
\n
52 
\n
54  typedef vec<1, double, aligned_highp> aligned_highp_dvec1;
\n
55 
\n
57  typedef vec<1, double, aligned_mediump> aligned_mediump_dvec1;
\n
58 
\n
60  typedef vec<1, double, aligned_lowp> aligned_lowp_dvec1;
\n
61 
\n
63  typedef vec<1, int, aligned_highp> aligned_highp_ivec1;
\n
64 
\n
66  typedef vec<1, int, aligned_mediump> aligned_mediump_ivec1;
\n
67 
\n
69  typedef vec<1, int, aligned_lowp> aligned_lowp_ivec1;
\n
70 
\n
72  typedef vec<1, uint, aligned_highp> aligned_highp_uvec1;
\n
73 
\n
75  typedef vec<1, uint, aligned_mediump> aligned_mediump_uvec1;
\n
76 
\n
78  typedef vec<1, uint, aligned_lowp> aligned_lowp_uvec1;
\n
79 
\n
81  typedef vec<1, bool, aligned_highp> aligned_highp_bvec1;
\n
82 
\n
84  typedef vec<1, bool, aligned_mediump> aligned_mediump_bvec1;
\n
85 
\n
87  typedef vec<1, bool, aligned_lowp> aligned_lowp_bvec1;
\n
88 
\n
90  typedef vec<1, float, packed_highp> packed_highp_vec1;
\n
91 
\n
93  typedef vec<1, float, packed_mediump> packed_mediump_vec1;
\n
94 
\n
96  typedef vec<1, float, packed_lowp> packed_lowp_vec1;
\n
97 
\n
99  typedef vec<1, double, packed_highp> packed_highp_dvec1;
\n
100 
\n
102  typedef vec<1, double, packed_mediump> packed_mediump_dvec1;
\n
103 
\n
105  typedef vec<1, double, packed_lowp> packed_lowp_dvec1;
\n
106 
\n
108  typedef vec<1, int, packed_highp> packed_highp_ivec1;
\n
109 
\n
111  typedef vec<1, int, packed_mediump> packed_mediump_ivec1;
\n
112 
\n
114  typedef vec<1, int, packed_lowp> packed_lowp_ivec1;
\n
115 
\n
117  typedef vec<1, uint, packed_highp> packed_highp_uvec1;
\n
118 
\n
120  typedef vec<1, uint, packed_mediump> packed_mediump_uvec1;
\n
121 
\n
123  typedef vec<1, uint, packed_lowp> packed_lowp_uvec1;
\n
124 
\n
126  typedef vec<1, bool, packed_highp> packed_highp_bvec1;
\n
127 
\n
129  typedef vec<1, bool, packed_mediump> packed_mediump_bvec1;
\n
130 
\n
132  typedef vec<1, bool, packed_lowp> packed_lowp_bvec1;
\n
133 
\n
134  // -- *vec2 --
\n
135 
\n
137  typedef vec<2, float, aligned_highp> aligned_highp_vec2;
\n
138 
\n
140  typedef vec<2, float, aligned_mediump> aligned_mediump_vec2;
\n
141 
\n
143  typedef vec<2, float, aligned_lowp> aligned_lowp_vec2;
\n
144 
\n
146  typedef vec<2, double, aligned_highp> aligned_highp_dvec2;
\n
147 
\n
149  typedef vec<2, double, aligned_mediump> aligned_mediump_dvec2;
\n
150 
\n
152  typedef vec<2, double, aligned_lowp> aligned_lowp_dvec2;
\n
153 
\n
155  typedef vec<2, int, aligned_highp> aligned_highp_ivec2;
\n
156 
\n
158  typedef vec<2, int, aligned_mediump> aligned_mediump_ivec2;
\n
159 
\n
161  typedef vec<2, int, aligned_lowp> aligned_lowp_ivec2;
\n
162 
\n
164  typedef vec<2, uint, aligned_highp> aligned_highp_uvec2;
\n
165 
\n
167  typedef vec<2, uint, aligned_mediump> aligned_mediump_uvec2;
\n
168 
\n
170  typedef vec<2, uint, aligned_lowp> aligned_lowp_uvec2;
\n
171 
\n
173  typedef vec<2, bool, aligned_highp> aligned_highp_bvec2;
\n
174 
\n
176  typedef vec<2, bool, aligned_mediump> aligned_mediump_bvec2;
\n
177 
\n
179  typedef vec<2, bool, aligned_lowp> aligned_lowp_bvec2;
\n
180 
\n
182  typedef vec<2, float, packed_highp> packed_highp_vec2;
\n
183 
\n
185  typedef vec<2, float, packed_mediump> packed_mediump_vec2;
\n
186 
\n
188  typedef vec<2, float, packed_lowp> packed_lowp_vec2;
\n
189 
\n
191  typedef vec<2, double, packed_highp> packed_highp_dvec2;
\n
192 
\n
194  typedef vec<2, double, packed_mediump> packed_mediump_dvec2;
\n
195 
\n
197  typedef vec<2, double, packed_lowp> packed_lowp_dvec2;
\n
198 
\n
200  typedef vec<2, int, packed_highp> packed_highp_ivec2;
\n
201 
\n
203  typedef vec<2, int, packed_mediump> packed_mediump_ivec2;
\n
204 
\n
206  typedef vec<2, int, packed_lowp> packed_lowp_ivec2;
\n
207 
\n
209  typedef vec<2, uint, packed_highp> packed_highp_uvec2;
\n
210 
\n
212  typedef vec<2, uint, packed_mediump> packed_mediump_uvec2;
\n
213 
\n
215  typedef vec<2, uint, packed_lowp> packed_lowp_uvec2;
\n
216 
\n
218  typedef vec<2, bool, packed_highp> packed_highp_bvec2;
\n
219 
\n
221  typedef vec<2, bool, packed_mediump> packed_mediump_bvec2;
\n
222 
\n
224  typedef vec<2, bool, packed_lowp> packed_lowp_bvec2;
\n
225 
\n
226  // -- *vec3 --
\n
227 
\n
229  typedef vec<3, float, aligned_highp> aligned_highp_vec3;
\n
230 
\n
232  typedef vec<3, float, aligned_mediump> aligned_mediump_vec3;
\n
233 
\n
235  typedef vec<3, float, aligned_lowp> aligned_lowp_vec3;
\n
236 
\n
238  typedef vec<3, double, aligned_highp> aligned_highp_dvec3;
\n
239 
\n
241  typedef vec<3, double, aligned_mediump> aligned_mediump_dvec3;
\n
242 
\n
244  typedef vec<3, double, aligned_lowp> aligned_lowp_dvec3;
\n
245 
\n
247  typedef vec<3, int, aligned_highp> aligned_highp_ivec3;
\n
248 
\n
250  typedef vec<3, int, aligned_mediump> aligned_mediump_ivec3;
\n
251 
\n
253  typedef vec<3, int, aligned_lowp> aligned_lowp_ivec3;
\n
254 
\n
256  typedef vec<3, uint, aligned_highp> aligned_highp_uvec3;
\n
257 
\n
259  typedef vec<3, uint, aligned_mediump> aligned_mediump_uvec3;
\n
260 
\n
262  typedef vec<3, uint, aligned_lowp> aligned_lowp_uvec3;
\n
263 
\n
265  typedef vec<3, bool, aligned_highp> aligned_highp_bvec3;
\n
266 
\n
268  typedef vec<3, bool, aligned_mediump> aligned_mediump_bvec3;
\n
269 
\n
271  typedef vec<3, bool, aligned_lowp> aligned_lowp_bvec3;
\n
272 
\n
274  typedef vec<3, float, packed_highp> packed_highp_vec3;
\n
275 
\n
277  typedef vec<3, float, packed_mediump> packed_mediump_vec3;
\n
278 
\n
280  typedef vec<3, float, packed_lowp> packed_lowp_vec3;
\n
281 
\n
283  typedef vec<3, double, packed_highp> packed_highp_dvec3;
\n
284 
\n
286  typedef vec<3, double, packed_mediump> packed_mediump_dvec3;
\n
287 
\n
289  typedef vec<3, double, packed_lowp> packed_lowp_dvec3;
\n
290 
\n
292  typedef vec<3, int, packed_highp> packed_highp_ivec3;
\n
293 
\n
295  typedef vec<3, int, packed_mediump> packed_mediump_ivec3;
\n
296 
\n
298  typedef vec<3, int, packed_lowp> packed_lowp_ivec3;
\n
299 
\n
301  typedef vec<3, uint, packed_highp> packed_highp_uvec3;
\n
302 
\n
304  typedef vec<3, uint, packed_mediump> packed_mediump_uvec3;
\n
305 
\n
307  typedef vec<3, uint, packed_lowp> packed_lowp_uvec3;
\n
308 
\n
310  typedef vec<3, bool, packed_highp> packed_highp_bvec3;
\n
311 
\n
313  typedef vec<3, bool, packed_mediump> packed_mediump_bvec3;
\n
314 
\n
316  typedef vec<3, bool, packed_lowp> packed_lowp_bvec3;
\n
317 
\n
318  // -- *vec4 --
\n
319 
\n
321  typedef vec<4, float, aligned_highp> aligned_highp_vec4;
\n
322 
\n
324  typedef vec<4, float, aligned_mediump> aligned_mediump_vec4;
\n
325 
\n
327  typedef vec<4, float, aligned_lowp> aligned_lowp_vec4;
\n
328 
\n
330  typedef vec<4, double, aligned_highp> aligned_highp_dvec4;
\n
331 
\n
333  typedef vec<4, double, aligned_mediump> aligned_mediump_dvec4;
\n
334 
\n
336  typedef vec<4, double, aligned_lowp> aligned_lowp_dvec4;
\n
337 
\n
339  typedef vec<4, int, aligned_highp> aligned_highp_ivec4;
\n
340 
\n
342  typedef vec<4, int, aligned_mediump> aligned_mediump_ivec4;
\n
343 
\n
345  typedef vec<4, int, aligned_lowp> aligned_lowp_ivec4;
\n
346 
\n
348  typedef vec<4, uint, aligned_highp> aligned_highp_uvec4;
\n
349 
\n
351  typedef vec<4, uint, aligned_mediump> aligned_mediump_uvec4;
\n
352 
\n
354  typedef vec<4, uint, aligned_lowp> aligned_lowp_uvec4;
\n
355 
\n
357  typedef vec<4, bool, aligned_highp> aligned_highp_bvec4;
\n
358 
\n
360  typedef vec<4, bool, aligned_mediump> aligned_mediump_bvec4;
\n
361 
\n
363  typedef vec<4, bool, aligned_lowp> aligned_lowp_bvec4;
\n
364 
\n
366  typedef vec<4, float, packed_highp> packed_highp_vec4;
\n
367 
\n
369  typedef vec<4, float, packed_mediump> packed_mediump_vec4;
\n
370 
\n
372  typedef vec<4, float, packed_lowp> packed_lowp_vec4;
\n
373 
\n
375  typedef vec<4, double, packed_highp> packed_highp_dvec4;
\n
376 
\n
378  typedef vec<4, double, packed_mediump> packed_mediump_dvec4;
\n
379 
\n
381  typedef vec<4, double, packed_lowp> packed_lowp_dvec4;
\n
382 
\n
384  typedef vec<4, int, packed_highp> packed_highp_ivec4;
\n
385 
\n
387  typedef vec<4, int, packed_mediump> packed_mediump_ivec4;
\n
388 
\n
390  typedef vec<4, int, packed_lowp> packed_lowp_ivec4;
\n
391 
\n
393  typedef vec<4, uint, packed_highp> packed_highp_uvec4;
\n
394 
\n
396  typedef vec<4, uint, packed_mediump> packed_mediump_uvec4;
\n
397 
\n
399  typedef vec<4, uint, packed_lowp> packed_lowp_uvec4;
\n
400 
\n
402  typedef vec<4, bool, packed_highp> packed_highp_bvec4;
\n
403 
\n
405  typedef vec<4, bool, packed_mediump> packed_mediump_bvec4;
\n
406 
\n
408  typedef vec<4, bool, packed_lowp> packed_lowp_bvec4;
\n
409 
\n
410  // -- *mat2 --
\n
411 
\n
413  typedef mat<2, 2, float, aligned_highp> aligned_highp_mat2;
\n
414 
\n
416  typedef mat<2, 2, float, aligned_mediump> aligned_mediump_mat2;
\n
417 
\n
419  typedef mat<2, 2, float, aligned_lowp> aligned_lowp_mat2;
\n
420 
\n
422  typedef mat<2, 2, double, aligned_highp> aligned_highp_dmat2;
\n
423 
\n
425  typedef mat<2, 2, double, aligned_mediump> aligned_mediump_dmat2;
\n
426 
\n
428  typedef mat<2, 2, double, aligned_lowp> aligned_lowp_dmat2;
\n
429 
\n
431  typedef mat<2, 2, float, packed_highp> packed_highp_mat2;
\n
432 
\n
434  typedef mat<2, 2, float, packed_mediump> packed_mediump_mat2;
\n
435 
\n
437  typedef mat<2, 2, float, packed_lowp> packed_lowp_mat2;
\n
438 
\n
440  typedef mat<2, 2, double, packed_highp> packed_highp_dmat2;
\n
441 
\n
443  typedef mat<2, 2, double, packed_mediump> packed_mediump_dmat2;
\n
444 
\n
446  typedef mat<2, 2, double, packed_lowp> packed_lowp_dmat2;
\n
447 
\n
448  // -- *mat3 --
\n
449 
\n
451  typedef mat<3, 3, float, aligned_highp> aligned_highp_mat3;
\n
452 
\n
454  typedef mat<3, 3, float, aligned_mediump> aligned_mediump_mat3;
\n
455 
\n
457  typedef mat<3, 3, float, aligned_lowp> aligned_lowp_mat3;
\n
458 
\n
460  typedef mat<3, 3, double, aligned_highp> aligned_highp_dmat3;
\n
461 
\n
463  typedef mat<3, 3, double, aligned_mediump> aligned_mediump_dmat3;
\n
464 
\n
466  typedef mat<3, 3, double, aligned_lowp> aligned_lowp_dmat3;
\n
467 
\n
469  typedef mat<3, 3, float, packed_highp> packed_highp_mat3;
\n
470 
\n
472  typedef mat<3, 3, float, packed_mediump> packed_mediump_mat3;
\n
473 
\n
475  typedef mat<3, 3, float, packed_lowp> packed_lowp_mat3;
\n
476 
\n
478  typedef mat<3, 3, double, packed_highp> packed_highp_dmat3;
\n
479 
\n
481  typedef mat<3, 3, double, packed_mediump> packed_mediump_dmat3;
\n
482 
\n
484  typedef mat<3, 3, double, packed_lowp> packed_lowp_dmat3;
\n
485 
\n
486  // -- *mat4 --
\n
487 
\n
489  typedef mat<4, 4, float, aligned_highp> aligned_highp_mat4;
\n
490 
\n
492  typedef mat<4, 4, float, aligned_mediump> aligned_mediump_mat4;
\n
493 
\n
495  typedef mat<4, 4, float, aligned_lowp> aligned_lowp_mat4;
\n
496 
\n
498  typedef mat<4, 4, double, aligned_highp> aligned_highp_dmat4;
\n
499 
\n
501  typedef mat<4, 4, double, aligned_mediump> aligned_mediump_dmat4;
\n
502 
\n
504  typedef mat<4, 4, double, aligned_lowp> aligned_lowp_dmat4;
\n
505 
\n
507  typedef mat<4, 4, float, packed_highp> packed_highp_mat4;
\n
508 
\n
510  typedef mat<4, 4, float, packed_mediump> packed_mediump_mat4;
\n
511 
\n
513  typedef mat<4, 4, float, packed_lowp> packed_lowp_mat4;
\n
514 
\n
516  typedef mat<4, 4, double, packed_highp> packed_highp_dmat4;
\n
517 
\n
519  typedef mat<4, 4, double, packed_mediump> packed_mediump_dmat4;
\n
520 
\n
522  typedef mat<4, 4, double, packed_lowp> packed_lowp_dmat4;
\n
523 
\n
524  // -- *mat2x2 --
\n
525 
\n
527  typedef mat<2, 2, float, aligned_highp> aligned_highp_mat2x2;
\n
528 
\n
530  typedef mat<2, 2, float, aligned_mediump> aligned_mediump_mat2x2;
\n
531 
\n
533  typedef mat<2, 2, float, aligned_lowp> aligned_lowp_mat2x2;
\n
534 
\n
536  typedef mat<2, 2, double, aligned_highp> aligned_highp_dmat2x2;
\n
537 
\n
539  typedef mat<2, 2, double, aligned_mediump> aligned_mediump_dmat2x2;
\n
540 
\n
542  typedef mat<2, 2, double, aligned_lowp> aligned_lowp_dmat2x2;
\n
543 
\n
545  typedef mat<2, 2, float, packed_highp> packed_highp_mat2x2;
\n
546 
\n
548  typedef mat<2, 2, float, packed_mediump> packed_mediump_mat2x2;
\n
549 
\n
551  typedef mat<2, 2, float, packed_lowp> packed_lowp_mat2x2;
\n
552 
\n
554  typedef mat<2, 2, double, packed_highp> packed_highp_dmat2x2;
\n
555 
\n
557  typedef mat<2, 2, double, packed_mediump> packed_mediump_dmat2x2;
\n
558 
\n
560  typedef mat<2, 2, double, packed_lowp> packed_lowp_dmat2x2;
\n
561 
\n
562  // -- *mat2x3 --
\n
563 
\n
565  typedef mat<2, 3, float, aligned_highp> aligned_highp_mat2x3;
\n
566 
\n
568  typedef mat<2, 3, float, aligned_mediump> aligned_mediump_mat2x3;
\n
569 
\n
571  typedef mat<2, 3, float, aligned_lowp> aligned_lowp_mat2x3;
\n
572 
\n
574  typedef mat<2, 3, double, aligned_highp> aligned_highp_dmat2x3;
\n
575 
\n
577  typedef mat<2, 3, double, aligned_mediump> aligned_mediump_dmat2x3;
\n
578 
\n
580  typedef mat<2, 3, double, aligned_lowp> aligned_lowp_dmat2x3;
\n
581 
\n
583  typedef mat<2, 3, float, packed_highp> packed_highp_mat2x3;
\n
584 
\n
586  typedef mat<2, 3, float, packed_mediump> packed_mediump_mat2x3;
\n
587 
\n
589  typedef mat<2, 3, float, packed_lowp> packed_lowp_mat2x3;
\n
590 
\n
592  typedef mat<2, 3, double, packed_highp> packed_highp_dmat2x3;
\n
593 
\n
595  typedef mat<2, 3, double, packed_mediump> packed_mediump_dmat2x3;
\n
596 
\n
598  typedef mat<2, 3, double, packed_lowp> packed_lowp_dmat2x3;
\n
599 
\n
600  // -- *mat2x4 --
\n
601 
\n
603  typedef mat<2, 4, float, aligned_highp> aligned_highp_mat2x4;
\n
604 
\n
606  typedef mat<2, 4, float, aligned_mediump> aligned_mediump_mat2x4;
\n
607 
\n
609  typedef mat<2, 4, float, aligned_lowp> aligned_lowp_mat2x4;
\n
610 
\n
612  typedef mat<2, 4, double, aligned_highp> aligned_highp_dmat2x4;
\n
613 
\n
615  typedef mat<2, 4, double, aligned_mediump> aligned_mediump_dmat2x4;
\n
616 
\n
618  typedef mat<2, 4, double, aligned_lowp> aligned_lowp_dmat2x4;
\n
619 
\n
621  typedef mat<2, 4, float, packed_highp> packed_highp_mat2x4;
\n
622 
\n
624  typedef mat<2, 4, float, packed_mediump> packed_mediump_mat2x4;
\n
625 
\n
627  typedef mat<2, 4, float, packed_lowp> packed_lowp_mat2x4;
\n
628 
\n
630  typedef mat<2, 4, double, packed_highp> packed_highp_dmat2x4;
\n
631 
\n
633  typedef mat<2, 4, double, packed_mediump> packed_mediump_dmat2x4;
\n
634 
\n
636  typedef mat<2, 4, double, packed_lowp> packed_lowp_dmat2x4;
\n
637 
\n
638  // -- *mat3x2 --
\n
639 
\n
641  typedef mat<3, 2, float, aligned_highp> aligned_highp_mat3x2;
\n
642 
\n
644  typedef mat<3, 2, float, aligned_mediump> aligned_mediump_mat3x2;
\n
645 
\n
647  typedef mat<3, 2, float, aligned_lowp> aligned_lowp_mat3x2;
\n
648 
\n
650  typedef mat<3, 2, double, aligned_highp> aligned_highp_dmat3x2;
\n
651 
\n
653  typedef mat<3, 2, double, aligned_mediump> aligned_mediump_dmat3x2;
\n
654 
\n
656  typedef mat<3, 2, double, aligned_lowp> aligned_lowp_dmat3x2;
\n
657 
\n
659  typedef mat<3, 2, float, packed_highp> packed_highp_mat3x2;
\n
660 
\n
662  typedef mat<3, 2, float, packed_mediump> packed_mediump_mat3x2;
\n
663 
\n
665  typedef mat<3, 2, float, packed_lowp> packed_lowp_mat3x2;
\n
666 
\n
668  typedef mat<3, 2, double, packed_highp> packed_highp_dmat3x2;
\n
669 
\n
671  typedef mat<3, 2, double, packed_mediump> packed_mediump_dmat3x2;
\n
672 
\n
674  typedef mat<3, 2, double, packed_lowp> packed_lowp_dmat3x2;
\n
675 
\n
676  // -- *mat3x3 --
\n
677 
\n
679  typedef mat<3, 3, float, aligned_highp> aligned_highp_mat3x3;
\n
680 
\n
682  typedef mat<3, 3, float, aligned_mediump> aligned_mediump_mat3x3;
\n
683 
\n
685  typedef mat<3, 3, float, aligned_lowp> aligned_lowp_mat3x3;
\n
686 
\n
688  typedef mat<3, 3, double, aligned_highp> aligned_highp_dmat3x3;
\n
689 
\n
691  typedef mat<3, 3, double, aligned_mediump> aligned_mediump_dmat3x3;
\n
692 
\n
694  typedef mat<3, 3, double, aligned_lowp> aligned_lowp_dmat3x3;
\n
695 
\n
697  typedef mat<3, 3, float, packed_highp> packed_highp_mat3x3;
\n
698 
\n
700  typedef mat<3, 3, float, packed_mediump> packed_mediump_mat3x3;
\n
701 
\n
703  typedef mat<3, 3, float, packed_lowp> packed_lowp_mat3x3;
\n
704 
\n
706  typedef mat<3, 3, double, packed_highp> packed_highp_dmat3x3;
\n
707 
\n
709  typedef mat<3, 3, double, packed_mediump> packed_mediump_dmat3x3;
\n
710 
\n
712  typedef mat<3, 3, double, packed_lowp> packed_lowp_dmat3x3;
\n
713 
\n
714  // -- *mat3x4 --
\n
715 
\n
717  typedef mat<3, 4, float, aligned_highp> aligned_highp_mat3x4;
\n
718 
\n
720  typedef mat<3, 4, float, aligned_mediump> aligned_mediump_mat3x4;
\n
721 
\n
723  typedef mat<3, 4, float, aligned_lowp> aligned_lowp_mat3x4;
\n
724 
\n
726  typedef mat<3, 4, double, aligned_highp> aligned_highp_dmat3x4;
\n
727 
\n
729  typedef mat<3, 4, double, aligned_mediump> aligned_mediump_dmat3x4;
\n
730 
\n
732  typedef mat<3, 4, double, aligned_lowp> aligned_lowp_dmat3x4;
\n
733 
\n
735  typedef mat<3, 4, float, packed_highp> packed_highp_mat3x4;
\n
736 
\n
738  typedef mat<3, 4, float, packed_mediump> packed_mediump_mat3x4;
\n
739 
\n
741  typedef mat<3, 4, float, packed_lowp> packed_lowp_mat3x4;
\n
742 
\n
744  typedef mat<3, 4, double, packed_highp> packed_highp_dmat3x4;
\n
745 
\n
747  typedef mat<3, 4, double, packed_mediump> packed_mediump_dmat3x4;
\n
748 
\n
750  typedef mat<3, 4, double, packed_lowp> packed_lowp_dmat3x4;
\n
751 
\n
752  // -- *mat4x2 --
\n
753 
\n
755  typedef mat<4, 2, float, aligned_highp> aligned_highp_mat4x2;
\n
756 
\n
758  typedef mat<4, 2, float, aligned_mediump> aligned_mediump_mat4x2;
\n
759 
\n
761  typedef mat<4, 2, float, aligned_lowp> aligned_lowp_mat4x2;
\n
762 
\n
764  typedef mat<4, 2, double, aligned_highp> aligned_highp_dmat4x2;
\n
765 
\n
767  typedef mat<4, 2, double, aligned_mediump> aligned_mediump_dmat4x2;
\n
768 
\n
770  typedef mat<4, 2, double, aligned_lowp> aligned_lowp_dmat4x2;
\n
771 
\n
773  typedef mat<4, 2, float, packed_highp> packed_highp_mat4x2;
\n
774 
\n
776  typedef mat<4, 2, float, packed_mediump> packed_mediump_mat4x2;
\n
777 
\n
779  typedef mat<4, 2, float, packed_lowp> packed_lowp_mat4x2;
\n
780 
\n
782  typedef mat<4, 2, double, packed_highp> packed_highp_dmat4x2;
\n
783 
\n
785  typedef mat<4, 2, double, packed_mediump> packed_mediump_dmat4x2;
\n
786 
\n
788  typedef mat<4, 2, double, packed_lowp> packed_lowp_dmat4x2;
\n
789 
\n
790  // -- *mat4x3 --
\n
791 
\n
793  typedef mat<4, 3, float, aligned_highp> aligned_highp_mat4x3;
\n
794 
\n
796  typedef mat<4, 3, float, aligned_mediump> aligned_mediump_mat4x3;
\n
797 
\n
799  typedef mat<4, 3, float, aligned_lowp> aligned_lowp_mat4x3;
\n
800 
\n
802  typedef mat<4, 3, double, aligned_highp> aligned_highp_dmat4x3;
\n
803 
\n
805  typedef mat<4, 3, double, aligned_mediump> aligned_mediump_dmat4x3;
\n
806 
\n
808  typedef mat<4, 3, double, aligned_lowp> aligned_lowp_dmat4x3;
\n
809 
\n
811  typedef mat<4, 3, float, packed_highp> packed_highp_mat4x3;
\n
812 
\n
814  typedef mat<4, 3, float, packed_mediump> packed_mediump_mat4x3;
\n
815 
\n
817  typedef mat<4, 3, float, packed_lowp> packed_lowp_mat4x3;
\n
818 
\n
820  typedef mat<4, 3, double, packed_highp> packed_highp_dmat4x3;
\n
821 
\n
823  typedef mat<4, 3, double, packed_mediump> packed_mediump_dmat4x3;
\n
824 
\n
826  typedef mat<4, 3, double, packed_lowp> packed_lowp_dmat4x3;
\n
827 
\n
828  // -- *mat4x4 --
\n
829 
\n
831  typedef mat<4, 4, float, aligned_highp> aligned_highp_mat4x4;
\n
832 
\n
834  typedef mat<4, 4, float, aligned_mediump> aligned_mediump_mat4x4;
\n
835 
\n
837  typedef mat<4, 4, float, aligned_lowp> aligned_lowp_mat4x4;
\n
838 
\n
840  typedef mat<4, 4, double, aligned_highp> aligned_highp_dmat4x4;
\n
841 
\n
843  typedef mat<4, 4, double, aligned_mediump> aligned_mediump_dmat4x4;
\n
844 
\n
846  typedef mat<4, 4, double, aligned_lowp> aligned_lowp_dmat4x4;
\n
847 
\n
849  typedef mat<4, 4, float, packed_highp> packed_highp_mat4x4;
\n
850 
\n
852  typedef mat<4, 4, float, packed_mediump> packed_mediump_mat4x4;
\n
853 
\n
855  typedef mat<4, 4, float, packed_lowp> packed_lowp_mat4x4;
\n
856 
\n
858  typedef mat<4, 4, double, packed_highp> packed_highp_dmat4x4;
\n
859 
\n
861  typedef mat<4, 4, double, packed_mediump> packed_mediump_dmat4x4;
\n
862 
\n
864  typedef mat<4, 4, double, packed_lowp> packed_lowp_dmat4x4;
\n
865 
\n
866  // -- default --
\n
867 
\n
868 #if(defined(GLM_PRECISION_LOWP_FLOAT))
\n
869  typedef aligned_lowp_vec1 aligned_vec1;
\n
870  typedef aligned_lowp_vec2 aligned_vec2;
\n
871  typedef aligned_lowp_vec3 aligned_vec3;
\n
872  typedef aligned_lowp_vec4 aligned_vec4;
\n
873  typedef packed_lowp_vec1 packed_vec1;
\n
874  typedef packed_lowp_vec2 packed_vec2;
\n
875  typedef packed_lowp_vec3 packed_vec3;
\n
876  typedef packed_lowp_vec4 packed_vec4;
\n
877 
\n
878  typedef aligned_lowp_mat2 aligned_mat2;
\n
879  typedef aligned_lowp_mat3 aligned_mat3;
\n
880  typedef aligned_lowp_mat4 aligned_mat4;
\n
881  typedef packed_lowp_mat2 packed_mat2;
\n
882  typedef packed_lowp_mat3 packed_mat3;
\n
883  typedef packed_lowp_mat4 packed_mat4;
\n
884 
\n
885  typedef aligned_lowp_mat2x2 aligned_mat2x2;
\n
886  typedef aligned_lowp_mat2x3 aligned_mat2x3;
\n
887  typedef aligned_lowp_mat2x4 aligned_mat2x4;
\n
888  typedef aligned_lowp_mat3x2 aligned_mat3x2;
\n
889  typedef aligned_lowp_mat3x3 aligned_mat3x3;
\n
890  typedef aligned_lowp_mat3x4 aligned_mat3x4;
\n
891  typedef aligned_lowp_mat4x2 aligned_mat4x2;
\n
892  typedef aligned_lowp_mat4x3 aligned_mat4x3;
\n
893  typedef aligned_lowp_mat4x4 aligned_mat4x4;
\n
894  typedef packed_lowp_mat2x2 packed_mat2x2;
\n
895  typedef packed_lowp_mat2x3 packed_mat2x3;
\n
896  typedef packed_lowp_mat2x4 packed_mat2x4;
\n
897  typedef packed_lowp_mat3x2 packed_mat3x2;
\n
898  typedef packed_lowp_mat3x3 packed_mat3x3;
\n
899  typedef packed_lowp_mat3x4 packed_mat3x4;
\n
900  typedef packed_lowp_mat4x2 packed_mat4x2;
\n
901  typedef packed_lowp_mat4x3 packed_mat4x3;
\n
902  typedef packed_lowp_mat4x4 packed_mat4x4;
\n
903 #elif(defined(GLM_PRECISION_MEDIUMP_FLOAT))
\n
904  typedef aligned_mediump_vec1 aligned_vec1;
\n
905  typedef aligned_mediump_vec2 aligned_vec2;
\n
906  typedef aligned_mediump_vec3 aligned_vec3;
\n
907  typedef aligned_mediump_vec4 aligned_vec4;
\n
908  typedef packed_mediump_vec1 packed_vec1;
\n
909  typedef packed_mediump_vec2 packed_vec2;
\n
910  typedef packed_mediump_vec3 packed_vec3;
\n
911  typedef packed_mediump_vec4 packed_vec4;
\n
912 
\n
913  typedef aligned_mediump_mat2 aligned_mat2;
\n
914  typedef aligned_mediump_mat3 aligned_mat3;
\n
915  typedef aligned_mediump_mat4 aligned_mat4;
\n
916  typedef packed_mediump_mat2 packed_mat2;
\n
917  typedef packed_mediump_mat3 packed_mat3;
\n
918  typedef packed_mediump_mat4 packed_mat4;
\n
919 
\n
920  typedef aligned_mediump_mat2x2 aligned_mat2x2;
\n
921  typedef aligned_mediump_mat2x3 aligned_mat2x3;
\n
922  typedef aligned_mediump_mat2x4 aligned_mat2x4;
\n
923  typedef aligned_mediump_mat3x2 aligned_mat3x2;
\n
924  typedef aligned_mediump_mat3x3 aligned_mat3x3;
\n
925  typedef aligned_mediump_mat3x4 aligned_mat3x4;
\n
926  typedef aligned_mediump_mat4x2 aligned_mat4x2;
\n
927  typedef aligned_mediump_mat4x3 aligned_mat4x3;
\n
928  typedef aligned_mediump_mat4x4 aligned_mat4x4;
\n
929  typedef packed_mediump_mat2x2 packed_mat2x2;
\n
930  typedef packed_mediump_mat2x3 packed_mat2x3;
\n
931  typedef packed_mediump_mat2x4 packed_mat2x4;
\n
932  typedef packed_mediump_mat3x2 packed_mat3x2;
\n
933  typedef packed_mediump_mat3x3 packed_mat3x3;
\n
934  typedef packed_mediump_mat3x4 packed_mat3x4;
\n
935  typedef packed_mediump_mat4x2 packed_mat4x2;
\n
936  typedef packed_mediump_mat4x3 packed_mat4x3;
\n
937  typedef packed_mediump_mat4x4 packed_mat4x4;
\n
938 #else //defined(GLM_PRECISION_HIGHP_FLOAT)
\n
939  typedef aligned_highp_vec1 aligned_vec1;
\n
941 
\n
943  typedef aligned_highp_vec2 aligned_vec2;
\n
944 
\n
946  typedef aligned_highp_vec3 aligned_vec3;
\n
947 
\n
949  typedef aligned_highp_vec4 aligned_vec4;
\n
950 
\n
952  typedef packed_highp_vec1 packed_vec1;
\n
953 
\n
955  typedef packed_highp_vec2 packed_vec2;
\n
956 
\n
958  typedef packed_highp_vec3 packed_vec3;
\n
959 
\n
961  typedef packed_highp_vec4 packed_vec4;
\n
962 
\n
964  typedef aligned_highp_mat2 aligned_mat2;
\n
965 
\n
967  typedef aligned_highp_mat3 aligned_mat3;
\n
968 
\n
970  typedef aligned_highp_mat4 aligned_mat4;
\n
971 
\n
973  typedef packed_highp_mat2 packed_mat2;
\n
974 
\n
976  typedef packed_highp_mat3 packed_mat3;
\n
977 
\n
979  typedef packed_highp_mat4 packed_mat4;
\n
980 
\n
982  typedef aligned_highp_mat2x2 aligned_mat2x2;
\n
983 
\n
985  typedef aligned_highp_mat2x3 aligned_mat2x3;
\n
986 
\n
988  typedef aligned_highp_mat2x4 aligned_mat2x4;
\n
989 
\n
991  typedef aligned_highp_mat3x2 aligned_mat3x2;
\n
992 
\n
994  typedef aligned_highp_mat3x3 aligned_mat3x3;
\n
995 
\n
997  typedef aligned_highp_mat3x4 aligned_mat3x4;
\n
998 
\n
1000  typedef aligned_highp_mat4x2 aligned_mat4x2;
\n
1001 
\n
1003  typedef aligned_highp_mat4x3 aligned_mat4x3;
\n
1004 
\n
1006  typedef aligned_highp_mat4x4 aligned_mat4x4;
\n
1007 
\n
1009  typedef packed_highp_mat2x2 packed_mat2x2;
\n
1010 
\n
1012  typedef packed_highp_mat2x3 packed_mat2x3;
\n
1013 
\n
1015  typedef packed_highp_mat2x4 packed_mat2x4;
\n
1016 
\n
1018  typedef packed_highp_mat3x2 packed_mat3x2;
\n
1019 
\n
1021  typedef packed_highp_mat3x3 packed_mat3x3;
\n
1022 
\n
1024  typedef packed_highp_mat3x4 packed_mat3x4;
\n
1025 
\n
1027  typedef packed_highp_mat4x2 packed_mat4x2;
\n
1028 
\n
1030  typedef packed_highp_mat4x3 packed_mat4x3;
\n
1031 
\n
1033  typedef packed_highp_mat4x4 packed_mat4x4;
\n
1034 #endif//GLM_PRECISION
\n
1035 
\n
1036 #if(defined(GLM_PRECISION_LOWP_DOUBLE))
\n
1037  typedef aligned_lowp_dvec1 aligned_dvec1;
\n
1038  typedef aligned_lowp_dvec2 aligned_dvec2;
\n
1039  typedef aligned_lowp_dvec3 aligned_dvec3;
\n
1040  typedef aligned_lowp_dvec4 aligned_dvec4;
\n
1041  typedef packed_lowp_dvec1 packed_dvec1;
\n
1042  typedef packed_lowp_dvec2 packed_dvec2;
\n
1043  typedef packed_lowp_dvec3 packed_dvec3;
\n
1044  typedef packed_lowp_dvec4 packed_dvec4;
\n
1045 
\n
1046  typedef aligned_lowp_dmat2 aligned_dmat2;
\n
1047  typedef aligned_lowp_dmat3 aligned_dmat3;
\n
1048  typedef aligned_lowp_dmat4 aligned_dmat4;
\n
1049  typedef packed_lowp_dmat2 packed_dmat2;
\n
1050  typedef packed_lowp_dmat3 packed_dmat3;
\n
1051  typedef packed_lowp_dmat4 packed_dmat4;
\n
1052 
\n
1053  typedef aligned_lowp_dmat2x2 aligned_dmat2x2;
\n
1054  typedef aligned_lowp_dmat2x3 aligned_dmat2x3;
\n
1055  typedef aligned_lowp_dmat2x4 aligned_dmat2x4;
\n
1056  typedef aligned_lowp_dmat3x2 aligned_dmat3x2;
\n
1057  typedef aligned_lowp_dmat3x3 aligned_dmat3x3;
\n
1058  typedef aligned_lowp_dmat3x4 aligned_dmat3x4;
\n
1059  typedef aligned_lowp_dmat4x2 aligned_dmat4x2;
\n
1060  typedef aligned_lowp_dmat4x3 aligned_dmat4x3;
\n
1061  typedef aligned_lowp_dmat4x4 aligned_dmat4x4;
\n
1062  typedef packed_lowp_dmat2x2 packed_dmat2x2;
\n
1063  typedef packed_lowp_dmat2x3 packed_dmat2x3;
\n
1064  typedef packed_lowp_dmat2x4 packed_dmat2x4;
\n
1065  typedef packed_lowp_dmat3x2 packed_dmat3x2;
\n
1066  typedef packed_lowp_dmat3x3 packed_dmat3x3;
\n
1067  typedef packed_lowp_dmat3x4 packed_dmat3x4;
\n
1068  typedef packed_lowp_dmat4x2 packed_dmat4x2;
\n
1069  typedef packed_lowp_dmat4x3 packed_dmat4x3;
\n
1070  typedef packed_lowp_dmat4x4 packed_dmat4x4;
\n
1071 #elif(defined(GLM_PRECISION_MEDIUMP_DOUBLE))
\n
1072  typedef aligned_mediump_dvec1 aligned_dvec1;
\n
1073  typedef aligned_mediump_dvec2 aligned_dvec2;
\n
1074  typedef aligned_mediump_dvec3 aligned_dvec3;
\n
1075  typedef aligned_mediump_dvec4 aligned_dvec4;
\n
1076  typedef packed_mediump_dvec1 packed_dvec1;
\n
1077  typedef packed_mediump_dvec2 packed_dvec2;
\n
1078  typedef packed_mediump_dvec3 packed_dvec3;
\n
1079  typedef packed_mediump_dvec4 packed_dvec4;
\n
1080 
\n
1081  typedef aligned_mediump_dmat2 aligned_dmat2;
\n
1082  typedef aligned_mediump_dmat3 aligned_dmat3;
\n
1083  typedef aligned_mediump_dmat4 aligned_dmat4;
\n
1084  typedef packed_mediump_dmat2 packed_dmat2;
\n
1085  typedef packed_mediump_dmat3 packed_dmat3;
\n
1086  typedef packed_mediump_dmat4 packed_dmat4;
\n
1087 
\n
1088  typedef aligned_mediump_dmat2x2 aligned_dmat2x2;
\n
1089  typedef aligned_mediump_dmat2x3 aligned_dmat2x3;
\n
1090  typedef aligned_mediump_dmat2x4 aligned_dmat2x4;
\n
1091  typedef aligned_mediump_dmat3x2 aligned_dmat3x2;
\n
1092  typedef aligned_mediump_dmat3x3 aligned_dmat3x3;
\n
1093  typedef aligned_mediump_dmat3x4 aligned_dmat3x4;
\n
1094  typedef aligned_mediump_dmat4x2 aligned_dmat4x2;
\n
1095  typedef aligned_mediump_dmat4x3 aligned_dmat4x3;
\n
1096  typedef aligned_mediump_dmat4x4 aligned_dmat4x4;
\n
1097  typedef packed_mediump_dmat2x2 packed_dmat2x2;
\n
1098  typedef packed_mediump_dmat2x3 packed_dmat2x3;
\n
1099  typedef packed_mediump_dmat2x4 packed_dmat2x4;
\n
1100  typedef packed_mediump_dmat3x2 packed_dmat3x2;
\n
1101  typedef packed_mediump_dmat3x3 packed_dmat3x3;
\n
1102  typedef packed_mediump_dmat3x4 packed_dmat3x4;
\n
1103  typedef packed_mediump_dmat4x2 packed_dmat4x2;
\n
1104  typedef packed_mediump_dmat4x3 packed_dmat4x3;
\n
1105  typedef packed_mediump_dmat4x4 packed_dmat4x4;
\n
1106 #else //defined(GLM_PRECISION_HIGHP_DOUBLE)
\n
1107  typedef aligned_highp_dvec1 aligned_dvec1;
\n
1109 
\n
1111  typedef aligned_highp_dvec2 aligned_dvec2;
\n
1112 
\n
1114  typedef aligned_highp_dvec3 aligned_dvec3;
\n
1115 
\n
1117  typedef aligned_highp_dvec4 aligned_dvec4;
\n
1118 
\n
1120  typedef packed_highp_dvec1 packed_dvec1;
\n
1121 
\n
1123  typedef packed_highp_dvec2 packed_dvec2;
\n
1124 
\n
1126  typedef packed_highp_dvec3 packed_dvec3;
\n
1127 
\n
1129  typedef packed_highp_dvec4 packed_dvec4;
\n
1130 
\n
1132  typedef aligned_highp_dmat2 aligned_dmat2;
\n
1133 
\n
1135  typedef aligned_highp_dmat3 aligned_dmat3;
\n
1136 
\n
1138  typedef aligned_highp_dmat4 aligned_dmat4;
\n
1139 
\n
1141  typedef packed_highp_dmat2 packed_dmat2;
\n
1142 
\n
1144  typedef packed_highp_dmat3 packed_dmat3;
\n
1145 
\n
1147  typedef packed_highp_dmat4 packed_dmat4;
\n
1148 
\n
1150  typedef aligned_highp_dmat2x2 aligned_dmat2x2;
\n
1151 
\n
1153  typedef aligned_highp_dmat2x3 aligned_dmat2x3;
\n
1154 
\n
1156  typedef aligned_highp_dmat2x4 aligned_dmat2x4;
\n
1157 
\n
1159  typedef aligned_highp_dmat3x2 aligned_dmat3x2;
\n
1160 
\n
1162  typedef aligned_highp_dmat3x3 aligned_dmat3x3;
\n
1163 
\n
1165  typedef aligned_highp_dmat3x4 aligned_dmat3x4;
\n
1166 
\n
1168  typedef aligned_highp_dmat4x2 aligned_dmat4x2;
\n
1169 
\n
1171  typedef aligned_highp_dmat4x3 aligned_dmat4x3;
\n
1172 
\n
1174  typedef aligned_highp_dmat4x4 aligned_dmat4x4;
\n
1175 
\n
1177  typedef packed_highp_dmat2x2 packed_dmat2x2;
\n
1178 
\n
1180  typedef packed_highp_dmat2x3 packed_dmat2x3;
\n
1181 
\n
1183  typedef packed_highp_dmat2x4 packed_dmat2x4;
\n
1184 
\n
1186  typedef packed_highp_dmat3x2 packed_dmat3x2;
\n
1187 
\n
1189  typedef packed_highp_dmat3x3 packed_dmat3x3;
\n
1190 
\n
1192  typedef packed_highp_dmat3x4 packed_dmat3x4;
\n
1193 
\n
1195  typedef packed_highp_dmat4x2 packed_dmat4x2;
\n
1196 
\n
1198  typedef packed_highp_dmat4x3 packed_dmat4x3;
\n
1199 
\n
1201  typedef packed_highp_dmat4x4 packed_dmat4x4;
\n
1202 #endif//GLM_PRECISION
\n
1203 
\n
1204 #if(defined(GLM_PRECISION_LOWP_INT))
\n
1205  typedef aligned_lowp_ivec1 aligned_ivec1;
\n
1206  typedef aligned_lowp_ivec2 aligned_ivec2;
\n
1207  typedef aligned_lowp_ivec3 aligned_ivec3;
\n
1208  typedef aligned_lowp_ivec4 aligned_ivec4;
\n
1209 #elif(defined(GLM_PRECISION_MEDIUMP_INT))
\n
1210  typedef aligned_mediump_ivec1 aligned_ivec1;
\n
1211  typedef aligned_mediump_ivec2 aligned_ivec2;
\n
1212  typedef aligned_mediump_ivec3 aligned_ivec3;
\n
1213  typedef aligned_mediump_ivec4 aligned_ivec4;
\n
1214 #else //defined(GLM_PRECISION_HIGHP_INT)
\n
1215  typedef aligned_highp_ivec1 aligned_ivec1;
\n
1217 
\n
1219  typedef aligned_highp_ivec2 aligned_ivec2;
\n
1220 
\n
1222  typedef aligned_highp_ivec3 aligned_ivec3;
\n
1223 
\n
1225  typedef aligned_highp_ivec4 aligned_ivec4;
\n
1226 
\n
1228  typedef packed_highp_ivec1 packed_ivec1;
\n
1229 
\n
1231  typedef packed_highp_ivec2 packed_ivec2;
\n
1232 
\n
1234  typedef packed_highp_ivec3 packed_ivec3;
\n
1235 
\n
1237  typedef packed_highp_ivec4 packed_ivec4;
\n
1238 #endif//GLM_PRECISION
\n
1239 
\n
1240  // -- Unsigned integer definition --
\n
1241 
\n
1242 #if(defined(GLM_PRECISION_LOWP_UINT))
\n
1243  typedef aligned_lowp_uvec1 aligned_uvec1;
\n
1244  typedef aligned_lowp_uvec2 aligned_uvec2;
\n
1245  typedef aligned_lowp_uvec3 aligned_uvec3;
\n
1246  typedef aligned_lowp_uvec4 aligned_uvec4;
\n
1247 #elif(defined(GLM_PRECISION_MEDIUMP_UINT))
\n
1248  typedef aligned_mediump_uvec1 aligned_uvec1;
\n
1249  typedef aligned_mediump_uvec2 aligned_uvec2;
\n
1250  typedef aligned_mediump_uvec3 aligned_uvec3;
\n
1251  typedef aligned_mediump_uvec4 aligned_uvec4;
\n
1252 #else //defined(GLM_PRECISION_HIGHP_UINT)
\n
1253  typedef aligned_highp_uvec1 aligned_uvec1;
\n
1255 
\n
1257  typedef aligned_highp_uvec2 aligned_uvec2;
\n
1258 
\n
1260  typedef aligned_highp_uvec3 aligned_uvec3;
\n
1261 
\n
1263  typedef aligned_highp_uvec4 aligned_uvec4;
\n
1264 
\n
1266  typedef packed_highp_uvec1 packed_uvec1;
\n
1267 
\n
1269  typedef packed_highp_uvec2 packed_uvec2;
\n
1270 
\n
1272  typedef packed_highp_uvec3 packed_uvec3;
\n
1273 
\n
1275  typedef packed_highp_uvec4 packed_uvec4;
\n
1276 #endif//GLM_PRECISION
\n
1277 
\n
1278 #if(defined(GLM_PRECISION_LOWP_BOOL))
\n
1279  typedef aligned_lowp_bvec1 aligned_bvec1;
\n
1280  typedef aligned_lowp_bvec2 aligned_bvec2;
\n
1281  typedef aligned_lowp_bvec3 aligned_bvec3;
\n
1282  typedef aligned_lowp_bvec4 aligned_bvec4;
\n
1283 #elif(defined(GLM_PRECISION_MEDIUMP_BOOL))
\n
1284  typedef aligned_mediump_bvec1 aligned_bvec1;
\n
1285  typedef aligned_mediump_bvec2 aligned_bvec2;
\n
1286  typedef aligned_mediump_bvec3 aligned_bvec3;
\n
1287  typedef aligned_mediump_bvec4 aligned_bvec4;
\n
1288 #else //defined(GLM_PRECISION_HIGHP_BOOL)
\n
1289  typedef aligned_highp_bvec1 aligned_bvec1;
\n
1291 
\n
1293  typedef aligned_highp_bvec2 aligned_bvec2;
\n
1294 
\n
1296  typedef aligned_highp_bvec3 aligned_bvec3;
\n
1297 
\n
1299  typedef aligned_highp_bvec4 aligned_bvec4;
\n
1300 
\n
1302  typedef packed_highp_bvec1 packed_bvec1;
\n
1303 
\n
1305  typedef packed_highp_bvec2 packed_bvec2;
\n
1306 
\n
1308  typedef packed_highp_bvec3 packed_bvec3;
\n
1309 
\n
1311  typedef packed_highp_bvec4 packed_bvec4;
\n
1312 #endif//GLM_PRECISION
\n
1313 
\n
1315 }//namespace glm
\n
glm::packed_uvec3
packed_highp_uvec3 packed_uvec3
3 components vector tightly packed in memory of unsigned integer numbers.
Definition: gtc/type_aligned.hpp:1272
\n
glm::packed_mat2x2
packed_highp_mat2x2 packed_mat2x2
2 by 2 matrix tightly packed in memory of single-precision floating-point numbers.
Definition: gtc/type_aligned.hpp:1009
\n
glm::aligned_lowp_mat2x4
mat< 2, 4, float, aligned_lowp > aligned_lowp_mat2x4
2 by 4 matrix aligned in memory of single-precision floating-point numbers using low precision arithm...
Definition: gtc/type_aligned.hpp:609
\n
glm::aligned_lowp_bvec4
vec< 4, bool, aligned_lowp > aligned_lowp_bvec4
4 components vector aligned in memory of bool values.
Definition: gtc/type_aligned.hpp:363
\n
glm::packed_highp_dvec4
vec< 4, double, packed_highp > packed_highp_dvec4
4 components vector tightly packed in memory of double-precision floating-point numbers using high pr...
Definition: gtc/type_aligned.hpp:375
\n
glm::packed_dmat2x3
packed_highp_dmat2x3 packed_dmat2x3
2 by 3 matrix tightly packed in memory of double-precision floating-point numbers.
Definition: gtc/type_aligned.hpp:1180
\n
glm::packed_lowp_bvec3
vec< 3, bool, packed_lowp > packed_lowp_bvec3
3 components vector tightly packed in memory of bool values.
Definition: gtc/type_aligned.hpp:316
\n
glm::packed_mat4
packed_highp_mat4 packed_mat4
4 by 4 matrix tightly packed in memory of single-precision floating-point numbers.
Definition: gtc/type_aligned.hpp:979
\n
glm::aligned_uvec2
aligned_highp_uvec2 aligned_uvec2
2 components vector aligned in memory of unsigned integer numbers.
Definition: gtc/type_aligned.hpp:1257
\n
glm::aligned_lowp_bvec2
vec< 2, bool, aligned_lowp > aligned_lowp_bvec2
2 components vector aligned in memory of bool values.
Definition: gtc/type_aligned.hpp:179
\n
glm::packed_highp_ivec3
vec< 3, int, packed_highp > packed_highp_ivec3
3 components vector tightly packed in memory of signed integer numbers.
Definition: gtc/type_aligned.hpp:292
\n
glm::packed_highp_dmat4x2
mat< 4, 2, double, packed_highp > packed_highp_dmat4x2
4 by 2 matrix tightly packed in memory of double-precision floating-point numbers using high precisio...
Definition: gtc/type_aligned.hpp:782
\n
glm::packed_dmat2
packed_highp_dmat2 packed_dmat2
2 by 2 matrix tightly packed in memory of double-precision floating-point numbers.
Definition: gtc/type_aligned.hpp:1141
\n
glm::packed_highp_dmat3
mat< 3, 3, double, packed_highp > packed_highp_dmat3
3 by 3 matrix tightly packed in memory of double-precision floating-point numbers using high precisio...
Definition: gtc/type_aligned.hpp:478
\n
glm::aligned_lowp_mat4x3
mat< 4, 3, float, aligned_lowp > aligned_lowp_mat4x3
4 by 3 matrix aligned in memory of single-precision floating-point numbers using low precision arithm...
Definition: gtc/type_aligned.hpp:799
\n
glm::packed_highp_dmat2x4
mat< 2, 4, double, packed_highp > packed_highp_dmat2x4
2 by 4 matrix tightly packed in memory of double-precision floating-point numbers using high precisio...
Definition: gtc/type_aligned.hpp:630
\n
glm::aligned_mediump_ivec2
vec< 2, int, aligned_mediump > aligned_mediump_ivec2
2 components vector aligned in memory of signed integer numbers.
Definition: gtc/type_aligned.hpp:158
\n
glm::packed_mediump_mat4x3
mat< 4, 3, float, packed_mediump > packed_mediump_mat4x3
4 by 3 matrix tightly packed in memory of single-precision floating-point numbers using medium precis...
Definition: gtc/type_aligned.hpp:814
\n
glm::packed_ivec3
packed_highp_ivec3 packed_ivec3
3 components vector tightly packed in memory of signed integer numbers.
Definition: gtc/type_aligned.hpp:1234
\n
glm::aligned_highp_dmat3x4
mat< 3, 4, double, aligned_highp > aligned_highp_dmat3x4
3 by 4 matrix aligned in memory of double-precision floating-point numbers using high precision arith...
Definition: gtc/type_aligned.hpp:726
\n
glm::packed_mediump_dmat3x3
mat< 3, 3, double, packed_mediump > packed_mediump_dmat3x3
3 by 3 matrix tightly packed in memory of double-precision floating-point numbers using medium precis...
Definition: gtc/type_aligned.hpp:709
\n
glm::packed_mat2
packed_highp_mat2 packed_mat2
2 by 2 matrix tightly packed in memory of single-precision floating-point numbers.
Definition: gtc/type_aligned.hpp:973
\n
glm::packed_lowp_dmat3x4
mat< 3, 4, double, packed_lowp > packed_lowp_dmat3x4
3 by 4 matrix tightly packed in memory of double-precision floating-point numbers using low precision...
Definition: gtc/type_aligned.hpp:750
\n
glm::aligned_mediump_vec2
vec< 2, float, aligned_mediump > aligned_mediump_vec2
2 components vector aligned in memory of single-precision floating-point numbers using medium precisi...
Definition: gtc/type_aligned.hpp:140
\n
glm::aligned_lowp_mat4x4
mat< 4, 4, float, aligned_lowp > aligned_lowp_mat4x4
4 by 4 matrix aligned in memory of single-precision floating-point numbers using low precision arithm...
Definition: gtc/type_aligned.hpp:837
\n
glm::packed_mat3
packed_highp_mat3 packed_mat3
3 by 3 matrix tightly packed in memory of single-precision floating-point numbers.
Definition: gtc/type_aligned.hpp:976
\n
glm::packed_dmat4
packed_highp_dmat4 packed_dmat4
4 by 4 matrix tightly packed in memory of double-precision floating-point numbers.
Definition: gtc/type_aligned.hpp:1147
\n
glm::packed_vec4
packed_highp_vec4 packed_vec4
4 components vector tightly packed in memory of single-precision floating-point numbers.
Definition: gtc/type_aligned.hpp:961
\n
glm::aligned_highp_vec4
vec< 4, float, aligned_highp > aligned_highp_vec4
4 components vector aligned in memory of single-precision floating-point numbers using high precision...
Definition: gtc/type_aligned.hpp:321
\n
glm::packed_highp_dmat4x4
mat< 4, 4, double, packed_highp > packed_highp_dmat4x4
4 by 4 matrix tightly packed in memory of double-precision floating-point numbers using high precisio...
Definition: gtc/type_aligned.hpp:858
\n
glm::aligned_mediump_dvec1
vec< 1, double, aligned_mediump > aligned_mediump_dvec1
1 component vector aligned in memory of double-precision floating-point numbers using medium precisio...
Definition: gtc/type_aligned.hpp:57
\n
glm::aligned_highp_dmat3x3
mat< 3, 3, double, aligned_highp > aligned_highp_dmat3x3
3 by 3 matrix aligned in memory of double-precision floating-point numbers using high precision arith...
Definition: gtc/type_aligned.hpp:688
\n
glm::packed_dvec3
packed_highp_dvec3 packed_dvec3
3 components vector tightly packed in memory of double-precision floating-point numbers.
Definition: gtc/type_aligned.hpp:1126
\n
glm::packed_mediump_dvec1
vec< 1, double, packed_mediump > packed_mediump_dvec1
1 component vector tightly packed in memory of double-precision floating-point numbers using medium p...
Definition: gtc/type_aligned.hpp:102
\n
glm::packed_uvec1
packed_highp_uvec1 packed_uvec1
1 component vector tightly packed in memory of unsigned integer numbers.
Definition: gtc/type_aligned.hpp:1266
\n
glm::packed_lowp_mat3x4
mat< 3, 4, float, packed_lowp > packed_lowp_mat3x4
3 by 4 matrix tightly packed in memory of single-precision floating-point numbers using low precision...
Definition: gtc/type_aligned.hpp:741
\n
glm::aligned_lowp_uvec1
vec< 1, uint, aligned_lowp > aligned_lowp_uvec1
1 component vector aligned in memory of unsigned integer numbers.
Definition: gtc/type_aligned.hpp:78
\n
glm::packed_lowp_dmat2x4
mat< 2, 4, double, packed_lowp > packed_lowp_dmat2x4
2 by 4 matrix tightly packed in memory of double-precision floating-point numbers using low precision...
Definition: gtc/type_aligned.hpp:636
\n
glm::aligned_ivec3
aligned_highp_ivec3 aligned_ivec3
3 components vector aligned in memory of signed integer numbers.
Definition: gtc/type_aligned.hpp:1222
\n
glm::packed_highp_dmat3x4
mat< 3, 4, double, packed_highp > packed_highp_dmat3x4
3 by 4 matrix tightly packed in memory of double-precision floating-point numbers using high precisio...
Definition: gtc/type_aligned.hpp:744
\n
glm::packed_vec2
packed_highp_vec2 packed_vec2
2 components vector tightly packed in memory of single-precision floating-point numbers.
Definition: gtc/type_aligned.hpp:955
\n
glm::packed_highp_uvec1
vec< 1, uint, packed_highp > packed_highp_uvec1
1 component vector tightly packed in memory of unsigned integer numbers.
Definition: gtc/type_aligned.hpp:117
\n
glm::packed_lowp_mat2x2
mat< 2, 2, float, packed_lowp > packed_lowp_mat2x2
2 by 2 matrix tightly packed in memory of single-precision floating-point numbers using low precision...
Definition: gtc/type_aligned.hpp:551
\n
glm::packed_highp_bvec1
vec< 1, bool, packed_highp > packed_highp_bvec1
1 component vector tightly packed in memory of bool values.
Definition: gtc/type_aligned.hpp:126
\n
glm::aligned_bvec4
aligned_highp_bvec4 aligned_bvec4
4 components vector aligned in memory of bool values.
Definition: gtc/type_aligned.hpp:1299
\n
glm::aligned_vec3
aligned_highp_vec3 aligned_vec3
3 components vector aligned in memory of single-precision floating-point numbers. ...
Definition: gtc/type_aligned.hpp:946
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glm::packed_lowp_dmat3x3
mat< 3, 3, double, packed_lowp > packed_lowp_dmat3x3
3 by 3 matrix tightly packed in memory of double-precision floating-point numbers using low precision...
Definition: gtc/type_aligned.hpp:712
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glm::aligned_uvec3
aligned_highp_uvec3 aligned_uvec3
3 components vector aligned in memory of unsigned integer numbers.
Definition: gtc/type_aligned.hpp:1260
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glm::aligned_mediump_dmat4x2
mat< 4, 2, double, aligned_mediump > aligned_mediump_dmat4x2
4 by 2 matrix aligned in memory of double-precision floating-point numbers using medium precision ari...
Definition: gtc/type_aligned.hpp:767
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glm::aligned_highp_mat3
mat< 3, 3, float, aligned_highp > aligned_highp_mat3
3 by 3 matrix aligned in memory of single-precision floating-point numbers using high precision arith...
Definition: gtc/type_aligned.hpp:451
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glm::packed_mediump_uvec2
vec< 2, uint, packed_mediump > packed_mediump_uvec2
2 components vector tightly packed in memory of unsigned integer numbers.
Definition: gtc/type_aligned.hpp:212
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glm::aligned_highp_vec3
vec< 3, float, aligned_highp > aligned_highp_vec3
3 components vector aligned in memory of single-precision floating-point numbers using high precision...
Definition: gtc/type_aligned.hpp:229
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glm::aligned_mat4x3
aligned_highp_mat4x3 aligned_mat4x3
4 by 3 matrix tightly aligned in memory of single-precision floating-point numbers.
Definition: gtc/type_aligned.hpp:1003
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glm::aligned_lowp_ivec4
vec< 4, int, aligned_lowp > aligned_lowp_ivec4
4 components vector aligned in memory of signed integer numbers.
Definition: gtc/type_aligned.hpp:345
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glm::aligned_highp_mat2
mat< 2, 2, float, aligned_highp > aligned_highp_mat2
2 by 2 matrix aligned in memory of single-precision floating-point numbers using high precision arith...
Definition: gtc/type_aligned.hpp:413
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glm::packed_vec1
packed_highp_vec1 packed_vec1
1 component vector tightly packed in memory of single-precision floating-point numbers.
Definition: gtc/type_aligned.hpp:952
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glm::aligned_lowp_ivec2
vec< 2, int, aligned_lowp > aligned_lowp_ivec2
2 components vector aligned in memory of signed integer numbers.
Definition: gtc/type_aligned.hpp:161
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glm::packed_lowp_dmat4x2
mat< 4, 2, double, packed_lowp > packed_lowp_dmat4x2
4 by 2 matrix tightly packed in memory of double-precision floating-point numbers using low precision...
Definition: gtc/type_aligned.hpp:788
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glm::aligned_dvec3
aligned_highp_dvec3 aligned_dvec3
3 components vector aligned in memory of double-precision floating-point numbers. ...
Definition: gtc/type_aligned.hpp:1114
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glm::packed_lowp_vec1
vec< 1, float, packed_lowp > packed_lowp_vec1
1 component vector tightly packed in memory of single-precision floating-point numbers using low prec...
Definition: gtc/type_aligned.hpp:96
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glm::packed_mediump_bvec3
vec< 3, bool, packed_mediump > packed_mediump_bvec3
3 components vector tightly packed in memory of bool values.
Definition: gtc/type_aligned.hpp:313
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glm::aligned_dvec1
aligned_highp_dvec1 aligned_dvec1
1 component vector aligned in memory of double-precision floating-point numbers.
Definition: gtc/type_aligned.hpp:1108
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glm::packed_uvec4
packed_highp_uvec4 packed_uvec4
4 components vector tightly packed in memory of unsigned integer numbers.
Definition: gtc/type_aligned.hpp:1275
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glm::packed_dmat3
packed_highp_dmat3 packed_dmat3
3 by 3 matrix tightly packed in memory of double-precision floating-point numbers.
Definition: gtc/type_aligned.hpp:1144
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glm::aligned_highp_vec2
vec< 2, float, aligned_highp > aligned_highp_vec2
2 components vector aligned in memory of single-precision floating-point numbers using high precision...
Definition: gtc/type_aligned.hpp:137
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glm::aligned_highp_dmat2x2
mat< 2, 2, double, aligned_highp > aligned_highp_dmat2x2
2 by 2 matrix aligned in memory of double-precision floating-point numbers using high precision arith...
Definition: gtc/type_aligned.hpp:536
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glm::aligned_lowp_uvec3
vec< 3, uint, aligned_lowp > aligned_lowp_uvec3
3 components vector aligned in memory of unsigned integer numbers.
Definition: gtc/type_aligned.hpp:262
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glm::aligned_lowp_dvec1
vec< 1, double, aligned_lowp > aligned_lowp_dvec1
1 component vector aligned in memory of double-precision floating-point numbers using low precision a...
Definition: gtc/type_aligned.hpp:60
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glm::aligned_mediump_vec1
vec< 1, float, aligned_mediump > aligned_mediump_vec1
1 component vector aligned in memory of single-precision floating-point numbers using medium precisio...
Definition: gtc/type_aligned.hpp:48
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glm::packed_lowp_dmat4x3
mat< 4, 3, double, packed_lowp > packed_lowp_dmat4x3
4 by 3 matrix tightly packed in memory of double-precision floating-point numbers using low precision...
Definition: gtc/type_aligned.hpp:826
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glm::aligned_lowp_dmat3x4
mat< 3, 4, double, aligned_lowp > aligned_lowp_dmat3x4
3 by 4 matrix aligned in memory of double-precision floating-point numbers using low precision arithm...
Definition: gtc/type_aligned.hpp:732
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glm::packed_highp_mat4x2
mat< 4, 2, float, packed_highp > packed_highp_mat4x2
4 by 2 matrix tightly packed in memory of single-precision floating-point numbers using high precisio...
Definition: gtc/type_aligned.hpp:773
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glm::aligned_mediump_mat3x2
mat< 3, 2, float, aligned_mediump > aligned_mediump_mat3x2
3 by 2 matrix aligned in memory of single-precision floating-point numbers using medium precision ari...
Definition: gtc/type_aligned.hpp:644
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glm::aligned_mediump_mat4x3
mat< 4, 3, float, aligned_mediump > aligned_mediump_mat4x3
4 by 3 matrix aligned in memory of single-precision floating-point numbers using medium precision ari...
Definition: gtc/type_aligned.hpp:796
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glm::aligned_lowp_dvec3
vec< 3, double, aligned_lowp > aligned_lowp_dvec3
3 components vector aligned in memory of double-precision floating-point numbers using low precision ...
Definition: gtc/type_aligned.hpp:244
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glm::packed_mediump_mat2
mat< 2, 2, float, packed_mediump > packed_mediump_mat2
2 by 2 matrix tightly packed in memory of single-precision floating-point numbers using medium precis...
Definition: gtc/type_aligned.hpp:434
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glm::packed_mat3x3
packed_highp_mat3x3 packed_mat3x3
3 by 3 matrix tightly packed in memory of single-precision floating-point numbers.
Definition: gtc/type_aligned.hpp:1021
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glm::packed_highp_dvec1
vec< 1, double, packed_highp > packed_highp_dvec1
1 component vector tightly packed in memory of double-precision floating-point numbers using high pre...
Definition: gtc/type_aligned.hpp:99
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glm::aligned_lowp_dmat3x2
mat< 3, 2, double, aligned_lowp > aligned_lowp_dmat3x2
3 by 2 matrix aligned in memory of double-precision floating-point numbers using low precision arithm...
Definition: gtc/type_aligned.hpp:656
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glm::packed_mediump_mat3
mat< 3, 3, float, packed_mediump > packed_mediump_mat3
3 by 3 matrix tightly packed in memory of single-precision floating-point numbers using medium precis...
Definition: gtc/type_aligned.hpp:472
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glm::aligned_lowp_mat2
mat< 2, 2, float, aligned_lowp > aligned_lowp_mat2
2 by 2 matrix aligned in memory of single-precision floating-point numbers using low precision arithm...
Definition: gtc/type_aligned.hpp:419
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glm::packed_dmat4x4
packed_highp_dmat4x4 packed_dmat4x4
4 by 4 matrix tightly packed in memory of double-precision floating-point numbers.
Definition: gtc/type_aligned.hpp:1201
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glm::aligned_lowp_dmat2x3
mat< 2, 3, double, aligned_lowp > aligned_lowp_dmat2x3
2 by 3 matrix aligned in memory of double-precision floating-point numbers using low precision arithm...
Definition: gtc/type_aligned.hpp:580
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glm::packed_lowp_mat4
mat< 4, 4, float, packed_lowp > packed_lowp_mat4
4 by 4 matrix tightly packed in memory of single-precision floating-point numbers using low precision...
Definition: gtc/type_aligned.hpp:513
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glm::packed_highp_vec1
vec< 1, float, packed_highp > packed_highp_vec1
1 component vector tightly packed in memory of single-precision floating-point numbers using high pre...
Definition: gtc/type_aligned.hpp:90
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glm::aligned_mediump_mat2x3
mat< 2, 3, float, aligned_mediump > aligned_mediump_mat2x3
2 by 3 matrix aligned in memory of single-precision floating-point numbers using medium precision ari...
Definition: gtc/type_aligned.hpp:568
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glm::packed_highp_mat2x2
mat< 2, 2, float, packed_highp > packed_highp_mat2x2
2 by 2 matrix tightly packed in memory of single-precision floating-point numbers using high precisio...
Definition: gtc/type_aligned.hpp:545
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glm::aligned_mediump_bvec2
vec< 2, bool, aligned_mediump > aligned_mediump_bvec2
2 components vector aligned in memory of bool values.
Definition: gtc/type_aligned.hpp:176
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glm::packed_lowp_dmat2
mat< 2, 2, double, packed_lowp > packed_lowp_dmat2
2 by 2 matrix tightly packed in memory of double-precision floating-point numbers using low precision...
Definition: gtc/type_aligned.hpp:446
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glm::aligned_mediump_dmat2
mat< 2, 2, double, aligned_mediump > aligned_mediump_dmat2
2 by 2 matrix aligned in memory of double-precision floating-point numbers using medium precision ari...
Definition: gtc/type_aligned.hpp:425
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glm::packed_mediump_vec4
vec< 4, float, packed_mediump > packed_mediump_vec4
4 components vector tightly packed in memory of single-precision floating-point numbers using medium ...
Definition: gtc/type_aligned.hpp:369
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glm::aligned_dmat4x2
aligned_highp_dmat4x2 aligned_dmat4x2
4 by 2 matrix tightly aligned in memory of double-precision floating-point numbers.
Definition: gtc/type_aligned.hpp:1168
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glm::packed_lowp_dmat4x4
mat< 4, 4, double, packed_lowp > packed_lowp_dmat4x4
4 by 4 matrix tightly packed in memory of double-precision floating-point numbers using low precision...
Definition: gtc/type_aligned.hpp:864
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glm::packed_highp_dmat2x2
mat< 2, 2, double, packed_highp > packed_highp_dmat2x2
2 by 2 matrix tightly packed in memory of double-precision floating-point numbers using high precisio...
Definition: gtc/type_aligned.hpp:554
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glm::packed_lowp_mat3x3
mat< 3, 3, float, packed_lowp > packed_lowp_mat3x3
3 by 3 matrix tightly packed in memory of single-precision floating-point numbers using low precision...
Definition: gtc/type_aligned.hpp:703
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glm::packed_highp_mat4x3
mat< 4, 3, float, packed_highp > packed_highp_mat4x3
4 by 3 matrix tightly packed in memory of single-precision floating-point numbers using high precisio...
Definition: gtc/type_aligned.hpp:811
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glm::aligned_mediump_mat3
mat< 3, 3, float, aligned_mediump > aligned_mediump_mat3
3 by 3 matrix aligned in memory of single-precision floating-point numbers using medium precision ari...
Definition: gtc/type_aligned.hpp:454
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glm::aligned_highp_dmat4x3
mat< 4, 3, double, aligned_highp > aligned_highp_dmat4x3
4 by 3 matrix aligned in memory of double-precision floating-point numbers using high precision arith...
Definition: gtc/type_aligned.hpp:802
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glm::aligned_lowp_bvec1
vec< 1, bool, aligned_lowp > aligned_lowp_bvec1
1 component vector aligned in memory of bool values.
Definition: gtc/type_aligned.hpp:87
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glm::aligned_mat2
aligned_highp_mat2 aligned_mat2
2 by 2 matrix tightly aligned in memory of single-precision floating-point numbers.
Definition: gtc/type_aligned.hpp:964
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glm::aligned_mediump_dmat4x4
mat< 4, 4, double, aligned_mediump > aligned_mediump_dmat4x4
4 by 4 matrix aligned in memory of double-precision floating-point numbers using medium precision ari...
Definition: gtc/type_aligned.hpp:843
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glm::aligned_mediump_ivec3
vec< 3, int, aligned_mediump > aligned_mediump_ivec3
3 components vector aligned in memory of signed integer numbers.
Definition: gtc/type_aligned.hpp:250
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glm::aligned_bvec3
aligned_highp_bvec3 aligned_bvec3
3 components vector aligned in memory of bool values.
Definition: gtc/type_aligned.hpp:1296
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glm::packed_uvec2
packed_highp_uvec2 packed_uvec2
2 components vector tightly packed in memory of unsigned integer numbers.
Definition: gtc/type_aligned.hpp:1269
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glm::aligned_lowp_dvec4
vec< 4, double, aligned_lowp > aligned_lowp_dvec4
4 components vector aligned in memory of double-precision floating-point numbers using low precision ...
Definition: gtc/type_aligned.hpp:336
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glm::aligned_lowp_dmat3
mat< 3, 3, double, aligned_lowp > aligned_lowp_dmat3
3 by 3 matrix aligned in memory of double-precision floating-point numbers using low precision arithm...
Definition: gtc/type_aligned.hpp:466
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glm::packed_mediump_mat4x4
mat< 4, 4, float, packed_mediump > packed_mediump_mat4x4
4 by 4 matrix tightly packed in memory of single-precision floating-point numbers using medium precis...
Definition: gtc/type_aligned.hpp:852
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glm::aligned_highp_uvec4
vec< 4, uint, aligned_highp > aligned_highp_uvec4
4 components vector aligned in memory of unsigned integer numbers.
Definition: gtc/type_aligned.hpp:348
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glm::packed_highp_dmat4x3
mat< 4, 3, double, packed_highp > packed_highp_dmat4x3
4 by 3 matrix tightly packed in memory of double-precision floating-point numbers using high precisio...
Definition: gtc/type_aligned.hpp:820
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glm::packed_lowp_mat4x3
mat< 4, 3, float, packed_lowp > packed_lowp_mat4x3
4 by 3 matrix tightly packed in memory of single-precision floating-point numbers using low precision...
Definition: gtc/type_aligned.hpp:817
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glm::aligned_lowp_vec2
vec< 2, float, aligned_lowp > aligned_lowp_vec2
2 components vector aligned in memory of single-precision floating-point numbers using low precision ...
Definition: gtc/type_aligned.hpp:143
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glm::packed_lowp_ivec1
vec< 1, int, packed_lowp > packed_lowp_ivec1
1 component vector tightly packed in memory of signed integer numbers.
Definition: gtc/type_aligned.hpp:114
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glm::aligned_lowp_bvec3
vec< 3, bool, aligned_lowp > aligned_lowp_bvec3
3 components vector aligned in memory of bool values.
Definition: gtc/type_aligned.hpp:271
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glm::aligned_mediump_dmat4
mat< 4, 4, double, aligned_mediump > aligned_mediump_dmat4
4 by 4 matrix aligned in memory of double-precision floating-point numbers using medium precision ari...
Definition: gtc/type_aligned.hpp:501
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glm::packed_mediump_mat2x4
mat< 2, 4, float, packed_mediump > packed_mediump_mat2x4
2 by 4 matrix tightly packed in memory of single-precision floating-point numbers using medium precis...
Definition: gtc/type_aligned.hpp:624
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glm::packed_highp_dmat4
mat< 4, 4, double, packed_highp > packed_highp_dmat4
4 by 4 matrix tightly packed in memory of double-precision floating-point numbers using high precisio...
Definition: gtc/type_aligned.hpp:516
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glm::aligned_mediump_mat4x2
mat< 4, 2, float, aligned_mediump > aligned_mediump_mat4x2
4 by 2 matrix aligned in memory of single-precision floating-point numbers using medium precision ari...
Definition: gtc/type_aligned.hpp:758
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glm::packed_mediump_mat3x4
mat< 3, 4, float, packed_mediump > packed_mediump_mat3x4
3 by 4 matrix tightly packed in memory of single-precision floating-point numbers using medium precis...
Definition: gtc/type_aligned.hpp:738
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glm::packed_highp_uvec3
vec< 3, uint, packed_highp > packed_highp_uvec3
3 components vector tightly packed in memory of unsigned integer numbers.
Definition: gtc/type_aligned.hpp:301
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glm::aligned_dmat2x2
aligned_highp_dmat2x2 aligned_dmat2x2
2 by 2 matrix tightly aligned in memory of double-precision floating-point numbers.
Definition: gtc/type_aligned.hpp:1150
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glm::packed_mediump_dmat2
mat< 2, 2, double, packed_mediump > packed_mediump_dmat2
2 by 2 matrix tightly packed in memory of double-precision floating-point numbers using medium precis...
Definition: gtc/type_aligned.hpp:443
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glm::packed_highp_mat3x4
mat< 3, 4, float, packed_highp > packed_highp_mat3x4
3 by 4 matrix tightly packed in memory of single-precision floating-point numbers using high precisio...
Definition: gtc/type_aligned.hpp:735
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glm::packed_mat3x4
packed_highp_mat3x4 packed_mat3x4
3 by 4 matrix tightly packed in memory of single-precision floating-point numbers.
Definition: gtc/type_aligned.hpp:1024
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glm::packed_mediump_dmat2x4
mat< 2, 4, double, packed_mediump > packed_mediump_dmat2x4
2 by 4 matrix tightly packed in memory of double-precision floating-point numbers using medium precis...
Definition: gtc/type_aligned.hpp:633
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glm::packed_mediump_uvec1
vec< 1, uint, packed_mediump > packed_mediump_uvec1
1 component vector tightly packed in memory of unsigned integer numbers.
Definition: gtc/type_aligned.hpp:120
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glm::aligned_lowp_dmat4
mat< 4, 4, double, aligned_lowp > aligned_lowp_dmat4
4 by 4 matrix aligned in memory of double-precision floating-point numbers using low precision arithm...
Definition: gtc/type_aligned.hpp:504
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glm::packed_mat4x3
packed_highp_mat4x3 packed_mat4x3
4 by 3 matrix tightly packed in memory of single-precision floating-point numbers.
Definition: gtc/type_aligned.hpp:1030
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glm::packed_lowp_ivec4
vec< 4, int, packed_lowp > packed_lowp_ivec4
4 components vector tightly packed in memory of signed integer numbers.
Definition: gtc/type_aligned.hpp:390
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glm::packed_mediump_ivec4
vec< 4, int, packed_mediump > packed_mediump_ivec4
4 components vector tightly packed in memory of signed integer numbers.
Definition: gtc/type_aligned.hpp:387
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glm::aligned_mediump_dvec2
vec< 2, double, aligned_mediump > aligned_mediump_dvec2
2 components vector aligned in memory of double-precision floating-point numbers using medium precisi...
Definition: gtc/type_aligned.hpp:149
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glm::packed_ivec2
packed_highp_ivec2 packed_ivec2
2 components vector tightly packed in memory of signed integer numbers.
Definition: gtc/type_aligned.hpp:1231
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glm::aligned_ivec1
aligned_highp_ivec1 aligned_ivec1
1 component vector aligned in memory of signed integer numbers.
Definition: gtc/type_aligned.hpp:1216
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glm::packed_mediump_ivec3
vec< 3, int, packed_mediump > packed_mediump_ivec3
3 components vector tightly packed in memory of signed integer numbers.
Definition: gtc/type_aligned.hpp:295
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glm::packed_lowp_uvec3
vec< 3, uint, packed_lowp > packed_lowp_uvec3
3 components vector tightly packed in memory of unsigned integer numbers.
Definition: gtc/type_aligned.hpp:307
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glm::packed_dmat4x2
packed_highp_dmat4x2 packed_dmat4x2
4 by 2 matrix tightly packed in memory of double-precision floating-point numbers.
Definition: gtc/type_aligned.hpp:1195
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glm::aligned_mediump_bvec4
vec< 4, bool, aligned_mediump > aligned_mediump_bvec4
4 components vector aligned in memory of bool values.
Definition: gtc/type_aligned.hpp:360
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glm::aligned_highp_bvec2
vec< 2, bool, aligned_highp > aligned_highp_bvec2
2 components vector aligned in memory of bool values.
Definition: gtc/type_aligned.hpp:173
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glm::packed_lowp_vec4
vec< 4, float, packed_lowp > packed_lowp_vec4
4 components vector tightly packed in memory of single-precision floating-point numbers using low pre...
Definition: gtc/type_aligned.hpp:372
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glm::aligned_highp_dvec4
vec< 4, double, aligned_highp > aligned_highp_dvec4
4 components vector aligned in memory of double-precision floating-point numbers using high precision...
Definition: gtc/type_aligned.hpp:330
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glm::packed_highp_dmat2x3
mat< 2, 3, double, packed_highp > packed_highp_dmat2x3
2 by 3 matrix tightly packed in memory of double-precision floating-point numbers using high precisio...
Definition: gtc/type_aligned.hpp:592
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glm::aligned_mediump_mat2
mat< 2, 2, float, aligned_mediump > aligned_mediump_mat2
2 by 2 matrix aligned in memory of single-precision floating-point numbers using medium precision ari...
Definition: gtc/type_aligned.hpp:416
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glm::aligned_mediump_dmat2x4
mat< 2, 4, double, aligned_mediump > aligned_mediump_dmat2x4
2 by 4 matrix aligned in memory of double-precision floating-point numbers using medium precision ari...
Definition: gtc/type_aligned.hpp:615
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glm::packed_lowp_bvec4
vec< 4, bool, packed_lowp > packed_lowp_bvec4
4 components vector tightly packed in memory of bool values.
Definition: gtc/type_aligned.hpp:408
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glm::packed_mediump_dvec2
vec< 2, double, packed_mediump > packed_mediump_dvec2
2 components vector tightly packed in memory of double-precision floating-point numbers using medium ...
Definition: gtc/type_aligned.hpp:194
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glm::aligned_highp_dvec2
vec< 2, double, aligned_highp > aligned_highp_dvec2
2 components vector aligned in memory of double-precision floating-point numbers using high precision...
Definition: gtc/type_aligned.hpp:146
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glm::aligned_lowp_dmat2x4
mat< 2, 4, double, aligned_lowp > aligned_lowp_dmat2x4
2 by 4 matrix aligned in memory of double-precision floating-point numbers using low precision arithm...
Definition: gtc/type_aligned.hpp:618
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glm::aligned_mat2x3
aligned_highp_mat2x3 aligned_mat2x3
2 by 3 matrix tightly aligned in memory of single-precision floating-point numbers.
Definition: gtc/type_aligned.hpp:985
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glm::packed_lowp_mat2
mat< 2, 2, float, packed_lowp > packed_lowp_mat2
2 by 2 matrix tightly packed in memory of single-precision floating-point numbers using low precision...
Definition: gtc/type_aligned.hpp:437
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glm::aligned_mediump_ivec4
vec< 4, int, aligned_mediump > aligned_mediump_ivec4
4 components vector aligned in memory of signed integer numbers.
Definition: gtc/type_aligned.hpp:342
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glm::packed_lowp_bvec2
vec< 2, bool, packed_lowp > packed_lowp_bvec2
2 components vector tightly packed in memory of bool values.
Definition: gtc/type_aligned.hpp:224
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glm::packed_highp_ivec2
vec< 2, int, packed_highp > packed_highp_ivec2
2 components vector tightly packed in memory of signed integer numbers.
Definition: gtc/type_aligned.hpp:200
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glm::packed_dmat3x4
packed_highp_dmat3x4 packed_dmat3x4
3 by 4 matrix tightly packed in memory of double-precision floating-point numbers.
Definition: gtc/type_aligned.hpp:1192
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glm::packed_mediump_dmat3
mat< 3, 3, double, packed_mediump > packed_mediump_dmat3
3 by 3 matrix tightly packed in memory of double-precision floating-point numbers using medium precis...
Definition: gtc/type_aligned.hpp:481
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glm::aligned_lowp_dmat4x3
mat< 4, 3, double, aligned_lowp > aligned_lowp_dmat4x3
4 by 3 matrix aligned in memory of double-precision floating-point numbers using low precision arithm...
Definition: gtc/type_aligned.hpp:808
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glm::aligned_highp_dmat4x4
mat< 4, 4, double, aligned_highp > aligned_highp_dmat4x4
4 by 4 matrix aligned in memory of double-precision floating-point numbers using high precision arith...
Definition: gtc/type_aligned.hpp:840
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glm::packed_mediump_dmat4x2
mat< 4, 2, double, packed_mediump > packed_mediump_dmat4x2
4 by 2 matrix tightly packed in memory of double-precision floating-point numbers using medium precis...
Definition: gtc/type_aligned.hpp:785
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glm::packed_mat4x4
packed_highp_mat4x4 packed_mat4x4
4 by 4 matrix tightly packed in memory of single-precision floating-point numbers.
Definition: gtc/type_aligned.hpp:1033
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glm::packed_highp_mat4
mat< 4, 4, float, packed_highp > packed_highp_mat4
4 by 4 matrix tightly packed in memory of single-precision floating-point numbers using high precisio...
Definition: gtc/type_aligned.hpp:507
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glm::aligned_highp_dmat4
mat< 4, 4, double, aligned_highp > aligned_highp_dmat4
4 by 4 matrix aligned in memory of double-precision floating-point numbers using high precision arith...
Definition: gtc/type_aligned.hpp:498
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glm::aligned_highp_dmat3x2
mat< 3, 2, double, aligned_highp > aligned_highp_dmat3x2
3 by 2 matrix aligned in memory of double-precision floating-point numbers using high precision arith...
Definition: gtc/type_aligned.hpp:650
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glm::packed_lowp_dvec1
vec< 1, double, packed_lowp > packed_lowp_dvec1
1 component vector tightly packed in memory of double-precision floating-point numbers using low prec...
Definition: gtc/type_aligned.hpp:105
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glm::aligned_lowp_mat4
mat< 4, 4, float, aligned_lowp > aligned_lowp_mat4
4 by 4 matrix aligned in memory of single-precision floating-point numbers using low precision arithm...
Definition: gtc/type_aligned.hpp:495
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glm::packed_mediump_uvec3
vec< 3, uint, packed_mediump > packed_mediump_uvec3
3 components vector tightly packed in memory of unsigned integer numbers.
Definition: gtc/type_aligned.hpp:304
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glm::aligned_dmat4x3
aligned_highp_dmat4x3 aligned_dmat4x3
4 by 3 matrix tightly aligned in memory of double-precision floating-point numbers.
Definition: gtc/type_aligned.hpp:1171
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glm::aligned_lowp_dmat4x2
mat< 4, 2, double, aligned_lowp > aligned_lowp_dmat4x2
4 by 2 matrix aligned in memory of double-precision floating-point numbers using low precision arithm...
Definition: gtc/type_aligned.hpp:770
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glm::packed_mediump_dmat2x3
mat< 2, 3, double, packed_mediump > packed_mediump_dmat2x3
2 by 3 matrix tightly packed in memory of double-precision floating-point numbers using medium precis...
Definition: gtc/type_aligned.hpp:595
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glm::aligned_highp_dmat4x2
mat< 4, 2, double, aligned_highp > aligned_highp_dmat4x2
4 by 2 matrix aligned in memory of double-precision floating-point numbers using high precision arith...
Definition: gtc/type_aligned.hpp:764
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glm::aligned_mat3x4
aligned_highp_mat3x4 aligned_mat3x4
3 by 4 matrix tightly aligned in memory of single-precision floating-point numbers.
Definition: gtc/type_aligned.hpp:997
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glm::aligned_lowp_dmat4x4
mat< 4, 4, double, aligned_lowp > aligned_lowp_dmat4x4
4 by 4 matrix aligned in memory of double-precision floating-point numbers using low precision arithm...
Definition: gtc/type_aligned.hpp:846
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glm::aligned_mediump_mat4x4
mat< 4, 4, float, aligned_mediump > aligned_mediump_mat4x4
4 by 4 matrix aligned in memory of single-precision floating-point numbers using medium precision ari...
Definition: gtc/type_aligned.hpp:834
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glm::aligned_mat4
aligned_highp_mat4 aligned_mat4
4 by 4 matrix tightly aligned in memory of single-precision floating-point numbers.
Definition: gtc/type_aligned.hpp:970
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glm::packed_lowp_dmat2x2
mat< 2, 2, double, packed_lowp > packed_lowp_dmat2x2
2 by 2 matrix tightly packed in memory of double-precision floating-point numbers using low precision...
Definition: gtc/type_aligned.hpp:560
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glm::packed_mediump_ivec2
vec< 2, int, packed_mediump > packed_mediump_ivec2
2 components vector tightly packed in memory of signed integer numbers.
Definition: gtc/type_aligned.hpp:203
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glm::packed_dmat3x2
packed_highp_dmat3x2 packed_dmat3x2
3 by 2 matrix tightly packed in memory of double-precision floating-point numbers.
Definition: gtc/type_aligned.hpp:1186
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glm::packed_mediump_dmat4
mat< 4, 4, double, packed_mediump > packed_mediump_dmat4
4 by 4 matrix tightly packed in memory of double-precision floating-point numbers using medium precis...
Definition: gtc/type_aligned.hpp:519
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glm::aligned_lowp_vec3
vec< 3, float, aligned_lowp > aligned_lowp_vec3
3 components vector aligned in memory of single-precision floating-point numbers using low precision ...
Definition: gtc/type_aligned.hpp:235
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glm::packed_highp_mat2x4
mat< 2, 4, float, packed_highp > packed_highp_mat2x4
2 by 4 matrix tightly packed in memory of single-precision floating-point numbers using high precisio...
Definition: gtc/type_aligned.hpp:621
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glm::aligned_highp_mat2x3
mat< 2, 3, float, aligned_highp > aligned_highp_mat2x3
2 by 3 matrix aligned in memory of single-precision floating-point numbers using high precision arith...
Definition: gtc/type_aligned.hpp:565
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glm::packed_mediump_mat3x3
mat< 3, 3, float, packed_mediump > packed_mediump_mat3x3
3 by 3 matrix tightly packed in memory of single-precision floating-point numbers using medium precis...
Definition: gtc/type_aligned.hpp:700
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glm::packed_highp_vec4
vec< 4, float, packed_highp > packed_highp_vec4
4 components vector tightly packed in memory of single-precision floating-point numbers using high pr...
Definition: gtc/type_aligned.hpp:366
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glm::aligned_uvec1
aligned_highp_uvec1 aligned_uvec1
1 component vector aligned in memory of unsigned integer numbers.
Definition: gtc/type_aligned.hpp:1254
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glm::aligned_highp_mat4x4
mat< 4, 4, float, aligned_highp > aligned_highp_mat4x4
4 by 4 matrix aligned in memory of single-precision floating-point numbers using high precision arith...
Definition: gtc/type_aligned.hpp:831
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glm::packed_mediump_mat4x2
mat< 4, 2, float, packed_mediump > packed_mediump_mat4x2
4 by 2 matrix tightly packed in memory of single-precision floating-point numbers using medium precis...
Definition: gtc/type_aligned.hpp:776
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glm::aligned_lowp_mat3x2
mat< 3, 2, float, aligned_lowp > aligned_lowp_mat3x2
3 by 2 matrix aligned in memory of single-precision floating-point numbers using low precision arithm...
Definition: gtc/type_aligned.hpp:647
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glm::packed_lowp_mat3
mat< 3, 3, float, packed_lowp > packed_lowp_mat3
3 by 3 matrix tightly packed in memory of single-precision floating-point numbers using low precision...
Definition: gtc/type_aligned.hpp:475
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glm::packed_highp_bvec4
vec< 4, bool, packed_highp > packed_highp_bvec4
4 components vector tightly packed in memory of bool values.
Definition: gtc/type_aligned.hpp:402
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glm::aligned_vec1
aligned_highp_vec1 aligned_vec1
1 component vector aligned in memory of single-precision floating-point numbers.
Definition: gtc/type_aligned.hpp:940
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glm::packed_vec3
packed_highp_vec3 packed_vec3
3 components vector tightly packed in memory of single-precision floating-point numbers.
Definition: gtc/type_aligned.hpp:958
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glm::packed_mat2x3
packed_highp_mat2x3 packed_mat2x3
2 by 3 matrix tightly packed in memory of single-precision floating-point numbers.
Definition: gtc/type_aligned.hpp:1012
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glm::aligned_mediump_bvec3
vec< 3, bool, aligned_mediump > aligned_mediump_bvec3
3 components vector aligned in memory of bool values.
Definition: gtc/type_aligned.hpp:268
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glm::aligned_mediump_uvec1
vec< 1, uint, aligned_mediump > aligned_mediump_uvec1
1 component vector aligned in memory of unsigned integer numbers.
Definition: gtc/type_aligned.hpp:75
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glm::aligned_bvec2
aligned_highp_bvec2 aligned_bvec2
2 components vector aligned in memory of bool values.
Definition: gtc/type_aligned.hpp:1293
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glm::packed_dmat2x2
packed_highp_dmat2x2 packed_dmat2x2
2 by 2 matrix tightly packed in memory of double-precision floating-point numbers.
Definition: gtc/type_aligned.hpp:1177
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glm::packed_lowp_mat4x2
mat< 4, 2, float, packed_lowp > packed_lowp_mat4x2
4 by 2 matrix tightly packed in memory of single-precision floating-point numbers using low precision...
Definition: gtc/type_aligned.hpp:779
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glm::packed_dmat2x4
packed_highp_dmat2x4 packed_dmat2x4
2 by 4 matrix tightly packed in memory of double-precision floating-point numbers.
Definition: gtc/type_aligned.hpp:1183
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glm::aligned_highp_uvec3
vec< 3, uint, aligned_highp > aligned_highp_uvec3
3 components vector aligned in memory of unsigned integer numbers.
Definition: gtc/type_aligned.hpp:256
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glm::packed_mediump_bvec2
vec< 2, bool, packed_mediump > packed_mediump_bvec2
2 components vector tightly packed in memory of bool values.
Definition: gtc/type_aligned.hpp:221
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glm::aligned_bvec1
aligned_highp_bvec1 aligned_bvec1
1 component vector aligned in memory of bool values.
Definition: gtc/type_aligned.hpp:1290
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glm::aligned_mat3x2
aligned_highp_mat3x2 aligned_mat3x2
3 by 2 matrix tightly aligned in memory of single-precision floating-point numbers.
Definition: gtc/type_aligned.hpp:991
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glm::aligned_lowp_ivec1
vec< 1, int, aligned_lowp > aligned_lowp_ivec1
1 component vector aligned in memory of signed integer numbers.
Definition: gtc/type_aligned.hpp:69
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glm::aligned_mediump_dmat3x3
mat< 3, 3, double, aligned_mediump > aligned_mediump_dmat3x3
3 by 3 matrix aligned in memory of double-precision floating-point numbers using medium precision ari...
Definition: gtc/type_aligned.hpp:691
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glm::packed_lowp_mat3x2
mat< 3, 2, float, packed_lowp > packed_lowp_mat3x2
3 by 2 matrix tightly packed in memory of single-precision floating-point numbers using low precision...
Definition: gtc/type_aligned.hpp:665
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glm::packed_highp_mat2x3
mat< 2, 3, float, packed_highp > packed_highp_mat2x3
2 by 3 matrix tightly packed in memory of single-precision floating-point numbers using high precisio...
Definition: gtc/type_aligned.hpp:583
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glm::packed_lowp_mat4x4
mat< 4, 4, float, packed_lowp > packed_lowp_mat4x4
4 by 4 matrix tightly packed in memory of single-precision floating-point numbers using low precision...
Definition: gtc/type_aligned.hpp:855
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glm::aligned_uvec4
aligned_highp_uvec4 aligned_uvec4
4 components vector aligned in memory of unsigned integer numbers.
Definition: gtc/type_aligned.hpp:1263
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glm::packed_bvec2
packed_highp_bvec2 packed_bvec2
2 components vector tightly packed in memory of bool values.
Definition: gtc/type_aligned.hpp:1305
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glm::aligned_highp_mat3x3
mat< 3, 3, float, aligned_highp > aligned_highp_mat3x3
3 by 3 matrix aligned in memory of single-precision floating-point numbers using high precision arith...
Definition: gtc/type_aligned.hpp:679
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glm::packed_bvec4
packed_highp_bvec4 packed_bvec4
4 components vector tightly packed in memory of bool values.
Definition: gtc/type_aligned.hpp:1311
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glm::aligned_ivec4
aligned_highp_ivec4 aligned_ivec4
4 components vector aligned in memory of signed integer numbers.
Definition: gtc/type_aligned.hpp:1225
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glm::packed_highp_mat3x3
mat< 3, 3, float, packed_highp > packed_highp_mat3x3
3 by 3 matrix tightly packed in memory of single-precision floating-point numbers using high precisio...
Definition: gtc/type_aligned.hpp:697
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glm::packed_highp_ivec4
vec< 4, int, packed_highp > packed_highp_ivec4
4 components vector tightly packed in memory of signed integer numbers.
Definition: gtc/type_aligned.hpp:384
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glm::packed_mat3x2
packed_highp_mat3x2 packed_mat3x2
3 by 2 matrix tightly packed in memory of single-precision floating-point numbers.
Definition: gtc/type_aligned.hpp:1018
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glm::aligned_highp_uvec2
vec< 2, uint, aligned_highp > aligned_highp_uvec2
2 components vector aligned in memory of unsigned integer numbers.
Definition: gtc/type_aligned.hpp:164
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glm::aligned_dmat3
aligned_highp_dmat3 aligned_dmat3
3 by 3 matrix tightly aligned in memory of double-precision floating-point numbers.
Definition: gtc/type_aligned.hpp:1135
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glm::aligned_highp_ivec3
vec< 3, int, aligned_highp > aligned_highp_ivec3
3 components vector aligned in memory of signed integer numbers.
Definition: gtc/type_aligned.hpp:247
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glm::aligned_lowp_dmat2x2
mat< 2, 2, double, aligned_lowp > aligned_lowp_dmat2x2
2 by 2 matrix aligned in memory of double-precision floating-point numbers using low precision arithm...
Definition: gtc/type_aligned.hpp:542
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glm::aligned_highp_mat3x2
mat< 3, 2, float, aligned_highp > aligned_highp_mat3x2
3 by 2 matrix aligned in memory of single-precision floating-point numbers using high precision arith...
Definition: gtc/type_aligned.hpp:641
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glm::aligned_highp_uvec1
vec< 1, uint, aligned_highp > aligned_highp_uvec1
1 component vector aligned in memory of unsigned integer numbers.
Definition: gtc/type_aligned.hpp:72
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glm::aligned_mat2x4
aligned_highp_mat2x4 aligned_mat2x4
2 by 4 matrix tightly aligned in memory of single-precision floating-point numbers.
Definition: gtc/type_aligned.hpp:988
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glm::aligned_mediump_mat4
mat< 4, 4, float, aligned_mediump > aligned_mediump_mat4
4 by 4 matrix aligned in memory of single-precision floating-point numbers using medium precision ari...
Definition: gtc/type_aligned.hpp:492
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glm::packed_dvec1
packed_highp_dvec1 packed_dvec1
1 component vector tightly packed in memory of double-precision floating-point numbers.
Definition: gtc/type_aligned.hpp:1120
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glm::aligned_dmat2x4
aligned_highp_dmat2x4 aligned_dmat2x4
2 by 4 matrix tightly aligned in memory of double-precision floating-point numbers.
Definition: gtc/type_aligned.hpp:1156
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glm::packed_mediump_dmat2x2
mat< 2, 2, double, packed_mediump > packed_mediump_dmat2x2
2 by 2 matrix tightly packed in memory of double-precision floating-point numbers using medium precis...
Definition: gtc/type_aligned.hpp:557
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glm::packed_lowp_dvec3
vec< 3, double, packed_lowp > packed_lowp_dvec3
3 components vector tightly packed in memory of double-precision floating-point numbers using low pre...
Definition: gtc/type_aligned.hpp:289
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glm::aligned_lowp_uvec4
vec< 4, uint, aligned_lowp > aligned_lowp_uvec4
4 components vector aligned in memory of unsigned integer numbers.
Definition: gtc/type_aligned.hpp:354
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glm::packed_highp_uvec4
vec< 4, uint, packed_highp > packed_highp_uvec4
4 components vector tightly packed in memory of unsigned integer numbers.
Definition: gtc/type_aligned.hpp:393
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glm::packed_lowp_mat2x4
mat< 2, 4, float, packed_lowp > packed_lowp_mat2x4
2 by 4 matrix tightly packed in memory of single-precision floating-point numbers using low precision...
Definition: gtc/type_aligned.hpp:627
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glm::aligned_vec2
aligned_highp_vec2 aligned_vec2
2 components vector aligned in memory of single-precision floating-point numbers. ...
Definition: gtc/type_aligned.hpp:943
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glm::aligned_mat2x2
aligned_highp_mat2x2 aligned_mat2x2
2 by 2 matrix tightly aligned in memory of single-precision floating-point numbers.
Definition: gtc/type_aligned.hpp:982
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glm::packed_lowp_dmat3
mat< 3, 3, double, packed_lowp > packed_lowp_dmat3
3 by 3 matrix tightly packed in memory of double-precision floating-point numbers using low precision...
Definition: gtc/type_aligned.hpp:484
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glm::aligned_dmat3x3
aligned_highp_dmat3x3 aligned_dmat3x3
3 by 3 matrix tightly aligned in memory of double-precision floating-point numbers.
Definition: gtc/type_aligned.hpp:1162
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glm::packed_highp_dvec2
vec< 2, double, packed_highp > packed_highp_dvec2
2 components vector tightly packed in memory of double-precision floating-point numbers using high pr...
Definition: gtc/type_aligned.hpp:191
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glm::aligned_mediump_dmat2x2
mat< 2, 2, double, aligned_mediump > aligned_mediump_dmat2x2
2 by 2 matrix aligned in memory of double-precision floating-point numbers using medium precision ari...
Definition: gtc/type_aligned.hpp:539
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glm::packed_lowp_uvec1
vec< 1, uint, packed_lowp > packed_lowp_uvec1
1 component vector tightly packed in memory of unsigned integer numbers.
Definition: gtc/type_aligned.hpp:123
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glm::packed_lowp_uvec2
vec< 2, uint, packed_lowp > packed_lowp_uvec2
2 components vector tightly packed in memory of unsigned integer numbers.
Definition: gtc/type_aligned.hpp:215
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glm::packed_dmat4x3
packed_highp_dmat4x3 packed_dmat4x3
4 by 3 matrix tightly packed in memory of double-precision floating-point numbers.
Definition: gtc/type_aligned.hpp:1198
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glm::aligned_lowp_dmat3x3
mat< 3, 3, double, aligned_lowp > aligned_lowp_dmat3x3
3 by 3 matrix aligned in memory of double-precision floating-point numbers using low precision arithm...
Definition: gtc/type_aligned.hpp:694
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glm::packed_highp_mat3
mat< 3, 3, float, packed_highp > packed_highp_mat3
3 by 3 matrix tightly packed in memory of single-precision floating-point numbers using high precisio...
Definition: gtc/type_aligned.hpp:469
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glm::aligned_dmat2x3
aligned_highp_dmat2x3 aligned_dmat2x3
2 by 3 matrix tightly aligned in memory of double-precision floating-point numbers.
Definition: gtc/type_aligned.hpp:1153
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glm::aligned_mediump_mat2x2
mat< 2, 2, float, aligned_mediump > aligned_mediump_mat2x2
2 by 2 matrix aligned in memory of single-precision floating-point numbers using medium precision ari...
Definition: gtc/type_aligned.hpp:530
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glm::aligned_lowp_mat2x2
mat< 2, 2, float, aligned_lowp > aligned_lowp_mat2x2
2 by 2 matrix aligned in memory of single-precision floating-point numbers using low precision arithm...
Definition: gtc/type_aligned.hpp:533
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glm::packed_mediump_bvec1
vec< 1, bool, packed_mediump > packed_mediump_bvec1
1 component vector tightly packed in memory of bool values.
Definition: gtc/type_aligned.hpp:129
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glm::packed_lowp_dmat4
mat< 4, 4, double, packed_lowp > packed_lowp_dmat4
4 by 4 matrix tightly packed in memory of double-precision floating-point numbers using low precision...
Definition: gtc/type_aligned.hpp:522
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glm::packed_ivec1
packed_highp_ivec1 packed_ivec1
1 component vector tightly packed in memory of signed integer numbers.
Definition: gtc/type_aligned.hpp:1228
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glm::packed_lowp_bvec1
vec< 1, bool, packed_lowp > packed_lowp_bvec1
1 component vector tightly packed in memory of bool values.
Definition: gtc/type_aligned.hpp:132
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glm::aligned_dmat3x2
aligned_highp_dmat3x2 aligned_dmat3x2
3 by 2 matrix tightly aligned in memory of double-precision floating-point numbers.
Definition: gtc/type_aligned.hpp:1159
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glm::packed_highp_dmat3x2
mat< 3, 2, double, packed_highp > packed_highp_dmat3x2
3 by 2 matrix tightly packed in memory of double-precision floating-point numbers using high precisio...
Definition: gtc/type_aligned.hpp:668
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glm::aligned_ivec2
aligned_highp_ivec2 aligned_ivec2
2 components vector aligned in memory of signed integer numbers.
Definition: gtc/type_aligned.hpp:1219
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glm::aligned_dmat4x4
aligned_highp_dmat4x4 aligned_dmat4x4
4 by 4 matrix tightly aligned in memory of double-precision floating-point numbers.
Definition: gtc/type_aligned.hpp:1174
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glm::packed_highp_dmat3x3
mat< 3, 3, double, packed_highp > packed_highp_dmat3x3
3 by 3 matrix tightly packed in memory of double-precision floating-point numbers using high precisio...
Definition: gtc/type_aligned.hpp:706
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glm::aligned_highp_bvec4
vec< 4, bool, aligned_highp > aligned_highp_bvec4
4 components vector aligned in memory of bool values.
Definition: gtc/type_aligned.hpp:357
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glm::packed_mediump_bvec4
vec< 4, bool, packed_mediump > packed_mediump_bvec4
4 components vector tightly packed in memory of bool values.
Definition: gtc/type_aligned.hpp:405
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glm::packed_highp_mat2
mat< 2, 2, float, packed_highp > packed_highp_mat2
2 by 2 matrix tightly packed in memory of single-precision floating-point numbers using high precisio...
Definition: gtc/type_aligned.hpp:431
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glm::packed_mat2x4
packed_highp_mat2x4 packed_mat2x4
2 by 4 matrix tightly packed in memory of single-precision floating-point numbers.
Definition: gtc/type_aligned.hpp:1015
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glm::aligned_lowp_mat4x2
mat< 4, 2, float, aligned_lowp > aligned_lowp_mat4x2
4 by 2 matrix aligned in memory of single-precision floating-point numbers using low precision arithm...
Definition: gtc/type_aligned.hpp:761
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glm::aligned_mediump_bvec1
vec< 1, bool, aligned_mediump > aligned_mediump_bvec1
1 component vector aligned in memory of bool values.
Definition: gtc/type_aligned.hpp:84
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glm::aligned_highp_dmat2x4
mat< 2, 4, double, aligned_highp > aligned_highp_dmat2x4
2 by 4 matrix aligned in memory of double-precision floating-point numbers using high precision arith...
Definition: gtc/type_aligned.hpp:612
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glm::aligned_mediump_mat3x3
mat< 3, 3, float, aligned_mediump > aligned_mediump_mat3x3
3 by 3 matrix aligned in memory of single-precision floating-point numbers using medium precision ari...
Definition: gtc/type_aligned.hpp:682
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glm::packed_mediump_mat4
mat< 4, 4, float, packed_mediump > packed_mediump_mat4
4 by 4 matrix tightly packed in memory of single-precision floating-point numbers using medium precis...
Definition: gtc/type_aligned.hpp:510
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glm::packed_mediump_vec1
vec< 1, float, packed_mediump > packed_mediump_vec1
1 component vector tightly packed in memory of single-precision floating-point numbers using medium p...
Definition: gtc/type_aligned.hpp:93
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glm::aligned_mat4x4
aligned_highp_mat4x4 aligned_mat4x4
4 by 4 matrix tightly aligned in memory of single-precision floating-point numbers.
Definition: gtc/type_aligned.hpp:1006
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glm::aligned_mat4x2
aligned_highp_mat4x2 aligned_mat4x2
4 by 2 matrix tightly aligned in memory of single-precision floating-point numbers.
Definition: gtc/type_aligned.hpp:1000
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glm::packed_highp_vec3
vec< 3, float, packed_highp > packed_highp_vec3
3 components vector tightly packed in memory of single-precision floating-point numbers using high pr...
Definition: gtc/type_aligned.hpp:274
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glm::aligned_dvec4
aligned_highp_dvec4 aligned_dvec4
4 components vector aligned in memory of double-precision floating-point numbers. ...
Definition: gtc/type_aligned.hpp:1117
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glm::aligned_highp_ivec1
vec< 1, int, aligned_highp > aligned_highp_ivec1
1 component vector aligned in memory of signed integer numbers.
Definition: gtc/type_aligned.hpp:63
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glm::aligned_lowp_mat3
mat< 3, 3, float, aligned_lowp > aligned_lowp_mat3
3 by 3 matrix aligned in memory of single-precision floating-point numbers using low precision arithm...
Definition: gtc/type_aligned.hpp:457
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glm::aligned_highp_dmat2
mat< 2, 2, double, aligned_highp > aligned_highp_dmat2
2 by 2 matrix aligned in memory of double-precision floating-point numbers using high precision arith...
Definition: gtc/type_aligned.hpp:422
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glm::aligned_dmat3x4
aligned_highp_dmat3x4 aligned_dmat3x4
3 by 4 matrix tightly aligned in memory of double-precision floating-point numbers.
Definition: gtc/type_aligned.hpp:1165
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glm::packed_bvec3
packed_highp_bvec3 packed_bvec3
3 components vector tightly packed in memory of bool values.
Definition: gtc/type_aligned.hpp:1308
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glm::packed_highp_mat4x4
mat< 4, 4, float, packed_highp > packed_highp_mat4x4
4 by 4 matrix tightly packed in memory of single-precision floating-point numbers using high precisio...
Definition: gtc/type_aligned.hpp:849
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glm::packed_mediump_vec3
vec< 3, float, packed_mediump > packed_mediump_vec3
3 components vector tightly packed in memory of single-precision floating-point numbers using medium ...
Definition: gtc/type_aligned.hpp:277
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glm::aligned_lowp_uvec2
vec< 2, uint, aligned_lowp > aligned_lowp_uvec2
2 components vector aligned in memory of unsigned integer numbers.
Definition: gtc/type_aligned.hpp:170
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glm::aligned_highp_bvec1
vec< 1, bool, aligned_highp > aligned_highp_bvec1
1 component vector aligned in memory of bool values.
Definition: gtc/type_aligned.hpp:81
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glm::packed_highp_bvec2
vec< 2, bool, packed_highp > packed_highp_bvec2
2 components vector tightly packed in memory of bool values.
Definition: gtc/type_aligned.hpp:218
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glm::packed_highp_ivec1
vec< 1, int, packed_highp > packed_highp_ivec1
1 component vector tightly packed in memory of signed integer numbers.
Definition: gtc/type_aligned.hpp:108
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glm::aligned_highp_mat2x4
mat< 2, 4, float, aligned_highp > aligned_highp_mat2x4
2 by 4 matrix aligned in memory of single-precision floating-point numbers using high precision arith...
Definition: gtc/type_aligned.hpp:603
\n
glm::packed_lowp_ivec2
vec< 2, int, packed_lowp > packed_lowp_ivec2
2 components vector tightly packed in memory of signed integer numbers.
Definition: gtc/type_aligned.hpp:206
\n
glm::packed_highp_dvec3
vec< 3, double, packed_highp > packed_highp_dvec3
3 components vector tightly packed in memory of double-precision floating-point numbers using high pr...
Definition: gtc/type_aligned.hpp:283
\n
glm::aligned_highp_ivec2
vec< 2, int, aligned_highp > aligned_highp_ivec2
2 components vector aligned in memory of signed integer numbers.
Definition: gtc/type_aligned.hpp:155
\n
glm::aligned_dmat2
aligned_highp_dmat2 aligned_dmat2
2 by 2 matrix tightly aligned in memory of double-precision floating-point numbers.
Definition: gtc/type_aligned.hpp:1132
\n
glm::packed_mediump_dmat3x2
mat< 3, 2, double, packed_mediump > packed_mediump_dmat3x2
3 by 2 matrix tightly packed in memory of double-precision floating-point numbers using medium precis...
Definition: gtc/type_aligned.hpp:671
\n
glm::aligned_mediump_uvec3
vec< 3, uint, aligned_mediump > aligned_mediump_uvec3
3 components vector aligned in memory of unsigned integer numbers.
Definition: gtc/type_aligned.hpp:259
\n
glm::packed_lowp_dvec4
vec< 4, double, packed_lowp > packed_lowp_dvec4
4 components vector tightly packed in memory of double-precision floating-point numbers using low pre...
Definition: gtc/type_aligned.hpp:381
\n
glm::packed_mediump_dvec3
vec< 3, double, packed_mediump > packed_mediump_dvec3
3 components vector tightly packed in memory of double-precision floating-point numbers using medium ...
Definition: gtc/type_aligned.hpp:286
\n
glm::aligned_mediump_dmat2x3
mat< 2, 3, double, aligned_mediump > aligned_mediump_dmat2x3
2 by 3 matrix aligned in memory of double-precision floating-point numbers using medium precision ari...
Definition: gtc/type_aligned.hpp:577
\n
glm::aligned_lowp_ivec3
vec< 3, int, aligned_lowp > aligned_lowp_ivec3
3 components vector aligned in memory of signed integer numbers.
Definition: gtc/type_aligned.hpp:253
\n
glm::aligned_highp_mat2x2
mat< 2, 2, float, aligned_highp > aligned_highp_mat2x2
2 by 2 matrix aligned in memory of single-precision floating-point numbers using high precision arith...
Definition: gtc/type_aligned.hpp:527
\n
glm::aligned_highp_mat4x3
mat< 4, 3, float, aligned_highp > aligned_highp_mat4x3
4 by 3 matrix aligned in memory of single-precision floating-point numbers using high precision arith...
Definition: gtc/type_aligned.hpp:793
\n
glm::aligned_highp_bvec3
vec< 3, bool, aligned_highp > aligned_highp_bvec3
3 components vector aligned in memory of bool values.
Definition: gtc/type_aligned.hpp:265
\n
glm::packed_lowp_vec3
vec< 3, float, packed_lowp > packed_lowp_vec3
3 components vector tightly packed in memory of single-precision floating-point numbers using low pre...
Definition: gtc/type_aligned.hpp:280
\n
glm::aligned_mediump_uvec2
vec< 2, uint, aligned_mediump > aligned_mediump_uvec2
2 components vector aligned in memory of unsigned integer numbers.
Definition: gtc/type_aligned.hpp:167
\n
glm::packed_mediump_ivec1
vec< 1, int, packed_mediump > packed_mediump_ivec1
1 component vector tightly packed in memory of signed integer numbers.
Definition: gtc/type_aligned.hpp:111
\n
glm::aligned_highp_dmat3
mat< 3, 3, double, aligned_highp > aligned_highp_dmat3
3 by 3 matrix aligned in memory of double-precision floating-point numbers using high precision arith...
Definition: gtc/type_aligned.hpp:460
\n
glm::packed_mediump_uvec4
vec< 4, uint, packed_mediump > packed_mediump_uvec4
4 components vector tightly packed in memory of unsigned integer numbers.
Definition: gtc/type_aligned.hpp:396
\n
glm::aligned_mediump_dmat3x2
mat< 3, 2, double, aligned_mediump > aligned_mediump_dmat3x2
3 by 2 matrix aligned in memory of double-precision floating-point numbers using medium precision ari...
Definition: gtc/type_aligned.hpp:653
\n
glm::aligned_mediump_dmat3x4
mat< 3, 4, double, aligned_mediump > aligned_mediump_dmat3x4
3 by 4 matrix aligned in memory of double-precision floating-point numbers using medium precision ari...
Definition: gtc/type_aligned.hpp:729
\n
glm::packed_mediump_dmat4x3
mat< 4, 3, double, packed_mediump > packed_mediump_dmat4x3
4 by 3 matrix tightly packed in memory of double-precision floating-point numbers using medium precis...
Definition: gtc/type_aligned.hpp:823
\n
glm::packed_highp_uvec2
vec< 2, uint, packed_highp > packed_highp_uvec2
2 components vector tightly packed in memory of unsigned integer numbers.
Definition: gtc/type_aligned.hpp:209
\n
glm::aligned_mediump_uvec4
vec< 4, uint, aligned_mediump > aligned_mediump_uvec4
4 components vector aligned in memory of unsigned integer numbers.
Definition: gtc/type_aligned.hpp:351
\n
glm::packed_mediump_dvec4
vec< 4, double, packed_mediump > packed_mediump_dvec4
4 components vector tightly packed in memory of double-precision floating-point numbers using medium ...
Definition: gtc/type_aligned.hpp:378
\n
glm::aligned_vec4
aligned_highp_vec4 aligned_vec4
4 components vector aligned in memory of single-precision floating-point numbers. ...
Definition: gtc/type_aligned.hpp:949
\n
glm::aligned_highp_ivec4
vec< 4, int, aligned_highp > aligned_highp_ivec4
4 components vector aligned in memory of signed integer numbers.
Definition: gtc/type_aligned.hpp:339
\n
glm::aligned_lowp_dvec2
vec< 2, double, aligned_lowp > aligned_lowp_dvec2
2 components vector aligned in memory of double-precision floating-point numbers using low precision ...
Definition: gtc/type_aligned.hpp:152
\n
glm::packed_bvec1
packed_highp_bvec1 packed_bvec1
1 components vector tightly packed in memory of bool values.
Definition: gtc/type_aligned.hpp:1302
\n
glm::packed_lowp_mat2x3
mat< 2, 3, float, packed_lowp > packed_lowp_mat2x3
2 by 3 matrix tightly packed in memory of single-precision floating-point numbers using low precision...
Definition: gtc/type_aligned.hpp:589
\n
glm::aligned_lowp_vec1
vec< 1, float, aligned_lowp > aligned_lowp_vec1
1 component vector aligned in memory of single-precision floating-point numbers using low precision a...
Definition: gtc/type_aligned.hpp:51
\n
glm::packed_lowp_vec2
vec< 2, float, packed_lowp > packed_lowp_vec2
2 components vector tightly packed in memory of single-precision floating-point numbers using low pre...
Definition: gtc/type_aligned.hpp:188
\n
glm::aligned_highp_dvec1
vec< 1, double, aligned_highp > aligned_highp_dvec1
1 component vector aligned in memory of double-precision floating-point numbers using high precision ...
Definition: gtc/type_aligned.hpp:54
\n
glm::packed_mediump_mat2x3
mat< 2, 3, float, packed_mediump > packed_mediump_mat2x3
2 by 3 matrix tightly packed in memory of single-precision floating-point numbers using medium precis...
Definition: gtc/type_aligned.hpp:586
\n
glm::packed_mediump_mat3x2
mat< 3, 2, float, packed_mediump > packed_mediump_mat3x2
3 by 2 matrix tightly packed in memory of single-precision floating-point numbers using medium precis...
Definition: gtc/type_aligned.hpp:662
\n
glm::aligned_mediump_vec4
vec< 4, float, aligned_mediump > aligned_mediump_vec4
4 components vector aligned in memory of single-precision floating-point numbers using medium precisi...
Definition: gtc/type_aligned.hpp:324
\n
glm::packed_dvec4
packed_highp_dvec4 packed_dvec4
4 components vector tightly packed in memory of double-precision floating-point numbers.
Definition: gtc/type_aligned.hpp:1129
\n
glm::aligned_mediump_dvec4
vec< 4, double, aligned_mediump > aligned_mediump_dvec4
4 components vector aligned in memory of double-precision floating-point numbers using medium precisi...
Definition: gtc/type_aligned.hpp:333
\n
glm::packed_mediump_dmat4x4
mat< 4, 4, double, packed_mediump > packed_mediump_dmat4x4
4 by 4 matrix tightly packed in memory of double-precision floating-point numbers using medium precis...
Definition: gtc/type_aligned.hpp:861
\n
glm::packed_mediump_dmat3x4
mat< 3, 4, double, packed_mediump > packed_mediump_dmat3x4
3 by 4 matrix tightly packed in memory of double-precision floating-point numbers using medium precis...
Definition: gtc/type_aligned.hpp:747
\n
glm::aligned_highp_mat3x4
mat< 3, 4, float, aligned_highp > aligned_highp_mat3x4
3 by 4 matrix aligned in memory of single-precision floating-point numbers using high precision arith...
Definition: gtc/type_aligned.hpp:717
\n
glm::aligned_highp_mat4
mat< 4, 4, float, aligned_highp > aligned_highp_mat4
4 by 4 matrix aligned in memory of single-precision floating-point numbers using high precision arith...
Definition: gtc/type_aligned.hpp:489
\n
glm::packed_lowp_dmat2x3
mat< 2, 3, double, packed_lowp > packed_lowp_dmat2x3
2 by 3 matrix tightly packed in memory of double-precision floating-point numbers using low precision...
Definition: gtc/type_aligned.hpp:598
\n
glm::aligned_mediump_mat3x4
mat< 3, 4, float, aligned_mediump > aligned_mediump_mat3x4
3 by 4 matrix aligned in memory of single-precision floating-point numbers using medium precision ari...
Definition: gtc/type_aligned.hpp:720
\n
glm::packed_dmat3x3
packed_highp_dmat3x3 packed_dmat3x3
3 by 3 matrix tightly packed in memory of double-precision floating-point numbers.
Definition: gtc/type_aligned.hpp:1189
\n
glm::aligned_mat3x3
aligned_highp_mat3x3 aligned_mat3x3
3 by 3 matrix tightly aligned in memory of single-precision floating-point numbers.
Definition: gtc/type_aligned.hpp:994
\n
glm::packed_lowp_ivec3
vec< 3, int, packed_lowp > packed_lowp_ivec3
3 components vector tightly packed in memory of signed integer numbers.
Definition: gtc/type_aligned.hpp:298
\n
glm::aligned_dmat4
aligned_highp_dmat4 aligned_dmat4
4 by 4 matrix tightly aligned in memory of double-precision floating-point numbers.
Definition: gtc/type_aligned.hpp:1138
\n
glm::aligned_highp_mat4x2
mat< 4, 2, float, aligned_highp > aligned_highp_mat4x2
4 by 2 matrix aligned in memory of single-precision floating-point numbers using high precision arith...
Definition: gtc/type_aligned.hpp:755
\n
glm::packed_highp_vec2
vec< 2, float, packed_highp > packed_highp_vec2
2 components vector tightly packed in memory of single-precision floating-point numbers using high pr...
Definition: gtc/type_aligned.hpp:182
\n
glm::aligned_lowp_dmat2
mat< 2, 2, double, aligned_lowp > aligned_lowp_dmat2
2 by 2 matrix aligned in memory of double-precision floating-point numbers using low precision arithm...
Definition: gtc/type_aligned.hpp:428
\n
glm::packed_lowp_uvec4
vec< 4, uint, packed_lowp > packed_lowp_uvec4
4 components vector tightly packed in memory of unsigned integer numbers.
Definition: gtc/type_aligned.hpp:399
\n
glm::packed_ivec4
packed_highp_ivec4 packed_ivec4
4 components vector tightly packed in memory of signed integer numbers.
Definition: gtc/type_aligned.hpp:1237
\n
glm::packed_dvec2
packed_highp_dvec2 packed_dvec2
2 components vector tightly packed in memory of double-precision floating-point numbers.
Definition: gtc/type_aligned.hpp:1123
\n
glm::aligned_lowp_mat3x3
mat< 3, 3, float, aligned_lowp > aligned_lowp_mat3x3
3 by 3 matrix aligned in memory of single-precision floating-point numbers using low precision arithm...
Definition: gtc/type_aligned.hpp:685
\n
glm::packed_lowp_dmat3x2
mat< 3, 2, double, packed_lowp > packed_lowp_dmat3x2
3 by 2 matrix tightly packed in memory of double-precision floating-point numbers using low precision...
Definition: gtc/type_aligned.hpp:674
\n
glm::packed_highp_mat3x2
mat< 3, 2, float, packed_highp > packed_highp_mat3x2
3 by 2 matrix tightly packed in memory of single-precision floating-point numbers using high precisio...
Definition: gtc/type_aligned.hpp:659
\n
glm::aligned_mediump_dmat3
mat< 3, 3, double, aligned_mediump > aligned_mediump_dmat3
3 by 3 matrix aligned in memory of double-precision floating-point numbers using medium precision ari...
Definition: gtc/type_aligned.hpp:463
\n
glm::aligned_lowp_mat2x3
mat< 2, 3, float, aligned_lowp > aligned_lowp_mat2x3
2 by 3 matrix aligned in memory of single-precision floating-point numbers using low precision arithm...
Definition: gtc/type_aligned.hpp:571
\n
glm::aligned_mediump_ivec1
vec< 1, int, aligned_mediump > aligned_mediump_ivec1
1 component vector aligned in memory of signed integer numbers.
Definition: gtc/type_aligned.hpp:66
\n
glm::packed_highp_dmat2
mat< 2, 2, double, packed_highp > packed_highp_dmat2
2 by 2 matrix tightly packed in memory of double-precision floating-point numbers using high precisio...
Definition: gtc/type_aligned.hpp:440
\n
glm::packed_mediump_vec2
vec< 2, float, packed_mediump > packed_mediump_vec2
2 components vector tightly packed in memory of single-precision floating-point numbers using medium ...
Definition: gtc/type_aligned.hpp:185
\n
glm::aligned_mat3
aligned_highp_mat3 aligned_mat3
3 by 3 matrix tightly aligned in memory of single-precision floating-point numbers.
Definition: gtc/type_aligned.hpp:967
\n
glm::aligned_mediump_dmat4x3
mat< 4, 3, double, aligned_mediump > aligned_mediump_dmat4x3
4 by 3 matrix aligned in memory of double-precision floating-point numbers using medium precision ari...
Definition: gtc/type_aligned.hpp:805
\n
glm::packed_mediump_mat2x2
mat< 2, 2, float, packed_mediump > packed_mediump_mat2x2
2 by 2 matrix tightly packed in memory of single-precision floating-point numbers using medium precis...
Definition: gtc/type_aligned.hpp:548
\n
glm::aligned_highp_dmat2x3
mat< 2, 3, double, aligned_highp > aligned_highp_dmat2x3
2 by 3 matrix aligned in memory of double-precision floating-point numbers using high precision arith...
Definition: gtc/type_aligned.hpp:574
\n
glm::aligned_dvec2
aligned_highp_dvec2 aligned_dvec2
2 components vector aligned in memory of double-precision floating-point numbers. ...
Definition: gtc/type_aligned.hpp:1111
\n
glm::aligned_lowp_mat3x4
mat< 3, 4, float, aligned_lowp > aligned_lowp_mat3x4
3 by 4 matrix aligned in memory of single-precision floating-point numbers using low precision arithm...
Definition: gtc/type_aligned.hpp:723
\n
glm::packed_mat4x2
packed_highp_mat4x2 packed_mat4x2
4 by 2 matrix tightly packed in memory of single-precision floating-point numbers.
Definition: gtc/type_aligned.hpp:1027
\n
glm::aligned_lowp_vec4
vec< 4, float, aligned_lowp > aligned_lowp_vec4
4 components vector aligned in memory of single-precision floating-point numbers using low precision ...
Definition: gtc/type_aligned.hpp:327
\n
glm::packed_highp_bvec3
vec< 3, bool, packed_highp > packed_highp_bvec3
3 components vector tightly packed in memory of bool values.
Definition: gtc/type_aligned.hpp:310
\n
glm::packed_lowp_dvec2
vec< 2, double, packed_lowp > packed_lowp_dvec2
2 components vector tightly packed in memory of double-precision floating-point numbers using low pre...
Definition: gtc/type_aligned.hpp:197
\n
glm::aligned_mediump_dvec3
vec< 3, double, aligned_mediump > aligned_mediump_dvec3
3 components vector aligned in memory of double-precision floating-point numbers using medium precisi...
Definition: gtc/type_aligned.hpp:241
\n
glm::aligned_mediump_vec3
vec< 3, float, aligned_mediump > aligned_mediump_vec3
3 components vector aligned in memory of single-precision floating-point numbers using medium precisi...
Definition: gtc/type_aligned.hpp:232
\n
glm::aligned_mediump_mat2x4
mat< 2, 4, float, aligned_mediump > aligned_mediump_mat2x4
2 by 4 matrix aligned in memory of single-precision floating-point numbers using medium precision ari...
Definition: gtc/type_aligned.hpp:606
\n
glm
Definition: common.hpp:20
\n
glm::aligned_highp_dvec3
vec< 3, double, aligned_highp > aligned_highp_dvec3
3 components vector aligned in memory of double-precision floating-point numbers using high precision...
Definition: gtc/type_aligned.hpp:238
\n
glm::aligned_highp_vec1
vec< 1, float, aligned_highp > aligned_highp_vec1
1 component vector aligned in memory of single-precision floating-point numbers using high precision ...
Definition: gtc/type_aligned.hpp:45
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\nFunctions

 GLM_ALIGNED_TYPEDEF (lowp_int8, aligned_lowp_int8, 1)
 Low qualifier 8 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (lowp_int16, aligned_lowp_int16, 2)
 Low qualifier 16 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (lowp_int32, aligned_lowp_int32, 4)
 Low qualifier 32 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (lowp_int64, aligned_lowp_int64, 8)
 Low qualifier 64 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (lowp_int8_t, aligned_lowp_int8_t, 1)
 Low qualifier 8 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (lowp_int16_t, aligned_lowp_int16_t, 2)
 Low qualifier 16 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (lowp_int32_t, aligned_lowp_int32_t, 4)
 Low qualifier 32 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (lowp_int64_t, aligned_lowp_int64_t, 8)
 Low qualifier 64 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (lowp_i8, aligned_lowp_i8, 1)
 Low qualifier 8 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (lowp_i16, aligned_lowp_i16, 2)
 Low qualifier 16 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (lowp_i32, aligned_lowp_i32, 4)
 Low qualifier 32 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (lowp_i64, aligned_lowp_i64, 8)
 Low qualifier 64 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (mediump_int8, aligned_mediump_int8, 1)
 Medium qualifier 8 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (mediump_int16, aligned_mediump_int16, 2)
 Medium qualifier 16 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (mediump_int32, aligned_mediump_int32, 4)
 Medium qualifier 32 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (mediump_int64, aligned_mediump_int64, 8)
 Medium qualifier 64 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (mediump_int8_t, aligned_mediump_int8_t, 1)
 Medium qualifier 8 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (mediump_int16_t, aligned_mediump_int16_t, 2)
 Medium qualifier 16 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (mediump_int32_t, aligned_mediump_int32_t, 4)
 Medium qualifier 32 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (mediump_int64_t, aligned_mediump_int64_t, 8)
 Medium qualifier 64 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (mediump_i8, aligned_mediump_i8, 1)
 Medium qualifier 8 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (mediump_i16, aligned_mediump_i16, 2)
 Medium qualifier 16 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (mediump_i32, aligned_mediump_i32, 4)
 Medium qualifier 32 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (mediump_i64, aligned_mediump_i64, 8)
 Medium qualifier 64 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (highp_int8, aligned_highp_int8, 1)
 High qualifier 8 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (highp_int16, aligned_highp_int16, 2)
 High qualifier 16 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (highp_int32, aligned_highp_int32, 4)
 High qualifier 32 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (highp_int64, aligned_highp_int64, 8)
 High qualifier 64 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (highp_int8_t, aligned_highp_int8_t, 1)
 High qualifier 8 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (highp_int16_t, aligned_highp_int16_t, 2)
 High qualifier 16 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (highp_int32_t, aligned_highp_int32_t, 4)
 High qualifier 32 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (highp_int64_t, aligned_highp_int64_t, 8)
 High qualifier 64 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (highp_i8, aligned_highp_i8, 1)
 High qualifier 8 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (highp_i16, aligned_highp_i16, 2)
 High qualifier 16 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (highp_i32, aligned_highp_i32, 4)
 High qualifier 32 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (highp_i64, aligned_highp_i64, 8)
 High qualifier 64 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (int8, aligned_int8, 1)
 Default qualifier 8 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (int16, aligned_int16, 2)
 Default qualifier 16 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (int32, aligned_int32, 4)
 Default qualifier 32 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (int64, aligned_int64, 8)
 Default qualifier 64 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (int8_t, aligned_int8_t, 1)
 Default qualifier 8 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (int16_t, aligned_int16_t, 2)
 Default qualifier 16 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (int32_t, aligned_int32_t, 4)
 Default qualifier 32 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (int64_t, aligned_int64_t, 8)
 Default qualifier 64 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (i8, aligned_i8, 1)
 Default qualifier 8 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (i16, aligned_i16, 2)
 Default qualifier 16 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (i32, aligned_i32, 4)
 Default qualifier 32 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (i64, aligned_i64, 8)
 Default qualifier 64 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (ivec1, aligned_ivec1, 4)
 Default qualifier 32 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (ivec2, aligned_ivec2, 8)
 Default qualifier 32 bit signed integer aligned vector of 2 components type. More...
 
 GLM_ALIGNED_TYPEDEF (ivec3, aligned_ivec3, 16)
 Default qualifier 32 bit signed integer aligned vector of 3 components type. More...
 
 GLM_ALIGNED_TYPEDEF (ivec4, aligned_ivec4, 16)
 Default qualifier 32 bit signed integer aligned vector of 4 components type. More...
 
 GLM_ALIGNED_TYPEDEF (i8vec1, aligned_i8vec1, 1)
 Default qualifier 8 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (i8vec2, aligned_i8vec2, 2)
 Default qualifier 8 bit signed integer aligned vector of 2 components type. More...
 
 GLM_ALIGNED_TYPEDEF (i8vec3, aligned_i8vec3, 4)
 Default qualifier 8 bit signed integer aligned vector of 3 components type. More...
 
 GLM_ALIGNED_TYPEDEF (i8vec4, aligned_i8vec4, 4)
 Default qualifier 8 bit signed integer aligned vector of 4 components type. More...
 
 GLM_ALIGNED_TYPEDEF (i16vec1, aligned_i16vec1, 2)
 Default qualifier 16 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (i16vec2, aligned_i16vec2, 4)
 Default qualifier 16 bit signed integer aligned vector of 2 components type. More...
 
 GLM_ALIGNED_TYPEDEF (i16vec3, aligned_i16vec3, 8)
 Default qualifier 16 bit signed integer aligned vector of 3 components type. More...
 
 GLM_ALIGNED_TYPEDEF (i16vec4, aligned_i16vec4, 8)
 Default qualifier 16 bit signed integer aligned vector of 4 components type. More...
 
 GLM_ALIGNED_TYPEDEF (i32vec1, aligned_i32vec1, 4)
 Default qualifier 32 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (i32vec2, aligned_i32vec2, 8)
 Default qualifier 32 bit signed integer aligned vector of 2 components type. More...
 
 GLM_ALIGNED_TYPEDEF (i32vec3, aligned_i32vec3, 16)
 Default qualifier 32 bit signed integer aligned vector of 3 components type. More...
 
 GLM_ALIGNED_TYPEDEF (i32vec4, aligned_i32vec4, 16)
 Default qualifier 32 bit signed integer aligned vector of 4 components type. More...
 
 GLM_ALIGNED_TYPEDEF (i64vec1, aligned_i64vec1, 8)
 Default qualifier 64 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (i64vec2, aligned_i64vec2, 16)
 Default qualifier 64 bit signed integer aligned vector of 2 components type. More...
 
 GLM_ALIGNED_TYPEDEF (i64vec3, aligned_i64vec3, 32)
 Default qualifier 64 bit signed integer aligned vector of 3 components type. More...
 
 GLM_ALIGNED_TYPEDEF (i64vec4, aligned_i64vec4, 32)
 Default qualifier 64 bit signed integer aligned vector of 4 components type. More...
 
 GLM_ALIGNED_TYPEDEF (lowp_uint8, aligned_lowp_uint8, 1)
 Low qualifier 8 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (lowp_uint16, aligned_lowp_uint16, 2)
 Low qualifier 16 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (lowp_uint32, aligned_lowp_uint32, 4)
 Low qualifier 32 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (lowp_uint64, aligned_lowp_uint64, 8)
 Low qualifier 64 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (lowp_uint8_t, aligned_lowp_uint8_t, 1)
 Low qualifier 8 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (lowp_uint16_t, aligned_lowp_uint16_t, 2)
 Low qualifier 16 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (lowp_uint32_t, aligned_lowp_uint32_t, 4)
 Low qualifier 32 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (lowp_uint64_t, aligned_lowp_uint64_t, 8)
 Low qualifier 64 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (lowp_u8, aligned_lowp_u8, 1)
 Low qualifier 8 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (lowp_u16, aligned_lowp_u16, 2)
 Low qualifier 16 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (lowp_u32, aligned_lowp_u32, 4)
 Low qualifier 32 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (lowp_u64, aligned_lowp_u64, 8)
 Low qualifier 64 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (mediump_uint8, aligned_mediump_uint8, 1)
 Medium qualifier 8 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (mediump_uint16, aligned_mediump_uint16, 2)
 Medium qualifier 16 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (mediump_uint32, aligned_mediump_uint32, 4)
 Medium qualifier 32 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (mediump_uint64, aligned_mediump_uint64, 8)
 Medium qualifier 64 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (mediump_uint8_t, aligned_mediump_uint8_t, 1)
 Medium qualifier 8 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (mediump_uint16_t, aligned_mediump_uint16_t, 2)
 Medium qualifier 16 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (mediump_uint32_t, aligned_mediump_uint32_t, 4)
 Medium qualifier 32 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (mediump_uint64_t, aligned_mediump_uint64_t, 8)
 Medium qualifier 64 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (mediump_u8, aligned_mediump_u8, 1)
 Medium qualifier 8 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (mediump_u16, aligned_mediump_u16, 2)
 Medium qualifier 16 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (mediump_u32, aligned_mediump_u32, 4)
 Medium qualifier 32 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (mediump_u64, aligned_mediump_u64, 8)
 Medium qualifier 64 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (highp_uint8, aligned_highp_uint8, 1)
 High qualifier 8 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (highp_uint16, aligned_highp_uint16, 2)
 High qualifier 16 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (highp_uint32, aligned_highp_uint32, 4)
 High qualifier 32 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (highp_uint64, aligned_highp_uint64, 8)
 High qualifier 64 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (highp_uint8_t, aligned_highp_uint8_t, 1)
 High qualifier 8 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (highp_uint16_t, aligned_highp_uint16_t, 2)
 High qualifier 16 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (highp_uint32_t, aligned_highp_uint32_t, 4)
 High qualifier 32 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (highp_uint64_t, aligned_highp_uint64_t, 8)
 High qualifier 64 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (highp_u8, aligned_highp_u8, 1)
 High qualifier 8 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (highp_u16, aligned_highp_u16, 2)
 High qualifier 16 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (highp_u32, aligned_highp_u32, 4)
 High qualifier 32 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (highp_u64, aligned_highp_u64, 8)
 High qualifier 64 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (uint8, aligned_uint8, 1)
 Default qualifier 8 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (uint16, aligned_uint16, 2)
 Default qualifier 16 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (uint32, aligned_uint32, 4)
 Default qualifier 32 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (uint64, aligned_uint64, 8)
 Default qualifier 64 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (uint8_t, aligned_uint8_t, 1)
 Default qualifier 8 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (uint16_t, aligned_uint16_t, 2)
 Default qualifier 16 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (uint32_t, aligned_uint32_t, 4)
 Default qualifier 32 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (uint64_t, aligned_uint64_t, 8)
 Default qualifier 64 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (u8, aligned_u8, 1)
 Default qualifier 8 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (u16, aligned_u16, 2)
 Default qualifier 16 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (u32, aligned_u32, 4)
 Default qualifier 32 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (u64, aligned_u64, 8)
 Default qualifier 64 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (uvec1, aligned_uvec1, 4)
 Default qualifier 32 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (uvec2, aligned_uvec2, 8)
 Default qualifier 32 bit unsigned integer aligned vector of 2 components type. More...
 
 GLM_ALIGNED_TYPEDEF (uvec3, aligned_uvec3, 16)
 Default qualifier 32 bit unsigned integer aligned vector of 3 components type. More...
 
 GLM_ALIGNED_TYPEDEF (uvec4, aligned_uvec4, 16)
 Default qualifier 32 bit unsigned integer aligned vector of 4 components type. More...
 
 GLM_ALIGNED_TYPEDEF (u8vec1, aligned_u8vec1, 1)
 Default qualifier 8 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (u8vec2, aligned_u8vec2, 2)
 Default qualifier 8 bit unsigned integer aligned vector of 2 components type. More...
 
 GLM_ALIGNED_TYPEDEF (u8vec3, aligned_u8vec3, 4)
 Default qualifier 8 bit unsigned integer aligned vector of 3 components type. More...
 
 GLM_ALIGNED_TYPEDEF (u8vec4, aligned_u8vec4, 4)
 Default qualifier 8 bit unsigned integer aligned vector of 4 components type. More...
 
 GLM_ALIGNED_TYPEDEF (u16vec1, aligned_u16vec1, 2)
 Default qualifier 16 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (u16vec2, aligned_u16vec2, 4)
 Default qualifier 16 bit unsigned integer aligned vector of 2 components type. More...
 
 GLM_ALIGNED_TYPEDEF (u16vec3, aligned_u16vec3, 8)
 Default qualifier 16 bit unsigned integer aligned vector of 3 components type. More...
 
 GLM_ALIGNED_TYPEDEF (u16vec4, aligned_u16vec4, 8)
 Default qualifier 16 bit unsigned integer aligned vector of 4 components type. More...
 
 GLM_ALIGNED_TYPEDEF (u32vec1, aligned_u32vec1, 4)
 Default qualifier 32 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (u32vec2, aligned_u32vec2, 8)
 Default qualifier 32 bit unsigned integer aligned vector of 2 components type. More...
 
 GLM_ALIGNED_TYPEDEF (u32vec3, aligned_u32vec3, 16)
 Default qualifier 32 bit unsigned integer aligned vector of 3 components type. More...
 
 GLM_ALIGNED_TYPEDEF (u32vec4, aligned_u32vec4, 16)
 Default qualifier 32 bit unsigned integer aligned vector of 4 components type. More...
 
 GLM_ALIGNED_TYPEDEF (u64vec1, aligned_u64vec1, 8)
 Default qualifier 64 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (u64vec2, aligned_u64vec2, 16)
 Default qualifier 64 bit unsigned integer aligned vector of 2 components type. More...
 
 GLM_ALIGNED_TYPEDEF (u64vec3, aligned_u64vec3, 32)
 Default qualifier 64 bit unsigned integer aligned vector of 3 components type. More...
 
 GLM_ALIGNED_TYPEDEF (u64vec4, aligned_u64vec4, 32)
 Default qualifier 64 bit unsigned integer aligned vector of 4 components type. More...
 
 GLM_ALIGNED_TYPEDEF (float32, aligned_float32, 4)
 32 bit single-qualifier floating-point aligned scalar. More...
 
 GLM_ALIGNED_TYPEDEF (float32_t, aligned_float32_t, 4)
 32 bit single-qualifier floating-point aligned scalar. More...
 
 GLM_ALIGNED_TYPEDEF (float32, aligned_f32, 4)
 32 bit single-qualifier floating-point aligned scalar. More...
 
 GLM_ALIGNED_TYPEDEF (float64, aligned_float64, 8)
 64 bit double-qualifier floating-point aligned scalar. More...
 
 GLM_ALIGNED_TYPEDEF (float64_t, aligned_float64_t, 8)
 64 bit double-qualifier floating-point aligned scalar. More...
 
 GLM_ALIGNED_TYPEDEF (float64, aligned_f64, 8)
 64 bit double-qualifier floating-point aligned scalar. More...
 
 GLM_ALIGNED_TYPEDEF (vec1, aligned_vec1, 4)
 Single-qualifier floating-point aligned vector of 1 component. More...
 
 GLM_ALIGNED_TYPEDEF (vec2, aligned_vec2, 8)
 Single-qualifier floating-point aligned vector of 2 components. More...
 
 GLM_ALIGNED_TYPEDEF (vec3, aligned_vec3, 16)
 Single-qualifier floating-point aligned vector of 3 components. More...
 
 GLM_ALIGNED_TYPEDEF (vec4, aligned_vec4, 16)
 Single-qualifier floating-point aligned vector of 4 components. More...
 
 GLM_ALIGNED_TYPEDEF (fvec1, aligned_fvec1, 4)
 Single-qualifier floating-point aligned vector of 1 component. More...
 
 GLM_ALIGNED_TYPEDEF (fvec2, aligned_fvec2, 8)
 Single-qualifier floating-point aligned vector of 2 components. More...
 
 GLM_ALIGNED_TYPEDEF (fvec3, aligned_fvec3, 16)
 Single-qualifier floating-point aligned vector of 3 components. More...
 
 GLM_ALIGNED_TYPEDEF (fvec4, aligned_fvec4, 16)
 Single-qualifier floating-point aligned vector of 4 components. More...
 
 GLM_ALIGNED_TYPEDEF (f32vec1, aligned_f32vec1, 4)
 Single-qualifier floating-point aligned vector of 1 component. More...
 
 GLM_ALIGNED_TYPEDEF (f32vec2, aligned_f32vec2, 8)
 Single-qualifier floating-point aligned vector of 2 components. More...
 
 GLM_ALIGNED_TYPEDEF (f32vec3, aligned_f32vec3, 16)
 Single-qualifier floating-point aligned vector of 3 components. More...
 
 GLM_ALIGNED_TYPEDEF (f32vec4, aligned_f32vec4, 16)
 Single-qualifier floating-point aligned vector of 4 components. More...
 
 GLM_ALIGNED_TYPEDEF (dvec1, aligned_dvec1, 8)
 Double-qualifier floating-point aligned vector of 1 component. More...
 
 GLM_ALIGNED_TYPEDEF (dvec2, aligned_dvec2, 16)
 Double-qualifier floating-point aligned vector of 2 components. More...
 
 GLM_ALIGNED_TYPEDEF (dvec3, aligned_dvec3, 32)
 Double-qualifier floating-point aligned vector of 3 components. More...
 
 GLM_ALIGNED_TYPEDEF (dvec4, aligned_dvec4, 32)
 Double-qualifier floating-point aligned vector of 4 components. More...
 
 GLM_ALIGNED_TYPEDEF (f64vec1, aligned_f64vec1, 8)
 Double-qualifier floating-point aligned vector of 1 component. More...
 
 GLM_ALIGNED_TYPEDEF (f64vec2, aligned_f64vec2, 16)
 Double-qualifier floating-point aligned vector of 2 components. More...
 
 GLM_ALIGNED_TYPEDEF (f64vec3, aligned_f64vec3, 32)
 Double-qualifier floating-point aligned vector of 3 components. More...
 
 GLM_ALIGNED_TYPEDEF (f64vec4, aligned_f64vec4, 32)
 Double-qualifier floating-point aligned vector of 4 components. More...
 
 GLM_ALIGNED_TYPEDEF (mat2, aligned_mat2, 16)
 Single-qualifier floating-point aligned 1x1 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (mat3, aligned_mat3, 16)
 Single-qualifier floating-point aligned 3x3 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (mat4, aligned_mat4, 16)
 Single-qualifier floating-point aligned 4x4 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (fmat2x2, aligned_fmat2, 16)
 Single-qualifier floating-point aligned 1x1 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (fmat3x3, aligned_fmat3, 16)
 Single-qualifier floating-point aligned 3x3 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (fmat4x4, aligned_fmat4, 16)
 Single-qualifier floating-point aligned 4x4 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (fmat2x2, aligned_fmat2x2, 16)
 Single-qualifier floating-point aligned 1x1 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (fmat2x3, aligned_fmat2x3, 16)
 Single-qualifier floating-point aligned 2x3 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (fmat2x4, aligned_fmat2x4, 16)
 Single-qualifier floating-point aligned 2x4 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (fmat3x2, aligned_fmat3x2, 16)
 Single-qualifier floating-point aligned 3x2 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (fmat3x3, aligned_fmat3x3, 16)
 Single-qualifier floating-point aligned 3x3 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (fmat3x4, aligned_fmat3x4, 16)
 Single-qualifier floating-point aligned 3x4 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (fmat4x2, aligned_fmat4x2, 16)
 Single-qualifier floating-point aligned 4x2 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (fmat4x3, aligned_fmat4x3, 16)
 Single-qualifier floating-point aligned 4x3 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (fmat4x4, aligned_fmat4x4, 16)
 Single-qualifier floating-point aligned 4x4 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (f32mat2x2, aligned_f32mat2, 16)
 Single-qualifier floating-point aligned 1x1 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (f32mat3x3, aligned_f32mat3, 16)
 Single-qualifier floating-point aligned 3x3 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (f32mat4x4, aligned_f32mat4, 16)
 Single-qualifier floating-point aligned 4x4 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (f32mat2x2, aligned_f32mat2x2, 16)
 Single-qualifier floating-point aligned 1x1 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (f32mat2x3, aligned_f32mat2x3, 16)
 Single-qualifier floating-point aligned 2x3 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (f32mat2x4, aligned_f32mat2x4, 16)
 Single-qualifier floating-point aligned 2x4 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (f32mat3x2, aligned_f32mat3x2, 16)
 Single-qualifier floating-point aligned 3x2 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (f32mat3x3, aligned_f32mat3x3, 16)
 Single-qualifier floating-point aligned 3x3 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (f32mat3x4, aligned_f32mat3x4, 16)
 Single-qualifier floating-point aligned 3x4 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (f32mat4x2, aligned_f32mat4x2, 16)
 Single-qualifier floating-point aligned 4x2 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (f32mat4x3, aligned_f32mat4x3, 16)
 Single-qualifier floating-point aligned 4x3 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (f32mat4x4, aligned_f32mat4x4, 16)
 Single-qualifier floating-point aligned 4x4 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (f64mat2x2, aligned_f64mat2, 32)
 Double-qualifier floating-point aligned 1x1 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (f64mat3x3, aligned_f64mat3, 32)
 Double-qualifier floating-point aligned 3x3 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (f64mat4x4, aligned_f64mat4, 32)
 Double-qualifier floating-point aligned 4x4 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (f64mat2x2, aligned_f64mat2x2, 32)
 Double-qualifier floating-point aligned 1x1 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (f64mat2x3, aligned_f64mat2x3, 32)
 Double-qualifier floating-point aligned 2x3 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (f64mat2x4, aligned_f64mat2x4, 32)
 Double-qualifier floating-point aligned 2x4 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (f64mat3x2, aligned_f64mat3x2, 32)
 Double-qualifier floating-point aligned 3x2 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (f64mat3x3, aligned_f64mat3x3, 32)
 Double-qualifier floating-point aligned 3x3 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (f64mat3x4, aligned_f64mat3x4, 32)
 Double-qualifier floating-point aligned 3x4 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (f64mat4x2, aligned_f64mat4x2, 32)
 Double-qualifier floating-point aligned 4x2 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (f64mat4x3, aligned_f64mat4x3, 32)
 Double-qualifier floating-point aligned 4x3 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (f64mat4x4, aligned_f64mat4x4, 32)
 Double-qualifier floating-point aligned 4x4 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (quat, aligned_quat, 16)
 Single-qualifier floating-point aligned quaternion. More...
 
 GLM_ALIGNED_TYPEDEF (quat, aligned_fquat, 16)
 Single-qualifier floating-point aligned quaternion. More...
 
 GLM_ALIGNED_TYPEDEF (dquat, aligned_dquat, 32)
 Double-qualifier floating-point aligned quaternion. More...
 
 GLM_ALIGNED_TYPEDEF (f32quat, aligned_f32quat, 16)
 Single-qualifier floating-point aligned quaternion. More...
 
 GLM_ALIGNED_TYPEDEF (f64quat, aligned_f64quat, 32)
 Double-qualifier floating-point aligned quaternion. More...
 
\n

Detailed Description

\n

GLM_GTX_type_aligned

\n
See also
Core features (dependence)
\n
\n to use the features of this extension. \">GLM_GTC_quaternion (dependence)
\n\n

Definition in file gtx/type_aligned.hpp.

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0.9.9 API documentation\n
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gtx/type_aligned.hpp
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\n
\nGo to the documentation of this file.
1 
\n
14 #pragma once
\n
15 
\n
16 // Dependency:
\n
17 #include "../gtc/type_precision.hpp"
\n
18 #include "../gtc/quaternion.hpp"
\n
19 
\n
20 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
21 # ifndef GLM_ENABLE_EXPERIMENTAL
\n
22 # pragma message("GLM: GLM_GTX_type_aligned is an experimental extension and may change in the future. Use #define GLM_ENABLE_EXPERIMENTAL before including it, if you really want to use it.")
\n
23 # else
\n
24 # pragma message("GLM: GLM_GTX_type_aligned extension included")
\n
25 # endif
\n
26 #endif
\n
27 
\n
28 namespace glm
\n
29 {
\n
31  // Signed int vector types
\n
32 
\n
35 
\n
38  GLM_ALIGNED_TYPEDEF(lowp_int8, aligned_lowp_int8, 1);
\n
39 
\n
42  GLM_ALIGNED_TYPEDEF(lowp_int16, aligned_lowp_int16, 2);
\n
43 
\n
46  GLM_ALIGNED_TYPEDEF(lowp_int32, aligned_lowp_int32, 4);
\n
47 
\n
50  GLM_ALIGNED_TYPEDEF(lowp_int64, aligned_lowp_int64, 8);
\n
51 
\n
52 
\n
55  GLM_ALIGNED_TYPEDEF(lowp_int8_t, aligned_lowp_int8_t, 1);
\n
56 
\n
59  GLM_ALIGNED_TYPEDEF(lowp_int16_t, aligned_lowp_int16_t, 2);
\n
60 
\n
63  GLM_ALIGNED_TYPEDEF(lowp_int32_t, aligned_lowp_int32_t, 4);
\n
64 
\n
67  GLM_ALIGNED_TYPEDEF(lowp_int64_t, aligned_lowp_int64_t, 8);
\n
68 
\n
69 
\n
72  GLM_ALIGNED_TYPEDEF(lowp_i8, aligned_lowp_i8, 1);
\n
73 
\n
76  GLM_ALIGNED_TYPEDEF(lowp_i16, aligned_lowp_i16, 2);
\n
77 
\n
80  GLM_ALIGNED_TYPEDEF(lowp_i32, aligned_lowp_i32, 4);
\n
81 
\n
84  GLM_ALIGNED_TYPEDEF(lowp_i64, aligned_lowp_i64, 8);
\n
85 
\n
86 
\n
89  GLM_ALIGNED_TYPEDEF(mediump_int8, aligned_mediump_int8, 1);
\n
90 
\n
93  GLM_ALIGNED_TYPEDEF(mediump_int16, aligned_mediump_int16, 2);
\n
94 
\n
97  GLM_ALIGNED_TYPEDEF(mediump_int32, aligned_mediump_int32, 4);
\n
98 
\n
101  GLM_ALIGNED_TYPEDEF(mediump_int64, aligned_mediump_int64, 8);
\n
102 
\n
103 
\n
106  GLM_ALIGNED_TYPEDEF(mediump_int8_t, aligned_mediump_int8_t, 1);
\n
107 
\n
110  GLM_ALIGNED_TYPEDEF(mediump_int16_t, aligned_mediump_int16_t, 2);
\n
111 
\n
114  GLM_ALIGNED_TYPEDEF(mediump_int32_t, aligned_mediump_int32_t, 4);
\n
115 
\n
118  GLM_ALIGNED_TYPEDEF(mediump_int64_t, aligned_mediump_int64_t, 8);
\n
119 
\n
120 
\n
123  GLM_ALIGNED_TYPEDEF(mediump_i8, aligned_mediump_i8, 1);
\n
124 
\n
127  GLM_ALIGNED_TYPEDEF(mediump_i16, aligned_mediump_i16, 2);
\n
128 
\n
131  GLM_ALIGNED_TYPEDEF(mediump_i32, aligned_mediump_i32, 4);
\n
132 
\n
135  GLM_ALIGNED_TYPEDEF(mediump_i64, aligned_mediump_i64, 8);
\n
136 
\n
137 
\n
140  GLM_ALIGNED_TYPEDEF(highp_int8, aligned_highp_int8, 1);
\n
141 
\n
144  GLM_ALIGNED_TYPEDEF(highp_int16, aligned_highp_int16, 2);
\n
145 
\n
148  GLM_ALIGNED_TYPEDEF(highp_int32, aligned_highp_int32, 4);
\n
149 
\n
152  GLM_ALIGNED_TYPEDEF(highp_int64, aligned_highp_int64, 8);
\n
153 
\n
154 
\n
157  GLM_ALIGNED_TYPEDEF(highp_int8_t, aligned_highp_int8_t, 1);
\n
158 
\n
161  GLM_ALIGNED_TYPEDEF(highp_int16_t, aligned_highp_int16_t, 2);
\n
162 
\n
165  GLM_ALIGNED_TYPEDEF(highp_int32_t, aligned_highp_int32_t, 4);
\n
166 
\n
169  GLM_ALIGNED_TYPEDEF(highp_int64_t, aligned_highp_int64_t, 8);
\n
170 
\n
171 
\n
174  GLM_ALIGNED_TYPEDEF(highp_i8, aligned_highp_i8, 1);
\n
175 
\n
178  GLM_ALIGNED_TYPEDEF(highp_i16, aligned_highp_i16, 2);
\n
179 
\n
182  GLM_ALIGNED_TYPEDEF(highp_i32, aligned_highp_i32, 4);
\n
183 
\n
186  GLM_ALIGNED_TYPEDEF(highp_i64, aligned_highp_i64, 8);
\n
187 
\n
188 
\n
191  GLM_ALIGNED_TYPEDEF(int8, aligned_int8, 1);
\n
192 
\n
195  GLM_ALIGNED_TYPEDEF(int16, aligned_int16, 2);
\n
196 
\n
199  GLM_ALIGNED_TYPEDEF(int32, aligned_int32, 4);
\n
200 
\n
203  GLM_ALIGNED_TYPEDEF(int64, aligned_int64, 8);
\n
204 
\n
205 
\n
208  GLM_ALIGNED_TYPEDEF(int8_t, aligned_int8_t, 1);
\n
209 
\n
212  GLM_ALIGNED_TYPEDEF(int16_t, aligned_int16_t, 2);
\n
213 
\n
216  GLM_ALIGNED_TYPEDEF(int32_t, aligned_int32_t, 4);
\n
217 
\n
220  GLM_ALIGNED_TYPEDEF(int64_t, aligned_int64_t, 8);
\n
221 
\n
222 
\n
225  GLM_ALIGNED_TYPEDEF(i8, aligned_i8, 1);
\n
226 
\n
229  GLM_ALIGNED_TYPEDEF(i16, aligned_i16, 2);
\n
230 
\n
233  GLM_ALIGNED_TYPEDEF(i32, aligned_i32, 4);
\n
234 
\n
237  GLM_ALIGNED_TYPEDEF(i64, aligned_i64, 8);
\n
238 
\n
239 
\n
242  GLM_ALIGNED_TYPEDEF(ivec1, aligned_ivec1, 4);
\n
243 
\n
246  GLM_ALIGNED_TYPEDEF(ivec2, aligned_ivec2, 8);
\n
247 
\n
250  GLM_ALIGNED_TYPEDEF(ivec3, aligned_ivec3, 16);
\n
251 
\n
254  GLM_ALIGNED_TYPEDEF(ivec4, aligned_ivec4, 16);
\n
255 
\n
256 
\n
259  GLM_ALIGNED_TYPEDEF(i8vec1, aligned_i8vec1, 1);
\n
260 
\n
263  GLM_ALIGNED_TYPEDEF(i8vec2, aligned_i8vec2, 2);
\n
264 
\n
267  GLM_ALIGNED_TYPEDEF(i8vec3, aligned_i8vec3, 4);
\n
268 
\n
271  GLM_ALIGNED_TYPEDEF(i8vec4, aligned_i8vec4, 4);
\n
272 
\n
273 
\n
276  GLM_ALIGNED_TYPEDEF(i16vec1, aligned_i16vec1, 2);
\n
277 
\n
280  GLM_ALIGNED_TYPEDEF(i16vec2, aligned_i16vec2, 4);
\n
281 
\n
284  GLM_ALIGNED_TYPEDEF(i16vec3, aligned_i16vec3, 8);
\n
285 
\n
288  GLM_ALIGNED_TYPEDEF(i16vec4, aligned_i16vec4, 8);
\n
289 
\n
290 
\n
293  GLM_ALIGNED_TYPEDEF(i32vec1, aligned_i32vec1, 4);
\n
294 
\n
297  GLM_ALIGNED_TYPEDEF(i32vec2, aligned_i32vec2, 8);
\n
298 
\n
301  GLM_ALIGNED_TYPEDEF(i32vec3, aligned_i32vec3, 16);
\n
302 
\n
305  GLM_ALIGNED_TYPEDEF(i32vec4, aligned_i32vec4, 16);
\n
306 
\n
307 
\n
310  GLM_ALIGNED_TYPEDEF(i64vec1, aligned_i64vec1, 8);
\n
311 
\n
314  GLM_ALIGNED_TYPEDEF(i64vec2, aligned_i64vec2, 16);
\n
315 
\n
318  GLM_ALIGNED_TYPEDEF(i64vec3, aligned_i64vec3, 32);
\n
319 
\n
322  GLM_ALIGNED_TYPEDEF(i64vec4, aligned_i64vec4, 32);
\n
323 
\n
324 
\n
326  // Unsigned int vector types
\n
327 
\n
330  GLM_ALIGNED_TYPEDEF(lowp_uint8, aligned_lowp_uint8, 1);
\n
331 
\n
334  GLM_ALIGNED_TYPEDEF(lowp_uint16, aligned_lowp_uint16, 2);
\n
335 
\n
338  GLM_ALIGNED_TYPEDEF(lowp_uint32, aligned_lowp_uint32, 4);
\n
339 
\n
342  GLM_ALIGNED_TYPEDEF(lowp_uint64, aligned_lowp_uint64, 8);
\n
343 
\n
344 
\n
347  GLM_ALIGNED_TYPEDEF(lowp_uint8_t, aligned_lowp_uint8_t, 1);
\n
348 
\n
351  GLM_ALIGNED_TYPEDEF(lowp_uint16_t, aligned_lowp_uint16_t, 2);
\n
352 
\n
355  GLM_ALIGNED_TYPEDEF(lowp_uint32_t, aligned_lowp_uint32_t, 4);
\n
356 
\n
359  GLM_ALIGNED_TYPEDEF(lowp_uint64_t, aligned_lowp_uint64_t, 8);
\n
360 
\n
361 
\n
364  GLM_ALIGNED_TYPEDEF(lowp_u8, aligned_lowp_u8, 1);
\n
365 
\n
368  GLM_ALIGNED_TYPEDEF(lowp_u16, aligned_lowp_u16, 2);
\n
369 
\n
372  GLM_ALIGNED_TYPEDEF(lowp_u32, aligned_lowp_u32, 4);
\n
373 
\n
376  GLM_ALIGNED_TYPEDEF(lowp_u64, aligned_lowp_u64, 8);
\n
377 
\n
378 
\n
381  GLM_ALIGNED_TYPEDEF(mediump_uint8, aligned_mediump_uint8, 1);
\n
382 
\n
385  GLM_ALIGNED_TYPEDEF(mediump_uint16, aligned_mediump_uint16, 2);
\n
386 
\n
389  GLM_ALIGNED_TYPEDEF(mediump_uint32, aligned_mediump_uint32, 4);
\n
390 
\n
393  GLM_ALIGNED_TYPEDEF(mediump_uint64, aligned_mediump_uint64, 8);
\n
394 
\n
395 
\n
398  GLM_ALIGNED_TYPEDEF(mediump_uint8_t, aligned_mediump_uint8_t, 1);
\n
399 
\n
402  GLM_ALIGNED_TYPEDEF(mediump_uint16_t, aligned_mediump_uint16_t, 2);
\n
403 
\n
406  GLM_ALIGNED_TYPEDEF(mediump_uint32_t, aligned_mediump_uint32_t, 4);
\n
407 
\n
410  GLM_ALIGNED_TYPEDEF(mediump_uint64_t, aligned_mediump_uint64_t, 8);
\n
411 
\n
412 
\n
415  GLM_ALIGNED_TYPEDEF(mediump_u8, aligned_mediump_u8, 1);
\n
416 
\n
419  GLM_ALIGNED_TYPEDEF(mediump_u16, aligned_mediump_u16, 2);
\n
420 
\n
423  GLM_ALIGNED_TYPEDEF(mediump_u32, aligned_mediump_u32, 4);
\n
424 
\n
427  GLM_ALIGNED_TYPEDEF(mediump_u64, aligned_mediump_u64, 8);
\n
428 
\n
429 
\n
432  GLM_ALIGNED_TYPEDEF(highp_uint8, aligned_highp_uint8, 1);
\n
433 
\n
436  GLM_ALIGNED_TYPEDEF(highp_uint16, aligned_highp_uint16, 2);
\n
437 
\n
440  GLM_ALIGNED_TYPEDEF(highp_uint32, aligned_highp_uint32, 4);
\n
441 
\n
444  GLM_ALIGNED_TYPEDEF(highp_uint64, aligned_highp_uint64, 8);
\n
445 
\n
446 
\n
449  GLM_ALIGNED_TYPEDEF(highp_uint8_t, aligned_highp_uint8_t, 1);
\n
450 
\n
453  GLM_ALIGNED_TYPEDEF(highp_uint16_t, aligned_highp_uint16_t, 2);
\n
454 
\n
457  GLM_ALIGNED_TYPEDEF(highp_uint32_t, aligned_highp_uint32_t, 4);
\n
458 
\n
461  GLM_ALIGNED_TYPEDEF(highp_uint64_t, aligned_highp_uint64_t, 8);
\n
462 
\n
463 
\n
466  GLM_ALIGNED_TYPEDEF(highp_u8, aligned_highp_u8, 1);
\n
467 
\n
470  GLM_ALIGNED_TYPEDEF(highp_u16, aligned_highp_u16, 2);
\n
471 
\n
474  GLM_ALIGNED_TYPEDEF(highp_u32, aligned_highp_u32, 4);
\n
475 
\n
478  GLM_ALIGNED_TYPEDEF(highp_u64, aligned_highp_u64, 8);
\n
479 
\n
480 
\n
483  GLM_ALIGNED_TYPEDEF(uint8, aligned_uint8, 1);
\n
484 
\n
487  GLM_ALIGNED_TYPEDEF(uint16, aligned_uint16, 2);
\n
488 
\n
491  GLM_ALIGNED_TYPEDEF(uint32, aligned_uint32, 4);
\n
492 
\n
495  GLM_ALIGNED_TYPEDEF(uint64, aligned_uint64, 8);
\n
496 
\n
497 
\n
500  GLM_ALIGNED_TYPEDEF(uint8_t, aligned_uint8_t, 1);
\n
501 
\n
504  GLM_ALIGNED_TYPEDEF(uint16_t, aligned_uint16_t, 2);
\n
505 
\n
508  GLM_ALIGNED_TYPEDEF(uint32_t, aligned_uint32_t, 4);
\n
509 
\n
512  GLM_ALIGNED_TYPEDEF(uint64_t, aligned_uint64_t, 8);
\n
513 
\n
514 
\n
517  GLM_ALIGNED_TYPEDEF(u8, aligned_u8, 1);
\n
518 
\n
521  GLM_ALIGNED_TYPEDEF(u16, aligned_u16, 2);
\n
522 
\n
525  GLM_ALIGNED_TYPEDEF(u32, aligned_u32, 4);
\n
526 
\n
529  GLM_ALIGNED_TYPEDEF(u64, aligned_u64, 8);
\n
530 
\n
531 
\n
534  GLM_ALIGNED_TYPEDEF(uvec1, aligned_uvec1, 4);
\n
535 
\n
538  GLM_ALIGNED_TYPEDEF(uvec2, aligned_uvec2, 8);
\n
539 
\n
542  GLM_ALIGNED_TYPEDEF(uvec3, aligned_uvec3, 16);
\n
543 
\n
546  GLM_ALIGNED_TYPEDEF(uvec4, aligned_uvec4, 16);
\n
547 
\n
548 
\n
551  GLM_ALIGNED_TYPEDEF(u8vec1, aligned_u8vec1, 1);
\n
552 
\n
555  GLM_ALIGNED_TYPEDEF(u8vec2, aligned_u8vec2, 2);
\n
556 
\n
559  GLM_ALIGNED_TYPEDEF(u8vec3, aligned_u8vec3, 4);
\n
560 
\n
563  GLM_ALIGNED_TYPEDEF(u8vec4, aligned_u8vec4, 4);
\n
564 
\n
565 
\n
568  GLM_ALIGNED_TYPEDEF(u16vec1, aligned_u16vec1, 2);
\n
569 
\n
572  GLM_ALIGNED_TYPEDEF(u16vec2, aligned_u16vec2, 4);
\n
573 
\n
576  GLM_ALIGNED_TYPEDEF(u16vec3, aligned_u16vec3, 8);
\n
577 
\n
580  GLM_ALIGNED_TYPEDEF(u16vec4, aligned_u16vec4, 8);
\n
581 
\n
582 
\n
585  GLM_ALIGNED_TYPEDEF(u32vec1, aligned_u32vec1, 4);
\n
586 
\n
589  GLM_ALIGNED_TYPEDEF(u32vec2, aligned_u32vec2, 8);
\n
590 
\n
593  GLM_ALIGNED_TYPEDEF(u32vec3, aligned_u32vec3, 16);
\n
594 
\n
597  GLM_ALIGNED_TYPEDEF(u32vec4, aligned_u32vec4, 16);
\n
598 
\n
599 
\n
602  GLM_ALIGNED_TYPEDEF(u64vec1, aligned_u64vec1, 8);
\n
603 
\n
606  GLM_ALIGNED_TYPEDEF(u64vec2, aligned_u64vec2, 16);
\n
607 
\n
610  GLM_ALIGNED_TYPEDEF(u64vec3, aligned_u64vec3, 32);
\n
611 
\n
614  GLM_ALIGNED_TYPEDEF(u64vec4, aligned_u64vec4, 32);
\n
615 
\n
616 
\n
618  // Float vector types
\n
619 
\n
622  GLM_ALIGNED_TYPEDEF(float32, aligned_float32, 4);
\n
623 
\n
626  GLM_ALIGNED_TYPEDEF(float32_t, aligned_float32_t, 4);
\n
627 
\n
630  GLM_ALIGNED_TYPEDEF(float32, aligned_f32, 4);
\n
631 
\n
632 # ifndef GLM_FORCE_SINGLE_ONLY
\n
633 
\n
636  GLM_ALIGNED_TYPEDEF(float64, aligned_float64, 8);
\n
637 
\n
640  GLM_ALIGNED_TYPEDEF(float64_t, aligned_float64_t, 8);
\n
641 
\n
644  GLM_ALIGNED_TYPEDEF(float64, aligned_f64, 8);
\n
645 
\n
646 # endif//GLM_FORCE_SINGLE_ONLY
\n
647 
\n
648 
\n
651  GLM_ALIGNED_TYPEDEF(vec1, aligned_vec1, 4);
\n
652 
\n
655  GLM_ALIGNED_TYPEDEF(vec2, aligned_vec2, 8);
\n
656 
\n
659  GLM_ALIGNED_TYPEDEF(vec3, aligned_vec3, 16);
\n
660 
\n
663  GLM_ALIGNED_TYPEDEF(vec4, aligned_vec4, 16);
\n
664 
\n
665 
\n
668  GLM_ALIGNED_TYPEDEF(fvec1, aligned_fvec1, 4);
\n
669 
\n
672  GLM_ALIGNED_TYPEDEF(fvec2, aligned_fvec2, 8);
\n
673 
\n
676  GLM_ALIGNED_TYPEDEF(fvec3, aligned_fvec3, 16);
\n
677 
\n
680  GLM_ALIGNED_TYPEDEF(fvec4, aligned_fvec4, 16);
\n
681 
\n
682 
\n
685  GLM_ALIGNED_TYPEDEF(f32vec1, aligned_f32vec1, 4);
\n
686 
\n
689  GLM_ALIGNED_TYPEDEF(f32vec2, aligned_f32vec2, 8);
\n
690 
\n
693  GLM_ALIGNED_TYPEDEF(f32vec3, aligned_f32vec3, 16);
\n
694 
\n
697  GLM_ALIGNED_TYPEDEF(f32vec4, aligned_f32vec4, 16);
\n
698 
\n
699 
\n
702  GLM_ALIGNED_TYPEDEF(dvec1, aligned_dvec1, 8);
\n
703 
\n
706  GLM_ALIGNED_TYPEDEF(dvec2, aligned_dvec2, 16);
\n
707 
\n
710  GLM_ALIGNED_TYPEDEF(dvec3, aligned_dvec3, 32);
\n
711 
\n
714  GLM_ALIGNED_TYPEDEF(dvec4, aligned_dvec4, 32);
\n
715 
\n
716 
\n
717 # ifndef GLM_FORCE_SINGLE_ONLY
\n
718 
\n
721  GLM_ALIGNED_TYPEDEF(f64vec1, aligned_f64vec1, 8);
\n
722 
\n
725  GLM_ALIGNED_TYPEDEF(f64vec2, aligned_f64vec2, 16);
\n
726 
\n
729  GLM_ALIGNED_TYPEDEF(f64vec3, aligned_f64vec3, 32);
\n
730 
\n
733  GLM_ALIGNED_TYPEDEF(f64vec4, aligned_f64vec4, 32);
\n
734 
\n
735 # endif//GLM_FORCE_SINGLE_ONLY
\n
736 
\n
738  // Float matrix types
\n
739 
\n
742  //typedef detail::tmat1<f32> mat1;
\n
743 
\n
746  GLM_ALIGNED_TYPEDEF(mat2, aligned_mat2, 16);
\n
747 
\n
750  GLM_ALIGNED_TYPEDEF(mat3, aligned_mat3, 16);
\n
751 
\n
754  GLM_ALIGNED_TYPEDEF(mat4, aligned_mat4, 16);
\n
755 
\n
756 
\n
759  //typedef detail::tmat1x1<f32> mat1;
\n
760 
\n
763  GLM_ALIGNED_TYPEDEF(mat2x2, aligned_mat2x2, 16);
\n
764 
\n
767  GLM_ALIGNED_TYPEDEF(mat3x3, aligned_mat3x3, 16);
\n
768 
\n
771  GLM_ALIGNED_TYPEDEF(mat4x4, aligned_mat4x4, 16);
\n
772 
\n
773 
\n
776  //typedef detail::tmat1x1<f32> fmat1;
\n
777 
\n
780  GLM_ALIGNED_TYPEDEF(fmat2x2, aligned_fmat2, 16);
\n
781 
\n
784  GLM_ALIGNED_TYPEDEF(fmat3x3, aligned_fmat3, 16);
\n
785 
\n
788  GLM_ALIGNED_TYPEDEF(fmat4x4, aligned_fmat4, 16);
\n
789 
\n
790 
\n
793  //typedef f32 fmat1x1;
\n
794 
\n
797  GLM_ALIGNED_TYPEDEF(fmat2x2, aligned_fmat2x2, 16);
\n
798 
\n
801  GLM_ALIGNED_TYPEDEF(fmat2x3, aligned_fmat2x3, 16);
\n
802 
\n
805  GLM_ALIGNED_TYPEDEF(fmat2x4, aligned_fmat2x4, 16);
\n
806 
\n
809  GLM_ALIGNED_TYPEDEF(fmat3x2, aligned_fmat3x2, 16);
\n
810 
\n
813  GLM_ALIGNED_TYPEDEF(fmat3x3, aligned_fmat3x3, 16);
\n
814 
\n
817  GLM_ALIGNED_TYPEDEF(fmat3x4, aligned_fmat3x4, 16);
\n
818 
\n
821  GLM_ALIGNED_TYPEDEF(fmat4x2, aligned_fmat4x2, 16);
\n
822 
\n
825  GLM_ALIGNED_TYPEDEF(fmat4x3, aligned_fmat4x3, 16);
\n
826 
\n
829  GLM_ALIGNED_TYPEDEF(fmat4x4, aligned_fmat4x4, 16);
\n
830 
\n
831 
\n
834  //typedef detail::tmat1x1<f32, defaultp> f32mat1;
\n
835 
\n
838  GLM_ALIGNED_TYPEDEF(f32mat2x2, aligned_f32mat2, 16);
\n
839 
\n
842  GLM_ALIGNED_TYPEDEF(f32mat3x3, aligned_f32mat3, 16);
\n
843 
\n
846  GLM_ALIGNED_TYPEDEF(f32mat4x4, aligned_f32mat4, 16);
\n
847 
\n
848 
\n
851  //typedef f32 f32mat1x1;
\n
852 
\n
855  GLM_ALIGNED_TYPEDEF(f32mat2x2, aligned_f32mat2x2, 16);
\n
856 
\n
859  GLM_ALIGNED_TYPEDEF(f32mat2x3, aligned_f32mat2x3, 16);
\n
860 
\n
863  GLM_ALIGNED_TYPEDEF(f32mat2x4, aligned_f32mat2x4, 16);
\n
864 
\n
867  GLM_ALIGNED_TYPEDEF(f32mat3x2, aligned_f32mat3x2, 16);
\n
868 
\n
871  GLM_ALIGNED_TYPEDEF(f32mat3x3, aligned_f32mat3x3, 16);
\n
872 
\n
875  GLM_ALIGNED_TYPEDEF(f32mat3x4, aligned_f32mat3x4, 16);
\n
876 
\n
879  GLM_ALIGNED_TYPEDEF(f32mat4x2, aligned_f32mat4x2, 16);
\n
880 
\n
883  GLM_ALIGNED_TYPEDEF(f32mat4x3, aligned_f32mat4x3, 16);
\n
884 
\n
887  GLM_ALIGNED_TYPEDEF(f32mat4x4, aligned_f32mat4x4, 16);
\n
888 
\n
889 
\n
890 # ifndef GLM_FORCE_SINGLE_ONLY
\n
891 
\n
894  //typedef detail::tmat1x1<f64, defaultp> f64mat1;
\n
895 
\n
898  GLM_ALIGNED_TYPEDEF(f64mat2x2, aligned_f64mat2, 32);
\n
899 
\n
902  GLM_ALIGNED_TYPEDEF(f64mat3x3, aligned_f64mat3, 32);
\n
903 
\n
906  GLM_ALIGNED_TYPEDEF(f64mat4x4, aligned_f64mat4, 32);
\n
907 
\n
908 
\n
911  //typedef f64 f64mat1x1;
\n
912 
\n
915  GLM_ALIGNED_TYPEDEF(f64mat2x2, aligned_f64mat2x2, 32);
\n
916 
\n
919  GLM_ALIGNED_TYPEDEF(f64mat2x3, aligned_f64mat2x3, 32);
\n
920 
\n
923  GLM_ALIGNED_TYPEDEF(f64mat2x4, aligned_f64mat2x4, 32);
\n
924 
\n
927  GLM_ALIGNED_TYPEDEF(f64mat3x2, aligned_f64mat3x2, 32);
\n
928 
\n
931  GLM_ALIGNED_TYPEDEF(f64mat3x3, aligned_f64mat3x3, 32);
\n
932 
\n
935  GLM_ALIGNED_TYPEDEF(f64mat3x4, aligned_f64mat3x4, 32);
\n
936 
\n
939  GLM_ALIGNED_TYPEDEF(f64mat4x2, aligned_f64mat4x2, 32);
\n
940 
\n
943  GLM_ALIGNED_TYPEDEF(f64mat4x3, aligned_f64mat4x3, 32);
\n
944 
\n
947  GLM_ALIGNED_TYPEDEF(f64mat4x4, aligned_f64mat4x4, 32);
\n
948 
\n
949 # endif//GLM_FORCE_SINGLE_ONLY
\n
950 
\n
951 
\n
953  // Quaternion types
\n
954 
\n
957  GLM_ALIGNED_TYPEDEF(quat, aligned_quat, 16);
\n
958 
\n
961  GLM_ALIGNED_TYPEDEF(quat, aligned_fquat, 16);
\n
962 
\n
965  GLM_ALIGNED_TYPEDEF(dquat, aligned_dquat, 32);
\n
966 
\n
969  GLM_ALIGNED_TYPEDEF(f32quat, aligned_f32quat, 16);
\n
970 
\n
971 # ifndef GLM_FORCE_SINGLE_ONLY
\n
972 
\n
975  GLM_ALIGNED_TYPEDEF(f64quat, aligned_f64quat, 32);
\n
976 
\n
977 # endif//GLM_FORCE_SINGLE_ONLY
\n
978 
\n
980 }//namespace glm
\n
981 
\n
982 #include "type_aligned.inl"
\n
glm::mat4x4
mat< 4, 4, float, defaultp > mat4x4
4 columns of 4 components matrix of single-precision floating-point numbers.
Definition: matrix_float4x4.hpp:15
\n
glm::highp_u64
uint64 highp_u64
High qualifier 64 bit unsigned integer type.
Definition: fwd.hpp:133
\n
glm::f32vec3
vec< 3, f32, defaultp > f32vec3
Single-qualifier floating-point vector of 3 components.
Definition: fwd.hpp:463
\n
glm::mat2x2
mat< 2, 2, float, defaultp > mat2x2
2 columns of 2 components matrix of single-precision floating-point numbers.
Definition: matrix_float2x2.hpp:15
\n
glm::mediump_uint32_t
uint32 mediump_uint32_t
Medium qualifier 32 bit unsigned integer type.
Definition: fwd.hpp:127
\n
glm::aligned_uvec2
aligned_highp_uvec2 aligned_uvec2
2 components vector aligned in memory of unsigned integer numbers.
Definition: gtc/type_aligned.hpp:1257
\n
glm::lowp_uint64
uint64 lowp_uint64
Low qualifier 64 bit unsigned integer type.
Definition: fwd.hpp:136
\n
glm::f32vec1
vec< 1, f32, defaultp > f32vec1
Single-qualifier floating-point vector of 1 component.
Definition: fwd.hpp:461
\n
glm::lowp_u8
uint8 lowp_u8
Low qualifier 8 bit unsigned integer type.
Definition: fwd.hpp:89
\n
glm::u32
uint32 u32
Default qualifier 32 bit unsigned integer type.
Definition: fwd.hpp:120
\n
glm::i32vec1
vec< 1, i32, defaultp > i32vec1
32 bit signed integer scalar type.
Definition: fwd.hpp:277
\n
glm::highp_uint16
uint16 highp_uint16
High qualifier 16 bit unsigned integer type.
Definition: fwd.hpp:110
\n
glm::f64mat3x4
mat< 3, 4, f64, defaultp > f64mat3x4
Double-qualifier floating-point 3x4 matrix.
Definition: fwd.hpp:787
\n
glm::i16vec3
vec< 3, i16, defaultp > i16vec3
16 bit signed integer vector of 3 components type.
Definition: fwd.hpp:259
\n
glm::lowp_uint32_t
uint32 lowp_uint32_t
Low qualifier 32 bit unsigned integer type.
Definition: fwd.hpp:126
\n
glm::mediump_uint32
uint32 mediump_uint32
Medium qualifier 32 bit unsigned integer type.
Definition: fwd.hpp:123
\n
glm::highp_uint64
uint64 highp_uint64
High qualifier 64 bit unsigned integer type.
Definition: fwd.hpp:138
\n
glm::lowp_uint32
uint32 lowp_uint32
Low qualifier 32 bit unsigned integer type.
Definition: fwd.hpp:122
\n
glm::vec2
vec< 2, float, defaultp > vec2
2 components vector of single-precision floating-point numbers.
Definition: vector_float2.hpp:15
\n
glm::i64vec4
vec< 4, i64, defaultp > i64vec4
64 bit signed integer vector of 4 components type.
Definition: fwd.hpp:300
\n
glm::u16vec3
vec< 3, u16, defaultp > u16vec3
Default qualifier 16 bit unsigned integer vector of 3 components type.
Definition: fwd.hpp:361
\n
glm::aligned_ivec3
aligned_highp_ivec3 aligned_ivec3
3 components vector aligned in memory of signed integer numbers.
Definition: gtc/type_aligned.hpp:1222
\n
glm::i8vec2
vec< 2, i8, defaultp > i8vec2
8 bit signed integer vector of 2 components type.
Definition: fwd.hpp:238
\n
glm::aligned_vec3
aligned_highp_vec3 aligned_vec3
3 components vector aligned in memory of single-precision floating-point numbers. ...
Definition: gtc/type_aligned.hpp:946
\n
glm::uvec3
vec< 3, unsigned int, defaultp > uvec3
3 components vector of unsigned integer numbers.
Definition: vector_uint3.hpp:15
\n
glm::aligned_uvec3
aligned_highp_uvec3 aligned_uvec3
3 components vector aligned in memory of unsigned integer numbers.
Definition: gtc/type_aligned.hpp:1260
\n
glm::highp_int64
int64 highp_int64
High qualifier 64 bit signed integer type.
Definition: fwd.hpp:80
\n
glm::lowp_int16_t
int16 lowp_int16_t
Low qualifier 16 bit signed integer type.
Definition: fwd.hpp:54
\n
glm::f32mat4x2
mat< 4, 2, f32, defaultp > f32mat4x2
Single-qualifier floating-point 4x2 matrix.
Definition: fwd.hpp:702
\n
glm::mediump_u32
uint32 mediump_u32
Medium qualifier 32 bit unsigned integer type.
Definition: fwd.hpp:118
\n
glm::GLM_ALIGNED_TYPEDEF
GLM_ALIGNED_TYPEDEF(f64quat, aligned_f64quat, 32)
Double-qualifier floating-point aligned quaternion.
\n
glm::aligned_dvec3
aligned_highp_dvec3 aligned_dvec3
3 components vector aligned in memory of double-precision floating-point numbers. ...
Definition: gtc/type_aligned.hpp:1114
\n
glm::aligned_dvec1
aligned_highp_dvec1 aligned_dvec1
1 component vector aligned in memory of double-precision floating-point numbers.
Definition: gtc/type_aligned.hpp:1108
\n
glm::ivec3
vec< 3, int, defaultp > ivec3
3 components vector of signed integer numbers.
Definition: vector_int3.hpp:15
\n
glm::u64vec3
vec< 3, u64, defaultp > u64vec3
Default qualifier 64 bit unsigned integer vector of 3 components type.
Definition: fwd.hpp:401
\n
glm::lowp_uint8
uint8 lowp_uint8
Low qualifier 8 bit unsigned integer type.
Definition: fwd.hpp:94
\n
glm::lowp_u64
uint64 lowp_u64
Low qualifier 64 bit unsigned integer type.
Definition: fwd.hpp:131
\n
glm::mediump_int8
int8 mediump_int8
Medium qualifier 8 bit signed integer type.
Definition: fwd.hpp:37
\n
glm::lowp_int64
int64 lowp_int64
Low qualifier 64 bit signed integer type.
Definition: fwd.hpp:78
\n
glm::u64vec2
vec< 2, u64, defaultp > u64vec2
Default qualifier 64 bit unsigned integer vector of 2 components type.
Definition: fwd.hpp:400
\n
glm::f32mat3x4
mat< 3, 4, f32, defaultp > f32mat3x4
Single-qualifier floating-point 3x4 matrix.
Definition: fwd.hpp:707
\n
glm::u64
uint64 u64
Default qualifier 64 bit unsigned integer type.
Definition: fwd.hpp:134
\n
glm::f64vec1
vec< 1, f64, defaultp > f64vec1
Double-qualifier floating-point vector of 1 component.
Definition: fwd.hpp:501
\n
glm::i16vec1
vec< 1, i16, defaultp > i16vec1
16 bit signed integer scalar type.
Definition: fwd.hpp:257
\n
glm::float64
double float64
Double-qualifier floating-point scalar.
Definition: fwd.hpp:171
\n
glm::fmat4x2
mat< 4, 2, f32, defaultp > fmat4x2
Single-qualifier floating-point 4x2 matrix.
Definition: fwd.hpp:662
\n
glm::fmat3x4
mat< 3, 4, f32, defaultp > fmat3x4
Single-qualifier floating-point 3x4 matrix.
Definition: fwd.hpp:667
\n
glm::f32mat2x4
mat< 2, 4, f32, defaultp > f32mat2x4
Single-qualifier floating-point 2x4 matrix.
Definition: fwd.hpp:706
\n
glm::i16vec4
vec< 4, i16, defaultp > i16vec4
16 bit signed integer vector of 4 components type.
Definition: fwd.hpp:260
\n
glm::lowp_uint8_t
uint8 lowp_uint8_t
Low qualifier 8 bit unsigned integer type.
Definition: fwd.hpp:98
\n
glm::highp_uint32_t
uint32 highp_uint32_t
High qualifier 32 bit unsigned integer type.
Definition: fwd.hpp:128
\n
glm::fmat3x3
mat< 3, 3, f32, defaultp > fmat3x3
Single-qualifier floating-point 3x3 matrix.
Definition: fwd.hpp:664
\n
glm::f32mat2x3
mat< 2, 3, f32, defaultp > f32mat2x3
Single-qualifier floating-point 2x3 matrix.
Definition: fwd.hpp:703
\n
glm::mediump_int16
int16 mediump_int16
Medium qualifier 16 bit signed integer type.
Definition: fwd.hpp:51
\n
glm::mediump_u16
uint16 mediump_u16
Medium qualifier 16 bit unsigned integer type.
Definition: fwd.hpp:104
\n
glm::f64quat
qua< f64, defaultp > f64quat
Double-qualifier floating-point quaternion.
Definition: fwd.hpp:815
\n
glm::dquat
qua< double, defaultp > dquat
Quaternion of double-precision floating-point numbers.
Definition: quaternion_double.hpp:35
\n
glm::u64vec1
vec< 1, u64, defaultp > u64vec1
Default qualifier 64 bit unsigned integer scalar type.
Definition: fwd.hpp:399
\n
glm::int64_t
int64 int64_t
64 bit signed integer type.
Definition: fwd.hpp:85
\n
glm::aligned_mat2
aligned_highp_mat2 aligned_mat2
2 by 2 matrix tightly aligned in memory of single-precision floating-point numbers.
Definition: gtc/type_aligned.hpp:964
\n
glm::u8vec1
vec< 1, u8, defaultp > u8vec1
Default qualifier 8 bit unsigned integer scalar type.
Definition: fwd.hpp:339
\n
glm::u8vec4
vec< 4, u8, defaultp > u8vec4
Default qualifier 8 bit unsigned integer vector of 4 components type.
Definition: fwd.hpp:342
\n
glm::int8_t
int8 int8_t
8 bit signed integer type.
Definition: fwd.hpp:43
\n
glm::i32
int32 i32
32 bit signed integer type.
Definition: fwd.hpp:62
\n
glm::f64mat2x2
mat< 2, 2, f64, defaultp > f64mat2x2
Double-qualifier floating-point 1x1 matrix.
Definition: fwd.hpp:780
\n
glm::i8vec4
vec< 4, i8, defaultp > i8vec4
8 bit signed integer vector of 4 components type.
Definition: fwd.hpp:240
\n
glm::highp_int32
int32 highp_int32
High qualifier 32 bit signed integer type.
Definition: fwd.hpp:66
\n
glm::highp_u32
uint32 highp_u32
High qualifier 32 bit unsigned integer type.
Definition: fwd.hpp:119
\n
glm::highp_i32
int32 highp_i32
High qualifier 32 bit signed integer type.
Definition: fwd.hpp:61
\n
glm::ivec4
vec< 4, int, defaultp > ivec4
4 components vector of signed integer numbers.
Definition: vector_int4.hpp:15
\n
glm::u64vec4
vec< 4, u64, defaultp > u64vec4
Default qualifier 64 bit unsigned integer vector of 4 components type.
Definition: fwd.hpp:402
\n
glm::f32vec4
vec< 4, f32, defaultp > f32vec4
Single-qualifier floating-point vector of 4 components.
Definition: fwd.hpp:464
\n
glm::f64mat2x3
mat< 2, 3, f64, defaultp > f64mat2x3
Double-qualifier floating-point 2x3 matrix.
Definition: fwd.hpp:783
\n
glm::highp_uint32
uint32 highp_uint32
High qualifier 32 bit unsigned integer type.
Definition: fwd.hpp:124
\n
glm::f64mat3x2
mat< 3, 2, f64, defaultp > f64mat3x2
Double-qualifier floating-point 3x2 matrix.
Definition: fwd.hpp:781
\n
glm::u32vec1
vec< 1, u32, defaultp > u32vec1
Default qualifier 32 bit unsigned integer scalar type.
Definition: fwd.hpp:379
\n
glm::f64mat3x3
mat< 3, 3, f64, defaultp > f64mat3x3
Double-qualifier floating-point 3x3 matrix.
Definition: fwd.hpp:784
\n
glm::highp_uint8
uint8 highp_uint8
High qualifier 8 bit unsigned integer type.
Definition: fwd.hpp:96
\n
glm::highp_i8
int8 highp_i8
High qualifier 8 bit signed integer type.
Definition: fwd.hpp:33
\n
glm::mediump_i8
int8 mediump_i8
Medium qualifier 8 bit signed integer type.
Definition: fwd.hpp:32
\n
glm::highp_int64_t
int64 highp_int64_t
High qualifier 64 bit signed integer type.
Definition: fwd.hpp:84
\n
glm::f32mat4x4
mat< 4, 4, f32, defaultp > f32mat4x4
Single-qualifier floating-point 4x4 matrix.
Definition: fwd.hpp:708
\n
glm::float32_t
float float32_t
Default 32 bit single-qualifier floating-point scalar.
Definition: fwd.hpp:160
\n
glm::f32mat2x2
mat< 2, 2, f32, defaultp > f32mat2x2
Single-qualifier floating-point 1x1 matrix.
Definition: fwd.hpp:700
\n
glm::uint32_t
uint32 uint32_t
Default qualifier 32 bit unsigned integer type.
Definition: fwd.hpp:129
\n
glm::aligned_ivec1
aligned_highp_ivec1 aligned_ivec1
1 component vector aligned in memory of signed integer numbers.
Definition: gtc/type_aligned.hpp:1216
\n
glm::vec4
vec< 4, float, defaultp > vec4
4 components vector of single-precision floating-point numbers.
Definition: vector_float4.hpp:15
\n
glm::u8
uint8 u8
Default qualifier 8 bit unsigned integer type.
Definition: fwd.hpp:92
\n
glm::float32
float float32
Single-qualifier floating-point scalar.
Definition: fwd.hpp:155
\n
glm::fvec4
vec< 4, f32, defaultp > fvec4
Single-qualifier floating-point vector of 4 components.
Definition: fwd.hpp:444
\n
glm::u16vec1
vec< 1, u16, defaultp > u16vec1
Default qualifier 16 bit unsigned integer scalar type.
Definition: fwd.hpp:359
\n
glm::dvec1
vec< 1, double, defaultp > dvec1
1 components vector of double-precision floating-point numbers.
Definition: vector_double1.hpp:28
\n
glm::i8vec1
vec< 1, i8, defaultp > i8vec1
8 bit signed integer scalar type.
Definition: fwd.hpp:237
\n
glm::i32vec2
vec< 2, i32, defaultp > i32vec2
32 bit signed integer vector of 2 components type.
Definition: fwd.hpp:278
\n
glm::highp_uint8_t
uint8 highp_uint8_t
High qualifier 8 bit unsigned integer type.
Definition: fwd.hpp:100
\n
glm::mediump_uint64
uint64 mediump_uint64
Medium qualifier 64 bit unsigned integer type.
Definition: fwd.hpp:137
\n
glm::highp_int32_t
int32 highp_int32_t
32 bit signed integer type.
Definition: fwd.hpp:70
\n
glm::f64vec3
vec< 3, f64, defaultp > f64vec3
Double-qualifier floating-point vector of 3 components.
Definition: fwd.hpp:503
\n
glm::f64mat2x4
mat< 2, 4, f64, defaultp > f64mat2x4
Double-qualifier floating-point 2x4 matrix.
Definition: fwd.hpp:786
\n
glm::mat3x3
mat< 3, 3, float, defaultp > mat3x3
3 columns of 3 components matrix of single-precision floating-point numbers.
Definition: matrix_float3x3.hpp:15
\n
glm::mediump_u64
uint64 mediump_u64
Medium qualifier 64 bit unsigned integer type.
Definition: fwd.hpp:132
\n
glm::uvec2
vec< 2, unsigned int, defaultp > uvec2
2 components vector of unsigned integer numbers.
Definition: vector_uint2.hpp:15
\n
glm::lowp_u16
uint16 lowp_u16
Low qualifier 16 bit unsigned integer type.
Definition: fwd.hpp:103
\n
glm::uvec1
vec< 1, unsigned int, defaultp > uvec1
1 component vector of unsigned integer numbers.
Definition: vector_uint1.hpp:28
\n
glm::highp_i16
int16 highp_i16
High qualifier 16 bit signed integer type.
Definition: fwd.hpp:47
\n
glm::highp_int8
int8 highp_int8
High qualifier 8 bit signed integer type.
Definition: fwd.hpp:38
\n
glm::f64mat4x4
mat< 4, 4, f64, defaultp > f64mat4x4
Double-qualifier floating-point 4x4 matrix.
Definition: fwd.hpp:788
\n
glm::fmat4x3
mat< 4, 3, f32, defaultp > fmat4x3
Single-qualifier floating-point 4x3 matrix.
Definition: fwd.hpp:665
\n
glm::fvec3
vec< 3, f32, defaultp > fvec3
Single-qualifier floating-point vector of 3 components.
Definition: fwd.hpp:443
\n
glm::i16vec2
vec< 2, i16, defaultp > i16vec2
16 bit signed integer vector of 2 components type.
Definition: fwd.hpp:258
\n
glm::f32mat4x3
mat< 4, 3, f32, defaultp > f32mat4x3
Single-qualifier floating-point 4x3 matrix.
Definition: fwd.hpp:705
\n
glm::lowp_i16
int16 lowp_i16
Low qualifier 16 bit signed integer type.
Definition: fwd.hpp:45
\n
glm::vec1
vec< 1, float, defaultp > vec1
1 components vector of single-precision floating-point numbers.
Definition: vector_float1.hpp:28
\n
glm::aligned_mat4
aligned_highp_mat4 aligned_mat4
4 by 4 matrix tightly aligned in memory of single-precision floating-point numbers.
Definition: gtc/type_aligned.hpp:970
\n
glm::float64_t
double float64_t
Default 64 bit double-qualifier floating-point scalar.
Definition: fwd.hpp:176
\n
glm::lowp_int16
int16 lowp_int16
Low qualifier 16 bit signed integer type.
Definition: fwd.hpp:50
\n
glm::aligned_uvec1
aligned_highp_uvec1 aligned_uvec1
1 component vector aligned in memory of unsigned integer numbers.
Definition: gtc/type_aligned.hpp:1254
\n
glm::lowp_int64_t
int64 lowp_int64_t
Low qualifier 64 bit signed integer type.
Definition: fwd.hpp:82
\n
glm::uint16_t
uint16 uint16_t
Default qualifier 16 bit unsigned integer type.
Definition: fwd.hpp:115
\n
glm::aligned_vec1
aligned_highp_vec1 aligned_vec1
1 component vector aligned in memory of single-precision floating-point numbers.
Definition: gtc/type_aligned.hpp:940
\n
glm::lowp_int32
int32 lowp_int32
Low qualifier 32 bit signed integer type.
Definition: fwd.hpp:64
\n
glm::uint8_t
uint8 uint8_t
Default qualifier 8 bit unsigned integer type.
Definition: fwd.hpp:101
\n
glm::mediump_int32_t
int32 mediump_int32_t
Medium qualifier 32 bit signed integer type.
Definition: fwd.hpp:69
\n
glm::f32mat3x3
mat< 3, 3, f32, defaultp > f32mat3x3
Single-qualifier floating-point 3x3 matrix.
Definition: fwd.hpp:704
\n
glm::highp_u8
uint8 highp_u8
High qualifier 8 bit unsigned integer type.
Definition: fwd.hpp:91
\n
glm::mediump_uint8
uint8 mediump_uint8
Medium qualifier 8 bit unsigned integer type.
Definition: fwd.hpp:95
\n
glm::aligned_uvec4
aligned_highp_uvec4 aligned_uvec4
4 components vector aligned in memory of unsigned integer numbers.
Definition: gtc/type_aligned.hpp:1263
\n
glm::mediump_int64_t
int64 mediump_int64_t
Medium qualifier 64 bit signed integer type.
Definition: fwd.hpp:83
\n
glm::aligned_ivec4
aligned_highp_ivec4 aligned_ivec4
4 components vector aligned in memory of signed integer numbers.
Definition: gtc/type_aligned.hpp:1225
\n
glm::highp_int8_t
int8 highp_int8_t
High qualifier 8 bit signed integer type.
Definition: fwd.hpp:42
\n
glm::f32mat3x2
mat< 3, 2, f32, defaultp > f32mat3x2
Single-qualifier floating-point 3x2 matrix.
Definition: fwd.hpp:701
\n
glm::i32vec4
vec< 4, i32, defaultp > i32vec4
32 bit signed integer vector of 4 components type.
Definition: fwd.hpp:280
\n
glm::u32vec3
vec< 3, u32, defaultp > u32vec3
Default qualifier 32 bit unsigned integer vector of 3 components type.
Definition: fwd.hpp:381
\n
glm::u8vec2
vec< 2, u8, defaultp > u8vec2
Default qualifier 8 bit unsigned integer vector of 2 components type.
Definition: fwd.hpp:340
\n
glm::mediump_i16
int16 mediump_i16
Medium qualifier 16 bit signed integer type.
Definition: fwd.hpp:46
\n
glm::i8vec3
vec< 3, i8, defaultp > i8vec3
8 bit signed integer vector of 3 components type.
Definition: fwd.hpp:239
\n
glm::aligned_vec2
aligned_highp_vec2 aligned_vec2
2 components vector aligned in memory of single-precision floating-point numbers. ...
Definition: gtc/type_aligned.hpp:943
\n
glm::mat4
mat< 4, 4, float, defaultp > mat4
4 columns of 4 components matrix of single-precision floating-point numbers.
Definition: matrix_float4x4.hpp:20
\n
glm::aligned_mat2x2
aligned_highp_mat2x2 aligned_mat2x2
2 by 2 matrix tightly aligned in memory of single-precision floating-point numbers.
Definition: gtc/type_aligned.hpp:982
\n
glm::mediump_uint16_t
uint16 mediump_uint16_t
Medium qualifier 16 bit unsigned integer type.
Definition: fwd.hpp:113
\n
glm::u8vec3
vec< 3, u8, defaultp > u8vec3
Default qualifier 8 bit unsigned integer vector of 3 components type.
Definition: fwd.hpp:341
\n
glm::mediump_int64
int64 mediump_int64
Medium qualifier 64 bit signed integer type.
Definition: fwd.hpp:79
\n
glm::uint64_t
uint64 uint64_t
Default qualifier 64 bit unsigned integer type.
Definition: fwd.hpp:143
\n
glm::i32vec3
vec< 3, i32, defaultp > i32vec3
32 bit signed integer vector of 3 components type.
Definition: fwd.hpp:279
\n
glm::lowp_uint16_t
uint16 lowp_uint16_t
Low qualifier 16 bit unsigned integer type.
Definition: fwd.hpp:112
\n
glm::dvec2
vec< 2, double, defaultp > dvec2
2 components vector of double-precision floating-point numbers.
Definition: vector_double2.hpp:15
\n
glm::lowp_uint16
uint16 lowp_uint16
Low qualifier 16 bit unsigned integer type.
Definition: fwd.hpp:108
\n
glm::f64vec4
vec< 4, f64, defaultp > f64vec4
Double-qualifier floating-point vector of 4 components.
Definition: fwd.hpp:504
\n
glm::lowp_i32
int32 lowp_i32
Low qualifier 32 bit signed integer type.
Definition: fwd.hpp:59
\n
glm::vec3
vec< 3, float, defaultp > vec3
3 components vector of single-precision floating-point numbers.
Definition: vector_float3.hpp:15
\n
glm::mediump_i64
int64 mediump_i64
Medium qualifier 64 bit signed integer type.
Definition: fwd.hpp:74
\n
glm::fvec2
vec< 2, f32, defaultp > fvec2
Single-qualifier floating-point vector of 2 components.
Definition: fwd.hpp:442
\n
glm::aligned_ivec2
aligned_highp_ivec2 aligned_ivec2
2 components vector aligned in memory of signed integer numbers.
Definition: gtc/type_aligned.hpp:1219
\n
glm::int16_t
int16 int16_t
16 bit signed integer type.
Definition: fwd.hpp:57
\n
glm::highp_i64
int64 highp_i64
High qualifier 64 bit signed integer type.
Definition: fwd.hpp:75
\n
glm::int32_t
int32 int32_t
32 bit signed integer type.
Definition: fwd.hpp:71
\n
glm::f64vec2
vec< 2, f64, defaultp > f64vec2
Double-qualifier floating-point vector of 2 components.
Definition: fwd.hpp:502
\n
glm::uvec4
vec< 4, unsigned int, defaultp > uvec4
4 components vector of unsigned integer numbers.
Definition: vector_uint4.hpp:15
\n
glm::lowp_uint64_t
uint64 lowp_uint64_t
Low qualifier 64 bit unsigned integer type.
Definition: fwd.hpp:140
\n
glm::uint64
detail::uint64 uint64
64 bit unsigned integer type.
Definition: scalar_uint_sized.hpp:67
\n
glm::highp_int16
int16 highp_int16
High qualifier 16 bit signed integer type.
Definition: fwd.hpp:52
\n
glm::aligned_mat4x4
aligned_highp_mat4x4 aligned_mat4x4
4 by 4 matrix tightly aligned in memory of single-precision floating-point numbers.
Definition: gtc/type_aligned.hpp:1006
\n
glm::fmat2x4
mat< 2, 4, f32, defaultp > fmat2x4
Single-qualifier floating-point 2x4 matrix.
Definition: fwd.hpp:666
\n
glm::mediump_i32
int32 mediump_i32
Medium qualifier 32 bit signed integer type.
Definition: fwd.hpp:60
\n
glm::aligned_dvec4
aligned_highp_dvec4 aligned_dvec4
4 components vector aligned in memory of double-precision floating-point numbers. ...
Definition: gtc/type_aligned.hpp:1117
\n
glm::highp_uint64_t
uint64 highp_uint64_t
High qualifier 64 bit unsigned integer type.
Definition: fwd.hpp:142
\n
glm::u32vec4
vec< 4, u32, defaultp > u32vec4
Default qualifier 32 bit unsigned integer vector of 4 components type.
Definition: fwd.hpp:382
\n
glm::f32quat
qua< f32, defaultp > f32quat
Single-qualifier floating-point quaternion.
Definition: fwd.hpp:805
\n
glm::int64
detail::int64 int64
64 bit signed integer type.
Definition: scalar_int_sized.hpp:67
\n
glm::f64mat4x2
mat< 4, 2, f64, defaultp > f64mat4x2
Double-qualifier floating-point 4x2 matrix.
Definition: fwd.hpp:782
\n
glm::fmat2x3
mat< 2, 3, f32, defaultp > fmat2x3
Single-qualifier floating-point 2x3 matrix.
Definition: fwd.hpp:663
\n
glm::u16
uint16 u16
Default qualifier 16 bit unsigned integer type.
Definition: fwd.hpp:106
\n
glm::lowp_i64
int64 lowp_i64
Low qualifier 64 bit signed integer type.
Definition: fwd.hpp:73
\n
glm::ivec2
vec< 2, int, defaultp > ivec2
2 components vector of signed integer numbers.
Definition: vector_int2.hpp:15
\n
glm::mediump_int8_t
int8 mediump_int8_t
Medium qualifier 8 bit signed integer type.
Definition: fwd.hpp:41
\n
glm::highp_int16_t
int16 highp_int16_t
High qualifier 16 bit signed integer type.
Definition: fwd.hpp:56
\n
glm::i64vec1
vec< 1, i64, defaultp > i64vec1
64 bit signed integer scalar type.
Definition: fwd.hpp:297
\n
glm::aligned_vec4
aligned_highp_vec4 aligned_vec4
4 components vector aligned in memory of single-precision floating-point numbers. ...
Definition: gtc/type_aligned.hpp:949
\n
glm::lowp_u32
uint32 lowp_u32
Low qualifier 32 bit unsigned integer type.
Definition: fwd.hpp:117
\n
glm::ivec1
vec< 1, int, defaultp > ivec1
1 component vector of signed integer numbers.
Definition: vector_int1.hpp:28
\n
glm::highp_u16
uint16 highp_u16
High qualifier 16 bit unsigned integer type.
Definition: fwd.hpp:105
\n
glm::fvec1
vec< 1, f32, defaultp > fvec1
Single-qualifier floating-point vector of 1 component.
Definition: fwd.hpp:441
\n
glm::lowp_int32_t
int32 lowp_int32_t
Low qualifier 32 bit signed integer type.
Definition: fwd.hpp:68
\n
glm::f32vec2
vec< 2, f32, defaultp > f32vec2
Single-qualifier floating-point vector of 2 components.
Definition: fwd.hpp:462
\n
glm::fmat2x2
mat< 2, 2, f32, defaultp > fmat2x2
Single-qualifier floating-point 1x1 matrix.
Definition: fwd.hpp:660
\n
glm::lowp_int8
int8 lowp_int8
Low qualifier 8 bit signed integer type.
Definition: fwd.hpp:36
\n
glm::dvec3
vec< 3, double, defaultp > dvec3
3 components vector of double-precision floating-point numbers.
Definition: vector_double3.hpp:15
\n
glm::lowp_int8_t
int8 lowp_int8_t
Low qualifier 8 bit signed integer type.
Definition: fwd.hpp:40
\n
glm::aligned_mat3x3
aligned_highp_mat3x3 aligned_mat3x3
3 by 3 matrix tightly aligned in memory of single-precision floating-point numbers.
Definition: gtc/type_aligned.hpp:994
\n
glm::f64mat4x3
mat< 4, 3, f64, defaultp > f64mat4x3
Double-qualifier floating-point 4x3 matrix.
Definition: fwd.hpp:785
\n
glm::i64
int64 i64
64 bit signed integer type.
Definition: fwd.hpp:76
\n
glm::u32vec2
vec< 2, u32, defaultp > u32vec2
Default qualifier 32 bit unsigned integer vector of 2 components type.
Definition: fwd.hpp:380
\n
glm::quat
qua< float, defaultp > quat
Quaternion of single-precision floating-point numbers.
Definition: quaternion_float.hpp:35
\n
glm::mediump_int32
int32 mediump_int32
Medium qualifier 32 bit signed integer type.
Definition: fwd.hpp:65
\n
glm::i64vec2
vec< 2, i64, defaultp > i64vec2
64 bit signed integer vector of 2 components type.
Definition: fwd.hpp:298
\n
glm::i16
int16 i16
16 bit signed integer type.
Definition: fwd.hpp:48
\n
glm::dvec4
vec< 4, double, defaultp > dvec4
4 components vector of double-precision floating-point numbers.
Definition: vector_double4.hpp:15
\n
glm::fmat4x4
mat< 4, 4, f32, defaultp > fmat4x4
Single-qualifier floating-point 4x4 matrix.
Definition: fwd.hpp:668
\n
glm::mat2
mat< 2, 2, float, defaultp > mat2
2 columns of 2 components matrix of single-precision floating-point numbers.
Definition: matrix_float2x2.hpp:20
\n
glm::aligned_mat3
aligned_highp_mat3 aligned_mat3
3 by 3 matrix tightly aligned in memory of single-precision floating-point numbers.
Definition: gtc/type_aligned.hpp:967
\n
glm::fmat3x2
mat< 3, 2, f32, defaultp > fmat3x2
Single-qualifier floating-point 3x2 matrix.
Definition: fwd.hpp:661
\n
glm::u16vec4
vec< 4, u16, defaultp > u16vec4
Default qualifier 16 bit unsigned integer vector of 4 components type.
Definition: fwd.hpp:362
\n
glm::u16vec2
vec< 2, u16, defaultp > u16vec2
Default qualifier 16 bit unsigned integer vector of 2 components type.
Definition: fwd.hpp:360
\n
glm::mediump_u8
uint8 mediump_u8
Medium qualifier 8 bit unsigned integer type.
Definition: fwd.hpp:90
\n
glm::aligned_dvec2
aligned_highp_dvec2 aligned_dvec2
2 components vector aligned in memory of double-precision floating-point numbers. ...
Definition: gtc/type_aligned.hpp:1111
\n
glm::mediump_int16_t
int16 mediump_int16_t
Medium qualifier 16 bit signed integer type.
Definition: fwd.hpp:55
\n
glm::lowp_i8
int8 lowp_i8
Low qualifier 8 bit signed integer type.
Definition: fwd.hpp:31
\n
glm::i64vec3
vec< 3, i64, defaultp > i64vec3
64 bit signed integer vector of 3 components type.
Definition: fwd.hpp:299
\n
glm::mat3
mat< 3, 3, float, defaultp > mat3
3 columns of 3 components matrix of single-precision floating-point numbers.
Definition: matrix_float3x3.hpp:20
\n
glm::highp_uint16_t
uint16 highp_uint16_t
High qualifier 16 bit unsigned integer type.
Definition: fwd.hpp:114
\n
glm::i8
int8 i8
8 bit signed integer type.
Definition: fwd.hpp:34
\n
glm::mediump_uint64_t
uint64 mediump_uint64_t
Medium qualifier 64 bit unsigned integer type.
Definition: fwd.hpp:141
\n
glm::mediump_uint8_t
uint8 mediump_uint8_t
Medium qualifier 8 bit unsigned integer type.
Definition: fwd.hpp:99
\n
glm
Definition: common.hpp:20
\n
glm::mediump_uint16
uint16 mediump_uint16
Medium qualifier 16 bit unsigned integer type.
Definition: fwd.hpp:109
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type_float.hpp
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1 #pragma once
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2 
\n
3 #include "setup.hpp"
\n
4 
\n
5 #if GLM_COMPILER == GLM_COMPILER_VC12
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6 # pragma warning(push)
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7 # pragma warning(disable: 4512) // assignment operator could not be generated
\n
8 #endif
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9 
\n
10 namespace glm{
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11 namespace detail
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12 {
\n
13  template <typename T>
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14  union float_t
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15  {};
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16 
\n
17  // https://randomascii.wordpress.com/2012/02/25/comparing-floating-point-numbers-2012-edition/
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18  template <>
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19  union float_t<float>
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20  {
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21  typedef int int_type;
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22  typedef float float_type;
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23 
\n
24  GLM_CONSTEXPR float_t(float_type Num = 0.0f) : f(Num) {}
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25 
\n
26  GLM_CONSTEXPR float_t& operator=(float_t const& x)
\n
27  {
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28  f = x.f;
\n
29  return *this;
\n
30  }
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31 
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32  // Portable extraction of components.
\n
33  GLM_CONSTEXPR bool negative() const { return i < 0; }
\n
34  GLM_CONSTEXPR int_type mantissa() const { return i & ((1 << 23) - 1); }
\n
35  GLM_CONSTEXPR int_type exponent() const { return (i >> 23) & ((1 << 8) - 1); }
\n
36 
\n
37  int_type i;
\n
38  float_type f;
\n
39  };
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40 
\n
41  template <>
\n
42  union float_t<double>
\n
43  {
\n
44  typedef detail::int64 int_type;
\n
45  typedef double float_type;
\n
46 
\n
47  GLM_CONSTEXPR float_t(float_type Num = static_cast<float_type>(0)) : f(Num) {}
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48 
\n
49  GLM_CONSTEXPR float_t& operator=(float_t const& x)
\n
50  {
\n
51  f = x.f;
\n
52  return *this;
\n
53  }
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54 
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55  // Portable extraction of components.
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56  GLM_CONSTEXPR bool negative() const { return i < 0; }
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57  GLM_CONSTEXPR int_type mantissa() const { return i & ((int_type(1) << 52) - 1); }
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58  GLM_CONSTEXPR int_type exponent() const { return (i >> 52) & ((int_type(1) << 11) - 1); }
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59 
\n
60  int_type i;
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61  float_type f;
\n
62  };
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63 }//namespace detail
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64 }//namespace glm
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65 
\n
66 #if GLM_COMPILER == GLM_COMPILER_VC12
\n
67 # pragma warning(pop)
\n
68 #endif
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glm::int64
detail::int64 int64
64 bit signed integer type.
Definition: scalar_int_sized.hpp:67
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glm
Definition: common.hpp:20
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type_half.hpp
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1 #pragma once
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2 
\n
3 #include "setup.hpp"
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4 
\n
5 namespace glm{
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6 namespace detail
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7 {
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8  typedef short hdata;
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9 
\n
10  GLM_FUNC_DECL float toFloat32(hdata value);
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11  GLM_FUNC_DECL hdata toFloat16(float const& value);
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12 
\n
13 }//namespace detail
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14 }//namespace glm
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15 
\n
16 #include "type_half.inl"
\n
glm
Definition: common.hpp:20
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type_mat2x2.hpp File Reference
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Core features \nMore...

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Core features

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Definition in file type_mat2x2.hpp.

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type_mat2x2.hpp
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\nGo to the documentation of this file.
1 
\n
4 #pragma once
\n
5 
\n
6 #include "type_vec2.hpp"
\n
7 #include <limits>
\n
8 #include <cstddef>
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9 
\n
10 namespace glm
\n
11 {
\n
12  template<typename T, qualifier Q>
\n
13  struct mat<2, 2, T, Q>
\n
14  {
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15  typedef vec<2, T, Q> col_type;
\n
16  typedef vec<2, T, Q> row_type;
\n
17  typedef mat<2, 2, T, Q> type;
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18  typedef mat<2, 2, T, Q> transpose_type;
\n
19  typedef T value_type;
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20 
\n
21  private:
\n
22  col_type value[2];
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23 
\n
24  public:
\n
25  // -- Accesses --
\n
26 
\n
27  typedef length_t length_type;
\n
28  GLM_FUNC_DECL static GLM_CONSTEXPR length_type length() { return 2; }
\n
29 
\n
30  GLM_FUNC_DECL col_type & operator[](length_type i);
\n
31  GLM_FUNC_DECL GLM_CONSTEXPR col_type const& operator[](length_type i) const;
\n
32 
\n
33  // -- Constructors --
\n
34 
\n
35  GLM_FUNC_DECL GLM_CONSTEXPR mat() GLM_DEFAULT;
\n
36  template<qualifier P>
\n
37  GLM_FUNC_DECL GLM_CONSTEXPR mat(mat<2, 2, T, P> const& m);
\n
38 
\n
39  GLM_FUNC_DECL explicit GLM_CONSTEXPR mat(T scalar);
\n
40  GLM_FUNC_DECL GLM_CONSTEXPR mat(
\n
41  T const& x1, T const& y1,
\n
42  T const& x2, T const& y2);
\n
43  GLM_FUNC_DECL GLM_CONSTEXPR mat(
\n
44  col_type const& v1,
\n
45  col_type const& v2);
\n
46 
\n
47  // -- Conversions --
\n
48 
\n
49  template<typename U, typename V, typename M, typename N>
\n
50  GLM_FUNC_DECL GLM_CONSTEXPR mat(
\n
51  U const& x1, V const& y1,
\n
52  M const& x2, N const& y2);
\n
53 
\n
54  template<typename U, typename V>
\n
55  GLM_FUNC_DECL GLM_CONSTEXPR mat(
\n
56  vec<2, U, Q> const& v1,
\n
57  vec<2, V, Q> const& v2);
\n
58 
\n
59  // -- Matrix conversions --
\n
60 
\n
61  template<typename U, qualifier P>
\n
62  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<2, 2, U, P> const& m);
\n
63 
\n
64  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<3, 3, T, Q> const& x);
\n
65  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<4, 4, T, Q> const& x);
\n
66  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<2, 3, T, Q> const& x);
\n
67  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<3, 2, T, Q> const& x);
\n
68  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<2, 4, T, Q> const& x);
\n
69  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<4, 2, T, Q> const& x);
\n
70  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<3, 4, T, Q> const& x);
\n
71  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<4, 3, T, Q> const& x);
\n
72 
\n
73  // -- Unary arithmetic operators --
\n
74 
\n
75  template<typename U>
\n
76  GLM_FUNC_DECL mat<2, 2, T, Q> & operator=(mat<2, 2, U, Q> const& m);
\n
77  template<typename U>
\n
78  GLM_FUNC_DECL mat<2, 2, T, Q> & operator+=(U s);
\n
79  template<typename U>
\n
80  GLM_FUNC_DECL mat<2, 2, T, Q> & operator+=(mat<2, 2, U, Q> const& m);
\n
81  template<typename U>
\n
82  GLM_FUNC_DECL mat<2, 2, T, Q> & operator-=(U s);
\n
83  template<typename U>
\n
84  GLM_FUNC_DECL mat<2, 2, T, Q> & operator-=(mat<2, 2, U, Q> const& m);
\n
85  template<typename U>
\n
86  GLM_FUNC_DECL mat<2, 2, T, Q> & operator*=(U s);
\n
87  template<typename U>
\n
88  GLM_FUNC_DECL mat<2, 2, T, Q> & operator*=(mat<2, 2, U, Q> const& m);
\n
89  template<typename U>
\n
90  GLM_FUNC_DECL mat<2, 2, T, Q> & operator/=(U s);
\n
91  template<typename U>
\n
92  GLM_FUNC_DECL mat<2, 2, T, Q> & operator/=(mat<2, 2, U, Q> const& m);
\n
93 
\n
94  // -- Increment and decrement operators --
\n
95 
\n
96  GLM_FUNC_DECL mat<2, 2, T, Q> & operator++ ();
\n
97  GLM_FUNC_DECL mat<2, 2, T, Q> & operator-- ();
\n
98  GLM_FUNC_DECL mat<2, 2, T, Q> operator++(int);
\n
99  GLM_FUNC_DECL mat<2, 2, T, Q> operator--(int);
\n
100  };
\n
101 
\n
102  // -- Unary operators --
\n
103 
\n
104  template<typename T, qualifier Q>
\n
105  GLM_FUNC_DECL mat<2, 2, T, Q> operator+(mat<2, 2, T, Q> const& m);
\n
106 
\n
107  template<typename T, qualifier Q>
\n
108  GLM_FUNC_DECL mat<2, 2, T, Q> operator-(mat<2, 2, T, Q> const& m);
\n
109 
\n
110  // -- Binary operators --
\n
111 
\n
112  template<typename T, qualifier Q>
\n
113  GLM_FUNC_DECL mat<2, 2, T, Q> operator+(mat<2, 2, T, Q> const& m, T scalar);
\n
114 
\n
115  template<typename T, qualifier Q>
\n
116  GLM_FUNC_DECL mat<2, 2, T, Q> operator+(T scalar, mat<2, 2, T, Q> const& m);
\n
117 
\n
118  template<typename T, qualifier Q>
\n
119  GLM_FUNC_DECL mat<2, 2, T, Q> operator+(mat<2, 2, T, Q> const& m1, mat<2, 2, T, Q> const& m2);
\n
120 
\n
121  template<typename T, qualifier Q>
\n
122  GLM_FUNC_DECL mat<2, 2, T, Q> operator-(mat<2, 2, T, Q> const& m, T scalar);
\n
123 
\n
124  template<typename T, qualifier Q>
\n
125  GLM_FUNC_DECL mat<2, 2, T, Q> operator-(T scalar, mat<2, 2, T, Q> const& m);
\n
126 
\n
127  template<typename T, qualifier Q>
\n
128  GLM_FUNC_DECL mat<2, 2, T, Q> operator-(mat<2, 2, T, Q> const& m1, mat<2, 2, T, Q> const& m2);
\n
129 
\n
130  template<typename T, qualifier Q>
\n
131  GLM_FUNC_DECL mat<2, 2, T, Q> operator*(mat<2, 2, T, Q> const& m, T scalar);
\n
132 
\n
133  template<typename T, qualifier Q>
\n
134  GLM_FUNC_DECL mat<2, 2, T, Q> operator*(T scalar, mat<2, 2, T, Q> const& m);
\n
135 
\n
136  template<typename T, qualifier Q>
\n
137  GLM_FUNC_DECL typename mat<2, 2, T, Q>::col_type operator*(mat<2, 2, T, Q> const& m, typename mat<2, 2, T, Q>::row_type const& v);
\n
138 
\n
139  template<typename T, qualifier Q>
\n
140  GLM_FUNC_DECL typename mat<2, 2, T, Q>::row_type operator*(typename mat<2, 2, T, Q>::col_type const& v, mat<2, 2, T, Q> const& m);
\n
141 
\n
142  template<typename T, qualifier Q>
\n
143  GLM_FUNC_DECL mat<2, 2, T, Q> operator*(mat<2, 2, T, Q> const& m1, mat<2, 2, T, Q> const& m2);
\n
144 
\n
145  template<typename T, qualifier Q>
\n
146  GLM_FUNC_DECL mat<3, 2, T, Q> operator*(mat<2, 2, T, Q> const& m1, mat<3, 2, T, Q> const& m2);
\n
147 
\n
148  template<typename T, qualifier Q>
\n
149  GLM_FUNC_DECL mat<4, 2, T, Q> operator*(mat<2, 2, T, Q> const& m1, mat<4, 2, T, Q> const& m2);
\n
150 
\n
151  template<typename T, qualifier Q>
\n
152  GLM_FUNC_DECL mat<2, 2, T, Q> operator/(mat<2, 2, T, Q> const& m, T scalar);
\n
153 
\n
154  template<typename T, qualifier Q>
\n
155  GLM_FUNC_DECL mat<2, 2, T, Q> operator/(T scalar, mat<2, 2, T, Q> const& m);
\n
156 
\n
157  template<typename T, qualifier Q>
\n
158  GLM_FUNC_DECL typename mat<2, 2, T, Q>::col_type operator/(mat<2, 2, T, Q> const& m, typename mat<2, 2, T, Q>::row_type const& v);
\n
159 
\n
160  template<typename T, qualifier Q>
\n
161  GLM_FUNC_DECL typename mat<2, 2, T, Q>::row_type operator/(typename mat<2, 2, T, Q>::col_type const& v, mat<2, 2, T, Q> const& m);
\n
162 
\n
163  template<typename T, qualifier Q>
\n
164  GLM_FUNC_DECL mat<2, 2, T, Q> operator/(mat<2, 2, T, Q> const& m1, mat<2, 2, T, Q> const& m2);
\n
165 
\n
166  // -- Boolean operators --
\n
167 
\n
168  template<typename T, qualifier Q>
\n
169  GLM_FUNC_DECL bool operator==(mat<2, 2, T, Q> const& m1, mat<2, 2, T, Q> const& m2);
\n
170 
\n
171  template<typename T, qualifier Q>
\n
172  GLM_FUNC_DECL bool operator!=(mat<2, 2, T, Q> const& m1, mat<2, 2, T, Q> const& m2);
\n
173 } //namespace glm
\n
174 
\n
175 #ifndef GLM_EXTERNAL_TEMPLATE
\n
176 #include "type_mat2x2.inl"
\n
177 #endif
\n
type_vec2.hpp
Core features
\n
glm::length
GLM_FUNC_DECL T length(qua< T, Q > const &q)
Returns the norm of a quaternions.
\n
glm
Definition: common.hpp:20
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Definition in file type_mat2x3.hpp.

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type_mat2x3.hpp
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1 
\n
4 #pragma once
\n
5 
\n
6 #include "type_vec2.hpp"
\n
7 #include "type_vec3.hpp"
\n
8 #include <limits>
\n
9 #include <cstddef>
\n
10 
\n
11 namespace glm
\n
12 {
\n
13  template<typename T, qualifier Q>
\n
14  struct mat<2, 3, T, Q>
\n
15  {
\n
16  typedef vec<3, T, Q> col_type;
\n
17  typedef vec<2, T, Q> row_type;
\n
18  typedef mat<2, 3, T, Q> type;
\n
19  typedef mat<3, 2, T, Q> transpose_type;
\n
20  typedef T value_type;
\n
21 
\n
22  private:
\n
23  col_type value[2];
\n
24 
\n
25  public:
\n
26  // -- Accesses --
\n
27 
\n
28  typedef length_t length_type;
\n
29  GLM_FUNC_DECL static GLM_CONSTEXPR length_type length() { return 2; }
\n
30 
\n
31  GLM_FUNC_DECL col_type & operator[](length_type i);
\n
32  GLM_FUNC_DECL GLM_CONSTEXPR col_type const& operator[](length_type i) const;
\n
33 
\n
34  // -- Constructors --
\n
35 
\n
36  GLM_FUNC_DECL GLM_CONSTEXPR mat() GLM_DEFAULT;
\n
37  template<qualifier P>
\n
38  GLM_FUNC_DECL GLM_CONSTEXPR mat(mat<2, 3, T, P> const& m);
\n
39 
\n
40  GLM_FUNC_DECL explicit GLM_CONSTEXPR mat(T scalar);
\n
41  GLM_FUNC_DECL GLM_CONSTEXPR mat(
\n
42  T x0, T y0, T z0,
\n
43  T x1, T y1, T z1);
\n
44  GLM_FUNC_DECL GLM_CONSTEXPR mat(
\n
45  col_type const& v0,
\n
46  col_type const& v1);
\n
47 
\n
48  // -- Conversions --
\n
49 
\n
50  template<typename X1, typename Y1, typename Z1, typename X2, typename Y2, typename Z2>
\n
51  GLM_FUNC_DECL GLM_CONSTEXPR mat(
\n
52  X1 x1, Y1 y1, Z1 z1,
\n
53  X2 x2, Y2 y2, Z2 z2);
\n
54 
\n
55  template<typename U, typename V>
\n
56  GLM_FUNC_DECL GLM_CONSTEXPR mat(
\n
57  vec<3, U, Q> const& v1,
\n
58  vec<3, V, Q> const& v2);
\n
59 
\n
60  // -- Matrix conversions --
\n
61 
\n
62  template<typename U, qualifier P>
\n
63  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<2, 3, U, P> const& m);
\n
64 
\n
65  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<2, 2, T, Q> const& x);
\n
66  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<3, 3, T, Q> const& x);
\n
67  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<4, 4, T, Q> const& x);
\n
68  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<2, 4, T, Q> const& x);
\n
69  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<3, 2, T, Q> const& x);
\n
70  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<3, 4, T, Q> const& x);
\n
71  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<4, 2, T, Q> const& x);
\n
72  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<4, 3, T, Q> const& x);
\n
73 
\n
74  // -- Unary arithmetic operators --
\n
75 
\n
76  template<typename U>
\n
77  GLM_FUNC_DECL mat<2, 3, T, Q> & operator=(mat<2, 3, U, Q> const& m);
\n
78  template<typename U>
\n
79  GLM_FUNC_DECL mat<2, 3, T, Q> & operator+=(U s);
\n
80  template<typename U>
\n
81  GLM_FUNC_DECL mat<2, 3, T, Q> & operator+=(mat<2, 3, U, Q> const& m);
\n
82  template<typename U>
\n
83  GLM_FUNC_DECL mat<2, 3, T, Q> & operator-=(U s);
\n
84  template<typename U>
\n
85  GLM_FUNC_DECL mat<2, 3, T, Q> & operator-=(mat<2, 3, U, Q> const& m);
\n
86  template<typename U>
\n
87  GLM_FUNC_DECL mat<2, 3, T, Q> & operator*=(U s);
\n
88  template<typename U>
\n
89  GLM_FUNC_DECL mat<2, 3, T, Q> & operator/=(U s);
\n
90 
\n
91  // -- Increment and decrement operators --
\n
92 
\n
93  GLM_FUNC_DECL mat<2, 3, T, Q> & operator++ ();
\n
94  GLM_FUNC_DECL mat<2, 3, T, Q> & operator-- ();
\n
95  GLM_FUNC_DECL mat<2, 3, T, Q> operator++(int);
\n
96  GLM_FUNC_DECL mat<2, 3, T, Q> operator--(int);
\n
97  };
\n
98 
\n
99  // -- Unary operators --
\n
100 
\n
101  template<typename T, qualifier Q>
\n
102  GLM_FUNC_DECL mat<2, 3, T, Q> operator+(mat<2, 3, T, Q> const& m);
\n
103 
\n
104  template<typename T, qualifier Q>
\n
105  GLM_FUNC_DECL mat<2, 3, T, Q> operator-(mat<2, 3, T, Q> const& m);
\n
106 
\n
107  // -- Binary operators --
\n
108 
\n
109  template<typename T, qualifier Q>
\n
110  GLM_FUNC_DECL mat<2, 3, T, Q> operator+(mat<2, 3, T, Q> const& m, T scalar);
\n
111 
\n
112  template<typename T, qualifier Q>
\n
113  GLM_FUNC_DECL mat<2, 3, T, Q> operator+(mat<2, 3, T, Q> const& m1, mat<2, 3, T, Q> const& m2);
\n
114 
\n
115  template<typename T, qualifier Q>
\n
116  GLM_FUNC_DECL mat<2, 3, T, Q> operator-(mat<2, 3, T, Q> const& m, T scalar);
\n
117 
\n
118  template<typename T, qualifier Q>
\n
119  GLM_FUNC_DECL mat<2, 3, T, Q> operator-(mat<2, 3, T, Q> const& m1, mat<2, 3, T, Q> const& m2);
\n
120 
\n
121  template<typename T, qualifier Q>
\n
122  GLM_FUNC_DECL mat<2, 3, T, Q> operator*(mat<2, 3, T, Q> const& m, T scalar);
\n
123 
\n
124  template<typename T, qualifier Q>
\n
125  GLM_FUNC_DECL mat<2, 3, T, Q> operator*(T scalar, mat<2, 3, T, Q> const& m);
\n
126 
\n
127  template<typename T, qualifier Q>
\n
128  GLM_FUNC_DECL typename mat<2, 3, T, Q>::col_type operator*(mat<2, 3, T, Q> const& m, typename mat<2, 3, T, Q>::row_type const& v);
\n
129 
\n
130  template<typename T, qualifier Q>
\n
131  GLM_FUNC_DECL typename mat<2, 3, T, Q>::row_type operator*(typename mat<2, 3, T, Q>::col_type const& v, mat<2, 3, T, Q> const& m);
\n
132 
\n
133  template<typename T, qualifier Q>
\n
134  GLM_FUNC_DECL mat<2, 3, T, Q> operator*(mat<2, 3, T, Q> const& m1, mat<2, 2, T, Q> const& m2);
\n
135 
\n
136  template<typename T, qualifier Q>
\n
137  GLM_FUNC_DECL mat<3, 3, T, Q> operator*(mat<2, 3, T, Q> const& m1, mat<3, 2, T, Q> const& m2);
\n
138 
\n
139  template<typename T, qualifier Q>
\n
140  GLM_FUNC_DECL mat<4, 3, T, Q> operator*(mat<2, 3, T, Q> const& m1, mat<4, 2, T, Q> const& m2);
\n
141 
\n
142  template<typename T, qualifier Q>
\n
143  GLM_FUNC_DECL mat<2, 3, T, Q> operator/(mat<2, 3, T, Q> const& m, T scalar);
\n
144 
\n
145  template<typename T, qualifier Q>
\n
146  GLM_FUNC_DECL mat<2, 3, T, Q> operator/(T scalar, mat<2, 3, T, Q> const& m);
\n
147 
\n
148  // -- Boolean operators --
\n
149 
\n
150  template<typename T, qualifier Q>
\n
151  GLM_FUNC_DECL bool operator==(mat<2, 3, T, Q> const& m1, mat<2, 3, T, Q> const& m2);
\n
152 
\n
153  template<typename T, qualifier Q>
\n
154  GLM_FUNC_DECL bool operator!=(mat<2, 3, T, Q> const& m1, mat<2, 3, T, Q> const& m2);
\n
155 }//namespace glm
\n
156 
\n
157 #ifndef GLM_EXTERNAL_TEMPLATE
\n
158 #include "type_mat2x3.inl"
\n
159 #endif
\n
type_vec2.hpp
Core features
\n
glm::length
GLM_FUNC_DECL T length(qua< T, Q > const &q)
Returns the norm of a quaternions.
\n
type_vec3.hpp
Core features
\n
glm
Definition: common.hpp:20
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1 
\n
4 #pragma once
\n
5 
\n
6 #include "type_vec2.hpp"
\n
7 #include "type_vec4.hpp"
\n
8 #include <limits>
\n
9 #include <cstddef>
\n
10 
\n
11 namespace glm
\n
12 {
\n
13  template<typename T, qualifier Q>
\n
14  struct mat<2, 4, T, Q>
\n
15  {
\n
16  typedef vec<4, T, Q> col_type;
\n
17  typedef vec<2, T, Q> row_type;
\n
18  typedef mat<2, 4, T, Q> type;
\n
19  typedef mat<4, 2, T, Q> transpose_type;
\n
20  typedef T value_type;
\n
21 
\n
22  private:
\n
23  col_type value[2];
\n
24 
\n
25  public:
\n
26  // -- Accesses --
\n
27 
\n
28  typedef length_t length_type;
\n
29  GLM_FUNC_DECL static GLM_CONSTEXPR length_type length() { return 2; }
\n
30 
\n
31  GLM_FUNC_DECL col_type & operator[](length_type i);
\n
32  GLM_FUNC_DECL GLM_CONSTEXPR col_type const& operator[](length_type i) const;
\n
33 
\n
34  // -- Constructors --
\n
35 
\n
36  GLM_FUNC_DECL GLM_CONSTEXPR mat() GLM_DEFAULT;
\n
37  template<qualifier P>
\n
38  GLM_FUNC_DECL GLM_CONSTEXPR mat(mat<2, 4, T, P> const& m);
\n
39 
\n
40  GLM_FUNC_DECL explicit GLM_CONSTEXPR mat(T scalar);
\n
41  GLM_FUNC_DECL GLM_CONSTEXPR mat(
\n
42  T x0, T y0, T z0, T w0,
\n
43  T x1, T y1, T z1, T w1);
\n
44  GLM_FUNC_DECL GLM_CONSTEXPR mat(
\n
45  col_type const& v0,
\n
46  col_type const& v1);
\n
47 
\n
48  // -- Conversions --
\n
49 
\n
50  template<
\n
51  typename X1, typename Y1, typename Z1, typename W1,
\n
52  typename X2, typename Y2, typename Z2, typename W2>
\n
53  GLM_FUNC_DECL GLM_CONSTEXPR mat(
\n
54  X1 x1, Y1 y1, Z1 z1, W1 w1,
\n
55  X2 x2, Y2 y2, Z2 z2, W2 w2);
\n
56 
\n
57  template<typename U, typename V>
\n
58  GLM_FUNC_DECL GLM_CONSTEXPR mat(
\n
59  vec<4, U, Q> const& v1,
\n
60  vec<4, V, Q> const& v2);
\n
61 
\n
62  // -- Matrix conversions --
\n
63 
\n
64  template<typename U, qualifier P>
\n
65  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<2, 4, U, P> const& m);
\n
66 
\n
67  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<2, 2, T, Q> const& x);
\n
68  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<3, 3, T, Q> const& x);
\n
69  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<4, 4, T, Q> const& x);
\n
70  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<2, 3, T, Q> const& x);
\n
71  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<3, 2, T, Q> const& x);
\n
72  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<3, 4, T, Q> const& x);
\n
73  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<4, 2, T, Q> const& x);
\n
74  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<4, 3, T, Q> const& x);
\n
75 
\n
76  // -- Unary arithmetic operators --
\n
77 
\n
78  template<typename U>
\n
79  GLM_FUNC_DECL mat<2, 4, T, Q> & operator=(mat<2, 4, U, Q> const& m);
\n
80  template<typename U>
\n
81  GLM_FUNC_DECL mat<2, 4, T, Q> & operator+=(U s);
\n
82  template<typename U>
\n
83  GLM_FUNC_DECL mat<2, 4, T, Q> & operator+=(mat<2, 4, U, Q> const& m);
\n
84  template<typename U>
\n
85  GLM_FUNC_DECL mat<2, 4, T, Q> & operator-=(U s);
\n
86  template<typename U>
\n
87  GLM_FUNC_DECL mat<2, 4, T, Q> & operator-=(mat<2, 4, U, Q> const& m);
\n
88  template<typename U>
\n
89  GLM_FUNC_DECL mat<2, 4, T, Q> & operator*=(U s);
\n
90  template<typename U>
\n
91  GLM_FUNC_DECL mat<2, 4, T, Q> & operator/=(U s);
\n
92 
\n
93  // -- Increment and decrement operators --
\n
94 
\n
95  GLM_FUNC_DECL mat<2, 4, T, Q> & operator++ ();
\n
96  GLM_FUNC_DECL mat<2, 4, T, Q> & operator-- ();
\n
97  GLM_FUNC_DECL mat<2, 4, T, Q> operator++(int);
\n
98  GLM_FUNC_DECL mat<2, 4, T, Q> operator--(int);
\n
99  };
\n
100 
\n
101  // -- Unary operators --
\n
102 
\n
103  template<typename T, qualifier Q>
\n
104  GLM_FUNC_DECL mat<2, 4, T, Q> operator+(mat<2, 4, T, Q> const& m);
\n
105 
\n
106  template<typename T, qualifier Q>
\n
107  GLM_FUNC_DECL mat<2, 4, T, Q> operator-(mat<2, 4, T, Q> const& m);
\n
108 
\n
109  // -- Binary operators --
\n
110 
\n
111  template<typename T, qualifier Q>
\n
112  GLM_FUNC_DECL mat<2, 4, T, Q> operator+(mat<2, 4, T, Q> const& m, T scalar);
\n
113 
\n
114  template<typename T, qualifier Q>
\n
115  GLM_FUNC_DECL mat<2, 4, T, Q> operator+(mat<2, 4, T, Q> const& m1, mat<2, 4, T, Q> const& m2);
\n
116 
\n
117  template<typename T, qualifier Q>
\n
118  GLM_FUNC_DECL mat<2, 4, T, Q> operator-(mat<2, 4, T, Q> const& m, T scalar);
\n
119 
\n
120  template<typename T, qualifier Q>
\n
121  GLM_FUNC_DECL mat<2, 4, T, Q> operator-(mat<2, 4, T, Q> const& m1, mat<2, 4, T, Q> const& m2);
\n
122 
\n
123  template<typename T, qualifier Q>
\n
124  GLM_FUNC_DECL mat<2, 4, T, Q> operator*(mat<2, 4, T, Q> const& m, T scalar);
\n
125 
\n
126  template<typename T, qualifier Q>
\n
127  GLM_FUNC_DECL mat<2, 4, T, Q> operator*(T scalar, mat<2, 4, T, Q> const& m);
\n
128 
\n
129  template<typename T, qualifier Q>
\n
130  GLM_FUNC_DECL typename mat<2, 4, T, Q>::col_type operator*(mat<2, 4, T, Q> const& m, typename mat<2, 4, T, Q>::row_type const& v);
\n
131 
\n
132  template<typename T, qualifier Q>
\n
133  GLM_FUNC_DECL typename mat<2, 4, T, Q>::row_type operator*(typename mat<2, 4, T, Q>::col_type const& v, mat<2, 4, T, Q> const& m);
\n
134 
\n
135  template<typename T, qualifier Q>
\n
136  GLM_FUNC_DECL mat<4, 4, T, Q> operator*(mat<2, 4, T, Q> const& m1, mat<4, 2, T, Q> const& m2);
\n
137 
\n
138  template<typename T, qualifier Q>
\n
139  GLM_FUNC_DECL mat<2, 4, T, Q> operator*(mat<2, 4, T, Q> const& m1, mat<2, 2, T, Q> const& m2);
\n
140 
\n
141  template<typename T, qualifier Q>
\n
142  GLM_FUNC_DECL mat<3, 4, T, Q> operator*(mat<2, 4, T, Q> const& m1, mat<3, 2, T, Q> const& m2);
\n
143 
\n
144  template<typename T, qualifier Q>
\n
145  GLM_FUNC_DECL mat<2, 4, T, Q> operator/(mat<2, 4, T, Q> const& m, T scalar);
\n
146 
\n
147  template<typename T, qualifier Q>
\n
148  GLM_FUNC_DECL mat<2, 4, T, Q> operator/(T scalar, mat<2, 4, T, Q> const& m);
\n
149 
\n
150  // -- Boolean operators --
\n
151 
\n
152  template<typename T, qualifier Q>
\n
153  GLM_FUNC_DECL bool operator==(mat<2, 4, T, Q> const& m1, mat<2, 4, T, Q> const& m2);
\n
154 
\n
155  template<typename T, qualifier Q>
\n
156  GLM_FUNC_DECL bool operator!=(mat<2, 4, T, Q> const& m1, mat<2, 4, T, Q> const& m2);
\n
157 }//namespace glm
\n
158 
\n
159 #ifndef GLM_EXTERNAL_TEMPLATE
\n
160 #include "type_mat2x4.inl"
\n
161 #endif
\n
type_vec2.hpp
Core features
\n
glm::length
GLM_FUNC_DECL T length(qua< T, Q > const &q)
Returns the norm of a quaternions.
\n
type_vec4.hpp
Core features
\n
glm
Definition: common.hpp:20
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Definition in file type_mat3x2.hpp.

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type_mat3x2.hpp
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1 
\n
4 #pragma once
\n
5 
\n
6 #include "type_vec2.hpp"
\n
7 #include "type_vec3.hpp"
\n
8 #include <limits>
\n
9 #include <cstddef>
\n
10 
\n
11 namespace glm
\n
12 {
\n
13  template<typename T, qualifier Q>
\n
14  struct mat<3, 2, T, Q>
\n
15  {
\n
16  typedef vec<2, T, Q> col_type;
\n
17  typedef vec<3, T, Q> row_type;
\n
18  typedef mat<3, 2, T, Q> type;
\n
19  typedef mat<2, 3, T, Q> transpose_type;
\n
20  typedef T value_type;
\n
21 
\n
22  private:
\n
23  col_type value[3];
\n
24 
\n
25  public:
\n
26  // -- Accesses --
\n
27 
\n
28  typedef length_t length_type;
\n
29  GLM_FUNC_DECL static GLM_CONSTEXPR length_type length() { return 3; }
\n
30 
\n
31  GLM_FUNC_DECL col_type & operator[](length_type i);
\n
32  GLM_FUNC_DECL GLM_CONSTEXPR col_type const& operator[](length_type i) const;
\n
33 
\n
34  // -- Constructors --
\n
35 
\n
36  GLM_FUNC_DECL GLM_CONSTEXPR mat() GLM_DEFAULT;
\n
37  template<qualifier P>
\n
38  GLM_FUNC_DECL GLM_CONSTEXPR mat(mat<3, 2, T, P> const& m);
\n
39 
\n
40  GLM_FUNC_DECL explicit GLM_CONSTEXPR mat(T scalar);
\n
41  GLM_FUNC_DECL GLM_CONSTEXPR mat(
\n
42  T x0, T y0,
\n
43  T x1, T y1,
\n
44  T x2, T y2);
\n
45  GLM_FUNC_DECL GLM_CONSTEXPR mat(
\n
46  col_type const& v0,
\n
47  col_type const& v1,
\n
48  col_type const& v2);
\n
49 
\n
50  // -- Conversions --
\n
51 
\n
52  template<
\n
53  typename X1, typename Y1,
\n
54  typename X2, typename Y2,
\n
55  typename X3, typename Y3>
\n
56  GLM_FUNC_DECL GLM_CONSTEXPR mat(
\n
57  X1 x1, Y1 y1,
\n
58  X2 x2, Y2 y2,
\n
59  X3 x3, Y3 y3);
\n
60 
\n
61  template<typename V1, typename V2, typename V3>
\n
62  GLM_FUNC_DECL GLM_CONSTEXPR mat(
\n
63  vec<2, V1, Q> const& v1,
\n
64  vec<2, V2, Q> const& v2,
\n
65  vec<2, V3, Q> const& v3);
\n
66 
\n
67  // -- Matrix conversions --
\n
68 
\n
69  template<typename U, qualifier P>
\n
70  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<3, 2, U, P> const& m);
\n
71 
\n
72  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<2, 2, T, Q> const& x);
\n
73  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<3, 3, T, Q> const& x);
\n
74  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<4, 4, T, Q> const& x);
\n
75  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<2, 3, T, Q> const& x);
\n
76  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<2, 4, T, Q> const& x);
\n
77  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<3, 4, T, Q> const& x);
\n
78  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<4, 2, T, Q> const& x);
\n
79  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<4, 3, T, Q> const& x);
\n
80 
\n
81  // -- Unary arithmetic operators --
\n
82 
\n
83  template<typename U>
\n
84  GLM_FUNC_DECL mat<3, 2, T, Q> & operator=(mat<3, 2, U, Q> const& m);
\n
85  template<typename U>
\n
86  GLM_FUNC_DECL mat<3, 2, T, Q> & operator+=(U s);
\n
87  template<typename U>
\n
88  GLM_FUNC_DECL mat<3, 2, T, Q> & operator+=(mat<3, 2, U, Q> const& m);
\n
89  template<typename U>
\n
90  GLM_FUNC_DECL mat<3, 2, T, Q> & operator-=(U s);
\n
91  template<typename U>
\n
92  GLM_FUNC_DECL mat<3, 2, T, Q> & operator-=(mat<3, 2, U, Q> const& m);
\n
93  template<typename U>
\n
94  GLM_FUNC_DECL mat<3, 2, T, Q> & operator*=(U s);
\n
95  template<typename U>
\n
96  GLM_FUNC_DECL mat<3, 2, T, Q> & operator/=(U s);
\n
97 
\n
98  // -- Increment and decrement operators --
\n
99 
\n
100  GLM_FUNC_DECL mat<3, 2, T, Q> & operator++ ();
\n
101  GLM_FUNC_DECL mat<3, 2, T, Q> & operator-- ();
\n
102  GLM_FUNC_DECL mat<3, 2, T, Q> operator++(int);
\n
103  GLM_FUNC_DECL mat<3, 2, T, Q> operator--(int);
\n
104  };
\n
105 
\n
106  // -- Unary operators --
\n
107 
\n
108  template<typename T, qualifier Q>
\n
109  GLM_FUNC_DECL mat<3, 2, T, Q> operator+(mat<3, 2, T, Q> const& m);
\n
110 
\n
111  template<typename T, qualifier Q>
\n
112  GLM_FUNC_DECL mat<3, 2, T, Q> operator-(mat<3, 2, T, Q> const& m);
\n
113 
\n
114  // -- Binary operators --
\n
115 
\n
116  template<typename T, qualifier Q>
\n
117  GLM_FUNC_DECL mat<3, 2, T, Q> operator+(mat<3, 2, T, Q> const& m, T scalar);
\n
118 
\n
119  template<typename T, qualifier Q>
\n
120  GLM_FUNC_DECL mat<3, 2, T, Q> operator+(mat<3, 2, T, Q> const& m1, mat<3, 2, T, Q> const& m2);
\n
121 
\n
122  template<typename T, qualifier Q>
\n
123  GLM_FUNC_DECL mat<3, 2, T, Q> operator-(mat<3, 2, T, Q> const& m, T scalar);
\n
124 
\n
125  template<typename T, qualifier Q>
\n
126  GLM_FUNC_DECL mat<3, 2, T, Q> operator-(mat<3, 2, T, Q> const& m1, mat<3, 2, T, Q> const& m2);
\n
127 
\n
128  template<typename T, qualifier Q>
\n
129  GLM_FUNC_DECL mat<3, 2, T, Q> operator*(mat<3, 2, T, Q> const& m, T scalar);
\n
130 
\n
131  template<typename T, qualifier Q>
\n
132  GLM_FUNC_DECL mat<3, 2, T, Q> operator*(T scalar, mat<3, 2, T, Q> const& m);
\n
133 
\n
134  template<typename T, qualifier Q>
\n
135  GLM_FUNC_DECL typename mat<3, 2, T, Q>::col_type operator*(mat<3, 2, T, Q> const& m, typename mat<3, 2, T, Q>::row_type const& v);
\n
136 
\n
137  template<typename T, qualifier Q>
\n
138  GLM_FUNC_DECL typename mat<3, 2, T, Q>::row_type operator*(typename mat<3, 2, T, Q>::col_type const& v, mat<3, 2, T, Q> const& m);
\n
139 
\n
140  template<typename T, qualifier Q>
\n
141  GLM_FUNC_DECL mat<2, 2, T, Q> operator*(mat<3, 2, T, Q> const& m1, mat<2, 3, T, Q> const& m2);
\n
142 
\n
143  template<typename T, qualifier Q>
\n
144  GLM_FUNC_DECL mat<3, 2, T, Q> operator*(mat<3, 2, T, Q> const& m1, mat<3, 3, T, Q> const& m2);
\n
145 
\n
146  template<typename T, qualifier Q>
\n
147  GLM_FUNC_DECL mat<4, 2, T, Q> operator*(mat<3, 2, T, Q> const& m1, mat<4, 3, T, Q> const& m2);
\n
148 
\n
149  template<typename T, qualifier Q>
\n
150  GLM_FUNC_DECL mat<3, 2, T, Q> operator/(mat<3, 2, T, Q> const& m, T scalar);
\n
151 
\n
152  template<typename T, qualifier Q>
\n
153  GLM_FUNC_DECL mat<3, 2, T, Q> operator/(T scalar, mat<3, 2, T, Q> const& m);
\n
154 
\n
155  // -- Boolean operators --
\n
156 
\n
157  template<typename T, qualifier Q>
\n
158  GLM_FUNC_DECL bool operator==(mat<3, 2, T, Q> const& m1, mat<3, 2, T, Q> const& m2);
\n
159 
\n
160  template<typename T, qualifier Q>
\n
161  GLM_FUNC_DECL bool operator!=(mat<3, 2, T, Q> const& m1, mat<3, 2, T, Q> const& m2);
\n
162 
\n
163 }//namespace glm
\n
164 
\n
165 #ifndef GLM_EXTERNAL_TEMPLATE
\n
166 #include "type_mat3x2.inl"
\n
167 #endif
\n
type_vec2.hpp
Core features
\n
glm::length
GLM_FUNC_DECL T length(qua< T, Q > const &q)
Returns the norm of a quaternions.
\n
type_vec3.hpp
Core features
\n
glm
Definition: common.hpp:20
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Definition in file type_mat3x3.hpp.

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1 
\n
4 #pragma once
\n
5 
\n
6 #include "type_vec3.hpp"
\n
7 #include <limits>
\n
8 #include <cstddef>
\n
9 
\n
10 namespace glm
\n
11 {
\n
12  template<typename T, qualifier Q>
\n
13  struct mat<3, 3, T, Q>
\n
14  {
\n
15  typedef vec<3, T, Q> col_type;
\n
16  typedef vec<3, T, Q> row_type;
\n
17  typedef mat<3, 3, T, Q> type;
\n
18  typedef mat<3, 3, T, Q> transpose_type;
\n
19  typedef T value_type;
\n
20 
\n
21  private:
\n
22  col_type value[3];
\n
23 
\n
24  public:
\n
25  // -- Accesses --
\n
26 
\n
27  typedef length_t length_type;
\n
28  GLM_FUNC_DECL static GLM_CONSTEXPR length_type length() { return 3; }
\n
29 
\n
30  GLM_FUNC_DECL col_type & operator[](length_type i);
\n
31  GLM_FUNC_DECL GLM_CONSTEXPR col_type const& operator[](length_type i) const;
\n
32 
\n
33  // -- Constructors --
\n
34 
\n
35  GLM_FUNC_DECL GLM_CONSTEXPR mat() GLM_DEFAULT;
\n
36  template<qualifier P>
\n
37  GLM_FUNC_DECL GLM_CONSTEXPR mat(mat<3, 3, T, P> const& m);
\n
38 
\n
39  GLM_FUNC_DECL explicit GLM_CONSTEXPR mat(T scalar);
\n
40  GLM_FUNC_DECL GLM_CONSTEXPR mat(
\n
41  T x0, T y0, T z0,
\n
42  T x1, T y1, T z1,
\n
43  T x2, T y2, T z2);
\n
44  GLM_FUNC_DECL GLM_CONSTEXPR mat(
\n
45  col_type const& v0,
\n
46  col_type const& v1,
\n
47  col_type const& v2);
\n
48 
\n
49  // -- Conversions --
\n
50 
\n
51  template<
\n
52  typename X1, typename Y1, typename Z1,
\n
53  typename X2, typename Y2, typename Z2,
\n
54  typename X3, typename Y3, typename Z3>
\n
55  GLM_FUNC_DECL GLM_CONSTEXPR mat(
\n
56  X1 x1, Y1 y1, Z1 z1,
\n
57  X2 x2, Y2 y2, Z2 z2,
\n
58  X3 x3, Y3 y3, Z3 z3);
\n
59 
\n
60  template<typename V1, typename V2, typename V3>
\n
61  GLM_FUNC_DECL GLM_CONSTEXPR mat(
\n
62  vec<3, V1, Q> const& v1,
\n
63  vec<3, V2, Q> const& v2,
\n
64  vec<3, V3, Q> const& v3);
\n
65 
\n
66  // -- Matrix conversions --
\n
67 
\n
68  template<typename U, qualifier P>
\n
69  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<3, 3, U, P> const& m);
\n
70 
\n
71  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<2, 2, T, Q> const& x);
\n
72  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<4, 4, T, Q> const& x);
\n
73  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<2, 3, T, Q> const& x);
\n
74  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<3, 2, T, Q> const& x);
\n
75  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<2, 4, T, Q> const& x);
\n
76  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<4, 2, T, Q> const& x);
\n
77  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<3, 4, T, Q> const& x);
\n
78  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<4, 3, T, Q> const& x);
\n
79 
\n
80  // -- Unary arithmetic operators --
\n
81 
\n
82  template<typename U>
\n
83  GLM_FUNC_DECL mat<3, 3, T, Q> & operator=(mat<3, 3, U, Q> const& m);
\n
84  template<typename U>
\n
85  GLM_FUNC_DECL mat<3, 3, T, Q> & operator+=(U s);
\n
86  template<typename U>
\n
87  GLM_FUNC_DECL mat<3, 3, T, Q> & operator+=(mat<3, 3, U, Q> const& m);
\n
88  template<typename U>
\n
89  GLM_FUNC_DECL mat<3, 3, T, Q> & operator-=(U s);
\n
90  template<typename U>
\n
91  GLM_FUNC_DECL mat<3, 3, T, Q> & operator-=(mat<3, 3, U, Q> const& m);
\n
92  template<typename U>
\n
93  GLM_FUNC_DECL mat<3, 3, T, Q> & operator*=(U s);
\n
94  template<typename U>
\n
95  GLM_FUNC_DECL mat<3, 3, T, Q> & operator*=(mat<3, 3, U, Q> const& m);
\n
96  template<typename U>
\n
97  GLM_FUNC_DECL mat<3, 3, T, Q> & operator/=(U s);
\n
98  template<typename U>
\n
99  GLM_FUNC_DECL mat<3, 3, T, Q> & operator/=(mat<3, 3, U, Q> const& m);
\n
100 
\n
101  // -- Increment and decrement operators --
\n
102 
\n
103  GLM_FUNC_DECL mat<3, 3, T, Q> & operator++();
\n
104  GLM_FUNC_DECL mat<3, 3, T, Q> & operator--();
\n
105  GLM_FUNC_DECL mat<3, 3, T, Q> operator++(int);
\n
106  GLM_FUNC_DECL mat<3, 3, T, Q> operator--(int);
\n
107  };
\n
108 
\n
109  // -- Unary operators --
\n
110 
\n
111  template<typename T, qualifier Q>
\n
112  GLM_FUNC_DECL mat<3, 3, T, Q> operator+(mat<3, 3, T, Q> const& m);
\n
113 
\n
114  template<typename T, qualifier Q>
\n
115  GLM_FUNC_DECL mat<3, 3, T, Q> operator-(mat<3, 3, T, Q> const& m);
\n
116 
\n
117  // -- Binary operators --
\n
118 
\n
119  template<typename T, qualifier Q>
\n
120  GLM_FUNC_DECL mat<3, 3, T, Q> operator+(mat<3, 3, T, Q> const& m, T scalar);
\n
121 
\n
122  template<typename T, qualifier Q>
\n
123  GLM_FUNC_DECL mat<3, 3, T, Q> operator+(T scalar, mat<3, 3, T, Q> const& m);
\n
124 
\n
125  template<typename T, qualifier Q>
\n
126  GLM_FUNC_DECL mat<3, 3, T, Q> operator+(mat<3, 3, T, Q> const& m1, mat<3, 3, T, Q> const& m2);
\n
127 
\n
128  template<typename T, qualifier Q>
\n
129  GLM_FUNC_DECL mat<3, 3, T, Q> operator-(mat<3, 3, T, Q> const& m, T scalar);
\n
130 
\n
131  template<typename T, qualifier Q>
\n
132  GLM_FUNC_DECL mat<3, 3, T, Q> operator-(T scalar, mat<3, 3, T, Q> const& m);
\n
133 
\n
134  template<typename T, qualifier Q>
\n
135  GLM_FUNC_DECL mat<3, 3, T, Q> operator-(mat<3, 3, T, Q> const& m1, mat<3, 3, T, Q> const& m2);
\n
136 
\n
137  template<typename T, qualifier Q>
\n
138  GLM_FUNC_DECL mat<3, 3, T, Q> operator*(mat<3, 3, T, Q> const& m, T scalar);
\n
139 
\n
140  template<typename T, qualifier Q>
\n
141  GLM_FUNC_DECL mat<3, 3, T, Q> operator*(T scalar, mat<3, 3, T, Q> const& m);
\n
142 
\n
143  template<typename T, qualifier Q>
\n
144  GLM_FUNC_DECL typename mat<3, 3, T, Q>::col_type operator*(mat<3, 3, T, Q> const& m, typename mat<3, 3, T, Q>::row_type const& v);
\n
145 
\n
146  template<typename T, qualifier Q>
\n
147  GLM_FUNC_DECL typename mat<3, 3, T, Q>::row_type operator*(typename mat<3, 3, T, Q>::col_type const& v, mat<3, 3, T, Q> const& m);
\n
148 
\n
149  template<typename T, qualifier Q>
\n
150  GLM_FUNC_DECL mat<3, 3, T, Q> operator*(mat<3, 3, T, Q> const& m1, mat<3, 3, T, Q> const& m2);
\n
151 
\n
152  template<typename T, qualifier Q>
\n
153  GLM_FUNC_DECL mat<2, 3, T, Q> operator*(mat<3, 3, T, Q> const& m1, mat<2, 3, T, Q> const& m2);
\n
154 
\n
155  template<typename T, qualifier Q>
\n
156  GLM_FUNC_DECL mat<4, 3, T, Q> operator*(mat<3, 3, T, Q> const& m1, mat<4, 3, T, Q> const& m2);
\n
157 
\n
158  template<typename T, qualifier Q>
\n
159  GLM_FUNC_DECL mat<3, 3, T, Q> operator/(mat<3, 3, T, Q> const& m, T scalar);
\n
160 
\n
161  template<typename T, qualifier Q>
\n
162  GLM_FUNC_DECL mat<3, 3, T, Q> operator/(T scalar, mat<3, 3, T, Q> const& m);
\n
163 
\n
164  template<typename T, qualifier Q>
\n
165  GLM_FUNC_DECL typename mat<3, 3, T, Q>::col_type operator/(mat<3, 3, T, Q> const& m, typename mat<3, 3, T, Q>::row_type const& v);
\n
166 
\n
167  template<typename T, qualifier Q>
\n
168  GLM_FUNC_DECL typename mat<3, 3, T, Q>::row_type operator/(typename mat<3, 3, T, Q>::col_type const& v, mat<3, 3, T, Q> const& m);
\n
169 
\n
170  template<typename T, qualifier Q>
\n
171  GLM_FUNC_DECL mat<3, 3, T, Q> operator/(mat<3, 3, T, Q> const& m1, mat<3, 3, T, Q> const& m2);
\n
172 
\n
173  // -- Boolean operators --
\n
174 
\n
175  template<typename T, qualifier Q>
\n
176  GLM_FUNC_DECL GLM_CONSTEXPR bool operator==(mat<3, 3, T, Q> const& m1, mat<3, 3, T, Q> const& m2);
\n
177 
\n
178  template<typename T, qualifier Q>
\n
179  GLM_FUNC_DECL bool operator!=(mat<3, 3, T, Q> const& m1, mat<3, 3, T, Q> const& m2);
\n
180 }//namespace glm
\n
181 
\n
182 #ifndef GLM_EXTERNAL_TEMPLATE
\n
183 #include "type_mat3x3.inl"
\n
184 #endif
\n
glm::length
GLM_FUNC_DECL T length(qua< T, Q > const &q)
Returns the norm of a quaternions.
\n
type_vec3.hpp
Core features
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Definition: common.hpp:20
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type_mat3x4.hpp
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1 
\n
4 #pragma once
\n
5 
\n
6 #include "type_vec3.hpp"
\n
7 #include "type_vec4.hpp"
\n
8 #include <limits>
\n
9 #include <cstddef>
\n
10 
\n
11 namespace glm
\n
12 {
\n
13  template<typename T, qualifier Q>
\n
14  struct mat<3, 4, T, Q>
\n
15  {
\n
16  typedef vec<4, T, Q> col_type;
\n
17  typedef vec<3, T, Q> row_type;
\n
18  typedef mat<3, 4, T, Q> type;
\n
19  typedef mat<4, 3, T, Q> transpose_type;
\n
20  typedef T value_type;
\n
21 
\n
22  private:
\n
23  col_type value[3];
\n
24 
\n
25  public:
\n
26  // -- Accesses --
\n
27 
\n
28  typedef length_t length_type;
\n
29  GLM_FUNC_DECL static GLM_CONSTEXPR length_type length() { return 3; }
\n
30 
\n
31  GLM_FUNC_DECL col_type & operator[](length_type i);
\n
32  GLM_FUNC_DECL GLM_CONSTEXPR col_type const& operator[](length_type i) const;
\n
33 
\n
34  // -- Constructors --
\n
35 
\n
36  GLM_FUNC_DECL GLM_CONSTEXPR mat() GLM_DEFAULT;
\n
37  template<qualifier P>
\n
38  GLM_FUNC_DECL GLM_CONSTEXPR mat(mat<3, 4, T, P> const& m);
\n
39 
\n
40  GLM_FUNC_DECL explicit GLM_CONSTEXPR mat(T scalar);
\n
41  GLM_FUNC_DECL GLM_CONSTEXPR mat(
\n
42  T x0, T y0, T z0, T w0,
\n
43  T x1, T y1, T z1, T w1,
\n
44  T x2, T y2, T z2, T w2);
\n
45  GLM_FUNC_DECL GLM_CONSTEXPR mat(
\n
46  col_type const& v0,
\n
47  col_type const& v1,
\n
48  col_type const& v2);
\n
49 
\n
50  // -- Conversions --
\n
51 
\n
52  template<
\n
53  typename X1, typename Y1, typename Z1, typename W1,
\n
54  typename X2, typename Y2, typename Z2, typename W2,
\n
55  typename X3, typename Y3, typename Z3, typename W3>
\n
56  GLM_FUNC_DECL GLM_CONSTEXPR mat(
\n
57  X1 x1, Y1 y1, Z1 z1, W1 w1,
\n
58  X2 x2, Y2 y2, Z2 z2, W2 w2,
\n
59  X3 x3, Y3 y3, Z3 z3, W3 w3);
\n
60 
\n
61  template<typename V1, typename V2, typename V3>
\n
62  GLM_FUNC_DECL GLM_CONSTEXPR mat(
\n
63  vec<4, V1, Q> const& v1,
\n
64  vec<4, V2, Q> const& v2,
\n
65  vec<4, V3, Q> const& v3);
\n
66 
\n
67  // -- Matrix conversions --
\n
68 
\n
69  template<typename U, qualifier P>
\n
70  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<3, 4, U, P> const& m);
\n
71 
\n
72  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<2, 2, T, Q> const& x);
\n
73  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<3, 3, T, Q> const& x);
\n
74  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<4, 4, T, Q> const& x);
\n
75  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<2, 3, T, Q> const& x);
\n
76  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<3, 2, T, Q> const& x);
\n
77  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<2, 4, T, Q> const& x);
\n
78  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<4, 2, T, Q> const& x);
\n
79  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<4, 3, T, Q> const& x);
\n
80 
\n
81  // -- Unary arithmetic operators --
\n
82 
\n
83  template<typename U>
\n
84  GLM_FUNC_DECL mat<3, 4, T, Q> & operator=(mat<3, 4, U, Q> const& m);
\n
85  template<typename U>
\n
86  GLM_FUNC_DECL mat<3, 4, T, Q> & operator+=(U s);
\n
87  template<typename U>
\n
88  GLM_FUNC_DECL mat<3, 4, T, Q> & operator+=(mat<3, 4, U, Q> const& m);
\n
89  template<typename U>
\n
90  GLM_FUNC_DECL mat<3, 4, T, Q> & operator-=(U s);
\n
91  template<typename U>
\n
92  GLM_FUNC_DECL mat<3, 4, T, Q> & operator-=(mat<3, 4, U, Q> const& m);
\n
93  template<typename U>
\n
94  GLM_FUNC_DECL mat<3, 4, T, Q> & operator*=(U s);
\n
95  template<typename U>
\n
96  GLM_FUNC_DECL mat<3, 4, T, Q> & operator/=(U s);
\n
97 
\n
98  // -- Increment and decrement operators --
\n
99 
\n
100  GLM_FUNC_DECL mat<3, 4, T, Q> & operator++();
\n
101  GLM_FUNC_DECL mat<3, 4, T, Q> & operator--();
\n
102  GLM_FUNC_DECL mat<3, 4, T, Q> operator++(int);
\n
103  GLM_FUNC_DECL mat<3, 4, T, Q> operator--(int);
\n
104  };
\n
105 
\n
106  // -- Unary operators --
\n
107 
\n
108  template<typename T, qualifier Q>
\n
109  GLM_FUNC_DECL mat<3, 4, T, Q> operator+(mat<3, 4, T, Q> const& m);
\n
110 
\n
111  template<typename T, qualifier Q>
\n
112  GLM_FUNC_DECL mat<3, 4, T, Q> operator-(mat<3, 4, T, Q> const& m);
\n
113 
\n
114  // -- Binary operators --
\n
115 
\n
116  template<typename T, qualifier Q>
\n
117  GLM_FUNC_DECL mat<3, 4, T, Q> operator+(mat<3, 4, T, Q> const& m, T scalar);
\n
118 
\n
119  template<typename T, qualifier Q>
\n
120  GLM_FUNC_DECL mat<3, 4, T, Q> operator+(mat<3, 4, T, Q> const& m1, mat<3, 4, T, Q> const& m2);
\n
121 
\n
122  template<typename T, qualifier Q>
\n
123  GLM_FUNC_DECL mat<3, 4, T, Q> operator-(mat<3, 4, T, Q> const& m, T scalar);
\n
124 
\n
125  template<typename T, qualifier Q>
\n
126  GLM_FUNC_DECL mat<3, 4, T, Q> operator-(mat<3, 4, T, Q> const& m1, mat<3, 4, T, Q> const& m2);
\n
127 
\n
128  template<typename T, qualifier Q>
\n
129  GLM_FUNC_DECL mat<3, 4, T, Q> operator*(mat<3, 4, T, Q> const& m, T scalar);
\n
130 
\n
131  template<typename T, qualifier Q>
\n
132  GLM_FUNC_DECL mat<3, 4, T, Q> operator*(T scalar, mat<3, 4, T, Q> const& m);
\n
133 
\n
134  template<typename T, qualifier Q>
\n
135  GLM_FUNC_DECL typename mat<3, 4, T, Q>::col_type operator*(mat<3, 4, T, Q> const& m, typename mat<3, 4, T, Q>::row_type const& v);
\n
136 
\n
137  template<typename T, qualifier Q>
\n
138  GLM_FUNC_DECL typename mat<3, 4, T, Q>::row_type operator*(typename mat<3, 4, T, Q>::col_type const& v, mat<3, 4, T, Q> const& m);
\n
139 
\n
140  template<typename T, qualifier Q>
\n
141  GLM_FUNC_DECL mat<4, 4, T, Q> operator*(mat<3, 4, T, Q> const& m1, mat<4, 3, T, Q> const& m2);
\n
142 
\n
143  template<typename T, qualifier Q>
\n
144  GLM_FUNC_DECL mat<2, 4, T, Q> operator*(mat<3, 4, T, Q> const& m1, mat<2, 3, T, Q> const& m2);
\n
145 
\n
146  template<typename T, qualifier Q>
\n
147  GLM_FUNC_DECL mat<3, 4, T, Q> operator*(mat<3, 4, T, Q> const& m1, mat<3, 3, T, Q> const& m2);
\n
148 
\n
149  template<typename T, qualifier Q>
\n
150  GLM_FUNC_DECL mat<3, 4, T, Q> operator/(mat<3, 4, T, Q> const& m, T scalar);
\n
151 
\n
152  template<typename T, qualifier Q>
\n
153  GLM_FUNC_DECL mat<3, 4, T, Q> operator/(T scalar, mat<3, 4, T, Q> const& m);
\n
154 
\n
155  // -- Boolean operators --
\n
156 
\n
157  template<typename T, qualifier Q>
\n
158  GLM_FUNC_DECL bool operator==(mat<3, 4, T, Q> const& m1, mat<3, 4, T, Q> const& m2);
\n
159 
\n
160  template<typename T, qualifier Q>
\n
161  GLM_FUNC_DECL bool operator!=(mat<3, 4, T, Q> const& m1, mat<3, 4, T, Q> const& m2);
\n
162 }//namespace glm
\n
163 
\n
164 #ifndef GLM_EXTERNAL_TEMPLATE
\n
165 #include "type_mat3x4.inl"
\n
166 #endif
\n
glm::length
GLM_FUNC_DECL T length(qua< T, Q > const &q)
Returns the norm of a quaternions.
\n
type_vec4.hpp
Core features
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type_vec3.hpp
Core features
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glm
Definition: common.hpp:20
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type_mat4x2.hpp
\n
\n
\nGo to the documentation of this file.
1 
\n
4 #pragma once
\n
5 
\n
6 #include "type_vec2.hpp"
\n
7 #include "type_vec4.hpp"
\n
8 #include <limits>
\n
9 #include <cstddef>
\n
10 
\n
11 namespace glm
\n
12 {
\n
13  template<typename T, qualifier Q>
\n
14  struct mat<4, 2, T, Q>
\n
15  {
\n
16  typedef vec<2, T, Q> col_type;
\n
17  typedef vec<4, T, Q> row_type;
\n
18  typedef mat<4, 2, T, Q> type;
\n
19  typedef mat<2, 4, T, Q> transpose_type;
\n
20  typedef T value_type;
\n
21 
\n
22  private:
\n
23  col_type value[4];
\n
24 
\n
25  public:
\n
26  // -- Accesses --
\n
27 
\n
28  typedef length_t length_type;
\n
29  GLM_FUNC_DECL static GLM_CONSTEXPR length_type length() { return 4; }
\n
30 
\n
31  GLM_FUNC_DECL col_type & operator[](length_type i);
\n
32  GLM_FUNC_DECL GLM_CONSTEXPR col_type const& operator[](length_type i) const;
\n
33 
\n
34  // -- Constructors --
\n
35 
\n
36  GLM_FUNC_DECL GLM_CONSTEXPR mat() GLM_DEFAULT;
\n
37  template<qualifier P>
\n
38  GLM_FUNC_DECL GLM_CONSTEXPR mat(mat<4, 2, T, P> const& m);
\n
39 
\n
40  GLM_FUNC_DECL explicit GLM_CONSTEXPR mat(T scalar);
\n
41  GLM_FUNC_DECL GLM_CONSTEXPR mat(
\n
42  T x0, T y0,
\n
43  T x1, T y1,
\n
44  T x2, T y2,
\n
45  T x3, T y3);
\n
46  GLM_FUNC_DECL GLM_CONSTEXPR mat(
\n
47  col_type const& v0,
\n
48  col_type const& v1,
\n
49  col_type const& v2,
\n
50  col_type const& v3);
\n
51 
\n
52  // -- Conversions --
\n
53 
\n
54  template<
\n
55  typename X0, typename Y0,
\n
56  typename X1, typename Y1,
\n
57  typename X2, typename Y2,
\n
58  typename X3, typename Y3>
\n
59  GLM_FUNC_DECL GLM_CONSTEXPR mat(
\n
60  X0 x0, Y0 y0,
\n
61  X1 x1, Y1 y1,
\n
62  X2 x2, Y2 y2,
\n
63  X3 x3, Y3 y3);
\n
64 
\n
65  template<typename V1, typename V2, typename V3, typename V4>
\n
66  GLM_FUNC_DECL GLM_CONSTEXPR mat(
\n
67  vec<2, V1, Q> const& v1,
\n
68  vec<2, V2, Q> const& v2,
\n
69  vec<2, V3, Q> const& v3,
\n
70  vec<2, V4, Q> const& v4);
\n
71 
\n
72  // -- Matrix conversions --
\n
73 
\n
74  template<typename U, qualifier P>
\n
75  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<4, 2, U, P> const& m);
\n
76 
\n
77  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<2, 2, T, Q> const& x);
\n
78  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<3, 3, T, Q> const& x);
\n
79  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<4, 4, T, Q> const& x);
\n
80  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<2, 3, T, Q> const& x);
\n
81  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<3, 2, T, Q> const& x);
\n
82  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<2, 4, T, Q> const& x);
\n
83  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<4, 3, T, Q> const& x);
\n
84  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<3, 4, T, Q> const& x);
\n
85 
\n
86  // -- Unary arithmetic operators --
\n
87 
\n
88  template<typename U>
\n
89  GLM_FUNC_DECL mat<4, 2, T, Q> & operator=(mat<4, 2, U, Q> const& m);
\n
90  template<typename U>
\n
91  GLM_FUNC_DECL mat<4, 2, T, Q> & operator+=(U s);
\n
92  template<typename U>
\n
93  GLM_FUNC_DECL mat<4, 2, T, Q> & operator+=(mat<4, 2, U, Q> const& m);
\n
94  template<typename U>
\n
95  GLM_FUNC_DECL mat<4, 2, T, Q> & operator-=(U s);
\n
96  template<typename U>
\n
97  GLM_FUNC_DECL mat<4, 2, T, Q> & operator-=(mat<4, 2, U, Q> const& m);
\n
98  template<typename U>
\n
99  GLM_FUNC_DECL mat<4, 2, T, Q> & operator*=(U s);
\n
100  template<typename U>
\n
101  GLM_FUNC_DECL mat<4, 2, T, Q> & operator/=(U s);
\n
102 
\n
103  // -- Increment and decrement operators --
\n
104 
\n
105  GLM_FUNC_DECL mat<4, 2, T, Q> & operator++ ();
\n
106  GLM_FUNC_DECL mat<4, 2, T, Q> & operator-- ();
\n
107  GLM_FUNC_DECL mat<4, 2, T, Q> operator++(int);
\n
108  GLM_FUNC_DECL mat<4, 2, T, Q> operator--(int);
\n
109  };
\n
110 
\n
111  // -- Unary operators --
\n
112 
\n
113  template<typename T, qualifier Q>
\n
114  GLM_FUNC_DECL mat<4, 2, T, Q> operator+(mat<4, 2, T, Q> const& m);
\n
115 
\n
116  template<typename T, qualifier Q>
\n
117  GLM_FUNC_DECL mat<4, 2, T, Q> operator-(mat<4, 2, T, Q> const& m);
\n
118 
\n
119  // -- Binary operators --
\n
120 
\n
121  template<typename T, qualifier Q>
\n
122  GLM_FUNC_DECL mat<4, 2, T, Q> operator+(mat<4, 2, T, Q> const& m, T scalar);
\n
123 
\n
124  template<typename T, qualifier Q>
\n
125  GLM_FUNC_DECL mat<4, 2, T, Q> operator+(mat<4, 2, T, Q> const& m1, mat<4, 2, T, Q> const& m2);
\n
126 
\n
127  template<typename T, qualifier Q>
\n
128  GLM_FUNC_DECL mat<4, 2, T, Q> operator-(mat<4, 2, T, Q> const& m, T scalar);
\n
129 
\n
130  template<typename T, qualifier Q>
\n
131  GLM_FUNC_DECL mat<4, 2, T, Q> operator-(mat<4, 2, T, Q> const& m1, mat<4, 2, T, Q> const& m2);
\n
132 
\n
133  template<typename T, qualifier Q>
\n
134  GLM_FUNC_DECL mat<4, 2, T, Q> operator*(mat<4, 2, T, Q> const& m, T scalar);
\n
135 
\n
136  template<typename T, qualifier Q>
\n
137  GLM_FUNC_DECL mat<4, 2, T, Q> operator*(T scalar, mat<4, 2, T, Q> const& m);
\n
138 
\n
139  template<typename T, qualifier Q>
\n
140  GLM_FUNC_DECL typename mat<4, 2, T, Q>::col_type operator*(mat<4, 2, T, Q> const& m, typename mat<4, 2, T, Q>::row_type const& v);
\n
141 
\n
142  template<typename T, qualifier Q>
\n
143  GLM_FUNC_DECL typename mat<4, 2, T, Q>::row_type operator*(typename mat<4, 2, T, Q>::col_type const& v, mat<4, 2, T, Q> const& m);
\n
144 
\n
145  template<typename T, qualifier Q>
\n
146  GLM_FUNC_DECL mat<2, 2, T, Q> operator*(mat<4, 2, T, Q> const& m1, mat<2, 4, T, Q> const& m2);
\n
147 
\n
148  template<typename T, qualifier Q>
\n
149  GLM_FUNC_DECL mat<3, 2, T, Q> operator*(mat<4, 2, T, Q> const& m1, mat<3, 4, T, Q> const& m2);
\n
150 
\n
151  template<typename T, qualifier Q>
\n
152  GLM_FUNC_DECL mat<4, 2, T, Q> operator*(mat<4, 2, T, Q> const& m1, mat<4, 4, T, Q> const& m2);
\n
153 
\n
154  template<typename T, qualifier Q>
\n
155  GLM_FUNC_DECL mat<4, 2, T, Q> operator/(mat<4, 2, T, Q> const& m, T scalar);
\n
156 
\n
157  template<typename T, qualifier Q>
\n
158  GLM_FUNC_DECL mat<4, 2, T, Q> operator/(T scalar, mat<4, 2, T, Q> const& m);
\n
159 
\n
160  // -- Boolean operators --
\n
161 
\n
162  template<typename T, qualifier Q>
\n
163  GLM_FUNC_DECL bool operator==(mat<4, 2, T, Q> const& m1, mat<4, 2, T, Q> const& m2);
\n
164 
\n
165  template<typename T, qualifier Q>
\n
166  GLM_FUNC_DECL bool operator!=(mat<4, 2, T, Q> const& m1, mat<4, 2, T, Q> const& m2);
\n
167 }//namespace glm
\n
168 
\n
169 #ifndef GLM_EXTERNAL_TEMPLATE
\n
170 #include "type_mat4x2.inl"
\n
171 #endif
\n
type_vec2.hpp
Core features
\n
glm::length
GLM_FUNC_DECL T length(qua< T, Q > const &q)
Returns the norm of a quaternions.
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type_vec4.hpp
Core features
\n
glm
Definition: common.hpp:20
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Definition in file type_mat4x3.hpp.

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type_mat4x3.hpp
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\nGo to the documentation of this file.
1 
\n
4 #pragma once
\n
5 
\n
6 #include "type_vec3.hpp"
\n
7 #include "type_vec4.hpp"
\n
8 #include <limits>
\n
9 #include <cstddef>
\n
10 
\n
11 namespace glm
\n
12 {
\n
13  template<typename T, qualifier Q>
\n
14  struct mat<4, 3, T, Q>
\n
15  {
\n
16  typedef vec<3, T, Q> col_type;
\n
17  typedef vec<4, T, Q> row_type;
\n
18  typedef mat<4, 3, T, Q> type;
\n
19  typedef mat<3, 4, T, Q> transpose_type;
\n
20  typedef T value_type;
\n
21 
\n
22  private:
\n
23  col_type value[4];
\n
24 
\n
25  public:
\n
26  // -- Accesses --
\n
27 
\n
28  typedef length_t length_type;
\n
29  GLM_FUNC_DECL static GLM_CONSTEXPR length_type length() { return 4; }
\n
30 
\n
31  GLM_FUNC_DECL col_type & operator[](length_type i);
\n
32  GLM_FUNC_DECL GLM_CONSTEXPR col_type const& operator[](length_type i) const;
\n
33 
\n
34  // -- Constructors --
\n
35 
\n
36  GLM_FUNC_DECL GLM_CONSTEXPR mat() GLM_DEFAULT;
\n
37  template<qualifier P>
\n
38  GLM_FUNC_DECL GLM_CONSTEXPR mat(mat<4, 3, T, P> const& m);
\n
39 
\n
40  GLM_FUNC_DECL explicit GLM_CONSTEXPR mat(T const& x);
\n
41  GLM_FUNC_DECL GLM_CONSTEXPR mat(
\n
42  T const& x0, T const& y0, T const& z0,
\n
43  T const& x1, T const& y1, T const& z1,
\n
44  T const& x2, T const& y2, T const& z2,
\n
45  T const& x3, T const& y3, T const& z3);
\n
46  GLM_FUNC_DECL GLM_CONSTEXPR mat(
\n
47  col_type const& v0,
\n
48  col_type const& v1,
\n
49  col_type const& v2,
\n
50  col_type const& v3);
\n
51 
\n
52  // -- Conversions --
\n
53 
\n
54  template<
\n
55  typename X1, typename Y1, typename Z1,
\n
56  typename X2, typename Y2, typename Z2,
\n
57  typename X3, typename Y3, typename Z3,
\n
58  typename X4, typename Y4, typename Z4>
\n
59  GLM_FUNC_DECL GLM_CONSTEXPR mat(
\n
60  X1 const& x1, Y1 const& y1, Z1 const& z1,
\n
61  X2 const& x2, Y2 const& y2, Z2 const& z2,
\n
62  X3 const& x3, Y3 const& y3, Z3 const& z3,
\n
63  X4 const& x4, Y4 const& y4, Z4 const& z4);
\n
64 
\n
65  template<typename V1, typename V2, typename V3, typename V4>
\n
66  GLM_FUNC_DECL GLM_CONSTEXPR mat(
\n
67  vec<3, V1, Q> const& v1,
\n
68  vec<3, V2, Q> const& v2,
\n
69  vec<3, V3, Q> const& v3,
\n
70  vec<3, V4, Q> const& v4);
\n
71 
\n
72  // -- Matrix conversions --
\n
73 
\n
74  template<typename U, qualifier P>
\n
75  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<4, 3, U, P> const& m);
\n
76 
\n
77  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<2, 2, T, Q> const& x);
\n
78  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<3, 3, T, Q> const& x);
\n
79  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<4, 4, T, Q> const& x);
\n
80  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<2, 3, T, Q> const& x);
\n
81  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<3, 2, T, Q> const& x);
\n
82  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<2, 4, T, Q> const& x);
\n
83  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<4, 2, T, Q> const& x);
\n
84  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<3, 4, T, Q> const& x);
\n
85 
\n
86  // -- Unary arithmetic operators --
\n
87 
\n
88  template<typename U>
\n
89  GLM_FUNC_DECL mat<4, 3, T, Q> & operator=(mat<4, 3, U, Q> const& m);
\n
90  template<typename U>
\n
91  GLM_FUNC_DECL mat<4, 3, T, Q> & operator+=(U s);
\n
92  template<typename U>
\n
93  GLM_FUNC_DECL mat<4, 3, T, Q> & operator+=(mat<4, 3, U, Q> const& m);
\n
94  template<typename U>
\n
95  GLM_FUNC_DECL mat<4, 3, T, Q> & operator-=(U s);
\n
96  template<typename U>
\n
97  GLM_FUNC_DECL mat<4, 3, T, Q> & operator-=(mat<4, 3, U, Q> const& m);
\n
98  template<typename U>
\n
99  GLM_FUNC_DECL mat<4, 3, T, Q> & operator*=(U s);
\n
100  template<typename U>
\n
101  GLM_FUNC_DECL mat<4, 3, T, Q> & operator/=(U s);
\n
102 
\n
103  // -- Increment and decrement operators --
\n
104 
\n
105  GLM_FUNC_DECL mat<4, 3, T, Q>& operator++();
\n
106  GLM_FUNC_DECL mat<4, 3, T, Q>& operator--();
\n
107  GLM_FUNC_DECL mat<4, 3, T, Q> operator++(int);
\n
108  GLM_FUNC_DECL mat<4, 3, T, Q> operator--(int);
\n
109  };
\n
110 
\n
111  // -- Unary operators --
\n
112 
\n
113  template<typename T, qualifier Q>
\n
114  GLM_FUNC_DECL mat<4, 3, T, Q> operator+(mat<4, 3, T, Q> const& m);
\n
115 
\n
116  template<typename T, qualifier Q>
\n
117  GLM_FUNC_DECL mat<4, 3, T, Q> operator-(mat<4, 3, T, Q> const& m);
\n
118 
\n
119  // -- Binary operators --
\n
120 
\n
121  template<typename T, qualifier Q>
\n
122  GLM_FUNC_DECL mat<4, 3, T, Q> operator+(mat<4, 3, T, Q> const& m, T const& s);
\n
123 
\n
124  template<typename T, qualifier Q>
\n
125  GLM_FUNC_DECL mat<4, 3, T, Q> operator+(mat<4, 3, T, Q> const& m1, mat<4, 3, T, Q> const& m2);
\n
126 
\n
127  template<typename T, qualifier Q>
\n
128  GLM_FUNC_DECL mat<4, 3, T, Q> operator-(mat<4, 3, T, Q> const& m, T const& s);
\n
129 
\n
130  template<typename T, qualifier Q>
\n
131  GLM_FUNC_DECL mat<4, 3, T, Q> operator-(mat<4, 3, T, Q> const& m1, mat<4, 3, T, Q> const& m2);
\n
132 
\n
133  template<typename T, qualifier Q>
\n
134  GLM_FUNC_DECL mat<4, 3, T, Q> operator*(mat<4, 3, T, Q> const& m, T const& s);
\n
135 
\n
136  template<typename T, qualifier Q>
\n
137  GLM_FUNC_DECL mat<4, 3, T, Q> operator*(T const& s, mat<4, 3, T, Q> const& m);
\n
138 
\n
139  template<typename T, qualifier Q>
\n
140  GLM_FUNC_DECL typename mat<4, 3, T, Q>::col_type operator*(mat<4, 3, T, Q> const& m, typename mat<4, 3, T, Q>::row_type const& v);
\n
141 
\n
142  template<typename T, qualifier Q>
\n
143  GLM_FUNC_DECL typename mat<4, 3, T, Q>::row_type operator*(typename mat<4, 3, T, Q>::col_type const& v, mat<4, 3, T, Q> const& m);
\n
144 
\n
145  template<typename T, qualifier Q>
\n
146  GLM_FUNC_DECL mat<2, 3, T, Q> operator*(mat<4, 3, T, Q> const& m1, mat<2, 4, T, Q> const& m2);
\n
147 
\n
148  template<typename T, qualifier Q>
\n
149  GLM_FUNC_DECL mat<3, 3, T, Q> operator*(mat<4, 3, T, Q> const& m1, mat<3, 4, T, Q> const& m2);
\n
150 
\n
151  template<typename T, qualifier Q>
\n
152  GLM_FUNC_DECL mat<4, 3, T, Q> operator*(mat<4, 3, T, Q> const& m1, mat<4, 4, T, Q> const& m2);
\n
153 
\n
154  template<typename T, qualifier Q>
\n
155  GLM_FUNC_DECL mat<4, 3, T, Q> operator/(mat<4, 3, T, Q> const& m, T const& s);
\n
156 
\n
157  template<typename T, qualifier Q>
\n
158  GLM_FUNC_DECL mat<4, 3, T, Q> operator/(T const& s, mat<4, 3, T, Q> const& m);
\n
159 
\n
160  // -- Boolean operators --
\n
161 
\n
162  template<typename T, qualifier Q>
\n
163  GLM_FUNC_DECL bool operator==(mat<4, 3, T, Q> const& m1, mat<4, 3, T, Q> const& m2);
\n
164 
\n
165  template<typename T, qualifier Q>
\n
166  GLM_FUNC_DECL bool operator!=(mat<4, 3, T, Q> const& m1, mat<4, 3, T, Q> const& m2);
\n
167 }//namespace glm
\n
168 
\n
169 #ifndef GLM_EXTERNAL_TEMPLATE
\n
170 #include "type_mat4x3.inl"
\n
171 #endif //GLM_EXTERNAL_TEMPLATE
\n
glm::length
GLM_FUNC_DECL T length(qua< T, Q > const &q)
Returns the norm of a quaternions.
\n
type_vec4.hpp
Core features
\n
type_vec3.hpp
Core features
\n
glm
Definition: common.hpp:20
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Core features

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Definition in file type_mat4x4.hpp.

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type_mat4x4.hpp
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\nGo to the documentation of this file.
1 
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4 #pragma once
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5 
\n
6 #include "type_vec4.hpp"
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7 #include <limits>
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8 #include <cstddef>
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9 
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10 namespace glm
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11 {
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12  template<typename T, qualifier Q>
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13  struct mat<4, 4, T, Q>
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14  {
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15  typedef vec<4, T, Q> col_type;
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16  typedef vec<4, T, Q> row_type;
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17  typedef mat<4, 4, T, Q> type;
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18  typedef mat<4, 4, T, Q> transpose_type;
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19  typedef T value_type;
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20 
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21  private:
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22  col_type value[4];
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23 
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24  public:
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25  // -- Accesses --
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26 
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27  typedef length_t length_type;
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28  GLM_FUNC_DECL static GLM_CONSTEXPR length_type length(){return 4;}
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29 
\n
30  GLM_FUNC_DECL col_type & operator[](length_type i);
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31  GLM_FUNC_DECL GLM_CONSTEXPR col_type const& operator[](length_type i) const;
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32 
\n
33  // -- Constructors --
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34 
\n
35  GLM_FUNC_DECL GLM_CONSTEXPR mat() GLM_DEFAULT;
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36  template<qualifier P>
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37  GLM_FUNC_DECL GLM_CONSTEXPR mat(mat<4, 4, T, P> const& m);
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38 
\n
39  GLM_FUNC_DECL explicit GLM_CONSTEXPR mat(T const& x);
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40  GLM_FUNC_DECL GLM_CONSTEXPR mat(
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41  T const& x0, T const& y0, T const& z0, T const& w0,
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42  T const& x1, T const& y1, T const& z1, T const& w1,
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43  T const& x2, T const& y2, T const& z2, T const& w2,
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44  T const& x3, T const& y3, T const& z3, T const& w3);
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45  GLM_FUNC_DECL GLM_CONSTEXPR mat(
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46  col_type const& v0,
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47  col_type const& v1,
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48  col_type const& v2,
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49  col_type const& v3);
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50 
\n
51  // -- Conversions --
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52 
\n
53  template<
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54  typename X1, typename Y1, typename Z1, typename W1,
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55  typename X2, typename Y2, typename Z2, typename W2,
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56  typename X3, typename Y3, typename Z3, typename W3,
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57  typename X4, typename Y4, typename Z4, typename W4>
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58  GLM_FUNC_DECL GLM_CONSTEXPR mat(
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59  X1 const& x1, Y1 const& y1, Z1 const& z1, W1 const& w1,
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60  X2 const& x2, Y2 const& y2, Z2 const& z2, W2 const& w2,
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61  X3 const& x3, Y3 const& y3, Z3 const& z3, W3 const& w3,
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62  X4 const& x4, Y4 const& y4, Z4 const& z4, W4 const& w4);
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63 
\n
64  template<typename V1, typename V2, typename V3, typename V4>
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65  GLM_FUNC_DECL GLM_CONSTEXPR mat(
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66  vec<4, V1, Q> const& v1,
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67  vec<4, V2, Q> const& v2,
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68  vec<4, V3, Q> const& v3,
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69  vec<4, V4, Q> const& v4);
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70 
\n
71  // -- Matrix conversions --
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72 
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73  template<typename U, qualifier P>
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74  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<4, 4, U, P> const& m);
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75 
\n
76  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<2, 2, T, Q> const& x);
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77  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<3, 3, T, Q> const& x);
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78  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<2, 3, T, Q> const& x);
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79  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<3, 2, T, Q> const& x);
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80  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<2, 4, T, Q> const& x);
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81  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<4, 2, T, Q> const& x);
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82  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<3, 4, T, Q> const& x);
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83  GLM_FUNC_DECL GLM_EXPLICIT GLM_CONSTEXPR mat(mat<4, 3, T, Q> const& x);
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84 
\n
85  // -- Unary arithmetic operators --
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86 
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87  template<typename U>
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88  GLM_FUNC_DECL mat<4, 4, T, Q> & operator=(mat<4, 4, U, Q> const& m);
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89  template<typename U>
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90  GLM_FUNC_DECL mat<4, 4, T, Q> & operator+=(U s);
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91  template<typename U>
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92  GLM_FUNC_DECL mat<4, 4, T, Q> & operator+=(mat<4, 4, U, Q> const& m);
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93  template<typename U>
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94  GLM_FUNC_DECL mat<4, 4, T, Q> & operator-=(U s);
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95  template<typename U>
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96  GLM_FUNC_DECL mat<4, 4, T, Q> & operator-=(mat<4, 4, U, Q> const& m);
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97  template<typename U>
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98  GLM_FUNC_DECL mat<4, 4, T, Q> & operator*=(U s);
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99  template<typename U>
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100  GLM_FUNC_DECL mat<4, 4, T, Q> & operator*=(mat<4, 4, U, Q> const& m);
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101  template<typename U>
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102  GLM_FUNC_DECL mat<4, 4, T, Q> & operator/=(U s);
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103  template<typename U>
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104  GLM_FUNC_DECL mat<4, 4, T, Q> & operator/=(mat<4, 4, U, Q> const& m);
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105 
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106  // -- Increment and decrement operators --
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107 
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108  GLM_FUNC_DECL mat<4, 4, T, Q> & operator++();
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109  GLM_FUNC_DECL mat<4, 4, T, Q> & operator--();
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110  GLM_FUNC_DECL mat<4, 4, T, Q> operator++(int);
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111  GLM_FUNC_DECL mat<4, 4, T, Q> operator--(int);
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112  };
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113 
\n
114  // -- Unary operators --
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115 
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116  template<typename T, qualifier Q>
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117  GLM_FUNC_DECL mat<4, 4, T, Q> operator+(mat<4, 4, T, Q> const& m);
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118 
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119  template<typename T, qualifier Q>
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120  GLM_FUNC_DECL mat<4, 4, T, Q> operator-(mat<4, 4, T, Q> const& m);
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121 
\n
122  // -- Binary operators --
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123 
\n
124  template<typename T, qualifier Q>
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125  GLM_FUNC_DECL mat<4, 4, T, Q> operator+(mat<4, 4, T, Q> const& m, T const& s);
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126 
\n
127  template<typename T, qualifier Q>
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128  GLM_FUNC_DECL mat<4, 4, T, Q> operator+(T const& s, mat<4, 4, T, Q> const& m);
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129 
\n
130  template<typename T, qualifier Q>
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131  GLM_FUNC_DECL mat<4, 4, T, Q> operator+(mat<4, 4, T, Q> const& m1, mat<4, 4, T, Q> const& m2);
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132 
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133  template<typename T, qualifier Q>
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134  GLM_FUNC_DECL mat<4, 4, T, Q> operator-(mat<4, 4, T, Q> const& m, T const& s);
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135 
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136  template<typename T, qualifier Q>
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137  GLM_FUNC_DECL mat<4, 4, T, Q> operator-(T const& s, mat<4, 4, T, Q> const& m);
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138 
\n
139  template<typename T, qualifier Q>
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140  GLM_FUNC_DECL mat<4, 4, T, Q> operator-(mat<4, 4, T, Q> const& m1, mat<4, 4, T, Q> const& m2);
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141 
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142  template<typename T, qualifier Q>
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143  GLM_FUNC_DECL mat<4, 4, T, Q> operator*(mat<4, 4, T, Q> const& m, T const& s);
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144 
\n
145  template<typename T, qualifier Q>
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146  GLM_FUNC_DECL mat<4, 4, T, Q> operator*(T const& s, mat<4, 4, T, Q> const& m);
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147 
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148  template<typename T, qualifier Q>
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149  GLM_FUNC_DECL typename mat<4, 4, T, Q>::col_type operator*(mat<4, 4, T, Q> const& m, typename mat<4, 4, T, Q>::row_type const& v);
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150 
\n
151  template<typename T, qualifier Q>
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152  GLM_FUNC_DECL typename mat<4, 4, T, Q>::row_type operator*(typename mat<4, 4, T, Q>::col_type const& v, mat<4, 4, T, Q> const& m);
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153 
\n
154  template<typename T, qualifier Q>
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155  GLM_FUNC_DECL mat<2, 4, T, Q> operator*(mat<4, 4, T, Q> const& m1, mat<2, 4, T, Q> const& m2);
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156 
\n
157  template<typename T, qualifier Q>
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158  GLM_FUNC_DECL mat<3, 4, T, Q> operator*(mat<4, 4, T, Q> const& m1, mat<3, 4, T, Q> const& m2);
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159 
\n
160  template<typename T, qualifier Q>
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161  GLM_FUNC_DECL mat<4, 4, T, Q> operator*(mat<4, 4, T, Q> const& m1, mat<4, 4, T, Q> const& m2);
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162 
\n
163  template<typename T, qualifier Q>
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164  GLM_FUNC_DECL mat<4, 4, T, Q> operator/(mat<4, 4, T, Q> const& m, T const& s);
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165 
\n
166  template<typename T, qualifier Q>
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167  GLM_FUNC_DECL mat<4, 4, T, Q> operator/(T const& s, mat<4, 4, T, Q> const& m);
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168 
\n
169  template<typename T, qualifier Q>
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170  GLM_FUNC_DECL typename mat<4, 4, T, Q>::col_type operator/(mat<4, 4, T, Q> const& m, typename mat<4, 4, T, Q>::row_type const& v);
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171 
\n
172  template<typename T, qualifier Q>
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173  GLM_FUNC_DECL typename mat<4, 4, T, Q>::row_type operator/(typename mat<4, 4, T, Q>::col_type const& v, mat<4, 4, T, Q> const& m);
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174 
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175  template<typename T, qualifier Q>
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176  GLM_FUNC_DECL mat<4, 4, T, Q> operator/(mat<4, 4, T, Q> const& m1, mat<4, 4, T, Q> const& m2);
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177 
\n
178  // -- Boolean operators --
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179 
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180  template<typename T, qualifier Q>
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181  GLM_FUNC_DECL bool operator==(mat<4, 4, T, Q> const& m1, mat<4, 4, T, Q> const& m2);
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182 
\n
183  template<typename T, qualifier Q>
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184  GLM_FUNC_DECL bool operator!=(mat<4, 4, T, Q> const& m1, mat<4, 4, T, Q> const& m2);
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185 }//namespace glm
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186 
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187 #ifndef GLM_EXTERNAL_TEMPLATE
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188 #include "type_mat4x4.inl"
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189 #endif//GLM_EXTERNAL_TEMPLATE
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glm::length
GLM_FUNC_DECL T length(qua< T, Q > const &q)
Returns the norm of a quaternions.
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type_vec4.hpp
Core features
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glm
Definition: common.hpp:20
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type_precision.hpp File Reference
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GLM_GTC_type_precision \nMore...

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Detailed Description

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GLM_GTC_type_precision

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See also
Core features (dependence)
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\n to use the features of this extension. \">GLM_GTC_quaternion (dependence)
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Definition in file type_precision.hpp.

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type_precision.hpp
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1 
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14 #pragma once
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15 
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16 // Dependency:
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17 #include "../gtc/quaternion.hpp"
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18 #include "../gtc/vec1.hpp"
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19 #include "../ext/scalar_int_sized.hpp"
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20 #include "../ext/scalar_uint_sized.hpp"
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21 #include "../detail/type_vec2.hpp"
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22 #include "../detail/type_vec3.hpp"
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23 #include "../detail/type_vec4.hpp"
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24 #include "../detail/type_mat2x2.hpp"
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25 #include "../detail/type_mat2x3.hpp"
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26 #include "../detail/type_mat2x4.hpp"
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27 #include "../detail/type_mat3x2.hpp"
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28 #include "../detail/type_mat3x3.hpp"
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29 #include "../detail/type_mat3x4.hpp"
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30 #include "../detail/type_mat4x2.hpp"
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31 #include "../detail/type_mat4x3.hpp"
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32 #include "../detail/type_mat4x4.hpp"
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33 
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34 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
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35 # pragma message("GLM: GLM_GTC_type_precision extension included")
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36 #endif
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37 
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38 namespace glm
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39 {
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41  // Signed int vector types
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42 
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45 
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48  typedef detail::int8 lowp_int8;
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49 
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52  typedef detail::int16 lowp_int16;
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53 
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56  typedef detail::int32 lowp_int32;
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57 
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60  typedef detail::int64 lowp_int64;
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61 
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64  typedef detail::int8 lowp_int8_t;
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65 
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68  typedef detail::int16 lowp_int16_t;
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69 
\n
72  typedef detail::int32 lowp_int32_t;
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73 
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76  typedef detail::int64 lowp_int64_t;
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77 
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80  typedef detail::int8 lowp_i8;
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81 
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84  typedef detail::int16 lowp_i16;
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85 
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88  typedef detail::int32 lowp_i32;
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89 
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92  typedef detail::int64 lowp_i64;
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93 
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96  typedef detail::int8 mediump_int8;
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97 
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100  typedef detail::int16 mediump_int16;
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101 
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104  typedef detail::int32 mediump_int32;
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105 
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108  typedef detail::int64 mediump_int64;
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109 
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112  typedef detail::int8 mediump_int8_t;
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113 
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116  typedef detail::int16 mediump_int16_t;
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117 
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120  typedef detail::int32 mediump_int32_t;
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121 
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124  typedef detail::int64 mediump_int64_t;
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125 
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128  typedef detail::int8 mediump_i8;
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129 
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132  typedef detail::int16 mediump_i16;
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133 
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136  typedef detail::int32 mediump_i32;
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137 
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140  typedef detail::int64 mediump_i64;
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141 
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144  typedef detail::int8 highp_int8;
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145 
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148  typedef detail::int16 highp_int16;
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149 
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152  typedef detail::int32 highp_int32;
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153 
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156  typedef detail::int64 highp_int64;
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157 
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160  typedef detail::int8 highp_int8_t;
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161 
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164  typedef detail::int16 highp_int16_t;
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165 
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168  typedef detail::int32 highp_int32_t;
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169 
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172  typedef detail::int64 highp_int64_t;
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173 
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176  typedef detail::int8 highp_i8;
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177 
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180  typedef detail::int16 highp_i16;
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181 
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184  typedef detail::int32 highp_i32;
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185 
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188  typedef detail::int64 highp_i64;
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189 
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190 
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191 #if GLM_HAS_EXTENDED_INTEGER_TYPE
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192  using std::int8_t;
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193  using std::int16_t;
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194  using std::int32_t;
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195  using std::int64_t;
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196 #else
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197  typedef detail::int8 int8_t;
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200 
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203  typedef detail::int16 int16_t;
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204 
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207  typedef detail::int32 int32_t;
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208 
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211  typedef detail::int64 int64_t;
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212 #endif
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213 
\n
216  typedef detail::int8 i8;
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217 
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220  typedef detail::int16 i16;
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221 
\n
224  typedef detail::int32 i32;
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225 
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228  typedef detail::int64 i64;
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229 
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230 
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231 
\n
234  typedef vec<1, i8, lowp> lowp_i8vec1;
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235 
\n
238  typedef vec<2, i8, lowp> lowp_i8vec2;
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239 
\n
242  typedef vec<3, i8, lowp> lowp_i8vec3;
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243 
\n
246  typedef vec<4, i8, lowp> lowp_i8vec4;
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247 
\n
248 
\n
251  typedef vec<1, i8, mediump> mediump_i8vec1;
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252 
\n
255  typedef vec<2, i8, mediump> mediump_i8vec2;
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256 
\n
259  typedef vec<3, i8, mediump> mediump_i8vec3;
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260 
\n
263  typedef vec<4, i8, mediump> mediump_i8vec4;
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264 
\n
265 
\n
268  typedef vec<1, i8, highp> highp_i8vec1;
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269 
\n
272  typedef vec<2, i8, highp> highp_i8vec2;
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273 
\n
276  typedef vec<3, i8, highp> highp_i8vec3;
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277 
\n
280  typedef vec<4, i8, highp> highp_i8vec4;
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281 
\n
282 
\n
283 
\n
286  typedef vec<1, i8, defaultp> i8vec1;
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287 
\n
290  typedef vec<2, i8, defaultp> i8vec2;
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291 
\n
294  typedef vec<3, i8, defaultp> i8vec3;
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295 
\n
298  typedef vec<4, i8, defaultp> i8vec4;
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299 
\n
300 
\n
301 
\n
302 
\n
303 
\n
306  typedef vec<1, i16, lowp> lowp_i16vec1;
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307 
\n
310  typedef vec<2, i16, lowp> lowp_i16vec2;
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311 
\n
314  typedef vec<3, i16, lowp> lowp_i16vec3;
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315 
\n
318  typedef vec<4, i16, lowp> lowp_i16vec4;
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319 
\n
320 
\n
323  typedef vec<1, i16, mediump> mediump_i16vec1;
\n
324 
\n
327  typedef vec<2, i16, mediump> mediump_i16vec2;
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328 
\n
331  typedef vec<3, i16, mediump> mediump_i16vec3;
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332 
\n
335  typedef vec<4, i16, mediump> mediump_i16vec4;
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336 
\n
337 
\n
340  typedef vec<1, i16, highp> highp_i16vec1;
\n
341 
\n
344  typedef vec<2, i16, highp> highp_i16vec2;
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345 
\n
348  typedef vec<3, i16, highp> highp_i16vec3;
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349 
\n
352  typedef vec<4, i16, highp> highp_i16vec4;
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353 
\n
354 
\n
355 
\n
356 
\n
359  typedef vec<1, i16, defaultp> i16vec1;
\n
360 
\n
363  typedef vec<2, i16, defaultp> i16vec2;
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364 
\n
367  typedef vec<3, i16, defaultp> i16vec3;
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368 
\n
371  typedef vec<4, i16, defaultp> i16vec4;
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372 
\n
373 
\n
374 
\n
377  typedef vec<1, i32, lowp> lowp_i32vec1;
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378 
\n
381  typedef vec<2, i32, lowp> lowp_i32vec2;
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382 
\n
385  typedef vec<3, i32, lowp> lowp_i32vec3;
\n
386 
\n
389  typedef vec<4, i32, lowp> lowp_i32vec4;
\n
390 
\n
391 
\n
394  typedef vec<1, i32, mediump> mediump_i32vec1;
\n
395 
\n
398  typedef vec<2, i32, mediump> mediump_i32vec2;
\n
399 
\n
402  typedef vec<3, i32, mediump> mediump_i32vec3;
\n
403 
\n
406  typedef vec<4, i32, mediump> mediump_i32vec4;
\n
407 
\n
408 
\n
411  typedef vec<1, i32, highp> highp_i32vec1;
\n
412 
\n
415  typedef vec<2, i32, highp> highp_i32vec2;
\n
416 
\n
419  typedef vec<3, i32, highp> highp_i32vec3;
\n
420 
\n
423  typedef vec<4, i32, highp> highp_i32vec4;
\n
424 
\n
425 
\n
428  typedef vec<1, i32, defaultp> i32vec1;
\n
429 
\n
432  typedef vec<2, i32, defaultp> i32vec2;
\n
433 
\n
436  typedef vec<3, i32, defaultp> i32vec3;
\n
437 
\n
440  typedef vec<4, i32, defaultp> i32vec4;
\n
441 
\n
442 
\n
443 
\n
444 
\n
447  typedef vec<1, i64, lowp> lowp_i64vec1;
\n
448 
\n
451  typedef vec<2, i64, lowp> lowp_i64vec2;
\n
452 
\n
455  typedef vec<3, i64, lowp> lowp_i64vec3;
\n
456 
\n
459  typedef vec<4, i64, lowp> lowp_i64vec4;
\n
460 
\n
461 
\n
464  typedef vec<1, i64, mediump> mediump_i64vec1;
\n
465 
\n
468  typedef vec<2, i64, mediump> mediump_i64vec2;
\n
469 
\n
472  typedef vec<3, i64, mediump> mediump_i64vec3;
\n
473 
\n
476  typedef vec<4, i64, mediump> mediump_i64vec4;
\n
477 
\n
478 
\n
481  typedef vec<1, i64, highp> highp_i64vec1;
\n
482 
\n
485  typedef vec<2, i64, highp> highp_i64vec2;
\n
486 
\n
489  typedef vec<3, i64, highp> highp_i64vec3;
\n
490 
\n
493  typedef vec<4, i64, highp> highp_i64vec4;
\n
494 
\n
495 
\n
498  typedef vec<1, i64, defaultp> i64vec1;
\n
499 
\n
502  typedef vec<2, i64, defaultp> i64vec2;
\n
503 
\n
506  typedef vec<3, i64, defaultp> i64vec3;
\n
507 
\n
510  typedef vec<4, i64, defaultp> i64vec4;
\n
511 
\n
512 
\n
514  // Unsigned int vector types
\n
515 
\n
518  typedef detail::uint8 lowp_uint8;
\n
519 
\n
522  typedef detail::uint16 lowp_uint16;
\n
523 
\n
526  typedef detail::uint32 lowp_uint32;
\n
527 
\n
530  typedef detail::uint64 lowp_uint64;
\n
531 
\n
534  typedef detail::uint8 lowp_uint8_t;
\n
535 
\n
538  typedef detail::uint16 lowp_uint16_t;
\n
539 
\n
542  typedef detail::uint32 lowp_uint32_t;
\n
543 
\n
546  typedef detail::uint64 lowp_uint64_t;
\n
547 
\n
550  typedef detail::uint8 lowp_u8;
\n
551 
\n
554  typedef detail::uint16 lowp_u16;
\n
555 
\n
558  typedef detail::uint32 lowp_u32;
\n
559 
\n
562  typedef detail::uint64 lowp_u64;
\n
563 
\n
566  typedef detail::uint8 mediump_uint8;
\n
567 
\n
570  typedef detail::uint16 mediump_uint16;
\n
571 
\n
574  typedef detail::uint32 mediump_uint32;
\n
575 
\n
578  typedef detail::uint64 mediump_uint64;
\n
579 
\n
582  typedef detail::uint8 mediump_uint8_t;
\n
583 
\n
586  typedef detail::uint16 mediump_uint16_t;
\n
587 
\n
590  typedef detail::uint32 mediump_uint32_t;
\n
591 
\n
594  typedef detail::uint64 mediump_uint64_t;
\n
595 
\n
598  typedef detail::uint8 mediump_u8;
\n
599 
\n
602  typedef detail::uint16 mediump_u16;
\n
603 
\n
606  typedef detail::uint32 mediump_u32;
\n
607 
\n
610  typedef detail::uint64 mediump_u64;
\n
611 
\n
614  typedef detail::uint8 highp_uint8;
\n
615 
\n
618  typedef detail::uint16 highp_uint16;
\n
619 
\n
622  typedef detail::uint32 highp_uint32;
\n
623 
\n
626  typedef detail::uint64 highp_uint64;
\n
627 
\n
630  typedef detail::uint8 highp_uint8_t;
\n
631 
\n
634  typedef detail::uint16 highp_uint16_t;
\n
635 
\n
638  typedef detail::uint32 highp_uint32_t;
\n
639 
\n
642  typedef detail::uint64 highp_uint64_t;
\n
643 
\n
646  typedef detail::uint8 highp_u8;
\n
647 
\n
650  typedef detail::uint16 highp_u16;
\n
651 
\n
654  typedef detail::uint32 highp_u32;
\n
655 
\n
658  typedef detail::uint64 highp_u64;
\n
659 
\n
660 #if GLM_HAS_EXTENDED_INTEGER_TYPE
\n
661  using std::uint8_t;
\n
662  using std::uint16_t;
\n
663  using std::uint32_t;
\n
664  using std::uint64_t;
\n
665 #else
\n
666  typedef detail::uint8 uint8_t;
\n
669 
\n
672  typedef detail::uint16 uint16_t;
\n
673 
\n
676  typedef detail::uint32 uint32_t;
\n
677 
\n
680  typedef detail::uint64 uint64_t;
\n
681 #endif
\n
682 
\n
685  typedef detail::uint8 u8;
\n
686 
\n
689  typedef detail::uint16 u16;
\n
690 
\n
693  typedef detail::uint32 u32;
\n
694 
\n
697  typedef detail::uint64 u64;
\n
698 
\n
699 
\n
700 
\n
701 
\n
702 
\n
704  // Float vector types
\n
705 
\n
708  typedef float float32;
\n
709 
\n
712  typedef double float64;
\n
713 
\n
716  typedef float32 lowp_float32;
\n
717 
\n
720  typedef float64 lowp_float64;
\n
721 
\n
724  typedef float32 lowp_float32_t;
\n
725 
\n
728  typedef float64 lowp_float64_t;
\n
729 
\n
732  typedef float32 lowp_f32;
\n
733 
\n
736  typedef float64 lowp_f64;
\n
737 
\n
740  typedef float32 lowp_float32;
\n
741 
\n
744  typedef float64 lowp_float64;
\n
745 
\n
748  typedef float32 lowp_float32_t;
\n
749 
\n
752  typedef float64 lowp_float64_t;
\n
753 
\n
756  typedef float32 lowp_f32;
\n
757 
\n
760  typedef float64 lowp_f64;
\n
761 
\n
762 
\n
765  typedef float32 lowp_float32;
\n
766 
\n
769  typedef float64 lowp_float64;
\n
770 
\n
773  typedef float32 lowp_float32_t;
\n
774 
\n
777  typedef float64 lowp_float64_t;
\n
778 
\n
781  typedef float32 lowp_f32;
\n
782 
\n
785  typedef float64 lowp_f64;
\n
786 
\n
787 
\n
790  typedef float32 mediump_float32;
\n
791 
\n
794  typedef float64 mediump_float64;
\n
795 
\n
798  typedef float32 mediump_float32_t;
\n
799 
\n
802  typedef float64 mediump_float64_t;
\n
803 
\n
806  typedef float32 mediump_f32;
\n
807 
\n
810  typedef float64 mediump_f64;
\n
811 
\n
812 
\n
815  typedef float32 highp_float32;
\n
816 
\n
819  typedef float64 highp_float64;
\n
820 
\n
823  typedef float32 highp_float32_t;
\n
824 
\n
827  typedef float64 highp_float64_t;
\n
828 
\n
831  typedef float32 highp_f32;
\n
832 
\n
835  typedef float64 highp_f64;
\n
836 
\n
837 
\n
838 #if(defined(GLM_PRECISION_LOWP_FLOAT))
\n
839  typedef lowp_float32_t float32_t;
\n
842 
\n
845  typedef lowp_float64_t float64_t;
\n
846 
\n
849  typedef lowp_f32 f32;
\n
850 
\n
853  typedef lowp_f64 f64;
\n
854 
\n
855 #elif(defined(GLM_PRECISION_MEDIUMP_FLOAT))
\n
856  typedef mediump_float32 float32_t;
\n
859 
\n
862  typedef mediump_float64 float64_t;
\n
863 
\n
866  typedef mediump_float32 f32;
\n
867 
\n
870  typedef mediump_float64 f64;
\n
871 
\n
872 #else//(defined(GLM_PRECISION_HIGHP_FLOAT))
\n
873 
\n
876  typedef highp_float32_t float32_t;
\n
877 
\n
880  typedef highp_float64_t float64_t;
\n
881 
\n
884  typedef highp_float32_t f32;
\n
885 
\n
888  typedef highp_float64_t f64;
\n
889 #endif
\n
890 
\n
891 
\n
894  typedef vec<1, float, lowp> lowp_fvec1;
\n
895 
\n
898  typedef vec<2, float, lowp> lowp_fvec2;
\n
899 
\n
902  typedef vec<3, float, lowp> lowp_fvec3;
\n
903 
\n
906  typedef vec<4, float, lowp> lowp_fvec4;
\n
907 
\n
908 
\n
911  typedef vec<1, float, mediump> mediump_fvec1;
\n
912 
\n
915  typedef vec<2, float, mediump> mediump_fvec2;
\n
916 
\n
919  typedef vec<3, float, mediump> mediump_fvec3;
\n
920 
\n
923  typedef vec<4, float, mediump> mediump_fvec4;
\n
924 
\n
925 
\n
928  typedef vec<1, float, highp> highp_fvec1;
\n
929 
\n
932  typedef vec<2, float, highp> highp_fvec2;
\n
933 
\n
936  typedef vec<3, float, highp> highp_fvec3;
\n
937 
\n
940  typedef vec<4, float, highp> highp_fvec4;
\n
941 
\n
942 
\n
945  typedef vec<1, f32, lowp> lowp_f32vec1;
\n
946 
\n
949  typedef vec<2, f32, lowp> lowp_f32vec2;
\n
950 
\n
953  typedef vec<3, f32, lowp> lowp_f32vec3;
\n
954 
\n
957  typedef vec<4, f32, lowp> lowp_f32vec4;
\n
958 
\n
961  typedef vec<1, f32, mediump> mediump_f32vec1;
\n
962 
\n
965  typedef vec<2, f32, mediump> mediump_f32vec2;
\n
966 
\n
969  typedef vec<3, f32, mediump> mediump_f32vec3;
\n
970 
\n
973  typedef vec<4, f32, mediump> mediump_f32vec4;
\n
974 
\n
977  typedef vec<1, f32, highp> highp_f32vec1;
\n
978 
\n
981  typedef vec<2, f32, highp> highp_f32vec2;
\n
982 
\n
985  typedef vec<3, f32, highp> highp_f32vec3;
\n
986 
\n
989  typedef vec<4, f32, highp> highp_f32vec4;
\n
990 
\n
991 
\n
994  typedef vec<1, f64, lowp> lowp_f64vec1;
\n
995 
\n
998  typedef vec<2, f64, lowp> lowp_f64vec2;
\n
999 
\n
1002  typedef vec<3, f64, lowp> lowp_f64vec3;
\n
1003 
\n
1006  typedef vec<4, f64, lowp> lowp_f64vec4;
\n
1007 
\n
1010  typedef vec<1, f64, mediump> mediump_f64vec1;
\n
1011 
\n
1014  typedef vec<2, f64, mediump> mediump_f64vec2;
\n
1015 
\n
1018  typedef vec<3, f64, mediump> mediump_f64vec3;
\n
1019 
\n
1022  typedef vec<4, f64, mediump> mediump_f64vec4;
\n
1023 
\n
1026  typedef vec<1, f64, highp> highp_f64vec1;
\n
1027 
\n
1030  typedef vec<2, f64, highp> highp_f64vec2;
\n
1031 
\n
1034  typedef vec<3, f64, highp> highp_f64vec3;
\n
1035 
\n
1038  typedef vec<4, f64, highp> highp_f64vec4;
\n
1039 
\n
1040 
\n
1041 
\n
1043  // Float matrix types
\n
1044 
\n
1047  //typedef lowp_f32 lowp_fmat1x1;
\n
1048 
\n
1051  typedef mat<2, 2, f32, lowp> lowp_fmat2x2;
\n
1052 
\n
1055  typedef mat<2, 3, f32, lowp> lowp_fmat2x3;
\n
1056 
\n
1059  typedef mat<2, 4, f32, lowp> lowp_fmat2x4;
\n
1060 
\n
1063  typedef mat<3, 2, f32, lowp> lowp_fmat3x2;
\n
1064 
\n
1067  typedef mat<3, 3, f32, lowp> lowp_fmat3x3;
\n
1068 
\n
1071  typedef mat<3, 4, f32, lowp> lowp_fmat3x4;
\n
1072 
\n
1075  typedef mat<4, 2, f32, lowp> lowp_fmat4x2;
\n
1076 
\n
1079  typedef mat<4, 3, f32, lowp> lowp_fmat4x3;
\n
1080 
\n
1083  typedef mat<4, 4, f32, lowp> lowp_fmat4x4;
\n
1084 
\n
1087  //typedef lowp_fmat1x1 lowp_fmat1;
\n
1088 
\n
1091  typedef lowp_fmat2x2 lowp_fmat2;
\n
1092 
\n
1095  typedef lowp_fmat3x3 lowp_fmat3;
\n
1096 
\n
1099  typedef lowp_fmat4x4 lowp_fmat4;
\n
1100 
\n
1101 
\n
1104  //typedef mediump_f32 mediump_fmat1x1;
\n
1105 
\n
1108  typedef mat<2, 2, f32, mediump> mediump_fmat2x2;
\n
1109 
\n
1112  typedef mat<2, 3, f32, mediump> mediump_fmat2x3;
\n
1113 
\n
1116  typedef mat<2, 4, f32, mediump> mediump_fmat2x4;
\n
1117 
\n
1120  typedef mat<3, 2, f32, mediump> mediump_fmat3x2;
\n
1121 
\n
1124  typedef mat<3, 3, f32, mediump> mediump_fmat3x3;
\n
1125 
\n
1128  typedef mat<3, 4, f32, mediump> mediump_fmat3x4;
\n
1129 
\n
1132  typedef mat<4, 2, f32, mediump> mediump_fmat4x2;
\n
1133 
\n
1136  typedef mat<4, 3, f32, mediump> mediump_fmat4x3;
\n
1137 
\n
1140  typedef mat<4, 4, f32, mediump> mediump_fmat4x4;
\n
1141 
\n
1144  //typedef mediump_fmat1x1 mediump_fmat1;
\n
1145 
\n
1148  typedef mediump_fmat2x2 mediump_fmat2;
\n
1149 
\n
1152  typedef mediump_fmat3x3 mediump_fmat3;
\n
1153 
\n
1156  typedef mediump_fmat4x4 mediump_fmat4;
\n
1157 
\n
1158 
\n
1161  //typedef highp_f32 highp_fmat1x1;
\n
1162 
\n
1165  typedef mat<2, 2, f32, highp> highp_fmat2x2;
\n
1166 
\n
1169  typedef mat<2, 3, f32, highp> highp_fmat2x3;
\n
1170 
\n
1173  typedef mat<2, 4, f32, highp> highp_fmat2x4;
\n
1174 
\n
1177  typedef mat<3, 2, f32, highp> highp_fmat3x2;
\n
1178 
\n
1181  typedef mat<3, 3, f32, highp> highp_fmat3x3;
\n
1182 
\n
1185  typedef mat<3, 4, f32, highp> highp_fmat3x4;
\n
1186 
\n
1189  typedef mat<4, 2, f32, highp> highp_fmat4x2;
\n
1190 
\n
1193  typedef mat<4, 3, f32, highp> highp_fmat4x3;
\n
1194 
\n
1197  typedef mat<4, 4, f32, highp> highp_fmat4x4;
\n
1198 
\n
1201  //typedef highp_fmat1x1 highp_fmat1;
\n
1202 
\n
1205  typedef highp_fmat2x2 highp_fmat2;
\n
1206 
\n
1209  typedef highp_fmat3x3 highp_fmat3;
\n
1210 
\n
1213  typedef highp_fmat4x4 highp_fmat4;
\n
1214 
\n
1215 
\n
1218  //typedef f32 lowp_f32mat1x1;
\n
1219 
\n
1222  typedef mat<2, 2, f32, lowp> lowp_f32mat2x2;
\n
1223 
\n
1226  typedef mat<2, 3, f32, lowp> lowp_f32mat2x3;
\n
1227 
\n
1230  typedef mat<2, 4, f32, lowp> lowp_f32mat2x4;
\n
1231 
\n
1234  typedef mat<3, 2, f32, lowp> lowp_f32mat3x2;
\n
1235 
\n
1238  typedef mat<3, 3, f32, lowp> lowp_f32mat3x3;
\n
1239 
\n
1242  typedef mat<3, 4, f32, lowp> lowp_f32mat3x4;
\n
1243 
\n
1246  typedef mat<4, 2, f32, lowp> lowp_f32mat4x2;
\n
1247 
\n
1250  typedef mat<4, 3, f32, lowp> lowp_f32mat4x3;
\n
1251 
\n
1254  typedef mat<4, 4, f32, lowp> lowp_f32mat4x4;
\n
1255 
\n
1258  //typedef detail::tmat1x1<f32, lowp> lowp_f32mat1;
\n
1259 
\n
1262  typedef lowp_f32mat2x2 lowp_f32mat2;
\n
1263 
\n
1266  typedef lowp_f32mat3x3 lowp_f32mat3;
\n
1267 
\n
1270  typedef lowp_f32mat4x4 lowp_f32mat4;
\n
1271 
\n
1272 
\n
1275  //typedef f32 mediump_f32mat1x1;
\n
1276 
\n
1279  typedef mat<2, 2, f32, mediump> mediump_f32mat2x2;
\n
1280 
\n
1283  typedef mat<2, 3, f32, mediump> mediump_f32mat2x3;
\n
1284 
\n
1287  typedef mat<2, 4, f32, mediump> mediump_f32mat2x4;
\n
1288 
\n
1291  typedef mat<3, 2, f32, mediump> mediump_f32mat3x2;
\n
1292 
\n
1295  typedef mat<3, 3, f32, mediump> mediump_f32mat3x3;
\n
1296 
\n
1299  typedef mat<3, 4, f32, mediump> mediump_f32mat3x4;
\n
1300 
\n
1303  typedef mat<4, 2, f32, mediump> mediump_f32mat4x2;
\n
1304 
\n
1307  typedef mat<4, 3, f32, mediump> mediump_f32mat4x3;
\n
1308 
\n
1311  typedef mat<4, 4, f32, mediump> mediump_f32mat4x4;
\n
1312 
\n
1315  //typedef detail::tmat1x1<f32, mediump> f32mat1;
\n
1316 
\n
1319  typedef mediump_f32mat2x2 mediump_f32mat2;
\n
1320 
\n
1323  typedef mediump_f32mat3x3 mediump_f32mat3;
\n
1324 
\n
1327  typedef mediump_f32mat4x4 mediump_f32mat4;
\n
1328 
\n
1329 
\n
1332  //typedef f32 highp_f32mat1x1;
\n
1333 
\n
1336  typedef mat<2, 2, f32, highp> highp_f32mat2x2;
\n
1337 
\n
1340  typedef mat<2, 3, f32, highp> highp_f32mat2x3;
\n
1341 
\n
1344  typedef mat<2, 4, f32, highp> highp_f32mat2x4;
\n
1345 
\n
1348  typedef mat<3, 2, f32, highp> highp_f32mat3x2;
\n
1349 
\n
1352  typedef mat<3, 3, f32, highp> highp_f32mat3x3;
\n
1353 
\n
1356  typedef mat<3, 4, f32, highp> highp_f32mat3x4;
\n
1357 
\n
1360  typedef mat<4, 2, f32, highp> highp_f32mat4x2;
\n
1361 
\n
1364  typedef mat<4, 3, f32, highp> highp_f32mat4x3;
\n
1365 
\n
1368  typedef mat<4, 4, f32, highp> highp_f32mat4x4;
\n
1369 
\n
1372  //typedef detail::tmat1x1<f32, highp> f32mat1;
\n
1373 
\n
1376  typedef highp_f32mat2x2 highp_f32mat2;
\n
1377 
\n
1380  typedef highp_f32mat3x3 highp_f32mat3;
\n
1381 
\n
1384  typedef highp_f32mat4x4 highp_f32mat4;
\n
1385 
\n
1386 
\n
1389  //typedef f64 lowp_f64mat1x1;
\n
1390 
\n
1393  typedef mat<2, 2, f64, lowp> lowp_f64mat2x2;
\n
1394 
\n
1397  typedef mat<2, 3, f64, lowp> lowp_f64mat2x3;
\n
1398 
\n
1401  typedef mat<2, 4, f64, lowp> lowp_f64mat2x4;
\n
1402 
\n
1405  typedef mat<3, 2, f64, lowp> lowp_f64mat3x2;
\n
1406 
\n
1409  typedef mat<3, 3, f64, lowp> lowp_f64mat3x3;
\n
1410 
\n
1413  typedef mat<3, 4, f64, lowp> lowp_f64mat3x4;
\n
1414 
\n
1417  typedef mat<4, 2, f64, lowp> lowp_f64mat4x2;
\n
1418 
\n
1421  typedef mat<4, 3, f64, lowp> lowp_f64mat4x3;
\n
1422 
\n
1425  typedef mat<4, 4, f64, lowp> lowp_f64mat4x4;
\n
1426 
\n
1429  //typedef lowp_f64mat1x1 lowp_f64mat1;
\n
1430 
\n
1433  typedef lowp_f64mat2x2 lowp_f64mat2;
\n
1434 
\n
1437  typedef lowp_f64mat3x3 lowp_f64mat3;
\n
1438 
\n
1441  typedef lowp_f64mat4x4 lowp_f64mat4;
\n
1442 
\n
1443 
\n
1446  //typedef f64 Highp_f64mat1x1;
\n
1447 
\n
1450  typedef mat<2, 2, f64, mediump> mediump_f64mat2x2;
\n
1451 
\n
1454  typedef mat<2, 3, f64, mediump> mediump_f64mat2x3;
\n
1455 
\n
1458  typedef mat<2, 4, f64, mediump> mediump_f64mat2x4;
\n
1459 
\n
1462  typedef mat<3, 2, f64, mediump> mediump_f64mat3x2;
\n
1463 
\n
1466  typedef mat<3, 3, f64, mediump> mediump_f64mat3x3;
\n
1467 
\n
1470  typedef mat<3, 4, f64, mediump> mediump_f64mat3x4;
\n
1471 
\n
1474  typedef mat<4, 2, f64, mediump> mediump_f64mat4x2;
\n
1475 
\n
1478  typedef mat<4, 3, f64, mediump> mediump_f64mat4x3;
\n
1479 
\n
1482  typedef mat<4, 4, f64, mediump> mediump_f64mat4x4;
\n
1483 
\n
1486  //typedef mediump_f64mat1x1 mediump_f64mat1;
\n
1487 
\n
1490  typedef mediump_f64mat2x2 mediump_f64mat2;
\n
1491 
\n
1494  typedef mediump_f64mat3x3 mediump_f64mat3;
\n
1495 
\n
1498  typedef mediump_f64mat4x4 mediump_f64mat4;
\n
1499 
\n
1502  //typedef f64 highp_f64mat1x1;
\n
1503 
\n
1506  typedef mat<2, 2, f64, highp> highp_f64mat2x2;
\n
1507 
\n
1510  typedef mat<2, 3, f64, highp> highp_f64mat2x3;
\n
1511 
\n
1514  typedef mat<2, 4, f64, highp> highp_f64mat2x4;
\n
1515 
\n
1518  typedef mat<3, 2, f64, highp> highp_f64mat3x2;
\n
1519 
\n
1522  typedef mat<3, 3, f64, highp> highp_f64mat3x3;
\n
1523 
\n
1526  typedef mat<3, 4, f64, highp> highp_f64mat3x4;
\n
1527 
\n
1530  typedef mat<4, 2, f64, highp> highp_f64mat4x2;
\n
1531 
\n
1534  typedef mat<4, 3, f64, highp> highp_f64mat4x3;
\n
1535 
\n
1538  typedef mat<4, 4, f64, highp> highp_f64mat4x4;
\n
1539 
\n
1542  //typedef highp_f64mat1x1 highp_f64mat1;
\n
1543 
\n
1546  typedef highp_f64mat2x2 highp_f64mat2;
\n
1547 
\n
1550  typedef highp_f64mat3x3 highp_f64mat3;
\n
1551 
\n
1554  typedef highp_f64mat4x4 highp_f64mat4;
\n
1555 
\n
1556 
\n
1557 
\n
1558 
\n
1561  typedef vec<1, u8, lowp> lowp_u8vec1;
\n
1562 
\n
1565  typedef vec<2, u8, lowp> lowp_u8vec2;
\n
1566 
\n
1569  typedef vec<3, u8, lowp> lowp_u8vec3;
\n
1570 
\n
1573  typedef vec<4, u8, lowp> lowp_u8vec4;
\n
1574 
\n
1575 
\n
1578  typedef vec<1, u8, mediump> mediump_u8vec1;
\n
1579 
\n
1582  typedef vec<2, u8, mediump> mediump_u8vec2;
\n
1583 
\n
1586  typedef vec<3, u8, mediump> mediump_u8vec3;
\n
1587 
\n
1590  typedef vec<4, u8, mediump> mediump_u8vec4;
\n
1591 
\n
1592 
\n
1595  typedef vec<1, u8, highp> highp_u8vec1;
\n
1596 
\n
1599  typedef vec<2, u8, highp> highp_u8vec2;
\n
1600 
\n
1603  typedef vec<3, u8, highp> highp_u8vec3;
\n
1604 
\n
1607  typedef vec<4, u8, highp> highp_u8vec4;
\n
1608 
\n
1609 
\n
1610 
\n
1613  typedef vec<1, u8, defaultp> u8vec1;
\n
1614 
\n
1617  typedef vec<2, u8, defaultp> u8vec2;
\n
1618 
\n
1621  typedef vec<3, u8, defaultp> u8vec3;
\n
1622 
\n
1625  typedef vec<4, u8, defaultp> u8vec4;
\n
1626 
\n
1627 
\n
1628 
\n
1629 
\n
1632  typedef vec<1, u16, lowp> lowp_u16vec1;
\n
1633 
\n
1636  typedef vec<2, u16, lowp> lowp_u16vec2;
\n
1637 
\n
1640  typedef vec<3, u16, lowp> lowp_u16vec3;
\n
1641 
\n
1644  typedef vec<4, u16, lowp> lowp_u16vec4;
\n
1645 
\n
1646 
\n
1649  typedef vec<1, u16, mediump> mediump_u16vec1;
\n
1650 
\n
1653  typedef vec<2, u16, mediump> mediump_u16vec2;
\n
1654 
\n
1657  typedef vec<3, u16, mediump> mediump_u16vec3;
\n
1658 
\n
1661  typedef vec<4, u16, mediump> mediump_u16vec4;
\n
1662 
\n
1663 
\n
1666  typedef vec<1, u16, highp> highp_u16vec1;
\n
1667 
\n
1670  typedef vec<2, u16, highp> highp_u16vec2;
\n
1671 
\n
1674  typedef vec<3, u16, highp> highp_u16vec3;
\n
1675 
\n
1678  typedef vec<4, u16, highp> highp_u16vec4;
\n
1679 
\n
1680 
\n
1681 
\n
1682 
\n
1685  typedef vec<1, u16, defaultp> u16vec1;
\n
1686 
\n
1689  typedef vec<2, u16, defaultp> u16vec2;
\n
1690 
\n
1693  typedef vec<3, u16, defaultp> u16vec3;
\n
1694 
\n
1697  typedef vec<4, u16, defaultp> u16vec4;
\n
1698 
\n
1699 
\n
1700 
\n
1703  typedef vec<1, u32, lowp> lowp_u32vec1;
\n
1704 
\n
1707  typedef vec<2, u32, lowp> lowp_u32vec2;
\n
1708 
\n
1711  typedef vec<3, u32, lowp> lowp_u32vec3;
\n
1712 
\n
1715  typedef vec<4, u32, lowp> lowp_u32vec4;
\n
1716 
\n
1717 
\n
1720  typedef vec<1, u32, mediump> mediump_u32vec1;
\n
1721 
\n
1724  typedef vec<2, u32, mediump> mediump_u32vec2;
\n
1725 
\n
1728  typedef vec<3, u32, mediump> mediump_u32vec3;
\n
1729 
\n
1732  typedef vec<4, u32, mediump> mediump_u32vec4;
\n
1733 
\n
1734 
\n
1737  typedef vec<1, u32, highp> highp_u32vec1;
\n
1738 
\n
1741  typedef vec<2, u32, highp> highp_u32vec2;
\n
1742 
\n
1745  typedef vec<3, u32, highp> highp_u32vec3;
\n
1746 
\n
1749  typedef vec<4, u32, highp> highp_u32vec4;
\n
1750 
\n
1751 
\n
1752 
\n
1755  typedef vec<1, u32, defaultp> u32vec1;
\n
1756 
\n
1759  typedef vec<2, u32, defaultp> u32vec2;
\n
1760 
\n
1763  typedef vec<3, u32, defaultp> u32vec3;
\n
1764 
\n
1767  typedef vec<4, u32, defaultp> u32vec4;
\n
1768 
\n
1769 
\n
1770 
\n
1771 
\n
1774  typedef vec<1, u64, lowp> lowp_u64vec1;
\n
1775 
\n
1778  typedef vec<2, u64, lowp> lowp_u64vec2;
\n
1779 
\n
1782  typedef vec<3, u64, lowp> lowp_u64vec3;
\n
1783 
\n
1786  typedef vec<4, u64, lowp> lowp_u64vec4;
\n
1787 
\n
1788 
\n
1791  typedef vec<1, u64, mediump> mediump_u64vec1;
\n
1792 
\n
1795  typedef vec<2, u64, mediump> mediump_u64vec2;
\n
1796 
\n
1799  typedef vec<3, u64, mediump> mediump_u64vec3;
\n
1800 
\n
1803  typedef vec<4, u64, mediump> mediump_u64vec4;
\n
1804 
\n
1805 
\n
1808  typedef vec<1, u64, highp> highp_u64vec1;
\n
1809 
\n
1812  typedef vec<2, u64, highp> highp_u64vec2;
\n
1813 
\n
1816  typedef vec<3, u64, highp> highp_u64vec3;
\n
1817 
\n
1820  typedef vec<4, u64, highp> highp_u64vec4;
\n
1821 
\n
1822 
\n
1823 
\n
1824 
\n
1827  typedef vec<1, u64, defaultp> u64vec1;
\n
1828 
\n
1831  typedef vec<2, u64, defaultp> u64vec2;
\n
1832 
\n
1835  typedef vec<3, u64, defaultp> u64vec3;
\n
1836 
\n
1839  typedef vec<4, u64, defaultp> u64vec4;
\n
1840 
\n
1841 
\n
1843  // Float vector types
\n
1844 
\n
1847  typedef float32 float32_t;
\n
1848 
\n
1851  typedef float32 f32;
\n
1852 
\n
1853 # ifndef GLM_FORCE_SINGLE_ONLY
\n
1854 
\n
1857  typedef float64 float64_t;
\n
1858 
\n
1861  typedef float64 f64;
\n
1862 # endif//GLM_FORCE_SINGLE_ONLY
\n
1863 
\n
1866  typedef vec<1, float, defaultp> fvec1;
\n
1867 
\n
1870  typedef vec<2, float, defaultp> fvec2;
\n
1871 
\n
1874  typedef vec<3, float, defaultp> fvec3;
\n
1875 
\n
1878  typedef vec<4, float, defaultp> fvec4;
\n
1879 
\n
1880 
\n
1883  typedef vec<1, f32, defaultp> f32vec1;
\n
1884 
\n
1887  typedef vec<2, f32, defaultp> f32vec2;
\n
1888 
\n
1891  typedef vec<3, f32, defaultp> f32vec3;
\n
1892 
\n
1895  typedef vec<4, f32, defaultp> f32vec4;
\n
1896 
\n
1897 # ifndef GLM_FORCE_SINGLE_ONLY
\n
1898  typedef vec<1, f64, defaultp> f64vec1;
\n
1901 
\n
1904  typedef vec<2, f64, defaultp> f64vec2;
\n
1905 
\n
1908  typedef vec<3, f64, defaultp> f64vec3;
\n
1909 
\n
1912  typedef vec<4, f64, defaultp> f64vec4;
\n
1913 # endif//GLM_FORCE_SINGLE_ONLY
\n
1914 
\n
1915 
\n
1917  // Float matrix types
\n
1918 
\n
1921  //typedef detail::tmat1x1<f32> fmat1;
\n
1922 
\n
1925  typedef mat<2, 2, f32, defaultp> fmat2;
\n
1926 
\n
1929  typedef mat<3, 3, f32, defaultp> fmat3;
\n
1930 
\n
1933  typedef mat<4, 4, f32, defaultp> fmat4;
\n
1934 
\n
1935 
\n
1938  //typedef f32 fmat1x1;
\n
1939 
\n
1942  typedef mat<2, 2, f32, defaultp> fmat2x2;
\n
1943 
\n
1946  typedef mat<2, 3, f32, defaultp> fmat2x3;
\n
1947 
\n
1950  typedef mat<2, 4, f32, defaultp> fmat2x4;
\n
1951 
\n
1954  typedef mat<3, 2, f32, defaultp> fmat3x2;
\n
1955 
\n
1958  typedef mat<3, 3, f32, defaultp> fmat3x3;
\n
1959 
\n
1962  typedef mat<3, 4, f32, defaultp> fmat3x4;
\n
1963 
\n
1966  typedef mat<4, 2, f32, defaultp> fmat4x2;
\n
1967 
\n
1970  typedef mat<4, 3, f32, defaultp> fmat4x3;
\n
1971 
\n
1974  typedef mat<4, 4, f32, defaultp> fmat4x4;
\n
1975 
\n
1976 
\n
1979  //typedef detail::tmat1x1<f32, defaultp> f32mat1;
\n
1980 
\n
1983  typedef mat<2, 2, f32, defaultp> f32mat2;
\n
1984 
\n
1987  typedef mat<3, 3, f32, defaultp> f32mat3;
\n
1988 
\n
1991  typedef mat<4, 4, f32, defaultp> f32mat4;
\n
1992 
\n
1993 
\n
1996  //typedef f32 f32mat1x1;
\n
1997 
\n
2000  typedef mat<2, 2, f32, defaultp> f32mat2x2;
\n
2001 
\n
2004  typedef mat<2, 3, f32, defaultp> f32mat2x3;
\n
2005 
\n
2008  typedef mat<2, 4, f32, defaultp> f32mat2x4;
\n
2009 
\n
2012  typedef mat<3, 2, f32, defaultp> f32mat3x2;
\n
2013 
\n
2016  typedef mat<3, 3, f32, defaultp> f32mat3x3;
\n
2017 
\n
2020  typedef mat<3, 4, f32, defaultp> f32mat3x4;
\n
2021 
\n
2024  typedef mat<4, 2, f32, defaultp> f32mat4x2;
\n
2025 
\n
2028  typedef mat<4, 3, f32, defaultp> f32mat4x3;
\n
2029 
\n
2032  typedef mat<4, 4, f32, defaultp> f32mat4x4;
\n
2033 
\n
2034 
\n
2035 # ifndef GLM_FORCE_SINGLE_ONLY
\n
2036 
\n
2039  //typedef detail::tmat1x1<f64, defaultp> f64mat1;
\n
2040 
\n
2043  typedef mat<2, 2, f64, defaultp> f64mat2;
\n
2044 
\n
2047  typedef mat<3, 3, f64, defaultp> f64mat3;
\n
2048 
\n
2051  typedef mat<4, 4, f64, defaultp> f64mat4;
\n
2052 
\n
2053 
\n
2056  //typedef f64 f64mat1x1;
\n
2057 
\n
2060  typedef mat<2, 2, f64, defaultp> f64mat2x2;
\n
2061 
\n
2064  typedef mat<2, 3, f64, defaultp> f64mat2x3;
\n
2065 
\n
2068  typedef mat<2, 4, f64, defaultp> f64mat2x4;
\n
2069 
\n
2072  typedef mat<3, 2, f64, defaultp> f64mat3x2;
\n
2073 
\n
2076  typedef mat<3, 3, f64, defaultp> f64mat3x3;
\n
2077 
\n
2080  typedef mat<3, 4, f64, defaultp> f64mat3x4;
\n
2081 
\n
2084  typedef mat<4, 2, f64, defaultp> f64mat4x2;
\n
2085 
\n
2088  typedef mat<4, 3, f64, defaultp> f64mat4x3;
\n
2089 
\n
2092  typedef mat<4, 4, f64, defaultp> f64mat4x4;
\n
2093 
\n
2094 # endif//GLM_FORCE_SINGLE_ONLY
\n
2095 
\n
2097  // Quaternion types
\n
2098 
\n
2101  typedef qua<f32, defaultp> f32quat;
\n
2102 
\n
2105  typedef qua<f32, lowp> lowp_f32quat;
\n
2106 
\n
2109  typedef qua<f64, lowp> lowp_f64quat;
\n
2110 
\n
2113  typedef qua<f32, mediump> mediump_f32quat;
\n
2114 
\n
2115 # ifndef GLM_FORCE_SINGLE_ONLY
\n
2116 
\n
2119  typedef qua<f64, mediump> mediump_f64quat;
\n
2120 
\n
2123  typedef qua<f32, highp> highp_f32quat;
\n
2124 
\n
2127  typedef qua<f64, highp> highp_f64quat;
\n
2128 
\n
2131  typedef qua<f64, defaultp> f64quat;
\n
2132 
\n
2133 # endif//GLM_FORCE_SINGLE_ONLY
\n
2134 
\n
2136 }//namespace glm
\n
2137 
\n
2138 #include "type_precision.inl"
\n
glm::highp_u16vec1
vec< 1, u16, highp > highp_u16vec1
High qualifier 16 bit unsigned integer scalar type.
Definition: fwd.hpp:354
\n
glm::highp_f32mat4x2
mat< 4, 2, f32, highp > highp_f32mat4x2
High single-qualifier floating-point 4x2 matrix.
Definition: fwd.hpp:696
\n
glm::highp_u64
uint64 highp_u64
High qualifier 64 bit unsigned integer type.
Definition: fwd.hpp:133
\n
glm::mediump_f64vec1
vec< 1, f64, mediump > mediump_f64vec1
Medium double-qualifier floating-point vector of 1 component.
Definition: fwd.hpp:491
\n
glm::f32vec3
vec< 3, f32, defaultp > f32vec3
Single-qualifier floating-point vector of 3 components.
Definition: fwd.hpp:463
\n
glm::mediump_fmat2
mat< 2, 2, f32, mediump > mediump_fmat2
Medium single-qualifier floating-point 1x1 matrix.
Definition: fwd.hpp:528
\n
glm::highp_float64_t
double highp_float64_t
High 64 bit double-qualifier floating-point scalar.
Definition: fwd.hpp:175
\n
glm::f64mat4
mat< 4, 4, f64, defaultp > f64mat4
Double-qualifier floating-point 4x4 matrix.
Definition: fwd.hpp:586
\n
glm::f64mat2
mat< 2, 2, f64, defaultp > f64mat2
Double-qualifier floating-point 1x1 matrix.
Definition: fwd.hpp:584
\n
glm::mediump_fmat4x3
mat< 4, 3, f32, mediump > mediump_fmat4x3
Medium single-qualifier floating-point 4x3 matrix.
Definition: fwd.hpp:647
\n
glm::mediump_f32mat3
mat< 3, 3, f32, mediump > mediump_f32mat3
Medium single-qualifier floating-point 3x3 matrix.
Definition: fwd.hpp:545
\n
glm::mediump_uint32_t
uint32 mediump_uint32_t
Medium qualifier 32 bit unsigned integer type.
Definition: fwd.hpp:127
\n
glm::lowp_uint64
uint64 lowp_uint64
Low qualifier 64 bit unsigned integer type.
Definition: fwd.hpp:136
\n
glm::mediump_fmat2x2
mat< 2, 2, f32, mediump > mediump_fmat2x2
Medium single-qualifier floating-point 1x1 matrix.
Definition: fwd.hpp:640
\n
glm::f32vec1
vec< 1, f32, defaultp > f32vec1
Single-qualifier floating-point vector of 1 component.
Definition: fwd.hpp:461
\n
glm::highp_f32mat4
mat< 4, 4, f32, highp > highp_f32mat4
High single-qualifier floating-point 4x4 matrix.
Definition: fwd.hpp:550
\n
glm::highp_float64
double highp_float64
High 64 bit double-qualifier floating-point scalar.
Definition: fwd.hpp:170
\n
glm::lowp_u8
uint8 lowp_u8
Low qualifier 8 bit unsigned integer type.
Definition: fwd.hpp:89
\n
glm::u32
uint32 u32
Default qualifier 32 bit unsigned integer type.
Definition: fwd.hpp:120
\n
glm::f64mat3
mat< 3, 3, f64, defaultp > f64mat3
Double-qualifier floating-point 3x3 matrix.
Definition: fwd.hpp:585
\n
glm::lowp_float64
double lowp_float64
Low 64 bit double-qualifier floating-point scalar.
Definition: fwd.hpp:168
\n
glm::i32vec1
vec< 1, i32, defaultp > i32vec1
32 bit signed integer scalar type.
Definition: fwd.hpp:277
\n
glm::highp_uint16
uint16 highp_uint16
High qualifier 16 bit unsigned integer type.
Definition: fwd.hpp:110
\n
glm::mediump_f64mat2x4
mat< 2, 4, f64, mediump > mediump_f64mat2x4
Medium double-qualifier floating-point 2x4 matrix.
Definition: fwd.hpp:762
\n
glm::highp_i64vec4
vec< 4, i64, highp > highp_i64vec4
High qualifier 64 bit signed integer vector of 4 components type.
Definition: fwd.hpp:295
\n
glm::f64mat3x4
mat< 3, 4, f64, defaultp > f64mat3x4
Double-qualifier floating-point 3x4 matrix.
Definition: fwd.hpp:787
\n
glm::fmat2
mat< 2, 2, f32, defaultp > fmat2
Single-qualifier floating-point 1x1 matrix.
Definition: fwd.hpp:536
\n
glm::i16vec3
vec< 3, i16, defaultp > i16vec3
16 bit signed integer vector of 3 components type.
Definition: fwd.hpp:259
\n
glm::lowp_uint32_t
uint32 lowp_uint32_t
Low qualifier 32 bit unsigned integer type.
Definition: fwd.hpp:126
\n
glm::lowp_fvec2
vec< 2, float, lowp > lowp_fvec2
Low single-qualifier floating-point vector of 2 components.
Definition: fwd.hpp:427
\n
glm::mediump_uint32
uint32 mediump_uint32
Medium qualifier 32 bit unsigned integer type.
Definition: fwd.hpp:123
\n
glm::mediump_fmat4
mat< 4, 4, f32, mediump > mediump_fmat4
Medium single-qualifier floating-point 4x4 matrix.
Definition: fwd.hpp:530
\n
glm::highp_uint64
uint64 highp_uint64
High qualifier 64 bit unsigned integer type.
Definition: fwd.hpp:138
\n
glm::lowp_fmat2
mat< 2, 2, f32, lowp > lowp_fmat2
Low single-qualifier floating-point 1x1 matrix.
Definition: fwd.hpp:524
\n
glm::lowp_uint32
uint32 lowp_uint32
Low qualifier 32 bit unsigned integer type.
Definition: fwd.hpp:122
\n
glm::lowp_fvec3
vec< 3, float, lowp > lowp_fvec3
Low single-qualifier floating-point vector of 3 components.
Definition: fwd.hpp:428
\n
glm::mediump_fvec2
vec< 2, float, mediump > mediump_fvec2
Medium Single-qualifier floating-point vector of 2 components.
Definition: fwd.hpp:432
\n
glm::lowp_fmat3x4
mat< 3, 4, f32, lowp > lowp_fmat3x4
Low single-qualifier floating-point 3x4 matrix.
Definition: fwd.hpp:635
\n
glm::lowp_f64mat2x2
mat< 2, 2, f64, lowp > lowp_f64mat2x2
Low double-qualifier floating-point 1x1 matrix.
Definition: fwd.hpp:750
\n
glm::i64vec4
vec< 4, i64, defaultp > i64vec4
64 bit signed integer vector of 4 components type.
Definition: fwd.hpp:300
\n
glm::u16vec3
vec< 3, u16, defaultp > u16vec3
Default qualifier 16 bit unsigned integer vector of 3 components type.
Definition: fwd.hpp:361
\n
glm::lowp_u64vec1
vec< 1, u64, lowp > lowp_u64vec1
Low qualifier 64 bit unsigned integer scalar type.
Definition: fwd.hpp:384
\n
glm::mediump_u16vec1
vec< 1, u16, mediump > mediump_u16vec1
Medium qualifier 16 bit unsigned integer scalar type.
Definition: fwd.hpp:349
\n
glm::i8vec2
vec< 2, i8, defaultp > i8vec2
8 bit signed integer vector of 2 components type.
Definition: fwd.hpp:238
\n
glm::mediump_f64mat2x3
mat< 2, 3, f64, mediump > mediump_f64mat2x3
Medium double-qualifier floating-point 2x3 matrix.
Definition: fwd.hpp:761
\n
glm::lowp_u32vec4
vec< 4, u32, lowp > lowp_u32vec4
Low qualifier 32 bit unsigned integer vector of 4 components type.
Definition: fwd.hpp:367
\n
glm::highp_f32vec4
vec< 4, f32, highp > highp_f32vec4
High single-qualifier floating-point vector of 4 components.
Definition: fwd.hpp:459
\n
glm::lowp_f32vec1
vec< 1, f32, lowp > lowp_f32vec1
Low single-qualifier floating-point vector of 1 component.
Definition: fwd.hpp:446
\n
glm::highp_f32mat2x3
mat< 2, 3, f32, highp > highp_f32mat2x3
High single-qualifier floating-point 2x3 matrix.
Definition: fwd.hpp:691
\n
glm::highp_int64
int64 highp_int64
High qualifier 64 bit signed integer type.
Definition: fwd.hpp:80
\n
glm::mediump_i32vec2
vec< 2, i32, mediump > mediump_i32vec2
Medium qualifier 32 bit signed integer vector of 2 components type.
Definition: fwd.hpp:268
\n
glm::lowp_f64mat4
mat< 4, 4, f64, lowp > lowp_f64mat4
Low double-qualifier floating-point 4x4 matrix.
Definition: fwd.hpp:574
\n
glm::fmat4
mat< 4, 4, f32, defaultp > fmat4
Single-qualifier floating-point 4x4 matrix.
Definition: fwd.hpp:538
\n
glm::mediump_fmat3x4
mat< 3, 4, f32, mediump > mediump_fmat3x4
Medium single-qualifier floating-point 3x4 matrix.
Definition: fwd.hpp:645
\n
glm::lowp_int16_t
int16 lowp_int16_t
Low qualifier 16 bit signed integer type.
Definition: fwd.hpp:54
\n
glm::highp_i32vec4
vec< 4, i32, highp > highp_i32vec4
High qualifier 32 bit signed integer vector of 4 components type.
Definition: fwd.hpp:275
\n
glm::f32mat4x2
mat< 4, 2, f32, defaultp > f32mat4x2
Single-qualifier floating-point 4x2 matrix.
Definition: fwd.hpp:702
\n
glm::highp_fmat3x2
mat< 3, 2, f32, highp > highp_fmat3x2
High single-qualifier floating-point 3x2 matrix.
Definition: fwd.hpp:653
\n
glm::mediump_fmat2x3
mat< 2, 3, f32, mediump > mediump_fmat2x3
Medium single-qualifier floating-point 2x3 matrix.
Definition: fwd.hpp:641
\n
glm::mediump_u32
uint32 mediump_u32
Medium qualifier 32 bit unsigned integer type.
Definition: fwd.hpp:118
\n
glm::lowp_fmat3x2
mat< 3, 2, f32, lowp > lowp_fmat3x2
Low single-qualifier floating-point 3x2 matrix.
Definition: fwd.hpp:633
\n
glm::mediump_f64mat4x2
mat< 4, 2, f64, mediump > mediump_f64mat4x2
Medium double-qualifier floating-point 4x2 matrix.
Definition: fwd.hpp:766
\n
glm::highp_u16vec2
vec< 2, u16, highp > highp_u16vec2
High qualifier 16 bit unsigned integer vector of 2 components type.
Definition: fwd.hpp:355
\n
glm::highp_f64vec1
vec< 1, f64, highp > highp_f64vec1
High double-qualifier floating-point vector of 1 component.
Definition: fwd.hpp:496
\n
glm::mediump_i16vec2
vec< 2, i16, mediump > mediump_i16vec2
Medium qualifier 16 bit signed integer vector of 2 components type.
Definition: fwd.hpp:248
\n
glm::highp_fmat2x4
mat< 2, 4, f32, highp > highp_fmat2x4
High single-qualifier floating-point 2x4 matrix.
Definition: fwd.hpp:652
\n
glm::u64vec3
vec< 3, u64, defaultp > u64vec3
Default qualifier 64 bit unsigned integer vector of 3 components type.
Definition: fwd.hpp:401
\n
glm::lowp_uint8
uint8 lowp_uint8
Low qualifier 8 bit unsigned integer type.
Definition: fwd.hpp:94
\n
glm::lowp_f32mat3x2
mat< 3, 2, f32, lowp > lowp_f32mat3x2
Low single-qualifier floating-point 3x2 matrix.
Definition: fwd.hpp:673
\n
glm::lowp_u64
uint64 lowp_u64
Low qualifier 64 bit unsigned integer type.
Definition: fwd.hpp:131
\n
glm::highp_i64vec3
vec< 3, i64, highp > highp_i64vec3
High qualifier 64 bit signed integer vector of 3 components type.
Definition: fwd.hpp:294
\n
glm::mediump_int8
int8 mediump_int8
Medium qualifier 8 bit signed integer type.
Definition: fwd.hpp:37
\n
glm::lowp_int64
int64 lowp_int64
Low qualifier 64 bit signed integer type.
Definition: fwd.hpp:78
\n
glm::mediump_f32mat4x2
mat< 4, 2, f32, mediump > mediump_f32mat4x2
Medium single-qualifier floating-point 4x2 matrix.
Definition: fwd.hpp:686
\n
glm::lowp_f64vec3
vec< 3, f64, lowp > lowp_f64vec3
Low double-qualifier floating-point vector of 3 components.
Definition: fwd.hpp:488
\n
glm::u64vec2
vec< 2, u64, defaultp > u64vec2
Default qualifier 64 bit unsigned integer vector of 2 components type.
Definition: fwd.hpp:400
\n
glm::lowp_i64vec3
vec< 3, i64, lowp > lowp_i64vec3
Low qualifier 64 bit signed integer vector of 3 components type.
Definition: fwd.hpp:284
\n
glm::mediump_i8vec2
vec< 2, i8, mediump > mediump_i8vec2
Medium qualifier 8 bit signed integer vector of 2 components type.
Definition: fwd.hpp:228
\n
glm::f32mat3x4
mat< 3, 4, f32, defaultp > f32mat3x4
Single-qualifier floating-point 3x4 matrix.
Definition: fwd.hpp:707
\n
glm::highp_i16vec3
vec< 3, i16, highp > highp_i16vec3
High qualifier 16 bit signed integer vector of 3 components type.
Definition: fwd.hpp:254
\n
glm::mediump_i16vec3
vec< 3, i16, mediump > mediump_i16vec3
Medium qualifier 16 bit signed integer vector of 3 components type.
Definition: fwd.hpp:249
\n
glm::u64
uint64 u64
Default qualifier 64 bit unsigned integer type.
Definition: fwd.hpp:134
\n
glm::f64vec1
vec< 1, f64, defaultp > f64vec1
Double-qualifier floating-point vector of 1 component.
Definition: fwd.hpp:501
\n
glm::mediump_fmat3x2
mat< 3, 2, f32, mediump > mediump_fmat3x2
Medium single-qualifier floating-point 3x2 matrix.
Definition: fwd.hpp:643
\n
glm::mediump_i64vec1
vec< 1, i64, mediump > mediump_i64vec1
Medium qualifier 64 bit signed integer scalar type.
Definition: fwd.hpp:287
\n
glm::i16vec1
vec< 1, i16, defaultp > i16vec1
16 bit signed integer scalar type.
Definition: fwd.hpp:257
\n
glm::lowp_f64mat3x3
mat< 3, 3, f64, lowp > lowp_f64mat3x3
Low double-qualifier floating-point 3x3 matrix.
Definition: fwd.hpp:754
\n
glm::lowp_f64vec2
vec< 2, f64, lowp > lowp_f64vec2
Low double-qualifier floating-point vector of 2 components.
Definition: fwd.hpp:487
\n
glm::highp_fmat2x3
mat< 2, 3, f32, highp > highp_fmat2x3
High single-qualifier floating-point 2x3 matrix.
Definition: fwd.hpp:651
\n
glm::lowp_f64mat3
mat< 3, 3, f64, lowp > lowp_f64mat3
Low double-qualifier floating-point 3x3 matrix.
Definition: fwd.hpp:573
\n
glm::lowp_f32mat4x3
mat< 4, 3, f32, lowp > lowp_f32mat4x3
Low single-qualifier floating-point 4x3 matrix.
Definition: fwd.hpp:677
\n
glm::mediump_u64vec3
vec< 3, u64, mediump > mediump_u64vec3
Medium qualifier 64 bit unsigned integer vector of 3 components type.
Definition: fwd.hpp:391
\n
glm::mediump_float64
double mediump_float64
Medium 64 bit double-qualifier floating-point scalar.
Definition: fwd.hpp:169
\n
glm::float64
double float64
Double-qualifier floating-point scalar.
Definition: fwd.hpp:171
\n
glm::highp_i16vec2
vec< 2, i16, highp > highp_i16vec2
High qualifier 16 bit signed integer vector of 2 components type.
Definition: fwd.hpp:253
\n
glm::fmat4x2
mat< 4, 2, f32, defaultp > fmat4x2
Single-qualifier floating-point 4x2 matrix.
Definition: fwd.hpp:662
\n
glm::lowp_f64mat2x3
mat< 2, 3, f64, lowp > lowp_f64mat2x3
Low double-qualifier floating-point 2x3 matrix.
Definition: fwd.hpp:751
\n
glm::fmat3x4
mat< 3, 4, f32, defaultp > fmat3x4
Single-qualifier floating-point 3x4 matrix.
Definition: fwd.hpp:667
\n
glm::lowp_u32vec3
vec< 3, u32, lowp > lowp_u32vec3
Low qualifier 32 bit unsigned integer vector of 3 components type.
Definition: fwd.hpp:366
\n
glm::f32mat2x4
mat< 2, 4, f32, defaultp > f32mat2x4
Single-qualifier floating-point 2x4 matrix.
Definition: fwd.hpp:706
\n
glm::lowp_fvec4
vec< 4, float, lowp > lowp_fvec4
Low single-qualifier floating-point vector of 4 components.
Definition: fwd.hpp:429
\n
glm::mediump_f32vec4
vec< 4, f32, mediump > mediump_f32vec4
Medium single-qualifier floating-point vector of 4 components.
Definition: fwd.hpp:454
\n
glm::i16vec4
vec< 4, i16, defaultp > i16vec4
16 bit signed integer vector of 4 components type.
Definition: fwd.hpp:260
\n
glm::lowp_uint8_t
uint8 lowp_uint8_t
Low qualifier 8 bit unsigned integer type.
Definition: fwd.hpp:98
\n
glm::highp_uint32_t
uint32 highp_uint32_t
High qualifier 32 bit unsigned integer type.
Definition: fwd.hpp:128
\n
glm::fmat3x3
mat< 3, 3, f32, defaultp > fmat3x3
Single-qualifier floating-point 3x3 matrix.
Definition: fwd.hpp:664
\n
glm::mediump_f64mat3x4
mat< 3, 4, f64, mediump > mediump_f64mat3x4
Medium double-qualifier floating-point 3x4 matrix.
Definition: fwd.hpp:765
\n
glm::lowp_fmat2x3
mat< 2, 3, f32, lowp > lowp_fmat2x3
Low single-qualifier floating-point 2x3 matrix.
Definition: fwd.hpp:631
\n
glm::lowp_u32vec1
vec< 1, u32, lowp > lowp_u32vec1
Low qualifier 32 bit unsigned integer scalar type.
Definition: fwd.hpp:364
\n
glm::f32mat2x3
mat< 2, 3, f32, defaultp > f32mat2x3
Single-qualifier floating-point 2x3 matrix.
Definition: fwd.hpp:703
\n
glm::mediump_i32vec1
vec< 1, i32, mediump > mediump_i32vec1
Medium qualifier 32 bit signed integer scalar type.
Definition: fwd.hpp:267
\n
glm::highp_u16vec4
vec< 4, u16, highp > highp_u16vec4
High qualifier 16 bit unsigned integer vector of 4 components type.
Definition: fwd.hpp:357
\n
glm::lowp_i32vec1
vec< 1, i32, lowp > lowp_i32vec1
Low qualifier 32 bit signed integer scalar type.
Definition: fwd.hpp:262
\n
glm::lowp_i64vec1
vec< 1, i64, lowp > lowp_i64vec1
Low qualifier 64 bit signed integer scalar type.
Definition: fwd.hpp:282
\n
glm::highp_u32vec1
vec< 1, u32, highp > highp_u32vec1
High qualifier 32 bit unsigned integer scalar type.
Definition: fwd.hpp:374
\n
glm::mediump_int16
int16 mediump_int16
Medium qualifier 16 bit signed integer type.
Definition: fwd.hpp:51
\n
glm::mediump_u16
uint16 mediump_u16
Medium qualifier 16 bit unsigned integer type.
Definition: fwd.hpp:104
\n
glm::f64quat
qua< f64, defaultp > f64quat
Double-qualifier floating-point quaternion.
Definition: fwd.hpp:815
\n
glm::mediump_f64vec3
vec< 3, f64, mediump > mediump_f64vec3
Medium double-qualifier floating-point vector of 3 components.
Definition: fwd.hpp:493
\n
glm::u64vec1
vec< 1, u64, defaultp > u64vec1
Default qualifier 64 bit unsigned integer scalar type.
Definition: fwd.hpp:399
\n
glm::int64_t
int64 int64_t
64 bit signed integer type.
Definition: fwd.hpp:85
\n
glm::u8vec1
vec< 1, u8, defaultp > u8vec1
Default qualifier 8 bit unsigned integer scalar type.
Definition: fwd.hpp:339
\n
glm::highp_i8vec1
vec< 1, i8, highp > highp_i8vec1
High qualifier 8 bit signed integer scalar type.
Definition: fwd.hpp:232
\n
glm::u8vec4
vec< 4, u8, defaultp > u8vec4
Default qualifier 8 bit unsigned integer vector of 4 components type.
Definition: fwd.hpp:342
\n
glm::int8_t
int8 int8_t
8 bit signed integer type.
Definition: fwd.hpp:43
\n
glm::i32
int32 i32
32 bit signed integer type.
Definition: fwd.hpp:62
\n
glm::mediump_u32vec1
vec< 1, u32, mediump > mediump_u32vec1
Medium qualifier 32 bit unsigned integer scalar type.
Definition: fwd.hpp:369
\n
glm::f64mat2x2
mat< 2, 2, f64, defaultp > f64mat2x2
Double-qualifier floating-point 1x1 matrix.
Definition: fwd.hpp:780
\n
glm::lowp_f32mat2x2
mat< 2, 2, f32, lowp > lowp_f32mat2x2
Low single-qualifier floating-point 1x1 matrix.
Definition: fwd.hpp:670
\n
glm::lowp_f32vec4
vec< 4, f32, lowp > lowp_f32vec4
Low single-qualifier floating-point vector of 4 components.
Definition: fwd.hpp:449
\n
glm::highp_fvec3
vec< 3, float, highp > highp_fvec3
High Single-qualifier floating-point vector of 3 components.
Definition: fwd.hpp:438
\n
glm::lowp_f64mat4x2
mat< 4, 2, f64, lowp > lowp_f64mat4x2
Low double-qualifier floating-point 4x2 matrix.
Definition: fwd.hpp:756
\n
glm::mediump_fmat3x3
mat< 3, 3, f32, mediump > mediump_fmat3x3
Medium single-qualifier floating-point 3x3 matrix.
Definition: fwd.hpp:644
\n
glm::highp_i64vec1
vec< 1, i64, highp > highp_i64vec1
High qualifier 64 bit signed integer scalar type.
Definition: fwd.hpp:292
\n
glm::i8vec4
vec< 4, i8, defaultp > i8vec4
8 bit signed integer vector of 4 components type.
Definition: fwd.hpp:240
\n
glm::highp_int32
int32 highp_int32
High qualifier 32 bit signed integer type.
Definition: fwd.hpp:66
\n
glm::mediump_f32mat2x3
mat< 2, 3, f32, mediump > mediump_f32mat2x3
Medium single-qualifier floating-point 2x3 matrix.
Definition: fwd.hpp:681
\n
glm::lowp_f64mat3x2
mat< 3, 2, f64, lowp > lowp_f64mat3x2
Low double-qualifier floating-point 3x2 matrix.
Definition: fwd.hpp:753
\n
glm::highp_u32
uint32 highp_u32
High qualifier 32 bit unsigned integer type.
Definition: fwd.hpp:119
\n
glm::highp_i32
int32 highp_i32
High qualifier 32 bit signed integer type.
Definition: fwd.hpp:61
\n
glm::u64vec4
vec< 4, u64, defaultp > u64vec4
Default qualifier 64 bit unsigned integer vector of 4 components type.
Definition: fwd.hpp:402
\n
glm::f32vec4
vec< 4, f32, defaultp > f32vec4
Single-qualifier floating-point vector of 4 components.
Definition: fwd.hpp:464
\n
glm::f64mat2x3
mat< 2, 3, f64, defaultp > f64mat2x3
Double-qualifier floating-point 2x3 matrix.
Definition: fwd.hpp:783
\n
glm::mediump_f64mat4x4
mat< 4, 4, f64, mediump > mediump_f64mat4x4
Medium double-qualifier floating-point 4x4 matrix.
Definition: fwd.hpp:768
\n
glm::lowp_u16vec4
vec< 4, u16, lowp > lowp_u16vec4
Low qualifier 16 bit unsigned integer vector of 4 components type.
Definition: fwd.hpp:347
\n
glm::highp_uint32
uint32 highp_uint32
High qualifier 32 bit unsigned integer type.
Definition: fwd.hpp:124
\n
glm::lowp_f32mat4
mat< 4, 4, f32, lowp > lowp_f32mat4
Low single-qualifier floating-point 4x4 matrix.
Definition: fwd.hpp:542
\n
glm::f64mat3x2
mat< 3, 2, f64, defaultp > f64mat3x2
Double-qualifier floating-point 3x2 matrix.
Definition: fwd.hpp:781
\n
glm::mediump_float32
float mediump_float32
Medium 32 bit single-qualifier floating-point scalar.
Definition: fwd.hpp:153
\n
glm::u32vec1
vec< 1, u32, defaultp > u32vec1
Default qualifier 32 bit unsigned integer scalar type.
Definition: fwd.hpp:379
\n
glm::mediump_f64vec4
vec< 4, f64, mediump > mediump_f64vec4
Medium double-qualifier floating-point vector of 4 components.
Definition: fwd.hpp:494
\n
glm::f64mat3x3
mat< 3, 3, f64, defaultp > f64mat3x3
Double-qualifier floating-point 3x3 matrix.
Definition: fwd.hpp:784
\n
glm::highp_float32
float highp_float32
High 32 bit single-qualifier floating-point scalar.
Definition: fwd.hpp:154
\n
glm::highp_uint8
uint8 highp_uint8
High qualifier 8 bit unsigned integer type.
Definition: fwd.hpp:96
\n
glm::highp_i8
int8 highp_i8
High qualifier 8 bit signed integer type.
Definition: fwd.hpp:33
\n
glm::lowp_f64mat2x4
mat< 2, 4, f64, lowp > lowp_f64mat2x4
Low double-qualifier floating-point 2x4 matrix.
Definition: fwd.hpp:752
\n
glm::lowp_f64mat3x4
mat< 3, 4, f64, lowp > lowp_f64mat3x4
Low double-qualifier floating-point 3x4 matrix.
Definition: fwd.hpp:755
\n
glm::mediump_i8
int8 mediump_i8
Medium qualifier 8 bit signed integer type.
Definition: fwd.hpp:32
\n
glm::highp_int64_t
int64 highp_int64_t
High qualifier 64 bit signed integer type.
Definition: fwd.hpp:84
\n
glm::f32mat4x4
mat< 4, 4, f32, defaultp > f32mat4x4
Single-qualifier floating-point 4x4 matrix.
Definition: fwd.hpp:708
\n
glm::float32_t
float float32_t
Default 32 bit single-qualifier floating-point scalar.
Definition: fwd.hpp:160
\n
glm::f32mat2x2
mat< 2, 2, f32, defaultp > f32mat2x2
Single-qualifier floating-point 1x1 matrix.
Definition: fwd.hpp:700
\n
glm::lowp_i64vec2
vec< 2, i64, lowp > lowp_i64vec2
Low qualifier 64 bit signed integer vector of 2 components type.
Definition: fwd.hpp:283
\n
glm::lowp_f32mat2x4
mat< 2, 4, f32, lowp > lowp_f32mat2x4
Low single-qualifier floating-point 2x4 matrix.
Definition: fwd.hpp:672
\n
glm::uint32_t
uint32 uint32_t
Default qualifier 32 bit unsigned integer type.
Definition: fwd.hpp:129
\n
glm::highp_f32mat3
mat< 3, 3, f32, highp > highp_f32mat3
High single-qualifier floating-point 3x3 matrix.
Definition: fwd.hpp:549
\n
glm::mediump_f64mat3x3
mat< 3, 3, f64, mediump > mediump_f64mat3x3
Medium double-qualifier floating-point 3x3 matrix.
Definition: fwd.hpp:764
\n
glm::u8
uint8 u8
Default qualifier 8 bit unsigned integer type.
Definition: fwd.hpp:92
\n
glm::highp_i32vec3
vec< 3, i32, highp > highp_i32vec3
High qualifier 32 bit signed integer vector of 3 components type.
Definition: fwd.hpp:274
\n
glm::float32
float float32
Single-qualifier floating-point scalar.
Definition: fwd.hpp:155
\n
glm::fvec4
vec< 4, f32, defaultp > fvec4
Single-qualifier floating-point vector of 4 components.
Definition: fwd.hpp:444
\n
glm::highp_i32vec1
vec< 1, i32, highp > highp_i32vec1
High qualifier 32 bit signed integer scalar type.
Definition: fwd.hpp:272
\n
glm::lowp_f32mat3
mat< 3, 3, f32, lowp > lowp_f32mat3
Low single-qualifier floating-point 3x3 matrix.
Definition: fwd.hpp:541
\n
glm::u16vec1
vec< 1, u16, defaultp > u16vec1
Default qualifier 16 bit unsigned integer scalar type.
Definition: fwd.hpp:359
\n
glm::i8vec1
vec< 1, i8, defaultp > i8vec1
8 bit signed integer scalar type.
Definition: fwd.hpp:237
\n
glm::mediump_i32vec3
vec< 3, i32, mediump > mediump_i32vec3
Medium qualifier 32 bit signed integer vector of 3 components type.
Definition: fwd.hpp:269
\n
glm::i32vec2
vec< 2, i32, defaultp > i32vec2
32 bit signed integer vector of 2 components type.
Definition: fwd.hpp:278
\n
glm::lowp_i16vec2
vec< 2, i16, lowp > lowp_i16vec2
Low qualifier 16 bit signed integer vector of 2 components type.
Definition: fwd.hpp:243
\n
glm::mediump_u64vec2
vec< 2, u64, mediump > mediump_u64vec2
Medium qualifier 64 bit unsigned integer vector of 2 components type.
Definition: fwd.hpp:390
\n
glm::lowp_u8vec4
vec< 4, u8, lowp > lowp_u8vec4
Low qualifier 8 bit unsigned integer vector of 4 components type.
Definition: fwd.hpp:327
\n
glm::highp_f32mat3x3
mat< 3, 3, f32, highp > highp_f32mat3x3
High single-qualifier floating-point 3x3 matrix.
Definition: fwd.hpp:694
\n
glm::highp_u8vec1
vec< 1, u8, highp > highp_u8vec1
High qualifier 8 bit unsigned integer scalar type.
Definition: fwd.hpp:334
\n
glm::highp_uint8_t
uint8 highp_uint8_t
High qualifier 8 bit unsigned integer type.
Definition: fwd.hpp:100
\n
glm::mediump_u32vec4
vec< 4, u32, mediump > mediump_u32vec4
Medium qualifier 32 bit unsigned integer vector of 4 components type.
Definition: fwd.hpp:372
\n
glm::highp_f32mat2x2
mat< 2, 2, f32, highp > highp_f32mat2x2
High single-qualifier floating-point 1x1 matrix.
Definition: fwd.hpp:690
\n
glm::highp_f64vec4
vec< 4, f64, highp > highp_f64vec4
High double-qualifier floating-point vector of 4 components.
Definition: fwd.hpp:499
\n
glm::lowp_u8vec3
vec< 3, u8, lowp > lowp_u8vec3
Low qualifier 8 bit unsigned integer vector of 3 components type.
Definition: fwd.hpp:326
\n
glm::highp_f32
float highp_f32
High 32 bit single-qualifier floating-point scalar.
Definition: fwd.hpp:149
\n
glm::mediump_uint64
uint64 mediump_uint64
Medium qualifier 64 bit unsigned integer type.
Definition: fwd.hpp:137
\n
glm::highp_int32_t
int32 highp_int32_t
32 bit signed integer type.
Definition: fwd.hpp:70
\n
glm::f64vec3
vec< 3, f64, defaultp > f64vec3
Double-qualifier floating-point vector of 3 components.
Definition: fwd.hpp:503
\n
glm::lowp_f32mat2x3
mat< 2, 3, f32, lowp > lowp_f32mat2x3
Low single-qualifier floating-point 2x3 matrix.
Definition: fwd.hpp:671
\n
glm::mediump_u16vec3
vec< 3, u16, mediump > mediump_u16vec3
Medium qualifier 16 bit unsigned integer vector of 3 components type.
Definition: fwd.hpp:351
\n
glm::f64mat2x4
mat< 2, 4, f64, defaultp > f64mat2x4
Double-qualifier floating-point 2x4 matrix.
Definition: fwd.hpp:786
\n
glm::f32mat3
mat< 3, 3, f32, defaultp > f32mat3
Single-qualifier floating-point 3x3 matrix.
Definition: fwd.hpp:553
\n
glm::mediump_f64mat2x2
mat< 2, 2, f64, mediump > mediump_f64mat2x2
Medium double-qualifier floating-point 1x1 matrix.
Definition: fwd.hpp:760
\n
glm::mediump_u64
uint64 mediump_u64
Medium qualifier 64 bit unsigned integer type.
Definition: fwd.hpp:132
\n
glm::highp_i16vec4
vec< 4, i16, highp > highp_i16vec4
High qualifier 16 bit signed integer vector of 4 components type.
Definition: fwd.hpp:255
\n
glm::lowp_fmat4
mat< 4, 4, f32, lowp > lowp_fmat4
Low single-qualifier floating-point 4x4 matrix.
Definition: fwd.hpp:526
\n
glm::mediump_u32vec2
vec< 2, u32, mediump > mediump_u32vec2
Medium qualifier 32 bit unsigned integer vector of 2 components type.
Definition: fwd.hpp:370
\n
glm::highp_u64vec3
vec< 3, u64, highp > highp_u64vec3
High qualifier 64 bit unsigned integer vector of 3 components type.
Definition: fwd.hpp:396
\n
glm::lowp_u16
uint16 lowp_u16
Low qualifier 16 bit unsigned integer type.
Definition: fwd.hpp:103
\n
glm::lowp_i16vec3
vec< 3, i16, lowp > lowp_i16vec3
Low qualifier 16 bit signed integer vector of 3 components type.
Definition: fwd.hpp:244
\n
glm::lowp_u16vec3
vec< 3, u16, lowp > lowp_u16vec3
Low qualifier 16 bit unsigned integer vector of 3 components type.
Definition: fwd.hpp:346
\n
glm::lowp_f32vec3
vec< 3, f32, lowp > lowp_f32vec3
Low single-qualifier floating-point vector of 3 components.
Definition: fwd.hpp:448
\n
glm::highp_fmat4
mat< 4, 4, f32, highp > highp_fmat4
High single-qualifier floating-point 4x4 matrix.
Definition: fwd.hpp:534
\n
glm::lowp_fmat3
mat< 3, 3, f32, lowp > lowp_fmat3
Low single-qualifier floating-point 3x3 matrix.
Definition: fwd.hpp:525
\n
glm::highp_i16
int16 highp_i16
High qualifier 16 bit signed integer type.
Definition: fwd.hpp:47
\n
glm::mediump_f32quat
qua< f32, mediump > mediump_f32quat
Medium single-qualifier floating-point quaternion.
Definition: fwd.hpp:803
\n
glm::highp_int8
int8 highp_int8
High qualifier 8 bit signed integer type.
Definition: fwd.hpp:38
\n
glm::f64mat4x4
mat< 4, 4, f64, defaultp > f64mat4x4
Double-qualifier floating-point 4x4 matrix.
Definition: fwd.hpp:788
\n
glm::fmat4x3
mat< 4, 3, f32, defaultp > fmat4x3
Single-qualifier floating-point 4x3 matrix.
Definition: fwd.hpp:665
\n
glm::lowp_fmat2x4
mat< 2, 4, f32, lowp > lowp_fmat2x4
Low single-qualifier floating-point 2x4 matrix.
Definition: fwd.hpp:632
\n
glm::highp_f64mat3
mat< 3, 3, f64, highp > highp_f64mat3
High double-qualifier floating-point 3x3 matrix.
Definition: fwd.hpp:581
\n
glm::mediump_i8vec3
vec< 3, i8, mediump > mediump_i8vec3
Medium qualifier 8 bit signed integer vector of 3 components type.
Definition: fwd.hpp:229
\n
glm::highp_f32vec1
vec< 1, f32, highp > highp_f32vec1
High single-qualifier floating-point vector of 1 component.
Definition: fwd.hpp:456
\n
glm::lowp_i8vec3
vec< 3, i8, lowp > lowp_i8vec3
Low qualifier 8 bit signed integer vector of 3 components type.
Definition: fwd.hpp:224
\n
glm::lowp_f64mat4x3
mat< 4, 3, f64, lowp > lowp_f64mat4x3
Low double-qualifier floating-point 4x3 matrix.
Definition: fwd.hpp:757
\n
glm::highp_u64vec4
vec< 4, u64, highp > highp_u64vec4
High qualifier 64 bit unsigned integer vector of 4 components type.
Definition: fwd.hpp:397
\n
glm::fvec3
vec< 3, f32, defaultp > fvec3
Single-qualifier floating-point vector of 3 components.
Definition: fwd.hpp:443
\n
glm::i16vec2
vec< 2, i16, defaultp > i16vec2
16 bit signed integer vector of 2 components type.
Definition: fwd.hpp:258
\n
glm::f32mat4x3
mat< 4, 3, f32, defaultp > f32mat4x3
Single-qualifier floating-point 4x3 matrix.
Definition: fwd.hpp:705
\n
glm::f32mat2
mat< 2, 2, f32, defaultp > f32mat2
Single-qualifier floating-point 1x1 matrix.
Definition: fwd.hpp:552
\n
glm::mediump_u16vec2
vec< 2, u16, mediump > mediump_u16vec2
Medium qualifier 16 bit unsigned integer vector of 2 components type.
Definition: fwd.hpp:350
\n
glm::mediump_fmat2x4
mat< 2, 4, f32, mediump > mediump_fmat2x4
Medium single-qualifier floating-point 2x4 matrix.
Definition: fwd.hpp:642
\n
glm::lowp_f32mat4x4
mat< 4, 4, f32, lowp > lowp_f32mat4x4
Low single-qualifier floating-point 4x4 matrix.
Definition: fwd.hpp:678
\n
glm::lowp_u8vec2
vec< 2, u8, lowp > lowp_u8vec2
Low qualifier 8 bit unsigned integer vector of 2 components type.
Definition: fwd.hpp:325
\n
glm::mediump_f64mat3
mat< 3, 3, f64, mediump > mediump_f64mat3
Medium double-qualifier floating-point 3x3 matrix.
Definition: fwd.hpp:577
\n
glm::lowp_i16
int16 lowp_i16
Low qualifier 16 bit signed integer type.
Definition: fwd.hpp:45
\n
glm::highp_fmat3x4
mat< 3, 4, f32, highp > highp_fmat3x4
High single-qualifier floating-point 3x4 matrix.
Definition: fwd.hpp:655
\n
glm::float64_t
double float64_t
Default 64 bit double-qualifier floating-point scalar.
Definition: fwd.hpp:176
\n
glm::highp_f64mat4x4
mat< 4, 4, f64, highp > highp_f64mat4x4
High double-qualifier floating-point 4x4 matrix.
Definition: fwd.hpp:778
\n
glm::mediump_f32mat4x3
mat< 4, 3, f32, mediump > mediump_f32mat4x3
Medium single-qualifier floating-point 4x3 matrix.
Definition: fwd.hpp:687
\n
glm::lowp_int16
int16 lowp_int16
Low qualifier 16 bit signed integer type.
Definition: fwd.hpp:50
\n
glm::mediump_fmat3
mat< 3, 3, f32, mediump > mediump_fmat3
Medium single-qualifier floating-point 3x3 matrix.
Definition: fwd.hpp:529
\n
glm::highp_f32mat4x4
mat< 4, 4, f32, highp > highp_f32mat4x4
High single-qualifier floating-point 4x4 matrix.
Definition: fwd.hpp:698
\n
glm::lowp_int64_t
int64 lowp_int64_t
Low qualifier 64 bit signed integer type.
Definition: fwd.hpp:82
\n
glm::uint16_t
uint16 uint16_t
Default qualifier 16 bit unsigned integer type.
Definition: fwd.hpp:115
\n
glm::highp_f64vec2
vec< 2, f64, highp > highp_f64vec2
High double-qualifier floating-point vector of 2 components.
Definition: fwd.hpp:497
\n
glm::lowp_u64vec2
vec< 2, u64, lowp > lowp_u64vec2
Low qualifier 64 bit unsigned integer vector of 2 components type.
Definition: fwd.hpp:385
\n
glm::fmat3
mat< 3, 3, f32, defaultp > fmat3
Single-qualifier floating-point 3x3 matrix.
Definition: fwd.hpp:537
\n
glm::mediump_f32mat3x2
mat< 3, 2, f32, mediump > mediump_f32mat3x2
Medium single-qualifier floating-point 3x2 matrix.
Definition: fwd.hpp:683
\n
glm::lowp_f32mat4x2
mat< 4, 2, f32, lowp > lowp_f32mat4x2
Low single-qualifier floating-point 4x2 matrix.
Definition: fwd.hpp:676
\n
glm::lowp_int32
int32 lowp_int32
Low qualifier 32 bit signed integer type.
Definition: fwd.hpp:64
\n
glm::mediump_i64vec4
vec< 4, i64, mediump > mediump_i64vec4
Medium qualifier 64 bit signed integer vector of 4 components type.
Definition: fwd.hpp:290
\n
glm::uint8_t
uint8 uint8_t
Default qualifier 8 bit unsigned integer type.
Definition: fwd.hpp:101
\n
glm::mediump_i8vec1
vec< 1, i8, mediump > mediump_i8vec1
Medium qualifier 8 bit signed integer scalar type.
Definition: fwd.hpp:227
\n
glm::mediump_int32_t
int32 mediump_int32_t
Medium qualifier 32 bit signed integer type.
Definition: fwd.hpp:69
\n
glm::highp_float32_t
float highp_float32_t
High 32 bit single-qualifier floating-point scalar.
Definition: fwd.hpp:159
\n
glm::f32mat3x3
mat< 3, 3, f32, defaultp > f32mat3x3
Single-qualifier floating-point 3x3 matrix.
Definition: fwd.hpp:704
\n
glm::highp_u8
uint8 highp_u8
High qualifier 8 bit unsigned integer type.
Definition: fwd.hpp:91
\n
glm::mediump_uint8
uint8 mediump_uint8
Medium qualifier 8 bit unsigned integer type.
Definition: fwd.hpp:95
\n
glm::highp_fmat4x2
mat< 4, 2, f32, highp > highp_fmat4x2
High single-qualifier floating-point 4x2 matrix.
Definition: fwd.hpp:656
\n
glm::highp_f32vec2
vec< 2, f32, highp > highp_f32vec2
High single-qualifier floating-point vector of 2 components.
Definition: fwd.hpp:457
\n
glm::mediump_int64_t
int64 mediump_int64_t
Medium qualifier 64 bit signed integer type.
Definition: fwd.hpp:83
\n
glm::lowp_u64vec3
vec< 3, u64, lowp > lowp_u64vec3
Low qualifier 64 bit unsigned integer vector of 3 components type.
Definition: fwd.hpp:386
\n
glm::highp_f64mat2x2
mat< 2, 2, f64, highp > highp_f64mat2x2
High double-qualifier floating-point 1x1 matrix.
Definition: fwd.hpp:770
\n
glm::highp_u32vec3
vec< 3, u32, highp > highp_u32vec3
High qualifier 32 bit unsigned integer vector of 3 components type.
Definition: fwd.hpp:376
\n
glm::highp_int8_t
int8 highp_int8_t
High qualifier 8 bit signed integer type.
Definition: fwd.hpp:42
\n
glm::lowp_f32quat
qua< f32, lowp > lowp_f32quat
Low single-qualifier floating-point quaternion.
Definition: fwd.hpp:802
\n
glm::lowp_i32vec4
vec< 4, i32, lowp > lowp_i32vec4
Low qualifier 32 bit signed integer vector of 4 components type.
Definition: fwd.hpp:265
\n
glm::highp_i16vec1
vec< 1, i16, highp > highp_i16vec1
High qualifier 16 bit signed integer scalar type.
Definition: fwd.hpp:252
\n
glm::lowp_fmat4x4
mat< 4, 4, f32, lowp > lowp_fmat4x4
Low single-qualifier floating-point 4x4 matrix.
Definition: fwd.hpp:638
\n
glm::f32mat3x2
mat< 3, 2, f32, defaultp > f32mat3x2
Single-qualifier floating-point 3x2 matrix.
Definition: fwd.hpp:701
\n
glm::lowp_f32mat3x3
mat< 3, 3, f32, lowp > lowp_f32mat3x3
Low single-qualifier floating-point 3x3 matrix.
Definition: fwd.hpp:674
\n
glm::lowp_i8vec2
vec< 2, i8, lowp > lowp_i8vec2
Low qualifier 8 bit signed integer vector of 2 components type.
Definition: fwd.hpp:223
\n
glm::i32vec4
vec< 4, i32, defaultp > i32vec4
32 bit signed integer vector of 4 components type.
Definition: fwd.hpp:280
\n
glm::highp_f32mat2
mat< 2, 2, f32, highp > highp_f32mat2
High single-qualifier floating-point 1x1 matrix.
Definition: fwd.hpp:548
\n
glm::lowp_f32
float lowp_f32
Low 32 bit single-qualifier floating-point scalar.
Definition: fwd.hpp:147
\n
glm::mediump_u16vec4
vec< 4, u16, mediump > mediump_u16vec4
Medium qualifier 16 bit unsigned integer vector of 4 components type.
Definition: fwd.hpp:352
\n
glm::u32vec3
vec< 3, u32, defaultp > u32vec3
Default qualifier 32 bit unsigned integer vector of 3 components type.
Definition: fwd.hpp:381
\n
glm::u8vec2
vec< 2, u8, defaultp > u8vec2
Default qualifier 8 bit unsigned integer vector of 2 components type.
Definition: fwd.hpp:340
\n
glm::mediump_i16
int16 mediump_i16
Medium qualifier 16 bit signed integer type.
Definition: fwd.hpp:46
\n
glm::highp_u64vec2
vec< 2, u64, highp > highp_u64vec2
High qualifier 64 bit unsigned integer vector of 2 components type.
Definition: fwd.hpp:395
\n
glm::i8vec3
vec< 3, i8, defaultp > i8vec3
8 bit signed integer vector of 3 components type.
Definition: fwd.hpp:239
\n
glm::mediump_f32mat2x2
mat< 2, 2, f32, mediump > mediump_f32mat2x2
High single-qualifier floating-point 1x1 matrix.
Definition: fwd.hpp:680
\n
glm::mediump_uint16_t
uint16 mediump_uint16_t
Medium qualifier 16 bit unsigned integer type.
Definition: fwd.hpp:113
\n
glm::mediump_f64mat4x3
mat< 4, 3, f64, mediump > mediump_f64mat4x3
Medium double-qualifier floating-point 4x3 matrix.
Definition: fwd.hpp:767
\n
glm::u8vec3
vec< 3, u8, defaultp > u8vec3
Default qualifier 8 bit unsigned integer vector of 3 components type.
Definition: fwd.hpp:341
\n
glm::highp_f64
double highp_f64
High 64 bit double-qualifier floating-point scalar.
Definition: fwd.hpp:165
\n
glm::mediump_fvec3
vec< 3, float, mediump > mediump_fvec3
Medium Single-qualifier floating-point vector of 3 components.
Definition: fwd.hpp:433
\n
glm::mediump_int64
int64 mediump_int64
Medium qualifier 64 bit signed integer type.
Definition: fwd.hpp:79
\n
glm::mediump_u64vec4
vec< 4, u64, mediump > mediump_u64vec4
Medium qualifier 64 bit unsigned integer vector of 4 components type.
Definition: fwd.hpp:392
\n
glm::uint64_t
uint64 uint64_t
Default qualifier 64 bit unsigned integer type.
Definition: fwd.hpp:143
\n
glm::highp_u32vec2
vec< 2, u32, highp > highp_u32vec2
High qualifier 32 bit unsigned integer vector of 2 components type.
Definition: fwd.hpp:375
\n
glm::highp_fvec1
vec< 1, float, highp > highp_fvec1
High single-qualifier floating-point vector of 1 component.
Definition: fwd.hpp:436
\n
glm::lowp_i64vec4
vec< 4, i64, lowp > lowp_i64vec4
Low qualifier 64 bit signed integer vector of 4 components type.
Definition: fwd.hpp:285
\n
glm::i32vec3
vec< 3, i32, defaultp > i32vec3
32 bit signed integer vector of 3 components type.
Definition: fwd.hpp:279
\n
glm::highp_f32mat2x4
mat< 2, 4, f32, highp > highp_f32mat2x4
High single-qualifier floating-point 2x4 matrix.
Definition: fwd.hpp:692
\n
glm::lowp_i8vec1
vec< 1, i8, lowp > lowp_i8vec1
Low qualifier 8 bit signed integer scalar type.
Definition: fwd.hpp:222
\n
glm::highp_f64mat2
mat< 2, 2, f64, highp > highp_f64mat2
High double-qualifier floating-point 1x1 matrix.
Definition: fwd.hpp:580
\n
glm::lowp_uint16_t
uint16 lowp_uint16_t
Low qualifier 16 bit unsigned integer type.
Definition: fwd.hpp:112
\n
glm::highp_f64mat3x2
mat< 3, 2, f64, highp > highp_f64mat3x2
High double-qualifier floating-point 3x2 matrix.
Definition: fwd.hpp:773
\n
glm::mediump_u32vec3
vec< 3, u32, mediump > mediump_u32vec3
Medium qualifier 32 bit unsigned integer vector of 3 components type.
Definition: fwd.hpp:371
\n
glm::lowp_uint16
uint16 lowp_uint16
Low qualifier 16 bit unsigned integer type.
Definition: fwd.hpp:108
\n
glm::highp_u8vec3
vec< 3, u8, highp > highp_u8vec3
High qualifier 8 bit unsigned integer vector of 3 components type.
Definition: fwd.hpp:336
\n
glm::f64vec4
vec< 4, f64, defaultp > f64vec4
Double-qualifier floating-point vector of 4 components.
Definition: fwd.hpp:504
\n
glm::highp_i8vec2
vec< 2, i8, highp > highp_i8vec2
High qualifier 8 bit signed integer vector of 2 components type.
Definition: fwd.hpp:233
\n
glm::lowp_i32vec3
vec< 3, i32, lowp > lowp_i32vec3
Low qualifier 32 bit signed integer vector of 3 components type.
Definition: fwd.hpp:264
\n
glm::lowp_i32
int32 lowp_i32
Low qualifier 32 bit signed integer type.
Definition: fwd.hpp:59
\n
glm::mediump_fmat4x4
mat< 4, 4, f32, mediump > mediump_fmat4x4
Medium single-qualifier floating-point 4x4 matrix.
Definition: fwd.hpp:648
\n
glm::mediump_i64
int64 mediump_i64
Medium qualifier 64 bit signed integer type.
Definition: fwd.hpp:74
\n
glm::lowp_i16vec4
vec< 4, i16, lowp > lowp_i16vec4
Low qualifier 16 bit signed integer vector of 4 components type.
Definition: fwd.hpp:245
\n
glm::highp_f64mat4x3
mat< 4, 3, f64, highp > highp_f64mat4x3
High double-qualifier floating-point 4x3 matrix.
Definition: fwd.hpp:777
\n
glm::highp_u8vec2
vec< 2, u8, highp > highp_u8vec2
High qualifier 8 bit unsigned integer vector of 2 components type.
Definition: fwd.hpp:335
\n
glm::highp_i8vec3
vec< 3, i8, highp > highp_i8vec3
High qualifier 8 bit signed integer vector of 3 components type.
Definition: fwd.hpp:234
\n
glm::highp_f64vec3
vec< 3, f64, highp > highp_f64vec3
High double-qualifier floating-point vector of 3 components.
Definition: fwd.hpp:498
\n
glm::fvec2
vec< 2, f32, defaultp > fvec2
Single-qualifier floating-point vector of 2 components.
Definition: fwd.hpp:442
\n
glm::lowp_f64vec4
vec< 4, f64, lowp > lowp_f64vec4
Low double-qualifier floating-point vector of 4 components.
Definition: fwd.hpp:489
\n
glm::mediump_f32vec3
vec< 3, f32, mediump > mediump_f32vec3
Medium single-qualifier floating-point vector of 3 components.
Definition: fwd.hpp:453
\n
glm::lowp_f64
double lowp_f64
Low 64 bit double-qualifier floating-point scalar.
Definition: fwd.hpp:163
\n
glm::lowp_fmat4x2
mat< 4, 2, f32, lowp > lowp_fmat4x2
Low single-qualifier floating-point 4x2 matrix.
Definition: fwd.hpp:636
\n
glm::highp_f64mat2x4
mat< 2, 4, f64, highp > highp_f64mat2x4
High double-qualifier floating-point 2x4 matrix.
Definition: fwd.hpp:772
\n
glm::highp_f64mat4
mat< 4, 4, f64, highp > highp_f64mat4
High double-qualifier floating-point 4x4 matrix.
Definition: fwd.hpp:582
\n
glm::mediump_i32vec4
vec< 4, i32, mediump > mediump_i32vec4
Medium qualifier 32 bit signed integer vector of 4 components type.
Definition: fwd.hpp:270
\n
glm::lowp_f32mat2
mat< 2, 2, f32, lowp > lowp_f32mat2
Low single-qualifier floating-point 1x1 matrix.
Definition: fwd.hpp:540
\n
glm::int16_t
int16 int16_t
16 bit signed integer type.
Definition: fwd.hpp:57
\n
glm::highp_i64
int64 highp_i64
High qualifier 64 bit signed integer type.
Definition: fwd.hpp:75
\n
glm::highp_f64mat3x4
mat< 3, 4, f64, highp > highp_f64mat3x4
High double-qualifier floating-point 3x4 matrix.
Definition: fwd.hpp:775
\n
glm::highp_fmat3
mat< 3, 3, f32, highp > highp_fmat3
High single-qualifier floating-point 3x3 matrix.
Definition: fwd.hpp:533
\n
glm::mediump_f32mat3x3
mat< 3, 3, f32, mediump > mediump_f32mat3x3
Medium single-qualifier floating-point 3x3 matrix.
Definition: fwd.hpp:684
\n
glm::mediump_f64quat
qua< f64, mediump > mediump_f64quat
Medium double-qualifier floating-point quaternion.
Definition: fwd.hpp:813
\n
glm::int32_t
int32 int32_t
32 bit signed integer type.
Definition: fwd.hpp:71
\n
glm::f64vec2
vec< 2, f64, defaultp > f64vec2
Double-qualifier floating-point vector of 2 components.
Definition: fwd.hpp:502
\n
glm::lowp_uint64_t
uint64 lowp_uint64_t
Low qualifier 64 bit unsigned integer type.
Definition: fwd.hpp:140
\n
glm::uint64
detail::uint64 uint64
64 bit unsigned integer type.
Definition: scalar_uint_sized.hpp:67
\n
glm::highp_int16
int16 highp_int16
High qualifier 16 bit signed integer type.
Definition: fwd.hpp:52
\n
glm::mediump_i16vec1
vec< 1, i16, mediump > mediump_i16vec1
Medium qualifier 16 bit signed integer scalar type.
Definition: fwd.hpp:247
\n
glm::fmat2x4
mat< 2, 4, f32, defaultp > fmat2x4
Single-qualifier floating-point 2x4 matrix.
Definition: fwd.hpp:666
\n
glm::highp_fmat2x2
mat< 2, 2, f32, highp > highp_fmat2x2
High single-qualifier floating-point 1x1 matrix.
Definition: fwd.hpp:650
\n
glm::highp_fvec4
vec< 4, float, highp > highp_fvec4
High Single-qualifier floating-point vector of 4 components.
Definition: fwd.hpp:439
\n
glm::highp_f64mat3x3
mat< 3, 3, f64, highp > highp_f64mat3x3
High double-qualifier floating-point 3x3 matrix.
Definition: fwd.hpp:774
\n
glm::mediump_i32
int32 mediump_i32
Medium qualifier 32 bit signed integer type.
Definition: fwd.hpp:60
\n
glm::lowp_u16vec2
vec< 2, u16, lowp > lowp_u16vec2
Low qualifier 16 bit unsigned integer vector of 2 components type.
Definition: fwd.hpp:345
\n
glm::highp_u32vec4
vec< 4, u32, highp > highp_u32vec4
High qualifier 32 bit unsigned integer vector of 4 components type.
Definition: fwd.hpp:377
\n
glm::lowp_float32_t
float lowp_float32_t
Low 32 bit single-qualifier floating-point scalar.
Definition: fwd.hpp:157
\n
glm::highp_uint64_t
uint64 highp_uint64_t
High qualifier 64 bit unsigned integer type.
Definition: fwd.hpp:142
\n
glm::lowp_f32vec2
vec< 2, f32, lowp > lowp_f32vec2
Low single-qualifier floating-point vector of 2 components.
Definition: fwd.hpp:447
\n
glm::u32vec4
vec< 4, u32, defaultp > u32vec4
Default qualifier 32 bit unsigned integer vector of 4 components type.
Definition: fwd.hpp:382
\n
glm::mediump_f64mat2
mat< 2, 2, f64, mediump > mediump_f64mat2
Medium double-qualifier floating-point 1x1 matrix.
Definition: fwd.hpp:576
\n
glm::highp_f32mat4x3
mat< 4, 3, f32, highp > highp_f32mat4x3
High single-qualifier floating-point 4x3 matrix.
Definition: fwd.hpp:697
\n
glm::f32quat
qua< f32, defaultp > f32quat
Single-qualifier floating-point quaternion.
Definition: fwd.hpp:805
\n
glm::int64
detail::int64 int64
64 bit signed integer type.
Definition: scalar_int_sized.hpp:67
\n
glm::highp_u64vec1
vec< 1, u64, highp > highp_u64vec1
High qualifier 64 bit unsigned integer scalar type.
Definition: fwd.hpp:394
\n
glm::highp_f64mat2x3
mat< 2, 3, f64, highp > highp_f64mat2x3
High double-qualifier floating-point 2x3 matrix.
Definition: fwd.hpp:771
\n
glm::lowp_i8vec4
vec< 4, i8, lowp > lowp_i8vec4
Low qualifier 8 bit signed integer vector of 4 components type.
Definition: fwd.hpp:225
\n
glm::lowp_fmat4x3
mat< 4, 3, f32, lowp > lowp_fmat4x3
Low single-qualifier floating-point 4x3 matrix.
Definition: fwd.hpp:637
\n
glm::f32
float f32
Default 32 bit single-qualifier floating-point scalar.
Definition: fwd.hpp:150
\n
glm::highp_i32vec2
vec< 2, i32, highp > highp_i32vec2
High qualifier 32 bit signed integer vector of 2 components type.
Definition: fwd.hpp:273
\n
glm::mediump_u8vec1
vec< 1, u8, mediump > mediump_u8vec1
Medium qualifier 8 bit unsigned integer scalar type.
Definition: fwd.hpp:329
\n
glm::highp_fmat4x3
mat< 4, 3, f32, highp > highp_fmat4x3
High single-qualifier floating-point 4x3 matrix.
Definition: fwd.hpp:657
\n
glm::mediump_i16vec4
vec< 4, i16, mediump > mediump_i16vec4
Medium qualifier 16 bit signed integer vector of 4 components type.
Definition: fwd.hpp:250
\n
glm::f64mat4x2
mat< 4, 2, f64, defaultp > f64mat4x2
Double-qualifier floating-point 4x2 matrix.
Definition: fwd.hpp:782
\n
glm::fmat2x3
mat< 2, 3, f32, defaultp > fmat2x3
Single-qualifier floating-point 2x3 matrix.
Definition: fwd.hpp:663
\n
glm::mediump_f64mat4
mat< 4, 4, f64, mediump > mediump_f64mat4
Medium double-qualifier floating-point 4x4 matrix.
Definition: fwd.hpp:578
\n
glm::mediump_u8vec4
vec< 4, u8, mediump > mediump_u8vec4
Medium qualifier 8 bit unsigned integer vector of 4 components type.
Definition: fwd.hpp:332
\n
glm::lowp_f32mat3x4
mat< 3, 4, f32, lowp > lowp_f32mat3x4
Low single-qualifier floating-point 3x4 matrix.
Definition: fwd.hpp:675
\n
glm::mediump_float64_t
double mediump_float64_t
Medium 64 bit double-qualifier floating-point scalar.
Definition: fwd.hpp:174
\n
glm::highp_fvec2
vec< 2, float, highp > highp_fvec2
High Single-qualifier floating-point vector of 2 components.
Definition: fwd.hpp:437
\n
glm::u16
uint16 u16
Default qualifier 16 bit unsigned integer type.
Definition: fwd.hpp:106
\n
glm::lowp_i64
int64 lowp_i64
Low qualifier 64 bit signed integer type.
Definition: fwd.hpp:73
\n
glm::f32mat4
mat< 4, 4, f32, defaultp > f32mat4
Single-qualifier floating-point 4x4 matrix.
Definition: fwd.hpp:554
\n
glm::mediump_fmat4x2
mat< 4, 2, f32, mediump > mediump_fmat4x2
Medium single-qualifier floating-point 4x2 matrix.
Definition: fwd.hpp:646
\n
glm::lowp_f64mat2
mat< 2, 2, f64, lowp > lowp_f64mat2
Low double-qualifier floating-point 1x1 matrix.
Definition: fwd.hpp:572
\n
glm::mediump_int8_t
int8 mediump_int8_t
Medium qualifier 8 bit signed integer type.
Definition: fwd.hpp:41
\n
glm::lowp_fmat3x3
mat< 3, 3, f32, lowp > lowp_fmat3x3
Low single-qualifier floating-point 3x3 matrix.
Definition: fwd.hpp:634
\n
glm::lowp_float64_t
double lowp_float64_t
Low 64 bit double-qualifier floating-point scalar.
Definition: fwd.hpp:173
\n
glm::highp_int16_t
int16 highp_int16_t
High qualifier 16 bit signed integer type.
Definition: fwd.hpp:56
\n
glm::highp_fmat3x3
mat< 3, 3, f32, highp > highp_fmat3x3
High single-qualifier floating-point 3x3 matrix.
Definition: fwd.hpp:654
\n
glm::i64vec1
vec< 1, i64, defaultp > i64vec1
64 bit signed integer scalar type.
Definition: fwd.hpp:297
\n
glm::lowp_u32
uint32 lowp_u32
Low qualifier 32 bit unsigned integer type.
Definition: fwd.hpp:117
\n
glm::lowp_u8vec1
vec< 1, u8, lowp > lowp_u8vec1
Low qualifier 8 bit unsigned integer scalar type.
Definition: fwd.hpp:324
\n
glm::mediump_i64vec3
vec< 3, i64, mediump > mediump_i64vec3
Medium qualifier 64 bit signed integer vector of 3 components type.
Definition: fwd.hpp:289
\n
glm::highp_f32quat
qua< f32, highp > highp_f32quat
High single-qualifier floating-point quaternion.
Definition: fwd.hpp:804
\n
glm::highp_u16
uint16 highp_u16
High qualifier 16 bit unsigned integer type.
Definition: fwd.hpp:105
\n
glm::fvec1
vec< 1, f32, defaultp > fvec1
Single-qualifier floating-point vector of 1 component.
Definition: fwd.hpp:441
\n
glm::mediump_u8vec2
vec< 2, u8, mediump > mediump_u8vec2
Medium qualifier 8 bit unsigned integer vector of 2 components type.
Definition: fwd.hpp:330
\n
glm::lowp_int32_t
int32 lowp_int32_t
Low qualifier 32 bit signed integer type.
Definition: fwd.hpp:68
\n
glm::lowp_u16vec1
vec< 1, u16, lowp > lowp_u16vec1
Low qualifier 16 bit unsigned integer scalar type.
Definition: fwd.hpp:344
\n
glm::highp_fmat4x4
mat< 4, 4, f32, highp > highp_fmat4x4
High single-qualifier floating-point 4x4 matrix.
Definition: fwd.hpp:658
\n
glm::highp_f32mat3x4
mat< 3, 4, f32, highp > highp_f32mat3x4
High single-qualifier floating-point 3x4 matrix.
Definition: fwd.hpp:695
\n
glm::f32vec2
vec< 2, f32, defaultp > f32vec2
Single-qualifier floating-point vector of 2 components.
Definition: fwd.hpp:462
\n
glm::highp_u16vec3
vec< 3, u16, highp > highp_u16vec3
High qualifier 16 bit unsigned integer vector of 3 components type.
Definition: fwd.hpp:356
\n
glm::mediump_float32_t
float mediump_float32_t
Medium 32 bit single-qualifier floating-point scalar.
Definition: fwd.hpp:158
\n
glm::fmat2x2
mat< 2, 2, f32, defaultp > fmat2x2
Single-qualifier floating-point 1x1 matrix.
Definition: fwd.hpp:660
\n
glm::mediump_f32
float mediump_f32
Medium 32 bit single-qualifier floating-point scalar.
Definition: fwd.hpp:148
\n
glm::mediump_f32mat4x4
mat< 4, 4, f32, mediump > mediump_f32mat4x4
Medium single-qualifier floating-point 4x4 matrix.
Definition: fwd.hpp:688
\n
glm::mediump_f32vec2
vec< 2, f32, mediump > mediump_f32vec2
Medium single-qualifier floating-point vector of 2 components.
Definition: fwd.hpp:452
\n
glm::lowp_int8
int8 lowp_int8
Low qualifier 8 bit signed integer type.
Definition: fwd.hpp:36
\n
glm::lowp_f64vec1
vec< 1, f64, lowp > lowp_f64vec1
Low double-qualifier floating-point vector of 1 component.
Definition: fwd.hpp:486
\n
glm::highp_f32mat3x2
mat< 3, 2, f32, highp > highp_f32mat3x2
High single-qualifier floating-point 3x2 matrix.
Definition: fwd.hpp:693
\n
glm::mediump_f64mat3x2
mat< 3, 2, f64, mediump > mediump_f64mat3x2
Medium double-qualifier floating-point 3x2 matrix.
Definition: fwd.hpp:763
\n
glm::mediump_u8vec3
vec< 3, u8, mediump > mediump_u8vec3
Medium qualifier 8 bit unsigned integer vector of 3 components type.
Definition: fwd.hpp:331
\n
glm::lowp_f64mat4x4
mat< 4, 4, f64, lowp > lowp_f64mat4x4
Low double-qualifier floating-point 4x4 matrix.
Definition: fwd.hpp:758
\n
glm::lowp_i16vec1
vec< 1, i16, lowp > lowp_i16vec1
Low qualifier 16 bit signed integer scalar type.
Definition: fwd.hpp:242
\n
glm::lowp_int8_t
int8 lowp_int8_t
Low qualifier 8 bit signed integer type.
Definition: fwd.hpp:40
\n
glm::lowp_u32vec2
vec< 2, u32, lowp > lowp_u32vec2
Low qualifier 32 bit unsigned integer vector of 2 components type.
Definition: fwd.hpp:365
\n
glm::mediump_f32mat2x4
mat< 2, 4, f32, mediump > mediump_f32mat2x4
Medium single-qualifier floating-point 2x4 matrix.
Definition: fwd.hpp:682
\n
glm::f64mat4x3
mat< 4, 3, f64, defaultp > f64mat4x3
Double-qualifier floating-point 4x3 matrix.
Definition: fwd.hpp:785
\n
glm::highp_i64vec2
vec< 2, i64, highp > highp_i64vec2
High qualifier 64 bit signed integer vector of 2 components type.
Definition: fwd.hpp:293
\n
glm::mediump_f32mat4
mat< 4, 4, f32, mediump > mediump_f32mat4
Medium single-qualifier floating-point 4x4 matrix.
Definition: fwd.hpp:546
\n
glm::i64
int64 i64
64 bit signed integer type.
Definition: fwd.hpp:76
\n
glm::f64
double f64
Default 64 bit double-qualifier floating-point scalar.
Definition: fwd.hpp:166
\n
glm::mediump_f32vec1
vec< 1, f32, mediump > mediump_f32vec1
Medium single-qualifier floating-point vector of 1 component.
Definition: fwd.hpp:451
\n
glm::mediump_f32mat3x4
mat< 3, 4, f32, mediump > mediump_f32mat3x4
Medium single-qualifier floating-point 3x4 matrix.
Definition: fwd.hpp:685
\n
glm::highp_fmat2
mat< 2, 2, f32, highp > highp_fmat2
High single-qualifier floating-point 1x1 matrix.
Definition: fwd.hpp:532
\n
glm::highp_f32vec3
vec< 3, f32, highp > highp_f32vec3
High single-qualifier floating-point vector of 3 components.
Definition: fwd.hpp:458
\n
glm::mediump_i8vec4
vec< 4, i8, mediump > mediump_i8vec4
Medium qualifier 8 bit signed integer vector of 4 components type.
Definition: fwd.hpp:230
\n
glm::lowp_float32
float lowp_float32
Low 32 bit single-qualifier floating-point scalar.
Definition: fwd.hpp:152
\n
glm::u32vec2
vec< 2, u32, defaultp > u32vec2
Default qualifier 32 bit unsigned integer vector of 2 components type.
Definition: fwd.hpp:380
\n
glm::mediump_fvec4
vec< 4, float, mediump > mediump_fvec4
Medium Single-qualifier floating-point vector of 4 components.
Definition: fwd.hpp:434
\n
glm::mediump_int32
int32 mediump_int32
Medium qualifier 32 bit signed integer type.
Definition: fwd.hpp:65
\n
glm::i64vec2
vec< 2, i64, defaultp > i64vec2
64 bit signed integer vector of 2 components type.
Definition: fwd.hpp:298
\n
glm::i16
int16 i16
16 bit signed integer type.
Definition: fwd.hpp:48
\n
glm::fmat4x4
mat< 4, 4, f32, defaultp > fmat4x4
Single-qualifier floating-point 4x4 matrix.
Definition: fwd.hpp:668
\n
glm::lowp_f64quat
qua< f64, lowp > lowp_f64quat
Low double-qualifier floating-point quaternion.
Definition: fwd.hpp:812
\n
glm::fmat3x2
mat< 3, 2, f32, defaultp > fmat3x2
Single-qualifier floating-point 3x2 matrix.
Definition: fwd.hpp:661
\n
glm::u16vec4
vec< 4, u16, defaultp > u16vec4
Default qualifier 16 bit unsigned integer vector of 4 components type.
Definition: fwd.hpp:362
\n
glm::u16vec2
vec< 2, u16, defaultp > u16vec2
Default qualifier 16 bit unsigned integer vector of 2 components type.
Definition: fwd.hpp:360
\n
glm::mediump_u8
uint8 mediump_u8
Medium qualifier 8 bit unsigned integer type.
Definition: fwd.hpp:90
\n
glm::lowp_fmat2x2
mat< 2, 2, f32, lowp > lowp_fmat2x2
Low single-qualifier floating-point 1x1 matrix.
Definition: fwd.hpp:630
\n
glm::highp_i8vec4
vec< 4, i8, highp > highp_i8vec4
High qualifier 8 bit signed integer vector of 4 components type.
Definition: fwd.hpp:235
\n
glm::lowp_u64vec4
vec< 4, u64, lowp > lowp_u64vec4
Low qualifier 64 bit unsigned integer vector of 4 components type.
Definition: fwd.hpp:387
\n
glm::mediump_i64vec2
vec< 2, i64, mediump > mediump_i64vec2
Medium qualifier 64 bit signed integer vector of 2 components type.
Definition: fwd.hpp:288
\n
glm::highp_f64mat4x2
mat< 4, 2, f64, highp > highp_f64mat4x2
High double-qualifier floating-point 4x2 matrix.
Definition: fwd.hpp:776
\n
glm::mediump_int16_t
int16 mediump_int16_t
Medium qualifier 16 bit signed integer type.
Definition: fwd.hpp:55
\n
glm::lowp_i8
int8 lowp_i8
Low qualifier 8 bit signed integer type.
Definition: fwd.hpp:31
\n
glm::i64vec3
vec< 3, i64, defaultp > i64vec3
64 bit signed integer vector of 3 components type.
Definition: fwd.hpp:299
\n
glm::lowp_i32vec2
vec< 2, i32, lowp > lowp_i32vec2
Low qualifier 32 bit signed integer vector of 2 components type.
Definition: fwd.hpp:263
\n
glm::highp_f64quat
qua< f64, highp > highp_f64quat
High double-qualifier floating-point quaternion.
Definition: fwd.hpp:814
\n
glm::mediump_f64vec2
vec< 2, f64, mediump > mediump_f64vec2
Medium double-qualifier floating-point vector of 2 components.
Definition: fwd.hpp:492
\n
glm::highp_uint16_t
uint16 highp_uint16_t
High qualifier 16 bit unsigned integer type.
Definition: fwd.hpp:114
\n
glm::lowp_fvec1
vec< 1, float, lowp > lowp_fvec1
Low single-qualifier floating-point vector of 1 component.
Definition: fwd.hpp:426
\n
glm::i8
int8 i8
8 bit signed integer type.
Definition: fwd.hpp:34
\n
glm::mediump_uint64_t
uint64 mediump_uint64_t
Medium qualifier 64 bit unsigned integer type.
Definition: fwd.hpp:141
\n
glm::mediump_u64vec1
vec< 1, u64, mediump > mediump_u64vec1
Medium qualifier 64 bit unsigned integer scalar type.
Definition: fwd.hpp:389
\n
glm::mediump_f32mat2
mat< 2, 2, f32, mediump > mediump_f32mat2
Medium single-qualifier floating-point 1x1 matrix.
Definition: fwd.hpp:544
\n
glm::mediump_uint8_t
uint8 mediump_uint8_t
Medium qualifier 8 bit unsigned integer type.
Definition: fwd.hpp:99
\n
glm
Definition: common.hpp:20
\n
glm::mediump_f64
double mediump_f64
Medium 64 bit double-qualifier floating-point scalar.
Definition: fwd.hpp:164
\n
glm::mediump_fvec1
vec< 1, float, mediump > mediump_fvec1
Medium single-qualifier floating-point vector of 1 component.
Definition: fwd.hpp:431
\n
glm::mediump_uint16
uint16 mediump_uint16
Medium qualifier 16 bit unsigned integer type.
Definition: fwd.hpp:109
\n
glm::highp_u8vec4
vec< 4, u8, highp > highp_u8vec4
High qualifier 8 bit unsigned integer vector of 4 components type.
Definition: fwd.hpp:337
\n
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\nFunctions
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type_ptr.hpp File Reference
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GLM_GTC_type_ptr \nMore...

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\nFunctions

template<typename T >
GLM_FUNC_DECL mat< 2, 2, T, defaultp > make_mat2 (T const *const ptr)
 Build a matrix from a pointer. More...
 
template<typename T >
GLM_FUNC_DECL mat< 2, 2, T, defaultp > make_mat2x2 (T const *const ptr)
 Build a matrix from a pointer. More...
 
template<typename T >
GLM_FUNC_DECL mat< 2, 3, T, defaultp > make_mat2x3 (T const *const ptr)
 Build a matrix from a pointer. More...
 
template<typename T >
GLM_FUNC_DECL mat< 2, 4, T, defaultp > make_mat2x4 (T const *const ptr)
 Build a matrix from a pointer. More...
 
template<typename T >
GLM_FUNC_DECL mat< 3, 3, T, defaultp > make_mat3 (T const *const ptr)
 Build a matrix from a pointer. More...
 
template<typename T >
GLM_FUNC_DECL mat< 3, 2, T, defaultp > make_mat3x2 (T const *const ptr)
 Build a matrix from a pointer. More...
 
template<typename T >
GLM_FUNC_DECL mat< 3, 3, T, defaultp > make_mat3x3 (T const *const ptr)
 Build a matrix from a pointer. More...
 
template<typename T >
GLM_FUNC_DECL mat< 3, 4, T, defaultp > make_mat3x4 (T const *const ptr)
 Build a matrix from a pointer. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > make_mat4 (T const *const ptr)
 Build a matrix from a pointer. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 2, T, defaultp > make_mat4x2 (T const *const ptr)
 Build a matrix from a pointer. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 3, T, defaultp > make_mat4x3 (T const *const ptr)
 Build a matrix from a pointer. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > make_mat4x4 (T const *const ptr)
 Build a matrix from a pointer. More...
 
template<typename T >
GLM_FUNC_DECL qua< T, defaultp > make_quat (T const *const ptr)
 Build a quaternion from a pointer. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 1, T, Q > make_vec1 (vec< 1, T, Q > const &v)
 Build a vector from a pointer. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 1, T, Q > make_vec1 (vec< 2, T, Q > const &v)
 Build a vector from a pointer. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 1, T, Q > make_vec1 (vec< 3, T, Q > const &v)
 Build a vector from a pointer. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 1, T, Q > make_vec1 (vec< 4, T, Q > const &v)
 Build a vector from a pointer. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 2, T, Q > make_vec2 (vec< 1, T, Q > const &v)
 Build a vector from a pointer. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 2, T, Q > make_vec2 (vec< 2, T, Q > const &v)
 Build a vector from a pointer. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 2, T, Q > make_vec2 (vec< 3, T, Q > const &v)
 Build a vector from a pointer. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 2, T, Q > make_vec2 (vec< 4, T, Q > const &v)
 Build a vector from a pointer. More...
 
template<typename T >
GLM_FUNC_DECL vec< 2, T, defaultp > make_vec2 (T const *const ptr)
 Build a vector from a pointer. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > make_vec3 (vec< 1, T, Q > const &v)
 Build a vector from a pointer. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > make_vec3 (vec< 2, T, Q > const &v)
 Build a vector from a pointer. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > make_vec3 (vec< 3, T, Q > const &v)
 Build a vector from a pointer. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > make_vec3 (vec< 4, T, Q > const &v)
 Build a vector from a pointer. More...
 
template<typename T >
GLM_FUNC_DECL vec< 3, T, defaultp > make_vec3 (T const *const ptr)
 Build a vector from a pointer. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 4, T, Q > make_vec4 (vec< 1, T, Q > const &v)
 Build a vector from a pointer. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 4, T, Q > make_vec4 (vec< 2, T, Q > const &v)
 Build a vector from a pointer. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 4, T, Q > make_vec4 (vec< 3, T, Q > const &v)
 Build a vector from a pointer. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 4, T, Q > make_vec4 (vec< 4, T, Q > const &v)
 Build a vector from a pointer. More...
 
template<typename T >
GLM_FUNC_DECL vec< 4, T, defaultp > make_vec4 (T const *const ptr)
 Build a vector from a pointer. More...
 
template<typename genType >
GLM_FUNC_DECL genType::value_type const * value_ptr (genType const &v)
 Return the constant address to the data of the input parameter. More...
 
\n

Detailed Description

\n

GLM_GTC_type_ptr

\n
See also
Core features (dependence)
\n
\n to use the features of this extension. \">GLM_GTC_quaternion (dependence)
\n\n

Definition in file type_ptr.hpp.

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type_ptr.hpp
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1 
\n
34 #pragma once
\n
35 
\n
36 // Dependency:
\n
37 #include "../gtc/quaternion.hpp"
\n
38 #include "../gtc/vec1.hpp"
\n
39 #include "../vec2.hpp"
\n
40 #include "../vec3.hpp"
\n
41 #include "../vec4.hpp"
\n
42 #include "../mat2x2.hpp"
\n
43 #include "../mat2x3.hpp"
\n
44 #include "../mat2x4.hpp"
\n
45 #include "../mat3x2.hpp"
\n
46 #include "../mat3x3.hpp"
\n
47 #include "../mat3x4.hpp"
\n
48 #include "../mat4x2.hpp"
\n
49 #include "../mat4x3.hpp"
\n
50 #include "../mat4x4.hpp"
\n
51 #include <cstring>
\n
52 
\n
53 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
54 # pragma message("GLM: GLM_GTC_type_ptr extension included")
\n
55 #endif
\n
56 
\n
57 namespace glm
\n
58 {
\n
61 
\n
64  template<typename genType>
\n
65  GLM_FUNC_DECL typename genType::value_type const * value_ptr(genType const& v);
\n
66 
\n
69  template <typename T, qualifier Q>
\n
70  GLM_FUNC_DECL vec<1, T, Q> make_vec1(vec<1, T, Q> const& v);
\n
71 
\n
74  template <typename T, qualifier Q>
\n
75  GLM_FUNC_DECL vec<1, T, Q> make_vec1(vec<2, T, Q> const& v);
\n
76 
\n
79  template <typename T, qualifier Q>
\n
80  GLM_FUNC_DECL vec<1, T, Q> make_vec1(vec<3, T, Q> const& v);
\n
81 
\n
84  template <typename T, qualifier Q>
\n
85  GLM_FUNC_DECL vec<1, T, Q> make_vec1(vec<4, T, Q> const& v);
\n
86 
\n
89  template <typename T, qualifier Q>
\n
90  GLM_FUNC_DECL vec<2, T, Q> make_vec2(vec<1, T, Q> const& v);
\n
91 
\n
94  template <typename T, qualifier Q>
\n
95  GLM_FUNC_DECL vec<2, T, Q> make_vec2(vec<2, T, Q> const& v);
\n
96 
\n
99  template <typename T, qualifier Q>
\n
100  GLM_FUNC_DECL vec<2, T, Q> make_vec2(vec<3, T, Q> const& v);
\n
101 
\n
104  template <typename T, qualifier Q>
\n
105  GLM_FUNC_DECL vec<2, T, Q> make_vec2(vec<4, T, Q> const& v);
\n
106 
\n
109  template <typename T, qualifier Q>
\n
110  GLM_FUNC_DECL vec<3, T, Q> make_vec3(vec<1, T, Q> const& v);
\n
111 
\n
114  template <typename T, qualifier Q>
\n
115  GLM_FUNC_DECL vec<3, T, Q> make_vec3(vec<2, T, Q> const& v);
\n
116 
\n
119  template <typename T, qualifier Q>
\n
120  GLM_FUNC_DECL vec<3, T, Q> make_vec3(vec<3, T, Q> const& v);
\n
121 
\n
124  template <typename T, qualifier Q>
\n
125  GLM_FUNC_DECL vec<3, T, Q> make_vec3(vec<4, T, Q> const& v);
\n
126 
\n
129  template <typename T, qualifier Q>
\n
130  GLM_FUNC_DECL vec<4, T, Q> make_vec4(vec<1, T, Q> const& v);
\n
131 
\n
134  template <typename T, qualifier Q>
\n
135  GLM_FUNC_DECL vec<4, T, Q> make_vec4(vec<2, T, Q> const& v);
\n
136 
\n
139  template <typename T, qualifier Q>
\n
140  GLM_FUNC_DECL vec<4, T, Q> make_vec4(vec<3, T, Q> const& v);
\n
141 
\n
144  template <typename T, qualifier Q>
\n
145  GLM_FUNC_DECL vec<4, T, Q> make_vec4(vec<4, T, Q> const& v);
\n
146 
\n
149  template<typename T>
\n
150  GLM_FUNC_DECL vec<2, T, defaultp> make_vec2(T const * const ptr);
\n
151 
\n
154  template<typename T>
\n
155  GLM_FUNC_DECL vec<3, T, defaultp> make_vec3(T const * const ptr);
\n
156 
\n
159  template<typename T>
\n
160  GLM_FUNC_DECL vec<4, T, defaultp> make_vec4(T const * const ptr);
\n
161 
\n
164  template<typename T>
\n
165  GLM_FUNC_DECL mat<2, 2, T, defaultp> make_mat2x2(T const * const ptr);
\n
166 
\n
169  template<typename T>
\n
170  GLM_FUNC_DECL mat<2, 3, T, defaultp> make_mat2x3(T const * const ptr);
\n
171 
\n
174  template<typename T>
\n
175  GLM_FUNC_DECL mat<2, 4, T, defaultp> make_mat2x4(T const * const ptr);
\n
176 
\n
179  template<typename T>
\n
180  GLM_FUNC_DECL mat<3, 2, T, defaultp> make_mat3x2(T const * const ptr);
\n
181 
\n
184  template<typename T>
\n
185  GLM_FUNC_DECL mat<3, 3, T, defaultp> make_mat3x3(T const * const ptr);
\n
186 
\n
189  template<typename T>
\n
190  GLM_FUNC_DECL mat<3, 4, T, defaultp> make_mat3x4(T const * const ptr);
\n
191 
\n
194  template<typename T>
\n
195  GLM_FUNC_DECL mat<4, 2, T, defaultp> make_mat4x2(T const * const ptr);
\n
196 
\n
199  template<typename T>
\n
200  GLM_FUNC_DECL mat<4, 3, T, defaultp> make_mat4x3(T const * const ptr);
\n
201 
\n
204  template<typename T>
\n
205  GLM_FUNC_DECL mat<4, 4, T, defaultp> make_mat4x4(T const * const ptr);
\n
206 
\n
209  template<typename T>
\n
210  GLM_FUNC_DECL mat<2, 2, T, defaultp> make_mat2(T const * const ptr);
\n
211 
\n
214  template<typename T>
\n
215  GLM_FUNC_DECL mat<3, 3, T, defaultp> make_mat3(T const * const ptr);
\n
216 
\n
219  template<typename T>
\n
220  GLM_FUNC_DECL mat<4, 4, T, defaultp> make_mat4(T const * const ptr);
\n
221 
\n
224  template<typename T>
\n
225  GLM_FUNC_DECL qua<T, defaultp> make_quat(T const * const ptr);
\n
226 
\n
228 }//namespace glm
\n
229 
\n
230 #include "type_ptr.inl"
\n
glm::make_mat3
GLM_FUNC_DECL mat< 3, 3, T, defaultp > make_mat3(T const *const ptr)
Build a matrix from a pointer.
\n
glm::make_vec3
GLM_FUNC_DECL vec< 3, T, defaultp > make_vec3(T const *const ptr)
Build a vector from a pointer.
\n
glm::make_mat3x2
GLM_FUNC_DECL mat< 3, 2, T, defaultp > make_mat3x2(T const *const ptr)
Build a matrix from a pointer.
\n
glm::make_vec1
GLM_FUNC_DECL vec< 1, T, Q > make_vec1(vec< 4, T, Q > const &v)
Build a vector from a pointer.
\n
glm::make_quat
GLM_FUNC_DECL qua< T, defaultp > make_quat(T const *const ptr)
Build a quaternion from a pointer.
\n
glm::make_mat4
GLM_FUNC_DECL mat< 4, 4, T, defaultp > make_mat4(T const *const ptr)
Build a matrix from a pointer.
\n
glm::make_vec2
GLM_FUNC_DECL vec< 2, T, defaultp > make_vec2(T const *const ptr)
Build a vector from a pointer.
\n
glm::make_mat2x4
GLM_FUNC_DECL mat< 2, 4, T, defaultp > make_mat2x4(T const *const ptr)
Build a matrix from a pointer.
\n
glm::make_mat2
GLM_FUNC_DECL mat< 2, 2, T, defaultp > make_mat2(T const *const ptr)
Build a matrix from a pointer.
\n
glm::value_ptr
GLM_FUNC_DECL genType::value_type const * value_ptr(genType const &v)
Return the constant address to the data of the input parameter.
\n
glm::make_mat2x2
GLM_FUNC_DECL mat< 2, 2, T, defaultp > make_mat2x2(T const *const ptr)
Build a matrix from a pointer.
\n
glm::make_mat2x3
GLM_FUNC_DECL mat< 2, 3, T, defaultp > make_mat2x3(T const *const ptr)
Build a matrix from a pointer.
\n
glm::make_mat3x4
GLM_FUNC_DECL mat< 3, 4, T, defaultp > make_mat3x4(T const *const ptr)
Build a matrix from a pointer.
\n
glm::make_vec4
GLM_FUNC_DECL vec< 4, T, defaultp > make_vec4(T const *const ptr)
Build a vector from a pointer.
\n
glm::make_mat4x3
GLM_FUNC_DECL mat< 4, 3, T, defaultp > make_mat4x3(T const *const ptr)
Build a matrix from a pointer.
\n
glm::make_mat3x3
GLM_FUNC_DECL mat< 3, 3, T, defaultp > make_mat3x3(T const *const ptr)
Build a matrix from a pointer.
\n
glm::make_mat4x4
GLM_FUNC_DECL mat< 4, 4, T, defaultp > make_mat4x4(T const *const ptr)
Build a matrix from a pointer.
\n
glm::make_mat4x2
GLM_FUNC_DECL mat< 4, 2, T, defaultp > make_mat4x2(T const *const ptr)
Build a matrix from a pointer.
\n
glm
Definition: common.hpp:20
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type_quat.hpp File Reference
\n
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\n\n

Core features \nMore...

\n\n

Go to the source code of this file.

\n

Detailed Description

\n

Core features

\n\n

Definition in file type_quat.hpp.

\n
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type_quat.hpp
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1 
\n
4 #pragma once
\n
5 
\n
6 // Dependency:
\n
7 #include "../detail/type_mat3x3.hpp"
\n
8 #include "../detail/type_mat4x4.hpp"
\n
9 #include "../detail/type_vec3.hpp"
\n
10 #include "../detail/type_vec4.hpp"
\n
11 #include "../ext/vector_relational.hpp"
\n
12 #include "../ext/quaternion_relational.hpp"
\n
13 #include "../gtc/constants.hpp"
\n
14 #include "../gtc/matrix_transform.hpp"
\n
15 
\n
16 namespace glm
\n
17 {
\n
18  template<typename T, qualifier Q>
\n
19  struct qua
\n
20  {
\n
21  // -- Implementation detail --
\n
22 
\n
23  typedef qua<T, Q> type;
\n
24  typedef T value_type;
\n
25 
\n
26  // -- Data --
\n
27 
\n
28 # if GLM_SILENT_WARNINGS == GLM_ENABLE
\n
29 # if GLM_COMPILER & GLM_COMPILER_GCC
\n
30 # pragma GCC diagnostic push
\n
31 # pragma GCC diagnostic ignored "-Wpedantic"
\n
32 # elif GLM_COMPILER & GLM_COMPILER_CLANG
\n
33 # pragma clang diagnostic push
\n
34 # pragma clang diagnostic ignored "-Wgnu-anonymous-struct"
\n
35 # pragma clang diagnostic ignored "-Wnested-anon-types"
\n
36 # elif GLM_COMPILER & GLM_COMPILER_VC
\n
37 # pragma warning(push)
\n
38 # pragma warning(disable: 4201) // nonstandard extension used : nameless struct/union
\n
39 # endif
\n
40 # endif
\n
41 
\n
42 # if GLM_LANG & GLM_LANG_CXXMS_FLAG
\n
43  union
\n
44  {
\n
45  struct { T x, y, z, w;};
\n
46 
\n
47  typename detail::storage<4, T, detail::is_aligned<Q>::value>::type data;
\n
48  };
\n
49 # else
\n
50  T x, y, z, w;
\n
51 # endif
\n
52 
\n
53 # if GLM_SILENT_WARNINGS == GLM_ENABLE
\n
54 # if GLM_COMPILER & GLM_COMPILER_CLANG
\n
55 # pragma clang diagnostic pop
\n
56 # elif GLM_COMPILER & GLM_COMPILER_GCC
\n
57 # pragma GCC diagnostic pop
\n
58 # elif GLM_COMPILER & GLM_COMPILER_VC
\n
59 # pragma warning(pop)
\n
60 # endif
\n
61 # endif
\n
62 
\n
63  // -- Component accesses --
\n
64 
\n
65  typedef length_t length_type;
\n
66 
\n
68  GLM_FUNC_DECL static GLM_CONSTEXPR length_type length(){return 4;}
\n
69 
\n
70  GLM_FUNC_DECL GLM_CONSTEXPR T & operator[](length_type i);
\n
71  GLM_FUNC_DECL GLM_CONSTEXPR T const& operator[](length_type i) const;
\n
72 
\n
73  // -- Implicit basic constructors --
\n
74 
\n
75  GLM_FUNC_DECL GLM_CONSTEXPR qua() GLM_DEFAULT;
\n
76  GLM_FUNC_DECL GLM_CONSTEXPR qua(qua<T, Q> const& q) GLM_DEFAULT;
\n
77  template<qualifier P>
\n
78  GLM_FUNC_DECL GLM_CONSTEXPR qua(qua<T, P> const& q);
\n
79 
\n
80  // -- Explicit basic constructors --
\n
81 
\n
82  GLM_FUNC_DECL GLM_CONSTEXPR qua(T s, vec<3, T, Q> const& v);
\n
83  GLM_FUNC_DECL GLM_CONSTEXPR qua(T w, T x, T y, T z);
\n
84 
\n
85  // -- Conversion constructors --
\n
86 
\n
87  template<typename U, qualifier P>
\n
88  GLM_FUNC_DECL GLM_CONSTEXPR GLM_EXPLICIT qua(qua<U, P> const& q);
\n
89 
\n
91 # if GLM_HAS_EXPLICIT_CONVERSION_OPERATORS
\n
92  GLM_FUNC_DECL explicit operator mat<3, 3, T, Q>() const;
\n
93  GLM_FUNC_DECL explicit operator mat<4, 4, T, Q>() const;
\n
94 # endif
\n
95 
\n
102  GLM_FUNC_DECL qua(vec<3, T, Q> const& u, vec<3, T, Q> const& v);
\n
103 
\n
105  GLM_FUNC_DECL GLM_EXPLICIT qua(vec<3, T, Q> const& eulerAngles);
\n
106  GLM_FUNC_DECL GLM_EXPLICIT qua(mat<3, 3, T, Q> const& q);
\n
107  GLM_FUNC_DECL GLM_EXPLICIT qua(mat<4, 4, T, Q> const& q);
\n
108 
\n
109  // -- Unary arithmetic operators --
\n
110 
\n
111  GLM_FUNC_DECL qua<T, Q>& operator=(qua<T, Q> const& q) GLM_DEFAULT;
\n
112 
\n
113  template<typename U>
\n
114  GLM_FUNC_DECL qua<T, Q>& operator=(qua<U, Q> const& q);
\n
115  template<typename U>
\n
116  GLM_FUNC_DECL qua<T, Q>& operator+=(qua<U, Q> const& q);
\n
117  template<typename U>
\n
118  GLM_FUNC_DECL qua<T, Q>& operator-=(qua<U, Q> const& q);
\n
119  template<typename U>
\n
120  GLM_FUNC_DECL qua<T, Q>& operator*=(qua<U, Q> const& q);
\n
121  template<typename U>
\n
122  GLM_FUNC_DECL qua<T, Q>& operator*=(U s);
\n
123  template<typename U>
\n
124  GLM_FUNC_DECL qua<T, Q>& operator/=(U s);
\n
125  };
\n
126 
\n
127  // -- Unary bit operators --
\n
128 
\n
129  template<typename T, qualifier Q>
\n
130  GLM_FUNC_DECL qua<T, Q> operator+(qua<T, Q> const& q);
\n
131 
\n
132  template<typename T, qualifier Q>
\n
133  GLM_FUNC_DECL qua<T, Q> operator-(qua<T, Q> const& q);
\n
134 
\n
135  // -- Binary operators --
\n
136 
\n
137  template<typename T, qualifier Q>
\n
138  GLM_FUNC_DECL qua<T, Q> operator+(qua<T, Q> const& q, qua<T, Q> const& p);
\n
139 
\n
140  template<typename T, qualifier Q>
\n
141  GLM_FUNC_DECL qua<T, Q> operator-(qua<T, Q> const& q, qua<T, Q> const& p);
\n
142 
\n
143  template<typename T, qualifier Q>
\n
144  GLM_FUNC_DECL qua<T, Q> operator*(qua<T, Q> const& q, qua<T, Q> const& p);
\n
145 
\n
146  template<typename T, qualifier Q>
\n
147  GLM_FUNC_DECL vec<3, T, Q> operator*(qua<T, Q> const& q, vec<3, T, Q> const& v);
\n
148 
\n
149  template<typename T, qualifier Q>
\n
150  GLM_FUNC_DECL vec<3, T, Q> operator*(vec<3, T, Q> const& v, qua<T, Q> const& q);
\n
151 
\n
152  template<typename T, qualifier Q>
\n
153  GLM_FUNC_DECL vec<4, T, Q> operator*(qua<T, Q> const& q, vec<4, T, Q> const& v);
\n
154 
\n
155  template<typename T, qualifier Q>
\n
156  GLM_FUNC_DECL vec<4, T, Q> operator*(vec<4, T, Q> const& v, qua<T, Q> const& q);
\n
157 
\n
158  template<typename T, qualifier Q>
\n
159  GLM_FUNC_DECL qua<T, Q> operator*(qua<T, Q> const& q, T const& s);
\n
160 
\n
161  template<typename T, qualifier Q>
\n
162  GLM_FUNC_DECL qua<T, Q> operator*(T const& s, qua<T, Q> const& q);
\n
163 
\n
164  template<typename T, qualifier Q>
\n
165  GLM_FUNC_DECL qua<T, Q> operator/(qua<T, Q> const& q, T const& s);
\n
166 
\n
167  // -- Boolean operators --
\n
168 
\n
169  template<typename T, qualifier Q>
\n
170  GLM_FUNC_DECL GLM_CONSTEXPR bool operator==(qua<T, Q> const& q1, qua<T, Q> const& q2);
\n
171 
\n
172  template<typename T, qualifier Q>
\n
173  GLM_FUNC_DECL GLM_CONSTEXPR bool operator!=(qua<T, Q> const& q1, qua<T, Q> const& q2);
\n
174 } //namespace glm
\n
175 
\n
176 #ifndef GLM_EXTERNAL_TEMPLATE
\n
177 #include "type_quat.inl"
\n
178 #endif//GLM_EXTERNAL_TEMPLATE
\n
glm::eulerAngles
GLM_FUNC_DECL vec< 3, T, Q > eulerAngles(qua< T, Q > const &x)
Returns euler angles, pitch as x, yaw as y, roll as z.
\n
glm::length
GLM_FUNC_DECL T length(qua< T, Q > const &q)
Returns the norm of a quaternions.
\n
glm
Definition: common.hpp:20
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GLM_GTX_type_trait

\n
See also
Core features (dependence)
\n\n

Definition in file type_trait.hpp.

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type_trait.hpp
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1 
\n
13 #pragma once
\n
14 
\n
15 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
16 # ifndef GLM_ENABLE_EXPERIMENTAL
\n
17 # pragma message("GLM: GLM_GTX_type_trait is an experimental extension and may change in the future. Use #define GLM_ENABLE_EXPERIMENTAL before including it, if you really want to use it.")
\n
18 # else
\n
19 # pragma message("GLM: GLM_GTX_type_trait extension included")
\n
20 # endif
\n
21 #endif
\n
22 
\n
23 // Dependency:
\n
24 #include "../detail/qualifier.hpp"
\n
25 #include "../gtc/quaternion.hpp"
\n
26 #include "../gtx/dual_quaternion.hpp"
\n
27 
\n
28 namespace glm
\n
29 {
\n
32 
\n
33  template<typename T>
\n
34  struct type
\n
35  {
\n
36  static bool const is_vec = false;
\n
37  static bool const is_mat = false;
\n
38  static bool const is_quat = false;
\n
39  static length_t const components = 0;
\n
40  static length_t const cols = 0;
\n
41  static length_t const rows = 0;
\n
42  };
\n
43 
\n
44  template<length_t L, typename T, qualifier Q>
\n
45  struct type<vec<L, T, Q> >
\n
46  {
\n
47  static bool const is_vec = true;
\n
48  static bool const is_mat = false;
\n
49  static bool const is_quat = false;
\n
50  static length_t const components = L;
\n
51  };
\n
52 
\n
53  template<length_t C, length_t R, typename T, qualifier Q>
\n
54  struct type<mat<C, R, T, Q> >
\n
55  {
\n
56  static bool const is_vec = false;
\n
57  static bool const is_mat = true;
\n
58  static bool const is_quat = false;
\n
59  static length_t const components = C;
\n
60  static length_t const cols = C;
\n
61  static length_t const rows = R;
\n
62  };
\n
63 
\n
64  template<typename T, qualifier Q>
\n
65  struct type<qua<T, Q> >
\n
66  {
\n
67  static bool const is_vec = false;
\n
68  static bool const is_mat = false;
\n
69  static bool const is_quat = true;
\n
70  static length_t const components = 4;
\n
71  };
\n
72 
\n
73  template<typename T, qualifier Q>
\n
74  struct type<tdualquat<T, Q> >
\n
75  {
\n
76  static bool const is_vec = false;
\n
77  static bool const is_mat = false;
\n
78  static bool const is_quat = true;
\n
79  static length_t const components = 8;
\n
80  };
\n
81 
\n
83 }//namespace glm
\n
84 
\n
85 #include "type_trait.inl"
\n
glm
Definition: common.hpp:20
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Core features \nMore...

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Core features

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Definition in file type_vec1.hpp.

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1 
\n
4 #pragma once
\n
5 
\n
6 #include "qualifier.hpp"
\n
7 #if GLM_CONFIG_SWIZZLE == GLM_SWIZZLE_OPERATOR
\n
8 # include "_swizzle.hpp"
\n
9 #elif GLM_CONFIG_SWIZZLE == GLM_SWIZZLE_FUNCTION
\n
10 # include "_swizzle_func.hpp"
\n
11 #endif
\n
12 #include <cstddef>
\n
13 
\n
14 namespace glm
\n
15 {
\n
16  template<typename T, qualifier Q>
\n
17  struct vec<1, T, Q>
\n
18  {
\n
19  // -- Implementation detail --
\n
20 
\n
21  typedef T value_type;
\n
22  typedef vec<1, T, Q> type;
\n
23  typedef vec<1, bool, Q> bool_type;
\n
24 
\n
25  // -- Data --
\n
26 
\n
27 # if GLM_SILENT_WARNINGS == GLM_ENABLE
\n
28 # if GLM_COMPILER & GLM_COMPILER_GCC
\n
29 # pragma GCC diagnostic push
\n
30 # pragma GCC diagnostic ignored "-Wpedantic"
\n
31 # elif GLM_COMPILER & GLM_COMPILER_CLANG
\n
32 # pragma clang diagnostic push
\n
33 # pragma clang diagnostic ignored "-Wgnu-anonymous-struct"
\n
34 # pragma clang diagnostic ignored "-Wnested-anon-types"
\n
35 # elif GLM_COMPILER & GLM_COMPILER_VC
\n
36 # pragma warning(push)
\n
37 # pragma warning(disable: 4201) // nonstandard extension used : nameless struct/union
\n
38 # endif
\n
39 # endif
\n
40 
\n
41 # if GLM_CONFIG_XYZW_ONLY
\n
42  T x;
\n
43 # elif GLM_CONFIG_ANONYMOUS_STRUCT == GLM_ENABLE
\n
44  union
\n
45  {
\n
46  T x;
\n
47  T r;
\n
48  T s;
\n
49 
\n
50  typename detail::storage<1, T, detail::is_aligned<Q>::value>::type data;
\n
51 /*
\n
52 # if GLM_CONFIG_SWIZZLE == GLM_SWIZZLE_OPERATOR
\n
53  _GLM_SWIZZLE1_2_MEMBERS(T, Q, x)
\n
54  _GLM_SWIZZLE1_2_MEMBERS(T, Q, r)
\n
55  _GLM_SWIZZLE1_2_MEMBERS(T, Q, s)
\n
56  _GLM_SWIZZLE1_3_MEMBERS(T, Q, x)
\n
57  _GLM_SWIZZLE1_3_MEMBERS(T, Q, r)
\n
58  _GLM_SWIZZLE1_3_MEMBERS(T, Q, s)
\n
59  _GLM_SWIZZLE1_4_MEMBERS(T, Q, x)
\n
60  _GLM_SWIZZLE1_4_MEMBERS(T, Q, r)
\n
61  _GLM_SWIZZLE1_4_MEMBERS(T, Q, s)
\n
62 # endif
\n
63 */
\n
64  };
\n
65 # else
\n
66  union {T x, r, s;};
\n
67 /*
\n
68 # if GLM_CONFIG_SWIZZLE == GLM_SWIZZLE_FUNCTION
\n
69  GLM_SWIZZLE_GEN_VEC_FROM_VEC1(T, Q)
\n
70 # endif
\n
71 */
\n
72 # endif
\n
73 
\n
74 # if GLM_SILENT_WARNINGS == GLM_ENABLE
\n
75 # if GLM_COMPILER & GLM_COMPILER_CLANG
\n
76 # pragma clang diagnostic pop
\n
77 # elif GLM_COMPILER & GLM_COMPILER_GCC
\n
78 # pragma GCC diagnostic pop
\n
79 # elif GLM_COMPILER & GLM_COMPILER_VC
\n
80 # pragma warning(pop)
\n
81 # endif
\n
82 # endif
\n
83 
\n
84  // -- Component accesses --
\n
85 
\n
87  typedef length_t length_type;
\n
88  GLM_FUNC_DECL static GLM_CONSTEXPR length_type length(){return 1;}
\n
89 
\n
90  GLM_FUNC_DECL GLM_CONSTEXPR T & operator[](length_type i);
\n
91  GLM_FUNC_DECL GLM_CONSTEXPR T const& operator[](length_type i) const;
\n
92 
\n
93  // -- Implicit basic constructors --
\n
94 
\n
95  GLM_FUNC_DECL GLM_CONSTEXPR vec() GLM_DEFAULT;
\n
96  GLM_FUNC_DECL GLM_CONSTEXPR vec(vec const& v) GLM_DEFAULT;
\n
97  template<qualifier P>
\n
98  GLM_FUNC_DECL GLM_CONSTEXPR vec(vec<1, T, P> const& v);
\n
99 
\n
100  // -- Explicit basic constructors --
\n
101 
\n
102  GLM_FUNC_DECL GLM_CONSTEXPR explicit vec(T scalar);
\n
103 
\n
104  // -- Conversion vector constructors --
\n
105 
\n
107  template<typename U, qualifier P>
\n
108  GLM_FUNC_DECL GLM_CONSTEXPR GLM_EXPLICIT vec(vec<2, U, P> const& v);
\n
110  template<typename U, qualifier P>
\n
111  GLM_FUNC_DECL GLM_CONSTEXPR GLM_EXPLICIT vec(vec<3, U, P> const& v);
\n
113  template<typename U, qualifier P>
\n
114  GLM_FUNC_DECL GLM_CONSTEXPR GLM_EXPLICIT vec(vec<4, U, P> const& v);
\n
115 
\n
117  template<typename U, qualifier P>
\n
118  GLM_FUNC_DECL GLM_CONSTEXPR GLM_EXPLICIT vec(vec<1, U, P> const& v);
\n
119 
\n
120  // -- Swizzle constructors --
\n
121 /*
\n
122 # if GLM_CONFIG_SWIZZLE == GLM_SWIZZLE_OPERATOR
\n
123  template<int E0>
\n
124  GLM_FUNC_DECL GLM_CONSTEXPR vec(detail::_swizzle<1, T, Q, E0, -1,-2,-3> const& that)
\n
125  {
\n
126  *this = that();
\n
127  }
\n
128 # endif//GLM_CONFIG_SWIZZLE == GLM_SWIZZLE_OPERATOR
\n
129 */
\n
130  // -- Unary arithmetic operators --
\n
131 
\n
132  GLM_FUNC_DECL GLM_CONSTEXPR vec<1, T, Q> & operator=(vec const& v) GLM_DEFAULT;
\n
133 
\n
134  template<typename U>
\n
135  GLM_FUNC_DECL GLM_CONSTEXPR vec<1, T, Q> & operator=(vec<1, U, Q> const& v);
\n
136  template<typename U>
\n
137  GLM_FUNC_DECL GLM_CONSTEXPR vec<1, T, Q> & operator+=(U scalar);
\n
138  template<typename U>
\n
139  GLM_FUNC_DECL GLM_CONSTEXPR vec<1, T, Q> & operator+=(vec<1, U, Q> const& v);
\n
140  template<typename U>
\n
141  GLM_FUNC_DECL GLM_CONSTEXPR vec<1, T, Q> & operator-=(U scalar);
\n
142  template<typename U>
\n
143  GLM_FUNC_DECL GLM_CONSTEXPR vec<1, T, Q> & operator-=(vec<1, U, Q> const& v);
\n
144  template<typename U>
\n
145  GLM_FUNC_DECL GLM_CONSTEXPR vec<1, T, Q> & operator*=(U scalar);
\n
146  template<typename U>
\n
147  GLM_FUNC_DECL GLM_CONSTEXPR vec<1, T, Q> & operator*=(vec<1, U, Q> const& v);
\n
148  template<typename U>
\n
149  GLM_FUNC_DECL GLM_CONSTEXPR vec<1, T, Q> & operator/=(U scalar);
\n
150  template<typename U>
\n
151  GLM_FUNC_DECL GLM_CONSTEXPR vec<1, T, Q> & operator/=(vec<1, U, Q> const& v);
\n
152 
\n
153  // -- Increment and decrement operators --
\n
154 
\n
155  GLM_FUNC_DECL GLM_CONSTEXPR vec<1, T, Q> & operator++();
\n
156  GLM_FUNC_DECL GLM_CONSTEXPR vec<1, T, Q> & operator--();
\n
157  GLM_FUNC_DECL GLM_CONSTEXPR vec<1, T, Q> operator++(int);
\n
158  GLM_FUNC_DECL GLM_CONSTEXPR vec<1, T, Q> operator--(int);
\n
159 
\n
160  // -- Unary bit operators --
\n
161 
\n
162  template<typename U>
\n
163  GLM_FUNC_DECL GLM_CONSTEXPR vec<1, T, Q> & operator%=(U scalar);
\n
164  template<typename U>
\n
165  GLM_FUNC_DECL GLM_CONSTEXPR vec<1, T, Q> & operator%=(vec<1, U, Q> const& v);
\n
166  template<typename U>
\n
167  GLM_FUNC_DECL GLM_CONSTEXPR vec<1, T, Q> & operator&=(U scalar);
\n
168  template<typename U>
\n
169  GLM_FUNC_DECL GLM_CONSTEXPR vec<1, T, Q> & operator&=(vec<1, U, Q> const& v);
\n
170  template<typename U>
\n
171  GLM_FUNC_DECL GLM_CONSTEXPR vec<1, T, Q> & operator|=(U scalar);
\n
172  template<typename U>
\n
173  GLM_FUNC_DECL GLM_CONSTEXPR vec<1, T, Q> & operator|=(vec<1, U, Q> const& v);
\n
174  template<typename U>
\n
175  GLM_FUNC_DECL GLM_CONSTEXPR vec<1, T, Q> & operator^=(U scalar);
\n
176  template<typename U>
\n
177  GLM_FUNC_DECL GLM_CONSTEXPR vec<1, T, Q> & operator^=(vec<1, U, Q> const& v);
\n
178  template<typename U>
\n
179  GLM_FUNC_DECL GLM_CONSTEXPR vec<1, T, Q> & operator<<=(U scalar);
\n
180  template<typename U>
\n
181  GLM_FUNC_DECL GLM_CONSTEXPR vec<1, T, Q> & operator<<=(vec<1, U, Q> const& v);
\n
182  template<typename U>
\n
183  GLM_FUNC_DECL GLM_CONSTEXPR vec<1, T, Q> & operator>>=(U scalar);
\n
184  template<typename U>
\n
185  GLM_FUNC_DECL GLM_CONSTEXPR vec<1, T, Q> & operator>>=(vec<1, U, Q> const& v);
\n
186  };
\n
187 
\n
188  // -- Unary operators --
\n
189 
\n
190  template<typename T, qualifier Q>
\n
191  GLM_FUNC_DECL GLM_CONSTEXPR vec<1, T, Q> operator+(vec<1, T, Q> const& v);
\n
192 
\n
193  template<typename T, qualifier Q>
\n
194  GLM_FUNC_DECL GLM_CONSTEXPR vec<1, T, Q> operator-(vec<1, T, Q> const& v);
\n
195 
\n
196  // -- Binary operators --
\n
197 
\n
198  template<typename T, qualifier Q>
\n
199  GLM_FUNC_DECL GLM_CONSTEXPR vec<1, T, Q> operator+(vec<1, T, Q> const& v, T scalar);
\n
200 
\n
201  template<typename T, qualifier Q>
\n
202  GLM_FUNC_DECL GLM_CONSTEXPR vec<1, T, Q> operator+(T scalar, vec<1, T, Q> const& v);
\n
203 
\n
204  template<typename T, qualifier Q>
\n
205  GLM_FUNC_DECL GLM_CONSTEXPR vec<1, T, Q> operator+(vec<1, T, Q> const& v1, vec<1, T, Q> const& v2);
\n
206 
\n
207  template<typename T, qualifier Q>
\n
208  GLM_FUNC_DECL GLM_CONSTEXPR vec<1, T, Q> operator-(vec<1, T, Q> const& v, T scalar);
\n
209 
\n
210  template<typename T, qualifier Q>
\n
211  GLM_FUNC_DECL GLM_CONSTEXPR vec<1, T, Q> operator-(T scalar, vec<1, T, Q> const& v);
\n
212 
\n
213  template<typename T, qualifier Q>
\n
214  GLM_FUNC_DECL GLM_CONSTEXPR vec<1, T, Q> operator-(vec<1, T, Q> const& v1, vec<1, T, Q> const& v2);
\n
215 
\n
216  template<typename T, qualifier Q>
\n
217  GLM_FUNC_DECL GLM_CONSTEXPR vec<1, T, Q> operator*(vec<1, T, Q> const& v, T scalar);
\n
218 
\n
219  template<typename T, qualifier Q>
\n
220  GLM_FUNC_DECL GLM_CONSTEXPR vec<1, T, Q> operator*(T scalar, vec<1, T, Q> const& v);
\n
221 
\n
222  template<typename T, qualifier Q>
\n
223  GLM_FUNC_DECL GLM_CONSTEXPR vec<1, T, Q> operator*(vec<1, T, Q> const& v1, vec<1, T, Q> const& v2);
\n
224 
\n
225  template<typename T, qualifier Q>
\n
226  GLM_FUNC_DECL GLM_CONSTEXPR vec<1, T, Q> operator/(vec<1, T, Q> const& v, T scalar);
\n
227 
\n
228  template<typename T, qualifier Q>
\n
229  GLM_FUNC_DECL GLM_CONSTEXPR vec<1, T, Q> operator/(T scalar, vec<1, T, Q> const& v);
\n
230 
\n
231  template<typename T, qualifier Q>
\n
232  GLM_FUNC_DECL GLM_CONSTEXPR vec<1, T, Q> operator/(vec<1, T, Q> const& v1, vec<1, T, Q> const& v2);
\n
233 
\n
234  template<typename T, qualifier Q>
\n
235  GLM_FUNC_DECL GLM_CONSTEXPR vec<1, T, Q> operator%(vec<1, T, Q> const& v, T scalar);
\n
236 
\n
237  template<typename T, qualifier Q>
\n
238  GLM_FUNC_DECL GLM_CONSTEXPR vec<1, T, Q> operator%(T scalar, vec<1, T, Q> const& v);
\n
239 
\n
240  template<typename T, qualifier Q>
\n
241  GLM_FUNC_DECL GLM_CONSTEXPR vec<1, T, Q> operator%(vec<1, T, Q> const& v1, vec<1, T, Q> const& v2);
\n
242 
\n
243  template<typename T, qualifier Q>
\n
244  GLM_FUNC_DECL GLM_CONSTEXPR vec<1, T, Q> operator&(vec<1, T, Q> const& v, T scalar);
\n
245 
\n
246  template<typename T, qualifier Q>
\n
247  GLM_FUNC_DECL GLM_CONSTEXPR vec<1, T, Q> operator&(T scalar, vec<1, T, Q> const& v);
\n
248 
\n
249  template<typename T, qualifier Q>
\n
250  GLM_FUNC_DECL GLM_CONSTEXPR vec<1, T, Q> operator&(vec<1, T, Q> const& v1, vec<1, T, Q> const& v2);
\n
251 
\n
252  template<typename T, qualifier Q>
\n
253  GLM_FUNC_DECL GLM_CONSTEXPR vec<1, T, Q> operator|(vec<1, T, Q> const& v, T scalar);
\n
254 
\n
255  template<typename T, qualifier Q>
\n
256  GLM_FUNC_DECL GLM_CONSTEXPR vec<1, T, Q> operator|(T scalar, vec<1, T, Q> const& v);
\n
257 
\n
258  template<typename T, qualifier Q>
\n
259  GLM_FUNC_DECL GLM_CONSTEXPR vec<1, T, Q> operator|(vec<1, T, Q> const& v1, vec<1, T, Q> const& v2);
\n
260 
\n
261  template<typename T, qualifier Q>
\n
262  GLM_FUNC_DECL GLM_CONSTEXPR vec<1, T, Q> operator^(vec<1, T, Q> const& v, T scalar);
\n
263 
\n
264  template<typename T, qualifier Q>
\n
265  GLM_FUNC_DECL GLM_CONSTEXPR vec<1, T, Q> operator^(T scalar, vec<1, T, Q> const& v);
\n
266 
\n
267  template<typename T, qualifier Q>
\n
268  GLM_FUNC_DECL GLM_CONSTEXPR vec<1, T, Q> operator^(vec<1, T, Q> const& v1, vec<1, T, Q> const& v2);
\n
269 
\n
270  template<typename T, qualifier Q>
\n
271  GLM_FUNC_DECL GLM_CONSTEXPR vec<1, T, Q> operator<<(vec<1, T, Q> const& v, T scalar);
\n
272 
\n
273  template<typename T, qualifier Q>
\n
274  GLM_FUNC_DECL GLM_CONSTEXPR vec<1, T, Q> operator<<(T scalar, vec<1, T, Q> const& v);
\n
275 
\n
276  template<typename T, qualifier Q>
\n
277  GLM_FUNC_DECL GLM_CONSTEXPR vec<1, T, Q> operator<<(vec<1, T, Q> const& v1, vec<1, T, Q> const& v2);
\n
278 
\n
279  template<typename T, qualifier Q>
\n
280  GLM_FUNC_DECL GLM_CONSTEXPR vec<1, T, Q> operator>>(vec<1, T, Q> const& v, T scalar);
\n
281 
\n
282  template<typename T, qualifier Q>
\n
283  GLM_FUNC_DECL GLM_CONSTEXPR vec<1, T, Q> operator>>(T scalar, vec<1, T, Q> const& v);
\n
284 
\n
285  template<typename T, qualifier Q>
\n
286  GLM_FUNC_DECL GLM_CONSTEXPR vec<1, T, Q> operator>>(vec<1, T, Q> const& v1, vec<1, T, Q> const& v2);
\n
287 
\n
288  template<typename T, qualifier Q>
\n
289  GLM_FUNC_DECL GLM_CONSTEXPR vec<1, T, Q> operator~(vec<1, T, Q> const& v);
\n
290 
\n
291  // -- Boolean operators --
\n
292 
\n
293  template<typename T, qualifier Q>
\n
294  GLM_FUNC_DECL GLM_CONSTEXPR bool operator==(vec<1, T, Q> const& v1, vec<1, T, Q> const& v2);
\n
295 
\n
296  template<typename T, qualifier Q>
\n
297  GLM_FUNC_DECL GLM_CONSTEXPR bool operator!=(vec<1, T, Q> const& v1, vec<1, T, Q> const& v2);
\n
298 
\n
299  template<qualifier Q>
\n
300  GLM_FUNC_DECL GLM_CONSTEXPR vec<1, bool, Q> operator&&(vec<1, bool, Q> const& v1, vec<1, bool, Q> const& v2);
\n
301 
\n
302  template<qualifier Q>
\n
303  GLM_FUNC_DECL GLM_CONSTEXPR vec<1, bool, Q> operator||(vec<1, bool, Q> const& v1, vec<1, bool, Q> const& v2);
\n
304 }//namespace glm
\n
305 
\n
306 #ifndef GLM_EXTERNAL_TEMPLATE
\n
307 #include "type_vec1.inl"
\n
308 #endif//GLM_EXTERNAL_TEMPLATE
\n
glm::length
GLM_FUNC_DECL T length(qua< T, Q > const &q)
Returns the norm of a quaternions.
\n
glm
Definition: common.hpp:20
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type_vec2.hpp File Reference
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Core features \nMore...

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Core features

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Definition in file type_vec2.hpp.

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type_vec2.hpp
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\nGo to the documentation of this file.
1 
\n
4 #pragma once
\n
5 
\n
6 #include "qualifier.hpp"
\n
7 #if GLM_CONFIG_SWIZZLE == GLM_SWIZZLE_OPERATOR
\n
8 # include "_swizzle.hpp"
\n
9 #elif GLM_CONFIG_SWIZZLE == GLM_SWIZZLE_FUNCTION
\n
10 # include "_swizzle_func.hpp"
\n
11 #endif
\n
12 #include <cstddef>
\n
13 
\n
14 namespace glm
\n
15 {
\n
16  template<typename T, qualifier Q>
\n
17  struct vec<2, T, Q>
\n
18  {
\n
19  // -- Implementation detail --
\n
20 
\n
21  typedef T value_type;
\n
22  typedef vec<2, T, Q> type;
\n
23  typedef vec<2, bool, Q> bool_type;
\n
24 
\n
25  // -- Data --
\n
26 
\n
27 # if GLM_SILENT_WARNINGS == GLM_ENABLE
\n
28 # if GLM_COMPILER & GLM_COMPILER_GCC
\n
29 # pragma GCC diagnostic push
\n
30 # pragma GCC diagnostic ignored "-Wpedantic"
\n
31 # elif GLM_COMPILER & GLM_COMPILER_CLANG
\n
32 # pragma clang diagnostic push
\n
33 # pragma clang diagnostic ignored "-Wgnu-anonymous-struct"
\n
34 # pragma clang diagnostic ignored "-Wnested-anon-types"
\n
35 # elif GLM_COMPILER & GLM_COMPILER_VC
\n
36 # pragma warning(push)
\n
37 # pragma warning(disable: 4201) // nonstandard extension used : nameless struct/union
\n
38 # endif
\n
39 # endif
\n
40 
\n
41 # if GLM_CONFIG_XYZW_ONLY
\n
42  T x, y;
\n
43 # elif GLM_CONFIG_ANONYMOUS_STRUCT == GLM_ENABLE
\n
44  union
\n
45  {
\n
46  struct{ T x, y; };
\n
47  struct{ T r, g; };
\n
48  struct{ T s, t; };
\n
49 
\n
50  typename detail::storage<2, T, detail::is_aligned<Q>::value>::type data;
\n
51 
\n
52 # if GLM_CONFIG_SWIZZLE == GLM_SWIZZLE_OPERATOR
\n
53  GLM_SWIZZLE2_2_MEMBERS(T, Q, x, y)
\n
54  GLM_SWIZZLE2_2_MEMBERS(T, Q, r, g)
\n
55  GLM_SWIZZLE2_2_MEMBERS(T, Q, s, t)
\n
56  GLM_SWIZZLE2_3_MEMBERS(T, Q, x, y)
\n
57  GLM_SWIZZLE2_3_MEMBERS(T, Q, r, g)
\n
58  GLM_SWIZZLE2_3_MEMBERS(T, Q, s, t)
\n
59  GLM_SWIZZLE2_4_MEMBERS(T, Q, x, y)
\n
60  GLM_SWIZZLE2_4_MEMBERS(T, Q, r, g)
\n
61  GLM_SWIZZLE2_4_MEMBERS(T, Q, s, t)
\n
62 # endif
\n
63  };
\n
64 # else
\n
65  union {T x, r, s;};
\n
66  union {T y, g, t;};
\n
67 
\n
68 # if GLM_CONFIG_SWIZZLE == GLM_SWIZZLE_FUNCTION
\n
69  GLM_SWIZZLE_GEN_VEC_FROM_VEC2(T, Q)
\n
70 # endif//GLM_CONFIG_SWIZZLE
\n
71 # endif
\n
72 
\n
73 # if GLM_SILENT_WARNINGS == GLM_ENABLE
\n
74 # if GLM_COMPILER & GLM_COMPILER_CLANG
\n
75 # pragma clang diagnostic pop
\n
76 # elif GLM_COMPILER & GLM_COMPILER_GCC
\n
77 # pragma GCC diagnostic pop
\n
78 # elif GLM_COMPILER & GLM_COMPILER_VC
\n
79 # pragma warning(pop)
\n
80 # endif
\n
81 # endif
\n
82 
\n
83  // -- Component accesses --
\n
84 
\n
86  typedef length_t length_type;
\n
87  GLM_FUNC_DECL static GLM_CONSTEXPR length_type length(){return 2;}
\n
88 
\n
89  GLM_FUNC_DECL GLM_CONSTEXPR T& operator[](length_type i);
\n
90  GLM_FUNC_DECL GLM_CONSTEXPR T const& operator[](length_type i) const;
\n
91 
\n
92  // -- Implicit basic constructors --
\n
93 
\n
94  GLM_FUNC_DECL GLM_CONSTEXPR vec() GLM_DEFAULT;
\n
95  GLM_FUNC_DECL GLM_CONSTEXPR vec(vec const& v) GLM_DEFAULT;
\n
96  template<qualifier P>
\n
97  GLM_FUNC_DECL GLM_CONSTEXPR vec(vec<2, T, P> const& v);
\n
98 
\n
99  // -- Explicit basic constructors --
\n
100 
\n
101  GLM_FUNC_DECL GLM_CONSTEXPR explicit vec(T scalar);
\n
102  GLM_FUNC_DECL GLM_CONSTEXPR vec(T x, T y);
\n
103 
\n
104  // -- Conversion constructors --
\n
105 
\n
106  template<typename U, qualifier P>
\n
107  GLM_FUNC_DECL GLM_CONSTEXPR explicit vec(vec<1, U, P> const& v);
\n
108 
\n
110  template<typename A, typename B>
\n
111  GLM_FUNC_DECL GLM_CONSTEXPR vec(A x, B y);
\n
112  template<typename A, typename B>
\n
113  GLM_FUNC_DECL GLM_CONSTEXPR vec(vec<1, A, Q> const& x, B y);
\n
114  template<typename A, typename B>
\n
115  GLM_FUNC_DECL GLM_CONSTEXPR vec(A x, vec<1, B, Q> const& y);
\n
116  template<typename A, typename B>
\n
117  GLM_FUNC_DECL GLM_CONSTEXPR vec(vec<1, A, Q> const& x, vec<1, B, Q> const& y);
\n
118 
\n
119  // -- Conversion vector constructors --
\n
120 
\n
122  template<typename U, qualifier P>
\n
123  GLM_FUNC_DECL GLM_CONSTEXPR GLM_EXPLICIT vec(vec<3, U, P> const& v);
\n
125  template<typename U, qualifier P>
\n
126  GLM_FUNC_DECL GLM_CONSTEXPR GLM_EXPLICIT vec(vec<4, U, P> const& v);
\n
127 
\n
129  template<typename U, qualifier P>
\n
130  GLM_FUNC_DECL GLM_CONSTEXPR GLM_EXPLICIT vec(vec<2, U, P> const& v);
\n
131 
\n
132  // -- Swizzle constructors --
\n
133 # if GLM_CONFIG_SWIZZLE == GLM_SWIZZLE_OPERATOR
\n
134  template<int E0, int E1>
\n
135  GLM_FUNC_DECL GLM_CONSTEXPR vec(detail::_swizzle<2, T, Q, E0, E1,-1,-2> const& that)
\n
136  {
\n
137  *this = that();
\n
138  }
\n
139 # endif//GLM_CONFIG_SWIZZLE == GLM_SWIZZLE_OPERATOR
\n
140 
\n
141  // -- Unary arithmetic operators --
\n
142 
\n
143  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> & operator=(vec const& v) GLM_DEFAULT;
\n
144 
\n
145  template<typename U>
\n
146  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> & operator=(vec<2, U, Q> const& v);
\n
147  template<typename U>
\n
148  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> & operator+=(U scalar);
\n
149  template<typename U>
\n
150  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> & operator+=(vec<1, U, Q> const& v);
\n
151  template<typename U>
\n
152  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> & operator+=(vec<2, U, Q> const& v);
\n
153  template<typename U>
\n
154  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> & operator-=(U scalar);
\n
155  template<typename U>
\n
156  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> & operator-=(vec<1, U, Q> const& v);
\n
157  template<typename U>
\n
158  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> & operator-=(vec<2, U, Q> const& v);
\n
159  template<typename U>
\n
160  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> & operator*=(U scalar);
\n
161  template<typename U>
\n
162  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> & operator*=(vec<1, U, Q> const& v);
\n
163  template<typename U>
\n
164  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> & operator*=(vec<2, U, Q> const& v);
\n
165  template<typename U>
\n
166  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> & operator/=(U scalar);
\n
167  template<typename U>
\n
168  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> & operator/=(vec<1, U, Q> const& v);
\n
169  template<typename U>
\n
170  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> & operator/=(vec<2, U, Q> const& v);
\n
171 
\n
172  // -- Increment and decrement operators --
\n
173 
\n
174  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> & operator++();
\n
175  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> & operator--();
\n
176  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> operator++(int);
\n
177  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> operator--(int);
\n
178 
\n
179  // -- Unary bit operators --
\n
180 
\n
181  template<typename U>
\n
182  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> & operator%=(U scalar);
\n
183  template<typename U>
\n
184  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> & operator%=(vec<1, U, Q> const& v);
\n
185  template<typename U>
\n
186  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> & operator%=(vec<2, U, Q> const& v);
\n
187  template<typename U>
\n
188  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> & operator&=(U scalar);
\n
189  template<typename U>
\n
190  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> & operator&=(vec<1, U, Q> const& v);
\n
191  template<typename U>
\n
192  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> & operator&=(vec<2, U, Q> const& v);
\n
193  template<typename U>
\n
194  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> & operator|=(U scalar);
\n
195  template<typename U>
\n
196  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> & operator|=(vec<1, U, Q> const& v);
\n
197  template<typename U>
\n
198  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> & operator|=(vec<2, U, Q> const& v);
\n
199  template<typename U>
\n
200  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> & operator^=(U scalar);
\n
201  template<typename U>
\n
202  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> & operator^=(vec<1, U, Q> const& v);
\n
203  template<typename U>
\n
204  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> & operator^=(vec<2, U, Q> const& v);
\n
205  template<typename U>
\n
206  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> & operator<<=(U scalar);
\n
207  template<typename U>
\n
208  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> & operator<<=(vec<1, U, Q> const& v);
\n
209  template<typename U>
\n
210  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> & operator<<=(vec<2, U, Q> const& v);
\n
211  template<typename U>
\n
212  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> & operator>>=(U scalar);
\n
213  template<typename U>
\n
214  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> & operator>>=(vec<1, U, Q> const& v);
\n
215  template<typename U>
\n
216  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> & operator>>=(vec<2, U, Q> const& v);
\n
217  };
\n
218 
\n
219  // -- Unary operators --
\n
220 
\n
221  template<typename T, qualifier Q>
\n
222  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> operator+(vec<2, T, Q> const& v);
\n
223 
\n
224  template<typename T, qualifier Q>
\n
225  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> operator-(vec<2, T, Q> const& v);
\n
226 
\n
227  // -- Binary operators --
\n
228 
\n
229  template<typename T, qualifier Q>
\n
230  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> operator+(vec<2, T, Q> const& v, T scalar);
\n
231 
\n
232  template<typename T, qualifier Q>
\n
233  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> operator+(vec<2, T, Q> const& v1, vec<1, T, Q> const& v2);
\n
234 
\n
235  template<typename T, qualifier Q>
\n
236  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> operator+(T scalar, vec<2, T, Q> const& v);
\n
237 
\n
238  template<typename T, qualifier Q>
\n
239  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> operator+(vec<1, T, Q> const& v1, vec<2, T, Q> const& v2);
\n
240 
\n
241  template<typename T, qualifier Q>
\n
242  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> operator+(vec<2, T, Q> const& v1, vec<2, T, Q> const& v2);
\n
243 
\n
244  template<typename T, qualifier Q>
\n
245  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> operator-(vec<2, T, Q> const& v, T scalar);
\n
246 
\n
247  template<typename T, qualifier Q>
\n
248  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> operator-(vec<2, T, Q> const& v1, vec<1, T, Q> const& v2);
\n
249 
\n
250  template<typename T, qualifier Q>
\n
251  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> operator-(T scalar, vec<2, T, Q> const& v);
\n
252 
\n
253  template<typename T, qualifier Q>
\n
254  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> operator-(vec<1, T, Q> const& v1, vec<2, T, Q> const& v2);
\n
255 
\n
256  template<typename T, qualifier Q>
\n
257  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> operator-(vec<2, T, Q> const& v1, vec<2, T, Q> const& v2);
\n
258 
\n
259  template<typename T, qualifier Q>
\n
260  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> operator*(vec<2, T, Q> const& v, T scalar);
\n
261 
\n
262  template<typename T, qualifier Q>
\n
263  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> operator*(vec<2, T, Q> const& v1, vec<1, T, Q> const& v2);
\n
264 
\n
265  template<typename T, qualifier Q>
\n
266  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> operator*(T scalar, vec<2, T, Q> const& v);
\n
267 
\n
268  template<typename T, qualifier Q>
\n
269  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> operator*(vec<1, T, Q> const& v1, vec<2, T, Q> const& v2);
\n
270 
\n
271  template<typename T, qualifier Q>
\n
272  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> operator*(vec<2, T, Q> const& v1, vec<2, T, Q> const& v2);
\n
273 
\n
274  template<typename T, qualifier Q>
\n
275  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> operator/(vec<2, T, Q> const& v, T scalar);
\n
276 
\n
277  template<typename T, qualifier Q>
\n
278  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> operator/(vec<2, T, Q> const& v1, vec<1, T, Q> const& v2);
\n
279 
\n
280  template<typename T, qualifier Q>
\n
281  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> operator/(T scalar, vec<2, T, Q> const& v);
\n
282 
\n
283  template<typename T, qualifier Q>
\n
284  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> operator/(vec<1, T, Q> const& v1, vec<2, T, Q> const& v2);
\n
285 
\n
286  template<typename T, qualifier Q>
\n
287  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> operator/(vec<2, T, Q> const& v1, vec<2, T, Q> const& v2);
\n
288 
\n
289  template<typename T, qualifier Q>
\n
290  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> operator%(vec<2, T, Q> const& v, T scalar);
\n
291 
\n
292  template<typename T, qualifier Q>
\n
293  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> operator%(vec<2, T, Q> const& v1, vec<1, T, Q> const& v2);
\n
294 
\n
295  template<typename T, qualifier Q>
\n
296  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> operator%(T scalar, vec<2, T, Q> const& v);
\n
297 
\n
298  template<typename T, qualifier Q>
\n
299  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> operator%(vec<1, T, Q> const& v1, vec<2, T, Q> const& v2);
\n
300 
\n
301  template<typename T, qualifier Q>
\n
302  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> operator%(vec<2, T, Q> const& v1, vec<2, T, Q> const& v2);
\n
303 
\n
304  template<typename T, qualifier Q>
\n
305  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> operator&(vec<2, T, Q> const& v, T scalar);
\n
306 
\n
307  template<typename T, qualifier Q>
\n
308  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> operator&(vec<2, T, Q> const& v1, vec<1, T, Q> const& v2);
\n
309 
\n
310  template<typename T, qualifier Q>
\n
311  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> operator&(T scalar, vec<2, T, Q> const& v);
\n
312 
\n
313  template<typename T, qualifier Q>
\n
314  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> operator&(vec<1, T, Q> const& v1, vec<2, T, Q> const& v2);
\n
315 
\n
316  template<typename T, qualifier Q>
\n
317  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> operator&(vec<2, T, Q> const& v1, vec<2, T, Q> const& v2);
\n
318 
\n
319  template<typename T, qualifier Q>
\n
320  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> operator|(vec<2, T, Q> const& v, T scalar);
\n
321 
\n
322  template<typename T, qualifier Q>
\n
323  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> operator|(vec<2, T, Q> const& v1, vec<1, T, Q> const& v2);
\n
324 
\n
325  template<typename T, qualifier Q>
\n
326  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> operator|(T scalar, vec<2, T, Q> const& v);
\n
327 
\n
328  template<typename T, qualifier Q>
\n
329  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> operator|(vec<1, T, Q> const& v1, vec<2, T, Q> const& v2);
\n
330 
\n
331  template<typename T, qualifier Q>
\n
332  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> operator|(vec<2, T, Q> const& v1, vec<2, T, Q> const& v2);
\n
333 
\n
334  template<typename T, qualifier Q>
\n
335  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> operator^(vec<2, T, Q> const& v, T scalar);
\n
336 
\n
337  template<typename T, qualifier Q>
\n
338  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> operator^(vec<2, T, Q> const& v1, vec<1, T, Q> const& v2);
\n
339 
\n
340  template<typename T, qualifier Q>
\n
341  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> operator^(T scalar, vec<2, T, Q> const& v);
\n
342 
\n
343  template<typename T, qualifier Q>
\n
344  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> operator^(vec<1, T, Q> const& v1, vec<2, T, Q> const& v2);
\n
345 
\n
346  template<typename T, qualifier Q>
\n
347  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> operator^(vec<2, T, Q> const& v1, vec<2, T, Q> const& v2);
\n
348 
\n
349  template<typename T, qualifier Q>
\n
350  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> operator<<(vec<2, T, Q> const& v, T scalar);
\n
351 
\n
352  template<typename T, qualifier Q>
\n
353  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> operator<<(vec<2, T, Q> const& v1, vec<1, T, Q> const& v2);
\n
354 
\n
355  template<typename T, qualifier Q>
\n
356  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> operator<<(T scalar, vec<2, T, Q> const& v);
\n
357 
\n
358  template<typename T, qualifier Q>
\n
359  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> operator<<(vec<1, T, Q> const& v1, vec<2, T, Q> const& v2);
\n
360 
\n
361  template<typename T, qualifier Q>
\n
362  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> operator<<(vec<2, T, Q> const& v1, vec<2, T, Q> const& v2);
\n
363 
\n
364  template<typename T, qualifier Q>
\n
365  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> operator>>(vec<2, T, Q> const& v, T scalar);
\n
366 
\n
367  template<typename T, qualifier Q>
\n
368  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> operator>>(vec<2, T, Q> const& v1, vec<1, T, Q> const& v2);
\n
369 
\n
370  template<typename T, qualifier Q>
\n
371  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> operator>>(T scalar, vec<2, T, Q> const& v);
\n
372 
\n
373  template<typename T, qualifier Q>
\n
374  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> operator>>(vec<1, T, Q> const& v1, vec<2, T, Q> const& v2);
\n
375 
\n
376  template<typename T, qualifier Q>
\n
377  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> operator>>(vec<2, T, Q> const& v1, vec<2, T, Q> const& v2);
\n
378 
\n
379  template<typename T, qualifier Q>
\n
380  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, T, Q> operator~(vec<2, T, Q> const& v);
\n
381 
\n
382  // -- Boolean operators --
\n
383 
\n
384  template<typename T, qualifier Q>
\n
385  GLM_FUNC_DECL GLM_CONSTEXPR bool operator==(vec<2, T, Q> const& v1, vec<2, T, Q> const& v2);
\n
386 
\n
387  template<typename T, qualifier Q>
\n
388  GLM_FUNC_DECL GLM_CONSTEXPR bool operator!=(vec<2, T, Q> const& v1, vec<2, T, Q> const& v2);
\n
389 
\n
390  template<qualifier Q>
\n
391  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, bool, Q> operator&&(vec<2, bool, Q> const& v1, vec<2, bool, Q> const& v2);
\n
392 
\n
393  template<qualifier Q>
\n
394  GLM_FUNC_DECL GLM_CONSTEXPR vec<2, bool, Q> operator||(vec<2, bool, Q> const& v1, vec<2, bool, Q> const& v2);
\n
395 }//namespace glm
\n
396 
\n
397 #ifndef GLM_EXTERNAL_TEMPLATE
\n
398 #include "type_vec2.inl"
\n
399 #endif//GLM_EXTERNAL_TEMPLATE
\n
glm::length
GLM_FUNC_DECL T length(qua< T, Q > const &q)
Returns the norm of a quaternions.
\n
glm
Definition: common.hpp:20
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type_vec3.hpp File Reference
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Core features \nMore...

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Core features

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Definition in file type_vec3.hpp.

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type_vec3.hpp
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\nGo to the documentation of this file.
1 
\n
4 #pragma once
\n
5 
\n
6 #include "qualifier.hpp"
\n
7 #if GLM_CONFIG_SWIZZLE == GLM_SWIZZLE_OPERATOR
\n
8 # include "_swizzle.hpp"
\n
9 #elif GLM_CONFIG_SWIZZLE == GLM_SWIZZLE_FUNCTION
\n
10 # include "_swizzle_func.hpp"
\n
11 #endif
\n
12 #include <cstddef>
\n
13 
\n
14 namespace glm
\n
15 {
\n
16  template<typename T, qualifier Q>
\n
17  struct vec<3, T, Q>
\n
18  {
\n
19  // -- Implementation detail --
\n
20 
\n
21  typedef T value_type;
\n
22  typedef vec<3, T, Q> type;
\n
23  typedef vec<3, bool, Q> bool_type;
\n
24 
\n
25  // -- Data --
\n
26 
\n
27 # if GLM_SILENT_WARNINGS == GLM_ENABLE
\n
28 # if GLM_COMPILER & GLM_COMPILER_GCC
\n
29 # pragma GCC diagnostic push
\n
30 # pragma GCC diagnostic ignored "-Wpedantic"
\n
31 # elif GLM_COMPILER & GLM_COMPILER_CLANG
\n
32 # pragma clang diagnostic push
\n
33 # pragma clang diagnostic ignored "-Wgnu-anonymous-struct"
\n
34 # pragma clang diagnostic ignored "-Wnested-anon-types"
\n
35 # elif GLM_COMPILER & GLM_COMPILER_VC
\n
36 # pragma warning(push)
\n
37 # pragma warning(disable: 4201) // nonstandard extension used : nameless struct/union
\n
38 # if GLM_CONFIG_ALIGNED_GENTYPES == GLM_ENABLE
\n
39 # pragma warning(disable: 4324) // structure was padded due to alignment specifier
\n
40 # endif
\n
41 # endif
\n
42 # endif
\n
43 
\n
44 # if GLM_CONFIG_XYZW_ONLY
\n
45  T x, y, z;
\n
46 # elif GLM_CONFIG_ANONYMOUS_STRUCT == GLM_ENABLE
\n
47  union
\n
48  {
\n
49  struct{ T x, y, z; };
\n
50  struct{ T r, g, b; };
\n
51  struct{ T s, t, p; };
\n
52 
\n
53  typename detail::storage<3, T, detail::is_aligned<Q>::value>::type data;
\n
54 
\n
55 # if GLM_CONFIG_SWIZZLE == GLM_SWIZZLE_OPERATOR
\n
56  GLM_SWIZZLE3_2_MEMBERS(T, Q, x, y, z)
\n
57  GLM_SWIZZLE3_2_MEMBERS(T, Q, r, g, b)
\n
58  GLM_SWIZZLE3_2_MEMBERS(T, Q, s, t, p)
\n
59  GLM_SWIZZLE3_3_MEMBERS(T, Q, x, y, z)
\n
60  GLM_SWIZZLE3_3_MEMBERS(T, Q, r, g, b)
\n
61  GLM_SWIZZLE3_3_MEMBERS(T, Q, s, t, p)
\n
62  GLM_SWIZZLE3_4_MEMBERS(T, Q, x, y, z)
\n
63  GLM_SWIZZLE3_4_MEMBERS(T, Q, r, g, b)
\n
64  GLM_SWIZZLE3_4_MEMBERS(T, Q, s, t, p)
\n
65 # endif
\n
66  };
\n
67 # else
\n
68  union { T x, r, s; };
\n
69  union { T y, g, t; };
\n
70  union { T z, b, p; };
\n
71 
\n
72 # if GLM_CONFIG_SWIZZLE == GLM_SWIZZLE_FUNCTION
\n
73  GLM_SWIZZLE_GEN_VEC_FROM_VEC3(T, Q)
\n
74 # endif//GLM_CONFIG_SWIZZLE
\n
75 # endif//GLM_LANG
\n
76 
\n
77 # if GLM_SILENT_WARNINGS == GLM_ENABLE
\n
78 # if GLM_COMPILER & GLM_COMPILER_CLANG
\n
79 # pragma clang diagnostic pop
\n
80 # elif GLM_COMPILER & GLM_COMPILER_GCC
\n
81 # pragma GCC diagnostic pop
\n
82 # elif GLM_COMPILER & GLM_COMPILER_VC
\n
83 # pragma warning(pop)
\n
84 # endif
\n
85 # endif
\n
86 
\n
87  // -- Component accesses --
\n
88 
\n
90  typedef length_t length_type;
\n
91  GLM_FUNC_DECL static GLM_CONSTEXPR length_type length(){return 3;}
\n
92 
\n
93  GLM_FUNC_DECL GLM_CONSTEXPR T & operator[](length_type i);
\n
94  GLM_FUNC_DECL GLM_CONSTEXPR T const& operator[](length_type i) const;
\n
95 
\n
96  // -- Implicit basic constructors --
\n
97 
\n
98  GLM_FUNC_DECL GLM_CONSTEXPR vec() GLM_DEFAULT;
\n
99  GLM_FUNC_DECL GLM_CONSTEXPR vec(vec const& v) GLM_DEFAULT;
\n
100  template<qualifier P>
\n
101  GLM_FUNC_DECL GLM_CONSTEXPR vec(vec<3, T, P> const& v);
\n
102 
\n
103  // -- Explicit basic constructors --
\n
104 
\n
105  GLM_FUNC_DECL GLM_CONSTEXPR explicit vec(T scalar);
\n
106  GLM_FUNC_DECL GLM_CONSTEXPR vec(T a, T b, T c);
\n
107 
\n
108  // -- Conversion scalar constructors --
\n
109 
\n
110  template<typename U, qualifier P>
\n
111  GLM_FUNC_DECL GLM_CONSTEXPR explicit vec(vec<1, U, P> const& v);
\n
112 
\n
114  template<typename X, typename Y, typename Z>
\n
115  GLM_FUNC_DECL GLM_CONSTEXPR vec(X x, Y y, Z z);
\n
116  template<typename X, typename Y, typename Z>
\n
117  GLM_FUNC_DECL GLM_CONSTEXPR vec(vec<1, X, Q> const& _x, Y _y, Z _z);
\n
118  template<typename X, typename Y, typename Z>
\n
119  GLM_FUNC_DECL GLM_CONSTEXPR vec(X _x, vec<1, Y, Q> const& _y, Z _z);
\n
120  template<typename X, typename Y, typename Z>
\n
121  GLM_FUNC_DECL GLM_CONSTEXPR vec(vec<1, X, Q> const& _x, vec<1, Y, Q> const& _y, Z _z);
\n
122  template<typename X, typename Y, typename Z>
\n
123  GLM_FUNC_DECL GLM_CONSTEXPR vec(X _x, Y _y, vec<1, Z, Q> const& _z);
\n
124  template<typename X, typename Y, typename Z>
\n
125  GLM_FUNC_DECL GLM_CONSTEXPR vec(vec<1, X, Q> const& _x, Y _y, vec<1, Z, Q> const& _z);
\n
126  template<typename X, typename Y, typename Z>
\n
127  GLM_FUNC_DECL GLM_CONSTEXPR vec(X _x, vec<1, Y, Q> const& _y, vec<1, Z, Q> const& _z);
\n
128  template<typename X, typename Y, typename Z>
\n
129  GLM_FUNC_DECL GLM_CONSTEXPR vec(vec<1, X, Q> const& _x, vec<1, Y, Q> const& _y, vec<1, Z, Q> const& _z);
\n
130 
\n
131  // -- Conversion vector constructors --
\n
132 
\n
134  template<typename A, typename B, qualifier P>
\n
135  GLM_FUNC_DECL GLM_CONSTEXPR vec(vec<2, A, P> const& _xy, B _z);
\n
137  template<typename A, typename B, qualifier P>
\n
138  GLM_FUNC_DECL GLM_CONSTEXPR vec(vec<2, A, P> const& _xy, vec<1, B, P> const& _z);
\n
140  template<typename A, typename B, qualifier P>
\n
141  GLM_FUNC_DECL GLM_CONSTEXPR vec(A _x, vec<2, B, P> const& _yz);
\n
143  template<typename A, typename B, qualifier P>
\n
144  GLM_FUNC_DECL GLM_CONSTEXPR vec(vec<1, A, P> const& _x, vec<2, B, P> const& _yz);
\n
146  template<typename U, qualifier P>
\n
147  GLM_FUNC_DECL GLM_CONSTEXPR GLM_EXPLICIT vec(vec<4, U, P> const& v);
\n
148 
\n
150  template<typename U, qualifier P>
\n
151  GLM_FUNC_DECL GLM_CONSTEXPR GLM_EXPLICIT vec(vec<3, U, P> const& v);
\n
152 
\n
153  // -- Swizzle constructors --
\n
154 # if GLM_CONFIG_SWIZZLE == GLM_SWIZZLE_OPERATOR
\n
155  template<int E0, int E1, int E2>
\n
156  GLM_FUNC_DECL GLM_CONSTEXPR vec(detail::_swizzle<3, T, Q, E0, E1, E2, -1> const& that)
\n
157  {
\n
158  *this = that();
\n
159  }
\n
160 
\n
161  template<int E0, int E1>
\n
162  GLM_FUNC_DECL GLM_CONSTEXPR vec(detail::_swizzle<2, T, Q, E0, E1, -1, -2> const& v, T const& scalar)
\n
163  {
\n
164  *this = vec(v(), scalar);
\n
165  }
\n
166 
\n
167  template<int E0, int E1>
\n
168  GLM_FUNC_DECL GLM_CONSTEXPR vec(T const& scalar, detail::_swizzle<2, T, Q, E0, E1, -1, -2> const& v)
\n
169  {
\n
170  *this = vec(scalar, v());
\n
171  }
\n
172 # endif//GLM_CONFIG_SWIZZLE == GLM_SWIZZLE_OPERATOR
\n
173 
\n
174  // -- Unary arithmetic operators --
\n
175 
\n
176  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q>& operator=(vec<3, T, Q> const& v) GLM_DEFAULT;
\n
177 
\n
178  template<typename U>
\n
179  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> & operator=(vec<3, U, Q> const& v);
\n
180  template<typename U>
\n
181  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> & operator+=(U scalar);
\n
182  template<typename U>
\n
183  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> & operator+=(vec<1, U, Q> const& v);
\n
184  template<typename U>
\n
185  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> & operator+=(vec<3, U, Q> const& v);
\n
186  template<typename U>
\n
187  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> & operator-=(U scalar);
\n
188  template<typename U>
\n
189  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> & operator-=(vec<1, U, Q> const& v);
\n
190  template<typename U>
\n
191  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> & operator-=(vec<3, U, Q> const& v);
\n
192  template<typename U>
\n
193  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> & operator*=(U scalar);
\n
194  template<typename U>
\n
195  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> & operator*=(vec<1, U, Q> const& v);
\n
196  template<typename U>
\n
197  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> & operator*=(vec<3, U, Q> const& v);
\n
198  template<typename U>
\n
199  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> & operator/=(U scalar);
\n
200  template<typename U>
\n
201  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> & operator/=(vec<1, U, Q> const& v);
\n
202  template<typename U>
\n
203  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> & operator/=(vec<3, U, Q> const& v);
\n
204 
\n
205  // -- Increment and decrement operators --
\n
206 
\n
207  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> & operator++();
\n
208  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> & operator--();
\n
209  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> operator++(int);
\n
210  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> operator--(int);
\n
211 
\n
212  // -- Unary bit operators --
\n
213 
\n
214  template<typename U>
\n
215  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> & operator%=(U scalar);
\n
216  template<typename U>
\n
217  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> & operator%=(vec<1, U, Q> const& v);
\n
218  template<typename U>
\n
219  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> & operator%=(vec<3, U, Q> const& v);
\n
220  template<typename U>
\n
221  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> & operator&=(U scalar);
\n
222  template<typename U>
\n
223  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> & operator&=(vec<1, U, Q> const& v);
\n
224  template<typename U>
\n
225  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> & operator&=(vec<3, U, Q> const& v);
\n
226  template<typename U>
\n
227  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> & operator|=(U scalar);
\n
228  template<typename U>
\n
229  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> & operator|=(vec<1, U, Q> const& v);
\n
230  template<typename U>
\n
231  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> & operator|=(vec<3, U, Q> const& v);
\n
232  template<typename U>
\n
233  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> & operator^=(U scalar);
\n
234  template<typename U>
\n
235  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> & operator^=(vec<1, U, Q> const& v);
\n
236  template<typename U>
\n
237  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> & operator^=(vec<3, U, Q> const& v);
\n
238  template<typename U>
\n
239  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> & operator<<=(U scalar);
\n
240  template<typename U>
\n
241  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> & operator<<=(vec<1, U, Q> const& v);
\n
242  template<typename U>
\n
243  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> & operator<<=(vec<3, U, Q> const& v);
\n
244  template<typename U>
\n
245  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> & operator>>=(U scalar);
\n
246  template<typename U>
\n
247  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> & operator>>=(vec<1, U, Q> const& v);
\n
248  template<typename U>
\n
249  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> & operator>>=(vec<3, U, Q> const& v);
\n
250  };
\n
251 
\n
252  // -- Unary operators --
\n
253 
\n
254  template<typename T, qualifier Q>
\n
255  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> operator+(vec<3, T, Q> const& v);
\n
256 
\n
257  template<typename T, qualifier Q>
\n
258  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> operator-(vec<3, T, Q> const& v);
\n
259 
\n
260  // -- Binary operators --
\n
261 
\n
262  template<typename T, qualifier Q>
\n
263  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> operator+(vec<3, T, Q> const& v, T scalar);
\n
264 
\n
265  template<typename T, qualifier Q>
\n
266  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> operator+(vec<3, T, Q> const& v, vec<1, T, Q> const& scalar);
\n
267 
\n
268  template<typename T, qualifier Q>
\n
269  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> operator+(T scalar, vec<3, T, Q> const& v);
\n
270 
\n
271  template<typename T, qualifier Q>
\n
272  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> operator+(vec<1, T, Q> const& v1, vec<3, T, Q> const& v2);
\n
273 
\n
274  template<typename T, qualifier Q>
\n
275  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> operator+(vec<3, T, Q> const& v1, vec<3, T, Q> const& v2);
\n
276 
\n
277  template<typename T, qualifier Q>
\n
278  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> operator-(vec<3, T, Q> const& v, T scalar);
\n
279 
\n
280  template<typename T, qualifier Q>
\n
281  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> operator-(vec<3, T, Q> const& v1, vec<1, T, Q> const& v2);
\n
282 
\n
283  template<typename T, qualifier Q>
\n
284  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> operator-(T scalar, vec<3, T, Q> const& v);
\n
285 
\n
286  template<typename T, qualifier Q>
\n
287  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> operator-(vec<1, T, Q> const& v1, vec<3, T, Q> const& v2);
\n
288 
\n
289  template<typename T, qualifier Q>
\n
290  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> operator-(vec<3, T, Q> const& v1, vec<3, T, Q> const& v2);
\n
291 
\n
292  template<typename T, qualifier Q>
\n
293  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> operator*(vec<3, T, Q> const& v, T scalar);
\n
294 
\n
295  template<typename T, qualifier Q>
\n
296  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> operator*(vec<3, T, Q> const& v1, vec<1, T, Q> const& v2);
\n
297 
\n
298  template<typename T, qualifier Q>
\n
299  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> operator*(T scalar, vec<3, T, Q> const& v);
\n
300 
\n
301  template<typename T, qualifier Q>
\n
302  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> operator*(vec<1, T, Q> const& v1, vec<3, T, Q> const& v2);
\n
303 
\n
304  template<typename T, qualifier Q>
\n
305  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> operator*(vec<3, T, Q> const& v1, vec<3, T, Q> const& v2);
\n
306 
\n
307  template<typename T, qualifier Q>
\n
308  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> operator/(vec<3, T, Q> const& v, T scalar);
\n
309 
\n
310  template<typename T, qualifier Q>
\n
311  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> operator/(vec<3, T, Q> const& v1, vec<1, T, Q> const& v2);
\n
312 
\n
313  template<typename T, qualifier Q>
\n
314  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> operator/(T scalar, vec<3, T, Q> const& v);
\n
315 
\n
316  template<typename T, qualifier Q>
\n
317  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> operator/(vec<1, T, Q> const& v1, vec<3, T, Q> const& v2);
\n
318 
\n
319  template<typename T, qualifier Q>
\n
320  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> operator/(vec<3, T, Q> const& v1, vec<3, T, Q> const& v2);
\n
321 
\n
322  template<typename T, qualifier Q>
\n
323  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> operator%(vec<3, T, Q> const& v, T scalar);
\n
324 
\n
325  template<typename T, qualifier Q>
\n
326  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> operator%(vec<3, T, Q> const& v1, vec<1, T, Q> const& v2);
\n
327 
\n
328  template<typename T, qualifier Q>
\n
329  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> operator%(T scalar, vec<3, T, Q> const& v);
\n
330 
\n
331  template<typename T, qualifier Q>
\n
332  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> operator%(vec<1, T, Q> const& v1, vec<3, T, Q> const& v2);
\n
333 
\n
334  template<typename T, qualifier Q>
\n
335  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> operator%(vec<3, T, Q> const& v1, vec<3, T, Q> const& v2);
\n
336 
\n
337  template<typename T, qualifier Q>
\n
338  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> operator&(vec<3, T, Q> const& v1, T scalar);
\n
339 
\n
340  template<typename T, qualifier Q>
\n
341  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> operator&(vec<3, T, Q> const& v1, vec<1, T, Q> const& v2);
\n
342 
\n
343  template<typename T, qualifier Q>
\n
344  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> operator&(T scalar, vec<3, T, Q> const& v);
\n
345 
\n
346  template<typename T, qualifier Q>
\n
347  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> operator&(vec<1, T, Q> const& v1, vec<3, T, Q> const& v2);
\n
348 
\n
349  template<typename T, qualifier Q>
\n
350  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> operator&(vec<3, T, Q> const& v1, vec<3, T, Q> const& v2);
\n
351 
\n
352  template<typename T, qualifier Q>
\n
353  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> operator|(vec<3, T, Q> const& v, T scalar);
\n
354 
\n
355  template<typename T, qualifier Q>
\n
356  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> operator|(vec<3, T, Q> const& v1, vec<1, T, Q> const& v2);
\n
357 
\n
358  template<typename T, qualifier Q>
\n
359  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> operator|(T scalar, vec<3, T, Q> const& v);
\n
360 
\n
361  template<typename T, qualifier Q>
\n
362  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> operator|(vec<1, T, Q> const& v1, vec<3, T, Q> const& v2);
\n
363 
\n
364  template<typename T, qualifier Q>
\n
365  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> operator|(vec<3, T, Q> const& v1, vec<3, T, Q> const& v2);
\n
366 
\n
367  template<typename T, qualifier Q>
\n
368  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> operator^(vec<3, T, Q> const& v, T scalar);
\n
369 
\n
370  template<typename T, qualifier Q>
\n
371  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> operator^(vec<3, T, Q> const& v1, vec<1, T, Q> const& v2);
\n
372 
\n
373  template<typename T, qualifier Q>
\n
374  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> operator^(T scalar, vec<3, T, Q> const& v);
\n
375 
\n
376  template<typename T, qualifier Q>
\n
377  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> operator^(vec<1, T, Q> const& v1, vec<3, T, Q> const& v2);
\n
378 
\n
379  template<typename T, qualifier Q>
\n
380  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> operator^(vec<3, T, Q> const& v1, vec<3, T, Q> const& v2);
\n
381 
\n
382  template<typename T, qualifier Q>
\n
383  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> operator<<(vec<3, T, Q> const& v, T scalar);
\n
384 
\n
385  template<typename T, qualifier Q>
\n
386  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> operator<<(vec<3, T, Q> const& v1, vec<1, T, Q> const& v2);
\n
387 
\n
388  template<typename T, qualifier Q>
\n
389  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> operator<<(T scalar, vec<3, T, Q> const& v);
\n
390 
\n
391  template<typename T, qualifier Q>
\n
392  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> operator<<(vec<1, T, Q> const& v1, vec<3, T, Q> const& v2);
\n
393 
\n
394  template<typename T, qualifier Q>
\n
395  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> operator<<(vec<3, T, Q> const& v1, vec<3, T, Q> const& v2);
\n
396 
\n
397  template<typename T, qualifier Q>
\n
398  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> operator>>(vec<3, T, Q> const& v, T scalar);
\n
399 
\n
400  template<typename T, qualifier Q>
\n
401  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> operator>>(vec<3, T, Q> const& v1, vec<1, T, Q> const& v2);
\n
402 
\n
403  template<typename T, qualifier Q>
\n
404  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> operator>>(T scalar, vec<3, T, Q> const& v);
\n
405 
\n
406  template<typename T, qualifier Q>
\n
407  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> operator>>(vec<1, T, Q> const& v1, vec<3, T, Q> const& v2);
\n
408 
\n
409  template<typename T, qualifier Q>
\n
410  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> operator>>(vec<3, T, Q> const& v1, vec<3, T, Q> const& v2);
\n
411 
\n
412  template<typename T, qualifier Q>
\n
413  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, T, Q> operator~(vec<3, T, Q> const& v);
\n
414 
\n
415  // -- Boolean operators --
\n
416 
\n
417  template<typename T, qualifier Q>
\n
418  GLM_FUNC_DECL GLM_CONSTEXPR bool operator==(vec<3, T, Q> const& v1, vec<3, T, Q> const& v2);
\n
419 
\n
420  template<typename T, qualifier Q>
\n
421  GLM_FUNC_DECL GLM_CONSTEXPR bool operator!=(vec<3, T, Q> const& v1, vec<3, T, Q> const& v2);
\n
422 
\n
423  template<qualifier Q>
\n
424  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, bool, Q> operator&&(vec<3, bool, Q> const& v1, vec<3, bool, Q> const& v2);
\n
425 
\n
426  template<qualifier Q>
\n
427  GLM_FUNC_DECL GLM_CONSTEXPR vec<3, bool, Q> operator||(vec<3, bool, Q> const& v1, vec<3, bool, Q> const& v2);
\n
428 }//namespace glm
\n
429 
\n
430 #ifndef GLM_EXTERNAL_TEMPLATE
\n
431 #include "type_vec3.inl"
\n
432 #endif//GLM_EXTERNAL_TEMPLATE
\n
glm::length
GLM_FUNC_DECL T length(qua< T, Q > const &q)
Returns the norm of a quaternions.
\n
glm
Definition: common.hpp:20
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type_vec4.hpp File Reference
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Core features \nMore...

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Core features

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Definition in file type_vec4.hpp.

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type_vec4.hpp
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\n
\nGo to the documentation of this file.
1 
\n
4 #pragma once
\n
5 
\n
6 #include "qualifier.hpp"
\n
7 #if GLM_CONFIG_SWIZZLE == GLM_SWIZZLE_OPERATOR
\n
8 # include "_swizzle.hpp"
\n
9 #elif GLM_CONFIG_SWIZZLE == GLM_SWIZZLE_FUNCTION
\n
10 # include "_swizzle_func.hpp"
\n
11 #endif
\n
12 #include <cstddef>
\n
13 
\n
14 namespace glm
\n
15 {
\n
16  template<typename T, qualifier Q>
\n
17  struct vec<4, T, Q>
\n
18  {
\n
19  // -- Implementation detail --
\n
20 
\n
21  typedef T value_type;
\n
22  typedef vec<4, T, Q> type;
\n
23  typedef vec<4, bool, Q> bool_type;
\n
24 
\n
25  // -- Data --
\n
26 
\n
27 # if GLM_SILENT_WARNINGS == GLM_ENABLE
\n
28 # if GLM_COMPILER & GLM_COMPILER_GCC
\n
29 # pragma GCC diagnostic push
\n
30 # pragma GCC diagnostic ignored "-Wpedantic"
\n
31 # elif GLM_COMPILER & GLM_COMPILER_CLANG
\n
32 # pragma clang diagnostic push
\n
33 # pragma clang diagnostic ignored "-Wgnu-anonymous-struct"
\n
34 # pragma clang diagnostic ignored "-Wnested-anon-types"
\n
35 # elif GLM_COMPILER & GLM_COMPILER_VC
\n
36 # pragma warning(push)
\n
37 # pragma warning(disable: 4201) // nonstandard extension used : nameless struct/union
\n
38 # endif
\n
39 # endif
\n
40 
\n
41 # if GLM_CONFIG_XYZW_ONLY
\n
42  T x, y, z, w;
\n
43 # elif GLM_CONFIG_ANONYMOUS_STRUCT == GLM_ENABLE
\n
44  union
\n
45  {
\n
46  struct { T x, y, z, w; };
\n
47  struct { T r, g, b, a; };
\n
48  struct { T s, t, p, q; };
\n
49 
\n
50  typename detail::storage<4, T, detail::is_aligned<Q>::value>::type data;
\n
51 
\n
52 # if GLM_CONFIG_SWIZZLE == GLM_SWIZZLE_OPERATOR
\n
53  GLM_SWIZZLE4_2_MEMBERS(T, Q, x, y, z, w)
\n
54  GLM_SWIZZLE4_2_MEMBERS(T, Q, r, g, b, a)
\n
55  GLM_SWIZZLE4_2_MEMBERS(T, Q, s, t, p, q)
\n
56  GLM_SWIZZLE4_3_MEMBERS(T, Q, x, y, z, w)
\n
57  GLM_SWIZZLE4_3_MEMBERS(T, Q, r, g, b, a)
\n
58  GLM_SWIZZLE4_3_MEMBERS(T, Q, s, t, p, q)
\n
59  GLM_SWIZZLE4_4_MEMBERS(T, Q, x, y, z, w)
\n
60  GLM_SWIZZLE4_4_MEMBERS(T, Q, r, g, b, a)
\n
61  GLM_SWIZZLE4_4_MEMBERS(T, Q, s, t, p, q)
\n
62 # endif
\n
63  };
\n
64 # else
\n
65  union { T x, r, s; };
\n
66  union { T y, g, t; };
\n
67  union { T z, b, p; };
\n
68  union { T w, a, q; };
\n
69 
\n
70 # if GLM_CONFIG_SWIZZLE == GLM_SWIZZLE_FUNCTION
\n
71  GLM_SWIZZLE_GEN_VEC_FROM_VEC4(T, Q)
\n
72 # endif
\n
73 # endif
\n
74 
\n
75 # if GLM_SILENT_WARNINGS == GLM_ENABLE
\n
76 # if GLM_COMPILER & GLM_COMPILER_CLANG
\n
77 # pragma clang diagnostic pop
\n
78 # elif GLM_COMPILER & GLM_COMPILER_GCC
\n
79 # pragma GCC diagnostic pop
\n
80 # elif GLM_COMPILER & GLM_COMPILER_VC
\n
81 # pragma warning(pop)
\n
82 # endif
\n
83 # endif
\n
84 
\n
85  // -- Component accesses --
\n
86 
\n
87  typedef length_t length_type;
\n
88 
\n
90  GLM_FUNC_DECL static GLM_CONSTEXPR length_type length(){return 4;}
\n
91 
\n
92  GLM_FUNC_DECL GLM_CONSTEXPR T & operator[](length_type i);
\n
93  GLM_FUNC_DECL GLM_CONSTEXPR T const& operator[](length_type i) const;
\n
94 
\n
95  // -- Implicit basic constructors --
\n
96 
\n
97  GLM_FUNC_DECL GLM_CONSTEXPR vec() GLM_DEFAULT;
\n
98  GLM_FUNC_DECL GLM_CONSTEXPR vec(vec<4, T, Q> const& v) GLM_DEFAULT;
\n
99  template<qualifier P>
\n
100  GLM_FUNC_DECL GLM_CONSTEXPR vec(vec<4, T, P> const& v);
\n
101 
\n
102  // -- Explicit basic constructors --
\n
103 
\n
104  GLM_FUNC_DECL GLM_CONSTEXPR explicit vec(T scalar);
\n
105  GLM_FUNC_DECL GLM_CONSTEXPR vec(T x, T y, T z, T w);
\n
106 
\n
107  // -- Conversion scalar constructors --
\n
108 
\n
109  template<typename U, qualifier P>
\n
110  GLM_FUNC_DECL GLM_CONSTEXPR explicit vec(vec<1, U, P> const& v);
\n
111 
\n
113  template<typename X, typename Y, typename Z, typename W>
\n
114  GLM_FUNC_DECL GLM_CONSTEXPR vec(X _x, Y _y, Z _z, W _w);
\n
115  template<typename X, typename Y, typename Z, typename W>
\n
116  GLM_FUNC_DECL GLM_CONSTEXPR vec(vec<1, X, Q> const& _x, Y _y, Z _z, W _w);
\n
117  template<typename X, typename Y, typename Z, typename W>
\n
118  GLM_FUNC_DECL GLM_CONSTEXPR vec(X _x, vec<1, Y, Q> const& _y, Z _z, W _w);
\n
119  template<typename X, typename Y, typename Z, typename W>
\n
120  GLM_FUNC_DECL GLM_CONSTEXPR vec(vec<1, X, Q> const& _x, vec<1, Y, Q> const& _y, Z _z, W _w);
\n
121  template<typename X, typename Y, typename Z, typename W>
\n
122  GLM_FUNC_DECL GLM_CONSTEXPR vec(X _x, Y _y, vec<1, Z, Q> const& _z, W _w);
\n
123  template<typename X, typename Y, typename Z, typename W>
\n
124  GLM_FUNC_DECL GLM_CONSTEXPR vec(vec<1, X, Q> const& _x, Y _y, vec<1, Z, Q> const& _z, W _w);
\n
125  template<typename X, typename Y, typename Z, typename W>
\n
126  GLM_FUNC_DECL GLM_CONSTEXPR vec(X _x, vec<1, Y, Q> const& _y, vec<1, Z, Q> const& _z, W _w);
\n
127  template<typename X, typename Y, typename Z, typename W>
\n
128  GLM_FUNC_DECL GLM_CONSTEXPR vec(vec<1, X, Q> const& _x, vec<1, Y, Q> const& _y, vec<1, Z, Q> const& _z, W _w);
\n
129  template<typename X, typename Y, typename Z, typename W>
\n
130  GLM_FUNC_DECL GLM_CONSTEXPR vec(vec<1, X, Q> const& _x, Y _y, Z _z, vec<1, W, Q> const& _w);
\n
131  template<typename X, typename Y, typename Z, typename W>
\n
132  GLM_FUNC_DECL GLM_CONSTEXPR vec(X _x, vec<1, Y, Q> const& _y, Z _z, vec<1, W, Q> const& _w);
\n
133  template<typename X, typename Y, typename Z, typename W>
\n
134  GLM_FUNC_DECL GLM_CONSTEXPR vec(vec<1, X, Q> const& _x, vec<1, Y, Q> const& _y, Z _z, vec<1, W, Q> const& _w);
\n
135  template<typename X, typename Y, typename Z, typename W>
\n
136  GLM_FUNC_DECL GLM_CONSTEXPR vec(X _x, Y _y, vec<1, Z, Q> const& _z, vec<1, W, Q> const& _w);
\n
137  template<typename X, typename Y, typename Z, typename W>
\n
138  GLM_FUNC_DECL GLM_CONSTEXPR vec(vec<1, X, Q> const& _x, Y _y, vec<1, Z, Q> const& _z, vec<1, W, Q> const& _w);
\n
139  template<typename X, typename Y, typename Z, typename W>
\n
140  GLM_FUNC_DECL GLM_CONSTEXPR vec(X _x, vec<1, Y, Q> const& _y, vec<1, Z, Q> const& _z, vec<1, W, Q> const& _w);
\n
141  template<typename X, typename Y, typename Z, typename W>
\n
142  GLM_FUNC_DECL GLM_CONSTEXPR vec(vec<1, X, Q> const& _x, vec<1, Y, Q> const& _Y, vec<1, Z, Q> const& _z, vec<1, W, Q> const& _w);
\n
143 
\n
144  // -- Conversion vector constructors --
\n
145 
\n
147  template<typename A, typename B, typename C, qualifier P>
\n
148  GLM_FUNC_DECL GLM_CONSTEXPR vec(vec<2, A, P> const& _xy, B _z, C _w);
\n
150  template<typename A, typename B, typename C, qualifier P>
\n
151  GLM_FUNC_DECL GLM_CONSTEXPR vec(vec<2, A, P> const& _xy, vec<1, B, P> const& _z, C _w);
\n
153  template<typename A, typename B, typename C, qualifier P>
\n
154  GLM_FUNC_DECL GLM_CONSTEXPR vec(vec<2, A, P> const& _xy, B _z, vec<1, C, P> const& _w);
\n
156  template<typename A, typename B, typename C, qualifier P>
\n
157  GLM_FUNC_DECL GLM_CONSTEXPR vec(vec<2, A, P> const& _xy, vec<1, B, P> const& _z, vec<1, C, P> const& _w);
\n
159  template<typename A, typename B, typename C, qualifier P>
\n
160  GLM_FUNC_DECL GLM_CONSTEXPR vec(A _x, vec<2, B, P> const& _yz, C _w);
\n
162  template<typename A, typename B, typename C, qualifier P>
\n
163  GLM_FUNC_DECL GLM_CONSTEXPR vec(vec<1, A, P> const& _x, vec<2, B, P> const& _yz, C _w);
\n
165  template<typename A, typename B, typename C, qualifier P>
\n
166  GLM_FUNC_DECL GLM_CONSTEXPR vec(A _x, vec<2, B, P> const& _yz, vec<1, C, P> const& _w);
\n
168  template<typename A, typename B, typename C, qualifier P>
\n
169  GLM_FUNC_DECL GLM_CONSTEXPR vec(vec<1, A, P> const& _x, vec<2, B, P> const& _yz, vec<1, C, P> const& _w);
\n
171  template<typename A, typename B, typename C, qualifier P>
\n
172  GLM_FUNC_DECL GLM_CONSTEXPR vec(A _x, B _y, vec<2, C, P> const& _zw);
\n
174  template<typename A, typename B, typename C, qualifier P>
\n
175  GLM_FUNC_DECL GLM_CONSTEXPR vec(vec<1, A, P> const& _x, B _y, vec<2, C, P> const& _zw);
\n
177  template<typename A, typename B, typename C, qualifier P>
\n
178  GLM_FUNC_DECL GLM_CONSTEXPR vec(A _x, vec<1, B, P> const& _y, vec<2, C, P> const& _zw);
\n
180  template<typename A, typename B, typename C, qualifier P>
\n
181  GLM_FUNC_DECL GLM_CONSTEXPR vec(vec<1, A, P> const& _x, vec<1, B, P> const& _y, vec<2, C, P> const& _zw);
\n
183  template<typename A, typename B, qualifier P>
\n
184  GLM_FUNC_DECL GLM_CONSTEXPR vec(vec<3, A, P> const& _xyz, B _w);
\n
186  template<typename A, typename B, qualifier P>
\n
187  GLM_FUNC_DECL GLM_CONSTEXPR vec(vec<3, A, P> const& _xyz, vec<1, B, P> const& _w);
\n
189  template<typename A, typename B, qualifier P>
\n
190  GLM_FUNC_DECL GLM_CONSTEXPR vec(A _x, vec<3, B, P> const& _yzw);
\n
192  template<typename A, typename B, qualifier P>
\n
193  GLM_FUNC_DECL GLM_CONSTEXPR vec(vec<1, A, P> const& _x, vec<3, B, P> const& _yzw);
\n
195  template<typename A, typename B, qualifier P>
\n
196  GLM_FUNC_DECL GLM_CONSTEXPR vec(vec<2, A, P> const& _xy, vec<2, B, P> const& _zw);
\n
197 
\n
199  template<typename U, qualifier P>
\n
200  GLM_FUNC_DECL GLM_CONSTEXPR GLM_EXPLICIT vec(vec<4, U, P> const& v);
\n
201 
\n
202  // -- Swizzle constructors --
\n
203 # if GLM_CONFIG_SWIZZLE == GLM_SWIZZLE_OPERATOR
\n
204  template<int E0, int E1, int E2, int E3>
\n
205  GLM_FUNC_DECL GLM_CONSTEXPR vec(detail::_swizzle<4, T, Q, E0, E1, E2, E3> const& that)
\n
206  {
\n
207  *this = that();
\n
208  }
\n
209 
\n
210  template<int E0, int E1, int F0, int F1>
\n
211  GLM_FUNC_DECL GLM_CONSTEXPR vec(detail::_swizzle<2, T, Q, E0, E1, -1, -2> const& v, detail::_swizzle<2, T, Q, F0, F1, -1, -2> const& u)
\n
212  {
\n
213  *this = vec<4, T, Q>(v(), u());
\n
214  }
\n
215 
\n
216  template<int E0, int E1>
\n
217  GLM_FUNC_DECL GLM_CONSTEXPR vec(T const& x, T const& y, detail::_swizzle<2, T, Q, E0, E1, -1, -2> const& v)
\n
218  {
\n
219  *this = vec<4, T, Q>(x, y, v());
\n
220  }
\n
221 
\n
222  template<int E0, int E1>
\n
223  GLM_FUNC_DECL GLM_CONSTEXPR vec(T const& x, detail::_swizzle<2, T, Q, E0, E1, -1, -2> const& v, T const& w)
\n
224  {
\n
225  *this = vec<4, T, Q>(x, v(), w);
\n
226  }
\n
227 
\n
228  template<int E0, int E1>
\n
229  GLM_FUNC_DECL GLM_CONSTEXPR vec(detail::_swizzle<2, T, Q, E0, E1, -1, -2> const& v, T const& z, T const& w)
\n
230  {
\n
231  *this = vec<4, T, Q>(v(), z, w);
\n
232  }
\n
233 
\n
234  template<int E0, int E1, int E2>
\n
235  GLM_FUNC_DECL GLM_CONSTEXPR vec(detail::_swizzle<3, T, Q, E0, E1, E2, -1> const& v, T const& w)
\n
236  {
\n
237  *this = vec<4, T, Q>(v(), w);
\n
238  }
\n
239 
\n
240  template<int E0, int E1, int E2>
\n
241  GLM_FUNC_DECL GLM_CONSTEXPR vec(T const& x, detail::_swizzle<3, T, Q, E0, E1, E2, -1> const& v)
\n
242  {
\n
243  *this = vec<4, T, Q>(x, v());
\n
244  }
\n
245 # endif//GLM_CONFIG_SWIZZLE == GLM_SWIZZLE_OPERATOR
\n
246 
\n
247  // -- Unary arithmetic operators --
\n
248 
\n
249  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q>& operator=(vec<4, T, Q> const& v) GLM_DEFAULT;
\n
250 
\n
251  template<typename U>
\n
252  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q>& operator=(vec<4, U, Q> const& v);
\n
253  template<typename U>
\n
254  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q>& operator+=(U scalar);
\n
255  template<typename U>
\n
256  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q>& operator+=(vec<1, U, Q> const& v);
\n
257  template<typename U>
\n
258  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q>& operator+=(vec<4, U, Q> const& v);
\n
259  template<typename U>
\n
260  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q>& operator-=(U scalar);
\n
261  template<typename U>
\n
262  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q>& operator-=(vec<1, U, Q> const& v);
\n
263  template<typename U>
\n
264  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q>& operator-=(vec<4, U, Q> const& v);
\n
265  template<typename U>
\n
266  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q>& operator*=(U scalar);
\n
267  template<typename U>
\n
268  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q>& operator*=(vec<1, U, Q> const& v);
\n
269  template<typename U>
\n
270  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q>& operator*=(vec<4, U, Q> const& v);
\n
271  template<typename U>
\n
272  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q>& operator/=(U scalar);
\n
273  template<typename U>
\n
274  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q>& operator/=(vec<1, U, Q> const& v);
\n
275  template<typename U>
\n
276  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q>& operator/=(vec<4, U, Q> const& v);
\n
277 
\n
278  // -- Increment and decrement operators --
\n
279 
\n
280  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> & operator++();
\n
281  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> & operator--();
\n
282  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> operator++(int);
\n
283  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> operator--(int);
\n
284 
\n
285  // -- Unary bit operators --
\n
286 
\n
287  template<typename U>
\n
288  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> & operator%=(U scalar);
\n
289  template<typename U>
\n
290  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> & operator%=(vec<1, U, Q> const& v);
\n
291  template<typename U>
\n
292  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> & operator%=(vec<4, U, Q> const& v);
\n
293  template<typename U>
\n
294  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> & operator&=(U scalar);
\n
295  template<typename U>
\n
296  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> & operator&=(vec<1, U, Q> const& v);
\n
297  template<typename U>
\n
298  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> & operator&=(vec<4, U, Q> const& v);
\n
299  template<typename U>
\n
300  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> & operator|=(U scalar);
\n
301  template<typename U>
\n
302  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> & operator|=(vec<1, U, Q> const& v);
\n
303  template<typename U>
\n
304  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> & operator|=(vec<4, U, Q> const& v);
\n
305  template<typename U>
\n
306  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> & operator^=(U scalar);
\n
307  template<typename U>
\n
308  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> & operator^=(vec<1, U, Q> const& v);
\n
309  template<typename U>
\n
310  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> & operator^=(vec<4, U, Q> const& v);
\n
311  template<typename U>
\n
312  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> & operator<<=(U scalar);
\n
313  template<typename U>
\n
314  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> & operator<<=(vec<1, U, Q> const& v);
\n
315  template<typename U>
\n
316  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> & operator<<=(vec<4, U, Q> const& v);
\n
317  template<typename U>
\n
318  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> & operator>>=(U scalar);
\n
319  template<typename U>
\n
320  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> & operator>>=(vec<1, U, Q> const& v);
\n
321  template<typename U>
\n
322  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> & operator>>=(vec<4, U, Q> const& v);
\n
323  };
\n
324 
\n
325  // -- Unary operators --
\n
326 
\n
327  template<typename T, qualifier Q>
\n
328  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> operator+(vec<4, T, Q> const& v);
\n
329 
\n
330  template<typename T, qualifier Q>
\n
331  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> operator-(vec<4, T, Q> const& v);
\n
332 
\n
333  // -- Binary operators --
\n
334 
\n
335  template<typename T, qualifier Q>
\n
336  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> operator+(vec<4, T, Q> const& v, T const & scalar);
\n
337 
\n
338  template<typename T, qualifier Q>
\n
339  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> operator+(vec<4, T, Q> const& v1, vec<1, T, Q> const& v2);
\n
340 
\n
341  template<typename T, qualifier Q>
\n
342  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> operator+(T scalar, vec<4, T, Q> const& v);
\n
343 
\n
344  template<typename T, qualifier Q>
\n
345  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> operator+(vec<1, T, Q> const& v1, vec<4, T, Q> const& v2);
\n
346 
\n
347  template<typename T, qualifier Q>
\n
348  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> operator+(vec<4, T, Q> const& v1, vec<4, T, Q> const& v2);
\n
349 
\n
350  template<typename T, qualifier Q>
\n
351  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> operator-(vec<4, T, Q> const& v, T const & scalar);
\n
352 
\n
353  template<typename T, qualifier Q>
\n
354  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> operator-(vec<4, T, Q> const& v1, vec<1, T, Q> const& v2);
\n
355 
\n
356  template<typename T, qualifier Q>
\n
357  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> operator-(T scalar, vec<4, T, Q> const& v);
\n
358 
\n
359  template<typename T, qualifier Q>
\n
360  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> operator-(vec<1, T, Q> const& v1, vec<4, T, Q> const& v2);
\n
361 
\n
362  template<typename T, qualifier Q>
\n
363  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> operator-(vec<4, T, Q> const& v1, vec<4, T, Q> const& v2);
\n
364 
\n
365  template<typename T, qualifier Q>
\n
366  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> operator*(vec<4, T, Q> const& v, T const & scalar);
\n
367 
\n
368  template<typename T, qualifier Q>
\n
369  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> operator*(vec<4, T, Q> const& v1, vec<1, T, Q> const& v2);
\n
370 
\n
371  template<typename T, qualifier Q>
\n
372  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> operator*(T scalar, vec<4, T, Q> const& v);
\n
373 
\n
374  template<typename T, qualifier Q>
\n
375  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> operator*(vec<1, T, Q> const& v1, vec<4, T, Q> const& v2);
\n
376 
\n
377  template<typename T, qualifier Q>
\n
378  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> operator*(vec<4, T, Q> const& v1, vec<4, T, Q> const& v2);
\n
379 
\n
380  template<typename T, qualifier Q>
\n
381  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> operator/(vec<4, T, Q> const& v, T const & scalar);
\n
382 
\n
383  template<typename T, qualifier Q>
\n
384  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> operator/(vec<4, T, Q> const& v1, vec<1, T, Q> const& v2);
\n
385 
\n
386  template<typename T, qualifier Q>
\n
387  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> operator/(T scalar, vec<4, T, Q> const& v);
\n
388 
\n
389  template<typename T, qualifier Q>
\n
390  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> operator/(vec<1, T, Q> const& v1, vec<4, T, Q> const& v2);
\n
391 
\n
392  template<typename T, qualifier Q>
\n
393  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> operator/(vec<4, T, Q> const& v1, vec<4, T, Q> const& v2);
\n
394 
\n
395  template<typename T, qualifier Q>
\n
396  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> operator%(vec<4, T, Q> const& v, T scalar);
\n
397 
\n
398  template<typename T, qualifier Q>
\n
399  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> operator%(vec<4, T, Q> const& v, vec<1, T, Q> const& scalar);
\n
400 
\n
401  template<typename T, qualifier Q>
\n
402  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> operator%(T scalar, vec<4, T, Q> const& v);
\n
403 
\n
404  template<typename T, qualifier Q>
\n
405  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> operator%(vec<1, T, Q> const& scalar, vec<4, T, Q> const& v);
\n
406 
\n
407  template<typename T, qualifier Q>
\n
408  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> operator%(vec<4, T, Q> const& v1, vec<4, T, Q> const& v2);
\n
409 
\n
410  template<typename T, qualifier Q>
\n
411  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> operator&(vec<4, T, Q> const& v, T scalar);
\n
412 
\n
413  template<typename T, qualifier Q>
\n
414  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> operator&(vec<4, T, Q> const& v, vec<1, T, Q> const& scalar);
\n
415 
\n
416  template<typename T, qualifier Q>
\n
417  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> operator&(T scalar, vec<4, T, Q> const& v);
\n
418 
\n
419  template<typename T, qualifier Q>
\n
420  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> operator&(vec<1, T, Q> const& scalar, vec<4, T, Q> const& v);
\n
421 
\n
422  template<typename T, qualifier Q>
\n
423  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> operator&(vec<4, T, Q> const& v1, vec<4, T, Q> const& v2);
\n
424 
\n
425  template<typename T, qualifier Q>
\n
426  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> operator|(vec<4, T, Q> const& v, T scalar);
\n
427 
\n
428  template<typename T, qualifier Q>
\n
429  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> operator|(vec<4, T, Q> const& v, vec<1, T, Q> const& scalar);
\n
430 
\n
431  template<typename T, qualifier Q>
\n
432  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> operator|(T scalar, vec<4, T, Q> const& v);
\n
433 
\n
434  template<typename T, qualifier Q>
\n
435  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> operator|(vec<1, T, Q> const& scalar, vec<4, T, Q> const& v);
\n
436 
\n
437  template<typename T, qualifier Q>
\n
438  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> operator|(vec<4, T, Q> const& v1, vec<4, T, Q> const& v2);
\n
439 
\n
440  template<typename T, qualifier Q>
\n
441  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> operator^(vec<4, T, Q> const& v, T scalar);
\n
442 
\n
443  template<typename T, qualifier Q>
\n
444  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> operator^(vec<4, T, Q> const& v, vec<1, T, Q> const& scalar);
\n
445 
\n
446  template<typename T, qualifier Q>
\n
447  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> operator^(T scalar, vec<4, T, Q> const& v);
\n
448 
\n
449  template<typename T, qualifier Q>
\n
450  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> operator^(vec<1, T, Q> const& scalar, vec<4, T, Q> const& v);
\n
451 
\n
452  template<typename T, qualifier Q>
\n
453  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> operator^(vec<4, T, Q> const& v1, vec<4, T, Q> const& v2);
\n
454 
\n
455  template<typename T, qualifier Q>
\n
456  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> operator<<(vec<4, T, Q> const& v, T scalar);
\n
457 
\n
458  template<typename T, qualifier Q>
\n
459  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> operator<<(vec<4, T, Q> const& v, vec<1, T, Q> const& scalar);
\n
460 
\n
461  template<typename T, qualifier Q>
\n
462  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> operator<<(T scalar, vec<4, T, Q> const& v);
\n
463 
\n
464  template<typename T, qualifier Q>
\n
465  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> operator<<(vec<1, T, Q> const& scalar, vec<4, T, Q> const& v);
\n
466 
\n
467  template<typename T, qualifier Q>
\n
468  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> operator<<(vec<4, T, Q> const& v1, vec<4, T, Q> const& v2);
\n
469 
\n
470  template<typename T, qualifier Q>
\n
471  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> operator>>(vec<4, T, Q> const& v, T scalar);
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472 
\n
473  template<typename T, qualifier Q>
\n
474  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> operator>>(vec<4, T, Q> const& v, vec<1, T, Q> const& scalar);
\n
475 
\n
476  template<typename T, qualifier Q>
\n
477  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> operator>>(T scalar, vec<4, T, Q> const& v);
\n
478 
\n
479  template<typename T, qualifier Q>
\n
480  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> operator>>(vec<1, T, Q> const& scalar, vec<4, T, Q> const& v);
\n
481 
\n
482  template<typename T, qualifier Q>
\n
483  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> operator>>(vec<4, T, Q> const& v1, vec<4, T, Q> const& v2);
\n
484 
\n
485  template<typename T, qualifier Q>
\n
486  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, T, Q> operator~(vec<4, T, Q> const& v);
\n
487 
\n
488  // -- Boolean operators --
\n
489 
\n
490  template<typename T, qualifier Q>
\n
491  GLM_FUNC_DECL GLM_CONSTEXPR bool operator==(vec<4, T, Q> const& v1, vec<4, T, Q> const& v2);
\n
492 
\n
493  template<typename T, qualifier Q>
\n
494  GLM_FUNC_DECL GLM_CONSTEXPR bool operator!=(vec<4, T, Q> const& v1, vec<4, T, Q> const& v2);
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495 
\n
496  template<qualifier Q>
\n
497  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, bool, Q> operator&&(vec<4, bool, Q> const& v1, vec<4, bool, Q> const& v2);
\n
498 
\n
499  template<qualifier Q>
\n
500  GLM_FUNC_DECL GLM_CONSTEXPR vec<4, bool, Q> operator||(vec<4, bool, Q> const& v1, vec<4, bool, Q> const& v2);
\n
501 }//namespace glm
\n
502 
\n
503 #ifndef GLM_EXTERNAL_TEMPLATE
\n
504 #include "type_vec4.inl"
\n
505 #endif//GLM_EXTERNAL_TEMPLATE
\n
glm::length
GLM_FUNC_DECL T length(qua< T, Q > const &q)
Returns the norm of a quaternions.
\n
glm
Definition: common.hpp:20
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\nFunctions
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ulp.hpp File Reference
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GLM_GTC_ulp \nMore...

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\nFunctions

GLM_FUNC_DECL int float_distance (float x, float y)
 Return the distance in the number of ULP between 2 single-precision floating-point scalars. More...
 
GLM_FUNC_DECL int64 float_distance (double x, double y)
 Return the distance in the number of ULP between 2 double-precision floating-point scalars. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, int, Q > float_distance (vec< L, float, Q > const &x, vec< L, float, Q > const &y)
 Return the distance in the number of ULP between 2 single-precision floating-point scalars. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, int64, Q > float_distance (vec< L, double, Q > const &x, vec< L, double, Q > const &y)
 Return the distance in the number of ULP between 2 double-precision floating-point scalars. More...
 
template<typename genType >
GLM_FUNC_DECL genType next_float (genType x)
 Return the next ULP value(s) after the input value(s). More...
 
template<typename genType >
GLM_FUNC_DECL genType next_float (genType x, int ULPs)
 Return the value(s) ULP distance after the input value(s). More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > next_float (vec< L, T, Q > const &x)
 Return the next ULP value(s) after the input value(s). More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > next_float (vec< L, T, Q > const &x, int ULPs)
 Return the value(s) ULP distance after the input value(s). More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > next_float (vec< L, T, Q > const &x, vec< L, int, Q > const &ULPs)
 Return the value(s) ULP distance after the input value(s). More...
 
template<typename genType >
GLM_FUNC_DECL genType prev_float (genType x)
 Return the previous ULP value(s) before the input value(s). More...
 
template<typename genType >
GLM_FUNC_DECL genType prev_float (genType x, int ULPs)
 Return the value(s) ULP distance before the input value(s). More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > prev_float (vec< L, T, Q > const &x)
 Return the previous ULP value(s) before the input value(s). More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > prev_float (vec< L, T, Q > const &x, int ULPs)
 Return the value(s) ULP distance before the input value(s). More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > prev_float (vec< L, T, Q > const &x, vec< L, int, Q > const &ULPs)
 Return the value(s) ULP distance before the input value(s). More...
 
\n

Detailed Description

\n

GLM_GTC_ulp

\n
See also
Core features (dependence)
\n\n

Definition in file ulp.hpp.

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ulp.hpp
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1 
\n
15 #pragma once
\n
16 
\n
17 // Dependencies
\n
18 #include "../detail/setup.hpp"
\n
19 #include "../detail/qualifier.hpp"
\n
20 #include "../detail/_vectorize.hpp"
\n
21 #include "../ext/scalar_int_sized.hpp"
\n
22 
\n
23 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
24 # pragma message("GLM: GLM_GTC_ulp extension included")
\n
25 #endif
\n
26 
\n
27 namespace glm
\n
28 {
\n
34  template<typename genType>
\n
35  GLM_FUNC_DECL genType next_float(genType x);
\n
36 
\n
42  template<typename genType>
\n
43  GLM_FUNC_DECL genType prev_float(genType x);
\n
44 
\n
50  template<typename genType>
\n
51  GLM_FUNC_DECL genType next_float(genType x, int ULPs);
\n
52 
\n
58  template<typename genType>
\n
59  GLM_FUNC_DECL genType prev_float(genType x, int ULPs);
\n
60 
\n
64  GLM_FUNC_DECL int float_distance(float x, float y);
\n
65 
\n
69  GLM_FUNC_DECL int64 float_distance(double x, double y);
\n
70 
\n
78  template<length_t L, typename T, qualifier Q>
\n
79  GLM_FUNC_DECL vec<L, T, Q> next_float(vec<L, T, Q> const& x);
\n
80 
\n
88  template<length_t L, typename T, qualifier Q>
\n
89  GLM_FUNC_DECL vec<L, T, Q> next_float(vec<L, T, Q> const& x, int ULPs);
\n
90 
\n
98  template<length_t L, typename T, qualifier Q>
\n
99  GLM_FUNC_DECL vec<L, T, Q> next_float(vec<L, T, Q> const& x, vec<L, int, Q> const& ULPs);
\n
100 
\n
108  template<length_t L, typename T, qualifier Q>
\n
109  GLM_FUNC_DECL vec<L, T, Q> prev_float(vec<L, T, Q> const& x);
\n
110 
\n
118  template<length_t L, typename T, qualifier Q>
\n
119  GLM_FUNC_DECL vec<L, T, Q> prev_float(vec<L, T, Q> const& x, int ULPs);
\n
120 
\n
128  template<length_t L, typename T, qualifier Q>
\n
129  GLM_FUNC_DECL vec<L, T, Q> prev_float(vec<L, T, Q> const& x, vec<L, int, Q> const& ULPs);
\n
130 
\n
137  template<length_t L, typename T, qualifier Q>
\n
138  GLM_FUNC_DECL vec<L, int, Q> float_distance(vec<L, float, Q> const& x, vec<L, float, Q> const& y);
\n
139 
\n
146  template<length_t L, typename T, qualifier Q>
\n
147  GLM_FUNC_DECL vec<L, int64, Q> float_distance(vec<L, double, Q> const& x, vec<L, double, Q> const& y);
\n
148 
\n
150 }//namespace glm
\n
151 
\n
152 #include "ulp.inl"
\n
glm::int64
detail::int64 int64
64 bit signed integer type.
Definition: scalar_int_sized.hpp:67
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glm
Definition: common.hpp:20
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GLM_GTC_vec1 \nMore...

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GLM_GTC_vec1

\n
See also
Core features (dependence)
\n\n

Definition in file vec1.hpp.

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vec1.hpp
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13 #pragma once
\n
14 
\n
15 // Dependency:
\n
16 #include "../ext/vector_bool1.hpp"
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17 #include "../ext/vector_bool1_precision.hpp"
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18 #include "../ext/vector_float1.hpp"
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19 #include "../ext/vector_float1_precision.hpp"
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20 #include "../ext/vector_double1.hpp"
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21 #include "../ext/vector_double1_precision.hpp"
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22 #include "../ext/vector_int1.hpp"
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23 #include "../ext/vector_int1_precision.hpp"
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24 #include "../ext/vector_uint1.hpp"
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25 #include "../ext/vector_uint1_precision.hpp"
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26 
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27 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
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28 # pragma message("GLM: GLM_GTC_vec1 extension included")
\n
29 #endif
\n
30 
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Definition in file vec2.hpp.

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4 #pragma once
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5 #include "./ext/vector_bool2.hpp"
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6 #include "./ext/vector_bool2_precision.hpp"
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7 #include "./ext/vector_float2.hpp"
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8 #include "./ext/vector_float2_precision.hpp"
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9 #include "./ext/vector_double2.hpp"
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10 #include "./ext/vector_double2_precision.hpp"
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11 #include "./ext/vector_int2.hpp"
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Definition in file vec3.hpp.

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4 #pragma once
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5 #include "./ext/vector_bool3.hpp"
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6 #include "./ext/vector_bool3_precision.hpp"
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8 #include "./ext/vector_float3_precision.hpp"
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9 #include "./ext/vector_double3.hpp"
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10 #include "./ext/vector_double3_precision.hpp"
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11 #include "./ext/vector_int3.hpp"
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13 #include "./ext/vector_uint3.hpp"
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14 #include "./ext/vector_uint3_precision.hpp"
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4 #pragma once
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5 #include "./ext/vector_bool4.hpp"
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9 #include "./ext/vector_double4.hpp"
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\n\n

Definition in file vec_swizzle.hpp.

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vec_swizzle.hpp
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\nGo to the documentation of this file.
1 
\n
13 #pragma once
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14 
\n
15 #include "../glm.hpp"
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16 
\n
17 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
18 # ifndef GLM_ENABLE_EXPERIMENTAL
\n
19 # pragma message("GLM: GLM_GTX_vec_swizzle is an experimental extension and may change in the future. Use #define GLM_ENABLE_EXPERIMENTAL before including it, if you really want to use it.")
\n
20 # else
\n
21 # pragma message("GLM: GLM_GTX_vec_swizzle extension included")
\n
22 # endif
\n
23 #endif
\n
24 
\n
25 namespace glm {
\n
26  // xx
\n
27  template<typename T, qualifier Q>
\n
28  GLM_INLINE glm::vec<2, T, Q> xx(const glm::vec<1, T, Q> &v) {
\n
29  return glm::vec<2, T, Q>(v.x, v.x);
\n
30  }
\n
31 
\n
32  template<typename T, qualifier Q>
\n
33  GLM_INLINE glm::vec<2, T, Q> xx(const glm::vec<2, T, Q> &v) {
\n
34  return glm::vec<2, T, Q>(v.x, v.x);
\n
35  }
\n
36 
\n
37  template<typename T, qualifier Q>
\n
38  GLM_INLINE glm::vec<2, T, Q> xx(const glm::vec<3, T, Q> &v) {
\n
39  return glm::vec<2, T, Q>(v.x, v.x);
\n
40  }
\n
41 
\n
42  template<typename T, qualifier Q>
\n
43  GLM_INLINE glm::vec<2, T, Q> xx(const glm::vec<4, T, Q> &v) {
\n
44  return glm::vec<2, T, Q>(v.x, v.x);
\n
45  }
\n
46 
\n
47  // xy
\n
48  template<typename T, qualifier Q>
\n
49  GLM_INLINE glm::vec<2, T, Q> xy(const glm::vec<2, T, Q> &v) {
\n
50  return glm::vec<2, T, Q>(v.x, v.y);
\n
51  }
\n
52 
\n
53  template<typename T, qualifier Q>
\n
54  GLM_INLINE glm::vec<2, T, Q> xy(const glm::vec<3, T, Q> &v) {
\n
55  return glm::vec<2, T, Q>(v.x, v.y);
\n
56  }
\n
57 
\n
58  template<typename T, qualifier Q>
\n
59  GLM_INLINE glm::vec<2, T, Q> xy(const glm::vec<4, T, Q> &v) {
\n
60  return glm::vec<2, T, Q>(v.x, v.y);
\n
61  }
\n
62 
\n
63  // xz
\n
64  template<typename T, qualifier Q>
\n
65  GLM_INLINE glm::vec<2, T, Q> xz(const glm::vec<3, T, Q> &v) {
\n
66  return glm::vec<2, T, Q>(v.x, v.z);
\n
67  }
\n
68 
\n
69  template<typename T, qualifier Q>
\n
70  GLM_INLINE glm::vec<2, T, Q> xz(const glm::vec<4, T, Q> &v) {
\n
71  return glm::vec<2, T, Q>(v.x, v.z);
\n
72  }
\n
73 
\n
74  // xw
\n
75  template<typename T, qualifier Q>
\n
76  GLM_INLINE glm::vec<2, T, Q> xw(const glm::vec<4, T, Q> &v) {
\n
77  return glm::vec<2, T, Q>(v.x, v.w);
\n
78  }
\n
79 
\n
80  // yx
\n
81  template<typename T, qualifier Q>
\n
82  GLM_INLINE glm::vec<2, T, Q> yx(const glm::vec<2, T, Q> &v) {
\n
83  return glm::vec<2, T, Q>(v.y, v.x);
\n
84  }
\n
85 
\n
86  template<typename T, qualifier Q>
\n
87  GLM_INLINE glm::vec<2, T, Q> yx(const glm::vec<3, T, Q> &v) {
\n
88  return glm::vec<2, T, Q>(v.y, v.x);
\n
89  }
\n
90 
\n
91  template<typename T, qualifier Q>
\n
92  GLM_INLINE glm::vec<2, T, Q> yx(const glm::vec<4, T, Q> &v) {
\n
93  return glm::vec<2, T, Q>(v.y, v.x);
\n
94  }
\n
95 
\n
96  // yy
\n
97  template<typename T, qualifier Q>
\n
98  GLM_INLINE glm::vec<2, T, Q> yy(const glm::vec<2, T, Q> &v) {
\n
99  return glm::vec<2, T, Q>(v.y, v.y);
\n
100  }
\n
101 
\n
102  template<typename T, qualifier Q>
\n
103  GLM_INLINE glm::vec<2, T, Q> yy(const glm::vec<3, T, Q> &v) {
\n
104  return glm::vec<2, T, Q>(v.y, v.y);
\n
105  }
\n
106 
\n
107  template<typename T, qualifier Q>
\n
108  GLM_INLINE glm::vec<2, T, Q> yy(const glm::vec<4, T, Q> &v) {
\n
109  return glm::vec<2, T, Q>(v.y, v.y);
\n
110  }
\n
111 
\n
112  // yz
\n
113  template<typename T, qualifier Q>
\n
114  GLM_INLINE glm::vec<2, T, Q> yz(const glm::vec<3, T, Q> &v) {
\n
115  return glm::vec<2, T, Q>(v.y, v.z);
\n
116  }
\n
117 
\n
118  template<typename T, qualifier Q>
\n
119  GLM_INLINE glm::vec<2, T, Q> yz(const glm::vec<4, T, Q> &v) {
\n
120  return glm::vec<2, T, Q>(v.y, v.z);
\n
121  }
\n
122 
\n
123  // yw
\n
124  template<typename T, qualifier Q>
\n
125  GLM_INLINE glm::vec<2, T, Q> yw(const glm::vec<4, T, Q> &v) {
\n
126  return glm::vec<2, T, Q>(v.y, v.w);
\n
127  }
\n
128 
\n
129  // zx
\n
130  template<typename T, qualifier Q>
\n
131  GLM_INLINE glm::vec<2, T, Q> zx(const glm::vec<3, T, Q> &v) {
\n
132  return glm::vec<2, T, Q>(v.z, v.x);
\n
133  }
\n
134 
\n
135  template<typename T, qualifier Q>
\n
136  GLM_INLINE glm::vec<2, T, Q> zx(const glm::vec<4, T, Q> &v) {
\n
137  return glm::vec<2, T, Q>(v.z, v.x);
\n
138  }
\n
139 
\n
140  // zy
\n
141  template<typename T, qualifier Q>
\n
142  GLM_INLINE glm::vec<2, T, Q> zy(const glm::vec<3, T, Q> &v) {
\n
143  return glm::vec<2, T, Q>(v.z, v.y);
\n
144  }
\n
145 
\n
146  template<typename T, qualifier Q>
\n
147  GLM_INLINE glm::vec<2, T, Q> zy(const glm::vec<4, T, Q> &v) {
\n
148  return glm::vec<2, T, Q>(v.z, v.y);
\n
149  }
\n
150 
\n
151  // zz
\n
152  template<typename T, qualifier Q>
\n
153  GLM_INLINE glm::vec<2, T, Q> zz(const glm::vec<3, T, Q> &v) {
\n
154  return glm::vec<2, T, Q>(v.z, v.z);
\n
155  }
\n
156 
\n
157  template<typename T, qualifier Q>
\n
158  GLM_INLINE glm::vec<2, T, Q> zz(const glm::vec<4, T, Q> &v) {
\n
159  return glm::vec<2, T, Q>(v.z, v.z);
\n
160  }
\n
161 
\n
162  // zw
\n
163  template<typename T, qualifier Q>
\n
164  GLM_INLINE glm::vec<2, T, Q> zw(const glm::vec<4, T, Q> &v) {
\n
165  return glm::vec<2, T, Q>(v.z, v.w);
\n
166  }
\n
167 
\n
168  // wx
\n
169  template<typename T, qualifier Q>
\n
170  GLM_INLINE glm::vec<2, T, Q> wx(const glm::vec<4, T, Q> &v) {
\n
171  return glm::vec<2, T, Q>(v.w, v.x);
\n
172  }
\n
173 
\n
174  // wy
\n
175  template<typename T, qualifier Q>
\n
176  GLM_INLINE glm::vec<2, T, Q> wy(const glm::vec<4, T, Q> &v) {
\n
177  return glm::vec<2, T, Q>(v.w, v.y);
\n
178  }
\n
179 
\n
180  // wz
\n
181  template<typename T, qualifier Q>
\n
182  GLM_INLINE glm::vec<2, T, Q> wz(const glm::vec<4, T, Q> &v) {
\n
183  return glm::vec<2, T, Q>(v.w, v.z);
\n
184  }
\n
185 
\n
186  // ww
\n
187  template<typename T, qualifier Q>
\n
188  GLM_INLINE glm::vec<2, T, Q> ww(const glm::vec<4, T, Q> &v) {
\n
189  return glm::vec<2, T, Q>(v.w, v.w);
\n
190  }
\n
191 
\n
192  // xxx
\n
193  template<typename T, qualifier Q>
\n
194  GLM_INLINE glm::vec<3, T, Q> xxx(const glm::vec<1, T, Q> &v) {
\n
195  return glm::vec<3, T, Q>(v.x, v.x, v.x);
\n
196  }
\n
197 
\n
198  template<typename T, qualifier Q>
\n
199  GLM_INLINE glm::vec<3, T, Q> xxx(const glm::vec<2, T, Q> &v) {
\n
200  return glm::vec<3, T, Q>(v.x, v.x, v.x);
\n
201  }
\n
202 
\n
203  template<typename T, qualifier Q>
\n
204  GLM_INLINE glm::vec<3, T, Q> xxx(const glm::vec<3, T, Q> &v) {
\n
205  return glm::vec<3, T, Q>(v.x, v.x, v.x);
\n
206  }
\n
207 
\n
208  template<typename T, qualifier Q>
\n
209  GLM_INLINE glm::vec<3, T, Q> xxx(const glm::vec<4, T, Q> &v) {
\n
210  return glm::vec<3, T, Q>(v.x, v.x, v.x);
\n
211  }
\n
212 
\n
213  // xxy
\n
214  template<typename T, qualifier Q>
\n
215  GLM_INLINE glm::vec<3, T, Q> xxy(const glm::vec<2, T, Q> &v) {
\n
216  return glm::vec<3, T, Q>(v.x, v.x, v.y);
\n
217  }
\n
218 
\n
219  template<typename T, qualifier Q>
\n
220  GLM_INLINE glm::vec<3, T, Q> xxy(const glm::vec<3, T, Q> &v) {
\n
221  return glm::vec<3, T, Q>(v.x, v.x, v.y);
\n
222  }
\n
223 
\n
224  template<typename T, qualifier Q>
\n
225  GLM_INLINE glm::vec<3, T, Q> xxy(const glm::vec<4, T, Q> &v) {
\n
226  return glm::vec<3, T, Q>(v.x, v.x, v.y);
\n
227  }
\n
228 
\n
229  // xxz
\n
230  template<typename T, qualifier Q>
\n
231  GLM_INLINE glm::vec<3, T, Q> xxz(const glm::vec<3, T, Q> &v) {
\n
232  return glm::vec<3, T, Q>(v.x, v.x, v.z);
\n
233  }
\n
234 
\n
235  template<typename T, qualifier Q>
\n
236  GLM_INLINE glm::vec<3, T, Q> xxz(const glm::vec<4, T, Q> &v) {
\n
237  return glm::vec<3, T, Q>(v.x, v.x, v.z);
\n
238  }
\n
239 
\n
240  // xxw
\n
241  template<typename T, qualifier Q>
\n
242  GLM_INLINE glm::vec<3, T, Q> xxw(const glm::vec<4, T, Q> &v) {
\n
243  return glm::vec<3, T, Q>(v.x, v.x, v.w);
\n
244  }
\n
245 
\n
246  // xyx
\n
247  template<typename T, qualifier Q>
\n
248  GLM_INLINE glm::vec<3, T, Q> xyx(const glm::vec<2, T, Q> &v) {
\n
249  return glm::vec<3, T, Q>(v.x, v.y, v.x);
\n
250  }
\n
251 
\n
252  template<typename T, qualifier Q>
\n
253  GLM_INLINE glm::vec<3, T, Q> xyx(const glm::vec<3, T, Q> &v) {
\n
254  return glm::vec<3, T, Q>(v.x, v.y, v.x);
\n
255  }
\n
256 
\n
257  template<typename T, qualifier Q>
\n
258  GLM_INLINE glm::vec<3, T, Q> xyx(const glm::vec<4, T, Q> &v) {
\n
259  return glm::vec<3, T, Q>(v.x, v.y, v.x);
\n
260  }
\n
261 
\n
262  // xyy
\n
263  template<typename T, qualifier Q>
\n
264  GLM_INLINE glm::vec<3, T, Q> xyy(const glm::vec<2, T, Q> &v) {
\n
265  return glm::vec<3, T, Q>(v.x, v.y, v.y);
\n
266  }
\n
267 
\n
268  template<typename T, qualifier Q>
\n
269  GLM_INLINE glm::vec<3, T, Q> xyy(const glm::vec<3, T, Q> &v) {
\n
270  return glm::vec<3, T, Q>(v.x, v.y, v.y);
\n
271  }
\n
272 
\n
273  template<typename T, qualifier Q>
\n
274  GLM_INLINE glm::vec<3, T, Q> xyy(const glm::vec<4, T, Q> &v) {
\n
275  return glm::vec<3, T, Q>(v.x, v.y, v.y);
\n
276  }
\n
277 
\n
278  // xyz
\n
279  template<typename T, qualifier Q>
\n
280  GLM_INLINE glm::vec<3, T, Q> xyz(const glm::vec<3, T, Q> &v) {
\n
281  return glm::vec<3, T, Q>(v.x, v.y, v.z);
\n
282  }
\n
283 
\n
284  template<typename T, qualifier Q>
\n
285  GLM_INLINE glm::vec<3, T, Q> xyz(const glm::vec<4, T, Q> &v) {
\n
286  return glm::vec<3, T, Q>(v.x, v.y, v.z);
\n
287  }
\n
288 
\n
289  // xyw
\n
290  template<typename T, qualifier Q>
\n
291  GLM_INLINE glm::vec<3, T, Q> xyw(const glm::vec<4, T, Q> &v) {
\n
292  return glm::vec<3, T, Q>(v.x, v.y, v.w);
\n
293  }
\n
294 
\n
295  // xzx
\n
296  template<typename T, qualifier Q>
\n
297  GLM_INLINE glm::vec<3, T, Q> xzx(const glm::vec<3, T, Q> &v) {
\n
298  return glm::vec<3, T, Q>(v.x, v.z, v.x);
\n
299  }
\n
300 
\n
301  template<typename T, qualifier Q>
\n
302  GLM_INLINE glm::vec<3, T, Q> xzx(const glm::vec<4, T, Q> &v) {
\n
303  return glm::vec<3, T, Q>(v.x, v.z, v.x);
\n
304  }
\n
305 
\n
306  // xzy
\n
307  template<typename T, qualifier Q>
\n
308  GLM_INLINE glm::vec<3, T, Q> xzy(const glm::vec<3, T, Q> &v) {
\n
309  return glm::vec<3, T, Q>(v.x, v.z, v.y);
\n
310  }
\n
311 
\n
312  template<typename T, qualifier Q>
\n
313  GLM_INLINE glm::vec<3, T, Q> xzy(const glm::vec<4, T, Q> &v) {
\n
314  return glm::vec<3, T, Q>(v.x, v.z, v.y);
\n
315  }
\n
316 
\n
317  // xzz
\n
318  template<typename T, qualifier Q>
\n
319  GLM_INLINE glm::vec<3, T, Q> xzz(const glm::vec<3, T, Q> &v) {
\n
320  return glm::vec<3, T, Q>(v.x, v.z, v.z);
\n
321  }
\n
322 
\n
323  template<typename T, qualifier Q>
\n
324  GLM_INLINE glm::vec<3, T, Q> xzz(const glm::vec<4, T, Q> &v) {
\n
325  return glm::vec<3, T, Q>(v.x, v.z, v.z);
\n
326  }
\n
327 
\n
328  // xzw
\n
329  template<typename T, qualifier Q>
\n
330  GLM_INLINE glm::vec<3, T, Q> xzw(const glm::vec<4, T, Q> &v) {
\n
331  return glm::vec<3, T, Q>(v.x, v.z, v.w);
\n
332  }
\n
333 
\n
334  // xwx
\n
335  template<typename T, qualifier Q>
\n
336  GLM_INLINE glm::vec<3, T, Q> xwx(const glm::vec<4, T, Q> &v) {
\n
337  return glm::vec<3, T, Q>(v.x, v.w, v.x);
\n
338  }
\n
339 
\n
340  // xwy
\n
341  template<typename T, qualifier Q>
\n
342  GLM_INLINE glm::vec<3, T, Q> xwy(const glm::vec<4, T, Q> &v) {
\n
343  return glm::vec<3, T, Q>(v.x, v.w, v.y);
\n
344  }
\n
345 
\n
346  // xwz
\n
347  template<typename T, qualifier Q>
\n
348  GLM_INLINE glm::vec<3, T, Q> xwz(const glm::vec<4, T, Q> &v) {
\n
349  return glm::vec<3, T, Q>(v.x, v.w, v.z);
\n
350  }
\n
351 
\n
352  // xww
\n
353  template<typename T, qualifier Q>
\n
354  GLM_INLINE glm::vec<3, T, Q> xww(const glm::vec<4, T, Q> &v) {
\n
355  return glm::vec<3, T, Q>(v.x, v.w, v.w);
\n
356  }
\n
357 
\n
358  // yxx
\n
359  template<typename T, qualifier Q>
\n
360  GLM_INLINE glm::vec<3, T, Q> yxx(const glm::vec<2, T, Q> &v) {
\n
361  return glm::vec<3, T, Q>(v.y, v.x, v.x);
\n
362  }
\n
363 
\n
364  template<typename T, qualifier Q>
\n
365  GLM_INLINE glm::vec<3, T, Q> yxx(const glm::vec<3, T, Q> &v) {
\n
366  return glm::vec<3, T, Q>(v.y, v.x, v.x);
\n
367  }
\n
368 
\n
369  template<typename T, qualifier Q>
\n
370  GLM_INLINE glm::vec<3, T, Q> yxx(const glm::vec<4, T, Q> &v) {
\n
371  return glm::vec<3, T, Q>(v.y, v.x, v.x);
\n
372  }
\n
373 
\n
374  // yxy
\n
375  template<typename T, qualifier Q>
\n
376  GLM_INLINE glm::vec<3, T, Q> yxy(const glm::vec<2, T, Q> &v) {
\n
377  return glm::vec<3, T, Q>(v.y, v.x, v.y);
\n
378  }
\n
379 
\n
380  template<typename T, qualifier Q>
\n
381  GLM_INLINE glm::vec<3, T, Q> yxy(const glm::vec<3, T, Q> &v) {
\n
382  return glm::vec<3, T, Q>(v.y, v.x, v.y);
\n
383  }
\n
384 
\n
385  template<typename T, qualifier Q>
\n
386  GLM_INLINE glm::vec<3, T, Q> yxy(const glm::vec<4, T, Q> &v) {
\n
387  return glm::vec<3, T, Q>(v.y, v.x, v.y);
\n
388  }
\n
389 
\n
390  // yxz
\n
391  template<typename T, qualifier Q>
\n
392  GLM_INLINE glm::vec<3, T, Q> yxz(const glm::vec<3, T, Q> &v) {
\n
393  return glm::vec<3, T, Q>(v.y, v.x, v.z);
\n
394  }
\n
395 
\n
396  template<typename T, qualifier Q>
\n
397  GLM_INLINE glm::vec<3, T, Q> yxz(const glm::vec<4, T, Q> &v) {
\n
398  return glm::vec<3, T, Q>(v.y, v.x, v.z);
\n
399  }
\n
400 
\n
401  // yxw
\n
402  template<typename T, qualifier Q>
\n
403  GLM_INLINE glm::vec<3, T, Q> yxw(const glm::vec<4, T, Q> &v) {
\n
404  return glm::vec<3, T, Q>(v.y, v.x, v.w);
\n
405  }
\n
406 
\n
407  // yyx
\n
408  template<typename T, qualifier Q>
\n
409  GLM_INLINE glm::vec<3, T, Q> yyx(const glm::vec<2, T, Q> &v) {
\n
410  return glm::vec<3, T, Q>(v.y, v.y, v.x);
\n
411  }
\n
412 
\n
413  template<typename T, qualifier Q>
\n
414  GLM_INLINE glm::vec<3, T, Q> yyx(const glm::vec<3, T, Q> &v) {
\n
415  return glm::vec<3, T, Q>(v.y, v.y, v.x);
\n
416  }
\n
417 
\n
418  template<typename T, qualifier Q>
\n
419  GLM_INLINE glm::vec<3, T, Q> yyx(const glm::vec<4, T, Q> &v) {
\n
420  return glm::vec<3, T, Q>(v.y, v.y, v.x);
\n
421  }
\n
422 
\n
423  // yyy
\n
424  template<typename T, qualifier Q>
\n
425  GLM_INLINE glm::vec<3, T, Q> yyy(const glm::vec<2, T, Q> &v) {
\n
426  return glm::vec<3, T, Q>(v.y, v.y, v.y);
\n
427  }
\n
428 
\n
429  template<typename T, qualifier Q>
\n
430  GLM_INLINE glm::vec<3, T, Q> yyy(const glm::vec<3, T, Q> &v) {
\n
431  return glm::vec<3, T, Q>(v.y, v.y, v.y);
\n
432  }
\n
433 
\n
434  template<typename T, qualifier Q>
\n
435  GLM_INLINE glm::vec<3, T, Q> yyy(const glm::vec<4, T, Q> &v) {
\n
436  return glm::vec<3, T, Q>(v.y, v.y, v.y);
\n
437  }
\n
438 
\n
439  // yyz
\n
440  template<typename T, qualifier Q>
\n
441  GLM_INLINE glm::vec<3, T, Q> yyz(const glm::vec<3, T, Q> &v) {
\n
442  return glm::vec<3, T, Q>(v.y, v.y, v.z);
\n
443  }
\n
444 
\n
445  template<typename T, qualifier Q>
\n
446  GLM_INLINE glm::vec<3, T, Q> yyz(const glm::vec<4, T, Q> &v) {
\n
447  return glm::vec<3, T, Q>(v.y, v.y, v.z);
\n
448  }
\n
449 
\n
450  // yyw
\n
451  template<typename T, qualifier Q>
\n
452  GLM_INLINE glm::vec<3, T, Q> yyw(const glm::vec<4, T, Q> &v) {
\n
453  return glm::vec<3, T, Q>(v.y, v.y, v.w);
\n
454  }
\n
455 
\n
456  // yzx
\n
457  template<typename T, qualifier Q>
\n
458  GLM_INLINE glm::vec<3, T, Q> yzx(const glm::vec<3, T, Q> &v) {
\n
459  return glm::vec<3, T, Q>(v.y, v.z, v.x);
\n
460  }
\n
461 
\n
462  template<typename T, qualifier Q>
\n
463  GLM_INLINE glm::vec<3, T, Q> yzx(const glm::vec<4, T, Q> &v) {
\n
464  return glm::vec<3, T, Q>(v.y, v.z, v.x);
\n
465  }
\n
466 
\n
467  // yzy
\n
468  template<typename T, qualifier Q>
\n
469  GLM_INLINE glm::vec<3, T, Q> yzy(const glm::vec<3, T, Q> &v) {
\n
470  return glm::vec<3, T, Q>(v.y, v.z, v.y);
\n
471  }
\n
472 
\n
473  template<typename T, qualifier Q>
\n
474  GLM_INLINE glm::vec<3, T, Q> yzy(const glm::vec<4, T, Q> &v) {
\n
475  return glm::vec<3, T, Q>(v.y, v.z, v.y);
\n
476  }
\n
477 
\n
478  // yzz
\n
479  template<typename T, qualifier Q>
\n
480  GLM_INLINE glm::vec<3, T, Q> yzz(const glm::vec<3, T, Q> &v) {
\n
481  return glm::vec<3, T, Q>(v.y, v.z, v.z);
\n
482  }
\n
483 
\n
484  template<typename T, qualifier Q>
\n
485  GLM_INLINE glm::vec<3, T, Q> yzz(const glm::vec<4, T, Q> &v) {
\n
486  return glm::vec<3, T, Q>(v.y, v.z, v.z);
\n
487  }
\n
488 
\n
489  // yzw
\n
490  template<typename T, qualifier Q>
\n
491  GLM_INLINE glm::vec<3, T, Q> yzw(const glm::vec<4, T, Q> &v) {
\n
492  return glm::vec<3, T, Q>(v.y, v.z, v.w);
\n
493  }
\n
494 
\n
495  // ywx
\n
496  template<typename T, qualifier Q>
\n
497  GLM_INLINE glm::vec<3, T, Q> ywx(const glm::vec<4, T, Q> &v) {
\n
498  return glm::vec<3, T, Q>(v.y, v.w, v.x);
\n
499  }
\n
500 
\n
501  // ywy
\n
502  template<typename T, qualifier Q>
\n
503  GLM_INLINE glm::vec<3, T, Q> ywy(const glm::vec<4, T, Q> &v) {
\n
504  return glm::vec<3, T, Q>(v.y, v.w, v.y);
\n
505  }
\n
506 
\n
507  // ywz
\n
508  template<typename T, qualifier Q>
\n
509  GLM_INLINE glm::vec<3, T, Q> ywz(const glm::vec<4, T, Q> &v) {
\n
510  return glm::vec<3, T, Q>(v.y, v.w, v.z);
\n
511  }
\n
512 
\n
513  // yww
\n
514  template<typename T, qualifier Q>
\n
515  GLM_INLINE glm::vec<3, T, Q> yww(const glm::vec<4, T, Q> &v) {
\n
516  return glm::vec<3, T, Q>(v.y, v.w, v.w);
\n
517  }
\n
518 
\n
519  // zxx
\n
520  template<typename T, qualifier Q>
\n
521  GLM_INLINE glm::vec<3, T, Q> zxx(const glm::vec<3, T, Q> &v) {
\n
522  return glm::vec<3, T, Q>(v.z, v.x, v.x);
\n
523  }
\n
524 
\n
525  template<typename T, qualifier Q>
\n
526  GLM_INLINE glm::vec<3, T, Q> zxx(const glm::vec<4, T, Q> &v) {
\n
527  return glm::vec<3, T, Q>(v.z, v.x, v.x);
\n
528  }
\n
529 
\n
530  // zxy
\n
531  template<typename T, qualifier Q>
\n
532  GLM_INLINE glm::vec<3, T, Q> zxy(const glm::vec<3, T, Q> &v) {
\n
533  return glm::vec<3, T, Q>(v.z, v.x, v.y);
\n
534  }
\n
535 
\n
536  template<typename T, qualifier Q>
\n
537  GLM_INLINE glm::vec<3, T, Q> zxy(const glm::vec<4, T, Q> &v) {
\n
538  return glm::vec<3, T, Q>(v.z, v.x, v.y);
\n
539  }
\n
540 
\n
541  // zxz
\n
542  template<typename T, qualifier Q>
\n
543  GLM_INLINE glm::vec<3, T, Q> zxz(const glm::vec<3, T, Q> &v) {
\n
544  return glm::vec<3, T, Q>(v.z, v.x, v.z);
\n
545  }
\n
546 
\n
547  template<typename T, qualifier Q>
\n
548  GLM_INLINE glm::vec<3, T, Q> zxz(const glm::vec<4, T, Q> &v) {
\n
549  return glm::vec<3, T, Q>(v.z, v.x, v.z);
\n
550  }
\n
551 
\n
552  // zxw
\n
553  template<typename T, qualifier Q>
\n
554  GLM_INLINE glm::vec<3, T, Q> zxw(const glm::vec<4, T, Q> &v) {
\n
555  return glm::vec<3, T, Q>(v.z, v.x, v.w);
\n
556  }
\n
557 
\n
558  // zyx
\n
559  template<typename T, qualifier Q>
\n
560  GLM_INLINE glm::vec<3, T, Q> zyx(const glm::vec<3, T, Q> &v) {
\n
561  return glm::vec<3, T, Q>(v.z, v.y, v.x);
\n
562  }
\n
563 
\n
564  template<typename T, qualifier Q>
\n
565  GLM_INLINE glm::vec<3, T, Q> zyx(const glm::vec<4, T, Q> &v) {
\n
566  return glm::vec<3, T, Q>(v.z, v.y, v.x);
\n
567  }
\n
568 
\n
569  // zyy
\n
570  template<typename T, qualifier Q>
\n
571  GLM_INLINE glm::vec<3, T, Q> zyy(const glm::vec<3, T, Q> &v) {
\n
572  return glm::vec<3, T, Q>(v.z, v.y, v.y);
\n
573  }
\n
574 
\n
575  template<typename T, qualifier Q>
\n
576  GLM_INLINE glm::vec<3, T, Q> zyy(const glm::vec<4, T, Q> &v) {
\n
577  return glm::vec<3, T, Q>(v.z, v.y, v.y);
\n
578  }
\n
579 
\n
580  // zyz
\n
581  template<typename T, qualifier Q>
\n
582  GLM_INLINE glm::vec<3, T, Q> zyz(const glm::vec<3, T, Q> &v) {
\n
583  return glm::vec<3, T, Q>(v.z, v.y, v.z);
\n
584  }
\n
585 
\n
586  template<typename T, qualifier Q>
\n
587  GLM_INLINE glm::vec<3, T, Q> zyz(const glm::vec<4, T, Q> &v) {
\n
588  return glm::vec<3, T, Q>(v.z, v.y, v.z);
\n
589  }
\n
590 
\n
591  // zyw
\n
592  template<typename T, qualifier Q>
\n
593  GLM_INLINE glm::vec<3, T, Q> zyw(const glm::vec<4, T, Q> &v) {
\n
594  return glm::vec<3, T, Q>(v.z, v.y, v.w);
\n
595  }
\n
596 
\n
597  // zzx
\n
598  template<typename T, qualifier Q>
\n
599  GLM_INLINE glm::vec<3, T, Q> zzx(const glm::vec<3, T, Q> &v) {
\n
600  return glm::vec<3, T, Q>(v.z, v.z, v.x);
\n
601  }
\n
602 
\n
603  template<typename T, qualifier Q>
\n
604  GLM_INLINE glm::vec<3, T, Q> zzx(const glm::vec<4, T, Q> &v) {
\n
605  return glm::vec<3, T, Q>(v.z, v.z, v.x);
\n
606  }
\n
607 
\n
608  // zzy
\n
609  template<typename T, qualifier Q>
\n
610  GLM_INLINE glm::vec<3, T, Q> zzy(const glm::vec<3, T, Q> &v) {
\n
611  return glm::vec<3, T, Q>(v.z, v.z, v.y);
\n
612  }
\n
613 
\n
614  template<typename T, qualifier Q>
\n
615  GLM_INLINE glm::vec<3, T, Q> zzy(const glm::vec<4, T, Q> &v) {
\n
616  return glm::vec<3, T, Q>(v.z, v.z, v.y);
\n
617  }
\n
618 
\n
619  // zzz
\n
620  template<typename T, qualifier Q>
\n
621  GLM_INLINE glm::vec<3, T, Q> zzz(const glm::vec<3, T, Q> &v) {
\n
622  return glm::vec<3, T, Q>(v.z, v.z, v.z);
\n
623  }
\n
624 
\n
625  template<typename T, qualifier Q>
\n
626  GLM_INLINE glm::vec<3, T, Q> zzz(const glm::vec<4, T, Q> &v) {
\n
627  return glm::vec<3, T, Q>(v.z, v.z, v.z);
\n
628  }
\n
629 
\n
630  // zzw
\n
631  template<typename T, qualifier Q>
\n
632  GLM_INLINE glm::vec<3, T, Q> zzw(const glm::vec<4, T, Q> &v) {
\n
633  return glm::vec<3, T, Q>(v.z, v.z, v.w);
\n
634  }
\n
635 
\n
636  // zwx
\n
637  template<typename T, qualifier Q>
\n
638  GLM_INLINE glm::vec<3, T, Q> zwx(const glm::vec<4, T, Q> &v) {
\n
639  return glm::vec<3, T, Q>(v.z, v.w, v.x);
\n
640  }
\n
641 
\n
642  // zwy
\n
643  template<typename T, qualifier Q>
\n
644  GLM_INLINE glm::vec<3, T, Q> zwy(const glm::vec<4, T, Q> &v) {
\n
645  return glm::vec<3, T, Q>(v.z, v.w, v.y);
\n
646  }
\n
647 
\n
648  // zwz
\n
649  template<typename T, qualifier Q>
\n
650  GLM_INLINE glm::vec<3, T, Q> zwz(const glm::vec<4, T, Q> &v) {
\n
651  return glm::vec<3, T, Q>(v.z, v.w, v.z);
\n
652  }
\n
653 
\n
654  // zww
\n
655  template<typename T, qualifier Q>
\n
656  GLM_INLINE glm::vec<3, T, Q> zww(const glm::vec<4, T, Q> &v) {
\n
657  return glm::vec<3, T, Q>(v.z, v.w, v.w);
\n
658  }
\n
659 
\n
660  // wxx
\n
661  template<typename T, qualifier Q>
\n
662  GLM_INLINE glm::vec<3, T, Q> wxx(const glm::vec<4, T, Q> &v) {
\n
663  return glm::vec<3, T, Q>(v.w, v.x, v.x);
\n
664  }
\n
665 
\n
666  // wxy
\n
667  template<typename T, qualifier Q>
\n
668  GLM_INLINE glm::vec<3, T, Q> wxy(const glm::vec<4, T, Q> &v) {
\n
669  return glm::vec<3, T, Q>(v.w, v.x, v.y);
\n
670  }
\n
671 
\n
672  // wxz
\n
673  template<typename T, qualifier Q>
\n
674  GLM_INLINE glm::vec<3, T, Q> wxz(const glm::vec<4, T, Q> &v) {
\n
675  return glm::vec<3, T, Q>(v.w, v.x, v.z);
\n
676  }
\n
677 
\n
678  // wxw
\n
679  template<typename T, qualifier Q>
\n
680  GLM_INLINE glm::vec<3, T, Q> wxw(const glm::vec<4, T, Q> &v) {
\n
681  return glm::vec<3, T, Q>(v.w, v.x, v.w);
\n
682  }
\n
683 
\n
684  // wyx
\n
685  template<typename T, qualifier Q>
\n
686  GLM_INLINE glm::vec<3, T, Q> wyx(const glm::vec<4, T, Q> &v) {
\n
687  return glm::vec<3, T, Q>(v.w, v.y, v.x);
\n
688  }
\n
689 
\n
690  // wyy
\n
691  template<typename T, qualifier Q>
\n
692  GLM_INLINE glm::vec<3, T, Q> wyy(const glm::vec<4, T, Q> &v) {
\n
693  return glm::vec<3, T, Q>(v.w, v.y, v.y);
\n
694  }
\n
695 
\n
696  // wyz
\n
697  template<typename T, qualifier Q>
\n
698  GLM_INLINE glm::vec<3, T, Q> wyz(const glm::vec<4, T, Q> &v) {
\n
699  return glm::vec<3, T, Q>(v.w, v.y, v.z);
\n
700  }
\n
701 
\n
702  // wyw
\n
703  template<typename T, qualifier Q>
\n
704  GLM_INLINE glm::vec<3, T, Q> wyw(const glm::vec<4, T, Q> &v) {
\n
705  return glm::vec<3, T, Q>(v.w, v.y, v.w);
\n
706  }
\n
707 
\n
708  // wzx
\n
709  template<typename T, qualifier Q>
\n
710  GLM_INLINE glm::vec<3, T, Q> wzx(const glm::vec<4, T, Q> &v) {
\n
711  return glm::vec<3, T, Q>(v.w, v.z, v.x);
\n
712  }
\n
713 
\n
714  // wzy
\n
715  template<typename T, qualifier Q>
\n
716  GLM_INLINE glm::vec<3, T, Q> wzy(const glm::vec<4, T, Q> &v) {
\n
717  return glm::vec<3, T, Q>(v.w, v.z, v.y);
\n
718  }
\n
719 
\n
720  // wzz
\n
721  template<typename T, qualifier Q>
\n
722  GLM_INLINE glm::vec<3, T, Q> wzz(const glm::vec<4, T, Q> &v) {
\n
723  return glm::vec<3, T, Q>(v.w, v.z, v.z);
\n
724  }
\n
725 
\n
726  // wzw
\n
727  template<typename T, qualifier Q>
\n
728  GLM_INLINE glm::vec<3, T, Q> wzw(const glm::vec<4, T, Q> &v) {
\n
729  return glm::vec<3, T, Q>(v.w, v.z, v.w);
\n
730  }
\n
731 
\n
732  // wwx
\n
733  template<typename T, qualifier Q>
\n
734  GLM_INLINE glm::vec<3, T, Q> wwx(const glm::vec<4, T, Q> &v) {
\n
735  return glm::vec<3, T, Q>(v.w, v.w, v.x);
\n
736  }
\n
737 
\n
738  // wwy
\n
739  template<typename T, qualifier Q>
\n
740  GLM_INLINE glm::vec<3, T, Q> wwy(const glm::vec<4, T, Q> &v) {
\n
741  return glm::vec<3, T, Q>(v.w, v.w, v.y);
\n
742  }
\n
743 
\n
744  // wwz
\n
745  template<typename T, qualifier Q>
\n
746  GLM_INLINE glm::vec<3, T, Q> wwz(const glm::vec<4, T, Q> &v) {
\n
747  return glm::vec<3, T, Q>(v.w, v.w, v.z);
\n
748  }
\n
749 
\n
750  // www
\n
751  template<typename T, qualifier Q>
\n
752  GLM_INLINE glm::vec<3, T, Q> www(const glm::vec<4, T, Q> &v) {
\n
753  return glm::vec<3, T, Q>(v.w, v.w, v.w);
\n
754  }
\n
755 
\n
756  // xxxx
\n
757  template<typename T, qualifier Q>
\n
758  GLM_INLINE glm::vec<4, T, Q> xxxx(const glm::vec<1, T, Q> &v) {
\n
759  return glm::vec<4, T, Q>(v.x, v.x, v.x, v.x);
\n
760  }
\n
761 
\n
762  template<typename T, qualifier Q>
\n
763  GLM_INLINE glm::vec<4, T, Q> xxxx(const glm::vec<2, T, Q> &v) {
\n
764  return glm::vec<4, T, Q>(v.x, v.x, v.x, v.x);
\n
765  }
\n
766 
\n
767  template<typename T, qualifier Q>
\n
768  GLM_INLINE glm::vec<4, T, Q> xxxx(const glm::vec<3, T, Q> &v) {
\n
769  return glm::vec<4, T, Q>(v.x, v.x, v.x, v.x);
\n
770  }
\n
771 
\n
772  template<typename T, qualifier Q>
\n
773  GLM_INLINE glm::vec<4, T, Q> xxxx(const glm::vec<4, T, Q> &v) {
\n
774  return glm::vec<4, T, Q>(v.x, v.x, v.x, v.x);
\n
775  }
\n
776 
\n
777  // xxxy
\n
778  template<typename T, qualifier Q>
\n
779  GLM_INLINE glm::vec<4, T, Q> xxxy(const glm::vec<2, T, Q> &v) {
\n
780  return glm::vec<4, T, Q>(v.x, v.x, v.x, v.y);
\n
781  }
\n
782 
\n
783  template<typename T, qualifier Q>
\n
784  GLM_INLINE glm::vec<4, T, Q> xxxy(const glm::vec<3, T, Q> &v) {
\n
785  return glm::vec<4, T, Q>(v.x, v.x, v.x, v.y);
\n
786  }
\n
787 
\n
788  template<typename T, qualifier Q>
\n
789  GLM_INLINE glm::vec<4, T, Q> xxxy(const glm::vec<4, T, Q> &v) {
\n
790  return glm::vec<4, T, Q>(v.x, v.x, v.x, v.y);
\n
791  }
\n
792 
\n
793  // xxxz
\n
794  template<typename T, qualifier Q>
\n
795  GLM_INLINE glm::vec<4, T, Q> xxxz(const glm::vec<3, T, Q> &v) {
\n
796  return glm::vec<4, T, Q>(v.x, v.x, v.x, v.z);
\n
797  }
\n
798 
\n
799  template<typename T, qualifier Q>
\n
800  GLM_INLINE glm::vec<4, T, Q> xxxz(const glm::vec<4, T, Q> &v) {
\n
801  return glm::vec<4, T, Q>(v.x, v.x, v.x, v.z);
\n
802  }
\n
803 
\n
804  // xxxw
\n
805  template<typename T, qualifier Q>
\n
806  GLM_INLINE glm::vec<4, T, Q> xxxw(const glm::vec<4, T, Q> &v) {
\n
807  return glm::vec<4, T, Q>(v.x, v.x, v.x, v.w);
\n
808  }
\n
809 
\n
810  // xxyx
\n
811  template<typename T, qualifier Q>
\n
812  GLM_INLINE glm::vec<4, T, Q> xxyx(const glm::vec<2, T, Q> &v) {
\n
813  return glm::vec<4, T, Q>(v.x, v.x, v.y, v.x);
\n
814  }
\n
815 
\n
816  template<typename T, qualifier Q>
\n
817  GLM_INLINE glm::vec<4, T, Q> xxyx(const glm::vec<3, T, Q> &v) {
\n
818  return glm::vec<4, T, Q>(v.x, v.x, v.y, v.x);
\n
819  }
\n
820 
\n
821  template<typename T, qualifier Q>
\n
822  GLM_INLINE glm::vec<4, T, Q> xxyx(const glm::vec<4, T, Q> &v) {
\n
823  return glm::vec<4, T, Q>(v.x, v.x, v.y, v.x);
\n
824  }
\n
825 
\n
826  // xxyy
\n
827  template<typename T, qualifier Q>
\n
828  GLM_INLINE glm::vec<4, T, Q> xxyy(const glm::vec<2, T, Q> &v) {
\n
829  return glm::vec<4, T, Q>(v.x, v.x, v.y, v.y);
\n
830  }
\n
831 
\n
832  template<typename T, qualifier Q>
\n
833  GLM_INLINE glm::vec<4, T, Q> xxyy(const glm::vec<3, T, Q> &v) {
\n
834  return glm::vec<4, T, Q>(v.x, v.x, v.y, v.y);
\n
835  }
\n
836 
\n
837  template<typename T, qualifier Q>
\n
838  GLM_INLINE glm::vec<4, T, Q> xxyy(const glm::vec<4, T, Q> &v) {
\n
839  return glm::vec<4, T, Q>(v.x, v.x, v.y, v.y);
\n
840  }
\n
841 
\n
842  // xxyz
\n
843  template<typename T, qualifier Q>
\n
844  GLM_INLINE glm::vec<4, T, Q> xxyz(const glm::vec<3, T, Q> &v) {
\n
845  return glm::vec<4, T, Q>(v.x, v.x, v.y, v.z);
\n
846  }
\n
847 
\n
848  template<typename T, qualifier Q>
\n
849  GLM_INLINE glm::vec<4, T, Q> xxyz(const glm::vec<4, T, Q> &v) {
\n
850  return glm::vec<4, T, Q>(v.x, v.x, v.y, v.z);
\n
851  }
\n
852 
\n
853  // xxyw
\n
854  template<typename T, qualifier Q>
\n
855  GLM_INLINE glm::vec<4, T, Q> xxyw(const glm::vec<4, T, Q> &v) {
\n
856  return glm::vec<4, T, Q>(v.x, v.x, v.y, v.w);
\n
857  }
\n
858 
\n
859  // xxzx
\n
860  template<typename T, qualifier Q>
\n
861  GLM_INLINE glm::vec<4, T, Q> xxzx(const glm::vec<3, T, Q> &v) {
\n
862  return glm::vec<4, T, Q>(v.x, v.x, v.z, v.x);
\n
863  }
\n
864 
\n
865  template<typename T, qualifier Q>
\n
866  GLM_INLINE glm::vec<4, T, Q> xxzx(const glm::vec<4, T, Q> &v) {
\n
867  return glm::vec<4, T, Q>(v.x, v.x, v.z, v.x);
\n
868  }
\n
869 
\n
870  // xxzy
\n
871  template<typename T, qualifier Q>
\n
872  GLM_INLINE glm::vec<4, T, Q> xxzy(const glm::vec<3, T, Q> &v) {
\n
873  return glm::vec<4, T, Q>(v.x, v.x, v.z, v.y);
\n
874  }
\n
875 
\n
876  template<typename T, qualifier Q>
\n
877  GLM_INLINE glm::vec<4, T, Q> xxzy(const glm::vec<4, T, Q> &v) {
\n
878  return glm::vec<4, T, Q>(v.x, v.x, v.z, v.y);
\n
879  }
\n
880 
\n
881  // xxzz
\n
882  template<typename T, qualifier Q>
\n
883  GLM_INLINE glm::vec<4, T, Q> xxzz(const glm::vec<3, T, Q> &v) {
\n
884  return glm::vec<4, T, Q>(v.x, v.x, v.z, v.z);
\n
885  }
\n
886 
\n
887  template<typename T, qualifier Q>
\n
888  GLM_INLINE glm::vec<4, T, Q> xxzz(const glm::vec<4, T, Q> &v) {
\n
889  return glm::vec<4, T, Q>(v.x, v.x, v.z, v.z);
\n
890  }
\n
891 
\n
892  // xxzw
\n
893  template<typename T, qualifier Q>
\n
894  GLM_INLINE glm::vec<4, T, Q> xxzw(const glm::vec<4, T, Q> &v) {
\n
895  return glm::vec<4, T, Q>(v.x, v.x, v.z, v.w);
\n
896  }
\n
897 
\n
898  // xxwx
\n
899  template<typename T, qualifier Q>
\n
900  GLM_INLINE glm::vec<4, T, Q> xxwx(const glm::vec<4, T, Q> &v) {
\n
901  return glm::vec<4, T, Q>(v.x, v.x, v.w, v.x);
\n
902  }
\n
903 
\n
904  // xxwy
\n
905  template<typename T, qualifier Q>
\n
906  GLM_INLINE glm::vec<4, T, Q> xxwy(const glm::vec<4, T, Q> &v) {
\n
907  return glm::vec<4, T, Q>(v.x, v.x, v.w, v.y);
\n
908  }
\n
909 
\n
910  // xxwz
\n
911  template<typename T, qualifier Q>
\n
912  GLM_INLINE glm::vec<4, T, Q> xxwz(const glm::vec<4, T, Q> &v) {
\n
913  return glm::vec<4, T, Q>(v.x, v.x, v.w, v.z);
\n
914  }
\n
915 
\n
916  // xxww
\n
917  template<typename T, qualifier Q>
\n
918  GLM_INLINE glm::vec<4, T, Q> xxww(const glm::vec<4, T, Q> &v) {
\n
919  return glm::vec<4, T, Q>(v.x, v.x, v.w, v.w);
\n
920  }
\n
921 
\n
922  // xyxx
\n
923  template<typename T, qualifier Q>
\n
924  GLM_INLINE glm::vec<4, T, Q> xyxx(const glm::vec<2, T, Q> &v) {
\n
925  return glm::vec<4, T, Q>(v.x, v.y, v.x, v.x);
\n
926  }
\n
927 
\n
928  template<typename T, qualifier Q>
\n
929  GLM_INLINE glm::vec<4, T, Q> xyxx(const glm::vec<3, T, Q> &v) {
\n
930  return glm::vec<4, T, Q>(v.x, v.y, v.x, v.x);
\n
931  }
\n
932 
\n
933  template<typename T, qualifier Q>
\n
934  GLM_INLINE glm::vec<4, T, Q> xyxx(const glm::vec<4, T, Q> &v) {
\n
935  return glm::vec<4, T, Q>(v.x, v.y, v.x, v.x);
\n
936  }
\n
937 
\n
938  // xyxy
\n
939  template<typename T, qualifier Q>
\n
940  GLM_INLINE glm::vec<4, T, Q> xyxy(const glm::vec<2, T, Q> &v) {
\n
941  return glm::vec<4, T, Q>(v.x, v.y, v.x, v.y);
\n
942  }
\n
943 
\n
944  template<typename T, qualifier Q>
\n
945  GLM_INLINE glm::vec<4, T, Q> xyxy(const glm::vec<3, T, Q> &v) {
\n
946  return glm::vec<4, T, Q>(v.x, v.y, v.x, v.y);
\n
947  }
\n
948 
\n
949  template<typename T, qualifier Q>
\n
950  GLM_INLINE glm::vec<4, T, Q> xyxy(const glm::vec<4, T, Q> &v) {
\n
951  return glm::vec<4, T, Q>(v.x, v.y, v.x, v.y);
\n
952  }
\n
953 
\n
954  // xyxz
\n
955  template<typename T, qualifier Q>
\n
956  GLM_INLINE glm::vec<4, T, Q> xyxz(const glm::vec<3, T, Q> &v) {
\n
957  return glm::vec<4, T, Q>(v.x, v.y, v.x, v.z);
\n
958  }
\n
959 
\n
960  template<typename T, qualifier Q>
\n
961  GLM_INLINE glm::vec<4, T, Q> xyxz(const glm::vec<4, T, Q> &v) {
\n
962  return glm::vec<4, T, Q>(v.x, v.y, v.x, v.z);
\n
963  }
\n
964 
\n
965  // xyxw
\n
966  template<typename T, qualifier Q>
\n
967  GLM_INLINE glm::vec<4, T, Q> xyxw(const glm::vec<4, T, Q> &v) {
\n
968  return glm::vec<4, T, Q>(v.x, v.y, v.x, v.w);
\n
969  }
\n
970 
\n
971  // xyyx
\n
972  template<typename T, qualifier Q>
\n
973  GLM_INLINE glm::vec<4, T, Q> xyyx(const glm::vec<2, T, Q> &v) {
\n
974  return glm::vec<4, T, Q>(v.x, v.y, v.y, v.x);
\n
975  }
\n
976 
\n
977  template<typename T, qualifier Q>
\n
978  GLM_INLINE glm::vec<4, T, Q> xyyx(const glm::vec<3, T, Q> &v) {
\n
979  return glm::vec<4, T, Q>(v.x, v.y, v.y, v.x);
\n
980  }
\n
981 
\n
982  template<typename T, qualifier Q>
\n
983  GLM_INLINE glm::vec<4, T, Q> xyyx(const glm::vec<4, T, Q> &v) {
\n
984  return glm::vec<4, T, Q>(v.x, v.y, v.y, v.x);
\n
985  }
\n
986 
\n
987  // xyyy
\n
988  template<typename T, qualifier Q>
\n
989  GLM_INLINE glm::vec<4, T, Q> xyyy(const glm::vec<2, T, Q> &v) {
\n
990  return glm::vec<4, T, Q>(v.x, v.y, v.y, v.y);
\n
991  }
\n
992 
\n
993  template<typename T, qualifier Q>
\n
994  GLM_INLINE glm::vec<4, T, Q> xyyy(const glm::vec<3, T, Q> &v) {
\n
995  return glm::vec<4, T, Q>(v.x, v.y, v.y, v.y);
\n
996  }
\n
997 
\n
998  template<typename T, qualifier Q>
\n
999  GLM_INLINE glm::vec<4, T, Q> xyyy(const glm::vec<4, T, Q> &v) {
\n
1000  return glm::vec<4, T, Q>(v.x, v.y, v.y, v.y);
\n
1001  }
\n
1002 
\n
1003  // xyyz
\n
1004  template<typename T, qualifier Q>
\n
1005  GLM_INLINE glm::vec<4, T, Q> xyyz(const glm::vec<3, T, Q> &v) {
\n
1006  return glm::vec<4, T, Q>(v.x, v.y, v.y, v.z);
\n
1007  }
\n
1008 
\n
1009  template<typename T, qualifier Q>
\n
1010  GLM_INLINE glm::vec<4, T, Q> xyyz(const glm::vec<4, T, Q> &v) {
\n
1011  return glm::vec<4, T, Q>(v.x, v.y, v.y, v.z);
\n
1012  }
\n
1013 
\n
1014  // xyyw
\n
1015  template<typename T, qualifier Q>
\n
1016  GLM_INLINE glm::vec<4, T, Q> xyyw(const glm::vec<4, T, Q> &v) {
\n
1017  return glm::vec<4, T, Q>(v.x, v.y, v.y, v.w);
\n
1018  }
\n
1019 
\n
1020  // xyzx
\n
1021  template<typename T, qualifier Q>
\n
1022  GLM_INLINE glm::vec<4, T, Q> xyzx(const glm::vec<3, T, Q> &v) {
\n
1023  return glm::vec<4, T, Q>(v.x, v.y, v.z, v.x);
\n
1024  }
\n
1025 
\n
1026  template<typename T, qualifier Q>
\n
1027  GLM_INLINE glm::vec<4, T, Q> xyzx(const glm::vec<4, T, Q> &v) {
\n
1028  return glm::vec<4, T, Q>(v.x, v.y, v.z, v.x);
\n
1029  }
\n
1030 
\n
1031  // xyzy
\n
1032  template<typename T, qualifier Q>
\n
1033  GLM_INLINE glm::vec<4, T, Q> xyzy(const glm::vec<3, T, Q> &v) {
\n
1034  return glm::vec<4, T, Q>(v.x, v.y, v.z, v.y);
\n
1035  }
\n
1036 
\n
1037  template<typename T, qualifier Q>
\n
1038  GLM_INLINE glm::vec<4, T, Q> xyzy(const glm::vec<4, T, Q> &v) {
\n
1039  return glm::vec<4, T, Q>(v.x, v.y, v.z, v.y);
\n
1040  }
\n
1041 
\n
1042  // xyzz
\n
1043  template<typename T, qualifier Q>
\n
1044  GLM_INLINE glm::vec<4, T, Q> xyzz(const glm::vec<3, T, Q> &v) {
\n
1045  return glm::vec<4, T, Q>(v.x, v.y, v.z, v.z);
\n
1046  }
\n
1047 
\n
1048  template<typename T, qualifier Q>
\n
1049  GLM_INLINE glm::vec<4, T, Q> xyzz(const glm::vec<4, T, Q> &v) {
\n
1050  return glm::vec<4, T, Q>(v.x, v.y, v.z, v.z);
\n
1051  }
\n
1052 
\n
1053  // xyzw
\n
1054  template<typename T, qualifier Q>
\n
1055  GLM_INLINE glm::vec<4, T, Q> xyzw(const glm::vec<4, T, Q> &v) {
\n
1056  return glm::vec<4, T, Q>(v.x, v.y, v.z, v.w);
\n
1057  }
\n
1058 
\n
1059  // xywx
\n
1060  template<typename T, qualifier Q>
\n
1061  GLM_INLINE glm::vec<4, T, Q> xywx(const glm::vec<4, T, Q> &v) {
\n
1062  return glm::vec<4, T, Q>(v.x, v.y, v.w, v.x);
\n
1063  }
\n
1064 
\n
1065  // xywy
\n
1066  template<typename T, qualifier Q>
\n
1067  GLM_INLINE glm::vec<4, T, Q> xywy(const glm::vec<4, T, Q> &v) {
\n
1068  return glm::vec<4, T, Q>(v.x, v.y, v.w, v.y);
\n
1069  }
\n
1070 
\n
1071  // xywz
\n
1072  template<typename T, qualifier Q>
\n
1073  GLM_INLINE glm::vec<4, T, Q> xywz(const glm::vec<4, T, Q> &v) {
\n
1074  return glm::vec<4, T, Q>(v.x, v.y, v.w, v.z);
\n
1075  }
\n
1076 
\n
1077  // xyww
\n
1078  template<typename T, qualifier Q>
\n
1079  GLM_INLINE glm::vec<4, T, Q> xyww(const glm::vec<4, T, Q> &v) {
\n
1080  return glm::vec<4, T, Q>(v.x, v.y, v.w, v.w);
\n
1081  }
\n
1082 
\n
1083  // xzxx
\n
1084  template<typename T, qualifier Q>
\n
1085  GLM_INLINE glm::vec<4, T, Q> xzxx(const glm::vec<3, T, Q> &v) {
\n
1086  return glm::vec<4, T, Q>(v.x, v.z, v.x, v.x);
\n
1087  }
\n
1088 
\n
1089  template<typename T, qualifier Q>
\n
1090  GLM_INLINE glm::vec<4, T, Q> xzxx(const glm::vec<4, T, Q> &v) {
\n
1091  return glm::vec<4, T, Q>(v.x, v.z, v.x, v.x);
\n
1092  }
\n
1093 
\n
1094  // xzxy
\n
1095  template<typename T, qualifier Q>
\n
1096  GLM_INLINE glm::vec<4, T, Q> xzxy(const glm::vec<3, T, Q> &v) {
\n
1097  return glm::vec<4, T, Q>(v.x, v.z, v.x, v.y);
\n
1098  }
\n
1099 
\n
1100  template<typename T, qualifier Q>
\n
1101  GLM_INLINE glm::vec<4, T, Q> xzxy(const glm::vec<4, T, Q> &v) {
\n
1102  return glm::vec<4, T, Q>(v.x, v.z, v.x, v.y);
\n
1103  }
\n
1104 
\n
1105  // xzxz
\n
1106  template<typename T, qualifier Q>
\n
1107  GLM_INLINE glm::vec<4, T, Q> xzxz(const glm::vec<3, T, Q> &v) {
\n
1108  return glm::vec<4, T, Q>(v.x, v.z, v.x, v.z);
\n
1109  }
\n
1110 
\n
1111  template<typename T, qualifier Q>
\n
1112  GLM_INLINE glm::vec<4, T, Q> xzxz(const glm::vec<4, T, Q> &v) {
\n
1113  return glm::vec<4, T, Q>(v.x, v.z, v.x, v.z);
\n
1114  }
\n
1115 
\n
1116  // xzxw
\n
1117  template<typename T, qualifier Q>
\n
1118  GLM_INLINE glm::vec<4, T, Q> xzxw(const glm::vec<4, T, Q> &v) {
\n
1119  return glm::vec<4, T, Q>(v.x, v.z, v.x, v.w);
\n
1120  }
\n
1121 
\n
1122  // xzyx
\n
1123  template<typename T, qualifier Q>
\n
1124  GLM_INLINE glm::vec<4, T, Q> xzyx(const glm::vec<3, T, Q> &v) {
\n
1125  return glm::vec<4, T, Q>(v.x, v.z, v.y, v.x);
\n
1126  }
\n
1127 
\n
1128  template<typename T, qualifier Q>
\n
1129  GLM_INLINE glm::vec<4, T, Q> xzyx(const glm::vec<4, T, Q> &v) {
\n
1130  return glm::vec<4, T, Q>(v.x, v.z, v.y, v.x);
\n
1131  }
\n
1132 
\n
1133  // xzyy
\n
1134  template<typename T, qualifier Q>
\n
1135  GLM_INLINE glm::vec<4, T, Q> xzyy(const glm::vec<3, T, Q> &v) {
\n
1136  return glm::vec<4, T, Q>(v.x, v.z, v.y, v.y);
\n
1137  }
\n
1138 
\n
1139  template<typename T, qualifier Q>
\n
1140  GLM_INLINE glm::vec<4, T, Q> xzyy(const glm::vec<4, T, Q> &v) {
\n
1141  return glm::vec<4, T, Q>(v.x, v.z, v.y, v.y);
\n
1142  }
\n
1143 
\n
1144  // xzyz
\n
1145  template<typename T, qualifier Q>
\n
1146  GLM_INLINE glm::vec<4, T, Q> xzyz(const glm::vec<3, T, Q> &v) {
\n
1147  return glm::vec<4, T, Q>(v.x, v.z, v.y, v.z);
\n
1148  }
\n
1149 
\n
1150  template<typename T, qualifier Q>
\n
1151  GLM_INLINE glm::vec<4, T, Q> xzyz(const glm::vec<4, T, Q> &v) {
\n
1152  return glm::vec<4, T, Q>(v.x, v.z, v.y, v.z);
\n
1153  }
\n
1154 
\n
1155  // xzyw
\n
1156  template<typename T, qualifier Q>
\n
1157  GLM_INLINE glm::vec<4, T, Q> xzyw(const glm::vec<4, T, Q> &v) {
\n
1158  return glm::vec<4, T, Q>(v.x, v.z, v.y, v.w);
\n
1159  }
\n
1160 
\n
1161  // xzzx
\n
1162  template<typename T, qualifier Q>
\n
1163  GLM_INLINE glm::vec<4, T, Q> xzzx(const glm::vec<3, T, Q> &v) {
\n
1164  return glm::vec<4, T, Q>(v.x, v.z, v.z, v.x);
\n
1165  }
\n
1166 
\n
1167  template<typename T, qualifier Q>
\n
1168  GLM_INLINE glm::vec<4, T, Q> xzzx(const glm::vec<4, T, Q> &v) {
\n
1169  return glm::vec<4, T, Q>(v.x, v.z, v.z, v.x);
\n
1170  }
\n
1171 
\n
1172  // xzzy
\n
1173  template<typename T, qualifier Q>
\n
1174  GLM_INLINE glm::vec<4, T, Q> xzzy(const glm::vec<3, T, Q> &v) {
\n
1175  return glm::vec<4, T, Q>(v.x, v.z, v.z, v.y);
\n
1176  }
\n
1177 
\n
1178  template<typename T, qualifier Q>
\n
1179  GLM_INLINE glm::vec<4, T, Q> xzzy(const glm::vec<4, T, Q> &v) {
\n
1180  return glm::vec<4, T, Q>(v.x, v.z, v.z, v.y);
\n
1181  }
\n
1182 
\n
1183  // xzzz
\n
1184  template<typename T, qualifier Q>
\n
1185  GLM_INLINE glm::vec<4, T, Q> xzzz(const glm::vec<3, T, Q> &v) {
\n
1186  return glm::vec<4, T, Q>(v.x, v.z, v.z, v.z);
\n
1187  }
\n
1188 
\n
1189  template<typename T, qualifier Q>
\n
1190  GLM_INLINE glm::vec<4, T, Q> xzzz(const glm::vec<4, T, Q> &v) {
\n
1191  return glm::vec<4, T, Q>(v.x, v.z, v.z, v.z);
\n
1192  }
\n
1193 
\n
1194  // xzzw
\n
1195  template<typename T, qualifier Q>
\n
1196  GLM_INLINE glm::vec<4, T, Q> xzzw(const glm::vec<4, T, Q> &v) {
\n
1197  return glm::vec<4, T, Q>(v.x, v.z, v.z, v.w);
\n
1198  }
\n
1199 
\n
1200  // xzwx
\n
1201  template<typename T, qualifier Q>
\n
1202  GLM_INLINE glm::vec<4, T, Q> xzwx(const glm::vec<4, T, Q> &v) {
\n
1203  return glm::vec<4, T, Q>(v.x, v.z, v.w, v.x);
\n
1204  }
\n
1205 
\n
1206  // xzwy
\n
1207  template<typename T, qualifier Q>
\n
1208  GLM_INLINE glm::vec<4, T, Q> xzwy(const glm::vec<4, T, Q> &v) {
\n
1209  return glm::vec<4, T, Q>(v.x, v.z, v.w, v.y);
\n
1210  }
\n
1211 
\n
1212  // xzwz
\n
1213  template<typename T, qualifier Q>
\n
1214  GLM_INLINE glm::vec<4, T, Q> xzwz(const glm::vec<4, T, Q> &v) {
\n
1215  return glm::vec<4, T, Q>(v.x, v.z, v.w, v.z);
\n
1216  }
\n
1217 
\n
1218  // xzww
\n
1219  template<typename T, qualifier Q>
\n
1220  GLM_INLINE glm::vec<4, T, Q> xzww(const glm::vec<4, T, Q> &v) {
\n
1221  return glm::vec<4, T, Q>(v.x, v.z, v.w, v.w);
\n
1222  }
\n
1223 
\n
1224  // xwxx
\n
1225  template<typename T, qualifier Q>
\n
1226  GLM_INLINE glm::vec<4, T, Q> xwxx(const glm::vec<4, T, Q> &v) {
\n
1227  return glm::vec<4, T, Q>(v.x, v.w, v.x, v.x);
\n
1228  }
\n
1229 
\n
1230  // xwxy
\n
1231  template<typename T, qualifier Q>
\n
1232  GLM_INLINE glm::vec<4, T, Q> xwxy(const glm::vec<4, T, Q> &v) {
\n
1233  return glm::vec<4, T, Q>(v.x, v.w, v.x, v.y);
\n
1234  }
\n
1235 
\n
1236  // xwxz
\n
1237  template<typename T, qualifier Q>
\n
1238  GLM_INLINE glm::vec<4, T, Q> xwxz(const glm::vec<4, T, Q> &v) {
\n
1239  return glm::vec<4, T, Q>(v.x, v.w, v.x, v.z);
\n
1240  }
\n
1241 
\n
1242  // xwxw
\n
1243  template<typename T, qualifier Q>
\n
1244  GLM_INLINE glm::vec<4, T, Q> xwxw(const glm::vec<4, T, Q> &v) {
\n
1245  return glm::vec<4, T, Q>(v.x, v.w, v.x, v.w);
\n
1246  }
\n
1247 
\n
1248  // xwyx
\n
1249  template<typename T, qualifier Q>
\n
1250  GLM_INLINE glm::vec<4, T, Q> xwyx(const glm::vec<4, T, Q> &v) {
\n
1251  return glm::vec<4, T, Q>(v.x, v.w, v.y, v.x);
\n
1252  }
\n
1253 
\n
1254  // xwyy
\n
1255  template<typename T, qualifier Q>
\n
1256  GLM_INLINE glm::vec<4, T, Q> xwyy(const glm::vec<4, T, Q> &v) {
\n
1257  return glm::vec<4, T, Q>(v.x, v.w, v.y, v.y);
\n
1258  }
\n
1259 
\n
1260  // xwyz
\n
1261  template<typename T, qualifier Q>
\n
1262  GLM_INLINE glm::vec<4, T, Q> xwyz(const glm::vec<4, T, Q> &v) {
\n
1263  return glm::vec<4, T, Q>(v.x, v.w, v.y, v.z);
\n
1264  }
\n
1265 
\n
1266  // xwyw
\n
1267  template<typename T, qualifier Q>
\n
1268  GLM_INLINE glm::vec<4, T, Q> xwyw(const glm::vec<4, T, Q> &v) {
\n
1269  return glm::vec<4, T, Q>(v.x, v.w, v.y, v.w);
\n
1270  }
\n
1271 
\n
1272  // xwzx
\n
1273  template<typename T, qualifier Q>
\n
1274  GLM_INLINE glm::vec<4, T, Q> xwzx(const glm::vec<4, T, Q> &v) {
\n
1275  return glm::vec<4, T, Q>(v.x, v.w, v.z, v.x);
\n
1276  }
\n
1277 
\n
1278  // xwzy
\n
1279  template<typename T, qualifier Q>
\n
1280  GLM_INLINE glm::vec<4, T, Q> xwzy(const glm::vec<4, T, Q> &v) {
\n
1281  return glm::vec<4, T, Q>(v.x, v.w, v.z, v.y);
\n
1282  }
\n
1283 
\n
1284  // xwzz
\n
1285  template<typename T, qualifier Q>
\n
1286  GLM_INLINE glm::vec<4, T, Q> xwzz(const glm::vec<4, T, Q> &v) {
\n
1287  return glm::vec<4, T, Q>(v.x, v.w, v.z, v.z);
\n
1288  }
\n
1289 
\n
1290  // xwzw
\n
1291  template<typename T, qualifier Q>
\n
1292  GLM_INLINE glm::vec<4, T, Q> xwzw(const glm::vec<4, T, Q> &v) {
\n
1293  return glm::vec<4, T, Q>(v.x, v.w, v.z, v.w);
\n
1294  }
\n
1295 
\n
1296  // xwwx
\n
1297  template<typename T, qualifier Q>
\n
1298  GLM_INLINE glm::vec<4, T, Q> xwwx(const glm::vec<4, T, Q> &v) {
\n
1299  return glm::vec<4, T, Q>(v.x, v.w, v.w, v.x);
\n
1300  }
\n
1301 
\n
1302  // xwwy
\n
1303  template<typename T, qualifier Q>
\n
1304  GLM_INLINE glm::vec<4, T, Q> xwwy(const glm::vec<4, T, Q> &v) {
\n
1305  return glm::vec<4, T, Q>(v.x, v.w, v.w, v.y);
\n
1306  }
\n
1307 
\n
1308  // xwwz
\n
1309  template<typename T, qualifier Q>
\n
1310  GLM_INLINE glm::vec<4, T, Q> xwwz(const glm::vec<4, T, Q> &v) {
\n
1311  return glm::vec<4, T, Q>(v.x, v.w, v.w, v.z);
\n
1312  }
\n
1313 
\n
1314  // xwww
\n
1315  template<typename T, qualifier Q>
\n
1316  GLM_INLINE glm::vec<4, T, Q> xwww(const glm::vec<4, T, Q> &v) {
\n
1317  return glm::vec<4, T, Q>(v.x, v.w, v.w, v.w);
\n
1318  }
\n
1319 
\n
1320  // yxxx
\n
1321  template<typename T, qualifier Q>
\n
1322  GLM_INLINE glm::vec<4, T, Q> yxxx(const glm::vec<2, T, Q> &v) {
\n
1323  return glm::vec<4, T, Q>(v.y, v.x, v.x, v.x);
\n
1324  }
\n
1325 
\n
1326  template<typename T, qualifier Q>
\n
1327  GLM_INLINE glm::vec<4, T, Q> yxxx(const glm::vec<3, T, Q> &v) {
\n
1328  return glm::vec<4, T, Q>(v.y, v.x, v.x, v.x);
\n
1329  }
\n
1330 
\n
1331  template<typename T, qualifier Q>
\n
1332  GLM_INLINE glm::vec<4, T, Q> yxxx(const glm::vec<4, T, Q> &v) {
\n
1333  return glm::vec<4, T, Q>(v.y, v.x, v.x, v.x);
\n
1334  }
\n
1335 
\n
1336  // yxxy
\n
1337  template<typename T, qualifier Q>
\n
1338  GLM_INLINE glm::vec<4, T, Q> yxxy(const glm::vec<2, T, Q> &v) {
\n
1339  return glm::vec<4, T, Q>(v.y, v.x, v.x, v.y);
\n
1340  }
\n
1341 
\n
1342  template<typename T, qualifier Q>
\n
1343  GLM_INLINE glm::vec<4, T, Q> yxxy(const glm::vec<3, T, Q> &v) {
\n
1344  return glm::vec<4, T, Q>(v.y, v.x, v.x, v.y);
\n
1345  }
\n
1346 
\n
1347  template<typename T, qualifier Q>
\n
1348  GLM_INLINE glm::vec<4, T, Q> yxxy(const glm::vec<4, T, Q> &v) {
\n
1349  return glm::vec<4, T, Q>(v.y, v.x, v.x, v.y);
\n
1350  }
\n
1351 
\n
1352  // yxxz
\n
1353  template<typename T, qualifier Q>
\n
1354  GLM_INLINE glm::vec<4, T, Q> yxxz(const glm::vec<3, T, Q> &v) {
\n
1355  return glm::vec<4, T, Q>(v.y, v.x, v.x, v.z);
\n
1356  }
\n
1357 
\n
1358  template<typename T, qualifier Q>
\n
1359  GLM_INLINE glm::vec<4, T, Q> yxxz(const glm::vec<4, T, Q> &v) {
\n
1360  return glm::vec<4, T, Q>(v.y, v.x, v.x, v.z);
\n
1361  }
\n
1362 
\n
1363  // yxxw
\n
1364  template<typename T, qualifier Q>
\n
1365  GLM_INLINE glm::vec<4, T, Q> yxxw(const glm::vec<4, T, Q> &v) {
\n
1366  return glm::vec<4, T, Q>(v.y, v.x, v.x, v.w);
\n
1367  }
\n
1368 
\n
1369  // yxyx
\n
1370  template<typename T, qualifier Q>
\n
1371  GLM_INLINE glm::vec<4, T, Q> yxyx(const glm::vec<2, T, Q> &v) {
\n
1372  return glm::vec<4, T, Q>(v.y, v.x, v.y, v.x);
\n
1373  }
\n
1374 
\n
1375  template<typename T, qualifier Q>
\n
1376  GLM_INLINE glm::vec<4, T, Q> yxyx(const glm::vec<3, T, Q> &v) {
\n
1377  return glm::vec<4, T, Q>(v.y, v.x, v.y, v.x);
\n
1378  }
\n
1379 
\n
1380  template<typename T, qualifier Q>
\n
1381  GLM_INLINE glm::vec<4, T, Q> yxyx(const glm::vec<4, T, Q> &v) {
\n
1382  return glm::vec<4, T, Q>(v.y, v.x, v.y, v.x);
\n
1383  }
\n
1384 
\n
1385  // yxyy
\n
1386  template<typename T, qualifier Q>
\n
1387  GLM_INLINE glm::vec<4, T, Q> yxyy(const glm::vec<2, T, Q> &v) {
\n
1388  return glm::vec<4, T, Q>(v.y, v.x, v.y, v.y);
\n
1389  }
\n
1390 
\n
1391  template<typename T, qualifier Q>
\n
1392  GLM_INLINE glm::vec<4, T, Q> yxyy(const glm::vec<3, T, Q> &v) {
\n
1393  return glm::vec<4, T, Q>(v.y, v.x, v.y, v.y);
\n
1394  }
\n
1395 
\n
1396  template<typename T, qualifier Q>
\n
1397  GLM_INLINE glm::vec<4, T, Q> yxyy(const glm::vec<4, T, Q> &v) {
\n
1398  return glm::vec<4, T, Q>(v.y, v.x, v.y, v.y);
\n
1399  }
\n
1400 
\n
1401  // yxyz
\n
1402  template<typename T, qualifier Q>
\n
1403  GLM_INLINE glm::vec<4, T, Q> yxyz(const glm::vec<3, T, Q> &v) {
\n
1404  return glm::vec<4, T, Q>(v.y, v.x, v.y, v.z);
\n
1405  }
\n
1406 
\n
1407  template<typename T, qualifier Q>
\n
1408  GLM_INLINE glm::vec<4, T, Q> yxyz(const glm::vec<4, T, Q> &v) {
\n
1409  return glm::vec<4, T, Q>(v.y, v.x, v.y, v.z);
\n
1410  }
\n
1411 
\n
1412  // yxyw
\n
1413  template<typename T, qualifier Q>
\n
1414  GLM_INLINE glm::vec<4, T, Q> yxyw(const glm::vec<4, T, Q> &v) {
\n
1415  return glm::vec<4, T, Q>(v.y, v.x, v.y, v.w);
\n
1416  }
\n
1417 
\n
1418  // yxzx
\n
1419  template<typename T, qualifier Q>
\n
1420  GLM_INLINE glm::vec<4, T, Q> yxzx(const glm::vec<3, T, Q> &v) {
\n
1421  return glm::vec<4, T, Q>(v.y, v.x, v.z, v.x);
\n
1422  }
\n
1423 
\n
1424  template<typename T, qualifier Q>
\n
1425  GLM_INLINE glm::vec<4, T, Q> yxzx(const glm::vec<4, T, Q> &v) {
\n
1426  return glm::vec<4, T, Q>(v.y, v.x, v.z, v.x);
\n
1427  }
\n
1428 
\n
1429  // yxzy
\n
1430  template<typename T, qualifier Q>
\n
1431  GLM_INLINE glm::vec<4, T, Q> yxzy(const glm::vec<3, T, Q> &v) {
\n
1432  return glm::vec<4, T, Q>(v.y, v.x, v.z, v.y);
\n
1433  }
\n
1434 
\n
1435  template<typename T, qualifier Q>
\n
1436  GLM_INLINE glm::vec<4, T, Q> yxzy(const glm::vec<4, T, Q> &v) {
\n
1437  return glm::vec<4, T, Q>(v.y, v.x, v.z, v.y);
\n
1438  }
\n
1439 
\n
1440  // yxzz
\n
1441  template<typename T, qualifier Q>
\n
1442  GLM_INLINE glm::vec<4, T, Q> yxzz(const glm::vec<3, T, Q> &v) {
\n
1443  return glm::vec<4, T, Q>(v.y, v.x, v.z, v.z);
\n
1444  }
\n
1445 
\n
1446  template<typename T, qualifier Q>
\n
1447  GLM_INLINE glm::vec<4, T, Q> yxzz(const glm::vec<4, T, Q> &v) {
\n
1448  return glm::vec<4, T, Q>(v.y, v.x, v.z, v.z);
\n
1449  }
\n
1450 
\n
1451  // yxzw
\n
1452  template<typename T, qualifier Q>
\n
1453  GLM_INLINE glm::vec<4, T, Q> yxzw(const glm::vec<4, T, Q> &v) {
\n
1454  return glm::vec<4, T, Q>(v.y, v.x, v.z, v.w);
\n
1455  }
\n
1456 
\n
1457  // yxwx
\n
1458  template<typename T, qualifier Q>
\n
1459  GLM_INLINE glm::vec<4, T, Q> yxwx(const glm::vec<4, T, Q> &v) {
\n
1460  return glm::vec<4, T, Q>(v.y, v.x, v.w, v.x);
\n
1461  }
\n
1462 
\n
1463  // yxwy
\n
1464  template<typename T, qualifier Q>
\n
1465  GLM_INLINE glm::vec<4, T, Q> yxwy(const glm::vec<4, T, Q> &v) {
\n
1466  return glm::vec<4, T, Q>(v.y, v.x, v.w, v.y);
\n
1467  }
\n
1468 
\n
1469  // yxwz
\n
1470  template<typename T, qualifier Q>
\n
1471  GLM_INLINE glm::vec<4, T, Q> yxwz(const glm::vec<4, T, Q> &v) {
\n
1472  return glm::vec<4, T, Q>(v.y, v.x, v.w, v.z);
\n
1473  }
\n
1474 
\n
1475  // yxww
\n
1476  template<typename T, qualifier Q>
\n
1477  GLM_INLINE glm::vec<4, T, Q> yxww(const glm::vec<4, T, Q> &v) {
\n
1478  return glm::vec<4, T, Q>(v.y, v.x, v.w, v.w);
\n
1479  }
\n
1480 
\n
1481  // yyxx
\n
1482  template<typename T, qualifier Q>
\n
1483  GLM_INLINE glm::vec<4, T, Q> yyxx(const glm::vec<2, T, Q> &v) {
\n
1484  return glm::vec<4, T, Q>(v.y, v.y, v.x, v.x);
\n
1485  }
\n
1486 
\n
1487  template<typename T, qualifier Q>
\n
1488  GLM_INLINE glm::vec<4, T, Q> yyxx(const glm::vec<3, T, Q> &v) {
\n
1489  return glm::vec<4, T, Q>(v.y, v.y, v.x, v.x);
\n
1490  }
\n
1491 
\n
1492  template<typename T, qualifier Q>
\n
1493  GLM_INLINE glm::vec<4, T, Q> yyxx(const glm::vec<4, T, Q> &v) {
\n
1494  return glm::vec<4, T, Q>(v.y, v.y, v.x, v.x);
\n
1495  }
\n
1496 
\n
1497  // yyxy
\n
1498  template<typename T, qualifier Q>
\n
1499  GLM_INLINE glm::vec<4, T, Q> yyxy(const glm::vec<2, T, Q> &v) {
\n
1500  return glm::vec<4, T, Q>(v.y, v.y, v.x, v.y);
\n
1501  }
\n
1502 
\n
1503  template<typename T, qualifier Q>
\n
1504  GLM_INLINE glm::vec<4, T, Q> yyxy(const glm::vec<3, T, Q> &v) {
\n
1505  return glm::vec<4, T, Q>(v.y, v.y, v.x, v.y);
\n
1506  }
\n
1507 
\n
1508  template<typename T, qualifier Q>
\n
1509  GLM_INLINE glm::vec<4, T, Q> yyxy(const glm::vec<4, T, Q> &v) {
\n
1510  return glm::vec<4, T, Q>(v.y, v.y, v.x, v.y);
\n
1511  }
\n
1512 
\n
1513  // yyxz
\n
1514  template<typename T, qualifier Q>
\n
1515  GLM_INLINE glm::vec<4, T, Q> yyxz(const glm::vec<3, T, Q> &v) {
\n
1516  return glm::vec<4, T, Q>(v.y, v.y, v.x, v.z);
\n
1517  }
\n
1518 
\n
1519  template<typename T, qualifier Q>
\n
1520  GLM_INLINE glm::vec<4, T, Q> yyxz(const glm::vec<4, T, Q> &v) {
\n
1521  return glm::vec<4, T, Q>(v.y, v.y, v.x, v.z);
\n
1522  }
\n
1523 
\n
1524  // yyxw
\n
1525  template<typename T, qualifier Q>
\n
1526  GLM_INLINE glm::vec<4, T, Q> yyxw(const glm::vec<4, T, Q> &v) {
\n
1527  return glm::vec<4, T, Q>(v.y, v.y, v.x, v.w);
\n
1528  }
\n
1529 
\n
1530  // yyyx
\n
1531  template<typename T, qualifier Q>
\n
1532  GLM_INLINE glm::vec<4, T, Q> yyyx(const glm::vec<2, T, Q> &v) {
\n
1533  return glm::vec<4, T, Q>(v.y, v.y, v.y, v.x);
\n
1534  }
\n
1535 
\n
1536  template<typename T, qualifier Q>
\n
1537  GLM_INLINE glm::vec<4, T, Q> yyyx(const glm::vec<3, T, Q> &v) {
\n
1538  return glm::vec<4, T, Q>(v.y, v.y, v.y, v.x);
\n
1539  }
\n
1540 
\n
1541  template<typename T, qualifier Q>
\n
1542  GLM_INLINE glm::vec<4, T, Q> yyyx(const glm::vec<4, T, Q> &v) {
\n
1543  return glm::vec<4, T, Q>(v.y, v.y, v.y, v.x);
\n
1544  }
\n
1545 
\n
1546  // yyyy
\n
1547  template<typename T, qualifier Q>
\n
1548  GLM_INLINE glm::vec<4, T, Q> yyyy(const glm::vec<2, T, Q> &v) {
\n
1549  return glm::vec<4, T, Q>(v.y, v.y, v.y, v.y);
\n
1550  }
\n
1551 
\n
1552  template<typename T, qualifier Q>
\n
1553  GLM_INLINE glm::vec<4, T, Q> yyyy(const glm::vec<3, T, Q> &v) {
\n
1554  return glm::vec<4, T, Q>(v.y, v.y, v.y, v.y);
\n
1555  }
\n
1556 
\n
1557  template<typename T, qualifier Q>
\n
1558  GLM_INLINE glm::vec<4, T, Q> yyyy(const glm::vec<4, T, Q> &v) {
\n
1559  return glm::vec<4, T, Q>(v.y, v.y, v.y, v.y);
\n
1560  }
\n
1561 
\n
1562  // yyyz
\n
1563  template<typename T, qualifier Q>
\n
1564  GLM_INLINE glm::vec<4, T, Q> yyyz(const glm::vec<3, T, Q> &v) {
\n
1565  return glm::vec<4, T, Q>(v.y, v.y, v.y, v.z);
\n
1566  }
\n
1567 
\n
1568  template<typename T, qualifier Q>
\n
1569  GLM_INLINE glm::vec<4, T, Q> yyyz(const glm::vec<4, T, Q> &v) {
\n
1570  return glm::vec<4, T, Q>(v.y, v.y, v.y, v.z);
\n
1571  }
\n
1572 
\n
1573  // yyyw
\n
1574  template<typename T, qualifier Q>
\n
1575  GLM_INLINE glm::vec<4, T, Q> yyyw(const glm::vec<4, T, Q> &v) {
\n
1576  return glm::vec<4, T, Q>(v.y, v.y, v.y, v.w);
\n
1577  }
\n
1578 
\n
1579  // yyzx
\n
1580  template<typename T, qualifier Q>
\n
1581  GLM_INLINE glm::vec<4, T, Q> yyzx(const glm::vec<3, T, Q> &v) {
\n
1582  return glm::vec<4, T, Q>(v.y, v.y, v.z, v.x);
\n
1583  }
\n
1584 
\n
1585  template<typename T, qualifier Q>
\n
1586  GLM_INLINE glm::vec<4, T, Q> yyzx(const glm::vec<4, T, Q> &v) {
\n
1587  return glm::vec<4, T, Q>(v.y, v.y, v.z, v.x);
\n
1588  }
\n
1589 
\n
1590  // yyzy
\n
1591  template<typename T, qualifier Q>
\n
1592  GLM_INLINE glm::vec<4, T, Q> yyzy(const glm::vec<3, T, Q> &v) {
\n
1593  return glm::vec<4, T, Q>(v.y, v.y, v.z, v.y);
\n
1594  }
\n
1595 
\n
1596  template<typename T, qualifier Q>
\n
1597  GLM_INLINE glm::vec<4, T, Q> yyzy(const glm::vec<4, T, Q> &v) {
\n
1598  return glm::vec<4, T, Q>(v.y, v.y, v.z, v.y);
\n
1599  }
\n
1600 
\n
1601  // yyzz
\n
1602  template<typename T, qualifier Q>
\n
1603  GLM_INLINE glm::vec<4, T, Q> yyzz(const glm::vec<3, T, Q> &v) {
\n
1604  return glm::vec<4, T, Q>(v.y, v.y, v.z, v.z);
\n
1605  }
\n
1606 
\n
1607  template<typename T, qualifier Q>
\n
1608  GLM_INLINE glm::vec<4, T, Q> yyzz(const glm::vec<4, T, Q> &v) {
\n
1609  return glm::vec<4, T, Q>(v.y, v.y, v.z, v.z);
\n
1610  }
\n
1611 
\n
1612  // yyzw
\n
1613  template<typename T, qualifier Q>
\n
1614  GLM_INLINE glm::vec<4, T, Q> yyzw(const glm::vec<4, T, Q> &v) {
\n
1615  return glm::vec<4, T, Q>(v.y, v.y, v.z, v.w);
\n
1616  }
\n
1617 
\n
1618  // yywx
\n
1619  template<typename T, qualifier Q>
\n
1620  GLM_INLINE glm::vec<4, T, Q> yywx(const glm::vec<4, T, Q> &v) {
\n
1621  return glm::vec<4, T, Q>(v.y, v.y, v.w, v.x);
\n
1622  }
\n
1623 
\n
1624  // yywy
\n
1625  template<typename T, qualifier Q>
\n
1626  GLM_INLINE glm::vec<4, T, Q> yywy(const glm::vec<4, T, Q> &v) {
\n
1627  return glm::vec<4, T, Q>(v.y, v.y, v.w, v.y);
\n
1628  }
\n
1629 
\n
1630  // yywz
\n
1631  template<typename T, qualifier Q>
\n
1632  GLM_INLINE glm::vec<4, T, Q> yywz(const glm::vec<4, T, Q> &v) {
\n
1633  return glm::vec<4, T, Q>(v.y, v.y, v.w, v.z);
\n
1634  }
\n
1635 
\n
1636  // yyww
\n
1637  template<typename T, qualifier Q>
\n
1638  GLM_INLINE glm::vec<4, T, Q> yyww(const glm::vec<4, T, Q> &v) {
\n
1639  return glm::vec<4, T, Q>(v.y, v.y, v.w, v.w);
\n
1640  }
\n
1641 
\n
1642  // yzxx
\n
1643  template<typename T, qualifier Q>
\n
1644  GLM_INLINE glm::vec<4, T, Q> yzxx(const glm::vec<3, T, Q> &v) {
\n
1645  return glm::vec<4, T, Q>(v.y, v.z, v.x, v.x);
\n
1646  }
\n
1647 
\n
1648  template<typename T, qualifier Q>
\n
1649  GLM_INLINE glm::vec<4, T, Q> yzxx(const glm::vec<4, T, Q> &v) {
\n
1650  return glm::vec<4, T, Q>(v.y, v.z, v.x, v.x);
\n
1651  }
\n
1652 
\n
1653  // yzxy
\n
1654  template<typename T, qualifier Q>
\n
1655  GLM_INLINE glm::vec<4, T, Q> yzxy(const glm::vec<3, T, Q> &v) {
\n
1656  return glm::vec<4, T, Q>(v.y, v.z, v.x, v.y);
\n
1657  }
\n
1658 
\n
1659  template<typename T, qualifier Q>
\n
1660  GLM_INLINE glm::vec<4, T, Q> yzxy(const glm::vec<4, T, Q> &v) {
\n
1661  return glm::vec<4, T, Q>(v.y, v.z, v.x, v.y);
\n
1662  }
\n
1663 
\n
1664  // yzxz
\n
1665  template<typename T, qualifier Q>
\n
1666  GLM_INLINE glm::vec<4, T, Q> yzxz(const glm::vec<3, T, Q> &v) {
\n
1667  return glm::vec<4, T, Q>(v.y, v.z, v.x, v.z);
\n
1668  }
\n
1669 
\n
1670  template<typename T, qualifier Q>
\n
1671  GLM_INLINE glm::vec<4, T, Q> yzxz(const glm::vec<4, T, Q> &v) {
\n
1672  return glm::vec<4, T, Q>(v.y, v.z, v.x, v.z);
\n
1673  }
\n
1674 
\n
1675  // yzxw
\n
1676  template<typename T, qualifier Q>
\n
1677  GLM_INLINE glm::vec<4, T, Q> yzxw(const glm::vec<4, T, Q> &v) {
\n
1678  return glm::vec<4, T, Q>(v.y, v.z, v.x, v.w);
\n
1679  }
\n
1680 
\n
1681  // yzyx
\n
1682  template<typename T, qualifier Q>
\n
1683  GLM_INLINE glm::vec<4, T, Q> yzyx(const glm::vec<3, T, Q> &v) {
\n
1684  return glm::vec<4, T, Q>(v.y, v.z, v.y, v.x);
\n
1685  }
\n
1686 
\n
1687  template<typename T, qualifier Q>
\n
1688  GLM_INLINE glm::vec<4, T, Q> yzyx(const glm::vec<4, T, Q> &v) {
\n
1689  return glm::vec<4, T, Q>(v.y, v.z, v.y, v.x);
\n
1690  }
\n
1691 
\n
1692  // yzyy
\n
1693  template<typename T, qualifier Q>
\n
1694  GLM_INLINE glm::vec<4, T, Q> yzyy(const glm::vec<3, T, Q> &v) {
\n
1695  return glm::vec<4, T, Q>(v.y, v.z, v.y, v.y);
\n
1696  }
\n
1697 
\n
1698  template<typename T, qualifier Q>
\n
1699  GLM_INLINE glm::vec<4, T, Q> yzyy(const glm::vec<4, T, Q> &v) {
\n
1700  return glm::vec<4, T, Q>(v.y, v.z, v.y, v.y);
\n
1701  }
\n
1702 
\n
1703  // yzyz
\n
1704  template<typename T, qualifier Q>
\n
1705  GLM_INLINE glm::vec<4, T, Q> yzyz(const glm::vec<3, T, Q> &v) {
\n
1706  return glm::vec<4, T, Q>(v.y, v.z, v.y, v.z);
\n
1707  }
\n
1708 
\n
1709  template<typename T, qualifier Q>
\n
1710  GLM_INLINE glm::vec<4, T, Q> yzyz(const glm::vec<4, T, Q> &v) {
\n
1711  return glm::vec<4, T, Q>(v.y, v.z, v.y, v.z);
\n
1712  }
\n
1713 
\n
1714  // yzyw
\n
1715  template<typename T, qualifier Q>
\n
1716  GLM_INLINE glm::vec<4, T, Q> yzyw(const glm::vec<4, T, Q> &v) {
\n
1717  return glm::vec<4, T, Q>(v.y, v.z, v.y, v.w);
\n
1718  }
\n
1719 
\n
1720  // yzzx
\n
1721  template<typename T, qualifier Q>
\n
1722  GLM_INLINE glm::vec<4, T, Q> yzzx(const glm::vec<3, T, Q> &v) {
\n
1723  return glm::vec<4, T, Q>(v.y, v.z, v.z, v.x);
\n
1724  }
\n
1725 
\n
1726  template<typename T, qualifier Q>
\n
1727  GLM_INLINE glm::vec<4, T, Q> yzzx(const glm::vec<4, T, Q> &v) {
\n
1728  return glm::vec<4, T, Q>(v.y, v.z, v.z, v.x);
\n
1729  }
\n
1730 
\n
1731  // yzzy
\n
1732  template<typename T, qualifier Q>
\n
1733  GLM_INLINE glm::vec<4, T, Q> yzzy(const glm::vec<3, T, Q> &v) {
\n
1734  return glm::vec<4, T, Q>(v.y, v.z, v.z, v.y);
\n
1735  }
\n
1736 
\n
1737  template<typename T, qualifier Q>
\n
1738  GLM_INLINE glm::vec<4, T, Q> yzzy(const glm::vec<4, T, Q> &v) {
\n
1739  return glm::vec<4, T, Q>(v.y, v.z, v.z, v.y);
\n
1740  }
\n
1741 
\n
1742  // yzzz
\n
1743  template<typename T, qualifier Q>
\n
1744  GLM_INLINE glm::vec<4, T, Q> yzzz(const glm::vec<3, T, Q> &v) {
\n
1745  return glm::vec<4, T, Q>(v.y, v.z, v.z, v.z);
\n
1746  }
\n
1747 
\n
1748  template<typename T, qualifier Q>
\n
1749  GLM_INLINE glm::vec<4, T, Q> yzzz(const glm::vec<4, T, Q> &v) {
\n
1750  return glm::vec<4, T, Q>(v.y, v.z, v.z, v.z);
\n
1751  }
\n
1752 
\n
1753  // yzzw
\n
1754  template<typename T, qualifier Q>
\n
1755  GLM_INLINE glm::vec<4, T, Q> yzzw(const glm::vec<4, T, Q> &v) {
\n
1756  return glm::vec<4, T, Q>(v.y, v.z, v.z, v.w);
\n
1757  }
\n
1758 
\n
1759  // yzwx
\n
1760  template<typename T, qualifier Q>
\n
1761  GLM_INLINE glm::vec<4, T, Q> yzwx(const glm::vec<4, T, Q> &v) {
\n
1762  return glm::vec<4, T, Q>(v.y, v.z, v.w, v.x);
\n
1763  }
\n
1764 
\n
1765  // yzwy
\n
1766  template<typename T, qualifier Q>
\n
1767  GLM_INLINE glm::vec<4, T, Q> yzwy(const glm::vec<4, T, Q> &v) {
\n
1768  return glm::vec<4, T, Q>(v.y, v.z, v.w, v.y);
\n
1769  }
\n
1770 
\n
1771  // yzwz
\n
1772  template<typename T, qualifier Q>
\n
1773  GLM_INLINE glm::vec<4, T, Q> yzwz(const glm::vec<4, T, Q> &v) {
\n
1774  return glm::vec<4, T, Q>(v.y, v.z, v.w, v.z);
\n
1775  }
\n
1776 
\n
1777  // yzww
\n
1778  template<typename T, qualifier Q>
\n
1779  GLM_INLINE glm::vec<4, T, Q> yzww(const glm::vec<4, T, Q> &v) {
\n
1780  return glm::vec<4, T, Q>(v.y, v.z, v.w, v.w);
\n
1781  }
\n
1782 
\n
1783  // ywxx
\n
1784  template<typename T, qualifier Q>
\n
1785  GLM_INLINE glm::vec<4, T, Q> ywxx(const glm::vec<4, T, Q> &v) {
\n
1786  return glm::vec<4, T, Q>(v.y, v.w, v.x, v.x);
\n
1787  }
\n
1788 
\n
1789  // ywxy
\n
1790  template<typename T, qualifier Q>
\n
1791  GLM_INLINE glm::vec<4, T, Q> ywxy(const glm::vec<4, T, Q> &v) {
\n
1792  return glm::vec<4, T, Q>(v.y, v.w, v.x, v.y);
\n
1793  }
\n
1794 
\n
1795  // ywxz
\n
1796  template<typename T, qualifier Q>
\n
1797  GLM_INLINE glm::vec<4, T, Q> ywxz(const glm::vec<4, T, Q> &v) {
\n
1798  return glm::vec<4, T, Q>(v.y, v.w, v.x, v.z);
\n
1799  }
\n
1800 
\n
1801  // ywxw
\n
1802  template<typename T, qualifier Q>
\n
1803  GLM_INLINE glm::vec<4, T, Q> ywxw(const glm::vec<4, T, Q> &v) {
\n
1804  return glm::vec<4, T, Q>(v.y, v.w, v.x, v.w);
\n
1805  }
\n
1806 
\n
1807  // ywyx
\n
1808  template<typename T, qualifier Q>
\n
1809  GLM_INLINE glm::vec<4, T, Q> ywyx(const glm::vec<4, T, Q> &v) {
\n
1810  return glm::vec<4, T, Q>(v.y, v.w, v.y, v.x);
\n
1811  }
\n
1812 
\n
1813  // ywyy
\n
1814  template<typename T, qualifier Q>
\n
1815  GLM_INLINE glm::vec<4, T, Q> ywyy(const glm::vec<4, T, Q> &v) {
\n
1816  return glm::vec<4, T, Q>(v.y, v.w, v.y, v.y);
\n
1817  }
\n
1818 
\n
1819  // ywyz
\n
1820  template<typename T, qualifier Q>
\n
1821  GLM_INLINE glm::vec<4, T, Q> ywyz(const glm::vec<4, T, Q> &v) {
\n
1822  return glm::vec<4, T, Q>(v.y, v.w, v.y, v.z);
\n
1823  }
\n
1824 
\n
1825  // ywyw
\n
1826  template<typename T, qualifier Q>
\n
1827  GLM_INLINE glm::vec<4, T, Q> ywyw(const glm::vec<4, T, Q> &v) {
\n
1828  return glm::vec<4, T, Q>(v.y, v.w, v.y, v.w);
\n
1829  }
\n
1830 
\n
1831  // ywzx
\n
1832  template<typename T, qualifier Q>
\n
1833  GLM_INLINE glm::vec<4, T, Q> ywzx(const glm::vec<4, T, Q> &v) {
\n
1834  return glm::vec<4, T, Q>(v.y, v.w, v.z, v.x);
\n
1835  }
\n
1836 
\n
1837  // ywzy
\n
1838  template<typename T, qualifier Q>
\n
1839  GLM_INLINE glm::vec<4, T, Q> ywzy(const glm::vec<4, T, Q> &v) {
\n
1840  return glm::vec<4, T, Q>(v.y, v.w, v.z, v.y);
\n
1841  }
\n
1842 
\n
1843  // ywzz
\n
1844  template<typename T, qualifier Q>
\n
1845  GLM_INLINE glm::vec<4, T, Q> ywzz(const glm::vec<4, T, Q> &v) {
\n
1846  return glm::vec<4, T, Q>(v.y, v.w, v.z, v.z);
\n
1847  }
\n
1848 
\n
1849  // ywzw
\n
1850  template<typename T, qualifier Q>
\n
1851  GLM_INLINE glm::vec<4, T, Q> ywzw(const glm::vec<4, T, Q> &v) {
\n
1852  return glm::vec<4, T, Q>(v.y, v.w, v.z, v.w);
\n
1853  }
\n
1854 
\n
1855  // ywwx
\n
1856  template<typename T, qualifier Q>
\n
1857  GLM_INLINE glm::vec<4, T, Q> ywwx(const glm::vec<4, T, Q> &v) {
\n
1858  return glm::vec<4, T, Q>(v.y, v.w, v.w, v.x);
\n
1859  }
\n
1860 
\n
1861  // ywwy
\n
1862  template<typename T, qualifier Q>
\n
1863  GLM_INLINE glm::vec<4, T, Q> ywwy(const glm::vec<4, T, Q> &v) {
\n
1864  return glm::vec<4, T, Q>(v.y, v.w, v.w, v.y);
\n
1865  }
\n
1866 
\n
1867  // ywwz
\n
1868  template<typename T, qualifier Q>
\n
1869  GLM_INLINE glm::vec<4, T, Q> ywwz(const glm::vec<4, T, Q> &v) {
\n
1870  return glm::vec<4, T, Q>(v.y, v.w, v.w, v.z);
\n
1871  }
\n
1872 
\n
1873  // ywww
\n
1874  template<typename T, qualifier Q>
\n
1875  GLM_INLINE glm::vec<4, T, Q> ywww(const glm::vec<4, T, Q> &v) {
\n
1876  return glm::vec<4, T, Q>(v.y, v.w, v.w, v.w);
\n
1877  }
\n
1878 
\n
1879  // zxxx
\n
1880  template<typename T, qualifier Q>
\n
1881  GLM_INLINE glm::vec<4, T, Q> zxxx(const glm::vec<3, T, Q> &v) {
\n
1882  return glm::vec<4, T, Q>(v.z, v.x, v.x, v.x);
\n
1883  }
\n
1884 
\n
1885  template<typename T, qualifier Q>
\n
1886  GLM_INLINE glm::vec<4, T, Q> zxxx(const glm::vec<4, T, Q> &v) {
\n
1887  return glm::vec<4, T, Q>(v.z, v.x, v.x, v.x);
\n
1888  }
\n
1889 
\n
1890  // zxxy
\n
1891  template<typename T, qualifier Q>
\n
1892  GLM_INLINE glm::vec<4, T, Q> zxxy(const glm::vec<3, T, Q> &v) {
\n
1893  return glm::vec<4, T, Q>(v.z, v.x, v.x, v.y);
\n
1894  }
\n
1895 
\n
1896  template<typename T, qualifier Q>
\n
1897  GLM_INLINE glm::vec<4, T, Q> zxxy(const glm::vec<4, T, Q> &v) {
\n
1898  return glm::vec<4, T, Q>(v.z, v.x, v.x, v.y);
\n
1899  }
\n
1900 
\n
1901  // zxxz
\n
1902  template<typename T, qualifier Q>
\n
1903  GLM_INLINE glm::vec<4, T, Q> zxxz(const glm::vec<3, T, Q> &v) {
\n
1904  return glm::vec<4, T, Q>(v.z, v.x, v.x, v.z);
\n
1905  }
\n
1906 
\n
1907  template<typename T, qualifier Q>
\n
1908  GLM_INLINE glm::vec<4, T, Q> zxxz(const glm::vec<4, T, Q> &v) {
\n
1909  return glm::vec<4, T, Q>(v.z, v.x, v.x, v.z);
\n
1910  }
\n
1911 
\n
1912  // zxxw
\n
1913  template<typename T, qualifier Q>
\n
1914  GLM_INLINE glm::vec<4, T, Q> zxxw(const glm::vec<4, T, Q> &v) {
\n
1915  return glm::vec<4, T, Q>(v.z, v.x, v.x, v.w);
\n
1916  }
\n
1917 
\n
1918  // zxyx
\n
1919  template<typename T, qualifier Q>
\n
1920  GLM_INLINE glm::vec<4, T, Q> zxyx(const glm::vec<3, T, Q> &v) {
\n
1921  return glm::vec<4, T, Q>(v.z, v.x, v.y, v.x);
\n
1922  }
\n
1923 
\n
1924  template<typename T, qualifier Q>
\n
1925  GLM_INLINE glm::vec<4, T, Q> zxyx(const glm::vec<4, T, Q> &v) {
\n
1926  return glm::vec<4, T, Q>(v.z, v.x, v.y, v.x);
\n
1927  }
\n
1928 
\n
1929  // zxyy
\n
1930  template<typename T, qualifier Q>
\n
1931  GLM_INLINE glm::vec<4, T, Q> zxyy(const glm::vec<3, T, Q> &v) {
\n
1932  return glm::vec<4, T, Q>(v.z, v.x, v.y, v.y);
\n
1933  }
\n
1934 
\n
1935  template<typename T, qualifier Q>
\n
1936  GLM_INLINE glm::vec<4, T, Q> zxyy(const glm::vec<4, T, Q> &v) {
\n
1937  return glm::vec<4, T, Q>(v.z, v.x, v.y, v.y);
\n
1938  }
\n
1939 
\n
1940  // zxyz
\n
1941  template<typename T, qualifier Q>
\n
1942  GLM_INLINE glm::vec<4, T, Q> zxyz(const glm::vec<3, T, Q> &v) {
\n
1943  return glm::vec<4, T, Q>(v.z, v.x, v.y, v.z);
\n
1944  }
\n
1945 
\n
1946  template<typename T, qualifier Q>
\n
1947  GLM_INLINE glm::vec<4, T, Q> zxyz(const glm::vec<4, T, Q> &v) {
\n
1948  return glm::vec<4, T, Q>(v.z, v.x, v.y, v.z);
\n
1949  }
\n
1950 
\n
1951  // zxyw
\n
1952  template<typename T, qualifier Q>
\n
1953  GLM_INLINE glm::vec<4, T, Q> zxyw(const glm::vec<4, T, Q> &v) {
\n
1954  return glm::vec<4, T, Q>(v.z, v.x, v.y, v.w);
\n
1955  }
\n
1956 
\n
1957  // zxzx
\n
1958  template<typename T, qualifier Q>
\n
1959  GLM_INLINE glm::vec<4, T, Q> zxzx(const glm::vec<3, T, Q> &v) {
\n
1960  return glm::vec<4, T, Q>(v.z, v.x, v.z, v.x);
\n
1961  }
\n
1962 
\n
1963  template<typename T, qualifier Q>
\n
1964  GLM_INLINE glm::vec<4, T, Q> zxzx(const glm::vec<4, T, Q> &v) {
\n
1965  return glm::vec<4, T, Q>(v.z, v.x, v.z, v.x);
\n
1966  }
\n
1967 
\n
1968  // zxzy
\n
1969  template<typename T, qualifier Q>
\n
1970  GLM_INLINE glm::vec<4, T, Q> zxzy(const glm::vec<3, T, Q> &v) {
\n
1971  return glm::vec<4, T, Q>(v.z, v.x, v.z, v.y);
\n
1972  }
\n
1973 
\n
1974  template<typename T, qualifier Q>
\n
1975  GLM_INLINE glm::vec<4, T, Q> zxzy(const glm::vec<4, T, Q> &v) {
\n
1976  return glm::vec<4, T, Q>(v.z, v.x, v.z, v.y);
\n
1977  }
\n
1978 
\n
1979  // zxzz
\n
1980  template<typename T, qualifier Q>
\n
1981  GLM_INLINE glm::vec<4, T, Q> zxzz(const glm::vec<3, T, Q> &v) {
\n
1982  return glm::vec<4, T, Q>(v.z, v.x, v.z, v.z);
\n
1983  }
\n
1984 
\n
1985  template<typename T, qualifier Q>
\n
1986  GLM_INLINE glm::vec<4, T, Q> zxzz(const glm::vec<4, T, Q> &v) {
\n
1987  return glm::vec<4, T, Q>(v.z, v.x, v.z, v.z);
\n
1988  }
\n
1989 
\n
1990  // zxzw
\n
1991  template<typename T, qualifier Q>
\n
1992  GLM_INLINE glm::vec<4, T, Q> zxzw(const glm::vec<4, T, Q> &v) {
\n
1993  return glm::vec<4, T, Q>(v.z, v.x, v.z, v.w);
\n
1994  }
\n
1995 
\n
1996  // zxwx
\n
1997  template<typename T, qualifier Q>
\n
1998  GLM_INLINE glm::vec<4, T, Q> zxwx(const glm::vec<4, T, Q> &v) {
\n
1999  return glm::vec<4, T, Q>(v.z, v.x, v.w, v.x);
\n
2000  }
\n
2001 
\n
2002  // zxwy
\n
2003  template<typename T, qualifier Q>
\n
2004  GLM_INLINE glm::vec<4, T, Q> zxwy(const glm::vec<4, T, Q> &v) {
\n
2005  return glm::vec<4, T, Q>(v.z, v.x, v.w, v.y);
\n
2006  }
\n
2007 
\n
2008  // zxwz
\n
2009  template<typename T, qualifier Q>
\n
2010  GLM_INLINE glm::vec<4, T, Q> zxwz(const glm::vec<4, T, Q> &v) {
\n
2011  return glm::vec<4, T, Q>(v.z, v.x, v.w, v.z);
\n
2012  }
\n
2013 
\n
2014  // zxww
\n
2015  template<typename T, qualifier Q>
\n
2016  GLM_INLINE glm::vec<4, T, Q> zxww(const glm::vec<4, T, Q> &v) {
\n
2017  return glm::vec<4, T, Q>(v.z, v.x, v.w, v.w);
\n
2018  }
\n
2019 
\n
2020  // zyxx
\n
2021  template<typename T, qualifier Q>
\n
2022  GLM_INLINE glm::vec<4, T, Q> zyxx(const glm::vec<3, T, Q> &v) {
\n
2023  return glm::vec<4, T, Q>(v.z, v.y, v.x, v.x);
\n
2024  }
\n
2025 
\n
2026  template<typename T, qualifier Q>
\n
2027  GLM_INLINE glm::vec<4, T, Q> zyxx(const glm::vec<4, T, Q> &v) {
\n
2028  return glm::vec<4, T, Q>(v.z, v.y, v.x, v.x);
\n
2029  }
\n
2030 
\n
2031  // zyxy
\n
2032  template<typename T, qualifier Q>
\n
2033  GLM_INLINE glm::vec<4, T, Q> zyxy(const glm::vec<3, T, Q> &v) {
\n
2034  return glm::vec<4, T, Q>(v.z, v.y, v.x, v.y);
\n
2035  }
\n
2036 
\n
2037  template<typename T, qualifier Q>
\n
2038  GLM_INLINE glm::vec<4, T, Q> zyxy(const glm::vec<4, T, Q> &v) {
\n
2039  return glm::vec<4, T, Q>(v.z, v.y, v.x, v.y);
\n
2040  }
\n
2041 
\n
2042  // zyxz
\n
2043  template<typename T, qualifier Q>
\n
2044  GLM_INLINE glm::vec<4, T, Q> zyxz(const glm::vec<3, T, Q> &v) {
\n
2045  return glm::vec<4, T, Q>(v.z, v.y, v.x, v.z);
\n
2046  }
\n
2047 
\n
2048  template<typename T, qualifier Q>
\n
2049  GLM_INLINE glm::vec<4, T, Q> zyxz(const glm::vec<4, T, Q> &v) {
\n
2050  return glm::vec<4, T, Q>(v.z, v.y, v.x, v.z);
\n
2051  }
\n
2052 
\n
2053  // zyxw
\n
2054  template<typename T, qualifier Q>
\n
2055  GLM_INLINE glm::vec<4, T, Q> zyxw(const glm::vec<4, T, Q> &v) {
\n
2056  return glm::vec<4, T, Q>(v.z, v.y, v.x, v.w);
\n
2057  }
\n
2058 
\n
2059  // zyyx
\n
2060  template<typename T, qualifier Q>
\n
2061  GLM_INLINE glm::vec<4, T, Q> zyyx(const glm::vec<3, T, Q> &v) {
\n
2062  return glm::vec<4, T, Q>(v.z, v.y, v.y, v.x);
\n
2063  }
\n
2064 
\n
2065  template<typename T, qualifier Q>
\n
2066  GLM_INLINE glm::vec<4, T, Q> zyyx(const glm::vec<4, T, Q> &v) {
\n
2067  return glm::vec<4, T, Q>(v.z, v.y, v.y, v.x);
\n
2068  }
\n
2069 
\n
2070  // zyyy
\n
2071  template<typename T, qualifier Q>
\n
2072  GLM_INLINE glm::vec<4, T, Q> zyyy(const glm::vec<3, T, Q> &v) {
\n
2073  return glm::vec<4, T, Q>(v.z, v.y, v.y, v.y);
\n
2074  }
\n
2075 
\n
2076  template<typename T, qualifier Q>
\n
2077  GLM_INLINE glm::vec<4, T, Q> zyyy(const glm::vec<4, T, Q> &v) {
\n
2078  return glm::vec<4, T, Q>(v.z, v.y, v.y, v.y);
\n
2079  }
\n
2080 
\n
2081  // zyyz
\n
2082  template<typename T, qualifier Q>
\n
2083  GLM_INLINE glm::vec<4, T, Q> zyyz(const glm::vec<3, T, Q> &v) {
\n
2084  return glm::vec<4, T, Q>(v.z, v.y, v.y, v.z);
\n
2085  }
\n
2086 
\n
2087  template<typename T, qualifier Q>
\n
2088  GLM_INLINE glm::vec<4, T, Q> zyyz(const glm::vec<4, T, Q> &v) {
\n
2089  return glm::vec<4, T, Q>(v.z, v.y, v.y, v.z);
\n
2090  }
\n
2091 
\n
2092  // zyyw
\n
2093  template<typename T, qualifier Q>
\n
2094  GLM_INLINE glm::vec<4, T, Q> zyyw(const glm::vec<4, T, Q> &v) {
\n
2095  return glm::vec<4, T, Q>(v.z, v.y, v.y, v.w);
\n
2096  }
\n
2097 
\n
2098  // zyzx
\n
2099  template<typename T, qualifier Q>
\n
2100  GLM_INLINE glm::vec<4, T, Q> zyzx(const glm::vec<3, T, Q> &v) {
\n
2101  return glm::vec<4, T, Q>(v.z, v.y, v.z, v.x);
\n
2102  }
\n
2103 
\n
2104  template<typename T, qualifier Q>
\n
2105  GLM_INLINE glm::vec<4, T, Q> zyzx(const glm::vec<4, T, Q> &v) {
\n
2106  return glm::vec<4, T, Q>(v.z, v.y, v.z, v.x);
\n
2107  }
\n
2108 
\n
2109  // zyzy
\n
2110  template<typename T, qualifier Q>
\n
2111  GLM_INLINE glm::vec<4, T, Q> zyzy(const glm::vec<3, T, Q> &v) {
\n
2112  return glm::vec<4, T, Q>(v.z, v.y, v.z, v.y);
\n
2113  }
\n
2114 
\n
2115  template<typename T, qualifier Q>
\n
2116  GLM_INLINE glm::vec<4, T, Q> zyzy(const glm::vec<4, T, Q> &v) {
\n
2117  return glm::vec<4, T, Q>(v.z, v.y, v.z, v.y);
\n
2118  }
\n
2119 
\n
2120  // zyzz
\n
2121  template<typename T, qualifier Q>
\n
2122  GLM_INLINE glm::vec<4, T, Q> zyzz(const glm::vec<3, T, Q> &v) {
\n
2123  return glm::vec<4, T, Q>(v.z, v.y, v.z, v.z);
\n
2124  }
\n
2125 
\n
2126  template<typename T, qualifier Q>
\n
2127  GLM_INLINE glm::vec<4, T, Q> zyzz(const glm::vec<4, T, Q> &v) {
\n
2128  return glm::vec<4, T, Q>(v.z, v.y, v.z, v.z);
\n
2129  }
\n
2130 
\n
2131  // zyzw
\n
2132  template<typename T, qualifier Q>
\n
2133  GLM_INLINE glm::vec<4, T, Q> zyzw(const glm::vec<4, T, Q> &v) {
\n
2134  return glm::vec<4, T, Q>(v.z, v.y, v.z, v.w);
\n
2135  }
\n
2136 
\n
2137  // zywx
\n
2138  template<typename T, qualifier Q>
\n
2139  GLM_INLINE glm::vec<4, T, Q> zywx(const glm::vec<4, T, Q> &v) {
\n
2140  return glm::vec<4, T, Q>(v.z, v.y, v.w, v.x);
\n
2141  }
\n
2142 
\n
2143  // zywy
\n
2144  template<typename T, qualifier Q>
\n
2145  GLM_INLINE glm::vec<4, T, Q> zywy(const glm::vec<4, T, Q> &v) {
\n
2146  return glm::vec<4, T, Q>(v.z, v.y, v.w, v.y);
\n
2147  }
\n
2148 
\n
2149  // zywz
\n
2150  template<typename T, qualifier Q>
\n
2151  GLM_INLINE glm::vec<4, T, Q> zywz(const glm::vec<4, T, Q> &v) {
\n
2152  return glm::vec<4, T, Q>(v.z, v.y, v.w, v.z);
\n
2153  }
\n
2154 
\n
2155  // zyww
\n
2156  template<typename T, qualifier Q>
\n
2157  GLM_INLINE glm::vec<4, T, Q> zyww(const glm::vec<4, T, Q> &v) {
\n
2158  return glm::vec<4, T, Q>(v.z, v.y, v.w, v.w);
\n
2159  }
\n
2160 
\n
2161  // zzxx
\n
2162  template<typename T, qualifier Q>
\n
2163  GLM_INLINE glm::vec<4, T, Q> zzxx(const glm::vec<3, T, Q> &v) {
\n
2164  return glm::vec<4, T, Q>(v.z, v.z, v.x, v.x);
\n
2165  }
\n
2166 
\n
2167  template<typename T, qualifier Q>
\n
2168  GLM_INLINE glm::vec<4, T, Q> zzxx(const glm::vec<4, T, Q> &v) {
\n
2169  return glm::vec<4, T, Q>(v.z, v.z, v.x, v.x);
\n
2170  }
\n
2171 
\n
2172  // zzxy
\n
2173  template<typename T, qualifier Q>
\n
2174  GLM_INLINE glm::vec<4, T, Q> zzxy(const glm::vec<3, T, Q> &v) {
\n
2175  return glm::vec<4, T, Q>(v.z, v.z, v.x, v.y);
\n
2176  }
\n
2177 
\n
2178  template<typename T, qualifier Q>
\n
2179  GLM_INLINE glm::vec<4, T, Q> zzxy(const glm::vec<4, T, Q> &v) {
\n
2180  return glm::vec<4, T, Q>(v.z, v.z, v.x, v.y);
\n
2181  }
\n
2182 
\n
2183  // zzxz
\n
2184  template<typename T, qualifier Q>
\n
2185  GLM_INLINE glm::vec<4, T, Q> zzxz(const glm::vec<3, T, Q> &v) {
\n
2186  return glm::vec<4, T, Q>(v.z, v.z, v.x, v.z);
\n
2187  }
\n
2188 
\n
2189  template<typename T, qualifier Q>
\n
2190  GLM_INLINE glm::vec<4, T, Q> zzxz(const glm::vec<4, T, Q> &v) {
\n
2191  return glm::vec<4, T, Q>(v.z, v.z, v.x, v.z);
\n
2192  }
\n
2193 
\n
2194  // zzxw
\n
2195  template<typename T, qualifier Q>
\n
2196  GLM_INLINE glm::vec<4, T, Q> zzxw(const glm::vec<4, T, Q> &v) {
\n
2197  return glm::vec<4, T, Q>(v.z, v.z, v.x, v.w);
\n
2198  }
\n
2199 
\n
2200  // zzyx
\n
2201  template<typename T, qualifier Q>
\n
2202  GLM_INLINE glm::vec<4, T, Q> zzyx(const glm::vec<3, T, Q> &v) {
\n
2203  return glm::vec<4, T, Q>(v.z, v.z, v.y, v.x);
\n
2204  }
\n
2205 
\n
2206  template<typename T, qualifier Q>
\n
2207  GLM_INLINE glm::vec<4, T, Q> zzyx(const glm::vec<4, T, Q> &v) {
\n
2208  return glm::vec<4, T, Q>(v.z, v.z, v.y, v.x);
\n
2209  }
\n
2210 
\n
2211  // zzyy
\n
2212  template<typename T, qualifier Q>
\n
2213  GLM_INLINE glm::vec<4, T, Q> zzyy(const glm::vec<3, T, Q> &v) {
\n
2214  return glm::vec<4, T, Q>(v.z, v.z, v.y, v.y);
\n
2215  }
\n
2216 
\n
2217  template<typename T, qualifier Q>
\n
2218  GLM_INLINE glm::vec<4, T, Q> zzyy(const glm::vec<4, T, Q> &v) {
\n
2219  return glm::vec<4, T, Q>(v.z, v.z, v.y, v.y);
\n
2220  }
\n
2221 
\n
2222  // zzyz
\n
2223  template<typename T, qualifier Q>
\n
2224  GLM_INLINE glm::vec<4, T, Q> zzyz(const glm::vec<3, T, Q> &v) {
\n
2225  return glm::vec<4, T, Q>(v.z, v.z, v.y, v.z);
\n
2226  }
\n
2227 
\n
2228  template<typename T, qualifier Q>
\n
2229  GLM_INLINE glm::vec<4, T, Q> zzyz(const glm::vec<4, T, Q> &v) {
\n
2230  return glm::vec<4, T, Q>(v.z, v.z, v.y, v.z);
\n
2231  }
\n
2232 
\n
2233  // zzyw
\n
2234  template<typename T, qualifier Q>
\n
2235  GLM_INLINE glm::vec<4, T, Q> zzyw(const glm::vec<4, T, Q> &v) {
\n
2236  return glm::vec<4, T, Q>(v.z, v.z, v.y, v.w);
\n
2237  }
\n
2238 
\n
2239  // zzzx
\n
2240  template<typename T, qualifier Q>
\n
2241  GLM_INLINE glm::vec<4, T, Q> zzzx(const glm::vec<3, T, Q> &v) {
\n
2242  return glm::vec<4, T, Q>(v.z, v.z, v.z, v.x);
\n
2243  }
\n
2244 
\n
2245  template<typename T, qualifier Q>
\n
2246  GLM_INLINE glm::vec<4, T, Q> zzzx(const glm::vec<4, T, Q> &v) {
\n
2247  return glm::vec<4, T, Q>(v.z, v.z, v.z, v.x);
\n
2248  }
\n
2249 
\n
2250  // zzzy
\n
2251  template<typename T, qualifier Q>
\n
2252  GLM_INLINE glm::vec<4, T, Q> zzzy(const glm::vec<3, T, Q> &v) {
\n
2253  return glm::vec<4, T, Q>(v.z, v.z, v.z, v.y);
\n
2254  }
\n
2255 
\n
2256  template<typename T, qualifier Q>
\n
2257  GLM_INLINE glm::vec<4, T, Q> zzzy(const glm::vec<4, T, Q> &v) {
\n
2258  return glm::vec<4, T, Q>(v.z, v.z, v.z, v.y);
\n
2259  }
\n
2260 
\n
2261  // zzzz
\n
2262  template<typename T, qualifier Q>
\n
2263  GLM_INLINE glm::vec<4, T, Q> zzzz(const glm::vec<3, T, Q> &v) {
\n
2264  return glm::vec<4, T, Q>(v.z, v.z, v.z, v.z);
\n
2265  }
\n
2266 
\n
2267  template<typename T, qualifier Q>
\n
2268  GLM_INLINE glm::vec<4, T, Q> zzzz(const glm::vec<4, T, Q> &v) {
\n
2269  return glm::vec<4, T, Q>(v.z, v.z, v.z, v.z);
\n
2270  }
\n
2271 
\n
2272  // zzzw
\n
2273  template<typename T, qualifier Q>
\n
2274  GLM_INLINE glm::vec<4, T, Q> zzzw(const glm::vec<4, T, Q> &v) {
\n
2275  return glm::vec<4, T, Q>(v.z, v.z, v.z, v.w);
\n
2276  }
\n
2277 
\n
2278  // zzwx
\n
2279  template<typename T, qualifier Q>
\n
2280  GLM_INLINE glm::vec<4, T, Q> zzwx(const glm::vec<4, T, Q> &v) {
\n
2281  return glm::vec<4, T, Q>(v.z, v.z, v.w, v.x);
\n
2282  }
\n
2283 
\n
2284  // zzwy
\n
2285  template<typename T, qualifier Q>
\n
2286  GLM_INLINE glm::vec<4, T, Q> zzwy(const glm::vec<4, T, Q> &v) {
\n
2287  return glm::vec<4, T, Q>(v.z, v.z, v.w, v.y);
\n
2288  }
\n
2289 
\n
2290  // zzwz
\n
2291  template<typename T, qualifier Q>
\n
2292  GLM_INLINE glm::vec<4, T, Q> zzwz(const glm::vec<4, T, Q> &v) {
\n
2293  return glm::vec<4, T, Q>(v.z, v.z, v.w, v.z);
\n
2294  }
\n
2295 
\n
2296  // zzww
\n
2297  template<typename T, qualifier Q>
\n
2298  GLM_INLINE glm::vec<4, T, Q> zzww(const glm::vec<4, T, Q> &v) {
\n
2299  return glm::vec<4, T, Q>(v.z, v.z, v.w, v.w);
\n
2300  }
\n
2301 
\n
2302  // zwxx
\n
2303  template<typename T, qualifier Q>
\n
2304  GLM_INLINE glm::vec<4, T, Q> zwxx(const glm::vec<4, T, Q> &v) {
\n
2305  return glm::vec<4, T, Q>(v.z, v.w, v.x, v.x);
\n
2306  }
\n
2307 
\n
2308  // zwxy
\n
2309  template<typename T, qualifier Q>
\n
2310  GLM_INLINE glm::vec<4, T, Q> zwxy(const glm::vec<4, T, Q> &v) {
\n
2311  return glm::vec<4, T, Q>(v.z, v.w, v.x, v.y);
\n
2312  }
\n
2313 
\n
2314  // zwxz
\n
2315  template<typename T, qualifier Q>
\n
2316  GLM_INLINE glm::vec<4, T, Q> zwxz(const glm::vec<4, T, Q> &v) {
\n
2317  return glm::vec<4, T, Q>(v.z, v.w, v.x, v.z);
\n
2318  }
\n
2319 
\n
2320  // zwxw
\n
2321  template<typename T, qualifier Q>
\n
2322  GLM_INLINE glm::vec<4, T, Q> zwxw(const glm::vec<4, T, Q> &v) {
\n
2323  return glm::vec<4, T, Q>(v.z, v.w, v.x, v.w);
\n
2324  }
\n
2325 
\n
2326  // zwyx
\n
2327  template<typename T, qualifier Q>
\n
2328  GLM_INLINE glm::vec<4, T, Q> zwyx(const glm::vec<4, T, Q> &v) {
\n
2329  return glm::vec<4, T, Q>(v.z, v.w, v.y, v.x);
\n
2330  }
\n
2331 
\n
2332  // zwyy
\n
2333  template<typename T, qualifier Q>
\n
2334  GLM_INLINE glm::vec<4, T, Q> zwyy(const glm::vec<4, T, Q> &v) {
\n
2335  return glm::vec<4, T, Q>(v.z, v.w, v.y, v.y);
\n
2336  }
\n
2337 
\n
2338  // zwyz
\n
2339  template<typename T, qualifier Q>
\n
2340  GLM_INLINE glm::vec<4, T, Q> zwyz(const glm::vec<4, T, Q> &v) {
\n
2341  return glm::vec<4, T, Q>(v.z, v.w, v.y, v.z);
\n
2342  }
\n
2343 
\n
2344  // zwyw
\n
2345  template<typename T, qualifier Q>
\n
2346  GLM_INLINE glm::vec<4, T, Q> zwyw(const glm::vec<4, T, Q> &v) {
\n
2347  return glm::vec<4, T, Q>(v.z, v.w, v.y, v.w);
\n
2348  }
\n
2349 
\n
2350  // zwzx
\n
2351  template<typename T, qualifier Q>
\n
2352  GLM_INLINE glm::vec<4, T, Q> zwzx(const glm::vec<4, T, Q> &v) {
\n
2353  return glm::vec<4, T, Q>(v.z, v.w, v.z, v.x);
\n
2354  }
\n
2355 
\n
2356  // zwzy
\n
2357  template<typename T, qualifier Q>
\n
2358  GLM_INLINE glm::vec<4, T, Q> zwzy(const glm::vec<4, T, Q> &v) {
\n
2359  return glm::vec<4, T, Q>(v.z, v.w, v.z, v.y);
\n
2360  }
\n
2361 
\n
2362  // zwzz
\n
2363  template<typename T, qualifier Q>
\n
2364  GLM_INLINE glm::vec<4, T, Q> zwzz(const glm::vec<4, T, Q> &v) {
\n
2365  return glm::vec<4, T, Q>(v.z, v.w, v.z, v.z);
\n
2366  }
\n
2367 
\n
2368  // zwzw
\n
2369  template<typename T, qualifier Q>
\n
2370  GLM_INLINE glm::vec<4, T, Q> zwzw(const glm::vec<4, T, Q> &v) {
\n
2371  return glm::vec<4, T, Q>(v.z, v.w, v.z, v.w);
\n
2372  }
\n
2373 
\n
2374  // zwwx
\n
2375  template<typename T, qualifier Q>
\n
2376  GLM_INLINE glm::vec<4, T, Q> zwwx(const glm::vec<4, T, Q> &v) {
\n
2377  return glm::vec<4, T, Q>(v.z, v.w, v.w, v.x);
\n
2378  }
\n
2379 
\n
2380  // zwwy
\n
2381  template<typename T, qualifier Q>
\n
2382  GLM_INLINE glm::vec<4, T, Q> zwwy(const glm::vec<4, T, Q> &v) {
\n
2383  return glm::vec<4, T, Q>(v.z, v.w, v.w, v.y);
\n
2384  }
\n
2385 
\n
2386  // zwwz
\n
2387  template<typename T, qualifier Q>
\n
2388  GLM_INLINE glm::vec<4, T, Q> zwwz(const glm::vec<4, T, Q> &v) {
\n
2389  return glm::vec<4, T, Q>(v.z, v.w, v.w, v.z);
\n
2390  }
\n
2391 
\n
2392  // zwww
\n
2393  template<typename T, qualifier Q>
\n
2394  GLM_INLINE glm::vec<4, T, Q> zwww(const glm::vec<4, T, Q> &v) {
\n
2395  return glm::vec<4, T, Q>(v.z, v.w, v.w, v.w);
\n
2396  }
\n
2397 
\n
2398  // wxxx
\n
2399  template<typename T, qualifier Q>
\n
2400  GLM_INLINE glm::vec<4, T, Q> wxxx(const glm::vec<4, T, Q> &v) {
\n
2401  return glm::vec<4, T, Q>(v.w, v.x, v.x, v.x);
\n
2402  }
\n
2403 
\n
2404  // wxxy
\n
2405  template<typename T, qualifier Q>
\n
2406  GLM_INLINE glm::vec<4, T, Q> wxxy(const glm::vec<4, T, Q> &v) {
\n
2407  return glm::vec<4, T, Q>(v.w, v.x, v.x, v.y);
\n
2408  }
\n
2409 
\n
2410  // wxxz
\n
2411  template<typename T, qualifier Q>
\n
2412  GLM_INLINE glm::vec<4, T, Q> wxxz(const glm::vec<4, T, Q> &v) {
\n
2413  return glm::vec<4, T, Q>(v.w, v.x, v.x, v.z);
\n
2414  }
\n
2415 
\n
2416  // wxxw
\n
2417  template<typename T, qualifier Q>
\n
2418  GLM_INLINE glm::vec<4, T, Q> wxxw(const glm::vec<4, T, Q> &v) {
\n
2419  return glm::vec<4, T, Q>(v.w, v.x, v.x, v.w);
\n
2420  }
\n
2421 
\n
2422  // wxyx
\n
2423  template<typename T, qualifier Q>
\n
2424  GLM_INLINE glm::vec<4, T, Q> wxyx(const glm::vec<4, T, Q> &v) {
\n
2425  return glm::vec<4, T, Q>(v.w, v.x, v.y, v.x);
\n
2426  }
\n
2427 
\n
2428  // wxyy
\n
2429  template<typename T, qualifier Q>
\n
2430  GLM_INLINE glm::vec<4, T, Q> wxyy(const glm::vec<4, T, Q> &v) {
\n
2431  return glm::vec<4, T, Q>(v.w, v.x, v.y, v.y);
\n
2432  }
\n
2433 
\n
2434  // wxyz
\n
2435  template<typename T, qualifier Q>
\n
2436  GLM_INLINE glm::vec<4, T, Q> wxyz(const glm::vec<4, T, Q> &v) {
\n
2437  return glm::vec<4, T, Q>(v.w, v.x, v.y, v.z);
\n
2438  }
\n
2439 
\n
2440  // wxyw
\n
2441  template<typename T, qualifier Q>
\n
2442  GLM_INLINE glm::vec<4, T, Q> wxyw(const glm::vec<4, T, Q> &v) {
\n
2443  return glm::vec<4, T, Q>(v.w, v.x, v.y, v.w);
\n
2444  }
\n
2445 
\n
2446  // wxzx
\n
2447  template<typename T, qualifier Q>
\n
2448  GLM_INLINE glm::vec<4, T, Q> wxzx(const glm::vec<4, T, Q> &v) {
\n
2449  return glm::vec<4, T, Q>(v.w, v.x, v.z, v.x);
\n
2450  }
\n
2451 
\n
2452  // wxzy
\n
2453  template<typename T, qualifier Q>
\n
2454  GLM_INLINE glm::vec<4, T, Q> wxzy(const glm::vec<4, T, Q> &v) {
\n
2455  return glm::vec<4, T, Q>(v.w, v.x, v.z, v.y);
\n
2456  }
\n
2457 
\n
2458  // wxzz
\n
2459  template<typename T, qualifier Q>
\n
2460  GLM_INLINE glm::vec<4, T, Q> wxzz(const glm::vec<4, T, Q> &v) {
\n
2461  return glm::vec<4, T, Q>(v.w, v.x, v.z, v.z);
\n
2462  }
\n
2463 
\n
2464  // wxzw
\n
2465  template<typename T, qualifier Q>
\n
2466  GLM_INLINE glm::vec<4, T, Q> wxzw(const glm::vec<4, T, Q> &v) {
\n
2467  return glm::vec<4, T, Q>(v.w, v.x, v.z, v.w);
\n
2468  }
\n
2469 
\n
2470  // wxwx
\n
2471  template<typename T, qualifier Q>
\n
2472  GLM_INLINE glm::vec<4, T, Q> wxwx(const glm::vec<4, T, Q> &v) {
\n
2473  return glm::vec<4, T, Q>(v.w, v.x, v.w, v.x);
\n
2474  }
\n
2475 
\n
2476  // wxwy
\n
2477  template<typename T, qualifier Q>
\n
2478  GLM_INLINE glm::vec<4, T, Q> wxwy(const glm::vec<4, T, Q> &v) {
\n
2479  return glm::vec<4, T, Q>(v.w, v.x, v.w, v.y);
\n
2480  }
\n
2481 
\n
2482  // wxwz
\n
2483  template<typename T, qualifier Q>
\n
2484  GLM_INLINE glm::vec<4, T, Q> wxwz(const glm::vec<4, T, Q> &v) {
\n
2485  return glm::vec<4, T, Q>(v.w, v.x, v.w, v.z);
\n
2486  }
\n
2487 
\n
2488  // wxww
\n
2489  template<typename T, qualifier Q>
\n
2490  GLM_INLINE glm::vec<4, T, Q> wxww(const glm::vec<4, T, Q> &v) {
\n
2491  return glm::vec<4, T, Q>(v.w, v.x, v.w, v.w);
\n
2492  }
\n
2493 
\n
2494  // wyxx
\n
2495  template<typename T, qualifier Q>
\n
2496  GLM_INLINE glm::vec<4, T, Q> wyxx(const glm::vec<4, T, Q> &v) {
\n
2497  return glm::vec<4, T, Q>(v.w, v.y, v.x, v.x);
\n
2498  }
\n
2499 
\n
2500  // wyxy
\n
2501  template<typename T, qualifier Q>
\n
2502  GLM_INLINE glm::vec<4, T, Q> wyxy(const glm::vec<4, T, Q> &v) {
\n
2503  return glm::vec<4, T, Q>(v.w, v.y, v.x, v.y);
\n
2504  }
\n
2505 
\n
2506  // wyxz
\n
2507  template<typename T, qualifier Q>
\n
2508  GLM_INLINE glm::vec<4, T, Q> wyxz(const glm::vec<4, T, Q> &v) {
\n
2509  return glm::vec<4, T, Q>(v.w, v.y, v.x, v.z);
\n
2510  }
\n
2511 
\n
2512  // wyxw
\n
2513  template<typename T, qualifier Q>
\n
2514  GLM_INLINE glm::vec<4, T, Q> wyxw(const glm::vec<4, T, Q> &v) {
\n
2515  return glm::vec<4, T, Q>(v.w, v.y, v.x, v.w);
\n
2516  }
\n
2517 
\n
2518  // wyyx
\n
2519  template<typename T, qualifier Q>
\n
2520  GLM_INLINE glm::vec<4, T, Q> wyyx(const glm::vec<4, T, Q> &v) {
\n
2521  return glm::vec<4, T, Q>(v.w, v.y, v.y, v.x);
\n
2522  }
\n
2523 
\n
2524  // wyyy
\n
2525  template<typename T, qualifier Q>
\n
2526  GLM_INLINE glm::vec<4, T, Q> wyyy(const glm::vec<4, T, Q> &v) {
\n
2527  return glm::vec<4, T, Q>(v.w, v.y, v.y, v.y);
\n
2528  }
\n
2529 
\n
2530  // wyyz
\n
2531  template<typename T, qualifier Q>
\n
2532  GLM_INLINE glm::vec<4, T, Q> wyyz(const glm::vec<4, T, Q> &v) {
\n
2533  return glm::vec<4, T, Q>(v.w, v.y, v.y, v.z);
\n
2534  }
\n
2535 
\n
2536  // wyyw
\n
2537  template<typename T, qualifier Q>
\n
2538  GLM_INLINE glm::vec<4, T, Q> wyyw(const glm::vec<4, T, Q> &v) {
\n
2539  return glm::vec<4, T, Q>(v.w, v.y, v.y, v.w);
\n
2540  }
\n
2541 
\n
2542  // wyzx
\n
2543  template<typename T, qualifier Q>
\n
2544  GLM_INLINE glm::vec<4, T, Q> wyzx(const glm::vec<4, T, Q> &v) {
\n
2545  return glm::vec<4, T, Q>(v.w, v.y, v.z, v.x);
\n
2546  }
\n
2547 
\n
2548  // wyzy
\n
2549  template<typename T, qualifier Q>
\n
2550  GLM_INLINE glm::vec<4, T, Q> wyzy(const glm::vec<4, T, Q> &v) {
\n
2551  return glm::vec<4, T, Q>(v.w, v.y, v.z, v.y);
\n
2552  }
\n
2553 
\n
2554  // wyzz
\n
2555  template<typename T, qualifier Q>
\n
2556  GLM_INLINE glm::vec<4, T, Q> wyzz(const glm::vec<4, T, Q> &v) {
\n
2557  return glm::vec<4, T, Q>(v.w, v.y, v.z, v.z);
\n
2558  }
\n
2559 
\n
2560  // wyzw
\n
2561  template<typename T, qualifier Q>
\n
2562  GLM_INLINE glm::vec<4, T, Q> wyzw(const glm::vec<4, T, Q> &v) {
\n
2563  return glm::vec<4, T, Q>(v.w, v.y, v.z, v.w);
\n
2564  }
\n
2565 
\n
2566  // wywx
\n
2567  template<typename T, qualifier Q>
\n
2568  GLM_INLINE glm::vec<4, T, Q> wywx(const glm::vec<4, T, Q> &v) {
\n
2569  return glm::vec<4, T, Q>(v.w, v.y, v.w, v.x);
\n
2570  }
\n
2571 
\n
2572  // wywy
\n
2573  template<typename T, qualifier Q>
\n
2574  GLM_INLINE glm::vec<4, T, Q> wywy(const glm::vec<4, T, Q> &v) {
\n
2575  return glm::vec<4, T, Q>(v.w, v.y, v.w, v.y);
\n
2576  }
\n
2577 
\n
2578  // wywz
\n
2579  template<typename T, qualifier Q>
\n
2580  GLM_INLINE glm::vec<4, T, Q> wywz(const glm::vec<4, T, Q> &v) {
\n
2581  return glm::vec<4, T, Q>(v.w, v.y, v.w, v.z);
\n
2582  }
\n
2583 
\n
2584  // wyww
\n
2585  template<typename T, qualifier Q>
\n
2586  GLM_INLINE glm::vec<4, T, Q> wyww(const glm::vec<4, T, Q> &v) {
\n
2587  return glm::vec<4, T, Q>(v.w, v.y, v.w, v.w);
\n
2588  }
\n
2589 
\n
2590  // wzxx
\n
2591  template<typename T, qualifier Q>
\n
2592  GLM_INLINE glm::vec<4, T, Q> wzxx(const glm::vec<4, T, Q> &v) {
\n
2593  return glm::vec<4, T, Q>(v.w, v.z, v.x, v.x);
\n
2594  }
\n
2595 
\n
2596  // wzxy
\n
2597  template<typename T, qualifier Q>
\n
2598  GLM_INLINE glm::vec<4, T, Q> wzxy(const glm::vec<4, T, Q> &v) {
\n
2599  return glm::vec<4, T, Q>(v.w, v.z, v.x, v.y);
\n
2600  }
\n
2601 
\n
2602  // wzxz
\n
2603  template<typename T, qualifier Q>
\n
2604  GLM_INLINE glm::vec<4, T, Q> wzxz(const glm::vec<4, T, Q> &v) {
\n
2605  return glm::vec<4, T, Q>(v.w, v.z, v.x, v.z);
\n
2606  }
\n
2607 
\n
2608  // wzxw
\n
2609  template<typename T, qualifier Q>
\n
2610  GLM_INLINE glm::vec<4, T, Q> wzxw(const glm::vec<4, T, Q> &v) {
\n
2611  return glm::vec<4, T, Q>(v.w, v.z, v.x, v.w);
\n
2612  }
\n
2613 
\n
2614  // wzyx
\n
2615  template<typename T, qualifier Q>
\n
2616  GLM_INLINE glm::vec<4, T, Q> wzyx(const glm::vec<4, T, Q> &v) {
\n
2617  return glm::vec<4, T, Q>(v.w, v.z, v.y, v.x);
\n
2618  }
\n
2619 
\n
2620  // wzyy
\n
2621  template<typename T, qualifier Q>
\n
2622  GLM_INLINE glm::vec<4, T, Q> wzyy(const glm::vec<4, T, Q> &v) {
\n
2623  return glm::vec<4, T, Q>(v.w, v.z, v.y, v.y);
\n
2624  }
\n
2625 
\n
2626  // wzyz
\n
2627  template<typename T, qualifier Q>
\n
2628  GLM_INLINE glm::vec<4, T, Q> wzyz(const glm::vec<4, T, Q> &v) {
\n
2629  return glm::vec<4, T, Q>(v.w, v.z, v.y, v.z);
\n
2630  }
\n
2631 
\n
2632  // wzyw
\n
2633  template<typename T, qualifier Q>
\n
2634  GLM_INLINE glm::vec<4, T, Q> wzyw(const glm::vec<4, T, Q> &v) {
\n
2635  return glm::vec<4, T, Q>(v.w, v.z, v.y, v.w);
\n
2636  }
\n
2637 
\n
2638  // wzzx
\n
2639  template<typename T, qualifier Q>
\n
2640  GLM_INLINE glm::vec<4, T, Q> wzzx(const glm::vec<4, T, Q> &v) {
\n
2641  return glm::vec<4, T, Q>(v.w, v.z, v.z, v.x);
\n
2642  }
\n
2643 
\n
2644  // wzzy
\n
2645  template<typename T, qualifier Q>
\n
2646  GLM_INLINE glm::vec<4, T, Q> wzzy(const glm::vec<4, T, Q> &v) {
\n
2647  return glm::vec<4, T, Q>(v.w, v.z, v.z, v.y);
\n
2648  }
\n
2649 
\n
2650  // wzzz
\n
2651  template<typename T, qualifier Q>
\n
2652  GLM_INLINE glm::vec<4, T, Q> wzzz(const glm::vec<4, T, Q> &v) {
\n
2653  return glm::vec<4, T, Q>(v.w, v.z, v.z, v.z);
\n
2654  }
\n
2655 
\n
2656  // wzzw
\n
2657  template<typename T, qualifier Q>
\n
2658  GLM_INLINE glm::vec<4, T, Q> wzzw(const glm::vec<4, T, Q> &v) {
\n
2659  return glm::vec<4, T, Q>(v.w, v.z, v.z, v.w);
\n
2660  }
\n
2661 
\n
2662  // wzwx
\n
2663  template<typename T, qualifier Q>
\n
2664  GLM_INLINE glm::vec<4, T, Q> wzwx(const glm::vec<4, T, Q> &v) {
\n
2665  return glm::vec<4, T, Q>(v.w, v.z, v.w, v.x);
\n
2666  }
\n
2667 
\n
2668  // wzwy
\n
2669  template<typename T, qualifier Q>
\n
2670  GLM_INLINE glm::vec<4, T, Q> wzwy(const glm::vec<4, T, Q> &v) {
\n
2671  return glm::vec<4, T, Q>(v.w, v.z, v.w, v.y);
\n
2672  }
\n
2673 
\n
2674  // wzwz
\n
2675  template<typename T, qualifier Q>
\n
2676  GLM_INLINE glm::vec<4, T, Q> wzwz(const glm::vec<4, T, Q> &v) {
\n
2677  return glm::vec<4, T, Q>(v.w, v.z, v.w, v.z);
\n
2678  }
\n
2679 
\n
2680  // wzww
\n
2681  template<typename T, qualifier Q>
\n
2682  GLM_INLINE glm::vec<4, T, Q> wzww(const glm::vec<4, T, Q> &v) {
\n
2683  return glm::vec<4, T, Q>(v.w, v.z, v.w, v.w);
\n
2684  }
\n
2685 
\n
2686  // wwxx
\n
2687  template<typename T, qualifier Q>
\n
2688  GLM_INLINE glm::vec<4, T, Q> wwxx(const glm::vec<4, T, Q> &v) {
\n
2689  return glm::vec<4, T, Q>(v.w, v.w, v.x, v.x);
\n
2690  }
\n
2691 
\n
2692  // wwxy
\n
2693  template<typename T, qualifier Q>
\n
2694  GLM_INLINE glm::vec<4, T, Q> wwxy(const glm::vec<4, T, Q> &v) {
\n
2695  return glm::vec<4, T, Q>(v.w, v.w, v.x, v.y);
\n
2696  }
\n
2697 
\n
2698  // wwxz
\n
2699  template<typename T, qualifier Q>
\n
2700  GLM_INLINE glm::vec<4, T, Q> wwxz(const glm::vec<4, T, Q> &v) {
\n
2701  return glm::vec<4, T, Q>(v.w, v.w, v.x, v.z);
\n
2702  }
\n
2703 
\n
2704  // wwxw
\n
2705  template<typename T, qualifier Q>
\n
2706  GLM_INLINE glm::vec<4, T, Q> wwxw(const glm::vec<4, T, Q> &v) {
\n
2707  return glm::vec<4, T, Q>(v.w, v.w, v.x, v.w);
\n
2708  }
\n
2709 
\n
2710  // wwyx
\n
2711  template<typename T, qualifier Q>
\n
2712  GLM_INLINE glm::vec<4, T, Q> wwyx(const glm::vec<4, T, Q> &v) {
\n
2713  return glm::vec<4, T, Q>(v.w, v.w, v.y, v.x);
\n
2714  }
\n
2715 
\n
2716  // wwyy
\n
2717  template<typename T, qualifier Q>
\n
2718  GLM_INLINE glm::vec<4, T, Q> wwyy(const glm::vec<4, T, Q> &v) {
\n
2719  return glm::vec<4, T, Q>(v.w, v.w, v.y, v.y);
\n
2720  }
\n
2721 
\n
2722  // wwyz
\n
2723  template<typename T, qualifier Q>
\n
2724  GLM_INLINE glm::vec<4, T, Q> wwyz(const glm::vec<4, T, Q> &v) {
\n
2725  return glm::vec<4, T, Q>(v.w, v.w, v.y, v.z);
\n
2726  }
\n
2727 
\n
2728  // wwyw
\n
2729  template<typename T, qualifier Q>
\n
2730  GLM_INLINE glm::vec<4, T, Q> wwyw(const glm::vec<4, T, Q> &v) {
\n
2731  return glm::vec<4, T, Q>(v.w, v.w, v.y, v.w);
\n
2732  }
\n
2733 
\n
2734  // wwzx
\n
2735  template<typename T, qualifier Q>
\n
2736  GLM_INLINE glm::vec<4, T, Q> wwzx(const glm::vec<4, T, Q> &v) {
\n
2737  return glm::vec<4, T, Q>(v.w, v.w, v.z, v.x);
\n
2738  }
\n
2739 
\n
2740  // wwzy
\n
2741  template<typename T, qualifier Q>
\n
2742  GLM_INLINE glm::vec<4, T, Q> wwzy(const glm::vec<4, T, Q> &v) {
\n
2743  return glm::vec<4, T, Q>(v.w, v.w, v.z, v.y);
\n
2744  }
\n
2745 
\n
2746  // wwzz
\n
2747  template<typename T, qualifier Q>
\n
2748  GLM_INLINE glm::vec<4, T, Q> wwzz(const glm::vec<4, T, Q> &v) {
\n
2749  return glm::vec<4, T, Q>(v.w, v.w, v.z, v.z);
\n
2750  }
\n
2751 
\n
2752  // wwzw
\n
2753  template<typename T, qualifier Q>
\n
2754  GLM_INLINE glm::vec<4, T, Q> wwzw(const glm::vec<4, T, Q> &v) {
\n
2755  return glm::vec<4, T, Q>(v.w, v.w, v.z, v.w);
\n
2756  }
\n
2757 
\n
2758  // wwwx
\n
2759  template<typename T, qualifier Q>
\n
2760  GLM_INLINE glm::vec<4, T, Q> wwwx(const glm::vec<4, T, Q> &v) {
\n
2761  return glm::vec<4, T, Q>(v.w, v.w, v.w, v.x);
\n
2762  }
\n
2763 
\n
2764  // wwwy
\n
2765  template<typename T, qualifier Q>
\n
2766  GLM_INLINE glm::vec<4, T, Q> wwwy(const glm::vec<4, T, Q> &v) {
\n
2767  return glm::vec<4, T, Q>(v.w, v.w, v.w, v.y);
\n
2768  }
\n
2769 
\n
2770  // wwwz
\n
2771  template<typename T, qualifier Q>
\n
2772  GLM_INLINE glm::vec<4, T, Q> wwwz(const glm::vec<4, T, Q> &v) {
\n
2773  return glm::vec<4, T, Q>(v.w, v.w, v.w, v.z);
\n
2774  }
\n
2775 
\n
2776  // wwww
\n
2777  template<typename T, qualifier Q>
\n
2778  GLM_INLINE glm::vec<4, T, Q> wwww(const glm::vec<4, T, Q> &v) {
\n
2779  return glm::vec<4, T, Q>(v.w, v.w, v.w, v.w);
\n
2780  }
\n
2781 
\n
2782 }
\n
glm
Definition: common.hpp:20
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vector_angle.hpp File Reference
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\nFunctions

template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL T angle (vec< L, T, Q > const &x, vec< L, T, Q > const &y)
 Returns the absolute angle between two vectors. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL T orientedAngle (vec< 2, T, Q > const &x, vec< 2, T, Q > const &y)
 Returns the oriented angle between two 2d vectors. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL T orientedAngle (vec< 3, T, Q > const &x, vec< 3, T, Q > const &y, vec< 3, T, Q > const &ref)
 Returns the oriented angle between two 3d vectors based from a reference axis. More...
 
\n

Detailed Description

\n

GLM_GTX_vector_angle

\n
See also
Core features (dependence)
\n
\n to use the features of this extension. \">GLM_GTX_quaternion (dependence)
\n
\ngtx_epsilon (dependence)
\n\n

Definition in file vector_angle.hpp.

\n
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vector_angle.hpp
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1 
\n
15 #pragma once
\n
16 
\n
17 // Dependency:
\n
18 #include "../glm.hpp"
\n
19 #include "../gtc/epsilon.hpp"
\n
20 #include "../gtx/quaternion.hpp"
\n
21 #include "../gtx/rotate_vector.hpp"
\n
22 
\n
23 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
24 # ifndef GLM_ENABLE_EXPERIMENTAL
\n
25 # pragma message("GLM: GLM_GTX_vector_angle is an experimental extension and may change in the future. Use #define GLM_ENABLE_EXPERIMENTAL before including it, if you really want to use it.")
\n
26 # else
\n
27 # pragma message("GLM: GLM_GTX_vector_angle extension included")
\n
28 # endif
\n
29 #endif
\n
30 
\n
31 namespace glm
\n
32 {
\n
35 
\n
39  template<length_t L, typename T, qualifier Q>
\n
40  GLM_FUNC_DECL T angle(vec<L, T, Q> const& x, vec<L, T, Q> const& y);
\n
41 
\n
45  template<typename T, qualifier Q>
\n
46  GLM_FUNC_DECL T orientedAngle(vec<2, T, Q> const& x, vec<2, T, Q> const& y);
\n
47 
\n
51  template<typename T, qualifier Q>
\n
52  GLM_FUNC_DECL T orientedAngle(vec<3, T, Q> const& x, vec<3, T, Q> const& y, vec<3, T, Q> const& ref);
\n
53 
\n
55 }// namespace glm
\n
56 
\n
57 #include "vector_angle.inl"
\n
glm::orientedAngle
GLM_FUNC_DECL T orientedAngle(vec< 3, T, Q > const &x, vec< 3, T, Q > const &y, vec< 3, T, Q > const &ref)
Returns the oriented angle between two 3d vectors based from a reference axis.
\n
glm::angle
GLM_FUNC_DECL T angle(vec< L, T, Q > const &x, vec< L, T, Q > const &y)
Returns the absolute angle between two vectors.
\n
glm
Definition: common.hpp:20
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\ntypedef vec< 1, bool, defaultp > bvec1
 1 components vector of boolean.
 
\n

Detailed Description

\n

GLM_EXT_vector_bool1

\n\n

Definition in file vector_bool1.hpp.

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vector_bool1.hpp
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1 
\n
13 #pragma once
\n
14 
\n
15 #include "../detail/type_vec1.hpp"
\n
16 
\n
17 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
18 # pragma message("GLM: GLM_EXT_vector_bool1 extension included")
\n
19 #endif
\n
20 
\n
21 namespace glm
\n
22 {
\n
25 
\n
27  typedef vec<1, bool, defaultp> bvec1;
\n
28 
\n
30 }//namespace glm
\n
glm::bvec1
vec< 1, bool, defaultp > bvec1
1 components vector of boolean.
Definition: vector_bool1.hpp:27
\n
glm
Definition: common.hpp:20
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\nTypedefs

\ntypedef vec< 1, bool, highp > highp_bvec1
 1 component vector of bool values.
 
\ntypedef vec< 1, bool, lowp > lowp_bvec1
 1 component vector of bool values.
 
\ntypedef vec< 1, bool, mediump > mediump_bvec1
 1 component vector of bool values.
 
\n

Detailed Description

\n

GLM_EXT_vector_bool1_precision

\n\n

Definition in file vector_bool1_precision.hpp.

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vector_bool1_precision.hpp
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1 
\n
11 #pragma once
\n
12 
\n
13 #include "../detail/type_vec1.hpp"
\n
14 
\n
15 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
16 # pragma message("GLM: GLM_EXT_vector_bool1_precision extension included")
\n
17 #endif
\n
18 
\n
19 namespace glm
\n
20 {
\n
23 
\n
25  typedef vec<1, bool, highp> highp_bvec1;
\n
26 
\n
28  typedef vec<1, bool, mediump> mediump_bvec1;
\n
29 
\n
31  typedef vec<1, bool, lowp> lowp_bvec1;
\n
32 
\n
34 }//namespace glm
\n
glm::highp_bvec1
vec< 1, bool, highp > highp_bvec1
1 component vector of bool values.
Definition: vector_bool1_precision.hpp:25
\n
glm::mediump_bvec1
vec< 1, bool, mediump > mediump_bvec1
1 component vector of bool values.
Definition: vector_bool1_precision.hpp:28
\n
glm::lowp_bvec1
vec< 1, bool, lowp > lowp_bvec1
1 component vector of bool values.
Definition: vector_bool1_precision.hpp:31
\n
glm
Definition: common.hpp:20
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typedef vec< 2, bool, defaultp > bvec2
 2 components vector of boolean. More...
 
\n

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Core features

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Definition in file vector_bool2.hpp.

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1 
\n
4 #pragma once
\n
5 #include "../detail/type_vec2.hpp"
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6 
\n
7 namespace glm
\n
8 {
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11 
\n
15  typedef vec<2, bool, defaultp> bvec2;
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16 
\n
18 }//namespace glm
\n
glm::bvec2
vec< 2, bool, defaultp > bvec2
2 components vector of boolean.
Definition: vector_bool2.hpp:15
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glm
Definition: common.hpp:20
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vector_bool2_precision.hpp File Reference
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Core features \nMore...

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typedef vec< 2, bool, highp > highp_bvec2
 2 components vector of high qualifier bool numbers. More...
 
typedef vec< 2, bool, lowp > lowp_bvec2
 2 components vector of low qualifier bool numbers. More...
 
typedef vec< 2, bool, mediump > mediump_bvec2
 2 components vector of medium qualifier bool numbers. More...
 
\n

Detailed Description

\n

Core features

\n\n

Definition in file vector_bool2_precision.hpp.

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vector_bool2_precision.hpp
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1 
\n
4 #pragma once
\n
5 #include "../detail/type_vec2.hpp"
\n
6 
\n
7 namespace glm
\n
8 {
\n
11 
\n
16  typedef vec<2, bool, highp> highp_bvec2;
\n
17 
\n
22  typedef vec<2, bool, mediump> mediump_bvec2;
\n
23 
\n
28  typedef vec<2, bool, lowp> lowp_bvec2;
\n
29 
\n
31 }//namespace glm
\n
glm::highp_bvec2
vec< 2, bool, highp > highp_bvec2
2 components vector of high qualifier bool numbers.
Definition: vector_bool2_precision.hpp:16
\n
glm::mediump_bvec2
vec< 2, bool, mediump > mediump_bvec2
2 components vector of medium qualifier bool numbers.
Definition: vector_bool2_precision.hpp:22
\n
glm::lowp_bvec2
vec< 2, bool, lowp > lowp_bvec2
2 components vector of low qualifier bool numbers.
Definition: vector_bool2_precision.hpp:28
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glm
Definition: common.hpp:20
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typedef vec< 3, bool, defaultp > bvec3
 3 components vector of boolean. More...
 
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Definition in file vector_bool3.hpp.

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vector_bool3.hpp
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1 
\n
4 #pragma once
\n
5 #include "../detail/type_vec3.hpp"
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6 
\n
7 namespace glm
\n
8 {
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11 
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15  typedef vec<3, bool, defaultp> bvec3;
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16 
\n
18 }//namespace glm
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glm::bvec3
vec< 3, bool, defaultp > bvec3
3 components vector of boolean.
Definition: vector_bool3.hpp:15
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glm
Definition: common.hpp:20
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typedef vec< 3, bool, highp > highp_bvec3
 3 components vector of high qualifier bool numbers. More...
 
typedef vec< 3, bool, lowp > lowp_bvec3
 3 components vector of low qualifier bool numbers. More...
 
typedef vec< 3, bool, mediump > mediump_bvec3
 3 components vector of medium qualifier bool numbers. More...
 
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Definition in file vector_bool3_precision.hpp.

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vector_bool3_precision.hpp
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1 
\n
4 #pragma once
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5 #include "../detail/type_vec3.hpp"
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6 
\n
7 namespace glm
\n
8 {
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11 
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16  typedef vec<3, bool, highp> highp_bvec3;
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17 
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22  typedef vec<3, bool, mediump> mediump_bvec3;
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28  typedef vec<3, bool, lowp> lowp_bvec3;
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29 
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31 }//namespace glm
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glm::mediump_bvec3
vec< 3, bool, mediump > mediump_bvec3
3 components vector of medium qualifier bool numbers.
Definition: vector_bool3_precision.hpp:22
\n
glm::highp_bvec3
vec< 3, bool, highp > highp_bvec3
3 components vector of high qualifier bool numbers.
Definition: vector_bool3_precision.hpp:16
\n
glm::lowp_bvec3
vec< 3, bool, lowp > lowp_bvec3
3 components vector of low qualifier bool numbers.
Definition: vector_bool3_precision.hpp:28
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glm
Definition: common.hpp:20
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typedef vec< 4, bool, defaultp > bvec4
 4 components vector of boolean. More...
 
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Definition in file vector_bool4.hpp.

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vector_bool4.hpp
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1 
\n
4 #pragma once
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5 #include "../detail/type_vec4.hpp"
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6 
\n
7 namespace glm
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8 {
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11 
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15  typedef vec<4, bool, defaultp> bvec4;
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18 }//namespace glm
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glm::bvec4
vec< 4, bool, defaultp > bvec4
4 components vector of boolean.
Definition: vector_bool4.hpp:15
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glm
Definition: common.hpp:20
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typedef vec< 4, bool, highp > highp_bvec4
 4 components vector of high qualifier bool numbers. More...
 
typedef vec< 4, bool, lowp > lowp_bvec4
 4 components vector of low qualifier bool numbers. More...
 
typedef vec< 4, bool, mediump > mediump_bvec4
 4 components vector of medium qualifier bool numbers. More...
 
\n

Detailed Description

\n

Core features

\n\n

Definition in file vector_bool4_precision.hpp.

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vector_bool4_precision.hpp
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1 
\n
4 #pragma once
\n
5 #include "../detail/type_vec4.hpp"
\n
6 
\n
7 namespace glm
\n
8 {
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11 
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16  typedef vec<4, bool, highp> highp_bvec4;
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17 
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22  typedef vec<4, bool, mediump> mediump_bvec4;
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28  typedef vec<4, bool, lowp> lowp_bvec4;
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29 
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31 }//namespace glm
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glm::lowp_bvec4
vec< 4, bool, lowp > lowp_bvec4
4 components vector of low qualifier bool numbers.
Definition: vector_bool4_precision.hpp:28
\n
glm::mediump_bvec4
vec< 4, bool, mediump > mediump_bvec4
4 components vector of medium qualifier bool numbers.
Definition: vector_bool4_precision.hpp:22
\n
glm::highp_bvec4
vec< 4, bool, highp > highp_bvec4
4 components vector of high qualifier bool numbers.
Definition: vector_bool4_precision.hpp:16
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glm
Definition: common.hpp:20
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\nFunctions

template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > fmax (vec< L, T, Q > const &a, T b)
 Returns y if x < y; otherwise, it returns x. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > fmax (vec< L, T, Q > const &a, vec< L, T, Q > const &b)
 Returns y if x < y; otherwise, it returns x. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > fmax (vec< L, T, Q > const &a, vec< L, T, Q > const &b, vec< L, T, Q > const &c)
 Returns y if x < y; otherwise, it returns x. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > fmax (vec< L, T, Q > const &a, vec< L, T, Q > const &b, vec< L, T, Q > const &c, vec< L, T, Q > const &d)
 Returns y if x < y; otherwise, it returns x. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > fmin (vec< L, T, Q > const &x, T y)
 Returns y if y < x; otherwise, it returns x. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > fmin (vec< L, T, Q > const &x, vec< L, T, Q > const &y)
 Returns y if y < x; otherwise, it returns x. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > fmin (vec< L, T, Q > const &a, vec< L, T, Q > const &b, vec< L, T, Q > const &c)
 Returns y if y < x; otherwise, it returns x. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > fmin (vec< L, T, Q > const &a, vec< L, T, Q > const &b, vec< L, T, Q > const &c, vec< L, T, Q > const &d)
 Returns y if y < x; otherwise, it returns x. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< L, T, Q > max (vec< L, T, Q > const &x, vec< L, T, Q > const &y, vec< L, T, Q > const &z)
 Return the maximum component-wise values of 3 inputs. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< L, T, Q > max (vec< L, T, Q > const &x, vec< L, T, Q > const &y, vec< L, T, Q > const &z, vec< L, T, Q > const &w)
 Return the maximum component-wise values of 4 inputs. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< L, T, Q > min (vec< L, T, Q > const &a, vec< L, T, Q > const &b, vec< L, T, Q > const &c)
 Return the minimum component-wise values of 3 inputs. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< L, T, Q > min (vec< L, T, Q > const &a, vec< L, T, Q > const &b, vec< L, T, Q > const &c, vec< L, T, Q > const &d)
 Return the minimum component-wise values of 4 inputs. More...
 
\n

Detailed Description

\n

GLM_EXT_vector_common

\n\n

Definition in file vector_common.hpp.

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vector_common.hpp
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1 
\n
14 #pragma once
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15 
\n
16 // Dependency:
\n
17 #include "../ext/scalar_common.hpp"
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18 #include "../common.hpp"
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19 
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20 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
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21 # pragma message("GLM: GLM_EXT_vector_common extension included")
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22 #endif
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23 
\n
24 namespace glm
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25 {
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28 
\n
34  template<length_t L, typename T, qualifier Q>
\n
35  GLM_FUNC_DECL GLM_CONSTEXPR vec<L, T, Q> min(vec<L, T, Q> const& a, vec<L, T, Q> const& b, vec<L, T, Q> const& c);
\n
36 
\n
42  template<length_t L, typename T, qualifier Q>
\n
43  GLM_FUNC_DECL GLM_CONSTEXPR vec<L, T, Q> min(vec<L, T, Q> const& a, vec<L, T, Q> const& b, vec<L, T, Q> const& c, vec<L, T, Q> const& d);
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44 
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50  template<length_t L, typename T, qualifier Q>
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51  GLM_FUNC_DECL GLM_CONSTEXPR vec<L, T, Q> max(vec<L, T, Q> const& x, vec<L, T, Q> const& y, vec<L, T, Q> const& z);
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52 
\n
58  template<length_t L, typename T, qualifier Q>
\n
59  GLM_FUNC_DECL GLM_CONSTEXPR vec<L, T, Q> max( vec<L, T, Q> const& x, vec<L, T, Q> const& y, vec<L, T, Q> const& z, vec<L, T, Q> const& w);
\n
60 
\n
68  template<length_t L, typename T, qualifier Q>
\n
69  GLM_FUNC_DECL vec<L, T, Q> fmin(vec<L, T, Q> const& x, T y);
\n
70 
\n
78  template<length_t L, typename T, qualifier Q>
\n
79  GLM_FUNC_DECL vec<L, T, Q> fmin(vec<L, T, Q> const& x, vec<L, T, Q> const& y);
\n
80 
\n
88  template<length_t L, typename T, qualifier Q>
\n
89  GLM_FUNC_DECL vec<L, T, Q> fmin(vec<L, T, Q> const& a, vec<L, T, Q> const& b, vec<L, T, Q> const& c);
\n
90 
\n
98  template<length_t L, typename T, qualifier Q>
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99  GLM_FUNC_DECL vec<L, T, Q> fmin(vec<L, T, Q> const& a, vec<L, T, Q> const& b, vec<L, T, Q> const& c, vec<L, T, Q> const& d);
\n
100 
\n
108  template<length_t L, typename T, qualifier Q>
\n
109  GLM_FUNC_DECL vec<L, T, Q> fmax(vec<L, T, Q> const& a, T b);
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110 
\n
118  template<length_t L, typename T, qualifier Q>
\n
119  GLM_FUNC_DECL vec<L, T, Q> fmax(vec<L, T, Q> const& a, vec<L, T, Q> const& b);
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120 
\n
128  template<length_t L, typename T, qualifier Q>
\n
129  GLM_FUNC_DECL vec<L, T, Q> fmax(vec<L, T, Q> const& a, vec<L, T, Q> const& b, vec<L, T, Q> const& c);
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130 
\n
138  template<length_t L, typename T, qualifier Q>
\n
139  GLM_FUNC_DECL vec<L, T, Q> fmax(vec<L, T, Q> const& a, vec<L, T, Q> const& b, vec<L, T, Q> const& c, vec<L, T, Q> const& d);
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140 
\n
142 }//namespace glm
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143 
\n
144 #include "vector_common.inl"
\n
glm::fmax
GLM_FUNC_DECL vec< L, T, Q > fmax(vec< L, T, Q > const &a, vec< L, T, Q > const &b, vec< L, T, Q > const &c, vec< L, T, Q > const &d)
Returns y if x < y; otherwise, it returns x.
\n
glm::fmin
GLM_FUNC_DECL vec< L, T, Q > fmin(vec< L, T, Q > const &a, vec< L, T, Q > const &b, vec< L, T, Q > const &c, vec< L, T, Q > const &d)
Returns y if y < x; otherwise, it returns x.
\n
glm::max
GLM_FUNC_DECL GLM_CONSTEXPR vec< L, T, Q > max(vec< L, T, Q > const &x, vec< L, T, Q > const &y, vec< L, T, Q > const &z, vec< L, T, Q > const &w)
Return the maximum component-wise values of 4 inputs.
\n
glm::min
GLM_FUNC_DECL GLM_CONSTEXPR vec< L, T, Q > min(vec< L, T, Q > const &a, vec< L, T, Q > const &b, vec< L, T, Q > const &c, vec< L, T, Q > const &d)
Return the minimum component-wise values of 4 inputs.
\n
glm
Definition: common.hpp:20
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GLM_EXT_vector_double1 \nMore...

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\ntypedef vec< 1, double, defaultp > dvec1
 1 components vector of double-precision floating-point numbers.
 
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Detailed Description

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GLM_EXT_vector_double1

\n\n

Definition in file vector_double1.hpp.

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vector_double1.hpp
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\nGo to the documentation of this file.
1 
\n
14 #pragma once
\n
15 
\n
16 #include "../detail/type_vec1.hpp"
\n
17 
\n
18 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
19 # pragma message("GLM: GLM_EXT_vector_double1 extension included")
\n
20 #endif
\n
21 
\n
22 namespace glm
\n
23 {
\n
26 
\n
28  typedef vec<1, double, defaultp> dvec1;
\n
29 
\n
31 }//namespace glm
\n
glm::dvec1
vec< 1, double, defaultp > dvec1
1 components vector of double-precision floating-point numbers.
Definition: vector_double1.hpp:28
\n
glm
Definition: common.hpp:20
\n
\n\n
\nGenerated by  \n\"doxygen\"/\n 1.8.10\n
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vector_double1_precision.hpp File Reference
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GLM_EXT_vector_double1_precision \nMore...

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\n\n\n\n\n\n\n\n\n\n\n\n

\nTypedefs

\ntypedef vec< 1, double, highp > highp_dvec1
 1 component vector of double-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef vec< 1, double, lowp > lowp_dvec1
 1 component vector of double-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef vec< 1, double, mediump > mediump_dvec1
 1 component vector of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\n

Detailed Description

\n

GLM_EXT_vector_double1_precision

\n\n

Definition in file vector_double1_precision.hpp.

\n
\n\n
\nGenerated by  \n\"doxygen\"/\n 1.8.10\n
\n\n\n" }, { "path": "field_construction/submodules/diff-langsurf-rasterizer/third_party/glm/doc/api/a00199_source.html", "content": "\n\n\n\n\n\n0.9.9 API documentation: vector_double1_precision.hpp Source File\n\n\n\n\n\n\n\n\n\n\n
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vector_double1_precision.hpp
\n
\n
\nGo to the documentation of this file.
1 
\n
13 #pragma once
\n
14 
\n
15 #include "../detail/type_vec1.hpp"
\n
16 
\n
17 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
18 # pragma message("GLM: GLM_EXT_vector_double1_precision extension included")
\n
19 #endif
\n
20 
\n
21 namespace glm
\n
22 {
\n
25 
\n
27  typedef vec<1, double, highp> highp_dvec1;
\n
28 
\n
30  typedef vec<1, double, mediump> mediump_dvec1;
\n
31 
\n
33  typedef vec<1, double, lowp> lowp_dvec1;
\n
34 
\n
36 }//namespace glm
\n
glm::lowp_dvec1
vec< 1, double, lowp > lowp_dvec1
1 component vector of double-precision floating-point numbers using low precision arithmetic in term ...
Definition: vector_double1_precision.hpp:33
\n
glm::highp_dvec1
vec< 1, double, highp > highp_dvec1
1 component vector of double-precision floating-point numbers using high precision arithmetic in term...
Definition: vector_double1_precision.hpp:27
\n
glm::mediump_dvec1
vec< 1, double, mediump > mediump_dvec1
1 component vector of double-precision floating-point numbers using medium precision arithmetic in te...
Definition: vector_double1_precision.hpp:30
\n
glm
Definition: common.hpp:20
\n
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\nGenerated by  \n\"doxygen\"/\n 1.8.10\n
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vector_double2.hpp File Reference
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typedef vec< 2, double, defaultp > dvec2
 2 components vector of double-precision floating-point numbers. More...
 
\n

Detailed Description

\n

Core features

\n\n

Definition in file vector_double2.hpp.

\n
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vector_double2.hpp
\n
\n
\nGo to the documentation of this file.
1 
\n
4 #pragma once
\n
5 #include "../detail/type_vec2.hpp"
\n
6 
\n
7 namespace glm
\n
8 {
\n
11 
\n
15  typedef vec<2, double, defaultp> dvec2;
\n
16 
\n
18 }//namespace glm
\n
glm::dvec2
vec< 2, double, defaultp > dvec2
2 components vector of double-precision floating-point numbers.
Definition: vector_double2.hpp:15
\n
glm
Definition: common.hpp:20
\n
\n\n
\nGenerated by  \n\"doxygen\"/\n 1.8.10\n
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vector_double2_precision.hpp File Reference
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Core features \nMore...

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\nTypedefs

typedef vec< 2, double, highp > highp_dvec2
 2 components vector of high double-qualifier floating-point numbers. More...
 
typedef vec< 2, double, lowp > lowp_dvec2
 2 components vector of low double-qualifier floating-point numbers. More...
 
typedef vec< 2, double, mediump > mediump_dvec2
 2 components vector of medium double-qualifier floating-point numbers. More...
 
\n

Detailed Description

\n

Core features

\n\n

Definition in file vector_double2_precision.hpp.

\n
\n\n
\nGenerated by  \n\"doxygen\"/\n 1.8.10\n
\n\n\n" }, { "path": "field_construction/submodules/diff-langsurf-rasterizer/third_party/glm/doc/api/a00201_source.html", "content": "\n\n\n\n\n\n0.9.9 API documentation: vector_double2_precision.hpp Source File\n\n\n\n\n\n\n\n\n\n\n
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vector_double2_precision.hpp
\n
\n
\nGo to the documentation of this file.
1 
\n
4 #pragma once
\n
5 #include "../detail/type_vec2.hpp"
\n
6 
\n
7 namespace glm
\n
8 {
\n
11 
\n
16  typedef vec<2, double, highp> highp_dvec2;
\n
17 
\n
22  typedef vec<2, double, mediump> mediump_dvec2;
\n
23 
\n
28  typedef vec<2, double, lowp> lowp_dvec2;
\n
29 
\n
31 }//namespace glm
\n
glm::lowp_dvec2
vec< 2, double, lowp > lowp_dvec2
2 components vector of low double-qualifier floating-point numbers.
Definition: vector_double2_precision.hpp:28
\n
glm::mediump_dvec2
vec< 2, double, mediump > mediump_dvec2
2 components vector of medium double-qualifier floating-point numbers.
Definition: vector_double2_precision.hpp:22
\n
glm::highp_dvec2
vec< 2, double, highp > highp_dvec2
2 components vector of high double-qualifier floating-point numbers.
Definition: vector_double2_precision.hpp:16
\n
glm
Definition: common.hpp:20
\n
\n\n
\nGenerated by  \n\"doxygen\"/\n 1.8.10\n
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vector_double3.hpp File Reference
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Core features \nMore...

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typedef vec< 3, double, defaultp > dvec3
 3 components vector of double-precision floating-point numbers. More...
 
\n

Detailed Description

\n

Core features

\n\n

Definition in file vector_double3.hpp.

\n
\n\n
\nGenerated by  \n\"doxygen\"/\n 1.8.10\n
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vector_double3.hpp
\n
\n
\nGo to the documentation of this file.
1 
\n
4 #pragma once
\n
5 #include "../detail/type_vec3.hpp"
\n
6 
\n
7 namespace glm
\n
8 {
\n
11 
\n
15  typedef vec<3, double, defaultp> dvec3;
\n
16 
\n
18 }//namespace glm
\n
glm::dvec3
vec< 3, double, defaultp > dvec3
3 components vector of double-precision floating-point numbers.
Definition: vector_double3.hpp:15
\n
glm
Definition: common.hpp:20
\n
\n\n
\nGenerated by  \n\"doxygen\"/\n 1.8.10\n
\n\n\n" }, { "path": "field_construction/submodules/diff-langsurf-rasterizer/third_party/glm/doc/api/a00203.html", "content": "\n\n\n\n\n\n0.9.9 API documentation: vector_double3_precision.hpp File Reference\n\n\n\n\n\n\n\n\n\n\n
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vector_double3_precision.hpp File Reference
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Core features \nMore...

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Go to the source code of this file.

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typedef vec< 3, double, highp > highp_dvec3
 3 components vector of high double-qualifier floating-point numbers. More...
 
typedef vec< 3, double, lowp > lowp_dvec3
 3 components vector of low double-qualifier floating-point numbers. More...
 
typedef vec< 3, double, mediump > mediump_dvec3
 3 components vector of medium double-qualifier floating-point numbers. More...
 
\n

Detailed Description

\n

Core features

\n\n

Definition in file vector_double3_precision.hpp.

\n
\n\n
\nGenerated by  \n\"doxygen\"/\n 1.8.10\n
\n\n\n" }, { "path": "field_construction/submodules/diff-langsurf-rasterizer/third_party/glm/doc/api/a00203_source.html", "content": "\n\n\n\n\n\n0.9.9 API documentation: vector_double3_precision.hpp Source File\n\n\n\n\n\n\n\n\n\n\n
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vector_double3_precision.hpp
\n
\n
\nGo to the documentation of this file.
1 
\n
4 #pragma once
\n
5 #include "../detail/type_vec3.hpp"
\n
6 
\n
7 namespace glm
\n
8 {
\n
11 
\n
17  typedef vec<3, double, highp> highp_dvec3;
\n
18 
\n
24  typedef vec<3, double, mediump> mediump_dvec3;
\n
25 
\n
31  typedef vec<3, double, lowp> lowp_dvec3;
\n
32 
\n
34 }//namespace glm
\n
glm::mediump_dvec3
vec< 3, double, mediump > mediump_dvec3
3 components vector of medium double-qualifier floating-point numbers.
Definition: vector_double3_precision.hpp:24
\n
glm::lowp_dvec3
vec< 3, double, lowp > lowp_dvec3
3 components vector of low double-qualifier floating-point numbers.
Definition: vector_double3_precision.hpp:31
\n
glm::highp_dvec3
vec< 3, double, highp > highp_dvec3
3 components vector of high double-qualifier floating-point numbers.
Definition: vector_double3_precision.hpp:17
\n
glm
Definition: common.hpp:20
\n
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\nGenerated by  \n\"doxygen\"/\n 1.8.10\n
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typedef vec< 4, double, defaultp > dvec4
 4 components vector of double-precision floating-point numbers. More...
 
\n

Detailed Description

\n

Core features

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Definition in file vector_double4.hpp.

\n
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\nGenerated by  \n\"doxygen\"/\n 1.8.10\n
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vector_double4.hpp
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\n
\nGo to the documentation of this file.
1 
\n
4 #pragma once
\n
5 #include "../detail/type_vec4.hpp"
\n
6 
\n
7 namespace glm
\n
8 {
\n
11 
\n
15  typedef vec<4, double, defaultp> dvec4;
\n
16 
\n
18 }//namespace glm
\n
glm::dvec4
vec< 4, double, defaultp > dvec4
4 components vector of double-precision floating-point numbers.
Definition: vector_double4.hpp:15
\n
glm
Definition: common.hpp:20
\n
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\nGenerated by  \n\"doxygen\"/\n 1.8.10\n
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vector_double4_precision.hpp File Reference
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Core features \nMore...

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typedef vec< 4, double, highp > highp_dvec4
 4 components vector of high double-qualifier floating-point numbers. More...
 
typedef vec< 4, double, lowp > lowp_dvec4
 4 components vector of low double-qualifier floating-point numbers. More...
 
typedef vec< 4, double, mediump > mediump_dvec4
 4 components vector of medium double-qualifier floating-point numbers. More...
 
\n

Detailed Description

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Definition in file vector_double4_precision.hpp.

\n
\n\n
\nGenerated by  \n\"doxygen\"/\n 1.8.10\n
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vector_double4_precision.hpp
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\nGo to the documentation of this file.
1 
\n
4 #pragma once
\n
5 #include "../detail/setup.hpp"
\n
6 #include "../detail/type_vec4.hpp"
\n
7 
\n
8 namespace glm
\n
9 {
\n
12 
\n
18  typedef vec<4, double, highp> highp_dvec4;
\n
19 
\n
25  typedef vec<4, double, mediump> mediump_dvec4;
\n
26 
\n
32  typedef vec<4, double, lowp> lowp_dvec4;
\n
33 
\n
35 }//namespace glm
\n
glm::mediump_dvec4
vec< 4, double, mediump > mediump_dvec4
4 components vector of medium double-qualifier floating-point numbers.
Definition: vector_double4_precision.hpp:25
\n
glm::highp_dvec4
vec< 4, double, highp > highp_dvec4
4 components vector of high double-qualifier floating-point numbers.
Definition: vector_double4_precision.hpp:18
\n
glm::lowp_dvec4
vec< 4, double, lowp > lowp_dvec4
4 components vector of low double-qualifier floating-point numbers.
Definition: vector_double4_precision.hpp:32
\n
glm
Definition: common.hpp:20
\n
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\ntypedef vec< 1, float, defaultp > vec1
 1 components vector of single-precision floating-point numbers.
 
\n

Detailed Description

\n

GLM_EXT_vector_float1

\n\n

Definition in file vector_float1.hpp.

\n
\n\n
\nGenerated by  \n\"doxygen\"/\n 1.8.10\n
\n\n\n" }, { "path": "field_construction/submodules/diff-langsurf-rasterizer/third_party/glm/doc/api/a00206_source.html", "content": "\n\n\n\n\n\n0.9.9 API documentation: vector_float1.hpp Source File\n\n\n\n\n\n\n\n\n\n\n
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vector_float1.hpp
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1 
\n
14 #pragma once
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15 
\n
16 #include "../detail/type_vec1.hpp"
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17 
\n
18 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
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19 # pragma message("GLM: GLM_EXT_vector_float1 extension included")
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20 #endif
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21 
\n
22 namespace glm
\n
23 {
\n
26 
\n
28  typedef vec<1, float, defaultp> vec1;
\n
29 
\n
31 }//namespace glm
\n
glm::vec1
vec< 1, float, defaultp > vec1
1 components vector of single-precision floating-point numbers.
Definition: vector_float1.hpp:28
\n
glm
Definition: common.hpp:20
\n
\n\n
\nGenerated by  \n\"doxygen\"/\n 1.8.10\n
\n\n\n" }, { "path": "field_construction/submodules/diff-langsurf-rasterizer/third_party/glm/doc/api/a00207.html", "content": "\n\n\n\n\n\n0.9.9 API documentation: vector_float1_precision.hpp File Reference\n\n\n\n\n\n\n\n\n\n\n
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vector_float1_precision.hpp File Reference
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GLM_EXT_vector_float1_precision \nMore...

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\n\n\n\n\n\n\n\n\n\n\n\n

\nTypedefs

\ntypedef vec< 1, float, highp > highp_vec1
 1 component vector of single-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef vec< 1, float, lowp > lowp_vec1
 1 component vector of single-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef vec< 1, float, mediump > mediump_vec1
 1 component vector of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\n

Detailed Description

\n

GLM_EXT_vector_float1_precision

\n\n

Definition in file vector_float1_precision.hpp.

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\n\n
\nGenerated by  \n\"doxygen\"/\n 1.8.10\n
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vector_float1_precision.hpp
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\n
\nGo to the documentation of this file.
1 
\n
13 #pragma once
\n
14 
\n
15 #include "../detail/type_vec1.hpp"
\n
16 
\n
17 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
18 # pragma message("GLM: GLM_EXT_vector_float1_precision extension included")
\n
19 #endif
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20 
\n
21 namespace glm
\n
22 {
\n
25 
\n
27  typedef vec<1, float, highp> highp_vec1;
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28 
\n
30  typedef vec<1, float, mediump> mediump_vec1;
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31 
\n
33  typedef vec<1, float, lowp> lowp_vec1;
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34 
\n
36 }//namespace glm
\n
glm::lowp_vec1
vec< 1, float, lowp > lowp_vec1
1 component vector of single-precision floating-point numbers using low precision arithmetic in term ...
Definition: vector_float1_precision.hpp:33
\n
glm::mediump_vec1
vec< 1, float, mediump > mediump_vec1
1 component vector of single-precision floating-point numbers using medium precision arithmetic in te...
Definition: vector_float1_precision.hpp:30
\n
glm::highp_vec1
vec< 1, float, highp > highp_vec1
1 component vector of single-precision floating-point numbers using high precision arithmetic in term...
Definition: vector_float1_precision.hpp:27
\n
glm
Definition: common.hpp:20
\n
\n\n
\nGenerated by  \n\"doxygen\"/\n 1.8.10\n
\n\n\n" }, { "path": "field_construction/submodules/diff-langsurf-rasterizer/third_party/glm/doc/api/a00208.html", "content": "\n\n\n\n\n\n0.9.9 API documentation: vector_float2.hpp File Reference\n\n\n\n\n\n\n\n\n\n\n
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vector_float2.hpp File Reference
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Core features \nMore...

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typedef vec< 2, float, defaultp > vec2
 2 components vector of single-precision floating-point numbers. More...
 
\n

Detailed Description

\n

Core features

\n\n

Definition in file vector_float2.hpp.

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vector_float2.hpp
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\n
\nGo to the documentation of this file.
1 
\n
4 #pragma once
\n
5 #include "../detail/type_vec2.hpp"
\n
6 
\n
7 namespace glm
\n
8 {
\n
11 
\n
15  typedef vec<2, float, defaultp> vec2;
\n
16 
\n
18 }//namespace glm
\n
glm::vec2
vec< 2, float, defaultp > vec2
2 components vector of single-precision floating-point numbers.
Definition: vector_float2.hpp:15
\n
glm
Definition: common.hpp:20
\n
\n\n
\nGenerated by  \n\"doxygen\"/\n 1.8.10\n
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vector_float2_precision.hpp File Reference
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Core features \nMore...

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\nTypedefs

typedef vec< 2, float, highp > highp_vec2
 2 components vector of high single-qualifier floating-point numbers. More...
 
typedef vec< 2, float, lowp > lowp_vec2
 2 components vector of low single-qualifier floating-point numbers. More...
 
typedef vec< 2, float, mediump > mediump_vec2
 2 components vector of medium single-qualifier floating-point numbers. More...
 
\n

Detailed Description

\n

Core features

\n\n

Definition in file vector_float2_precision.hpp.

\n
\n\n
\nGenerated by  \n\"doxygen\"/\n 1.8.10\n
\n\n\n" }, { "path": "field_construction/submodules/diff-langsurf-rasterizer/third_party/glm/doc/api/a00209_source.html", "content": "\n\n\n\n\n\n0.9.9 API documentation: vector_float2_precision.hpp Source File\n\n\n\n\n\n\n\n\n\n\n
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vector_float2_precision.hpp
\n
\n
\nGo to the documentation of this file.
1 
\n
4 #pragma once
\n
5 #include "../detail/type_vec2.hpp"
\n
6 
\n
7 namespace glm
\n
8 {
\n
11 
\n
16  typedef vec<2, float, highp> highp_vec2;
\n
17 
\n
22  typedef vec<2, float, mediump> mediump_vec2;
\n
23 
\n
28  typedef vec<2, float, lowp> lowp_vec2;
\n
29 
\n
31 }//namespace glm
\n
glm::highp_vec2
vec< 2, float, highp > highp_vec2
2 components vector of high single-qualifier floating-point numbers.
Definition: vector_float2_precision.hpp:16
\n
glm::lowp_vec2
vec< 2, float, lowp > lowp_vec2
2 components vector of low single-qualifier floating-point numbers.
Definition: vector_float2_precision.hpp:28
\n
glm::mediump_vec2
vec< 2, float, mediump > mediump_vec2
2 components vector of medium single-qualifier floating-point numbers.
Definition: vector_float2_precision.hpp:22
\n
glm
Definition: common.hpp:20
\n
\n\n
\nGenerated by  \n\"doxygen\"/\n 1.8.10\n
\n\n\n" }, { "path": "field_construction/submodules/diff-langsurf-rasterizer/third_party/glm/doc/api/a00210.html", "content": "\n\n\n\n\n\n0.9.9 API documentation: vector_float3.hpp File Reference\n\n\n\n\n\n\n\n\n\n\n
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vector_float3.hpp File Reference
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Core features \nMore...

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Go to the source code of this file.

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\nTypedefs

typedef vec< 3, float, defaultp > vec3
 3 components vector of single-precision floating-point numbers. More...
 
\n

Detailed Description

\n

Core features

\n\n

Definition in file vector_float3.hpp.

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\n\n
\nGenerated by  \n\"doxygen\"/\n 1.8.10\n
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vector_float3.hpp
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\n
\nGo to the documentation of this file.
1 
\n
4 #pragma once
\n
5 #include "../detail/type_vec3.hpp"
\n
6 
\n
7 namespace glm
\n
8 {
\n
11 
\n
15  typedef vec<3, float, defaultp> vec3;
\n
16 
\n
18 }//namespace glm
\n
glm::vec3
vec< 3, float, defaultp > vec3
3 components vector of single-precision floating-point numbers.
Definition: vector_float3.hpp:15
\n
glm
Definition: common.hpp:20
\n
\n\n
\nGenerated by  \n\"doxygen\"/\n 1.8.10\n
\n\n\n" }, { "path": "field_construction/submodules/diff-langsurf-rasterizer/third_party/glm/doc/api/a00211.html", "content": "\n\n\n\n\n\n0.9.9 API documentation: vector_float3_precision.hpp File Reference\n\n\n\n\n\n\n\n\n\n\n
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vector_float3_precision.hpp File Reference
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Core features \nMore...

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typedef vec< 3, float, highp > highp_vec3
 3 components vector of high single-qualifier floating-point numbers. More...
 
typedef vec< 3, float, lowp > lowp_vec3
 3 components vector of low single-qualifier floating-point numbers. More...
 
typedef vec< 3, float, mediump > mediump_vec3
 3 components vector of medium single-qualifier floating-point numbers. More...
 
\n

Detailed Description

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Core features

\n\n

Definition in file vector_float3_precision.hpp.

\n
\n\n
\nGenerated by  \n\"doxygen\"/\n 1.8.10\n
\n\n\n" }, { "path": "field_construction/submodules/diff-langsurf-rasterizer/third_party/glm/doc/api/a00211_source.html", "content": "\n\n\n\n\n\n0.9.9 API documentation: vector_float3_precision.hpp Source File\n\n\n\n\n\n\n\n\n\n\n
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vector_float3_precision.hpp
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\nGo to the documentation of this file.
1 
\n
4 #pragma once
\n
5 #include "../detail/type_vec3.hpp"
\n
6 
\n
7 namespace glm
\n
8 {
\n
11 
\n
16  typedef vec<3, float, highp> highp_vec3;
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17 
\n
22  typedef vec<3, float, mediump> mediump_vec3;
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23 
\n
28  typedef vec<3, float, lowp> lowp_vec3;
\n
29 
\n
31 }//namespace glm
\n
glm::highp_vec3
vec< 3, float, highp > highp_vec3
3 components vector of high single-qualifier floating-point numbers.
Definition: vector_float3_precision.hpp:16
\n
glm::lowp_vec3
vec< 3, float, lowp > lowp_vec3
3 components vector of low single-qualifier floating-point numbers.
Definition: vector_float3_precision.hpp:28
\n
glm::mediump_vec3
vec< 3, float, mediump > mediump_vec3
3 components vector of medium single-qualifier floating-point numbers.
Definition: vector_float3_precision.hpp:22
\n
glm
Definition: common.hpp:20
\n
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\nGenerated by  \n\"doxygen\"/\n 1.8.10\n
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Core features \nMore...

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typedef vec< 4, float, defaultp > vec4
 4 components vector of single-precision floating-point numbers. More...
 
\n

Detailed Description

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Core features

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Definition in file vector_float4.hpp.

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vector_float4.hpp
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\nGo to the documentation of this file.
1 
\n
4 #pragma once
\n
5 #include "../detail/type_vec4.hpp"
\n
6 
\n
7 namespace glm
\n
8 {
\n
11 
\n
15  typedef vec<4, float, defaultp> vec4;
\n
16 
\n
18 }//namespace glm
\n
glm::vec4
vec< 4, float, defaultp > vec4
4 components vector of single-precision floating-point numbers.
Definition: vector_float4.hpp:15
\n
glm
Definition: common.hpp:20
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\nGenerated by  \n\"doxygen\"/\n 1.8.10\n
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vector_float4_precision.hpp File Reference
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Core features \nMore...

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Go to the source code of this file.

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typedef vec< 4, float, highp > highp_vec4
 4 components vector of high single-qualifier floating-point numbers. More...
 
typedef vec< 4, float, lowp > lowp_vec4
 4 components vector of low single-qualifier floating-point numbers. More...
 
typedef vec< 4, float, mediump > mediump_vec4
 4 components vector of medium single-qualifier floating-point numbers. More...
 
\n

Detailed Description

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Core features

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Definition in file vector_float4_precision.hpp.

\n
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\nGenerated by  \n\"doxygen\"/\n 1.8.10\n
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vector_float4_precision.hpp
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\nGo to the documentation of this file.
1 
\n
4 #pragma once
\n
5 #include "../detail/type_vec4.hpp"
\n
6 
\n
7 namespace glm
\n
8 {
\n
11 
\n
16  typedef vec<4, float, highp> highp_vec4;
\n
17 
\n
22  typedef vec<4, float, mediump> mediump_vec4;
\n
23 
\n
28  typedef vec<4, float, lowp> lowp_vec4;
\n
29 
\n
31 }//namespace glm
\n
glm::lowp_vec4
vec< 4, float, lowp > lowp_vec4
4 components vector of low single-qualifier floating-point numbers.
Definition: vector_float4_precision.hpp:28
\n
glm::mediump_vec4
vec< 4, float, mediump > mediump_vec4
4 components vector of medium single-qualifier floating-point numbers.
Definition: vector_float4_precision.hpp:22
\n
glm::highp_vec4
vec< 4, float, highp > highp_vec4
4 components vector of high single-qualifier floating-point numbers.
Definition: vector_float4_precision.hpp:16
\n
glm
Definition: common.hpp:20
\n
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\nGenerated by  \n\"doxygen\"/\n 1.8.10\n
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vector_int1.hpp File Reference
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GLM_EXT_vector_int1 \nMore...

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\ntypedef vec< 1, int, defaultp > ivec1
 1 component vector of signed integer numbers.
 
\n

Detailed Description

\n

GLM_EXT_vector_int1

\n\n

Definition in file vector_int1.hpp.

\n
\n\n
\nGenerated by  \n\"doxygen\"/\n 1.8.10\n
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vector_int1.hpp
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1 
\n
14 #pragma once
\n
15 
\n
16 #include "../detail/type_vec1.hpp"
\n
17 
\n
18 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
19 # pragma message("GLM: GLM_EXT_vector_int1 extension included")
\n
20 #endif
\n
21 
\n
22 namespace glm
\n
23 {
\n
26 
\n
28  typedef vec<1, int, defaultp> ivec1;
\n
29 
\n
31 }//namespace glm
\n
32 
\n
glm::ivec1
vec< 1, int, defaultp > ivec1
1 component vector of signed integer numbers.
Definition: vector_int1.hpp:28
\n
glm
Definition: common.hpp:20
\n
\n\n
\nGenerated by  \n\"doxygen\"/\n 1.8.10\n
\n\n\n" }, { "path": "field_construction/submodules/diff-langsurf-rasterizer/third_party/glm/doc/api/a00215.html", "content": "\n\n\n\n\n\n0.9.9 API documentation: vector_int1_precision.hpp File Reference\n\n\n\n\n\n\n\n\n\n\n
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vector_int1_precision.hpp File Reference
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GLM_EXT_vector_int1_precision \nMore...

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\n\n\n\n\n\n\n\n\n\n\n\n

\nTypedefs

\ntypedef vec< 1, int, highp > highp_ivec1
 1 component vector of signed integer values.
 
\ntypedef vec< 1, int, lowp > lowp_ivec1
 1 component vector of signed integer values.
 
\ntypedef vec< 1, int, mediump > mediump_ivec1
 1 component vector of signed integer values.
 
\n

Detailed Description

\n

GLM_EXT_vector_int1_precision

\n\n

Definition in file vector_int1_precision.hpp.

\n
\n\n
\nGenerated by  \n\"doxygen\"/\n 1.8.10\n
\n\n\n" }, { "path": "field_construction/submodules/diff-langsurf-rasterizer/third_party/glm/doc/api/a00215_source.html", "content": "\n\n\n\n\n\n0.9.9 API documentation: vector_int1_precision.hpp Source File\n\n\n\n\n\n\n\n\n\n\n
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vector_int1_precision.hpp
\n
\n
\nGo to the documentation of this file.
1 
\n
11 #pragma once
\n
12 
\n
13 #include "../detail/type_vec1.hpp"
\n
14 
\n
15 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
16 # pragma message("GLM: GLM_EXT_vector_int1_precision extension included")
\n
17 #endif
\n
18 
\n
19 namespace glm
\n
20 {
\n
23 
\n
25  typedef vec<1, int, highp> highp_ivec1;
\n
26 
\n
28  typedef vec<1, int, mediump> mediump_ivec1;
\n
29 
\n
31  typedef vec<1, int, lowp> lowp_ivec1;
\n
32 
\n
34 }//namespace glm
\n
glm::mediump_ivec1
vec< 1, int, mediump > mediump_ivec1
1 component vector of signed integer values.
Definition: vector_int1_precision.hpp:28
\n
glm::highp_ivec1
vec< 1, int, highp > highp_ivec1
1 component vector of signed integer values.
Definition: vector_int1_precision.hpp:25
\n
glm::lowp_ivec1
vec< 1, int, lowp > lowp_ivec1
1 component vector of signed integer values.
Definition: vector_int1_precision.hpp:31
\n
glm
Definition: common.hpp:20
\n
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\nGenerated by  \n\"doxygen\"/\n 1.8.10\n
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vector_int2.hpp File Reference
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Core features \nMore...

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typedef vec< 2, int, defaultp > ivec2
 2 components vector of signed integer numbers. More...
 
\n

Detailed Description

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Core features

\n\n

Definition in file vector_int2.hpp.

\n
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\nGenerated by  \n\"doxygen\"/\n 1.8.10\n
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vector_int2.hpp
\n
\n
\nGo to the documentation of this file.
1 
\n
4 #pragma once
\n
5 #include "../detail/type_vec2.hpp"
\n
6 
\n
7 namespace glm
\n
8 {
\n
11 
\n
15  typedef vec<2, int, defaultp> ivec2;
\n
16 
\n
18 }//namespace glm
\n
glm::ivec2
vec< 2, int, defaultp > ivec2
2 components vector of signed integer numbers.
Definition: vector_int2.hpp:15
\n
glm
Definition: common.hpp:20
\n
\n\n
\nGenerated by  \n\"doxygen\"/\n 1.8.10\n
\n\n\n" }, { "path": "field_construction/submodules/diff-langsurf-rasterizer/third_party/glm/doc/api/a00217.html", "content": "\n\n\n\n\n\n0.9.9 API documentation: vector_int2_precision.hpp File Reference\n\n\n\n\n\n\n\n\n\n\n
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vector_int2_precision.hpp File Reference
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Core features \nMore...

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typedef vec< 2, int, highp > highp_ivec2
 2 components vector of high qualifier signed integer numbers. More...
 
typedef vec< 2, int, lowp > lowp_ivec2
 2 components vector of low qualifier signed integer numbers. More...
 
typedef vec< 2, int, mediump > mediump_ivec2
 2 components vector of medium qualifier signed integer numbers. More...
 
\n

Detailed Description

\n

Core features

\n\n

Definition in file vector_int2_precision.hpp.

\n
\n\n
\nGenerated by  \n\"doxygen\"/\n 1.8.10\n
\n\n\n" }, { "path": "field_construction/submodules/diff-langsurf-rasterizer/third_party/glm/doc/api/a00217_source.html", "content": "\n\n\n\n\n\n0.9.9 API documentation: vector_int2_precision.hpp Source File\n\n\n\n\n\n\n\n\n\n\n
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vector_int2_precision.hpp
\n
\n
\nGo to the documentation of this file.
1 
\n
4 #pragma once
\n
5 #include "../detail/type_vec2.hpp"
\n
6 
\n
7 namespace glm
\n
8 {
\n
11 
\n
16  typedef vec<2, int, highp> highp_ivec2;
\n
17 
\n
22  typedef vec<2, int, mediump> mediump_ivec2;
\n
23 
\n
28  typedef vec<2, int, lowp> lowp_ivec2;
\n
29 
\n
31 }//namespace glm
\n
glm::highp_ivec2
vec< 2, int, highp > highp_ivec2
2 components vector of high qualifier signed integer numbers.
Definition: vector_int2_precision.hpp:16
\n
glm::mediump_ivec2
vec< 2, int, mediump > mediump_ivec2
2 components vector of medium qualifier signed integer numbers.
Definition: vector_int2_precision.hpp:22
\n
glm::lowp_ivec2
vec< 2, int, lowp > lowp_ivec2
2 components vector of low qualifier signed integer numbers.
Definition: vector_int2_precision.hpp:28
\n
glm
Definition: common.hpp:20
\n
\n\n
\nGenerated by  \n\"doxygen\"/\n 1.8.10\n
\n\n\n" }, { "path": "field_construction/submodules/diff-langsurf-rasterizer/third_party/glm/doc/api/a00218.html", "content": "\n\n\n\n\n\n0.9.9 API documentation: vector_int3.hpp File Reference\n\n\n\n\n\n\n\n\n\n\n
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vector_int3.hpp File Reference
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Core features \nMore...

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Go to the source code of this file.

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typedef vec< 3, int, defaultp > ivec3
 3 components vector of signed integer numbers. More...
 
\n

Detailed Description

\n

Core features

\n\n

Definition in file vector_int3.hpp.

\n
\n\n
\nGenerated by  \n\"doxygen\"/\n 1.8.10\n
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vector_int3.hpp
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\n
\nGo to the documentation of this file.
1 
\n
4 #pragma once
\n
5 #include "../detail/type_vec3.hpp"
\n
6 
\n
7 namespace glm
\n
8 {
\n
11 
\n
15  typedef vec<3, int, defaultp> ivec3;
\n
16 
\n
18 }//namespace glm
\n
glm::ivec3
vec< 3, int, defaultp > ivec3
3 components vector of signed integer numbers.
Definition: vector_int3.hpp:15
\n
glm
Definition: common.hpp:20
\n
\n\n
\nGenerated by  \n\"doxygen\"/\n 1.8.10\n
\n\n\n" }, { "path": "field_construction/submodules/diff-langsurf-rasterizer/third_party/glm/doc/api/a00219.html", "content": "\n\n\n\n\n\n0.9.9 API documentation: vector_int3_precision.hpp File Reference\n\n\n\n\n\n\n\n\n\n\n
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vector_int3_precision.hpp File Reference
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Core features \nMore...

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Go to the source code of this file.

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typedef vec< 3, int, highp > highp_ivec3
 3 components vector of high qualifier signed integer numbers. More...
 
typedef vec< 3, int, lowp > lowp_ivec3
 3 components vector of low qualifier signed integer numbers. More...
 
typedef vec< 3, int, mediump > mediump_ivec3
 3 components vector of medium qualifier signed integer numbers. More...
 
\n

Detailed Description

\n

Core features

\n\n

Definition in file vector_int3_precision.hpp.

\n
\n\n
\nGenerated by  \n\"doxygen\"/\n 1.8.10\n
\n\n\n" }, { "path": "field_construction/submodules/diff-langsurf-rasterizer/third_party/glm/doc/api/a00219_source.html", "content": "\n\n\n\n\n\n0.9.9 API documentation: vector_int3_precision.hpp Source File\n\n\n\n\n\n\n\n\n\n\n
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vector_int3_precision.hpp
\n
\n
\nGo to the documentation of this file.
1 
\n
4 #pragma once
\n
5 #include "../detail/type_vec3.hpp"
\n
6 
\n
7 namespace glm
\n
8 {
\n
11 
\n
16  typedef vec<3, int, highp> highp_ivec3;
\n
17 
\n
22  typedef vec<3, int, mediump> mediump_ivec3;
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23 
\n
28  typedef vec<3, int, lowp> lowp_ivec3;
\n
29 
\n
31 }//namespace glm
\n
glm::lowp_ivec3
vec< 3, int, lowp > lowp_ivec3
3 components vector of low qualifier signed integer numbers.
Definition: vector_int3_precision.hpp:28
\n
glm::mediump_ivec3
vec< 3, int, mediump > mediump_ivec3
3 components vector of medium qualifier signed integer numbers.
Definition: vector_int3_precision.hpp:22
\n
glm::highp_ivec3
vec< 3, int, highp > highp_ivec3
3 components vector of high qualifier signed integer numbers.
Definition: vector_int3_precision.hpp:16
\n
glm
Definition: common.hpp:20
\n
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\nGenerated by  \n\"doxygen\"/\n 1.8.10\n
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typedef vec< 4, int, defaultp > ivec4
 4 components vector of signed integer numbers. More...
 
\n

Detailed Description

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Core features

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Definition in file vector_int4.hpp.

\n
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\nGenerated by  \n\"doxygen\"/\n 1.8.10\n
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vector_int4.hpp
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\nGo to the documentation of this file.
1 
\n
4 #pragma once
\n
5 #include "../detail/type_vec4.hpp"
\n
6 
\n
7 namespace glm
\n
8 {
\n
11 
\n
15  typedef vec<4, int, defaultp> ivec4;
\n
16 
\n
18 }//namespace glm
\n
glm::ivec4
vec< 4, int, defaultp > ivec4
4 components vector of signed integer numbers.
Definition: vector_int4.hpp:15
\n
glm
Definition: common.hpp:20
\n
\n\n
\nGenerated by  \n\"doxygen\"/\n 1.8.10\n
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vector_int4_precision.hpp File Reference
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Core features \nMore...

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Go to the source code of this file.

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typedef vec< 4, int, highp > highp_ivec4
 4 components vector of high qualifier signed integer numbers. More...
 
typedef vec< 4, int, lowp > lowp_ivec4
 4 components vector of low qualifier signed integer numbers. More...
 
typedef vec< 4, int, mediump > mediump_ivec4
 4 components vector of medium qualifier signed integer numbers. More...
 
\n

Detailed Description

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Definition in file vector_int4_precision.hpp.

\n
\n\n
\nGenerated by  \n\"doxygen\"/\n 1.8.10\n
\n\n\n" }, { "path": "field_construction/submodules/diff-langsurf-rasterizer/third_party/glm/doc/api/a00221_source.html", "content": "\n\n\n\n\n\n0.9.9 API documentation: vector_int4_precision.hpp Source File\n\n\n\n\n\n\n\n\n\n\n
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vector_int4_precision.hpp
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\nGo to the documentation of this file.
1 
\n
4 #pragma once
\n
5 #include "../detail/type_vec4.hpp"
\n
6 
\n
7 namespace glm
\n
8 {
\n
11 
\n
16  typedef vec<4, int, highp> highp_ivec4;
\n
17 
\n
22  typedef vec<4, int, mediump> mediump_ivec4;
\n
23 
\n
28  typedef vec<4, int, lowp> lowp_ivec4;
\n
29 
\n
31 }//namespace glm
\n
glm::lowp_ivec4
vec< 4, int, lowp > lowp_ivec4
4 components vector of low qualifier signed integer numbers.
Definition: vector_int4_precision.hpp:28
\n
glm::highp_ivec4
vec< 4, int, highp > highp_ivec4
4 components vector of high qualifier signed integer numbers.
Definition: vector_int4_precision.hpp:16
\n
glm::mediump_ivec4
vec< 4, int, mediump > mediump_ivec4
4 components vector of medium qualifier signed integer numbers.
Definition: vector_int4_precision.hpp:22
\n
glm
Definition: common.hpp:20
\n
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vector_integer.hpp File Reference
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GLM_EXT_vector_integer \nMore...

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\nFunctions

template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, int, Q > findNSB (vec< L, T, Q > const &Source, vec< L, int, Q > SignificantBitCount)
 Returns the bit number of the Nth significant bit set to 1 in the binary representation of value. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, bool, Q > isMultiple (vec< L, T, Q > const &v, T Multiple)
 Return true if the 'Value' is a multiple of 'Multiple'. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, bool, Q > isMultiple (vec< L, T, Q > const &v, vec< L, T, Q > const &Multiple)
 Return true if the 'Value' is a multiple of 'Multiple'. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, bool, Q > isPowerOfTwo (vec< L, T, Q > const &v)
 Return true if the value is a power of two number. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > nextMultiple (vec< L, T, Q > const &v, T Multiple)
 Higher multiple number of Source. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > nextMultiple (vec< L, T, Q > const &v, vec< L, T, Q > const &Multiple)
 Higher multiple number of Source. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > nextPowerOfTwo (vec< L, T, Q > const &v)
 Return the power of two number which value is just higher the input value, round up to a power of two. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > prevMultiple (vec< L, T, Q > const &v, T Multiple)
 Lower multiple number of Source. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > prevMultiple (vec< L, T, Q > const &v, vec< L, T, Q > const &Multiple)
 Lower multiple number of Source. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > prevPowerOfTwo (vec< L, T, Q > const &v)
 Return the power of two number which value is just lower the input value, round down to a power of two. More...
 
\n

Detailed Description

\n

GLM_EXT_vector_integer

\n
See also
Core features (dependence)
\n
\n to use the features of this extension. \">GLM_EXT_vector_integer (dependence)
\n\n

Definition in file vector_integer.hpp.

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vector_integer.hpp
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1 
\n
12 #pragma once
\n
13 
\n
14 // Dependencies
\n
15 #include "../detail/setup.hpp"
\n
16 #include "../detail/qualifier.hpp"
\n
17 #include "../detail/_vectorize.hpp"
\n
18 #include "../vector_relational.hpp"
\n
19 #include "../common.hpp"
\n
20 #include <limits>
\n
21 
\n
22 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
23 # pragma message("GLM: GLM_EXT_vector_integer extension included")
\n
24 #endif
\n
25 
\n
26 namespace glm
\n
27 {
\n
30 
\n
38  template<length_t L, typename T, qualifier Q>
\n
39  GLM_FUNC_DECL vec<L, bool, Q> isPowerOfTwo(vec<L, T, Q> const& v);
\n
40 
\n
49  template<length_t L, typename T, qualifier Q>
\n
50  GLM_FUNC_DECL vec<L, T, Q> nextPowerOfTwo(vec<L, T, Q> const& v);
\n
51 
\n
60  template<length_t L, typename T, qualifier Q>
\n
61  GLM_FUNC_DECL vec<L, T, Q> prevPowerOfTwo(vec<L, T, Q> const& v);
\n
62 
\n
70  template<length_t L, typename T, qualifier Q>
\n
71  GLM_FUNC_DECL vec<L, bool, Q> isMultiple(vec<L, T, Q> const& v, T Multiple);
\n
72 
\n
80  template<length_t L, typename T, qualifier Q>
\n
81  GLM_FUNC_DECL vec<L, bool, Q> isMultiple(vec<L, T, Q> const& v, vec<L, T, Q> const& Multiple);
\n
82 
\n
93  template<length_t L, typename T, qualifier Q>
\n
94  GLM_FUNC_DECL vec<L, T, Q> nextMultiple(vec<L, T, Q> const& v, T Multiple);
\n
95 
\n
106  template<length_t L, typename T, qualifier Q>
\n
107  GLM_FUNC_DECL vec<L, T, Q> nextMultiple(vec<L, T, Q> const& v, vec<L, T, Q> const& Multiple);
\n
108 
\n
119  template<length_t L, typename T, qualifier Q>
\n
120  GLM_FUNC_DECL vec<L, T, Q> prevMultiple(vec<L, T, Q> const& v, T Multiple);
\n
121 
\n
132  template<length_t L, typename T, qualifier Q>
\n
133  GLM_FUNC_DECL vec<L, T, Q> prevMultiple(vec<L, T, Q> const& v, vec<L, T, Q> const& Multiple);
\n
134 
\n
143  template<length_t L, typename T, qualifier Q>
\n
144  GLM_FUNC_DECL vec<L, int, Q> findNSB(vec<L, T, Q> const& Source, vec<L, int, Q> SignificantBitCount);
\n
145 
\n
147 } //namespace glm
\n
148 
\n
149 #include "vector_integer.inl"
\n
glm::isPowerOfTwo
GLM_FUNC_DECL vec< L, bool, Q > isPowerOfTwo(vec< L, T, Q > const &v)
Return true if the value is a power of two number.
\n
glm::nextPowerOfTwo
GLM_FUNC_DECL vec< L, T, Q > nextPowerOfTwo(vec< L, T, Q > const &v)
Return the power of two number which value is just higher the input value, round up to a power of two...
\n
glm::nextMultiple
GLM_FUNC_DECL vec< L, T, Q > nextMultiple(vec< L, T, Q > const &v, vec< L, T, Q > const &Multiple)
Higher multiple number of Source.
\n
glm::prevPowerOfTwo
GLM_FUNC_DECL vec< L, T, Q > prevPowerOfTwo(vec< L, T, Q > const &v)
Return the power of two number which value is just lower the input value, round down to a power of tw...
\n
glm::findNSB
GLM_FUNC_DECL vec< L, int, Q > findNSB(vec< L, T, Q > const &Source, vec< L, int, Q > SignificantBitCount)
Returns the bit number of the Nth significant bit set to 1 in the binary representation of value...
\n
glm::prevMultiple
GLM_FUNC_DECL vec< L, T, Q > prevMultiple(vec< L, T, Q > const &v, vec< L, T, Q > const &Multiple)
Lower multiple number of Source.
\n
glm::isMultiple
GLM_FUNC_DECL vec< L, bool, Q > isMultiple(vec< L, T, Q > const &v, vec< L, T, Q > const &Multiple)
Return true if the 'Value' is a multiple of 'Multiple'.
\n
glm
Definition: common.hpp:20
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vector_query.hpp File Reference
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GLM_GTX_vector_query \nMore...

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Go to the source code of this file.

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\nFunctions

template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL bool areCollinear (vec< L, T, Q > const &v0, vec< L, T, Q > const &v1, T const &epsilon)
 Check whether two vectors are collinears. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL bool areOrthogonal (vec< L, T, Q > const &v0, vec< L, T, Q > const &v1, T const &epsilon)
 Check whether two vectors are orthogonals. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL bool areOrthonormal (vec< L, T, Q > const &v0, vec< L, T, Q > const &v1, T const &epsilon)
 Check whether two vectors are orthonormal. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, bool, Q > isCompNull (vec< L, T, Q > const &v, T const &epsilon)
 Check whether a each component of a vector is null. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL bool isNormalized (vec< L, T, Q > const &v, T const &epsilon)
 Check whether a vector is normalized. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL bool isNull (vec< L, T, Q > const &v, T const &epsilon)
 Check whether a vector is null. More...
 
\n

Detailed Description

\n

GLM_GTX_vector_query

\n
See also
Core features (dependence)
\n\n

Definition in file vector_query.hpp.

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vector_query.hpp
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1 
\n
13 #pragma once
\n
14 
\n
15 // Dependency:
\n
16 #include "../glm.hpp"
\n
17 #include <cfloat>
\n
18 #include <limits>
\n
19 
\n
20 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
21 # ifndef GLM_ENABLE_EXPERIMENTAL
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22 # pragma message("GLM: GLM_GTX_vector_query is an experimental extension and may change in the future. Use #define GLM_ENABLE_EXPERIMENTAL before including it, if you really want to use it.")
\n
23 # else
\n
24 # pragma message("GLM: GLM_GTX_vector_query extension included")
\n
25 # endif
\n
26 #endif
\n
27 
\n
28 namespace glm
\n
29 {
\n
32 
\n
35  template<length_t L, typename T, qualifier Q>
\n
36  GLM_FUNC_DECL bool areCollinear(vec<L, T, Q> const& v0, vec<L, T, Q> const& v1, T const& epsilon);
\n
37 
\n
40  template<length_t L, typename T, qualifier Q>
\n
41  GLM_FUNC_DECL bool areOrthogonal(vec<L, T, Q> const& v0, vec<L, T, Q> const& v1, T const& epsilon);
\n
42 
\n
45  template<length_t L, typename T, qualifier Q>
\n
46  GLM_FUNC_DECL bool isNormalized(vec<L, T, Q> const& v, T const& epsilon);
\n
47 
\n
50  template<length_t L, typename T, qualifier Q>
\n
51  GLM_FUNC_DECL bool isNull(vec<L, T, Q> const& v, T const& epsilon);
\n
52 
\n
55  template<length_t L, typename T, qualifier Q>
\n
56  GLM_FUNC_DECL vec<L, bool, Q> isCompNull(vec<L, T, Q> const& v, T const& epsilon);
\n
57 
\n
60  template<length_t L, typename T, qualifier Q>
\n
61  GLM_FUNC_DECL bool areOrthonormal(vec<L, T, Q> const& v0, vec<L, T, Q> const& v1, T const& epsilon);
\n
62 
\n
64 }// namespace glm
\n
65 
\n
66 #include "vector_query.inl"
\n
glm::isNull
GLM_FUNC_DECL bool isNull(vec< L, T, Q > const &v, T const &epsilon)
Check whether a vector is null.
\n
glm::areCollinear
GLM_FUNC_DECL bool areCollinear(vec< L, T, Q > const &v0, vec< L, T, Q > const &v1, T const &epsilon)
Check whether two vectors are collinears.
\n
glm::isNormalized
GLM_FUNC_DECL bool isNormalized(vec< L, T, Q > const &v, T const &epsilon)
Check whether a vector is normalized.
\n
glm::areOrthonormal
GLM_FUNC_DECL bool areOrthonormal(vec< L, T, Q > const &v0, vec< L, T, Q > const &v1, T const &epsilon)
Check whether two vectors are orthonormal.
\n
glm::isCompNull
GLM_FUNC_DECL vec< L, bool, Q > isCompNull(vec< L, T, Q > const &v, T const &epsilon)
Check whether a each component of a vector is null.
\n
glm::areOrthogonal
GLM_FUNC_DECL bool areOrthogonal(vec< L, T, Q > const &v0, vec< L, T, Q > const &v1, T const &epsilon)
Check whether two vectors are orthogonals.
\n
glm::epsilon
GLM_FUNC_DECL GLM_CONSTEXPR genType epsilon()
Return the epsilon constant for floating point types.
\n
glm
Definition: common.hpp:20
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ext/vector_relational.hpp File Reference
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GLM_EXT_vector_relational \nMore...

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Go to the source code of this file.

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\nFunctions

template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< L, bool, Q > equal (vec< L, T, Q > const &x, vec< L, T, Q > const &y, T epsilon)
 Returns the component-wise comparison of |x - y| < epsilon. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< L, bool, Q > equal (vec< L, T, Q > const &x, vec< L, T, Q > const &y, vec< L, T, Q > const &epsilon)
 Returns the component-wise comparison of |x - y| < epsilon. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< L, bool, Q > equal (vec< L, T, Q > const &x, vec< L, T, Q > const &y, int ULPs)
 Returns the component-wise comparison between two vectors in term of ULPs. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< L, bool, Q > equal (vec< L, T, Q > const &x, vec< L, T, Q > const &y, vec< L, int, Q > const &ULPs)
 Returns the component-wise comparison between two vectors in term of ULPs. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< L, bool, Q > notEqual (vec< L, T, Q > const &x, vec< L, T, Q > const &y, T epsilon)
 Returns the component-wise comparison of |x - y| >= epsilon. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< L, bool, Q > notEqual (vec< L, T, Q > const &x, vec< L, T, Q > const &y, vec< L, T, Q > const &epsilon)
 Returns the component-wise comparison of |x - y| >= epsilon. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< L, bool, Q > notEqual (vec< L, T, Q > const &x, vec< L, T, Q > const &y, int ULPs)
 Returns the component-wise comparison between two vectors in term of ULPs. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< L, bool, Q > notEqual (vec< L, T, Q > const &x, vec< L, T, Q > const &y, vec< L, int, Q > const &ULPs)
 Returns the component-wise comparison between two vectors in term of ULPs. More...
 
\n

Detailed Description

\n

GLM_EXT_vector_relational

\n
See also
Core features (dependence)
\n
\n to use the features of this extension. \">GLM_EXT_scalar_integer (dependence)
\n\n

Definition in file ext/vector_relational.hpp.

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1 
\n
18 #pragma once
\n
19 
\n
20 // Dependencies
\n
21 #include "../detail/qualifier.hpp"
\n
22 
\n
23 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
24 # pragma message("GLM: GLM_EXT_vector_relational extension included")
\n
25 #endif
\n
26 
\n
27 namespace glm
\n
28 {
\n
31 
\n
38  template<length_t L, typename T, qualifier Q>
\n
39  GLM_FUNC_DECL GLM_CONSTEXPR vec<L, bool, Q> equal(vec<L, T, Q> const& x, vec<L, T, Q> const& y, T epsilon);
\n
40 
\n
47  template<length_t L, typename T, qualifier Q>
\n
48  GLM_FUNC_DECL GLM_CONSTEXPR vec<L, bool, Q> equal(vec<L, T, Q> const& x, vec<L, T, Q> const& y, vec<L, T, Q> const& epsilon);
\n
49 
\n
56  template<length_t L, typename T, qualifier Q>
\n
57  GLM_FUNC_DECL GLM_CONSTEXPR vec<L, bool, Q> notEqual(vec<L, T, Q> const& x, vec<L, T, Q> const& y, T epsilon);
\n
58 
\n
65  template<length_t L, typename T, qualifier Q>
\n
66  GLM_FUNC_DECL GLM_CONSTEXPR vec<L, bool, Q> notEqual(vec<L, T, Q> const& x, vec<L, T, Q> const& y, vec<L, T, Q> const& epsilon);
\n
67 
\n
74  template<length_t L, typename T, qualifier Q>
\n
75  GLM_FUNC_DECL GLM_CONSTEXPR vec<L, bool, Q> equal(vec<L, T, Q> const& x, vec<L, T, Q> const& y, int ULPs);
\n
76 
\n
83  template<length_t L, typename T, qualifier Q>
\n
84  GLM_FUNC_DECL GLM_CONSTEXPR vec<L, bool, Q> equal(vec<L, T, Q> const& x, vec<L, T, Q> const& y, vec<L, int, Q> const& ULPs);
\n
85 
\n
92  template<length_t L, typename T, qualifier Q>
\n
93  GLM_FUNC_DECL GLM_CONSTEXPR vec<L, bool, Q> notEqual(vec<L, T, Q> const& x, vec<L, T, Q> const& y, int ULPs);
\n
94 
\n
101  template<length_t L, typename T, qualifier Q>
\n
102  GLM_FUNC_DECL GLM_CONSTEXPR vec<L, bool, Q> notEqual(vec<L, T, Q> const& x, vec<L, T, Q> const& y, vec<L, int, Q> const& ULPs);
\n
103 
\n
105 }//namespace glm
\n
106 
\n
107 #include "vector_relational.inl"
\n
glm::notEqual
GLM_FUNC_DECL GLM_CONSTEXPR vec< L, bool, Q > notEqual(vec< L, T, Q > const &x, vec< L, T, Q > const &y, vec< L, int, Q > const &ULPs)
Returns the component-wise comparison between two vectors in term of ULPs.
\n
glm::equal
GLM_FUNC_DECL GLM_CONSTEXPR vec< L, bool, Q > equal(vec< L, T, Q > const &x, vec< L, T, Q > const &y, vec< L, int, Q > const &ULPs)
Returns the component-wise comparison between two vectors in term of ULPs.
\n
glm::epsilon
GLM_FUNC_DECL GLM_CONSTEXPR genType epsilon()
Return the epsilon constant for floating point types.
\n
glm
Definition: common.hpp:20
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\nFunctions
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vector_relational.hpp File Reference
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Core features \nMore...

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\nFunctions

template<length_t L, qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR bool all (vec< L, bool, Q > const &v)
 Returns true if all components of x are true. More...
 
template<length_t L, qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR bool any (vec< L, bool, Q > const &v)
 Returns true if any component of x is true. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< L, bool, Q > equal (vec< L, T, Q > const &x, vec< L, T, Q > const &y)
 Returns the component-wise comparison of result x == y. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< L, bool, Q > greaterThan (vec< L, T, Q > const &x, vec< L, T, Q > const &y)
 Returns the component-wise comparison of result x > y. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< L, bool, Q > greaterThanEqual (vec< L, T, Q > const &x, vec< L, T, Q > const &y)
 Returns the component-wise comparison of result x >= y. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< L, bool, Q > lessThan (vec< L, T, Q > const &x, vec< L, T, Q > const &y)
 Returns the component-wise comparison result of x < y. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< L, bool, Q > lessThanEqual (vec< L, T, Q > const &x, vec< L, T, Q > const &y)
 Returns the component-wise comparison of result x <= y. More...
 
template<length_t L, qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< L, bool, Q > not_ (vec< L, bool, Q > const &v)
 Returns the component-wise logical complement of x. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< L, bool, Q > notEqual (vec< L, T, Q > const &x, vec< L, T, Q > const &y)
 Returns the component-wise comparison of result x != y. More...
 
\n

Detailed Description

\n

Core features

\n
See also
GLSL 4.20.8 specification, section 8.7 Vector Relational Functions
\n\n

Definition in file vector_relational.hpp.

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vector_relational.hpp
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1 
\n
20 #pragma once
\n
21 
\n
22 #include "detail/qualifier.hpp"
\n
23 #include "detail/setup.hpp"
\n
24 
\n
25 namespace glm
\n
26 {
\n
29 
\n
37  template<length_t L, typename T, qualifier Q>
\n
38  GLM_FUNC_DECL GLM_CONSTEXPR vec<L, bool, Q> lessThan(vec<L, T, Q> const& x, vec<L, T, Q> const& y);
\n
39 
\n
47  template<length_t L, typename T, qualifier Q>
\n
48  GLM_FUNC_DECL GLM_CONSTEXPR vec<L, bool, Q> lessThanEqual(vec<L, T, Q> const& x, vec<L, T, Q> const& y);
\n
49 
\n
57  template<length_t L, typename T, qualifier Q>
\n
58  GLM_FUNC_DECL GLM_CONSTEXPR vec<L, bool, Q> greaterThan(vec<L, T, Q> const& x, vec<L, T, Q> const& y);
\n
59 
\n
67  template<length_t L, typename T, qualifier Q>
\n
68  GLM_FUNC_DECL GLM_CONSTEXPR vec<L, bool, Q> greaterThanEqual(vec<L, T, Q> const& x, vec<L, T, Q> const& y);
\n
69 
\n
77  template<length_t L, typename T, qualifier Q>
\n
78  GLM_FUNC_DECL GLM_CONSTEXPR vec<L, bool, Q> equal(vec<L, T, Q> const& x, vec<L, T, Q> const& y);
\n
79 
\n
87  template<length_t L, typename T, qualifier Q>
\n
88  GLM_FUNC_DECL GLM_CONSTEXPR vec<L, bool, Q> notEqual(vec<L, T, Q> const& x, vec<L, T, Q> const& y);
\n
89 
\n
96  template<length_t L, qualifier Q>
\n
97  GLM_FUNC_DECL GLM_CONSTEXPR bool any(vec<L, bool, Q> const& v);
\n
98 
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105  template<length_t L, qualifier Q>
\n
106  GLM_FUNC_DECL GLM_CONSTEXPR bool all(vec<L, bool, Q> const& v);
\n
107 
\n
115  template<length_t L, qualifier Q>
\n
116  GLM_FUNC_DECL GLM_CONSTEXPR vec<L, bool, Q> not_(vec<L, bool, Q> const& v);
\n
117 
\n
119 }//namespace glm
\n
120 
\n
121 #include "detail/func_vector_relational.inl"
\n
glm::all
GLM_FUNC_DECL GLM_CONSTEXPR bool all(vec< L, bool, Q > const &v)
Returns true if all components of x are true.
\n
glm::greaterThan
GLM_FUNC_DECL GLM_CONSTEXPR vec< L, bool, Q > greaterThan(vec< L, T, Q > const &x, vec< L, T, Q > const &y)
Returns the component-wise comparison of result x > y.
\n
glm::notEqual
GLM_FUNC_DECL GLM_CONSTEXPR vec< L, bool, Q > notEqual(vec< L, T, Q > const &x, vec< L, T, Q > const &y)
Returns the component-wise comparison of result x != y.
\n
glm::lessThanEqual
GLM_FUNC_DECL GLM_CONSTEXPR vec< L, bool, Q > lessThanEqual(vec< L, T, Q > const &x, vec< L, T, Q > const &y)
Returns the component-wise comparison of result x <= y.
\n
glm::not_
GLM_FUNC_DECL GLM_CONSTEXPR vec< L, bool, Q > not_(vec< L, bool, Q > const &v)
Returns the component-wise logical complement of x.
\n
glm::any
GLM_FUNC_DECL GLM_CONSTEXPR bool any(vec< L, bool, Q > const &v)
Returns true if any component of x is true.
\n
glm::equal
GLM_FUNC_DECL GLM_CONSTEXPR vec< L, bool, Q > equal(vec< L, T, Q > const &x, vec< L, T, Q > const &y)
Returns the component-wise comparison of result x == y.
\n
glm::greaterThanEqual
GLM_FUNC_DECL GLM_CONSTEXPR vec< L, bool, Q > greaterThanEqual(vec< L, T, Q > const &x, vec< L, T, Q > const &y)
Returns the component-wise comparison of result x >= y.
\n
glm::lessThan
GLM_FUNC_DECL GLM_CONSTEXPR vec< L, bool, Q > lessThan(vec< L, T, Q > const &x, vec< L, T, Q > const &y)
Returns the component-wise comparison result of x < y.
\n
glm
Definition: common.hpp:20
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GLM_EXT_vector_uint1 \nMore...

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\ntypedef vec< 1, unsigned int, defaultp > uvec1
 1 component vector of unsigned integer numbers.
 
\n

Detailed Description

\n

GLM_EXT_vector_uint1

\n\n

Definition in file vector_uint1.hpp.

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vector_uint1.hpp
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\n
14 #pragma once
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15 
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16 #include "../detail/type_vec1.hpp"
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17 
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18 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
19 # pragma message("GLM: GLM_EXT_vector_uint1 extension included")
\n
20 #endif
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21 
\n
22 namespace glm
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23 {
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26 
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28  typedef vec<1, unsigned int, defaultp> uvec1;
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29 
\n
31 }//namespace glm
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32 
\n
glm::uvec1
vec< 1, unsigned int, defaultp > uvec1
1 component vector of unsigned integer numbers.
Definition: vector_uint1.hpp:28
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glm
Definition: common.hpp:20
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vector_uint1_precision.hpp File Reference
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GLM_EXT_vector_uint1_precision \nMore...

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\nTypedefs

typedef vec< 1, unsigned int, highp > highp_uvec1
 1 component vector of unsigned integer values. More...
 
typedef vec< 1, unsigned int, lowp > lowp_uvec1
 1 component vector of unsigned integer values. More...
 
typedef vec< 1, unsigned int, mediump > mediump_uvec1
 1 component vector of unsigned integer values. More...
 
\n

Detailed Description

\n

GLM_EXT_vector_uint1_precision

\n\n

Definition in file vector_uint1_precision.hpp.

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vector_uint1_precision.hpp
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1 
\n
11 #pragma once
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12 
\n
13 #include "../detail/type_vec1.hpp"
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14 
\n
15 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
16 # pragma message("GLM: GLM_EXT_vector_uint1_precision extension included")
\n
17 #endif
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18 
\n
19 namespace glm
\n
20 {
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23 
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27  typedef vec<1, unsigned int, highp> highp_uvec1;
\n
28 
\n
32  typedef vec<1, unsigned int, mediump> mediump_uvec1;
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33 
\n
37  typedef vec<1, unsigned int, lowp> lowp_uvec1;
\n
38 
\n
40 }//namespace glm
\n
glm::mediump_uvec1
vec< 1, unsigned int, mediump > mediump_uvec1
1 component vector of unsigned integer values.
Definition: vector_uint1_precision.hpp:32
\n
glm::highp_uvec1
vec< 1, unsigned int, highp > highp_uvec1
1 component vector of unsigned integer values.
Definition: vector_uint1_precision.hpp:27
\n
glm::lowp_uvec1
vec< 1, unsigned int, lowp > lowp_uvec1
1 component vector of unsigned integer values.
Definition: vector_uint1_precision.hpp:37
\n
glm
Definition: common.hpp:20
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typedef vec< 2, unsigned int, defaultp > uvec2
 2 components vector of unsigned integer numbers. More...
 
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Definition in file vector_uint2.hpp.

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\n
4 #pragma once
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5 #include "../detail/type_vec2.hpp"
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6 
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7 namespace glm
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8 {
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11 
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15  typedef vec<2, unsigned int, defaultp> uvec2;
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16 
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18 }//namespace glm
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glm::uvec2
vec< 2, unsigned int, defaultp > uvec2
2 components vector of unsigned integer numbers.
Definition: vector_uint2.hpp:15
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glm
Definition: common.hpp:20
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typedef vec< 2, unsigned int, highp > highp_uvec2
 2 components vector of high qualifier unsigned integer numbers. More...
 
typedef vec< 2, unsigned int, lowp > lowp_uvec2
 2 components vector of low qualifier unsigned integer numbers. More...
 
typedef vec< 2, unsigned int, mediump > mediump_uvec2
 2 components vector of medium qualifier unsigned integer numbers. More...
 
\n

Detailed Description

\n

Core features

\n\n

Definition in file vector_uint2_precision.hpp.

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vector_uint2_precision.hpp
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1 
\n
4 #pragma once
\n
5 #include "../detail/type_vec2.hpp"
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6 
\n
7 namespace glm
\n
8 {
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11 
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16  typedef vec<2, unsigned int, highp> highp_uvec2;
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17 
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22  typedef vec<2, unsigned int, mediump> mediump_uvec2;
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23 
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28  typedef vec<2, unsigned int, lowp> lowp_uvec2;
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29 
\n
31 }//namespace glm
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glm::lowp_uvec2
vec< 2, unsigned int, lowp > lowp_uvec2
2 components vector of low qualifier unsigned integer numbers.
Definition: vector_uint2_precision.hpp:28
\n
glm::highp_uvec2
vec< 2, unsigned int, highp > highp_uvec2
2 components vector of high qualifier unsigned integer numbers.
Definition: vector_uint2_precision.hpp:16
\n
glm::mediump_uvec2
vec< 2, unsigned int, mediump > mediump_uvec2
2 components vector of medium qualifier unsigned integer numbers.
Definition: vector_uint2_precision.hpp:22
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glm
Definition: common.hpp:20
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typedef vec< 3, unsigned int, defaultp > uvec3
 3 components vector of unsigned integer numbers. More...
 
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Definition in file vector_uint3.hpp.

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vector_uint3.hpp
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\n
4 #pragma once
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5 #include "../detail/type_vec3.hpp"
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6 
\n
7 namespace glm
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8 {
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11 
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15  typedef vec<3, unsigned int, defaultp> uvec3;
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16 
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18 }//namespace glm
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glm::uvec3
vec< 3, unsigned int, defaultp > uvec3
3 components vector of unsigned integer numbers.
Definition: vector_uint3.hpp:15
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glm
Definition: common.hpp:20
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vector_uint3_precision.hpp File Reference
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Core features \nMore...

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typedef vec< 3, unsigned int, highp > highp_uvec3
 3 components vector of high qualifier unsigned integer numbers. More...
 
typedef vec< 3, unsigned int, lowp > lowp_uvec3
 3 components vector of low qualifier unsigned integer numbers. More...
 
typedef vec< 3, unsigned int, mediump > mediump_uvec3
 3 components vector of medium qualifier unsigned integer numbers. More...
 
\n

Detailed Description

\n

Core features

\n\n

Definition in file vector_uint3_precision.hpp.

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vector_uint3_precision.hpp
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1 
\n
4 #pragma once
\n
5 #include "../detail/type_vec3.hpp"
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6 
\n
7 namespace glm
\n
8 {
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11 
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16  typedef vec<3, unsigned int, highp> highp_uvec3;
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17 
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22  typedef vec<3, unsigned int, mediump> mediump_uvec3;
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23 
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28  typedef vec<3, unsigned int, lowp> lowp_uvec3;
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29 
\n
31 }//namespace glm
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glm::mediump_uvec3
vec< 3, unsigned int, mediump > mediump_uvec3
3 components vector of medium qualifier unsigned integer numbers.
Definition: vector_uint3_precision.hpp:22
\n
glm::highp_uvec3
vec< 3, unsigned int, highp > highp_uvec3
3 components vector of high qualifier unsigned integer numbers.
Definition: vector_uint3_precision.hpp:16
\n
glm::lowp_uvec3
vec< 3, unsigned int, lowp > lowp_uvec3
3 components vector of low qualifier unsigned integer numbers.
Definition: vector_uint3_precision.hpp:28
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glm
Definition: common.hpp:20
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typedef vec< 4, unsigned int, defaultp > uvec4
 4 components vector of unsigned integer numbers. More...
 
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Detailed Description

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Core features

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Definition in file vector_uint4.hpp.

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vector_uint4.hpp
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1 
\n
4 #pragma once
\n
5 #include "../detail/type_vec4.hpp"
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6 
\n
7 namespace glm
\n
8 {
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11 
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15  typedef vec<4, unsigned int, defaultp> uvec4;
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16 
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18 }//namespace glm
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glm::uvec4
vec< 4, unsigned int, defaultp > uvec4
4 components vector of unsigned integer numbers.
Definition: vector_uint4.hpp:15
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glm
Definition: common.hpp:20
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vector_uint4_precision.hpp File Reference
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Core features \nMore...

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Go to the source code of this file.

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\nTypedefs

typedef vec< 4, unsigned int, highp > highp_uvec4
 4 components vector of high qualifier unsigned integer numbers. More...
 
typedef vec< 4, unsigned int, lowp > lowp_uvec4
 4 components vector of low qualifier unsigned integer numbers. More...
 
typedef vec< 4, unsigned int, mediump > mediump_uvec4
 4 components vector of medium qualifier unsigned integer numbers. More...
 
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Detailed Description

\n

Core features

\n\n

Definition in file vector_uint4_precision.hpp.

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vector_uint4_precision.hpp
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1 
\n
4 #pragma once
\n
5 #include "../detail/type_vec4.hpp"
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6 
\n
7 namespace glm
\n
8 {
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11 
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16  typedef vec<4, unsigned int, highp> highp_uvec4;
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17 
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22  typedef vec<4, unsigned int, mediump> mediump_uvec4;
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23 
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28  typedef vec<4, unsigned int, lowp> lowp_uvec4;
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29 
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31 }//namespace glm
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glm::mediump_uvec4
vec< 4, unsigned int, mediump > mediump_uvec4
4 components vector of medium qualifier unsigned integer numbers.
Definition: vector_uint4_precision.hpp:22
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glm::highp_uvec4
vec< 4, unsigned int, highp > highp_uvec4
4 components vector of high qualifier unsigned integer numbers.
Definition: vector_uint4_precision.hpp:16
\n
glm::lowp_uvec4
vec< 4, unsigned int, lowp > lowp_uvec4
4 components vector of low qualifier unsigned integer numbers.
Definition: vector_uint4_precision.hpp:28
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glm
Definition: common.hpp:20
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GLM_EXT_vector_ulp \nMore...

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\nFunctions

template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, int, Q > floatDistance (vec< L, float, Q > const &x, vec< L, float, Q > const &y)
 Return the distance in the number of ULP between 2 single-precision floating-point scalars. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, int64, Q > floatDistance (vec< L, double, Q > const &x, vec< L, double, Q > const &y)
 Return the distance in the number of ULP between 2 double-precision floating-point scalars. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > nextFloat (vec< L, T, Q > const &x)
 Return the next ULP value(s) after the input value(s). More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > nextFloat (vec< L, T, Q > const &x, int ULPs)
 Return the value(s) ULP distance after the input value(s). More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > nextFloat (vec< L, T, Q > const &x, vec< L, int, Q > const &ULPs)
 Return the value(s) ULP distance after the input value(s). More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > prevFloat (vec< L, T, Q > const &x)
 Return the previous ULP value(s) before the input value(s). More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > prevFloat (vec< L, T, Q > const &x, int ULPs)
 Return the value(s) ULP distance before the input value(s). More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > prevFloat (vec< L, T, Q > const &x, vec< L, int, Q > const &ULPs)
 Return the value(s) ULP distance before the input value(s). More...
 
\n

Detailed Description

\n

GLM_EXT_vector_ulp

\n\n

Definition in file vector_ulp.hpp.

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vector_ulp.hpp
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\n
17 #pragma once
\n
18 
\n
19 // Dependencies
\n
20 #include "../ext/scalar_ulp.hpp"
\n
21 
\n
22 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
23 # pragma message("GLM: GLM_EXT_vector_ulp extension included")
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24 #endif
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25 
\n
26 namespace glm
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27 {
\n
35  template<length_t L, typename T, qualifier Q>
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36  GLM_FUNC_DECL vec<L, T, Q> nextFloat(vec<L, T, Q> const& x);
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37 
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45  template<length_t L, typename T, qualifier Q>
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46  GLM_FUNC_DECL vec<L, T, Q> nextFloat(vec<L, T, Q> const& x, int ULPs);
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47 
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55  template<length_t L, typename T, qualifier Q>
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56  GLM_FUNC_DECL vec<L, T, Q> nextFloat(vec<L, T, Q> const& x, vec<L, int, Q> const& ULPs);
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57 
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65  template<length_t L, typename T, qualifier Q>
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66  GLM_FUNC_DECL vec<L, T, Q> prevFloat(vec<L, T, Q> const& x);
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67 
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75  template<length_t L, typename T, qualifier Q>
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76  GLM_FUNC_DECL vec<L, T, Q> prevFloat(vec<L, T, Q> const& x, int ULPs);
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77 
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85  template<length_t L, typename T, qualifier Q>
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86  GLM_FUNC_DECL vec<L, T, Q> prevFloat(vec<L, T, Q> const& x, vec<L, int, Q> const& ULPs);
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87 
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94  template<length_t L, typename T, qualifier Q>
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95  GLM_FUNC_DECL vec<L, int, Q> floatDistance(vec<L, float, Q> const& x, vec<L, float, Q> const& y);
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96 
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103  template<length_t L, typename T, qualifier Q>
\n
104  GLM_FUNC_DECL vec<L, int64, Q> floatDistance(vec<L, double, Q> const& x, vec<L, double, Q> const& y);
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105 
\n
107 }//namespace glm
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108 
\n
109 #include "vector_ulp.inl"
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glm
Definition: common.hpp:20
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wrap.hpp File Reference
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GLM_GTX_wrap \nMore...

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\nFunctions

template<typename genType >
GLM_FUNC_DECL genType clamp (genType const &Texcoord)
 Simulate GL_CLAMP OpenGL wrap mode. More...
 
template<typename genType >
GLM_FUNC_DECL genType mirrorClamp (genType const &Texcoord)
 Simulate GL_MIRRORED_REPEAT OpenGL wrap mode. More...
 
template<typename genType >
GLM_FUNC_DECL genType mirrorRepeat (genType const &Texcoord)
 Simulate GL_MIRROR_REPEAT OpenGL wrap mode. More...
 
template<typename genType >
GLM_FUNC_DECL genType repeat (genType const &Texcoord)
 Simulate GL_REPEAT OpenGL wrap mode. More...
 
\n

Detailed Description

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GLM_GTX_wrap

\n
See also
Core features (dependence)
\n\n

Definition in file wrap.hpp.

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wrap.hpp
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1 
\n
13 #pragma once
\n
14 
\n
15 // Dependency:
\n
16 #include "../glm.hpp"
\n
17 #include "../gtc/vec1.hpp"
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18 
\n
19 #if GLM_MESSAGES == GLM_ENABLE && !defined(GLM_EXT_INCLUDED)
\n
20 # ifndef GLM_ENABLE_EXPERIMENTAL
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21 # pragma message("GLM: GLM_GTX_wrap is an experimental extension and may change in the future. Use #define GLM_ENABLE_EXPERIMENTAL before including it, if you really want to use it.")
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22 # else
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23 # pragma message("GLM: GLM_GTX_wrap extension included")
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24 # endif
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25 #endif
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26 
\n
27 namespace glm
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28 {
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31 
\n
34  template<typename genType>
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35  GLM_FUNC_DECL genType clamp(genType const& Texcoord);
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36 
\n
39  template<typename genType>
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40  GLM_FUNC_DECL genType repeat(genType const& Texcoord);
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41 
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44  template<typename genType>
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45  GLM_FUNC_DECL genType mirrorClamp(genType const& Texcoord);
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46 
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49  template<typename genType>
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50  GLM_FUNC_DECL genType mirrorRepeat(genType const& Texcoord);
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51 
\n
53 }// namespace glm
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54 
\n
55 #include "wrap.inl"
\n
glm::mirrorRepeat
GLM_FUNC_DECL genType mirrorRepeat(genType const &Texcoord)
Simulate GL_MIRROR_REPEAT OpenGL wrap mode.
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glm::repeat
GLM_FUNC_DECL genType repeat(genType const &Texcoord)
Simulate GL_REPEAT OpenGL wrap mode.
\n
glm::mirrorClamp
GLM_FUNC_DECL genType mirrorClamp(genType const &Texcoord)
Simulate GL_MIRRORED_REPEAT OpenGL wrap mode.
\n
glm::clamp
GLM_FUNC_DECL genType clamp(genType const &Texcoord)
Simulate GL_CLAMP OpenGL wrap mode.
\n
glm
Definition: common.hpp:20
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\nFunctions

template<typename genType >
GLM_FUNC_DECL GLM_CONSTEXPR genType abs (genType x)
 Returns x if x >= 0; otherwise, it returns -x. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< L, T, Q > abs (vec< L, T, Q > const &x)
 Returns x if x >= 0; otherwise, it returns -x. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > ceil (vec< L, T, Q > const &x)
 Returns a value equal to the nearest integer that is greater than or equal to x. More...
 
template<typename genType >
GLM_FUNC_DECL GLM_CONSTEXPR genType clamp (genType x, genType minVal, genType maxVal)
 Returns min(max(x, minVal), maxVal) for each component in x using the floating-point values minVal and maxVal. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< L, T, Q > clamp (vec< L, T, Q > const &x, T minVal, T maxVal)
 Returns min(max(x, minVal), maxVal) for each component in x using the floating-point values minVal and maxVal. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< L, T, Q > clamp (vec< L, T, Q > const &x, vec< L, T, Q > const &minVal, vec< L, T, Q > const &maxVal)
 Returns min(max(x, minVal), maxVal) for each component in x using the floating-point values minVal and maxVal. More...
 
GLM_FUNC_DECL int floatBitsToInt (float const &v)
 Returns a signed integer value representing the encoding of a floating-point value. More...
 
template<length_t L, qualifier Q>
GLM_FUNC_DECL vec< L, int, Q > floatBitsToInt (vec< L, float, Q > const &v)
 Returns a signed integer value representing the encoding of a floating-point value. More...
 
GLM_FUNC_DECL uint floatBitsToUint (float const &v)
 Returns a unsigned integer value representing the encoding of a floating-point value. More...
 
template<length_t L, qualifier Q>
GLM_FUNC_DECL vec< L, uint, Q > floatBitsToUint (vec< L, float, Q > const &v)
 Returns a unsigned integer value representing the encoding of a floating-point value. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > floor (vec< L, T, Q > const &x)
 Returns a value equal to the nearest integer that is less then or equal to x. More...
 
template<typename genType >
GLM_FUNC_DECL genType fma (genType const &a, genType const &b, genType const &c)
 Computes and returns a * b + c. More...
 
template<typename genType >
GLM_FUNC_DECL genType fract (genType x)
 Return x - floor(x). More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > fract (vec< L, T, Q > const &x)
 Return x - floor(x). More...
 
template<typename genType >
GLM_FUNC_DECL genType frexp (genType x, int &exp)
 Splits x into a floating-point significand in the range [0.5, 1.0) and an integral exponent of two, such that: x = significand * exp(2, exponent) More...
 
GLM_FUNC_DECL float intBitsToFloat (int const &v)
 Returns a floating-point value corresponding to a signed integer encoding of a floating-point value. More...
 
template<length_t L, qualifier Q>
GLM_FUNC_DECL vec< L, float, Q > intBitsToFloat (vec< L, int, Q > const &v)
 Returns a floating-point value corresponding to a signed integer encoding of a floating-point value. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, bool, Q > isinf (vec< L, T, Q > const &x)
 Returns true if x holds a positive infinity or negative infinity representation in the underlying implementation's set of floating point representations. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, bool, Q > isnan (vec< L, T, Q > const &x)
 Returns true if x holds a NaN (not a number) representation in the underlying implementation's set of floating point representations. More...
 
template<typename genType >
GLM_FUNC_DECL genType ldexp (genType const &x, int const &exp)
 Builds a floating-point number from x and the corresponding integral exponent of two in exp, returning: significand * exp(2, exponent) More...
 
template<typename genType >
GLM_FUNC_DECL GLM_CONSTEXPR genType max (genType x, genType y)
 Returns y if x < y; otherwise, it returns x. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< L, T, Q > max (vec< L, T, Q > const &x, T y)
 Returns y if x < y; otherwise, it returns x. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< L, T, Q > max (vec< L, T, Q > const &x, vec< L, T, Q > const &y)
 Returns y if x < y; otherwise, it returns x. More...
 
template<typename genType >
GLM_FUNC_DECL GLM_CONSTEXPR genType min (genType x, genType y)
 Returns y if y < x; otherwise, it returns x. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< L, T, Q > min (vec< L, T, Q > const &x, T y)
 Returns y if y < x; otherwise, it returns x. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< L, T, Q > min (vec< L, T, Q > const &x, vec< L, T, Q > const &y)
 Returns y if y < x; otherwise, it returns x. More...
 
template<typename genTypeT , typename genTypeU >
GLM_FUNC_DECL genTypeT mix (genTypeT x, genTypeT y, genTypeU a)
 If genTypeU is a floating scalar or vector: Returns x * (1.0 - a) + y * a, i.e., the linear blend of x and y using the floating-point value a. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > mod (vec< L, T, Q > const &x, vec< L, T, Q > const &y)
 Modulus. More...
 
template<typename genType >
GLM_FUNC_DECL genType modf (genType x, genType &i)
 Returns the fractional part of x and sets i to the integer part (as a whole number floating point value). More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > round (vec< L, T, Q > const &x)
 Returns a value equal to the nearest integer to x. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > roundEven (vec< L, T, Q > const &x)
 Returns a value equal to the nearest integer to x. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > sign (vec< L, T, Q > const &x)
 Returns 1.0 if x > 0, 0.0 if x == 0, or -1.0 if x < 0. More...
 
template<typename genType >
GLM_FUNC_DECL genType smoothstep (genType edge0, genType edge1, genType x)
 Returns 0.0 if x <= edge0 and 1.0 if x >= edge1 and performs smooth Hermite interpolation between 0 and 1 when edge0 < x < edge1. More...
 
template<typename genType >
GLM_FUNC_DECL genType step (genType edge, genType x)
 Returns 0.0 if x < edge, otherwise it returns 1.0 for each component of a genType. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > step (T edge, vec< L, T, Q > const &x)
 Returns 0.0 if x < edge, otherwise it returns 1.0. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > step (vec< L, T, Q > const &edge, vec< L, T, Q > const &x)
 Returns 0.0 if x < edge, otherwise it returns 1.0. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > trunc (vec< L, T, Q > const &x)
 Returns a value equal to the nearest integer to x whose absolute value is not larger than the absolute value of x. More...
 
GLM_FUNC_DECL float uintBitsToFloat (uint const &v)
 Returns a floating-point value corresponding to a unsigned integer encoding of a floating-point value. More...
 
template<length_t L, qualifier Q>
GLM_FUNC_DECL vec< L, float, Q > uintBitsToFloat (vec< L, uint, Q > const &v)
 Returns a floating-point value corresponding to a unsigned integer encoding of a floating-point value. More...
 
\n

Detailed Description

\n

Provides GLSL common functions.

\n

These all operate component-wise. The description is per component.

\n

Include <glm/common.hpp> to use these core features.

\n

Function Documentation

\n\n
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GLM_FUNC_DECL GLM_CONSTEXPR genType glm::abs (genType x)
\n
\n\n

Returns x if x >= 0; otherwise, it returns -x.

\n
Template Parameters
\n \n \n
genTypefloating-point or signed integer; scalar or vector types.
\n
\n
\n
See also
GLSL abs man page
\n
\nGLSL 4.20.8 specification, section 8.3 Common Functions
\n\n
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\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL GLM_CONSTEXPR vec<L, T, Q> glm::abs (vec< L, T, Q > const & x)
\n
\n\n

Returns x if x >= 0; otherwise, it returns -x.

\n
Template Parameters
\n \n \n \n \n
LInteger between 1 and 4 included that qualify the dimension of the vector
TFloating-point or signed integer scalar types
QValue from qualifier enum
\n
\n
\n
See also
GLSL abs man page
\n
\nGLSL 4.20.8 specification, section 8.3 Common Functions
\n\n
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\n\n
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\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL vec<L, T, Q> glm::ceil (vec< L, T, Q > const & x)
\n
\n\n

Returns a value equal to the nearest integer that is greater than or equal to x.

\n
Template Parameters
\n \n \n \n \n
LInteger between 1 and 4 included that qualify the dimension of the vector
TFloating-point scalar types
QValue from qualifier enum
\n
\n
\n
See also
GLSL ceil man page
\n
\nGLSL 4.20.8 specification, section 8.3 Common Functions
\n\n
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\n\n
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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL GLM_CONSTEXPR genType glm::clamp (genType x,
genType minVal,
genType maxVal 
)
\n
\n\n

Returns min(max(x, minVal), maxVal) for each component in x using the floating-point values minVal and maxVal.

\n
Template Parameters
\n \n \n
genTypeFloating-point or integer; scalar or vector types.
\n
\n
\n
See also
GLSL clamp man page
\n
\nGLSL 4.20.8 specification, section 8.3 Common Functions
\n\n

Referenced by glm::saturate().

\n\n
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\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL GLM_CONSTEXPR vec<L, T, Q> glm::clamp (vec< L, T, Q > const & x,
T minVal,
T maxVal 
)
\n
\n\n

Returns min(max(x, minVal), maxVal) for each component in x using the floating-point values minVal and maxVal.

\n
Template Parameters
\n \n \n \n \n
LInteger between 1 and 4 included that qualify the dimension of the vector
TFloating-point or integer scalar types
QValue from qualifier enum
\n
\n
\n
See also
GLSL clamp man page
\n
\nGLSL 4.20.8 specification, section 8.3 Common Functions
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL GLM_CONSTEXPR vec<L, T, Q> glm::clamp (vec< L, T, Q > const & x,
vec< L, T, Q > const & minVal,
vec< L, T, Q > const & maxVal 
)
\n
\n\n

Returns min(max(x, minVal), maxVal) for each component in x using the floating-point values minVal and maxVal.

\n
Template Parameters
\n \n \n \n \n
LInteger between 1 and 4 included that qualify the dimension of the vector
TFloating-point or integer scalar types
QValue from qualifier enum
\n
\n
\n
See also
GLSL clamp man page
\n
\nGLSL 4.20.8 specification, section 8.3 Common Functions
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL int glm::floatBitsToInt (float const & v)
\n
\n\n

Returns a signed integer value representing the encoding of a floating-point value.

\n

The floating-point value's bit-level representation is preserved.

\n
See also
GLSL floatBitsToInt man page
\n
\nGLSL 4.20.8 specification, section 8.3 Common Functions
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL vec<L, int, Q> glm::floatBitsToInt (vec< L, float, Q > const & v)
\n
\n\n

Returns a signed integer value representing the encoding of a floating-point value.

\n

The floatingpoint value's bit-level representation is preserved.

\n
Template Parameters
\n \n \n \n
LInteger between 1 and 4 included that qualify the dimension of the vector
QValue from qualifier enum
\n
\n
\n
See also
GLSL floatBitsToInt man page
\n
\nGLSL 4.20.8 specification, section 8.3 Common Functions
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL uint glm::floatBitsToUint (float const & v)
\n
\n\n

Returns a unsigned integer value representing the encoding of a floating-point value.

\n

The floatingpoint value's bit-level representation is preserved.

\n
See also
GLSL floatBitsToUint man page
\n
\nGLSL 4.20.8 specification, section 8.3 Common Functions
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL vec<L, uint, Q> glm::floatBitsToUint (vec< L, float, Q > const & v)
\n
\n\n

Returns a unsigned integer value representing the encoding of a floating-point value.

\n

The floatingpoint value's bit-level representation is preserved.

\n
Template Parameters
\n \n \n \n
LInteger between 1 and 4 included that qualify the dimension of the vector
QValue from qualifier enum
\n
\n
\n
See also
GLSL floatBitsToUint man page
\n
\nGLSL 4.20.8 specification, section 8.3 Common Functions
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL vec<L, T, Q> glm::floor (vec< L, T, Q > const & x)
\n
\n\n

Returns a value equal to the nearest integer that is less then or equal to x.

\n
Template Parameters
\n \n \n \n \n
LInteger between 1 and 4 included that qualify the dimension of the vector
TFloating-point scalar types
QValue from qualifier enum
\n
\n
\n
See also
GLSL floor man page
\n
\nGLSL 4.20.8 specification, section 8.3 Common Functions
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL genType glm::fma (genType const & a,
genType const & b,
genType const & c 
)
\n
\n\n

Computes and returns a * b + c.

\n
Template Parameters
\n \n \n
genTypeFloating-point scalar or vector types.
\n
\n
\n
See also
GLSL fma man page
\n
\nGLSL 4.20.8 specification, section 8.3 Common Functions
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL genType glm::fract (genType x)
\n
\n\n

Return x - floor(x).

\n
Template Parameters
\n \n \n
genTypeFloating-point scalar or vector types.
\n
\n
\n
See also
GLSL fract man page
\n
\nGLSL 4.20.8 specification, section 8.3 Common Functions
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL vec<L, T, Q> glm::fract (vec< L, T, Q > const & x)
\n
\n\n

Return x - floor(x).

\n
Template Parameters
\n \n \n \n \n
LInteger between 1 and 4 included that qualify the dimension of the vector
TFloating-point scalar types
QValue from qualifier enum
\n
\n
\n
See also
GLSL fract man page
\n
\nGLSL 4.20.8 specification, section 8.3 Common Functions
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL genType glm::frexp (genType x,
int & exp 
)
\n
\n\n

Splits x into a floating-point significand in the range [0.5, 1.0) and an integral exponent of two, such that: x = significand * exp(2, exponent)

\n

The significand is returned by the function and the exponent is returned in the parameter exp. For a floating-point value of zero, the significant and exponent are both zero. For a floating-point value that is an infinity or is not a number, the results are undefined.

\n
Template Parameters
\n \n \n
genTypeFloating-point scalar or vector types.
\n
\n
\n
See also
GLSL frexp man page
\n
\nGLSL 4.20.8 specification, section 8.3 Common Functions
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL float glm::intBitsToFloat (int const & v)
\n
\n\n

Returns a floating-point value corresponding to a signed integer encoding of a floating-point value.

\n

If an inf or NaN is passed in, it will not signal, and the resulting floating point value is unspecified. Otherwise, the bit-level representation is preserved.

\n
See also
GLSL intBitsToFloat man page
\n
\nGLSL 4.20.8 specification, section 8.3 Common Functions
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL vec<L, float, Q> glm::intBitsToFloat (vec< L, int, Q > const & v)
\n
\n\n

Returns a floating-point value corresponding to a signed integer encoding of a floating-point value.

\n

If an inf or NaN is passed in, it will not signal, and the resulting floating point value is unspecified. Otherwise, the bit-level representation is preserved.

\n
Template Parameters
\n \n \n \n
LInteger between 1 and 4 included that qualify the dimension of the vector
QValue from qualifier enum
\n
\n
\n
See also
GLSL intBitsToFloat man page
\n
\nGLSL 4.20.8 specification, section 8.3 Common Functions
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL vec<L, bool, Q> glm::isinf (vec< L, T, Q > const & x)
\n
\n\n

Returns true if x holds a positive infinity or negative infinity representation in the underlying implementation's set of floating point representations.

\n

Returns false otherwise, including for implementations with no infinity representations.

\n
Template Parameters
\n \n \n \n \n
LInteger between 1 and 4 included that qualify the dimension of the vector
TFloating-point scalar types
QValue from qualifier enum
\n
\n
\n
See also
GLSL isinf man page
\n
\nGLSL 4.20.8 specification, section 8.3 Common Functions
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL vec<L, bool, Q> glm::isnan (vec< L, T, Q > const & x)
\n
\n\n

Returns true if x holds a NaN (not a number) representation in the underlying implementation's set of floating point representations.

\n

Returns false otherwise, including for implementations with no NaN representations.

\n

/!\\ When using compiler fast math, this function may fail.

\n
Template Parameters
\n \n \n \n \n
LInteger between 1 and 4 included that qualify the dimension of the vector
TFloating-point scalar types
QValue from qualifier enum
\n
\n
\n
See also
GLSL isnan man page
\n
\nGLSL 4.20.8 specification, section 8.3 Common Functions
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL genType glm::ldexp (genType const & x,
int const & exp 
)
\n
\n\n

Builds a floating-point number from x and the corresponding integral exponent of two in exp, returning: significand * exp(2, exponent)

\n

If this product is too large to be represented in the floating-point type, the result is undefined.

\n
Template Parameters
\n \n \n
genTypeFloating-point scalar or vector types.
\n
\n
\n
See also
GLSL ldexp man page;
\n
\nGLSL 4.20.8 specification, section 8.3 Common Functions
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL GLM_CONSTEXPR genType glm::max (genType x,
genType y 
)
\n
\n\n

Returns y if x < y; otherwise, it returns x.

\n
Template Parameters
\n \n \n
genTypeFloating-point or integer; scalar or vector types.
\n
\n
\n
See also
GLSL max man page
\n
\nGLSL 4.20.8 specification, section 8.3 Common Functions
\n\n
\n
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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL GLM_CONSTEXPR vec<L, T, Q> glm::max (vec< L, T, Q > const & x,
T y 
)
\n
\n\n

Returns y if x < y; otherwise, it returns x.

\n
Template Parameters
\n \n \n \n \n
LInteger between 1 and 4 included that qualify the dimension of the vector
TFloating-point or integer scalar types
QValue from qualifier enum
\n
\n
\n
See also
GLSL max man page
\n
\nGLSL 4.20.8 specification, section 8.3 Common Functions
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL GLM_CONSTEXPR vec<L, T, Q> glm::max (vec< L, T, Q > const & x,
vec< L, T, Q > const & y 
)
\n
\n\n

Returns y if x < y; otherwise, it returns x.

\n
Template Parameters
\n \n \n \n \n
LInteger between 1 and 4 included that qualify the dimension of the vector
TFloating-point or integer scalar types
QValue from qualifier enum
\n
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\n
See also
GLSL max man page
\n
\nGLSL 4.20.8 specification, section 8.3 Common Functions
\n\n
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GLM_FUNC_DECL GLM_CONSTEXPR genType glm::min (genType x,
genType y 
)
\n
\n\n

Returns y if y < x; otherwise, it returns x.

\n
Template Parameters
\n \n \n
genTypeFloating-point or integer; scalar or vector types.
\n
\n
\n
See also
GLSL min man page
\n
\nGLSL 4.20.8 specification, section 8.3 Common Functions
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL GLM_CONSTEXPR vec<L, T, Q> glm::min (vec< L, T, Q > const & x,
T y 
)
\n
\n\n

Returns y if y < x; otherwise, it returns x.

\n
Template Parameters
\n \n \n \n \n
LInteger between 1 and 4 included that qualify the dimension of the vector
TFloating-point or integer scalar types
QValue from qualifier enum
\n
\n
\n
See also
GLSL min man page
\n
\nGLSL 4.20.8 specification, section 8.3 Common Functions
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL GLM_CONSTEXPR vec<L, T, Q> glm::min (vec< L, T, Q > const & x,
vec< L, T, Q > const & y 
)
\n
\n\n

Returns y if y < x; otherwise, it returns x.

\n
Template Parameters
\n \n \n \n \n
LInteger between 1 and 4 included that qualify the dimension of the vector
TFloating-point or integer scalar types
QValue from qualifier enum
\n
\n
\n
See also
GLSL min man page
\n
\nGLSL 4.20.8 specification, section 8.3 Common Functions
\n\n
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\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL genTypeT glm::mix (genTypeT x,
genTypeT y,
genTypeU a 
)
\n
\n\n

If genTypeU is a floating scalar or vector: Returns x * (1.0 - a) + y * a, i.e., the linear blend of x and y using the floating-point value a.

\n

The value for a is not restricted to the range [0, 1].

\n

If genTypeU is a boolean scalar or vector: Selects which vector each returned component comes from. For a component of 'a' that is false, the corresponding component of 'x' is returned. For a component of 'a' that is true, the corresponding component of 'y' is returned. Components of 'x' and 'y' that are not selected are allowed to be invalid floating point values and will have no effect on the results. Thus, this provides different functionality than genType mix(genType x, genType y, genType(a)) where a is a Boolean vector.

\n
See also
GLSL mix man page
\n
\nGLSL 4.20.8 specification, section 8.3 Common Functions
\n
Parameters
\n \n \n \n \n
[in]xValue to interpolate.
[in]yValue to interpolate.
[in]aInterpolant.
\n
\n
\n
Template Parameters
\n \n \n \n
genTypeTFloating point scalar or vector.
genTypeUFloating point or boolean scalar or vector. It can't be a vector if it is the length of genTypeT.
\n
\n
\n
#include <glm/glm.hpp>
\n
...
\n
float a;
\n
bool b;
\n
glm::dvec3 e;
\n
glm::dvec3 f;
\n
glm::vec4 g;
\n
glm::vec4 h;
\n
...
\n
glm::vec4 r = glm::mix(g, h, a); // Interpolate with a floating-point scalar two vectors.
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glm::vec4 s = glm::mix(g, h, b); // Returns g or h;
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glm::dvec3 t = glm::mix(e, f, a); // Types of the third parameter is not required to match with the first and the second.
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glm::vec4 u = glm::mix(g, h, r); // Interpolations can be perform per component with a vector for the last parameter.
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Referenced by glm::lerp().

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GLM_FUNC_DECL vec<L, T, Q> glm::mod (vec< L, T, Q > const & x,
vec< L, T, Q > const & y 
)
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Modulus.

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Returns x - y * floor(x / y) for each component in x using the floating point value y.

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LInteger between 1 and 4 included that qualify the dimension of the vector
TFloating-point scalar types, include glm/gtc/integer for integer scalar types support
QValue from qualifier enum
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GLSL mod man page
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\nGLSL 4.20.8 specification, section 8.3 Common Functions
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GLM_FUNC_DECL genType glm::modf (genType x,
genType & i 
)
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Returns the fractional part of x and sets i to the integer part (as a whole number floating point value).

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Both the return value and the output parameter will have the same sign as x.

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genTypeFloating-point scalar or vector types.
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GLSL modf man page
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GLM_FUNC_DECL vec<L, T, Q> glm::round (vec< L, T, Q > const & x)
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Returns a value equal to the nearest integer to x.

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The fraction 0.5 will round in a direction chosen by the implementation, presumably the direction that is fastest. This includes the possibility that round(x) returns the same value as roundEven(x) for all values of x.

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LInteger between 1 and 4 included that qualify the dimension of the vector
TFloating-point scalar types
QValue from qualifier enum
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GLSL round man page
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GLM_FUNC_DECL vec<L, T, Q> glm::roundEven (vec< L, T, Q > const & x)
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Returns a value equal to the nearest integer to x.

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A fractional part of 0.5 will round toward the nearest even integer. (Both 3.5 and 4.5 for x will return 4.0.)

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LInteger between 1 and 4 included that qualify the dimension of the vector
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QValue from qualifier enum
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GLSL roundEven man page
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GLM_FUNC_DECL vec<L, T, Q> glm::sign (vec< L, T, Q > const & x)
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Returns 1.0 if x > 0, 0.0 if x == 0, or -1.0 if x < 0.

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LInteger between 1 and 4 included that qualify the dimension of the vector
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QValue from qualifier enum
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GLSL sign man page
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GLM_FUNC_DECL genType glm::smoothstep (genType edge0,
genType edge1,
genType x 
)
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Returns 0.0 if x <= edge0 and 1.0 if x >= edge1 and performs smooth Hermite interpolation between 0 and 1 when edge0 < x < edge1.

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This is useful in cases where you would want a threshold function with a smooth transition. This is equivalent to: genType t; t = clamp ((x - edge0) / (edge1 - edge0), 0, 1); return t * t * (3 - 2 * t); Results are undefined if edge0 >= edge1.

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genTypeFloating-point scalar or vector types.
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GLSL smoothstep man page
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GLM_FUNC_DECL genType glm::step (genType edge,
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)
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Returns 0.0 if x < edge, otherwise it returns 1.0 for each component of a genType.

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GLSL step man page
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GLM_FUNC_DECL vec<L, T, Q> glm::step (T edge,
vec< L, T, Q > const & x 
)
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Returns 0.0 if x < edge, otherwise it returns 1.0.

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LInteger between 1 and 4 included that qualify the dimension of the vector
TFloating-point scalar types
QValue from qualifier enum
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GLSL step man page
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GLM_FUNC_DECL vec<L, T, Q> glm::step (vec< L, T, Q > const & edge,
vec< L, T, Q > const & x 
)
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Returns 0.0 if x < edge, otherwise it returns 1.0.

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LInteger between 1 and 4 included that qualify the dimension of the vector
TFloating-point scalar types
QValue from qualifier enum
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GLSL step man page
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GLM_FUNC_DECL vec<L, T, Q> glm::trunc (vec< L, T, Q > const & x)
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Returns a value equal to the nearest integer to x whose absolute value is not larger than the absolute value of x.

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Template Parameters
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LInteger between 1 and 4 included that qualify the dimension of the vector
TFloating-point scalar types
QValue from qualifier enum
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GLSL trunc man page
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GLM_FUNC_DECL float glm::uintBitsToFloat (uint const & v)
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Returns a floating-point value corresponding to a unsigned integer encoding of a floating-point value.

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If an inf or NaN is passed in, it will not signal, and the resulting floating point value is unspecified. Otherwise, the bit-level representation is preserved.

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GLSL uintBitsToFloat man page
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GLM_FUNC_DECL vec<L, float, Q> glm::uintBitsToFloat (vec< L, uint, Q > const & v)
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Returns a floating-point value corresponding to a unsigned integer encoding of a floating-point value.

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If an inf or NaN is passed in, it will not signal, and the resulting floating point value is unspecified. Otherwise, the bit-level representation is preserved.

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LInteger between 1 and 4 included that qualify the dimension of the vector
QValue from qualifier enum
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GLSL uintBitsToFloat man page
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Exponential functions
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Provides GLSL exponential functions. \nMore...

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\nFunctions

template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > exp (vec< L, T, Q > const &v)
 Returns the natural exponentiation of v, i.e., e^v. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > exp2 (vec< L, T, Q > const &v)
 Returns 2 raised to the v power. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > inversesqrt (vec< L, T, Q > const &v)
 Returns the reciprocal of the positive square root of v. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > log (vec< L, T, Q > const &v)
 Returns the natural logarithm of v, i.e., returns the value y which satisfies the equation x = e^y. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > log2 (vec< L, T, Q > const &v)
 Returns the base 2 log of x, i.e., returns the value y, which satisfies the equation x = 2 ^ y. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > pow (vec< L, T, Q > const &base, vec< L, T, Q > const &exponent)
 Returns 'base' raised to the power 'exponent'. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > sqrt (vec< L, T, Q > const &v)
 Returns the positive square root of v. More...
 
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Detailed Description

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Provides GLSL exponential functions.

\n

These all operate component-wise. The description is per component.

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Include <glm/exponential.hpp> to use these core features.

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Function Documentation

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GLM_FUNC_DECL vec<L, T, Q> glm::exp (vec< L, T, Q > const & v)
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Returns the natural exponentiation of x, i.e., e^x.

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LAn integer between 1 and 4 included that qualify the dimension of the vector.
TFloating-point scalar types.
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GLSL exp man page
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GLM_FUNC_DECL vec<L, T, Q> glm::exp2 (vec< L, T, Q > const & v)
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Returns 2 raised to the v power.

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vexp2 function is defined for input values of v defined in the range (inf-, inf+) in the limit of the type qualifier.
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LAn integer between 1 and 4 included that qualify the dimension of the vector.
TFloating-point scalar types.
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GLSL exp2 man page
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GLM_FUNC_DECL vec<L, T, Q> glm::inversesqrt (vec< L, T, Q > const & v)
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Returns the reciprocal of the positive square root of v.

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vinversesqrt function is defined for input values of v defined in the range [0, inf+) in the limit of the type qualifier.
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LAn integer between 1 and 4 included that qualify the dimension of the vector.
TFloating-point scalar types.
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GLSL inversesqrt man page
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GLM_FUNC_DECL vec<L, T, Q> glm::log (vec< L, T, Q > const & v)
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Returns the natural logarithm of v, i.e., returns the value y which satisfies the equation x = e^y.

\n

Results are undefined if v <= 0.

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GLSL log man page
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GLM_FUNC_DECL vec<L, T, Q> glm::log2 (vec< L, T, Q > const & v)
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Returns the base 2 log of x, i.e., returns the value y, which satisfies the equation x = 2 ^ y.

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vlog2 function is defined for input values of v defined in the range (0, inf+) in the limit of the type qualifier.
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LAn integer between 1 and 4 included that qualify the dimension of the vector.
TFloating-point scalar types.
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GLSL log2 man page
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\nGLSL 4.20.8 specification, section 8.2 Exponential Functions
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GLM_FUNC_DECL vec<L, T, Q> glm::pow (vec< L, T, Q > const & base,
vec< L, T, Q > const & exponent 
)
\n
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Returns 'base' raised to the power 'exponent'.

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Parameters
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baseFloating point value. pow function is defined for input values of 'base' defined in the range (inf-, inf+) in the limit of the type qualifier.
exponentFloating point value representing the 'exponent'.
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GLSL pow man page
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\nGLSL 4.20.8 specification, section 8.2 Exponential Functions
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GLM_FUNC_DECL vec<L, T, Q> glm::sqrt (vec< L, T, Q > const & v)
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Returns the positive square root of v.

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LAn integer between 1 and 4 included that qualify the dimension of the vector.
TFloating-point scalar types.
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GLSL sqrt man page
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GLM_EXT_matrix_clip_space
Stable extensions
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Defines functions that generate clip space transformation matrices. \nMore...

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\nFunctions

template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > frustum (T left, T right, T bottom, T top, T near, T far)
 Creates a frustum matrix with default handedness, using the default handedness and default near and far clip planes definition. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > frustumLH (T left, T right, T bottom, T top, T near, T far)
 Creates a left handed frustum matrix. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > frustumLH_NO (T left, T right, T bottom, T top, T near, T far)
 Creates a left handed frustum matrix. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > frustumLH_ZO (T left, T right, T bottom, T top, T near, T far)
 Creates a left handed frustum matrix. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > frustumNO (T left, T right, T bottom, T top, T near, T far)
 Creates a frustum matrix using left-handed coordinates if GLM_FORCE_LEFT_HANDED if defined or right-handed coordinates otherwise. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > frustumRH (T left, T right, T bottom, T top, T near, T far)
 Creates a right handed frustum matrix. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > frustumRH_NO (T left, T right, T bottom, T top, T near, T far)
 Creates a right handed frustum matrix. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > frustumRH_ZO (T left, T right, T bottom, T top, T near, T far)
 Creates a right handed frustum matrix. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > frustumZO (T left, T right, T bottom, T top, T near, T far)
 Creates a frustum matrix using left-handed coordinates if GLM_FORCE_LEFT_HANDED if defined or right-handed coordinates otherwise. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > infinitePerspective (T fovy, T aspect, T near)
 Creates a matrix for a symmetric perspective-view frustum with far plane at infinite with default handedness. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > infinitePerspectiveLH (T fovy, T aspect, T near)
 Creates a matrix for a left handed, symmetric perspective-view frustum with far plane at infinite. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > infinitePerspectiveRH (T fovy, T aspect, T near)
 Creates a matrix for a right handed, symmetric perspective-view frustum with far plane at infinite. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > ortho (T left, T right, T bottom, T top)
 Creates a matrix for projecting two-dimensional coordinates onto the screen. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > ortho (T left, T right, T bottom, T top, T zNear, T zFar)
 Creates a matrix for an orthographic parallel viewing volume, using the default handedness and default near and far clip planes definition. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > orthoLH (T left, T right, T bottom, T top, T zNear, T zFar)
 Creates a matrix for an orthographic parallel viewing volume, using left-handed coordinates. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > orthoLH_NO (T left, T right, T bottom, T top, T zNear, T zFar)
 Creates a matrix for an orthographic parallel viewing volume using right-handed coordinates. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > orthoLH_ZO (T left, T right, T bottom, T top, T zNear, T zFar)
 Creates a matrix for an orthographic parallel viewing volume, using left-handed coordinates. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > orthoNO (T left, T right, T bottom, T top, T zNear, T zFar)
 Creates a matrix for an orthographic parallel viewing volume, using left-handed coordinates if GLM_FORCE_LEFT_HANDED if defined or right-handed coordinates otherwise. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > orthoRH (T left, T right, T bottom, T top, T zNear, T zFar)
 Creates a matrix for an orthographic parallel viewing volume, using right-handed coordinates. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > orthoRH_NO (T left, T right, T bottom, T top, T zNear, T zFar)
 Creates a matrix for an orthographic parallel viewing volume, using right-handed coordinates. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > orthoRH_ZO (T left, T right, T bottom, T top, T zNear, T zFar)
 Creates a matrix for an orthographic parallel viewing volume, using left-handed coordinates. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > orthoZO (T left, T right, T bottom, T top, T zNear, T zFar)
 Creates a matrix for an orthographic parallel viewing volume, using left-handed coordinates. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > perspective (T fovy, T aspect, T near, T far)
 Creates a matrix for a symetric perspective-view frustum based on the default handedness and default near and far clip planes definition. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > perspectiveFov (T fov, T width, T height, T near, T far)
 Builds a perspective projection matrix based on a field of view and the default handedness and default near and far clip planes definition. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > perspectiveFovLH (T fov, T width, T height, T near, T far)
 Builds a left handed perspective projection matrix based on a field of view. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > perspectiveFovLH_NO (T fov, T width, T height, T near, T far)
 Builds a perspective projection matrix based on a field of view using left-handed coordinates. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > perspectiveFovLH_ZO (T fov, T width, T height, T near, T far)
 Builds a perspective projection matrix based on a field of view using left-handed coordinates. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > perspectiveFovNO (T fov, T width, T height, T near, T far)
 Builds a perspective projection matrix based on a field of view using left-handed coordinates if GLM_FORCE_LEFT_HANDED if defined or right-handed coordinates otherwise. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > perspectiveFovRH (T fov, T width, T height, T near, T far)
 Builds a right handed perspective projection matrix based on a field of view. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > perspectiveFovRH_NO (T fov, T width, T height, T near, T far)
 Builds a perspective projection matrix based on a field of view using right-handed coordinates. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > perspectiveFovRH_ZO (T fov, T width, T height, T near, T far)
 Builds a perspective projection matrix based on a field of view using right-handed coordinates. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > perspectiveFovZO (T fov, T width, T height, T near, T far)
 Builds a perspective projection matrix based on a field of view using left-handed coordinates if GLM_FORCE_LEFT_HANDED if defined or right-handed coordinates otherwise. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > perspectiveLH (T fovy, T aspect, T near, T far)
 Creates a matrix for a left handed, symetric perspective-view frustum. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > perspectiveLH_NO (T fovy, T aspect, T near, T far)
 Creates a matrix for a left handed, symetric perspective-view frustum. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > perspectiveLH_ZO (T fovy, T aspect, T near, T far)
 Creates a matrix for a left handed, symetric perspective-view frustum. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > perspectiveNO (T fovy, T aspect, T near, T far)
 Creates a matrix for a symetric perspective-view frustum using left-handed coordinates if GLM_FORCE_LEFT_HANDED if defined or right-handed coordinates otherwise. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > perspectiveRH (T fovy, T aspect, T near, T far)
 Creates a matrix for a right handed, symetric perspective-view frustum. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > perspectiveRH_NO (T fovy, T aspect, T near, T far)
 Creates a matrix for a right handed, symetric perspective-view frustum. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > perspectiveRH_ZO (T fovy, T aspect, T near, T far)
 Creates a matrix for a right handed, symetric perspective-view frustum. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > perspectiveZO (T fovy, T aspect, T near, T far)
 Creates a matrix for a symetric perspective-view frustum using left-handed coordinates if GLM_FORCE_LEFT_HANDED if defined or right-handed coordinates otherwise. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > tweakedInfinitePerspective (T fovy, T aspect, T near)
 Creates a matrix for a symmetric perspective-view frustum with far plane at infinite for graphics hardware that doesn't support depth clamping. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > tweakedInfinitePerspective (T fovy, T aspect, T near, T ep)
 Creates a matrix for a symmetric perspective-view frustum with far plane at infinite for graphics hardware that doesn't support depth clamping. More...
 
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Detailed Description

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Defines functions that generate clip space transformation matrices.

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The matrices generated by this extension use standard OpenGL fixed-function conventions. For example, the lookAt function generates a transform from world space into the specific eye space that the projective matrix functions (perspective, ortho, etc) are designed to expect. The OpenGL compatibility specifications defines the particular layout of this eye space.

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Include <glm/ext/matrix_clip_space.hpp> to use the features of this extension.

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See also
GLM_EXT_matrix_transform
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\nGLM_EXT_matrix_projection
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Function Documentation

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GLM_FUNC_DECL mat<4, 4, T, defaultp> glm::frustum (T left,
T right,
T bottom,
T top,
T near,
T far 
)
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Creates a frustum matrix with default handedness, using the default handedness and default near and far clip planes definition.

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To change default handedness use GLM_FORCE_LEFT_HANDED. To change default near and far clip planes definition use GLM_FORCE_DEPTH_ZERO_TO_ONE.

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Template Parameters
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See also
glFrustum man page
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GLM_FUNC_DECL mat<4, 4, T, defaultp> glm::frustumLH (T left,
T right,
T bottom,
T top,
T near,
T far 
)
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Creates a left handed frustum matrix.

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If GLM_FORCE_DEPTH_ZERO_TO_ONE is defined, the near and far clip planes correspond to z normalized device coordinates of 0 and +1 respectively. (Direct3D clip volume definition) Otherwise, the near and far clip planes correspond to z normalized device coordinates of -1 and +1 respectively. (OpenGL clip volume definition)

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GLM_FUNC_DECL mat<4, 4, T, defaultp> glm::frustumLH_NO (T left,
T right,
T bottom,
T top,
T near,
T far 
)
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Creates a left handed frustum matrix.

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The near and far clip planes correspond to z normalized device coordinates of -1 and +1 respectively. (OpenGL clip volume definition)

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GLM_FUNC_DECL mat<4, 4, T, defaultp> glm::frustumLH_ZO (T left,
T right,
T bottom,
T top,
T near,
T far 
)
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Creates a left handed frustum matrix.

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The near and far clip planes correspond to z normalized device coordinates of 0 and +1 respectively. (Direct3D clip volume definition)

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GLM_FUNC_DECL mat<4, 4, T, defaultp> glm::frustumNO (T left,
T right,
T bottom,
T top,
T near,
T far 
)
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Creates a frustum matrix using left-handed coordinates if GLM_FORCE_LEFT_HANDED if defined or right-handed coordinates otherwise.

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The near and far clip planes correspond to z normalized device coordinates of -1 and +1 respectively. (OpenGL clip volume definition)

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GLM_FUNC_DECL mat<4, 4, T, defaultp> glm::frustumRH (T left,
T right,
T bottom,
T top,
T near,
T far 
)
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Creates a right handed frustum matrix.

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If GLM_FORCE_DEPTH_ZERO_TO_ONE is defined, the near and far clip planes correspond to z normalized device coordinates of 0 and +1 respectively. (Direct3D clip volume definition) Otherwise, the near and far clip planes correspond to z normalized device coordinates of -1 and +1 respectively. (OpenGL clip volume definition)

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GLM_FUNC_DECL mat<4, 4, T, defaultp> glm::frustumRH_NO (T left,
T right,
T bottom,
T top,
T near,
T far 
)
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Creates a right handed frustum matrix.

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The near and far clip planes correspond to z normalized device coordinates of -1 and +1 respectively. (OpenGL clip volume definition)

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TA floating-point scalar type
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GLM_FUNC_DECL mat<4, 4, T, defaultp> glm::frustumRH_ZO (T left,
T right,
T bottom,
T top,
T near,
T far 
)
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Creates a right handed frustum matrix.

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The near and far clip planes correspond to z normalized device coordinates of 0 and +1 respectively. (Direct3D clip volume definition)

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Template Parameters
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TA floating-point scalar type
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GLM_FUNC_DECL mat<4, 4, T, defaultp> glm::frustumZO (T left,
T right,
T bottom,
T top,
T near,
T far 
)
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Creates a frustum matrix using left-handed coordinates if GLM_FORCE_LEFT_HANDED if defined or right-handed coordinates otherwise.

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The near and far clip planes correspond to z normalized device coordinates of 0 and +1 respectively. (Direct3D clip volume definition)

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GLM_FUNC_DECL mat<4, 4, T, defaultp> glm::infinitePerspective (T fovy,
T aspect,
T near 
)
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Creates a matrix for a symmetric perspective-view frustum with far plane at infinite with default handedness.

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fovySpecifies the field of view angle, in degrees, in the y direction. Expressed in radians.
aspectSpecifies the aspect ratio that determines the field of view in the x direction. The aspect ratio is the ratio of x (width) to y (height).
nearSpecifies the distance from the viewer to the near clipping plane (always positive).
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GLM_FUNC_DECL mat<4, 4, T, defaultp> glm::infinitePerspectiveLH (T fovy,
T aspect,
T near 
)
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Creates a matrix for a left handed, symmetric perspective-view frustum with far plane at infinite.

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fovySpecifies the field of view angle, in degrees, in the y direction. Expressed in radians.
aspectSpecifies the aspect ratio that determines the field of view in the x direction. The aspect ratio is the ratio of x (width) to y (height).
nearSpecifies the distance from the viewer to the near clipping plane (always positive).
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GLM_FUNC_DECL mat<4, 4, T, defaultp> glm::infinitePerspectiveRH (T fovy,
T aspect,
T near 
)
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Creates a matrix for a right handed, symmetric perspective-view frustum with far plane at infinite.

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fovySpecifies the field of view angle, in degrees, in the y direction. Expressed in radians.
aspectSpecifies the aspect ratio that determines the field of view in the x direction. The aspect ratio is the ratio of x (width) to y (height).
nearSpecifies the distance from the viewer to the near clipping plane (always positive).
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GLM_FUNC_DECL mat<4, 4, T, defaultp> glm::ortho (T left,
T right,
T bottom,
T top 
)
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Creates a matrix for projecting two-dimensional coordinates onto the screen.

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Template Parameters
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TA floating-point scalar type
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See also
- glm::ortho(T const& left, T const& right, T const& bottom, T const& top, T const& zNear, T const& zFar)
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\ngluOrtho2D man page
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GLM_FUNC_DECL mat<4, 4, T, defaultp> glm::ortho (T left,
T right,
T bottom,
T top,
T zNear,
T zFar 
)
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Creates a matrix for an orthographic parallel viewing volume, using the default handedness and default near and far clip planes definition.

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To change default handedness use GLM_FORCE_LEFT_HANDED. To change default near and far clip planes definition use GLM_FORCE_DEPTH_ZERO_TO_ONE.

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Template Parameters
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TA floating-point scalar type
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See also
- glm::ortho(T const& left, T const& right, T const& bottom, T const& top)
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\nglOrtho man page
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GLM_FUNC_DECL mat<4, 4, T, defaultp> glm::orthoLH (T left,
T right,
T bottom,
T top,
T zNear,
T zFar 
)
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Creates a matrix for an orthographic parallel viewing volume, using left-handed coordinates.

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If GLM_FORCE_DEPTH_ZERO_TO_ONE is defined, the near and far clip planes correspond to z normalized device coordinates of 0 and +1 respectively. (Direct3D clip volume definition) Otherwise, the near and far clip planes correspond to z normalized device coordinates of -1 and +1 respectively. (OpenGL clip volume definition)

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See also
- glm::ortho(T const& left, T const& right, T const& bottom, T const& top)
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GLM_FUNC_DECL mat<4, 4, T, defaultp> glm::orthoLH_NO (T left,
T right,
T bottom,
T top,
T zNear,
T zFar 
)
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Creates a matrix for an orthographic parallel viewing volume using right-handed coordinates.

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The near and far clip planes correspond to z normalized device coordinates of -1 and +1 respectively. (OpenGL clip volume definition)

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TA floating-point scalar type
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See also
- glm::ortho(T const& left, T const& right, T const& bottom, T const& top)
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GLM_FUNC_DECL mat<4, 4, T, defaultp> glm::orthoLH_ZO (T left,
T right,
T bottom,
T top,
T zNear,
T zFar 
)
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Creates a matrix for an orthographic parallel viewing volume, using left-handed coordinates.

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The near and far clip planes correspond to z normalized device coordinates of 0 and +1 respectively. (Direct3D clip volume definition)

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See also
- glm::ortho(T const& left, T const& right, T const& bottom, T const& top)
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GLM_FUNC_DECL mat<4, 4, T, defaultp> glm::orthoNO (T left,
T right,
T bottom,
T top,
T zNear,
T zFar 
)
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Creates a matrix for an orthographic parallel viewing volume, using left-handed coordinates if GLM_FORCE_LEFT_HANDED if defined or right-handed coordinates otherwise.

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The near and far clip planes correspond to z normalized device coordinates of -1 and +1 respectively. (OpenGL clip volume definition)

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See also
- glm::ortho(T const& left, T const& right, T const& bottom, T const& top)
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GLM_FUNC_DECL mat<4, 4, T, defaultp> glm::orthoRH (T left,
T right,
T bottom,
T top,
T zNear,
T zFar 
)
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Creates a matrix for an orthographic parallel viewing volume, using right-handed coordinates.

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If GLM_FORCE_DEPTH_ZERO_TO_ONE is defined, the near and far clip planes correspond to z normalized device coordinates of 0 and +1 respectively. (Direct3D clip volume definition) Otherwise, the near and far clip planes correspond to z normalized device coordinates of -1 and +1 respectively. (OpenGL clip volume definition)

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See also
- glm::ortho(T const& left, T const& right, T const& bottom, T const& top)
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GLM_FUNC_DECL mat<4, 4, T, defaultp> glm::orthoRH_NO (T left,
T right,
T bottom,
T top,
T zNear,
T zFar 
)
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Creates a matrix for an orthographic parallel viewing volume, using right-handed coordinates.

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The near and far clip planes correspond to z normalized device coordinates of -1 and +1 respectively. (OpenGL clip volume definition)

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See also
- glm::ortho(T const& left, T const& right, T const& bottom, T const& top)
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GLM_FUNC_DECL mat<4, 4, T, defaultp> glm::orthoRH_ZO (T left,
T right,
T bottom,
T top,
T zNear,
T zFar 
)
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Creates a matrix for an orthographic parallel viewing volume, using left-handed coordinates.

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The near and far clip planes correspond to z normalized device coordinates of 0 and +1 respectively. (Direct3D clip volume definition)

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Template Parameters
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See also
- glm::ortho(T const& left, T const& right, T const& bottom, T const& top)
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GLM_FUNC_DECL mat<4, 4, T, defaultp> glm::orthoZO (T left,
T right,
T bottom,
T top,
T zNear,
T zFar 
)
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Creates a matrix for an orthographic parallel viewing volume, using left-handed coordinates.

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The near and far clip planes correspond to z normalized device coordinates of 0 and +1 respectively. (Direct3D clip volume definition)

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Template Parameters
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TA floating-point scalar type
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See also
- glm::ortho(T const& left, T const& right, T const& bottom, T const& top)
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GLM_FUNC_DECL mat<4, 4, T, defaultp> glm::perspective (T fovy,
T aspect,
T near,
T far 
)
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Creates a matrix for a symetric perspective-view frustum based on the default handedness and default near and far clip planes definition.

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To change default handedness use GLM_FORCE_LEFT_HANDED. To change default near and far clip planes definition use GLM_FORCE_DEPTH_ZERO_TO_ONE.

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Parameters
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fovySpecifies the field of view angle in the y direction. Expressed in radians.
aspectSpecifies the aspect ratio that determines the field of view in the x direction. The aspect ratio is the ratio of x (width) to y (height).
nearSpecifies the distance from the viewer to the near clipping plane (always positive).
farSpecifies the distance from the viewer to the far clipping plane (always positive).
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See also
gluPerspective man page
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GLM_FUNC_DECL mat<4, 4, T, defaultp> glm::perspectiveFov (T fov,
T width,
T height,
T near,
T far 
)
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Builds a perspective projection matrix based on a field of view and the default handedness and default near and far clip planes definition.

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To change default handedness use GLM_FORCE_LEFT_HANDED. To change default near and far clip planes definition use GLM_FORCE_DEPTH_ZERO_TO_ONE.

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Parameters
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fovExpressed in radians.
widthWidth of the viewport
heightHeight of the viewport
nearSpecifies the distance from the viewer to the near clipping plane (always positive).
farSpecifies the distance from the viewer to the far clipping plane (always positive).
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GLM_FUNC_DECL mat<4, 4, T, defaultp> glm::perspectiveFovLH (T fov,
T width,
T height,
T near,
T far 
)
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Builds a left handed perspective projection matrix based on a field of view.

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If GLM_FORCE_DEPTH_ZERO_TO_ONE is defined, the near and far clip planes correspond to z normalized device coordinates of 0 and +1 respectively. (Direct3D clip volume definition) Otherwise, the near and far clip planes correspond to z normalized device coordinates of -1 and +1 respectively. (OpenGL clip volume definition)

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Parameters
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fovExpressed in radians.
widthWidth of the viewport
heightHeight of the viewport
nearSpecifies the distance from the viewer to the near clipping plane (always positive).
farSpecifies the distance from the viewer to the far clipping plane (always positive).
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GLM_FUNC_DECL mat<4, 4, T, defaultp> glm::perspectiveFovLH_NO (T fov,
T width,
T height,
T near,
T far 
)
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Builds a perspective projection matrix based on a field of view using left-handed coordinates.

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The near and far clip planes correspond to z normalized device coordinates of -1 and +1 respectively. (OpenGL clip volume definition)

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Parameters
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fovExpressed in radians.
widthWidth of the viewport
heightHeight of the viewport
nearSpecifies the distance from the viewer to the near clipping plane (always positive).
farSpecifies the distance from the viewer to the far clipping plane (always positive).
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GLM_FUNC_DECL mat<4, 4, T, defaultp> glm::perspectiveFovLH_ZO (T fov,
T width,
T height,
T near,
T far 
)
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Builds a perspective projection matrix based on a field of view using left-handed coordinates.

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The near and far clip planes correspond to z normalized device coordinates of 0 and +1 respectively. (Direct3D clip volume definition)

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Parameters
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fovExpressed in radians.
widthWidth of the viewport
heightHeight of the viewport
nearSpecifies the distance from the viewer to the near clipping plane (always positive).
farSpecifies the distance from the viewer to the far clipping plane (always positive).
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GLM_FUNC_DECL mat<4, 4, T, defaultp> glm::perspectiveFovNO (T fov,
T width,
T height,
T near,
T far 
)
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Builds a perspective projection matrix based on a field of view using left-handed coordinates if GLM_FORCE_LEFT_HANDED if defined or right-handed coordinates otherwise.

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The near and far clip planes correspond to z normalized device coordinates of -1 and +1 respectively. (OpenGL clip volume definition)

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Parameters
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fovExpressed in radians.
widthWidth of the viewport
heightHeight of the viewport
nearSpecifies the distance from the viewer to the near clipping plane (always positive).
farSpecifies the distance from the viewer to the far clipping plane (always positive).
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GLM_FUNC_DECL mat<4, 4, T, defaultp> glm::perspectiveFovRH (T fov,
T width,
T height,
T near,
T far 
)
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Builds a right handed perspective projection matrix based on a field of view.

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If GLM_FORCE_DEPTH_ZERO_TO_ONE is defined, the near and far clip planes correspond to z normalized device coordinates of 0 and +1 respectively. (Direct3D clip volume definition) Otherwise, the near and far clip planes correspond to z normalized device coordinates of -1 and +1 respectively. (OpenGL clip volume definition)

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Parameters
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fovExpressed in radians.
widthWidth of the viewport
heightHeight of the viewport
nearSpecifies the distance from the viewer to the near clipping plane (always positive).
farSpecifies the distance from the viewer to the far clipping plane (always positive).
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GLM_FUNC_DECL mat<4, 4, T, defaultp> glm::perspectiveFovRH_NO (T fov,
T width,
T height,
T near,
T far 
)
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Builds a perspective projection matrix based on a field of view using right-handed coordinates.

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The near and far clip planes correspond to z normalized device coordinates of -1 and +1 respectively. (OpenGL clip volume definition)

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Parameters
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fovExpressed in radians.
widthWidth of the viewport
heightHeight of the viewport
nearSpecifies the distance from the viewer to the near clipping plane (always positive).
farSpecifies the distance from the viewer to the far clipping plane (always positive).
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GLM_FUNC_DECL mat<4, 4, T, defaultp> glm::perspectiveFovRH_ZO (T fov,
T width,
T height,
T near,
T far 
)
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Builds a perspective projection matrix based on a field of view using right-handed coordinates.

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The near and far clip planes correspond to z normalized device coordinates of 0 and +1 respectively. (Direct3D clip volume definition)

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Parameters
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fovExpressed in radians.
widthWidth of the viewport
heightHeight of the viewport
nearSpecifies the distance from the viewer to the near clipping plane (always positive).
farSpecifies the distance from the viewer to the far clipping plane (always positive).
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GLM_FUNC_DECL mat<4, 4, T, defaultp> glm::perspectiveFovZO (T fov,
T width,
T height,
T near,
T far 
)
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Builds a perspective projection matrix based on a field of view using left-handed coordinates if GLM_FORCE_LEFT_HANDED if defined or right-handed coordinates otherwise.

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The near and far clip planes correspond to z normalized device coordinates of 0 and +1 respectively. (Direct3D clip volume definition)

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Parameters
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fovExpressed in radians.
widthWidth of the viewport
heightHeight of the viewport
nearSpecifies the distance from the viewer to the near clipping plane (always positive).
farSpecifies the distance from the viewer to the far clipping plane (always positive).
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GLM_FUNC_DECL mat<4, 4, T, defaultp> glm::perspectiveLH (T fovy,
T aspect,
T near,
T far 
)
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Creates a matrix for a left handed, symetric perspective-view frustum.

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If GLM_FORCE_DEPTH_ZERO_TO_ONE is defined, the near and far clip planes correspond to z normalized device coordinates of 0 and +1 respectively. (Direct3D clip volume definition) Otherwise, the near and far clip planes correspond to z normalized device coordinates of -1 and +1 respectively. (OpenGL clip volume definition)

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Parameters
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fovySpecifies the field of view angle, in degrees, in the y direction. Expressed in radians.
aspectSpecifies the aspect ratio that determines the field of view in the x direction. The aspect ratio is the ratio of x (width) to y (height).
nearSpecifies the distance from the viewer to the near clipping plane (always positive).
farSpecifies the distance from the viewer to the far clipping plane (always positive).
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GLM_FUNC_DECL mat<4, 4, T, defaultp> glm::perspectiveLH_NO (T fovy,
T aspect,
T near,
T far 
)
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Creates a matrix for a left handed, symetric perspective-view frustum.

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The near and far clip planes correspond to z normalized device coordinates of -1 and +1 respectively. (OpenGL clip volume definition)

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Parameters
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fovySpecifies the field of view angle, in degrees, in the y direction. Expressed in radians.
aspectSpecifies the aspect ratio that determines the field of view in the x direction. The aspect ratio is the ratio of x (width) to y (height).
nearSpecifies the distance from the viewer to the near clipping plane (always positive).
farSpecifies the distance from the viewer to the far clipping plane (always positive).
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GLM_FUNC_DECL mat<4, 4, T, defaultp> glm::perspectiveLH_ZO (T fovy,
T aspect,
T near,
T far 
)
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Creates a matrix for a left handed, symetric perspective-view frustum.

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The near and far clip planes correspond to z normalized device coordinates of 0 and +1 respectively. (Direct3D clip volume definition)

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Parameters
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fovySpecifies the field of view angle, in degrees, in the y direction. Expressed in radians.
aspectSpecifies the aspect ratio that determines the field of view in the x direction. The aspect ratio is the ratio of x (width) to y (height).
nearSpecifies the distance from the viewer to the near clipping plane (always positive).
farSpecifies the distance from the viewer to the far clipping plane (always positive).
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GLM_FUNC_DECL mat<4, 4, T, defaultp> glm::perspectiveNO (T fovy,
T aspect,
T near,
T far 
)
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Creates a matrix for a symetric perspective-view frustum using left-handed coordinates if GLM_FORCE_LEFT_HANDED if defined or right-handed coordinates otherwise.

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The near and far clip planes correspond to z normalized device coordinates of -1 and +1 respectively. (OpenGL clip volume definition)

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Parameters
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fovySpecifies the field of view angle, in degrees, in the y direction. Expressed in radians.
aspectSpecifies the aspect ratio that determines the field of view in the x direction. The aspect ratio is the ratio of x (width) to y (height).
nearSpecifies the distance from the viewer to the near clipping plane (always positive).
farSpecifies the distance from the viewer to the far clipping plane (always positive).
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GLM_FUNC_DECL mat<4, 4, T, defaultp> glm::perspectiveRH (T fovy,
T aspect,
T near,
T far 
)
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Creates a matrix for a right handed, symetric perspective-view frustum.

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If GLM_FORCE_DEPTH_ZERO_TO_ONE is defined, the near and far clip planes correspond to z normalized device coordinates of 0 and +1 respectively. (Direct3D clip volume definition) Otherwise, the near and far clip planes correspond to z normalized device coordinates of -1 and +1 respectively. (OpenGL clip volume definition)

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Parameters
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fovySpecifies the field of view angle, in degrees, in the y direction. Expressed in radians.
aspectSpecifies the aspect ratio that determines the field of view in the x direction. The aspect ratio is the ratio of x (width) to y (height).
nearSpecifies the distance from the viewer to the near clipping plane (always positive).
farSpecifies the distance from the viewer to the far clipping plane (always positive).
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GLM_FUNC_DECL mat<4, 4, T, defaultp> glm::perspectiveRH_NO (T fovy,
T aspect,
T near,
T far 
)
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Creates a matrix for a right handed, symetric perspective-view frustum.

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Parameters
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fovySpecifies the field of view angle, in degrees, in the y direction. Expressed in radians.
aspectSpecifies the aspect ratio that determines the field of view in the x direction. The aspect ratio is the ratio of x (width) to y (height).
nearSpecifies the distance from the viewer to the near clipping plane (always positive).
farSpecifies the distance from the viewer to the far clipping plane (always positive).
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GLM_FUNC_DECL mat<4, 4, T, defaultp> glm::perspectiveRH_ZO (T fovy,
T aspect,
T near,
T far 
)
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Creates a matrix for a right handed, symetric perspective-view frustum.

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Parameters
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fovySpecifies the field of view angle, in degrees, in the y direction. Expressed in radians.
aspectSpecifies the aspect ratio that determines the field of view in the x direction. The aspect ratio is the ratio of x (width) to y (height).
nearSpecifies the distance from the viewer to the near clipping plane (always positive).
farSpecifies the distance from the viewer to the far clipping plane (always positive).
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GLM_FUNC_DECL mat<4, 4, T, defaultp> glm::perspectiveZO (T fovy,
T aspect,
T near,
T far 
)
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Creates a matrix for a symetric perspective-view frustum using left-handed coordinates if GLM_FORCE_LEFT_HANDED if defined or right-handed coordinates otherwise.

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Parameters
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fovySpecifies the field of view angle, in degrees, in the y direction. Expressed in radians.
aspectSpecifies the aspect ratio that determines the field of view in the x direction. The aspect ratio is the ratio of x (width) to y (height).
nearSpecifies the distance from the viewer to the near clipping plane (always positive).
farSpecifies the distance from the viewer to the far clipping plane (always positive).
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GLM_FUNC_DECL mat<4, 4, T, defaultp> glm::tweakedInfinitePerspective (T fovy,
T aspect,
T near 
)
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Creates a matrix for a symmetric perspective-view frustum with far plane at infinite for graphics hardware that doesn't support depth clamping.

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Parameters
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fovySpecifies the field of view angle, in degrees, in the y direction. Expressed in radians.
aspectSpecifies the aspect ratio that determines the field of view in the x direction. The aspect ratio is the ratio of x (width) to y (height).
nearSpecifies the distance from the viewer to the near clipping plane (always positive).
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GLM_FUNC_DECL mat<4, 4, T, defaultp> glm::tweakedInfinitePerspective (T fovy,
T aspect,
T near,
T ep 
)
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Creates a matrix for a symmetric perspective-view frustum with far plane at infinite for graphics hardware that doesn't support depth clamping.

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Parameters
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fovySpecifies the field of view angle, in degrees, in the y direction. Expressed in radians.
aspectSpecifies the aspect ratio that determines the field of view in the x direction. The aspect ratio is the ratio of x (width) to y (height).
nearSpecifies the distance from the viewer to the near clipping plane (always positive).
epEpsilon
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GLM_EXT_matrix_common
Stable extensions
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Defines functions for common matrix operations. \nMore...

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Detailed Description

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Defines functions for common matrix operations.

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Include <glm/ext/matrix_common.hpp> to use the features of this extension.

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See also
GLM_EXT_matrix_common
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\nFunctions
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GLM_EXT_matrix_projection
Stable extensions
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Functions that generate common projection transformation matrices. \nMore...

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\nFunctions

template<typename T , qualifier Q, typename U >
GLM_FUNC_DECL mat< 4, 4, T, Q > pickMatrix (vec< 2, T, Q > const &center, vec< 2, T, Q > const &delta, vec< 4, U, Q > const &viewport)
 Define a picking region. More...
 
template<typename T , typename U , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > project (vec< 3, T, Q > const &obj, mat< 4, 4, T, Q > const &model, mat< 4, 4, T, Q > const &proj, vec< 4, U, Q > const &viewport)
 Map the specified object coordinates (obj.x, obj.y, obj.z) into window coordinates using default near and far clip planes definition. More...
 
template<typename T , typename U , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > projectNO (vec< 3, T, Q > const &obj, mat< 4, 4, T, Q > const &model, mat< 4, 4, T, Q > const &proj, vec< 4, U, Q > const &viewport)
 Map the specified object coordinates (obj.x, obj.y, obj.z) into window coordinates. More...
 
template<typename T , typename U , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > projectZO (vec< 3, T, Q > const &obj, mat< 4, 4, T, Q > const &model, mat< 4, 4, T, Q > const &proj, vec< 4, U, Q > const &viewport)
 Map the specified object coordinates (obj.x, obj.y, obj.z) into window coordinates. More...
 
template<typename T , typename U , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > unProject (vec< 3, T, Q > const &win, mat< 4, 4, T, Q > const &model, mat< 4, 4, T, Q > const &proj, vec< 4, U, Q > const &viewport)
 Map the specified window coordinates (win.x, win.y, win.z) into object coordinates using default near and far clip planes definition. More...
 
template<typename T , typename U , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > unProjectNO (vec< 3, T, Q > const &win, mat< 4, 4, T, Q > const &model, mat< 4, 4, T, Q > const &proj, vec< 4, U, Q > const &viewport)
 Map the specified window coordinates (win.x, win.y, win.z) into object coordinates. More...
 
template<typename T , typename U , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > unProjectZO (vec< 3, T, Q > const &win, mat< 4, 4, T, Q > const &model, mat< 4, 4, T, Q > const &proj, vec< 4, U, Q > const &viewport)
 Map the specified window coordinates (win.x, win.y, win.z) into object coordinates. More...
 
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Detailed Description

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Functions that generate common projection transformation matrices.

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The matrices generated by this extension use standard OpenGL fixed-function conventions. For example, the lookAt function generates a transform from world space into the specific eye space that the projective matrix functions (perspective, ortho, etc) are designed to expect. The OpenGL compatibility specifications defines the particular layout of this eye space.

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Include <glm/ext/matrix_projection.hpp> to use the features of this extension.

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See also
GLM_EXT_matrix_transform
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\nGLM_EXT_matrix_clip_space
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Function Documentation

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GLM_FUNC_DECL mat<4, 4, T, Q> glm::pickMatrix (vec< 2, T, Q > const & center,
vec< 2, T, Q > const & delta,
vec< 4, U, Q > const & viewport 
)
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Define a picking region.

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centerSpecify the center of a picking region in window coordinates.
deltaSpecify the width and height, respectively, of the picking region in window coordinates.
viewportRendering viewport
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TNative type used for the computation. Currently supported: half (not recommended), float or double.
UCurrently supported: Floating-point types and integer types.
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gluPickMatrix man page
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GLM_FUNC_DECL vec<3, T, Q> glm::project (vec< 3, T, Q > const & obj,
mat< 4, 4, T, Q > const & model,
mat< 4, 4, T, Q > const & proj,
vec< 4, U, Q > const & viewport 
)
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Map the specified object coordinates (obj.x, obj.y, obj.z) into window coordinates using default near and far clip planes definition.

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To change default near and far clip planes definition use GLM_FORCE_DEPTH_ZERO_TO_ONE.

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Parameters
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objSpecify the object coordinates.
modelSpecifies the current modelview matrix
projSpecifies the current projection matrix
viewportSpecifies the current viewport
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Returns
Return the computed window coordinates.
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Template Parameters
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gluProject man page
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GLM_FUNC_DECL vec<3, T, Q> glm::projectNO (vec< 3, T, Q > const & obj,
mat< 4, 4, T, Q > const & model,
mat< 4, 4, T, Q > const & proj,
vec< 4, U, Q > const & viewport 
)
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Map the specified object coordinates (obj.x, obj.y, obj.z) into window coordinates.

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The near and far clip planes correspond to z normalized device coordinates of -1 and +1 respectively. (OpenGL clip volume definition)

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objSpecify the object coordinates.
modelSpecifies the current modelview matrix
projSpecifies the current projection matrix
viewportSpecifies the current viewport
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Return the computed window coordinates.
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gluProject man page
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GLM_FUNC_DECL vec<3, T, Q> glm::projectZO (vec< 3, T, Q > const & obj,
mat< 4, 4, T, Q > const & model,
mat< 4, 4, T, Q > const & proj,
vec< 4, U, Q > const & viewport 
)
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Map the specified object coordinates (obj.x, obj.y, obj.z) into window coordinates.

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The near and far clip planes correspond to z normalized device coordinates of 0 and +1 respectively. (Direct3D clip volume definition)

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Parameters
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objSpecify the object coordinates.
modelSpecifies the current modelview matrix
projSpecifies the current projection matrix
viewportSpecifies the current viewport
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Return the computed window coordinates.
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TNative type used for the computation. Currently supported: half (not recommended), float or double.
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gluProject man page
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GLM_FUNC_DECL vec<3, T, Q> glm::unProject (vec< 3, T, Q > const & win,
mat< 4, 4, T, Q > const & model,
mat< 4, 4, T, Q > const & proj,
vec< 4, U, Q > const & viewport 
)
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Map the specified window coordinates (win.x, win.y, win.z) into object coordinates using default near and far clip planes definition.

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To change default near and far clip planes definition use GLM_FORCE_DEPTH_ZERO_TO_ONE.

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winSpecify the window coordinates to be mapped.
modelSpecifies the modelview matrix
projSpecifies the projection matrix
viewportSpecifies the viewport
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Returns the computed object coordinates.
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TNative type used for the computation. Currently supported: half (not recommended), float or double.
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gluUnProject man page
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GLM_FUNC_DECL vec<3, T, Q> glm::unProjectNO (vec< 3, T, Q > const & win,
mat< 4, 4, T, Q > const & model,
mat< 4, 4, T, Q > const & proj,
vec< 4, U, Q > const & viewport 
)
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Map the specified window coordinates (win.x, win.y, win.z) into object coordinates.

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The near and far clip planes correspond to z normalized device coordinates of -1 and +1 respectively. (OpenGL clip volume definition)

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Parameters
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winSpecify the window coordinates to be mapped.
modelSpecifies the modelview matrix
projSpecifies the projection matrix
viewportSpecifies the viewport
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Returns
Returns the computed object coordinates.
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Template Parameters
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TNative type used for the computation. Currently supported: half (not recommended), float or double.
UCurrently supported: Floating-point types and integer types.
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gluUnProject man page
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GLM_FUNC_DECL vec<3, T, Q> glm::unProjectZO (vec< 3, T, Q > const & win,
mat< 4, 4, T, Q > const & model,
mat< 4, 4, T, Q > const & proj,
vec< 4, U, Q > const & viewport 
)
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Map the specified window coordinates (win.x, win.y, win.z) into object coordinates.

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The near and far clip planes correspond to z normalized device coordinates of 0 and +1 respectively. (Direct3D clip volume definition)

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winSpecify the window coordinates to be mapped.
modelSpecifies the modelview matrix
projSpecifies the projection matrix
viewportSpecifies the viewport
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Returns the computed object coordinates.
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Template Parameters
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TNative type used for the computation. Currently supported: half (not recommended), float or double.
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See also
gluUnProject man page
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\nGenerated by  \n\"doxygen\"/\n 1.8.10\n
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GLM_EXT_matrix_relational
Stable extensions
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Exposes comparison functions for matrix types that take a user defined epsilon values. \nMore...

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\nFunctions

template<length_t C, length_t R, typename T , qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< C, bool, Q > equal (mat< C, R, T, Q > const &x, mat< C, R, T, Q > const &y)
 Perform a component-wise equal-to comparison of two matrices. More...
 
template<length_t C, length_t R, typename T , qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< C, bool, Q > equal (mat< C, R, T, Q > const &x, mat< C, R, T, Q > const &y, T epsilon)
 Returns the component-wise comparison of |x - y| < epsilon. More...
 
template<length_t C, length_t R, typename T , qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< C, bool, Q > equal (mat< C, R, T, Q > const &x, mat< C, R, T, Q > const &y, vec< C, T, Q > const &epsilon)
 Returns the component-wise comparison of |x - y| < epsilon. More...
 
template<length_t C, length_t R, typename T , qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< C, bool, Q > equal (mat< C, R, T, Q > const &x, mat< C, R, T, Q > const &y, int ULPs)
 Returns the component-wise comparison between two vectors in term of ULPs. More...
 
template<length_t C, length_t R, typename T , qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< C, bool, Q > equal (mat< C, R, T, Q > const &x, mat< C, R, T, Q > const &y, vec< C, int, Q > const &ULPs)
 Returns the component-wise comparison between two vectors in term of ULPs. More...
 
template<length_t C, length_t R, typename T , qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< C, bool, Q > notEqual (mat< C, R, T, Q > const &x, mat< C, R, T, Q > const &y)
 Perform a component-wise not-equal-to comparison of two matrices. More...
 
template<length_t C, length_t R, typename T , qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< C, bool, Q > notEqual (mat< C, R, T, Q > const &x, mat< C, R, T, Q > const &y, T epsilon)
 Returns the component-wise comparison of |x - y| < epsilon. More...
 
template<length_t C, length_t R, typename T , qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< C, bool, Q > notEqual (mat< C, R, T, Q > const &x, mat< C, R, T, Q > const &y, vec< C, T, Q > const &epsilon)
 Returns the component-wise comparison of |x - y| >= epsilon. More...
 
template<length_t C, length_t R, typename T , qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< C, bool, Q > notEqual (mat< C, R, T, Q > const &x, mat< C, R, T, Q > const &y, int ULPs)
 Returns the component-wise comparison between two vectors in term of ULPs. More...
 
template<length_t C, length_t R, typename T , qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< C, bool, Q > notEqual (mat< C, R, T, Q > const &x, mat< C, R, T, Q > const &y, vec< C, int, Q > const &ULPs)
 Returns the component-wise comparison between two vectors in term of ULPs. More...
 
\n

Detailed Description

\n

Exposes comparison functions for matrix types that take a user defined epsilon values.

\n

Include <glm/ext/matrix_relational.hpp> to use the features of this extension.

\n
See also
GLM_EXT_vector_relational
\n
\nGLM_EXT_scalar_relational
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\nGLM_EXT_quaternion_relational
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Function Documentation

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GLM_FUNC_DECL GLM_CONSTEXPR vec<C, bool, Q> glm::equal (mat< C, R, T, Q > const & x,
mat< C, R, T, Q > const & y 
)
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Perform a component-wise equal-to comparison of two matrices.

\n

Return a boolean vector which components value is True if this expression is satisfied per column of the matrices.

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Template Parameters
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CInteger between 1 and 4 included that qualify the number of columns of the matrix
RInteger between 1 and 4 included that qualify the number of rows of the matrix
TFloating-point or integer scalar types
QValue from qualifier enum
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GLM_FUNC_DECL GLM_CONSTEXPR vec<C, bool, Q> glm::equal (mat< C, R, T, Q > const & x,
mat< C, R, T, Q > const & y,
T epsilon 
)
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Returns the component-wise comparison of |x - y| < epsilon.

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True if this expression is satisfied.

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Template Parameters
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CInteger between 1 and 4 included that qualify the number of columns of the matrix
RInteger between 1 and 4 included that qualify the number of rows of the matrix
TFloating-point or integer scalar types
QValue from qualifier enum
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GLM_FUNC_DECL GLM_CONSTEXPR vec<C, bool, Q> glm::equal (mat< C, R, T, Q > const & x,
mat< C, R, T, Q > const & y,
vec< C, T, Q > const & epsilon 
)
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Returns the component-wise comparison of |x - y| < epsilon.

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True if this expression is satisfied.

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Template Parameters
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CInteger between 1 and 4 included that qualify the number of columns of the matrix
RInteger between 1 and 4 included that qualify the number of rows of the matrix
TFloating-point or integer scalar types
QValue from qualifier enum
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GLM_FUNC_DECL GLM_CONSTEXPR vec<C, bool, Q> glm::equal (mat< C, R, T, Q > const & x,
mat< C, R, T, Q > const & y,
int ULPs 
)
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Returns the component-wise comparison between two vectors in term of ULPs.

\n

True if this expression is satisfied.

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CInteger between 1 and 4 included that qualify the number of columns of the matrix
RInteger between 1 and 4 included that qualify the number of rows of the matrix
TFloating-point
QValue from qualifier enum
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GLM_FUNC_DECL GLM_CONSTEXPR vec<C, bool, Q> glm::equal (mat< C, R, T, Q > const & x,
mat< C, R, T, Q > const & y,
vec< C, int, Q > const & ULPs 
)
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Returns the component-wise comparison between two vectors in term of ULPs.

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True if this expression is satisfied.

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Template Parameters
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CInteger between 1 and 4 included that qualify the number of columns of the matrix
RInteger between 1 and 4 included that qualify the number of rows of the matrix
TFloating-point
QValue from qualifier enum
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GLM_FUNC_DECL GLM_CONSTEXPR vec<C, bool, Q> glm::notEqual (mat< C, R, T, Q > const & x,
mat< C, R, T, Q > const & y 
)
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Perform a component-wise not-equal-to comparison of two matrices.

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Return a boolean vector which components value is True if this expression is satisfied per column of the matrices.

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Template Parameters
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TFloating-point or integer scalar types
QValue from qualifier enum
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GLM_FUNC_DECL GLM_CONSTEXPR vec<C, bool, Q> glm::notEqual (mat< C, R, T, Q > const & x,
mat< C, R, T, Q > const & y,
T epsilon 
)
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Returns the component-wise comparison of |x - y| < epsilon.

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True if this expression is not satisfied.

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TFloating-point or integer scalar types
QValue from qualifier enum
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GLM_FUNC_DECL GLM_CONSTEXPR vec<C, bool, Q> glm::notEqual (mat< C, R, T, Q > const & x,
mat< C, R, T, Q > const & y,
vec< C, T, Q > const & epsilon 
)
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True if this expression is not satisfied.

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TFloating-point or integer scalar types
QValue from qualifier enum
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GLM_FUNC_DECL GLM_CONSTEXPR vec<C, bool, Q> glm::notEqual (mat< C, R, T, Q > const & x,
mat< C, R, T, Q > const & y,
int ULPs 
)
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Returns the component-wise comparison between two vectors in term of ULPs.

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True if this expression is not satisfied.

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RInteger between 1 and 4 included that qualify the number of rows of the matrix
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GLM_FUNC_DECL GLM_CONSTEXPR vec<C, bool, Q> glm::notEqual (mat< C, R, T, Q > const & x,
mat< C, R, T, Q > const & y,
vec< C, int, Q > const & ULPs 
)
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True if this expression is not satisfied.

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CInteger between 1 and 4 included that qualify the number of columns of the matrix
RInteger between 1 and 4 included that qualify the number of rows of the matrix
TFloating-point
QValue from qualifier enum
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\nGenerated by  \n\"doxygen\"/\n 1.8.10\n
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GLM_EXT_matrix_transform
Stable extensions
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Defines functions that generate common transformation matrices. \nMore...

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\ntemplate<typename genType >
GLM_FUNC_DECL GLM_CONSTEXPR genType identity ()
 Builds an identity matrix.
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 4, 4, T, Q > lookAt (vec< 3, T, Q > const &eye, vec< 3, T, Q > const &center, vec< 3, T, Q > const &up)
 Build a look at view matrix based on the default handedness. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 4, 4, T, Q > lookAtLH (vec< 3, T, Q > const &eye, vec< 3, T, Q > const &center, vec< 3, T, Q > const &up)
 Build a left handed look at view matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 4, 4, T, Q > lookAtRH (vec< 3, T, Q > const &eye, vec< 3, T, Q > const &center, vec< 3, T, Q > const &up)
 Build a right handed look at view matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 4, 4, T, Q > rotate (mat< 4, 4, T, Q > const &m, T angle, vec< 3, T, Q > const &axis)
 Builds a rotation 4 * 4 matrix created from an axis vector and an angle. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 4, 4, T, Q > scale (mat< 4, 4, T, Q > const &m, vec< 3, T, Q > const &v)
 Builds a scale 4 * 4 matrix created from 3 scalars. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 4, 4, T, Q > translate (mat< 4, 4, T, Q > const &m, vec< 3, T, Q > const &v)
 Builds a translation 4 * 4 matrix created from a vector of 3 components. More...
 
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Detailed Description

\n

Defines functions that generate common transformation matrices.

\n

The matrices generated by this extension use standard OpenGL fixed-function conventions. For example, the lookAt function generates a transform from world space into the specific eye space that the projective matrix functions (perspective, ortho, etc) are designed to expect. The OpenGL compatibility specifications defines the particular layout of this eye space.

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Include <glm/ext/matrix_transform.hpp> to use the features of this extension.

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See also
GLM_EXT_matrix_projection
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\nGLM_EXT_matrix_clip_space
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Function Documentation

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GLM_FUNC_DECL mat<4, 4, T, Q> glm::lookAt (vec< 3, T, Q > const & eye,
vec< 3, T, Q > const & center,
vec< 3, T, Q > const & up 
)
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\n\n

Build a look at view matrix based on the default handedness.

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Parameters
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eyePosition of the camera
centerPosition where the camera is looking at
upNormalized up vector, how the camera is oriented. Typically (0, 0, 1)
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QA value from qualifier enum
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- frustum(T const& left, T const& right, T const& bottom, T const& top, T const& nearVal, T const& farVal) frustum(T const& left, T const& right, T const& bottom, T const& top, T const& nearVal, T const& farVal)
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GLM_FUNC_DECL mat<4, 4, T, Q> glm::lookAtLH (vec< 3, T, Q > const & eye,
vec< 3, T, Q > const & center,
vec< 3, T, Q > const & up 
)
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Build a left handed look at view matrix.

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eyePosition of the camera
centerPosition where the camera is looking at
upNormalized up vector, how the camera is oriented. Typically (0, 0, 1)
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QA value from qualifier enum
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See also
- frustum(T const& left, T const& right, T const& bottom, T const& top, T const& nearVal, T const& farVal) frustum(T const& left, T const& right, T const& bottom, T const& top, T const& nearVal, T const& farVal)
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GLM_FUNC_DECL mat<4, 4, T, Q> glm::lookAtRH (vec< 3, T, Q > const & eye,
vec< 3, T, Q > const & center,
vec< 3, T, Q > const & up 
)
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Build a right handed look at view matrix.

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eyePosition of the camera
centerPosition where the camera is looking at
upNormalized up vector, how the camera is oriented. Typically (0, 0, 1)
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QA value from qualifier enum
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See also
- frustum(T const& left, T const& right, T const& bottom, T const& top, T const& nearVal, T const& farVal) frustum(T const& left, T const& right, T const& bottom, T const& top, T const& nearVal, T const& farVal)
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GLM_FUNC_DECL mat<4, 4, T, Q> glm::rotate (mat< 4, 4, T, Q > const & m,
T angle,
vec< 3, T, Q > const & axis 
)
\n
\n\n

Builds a rotation 4 * 4 matrix created from an axis vector and an angle.

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mInput matrix multiplied by this rotation matrix.
angleRotation angle expressed in radians.
axisRotation axis, recommended to be normalized.
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QA value from qualifier enum
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See also
- rotate(mat<4, 4, T, Q> const& m, T angle, T x, T y, T z)
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vec< 3, T, Q > const & v 
)
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Builds a scale 4 * 4 matrix created from 3 scalars.

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mInput matrix multiplied by this scale matrix.
vRatio of scaling for each axis.
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TA floating-point scalar type
QA value from qualifier enum
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- scale(mat<4, 4, T, Q> const& m, T x, T y, T z)
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GLM_FUNC_DECL mat<4, 4, T, Q> glm::translate (mat< 4, 4, T, Q > const & m,
vec< 3, T, Q > const & v 
)
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Builds a translation 4 * 4 matrix created from a vector of 3 components.

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mInput matrix multiplied by this translation matrix.
vCoordinates of a translation vector.
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See also
- translate(mat<4, 4, T, Q> const& m, T x, T y, T z)
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Provides common functions for quaternion types. \nMore...

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template<typename T , qualifier Q>
GLM_FUNC_DECL qua< T, Q > conjugate (qua< T, Q > const &q)
 Returns the q conjugate. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL qua< T, Q > inverse (qua< T, Q > const &q)
 Returns the q inverse. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 4, bool, Q > isinf (qua< T, Q > const &x)
 Returns true if x holds a positive infinity or negative infinity representation in the underlying implementation's set of floating point representations. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 4, bool, Q > isnan (qua< T, Q > const &x)
 Returns true if x holds a NaN (not a number) representation in the underlying implementation's set of floating point representations. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL qua< T, Q > lerp (qua< T, Q > const &x, qua< T, Q > const &y, T a)
 Linear interpolation of two quaternions. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL qua< T, Q > mix (qua< T, Q > const &x, qua< T, Q > const &y, T a)
 Spherical linear interpolation of two quaternions. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL qua< T, Q > slerp (qua< T, Q > const &x, qua< T, Q > const &y, T a)
 Spherical linear interpolation of two quaternions. More...
 
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Detailed Description

\n

Provides common functions for quaternion types.

\n

Include <glm/ext/quaternion_common.hpp> to use the features of this extension.

\n
See also
GLM_EXT_scalar_common
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\nGLM_EXT_quaternion_exponential
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Function Documentation

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GLM_FUNC_DECL qua<T, Q> glm::conjugate (qua< T, Q > const & q)
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Returns the q conjugate.

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GLM_FUNC_DECL qua<T, Q> glm::inverse (qua< T, Q > const & q)
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Returns the q inverse.

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QA value from qualifier enum
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GLM_FUNC_DECL vec<4, bool, Q> glm::isinf (qua< T, Q > const & x)
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Returns true if x holds a positive infinity or negative infinity representation in the underlying implementation's set of floating point representations.

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Returns false otherwise, including for implementations with no infinity representations.

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GLM_FUNC_DECL vec<4, bool, Q> glm::isnan (qua< T, Q > const & x)
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Returns true if x holds a NaN (not a number) representation in the underlying implementation's set of floating point representations.

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Returns false otherwise, including for implementations with no NaN representations.

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/!\\ When using compiler fast math, this function may fail.

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GLM_FUNC_DECL qua<T, Q> glm::lerp (qua< T, Q > const & x,
qua< T, Q > const & y,
T a 
)
\n
\n\n

Linear interpolation of two quaternions.

\n

The interpolation is oriented.

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xA quaternion
yA quaternion
aInterpolation factor. The interpolation is defined in the range [0, 1].
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TA floating-point scalar type
QA value from qualifier enum
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GLM_FUNC_DECL qua<T, Q> glm::mix (qua< T, Q > const & x,
qua< T, Q > const & y,
T a 
)
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\n\n

Spherical linear interpolation of two quaternions.

\n

The interpolation is oriented and the rotation is performed at constant speed. For short path spherical linear interpolation, use the slerp function.

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xA quaternion
yA quaternion
aInterpolation factor. The interpolation is defined beyond the range [0, 1].
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- slerp(qua<T, Q> const& x, qua<T, Q> const& y, T const& a)
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GLM_FUNC_DECL qua<T, Q> glm::slerp (qua< T, Q > const & x,
qua< T, Q > const & y,
T a 
)
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Spherical linear interpolation of two quaternions.

\n

The interpolation always take the short path and the rotation is performed at constant speed.

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xA quaternion
yA quaternion
aInterpolation factor. The interpolation is defined beyond the range [0, 1].
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Exposes double-precision floating point quaternion type. \nMore...

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\ntypedef qua< double, defaultp > dquat
 Quaternion of double-precision floating-point numbers.
 
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Exposes double-precision floating point quaternion type.

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Exposes double-precision floating point quaternion type with various precision in term of ULPs. \nMore...

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typedef qua< double, highp > highp_dquat
 Quaternion of high double-qualifier floating-point numbers using high precision arithmetic in term of ULPs. More...
 
typedef qua< double, lowp > lowp_dquat
 Quaternion of double-precision floating-point numbers using high precision arithmetic in term of ULPs. More...
 
typedef qua< double, mediump > mediump_dquat
 Quaternion of medium double-qualifier floating-point numbers using high precision arithmetic in term of ULPs. More...
 
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Exposes double-precision floating point quaternion type with various precision in term of ULPs.

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Typedef Documentation

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typedef qua< double, highp > highp_dquat
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Quaternion of high double-qualifier floating-point numbers using high precision arithmetic in term of ULPs.

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GLM_EXT_quaternion_double_precision
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Definition at line 38 of file quaternion_double_precision.hpp.

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typedef qua< double, lowp > lowp_dquat
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Quaternion of double-precision floating-point numbers using high precision arithmetic in term of ULPs.

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Definition at line 28 of file quaternion_double_precision.hpp.

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Quaternion of medium double-qualifier floating-point numbers using high precision arithmetic in term of ULPs.

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Definition at line 33 of file quaternion_double_precision.hpp.

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Provides exponential functions for quaternion types.

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\ntypedef qua< float, defaultp > quat
 Quaternion of single-precision floating-point numbers.
 
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Detailed Description

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Exposes single-precision floating point quaternion type.

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GLM_EXT_quaternion_double
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GLM_EXT_quaternion_float_precision
Stable extensions
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Exposes single-precision floating point quaternion type with various precision in term of ULPs. \nMore...

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\nTypedefs

\ntypedef qua< float, highp > highp_quat
 Quaternion of single-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef qua< float, lowp > lowp_quat
 Quaternion of single-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef qua< float, mediump > mediump_quat
 Quaternion of single-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\n

Detailed Description

\n

Exposes single-precision floating point quaternion type with various precision in term of ULPs.

\n

Include <glm/ext/quaternion_float_precision.hpp> to use the features of this extension.

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GLM_EXT_quaternion_geometric
Stable extensions
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Provides geometric functions for quaternion types. \nMore...

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\nFunctions

template<typename T , qualifier Q>
GLM_FUNC_QUALIFIER qua< T, Q > cross (qua< T, Q > const &q1, qua< T, Q > const &q2)
 Compute a cross product. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL T dot (qua< T, Q > const &x, qua< T, Q > const &y)
 Returns dot product of q1 and q2, i.e., q1[0] * q2[0] + q1[1] * q2[1] + ... More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL T length (qua< T, Q > const &q)
 Returns the norm of a quaternions. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL qua< T, Q > normalize (qua< T, Q > const &q)
 Returns the normalized quaternion. More...
 
\n

Detailed Description

\n

Provides geometric functions for quaternion types.

\n

Include <glm/ext/quaternion_geometric.hpp> to use the features of this extension.

\n
See also
core_geometric
\n
\nGLM_EXT_quaternion_float
\n
\nGLM_EXT_quaternion_double
\n

Function Documentation

\n\n
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GLM_FUNC_QUALIFIER qua<T, Q> glm::cross (qua< T, Q > const & q1,
qua< T, Q > const & q2 
)
\n
\n\n

Compute a cross product.

\n
Template Parameters
\n \n \n \n
TFloating-point scalar types
QValue from qualifier enum
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See also
GLM_EXT_quaternion_geometric
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GLM_FUNC_DECL T glm::dot (qua< T, Q > const & x,
qua< T, Q > const & y 
)
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Returns dot product of q1 and q2, i.e., q1[0] * q2[0] + q1[1] * q2[1] + ...

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Template Parameters
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TFloating-point scalar types.
QValue from qualifier enum
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See also
GLM_EXT_quaternion_geometric
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GLM_FUNC_DECL T glm::length (qua< T, Q > const & q)
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Returns the norm of a quaternions.

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TFloating-point scalar types
QValue from qualifier enum
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See also
GLM_EXT_quaternion_geometric
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GLM_FUNC_DECL qua<T, Q> glm::normalize (qua< T, Q > const & q)
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Returns the normalized quaternion.

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TFloating-point scalar types
QValue from qualifier enum
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GLM_EXT_quaternion_geometric
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GLM_EXT_quaternion_relational
Stable extensions
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Exposes comparison functions for quaternion types that take a user defined epsilon values. \nMore...

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\nFunctions

template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 4, bool, Q > equal (qua< T, Q > const &x, qua< T, Q > const &y)
 Returns the component-wise comparison of result x == y. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 4, bool, Q > equal (qua< T, Q > const &x, qua< T, Q > const &y, T epsilon)
 Returns the component-wise comparison of |x - y| < epsilon. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 4, bool, Q > notEqual (qua< T, Q > const &x, qua< T, Q > const &y)
 Returns the component-wise comparison of result x != y. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 4, bool, Q > notEqual (qua< T, Q > const &x, qua< T, Q > const &y, T epsilon)
 Returns the component-wise comparison of |x - y| >= epsilon. More...
 
\n

Detailed Description

\n

Exposes comparison functions for quaternion types that take a user defined epsilon values.

\n

Include <glm/ext/quaternion_relational.hpp> to use the features of this extension.

\n
See also
core_vector_relational
\n
\nGLM_EXT_vector_relational
\n
\nGLM_EXT_matrix_relational
\n
\nGLM_EXT_quaternion_float
\n
\nGLM_EXT_quaternion_double
\n

Function Documentation

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GLM_FUNC_DECL vec<4, bool, Q> glm::equal (qua< T, Q > const & x,
qua< T, Q > const & y 
)
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Returns the component-wise comparison of result x == y.

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QValue from qualifier enum
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GLM_FUNC_DECL vec<4, bool, Q> glm::equal (qua< T, Q > const & x,
qua< T, Q > const & y,
T epsilon 
)
\n
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Returns the component-wise comparison of |x - y| < epsilon.

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TFloating-point scalar types
QValue from qualifier enum
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GLM_FUNC_DECL vec<4, bool, Q> glm::notEqual (qua< T, Q > const & x,
qua< T, Q > const & y 
)
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Returns the component-wise comparison of result x != y.

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QValue from qualifier enum
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GLM_FUNC_DECL vec<4, bool, Q> glm::notEqual (qua< T, Q > const & x,
qua< T, Q > const & y,
T epsilon 
)
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Returns the component-wise comparison of |x - y| >= epsilon.

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QValue from qualifier enum
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GLM_EXT_quaternion_transform
Stable extensions
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Provides transformation functions for quaternion types. \nMore...

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\nFunctions

template<typename T , qualifier Q>
GLM_FUNC_DECL qua< T, Q > exp (qua< T, Q > const &q)
 Returns a exponential of a quaternion. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL qua< T, Q > log (qua< T, Q > const &q)
 Returns a logarithm of a quaternion. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL qua< T, Q > pow (qua< T, Q > const &q, T y)
 Returns a quaternion raised to a power. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL qua< T, Q > rotate (qua< T, Q > const &q, T const &angle, vec< 3, T, Q > const &axis)
 Rotates a quaternion from a vector of 3 components axis and an angle. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL qua< T, Q > sqrt (qua< T, Q > const &q)
 Returns the square root of a quaternion. More...
 
\n

Detailed Description

\n

Provides transformation functions for quaternion types.

\n

Include <glm/ext/quaternion_transform.hpp> to use the features of this extension.

\n
See also
GLM_EXT_quaternion_float
\n
\nGLM_EXT_quaternion_double
\n
\nGLM_EXT_quaternion_exponential
\n
\nGLM_EXT_quaternion_geometric
\n
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\n
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\n

Function Documentation

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GLM_FUNC_DECL qua<T, Q> glm::exp (qua< T, Q > const & q)
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Returns a exponential of a quaternion.

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QA value from qualifier enum
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GLM_FUNC_DECL qua<T, Q> glm::log (qua< T, Q > const & q)
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Returns a logarithm of a quaternion.

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TA floating-point scalar type
QA value from qualifier enum
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GLM_FUNC_DECL qua<T, Q> glm::pow (qua< T, Q > const & q,
T y 
)
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Returns a quaternion raised to a power.

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TA floating-point scalar type
QA value from qualifier enum
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GLM_FUNC_DECL qua<T, Q> glm::rotate (qua< T, Q > const & q,
T const & angle,
vec< 3, T, Q > const & axis 
)
\n
\n\n

Rotates a quaternion from a vector of 3 components axis and an angle.

\n
Parameters
\n \n \n \n \n
qSource orientation
angleAngle expressed in radians.
axisAxis of the rotation
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QValue from qualifier enum
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GLM_FUNC_DECL qua<T, Q> glm::sqrt (qua< T, Q > const & q)
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Returns the square root of a quaternion.

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TA floating-point scalar type
QA value from qualifier enum
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GLM_EXT_quaternion_trigonometric
Stable extensions
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Provides trigonometric functions for quaternion types. \nMore...

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template<typename T , qualifier Q>
GLM_FUNC_DECL T angle (qua< T, Q > const &x)
 Returns the quaternion rotation angle. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL qua< T, Q > angleAxis (T const &angle, vec< 3, T, Q > const &axis)
 Build a quaternion from an angle and a normalized axis. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > axis (qua< T, Q > const &x)
 Returns the q rotation axis. More...
 
\n

Detailed Description

\n

Provides trigonometric functions for quaternion types.

\n

Include <glm/ext/quaternion_trigonometric.hpp> to use the features of this extension.

\n
See also
GLM_EXT_quaternion_float
\n
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\n
\nGLM_EXT_quaternion_geometric
\n
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\n
\nGLM_EXT_quaternion_transform
\n

Function Documentation

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GLM_FUNC_DECL T glm::angle (qua< T, Q > const & x)
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Returns the quaternion rotation angle.

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TA floating-point scalar type
QA value from qualifier enum
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GLM_FUNC_DECL qua<T, Q> glm::angleAxis (T const & angle,
vec< 3, T, Q > const & axis 
)
\n
\n\n

Build a quaternion from an angle and a normalized axis.

\n
Parameters
\n \n \n \n
angleAngle expressed in radians.
axisAxis of the quaternion, must be normalized.
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TA floating-point scalar type
QA value from qualifier enum
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GLM_FUNC_DECL vec<3, T, Q> glm::axis (qua< T, Q > const & x)
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Returns the q rotation axis.

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TA floating-point scalar type
QA value from qualifier enum
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GLM_EXT_scalar_common
Stable extensions
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Exposes min and max functions for 3 to 4 scalar parameters. \nMore...

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template<typename T >
GLM_FUNC_DECL T fmax (T a, T b)
 Returns the maximum component-wise values of 2 inputs. More...
 
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GLM_FUNC_DECL T fmax (T a, T b, T C)
 Returns the maximum component-wise values of 3 inputs. More...
 
template<typename T >
GLM_FUNC_DECL T fmax (T a, T b, T C, T D)
 Returns the maximum component-wise values of 4 inputs. More...
 
template<typename T >
GLM_FUNC_DECL T fmin (T a, T b)
 Returns the minimum component-wise values of 2 inputs. More...
 
template<typename T >
GLM_FUNC_DECL T fmin (T a, T b, T c)
 Returns the minimum component-wise values of 3 inputs. More...
 
template<typename T >
GLM_FUNC_DECL T fmin (T a, T b, T c, T d)
 Returns the minimum component-wise values of 4 inputs. More...
 
template<typename T >
GLM_FUNC_DECL T max (T a, T b, T c)
 Returns the maximum component-wise values of 3 inputs. More...
 
template<typename T >
GLM_FUNC_DECL T max (T a, T b, T c, T d)
 Returns the maximum component-wise values of 4 inputs. More...
 
template<typename T >
GLM_FUNC_DECL T min (T a, T b, T c)
 Returns the minimum component-wise values of 3 inputs. More...
 
template<typename T >
GLM_FUNC_DECL T min (T a, T b, T c, T d)
 Returns the minimum component-wise values of 4 inputs. More...
 
\n

Detailed Description

\n

Exposes min and max functions for 3 to 4 scalar parameters.

\n

Include <glm/ext/scalar_common.hpp> to use the features of this extension.

\n
See also
Common functions
\n
\nGLM_EXT_vector_common
\n

Function Documentation

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GLM_FUNC_DECL T glm::fmax (T a,
T b 
)
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Returns the maximum component-wise values of 2 inputs.

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GLM_FUNC_DECL T glm::fmax (T a,
T b,
T C 
)
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Returns the maximum component-wise values of 3 inputs.

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GLM_FUNC_DECL T glm::fmax (T a,
T b,
T C,
T D 
)
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Returns the maximum component-wise values of 4 inputs.

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GLM_FUNC_DECL T glm::fmin (T a,
T b 
)
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Returns the minimum component-wise values of 2 inputs.

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GLM_FUNC_DECL T glm::fmin (T a,
T b,
T c 
)
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Returns the minimum component-wise values of 3 inputs.

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GLM_FUNC_DECL T glm::fmin (T a,
T b,
T c,
T d 
)
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Returns the minimum component-wise values of 4 inputs.

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std::fmin documentation
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GLM_FUNC_DECL T glm::max (T a,
T b,
T c 
)
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Returns the maximum component-wise values of 3 inputs.

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GLM_FUNC_DECL T glm::max (T a,
T b,
T c,
T d 
)
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Returns the maximum component-wise values of 4 inputs.

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TA floating-point scalar type.
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GLM_FUNC_DECL T glm::min (T a,
T b,
T c 
)
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Returns the minimum component-wise values of 3 inputs.

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GLM_FUNC_DECL T glm::min (T a,
T b,
T c,
T d 
)
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Returns the minimum component-wise values of 4 inputs.

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Provides a list of constants and precomputed useful values. \nMore...

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\ntemplate<typename genType >
GLM_FUNC_DECL GLM_CONSTEXPR genType epsilon ()
 Return the epsilon constant for floating point types.
 
\ntemplate<typename genType >
GLM_FUNC_DECL GLM_CONSTEXPR genType pi ()
 Return the pi constant for floating point types.
 
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Detailed Description

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Provides a list of constants and precomputed useful values.

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Include <glm/ext/scalar_constants.hpp> to use the features of this extension.

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Exposes sized signed integer scalar types. \nMore...

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\ntypedef detail::int16 int16
 16 bit signed integer type.
 
\ntypedef detail::int32 int32
 32 bit signed integer type.
 
\ntypedef detail::int64 int64
 64 bit signed integer type.
 
\ntypedef detail::int8 int8
 8 bit signed integer type.
 
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Detailed Description

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Exposes sized signed integer scalar types.

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Include <glm/ext/scalar_int_sized.hpp> to use the features of this extension.

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See also
GLM_EXT_scalar_uint_sized
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\nFunctions

template<typename genIUType >
GLM_FUNC_DECL int findNSB (genIUType x, int significantBitCount)
 Returns the bit number of the Nth significant bit set to 1 in the binary representation of value. More...
 
template<typename genIUType >
GLM_FUNC_DECL bool isMultiple (genIUType v, genIUType Multiple)
 Return true if the 'Value' is a multiple of 'Multiple'. More...
 
template<typename genIUType >
GLM_FUNC_DECL bool isPowerOfTwo (genIUType v)
 Return true if the value is a power of two number. More...
 
template<typename genIUType >
GLM_FUNC_DECL genIUType nextMultiple (genIUType v, genIUType Multiple)
 Higher multiple number of Source. More...
 
template<typename genIUType >
GLM_FUNC_DECL genIUType nextPowerOfTwo (genIUType v)
 Return the power of two number which value is just higher the input value, round up to a power of two. More...
 
template<typename genIUType >
GLM_FUNC_DECL genIUType prevMultiple (genIUType v, genIUType Multiple)
 Lower multiple number of Source. More...
 
template<typename genIUType >
GLM_FUNC_DECL genIUType prevPowerOfTwo (genIUType v)
 Return the power of two number which value is just lower the input value, round down to a power of two. More...
 
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Detailed Description

\n

Include <glm/ext/scalar_integer.hpp> to use the features of this extension.

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Function Documentation

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GLM_FUNC_DECL int glm::findNSB (genIUType x,
int significantBitCount 
)
\n
\n\n

Returns the bit number of the Nth significant bit set to 1 in the binary representation of value.

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If value bitcount is less than the Nth significant bit, -1 will be returned.

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Template Parameters
\n \n \n
genIUTypeSigned or unsigned integer scalar types.
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See also
to use the features of this extension. \">GLM_EXT_scalar_integer
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GLM_FUNC_DECL bool glm::isMultiple (genIUType v,
genIUType Multiple 
)
\n
\n\n

Return true if the 'Value' is a multiple of 'Multiple'.

\n
See also
to use the features of this extension. \">GLM_EXT_scalar_integer
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GLM_FUNC_DECL bool glm::isPowerOfTwo (genIUType v)
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Return true if the value is a power of two number.

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See also
to use the features of this extension. \">GLM_EXT_scalar_integer
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GLM_FUNC_DECL genIUType glm::nextMultiple (genIUType v,
genIUType Multiple 
)
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Higher multiple number of Source.

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Template Parameters
\n \n \n
genIUTypeInteger scalar or vector types.
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Parameters
\n \n \n \n
vSource value to which is applied the function
MultipleMust be a null or positive value
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to use the features of this extension. \">GLM_EXT_scalar_integer
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GLM_FUNC_DECL genIUType glm::nextPowerOfTwo (genIUType v)
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Return the power of two number which value is just higher the input value, round up to a power of two.

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to use the features of this extension. \">GLM_EXT_scalar_integer
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GLM_FUNC_DECL genIUType glm::prevMultiple (genIUType v,
genIUType Multiple 
)
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Lower multiple number of Source.

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genIUTypeInteger scalar or vector types.
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Parameters
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vSource value to which is applied the function
MultipleMust be a null or positive value
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to use the features of this extension. \">GLM_EXT_scalar_integer
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GLM_FUNC_DECL genIUType glm::prevPowerOfTwo (genIUType v)
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Return the power of two number which value is just lower the input value, round down to a power of two.

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to use the features of this extension. \">GLM_EXT_scalar_integer
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Exposes comparison functions for scalar types that take a user defined epsilon values. \nMore...

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Exposes comparison functions for scalar types that take a user defined epsilon values.

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Include <glm/ext/scalar_relational.hpp> to use the features of this extension.

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core_vector_relational
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Exposes sized unsigned integer scalar types. \nMore...

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\nTypedefs

\ntypedef detail::uint16 uint16
 16 bit unsigned integer type.
 
\ntypedef detail::uint32 uint32
 32 bit unsigned integer type.
 
\ntypedef detail::uint64 uint64
 64 bit unsigned integer type.
 
\ntypedef detail::uint8 uint8
 8 bit unsigned integer type.
 
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Detailed Description

\n

Exposes sized unsigned integer scalar types.

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Include <glm/ext/scalar_uint_sized.hpp> to use the features of this extension.

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GLM_EXT_scalar_int_sized
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Allow the measurement of the accuracy of a function against a reference implementation.

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This extension works on floating-point data and provide results in ULP.

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GLM_EXT_vector_ulp
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\ntypedef vec< 1, bool, defaultp > bvec1
 1 components vector of boolean.
 
\n

Detailed Description

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Exposes bvec1 vector type.

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Include <glm/ext/vector_bool1.hpp> to use the features of this extension.

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See also
GLM_EXT_vector_bool1_precision extension.
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Exposes highp_bvec1, mediump_bvec1 and lowp_bvec1 types. \nMore...

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\ntypedef vec< 1, bool, highp > highp_bvec1
 1 component vector of bool values.
 
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 1 component vector of bool values.
 
\ntypedef vec< 1, bool, mediump > mediump_bvec1
 1 component vector of bool values.
 
\n

Detailed Description

\n

Exposes highp_bvec1, mediump_bvec1 and lowp_bvec1 types.

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Include <glm/ext/vector_bool1_precision.hpp> to use the features of this extension.

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Exposes min and max functions for 3 to 4 vector parameters. \nMore...

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template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > fmax (vec< L, T, Q > const &a, T b)
 Returns y if x < y; otherwise, it returns x. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > fmax (vec< L, T, Q > const &a, vec< L, T, Q > const &b)
 Returns y if x < y; otherwise, it returns x. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > fmax (vec< L, T, Q > const &a, vec< L, T, Q > const &b, vec< L, T, Q > const &c)
 Returns y if x < y; otherwise, it returns x. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > fmax (vec< L, T, Q > const &a, vec< L, T, Q > const &b, vec< L, T, Q > const &c, vec< L, T, Q > const &d)
 Returns y if x < y; otherwise, it returns x. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > fmin (vec< L, T, Q > const &x, T y)
 Returns y if y < x; otherwise, it returns x. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > fmin (vec< L, T, Q > const &x, vec< L, T, Q > const &y)
 Returns y if y < x; otherwise, it returns x. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > fmin (vec< L, T, Q > const &a, vec< L, T, Q > const &b, vec< L, T, Q > const &c)
 Returns y if y < x; otherwise, it returns x. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > fmin (vec< L, T, Q > const &a, vec< L, T, Q > const &b, vec< L, T, Q > const &c, vec< L, T, Q > const &d)
 Returns y if y < x; otherwise, it returns x. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< L, T, Q > max (vec< L, T, Q > const &x, vec< L, T, Q > const &y, vec< L, T, Q > const &z)
 Return the maximum component-wise values of 3 inputs. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< L, T, Q > max (vec< L, T, Q > const &x, vec< L, T, Q > const &y, vec< L, T, Q > const &z, vec< L, T, Q > const &w)
 Return the maximum component-wise values of 4 inputs. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< L, T, Q > min (vec< L, T, Q > const &a, vec< L, T, Q > const &b, vec< L, T, Q > const &c)
 Return the minimum component-wise values of 3 inputs. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< L, T, Q > min (vec< L, T, Q > const &a, vec< L, T, Q > const &b, vec< L, T, Q > const &c, vec< L, T, Q > const &d)
 Return the minimum component-wise values of 4 inputs. More...
 
\n

Detailed Description

\n

Exposes min and max functions for 3 to 4 vector parameters.

\n

Include <glm/ext/vector_common.hpp> to use the features of this extension.

\n
See also
core_common
\n
\nGLM_EXT_scalar_common
\n

Function Documentation

\n\n
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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL vec<L, T, Q> glm::fmax (vec< L, T, Q > const & a,
T b 
)
\n
\n\n

Returns y if x < y; otherwise, it returns x.

\n

If one of the two arguments is NaN, the value of the other argument is returned.

\n
Template Parameters
\n \n \n \n \n
LInteger between 1 and 4 included that qualify the dimension of the vector
TFloating-point scalar types
QValue from qualifier enum
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See also
std::fmax documentation
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GLM_FUNC_DECL vec<L, T, Q> glm::fmax (vec< L, T, Q > const & a,
vec< L, T, Q > const & b 
)
\n
\n\n

Returns y if x < y; otherwise, it returns x.

\n

If one of the two arguments is NaN, the value of the other argument is returned.

\n
Template Parameters
\n \n \n \n \n
LInteger between 1 and 4 included that qualify the dimension of the vector
TFloating-point scalar types
QValue from qualifier enum
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See also
std::fmax documentation
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GLM_FUNC_DECL vec<L, T, Q> glm::fmax (vec< L, T, Q > const & a,
vec< L, T, Q > const & b,
vec< L, T, Q > const & c 
)
\n
\n\n

Returns y if x < y; otherwise, it returns x.

\n

If one of the two arguments is NaN, the value of the other argument is returned.

\n
Template Parameters
\n \n \n \n \n
LInteger between 1 and 4 included that qualify the dimension of the vector
TFloating-point scalar types
QValue from qualifier enum
\n
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See also
std::fmax documentation
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GLM_FUNC_DECL vec<L, T, Q> glm::fmax (vec< L, T, Q > const & a,
vec< L, T, Q > const & b,
vec< L, T, Q > const & c,
vec< L, T, Q > const & d 
)
\n
\n\n

Returns y if x < y; otherwise, it returns x.

\n

If one of the two arguments is NaN, the value of the other argument is returned.

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LInteger between 1 and 4 included that qualify the dimension of the vector
TFloating-point scalar types
QValue from qualifier enum
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See also
std::fmax documentation
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GLM_FUNC_DECL vec<L, T, Q> glm::fmin (vec< L, T, Q > const & x,
T y 
)
\n
\n\n

Returns y if y < x; otherwise, it returns x.

\n

If one of the two arguments is NaN, the value of the other argument is returned.

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Template Parameters
\n \n \n \n \n
LInteger between 1 and 4 included that qualify the dimension of the vector
TFloating-point scalar types
QValue from qualifier enum
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See also
std::fmin documentation
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GLM_FUNC_DECL vec<L, T, Q> glm::fmin (vec< L, T, Q > const & x,
vec< L, T, Q > const & y 
)
\n
\n\n

Returns y if y < x; otherwise, it returns x.

\n

If one of the two arguments is NaN, the value of the other argument is returned.

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Template Parameters
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LInteger between 1 and 4 included that qualify the dimension of the vector
TFloating-point scalar types
QValue from qualifier enum
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See also
std::fmin documentation
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GLM_FUNC_DECL vec<L, T, Q> glm::fmin (vec< L, T, Q > const & a,
vec< L, T, Q > const & b,
vec< L, T, Q > const & c 
)
\n
\n\n

Returns y if y < x; otherwise, it returns x.

\n

If one of the two arguments is NaN, the value of the other argument is returned.

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Template Parameters
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LInteger between 1 and 4 included that qualify the dimension of the vector
TFloating-point scalar types
QValue from qualifier enum
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See also
std::fmin documentation
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GLM_FUNC_DECL vec<L, T, Q> glm::fmin (vec< L, T, Q > const & a,
vec< L, T, Q > const & b,
vec< L, T, Q > const & c,
vec< L, T, Q > const & d 
)
\n
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Returns y if y < x; otherwise, it returns x.

\n

If one of the two arguments is NaN, the value of the other argument is returned.

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Template Parameters
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LInteger between 1 and 4 included that qualify the dimension of the vector
TFloating-point scalar types
QValue from qualifier enum
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See also
std::fmin documentation
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GLM_FUNC_DECL GLM_CONSTEXPR vec<L, T, Q> glm::max (vec< L, T, Q > const & x,
vec< L, T, Q > const & y,
vec< L, T, Q > const & z 
)
\n
\n\n

Return the maximum component-wise values of 3 inputs.

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Template Parameters
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LInteger between 1 and 4 included that qualify the dimension of the vector
TFloating-point or integer scalar types
QValue from qualifier enum
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GLM_FUNC_DECL GLM_CONSTEXPR vec<L, T, Q> glm::max (vec< L, T, Q > const & x,
vec< L, T, Q > const & y,
vec< L, T, Q > const & z,
vec< L, T, Q > const & w 
)
\n
\n\n

Return the maximum component-wise values of 4 inputs.

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Template Parameters
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LInteger between 1 and 4 included that qualify the dimension of the vector
TFloating-point or integer scalar types
QValue from qualifier enum
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GLM_FUNC_DECL GLM_CONSTEXPR vec<L, T, Q> glm::min (vec< L, T, Q > const & a,
vec< L, T, Q > const & b,
vec< L, T, Q > const & c 
)
\n
\n\n

Return the minimum component-wise values of 3 inputs.

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Template Parameters
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LInteger between 1 and 4 included that qualify the dimension of the vector
TFloating-point or integer scalar types
QValue from qualifier enum
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GLM_FUNC_DECL GLM_CONSTEXPR vec<L, T, Q> glm::min (vec< L, T, Q > const & a,
vec< L, T, Q > const & b,
vec< L, T, Q > const & c,
vec< L, T, Q > const & d 
)
\n
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Return the minimum component-wise values of 4 inputs.

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Template Parameters
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LInteger between 1 and 4 included that qualify the dimension of the vector
TFloating-point or integer scalar types
QValue from qualifier enum
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GLM_EXT_vector_double1
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Exposes double-precision floating point vector type with one component. \nMore...

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\nTypedefs

\ntypedef vec< 1, double, defaultp > dvec1
 1 components vector of double-precision floating-point numbers.
 
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Detailed Description

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Exposes double-precision floating point vector type with one component.

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Include <glm/ext/vector_double1.hpp> to use the features of this extension.

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See also
GLM_EXT_vector_double1_precision extension.
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Exposes highp_dvec1, mediump_dvec1 and lowp_dvec1 types. \nMore...

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\ntypedef vec< 1, double, highp > highp_dvec1
 1 component vector of double-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef vec< 1, double, lowp > lowp_dvec1
 1 component vector of double-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef vec< 1, double, mediump > mediump_dvec1
 1 component vector of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
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Detailed Description

\n

Exposes highp_dvec1, mediump_dvec1 and lowp_dvec1 types.

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Include <glm/ext/vector_double1_precision.hpp> to use the features of this extension.

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See also
GLM_EXT_vector_double1
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Exposes single-precision floating point vector type with one component. \nMore...

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\ntypedef vec< 1, float, defaultp > vec1
 1 components vector of single-precision floating-point numbers.
 
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Detailed Description

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Exposes single-precision floating point vector type with one component.

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Include <glm/ext/vector_float1.hpp> to use the features of this extension.

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See also
GLM_EXT_vector_float1_precision extension.
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Stable extensions
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Exposes highp_vec1, mediump_vec1 and lowp_vec1 types. \nMore...

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\ntypedef vec< 1, float, highp > highp_vec1
 1 component vector of single-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef vec< 1, float, lowp > lowp_vec1
 1 component vector of single-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef vec< 1, float, mediump > mediump_vec1
 1 component vector of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
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Detailed Description

\n

Exposes highp_vec1, mediump_vec1 and lowp_vec1 types.

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Include <glm/ext/vector_float1_precision.hpp> to use the features of this extension.

\n
See also
GLM_EXT_vector_float1 extension.
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GLM_EXT_vector_int1
Stable extensions
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Exposes ivec1 vector type. \nMore...

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\ntypedef vec< 1, int, defaultp > ivec1
 1 component vector of signed integer numbers.
 
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Detailed Description

\n

Exposes ivec1 vector type.

\n

Include <glm/ext/vector_int1.hpp> to use the features of this extension.

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See also
GLM_EXT_vector_uint1 extension.
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Exposes highp_ivec1, mediump_ivec1 and lowp_ivec1 types. \nMore...

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\ntypedef vec< 1, int, highp > highp_ivec1
 1 component vector of signed integer values.
 
\ntypedef vec< 1, int, lowp > lowp_ivec1
 1 component vector of signed integer values.
 
\ntypedef vec< 1, int, mediump > mediump_ivec1
 1 component vector of signed integer values.
 
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Detailed Description

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Exposes highp_ivec1, mediump_ivec1 and lowp_ivec1 types.

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Include <glm/ext/vector_int1_precision.hpp> to use the features of this extension.

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GLM_EXT_vector_integer
Stable extensions
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Include <glm/ext/vector_integer.hpp> to use the features of this extension. \nMore...

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\nFunctions

template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, int, Q > findNSB (vec< L, T, Q > const &Source, vec< L, int, Q > SignificantBitCount)
 Returns the bit number of the Nth significant bit set to 1 in the binary representation of value. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, bool, Q > isMultiple (vec< L, T, Q > const &v, T Multiple)
 Return true if the 'Value' is a multiple of 'Multiple'. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, bool, Q > isMultiple (vec< L, T, Q > const &v, vec< L, T, Q > const &Multiple)
 Return true if the 'Value' is a multiple of 'Multiple'. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, bool, Q > isPowerOfTwo (vec< L, T, Q > const &v)
 Return true if the value is a power of two number. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > nextMultiple (vec< L, T, Q > const &v, T Multiple)
 Higher multiple number of Source. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > nextMultiple (vec< L, T, Q > const &v, vec< L, T, Q > const &Multiple)
 Higher multiple number of Source. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > nextPowerOfTwo (vec< L, T, Q > const &v)
 Return the power of two number which value is just higher the input value, round up to a power of two. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > prevMultiple (vec< L, T, Q > const &v, T Multiple)
 Lower multiple number of Source. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > prevMultiple (vec< L, T, Q > const &v, vec< L, T, Q > const &Multiple)
 Lower multiple number of Source. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > prevPowerOfTwo (vec< L, T, Q > const &v)
 Return the power of two number which value is just lower the input value, round down to a power of two. More...
 
\n

Detailed Description

\n

Include <glm/ext/vector_integer.hpp> to use the features of this extension.

\n

Function Documentation

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GLM_FUNC_DECL vec<L, int, Q> glm::findNSB (vec< L, T, Q > const & Source,
vec< L, int, Q > SignificantBitCount 
)
\n
\n\n

Returns the bit number of the Nth significant bit set to 1 in the binary representation of value.

\n

If value bitcount is less than the Nth significant bit, -1 will be returned.

\n
Template Parameters
\n \n \n \n
LAn integer between 1 and 4 included that qualify the dimension of the vector.
TSigned or unsigned integer scalar types.
\n
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See also
to use the features of this extension. \">GLM_EXT_vector_integer
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GLM_FUNC_DECL vec<L, bool, Q> glm::isMultiple (vec< L, T, Q > const & v,
T Multiple 
)
\n
\n\n

Return true if the 'Value' is a multiple of 'Multiple'.

\n
Template Parameters
\n \n \n \n \n
LInteger between 1 and 4 included that qualify the dimension of the vector
TSigned or unsigned integer scalar types.
QValue from qualifier enum
\n
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See also
to use the features of this extension. \">GLM_EXT_vector_integer
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GLM_FUNC_DECL vec<L, bool, Q> glm::isMultiple (vec< L, T, Q > const & v,
vec< L, T, Q > const & Multiple 
)
\n
\n\n

Return true if the 'Value' is a multiple of 'Multiple'.

\n
Template Parameters
\n \n \n \n \n
LInteger between 1 and 4 included that qualify the dimension of the vector
TSigned or unsigned integer scalar types.
QValue from qualifier enum
\n
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See also
to use the features of this extension. \">GLM_EXT_vector_integer
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GLM_FUNC_DECL vec<L, bool, Q> glm::isPowerOfTwo (vec< L, T, Q > const & v)
\n
\n\n

Return true if the value is a power of two number.

\n
Template Parameters
\n \n \n \n \n
LInteger between 1 and 4 included that qualify the dimension of the vector
TSigned or unsigned integer scalar types.
QValue from qualifier enum
\n
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See also
to use the features of this extension. \">GLM_EXT_vector_integer
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GLM_FUNC_DECL vec<L, T, Q> glm::nextMultiple (vec< L, T, Q > const & v,
T Multiple 
)
\n
\n\n

Higher multiple number of Source.

\n
Template Parameters
\n \n \n \n \n
LInteger between 1 and 4 included that qualify the dimension of the vector
TSigned or unsigned integer scalar types.
QValue from qualifier enum
\n
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Parameters
\n \n \n \n
vSource values to which is applied the function
MultipleMust be a null or positive value
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to use the features of this extension. \">GLM_EXT_vector_integer
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GLM_FUNC_DECL vec<L, T, Q> glm::nextMultiple (vec< L, T, Q > const & v,
vec< L, T, Q > const & Multiple 
)
\n
\n\n

Higher multiple number of Source.

\n
Template Parameters
\n \n \n \n \n
LInteger between 1 and 4 included that qualify the dimension of the vector
TSigned or unsigned integer scalar types.
QValue from qualifier enum
\n
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Parameters
\n \n \n \n
vSource values to which is applied the function
MultipleMust be a null or positive value
\n
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See also
to use the features of this extension. \">GLM_EXT_vector_integer
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GLM_FUNC_DECL vec<L, T, Q> glm::nextPowerOfTwo (vec< L, T, Q > const & v)
\n
\n\n

Return the power of two number which value is just higher the input value, round up to a power of two.

\n
Template Parameters
\n \n \n \n \n
LInteger between 1 and 4 included that qualify the dimension of the vector
TSigned or unsigned integer scalar types.
QValue from qualifier enum
\n
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See also
to use the features of this extension. \">GLM_EXT_vector_integer
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GLM_FUNC_DECL vec<L, T, Q> glm::prevMultiple (vec< L, T, Q > const & v,
T Multiple 
)
\n
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Lower multiple number of Source.

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LInteger between 1 and 4 included that qualify the dimension of the vector
TSigned or unsigned integer scalar types.
QValue from qualifier enum
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Parameters
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vSource values to which is applied the function
MultipleMust be a null or positive value
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to use the features of this extension. \">GLM_EXT_vector_integer
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GLM_FUNC_DECL vec<L, T, Q> glm::prevMultiple (vec< L, T, Q > const & v,
vec< L, T, Q > const & Multiple 
)
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Lower multiple number of Source.

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LInteger between 1 and 4 included that qualify the dimension of the vector
TSigned or unsigned integer scalar types.
QValue from qualifier enum
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vSource values to which is applied the function
MultipleMust be a null or positive value
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to use the features of this extension. \">GLM_EXT_vector_integer
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GLM_FUNC_DECL vec<L, T, Q> glm::prevPowerOfTwo (vec< L, T, Q > const & v)
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Return the power of two number which value is just lower the input value, round down to a power of two.

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LInteger between 1 and 4 included that qualify the dimension of the vector
TSigned or unsigned integer scalar types.
QValue from qualifier enum
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to use the features of this extension. \">GLM_EXT_vector_integer
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Exposes comparison functions for vector types that take a user defined epsilon values. \nMore...

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\nFunctions

template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< L, bool, Q > equal (vec< L, T, Q > const &x, vec< L, T, Q > const &y, T epsilon)
 Returns the component-wise comparison of |x - y| < epsilon. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< L, bool, Q > equal (vec< L, T, Q > const &x, vec< L, T, Q > const &y, vec< L, T, Q > const &epsilon)
 Returns the component-wise comparison of |x - y| < epsilon. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< L, bool, Q > equal (vec< L, T, Q > const &x, vec< L, T, Q > const &y, int ULPs)
 Returns the component-wise comparison between two vectors in term of ULPs. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< L, bool, Q > equal (vec< L, T, Q > const &x, vec< L, T, Q > const &y, vec< L, int, Q > const &ULPs)
 Returns the component-wise comparison between two vectors in term of ULPs. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< L, bool, Q > notEqual (vec< L, T, Q > const &x, vec< L, T, Q > const &y, T epsilon)
 Returns the component-wise comparison of |x - y| >= epsilon. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< L, bool, Q > notEqual (vec< L, T, Q > const &x, vec< L, T, Q > const &y, vec< L, T, Q > const &epsilon)
 Returns the component-wise comparison of |x - y| >= epsilon. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< L, bool, Q > notEqual (vec< L, T, Q > const &x, vec< L, T, Q > const &y, int ULPs)
 Returns the component-wise comparison between two vectors in term of ULPs. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< L, bool, Q > notEqual (vec< L, T, Q > const &x, vec< L, T, Q > const &y, vec< L, int, Q > const &ULPs)
 Returns the component-wise comparison between two vectors in term of ULPs. More...
 
\n

Detailed Description

\n

Exposes comparison functions for vector types that take a user defined epsilon values.

\n

Include <glm/ext/vector_relational.hpp> to use the features of this extension.

\n
See also
core_vector_relational
\n
\nGLM_EXT_scalar_relational
\n
\nGLM_EXT_matrix_relational
\n

Function Documentation

\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL GLM_CONSTEXPR vec<L, bool, Q> glm::equal (vec< L, T, Q > const & x,
vec< L, T, Q > const & y,
T epsilon 
)
\n
\n\n

Returns the component-wise comparison of |x - y| < epsilon.

\n

True if this expression is satisfied.

\n
Template Parameters
\n \n \n \n \n
LInteger between 1 and 4 included that qualify the dimension of the vector
TFloating-point or integer scalar types
QValue from qualifier enum
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GLM_FUNC_DECL GLM_CONSTEXPR vec<L, bool, Q> glm::equal (vec< L, T, Q > const & x,
vec< L, T, Q > const & y,
vec< L, T, Q > const & epsilon 
)
\n
\n\n

Returns the component-wise comparison of |x - y| < epsilon.

\n

True if this expression is satisfied.

\n
Template Parameters
\n \n \n \n \n
LInteger between 1 and 4 included that qualify the dimension of the vector
TFloating-point or integer scalar types
QValue from qualifier enum
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GLM_FUNC_DECL GLM_CONSTEXPR vec<L, bool, Q> glm::equal (vec< L, T, Q > const & x,
vec< L, T, Q > const & y,
int ULPs 
)
\n
\n\n

Returns the component-wise comparison between two vectors in term of ULPs.

\n

True if this expression is satisfied.

\n
Template Parameters
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LInteger between 1 and 4 included that qualify the dimension of the vector
TFloating-point
QValue from qualifier enum
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GLM_FUNC_DECL GLM_CONSTEXPR vec<L, bool, Q> glm::equal (vec< L, T, Q > const & x,
vec< L, T, Q > const & y,
vec< L, int, Q > const & ULPs 
)
\n
\n\n

Returns the component-wise comparison between two vectors in term of ULPs.

\n

True if this expression is satisfied.

\n
Template Parameters
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LInteger between 1 and 4 included that qualify the dimension of the vector
TFloating-point
QValue from qualifier enum
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GLM_FUNC_DECL GLM_CONSTEXPR vec<L, bool, Q> glm::notEqual (vec< L, T, Q > const & x,
vec< L, T, Q > const & y,
T epsilon 
)
\n
\n\n

Returns the component-wise comparison of |x - y| >= epsilon.

\n

True if this expression is not satisfied.

\n
Template Parameters
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LInteger between 1 and 4 included that qualify the dimension of the vector
TFloating-point or integer scalar types
QValue from qualifier enum
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GLM_FUNC_DECL GLM_CONSTEXPR vec<L, bool, Q> glm::notEqual (vec< L, T, Q > const & x,
vec< L, T, Q > const & y,
vec< L, T, Q > const & epsilon 
)
\n
\n\n

Returns the component-wise comparison of |x - y| >= epsilon.

\n

True if this expression is not satisfied.

\n
Template Parameters
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LInteger between 1 and 4 included that qualify the dimension of the vector
TFloating-point or integer scalar types
QValue from qualifier enum
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GLM_FUNC_DECL GLM_CONSTEXPR vec<L, bool, Q> glm::notEqual (vec< L, T, Q > const & x,
vec< L, T, Q > const & y,
int ULPs 
)
\n
\n\n

Returns the component-wise comparison between two vectors in term of ULPs.

\n

True if this expression is not satisfied.

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LInteger between 1 and 4 included that qualify the dimension of the vector
TFloating-point
QValue from qualifier enum
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GLM_FUNC_DECL GLM_CONSTEXPR vec<L, bool, Q> glm::notEqual (vec< L, T, Q > const & x,
vec< L, T, Q > const & y,
vec< L, int, Q > const & ULPs 
)
\n
\n\n

Returns the component-wise comparison between two vectors in term of ULPs.

\n

True if this expression is not satisfied.

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LInteger between 1 and 4 included that qualify the dimension of the vector
TFloating-point
QValue from qualifier enum
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GLM_EXT_vector_uint1
Stable extensions
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Exposes uvec1 vector type. \nMore...

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\nTypedefs

\ntypedef vec< 1, unsigned int, defaultp > uvec1
 1 component vector of unsigned integer numbers.
 
\n

Detailed Description

\n

Exposes uvec1 vector type.

\n

Include <glm/ext/vector_uvec1.hpp> to use the features of this extension.

\n
See also
GLM_EXT_vector_int1 extension.
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\nGLM_EXT_vector_uint1_precision extension.
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Exposes highp_uvec1, mediump_uvec1 and lowp_uvec1 types. \nMore...

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\nTypedefs

typedef vec< 1, unsigned int, highp > highp_uvec1
 1 component vector of unsigned integer values. More...
 
typedef vec< 1, unsigned int, lowp > lowp_uvec1
 1 component vector of unsigned integer values. More...
 
typedef vec< 1, unsigned int, mediump > mediump_uvec1
 1 component vector of unsigned integer values. More...
 
\n

Detailed Description

\n

Exposes highp_uvec1, mediump_uvec1 and lowp_uvec1 types.

\n

Include <glm/ext/vector_uint1_precision.hpp> to use the features of this extension.

\n

Typedef Documentation

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typedef vec< 1, u32, highp > highp_uvec1
\n
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1 component vector of unsigned integer values.

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See also
GLM_EXT_vector_uint1_precision
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Definition at line 27 of file vector_uint1_precision.hpp.

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typedef vec< 1, u32, lowp > lowp_uvec1
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1 component vector of unsigned integer values.

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See also
GLM_EXT_vector_uint1_precision
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Definition at line 37 of file vector_uint1_precision.hpp.

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typedef vec< 1, u32, mediump > mediump_uvec1
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1 component vector of unsigned integer values.

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GLM_EXT_vector_uint1_precision
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Definition at line 32 of file vector_uint1_precision.hpp.

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GLM_EXT_vector_ulp
Stable extensions
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Allow the measurement of the accuracy of a function against a reference implementation. \nMore...

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Allow the measurement of the accuracy of a function against a reference implementation.

\n

This extension works on floating-point data and provide results in ULP.

\n

Include <glm/ext/vector_ulp.hpp> to use the features of this extension.

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See also
GLM_EXT_scalar_ulp
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Core features
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\nFunctions

template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > cross (vec< 3, T, Q > const &x, vec< 3, T, Q > const &y)
 Returns the cross product of x and y. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL T distance (vec< L, T, Q > const &p0, vec< L, T, Q > const &p1)
 Returns the distance betwwen p0 and p1, i.e., length(p0 - p1). More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL T dot (vec< L, T, Q > const &x, vec< L, T, Q > const &y)
 Returns the dot product of x and y, i.e., result = x * y. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > faceforward (vec< L, T, Q > const &N, vec< L, T, Q > const &I, vec< L, T, Q > const &Nref)
 If dot(Nref, I) < 0.0, return N, otherwise, return -N. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL T length (vec< L, T, Q > const &x)
 Returns the length of x, i.e., sqrt(x * x). More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > normalize (vec< L, T, Q > const &x)
 Returns a vector in the same direction as x but with length of 1. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > reflect (vec< L, T, Q > const &I, vec< L, T, Q > const &N)
 For the incident vector I and surface orientation N, returns the reflection direction : result = I - 2.0 * dot(N, I) * N. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > refract (vec< L, T, Q > const &I, vec< L, T, Q > const &N, T eta)
 For the incident vector I and surface normal N, and the ratio of indices of refraction eta, return the refraction vector. More...
 
\n

Detailed Description

\n

These operate on vectors as vectors, not component-wise.

\n

Include <glm/geometric.hpp> to use these core features.

\n

Function Documentation

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GLM_FUNC_DECL vec<3, T, Q> glm::cross (vec< 3, T, Q > const & x,
vec< 3, T, Q > const & y 
)
\n
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Returns the cross product of x and y.

\n
Template Parameters
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TFloating-point scalar types.
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See also
GLSL cross man page
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\nGLSL 4.20.8 specification, section 8.5 Geometric Functions
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GLM_FUNC_DECL T glm::distance (vec< L, T, Q > const & p0,
vec< L, T, Q > const & p1 
)
\n
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Returns the distance betwwen p0 and p1, i.e., length(p0 - p1).

\n
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\n \n \n \n
LAn integer between 1 and 4 included that qualify the dimension of the vector.
TFloating-point scalar types.
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See also
GLSL distance man page
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\nGLSL 4.20.8 specification, section 8.5 Geometric Functions
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GLM_FUNC_DECL T glm::dot (vec< L, T, Q > const & x,
vec< L, T, Q > const & y 
)
\n
\n\n

Returns the dot product of x and y, i.e., result = x * y.

\n
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\n \n \n \n
LAn integer between 1 and 4 included that qualify the dimension of the vector.
TFloating-point scalar types.
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See also
GLSL dot man page
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\nGLSL 4.20.8 specification, section 8.5 Geometric Functions
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GLM_FUNC_DECL vec<L, T, Q> glm::faceforward (vec< L, T, Q > const & N,
vec< L, T, Q > const & I,
vec< L, T, Q > const & Nref 
)
\n
\n\n

If dot(Nref, I) < 0.0, return N, otherwise, return -N.

\n
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LAn integer between 1 and 4 included that qualify the dimension of the vector.
TFloating-point scalar types.
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See also
GLSL faceforward man page
\n
\nGLSL 4.20.8 specification, section 8.5 Geometric Functions
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GLM_FUNC_DECL T glm::length (vec< L, T, Q > const & x)
\n
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Returns the length of x, i.e., sqrt(x * x).

\n
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LAn integer between 1 and 4 included that qualify the dimension of the vector.
TFloating-point scalar types.
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See also
GLSL length man page
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\nGLSL 4.20.8 specification, section 8.5 Geometric Functions
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GLM_FUNC_DECL vec<L, T, Q> glm::normalize (vec< L, T, Q > const & x)
\n
\n\n

Returns a vector in the same direction as x but with length of 1.

\n

According to issue 10 GLSL 1.10 specification, if length(x) == 0 then result is undefined and generate an error.

\n
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LAn integer between 1 and 4 included that qualify the dimension of the vector.
TFloating-point scalar types.
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See also
GLSL normalize man page
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\nGLSL 4.20.8 specification, section 8.5 Geometric Functions
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GLM_FUNC_DECL vec<L, T, Q> glm::reflect (vec< L, T, Q > const & I,
vec< L, T, Q > const & N 
)
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For the incident vector I and surface orientation N, returns the reflection direction : result = I - 2.0 * dot(N, I) * N.

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LAn integer between 1 and 4 included that qualify the dimension of the vector.
TFloating-point scalar types.
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GLSL reflect man page
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vec< L, T, Q > const & N,
T eta 
)
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For the incident vector I and surface normal N, and the ratio of indices of refraction eta, return the refraction vector.

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LAn integer between 1 and 4 included that qualify the dimension of the vector.
TFloating-point scalar types.
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GLSL refract man page
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Features that implement in C++ the GLSL specification as closely as possible. \nMore...

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\nModules

 Common functions
 Provides GLSL common functions.
 
 Exponential functions
 Provides GLSL exponential functions.
 
 Geometric functions
 These operate on vectors as vectors, not component-wise.
 
 Vector types
 Vector types of two to four components with an exhaustive set of operators.
 
 Vector types with precision qualifiers
 Vector types with precision qualifiers which may result in various precision in term of ULPs.
 
 Matrix types
 Matrix types of with C columns and R rows where C and R are values between 2 to 4 included.
 
 Matrix types with precision qualifiers
 Matrix types with precision qualifiers which may result in various precision in term of ULPs.
 
 Integer functions
 Provides GLSL functions on integer types.
 
 Matrix functions
 Provides GLSL matrix functions.
 
 Floating-Point Pack and Unpack Functions
 Provides GLSL functions to pack and unpack half, single and double-precision floating point values into more compact integer types.
 
 Angle and Trigonometry Functions
 Function parameters specified as angle are assumed to be in units of radians.
 
 Vector Relational Functions
 Relational and equality operators (<, <=, >, >=, ==, !=) are defined to operate on scalars and produce scalar Boolean results.
 
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\nTypedefs

typedef mat< 3, 2, float, defaultp > mat3x2
 3 columns of 2 components matrix of single-precision floating-point numbers. More...
 
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Detailed Description

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Features that implement in C++ the GLSL specification as closely as possible.

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The GLM core consists of C++ types that mirror GLSL types and C++ functions that mirror the GLSL functions.

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The best documentation for GLM Core is the current GLSL specification, version 4.2 (pdf file).

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GLM core functionalities require <glm/glm.hpp> to be included to be used.

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Typedef Documentation

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typedef mat< 3, 2, f32, defaultp > mat3x2
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3 columns of 2 components matrix of single-precision floating-point numbers.

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GLSL 4.20.8 specification, section 4.1.6 Matrices
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Definition at line 15 of file matrix_float3x2.hpp.

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\nTypedefs

typedef vec< 2, bool, defaultp > bvec2
 2 components vector of boolean. More...
 
typedef vec< 3, bool, defaultp > bvec3
 3 components vector of boolean. More...
 
typedef vec< 4, bool, defaultp > bvec4
 4 components vector of boolean. More...
 
typedef vec< 2, double, defaultp > dvec2
 2 components vector of double-precision floating-point numbers. More...
 
typedef vec< 3, double, defaultp > dvec3
 3 components vector of double-precision floating-point numbers. More...
 
typedef vec< 4, double, defaultp > dvec4
 4 components vector of double-precision floating-point numbers. More...
 
typedef vec< 2, int, defaultp > ivec2
 2 components vector of signed integer numbers. More...
 
typedef vec< 3, int, defaultp > ivec3
 3 components vector of signed integer numbers. More...
 
typedef vec< 4, int, defaultp > ivec4
 4 components vector of signed integer numbers. More...
 
typedef vec< 2, unsigned int, defaultp > uvec2
 2 components vector of unsigned integer numbers. More...
 
typedef vec< 3, unsigned int, defaultp > uvec3
 3 components vector of unsigned integer numbers. More...
 
typedef vec< 4, unsigned int, defaultp > uvec4
 4 components vector of unsigned integer numbers. More...
 
typedef vec< 2, float, defaultp > vec2
 2 components vector of single-precision floating-point numbers. More...
 
typedef vec< 3, float, defaultp > vec3
 3 components vector of single-precision floating-point numbers. More...
 
typedef vec< 4, float, defaultp > vec4
 4 components vector of single-precision floating-point numbers. More...
 
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Detailed Description

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Vector types of two to four components with an exhaustive set of operators.

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Typedef Documentation

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typedef vec< 2, bool, defaultp > bvec2
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2 components vector of boolean.

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GLSL 4.20.8 specification, section 4.1.5 Vectors
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Definition at line 15 of file vector_bool2.hpp.

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3 components vector of boolean.

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GLSL 4.20.8 specification, section 4.1.5 Vectors
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Definition at line 15 of file vector_bool3.hpp.

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typedef vec< 4, bool, defaultp > bvec4
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4 components vector of boolean.

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GLSL 4.20.8 specification, section 4.1.5 Vectors
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Definition at line 15 of file vector_bool4.hpp.

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typedef vec< 2, f64, defaultp > dvec2
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2 components vector of double-precision floating-point numbers.

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See also
GLSL 4.20.8 specification, section 4.1.5 Vectors
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Definition at line 15 of file vector_double2.hpp.

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typedef vec< 3, f64, defaultp > dvec3
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3 components vector of double-precision floating-point numbers.

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GLSL 4.20.8 specification, section 4.1.5 Vectors
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Definition at line 15 of file vector_double3.hpp.

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4 components vector of double-precision floating-point numbers.

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GLSL 4.20.8 specification, section 4.1.5 Vectors
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Definition at line 15 of file vector_double4.hpp.

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2 components vector of signed integer numbers.

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GLSL 4.20.8 specification, section 4.1.5 Vectors
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Definition at line 15 of file vector_int2.hpp.

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3 components vector of signed integer numbers.

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GLSL 4.20.8 specification, section 4.1.5 Vectors
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Definition at line 15 of file vector_int3.hpp.

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4 components vector of signed integer numbers.

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GLSL 4.20.8 specification, section 4.1.5 Vectors
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Definition at line 15 of file vector_int4.hpp.

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2 components vector of unsigned integer numbers.

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GLSL 4.20.8 specification, section 4.1.5 Vectors
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Definition at line 15 of file vector_uint2.hpp.

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3 components vector of unsigned integer numbers.

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GLSL 4.20.8 specification, section 4.1.5 Vectors
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Definition at line 15 of file vector_uint3.hpp.

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4 components vector of unsigned integer numbers.

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GLSL 4.20.8 specification, section 4.1.5 Vectors
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Definition at line 15 of file vector_uint4.hpp.

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2 components vector of single-precision floating-point numbers.

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GLSL 4.20.8 specification, section 4.1.5 Vectors
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Definition at line 15 of file vector_float2.hpp.

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typedef vec< 3, float, defaultp > vec3
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3 components vector of single-precision floating-point numbers.

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GLSL 4.20.8 specification, section 4.1.5 Vectors
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Definition at line 15 of file vector_float3.hpp.

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4 components vector of single-precision floating-point numbers.

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GLSL 4.20.8 specification, section 4.1.5 Vectors
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Definition at line 15 of file vector_float4.hpp.

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Vector types with precision qualifiers
Core features
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Vector types with precision qualifiers which may result in various precision in term of ULPs. \nMore...

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\nTypedefs

typedef vec< 2, bool, highp > highp_bvec2
 2 components vector of high qualifier bool numbers. More...
 
typedef vec< 3, bool, highp > highp_bvec3
 3 components vector of high qualifier bool numbers. More...
 
typedef vec< 4, bool, highp > highp_bvec4
 4 components vector of high qualifier bool numbers. More...
 
typedef vec< 2, double, highp > highp_dvec2
 2 components vector of high double-qualifier floating-point numbers. More...
 
typedef vec< 3, double, highp > highp_dvec3
 3 components vector of high double-qualifier floating-point numbers. More...
 
typedef vec< 4, double, highp > highp_dvec4
 4 components vector of high double-qualifier floating-point numbers. More...
 
typedef vec< 2, int, highp > highp_ivec2
 2 components vector of high qualifier signed integer numbers. More...
 
typedef vec< 3, int, highp > highp_ivec3
 3 components vector of high qualifier signed integer numbers. More...
 
typedef vec< 4, int, highp > highp_ivec4
 4 components vector of high qualifier signed integer numbers. More...
 
typedef vec< 2, unsigned int, highp > highp_uvec2
 2 components vector of high qualifier unsigned integer numbers. More...
 
typedef vec< 3, unsigned int, highp > highp_uvec3
 3 components vector of high qualifier unsigned integer numbers. More...
 
typedef vec< 4, unsigned int, highp > highp_uvec4
 4 components vector of high qualifier unsigned integer numbers. More...
 
typedef vec< 2, float, highp > highp_vec2
 2 components vector of high single-qualifier floating-point numbers. More...
 
typedef vec< 3, float, highp > highp_vec3
 3 components vector of high single-qualifier floating-point numbers. More...
 
typedef vec< 4, float, highp > highp_vec4
 4 components vector of high single-qualifier floating-point numbers. More...
 
typedef vec< 2, bool, lowp > lowp_bvec2
 2 components vector of low qualifier bool numbers. More...
 
typedef vec< 3, bool, lowp > lowp_bvec3
 3 components vector of low qualifier bool numbers. More...
 
typedef vec< 4, bool, lowp > lowp_bvec4
 4 components vector of low qualifier bool numbers. More...
 
typedef vec< 2, double, lowp > lowp_dvec2
 2 components vector of low double-qualifier floating-point numbers. More...
 
typedef vec< 3, double, lowp > lowp_dvec3
 3 components vector of low double-qualifier floating-point numbers. More...
 
typedef vec< 4, double, lowp > lowp_dvec4
 4 components vector of low double-qualifier floating-point numbers. More...
 
typedef vec< 2, int, lowp > lowp_ivec2
 2 components vector of low qualifier signed integer numbers. More...
 
typedef vec< 3, int, lowp > lowp_ivec3
 3 components vector of low qualifier signed integer numbers. More...
 
typedef vec< 4, int, lowp > lowp_ivec4
 4 components vector of low qualifier signed integer numbers. More...
 
typedef vec< 2, unsigned int, lowp > lowp_uvec2
 2 components vector of low qualifier unsigned integer numbers. More...
 
typedef vec< 3, unsigned int, lowp > lowp_uvec3
 3 components vector of low qualifier unsigned integer numbers. More...
 
typedef vec< 4, unsigned int, lowp > lowp_uvec4
 4 components vector of low qualifier unsigned integer numbers. More...
 
typedef vec< 2, float, lowp > lowp_vec2
 2 components vector of low single-qualifier floating-point numbers. More...
 
typedef vec< 3, float, lowp > lowp_vec3
 3 components vector of low single-qualifier floating-point numbers. More...
 
typedef vec< 4, float, lowp > lowp_vec4
 4 components vector of low single-qualifier floating-point numbers. More...
 
typedef vec< 2, bool, mediump > mediump_bvec2
 2 components vector of medium qualifier bool numbers. More...
 
typedef vec< 3, bool, mediump > mediump_bvec3
 3 components vector of medium qualifier bool numbers. More...
 
typedef vec< 4, bool, mediump > mediump_bvec4
 4 components vector of medium qualifier bool numbers. More...
 
typedef vec< 2, double, mediump > mediump_dvec2
 2 components vector of medium double-qualifier floating-point numbers. More...
 
typedef vec< 3, double, mediump > mediump_dvec3
 3 components vector of medium double-qualifier floating-point numbers. More...
 
typedef vec< 4, double, mediump > mediump_dvec4
 4 components vector of medium double-qualifier floating-point numbers. More...
 
typedef vec< 2, int, mediump > mediump_ivec2
 2 components vector of medium qualifier signed integer numbers. More...
 
typedef vec< 3, int, mediump > mediump_ivec3
 3 components vector of medium qualifier signed integer numbers. More...
 
typedef vec< 4, int, mediump > mediump_ivec4
 4 components vector of medium qualifier signed integer numbers. More...
 
typedef vec< 2, unsigned int, mediump > mediump_uvec2
 2 components vector of medium qualifier unsigned integer numbers. More...
 
typedef vec< 3, unsigned int, mediump > mediump_uvec3
 3 components vector of medium qualifier unsigned integer numbers. More...
 
typedef vec< 4, unsigned int, mediump > mediump_uvec4
 4 components vector of medium qualifier unsigned integer numbers. More...
 
typedef vec< 2, float, mediump > mediump_vec2
 2 components vector of medium single-qualifier floating-point numbers. More...
 
typedef vec< 3, float, mediump > mediump_vec3
 3 components vector of medium single-qualifier floating-point numbers. More...
 
typedef vec< 4, float, mediump > mediump_vec4
 4 components vector of medium single-qualifier floating-point numbers. More...
 
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Detailed Description

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Vector types with precision qualifiers which may result in various precision in term of ULPs.

\n

GLSL allows defining qualifiers for particular variables. With OpenGL's GLSL, these qualifiers have no effect; they are there for compatibility, with OpenGL ES's GLSL, these qualifiers do have an effect.

\n

C++ has no language equivalent to qualifier qualifiers. So GLM provides the next-best thing: a number of typedefs that use a particular qualifier.

\n

None of these types make any guarantees about the actual qualifier used.

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Typedef Documentation

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typedef vec< 2, bool, highp > highp_bvec2
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2 components vector of high qualifier bool numbers.

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See also
GLSL 4.20.8 specification, section 4.1.5 Vectors
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Definition at line 16 of file vector_bool2_precision.hpp.

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typedef vec< 3, bool, highp > highp_bvec3
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3 components vector of high qualifier bool numbers.

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GLSL 4.20.8 specification, section 4.1.5 Vectors
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Definition at line 16 of file vector_bool3_precision.hpp.

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typedef vec< 4, bool, highp > highp_bvec4
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4 components vector of high qualifier bool numbers.

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GLSL 4.20.8 specification, section 4.1.5 Vectors
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Definition at line 16 of file vector_bool4_precision.hpp.

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typedef vec< 2, f64, highp > highp_dvec2
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2 components vector of high double-qualifier floating-point numbers.

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See also
GLSL 4.20.8 specification, section 4.1.5 Vectors
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Definition at line 16 of file vector_double2_precision.hpp.

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typedef vec< 3, f64, highp > highp_dvec3
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3 components vector of high double-qualifier floating-point numbers.

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There is no guarantee on the actual qualifier.

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See also
GLSL 4.20.8 specification, section 4.1.5 Vectors
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Definition at line 17 of file vector_double3_precision.hpp.

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typedef vec< 4, f64, highp > highp_dvec4
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4 components vector of high double-qualifier floating-point numbers.

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There is no guarantee on the actual qualifier.

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See also
GLSL 4.20.8 specification, section 4.1.5 Vectors
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Definition at line 18 of file vector_double4_precision.hpp.

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2 components vector of high qualifier signed integer numbers.

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GLSL 4.20.8 specification, section 4.1.5 Vectors
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Definition at line 16 of file vector_int2_precision.hpp.

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3 components vector of high qualifier signed integer numbers.

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GLSL 4.20.8 specification, section 4.1.5 Vectors
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Definition at line 16 of file vector_int3_precision.hpp.

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4 components vector of high qualifier signed integer numbers.

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GLSL 4.20.8 specification, section 4.1.5 Vectors
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Definition at line 16 of file vector_int4_precision.hpp.

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2 components vector of high qualifier unsigned integer numbers.

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GLSL 4.20.8 specification, section 4.1.5 Vectors
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Definition at line 16 of file vector_uint2_precision.hpp.

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3 components vector of high qualifier unsigned integer numbers.

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GLSL 4.20.8 specification, section 4.1.5 Vectors
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Definition at line 16 of file vector_uint3_precision.hpp.

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typedef vec< 4, u32, highp > highp_uvec4
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4 components vector of high qualifier unsigned integer numbers.

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See also
GLSL 4.20.8 specification, section 4.1.5 Vectors
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\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
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Definition at line 16 of file vector_uint4_precision.hpp.

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typedef vec< 2, float, highp > highp_vec2
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2 components vector of high single-qualifier floating-point numbers.

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See also
GLSL 4.20.8 specification, section 4.1.5 Vectors
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\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
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Definition at line 16 of file vector_float2_precision.hpp.

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typedef vec< 3, float, highp > highp_vec3
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3 components vector of high single-qualifier floating-point numbers.

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See also
GLSL 4.20.8 specification, section 4.1.5 Vectors
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\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
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Definition at line 16 of file vector_float3_precision.hpp.

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typedef vec< 4, float, highp > highp_vec4
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4 components vector of high single-qualifier floating-point numbers.

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See also
GLSL 4.20.8 specification, section 4.1.5 Vectors
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\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
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Definition at line 16 of file vector_float4_precision.hpp.

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typedef vec< 2, bool, lowp > lowp_bvec2
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2 components vector of low qualifier bool numbers.

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See also
GLSL 4.20.8 specification, section 4.1.5 Vectors
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\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
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Definition at line 28 of file vector_bool2_precision.hpp.

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typedef vec< 3, bool, lowp > lowp_bvec3
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3 components vector of low qualifier bool numbers.

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See also
GLSL 4.20.8 specification, section 4.1.5 Vectors
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Definition at line 28 of file vector_bool3_precision.hpp.

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typedef vec< 4, bool, lowp > lowp_bvec4
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4 components vector of low qualifier bool numbers.

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GLSL 4.20.8 specification, section 4.1.5 Vectors
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\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
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Definition at line 28 of file vector_bool4_precision.hpp.

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typedef vec< 2, f64, lowp > lowp_dvec2
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2 components vector of low double-qualifier floating-point numbers.

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See also
GLSL 4.20.8 specification, section 4.1.5 Vectors
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\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
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Definition at line 28 of file vector_double2_precision.hpp.

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typedef vec< 3, f64, lowp > lowp_dvec3
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3 components vector of low double-qualifier floating-point numbers.

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There is no guarantee on the actual qualifier.

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See also
GLSL 4.20.8 specification, section 4.1.5 Vectors
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\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
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Definition at line 31 of file vector_double3_precision.hpp.

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typedef vec< 4, f64, lowp > lowp_dvec4
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4 components vector of low double-qualifier floating-point numbers.

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There is no guarantee on the actual qualifier.

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See also
GLSL 4.20.8 specification, section 4.1.5 Vectors
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\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
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Definition at line 32 of file vector_double4_precision.hpp.

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typedef vec< 2, i32, lowp > lowp_ivec2
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2 components vector of low qualifier signed integer numbers.

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See also
GLSL 4.20.8 specification, section 4.1.5 Vectors
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\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
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Definition at line 28 of file vector_int2_precision.hpp.

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typedef vec< 3, i32, lowp > lowp_ivec3
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3 components vector of low qualifier signed integer numbers.

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See also
GLSL 4.20.8 specification, section 4.1.5 Vectors
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\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
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Definition at line 28 of file vector_int3_precision.hpp.

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typedef vec< 4, i32, lowp > lowp_ivec4
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4 components vector of low qualifier signed integer numbers.

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See also
GLSL 4.20.8 specification, section 4.1.5 Vectors
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\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
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Definition at line 28 of file vector_int4_precision.hpp.

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typedef vec< 2, u32, lowp > lowp_uvec2
\n
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2 components vector of low qualifier unsigned integer numbers.

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See also
GLSL 4.20.8 specification, section 4.1.5 Vectors
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\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
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Definition at line 28 of file vector_uint2_precision.hpp.

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typedef vec< 3, u32, lowp > lowp_uvec3
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3 components vector of low qualifier unsigned integer numbers.

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See also
GLSL 4.20.8 specification, section 4.1.5 Vectors
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\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
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Definition at line 28 of file vector_uint3_precision.hpp.

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typedef vec< 4, u32, lowp > lowp_uvec4
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4 components vector of low qualifier unsigned integer numbers.

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See also
GLSL 4.20.8 specification, section 4.1.5 Vectors
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\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
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Definition at line 28 of file vector_uint4_precision.hpp.

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typedef vec< 2, float, lowp > lowp_vec2
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2 components vector of low single-qualifier floating-point numbers.

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See also
GLSL 4.20.8 specification, section 4.1.5 Vectors
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\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
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Definition at line 28 of file vector_float2_precision.hpp.

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typedef vec< 3, float, lowp > lowp_vec3
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3 components vector of low single-qualifier floating-point numbers.

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See also
GLSL 4.20.8 specification, section 4.1.5 Vectors
\n
\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
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Definition at line 28 of file vector_float3_precision.hpp.

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typedef vec< 4, float, lowp > lowp_vec4
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4 components vector of low single-qualifier floating-point numbers.

\n
See also
GLSL 4.20.8 specification, section 4.1.5 Vectors
\n
\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
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Definition at line 28 of file vector_float4_precision.hpp.

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typedef vec< 2, bool, mediump > mediump_bvec2
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2 components vector of medium qualifier bool numbers.

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See also
GLSL 4.20.8 specification, section 4.1.5 Vectors
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\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
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Definition at line 22 of file vector_bool2_precision.hpp.

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typedef vec< 3, bool, mediump > mediump_bvec3
\n
\n\n

3 components vector of medium qualifier bool numbers.

\n
See also
GLSL 4.20.8 specification, section 4.1.5 Vectors
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\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
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Definition at line 22 of file vector_bool3_precision.hpp.

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typedef vec< 4, bool, mediump > mediump_bvec4
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4 components vector of medium qualifier bool numbers.

\n
See also
GLSL 4.20.8 specification, section 4.1.5 Vectors
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\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
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Definition at line 22 of file vector_bool4_precision.hpp.

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typedef vec< 2, f64, mediump > mediump_dvec2
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\n\n

2 components vector of medium double-qualifier floating-point numbers.

\n
See also
GLSL 4.20.8 specification, section 4.1.5 Vectors
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\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
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Definition at line 22 of file vector_double2_precision.hpp.

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typedef vec< 3, f64, mediump > mediump_dvec3
\n
\n\n

3 components vector of medium double-qualifier floating-point numbers.

\n

There is no guarantee on the actual qualifier.

\n
See also
GLSL 4.20.8 specification, section 4.1.5 Vectors
\n
\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
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Definition at line 24 of file vector_double3_precision.hpp.

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typedef vec< 4, f64, mediump > mediump_dvec4
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4 components vector of medium double-qualifier floating-point numbers.

\n

There is no guarantee on the actual qualifier.

\n
See also
GLSL 4.20.8 specification, section 4.1.5 Vectors
\n
\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
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Definition at line 25 of file vector_double4_precision.hpp.

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typedef vec< 2, i32, mediump > mediump_ivec2
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2 components vector of medium qualifier signed integer numbers.

\n
See also
GLSL 4.20.8 specification, section 4.1.5 Vectors
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\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
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Definition at line 22 of file vector_int2_precision.hpp.

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typedef vec< 3, i32, mediump > mediump_ivec3
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3 components vector of medium qualifier signed integer numbers.

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See also
GLSL 4.20.8 specification, section 4.1.5 Vectors
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\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
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Definition at line 22 of file vector_int3_precision.hpp.

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typedef vec< 4, i32, mediump > mediump_ivec4
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4 components vector of medium qualifier signed integer numbers.

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See also
GLSL 4.20.8 specification, section 4.1.5 Vectors
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\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
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Definition at line 22 of file vector_int4_precision.hpp.

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typedef vec< 2, u32, mediump > mediump_uvec2
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2 components vector of medium qualifier unsigned integer numbers.

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See also
GLSL 4.20.8 specification, section 4.1.5 Vectors
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\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
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Definition at line 22 of file vector_uint2_precision.hpp.

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typedef vec< 3, u32, mediump > mediump_uvec3
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3 components vector of medium qualifier unsigned integer numbers.

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See also
GLSL 4.20.8 specification, section 4.1.5 Vectors
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\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
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Definition at line 22 of file vector_uint3_precision.hpp.

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typedef vec< 4, u32, mediump > mediump_uvec4
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4 components vector of medium qualifier unsigned integer numbers.

\n
See also
GLSL 4.20.8 specification, section 4.1.5 Vectors
\n
\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
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Definition at line 22 of file vector_uint4_precision.hpp.

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typedef vec< 2, float, mediump > mediump_vec2
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2 components vector of medium single-qualifier floating-point numbers.

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See also
GLSL 4.20.8 specification, section 4.1.5 Vectors
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\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
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Definition at line 22 of file vector_float2_precision.hpp.

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typedef vec< 3, float, mediump > mediump_vec3
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3 components vector of medium single-qualifier floating-point numbers.

\n
See also
GLSL 4.20.8 specification, section 4.1.5 Vectors
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\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
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Definition at line 22 of file vector_float3_precision.hpp.

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typedef vec< 4, float, mediump > mediump_vec4
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4 components vector of medium single-qualifier floating-point numbers.

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See also
GLSL 4.20.8 specification, section 4.1.5 Vectors
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\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
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Definition at line 22 of file vector_float4_precision.hpp.

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Matrix types
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Matrix types of with C columns and R rows where C and R are values between 2 to 4 included. \nMore...

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\nTypedefs

typedef mat< 2, 2, double, defaultp > dmat2
 2 columns of 2 components matrix of double-precision floating-point numbers. More...
 
typedef mat< 2, 2, double, defaultp > dmat2x2
 2 columns of 2 components matrix of double-precision floating-point numbers. More...
 
typedef mat< 2, 3, double, defaultp > dmat2x3
 2 columns of 3 components matrix of double-precision floating-point numbers. More...
 
typedef mat< 2, 4, double, defaultp > dmat2x4
 2 columns of 4 components matrix of double-precision floating-point numbers. More...
 
typedef mat< 3, 3, double, defaultp > dmat3
 3 columns of 3 components matrix of double-precision floating-point numbers. More...
 
typedef mat< 3, 2, double, defaultp > dmat3x2
 3 columns of 2 components matrix of double-precision floating-point numbers. More...
 
typedef mat< 3, 3, double, defaultp > dmat3x3
 3 columns of 3 components matrix of double-precision floating-point numbers. More...
 
typedef mat< 3, 4, double, defaultp > dmat3x4
 3 columns of 4 components matrix of double-precision floating-point numbers. More...
 
typedef mat< 4, 4, double, defaultp > dmat4
 4 columns of 4 components matrix of double-precision floating-point numbers. More...
 
typedef mat< 4, 2, double, defaultp > dmat4x2
 4 columns of 2 components matrix of double-precision floating-point numbers. More...
 
typedef mat< 4, 3, double, defaultp > dmat4x3
 4 columns of 3 components matrix of double-precision floating-point numbers. More...
 
typedef mat< 4, 4, double, defaultp > dmat4x4
 4 columns of 4 components matrix of double-precision floating-point numbers. More...
 
typedef mat< 2, 2, float, defaultp > mat2
 2 columns of 2 components matrix of single-precision floating-point numbers. More...
 
typedef mat< 2, 2, float, defaultp > mat2x2
 2 columns of 2 components matrix of single-precision floating-point numbers. More...
 
typedef mat< 2, 3, float, defaultp > mat2x3
 2 columns of 3 components matrix of single-precision floating-point numbers. More...
 
typedef mat< 2, 4, float, defaultp > mat2x4
 2 columns of 4 components matrix of single-precision floating-point numbers. More...
 
typedef mat< 3, 3, float, defaultp > mat3
 3 columns of 3 components matrix of single-precision floating-point numbers. More...
 
typedef mat< 3, 3, float, defaultp > mat3x3
 3 columns of 3 components matrix of single-precision floating-point numbers. More...
 
typedef mat< 3, 4, float, defaultp > mat3x4
 3 columns of 4 components matrix of single-precision floating-point numbers. More...
 
typedef mat< 4, 2, float, defaultp > mat4x2
 4 columns of 2 components matrix of single-precision floating-point numbers. More...
 
typedef mat< 4, 3, float, defaultp > mat4x3
 4 columns of 3 components matrix of single-precision floating-point numbers. More...
 
typedef mat< 4, 4, float, defaultp > mat4x4
 4 columns of 4 components matrix of single-precision floating-point numbers. More...
 
typedef mat< 4, 4, float, defaultp > mat4
 4 columns of 4 components matrix of single-precision floating-point numbers. More...
 
\n

Detailed Description

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Matrix types of with C columns and R rows where C and R are values between 2 to 4 included.

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These types have exhaustive sets of operators.

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Typedef Documentation

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typedef mat< 2, 2, f64, defaultp > dmat2
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2 columns of 2 components matrix of double-precision floating-point numbers.

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See also
GLSL 4.20.8 specification, section 4.1.6 Matrices
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Definition at line 20 of file matrix_double2x2.hpp.

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typedef mat< 2, 2, double, defaultp > dmat2x2
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2 columns of 2 components matrix of double-precision floating-point numbers.

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See also
GLSL 4.20.8 specification, section 4.1.6 Matrices
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Definition at line 15 of file matrix_double2x2.hpp.

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typedef mat< 2, 3, double, defaultp > dmat2x3
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2 columns of 3 components matrix of double-precision floating-point numbers.

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See also
GLSL 4.20.8 specification, section 4.1.6 Matrices
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Definition at line 15 of file matrix_double2x3.hpp.

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typedef mat< 2, 4, double, defaultp > dmat2x4
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2 columns of 4 components matrix of double-precision floating-point numbers.

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See also
GLSL 4.20.8 specification, section 4.1.6 Matrices
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Definition at line 15 of file matrix_double2x4.hpp.

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typedef mat< 3, 3, f64, defaultp > dmat3
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3 columns of 3 components matrix of double-precision floating-point numbers.

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See also
GLSL 4.20.8 specification, section 4.1.6 Matrices
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Definition at line 20 of file matrix_double3x3.hpp.

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typedef mat< 3, 2, double, defaultp > dmat3x2
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3 columns of 2 components matrix of double-precision floating-point numbers.

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See also
GLSL 4.20.8 specification, section 4.1.6 Matrices
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Definition at line 15 of file matrix_double3x2.hpp.

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typedef mat< 3, 3, double, defaultp > dmat3x3
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3 columns of 3 components matrix of double-precision floating-point numbers.

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See also
GLSL 4.20.8 specification, section 4.1.6 Matrices
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Definition at line 15 of file matrix_double3x3.hpp.

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typedef mat< 3, 4, double, defaultp > dmat3x4
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3 columns of 4 components matrix of double-precision floating-point numbers.

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See also
GLSL 4.20.8 specification, section 4.1.6 Matrices
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Definition at line 15 of file matrix_double3x4.hpp.

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typedef mat< 4, 4, f64, defaultp > dmat4
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4 columns of 4 components matrix of double-precision floating-point numbers.

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See also
GLSL 4.20.8 specification, section 4.1.6 Matrices
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Definition at line 20 of file matrix_double4x4.hpp.

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typedef mat< 4, 2, double, defaultp > dmat4x2
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4 columns of 2 components matrix of double-precision floating-point numbers.

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See also
GLSL 4.20.8 specification, section 4.1.6 Matrices
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Definition at line 15 of file matrix_double4x2.hpp.

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typedef mat< 4, 3, double, defaultp > dmat4x3
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4 columns of 3 components matrix of double-precision floating-point numbers.

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See also
GLSL 4.20.8 specification, section 4.1.6 Matrices
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Definition at line 15 of file matrix_double4x3.hpp.

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typedef mat< 4, 4, double, defaultp > dmat4x4
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4 columns of 4 components matrix of double-precision floating-point numbers.

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See also
GLSL 4.20.8 specification, section 4.1.6 Matrices
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Definition at line 15 of file matrix_double4x4.hpp.

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typedef mat< 2, 2, f32, defaultp > mat2
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2 columns of 2 components matrix of single-precision floating-point numbers.

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Definition at line 20 of file matrix_float2x2.hpp.

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typedef mat< 2, 2, f32, defaultp > mat2x2
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2 columns of 2 components matrix of single-precision floating-point numbers.

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GLSL 4.20.8 specification, section 4.1.6 Matrices
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Definition at line 15 of file matrix_float2x2.hpp.

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typedef mat< 2, 3, f32, defaultp > mat2x3
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2 columns of 3 components matrix of single-precision floating-point numbers.

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GLSL 4.20.8 specification, section 4.1.6 Matrices
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Definition at line 15 of file matrix_float2x3.hpp.

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typedef mat< 2, 4, f32, defaultp > mat2x4
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2 columns of 4 components matrix of single-precision floating-point numbers.

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GLSL 4.20.8 specification, section 4.1.6 Matrices
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Definition at line 15 of file matrix_float2x4.hpp.

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3 columns of 3 components matrix of single-precision floating-point numbers.

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GLSL 4.20.8 specification, section 4.1.6 Matrices
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Definition at line 20 of file matrix_float3x3.hpp.

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typedef mat< 3, 3, f32, defaultp > mat3x3
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3 columns of 3 components matrix of single-precision floating-point numbers.

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GLSL 4.20.8 specification, section 4.1.6 Matrices
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Definition at line 15 of file matrix_float3x3.hpp.

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typedef mat< 3, 4, f32, defaultp > mat3x4
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3 columns of 4 components matrix of single-precision floating-point numbers.

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GLSL 4.20.8 specification, section 4.1.6 Matrices
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Definition at line 15 of file matrix_float3x4.hpp.

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typedef mat< 4, 4, f32, defaultp > mat4
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4 columns of 4 components matrix of single-precision floating-point numbers.

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GLSL 4.20.8 specification, section 4.1.6 Matrices
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Definition at line 20 of file matrix_float4x4.hpp.

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typedef mat< 4, 2, f32, defaultp > mat4x2
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4 columns of 2 components matrix of single-precision floating-point numbers.

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GLSL 4.20.8 specification, section 4.1.6 Matrices
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Definition at line 15 of file matrix_float4x2.hpp.

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typedef mat< 4, 3, f32, defaultp > mat4x3
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4 columns of 3 components matrix of single-precision floating-point numbers.

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GLSL 4.20.8 specification, section 4.1.6 Matrices
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Definition at line 15 of file matrix_float4x3.hpp.

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typedef mat< 4, 4, f32, defaultp > mat4x4
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4 columns of 4 components matrix of single-precision floating-point numbers.

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GLSL 4.20.8 specification, section 4.1.6 Matrices
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Definition at line 15 of file matrix_float4x4.hpp.

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\nTypedefs
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Matrix types with precision qualifiers
Core features
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Matrix types with precision qualifiers which may result in various precision in term of ULPs. \nMore...

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\nTypedefs

typedef mat< 2, 2, double, highp > highp_dmat2
 2 columns of 2 components matrix of double-precision floating-point numbers using medium precision arithmetic in term of ULPs. More...
 
typedef mat< 2, 2, double, highp > highp_dmat2x2
 2 columns of 2 components matrix of double-precision floating-point numbers using medium precision arithmetic in term of ULPs. More...
 
typedef mat< 2, 3, double, highp > highp_dmat2x3
 2 columns of 3 components matrix of double-precision floating-point numbers using medium precision arithmetic in term of ULPs. More...
 
typedef mat< 2, 4, double, highp > highp_dmat2x4
 2 columns of 4 components matrix of double-precision floating-point numbers using medium precision arithmetic in term of ULPs. More...
 
typedef mat< 3, 3, double, highp > highp_dmat3
 3 columns of 3 components matrix of double-precision floating-point numbers using medium precision arithmetic in term of ULPs. More...
 
typedef mat< 3, 2, double, highp > highp_dmat3x2
 3 columns of 2 components matrix of double-precision floating-point numbers using medium precision arithmetic in term of ULPs. More...
 
typedef mat< 3, 3, double, highp > highp_dmat3x3
 3 columns of 3 components matrix of double-precision floating-point numbers using medium precision arithmetic in term of ULPs. More...
 
typedef mat< 3, 4, double, highp > highp_dmat3x4
 3 columns of 4 components matrix of double-precision floating-point numbers using medium precision arithmetic in term of ULPs. More...
 
typedef mat< 4, 4, double, highp > highp_dmat4
 4 columns of 4 components matrix of double-precision floating-point numbers using medium precision arithmetic in term of ULPs. More...
 
typedef mat< 4, 2, double, highp > highp_dmat4x2
 4 columns of 2 components matrix of double-precision floating-point numbers using medium precision arithmetic in term of ULPs. More...
 
typedef mat< 4, 3, double, highp > highp_dmat4x3
 4 columns of 3 components matrix of double-precision floating-point numbers using medium precision arithmetic in term of ULPs. More...
 
typedef mat< 4, 4, double, highp > highp_dmat4x4
 4 columns of 4 components matrix of double-precision floating-point numbers using medium precision arithmetic in term of ULPs. More...
 
typedef mat< 2, 2, float, highp > highp_mat2
 2 columns of 2 components matrix of single-precision floating-point numbers using high precision arithmetic in term of ULPs. More...
 
typedef mat< 2, 2, float, highp > highp_mat2x2
 2 columns of 2 components matrix of single-precision floating-point numbers using high precision arithmetic in term of ULPs. More...
 
typedef mat< 2, 3, float, highp > highp_mat2x3
 2 columns of 3 components matrix of single-precision floating-point numbers using high precision arithmetic in term of ULPs. More...
 
typedef mat< 2, 4, float, highp > highp_mat2x4
 2 columns of 4 components matrix of single-precision floating-point numbers using high precision arithmetic in term of ULPs. More...
 
typedef mat< 3, 3, float, highp > highp_mat3
 3 columns of 3 components matrix of single-precision floating-point numbers using high precision arithmetic in term of ULPs. More...
 
typedef mat< 3, 2, float, highp > highp_mat3x2
 3 columns of 2 components matrix of single-precision floating-point numbers using high precision arithmetic in term of ULPs. More...
 
typedef mat< 3, 3, float, highp > highp_mat3x3
 3 columns of 3 components matrix of single-precision floating-point numbers using high precision arithmetic in term of ULPs. More...
 
typedef mat< 3, 4, float, highp > highp_mat3x4
 3 columns of 4 components matrix of single-precision floating-point numbers using high precision arithmetic in term of ULPs. More...
 
typedef mat< 4, 4, float, highp > highp_mat4
 4 columns of 4 components matrix of single-precision floating-point numbers using high precision arithmetic in term of ULPs. More...
 
typedef mat< 4, 2, float, highp > highp_mat4x2
 4 columns of 2 components matrix of single-precision floating-point numbers using high precision arithmetic in term of ULPs. More...
 
typedef mat< 4, 3, float, highp > highp_mat4x3
 4 columns of 3 components matrix of single-precision floating-point numbers using high precision arithmetic in term of ULPs. More...
 
typedef mat< 4, 4, float, highp > highp_mat4x4
 4 columns of 4 components matrix of single-precision floating-point numbers using high precision arithmetic in term of ULPs. More...
 
typedef mat< 2, 2, double, lowp > lowp_dmat2
 2 columns of 2 components matrix of double-precision floating-point numbers using low precision arithmetic in term of ULPs. More...
 
typedef mat< 2, 2, double, lowp > lowp_dmat2x2
 2 columns of 2 components matrix of double-precision floating-point numbers using low precision arithmetic in term of ULPs. More...
 
typedef mat< 2, 3, double, lowp > lowp_dmat2x3
 2 columns of 3 components matrix of double-precision floating-point numbers using low precision arithmetic in term of ULPs. More...
 
typedef mat< 2, 4, double, lowp > lowp_dmat2x4
 2 columns of 4 components matrix of double-precision floating-point numbers using low precision arithmetic in term of ULPs. More...
 
typedef mat< 3, 3, double, lowp > lowp_dmat3
 3 columns of 3 components matrix of double-precision floating-point numbers using low precision arithmetic in term of ULPs. More...
 
typedef mat< 3, 2, double, lowp > lowp_dmat3x2
 3 columns of 2 components matrix of double-precision floating-point numbers using low precision arithmetic in term of ULPs. More...
 
typedef mat< 3, 3, double, lowp > lowp_dmat3x3
 3 columns of 3 components matrix of double-precision floating-point numbers using low precision arithmetic in term of ULPs. More...
 
typedef mat< 3, 4, double, lowp > lowp_dmat3x4
 3 columns of 4 components matrix of double-precision floating-point numbers using low precision arithmetic in term of ULPs. More...
 
typedef mat< 4, 4, double, lowp > lowp_dmat4
 4 columns of 4 components matrix of double-precision floating-point numbers using low precision arithmetic in term of ULPs. More...
 
typedef mat< 4, 2, double, lowp > lowp_dmat4x2
 4 columns of 2 components matrix of double-precision floating-point numbers using low precision arithmetic in term of ULPs. More...
 
typedef mat< 4, 3, double, lowp > lowp_dmat4x3
 4 columns of 3 components matrix of double-precision floating-point numbers using low precision arithmetic in term of ULPs. More...
 
typedef mat< 4, 4, double, lowp > lowp_dmat4x4
 4 columns of 4 components matrix of double-precision floating-point numbers using low precision arithmetic in term of ULPs. More...
 
typedef mat< 2, 2, float, lowp > lowp_mat2
 2 columns of 2 components matrix of single-precision floating-point numbers using low precision arithmetic in term of ULPs. More...
 
typedef mat< 2, 2, float, lowp > lowp_mat2x2
 2 columns of 2 components matrix of single-precision floating-point numbers using low precision arithmetic in term of ULPs. More...
 
typedef mat< 2, 3, float, lowp > lowp_mat2x3
 2 columns of 3 components matrix of single-precision floating-point numbers using low precision arithmetic in term of ULPs. More...
 
typedef mat< 2, 4, float, lowp > lowp_mat2x4
 2 columns of 4 components matrix of single-precision floating-point numbers using low precision arithmetic in term of ULPs. More...
 
typedef mat< 3, 3, float, lowp > lowp_mat3
 3 columns of 3 components matrix of single-precision floating-point numbers using low precision arithmetic in term of ULPs. More...
 
typedef mat< 3, 2, float, lowp > lowp_mat3x2
 3 columns of 2 components matrix of single-precision floating-point numbers using low precision arithmetic in term of ULPs. More...
 
typedef mat< 3, 3, float, lowp > lowp_mat3x3
 3 columns of 3 components matrix of single-precision floating-point numbers using low precision arithmetic in term of ULPs. More...
 
typedef mat< 3, 4, float, lowp > lowp_mat3x4
 3 columns of 4 components matrix of single-precision floating-point numbers using low precision arithmetic in term of ULPs. More...
 
typedef mat< 4, 4, float, lowp > lowp_mat4
 4 columns of 4 components matrix of single-precision floating-point numbers using low precision arithmetic in term of ULPs. More...
 
typedef mat< 4, 2, float, lowp > lowp_mat4x2
 4 columns of 2 components matrix of single-precision floating-point numbers using low precision arithmetic in term of ULPs. More...
 
typedef mat< 4, 3, float, lowp > lowp_mat4x3
 4 columns of 3 components matrix of single-precision floating-point numbers using low precision arithmetic in term of ULPs. More...
 
typedef mat< 4, 4, float, lowp > lowp_mat4x4
 4 columns of 4 components matrix of single-precision floating-point numbers using low precision arithmetic in term of ULPs. More...
 
typedef mat< 2, 2, double, mediump > mediump_dmat2
 2 columns of 2 components matrix of double-precision floating-point numbers using medium precision arithmetic in term of ULPs. More...
 
typedef mat< 2, 2, double, mediump > mediump_dmat2x2
 2 columns of 2 components matrix of double-precision floating-point numbers using medium precision arithmetic in term of ULPs. More...
 
typedef mat< 2, 3, double, mediump > mediump_dmat2x3
 2 columns of 3 components matrix of double-precision floating-point numbers using medium precision arithmetic in term of ULPs. More...
 
typedef mat< 2, 4, double, mediump > mediump_dmat2x4
 2 columns of 4 components matrix of double-precision floating-point numbers using medium precision arithmetic in term of ULPs. More...
 
typedef mat< 3, 3, double, mediump > mediump_dmat3
 3 columns of 3 components matrix of double-precision floating-point numbers using medium precision arithmetic in term of ULPs. More...
 
typedef mat< 3, 2, double, mediump > mediump_dmat3x2
 3 columns of 2 components matrix of double-precision floating-point numbers using medium precision arithmetic in term of ULPs. More...
 
typedef mat< 3, 3, double, mediump > mediump_dmat3x3
 3 columns of 3 components matrix of double-precision floating-point numbers using medium precision arithmetic in term of ULPs. More...
 
typedef mat< 3, 4, double, mediump > mediump_dmat3x4
 3 columns of 4 components matrix of double-precision floating-point numbers using medium precision arithmetic in term of ULPs. More...
 
typedef mat< 4, 4, double, mediump > mediump_dmat4
 4 columns of 4 components matrix of double-precision floating-point numbers using medium precision arithmetic in term of ULPs. More...
 
typedef mat< 4, 2, double, mediump > mediump_dmat4x2
 4 columns of 2 components matrix of double-precision floating-point numbers using medium precision arithmetic in term of ULPs. More...
 
typedef mat< 4, 3, double, mediump > mediump_dmat4x3
 4 columns of 3 components matrix of double-precision floating-point numbers using medium precision arithmetic in term of ULPs. More...
 
typedef mat< 4, 4, double, mediump > mediump_dmat4x4
 4 columns of 4 components matrix of double-precision floating-point numbers using medium precision arithmetic in term of ULPs. More...
 
typedef mat< 2, 2, float, mediump > mediump_mat2
 2 columns of 2 components matrix of single-precision floating-point numbers using medium precision arithmetic in term of ULPs. More...
 
typedef mat< 2, 2, float, mediump > mediump_mat2x2
 2 columns of 2 components matrix of single-precision floating-point numbers using medium precision arithmetic in term of ULPs. More...
 
typedef mat< 2, 3, float, mediump > mediump_mat2x3
 2 columns of 3 components matrix of single-precision floating-point numbers using medium precision arithmetic in term of ULPs. More...
 
typedef mat< 2, 4, float, mediump > mediump_mat2x4
 2 columns of 4 components matrix of single-precision floating-point numbers using medium precision arithmetic in term of ULPs. More...
 
typedef mat< 3, 3, float, mediump > mediump_mat3
 3 columns of 3 components matrix of single-precision floating-point numbers using medium precision arithmetic in term of ULPs. More...
 
typedef mat< 3, 2, float, mediump > mediump_mat3x2
 3 columns of 2 components matrix of single-precision floating-point numbers using medium precision arithmetic in term of ULPs. More...
 
typedef mat< 3, 3, float, mediump > mediump_mat3x3
 3 columns of 3 components matrix of single-precision floating-point numbers using medium precision arithmetic in term of ULPs. More...
 
typedef mat< 3, 4, float, mediump > mediump_mat3x4
 3 columns of 4 components matrix of single-precision floating-point numbers using medium precision arithmetic in term of ULPs. More...
 
typedef mat< 4, 4, float, mediump > mediump_mat4
 4 columns of 4 components matrix of single-precision floating-point numbers using medium precision arithmetic in term of ULPs. More...
 
typedef mat< 4, 2, float, mediump > mediump_mat4x2
 4 columns of 2 components matrix of single-precision floating-point numbers using medium precision arithmetic in term of ULPs. More...
 
typedef mat< 4, 3, float, mediump > mediump_mat4x3
 4 columns of 3 components matrix of single-precision floating-point numbers using medium precision arithmetic in term of ULPs. More...
 
typedef mat< 4, 4, float, mediump > mediump_mat4x4
 4 columns of 4 components matrix of single-precision floating-point numbers using medium precision arithmetic in term of ULPs. More...
 
\n

Detailed Description

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Matrix types with precision qualifiers which may result in various precision in term of ULPs.

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GLSL allows defining qualifiers for particular variables. With OpenGL's GLSL, these qualifiers have no effect; they are there for compatibility, with OpenGL ES's GLSL, these qualifiers do have an effect.

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C++ has no language equivalent to qualifier qualifiers. So GLM provides the next-best thing: a number of typedefs that use a particular qualifier.

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None of these types make any guarantees about the actual qualifier used.

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Typedef Documentation

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typedef mat< 2, 2, f64, highp > highp_dmat2
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2 columns of 2 components matrix of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.

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See also
GLSL 4.20.8 specification, section 4.1.6 Matrices
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\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
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Definition at line 28 of file matrix_double2x2_precision.hpp.

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typedef mat< 2, 2, double, highp > highp_dmat2x2
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2 columns of 2 components matrix of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.

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See also
GLSL 4.20.8 specification, section 4.1.6 Matrices
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Definition at line 46 of file matrix_double2x2_precision.hpp.

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typedef mat< 2, 3, double, highp > highp_dmat2x3
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2 columns of 3 components matrix of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.

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GLSL 4.20.8 specification, section 4.1.6 Matrices
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Definition at line 28 of file matrix_double2x3_precision.hpp.

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typedef mat< 2, 4, double, highp > highp_dmat2x4
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2 columns of 4 components matrix of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.

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GLSL 4.20.8 specification, section 4.1.6 Matrices
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Definition at line 28 of file matrix_double2x4_precision.hpp.

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typedef mat< 3, 3, f64, highp > highp_dmat3
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3 columns of 3 components matrix of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.

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GLSL 4.20.8 specification, section 4.1.6 Matrices
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Definition at line 28 of file matrix_double3x3_precision.hpp.

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typedef mat< 3, 2, double, highp > highp_dmat3x2
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3 columns of 2 components matrix of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.

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GLSL 4.20.8 specification, section 4.1.6 Matrices
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Definition at line 28 of file matrix_double3x2_precision.hpp.

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typedef mat< 3, 3, double, highp > highp_dmat3x3
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3 columns of 3 components matrix of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.

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GLSL 4.20.8 specification, section 4.1.6 Matrices
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Definition at line 46 of file matrix_double3x3_precision.hpp.

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typedef mat< 3, 4, double, highp > highp_dmat3x4
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3 columns of 4 components matrix of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.

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GLSL 4.20.8 specification, section 4.1.6 Matrices
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Definition at line 28 of file matrix_double3x4_precision.hpp.

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typedef mat< 4, 4, f64, highp > highp_dmat4
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4 columns of 4 components matrix of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.

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GLSL 4.20.8 specification, section 4.1.6 Matrices
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Definition at line 28 of file matrix_double4x4_precision.hpp.

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typedef mat< 4, 2, double, highp > highp_dmat4x2
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4 columns of 2 components matrix of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.

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GLSL 4.20.8 specification, section 4.1.6 Matrices
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Definition at line 28 of file matrix_double4x2_precision.hpp.

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typedef mat< 4, 3, double, highp > highp_dmat4x3
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4 columns of 3 components matrix of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.

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GLSL 4.20.8 specification, section 4.1.6 Matrices
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Definition at line 28 of file matrix_double4x3_precision.hpp.

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typedef mat< 4, 4, double, highp > highp_dmat4x4
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4 columns of 4 components matrix of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.

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GLSL 4.20.8 specification, section 4.1.6 Matrices
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Definition at line 46 of file matrix_double4x4_precision.hpp.

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typedef mat< 2, 2, f32, highp > highp_mat2
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2 columns of 2 components matrix of single-precision floating-point numbers using high precision arithmetic in term of ULPs.

\n
See also
GLSL 4.20.8 specification, section 4.1.6 Matrices
\n
\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
\n\n

Definition at line 28 of file matrix_float2x2_precision.hpp.

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typedef mat< 2, 2, f32, highp > highp_mat2x2
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2 columns of 2 components matrix of single-precision floating-point numbers using high precision arithmetic in term of ULPs.

\n
See also
GLSL 4.20.8 specification, section 4.1.6 Matrices
\n
\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
\n\n

Definition at line 46 of file matrix_float2x2_precision.hpp.

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typedef mat< 2, 3, f32, highp > highp_mat2x3
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2 columns of 3 components matrix of single-precision floating-point numbers using high precision arithmetic in term of ULPs.

\n
See also
GLSL 4.20.8 specification, section 4.1.6 Matrices
\n
\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
\n\n

Definition at line 28 of file matrix_float2x3_precision.hpp.

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typedef mat< 2, 4, f32, highp > highp_mat2x4
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2 columns of 4 components matrix of single-precision floating-point numbers using high precision arithmetic in term of ULPs.

\n
See also
GLSL 4.20.8 specification, section 4.1.6 Matrices
\n
\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
\n\n

Definition at line 28 of file matrix_float2x4_precision.hpp.

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typedef mat< 3, 3, f32, highp > highp_mat3
\n
\n\n

3 columns of 3 components matrix of single-precision floating-point numbers using high precision arithmetic in term of ULPs.

\n
See also
GLSL 4.20.8 specification, section 4.1.6 Matrices
\n
\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
\n\n

Definition at line 28 of file matrix_float3x3_precision.hpp.

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typedef mat< 3, 2, f32, highp > highp_mat3x2
\n
\n\n

3 columns of 2 components matrix of single-precision floating-point numbers using high precision arithmetic in term of ULPs.

\n
See also
GLSL 4.20.8 specification, section 4.1.6 Matrices
\n
\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
\n\n

Definition at line 28 of file matrix_float3x2_precision.hpp.

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typedef mat< 3, 3, f32, highp > highp_mat3x3
\n
\n\n

3 columns of 3 components matrix of single-precision floating-point numbers using high precision arithmetic in term of ULPs.

\n
See also
GLSL 4.20.8 specification, section 4.1.6 Matrices
\n
\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
\n\n

Definition at line 46 of file matrix_float3x3_precision.hpp.

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typedef mat< 3, 4, f32, highp > highp_mat3x4
\n
\n\n

3 columns of 4 components matrix of single-precision floating-point numbers using high precision arithmetic in term of ULPs.

\n
See also
GLSL 4.20.8 specification, section 4.1.6 Matrices
\n
\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
\n\n

Definition at line 28 of file matrix_float3x4_precision.hpp.

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typedef mat< 4, 4, f32, highp > highp_mat4
\n
\n\n

4 columns of 4 components matrix of single-precision floating-point numbers using high precision arithmetic in term of ULPs.

\n
See also
GLSL 4.20.8 specification, section 4.1.6 Matrices
\n
\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
\n\n

Definition at line 28 of file matrix_float4x4_precision.hpp.

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typedef mat< 4, 2, f32, highp > highp_mat4x2
\n
\n\n

4 columns of 2 components matrix of single-precision floating-point numbers using high precision arithmetic in term of ULPs.

\n
See also
GLSL 4.20.8 specification, section 4.1.6 Matrices
\n
\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
\n\n

Definition at line 28 of file matrix_float4x2_precision.hpp.

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typedef mat< 4, 3, f32, highp > highp_mat4x3
\n
\n\n

4 columns of 3 components matrix of single-precision floating-point numbers using high precision arithmetic in term of ULPs.

\n
See also
GLSL 4.20.8 specification, section 4.1.6 Matrices
\n
\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
\n\n

Definition at line 28 of file matrix_float4x3_precision.hpp.

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typedef mat< 4, 4, f32, highp > highp_mat4x4
\n
\n\n

4 columns of 4 components matrix of single-precision floating-point numbers using high precision arithmetic in term of ULPs.

\n
See also
GLSL 4.20.8 specification, section 4.1.6 Matrices
\n
\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
\n\n

Definition at line 46 of file matrix_float4x4_precision.hpp.

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typedef mat< 2, 2, f64, lowp > lowp_dmat2
\n
\n\n

2 columns of 2 components matrix of double-precision floating-point numbers using low precision arithmetic in term of ULPs.

\n
See also
GLSL 4.20.8 specification, section 4.1.6 Matrices
\n
\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
\n\n

Definition at line 16 of file matrix_double2x2_precision.hpp.

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typedef mat< 2, 2, double, lowp > lowp_dmat2x2
\n
\n\n

2 columns of 2 components matrix of double-precision floating-point numbers using low precision arithmetic in term of ULPs.

\n
See also
GLSL 4.20.8 specification, section 4.1.6 Matrices
\n
\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
\n\n

Definition at line 34 of file matrix_double2x2_precision.hpp.

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typedef mat< 2, 3, double, lowp > lowp_dmat2x3
\n
\n\n

2 columns of 3 components matrix of double-precision floating-point numbers using low precision arithmetic in term of ULPs.

\n
See also
GLSL 4.20.8 specification, section 4.1.6 Matrices
\n
\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
\n\n

Definition at line 16 of file matrix_double2x3_precision.hpp.

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typedef mat< 2, 4, double, lowp > lowp_dmat2x4
\n
\n\n

2 columns of 4 components matrix of double-precision floating-point numbers using low precision arithmetic in term of ULPs.

\n
See also
GLSL 4.20.8 specification, section 4.1.6 Matrices
\n
\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
\n\n

Definition at line 16 of file matrix_double2x4_precision.hpp.

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typedef mat< 3, 3, f64, lowp > lowp_dmat3
\n
\n\n

3 columns of 3 components matrix of double-precision floating-point numbers using low precision arithmetic in term of ULPs.

\n
See also
GLSL 4.20.8 specification, section 4.1.6 Matrices
\n
\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
\n\n

Definition at line 16 of file matrix_double3x3_precision.hpp.

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typedef mat< 3, 2, double, lowp > lowp_dmat3x2
\n
\n\n

3 columns of 2 components matrix of double-precision floating-point numbers using low precision arithmetic in term of ULPs.

\n
See also
GLSL 4.20.8 specification, section 4.1.6 Matrices
\n
\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
\n\n

Definition at line 16 of file matrix_double3x2_precision.hpp.

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typedef mat< 3, 3, double, lowp > lowp_dmat3x3
\n
\n\n

3 columns of 3 components matrix of double-precision floating-point numbers using low precision arithmetic in term of ULPs.

\n
See also
GLSL 4.20.8 specification, section 4.1.6 Matrices
\n
\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
\n\n

Definition at line 34 of file matrix_double3x3_precision.hpp.

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typedef mat< 3, 4, double, lowp > lowp_dmat3x4
\n
\n\n

3 columns of 4 components matrix of double-precision floating-point numbers using low precision arithmetic in term of ULPs.

\n
See also
GLSL 4.20.8 specification, section 4.1.6 Matrices
\n
\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
\n\n

Definition at line 16 of file matrix_double3x4_precision.hpp.

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typedef mat< 4, 4, f64, lowp > lowp_dmat4
\n
\n\n

4 columns of 4 components matrix of double-precision floating-point numbers using low precision arithmetic in term of ULPs.

\n
See also
GLSL 4.20.8 specification, section 4.1.6 Matrices
\n
\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
\n\n

Definition at line 16 of file matrix_double4x4_precision.hpp.

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typedef mat< 4, 2, double, lowp > lowp_dmat4x2
\n
\n\n

4 columns of 2 components matrix of double-precision floating-point numbers using low precision arithmetic in term of ULPs.

\n
See also
GLSL 4.20.8 specification, section 4.1.6 Matrices
\n
\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
\n\n

Definition at line 16 of file matrix_double4x2_precision.hpp.

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\n \n \n \n \n
typedef mat< 4, 3, double, lowp > lowp_dmat4x3
\n
\n\n

4 columns of 3 components matrix of double-precision floating-point numbers using low precision arithmetic in term of ULPs.

\n
See also
GLSL 4.20.8 specification, section 4.1.6 Matrices
\n
\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
\n\n

Definition at line 16 of file matrix_double4x3_precision.hpp.

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typedef mat< 4, 4, double, lowp > lowp_dmat4x4
\n
\n\n

4 columns of 4 components matrix of double-precision floating-point numbers using low precision arithmetic in term of ULPs.

\n
See also
GLSL 4.20.8 specification, section 4.1.6 Matrices
\n
\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
\n\n

Definition at line 34 of file matrix_double4x4_precision.hpp.

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typedef mat< 2, 2, f32, lowp > lowp_mat2
\n
\n\n

2 columns of 2 components matrix of single-precision floating-point numbers using low precision arithmetic in term of ULPs.

\n
See also
GLSL 4.20.8 specification, section 4.1.6 Matrices
\n
\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
\n\n

Definition at line 16 of file matrix_float2x2_precision.hpp.

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typedef mat< 2, 2, f32, lowp > lowp_mat2x2
\n
\n\n

2 columns of 2 components matrix of single-precision floating-point numbers using low precision arithmetic in term of ULPs.

\n
See also
GLSL 4.20.8 specification, section 4.1.6 Matrices
\n
\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
\n\n

Definition at line 34 of file matrix_float2x2_precision.hpp.

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\n \n \n \n \n
typedef mat< 2, 3, f32, lowp > lowp_mat2x3
\n
\n\n

2 columns of 3 components matrix of single-precision floating-point numbers using low precision arithmetic in term of ULPs.

\n
See also
GLSL 4.20.8 specification, section 4.1.6 Matrices
\n
\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
\n\n

Definition at line 16 of file matrix_float2x3_precision.hpp.

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typedef mat< 2, 4, f32, lowp > lowp_mat2x4
\n
\n\n

2 columns of 4 components matrix of single-precision floating-point numbers using low precision arithmetic in term of ULPs.

\n
See also
GLSL 4.20.8 specification, section 4.1.6 Matrices
\n
\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
\n\n

Definition at line 16 of file matrix_float2x4_precision.hpp.

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typedef mat< 3, 3, f32, lowp > lowp_mat3
\n
\n\n

3 columns of 3 components matrix of single-precision floating-point numbers using low precision arithmetic in term of ULPs.

\n
See also
GLSL 4.20.8 specification, section 4.1.6 Matrices
\n
\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
\n\n

Definition at line 16 of file matrix_float3x3_precision.hpp.

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typedef mat< 3, 2, f32, lowp > lowp_mat3x2
\n
\n\n

3 columns of 2 components matrix of single-precision floating-point numbers using low precision arithmetic in term of ULPs.

\n
See also
GLSL 4.20.8 specification, section 4.1.6 Matrices
\n
\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
\n\n

Definition at line 16 of file matrix_float3x2_precision.hpp.

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typedef mat< 3, 3, f32, lowp > lowp_mat3x3
\n
\n\n

3 columns of 3 components matrix of single-precision floating-point numbers using low precision arithmetic in term of ULPs.

\n
See also
GLSL 4.20.8 specification, section 4.1.6 Matrices
\n
\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
\n\n

Definition at line 34 of file matrix_float3x3_precision.hpp.

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typedef mat< 3, 4, f32, lowp > lowp_mat3x4
\n
\n\n

3 columns of 4 components matrix of single-precision floating-point numbers using low precision arithmetic in term of ULPs.

\n
See also
GLSL 4.20.8 specification, section 4.1.6 Matrices
\n
\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
\n\n

Definition at line 16 of file matrix_float3x4_precision.hpp.

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typedef mat< 4, 4, f32, lowp > lowp_mat4
\n
\n\n

4 columns of 4 components matrix of single-precision floating-point numbers using low precision arithmetic in term of ULPs.

\n
See also
GLSL 4.20.8 specification, section 4.1.6 Matrices
\n
\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
\n\n

Definition at line 16 of file matrix_float4x4_precision.hpp.

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typedef mat< 4, 2, f32, lowp > lowp_mat4x2
\n
\n\n

4 columns of 2 components matrix of single-precision floating-point numbers using low precision arithmetic in term of ULPs.

\n
See also
GLSL 4.20.8 specification, section 4.1.6 Matrices
\n
\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
\n\n

Definition at line 16 of file matrix_float4x2_precision.hpp.

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typedef mat< 4, 3, f32, lowp > lowp_mat4x3
\n
\n\n

4 columns of 3 components matrix of single-precision floating-point numbers using low precision arithmetic in term of ULPs.

\n
See also
GLSL 4.20.8 specification, section 4.1.6 Matrices
\n
\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
\n\n

Definition at line 16 of file matrix_float4x3_precision.hpp.

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typedef mat< 4, 4, f32, lowp > lowp_mat4x4
\n
\n\n

4 columns of 4 components matrix of single-precision floating-point numbers using low precision arithmetic in term of ULPs.

\n
See also
GLSL 4.20.8 specification, section 4.1.6 Matrices
\n
\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
\n\n

Definition at line 34 of file matrix_float4x4_precision.hpp.

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typedef mat< 2, 2, f64, mediump > mediump_dmat2
\n
\n\n

2 columns of 2 components matrix of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.

\n
See also
GLSL 4.20.8 specification, section 4.1.6 Matrices
\n
\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
\n\n

Definition at line 22 of file matrix_double2x2_precision.hpp.

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typedef mat< 2, 2, double, mediump > mediump_dmat2x2
\n
\n\n

2 columns of 2 components matrix of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.

\n
See also
GLSL 4.20.8 specification, section 4.1.6 Matrices
\n
\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
\n\n

Definition at line 40 of file matrix_double2x2_precision.hpp.

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typedef mat< 2, 3, double, mediump > mediump_dmat2x3
\n
\n\n

2 columns of 3 components matrix of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.

\n
See also
GLSL 4.20.8 specification, section 4.1.6 Matrices
\n
\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
\n\n

Definition at line 22 of file matrix_double2x3_precision.hpp.

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typedef mat< 2, 4, double, mediump > mediump_dmat2x4
\n
\n\n

2 columns of 4 components matrix of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.

\n
See also
GLSL 4.20.8 specification, section 4.1.6 Matrices
\n
\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
\n\n

Definition at line 22 of file matrix_double2x4_precision.hpp.

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typedef mat< 3, 3, f64, mediump > mediump_dmat3
\n
\n\n

3 columns of 3 components matrix of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.

\n
See also
GLSL 4.20.8 specification, section 4.1.6 Matrices
\n
\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
\n\n

Definition at line 22 of file matrix_double3x3_precision.hpp.

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typedef mat< 3, 2, double, mediump > mediump_dmat3x2
\n
\n\n

3 columns of 2 components matrix of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.

\n
See also
GLSL 4.20.8 specification, section 4.1.6 Matrices
\n
\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
\n\n

Definition at line 22 of file matrix_double3x2_precision.hpp.

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typedef mat< 3, 3, double, mediump > mediump_dmat3x3
\n
\n\n

3 columns of 3 components matrix of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.

\n
See also
GLSL 4.20.8 specification, section 4.1.6 Matrices
\n
\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
\n\n

Definition at line 40 of file matrix_double3x3_precision.hpp.

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typedef mat< 3, 4, double, mediump > mediump_dmat3x4
\n
\n\n

3 columns of 4 components matrix of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.

\n
See also
GLSL 4.20.8 specification, section 4.1.6 Matrices
\n
\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
\n\n

Definition at line 22 of file matrix_double3x4_precision.hpp.

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typedef mat< 4, 4, f64, mediump > mediump_dmat4
\n
\n\n

4 columns of 4 components matrix of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.

\n
See also
GLSL 4.20.8 specification, section 4.1.6 Matrices
\n
\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
\n\n

Definition at line 22 of file matrix_double4x4_precision.hpp.

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typedef mat< 4, 2, double, mediump > mediump_dmat4x2
\n
\n\n

4 columns of 2 components matrix of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.

\n
See also
GLSL 4.20.8 specification, section 4.1.6 Matrices
\n
\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
\n\n

Definition at line 22 of file matrix_double4x2_precision.hpp.

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typedef mat< 4, 3, double, mediump > mediump_dmat4x3
\n
\n\n

4 columns of 3 components matrix of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.

\n
See also
GLSL 4.20.8 specification, section 4.1.6 Matrices
\n
\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
\n\n

Definition at line 22 of file matrix_double4x3_precision.hpp.

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typedef mat< 4, 4, double, mediump > mediump_dmat4x4
\n
\n\n

4 columns of 4 components matrix of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.

\n
See also
GLSL 4.20.8 specification, section 4.1.6 Matrices
\n
\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
\n\n

Definition at line 40 of file matrix_double4x4_precision.hpp.

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typedef mat< 2, 2, f32, mediump > mediump_mat2
\n
\n\n

2 columns of 2 components matrix of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.

\n
See also
GLSL 4.20.8 specification, section 4.1.6 Matrices
\n
\nGLSL 4.20.8 specification, section 4.7.2 Precision Qualifier
\n\n

Definition at line 22 of file matrix_float2x2_precision.hpp.

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typedef mat< 2, 2, f32, mediump > mediump_mat2x2
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2 columns of 2 components matrix of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.

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GLSL 4.20.8 specification, section 4.1.6 Matrices
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Definition at line 40 of file matrix_float2x2_precision.hpp.

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typedef mat< 2, 3, f32, mediump > mediump_mat2x3
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2 columns of 3 components matrix of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.

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GLSL 4.20.8 specification, section 4.1.6 Matrices
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Definition at line 22 of file matrix_float2x3_precision.hpp.

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typedef mat< 2, 4, f32, mediump > mediump_mat2x4
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2 columns of 4 components matrix of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.

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GLSL 4.20.8 specification, section 4.1.6 Matrices
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Definition at line 22 of file matrix_float2x4_precision.hpp.

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typedef mat< 3, 3, f32, mediump > mediump_mat3
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3 columns of 3 components matrix of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.

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GLSL 4.20.8 specification, section 4.1.6 Matrices
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Definition at line 22 of file matrix_float3x3_precision.hpp.

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typedef mat< 3, 2, f32, mediump > mediump_mat3x2
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3 columns of 2 components matrix of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.

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Definition at line 22 of file matrix_float3x2_precision.hpp.

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3 columns of 3 components matrix of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.

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GLSL 4.20.8 specification, section 4.1.6 Matrices
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Definition at line 40 of file matrix_float3x3_precision.hpp.

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typedef mat< 3, 4, f32, mediump > mediump_mat3x4
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3 columns of 4 components matrix of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.

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See also
GLSL 4.20.8 specification, section 4.1.6 Matrices
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Definition at line 22 of file matrix_float3x4_precision.hpp.

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typedef mat< 4, 4, f32, mediump > mediump_mat4
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4 columns of 4 components matrix of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.

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GLSL 4.20.8 specification, section 4.1.6 Matrices
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Definition at line 22 of file matrix_float4x4_precision.hpp.

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typedef mat< 4, 2, f32, mediump > mediump_mat4x2
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4 columns of 2 components matrix of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.

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See also
GLSL 4.20.8 specification, section 4.1.6 Matrices
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Definition at line 22 of file matrix_float4x2_precision.hpp.

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typedef mat< 4, 3, f32, mediump > mediump_mat4x3
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4 columns of 3 components matrix of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.

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See also
GLSL 4.20.8 specification, section 4.1.6 Matrices
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Definition at line 22 of file matrix_float4x3_precision.hpp.

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typedef mat< 4, 4, f32, mediump > mediump_mat4x4
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4 columns of 4 components matrix of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.

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See also
GLSL 4.20.8 specification, section 4.1.6 Matrices
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Definition at line 40 of file matrix_float4x4_precision.hpp.

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Additional features not specified by GLSL specification. \nMore...

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\nModules

 GLM_EXT_matrix_clip_space
 Defines functions that generate clip space transformation matrices.
 
 GLM_EXT_matrix_common
 Defines functions for common matrix operations.
 
 GLM_EXT_matrix_projection
 Functions that generate common projection transformation matrices.
 
 GLM_EXT_matrix_relational
 Exposes comparison functions for matrix types that take a user defined epsilon values.
 
 GLM_EXT_matrix_transform
 Defines functions that generate common transformation matrices.
 
 GLM_EXT_quaternion_common
 Provides common functions for quaternion types.
 
 GLM_EXT_quaternion_double
 Exposes double-precision floating point quaternion type.
 
 GLM_EXT_quaternion_double_precision
 Exposes double-precision floating point quaternion type with various precision in term of ULPs.
 
 GLM_EXT_quaternion_exponential
 Provides exponential functions for quaternion types.
 
 GLM_EXT_quaternion_float
 Exposes single-precision floating point quaternion type.
 
 GLM_EXT_quaternion_float_precision
 Exposes single-precision floating point quaternion type with various precision in term of ULPs.
 
 GLM_EXT_quaternion_geometric
 Provides geometric functions for quaternion types.
 
 GLM_EXT_quaternion_relational
 Exposes comparison functions for quaternion types that take a user defined epsilon values.
 
 GLM_EXT_quaternion_transform
 Provides transformation functions for quaternion types.
 
 GLM_EXT_quaternion_trigonometric
 Provides trigonometric functions for quaternion types.
 
 GLM_EXT_scalar_common
 Exposes min and max functions for 3 to 4 scalar parameters.
 
 GLM_EXT_scalar_constants
 Provides a list of constants and precomputed useful values.
 
 GLM_EXT_scalar_int_sized
 Exposes sized signed integer scalar types.
 
 GLM_EXT_scalar_integer
 Include <glm/ext/scalar_integer.hpp> to use the features of this extension.
 
 GLM_EXT_scalar_relational
 Exposes comparison functions for scalar types that take a user defined epsilon values.
 
 GLM_EXT_scalar_uint_sized
 Exposes sized unsigned integer scalar types.
 
 GLM_EXT_scalar_ulp
 Allow the measurement of the accuracy of a function against a reference implementation.
 
 GLM_EXT_vector_bool1
 Exposes bvec1 vector type.
 
 GLM_EXT_vector_bool1_precision
 Exposes highp_bvec1, mediump_bvec1 and lowp_bvec1 types.
 
 GLM_EXT_vector_common
 Exposes min and max functions for 3 to 4 vector parameters.
 
 GLM_EXT_vector_double1
 Exposes double-precision floating point vector type with one component.
 
 GLM_EXT_vector_double1_precision
 Exposes highp_dvec1, mediump_dvec1 and lowp_dvec1 types.
 
 GLM_EXT_vector_float1
 Exposes single-precision floating point vector type with one component.
 
 GLM_EXT_vector_float1_precision
 Exposes highp_vec1, mediump_vec1 and lowp_vec1 types.
 
 GLM_EXT_vector_int1
 Exposes ivec1 vector type.
 
 GLM_EXT_vector_int1_precision
 Exposes highp_ivec1, mediump_ivec1 and lowp_ivec1 types.
 
 GLM_EXT_vector_integer
 Include <glm/ext/vector_integer.hpp> to use the features of this extension.
 
 GLM_EXT_vector_relational
 Exposes comparison functions for vector types that take a user defined epsilon values.
 
 GLM_EXT_vector_uint1
 Exposes uvec1 vector type.
 
 GLM_EXT_vector_uint1_precision
 Exposes highp_uvec1, mediump_uvec1 and lowp_uvec1 types.
 
 GLM_EXT_vector_ulp
 Allow the measurement of the accuracy of a function against a reference implementation.
 
\n

Detailed Description

\n

Additional features not specified by GLSL specification.

\n

EXT extensions are fully tested and documented.

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Even if it's highly unrecommended, it's possible to include all the extensions at once by including <glm/ext.hpp>. Otherwise, each extension needs to be included a specific file.

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\nModules

 GLM_GTC_bitfield
 Include <glm/gtc/bitfield.hpp> to use the features of this extension.
 
 GLM_GTC_color_space
 Include <glm/gtc/color_space.hpp> to use the features of this extension.
 
 GLM_GTC_constants
 Include <glm/gtc/constants.hpp> to use the features of this extension.
 
 GLM_GTC_epsilon
 Include <glm/gtc/epsilon.hpp> to use the features of this extension.
 
 GLM_GTC_integer
 Include <glm/gtc/integer.hpp> to use the features of this extension.
 
 GLM_GTC_matrix_access
 Include <glm/gtc/matrix_access.hpp> to use the features of this extension.
 
 GLM_GTC_matrix_integer
 Include <glm/gtc/matrix_integer.hpp> to use the features of this extension.
 
 GLM_GTC_matrix_inverse
 Include <glm/gtc/matrix_integer.hpp> to use the features of this extension.
 
 GLM_GTC_matrix_transform
 Include <glm/gtc/matrix_transform.hpp> to use the features of this extension.
 
 GLM_GTC_noise
 Include <glm/gtc/noise.hpp> to use the features of this extension.
 
 GLM_GTC_packing
 Include <glm/gtc/packing.hpp> to use the features of this extension.
 
 GLM_GTC_quaternion
 Include <glm/gtc/quaternion.hpp> to use the features of this extension.
 
 GLM_GTC_random
 Include <glm/gtc/random.hpp> to use the features of this extension.
 
 GLM_GTC_reciprocal
 Include <glm/gtc/reciprocal.hpp> to use the features of this extension.
 
 GLM_GTC_round
 Include <glm/gtc/round.hpp> to use the features of this extension.
 
 GLM_GTC_type_aligned
 Include <glm/gtc/type_aligned.hpp> to use the features of this extension.
 
 GLM_GTC_type_precision
 Include <glm/gtc/type_precision.hpp> to use the features of this extension.
 
 GLM_GTC_type_ptr
 Include <glm/gtc/type_ptr.hpp> to use the features of this extension.
 
 GLM_GTC_ulp
 Include <glm/gtc/ulp.hpp> to use the features of this extension.
 
 GLM_GTC_vec1
 Include <glm/gtc/vec1.hpp> to use the features of this extension.
 
\n

Detailed Description

\n

Additional features not specified by GLSL specification.

\n

GTC extensions aim to be stable with tests and documentation.

\n

Even if it's highly unrecommended, it's possible to include all the extensions at once by including <glm/ext.hpp>. Otherwise, each extension needs to be included a specific file.

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\nModules

 GLM_GTX_associated_min_max
 Include <glm/gtx/associated_min_max.hpp> to use the features of this extension.
 
 GLM_GTX_bit
 Include <glm/gtx/bit.hpp> to use the features of this extension.
 
 GLM_GTX_closest_point
 Include <glm/gtx/closest_point.hpp> to use the features of this extension.
 
 GLM_GTX_color_encoding
 Include <glm/gtx/color_encoding.hpp> to use the features of this extension.
 
 GLM_GTX_color_space
 Include <glm/gtx/color_space.hpp> to use the features of this extension.
 
 GLM_GTX_color_space_YCoCg
 Include <glm/gtx/color_space_YCoCg.hpp> to use the features of this extension.
 
 GLM_GTX_common
 Include <glm/gtx/common.hpp> to use the features of this extension.
 
 GLM_GTX_compatibility
 Include <glm/gtx/compatibility.hpp> to use the features of this extension.
 
 GLM_GTX_component_wise
 Include <glm/gtx/component_wise.hpp> to use the features of this extension.
 
 GLM_GTX_dual_quaternion
 Include <glm/gtx/dual_quaternion.hpp> to use the features of this extension.
 
 GLM_GTX_easing
 Include <glm/gtx/easing.hpp> to use the features of this extension.
 
 GLM_GTX_euler_angles
 Include <glm/gtx/euler_angles.hpp> to use the features of this extension.
 
 GLM_GTX_extend
 Include <glm/gtx/extend.hpp> to use the features of this extension.
 
 GLM_GTX_extented_min_max
 Include <glm/gtx/extented_min_max.hpp> to use the features of this extension.
 
 GLM_GTX_exterior_product
 Include <glm/gtx/exterior_product.hpp> to use the features of this extension.
 
 GLM_GTX_fast_exponential
 Include <glm/gtx/fast_exponential.hpp> to use the features of this extension.
 
 GLM_GTX_fast_square_root
 Include <glm/gtx/fast_square_root.hpp> to use the features of this extension.
 
 GLM_GTX_fast_trigonometry
 Include <glm/gtx/fast_trigonometry.hpp> to use the features of this extension.
 
 GLM_GTX_functions
 Include <glm/gtx/functions.hpp> to use the features of this extension.
 
 GLM_GTX_gradient_paint
 Include <glm/gtx/gradient_paint.hpp> to use the features of this extension.
 
 GLM_GTX_handed_coordinate_space
 Include <glm/gtx/handed_coordinate_system.hpp> to use the features of this extension.
 
 GLM_GTX_hash
 Include <glm/gtx/hash.hpp> to use the features of this extension.
 
 GLM_GTX_integer
 Include <glm/gtx/integer.hpp> to use the features of this extension.
 
 GLM_GTX_intersect
 Include <glm/gtx/intersect.hpp> to use the features of this extension.
 
 GLM_GTX_io
 Include <glm/gtx/io.hpp> to use the features of this extension.
 
 GLM_GTX_log_base
 Include <glm/gtx/log_base.hpp> to use the features of this extension.
 
 GLM_GTX_matrix_cross_product
 Include <glm/gtx/matrix_cross_product.hpp> to use the features of this extension.
 
 GLM_GTX_matrix_decompose
 Include <glm/gtx/matrix_decompose.hpp> to use the features of this extension.
 
 GLM_GTX_matrix_factorisation
 Include <glm/gtx/matrix_factorisation.hpp> to use the features of this extension.
 
 GLM_GTX_matrix_interpolation
 Include <glm/gtx/matrix_interpolation.hpp> to use the features of this extension.
 
 GLM_GTX_matrix_major_storage
 Include <glm/gtx/matrix_major_storage.hpp> to use the features of this extension.
 
 GLM_GTX_matrix_operation
 Include <glm/gtx/matrix_operation.hpp> to use the features of this extension.
 
 GLM_GTX_matrix_query
 Include <glm/gtx/matrix_query.hpp> to use the features of this extension.
 
 GLM_GTX_matrix_transform_2d
 Include <glm/gtx/matrix_transform_2d.hpp> to use the features of this extension.
 
 GLM_GTX_mixed_producte
 Include <glm/gtx/mixed_product.hpp> to use the features of this extension.
 
 GLM_GTX_norm
 Include <glm/gtx/norm.hpp> to use the features of this extension.
 
 GLM_GTX_normal
 Include <glm/gtx/normal.hpp> to use the features of this extension.
 
 GLM_GTX_normalize_dot
 Include <glm/gtx/normalized_dot.hpp> to use the features of this extension.
 
 GLM_GTX_number_precision
 Include <glm/gtx/number_precision.hpp> to use the features of this extension.
 
 GLM_GTX_optimum_pow
 Include <glm/gtx/optimum_pow.hpp> to use the features of this extension.
 
 GLM_GTX_orthonormalize
 Include <glm/gtx/orthonormalize.hpp> to use the features of this extension.
 
 GLM_GTX_perpendicular
 Include <glm/gtx/perpendicular.hpp> to use the features of this extension.
 
 GLM_GTX_polar_coordinates
 Include <glm/gtx/polar_coordinates.hpp> to use the features of this extension.
 
 GLM_GTX_projection
 Include <glm/gtx/projection.hpp> to use the features of this extension.
 
 GLM_GTX_quaternion
 Include <glm/gtx/quaternion.hpp> to use the features of this extension.
 
 GLM_GTX_range
 Include <glm/gtx/range.hpp> to use the features of this extension.
 
 GLM_GTX_raw_data
 Include <glm/gtx/raw_data.hpp> to use the features of this extension.
 
 GLM_GTX_rotate_normalized_axis
 Include <glm/gtx/rotate_normalized_axis.hpp> to use the features of this extension.
 
 GLM_GTX_rotate_vector
 Include <glm/gtx/rotate_vector.hpp> to use the features of this extension.
 
 GLM_GTX_scalar_relational
 Include <glm/gtx/scalar_relational.hpp> to use the features of this extension.
 
 GLM_GTX_spline
 Include <glm/gtx/spline.hpp> to use the features of this extension.
 
 GLM_GTX_std_based_type
 Include <glm/gtx/std_based_type.hpp> to use the features of this extension.
 
 GLM_GTX_string_cast
 Include <glm/gtx/string_cast.hpp> to use the features of this extension.
 
 GLM_GTX_texture
 Include <glm/gtx/texture.hpp> to use the features of this extension.
 
 GLM_GTX_transform
 Include <glm/gtx/transform.hpp> to use the features of this extension.
 
 GLM_GTX_transform2
 Include <glm/gtx/transform2.hpp> to use the features of this extension.
 
 GLM_GTX_type_aligned
 Include <glm/gtx/type_aligned.hpp> to use the features of this extension.
 
 GLM_GTX_type_trait
 Include <glm/gtx/type_trait.hpp> to use the features of this extension.
 
 GLM_GTX_vec_swizzle
 Include <glm/gtx/vec_swizzle.hpp> to use the features of this extension.
 
 GLM_GTX_vector_angle
 Include <glm/gtx/vector_angle.hpp> to use the features of this extension.
 
 GLM_GTX_vector_query
 Include <glm/gtx/vector_query.hpp> to use the features of this extension.
 
 GLM_GTX_wrap
 Include <glm/gtx/wrap.hpp> to use the features of this extension.
 
\n

Detailed Description

\n

Experimental features not specified by GLSL specification.

\n

Experimental extensions are useful functions and types, but the development of their API and functionality is not necessarily stable. They can change substantially between versions. Backwards compatibility is not much of an issue for them.

\n

Even if it's highly unrecommended, it's possible to include all the extensions at once by including <glm/ext.hpp>. Otherwise, each extension needs to be included a specific file.

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\nFunctions
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GLM_GTC_bitfield
Recommended extensions
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Include <glm/gtc/bitfield.hpp> to use the features of this extension. \nMore...

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\nFunctions

GLM_FUNC_DECL glm::u8vec2 bitfieldDeinterleave (glm::uint16 x)
 Deinterleaves the bits of x. More...
 
GLM_FUNC_DECL glm::u16vec2 bitfieldDeinterleave (glm::uint32 x)
 Deinterleaves the bits of x. More...
 
GLM_FUNC_DECL glm::u32vec2 bitfieldDeinterleave (glm::uint64 x)
 Deinterleaves the bits of x. More...
 
template<typename genIUType >
GLM_FUNC_DECL genIUType bitfieldFillOne (genIUType Value, int FirstBit, int BitCount)
 Set to 1 a range of bits. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > bitfieldFillOne (vec< L, T, Q > const &Value, int FirstBit, int BitCount)
 Set to 1 a range of bits. More...
 
template<typename genIUType >
GLM_FUNC_DECL genIUType bitfieldFillZero (genIUType Value, int FirstBit, int BitCount)
 Set to 0 a range of bits. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > bitfieldFillZero (vec< L, T, Q > const &Value, int FirstBit, int BitCount)
 Set to 0 a range of bits. More...
 
GLM_FUNC_DECL int16 bitfieldInterleave (int8 x, int8 y)
 Interleaves the bits of x and y. More...
 
GLM_FUNC_DECL uint16 bitfieldInterleave (uint8 x, uint8 y)
 Interleaves the bits of x and y. More...
 
GLM_FUNC_DECL uint16 bitfieldInterleave (u8vec2 const &v)
 Interleaves the bits of x and y. More...
 
GLM_FUNC_DECL int32 bitfieldInterleave (int16 x, int16 y)
 Interleaves the bits of x and y. More...
 
GLM_FUNC_DECL uint32 bitfieldInterleave (uint16 x, uint16 y)
 Interleaves the bits of x and y. More...
 
GLM_FUNC_DECL uint32 bitfieldInterleave (u16vec2 const &v)
 Interleaves the bits of x and y. More...
 
GLM_FUNC_DECL int64 bitfieldInterleave (int32 x, int32 y)
 Interleaves the bits of x and y. More...
 
GLM_FUNC_DECL uint64 bitfieldInterleave (uint32 x, uint32 y)
 Interleaves the bits of x and y. More...
 
GLM_FUNC_DECL uint64 bitfieldInterleave (u32vec2 const &v)
 Interleaves the bits of x and y. More...
 
GLM_FUNC_DECL int32 bitfieldInterleave (int8 x, int8 y, int8 z)
 Interleaves the bits of x, y and z. More...
 
GLM_FUNC_DECL uint32 bitfieldInterleave (uint8 x, uint8 y, uint8 z)
 Interleaves the bits of x, y and z. More...
 
GLM_FUNC_DECL int64 bitfieldInterleave (int16 x, int16 y, int16 z)
 Interleaves the bits of x, y and z. More...
 
GLM_FUNC_DECL uint64 bitfieldInterleave (uint16 x, uint16 y, uint16 z)
 Interleaves the bits of x, y and z. More...
 
GLM_FUNC_DECL int64 bitfieldInterleave (int32 x, int32 y, int32 z)
 Interleaves the bits of x, y and z. More...
 
GLM_FUNC_DECL uint64 bitfieldInterleave (uint32 x, uint32 y, uint32 z)
 Interleaves the bits of x, y and z. More...
 
GLM_FUNC_DECL int32 bitfieldInterleave (int8 x, int8 y, int8 z, int8 w)
 Interleaves the bits of x, y, z and w. More...
 
GLM_FUNC_DECL uint32 bitfieldInterleave (uint8 x, uint8 y, uint8 z, uint8 w)
 Interleaves the bits of x, y, z and w. More...
 
GLM_FUNC_DECL int64 bitfieldInterleave (int16 x, int16 y, int16 z, int16 w)
 Interleaves the bits of x, y, z and w. More...
 
GLM_FUNC_DECL uint64 bitfieldInterleave (uint16 x, uint16 y, uint16 z, uint16 w)
 Interleaves the bits of x, y, z and w. More...
 
template<typename genIUType >
GLM_FUNC_DECL genIUType bitfieldRotateLeft (genIUType In, int Shift)
 Rotate all bits to the left. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > bitfieldRotateLeft (vec< L, T, Q > const &In, int Shift)
 Rotate all bits to the left. More...
 
template<typename genIUType >
GLM_FUNC_DECL genIUType bitfieldRotateRight (genIUType In, int Shift)
 Rotate all bits to the right. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > bitfieldRotateRight (vec< L, T, Q > const &In, int Shift)
 Rotate all bits to the right. More...
 
template<typename genIUType >
GLM_FUNC_DECL genIUType mask (genIUType Bits)
 Build a mask of 'count' bits. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > mask (vec< L, T, Q > const &v)
 Build a mask of 'count' bits. More...
 
\n

Detailed Description

\n

Include <glm/gtc/bitfield.hpp> to use the features of this extension.

\n

Allow to perform bit operations on integer values

\n

Function Documentation

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GLM_FUNC_DECL glm::u8vec2 glm::bitfieldDeinterleave (glm::uint16 x)
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Deinterleaves the bits of x.

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See also
to use the features of this extension. \">GLM_GTC_bitfield
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GLM_FUNC_DECL glm::u16vec2 glm::bitfieldDeinterleave (glm::uint32 x)
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Deinterleaves the bits of x.

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See also
to use the features of this extension. \">GLM_GTC_bitfield
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GLM_FUNC_DECL glm::u32vec2 glm::bitfieldDeinterleave (glm::uint64 x)
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Deinterleaves the bits of x.

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See also
to use the features of this extension. \">GLM_GTC_bitfield
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GLM_FUNC_DECL genIUType glm::bitfieldFillOne (genIUType Value,
int FirstBit,
int BitCount 
)
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Set to 1 a range of bits.

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See also
to use the features of this extension. \">GLM_GTC_bitfield
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GLM_FUNC_DECL vec<L, T, Q> glm::bitfieldFillOne (vec< L, T, Q > const & Value,
int FirstBit,
int BitCount 
)
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Set to 1 a range of bits.

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LInteger between 1 and 4 included that qualify the dimension of the vector
TSigned and unsigned integer scalar types
QValue from qualifier enum
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See also
to use the features of this extension. \">GLM_GTC_bitfield
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GLM_FUNC_DECL genIUType glm::bitfieldFillZero (genIUType Value,
int FirstBit,
int BitCount 
)
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Set to 0 a range of bits.

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See also
to use the features of this extension. \">GLM_GTC_bitfield
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GLM_FUNC_DECL vec<L, T, Q> glm::bitfieldFillZero (vec< L, T, Q > const & Value,
int FirstBit,
int BitCount 
)
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Set to 0 a range of bits.

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LInteger between 1 and 4 included that qualify the dimension of the vector
TSigned and unsigned integer scalar types
QValue from qualifier enum
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See also
to use the features of this extension. \">GLM_GTC_bitfield
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GLM_FUNC_DECL int16 glm::bitfieldInterleave (int8 x,
int8 y 
)
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Interleaves the bits of x and y.

\n

The first bit is the first bit of x followed by the first bit of y. The other bits are interleaved following the previous sequence.

\n
See also
to use the features of this extension. \">GLM_GTC_bitfield
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GLM_FUNC_DECL uint16 glm::bitfieldInterleave (uint8 x,
uint8 y 
)
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Interleaves the bits of x and y.

\n

The first bit is the first bit of x followed by the first bit of y. The other bits are interleaved following the previous sequence.

\n
See also
to use the features of this extension. \">GLM_GTC_bitfield
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GLM_FUNC_DECL uint16 glm::bitfieldInterleave (u8vec2 const & v)
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Interleaves the bits of x and y.

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The first bit is the first bit of v.x followed by the first bit of v.y. The other bits are interleaved following the previous sequence.

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See also
to use the features of this extension. \">GLM_GTC_bitfield
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GLM_FUNC_DECL int32 glm::bitfieldInterleave (int16 x,
int16 y 
)
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Interleaves the bits of x and y.

\n

The first bit is the first bit of x followed by the first bit of y. The other bits are interleaved following the previous sequence.

\n
See also
to use the features of this extension. \">GLM_GTC_bitfield
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GLM_FUNC_DECL uint32 glm::bitfieldInterleave (uint16 x,
uint16 y 
)
\n
\n\n

Interleaves the bits of x and y.

\n

The first bit is the first bit of x followed by the first bit of y. The other bits are interleaved following the previous sequence.

\n
See also
to use the features of this extension. \">GLM_GTC_bitfield
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GLM_FUNC_DECL uint32 glm::bitfieldInterleave (u16vec2 const & v)
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Interleaves the bits of x and y.

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The first bit is the first bit of v.x followed by the first bit of v.y. The other bits are interleaved following the previous sequence.

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See also
to use the features of this extension. \">GLM_GTC_bitfield
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GLM_FUNC_DECL int64 glm::bitfieldInterleave (int32 x,
int32 y 
)
\n
\n\n

Interleaves the bits of x and y.

\n

The first bit is the first bit of x followed by the first bit of y. The other bits are interleaved following the previous sequence.

\n
See also
to use the features of this extension. \">GLM_GTC_bitfield
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GLM_FUNC_DECL uint64 glm::bitfieldInterleave (uint32 x,
uint32 y 
)
\n
\n\n

Interleaves the bits of x and y.

\n

The first bit is the first bit of x followed by the first bit of y. The other bits are interleaved following the previous sequence.

\n
See also
to use the features of this extension. \">GLM_GTC_bitfield
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GLM_FUNC_DECL uint64 glm::bitfieldInterleave (u32vec2 const & v)
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Interleaves the bits of x and y.

\n

The first bit is the first bit of v.x followed by the first bit of v.y. The other bits are interleaved following the previous sequence.

\n
See also
to use the features of this extension. \">GLM_GTC_bitfield
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GLM_FUNC_DECL int32 glm::bitfieldInterleave (int8 x,
int8 y,
int8 z 
)
\n
\n\n

Interleaves the bits of x, y and z.

\n

The first bit is the first bit of x followed by the first bit of y and the first bit of z. The other bits are interleaved following the previous sequence.

\n
See also
to use the features of this extension. \">GLM_GTC_bitfield
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GLM_FUNC_DECL uint32 glm::bitfieldInterleave (uint8 x,
uint8 y,
uint8 z 
)
\n
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Interleaves the bits of x, y and z.

\n

The first bit is the first bit of x followed by the first bit of y and the first bit of z. The other bits are interleaved following the previous sequence.

\n
See also
to use the features of this extension. \">GLM_GTC_bitfield
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GLM_FUNC_DECL int64 glm::bitfieldInterleave (int16 x,
int16 y,
int16 z 
)
\n
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Interleaves the bits of x, y and z.

\n

The first bit is the first bit of x followed by the first bit of y and the first bit of z. The other bits are interleaved following the previous sequence.

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See also
to use the features of this extension. \">GLM_GTC_bitfield
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GLM_FUNC_DECL uint64 glm::bitfieldInterleave (uint16 x,
uint16 y,
uint16 z 
)
\n
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Interleaves the bits of x, y and z.

\n

The first bit is the first bit of x followed by the first bit of y and the first bit of z. The other bits are interleaved following the previous sequence.

\n
See also
to use the features of this extension. \">GLM_GTC_bitfield
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GLM_FUNC_DECL int64 glm::bitfieldInterleave (int32 x,
int32 y,
int32 z 
)
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Interleaves the bits of x, y and z.

\n

The first bit is the first bit of x followed by the first bit of y and the first bit of z. The other bits are interleaved following the previous sequence.

\n
See also
to use the features of this extension. \">GLM_GTC_bitfield
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GLM_FUNC_DECL uint64 glm::bitfieldInterleave (uint32 x,
uint32 y,
uint32 z 
)
\n
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Interleaves the bits of x, y and z.

\n

The first bit is the first bit of x followed by the first bit of y and the first bit of z. The other bits are interleaved following the previous sequence.

\n
See also
to use the features of this extension. \">GLM_GTC_bitfield
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GLM_FUNC_DECL int32 glm::bitfieldInterleave (int8 x,
int8 y,
int8 z,
int8 w 
)
\n
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Interleaves the bits of x, y, z and w.

\n

The first bit is the first bit of x followed by the first bit of y, the first bit of z and finally the first bit of w. The other bits are interleaved following the previous sequence.

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See also
to use the features of this extension. \">GLM_GTC_bitfield
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GLM_FUNC_DECL uint32 glm::bitfieldInterleave (uint8 x,
uint8 y,
uint8 z,
uint8 w 
)
\n
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Interleaves the bits of x, y, z and w.

\n

The first bit is the first bit of x followed by the first bit of y, the first bit of z and finally the first bit of w. The other bits are interleaved following the previous sequence.

\n
See also
to use the features of this extension. \">GLM_GTC_bitfield
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GLM_FUNC_DECL int64 glm::bitfieldInterleave (int16 x,
int16 y,
int16 z,
int16 w 
)
\n
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Interleaves the bits of x, y, z and w.

\n

The first bit is the first bit of x followed by the first bit of y, the first bit of z and finally the first bit of w. The other bits are interleaved following the previous sequence.

\n
See also
to use the features of this extension. \">GLM_GTC_bitfield
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GLM_FUNC_DECL uint64 glm::bitfieldInterleave (uint16 x,
uint16 y,
uint16 z,
uint16 w 
)
\n
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Interleaves the bits of x, y, z and w.

\n

The first bit is the first bit of x followed by the first bit of y, the first bit of z and finally the first bit of w. The other bits are interleaved following the previous sequence.

\n
See also
to use the features of this extension. \">GLM_GTC_bitfield
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GLM_FUNC_DECL genIUType glm::bitfieldRotateLeft (genIUType In,
int Shift 
)
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Rotate all bits to the left.

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All the bits dropped in the left side are inserted back on the right side.

\n
See also
to use the features of this extension. \">GLM_GTC_bitfield
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GLM_FUNC_DECL vec<L, T, Q> glm::bitfieldRotateLeft (vec< L, T, Q > const & In,
int Shift 
)
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Rotate all bits to the left.

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All the bits dropped in the left side are inserted back on the right side.

\n
Template Parameters
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LInteger between 1 and 4 included that qualify the dimension of the vector
TSigned and unsigned integer scalar types
QValue from qualifier enum
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See also
to use the features of this extension. \">GLM_GTC_bitfield
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GLM_FUNC_DECL genIUType glm::bitfieldRotateRight (genIUType In,
int Shift 
)
\n
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Rotate all bits to the right.

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All the bits dropped in the right side are inserted back on the left side.

\n
See also
to use the features of this extension. \">GLM_GTC_bitfield
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GLM_FUNC_DECL vec<L, T, Q> glm::bitfieldRotateRight (vec< L, T, Q > const & In,
int Shift 
)
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Rotate all bits to the right.

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All the bits dropped in the right side are inserted back on the left side.

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Template Parameters
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LInteger between 1 and 4 included that qualify the dimension of the vector
TSigned and unsigned integer scalar types
QValue from qualifier enum
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See also
to use the features of this extension. \">GLM_GTC_bitfield
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GLM_FUNC_DECL genIUType glm::mask (genIUType Bits)
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Build a mask of 'count' bits.

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See also
to use the features of this extension. \">GLM_GTC_bitfield
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GLM_FUNC_DECL vec<L, T, Q> glm::mask (vec< L, T, Q > const & v)
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Build a mask of 'count' bits.

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Template Parameters
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LInteger between 1 and 4 included that qualify the dimension of the vector
TSigned and unsigned integer scalar types
QValue from qualifier enum
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See also
to use the features of this extension. \">GLM_GTC_bitfield
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GLM_GTC_color_space
Recommended extensions
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Include <glm/gtc/color_space.hpp> to use the features of this extension. \nMore...

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\nFunctions

template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > convertLinearToSRGB (vec< L, T, Q > const &ColorLinear)
 Convert a linear color to sRGB color using a standard gamma correction. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > convertLinearToSRGB (vec< L, T, Q > const &ColorLinear, T Gamma)
 Convert a linear color to sRGB color using a custom gamma correction. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > convertSRGBToLinear (vec< L, T, Q > const &ColorSRGB)
 Convert a sRGB color to linear color using a standard gamma correction. More...
 
\ntemplate<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > convertSRGBToLinear (vec< L, T, Q > const &ColorSRGB, T Gamma)
 Convert a sRGB color to linear color using a custom gamma correction.
 
\n

Detailed Description

\n

Include <glm/gtc/color_space.hpp> to use the features of this extension.

\n

Allow to perform bit operations on integer values

\n

Function Documentation

\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL vec<L, T, Q> glm::convertLinearToSRGB (vec< L, T, Q > const & ColorLinear)
\n
\n\n

Convert a linear color to sRGB color using a standard gamma correction.

\n

IEC 61966-2-1:1999 / Rec. 709 specification https://www.w3.org/Graphics/Color/srgb

\n\n
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GLM_FUNC_DECL vec<L, T, Q> glm::convertLinearToSRGB (vec< L, T, Q > const & ColorLinear,
T Gamma 
)
\n
\n\n

Convert a linear color to sRGB color using a custom gamma correction.

\n

IEC 61966-2-1:1999 / Rec. 709 specification https://www.w3.org/Graphics/Color/srgb

\n\n
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GLM_FUNC_DECL vec<L, T, Q> glm::convertSRGBToLinear (vec< L, T, Q > const & ColorSRGB)
\n
\n\n

Convert a sRGB color to linear color using a standard gamma correction.

\n

IEC 61966-2-1:1999 / Rec. 709 specification https://www.w3.org/Graphics/Color/srgb

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\nGenerated by  \n\"doxygen\"/\n 1.8.10\n
\n\n\n" }, { "path": "field_construction/submodules/diff-langsurf-rasterizer/third_party/glm/doc/api/a00290.html", "content": "\n\n\n\n\n\n0.9.9 API documentation: GLM_GTC_constants\n\n\n\n\n\n\n\n\n\n\n
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Recommended extensions
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Include <glm/gtc/constants.hpp> to use the features of this extension. \nMore...

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\nFunctions

template<typename genType >
GLM_FUNC_DECL GLM_CONSTEXPR genType e ()
 Return e constant. More...
 
template<typename genType >
GLM_FUNC_DECL GLM_CONSTEXPR genType euler ()
 Return Euler's constant. More...
 
template<typename genType >
GLM_FUNC_DECL GLM_CONSTEXPR genType four_over_pi ()
 Return 4 / pi. More...
 
template<typename genType >
GLM_FUNC_DECL GLM_CONSTEXPR genType golden_ratio ()
 Return the golden ratio constant. More...
 
template<typename genType >
GLM_FUNC_DECL GLM_CONSTEXPR genType half_pi ()
 Return pi / 2. More...
 
template<typename genType >
GLM_FUNC_DECL GLM_CONSTEXPR genType ln_ln_two ()
 Return ln(ln(2)). More...
 
template<typename genType >
GLM_FUNC_DECL GLM_CONSTEXPR genType ln_ten ()
 Return ln(10). More...
 
template<typename genType >
GLM_FUNC_DECL GLM_CONSTEXPR genType ln_two ()
 Return ln(2). More...
 
template<typename genType >
GLM_FUNC_DECL GLM_CONSTEXPR genType one ()
 Return 1. More...
 
template<typename genType >
GLM_FUNC_DECL GLM_CONSTEXPR genType one_over_pi ()
 Return 1 / pi. More...
 
template<typename genType >
GLM_FUNC_DECL GLM_CONSTEXPR genType one_over_root_two ()
 Return 1 / sqrt(2). More...
 
template<typename genType >
GLM_FUNC_DECL GLM_CONSTEXPR genType one_over_two_pi ()
 Return 1 / (pi * 2). More...
 
template<typename genType >
GLM_FUNC_DECL GLM_CONSTEXPR genType quarter_pi ()
 Return pi / 4. More...
 
template<typename genType >
GLM_FUNC_DECL GLM_CONSTEXPR genType root_five ()
 Return sqrt(5). More...
 
template<typename genType >
GLM_FUNC_DECL GLM_CONSTEXPR genType root_half_pi ()
 Return sqrt(pi / 2). More...
 
template<typename genType >
GLM_FUNC_DECL GLM_CONSTEXPR genType root_ln_four ()
 Return sqrt(ln(4)). More...
 
template<typename genType >
GLM_FUNC_DECL GLM_CONSTEXPR genType root_pi ()
 Return square root of pi. More...
 
template<typename genType >
GLM_FUNC_DECL GLM_CONSTEXPR genType root_three ()
 Return sqrt(3). More...
 
template<typename genType >
GLM_FUNC_DECL GLM_CONSTEXPR genType root_two ()
 Return sqrt(2). More...
 
template<typename genType >
GLM_FUNC_DECL GLM_CONSTEXPR genType root_two_pi ()
 Return sqrt(2 * pi). More...
 
template<typename genType >
GLM_FUNC_DECL GLM_CONSTEXPR genType third ()
 Return 1 / 3. More...
 
template<typename genType >
GLM_FUNC_DECL GLM_CONSTEXPR genType three_over_two_pi ()
 Return pi / 2 * 3. More...
 
template<typename genType >
GLM_FUNC_DECL GLM_CONSTEXPR genType two_over_pi ()
 Return 2 / pi. More...
 
template<typename genType >
GLM_FUNC_DECL GLM_CONSTEXPR genType two_over_root_pi ()
 Return 2 / sqrt(pi). More...
 
template<typename genType >
GLM_FUNC_DECL GLM_CONSTEXPR genType two_pi ()
 Return pi * 2. More...
 
template<typename genType >
GLM_FUNC_DECL GLM_CONSTEXPR genType two_thirds ()
 Return 2 / 3. More...
 
template<typename genType >
GLM_FUNC_DECL GLM_CONSTEXPR genType zero ()
 Return 0. More...
 
\n

Detailed Description

\n

Include <glm/gtc/constants.hpp> to use the features of this extension.

\n

Provide a list of constants and precomputed useful values.

\n

Function Documentation

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\n \n \n \n \n \n \n \n
GLM_FUNC_DECL GLM_CONSTEXPR genType glm::e ()
\n
\n\n

Return e constant.

\n
See also
to use the features of this extension. \">GLM_GTC_constants
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GLM_FUNC_DECL GLM_CONSTEXPR genType glm::euler ()
\n
\n\n

Return Euler's constant.

\n
See also
to use the features of this extension. \">GLM_GTC_constants
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GLM_FUNC_DECL GLM_CONSTEXPR genType glm::four_over_pi ()
\n
\n\n

Return 4 / pi.

\n
See also
to use the features of this extension. \">GLM_GTC_constants
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GLM_FUNC_DECL GLM_CONSTEXPR genType glm::golden_ratio ()
\n
\n\n

Return the golden ratio constant.

\n
See also
to use the features of this extension. \">GLM_GTC_constants
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GLM_FUNC_DECL GLM_CONSTEXPR genType glm::half_pi ()
\n
\n\n

Return pi / 2.

\n
See also
to use the features of this extension. \">GLM_GTC_constants
\n\n
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\n\n
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GLM_FUNC_DECL GLM_CONSTEXPR genType glm::ln_ln_two ()
\n
\n\n

Return ln(ln(2)).

\n
See also
to use the features of this extension. \">GLM_GTC_constants
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n
GLM_FUNC_DECL GLM_CONSTEXPR genType glm::ln_ten ()
\n
\n\n

Return ln(10).

\n
See also
to use the features of this extension. \">GLM_GTC_constants
\n\n
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GLM_FUNC_DECL GLM_CONSTEXPR genType glm::ln_two ()
\n
\n\n

Return ln(2).

\n
See also
to use the features of this extension. \">GLM_GTC_constants
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GLM_FUNC_DECL GLM_CONSTEXPR genType glm::one ()
\n
\n\n

Return 1.

\n
See also
to use the features of this extension. \">GLM_GTC_constants
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GLM_FUNC_DECL GLM_CONSTEXPR genType glm::one_over_pi ()
\n
\n\n

Return 1 / pi.

\n
See also
to use the features of this extension. \">GLM_GTC_constants
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GLM_FUNC_DECL GLM_CONSTEXPR genType glm::one_over_root_two ()
\n
\n\n

Return 1 / sqrt(2).

\n
See also
to use the features of this extension. \">GLM_GTC_constants
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GLM_FUNC_DECL GLM_CONSTEXPR genType glm::one_over_two_pi ()
\n
\n\n

Return 1 / (pi * 2).

\n
See also
to use the features of this extension. \">GLM_GTC_constants
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GLM_FUNC_DECL GLM_CONSTEXPR genType glm::quarter_pi ()
\n
\n\n

Return pi / 4.

\n
See also
to use the features of this extension. \">GLM_GTC_constants
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GLM_FUNC_DECL GLM_CONSTEXPR genType glm::root_five ()
\n
\n\n

Return sqrt(5).

\n
See also
to use the features of this extension. \">GLM_GTC_constants
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GLM_FUNC_DECL GLM_CONSTEXPR genType glm::root_half_pi ()
\n
\n\n

Return sqrt(pi / 2).

\n
See also
to use the features of this extension. \">GLM_GTC_constants
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GLM_FUNC_DECL GLM_CONSTEXPR genType glm::root_ln_four ()
\n
\n\n

Return sqrt(ln(4)).

\n
See also
to use the features of this extension. \">GLM_GTC_constants
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GLM_FUNC_DECL GLM_CONSTEXPR genType glm::root_pi ()
\n
\n\n

Return square root of pi.

\n
See also
to use the features of this extension. \">GLM_GTC_constants
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GLM_FUNC_DECL GLM_CONSTEXPR genType glm::root_three ()
\n
\n\n

Return sqrt(3).

\n
See also
to use the features of this extension. \">GLM_GTC_constants
\n\n
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\n\n
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GLM_FUNC_DECL GLM_CONSTEXPR genType glm::root_two ()
\n
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Return sqrt(2).

\n
See also
to use the features of this extension. \">GLM_GTC_constants
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GLM_FUNC_DECL GLM_CONSTEXPR genType glm::root_two_pi ()
\n
\n\n

Return sqrt(2 * pi).

\n
See also
to use the features of this extension. \">GLM_GTC_constants
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GLM_FUNC_DECL GLM_CONSTEXPR genType glm::third ()
\n
\n\n

Return 1 / 3.

\n
See also
to use the features of this extension. \">GLM_GTC_constants
\n\n
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GLM_FUNC_DECL GLM_CONSTEXPR genType glm::three_over_two_pi ()
\n
\n\n

Return pi / 2 * 3.

\n
See also
to use the features of this extension. \">GLM_GTC_constants
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GLM_FUNC_DECL GLM_CONSTEXPR genType glm::two_over_pi ()
\n
\n\n

Return 2 / pi.

\n
See also
to use the features of this extension. \">GLM_GTC_constants
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GLM_FUNC_DECL GLM_CONSTEXPR genType glm::two_over_root_pi ()
\n
\n\n

Return 2 / sqrt(pi).

\n
See also
to use the features of this extension. \">GLM_GTC_constants
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GLM_FUNC_DECL GLM_CONSTEXPR genType glm::two_pi ()
\n
\n\n

Return pi * 2.

\n
See also
to use the features of this extension. \">GLM_GTC_constants
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GLM_FUNC_DECL GLM_CONSTEXPR genType glm::two_thirds ()
\n
\n\n

Return 2 / 3.

\n
See also
to use the features of this extension. \">GLM_GTC_constants
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GLM_FUNC_DECL GLM_CONSTEXPR genType glm::zero ()
\n
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Return 0.

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See also
to use the features of this extension. \">GLM_GTC_constants
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Recommended extensions
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Include <glm/gtc/epsilon.hpp> to use the features of this extension. \nMore...

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\nFunctions

template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, bool, Q > epsilonEqual (vec< L, T, Q > const &x, vec< L, T, Q > const &y, T const &epsilon)
 Returns the component-wise comparison of |x - y| < epsilon. More...
 
template<typename genType >
GLM_FUNC_DECL bool epsilonEqual (genType const &x, genType const &y, genType const &epsilon)
 Returns the component-wise comparison of |x - y| < epsilon. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, bool, Q > epsilonNotEqual (vec< L, T, Q > const &x, vec< L, T, Q > const &y, T const &epsilon)
 Returns the component-wise comparison of |x - y| < epsilon. More...
 
template<typename genType >
GLM_FUNC_DECL bool epsilonNotEqual (genType const &x, genType const &y, genType const &epsilon)
 Returns the component-wise comparison of |x - y| >= epsilon. More...
 
\n

Detailed Description

\n

Include <glm/gtc/epsilon.hpp> to use the features of this extension.

\n

Comparison functions for a user defined epsilon values.

\n

Function Documentation

\n\n
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GLM_FUNC_DECL vec<L, bool, Q> glm::epsilonEqual (vec< L, T, Q > const & x,
vec< L, T, Q > const & y,
T const & epsilon 
)
\n
\n\n

Returns the component-wise comparison of |x - y| < epsilon.

\n

True if this expression is satisfied.

\n
See also
to use the features of this extension. \">GLM_GTC_epsilon
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GLM_FUNC_DECL bool glm::epsilonEqual (genType const & x,
genType const & y,
genType const & epsilon 
)
\n
\n\n

Returns the component-wise comparison of |x - y| < epsilon.

\n

True if this expression is satisfied.

\n
See also
to use the features of this extension. \">GLM_GTC_epsilon
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GLM_FUNC_DECL vec<L, bool, Q> glm::epsilonNotEqual (vec< L, T, Q > const & x,
vec< L, T, Q > const & y,
T const & epsilon 
)
\n
\n\n

Returns the component-wise comparison of |x - y| < epsilon.

\n

True if this expression is not satisfied.

\n
See also
to use the features of this extension. \">GLM_GTC_epsilon
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GLM_FUNC_DECL bool glm::epsilonNotEqual (genType const & x,
genType const & y,
genType const & epsilon 
)
\n
\n\n

Returns the component-wise comparison of |x - y| >= epsilon.

\n

True if this expression is not satisfied.

\n
See also
to use the features of this extension. \">GLM_GTC_epsilon
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Recommended extensions
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Include <glm/gtc/integer.hpp> to use the features of this extension. \nMore...

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\nFunctions

template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, int, Q > iround (vec< L, T, Q > const &x)
 Returns a value equal to the nearest integer to x. More...
 
template<typename genIUType >
GLM_FUNC_DECL genIUType log2 (genIUType x)
 Returns the log2 of x for integer values. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, uint, Q > uround (vec< L, T, Q > const &x)
 Returns a value equal to the nearest integer to x. More...
 
\n

Detailed Description

\n

Include <glm/gtc/integer.hpp> to use the features of this extension.

\n

Allow to perform bit operations on integer values

\n

Function Documentation

\n\n
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GLM_FUNC_DECL vec<L, int, Q> glm::iround (vec< L, T, Q > const & x)
\n
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Returns a value equal to the nearest integer to x.

\n

The fraction 0.5 will round in a direction chosen by the implementation, presumably the direction that is fastest.

\n
Parameters
\n \n \n
xThe values of the argument must be greater or equal to zero.
\n
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Template Parameters
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Tfloating point scalar types.
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See also
GLSL round man page
\n
\n to use the features of this extension. \">GLM_GTC_integer
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GLM_FUNC_DECL genIUType glm::log2 (genIUType x)
\n
\n\n

Returns the log2 of x for integer values.

\n

Usefull to compute mipmap count from the texture size.

See also
to use the features of this extension. \">GLM_GTC_integer
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GLM_FUNC_DECL vec<L, uint, Q> glm::uround (vec< L, T, Q > const & x)
\n
\n\n

Returns a value equal to the nearest integer to x.

\n

The fraction 0.5 will round in a direction chosen by the implementation, presumably the direction that is fastest.

\n
Parameters
\n \n \n
xThe values of the argument must be greater or equal to zero.
\n
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Template Parameters
\n \n \n
Tfloating point scalar types.
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See also
GLSL round man page
\n
\n to use the features of this extension. \">GLM_GTC_integer
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GLM_GTC_matrix_access
Recommended extensions
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Include <glm/gtc/matrix_access.hpp> to use the features of this extension. \nMore...

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\nFunctions

template<typename genType >
GLM_FUNC_DECL genType::col_type column (genType const &m, length_t index)
 Get a specific column of a matrix. More...
 
template<typename genType >
GLM_FUNC_DECL genType column (genType const &m, length_t index, typename genType::col_type const &x)
 Set a specific column to a matrix. More...
 
template<typename genType >
GLM_FUNC_DECL genType::row_type row (genType const &m, length_t index)
 Get a specific row of a matrix. More...
 
template<typename genType >
GLM_FUNC_DECL genType row (genType const &m, length_t index, typename genType::row_type const &x)
 Set a specific row to a matrix. More...
 
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Detailed Description

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Include <glm/gtc/matrix_access.hpp> to use the features of this extension.

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Defines functions to access rows or columns of a matrix easily.

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Function Documentation

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GLM_FUNC_DECL genType::col_type glm::column (genType const & m,
length_t index 
)
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Get a specific column of a matrix.

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See also
to use the features of this extension. \">GLM_GTC_matrix_access
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GLM_FUNC_DECL genType glm::column (genType const & m,
length_t index,
typename genType::col_type const & x 
)
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Set a specific column to a matrix.

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to use the features of this extension. \">GLM_GTC_matrix_access
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length_t index 
)
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Get a specific row of a matrix.

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to use the features of this extension. \">GLM_GTC_matrix_access
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GLM_FUNC_DECL genType glm::row (genType const & m,
length_t index,
typename genType::row_type const & x 
)
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Set a specific row to a matrix.

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See also
to use the features of this extension. \">GLM_GTC_matrix_access
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\nGenerated by  \n\"doxygen\"/\n 1.8.10\n
\n\n\n" }, { "path": "field_construction/submodules/diff-langsurf-rasterizer/third_party/glm/doc/api/a00294.html", "content": "\n\n\n\n\n\n0.9.9 API documentation: GLM_GTC_matrix_integer\n\n\n\n\n\n\n\n\n\n\n
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GLM_GTC_matrix_integer
Recommended extensions
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Include <glm/gtc/matrix_integer.hpp> to use the features of this extension. \nMore...

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\nTypedefs

typedef mat< 2, 2, int, highp > highp_imat2
 High-qualifier signed integer 2x2 matrix. More...
 
typedef mat< 2, 2, int, highp > highp_imat2x2
 High-qualifier signed integer 2x2 matrix. More...
 
typedef mat< 2, 3, int, highp > highp_imat2x3
 High-qualifier signed integer 2x3 matrix. More...
 
typedef mat< 2, 4, int, highp > highp_imat2x4
 High-qualifier signed integer 2x4 matrix. More...
 
typedef mat< 3, 3, int, highp > highp_imat3
 High-qualifier signed integer 3x3 matrix. More...
 
typedef mat< 3, 2, int, highp > highp_imat3x2
 High-qualifier signed integer 3x2 matrix. More...
 
typedef mat< 3, 3, int, highp > highp_imat3x3
 High-qualifier signed integer 3x3 matrix. More...
 
typedef mat< 3, 4, int, highp > highp_imat3x4
 High-qualifier signed integer 3x4 matrix. More...
 
typedef mat< 4, 4, int, highp > highp_imat4
 High-qualifier signed integer 4x4 matrix. More...
 
typedef mat< 4, 2, int, highp > highp_imat4x2
 High-qualifier signed integer 4x2 matrix. More...
 
typedef mat< 4, 3, int, highp > highp_imat4x3
 High-qualifier signed integer 4x3 matrix. More...
 
typedef mat< 4, 4, int, highp > highp_imat4x4
 High-qualifier signed integer 4x4 matrix. More...
 
typedef mat< 2, 2, uint, highp > highp_umat2
 High-qualifier unsigned integer 2x2 matrix. More...
 
typedef mat< 2, 2, uint, highp > highp_umat2x2
 High-qualifier unsigned integer 2x2 matrix. More...
 
typedef mat< 2, 3, uint, highp > highp_umat2x3
 High-qualifier unsigned integer 2x3 matrix. More...
 
typedef mat< 2, 4, uint, highp > highp_umat2x4
 High-qualifier unsigned integer 2x4 matrix. More...
 
typedef mat< 3, 3, uint, highp > highp_umat3
 High-qualifier unsigned integer 3x3 matrix. More...
 
typedef mat< 3, 2, uint, highp > highp_umat3x2
 High-qualifier unsigned integer 3x2 matrix. More...
 
typedef mat< 3, 3, uint, highp > highp_umat3x3
 High-qualifier unsigned integer 3x3 matrix. More...
 
typedef mat< 3, 4, uint, highp > highp_umat3x4
 High-qualifier unsigned integer 3x4 matrix. More...
 
typedef mat< 4, 4, uint, highp > highp_umat4
 High-qualifier unsigned integer 4x4 matrix. More...
 
typedef mat< 4, 2, uint, highp > highp_umat4x2
 High-qualifier unsigned integer 4x2 matrix. More...
 
typedef mat< 4, 3, uint, highp > highp_umat4x3
 High-qualifier unsigned integer 4x3 matrix. More...
 
typedef mat< 4, 4, uint, highp > highp_umat4x4
 High-qualifier unsigned integer 4x4 matrix. More...
 
typedef mediump_imat2 imat2
 Signed integer 2x2 matrix. More...
 
typedef mediump_imat2x2 imat2x2
 Signed integer 2x2 matrix. More...
 
typedef mediump_imat2x3 imat2x3
 Signed integer 2x3 matrix. More...
 
typedef mediump_imat2x4 imat2x4
 Signed integer 2x4 matrix. More...
 
typedef mediump_imat3 imat3
 Signed integer 3x3 matrix. More...
 
typedef mediump_imat3x2 imat3x2
 Signed integer 3x2 matrix. More...
 
typedef mediump_imat3x3 imat3x3
 Signed integer 3x3 matrix. More...
 
typedef mediump_imat3x4 imat3x4
 Signed integer 3x4 matrix. More...
 
typedef mediump_imat4 imat4
 Signed integer 4x4 matrix. More...
 
typedef mediump_imat4x2 imat4x2
 Signed integer 4x2 matrix. More...
 
typedef mediump_imat4x3 imat4x3
 Signed integer 4x3 matrix. More...
 
typedef mediump_imat4x4 imat4x4
 Signed integer 4x4 matrix. More...
 
typedef mat< 2, 2, int, lowp > lowp_imat2
 Low-qualifier signed integer 2x2 matrix. More...
 
typedef mat< 2, 2, int, lowp > lowp_imat2x2
 Low-qualifier signed integer 2x2 matrix. More...
 
typedef mat< 2, 3, int, lowp > lowp_imat2x3
 Low-qualifier signed integer 2x3 matrix. More...
 
typedef mat< 2, 4, int, lowp > lowp_imat2x4
 Low-qualifier signed integer 2x4 matrix. More...
 
typedef mat< 3, 3, int, lowp > lowp_imat3
 Low-qualifier signed integer 3x3 matrix. More...
 
typedef mat< 3, 2, int, lowp > lowp_imat3x2
 Low-qualifier signed integer 3x2 matrix. More...
 
typedef mat< 3, 3, int, lowp > lowp_imat3x3
 Low-qualifier signed integer 3x3 matrix. More...
 
typedef mat< 3, 4, int, lowp > lowp_imat3x4
 Low-qualifier signed integer 3x4 matrix. More...
 
typedef mat< 4, 4, int, lowp > lowp_imat4
 Low-qualifier signed integer 4x4 matrix. More...
 
typedef mat< 4, 2, int, lowp > lowp_imat4x2
 Low-qualifier signed integer 4x2 matrix. More...
 
typedef mat< 4, 3, int, lowp > lowp_imat4x3
 Low-qualifier signed integer 4x3 matrix. More...
 
typedef mat< 4, 4, int, lowp > lowp_imat4x4
 Low-qualifier signed integer 4x4 matrix. More...
 
typedef mat< 2, 2, uint, lowp > lowp_umat2
 Low-qualifier unsigned integer 2x2 matrix. More...
 
typedef mat< 2, 2, uint, lowp > lowp_umat2x2
 Low-qualifier unsigned integer 2x2 matrix. More...
 
typedef mat< 2, 3, uint, lowp > lowp_umat2x3
 Low-qualifier unsigned integer 2x3 matrix. More...
 
typedef mat< 2, 4, uint, lowp > lowp_umat2x4
 Low-qualifier unsigned integer 2x4 matrix. More...
 
typedef mat< 3, 3, uint, lowp > lowp_umat3
 Low-qualifier unsigned integer 3x3 matrix. More...
 
typedef mat< 3, 2, uint, lowp > lowp_umat3x2
 Low-qualifier unsigned integer 3x2 matrix. More...
 
typedef mat< 3, 3, uint, lowp > lowp_umat3x3
 Low-qualifier unsigned integer 3x3 matrix. More...
 
typedef mat< 3, 4, uint, lowp > lowp_umat3x4
 Low-qualifier unsigned integer 3x4 matrix. More...
 
typedef mat< 4, 4, uint, lowp > lowp_umat4
 Low-qualifier unsigned integer 4x4 matrix. More...
 
typedef mat< 4, 2, uint, lowp > lowp_umat4x2
 Low-qualifier unsigned integer 4x2 matrix. More...
 
typedef mat< 4, 3, uint, lowp > lowp_umat4x3
 Low-qualifier unsigned integer 4x3 matrix. More...
 
typedef mat< 4, 4, uint, lowp > lowp_umat4x4
 Low-qualifier unsigned integer 4x4 matrix. More...
 
typedef mat< 2, 2, int, mediump > mediump_imat2
 Medium-qualifier signed integer 2x2 matrix. More...
 
typedef mat< 2, 2, int, mediump > mediump_imat2x2
 Medium-qualifier signed integer 2x2 matrix. More...
 
typedef mat< 2, 3, int, mediump > mediump_imat2x3
 Medium-qualifier signed integer 2x3 matrix. More...
 
typedef mat< 2, 4, int, mediump > mediump_imat2x4
 Medium-qualifier signed integer 2x4 matrix. More...
 
typedef mat< 3, 3, int, mediump > mediump_imat3
 Medium-qualifier signed integer 3x3 matrix. More...
 
typedef mat< 3, 2, int, mediump > mediump_imat3x2
 Medium-qualifier signed integer 3x2 matrix. More...
 
typedef mat< 3, 3, int, mediump > mediump_imat3x3
 Medium-qualifier signed integer 3x3 matrix. More...
 
typedef mat< 3, 4, int, mediump > mediump_imat3x4
 Medium-qualifier signed integer 3x4 matrix. More...
 
typedef mat< 4, 4, int, mediump > mediump_imat4
 Medium-qualifier signed integer 4x4 matrix. More...
 
typedef mat< 4, 2, int, mediump > mediump_imat4x2
 Medium-qualifier signed integer 4x2 matrix. More...
 
typedef mat< 4, 3, int, mediump > mediump_imat4x3
 Medium-qualifier signed integer 4x3 matrix. More...
 
typedef mat< 4, 4, int, mediump > mediump_imat4x4
 Medium-qualifier signed integer 4x4 matrix. More...
 
typedef mat< 2, 2, uint, mediump > mediump_umat2
 Medium-qualifier unsigned integer 2x2 matrix. More...
 
typedef mat< 2, 2, uint, mediump > mediump_umat2x2
 Medium-qualifier unsigned integer 2x2 matrix. More...
 
typedef mat< 2, 3, uint, mediump > mediump_umat2x3
 Medium-qualifier unsigned integer 2x3 matrix. More...
 
typedef mat< 2, 4, uint, mediump > mediump_umat2x4
 Medium-qualifier unsigned integer 2x4 matrix. More...
 
typedef mat< 3, 3, uint, mediump > mediump_umat3
 Medium-qualifier unsigned integer 3x3 matrix. More...
 
typedef mat< 3, 2, uint, mediump > mediump_umat3x2
 Medium-qualifier unsigned integer 3x2 matrix. More...
 
typedef mat< 3, 3, uint, mediump > mediump_umat3x3
 Medium-qualifier unsigned integer 3x3 matrix. More...
 
typedef mat< 3, 4, uint, mediump > mediump_umat3x4
 Medium-qualifier unsigned integer 3x4 matrix. More...
 
typedef mat< 4, 4, uint, mediump > mediump_umat4
 Medium-qualifier unsigned integer 4x4 matrix. More...
 
typedef mat< 4, 2, uint, mediump > mediump_umat4x2
 Medium-qualifier unsigned integer 4x2 matrix. More...
 
typedef mat< 4, 3, uint, mediump > mediump_umat4x3
 Medium-qualifier unsigned integer 4x3 matrix. More...
 
typedef mat< 4, 4, uint, mediump > mediump_umat4x4
 Medium-qualifier unsigned integer 4x4 matrix. More...
 
typedef mediump_umat2 umat2
 Unsigned integer 2x2 matrix. More...
 
typedef mediump_umat2x2 umat2x2
 Unsigned integer 2x2 matrix. More...
 
typedef mediump_umat2x3 umat2x3
 Unsigned integer 2x3 matrix. More...
 
typedef mediump_umat2x4 umat2x4
 Unsigned integer 2x4 matrix. More...
 
typedef mediump_umat3 umat3
 Unsigned integer 3x3 matrix. More...
 
typedef mediump_umat3x2 umat3x2
 Unsigned integer 3x2 matrix. More...
 
typedef mediump_umat3x3 umat3x3
 Unsigned integer 3x3 matrix. More...
 
typedef mediump_umat3x4 umat3x4
 Unsigned integer 3x4 matrix. More...
 
typedef mediump_umat4 umat4
 Unsigned integer 4x4 matrix. More...
 
typedef mediump_umat4x2 umat4x2
 Unsigned integer 4x2 matrix. More...
 
typedef mediump_umat4x3 umat4x3
 Unsigned integer 4x3 matrix. More...
 
typedef mediump_umat4x4 umat4x4
 Unsigned integer 4x4 matrix. More...
 
\n

Detailed Description

\n

Include <glm/gtc/matrix_integer.hpp> to use the features of this extension.

\n

Defines a number of matrices with integer types.

\n

Typedef Documentation

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typedef mat<2, 2, int, highp> highp_imat2
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High-qualifier signed integer 2x2 matrix.

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See also
to use the features of this extension. \">GLM_GTC_matrix_integer
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Definition at line 37 of file matrix_integer.hpp.

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typedef mat<2, 2, int, highp> highp_imat2x2
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High-qualifier signed integer 2x2 matrix.

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See also
to use the features of this extension. \">GLM_GTC_matrix_integer
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Definition at line 49 of file matrix_integer.hpp.

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typedef mat<2, 3, int, highp> highp_imat2x3
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High-qualifier signed integer 2x3 matrix.

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See also
to use the features of this extension. \">GLM_GTC_matrix_integer
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Definition at line 53 of file matrix_integer.hpp.

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typedef mat<2, 4, int, highp> highp_imat2x4
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High-qualifier signed integer 2x4 matrix.

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to use the features of this extension. \">GLM_GTC_matrix_integer
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Definition at line 57 of file matrix_integer.hpp.

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typedef mat<3, 3, int, highp> highp_imat3
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High-qualifier signed integer 3x3 matrix.

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See also
to use the features of this extension. \">GLM_GTC_matrix_integer
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Definition at line 41 of file matrix_integer.hpp.

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typedef mat<3, 2, int, highp> highp_imat3x2
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High-qualifier signed integer 3x2 matrix.

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See also
to use the features of this extension. \">GLM_GTC_matrix_integer
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Definition at line 61 of file matrix_integer.hpp.

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typedef mat<3, 3, int, highp> highp_imat3x3
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High-qualifier signed integer 3x3 matrix.

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See also
to use the features of this extension. \">GLM_GTC_matrix_integer
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Definition at line 65 of file matrix_integer.hpp.

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typedef mat<3, 4, int, highp> highp_imat3x4
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High-qualifier signed integer 3x4 matrix.

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See also
to use the features of this extension. \">GLM_GTC_matrix_integer
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Definition at line 69 of file matrix_integer.hpp.

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typedef mat<4, 4, int, highp> highp_imat4
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High-qualifier signed integer 4x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_matrix_integer
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Definition at line 45 of file matrix_integer.hpp.

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typedef mat<4, 2, int, highp> highp_imat4x2
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High-qualifier signed integer 4x2 matrix.

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See also
to use the features of this extension. \">GLM_GTC_matrix_integer
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Definition at line 73 of file matrix_integer.hpp.

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typedef mat<4, 3, int, highp> highp_imat4x3
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High-qualifier signed integer 4x3 matrix.

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See also
to use the features of this extension. \">GLM_GTC_matrix_integer
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Definition at line 77 of file matrix_integer.hpp.

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typedef mat<4, 4, int, highp> highp_imat4x4
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High-qualifier signed integer 4x4 matrix.

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to use the features of this extension. \">GLM_GTC_matrix_integer
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Definition at line 81 of file matrix_integer.hpp.

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typedef mat<2, 2, uint, highp> highp_umat2
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High-qualifier unsigned integer 2x2 matrix.

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to use the features of this extension. \">GLM_GTC_matrix_integer
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Definition at line 186 of file matrix_integer.hpp.

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typedef mat<2, 2, uint, highp> highp_umat2x2
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High-qualifier unsigned integer 2x2 matrix.

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to use the features of this extension. \">GLM_GTC_matrix_integer
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Definition at line 198 of file matrix_integer.hpp.

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typedef mat<2, 3, uint, highp> highp_umat2x3
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High-qualifier unsigned integer 2x3 matrix.

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to use the features of this extension. \">GLM_GTC_matrix_integer
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Definition at line 202 of file matrix_integer.hpp.

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typedef mat<2, 4, uint, highp> highp_umat2x4
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High-qualifier unsigned integer 2x4 matrix.

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to use the features of this extension. \">GLM_GTC_matrix_integer
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Definition at line 206 of file matrix_integer.hpp.

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typedef mat<3, 3, uint, highp> highp_umat3
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High-qualifier unsigned integer 3x3 matrix.

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to use the features of this extension. \">GLM_GTC_matrix_integer
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Definition at line 190 of file matrix_integer.hpp.

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typedef mat<3, 2, uint, highp> highp_umat3x2
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High-qualifier unsigned integer 3x2 matrix.

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to use the features of this extension. \">GLM_GTC_matrix_integer
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Definition at line 210 of file matrix_integer.hpp.

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typedef mat<3, 3, uint, highp> highp_umat3x3
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High-qualifier unsigned integer 3x3 matrix.

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See also
to use the features of this extension. \">GLM_GTC_matrix_integer
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Definition at line 214 of file matrix_integer.hpp.

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typedef mat<3, 4, uint, highp> highp_umat3x4
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High-qualifier unsigned integer 3x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_matrix_integer
\n\n

Definition at line 218 of file matrix_integer.hpp.

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typedef mat<4, 4, uint, highp> highp_umat4
\n
\n\n

High-qualifier unsigned integer 4x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_matrix_integer
\n\n

Definition at line 194 of file matrix_integer.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat<4, 2, uint, highp> highp_umat4x2
\n
\n\n

High-qualifier unsigned integer 4x2 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_matrix_integer
\n\n

Definition at line 222 of file matrix_integer.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat<4, 3, uint, highp> highp_umat4x3
\n
\n\n

High-qualifier unsigned integer 4x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_matrix_integer
\n\n

Definition at line 226 of file matrix_integer.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat<4, 4, uint, highp> highp_umat4x4
\n
\n\n

High-qualifier unsigned integer 4x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_matrix_integer
\n\n

Definition at line 230 of file matrix_integer.hpp.

\n\n
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\n\n
\n
\n \n \n \n \n
typedef mediump_imat2 imat2
\n
\n\n

Signed integer 2x2 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_matrix_integer
\n\n

Definition at line 362 of file matrix_integer.hpp.

\n\n
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\n\n
\n
\n \n \n \n \n
typedef mediump_imat2x2 imat2x2
\n
\n\n

Signed integer 2x2 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_matrix_integer
\n\n

Definition at line 374 of file matrix_integer.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mediump_imat2x3 imat2x3
\n
\n\n

Signed integer 2x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_matrix_integer
\n\n

Definition at line 378 of file matrix_integer.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mediump_imat2x4 imat2x4
\n
\n\n

Signed integer 2x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_matrix_integer
\n\n

Definition at line 382 of file matrix_integer.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mediump_imat3 imat3
\n
\n\n

Signed integer 3x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_matrix_integer
\n\n

Definition at line 366 of file matrix_integer.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mediump_imat3x2 imat3x2
\n
\n\n

Signed integer 3x2 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_matrix_integer
\n\n

Definition at line 386 of file matrix_integer.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mediump_imat3x3 imat3x3
\n
\n\n

Signed integer 3x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_matrix_integer
\n\n

Definition at line 390 of file matrix_integer.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mediump_imat3x4 imat3x4
\n
\n\n

Signed integer 3x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_matrix_integer
\n\n

Definition at line 394 of file matrix_integer.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mediump_imat4 imat4
\n
\n\n

Signed integer 4x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_matrix_integer
\n\n

Definition at line 370 of file matrix_integer.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mediump_imat4x2 imat4x2
\n
\n\n

Signed integer 4x2 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_matrix_integer
\n\n

Definition at line 398 of file matrix_integer.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mediump_imat4x3 imat4x3
\n
\n\n

Signed integer 4x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_matrix_integer
\n\n

Definition at line 402 of file matrix_integer.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mediump_imat4x4 imat4x4
\n
\n\n

Signed integer 4x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_matrix_integer
\n\n

Definition at line 406 of file matrix_integer.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat<2, 2, int, lowp> lowp_imat2
\n
\n\n

Low-qualifier signed integer 2x2 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_matrix_integer
\n\n

Definition at line 136 of file matrix_integer.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat<2, 2, int, lowp> lowp_imat2x2
\n
\n\n

Low-qualifier signed integer 2x2 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_matrix_integer
\n\n

Definition at line 149 of file matrix_integer.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat<2, 3, int, lowp> lowp_imat2x3
\n
\n\n

Low-qualifier signed integer 2x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_matrix_integer
\n\n

Definition at line 153 of file matrix_integer.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat<2, 4, int, lowp> lowp_imat2x4
\n
\n\n

Low-qualifier signed integer 2x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_matrix_integer
\n\n

Definition at line 157 of file matrix_integer.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat<3, 3, int, lowp> lowp_imat3
\n
\n\n

Low-qualifier signed integer 3x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_matrix_integer
\n\n

Definition at line 140 of file matrix_integer.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat<3, 2, int, lowp> lowp_imat3x2
\n
\n\n

Low-qualifier signed integer 3x2 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_matrix_integer
\n\n

Definition at line 161 of file matrix_integer.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat<3, 3, int, lowp> lowp_imat3x3
\n
\n\n

Low-qualifier signed integer 3x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_matrix_integer
\n\n

Definition at line 165 of file matrix_integer.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat<3, 4, int, lowp> lowp_imat3x4
\n
\n\n

Low-qualifier signed integer 3x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_matrix_integer
\n\n

Definition at line 169 of file matrix_integer.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat<4, 4, int, lowp> lowp_imat4
\n
\n\n

Low-qualifier signed integer 4x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_matrix_integer
\n\n

Definition at line 144 of file matrix_integer.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat<4, 2, int, lowp> lowp_imat4x2
\n
\n\n

Low-qualifier signed integer 4x2 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_matrix_integer
\n\n

Definition at line 173 of file matrix_integer.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat<4, 3, int, lowp> lowp_imat4x3
\n
\n\n

Low-qualifier signed integer 4x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_matrix_integer
\n\n

Definition at line 177 of file matrix_integer.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat<4, 4, int, lowp> lowp_imat4x4
\n
\n\n

Low-qualifier signed integer 4x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_matrix_integer
\n\n

Definition at line 181 of file matrix_integer.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat<2, 2, uint, lowp> lowp_umat2
\n
\n\n

Low-qualifier unsigned integer 2x2 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_matrix_integer
\n\n

Definition at line 285 of file matrix_integer.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat<2, 2, uint, lowp> lowp_umat2x2
\n
\n\n

Low-qualifier unsigned integer 2x2 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_matrix_integer
\n\n

Definition at line 298 of file matrix_integer.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat<2, 3, uint, lowp> lowp_umat2x3
\n
\n\n

Low-qualifier unsigned integer 2x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_matrix_integer
\n\n

Definition at line 302 of file matrix_integer.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat<2, 4, uint, lowp> lowp_umat2x4
\n
\n\n

Low-qualifier unsigned integer 2x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_matrix_integer
\n\n

Definition at line 306 of file matrix_integer.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat<3, 3, uint, lowp> lowp_umat3
\n
\n\n

Low-qualifier unsigned integer 3x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_matrix_integer
\n\n

Definition at line 289 of file matrix_integer.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat<3, 2, uint, lowp> lowp_umat3x2
\n
\n\n

Low-qualifier unsigned integer 3x2 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_matrix_integer
\n\n

Definition at line 310 of file matrix_integer.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat<3, 3, uint, lowp> lowp_umat3x3
\n
\n\n

Low-qualifier unsigned integer 3x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_matrix_integer
\n\n

Definition at line 314 of file matrix_integer.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat<3, 4, uint, lowp> lowp_umat3x4
\n
\n\n

Low-qualifier unsigned integer 3x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_matrix_integer
\n\n

Definition at line 318 of file matrix_integer.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat<4, 4, uint, lowp> lowp_umat4
\n
\n\n

Low-qualifier unsigned integer 4x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_matrix_integer
\n\n

Definition at line 293 of file matrix_integer.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat<4, 2, uint, lowp> lowp_umat4x2
\n
\n\n

Low-qualifier unsigned integer 4x2 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_matrix_integer
\n\n

Definition at line 322 of file matrix_integer.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat<4, 3, uint, lowp> lowp_umat4x3
\n
\n\n

Low-qualifier unsigned integer 4x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_matrix_integer
\n\n

Definition at line 326 of file matrix_integer.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat<4, 4, uint, lowp> lowp_umat4x4
\n
\n\n

Low-qualifier unsigned integer 4x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_matrix_integer
\n\n

Definition at line 330 of file matrix_integer.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat<2, 2, int, mediump> mediump_imat2
\n
\n\n

Medium-qualifier signed integer 2x2 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_matrix_integer
\n\n

Definition at line 86 of file matrix_integer.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat<2, 2, int, mediump> mediump_imat2x2
\n
\n\n

Medium-qualifier signed integer 2x2 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_matrix_integer
\n\n

Definition at line 99 of file matrix_integer.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat<2, 3, int, mediump> mediump_imat2x3
\n
\n\n

Medium-qualifier signed integer 2x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_matrix_integer
\n\n

Definition at line 103 of file matrix_integer.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat<2, 4, int, mediump> mediump_imat2x4
\n
\n\n

Medium-qualifier signed integer 2x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_matrix_integer
\n\n

Definition at line 107 of file matrix_integer.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat<3, 3, int, mediump> mediump_imat3
\n
\n\n

Medium-qualifier signed integer 3x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_matrix_integer
\n\n

Definition at line 90 of file matrix_integer.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat<3, 2, int, mediump> mediump_imat3x2
\n
\n\n

Medium-qualifier signed integer 3x2 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_matrix_integer
\n\n

Definition at line 111 of file matrix_integer.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat<3, 3, int, mediump> mediump_imat3x3
\n
\n\n

Medium-qualifier signed integer 3x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_matrix_integer
\n\n

Definition at line 115 of file matrix_integer.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat<3, 4, int, mediump> mediump_imat3x4
\n
\n\n

Medium-qualifier signed integer 3x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_matrix_integer
\n\n

Definition at line 119 of file matrix_integer.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat<4, 4, int, mediump> mediump_imat4
\n
\n\n

Medium-qualifier signed integer 4x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_matrix_integer
\n\n

Definition at line 94 of file matrix_integer.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat<4, 2, int, mediump> mediump_imat4x2
\n
\n\n

Medium-qualifier signed integer 4x2 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_matrix_integer
\n\n

Definition at line 123 of file matrix_integer.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat<4, 3, int, mediump> mediump_imat4x3
\n
\n\n

Medium-qualifier signed integer 4x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_matrix_integer
\n\n

Definition at line 127 of file matrix_integer.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat<4, 4, int, mediump> mediump_imat4x4
\n
\n\n

Medium-qualifier signed integer 4x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_matrix_integer
\n\n

Definition at line 131 of file matrix_integer.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat<2, 2, uint, mediump> mediump_umat2
\n
\n\n

Medium-qualifier unsigned integer 2x2 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_matrix_integer
\n\n

Definition at line 235 of file matrix_integer.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat<2, 2, uint, mediump> mediump_umat2x2
\n
\n\n

Medium-qualifier unsigned integer 2x2 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_matrix_integer
\n\n

Definition at line 248 of file matrix_integer.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat<2, 3, uint, mediump> mediump_umat2x3
\n
\n\n

Medium-qualifier unsigned integer 2x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_matrix_integer
\n\n

Definition at line 252 of file matrix_integer.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat<2, 4, uint, mediump> mediump_umat2x4
\n
\n\n

Medium-qualifier unsigned integer 2x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_matrix_integer
\n\n

Definition at line 256 of file matrix_integer.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat<3, 3, uint, mediump> mediump_umat3
\n
\n\n

Medium-qualifier unsigned integer 3x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_matrix_integer
\n\n

Definition at line 239 of file matrix_integer.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat<3, 2, uint, mediump> mediump_umat3x2
\n
\n\n

Medium-qualifier unsigned integer 3x2 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_matrix_integer
\n\n

Definition at line 260 of file matrix_integer.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat<3, 3, uint, mediump> mediump_umat3x3
\n
\n\n

Medium-qualifier unsigned integer 3x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_matrix_integer
\n\n

Definition at line 264 of file matrix_integer.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat<3, 4, uint, mediump> mediump_umat3x4
\n
\n\n

Medium-qualifier unsigned integer 3x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_matrix_integer
\n\n

Definition at line 268 of file matrix_integer.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat<4, 4, uint, mediump> mediump_umat4
\n
\n\n

Medium-qualifier unsigned integer 4x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_matrix_integer
\n\n

Definition at line 243 of file matrix_integer.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat<4, 2, uint, mediump> mediump_umat4x2
\n
\n\n

Medium-qualifier unsigned integer 4x2 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_matrix_integer
\n\n

Definition at line 272 of file matrix_integer.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat<4, 3, uint, mediump> mediump_umat4x3
\n
\n\n

Medium-qualifier unsigned integer 4x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_matrix_integer
\n\n

Definition at line 276 of file matrix_integer.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat<4, 4, uint, mediump> mediump_umat4x4
\n
\n\n

Medium-qualifier unsigned integer 4x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_matrix_integer
\n\n

Definition at line 280 of file matrix_integer.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mediump_umat2 umat2
\n
\n\n

Unsigned integer 2x2 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_matrix_integer
\n\n

Definition at line 439 of file matrix_integer.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mediump_umat2x2 umat2x2
\n
\n\n

Unsigned integer 2x2 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_matrix_integer
\n\n

Definition at line 451 of file matrix_integer.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mediump_umat2x3 umat2x3
\n
\n\n

Unsigned integer 2x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_matrix_integer
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Definition at line 455 of file matrix_integer.hpp.

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typedef mediump_umat2x4 umat2x4
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Unsigned integer 2x4 matrix.

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Definition at line 459 of file matrix_integer.hpp.

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typedef mediump_umat3 umat3
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Unsigned integer 3x3 matrix.

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Definition at line 443 of file matrix_integer.hpp.

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typedef mediump_umat3x2 umat3x2
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Unsigned integer 3x2 matrix.

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to use the features of this extension. \">GLM_GTC_matrix_integer
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Definition at line 463 of file matrix_integer.hpp.

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typedef mediump_umat3x3 umat3x3
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Unsigned integer 3x3 matrix.

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Definition at line 467 of file matrix_integer.hpp.

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typedef mediump_umat3x4 umat3x4
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Unsigned integer 3x4 matrix.

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Definition at line 471 of file matrix_integer.hpp.

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typedef mediump_umat4 umat4
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Unsigned integer 4x4 matrix.

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Definition at line 447 of file matrix_integer.hpp.

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typedef mediump_umat4x2 umat4x2
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Unsigned integer 4x2 matrix.

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to use the features of this extension. \">GLM_GTC_matrix_integer
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Definition at line 475 of file matrix_integer.hpp.

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typedef mediump_umat4x3 umat4x3
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Unsigned integer 4x3 matrix.

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to use the features of this extension. \">GLM_GTC_matrix_integer
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Definition at line 479 of file matrix_integer.hpp.

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typedef mediump_umat4x4 umat4x4
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Unsigned integer 4x4 matrix.

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Definition at line 483 of file matrix_integer.hpp.

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GLM_GTC_matrix_inverse
Recommended extensions
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Include <glm/gtc/matrix_integer.hpp> to use the features of this extension. \nMore...

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template<typename genType >
GLM_FUNC_DECL genType affineInverse (genType const &m)
 Fast matrix inverse for affine matrix. More...
 
template<typename genType >
GLM_FUNC_DECL genType inverseTranspose (genType const &m)
 Compute the inverse transpose of a matrix. More...
 
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Include <glm/gtc/matrix_integer.hpp> to use the features of this extension.

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Defines additional matrix inverting functions.

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GLM_FUNC_DECL genType glm::affineInverse (genType const & m)
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Fast matrix inverse for affine matrix.

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mInput matrix to invert.
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genTypeSquared floating-point matrix: half, float or double. Inverse of matrix based of half-qualifier floating point value is highly innacurate.
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to use the features of this extension. \">GLM_GTC_matrix_inverse
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GLM_FUNC_DECL genType glm::inverseTranspose (genType const & m)
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Compute the inverse transpose of a matrix.

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mInput matrix to invert transpose.
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genTypeSquared floating-point matrix: half, float or double. Inverse of matrix based of half-qualifier floating point value is highly innacurate.
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Include <glm/gtc/matrix_transform.hpp> to use the features of this extension. \nMore...

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Include <glm/gtc/matrix_transform.hpp> to use the features of this extension.

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Defines functions that generate common transformation matrices.

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The matrices generated by this extension use standard OpenGL fixed-function conventions. For example, the lookAt function generates a transform from world space into the specific eye space that the projective matrix functions (perspective, ortho, etc) are designed to expect. The OpenGL compatibility specifications defines the particular layout of this eye space.

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template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL T perlin (vec< L, T, Q > const &p)
 Classic perlin noise. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL T perlin (vec< L, T, Q > const &p, vec< L, T, Q > const &rep)
 Periodic perlin noise. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL T simplex (vec< L, T, Q > const &p)
 Simplex noise. More...
 
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Detailed Description

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Include <glm/gtc/noise.hpp> to use the features of this extension.

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Defines 2D, 3D and 4D procedural noise functions Based on the work of Stefan Gustavson and Ashima Arts on \"webgl-noise\": https://github.com/ashima/webgl-noise Following Stefan Gustavson's paper \"Simplex noise demystified\": http://www.itn.liu.se/~stegu/simplexnoise/simplexnoise.pdf

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GLM_FUNC_DECL T glm::perlin (vec< L, T, Q > const & p)
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Classic perlin noise.

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to use the features of this extension. \">GLM_GTC_noise
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GLM_FUNC_DECL T glm::perlin (vec< L, T, Q > const & p,
vec< L, T, Q > const & rep 
)
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Periodic perlin noise.

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to use the features of this extension. \">GLM_GTC_noise
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GLM_FUNC_DECL T glm::simplex (vec< L, T, Q > const & p)
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Simplex noise.

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to use the features of this extension. \">GLM_GTC_noise
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Include <glm/gtc/packing.hpp> to use the features of this extension. \nMore...

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\nFunctions

GLM_FUNC_DECL uint32 packF2x11_1x10 (vec3 const &v)
 First, converts the first two components of the normalized floating-point value v into 11-bit signless floating-point values. More...
 
GLM_FUNC_DECL uint32 packF3x9_E1x5 (vec3 const &v)
 First, converts the first two components of the normalized floating-point value v into 11-bit signless floating-point values. More...
 
template<length_t L, qualifier Q>
GLM_FUNC_DECL vec< L, uint16, Q > packHalf (vec< L, float, Q > const &v)
 Returns an unsigned integer vector obtained by converting the components of a floating-point vector to the 16-bit floating-point representation found in the OpenGL Specification. More...
 
GLM_FUNC_DECL uint16 packHalf1x16 (float v)
 Returns an unsigned integer obtained by converting the components of a floating-point scalar to the 16-bit floating-point representation found in the OpenGL Specification, and then packing this 16-bit value into a 16-bit unsigned integer. More...
 
GLM_FUNC_DECL uint64 packHalf4x16 (vec4 const &v)
 Returns an unsigned integer obtained by converting the components of a four-component floating-point vector to the 16-bit floating-point representation found in the OpenGL Specification, and then packing these four 16-bit values into a 64-bit unsigned integer. More...
 
GLM_FUNC_DECL uint32 packI3x10_1x2 (ivec4 const &v)
 Returns an unsigned integer obtained by converting the components of a four-component signed integer vector to the 10-10-10-2-bit signed integer representation found in the OpenGL Specification, and then packing these four values into a 32-bit unsigned integer. More...
 
GLM_FUNC_DECL int packInt2x16 (i16vec2 const &v)
 Convert each component from an integer vector into a packed integer. More...
 
GLM_FUNC_DECL int64 packInt2x32 (i32vec2 const &v)
 Convert each component from an integer vector into a packed integer. More...
 
GLM_FUNC_DECL int16 packInt2x8 (i8vec2 const &v)
 Convert each component from an integer vector into a packed integer. More...
 
GLM_FUNC_DECL int64 packInt4x16 (i16vec4 const &v)
 Convert each component from an integer vector into a packed integer. More...
 
GLM_FUNC_DECL int32 packInt4x8 (i8vec4 const &v)
 Convert each component from an integer vector into a packed integer. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< 4, T, Q > packRGBM (vec< 3, T, Q > const &rgb)
 Returns an unsigned integer vector obtained by converting the components of a floating-point vector to the 16-bit floating-point representation found in the OpenGL Specification. More...
 
template<typename intType , length_t L, typename floatType , qualifier Q>
GLM_FUNC_DECL vec< L, intType, Q > packSnorm (vec< L, floatType, Q > const &v)
 Convert each component of the normalized floating-point vector into signed integer values. More...
 
GLM_FUNC_DECL uint16 packSnorm1x16 (float v)
 First, converts the normalized floating-point value v into 16-bit integer value. More...
 
GLM_FUNC_DECL uint8 packSnorm1x8 (float s)
 First, converts the normalized floating-point value v into 8-bit integer value. More...
 
GLM_FUNC_DECL uint16 packSnorm2x8 (vec2 const &v)
 First, converts each component of the normalized floating-point value v into 8-bit integer values. More...
 
GLM_FUNC_DECL uint32 packSnorm3x10_1x2 (vec4 const &v)
 First, converts the first three components of the normalized floating-point value v into 10-bit signed integer values. More...
 
GLM_FUNC_DECL uint64 packSnorm4x16 (vec4 const &v)
 First, converts each component of the normalized floating-point value v into 16-bit integer values. More...
 
GLM_FUNC_DECL uint32 packU3x10_1x2 (uvec4 const &v)
 Returns an unsigned integer obtained by converting the components of a four-component unsigned integer vector to the 10-10-10-2-bit unsigned integer representation found in the OpenGL Specification, and then packing these four values into a 32-bit unsigned integer. More...
 
GLM_FUNC_DECL uint packUint2x16 (u16vec2 const &v)
 Convert each component from an integer vector into a packed unsigned integer. More...
 
GLM_FUNC_DECL uint64 packUint2x32 (u32vec2 const &v)
 Convert each component from an integer vector into a packed unsigned integer. More...
 
GLM_FUNC_DECL uint16 packUint2x8 (u8vec2 const &v)
 Convert each component from an integer vector into a packed unsigned integer. More...
 
GLM_FUNC_DECL uint64 packUint4x16 (u16vec4 const &v)
 Convert each component from an integer vector into a packed unsigned integer. More...
 
GLM_FUNC_DECL uint32 packUint4x8 (u8vec4 const &v)
 Convert each component from an integer vector into a packed unsigned integer. More...
 
template<typename uintType , length_t L, typename floatType , qualifier Q>
GLM_FUNC_DECL vec< L, uintType, Q > packUnorm (vec< L, floatType, Q > const &v)
 Convert each component of the normalized floating-point vector into unsigned integer values. More...
 
GLM_FUNC_DECL uint16 packUnorm1x16 (float v)
 First, converts the normalized floating-point value v into a 16-bit integer value. More...
 
GLM_FUNC_DECL uint16 packUnorm1x5_1x6_1x5 (vec3 const &v)
 Convert each component of the normalized floating-point vector into unsigned integer values. More...
 
GLM_FUNC_DECL uint8 packUnorm1x8 (float v)
 First, converts the normalized floating-point value v into a 8-bit integer value. More...
 
GLM_FUNC_DECL uint8 packUnorm2x3_1x2 (vec3 const &v)
 Convert each component of the normalized floating-point vector into unsigned integer values. More...
 
GLM_FUNC_DECL uint8 packUnorm2x4 (vec2 const &v)
 Convert each component of the normalized floating-point vector into unsigned integer values. More...
 
GLM_FUNC_DECL uint16 packUnorm2x8 (vec2 const &v)
 First, converts each component of the normalized floating-point value v into 8-bit integer values. More...
 
GLM_FUNC_DECL uint32 packUnorm3x10_1x2 (vec4 const &v)
 First, converts the first three components of the normalized floating-point value v into 10-bit unsigned integer values. More...
 
GLM_FUNC_DECL uint16 packUnorm3x5_1x1 (vec4 const &v)
 Convert each component of the normalized floating-point vector into unsigned integer values. More...
 
GLM_FUNC_DECL uint64 packUnorm4x16 (vec4 const &v)
 First, converts each component of the normalized floating-point value v into 16-bit integer values. More...
 
GLM_FUNC_DECL uint16 packUnorm4x4 (vec4 const &v)
 Convert each component of the normalized floating-point vector into unsigned integer values. More...
 
GLM_FUNC_DECL vec3 unpackF2x11_1x10 (uint32 p)
 First, unpacks a single 32-bit unsigned integer p into two 11-bit signless floating-point values and one 10-bit signless floating-point value . More...
 
GLM_FUNC_DECL vec3 unpackF3x9_E1x5 (uint32 p)
 First, unpacks a single 32-bit unsigned integer p into two 11-bit signless floating-point values and one 10-bit signless floating-point value . More...
 
template<length_t L, qualifier Q>
GLM_FUNC_DECL vec< L, float, Q > unpackHalf (vec< L, uint16, Q > const &p)
 Returns a floating-point vector with components obtained by reinterpreting an integer vector as 16-bit floating-point numbers and converting them to 32-bit floating-point values. More...
 
GLM_FUNC_DECL float unpackHalf1x16 (uint16 v)
 Returns a floating-point scalar with components obtained by unpacking a 16-bit unsigned integer into a 16-bit value, interpreted as a 16-bit floating-point number according to the OpenGL Specification, and converting it to 32-bit floating-point values. More...
 
GLM_FUNC_DECL vec4 unpackHalf4x16 (uint64 p)
 Returns a four-component floating-point vector with components obtained by unpacking a 64-bit unsigned integer into four 16-bit values, interpreting those values as 16-bit floating-point numbers according to the OpenGL Specification, and converting them to 32-bit floating-point values. More...
 
GLM_FUNC_DECL ivec4 unpackI3x10_1x2 (uint32 p)
 Unpacks a single 32-bit unsigned integer p into three 10-bit and one 2-bit signed integers. More...
 
GLM_FUNC_DECL i16vec2 unpackInt2x16 (int p)
 Convert a packed integer into an integer vector. More...
 
GLM_FUNC_DECL i32vec2 unpackInt2x32 (int64 p)
 Convert a packed integer into an integer vector. More...
 
GLM_FUNC_DECL i8vec2 unpackInt2x8 (int16 p)
 Convert a packed integer into an integer vector. More...
 
GLM_FUNC_DECL i16vec4 unpackInt4x16 (int64 p)
 Convert a packed integer into an integer vector. More...
 
GLM_FUNC_DECL i8vec4 unpackInt4x8 (int32 p)
 Convert a packed integer into an integer vector. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > unpackRGBM (vec< 4, T, Q > const &rgbm)
 Returns a floating-point vector with components obtained by reinterpreting an integer vector as 16-bit floating-point numbers and converting them to 32-bit floating-point values. More...
 
template<typename floatType , length_t L, typename intType , qualifier Q>
GLM_FUNC_DECL vec< L, floatType, Q > unpackSnorm (vec< L, intType, Q > const &v)
 Convert a packed integer to a normalized floating-point vector. More...
 
GLM_FUNC_DECL float unpackSnorm1x16 (uint16 p)
 First, unpacks a single 16-bit unsigned integer p into a single 16-bit signed integers. More...
 
GLM_FUNC_DECL float unpackSnorm1x8 (uint8 p)
 First, unpacks a single 8-bit unsigned integer p into a single 8-bit signed integers. More...
 
GLM_FUNC_DECL vec2 unpackSnorm2x8 (uint16 p)
 First, unpacks a single 16-bit unsigned integer p into a pair of 8-bit signed integers. More...
 
GLM_FUNC_DECL vec4 unpackSnorm3x10_1x2 (uint32 p)
 First, unpacks a single 32-bit unsigned integer p into four 16-bit signed integers. More...
 
GLM_FUNC_DECL vec4 unpackSnorm4x16 (uint64 p)
 First, unpacks a single 64-bit unsigned integer p into four 16-bit signed integers. More...
 
GLM_FUNC_DECL uvec4 unpackU3x10_1x2 (uint32 p)
 Unpacks a single 32-bit unsigned integer p into three 10-bit and one 2-bit unsigned integers. More...
 
GLM_FUNC_DECL u16vec2 unpackUint2x16 (uint p)
 Convert a packed integer into an integer vector. More...
 
GLM_FUNC_DECL u32vec2 unpackUint2x32 (uint64 p)
 Convert a packed integer into an integer vector. More...
 
GLM_FUNC_DECL u8vec2 unpackUint2x8 (uint16 p)
 Convert a packed integer into an integer vector. More...
 
GLM_FUNC_DECL u16vec4 unpackUint4x16 (uint64 p)
 Convert a packed integer into an integer vector. More...
 
GLM_FUNC_DECL u8vec4 unpackUint4x8 (uint32 p)
 Convert a packed integer into an integer vector. More...
 
template<typename floatType , length_t L, typename uintType , qualifier Q>
GLM_FUNC_DECL vec< L, floatType, Q > unpackUnorm (vec< L, uintType, Q > const &v)
 Convert a packed integer to a normalized floating-point vector. More...
 
GLM_FUNC_DECL float unpackUnorm1x16 (uint16 p)
 First, unpacks a single 16-bit unsigned integer p into a of 16-bit unsigned integers. More...
 
GLM_FUNC_DECL vec3 unpackUnorm1x5_1x6_1x5 (uint16 p)
 Convert a packed integer to a normalized floating-point vector. More...
 
GLM_FUNC_DECL float unpackUnorm1x8 (uint8 p)
 Convert a single 8-bit integer to a normalized floating-point value. More...
 
GLM_FUNC_DECL vec3 unpackUnorm2x3_1x2 (uint8 p)
 Convert a packed integer to a normalized floating-point vector. More...
 
GLM_FUNC_DECL vec2 unpackUnorm2x4 (uint8 p)
 Convert a packed integer to a normalized floating-point vector. More...
 
GLM_FUNC_DECL vec2 unpackUnorm2x8 (uint16 p)
 First, unpacks a single 16-bit unsigned integer p into a pair of 8-bit unsigned integers. More...
 
GLM_FUNC_DECL vec4 unpackUnorm3x10_1x2 (uint32 p)
 First, unpacks a single 32-bit unsigned integer p into four 16-bit signed integers. More...
 
GLM_FUNC_DECL vec4 unpackUnorm3x5_1x1 (uint16 p)
 Convert a packed integer to a normalized floating-point vector. More...
 
GLM_FUNC_DECL vec4 unpackUnorm4x16 (uint64 p)
 First, unpacks a single 64-bit unsigned integer p into four 16-bit unsigned integers. More...
 
GLM_FUNC_DECL vec4 unpackUnorm4x4 (uint16 p)
 Convert a packed integer to a normalized floating-point vector. More...
 
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Detailed Description

\n

Include <glm/gtc/packing.hpp> to use the features of this extension.

\n

This extension provides a set of function to convert vertors to packed formats.

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Function Documentation

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GLM_FUNC_DECL uint32 glm::packF2x11_1x10 (vec3 const & v)
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First, converts the first two components of the normalized floating-point value v into 11-bit signless floating-point values.

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Then, converts the third component of the normalized floating-point value v into a 10-bit signless floating-point value. Then, the results are packed into the returned 32-bit unsigned integer.

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The first vector component specifies the 11 least-significant bits of the result; the last component specifies the 10 most-significant bits.

\n
See also
to use the features of this extension. \">GLM_GTC_packing
\n
\nvec3 unpackF2x11_1x10(uint32 const& p)
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL uint32 glm::packF3x9_E1x5 (vec3 const & v)
\n
\n\n

First, converts the first two components of the normalized floating-point value v into 11-bit signless floating-point values.

\n

Then, converts the third component of the normalized floating-point value v into a 10-bit signless floating-point value. Then, the results are packed into the returned 32-bit unsigned integer.

\n

The first vector component specifies the 11 least-significant bits of the result; the last component specifies the 10 most-significant bits.

\n

packF3x9_E1x5 allows encoding into RGBE / RGB9E5 format

\n
See also
to use the features of this extension. \">GLM_GTC_packing
\n
\nvec3 unpackF3x9_E1x5(uint32 const& p)
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL vec<L, uint16, Q> glm::packHalf (vec< L, float, Q > const & v)
\n
\n\n

Returns an unsigned integer vector obtained by converting the components of a floating-point vector to the 16-bit floating-point representation found in the OpenGL Specification.

\n

The first vector component specifies the 16 least-significant bits of the result; the forth component specifies the 16 most-significant bits.

\n
See also
to use the features of this extension. \">GLM_GTC_packing
\n
\nvec<L, float, Q> unpackHalf(vec<L, uint16, Q> const& p)
\n
\nGLSL 4.20.8 specification, section 8.4 Floating-Point Pack and Unpack Functions
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL uint16 glm::packHalf1x16 (float v)
\n
\n\n

Returns an unsigned integer obtained by converting the components of a floating-point scalar to the 16-bit floating-point representation found in the OpenGL Specification, and then packing this 16-bit value into a 16-bit unsigned integer.

\n
See also
to use the features of this extension. \">GLM_GTC_packing
\n
\nuint32 packHalf2x16(vec2 const& v)
\n
\nuint64 packHalf4x16(vec4 const& v)
\n
\nGLSL packHalf2x16 man page
\n
\nGLSL 4.20.8 specification, section 8.4 Floating-Point Pack and Unpack Functions
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL uint64 glm::packHalf4x16 (vec4 const & v)
\n
\n\n

Returns an unsigned integer obtained by converting the components of a four-component floating-point vector to the 16-bit floating-point representation found in the OpenGL Specification, and then packing these four 16-bit values into a 64-bit unsigned integer.

\n

The first vector component specifies the 16 least-significant bits of the result; the forth component specifies the 16 most-significant bits.

\n
See also
to use the features of this extension. \">GLM_GTC_packing
\n
\nuint16 packHalf1x16(float const& v)
\n
\nuint32 packHalf2x16(vec2 const& v)
\n
\nGLSL packHalf2x16 man page
\n
\nGLSL 4.20.8 specification, section 8.4 Floating-Point Pack and Unpack Functions
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL uint32 glm::packI3x10_1x2 (ivec4 const & v)
\n
\n\n

Returns an unsigned integer obtained by converting the components of a four-component signed integer vector to the 10-10-10-2-bit signed integer representation found in the OpenGL Specification, and then packing these four values into a 32-bit unsigned integer.

\n

The first vector component specifies the 10 least-significant bits of the result; the forth component specifies the 2 most-significant bits.

\n
See also
to use the features of this extension. \">GLM_GTC_packing
\n
\nuint32 packI3x10_1x2(uvec4 const& v)
\n
\nuint32 packSnorm3x10_1x2(vec4 const& v)
\n
\nuint32 packUnorm3x10_1x2(vec4 const& v)
\n
\nivec4 unpackI3x10_1x2(uint32 const& p)
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL int glm::packInt2x16 (i16vec2 const & v)
\n
\n\n

Convert each component from an integer vector into a packed integer.

\n
See also
to use the features of this extension. \">GLM_GTC_packing
\n
\ni16vec2 unpackInt2x16(int p)
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL int64 glm::packInt2x32 (i32vec2 const & v)
\n
\n\n

Convert each component from an integer vector into a packed integer.

\n
See also
to use the features of this extension. \">GLM_GTC_packing
\n
\ni32vec2 unpackInt2x32(int p)
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL int16 glm::packInt2x8 (i8vec2 const & v)
\n
\n\n

Convert each component from an integer vector into a packed integer.

\n
See also
to use the features of this extension. \">GLM_GTC_packing
\n
\ni8vec2 unpackInt2x8(int16 p)
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL int64 glm::packInt4x16 (i16vec4 const & v)
\n
\n\n

Convert each component from an integer vector into a packed integer.

\n
See also
to use the features of this extension. \">GLM_GTC_packing
\n
\ni16vec4 unpackInt4x16(int64 p)
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL int32 glm::packInt4x8 (i8vec4 const & v)
\n
\n\n

Convert each component from an integer vector into a packed integer.

\n
See also
to use the features of this extension. \">GLM_GTC_packing
\n
\ni8vec4 unpackInt4x8(int32 p)
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL vec<4, T, Q> glm::packRGBM (vec< 3, T, Q > const & rgb)
\n
\n\n

Returns an unsigned integer vector obtained by converting the components of a floating-point vector to the 16-bit floating-point representation found in the OpenGL Specification.

\n

The first vector component specifies the 16 least-significant bits of the result; the forth component specifies the 16 most-significant bits.

\n
See also
to use the features of this extension. \">GLM_GTC_packing
\n
\nvec<3, T, Q> unpackRGBM(vec<4, T, Q> const& p)
\n
\nGLSL 4.20.8 specification, section 8.4 Floating-Point Pack and Unpack Functions
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL vec<L, intType, Q> glm::packSnorm (vec< L, floatType, Q > const & v)
\n
\n\n

Convert each component of the normalized floating-point vector into signed integer values.

\n
See also
to use the features of this extension. \">GLM_GTC_packing
\n
\nvec<L, floatType, Q> unpackSnorm(vec<L, intType, Q> const& p);
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL uint16 glm::packSnorm1x16 (float v)
\n
\n\n

First, converts the normalized floating-point value v into 16-bit integer value.

\n

Then, the results are packed into the returned 16-bit unsigned integer.

\n

The conversion to fixed point is done as follows: packSnorm1x8: round(clamp(s, -1, +1) * 32767.0)

\n
See also
to use the features of this extension. \">GLM_GTC_packing
\n
\nuint32 packSnorm2x16(vec2 const& v)
\n
\nuint64 packSnorm4x16(vec4 const& v)
\n
\nGLSL packSnorm4x8 man page
\n
\nGLSL 4.20.8 specification, section 8.4 Floating-Point Pack and Unpack Functions
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL uint8 glm::packSnorm1x8 (float s)
\n
\n\n

First, converts the normalized floating-point value v into 8-bit integer value.

\n

Then, the results are packed into the returned 8-bit unsigned integer.

\n

The conversion to fixed point is done as follows: packSnorm1x8: round(clamp(s, -1, +1) * 127.0)

\n
See also
to use the features of this extension. \">GLM_GTC_packing
\n
\nuint16 packSnorm2x8(vec2 const& v)
\n
\nuint32 packSnorm4x8(vec4 const& v)
\n
\nGLSL packSnorm4x8 man page
\n
\nGLSL 4.20.8 specification, section 8.4 Floating-Point Pack and Unpack Functions
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL uint16 glm::packSnorm2x8 (vec2 const & v)
\n
\n\n

First, converts each component of the normalized floating-point value v into 8-bit integer values.

\n

Then, the results are packed into the returned 16-bit unsigned integer.

\n

The conversion for component c of v to fixed point is done as follows: packSnorm2x8: round(clamp(c, -1, +1) * 127.0)

\n

The first component of the vector will be written to the least significant bits of the output; the last component will be written to the most significant bits.

\n
See also
to use the features of this extension. \">GLM_GTC_packing
\n
\nuint8 packSnorm1x8(float const& v)
\n
\nuint32 packSnorm4x8(vec4 const& v)
\n
\nGLSL packSnorm4x8 man page
\n
\nGLSL 4.20.8 specification, section 8.4 Floating-Point Pack and Unpack Functions
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL uint32 glm::packSnorm3x10_1x2 (vec4 const & v)
\n
\n\n

First, converts the first three components of the normalized floating-point value v into 10-bit signed integer values.

\n

Then, converts the forth component of the normalized floating-point value v into 2-bit signed integer values. Then, the results are packed into the returned 32-bit unsigned integer.

\n

The conversion for component c of v to fixed point is done as follows: packSnorm3x10_1x2(xyz): round(clamp(c, -1, +1) * 511.0) packSnorm3x10_1x2(w): round(clamp(c, -1, +1) * 1.0)

\n

The first vector component specifies the 10 least-significant bits of the result; the forth component specifies the 2 most-significant bits.

\n
See also
to use the features of this extension. \">GLM_GTC_packing
\n
\nvec4 unpackSnorm3x10_1x2(uint32 const& p)
\n
\nuint32 packUnorm3x10_1x2(vec4 const& v)
\n
\nuint32 packU3x10_1x2(uvec4 const& v)
\n
\nuint32 packI3x10_1x2(ivec4 const& v)
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL uint64 glm::packSnorm4x16 (vec4 const & v)
\n
\n\n

First, converts each component of the normalized floating-point value v into 16-bit integer values.

\n

Then, the results are packed into the returned 64-bit unsigned integer.

\n

The conversion for component c of v to fixed point is done as follows: packSnorm2x8: round(clamp(c, -1, +1) * 32767.0)

\n

The first component of the vector will be written to the least significant bits of the output; the last component will be written to the most significant bits.

\n
See also
to use the features of this extension. \">GLM_GTC_packing
\n
\nuint16 packSnorm1x16(float const& v)
\n
\nuint32 packSnorm2x16(vec2 const& v)
\n
\nGLSL packSnorm4x8 man page
\n
\nGLSL 4.20.8 specification, section 8.4 Floating-Point Pack and Unpack Functions
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL uint32 glm::packU3x10_1x2 (uvec4 const & v)
\n
\n\n

Returns an unsigned integer obtained by converting the components of a four-component unsigned integer vector to the 10-10-10-2-bit unsigned integer representation found in the OpenGL Specification, and then packing these four values into a 32-bit unsigned integer.

\n

The first vector component specifies the 10 least-significant bits of the result; the forth component specifies the 2 most-significant bits.

\n
See also
to use the features of this extension. \">GLM_GTC_packing
\n
\nuint32 packI3x10_1x2(ivec4 const& v)
\n
\nuint32 packSnorm3x10_1x2(vec4 const& v)
\n
\nuint32 packUnorm3x10_1x2(vec4 const& v)
\n
\nivec4 unpackU3x10_1x2(uint32 const& p)
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL uint glm::packUint2x16 (u16vec2 const & v)
\n
\n\n

Convert each component from an integer vector into a packed unsigned integer.

\n
See also
to use the features of this extension. \">GLM_GTC_packing
\n
\nu16vec2 unpackUint2x16(uint p)
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL uint64 glm::packUint2x32 (u32vec2 const & v)
\n
\n\n

Convert each component from an integer vector into a packed unsigned integer.

\n
See also
to use the features of this extension. \">GLM_GTC_packing
\n
\nu32vec2 unpackUint2x32(int p)
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL uint16 glm::packUint2x8 (u8vec2 const & v)
\n
\n\n

Convert each component from an integer vector into a packed unsigned integer.

\n
See also
to use the features of this extension. \">GLM_GTC_packing
\n
\nu8vec2 unpackInt2x8(uint16 p)
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL uint64 glm::packUint4x16 (u16vec4 const & v)
\n
\n\n

Convert each component from an integer vector into a packed unsigned integer.

\n
See also
to use the features of this extension. \">GLM_GTC_packing
\n
\nu16vec4 unpackUint4x16(uint64 p)
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL uint32 glm::packUint4x8 (u8vec4 const & v)
\n
\n\n

Convert each component from an integer vector into a packed unsigned integer.

\n
See also
to use the features of this extension. \">GLM_GTC_packing
\n
\nu8vec4 unpackUint4x8(uint32 p)
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL vec<L, uintType, Q> glm::packUnorm (vec< L, floatType, Q > const & v)
\n
\n\n

Convert each component of the normalized floating-point vector into unsigned integer values.

\n
See also
to use the features of this extension. \">GLM_GTC_packing
\n
\nvec<L, floatType, Q> unpackUnorm(vec<L, intType, Q> const& p);
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL uint16 glm::packUnorm1x16 (float v)
\n
\n\n

First, converts the normalized floating-point value v into a 16-bit integer value.

\n

Then, the results are packed into the returned 16-bit unsigned integer.

\n

The conversion for component c of v to fixed point is done as follows: packUnorm1x16: round(clamp(c, 0, +1) * 65535.0)

\n
See also
to use the features of this extension. \">GLM_GTC_packing
\n
\nuint16 packSnorm1x16(float const& v)
\n
\nuint64 packSnorm4x16(vec4 const& v)
\n
\nGLSL packUnorm4x8 man page
\n
\nGLSL 4.20.8 specification, section 8.4 Floating-Point Pack and Unpack Functions
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL uint16 glm::packUnorm1x5_1x6_1x5 (vec3 const & v)
\n
\n\n

Convert each component of the normalized floating-point vector into unsigned integer values.

\n
See also
to use the features of this extension. \">GLM_GTC_packing
\n
\nvec3 unpackUnorm1x5_1x6_1x5(uint16 p)
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL uint8 glm::packUnorm1x8 (float v)
\n
\n\n

First, converts the normalized floating-point value v into a 8-bit integer value.

\n

Then, the results are packed into the returned 8-bit unsigned integer.

\n

The conversion for component c of v to fixed point is done as follows: packUnorm1x8: round(clamp(c, 0, +1) * 255.0)

\n
See also
to use the features of this extension. \">GLM_GTC_packing
\n
\nuint16 packUnorm2x8(vec2 const& v)
\n
\nuint32 packUnorm4x8(vec4 const& v)
\n
\nGLSL packUnorm4x8 man page
\n
\nGLSL 4.20.8 specification, section 8.4 Floating-Point Pack and Unpack Functions
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL uint8 glm::packUnorm2x3_1x2 (vec3 const & v)
\n
\n\n

Convert each component of the normalized floating-point vector into unsigned integer values.

\n
See also
to use the features of this extension. \">GLM_GTC_packing
\n
\nvec3 unpackUnorm2x3_1x2(uint8 p)
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL uint8 glm::packUnorm2x4 (vec2 const & v)
\n
\n\n

Convert each component of the normalized floating-point vector into unsigned integer values.

\n
See also
to use the features of this extension. \">GLM_GTC_packing
\n
\nvec2 unpackUnorm2x4(uint8 p)
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL uint16 glm::packUnorm2x8 (vec2 const & v)
\n
\n\n

First, converts each component of the normalized floating-point value v into 8-bit integer values.

\n

Then, the results are packed into the returned 16-bit unsigned integer.

\n

The conversion for component c of v to fixed point is done as follows: packUnorm2x8: round(clamp(c, 0, +1) * 255.0)

\n

The first component of the vector will be written to the least significant bits of the output; the last component will be written to the most significant bits.

\n
See also
to use the features of this extension. \">GLM_GTC_packing
\n
\nuint8 packUnorm1x8(float const& v)
\n
\nuint32 packUnorm4x8(vec4 const& v)
\n
\nGLSL packUnorm4x8 man page
\n
\nGLSL 4.20.8 specification, section 8.4 Floating-Point Pack and Unpack Functions
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL uint32 glm::packUnorm3x10_1x2 (vec4 const & v)
\n
\n\n

First, converts the first three components of the normalized floating-point value v into 10-bit unsigned integer values.

\n

Then, converts the forth component of the normalized floating-point value v into 2-bit signed uninteger values. Then, the results are packed into the returned 32-bit unsigned integer.

\n

The conversion for component c of v to fixed point is done as follows: packUnorm3x10_1x2(xyz): round(clamp(c, 0, +1) * 1023.0) packUnorm3x10_1x2(w): round(clamp(c, 0, +1) * 3.0)

\n

The first vector component specifies the 10 least-significant bits of the result; the forth component specifies the 2 most-significant bits.

\n
See also
to use the features of this extension. \">GLM_GTC_packing
\n
\nvec4 unpackUnorm3x10_1x2(uint32 const& p)
\n
\nuint32 packUnorm3x10_1x2(vec4 const& v)
\n
\nuint32 packU3x10_1x2(uvec4 const& v)
\n
\nuint32 packI3x10_1x2(ivec4 const& v)
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL uint16 glm::packUnorm3x5_1x1 (vec4 const & v)
\n
\n\n

Convert each component of the normalized floating-point vector into unsigned integer values.

\n
See also
to use the features of this extension. \">GLM_GTC_packing
\n
\nvec4 unpackUnorm3x5_1x1(uint16 p)
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL uint64 glm::packUnorm4x16 (vec4 const & v)
\n
\n\n

First, converts each component of the normalized floating-point value v into 16-bit integer values.

\n

Then, the results are packed into the returned 64-bit unsigned integer.

\n

The conversion for component c of v to fixed point is done as follows: packUnorm4x16: round(clamp(c, 0, +1) * 65535.0)

\n

The first component of the vector will be written to the least significant bits of the output; the last component will be written to the most significant bits.

\n
See also
to use the features of this extension. \">GLM_GTC_packing
\n
\nuint16 packUnorm1x16(float const& v)
\n
\nuint32 packUnorm2x16(vec2 const& v)
\n
\nGLSL packUnorm4x8 man page
\n
\nGLSL 4.20.8 specification, section 8.4 Floating-Point Pack and Unpack Functions
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL uint16 glm::packUnorm4x4 (vec4 const & v)
\n
\n\n

Convert each component of the normalized floating-point vector into unsigned integer values.

\n
See also
to use the features of this extension. \">GLM_GTC_packing
\n
\nvec4 unpackUnorm4x4(uint16 p)
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL vec3 glm::unpackF2x11_1x10 (uint32 p)
\n
\n\n

First, unpacks a single 32-bit unsigned integer p into two 11-bit signless floating-point values and one 10-bit signless floating-point value .

\n

Then, each component is converted to a normalized floating-point value to generate the returned three-component vector.

\n

The first component of the returned vector will be extracted from the least significant bits of the input; the last component will be extracted from the most significant bits.

\n
See also
to use the features of this extension. \">GLM_GTC_packing
\n
\nuint32 packF2x11_1x10(vec3 const& v)
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL vec3 glm::unpackF3x9_E1x5 (uint32 p)
\n
\n\n

First, unpacks a single 32-bit unsigned integer p into two 11-bit signless floating-point values and one 10-bit signless floating-point value .

\n

Then, each component is converted to a normalized floating-point value to generate the returned three-component vector.

\n

The first component of the returned vector will be extracted from the least significant bits of the input; the last component will be extracted from the most significant bits.

\n

unpackF3x9_E1x5 allows decoding RGBE / RGB9E5 data

\n
See also
to use the features of this extension. \">GLM_GTC_packing
\n
\nuint32 packF3x9_E1x5(vec3 const& v)
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL vec<L, float, Q> glm::unpackHalf (vec< L, uint16, Q > const & p)
\n
\n\n

Returns a floating-point vector with components obtained by reinterpreting an integer vector as 16-bit floating-point numbers and converting them to 32-bit floating-point values.

\n

The first component of the vector is obtained from the 16 least-significant bits of v; the forth component is obtained from the 16 most-significant bits of v.

\n
See also
to use the features of this extension. \">GLM_GTC_packing
\n
\nvec<L, uint16, Q> packHalf(vec<L, float, Q> const& v)
\n
\nGLSL 4.20.8 specification, section 8.4 Floating-Point Pack and Unpack Functions
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL float glm::unpackHalf1x16 (uint16 v)
\n
\n\n

Returns a floating-point scalar with components obtained by unpacking a 16-bit unsigned integer into a 16-bit value, interpreted as a 16-bit floating-point number according to the OpenGL Specification, and converting it to 32-bit floating-point values.

\n
See also
to use the features of this extension. \">GLM_GTC_packing
\n
\nvec2 unpackHalf2x16(uint32 const& v)
\n
\nvec4 unpackHalf4x16(uint64 const& v)
\n
\nGLSL unpackHalf2x16 man page
\n
\nGLSL 4.20.8 specification, section 8.4 Floating-Point Pack and Unpack Functions
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL vec4 glm::unpackHalf4x16 (uint64 p)
\n
\n\n

Returns a four-component floating-point vector with components obtained by unpacking a 64-bit unsigned integer into four 16-bit values, interpreting those values as 16-bit floating-point numbers according to the OpenGL Specification, and converting them to 32-bit floating-point values.

\n

The first component of the vector is obtained from the 16 least-significant bits of v; the forth component is obtained from the 16 most-significant bits of v.

\n
See also
to use the features of this extension. \">GLM_GTC_packing
\n
\nfloat unpackHalf1x16(uint16 const& v)
\n
\nvec2 unpackHalf2x16(uint32 const& v)
\n
\nGLSL unpackHalf2x16 man page
\n
\nGLSL 4.20.8 specification, section 8.4 Floating-Point Pack and Unpack Functions
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL ivec4 glm::unpackI3x10_1x2 (uint32 p)
\n
\n\n

Unpacks a single 32-bit unsigned integer p into three 10-bit and one 2-bit signed integers.

\n

The first component of the returned vector will be extracted from the least significant bits of the input; the last component will be extracted from the most significant bits.

\n
See also
to use the features of this extension. \">GLM_GTC_packing
\n
\nuint32 packU3x10_1x2(uvec4 const& v)
\n
\nvec4 unpackSnorm3x10_1x2(uint32 const& p);
\n
\nuvec4 unpackI3x10_1x2(uint32 const& p);
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL i16vec2 glm::unpackInt2x16 (int p)
\n
\n\n

Convert a packed integer into an integer vector.

\n
See also
to use the features of this extension. \">GLM_GTC_packing
\n
\nint packInt2x16(i16vec2 const& v)
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL i32vec2 glm::unpackInt2x32 (int64 p)
\n
\n\n

Convert a packed integer into an integer vector.

\n
See also
to use the features of this extension. \">GLM_GTC_packing
\n
\nint packInt2x16(i32vec2 const& v)
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL i8vec2 glm::unpackInt2x8 (int16 p)
\n
\n\n

Convert a packed integer into an integer vector.

\n
See also
to use the features of this extension. \">GLM_GTC_packing
\n
\nint16 packInt2x8(i8vec2 const& v)
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL i16vec4 glm::unpackInt4x16 (int64 p)
\n
\n\n

Convert a packed integer into an integer vector.

\n
See also
to use the features of this extension. \">GLM_GTC_packing
\n
\nint64 packInt4x16(i16vec4 const& v)
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL i8vec4 glm::unpackInt4x8 (int32 p)
\n
\n\n

Convert a packed integer into an integer vector.

\n
See also
to use the features of this extension. \">GLM_GTC_packing
\n
\nint32 packInt2x8(i8vec4 const& v)
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL vec<3, T, Q> glm::unpackRGBM (vec< 4, T, Q > const & rgbm)
\n
\n\n

Returns a floating-point vector with components obtained by reinterpreting an integer vector as 16-bit floating-point numbers and converting them to 32-bit floating-point values.

\n

The first component of the vector is obtained from the 16 least-significant bits of v; the forth component is obtained from the 16 most-significant bits of v.

\n
See also
to use the features of this extension. \">GLM_GTC_packing
\n
\nvec<4, T, Q> packRGBM(vec<3, float, Q> const& v)
\n
\nGLSL 4.20.8 specification, section 8.4 Floating-Point Pack and Unpack Functions
\n\n
\n
\n\n
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\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL vec<L, floatType, Q> glm::unpackSnorm (vec< L, intType, Q > const & v)
\n
\n\n

Convert a packed integer to a normalized floating-point vector.

\n
See also
to use the features of this extension. \">GLM_GTC_packing
\n
\nvec<L, intType, Q> packSnorm(vec<L, floatType, Q> const& v)
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL float glm::unpackSnorm1x16 (uint16 p)
\n
\n\n

First, unpacks a single 16-bit unsigned integer p into a single 16-bit signed integers.

\n

Then, each component is converted to a normalized floating-point value to generate the returned scalar.

\n

The conversion for unpacked fixed-point value f to floating point is done as follows: unpackSnorm1x16: clamp(f / 32767.0, -1, +1)

\n
See also
to use the features of this extension. \">GLM_GTC_packing
\n
\nvec2 unpackSnorm2x16(uint32 p)
\n
\nvec4 unpackSnorm4x16(uint64 p)
\n
\nGLSL unpackSnorm4x8 man page
\n
\nGLSL 4.20.8 specification, section 8.4 Floating-Point Pack and Unpack Functions
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL float glm::unpackSnorm1x8 (uint8 p)
\n
\n\n

First, unpacks a single 8-bit unsigned integer p into a single 8-bit signed integers.

\n

Then, the value is converted to a normalized floating-point value to generate the returned scalar.

\n

The conversion for unpacked fixed-point value f to floating point is done as follows: unpackSnorm1x8: clamp(f / 127.0, -1, +1)

\n
See also
to use the features of this extension. \">GLM_GTC_packing
\n
\nvec2 unpackSnorm2x8(uint16 p)
\n
\nvec4 unpackSnorm4x8(uint32 p)
\n
\nGLSL unpackSnorm4x8 man page
\n
\nGLSL 4.20.8 specification, section 8.4 Floating-Point Pack and Unpack Functions
\n\n
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GLM_FUNC_DECL vec2 glm::unpackSnorm2x8 (uint16 p)
\n
\n\n

First, unpacks a single 16-bit unsigned integer p into a pair of 8-bit signed integers.

\n

Then, each component is converted to a normalized floating-point value to generate the returned two-component vector.

\n

The conversion for unpacked fixed-point value f to floating point is done as follows: unpackSnorm2x8: clamp(f / 127.0, -1, +1)

\n

The first component of the returned vector will be extracted from the least significant bits of the input; the last component will be extracted from the most significant bits.

\n
See also
to use the features of this extension. \">GLM_GTC_packing
\n
\nfloat unpackSnorm1x8(uint8 p)
\n
\nvec4 unpackSnorm4x8(uint32 p)
\n
\nGLSL unpackSnorm4x8 man page
\n
\nGLSL 4.20.8 specification, section 8.4 Floating-Point Pack and Unpack Functions
\n\n
\n
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\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL vec4 glm::unpackSnorm3x10_1x2 (uint32 p)
\n
\n\n

First, unpacks a single 32-bit unsigned integer p into four 16-bit signed integers.

\n

Then, each component is converted to a normalized floating-point value to generate the returned four-component vector.

\n

The conversion for unpacked fixed-point value f to floating point is done as follows: unpackSnorm3x10_1x2(xyz): clamp(f / 511.0, -1, +1) unpackSnorm3x10_1x2(w): clamp(f / 511.0, -1, +1)

\n

The first component of the returned vector will be extracted from the least significant bits of the input; the last component will be extracted from the most significant bits.

\n
See also
to use the features of this extension. \">GLM_GTC_packing
\n
\nuint32 packSnorm3x10_1x2(vec4 const& v)
\n
\nvec4 unpackUnorm3x10_1x2(uint32 const& p))
\n
\nuvec4 unpackI3x10_1x2(uint32 const& p)
\n
\nuvec4 unpackU3x10_1x2(uint32 const& p)
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL vec4 glm::unpackSnorm4x16 (uint64 p)
\n
\n\n

First, unpacks a single 64-bit unsigned integer p into four 16-bit signed integers.

\n

Then, each component is converted to a normalized floating-point value to generate the returned four-component vector.

\n

The conversion for unpacked fixed-point value f to floating point is done as follows: unpackSnorm4x16: clamp(f / 32767.0, -1, +1)

\n

The first component of the returned vector will be extracted from the least significant bits of the input; the last component will be extracted from the most significant bits.

\n
See also
to use the features of this extension. \">GLM_GTC_packing
\n
\nfloat unpackSnorm1x16(uint16 p)
\n
\nvec2 unpackSnorm2x16(uint32 p)
\n
\nGLSL unpackSnorm4x8 man page
\n
\nGLSL 4.20.8 specification, section 8.4 Floating-Point Pack and Unpack Functions
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL uvec4 glm::unpackU3x10_1x2 (uint32 p)
\n
\n\n

Unpacks a single 32-bit unsigned integer p into three 10-bit and one 2-bit unsigned integers.

\n

The first component of the returned vector will be extracted from the least significant bits of the input; the last component will be extracted from the most significant bits.

\n
See also
to use the features of this extension. \">GLM_GTC_packing
\n
\nuint32 packU3x10_1x2(uvec4 const& v)
\n
\nvec4 unpackSnorm3x10_1x2(uint32 const& p);
\n
\nuvec4 unpackI3x10_1x2(uint32 const& p);
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL u16vec2 glm::unpackUint2x16 (uint p)
\n
\n\n

Convert a packed integer into an integer vector.

\n
See also
to use the features of this extension. \">GLM_GTC_packing
\n
\nuint packUint2x16(u16vec2 const& v)
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL u32vec2 glm::unpackUint2x32 (uint64 p)
\n
\n\n

Convert a packed integer into an integer vector.

\n
See also
to use the features of this extension. \">GLM_GTC_packing
\n
\nint packUint2x16(u32vec2 const& v)
\n\n
\n
\n\n
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GLM_FUNC_DECL u8vec2 glm::unpackUint2x8 (uint16 p)
\n
\n\n

Convert a packed integer into an integer vector.

\n
See also
to use the features of this extension. \">GLM_GTC_packing
\n
\nuint16 packInt2x8(u8vec2 const& v)
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL u16vec4 glm::unpackUint4x16 (uint64 p)
\n
\n\n

Convert a packed integer into an integer vector.

\n
See also
to use the features of this extension. \">GLM_GTC_packing
\n
\nuint64 packUint4x16(u16vec4 const& v)
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL u8vec4 glm::unpackUint4x8 (uint32 p)
\n
\n\n

Convert a packed integer into an integer vector.

\n
See also
to use the features of this extension. \">GLM_GTC_packing
\n
\nuint32 packUint4x8(u8vec2 const& v)
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL vec<L, floatType, Q> glm::unpackUnorm (vec< L, uintType, Q > const & v)
\n
\n\n

Convert a packed integer to a normalized floating-point vector.

\n
See also
to use the features of this extension. \">GLM_GTC_packing
\n
\nvec<L, intType, Q> packUnorm(vec<L, floatType, Q> const& v)
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL float glm::unpackUnorm1x16 (uint16 p)
\n
\n\n

First, unpacks a single 16-bit unsigned integer p into a of 16-bit unsigned integers.

\n

Then, the value is converted to a normalized floating-point value to generate the returned scalar.

\n

The conversion for unpacked fixed-point value f to floating point is done as follows: unpackUnorm1x16: f / 65535.0

\n
See also
to use the features of this extension. \">GLM_GTC_packing
\n
\nvec2 unpackUnorm2x16(uint32 p)
\n
\nvec4 unpackUnorm4x16(uint64 p)
\n
\nGLSL unpackUnorm2x16 man page
\n
\nGLSL 4.20.8 specification, section 8.4 Floating-Point Pack and Unpack Functions
\n\n
\n
\n\n
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\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL vec3 glm::unpackUnorm1x5_1x6_1x5 (uint16 p)
\n
\n\n

Convert a packed integer to a normalized floating-point vector.

\n
See also
to use the features of this extension. \">GLM_GTC_packing
\n
\nuint16 packUnorm1x5_1x6_1x5(vec3 const& v)
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL float glm::unpackUnorm1x8 (uint8 p)
\n
\n\n

Convert a single 8-bit integer to a normalized floating-point value.

\n

The conversion for unpacked fixed-point value f to floating point is done as follows: unpackUnorm4x8: f / 255.0

\n
See also
to use the features of this extension. \">GLM_GTC_packing
\n
\nvec2 unpackUnorm2x8(uint16 p)
\n
\nvec4 unpackUnorm4x8(uint32 p)
\n
\nGLSL unpackUnorm4x8 man page
\n
\nGLSL 4.20.8 specification, section 8.4 Floating-Point Pack and Unpack Functions
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL vec3 glm::unpackUnorm2x3_1x2 (uint8 p)
\n
\n\n

Convert a packed integer to a normalized floating-point vector.

\n
See also
to use the features of this extension. \">GLM_GTC_packing
\n
\nuint8 packUnorm2x3_1x2(vec3 const& v)
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL vec2 glm::unpackUnorm2x4 (uint8 p)
\n
\n\n

Convert a packed integer to a normalized floating-point vector.

\n
See also
to use the features of this extension. \">GLM_GTC_packing
\n
\nuint8 packUnorm2x4(vec2 const& v)
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL vec2 glm::unpackUnorm2x8 (uint16 p)
\n
\n\n

First, unpacks a single 16-bit unsigned integer p into a pair of 8-bit unsigned integers.

\n

Then, each component is converted to a normalized floating-point value to generate the returned two-component vector.

\n

The conversion for unpacked fixed-point value f to floating point is done as follows: unpackUnorm4x8: f / 255.0

\n

The first component of the returned vector will be extracted from the least significant bits of the input; the last component will be extracted from the most significant bits.

\n
See also
to use the features of this extension. \">GLM_GTC_packing
\n
\nfloat unpackUnorm1x8(uint8 v)
\n
\nvec4 unpackUnorm4x8(uint32 p)
\n
\nGLSL unpackUnorm4x8 man page
\n
\nGLSL 4.20.8 specification, section 8.4 Floating-Point Pack and Unpack Functions
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL vec4 glm::unpackUnorm3x10_1x2 (uint32 p)
\n
\n\n

First, unpacks a single 32-bit unsigned integer p into four 16-bit signed integers.

\n

Then, each component is converted to a normalized floating-point value to generate the returned four-component vector.

\n

The conversion for unpacked fixed-point value f to floating point is done as follows: unpackSnorm3x10_1x2(xyz): clamp(f / 1023.0, 0, +1) unpackSnorm3x10_1x2(w): clamp(f / 3.0, 0, +1)

\n

The first component of the returned vector will be extracted from the least significant bits of the input; the last component will be extracted from the most significant bits.

\n
See also
to use the features of this extension. \">GLM_GTC_packing
\n
\nuint32 packSnorm3x10_1x2(vec4 const& v)
\n
\nvec4 unpackInorm3x10_1x2(uint32 const& p))
\n
\nuvec4 unpackI3x10_1x2(uint32 const& p)
\n
\nuvec4 unpackU3x10_1x2(uint32 const& p)
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL vec4 glm::unpackUnorm3x5_1x1 (uint16 p)
\n
\n\n

Convert a packed integer to a normalized floating-point vector.

\n
See also
to use the features of this extension. \">GLM_GTC_packing
\n
\nuint16 packUnorm3x5_1x1(vec4 const& v)
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL vec4 glm::unpackUnorm4x16 (uint64 p)
\n
\n\n

First, unpacks a single 64-bit unsigned integer p into four 16-bit unsigned integers.

\n

Then, each component is converted to a normalized floating-point value to generate the returned four-component vector.

\n

The conversion for unpacked fixed-point value f to floating point is done as follows: unpackUnormx4x16: f / 65535.0

\n

The first component of the returned vector will be extracted from the least significant bits of the input; the last component will be extracted from the most significant bits.

\n
See also
to use the features of this extension. \">GLM_GTC_packing
\n
\nfloat unpackUnorm1x16(uint16 p)
\n
\nvec2 unpackUnorm2x16(uint32 p)
\n
\nGLSL unpackUnorm2x16 man page
\n
\nGLSL 4.20.8 specification, section 8.4 Floating-Point Pack and Unpack Functions
\n\n
\n
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GLM_FUNC_DECL vec4 glm::unpackUnorm4x4 (uint16 p)
\n
\n\n

Convert a packed integer to a normalized floating-point vector.

\n
See also
to use the features of this extension. \">GLM_GTC_packing
\n
\nuint16 packUnorm4x4(vec4 const& v)
\n\n
\n
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\n\n
\nGenerated by  \n\"doxygen\"/\n 1.8.10\n
\n\n\n" }, { "path": "field_construction/submodules/diff-langsurf-rasterizer/third_party/glm/doc/api/a00299.html", "content": "\n\n\n\n\n\n0.9.9 API documentation: GLM_GTC_quaternion\n\n\n\n\n\n\n\n\n\n\n
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\"Logo\"\n
0.9.9 API documentation\n
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\nFunctions
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GLM_GTC_quaternion
Recommended extensions
\n
\n
\n\n

Include <glm/gtc/quaternion.hpp> to use the features of this extension. \nMore...

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

\nFunctions

template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > eulerAngles (qua< T, Q > const &x)
 Returns euler angles, pitch as x, yaw as y, roll as z. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 4, bool, Q > greaterThan (qua< T, Q > const &x, qua< T, Q > const &y)
 Returns the component-wise comparison of result x > y. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 4, bool, Q > greaterThanEqual (qua< T, Q > const &x, qua< T, Q > const &y)
 Returns the component-wise comparison of result x >= y. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 4, bool, Q > lessThan (qua< T, Q > const &x, qua< T, Q > const &y)
 Returns the component-wise comparison result of x < y. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 4, bool, Q > lessThanEqual (qua< T, Q > const &x, qua< T, Q > const &y)
 Returns the component-wise comparison of result x <= y. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 3, 3, T, Q > mat3_cast (qua< T, Q > const &x)
 Converts a quaternion to a 3 * 3 matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 4, 4, T, Q > mat4_cast (qua< T, Q > const &x)
 Converts a quaternion to a 4 * 4 matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL T pitch (qua< T, Q > const &x)
 Returns pitch value of euler angles expressed in radians. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL qua< T, Q > quat_cast (mat< 3, 3, T, Q > const &x)
 Converts a pure rotation 3 * 3 matrix to a quaternion. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL qua< T, Q > quat_cast (mat< 4, 4, T, Q > const &x)
 Converts a pure rotation 4 * 4 matrix to a quaternion. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL qua< T, Q > quatLookAt (vec< 3, T, Q > const &direction, vec< 3, T, Q > const &up)
 Build a look at quaternion based on the default handedness. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL qua< T, Q > quatLookAtLH (vec< 3, T, Q > const &direction, vec< 3, T, Q > const &up)
 Build a left-handed look at quaternion. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL qua< T, Q > quatLookAtRH (vec< 3, T, Q > const &direction, vec< 3, T, Q > const &up)
 Build a right-handed look at quaternion. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL T roll (qua< T, Q > const &x)
 Returns roll value of euler angles expressed in radians. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL T yaw (qua< T, Q > const &x)
 Returns yaw value of euler angles expressed in radians. More...
 
\n

Detailed Description

\n

Include <glm/gtc/quaternion.hpp> to use the features of this extension.

\n

Defines a templated quaternion type and several quaternion operations.

\n

Function Documentation

\n\n
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GLM_FUNC_DECL vec<3, T, Q> glm::eulerAngles (qua< T, Q > const & x)
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Returns euler angles, pitch as x, yaw as y, roll as z.

\n

The result is expressed in radians.

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See also
to use the features of this extension. \">GLM_GTC_quaternion
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GLM_FUNC_DECL vec<4, bool, Q> glm::greaterThan (qua< T, Q > const & x,
qua< T, Q > const & y 
)
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Returns the component-wise comparison of result x > y.

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TFloating-point scalar types
QValue from qualifier enum
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See also
GLM_EXT_quaternion_relational
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GLM_FUNC_DECL vec<4, bool, Q> glm::greaterThanEqual (qua< T, Q > const & x,
qua< T, Q > const & y 
)
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Returns the component-wise comparison of result x >= y.

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Template Parameters
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TFloating-point scalar types
QValue from qualifier enum
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See also
GLM_EXT_quaternion_relational
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GLM_FUNC_DECL vec<4, bool, Q> glm::lessThan (qua< T, Q > const & x,
qua< T, Q > const & y 
)
\n
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Returns the component-wise comparison result of x < y.

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TFloating-point scalar types
QValue from qualifier enum
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GLM_EXT_quaternion_relational
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GLM_FUNC_DECL vec<4, bool, Q> glm::lessThanEqual (qua< T, Q > const & x,
qua< T, Q > const & y 
)
\n
\n\n

Returns the component-wise comparison of result x <= y.

\n
Template Parameters
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TFloating-point scalar types
QValue from qualifier enum
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GLM_EXT_quaternion_relational
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GLM_FUNC_DECL mat<3, 3, T, Q> glm::mat3_cast (qua< T, Q > const & x)
\n
\n\n

Converts a quaternion to a 3 * 3 matrix.

\n
Template Parameters
\n \n \n
TFloating-point scalar types.
\n
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See also
to use the features of this extension. \">GLM_GTC_quaternion
\n\n

Referenced by glm::toMat3().

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GLM_FUNC_DECL mat<4, 4, T, Q> glm::mat4_cast (qua< T, Q > const & x)
\n
\n\n

Converts a quaternion to a 4 * 4 matrix.

\n
Template Parameters
\n \n \n
TFloating-point scalar types.
\n
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See also
to use the features of this extension. \">GLM_GTC_quaternion
\n\n

Referenced by glm::toMat4().

\n\n
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GLM_FUNC_DECL T glm::pitch (qua< T, Q > const & x)
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Returns pitch value of euler angles expressed in radians.

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Template Parameters
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TFloating-point scalar types.
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See also
to use the features of this extension. \">GLM_GTC_quaternion
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GLM_FUNC_DECL qua<T, Q> glm::quat_cast (mat< 3, 3, T, Q > const & x)
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Converts a pure rotation 3 * 3 matrix to a quaternion.

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TFloating-point scalar types.
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See also
to use the features of this extension. \">GLM_GTC_quaternion
\n\n

Referenced by glm::toQuat().

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GLM_FUNC_DECL qua<T, Q> glm::quat_cast (mat< 4, 4, T, Q > const & x)
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Converts a pure rotation 4 * 4 matrix to a quaternion.

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TFloating-point scalar types.
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to use the features of this extension. \">GLM_GTC_quaternion
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GLM_FUNC_DECL qua<T, Q> glm::quatLookAt (vec< 3, T, Q > const & direction,
vec< 3, T, Q > const & up 
)
\n
\n\n

Build a look at quaternion based on the default handedness.

\n
Parameters
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directionDesired forward direction. Needs to be normalized.
upUp vector, how the camera is oriented. Typically (0, 1, 0).
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GLM_FUNC_DECL qua<T, Q> glm::quatLookAtLH (vec< 3, T, Q > const & direction,
vec< 3, T, Q > const & up 
)
\n
\n\n

Build a left-handed look at quaternion.

\n
Parameters
\n \n \n \n
directionDesired forward direction onto which the +z-axis gets mapped. Needs to be normalized.
upUp vector, how the camera is oriented. Typically (0, 1, 0).
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GLM_FUNC_DECL qua<T, Q> glm::quatLookAtRH (vec< 3, T, Q > const & direction,
vec< 3, T, Q > const & up 
)
\n
\n\n

Build a right-handed look at quaternion.

\n
Parameters
\n \n \n \n
directionDesired forward direction onto which the -z-axis gets mapped. Needs to be normalized.
upUp vector, how the camera is oriented. Typically (0, 1, 0).
\n
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GLM_FUNC_DECL T glm::roll (qua< T, Q > const & x)
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Returns roll value of euler angles expressed in radians.

\n
Template Parameters
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TFloating-point scalar types.
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See also
to use the features of this extension. \">GLM_GTC_quaternion
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GLM_FUNC_DECL T glm::yaw (qua< T, Q > const & x)
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Returns yaw value of euler angles expressed in radians.

\n
Template Parameters
\n \n \n
TFloating-point scalar types.
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See also
to use the features of this extension. \">GLM_GTC_quaternion
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GLM_GTC_random
Recommended extensions
\n
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Include <glm/gtc/random.hpp> to use the features of this extension. \nMore...

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

\nFunctions

template<typename T >
GLM_FUNC_DECL vec< 3, T, defaultp > ballRand (T Radius)
 Generate a random 3D vector which coordinates are regulary distributed within the volume of a ball of a given radius. More...
 
template<typename T >
GLM_FUNC_DECL vec< 2, T, defaultp > circularRand (T Radius)
 Generate a random 2D vector which coordinates are regulary distributed on a circle of a given radius. More...
 
template<typename T >
GLM_FUNC_DECL vec< 2, T, defaultp > diskRand (T Radius)
 Generate a random 2D vector which coordinates are regulary distributed within the area of a disk of a given radius. More...
 
template<typename genType >
GLM_FUNC_DECL genType gaussRand (genType Mean, genType Deviation)
 Generate random numbers in the interval [Min, Max], according a gaussian distribution. More...
 
template<typename genType >
GLM_FUNC_DECL genType linearRand (genType Min, genType Max)
 Generate random numbers in the interval [Min, Max], according a linear distribution. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > linearRand (vec< L, T, Q > const &Min, vec< L, T, Q > const &Max)
 Generate random numbers in the interval [Min, Max], according a linear distribution. More...
 
template<typename T >
GLM_FUNC_DECL vec< 3, T, defaultp > sphericalRand (T Radius)
 Generate a random 3D vector which coordinates are regulary distributed on a sphere of a given radius. More...
 
\n

Detailed Description

\n

Include <glm/gtc/random.hpp> to use the features of this extension.

\n

Generate random number from various distribution methods.

\n

Function Documentation

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GLM_FUNC_DECL vec<3, T, defaultp> glm::ballRand (T Radius)
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Generate a random 3D vector which coordinates are regulary distributed within the volume of a ball of a given radius.

\n
See also
to use the features of this extension. \">GLM_GTC_random
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GLM_FUNC_DECL vec<2, T, defaultp> glm::circularRand (T Radius)
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Generate a random 2D vector which coordinates are regulary distributed on a circle of a given radius.

\n
See also
to use the features of this extension. \">GLM_GTC_random
\n\n
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GLM_FUNC_DECL vec<2, T, defaultp> glm::diskRand (T Radius)
\n
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Generate a random 2D vector which coordinates are regulary distributed within the area of a disk of a given radius.

\n
See also
to use the features of this extension. \">GLM_GTC_random
\n\n
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GLM_FUNC_DECL genType glm::gaussRand (genType Mean,
genType Deviation 
)
\n
\n\n

Generate random numbers in the interval [Min, Max], according a gaussian distribution.

\n
See also
to use the features of this extension. \">GLM_GTC_random
\n\n
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GLM_FUNC_DECL genType glm::linearRand (genType Min,
genType Max 
)
\n
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Generate random numbers in the interval [Min, Max], according a linear distribution.

\n
Parameters
\n \n \n \n
MinMinimum value included in the sampling
MaxMaximum value included in the sampling
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Template Parameters
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genTypeValue type. Currently supported: float or double scalars.
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See also
to use the features of this extension. \">GLM_GTC_random
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GLM_FUNC_DECL vec<L, T, Q> glm::linearRand (vec< L, T, Q > const & Min,
vec< L, T, Q > const & Max 
)
\n
\n\n

Generate random numbers in the interval [Min, Max], according a linear distribution.

\n
Parameters
\n \n \n \n
MinMinimum value included in the sampling
MaxMaximum value included in the sampling
\n
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Template Parameters
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TValue type. Currently supported: float or double.
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See also
to use the features of this extension. \">GLM_GTC_random
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GLM_FUNC_DECL vec<3, T, defaultp> glm::sphericalRand (T Radius)
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Generate a random 3D vector which coordinates are regulary distributed on a sphere of a given radius.

\n
See also
to use the features of this extension. \">GLM_GTC_random
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GLM_GTC_reciprocal
Recommended extensions
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Include <glm/gtc/reciprocal.hpp> to use the features of this extension. \nMore...

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\nFunctions

template<typename genType >
GLM_FUNC_DECL genType acot (genType x)
 Inverse cotangent function. More...
 
template<typename genType >
GLM_FUNC_DECL genType acoth (genType x)
 Inverse cotangent hyperbolic function. More...
 
template<typename genType >
GLM_FUNC_DECL genType acsc (genType x)
 Inverse cosecant function. More...
 
template<typename genType >
GLM_FUNC_DECL genType acsch (genType x)
 Inverse cosecant hyperbolic function. More...
 
template<typename genType >
GLM_FUNC_DECL genType asec (genType x)
 Inverse secant function. More...
 
template<typename genType >
GLM_FUNC_DECL genType asech (genType x)
 Inverse secant hyperbolic function. More...
 
template<typename genType >
GLM_FUNC_DECL genType cot (genType angle)
 Cotangent function. More...
 
template<typename genType >
GLM_FUNC_DECL genType coth (genType angle)
 Cotangent hyperbolic function. More...
 
template<typename genType >
GLM_FUNC_DECL genType csc (genType angle)
 Cosecant function. More...
 
template<typename genType >
GLM_FUNC_DECL genType csch (genType angle)
 Cosecant hyperbolic function. More...
 
template<typename genType >
GLM_FUNC_DECL genType sec (genType angle)
 Secant function. More...
 
template<typename genType >
GLM_FUNC_DECL genType sech (genType angle)
 Secant hyperbolic function. More...
 
\n

Detailed Description

\n

Include <glm/gtc/reciprocal.hpp> to use the features of this extension.

\n

Define secant, cosecant and cotangent functions.

\n

Function Documentation

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GLM_FUNC_DECL genType glm::acot (genType x)
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Inverse cotangent function.

\n
Returns
Return an angle expressed in radians.
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Template Parameters
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genTypeFloating-point scalar or vector types.
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See also
to use the features of this extension. \">GLM_GTC_reciprocal
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GLM_FUNC_DECL genType glm::acoth (genType x)
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Inverse cotangent hyperbolic function.

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Returns
Return an angle expressed in radians.
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Template Parameters
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genTypeFloating-point scalar or vector types.
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See also
to use the features of this extension. \">GLM_GTC_reciprocal
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GLM_FUNC_DECL genType glm::acsc (genType x)
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Inverse cosecant function.

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Returns
Return an angle expressed in radians.
\n
Template Parameters
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genTypeFloating-point scalar or vector types.
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See also
to use the features of this extension. \">GLM_GTC_reciprocal
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GLM_FUNC_DECL genType glm::acsch (genType x)
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Inverse cosecant hyperbolic function.

\n
Returns
Return an angle expressed in radians.
\n
Template Parameters
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genTypeFloating-point scalar or vector types.
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See also
to use the features of this extension. \">GLM_GTC_reciprocal
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GLM_FUNC_DECL genType glm::asec (genType x)
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Inverse secant function.

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Returns
Return an angle expressed in radians.
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genTypeFloating-point scalar or vector types.
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See also
to use the features of this extension. \">GLM_GTC_reciprocal
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GLM_FUNC_DECL genType glm::asech (genType x)
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Inverse secant hyperbolic function.

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Returns
Return an angle expressed in radians.
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genTypeFloating-point scalar or vector types.
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See also
to use the features of this extension. \">GLM_GTC_reciprocal
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GLM_FUNC_DECL genType glm::cot (genType angle)
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Cotangent function.

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adjacent / opposite or 1 / tan(x)

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genTypeFloating-point scalar or vector types.
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to use the features of this extension. \">GLM_GTC_reciprocal
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GLM_FUNC_DECL genType glm::coth (genType angle)
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Cotangent hyperbolic function.

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genTypeFloating-point scalar or vector types.
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See also
to use the features of this extension. \">GLM_GTC_reciprocal
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GLM_FUNC_DECL genType glm::csc (genType angle)
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Cosecant function.

\n

hypotenuse / opposite or 1 / sin(x)

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Template Parameters
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genTypeFloating-point scalar or vector types.
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See also
to use the features of this extension. \">GLM_GTC_reciprocal
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GLM_FUNC_DECL genType glm::csch (genType angle)
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Cosecant hyperbolic function.

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Template Parameters
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genTypeFloating-point scalar or vector types.
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See also
to use the features of this extension. \">GLM_GTC_reciprocal
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GLM_FUNC_DECL genType glm::sec (genType angle)
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Secant function.

\n

hypotenuse / adjacent or 1 / cos(x)

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Template Parameters
\n \n \n
genTypeFloating-point scalar or vector types.
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See also
to use the features of this extension. \">GLM_GTC_reciprocal
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GLM_FUNC_DECL genType glm::sech (genType angle)
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Secant hyperbolic function.

\n
Template Parameters
\n \n \n
genTypeFloating-point scalar or vector types.
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See also
to use the features of this extension. \">GLM_GTC_reciprocal
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\nFunctions
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GLM_GTC_round
Recommended extensions
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Include <glm/gtc/round.hpp> to use the features of this extension. \nMore...

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\nFunctions

template<typename genType >
GLM_FUNC_DECL genType ceilMultiple (genType v, genType Multiple)
 Higher multiple number of Source. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > ceilMultiple (vec< L, T, Q > const &v, vec< L, T, Q > const &Multiple)
 Higher multiple number of Source. More...
 
template<typename genIUType >
GLM_FUNC_DECL genIUType ceilPowerOfTwo (genIUType v)
 Return the power of two number which value is just higher the input value, round up to a power of two. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > ceilPowerOfTwo (vec< L, T, Q > const &v)
 Return the power of two number which value is just higher the input value, round up to a power of two. More...
 
template<typename genType >
GLM_FUNC_DECL genType floorMultiple (genType v, genType Multiple)
 Lower multiple number of Source. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > floorMultiple (vec< L, T, Q > const &v, vec< L, T, Q > const &Multiple)
 Lower multiple number of Source. More...
 
template<typename genIUType >
GLM_FUNC_DECL genIUType floorPowerOfTwo (genIUType v)
 Return the power of two number which value is just lower the input value, round down to a power of two. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > floorPowerOfTwo (vec< L, T, Q > const &v)
 Return the power of two number which value is just lower the input value, round down to a power of two. More...
 
template<typename genType >
GLM_FUNC_DECL genType roundMultiple (genType v, genType Multiple)
 Lower multiple number of Source. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > roundMultiple (vec< L, T, Q > const &v, vec< L, T, Q > const &Multiple)
 Lower multiple number of Source. More...
 
template<typename genIUType >
GLM_FUNC_DECL genIUType roundPowerOfTwo (genIUType v)
 Return the power of two number which value is the closet to the input value. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > roundPowerOfTwo (vec< L, T, Q > const &v)
 Return the power of two number which value is the closet to the input value. More...
 
\n

Detailed Description

\n

Include <glm/gtc/round.hpp> to use the features of this extension.

\n

Rounding value to specific boundings

\n

Function Documentation

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GLM_FUNC_DECL genType glm::ceilMultiple (genType v,
genType Multiple 
)
\n
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Higher multiple number of Source.

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Template Parameters
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genTypeFloating-point or integer scalar or vector types.
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Parameters
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vSource value to which is applied the function
MultipleMust be a null or positive value
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See also
to use the features of this extension. \">GLM_GTC_round
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GLM_FUNC_DECL vec<L, T, Q> glm::ceilMultiple (vec< L, T, Q > const & v,
vec< L, T, Q > const & Multiple 
)
\n
\n\n

Higher multiple number of Source.

\n
Template Parameters
\n \n \n \n \n
LInteger between 1 and 4 included that qualify the dimension of the vector
TFloating-point or integer scalar types
QValue from qualifier enum
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Parameters
\n \n \n \n
vSource values to which is applied the function
MultipleMust be a null or positive value
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See also
to use the features of this extension. \">GLM_GTC_round
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GLM_FUNC_DECL genIUType glm::ceilPowerOfTwo (genIUType v)
\n
\n\n

Return the power of two number which value is just higher the input value, round up to a power of two.

\n
See also
to use the features of this extension. \">GLM_GTC_round
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GLM_FUNC_DECL vec<L, T, Q> glm::ceilPowerOfTwo (vec< L, T, Q > const & v)
\n
\n\n

Return the power of two number which value is just higher the input value, round up to a power of two.

\n
Template Parameters
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LInteger between 1 and 4 included that qualify the dimension of the vector
TFloating-point or integer scalar types
QValue from qualifier enum
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See also
to use the features of this extension. \">GLM_GTC_round
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GLM_FUNC_DECL genType glm::floorMultiple (genType v,
genType Multiple 
)
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Lower multiple number of Source.

\n
Template Parameters
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genTypeFloating-point or integer scalar or vector types.
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Parameters
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vSource value to which is applied the function
MultipleMust be a null or positive value
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See also
to use the features of this extension. \">GLM_GTC_round
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GLM_FUNC_DECL vec<L, T, Q> glm::floorMultiple (vec< L, T, Q > const & v,
vec< L, T, Q > const & Multiple 
)
\n
\n\n

Lower multiple number of Source.

\n
Template Parameters
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LInteger between 1 and 4 included that qualify the dimension of the vector
TFloating-point or integer scalar types
QValue from qualifier enum
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Parameters
\n \n \n \n
vSource values to which is applied the function
MultipleMust be a null or positive value
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See also
to use the features of this extension. \">GLM_GTC_round
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GLM_FUNC_DECL genIUType glm::floorPowerOfTwo (genIUType v)
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Return the power of two number which value is just lower the input value, round down to a power of two.

\n
See also
to use the features of this extension. \">GLM_GTC_round
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GLM_FUNC_DECL vec<L, T, Q> glm::floorPowerOfTwo (vec< L, T, Q > const & v)
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Return the power of two number which value is just lower the input value, round down to a power of two.

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Template Parameters
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LInteger between 1 and 4 included that qualify the dimension of the vector
TFloating-point or integer scalar types
QValue from qualifier enum
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See also
to use the features of this extension. \">GLM_GTC_round
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GLM_FUNC_DECL genType glm::roundMultiple (genType v,
genType Multiple 
)
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Lower multiple number of Source.

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Template Parameters
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genTypeFloating-point or integer scalar or vector types.
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Parameters
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vSource value to which is applied the function
MultipleMust be a null or positive value
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See also
to use the features of this extension. \">GLM_GTC_round
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GLM_FUNC_DECL vec<L, T, Q> glm::roundMultiple (vec< L, T, Q > const & v,
vec< L, T, Q > const & Multiple 
)
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Lower multiple number of Source.

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Template Parameters
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LInteger between 1 and 4 included that qualify the dimension of the vector
TFloating-point or integer scalar types
QValue from qualifier enum
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Parameters
\n \n \n \n
vSource values to which is applied the function
MultipleMust be a null or positive value
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See also
to use the features of this extension. \">GLM_GTC_round
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GLM_FUNC_DECL genIUType glm::roundPowerOfTwo (genIUType v)
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Return the power of two number which value is the closet to the input value.

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See also
to use the features of this extension. \">GLM_GTC_round
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GLM_FUNC_DECL vec<L, T, Q> glm::roundPowerOfTwo (vec< L, T, Q > const & v)
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Return the power of two number which value is the closet to the input value.

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Template Parameters
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LInteger between 1 and 4 included that qualify the dimension of the vector
TFloating-point or integer scalar types
QValue from qualifier enum
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See also
to use the features of this extension. \">GLM_GTC_round
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\nGenerated by  \n\"doxygen\"/\n 1.8.10\n
\n\n\n" }, { "path": "field_construction/submodules/diff-langsurf-rasterizer/third_party/glm/doc/api/a00303.html", "content": "\n\n\n\n\n\n0.9.9 API documentation: GLM_GTC_type_aligned\n\n\n\n\n\n\n\n\n\n\n
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0.9.9 API documentation\n
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\nTypedefs
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GLM_GTC_type_aligned
Recommended extensions
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Include <glm/gtc/type_aligned.hpp> to use the features of this extension. \nMore...

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\nTypedefs

\ntypedef aligned_highp_bvec1 aligned_bvec1
 1 component vector aligned in memory of bool values.
 
\ntypedef aligned_highp_bvec2 aligned_bvec2
 2 components vector aligned in memory of bool values.
 
\ntypedef aligned_highp_bvec3 aligned_bvec3
 3 components vector aligned in memory of bool values.
 
\ntypedef aligned_highp_bvec4 aligned_bvec4
 4 components vector aligned in memory of bool values.
 
\ntypedef aligned_highp_dmat2 aligned_dmat2
 2 by 2 matrix tightly aligned in memory of double-precision floating-point numbers.
 
\ntypedef aligned_highp_dmat2x2 aligned_dmat2x2
 2 by 2 matrix tightly aligned in memory of double-precision floating-point numbers.
 
\ntypedef aligned_highp_dmat2x3 aligned_dmat2x3
 2 by 3 matrix tightly aligned in memory of double-precision floating-point numbers.
 
\ntypedef aligned_highp_dmat2x4 aligned_dmat2x4
 2 by 4 matrix tightly aligned in memory of double-precision floating-point numbers.
 
\ntypedef aligned_highp_dmat3 aligned_dmat3
 3 by 3 matrix tightly aligned in memory of double-precision floating-point numbers.
 
\ntypedef aligned_highp_dmat3x2 aligned_dmat3x2
 3 by 2 matrix tightly aligned in memory of double-precision floating-point numbers.
 
\ntypedef aligned_highp_dmat3x3 aligned_dmat3x3
 3 by 3 matrix tightly aligned in memory of double-precision floating-point numbers.
 
\ntypedef aligned_highp_dmat3x4 aligned_dmat3x4
 3 by 4 matrix tightly aligned in memory of double-precision floating-point numbers.
 
\ntypedef aligned_highp_dmat4 aligned_dmat4
 4 by 4 matrix tightly aligned in memory of double-precision floating-point numbers.
 
\ntypedef aligned_highp_dmat4x2 aligned_dmat4x2
 4 by 2 matrix tightly aligned in memory of double-precision floating-point numbers.
 
\ntypedef aligned_highp_dmat4x3 aligned_dmat4x3
 4 by 3 matrix tightly aligned in memory of double-precision floating-point numbers.
 
\ntypedef aligned_highp_dmat4x4 aligned_dmat4x4
 4 by 4 matrix tightly aligned in memory of double-precision floating-point numbers.
 
\ntypedef aligned_highp_dvec1 aligned_dvec1
 1 component vector aligned in memory of double-precision floating-point numbers.
 
\ntypedef aligned_highp_dvec2 aligned_dvec2
 2 components vector aligned in memory of double-precision floating-point numbers.
 
\ntypedef aligned_highp_dvec3 aligned_dvec3
 3 components vector aligned in memory of double-precision floating-point numbers.
 
\ntypedef aligned_highp_dvec4 aligned_dvec4
 4 components vector aligned in memory of double-precision floating-point numbers.
 
\ntypedef vec< 1, bool, aligned_highp > aligned_highp_bvec1
 1 component vector aligned in memory of bool values.
 
\ntypedef vec< 2, bool, aligned_highp > aligned_highp_bvec2
 2 components vector aligned in memory of bool values.
 
\ntypedef vec< 3, bool, aligned_highp > aligned_highp_bvec3
 3 components vector aligned in memory of bool values.
 
\ntypedef vec< 4, bool, aligned_highp > aligned_highp_bvec4
 4 components vector aligned in memory of bool values.
 
\ntypedef mat< 2, 2, double, aligned_highp > aligned_highp_dmat2
 2 by 2 matrix aligned in memory of double-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 2, 2, double, aligned_highp > aligned_highp_dmat2x2
 2 by 2 matrix aligned in memory of double-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 2, 3, double, aligned_highp > aligned_highp_dmat2x3
 2 by 3 matrix aligned in memory of double-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 2, 4, double, aligned_highp > aligned_highp_dmat2x4
 2 by 4 matrix aligned in memory of double-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 3, double, aligned_highp > aligned_highp_dmat3
 3 by 3 matrix aligned in memory of double-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 2, double, aligned_highp > aligned_highp_dmat3x2
 3 by 2 matrix aligned in memory of double-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 3, double, aligned_highp > aligned_highp_dmat3x3
 3 by 3 matrix aligned in memory of double-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 4, double, aligned_highp > aligned_highp_dmat3x4
 3 by 4 matrix aligned in memory of double-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 4, double, aligned_highp > aligned_highp_dmat4
 4 by 4 matrix aligned in memory of double-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 2, double, aligned_highp > aligned_highp_dmat4x2
 4 by 2 matrix aligned in memory of double-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 3, double, aligned_highp > aligned_highp_dmat4x3
 4 by 3 matrix aligned in memory of double-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 4, double, aligned_highp > aligned_highp_dmat4x4
 4 by 4 matrix aligned in memory of double-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef vec< 1, double, aligned_highp > aligned_highp_dvec1
 1 component vector aligned in memory of double-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef vec< 2, double, aligned_highp > aligned_highp_dvec2
 2 components vector aligned in memory of double-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef vec< 3, double, aligned_highp > aligned_highp_dvec3
 3 components vector aligned in memory of double-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef vec< 4, double, aligned_highp > aligned_highp_dvec4
 4 components vector aligned in memory of double-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef vec< 1, int, aligned_highp > aligned_highp_ivec1
 1 component vector aligned in memory of signed integer numbers.
 
\ntypedef vec< 2, int, aligned_highp > aligned_highp_ivec2
 2 components vector aligned in memory of signed integer numbers.
 
\ntypedef vec< 3, int, aligned_highp > aligned_highp_ivec3
 3 components vector aligned in memory of signed integer numbers.
 
\ntypedef vec< 4, int, aligned_highp > aligned_highp_ivec4
 4 components vector aligned in memory of signed integer numbers.
 
\ntypedef mat< 2, 2, float, aligned_highp > aligned_highp_mat2
 2 by 2 matrix aligned in memory of single-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 2, 2, float, aligned_highp > aligned_highp_mat2x2
 2 by 2 matrix aligned in memory of single-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 2, 3, float, aligned_highp > aligned_highp_mat2x3
 2 by 3 matrix aligned in memory of single-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 2, 4, float, aligned_highp > aligned_highp_mat2x4
 2 by 4 matrix aligned in memory of single-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 3, float, aligned_highp > aligned_highp_mat3
 3 by 3 matrix aligned in memory of single-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 2, float, aligned_highp > aligned_highp_mat3x2
 3 by 2 matrix aligned in memory of single-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 3, float, aligned_highp > aligned_highp_mat3x3
 3 by 3 matrix aligned in memory of single-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 4, float, aligned_highp > aligned_highp_mat3x4
 3 by 4 matrix aligned in memory of single-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 4, float, aligned_highp > aligned_highp_mat4
 4 by 4 matrix aligned in memory of single-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 2, float, aligned_highp > aligned_highp_mat4x2
 4 by 2 matrix aligned in memory of single-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 3, float, aligned_highp > aligned_highp_mat4x3
 4 by 3 matrix aligned in memory of single-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 4, float, aligned_highp > aligned_highp_mat4x4
 4 by 4 matrix aligned in memory of single-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef vec< 1, uint, aligned_highp > aligned_highp_uvec1
 1 component vector aligned in memory of unsigned integer numbers.
 
\ntypedef vec< 2, uint, aligned_highp > aligned_highp_uvec2
 2 components vector aligned in memory of unsigned integer numbers.
 
\ntypedef vec< 3, uint, aligned_highp > aligned_highp_uvec3
 3 components vector aligned in memory of unsigned integer numbers.
 
\ntypedef vec< 4, uint, aligned_highp > aligned_highp_uvec4
 4 components vector aligned in memory of unsigned integer numbers.
 
\ntypedef vec< 1, float, aligned_highp > aligned_highp_vec1
 1 component vector aligned in memory of single-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef vec< 2, float, aligned_highp > aligned_highp_vec2
 2 components vector aligned in memory of single-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef vec< 3, float, aligned_highp > aligned_highp_vec3
 3 components vector aligned in memory of single-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef vec< 4, float, aligned_highp > aligned_highp_vec4
 4 components vector aligned in memory of single-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef aligned_highp_ivec1 aligned_ivec1
 1 component vector aligned in memory of signed integer numbers.
 
\ntypedef aligned_highp_ivec2 aligned_ivec2
 2 components vector aligned in memory of signed integer numbers.
 
\ntypedef aligned_highp_ivec3 aligned_ivec3
 3 components vector aligned in memory of signed integer numbers.
 
\ntypedef aligned_highp_ivec4 aligned_ivec4
 4 components vector aligned in memory of signed integer numbers.
 
\ntypedef vec< 1, bool, aligned_lowp > aligned_lowp_bvec1
 1 component vector aligned in memory of bool values.
 
\ntypedef vec< 2, bool, aligned_lowp > aligned_lowp_bvec2
 2 components vector aligned in memory of bool values.
 
\ntypedef vec< 3, bool, aligned_lowp > aligned_lowp_bvec3
 3 components vector aligned in memory of bool values.
 
\ntypedef vec< 4, bool, aligned_lowp > aligned_lowp_bvec4
 4 components vector aligned in memory of bool values.
 
\ntypedef mat< 2, 2, double, aligned_lowp > aligned_lowp_dmat2
 2 by 2 matrix aligned in memory of double-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 2, 2, double, aligned_lowp > aligned_lowp_dmat2x2
 2 by 2 matrix aligned in memory of double-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 2, 3, double, aligned_lowp > aligned_lowp_dmat2x3
 2 by 3 matrix aligned in memory of double-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 2, 4, double, aligned_lowp > aligned_lowp_dmat2x4
 2 by 4 matrix aligned in memory of double-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 3, double, aligned_lowp > aligned_lowp_dmat3
 3 by 3 matrix aligned in memory of double-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 2, double, aligned_lowp > aligned_lowp_dmat3x2
 3 by 2 matrix aligned in memory of double-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 3, double, aligned_lowp > aligned_lowp_dmat3x3
 3 by 3 matrix aligned in memory of double-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 4, double, aligned_lowp > aligned_lowp_dmat3x4
 3 by 4 matrix aligned in memory of double-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 4, double, aligned_lowp > aligned_lowp_dmat4
 4 by 4 matrix aligned in memory of double-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 2, double, aligned_lowp > aligned_lowp_dmat4x2
 4 by 2 matrix aligned in memory of double-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 3, double, aligned_lowp > aligned_lowp_dmat4x3
 4 by 3 matrix aligned in memory of double-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 4, double, aligned_lowp > aligned_lowp_dmat4x4
 4 by 4 matrix aligned in memory of double-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef vec< 1, double, aligned_lowp > aligned_lowp_dvec1
 1 component vector aligned in memory of double-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef vec< 2, double, aligned_lowp > aligned_lowp_dvec2
 2 components vector aligned in memory of double-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef vec< 3, double, aligned_lowp > aligned_lowp_dvec3
 3 components vector aligned in memory of double-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef vec< 4, double, aligned_lowp > aligned_lowp_dvec4
 4 components vector aligned in memory of double-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef vec< 1, int, aligned_lowp > aligned_lowp_ivec1
 1 component vector aligned in memory of signed integer numbers.
 
\ntypedef vec< 2, int, aligned_lowp > aligned_lowp_ivec2
 2 components vector aligned in memory of signed integer numbers.
 
\ntypedef vec< 3, int, aligned_lowp > aligned_lowp_ivec3
 3 components vector aligned in memory of signed integer numbers.
 
\ntypedef vec< 4, int, aligned_lowp > aligned_lowp_ivec4
 4 components vector aligned in memory of signed integer numbers.
 
\ntypedef mat< 2, 2, float, aligned_lowp > aligned_lowp_mat2
 2 by 2 matrix aligned in memory of single-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 2, 2, float, aligned_lowp > aligned_lowp_mat2x2
 2 by 2 matrix aligned in memory of single-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 2, 3, float, aligned_lowp > aligned_lowp_mat2x3
 2 by 3 matrix aligned in memory of single-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 2, 4, float, aligned_lowp > aligned_lowp_mat2x4
 2 by 4 matrix aligned in memory of single-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 3, float, aligned_lowp > aligned_lowp_mat3
 3 by 3 matrix aligned in memory of single-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 2, float, aligned_lowp > aligned_lowp_mat3x2
 3 by 2 matrix aligned in memory of single-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 3, float, aligned_lowp > aligned_lowp_mat3x3
 3 by 3 matrix aligned in memory of single-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 4, float, aligned_lowp > aligned_lowp_mat3x4
 3 by 4 matrix aligned in memory of single-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 4, float, aligned_lowp > aligned_lowp_mat4
 4 by 4 matrix aligned in memory of single-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 2, float, aligned_lowp > aligned_lowp_mat4x2
 4 by 2 matrix aligned in memory of single-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 3, float, aligned_lowp > aligned_lowp_mat4x3
 4 by 3 matrix aligned in memory of single-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 4, float, aligned_lowp > aligned_lowp_mat4x4
 4 by 4 matrix aligned in memory of single-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef vec< 1, uint, aligned_lowp > aligned_lowp_uvec1
 1 component vector aligned in memory of unsigned integer numbers.
 
\ntypedef vec< 2, uint, aligned_lowp > aligned_lowp_uvec2
 2 components vector aligned in memory of unsigned integer numbers.
 
\ntypedef vec< 3, uint, aligned_lowp > aligned_lowp_uvec3
 3 components vector aligned in memory of unsigned integer numbers.
 
\ntypedef vec< 4, uint, aligned_lowp > aligned_lowp_uvec4
 4 components vector aligned in memory of unsigned integer numbers.
 
\ntypedef vec< 1, float, aligned_lowp > aligned_lowp_vec1
 1 component vector aligned in memory of single-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef vec< 2, float, aligned_lowp > aligned_lowp_vec2
 2 components vector aligned in memory of single-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef vec< 3, float, aligned_lowp > aligned_lowp_vec3
 3 components vector aligned in memory of single-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef vec< 4, float, aligned_lowp > aligned_lowp_vec4
 4 components vector aligned in memory of single-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef aligned_highp_mat2 aligned_mat2
 2 by 2 matrix tightly aligned in memory of single-precision floating-point numbers.
 
\ntypedef aligned_highp_mat2x2 aligned_mat2x2
 2 by 2 matrix tightly aligned in memory of single-precision floating-point numbers.
 
\ntypedef aligned_highp_mat2x3 aligned_mat2x3
 2 by 3 matrix tightly aligned in memory of single-precision floating-point numbers.
 
\ntypedef aligned_highp_mat2x4 aligned_mat2x4
 2 by 4 matrix tightly aligned in memory of single-precision floating-point numbers.
 
\ntypedef aligned_highp_mat3 aligned_mat3
 3 by 3 matrix tightly aligned in memory of single-precision floating-point numbers.
 
\ntypedef aligned_highp_mat3x2 aligned_mat3x2
 3 by 2 matrix tightly aligned in memory of single-precision floating-point numbers.
 
\ntypedef aligned_highp_mat3x3 aligned_mat3x3
 3 by 3 matrix tightly aligned in memory of single-precision floating-point numbers.
 
\ntypedef aligned_highp_mat3x4 aligned_mat3x4
 3 by 4 matrix tightly aligned in memory of single-precision floating-point numbers.
 
\ntypedef aligned_highp_mat4 aligned_mat4
 4 by 4 matrix tightly aligned in memory of single-precision floating-point numbers.
 
\ntypedef aligned_highp_mat4x2 aligned_mat4x2
 4 by 2 matrix tightly aligned in memory of single-precision floating-point numbers.
 
\ntypedef aligned_highp_mat4x3 aligned_mat4x3
 4 by 3 matrix tightly aligned in memory of single-precision floating-point numbers.
 
\ntypedef aligned_highp_mat4x4 aligned_mat4x4
 4 by 4 matrix tightly aligned in memory of single-precision floating-point numbers.
 
\ntypedef vec< 1, bool, aligned_mediump > aligned_mediump_bvec1
 1 component vector aligned in memory of bool values.
 
\ntypedef vec< 2, bool, aligned_mediump > aligned_mediump_bvec2
 2 components vector aligned in memory of bool values.
 
\ntypedef vec< 3, bool, aligned_mediump > aligned_mediump_bvec3
 3 components vector aligned in memory of bool values.
 
\ntypedef vec< 4, bool, aligned_mediump > aligned_mediump_bvec4
 4 components vector aligned in memory of bool values.
 
\ntypedef mat< 2, 2, double, aligned_mediump > aligned_mediump_dmat2
 2 by 2 matrix aligned in memory of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 2, 2, double, aligned_mediump > aligned_mediump_dmat2x2
 2 by 2 matrix aligned in memory of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 2, 3, double, aligned_mediump > aligned_mediump_dmat2x3
 2 by 3 matrix aligned in memory of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 2, 4, double, aligned_mediump > aligned_mediump_dmat2x4
 2 by 4 matrix aligned in memory of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 3, double, aligned_mediump > aligned_mediump_dmat3
 3 by 3 matrix aligned in memory of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 2, double, aligned_mediump > aligned_mediump_dmat3x2
 3 by 2 matrix aligned in memory of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 3, double, aligned_mediump > aligned_mediump_dmat3x3
 3 by 3 matrix aligned in memory of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 4, double, aligned_mediump > aligned_mediump_dmat3x4
 3 by 4 matrix aligned in memory of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 4, double, aligned_mediump > aligned_mediump_dmat4
 4 by 4 matrix aligned in memory of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 2, double, aligned_mediump > aligned_mediump_dmat4x2
 4 by 2 matrix aligned in memory of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 3, double, aligned_mediump > aligned_mediump_dmat4x3
 4 by 3 matrix aligned in memory of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 4, double, aligned_mediump > aligned_mediump_dmat4x4
 4 by 4 matrix aligned in memory of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef vec< 1, double, aligned_mediump > aligned_mediump_dvec1
 1 component vector aligned in memory of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef vec< 2, double, aligned_mediump > aligned_mediump_dvec2
 2 components vector aligned in memory of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef vec< 3, double, aligned_mediump > aligned_mediump_dvec3
 3 components vector aligned in memory of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef vec< 4, double, aligned_mediump > aligned_mediump_dvec4
 4 components vector aligned in memory of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef vec< 1, int, aligned_mediump > aligned_mediump_ivec1
 1 component vector aligned in memory of signed integer numbers.
 
\ntypedef vec< 2, int, aligned_mediump > aligned_mediump_ivec2
 2 components vector aligned in memory of signed integer numbers.
 
\ntypedef vec< 3, int, aligned_mediump > aligned_mediump_ivec3
 3 components vector aligned in memory of signed integer numbers.
 
\ntypedef vec< 4, int, aligned_mediump > aligned_mediump_ivec4
 4 components vector aligned in memory of signed integer numbers.
 
\ntypedef mat< 2, 2, float, aligned_mediump > aligned_mediump_mat2
 2 by 2 matrix aligned in memory of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 2, 2, float, aligned_mediump > aligned_mediump_mat2x2
 2 by 2 matrix aligned in memory of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 2, 3, float, aligned_mediump > aligned_mediump_mat2x3
 2 by 3 matrix aligned in memory of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 2, 4, float, aligned_mediump > aligned_mediump_mat2x4
 2 by 4 matrix aligned in memory of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 3, float, aligned_mediump > aligned_mediump_mat3
 3 by 3 matrix aligned in memory of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 2, float, aligned_mediump > aligned_mediump_mat3x2
 3 by 2 matrix aligned in memory of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 3, float, aligned_mediump > aligned_mediump_mat3x3
 3 by 3 matrix aligned in memory of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 4, float, aligned_mediump > aligned_mediump_mat3x4
 3 by 4 matrix aligned in memory of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 4, float, aligned_mediump > aligned_mediump_mat4
 4 by 4 matrix aligned in memory of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 2, float, aligned_mediump > aligned_mediump_mat4x2
 4 by 2 matrix aligned in memory of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 3, float, aligned_mediump > aligned_mediump_mat4x3
 4 by 3 matrix aligned in memory of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 4, float, aligned_mediump > aligned_mediump_mat4x4
 4 by 4 matrix aligned in memory of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef vec< 1, uint, aligned_mediump > aligned_mediump_uvec1
 1 component vector aligned in memory of unsigned integer numbers.
 
\ntypedef vec< 2, uint, aligned_mediump > aligned_mediump_uvec2
 2 components vector aligned in memory of unsigned integer numbers.
 
\ntypedef vec< 3, uint, aligned_mediump > aligned_mediump_uvec3
 3 components vector aligned in memory of unsigned integer numbers.
 
\ntypedef vec< 4, uint, aligned_mediump > aligned_mediump_uvec4
 4 components vector aligned in memory of unsigned integer numbers.
 
\ntypedef vec< 1, float, aligned_mediump > aligned_mediump_vec1
 1 component vector aligned in memory of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef vec< 2, float, aligned_mediump > aligned_mediump_vec2
 2 components vector aligned in memory of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef vec< 3, float, aligned_mediump > aligned_mediump_vec3
 3 components vector aligned in memory of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef vec< 4, float, aligned_mediump > aligned_mediump_vec4
 4 components vector aligned in memory of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef aligned_highp_uvec1 aligned_uvec1
 1 component vector aligned in memory of unsigned integer numbers.
 
\ntypedef aligned_highp_uvec2 aligned_uvec2
 2 components vector aligned in memory of unsigned integer numbers.
 
\ntypedef aligned_highp_uvec3 aligned_uvec3
 3 components vector aligned in memory of unsigned integer numbers.
 
\ntypedef aligned_highp_uvec4 aligned_uvec4
 4 components vector aligned in memory of unsigned integer numbers.
 
\ntypedef aligned_highp_vec1 aligned_vec1
 1 component vector aligned in memory of single-precision floating-point numbers.
 
\ntypedef aligned_highp_vec2 aligned_vec2
 2 components vector aligned in memory of single-precision floating-point numbers.
 
\ntypedef aligned_highp_vec3 aligned_vec3
 3 components vector aligned in memory of single-precision floating-point numbers.
 
\ntypedef aligned_highp_vec4 aligned_vec4
 4 components vector aligned in memory of single-precision floating-point numbers.
 
\ntypedef packed_highp_bvec1 packed_bvec1
 1 components vector tightly packed in memory of bool values.
 
\ntypedef packed_highp_bvec2 packed_bvec2
 2 components vector tightly packed in memory of bool values.
 
\ntypedef packed_highp_bvec3 packed_bvec3
 3 components vector tightly packed in memory of bool values.
 
\ntypedef packed_highp_bvec4 packed_bvec4
 4 components vector tightly packed in memory of bool values.
 
\ntypedef packed_highp_dmat2 packed_dmat2
 2 by 2 matrix tightly packed in memory of double-precision floating-point numbers.
 
\ntypedef packed_highp_dmat2x2 packed_dmat2x2
 2 by 2 matrix tightly packed in memory of double-precision floating-point numbers.
 
\ntypedef packed_highp_dmat2x3 packed_dmat2x3
 2 by 3 matrix tightly packed in memory of double-precision floating-point numbers.
 
\ntypedef packed_highp_dmat2x4 packed_dmat2x4
 2 by 4 matrix tightly packed in memory of double-precision floating-point numbers.
 
\ntypedef packed_highp_dmat3 packed_dmat3
 3 by 3 matrix tightly packed in memory of double-precision floating-point numbers.
 
\ntypedef packed_highp_dmat3x2 packed_dmat3x2
 3 by 2 matrix tightly packed in memory of double-precision floating-point numbers.
 
\ntypedef packed_highp_dmat3x3 packed_dmat3x3
 3 by 3 matrix tightly packed in memory of double-precision floating-point numbers.
 
\ntypedef packed_highp_dmat3x4 packed_dmat3x4
 3 by 4 matrix tightly packed in memory of double-precision floating-point numbers.
 
\ntypedef packed_highp_dmat4 packed_dmat4
 4 by 4 matrix tightly packed in memory of double-precision floating-point numbers.
 
\ntypedef packed_highp_dmat4x2 packed_dmat4x2
 4 by 2 matrix tightly packed in memory of double-precision floating-point numbers.
 
\ntypedef packed_highp_dmat4x3 packed_dmat4x3
 4 by 3 matrix tightly packed in memory of double-precision floating-point numbers.
 
\ntypedef packed_highp_dmat4x4 packed_dmat4x4
 4 by 4 matrix tightly packed in memory of double-precision floating-point numbers.
 
\ntypedef packed_highp_dvec1 packed_dvec1
 1 component vector tightly packed in memory of double-precision floating-point numbers.
 
\ntypedef packed_highp_dvec2 packed_dvec2
 2 components vector tightly packed in memory of double-precision floating-point numbers.
 
\ntypedef packed_highp_dvec3 packed_dvec3
 3 components vector tightly packed in memory of double-precision floating-point numbers.
 
\ntypedef packed_highp_dvec4 packed_dvec4
 4 components vector tightly packed in memory of double-precision floating-point numbers.
 
\ntypedef vec< 1, bool, packed_highp > packed_highp_bvec1
 1 component vector tightly packed in memory of bool values.
 
\ntypedef vec< 2, bool, packed_highp > packed_highp_bvec2
 2 components vector tightly packed in memory of bool values.
 
\ntypedef vec< 3, bool, packed_highp > packed_highp_bvec3
 3 components vector tightly packed in memory of bool values.
 
\ntypedef vec< 4, bool, packed_highp > packed_highp_bvec4
 4 components vector tightly packed in memory of bool values.
 
\ntypedef mat< 2, 2, double, packed_highp > packed_highp_dmat2
 2 by 2 matrix tightly packed in memory of double-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 2, 2, double, packed_highp > packed_highp_dmat2x2
 2 by 2 matrix tightly packed in memory of double-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 2, 3, double, packed_highp > packed_highp_dmat2x3
 2 by 3 matrix tightly packed in memory of double-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 2, 4, double, packed_highp > packed_highp_dmat2x4
 2 by 4 matrix tightly packed in memory of double-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 3, double, packed_highp > packed_highp_dmat3
 3 by 3 matrix tightly packed in memory of double-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 2, double, packed_highp > packed_highp_dmat3x2
 3 by 2 matrix tightly packed in memory of double-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 3, double, packed_highp > packed_highp_dmat3x3
 3 by 3 matrix tightly packed in memory of double-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 4, double, packed_highp > packed_highp_dmat3x4
 3 by 4 matrix tightly packed in memory of double-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 4, double, packed_highp > packed_highp_dmat4
 4 by 4 matrix tightly packed in memory of double-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 2, double, packed_highp > packed_highp_dmat4x2
 4 by 2 matrix tightly packed in memory of double-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 3, double, packed_highp > packed_highp_dmat4x3
 4 by 3 matrix tightly packed in memory of double-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 4, double, packed_highp > packed_highp_dmat4x4
 4 by 4 matrix tightly packed in memory of double-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef vec< 1, double, packed_highp > packed_highp_dvec1
 1 component vector tightly packed in memory of double-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef vec< 2, double, packed_highp > packed_highp_dvec2
 2 components vector tightly packed in memory of double-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef vec< 3, double, packed_highp > packed_highp_dvec3
 3 components vector tightly packed in memory of double-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef vec< 4, double, packed_highp > packed_highp_dvec4
 4 components vector tightly packed in memory of double-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef vec< 1, int, packed_highp > packed_highp_ivec1
 1 component vector tightly packed in memory of signed integer numbers.
 
\ntypedef vec< 2, int, packed_highp > packed_highp_ivec2
 2 components vector tightly packed in memory of signed integer numbers.
 
\ntypedef vec< 3, int, packed_highp > packed_highp_ivec3
 3 components vector tightly packed in memory of signed integer numbers.
 
\ntypedef vec< 4, int, packed_highp > packed_highp_ivec4
 4 components vector tightly packed in memory of signed integer numbers.
 
\ntypedef mat< 2, 2, float, packed_highp > packed_highp_mat2
 2 by 2 matrix tightly packed in memory of single-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 2, 2, float, packed_highp > packed_highp_mat2x2
 2 by 2 matrix tightly packed in memory of single-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 2, 3, float, packed_highp > packed_highp_mat2x3
 2 by 3 matrix tightly packed in memory of single-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 2, 4, float, packed_highp > packed_highp_mat2x4
 2 by 4 matrix tightly packed in memory of single-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 3, float, packed_highp > packed_highp_mat3
 3 by 3 matrix tightly packed in memory of single-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 2, float, packed_highp > packed_highp_mat3x2
 3 by 2 matrix tightly packed in memory of single-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 3, float, packed_highp > packed_highp_mat3x3
 3 by 3 matrix tightly packed in memory of single-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 4, float, packed_highp > packed_highp_mat3x4
 3 by 4 matrix tightly packed in memory of single-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 4, float, packed_highp > packed_highp_mat4
 4 by 4 matrix tightly packed in memory of single-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 2, float, packed_highp > packed_highp_mat4x2
 4 by 2 matrix tightly packed in memory of single-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 3, float, packed_highp > packed_highp_mat4x3
 4 by 3 matrix tightly packed in memory of single-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 4, float, packed_highp > packed_highp_mat4x4
 4 by 4 matrix tightly packed in memory of single-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef vec< 1, uint, packed_highp > packed_highp_uvec1
 1 component vector tightly packed in memory of unsigned integer numbers.
 
\ntypedef vec< 2, uint, packed_highp > packed_highp_uvec2
 2 components vector tightly packed in memory of unsigned integer numbers.
 
\ntypedef vec< 3, uint, packed_highp > packed_highp_uvec3
 3 components vector tightly packed in memory of unsigned integer numbers.
 
\ntypedef vec< 4, uint, packed_highp > packed_highp_uvec4
 4 components vector tightly packed in memory of unsigned integer numbers.
 
\ntypedef vec< 1, float, packed_highp > packed_highp_vec1
 1 component vector tightly packed in memory of single-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef vec< 2, float, packed_highp > packed_highp_vec2
 2 components vector tightly packed in memory of single-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef vec< 3, float, packed_highp > packed_highp_vec3
 3 components vector tightly packed in memory of single-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef vec< 4, float, packed_highp > packed_highp_vec4
 4 components vector tightly packed in memory of single-precision floating-point numbers using high precision arithmetic in term of ULPs.
 
\ntypedef packed_highp_ivec1 packed_ivec1
 1 component vector tightly packed in memory of signed integer numbers.
 
\ntypedef packed_highp_ivec2 packed_ivec2
 2 components vector tightly packed in memory of signed integer numbers.
 
\ntypedef packed_highp_ivec3 packed_ivec3
 3 components vector tightly packed in memory of signed integer numbers.
 
\ntypedef packed_highp_ivec4 packed_ivec4
 4 components vector tightly packed in memory of signed integer numbers.
 
\ntypedef vec< 1, bool, packed_lowp > packed_lowp_bvec1
 1 component vector tightly packed in memory of bool values.
 
\ntypedef vec< 2, bool, packed_lowp > packed_lowp_bvec2
 2 components vector tightly packed in memory of bool values.
 
\ntypedef vec< 3, bool, packed_lowp > packed_lowp_bvec3
 3 components vector tightly packed in memory of bool values.
 
\ntypedef vec< 4, bool, packed_lowp > packed_lowp_bvec4
 4 components vector tightly packed in memory of bool values.
 
\ntypedef mat< 2, 2, double, packed_lowp > packed_lowp_dmat2
 2 by 2 matrix tightly packed in memory of double-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 2, 2, double, packed_lowp > packed_lowp_dmat2x2
 2 by 2 matrix tightly packed in memory of double-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 2, 3, double, packed_lowp > packed_lowp_dmat2x3
 2 by 3 matrix tightly packed in memory of double-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 2, 4, double, packed_lowp > packed_lowp_dmat2x4
 2 by 4 matrix tightly packed in memory of double-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 3, double, packed_lowp > packed_lowp_dmat3
 3 by 3 matrix tightly packed in memory of double-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 2, double, packed_lowp > packed_lowp_dmat3x2
 3 by 2 matrix tightly packed in memory of double-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 3, double, packed_lowp > packed_lowp_dmat3x3
 3 by 3 matrix tightly packed in memory of double-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 4, double, packed_lowp > packed_lowp_dmat3x4
 3 by 4 matrix tightly packed in memory of double-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 4, double, packed_lowp > packed_lowp_dmat4
 4 by 4 matrix tightly packed in memory of double-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 2, double, packed_lowp > packed_lowp_dmat4x2
 4 by 2 matrix tightly packed in memory of double-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 3, double, packed_lowp > packed_lowp_dmat4x3
 4 by 3 matrix tightly packed in memory of double-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 4, double, packed_lowp > packed_lowp_dmat4x4
 4 by 4 matrix tightly packed in memory of double-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef vec< 1, double, packed_lowp > packed_lowp_dvec1
 1 component vector tightly packed in memory of double-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef vec< 2, double, packed_lowp > packed_lowp_dvec2
 2 components vector tightly packed in memory of double-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef vec< 3, double, packed_lowp > packed_lowp_dvec3
 3 components vector tightly packed in memory of double-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef vec< 4, double, packed_lowp > packed_lowp_dvec4
 4 components vector tightly packed in memory of double-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef vec< 1, int, packed_lowp > packed_lowp_ivec1
 1 component vector tightly packed in memory of signed integer numbers.
 
\ntypedef vec< 2, int, packed_lowp > packed_lowp_ivec2
 2 components vector tightly packed in memory of signed integer numbers.
 
\ntypedef vec< 3, int, packed_lowp > packed_lowp_ivec3
 3 components vector tightly packed in memory of signed integer numbers.
 
\ntypedef vec< 4, int, packed_lowp > packed_lowp_ivec4
 4 components vector tightly packed in memory of signed integer numbers.
 
\ntypedef mat< 2, 2, float, packed_lowp > packed_lowp_mat2
 2 by 2 matrix tightly packed in memory of single-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 2, 2, float, packed_lowp > packed_lowp_mat2x2
 2 by 2 matrix tightly packed in memory of single-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 2, 3, float, packed_lowp > packed_lowp_mat2x3
 2 by 3 matrix tightly packed in memory of single-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 2, 4, float, packed_lowp > packed_lowp_mat2x4
 2 by 4 matrix tightly packed in memory of single-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 3, float, packed_lowp > packed_lowp_mat3
 3 by 3 matrix tightly packed in memory of single-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 2, float, packed_lowp > packed_lowp_mat3x2
 3 by 2 matrix tightly packed in memory of single-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 3, float, packed_lowp > packed_lowp_mat3x3
 3 by 3 matrix tightly packed in memory of single-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 4, float, packed_lowp > packed_lowp_mat3x4
 3 by 4 matrix tightly packed in memory of single-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 4, float, packed_lowp > packed_lowp_mat4
 4 by 4 matrix tightly packed in memory of single-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 2, float, packed_lowp > packed_lowp_mat4x2
 4 by 2 matrix tightly packed in memory of single-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 3, float, packed_lowp > packed_lowp_mat4x3
 4 by 3 matrix tightly packed in memory of single-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 4, float, packed_lowp > packed_lowp_mat4x4
 4 by 4 matrix tightly packed in memory of single-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef vec< 1, uint, packed_lowp > packed_lowp_uvec1
 1 component vector tightly packed in memory of unsigned integer numbers.
 
\ntypedef vec< 2, uint, packed_lowp > packed_lowp_uvec2
 2 components vector tightly packed in memory of unsigned integer numbers.
 
\ntypedef vec< 3, uint, packed_lowp > packed_lowp_uvec3
 3 components vector tightly packed in memory of unsigned integer numbers.
 
\ntypedef vec< 4, uint, packed_lowp > packed_lowp_uvec4
 4 components vector tightly packed in memory of unsigned integer numbers.
 
\ntypedef vec< 1, float, packed_lowp > packed_lowp_vec1
 1 component vector tightly packed in memory of single-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef vec< 2, float, packed_lowp > packed_lowp_vec2
 2 components vector tightly packed in memory of single-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef vec< 3, float, packed_lowp > packed_lowp_vec3
 3 components vector tightly packed in memory of single-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef vec< 4, float, packed_lowp > packed_lowp_vec4
 4 components vector tightly packed in memory of single-precision floating-point numbers using low precision arithmetic in term of ULPs.
 
\ntypedef packed_highp_mat2 packed_mat2
 2 by 2 matrix tightly packed in memory of single-precision floating-point numbers.
 
\ntypedef packed_highp_mat2x2 packed_mat2x2
 2 by 2 matrix tightly packed in memory of single-precision floating-point numbers.
 
\ntypedef packed_highp_mat2x3 packed_mat2x3
 2 by 3 matrix tightly packed in memory of single-precision floating-point numbers.
 
\ntypedef packed_highp_mat2x4 packed_mat2x4
 2 by 4 matrix tightly packed in memory of single-precision floating-point numbers.
 
\ntypedef packed_highp_mat3 packed_mat3
 3 by 3 matrix tightly packed in memory of single-precision floating-point numbers.
 
\ntypedef packed_highp_mat3x2 packed_mat3x2
 3 by 2 matrix tightly packed in memory of single-precision floating-point numbers.
 
\ntypedef packed_highp_mat3x3 packed_mat3x3
 3 by 3 matrix tightly packed in memory of single-precision floating-point numbers.
 
\ntypedef packed_highp_mat3x4 packed_mat3x4
 3 by 4 matrix tightly packed in memory of single-precision floating-point numbers.
 
\ntypedef packed_highp_mat4 packed_mat4
 4 by 4 matrix tightly packed in memory of single-precision floating-point numbers.
 
\ntypedef packed_highp_mat4x2 packed_mat4x2
 4 by 2 matrix tightly packed in memory of single-precision floating-point numbers.
 
\ntypedef packed_highp_mat4x3 packed_mat4x3
 4 by 3 matrix tightly packed in memory of single-precision floating-point numbers.
 
\ntypedef packed_highp_mat4x4 packed_mat4x4
 4 by 4 matrix tightly packed in memory of single-precision floating-point numbers.
 
\ntypedef vec< 1, bool, packed_mediump > packed_mediump_bvec1
 1 component vector tightly packed in memory of bool values.
 
\ntypedef vec< 2, bool, packed_mediump > packed_mediump_bvec2
 2 components vector tightly packed in memory of bool values.
 
\ntypedef vec< 3, bool, packed_mediump > packed_mediump_bvec3
 3 components vector tightly packed in memory of bool values.
 
\ntypedef vec< 4, bool, packed_mediump > packed_mediump_bvec4
 4 components vector tightly packed in memory of bool values.
 
\ntypedef mat< 2, 2, double, packed_mediump > packed_mediump_dmat2
 2 by 2 matrix tightly packed in memory of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 2, 2, double, packed_mediump > packed_mediump_dmat2x2
 2 by 2 matrix tightly packed in memory of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 2, 3, double, packed_mediump > packed_mediump_dmat2x3
 2 by 3 matrix tightly packed in memory of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 2, 4, double, packed_mediump > packed_mediump_dmat2x4
 2 by 4 matrix tightly packed in memory of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 3, double, packed_mediump > packed_mediump_dmat3
 3 by 3 matrix tightly packed in memory of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 2, double, packed_mediump > packed_mediump_dmat3x2
 3 by 2 matrix tightly packed in memory of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 3, double, packed_mediump > packed_mediump_dmat3x3
 3 by 3 matrix tightly packed in memory of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 4, double, packed_mediump > packed_mediump_dmat3x4
 3 by 4 matrix tightly packed in memory of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 4, double, packed_mediump > packed_mediump_dmat4
 4 by 4 matrix tightly packed in memory of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 2, double, packed_mediump > packed_mediump_dmat4x2
 4 by 2 matrix tightly packed in memory of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 3, double, packed_mediump > packed_mediump_dmat4x3
 4 by 3 matrix tightly packed in memory of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 4, double, packed_mediump > packed_mediump_dmat4x4
 4 by 4 matrix tightly packed in memory of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef vec< 1, double, packed_mediump > packed_mediump_dvec1
 1 component vector tightly packed in memory of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef vec< 2, double, packed_mediump > packed_mediump_dvec2
 2 components vector tightly packed in memory of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef vec< 3, double, packed_mediump > packed_mediump_dvec3
 3 components vector tightly packed in memory of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef vec< 4, double, packed_mediump > packed_mediump_dvec4
 4 components vector tightly packed in memory of double-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef vec< 1, int, packed_mediump > packed_mediump_ivec1
 1 component vector tightly packed in memory of signed integer numbers.
 
\ntypedef vec< 2, int, packed_mediump > packed_mediump_ivec2
 2 components vector tightly packed in memory of signed integer numbers.
 
\ntypedef vec< 3, int, packed_mediump > packed_mediump_ivec3
 3 components vector tightly packed in memory of signed integer numbers.
 
\ntypedef vec< 4, int, packed_mediump > packed_mediump_ivec4
 4 components vector tightly packed in memory of signed integer numbers.
 
\ntypedef mat< 2, 2, float, packed_mediump > packed_mediump_mat2
 2 by 2 matrix tightly packed in memory of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 2, 2, float, packed_mediump > packed_mediump_mat2x2
 2 by 2 matrix tightly packed in memory of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 2, 3, float, packed_mediump > packed_mediump_mat2x3
 2 by 3 matrix tightly packed in memory of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 2, 4, float, packed_mediump > packed_mediump_mat2x4
 2 by 4 matrix tightly packed in memory of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 3, float, packed_mediump > packed_mediump_mat3
 3 by 3 matrix tightly packed in memory of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 2, float, packed_mediump > packed_mediump_mat3x2
 3 by 2 matrix tightly packed in memory of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 3, float, packed_mediump > packed_mediump_mat3x3
 3 by 3 matrix tightly packed in memory of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 3, 4, float, packed_mediump > packed_mediump_mat3x4
 3 by 4 matrix tightly packed in memory of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 4, float, packed_mediump > packed_mediump_mat4
 4 by 4 matrix tightly packed in memory of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 2, float, packed_mediump > packed_mediump_mat4x2
 4 by 2 matrix tightly packed in memory of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 3, float, packed_mediump > packed_mediump_mat4x3
 4 by 3 matrix tightly packed in memory of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef mat< 4, 4, float, packed_mediump > packed_mediump_mat4x4
 4 by 4 matrix tightly packed in memory of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef vec< 1, uint, packed_mediump > packed_mediump_uvec1
 1 component vector tightly packed in memory of unsigned integer numbers.
 
\ntypedef vec< 2, uint, packed_mediump > packed_mediump_uvec2
 2 components vector tightly packed in memory of unsigned integer numbers.
 
\ntypedef vec< 3, uint, packed_mediump > packed_mediump_uvec3
 3 components vector tightly packed in memory of unsigned integer numbers.
 
\ntypedef vec< 4, uint, packed_mediump > packed_mediump_uvec4
 4 components vector tightly packed in memory of unsigned integer numbers.
 
\ntypedef vec< 1, float, packed_mediump > packed_mediump_vec1
 1 component vector tightly packed in memory of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef vec< 2, float, packed_mediump > packed_mediump_vec2
 2 components vector tightly packed in memory of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef vec< 3, float, packed_mediump > packed_mediump_vec3
 3 components vector tightly packed in memory of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef vec< 4, float, packed_mediump > packed_mediump_vec4
 4 components vector tightly packed in memory of single-precision floating-point numbers using medium precision arithmetic in term of ULPs.
 
\ntypedef packed_highp_uvec1 packed_uvec1
 1 component vector tightly packed in memory of unsigned integer numbers.
 
\ntypedef packed_highp_uvec2 packed_uvec2
 2 components vector tightly packed in memory of unsigned integer numbers.
 
\ntypedef packed_highp_uvec3 packed_uvec3
 3 components vector tightly packed in memory of unsigned integer numbers.
 
\ntypedef packed_highp_uvec4 packed_uvec4
 4 components vector tightly packed in memory of unsigned integer numbers.
 
\ntypedef packed_highp_vec1 packed_vec1
 1 component vector tightly packed in memory of single-precision floating-point numbers.
 
\ntypedef packed_highp_vec2 packed_vec2
 2 components vector tightly packed in memory of single-precision floating-point numbers.
 
\ntypedef packed_highp_vec3 packed_vec3
 3 components vector tightly packed in memory of single-precision floating-point numbers.
 
\ntypedef packed_highp_vec4 packed_vec4
 4 components vector tightly packed in memory of single-precision floating-point numbers.
 
\n

Detailed Description

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Include <glm/gtc/type_aligned.hpp> to use the features of this extension.

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Aligned types allowing SIMD optimizations of vectors and matrices types

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0.9.9 API documentation\n
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\nTypedefs
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GLM_GTC_type_precision
Recommended extensions
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\n\n

Include <glm/gtc/type_precision.hpp> to use the features of this extension. \nMore...

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\nTypedefs

typedef float f32
 Default 32 bit single-qualifier floating-point scalar. More...
 
typedef mat< 2, 2, f32, defaultp > f32mat2
 Single-qualifier floating-point 1x1 matrix. More...
 
typedef mat< 2, 2, f32, defaultp > f32mat2x2
 Single-qualifier floating-point 1x1 matrix. More...
 
typedef mat< 2, 3, f32, defaultp > f32mat2x3
 Single-qualifier floating-point 2x3 matrix. More...
 
typedef mat< 2, 4, f32, defaultp > f32mat2x4
 Single-qualifier floating-point 2x4 matrix. More...
 
typedef mat< 3, 3, f32, defaultp > f32mat3
 Single-qualifier floating-point 3x3 matrix. More...
 
typedef mat< 3, 2, f32, defaultp > f32mat3x2
 Single-qualifier floating-point 3x2 matrix. More...
 
typedef mat< 3, 3, f32, defaultp > f32mat3x3
 Single-qualifier floating-point 3x3 matrix. More...
 
typedef mat< 3, 4, f32, defaultp > f32mat3x4
 Single-qualifier floating-point 3x4 matrix. More...
 
typedef mat< 4, 4, f32, defaultp > f32mat4
 Single-qualifier floating-point 4x4 matrix. More...
 
typedef mat< 4, 2, f32, defaultp > f32mat4x2
 Single-qualifier floating-point 4x2 matrix. More...
 
typedef mat< 4, 3, f32, defaultp > f32mat4x3
 Single-qualifier floating-point 4x3 matrix. More...
 
typedef mat< 4, 4, f32, defaultp > f32mat4x4
 Single-qualifier floating-point 4x4 matrix. More...
 
typedef qua< f32, defaultp > f32quat
 Single-qualifier floating-point quaternion. More...
 
typedef vec< 1, f32, defaultp > f32vec1
 Single-qualifier floating-point vector of 1 component. More...
 
typedef vec< 2, f32, defaultp > f32vec2
 Single-qualifier floating-point vector of 2 components. More...
 
typedef vec< 3, f32, defaultp > f32vec3
 Single-qualifier floating-point vector of 3 components. More...
 
typedef vec< 4, f32, defaultp > f32vec4
 Single-qualifier floating-point vector of 4 components. More...
 
typedef double f64
 Default 64 bit double-qualifier floating-point scalar. More...
 
typedef mat< 2, 2, f64, defaultp > f64mat2
 Double-qualifier floating-point 1x1 matrix. More...
 
typedef mat< 2, 2, f64, defaultp > f64mat2x2
 Double-qualifier floating-point 1x1 matrix. More...
 
typedef mat< 2, 3, f64, defaultp > f64mat2x3
 Double-qualifier floating-point 2x3 matrix. More...
 
typedef mat< 2, 4, f64, defaultp > f64mat2x4
 Double-qualifier floating-point 2x4 matrix. More...
 
typedef mat< 3, 3, f64, defaultp > f64mat3
 Double-qualifier floating-point 3x3 matrix. More...
 
typedef mat< 3, 2, f64, defaultp > f64mat3x2
 Double-qualifier floating-point 3x2 matrix. More...
 
typedef mat< 3, 3, f64, defaultp > f64mat3x3
 Double-qualifier floating-point 3x3 matrix. More...
 
typedef mat< 3, 4, f64, defaultp > f64mat3x4
 Double-qualifier floating-point 3x4 matrix. More...
 
typedef mat< 4, 4, f64, defaultp > f64mat4
 Double-qualifier floating-point 4x4 matrix. More...
 
typedef mat< 4, 2, f64, defaultp > f64mat4x2
 Double-qualifier floating-point 4x2 matrix. More...
 
typedef mat< 4, 3, f64, defaultp > f64mat4x3
 Double-qualifier floating-point 4x3 matrix. More...
 
typedef mat< 4, 4, f64, defaultp > f64mat4x4
 Double-qualifier floating-point 4x4 matrix. More...
 
typedef qua< f64, defaultp > f64quat
 Double-qualifier floating-point quaternion. More...
 
typedef vec< 1, f64, defaultp > f64vec1
 Double-qualifier floating-point vector of 1 component. More...
 
typedef vec< 2, f64, defaultp > f64vec2
 Double-qualifier floating-point vector of 2 components. More...
 
typedef vec< 3, f64, defaultp > f64vec3
 Double-qualifier floating-point vector of 3 components. More...
 
typedef vec< 4, f64, defaultp > f64vec4
 Double-qualifier floating-point vector of 4 components. More...
 
typedef float float32
 Single-qualifier floating-point scalar. More...
 
typedef float float32_t
 Default 32 bit single-qualifier floating-point scalar. More...
 
typedef double float64
 Double-qualifier floating-point scalar. More...
 
typedef double float64_t
 Default 64 bit double-qualifier floating-point scalar. More...
 
typedef mat< 2, 2, f32, defaultp > fmat2
 Single-qualifier floating-point 1x1 matrix. More...
 
typedef mat< 2, 2, f32, defaultp > fmat2x2
 Single-qualifier floating-point 1x1 matrix. More...
 
typedef mat< 2, 3, f32, defaultp > fmat2x3
 Single-qualifier floating-point 2x3 matrix. More...
 
typedef mat< 2, 4, f32, defaultp > fmat2x4
 Single-qualifier floating-point 2x4 matrix. More...
 
typedef mat< 3, 3, f32, defaultp > fmat3
 Single-qualifier floating-point 3x3 matrix. More...
 
typedef mat< 3, 2, f32, defaultp > fmat3x2
 Single-qualifier floating-point 3x2 matrix. More...
 
typedef mat< 3, 3, f32, defaultp > fmat3x3
 Single-qualifier floating-point 3x3 matrix. More...
 
typedef mat< 3, 4, f32, defaultp > fmat3x4
 Single-qualifier floating-point 3x4 matrix. More...
 
typedef mat< 4, 4, f32, defaultp > fmat4
 Single-qualifier floating-point 4x4 matrix. More...
 
typedef mat< 4, 2, f32, defaultp > fmat4x2
 Single-qualifier floating-point 4x2 matrix. More...
 
typedef mat< 4, 3, f32, defaultp > fmat4x3
 Single-qualifier floating-point 4x3 matrix. More...
 
typedef mat< 4, 4, f32, defaultp > fmat4x4
 Single-qualifier floating-point 4x4 matrix. More...
 
typedef vec< 1, f32, defaultp > fvec1
 Single-qualifier floating-point vector of 1 component. More...
 
typedef vec< 2, f32, defaultp > fvec2
 Single-qualifier floating-point vector of 2 components. More...
 
typedef vec< 3, f32, defaultp > fvec3
 Single-qualifier floating-point vector of 3 components. More...
 
typedef vec< 4, f32, defaultp > fvec4
 Single-qualifier floating-point vector of 4 components. More...
 
typedef float highp_f32
 High 32 bit single-qualifier floating-point scalar. More...
 
typedef mat< 2, 2, f32, highp > highp_f32mat2
 High single-qualifier floating-point 1x1 matrix. More...
 
typedef mat< 2, 2, f32, highp > highp_f32mat2x2
 High single-qualifier floating-point 1x1 matrix. More...
 
typedef mat< 2, 3, f32, highp > highp_f32mat2x3
 High single-qualifier floating-point 2x3 matrix. More...
 
typedef mat< 2, 4, f32, highp > highp_f32mat2x4
 High single-qualifier floating-point 2x4 matrix. More...
 
typedef mat< 3, 3, f32, highp > highp_f32mat3
 High single-qualifier floating-point 3x3 matrix. More...
 
typedef mat< 3, 2, f32, highp > highp_f32mat3x2
 High single-qualifier floating-point 3x2 matrix. More...
 
typedef mat< 3, 3, f32, highp > highp_f32mat3x3
 High single-qualifier floating-point 3x3 matrix. More...
 
typedef mat< 3, 4, f32, highp > highp_f32mat3x4
 High single-qualifier floating-point 3x4 matrix. More...
 
typedef mat< 4, 4, f32, highp > highp_f32mat4
 High single-qualifier floating-point 4x4 matrix. More...
 
typedef mat< 4, 2, f32, highp > highp_f32mat4x2
 High single-qualifier floating-point 4x2 matrix. More...
 
typedef mat< 4, 3, f32, highp > highp_f32mat4x3
 High single-qualifier floating-point 4x3 matrix. More...
 
typedef mat< 4, 4, f32, highp > highp_f32mat4x4
 High single-qualifier floating-point 4x4 matrix. More...
 
typedef qua< f32, highp > highp_f32quat
 High single-qualifier floating-point quaternion. More...
 
typedef vec< 1, f32, highp > highp_f32vec1
 High single-qualifier floating-point vector of 1 component. More...
 
typedef vec< 2, f32, highp > highp_f32vec2
 High single-qualifier floating-point vector of 2 components. More...
 
typedef vec< 3, f32, highp > highp_f32vec3
 High single-qualifier floating-point vector of 3 components. More...
 
typedef vec< 4, f32, highp > highp_f32vec4
 High single-qualifier floating-point vector of 4 components. More...
 
typedef double highp_f64
 High 64 bit double-qualifier floating-point scalar. More...
 
typedef mat< 2, 2, f64, highp > highp_f64mat2
 High double-qualifier floating-point 1x1 matrix. More...
 
typedef mat< 2, 2, f64, highp > highp_f64mat2x2
 High double-qualifier floating-point 1x1 matrix. More...
 
typedef mat< 2, 3, f64, highp > highp_f64mat2x3
 High double-qualifier floating-point 2x3 matrix. More...
 
typedef mat< 2, 4, f64, highp > highp_f64mat2x4
 High double-qualifier floating-point 2x4 matrix. More...
 
typedef mat< 3, 3, f64, highp > highp_f64mat3
 High double-qualifier floating-point 3x3 matrix. More...
 
typedef mat< 3, 2, f64, highp > highp_f64mat3x2
 High double-qualifier floating-point 3x2 matrix. More...
 
typedef mat< 3, 3, f64, highp > highp_f64mat3x3
 High double-qualifier floating-point 3x3 matrix. More...
 
typedef mat< 3, 4, f64, highp > highp_f64mat3x4
 High double-qualifier floating-point 3x4 matrix. More...
 
typedef mat< 4, 4, f64, highp > highp_f64mat4
 High double-qualifier floating-point 4x4 matrix. More...
 
typedef mat< 4, 2, f64, highp > highp_f64mat4x2
 High double-qualifier floating-point 4x2 matrix. More...
 
typedef mat< 4, 3, f64, highp > highp_f64mat4x3
 High double-qualifier floating-point 4x3 matrix. More...
 
typedef mat< 4, 4, f64, highp > highp_f64mat4x4
 High double-qualifier floating-point 4x4 matrix. More...
 
typedef qua< f64, highp > highp_f64quat
 High double-qualifier floating-point quaternion. More...
 
typedef vec< 1, f64, highp > highp_f64vec1
 High double-qualifier floating-point vector of 1 component. More...
 
typedef vec< 2, f64, highp > highp_f64vec2
 High double-qualifier floating-point vector of 2 components. More...
 
typedef vec< 3, f64, highp > highp_f64vec3
 High double-qualifier floating-point vector of 3 components. More...
 
typedef vec< 4, f64, highp > highp_f64vec4
 High double-qualifier floating-point vector of 4 components. More...
 
typedef float highp_float32
 High 32 bit single-qualifier floating-point scalar. More...
 
typedef float highp_float32_t
 High 32 bit single-qualifier floating-point scalar. More...
 
typedef double highp_float64
 High 64 bit double-qualifier floating-point scalar. More...
 
typedef double highp_float64_t
 High 64 bit double-qualifier floating-point scalar. More...
 
typedef mat< 2, 2, f32, highp > highp_fmat2
 High single-qualifier floating-point 1x1 matrix. More...
 
typedef mat< 2, 2, f32, highp > highp_fmat2x2
 High single-qualifier floating-point 1x1 matrix. More...
 
typedef mat< 2, 3, f32, highp > highp_fmat2x3
 High single-qualifier floating-point 2x3 matrix. More...
 
typedef mat< 2, 4, f32, highp > highp_fmat2x4
 High single-qualifier floating-point 2x4 matrix. More...
 
typedef mat< 3, 3, f32, highp > highp_fmat3
 High single-qualifier floating-point 3x3 matrix. More...
 
typedef mat< 3, 2, f32, highp > highp_fmat3x2
 High single-qualifier floating-point 3x2 matrix. More...
 
typedef mat< 3, 3, f32, highp > highp_fmat3x3
 High single-qualifier floating-point 3x3 matrix. More...
 
typedef mat< 3, 4, f32, highp > highp_fmat3x4
 High single-qualifier floating-point 3x4 matrix. More...
 
typedef mat< 4, 4, f32, highp > highp_fmat4
 High single-qualifier floating-point 4x4 matrix. More...
 
typedef mat< 4, 2, f32, highp > highp_fmat4x2
 High single-qualifier floating-point 4x2 matrix. More...
 
typedef mat< 4, 3, f32, highp > highp_fmat4x3
 High single-qualifier floating-point 4x3 matrix. More...
 
typedef mat< 4, 4, f32, highp > highp_fmat4x4
 High single-qualifier floating-point 4x4 matrix. More...
 
typedef vec< 1, float, highp > highp_fvec1
 High single-qualifier floating-point vector of 1 component. More...
 
typedef vec< 2, float, highp > highp_fvec2
 High Single-qualifier floating-point vector of 2 components. More...
 
typedef vec< 3, float, highp > highp_fvec3
 High Single-qualifier floating-point vector of 3 components. More...
 
typedef vec< 4, float, highp > highp_fvec4
 High Single-qualifier floating-point vector of 4 components. More...
 
typedef int16 highp_i16
 High qualifier 16 bit signed integer type. More...
 
typedef vec< 1, i16, highp > highp_i16vec1
 High qualifier 16 bit signed integer scalar type. More...
 
typedef vec< 2, i16, highp > highp_i16vec2
 High qualifier 16 bit signed integer vector of 2 components type. More...
 
typedef vec< 3, i16, highp > highp_i16vec3
 High qualifier 16 bit signed integer vector of 3 components type. More...
 
typedef vec< 4, i16, highp > highp_i16vec4
 High qualifier 16 bit signed integer vector of 4 components type. More...
 
typedef int32 highp_i32
 High qualifier 32 bit signed integer type. More...
 
typedef vec< 1, i32, highp > highp_i32vec1
 High qualifier 32 bit signed integer scalar type. More...
 
typedef vec< 2, i32, highp > highp_i32vec2
 High qualifier 32 bit signed integer vector of 2 components type. More...
 
typedef vec< 3, i32, highp > highp_i32vec3
 High qualifier 32 bit signed integer vector of 3 components type. More...
 
typedef vec< 4, i32, highp > highp_i32vec4
 High qualifier 32 bit signed integer vector of 4 components type. More...
 
typedef int64 highp_i64
 High qualifier 64 bit signed integer type. More...
 
typedef vec< 1, i64, highp > highp_i64vec1
 High qualifier 64 bit signed integer scalar type. More...
 
typedef vec< 2, i64, highp > highp_i64vec2
 High qualifier 64 bit signed integer vector of 2 components type. More...
 
typedef vec< 3, i64, highp > highp_i64vec3
 High qualifier 64 bit signed integer vector of 3 components type. More...
 
typedef vec< 4, i64, highp > highp_i64vec4
 High qualifier 64 bit signed integer vector of 4 components type. More...
 
typedef int8 highp_i8
 High qualifier 8 bit signed integer type. More...
 
typedef vec< 1, i8, highp > highp_i8vec1
 High qualifier 8 bit signed integer scalar type. More...
 
typedef vec< 2, i8, highp > highp_i8vec2
 High qualifier 8 bit signed integer vector of 2 components type. More...
 
typedef vec< 3, i8, highp > highp_i8vec3
 High qualifier 8 bit signed integer vector of 3 components type. More...
 
typedef vec< 4, i8, highp > highp_i8vec4
 High qualifier 8 bit signed integer vector of 4 components type. More...
 
typedef int16 highp_int16
 High qualifier 16 bit signed integer type. More...
 
typedef int16 highp_int16_t
 High qualifier 16 bit signed integer type. More...
 
typedef int32 highp_int32
 High qualifier 32 bit signed integer type. More...
 
typedef int32 highp_int32_t
 32 bit signed integer type. More...
 
typedef int64 highp_int64
 High qualifier 64 bit signed integer type. More...
 
typedef int64 highp_int64_t
 High qualifier 64 bit signed integer type. More...
 
typedef int8 highp_int8
 High qualifier 8 bit signed integer type. More...
 
typedef int8 highp_int8_t
 High qualifier 8 bit signed integer type. More...
 
typedef uint16 highp_u16
 High qualifier 16 bit unsigned integer type. More...
 
typedef vec< 1, u16, highp > highp_u16vec1
 High qualifier 16 bit unsigned integer scalar type. More...
 
typedef vec< 2, u16, highp > highp_u16vec2
 High qualifier 16 bit unsigned integer vector of 2 components type. More...
 
typedef vec< 3, u16, highp > highp_u16vec3
 High qualifier 16 bit unsigned integer vector of 3 components type. More...
 
typedef vec< 4, u16, highp > highp_u16vec4
 High qualifier 16 bit unsigned integer vector of 4 components type. More...
 
typedef uint32 highp_u32
 High qualifier 32 bit unsigned integer type. More...
 
typedef vec< 1, u32, highp > highp_u32vec1
 High qualifier 32 bit unsigned integer scalar type. More...
 
typedef vec< 2, u32, highp > highp_u32vec2
 High qualifier 32 bit unsigned integer vector of 2 components type. More...
 
typedef vec< 3, u32, highp > highp_u32vec3
 High qualifier 32 bit unsigned integer vector of 3 components type. More...
 
typedef vec< 4, u32, highp > highp_u32vec4
 High qualifier 32 bit unsigned integer vector of 4 components type. More...
 
typedef uint64 highp_u64
 High qualifier 64 bit unsigned integer type. More...
 
typedef vec< 1, u64, highp > highp_u64vec1
 High qualifier 64 bit unsigned integer scalar type. More...
 
typedef vec< 2, u64, highp > highp_u64vec2
 High qualifier 64 bit unsigned integer vector of 2 components type. More...
 
typedef vec< 3, u64, highp > highp_u64vec3
 High qualifier 64 bit unsigned integer vector of 3 components type. More...
 
typedef vec< 4, u64, highp > highp_u64vec4
 High qualifier 64 bit unsigned integer vector of 4 components type. More...
 
typedef uint8 highp_u8
 High qualifier 8 bit unsigned integer type. More...
 
typedef vec< 1, u8, highp > highp_u8vec1
 High qualifier 8 bit unsigned integer scalar type. More...
 
typedef vec< 2, u8, highp > highp_u8vec2
 High qualifier 8 bit unsigned integer vector of 2 components type. More...
 
typedef vec< 3, u8, highp > highp_u8vec3
 High qualifier 8 bit unsigned integer vector of 3 components type. More...
 
typedef vec< 4, u8, highp > highp_u8vec4
 High qualifier 8 bit unsigned integer vector of 4 components type. More...
 
typedef uint16 highp_uint16
 High qualifier 16 bit unsigned integer type. More...
 
typedef uint16 highp_uint16_t
 High qualifier 16 bit unsigned integer type. More...
 
typedef uint32 highp_uint32
 High qualifier 32 bit unsigned integer type. More...
 
typedef uint32 highp_uint32_t
 High qualifier 32 bit unsigned integer type. More...
 
typedef uint64 highp_uint64
 High qualifier 64 bit unsigned integer type. More...
 
typedef uint64 highp_uint64_t
 High qualifier 64 bit unsigned integer type. More...
 
typedef uint8 highp_uint8
 High qualifier 8 bit unsigned integer type. More...
 
typedef uint8 highp_uint8_t
 High qualifier 8 bit unsigned integer type. More...
 
typedef int16 i16
 16 bit signed integer type. More...
 
typedef vec< 1, i16, defaultp > i16vec1
 16 bit signed integer scalar type. More...
 
typedef vec< 2, i16, defaultp > i16vec2
 16 bit signed integer vector of 2 components type. More...
 
typedef vec< 3, i16, defaultp > i16vec3
 16 bit signed integer vector of 3 components type. More...
 
typedef vec< 4, i16, defaultp > i16vec4
 16 bit signed integer vector of 4 components type. More...
 
typedef int32 i32
 32 bit signed integer type. More...
 
typedef vec< 1, i32, defaultp > i32vec1
 32 bit signed integer scalar type. More...
 
typedef vec< 2, i32, defaultp > i32vec2
 32 bit signed integer vector of 2 components type. More...
 
typedef vec< 3, i32, defaultp > i32vec3
 32 bit signed integer vector of 3 components type. More...
 
typedef vec< 4, i32, defaultp > i32vec4
 32 bit signed integer vector of 4 components type. More...
 
typedef int64 i64
 64 bit signed integer type. More...
 
typedef vec< 1, i64, defaultp > i64vec1
 64 bit signed integer scalar type. More...
 
typedef vec< 2, i64, defaultp > i64vec2
 64 bit signed integer vector of 2 components type. More...
 
typedef vec< 3, i64, defaultp > i64vec3
 64 bit signed integer vector of 3 components type. More...
 
typedef vec< 4, i64, defaultp > i64vec4
 64 bit signed integer vector of 4 components type. More...
 
typedef int8 i8
 8 bit signed integer type. More...
 
typedef vec< 1, i8, defaultp > i8vec1
 8 bit signed integer scalar type. More...
 
typedef vec< 2, i8, defaultp > i8vec2
 8 bit signed integer vector of 2 components type. More...
 
typedef vec< 3, i8, defaultp > i8vec3
 8 bit signed integer vector of 3 components type. More...
 
typedef vec< 4, i8, defaultp > i8vec4
 8 bit signed integer vector of 4 components type. More...
 
typedef int16 int16_t
 16 bit signed integer type. More...
 
typedef int32 int32_t
 32 bit signed integer type. More...
 
typedef int64 int64_t
 64 bit signed integer type. More...
 
typedef int8 int8_t
 8 bit signed integer type. More...
 
typedef float lowp_f32
 Low 32 bit single-qualifier floating-point scalar. More...
 
typedef mat< 2, 2, f32, lowp > lowp_f32mat2
 Low single-qualifier floating-point 1x1 matrix. More...
 
typedef mat< 2, 2, f32, lowp > lowp_f32mat2x2
 Low single-qualifier floating-point 1x1 matrix. More...
 
typedef mat< 2, 3, f32, lowp > lowp_f32mat2x3
 Low single-qualifier floating-point 2x3 matrix. More...
 
typedef mat< 2, 4, f32, lowp > lowp_f32mat2x4
 Low single-qualifier floating-point 2x4 matrix. More...
 
typedef mat< 3, 3, f32, lowp > lowp_f32mat3
 Low single-qualifier floating-point 3x3 matrix. More...
 
typedef mat< 3, 2, f32, lowp > lowp_f32mat3x2
 Low single-qualifier floating-point 3x2 matrix. More...
 
typedef mat< 3, 3, f32, lowp > lowp_f32mat3x3
 Low single-qualifier floating-point 3x3 matrix. More...
 
typedef mat< 3, 4, f32, lowp > lowp_f32mat3x4
 Low single-qualifier floating-point 3x4 matrix. More...
 
typedef mat< 4, 4, f32, lowp > lowp_f32mat4
 Low single-qualifier floating-point 4x4 matrix. More...
 
typedef mat< 4, 2, f32, lowp > lowp_f32mat4x2
 Low single-qualifier floating-point 4x2 matrix. More...
 
typedef mat< 4, 3, f32, lowp > lowp_f32mat4x3
 Low single-qualifier floating-point 4x3 matrix. More...
 
typedef mat< 4, 4, f32, lowp > lowp_f32mat4x4
 Low single-qualifier floating-point 4x4 matrix. More...
 
typedef qua< f32, lowp > lowp_f32quat
 Low single-qualifier floating-point quaternion. More...
 
typedef vec< 1, f32, lowp > lowp_f32vec1
 Low single-qualifier floating-point vector of 1 component. More...
 
typedef vec< 2, f32, lowp > lowp_f32vec2
 Low single-qualifier floating-point vector of 2 components. More...
 
typedef vec< 3, f32, lowp > lowp_f32vec3
 Low single-qualifier floating-point vector of 3 components. More...
 
typedef vec< 4, f32, lowp > lowp_f32vec4
 Low single-qualifier floating-point vector of 4 components. More...
 
typedef double lowp_f64
 Low 64 bit double-qualifier floating-point scalar. More...
 
typedef mat< 2, 2, f64, lowp > lowp_f64mat2
 Low double-qualifier floating-point 1x1 matrix. More...
 
typedef mat< 2, 2, f64, lowp > lowp_f64mat2x2
 Low double-qualifier floating-point 1x1 matrix. More...
 
typedef mat< 2, 3, f64, lowp > lowp_f64mat2x3
 Low double-qualifier floating-point 2x3 matrix. More...
 
typedef mat< 2, 4, f64, lowp > lowp_f64mat2x4
 Low double-qualifier floating-point 2x4 matrix. More...
 
typedef mat< 3, 3, f64, lowp > lowp_f64mat3
 Low double-qualifier floating-point 3x3 matrix. More...
 
typedef mat< 3, 2, f64, lowp > lowp_f64mat3x2
 Low double-qualifier floating-point 3x2 matrix. More...
 
typedef mat< 3, 3, f64, lowp > lowp_f64mat3x3
 Low double-qualifier floating-point 3x3 matrix. More...
 
typedef mat< 3, 4, f64, lowp > lowp_f64mat3x4
 Low double-qualifier floating-point 3x4 matrix. More...
 
typedef mat< 4, 4, f64, lowp > lowp_f64mat4
 Low double-qualifier floating-point 4x4 matrix. More...
 
typedef mat< 4, 2, f64, lowp > lowp_f64mat4x2
 Low double-qualifier floating-point 4x2 matrix. More...
 
typedef mat< 4, 3, f64, lowp > lowp_f64mat4x3
 Low double-qualifier floating-point 4x3 matrix. More...
 
typedef mat< 4, 4, f64, lowp > lowp_f64mat4x4
 Low double-qualifier floating-point 4x4 matrix. More...
 
typedef qua< f64, lowp > lowp_f64quat
 Low double-qualifier floating-point quaternion. More...
 
typedef vec< 1, f64, lowp > lowp_f64vec1
 Low double-qualifier floating-point vector of 1 component. More...
 
typedef vec< 2, f64, lowp > lowp_f64vec2
 Low double-qualifier floating-point vector of 2 components. More...
 
typedef vec< 3, f64, lowp > lowp_f64vec3
 Low double-qualifier floating-point vector of 3 components. More...
 
typedef vec< 4, f64, lowp > lowp_f64vec4
 Low double-qualifier floating-point vector of 4 components. More...
 
typedef float lowp_float32
 Low 32 bit single-qualifier floating-point scalar. More...
 
typedef float lowp_float32_t
 Low 32 bit single-qualifier floating-point scalar. More...
 
typedef double lowp_float64
 Low 64 bit double-qualifier floating-point scalar. More...
 
typedef double lowp_float64_t
 Low 64 bit double-qualifier floating-point scalar. More...
 
typedef mat< 2, 2, f32, lowp > lowp_fmat2
 Low single-qualifier floating-point 1x1 matrix. More...
 
typedef mat< 2, 2, f32, lowp > lowp_fmat2x2
 Low single-qualifier floating-point 1x1 matrix. More...
 
typedef mat< 2, 3, f32, lowp > lowp_fmat2x3
 Low single-qualifier floating-point 2x3 matrix. More...
 
typedef mat< 2, 4, f32, lowp > lowp_fmat2x4
 Low single-qualifier floating-point 2x4 matrix. More...
 
typedef mat< 3, 3, f32, lowp > lowp_fmat3
 Low single-qualifier floating-point 3x3 matrix. More...
 
typedef mat< 3, 2, f32, lowp > lowp_fmat3x2
 Low single-qualifier floating-point 3x2 matrix. More...
 
typedef mat< 3, 3, f32, lowp > lowp_fmat3x3
 Low single-qualifier floating-point 3x3 matrix. More...
 
typedef mat< 3, 4, f32, lowp > lowp_fmat3x4
 Low single-qualifier floating-point 3x4 matrix. More...
 
typedef mat< 4, 4, f32, lowp > lowp_fmat4
 Low single-qualifier floating-point 4x4 matrix. More...
 
typedef mat< 4, 2, f32, lowp > lowp_fmat4x2
 Low single-qualifier floating-point 4x2 matrix. More...
 
typedef mat< 4, 3, f32, lowp > lowp_fmat4x3
 Low single-qualifier floating-point 4x3 matrix. More...
 
typedef mat< 4, 4, f32, lowp > lowp_fmat4x4
 Low single-qualifier floating-point 4x4 matrix. More...
 
typedef vec< 1, float, lowp > lowp_fvec1
 Low single-qualifier floating-point vector of 1 component. More...
 
typedef vec< 2, float, lowp > lowp_fvec2
 Low single-qualifier floating-point vector of 2 components. More...
 
typedef vec< 3, float, lowp > lowp_fvec3
 Low single-qualifier floating-point vector of 3 components. More...
 
typedef vec< 4, float, lowp > lowp_fvec4
 Low single-qualifier floating-point vector of 4 components. More...
 
typedef int16 lowp_i16
 Low qualifier 16 bit signed integer type. More...
 
typedef vec< 1, i16, lowp > lowp_i16vec1
 Low qualifier 16 bit signed integer scalar type. More...
 
typedef vec< 2, i16, lowp > lowp_i16vec2
 Low qualifier 16 bit signed integer vector of 2 components type. More...
 
typedef vec< 3, i16, lowp > lowp_i16vec3
 Low qualifier 16 bit signed integer vector of 3 components type. More...
 
typedef vec< 4, i16, lowp > lowp_i16vec4
 Low qualifier 16 bit signed integer vector of 4 components type. More...
 
typedef int32 lowp_i32
 Low qualifier 32 bit signed integer type. More...
 
typedef vec< 1, i32, lowp > lowp_i32vec1
 Low qualifier 32 bit signed integer scalar type. More...
 
typedef vec< 2, i32, lowp > lowp_i32vec2
 Low qualifier 32 bit signed integer vector of 2 components type. More...
 
typedef vec< 3, i32, lowp > lowp_i32vec3
 Low qualifier 32 bit signed integer vector of 3 components type. More...
 
typedef vec< 4, i32, lowp > lowp_i32vec4
 Low qualifier 32 bit signed integer vector of 4 components type. More...
 
typedef int64 lowp_i64
 Low qualifier 64 bit signed integer type. More...
 
typedef vec< 1, i64, lowp > lowp_i64vec1
 Low qualifier 64 bit signed integer scalar type. More...
 
typedef vec< 2, i64, lowp > lowp_i64vec2
 Low qualifier 64 bit signed integer vector of 2 components type. More...
 
typedef vec< 3, i64, lowp > lowp_i64vec3
 Low qualifier 64 bit signed integer vector of 3 components type. More...
 
typedef vec< 4, i64, lowp > lowp_i64vec4
 Low qualifier 64 bit signed integer vector of 4 components type. More...
 
typedef int8 lowp_i8
 Low qualifier 8 bit signed integer type. More...
 
typedef vec< 1, i8, lowp > lowp_i8vec1
 Low qualifier 8 bit signed integer scalar type. More...
 
typedef vec< 2, i8, lowp > lowp_i8vec2
 Low qualifier 8 bit signed integer vector of 2 components type. More...
 
typedef vec< 3, i8, lowp > lowp_i8vec3
 Low qualifier 8 bit signed integer vector of 3 components type. More...
 
typedef vec< 4, i8, lowp > lowp_i8vec4
 Low qualifier 8 bit signed integer vector of 4 components type. More...
 
typedef int16 lowp_int16
 Low qualifier 16 bit signed integer type. More...
 
typedef int16 lowp_int16_t
 Low qualifier 16 bit signed integer type. More...
 
typedef int32 lowp_int32
 Low qualifier 32 bit signed integer type. More...
 
typedef int32 lowp_int32_t
 Low qualifier 32 bit signed integer type. More...
 
typedef int64 lowp_int64
 Low qualifier 64 bit signed integer type. More...
 
typedef int64 lowp_int64_t
 Low qualifier 64 bit signed integer type. More...
 
typedef int8 lowp_int8
 Low qualifier 8 bit signed integer type. More...
 
typedef int8 lowp_int8_t
 Low qualifier 8 bit signed integer type. More...
 
typedef uint16 lowp_u16
 Low qualifier 16 bit unsigned integer type. More...
 
typedef vec< 1, u16, lowp > lowp_u16vec1
 Low qualifier 16 bit unsigned integer scalar type. More...
 
typedef vec< 2, u16, lowp > lowp_u16vec2
 Low qualifier 16 bit unsigned integer vector of 2 components type. More...
 
typedef vec< 3, u16, lowp > lowp_u16vec3
 Low qualifier 16 bit unsigned integer vector of 3 components type. More...
 
typedef vec< 4, u16, lowp > lowp_u16vec4
 Low qualifier 16 bit unsigned integer vector of 4 components type. More...
 
typedef uint32 lowp_u32
 Low qualifier 32 bit unsigned integer type. More...
 
typedef vec< 1, u32, lowp > lowp_u32vec1
 Low qualifier 32 bit unsigned integer scalar type. More...
 
typedef vec< 2, u32, lowp > lowp_u32vec2
 Low qualifier 32 bit unsigned integer vector of 2 components type. More...
 
typedef vec< 3, u32, lowp > lowp_u32vec3
 Low qualifier 32 bit unsigned integer vector of 3 components type. More...
 
typedef vec< 4, u32, lowp > lowp_u32vec4
 Low qualifier 32 bit unsigned integer vector of 4 components type. More...
 
typedef uint64 lowp_u64
 Low qualifier 64 bit unsigned integer type. More...
 
typedef vec< 1, u64, lowp > lowp_u64vec1
 Low qualifier 64 bit unsigned integer scalar type. More...
 
typedef vec< 2, u64, lowp > lowp_u64vec2
 Low qualifier 64 bit unsigned integer vector of 2 components type. More...
 
typedef vec< 3, u64, lowp > lowp_u64vec3
 Low qualifier 64 bit unsigned integer vector of 3 components type. More...
 
typedef vec< 4, u64, lowp > lowp_u64vec4
 Low qualifier 64 bit unsigned integer vector of 4 components type. More...
 
typedef uint8 lowp_u8
 Low qualifier 8 bit unsigned integer type. More...
 
typedef vec< 1, u8, lowp > lowp_u8vec1
 Low qualifier 8 bit unsigned integer scalar type. More...
 
typedef vec< 2, u8, lowp > lowp_u8vec2
 Low qualifier 8 bit unsigned integer vector of 2 components type. More...
 
typedef vec< 3, u8, lowp > lowp_u8vec3
 Low qualifier 8 bit unsigned integer vector of 3 components type. More...
 
typedef vec< 4, u8, lowp > lowp_u8vec4
 Low qualifier 8 bit unsigned integer vector of 4 components type. More...
 
typedef uint16 lowp_uint16
 Low qualifier 16 bit unsigned integer type. More...
 
typedef uint16 lowp_uint16_t
 Low qualifier 16 bit unsigned integer type. More...
 
typedef uint32 lowp_uint32
 Low qualifier 32 bit unsigned integer type. More...
 
typedef uint32 lowp_uint32_t
 Low qualifier 32 bit unsigned integer type. More...
 
typedef uint64 lowp_uint64
 Low qualifier 64 bit unsigned integer type. More...
 
typedef uint64 lowp_uint64_t
 Low qualifier 64 bit unsigned integer type. More...
 
typedef uint8 lowp_uint8
 Low qualifier 8 bit unsigned integer type. More...
 
typedef uint8 lowp_uint8_t
 Low qualifier 8 bit unsigned integer type. More...
 
typedef float mediump_f32
 Medium 32 bit single-qualifier floating-point scalar. More...
 
typedef mat< 2, 2, f32, mediump > mediump_f32mat2
 Medium single-qualifier floating-point 1x1 matrix. More...
 
typedef mat< 2, 2, f32, mediump > mediump_f32mat2x2
 High single-qualifier floating-point 1x1 matrix. More...
 
typedef mat< 2, 3, f32, mediump > mediump_f32mat2x3
 Medium single-qualifier floating-point 2x3 matrix. More...
 
typedef mat< 2, 4, f32, mediump > mediump_f32mat2x4
 Medium single-qualifier floating-point 2x4 matrix. More...
 
typedef mat< 3, 3, f32, mediump > mediump_f32mat3
 Medium single-qualifier floating-point 3x3 matrix. More...
 
typedef mat< 3, 2, f32, mediump > mediump_f32mat3x2
 Medium single-qualifier floating-point 3x2 matrix. More...
 
typedef mat< 3, 3, f32, mediump > mediump_f32mat3x3
 Medium single-qualifier floating-point 3x3 matrix. More...
 
typedef mat< 3, 4, f32, mediump > mediump_f32mat3x4
 Medium single-qualifier floating-point 3x4 matrix. More...
 
typedef mat< 4, 4, f32, mediump > mediump_f32mat4
 Medium single-qualifier floating-point 4x4 matrix. More...
 
typedef mat< 4, 2, f32, mediump > mediump_f32mat4x2
 Medium single-qualifier floating-point 4x2 matrix. More...
 
typedef mat< 4, 3, f32, mediump > mediump_f32mat4x3
 Medium single-qualifier floating-point 4x3 matrix. More...
 
typedef mat< 4, 4, f32, mediump > mediump_f32mat4x4
 Medium single-qualifier floating-point 4x4 matrix. More...
 
typedef qua< f32, mediump > mediump_f32quat
 Medium single-qualifier floating-point quaternion. More...
 
typedef vec< 1, f32, mediump > mediump_f32vec1
 Medium single-qualifier floating-point vector of 1 component. More...
 
typedef vec< 2, f32, mediump > mediump_f32vec2
 Medium single-qualifier floating-point vector of 2 components. More...
 
typedef vec< 3, f32, mediump > mediump_f32vec3
 Medium single-qualifier floating-point vector of 3 components. More...
 
typedef vec< 4, f32, mediump > mediump_f32vec4
 Medium single-qualifier floating-point vector of 4 components. More...
 
typedef double mediump_f64
 Medium 64 bit double-qualifier floating-point scalar. More...
 
typedef mat< 2, 2, f64, mediump > mediump_f64mat2
 Medium double-qualifier floating-point 1x1 matrix. More...
 
typedef mat< 2, 2, f64, mediump > mediump_f64mat2x2
 Medium double-qualifier floating-point 1x1 matrix. More...
 
typedef mat< 2, 3, f64, mediump > mediump_f64mat2x3
 Medium double-qualifier floating-point 2x3 matrix. More...
 
typedef mat< 2, 4, f64, mediump > mediump_f64mat2x4
 Medium double-qualifier floating-point 2x4 matrix. More...
 
typedef mat< 3, 3, f64, mediump > mediump_f64mat3
 Medium double-qualifier floating-point 3x3 matrix. More...
 
typedef mat< 3, 2, f64, mediump > mediump_f64mat3x2
 Medium double-qualifier floating-point 3x2 matrix. More...
 
typedef mat< 3, 3, f64, mediump > mediump_f64mat3x3
 Medium double-qualifier floating-point 3x3 matrix. More...
 
typedef mat< 3, 4, f64, mediump > mediump_f64mat3x4
 Medium double-qualifier floating-point 3x4 matrix. More...
 
typedef mat< 4, 4, f64, mediump > mediump_f64mat4
 Medium double-qualifier floating-point 4x4 matrix. More...
 
typedef mat< 4, 2, f64, mediump > mediump_f64mat4x2
 Medium double-qualifier floating-point 4x2 matrix. More...
 
typedef mat< 4, 3, f64, mediump > mediump_f64mat4x3
 Medium double-qualifier floating-point 4x3 matrix. More...
 
typedef mat< 4, 4, f64, mediump > mediump_f64mat4x4
 Medium double-qualifier floating-point 4x4 matrix. More...
 
typedef qua< f64, mediump > mediump_f64quat
 Medium double-qualifier floating-point quaternion. More...
 
typedef vec< 1, f64, mediump > mediump_f64vec1
 Medium double-qualifier floating-point vector of 1 component. More...
 
typedef vec< 2, f64, mediump > mediump_f64vec2
 Medium double-qualifier floating-point vector of 2 components. More...
 
typedef vec< 3, f64, mediump > mediump_f64vec3
 Medium double-qualifier floating-point vector of 3 components. More...
 
typedef vec< 4, f64, mediump > mediump_f64vec4
 Medium double-qualifier floating-point vector of 4 components. More...
 
typedef float mediump_float32
 Medium 32 bit single-qualifier floating-point scalar. More...
 
typedef float mediump_float32_t
 Medium 32 bit single-qualifier floating-point scalar. More...
 
typedef double mediump_float64
 Medium 64 bit double-qualifier floating-point scalar. More...
 
typedef double mediump_float64_t
 Medium 64 bit double-qualifier floating-point scalar. More...
 
typedef mat< 2, 2, f32, mediump > mediump_fmat2
 Medium single-qualifier floating-point 1x1 matrix. More...
 
typedef mat< 2, 2, f32, mediump > mediump_fmat2x2
 Medium single-qualifier floating-point 1x1 matrix. More...
 
typedef mat< 2, 3, f32, mediump > mediump_fmat2x3
 Medium single-qualifier floating-point 2x3 matrix. More...
 
typedef mat< 2, 4, f32, mediump > mediump_fmat2x4
 Medium single-qualifier floating-point 2x4 matrix. More...
 
typedef mat< 3, 3, f32, mediump > mediump_fmat3
 Medium single-qualifier floating-point 3x3 matrix. More...
 
typedef mat< 3, 2, f32, mediump > mediump_fmat3x2
 Medium single-qualifier floating-point 3x2 matrix. More...
 
typedef mat< 3, 3, f32, mediump > mediump_fmat3x3
 Medium single-qualifier floating-point 3x3 matrix. More...
 
typedef mat< 3, 4, f32, mediump > mediump_fmat3x4
 Medium single-qualifier floating-point 3x4 matrix. More...
 
typedef mat< 4, 4, f32, mediump > mediump_fmat4
 Medium single-qualifier floating-point 4x4 matrix. More...
 
typedef mat< 4, 2, f32, mediump > mediump_fmat4x2
 Medium single-qualifier floating-point 4x2 matrix. More...
 
typedef mat< 4, 3, f32, mediump > mediump_fmat4x3
 Medium single-qualifier floating-point 4x3 matrix. More...
 
typedef mat< 4, 4, f32, mediump > mediump_fmat4x4
 Medium single-qualifier floating-point 4x4 matrix. More...
 
typedef vec< 1, float, mediump > mediump_fvec1
 Medium single-qualifier floating-point vector of 1 component. More...
 
typedef vec< 2, float, mediump > mediump_fvec2
 Medium Single-qualifier floating-point vector of 2 components. More...
 
typedef vec< 3, float, mediump > mediump_fvec3
 Medium Single-qualifier floating-point vector of 3 components. More...
 
typedef vec< 4, float, mediump > mediump_fvec4
 Medium Single-qualifier floating-point vector of 4 components. More...
 
typedef int16 mediump_i16
 Medium qualifier 16 bit signed integer type. More...
 
typedef vec< 1, i16, mediump > mediump_i16vec1
 Medium qualifier 16 bit signed integer scalar type. More...
 
typedef vec< 2, i16, mediump > mediump_i16vec2
 Medium qualifier 16 bit signed integer vector of 2 components type. More...
 
typedef vec< 3, i16, mediump > mediump_i16vec3
 Medium qualifier 16 bit signed integer vector of 3 components type. More...
 
typedef vec< 4, i16, mediump > mediump_i16vec4
 Medium qualifier 16 bit signed integer vector of 4 components type. More...
 
typedef int32 mediump_i32
 Medium qualifier 32 bit signed integer type. More...
 
typedef vec< 1, i32, mediump > mediump_i32vec1
 Medium qualifier 32 bit signed integer scalar type. More...
 
typedef vec< 2, i32, mediump > mediump_i32vec2
 Medium qualifier 32 bit signed integer vector of 2 components type. More...
 
typedef vec< 3, i32, mediump > mediump_i32vec3
 Medium qualifier 32 bit signed integer vector of 3 components type. More...
 
typedef vec< 4, i32, mediump > mediump_i32vec4
 Medium qualifier 32 bit signed integer vector of 4 components type. More...
 
typedef int64 mediump_i64
 Medium qualifier 64 bit signed integer type. More...
 
typedef vec< 1, i64, mediump > mediump_i64vec1
 Medium qualifier 64 bit signed integer scalar type. More...
 
typedef vec< 2, i64, mediump > mediump_i64vec2
 Medium qualifier 64 bit signed integer vector of 2 components type. More...
 
typedef vec< 3, i64, mediump > mediump_i64vec3
 Medium qualifier 64 bit signed integer vector of 3 components type. More...
 
typedef vec< 4, i64, mediump > mediump_i64vec4
 Medium qualifier 64 bit signed integer vector of 4 components type. More...
 
typedef int8 mediump_i8
 Medium qualifier 8 bit signed integer type. More...
 
typedef vec< 1, i8, mediump > mediump_i8vec1
 Medium qualifier 8 bit signed integer scalar type. More...
 
typedef vec< 2, i8, mediump > mediump_i8vec2
 Medium qualifier 8 bit signed integer vector of 2 components type. More...
 
typedef vec< 3, i8, mediump > mediump_i8vec3
 Medium qualifier 8 bit signed integer vector of 3 components type. More...
 
typedef vec< 4, i8, mediump > mediump_i8vec4
 Medium qualifier 8 bit signed integer vector of 4 components type. More...
 
typedef int16 mediump_int16
 Medium qualifier 16 bit signed integer type. More...
 
typedef int16 mediump_int16_t
 Medium qualifier 16 bit signed integer type. More...
 
typedef int32 mediump_int32
 Medium qualifier 32 bit signed integer type. More...
 
typedef int32 mediump_int32_t
 Medium qualifier 32 bit signed integer type. More...
 
typedef int64 mediump_int64
 Medium qualifier 64 bit signed integer type. More...
 
typedef int64 mediump_int64_t
 Medium qualifier 64 bit signed integer type. More...
 
typedef int8 mediump_int8
 Medium qualifier 8 bit signed integer type. More...
 
typedef int8 mediump_int8_t
 Medium qualifier 8 bit signed integer type. More...
 
typedef uint16 mediump_u16
 Medium qualifier 16 bit unsigned integer type. More...
 
typedef vec< 1, u16, mediump > mediump_u16vec1
 Medium qualifier 16 bit unsigned integer scalar type. More...
 
typedef vec< 2, u16, mediump > mediump_u16vec2
 Medium qualifier 16 bit unsigned integer vector of 2 components type. More...
 
typedef vec< 3, u16, mediump > mediump_u16vec3
 Medium qualifier 16 bit unsigned integer vector of 3 components type. More...
 
typedef vec< 4, u16, mediump > mediump_u16vec4
 Medium qualifier 16 bit unsigned integer vector of 4 components type. More...
 
typedef uint32 mediump_u32
 Medium qualifier 32 bit unsigned integer type. More...
 
typedef vec< 1, u32, mediump > mediump_u32vec1
 Medium qualifier 32 bit unsigned integer scalar type. More...
 
typedef vec< 2, u32, mediump > mediump_u32vec2
 Medium qualifier 32 bit unsigned integer vector of 2 components type. More...
 
typedef vec< 3, u32, mediump > mediump_u32vec3
 Medium qualifier 32 bit unsigned integer vector of 3 components type. More...
 
typedef vec< 4, u32, mediump > mediump_u32vec4
 Medium qualifier 32 bit unsigned integer vector of 4 components type. More...
 
typedef uint64 mediump_u64
 Medium qualifier 64 bit unsigned integer type. More...
 
typedef vec< 1, u64, mediump > mediump_u64vec1
 Medium qualifier 64 bit unsigned integer scalar type. More...
 
typedef vec< 2, u64, mediump > mediump_u64vec2
 Medium qualifier 64 bit unsigned integer vector of 2 components type. More...
 
typedef vec< 3, u64, mediump > mediump_u64vec3
 Medium qualifier 64 bit unsigned integer vector of 3 components type. More...
 
typedef vec< 4, u64, mediump > mediump_u64vec4
 Medium qualifier 64 bit unsigned integer vector of 4 components type. More...
 
typedef uint8 mediump_u8
 Medium qualifier 8 bit unsigned integer type. More...
 
typedef vec< 1, u8, mediump > mediump_u8vec1
 Medium qualifier 8 bit unsigned integer scalar type. More...
 
typedef vec< 2, u8, mediump > mediump_u8vec2
 Medium qualifier 8 bit unsigned integer vector of 2 components type. More...
 
typedef vec< 3, u8, mediump > mediump_u8vec3
 Medium qualifier 8 bit unsigned integer vector of 3 components type. More...
 
typedef vec< 4, u8, mediump > mediump_u8vec4
 Medium qualifier 8 bit unsigned integer vector of 4 components type. More...
 
typedef uint16 mediump_uint16
 Medium qualifier 16 bit unsigned integer type. More...
 
typedef uint16 mediump_uint16_t
 Medium qualifier 16 bit unsigned integer type. More...
 
typedef uint32 mediump_uint32
 Medium qualifier 32 bit unsigned integer type. More...
 
typedef uint32 mediump_uint32_t
 Medium qualifier 32 bit unsigned integer type. More...
 
typedef uint64 mediump_uint64
 Medium qualifier 64 bit unsigned integer type. More...
 
typedef uint64 mediump_uint64_t
 Medium qualifier 64 bit unsigned integer type. More...
 
typedef uint8 mediump_uint8
 Medium qualifier 8 bit unsigned integer type. More...
 
typedef uint8 mediump_uint8_t
 Medium qualifier 8 bit unsigned integer type. More...
 
typedef uint16 u16
 Default qualifier 16 bit unsigned integer type. More...
 
typedef vec< 1, u16, defaultp > u16vec1
 Default qualifier 16 bit unsigned integer scalar type. More...
 
typedef vec< 2, u16, defaultp > u16vec2
 Default qualifier 16 bit unsigned integer vector of 2 components type. More...
 
typedef vec< 3, u16, defaultp > u16vec3
 Default qualifier 16 bit unsigned integer vector of 3 components type. More...
 
typedef vec< 4, u16, defaultp > u16vec4
 Default qualifier 16 bit unsigned integer vector of 4 components type. More...
 
typedef uint32 u32
 Default qualifier 32 bit unsigned integer type. More...
 
typedef vec< 1, u32, defaultp > u32vec1
 Default qualifier 32 bit unsigned integer scalar type. More...
 
typedef vec< 2, u32, defaultp > u32vec2
 Default qualifier 32 bit unsigned integer vector of 2 components type. More...
 
typedef vec< 3, u32, defaultp > u32vec3
 Default qualifier 32 bit unsigned integer vector of 3 components type. More...
 
typedef vec< 4, u32, defaultp > u32vec4
 Default qualifier 32 bit unsigned integer vector of 4 components type. More...
 
typedef uint64 u64
 Default qualifier 64 bit unsigned integer type. More...
 
typedef vec< 1, u64, defaultp > u64vec1
 Default qualifier 64 bit unsigned integer scalar type. More...
 
typedef vec< 2, u64, defaultp > u64vec2
 Default qualifier 64 bit unsigned integer vector of 2 components type. More...
 
typedef vec< 3, u64, defaultp > u64vec3
 Default qualifier 64 bit unsigned integer vector of 3 components type. More...
 
typedef vec< 4, u64, defaultp > u64vec4
 Default qualifier 64 bit unsigned integer vector of 4 components type. More...
 
typedef uint8 u8
 Default qualifier 8 bit unsigned integer type. More...
 
typedef vec< 1, u8, defaultp > u8vec1
 Default qualifier 8 bit unsigned integer scalar type. More...
 
typedef vec< 2, u8, defaultp > u8vec2
 Default qualifier 8 bit unsigned integer vector of 2 components type. More...
 
typedef vec< 3, u8, defaultp > u8vec3
 Default qualifier 8 bit unsigned integer vector of 3 components type. More...
 
typedef vec< 4, u8, defaultp > u8vec4
 Default qualifier 8 bit unsigned integer vector of 4 components type. More...
 
typedef uint16 uint16_t
 Default qualifier 16 bit unsigned integer type. More...
 
typedef uint32 uint32_t
 Default qualifier 32 bit unsigned integer type. More...
 
typedef uint64 uint64_t
 Default qualifier 64 bit unsigned integer type. More...
 
typedef uint8 uint8_t
 Default qualifier 8 bit unsigned integer type. More...
 
\n

Detailed Description

\n

Include <glm/gtc/type_precision.hpp> to use the features of this extension.

\n

Defines specific C++-based qualifier types.

\n

Typedef Documentation

\n\n
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typedef float32 f32
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Default 32 bit single-qualifier floating-point scalar.

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32 bit single-qualifier floating-point scalar.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 150 of file fwd.hpp.

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typedef mat< 2, 2, f32, defaultp > f32mat2
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Single-qualifier floating-point 1x1 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision Single-qualifier floating-point 2x2 matrix.
\n
\n to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 552 of file fwd.hpp.

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typedef mat< 2, 2, f32, defaultp > f32mat2x2
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Single-qualifier floating-point 1x1 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision Single-qualifier floating-point 2x2 matrix.
\n
\n to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 700 of file fwd.hpp.

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typedef mat< 2, 3, f32, defaultp > f32mat2x3
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Single-qualifier floating-point 2x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 703 of file fwd.hpp.

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typedef mat< 2, 4, f32, defaultp > f32mat2x4
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Single-qualifier floating-point 2x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 706 of file fwd.hpp.

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typedef mat< 3, 3, f32, defaultp > f32mat3
\n
\n\n

Single-qualifier floating-point 3x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 553 of file fwd.hpp.

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typedef mat< 3, 2, f32, defaultp > f32mat3x2
\n
\n\n

Single-qualifier floating-point 3x2 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 701 of file fwd.hpp.

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typedef mat< 3, 3, f32, defaultp > f32mat3x3
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\n\n

Single-qualifier floating-point 3x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 704 of file fwd.hpp.

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typedef mat< 3, 4, f32, defaultp > f32mat3x4
\n
\n\n

Single-qualifier floating-point 3x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 707 of file fwd.hpp.

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typedef mat< 4, 4, f32, defaultp > f32mat4
\n
\n\n

Single-qualifier floating-point 4x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 554 of file fwd.hpp.

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typedef mat< 4, 2, f32, defaultp > f32mat4x2
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\n\n

Single-qualifier floating-point 4x2 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 702 of file fwd.hpp.

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typedef mat< 4, 3, f32, defaultp > f32mat4x3
\n
\n\n

Single-qualifier floating-point 4x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 705 of file fwd.hpp.

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typedef mat< 4, 4, f32, defaultp > f32mat4x4
\n
\n\n

Single-qualifier floating-point 4x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 708 of file fwd.hpp.

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typedef qua< f32, defaultp > f32quat
\n
\n\n

Single-qualifier floating-point quaternion.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 805 of file fwd.hpp.

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typedef vec< 1, f32, defaultp > f32vec1
\n
\n\n

Single-qualifier floating-point vector of 1 component.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 461 of file fwd.hpp.

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typedef vec< 2, f32, defaultp > f32vec2
\n
\n\n

Single-qualifier floating-point vector of 2 components.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 462 of file fwd.hpp.

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typedef vec< 3, f32, defaultp > f32vec3
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\n\n

Single-qualifier floating-point vector of 3 components.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 463 of file fwd.hpp.

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typedef vec< 4, f32, defaultp > f32vec4
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\n\n

Single-qualifier floating-point vector of 4 components.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 464 of file fwd.hpp.

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typedef float64 f64
\n
\n\n

Default 64 bit double-qualifier floating-point scalar.

\n

64 bit double-qualifier floating-point scalar.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 166 of file fwd.hpp.

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typedef mat< 2, 2, f64, defaultp > f64mat2
\n
\n\n

Double-qualifier floating-point 1x1 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision Double-qualifier floating-point 2x2 matrix.
\n
\n to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 584 of file fwd.hpp.

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typedef mat< 2, 2, f64, defaultp > f64mat2x2
\n
\n\n

Double-qualifier floating-point 1x1 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision Double-qualifier floating-point 2x2 matrix.
\n
\n to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 780 of file fwd.hpp.

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typedef mat< 2, 3, f64, defaultp > f64mat2x3
\n
\n\n

Double-qualifier floating-point 2x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 783 of file fwd.hpp.

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typedef mat< 2, 4, f64, defaultp > f64mat2x4
\n
\n\n

Double-qualifier floating-point 2x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 786 of file fwd.hpp.

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typedef mat< 3, 3, f64, defaultp > f64mat3
\n
\n\n

Double-qualifier floating-point 3x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 585 of file fwd.hpp.

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typedef mat< 3, 2, f64, defaultp > f64mat3x2
\n
\n\n

Double-qualifier floating-point 3x2 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 781 of file fwd.hpp.

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typedef mat< 3, 3, f64, defaultp > f64mat3x3
\n
\n\n

Double-qualifier floating-point 3x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 784 of file fwd.hpp.

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typedef mat< 3, 4, f64, defaultp > f64mat3x4
\n
\n\n

Double-qualifier floating-point 3x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 787 of file fwd.hpp.

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typedef mat< 4, 4, f64, defaultp > f64mat4
\n
\n\n

Double-qualifier floating-point 4x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 586 of file fwd.hpp.

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typedef mat< 4, 2, f64, defaultp > f64mat4x2
\n
\n\n

Double-qualifier floating-point 4x2 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 782 of file fwd.hpp.

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typedef mat< 4, 3, f64, defaultp > f64mat4x3
\n
\n\n

Double-qualifier floating-point 4x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 785 of file fwd.hpp.

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typedef mat< 4, 4, f64, defaultp > f64mat4x4
\n
\n\n

Double-qualifier floating-point 4x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 788 of file fwd.hpp.

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typedef qua< f64, defaultp > f64quat
\n
\n\n

Double-qualifier floating-point quaternion.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 815 of file fwd.hpp.

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typedef vec< 1, f64, defaultp > f64vec1
\n
\n\n

Double-qualifier floating-point vector of 1 component.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 501 of file fwd.hpp.

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typedef vec< 2, f64, defaultp > f64vec2
\n
\n\n

Double-qualifier floating-point vector of 2 components.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 502 of file fwd.hpp.

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typedef vec< 3, f64, defaultp > f64vec3
\n
\n\n

Double-qualifier floating-point vector of 3 components.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 503 of file fwd.hpp.

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typedef vec< 4, f64, defaultp > f64vec4
\n
\n\n

Double-qualifier floating-point vector of 4 components.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 504 of file fwd.hpp.

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typedef float float32
\n
\n\n

Single-qualifier floating-point scalar.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 155 of file fwd.hpp.

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typedef float32 float32_t
\n
\n\n

Default 32 bit single-qualifier floating-point scalar.

\n

32 bit single-qualifier floating-point scalar.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 160 of file fwd.hpp.

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typedef double float64
\n
\n\n

Double-qualifier floating-point scalar.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 171 of file fwd.hpp.

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typedef float64 float64_t
\n
\n\n

Default 64 bit double-qualifier floating-point scalar.

\n

64 bit double-qualifier floating-point scalar.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 176 of file fwd.hpp.

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typedef mat< 2, 2, f32, defaultp > fmat2
\n
\n\n

Single-qualifier floating-point 1x1 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision Single-qualifier floating-point 2x2 matrix.
\n
\n to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 536 of file fwd.hpp.

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typedef mat< 2, 2, f32, defaultp > fmat2x2
\n
\n\n

Single-qualifier floating-point 1x1 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision Single-qualifier floating-point 2x2 matrix.
\n
\n to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 660 of file fwd.hpp.

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typedef mat< 2, 3, f32, defaultp > fmat2x3
\n
\n\n

Single-qualifier floating-point 2x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 663 of file fwd.hpp.

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typedef mat< 2, 4, f32, defaultp > fmat2x4
\n
\n\n

Single-qualifier floating-point 2x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 666 of file fwd.hpp.

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typedef mat< 3, 3, f32, defaultp > fmat3
\n
\n\n

Single-qualifier floating-point 3x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 537 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat< 3, 2, f32, defaultp > fmat3x2
\n
\n\n

Single-qualifier floating-point 3x2 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 661 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat< 3, 3, f32, defaultp > fmat3x3
\n
\n\n

Single-qualifier floating-point 3x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 664 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat< 3, 4, f32, defaultp > fmat3x4
\n
\n\n

Single-qualifier floating-point 3x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 667 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat< 4, 4, f32, defaultp > fmat4
\n
\n\n

Single-qualifier floating-point 4x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 538 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat< 4, 2, f32, defaultp > fmat4x2
\n
\n\n

Single-qualifier floating-point 4x2 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 662 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat< 4, 3, f32, defaultp > fmat4x3
\n
\n\n

Single-qualifier floating-point 4x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 665 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat< 4, 4, f32, defaultp > fmat4x4
\n
\n\n

Single-qualifier floating-point 4x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 668 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 1, float, defaultp > fvec1
\n
\n\n

Single-qualifier floating-point vector of 1 component.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 441 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 2, float, defaultp > fvec2
\n
\n\n

Single-qualifier floating-point vector of 2 components.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 442 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 3, float, defaultp > fvec3
\n
\n\n

Single-qualifier floating-point vector of 3 components.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 443 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 4, float, defaultp > fvec4
\n
\n\n

Single-qualifier floating-point vector of 4 components.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 444 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef float32 highp_f32
\n
\n\n

High 32 bit single-qualifier floating-point scalar.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 149 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef highp_f32mat2x2 highp_f32mat2
\n
\n\n

High single-qualifier floating-point 1x1 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision High single-qualifier floating-point 2x2 matrix.
\n
\n to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 548 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat< 2, 2, f32, highp > highp_f32mat2x2
\n
\n\n

High single-qualifier floating-point 1x1 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision High single-qualifier floating-point 2x2 matrix.
\n
\n to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 690 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat< 2, 3, f32, highp > highp_f32mat2x3
\n
\n\n

High single-qualifier floating-point 2x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 691 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat< 2, 4, f32, highp > highp_f32mat2x4
\n
\n\n

High single-qualifier floating-point 2x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 692 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef highp_f32mat3x3 highp_f32mat3
\n
\n\n

High single-qualifier floating-point 3x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 549 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat< 3, 2, f32, highp > highp_f32mat3x2
\n
\n\n

High single-qualifier floating-point 3x2 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 693 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat< 3, 3, f32, highp > highp_f32mat3x3
\n
\n\n

High single-qualifier floating-point 3x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 694 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat< 3, 4, f32, highp > highp_f32mat3x4
\n
\n\n

High single-qualifier floating-point 3x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 695 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef highp_f32mat4x4 highp_f32mat4
\n
\n\n

High single-qualifier floating-point 4x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 550 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat< 4, 2, f32, highp > highp_f32mat4x2
\n
\n\n

High single-qualifier floating-point 4x2 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 696 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat< 4, 3, f32, highp > highp_f32mat4x3
\n
\n\n

High single-qualifier floating-point 4x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 697 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat< 4, 4, f32, highp > highp_f32mat4x4
\n
\n\n

High single-qualifier floating-point 4x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 698 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef qua< f32, highp > highp_f32quat
\n
\n\n

High single-qualifier floating-point quaternion.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 804 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 1, f32, highp > highp_f32vec1
\n
\n\n

High single-qualifier floating-point vector of 1 component.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 456 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 2, f32, highp > highp_f32vec2
\n
\n\n

High single-qualifier floating-point vector of 2 components.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 457 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 3, f32, highp > highp_f32vec3
\n
\n\n

High single-qualifier floating-point vector of 3 components.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 458 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 4, f32, highp > highp_f32vec4
\n
\n\n

High single-qualifier floating-point vector of 4 components.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 459 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef float64 highp_f64
\n
\n\n

High 64 bit double-qualifier floating-point scalar.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 165 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef highp_f64mat2x2 highp_f64mat2
\n
\n\n

High double-qualifier floating-point 1x1 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision High double-qualifier floating-point 2x2 matrix.
\n
\n to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 580 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat< 2, 2, f64, highp > highp_f64mat2x2
\n
\n\n

High double-qualifier floating-point 1x1 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision High double-qualifier floating-point 2x2 matrix.
\n
\n to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 770 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat< 2, 3, f64, highp > highp_f64mat2x3
\n
\n\n

High double-qualifier floating-point 2x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 771 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat< 2, 4, f64, highp > highp_f64mat2x4
\n
\n\n

High double-qualifier floating-point 2x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 772 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef highp_f64mat3x3 highp_f64mat3
\n
\n\n

High double-qualifier floating-point 3x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 581 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat< 3, 2, f64, highp > highp_f64mat3x2
\n
\n\n

High double-qualifier floating-point 3x2 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 773 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat< 3, 3, f64, highp > highp_f64mat3x3
\n
\n\n

High double-qualifier floating-point 3x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 774 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat< 3, 4, f64, highp > highp_f64mat3x4
\n
\n\n

High double-qualifier floating-point 3x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 775 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef highp_f64mat4x4 highp_f64mat4
\n
\n\n

High double-qualifier floating-point 4x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 582 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat< 4, 2, f64, highp > highp_f64mat4x2
\n
\n\n

High double-qualifier floating-point 4x2 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 776 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat< 4, 3, f64, highp > highp_f64mat4x3
\n
\n\n

High double-qualifier floating-point 4x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 777 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat< 4, 4, f64, highp > highp_f64mat4x4
\n
\n\n

High double-qualifier floating-point 4x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 778 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef qua< f64, highp > highp_f64quat
\n
\n\n

High double-qualifier floating-point quaternion.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 814 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 1, f64, highp > highp_f64vec1
\n
\n\n

High double-qualifier floating-point vector of 1 component.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 496 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 2, f64, highp > highp_f64vec2
\n
\n\n

High double-qualifier floating-point vector of 2 components.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 497 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 3, f64, highp > highp_f64vec3
\n
\n\n

High double-qualifier floating-point vector of 3 components.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 498 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 4, f64, highp > highp_f64vec4
\n
\n\n

High double-qualifier floating-point vector of 4 components.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 499 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef float32 highp_float32
\n
\n\n

High 32 bit single-qualifier floating-point scalar.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 154 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef float32 highp_float32_t
\n
\n\n

High 32 bit single-qualifier floating-point scalar.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 159 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef float64 highp_float64
\n
\n\n

High 64 bit double-qualifier floating-point scalar.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 170 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef float64 highp_float64_t
\n
\n\n

High 64 bit double-qualifier floating-point scalar.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 175 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef highp_fmat2x2 highp_fmat2
\n
\n\n

High single-qualifier floating-point 1x1 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision High single-qualifier floating-point 2x2 matrix.
\n
\n to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 532 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat< 2, 2, f32, highp > highp_fmat2x2
\n
\n\n

High single-qualifier floating-point 1x1 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision High single-qualifier floating-point 2x2 matrix.
\n
\n to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 650 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat< 2, 3, f32, highp > highp_fmat2x3
\n
\n\n

High single-qualifier floating-point 2x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 651 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat< 2, 4, f32, highp > highp_fmat2x4
\n
\n\n

High single-qualifier floating-point 2x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 652 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef highp_fmat3x3 highp_fmat3
\n
\n\n

High single-qualifier floating-point 3x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 533 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat< 3, 2, f32, highp > highp_fmat3x2
\n
\n\n

High single-qualifier floating-point 3x2 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 653 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat< 3, 3, f32, highp > highp_fmat3x3
\n
\n\n

High single-qualifier floating-point 3x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 654 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat< 3, 4, f32, highp > highp_fmat3x4
\n
\n\n

High single-qualifier floating-point 3x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 655 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef highp_fmat4x4 highp_fmat4
\n
\n\n

High single-qualifier floating-point 4x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 534 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat< 4, 2, f32, highp > highp_fmat4x2
\n
\n\n

High single-qualifier floating-point 4x2 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 656 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat< 4, 3, f32, highp > highp_fmat4x3
\n
\n\n

High single-qualifier floating-point 4x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 657 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat< 4, 4, f32, highp > highp_fmat4x4
\n
\n\n

High single-qualifier floating-point 4x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 658 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 1, float, highp > highp_fvec1
\n
\n\n

High single-qualifier floating-point vector of 1 component.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 436 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 2, float, highp > highp_fvec2
\n
\n\n

High Single-qualifier floating-point vector of 2 components.

\n
See also
core_precision
\n\n

Definition at line 437 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 3, float, highp > highp_fvec3
\n
\n\n

High Single-qualifier floating-point vector of 3 components.

\n
See also
core_precision
\n\n

Definition at line 438 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 4, float, highp > highp_fvec4
\n
\n\n

High Single-qualifier floating-point vector of 4 components.

\n
See also
core_precision
\n\n

Definition at line 439 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef detail::int16 highp_i16
\n
\n\n

High qualifier 16 bit signed integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 47 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 1, i16, highp > highp_i16vec1
\n
\n\n

High qualifier 16 bit signed integer scalar type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 252 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 2, i16, highp > highp_i16vec2
\n
\n\n

High qualifier 16 bit signed integer vector of 2 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 253 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 3, i16, highp > highp_i16vec3
\n
\n\n

High qualifier 16 bit signed integer vector of 3 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 254 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 4, i16, highp > highp_i16vec4
\n
\n\n

High qualifier 16 bit signed integer vector of 4 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 255 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef detail::int32 highp_i32
\n
\n\n

High qualifier 32 bit signed integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 61 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 1, i32, highp > highp_i32vec1
\n
\n\n

High qualifier 32 bit signed integer scalar type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 272 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 2, i32, highp > highp_i32vec2
\n
\n\n

High qualifier 32 bit signed integer vector of 2 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 273 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 3, i32, highp > highp_i32vec3
\n
\n\n

High qualifier 32 bit signed integer vector of 3 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 274 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 4, i32, highp > highp_i32vec4
\n
\n\n

High qualifier 32 bit signed integer vector of 4 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 275 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef detail::int64 highp_i64
\n
\n\n

High qualifier 64 bit signed integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 75 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 1, i64, highp > highp_i64vec1
\n
\n\n

High qualifier 64 bit signed integer scalar type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 292 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 2, i64, highp > highp_i64vec2
\n
\n\n

High qualifier 64 bit signed integer vector of 2 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 293 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 3, i64, highp > highp_i64vec3
\n
\n\n

High qualifier 64 bit signed integer vector of 3 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 294 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 4, i64, highp > highp_i64vec4
\n
\n\n

High qualifier 64 bit signed integer vector of 4 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 295 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef detail::int8 highp_i8
\n
\n\n

High qualifier 8 bit signed integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 33 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 1, i8, highp > highp_i8vec1
\n
\n\n

High qualifier 8 bit signed integer scalar type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 232 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 2, i8, highp > highp_i8vec2
\n
\n\n

High qualifier 8 bit signed integer vector of 2 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 233 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 3, i8, highp > highp_i8vec3
\n
\n\n

High qualifier 8 bit signed integer vector of 3 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 234 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 4, i8, highp > highp_i8vec4
\n
\n\n

High qualifier 8 bit signed integer vector of 4 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 235 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef detail::int16 highp_int16
\n
\n\n

High qualifier 16 bit signed integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 52 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef detail::int16 highp_int16_t
\n
\n\n

High qualifier 16 bit signed integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 56 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef detail::int32 highp_int32
\n
\n\n

High qualifier 32 bit signed integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 66 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef detail::int32 highp_int32_t
\n
\n\n

32 bit signed integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 70 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef detail::int64 highp_int64
\n
\n\n

High qualifier 64 bit signed integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 80 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef detail::int64 highp_int64_t
\n
\n\n

High qualifier 64 bit signed integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 84 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef detail::int8 highp_int8
\n
\n\n

High qualifier 8 bit signed integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 38 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef detail::int8 highp_int8_t
\n
\n\n

High qualifier 8 bit signed integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 42 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef detail::uint16 highp_u16
\n
\n\n

High qualifier 16 bit unsigned integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 105 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 1, u16, highp > highp_u16vec1
\n
\n\n

High qualifier 16 bit unsigned integer scalar type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 354 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 2, u16, highp > highp_u16vec2
\n
\n\n

High qualifier 16 bit unsigned integer vector of 2 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 355 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 3, u16, highp > highp_u16vec3
\n
\n\n

High qualifier 16 bit unsigned integer vector of 3 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 356 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 4, u16, highp > highp_u16vec4
\n
\n\n

High qualifier 16 bit unsigned integer vector of 4 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 357 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef detail::uint32 highp_u32
\n
\n\n

High qualifier 32 bit unsigned integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 119 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 1, u32, highp > highp_u32vec1
\n
\n\n

High qualifier 32 bit unsigned integer scalar type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 374 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 2, u32, highp > highp_u32vec2
\n
\n\n

High qualifier 32 bit unsigned integer vector of 2 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 375 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 3, u32, highp > highp_u32vec3
\n
\n\n

High qualifier 32 bit unsigned integer vector of 3 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 376 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 4, u32, highp > highp_u32vec4
\n
\n\n

High qualifier 32 bit unsigned integer vector of 4 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 377 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef detail::uint64 highp_u64
\n
\n\n

High qualifier 64 bit unsigned integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 133 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 1, u64, highp > highp_u64vec1
\n
\n\n

High qualifier 64 bit unsigned integer scalar type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 394 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 2, u64, highp > highp_u64vec2
\n
\n\n

High qualifier 64 bit unsigned integer vector of 2 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 395 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 3, u64, highp > highp_u64vec3
\n
\n\n

High qualifier 64 bit unsigned integer vector of 3 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 396 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 4, u64, highp > highp_u64vec4
\n
\n\n

High qualifier 64 bit unsigned integer vector of 4 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 397 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef detail::uint8 highp_u8
\n
\n\n

High qualifier 8 bit unsigned integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 91 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 1, u8, highp > highp_u8vec1
\n
\n\n

High qualifier 8 bit unsigned integer scalar type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 334 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 2, u8, highp > highp_u8vec2
\n
\n\n

High qualifier 8 bit unsigned integer vector of 2 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 335 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 3, u8, highp > highp_u8vec3
\n
\n\n

High qualifier 8 bit unsigned integer vector of 3 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 336 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 4, u8, highp > highp_u8vec4
\n
\n\n

High qualifier 8 bit unsigned integer vector of 4 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 337 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef detail::uint16 highp_uint16
\n
\n\n

High qualifier 16 bit unsigned integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 110 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef detail::uint16 highp_uint16_t
\n
\n\n

High qualifier 16 bit unsigned integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 114 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef detail::uint32 highp_uint32
\n
\n\n

High qualifier 32 bit unsigned integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 124 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef detail::uint32 highp_uint32_t
\n
\n\n

High qualifier 32 bit unsigned integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 128 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef detail::uint64 highp_uint64
\n
\n\n

High qualifier 64 bit unsigned integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 138 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef detail::uint64 highp_uint64_t
\n
\n\n

High qualifier 64 bit unsigned integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 142 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef detail::uint8 highp_uint8
\n
\n\n

High qualifier 8 bit unsigned integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 96 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef detail::uint8 highp_uint8_t
\n
\n\n

High qualifier 8 bit unsigned integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 100 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef detail::int16 i16
\n
\n\n

16 bit signed integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 48 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 1, i16, defaultp > i16vec1
\n
\n\n

16 bit signed integer scalar type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 257 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 2, i16, defaultp > i16vec2
\n
\n\n

16 bit signed integer vector of 2 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 258 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 3, i16, defaultp > i16vec3
\n
\n\n

16 bit signed integer vector of 3 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 259 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 4, i16, defaultp > i16vec4
\n
\n\n

16 bit signed integer vector of 4 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 260 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef detail::int32 i32
\n
\n\n

32 bit signed integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 62 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 1, i32, defaultp > i32vec1
\n
\n\n

32 bit signed integer scalar type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 277 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 2, i32, defaultp > i32vec2
\n
\n\n

32 bit signed integer vector of 2 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 278 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 3, i32, defaultp > i32vec3
\n
\n\n

32 bit signed integer vector of 3 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 279 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 4, i32, defaultp > i32vec4
\n
\n\n

32 bit signed integer vector of 4 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 280 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef detail::int64 i64
\n
\n\n

64 bit signed integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 76 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 1, i64, defaultp > i64vec1
\n
\n\n

64 bit signed integer scalar type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 297 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 2, i64, defaultp > i64vec2
\n
\n\n

64 bit signed integer vector of 2 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 298 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 3, i64, defaultp > i64vec3
\n
\n\n

64 bit signed integer vector of 3 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 299 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 4, i64, defaultp > i64vec4
\n
\n\n

64 bit signed integer vector of 4 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 300 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef detail::int8 i8
\n
\n\n

8 bit signed integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 34 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 1, i8, defaultp > i8vec1
\n
\n\n

8 bit signed integer scalar type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 237 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 2, i8, defaultp > i8vec2
\n
\n\n

8 bit signed integer vector of 2 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 238 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 3, i8, defaultp > i8vec3
\n
\n\n

8 bit signed integer vector of 3 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 239 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 4, i8, defaultp > i8vec4
\n
\n\n

8 bit signed integer vector of 4 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 240 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef detail::int16 int16_t
\n
\n\n

16 bit signed integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 57 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef detail::int32 int32_t
\n
\n\n

32 bit signed integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 71 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef detail::int64 int64_t
\n
\n\n

64 bit signed integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 85 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef detail::int8 int8_t
\n
\n\n

8 bit signed integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 43 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef float32 lowp_f32
\n
\n\n

Low 32 bit single-qualifier floating-point scalar.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 147 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef lowp_f32mat2x2 lowp_f32mat2
\n
\n\n

Low single-qualifier floating-point 1x1 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision Low single-qualifier floating-point 2x2 matrix.
\n
\n to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 540 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat< 2, 2, f32, lowp > lowp_f32mat2x2
\n
\n\n

Low single-qualifier floating-point 1x1 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision Low single-qualifier floating-point 2x2 matrix.
\n
\n to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 670 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat< 2, 3, f32, lowp > lowp_f32mat2x3
\n
\n\n

Low single-qualifier floating-point 2x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 671 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat< 2, 4, f32, lowp > lowp_f32mat2x4
\n
\n\n

Low single-qualifier floating-point 2x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 672 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef lowp_f32mat3x3 lowp_f32mat3
\n
\n\n

Low single-qualifier floating-point 3x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 541 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat< 3, 2, f32, lowp > lowp_f32mat3x2
\n
\n\n

Low single-qualifier floating-point 3x2 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 673 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat< 3, 3, f32, lowp > lowp_f32mat3x3
\n
\n\n

Low single-qualifier floating-point 3x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 674 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat< 3, 4, f32, lowp > lowp_f32mat3x4
\n
\n\n

Low single-qualifier floating-point 3x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 675 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef lowp_f32mat4x4 lowp_f32mat4
\n
\n\n

Low single-qualifier floating-point 4x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 542 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat< 4, 2, f32, lowp > lowp_f32mat4x2
\n
\n\n

Low single-qualifier floating-point 4x2 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 676 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat< 4, 3, f32, lowp > lowp_f32mat4x3
\n
\n\n

Low single-qualifier floating-point 4x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 677 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat< 4, 4, f32, lowp > lowp_f32mat4x4
\n
\n\n

Low single-qualifier floating-point 4x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 678 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef qua< f32, lowp > lowp_f32quat
\n
\n\n

Low single-qualifier floating-point quaternion.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 802 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 1, f32, lowp > lowp_f32vec1
\n
\n\n

Low single-qualifier floating-point vector of 1 component.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 446 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 2, f32, lowp > lowp_f32vec2
\n
\n\n

Low single-qualifier floating-point vector of 2 components.

\n
See also
core_precision
\n\n

Definition at line 447 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 3, f32, lowp > lowp_f32vec3
\n
\n\n

Low single-qualifier floating-point vector of 3 components.

\n
See also
core_precision
\n\n

Definition at line 448 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 4, f32, lowp > lowp_f32vec4
\n
\n\n

Low single-qualifier floating-point vector of 4 components.

\n
See also
core_precision
\n\n

Definition at line 449 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef float64 lowp_f64
\n
\n\n

Low 64 bit double-qualifier floating-point scalar.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 163 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef lowp_f64mat2x2 lowp_f64mat2
\n
\n\n

Low double-qualifier floating-point 1x1 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision Low double-qualifier floating-point 2x2 matrix.
\n
\n to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 572 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat< 2, 2, f64, lowp > lowp_f64mat2x2
\n
\n\n

Low double-qualifier floating-point 1x1 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision Low double-qualifier floating-point 2x2 matrix.
\n
\n to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 750 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat< 2, 3, f64, lowp > lowp_f64mat2x3
\n
\n\n

Low double-qualifier floating-point 2x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 751 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat< 2, 4, f64, lowp > lowp_f64mat2x4
\n
\n\n

Low double-qualifier floating-point 2x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 752 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef lowp_f64mat3x3 lowp_f64mat3
\n
\n\n

Low double-qualifier floating-point 3x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 573 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat< 3, 2, f64, lowp > lowp_f64mat3x2
\n
\n\n

Low double-qualifier floating-point 3x2 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 753 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat< 3, 3, f64, lowp > lowp_f64mat3x3
\n
\n\n

Low double-qualifier floating-point 3x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 754 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat< 3, 4, f64, lowp > lowp_f64mat3x4
\n
\n\n

Low double-qualifier floating-point 3x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 755 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef lowp_f64mat4x4 lowp_f64mat4
\n
\n\n

Low double-qualifier floating-point 4x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 574 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat< 4, 2, f64, lowp > lowp_f64mat4x2
\n
\n\n

Low double-qualifier floating-point 4x2 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 756 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat< 4, 3, f64, lowp > lowp_f64mat4x3
\n
\n\n

Low double-qualifier floating-point 4x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 757 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat< 4, 4, f64, lowp > lowp_f64mat4x4
\n
\n\n

Low double-qualifier floating-point 4x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 758 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef qua< f64, lowp > lowp_f64quat
\n
\n\n

Low double-qualifier floating-point quaternion.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 812 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 1, f64, lowp > lowp_f64vec1
\n
\n\n

Low double-qualifier floating-point vector of 1 component.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 486 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 2, f64, lowp > lowp_f64vec2
\n
\n\n

Low double-qualifier floating-point vector of 2 components.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 487 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 3, f64, lowp > lowp_f64vec3
\n
\n\n

Low double-qualifier floating-point vector of 3 components.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 488 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 4, f64, lowp > lowp_f64vec4
\n
\n\n

Low double-qualifier floating-point vector of 4 components.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 489 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef float32 lowp_float32
\n
\n\n

Low 32 bit single-qualifier floating-point scalar.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 152 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef float32 lowp_float32_t
\n
\n\n

Low 32 bit single-qualifier floating-point scalar.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 157 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef float64 lowp_float64
\n
\n\n

Low 64 bit double-qualifier floating-point scalar.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 168 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef float64 lowp_float64_t
\n
\n\n

Low 64 bit double-qualifier floating-point scalar.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 173 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef lowp_fmat2x2 lowp_fmat2
\n
\n\n

Low single-qualifier floating-point 1x1 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision Low single-qualifier floating-point 2x2 matrix.
\n
\n to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 524 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat< 2, 2, f32, lowp > lowp_fmat2x2
\n
\n\n

Low single-qualifier floating-point 1x1 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision Low single-qualifier floating-point 2x2 matrix.
\n
\n to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 630 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat< 2, 3, f32, lowp > lowp_fmat2x3
\n
\n\n

Low single-qualifier floating-point 2x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 631 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat< 2, 4, f32, lowp > lowp_fmat2x4
\n
\n\n

Low single-qualifier floating-point 2x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 632 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef lowp_fmat3x3 lowp_fmat3
\n
\n\n

Low single-qualifier floating-point 3x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 525 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat< 3, 2, f32, lowp > lowp_fmat3x2
\n
\n\n

Low single-qualifier floating-point 3x2 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 633 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat< 3, 3, f32, lowp > lowp_fmat3x3
\n
\n\n

Low single-qualifier floating-point 3x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 634 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat< 3, 4, f32, lowp > lowp_fmat3x4
\n
\n\n

Low single-qualifier floating-point 3x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 635 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef lowp_fmat4x4 lowp_fmat4
\n
\n\n

Low single-qualifier floating-point 4x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 526 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat< 4, 2, f32, lowp > lowp_fmat4x2
\n
\n\n

Low single-qualifier floating-point 4x2 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 636 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat< 4, 3, f32, lowp > lowp_fmat4x3
\n
\n\n

Low single-qualifier floating-point 4x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 637 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat< 4, 4, f32, lowp > lowp_fmat4x4
\n
\n\n

Low single-qualifier floating-point 4x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 638 of file fwd.hpp.

\n\n
\n
\n\n
\n
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typedef vec< 1, float, lowp > lowp_fvec1
\n
\n\n

Low single-qualifier floating-point vector of 1 component.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 426 of file fwd.hpp.

\n\n
\n
\n\n
\n
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typedef vec< 2, float, lowp > lowp_fvec2
\n
\n\n

Low single-qualifier floating-point vector of 2 components.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 427 of file fwd.hpp.

\n\n
\n
\n\n
\n
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typedef vec< 3, float, lowp > lowp_fvec3
\n
\n\n

Low single-qualifier floating-point vector of 3 components.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 428 of file fwd.hpp.

\n\n
\n
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\n
\n \n \n \n \n
typedef vec< 4, float, lowp > lowp_fvec4
\n
\n\n

Low single-qualifier floating-point vector of 4 components.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 429 of file fwd.hpp.

\n\n
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\n\n
\n
\n \n \n \n \n
typedef detail::int16 lowp_i16
\n
\n\n

Low qualifier 16 bit signed integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 45 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 1, i16, lowp > lowp_i16vec1
\n
\n\n

Low qualifier 16 bit signed integer scalar type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 242 of file fwd.hpp.

\n\n
\n
\n\n
\n
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typedef vec< 2, i16, lowp > lowp_i16vec2
\n
\n\n

Low qualifier 16 bit signed integer vector of 2 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 243 of file fwd.hpp.

\n\n
\n
\n\n
\n
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typedef vec< 3, i16, lowp > lowp_i16vec3
\n
\n\n

Low qualifier 16 bit signed integer vector of 3 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 244 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 4, i16, lowp > lowp_i16vec4
\n
\n\n

Low qualifier 16 bit signed integer vector of 4 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 245 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef detail::int32 lowp_i32
\n
\n\n

Low qualifier 32 bit signed integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 59 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 1, i32, lowp > lowp_i32vec1
\n
\n\n

Low qualifier 32 bit signed integer scalar type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 262 of file fwd.hpp.

\n\n
\n
\n\n
\n
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typedef vec< 2, i32, lowp > lowp_i32vec2
\n
\n\n

Low qualifier 32 bit signed integer vector of 2 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 263 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 3, i32, lowp > lowp_i32vec3
\n
\n\n

Low qualifier 32 bit signed integer vector of 3 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 264 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 4, i32, lowp > lowp_i32vec4
\n
\n\n

Low qualifier 32 bit signed integer vector of 4 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 265 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef detail::int64 lowp_i64
\n
\n\n

Low qualifier 64 bit signed integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 73 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 1, i64, lowp > lowp_i64vec1
\n
\n\n

Low qualifier 64 bit signed integer scalar type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 282 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 2, i64, lowp > lowp_i64vec2
\n
\n\n

Low qualifier 64 bit signed integer vector of 2 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 283 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 3, i64, lowp > lowp_i64vec3
\n
\n\n

Low qualifier 64 bit signed integer vector of 3 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 284 of file fwd.hpp.

\n\n
\n
\n\n
\n
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typedef vec< 4, i64, lowp > lowp_i64vec4
\n
\n\n

Low qualifier 64 bit signed integer vector of 4 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 285 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef detail::int8 lowp_i8
\n
\n\n

Low qualifier 8 bit signed integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 31 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 1, i8, lowp > lowp_i8vec1
\n
\n\n

Low qualifier 8 bit signed integer scalar type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 222 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 2, i8, lowp > lowp_i8vec2
\n
\n\n

Low qualifier 8 bit signed integer vector of 2 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 223 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 3, i8, lowp > lowp_i8vec3
\n
\n\n

Low qualifier 8 bit signed integer vector of 3 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 224 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 4, i8, lowp > lowp_i8vec4
\n
\n\n

Low qualifier 8 bit signed integer vector of 4 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 225 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef detail::int16 lowp_int16
\n
\n\n

Low qualifier 16 bit signed integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 50 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef detail::int16 lowp_int16_t
\n
\n\n

Low qualifier 16 bit signed integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 54 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef detail::int32 lowp_int32
\n
\n\n

Low qualifier 32 bit signed integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 64 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef detail::int32 lowp_int32_t
\n
\n\n

Low qualifier 32 bit signed integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 68 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef detail::int64 lowp_int64
\n
\n\n

Low qualifier 64 bit signed integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 78 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef detail::int64 lowp_int64_t
\n
\n\n

Low qualifier 64 bit signed integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 82 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef detail::int8 lowp_int8
\n
\n\n

Low qualifier 8 bit signed integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 36 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef detail::int8 lowp_int8_t
\n
\n\n

Low qualifier 8 bit signed integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 40 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef detail::uint16 lowp_u16
\n
\n\n

Low qualifier 16 bit unsigned integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 103 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 1, u16, lowp > lowp_u16vec1
\n
\n\n

Low qualifier 16 bit unsigned integer scalar type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 344 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 2, u16, lowp > lowp_u16vec2
\n
\n\n

Low qualifier 16 bit unsigned integer vector of 2 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 345 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 3, u16, lowp > lowp_u16vec3
\n
\n\n

Low qualifier 16 bit unsigned integer vector of 3 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 346 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 4, u16, lowp > lowp_u16vec4
\n
\n\n

Low qualifier 16 bit unsigned integer vector of 4 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 347 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef detail::uint32 lowp_u32
\n
\n\n

Low qualifier 32 bit unsigned integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 117 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 1, u32, lowp > lowp_u32vec1
\n
\n\n

Low qualifier 32 bit unsigned integer scalar type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 364 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 2, u32, lowp > lowp_u32vec2
\n
\n\n

Low qualifier 32 bit unsigned integer vector of 2 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 365 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 3, u32, lowp > lowp_u32vec3
\n
\n\n

Low qualifier 32 bit unsigned integer vector of 3 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 366 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 4, u32, lowp > lowp_u32vec4
\n
\n\n

Low qualifier 32 bit unsigned integer vector of 4 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 367 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef detail::uint64 lowp_u64
\n
\n\n

Low qualifier 64 bit unsigned integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 131 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 1, u64, lowp > lowp_u64vec1
\n
\n\n

Low qualifier 64 bit unsigned integer scalar type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 384 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 2, u64, lowp > lowp_u64vec2
\n
\n\n

Low qualifier 64 bit unsigned integer vector of 2 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 385 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 3, u64, lowp > lowp_u64vec3
\n
\n\n

Low qualifier 64 bit unsigned integer vector of 3 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 386 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 4, u64, lowp > lowp_u64vec4
\n
\n\n

Low qualifier 64 bit unsigned integer vector of 4 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 387 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef detail::uint8 lowp_u8
\n
\n\n

Low qualifier 8 bit unsigned integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 89 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 1, u8, lowp > lowp_u8vec1
\n
\n\n

Low qualifier 8 bit unsigned integer scalar type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 324 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 2, u8, lowp > lowp_u8vec2
\n
\n\n

Low qualifier 8 bit unsigned integer vector of 2 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 325 of file fwd.hpp.

\n\n
\n
\n\n
\n
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typedef vec< 3, u8, lowp > lowp_u8vec3
\n
\n\n

Low qualifier 8 bit unsigned integer vector of 3 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 326 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 4, u8, lowp > lowp_u8vec4
\n
\n\n

Low qualifier 8 bit unsigned integer vector of 4 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 327 of file fwd.hpp.

\n\n
\n
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\n
\n \n \n \n \n
typedef detail::uint16 lowp_uint16
\n
\n\n

Low qualifier 16 bit unsigned integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 108 of file fwd.hpp.

\n\n
\n
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\n
\n \n \n \n \n
typedef detail::uint16 lowp_uint16_t
\n
\n\n

Low qualifier 16 bit unsigned integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 112 of file fwd.hpp.

\n\n
\n
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\n
\n \n \n \n \n
typedef detail::uint32 lowp_uint32
\n
\n\n

Low qualifier 32 bit unsigned integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 122 of file fwd.hpp.

\n\n
\n
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\n
\n \n \n \n \n
typedef detail::uint32 lowp_uint32_t
\n
\n\n

Low qualifier 32 bit unsigned integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 126 of file fwd.hpp.

\n\n
\n
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\n
\n \n \n \n \n
typedef detail::uint64 lowp_uint64
\n
\n\n

Low qualifier 64 bit unsigned integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 136 of file fwd.hpp.

\n\n
\n
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\n
\n \n \n \n \n
typedef detail::uint64 lowp_uint64_t
\n
\n\n

Low qualifier 64 bit unsigned integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 140 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef detail::uint8 lowp_uint8
\n
\n\n

Low qualifier 8 bit unsigned integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 94 of file fwd.hpp.

\n\n
\n
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\n
\n \n \n \n \n
typedef detail::uint8 lowp_uint8_t
\n
\n\n

Low qualifier 8 bit unsigned integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 98 of file fwd.hpp.

\n\n
\n
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\n
\n \n \n \n \n
typedef float32 mediump_f32
\n
\n\n

Medium 32 bit single-qualifier floating-point scalar.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 148 of file fwd.hpp.

\n\n
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\n \n \n \n \n
typedef mediump_f32mat2x2 mediump_f32mat2
\n
\n\n

Medium single-qualifier floating-point 1x1 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision Medium single-qualifier floating-point 2x2 matrix.
\n
\n to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 544 of file fwd.hpp.

\n\n
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\n
\n \n \n \n \n
typedef mat< 2, 2, f32, mediump > mediump_f32mat2x2
\n
\n\n

High single-qualifier floating-point 1x1 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision Low single-qualifier floating-point 2x2 matrix.
\n
\n to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 680 of file fwd.hpp.

\n\n
\n
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\n
\n \n \n \n \n
typedef mat< 2, 3, f32, mediump > mediump_f32mat2x3
\n
\n\n

Medium single-qualifier floating-point 2x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 681 of file fwd.hpp.

\n\n
\n
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\n
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typedef mat< 2, 4, f32, mediump > mediump_f32mat2x4
\n
\n\n

Medium single-qualifier floating-point 2x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 682 of file fwd.hpp.

\n\n
\n
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\n
\n \n \n \n \n
typedef mediump_f32mat3x3 mediump_f32mat3
\n
\n\n

Medium single-qualifier floating-point 3x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 545 of file fwd.hpp.

\n\n
\n
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\n
\n \n \n \n \n
typedef mat< 3, 2, f32, mediump > mediump_f32mat3x2
\n
\n\n

Medium single-qualifier floating-point 3x2 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 683 of file fwd.hpp.

\n\n
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typedef mat< 3, 3, f32, mediump > mediump_f32mat3x3
\n
\n\n

Medium single-qualifier floating-point 3x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 684 of file fwd.hpp.

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typedef mat< 3, 4, f32, mediump > mediump_f32mat3x4
\n
\n\n

Medium single-qualifier floating-point 3x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 685 of file fwd.hpp.

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typedef mediump_f32mat4x4 mediump_f32mat4
\n
\n\n

Medium single-qualifier floating-point 4x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 546 of file fwd.hpp.

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typedef mat< 4, 2, f32, mediump > mediump_f32mat4x2
\n
\n\n

Medium single-qualifier floating-point 4x2 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 686 of file fwd.hpp.

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typedef mat< 4, 3, f32, mediump > mediump_f32mat4x3
\n
\n\n

Medium single-qualifier floating-point 4x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 687 of file fwd.hpp.

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typedef mat< 4, 4, f32, mediump > mediump_f32mat4x4
\n
\n\n

Medium single-qualifier floating-point 4x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 688 of file fwd.hpp.

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typedef qua< f32, mediump > mediump_f32quat
\n
\n\n

Medium single-qualifier floating-point quaternion.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 803 of file fwd.hpp.

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typedef vec< 1, f32, mediump > mediump_f32vec1
\n
\n\n

Medium single-qualifier floating-point vector of 1 component.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 451 of file fwd.hpp.

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typedef vec< 2, f32, mediump > mediump_f32vec2
\n
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Medium single-qualifier floating-point vector of 2 components.

\n
See also
core_precision
\n\n

Definition at line 452 of file fwd.hpp.

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typedef vec< 3, f32, mediump > mediump_f32vec3
\n
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Medium single-qualifier floating-point vector of 3 components.

\n
See also
core_precision
\n\n

Definition at line 453 of file fwd.hpp.

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typedef vec< 4, f32, mediump > mediump_f32vec4
\n
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Medium single-qualifier floating-point vector of 4 components.

\n
See also
core_precision
\n\n

Definition at line 454 of file fwd.hpp.

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typedef float64 mediump_f64
\n
\n\n

Medium 64 bit double-qualifier floating-point scalar.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 164 of file fwd.hpp.

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\n \n \n \n \n
typedef mediump_f64mat2x2 mediump_f64mat2
\n
\n\n

Medium double-qualifier floating-point 1x1 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision Medium double-qualifier floating-point 2x2 matrix.
\n
\n to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 576 of file fwd.hpp.

\n\n
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\n \n \n \n \n
typedef mat< 2, 2, f64, mediump > mediump_f64mat2x2
\n
\n\n

Medium double-qualifier floating-point 1x1 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision Medium double-qualifier floating-point 2x2 matrix.
\n
\n to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 760 of file fwd.hpp.

\n\n
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typedef mat< 2, 3, f64, mediump > mediump_f64mat2x3
\n
\n\n

Medium double-qualifier floating-point 2x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 761 of file fwd.hpp.

\n\n
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\n\n
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typedef mat< 2, 4, f64, mediump > mediump_f64mat2x4
\n
\n\n

Medium double-qualifier floating-point 2x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 762 of file fwd.hpp.

\n\n
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typedef mediump_f64mat3x3 mediump_f64mat3
\n
\n\n

Medium double-qualifier floating-point 3x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 577 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat< 3, 2, f64, mediump > mediump_f64mat3x2
\n
\n\n

Medium double-qualifier floating-point 3x2 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 763 of file fwd.hpp.

\n\n
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\n\n
\n
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typedef mat< 3, 3, f64, mediump > mediump_f64mat3x3
\n
\n\n

Medium double-qualifier floating-point 3x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 764 of file fwd.hpp.

\n\n
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\n\n
\n
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typedef mat< 3, 4, f64, mediump > mediump_f64mat3x4
\n
\n\n

Medium double-qualifier floating-point 3x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 765 of file fwd.hpp.

\n\n
\n
\n\n
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typedef mediump_f64mat4x4 mediump_f64mat4
\n
\n\n

Medium double-qualifier floating-point 4x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 578 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat< 4, 2, f64, mediump > mediump_f64mat4x2
\n
\n\n

Medium double-qualifier floating-point 4x2 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 766 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat< 4, 3, f64, mediump > mediump_f64mat4x3
\n
\n\n

Medium double-qualifier floating-point 4x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 767 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat< 4, 4, f64, mediump > mediump_f64mat4x4
\n
\n\n

Medium double-qualifier floating-point 4x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 768 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef qua< f64, mediump > mediump_f64quat
\n
\n\n

Medium double-qualifier floating-point quaternion.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 813 of file fwd.hpp.

\n\n
\n
\n\n
\n
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typedef vec< 1, f64, mediump > mediump_f64vec1
\n
\n\n

Medium double-qualifier floating-point vector of 1 component.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 491 of file fwd.hpp.

\n\n
\n
\n\n
\n
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typedef vec< 2, f64, mediump > mediump_f64vec2
\n
\n\n

Medium double-qualifier floating-point vector of 2 components.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 492 of file fwd.hpp.

\n\n
\n
\n\n
\n
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typedef vec< 3, f64, mediump > mediump_f64vec3
\n
\n\n

Medium double-qualifier floating-point vector of 3 components.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 493 of file fwd.hpp.

\n\n
\n
\n\n
\n
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typedef vec< 4, f64, mediump > mediump_f64vec4
\n
\n\n

Medium double-qualifier floating-point vector of 4 components.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 494 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef float32 mediump_float32
\n
\n\n

Medium 32 bit single-qualifier floating-point scalar.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 153 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef float32 mediump_float32_t
\n
\n\n

Medium 32 bit single-qualifier floating-point scalar.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 158 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef float64 mediump_float64
\n
\n\n

Medium 64 bit double-qualifier floating-point scalar.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 169 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef float64 mediump_float64_t
\n
\n\n

Medium 64 bit double-qualifier floating-point scalar.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 174 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mediump_fmat2x2 mediump_fmat2
\n
\n\n

Medium single-qualifier floating-point 1x1 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision Medium single-qualifier floating-point 2x2 matrix.
\n
\n to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 528 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat< 2, 2, f32, mediump > mediump_fmat2x2
\n
\n\n

Medium single-qualifier floating-point 1x1 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision Medium single-qualifier floating-point 2x2 matrix.
\n
\n to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 640 of file fwd.hpp.

\n\n
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\n
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typedef mat< 2, 3, f32, mediump > mediump_fmat2x3
\n
\n\n

Medium single-qualifier floating-point 2x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 641 of file fwd.hpp.

\n\n
\n
\n\n
\n
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typedef mat< 2, 4, f32, mediump > mediump_fmat2x4
\n
\n\n

Medium single-qualifier floating-point 2x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 642 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mediump_fmat3x3 mediump_fmat3
\n
\n\n

Medium single-qualifier floating-point 3x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 529 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef mat< 3, 2, f32, mediump > mediump_fmat3x2
\n
\n\n

Medium single-qualifier floating-point 3x2 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 643 of file fwd.hpp.

\n\n
\n
\n\n
\n
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typedef mat< 3, 3, f32, mediump > mediump_fmat3x3
\n
\n\n

Medium single-qualifier floating-point 3x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 644 of file fwd.hpp.

\n\n
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\n\n
\n
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typedef mat< 3, 4, f32, mediump > mediump_fmat3x4
\n
\n\n

Medium single-qualifier floating-point 3x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 645 of file fwd.hpp.

\n\n
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\n\n
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typedef mediump_fmat4x4 mediump_fmat4
\n
\n\n

Medium single-qualifier floating-point 4x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 530 of file fwd.hpp.

\n\n
\n
\n\n
\n
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typedef mat< 4, 2, f32, mediump > mediump_fmat4x2
\n
\n\n

Medium single-qualifier floating-point 4x2 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 646 of file fwd.hpp.

\n\n
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\n\n
\n
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typedef mat< 4, 3, f32, mediump > mediump_fmat4x3
\n
\n\n

Medium single-qualifier floating-point 4x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 647 of file fwd.hpp.

\n\n
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\n\n
\n
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typedef mat< 4, 4, f32, mediump > mediump_fmat4x4
\n
\n\n

Medium single-qualifier floating-point 4x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 648 of file fwd.hpp.

\n\n
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typedef vec< 1, float, mediump > mediump_fvec1
\n
\n\n

Medium single-qualifier floating-point vector of 1 component.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 431 of file fwd.hpp.

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typedef vec< 2, float, mediump > mediump_fvec2
\n
\n\n

Medium Single-qualifier floating-point vector of 2 components.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 432 of file fwd.hpp.

\n\n
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typedef vec< 3, float, mediump > mediump_fvec3
\n
\n\n

Medium Single-qualifier floating-point vector of 3 components.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 433 of file fwd.hpp.

\n\n
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typedef vec< 4, float, mediump > mediump_fvec4
\n
\n\n

Medium Single-qualifier floating-point vector of 4 components.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 434 of file fwd.hpp.

\n\n
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typedef detail::int16 mediump_i16
\n
\n\n

Medium qualifier 16 bit signed integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 46 of file fwd.hpp.

\n\n
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\n
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typedef vec< 1, i16, mediump > mediump_i16vec1
\n
\n\n

Medium qualifier 16 bit signed integer scalar type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 247 of file fwd.hpp.

\n\n
\n
\n\n
\n
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typedef vec< 2, i16, mediump > mediump_i16vec2
\n
\n\n

Medium qualifier 16 bit signed integer vector of 2 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 248 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 3, i16, mediump > mediump_i16vec3
\n
\n\n

Medium qualifier 16 bit signed integer vector of 3 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 249 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 4, i16, mediump > mediump_i16vec4
\n
\n\n

Medium qualifier 16 bit signed integer vector of 4 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 250 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef detail::int32 mediump_i32
\n
\n\n

Medium qualifier 32 bit signed integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 60 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 1, i32, mediump > mediump_i32vec1
\n
\n\n

Medium qualifier 32 bit signed integer scalar type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 267 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 2, i32, mediump > mediump_i32vec2
\n
\n\n

Medium qualifier 32 bit signed integer vector of 2 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 268 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 3, i32, mediump > mediump_i32vec3
\n
\n\n

Medium qualifier 32 bit signed integer vector of 3 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 269 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 4, i32, mediump > mediump_i32vec4
\n
\n\n

Medium qualifier 32 bit signed integer vector of 4 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 270 of file fwd.hpp.

\n\n
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\n
\n \n \n \n \n
typedef detail::int64 mediump_i64
\n
\n\n

Medium qualifier 64 bit signed integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 74 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 1, i64, mediump > mediump_i64vec1
\n
\n\n

Medium qualifier 64 bit signed integer scalar type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 287 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 2, i64, mediump > mediump_i64vec2
\n
\n\n

Medium qualifier 64 bit signed integer vector of 2 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 288 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 3, i64, mediump > mediump_i64vec3
\n
\n\n

Medium qualifier 64 bit signed integer vector of 3 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 289 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 4, i64, mediump > mediump_i64vec4
\n
\n\n

Medium qualifier 64 bit signed integer vector of 4 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 290 of file fwd.hpp.

\n\n
\n
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\n \n \n \n \n
typedef detail::int8 mediump_i8
\n
\n\n

Medium qualifier 8 bit signed integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 32 of file fwd.hpp.

\n\n
\n
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\n
\n \n \n \n \n
typedef vec< 1, i8, mediump > mediump_i8vec1
\n
\n\n

Medium qualifier 8 bit signed integer scalar type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 227 of file fwd.hpp.

\n\n
\n
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\n
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typedef vec< 2, i8, mediump > mediump_i8vec2
\n
\n\n

Medium qualifier 8 bit signed integer vector of 2 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 228 of file fwd.hpp.

\n\n
\n
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\n
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typedef vec< 3, i8, mediump > mediump_i8vec3
\n
\n\n

Medium qualifier 8 bit signed integer vector of 3 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 229 of file fwd.hpp.

\n\n
\n
\n\n
\n
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typedef vec< 4, i8, mediump > mediump_i8vec4
\n
\n\n

Medium qualifier 8 bit signed integer vector of 4 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 230 of file fwd.hpp.

\n\n
\n
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\n
\n \n \n \n \n
typedef detail::int16 mediump_int16
\n
\n\n

Medium qualifier 16 bit signed integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 51 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef detail::int16 mediump_int16_t
\n
\n\n

Medium qualifier 16 bit signed integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 55 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef detail::int32 mediump_int32
\n
\n\n

Medium qualifier 32 bit signed integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 65 of file fwd.hpp.

\n\n
\n
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\n
\n \n \n \n \n
typedef detail::int32 mediump_int32_t
\n
\n\n

Medium qualifier 32 bit signed integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 69 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef detail::int64 mediump_int64
\n
\n\n

Medium qualifier 64 bit signed integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 79 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef detail::int64 mediump_int64_t
\n
\n\n

Medium qualifier 64 bit signed integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 83 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef detail::int8 mediump_int8
\n
\n\n

Medium qualifier 8 bit signed integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 37 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef detail::int8 mediump_int8_t
\n
\n\n

Medium qualifier 8 bit signed integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 41 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef detail::uint16 mediump_u16
\n
\n\n

Medium qualifier 16 bit unsigned integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 104 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 1, u16, mediump > mediump_u16vec1
\n
\n\n

Medium qualifier 16 bit unsigned integer scalar type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 349 of file fwd.hpp.

\n\n
\n
\n\n
\n
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typedef vec< 2, u16, mediump > mediump_u16vec2
\n
\n\n

Medium qualifier 16 bit unsigned integer vector of 2 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 350 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 3, u16, mediump > mediump_u16vec3
\n
\n\n

Medium qualifier 16 bit unsigned integer vector of 3 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 351 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 4, u16, mediump > mediump_u16vec4
\n
\n\n

Medium qualifier 16 bit unsigned integer vector of 4 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 352 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef detail::uint32 mediump_u32
\n
\n\n

Medium qualifier 32 bit unsigned integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 118 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 1, u32, mediump > mediump_u32vec1
\n
\n\n

Medium qualifier 32 bit unsigned integer scalar type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 369 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 2, u32, mediump > mediump_u32vec2
\n
\n\n

Medium qualifier 32 bit unsigned integer vector of 2 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 370 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 3, u32, mediump > mediump_u32vec3
\n
\n\n

Medium qualifier 32 bit unsigned integer vector of 3 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 371 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 4, u32, mediump > mediump_u32vec4
\n
\n\n

Medium qualifier 32 bit unsigned integer vector of 4 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 372 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef detail::uint64 mediump_u64
\n
\n\n

Medium qualifier 64 bit unsigned integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 132 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 1, u64, mediump > mediump_u64vec1
\n
\n\n

Medium qualifier 64 bit unsigned integer scalar type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 389 of file fwd.hpp.

\n\n
\n
\n\n
\n
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typedef vec< 2, u64, mediump > mediump_u64vec2
\n
\n\n

Medium qualifier 64 bit unsigned integer vector of 2 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 390 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 3, u64, mediump > mediump_u64vec3
\n
\n\n

Medium qualifier 64 bit unsigned integer vector of 3 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 391 of file fwd.hpp.

\n\n
\n
\n\n
\n
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typedef vec< 4, u64, mediump > mediump_u64vec4
\n
\n\n

Medium qualifier 64 bit unsigned integer vector of 4 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 392 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef detail::uint8 mediump_u8
\n
\n\n

Medium qualifier 8 bit unsigned integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 90 of file fwd.hpp.

\n\n
\n
\n\n
\n
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typedef vec< 1, u8, mediump > mediump_u8vec1
\n
\n\n

Medium qualifier 8 bit unsigned integer scalar type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 329 of file fwd.hpp.

\n\n
\n
\n\n
\n
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typedef vec< 2, u8, mediump > mediump_u8vec2
\n
\n\n

Medium qualifier 8 bit unsigned integer vector of 2 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 330 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 3, u8, mediump > mediump_u8vec3
\n
\n\n

Medium qualifier 8 bit unsigned integer vector of 3 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 331 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef vec< 4, u8, mediump > mediump_u8vec4
\n
\n\n

Medium qualifier 8 bit unsigned integer vector of 4 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 332 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef detail::uint16 mediump_uint16
\n
\n\n

Medium qualifier 16 bit unsigned integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 109 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef detail::uint16 mediump_uint16_t
\n
\n\n

Medium qualifier 16 bit unsigned integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 113 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef detail::uint32 mediump_uint32
\n
\n\n

Medium qualifier 32 bit unsigned integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 123 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef detail::uint32 mediump_uint32_t
\n
\n\n

Medium qualifier 32 bit unsigned integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 127 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef detail::uint64 mediump_uint64
\n
\n\n

Medium qualifier 64 bit unsigned integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 137 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef detail::uint64 mediump_uint64_t
\n
\n\n

Medium qualifier 64 bit unsigned integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 141 of file fwd.hpp.

\n\n
\n
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\n
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typedef detail::uint8 mediump_uint8
\n
\n\n

Medium qualifier 8 bit unsigned integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 95 of file fwd.hpp.

\n\n
\n
\n\n
\n
\n \n \n \n \n
typedef detail::uint8 mediump_uint8_t
\n
\n\n

Medium qualifier 8 bit unsigned integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 99 of file fwd.hpp.

\n\n
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\n
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typedef detail::uint16 u16
\n
\n\n

Default qualifier 16 bit unsigned integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 106 of file fwd.hpp.

\n\n
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\n
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typedef vec< 1, u16, defaultp > u16vec1
\n
\n\n

Default qualifier 16 bit unsigned integer scalar type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 359 of file fwd.hpp.

\n\n
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typedef vec< 2, u16, defaultp > u16vec2
\n
\n\n

Default qualifier 16 bit unsigned integer vector of 2 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 360 of file fwd.hpp.

\n\n
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\n
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typedef vec< 3, u16, defaultp > u16vec3
\n
\n\n

Default qualifier 16 bit unsigned integer vector of 3 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 361 of file fwd.hpp.

\n\n
\n
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\n
\n \n \n \n \n
typedef vec< 4, u16, defaultp > u16vec4
\n
\n\n

Default qualifier 16 bit unsigned integer vector of 4 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 362 of file fwd.hpp.

\n\n
\n
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\n
\n \n \n \n \n
typedef detail::uint32 u32
\n
\n\n

Default qualifier 32 bit unsigned integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 120 of file fwd.hpp.

\n\n
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\n
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typedef vec< 1, u32, defaultp > u32vec1
\n
\n\n

Default qualifier 32 bit unsigned integer scalar type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 379 of file fwd.hpp.

\n\n
\n
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\n
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typedef vec< 2, u32, defaultp > u32vec2
\n
\n\n

Default qualifier 32 bit unsigned integer vector of 2 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 380 of file fwd.hpp.

\n\n
\n
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typedef vec< 3, u32, defaultp > u32vec3
\n
\n\n

Default qualifier 32 bit unsigned integer vector of 3 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 381 of file fwd.hpp.

\n\n
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\n
\n \n \n \n \n
typedef vec< 4, u32, defaultp > u32vec4
\n
\n\n

Default qualifier 32 bit unsigned integer vector of 4 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 382 of file fwd.hpp.

\n\n
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\n
\n \n \n \n \n
typedef detail::uint64 u64
\n
\n\n

Default qualifier 64 bit unsigned integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 134 of file fwd.hpp.

\n\n
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\n
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typedef vec< 1, u64, defaultp > u64vec1
\n
\n\n

Default qualifier 64 bit unsigned integer scalar type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 399 of file fwd.hpp.

\n\n
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typedef vec< 2, u64, defaultp > u64vec2
\n
\n\n

Default qualifier 64 bit unsigned integer vector of 2 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 400 of file fwd.hpp.

\n\n
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\n
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typedef vec< 3, u64, defaultp > u64vec3
\n
\n\n

Default qualifier 64 bit unsigned integer vector of 3 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 401 of file fwd.hpp.

\n\n
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typedef vec< 4, u64, defaultp > u64vec4
\n
\n\n

Default qualifier 64 bit unsigned integer vector of 4 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 402 of file fwd.hpp.

\n\n
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typedef detail::uint8 u8
\n
\n\n

Default qualifier 8 bit unsigned integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 92 of file fwd.hpp.

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typedef vec< 1, u8, defaultp > u8vec1
\n
\n\n

Default qualifier 8 bit unsigned integer scalar type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 339 of file fwd.hpp.

\n\n
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typedef vec< 2, u8, defaultp > u8vec2
\n
\n\n

Default qualifier 8 bit unsigned integer vector of 2 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 340 of file fwd.hpp.

\n\n
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typedef vec< 3, u8, defaultp > u8vec3
\n
\n\n

Default qualifier 8 bit unsigned integer vector of 3 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 341 of file fwd.hpp.

\n\n
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\n\n
\n
\n \n \n \n \n
typedef vec< 4, u8, defaultp > u8vec4
\n
\n\n

Default qualifier 8 bit unsigned integer vector of 4 components type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 342 of file fwd.hpp.

\n\n
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\n
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typedef detail::uint16 uint16_t
\n
\n\n

Default qualifier 16 bit unsigned integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 115 of file fwd.hpp.

\n\n
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typedef detail::uint32 uint32_t
\n
\n\n

Default qualifier 32 bit unsigned integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 129 of file fwd.hpp.

\n\n
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typedef detail::uint64 uint64_t
\n
\n\n

Default qualifier 64 bit unsigned integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 143 of file fwd.hpp.

\n\n
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\n
\n \n \n \n \n
typedef detail::uint8 uint8_t
\n
\n\n

Default qualifier 8 bit unsigned integer type.

\n
See also
to use the features of this extension. \">GLM_GTC_type_precision
\n\n

Definition at line 101 of file fwd.hpp.

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\nGenerated by  \n\"doxygen\"/\n 1.8.10\n
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0.9.9 API documentation\n
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\nFunctions
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GLM_GTC_type_ptr
Recommended extensions
\n
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Include <glm/gtc/type_ptr.hpp> to use the features of this extension. \nMore...

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

\nFunctions

template<typename T >
GLM_FUNC_DECL mat< 2, 2, T, defaultp > make_mat2 (T const *const ptr)
 Build a matrix from a pointer. More...
 
template<typename T >
GLM_FUNC_DECL mat< 2, 2, T, defaultp > make_mat2x2 (T const *const ptr)
 Build a matrix from a pointer. More...
 
template<typename T >
GLM_FUNC_DECL mat< 2, 3, T, defaultp > make_mat2x3 (T const *const ptr)
 Build a matrix from a pointer. More...
 
template<typename T >
GLM_FUNC_DECL mat< 2, 4, T, defaultp > make_mat2x4 (T const *const ptr)
 Build a matrix from a pointer. More...
 
template<typename T >
GLM_FUNC_DECL mat< 3, 3, T, defaultp > make_mat3 (T const *const ptr)
 Build a matrix from a pointer. More...
 
template<typename T >
GLM_FUNC_DECL mat< 3, 2, T, defaultp > make_mat3x2 (T const *const ptr)
 Build a matrix from a pointer. More...
 
template<typename T >
GLM_FUNC_DECL mat< 3, 3, T, defaultp > make_mat3x3 (T const *const ptr)
 Build a matrix from a pointer. More...
 
template<typename T >
GLM_FUNC_DECL mat< 3, 4, T, defaultp > make_mat3x4 (T const *const ptr)
 Build a matrix from a pointer. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > make_mat4 (T const *const ptr)
 Build a matrix from a pointer. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 2, T, defaultp > make_mat4x2 (T const *const ptr)
 Build a matrix from a pointer. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 3, T, defaultp > make_mat4x3 (T const *const ptr)
 Build a matrix from a pointer. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > make_mat4x4 (T const *const ptr)
 Build a matrix from a pointer. More...
 
template<typename T >
GLM_FUNC_DECL qua< T, defaultp > make_quat (T const *const ptr)
 Build a quaternion from a pointer. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 1, T, Q > make_vec1 (vec< 1, T, Q > const &v)
 Build a vector from a pointer. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 1, T, Q > make_vec1 (vec< 2, T, Q > const &v)
 Build a vector from a pointer. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 1, T, Q > make_vec1 (vec< 3, T, Q > const &v)
 Build a vector from a pointer. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 1, T, Q > make_vec1 (vec< 4, T, Q > const &v)
 Build a vector from a pointer. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 2, T, Q > make_vec2 (vec< 1, T, Q > const &v)
 Build a vector from a pointer. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 2, T, Q > make_vec2 (vec< 2, T, Q > const &v)
 Build a vector from a pointer. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 2, T, Q > make_vec2 (vec< 3, T, Q > const &v)
 Build a vector from a pointer. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 2, T, Q > make_vec2 (vec< 4, T, Q > const &v)
 Build a vector from a pointer. More...
 
template<typename T >
GLM_FUNC_DECL vec< 2, T, defaultp > make_vec2 (T const *const ptr)
 Build a vector from a pointer. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > make_vec3 (vec< 1, T, Q > const &v)
 Build a vector from a pointer. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > make_vec3 (vec< 2, T, Q > const &v)
 Build a vector from a pointer. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > make_vec3 (vec< 3, T, Q > const &v)
 Build a vector from a pointer. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > make_vec3 (vec< 4, T, Q > const &v)
 Build a vector from a pointer. More...
 
template<typename T >
GLM_FUNC_DECL vec< 3, T, defaultp > make_vec3 (T const *const ptr)
 Build a vector from a pointer. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 4, T, Q > make_vec4 (vec< 1, T, Q > const &v)
 Build a vector from a pointer. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 4, T, Q > make_vec4 (vec< 2, T, Q > const &v)
 Build a vector from a pointer. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 4, T, Q > make_vec4 (vec< 3, T, Q > const &v)
 Build a vector from a pointer. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 4, T, Q > make_vec4 (vec< 4, T, Q > const &v)
 Build a vector from a pointer. More...
 
template<typename T >
GLM_FUNC_DECL vec< 4, T, defaultp > make_vec4 (T const *const ptr)
 Build a vector from a pointer. More...
 
template<typename genType >
GLM_FUNC_DECL genType::value_type const * value_ptr (genType const &v)
 Return the constant address to the data of the input parameter. More...
 
\n

Detailed Description

\n

Include <glm/gtc/type_ptr.hpp> to use the features of this extension.

\n

Handles the interaction between pointers and vector, matrix types.

\n

This extension defines an overloaded function, glm::value_ptr. It returns a pointer to the memory layout of the object. Matrix types store their values in column-major order.

\n

This is useful for uploading data to matrices or copying data to buffer objects.

\n

Example:

#include <glm/glm.hpp>
\n
#include <glm/gtc/type_ptr.hpp>
\n
\n
glm::vec3 aVector(3);
\n
glm::mat4 someMatrix(1.0);
\n
\n
glUniform3fv(uniformLoc, 1, glm::value_ptr(aVector));
\n
glUniformMatrix4fv(uniformMatrixLoc, 1, GL_FALSE, glm::value_ptr(someMatrix));
\n

<glm/gtc/type_ptr.hpp> need to be included to use the features of this extension.

\n

Function Documentation

\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL mat<2, 2, T, defaultp> glm::make_mat2 (T const *const ptr)
\n
\n\n

Build a matrix from a pointer.

\n
See also
to use the features of this extension. \">GLM_GTC_type_ptr
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GLM_FUNC_DECL mat<2, 2, T, defaultp> glm::make_mat2x2 (T const *const ptr)
\n
\n\n

Build a matrix from a pointer.

\n
See also
to use the features of this extension. \">GLM_GTC_type_ptr
\n\n
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GLM_FUNC_DECL mat<2, 3, T, defaultp> glm::make_mat2x3 (T const *const ptr)
\n
\n\n

Build a matrix from a pointer.

\n
See also
to use the features of this extension. \">GLM_GTC_type_ptr
\n\n
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GLM_FUNC_DECL mat<2, 4, T, defaultp> glm::make_mat2x4 (T const *const ptr)
\n
\n\n

Build a matrix from a pointer.

\n
See also
to use the features of this extension. \">GLM_GTC_type_ptr
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GLM_FUNC_DECL mat<3, 3, T, defaultp> glm::make_mat3 (T const *const ptr)
\n
\n\n

Build a matrix from a pointer.

\n
See also
to use the features of this extension. \">GLM_GTC_type_ptr
\n\n
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GLM_FUNC_DECL mat<3, 2, T, defaultp> glm::make_mat3x2 (T const *const ptr)
\n
\n\n

Build a matrix from a pointer.

\n
See also
to use the features of this extension. \">GLM_GTC_type_ptr
\n\n
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\n\n
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\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL mat<3, 3, T, defaultp> glm::make_mat3x3 (T const *const ptr)
\n
\n\n

Build a matrix from a pointer.

\n
See also
to use the features of this extension. \">GLM_GTC_type_ptr
\n\n
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GLM_FUNC_DECL mat<3, 4, T, defaultp> glm::make_mat3x4 (T const *const ptr)
\n
\n\n

Build a matrix from a pointer.

\n
See also
to use the features of this extension. \">GLM_GTC_type_ptr
\n\n
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GLM_FUNC_DECL mat<4, 4, T, defaultp> glm::make_mat4 (T const *const ptr)
\n
\n\n

Build a matrix from a pointer.

\n
See also
to use the features of this extension. \">GLM_GTC_type_ptr
\n\n
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GLM_FUNC_DECL mat<4, 2, T, defaultp> glm::make_mat4x2 (T const *const ptr)
\n
\n\n

Build a matrix from a pointer.

\n
See also
to use the features of this extension. \">GLM_GTC_type_ptr
\n\n
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GLM_FUNC_DECL mat<4, 3, T, defaultp> glm::make_mat4x3 (T const *const ptr)
\n
\n\n

Build a matrix from a pointer.

\n
See also
to use the features of this extension. \">GLM_GTC_type_ptr
\n\n
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GLM_FUNC_DECL mat<4, 4, T, defaultp> glm::make_mat4x4 (T const *const ptr)
\n
\n\n

Build a matrix from a pointer.

\n
See also
to use the features of this extension. \">GLM_GTC_type_ptr
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GLM_FUNC_DECL qua<T, defaultp> glm::make_quat (T const *const ptr)
\n
\n\n

Build a quaternion from a pointer.

\n
See also
to use the features of this extension. \">GLM_GTC_type_ptr
\n\n
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GLM_FUNC_DECL vec<1, T, Q> glm::make_vec1 (vec< 1, T, Q > const & v)
\n
\n\n

Build a vector from a pointer.

\n
See also
to use the features of this extension. \">GLM_GTC_type_ptr
\n\n
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GLM_FUNC_DECL vec<1, T, Q> glm::make_vec1 (vec< 2, T, Q > const & v)
\n
\n\n

Build a vector from a pointer.

\n
See also
to use the features of this extension. \">GLM_GTC_type_ptr
\n\n
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GLM_FUNC_DECL vec<1, T, Q> glm::make_vec1 (vec< 3, T, Q > const & v)
\n
\n\n

Build a vector from a pointer.

\n
See also
to use the features of this extension. \">GLM_GTC_type_ptr
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GLM_FUNC_DECL vec<1, T, Q> glm::make_vec1 (vec< 4, T, Q > const & v)
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\n\n

Build a vector from a pointer.

\n
See also
to use the features of this extension. \">GLM_GTC_type_ptr
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GLM_FUNC_DECL vec<2, T, Q> glm::make_vec2 (vec< 1, T, Q > const & v)
\n
\n\n

Build a vector from a pointer.

\n
See also
to use the features of this extension. \">GLM_GTC_type_ptr
\n\n
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GLM_FUNC_DECL vec<2, T, Q> glm::make_vec2 (vec< 2, T, Q > const & v)
\n
\n\n

Build a vector from a pointer.

\n
See also
to use the features of this extension. \">GLM_GTC_type_ptr
\n\n
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GLM_FUNC_DECL vec<2, T, Q> glm::make_vec2 (vec< 3, T, Q > const & v)
\n
\n\n

Build a vector from a pointer.

\n
See also
to use the features of this extension. \">GLM_GTC_type_ptr
\n\n
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GLM_FUNC_DECL vec<2, T, Q> glm::make_vec2 (vec< 4, T, Q > const & v)
\n
\n\n

Build a vector from a pointer.

\n
See also
to use the features of this extension. \">GLM_GTC_type_ptr
\n\n
\n
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\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL vec<2, T, defaultp> glm::make_vec2 (T const *const ptr)
\n
\n\n

Build a vector from a pointer.

\n
See also
to use the features of this extension. \">GLM_GTC_type_ptr
\n\n
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GLM_FUNC_DECL vec<3, T, Q> glm::make_vec3 (vec< 1, T, Q > const & v)
\n
\n\n

Build a vector from a pointer.

\n
See also
to use the features of this extension. \">GLM_GTC_type_ptr
\n\n
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GLM_FUNC_DECL vec<3, T, Q> glm::make_vec3 (vec< 2, T, Q > const & v)
\n
\n\n

Build a vector from a pointer.

\n
See also
to use the features of this extension. \">GLM_GTC_type_ptr
\n\n
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GLM_FUNC_DECL vec<3, T, Q> glm::make_vec3 (vec< 3, T, Q > const & v)
\n
\n\n

Build a vector from a pointer.

\n
See also
to use the features of this extension. \">GLM_GTC_type_ptr
\n\n
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GLM_FUNC_DECL vec<3, T, Q> glm::make_vec3 (vec< 4, T, Q > const & v)
\n
\n\n

Build a vector from a pointer.

\n
See also
to use the features of this extension. \">GLM_GTC_type_ptr
\n\n
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GLM_FUNC_DECL vec<3, T, defaultp> glm::make_vec3 (T const *const ptr)
\n
\n\n

Build a vector from a pointer.

\n
See also
to use the features of this extension. \">GLM_GTC_type_ptr
\n\n
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GLM_FUNC_DECL vec<4, T, Q> glm::make_vec4 (vec< 1, T, Q > const & v)
\n
\n\n

Build a vector from a pointer.

\n
See also
to use the features of this extension. \">GLM_GTC_type_ptr
\n\n
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GLM_FUNC_DECL vec<4, T, Q> glm::make_vec4 (vec< 2, T, Q > const & v)
\n
\n\n

Build a vector from a pointer.

\n
See also
to use the features of this extension. \">GLM_GTC_type_ptr
\n\n
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GLM_FUNC_DECL vec<4, T, Q> glm::make_vec4 (vec< 3, T, Q > const & v)
\n
\n\n

Build a vector from a pointer.

\n
See also
to use the features of this extension. \">GLM_GTC_type_ptr
\n\n
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\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL vec<4, T, Q> glm::make_vec4 (vec< 4, T, Q > const & v)
\n
\n\n

Build a vector from a pointer.

\n
See also
to use the features of this extension. \">GLM_GTC_type_ptr
\n\n
\n
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\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL vec<4, T, defaultp> glm::make_vec4 (T const *const ptr)
\n
\n\n

Build a vector from a pointer.

\n
See also
to use the features of this extension. \">GLM_GTC_type_ptr
\n\n
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GLM_FUNC_DECL genType::value_type const* glm::value_ptr (genType const & v)
\n
\n\n

Return the constant address to the data of the input parameter.

\n
See also
to use the features of this extension. \">GLM_GTC_type_ptr
\n\n
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\nGenerated by  \n\"doxygen\"/\n 1.8.10\n
\n\n\n" }, { "path": "field_construction/submodules/diff-langsurf-rasterizer/third_party/glm/doc/api/a00306.html", "content": "\n\n\n\n\n\n0.9.9 API documentation: GLM_GTC_ulp\n\n\n\n\n\n\n\n\n\n\n
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0.9.9 API documentation\n
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GLM_GTC_ulp
Recommended extensions
\n
\n
\n\n

Include <glm/gtc/ulp.hpp> to use the features of this extension. \nMore...

\n

Include <glm/gtc/ulp.hpp> to use the features of this extension.

\n

Allow the measurement of the accuracy of a function against a reference implementation. This extension works on floating-point data and provide results in ULP.

\n
\n\n
\nGenerated by  \n\"doxygen\"/\n 1.8.10\n
\n\n\n" }, { "path": "field_construction/submodules/diff-langsurf-rasterizer/third_party/glm/doc/api/a00307.html", "content": "\n\n\n\n\n\n0.9.9 API documentation: GLM_GTC_vec1\n\n\n\n\n\n\n\n\n\n\n
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0.9.9 API documentation\n
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GLM_GTC_vec1
Recommended extensions
\n
\n
\n\n

Include <glm/gtc/vec1.hpp> to use the features of this extension. \nMore...

\n

Include <glm/gtc/vec1.hpp> to use the features of this extension.

\n

Add vec1, ivec1, uvec1 and bvec1 types.

\n
\n\n
\nGenerated by  \n\"doxygen\"/\n 1.8.10\n
\n\n\n" }, { "path": "field_construction/submodules/diff-langsurf-rasterizer/third_party/glm/doc/api/a00308.html", "content": "\n\n\n\n\n\n0.9.9 API documentation: GLM_GTX_associated_min_max\n\n\n\n\n\n\n\n\n\n\n
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0.9.9 API documentation\n
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GLM_GTX_associated_min_max
Experimental extensions
\n
\n
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Include <glm/gtx/associated_min_max.hpp> to use the features of this extension. \nMore...

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\nFunctions

template<typename T , typename U >
GLM_FUNC_DECL U associatedMax (T x, U a, T y, U b)
 Maximum comparison between 2 variables and returns 2 associated variable values. More...
 
template<length_t L, typename T , typename U , qualifier Q>
GLM_FUNC_DECL vec< 2, U, Q > associatedMax (vec< L, T, Q > const &x, vec< L, U, Q > const &a, vec< L, T, Q > const &y, vec< L, U, Q > const &b)
 Maximum comparison between 2 variables and returns 2 associated variable values. More...
 
template<length_t L, typename T , typename U , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > associatedMax (T x, vec< L, U, Q > const &a, T y, vec< L, U, Q > const &b)
 Maximum comparison between 2 variables and returns 2 associated variable values. More...
 
template<length_t L, typename T , typename U , qualifier Q>
GLM_FUNC_DECL vec< L, U, Q > associatedMax (vec< L, T, Q > const &x, U a, vec< L, T, Q > const &y, U b)
 Maximum comparison between 2 variables and returns 2 associated variable values. More...
 
template<typename T , typename U >
GLM_FUNC_DECL U associatedMax (T x, U a, T y, U b, T z, U c)
 Maximum comparison between 3 variables and returns 3 associated variable values. More...
 
template<length_t L, typename T , typename U , qualifier Q>
GLM_FUNC_DECL vec< L, U, Q > associatedMax (vec< L, T, Q > const &x, vec< L, U, Q > const &a, vec< L, T, Q > const &y, vec< L, U, Q > const &b, vec< L, T, Q > const &z, vec< L, U, Q > const &c)
 Maximum comparison between 3 variables and returns 3 associated variable values. More...
 
template<length_t L, typename T , typename U , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > associatedMax (T x, vec< L, U, Q > const &a, T y, vec< L, U, Q > const &b, T z, vec< L, U, Q > const &c)
 Maximum comparison between 3 variables and returns 3 associated variable values. More...
 
template<length_t L, typename T , typename U , qualifier Q>
GLM_FUNC_DECL vec< L, U, Q > associatedMax (vec< L, T, Q > const &x, U a, vec< L, T, Q > const &y, U b, vec< L, T, Q > const &z, U c)
 Maximum comparison between 3 variables and returns 3 associated variable values. More...
 
template<typename T , typename U >
GLM_FUNC_DECL U associatedMax (T x, U a, T y, U b, T z, U c, T w, U d)
 Maximum comparison between 4 variables and returns 4 associated variable values. More...
 
template<length_t L, typename T , typename U , qualifier Q>
GLM_FUNC_DECL vec< L, U, Q > associatedMax (vec< L, T, Q > const &x, vec< L, U, Q > const &a, vec< L, T, Q > const &y, vec< L, U, Q > const &b, vec< L, T, Q > const &z, vec< L, U, Q > const &c, vec< L, T, Q > const &w, vec< L, U, Q > const &d)
 Maximum comparison between 4 variables and returns 4 associated variable values. More...
 
template<length_t L, typename T , typename U , qualifier Q>
GLM_FUNC_DECL vec< L, U, Q > associatedMax (T x, vec< L, U, Q > const &a, T y, vec< L, U, Q > const &b, T z, vec< L, U, Q > const &c, T w, vec< L, U, Q > const &d)
 Maximum comparison between 4 variables and returns 4 associated variable values. More...
 
template<length_t L, typename T , typename U , qualifier Q>
GLM_FUNC_DECL vec< L, U, Q > associatedMax (vec< L, T, Q > const &x, U a, vec< L, T, Q > const &y, U b, vec< L, T, Q > const &z, U c, vec< L, T, Q > const &w, U d)
 Maximum comparison between 4 variables and returns 4 associated variable values. More...
 
template<typename T , typename U , qualifier Q>
GLM_FUNC_DECL U associatedMin (T x, U a, T y, U b)
 Minimum comparison between 2 variables and returns 2 associated variable values. More...
 
template<length_t L, typename T , typename U , qualifier Q>
GLM_FUNC_DECL vec< 2, U, Q > associatedMin (vec< L, T, Q > const &x, vec< L, U, Q > const &a, vec< L, T, Q > const &y, vec< L, U, Q > const &b)
 Minimum comparison between 2 variables and returns 2 associated variable values. More...
 
template<length_t L, typename T , typename U , qualifier Q>
GLM_FUNC_DECL vec< L, U, Q > associatedMin (T x, const vec< L, U, Q > &a, T y, const vec< L, U, Q > &b)
 Minimum comparison between 2 variables and returns 2 associated variable values. More...
 
template<length_t L, typename T , typename U , qualifier Q>
GLM_FUNC_DECL vec< L, U, Q > associatedMin (vec< L, T, Q > const &x, U a, vec< L, T, Q > const &y, U b)
 Minimum comparison between 2 variables and returns 2 associated variable values. More...
 
template<typename T , typename U >
GLM_FUNC_DECL U associatedMin (T x, U a, T y, U b, T z, U c)
 Minimum comparison between 3 variables and returns 3 associated variable values. More...
 
template<length_t L, typename T , typename U , qualifier Q>
GLM_FUNC_DECL vec< L, U, Q > associatedMin (vec< L, T, Q > const &x, vec< L, U, Q > const &a, vec< L, T, Q > const &y, vec< L, U, Q > const &b, vec< L, T, Q > const &z, vec< L, U, Q > const &c)
 Minimum comparison between 3 variables and returns 3 associated variable values. More...
 
template<typename T , typename U >
GLM_FUNC_DECL U associatedMin (T x, U a, T y, U b, T z, U c, T w, U d)
 Minimum comparison between 4 variables and returns 4 associated variable values. More...
 
template<length_t L, typename T , typename U , qualifier Q>
GLM_FUNC_DECL vec< L, U, Q > associatedMin (vec< L, T, Q > const &x, vec< L, U, Q > const &a, vec< L, T, Q > const &y, vec< L, U, Q > const &b, vec< L, T, Q > const &z, vec< L, U, Q > const &c, vec< L, T, Q > const &w, vec< L, U, Q > const &d)
 Minimum comparison between 4 variables and returns 4 associated variable values. More...
 
template<length_t L, typename T , typename U , qualifier Q>
GLM_FUNC_DECL vec< L, U, Q > associatedMin (T x, vec< L, U, Q > const &a, T y, vec< L, U, Q > const &b, T z, vec< L, U, Q > const &c, T w, vec< L, U, Q > const &d)
 Minimum comparison between 4 variables and returns 4 associated variable values. More...
 
template<length_t L, typename T , typename U , qualifier Q>
GLM_FUNC_DECL vec< L, U, Q > associatedMin (vec< L, T, Q > const &x, U a, vec< L, T, Q > const &y, U b, vec< L, T, Q > const &z, U c, vec< L, T, Q > const &w, U d)
 Minimum comparison between 4 variables and returns 4 associated variable values. More...
 
\n

Detailed Description

\n

Include <glm/gtx/associated_min_max.hpp> to use the features of this extension.

\n

Min and max functions that return associated values not the compared onces.

\n

Function Documentation

\n\n
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GLM_FUNC_DECL U glm::associatedMax (T x,
U a,
T y,
U b 
)
\n
\n\n

Maximum comparison between 2 variables and returns 2 associated variable values.

\n
See also
to use the features of this extension. \">GLM_GTX_associated_min_max
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GLM_FUNC_DECL vec<2, U, Q> glm::associatedMax (vec< L, T, Q > const & x,
vec< L, U, Q > const & a,
vec< L, T, Q > const & y,
vec< L, U, Q > const & b 
)
\n
\n\n

Maximum comparison between 2 variables and returns 2 associated variable values.

\n
See also
to use the features of this extension. \">GLM_GTX_associated_min_max
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GLM_FUNC_DECL vec<L, T, Q> glm::associatedMax (T x,
vec< L, U, Q > const & a,
T y,
vec< L, U, Q > const & b 
)
\n
\n\n

Maximum comparison between 2 variables and returns 2 associated variable values.

\n
See also
to use the features of this extension. \">GLM_GTX_associated_min_max
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GLM_FUNC_DECL vec<L, U, Q> glm::associatedMax (vec< L, T, Q > const & x,
U a,
vec< L, T, Q > const & y,
U b 
)
\n
\n\n

Maximum comparison between 2 variables and returns 2 associated variable values.

\n
See also
to use the features of this extension. \">GLM_GTX_associated_min_max
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GLM_FUNC_DECL U glm::associatedMax (T x,
U a,
T y,
U b,
T z,
U c 
)
\n
\n\n

Maximum comparison between 3 variables and returns 3 associated variable values.

\n
See also
to use the features of this extension. \">GLM_GTX_associated_min_max
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GLM_FUNC_DECL vec<L, U, Q> glm::associatedMax (vec< L, T, Q > const & x,
vec< L, U, Q > const & a,
vec< L, T, Q > const & y,
vec< L, U, Q > const & b,
vec< L, T, Q > const & z,
vec< L, U, Q > const & c 
)
\n
\n\n

Maximum comparison between 3 variables and returns 3 associated variable values.

\n
See also
to use the features of this extension. \">GLM_GTX_associated_min_max
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GLM_FUNC_DECL vec<L, T, Q> glm::associatedMax (T x,
vec< L, U, Q > const & a,
T y,
vec< L, U, Q > const & b,
T z,
vec< L, U, Q > const & c 
)
\n
\n\n

Maximum comparison between 3 variables and returns 3 associated variable values.

\n
See also
to use the features of this extension. \">GLM_GTX_associated_min_max
\n\n
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GLM_FUNC_DECL vec<L, U, Q> glm::associatedMax (vec< L, T, Q > const & x,
U a,
vec< L, T, Q > const & y,
U b,
vec< L, T, Q > const & z,
U c 
)
\n
\n\n

Maximum comparison between 3 variables and returns 3 associated variable values.

\n
See also
to use the features of this extension. \">GLM_GTX_associated_min_max
\n\n
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GLM_FUNC_DECL U glm::associatedMax (T x,
U a,
T y,
U b,
T z,
U c,
T w,
U d 
)
\n
\n\n

Maximum comparison between 4 variables and returns 4 associated variable values.

\n
See also
to use the features of this extension. \">GLM_GTX_associated_min_max
\n\n
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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL vec<L, U, Q> glm::associatedMax (vec< L, T, Q > const & x,
vec< L, U, Q > const & a,
vec< L, T, Q > const & y,
vec< L, U, Q > const & b,
vec< L, T, Q > const & z,
vec< L, U, Q > const & c,
vec< L, T, Q > const & w,
vec< L, U, Q > const & d 
)
\n
\n\n

Maximum comparison between 4 variables and returns 4 associated variable values.

\n
See also
to use the features of this extension. \">GLM_GTX_associated_min_max
\n\n
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GLM_FUNC_DECL vec<L, U, Q> glm::associatedMax (T x,
vec< L, U, Q > const & a,
T y,
vec< L, U, Q > const & b,
T z,
vec< L, U, Q > const & c,
T w,
vec< L, U, Q > const & d 
)
\n
\n\n

Maximum comparison between 4 variables and returns 4 associated variable values.

\n
See also
to use the features of this extension. \">GLM_GTX_associated_min_max
\n\n
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\n
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GLM_FUNC_DECL vec<L, U, Q> glm::associatedMax (vec< L, T, Q > const & x,
U a,
vec< L, T, Q > const & y,
U b,
vec< L, T, Q > const & z,
U c,
vec< L, T, Q > const & w,
U d 
)
\n
\n\n

Maximum comparison between 4 variables and returns 4 associated variable values.

\n
See also
to use the features of this extension. \">GLM_GTX_associated_min_max
\n\n
\n
\n\n
\n
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GLM_FUNC_DECL U glm::associatedMin (T x,
U a,
T y,
U b 
)
\n
\n\n

Minimum comparison between 2 variables and returns 2 associated variable values.

\n
See also
to use the features of this extension. \">GLM_GTX_associated_min_max
\n\n
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\n
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GLM_FUNC_DECL vec<2, U, Q> glm::associatedMin (vec< L, T, Q > const & x,
vec< L, U, Q > const & a,
vec< L, T, Q > const & y,
vec< L, U, Q > const & b 
)
\n
\n\n

Minimum comparison between 2 variables and returns 2 associated variable values.

\n
See also
to use the features of this extension. \">GLM_GTX_associated_min_max
\n\n
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GLM_FUNC_DECL vec<L, U, Q> glm::associatedMin (T x,
const vec< L, U, Q > & a,
T y,
const vec< L, U, Q > & b 
)
\n
\n\n

Minimum comparison between 2 variables and returns 2 associated variable values.

\n
See also
to use the features of this extension. \">GLM_GTX_associated_min_max
\n\n
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GLM_FUNC_DECL vec<L, U, Q> glm::associatedMin (vec< L, T, Q > const & x,
U a,
vec< L, T, Q > const & y,
U b 
)
\n
\n\n

Minimum comparison between 2 variables and returns 2 associated variable values.

\n
See also
to use the features of this extension. \">GLM_GTX_associated_min_max
\n\n
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GLM_FUNC_DECL U glm::associatedMin (T x,
U a,
T y,
U b,
T z,
U c 
)
\n
\n\n

Minimum comparison between 3 variables and returns 3 associated variable values.

\n
See also
to use the features of this extension. \">GLM_GTX_associated_min_max
\n\n
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GLM_FUNC_DECL vec<L, U, Q> glm::associatedMin (vec< L, T, Q > const & x,
vec< L, U, Q > const & a,
vec< L, T, Q > const & y,
vec< L, U, Q > const & b,
vec< L, T, Q > const & z,
vec< L, U, Q > const & c 
)
\n
\n\n

Minimum comparison between 3 variables and returns 3 associated variable values.

\n
See also
to use the features of this extension. \">GLM_GTX_associated_min_max
\n\n
\n
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\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL U glm::associatedMin (T x,
U a,
T y,
U b,
T z,
U c,
T w,
U d 
)
\n
\n\n

Minimum comparison between 4 variables and returns 4 associated variable values.

\n
See also
to use the features of this extension. \">GLM_GTX_associated_min_max
\n\n
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GLM_FUNC_DECL vec<L, U, Q> glm::associatedMin (vec< L, T, Q > const & x,
vec< L, U, Q > const & a,
vec< L, T, Q > const & y,
vec< L, U, Q > const & b,
vec< L, T, Q > const & z,
vec< L, U, Q > const & c,
vec< L, T, Q > const & w,
vec< L, U, Q > const & d 
)
\n
\n\n

Minimum comparison between 4 variables and returns 4 associated variable values.

\n
See also
to use the features of this extension. \">GLM_GTX_associated_min_max
\n\n
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GLM_FUNC_DECL vec<L, U, Q> glm::associatedMin (T x,
vec< L, U, Q > const & a,
T y,
vec< L, U, Q > const & b,
T z,
vec< L, U, Q > const & c,
T w,
vec< L, U, Q > const & d 
)
\n
\n\n

Minimum comparison between 4 variables and returns 4 associated variable values.

\n
See also
to use the features of this extension. \">GLM_GTX_associated_min_max
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GLM_FUNC_DECL vec<L, U, Q> glm::associatedMin (vec< L, T, Q > const & x,
U a,
vec< L, T, Q > const & y,
U b,
vec< L, T, Q > const & z,
U c,
vec< L, T, Q > const & w,
U d 
)
\n
\n\n

Minimum comparison between 4 variables and returns 4 associated variable values.

\n
See also
to use the features of this extension. \">GLM_GTX_associated_min_max
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\nFunctions
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GLM_GTX_bit
Experimental extensions
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Include <glm/gtx/bit.hpp> to use the features of this extension. \nMore...

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\nFunctions

template<typename genIUType >
GLM_FUNC_DECL genIUType highestBitValue (genIUType Value)
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > highestBitValue (vec< L, T, Q > const &value)
 Find the highest bit set to 1 in a integer variable and return its value. More...
 
template<typename genIUType >
GLM_FUNC_DECL genIUType lowestBitValue (genIUType Value)
 
template<typename genIUType >
GLM_DEPRECATED GLM_FUNC_DECL genIUType powerOfTwoAbove (genIUType Value)
 Return the power of two number which value is just higher the input value. More...
 
template<length_t L, typename T , qualifier Q>
GLM_DEPRECATED GLM_FUNC_DECL vec< L, T, Q > powerOfTwoAbove (vec< L, T, Q > const &value)
 Return the power of two number which value is just higher the input value. More...
 
template<typename genIUType >
GLM_DEPRECATED GLM_FUNC_DECL genIUType powerOfTwoBelow (genIUType Value)
 Return the power of two number which value is just lower the input value. More...
 
template<length_t L, typename T , qualifier Q>
GLM_DEPRECATED GLM_FUNC_DECL vec< L, T, Q > powerOfTwoBelow (vec< L, T, Q > const &value)
 Return the power of two number which value is just lower the input value. More...
 
template<typename genIUType >
GLM_DEPRECATED GLM_FUNC_DECL genIUType powerOfTwoNearest (genIUType Value)
 Return the power of two number which value is the closet to the input value. More...
 
template<length_t L, typename T , qualifier Q>
GLM_DEPRECATED GLM_FUNC_DECL vec< L, T, Q > powerOfTwoNearest (vec< L, T, Q > const &value)
 Return the power of two number which value is the closet to the input value. More...
 
\n

Detailed Description

\n

Include <glm/gtx/bit.hpp> to use the features of this extension.

\n

Allow to perform bit operations on integer values

\n

Function Documentation

\n\n
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\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL genIUType glm::highestBitValue (genIUType Value)
\n
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See also
to use the features of this extension. \">GLM_GTX_bit
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GLM_FUNC_DECL vec<L, T, Q> glm::highestBitValue (vec< L, T, Q > const & value)
\n
\n\n

Find the highest bit set to 1 in a integer variable and return its value.

\n
See also
to use the features of this extension. \">GLM_GTX_bit
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GLM_FUNC_DECL genIUType glm::lowestBitValue (genIUType Value)
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See also
to use the features of this extension. \">GLM_GTX_bit
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GLM_DEPRECATED GLM_FUNC_DECL genIUType glm::powerOfTwoAbove (genIUType Value)
\n
\n\n

Return the power of two number which value is just higher the input value.

\n

Deprecated, use ceilPowerOfTwo from GTC_round instead

\n
See also
to use the features of this extension. \">GLM_GTC_round
\n
\n to use the features of this extension. \">GLM_GTX_bit
\n\n
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GLM_DEPRECATED GLM_FUNC_DECL vec<L, T, Q> glm::powerOfTwoAbove (vec< L, T, Q > const & value)
\n
\n\n

Return the power of two number which value is just higher the input value.

\n

Deprecated, use ceilPowerOfTwo from GTC_round instead

\n
See also
to use the features of this extension. \">GLM_GTC_round
\n
\n to use the features of this extension. \">GLM_GTX_bit
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_DEPRECATED GLM_FUNC_DECL genIUType glm::powerOfTwoBelow (genIUType Value)
\n
\n\n

Return the power of two number which value is just lower the input value.

\n

Deprecated, use floorPowerOfTwo from GTC_round instead

\n
See also
to use the features of this extension. \">GLM_GTC_round
\n
\n to use the features of this extension. \">GLM_GTX_bit
\n\n
\n
\n\n
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GLM_DEPRECATED GLM_FUNC_DECL vec<L, T, Q> glm::powerOfTwoBelow (vec< L, T, Q > const & value)
\n
\n\n

Return the power of two number which value is just lower the input value.

\n

Deprecated, use floorPowerOfTwo from GTC_round instead

\n
See also
to use the features of this extension. \">GLM_GTC_round
\n
\n to use the features of this extension. \">GLM_GTX_bit
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GLM_DEPRECATED GLM_FUNC_DECL genIUType glm::powerOfTwoNearest (genIUType Value)
\n
\n\n

Return the power of two number which value is the closet to the input value.

\n

Deprecated, use roundPowerOfTwo from GTC_round instead

\n
See also
to use the features of this extension. \">GLM_GTC_round
\n
\n to use the features of this extension. \">GLM_GTX_bit
\n\n
\n
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GLM_DEPRECATED GLM_FUNC_DECL vec<L, T, Q> glm::powerOfTwoNearest (vec< L, T, Q > const & value)
\n
\n\n

Return the power of two number which value is the closet to the input value.

\n

Deprecated, use roundPowerOfTwo from GTC_round instead

\n
See also
to use the features of this extension. \">GLM_GTC_round
\n
\n to use the features of this extension. \">GLM_GTX_bit
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\nGenerated by  \n\"doxygen\"/\n 1.8.10\n
\n\n\n" }, { "path": "field_construction/submodules/diff-langsurf-rasterizer/third_party/glm/doc/api/a00310.html", "content": "\n\n\n\n\n\n0.9.9 API documentation: GLM_GTX_closest_point\n\n\n\n\n\n\n\n\n\n\n
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GLM_GTX_closest_point
Experimental extensions
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Include <glm/gtx/closest_point.hpp> to use the features of this extension. \nMore...

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template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > closestPointOnLine (vec< 3, T, Q > const &point, vec< 3, T, Q > const &a, vec< 3, T, Q > const &b)
 Find the point on a straight line which is the closet of a point. More...
 
\ntemplate<typename T , qualifier Q>
GLM_FUNC_DECL vec< 2, T, Q > closestPointOnLine (vec< 2, T, Q > const &point, vec< 2, T, Q > const &a, vec< 2, T, Q > const &b)
 2d lines work as well
 
\n

Detailed Description

\n

Include <glm/gtx/closest_point.hpp> to use the features of this extension.

\n

Find the point on a straight line which is the closet of a point.

\n

Function Documentation

\n\n
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GLM_FUNC_DECL vec<3, T, Q> glm::closestPointOnLine (vec< 3, T, Q > const & point,
vec< 3, T, Q > const & a,
vec< 3, T, Q > const & b 
)
\n
\n\n

Find the point on a straight line which is the closet of a point.

\n
See also
to use the features of this extension. \">GLM_GTX_closest_point
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GLM_GTX_color_encoding
Experimental extensions
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Include <glm/gtx/color_encoding.hpp> to use the features of this extension. \nMore...

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\nFunctions

\ntemplate<typename T , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > convertD65XYZToD50XYZ (vec< 3, T, Q > const &ColorD65XYZ)
 Convert a D65 YUV color to D50 YUV.
 
\ntemplate<typename T , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > convertD65XYZToLinearSRGB (vec< 3, T, Q > const &ColorD65XYZ)
 Convert a D65 YUV color to linear sRGB.
 
\ntemplate<typename T , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > convertLinearSRGBToD50XYZ (vec< 3, T, Q > const &ColorLinearSRGB)
 Convert a linear sRGB color to D50 YUV.
 
\ntemplate<typename T , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > convertLinearSRGBToD65XYZ (vec< 3, T, Q > const &ColorLinearSRGB)
 Convert a linear sRGB color to D65 YUV.
 
\n

Detailed Description

\n

Include <glm/gtx/color_encoding.hpp> to use the features of this extension.

\n

Allow to perform bit operations on integer values

\n
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Experimental extensions
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\nFunctions

template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > hsvColor (vec< 3, T, Q > const &rgbValue)
 Converts a color from RGB color space to its color in HSV color space. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL T luminosity (vec< 3, T, Q > const &color)
 Compute color luminosity associating ratios (0.33, 0.59, 0.11) to RGB canals. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > rgbColor (vec< 3, T, Q > const &hsvValue)
 Converts a color from HSV color space to its color in RGB color space. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > saturation (T const s)
 Build a saturation matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > saturation (T const s, vec< 3, T, Q > const &color)
 Modify the saturation of a color. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 4, T, Q > saturation (T const s, vec< 4, T, Q > const &color)
 Modify the saturation of a color. More...
 
\n

Detailed Description

\n

Include <glm/gtx/color_space.hpp> to use the features of this extension.

\n

Related to RGB to HSV conversions and operations.

\n

Function Documentation

\n\n
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GLM_FUNC_DECL vec<3, T, Q> glm::hsvColor (vec< 3, T, Q > const & rgbValue)
\n
\n\n

Converts a color from RGB color space to its color in HSV color space.

\n
See also
to use the features of this extension. \">GLM_GTX_color_space
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GLM_FUNC_DECL T glm::luminosity (vec< 3, T, Q > const & color)
\n
\n\n

Compute color luminosity associating ratios (0.33, 0.59, 0.11) to RGB canals.

\n
See also
to use the features of this extension. \">GLM_GTX_color_space
\n\n
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GLM_FUNC_DECL vec<3, T, Q> glm::rgbColor (vec< 3, T, Q > const & hsvValue)
\n
\n\n

Converts a color from HSV color space to its color in RGB color space.

\n
See also
to use the features of this extension. \">GLM_GTX_color_space
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GLM_FUNC_DECL mat<4, 4, T, defaultp> glm::saturation (T const s)
\n
\n\n

Build a saturation matrix.

\n
See also
to use the features of this extension. \">GLM_GTX_color_space
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GLM_FUNC_DECL vec<3, T, Q> glm::saturation (T const s,
vec< 3, T, Q > const & color 
)
\n
\n\n

Modify the saturation of a color.

\n
See also
to use the features of this extension. \">GLM_GTX_color_space
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GLM_FUNC_DECL vec<4, T, Q> glm::saturation (T const s,
vec< 4, T, Q > const & color 
)
\n
\n\n

Modify the saturation of a color.

\n
See also
to use the features of this extension. \">GLM_GTX_color_space
\n\n
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GLM_GTX_color_space_YCoCg
Experimental extensions
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Include <glm/gtx/color_space_YCoCg.hpp> to use the features of this extension. \nMore...

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\nFunctions

template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > rgb2YCoCg (vec< 3, T, Q > const &rgbColor)
 Convert a color from RGB color space to YCoCg color space. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > rgb2YCoCgR (vec< 3, T, Q > const &rgbColor)
 Convert a color from RGB color space to YCoCgR color space. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > YCoCg2rgb (vec< 3, T, Q > const &YCoCgColor)
 Convert a color from YCoCg color space to RGB color space. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > YCoCgR2rgb (vec< 3, T, Q > const &YCoCgColor)
 Convert a color from YCoCgR color space to RGB color space. More...
 
\n

Detailed Description

\n

Include <glm/gtx/color_space_YCoCg.hpp> to use the features of this extension.

\n

RGB to YCoCg conversions and operations

\n

Function Documentation

\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL vec<3, T, Q> glm::rgb2YCoCg (vec< 3, T, Q > const & rgbColor)
\n
\n\n

Convert a color from RGB color space to YCoCg color space.

\n
See also
to use the features of this extension. \">GLM_GTX_color_space_YCoCg
\n\n
\n
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\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL vec<3, T, Q> glm::rgb2YCoCgR (vec< 3, T, Q > const & rgbColor)
\n
\n\n

Convert a color from RGB color space to YCoCgR color space.

\n
See also
\"YCoCg-R: A Color Space with RGB Reversibility and Low Dynamic Range\"
\n
\n to use the features of this extension. \">GLM_GTX_color_space_YCoCg
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GLM_FUNC_DECL vec<3, T, Q> glm::YCoCg2rgb (vec< 3, T, Q > const & YCoCgColor)
\n
\n\n

Convert a color from YCoCg color space to RGB color space.

\n
See also
to use the features of this extension. \">GLM_GTX_color_space_YCoCg
\n\n
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GLM_FUNC_DECL vec<3, T, Q> glm::YCoCgR2rgb (vec< 3, T, Q > const & YCoCgColor)
\n
\n\n

Convert a color from YCoCgR color space to RGB color space.

\n
See also
\"YCoCg-R: A Color Space with RGB Reversibility and Low Dynamic Range\"
\n
\n to use the features of this extension. \">GLM_GTX_color_space_YCoCg
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\nGenerated by  \n\"doxygen\"/\n 1.8.10\n
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GLM_GTX_common
Experimental extensions
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Include <glm/gtx/common.hpp> to use the features of this extension. \nMore...

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\nFunctions

template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, bool, Q > closeBounded (vec< L, T, Q > const &Value, vec< L, T, Q > const &Min, vec< L, T, Q > const &Max)
 Returns whether vector components values are within an interval. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > fmod (vec< L, T, Q > const &v)
 Similar to 'mod' but with a different rounding and integer support. More...
 
template<typename genType >
GLM_FUNC_DECL genType::bool_type isdenormal (genType const &x)
 Returns true if x is a denormalized number Numbers whose absolute value is too small to be represented in the normal format are represented in an alternate, denormalized format. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, bool, Q > openBounded (vec< L, T, Q > const &Value, vec< L, T, Q > const &Min, vec< L, T, Q > const &Max)
 Returns whether vector components values are within an interval. More...
 
\n

Detailed Description

\n

Include <glm/gtx/common.hpp> to use the features of this extension.

\n

Provide functions to increase the compatibility with Cg and HLSL languages

\n

Function Documentation

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GLM_FUNC_DECL vec<L, bool, Q> glm::closeBounded (vec< L, T, Q > const & Value,
vec< L, T, Q > const & Min,
vec< L, T, Q > const & Max 
)
\n
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Returns whether vector components values are within an interval.

\n

A closed interval includes its endpoints, and is denoted with square brackets.

\n
Template Parameters
\n \n \n \n \n
LInteger between 1 and 4 included that qualify the dimension of the vector
TFloating-point or integer scalar types
QValue from qualifier enum
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See also
GLM_EXT_vector_relational
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GLM_FUNC_DECL vec<L, T, Q> glm::fmod (vec< L, T, Q > const & v)
\n
\n\n

Similar to 'mod' but with a different rounding and integer support.

\n

Returns 'x - y * trunc(x/y)' instead of 'x - y * floor(x/y)'

\n
See also
GLSL mod vs HLSL fmod
\n
\nGLSL mod man page
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GLM_FUNC_DECL genType::bool_type glm::isdenormal (genType const & x)
\n
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Returns true if x is a denormalized number Numbers whose absolute value is too small to be represented in the normal format are represented in an alternate, denormalized format.

\n

This format is less precise but can represent values closer to zero.

\n
Template Parameters
\n \n \n
genTypeFloating-point scalar or vector types.
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See also
GLSL isnan man page
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GLM_FUNC_DECL vec<L, bool, Q> glm::openBounded (vec< L, T, Q > const & Value,
vec< L, T, Q > const & Min,
vec< L, T, Q > const & Max 
)
\n
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Returns whether vector components values are within an interval.

\n

A open interval excludes its endpoints, and is denoted with square brackets.

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Template Parameters
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LInteger between 1 and 4 included that qualify the dimension of the vector
TFloating-point or integer scalar types
QValue from qualifier enum
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See also
GLM_EXT_vector_relational
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GLM_GTX_compatibility
Experimental extensions
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\nTypedefs

\ntypedef bool bool1
 boolean type with 1 component. (From GLM_GTX_compatibility extension)
 
\ntypedef bool bool1x1
 boolean matrix with 1 x 1 component. (From GLM_GTX_compatibility extension)
 
\ntypedef vec< 2, bool, highp > bool2
 boolean type with 2 components. (From GLM_GTX_compatibility extension)
 
\ntypedef mat< 2, 2, bool, highp > bool2x2
 boolean matrix with 2 x 2 components. (From GLM_GTX_compatibility extension)
 
\ntypedef mat< 2, 3, bool, highp > bool2x3
 boolean matrix with 2 x 3 components. (From GLM_GTX_compatibility extension)
 
\ntypedef mat< 2, 4, bool, highp > bool2x4
 boolean matrix with 2 x 4 components. (From GLM_GTX_compatibility extension)
 
\ntypedef vec< 3, bool, highp > bool3
 boolean type with 3 components. (From GLM_GTX_compatibility extension)
 
\ntypedef mat< 3, 2, bool, highp > bool3x2
 boolean matrix with 3 x 2 components. (From GLM_GTX_compatibility extension)
 
\ntypedef mat< 3, 3, bool, highp > bool3x3
 boolean matrix with 3 x 3 components. (From GLM_GTX_compatibility extension)
 
\ntypedef mat< 3, 4, bool, highp > bool3x4
 boolean matrix with 3 x 4 components. (From GLM_GTX_compatibility extension)
 
\ntypedef vec< 4, bool, highp > bool4
 boolean type with 4 components. (From GLM_GTX_compatibility extension)
 
\ntypedef mat< 4, 2, bool, highp > bool4x2
 boolean matrix with 4 x 2 components. (From GLM_GTX_compatibility extension)
 
\ntypedef mat< 4, 3, bool, highp > bool4x3
 boolean matrix with 4 x 3 components. (From GLM_GTX_compatibility extension)
 
\ntypedef mat< 4, 4, bool, highp > bool4x4
 boolean matrix with 4 x 4 components. (From GLM_GTX_compatibility extension)
 
\ntypedef double double1
 double-qualifier floating-point vector with 1 component. (From GLM_GTX_compatibility extension)
 
\ntypedef double double1x1
 double-qualifier floating-point matrix with 1 component. (From GLM_GTX_compatibility extension)
 
\ntypedef vec< 2, double, highp > double2
 double-qualifier floating-point vector with 2 components. (From GLM_GTX_compatibility extension)
 
\ntypedef mat< 2, 2, double, highp > double2x2
 double-qualifier floating-point matrix with 2 x 2 components. (From GLM_GTX_compatibility extension)
 
\ntypedef mat< 2, 3, double, highp > double2x3
 double-qualifier floating-point matrix with 2 x 3 components. (From GLM_GTX_compatibility extension)
 
\ntypedef mat< 2, 4, double, highp > double2x4
 double-qualifier floating-point matrix with 2 x 4 components. (From GLM_GTX_compatibility extension)
 
\ntypedef vec< 3, double, highp > double3
 double-qualifier floating-point vector with 3 components. (From GLM_GTX_compatibility extension)
 
\ntypedef mat< 3, 2, double, highp > double3x2
 double-qualifier floating-point matrix with 3 x 2 components. (From GLM_GTX_compatibility extension)
 
\ntypedef mat< 3, 3, double, highp > double3x3
 double-qualifier floating-point matrix with 3 x 3 components. (From GLM_GTX_compatibility extension)
 
\ntypedef mat< 3, 4, double, highp > double3x4
 double-qualifier floating-point matrix with 3 x 4 components. (From GLM_GTX_compatibility extension)
 
\ntypedef vec< 4, double, highp > double4
 double-qualifier floating-point vector with 4 components. (From GLM_GTX_compatibility extension)
 
\ntypedef mat< 4, 2, double, highp > double4x2
 double-qualifier floating-point matrix with 4 x 2 components. (From GLM_GTX_compatibility extension)
 
\ntypedef mat< 4, 3, double, highp > double4x3
 double-qualifier floating-point matrix with 4 x 3 components. (From GLM_GTX_compatibility extension)
 
\ntypedef mat< 4, 4, double, highp > double4x4
 double-qualifier floating-point matrix with 4 x 4 components. (From GLM_GTX_compatibility extension)
 
\ntypedef float float1
 single-qualifier floating-point vector with 1 component. (From GLM_GTX_compatibility extension)
 
\ntypedef float float1x1
 single-qualifier floating-point matrix with 1 component. (From GLM_GTX_compatibility extension)
 
\ntypedef vec< 2, float, highp > float2
 single-qualifier floating-point vector with 2 components. (From GLM_GTX_compatibility extension)
 
\ntypedef mat< 2, 2, float, highp > float2x2
 single-qualifier floating-point matrix with 2 x 2 components. (From GLM_GTX_compatibility extension)
 
\ntypedef mat< 2, 3, float, highp > float2x3
 single-qualifier floating-point matrix with 2 x 3 components. (From GLM_GTX_compatibility extension)
 
\ntypedef mat< 2, 4, float, highp > float2x4
 single-qualifier floating-point matrix with 2 x 4 components. (From GLM_GTX_compatibility extension)
 
\ntypedef vec< 3, float, highp > float3
 single-qualifier floating-point vector with 3 components. (From GLM_GTX_compatibility extension)
 
\ntypedef mat< 3, 2, float, highp > float3x2
 single-qualifier floating-point matrix with 3 x 2 components. (From GLM_GTX_compatibility extension)
 
\ntypedef mat< 3, 3, float, highp > float3x3
 single-qualifier floating-point matrix with 3 x 3 components. (From GLM_GTX_compatibility extension)
 
\ntypedef mat< 3, 4, float, highp > float3x4
 single-qualifier floating-point matrix with 3 x 4 components. (From GLM_GTX_compatibility extension)
 
\ntypedef vec< 4, float, highp > float4
 single-qualifier floating-point vector with 4 components. (From GLM_GTX_compatibility extension)
 
\ntypedef mat< 4, 2, float, highp > float4x2
 single-qualifier floating-point matrix with 4 x 2 components. (From GLM_GTX_compatibility extension)
 
\ntypedef mat< 4, 3, float, highp > float4x3
 single-qualifier floating-point matrix with 4 x 3 components. (From GLM_GTX_compatibility extension)
 
\ntypedef mat< 4, 4, float, highp > float4x4
 single-qualifier floating-point matrix with 4 x 4 components. (From GLM_GTX_compatibility extension)
 
\ntypedef int int1
 integer vector with 1 component. (From GLM_GTX_compatibility extension)
 
\ntypedef int int1x1
 integer matrix with 1 component. (From GLM_GTX_compatibility extension)
 
\ntypedef vec< 2, int, highp > int2
 integer vector with 2 components. (From GLM_GTX_compatibility extension)
 
\ntypedef mat< 2, 2, int, highp > int2x2
 integer matrix with 2 x 2 components. (From GLM_GTX_compatibility extension)
 
\ntypedef mat< 2, 3, int, highp > int2x3
 integer matrix with 2 x 3 components. (From GLM_GTX_compatibility extension)
 
\ntypedef mat< 2, 4, int, highp > int2x4
 integer matrix with 2 x 4 components. (From GLM_GTX_compatibility extension)
 
\ntypedef vec< 3, int, highp > int3
 integer vector with 3 components. (From GLM_GTX_compatibility extension)
 
\ntypedef mat< 3, 2, int, highp > int3x2
 integer matrix with 3 x 2 components. (From GLM_GTX_compatibility extension)
 
\ntypedef mat< 3, 3, int, highp > int3x3
 integer matrix with 3 x 3 components. (From GLM_GTX_compatibility extension)
 
\ntypedef mat< 3, 4, int, highp > int3x4
 integer matrix with 3 x 4 components. (From GLM_GTX_compatibility extension)
 
\ntypedef vec< 4, int, highp > int4
 integer vector with 4 components. (From GLM_GTX_compatibility extension)
 
\ntypedef mat< 4, 2, int, highp > int4x2
 integer matrix with 4 x 2 components. (From GLM_GTX_compatibility extension)
 
\ntypedef mat< 4, 3, int, highp > int4x3
 integer matrix with 4 x 3 components. (From GLM_GTX_compatibility extension)
 
\ntypedef mat< 4, 4, int, highp > int4x4
 integer matrix with 4 x 4 components. (From GLM_GTX_compatibility extension)
 
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\nFunctions

\ntemplate<typename T , qualifier Q>
GLM_FUNC_QUALIFIER T atan2 (T x, T y)
 Arc tangent. Returns an angle whose tangent is y/x. The signs of x and y are used to determine what quadrant the angle is in. The range of values returned by this function is [-PI, PI]. Results are undefined if x and y are both 0. (From GLM_GTX_compatibility)
 
\ntemplate<typename T , qualifier Q>
GLM_FUNC_QUALIFIER vec< 2, T, Q > atan2 (const vec< 2, T, Q > &x, const vec< 2, T, Q > &y)
 Arc tangent. Returns an angle whose tangent is y/x. The signs of x and y are used to determine what quadrant the angle is in. The range of values returned by this function is [-PI, PI]. Results are undefined if x and y are both 0. (From GLM_GTX_compatibility)
 
\ntemplate<typename T , qualifier Q>
GLM_FUNC_QUALIFIER vec< 3, T, Q > atan2 (const vec< 3, T, Q > &x, const vec< 3, T, Q > &y)
 Arc tangent. Returns an angle whose tangent is y/x. The signs of x and y are used to determine what quadrant the angle is in. The range of values returned by this function is [-PI, PI]. Results are undefined if x and y are both 0. (From GLM_GTX_compatibility)
 
\ntemplate<typename T , qualifier Q>
GLM_FUNC_QUALIFIER vec< 4, T, Q > atan2 (const vec< 4, T, Q > &x, const vec< 4, T, Q > &y)
 Arc tangent. Returns an angle whose tangent is y/x. The signs of x and y are used to determine what quadrant the angle is in. The range of values returned by this function is [-PI, PI]. Results are undefined if x and y are both 0. (From GLM_GTX_compatibility)
 
\ntemplate<typename genType >
GLM_FUNC_DECL bool isfinite (genType const &x)
 Test whether or not a scalar or each vector component is a finite value. (From GLM_GTX_compatibility)
 
\ntemplate<typename T , qualifier Q>
GLM_FUNC_DECL vec< 1, bool, Q > isfinite (const vec< 1, T, Q > &x)
 Test whether or not a scalar or each vector component is a finite value. (From GLM_GTX_compatibility)
 
\ntemplate<typename T , qualifier Q>
GLM_FUNC_DECL vec< 2, bool, Q > isfinite (const vec< 2, T, Q > &x)
 Test whether or not a scalar or each vector component is a finite value. (From GLM_GTX_compatibility)
 
\ntemplate<typename T , qualifier Q>
GLM_FUNC_DECL vec< 3, bool, Q > isfinite (const vec< 3, T, Q > &x)
 Test whether or not a scalar or each vector component is a finite value. (From GLM_GTX_compatibility)
 
\ntemplate<typename T , qualifier Q>
GLM_FUNC_DECL vec< 4, bool, Q > isfinite (const vec< 4, T, Q > &x)
 Test whether or not a scalar or each vector component is a finite value. (From GLM_GTX_compatibility)
 
\ntemplate<typename T >
GLM_FUNC_QUALIFIER T lerp (T x, T y, T a)
 Returns x * (1.0 - a) + y * a, i.e., the linear blend of x and y using the floating-point value a. The value for a is not restricted to the range [0, 1]. (From GLM_GTX_compatibility)
 
\ntemplate<typename T , qualifier Q>
GLM_FUNC_QUALIFIER vec< 2, T, Q > lerp (const vec< 2, T, Q > &x, const vec< 2, T, Q > &y, T a)
 Returns x * (1.0 - a) + y * a, i.e., the linear blend of x and y using the floating-point value a. The value for a is not restricted to the range [0, 1]. (From GLM_GTX_compatibility)
 
\ntemplate<typename T , qualifier Q>
GLM_FUNC_QUALIFIER vec< 3, T, Q > lerp (const vec< 3, T, Q > &x, const vec< 3, T, Q > &y, T a)
 Returns x * (1.0 - a) + y * a, i.e., the linear blend of x and y using the floating-point value a. The value for a is not restricted to the range [0, 1]. (From GLM_GTX_compatibility)
 
\ntemplate<typename T , qualifier Q>
GLM_FUNC_QUALIFIER vec< 4, T, Q > lerp (const vec< 4, T, Q > &x, const vec< 4, T, Q > &y, T a)
 Returns x * (1.0 - a) + y * a, i.e., the linear blend of x and y using the floating-point value a. The value for a is not restricted to the range [0, 1]. (From GLM_GTX_compatibility)
 
\ntemplate<typename T , qualifier Q>
GLM_FUNC_QUALIFIER vec< 2, T, Q > lerp (const vec< 2, T, Q > &x, const vec< 2, T, Q > &y, const vec< 2, T, Q > &a)
 Returns the component-wise result of x * (1.0 - a) + y * a, i.e., the linear blend of x and y using vector a. The value for a is not restricted to the range [0, 1]. (From GLM_GTX_compatibility)
 
\ntemplate<typename T , qualifier Q>
GLM_FUNC_QUALIFIER vec< 3, T, Q > lerp (const vec< 3, T, Q > &x, const vec< 3, T, Q > &y, const vec< 3, T, Q > &a)
 Returns the component-wise result of x * (1.0 - a) + y * a, i.e., the linear blend of x and y using vector a. The value for a is not restricted to the range [0, 1]. (From GLM_GTX_compatibility)
 
\ntemplate<typename T , qualifier Q>
GLM_FUNC_QUALIFIER vec< 4, T, Q > lerp (const vec< 4, T, Q > &x, const vec< 4, T, Q > &y, const vec< 4, T, Q > &a)
 Returns the component-wise result of x * (1.0 - a) + y * a, i.e., the linear blend of x and y using vector a. The value for a is not restricted to the range [0, 1]. (From GLM_GTX_compatibility)
 
\ntemplate<typename T , qualifier Q>
GLM_FUNC_QUALIFIER T saturate (T x)
 Returns clamp(x, 0, 1) for each component in x. (From GLM_GTX_compatibility)
 
\ntemplate<typename T , qualifier Q>
GLM_FUNC_QUALIFIER vec< 2, T, Q > saturate (const vec< 2, T, Q > &x)
 Returns clamp(x, 0, 1) for each component in x. (From GLM_GTX_compatibility)
 
\ntemplate<typename T , qualifier Q>
GLM_FUNC_QUALIFIER vec< 3, T, Q > saturate (const vec< 3, T, Q > &x)
 Returns clamp(x, 0, 1) for each component in x. (From GLM_GTX_compatibility)
 
\ntemplate<typename T , qualifier Q>
GLM_FUNC_QUALIFIER vec< 4, T, Q > saturate (const vec< 4, T, Q > &x)
 Returns clamp(x, 0, 1) for each component in x. (From GLM_GTX_compatibility)
 
\n

Detailed Description

\n

Include <glm/gtx/compatibility.hpp> to use the features of this extension.

\n

Provide functions to increase the compatibility with Cg and HLSL languages

\n
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GLM_GTX_component_wise
Experimental extensions
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Include <glm/gtx/component_wise.hpp> to use the features of this extension. \nMore...

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\nFunctions

template<typename genType >
GLM_FUNC_DECL genType::value_type compAdd (genType const &v)
 Add all vector components together. More...
 
template<typename genType >
GLM_FUNC_DECL genType::value_type compMax (genType const &v)
 Find the maximum value between single vector components. More...
 
template<typename genType >
GLM_FUNC_DECL genType::value_type compMin (genType const &v)
 Find the minimum value between single vector components. More...
 
template<typename genType >
GLM_FUNC_DECL genType::value_type compMul (genType const &v)
 Multiply all vector components together. More...
 
template<typename floatType , length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, floatType, Q > compNormalize (vec< L, T, Q > const &v)
 Convert an integer vector to a normalized float vector. More...
 
template<length_t L, typename T , typename floatType , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > compScale (vec< L, floatType, Q > const &v)
 Convert a normalized float vector to an integer vector. More...
 
\n

Detailed Description

\n

Include <glm/gtx/component_wise.hpp> to use the features of this extension.

\n

Operations between components of a type

\n

Function Documentation

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GLM_FUNC_DECL genType::value_type glm::compAdd (genType const & v)
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Add all vector components together.

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See also
to use the features of this extension. \">GLM_GTX_component_wise
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GLM_FUNC_DECL genType::value_type glm::compMax (genType const & v)
\n
\n\n

Find the maximum value between single vector components.

\n
See also
to use the features of this extension. \">GLM_GTX_component_wise
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GLM_FUNC_DECL genType::value_type glm::compMin (genType const & v)
\n
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Find the minimum value between single vector components.

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See also
to use the features of this extension. \">GLM_GTX_component_wise
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GLM_FUNC_DECL genType::value_type glm::compMul (genType const & v)
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Multiply all vector components together.

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to use the features of this extension. \">GLM_GTX_component_wise
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GLM_FUNC_DECL vec<L, floatType, Q> glm::compNormalize (vec< L, T, Q > const & v)
\n
\n\n

Convert an integer vector to a normalized float vector.

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If the parameter value type is already a floating qualifier type, the value is passed through.

See also
to use the features of this extension. \">GLM_GTX_component_wise
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GLM_FUNC_DECL vec<L, T, Q> glm::compScale (vec< L, floatType, Q > const & v)
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Convert a normalized float vector to an integer vector.

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If the parameter value type is already a floating qualifier type, the value is passed through.

See also
to use the features of this extension. \">GLM_GTX_component_wise
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GLM_GTX_dual_quaternion
Experimental extensions
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Include <glm/gtx/dual_quaternion.hpp> to use the features of this extension. \nMore...

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\nTypedefs

typedef highp_ddualquat ddualquat
 Dual-quaternion of default double-qualifier floating-point numbers. More...
 
typedef highp_fdualquat dualquat
 Dual-quaternion of floating-point numbers. More...
 
typedef highp_fdualquat fdualquat
 Dual-quaternion of single-qualifier floating-point numbers. More...
 
typedef tdualquat< double, highp > highp_ddualquat
 Dual-quaternion of high double-qualifier floating-point numbers. More...
 
typedef tdualquat< float, highp > highp_dualquat
 Dual-quaternion of high single-qualifier floating-point numbers. More...
 
typedef tdualquat< float, highp > highp_fdualquat
 Dual-quaternion of high single-qualifier floating-point numbers. More...
 
typedef tdualquat< double, lowp > lowp_ddualquat
 Dual-quaternion of low double-qualifier floating-point numbers. More...
 
typedef tdualquat< float, lowp > lowp_dualquat
 Dual-quaternion of low single-qualifier floating-point numbers. More...
 
typedef tdualquat< float, lowp > lowp_fdualquat
 Dual-quaternion of low single-qualifier floating-point numbers. More...
 
typedef tdualquat< double, mediump > mediump_ddualquat
 Dual-quaternion of medium double-qualifier floating-point numbers. More...
 
typedef tdualquat< float, mediump > mediump_dualquat
 Dual-quaternion of medium single-qualifier floating-point numbers. More...
 
typedef tdualquat< float, mediump > mediump_fdualquat
 Dual-quaternion of medium single-qualifier floating-point numbers. More...
 
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\nFunctions

template<typename T , qualifier Q>
GLM_FUNC_DECL tdualquat< T, Q > dual_quat_identity ()
 Creates an identity dual quaternion. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL tdualquat< T, Q > dualquat_cast (mat< 2, 4, T, Q > const &x)
 Converts a 2 * 4 matrix (matrix which holds real and dual parts) to a quaternion. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL tdualquat< T, Q > dualquat_cast (mat< 3, 4, T, Q > const &x)
 Converts a 3 * 4 matrix (augmented matrix rotation + translation) to a quaternion. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL tdualquat< T, Q > inverse (tdualquat< T, Q > const &q)
 Returns the q inverse. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL tdualquat< T, Q > lerp (tdualquat< T, Q > const &x, tdualquat< T, Q > const &y, T const &a)
 Returns the linear interpolation of two dual quaternion. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 2, 4, T, Q > mat2x4_cast (tdualquat< T, Q > const &x)
 Converts a quaternion to a 2 * 4 matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 3, 4, T, Q > mat3x4_cast (tdualquat< T, Q > const &x)
 Converts a quaternion to a 3 * 4 matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL tdualquat< T, Q > normalize (tdualquat< T, Q > const &q)
 Returns the normalized quaternion. More...
 
\n

Detailed Description

\n

Include <glm/gtx/dual_quaternion.hpp> to use the features of this extension.

\n

Defines a templated dual-quaternion type and several dual-quaternion operations.

\n

Typedef Documentation

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typedef highp_ddualquat ddualquat
\n
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Dual-quaternion of default double-qualifier floating-point numbers.

\n
See also
to use the features of this extension. \">GLM_GTX_dual_quaternion
\n\n

Definition at line 260 of file dual_quaternion.hpp.

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typedef highp_fdualquat dualquat
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Dual-quaternion of floating-point numbers.

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See also
to use the features of this extension. \">GLM_GTX_dual_quaternion
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Definition at line 236 of file dual_quaternion.hpp.

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typedef highp_fdualquat fdualquat
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Dual-quaternion of single-qualifier floating-point numbers.

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See also
to use the features of this extension. \">GLM_GTX_dual_quaternion
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Definition at line 241 of file dual_quaternion.hpp.

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typedef tdualquat<double, highp> highp_ddualquat
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Dual-quaternion of high double-qualifier floating-point numbers.

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See also
to use the features of this extension. \">GLM_GTX_dual_quaternion
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Definition at line 229 of file dual_quaternion.hpp.

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typedef tdualquat<float, highp> highp_dualquat
\n
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Dual-quaternion of high single-qualifier floating-point numbers.

\n
See also
to use the features of this extension. \">GLM_GTX_dual_quaternion
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Definition at line 197 of file dual_quaternion.hpp.

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typedef tdualquat<float, highp> highp_fdualquat
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Dual-quaternion of high single-qualifier floating-point numbers.

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See also
to use the features of this extension. \">GLM_GTX_dual_quaternion
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Definition at line 213 of file dual_quaternion.hpp.

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typedef tdualquat<double, lowp> lowp_ddualquat
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Dual-quaternion of low double-qualifier floating-point numbers.

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See also
to use the features of this extension. \">GLM_GTX_dual_quaternion
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Definition at line 219 of file dual_quaternion.hpp.

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typedef tdualquat<float, lowp> lowp_dualquat
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Dual-quaternion of low single-qualifier floating-point numbers.

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See also
to use the features of this extension. \">GLM_GTX_dual_quaternion
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Definition at line 187 of file dual_quaternion.hpp.

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typedef tdualquat<float, lowp> lowp_fdualquat
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Dual-quaternion of low single-qualifier floating-point numbers.

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See also
to use the features of this extension. \">GLM_GTX_dual_quaternion
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Definition at line 203 of file dual_quaternion.hpp.

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typedef tdualquat<double, mediump> mediump_ddualquat
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Dual-quaternion of medium double-qualifier floating-point numbers.

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See also
to use the features of this extension. \">GLM_GTX_dual_quaternion
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Definition at line 224 of file dual_quaternion.hpp.

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typedef tdualquat<float, mediump> mediump_dualquat
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Dual-quaternion of medium single-qualifier floating-point numbers.

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See also
to use the features of this extension. \">GLM_GTX_dual_quaternion
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Definition at line 192 of file dual_quaternion.hpp.

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typedef tdualquat<float, mediump> mediump_fdualquat
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Dual-quaternion of medium single-qualifier floating-point numbers.

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See also
to use the features of this extension. \">GLM_GTX_dual_quaternion
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Definition at line 208 of file dual_quaternion.hpp.

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Function Documentation

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GLM_FUNC_DECL tdualquat<T, Q> glm::dual_quat_identity ()
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Creates an identity dual quaternion.

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See also
to use the features of this extension. \">GLM_GTX_dual_quaternion
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GLM_FUNC_DECL tdualquat<T, Q> glm::dualquat_cast (mat< 2, 4, T, Q > const & x)
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Converts a 2 * 4 matrix (matrix which holds real and dual parts) to a quaternion.

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See also
to use the features of this extension. \">GLM_GTX_dual_quaternion
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GLM_FUNC_DECL tdualquat<T, Q> glm::dualquat_cast (mat< 3, 4, T, Q > const & x)
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Converts a 3 * 4 matrix (augmented matrix rotation + translation) to a quaternion.

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See also
to use the features of this extension. \">GLM_GTX_dual_quaternion
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GLM_FUNC_DECL tdualquat<T, Q> glm::inverse (tdualquat< T, Q > const & q)
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Returns the q inverse.

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See also
to use the features of this extension. \">GLM_GTX_dual_quaternion
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GLM_FUNC_DECL tdualquat<T, Q> glm::lerp (tdualquat< T, Q > const & x,
tdualquat< T, Q > const & y,
T const & a 
)
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Returns the linear interpolation of two dual quaternion.

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See also
gtc_dual_quaternion
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GLM_FUNC_DECL mat<2, 4, T, Q> glm::mat2x4_cast (tdualquat< T, Q > const & x)
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Converts a quaternion to a 2 * 4 matrix.

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See also
to use the features of this extension. \">GLM_GTX_dual_quaternion
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GLM_FUNC_DECL mat<3, 4, T, Q> glm::mat3x4_cast (tdualquat< T, Q > const & x)
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Converts a quaternion to a 3 * 4 matrix.

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See also
to use the features of this extension. \">GLM_GTX_dual_quaternion
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GLM_FUNC_DECL tdualquat<T, Q> glm::normalize (tdualquat< T, Q > const & q)
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Returns the normalized quaternion.

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See also
to use the features of this extension. \">GLM_GTX_dual_quaternion
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\nGenerated by  \n\"doxygen\"/\n 1.8.10\n
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0.9.9 API documentation\n
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\nFunctions
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GLM_GTX_easing
Experimental extensions
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Include <glm/gtx/easing.hpp> to use the features of this extension. \nMore...

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\nFunctions

template<typename genType >
GLM_FUNC_DECL genType backEaseIn (genType const &a)
 
template<typename genType >
GLM_FUNC_DECL genType backEaseIn (genType const &a, genType const &o)
 
template<typename genType >
GLM_FUNC_DECL genType backEaseInOut (genType const &a)
 
template<typename genType >
GLM_FUNC_DECL genType backEaseInOut (genType const &a, genType const &o)
 
template<typename genType >
GLM_FUNC_DECL genType backEaseOut (genType const &a)
 
template<typename genType >
GLM_FUNC_DECL genType backEaseOut (genType const &a, genType const &o)
 
template<typename genType >
GLM_FUNC_DECL genType bounceEaseIn (genType const &a)
 
template<typename genType >
GLM_FUNC_DECL genType bounceEaseInOut (genType const &a)
 
template<typename genType >
GLM_FUNC_DECL genType bounceEaseOut (genType const &a)
 
template<typename genType >
GLM_FUNC_DECL genType circularEaseIn (genType const &a)
 Modelled after shifted quadrant IV of unit circle. More...
 
template<typename genType >
GLM_FUNC_DECL genType circularEaseInOut (genType const &a)
 Modelled after the piecewise circular function y = (1/2)(1 - sqrt(1 - 4x^2)) ; [0, 0.5) y = (1/2)(sqrt(-(2x - 3)*(2x - 1)) + 1) ; [0.5, 1]. More...
 
template<typename genType >
GLM_FUNC_DECL genType circularEaseOut (genType const &a)
 Modelled after shifted quadrant II of unit circle. More...
 
\ntemplate<typename genType >
GLM_FUNC_DECL genType cubicEaseIn (genType const &a)
 Modelled after the cubic y = x^3.
 
template<typename genType >
GLM_FUNC_DECL genType cubicEaseInOut (genType const &a)
 Modelled after the piecewise cubic y = (1/2)((2x)^3) ; [0, 0.5) y = (1/2)((2x-2)^3 + 2) ; [0.5, 1]. More...
 
template<typename genType >
GLM_FUNC_DECL genType cubicEaseOut (genType const &a)
 Modelled after the cubic y = (x - 1)^3 + 1. More...
 
template<typename genType >
GLM_FUNC_DECL genType elasticEaseIn (genType const &a)
 Modelled after the damped sine wave y = sin(13pi/2*x)*pow(2, 10 * (x - 1)) More...
 
template<typename genType >
GLM_FUNC_DECL genType elasticEaseInOut (genType const &a)
 Modelled after the piecewise exponentially-damped sine wave: y = (1/2)*sin(13pi/2*(2*x))*pow(2, 10 * ((2*x) - 1)) ; [0,0.5) y = (1/2)*(sin(-13pi/2*((2x-1)+1))*pow(2,-10(2*x-1)) + 2) ; [0.5, 1]. More...
 
template<typename genType >
GLM_FUNC_DECL genType elasticEaseOut (genType const &a)
 Modelled after the damped sine wave y = sin(-13pi/2*(x + 1))*pow(2, -10x) + 1. More...
 
template<typename genType >
GLM_FUNC_DECL genType exponentialEaseIn (genType const &a)
 Modelled after the exponential function y = 2^(10(x - 1)) More...
 
template<typename genType >
GLM_FUNC_DECL genType exponentialEaseInOut (genType const &a)
 Modelled after the piecewise exponential y = (1/2)2^(10(2x - 1)) ; [0,0.5) y = -(1/2)*2^(-10(2x - 1))) + 1 ; [0.5,1]. More...
 
template<typename genType >
GLM_FUNC_DECL genType exponentialEaseOut (genType const &a)
 Modelled after the exponential function y = -2^(-10x) + 1. More...
 
template<typename genType >
GLM_FUNC_DECL genType linearInterpolation (genType const &a)
 Modelled after the line y = x. More...
 
template<typename genType >
GLM_FUNC_DECL genType quadraticEaseIn (genType const &a)
 Modelled after the parabola y = x^2. More...
 
template<typename genType >
GLM_FUNC_DECL genType quadraticEaseInOut (genType const &a)
 Modelled after the piecewise quadratic y = (1/2)((2x)^2) ; [0, 0.5) y = -(1/2)((2x-1)*(2x-3) - 1) ; [0.5, 1]. More...
 
template<typename genType >
GLM_FUNC_DECL genType quadraticEaseOut (genType const &a)
 Modelled after the parabola y = -x^2 + 2x. More...
 
template<typename genType >
GLM_FUNC_DECL genType quarticEaseIn (genType const &a)
 Modelled after the quartic x^4. More...
 
template<typename genType >
GLM_FUNC_DECL genType quarticEaseInOut (genType const &a)
 Modelled after the piecewise quartic y = (1/2)((2x)^4) ; [0, 0.5) y = -(1/2)((2x-2)^4 - 2) ; [0.5, 1]. More...
 
template<typename genType >
GLM_FUNC_DECL genType quarticEaseOut (genType const &a)
 Modelled after the quartic y = 1 - (x - 1)^4. More...
 
template<typename genType >
GLM_FUNC_DECL genType quinticEaseIn (genType const &a)
 Modelled after the quintic y = x^5. More...
 
template<typename genType >
GLM_FUNC_DECL genType quinticEaseInOut (genType const &a)
 Modelled after the piecewise quintic y = (1/2)((2x)^5) ; [0, 0.5) y = (1/2)((2x-2)^5 + 2) ; [0.5, 1]. More...
 
template<typename genType >
GLM_FUNC_DECL genType quinticEaseOut (genType const &a)
 Modelled after the quintic y = (x - 1)^5 + 1. More...
 
template<typename genType >
GLM_FUNC_DECL genType sineEaseIn (genType const &a)
 Modelled after quarter-cycle of sine wave. More...
 
template<typename genType >
GLM_FUNC_DECL genType sineEaseInOut (genType const &a)
 Modelled after half sine wave. More...
 
template<typename genType >
GLM_FUNC_DECL genType sineEaseOut (genType const &a)
 Modelled after quarter-cycle of sine wave (different phase) More...
 
\n

Detailed Description

\n

Include <glm/gtx/easing.hpp> to use the features of this extension.

\n

Easing functions for animations and transitons All functions take a parameter x in the range [0.0,1.0]

\n

Based on the AHEasing project of Warren Moore (https://github.com/warrenm/AHEasing)

\n

Function Documentation

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GLM_FUNC_DECL genType glm::backEaseIn (genType const & a)
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See also
to use the features of this extension. \">GLM_GTX_easing
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GLM_FUNC_DECL genType glm::backEaseIn (genType const & a,
genType const & o 
)
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Parameters
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aparameter
oOptional overshoot modifier
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See also
to use the features of this extension. \">GLM_GTX_easing
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GLM_FUNC_DECL genType glm::backEaseInOut (genType const & a)
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See also
to use the features of this extension. \">GLM_GTX_easing
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GLM_FUNC_DECL genType glm::backEaseInOut (genType const & a,
genType const & o 
)
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Parameters
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aparameter
oOptional overshoot modifier
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See also
to use the features of this extension. \">GLM_GTX_easing
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GLM_FUNC_DECL genType glm::backEaseOut (genType const & a)
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See also
to use the features of this extension. \">GLM_GTX_easing
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GLM_FUNC_DECL genType glm::backEaseOut (genType const & a,
genType const & o 
)
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aparameter
oOptional overshoot modifier
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See also
to use the features of this extension. \">GLM_GTX_easing
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GLM_FUNC_DECL genType glm::bounceEaseIn (genType const & a)
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See also
to use the features of this extension. \">GLM_GTX_easing
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GLM_FUNC_DECL genType glm::bounceEaseInOut (genType const & a)
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See also
to use the features of this extension. \">GLM_GTX_easing
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GLM_FUNC_DECL genType glm::bounceEaseOut (genType const & a)
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See also
to use the features of this extension. \">GLM_GTX_easing
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GLM_FUNC_DECL genType glm::circularEaseIn (genType const & a)
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Modelled after shifted quadrant IV of unit circle.

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See also
to use the features of this extension. \">GLM_GTX_easing
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GLM_FUNC_DECL genType glm::circularEaseInOut (genType const & a)
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Modelled after the piecewise circular function y = (1/2)(1 - sqrt(1 - 4x^2)) ; [0, 0.5) y = (1/2)(sqrt(-(2x - 3)*(2x - 1)) + 1) ; [0.5, 1].

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See also
to use the features of this extension. \">GLM_GTX_easing
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GLM_FUNC_DECL genType glm::circularEaseOut (genType const & a)
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Modelled after shifted quadrant II of unit circle.

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See also
to use the features of this extension. \">GLM_GTX_easing
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GLM_FUNC_DECL genType glm::cubicEaseInOut (genType const & a)
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Modelled after the piecewise cubic y = (1/2)((2x)^3) ; [0, 0.5) y = (1/2)((2x-2)^3 + 2) ; [0.5, 1].

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See also
to use the features of this extension. \">GLM_GTX_easing
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GLM_FUNC_DECL genType glm::cubicEaseOut (genType const & a)
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Modelled after the cubic y = (x - 1)^3 + 1.

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See also
to use the features of this extension. \">GLM_GTX_easing
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GLM_FUNC_DECL genType glm::elasticEaseIn (genType const & a)
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Modelled after the damped sine wave y = sin(13pi/2*x)*pow(2, 10 * (x - 1))

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See also
to use the features of this extension. \">GLM_GTX_easing
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GLM_FUNC_DECL genType glm::elasticEaseInOut (genType const & a)
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Modelled after the piecewise exponentially-damped sine wave: y = (1/2)*sin(13pi/2*(2*x))*pow(2, 10 * ((2*x) - 1)) ; [0,0.5) y = (1/2)*(sin(-13pi/2*((2x-1)+1))*pow(2,-10(2*x-1)) + 2) ; [0.5, 1].

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See also
to use the features of this extension. \">GLM_GTX_easing
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GLM_FUNC_DECL genType glm::elasticEaseOut (genType const & a)
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Modelled after the damped sine wave y = sin(-13pi/2*(x + 1))*pow(2, -10x) + 1.

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See also
to use the features of this extension. \">GLM_GTX_easing
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GLM_FUNC_DECL genType glm::exponentialEaseIn (genType const & a)
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Modelled after the exponential function y = 2^(10(x - 1))

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See also
to use the features of this extension. \">GLM_GTX_easing
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GLM_FUNC_DECL genType glm::exponentialEaseInOut (genType const & a)
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Modelled after the piecewise exponential y = (1/2)2^(10(2x - 1)) ; [0,0.5) y = -(1/2)*2^(-10(2x - 1))) + 1 ; [0.5,1].

\n
See also
to use the features of this extension. \">GLM_GTX_easing
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GLM_FUNC_DECL genType glm::exponentialEaseOut (genType const & a)
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Modelled after the exponential function y = -2^(-10x) + 1.

\n
See also
to use the features of this extension. \">GLM_GTX_easing
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GLM_FUNC_DECL genType glm::linearInterpolation (genType const & a)
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Modelled after the line y = x.

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See also
to use the features of this extension. \">GLM_GTX_easing
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GLM_FUNC_DECL genType glm::quadraticEaseIn (genType const & a)
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Modelled after the parabola y = x^2.

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See also
to use the features of this extension. \">GLM_GTX_easing
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GLM_FUNC_DECL genType glm::quadraticEaseInOut (genType const & a)
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Modelled after the piecewise quadratic y = (1/2)((2x)^2) ; [0, 0.5) y = -(1/2)((2x-1)*(2x-3) - 1) ; [0.5, 1].

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See also
to use the features of this extension. \">GLM_GTX_easing
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GLM_FUNC_DECL genType glm::quadraticEaseOut (genType const & a)
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Modelled after the parabola y = -x^2 + 2x.

\n
See also
to use the features of this extension. \">GLM_GTX_easing
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GLM_FUNC_DECL genType glm::quarticEaseIn (genType const & a)
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Modelled after the quartic x^4.

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See also
to use the features of this extension. \">GLM_GTX_easing
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GLM_FUNC_DECL genType glm::quarticEaseInOut (genType const & a)
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Modelled after the piecewise quartic y = (1/2)((2x)^4) ; [0, 0.5) y = -(1/2)((2x-2)^4 - 2) ; [0.5, 1].

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See also
to use the features of this extension. \">GLM_GTX_easing
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GLM_FUNC_DECL genType glm::quarticEaseOut (genType const & a)
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Modelled after the quartic y = 1 - (x - 1)^4.

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See also
to use the features of this extension. \">GLM_GTX_easing
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GLM_FUNC_DECL genType glm::quinticEaseIn (genType const & a)
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Modelled after the quintic y = x^5.

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to use the features of this extension. \">GLM_GTX_easing
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GLM_FUNC_DECL genType glm::quinticEaseInOut (genType const & a)
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Modelled after the piecewise quintic y = (1/2)((2x)^5) ; [0, 0.5) y = (1/2)((2x-2)^5 + 2) ; [0.5, 1].

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See also
to use the features of this extension. \">GLM_GTX_easing
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GLM_FUNC_DECL genType glm::quinticEaseOut (genType const & a)
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Modelled after the quintic y = (x - 1)^5 + 1.

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to use the features of this extension. \">GLM_GTX_easing
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GLM_FUNC_DECL genType glm::sineEaseIn (genType const & a)
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Modelled after quarter-cycle of sine wave.

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to use the features of this extension. \">GLM_GTX_easing
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GLM_FUNC_DECL genType glm::sineEaseInOut (genType const & a)
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Modelled after half sine wave.

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to use the features of this extension. \">GLM_GTX_easing
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GLM_FUNC_DECL genType glm::sineEaseOut (genType const & a)
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Modelled after quarter-cycle of sine wave (different phase)

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See also
to use the features of this extension. \">GLM_GTX_easing
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\nGenerated by  \n\"doxygen\"/\n 1.8.10\n
\n\n\n" }, { "path": "field_construction/submodules/diff-langsurf-rasterizer/third_party/glm/doc/api/a00319.html", "content": "\n\n\n\n\n\n0.9.9 API documentation: GLM_GTX_euler_angles\n\n\n\n\n\n\n\n\n\n\n
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0.9.9 API documentation\n
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\nFunctions
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GLM_GTX_euler_angles
Experimental extensions
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Include <glm/gtx/euler_angles.hpp> to use the features of this extension. \nMore...

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\nFunctions

template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > derivedEulerAngleX (T const &angleX, T const &angularVelocityX)
 Creates a 3D 4 * 4 homogeneous derived matrix from the rotation matrix about X-axis. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > derivedEulerAngleY (T const &angleY, T const &angularVelocityY)
 Creates a 3D 4 * 4 homogeneous derived matrix from the rotation matrix about Y-axis. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > derivedEulerAngleZ (T const &angleZ, T const &angularVelocityZ)
 Creates a 3D 4 * 4 homogeneous derived matrix from the rotation matrix about Z-axis. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > eulerAngleX (T const &angleX)
 Creates a 3D 4 * 4 homogeneous rotation matrix from an euler angle X. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > eulerAngleXY (T const &angleX, T const &angleY)
 Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (X * Y). More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > eulerAngleXYX (T const &t1, T const &t2, T const &t3)
 Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (X * Y * X). More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > eulerAngleXYZ (T const &t1, T const &t2, T const &t3)
 Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (X * Y * Z). More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > eulerAngleXZ (T const &angleX, T const &angleZ)
 Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (X * Z). More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > eulerAngleXZX (T const &t1, T const &t2, T const &t3)
 Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (X * Z * X). More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > eulerAngleXZY (T const &t1, T const &t2, T const &t3)
 Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (X * Z * Y). More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > eulerAngleY (T const &angleY)
 Creates a 3D 4 * 4 homogeneous rotation matrix from an euler angle Y. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > eulerAngleYX (T const &angleY, T const &angleX)
 Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (Y * X). More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > eulerAngleYXY (T const &t1, T const &t2, T const &t3)
 Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (Y * X * Y). More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > eulerAngleYXZ (T const &yaw, T const &pitch, T const &roll)
 Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (Y * X * Z). More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > eulerAngleYZ (T const &angleY, T const &angleZ)
 Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (Y * Z). More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > eulerAngleYZX (T const &t1, T const &t2, T const &t3)
 Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (Y * Z * X). More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > eulerAngleYZY (T const &t1, T const &t2, T const &t3)
 Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (Y * Z * Y). More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > eulerAngleZ (T const &angleZ)
 Creates a 3D 4 * 4 homogeneous rotation matrix from an euler angle Z. More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > eulerAngleZX (T const &angle, T const &angleX)
 Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (Z * X). More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > eulerAngleZXY (T const &t1, T const &t2, T const &t3)
 Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (Z * X * Y). More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > eulerAngleZXZ (T const &t1, T const &t2, T const &t3)
 Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (Z * X * Z). More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > eulerAngleZY (T const &angleZ, T const &angleY)
 Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (Z * Y). More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > eulerAngleZYX (T const &t1, T const &t2, T const &t3)
 Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (Z * Y * X). More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > eulerAngleZYZ (T const &t1, T const &t2, T const &t3)
 Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (Z * Y * Z). More...
 
template<typename T >
GLM_FUNC_DECL void extractEulerAngleXYX (mat< 4, 4, T, defaultp > const &M, T &t1, T &t2, T &t3)
 Extracts the (X * Y * X) Euler angles from the rotation matrix M. More...
 
template<typename T >
GLM_FUNC_DECL void extractEulerAngleXYZ (mat< 4, 4, T, defaultp > const &M, T &t1, T &t2, T &t3)
 Extracts the (X * Y * Z) Euler angles from the rotation matrix M. More...
 
template<typename T >
GLM_FUNC_DECL void extractEulerAngleXZX (mat< 4, 4, T, defaultp > const &M, T &t1, T &t2, T &t3)
 Extracts the (X * Z * X) Euler angles from the rotation matrix M. More...
 
template<typename T >
GLM_FUNC_DECL void extractEulerAngleXZY (mat< 4, 4, T, defaultp > const &M, T &t1, T &t2, T &t3)
 Extracts the (X * Z * Y) Euler angles from the rotation matrix M. More...
 
template<typename T >
GLM_FUNC_DECL void extractEulerAngleYXY (mat< 4, 4, T, defaultp > const &M, T &t1, T &t2, T &t3)
 Extracts the (Y * X * Y) Euler angles from the rotation matrix M. More...
 
template<typename T >
GLM_FUNC_DECL void extractEulerAngleYXZ (mat< 4, 4, T, defaultp > const &M, T &t1, T &t2, T &t3)
 Extracts the (Y * X * Z) Euler angles from the rotation matrix M. More...
 
template<typename T >
GLM_FUNC_DECL void extractEulerAngleYZX (mat< 4, 4, T, defaultp > const &M, T &t1, T &t2, T &t3)
 Extracts the (Y * Z * X) Euler angles from the rotation matrix M. More...
 
template<typename T >
GLM_FUNC_DECL void extractEulerAngleYZY (mat< 4, 4, T, defaultp > const &M, T &t1, T &t2, T &t3)
 Extracts the (Y * Z * Y) Euler angles from the rotation matrix M. More...
 
template<typename T >
GLM_FUNC_DECL void extractEulerAngleZXY (mat< 4, 4, T, defaultp > const &M, T &t1, T &t2, T &t3)
 Extracts the (Z * X * Y) Euler angles from the rotation matrix M. More...
 
template<typename T >
GLM_FUNC_DECL void extractEulerAngleZXZ (mat< 4, 4, T, defaultp > const &M, T &t1, T &t2, T &t3)
 Extracts the (Z * X * Z) Euler angles from the rotation matrix M. More...
 
template<typename T >
GLM_FUNC_DECL void extractEulerAngleZYX (mat< 4, 4, T, defaultp > const &M, T &t1, T &t2, T &t3)
 Extracts the (Z * Y * X) Euler angles from the rotation matrix M. More...
 
template<typename T >
GLM_FUNC_DECL void extractEulerAngleZYZ (mat< 4, 4, T, defaultp > const &M, T &t1, T &t2, T &t3)
 Extracts the (Z * Y * Z) Euler angles from the rotation matrix M. More...
 
template<typename T >
GLM_FUNC_DECL mat< 2, 2, T, defaultp > orientate2 (T const &angle)
 Creates a 2D 2 * 2 rotation matrix from an euler angle. More...
 
template<typename T >
GLM_FUNC_DECL mat< 3, 3, T, defaultp > orientate3 (T const &angle)
 Creates a 2D 4 * 4 homogeneous rotation matrix from an euler angle. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 3, 3, T, Q > orientate3 (vec< 3, T, Q > const &angles)
 Creates a 3D 3 * 3 rotation matrix from euler angles (Y * X * Z). More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 4, 4, T, Q > orientate4 (vec< 3, T, Q > const &angles)
 Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (Y * X * Z). More...
 
template<typename T >
GLM_FUNC_DECL mat< 4, 4, T, defaultp > yawPitchRoll (T const &yaw, T const &pitch, T const &roll)
 Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (Y * X * Z). More...
 
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Detailed Description

\n

Include <glm/gtx/euler_angles.hpp> to use the features of this extension.

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Build matrices from Euler angles.

\n

Extraction of Euler angles from rotation matrix. Based on the original paper 2014 Mike Day - Extracting Euler Angles from a Rotation Matrix.

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Function Documentation

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GLM_FUNC_DECL mat<4, 4, T, defaultp> glm::derivedEulerAngleX (T const & angleX,
T const & angularVelocityX 
)
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Creates a 3D 4 * 4 homogeneous derived matrix from the rotation matrix about X-axis.

\n
See also
to use the features of this extension. \">GLM_GTX_euler_angles
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GLM_FUNC_DECL mat<4, 4, T, defaultp> glm::derivedEulerAngleY (T const & angleY,
T const & angularVelocityY 
)
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Creates a 3D 4 * 4 homogeneous derived matrix from the rotation matrix about Y-axis.

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See also
to use the features of this extension. \">GLM_GTX_euler_angles
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GLM_FUNC_DECL mat<4, 4, T, defaultp> glm::derivedEulerAngleZ (T const & angleZ,
T const & angularVelocityZ 
)
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Creates a 3D 4 * 4 homogeneous derived matrix from the rotation matrix about Z-axis.

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See also
to use the features of this extension. \">GLM_GTX_euler_angles
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GLM_FUNC_DECL mat<4, 4, T, defaultp> glm::eulerAngleX (T const & angleX)
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Creates a 3D 4 * 4 homogeneous rotation matrix from an euler angle X.

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See also
to use the features of this extension. \">GLM_GTX_euler_angles
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GLM_FUNC_DECL mat<4, 4, T, defaultp> glm::eulerAngleXY (T const & angleX,
T const & angleY 
)
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Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (X * Y).

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See also
to use the features of this extension. \">GLM_GTX_euler_angles
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GLM_FUNC_DECL mat<4, 4, T, defaultp> glm::eulerAngleXYX (T const & t1,
T const & t2,
T const & t3 
)
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Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (X * Y * X).

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See also
to use the features of this extension. \">GLM_GTX_euler_angles
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GLM_FUNC_DECL mat<4, 4, T, defaultp> glm::eulerAngleXYZ (T const & t1,
T const & t2,
T const & t3 
)
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Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (X * Y * Z).

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See also
to use the features of this extension. \">GLM_GTX_euler_angles
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GLM_FUNC_DECL mat<4, 4, T, defaultp> glm::eulerAngleXZ (T const & angleX,
T const & angleZ 
)
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Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (X * Z).

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See also
to use the features of this extension. \">GLM_GTX_euler_angles
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GLM_FUNC_DECL mat<4, 4, T, defaultp> glm::eulerAngleXZX (T const & t1,
T const & t2,
T const & t3 
)
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Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (X * Z * X).

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See also
to use the features of this extension. \">GLM_GTX_euler_angles
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GLM_FUNC_DECL mat<4, 4, T, defaultp> glm::eulerAngleXZY (T const & t1,
T const & t2,
T const & t3 
)
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Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (X * Z * Y).

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See also
to use the features of this extension. \">GLM_GTX_euler_angles
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GLM_FUNC_DECL mat<4, 4, T, defaultp> glm::eulerAngleY (T const & angleY)
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Creates a 3D 4 * 4 homogeneous rotation matrix from an euler angle Y.

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See also
to use the features of this extension. \">GLM_GTX_euler_angles
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GLM_FUNC_DECL mat<4, 4, T, defaultp> glm::eulerAngleYX (T const & angleY,
T const & angleX 
)
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Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (Y * X).

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See also
to use the features of this extension. \">GLM_GTX_euler_angles
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GLM_FUNC_DECL mat<4, 4, T, defaultp> glm::eulerAngleYXY (T const & t1,
T const & t2,
T const & t3 
)
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Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (Y * X * Y).

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See also
to use the features of this extension. \">GLM_GTX_euler_angles
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GLM_FUNC_DECL mat<4, 4, T, defaultp> glm::eulerAngleYXZ (T const & yaw,
T const & pitch,
T const & roll 
)
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Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (Y * X * Z).

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See also
to use the features of this extension. \">GLM_GTX_euler_angles
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GLM_FUNC_DECL mat<4, 4, T, defaultp> glm::eulerAngleYZ (T const & angleY,
T const & angleZ 
)
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Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (Y * Z).

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See also
to use the features of this extension. \">GLM_GTX_euler_angles
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GLM_FUNC_DECL mat<4, 4, T, defaultp> glm::eulerAngleYZX (T const & t1,
T const & t2,
T const & t3 
)
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Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (Y * Z * X).

\n
See also
to use the features of this extension. \">GLM_GTX_euler_angles
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GLM_FUNC_DECL mat<4, 4, T, defaultp> glm::eulerAngleYZY (T const & t1,
T const & t2,
T const & t3 
)
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Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (Y * Z * Y).

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See also
to use the features of this extension. \">GLM_GTX_euler_angles
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GLM_FUNC_DECL mat<4, 4, T, defaultp> glm::eulerAngleZ (T const & angleZ)
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Creates a 3D 4 * 4 homogeneous rotation matrix from an euler angle Z.

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See also
to use the features of this extension. \">GLM_GTX_euler_angles
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GLM_FUNC_DECL mat<4, 4, T, defaultp> glm::eulerAngleZX (T const & angle,
T const & angleX 
)
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Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (Z * X).

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See also
to use the features of this extension. \">GLM_GTX_euler_angles
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GLM_FUNC_DECL mat<4, 4, T, defaultp> glm::eulerAngleZXY (T const & t1,
T const & t2,
T const & t3 
)
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Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (Z * X * Y).

\n
See also
to use the features of this extension. \">GLM_GTX_euler_angles
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GLM_FUNC_DECL mat<4, 4, T, defaultp> glm::eulerAngleZXZ (T const & t1,
T const & t2,
T const & t3 
)
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Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (Z * X * Z).

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to use the features of this extension. \">GLM_GTX_euler_angles
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GLM_FUNC_DECL mat<4, 4, T, defaultp> glm::eulerAngleZY (T const & angleZ,
T const & angleY 
)
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Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (Z * Y).

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to use the features of this extension. \">GLM_GTX_euler_angles
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GLM_FUNC_DECL mat<4, 4, T, defaultp> glm::eulerAngleZYX (T const & t1,
T const & t2,
T const & t3 
)
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Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (Z * Y * X).

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to use the features of this extension. \">GLM_GTX_euler_angles
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GLM_FUNC_DECL mat<4, 4, T, defaultp> glm::eulerAngleZYZ (T const & t1,
T const & t2,
T const & t3 
)
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Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (Z * Y * Z).

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to use the features of this extension. \">GLM_GTX_euler_angles
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GLM_FUNC_DECL void glm::extractEulerAngleXYX (mat< 4, 4, T, defaultp > const & M,
T & t1,
T & t2,
T & t3 
)
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Extracts the (X * Y * X) Euler angles from the rotation matrix M.

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to use the features of this extension. \">GLM_GTX_euler_angles
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GLM_FUNC_DECL void glm::extractEulerAngleXYZ (mat< 4, 4, T, defaultp > const & M,
T & t1,
T & t2,
T & t3 
)
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Extracts the (X * Y * Z) Euler angles from the rotation matrix M.

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to use the features of this extension. \">GLM_GTX_euler_angles
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GLM_FUNC_DECL void glm::extractEulerAngleXZX (mat< 4, 4, T, defaultp > const & M,
T & t1,
T & t2,
T & t3 
)
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Extracts the (X * Z * X) Euler angles from the rotation matrix M.

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to use the features of this extension. \">GLM_GTX_euler_angles
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GLM_FUNC_DECL void glm::extractEulerAngleXZY (mat< 4, 4, T, defaultp > const & M,
T & t1,
T & t2,
T & t3 
)
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Extracts the (X * Z * Y) Euler angles from the rotation matrix M.

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to use the features of this extension. \">GLM_GTX_euler_angles
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GLM_FUNC_DECL void glm::extractEulerAngleYXY (mat< 4, 4, T, defaultp > const & M,
T & t1,
T & t2,
T & t3 
)
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Extracts the (Y * X * Y) Euler angles from the rotation matrix M.

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to use the features of this extension. \">GLM_GTX_euler_angles
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GLM_FUNC_DECL void glm::extractEulerAngleYXZ (mat< 4, 4, T, defaultp > const & M,
T & t1,
T & t2,
T & t3 
)
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Extracts the (Y * X * Z) Euler angles from the rotation matrix M.

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to use the features of this extension. \">GLM_GTX_euler_angles
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GLM_FUNC_DECL void glm::extractEulerAngleYZX (mat< 4, 4, T, defaultp > const & M,
T & t1,
T & t2,
T & t3 
)
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Extracts the (Y * Z * X) Euler angles from the rotation matrix M.

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to use the features of this extension. \">GLM_GTX_euler_angles
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GLM_FUNC_DECL void glm::extractEulerAngleYZY (mat< 4, 4, T, defaultp > const & M,
T & t1,
T & t2,
T & t3 
)
\n
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Extracts the (Y * Z * Y) Euler angles from the rotation matrix M.

\n
See also
to use the features of this extension. \">GLM_GTX_euler_angles
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GLM_FUNC_DECL void glm::extractEulerAngleZXY (mat< 4, 4, T, defaultp > const & M,
T & t1,
T & t2,
T & t3 
)
\n
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Extracts the (Z * X * Y) Euler angles from the rotation matrix M.

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See also
to use the features of this extension. \">GLM_GTX_euler_angles
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GLM_FUNC_DECL void glm::extractEulerAngleZXZ (mat< 4, 4, T, defaultp > const & M,
T & t1,
T & t2,
T & t3 
)
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Extracts the (Z * X * Z) Euler angles from the rotation matrix M.

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See also
to use the features of this extension. \">GLM_GTX_euler_angles
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GLM_FUNC_DECL void glm::extractEulerAngleZYX (mat< 4, 4, T, defaultp > const & M,
T & t1,
T & t2,
T & t3 
)
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Extracts the (Z * Y * X) Euler angles from the rotation matrix M.

\n
See also
to use the features of this extension. \">GLM_GTX_euler_angles
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GLM_FUNC_DECL void glm::extractEulerAngleZYZ (mat< 4, 4, T, defaultp > const & M,
T & t1,
T & t2,
T & t3 
)
\n
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Extracts the (Z * Y * Z) Euler angles from the rotation matrix M.

\n
See also
to use the features of this extension. \">GLM_GTX_euler_angles
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GLM_FUNC_DECL mat<2, 2, T, defaultp> glm::orientate2 (T const & angle)
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Creates a 2D 2 * 2 rotation matrix from an euler angle.

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See also
to use the features of this extension. \">GLM_GTX_euler_angles
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GLM_FUNC_DECL mat<3, 3, T, defaultp> glm::orientate3 (T const & angle)
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Creates a 2D 4 * 4 homogeneous rotation matrix from an euler angle.

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See also
to use the features of this extension. \">GLM_GTX_euler_angles
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GLM_FUNC_DECL mat<3, 3, T, Q> glm::orientate3 (vec< 3, T, Q > const & angles)
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Creates a 3D 3 * 3 rotation matrix from euler angles (Y * X * Z).

\n
See also
to use the features of this extension. \">GLM_GTX_euler_angles
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GLM_FUNC_DECL mat<4, 4, T, Q> glm::orientate4 (vec< 3, T, Q > const & angles)
\n
\n\n

Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (Y * X * Z).

\n
See also
to use the features of this extension. \">GLM_GTX_euler_angles
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GLM_FUNC_DECL mat<4, 4, T, defaultp> glm::yawPitchRoll (T const & yaw,
T const & pitch,
T const & roll 
)
\n
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Creates a 3D 4 * 4 homogeneous rotation matrix from euler angles (Y * X * Z).

\n
See also
to use the features of this extension. \">GLM_GTX_euler_angles
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GLM_GTX_extend
Experimental extensions
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Include <glm/gtx/extend.hpp> to use the features of this extension. \nMore...

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\nFunctions

template<typename genType >
GLM_FUNC_DECL genType extend (genType const &Origin, genType const &Source, typename genType::value_type const Length)
 Extends of Length the Origin position using the (Source - Origin) direction. More...
 
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Detailed Description

\n

Include <glm/gtx/extend.hpp> to use the features of this extension.

\n

Extend a position from a source to a position at a defined length.

\n

Function Documentation

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GLM_FUNC_DECL genType glm::extend (genType const & Origin,
genType const & Source,
typename genType::value_type const Length 
)
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Extends of Length the Origin position using the (Source - Origin) direction.

\n
See also
to use the features of this extension. \">GLM_GTX_extend
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\nGenerated by  \n\"doxygen\"/\n 1.8.10\n
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GLM_GTX_extented_min_max
Experimental extensions
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Include <glm/gtx/extented_min_max.hpp> to use the features of this extension. \nMore...

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\nFunctions

template<typename genType >
GLM_FUNC_DECL genType fclamp (genType x, genType minVal, genType maxVal)
 Returns min(max(x, minVal), maxVal) for each component in x. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > fclamp (vec< L, T, Q > const &x, T minVal, T maxVal)
 Returns min(max(x, minVal), maxVal) for each component in x. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > fclamp (vec< L, T, Q > const &x, vec< L, T, Q > const &minVal, vec< L, T, Q > const &maxVal)
 Returns min(max(x, minVal), maxVal) for each component in x. More...
 
template<typename genType >
GLM_FUNC_DECL genType fmax (genType x, genType y)
 Returns y if x < y; otherwise, it returns x. More...
 
template<typename genType >
GLM_FUNC_DECL genType fmin (genType x, genType y)
 Returns y if y < x; otherwise, it returns x. More...
 
template<typename T >
GLM_FUNC_DECL T max (T const &x, T const &y, T const &z)
 Return the maximum component-wise values of 3 inputs. More...
 
template<typename T , template< typename > class C>
GLM_FUNC_DECL C< T > max (C< T > const &x, typename C< T >::T const &y, typename C< T >::T const &z)
 Return the maximum component-wise values of 3 inputs. More...
 
template<typename T , template< typename > class C>
GLM_FUNC_DECL C< T > max (C< T > const &x, C< T > const &y, C< T > const &z)
 Return the maximum component-wise values of 3 inputs. More...
 
template<typename T >
GLM_FUNC_DECL T max (T const &x, T const &y, T const &z, T const &w)
 Return the maximum component-wise values of 4 inputs. More...
 
template<typename T , template< typename > class C>
GLM_FUNC_DECL C< T > max (C< T > const &x, typename C< T >::T const &y, typename C< T >::T const &z, typename C< T >::T const &w)
 Return the maximum component-wise values of 4 inputs. More...
 
template<typename T , template< typename > class C>
GLM_FUNC_DECL C< T > max (C< T > const &x, C< T > const &y, C< T > const &z, C< T > const &w)
 Return the maximum component-wise values of 4 inputs. More...
 
template<typename T >
GLM_FUNC_DECL T min (T const &x, T const &y, T const &z)
 Return the minimum component-wise values of 3 inputs. More...
 
template<typename T , template< typename > class C>
GLM_FUNC_DECL C< T > min (C< T > const &x, typename C< T >::T const &y, typename C< T >::T const &z)
 Return the minimum component-wise values of 3 inputs. More...
 
template<typename T , template< typename > class C>
GLM_FUNC_DECL C< T > min (C< T > const &x, C< T > const &y, C< T > const &z)
 Return the minimum component-wise values of 3 inputs. More...
 
template<typename T >
GLM_FUNC_DECL T min (T const &x, T const &y, T const &z, T const &w)
 Return the minimum component-wise values of 4 inputs. More...
 
template<typename T , template< typename > class C>
GLM_FUNC_DECL C< T > min (C< T > const &x, typename C< T >::T const &y, typename C< T >::T const &z, typename C< T >::T const &w)
 Return the minimum component-wise values of 4 inputs. More...
 
template<typename T , template< typename > class C>
GLM_FUNC_DECL C< T > min (C< T > const &x, C< T > const &y, C< T > const &z, C< T > const &w)
 Return the minimum component-wise values of 4 inputs. More...
 
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Detailed Description

\n

Include <glm/gtx/extented_min_max.hpp> to use the features of this extension.

\n

Min and max functions for 3 to 4 parameters.

\n

Function Documentation

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GLM_FUNC_DECL genType glm::fclamp (genType x,
genType minVal,
genType maxVal 
)
\n
\n\n

Returns min(max(x, minVal), maxVal) for each component in x.

\n

If one of the two arguments is NaN, the value of the other argument is returned.

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Template Parameters
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genTypeFloating-point scalar or vector types.
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See also
gtx_extented_min_max
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GLM_FUNC_DECL vec<L, T, Q> glm::fclamp (vec< L, T, Q > const & x,
T minVal,
T maxVal 
)
\n
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Returns min(max(x, minVal), maxVal) for each component in x.

\n

If one of the two arguments is NaN, the value of the other argument is returned.

\n
Template Parameters
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LInteger between 1 and 4 included that qualify the dimension of the vector
TFloating-point scalar types
QValue from qualifier enum
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gtx_extented_min_max
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GLM_FUNC_DECL vec<L, T, Q> glm::fclamp (vec< L, T, Q > const & x,
vec< L, T, Q > const & minVal,
vec< L, T, Q > const & maxVal 
)
\n
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Returns min(max(x, minVal), maxVal) for each component in x.

\n

If one of the two arguments is NaN, the value of the other argument is returned.

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Template Parameters
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LInteger between 1 and 4 included that qualify the dimension of the vector
TFloating-point scalar types
QValue from qualifier enum
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See also
gtx_extented_min_max
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GLM_FUNC_DECL genType glm::fmax (genType x,
genType y 
)
\n
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Returns y if x < y; otherwise, it returns x.

\n

If one of the two arguments is NaN, the value of the other argument is returned.

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genTypeFloating-point; scalar or vector types.
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See also
gtx_extented_min_max
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\nstd::fmax documentation
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GLM_FUNC_DECL genType glm::fmin (genType x,
genType y 
)
\n
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Returns y if y < x; otherwise, it returns x.

\n

If one of the two arguments is NaN, the value of the other argument is returned.

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Template Parameters
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genTypeFloating-point or integer; scalar or vector types.
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See also
gtx_extented_min_max
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GLM_FUNC_DECL T glm::max (T const & x,
T const & y,
T const & z 
)
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Return the maximum component-wise values of 3 inputs.

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gtx_extented_min_max
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GLM_FUNC_DECL C<T> glm::max (C< T > const & x,
typename C< T >::T const & y,
typename C< T >::T const & z 
)
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Return the maximum component-wise values of 3 inputs.

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gtx_extented_min_max
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GLM_FUNC_DECL C<T> glm::max (C< T > const & x,
C< T > const & y,
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)
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Return the maximum component-wise values of 3 inputs.

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gtx_extented_min_max
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GLM_FUNC_DECL T glm::max (T const & x,
T const & y,
T const & z,
T const & w 
)
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Return the maximum component-wise values of 4 inputs.

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gtx_extented_min_max
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GLM_FUNC_DECL C<T> glm::max (C< T > const & x,
typename C< T >::T const & y,
typename C< T >::T const & z,
typename C< T >::T const & w 
)
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Return the maximum component-wise values of 4 inputs.

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See also
gtx_extented_min_max
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GLM_FUNC_DECL C<T> glm::max (C< T > const & x,
C< T > const & y,
C< T > const & z,
C< T > const & w 
)
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Return the maximum component-wise values of 4 inputs.

\n
See also
gtx_extented_min_max
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GLM_FUNC_DECL T glm::min (T const & x,
T const & y,
T const & z 
)
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Return the minimum component-wise values of 3 inputs.

\n
See also
gtx_extented_min_max
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GLM_FUNC_DECL C<T> glm::min (C< T > const & x,
typename C< T >::T const & y,
typename C< T >::T const & z 
)
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\n\n

Return the minimum component-wise values of 3 inputs.

\n
See also
gtx_extented_min_max
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GLM_FUNC_DECL C<T> glm::min (C< T > const & x,
C< T > const & y,
C< T > const & z 
)
\n
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Return the minimum component-wise values of 3 inputs.

\n
See also
gtx_extented_min_max
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GLM_FUNC_DECL T glm::min (T const & x,
T const & y,
T const & z,
T const & w 
)
\n
\n\n

Return the minimum component-wise values of 4 inputs.

\n
See also
gtx_extented_min_max
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GLM_FUNC_DECL C<T> glm::min (C< T > const & x,
typename C< T >::T const & y,
typename C< T >::T const & z,
typename C< T >::T const & w 
)
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Return the minimum component-wise values of 4 inputs.

\n
See also
gtx_extented_min_max
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GLM_FUNC_DECL C<T> glm::min (C< T > const & x,
C< T > const & y,
C< T > const & z,
C< T > const & w 
)
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\n\n

Return the minimum component-wise values of 4 inputs.

\n
See also
gtx_extented_min_max
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Experimental extensions
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Include <glm/gtx/exterior_product.hpp> to use the features of this extension. \nMore...

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\nFunctions

template<typename T , qualifier Q>
GLM_FUNC_DECL T cross (vec< 2, T, Q > const &v, vec< 2, T, Q > const &u)
 Returns the cross product of x and y. More...
 
\n

Detailed Description

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Include <glm/gtx/exterior_product.hpp> to use the features of this extension.

\n

Allow to perform bit operations on integer values

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Function Documentation

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GLM_FUNC_DECL T glm::cross (vec< 2, T, Q > const & v,
vec< 2, T, Q > const & u 
)
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Returns the cross product of x and y.

\n
Template Parameters
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TFloating-point scalar types
QValue from qualifier enum
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See also
Exterior product
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Experimental extensions
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Include <glm/gtx/fast_exponential.hpp> to use the features of this extension. \nMore...

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\nFunctions

template<typename T >
GLM_FUNC_DECL T fastExp (T x)
 Faster than the common exp function but less accurate. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > fastExp (vec< L, T, Q > const &x)
 Faster than the common exp function but less accurate. More...
 
template<typename T >
GLM_FUNC_DECL T fastExp2 (T x)
 Faster than the common exp2 function but less accurate. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > fastExp2 (vec< L, T, Q > const &x)
 Faster than the common exp2 function but less accurate. More...
 
template<typename T >
GLM_FUNC_DECL T fastLog (T x)
 Faster than the common log function but less accurate. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > fastLog (vec< L, T, Q > const &x)
 Faster than the common exp2 function but less accurate. More...
 
template<typename T >
GLM_FUNC_DECL T fastLog2 (T x)
 Faster than the common log2 function but less accurate. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > fastLog2 (vec< L, T, Q > const &x)
 Faster than the common log2 function but less accurate. More...
 
template<typename genType >
GLM_FUNC_DECL genType fastPow (genType x, genType y)
 Faster than the common pow function but less accurate. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > fastPow (vec< L, T, Q > const &x, vec< L, T, Q > const &y)
 Faster than the common pow function but less accurate. More...
 
template<typename genTypeT , typename genTypeU >
GLM_FUNC_DECL genTypeT fastPow (genTypeT x, genTypeU y)
 Faster than the common pow function but less accurate. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > fastPow (vec< L, T, Q > const &x)
 Faster than the common pow function but less accurate. More...
 
\n

Detailed Description

\n

Include <glm/gtx/fast_exponential.hpp> to use the features of this extension.

\n

Fast but less accurate implementations of exponential based functions.

\n

Function Documentation

\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL T glm::fastExp (T x)
\n
\n\n

Faster than the common exp function but less accurate.

\n
See also
to use the features of this extension. \">GLM_GTX_fast_exponential
\n\n
\n
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\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL vec<L, T, Q> glm::fastExp (vec< L, T, Q > const & x)
\n
\n\n

Faster than the common exp function but less accurate.

\n
See also
to use the features of this extension. \">GLM_GTX_fast_exponential
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GLM_FUNC_DECL T glm::fastExp2 (T x)
\n
\n\n

Faster than the common exp2 function but less accurate.

\n
See also
to use the features of this extension. \">GLM_GTX_fast_exponential
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GLM_FUNC_DECL vec<L, T, Q> glm::fastExp2 (vec< L, T, Q > const & x)
\n
\n\n

Faster than the common exp2 function but less accurate.

\n
See also
to use the features of this extension. \">GLM_GTX_fast_exponential
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\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL T glm::fastLog (T x)
\n
\n\n

Faster than the common log function but less accurate.

\n
See also
to use the features of this extension. \">GLM_GTX_fast_exponential
\n\n
\n
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\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL vec<L, T, Q> glm::fastLog (vec< L, T, Q > const & x)
\n
\n\n

Faster than the common exp2 function but less accurate.

\n
See also
to use the features of this extension. \">GLM_GTX_fast_exponential
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GLM_FUNC_DECL T glm::fastLog2 (T x)
\n
\n\n

Faster than the common log2 function but less accurate.

\n
See also
to use the features of this extension. \">GLM_GTX_fast_exponential
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GLM_FUNC_DECL vec<L, T, Q> glm::fastLog2 (vec< L, T, Q > const & x)
\n
\n\n

Faster than the common log2 function but less accurate.

\n
See also
to use the features of this extension. \">GLM_GTX_fast_exponential
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GLM_FUNC_DECL genType glm::fastPow (genType x,
genType y 
)
\n
\n\n

Faster than the common pow function but less accurate.

\n
See also
to use the features of this extension. \">GLM_GTX_fast_exponential
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GLM_FUNC_DECL vec<L, T, Q> glm::fastPow (vec< L, T, Q > const & x,
vec< L, T, Q > const & y 
)
\n
\n\n

Faster than the common pow function but less accurate.

\n
See also
to use the features of this extension. \">GLM_GTX_fast_exponential
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GLM_FUNC_DECL genTypeT glm::fastPow (genTypeT x,
genTypeU y 
)
\n
\n\n

Faster than the common pow function but less accurate.

\n
See also
to use the features of this extension. \">GLM_GTX_fast_exponential
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GLM_FUNC_DECL vec<L, T, Q> glm::fastPow (vec< L, T, Q > const & x)
\n
\n\n

Faster than the common pow function but less accurate.

\n
See also
to use the features of this extension. \">GLM_GTX_fast_exponential
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\nGenerated by  \n\"doxygen\"/\n 1.8.10\n
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Experimental extensions
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\nFunctions

template<typename genType >
GLM_FUNC_DECL genType fastDistance (genType x, genType y)
 Faster than the common distance function but less accurate. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL T fastDistance (vec< L, T, Q > const &x, vec< L, T, Q > const &y)
 Faster than the common distance function but less accurate. More...
 
template<typename genType >
GLM_FUNC_DECL genType fastInverseSqrt (genType x)
 Faster than the common inversesqrt function but less accurate. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > fastInverseSqrt (vec< L, T, Q > const &x)
 Faster than the common inversesqrt function but less accurate. More...
 
template<typename genType >
GLM_FUNC_DECL genType fastLength (genType x)
 Faster than the common length function but less accurate. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL T fastLength (vec< L, T, Q > const &x)
 Faster than the common length function but less accurate. More...
 
template<typename genType >
GLM_FUNC_DECL genType fastNormalize (genType const &x)
 Faster than the common normalize function but less accurate. More...
 
template<typename genType >
GLM_FUNC_DECL genType fastSqrt (genType x)
 Faster than the common sqrt function but less accurate. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > fastSqrt (vec< L, T, Q > const &x)
 Faster than the common sqrt function but less accurate. More...
 
\n

Detailed Description

\n

Include <glm/gtx/fast_square_root.hpp> to use the features of this extension.

\n

Fast but less accurate implementations of square root based functions.

    \n
  • Sqrt optimisation based on Newton's method, www.gamedev.net/community/forums/topic.asp?topic id=139956
    • \n
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Function Documentation

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GLM_FUNC_DECL genType glm::fastDistance (genType x,
genType y 
)
\n
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Faster than the common distance function but less accurate.

\n
See also
to use the features of this extension. \">GLM_GTX_fast_square_root extension.
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GLM_FUNC_DECL T glm::fastDistance (vec< L, T, Q > const & x,
vec< L, T, Q > const & y 
)
\n
\n\n

Faster than the common distance function but less accurate.

\n
See also
to use the features of this extension. \">GLM_GTX_fast_square_root extension.
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GLM_FUNC_DECL genType glm::fastInverseSqrt (genType x)
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Faster than the common inversesqrt function but less accurate.

\n
See also
to use the features of this extension. \">GLM_GTX_fast_square_root extension.
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GLM_FUNC_DECL vec<L, T, Q> glm::fastInverseSqrt (vec< L, T, Q > const & x)
\n
\n\n

Faster than the common inversesqrt function but less accurate.

\n
See also
to use the features of this extension. \">GLM_GTX_fast_square_root extension.
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GLM_FUNC_DECL genType glm::fastLength (genType x)
\n
\n\n

Faster than the common length function but less accurate.

\n
See also
to use the features of this extension. \">GLM_GTX_fast_square_root extension.
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GLM_FUNC_DECL T glm::fastLength (vec< L, T, Q > const & x)
\n
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Faster than the common length function but less accurate.

\n
See also
to use the features of this extension. \">GLM_GTX_fast_square_root extension.
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GLM_FUNC_DECL genType glm::fastNormalize (genType const & x)
\n
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Faster than the common normalize function but less accurate.

\n
See also
to use the features of this extension. \">GLM_GTX_fast_square_root extension.
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GLM_FUNC_DECL genType glm::fastSqrt (genType x)
\n
\n\n

Faster than the common sqrt function but less accurate.

\n
See also
to use the features of this extension. \">GLM_GTX_fast_square_root extension.
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GLM_FUNC_DECL vec<L, T, Q> glm::fastSqrt (vec< L, T, Q > const & x)
\n
\n\n

Faster than the common sqrt function but less accurate.

\n
See also
to use the features of this extension. \">GLM_GTX_fast_square_root extension.
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Experimental extensions
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Include <glm/gtx/fast_trigonometry.hpp> to use the features of this extension. \nMore...

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\nFunctions

template<typename T >
GLM_FUNC_DECL T fastAcos (T angle)
 Faster than the common acos function but less accurate. More...
 
template<typename T >
GLM_FUNC_DECL T fastAsin (T angle)
 Faster than the common asin function but less accurate. More...
 
template<typename T >
GLM_FUNC_DECL T fastAtan (T y, T x)
 Faster than the common atan function but less accurate. More...
 
template<typename T >
GLM_FUNC_DECL T fastAtan (T angle)
 Faster than the common atan function but less accurate. More...
 
template<typename T >
GLM_FUNC_DECL T fastCos (T angle)
 Faster than the common cos function but less accurate. More...
 
template<typename T >
GLM_FUNC_DECL T fastSin (T angle)
 Faster than the common sin function but less accurate. More...
 
template<typename T >
GLM_FUNC_DECL T fastTan (T angle)
 Faster than the common tan function but less accurate. More...
 
template<typename T >
GLM_FUNC_DECL T wrapAngle (T angle)
 Wrap an angle to [0 2pi[ From GLM_GTX_fast_trigonometry extension. More...
 
\n

Detailed Description

\n

Include <glm/gtx/fast_trigonometry.hpp> to use the features of this extension.

\n

Fast but less accurate implementations of trigonometric functions.

\n

Function Documentation

\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL T glm::fastAcos (T angle)
\n
\n\n

Faster than the common acos function but less accurate.

\n

Defined between -2pi and 2pi. From GLM_GTX_fast_trigonometry extension.

\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL T glm::fastAsin (T angle)
\n
\n\n

Faster than the common asin function but less accurate.

\n

Defined between -2pi and 2pi. From GLM_GTX_fast_trigonometry extension.

\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL T glm::fastAtan (T y,
T x 
)
\n
\n\n

Faster than the common atan function but less accurate.

\n

Defined between -2pi and 2pi. From GLM_GTX_fast_trigonometry extension.

\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL T glm::fastAtan (T angle)
\n
\n\n

Faster than the common atan function but less accurate.

\n

Defined between -2pi and 2pi. From GLM_GTX_fast_trigonometry extension.

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GLM_FUNC_DECL T glm::fastCos (T angle)
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Faster than the common cos function but less accurate.

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From GLM_GTX_fast_trigonometry extension.

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GLM_FUNC_DECL T glm::fastSin (T angle)
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Faster than the common sin function but less accurate.

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From GLM_GTX_fast_trigonometry extension.

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GLM_FUNC_DECL T glm::fastTan (T angle)
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Faster than the common tan function but less accurate.

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Defined between -2pi and 2pi. From GLM_GTX_fast_trigonometry extension.

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GLM_FUNC_DECL T glm::wrapAngle (T angle)
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Wrap an angle to [0 2pi[ From GLM_GTX_fast_trigonometry extension.

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template<typename T >
GLM_FUNC_DECL T gauss (T x, T ExpectedValue, T StandardDeviation)
 1D gauss function More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL T gauss (vec< 2, T, Q > const &Coord, vec< 2, T, Q > const &ExpectedValue, vec< 2, T, Q > const &StandardDeviation)
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Detailed Description

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Include <glm/gtx/functions.hpp> to use the features of this extension.

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List of useful common functions.

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Function Documentation

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GLM_FUNC_DECL T glm::gauss (T x,
T ExpectedValue,
T StandardDeviation 
)
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1D gauss function

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GLM_FUNC_DECL T glm::gauss (vec< 2, T, Q > const & Coord,
vec< 2, T, Q > const & ExpectedValue,
vec< 2, T, Q > const & StandardDeviation 
)
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2D gauss function

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template<typename T , qualifier Q>
GLM_FUNC_DECL T linearGradient (vec< 2, T, Q > const &Point0, vec< 2, T, Q > const &Point1, vec< 2, T, Q > const &Position)
 Return a color from a linear gradient. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL T radialGradient (vec< 2, T, Q > const &Center, T const &Radius, vec< 2, T, Q > const &Focal, vec< 2, T, Q > const &Position)
 Return a color from a radial gradient. More...
 
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Detailed Description

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Include <glm/gtx/gradient_paint.hpp> to use the features of this extension.

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Functions that return the color of procedural gradient for specific coordinates.

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Function Documentation

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GLM_FUNC_DECL T glm::linearGradient (vec< 2, T, Q > const & Point0,
vec< 2, T, Q > const & Point1,
vec< 2, T, Q > const & Position 
)
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Return a color from a linear gradient.

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GLM_FUNC_DECL T glm::radialGradient (vec< 2, T, Q > const & Center,
T const & Radius,
vec< 2, T, Q > const & Focal,
vec< 2, T, Q > const & Position 
)
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Return a color from a radial gradient.

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template<typename T , qualifier Q>
GLM_FUNC_DECL bool leftHanded (vec< 3, T, Q > const &tangent, vec< 3, T, Q > const &binormal, vec< 3, T, Q > const &normal)
 Return if a trihedron left handed or not. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL bool rightHanded (vec< 3, T, Q > const &tangent, vec< 3, T, Q > const &binormal, vec< 3, T, Q > const &normal)
 Return if a trihedron right handed or not. More...
 
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Detailed Description

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Include <glm/gtx/handed_coordinate_system.hpp> to use the features of this extension.

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To know if a set of three basis vectors defines a right or left-handed coordinate system.

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Function Documentation

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GLM_FUNC_DECL bool glm::leftHanded (vec< 3, T, Q > const & tangent,
vec< 3, T, Q > const & binormal,
vec< 3, T, Q > const & normal 
)
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Return if a trihedron left handed or not.

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From GLM_GTX_handed_coordinate_space extension.

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GLM_FUNC_DECL bool glm::rightHanded (vec< 3, T, Q > const & tangent,
vec< 3, T, Q > const & binormal,
vec< 3, T, Q > const & normal 
)
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Return if a trihedron right handed or not.

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From GLM_GTX_handed_coordinate_space extension.

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Experimental extensions
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Include <glm/gtx/hash.hpp> to use the features of this extension.

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Add std::hash support for glm types

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Experimental extensions
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typedef signed int sint
 32bit signed integer. More...
 
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template<typename genType >
GLM_FUNC_DECL genType factorial (genType const &x)
 Return the factorial value of a number (!12 max, integer only) From GLM_GTX_integer extension. More...
 
GLM_FUNC_DECL unsigned int floor_log2 (unsigned int x)
 Returns the floor log2 of x. More...
 
GLM_FUNC_DECL int mod (int x, int y)
 Modulus. More...
 
GLM_FUNC_DECL uint mod (uint x, uint y)
 Modulus. More...
 
GLM_FUNC_DECL uint nlz (uint x)
 Returns the number of leading zeros. More...
 
GLM_FUNC_DECL int pow (int x, uint y)
 Returns x raised to the y power. More...
 
GLM_FUNC_DECL uint pow (uint x, uint y)
 Returns x raised to the y power. More...
 
GLM_FUNC_DECL int sqrt (int x)
 Returns the positive square root of x. More...
 
GLM_FUNC_DECL uint sqrt (uint x)
 Returns the positive square root of x. More...
 
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Detailed Description

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Include <glm/gtx/integer.hpp> to use the features of this extension.

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Add support for integer for core functions

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Typedef Documentation

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typedef signed int sint
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32bit signed integer.

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From GLM_GTX_integer extension.

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Definition at line 55 of file gtx/integer.hpp.

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Function Documentation

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GLM_FUNC_DECL genType glm::factorial (genType const & x)
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Return the factorial value of a number (!12 max, integer only) From GLM_GTX_integer extension.

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GLM_FUNC_DECL unsigned int glm::floor_log2 (unsigned int x)
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Returns the floor log2 of x.

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From GLM_GTX_integer extension.

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GLM_FUNC_DECL int glm::mod (int x,
int y 
)
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Modulus.

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Returns x - y * floor(x / y) for each component in x using the floating point value y. From GLM_GTX_integer extension.

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GLM_FUNC_DECL uint glm::mod (uint x,
uint y 
)
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Modulus.

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Returns x - y * floor(x / y) for each component in x using the floating point value y. From GLM_GTX_integer extension.

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GLM_FUNC_DECL uint glm::nlz (uint x)
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Returns the number of leading zeros.

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From GLM_GTX_integer extension.

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GLM_FUNC_DECL int glm::pow (int x,
uint y 
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Returns x raised to the y power.

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From GLM_GTX_integer extension.

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uint y 
)
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Returns x raised to the y power.

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From GLM_GTX_integer extension.

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GLM_FUNC_DECL int glm::sqrt (int x)
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Returns the positive square root of x.

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From GLM_GTX_integer extension.

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Returns the positive square root of x.

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From GLM_GTX_integer extension.

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Experimental extensions
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template<typename genType >
GLM_FUNC_DECL bool intersectLineSphere (genType const &point0, genType const &point1, genType const &sphereCenter, typename genType::value_type sphereRadius, genType &intersectionPosition1, genType &intersectionNormal1, genType &intersectionPosition2=genType(), genType &intersectionNormal2=genType())
 Compute the intersection of a line and a sphere. More...
 
template<typename genType >
GLM_FUNC_DECL bool intersectLineTriangle (genType const &orig, genType const &dir, genType const &vert0, genType const &vert1, genType const &vert2, genType &position)
 Compute the intersection of a line and a triangle. More...
 
template<typename genType >
GLM_FUNC_DECL bool intersectRayPlane (genType const &orig, genType const &dir, genType const &planeOrig, genType const &planeNormal, typename genType::value_type &intersectionDistance)
 Compute the intersection of a ray and a plane. More...
 
template<typename genType >
GLM_FUNC_DECL bool intersectRaySphere (genType const &rayStarting, genType const &rayNormalizedDirection, genType const &sphereCenter, typename genType::value_type const sphereRadiusSquared, typename genType::value_type &intersectionDistance)
 Compute the intersection distance of a ray and a sphere. More...
 
template<typename genType >
GLM_FUNC_DECL bool intersectRaySphere (genType const &rayStarting, genType const &rayNormalizedDirection, genType const &sphereCenter, const typename genType::value_type sphereRadius, genType &intersectionPosition, genType &intersectionNormal)
 Compute the intersection of a ray and a sphere. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL bool intersectRayTriangle (vec< 3, T, Q > const &orig, vec< 3, T, Q > const &dir, vec< 3, T, Q > const &v0, vec< 3, T, Q > const &v1, vec< 3, T, Q > const &v2, vec< 2, T, Q > &baryPosition, T &distance)
 Compute the intersection of a ray and a triangle. More...
 
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Detailed Description

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Include <glm/gtx/intersect.hpp> to use the features of this extension.

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Add intersection functions

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Function Documentation

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GLM_FUNC_DECL bool glm::intersectLineSphere (genType const & point0,
genType const & point1,
genType const & sphereCenter,
typename genType::value_type sphereRadius,
genType & intersectionPosition1,
genType & intersectionNormal1,
genType & intersectionPosition2 = genType(),
genType & intersectionNormal2 = genType() 
)
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Compute the intersection of a line and a sphere.

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From GLM_GTX_intersect extension

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GLM_FUNC_DECL bool glm::intersectLineTriangle (genType const & orig,
genType const & dir,
genType const & vert0,
genType const & vert1,
genType const & vert2,
genType & position 
)
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Compute the intersection of a line and a triangle.

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From GLM_GTX_intersect extension.

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GLM_FUNC_DECL bool glm::intersectRayPlane (genType const & orig,
genType const & dir,
genType const & planeOrig,
genType const & planeNormal,
typename genType::value_type & intersectionDistance 
)
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Compute the intersection of a ray and a plane.

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Ray direction and plane normal must be unit length. From GLM_GTX_intersect extension.

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GLM_FUNC_DECL bool glm::intersectRaySphere (genType const & rayStarting,
genType const & rayNormalizedDirection,
genType const & sphereCenter,
typename genType::value_type const sphereRadiusSquared,
typename genType::value_type & intersectionDistance 
)
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Compute the intersection distance of a ray and a sphere.

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The ray direction vector is unit length. From GLM_GTX_intersect extension.

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GLM_FUNC_DECL bool glm::intersectRaySphere (genType const & rayStarting,
genType const & rayNormalizedDirection,
genType const & sphereCenter,
const typename genType::value_type sphereRadius,
genType & intersectionPosition,
genType & intersectionNormal 
)
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Compute the intersection of a ray and a sphere.

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From GLM_GTX_intersect extension.

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GLM_FUNC_DECL bool glm::intersectRayTriangle (vec< 3, T, Q > const & orig,
vec< 3, T, Q > const & dir,
vec< 3, T, Q > const & v0,
vec< 3, T, Q > const & v1,
vec< 3, T, Q > const & v2,
vec< 2, T, Q > & baryPosition,
T & distance 
)
\n
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Compute the intersection of a ray and a triangle.

\n

Based om Tomas Möller implementation http://fileadmin.cs.lth.se/cs/Personal/Tomas_Akenine-Moller/raytri/ From GLM_GTX_intersect extension.

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GLM_GTX_io
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Include <glm/gtx/io.hpp> to use the features of this extension. \nMore...

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Detailed Description

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Include <glm/gtx/io.hpp> to use the features of this extension.

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std::[w]ostream support for glm types

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std::[w]ostream support for glm types + qualifier/width/etc. manipulators based on howard hinnant's std::chrono io proposal [http://home.roadrunner.com/~hinnant/bloomington/chrono_io.html]

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GLM_GTX_log_base
Experimental extensions
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Include <glm/gtx/log_base.hpp> to use the features of this extension. \nMore...

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template<typename genType >
GLM_FUNC_DECL genType log (genType const &x, genType const &base)
 Logarithm for any base. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > sign (vec< L, T, Q > const &x, vec< L, T, Q > const &base)
 Logarithm for any base. More...
 
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Detailed Description

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Include <glm/gtx/log_base.hpp> to use the features of this extension.

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Logarithm for any base. base can be a vector or a scalar.

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Function Documentation

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GLM_FUNC_DECL genType glm::log (genType const & x,
genType const & base 
)
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Logarithm for any base.

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From GLM_GTX_log_base.

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GLM_FUNC_DECL vec<L, T, Q> glm::sign (vec< L, T, Q > const & x,
vec< L, T, Q > const & base 
)
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Logarithm for any base.

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From GLM_GTX_log_base.

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GLM_GTX_matrix_cross_product
Experimental extensions
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template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 3, 3, T, Q > matrixCross3 (vec< 3, T, Q > const &x)
 Build a cross product matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 4, 4, T, Q > matrixCross4 (vec< 3, T, Q > const &x)
 Build a cross product matrix. More...
 
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Detailed Description

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Include <glm/gtx/matrix_cross_product.hpp> to use the features of this extension.

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Build cross product matrices

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Function Documentation

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GLM_FUNC_DECL mat<3, 3, T, Q> glm::matrixCross3 (vec< 3, T, Q > const & x)
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Build a cross product matrix.

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From GLM_GTX_matrix_cross_product extension.

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GLM_FUNC_DECL mat<4, 4, T, Q> glm::matrixCross4 (vec< 3, T, Q > const & x)
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Build a cross product matrix.

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From GLM_GTX_matrix_cross_product extension.

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GLM_GTX_matrix_decompose
Experimental extensions
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Include <glm/gtx/matrix_decompose.hpp> to use the features of this extension. \nMore...

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template<typename T , qualifier Q>
GLM_FUNC_DECL bool decompose (mat< 4, 4, T, Q > const &modelMatrix, vec< 3, T, Q > &scale, qua< T, Q > &orientation, vec< 3, T, Q > &translation, vec< 3, T, Q > &skew, vec< 4, T, Q > &perspective)
 Decomposes a model matrix to translations, rotation and scale components. More...
 
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Detailed Description

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Include <glm/gtx/matrix_decompose.hpp> to use the features of this extension.

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Decomposes a model matrix to translations, rotation and scale components

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Function Documentation

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GLM_FUNC_DECL bool glm::decompose (mat< 4, 4, T, Q > const & modelMatrix,
vec< 3, T, Q > & scale,
qua< T, Q > & orientation,
vec< 3, T, Q > & translation,
vec< 3, T, Q > & skew,
vec< 4, T, Q > & perspective 
)
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Decomposes a model matrix to translations, rotation and scale components.

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See also
to use the features of this extension. \">GLM_GTX_matrix_decompose
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Experimental extensions
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template<length_t C, length_t R, typename T , qualifier Q>
GLM_FUNC_DECL mat< C, R, T, Q > fliplr (mat< C, R, T, Q > const &in)
 Flips the matrix columns right and left. More...
 
template<length_t C, length_t R, typename T , qualifier Q>
GLM_FUNC_DECL mat< C, R, T, Q > flipud (mat< C, R, T, Q > const &in)
 Flips the matrix rows up and down. More...
 
template<length_t C, length_t R, typename T , qualifier Q>
GLM_FUNC_DECL void qr_decompose (mat< C, R, T, Q > const &in, mat<(C< R?C:R), R, T, Q > &q, mat< C,(C< R?C:R), T, Q > &r)
 Performs QR factorisation of a matrix. More...
 
template<length_t C, length_t R, typename T , qualifier Q>
GLM_FUNC_DECL void rq_decompose (mat< C, R, T, Q > const &in, mat<(C< R?C:R), R, T, Q > &r, mat< C,(C< R?C:R), T, Q > &q)
 Performs RQ factorisation of a matrix. More...
 
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Detailed Description

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Include <glm/gtx/matrix_factorisation.hpp> to use the features of this extension.

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Functions to factor matrices in various forms

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Function Documentation

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GLM_FUNC_DECL mat<C, R, T, Q> glm::fliplr (mat< C, R, T, Q > const & in)
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Flips the matrix columns right and left.

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From GLM_GTX_matrix_factorisation extension.

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GLM_FUNC_DECL mat<C, R, T, Q> glm::flipud (mat< C, R, T, Q > const & in)
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Flips the matrix rows up and down.

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From GLM_GTX_matrix_factorisation extension.

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GLM_FUNC_DECL void glm::qr_decompose (mat< C, R, T, Q > const & in)
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Performs QR factorisation of a matrix.

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Returns 2 matrices, q and r, such that the columns of q are orthonormal and span the same subspace than those of the input matrix, r is an upper triangular matrix, and q*r=in. Given an n-by-m input matrix, q has dimensions min(n,m)-by-m, and r has dimensions n-by-min(n,m).

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From GLM_GTX_matrix_factorisation extension.

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GLM_FUNC_DECL void glm::rq_decompose (mat< C, R, T, Q > const & in)
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Performs RQ factorisation of a matrix.

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Returns 2 matrices, r and q, such that r is an upper triangular matrix, the rows of q are orthonormal and span the same subspace than those of the input matrix, and r*q=in. Note that in the context of RQ factorisation, the diagonal is seen as starting in the lower-right corner of the matrix, instead of the usual upper-left. Given an n-by-m input matrix, r has dimensions min(n,m)-by-m, and q has dimensions n-by-min(n,m).

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From GLM_GTX_matrix_factorisation extension.

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GLM_GTX_matrix_interpolation
Experimental extensions
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\nFunctions

template<typename T , qualifier Q>
GLM_FUNC_DECL void axisAngle (mat< 4, 4, T, Q > const &Mat, vec< 3, T, Q > &Axis, T &Angle)
 Get the axis and angle of the rotation from a matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 4, 4, T, Q > axisAngleMatrix (vec< 3, T, Q > const &Axis, T const Angle)
 Build a matrix from axis and angle. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 4, 4, T, Q > extractMatrixRotation (mat< 4, 4, T, Q > const &Mat)
 Extracts the rotation part of a matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 4, 4, T, Q > interpolate (mat< 4, 4, T, Q > const &m1, mat< 4, 4, T, Q > const &m2, T const Delta)
 Build a interpolation of 4 * 4 matrixes. More...
 
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Detailed Description

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Include <glm/gtx/matrix_interpolation.hpp> to use the features of this extension.

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Allows to directly interpolate two matrices.

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Function Documentation

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GLM_FUNC_DECL void glm::axisAngle (mat< 4, 4, T, Q > const & Mat,
vec< 3, T, Q > & Axis,
T & Angle 
)
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Get the axis and angle of the rotation from a matrix.

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From GLM_GTX_matrix_interpolation extension.

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GLM_FUNC_DECL mat<4, 4, T, Q> glm::axisAngleMatrix (vec< 3, T, Q > const & Axis,
T const Angle 
)
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Build a matrix from axis and angle.

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From GLM_GTX_matrix_interpolation extension.

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GLM_FUNC_DECL mat<4, 4, T, Q> glm::extractMatrixRotation (mat< 4, 4, T, Q > const & Mat)
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Extracts the rotation part of a matrix.

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From GLM_GTX_matrix_interpolation extension.

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GLM_FUNC_DECL mat<4, 4, T, Q> glm::interpolate (mat< 4, 4, T, Q > const & m1,
mat< 4, 4, T, Q > const & m2,
T const Delta 
)
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Build a interpolation of 4 * 4 matrixes.

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From GLM_GTX_matrix_interpolation extension. Warning! works only with rotation and/or translation matrixes, scale will generate unexpected results.

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Experimental extensions
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template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 2, 2, T, Q > colMajor2 (vec< 2, T, Q > const &v1, vec< 2, T, Q > const &v2)
 Build a column major matrix from column vectors. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 2, 2, T, Q > colMajor2 (mat< 2, 2, T, Q > const &m)
 Build a column major matrix from other matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 3, 3, T, Q > colMajor3 (vec< 3, T, Q > const &v1, vec< 3, T, Q > const &v2, vec< 3, T, Q > const &v3)
 Build a column major matrix from column vectors. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 3, 3, T, Q > colMajor3 (mat< 3, 3, T, Q > const &m)
 Build a column major matrix from other matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 4, 4, T, Q > colMajor4 (vec< 4, T, Q > const &v1, vec< 4, T, Q > const &v2, vec< 4, T, Q > const &v3, vec< 4, T, Q > const &v4)
 Build a column major matrix from column vectors. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 4, 4, T, Q > colMajor4 (mat< 4, 4, T, Q > const &m)
 Build a column major matrix from other matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 2, 2, T, Q > rowMajor2 (vec< 2, T, Q > const &v1, vec< 2, T, Q > const &v2)
 Build a row major matrix from row vectors. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 2, 2, T, Q > rowMajor2 (mat< 2, 2, T, Q > const &m)
 Build a row major matrix from other matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 3, 3, T, Q > rowMajor3 (vec< 3, T, Q > const &v1, vec< 3, T, Q > const &v2, vec< 3, T, Q > const &v3)
 Build a row major matrix from row vectors. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 3, 3, T, Q > rowMajor3 (mat< 3, 3, T, Q > const &m)
 Build a row major matrix from other matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 4, 4, T, Q > rowMajor4 (vec< 4, T, Q > const &v1, vec< 4, T, Q > const &v2, vec< 4, T, Q > const &v3, vec< 4, T, Q > const &v4)
 Build a row major matrix from row vectors. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 4, 4, T, Q > rowMajor4 (mat< 4, 4, T, Q > const &m)
 Build a row major matrix from other matrix. More...
 
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Detailed Description

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Include <glm/gtx/matrix_major_storage.hpp> to use the features of this extension.

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Build matrices with specific matrix order, row or column

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Function Documentation

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GLM_FUNC_DECL mat<2, 2, T, Q> glm::colMajor2 (vec< 2, T, Q > const & v1,
vec< 2, T, Q > const & v2 
)
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Build a column major matrix from column vectors.

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From GLM_GTX_matrix_major_storage extension.

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GLM_FUNC_DECL mat<2, 2, T, Q> glm::colMajor2 (mat< 2, 2, T, Q > const & m)
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Build a column major matrix from other matrix.

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From GLM_GTX_matrix_major_storage extension.

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GLM_FUNC_DECL mat<3, 3, T, Q> glm::colMajor3 (vec< 3, T, Q > const & v1,
vec< 3, T, Q > const & v2,
vec< 3, T, Q > const & v3 
)
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Build a column major matrix from column vectors.

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From GLM_GTX_matrix_major_storage extension.

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GLM_FUNC_DECL mat<3, 3, T, Q> glm::colMajor3 (mat< 3, 3, T, Q > const & m)
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Build a column major matrix from other matrix.

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From GLM_GTX_matrix_major_storage extension.

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GLM_FUNC_DECL mat<4, 4, T, Q> glm::colMajor4 (vec< 4, T, Q > const & v1,
vec< 4, T, Q > const & v2,
vec< 4, T, Q > const & v3,
vec< 4, T, Q > const & v4 
)
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Build a column major matrix from column vectors.

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From GLM_GTX_matrix_major_storage extension.

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GLM_FUNC_DECL mat<4, 4, T, Q> glm::colMajor4 (mat< 4, 4, T, Q > const & m)
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Build a column major matrix from other matrix.

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From GLM_GTX_matrix_major_storage extension.

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GLM_FUNC_DECL mat<2, 2, T, Q> glm::rowMajor2 (vec< 2, T, Q > const & v1,
vec< 2, T, Q > const & v2 
)
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Build a row major matrix from row vectors.

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From GLM_GTX_matrix_major_storage extension.

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GLM_FUNC_DECL mat<2, 2, T, Q> glm::rowMajor2 (mat< 2, 2, T, Q > const & m)
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Build a row major matrix from other matrix.

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From GLM_GTX_matrix_major_storage extension.

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GLM_FUNC_DECL mat<3, 3, T, Q> glm::rowMajor3 (vec< 3, T, Q > const & v1,
vec< 3, T, Q > const & v2,
vec< 3, T, Q > const & v3 
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Build a row major matrix from row vectors.

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From GLM_GTX_matrix_major_storage extension.

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GLM_FUNC_DECL mat<3, 3, T, Q> glm::rowMajor3 (mat< 3, 3, T, Q > const & m)
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Build a row major matrix from other matrix.

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From GLM_GTX_matrix_major_storage extension.

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GLM_FUNC_DECL mat<4, 4, T, Q> glm::rowMajor4 (vec< 4, T, Q > const & v1,
vec< 4, T, Q > const & v2,
vec< 4, T, Q > const & v3,
vec< 4, T, Q > const & v4 
)
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Build a row major matrix from row vectors.

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From GLM_GTX_matrix_major_storage extension.

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GLM_FUNC_DECL mat<4, 4, T, Q> glm::rowMajor4 (mat< 4, 4, T, Q > const & m)
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Build a row major matrix from other matrix.

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From GLM_GTX_matrix_major_storage extension.

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\nFunctions

template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 2, 2, T, Q > adjugate (mat< 2, 2, T, Q > const &m)
 Build an adjugate matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 3, 3, T, Q > adjugate (mat< 3, 3, T, Q > const &m)
 Build an adjugate matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 4, 4, T, Q > adjugate (mat< 4, 4, T, Q > const &m)
 Build an adjugate matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 2, 2, T, Q > diagonal2x2 (vec< 2, T, Q > const &v)
 Build a diagonal matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 2, 3, T, Q > diagonal2x3 (vec< 2, T, Q > const &v)
 Build a diagonal matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 2, 4, T, Q > diagonal2x4 (vec< 2, T, Q > const &v)
 Build a diagonal matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 3, 2, T, Q > diagonal3x2 (vec< 2, T, Q > const &v)
 Build a diagonal matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 3, 3, T, Q > diagonal3x3 (vec< 3, T, Q > const &v)
 Build a diagonal matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 3, 4, T, Q > diagonal3x4 (vec< 3, T, Q > const &v)
 Build a diagonal matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 4, 2, T, Q > diagonal4x2 (vec< 2, T, Q > const &v)
 Build a diagonal matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 4, 3, T, Q > diagonal4x3 (vec< 3, T, Q > const &v)
 Build a diagonal matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 4, 4, T, Q > diagonal4x4 (vec< 4, T, Q > const &v)
 Build a diagonal matrix. More...
 
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Detailed Description

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Include <glm/gtx/matrix_operation.hpp> to use the features of this extension.

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Build diagonal matrices from vectors.

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Function Documentation

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GLM_FUNC_DECL mat<2, 2, T, Q> glm::adjugate (mat< 2, 2, T, Q > const & m)
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Build an adjugate matrix.

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From GLM_GTX_matrix_operation extension.

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GLM_FUNC_DECL mat<3, 3, T, Q> glm::adjugate (mat< 3, 3, T, Q > const & m)
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Build an adjugate matrix.

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From GLM_GTX_matrix_operation extension.

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GLM_FUNC_DECL mat<4, 4, T, Q> glm::adjugate (mat< 4, 4, T, Q > const & m)
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Build an adjugate matrix.

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From GLM_GTX_matrix_operation extension.

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GLM_FUNC_DECL mat<2, 2, T, Q> glm::diagonal2x2 (vec< 2, T, Q > const & v)
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Build a diagonal matrix.

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From GLM_GTX_matrix_operation extension.

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GLM_FUNC_DECL mat<2, 3, T, Q> glm::diagonal2x3 (vec< 2, T, Q > const & v)
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Build a diagonal matrix.

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From GLM_GTX_matrix_operation extension.

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GLM_FUNC_DECL mat<2, 4, T, Q> glm::diagonal2x4 (vec< 2, T, Q > const & v)
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Build a diagonal matrix.

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From GLM_GTX_matrix_operation extension.

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GLM_FUNC_DECL mat<3, 2, T, Q> glm::diagonal3x2 (vec< 2, T, Q > const & v)
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Build a diagonal matrix.

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From GLM_GTX_matrix_operation extension.

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GLM_FUNC_DECL mat<3, 3, T, Q> glm::diagonal3x3 (vec< 3, T, Q > const & v)
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Build a diagonal matrix.

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From GLM_GTX_matrix_operation extension.

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GLM_FUNC_DECL mat<3, 4, T, Q> glm::diagonal3x4 (vec< 3, T, Q > const & v)
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Build a diagonal matrix.

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From GLM_GTX_matrix_operation extension.

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GLM_FUNC_DECL mat<4, 2, T, Q> glm::diagonal4x2 (vec< 2, T, Q > const & v)
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Build a diagonal matrix.

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From GLM_GTX_matrix_operation extension.

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GLM_FUNC_DECL mat<4, 3, T, Q> glm::diagonal4x3 (vec< 3, T, Q > const & v)
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Build a diagonal matrix.

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From GLM_GTX_matrix_operation extension.

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GLM_FUNC_DECL mat<4, 4, T, Q> glm::diagonal4x4 (vec< 4, T, Q > const & v)
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Build a diagonal matrix.

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From GLM_GTX_matrix_operation extension.

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template<length_t C, length_t R, typename T , qualifier Q, template< length_t, length_t, typename, qualifier > class matType>
GLM_FUNC_DECL bool isIdentity (matType< C, R, T, Q > const &m, T const &epsilon)
 Return whether a matrix is an identity matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL bool isNormalized (mat< 2, 2, T, Q > const &m, T const &epsilon)
 Return whether a matrix is a normalized matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL bool isNormalized (mat< 3, 3, T, Q > const &m, T const &epsilon)
 Return whether a matrix is a normalized matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL bool isNormalized (mat< 4, 4, T, Q > const &m, T const &epsilon)
 Return whether a matrix is a normalized matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL bool isNull (mat< 2, 2, T, Q > const &m, T const &epsilon)
 Return whether a matrix a null matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL bool isNull (mat< 3, 3, T, Q > const &m, T const &epsilon)
 Return whether a matrix a null matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL bool isNull (mat< 4, 4, T, Q > const &m, T const &epsilon)
 Return whether a matrix is a null matrix. More...
 
template<length_t C, length_t R, typename T , qualifier Q, template< length_t, length_t, typename, qualifier > class matType>
GLM_FUNC_DECL bool isOrthogonal (matType< C, R, T, Q > const &m, T const &epsilon)
 Return whether a matrix is an orthonormalized matrix. More...
 
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Detailed Description

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Include <glm/gtx/matrix_query.hpp> to use the features of this extension.

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Query to evaluate matrix properties

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Function Documentation

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GLM_FUNC_DECL bool glm::isIdentity (matType< C, R, T, Q > const & m,
T const & epsilon 
)
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Return whether a matrix is an identity matrix.

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From GLM_GTX_matrix_query extension.

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GLM_FUNC_DECL bool glm::isNormalized (mat< 2, 2, T, Q > const & m,
T const & epsilon 
)
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Return whether a matrix is a normalized matrix.

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From GLM_GTX_matrix_query extension.

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GLM_FUNC_DECL bool glm::isNormalized (mat< 3, 3, T, Q > const & m,
T const & epsilon 
)
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Return whether a matrix is a normalized matrix.

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From GLM_GTX_matrix_query extension.

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GLM_FUNC_DECL bool glm::isNormalized (mat< 4, 4, T, Q > const & m,
T const & epsilon 
)
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Return whether a matrix is a normalized matrix.

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From GLM_GTX_matrix_query extension.

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GLM_FUNC_DECL bool glm::isNull (mat< 2, 2, T, Q > const & m,
T const & epsilon 
)
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Return whether a matrix a null matrix.

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From GLM_GTX_matrix_query extension.

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GLM_FUNC_DECL bool glm::isNull (mat< 3, 3, T, Q > const & m,
T const & epsilon 
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Return whether a matrix a null matrix.

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From GLM_GTX_matrix_query extension.

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GLM_FUNC_DECL bool glm::isNull (mat< 4, 4, T, Q > const & m,
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Return whether a matrix is a null matrix.

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From GLM_GTX_matrix_query extension.

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GLM_FUNC_DECL bool glm::isOrthogonal (matType< C, R, T, Q > const & m,
T const & epsilon 
)
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Return whether a matrix is an orthonormalized matrix.

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From GLM_GTX_matrix_query extension.

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\nFunctions

template<typename T , qualifier Q>
GLM_FUNC_QUALIFIER mat< 3, 3, T, Q > rotate (mat< 3, 3, T, Q > const &m, T angle)
 Builds a rotation 3 * 3 matrix created from an angle. More...
 
template<typename T , qualifier Q>
GLM_FUNC_QUALIFIER mat< 3, 3, T, Q > scale (mat< 3, 3, T, Q > const &m, vec< 2, T, Q > const &v)
 Builds a scale 3 * 3 matrix created from a vector of 2 components. More...
 
template<typename T , qualifier Q>
GLM_FUNC_QUALIFIER mat< 3, 3, T, Q > shearX (mat< 3, 3, T, Q > const &m, T y)
 Builds an horizontal (parallel to the x axis) shear 3 * 3 matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_QUALIFIER mat< 3, 3, T, Q > shearY (mat< 3, 3, T, Q > const &m, T x)
 Builds a vertical (parallel to the y axis) shear 3 * 3 matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_QUALIFIER mat< 3, 3, T, Q > translate (mat< 3, 3, T, Q > const &m, vec< 2, T, Q > const &v)
 Builds a translation 3 * 3 matrix created from a vector of 2 components. More...
 
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Detailed Description

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Include <glm/gtx/matrix_transform_2d.hpp> to use the features of this extension.

\n

Defines functions that generate common 2d transformation matrices.

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Function Documentation

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GLM_FUNC_QUALIFIER mat<3, 3, T, Q> glm::rotate (mat< 3, 3, T, Q > const & m,
T angle 
)
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Builds a rotation 3 * 3 matrix created from an angle.

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mInput matrix multiplied by this translation matrix.
angleRotation angle expressed in radians.
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GLM_FUNC_QUALIFIER mat<3, 3, T, Q> glm::scale (mat< 3, 3, T, Q > const & m,
vec< 2, T, Q > const & v 
)
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Builds a scale 3 * 3 matrix created from a vector of 2 components.

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mInput matrix multiplied by this translation matrix.
vCoordinates of a scale vector.
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GLM_FUNC_QUALIFIER mat<3, 3, T, Q> glm::shearX (mat< 3, 3, T, Q > const & m,
T y 
)
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Builds an horizontal (parallel to the x axis) shear 3 * 3 matrix.

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mInput matrix multiplied by this translation matrix.
yShear factor.
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GLM_FUNC_QUALIFIER mat<3, 3, T, Q> glm::shearY (mat< 3, 3, T, Q > const & m,
T x 
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Builds a vertical (parallel to the y axis) shear 3 * 3 matrix.

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mInput matrix multiplied by this translation matrix.
xShear factor.
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vec< 2, T, Q > const & v 
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Builds a translation 3 * 3 matrix created from a vector of 2 components.

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mInput matrix multiplied by this translation matrix.
vCoordinates of a translation vector.
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\ntemplate<typename T , qualifier Q>
GLM_FUNC_DECL T mixedProduct (vec< 3, T, Q > const &v1, vec< 3, T, Q > const &v2, vec< 3, T, Q > const &v3)
 Mixed product of 3 vectors (from GLM_GTX_mixed_product extension)
 
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Detailed Description

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Include <glm/gtx/mixed_product.hpp> to use the features of this extension.

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Mixed product of 3 vectors.

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template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL T distance2 (vec< L, T, Q > const &p0, vec< L, T, Q > const &p1)
 Returns the squared distance between p0 and p1, i.e., length2(p0 - p1). More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL T l1Norm (vec< 3, T, Q > const &x, vec< 3, T, Q > const &y)
 Returns the L1 norm between x and y. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL T l1Norm (vec< 3, T, Q > const &v)
 Returns the L1 norm of v. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL T l2Norm (vec< 3, T, Q > const &x, vec< 3, T, Q > const &y)
 Returns the L2 norm between x and y. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL T l2Norm (vec< 3, T, Q > const &x)
 Returns the L2 norm of v. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL T length2 (vec< L, T, Q > const &x)
 Returns the squared length of x. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL T lMaxNorm (vec< 3, T, Q > const &x, vec< 3, T, Q > const &y)
 Returns the LMax norm between x and y. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL T lMaxNorm (vec< 3, T, Q > const &x)
 Returns the LMax norm of v. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL T lxNorm (vec< 3, T, Q > const &x, vec< 3, T, Q > const &y, unsigned int Depth)
 Returns the L norm between x and y. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL T lxNorm (vec< 3, T, Q > const &x, unsigned int Depth)
 Returns the L norm of v. More...
 
\n

Detailed Description

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Include <glm/gtx/norm.hpp> to use the features of this extension.

\n

Various ways to compute vector norms.

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Function Documentation

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GLM_FUNC_DECL T glm::distance2 (vec< L, T, Q > const & p0,
vec< L, T, Q > const & p1 
)
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Returns the squared distance between p0 and p1, i.e., length2(p0 - p1).

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From GLM_GTX_norm extension.

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GLM_FUNC_DECL T glm::l1Norm (vec< 3, T, Q > const & x,
vec< 3, T, Q > const & y 
)
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Returns the L1 norm between x and y.

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From GLM_GTX_norm extension.

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GLM_FUNC_DECL T glm::l1Norm (vec< 3, T, Q > const & v)
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Returns the L1 norm of v.

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From GLM_GTX_norm extension.

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GLM_FUNC_DECL T glm::l2Norm (vec< 3, T, Q > const & x,
vec< 3, T, Q > const & y 
)
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Returns the L2 norm between x and y.

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From GLM_GTX_norm extension.

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GLM_FUNC_DECL T glm::l2Norm (vec< 3, T, Q > const & x)
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Returns the L2 norm of v.

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From GLM_GTX_norm extension.

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GLM_FUNC_DECL T glm::length2 (vec< L, T, Q > const & x)
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Returns the squared length of x.

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From GLM_GTX_norm extension.

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GLM_FUNC_DECL T glm::lMaxNorm (vec< 3, T, Q > const & x,
vec< 3, T, Q > const & y 
)
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Returns the LMax norm between x and y.

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From GLM_GTX_norm extension.

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GLM_FUNC_DECL T glm::lMaxNorm (vec< 3, T, Q > const & x)
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Returns the LMax norm of v.

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From GLM_GTX_norm extension.

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GLM_FUNC_DECL T glm::lxNorm (vec< 3, T, Q > const & x,
vec< 3, T, Q > const & y,
unsigned int Depth 
)
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Returns the L norm between x and y.

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From GLM_GTX_norm extension.

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GLM_FUNC_DECL T glm::lxNorm (vec< 3, T, Q > const & x,
unsigned int Depth 
)
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Returns the L norm of v.

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From GLM_GTX_norm extension.

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Include <glm/gtx/normal.hpp> to use the features of this extension. \nMore...

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template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > triangleNormal (vec< 3, T, Q > const &p1, vec< 3, T, Q > const &p2, vec< 3, T, Q > const &p3)
 Computes triangle normal from triangle points. More...
 
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Detailed Description

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Include <glm/gtx/normal.hpp> to use the features of this extension.

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Compute the normal of a triangle.

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Function Documentation

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GLM_FUNC_DECL vec<3, T, Q> glm::triangleNormal (vec< 3, T, Q > const & p1,
vec< 3, T, Q > const & p2,
vec< 3, T, Q > const & p3 
)
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Computes triangle normal from triangle points.

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See also
to use the features of this extension. \">GLM_GTX_normal
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GLM_GTX_normalize_dot
Experimental extensions
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Include <glm/gtx/normalized_dot.hpp> to use the features of this extension. \nMore...

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template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL T fastNormalizeDot (vec< L, T, Q > const &x, vec< L, T, Q > const &y)
 Normalize parameters and returns the dot product of x and y. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL T normalizeDot (vec< L, T, Q > const &x, vec< L, T, Q > const &y)
 Normalize parameters and returns the dot product of x and y. More...
 
\n

Detailed Description

\n

Include <glm/gtx/normalized_dot.hpp> to use the features of this extension.

\n

Dot product of vectors that need to be normalize with a single square root.

\n

Function Documentation

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GLM_FUNC_DECL T glm::fastNormalizeDot (vec< L, T, Q > const & x,
vec< L, T, Q > const & y 
)
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Normalize parameters and returns the dot product of x and y.

\n

Faster that dot(fastNormalize(x), fastNormalize(y)).

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See also
to use the features of this extension. \">GLM_GTX_normalize_dot extension.
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GLM_FUNC_DECL T glm::normalizeDot (vec< L, T, Q > const & x,
vec< L, T, Q > const & y 
)
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Normalize parameters and returns the dot product of x and y.

\n

It's faster that dot(normalize(x), normalize(y)).

\n
See also
to use the features of this extension. \">GLM_GTX_normalize_dot extension.
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Experimental extensions
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\ntypedef f32 f32mat1
 Single-qualifier floating-point scalar. (from GLM_GTX_number_precision extension)
 
\ntypedef f32 f32mat1x1
 Single-qualifier floating-point scalar. (from GLM_GTX_number_precision extension)
 
\ntypedef f32 f32vec1
 Single-qualifier floating-point scalar. (from GLM_GTX_number_precision extension)
 
\ntypedef f64 f64mat1
 Double-qualifier floating-point scalar. (from GLM_GTX_number_precision extension)
 
\ntypedef f64 f64mat1x1
 Double-qualifier floating-point scalar. (from GLM_GTX_number_precision extension)
 
\ntypedef f64 f64vec1
 Single-qualifier floating-point scalar. (from GLM_GTX_number_precision extension)
 
\ntypedef u16 u16vec1
 16bit unsigned integer scalar. (from GLM_GTX_number_precision extension)
 
\ntypedef u32 u32vec1
 32bit unsigned integer scalar. (from GLM_GTX_number_precision extension)
 
\ntypedef u64 u64vec1
 64bit unsigned integer scalar. (from GLM_GTX_number_precision extension)
 
\ntypedef u8 u8vec1
 8bit unsigned integer scalar. (from GLM_GTX_number_precision extension)
 
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Detailed Description

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Include <glm/gtx/number_precision.hpp> to use the features of this extension.

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Defined size types.

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Experimental extensions
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template<typename genType >
GLM_FUNC_DECL genType pow2 (genType const &x)
 Returns x raised to the power of 2. More...
 
template<typename genType >
GLM_FUNC_DECL genType pow3 (genType const &x)
 Returns x raised to the power of 3. More...
 
template<typename genType >
GLM_FUNC_DECL genType pow4 (genType const &x)
 Returns x raised to the power of 4. More...
 
\n

Detailed Description

\n

Include <glm/gtx/optimum_pow.hpp> to use the features of this extension.

\n

Integer exponentiation of power functions.

\n

Function Documentation

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GLM_FUNC_DECL genType glm::gtx::pow2 (genType const & x)
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Returns x raised to the power of 2.

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See also
to use the features of this extension. \">GLM_GTX_optimum_pow
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GLM_FUNC_DECL genType glm::gtx::pow3 (genType const & x)
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Returns x raised to the power of 3.

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See also
to use the features of this extension. \">GLM_GTX_optimum_pow
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GLM_FUNC_DECL genType glm::gtx::pow4 (genType const & x)
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Returns x raised to the power of 4.

\n
See also
to use the features of this extension. \">GLM_GTX_optimum_pow
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Experimental extensions
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Include <glm/gtx/orthonormalize.hpp> to use the features of this extension. \nMore...

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template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 3, 3, T, Q > orthonormalize (mat< 3, 3, T, Q > const &m)
 Returns the orthonormalized matrix of m. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > orthonormalize (vec< 3, T, Q > const &x, vec< 3, T, Q > const &y)
 Orthonormalizes x according y. More...
 
\n

Detailed Description

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Include <glm/gtx/orthonormalize.hpp> to use the features of this extension.

\n

Orthonormalize matrices.

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Function Documentation

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GLM_FUNC_DECL mat<3, 3, T, Q> glm::orthonormalize (mat< 3, 3, T, Q > const & m)
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Returns the orthonormalized matrix of m.

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to use the features of this extension. \">GLM_GTX_orthonormalize
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GLM_FUNC_DECL vec<3, T, Q> glm::orthonormalize (vec< 3, T, Q > const & x,
vec< 3, T, Q > const & y 
)
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Orthonormalizes x according y.

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to use the features of this extension. \">GLM_GTX_orthonormalize
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template<typename genType >
GLM_FUNC_DECL genType perp (genType const &x, genType const &Normal)
 Projects x a perpendicular axis of Normal. More...
 
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Detailed Description

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Include <glm/gtx/perpendicular.hpp> to use the features of this extension.

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Perpendicular of a vector from other one

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Function Documentation

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GLM_FUNC_DECL genType glm::perp (genType const & x,
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)
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Projects x a perpendicular axis of Normal.

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From GLM_GTX_perpendicular extension.

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template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > euclidean (vec< 2, T, Q > const &polar)
 Convert Polar to Euclidean coordinates. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > polar (vec< 3, T, Q > const &euclidean)
 Convert Euclidean to Polar coordinates, x is the xz distance, y, the latitude and z the longitude. More...
 
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Detailed Description

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Conversion from Euclidean space to polar space and revert.

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Function Documentation

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GLM_FUNC_DECL vec<3, T, Q> glm::euclidean (vec< 2, T, Q > const & polar)
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Convert Polar to Euclidean coordinates.

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to use the features of this extension. \">GLM_GTX_polar_coordinates
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GLM_FUNC_DECL vec<3, T, Q> glm::polar (vec< 3, T, Q > const & euclidean)
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Convert Euclidean to Polar coordinates, x is the xz distance, y, the latitude and z the longitude.

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See also
to use the features of this extension. \">GLM_GTX_polar_coordinates
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template<typename genType >
GLM_FUNC_DECL genType proj (genType const &x, genType const &Normal)
 Projects x on Normal. More...
 
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Detailed Description

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Include <glm/gtx/projection.hpp> to use the features of this extension.

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Projection of a vector to other one

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Function Documentation

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GLM_FUNC_DECL genType glm::proj (genType const & x,
genType const & Normal 
)
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Projects x on Normal.

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Parameters
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See also
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Experimental extensions
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template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > cross (qua< T, Q > const &q, vec< 3, T, Q > const &v)
 Compute a cross product between a quaternion and a vector. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > cross (vec< 3, T, Q > const &v, qua< T, Q > const &q)
 Compute a cross product between a vector and a quaternion. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL T extractRealComponent (qua< T, Q > const &q)
 Extract the real component of a quaternion. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL qua< T, Q > fastMix (qua< T, Q > const &x, qua< T, Q > const &y, T const &a)
 Quaternion normalized linear interpolation. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL qua< T, Q > intermediate (qua< T, Q > const &prev, qua< T, Q > const &curr, qua< T, Q > const &next)
 Returns an intermediate control point for squad interpolation. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL T length2 (qua< T, Q > const &q)
 Returns the squared length of x. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL qua< T, Q > quat_identity ()
 Create an identity quaternion. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > rotate (qua< T, Q > const &q, vec< 3, T, Q > const &v)
 Returns quarternion square root. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 4, T, Q > rotate (qua< T, Q > const &q, vec< 4, T, Q > const &v)
 Rotates a 4 components vector by a quaternion. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL qua< T, Q > rotation (vec< 3, T, Q > const &orig, vec< 3, T, Q > const &dest)
 Compute the rotation between two vectors. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL qua< T, Q > shortMix (qua< T, Q > const &x, qua< T, Q > const &y, T const &a)
 Quaternion interpolation using the rotation short path. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL qua< T, Q > squad (qua< T, Q > const &q1, qua< T, Q > const &q2, qua< T, Q > const &s1, qua< T, Q > const &s2, T const &h)
 Compute a point on a path according squad equation. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 3, 3, T, Q > toMat3 (qua< T, Q > const &x)
 Converts a quaternion to a 3 * 3 matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 4, 4, T, Q > toMat4 (qua< T, Q > const &x)
 Converts a quaternion to a 4 * 4 matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL qua< T, Q > toQuat (mat< 3, 3, T, Q > const &x)
 Converts a 3 * 3 matrix to a quaternion. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL qua< T, Q > toQuat (mat< 4, 4, T, Q > const &x)
 Converts a 4 * 4 matrix to a quaternion. More...
 
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Detailed Description

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Include <glm/gtx/quaternion.hpp> to use the features of this extension.

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Extented quaternion types and functions

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Function Documentation

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GLM_FUNC_DECL vec<3, T, Q> glm::cross (qua< T, Q > const & q,
vec< 3, T, Q > const & v 
)
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Compute a cross product between a quaternion and a vector.

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See also
to use the features of this extension. \">GLM_GTX_quaternion
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GLM_FUNC_DECL vec<3, T, Q> glm::cross (vec< 3, T, Q > const & v,
qua< T, Q > const & q 
)
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Compute a cross product between a vector and a quaternion.

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See also
to use the features of this extension. \">GLM_GTX_quaternion
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GLM_FUNC_DECL T glm::extractRealComponent (qua< T, Q > const & q)
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Extract the real component of a quaternion.

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See also
to use the features of this extension. \">GLM_GTX_quaternion
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GLM_FUNC_DECL qua<T, Q> glm::fastMix (qua< T, Q > const & x,
qua< T, Q > const & y,
T const & a 
)
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Quaternion normalized linear interpolation.

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See also
to use the features of this extension. \">GLM_GTX_quaternion
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GLM_FUNC_DECL qua<T, Q> glm::intermediate (qua< T, Q > const & prev,
qua< T, Q > const & curr,
qua< T, Q > const & next 
)
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Returns an intermediate control point for squad interpolation.

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to use the features of this extension. \">GLM_GTX_quaternion
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GLM_FUNC_DECL T glm::length2 (qua< T, Q > const & q)
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Returns the squared length of x.

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GLM_FUNC_DECL qua<T, Q> glm::quat_identity ()
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Create an identity quaternion.

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GLM_FUNC_DECL vec<3, T, Q> glm::rotate (qua< T, Q > const & q,
vec< 3, T, Q > const & v 
)
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Returns quarternion square root.

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to use the features of this extension. \">GLM_GTX_quaternion Rotates a 3 components vector by a quaternion.
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GLM_FUNC_DECL vec<4, T, Q> glm::rotate (qua< T, Q > const & q,
vec< 4, T, Q > const & v 
)
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Rotates a 4 components vector by a quaternion.

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to use the features of this extension. \">GLM_GTX_quaternion
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GLM_FUNC_DECL qua<T, Q> glm::rotation (vec< 3, T, Q > const & orig,
vec< 3, T, Q > const & dest 
)
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Compute the rotation between two vectors.

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origvector, needs to be normalized
destvector, needs to be normalized
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GLM_FUNC_DECL qua<T, Q> glm::shortMix (qua< T, Q > const & x,
qua< T, Q > const & y,
T const & a 
)
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Quaternion interpolation using the rotation short path.

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See also
to use the features of this extension. \">GLM_GTX_quaternion
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GLM_FUNC_DECL qua<T, Q> glm::squad (qua< T, Q > const & q1,
qua< T, Q > const & q2,
qua< T, Q > const & s1,
qua< T, Q > const & s2,
T const & h 
)
\n
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Compute a point on a path according squad equation.

\n

q1 and q2 are control points; s1 and s2 are intermediate control points.

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See also
to use the features of this extension. \">GLM_GTX_quaternion
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GLM_FUNC_DECL mat<3, 3, T, Q> glm::toMat3 (qua< T, Q > const & x)
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Converts a quaternion to a 3 * 3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTX_quaternion
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Definition at line 113 of file gtx/quaternion.hpp.

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References glm::mat3_cast().

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GLM_FUNC_DECL mat<4, 4, T, Q> glm::toMat4 (qua< T, Q > const & x)
\n
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Converts a quaternion to a 4 * 4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTX_quaternion
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Definition at line 120 of file gtx/quaternion.hpp.

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References glm::mat4_cast().

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GLM_FUNC_DECL qua<T, Q> glm::toQuat (mat< 3, 3, T, Q > const & x)
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Converts a 3 * 3 matrix to a quaternion.

\n
See also
to use the features of this extension. \">GLM_GTX_quaternion
\n\n

Definition at line 127 of file gtx/quaternion.hpp.

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References glm::quat_cast().

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GLM_FUNC_DECL qua<T, Q> glm::toQuat (mat< 4, 4, T, Q > const & x)
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Converts a 4 * 4 matrix to a quaternion.

\n
See also
to use the features of this extension. \">GLM_GTX_quaternion
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Definition at line 134 of file gtx/quaternion.hpp.

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References glm::quat_cast().

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GLM_GTX_range
Experimental extensions
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Include <glm/gtx/range.hpp> to use the features of this extension. \nMore...

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Detailed Description

\n

Include <glm/gtx/range.hpp> to use the features of this extension.

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Defines begin and end for vectors and matrices. Useful for range-based for loop. The range is defined over the elements, not over columns or rows (e.g. mat4 has 16 elements).

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GLM_GTX_raw_data
Experimental extensions
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Include <glm/gtx/raw_data.hpp> to use the features of this extension. \nMore...

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\nTypedefs

typedef detail::uint8 byte
 Type for byte numbers. More...
 
typedef detail::uint32 dword
 Type for dword numbers. More...
 
typedef detail::uint64 qword
 Type for qword numbers. More...
 
typedef detail::uint16 word
 Type for word numbers. More...
 
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Detailed Description

\n

Include <glm/gtx/raw_data.hpp> to use the features of this extension.

\n

Projection of a vector to other one

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Typedef Documentation

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typedef detail::uint8 byte
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Type for byte numbers.

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From GLM_GTX_raw_data extension.

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Definition at line 34 of file raw_data.hpp.

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typedef detail::uint32 dword
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Type for dword numbers.

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From GLM_GTX_raw_data extension.

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Definition at line 42 of file raw_data.hpp.

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typedef detail::uint64 qword
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Type for qword numbers.

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From GLM_GTX_raw_data extension.

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Definition at line 46 of file raw_data.hpp.

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typedef detail::uint16 word
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Type for word numbers.

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From GLM_GTX_raw_data extension.

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Definition at line 38 of file raw_data.hpp.

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GLM_GTX_rotate_normalized_axis
Experimental extensions
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Include <glm/gtx/rotate_normalized_axis.hpp> to use the features of this extension. \nMore...

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\nFunctions

template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 4, 4, T, Q > rotateNormalizedAxis (mat< 4, 4, T, Q > const &m, T const &angle, vec< 3, T, Q > const &axis)
 Builds a rotation 4 * 4 matrix created from a normalized axis and an angle. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL qua< T, Q > rotateNormalizedAxis (qua< T, Q > const &q, T const &angle, vec< 3, T, Q > const &axis)
 Rotates a quaternion from a vector of 3 components normalized axis and an angle. More...
 
\n

Detailed Description

\n

Include <glm/gtx/rotate_normalized_axis.hpp> to use the features of this extension.

\n

Quaternions and matrices rotations around normalized axis.

\n

Function Documentation

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GLM_FUNC_DECL mat<4, 4, T, Q> glm::rotateNormalizedAxis (mat< 4, 4, T, Q > const & m,
T const & angle,
vec< 3, T, Q > const & axis 
)
\n
\n\n

Builds a rotation 4 * 4 matrix created from a normalized axis and an angle.

\n
Parameters
\n \n \n \n \n
mInput matrix multiplied by this rotation matrix.
angleRotation angle expressed in radians.
axisRotation axis, must be normalized.
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Template Parameters
\n \n \n
TValue type used to build the matrix. Currently supported: half (not recommended), float or double.
\n
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See also
to use the features of this extension. \">GLM_GTX_rotate_normalized_axis
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\n- rotate(T angle, T x, T y, T z)
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\n- rotate(mat<4, 4, T, Q> const& m, T angle, T x, T y, T z)
\n
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GLM_FUNC_DECL qua<T, Q> glm::rotateNormalizedAxis (qua< T, Q > const & q,
T const & angle,
vec< 3, T, Q > const & axis 
)
\n
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Rotates a quaternion from a vector of 3 components normalized axis and an angle.

\n
Parameters
\n \n \n \n \n
qSource orientation
angleAngle expressed in radians.
axisNormalized axis of the rotation, must be normalized.
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See also
to use the features of this extension. \">GLM_GTX_rotate_normalized_axis
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GLM_GTX_rotate_vector
Experimental extensions
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Include <glm/gtx/rotate_vector.hpp> to use the features of this extension. \nMore...

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\nFunctions

template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 4, 4, T, Q > orientation (vec< 3, T, Q > const &Normal, vec< 3, T, Q > const &Up)
 Build a rotation matrix from a normal and a up vector. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 2, T, Q > rotate (vec< 2, T, Q > const &v, T const &angle)
 Rotate a two dimensional vector. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > rotate (vec< 3, T, Q > const &v, T const &angle, vec< 3, T, Q > const &normal)
 Rotate a three dimensional vector around an axis. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 4, T, Q > rotate (vec< 4, T, Q > const &v, T const &angle, vec< 3, T, Q > const &normal)
 Rotate a four dimensional vector around an axis. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > rotateX (vec< 3, T, Q > const &v, T const &angle)
 Rotate a three dimensional vector around the X axis. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 4, T, Q > rotateX (vec< 4, T, Q > const &v, T const &angle)
 Rotate a four dimensional vector around the X axis. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > rotateY (vec< 3, T, Q > const &v, T const &angle)
 Rotate a three dimensional vector around the Y axis. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 4, T, Q > rotateY (vec< 4, T, Q > const &v, T const &angle)
 Rotate a four dimensional vector around the Y axis. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > rotateZ (vec< 3, T, Q > const &v, T const &angle)
 Rotate a three dimensional vector around the Z axis. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 4, T, Q > rotateZ (vec< 4, T, Q > const &v, T const &angle)
 Rotate a four dimensional vector around the Z axis. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL vec< 3, T, Q > slerp (vec< 3, T, Q > const &x, vec< 3, T, Q > const &y, T const &a)
 Returns Spherical interpolation between two vectors. More...
 
\n

Detailed Description

\n

Include <glm/gtx/rotate_vector.hpp> to use the features of this extension.

\n

Function to directly rotate a vector

\n

Function Documentation

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GLM_FUNC_DECL mat<4, 4, T, Q> glm::orientation (vec< 3, T, Q > const & Normal,
vec< 3, T, Q > const & Up 
)
\n
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Build a rotation matrix from a normal and a up vector.

\n

From GLM_GTX_rotate_vector extension.

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GLM_FUNC_DECL vec<2, T, Q> glm::rotate (vec< 2, T, Q > const & v,
T const & angle 
)
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Rotate a two dimensional vector.

\n

From GLM_GTX_rotate_vector extension.

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GLM_FUNC_DECL vec<3, T, Q> glm::rotate (vec< 3, T, Q > const & v,
T const & angle,
vec< 3, T, Q > const & normal 
)
\n
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Rotate a three dimensional vector around an axis.

\n

From GLM_GTX_rotate_vector extension.

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GLM_FUNC_DECL vec<4, T, Q> glm::rotate (vec< 4, T, Q > const & v,
T const & angle,
vec< 3, T, Q > const & normal 
)
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Rotate a four dimensional vector around an axis.

\n

From GLM_GTX_rotate_vector extension.

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GLM_FUNC_DECL vec<3, T, Q> glm::rotateX (vec< 3, T, Q > const & v,
T const & angle 
)
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Rotate a three dimensional vector around the X axis.

\n

From GLM_GTX_rotate_vector extension.

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GLM_FUNC_DECL vec<4, T, Q> glm::rotateX (vec< 4, T, Q > const & v,
T const & angle 
)
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Rotate a four dimensional vector around the X axis.

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From GLM_GTX_rotate_vector extension.

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GLM_FUNC_DECL vec<3, T, Q> glm::rotateY (vec< 3, T, Q > const & v,
T const & angle 
)
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Rotate a three dimensional vector around the Y axis.

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From GLM_GTX_rotate_vector extension.

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GLM_FUNC_DECL vec<4, T, Q> glm::rotateY (vec< 4, T, Q > const & v,
T const & angle 
)
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Rotate a four dimensional vector around the Y axis.

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From GLM_GTX_rotate_vector extension.

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GLM_FUNC_DECL vec<3, T, Q> glm::rotateZ (vec< 3, T, Q > const & v,
T const & angle 
)
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Rotate a three dimensional vector around the Z axis.

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From GLM_GTX_rotate_vector extension.

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GLM_FUNC_DECL vec<4, T, Q> glm::rotateZ (vec< 4, T, Q > const & v,
T const & angle 
)
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Rotate a four dimensional vector around the Z axis.

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From GLM_GTX_rotate_vector extension.

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GLM_FUNC_DECL vec<3, T, Q> glm::slerp (vec< 3, T, Q > const & x,
vec< 3, T, Q > const & y,
T const & a 
)
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Returns Spherical interpolation between two vectors.

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xA first vector
yA second vector
aInterpolation factor. The interpolation is defined beyond the range [0, 1].
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See also
to use the features of this extension. \">GLM_GTX_rotate_vector
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GLM_GTX_scalar_relational
Experimental extensions
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Include <glm/gtx/scalar_relational.hpp> to use the features of this extension. \nMore...

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Include <glm/gtx/scalar_relational.hpp> to use the features of this extension.

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Extend a position from a source to a position at a defined length.

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GLM_GTX_spline
Experimental extensions
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Include <glm/gtx/spline.hpp> to use the features of this extension. \nMore...

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\nFunctions

template<typename genType >
GLM_FUNC_DECL genType catmullRom (genType const &v1, genType const &v2, genType const &v3, genType const &v4, typename genType::value_type const &s)
 Return a point from a catmull rom curve. More...
 
template<typename genType >
GLM_FUNC_DECL genType cubic (genType const &v1, genType const &v2, genType const &v3, genType const &v4, typename genType::value_type const &s)
 Return a point from a cubic curve. More...
 
template<typename genType >
GLM_FUNC_DECL genType hermite (genType const &v1, genType const &t1, genType const &v2, genType const &t2, typename genType::value_type const &s)
 Return a point from a hermite curve. More...
 
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Detailed Description

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Include <glm/gtx/spline.hpp> to use the features of this extension.

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Spline functions

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Function Documentation

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GLM_FUNC_DECL genType glm::catmullRom (genType const & v1,
genType const & v2,
genType const & v3,
genType const & v4,
typename genType::value_type const & s 
)
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Return a point from a catmull rom curve.

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See also
to use the features of this extension. \">GLM_GTX_spline extension.
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GLM_FUNC_DECL genType glm::cubic (genType const & v1,
genType const & v2,
genType const & v3,
genType const & v4,
typename genType::value_type const & s 
)
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Return a point from a cubic curve.

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See also
to use the features of this extension. \">GLM_GTX_spline extension.
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GLM_FUNC_DECL genType glm::hermite (genType const & v1,
genType const & t1,
genType const & v2,
genType const & t2,
typename genType::value_type const & s 
)
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Return a point from a hermite curve.

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See also
to use the features of this extension. \">GLM_GTX_spline extension.
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GLM_GTX_std_based_type
Experimental extensions
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Include <glm/gtx/std_based_type.hpp> to use the features of this extension. \nMore...

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\nTypedefs

typedef vec< 1, std::size_t, defaultp > size1
 Vector type based of one std::size_t component. More...
 
typedef vec< 1, std::size_t, defaultp > size1_t
 Vector type based of one std::size_t component. More...
 
typedef vec< 2, std::size_t, defaultp > size2
 Vector type based of two std::size_t components. More...
 
typedef vec< 2, std::size_t, defaultp > size2_t
 Vector type based of two std::size_t components. More...
 
typedef vec< 3, std::size_t, defaultp > size3
 Vector type based of three std::size_t components. More...
 
typedef vec< 3, std::size_t, defaultp > size3_t
 Vector type based of three std::size_t components. More...
 
typedef vec< 4, std::size_t, defaultp > size4
 Vector type based of four std::size_t components. More...
 
typedef vec< 4, std::size_t, defaultp > size4_t
 Vector type based of four std::size_t components. More...
 
\n

Detailed Description

\n

Include <glm/gtx/std_based_type.hpp> to use the features of this extension.

\n

Adds vector types based on STL value types.

\n

Typedef Documentation

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typedef vec<1, std::size_t, defaultp> size1
\n
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Vector type based of one std::size_t component.

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GLM_GTX_std_based_type
\n\n

Definition at line 35 of file std_based_type.hpp.

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typedef vec<1, std::size_t, defaultp> size1_t
\n
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Vector type based of one std::size_t component.

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GLM_GTX_std_based_type
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Definition at line 51 of file std_based_type.hpp.

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typedef vec<2, std::size_t, defaultp> size2
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Vector type based of two std::size_t components.

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See also
GLM_GTX_std_based_type
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Definition at line 39 of file std_based_type.hpp.

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typedef vec<2, std::size_t, defaultp> size2_t
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Vector type based of two std::size_t components.

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GLM_GTX_std_based_type
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Definition at line 55 of file std_based_type.hpp.

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typedef vec<3, std::size_t, defaultp> size3
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Vector type based of three std::size_t components.

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GLM_GTX_std_based_type
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Definition at line 43 of file std_based_type.hpp.

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typedef vec<3, std::size_t, defaultp> size3_t
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Vector type based of three std::size_t components.

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GLM_GTX_std_based_type
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Definition at line 59 of file std_based_type.hpp.

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typedef vec<4, std::size_t, defaultp> size4
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Vector type based of four std::size_t components.

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See also
GLM_GTX_std_based_type
\n\n

Definition at line 47 of file std_based_type.hpp.

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typedef vec<4, std::size_t, defaultp> size4_t
\n
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Vector type based of four std::size_t components.

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See also
GLM_GTX_std_based_type
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Definition at line 63 of file std_based_type.hpp.

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GLM_GTX_string_cast
Experimental extensions
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Include <glm/gtx/string_cast.hpp> to use the features of this extension. \nMore...

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template<typename genType >
GLM_FUNC_DECL std::string to_string (genType const &x)
 Create a string from a GLM vector or matrix typed variable. More...
 
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Detailed Description

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Include <glm/gtx/string_cast.hpp> to use the features of this extension.

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Setup strings for GLM type values

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This extension is not supported with CUDA

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Function Documentation

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GLM_FUNC_DECL std::string glm::to_string (genType const & x)
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Create a string from a GLM vector or matrix typed variable.

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See also
to use the features of this extension. \">GLM_GTX_string_cast extension.
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GLM_GTX_texture
Experimental extensions
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\nFunctions

template<length_t L, typename T , qualifier Q>
T levels (vec< L, T, Q > const &Extent)
 Compute the number of mipmaps levels necessary to create a mipmap complete texture. More...
 
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Detailed Description

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Include <glm/gtx/texture.hpp> to use the features of this extension.

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Wrapping mode of texture coordinates.

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Function Documentation

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T glm::levels (vec< L, T, Q > const & Extent)
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Compute the number of mipmaps levels necessary to create a mipmap complete texture.

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ExtentExtent of the texture base level mipmap
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LInteger between 1 and 4 included that qualify the dimension of the vector
TFloating-point or signed integer scalar types
QValue from qualifier enum
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Experimental extensions
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template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 4, 4, T, Q > rotate (T angle, vec< 3, T, Q > const &v)
 Builds a rotation 4 * 4 matrix created from an axis of 3 scalars and an angle expressed in radians. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 4, 4, T, Q > scale (vec< 3, T, Q > const &v)
 Transforms a matrix with a scale 4 * 4 matrix created from a vector of 3 components. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 4, 4, T, Q > translate (vec< 3, T, Q > const &v)
 Transforms a matrix with a translation 4 * 4 matrix created from 3 scalars. More...
 
\n

Detailed Description

\n

Include <glm/gtx/transform.hpp> to use the features of this extension.

\n

Add transformation matrices

\n

Function Documentation

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GLM_FUNC_DECL mat<4, 4, T, Q> glm::rotate (T angle,
vec< 3, T, Q > const & v 
)
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Builds a rotation 4 * 4 matrix created from an axis of 3 scalars and an angle expressed in radians.

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See also
to use the features of this extension. \">GLM_GTC_matrix_transform
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GLM_FUNC_DECL mat<4, 4, T, Q> glm::scale (vec< 3, T, Q > const & v)
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Transforms a matrix with a scale 4 * 4 matrix created from a vector of 3 components.

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See also
to use the features of this extension. \">GLM_GTC_matrix_transform
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GLM_FUNC_DECL mat<4, 4, T, Q> glm::translate (vec< 3, T, Q > const & v)
\n
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Transforms a matrix with a translation 4 * 4 matrix created from 3 scalars.

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See also
to use the features of this extension. \">GLM_GTC_matrix_transform
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Experimental extensions
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\nFunctions

template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 3, 3, T, Q > proj2D (mat< 3, 3, T, Q > const &m, vec< 3, T, Q > const &normal)
 Build planar projection matrix along normal axis. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 4, 4, T, Q > proj3D (mat< 4, 4, T, Q > const &m, vec< 3, T, Q > const &normal)
 Build planar projection matrix along normal axis. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 4, 4, T, Q > scaleBias (T scale, T bias)
 Build a scale bias matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 4, 4, T, Q > scaleBias (mat< 4, 4, T, Q > const &m, T scale, T bias)
 Build a scale bias matrix. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 3, 3, T, Q > shearX2D (mat< 3, 3, T, Q > const &m, T y)
 Transforms a matrix with a shearing on X axis. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 4, 4, T, Q > shearX3D (mat< 4, 4, T, Q > const &m, T y, T z)
 Transforms a matrix with a shearing on X axis From GLM_GTX_transform2 extension. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 3, 3, T, Q > shearY2D (mat< 3, 3, T, Q > const &m, T x)
 Transforms a matrix with a shearing on Y axis. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 4, 4, T, Q > shearY3D (mat< 4, 4, T, Q > const &m, T x, T z)
 Transforms a matrix with a shearing on Y axis. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL mat< 4, 4, T, Q > shearZ3D (mat< 4, 4, T, Q > const &m, T x, T y)
 Transforms a matrix with a shearing on Z axis. More...
 
\n

Detailed Description

\n

Include <glm/gtx/transform2.hpp> to use the features of this extension.

\n

Add extra transformation matrices

\n

Function Documentation

\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL mat<3, 3, T, Q> glm::proj2D (mat< 3, 3, T, Q > const & m,
vec< 3, T, Q > const & normal 
)
\n
\n\n

Build planar projection matrix along normal axis.

\n

From GLM_GTX_transform2 extension.

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GLM_FUNC_DECL mat<4, 4, T, Q> glm::proj3D (mat< 4, 4, T, Q > const & m,
vec< 3, T, Q > const & normal 
)
\n
\n\n

Build planar projection matrix along normal axis.

\n

From GLM_GTX_transform2 extension.

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GLM_FUNC_DECL mat<4, 4, T, Q> glm::scaleBias (T scale,
T bias 
)
\n
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Build a scale bias matrix.

\n

From GLM_GTX_transform2 extension.

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GLM_FUNC_DECL mat<4, 4, T, Q> glm::scaleBias (mat< 4, 4, T, Q > const & m,
T scale,
T bias 
)
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Build a scale bias matrix.

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GLM_FUNC_DECL mat<3, 3, T, Q> glm::shearX2D (mat< 3, 3, T, Q > const & m,
T y 
)
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Transforms a matrix with a shearing on X axis.

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From GLM_GTX_transform2 extension.

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GLM_FUNC_DECL mat<4, 4, T, Q> glm::shearX3D (mat< 4, 4, T, Q > const & m,
T y,
T z 
)
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Transforms a matrix with a shearing on X axis From GLM_GTX_transform2 extension.

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GLM_FUNC_DECL mat<3, 3, T, Q> glm::shearY2D (mat< 3, 3, T, Q > const & m,
T x 
)
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Transforms a matrix with a shearing on Y axis.

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GLM_FUNC_DECL mat<4, 4, T, Q> glm::shearY3D (mat< 4, 4, T, Q > const & m,
T x,
T z 
)
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Transforms a matrix with a shearing on Y axis.

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From GLM_GTX_transform2 extension.

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GLM_FUNC_DECL mat<4, 4, T, Q> glm::shearZ3D (mat< 4, 4, T, Q > const & m,
T x,
T y 
)
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Transforms a matrix with a shearing on Z axis.

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From GLM_GTX_transform2 extension.

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\nFunctions
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GLM_GTX_type_aligned
Experimental extensions
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Include <glm/gtx/type_aligned.hpp> to use the features of this extension. \nMore...

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\nFunctions

 GLM_ALIGNED_TYPEDEF (lowp_int8, aligned_lowp_int8, 1)
 Low qualifier 8 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (lowp_int16, aligned_lowp_int16, 2)
 Low qualifier 16 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (lowp_int32, aligned_lowp_int32, 4)
 Low qualifier 32 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (lowp_int64, aligned_lowp_int64, 8)
 Low qualifier 64 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (lowp_int8_t, aligned_lowp_int8_t, 1)
 Low qualifier 8 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (lowp_int16_t, aligned_lowp_int16_t, 2)
 Low qualifier 16 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (lowp_int32_t, aligned_lowp_int32_t, 4)
 Low qualifier 32 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (lowp_int64_t, aligned_lowp_int64_t, 8)
 Low qualifier 64 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (lowp_i8, aligned_lowp_i8, 1)
 Low qualifier 8 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (lowp_i16, aligned_lowp_i16, 2)
 Low qualifier 16 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (lowp_i32, aligned_lowp_i32, 4)
 Low qualifier 32 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (lowp_i64, aligned_lowp_i64, 8)
 Low qualifier 64 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (mediump_int8, aligned_mediump_int8, 1)
 Medium qualifier 8 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (mediump_int16, aligned_mediump_int16, 2)
 Medium qualifier 16 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (mediump_int32, aligned_mediump_int32, 4)
 Medium qualifier 32 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (mediump_int64, aligned_mediump_int64, 8)
 Medium qualifier 64 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (mediump_int8_t, aligned_mediump_int8_t, 1)
 Medium qualifier 8 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (mediump_int16_t, aligned_mediump_int16_t, 2)
 Medium qualifier 16 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (mediump_int32_t, aligned_mediump_int32_t, 4)
 Medium qualifier 32 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (mediump_int64_t, aligned_mediump_int64_t, 8)
 Medium qualifier 64 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (mediump_i8, aligned_mediump_i8, 1)
 Medium qualifier 8 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (mediump_i16, aligned_mediump_i16, 2)
 Medium qualifier 16 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (mediump_i32, aligned_mediump_i32, 4)
 Medium qualifier 32 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (mediump_i64, aligned_mediump_i64, 8)
 Medium qualifier 64 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (highp_int8, aligned_highp_int8, 1)
 High qualifier 8 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (highp_int16, aligned_highp_int16, 2)
 High qualifier 16 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (highp_int32, aligned_highp_int32, 4)
 High qualifier 32 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (highp_int64, aligned_highp_int64, 8)
 High qualifier 64 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (highp_int8_t, aligned_highp_int8_t, 1)
 High qualifier 8 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (highp_int16_t, aligned_highp_int16_t, 2)
 High qualifier 16 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (highp_int32_t, aligned_highp_int32_t, 4)
 High qualifier 32 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (highp_int64_t, aligned_highp_int64_t, 8)
 High qualifier 64 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (highp_i8, aligned_highp_i8, 1)
 High qualifier 8 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (highp_i16, aligned_highp_i16, 2)
 High qualifier 16 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (highp_i32, aligned_highp_i32, 4)
 High qualifier 32 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (highp_i64, aligned_highp_i64, 8)
 High qualifier 64 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (int8, aligned_int8, 1)
 Default qualifier 8 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (int16, aligned_int16, 2)
 Default qualifier 16 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (int32, aligned_int32, 4)
 Default qualifier 32 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (int64, aligned_int64, 8)
 Default qualifier 64 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (int8_t, aligned_int8_t, 1)
 Default qualifier 8 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (int16_t, aligned_int16_t, 2)
 Default qualifier 16 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (int32_t, aligned_int32_t, 4)
 Default qualifier 32 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (int64_t, aligned_int64_t, 8)
 Default qualifier 64 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (i8, aligned_i8, 1)
 Default qualifier 8 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (i16, aligned_i16, 2)
 Default qualifier 16 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (i32, aligned_i32, 4)
 Default qualifier 32 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (i64, aligned_i64, 8)
 Default qualifier 64 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (ivec1, aligned_ivec1, 4)
 Default qualifier 32 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (ivec2, aligned_ivec2, 8)
 Default qualifier 32 bit signed integer aligned vector of 2 components type. More...
 
 GLM_ALIGNED_TYPEDEF (ivec3, aligned_ivec3, 16)
 Default qualifier 32 bit signed integer aligned vector of 3 components type. More...
 
 GLM_ALIGNED_TYPEDEF (ivec4, aligned_ivec4, 16)
 Default qualifier 32 bit signed integer aligned vector of 4 components type. More...
 
 GLM_ALIGNED_TYPEDEF (i8vec1, aligned_i8vec1, 1)
 Default qualifier 8 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (i8vec2, aligned_i8vec2, 2)
 Default qualifier 8 bit signed integer aligned vector of 2 components type. More...
 
 GLM_ALIGNED_TYPEDEF (i8vec3, aligned_i8vec3, 4)
 Default qualifier 8 bit signed integer aligned vector of 3 components type. More...
 
 GLM_ALIGNED_TYPEDEF (i8vec4, aligned_i8vec4, 4)
 Default qualifier 8 bit signed integer aligned vector of 4 components type. More...
 
 GLM_ALIGNED_TYPEDEF (i16vec1, aligned_i16vec1, 2)
 Default qualifier 16 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (i16vec2, aligned_i16vec2, 4)
 Default qualifier 16 bit signed integer aligned vector of 2 components type. More...
 
 GLM_ALIGNED_TYPEDEF (i16vec3, aligned_i16vec3, 8)
 Default qualifier 16 bit signed integer aligned vector of 3 components type. More...
 
 GLM_ALIGNED_TYPEDEF (i16vec4, aligned_i16vec4, 8)
 Default qualifier 16 bit signed integer aligned vector of 4 components type. More...
 
 GLM_ALIGNED_TYPEDEF (i32vec1, aligned_i32vec1, 4)
 Default qualifier 32 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (i32vec2, aligned_i32vec2, 8)
 Default qualifier 32 bit signed integer aligned vector of 2 components type. More...
 
 GLM_ALIGNED_TYPEDEF (i32vec3, aligned_i32vec3, 16)
 Default qualifier 32 bit signed integer aligned vector of 3 components type. More...
 
 GLM_ALIGNED_TYPEDEF (i32vec4, aligned_i32vec4, 16)
 Default qualifier 32 bit signed integer aligned vector of 4 components type. More...
 
 GLM_ALIGNED_TYPEDEF (i64vec1, aligned_i64vec1, 8)
 Default qualifier 64 bit signed integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (i64vec2, aligned_i64vec2, 16)
 Default qualifier 64 bit signed integer aligned vector of 2 components type. More...
 
 GLM_ALIGNED_TYPEDEF (i64vec3, aligned_i64vec3, 32)
 Default qualifier 64 bit signed integer aligned vector of 3 components type. More...
 
 GLM_ALIGNED_TYPEDEF (i64vec4, aligned_i64vec4, 32)
 Default qualifier 64 bit signed integer aligned vector of 4 components type. More...
 
 GLM_ALIGNED_TYPEDEF (lowp_uint8, aligned_lowp_uint8, 1)
 Low qualifier 8 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (lowp_uint16, aligned_lowp_uint16, 2)
 Low qualifier 16 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (lowp_uint32, aligned_lowp_uint32, 4)
 Low qualifier 32 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (lowp_uint64, aligned_lowp_uint64, 8)
 Low qualifier 64 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (lowp_uint8_t, aligned_lowp_uint8_t, 1)
 Low qualifier 8 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (lowp_uint16_t, aligned_lowp_uint16_t, 2)
 Low qualifier 16 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (lowp_uint32_t, aligned_lowp_uint32_t, 4)
 Low qualifier 32 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (lowp_uint64_t, aligned_lowp_uint64_t, 8)
 Low qualifier 64 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (lowp_u8, aligned_lowp_u8, 1)
 Low qualifier 8 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (lowp_u16, aligned_lowp_u16, 2)
 Low qualifier 16 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (lowp_u32, aligned_lowp_u32, 4)
 Low qualifier 32 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (lowp_u64, aligned_lowp_u64, 8)
 Low qualifier 64 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (mediump_uint8, aligned_mediump_uint8, 1)
 Medium qualifier 8 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (mediump_uint16, aligned_mediump_uint16, 2)
 Medium qualifier 16 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (mediump_uint32, aligned_mediump_uint32, 4)
 Medium qualifier 32 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (mediump_uint64, aligned_mediump_uint64, 8)
 Medium qualifier 64 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (mediump_uint8_t, aligned_mediump_uint8_t, 1)
 Medium qualifier 8 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (mediump_uint16_t, aligned_mediump_uint16_t, 2)
 Medium qualifier 16 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (mediump_uint32_t, aligned_mediump_uint32_t, 4)
 Medium qualifier 32 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (mediump_uint64_t, aligned_mediump_uint64_t, 8)
 Medium qualifier 64 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (mediump_u8, aligned_mediump_u8, 1)
 Medium qualifier 8 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (mediump_u16, aligned_mediump_u16, 2)
 Medium qualifier 16 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (mediump_u32, aligned_mediump_u32, 4)
 Medium qualifier 32 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (mediump_u64, aligned_mediump_u64, 8)
 Medium qualifier 64 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (highp_uint8, aligned_highp_uint8, 1)
 High qualifier 8 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (highp_uint16, aligned_highp_uint16, 2)
 High qualifier 16 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (highp_uint32, aligned_highp_uint32, 4)
 High qualifier 32 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (highp_uint64, aligned_highp_uint64, 8)
 High qualifier 64 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (highp_uint8_t, aligned_highp_uint8_t, 1)
 High qualifier 8 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (highp_uint16_t, aligned_highp_uint16_t, 2)
 High qualifier 16 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (highp_uint32_t, aligned_highp_uint32_t, 4)
 High qualifier 32 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (highp_uint64_t, aligned_highp_uint64_t, 8)
 High qualifier 64 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (highp_u8, aligned_highp_u8, 1)
 High qualifier 8 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (highp_u16, aligned_highp_u16, 2)
 High qualifier 16 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (highp_u32, aligned_highp_u32, 4)
 High qualifier 32 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (highp_u64, aligned_highp_u64, 8)
 High qualifier 64 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (uint8, aligned_uint8, 1)
 Default qualifier 8 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (uint16, aligned_uint16, 2)
 Default qualifier 16 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (uint32, aligned_uint32, 4)
 Default qualifier 32 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (uint64, aligned_uint64, 8)
 Default qualifier 64 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (uint8_t, aligned_uint8_t, 1)
 Default qualifier 8 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (uint16_t, aligned_uint16_t, 2)
 Default qualifier 16 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (uint32_t, aligned_uint32_t, 4)
 Default qualifier 32 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (uint64_t, aligned_uint64_t, 8)
 Default qualifier 64 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (u8, aligned_u8, 1)
 Default qualifier 8 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (u16, aligned_u16, 2)
 Default qualifier 16 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (u32, aligned_u32, 4)
 Default qualifier 32 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (u64, aligned_u64, 8)
 Default qualifier 64 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (uvec1, aligned_uvec1, 4)
 Default qualifier 32 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (uvec2, aligned_uvec2, 8)
 Default qualifier 32 bit unsigned integer aligned vector of 2 components type. More...
 
 GLM_ALIGNED_TYPEDEF (uvec3, aligned_uvec3, 16)
 Default qualifier 32 bit unsigned integer aligned vector of 3 components type. More...
 
 GLM_ALIGNED_TYPEDEF (uvec4, aligned_uvec4, 16)
 Default qualifier 32 bit unsigned integer aligned vector of 4 components type. More...
 
 GLM_ALIGNED_TYPEDEF (u8vec1, aligned_u8vec1, 1)
 Default qualifier 8 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (u8vec2, aligned_u8vec2, 2)
 Default qualifier 8 bit unsigned integer aligned vector of 2 components type. More...
 
 GLM_ALIGNED_TYPEDEF (u8vec3, aligned_u8vec3, 4)
 Default qualifier 8 bit unsigned integer aligned vector of 3 components type. More...
 
 GLM_ALIGNED_TYPEDEF (u8vec4, aligned_u8vec4, 4)
 Default qualifier 8 bit unsigned integer aligned vector of 4 components type. More...
 
 GLM_ALIGNED_TYPEDEF (u16vec1, aligned_u16vec1, 2)
 Default qualifier 16 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (u16vec2, aligned_u16vec2, 4)
 Default qualifier 16 bit unsigned integer aligned vector of 2 components type. More...
 
 GLM_ALIGNED_TYPEDEF (u16vec3, aligned_u16vec3, 8)
 Default qualifier 16 bit unsigned integer aligned vector of 3 components type. More...
 
 GLM_ALIGNED_TYPEDEF (u16vec4, aligned_u16vec4, 8)
 Default qualifier 16 bit unsigned integer aligned vector of 4 components type. More...
 
 GLM_ALIGNED_TYPEDEF (u32vec1, aligned_u32vec1, 4)
 Default qualifier 32 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (u32vec2, aligned_u32vec2, 8)
 Default qualifier 32 bit unsigned integer aligned vector of 2 components type. More...
 
 GLM_ALIGNED_TYPEDEF (u32vec3, aligned_u32vec3, 16)
 Default qualifier 32 bit unsigned integer aligned vector of 3 components type. More...
 
 GLM_ALIGNED_TYPEDEF (u32vec4, aligned_u32vec4, 16)
 Default qualifier 32 bit unsigned integer aligned vector of 4 components type. More...
 
 GLM_ALIGNED_TYPEDEF (u64vec1, aligned_u64vec1, 8)
 Default qualifier 64 bit unsigned integer aligned scalar type. More...
 
 GLM_ALIGNED_TYPEDEF (u64vec2, aligned_u64vec2, 16)
 Default qualifier 64 bit unsigned integer aligned vector of 2 components type. More...
 
 GLM_ALIGNED_TYPEDEF (u64vec3, aligned_u64vec3, 32)
 Default qualifier 64 bit unsigned integer aligned vector of 3 components type. More...
 
 GLM_ALIGNED_TYPEDEF (u64vec4, aligned_u64vec4, 32)
 Default qualifier 64 bit unsigned integer aligned vector of 4 components type. More...
 
 GLM_ALIGNED_TYPEDEF (float32, aligned_float32, 4)
 32 bit single-qualifier floating-point aligned scalar. More...
 
 GLM_ALIGNED_TYPEDEF (float32_t, aligned_float32_t, 4)
 32 bit single-qualifier floating-point aligned scalar. More...
 
 GLM_ALIGNED_TYPEDEF (float32, aligned_f32, 4)
 32 bit single-qualifier floating-point aligned scalar. More...
 
 GLM_ALIGNED_TYPEDEF (float64, aligned_float64, 8)
 64 bit double-qualifier floating-point aligned scalar. More...
 
 GLM_ALIGNED_TYPEDEF (float64_t, aligned_float64_t, 8)
 64 bit double-qualifier floating-point aligned scalar. More...
 
 GLM_ALIGNED_TYPEDEF (float64, aligned_f64, 8)
 64 bit double-qualifier floating-point aligned scalar. More...
 
 GLM_ALIGNED_TYPEDEF (vec1, aligned_vec1, 4)
 Single-qualifier floating-point aligned vector of 1 component. More...
 
 GLM_ALIGNED_TYPEDEF (vec2, aligned_vec2, 8)
 Single-qualifier floating-point aligned vector of 2 components. More...
 
 GLM_ALIGNED_TYPEDEF (vec3, aligned_vec3, 16)
 Single-qualifier floating-point aligned vector of 3 components. More...
 
 GLM_ALIGNED_TYPEDEF (vec4, aligned_vec4, 16)
 Single-qualifier floating-point aligned vector of 4 components. More...
 
 GLM_ALIGNED_TYPEDEF (fvec1, aligned_fvec1, 4)
 Single-qualifier floating-point aligned vector of 1 component. More...
 
 GLM_ALIGNED_TYPEDEF (fvec2, aligned_fvec2, 8)
 Single-qualifier floating-point aligned vector of 2 components. More...
 
 GLM_ALIGNED_TYPEDEF (fvec3, aligned_fvec3, 16)
 Single-qualifier floating-point aligned vector of 3 components. More...
 
 GLM_ALIGNED_TYPEDEF (fvec4, aligned_fvec4, 16)
 Single-qualifier floating-point aligned vector of 4 components. More...
 
 GLM_ALIGNED_TYPEDEF (f32vec1, aligned_f32vec1, 4)
 Single-qualifier floating-point aligned vector of 1 component. More...
 
 GLM_ALIGNED_TYPEDEF (f32vec2, aligned_f32vec2, 8)
 Single-qualifier floating-point aligned vector of 2 components. More...
 
 GLM_ALIGNED_TYPEDEF (f32vec3, aligned_f32vec3, 16)
 Single-qualifier floating-point aligned vector of 3 components. More...
 
 GLM_ALIGNED_TYPEDEF (f32vec4, aligned_f32vec4, 16)
 Single-qualifier floating-point aligned vector of 4 components. More...
 
 GLM_ALIGNED_TYPEDEF (dvec1, aligned_dvec1, 8)
 Double-qualifier floating-point aligned vector of 1 component. More...
 
 GLM_ALIGNED_TYPEDEF (dvec2, aligned_dvec2, 16)
 Double-qualifier floating-point aligned vector of 2 components. More...
 
 GLM_ALIGNED_TYPEDEF (dvec3, aligned_dvec3, 32)
 Double-qualifier floating-point aligned vector of 3 components. More...
 
 GLM_ALIGNED_TYPEDEF (dvec4, aligned_dvec4, 32)
 Double-qualifier floating-point aligned vector of 4 components. More...
 
 GLM_ALIGNED_TYPEDEF (f64vec1, aligned_f64vec1, 8)
 Double-qualifier floating-point aligned vector of 1 component. More...
 
 GLM_ALIGNED_TYPEDEF (f64vec2, aligned_f64vec2, 16)
 Double-qualifier floating-point aligned vector of 2 components. More...
 
 GLM_ALIGNED_TYPEDEF (f64vec3, aligned_f64vec3, 32)
 Double-qualifier floating-point aligned vector of 3 components. More...
 
 GLM_ALIGNED_TYPEDEF (f64vec4, aligned_f64vec4, 32)
 Double-qualifier floating-point aligned vector of 4 components. More...
 
 GLM_ALIGNED_TYPEDEF (mat2, aligned_mat2, 16)
 Single-qualifier floating-point aligned 1x1 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (mat3, aligned_mat3, 16)
 Single-qualifier floating-point aligned 3x3 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (mat4, aligned_mat4, 16)
 Single-qualifier floating-point aligned 4x4 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (fmat2x2, aligned_fmat2, 16)
 Single-qualifier floating-point aligned 1x1 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (fmat3x3, aligned_fmat3, 16)
 Single-qualifier floating-point aligned 3x3 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (fmat4x4, aligned_fmat4, 16)
 Single-qualifier floating-point aligned 4x4 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (fmat2x2, aligned_fmat2x2, 16)
 Single-qualifier floating-point aligned 1x1 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (fmat2x3, aligned_fmat2x3, 16)
 Single-qualifier floating-point aligned 2x3 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (fmat2x4, aligned_fmat2x4, 16)
 Single-qualifier floating-point aligned 2x4 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (fmat3x2, aligned_fmat3x2, 16)
 Single-qualifier floating-point aligned 3x2 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (fmat3x3, aligned_fmat3x3, 16)
 Single-qualifier floating-point aligned 3x3 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (fmat3x4, aligned_fmat3x4, 16)
 Single-qualifier floating-point aligned 3x4 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (fmat4x2, aligned_fmat4x2, 16)
 Single-qualifier floating-point aligned 4x2 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (fmat4x3, aligned_fmat4x3, 16)
 Single-qualifier floating-point aligned 4x3 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (fmat4x4, aligned_fmat4x4, 16)
 Single-qualifier floating-point aligned 4x4 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (f32mat2x2, aligned_f32mat2, 16)
 Single-qualifier floating-point aligned 1x1 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (f32mat3x3, aligned_f32mat3, 16)
 Single-qualifier floating-point aligned 3x3 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (f32mat4x4, aligned_f32mat4, 16)
 Single-qualifier floating-point aligned 4x4 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (f32mat2x2, aligned_f32mat2x2, 16)
 Single-qualifier floating-point aligned 1x1 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (f32mat2x3, aligned_f32mat2x3, 16)
 Single-qualifier floating-point aligned 2x3 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (f32mat2x4, aligned_f32mat2x4, 16)
 Single-qualifier floating-point aligned 2x4 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (f32mat3x2, aligned_f32mat3x2, 16)
 Single-qualifier floating-point aligned 3x2 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (f32mat3x3, aligned_f32mat3x3, 16)
 Single-qualifier floating-point aligned 3x3 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (f32mat3x4, aligned_f32mat3x4, 16)
 Single-qualifier floating-point aligned 3x4 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (f32mat4x2, aligned_f32mat4x2, 16)
 Single-qualifier floating-point aligned 4x2 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (f32mat4x3, aligned_f32mat4x3, 16)
 Single-qualifier floating-point aligned 4x3 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (f32mat4x4, aligned_f32mat4x4, 16)
 Single-qualifier floating-point aligned 4x4 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (f64mat2x2, aligned_f64mat2, 32)
 Double-qualifier floating-point aligned 1x1 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (f64mat3x3, aligned_f64mat3, 32)
 Double-qualifier floating-point aligned 3x3 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (f64mat4x4, aligned_f64mat4, 32)
 Double-qualifier floating-point aligned 4x4 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (f64mat2x2, aligned_f64mat2x2, 32)
 Double-qualifier floating-point aligned 1x1 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (f64mat2x3, aligned_f64mat2x3, 32)
 Double-qualifier floating-point aligned 2x3 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (f64mat2x4, aligned_f64mat2x4, 32)
 Double-qualifier floating-point aligned 2x4 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (f64mat3x2, aligned_f64mat3x2, 32)
 Double-qualifier floating-point aligned 3x2 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (f64mat3x3, aligned_f64mat3x3, 32)
 Double-qualifier floating-point aligned 3x3 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (f64mat3x4, aligned_f64mat3x4, 32)
 Double-qualifier floating-point aligned 3x4 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (f64mat4x2, aligned_f64mat4x2, 32)
 Double-qualifier floating-point aligned 4x2 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (f64mat4x3, aligned_f64mat4x3, 32)
 Double-qualifier floating-point aligned 4x3 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (f64mat4x4, aligned_f64mat4x4, 32)
 Double-qualifier floating-point aligned 4x4 matrix. More...
 
 GLM_ALIGNED_TYPEDEF (quat, aligned_quat, 16)
 Single-qualifier floating-point aligned quaternion. More...
 
 GLM_ALIGNED_TYPEDEF (quat, aligned_fquat, 16)
 Single-qualifier floating-point aligned quaternion. More...
 
 GLM_ALIGNED_TYPEDEF (dquat, aligned_dquat, 32)
 Double-qualifier floating-point aligned quaternion. More...
 
 GLM_ALIGNED_TYPEDEF (f32quat, aligned_f32quat, 16)
 Single-qualifier floating-point aligned quaternion. More...
 
 GLM_ALIGNED_TYPEDEF (f64quat, aligned_f64quat, 32)
 Double-qualifier floating-point aligned quaternion. More...
 
\n

Detailed Description

\n

Include <glm/gtx/type_aligned.hpp> to use the features of this extension.

\n

Defines aligned types.

\n

Function Documentation

\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (lowp_int8 ,
aligned_lowp_int8 ,
1  
)
\n
\n\n

Low qualifier 8 bit signed integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
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\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (lowp_int16 ,
aligned_lowp_int16 ,
2  
)
\n
\n\n

Low qualifier 16 bit signed integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
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\n
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\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (lowp_int32 ,
aligned_lowp_int32 ,
4  
)
\n
\n\n

Low qualifier 32 bit signed integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (lowp_int64 ,
aligned_lowp_int64 ,
8  
)
\n
\n\n

Low qualifier 64 bit signed integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
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\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (lowp_int8_t ,
aligned_lowp_int8_t ,
1  
)
\n
\n\n

Low qualifier 8 bit signed integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (lowp_int16_t ,
aligned_lowp_int16_t ,
2  
)
\n
\n\n

Low qualifier 16 bit signed integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
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\n
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\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (lowp_int32_t ,
aligned_lowp_int32_t ,
4  
)
\n
\n\n

Low qualifier 32 bit signed integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
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\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (lowp_int64_t ,
aligned_lowp_int64_t ,
8  
)
\n
\n\n

Low qualifier 64 bit signed integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
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\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (lowp_i8 ,
aligned_lowp_i8 ,
1  
)
\n
\n\n

Low qualifier 8 bit signed integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
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\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (lowp_i16 ,
aligned_lowp_i16 ,
2  
)
\n
\n\n

Low qualifier 16 bit signed integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
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\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (lowp_i32 ,
aligned_lowp_i32 ,
4  
)
\n
\n\n

Low qualifier 32 bit signed integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (lowp_i64 ,
aligned_lowp_i64 ,
8  
)
\n
\n\n

Low qualifier 64 bit signed integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
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\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (mediump_int8 ,
aligned_mediump_int8 ,
1  
)
\n
\n\n

Medium qualifier 8 bit signed integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
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\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (mediump_int16 ,
aligned_mediump_int16 ,
2  
)
\n
\n\n

Medium qualifier 16 bit signed integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (mediump_int32 ,
aligned_mediump_int32 ,
4  
)
\n
\n\n

Medium qualifier 32 bit signed integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (mediump_int64 ,
aligned_mediump_int64 ,
8  
)
\n
\n\n

Medium qualifier 64 bit signed integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (mediump_int8_t ,
aligned_mediump_int8_t ,
1  
)
\n
\n\n

Medium qualifier 8 bit signed integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (mediump_int16_t ,
aligned_mediump_int16_t ,
2  
)
\n
\n\n

Medium qualifier 16 bit signed integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
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\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (mediump_int32_t ,
aligned_mediump_int32_t ,
4  
)
\n
\n\n

Medium qualifier 32 bit signed integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (mediump_int64_t ,
aligned_mediump_int64_t ,
8  
)
\n
\n\n

Medium qualifier 64 bit signed integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (mediump_i8 ,
aligned_mediump_i8 ,
1  
)
\n
\n\n

Medium qualifier 8 bit signed integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (mediump_i16 ,
aligned_mediump_i16 ,
2  
)
\n
\n\n

Medium qualifier 16 bit signed integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (mediump_i32 ,
aligned_mediump_i32 ,
4  
)
\n
\n\n

Medium qualifier 32 bit signed integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (mediump_i64 ,
aligned_mediump_i64 ,
8  
)
\n
\n\n

Medium qualifier 64 bit signed integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (highp_int8 ,
aligned_highp_int8 ,
1  
)
\n
\n\n

High qualifier 8 bit signed integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (highp_int16 ,
aligned_highp_int16 ,
2  
)
\n
\n\n

High qualifier 16 bit signed integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (highp_int32 ,
aligned_highp_int32 ,
4  
)
\n
\n\n

High qualifier 32 bit signed integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (highp_int64 ,
aligned_highp_int64 ,
8  
)
\n
\n\n

High qualifier 64 bit signed integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (highp_int8_t ,
aligned_highp_int8_t ,
1  
)
\n
\n\n

High qualifier 8 bit signed integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (highp_int16_t ,
aligned_highp_int16_t ,
2  
)
\n
\n\n

High qualifier 16 bit signed integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (highp_int32_t ,
aligned_highp_int32_t ,
4  
)
\n
\n\n

High qualifier 32 bit signed integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (highp_int64_t ,
aligned_highp_int64_t ,
8  
)
\n
\n\n

High qualifier 64 bit signed integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (highp_i8 ,
aligned_highp_i8 ,
1  
)
\n
\n\n

High qualifier 8 bit signed integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (highp_i16 ,
aligned_highp_i16 ,
2  
)
\n
\n\n

High qualifier 16 bit signed integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (highp_i32 ,
aligned_highp_i32 ,
4  
)
\n
\n\n

High qualifier 32 bit signed integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (highp_i64 ,
aligned_highp_i64 ,
8  
)
\n
\n\n

High qualifier 64 bit signed integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (int8 ,
aligned_int8 ,
1  
)
\n
\n\n

Default qualifier 8 bit signed integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (int16 ,
aligned_int16 ,
2  
)
\n
\n\n

Default qualifier 16 bit signed integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (int32 ,
aligned_int32 ,
4  
)
\n
\n\n

Default qualifier 32 bit signed integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (int64 ,
aligned_int64 ,
8  
)
\n
\n\n

Default qualifier 64 bit signed integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (int8_t ,
aligned_int8_t ,
1  
)
\n
\n\n

Default qualifier 8 bit signed integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (int16_t ,
aligned_int16_t ,
2  
)
\n
\n\n

Default qualifier 16 bit signed integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (int32_t ,
aligned_int32_t ,
4  
)
\n
\n\n

Default qualifier 32 bit signed integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (int64_t ,
aligned_int64_t ,
8  
)
\n
\n\n

Default qualifier 64 bit signed integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (i8 ,
aligned_i8 ,
1  
)
\n
\n\n

Default qualifier 8 bit signed integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (i16 ,
aligned_i16 ,
2  
)
\n
\n\n

Default qualifier 16 bit signed integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (i32 ,
aligned_i32 ,
4  
)
\n
\n\n

Default qualifier 32 bit signed integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (i64 ,
aligned_i64 ,
8  
)
\n
\n\n

Default qualifier 64 bit signed integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (ivec1 ,
aligned_ivec1 ,
4  
)
\n
\n\n

Default qualifier 32 bit signed integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (ivec2 ,
aligned_ivec2 ,
8  
)
\n
\n\n

Default qualifier 32 bit signed integer aligned vector of 2 components type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (ivec3 ,
aligned_ivec3 ,
16  
)
\n
\n\n

Default qualifier 32 bit signed integer aligned vector of 3 components type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (ivec4 ,
aligned_ivec4 ,
16  
)
\n
\n\n

Default qualifier 32 bit signed integer aligned vector of 4 components type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (i8vec1 ,
aligned_i8vec1 ,
1  
)
\n
\n\n

Default qualifier 8 bit signed integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (i8vec2 ,
aligned_i8vec2 ,
2  
)
\n
\n\n

Default qualifier 8 bit signed integer aligned vector of 2 components type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (i8vec3 ,
aligned_i8vec3 ,
4  
)
\n
\n\n

Default qualifier 8 bit signed integer aligned vector of 3 components type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (i8vec4 ,
aligned_i8vec4 ,
4  
)
\n
\n\n

Default qualifier 8 bit signed integer aligned vector of 4 components type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (i16vec1 ,
aligned_i16vec1 ,
2  
)
\n
\n\n

Default qualifier 16 bit signed integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (i16vec2 ,
aligned_i16vec2 ,
4  
)
\n
\n\n

Default qualifier 16 bit signed integer aligned vector of 2 components type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (i16vec3 ,
aligned_i16vec3 ,
8  
)
\n
\n\n

Default qualifier 16 bit signed integer aligned vector of 3 components type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (i16vec4 ,
aligned_i16vec4 ,
8  
)
\n
\n\n

Default qualifier 16 bit signed integer aligned vector of 4 components type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (i32vec1 ,
aligned_i32vec1 ,
4  
)
\n
\n\n

Default qualifier 32 bit signed integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (i32vec2 ,
aligned_i32vec2 ,
8  
)
\n
\n\n

Default qualifier 32 bit signed integer aligned vector of 2 components type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (i32vec3 ,
aligned_i32vec3 ,
16  
)
\n
\n\n

Default qualifier 32 bit signed integer aligned vector of 3 components type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (i32vec4 ,
aligned_i32vec4 ,
16  
)
\n
\n\n

Default qualifier 32 bit signed integer aligned vector of 4 components type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (i64vec1 ,
aligned_i64vec1 ,
8  
)
\n
\n\n

Default qualifier 64 bit signed integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (i64vec2 ,
aligned_i64vec2 ,
16  
)
\n
\n\n

Default qualifier 64 bit signed integer aligned vector of 2 components type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (i64vec3 ,
aligned_i64vec3 ,
32  
)
\n
\n\n

Default qualifier 64 bit signed integer aligned vector of 3 components type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (i64vec4 ,
aligned_i64vec4 ,
32  
)
\n
\n\n

Default qualifier 64 bit signed integer aligned vector of 4 components type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (lowp_uint8 ,
aligned_lowp_uint8 ,
1  
)
\n
\n\n

Low qualifier 8 bit unsigned integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (lowp_uint16 ,
aligned_lowp_uint16 ,
2  
)
\n
\n\n

Low qualifier 16 bit unsigned integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (lowp_uint32 ,
aligned_lowp_uint32 ,
4  
)
\n
\n\n

Low qualifier 32 bit unsigned integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (lowp_uint64 ,
aligned_lowp_uint64 ,
8  
)
\n
\n\n

Low qualifier 64 bit unsigned integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (lowp_uint8_t ,
aligned_lowp_uint8_t ,
1  
)
\n
\n\n

Low qualifier 8 bit unsigned integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (lowp_uint16_t ,
aligned_lowp_uint16_t ,
2  
)
\n
\n\n

Low qualifier 16 bit unsigned integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (lowp_uint32_t ,
aligned_lowp_uint32_t ,
4  
)
\n
\n\n

Low qualifier 32 bit unsigned integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (lowp_uint64_t ,
aligned_lowp_uint64_t ,
8  
)
\n
\n\n

Low qualifier 64 bit unsigned integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (lowp_u8 ,
aligned_lowp_u8 ,
1  
)
\n
\n\n

Low qualifier 8 bit unsigned integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (lowp_u16 ,
aligned_lowp_u16 ,
2  
)
\n
\n\n

Low qualifier 16 bit unsigned integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (lowp_u32 ,
aligned_lowp_u32 ,
4  
)
\n
\n\n

Low qualifier 32 bit unsigned integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (lowp_u64 ,
aligned_lowp_u64 ,
8  
)
\n
\n\n

Low qualifier 64 bit unsigned integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (mediump_uint8 ,
aligned_mediump_uint8 ,
1  
)
\n
\n\n

Medium qualifier 8 bit unsigned integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (mediump_uint16 ,
aligned_mediump_uint16 ,
2  
)
\n
\n\n

Medium qualifier 16 bit unsigned integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (mediump_uint32 ,
aligned_mediump_uint32 ,
4  
)
\n
\n\n

Medium qualifier 32 bit unsigned integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (mediump_uint64 ,
aligned_mediump_uint64 ,
8  
)
\n
\n\n

Medium qualifier 64 bit unsigned integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (mediump_uint8_t ,
aligned_mediump_uint8_t ,
1  
)
\n
\n\n

Medium qualifier 8 bit unsigned integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (mediump_uint16_t ,
aligned_mediump_uint16_t ,
2  
)
\n
\n\n

Medium qualifier 16 bit unsigned integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (mediump_uint32_t ,
aligned_mediump_uint32_t ,
4  
)
\n
\n\n

Medium qualifier 32 bit unsigned integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (mediump_uint64_t ,
aligned_mediump_uint64_t ,
8  
)
\n
\n\n

Medium qualifier 64 bit unsigned integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (mediump_u8 ,
aligned_mediump_u8 ,
1  
)
\n
\n\n

Medium qualifier 8 bit unsigned integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (mediump_u16 ,
aligned_mediump_u16 ,
2  
)
\n
\n\n

Medium qualifier 16 bit unsigned integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (mediump_u32 ,
aligned_mediump_u32 ,
4  
)
\n
\n\n

Medium qualifier 32 bit unsigned integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (mediump_u64 ,
aligned_mediump_u64 ,
8  
)
\n
\n\n

Medium qualifier 64 bit unsigned integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (highp_uint8 ,
aligned_highp_uint8 ,
1  
)
\n
\n\n

High qualifier 8 bit unsigned integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (highp_uint16 ,
aligned_highp_uint16 ,
2  
)
\n
\n\n

High qualifier 16 bit unsigned integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (highp_uint32 ,
aligned_highp_uint32 ,
4  
)
\n
\n\n

High qualifier 32 bit unsigned integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (highp_uint64 ,
aligned_highp_uint64 ,
8  
)
\n
\n\n

High qualifier 64 bit unsigned integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (highp_uint8_t ,
aligned_highp_uint8_t ,
1  
)
\n
\n\n

High qualifier 8 bit unsigned integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (highp_uint16_t ,
aligned_highp_uint16_t ,
2  
)
\n
\n\n

High qualifier 16 bit unsigned integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (highp_uint32_t ,
aligned_highp_uint32_t ,
4  
)
\n
\n\n

High qualifier 32 bit unsigned integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (highp_uint64_t ,
aligned_highp_uint64_t ,
8  
)
\n
\n\n

High qualifier 64 bit unsigned integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (highp_u8 ,
aligned_highp_u8 ,
1  
)
\n
\n\n

High qualifier 8 bit unsigned integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (highp_u16 ,
aligned_highp_u16 ,
2  
)
\n
\n\n

High qualifier 16 bit unsigned integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (highp_u32 ,
aligned_highp_u32 ,
4  
)
\n
\n\n

High qualifier 32 bit unsigned integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (highp_u64 ,
aligned_highp_u64 ,
8  
)
\n
\n\n

High qualifier 64 bit unsigned integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (uint8 ,
aligned_uint8 ,
1  
)
\n
\n\n

Default qualifier 8 bit unsigned integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (uint16 ,
aligned_uint16 ,
2  
)
\n
\n\n

Default qualifier 16 bit unsigned integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (uint32 ,
aligned_uint32 ,
4  
)
\n
\n\n

Default qualifier 32 bit unsigned integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (uint64 ,
aligned_uint64 ,
8  
)
\n
\n\n

Default qualifier 64 bit unsigned integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (uint8_t ,
aligned_uint8_t ,
1  
)
\n
\n\n

Default qualifier 8 bit unsigned integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (uint16_t ,
aligned_uint16_t ,
2  
)
\n
\n\n

Default qualifier 16 bit unsigned integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (uint32_t ,
aligned_uint32_t ,
4  
)
\n
\n\n

Default qualifier 32 bit unsigned integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (uint64_t ,
aligned_uint64_t ,
8  
)
\n
\n\n

Default qualifier 64 bit unsigned integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (u8 ,
aligned_u8 ,
1  
)
\n
\n\n

Default qualifier 8 bit unsigned integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (u16 ,
aligned_u16 ,
2  
)
\n
\n\n

Default qualifier 16 bit unsigned integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (u32 ,
aligned_u32 ,
4  
)
\n
\n\n

Default qualifier 32 bit unsigned integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (u64 ,
aligned_u64 ,
8  
)
\n
\n\n

Default qualifier 64 bit unsigned integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (uvec1 ,
aligned_uvec1 ,
4  
)
\n
\n\n

Default qualifier 32 bit unsigned integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (uvec2 ,
aligned_uvec2 ,
8  
)
\n
\n\n

Default qualifier 32 bit unsigned integer aligned vector of 2 components type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (uvec3 ,
aligned_uvec3 ,
16  
)
\n
\n\n

Default qualifier 32 bit unsigned integer aligned vector of 3 components type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (uvec4 ,
aligned_uvec4 ,
16  
)
\n
\n\n

Default qualifier 32 bit unsigned integer aligned vector of 4 components type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (u8vec1 ,
aligned_u8vec1 ,
1  
)
\n
\n\n

Default qualifier 8 bit unsigned integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (u8vec2 ,
aligned_u8vec2 ,
2  
)
\n
\n\n

Default qualifier 8 bit unsigned integer aligned vector of 2 components type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (u8vec3 ,
aligned_u8vec3 ,
4  
)
\n
\n\n

Default qualifier 8 bit unsigned integer aligned vector of 3 components type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (u8vec4 ,
aligned_u8vec4 ,
4  
)
\n
\n\n

Default qualifier 8 bit unsigned integer aligned vector of 4 components type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (u16vec1 ,
aligned_u16vec1 ,
2  
)
\n
\n\n

Default qualifier 16 bit unsigned integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (u16vec2 ,
aligned_u16vec2 ,
4  
)
\n
\n\n

Default qualifier 16 bit unsigned integer aligned vector of 2 components type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (u16vec3 ,
aligned_u16vec3 ,
8  
)
\n
\n\n

Default qualifier 16 bit unsigned integer aligned vector of 3 components type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (u16vec4 ,
aligned_u16vec4 ,
8  
)
\n
\n\n

Default qualifier 16 bit unsigned integer aligned vector of 4 components type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (u32vec1 ,
aligned_u32vec1 ,
4  
)
\n
\n\n

Default qualifier 32 bit unsigned integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (u32vec2 ,
aligned_u32vec2 ,
8  
)
\n
\n\n

Default qualifier 32 bit unsigned integer aligned vector of 2 components type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (u32vec3 ,
aligned_u32vec3 ,
16  
)
\n
\n\n

Default qualifier 32 bit unsigned integer aligned vector of 3 components type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (u32vec4 ,
aligned_u32vec4 ,
16  
)
\n
\n\n

Default qualifier 32 bit unsigned integer aligned vector of 4 components type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (u64vec1 ,
aligned_u64vec1 ,
8  
)
\n
\n\n

Default qualifier 64 bit unsigned integer aligned scalar type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (u64vec2 ,
aligned_u64vec2 ,
16  
)
\n
\n\n

Default qualifier 64 bit unsigned integer aligned vector of 2 components type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (u64vec3 ,
aligned_u64vec3 ,
32  
)
\n
\n\n

Default qualifier 64 bit unsigned integer aligned vector of 3 components type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (u64vec4 ,
aligned_u64vec4 ,
32  
)
\n
\n\n

Default qualifier 64 bit unsigned integer aligned vector of 4 components type.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (float32 ,
aligned_float32 ,
4  
)
\n
\n\n

32 bit single-qualifier floating-point aligned scalar.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (float32_t ,
aligned_float32_t ,
4  
)
\n
\n\n

32 bit single-qualifier floating-point aligned scalar.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (float32 ,
aligned_f32 ,
4  
)
\n
\n\n

32 bit single-qualifier floating-point aligned scalar.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (float64 ,
aligned_float64 ,
8  
)
\n
\n\n

64 bit double-qualifier floating-point aligned scalar.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (float64_t ,
aligned_float64_t ,
8  
)
\n
\n\n

64 bit double-qualifier floating-point aligned scalar.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (float64 ,
aligned_f64 ,
8  
)
\n
\n\n

64 bit double-qualifier floating-point aligned scalar.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (vec1 ,
aligned_vec1 ,
4  
)
\n
\n\n

Single-qualifier floating-point aligned vector of 1 component.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (vec2 ,
aligned_vec2 ,
8  
)
\n
\n\n

Single-qualifier floating-point aligned vector of 2 components.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (vec3 ,
aligned_vec3 ,
16  
)
\n
\n\n

Single-qualifier floating-point aligned vector of 3 components.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (vec4 ,
aligned_vec4 ,
16  
)
\n
\n\n

Single-qualifier floating-point aligned vector of 4 components.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (fvec1 ,
aligned_fvec1 ,
4  
)
\n
\n\n

Single-qualifier floating-point aligned vector of 1 component.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (fvec2 ,
aligned_fvec2 ,
8  
)
\n
\n\n

Single-qualifier floating-point aligned vector of 2 components.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (fvec3 ,
aligned_fvec3 ,
16  
)
\n
\n\n

Single-qualifier floating-point aligned vector of 3 components.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (fvec4 ,
aligned_fvec4 ,
16  
)
\n
\n\n

Single-qualifier floating-point aligned vector of 4 components.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (f32vec1 ,
aligned_f32vec1 ,
4  
)
\n
\n\n

Single-qualifier floating-point aligned vector of 1 component.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (f32vec2 ,
aligned_f32vec2 ,
8  
)
\n
\n\n

Single-qualifier floating-point aligned vector of 2 components.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (f32vec3 ,
aligned_f32vec3 ,
16  
)
\n
\n\n

Single-qualifier floating-point aligned vector of 3 components.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (f32vec4 ,
aligned_f32vec4 ,
16  
)
\n
\n\n

Single-qualifier floating-point aligned vector of 4 components.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (dvec1 ,
aligned_dvec1 ,
8  
)
\n
\n\n

Double-qualifier floating-point aligned vector of 1 component.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (dvec2 ,
aligned_dvec2 ,
16  
)
\n
\n\n

Double-qualifier floating-point aligned vector of 2 components.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (dvec3 ,
aligned_dvec3 ,
32  
)
\n
\n\n

Double-qualifier floating-point aligned vector of 3 components.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (dvec4 ,
aligned_dvec4 ,
32  
)
\n
\n\n

Double-qualifier floating-point aligned vector of 4 components.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (f64vec1 ,
aligned_f64vec1 ,
8  
)
\n
\n\n

Double-qualifier floating-point aligned vector of 1 component.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (f64vec2 ,
aligned_f64vec2 ,
16  
)
\n
\n\n

Double-qualifier floating-point aligned vector of 2 components.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (f64vec3 ,
aligned_f64vec3 ,
32  
)
\n
\n\n

Double-qualifier floating-point aligned vector of 3 components.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (f64vec4 ,
aligned_f64vec4 ,
32  
)
\n
\n\n

Double-qualifier floating-point aligned vector of 4 components.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
GLM_ALIGNED_TYPEDEF (mat2 ,
aligned_mat2 ,
16  
)
\n
\n\n

Single-qualifier floating-point aligned 1x1 matrix.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned Single-qualifier floating-point aligned 2x2 matrix.
\n
\n to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
GLM_ALIGNED_TYPEDEF (mat3 ,
aligned_mat3 ,
16  
)
\n
\n\n

Single-qualifier floating-point aligned 3x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
GLM_ALIGNED_TYPEDEF (mat4 ,
aligned_mat4 ,
16  
)
\n
\n\n

Single-qualifier floating-point aligned 4x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (fmat2x2 ,
aligned_fmat2 ,
16  
)
\n
\n\n

Single-qualifier floating-point aligned 1x1 matrix.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned Single-qualifier floating-point aligned 2x2 matrix.
\n
\n to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (fmat3x3 ,
aligned_fmat3 ,
16  
)
\n
\n\n

Single-qualifier floating-point aligned 3x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (fmat4x4 ,
aligned_fmat4 ,
16  
)
\n
\n\n

Single-qualifier floating-point aligned 4x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (fmat2x2 ,
aligned_fmat2x2 ,
16  
)
\n
\n\n

Single-qualifier floating-point aligned 1x1 matrix.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned Single-qualifier floating-point aligned 2x2 matrix.
\n
\n to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (fmat2x3 ,
aligned_fmat2x3 ,
16  
)
\n
\n\n

Single-qualifier floating-point aligned 2x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (fmat2x4 ,
aligned_fmat2x4 ,
16  
)
\n
\n\n

Single-qualifier floating-point aligned 2x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (fmat3x2 ,
aligned_fmat3x2 ,
16  
)
\n
\n\n

Single-qualifier floating-point aligned 3x2 matrix.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
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glm::GLM_ALIGNED_TYPEDEF (fmat3x3 ,
aligned_fmat3x3 ,
16  
)
\n
\n\n

Single-qualifier floating-point aligned 3x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (fmat3x4 ,
aligned_fmat3x4 ,
16  
)
\n
\n\n

Single-qualifier floating-point aligned 3x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (fmat4x2 ,
aligned_fmat4x2 ,
16  
)
\n
\n\n

Single-qualifier floating-point aligned 4x2 matrix.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (fmat4x3 ,
aligned_fmat4x3 ,
16  
)
\n
\n\n

Single-qualifier floating-point aligned 4x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (fmat4x4 ,
aligned_fmat4x4 ,
16  
)
\n
\n\n

Single-qualifier floating-point aligned 4x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (f32mat2x2 ,
aligned_f32mat2 ,
16  
)
\n
\n\n

Single-qualifier floating-point aligned 1x1 matrix.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned Single-qualifier floating-point aligned 2x2 matrix.
\n
\n to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (f32mat3x3 ,
aligned_f32mat3 ,
16  
)
\n
\n\n

Single-qualifier floating-point aligned 3x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (f32mat4x4 ,
aligned_f32mat4 ,
16  
)
\n
\n\n

Single-qualifier floating-point aligned 4x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (f32mat2x2 ,
aligned_f32mat2x2 ,
16  
)
\n
\n\n

Single-qualifier floating-point aligned 1x1 matrix.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned Single-qualifier floating-point aligned 2x2 matrix.
\n
\n to use the features of this extension. \">GLM_GTX_type_aligned
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glm::GLM_ALIGNED_TYPEDEF (f32mat2x3 ,
aligned_f32mat2x3 ,
16  
)
\n
\n\n

Single-qualifier floating-point aligned 2x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (f32mat2x4 ,
aligned_f32mat2x4 ,
16  
)
\n
\n\n

Single-qualifier floating-point aligned 2x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
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glm::GLM_ALIGNED_TYPEDEF (f32mat3x2 ,
aligned_f32mat3x2 ,
16  
)
\n
\n\n

Single-qualifier floating-point aligned 3x2 matrix.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (f32mat3x3 ,
aligned_f32mat3x3 ,
16  
)
\n
\n\n

Single-qualifier floating-point aligned 3x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
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glm::GLM_ALIGNED_TYPEDEF (f32mat3x4 ,
aligned_f32mat3x4 ,
16  
)
\n
\n\n

Single-qualifier floating-point aligned 3x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
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glm::GLM_ALIGNED_TYPEDEF (f32mat4x2 ,
aligned_f32mat4x2 ,
16  
)
\n
\n\n

Single-qualifier floating-point aligned 4x2 matrix.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
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glm::GLM_ALIGNED_TYPEDEF (f32mat4x3 ,
aligned_f32mat4x3 ,
16  
)
\n
\n\n

Single-qualifier floating-point aligned 4x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (f32mat4x4 ,
aligned_f32mat4x4 ,
16  
)
\n
\n\n

Single-qualifier floating-point aligned 4x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
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glm::GLM_ALIGNED_TYPEDEF (f64mat2x2 ,
aligned_f64mat2 ,
32  
)
\n
\n\n

Double-qualifier floating-point aligned 1x1 matrix.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned Double-qualifier floating-point aligned 2x2 matrix.
\n
\n to use the features of this extension. \">GLM_GTX_type_aligned
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glm::GLM_ALIGNED_TYPEDEF (f64mat3x3 ,
aligned_f64mat3 ,
32  
)
\n
\n\n

Double-qualifier floating-point aligned 3x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
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glm::GLM_ALIGNED_TYPEDEF (f64mat4x4 ,
aligned_f64mat4 ,
32  
)
\n
\n\n

Double-qualifier floating-point aligned 4x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
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glm::GLM_ALIGNED_TYPEDEF (f64mat2x2 ,
aligned_f64mat2x2 ,
32  
)
\n
\n\n

Double-qualifier floating-point aligned 1x1 matrix.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned Double-qualifier floating-point aligned 2x2 matrix.
\n
\n to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
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glm::GLM_ALIGNED_TYPEDEF (f64mat2x3 ,
aligned_f64mat2x3 ,
32  
)
\n
\n\n

Double-qualifier floating-point aligned 2x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
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glm::GLM_ALIGNED_TYPEDEF (f64mat2x4 ,
aligned_f64mat2x4 ,
32  
)
\n
\n\n

Double-qualifier floating-point aligned 2x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (f64mat3x2 ,
aligned_f64mat3x2 ,
32  
)
\n
\n\n

Double-qualifier floating-point aligned 3x2 matrix.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (f64mat3x3 ,
aligned_f64mat3x3 ,
32  
)
\n
\n\n

Double-qualifier floating-point aligned 3x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (f64mat3x4 ,
aligned_f64mat3x4 ,
32  
)
\n
\n\n

Double-qualifier floating-point aligned 3x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (f64mat4x2 ,
aligned_f64mat4x2 ,
32  
)
\n
\n\n

Double-qualifier floating-point aligned 4x2 matrix.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (f64mat4x3 ,
aligned_f64mat4x3 ,
32  
)
\n
\n\n

Double-qualifier floating-point aligned 4x3 matrix.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
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\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (f64mat4x4 ,
aligned_f64mat4x4 ,
32  
)
\n
\n\n

Double-qualifier floating-point aligned 4x4 matrix.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (quat ,
aligned_quat ,
16  
)
\n
\n\n

Single-qualifier floating-point aligned quaternion.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (quat ,
aligned_fquat ,
16  
)
\n
\n\n

Single-qualifier floating-point aligned quaternion.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (dquat ,
aligned_dquat ,
32  
)
\n
\n\n

Double-qualifier floating-point aligned quaternion.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
glm::GLM_ALIGNED_TYPEDEF (f32quat ,
aligned_f32quat ,
16  
)
\n
\n\n

Single-qualifier floating-point aligned quaternion.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
\n\n
\n
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glm::GLM_ALIGNED_TYPEDEF (f64quat ,
aligned_f64quat ,
32  
)
\n
\n\n

Double-qualifier floating-point aligned quaternion.

\n
See also
to use the features of this extension. \">GLM_GTX_type_aligned
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\nGenerated by  \n\"doxygen\"/\n 1.8.10\n
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GLM_GTX_type_trait
Experimental extensions
\n
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\n\n

Include <glm/gtx/type_trait.hpp> to use the features of this extension. \nMore...

\n

Detailed Description

\n

Include <glm/gtx/type_trait.hpp> to use the features of this extension.

\n

Defines traits for each type.

\n
\n\n
\nGenerated by  \n\"doxygen\"/\n 1.8.10\n
\n\n\n" }, { "path": "field_construction/submodules/diff-langsurf-rasterizer/third_party/glm/doc/api/a00366.html", "content": "\n\n\n\n\n\n0.9.9 API documentation: GLM_GTX_vec_swizzle\n\n\n\n\n\n\n\n\n\n\n
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GLM_GTX_vec_swizzle
Experimental extensions
\n
\n
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Include <glm/gtx/vec_swizzle.hpp> to use the features of this extension. \nMore...

\n

Include <glm/gtx/vec_swizzle.hpp> to use the features of this extension.

\n

Functions to perform swizzle operation.

\n
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\nGenerated by  \n\"doxygen\"/\n 1.8.10\n
\n\n\n" }, { "path": "field_construction/submodules/diff-langsurf-rasterizer/third_party/glm/doc/api/a00367.html", "content": "\n\n\n\n\n\n0.9.9 API documentation: GLM_GTX_vector_angle\n\n\n\n\n\n\n\n\n\n\n
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GLM_GTX_vector_angle
Experimental extensions
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Include <glm/gtx/vector_angle.hpp> to use the features of this extension. \nMore...

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

\nFunctions

template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL T angle (vec< L, T, Q > const &x, vec< L, T, Q > const &y)
 Returns the absolute angle between two vectors. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL T orientedAngle (vec< 2, T, Q > const &x, vec< 2, T, Q > const &y)
 Returns the oriented angle between two 2d vectors. More...
 
template<typename T , qualifier Q>
GLM_FUNC_DECL T orientedAngle (vec< 3, T, Q > const &x, vec< 3, T, Q > const &y, vec< 3, T, Q > const &ref)
 Returns the oriented angle between two 3d vectors based from a reference axis. More...
 
\n

Detailed Description

\n

Include <glm/gtx/vector_angle.hpp> to use the features of this extension.

\n

Compute angle between vectors

\n

Function Documentation

\n\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL T glm::angle (vec< L, T, Q > const & x,
vec< L, T, Q > const & y 
)
\n
\n\n

Returns the absolute angle between two vectors.

\n

Parameters need to be normalized.

See also
to use the features of this extension. \">GLM_GTX_vector_angle extension.
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GLM_FUNC_DECL T glm::orientedAngle (vec< 2, T, Q > const & x,
vec< 2, T, Q > const & y 
)
\n
\n\n

Returns the oriented angle between two 2d vectors.

\n

Parameters need to be normalized.

See also
to use the features of this extension. \">GLM_GTX_vector_angle extension.
\n\n
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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
GLM_FUNC_DECL T glm::orientedAngle (vec< 3, T, Q > const & x,
vec< 3, T, Q > const & y,
vec< 3, T, Q > const & ref 
)
\n
\n\n

Returns the oriented angle between two 3d vectors based from a reference axis.

\n

Parameters need to be normalized.

See also
to use the features of this extension. \">GLM_GTX_vector_angle extension.
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\nGenerated by  \n\"doxygen\"/\n 1.8.10\n
\n\n\n" }, { "path": "field_construction/submodules/diff-langsurf-rasterizer/third_party/glm/doc/api/a00368.html", "content": "\n\n\n\n\n\n0.9.9 API documentation: GLM_GTX_vector_query\n\n\n\n\n\n\n\n\n\n\n
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GLM_GTX_vector_query
Experimental extensions
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\n
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Include <glm/gtx/vector_query.hpp> to use the features of this extension. \nMore...

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

\nFunctions

template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL bool areCollinear (vec< L, T, Q > const &v0, vec< L, T, Q > const &v1, T const &epsilon)
 Check whether two vectors are collinears. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL bool areOrthogonal (vec< L, T, Q > const &v0, vec< L, T, Q > const &v1, T const &epsilon)
 Check whether two vectors are orthogonals. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL bool areOrthonormal (vec< L, T, Q > const &v0, vec< L, T, Q > const &v1, T const &epsilon)
 Check whether two vectors are orthonormal. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, bool, Q > isCompNull (vec< L, T, Q > const &v, T const &epsilon)
 Check whether a each component of a vector is null. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL bool isNormalized (vec< L, T, Q > const &v, T const &epsilon)
 Check whether a vector is normalized. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL bool isNull (vec< L, T, Q > const &v, T const &epsilon)
 Check whether a vector is null. More...
 
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Detailed Description

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Include <glm/gtx/vector_query.hpp> to use the features of this extension.

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Query informations of vector types

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Function Documentation

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GLM_FUNC_DECL bool glm::areCollinear (vec< L, T, Q > const & v0,
vec< L, T, Q > const & v1,
T const & epsilon 
)
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Check whether two vectors are collinears.

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See also
to use the features of this extension. \">GLM_GTX_vector_query extensions.
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GLM_FUNC_DECL bool glm::areOrthogonal (vec< L, T, Q > const & v0,
vec< L, T, Q > const & v1,
T const & epsilon 
)
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Check whether two vectors are orthogonals.

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See also
to use the features of this extension. \">GLM_GTX_vector_query extensions.
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GLM_FUNC_DECL bool glm::areOrthonormal (vec< L, T, Q > const & v0,
vec< L, T, Q > const & v1,
T const & epsilon 
)
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Check whether two vectors are orthonormal.

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See also
to use the features of this extension. \">GLM_GTX_vector_query extensions.
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GLM_FUNC_DECL vec<L, bool, Q> glm::isCompNull (vec< L, T, Q > const & v,
T const & epsilon 
)
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Check whether a each component of a vector is null.

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See also
to use the features of this extension. \">GLM_GTX_vector_query extensions.
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GLM_FUNC_DECL bool glm::isNormalized (vec< L, T, Q > const & v,
T const & epsilon 
)
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Check whether a vector is normalized.

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to use the features of this extension. \">GLM_GTX_vector_query extensions.
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T const & epsilon 
)
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Check whether a vector is null.

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See also
to use the features of this extension. \">GLM_GTX_vector_query extensions.
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template<typename genType >
GLM_FUNC_DECL genType clamp (genType const &Texcoord)
 Simulate GL_CLAMP OpenGL wrap mode. More...
 
template<typename genType >
GLM_FUNC_DECL genType mirrorClamp (genType const &Texcoord)
 Simulate GL_MIRRORED_REPEAT OpenGL wrap mode. More...
 
template<typename genType >
GLM_FUNC_DECL genType mirrorRepeat (genType const &Texcoord)
 Simulate GL_MIRROR_REPEAT OpenGL wrap mode. More...
 
template<typename genType >
GLM_FUNC_DECL genType repeat (genType const &Texcoord)
 Simulate GL_REPEAT OpenGL wrap mode. More...
 
\n

Detailed Description

\n

Include <glm/gtx/wrap.hpp> to use the features of this extension.

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Wrapping mode of texture coordinates.

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Function Documentation

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GLM_FUNC_DECL genType glm::clamp (genType const & Texcoord)
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Simulate GL_CLAMP OpenGL wrap mode.

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to use the features of this extension. \">GLM_GTX_wrap extension.
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GLM_FUNC_DECL genType glm::mirrorClamp (genType const & Texcoord)
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Simulate GL_MIRRORED_REPEAT OpenGL wrap mode.

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See also
to use the features of this extension. \">GLM_GTX_wrap extension.
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GLM_FUNC_DECL genType glm::mirrorRepeat (genType const & Texcoord)
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Simulate GL_MIRROR_REPEAT OpenGL wrap mode.

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See also
to use the features of this extension. \">GLM_GTX_wrap extension.
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GLM_FUNC_DECL genType glm::repeat (genType const & Texcoord)
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Simulate GL_REPEAT OpenGL wrap mode.

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See also
to use the features of this extension. \">GLM_GTX_wrap extension.
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Provides GLSL functions on integer types. \nMore...

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template<typename genType >
GLM_FUNC_DECL int bitCount (genType v)
 Returns the number of bits set to 1 in the binary representation of value. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, int, Q > bitCount (vec< L, T, Q > const &v)
 Returns the number of bits set to 1 in the binary representation of value. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > bitfieldExtract (vec< L, T, Q > const &Value, int Offset, int Bits)
 Extracts bits [offset, offset + bits - 1] from value, returning them in the least significant bits of the result. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > bitfieldInsert (vec< L, T, Q > const &Base, vec< L, T, Q > const &Insert, int Offset, int Bits)
 Returns the insertion the bits least-significant bits of insert into base. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > bitfieldReverse (vec< L, T, Q > const &v)
 Returns the reversal of the bits of value. More...
 
template<typename genIUType >
GLM_FUNC_DECL int findLSB (genIUType x)
 Returns the bit number of the least significant bit set to 1 in the binary representation of value. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, int, Q > findLSB (vec< L, T, Q > const &v)
 Returns the bit number of the least significant bit set to 1 in the binary representation of value. More...
 
template<typename genIUType >
GLM_FUNC_DECL int findMSB (genIUType x)
 Returns the bit number of the most significant bit in the binary representation of value. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, int, Q > findMSB (vec< L, T, Q > const &v)
 Returns the bit number of the most significant bit in the binary representation of value. More...
 
template<length_t L, qualifier Q>
GLM_FUNC_DECL void imulExtended (vec< L, int, Q > const &x, vec< L, int, Q > const &y, vec< L, int, Q > &msb, vec< L, int, Q > &lsb)
 Multiplies 32-bit integers x and y, producing a 64-bit result. More...
 
template<length_t L, qualifier Q>
GLM_FUNC_DECL vec< L, uint, Q > uaddCarry (vec< L, uint, Q > const &x, vec< L, uint, Q > const &y, vec< L, uint, Q > &carry)
 Adds 32-bit unsigned integer x and y, returning the sum modulo pow(2, 32). More...
 
template<length_t L, qualifier Q>
GLM_FUNC_DECL void umulExtended (vec< L, uint, Q > const &x, vec< L, uint, Q > const &y, vec< L, uint, Q > &msb, vec< L, uint, Q > &lsb)
 Multiplies 32-bit integers x and y, producing a 64-bit result. More...
 
template<length_t L, qualifier Q>
GLM_FUNC_DECL vec< L, uint, Q > usubBorrow (vec< L, uint, Q > const &x, vec< L, uint, Q > const &y, vec< L, uint, Q > &borrow)
 Subtracts the 32-bit unsigned integer y from x, returning the difference if non-negative, or pow(2, 32) plus the difference otherwise. More...
 
\n

Detailed Description

\n

Provides GLSL functions on integer types.

\n

These all operate component-wise. The description is per component. The notation [a, b] means the set of bits from bit-number a through bit-number b, inclusive. The lowest-order bit is bit 0.

\n

Include <glm/integer.hpp> to use these core features.

\n

Function Documentation

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GLM_FUNC_DECL int glm::bitCount (genType v)
\n
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Returns the number of bits set to 1 in the binary representation of value.

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Template Parameters
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genTypeSigned or unsigned integer scalar or vector types.
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See also
GLSL bitCount man page
\n
\nGLSL 4.20.8 specification, section 8.8 Integer Functions
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GLM_FUNC_DECL vec<L, int, Q> glm::bitCount (vec< L, T, Q > const & v)
\n
\n\n

Returns the number of bits set to 1 in the binary representation of value.

\n
Template Parameters
\n \n \n \n
LAn integer between 1 and 4 included that qualify the dimension of the vector.
TSigned or unsigned integer scalar or vector types.
\n
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See also
GLSL bitCount man page
\n
\nGLSL 4.20.8 specification, section 8.8 Integer Functions
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GLM_FUNC_DECL vec<L, T, Q> glm::bitfieldExtract (vec< L, T, Q > const & Value,
int Offset,
int Bits 
)
\n
\n\n

Extracts bits [offset, offset + bits - 1] from value, returning them in the least significant bits of the result.

\n

For unsigned data types, the most significant bits of the result will be set to zero. For signed data types, the most significant bits will be set to the value of bit offset + base - 1.

\n

If bits is zero, the result will be zero. The result will be undefined if offset or bits is negative, or if the sum of offset and bits is greater than the number of bits used to store the operand.

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Template Parameters
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LAn integer between 1 and 4 included that qualify the dimension of the vector.
TSigned or unsigned integer scalar types.
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See also
GLSL bitfieldExtract man page
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\nGLSL 4.20.8 specification, section 8.8 Integer Functions
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GLM_FUNC_DECL vec<L, T, Q> glm::bitfieldInsert (vec< L, T, Q > const & Base,
vec< L, T, Q > const & Insert,
int Offset,
int Bits 
)
\n
\n\n

Returns the insertion the bits least-significant bits of insert into base.

\n

The result will have bits [offset, offset + bits - 1] taken from bits [0, bits - 1] of insert, and all other bits taken directly from the corresponding bits of base. If bits is zero, the result will simply be base. The result will be undefined if offset or bits is negative, or if the sum of offset and bits is greater than the number of bits used to store the operand.

\n
Template Parameters
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LAn integer between 1 and 4 included that qualify the dimension of the vector.
TSigned or unsigned integer scalar or vector types.
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See also
GLSL bitfieldInsert man page
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\nGLSL 4.20.8 specification, section 8.8 Integer Functions
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GLM_FUNC_DECL vec<L, T, Q> glm::bitfieldReverse (vec< L, T, Q > const & v)
\n
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Returns the reversal of the bits of value.

\n

The bit numbered n of the result will be taken from bit (bits - 1) - n of value, where bits is the total number of bits used to represent value.

\n
Template Parameters
\n \n \n \n
LAn integer between 1 and 4 included that qualify the dimension of the vector.
TSigned or unsigned integer scalar or vector types.
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See also
GLSL bitfieldReverse man page
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GLM_FUNC_DECL int glm::findLSB (genIUType x)
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Returns the bit number of the least significant bit set to 1 in the binary representation of value.

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If value is zero, -1 will be returned.

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genIUTypeSigned or unsigned integer scalar types.
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See also
GLSL findLSB man page
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GLM_FUNC_DECL vec<L, int, Q> glm::findLSB (vec< L, T, Q > const & v)
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Returns the bit number of the least significant bit set to 1 in the binary representation of value.

\n

If value is zero, -1 will be returned.

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LAn integer between 1 and 4 included that qualify the dimension of the vector.
TSigned or unsigned integer scalar types.
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See also
GLSL findLSB man page
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GLM_FUNC_DECL int glm::findMSB (genIUType x)
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Returns the bit number of the most significant bit in the binary representation of value.

\n

For positive integers, the result will be the bit number of the most significant bit set to 1. For negative integers, the result will be the bit number of the most significant bit set to 0. For a value of zero or negative one, -1 will be returned.

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genIUTypeSigned or unsigned integer scalar types.
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See also
GLSL findMSB man page
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GLM_FUNC_DECL vec<L, int, Q> glm::findMSB (vec< L, T, Q > const & v)
\n
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Returns the bit number of the most significant bit in the binary representation of value.

\n

For positive integers, the result will be the bit number of the most significant bit set to 1. For negative integers, the result will be the bit number of the most significant bit set to 0. For a value of zero or negative one, -1 will be returned.

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LAn integer between 1 and 4 included that qualify the dimension of the vector.
TSigned or unsigned integer scalar types.
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See also
GLSL findMSB man page
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GLM_FUNC_DECL void glm::imulExtended (vec< L, int, Q > const & x,
vec< L, int, Q > const & y,
vec< L, int, Q > & msb,
vec< L, int, Q > & lsb 
)
\n
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Multiplies 32-bit integers x and y, producing a 64-bit result.

\n

The 32 least-significant bits are returned in lsb. The 32 most-significant bits are returned in msb.

\n
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LAn integer between 1 and 4 included that qualify the dimension of the vector.
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See also
GLSL imulExtended man page
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GLM_FUNC_DECL vec<L, uint, Q> glm::uaddCarry (vec< L, uint, Q > const & x,
vec< L, uint, Q > const & y,
vec< L, uint, Q > & carry 
)
\n
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Adds 32-bit unsigned integer x and y, returning the sum modulo pow(2, 32).

\n

The value carry is set to 0 if the sum was less than pow(2, 32), or to 1 otherwise.

\n
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\n \n \n
LAn integer between 1 and 4 included that qualify the dimension of the vector.
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See also
GLSL uaddCarry man page
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GLM_FUNC_DECL void glm::umulExtended (vec< L, uint, Q > const & x,
vec< L, uint, Q > const & y,
vec< L, uint, Q > & msb,
vec< L, uint, Q > & lsb 
)
\n
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Multiplies 32-bit integers x and y, producing a 64-bit result.

\n

The 32 least-significant bits are returned in lsb. The 32 most-significant bits are returned in msb.

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\n \n \n
LAn integer between 1 and 4 included that qualify the dimension of the vector.
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See also
GLSL umulExtended man page
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GLM_FUNC_DECL vec<L, uint, Q> glm::usubBorrow (vec< L, uint, Q > const & x,
vec< L, uint, Q > const & y,
vec< L, uint, Q > & borrow 
)
\n
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Subtracts the 32-bit unsigned integer y from x, returning the difference if non-negative, or pow(2, 32) plus the difference otherwise.

\n

The value borrow is set to 0 if x >= y, or to 1 otherwise.

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LAn integer between 1 and 4 included that qualify the dimension of the vector.
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GLSL usubBorrow man page
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template<length_t C, length_t R, typename T , qualifier Q>
GLM_FUNC_DECL T determinant (mat< C, R, T, Q > const &m)
 Return the determinant of a squared matrix. More...
 
template<length_t C, length_t R, typename T , qualifier Q>
GLM_FUNC_DECL mat< C, R, T, Q > inverse (mat< C, R, T, Q > const &m)
 Return the inverse of a squared matrix. More...
 
template<length_t C, length_t R, typename T , qualifier Q>
GLM_FUNC_DECL mat< C, R, T, Q > matrixCompMult (mat< C, R, T, Q > const &x, mat< C, R, T, Q > const &y)
 Multiply matrix x by matrix y component-wise, i.e., result[i][j] is the scalar product of x[i][j] and y[i][j]. More...
 
template<length_t C, length_t R, typename T , qualifier Q>
GLM_FUNC_DECL detail::outerProduct_trait< C, R, T, Q >::type outerProduct (vec< C, T, Q > const &c, vec< R, T, Q > const &r)
 Treats the first parameter c as a column vector and the second parameter r as a row vector and does a linear algebraic matrix multiply c * r. More...
 
template<length_t C, length_t R, typename T , qualifier Q>
GLM_FUNC_DECL mat< C, R, T, Q >::transpose_type transpose (mat< C, R, T, Q > const &x)
 Returns the transposed matrix of x. More...
 
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Detailed Description

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Provides GLSL matrix functions.

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Include <glm/matrix.hpp> to use these core features.

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Function Documentation

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GLM_FUNC_DECL T glm::determinant (mat< C, R, T, Q > const & m)
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Return the determinant of a squared matrix.

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CInteger between 1 and 4 included that qualify the number a column
RInteger between 1 and 4 included that qualify the number a row
TFloating-point or signed integer scalar types
QValue from qualifier enum
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See also
GLSL determinant man page
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\nGLSL 4.20.8 specification, section 8.6 Matrix Functions
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GLM_FUNC_DECL mat<C, R, T, Q> glm::inverse (mat< C, R, T, Q > const & m)
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Return the inverse of a squared matrix.

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CInteger between 1 and 4 included that qualify the number a column
RInteger between 1 and 4 included that qualify the number a row
TFloating-point or signed integer scalar types
QValue from qualifier enum
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See also
GLSL inverse man page
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\nGLSL 4.20.8 specification, section 8.6 Matrix Functions
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GLM_FUNC_DECL mat<C, R, T, Q> glm::matrixCompMult (mat< C, R, T, Q > const & x,
mat< C, R, T, Q > const & y 
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Multiply matrix x by matrix y component-wise, i.e., result[i][j] is the scalar product of x[i][j] and y[i][j].

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Template Parameters
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CInteger between 1 and 4 included that qualify the number a column
RInteger between 1 and 4 included that qualify the number a row
TFloating-point or signed integer scalar types
QValue from qualifier enum
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See also
GLSL matrixCompMult man page
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\nGLSL 4.20.8 specification, section 8.6 Matrix Functions
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GLM_FUNC_DECL detail::outerProduct_trait<C, R, T, Q>::type glm::outerProduct (vec< C, T, Q > const & c,
vec< R, T, Q > const & r 
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Treats the first parameter c as a column vector and the second parameter r as a row vector and does a linear algebraic matrix multiply c * r.

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CInteger between 1 and 4 included that qualify the number a column
RInteger between 1 and 4 included that qualify the number a row
TFloating-point or signed integer scalar types
QValue from qualifier enum
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GLSL outerProduct man page
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\nGLSL 4.20.8 specification, section 8.6 Matrix Functions
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GLM_FUNC_DECL mat<C, R, T, Q>::transpose_type glm::transpose (mat< C, R, T, Q > const & x)
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Returns the transposed matrix of x.

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CInteger between 1 and 4 included that qualify the number a column
RInteger between 1 and 4 included that qualify the number a row
TFloating-point or signed integer scalar types
QValue from qualifier enum
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See also
GLSL transpose man page
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\nGLSL 4.20.8 specification, section 8.6 Matrix Functions
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\nFunctions
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Floating-Point Pack and Unpack Functions
Core features
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Provides GLSL functions to pack and unpack half, single and double-precision floating point values into more compact integer types. \nMore...

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\nFunctions

GLM_FUNC_DECL double packDouble2x32 (uvec2 const &v)
 Returns a double-qualifier value obtained by packing the components of v into a 64-bit value. More...
 
GLM_FUNC_DECL uint packHalf2x16 (vec2 const &v)
 Returns an unsigned integer obtained by converting the components of a two-component floating-point vector to the 16-bit floating-point representation found in the OpenGL Specification, and then packing these two 16- bit integers into a 32-bit unsigned integer. More...
 
GLM_FUNC_DECL uint packSnorm2x16 (vec2 const &v)
 First, converts each component of the normalized floating-point value v into 8- or 16-bit integer values. More...
 
GLM_FUNC_DECL uint packSnorm4x8 (vec4 const &v)
 First, converts each component of the normalized floating-point value v into 8- or 16-bit integer values. More...
 
GLM_FUNC_DECL uint packUnorm2x16 (vec2 const &v)
 First, converts each component of the normalized floating-point value v into 8- or 16-bit integer values. More...
 
GLM_FUNC_DECL uint packUnorm4x8 (vec4 const &v)
 First, converts each component of the normalized floating-point value v into 8- or 16-bit integer values. More...
 
GLM_FUNC_DECL uvec2 unpackDouble2x32 (double v)
 Returns a two-component unsigned integer vector representation of v. More...
 
GLM_FUNC_DECL vec2 unpackHalf2x16 (uint v)
 Returns a two-component floating-point vector with components obtained by unpacking a 32-bit unsigned integer into a pair of 16-bit values, interpreting those values as 16-bit floating-point numbers according to the OpenGL Specification, and converting them to 32-bit floating-point values. More...
 
GLM_FUNC_DECL vec2 unpackSnorm2x16 (uint p)
 First, unpacks a single 32-bit unsigned integer p into a pair of 16-bit unsigned integers, four 8-bit unsigned integers, or four 8-bit signed integers. More...
 
GLM_FUNC_DECL vec4 unpackSnorm4x8 (uint p)
 First, unpacks a single 32-bit unsigned integer p into a pair of 16-bit unsigned integers, four 8-bit unsigned integers, or four 8-bit signed integers. More...
 
GLM_FUNC_DECL vec2 unpackUnorm2x16 (uint p)
 First, unpacks a single 32-bit unsigned integer p into a pair of 16-bit unsigned integers, four 8-bit unsigned integers, or four 8-bit signed integers. More...
 
GLM_FUNC_DECL vec4 unpackUnorm4x8 (uint p)
 First, unpacks a single 32-bit unsigned integer p into a pair of 16-bit unsigned integers, four 8-bit unsigned integers, or four 8-bit signed integers. More...
 
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Detailed Description

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Provides GLSL functions to pack and unpack half, single and double-precision floating point values into more compact integer types.

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These functions do not operate component-wise, rather as described in each case.

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Include <glm/packing.hpp> to use these core features.

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Function Documentation

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GLM_FUNC_DECL double glm::packDouble2x32 (uvec2 const & v)
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Returns a double-qualifier value obtained by packing the components of v into a 64-bit value.

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If an IEEE 754 Inf or NaN is created, it will not signal, and the resulting floating point value is unspecified. Otherwise, the bit- level representation of v is preserved. The first vector component specifies the 32 least significant bits; the second component specifies the 32 most significant bits.

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See also
GLSL packDouble2x32 man page
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\nGLSL 4.20.8 specification, section 8.4 Floating-Point Pack and Unpack Functions
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GLM_FUNC_DECL uint glm::packHalf2x16 (vec2 const & v)
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Returns an unsigned integer obtained by converting the components of a two-component floating-point vector to the 16-bit floating-point representation found in the OpenGL Specification, and then packing these two 16- bit integers into a 32-bit unsigned integer.

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The first vector component specifies the 16 least-significant bits of the result; the second component specifies the 16 most-significant bits.

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See also
GLSL packHalf2x16 man page
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\nGLSL 4.20.8 specification, section 8.4 Floating-Point Pack and Unpack Functions
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GLM_FUNC_DECL uint glm::packSnorm2x16 (vec2 const & v)
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First, converts each component of the normalized floating-point value v into 8- or 16-bit integer values.

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Then, the results are packed into the returned 32-bit unsigned integer.

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The conversion for component c of v to fixed point is done as follows: packSnorm2x16: round(clamp(v, -1, +1) * 32767.0)

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The first component of the vector will be written to the least significant bits of the output; the last component will be written to the most significant bits.

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See also
GLSL packSnorm2x16 man page
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\nGLSL 4.20.8 specification, section 8.4 Floating-Point Pack and Unpack Functions
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GLM_FUNC_DECL uint glm::packSnorm4x8 (vec4 const & v)
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First, converts each component of the normalized floating-point value v into 8- or 16-bit integer values.

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Then, the results are packed into the returned 32-bit unsigned integer.

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The conversion for component c of v to fixed point is done as follows: packSnorm4x8: round(clamp(c, -1, +1) * 127.0)

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The first component of the vector will be written to the least significant bits of the output; the last component will be written to the most significant bits.

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See also
GLSL packSnorm4x8 man page
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GLM_FUNC_DECL uint glm::packUnorm2x16 (vec2 const & v)
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First, converts each component of the normalized floating-point value v into 8- or 16-bit integer values.

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Then, the results are packed into the returned 32-bit unsigned integer.

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The conversion for component c of v to fixed point is done as follows: packUnorm2x16: round(clamp(c, 0, +1) * 65535.0)

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The first component of the vector will be written to the least significant bits of the output; the last component will be written to the most significant bits.

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See also
GLSL packUnorm2x16 man page
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GLM_FUNC_DECL uint glm::packUnorm4x8 (vec4 const & v)
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First, converts each component of the normalized floating-point value v into 8- or 16-bit integer values.

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Then, the results are packed into the returned 32-bit unsigned integer.

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The conversion for component c of v to fixed point is done as follows: packUnorm4x8: round(clamp(c, 0, +1) * 255.0)

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The first component of the vector will be written to the least significant bits of the output; the last component will be written to the most significant bits.

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See also
GLSL packUnorm4x8 man page
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\nGLSL 4.20.8 specification, section 8.4 Floating-Point Pack and Unpack Functions
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GLM_FUNC_DECL uvec2 glm::unpackDouble2x32 (double v)
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Returns a two-component unsigned integer vector representation of v.

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The bit-level representation of v is preserved. The first component of the vector contains the 32 least significant bits of the double; the second component consists the 32 most significant bits.

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See also
GLSL unpackDouble2x32 man page
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GLM_FUNC_DECL vec2 glm::unpackHalf2x16 (uint v)
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Returns a two-component floating-point vector with components obtained by unpacking a 32-bit unsigned integer into a pair of 16-bit values, interpreting those values as 16-bit floating-point numbers according to the OpenGL Specification, and converting them to 32-bit floating-point values.

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The first component of the vector is obtained from the 16 least-significant bits of v; the second component is obtained from the 16 most-significant bits of v.

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See also
GLSL unpackHalf2x16 man page
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GLM_FUNC_DECL vec2 glm::unpackSnorm2x16 (uint p)
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First, unpacks a single 32-bit unsigned integer p into a pair of 16-bit unsigned integers, four 8-bit unsigned integers, or four 8-bit signed integers.

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Then, each component is converted to a normalized floating-point value to generate the returned two- or four-component vector.

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The conversion for unpacked fixed-point value f to floating point is done as follows: unpackSnorm2x16: clamp(f / 32767.0, -1, +1)

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The first component of the returned vector will be extracted from the least significant bits of the input; the last component will be extracted from the most significant bits.

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See also
GLSL unpackSnorm2x16 man page
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GLM_FUNC_DECL vec4 glm::unpackSnorm4x8 (uint p)
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First, unpacks a single 32-bit unsigned integer p into a pair of 16-bit unsigned integers, four 8-bit unsigned integers, or four 8-bit signed integers.

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Then, each component is converted to a normalized floating-point value to generate the returned two- or four-component vector.

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The conversion for unpacked fixed-point value f to floating point is done as follows: unpackSnorm4x8: clamp(f / 127.0, -1, +1)

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The first component of the returned vector will be extracted from the least significant bits of the input; the last component will be extracted from the most significant bits.

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GLSL unpackSnorm4x8 man page
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GLM_FUNC_DECL vec2 glm::unpackUnorm2x16 (uint p)
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First, unpacks a single 32-bit unsigned integer p into a pair of 16-bit unsigned integers, four 8-bit unsigned integers, or four 8-bit signed integers.

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Then, each component is converted to a normalized floating-point value to generate the returned two- or four-component vector.

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The conversion for unpacked fixed-point value f to floating point is done as follows: unpackUnorm2x16: f / 65535.0

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The first component of the returned vector will be extracted from the least significant bits of the input; the last component will be extracted from the most significant bits.

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GLSL unpackUnorm2x16 man page
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GLM_FUNC_DECL vec4 glm::unpackUnorm4x8 (uint p)
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First, unpacks a single 32-bit unsigned integer p into a pair of 16-bit unsigned integers, four 8-bit unsigned integers, or four 8-bit signed integers.

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Then, each component is converted to a normalized floating-point value to generate the returned two- or four-component vector.

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The conversion for unpacked fixed-point value f to floating point is done as follows: unpackUnorm4x8: f / 255.0

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The first component of the returned vector will be extracted from the least significant bits of the input; the last component will be extracted from the most significant bits.

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GLSL unpackUnorm4x8 man page
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Angle and Trigonometry Functions
Core features
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Function parameters specified as angle are assumed to be in units of radians. \nMore...

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\nFunctions

template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > acos (vec< L, T, Q > const &x)
 Arc cosine. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > acosh (vec< L, T, Q > const &x)
 Arc hyperbolic cosine; returns the non-negative inverse of cosh. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > asin (vec< L, T, Q > const &x)
 Arc sine. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > asinh (vec< L, T, Q > const &x)
 Arc hyperbolic sine; returns the inverse of sinh. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > atan (vec< L, T, Q > const &y, vec< L, T, Q > const &x)
 Arc tangent. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > atan (vec< L, T, Q > const &y_over_x)
 Arc tangent. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > atanh (vec< L, T, Q > const &x)
 Arc hyperbolic tangent; returns the inverse of tanh. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > cos (vec< L, T, Q > const &angle)
 The standard trigonometric cosine function. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > cosh (vec< L, T, Q > const &angle)
 Returns the hyperbolic cosine function, (exp(x) + exp(-x)) / 2. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< L, T, Q > degrees (vec< L, T, Q > const &radians)
 Converts radians to degrees and returns the result. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< L, T, Q > radians (vec< L, T, Q > const &degrees)
 Converts degrees to radians and returns the result. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > sin (vec< L, T, Q > const &angle)
 The standard trigonometric sine function. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > sinh (vec< L, T, Q > const &angle)
 Returns the hyperbolic sine function, (exp(x) - exp(-x)) / 2. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > tan (vec< L, T, Q > const &angle)
 The standard trigonometric tangent function. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL vec< L, T, Q > tanh (vec< L, T, Q > const &angle)
 Returns the hyperbolic tangent function, sinh(angle) / cosh(angle) More...
 
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Detailed Description

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Function parameters specified as angle are assumed to be in units of radians.

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In no case will any of these functions result in a divide by zero error. If the divisor of a ratio is 0, then results will be undefined.

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These all operate component-wise. The description is per component.

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Include <glm/trigonometric.hpp> to use these core features.

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See also
ext_vector_trigonometric
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Function Documentation

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GLM_FUNC_DECL vec<L, T, Q> glm::acos (vec< L, T, Q > const & x)
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Arc cosine.

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Returns an angle whose sine is x. The range of values returned by this function is [0, PI]. Results are undefined if |x| > 1.

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GLSL acos man page
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\nGLSL 4.20.8 specification, section 8.1 Angle and Trigonometry Functions
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GLM_FUNC_DECL vec<L, T, Q> glm::acosh (vec< L, T, Q > const & x)
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Arc hyperbolic cosine; returns the non-negative inverse of cosh.

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Results are undefined if x < 1.

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LInteger between 1 and 4 included that qualify the dimension of the vector
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GLSL acosh man page
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GLM_FUNC_DECL vec<L, T, Q> glm::asin (vec< L, T, Q > const & x)
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Arc sine.

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Returns an angle whose sine is x. The range of values returned by this function is [-PI/2, PI/2]. Results are undefined if |x| > 1.

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LInteger between 1 and 4 included that qualify the dimension of the vector
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GLSL asin man page
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GLM_FUNC_DECL vec<L, T, Q> glm::asinh (vec< L, T, Q > const & x)
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Arc hyperbolic sine; returns the inverse of sinh.

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LInteger between 1 and 4 included that qualify the dimension of the vector
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GLSL asinh man page
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GLM_FUNC_DECL vec<L, T, Q> glm::atan (vec< L, T, Q > const & y,
vec< L, T, Q > const & x 
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Arc tangent.

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Returns an angle whose tangent is y/x. The signs of x and y are used to determine what quadrant the angle is in. The range of values returned by this function is [-PI, PI]. Results are undefined if x and y are both 0.

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LInteger between 1 and 4 included that qualify the dimension of the vector
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GLSL atan man page
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\nGLSL 4.20.8 specification, section 8.1 Angle and Trigonometry Functions
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Referenced by glm::atan2().

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GLM_FUNC_DECL vec<L, T, Q> glm::atan (vec< L, T, Q > const & y_over_x)
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Arc tangent.

\n

Returns an angle whose tangent is y_over_x. The range of values returned by this function is [-PI/2, PI/2].

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Template Parameters
\n \n \n \n \n
LInteger between 1 and 4 included that qualify the dimension of the vector
TFloating-point scalar types
QValue from qualifier enum
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GLSL atan man page
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GLM_FUNC_DECL vec<L, T, Q> glm::atanh (vec< L, T, Q > const & x)
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Arc hyperbolic tangent; returns the inverse of tanh.

\n

Results are undefined if abs(x) >= 1.

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LInteger between 1 and 4 included that qualify the dimension of the vector
TFloating-point scalar types
QValue from qualifier enum
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GLSL atanh man page
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\nGLSL 4.20.8 specification, section 8.1 Angle and Trigonometry Functions
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GLM_FUNC_DECL vec<L, T, Q> glm::cos (vec< L, T, Q > const & angle)
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The standard trigonometric cosine function.

\n

The values returned by this function will range from [-1, 1].

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Template Parameters
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LInteger between 1 and 4 included that qualify the dimension of the vector
TFloating-point scalar types
QValue from qualifier enum
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See also
GLSL cos man page
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\nGLSL 4.20.8 specification, section 8.1 Angle and Trigonometry Functions
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GLM_FUNC_DECL vec<L, T, Q> glm::cosh (vec< L, T, Q > const & angle)
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Returns the hyperbolic cosine function, (exp(x) + exp(-x)) / 2.

\n
Template Parameters
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LInteger between 1 and 4 included that qualify the dimension of the vector
TFloating-point scalar types
QValue from qualifier enum
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GLSL cosh man page
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GLM_FUNC_DECL GLM_CONSTEXPR vec<L, T, Q> glm::degrees (vec< L, T, Q > const & radians)
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Converts radians to degrees and returns the result.

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Template Parameters
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LInteger between 1 and 4 included that qualify the dimension of the vector
TFloating-point scalar types
QValue from qualifier enum
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GLSL degrees man page
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GLM_FUNC_DECL GLM_CONSTEXPR vec<L, T, Q> glm::radians (vec< L, T, Q > const & degrees)
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Converts degrees to radians and returns the result.

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Template Parameters
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LInteger between 1 and 4 included that qualify the dimension of the vector
TFloating-point scalar types
QValue from qualifier enum
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See also
GLSL radians man page
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GLM_FUNC_DECL vec<L, T, Q> glm::sin (vec< L, T, Q > const & angle)
\n
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The standard trigonometric sine function.

\n

The values returned by this function will range from [-1, 1].

\n
Template Parameters
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LInteger between 1 and 4 included that qualify the dimension of the vector
TFloating-point scalar types
QValue from qualifier enum
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See also
GLSL sin man page
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\nGLSL 4.20.8 specification, section 8.1 Angle and Trigonometry Functions
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GLM_FUNC_DECL vec<L, T, Q> glm::sinh (vec< L, T, Q > const & angle)
\n
\n\n

Returns the hyperbolic sine function, (exp(x) - exp(-x)) / 2.

\n
Template Parameters
\n \n \n \n \n
LInteger between 1 and 4 included that qualify the dimension of the vector
TFloating-point scalar types
QValue from qualifier enum
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See also
GLSL sinh man page
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\nGLSL 4.20.8 specification, section 8.1 Angle and Trigonometry Functions
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GLM_FUNC_DECL vec<L, T, Q> glm::tan (vec< L, T, Q > const & angle)
\n
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The standard trigonometric tangent function.

\n
Template Parameters
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LInteger between 1 and 4 included that qualify the dimension of the vector
TFloating-point scalar types
QValue from qualifier enum
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GLSL tan man page
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GLM_FUNC_DECL vec<L, T, Q> glm::tanh (vec< L, T, Q > const & angle)
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Returns the hyperbolic tangent function, sinh(angle) / cosh(angle)

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LInteger between 1 and 4 included that qualify the dimension of the vector
TFloating-point scalar types
QValue from qualifier enum
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GLSL tanh man page
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\nFunctions
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Vector Relational Functions
Core features
\n
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Relational and equality operators (<, <=, >, >=, ==, !=) are defined to operate on scalars and produce scalar Boolean results. \nMore...

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\nFunctions

template<length_t L, qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR bool all (vec< L, bool, Q > const &v)
 Returns true if all components of x are true. More...
 
template<length_t L, qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR bool any (vec< L, bool, Q > const &v)
 Returns true if any component of x is true. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< L, bool, Q > equal (vec< L, T, Q > const &x, vec< L, T, Q > const &y)
 Returns the component-wise comparison of result x == y. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< L, bool, Q > greaterThan (vec< L, T, Q > const &x, vec< L, T, Q > const &y)
 Returns the component-wise comparison of result x > y. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< L, bool, Q > greaterThanEqual (vec< L, T, Q > const &x, vec< L, T, Q > const &y)
 Returns the component-wise comparison of result x >= y. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< L, bool, Q > lessThan (vec< L, T, Q > const &x, vec< L, T, Q > const &y)
 Returns the component-wise comparison result of x < y. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< L, bool, Q > lessThanEqual (vec< L, T, Q > const &x, vec< L, T, Q > const &y)
 Returns the component-wise comparison of result x <= y. More...
 
template<length_t L, qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< L, bool, Q > not_ (vec< L, bool, Q > const &v)
 Returns the component-wise logical complement of x. More...
 
template<length_t L, typename T , qualifier Q>
GLM_FUNC_DECL GLM_CONSTEXPR vec< L, bool, Q > notEqual (vec< L, T, Q > const &x, vec< L, T, Q > const &y)
 Returns the component-wise comparison of result x != y. More...
 
\n

Detailed Description

\n

Relational and equality operators (<, <=, >, >=, ==, !=) are defined to operate on scalars and produce scalar Boolean results.

\n

For vector results, use the following built-in functions.

\n

In all cases, the sizes of all the input and return vectors for any particular call must match.

\n

Include <glm/vector_relational.hpp> to use these core features.

\n
See also
GLM_EXT_vector_relational
\n

Function Documentation

\n\n
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GLM_FUNC_DECL GLM_CONSTEXPR bool glm::all (vec< L, bool, Q > const & v)
\n
\n\n

Returns true if all components of x are true.

\n
Template Parameters
\n \n \n
LAn integer between 1 and 4 included that qualify the dimension of the vector.
\n
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See also
GLSL all man page
\n
\nGLSL 4.20.8 specification, section 8.7 Vector Relational Functions
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GLM_FUNC_DECL GLM_CONSTEXPR bool glm::any (vec< L, bool, Q > const & v)
\n
\n\n

Returns true if any component of x is true.

\n
Template Parameters
\n \n \n
LAn integer between 1 and 4 included that qualify the dimension of the vector.
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See also
GLSL any man page
\n
\nGLSL 4.20.8 specification, section 8.7 Vector Relational Functions
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GLM_FUNC_DECL GLM_CONSTEXPR vec<L, bool, Q> glm::equal (vec< L, T, Q > const & x,
vec< L, T, Q > const & y 
)
\n
\n\n

Returns the component-wise comparison of result x == y.

\n
Template Parameters
\n \n \n \n
LAn integer between 1 and 4 included that qualify the dimension of the vector.
TA floating-point, integer or bool scalar type.
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See also
GLSL equal man page
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\nGLSL 4.20.8 specification, section 8.7 Vector Relational Functions
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GLM_FUNC_DECL GLM_CONSTEXPR vec<L, bool, Q> glm::greaterThan (vec< L, T, Q > const & x,
vec< L, T, Q > const & y 
)
\n
\n\n

Returns the component-wise comparison of result x > y.

\n
Template Parameters
\n \n \n \n
LAn integer between 1 and 4 included that qualify the dimension of the vector.
TA floating-point or integer scalar type.
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See also
GLSL greaterThan man page
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\nGLSL 4.20.8 specification, section 8.7 Vector Relational Functions
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GLM_FUNC_DECL GLM_CONSTEXPR vec<L, bool, Q> glm::greaterThanEqual (vec< L, T, Q > const & x,
vec< L, T, Q > const & y 
)
\n
\n\n

Returns the component-wise comparison of result x >= y.

\n
Template Parameters
\n \n \n \n
LAn integer between 1 and 4 included that qualify the dimension of the vector.
TA floating-point or integer scalar type.
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See also
GLSL greaterThanEqual man page
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\nGLSL 4.20.8 specification, section 8.7 Vector Relational Functions
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GLM_FUNC_DECL GLM_CONSTEXPR vec<L, bool, Q> glm::lessThan (vec< L, T, Q > const & x,
vec< L, T, Q > const & y 
)
\n
\n\n

Returns the component-wise comparison result of x < y.

\n
Template Parameters
\n \n \n \n
LAn integer between 1 and 4 included that qualify the dimension of the vector.
TA floating-point or integer scalar type.
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See also
GLSL lessThan man page
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\nGLSL 4.20.8 specification, section 8.7 Vector Relational Functions
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GLM_FUNC_DECL GLM_CONSTEXPR vec<L, bool, Q> glm::lessThanEqual (vec< L, T, Q > const & x,
vec< L, T, Q > const & y 
)
\n
\n\n

Returns the component-wise comparison of result x <= y.

\n
Template Parameters
\n \n \n \n
LAn integer between 1 and 4 included that qualify the dimension of the vector.
TA floating-point or integer scalar type.
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See also
GLSL lessThanEqual man page
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\nGLSL 4.20.8 specification, section 8.7 Vector Relational Functions
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GLM_FUNC_DECL GLM_CONSTEXPR vec<L, bool, Q> glm::not_ (vec< L, bool, Q > const & v)
\n
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Returns the component-wise logical complement of x.

\n

/!\\ Because of language incompatibilities between C++ and GLSL, GLM defines the function not but not_ instead.

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LAn integer between 1 and 4 included that qualify the dimension of the vector.
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vec< L, T, Q > const & y 
)
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Returns the component-wise comparison of result x != y.

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LAn integer between 1 and 4 included that qualify the dimension of the vector.
TA floating-point, integer or bool scalar type.
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GLSL notEqual man page
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\nFiles

file  _features.hpp [code]
 
file  _fixes.hpp [code]
 
file  _noise.hpp [code]
 
file  _swizzle.hpp [code]
 
file  _swizzle_func.hpp [code]
 
file  _vectorize.hpp [code]
 
file  compute_common.hpp [code]
 
file  compute_vector_relational.hpp [code]
 
file  qualifier.hpp [code]
 
file  setup.hpp [code]
 
file  type_float.hpp [code]
 
file  type_half.hpp [code]
 
file  type_mat2x2.hpp [code]
 Core features
 
file  type_mat2x3.hpp [code]
 Core features
 
file  type_mat2x4.hpp [code]
 Core features
 
file  type_mat3x2.hpp [code]
 Core features
 
file  type_mat3x3.hpp [code]
 Core features
 
file  type_mat3x4.hpp [code]
 Core features
 
file  type_mat4x2.hpp [code]
 Core features
 
file  type_mat4x3.hpp [code]
 Core features
 
file  type_mat4x4.hpp [code]
 Core features
 
file  type_quat.hpp [code]
 Core features
 
file  type_vec1.hpp [code]
 Core features
 
file  type_vec2.hpp [code]
 Core features
 
file  type_vec3.hpp [code]
 Core features
 
file  type_vec4.hpp [code]
 Core features
 
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directory  doc
 
directory  glm
 
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\nFiles

file  bitfield.hpp [code]
 GLM_GTC_bitfield
 
file  gtc/color_space.hpp [code]
 GLM_GTC_color_space
 
file  constants.hpp [code]
 GLM_GTC_constants
 
file  epsilon.hpp [code]
 GLM_GTC_epsilon
 
file  gtc/integer.hpp [code]
 GLM_GTC_integer
 
file  matrix_access.hpp [code]
 GLM_GTC_matrix_access
 
file  matrix_integer.hpp [code]
 GLM_GTC_matrix_integer
 
file  matrix_inverse.hpp [code]
 GLM_GTC_matrix_inverse
 
file  gtc/matrix_transform.hpp [code]
 GLM_GTC_matrix_transform
 
file  noise.hpp [code]
 GLM_GTC_noise
 
file  gtc/packing.hpp [code]
 GLM_GTC_packing
 
file  gtc/quaternion.hpp [code]
 GLM_GTC_quaternion
 
file  random.hpp [code]
 GLM_GTC_random
 
file  reciprocal.hpp [code]
 GLM_GTC_reciprocal
 
file  round.hpp [code]
 GLM_GTC_round
 
file  gtc/type_aligned.hpp [code]
 GLM_GTC_type_aligned
 
file  type_precision.hpp [code]
 GLM_GTC_type_precision
 
file  type_ptr.hpp [code]
 GLM_GTC_type_ptr
 
file  ulp.hpp [code]
 GLM_GTC_ulp
 
file  vec1.hpp [code]
 GLM_GTC_vec1
 
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file  man.doxy [code]
 
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\nFiles

file  matrix_clip_space.hpp [code]
 GLM_EXT_matrix_clip_space
 
file  matrix_common.hpp [code]
 GLM_EXT_matrix_common
 
file  matrix_double2x2.hpp [code]
 Core features
 
file  matrix_double2x2_precision.hpp [code]
 Core features
 
file  matrix_double2x3.hpp [code]
 Core features
 
file  matrix_double2x3_precision.hpp [code]
 Core features
 
file  matrix_double2x4.hpp [code]
 Core features
 
file  matrix_double2x4_precision.hpp [code]
 Core features
 
file  matrix_double3x2.hpp [code]
 Core features
 
file  matrix_double3x2_precision.hpp [code]
 Core features
 
file  matrix_double3x3.hpp [code]
 Core features
 
file  matrix_double3x3_precision.hpp [code]
 Core features
 
file  matrix_double3x4.hpp [code]
 Core features
 
file  matrix_double3x4_precision.hpp [code]
 Core features
 
file  matrix_double4x2.hpp [code]
 Core features
 
file  matrix_double4x2_precision.hpp [code]
 Core features
 
file  matrix_double4x3.hpp [code]
 Core features
 
file  matrix_double4x3_precision.hpp [code]
 Core features
 
file  matrix_double4x4.hpp [code]
 Core features
 
file  matrix_double4x4_precision.hpp [code]
 Core features
 
file  matrix_float2x2.hpp [code]
 Core features
 
file  matrix_float2x2_precision.hpp [code]
 Core features
 
file  matrix_float2x3.hpp [code]
 Core features
 
file  matrix_float2x3_precision.hpp [code]
 Core features
 
file  matrix_float2x4.hpp [code]
 Core features
 
file  matrix_float2x4_precision.hpp [code]
 Core features
 
file  matrix_float3x2.hpp [code]
 Core features
 
file  matrix_float3x2_precision.hpp [code]
 Core features
 
file  matrix_float3x3.hpp [code]
 Core features
 
file  matrix_float3x3_precision.hpp [code]
 Core features
 
file  matrix_float3x4.hpp [code]
 Core features
 
file  matrix_float3x4_precision.hpp [code]
 Core features
 
file  matrix_float4x2.hpp [code]
 Core features
 
file  matrix_float4x2_precision.hpp [code]
 
file  matrix_float4x3.hpp [code]
 Core features
 
file  matrix_float4x3_precision.hpp [code]
 Core features
 
file  matrix_float4x4.hpp [code]
 Core features
 
file  matrix_float4x4_precision.hpp [code]
 Core features
 
file  matrix_projection.hpp [code]
 GLM_EXT_matrix_projection
 
file  matrix_relational.hpp [code]
 GLM_EXT_matrix_relational
 
file  ext/matrix_transform.hpp [code]
 GLM_EXT_matrix_transform
 
file  quaternion_common.hpp [code]
 GLM_EXT_quaternion_common
 
file  quaternion_double.hpp [code]
 GLM_EXT_quaternion_double
 
file  quaternion_double_precision.hpp [code]
 GLM_EXT_quaternion_double_precision
 
file  quaternion_exponential.hpp [code]
 GLM_EXT_quaternion_exponential
 
file  quaternion_float.hpp [code]
 GLM_EXT_quaternion_float
 
file  quaternion_float_precision.hpp [code]
 GLM_EXT_quaternion_float_precision
 
file  quaternion_geometric.hpp [code]
 GLM_EXT_quaternion_geometric
 
file  quaternion_relational.hpp [code]
 GLM_EXT_quaternion_relational
 
file  quaternion_transform.hpp [code]
 GLM_EXT_quaternion_transform
 
file  quaternion_trigonometric.hpp [code]
 GLM_EXT_quaternion_trigonometric
 
file  scalar_common.hpp [code]
 GLM_EXT_scalar_common
 
file  scalar_constants.hpp [code]
 GLM_EXT_scalar_constants
 
file  scalar_int_sized.hpp [code]
 GLM_EXT_scalar_int_sized
 
file  scalar_integer.hpp [code]
 GLM_EXT_scalar_integer
 
file  ext/scalar_relational.hpp [code]
 GLM_EXT_scalar_relational
 
file  scalar_uint_sized.hpp [code]
 GLM_EXT_scalar_uint_sized
 
file  scalar_ulp.hpp [code]
 GLM_EXT_scalar_ulp
 
file  vector_bool1.hpp [code]
 GLM_EXT_vector_bool1
 
file  vector_bool1_precision.hpp [code]
 GLM_EXT_vector_bool1_precision
 
file  vector_bool2.hpp [code]
 Core features
 
file  vector_bool2_precision.hpp [code]
 Core features
 
file  vector_bool3.hpp [code]
 Core features
 
file  vector_bool3_precision.hpp [code]
 Core features
 
file  vector_bool4.hpp [code]
 Core features
 
file  vector_bool4_precision.hpp [code]
 Core features
 
file  vector_common.hpp [code]
 GLM_EXT_vector_common
 
file  vector_double1.hpp [code]
 GLM_EXT_vector_double1
 
file  vector_double1_precision.hpp [code]
 GLM_EXT_vector_double1_precision
 
file  vector_double2.hpp [code]
 Core features
 
file  vector_double2_precision.hpp [code]
 Core features
 
file  vector_double3.hpp [code]
 Core features
 
file  vector_double3_precision.hpp [code]
 Core features
 
file  vector_double4.hpp [code]
 Core features
 
file  vector_double4_precision.hpp [code]
 Core features
 
file  vector_float1.hpp [code]
 GLM_EXT_vector_float1
 
file  vector_float1_precision.hpp [code]
 GLM_EXT_vector_float1_precision
 
file  vector_float2.hpp [code]
 Core features
 
file  vector_float2_precision.hpp [code]
 Core features
 
file  vector_float3.hpp [code]
 Core features
 
file  vector_float3_precision.hpp [code]
 Core features
 
file  vector_float4.hpp [code]
 Core features
 
file  vector_float4_precision.hpp [code]
 Core features
 
file  vector_int1.hpp [code]
 GLM_EXT_vector_int1
 
file  vector_int1_precision.hpp [code]
 GLM_EXT_vector_int1_precision
 
file  vector_int2.hpp [code]
 Core features
 
file  vector_int2_precision.hpp [code]
 Core features
 
file  vector_int3.hpp [code]
 Core features
 
file  vector_int3_precision.hpp [code]
 Core features
 
file  vector_int4.hpp [code]
 Core features
 
file  vector_int4_precision.hpp [code]
 Core features
 
file  vector_integer.hpp [code]
 GLM_EXT_vector_integer
 
file  ext/vector_relational.hpp [code]
 GLM_EXT_vector_relational
 
file  vector_uint1.hpp [code]
 GLM_EXT_vector_uint1
 
file  vector_uint1_precision.hpp [code]
 GLM_EXT_vector_uint1_precision
 
file  vector_uint2.hpp [code]
 Core features
 
file  vector_uint2_precision.hpp [code]
 Core features
 
file  vector_uint3.hpp [code]
 Core features
 
file  vector_uint3_precision.hpp [code]
 Core features
 
file  vector_uint4.hpp [code]
 Core features
 
file  vector_uint4_precision.hpp [code]
 Core features
 
file  vector_ulp.hpp [code]
 GLM_EXT_vector_ulp
 
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\nDirectories

directory  glm
 
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glm Directory Reference
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directory  detail
 
directory  ext
 
directory  gtc
 
directory  gtx
 
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\nFiles

file  common.hpp [code]
 Core features
 
file  exponential.hpp [code]
 Core features
 
file  ext.hpp [code]
 Core features (Dependence)
 
file  fwd.hpp [code]
 
file  geometric.hpp [code]
 Core features
 
file  glm.hpp [code]
 Core features
 
file  integer.hpp [code]
 Core features
 
file  mat2x2.hpp [code]
 Core features
 
file  mat2x3.hpp [code]
 Core features
 
file  mat2x4.hpp [code]
 Core features
 
file  mat3x2.hpp [code]
 Core features
 
file  mat3x3.hpp [code]
 Core features
 
file  mat3x4.hpp [code]
 Core features
 
file  mat4x2.hpp [code]
 Core features
 
file  mat4x3.hpp [code]
 Core features
 
file  mat4x4.hpp [code]
 Core features
 
file  matrix.hpp [code]
 Core features
 
file  packing.hpp [code]
 Core features
 
file  trigonometric.hpp [code]
 Core features
 
file  vec2.hpp [code]
 Core features
 
file  vec3.hpp [code]
 Core features
 
file  vec4.hpp [code]
 Core features
 
file  vector_relational.hpp [code]
 Core features
 
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\nDirectories

directory  G-Truc
 
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\nFiles

file  associated_min_max.hpp [code]
 GLM_GTX_associated_min_max
 
file  bit.hpp [code]
 GLM_GTX_bit
 
file  closest_point.hpp [code]
 GLM_GTX_closest_point
 
file  color_encoding.hpp [code]
 GLM_GTX_color_encoding
 
file  gtx/color_space.hpp [code]
 GLM_GTX_color_space
 
file  color_space_YCoCg.hpp [code]
 GLM_GTX_color_space_YCoCg
 
file  gtx/common.hpp [code]
 GLM_GTX_common
 
file  compatibility.hpp [code]
 GLM_GTX_compatibility
 
file  component_wise.hpp [code]
 GLM_GTX_component_wise
 
file  dual_quaternion.hpp [code]
 GLM_GTX_dual_quaternion
 
file  easing.hpp [code]
 GLM_GTX_easing
 
file  euler_angles.hpp [code]
 GLM_GTX_euler_angles
 
file  extend.hpp [code]
 GLM_GTX_extend
 
file  extended_min_max.hpp [code]
 GLM_GTX_extented_min_max
 
file  exterior_product.hpp [code]
 GLM_GTX_exterior_product
 
file  fast_exponential.hpp [code]
 GLM_GTX_fast_exponential
 
file  fast_square_root.hpp [code]
 GLM_GTX_fast_square_root
 
file  fast_trigonometry.hpp [code]
 GLM_GTX_fast_trigonometry
 
file  functions.hpp [code]
 GLM_GTX_functions
 
file  gradient_paint.hpp [code]
 GLM_GTX_gradient_paint
 
file  handed_coordinate_space.hpp [code]
 GLM_GTX_handed_coordinate_space
 
file  hash.hpp [code]
 GLM_GTX_hash
 
file  gtx/integer.hpp [code]
 GLM_GTX_integer
 
file  intersect.hpp [code]
 GLM_GTX_intersect
 
file  io.hpp [code]
 GLM_GTX_io
 
file  log_base.hpp [code]
 GLM_GTX_log_base
 
file  matrix_cross_product.hpp [code]
 GLM_GTX_matrix_cross_product
 
file  matrix_decompose.hpp [code]
 GLM_GTX_matrix_decompose
 
file  matrix_factorisation.hpp [code]
 GLM_GTX_matrix_factorisation
 
file  matrix_interpolation.hpp [code]
 GLM_GTX_matrix_interpolation
 
file  matrix_major_storage.hpp [code]
 GLM_GTX_matrix_major_storage
 
file  matrix_operation.hpp [code]
 GLM_GTX_matrix_operation
 
file  matrix_query.hpp [code]
 GLM_GTX_matrix_query
 
file  matrix_transform_2d.hpp [code]
 GLM_GTX_matrix_transform_2d
 
file  mixed_product.hpp [code]
 GLM_GTX_mixed_producte
 
file  norm.hpp [code]
 GLM_GTX_norm
 
file  normal.hpp [code]
 GLM_GTX_normal
 
file  normalize_dot.hpp [code]
 GLM_GTX_normalize_dot
 
file  number_precision.hpp [code]
 GLM_GTX_number_precision
 
file  optimum_pow.hpp [code]
 GLM_GTX_optimum_pow
 
file  orthonormalize.hpp [code]
 GLM_GTX_orthonormalize
 
file  perpendicular.hpp [code]
 GLM_GTX_perpendicular
 
file  polar_coordinates.hpp [code]
 GLM_GTX_polar_coordinates
 
file  projection.hpp [code]
 GLM_GTX_projection
 
file  gtx/quaternion.hpp [code]
 GLM_GTX_quaternion
 
file  range.hpp [code]
 GLM_GTX_range
 
file  raw_data.hpp [code]
 GLM_GTX_raw_data
 
file  rotate_normalized_axis.hpp [code]
 GLM_GTX_rotate_normalized_axis
 
file  rotate_vector.hpp [code]
 GLM_GTX_rotate_vector
 
file  scalar_multiplication.hpp [code]
 Experimental extensions
 
file  gtx/scalar_relational.hpp [code]
 GLM_GTX_scalar_relational
 
file  spline.hpp [code]
 GLM_GTX_spline
 
file  std_based_type.hpp [code]
 GLM_GTX_std_based_type
 
file  string_cast.hpp [code]
 GLM_GTX_string_cast
 
file  texture.hpp [code]
 GLM_GTX_texture
 
file  transform.hpp [code]
 GLM_GTX_transform
 
file  transform2.hpp [code]
 GLM_GTX_transform2
 
file  gtx/type_aligned.hpp [code]
 GLM_GTX_type_aligned
 
file  type_trait.hpp [code]
 GLM_GTX_type_trait
 
file  vec_swizzle.hpp [code]
 GLM_GTX_vec_swizzle
 
file  vector_angle.hpp [code]
 GLM_GTX_vector_angle
 
file  vector_query.hpp [code]
 GLM_GTX_vector_query
 
file  wrap.hpp [code]
 GLM_GTX_wrap
 
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li.footer\n{\n list-style-type:none;\n float:right;\n padding-left:10px;\n padding-right:15px;\n background-image:none;\n background-repeat:no-repeat;\n background-position:right;\n color:#bf6000;\n font-size: 8pt;\n}\n\ndiv.summary\n{\n\tfloat: right;\n\tfont-size: 8pt;\n\tpadding-right: 5px;\n\twidth: 50%;\n\ttext-align: right;\n}\n\ndiv.summary a\n{\n\twhite-space: nowrap;\n}\n\ndiv.ingroups\n{\n\tfont-size: 8pt;\n\twidth: 50%;\n\ttext-align: left;\n}\n\ndiv.ingroups a\n{\n\twhite-space: nowrap;\n}\n\ndiv.header\n{\n\tbackground-repeat:repeat-x;\n\tbackground-color: #FFFCF8;\n\n\tpadding:0px;\n\tmargin:0px;\n\tmargin-left:auto; \n\tmargin-right:auto;\n\twidth:1280px;\n}\n\ndiv.headertitle\n{\n\tpadding: 5px 5px 5px 10px;\n}\n\ndl\n{\n padding: 0 0 0 10px;\n}\n\n/* dl.note, dl.warning, dl.attention, dl.pre, dl.post, dl.invariant, dl.deprecated, dl.todo, dl.test, dl.bug */\ndl.section\n{\n\tmargin-left: 0px;\n\tpadding-left: 0px;\n}\n\ndl.note\n{\n margin-left:-7px;\n padding-left: 3px;\n border-left:4px solid;\n border-color: #D0C000;\n}\n\ndl.warning, dl.attention\n{\n margin-left:-7px;\n padding-left: 3px;\n border-left:4px solid;\n border-color: #FF0000;\n}\n\ndl.pre, dl.post, dl.invariant\n{\n margin-left:-7px;\n padding-left: 3px;\n border-left:4px solid;\n border-color: #00D000;\n}\n\ndl.deprecated\n{\n margin-left:-7px;\n padding-left: 3px;\n border-left:4px solid;\n border-color: #505050;\n}\n\ndl.todo\n{\n margin-left:-7px;\n padding-left: 3px;\n border-left:4px solid;\n border-color: #E0C000;\n}\n\ndl.test\n{\n margin-left:-7px;\n padding-left: 3px;\n border-left:4px solid;\n border-color: #3030E0;\n}\n\ndl.bug\n{\n margin-left:-7px;\n padding-left: 3px;\n border-left:4px solid;\n border-color: #C08050;\n}\n\ndl.section dd {\n\tmargin-bottom: 6px;\n}\n\n\n#projectlogo\n{\n\ttext-align: center;\n\tvertical-align: bottom;\n\tborder-collapse: separate;\n}\n \n#projectlogo img\n{ \n\tborder: 0px none;\n}\n \n#projectalign\n{\n vertical-align: middle;\n}\n\n#projectname\n{\n\tfont: 300% Tahoma, Arial,sans-serif;\n\tmargin: 0px;\n\tpadding: 2px 0px;\n\tcolor: #FF8000;\n}\n \n#projectbrief\n{\n\tfont: 120% Tahoma, Arial,sans-serif;\n\tmargin: 0px;\n\tpadding: 0px;\n}\n\n#projectnumber\n{\n\tfont: 50% Tahoma, Arial,sans-serif;\n\tmargin: 0px;\n\tpadding: 0px;\n}\n\n#titlearea\n{\n\tpadding: 0px;\n\tmargin: 0px;\n\twidth: 100%;\n\tborder-bottom: 1px solid #5373B4;\n}\n\n.image\n{\n text-align: center;\n}\n\n.dotgraph\n{\n text-align: center;\n}\n\n.mscgraph\n{\n text-align: center;\n}\n\n.diagraph\n{\n text-align: center;\n}\n\n.caption\n{\n\tfont-weight: bold;\n}\n\ndiv.zoom\n{\n\tborder: 1px solid #90A5CE;\n}\n\ndl.citelist {\n margin-bottom:50px;\n}\n\ndl.citelist dt {\n color:#334975;\n float:left;\n font-weight:bold;\n margin-right:10px;\n padding:5px;\n}\n\ndl.citelist dd {\n margin:2px 0;\n padding:5px 0;\n}\n\ndiv.toc {\n padding: 14px 25px;\n background-color: #F4F6FA;\n border: 1px solid #D8DFEE;\n border-radius: 7px 7px 7px 7px;\n float: right;\n height: auto;\n margin: 0 20px 10px 10px;\n width: 200px;\n}\n\ndiv.toc li {\n background: url(\"bdwn.png\") no-repeat scroll 0 5px transparent;\n font: 10px/1.2 Verdana,DejaVu Sans,Geneva,sans-serif;\n margin-top: 5px;\n padding-left: 10px;\n padding-top: 2px;\n}\n\ndiv.toc h3 {\n font: bold 12px/1.2 Arial,FreeSans,sans-serif;\n\tcolor: #4665A2;\n border-bottom: 0 none;\n margin: 0;\n}\n\ndiv.toc ul {\n list-style: none outside none;\n border: medium none;\n padding: 0px;\n} \n\ndiv.toc li.level1 {\n margin-left: 0px;\n}\n\ndiv.toc li.level2 {\n margin-left: 15px;\n}\n\ndiv.toc li.level3 {\n margin-left: 30px;\n}\n\ndiv.toc li.level4 {\n margin-left: 45px;\n}\n\n.inherit_header {\n\tfont-weight: bold;\n\tcolor: gray;\n\tcursor: pointer;\n\t-webkit-touch-callout: none;\n\t-webkit-user-select: none;\n\t-khtml-user-select: none;\n\t-moz-user-select: none;\n\t-ms-user-select: none;\n\tuser-select: none;\n}\n\n.inherit_header td {\n padding: 6px 0px 2px 5px;\n}\n\n.inherit {\n display: none;\n}\n\ntr.heading h2 {\n margin-top: 12px;\n margin-bottom: 4px;\n}\n\n/* tooltip related style info */\n\n.ttc {\n position: absolute;\n display: none;\n}\n\n#powerTip {\n\tcursor: default;\n\twhite-space: nowrap;\n\tbackground-color: white;\n\tborder: 1px solid gray;\n\tborder-radius: 4px 4px 4px 4px;\n\tbox-shadow: 1px 1px 7px gray;\n\tdisplay: none;\n\tfont-size: smaller;\n\tmax-width: 80%;\n\topacity: 0.9;\n\tpadding: 1ex 1em 1em;\n\tposition: absolute;\n\tz-index: 2147483647;\n}\n\n#powerTip div.ttdoc {\n color: grey;\n\tfont-style: italic;\n}\n\n#powerTip div.ttname a {\n font-weight: bold;\n}\n\n#powerTip div.ttname {\n font-weight: bold;\n}\n\n#powerTip div.ttdeci {\n color: #006318;\n}\n\n#powerTip div {\n margin: 0px;\n padding: 0px;\n font: 12px/16px Roboto,sans-serif;\n}\n\n#powerTip:before, #powerTip:after {\n\tcontent: \"\";\n\tposition: absolute;\n\tmargin: 0px;\n}\n\n#powerTip.n:after, #powerTip.n:before,\n#powerTip.s:after, #powerTip.s:before,\n#powerTip.w:after, #powerTip.w:before,\n#powerTip.e:after, #powerTip.e:before,\n#powerTip.ne:after, #powerTip.ne:before,\n#powerTip.se:after, #powerTip.se:before,\n#powerTip.nw:after, #powerTip.nw:before,\n#powerTip.sw:after, #powerTip.sw:before {\n\tborder: solid transparent;\n\tcontent: \" \";\n\theight: 0;\n\twidth: 0;\n\tposition: absolute;\n}\n\n#powerTip.n:after, #powerTip.s:after,\n#powerTip.w:after, #powerTip.e:after,\n#powerTip.nw:after, #powerTip.ne:after,\n#powerTip.sw:after, #powerTip.se:after {\n\tborder-color: rgba(255, 255, 255, 0);\n}\n\n#powerTip.n:before, #powerTip.s:before,\n#powerTip.w:before, #powerTip.e:before,\n#powerTip.nw:before, #powerTip.ne:before,\n#powerTip.sw:before, #powerTip.se:before {\n\tborder-color: rgba(128, 128, 128, 0);\n}\n\n#powerTip.n:after, #powerTip.n:before,\n#powerTip.ne:after, #powerTip.ne:before,\n#powerTip.nw:after, #powerTip.nw:before {\n\ttop: 100%;\n}\n\n#powerTip.n:after, #powerTip.ne:after, #powerTip.nw:after 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{ display: none; }\n body { overflow:visible; }\n h1, h2, h3, h4, h5, h6 { page-break-after: avoid; }\n .summary { display: none; }\n .memitem { page-break-inside: avoid; }\n #doc-content\n {\n margin-left:0 !important;\n height:auto !important;\n width:auto !important;\n overflow:inherit;\n display:inline;\n }\n}\n\n" }, { "path": "field_construction/submodules/diff-langsurf-rasterizer/third_party/glm/doc/api/dynsections.js", "content": "function toggleVisibility(linkObj)\n{\n var base = $(linkObj).attr('id');\n var summary = $('#'+base+'-summary');\n var content = $('#'+base+'-content');\n var trigger = $('#'+base+'-trigger');\n var src=$(trigger).attr('src');\n if (content.is(':visible')===true) {\n content.hide();\n summary.show();\n $(linkObj).addClass('closed').removeClass('opened');\n $(trigger).attr('src',src.substring(0,src.length-8)+'closed.png');\n } else {\n content.show();\n summary.hide();\n $(linkObj).removeClass('closed').addClass('opened');\n $(trigger).attr('src',src.substring(0,src.length-10)+'open.png');\n } \n return false;\n}\n\nfunction updateStripes()\n{\n $('table.directory tr').\n removeClass('even').filter(':visible:even').addClass('even');\n}\n\nfunction toggleLevel(level)\n{\n $('table.directory tr').each(function() {\n var l = this.id.split('_').length-1;\n var i = $('#img'+this.id.substring(3));\n var a = $('#arr'+this.id.substring(3));\n if (l\n\n\n\n\n\n0.9.9 API documentation: File List\n\n\n\n\n\n\n\n\n\n\n
\n
\n\n \n \n \n \n \n \n
\"Logo\"\n
0.9.9 API documentation\n
\n
\n
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\n \n
\n
\n \n
\n
\n\n
\n
\n\n\n
\n\n
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File List
\n
\n
\n
Here is a list of all documented files with brief descriptions:
\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
 _features.hpp
 _fixes.hpp
 _noise.hpp
 _swizzle.hpp
 _swizzle_func.hpp
 _vectorize.hpp
 associated_min_max.hppGLM_GTX_associated_min_max
 bit.hppGLM_GTX_bit
 bitfield.hppGLM_GTC_bitfield
 closest_point.hppGLM_GTX_closest_point
 color_encoding.hppGLM_GTX_color_encoding
 gtc/color_space.hppGLM_GTC_color_space
 gtx/color_space.hppGLM_GTX_color_space
 color_space_YCoCg.hppGLM_GTX_color_space_YCoCg
 common.hppCore features
 gtx/common.hppGLM_GTX_common
 compatibility.hppGLM_GTX_compatibility
 component_wise.hppGLM_GTX_component_wise
 compute_common.hpp
 compute_vector_relational.hpp
 constants.hppGLM_GTC_constants
 dual_quaternion.hppGLM_GTX_dual_quaternion
 easing.hppGLM_GTX_easing
 epsilon.hppGLM_GTC_epsilon
 euler_angles.hppGLM_GTX_euler_angles
 exponential.hppCore features
 ext.hppCore features (Dependence)
 extend.hppGLM_GTX_extend
 extended_min_max.hppGLM_GTX_extented_min_max
 exterior_product.hppGLM_GTX_exterior_product
 fast_exponential.hppGLM_GTX_fast_exponential
 fast_square_root.hppGLM_GTX_fast_square_root
 fast_trigonometry.hppGLM_GTX_fast_trigonometry
 functions.hppGLM_GTX_functions
 fwd.hpp
 geometric.hppCore features
 glm.hppCore features
 gradient_paint.hppGLM_GTX_gradient_paint
 handed_coordinate_space.hppGLM_GTX_handed_coordinate_space
 hash.hppGLM_GTX_hash
 gtc/integer.hppGLM_GTC_integer
 gtx/integer.hppGLM_GTX_integer
 integer.hppCore features
 intersect.hppGLM_GTX_intersect
 io.hppGLM_GTX_io
 log_base.hppGLM_GTX_log_base
 man.doxy
 mat2x2.hppCore features
 mat2x3.hppCore features
 mat2x4.hppCore features
 mat3x2.hppCore features
 mat3x3.hppCore features
 mat3x4.hppCore features
 mat4x2.hppCore features
 mat4x3.hppCore features
 mat4x4.hppCore features
 matrix.hppCore features
 matrix_access.hppGLM_GTC_matrix_access
 matrix_clip_space.hppGLM_EXT_matrix_clip_space
 matrix_common.hppGLM_EXT_matrix_common
 matrix_cross_product.hppGLM_GTX_matrix_cross_product
 matrix_decompose.hppGLM_GTX_matrix_decompose
 matrix_double2x2.hppCore features
 matrix_double2x2_precision.hppCore features
 matrix_double2x3.hppCore features
 matrix_double2x3_precision.hppCore features
 matrix_double2x4.hppCore features
 matrix_double2x4_precision.hppCore features
 matrix_double3x2.hppCore features
 matrix_double3x2_precision.hppCore features
 matrix_double3x3.hppCore features
 matrix_double3x3_precision.hppCore features
 matrix_double3x4.hppCore features
 matrix_double3x4_precision.hppCore features
 matrix_double4x2.hppCore features
 matrix_double4x2_precision.hppCore features
 matrix_double4x3.hppCore features
 matrix_double4x3_precision.hppCore features
 matrix_double4x4.hppCore features
 matrix_double4x4_precision.hppCore features
 matrix_factorisation.hppGLM_GTX_matrix_factorisation
 matrix_float2x2.hppCore features
 matrix_float2x2_precision.hppCore features
 matrix_float2x3.hppCore features
 matrix_float2x3_precision.hppCore features
 matrix_float2x4.hppCore features
 matrix_float2x4_precision.hppCore features
 matrix_float3x2.hppCore features
 matrix_float3x2_precision.hppCore features
 matrix_float3x3.hppCore features
 matrix_float3x3_precision.hppCore features
 matrix_float3x4.hppCore features
 matrix_float3x4_precision.hppCore features
 matrix_float4x2.hppCore features
 matrix_float4x2_precision.hpp
 matrix_float4x3.hppCore features
 matrix_float4x3_precision.hppCore features
 matrix_float4x4.hppCore features
 matrix_float4x4_precision.hppCore features
 matrix_integer.hppGLM_GTC_matrix_integer
 matrix_interpolation.hppGLM_GTX_matrix_interpolation
 matrix_inverse.hppGLM_GTC_matrix_inverse
 matrix_major_storage.hppGLM_GTX_matrix_major_storage
 matrix_operation.hppGLM_GTX_matrix_operation
 matrix_projection.hppGLM_EXT_matrix_projection
 matrix_query.hppGLM_GTX_matrix_query
 matrix_relational.hppGLM_EXT_matrix_relational
 ext/matrix_transform.hppGLM_EXT_matrix_transform
 gtc/matrix_transform.hppGLM_GTC_matrix_transform
 matrix_transform_2d.hppGLM_GTX_matrix_transform_2d
 mixed_product.hppGLM_GTX_mixed_producte
 noise.hppGLM_GTC_noise
 norm.hppGLM_GTX_norm
 normal.hppGLM_GTX_normal
 normalize_dot.hppGLM_GTX_normalize_dot
 number_precision.hppGLM_GTX_number_precision
 optimum_pow.hppGLM_GTX_optimum_pow
 orthonormalize.hppGLM_GTX_orthonormalize
 gtc/packing.hppGLM_GTC_packing
 packing.hppCore features
 perpendicular.hppGLM_GTX_perpendicular
 polar_coordinates.hppGLM_GTX_polar_coordinates
 projection.hppGLM_GTX_projection
 qualifier.hpp
 gtc/quaternion.hppGLM_GTC_quaternion
 gtx/quaternion.hppGLM_GTX_quaternion
 quaternion_common.hppGLM_EXT_quaternion_common
 quaternion_double.hppGLM_EXT_quaternion_double
 quaternion_double_precision.hppGLM_EXT_quaternion_double_precision
 quaternion_exponential.hppGLM_EXT_quaternion_exponential
 quaternion_float.hppGLM_EXT_quaternion_float
 quaternion_float_precision.hppGLM_EXT_quaternion_float_precision
 quaternion_geometric.hppGLM_EXT_quaternion_geometric
 quaternion_relational.hppGLM_EXT_quaternion_relational
 quaternion_transform.hppGLM_EXT_quaternion_transform
 quaternion_trigonometric.hppGLM_EXT_quaternion_trigonometric
 random.hppGLM_GTC_random
 range.hppGLM_GTX_range
 raw_data.hppGLM_GTX_raw_data
 reciprocal.hppGLM_GTC_reciprocal
 rotate_normalized_axis.hppGLM_GTX_rotate_normalized_axis
 rotate_vector.hppGLM_GTX_rotate_vector
 round.hppGLM_GTC_round
 scalar_common.hppGLM_EXT_scalar_common
 scalar_constants.hppGLM_EXT_scalar_constants
 scalar_int_sized.hppGLM_EXT_scalar_int_sized
 scalar_integer.hppGLM_EXT_scalar_integer
 scalar_multiplication.hppExperimental extensions
 ext/scalar_relational.hppGLM_EXT_scalar_relational
 gtx/scalar_relational.hppGLM_GTX_scalar_relational
 scalar_uint_sized.hppGLM_EXT_scalar_uint_sized
 scalar_ulp.hppGLM_EXT_scalar_ulp
 setup.hpp
 spline.hppGLM_GTX_spline
 std_based_type.hppGLM_GTX_std_based_type
 string_cast.hppGLM_GTX_string_cast
 texture.hppGLM_GTX_texture
 transform.hppGLM_GTX_transform
 transform2.hppGLM_GTX_transform2
 trigonometric.hppCore features
 gtc/type_aligned.hppGLM_GTC_type_aligned
 gtx/type_aligned.hppGLM_GTX_type_aligned
 type_float.hpp
 type_half.hpp
 type_mat2x2.hppCore features
 type_mat2x3.hppCore features
 type_mat2x4.hppCore features
 type_mat3x2.hppCore features
 type_mat3x3.hppCore features
 type_mat3x4.hppCore features
 type_mat4x2.hppCore features
 type_mat4x3.hppCore features
 type_mat4x4.hppCore features
 type_precision.hppGLM_GTC_type_precision
 type_ptr.hppGLM_GTC_type_ptr
 type_quat.hppCore features
 type_trait.hppGLM_GTX_type_trait
 type_vec1.hppCore features
 type_vec2.hppCore features
 type_vec3.hppCore features
 type_vec4.hppCore features
 ulp.hppGLM_GTC_ulp
 vec1.hppGLM_GTC_vec1
 vec2.hppCore features
 vec3.hppCore features
 vec4.hppCore features
 vec_swizzle.hppGLM_GTX_vec_swizzle
 vector_angle.hppGLM_GTX_vector_angle
 vector_bool1.hppGLM_EXT_vector_bool1
 vector_bool1_precision.hppGLM_EXT_vector_bool1_precision
 vector_bool2.hppCore features
 vector_bool2_precision.hppCore features
 vector_bool3.hppCore features
 vector_bool3_precision.hppCore features
 vector_bool4.hppCore features
 vector_bool4_precision.hppCore features
 vector_common.hppGLM_EXT_vector_common
 vector_double1.hppGLM_EXT_vector_double1
 vector_double1_precision.hppGLM_EXT_vector_double1_precision
 vector_double2.hppCore features
 vector_double2_precision.hppCore features
 vector_double3.hppCore features
 vector_double3_precision.hppCore features
 vector_double4.hppCore features
 vector_double4_precision.hppCore features
 vector_float1.hppGLM_EXT_vector_float1
 vector_float1_precision.hppGLM_EXT_vector_float1_precision
 vector_float2.hppCore features
 vector_float2_precision.hppCore features
 vector_float3.hppCore features
 vector_float3_precision.hppCore features
 vector_float4.hppCore features
 vector_float4_precision.hppCore features
 vector_int1.hppGLM_EXT_vector_int1
 vector_int1_precision.hppGLM_EXT_vector_int1_precision
 vector_int2.hppCore features
 vector_int2_precision.hppCore features
 vector_int3.hppCore features
 vector_int3_precision.hppCore features
 vector_int4.hppCore features
 vector_int4_precision.hppCore features
 vector_integer.hppGLM_EXT_vector_integer
 vector_query.hppGLM_GTX_vector_query
 ext/vector_relational.hppGLM_EXT_vector_relational
 vector_relational.hppCore features
 vector_uint1.hppGLM_EXT_vector_uint1
 vector_uint1_precision.hppGLM_EXT_vector_uint1_precision
 vector_uint2.hppCore features
 vector_uint2_precision.hppCore features
 vector_uint3.hppCore features
 vector_uint3_precision.hppCore features
 vector_uint4.hppCore features
 vector_uint4_precision.hppCore features
 vector_ulp.hppGLM_EXT_vector_ulp
 wrap.hppGLM_GTX_wrap
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bw=this[0],bv=this.offsetParent(),bx=this.offset(),e=ad.test(bv[0].nodeName)?{top:0,left:0}:bv.offset();bx.top-=parseFloat(b.css(bw,\"marginTop\"))||0;bx.left-=parseFloat(b.css(bw,\"marginLeft\"))||0;e.top+=parseFloat(b.css(bv[0],\"borderTopWidth\"))||0;e.left+=parseFloat(b.css(bv[0],\"borderLeftWidth\"))||0;return{top:bx.top-e.top,left:bx.left-e.left}},offsetParent:function(){return this.map(function(){var e=this.offsetParent||av.body;while(e&&(!ad.test(e.nodeName)&&b.css(e,\"position\")===\"static\")){e=e.offsetParent}return e})}});b.each([\"Left\",\"Top\"],function(bv,e){var bw=\"scroll\"+e;b.fn[bw]=function(bz){var bx,by;if(bz===L){bx=this[0];if(!bx){return null}by=aK(bx);return by?(\"pageXOffset\" in by)?by[bv?\"pageYOffset\":\"pageXOffset\"]:b.support.boxModel&&by.document.documentElement[bw]||by.document.body[bw]:bx[bw]}return this.each(function(){by=aK(this);if(by){by.scrollTo(!bv?bz:b(by).scrollLeft(),bv?bz:b(by).scrollTop())}else{this[bw]=bz}})}});function aK(e){return b.isWindow(e)?e:e.nodeType===9?e.defaultView||e.parentWindow:false}b.each([\"Height\",\"Width\"],function(bv,e){var bw=e.toLowerCase();b.fn[\"inner\"+e]=function(){var bx=this[0];return bx?bx.style?parseFloat(b.css(bx,bw,\"padding\")):this[bw]():null};b.fn[\"outer\"+e]=function(by){var bx=this[0];return bx?bx.style?parseFloat(b.css(bx,bw,by?\"margin\":\"border\")):this[bw]():null};b.fn[bw]=function(bz){var bA=this[0];if(!bA){return bz==null?null:this}if(b.isFunction(bz)){return this.each(function(bE){var bD=b(this);bD[bw](bz.call(this,bE,bD[bw]()))})}if(b.isWindow(bA)){var bB=bA.document.documentElement[\"client\"+e],bx=bA.document.body;return bA.document.compatMode===\"CSS1Compat\"&&bB||bx&&bx[\"client\"+e]||bB}else{if(bA.nodeType===9){return Math.max(bA.documentElement[\"client\"+e],bA.body[\"scroll\"+e],bA.documentElement[\"scroll\"+e],bA.body[\"offset\"+e],bA.documentElement[\"offset\"+e])}else{if(bz===L){var bC=b.css(bA,bw),by=parseFloat(bC);return b.isNumeric(by)?by:bC}else{return this.css(bw,typeof bz===\"string\"?bz:bz+\"px\")}}}}});bb.jQuery=bb.$=b;if(typeof define===\"function\"&&define.amd&&define.amd.jQuery){define(\"jquery\",[],function(){return b})}})(window);/*!\n * jQuery UI 1.8.18\n *\n * Copyright 2011, AUTHORS.txt (http://jqueryui.com/about)\n * Dual licensed under the MIT or GPL Version 2 licenses.\n * http://jquery.org/license\n *\n * http://docs.jquery.com/UI\n */\n(function(a,d){a.ui=a.ui||{};if(a.ui.version){return}a.extend(a.ui,{version:\"1.8.18\",keyCode:{ALT:18,BACKSPACE:8,CAPS_LOCK:20,COMMA:188,COMMAND:91,COMMAND_LEFT:91,COMMAND_RIGHT:93,CONTROL:17,DELETE:46,DOWN:40,END:35,ENTER:13,ESCAPE:27,HOME:36,INSERT:45,LEFT:37,MENU:93,NUMPAD_ADD:107,NUMPAD_DECIMAL:110,NUMPAD_DIVIDE:111,NUMPAD_ENTER:108,NUMPAD_MULTIPLY:106,NUMPAD_SUBTRACT:109,PAGE_DOWN:34,PAGE_UP:33,PERIOD:190,RIGHT:39,SHIFT:16,SPACE:32,TAB:9,UP:38,WINDOWS:91}});a.fn.extend({propAttr:a.fn.prop||a.fn.attr,_focus:a.fn.focus,focus:function(e,f){return typeof e===\"number\"?this.each(function(){var g=this;setTimeout(function(){a(g).focus();if(f){f.call(g)}},e)}):this._focus.apply(this,arguments)},scrollParent:function(){var e;if((a.browser.msie&&(/(static|relative)/).test(this.css(\"position\")))||(/absolute/).test(this.css(\"position\"))){e=this.parents().filter(function(){return(/(relative|absolute|fixed)/).test(a.curCSS(this,\"position\",1))&&(/(auto|scroll)/).test(a.curCSS(this,\"overflow\",1)+a.curCSS(this,\"overflow-y\",1)+a.curCSS(this,\"overflow-x\",1))}).eq(0)}else{e=this.parents().filter(function(){return(/(auto|scroll)/).test(a.curCSS(this,\"overflow\",1)+a.curCSS(this,\"overflow-y\",1)+a.curCSS(this,\"overflow-x\",1))}).eq(0)}return(/fixed/).test(this.css(\"position\"))||!e.length?a(document):e},zIndex:function(h){if(h!==d){return this.css(\"zIndex\",h)}if(this.length){var f=a(this[0]),e,g;while(f.length&&f[0]!==document){e=f.css(\"position\");if(e===\"absolute\"||e===\"relative\"||e===\"fixed\"){g=parseInt(f.css(\"zIndex\"),10);if(!isNaN(g)&&g!==0){return g}}f=f.parent()}}return 0},disableSelection:function(){return this.bind((a.support.selectstart?\"selectstart\":\"mousedown\")+\".ui-disableSelection\",function(e){e.preventDefault()})},enableSelection:function(){return this.unbind(\".ui-disableSelection\")}});a.each([\"Width\",\"Height\"],function(g,e){var f=e===\"Width\"?[\"Left\",\"Right\"]:[\"Top\",\"Bottom\"],h=e.toLowerCase(),k={innerWidth:a.fn.innerWidth,innerHeight:a.fn.innerHeight,outerWidth:a.fn.outerWidth,outerHeight:a.fn.outerHeight};function j(m,l,i,n){a.each(f,function(){l-=parseFloat(a.curCSS(m,\"padding\"+this,true))||0;if(i){l-=parseFloat(a.curCSS(m,\"border\"+this+\"Width\",true))||0}if(n){l-=parseFloat(a.curCSS(m,\"margin\"+this,true))||0}});return l}a.fn[\"inner\"+e]=function(i){if(i===d){return k[\"inner\"+e].call(this)}return this.each(function(){a(this).css(h,j(this,i)+\"px\")})};a.fn[\"outer\"+e]=function(i,l){if(typeof i!==\"number\"){return k[\"outer\"+e].call(this,i)}return this.each(function(){a(this).css(h,j(this,i,true,l)+\"px\")})}});function c(g,e){var j=g.nodeName.toLowerCase();if(\"area\"===j){var i=g.parentNode,h=i.name,f;if(!g.href||!h||i.nodeName.toLowerCase()!==\"map\"){return false}f=a(\"img[usemap=#\"+h+\"]\")[0];return !!f&&b(f)}return(/input|select|textarea|button|object/.test(j)?!g.disabled:\"a\"==j?g.href||e:e)&&b(g)}function b(e){return !a(e).parents().andSelf().filter(function(){return a.curCSS(this,\"visibility\")===\"hidden\"||a.expr.filters.hidden(this)}).length}a.extend(a.expr[\":\"],{data:function(g,f,e){return !!a.data(g,e[3])},focusable:function(e){return c(e,!isNaN(a.attr(e,\"tabindex\")))},tabbable:function(g){var e=a.attr(g,\"tabindex\"),f=isNaN(e);return(f||e>=0)&&c(g,!f)}});a(function(){var e=document.body,f=e.appendChild(f=document.createElement(\"div\"));f.offsetHeight;a.extend(f.style,{minHeight:\"100px\",height:\"auto\",padding:0,borderWidth:0});a.support.minHeight=f.offsetHeight===100;a.support.selectstart=\"onselectstart\" in f;e.removeChild(f).style.display=\"none\"});a.extend(a.ui,{plugin:{add:function(f,g,j){var h=a.ui[f].prototype;for(var e in j){h.plugins[e]=h.plugins[e]||[];h.plugins[e].push([g,j[e]])}},call:function(e,g,f){var j=e.plugins[g];if(!j||!e.element[0].parentNode){return}for(var h=0;h0){return true}h[e]=1;g=(h[e]>0);h[e]=0;return g},isOverAxis:function(f,e,g){return(f>e)&&(f<(e+g))},isOver:function(j,f,i,h,e,g){return a.ui.isOverAxis(j,i,e)&&a.ui.isOverAxis(f,h,g)}})})(jQuery);/*!\n * jQuery UI Widget 1.8.18\n *\n * Copyright 2011, AUTHORS.txt (http://jqueryui.com/about)\n * Dual licensed under the MIT or GPL Version 2 licenses.\n * http://jquery.org/license\n *\n * http://docs.jquery.com/UI/Widget\n */\n(function(b,d){if(b.cleanData){var c=b.cleanData;b.cleanData=function(f){for(var g=0,h;(h=f[g])!=null;g++){try{b(h).triggerHandler(\"remove\")}catch(j){}}c(f)}}else{var a=b.fn.remove;b.fn.remove=function(e,f){return this.each(function(){if(!f){if(!e||b.filter(e,[this]).length){b(\"*\",this).add([this]).each(function(){try{b(this).triggerHandler(\"remove\")}catch(g){}})}}return a.call(b(this),e,f)})}}b.widget=function(f,h,e){var g=f.split(\".\")[0],j;f=f.split(\".\")[1];j=g+\"-\"+f;if(!e){e=h;h=b.Widget}b.expr[\":\"][j]=function(k){return !!b.data(k,f)};b[g]=b[g]||{};b[g][f]=function(k,l){if(arguments.length){this._createWidget(k,l)}};var i=new h();i.options=b.extend(true,{},i.options);b[g][f].prototype=b.extend(true,i,{namespace:g,widgetName:f,widgetEventPrefix:b[g][f].prototype.widgetEventPrefix||f,widgetBaseClass:j},e);b.widget.bridge(f,b[g][f])};b.widget.bridge=function(f,e){b.fn[f]=function(i){var g=typeof i===\"string\",h=Array.prototype.slice.call(arguments,1),j=this;i=!g&&h.length?b.extend.apply(null,[true,i].concat(h)):i;if(g&&i.charAt(0)===\"_\"){return j}if(g){this.each(function(){var k=b.data(this,f),l=k&&b.isFunction(k[i])?k[i].apply(k,h):k;if(l!==k&&l!==d){j=l;return false}})}else{this.each(function(){var k=b.data(this,f);if(k){k.option(i||{})._init()}else{b.data(this,f,new e(i,this))}})}return j}};b.Widget=function(e,f){if(arguments.length){this._createWidget(e,f)}};b.Widget.prototype={widgetName:\"widget\",widgetEventPrefix:\"\",options:{disabled:false},_createWidget:function(f,g){b.data(g,this.widgetName,this);this.element=b(g);this.options=b.extend(true,{},this.options,this._getCreateOptions(),f);var e=this;this.element.bind(\"remove.\"+this.widgetName,function(){e.destroy()});this._create();this._trigger(\"create\");this._init()},_getCreateOptions:function(){return b.metadata&&b.metadata.get(this.element[0])[this.widgetName]},_create:function(){},_init:function(){},destroy:function(){this.element.unbind(\".\"+this.widgetName).removeData(this.widgetName);this.widget().unbind(\".\"+this.widgetName).removeAttr(\"aria-disabled\").removeClass(this.widgetBaseClass+\"-disabled ui-state-disabled\")},widget:function(){return this.element},option:function(f,g){var e=f;if(arguments.length===0){return b.extend({},this.options)}if(typeof f===\"string\"){if(g===d){return this.options[f]}e={};e[f]=g}this._setOptions(e);return this},_setOptions:function(f){var e=this;b.each(f,function(g,h){e._setOption(g,h)});return this},_setOption:function(e,f){this.options[e]=f;if(e===\"disabled\"){this.widget()[f?\"addClass\":\"removeClass\"](this.widgetBaseClass+\"-disabled ui-state-disabled\").attr(\"aria-disabled\",f)}return this},enable:function(){return this._setOption(\"disabled\",false)},disable:function(){return this._setOption(\"disabled\",true)},_trigger:function(e,f,g){var j,i,h=this.options[e];g=g||{};f=b.Event(f);f.type=(e===this.widgetEventPrefix?e:this.widgetEventPrefix+e).toLowerCase();f.target=this.element[0];i=f.originalEvent;if(i){for(j in i){if(!(j in f)){f[j]=i[j]}}}this.element.trigger(f,g);return !(b.isFunction(h)&&h.call(this.element[0],f,g)===false||f.isDefaultPrevented())}}})(jQuery);/*!\n * jQuery UI Mouse 1.8.18\n *\n * Copyright 2011, AUTHORS.txt (http://jqueryui.com/about)\n * Dual licensed under the MIT or GPL Version 2 licenses.\n * http://jquery.org/license\n *\n * http://docs.jquery.com/UI/Mouse\n *\n * Depends:\n *\tjquery.ui.widget.js\n */\n(function(b,c){var a=false;b(document).mouseup(function(d){a=false});b.widget(\"ui.mouse\",{options:{cancel:\":input,option\",distance:1,delay:0},_mouseInit:function(){var d=this;this.element.bind(\"mousedown.\"+this.widgetName,function(e){return d._mouseDown(e)}).bind(\"click.\"+this.widgetName,function(e){if(true===b.data(e.target,d.widgetName+\".preventClickEvent\")){b.removeData(e.target,d.widgetName+\".preventClickEvent\");e.stopImmediatePropagation();return false}});this.started=false},_mouseDestroy:function(){this.element.unbind(\".\"+this.widgetName)},_mouseDown:function(f){if(a){return}(this._mouseStarted&&this._mouseUp(f));this._mouseDownEvent=f;var e=this,g=(f.which==1),d=(typeof this.options.cancel==\"string\"&&f.target.nodeName?b(f.target).closest(this.options.cancel).length:false);if(!g||d||!this._mouseCapture(f)){return true}this.mouseDelayMet=!this.options.delay;if(!this.mouseDelayMet){this._mouseDelayTimer=setTimeout(function(){e.mouseDelayMet=true},this.options.delay)}if(this._mouseDistanceMet(f)&&this._mouseDelayMet(f)){this._mouseStarted=(this._mouseStart(f)!==false);if(!this._mouseStarted){f.preventDefault();return true}}if(true===b.data(f.target,this.widgetName+\".preventClickEvent\")){b.removeData(f.target,this.widgetName+\".preventClickEvent\")}this._mouseMoveDelegate=function(h){return e._mouseMove(h)};this._mouseUpDelegate=function(h){return e._mouseUp(h)};b(document).bind(\"mousemove.\"+this.widgetName,this._mouseMoveDelegate).bind(\"mouseup.\"+this.widgetName,this._mouseUpDelegate);f.preventDefault();a=true;return true},_mouseMove:function(d){if(b.browser.msie&&!(document.documentMode>=9)&&!d.button){return this._mouseUp(d)}if(this._mouseStarted){this._mouseDrag(d);return d.preventDefault()}if(this._mouseDistanceMet(d)&&this._mouseDelayMet(d)){this._mouseStarted=(this._mouseStart(this._mouseDownEvent,d)!==false);(this._mouseStarted?this._mouseDrag(d):this._mouseUp(d))}return !this._mouseStarted},_mouseUp:function(d){b(document).unbind(\"mousemove.\"+this.widgetName,this._mouseMoveDelegate).unbind(\"mouseup.\"+this.widgetName,this._mouseUpDelegate);if(this._mouseStarted){this._mouseStarted=false;if(d.target==this._mouseDownEvent.target){b.data(d.target,this.widgetName+\".preventClickEvent\",true)}this._mouseStop(d)}return false},_mouseDistanceMet:function(d){return(Math.max(Math.abs(this._mouseDownEvent.pageX-d.pageX),Math.abs(this._mouseDownEvent.pageY-d.pageY))>=this.options.distance)},_mouseDelayMet:function(d){return this.mouseDelayMet},_mouseStart:function(d){},_mouseDrag:function(d){},_mouseStop:function(d){},_mouseCapture:function(d){return true}})})(jQuery);(function(c,d){c.widget(\"ui.resizable\",c.ui.mouse,{widgetEventPrefix:\"resize\",options:{alsoResize:false,animate:false,animateDuration:\"slow\",animateEasing:\"swing\",aspectRatio:false,autoHide:false,containment:false,ghost:false,grid:false,handles:\"e,s,se\",helper:false,maxHeight:null,maxWidth:null,minHeight:10,minWidth:10,zIndex:1000},_create:function(){var f=this,k=this.options;this.element.addClass(\"ui-resizable\");c.extend(this,{_aspectRatio:!!(k.aspectRatio),aspectRatio:k.aspectRatio,originalElement:this.element,_proportionallyResizeElements:[],_helper:k.helper||k.ghost||k.animate?k.helper||\"ui-resizable-helper\":null});if(this.element[0].nodeName.match(/canvas|textarea|input|select|button|img/i)){this.element.wrap(c('
').css({position:this.element.css(\"position\"),width:this.element.outerWidth(),height:this.element.outerHeight(),top:this.element.css(\"top\"),left:this.element.css(\"left\")}));this.element=this.element.parent().data(\"resizable\",this.element.data(\"resizable\"));this.elementIsWrapper=true;this.element.css({marginLeft:this.originalElement.css(\"marginLeft\"),marginTop:this.originalElement.css(\"marginTop\"),marginRight:this.originalElement.css(\"marginRight\"),marginBottom:this.originalElement.css(\"marginBottom\")});this.originalElement.css({marginLeft:0,marginTop:0,marginRight:0,marginBottom:0});this.originalResizeStyle=this.originalElement.css(\"resize\");this.originalElement.css(\"resize\",\"none\");this._proportionallyResizeElements.push(this.originalElement.css({position:\"static\",zoom:1,display:\"block\"}));this.originalElement.css({margin:this.originalElement.css(\"margin\")});this._proportionallyResize()}this.handles=k.handles||(!c(\".ui-resizable-handle\",this.element).length?\"e,s,se\":{n:\".ui-resizable-n\",e:\".ui-resizable-e\",s:\".ui-resizable-s\",w:\".ui-resizable-w\",se:\".ui-resizable-se\",sw:\".ui-resizable-sw\",ne:\".ui-resizable-ne\",nw:\".ui-resizable-nw\"});if(this.handles.constructor==String){if(this.handles==\"all\"){this.handles=\"n,e,s,w,se,sw,ne,nw\"}var l=this.handles.split(\",\");this.handles={};for(var g=0;g
');if(/sw|se|ne|nw/.test(j)){h.css({zIndex:++k.zIndex})}if(\"se\"==j){h.addClass(\"ui-icon ui-icon-gripsmall-diagonal-se\")}this.handles[j]=\".ui-resizable-\"+j;this.element.append(h)}}this._renderAxis=function(q){q=q||this.element;for(var n in this.handles){if(this.handles[n].constructor==String){this.handles[n]=c(this.handles[n],this.element).show()}if(this.elementIsWrapper&&this.originalElement[0].nodeName.match(/textarea|input|select|button/i)){var o=c(this.handles[n],this.element),p=0;p=/sw|ne|nw|se|n|s/.test(n)?o.outerHeight():o.outerWidth();var m=[\"padding\",/ne|nw|n/.test(n)?\"Top\":/se|sw|s/.test(n)?\"Bottom\":/^e$/.test(n)?\"Right\":\"Left\"].join(\"\");q.css(m,p);this._proportionallyResize()}if(!c(this.handles[n]).length){continue}}};this._renderAxis(this.element);this._handles=c(\".ui-resizable-handle\",this.element).disableSelection();this._handles.mouseover(function(){if(!f.resizing){if(this.className){var i=this.className.match(/ui-resizable-(se|sw|ne|nw|n|e|s|w)/i)}f.axis=i&&i[1]?i[1]:\"se\"}});if(k.autoHide){this._handles.hide();c(this.element).addClass(\"ui-resizable-autohide\").hover(function(){if(k.disabled){return}c(this).removeClass(\"ui-resizable-autohide\");f._handles.show()},function(){if(k.disabled){return}if(!f.resizing){c(this).addClass(\"ui-resizable-autohide\");f._handles.hide()}})}this._mouseInit()},destroy:function(){this._mouseDestroy();var e=function(g){c(g).removeClass(\"ui-resizable ui-resizable-disabled ui-resizable-resizing\").removeData(\"resizable\").unbind(\".resizable\").find(\".ui-resizable-handle\").remove()};if(this.elementIsWrapper){e(this.element);var f=this.element;f.after(this.originalElement.css({position:f.css(\"position\"),width:f.outerWidth(),height:f.outerHeight(),top:f.css(\"top\"),left:f.css(\"left\")})).remove()}this.originalElement.css(\"resize\",this.originalResizeStyle);e(this.originalElement);return this},_mouseCapture:function(f){var 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a(j){j=j||location.href;return\"#\"+j.replace(/^[^#]*#?(.*)$/,\"$1\")}$.fn[c]=function(j){return j?this.bind(c,j):this.trigger(c)};$.fn[c].delay=50;g[c]=$.extend(g[c],{setup:function(){if(d){return false}$(f.start)},teardown:function(){if(d){return false}$(f.stop)}});f=(function(){var j={},p,m=a(),k=function(q){return q},l=k,o=k;j.start=function(){p||n()};j.stop=function(){p&&clearTimeout(p);p=b};function n(){var r=a(),q=o(m);if(r!==m){l(m=r,q);$(e).trigger(c)}else{if(q!==m){location.href=location.href.replace(/#.*/,\"\")+q}}p=setTimeout(n,$.fn[c].delay)}$.browser.msie&&!d&&(function(){var q,r;j.start=function(){if(!q){r=$.fn[c].src;r=r&&r+a();q=$('\n\n\n
\n
\n
Modules
\n
\n
\n
Here is a list of all modules:
\n
[detail level 12]
\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
 ►Core featuresFeatures that implement in C++ the GLSL specification as closely as possible
 Common functionsProvides GLSL common functions
 Exponential functionsProvides GLSL exponential functions
 Geometric functionsThese operate on vectors as vectors, not component-wise
 Vector typesVector types of two to four components with an exhaustive set of operators
 Vector types with precision qualifiersVector types with precision qualifiers which may result in various precision in term of ULPs
 Matrix typesMatrix types of with C columns and R rows where C and R are values between 2 to 4 included
 Matrix types with precision qualifiersMatrix types with precision qualifiers which may result in various precision in term of ULPs
 Integer functionsProvides GLSL functions on integer types
 Matrix functionsProvides GLSL matrix functions
 Floating-Point Pack and Unpack FunctionsProvides GLSL functions to pack and unpack half, single and double-precision floating point values into more compact integer types
 Angle and Trigonometry FunctionsFunction parameters specified as angle are assumed to be in units of radians
 Vector Relational FunctionsRelational and equality operators (<, <=, >, >=, ==, !=) are defined to operate on scalars and produce scalar Boolean results
 ►Stable extensionsAdditional features not specified by GLSL specification
 GLM_EXT_matrix_clip_spaceDefines functions that generate clip space transformation matrices
 GLM_EXT_matrix_commonDefines functions for common matrix operations
 GLM_EXT_matrix_projectionFunctions that generate common projection transformation matrices
 GLM_EXT_matrix_relationalExposes comparison functions for matrix types that take a user defined epsilon values
 GLM_EXT_matrix_transformDefines functions that generate common transformation matrices
 GLM_EXT_quaternion_commonProvides common functions for quaternion types
 GLM_EXT_quaternion_doubleExposes double-precision floating point quaternion type
 GLM_EXT_quaternion_double_precisionExposes double-precision floating point quaternion type with various precision in term of ULPs
 GLM_EXT_quaternion_exponentialProvides exponential functions for quaternion types
 GLM_EXT_quaternion_floatExposes single-precision floating point quaternion type
 GLM_EXT_quaternion_float_precisionExposes single-precision floating point quaternion type with various precision in term of ULPs
 GLM_EXT_quaternion_geometricProvides geometric functions for quaternion types
 GLM_EXT_quaternion_relationalExposes comparison functions for quaternion types that take a user defined epsilon values
 GLM_EXT_quaternion_transformProvides transformation functions for quaternion types
 GLM_EXT_quaternion_trigonometricProvides trigonometric functions for quaternion types
 GLM_EXT_scalar_commonExposes min and max functions for 3 to 4 scalar parameters
 GLM_EXT_scalar_constantsProvides a list of constants and precomputed useful values
 GLM_EXT_scalar_int_sizedExposes sized signed integer scalar types
 GLM_EXT_scalar_integerInclude <glm/ext/scalar_integer.hpp> to use the features of this extension
 GLM_EXT_scalar_relationalExposes comparison functions for scalar types that take a user defined epsilon values
 GLM_EXT_scalar_uint_sizedExposes sized unsigned integer scalar types
 GLM_EXT_scalar_ulpAllow the measurement of the accuracy of a function against a reference implementation
 GLM_EXT_vector_bool1Exposes bvec1 vector type
 GLM_EXT_vector_bool1_precisionExposes highp_bvec1, mediump_bvec1 and lowp_bvec1 types
 GLM_EXT_vector_commonExposes min and max functions for 3 to 4 vector parameters
 GLM_EXT_vector_double1Exposes double-precision floating point vector type with one component
 GLM_EXT_vector_double1_precisionExposes highp_dvec1, mediump_dvec1 and lowp_dvec1 types
 GLM_EXT_vector_float1Exposes single-precision floating point vector type with one component
 GLM_EXT_vector_float1_precisionExposes highp_vec1, mediump_vec1 and lowp_vec1 types
 GLM_EXT_vector_int1Exposes ivec1 vector type
 GLM_EXT_vector_int1_precisionExposes highp_ivec1, mediump_ivec1 and lowp_ivec1 types
 GLM_EXT_vector_integerInclude <glm/ext/vector_integer.hpp> to use the features of this extension
 GLM_EXT_vector_relationalExposes comparison functions for vector types that take a user defined epsilon values
 GLM_EXT_vector_uint1Exposes uvec1 vector type
 GLM_EXT_vector_uint1_precisionExposes highp_uvec1, mediump_uvec1 and lowp_uvec1 types
 GLM_EXT_vector_ulpAllow the measurement of the accuracy of a function against a reference implementation
 ►Recommended extensionsAdditional features not specified by GLSL specification
 GLM_GTC_bitfieldInclude <glm/gtc/bitfield.hpp> to use the features of this extension
 GLM_GTC_color_spaceInclude <glm/gtc/color_space.hpp> to use the features of this extension
 GLM_GTC_constantsInclude <glm/gtc/constants.hpp> to use the features of this extension
 GLM_GTC_epsilonInclude <glm/gtc/epsilon.hpp> to use the features of this extension
 GLM_GTC_integerInclude <glm/gtc/integer.hpp> to use the features of this extension
 GLM_GTC_matrix_accessInclude <glm/gtc/matrix_access.hpp> to use the features of this extension
 GLM_GTC_matrix_integerInclude <glm/gtc/matrix_integer.hpp> to use the features of this extension
 GLM_GTC_matrix_inverseInclude <glm/gtc/matrix_integer.hpp> to use the features of this extension
 GLM_GTC_matrix_transformInclude <glm/gtc/matrix_transform.hpp> to use the features of this extension
 GLM_GTC_noiseInclude <glm/gtc/noise.hpp> to use the features of this extension
 GLM_GTC_packingInclude <glm/gtc/packing.hpp> to use the features of this extension
 GLM_GTC_quaternionInclude <glm/gtc/quaternion.hpp> to use the features of this extension
 GLM_GTC_randomInclude <glm/gtc/random.hpp> to use the features of this extension
 GLM_GTC_reciprocalInclude <glm/gtc/reciprocal.hpp> to use the features of this extension
 GLM_GTC_roundInclude <glm/gtc/round.hpp> to use the features of this extension
 GLM_GTC_type_alignedInclude <glm/gtc/type_aligned.hpp> to use the features of this extension
 GLM_GTC_type_precisionInclude <glm/gtc/type_precision.hpp> to use the features of this extension
 GLM_GTC_type_ptrInclude <glm/gtc/type_ptr.hpp> to use the features of this extension
 GLM_GTC_ulpInclude <glm/gtc/ulp.hpp> to use the features of this extension
 GLM_GTC_vec1Include <glm/gtc/vec1.hpp> to use the features of this extension
 ►Experimental extensionsExperimental features not specified by GLSL specification
 GLM_GTX_associated_min_maxInclude <glm/gtx/associated_min_max.hpp> to use the features of this extension
 GLM_GTX_bitInclude <glm/gtx/bit.hpp> to use the features of this extension
 GLM_GTX_closest_pointInclude <glm/gtx/closest_point.hpp> to use the features of this extension
 GLM_GTX_color_encodingInclude <glm/gtx/color_encoding.hpp> to use the features of this extension
 GLM_GTX_color_spaceInclude <glm/gtx/color_space.hpp> to use the features of this extension
 GLM_GTX_color_space_YCoCgInclude <glm/gtx/color_space_YCoCg.hpp> to use the features of this extension
 GLM_GTX_commonInclude <glm/gtx/common.hpp> to use the features of this extension
 GLM_GTX_compatibilityInclude <glm/gtx/compatibility.hpp> to use the features of this extension
 GLM_GTX_component_wiseInclude <glm/gtx/component_wise.hpp> to use the features of this extension
 GLM_GTX_dual_quaternionInclude <glm/gtx/dual_quaternion.hpp> to use the features of this extension
 GLM_GTX_easingInclude <glm/gtx/easing.hpp> to use the features of this extension
 GLM_GTX_euler_anglesInclude <glm/gtx/euler_angles.hpp> to use the features of this extension
 GLM_GTX_extendInclude <glm/gtx/extend.hpp> to use the features of this extension
 GLM_GTX_extented_min_maxInclude <glm/gtx/extented_min_max.hpp> to use the features of this extension
 GLM_GTX_exterior_productInclude <glm/gtx/exterior_product.hpp> to use the features of this extension
 GLM_GTX_fast_exponentialInclude <glm/gtx/fast_exponential.hpp> to use the features of this extension
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Alternatively, this can be done per class using the\n# \\nosubgrouping command.\n# The default value is: YES.\n\nSUBGROUPING = NO\n\n# When the INLINE_GROUPED_CLASSES tag is set to YES, classes, structs and unions\n# are shown inside the group in which they are included (e.g. using \\ingroup)\n# instead of on a separate page (for HTML and Man pages) or section (for LaTeX\n# and RTF).\n#\n# Note that this feature does not work in combination with\n# SEPARATE_MEMBER_PAGES.\n# The default value is: NO.\n\nINLINE_GROUPED_CLASSES = NO\n\n# When the INLINE_SIMPLE_STRUCTS tag is set to YES, structs, classes, and unions\n# with only public data fields or simple typedef fields will be shown inline in\n# the documentation of the scope in which they are defined (i.e. file,\n# namespace, or group documentation), provided this scope is documented. If set\n# to NO, structs, classes, and unions are shown on a separate page (for HTML and\n# Man pages) or section (for LaTeX and RTF).\n# The default value is: NO.\n\nINLINE_SIMPLE_STRUCTS = NO\n\n# When TYPEDEF_HIDES_STRUCT tag is enabled, a typedef of a struct, union, or\n# enum is documented as struct, union, or enum with the name of the typedef. So\n# typedef struct TypeS {} TypeT, will appear in the documentation as a struct\n# with name TypeT. When disabled the typedef will appear as a member of a file,\n# namespace, or class. And the struct will be named TypeS. This can typically be\n# useful for C code in case the coding convention dictates that all compound\n# types are typedef'ed and only the typedef is referenced, never the tag name.\n# The default value is: NO.\n\nTYPEDEF_HIDES_STRUCT = NO\n\n# The size of the symbol lookup cache can be set using LOOKUP_CACHE_SIZE. This\n# cache is used to resolve symbols given their name and scope. Since this can be\n# an expensive process and often the same symbol appears multiple times in the\n# code, doxygen keeps a cache of pre-resolved symbols. If the cache is too small\n# doxygen will become slower. If the cache is too large, memory is wasted. The\n# cache size is given by this formula: 2^(16+LOOKUP_CACHE_SIZE). The valid range\n# is 0..9, the default is 0, corresponding to a cache size of 2^16=65536\n# symbols. At the end of a run doxygen will report the cache usage and suggest\n# the optimal cache size from a speed point of view.\n# Minimum value: 0, maximum value: 9, default value: 0.\n\nLOOKUP_CACHE_SIZE = 0\n\n#---------------------------------------------------------------------------\n# Build related configuration options\n#---------------------------------------------------------------------------\n\n# If the EXTRACT_ALL tag is set to YES, doxygen will assume all entities in\n# documentation are documented, even if no documentation was available. Private\n# class members and static file members will be hidden unless the\n# EXTRACT_PRIVATE respectively EXTRACT_STATIC tags are set to YES.\n# Note: This will also disable the warnings about undocumented members that are\n# normally produced when WARNINGS is set to YES.\n# The default value is: NO.\n\nEXTRACT_ALL = NO\n\n# If the EXTRACT_PRIVATE tag is set to YES, all private members of a class will\n# be included in the documentation.\n# The default value is: NO.\n\nEXTRACT_PRIVATE = NO\n\n# If the EXTRACT_PACKAGE tag is set to YES, all members with package or internal\n# scope will be included in the documentation.\n# The default value is: NO.\n\nEXTRACT_PACKAGE = NO\n\n# If the EXTRACT_STATIC tag is set to YES, all static members of a file will be\n# included in the documentation.\n# The default value is: NO.\n\nEXTRACT_STATIC = YES\n\n# If the EXTRACT_LOCAL_CLASSES tag is set to YES, classes (and structs) defined\n# locally in source files will be included in the documentation. If set to NO,\n# only classes defined in header files are included. Does not have any effect\n# for Java sources.\n# The default value is: YES.\n\nEXTRACT_LOCAL_CLASSES = NO\n\n# This flag is only useful for Objective-C code. If set to YES, local methods,\n# which are defined in the implementation section but not in the interface are\n# included in the documentation. If set to NO, only methods in the interface are\n# included.\n# The default value is: NO.\n\nEXTRACT_LOCAL_METHODS = NO\n\n# If this flag is set to YES, the members of anonymous namespaces will be\n# extracted and appear in the documentation as a namespace called\n# 'anonymous_namespace{file}', where file will be replaced with the base name of\n# the file that contains the anonymous namespace. By default anonymous namespace\n# are hidden.\n# The default value is: NO.\n\nEXTRACT_ANON_NSPACES = NO\n\n# If the HIDE_UNDOC_MEMBERS tag is set to YES, doxygen will hide all\n# undocumented members inside documented classes or files. If set to NO these\n# members will be included in the various overviews, but no documentation\n# section is generated. This option has no effect if EXTRACT_ALL is enabled.\n# The default value is: NO.\n\nHIDE_UNDOC_MEMBERS = YES\n\n# If the HIDE_UNDOC_CLASSES tag is set to YES, doxygen will hide all\n# undocumented classes that are normally visible in the class hierarchy. If set\n# to NO, these classes will be included in the various overviews. This option\n# has no effect if EXTRACT_ALL is enabled.\n# The default value is: NO.\n\nHIDE_UNDOC_CLASSES = YES\n\n# If the HIDE_FRIEND_COMPOUNDS tag is set to YES, doxygen will hide all friend\n# (class|struct|union) declarations. If set to NO, these declarations will be\n# included in the documentation.\n# The default value is: NO.\n\nHIDE_FRIEND_COMPOUNDS = YES\n\n# If the HIDE_IN_BODY_DOCS tag is set to YES, doxygen will hide any\n# documentation blocks found inside the body of a function. If set to NO, these\n# blocks will be appended to the function's detailed documentation block.\n# The default value is: NO.\n\nHIDE_IN_BODY_DOCS = YES\n\n# The INTERNAL_DOCS tag determines if documentation that is typed after a\n# \\internal command is included. If the tag is set to NO then the documentation\n# will be excluded. Set it to YES to include the internal documentation.\n# The default value is: NO.\n\nINTERNAL_DOCS = NO\n\n# If the CASE_SENSE_NAMES tag is set to NO then doxygen will only generate file\n# names in lower-case letters. If set to YES, upper-case letters are also\n# allowed. This is useful if you have classes or files whose names only differ\n# in case and if your file system supports case sensitive file names. Windows\n# and Mac users are advised to set this option to NO.\n# The default value is: system dependent.\n\nCASE_SENSE_NAMES = YES\n\n# If the HIDE_SCOPE_NAMES tag is set to NO then doxygen will show members with\n# their full class and namespace scopes in the documentation. If set to YES, the\n# scope will be hidden.\n# The default value is: NO.\n\nHIDE_SCOPE_NAMES = YES\n\n# If the HIDE_COMPOUND_REFERENCE tag is set to NO (default) then doxygen will\n# append additional text to a page's title, such as Class Reference. If set to\n# YES the compound reference will be hidden.\n# The default value is: NO.\n\nHIDE_COMPOUND_REFERENCE= NO\n\n# If the SHOW_INCLUDE_FILES tag is set to YES then doxygen will put a list of\n# the files that are included by a file in the documentation of that file.\n# The default value is: YES.\n\nSHOW_INCLUDE_FILES = NO\n\n# If the SHOW_GROUPED_MEMB_INC tag is set to YES then Doxygen will add for each\n# grouped member an include statement to the documentation, telling the reader\n# which file to include in order to use the member.\n# The default value is: NO.\n\nSHOW_GROUPED_MEMB_INC = NO\n\n# If the FORCE_LOCAL_INCLUDES tag is set to YES then doxygen will list include\n# files with double quotes in the documentation rather than with sharp brackets.\n# The default value is: NO.\n\nFORCE_LOCAL_INCLUDES = NO\n\n# If the INLINE_INFO tag is set to YES then a tag [inline] is inserted in the\n# documentation for inline members.\n# The default value is: YES.\n\nINLINE_INFO = NO\n\n# If the SORT_MEMBER_DOCS tag is set to YES then doxygen will sort the\n# (detailed) documentation of file and class members alphabetically by member\n# name. If set to NO, the members will appear in declaration order.\n# The default value is: YES.\n\nSORT_MEMBER_DOCS = YES\n\n# If the SORT_BRIEF_DOCS tag is set to YES then doxygen will sort the brief\n# descriptions of file, namespace and class members alphabetically by member\n# name. If set to NO, the members will appear in declaration order. Note that\n# this will also influence the order of the classes in the class list.\n# The default value is: NO.\n\nSORT_BRIEF_DOCS = YES\n\n# If the SORT_MEMBERS_CTORS_1ST tag is set to YES then doxygen will sort the\n# (brief and detailed) documentation of class members so that constructors and\n# destructors are listed first. If set to NO the constructors will appear in the\n# respective orders defined by SORT_BRIEF_DOCS and SORT_MEMBER_DOCS.\n# Note: If SORT_BRIEF_DOCS is set to NO this option is ignored for sorting brief\n# member documentation.\n# Note: If SORT_MEMBER_DOCS is set to NO this option is ignored for sorting\n# detailed member documentation.\n# The default value is: NO.\n\nSORT_MEMBERS_CTORS_1ST = NO\n\n# If the SORT_GROUP_NAMES tag is set to YES then doxygen will sort the hierarchy\n# of group names into alphabetical order. If set to NO the group names will\n# appear in their defined order.\n# The default value is: NO.\n\nSORT_GROUP_NAMES = NO\n\n# If the SORT_BY_SCOPE_NAME tag is set to YES, the class list will be sorted by\n# fully-qualified names, including namespaces. If set to NO, the class list will\n# be sorted only by class name, not including the namespace part.\n# Note: This option is not very useful if HIDE_SCOPE_NAMES is set to YES.\n# Note: This option applies only to the class list, not to the alphabetical\n# list.\n# The default value is: NO.\n\nSORT_BY_SCOPE_NAME = YES\n\n# If the STRICT_PROTO_MATCHING option is enabled and doxygen fails to do proper\n# type resolution of all parameters of a function it will reject a match between\n# the prototype and the implementation of a member function even if there is\n# only one candidate or it is obvious which candidate to choose by doing a\n# simple string match. By disabling STRICT_PROTO_MATCHING doxygen will still\n# accept a match between prototype and implementation in such cases.\n# The default value is: NO.\n\nSTRICT_PROTO_MATCHING = NO\n\n# The GENERATE_TODOLIST tag can be used to enable (YES) or disable (NO) the todo\n# list. This list is created by putting \\todo commands in the documentation.\n# The default value is: YES.\n\nGENERATE_TODOLIST = YES\n\n# The GENERATE_TESTLIST tag can be used to enable (YES) or disable (NO) the test\n# list. This list is created by putting \\test commands in the documentation.\n# The default value is: YES.\n\nGENERATE_TESTLIST = YES\n\n# The GENERATE_BUGLIST tag can be used to enable (YES) or disable (NO) the bug\n# list. This list is created by putting \\bug commands in the documentation.\n# The default value is: YES.\n\nGENERATE_BUGLIST = YES\n\n# The GENERATE_DEPRECATEDLIST tag can be used to enable (YES) or disable (NO)\n# the deprecated list. This list is created by putting \\deprecated commands in\n# the documentation.\n# The default value is: YES.\n\nGENERATE_DEPRECATEDLIST= YES\n\n# The ENABLED_SECTIONS tag can be used to enable conditional documentation\n# sections, marked by \\if ... \\endif and \\cond \n# ... \\endcond blocks.\n\nENABLED_SECTIONS = \n\n# The MAX_INITIALIZER_LINES tag determines the maximum number of lines that the\n# initial value of a variable or macro / define can have for it to appear in the\n# documentation. If the initializer consists of more lines than specified here\n# it will be hidden. Use a value of 0 to hide initializers completely. The\n# appearance of the value of individual variables and macros / defines can be\n# controlled using \\showinitializer or \\hideinitializer command in the\n# documentation regardless of this setting.\n# Minimum value: 0, maximum value: 10000, default value: 30.\n\nMAX_INITIALIZER_LINES = 30\n\n# Set the SHOW_USED_FILES tag to NO to disable the list of files generated at\n# the bottom of the documentation of classes and structs. If set to YES, the\n# list will mention the files that were used to generate the documentation.\n# The default value is: YES.\n\nSHOW_USED_FILES = NO\n\n# Set the SHOW_FILES tag to NO to disable the generation of the Files page. This\n# will remove the Files entry from the Quick Index and from the Folder Tree View\n# (if specified).\n# The default value is: YES.\n\nSHOW_FILES = YES\n\n# Set the SHOW_NAMESPACES tag to NO to disable the generation of the Namespaces\n# page. This will remove the Namespaces entry from the Quick Index and from the\n# Folder Tree View (if specified).\n# The default value is: YES.\n\nSHOW_NAMESPACES = YES\n\n# The FILE_VERSION_FILTER tag can be used to specify a program or script that\n# doxygen should invoke to get the current version for each file (typically from\n# the version control system). Doxygen will invoke the program by executing (via\n# popen()) the command command input-file, where command is the value of the\n# FILE_VERSION_FILTER tag, and input-file is the name of an input file provided\n# by doxygen. Whatever the program writes to standard output is used as the file\n# version. For an example see the documentation.\n\nFILE_VERSION_FILTER = \n\n# The LAYOUT_FILE tag can be used to specify a layout file which will be parsed\n# by doxygen. The layout file controls the global structure of the generated\n# output files in an output format independent way. To create the layout file\n# that represents doxygen's defaults, run doxygen with the -l option. You can\n# optionally specify a file name after the option, if omitted DoxygenLayout.xml\n# will be used as the name of the layout file.\n#\n# Note that if you run doxygen from a directory containing a file called\n# DoxygenLayout.xml, doxygen will parse it automatically even if the LAYOUT_FILE\n# tag is left empty.\n\nLAYOUT_FILE = \n\n# The CITE_BIB_FILES tag can be used to specify one or more bib files containing\n# the reference definitions. This must be a list of .bib files. The .bib\n# extension is automatically appended if omitted. This requires the bibtex tool\n# to be installed. See also http://en.wikipedia.org/wiki/BibTeX for more info.\n# For LaTeX the style of the bibliography can be controlled using\n# LATEX_BIB_STYLE. To use this feature you need bibtex and perl available in the\n# search path. See also \\cite for info how to create references.\n\nCITE_BIB_FILES = \n\n#---------------------------------------------------------------------------\n# Configuration options related to warning and progress messages\n#---------------------------------------------------------------------------\n\n# The QUIET tag can be used to turn on/off the messages that are generated to\n# standard output by doxygen. If QUIET is set to YES this implies that the\n# messages are off.\n# The default value is: NO.\n\nQUIET = NO\n\n# The WARNINGS tag can be used to turn on/off the warning messages that are\n# generated to standard error (stderr) by doxygen. If WARNINGS is set to YES\n# this implies that the warnings are on.\n#\n# Tip: Turn warnings on while writing the documentation.\n# The default value is: YES.\n\nWARNINGS = YES\n\n# If the WARN_IF_UNDOCUMENTED tag is set to YES then doxygen will generate\n# warnings for undocumented members. If EXTRACT_ALL is set to YES then this flag\n# will automatically be disabled.\n# The default value is: YES.\n\nWARN_IF_UNDOCUMENTED = YES\n\n# If the WARN_IF_DOC_ERROR tag is set to YES, doxygen will generate warnings for\n# potential errors in the documentation, such as not documenting some parameters\n# in a documented function, or documenting parameters that don't exist or using\n# markup commands wrongly.\n# The default value is: YES.\n\nWARN_IF_DOC_ERROR = YES\n\n# This WARN_NO_PARAMDOC option can be enabled to get warnings for functions that\n# are documented, but have no documentation for their parameters or return\n# value. If set to NO, doxygen will only warn about wrong or incomplete\n# parameter documentation, but not about the absence of documentation.\n# The default value is: NO.\n\nWARN_NO_PARAMDOC = NO\n\n# The WARN_FORMAT tag determines the format of the warning messages that doxygen\n# can produce. The string should contain the $file, $line, and $text tags, which\n# will be replaced by the file and line number from which the warning originated\n# and the warning text. Optionally the format may contain $version, which will\n# be replaced by the version of the file (if it could be obtained via\n# FILE_VERSION_FILTER)\n# The default value is: $file:$line: $text.\n\nWARN_FORMAT = \"$file:$line: $text\"\n\n# The WARN_LOGFILE tag can be used to specify a file to which warning and error\n# messages should be written. If left blank the output is written to standard\n# error (stderr).\n\nWARN_LOGFILE = \n\n#---------------------------------------------------------------------------\n# Configuration options related to the input files\n#---------------------------------------------------------------------------\n\n# The INPUT tag is used to specify the files and/or directories that contain\n# documented source files. You may enter file names like myfile.cpp or\n# directories like /usr/src/myproject. Separate the files or directories with\n# spaces. See also FILE_PATTERNS and EXTENSION_MAPPING\n# Note: If this tag is empty the current directory is searched.\n\nINPUT = ../glm \\\n .\n\n# This tag can be used to specify the character encoding of the source files\n# that doxygen parses. Internally doxygen uses the UTF-8 encoding. Doxygen uses\n# libiconv (or the iconv built into libc) for the transcoding. See the libiconv\n# documentation (see: http://www.gnu.org/software/libiconv) for the list of\n# possible encodings.\n# The default value is: UTF-8.\n\nINPUT_ENCODING = UTF-8\n\n# If the value of the INPUT tag contains directories, you can use the\n# FILE_PATTERNS tag to specify one or more wildcard patterns (like *.cpp and\n# *.h) to filter out the source-files in the directories.\n#\n# Note that for custom extensions or not directly supported extensions you also\n# need to set EXTENSION_MAPPING for the extension otherwise the files are not\n# read by doxygen.\n#\n# If left blank the following patterns are tested:*.c, *.cc, *.cxx, *.cpp,\n# *.c++, *.java, *.ii, *.ixx, *.ipp, *.i++, *.inl, *.idl, *.ddl, *.odl, *.h,\n# *.hh, *.hxx, *.hpp, *.h++, *.cs, *.d, *.php, *.php4, *.php5, *.phtml, *.inc,\n# *.m, *.markdown, *.md, *.mm, *.dox, *.py, *.f90, *.f, *.for, *.tcl, *.vhd,\n# *.vhdl, *.ucf, *.qsf, *.as and *.js.\n\nFILE_PATTERNS = *.hpp \\\n *.doxy\n\n# The RECURSIVE tag can be used to specify whether or not subdirectories should\n# be searched for input files as well.\n# The default value is: NO.\n\nRECURSIVE = YES\n\n# The EXCLUDE tag can be used to specify files and/or directories that should be\n# excluded from the INPUT source files. This way you can easily exclude a\n# subdirectory from a directory tree whose root is specified with the INPUT tag.\n#\n# Note that relative paths are relative to the directory from which doxygen is\n# run.\n\nEXCLUDE = \n\n# The EXCLUDE_SYMLINKS tag can be used to select whether or not files or\n# directories that are symbolic links (a Unix file system feature) are excluded\n# from the input.\n# The default value is: NO.\n\nEXCLUDE_SYMLINKS = NO\n\n# If the value of the INPUT tag contains directories, you can use the\n# EXCLUDE_PATTERNS tag to specify one or more wildcard patterns to exclude\n# certain files from those directories.\n#\n# Note that the wildcards are matched against the file with absolute path, so to\n# exclude all test directories for example use the pattern */test/*\n\nEXCLUDE_PATTERNS = \n\n# The EXCLUDE_SYMBOLS tag can be used to specify one or more symbol names\n# (namespaces, classes, functions, etc.) that should be excluded from the\n# output. The symbol name can be a fully qualified name, a word, or if the\n# wildcard * is used, a substring. Examples: ANamespace, AClass,\n# AClass::ANamespace, ANamespace::*Test\n#\n# Note that the wildcards are matched against the file with absolute path, so to\n# exclude all test directories use the pattern */test/*\n\nEXCLUDE_SYMBOLS = \n\n# The EXAMPLE_PATH tag can be used to specify one or more files or directories\n# that contain example code fragments that are included (see the \\include\n# command).\n\nEXAMPLE_PATH = \n\n# If the value of the EXAMPLE_PATH tag contains directories, you can use the\n# EXAMPLE_PATTERNS tag to specify one or more wildcard pattern (like *.cpp and\n# *.h) to filter out the source-files in the directories. If left blank all\n# files are included.\n\nEXAMPLE_PATTERNS = *\n\n# If the EXAMPLE_RECURSIVE tag is set to YES then subdirectories will be\n# searched for input files to be used with the \\include or \\dontinclude commands\n# irrespective of the value of the RECURSIVE tag.\n# The default value is: NO.\n\nEXAMPLE_RECURSIVE = NO\n\n# The IMAGE_PATH tag can be used to specify one or more files or directories\n# that contain images that are to be included in the documentation (see the\n# \\image command).\n\nIMAGE_PATH = \n\n# The INPUT_FILTER tag can be used to specify a program that doxygen should\n# invoke to filter for each input file. Doxygen will invoke the filter program\n# by executing (via popen()) the command:\n#\n# \n#\n# where is the value of the INPUT_FILTER tag, and is the\n# name of an input file. Doxygen will then use the output that the filter\n# program writes to standard output. If FILTER_PATTERNS is specified, this tag\n# will be ignored.\n#\n# Note that the filter must not add or remove lines; it is applied before the\n# code is scanned, but not when the output code is generated. If lines are added\n# or removed, the anchors will not be placed correctly.\n\nINPUT_FILTER = \n\n# The FILTER_PATTERNS tag can be used to specify filters on a per file pattern\n# basis. Doxygen will compare the file name with each pattern and apply the\n# filter if there is a match. The filters are a list of the form: pattern=filter\n# (like *.cpp=my_cpp_filter). See INPUT_FILTER for further information on how\n# filters are used. If the FILTER_PATTERNS tag is empty or if none of the\n# patterns match the file name, INPUT_FILTER is applied.\n\nFILTER_PATTERNS = \n\n# If the FILTER_SOURCE_FILES tag is set to YES, the input filter (if set using\n# INPUT_FILTER) will also be used to filter the input files that are used for\n# producing the source files to browse (i.e. when SOURCE_BROWSER is set to YES).\n# The default value is: NO.\n\nFILTER_SOURCE_FILES = NO\n\n# The FILTER_SOURCE_PATTERNS tag can be used to specify source filters per file\n# pattern. A pattern will override the setting for FILTER_PATTERN (if any) and\n# it is also possible to disable source filtering for a specific pattern using\n# *.ext= (so without naming a filter).\n# This tag requires that the tag FILTER_SOURCE_FILES is set to YES.\n\nFILTER_SOURCE_PATTERNS = \n\n# If the USE_MDFILE_AS_MAINPAGE tag refers to the name of a markdown file that\n# is part of the input, its contents will be placed on the main page\n# (index.html). This can be useful if you have a project on for instance GitHub\n# and want to reuse the introduction page also for the doxygen output.\n\nUSE_MDFILE_AS_MAINPAGE = \n\n#---------------------------------------------------------------------------\n# Configuration options related to source browsing\n#---------------------------------------------------------------------------\n\n# If the SOURCE_BROWSER tag is set to YES then a list of source files will be\n# generated. Documented entities will be cross-referenced with these sources.\n#\n# Note: To get rid of all source code in the generated output, make sure that\n# also VERBATIM_HEADERS is set to NO.\n# The default value is: NO.\n\nSOURCE_BROWSER = YES\n\n# Setting the INLINE_SOURCES tag to YES will include the body of functions,\n# classes and enums directly into the documentation.\n# The default value is: NO.\n\nINLINE_SOURCES = NO\n\n# Setting the STRIP_CODE_COMMENTS tag to YES will instruct doxygen to hide any\n# special comment blocks from generated source code fragments. Normal C, C++ and\n# Fortran comments will always remain visible.\n# The default value is: YES.\n\nSTRIP_CODE_COMMENTS = YES\n\n# If the REFERENCED_BY_RELATION tag is set to YES then for each documented\n# function all documented functions referencing it will be listed.\n# The default value is: NO.\n\nREFERENCED_BY_RELATION = YES\n\n# If the REFERENCES_RELATION tag is set to YES then for each documented function\n# all documented entities called/used by that function will be listed.\n# The default value is: NO.\n\nREFERENCES_RELATION = YES\n\n# If the REFERENCES_LINK_SOURCE tag is set to YES and SOURCE_BROWSER tag is set\n# to YES then the hyperlinks from functions in REFERENCES_RELATION and\n# REFERENCED_BY_RELATION lists will link to the source code. Otherwise they will\n# link to the documentation.\n# The default value is: YES.\n\nREFERENCES_LINK_SOURCE = YES\n\n# If SOURCE_TOOLTIPS is enabled (the default) then hovering a hyperlink in the\n# source code will show a tooltip with additional information such as prototype,\n# brief description and links to the definition and documentation. Since this\n# will make the HTML file larger and loading of large files a bit slower, you\n# can opt to disable this feature.\n# The default value is: YES.\n# This tag requires that the tag SOURCE_BROWSER is set to YES.\n\nSOURCE_TOOLTIPS = YES\n\n# If the USE_HTAGS tag is set to YES then the references to source code will\n# point to the HTML generated by the htags(1) tool instead of doxygen built-in\n# source browser. The htags tool is part of GNU's global source tagging system\n# (see http://www.gnu.org/software/global/global.html). You will need version\n# 4.8.6 or higher.\n#\n# To use it do the following:\n# - Install the latest version of global\n# - Enable SOURCE_BROWSER and USE_HTAGS in the config file\n# - Make sure the INPUT points to the root of the source tree\n# - Run doxygen as normal\n#\n# Doxygen will invoke htags (and that will in turn invoke gtags), so these\n# tools must be available from the command line (i.e. in the search path).\n#\n# The result: instead of the source browser generated by doxygen, the links to\n# source code will now point to the output of htags.\n# The default value is: NO.\n# This tag requires that the tag SOURCE_BROWSER is set to YES.\n\nUSE_HTAGS = NO\n\n# If the VERBATIM_HEADERS tag is set the YES then doxygen will generate a\n# verbatim copy of the header file for each class for which an include is\n# specified. Set to NO to disable this.\n# See also: Section \\class.\n# The default value is: YES.\n\nVERBATIM_HEADERS = YES\n\n# If the CLANG_ASSISTED_PARSING tag is set to YES then doxygen will use the\n# clang parser (see: http://clang.llvm.org/) for more accurate parsing at the\n# cost of reduced performance. This can be particularly helpful with template\n# rich C++ code for which doxygen's built-in parser lacks the necessary type\n# information.\n# Note: The availability of this option depends on whether or not doxygen was\n# compiled with the --with-libclang option.\n# The default value is: NO.\n\nCLANG_ASSISTED_PARSING = NO\n\n# If clang assisted parsing is enabled you can provide the compiler with command\n# line options that you would normally use when invoking the compiler. Note that\n# the include paths will already be set by doxygen for the files and directories\n# specified with INPUT and INCLUDE_PATH.\n# This tag requires that the tag CLANG_ASSISTED_PARSING is set to YES.\n\nCLANG_OPTIONS = \n\n#---------------------------------------------------------------------------\n# Configuration options related to the alphabetical class index\n#---------------------------------------------------------------------------\n\n# If the ALPHABETICAL_INDEX tag is set to YES, an alphabetical index of all\n# compounds will be generated. Enable this if the project contains a lot of\n# classes, structs, unions or interfaces.\n# The default value is: YES.\n\nALPHABETICAL_INDEX = NO\n\n# The COLS_IN_ALPHA_INDEX tag can be used to specify the number of columns in\n# which the alphabetical index list will be split.\n# Minimum value: 1, maximum value: 20, default value: 5.\n# This tag requires that the tag ALPHABETICAL_INDEX is set to YES.\n\nCOLS_IN_ALPHA_INDEX = 5\n\n# In case all classes in a project start with a common prefix, all classes will\n# be put under the same header in the alphabetical index. The IGNORE_PREFIX tag\n# can be used to specify a prefix (or a list of prefixes) that should be ignored\n# while generating the index headers.\n# This tag requires that the tag ALPHABETICAL_INDEX is set to YES.\n\nIGNORE_PREFIX = \n\n#---------------------------------------------------------------------------\n# Configuration options related to the HTML output\n#---------------------------------------------------------------------------\n\n# If the GENERATE_HTML tag is set to YES, doxygen will generate HTML output\n# The default value is: YES.\n\nGENERATE_HTML = YES\n\n# The HTML_OUTPUT tag is used to specify where the HTML docs will be put. If a\n# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of\n# it.\n# The default directory is: html.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nHTML_OUTPUT = html\n\n# The HTML_FILE_EXTENSION tag can be used to specify the file extension for each\n# generated HTML page (for example: .htm, .php, .asp).\n# The default value is: .html.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nHTML_FILE_EXTENSION = .html\n\n# The HTML_HEADER tag can be used to specify a user-defined HTML header file for\n# each generated HTML page. If the tag is left blank doxygen will generate a\n# standard header.\n#\n# To get valid HTML the header file that includes any scripts and style sheets\n# that doxygen needs, which is dependent on the configuration options used (e.g.\n# the setting GENERATE_TREEVIEW). It is highly recommended to start with a\n# default header using\n# doxygen -w html new_header.html new_footer.html new_stylesheet.css\n# YourConfigFile\n# and then modify the file new_header.html. See also section \"Doxygen usage\"\n# for information on how to generate the default header that doxygen normally\n# uses.\n# Note: The header is subject to change so you typically have to regenerate the\n# default header when upgrading to a newer version of doxygen. For a description\n# of the possible markers and block names see the documentation.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nHTML_HEADER = \n\n# The HTML_FOOTER tag can be used to specify a user-defined HTML footer for each\n# generated HTML page. If the tag is left blank doxygen will generate a standard\n# footer. See HTML_HEADER for more information on how to generate a default\n# footer and what special commands can be used inside the footer. See also\n# section \"Doxygen usage\" for information on how to generate the default footer\n# that doxygen normally uses.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nHTML_FOOTER = \n\n# The HTML_STYLESHEET tag can be used to specify a user-defined cascading style\n# sheet that is used by each HTML page. It can be used to fine-tune the look of\n# the HTML output. If left blank doxygen will generate a default style sheet.\n# See also section \"Doxygen usage\" for information on how to generate the style\n# sheet that doxygen normally uses.\n# Note: It is recommended to use HTML_EXTRA_STYLESHEET instead of this tag, as\n# it is more robust and this tag (HTML_STYLESHEET) will in the future become\n# obsolete.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nHTML_STYLESHEET = \n\n# The HTML_EXTRA_STYLESHEET tag can be used to specify additional user-defined\n# cascading style sheets that are included after the standard style sheets\n# created by doxygen. Using this option one can overrule certain style aspects.\n# This is preferred over using HTML_STYLESHEET since it does not replace the\n# standard style sheet and is therefore more robust against future updates.\n# Doxygen will copy the style sheet files to the output directory.\n# Note: The order of the extra style sheet files is of importance (e.g. the last\n# style sheet in the list overrules the setting of the previous ones in the\n# list). For an example see the documentation.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nHTML_EXTRA_STYLESHEET = \n\n# The HTML_EXTRA_FILES tag can be used to specify one or more extra images or\n# other source files which should be copied to the HTML output directory. Note\n# that these files will be copied to the base HTML output directory. Use the\n# $relpath^ marker in the HTML_HEADER and/or HTML_FOOTER files to load these\n# files. In the HTML_STYLESHEET file, use the file name only. Also note that the\n# files will be copied as-is; there are no commands or markers available.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nHTML_EXTRA_FILES = \n\n# The HTML_COLORSTYLE_HUE tag controls the color of the HTML output. Doxygen\n# will adjust the colors in the style sheet and background images according to\n# this color. Hue is specified as an angle on a colorwheel, see\n# http://en.wikipedia.org/wiki/Hue for more information. For instance the value\n# 0 represents red, 60 is yellow, 120 is green, 180 is cyan, 240 is blue, 300\n# purple, and 360 is red again.\n# Minimum value: 0, maximum value: 359, default value: 220.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nHTML_COLORSTYLE_HUE = 220\n\n# The HTML_COLORSTYLE_SAT tag controls the purity (or saturation) of the colors\n# in the HTML output. For a value of 0 the output will use grayscales only. A\n# value of 255 will produce the most vivid colors.\n# Minimum value: 0, maximum value: 255, default value: 100.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nHTML_COLORSTYLE_SAT = 100\n\n# The HTML_COLORSTYLE_GAMMA tag controls the gamma correction applied to the\n# luminance component of the colors in the HTML output. Values below 100\n# gradually make the output lighter, whereas values above 100 make the output\n# darker. The value divided by 100 is the actual gamma applied, so 80 represents\n# a gamma of 0.8, The value 220 represents a gamma of 2.2, and 100 does not\n# change the gamma.\n# Minimum value: 40, maximum value: 240, default value: 80.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nHTML_COLORSTYLE_GAMMA = 80\n\n# If the HTML_TIMESTAMP tag is set to YES then the footer of each generated HTML\n# page will contain the date and time when the page was generated. Setting this\n# to YES can help to show when doxygen was last run and thus if the\n# documentation is up to date.\n# The default value is: NO.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nHTML_TIMESTAMP = NO\n\n# If the HTML_DYNAMIC_SECTIONS tag is set to YES then the generated HTML\n# documentation will contain sections that can be hidden and shown after the\n# page has loaded.\n# The default value is: NO.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nHTML_DYNAMIC_SECTIONS = NO\n\n# With HTML_INDEX_NUM_ENTRIES one can control the preferred number of entries\n# shown in the various tree structured indices initially; the user can expand\n# and collapse entries dynamically later on. Doxygen will expand the tree to\n# such a level that at most the specified number of entries are visible (unless\n# a fully collapsed tree already exceeds this amount). So setting the number of\n# entries 1 will produce a full collapsed tree by default. 0 is a special value\n# representing an infinite number of entries and will result in a full expanded\n# tree by default.\n# Minimum value: 0, maximum value: 9999, default value: 100.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nHTML_INDEX_NUM_ENTRIES = 100\n\n# If the GENERATE_DOCSET tag is set to YES, additional index files will be\n# generated that can be used as input for Apple's Xcode 3 integrated development\n# environment (see: http://developer.apple.com/tools/xcode/), introduced with\n# OSX 10.5 (Leopard). To create a documentation set, doxygen will generate a\n# Makefile in the HTML output directory. Running make will produce the docset in\n# that directory and running make install will install the docset in\n# ~/Library/Developer/Shared/Documentation/DocSets so that Xcode will find it at\n# startup. See http://developer.apple.com/tools/creatingdocsetswithdoxygen.html\n# for more information.\n# The default value is: NO.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nGENERATE_DOCSET = NO\n\n# This tag determines the name of the docset feed. A documentation feed provides\n# an umbrella under which multiple documentation sets from a single provider\n# (such as a company or product suite) can be grouped.\n# The default value is: Doxygen generated docs.\n# This tag requires that the tag GENERATE_DOCSET is set to YES.\n\nDOCSET_FEEDNAME = \"Doxygen generated docs\"\n\n# This tag specifies a string that should uniquely identify the documentation\n# set bundle. This should be a reverse domain-name style string, e.g.\n# com.mycompany.MyDocSet. Doxygen will append .docset to the name.\n# The default value is: org.doxygen.Project.\n# This tag requires that the tag GENERATE_DOCSET is set to YES.\n\nDOCSET_BUNDLE_ID = org.doxygen.Project\n\n# The DOCSET_PUBLISHER_ID tag specifies a string that should uniquely identify\n# the documentation publisher. This should be a reverse domain-name style\n# string, e.g. com.mycompany.MyDocSet.documentation.\n# The default value is: org.doxygen.Publisher.\n# This tag requires that the tag GENERATE_DOCSET is set to YES.\n\nDOCSET_PUBLISHER_ID = org.doxygen.Publisher\n\n# The DOCSET_PUBLISHER_NAME tag identifies the documentation publisher.\n# The default value is: Publisher.\n# This tag requires that the tag GENERATE_DOCSET is set to YES.\n\nDOCSET_PUBLISHER_NAME = Publisher\n\n# If the GENERATE_HTMLHELP tag is set to YES then doxygen generates three\n# additional HTML index files: index.hhp, index.hhc, and index.hhk. The\n# index.hhp is a project file that can be read by Microsoft's HTML Help Workshop\n# (see: http://www.microsoft.com/en-us/download/details.aspx?id=21138) on\n# Windows.\n#\n# The HTML Help Workshop contains a compiler that can convert all HTML output\n# generated by doxygen into a single compiled HTML file (.chm). Compiled HTML\n# files are now used as the Windows 98 help format, and will replace the old\n# Windows help format (.hlp) on all Windows platforms in the future. Compressed\n# HTML files also contain an index, a table of contents, and you can search for\n# words in the documentation. The HTML workshop also contains a viewer for\n# compressed HTML files.\n# The default value is: NO.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nGENERATE_HTMLHELP = NO\n\n# The CHM_FILE tag can be used to specify the file name of the resulting .chm\n# file. You can add a path in front of the file if the result should not be\n# written to the html output directory.\n# This tag requires that the tag GENERATE_HTMLHELP is set to YES.\n\nCHM_FILE = \n\n# The HHC_LOCATION tag can be used to specify the location (absolute path\n# including file name) of the HTML help compiler (hhc.exe). If non-empty,\n# doxygen will try to run the HTML help compiler on the generated index.hhp.\n# The file has to be specified with full path.\n# This tag requires that the tag GENERATE_HTMLHELP is set to YES.\n\nHHC_LOCATION = \n\n# The GENERATE_CHI flag controls if a separate .chi index file is generated\n# (YES) or that it should be included in the master .chm file (NO).\n# The default value is: NO.\n# This tag requires that the tag GENERATE_HTMLHELP is set to YES.\n\nGENERATE_CHI = NO\n\n# The CHM_INDEX_ENCODING is used to encode HtmlHelp index (hhk), content (hhc)\n# and project file content.\n# This tag requires that the tag GENERATE_HTMLHELP is set to YES.\n\nCHM_INDEX_ENCODING = \n\n# The BINARY_TOC flag controls whether a binary table of contents is generated\n# (YES) or a normal table of contents (NO) in the .chm file. Furthermore it\n# enables the Previous and Next buttons.\n# The default value is: NO.\n# This tag requires that the tag GENERATE_HTMLHELP is set to YES.\n\nBINARY_TOC = NO\n\n# The TOC_EXPAND flag can be set to YES to add extra items for group members to\n# the table of contents of the HTML help documentation and to the tree view.\n# The default value is: NO.\n# This tag requires that the tag GENERATE_HTMLHELP is set to YES.\n\nTOC_EXPAND = NO\n\n# If the GENERATE_QHP tag is set to YES and both QHP_NAMESPACE and\n# QHP_VIRTUAL_FOLDER are set, an additional index file will be generated that\n# can be used as input for Qt's qhelpgenerator to generate a Qt Compressed Help\n# (.qch) of the generated HTML documentation.\n# The default value is: NO.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nGENERATE_QHP = NO\n\n# If the QHG_LOCATION tag is specified, the QCH_FILE tag can be used to specify\n# the file name of the resulting .qch file. The path specified is relative to\n# the HTML output folder.\n# This tag requires that the tag GENERATE_QHP is set to YES.\n\nQCH_FILE = \n\n# The QHP_NAMESPACE tag specifies the namespace to use when generating Qt Help\n# Project output. For more information please see Qt Help Project / Namespace\n# (see: http://qt-project.org/doc/qt-4.8/qthelpproject.html#namespace).\n# The default value is: org.doxygen.Project.\n# This tag requires that the tag GENERATE_QHP is set to YES.\n\nQHP_NAMESPACE = org.doxygen.Project\n\n# The QHP_VIRTUAL_FOLDER tag specifies the namespace to use when generating Qt\n# Help Project output. For more information please see Qt Help Project / Virtual\n# Folders (see: http://qt-project.org/doc/qt-4.8/qthelpproject.html#virtual-\n# folders).\n# The default value is: doc.\n# This tag requires that the tag GENERATE_QHP is set to YES.\n\nQHP_VIRTUAL_FOLDER = doc\n\n# If the QHP_CUST_FILTER_NAME tag is set, it specifies the name of a custom\n# filter to add. For more information please see Qt Help Project / Custom\n# Filters (see: http://qt-project.org/doc/qt-4.8/qthelpproject.html#custom-\n# filters).\n# This tag requires that the tag GENERATE_QHP is set to YES.\n\nQHP_CUST_FILTER_NAME = \n\n# The QHP_CUST_FILTER_ATTRS tag specifies the list of the attributes of the\n# custom filter to add. For more information please see Qt Help Project / Custom\n# Filters (see: http://qt-project.org/doc/qt-4.8/qthelpproject.html#custom-\n# filters).\n# This tag requires that the tag GENERATE_QHP is set to YES.\n\nQHP_CUST_FILTER_ATTRS = \n\n# The QHP_SECT_FILTER_ATTRS tag specifies the list of the attributes this\n# project's filter section matches. Qt Help Project / Filter Attributes (see:\n# http://qt-project.org/doc/qt-4.8/qthelpproject.html#filter-attributes).\n# This tag requires that the tag GENERATE_QHP is set to YES.\n\nQHP_SECT_FILTER_ATTRS = \n\n# The QHG_LOCATION tag can be used to specify the location of Qt's\n# qhelpgenerator. If non-empty doxygen will try to run qhelpgenerator on the\n# generated .qhp file.\n# This tag requires that the tag GENERATE_QHP is set to YES.\n\nQHG_LOCATION = \n\n# If the GENERATE_ECLIPSEHELP tag is set to YES, additional index files will be\n# generated, together with the HTML files, they form an Eclipse help plugin. To\n# install this plugin and make it available under the help contents menu in\n# Eclipse, the contents of the directory containing the HTML and XML files needs\n# to be copied into the plugins directory of eclipse. The name of the directory\n# within the plugins directory should be the same as the ECLIPSE_DOC_ID value.\n# After copying Eclipse needs to be restarted before the help appears.\n# The default value is: NO.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nGENERATE_ECLIPSEHELP = NO\n\n# A unique identifier for the Eclipse help plugin. When installing the plugin\n# the directory name containing the HTML and XML files should also have this\n# name. Each documentation set should have its own identifier.\n# The default value is: org.doxygen.Project.\n# This tag requires that the tag GENERATE_ECLIPSEHELP is set to YES.\n\nECLIPSE_DOC_ID = org.doxygen.Project\n\n# If you want full control over the layout of the generated HTML pages it might\n# be necessary to disable the index and replace it with your own. The\n# DISABLE_INDEX tag can be used to turn on/off the condensed index (tabs) at top\n# of each HTML page. A value of NO enables the index and the value YES disables\n# it. Since the tabs in the index contain the same information as the navigation\n# tree, you can set this option to YES if you also set GENERATE_TREEVIEW to YES.\n# The default value is: NO.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nDISABLE_INDEX = NO\n\n# The GENERATE_TREEVIEW tag is used to specify whether a tree-like index\n# structure should be generated to display hierarchical information. If the tag\n# value is set to YES, a side panel will be generated containing a tree-like\n# index structure (just like the one that is generated for HTML Help). For this\n# to work a browser that supports JavaScript, DHTML, CSS and frames is required\n# (i.e. any modern browser). Windows users are probably better off using the\n# HTML help feature. Via custom style sheets (see HTML_EXTRA_STYLESHEET) one can\n# further fine-tune the look of the index. As an example, the default style\n# sheet generated by doxygen has an example that shows how to put an image at\n# the root of the tree instead of the PROJECT_NAME. Since the tree basically has\n# the same information as the tab index, you could consider setting\n# DISABLE_INDEX to YES when enabling this option.\n# The default value is: NO.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nGENERATE_TREEVIEW = NO\n\n# The ENUM_VALUES_PER_LINE tag can be used to set the number of enum values that\n# doxygen will group on one line in the generated HTML documentation.\n#\n# Note that a value of 0 will completely suppress the enum values from appearing\n# in the overview section.\n# Minimum value: 0, maximum value: 20, default value: 4.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nENUM_VALUES_PER_LINE = 4\n\n# If the treeview is enabled (see GENERATE_TREEVIEW) then this tag can be used\n# to set the initial width (in pixels) of the frame in which the tree is shown.\n# Minimum value: 0, maximum value: 1500, default value: 250.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nTREEVIEW_WIDTH = 250\n\n# If the EXT_LINKS_IN_WINDOW option is set to YES, doxygen will open links to\n# external symbols imported via tag files in a separate window.\n# The default value is: NO.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nEXT_LINKS_IN_WINDOW = NO\n\n# Use this tag to change the font size of LaTeX formulas included as images in\n# the HTML documentation. When you change the font size after a successful\n# doxygen run you need to manually remove any form_*.png images from the HTML\n# output directory to force them to be regenerated.\n# Minimum value: 8, maximum value: 50, default value: 10.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nFORMULA_FONTSIZE = 10\n\n# Use the FORMULA_TRANPARENT tag to determine whether or not the images\n# generated for formulas are transparent PNGs. Transparent PNGs are not\n# supported properly for IE 6.0, but are supported on all modern browsers.\n#\n# Note that when changing this option you need to delete any form_*.png files in\n# the HTML output directory before the changes have effect.\n# The default value is: YES.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nFORMULA_TRANSPARENT = YES\n\n# Enable the USE_MATHJAX option to render LaTeX formulas using MathJax (see\n# http://www.mathjax.org) which uses client side Javascript for the rendering\n# instead of using pre-rendered bitmaps. Use this if you do not have LaTeX\n# installed or if you want to formulas look prettier in the HTML output. When\n# enabled you may also need to install MathJax separately and configure the path\n# to it using the MATHJAX_RELPATH option.\n# The default value is: NO.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nUSE_MATHJAX = NO\n\n# When MathJax is enabled you can set the default output format to be used for\n# the MathJax output. See the MathJax site (see:\n# http://docs.mathjax.org/en/latest/output.html) for more details.\n# Possible values are: HTML-CSS (which is slower, but has the best\n# compatibility), NativeMML (i.e. MathML) and SVG.\n# The default value is: HTML-CSS.\n# This tag requires that the tag USE_MATHJAX is set to YES.\n\nMATHJAX_FORMAT = HTML-CSS\n\n# When MathJax is enabled you need to specify the location relative to the HTML\n# output directory using the MATHJAX_RELPATH option. The destination directory\n# should contain the MathJax.js script. For instance, if the mathjax directory\n# is located at the same level as the HTML output directory, then\n# MATHJAX_RELPATH should be ../mathjax. The default value points to the MathJax\n# Content Delivery Network so you can quickly see the result without installing\n# MathJax. However, it is strongly recommended to install a local copy of\n# MathJax from http://www.mathjax.org before deployment.\n# The default value is: http://cdn.mathjax.org/mathjax/latest.\n# This tag requires that the tag USE_MATHJAX is set to YES.\n\nMATHJAX_RELPATH = http://www.mathjax.org/mathjax\n\n# The MATHJAX_EXTENSIONS tag can be used to specify one or more MathJax\n# extension names that should be enabled during MathJax rendering. For example\n# MATHJAX_EXTENSIONS = TeX/AMSmath TeX/AMSsymbols\n# This tag requires that the tag USE_MATHJAX is set to YES.\n\nMATHJAX_EXTENSIONS = \n\n# The MATHJAX_CODEFILE tag can be used to specify a file with javascript pieces\n# of code that will be used on startup of the MathJax code. See the MathJax site\n# (see: http://docs.mathjax.org/en/latest/output.html) for more details. For an\n# example see the documentation.\n# This tag requires that the tag USE_MATHJAX is set to YES.\n\nMATHJAX_CODEFILE = \n\n# When the SEARCHENGINE tag is enabled doxygen will generate a search box for\n# the HTML output. The underlying search engine uses javascript and DHTML and\n# should work on any modern browser. Note that when using HTML help\n# (GENERATE_HTMLHELP), Qt help (GENERATE_QHP), or docsets (GENERATE_DOCSET)\n# there is already a search function so this one should typically be disabled.\n# For large projects the javascript based search engine can be slow, then\n# enabling SERVER_BASED_SEARCH may provide a better solution. It is possible to\n# search using the keyboard; to jump to the search box use + S\n# (what the is depends on the OS and browser, but it is typically\n# , /