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Repository: Aleafy/Make_it_Real
Branch: main
Commit: a8e93d308127
Files: 85
Total size: 30.3 MB
Directory structure:
gitextract_mhywed_l/
├── .gitignore
├── INSTALL.md
├── LICENSE
├── README.md
├── configs/
│ ├── eval/
│ │ └── render_mv_image.yaml
│ └── train/
│ └── gen_mat_masks.yaml
├── data/
│ ├── assets/
│ │ └── env_sphere.obj
│ ├── hdr/
│ │ ├── basement_boxing_ring_2k.hdr
│ │ └── poly_haven_studio_2k.hdr
│ └── mesh/
│ ├── globe/
│ │ ├── globe.mtl
│ │ └── globe.obj
│ ├── gold_gun/
│ │ ├── gold_gun.mtl
│ │ └── gold_gun.obj
│ ├── kettle/
│ │ ├── kettle.mtl
│ │ └── kettle.obj
│ ├── red_oil/
│ │ ├── red_oil.mtl
│ │ └── red_oil.obj
│ └── vase/
│ ├── vase.mtl
│ └── vase.obj
├── main.py
├── requirements.txt
├── scripts/
│ ├── blender_scripts/
│ │ ├── paste_pbr.blend
│ │ ├── render_ori.py
│ │ └── render_pbr.py
│ ├── gpt_anno/
│ │ └── gpt4_query_mat.py
│ └── kaolin_scripts/
│ ├── load_cfg.py
│ └── src/
│ ├── __init__.py
│ ├── configs/
│ │ ├── __init__.py
│ │ └── train_config.py
│ ├── models/
│ │ ├── __init__.py
│ │ ├── mesh.py
│ │ ├── render.py
│ │ └── textured_mesh.py
│ ├── training/
│ │ ├── __init__.py
│ │ ├── trainer.py
│ │ └── views_dataset.py
│ └── utils.py
├── som/
│ ├── LICENSE
│ ├── ckpts/
│ │ └── download_ckpt.sh
│ ├── configs/
│ │ └── semantic_sam_only_sa-1b_swinL.yaml
│ ├── ops/
│ │ ├── MultiScaleDeformableAttention.egg-info/
│ │ │ ├── PKG-INFO
│ │ │ ├── SOURCES.txt
│ │ │ ├── dependency_links.txt
│ │ │ └── top_level.txt
│ │ ├── build/
│ │ │ ├── lib.linux-x86_64-cpython-38/
│ │ │ │ ├── functions/
│ │ │ │ │ ├── __init__.py
│ │ │ │ │ └── ms_deform_attn_func.py
│ │ │ │ └── modules/
│ │ │ │ ├── __init__.py
│ │ │ │ └── ms_deform_attn.py
│ │ │ └── temp.linux-x86_64-cpython-38/
│ │ │ ├── .ninja_deps
│ │ │ ├── .ninja_log
│ │ │ ├── build.ninja
│ │ │ ├── home/
│ │ │ │ └── fangye/
│ │ │ │ └── SoM/
│ │ │ │ └── ops/
│ │ │ │ └── src/
│ │ │ │ ├── cpu/
│ │ │ │ │ └── ms_deform_attn_cpu.o
│ │ │ │ ├── cuda/
│ │ │ │ │ └── ms_deform_attn_cuda.o
│ │ │ │ └── vision.o
│ │ │ └── mnt/
│ │ │ └── petrelfs/
│ │ │ └── fangye/
│ │ │ └── Make-it-Real_project/
│ │ │ └── som/
│ │ │ └── ops/
│ │ │ └── src/
│ │ │ ├── cpu/
│ │ │ │ └── ms_deform_attn_cpu.o
│ │ │ ├── cuda/
│ │ │ │ └── ms_deform_attn_cuda.o
│ │ │ └── vision.o
│ │ ├── dist/
│ │ │ └── MultiScaleDeformableAttention-1.0-py3.8-linux-x86_64.egg
│ │ ├── functions/
│ │ │ ├── __init__.py
│ │ │ └── ms_deform_attn_func.py
│ │ ├── make.sh
│ │ ├── modules/
│ │ │ ├── __init__.py
│ │ │ └── ms_deform_attn.py
│ │ ├── setup.py
│ │ ├── src/
│ │ │ ├── cpu/
│ │ │ │ ├── ms_deform_attn_cpu.cpp
│ │ │ │ └── ms_deform_attn_cpu.h
│ │ │ ├── cuda/
│ │ │ │ ├── ms_deform_attn_cuda.cu
│ │ │ │ ├── ms_deform_attn_cuda.h
│ │ │ │ └── ms_deform_im2col_cuda.cuh
│ │ │ ├── ms_deform_attn.h
│ │ │ └── vision.cpp
│ │ └── test.py
│ ├── sesam_serial.py
│ └── task_adapter/
│ ├── semantic_sam/
│ │ └── tasks/
│ │ ├── __init__.py
│ │ ├── automatic_mask_generator.py
│ │ ├── inference_semsam_m2m_auto.py
│ │ ├── interactive_idino_1o1_box.py
│ │ ├── interactive_idino_m2m.py
│ │ └── interactive_predictor.py
│ └── utils/
│ └── visualizer.py
└── utils/
├── gen_config.py
├── gpt4_query.py
├── mask_postprocess.py
├── texture_postprocess.py
└── tools.py
================================================
FILE CONTENTS
================================================
================================================
FILE: .gitignore
================================================
**/.ipynb_checkpoints
**/__pycache__
*.mp4
*.npy
*.npz
*.dae
*.json
blender-3.2.2-linux-x64
data/material_lib/mat_images
data/material_lib/pbr_maps
experiments
experiments_szy
experiments_tmp
data_0
output
tmp
main_mod.py
scripts/gpt_eval
som/ckpts/swinl_only_sam_many2many.pth
.idea
.vscode
================================================
FILE: INSTALL.md
================================================
### 💾 Installation
1. Install basic modules: torch and packages in requirements.txt
```bash
git clone https://github.com/Aleafy/Make_it_Real.git
cd Make_it_Real
conda create -n mkreal python=3.8
pip install torch==1.10.1+cu111 torchvision==0.11.2+cu111 torchaudio==0.10.1 -f https://download.pytorch.org/whl/cu111/torch_stable.html
pip install -r requirements.txt
```
2. Install rendering utils: Blender and Kaolin
Download [blender-3.2.2-linux-x64.tar.xz_original](https://download.blender.org/release/Blender3.2/) or [blender_3.2.2_with_installed_modules](https://drive.google.com/file/d/1PKbCS7VymPo_xVYavT0CDd42OmXbfurY/view?usp=sharing)
```bash
tar -xvf blender-3.2.2-linux-x64.tar.xz
export PATH=$PATH:path_to_blender/blender-3.2.2-linux-x64
```
and Kaolin
```bash
pip install kaolin==0.13.0 -f https://nvidia-kaolin.s3.us-east-2.amazonaws.com/torch-1.10.1_cu111.html
```
3. Install segment & mark utils:
```
# install Semantic-SAM
pip install git+https://github.com/UX-Decoder/Semantic-SAM.git@package
# install Deformable Convolution for Semantic-SAM
cd som/ops && sh make.sh && cd ..
# install detectron2
python -m pip install detectron2 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu111/torch1.10/index.html
```
Get model weight through `bash som/ckpts/download_ckpt.sh`
================================================
FILE: LICENSE
================================================
MIT License
Copyright (c) 2024 Aleafy
Permission 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:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE 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.
================================================
FILE: README.md
================================================
# <img src="assets/icon.png" style="vertical-align: -14px;" :height="50px" width="50px"> Make-it-Real
**[Make-it-Real: Unleashing Large Multimodal Model for Painting 3D Objects with Realistic Materials](https://arxiv.org/abs/2404.16829)**
</br>
[Ye Fang](https://github.com/Aleafy)\*,
[Zeyi Sun](https://github.com/SunzeY)\*,
[Tong Wu](https://wutong16.github.io/),
[Jiaqi Wang](https://myownskyw7.github.io/),
[Ziwei Liu](https://liuziwei7.github.io/),
[Gordon Wetzstein](https://web.stanford.edu/~gordonwz/),
[Dahua Lin](http://dahua.site/)
<p style="font-size: 0.6em; margin-top: -1em">*Equal Contribution</p>
<p align="center">
<a href="https://arxiv.org/abs/2404.16829"><img src="https://img.shields.io/badge/arXiv-Paper-<color>"></a>
<a href="https://sunzey.github.io/Make-it-Real"><img src="https://img.shields.io/badge/Project-Website-red"></a>
<a href="https://www.youtube.com/watch?v=_j-t8592GCM"><img src="https://img.shields.io/static/v1?label=Demo&message=Video&color=orange"></a>
<a href="" target='_blank'>
<img src="https://visitor-badge.laobi.icu/badge?page_id=Aleafy.Make_it_Real&left_color=gray&right_color=blue">
</a>
</p>
## 📜 News
🚀 [2024/6/8] We release our [inference pipeline of Make-it-Real](#⚡-quick-start), including material matching and generation of albedo-only 3D objects.
🚀 [2024/6/8] [Material library annotations](#📦-data-preparation) generated by GPT-4V and [data engine](#⚡-quick-start) are released!
🚀 [2024/4/26] The [paper](https://arxiv.org/abs/2404.16829) and [project page](https://sunzey.github.io/Make-it-Real) are released!
## 💡 Highlights
- 🔥 We first demonstrate that **GPT-4V** can effectively **recognize and describe materials**, allowing our model to precisely identifies and aligns materials with the corresponding components of 3D objects.
- 🔥 We construct a **Material Library** containing thousands of materials with highly
detailed descriptions readily for MLLMs to look up and assign.
- 🔥 **An effective pipeline** for texture segmentation, material identification and matching, enabling the high-quality application of materials to
3D assets.
## 👨💻 Todo
- [ ] Evaluation for Existed and Model-Generated Assets (both code & test assets)
- [ ] More Interactive Demos (huggingface, jupyter)
- [x] Make-it-Real Pipeline Inference Code
- [x] Highly detailed Material Library annotations (generated by GPT-4V)
- [x] Paper and Web Demos
## 💾 Installation
Prepare basic modules for deep learning 3d modeling tool(kaolin), rendering engine(blender), and segmentation model. See details in [INSTALL.md](INSTALL.md).
## 📦 Data Preparation
1. **Annotations**: in `data/material_lib/annotations` [folder](data/material_lib/annotations), include:
- Highly-detailed descriptions by GPT-4V: offering thorough descriptions of the material’s visual characteristics and rich semantic information.
- Category-tree: Divided into a hierarchical structure with coarse and fine granularity, it includes over 80 subcategories.
2. **PBR Maps**: You can download the complete PBR data collection at [Huggingface](https://huggingface.co/datasets/gvecchio/MatSynth/tree/main), or download the data used in our project at [OpenXLab](https://openxlab.org.cn/datasets/YeFang/MatSynth/tree/main) (Recommended). (If you have any questions, please refer to [issue#5](https://github.com/Aleafy/Make_it_Real/issues/5))
3. **Material Images(optinal)**: You can download the material images file [here](https://drive.google.com/file/d/1ob7CV6JiaqFyjuCzlmSnBuNRkzt2qMSG/view?usp=sharing), to check and visualize the material appearance.
<pre>
Make_it_Real
└── data
└── material_lib
├── annotations
├── mat_images
└── pbr_maps
└── train
├── Ceremic
├── Concrete
├── ...
└── Wood
</pre>
## ⚡ Quick Start
#### Inference
```bash
python main.py --obj_dir <object_dir> --exp_name <unique_exp_name> --api_key <your_own_gpt4_api_key>
```
- To ensure proper network connectivity for GPT-4V, add proxy environment settings in [main.py](https://github.com/Aleafy/Make_it_Real/blob/feb3563d57fbe18abbff8d4abfb48f71cc8f967b/main.py#L18) (optional). Also, please verify the reachability of your [API host](https://github.com/Aleafy/Make_it_Real/blob/feb3563d57fbe18abbff8d4abfb48f71cc8f967b/utils/gpt4_query.py#L68).
- Result visualization (blender engine) is located in the `output/refine_output` dir. You can compare the result with that in `output/ori_output`.
#### Annotation Engine
```bash
cd scripts/gpt_anno
python gpt4_query_mat.py
```
`Note`: Besides functinoning as annotation engine, you can also use this code ([gpt4_query_mat.py](https://github.com/Aleafy/Make_it_Real/blob/main/scripts/gpt_anno/gpt4_query_mat.py)) to test the GPT-4V connection simply.
<!-- [annotation code](scripts/gpt_anno) -->
<!-- #### Evalutation -->
## ❤️ Acknowledgments
- [MatSynth](https://huggingface.co/datasets/gvecchio/MatSynth/tree/main): a Physically Based Rendering (PBR) materials dataset, which offers extensive high-resolusion tilable pbr maps to look up.
- [TEXTure](https://github.com/TEXTurePaper/TEXTurePaper): Wonderful text-guided texture generation model, and the codebase we built upon.
- [SoM](https://som-gpt4v.github.io/): Draw visual cues on images to facilate GPT-4V query better.
- [Material Palette](https://github.com/astra-vision/MaterialPalette): Excellent exploration of material extraction and generation, offers good insights and comparable setting.
## ✒️ Citation
If you find our work helpful for your research, please consider giving a star ⭐ and citation 📝
```bibtex
@misc{fang2024makeitreal,
title={Make-it-Real: Unleashing Large Multimodal Model for Painting 3D Objects with Realistic Materials},
author={Ye Fang and Zeyi Sun and Tong Wu and Jiaqi Wang and Ziwei Liu and Gordon Wetzstein and Dahua Lin},
year={2024},
eprint={2404.16829},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
================================================
FILE: configs/eval/render_mv_image.yaml
================================================
log:
exp_name: kettle4
eval_only: true
render:
eval_grid_size: 1200
guide:
shape_path: data/mesh/kettle/kettle.obj
initial_texture: data/mesh/kettle/textures/Image_0.png
texture_resolution: 1024
optim:
seed: 3
================================================
FILE: configs/train/gen_mat_masks.yaml
================================================
log:
exp_name: kettle4_train
render:
eval_grid_size: 1200
guide:
shape_path: data/mesh/kettle/kettle.obj
mat2im_path: experiments/kettle4/gpt4_query/result_mat2im.json
result_path: experiments/kettle4/results
texture_resolution: 1024
optim:
seed: 3
================================================
FILE: data/assets/env_sphere.obj
================================================
####
#
# OBJ File Generated by Meshlab
#
####
# Object env_sphere.obj
#
# Vertices: 2562
# Faces: 5120
#
####
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v -13.578262 -6.693831 -13.069944
vn 4.608671 1.914318 3.800496
v -14.695885 -6.104250 -12.114830
# 2562 vertices, 0 vertices normals
f 2//2 1//1 3//3
f 1//1 4//4 3//3
f 2//2 5//5 1//1
f 3//3 6//6 2//2
f 3//3 7//7 8//8
f 7//7 9//9 8//8
f 3//3 4//4 7//7
f 8//8 6//6 3//3
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gitextract_mhywed_l/
├── .gitignore
├── INSTALL.md
├── LICENSE
├── README.md
├── configs/
│ ├── eval/
│ │ └── render_mv_image.yaml
│ └── train/
│ └── gen_mat_masks.yaml
├── data/
│ ├── assets/
│ │ └── env_sphere.obj
│ ├── hdr/
│ │ ├── basement_boxing_ring_2k.hdr
│ │ └── poly_haven_studio_2k.hdr
│ └── mesh/
│ ├── globe/
│ │ ├── globe.mtl
│ │ └── globe.obj
│ ├── gold_gun/
│ │ ├── gold_gun.mtl
│ │ └── gold_gun.obj
│ ├── kettle/
│ │ ├── kettle.mtl
│ │ └── kettle.obj
│ ├── red_oil/
│ │ ├── red_oil.mtl
│ │ └── red_oil.obj
│ └── vase/
│ ├── vase.mtl
│ └── vase.obj
├── main.py
├── requirements.txt
├── scripts/
│ ├── blender_scripts/
│ │ ├── paste_pbr.blend
│ │ ├── render_ori.py
│ │ └── render_pbr.py
│ ├── gpt_anno/
│ │ └── gpt4_query_mat.py
│ └── kaolin_scripts/
│ ├── load_cfg.py
│ └── src/
│ ├── __init__.py
│ ├── configs/
│ │ ├── __init__.py
│ │ └── train_config.py
│ ├── models/
│ │ ├── __init__.py
│ │ ├── mesh.py
│ │ ├── render.py
│ │ └── textured_mesh.py
│ ├── training/
│ │ ├── __init__.py
│ │ ├── trainer.py
│ │ └── views_dataset.py
│ └── utils.py
├── som/
│ ├── LICENSE
│ ├── ckpts/
│ │ └── download_ckpt.sh
│ ├── configs/
│ │ └── semantic_sam_only_sa-1b_swinL.yaml
│ ├── ops/
│ │ ├── MultiScaleDeformableAttention.egg-info/
│ │ │ ├── PKG-INFO
│ │ │ ├── SOURCES.txt
│ │ │ ├── dependency_links.txt
│ │ │ └── top_level.txt
│ │ ├── build/
│ │ │ ├── lib.linux-x86_64-cpython-38/
│ │ │ │ ├── functions/
│ │ │ │ │ ├── __init__.py
│ │ │ │ │ └── ms_deform_attn_func.py
│ │ │ │ └── modules/
│ │ │ │ ├── __init__.py
│ │ │ │ └── ms_deform_attn.py
│ │ │ └── temp.linux-x86_64-cpython-38/
│ │ │ ├── .ninja_deps
│ │ │ ├── .ninja_log
│ │ │ ├── build.ninja
│ │ │ ├── home/
│ │ │ │ └── fangye/
│ │ │ │ └── SoM/
│ │ │ │ └── ops/
│ │ │ │ └── src/
│ │ │ │ ├── cpu/
│ │ │ │ │ └── ms_deform_attn_cpu.o
│ │ │ │ ├── cuda/
│ │ │ │ │ └── ms_deform_attn_cuda.o
│ │ │ │ └── vision.o
│ │ │ └── mnt/
│ │ │ └── petrelfs/
│ │ │ └── fangye/
│ │ │ └── Make-it-Real_project/
│ │ │ └── som/
│ │ │ └── ops/
│ │ │ └── src/
│ │ │ ├── cpu/
│ │ │ │ └── ms_deform_attn_cpu.o
│ │ │ ├── cuda/
│ │ │ │ └── ms_deform_attn_cuda.o
│ │ │ └── vision.o
│ │ ├── dist/
│ │ │ └── MultiScaleDeformableAttention-1.0-py3.8-linux-x86_64.egg
│ │ ├── functions/
│ │ │ ├── __init__.py
│ │ │ └── ms_deform_attn_func.py
│ │ ├── make.sh
│ │ ├── modules/
│ │ │ ├── __init__.py
│ │ │ └── ms_deform_attn.py
│ │ ├── setup.py
│ │ ├── src/
│ │ │ ├── cpu/
│ │ │ │ ├── ms_deform_attn_cpu.cpp
│ │ │ │ └── ms_deform_attn_cpu.h
│ │ │ ├── cuda/
│ │ │ │ ├── ms_deform_attn_cuda.cu
│ │ │ │ ├── ms_deform_attn_cuda.h
│ │ │ │ └── ms_deform_im2col_cuda.cuh
│ │ │ ├── ms_deform_attn.h
│ │ │ └── vision.cpp
│ │ └── test.py
│ ├── sesam_serial.py
│ └── task_adapter/
│ ├── semantic_sam/
│ │ └── tasks/
│ │ ├── __init__.py
│ │ ├── automatic_mask_generator.py
│ │ ├── inference_semsam_m2m_auto.py
│ │ ├── interactive_idino_1o1_box.py
│ │ ├── interactive_idino_m2m.py
│ │ └── interactive_predictor.py
│ └── utils/
│ └── visualizer.py
└── utils/
├── gen_config.py
├── gpt4_query.py
├── mask_postprocess.py
├── texture_postprocess.py
└── tools.py
SYMBOL INDEX (281 symbols across 33 files)
FILE: main.py
function args (line 24) | def args():
FILE: scripts/blender_scripts/render_ori.py
function clear_scene (line 39) | def clear_scene():
function clear_lights (line 44) | def clear_lights():
function AddBackground (line 51) | def AddBackground(hdr_pth):
function import_model (line 75) | def import_model(path):
function scene_root_objects (line 95) | def scene_root_objects():
function scene_bbox (line 101) | def scene_bbox(single_obj=None, ignore_matrix=False):
function rotate_to_zzz (line 117) | def rotate_to_zzz():
function scene_meshes (line 122) | def scene_meshes():
function normalize_scene (line 128) | def normalize_scene(rotate=False):
function create_camera (line 150) | def create_camera():
function set_camera (line 158) | def set_camera(direction, camera_dist=2.0):
function randomize_camera (line 169) | def randomize_camera(camera_dist=2.0):
function pan_camera (line 174) | def pan_camera(time, axis="Z", camera_dist=2.0, elevation=-0.1):
function place_camera (line 186) | def place_camera(time, camera_pose_mode="random", camera_dist_min=2.0, c...
function create_light (line 198) | def create_light(location, energy=1.0, angle=0.5 * math.pi / 180):
function create_light_cap3d (line 213) | def create_light_cap3d(name, light_type, energy, location, rotation):
function three_point_lighting (line 221) | def three_point_lighting():
function _create_light (line 249) | def _create_light(
function randomize_lighting (line 284) | def randomize_lighting() -> Dict[str, bpy.types.Object]:
function create_random_lights (line 341) | def create_random_lights(count=4, distance=2.0, energy=1.5):
function create_camera_light (line 347) | def create_camera_light():
function create_uniform_light (line 352) | def create_uniform_light(backend):
function create_vertex_color_shaders (line 361) | def create_vertex_color_shaders():
function create_default_materials (line 407) | def create_default_materials():
function AddWhiteBackground (line 415) | def AddWhiteBackground():
function find_materials (line 440) | def find_materials():
function get_socket_value (line 450) | def get_socket_value(tree, socket):
function clear_socket_input (line 460) | def clear_socket_input(tree, socket):
function set_socket_value (line 466) | def set_socket_value(tree, socket, socket_and_default):
function setup_nodes (line 478) | def setup_nodes(output_path, capturing_material_alpha: bool = False):
function render_scene (line 544) | def render_scene(output_path, fast_mode: bool):
function scene_fov (line 597) | def scene_fov():
function write_camera_metadata (line 609) | def write_camera_metadata(path):
function save_rendering_dataset (line 630) | def save_rendering_dataset(
function main (line 705) | def main():
FILE: scripts/blender_scripts/render_pbr.py
function CleanScene (line 30) | def CleanScene():
function AddBackground (line 62) | def AddBackground(hdr_pth):
function paste_mat (line 86) | def paste_mat(args):
function clear_lights (line 186) | def clear_lights():
function scene_root_objects (line 193) | def scene_root_objects():
function scene_bbox (line 199) | def scene_bbox(single_obj=None, ignore_matrix=False):
function rotate_to_zzz (line 215) | def rotate_to_zzz():
function scene_meshes (line 220) | def scene_meshes():
function normalize_scene (line 226) | def normalize_scene(rotate=False):
function create_camera (line 250) | def create_camera():
function set_camera (line 258) | def set_camera(direction, camera_dist=2.0):
function randomize_camera (line 269) | def randomize_camera(camera_dist=2.0):
function pan_camera (line 274) | def pan_camera(time, axis="Z", camera_dist=2.0, elevation=-0.1):
function place_camera (line 286) | def place_camera(time, camera_pose_mode="random", camera_dist_min=2.0, c...
function create_light (line 298) | def create_light(location, energy=1.0, angle=0.5 * math.pi / 180):
function create_light_cap3d (line 313) | def create_light_cap3d(name, light_type, energy, location, rotation):
function three_point_lighting (line 321) | def three_point_lighting():
function _create_light (line 349) | def _create_light(
function randomize_lighting (line 384) | def randomize_lighting() -> Dict[str, bpy.types.Object]:
function create_random_lights (line 441) | def create_random_lights(count=4, distance=2.0, energy=1.5):
function create_camera_light (line 447) | def create_camera_light():
function create_uniform_light (line 452) | def create_uniform_light(backend):
function create_vertex_color_shaders (line 461) | def create_vertex_color_shaders():
function create_default_materials (line 507) | def create_default_materials():
function AddWhiteBackground (line 515) | def AddWhiteBackground():
function find_materials (line 540) | def find_materials():
function get_socket_value (line 550) | def get_socket_value(tree, socket):
function clear_socket_input (line 560) | def clear_socket_input(tree, socket):
function set_socket_value (line 566) | def set_socket_value(tree, socket, socket_and_default):
function setup_nodes (line 578) | def setup_nodes(output_path, capturing_material_alpha: bool = False):
function render_scene (line 644) | def render_scene(output_path, fast_mode: bool):
function scene_fov (line 697) | def scene_fov():
function write_camera_metadata (line 709) | def write_camera_metadata(path):
function save_rendering_dataset (line 730) | def save_rendering_dataset(
function main (line 817) | def main():
FILE: scripts/gpt_anno/gpt4_query_mat.py
function process_image (line 23) | def process_image(image_path):
FILE: scripts/kaolin_scripts/load_cfg.py
function load_config (line 7) | def load_config(yaml_path):
function render_model (line 18) | def render_model(eval_cfg):
function paint_model_w_mask (line 22) | def paint_model_w_mask(train_cfg):
FILE: scripts/kaolin_scripts/src/configs/train_config.py
class RenderConfig (line 8) | class RenderConfig:
class GuideConfig (line 35) | class GuideConfig:
class OptimConfig (line 78) | class OptimConfig:
class LogConfig (line 93) | class LogConfig:
method exp_dir (line 113) | def exp_dir(self) -> Path:
class TrainConfig (line 118) | class TrainConfig:
FILE: scripts/kaolin_scripts/src/models/mesh.py
class Mesh (line 6) | class Mesh:
method __init__ (line 7) | def __init__(self,obj_path, device):
method calculate_face_normals (line 31) | def calculate_face_normals(vertices: torch.Tensor, faces: torch.Tensor):
method standardize_mesh (line 45) | def standardize_mesh(self,inplace=False):
method normalize_mesh (line 56) | def normalize_mesh(self,inplace=False, target_scale=1, dy=0, use_origi...
FILE: scripts/kaolin_scripts/src/models/render.py
class Renderer (line 5) | class Renderer:
method __init__ (line 8) | def __init__(self, device, dim=(224, 224), interpolation_mode='nearest'):
method get_camera_from_view (line 20) | def get_camera_from_view(elev, azim, r=3.0, look_at_height=0.0):
method normalize_depth (line 34) | def normalize_depth(self, depth_map):
method render_single_view (line 45) | def render_single_view(self, mesh, face_attributes, elev=0, azim=0, ra...
method render_single_view_texture (line 72) | def render_single_view_texture(self, verts, faces, uv_face_attr, textu...
method project_uv_single_view (line 109) | def project_uv_single_view(self, verts, faces, uv_face_attr, elev=0, a...
method project_single_view (line 123) | def project_single_view(self, verts, faces, elev=0, azim=0, radius=2,
FILE: scripts/kaolin_scripts/src/models/textured_mesh.py
function build_cotan_laplacian_torch (line 19) | def build_cotan_laplacian_torch(points_tensor: torch.Tensor, tris_tensor...
function build_graph_laplacian_torch (line 44) | def build_graph_laplacian_torch(tris_tensor: torch.Tensor) -> np.ndarray:
function eigen_problem (line 70) | def eigen_problem(Lap, k=20, e=0.0) -> (torch.Tensor, torch.Tensor):
function choose_multi_modal (line 83) | def choose_multi_modal(n: int, k: int):
class TexturedMeshModel (line 97) | class TexturedMeshModel(nn.Module):
method __init__ (line 98) | def __init__(self,
method L (line 149) | def L(self) -> np.ndarray:
method eigens (line 154) | def eigens(self, k: int, e: float) -> (torch.Tensor, torch.Tensor):
method normalize_vertices (line 163) | def normalize_vertices(vertices: torch.Tensor, mesh_scale: float = 1.0...
method spectral_augmentations (line 170) | def spectral_augmentations(self, vertices: torch.Tensor) -> torch.Tensor:
method axis_augmentations (line 185) | def axis_augmentations(self, vertices: torch.Tensor, stretch_factor: f...
method augment_vertices (line 196) | def augment_vertices(self):
method init_meshes (line 204) | def init_meshes(self, env_sphere_path='data/assets/env_sphere.obj'):
method zero_meta (line 212) | def zero_meta(self):
method init_paint (line 216) | def init_paint(self, num_backgrounds=1):
method invert_color (line 234) | def invert_color(self, color: torch.Tensor) -> torch.Tensor:
method change_default_to_median (line 247) | def change_default_to_median(self, save_name='./tmp/diff_mask_test.png'):
method init_texture_map (line 263) | def init_texture_map(self):
method forward (line 301) | def forward(self, x):
method get_params (line 304) | def get_params(self):
method export_mesh (line 308) | def export_mesh(self, path):
method render (line 365) | def render(self, theta=None, phi=None, radius=None, background=None,
FILE: scripts/kaolin_scripts/src/training/trainer.py
class TEXTure (line 25) | class TEXTure:
method __init__ (line 27) | def __init__(self, cfg: TrainConfig):
method init_mesh_model (line 50) | def init_mesh_model(self) -> nn.Module:
method init_dataloaders (line 64) | def init_dataloaders(self) -> Dict[str, DataLoader]:
method init_logger (line 76) | def init_logger(self):
method paint (line 82) | def paint(self):
method evaluate (line 136) | def evaluate(self, dataloader: DataLoader, save_path: Path, save_as_vi...
method full_eval (line 162) | def full_eval(self, output_dir: Path = None):
method paint_viewpoint (line 175) | def paint_viewpoint(self, data: Dict[str, Any], pre_mask=None, pre_out...
method eval_render (line 218) | def eval_render(self, data):
method calculate_trimap (line 243) | def calculate_trimap(self, rgb_render_raw: torch.Tensor,
method project_back (line 294) | def project_back(self, render_cache: Dict[str, Any], background: Any, ...
FILE: scripts/kaolin_scripts/src/training/views_dataset.py
function rand_poses (line 12) | def rand_poses(size, device, radius_range=(1.0, 1.5), theta_range=(0.0, ...
function rand_modal_poses (line 47) | def rand_modal_poses(size, device, radius_range=(1.4, 1.6), theta_range=...
function circle_poses (line 74) | def circle_poses(device, radius=1.25, theta=60.0, phi=0.0, angle_overhea...
class MultiviewDataset (line 86) | class MultiviewDataset:
method __init__ (line 87) | def __init__(self, cfg: RenderConfig, device):
method collate (line 116) | def collate(self, index):
method dataloader (line 138) | def dataloader(self):
class ViewsDataset (line 145) | class ViewsDataset:
method __init__ (line 146) | def __init__(self, cfg: RenderConfig, device, size=100, random_views=F...
method collate (line 154) | def collate(self, index):
method dataloader (line 177) | def dataloader(self):
FILE: scripts/kaolin_scripts/src/utils.py
function get_view_direction (line 15) | def get_view_direction(thetas, phis, overhead, front):
function tensor2numpy (line 43) | def tensor2numpy(tensor: torch.Tensor) -> np.ndarray:
function make_path (line 49) | def make_path(path: Path) -> Path:
function save_colormap (line 54) | def save_colormap(tensor: torch.Tensor, path: Path):
function seed_everything (line 58) | def seed_everything(seed):
function smooth_image (line 68) | def smooth_image(self, img: torch.Tensor, sigma: float) -> torch.Tensor:
function get_nonzero_region (line 74) | def get_nonzero_region(mask: torch.Tensor):
function gaussian_fn (line 95) | def gaussian_fn(M, std):
function gkern (line 102) | def gkern(kernlen=256, std=128):
function gaussian_blur (line 109) | def gaussian_blur(image: torch.Tensor, kernel_size: int, std: int) -> to...
function color_with_shade (line 118) | def color_with_shade(color: List[float], z_normals: torch.Tensor, light_...
FILE: som/ops/build/lib.linux-x86_64-cpython-38/functions/ms_deform_attn_func.py
class MSDeformAttnFunction (line 32) | class MSDeformAttnFunction(Function):
method forward (line 34) | def forward(ctx, value, value_spatial_shapes, value_level_start_index,...
method backward (line 43) | def backward(ctx, grad_output):
function ms_deform_attn_core_pytorch (line 52) | def ms_deform_attn_core_pytorch(value, value_spatial_shapes, sampling_lo...
FILE: som/ops/build/lib.linux-x86_64-cpython-38/modules/ms_deform_attn.py
function _is_power_of_2 (line 28) | def _is_power_of_2(n):
class MSDeformAttn (line 34) | class MSDeformAttn(nn.Module):
method __init__ (line 35) | def __init__(self, d_model=256, n_levels=4, n_heads=8, n_points=4):
method _reset_parameters (line 66) | def _reset_parameters(self):
method forward (line 82) | def forward(self, query, reference_points, input_flatten, input_spatia...
FILE: som/ops/functions/ms_deform_attn_func.py
class MSDeformAttnFunction (line 32) | class MSDeformAttnFunction(Function):
method forward (line 34) | def forward(ctx, value, value_spatial_shapes, value_level_start_index,...
method backward (line 43) | def backward(ctx, grad_output):
function ms_deform_attn_core_pytorch (line 52) | def ms_deform_attn_core_pytorch(value, value_spatial_shapes, sampling_lo...
FILE: som/ops/modules/ms_deform_attn.py
function _is_power_of_2 (line 28) | def _is_power_of_2(n):
class MSDeformAttn (line 34) | class MSDeformAttn(nn.Module):
method __init__ (line 35) | def __init__(self, d_model=256, n_levels=4, n_heads=8, n_points=4):
method _reset_parameters (line 66) | def _reset_parameters(self):
method forward (line 82) | def forward(self, query, reference_points, input_flatten, input_spatia...
FILE: som/ops/setup.py
function get_extensions (line 26) | def get_extensions():
FILE: som/ops/src/cpu/ms_deform_attn_cpu.cpp
function ms_deform_attn_cpu_forward (line 22) | at::Tensor
function ms_deform_attn_cpu_backward (line 34) | std::vector<at::Tensor>
FILE: som/ops/src/ms_deform_attn.h
function im2col_step (line 32) | int im2col_step)
FILE: som/ops/src/vision.cpp
function PYBIND11_MODULE (line 18) | PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
FILE: som/ops/test.py
function check_forward_equal_with_pytorch_double (line 35) | def check_forward_equal_with_pytorch_double():
function check_forward_equal_with_pytorch_float (line 51) | def check_forward_equal_with_pytorch_float():
function check_gradient_numerical (line 66) | def check_gradient_numerical(channels=4, grad_value=True, grad_sampling_...
FILE: som/sesam_serial.py
function inference (line 25) | def inference(image, slider, mode, alpha, label_mode, anno_mode,points_p...
function seg_serial (line 68) | def seg_serial(input_dir, fine_grain=False):
function seg_serial_single (line 90) | def seg_serial_single(input_dir, img_index, seg_slider=1.92):
FILE: som/task_adapter/semantic_sam/tasks/automatic_mask_generator.py
function prompt_switch (line 35) | def prompt_switch(p):
class SemanticSamAutomaticMaskGenerator (line 53) | class SemanticSamAutomaticMaskGenerator:
method __init__ (line 54) | def __init__(
method generate (line 156) | def generate(self, image: np.ndarray) -> List[Dict[str, Any]]:
method _generate_masks (line 215) | def _generate_masks(self, image: np.ndarray) -> MaskData:
method _process_crop (line 246) | def _process_crop(
method _process_batch (line 287) | def _process_batch(
method postprocess_small_regions (line 345) | def postprocess_small_regions(
FILE: som/task_adapter/semantic_sam/tasks/inference_semsam_m2m_auto.py
function get_white_ratio (line 15) | def get_white_ratio(image_ori, mask):
function inference_semsam_auto (line 24) | def inference_semsam_auto(model, image, level, all_classes, all_parts, t...
FILE: som/task_adapter/semantic_sam/tasks/interactive_idino_1o1_box.py
function interactive_infer_image_box (line 20) | def interactive_infer_image_box(model, image,all_classes,all_parts, thre...
function remove_small_regions (line 120) | def remove_small_regions(
FILE: som/task_adapter/semantic_sam/tasks/interactive_idino_m2m.py
function interactive_infer_image (line 17) | def interactive_infer_image(model, image,all_classes,all_parts, thresh,t...
function interactive_infer_image_3l (line 109) | def interactive_infer_image_3l(model, image,all_classes,all_parts, thres...
function interactive_infer_image_semantic (line 206) | def interactive_infer_image_semantic(model, image,all_classes,all_parts,...
function remove_small_regions (line 298) | def remove_small_regions(
FILE: som/task_adapter/semantic_sam/tasks/interactive_predictor.py
class SemanticSAMPredictor (line 11) | class SemanticSAMPredictor:
method __init__ (line 12) | def __init__(self, model, thresh=0.5, text_size=640, hole_scale=100, i...
method predict (line 26) | def predict(self, image_ori, image, point=None):
method process_multi_mask (line 57) | def process_multi_mask(self, masks, ious, image_ori):
method predict_masks (line 110) | def predict_masks(self, image_ori, image, point=None):
method remove_small_regions (line 115) | def remove_small_regions(
FILE: som/task_adapter/utils/visualizer.py
class ColorMode (line 38) | class ColorMode(Enum):
class GenericMask (line 60) | class GenericMask:
method __init__ (line 68) | def __init__(self, mask_or_polygons, height, width):
method mask (line 100) | def mask(self):
method polygons (line 106) | def polygons(self):
method has_holes (line 112) | def has_holes(self):
method mask_to_polygons (line 120) | def mask_to_polygons(self, mask):
method polygons_to_mask (line 139) | def polygons_to_mask(self, polygons):
method area (line 144) | def area(self):
method bbox (line 147) | def bbox(self):
class _PanopticPrediction (line 156) | class _PanopticPrediction:
method __init__ (line 161) | def __init__(self, panoptic_seg, segments_info, metadata=None):
method non_empty_mask (line 197) | def non_empty_mask(self):
method semantic_masks (line 213) | def semantic_masks(self):
method instance_masks (line 221) | def instance_masks(self):
function _create_text_labels (line 231) | def _create_text_labels(classes, scores, class_names, is_crowd=None):
class VisImage (line 258) | class VisImage:
method __init__ (line 259) | def __init__(self, img, scale=1.5):#! Scale这里也改了
method _setup_figure (line 270) | def _setup_figure(self, img):
method reset_image (line 295) | def reset_image(self, img):
method save (line 303) | def save(self, filepath):
method get_image (line 311) | def get_image(self):
class Visualizer (line 332) | class Visualizer:
method __init__ (line 358) | def __init__(self, img_rgb, metadata=None, scale=1.0, instance_mode=Co...
method draw_instance_predictions (line 389) | def draw_instance_predictions(self, predictions):
method draw_sem_seg (line 452) | def draw_sem_seg(self, sem_seg, area_threshold=None, alpha=0.7):
method draw_panoptic_seg (line 488) | def draw_panoptic_seg(self, panoptic_seg, segments_info, area_threshol...
method draw_dataset_dict (line 555) | def draw_dataset_dict(self, dic):
method overlay_instances (line 624) | def overlay_instances(
method overlay_rotated_instances (line 766) | def overlay_rotated_instances(self, boxes=None, labels=None, assigned_...
method draw_and_connect_keypoints (line 804) | def draw_and_connect_keypoints(self, keypoints):
method draw_text (line 867) | def draw_text(
method draw_box (line 928) | def draw_box(self, box_coord, alpha=0.5, edge_color="g", line_style="-"):
method draw_rotated_box_with_label (line 962) | def draw_rotated_box_with_label(
method draw_circle (line 1017) | def draw_circle(self, circle_coord, color, radius=3):
method draw_line (line 1035) | def draw_line(self, x_data, y_data, color, linestyle="-", linewidth=No...
method draw_binary_mask (line 1066) | def draw_binary_mask(
method draw_binary_mask_with_number (line 1117) | def draw_binary_mask_with_number(
method draw_soft_mask (line 1180) | def draw_soft_mask(self, soft_mask, color=None, *, text=None, alpha=0.5):
method draw_polygon (line 1208) | def draw_polygon(self, segment, color, edge_color=None, alpha=0.5):
method _jitter (line 1244) | def _jitter(self, color):
method _create_grayscale_image (line 1264) | def _create_grayscale_image(self, mask=None):
method _change_color_brightness (line 1275) | def _change_color_brightness(self, color, brightness_factor):
method _convert_boxes (line 1300) | def _convert_boxes(self, boxes):
method _convert_masks (line 1309) | def _convert_masks(self, masks_or_polygons):
method _draw_number_in_mask (line 1332) | def _draw_number_in_mask(self, binary_mask, text, color, label_mode='1'):
method _draw_text_in_mask (line 1373) | def _draw_text_in_mask(self, binary_mask, text, color):
method _convert_keypoints (line 1393) | def _convert_keypoints(self, keypoints):
method get_output (line 1399) | def get_output(self):
FILE: utils/gen_config.py
function gen_eval_yaml (line 8) | def gen_eval_yaml(input_str, texture_pth, pixel, obj_pth):
function gen_train_yaml (line 37) | def gen_train_yaml(input_str, mat2im_path, result_path, pixel, obj_pth):
function remove_file (line 65) | def remove_file(folder_path, suffix):
function remove_dir (line 75) | def remove_dir(folder_path):
FILE: utils/gpt4_query.py
function process_image (line 32) | def process_image(image_path, api_key, prompt):
function query_shape (line 78) | def query_shape(folder_path, img_pth, api_key, type_str=None):
function query_overall (line 117) | def query_overall(folder_path, leave_list, api_key, obj_info=None):
function query_refine (line 170) | def query_refine(folder_path, leave_list, api_key, obj_info=None, force=...
function query_description (line 224) | def query_description(folder_path, leave_list, api_key, obj_info=None, f...
FILE: utils/mask_postprocess.py
function process_mask_list (line 8) | def process_mask_list(image, mask_list, mean_threshold, stddev_threshold):
function refine_masks (line 42) | def refine_masks(result_path, leave_index=None):
FILE: utils/texture_postprocess.py
function region_refine (line 15) | def region_refine(masks_dir = 'experiments/obj_train/masks', albedo_pth ...
function pixel_estimate (line 85) | def pixel_estimate(masks_refine_dir = 'experiments/obj_train/masks_refin...
function mask_hist (line 165) | def mask_hist(image_pth, mask_pth, pixel):
function normal_hist (line 185) | def normal_hist(img_pth, pixel):
function convert_cv2_to_pil (line 197) | def convert_cv2_to_pil(opencv_img):
function divide_masks (line 204) | def divide_masks(mask_dir='experiments/obj_train/masks'):
function find_closest_key (line 235) | def find_closest_key(albedo_avgs, number):
function merge (line 248) | def merge(mat_parts_dir):
function format_string (line 292) | def format_string(s):
FILE: utils/tools.py
function sort_gpt_result (line 9) | def sort_gpt_result(input_pth, leave_list):
function count_materials (line 37) | def count_materials(materials_data):
function select_most_frequent_materials (line 50) | def select_most_frequent_materials(materials_data):
function paste_image (line 67) | def paste_image(result_pth, leave_list=[0,3,4,7]):
function sort_categories (line 91) | def sort_categories(im2mat_pth):
function get_texture_pth (line 105) | def get_texture_pth(obj_dir):
function calculate_non_white_area (line 136) | def calculate_non_white_area(image_path):
function get_max_area (line 148) | def get_max_area(result_path):
function pp_dic_result (line 163) | def pp_dic_result(output_str):
function check_mat_valid (line 189) | def check_mat_valid(mat, count):
function get_texture_img (line 201) | def get_texture_img(texture_pth):
Copy disabled (too large)
Download .json
Condensed preview — 85 files, each showing path, character count, and a content snippet. Download the .json file for the full structured content (10,424K chars).
[
{
"path": ".gitignore",
"chars": 291,
"preview": "**/.ipynb_checkpoints\n**/__pycache__\n*.mp4\n*.npy\n*.npz\n*.dae\n*.json\nblender-3.2.2-linux-x64\ndata/material_lib/mat_images"
},
{
"path": "INSTALL.md",
"chars": 1381,
"preview": "### 💾 Installation\n\n1. Install basic modules: torch and packages in requirements.txt\n \n ```bash\n git clone https:/"
},
{
"path": "LICENSE",
"chars": 1063,
"preview": "MIT License\n\nCopyright (c) 2024 Aleafy\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof "
},
{
"path": "README.md",
"chars": 6027,
"preview": "# <img src=\"assets/icon.png\" style=\"vertical-align: -14px;\" :height=\"50px\" width=\"50px\"> Make-it-Real\n\n**[Make-it-Real: "
},
{
"path": "configs/eval/render_mv_image.yaml",
"chars": 224,
"preview": "log:\n exp_name: kettle4\n eval_only: true\nrender:\n eval_grid_size: 1200\nguide:\n shape_path: data/mesh/kettle/kettle.o"
},
{
"path": "configs/train/gen_mat_masks.yaml",
"chars": 263,
"preview": "log:\n exp_name: kettle4_train\nrender:\n eval_grid_size: 1200\nguide:\n shape_path: data/mesh/kettle/kettle.obj\n mat2im_"
},
{
"path": "data/assets/env_sphere.obj",
"chars": 328735,
"preview": "####\n#\n# OBJ File Generated by Meshlab\n#\n####\n# Object env_sphere.obj\n#\n# Vertices: 2562\n# Faces: 5120\n#\n####\nvn -2.5128"
},
{
"path": "data/mesh/globe/globe.mtl",
"chars": 263,
"preview": "# Blender MTL File: 'load.blend'\n# Material Count: 1\n\nnewmtl GlobeMat\nNs 250.000000\nKa 1.000000 1.000000 1.000000\nKd 0.8"
},
{
"path": "data/mesh/globe/globe.obj",
"chars": 879391,
"preview": "# Blender v3.2.2 OBJ File: 'load.blend'\n# www.blender.org\nmtllib globe.mtl\no Object_8_Object_2\nv 0.000000 -0.021963 0.00"
},
{
"path": "data/mesh/gold_gun/gold_gun.mtl",
"chars": 595,
"preview": "# Blender 3.5.0 MTL File: 'None'\n# www.blender.org\n\nnewmtl material\nNs 250.000000\nKa 1.000000 1.000000 1.000000\nKs 0.500"
},
{
"path": "data/mesh/gold_gun/gold_gun.obj",
"chars": 1592100,
"preview": "# Blender 3.5.0\n# www.blender.org\nmtllib gold_gun.mtl\no defaultMaterial.001\nv 0.255250 -24.260363 -47.998405\nv 0.260059 "
},
{
"path": "data/mesh/kettle/kettle.mtl",
"chars": 263,
"preview": "# Blender MTL File: 'load.blend'\n# Material Count: 1\n\nnewmtl Default\nNs 250.000000\nKa 1.000000 1.000000 1.000000\nKd 0.80"
},
{
"path": "data/mesh/kettle/kettle.obj",
"chars": 4950715,
"preview": "# Blender v3.2.2 OBJ File: 'load.blend'\n# www.blender.org\nmtllib kettle.mtl\no Object_2_Object_0\nv 22.464100 -77.393394 8"
},
{
"path": "data/mesh/red_oil/red_oil.mtl",
"chars": 238,
"preview": "# Blender 3.5.0 MTL File: 'None'\n# www.blender.org\n\nnewmtl Material__1.001\nNs 250.000000\nKa 1.000000 1.000000 1.000000\nK"
},
{
"path": "data/mesh/red_oil/red_oil.obj",
"chars": 258580,
"preview": "# Blender 3.5.0\n# www.blender.org\nmtllib red_oil.mtl\no defaultMaterial.002\nv 0.236279 -0.512363 0.236279\nv 0.127873 -0.5"
},
{
"path": "data/mesh/vase/vase.mtl",
"chars": 278,
"preview": "# Blender MTL File: 'load.blend'\n# Material Count: 1\n\nnewmtl Saffrito_Fish_Vase_Mat\nNs 250.000000\nKa 1.000000 1.000000 1"
},
{
"path": "data/mesh/vase/vase.obj",
"chars": 1715994,
"preview": "# Blender v3.2.2 OBJ File: 'load.blend'\n# www.blender.org\nmtllib vase.mtl\no Object_2_Object_0\nv -0.080028 0.122457 -0.00"
},
{
"path": "main.py",
"chars": 4558,
"preview": "from utils.gen_config import *\nimport sys \nsys.path.extend(['./som', './scripts/kaolin_scripts'])\nfrom som.sesam_serial "
},
{
"path": "requirements.txt",
"chars": 127,
"preview": "transformers\naccelerate\nhuggingface-hub\nninja\nxatlas\nimageio\nmatplotlib\npyrallis\nloguru\ntqdm\neinops\nopencv-python\nopenai"
},
{
"path": "scripts/blender_scripts/render_ori.py",
"chars": 24764,
"preview": "\"\"\"\nScript to run within Blender to render a 3D model as RGBAD images.\nhdr\nExample usage\n\n blender -b -P render_ori.p"
},
{
"path": "scripts/blender_scripts/render_pbr.py",
"chars": 30309,
"preview": "\"\"\"\nScript to run within Blender to render a 3D model as RGBAD images.\n\nExample usage\n\n blender paste_pbr.blend -b -P"
},
{
"path": "scripts/gpt_anno/gpt4_query_mat.py",
"chars": 3617,
"preview": "import base64\nimport requests\nimport json\nimport os\nfrom tqdm import tqdm\nimport time\nimport re\n\nfrom openai import Open"
},
{
"path": "scripts/kaolin_scripts/load_cfg.py",
"chars": 743,
"preview": "import yaml\n\nfrom src.configs.train_config import TrainConfig, LogConfig, RenderConfig, OptimConfig, GuideConfig\n\nfrom s"
},
{
"path": "scripts/kaolin_scripts/src/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "scripts/kaolin_scripts/src/configs/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "scripts/kaolin_scripts/src/configs/train_config.py",
"chars": 4068,
"preview": "from dataclasses import dataclass, field\nfrom pathlib import Path\nfrom typing import Optional, Tuple, List\nfrom loguru i"
},
{
"path": "scripts/kaolin_scripts/src/models/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "scripts/kaolin_scripts/src/models/mesh.py",
"chars": 2322,
"preview": "import kaolin as kal\nimport torch\n\nimport copy\n\nclass Mesh:\n def __init__(self,obj_path, device):\n # from http"
},
{
"path": "scripts/kaolin_scripts/src/models/render.py",
"chars": 7338,
"preview": "import kaolin as kal\nimport torch\nimport numpy as np\nfrom loguru import logger\nclass Renderer:\n # from https://github"
},
{
"path": "scripts/kaolin_scripts/src/models/textured_mesh.py",
"chars": 19259,
"preview": "import os\n\nimport kaolin as kal\nimport numpy as np\nimport scipy\nfrom scipy import sparse\nfrom scipy.sparse.linalg import"
},
{
"path": "scripts/kaolin_scripts/src/training/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "scripts/kaolin_scripts/src/training/trainer.py",
"chars": 16456,
"preview": "from pathlib import Path\nfrom typing import Any, Dict, Union, List\n\nimport cv2\nimport imageio\nimport numpy as np\nimport "
},
{
"path": "scripts/kaolin_scripts/src/training/views_dataset.py",
"chars": 7245,
"preview": "import warnings\n\nimport numpy as np\nimport torch\nfrom torch.utils.data import DataLoader\n\nfrom src.configs.train_config "
},
{
"path": "scripts/kaolin_scripts/src/utils.py",
"chars": 3989,
"preview": "import random\nimport os\nfrom pathlib import Path\nfrom typing import List\n\nimport numpy as np\nimport torch\nimport torchvi"
},
{
"path": "som/LICENSE",
"chars": 1141,
"preview": " MIT License\n\n Copyright (c) Microsoft Corporation.\n\n Permission is hereby granted, free of charge, to any pers"
},
{
"path": "som/ckpts/download_ckpt.sh",
"chars": 118,
"preview": "wget -P som/ckpts https://github.com/UX-Decoder/Semantic-SAM/releases/download/checkpoint/swinl_only_sam_many2many.pth"
},
{
"path": "som/configs/semantic_sam_only_sa-1b_swinL.yaml",
"chars": 12158,
"preview": "# ------------------------------------------------------------------------\n# Semantic SAM\n# Copyright (c) MicroSoft, Inc"
},
{
"path": "som/ops/MultiScaleDeformableAttention.egg-info/PKG-INFO",
"chars": 233,
"preview": "Metadata-Version: 2.1\nName: MultiScaleDeformableAttention\nVersion: 1.0\nSummary: PyTorch Wrapper for CUDA Functions of Mu"
},
{
"path": "som/ops/MultiScaleDeformableAttention.egg-info/SOURCES.txt",
"chars": 549,
"preview": "setup.py\n/mnt/petrelfs/fangye/Make-it-Real_project/som/ops/src/vision.cpp\n/mnt/petrelfs/fangye/Make-it-Real_project/som/"
},
{
"path": "som/ops/MultiScaleDeformableAttention.egg-info/dependency_links.txt",
"chars": 1,
"preview": "\n"
},
{
"path": "som/ops/MultiScaleDeformableAttention.egg-info/top_level.txt",
"chars": 48,
"preview": "MultiScaleDeformableAttention\nfunctions\nmodules\n"
},
{
"path": "som/ops/build/lib.linux-x86_64-cpython-38/functions/__init__.py",
"chars": 734,
"preview": "# ------------------------------------------------------------------------------------------------\n# Deformable DETR\n# C"
},
{
"path": "som/ops/build/lib.linux-x86_64-cpython-38/functions/ms_deform_attn_func.py",
"chars": 3728,
"preview": "# ------------------------------------------------------------------------------------------------\n# Deformable DETR\n# C"
},
{
"path": "som/ops/build/lib.linux-x86_64-cpython-38/modules/__init__.py",
"chars": 720,
"preview": "# ------------------------------------------------------------------------------------------------\n# Deformable DETR\n# C"
},
{
"path": "som/ops/build/lib.linux-x86_64-cpython-38/modules/ms_deform_attn.py",
"chars": 6805,
"preview": "# ------------------------------------------------------------------------------------------------\n# Deformable DETR\n# C"
},
{
"path": "som/ops/build/temp.linux-x86_64-cpython-38/.ninja_log",
"chars": 1500,
"preview": "# ninja log v5\n0\t5873\t1702389639300045861\t/home/fangye/SoM/ops/build/temp.linux-x86_64-cpython-38/home/fangye/SoM/ops/sr"
},
{
"path": "som/ops/build/temp.linux-x86_64-cpython-38/build.ninja",
"chars": 3191,
"preview": "ninja_required_version = 1.3\ncxx = c++\nnvcc = /mnt/cache/share/cuda-11.1/bin/nvcc\n\ncflags = -pthread -B /mnt/cache/fangy"
},
{
"path": "som/ops/functions/__init__.py",
"chars": 734,
"preview": "# ------------------------------------------------------------------------------------------------\n# Deformable DETR\n# C"
},
{
"path": "som/ops/functions/ms_deform_attn_func.py",
"chars": 3728,
"preview": "# ------------------------------------------------------------------------------------------------\n# Deformable DETR\n# C"
},
{
"path": "som/ops/make.sh",
"chars": 729,
"preview": "#!/usr/bin/env bash\n# ------------------------------------------------------------------------------------------------\n#"
},
{
"path": "som/ops/modules/__init__.py",
"chars": 720,
"preview": "# ------------------------------------------------------------------------------------------------\n# Deformable DETR\n# C"
},
{
"path": "som/ops/modules/ms_deform_attn.py",
"chars": 6805,
"preview": "# ------------------------------------------------------------------------------------------------\n# Deformable DETR\n# C"
},
{
"path": "som/ops/setup.py",
"chars": 3038,
"preview": "# ------------------------------------------------------------------------------------------------\n# Deformable DETR\n# C"
},
{
"path": "som/ops/src/cpu/ms_deform_attn_cpu.cpp",
"chars": 1399,
"preview": "/*!\n**************************************************************************************************\n* Deformable DETR"
},
{
"path": "som/ops/src/cpu/ms_deform_attn_cpu.h",
"chars": 1282,
"preview": "/*!\n**************************************************************************************************\n* Deformable DETR"
},
{
"path": "som/ops/src/cuda/ms_deform_attn_cuda.cu",
"chars": 7459,
"preview": "/*!\n**************************************************************************************************\n* Deformable DETR"
},
{
"path": "som/ops/src/cuda/ms_deform_attn_cuda.h",
"chars": 1283,
"preview": "/*!\n**************************************************************************************************\n* Deformable DETR"
},
{
"path": "som/ops/src/cuda/ms_deform_im2col_cuda.cuh",
"chars": 54837,
"preview": "/*!\n**************************************************************************\n* Deformable DETR\n* Copyright (c) 2020 Se"
},
{
"path": "som/ops/src/ms_deform_attn.h",
"chars": 1981,
"preview": "/*!\n**************************************************************************************************\n* Deformable DETR"
},
{
"path": "som/ops/src/vision.cpp",
"chars": 942,
"preview": "/*!\n**************************************************************************************************\n* Deformable DETR"
},
{
"path": "som/ops/test.py",
"chars": 4223,
"preview": "# ------------------------------------------------------------------------------------------------\n# Deformable DETR\n# C"
},
{
"path": "som/sesam_serial.py",
"chars": 3397,
"preview": "import torch\nfrom PIL import Image\n\nfrom semantic_sam.BaseModel import BaseModel\nfrom semantic_sam import build_model\nfr"
},
{
"path": "som/task_adapter/semantic_sam/tasks/__init__.py",
"chars": 468,
"preview": "from .interactive_idino_m2m import interactive_infer_image as interactive_infer_image_idino_m2m\nfrom .interactive_idino_"
},
{
"path": "som/task_adapter/semantic_sam/tasks/automatic_mask_generator.py",
"chars": 15853,
"preview": "# Copyright (c) Meta Platforms, Inc. and affiliates.\n# All rights reserved.\n\n# This source code is licensed under the li"
},
{
"path": "som/task_adapter/semantic_sam/tasks/inference_semsam_m2m_auto.py",
"chars": 2359,
"preview": "# --------------------------------------------------------\n# Semantic-SAM: Segment and Recognize Anything at Any Granula"
},
{
"path": "som/task_adapter/semantic_sam/tasks/interactive_idino_1o1_box.py",
"chars": 5530,
"preview": "# --------------------------------------------------------\n# Semantic-SAM: Segment and Recognize Anything at Any Granula"
},
{
"path": "som/task_adapter/semantic_sam/tasks/interactive_idino_m2m.py",
"chars": 12495,
"preview": "# --------------------------------------------------------\n# Semantic-SAM: Segment and Recognize Anything at Any Granula"
},
{
"path": "som/task_adapter/semantic_sam/tasks/interactive_predictor.py",
"chars": 5508,
"preview": "import torch\nimport numpy as np\nfrom torchvision import transforms\nfrom som.task_adapter.utils.visualizer import Visuali"
},
{
"path": "som/task_adapter/utils/visualizer.py",
"chars": 57518,
"preview": "# Copyright (c) Facebook, Inc. and its affiliates.\nimport colorsys\nimport logging\nimport math\nimport numpy as np\nfrom en"
},
{
"path": "utils/gen_config.py",
"chars": 2371,
"preview": "import yaml\nimport shutil\n\nimport os\nimport glob \n\n\ndef gen_eval_yaml(input_str, texture_pth, pixel, obj_pth):\n # The"
},
{
"path": "utils/gpt4_query.py",
"chars": 12852,
"preview": "import base64\nimport requests\nimport json\nimport os\nfrom tqdm import tqdm\nimport time\nfrom utils.tools import pp_dic_res"
},
{
"path": "utils/mask_postprocess.py",
"chars": 5951,
"preview": "import os\nimport numpy as np\nfrom PIL import Image\n\nimport torch\nimport cv2\n\ndef process_mask_list(image, mask_list, mea"
},
{
"path": "utils/texture_postprocess.py",
"chars": 11848,
"preview": "import re\nimport os\nfrom PIL import Image\n\nimport numpy as np\n\nimport cv2\n\n\nfrom tqdm import tqdm\nfrom scipy.spatial imp"
},
{
"path": "utils/tools.py",
"chars": 7374,
"preview": "import json\nimport os\n\nfrom PIL import Image\nimport re\n\nimport numpy as np\n\ndef sort_gpt_result(input_pth, leave_list):\n"
}
]
// ... and 11 more files (download for full content)
About this extraction
This page contains the full source code of the Aleafy/Make_it_Real GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 85 files (30.3 MB), approximately 2.5M tokens, and a symbol index with 281 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.