[
{
"path": ".gitattributes",
"content": "# Auto detect text files and perform LF normalization\n* text=auto\n"
},
{
"path": ".gitignore",
"content": "*.m~\n*.asv\ndata/Cityscapes/gtFine_bboxes/\nresults/pred_ins_clean/\nresults/pred_sem_clean/\nresults/pred_ins_cam_mandg_merged/\nresults/pred_sem_cam_mandg_merged/\n"
},
{
"path": "LICENSE",
"content": "MIT License\n\nCopyright (c) 2018 Qizhu Li, Anurag Arnab, Philip H.S Torr\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": "# Weakly- and Semi-Supervised Panoptic Segmentation\nby [Qizhu Li](http://www.robots.ox.ac.uk/~liqizhu/)\\*, [Anurag Arnab](http://www.robots.ox.ac.uk/~aarnab/)\\*, [Philip H.S. Torr](https://scholar.google.com/citations?user=kPxa2w0AAAAJ&hl=en)\n\nThis repository demonstrates the weakly supervised ground truth generation scheme presented in our paper *Weakly- and Semi-Supervised Panoptic Segmentation* published at ECCV 2018. The code has been cleaned-up and refactored, and should reproduce the results presented in the paper.\n\nFor details, please refer to our [paper](https://arxiv.org/abs/1808.03575), and [project page](https://qizhuli.github.io/publication/weakly-supervised-panoptic-segmentation/). Please check the [Downloads](#downloads) section for all the additional data we release.\n\n\n\n\n \\* Equal first authorship \n\n## Introduction\nIn our weakly-supervised *panoptic* segmentation experiments, our models are supervised by 1) image-level tags and 2) bounding boxes, as shown in the figure above.\nWe used image-level tags as supervision for \"stuff\" classes which do not have a defined extent and cannot be described well by tight bounding boxes. For \"thing\" classes, we used bounding boxes as our weak supervision. This code release clarifies the implementation details of the method presented in the paper.\n\n## Iterative ground truth generation \nFor readers' convenience, we will give an outline of the proposed iterative ground truth generation pipeline, and provide demos for some of the key steps.\n\n1. We train a multi-class classifier for all classes to obtain rough localisation cues. As it is not possible to fit an entire Cityscapes image (1024x2048) into a network due to GPU memory constraints, we took 15 fixed 400x500 crops per training image, and derived their classification ground truth accordingly, which we use to train the multi-class classifier. From the trained classifier, we extract the Class Activation Maps (CAMs) using Grad-CAM, which has the advantage of being agnostic to network architecture over CAM.\n - Download the fixed image crops with image-level tags [here](#downloads-crops) to train your own classifier. For convenience, the pixel-level semantic label of the crops are also included, though they should not be used in training.\n - The CAMs we produced are available for download [here](#downloads-cam).\n2. In parallel, we extract bounding box annotations from Cityscapes ground truth files, and then run MCG (a segment-proposal algorithm) and Grabcut (a classic foreground segmentation technique given a bounding-box prior) on the training images to generate foreground masks inside each annotated bounding box. MCG and Grabcut masks are merged following the rule that only regions where both have consensus are given the predicted label; otherwise an \"ignore\" label is assigned.\n - The extracted bounding boxes (saved in .mat format) can be downloaded [here](#downloads-bboxes). Alternatively, we also provide a demo script `demo_instanceTrainId_to_dets.m` and a batch script `batch_instanceTrainId_to_dets.m` for you to make them yourself. The demo is self-contained; However, before running the batch script, make sure to\n 1. Download the [official Cityscapes scripts repository](https://github.com/mcordts/cityscapesScripts);\n 2. Inside the above repository, navigate to `cityscapesscripts/preparation` and run \n \n ```sh\n python createTrainIdInstanceImgs.py\n ```\n This command requires an environment variable `CITYSCAPES_DATASTET=path/to/your/cityscapes/data/folder` to be set. These two steps produce the `*_instanceTrainIds.png` files required by our batch script;\n 3. Navigate back to this repository, and place/symlink your `gtFine` and `gtCoarse` folders inside `data/Cityscapes/` folder so that they are visible to our batch script.\n - Please see [here](https://github.com/jponttuset/mcg) for details on MCG.\n - We use the [OpenCV implementation](https://docs.opencv.org/3.2.0/d8/d83/tutorial_py_grabcut.html) of Grabcut in our experiments.\n - The merged M&G masks we produced are available for download [here](#downloads-mandg).\n3. The CAMs (step 1) and M&G masks (step 2) are merged to produce the ground truth needed to kick off iterative training. To see a demo of merging, navigate to the root folder of this repo in MATLAB and run:\n ```matlab\n demo_merge_cam_mandg;\n ```\n When post-processing network predictions of images from the Cityscapes `train_extra` split, make sure to use the following settings:\n ```matlab\n opts.run_apply_bbox_prior = false;\n opts.run_check_image_level_tags = false;\n opts.save_ins = false;\n ```\n because the coarse annotation provided on the `train_extra` split trades off recall for precision, leading to inaccurate bounding box coordinates, and frequent occurrences of false negatives. This also applies to step 5.\n - The results from merging CAMs with M&G masks can be downloaded [here](#downloads-cam-mandg-merged).\n4. Using the generated ground truth, weakly-supervised models can be trained in the same way as a fully-supervised model. When the training loss converges, we make dense predictions using the model and also save the prediction scores. \n - An example of dense prediction made by a weakly-supervised model is included at `results/pred_sem_raw/`, and an example of the corresponding prediction scores is provided at `results/pred_flat_feat/`. \n5. The prediction and prediction scores (and optionally, the M&G masks) are used to generate the ground truth labels for next stage of iterative training. To see a demo of iterative ground truth generation, navigate to the root folder of this repo in MATLAB and run:\n ```matlab\n demo_make_iterative_gt;\n ```\n The generated semantic and instance ground truth labels are saved at `results/pred_sem_clean` and `results/pred_ins_clean` respectively. \n \n Please refer to `scripts/get_opts.m` for the options available. To reproduce the results presented in the paper, use the default setting, and set `opts.run_merge_with_mcg_and_grabcut` to `false` after five iterations of training, as the weakly supervised model by then produces better quality segmentation of ''thing'' classes than the original M&G masks. \n6. Repeat step 4 and 5 until training loss no longer reduces.\n\n## Downloads\n1. Image crops and tags for training multi-class classifier:\n - Images \n - train (9.3GB): [Dropbox](https://www.dropbox.com/s/xvumnk14qmctb41/leftImg8bit_400x500crops_train.zip?dl=0) or [BaiduYun](https://pan.baidu.com/s/1T0xTuq88RITHqZHW1Tdo-g)\n - train_extra (63.3GB): [Dropbox](https://www.dropbox.com/s/rana9b0e0k1d467/leftImg8bit_400x500crops_train_extra.zip?dl=0) or [BaiduYun](https://pan.baidu.com/s/1yy0I-0R5IBI98QLGOdjkiQ)\n - val (1.6GB): [Dropbox](https://www.dropbox.com/s/hudd1k4i4zr53qj/leftImg8bit_400x500crops_val.zip?dl=0) or [BaiduYun](https://pan.baidu.com/s/1jSCps4wNg45mbgM0ggM7AQ)\n - Ground truth tags\n - train+train_extra+val (90.9MB): [Dropbox](https://www.dropbox.com/s/z9ak8rtwjldyerv/gtWeak_tags_400x500crops.zip?dl=0) or [BaiduYun](https://pan.baidu.com/s/19VcJrQU2GvwX6NZu8jLWfg)\n - Lists\n - train+train_extra+val (827kB): [Dropbox](https://www.dropbox.com/s/8itgdm0nau0rixz/lists.zip?dl=0) or [BaiduYun](https://pan.baidu.com/s/14j9rV3S8599YwYILzEfrCw)\n - Semantic labels (provided for convenience; **not** to be used in training)\n - train (87.8MB): [Dropbox](https://www.dropbox.com/s/v9nsuazh60mwm4g/gtFine_semantic_400x500crops_train.zip?dl=0) or [BaiduYun](https://pan.baidu.com/s/1dOX7CO9J0ep94TJjUsSYzg)\n - train_extra (608MB): [Dropbox](https://www.dropbox.com/s/u45mtdvb3xqt2di/gtCoarse_semantic_400x500crops_train_extra.zip?dl=0) or [BaiduYun](https://pan.baidu.com/s/12Jf0XwvValq2MtFKDRMTmg)\n - val (16.2MB): [Dropbox](https://www.dropbox.com/s/9o9unhqnijz3bmm/gtFine_semantic_400x500crops_val.zip?dl=0) or [BaiduYun](https://pan.baidu.com/s/14RLV--kmnyhRQXQrTtwQ_A)\n2. CAMs:\n - train+train_extra (682MB): [Dropbox](https://www.dropbox.com/s/24p60caieq3skik/cam.zip?dl=0) or [BaiduYun](https://pan.baidu.com/s/1zhgzw0IU7r9YBBBmwwRTzA)\n3. Extracted Cityscapes bounding boxes (.mat format):\n - train+val (7.6GB): [Dropbox](https://www.dropbox.com/s/bt7tpom8nohtwk8/gtFine_bboxes.zip?dl=0) or [BaiduYun](https://pan.baidu.com/s/1jXEp-ibmXkQz-bi1Oe6FtA)\n - train_extra (44.2GB): [Dropbox](https://www.dropbox.com/s/tuv4r44sr5vt15z/gtCoarse_bboxes.zip?dl=0) or [BaiduYun](https://pan.baidu.com/s/1p8bpZ2srcNmHlkCrLnPH2Q)\n4. Merged MCG&Grabcut masks:\n - train+train_extra (99.8MB): [Dropbox](https://www.dropbox.com/s/skwv2f8ny0aym9j/mcg_and_grabcut.zip?dl=0) or [BaiduYun](https://pan.baidu.com/s/1VvcodHbuZz4nJhVego5jwA)\n5. CAMs merged with MCG&Grabcut masks:\n - train+train_extra (764MB): [Dropbox](https://www.dropbox.com/s/t24gqpkyrytr7ai/cam_mandg_merged.zip?dl=0) or [BaiduYun](https://pan.baidu.com/s/1vI2tzbzXCEO3tij4RnMzgg)\n\nNote that due to file size limit set by **BaiduYun**, some of the larger files had to be split into several chunks in order to be uploaded. These files are named as `filename.zip.part##`, where `filename` is the original file name excluding the extension, and `##` is a two digit part index. After you have downloaded all the parts, `cd` to the folder where they are saved, and use the following command to join them back together:\n```sh\ncat filename.zip.part* > filename.zip\n```\nThe joining operation may take several minutes, depending on file size. \n\nThe above does not apply to files downloaded from Dropbox.\n\n## Reference\nIf you find the code helpful in your research, please cite our paper:\n\n```tex\n@InProceedings{Li_2018_ECCV,\n author = {Li, Qizhu and \n Arnab, Anurag and \n Torr, Philip H.S.},\n title = {Weakly- and Semi-Supervised Panoptic Segmentation},\n booktitle = {The European Conference on Computer Vision (ECCV)},\n month = {September},\n year = {2018}\n}\n```\n## Questions\nPlease contact Qizhu Li and Anurag Arnab for enquires, issues, and suggestions.\n"
},
{
"path": "batch_instanceTrainId_to_dets.m",
"content": "% ------------------------------------------------------------------------ \n% Copyright (C)\n% Torr Vision Group (TVG)\n% University of Oxford - UK\n% \n% Qizhu Li \n% August 2018\n% ------------------------------------------------------------------------ \n% This file is part of the weakly-supervised training method presented in:\n% Qizhu Li*, Anurag Arnab*, Philip H.S. Torr,\n% \"Weakly- and Semi-Supervised Panoptic Segmentation,\"\n% European Conference on Computer Vision (ECCV) 2018.\n% Please consider citing the paper if you use this code.\n% ------------------------------------------------------------------------\n% This script demos extracting bounding box information from Cityscapes\n% instance ground truth (*_gt_instanceTrainIds.png files) for\n% generation of iterative ground truths for weakly-supervised experiments\n%\n% It is a batch processor. Set split to 'train', 'val', or 'train_extra' \n% as desired. Make sure the instance ground truth files are found in\n% data/Cityscapes/gt// respectively.\n% If `visualise` is set to true, make sure the original RGB files are \n% available in data/Cityscapes/leftImg8bit// \n% following the standard Cityscapes data folder organisation.\n% -----------------------------------------------------------------------\n\nclearvars;\naddpath utils\naddpath scripts\naddpath visualisation\n\n%% Configure\n\nsplit = 'train'; % train, val or train_extra\n\n% set is_panoptic flag to include image-level stuff class dets\nis_panoptic = true;\n\n% set incl_grps flag to include thing groups present in Cityscapes\n% annotation\nincl_grps = true;\n\n% trainIds of stuff classes\nstuff_classes = 0:10;\n\n% trainIds of thing classes\nthing_classes = 11:18;\n\n% class names\nload objectName19.mat\n\n% ignore label\nignore_label = 255;\n\n% force overwrite\nforce_overwrite = true;\n\n% visualise\nvisualise = false;\nvisualise_save_template = '%s_leftImg8bit.png';\nload utils/colormapcs.mat\n\n%% Parse\nswitch split\n case 'train'\n instanceTrainId_dir = 'data/Cityscapes/gtFine/train';\n instanceTrainId_template = '%s_gtFine_instanceTrainIds.png';\n list_path = 'data/Cityscapes/lists/train_id.txt';\n save_dir = 'data/Cityscapes/gtFine_bboxes/train/%s'; % panoptic or thing-only as the child folder\n save_template = '%s_leftImg8bit.mat';\n rgb_dir = 'data/Cityscapes/leftImg8bit/train';\n case 'val'\n instanceTrainId_dir = 'data/Cityscapes/gtFine/val';\n instanceTrainId_template = '%s_gtFine_instanceTrainIds.png';\n list_path = 'data/Cityscapes/lists/val_id.txt';\n save_dir = 'data/Cityscapes/gtFine_bboxes/val/%s'; % panoptic or thing-only as the child folder\n save_template = '%s_leftImg8bit.mat';\n rgb_dir = 'data/Cityscapes/leftImg8bit/val';\n case 'train_extra'\n instanceTrainId_dir = 'data/Cityscapes/gtCoarse/train_extra';\n instanceTrainId_template = '%s_gtCoarse_instanceTrainIds.png';\n list_path = 'data/Cityscapes/lists/train_extra_id.txt';\n save_dir = 'data/Cityscapes/gtCoarse_bboxes/train_extra/%s'; % panoptic or thing-only as the child folder\n save_template = '%s_leftImg8bit.mat';\n rgb_dir = 'data/Cityscapes/leftImg8bit/train_extra';\n otherwise\n error('Unrecognised data split.');\nend\n\nif is_panoptic\n save_dir = sprintf(save_dir, 'panoptic');\nelse\n save_dir = sprintf(save_dir, 'thing-only');\nend\nif ~exist(save_dir, 'dir')\n mkdir(save_dir);\nend\n\nlist = importdata(list_path);\n\n%% Run the extraction\nfor k = 1:length(list)\n id = list{k};\n save_path = fullfile(save_dir, sprintf(save_template, id));\n % if the save_path file exists, and we don't require force-overwrite, skip the current file\n if ~force_overwrite && exist(save_path, 'file')\n continue;\n end\n city = strtok(id, '_');\n label_path = fullfile(instanceTrainId_dir, city, sprintf(instanceTrainId_template, id));\n label = imread(label_path);\n dets = instanceTrainId_to_dets(label, is_panoptic, incl_grps, ...\n stuff_classes, thing_classes, objectNames, ignore_label);\n save(fullfile(save_path), 'dets');\n if visualise\n vis_im = visualise_bboxes(id, objectNames, cmap, rgb_dir, save_dir);\n imwrite(vis_im, fullfile(save_dir, sprintf(visualise_save_template, id)));\n end\n if mod(k, 100) == 0\n fprintf('[%s] Processed %d/%d\\n', char(datetime), k, length(list));\n end\nend\n"
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},
{
"path": "data/Cityscapes/lists/val_id.txt",
"content": 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},
{
"path": "demo_instanceTrainId_to_dets.m",
"content": "% ------------------------------------------------------------------------ \n% Copyright (C)\n% Torr Vision Group (TVG)\n% University of Oxford - UK\n% \n% Qizhu Li \n% August 2018\n% ------------------------------------------------------------------------ \n% This file is part of the weakly-supervised training method presented in:\n% Qizhu Li*, Anurag Arnab*, Philip H.S. Torr,\n% \"Weakly- and Semi-Supervised Panoptic Segmentation,\"\n% European Conference on Computer Vision (ECCV) 2018.\n% Please consider citing the paper if you use this code.\n% ------------------------------------------------------------------------\n% This script demos extracting bounding box information from Cityscapes\n% instance ground truth (*_gtFine_instanceTrainIds.png files) for\n% generation of iterative ground truths for weakly-supervised experiments\n% ------------------------------------------------------------------------\n\nclearvars;\naddpath utils\naddpath scripts\n\ninstanceTrainId_file_path = 'data/Cityscapes/gtFine/train/aachen/aachen_000000_000019_gtFine_instanceTrainIds.png';\nlabel = imread(instanceTrainId_file_path);\n\n% set is_panoptic flag to include image-level stuff class dets\nis_panoptic = true;\n\n% set incl_grps flag to include thing groups present in Cityscapes\n% annotation\nincl_grps = true;\n\n% trainId of stuff classes\nstuff_classes = 0:10;\n\n% trainId of thing classes\nthing_classes = 11:18;\n\n% class names\nload objectName19.mat\n\n% ignore label\nignore_label = 255;\n\n% run the extraction\ndets = instanceTrainId_to_dets(label, is_panoptic, incl_grps, ...\n stuff_classes, thing_classes, objectNames, ignore_label);\nsave_dir = 'data/Cityscapes/gtFine_bboxes/train/panoptic';\nif ~exist(save_dir, 'dir')\n mkdir(save_dir);\nend\nsave(fullfile(save_dir, 'aachen_000000_000019_leftImg8bit.mat'), 'dets');\n"
},
{
"path": "demo_make_iterative_gt.m",
"content": "% ------------------------------------------------------------------------ \n% Copyright (C)\n% Torr Vision Group (TVG)\n% University of Oxford - UK\n% \n% Qizhu Li \n% August 2018\n% ------------------------------------------------------------------------ \n% This file is part of the weakly-supervised training method presented in:\n% Qizhu Li*, Anurag Arnab*, Philip H.S. Torr,\n% \"Weakly- and Semi-Supervised Panoptic Segmentation,\"\n% European Conference on Computer Vision (ECCV) 2018.\n% Please consider citing the paper if you use this code.\n% ------------------------------------------------------------------------\n% This script demos generation of iterative ground truths for weakly-\n% supervised experiments.\n%\n% It post-processes network predictions to produce iterative GT for \n% training in the next iteration.\n% INPUT:\n% - results/pred_flat_feat/*.mat: the softmax scores of the predicted\n% classes.\n% - results/pred_sem_raw/*.png: the prediction made by the current\n% weakly-supervised model.\n% - results/mcg_and_grabcut//*.png: the combined cues from MCG \n% and Grabcut. Optional. Set opts.run_merge_with_mcg_and_grabcut = false\n% to disable. To reproduce the results in our paper, disable after first \n% 5 iterations.\n% ------------------------------------------------------------------------\n\nclearvars;\naddpath scripts\naddpath utils\naddpath visualisation\n\n% Extract detections from Cityscapes ground truth file and save as .mat\n% this only need to be done once, as they do not change over iterative\n% training stages\ndemo_instanceTrainId_to_dets;\n\nclearvars;\ndataset = 'cityscapes';\nsplit = 'train';\nopts = get_opts(dataset, split);\nopts.list_path = 'lists/demo_id.txt';\nopts.visualise_results = true;\n[opts, results] = run_sub(opts);\n\n% visualise\nvisualise_results_iterative_gt(opts, results);"
},
{
"path": "demo_merge_cam_mandg.m",
"content": "% ------------------------------------------------------------------------ \n% Copyright (C)\n% Torr Vision Group (TVG)\n% University of Oxford - UK\n% \n% Qizhu Li \n% August 2018\n% ------------------------------------------------------------------------ \n% This file is part of the weakly-supervised training method presented in:\n% Qizhu Li*, Anurag Arnab*, Philip H.S. Torr,\n% \"Weakly- and Semi-Supervised Panoptic Segmentation,\"\n% European Conference on Computer Vision (ECCV) 2018.\n% Please consider citing the paper if you use this code.\n% ------------------------------------------------------------------------\n% This script demos generation of iterative ground truths for weakly-\n% supervised experiments.\n%\n% It merges CAM predictions with MCG&Grabcut masks to produce GT for \n% the first round of iterative training.\n% INPUT:\n% - results/cam//*.png: the CAMs obtained from a multi-class \n% classifier.\n% - results/mcg_and_grabcut//*.png: the combined cues from MCG\n% and Grabcut.\n% ------------------------------------------------------------------------\n\nclearvars;\naddpath scripts\naddpath utils\naddpath visualisation\n\n% Extract detections from Cityscapes ground truth file and save as .mat\n% this only need to be done once, as they do not change over iterative\n% training stages\ndemo_instanceTrainId_to_dets;\n\nclearvars;\ndataset = 'cityscapes';\nsplit = 'train';\nopts = get_opts(dataset, split);\nopts.list_path = 'lists/demo_id.txt';\nopts.pred_dir = fullfile('cam', split);\nopts.sem_save_dir = 'pred_sem_cam_mandg_merged';\nopts.ins_save_dir = 'pred_ins_cam_mandg_merged';\nopts.run_score_thresh = false;\nopts.visualise_results = true;\n[opts, results] = run_sub(opts);\n\n% visualise\nvisualise_results_cam_mandg(opts, results);"
},
{
"path": "scripts/apply_bbox_prior.m",
"content": "% ------------------------------------------------------------------------ \n% Copyright (C)\n% Torr Vision Group (TVG)\n% University of Oxford - UK\n% \n% Qizhu Li \n% August 2018\n% ------------------------------------------------------------------------ \n% This file is part of the weakly-supervised training method presented in:\n% Qizhu Li*, Anurag Arnab*, Philip H.S. Torr,\n% \"Weakly- and Semi-Supervised Panoptic Segmentation,\"\n% European Conference on Computer Vision (ECCV) 2018.\n% Please consider citing the paper if you use this code.\n% ------------------------------------------------------------------------\n% This function removes thing-class predictions outside their bounding \n% boxes.\n% INPUT:\n% - pred_label : prediction\n% - bbox_masks : object bounding box masks produced by make_bbox_masks.m \n% - thing_classes : list of train ids for thing classes\n% - ignore_label : label to ignore, normally 255\n%\n% OUTPUT:\n% - modified_label: processed prediction\n%\n% DEMO:\n% - See scripts/clean_label.m\n% ------------------------------------------------------------------------\n\nfunction modified_label = apply_bbox_prior(pred_label, bbox_masks, thing_classes, ignore_label)\nmodified_label = pred_label;\nfor j = reshape(thing_classes, 1, [])\n if isempty(bbox_masks{j+1})\n modified_label(modified_label == j) = ignore_label;\n else\n prediction_bbox_prior_mask = modified_label == j;\n gt_bbox_mask = bbox_masks{j+1};\n ignore_mask = and(prediction_bbox_prior_mask, ~gt_bbox_mask);\n modified_label(ignore_mask) = ignore_label;\n end\nend\nend"
},
{
"path": "scripts/check_image_level_tags.m",
"content": "% ------------------------------------------------------------------------ \n% Copyright (C)\n% Torr Vision Group (TVG)\n% University of Oxford - UK\n% \n% Qizhu Li \n% August 2018\n% ------------------------------------------------------------------------ \n% This file is part of the weakly-supervised training method presented in:\n% Qizhu Li*, Anurag Arnab*, Philip H.S. Torr,\n% \"Weakly- and Semi-Supervised Panoptic Segmentation,\"\n% European Conference on Computer Vision (ECCV) 2018.\n% Please consider citing the paper if you use this code.\n% ------------------------------------------------------------------------\n% This function checks prediction against the image-level tags. If a class\n% is predicted by network but not present as indicated by image-level \n% tags, the predicted label is changed to ignore_label.\n% INPUT:\n% - pred_label : prediction\n% - gt_label : ground truth label to generate image level tags\n% - crop_size : size of crops used in training the multi-class \n% classifier. In our experiments, [w,h] = [500, 400]\n% - canvas_size : size of original full image\n% - ignore_label : label to ignore, normally 255\n%\n% OUTPUT:\n% - modified_label: processed prediction\n%\n% DEMO:\n% - See scripts/clean_label.m\n% ------------------------------------------------------------------------\n\nfunction modified_label = check_image_level_tags(pred_label, gt_label, crop_size, canvas_size, ignore_label)\nmodified_label = pred_label;\n\nn_tiles_width = ceil(canvas_size(2) / crop_size(2));\nn_tiles_height = ceil(canvas_size(1) / crop_size(1));\n\noverlap_width = ceil((n_tiles_width * crop_size(2) - canvas_size(2)) / (n_tiles_width - 1));\noverlap_height = ceil((n_tiles_height * crop_size(1) - canvas_size(1)) / (n_tiles_height - 1));\n\nfor j = 1:n_tiles_width\n for k = 1:n_tiles_height\n w_start = 1 + (j - 1) * (crop_size(2) - overlap_width);\n w_end = w_start + crop_size(2) - 1;\n h_start = 1 + (k - 1) * (crop_size(1) - overlap_height);\n h_end = h_start + crop_size(1) - 1;\n % check everything is within range\n assert(w_start >= 1 && h_start >= 1 && w_end <= canvas_size(2) && h_end <= canvas_size(1));\n gt_label_crop = gt_label(h_start:h_end, w_start:w_end);\n pred_label_crop = modified_label(h_start:h_end, w_start:w_end);\n present_gt_labels = unique(gt_label_crop);\n present_pred_labels = unique(pred_label_crop);\n for i = 1:numel(present_pred_labels)\n label = present_pred_labels(i);\n if ~ismember(label, present_gt_labels)\n pred_label_crop(pred_label_crop==label) = ignore_label;\n end\n end\n modified_label(h_start:h_end, w_start:w_end) = pred_label_crop; \n end\nend\nend"
},
{
"path": "scripts/check_low_iou.m",
"content": "% ------------------------------------------------------------------------ \n% Copyright (C)\n% Torr Vision Group (TVG)\n% University of Oxford - UK\n% \n% Qizhu Li \n% August 2018\n% ------------------------------------------------------------------------ \n% This file is part of the weakly-supervised training method presented in:\n% Qizhu Li*, Anurag Arnab*, Philip H.S. Torr,\n% \"Weakly- and Semi-Supervised Panoptic Segmentation,\"\n% European Conference on Computer Vision (ECCV) 2018.\n% Please consider citing the paper if you use this code.\n% ------------------------------------------------------------------------\n% This function checks prediction against the bounding boxes of thing \n% classes. If the IoU between prediction and GT bounding box is less than\n% thresh, then the bounding box is filled with appropriate train id. \n% INPUT:\n% - pred_label : prediction\n% - annotation : GT boxes, area, class, and whether is thing group\n% - thresh : IoU threshold below which filling is carried out \n% - do_not_alter_existing_things : when set to true, the filling will \n% not overwrite existing thing predictions\n% of a different class\n% - ignore_label : label to ignore, normally 255\n%\n% OUTPUT:\n% - modified_label: processed prediction\n%\n% DEMO:\n% - See scripts/clean_label.m\n% ------------------------------------------------------------------------\n\nfunction modified_label = check_low_iou(pred_label, annotation, thresh, do_not_alter_existing_things, ignore_label)\n% label: HxW label map\n% annotation\n%\tL> object\n% L> bbox: [xmin, ymin, xmax, ymax]\n% area: rectangular area of bbox\n% class: class_id as used in label\n%..........is_grp:.flag whether the bbox is for a thing group\n\nmodified_label = pred_label;\nstuff_classes = 0:10;\nif do_not_alter_existing_things\n ignore_or_stuff_mask = or(pred_label==ignore_label, ismember(pred_label, stuff_classes));\nend\n\nif isempty(annotation) || isempty(annotation.object)\n return;\nend\n\nbox_areas = extractfield(annotation.object, 'area');\n% we assume the smaller boxes are in the front of bigger boxes\n[~, ranking] = sort(box_areas, 'descend');\n\nfor k = 1:numel(annotation.object)\n % go from big to small\n ind = ranking(k);\n % get the gt class\n gt_class = annotation.object(ind).class;\n % skip stuff classes since their bounding boxes are image-level\n if ismember(gt_class, stuff_classes)\n continue;\n end\n % skip thing groups if is_grp is a field in annotation\n % thing group boxes typically have very low precision and high recall,\n % thus unsuitable for this post-processing option.\n if isfield(annotation.object(ind), 'is_grp') && annotation.object(ind).is_grp\n continue;\n end\n % make a binary mask for the gt bbox\n xmin = annotation.object(ind).bbox(1)+1;\n ymin = annotation.object(ind).bbox(2)+1;\n xmax = annotation.object(ind).bbox(3)+1;\n ymax = annotation.object(ind).bbox(4)+1;\n cols = double([xmin xmax xmax xmin]);\n rows = double([ymin ymin ymax ymax]);\n gt_bbox_mask = poly2mask(cols, rows, size(pred_label, 1), size(pred_label, 2));\n % make a binary class mask for the region inside the gt bbox\n label_mask = and(pred_label==gt_class, gt_bbox_mask);\n % calculate the IoU\n bbox_iou = nnz(label_mask) / nnz(gt_bbox_mask);\n % if it is smaller than a threshold\n if bbox_iou < thresh\n % we fill the region inside the box which is not gt_class with\n % gt_class\n rectify_region = and(pred_label~=gt_class, gt_bbox_mask);\n if do_not_alter_existing_things\n rectify_region = and(rectify_region, ignore_or_stuff_mask);\n end\n modified_label(rectify_region) = gt_class;\n end\nend\nend"
},
{
"path": "scripts/clean_label.m",
"content": "% ------------------------------------------------------------------------ \n% Copyright (C)\n% Torr Vision Group (TVG)\n% University of Oxford - UK\n% \n% Qizhu Li \n% August 2018\n% ------------------------------------------------------------------------ \n% This file is part of the weakly-supervised training method presented in:\n% Qizhu Li*, Anurag Arnab*, Philip H.S. Torr,\n% \"Weakly- and Semi-Supervised Panoptic Segmentation,\"\n% European Conference on Computer Vision (ECCV) 2018.\n% Please consider citing the paper if you use this code.\n% ------------------------------------------------------------------------\n% This function cleans up raw network predictions by performance 5 \n% operations as numbered below. \n% INPUT:\n% - opts : options as produced by scripts/get_opts.m\n% - results : struct containing required data and original prediction\n%\n% OUTPUT:\n% - results: struct containing required data, original prediction and \n% processed prediction\n%\n% DEMO:\n% - See scripts/run_sub.m\n% ------------------------------------------------------------------------\n\nfunction results = clean_label(opts, results)\n%% 1. Confidence threshold\n% requires pred_scores + prediction + conf_thresh\nif opts.run_score_thresh\n results.pred_threshed = results.final_pred;\n results.pred_threshed(results.pred_scores < opts.score_thresh) = opts.ignore_label;\n results.final_pred = results.pred_threshed;\nend\n\n%% 2. Stuff class check, using image tags of crops\n% requires gt_label + prediction\nif opts.run_check_image_level_tags\n results.pred_schk = check_image_level_tags(results.final_pred, results.gt_label, ...\n opts.schk_crop_size, opts.canvas_size, opts.ignore_label);\n results.final_pred = results.pred_schk;\nend\n\n%% 3. Merge with MCG&Grabcut cues\nif opts.run_merge_with_mcg_and_grabcut\n results.pred_merge_w_mandg = merge_mag_and_pred(results.mandg_pred, results.final_pred, ...\n opts.stuff_classes, opts.thing_classes, opts.ignore_label);\n results.final_pred = results.pred_merge_w_mandg;\nend\n\n%% 4. Remove things outside bounding boxes\n% requires gt_bbox_masks + prediction\nif opts.run_apply_bbox_prior\n results.pred_bbox_prior = apply_bbox_prior(results.final_pred, results.gt_bbox_masks, ...\n opts.thing_classes, opts.ignore_label);\n results.final_pred = results.pred_bbox_prior;\nend\n\n%% 5. IoU check, fill low IoU with solid colour\n% requires gt_bboxes + prediction\nif opts.run_check_low_iou\n results.pred_ichk = check_low_iou(results.final_pred, results.gt_bboxes, opts.ichk_thresh, ...\n opts.ichk_do_not_alter_existing_things, opts.ignore_label);\n results.final_pred = results.pred_ichk;\nend\n\nend"
},
{
"path": "scripts/get_opts.m",
"content": "% ------------------------------------------------------------------------ \n% Copyright (C)\n% Torr Vision Group (TVG)\n% University of Oxford - UK\n% \n% Qizhu Li \n% August 2018\n% ------------------------------------------------------------------------ \n% This file is part of the weakly-supervised training method presented in:\n% Qizhu Li*, Anurag Arnab*, Philip H.S. Torr,\n% \"Weakly- and Semi-Supervised Panoptic Segmentation,\"\n% European Conference on Computer Vision (ECCV) 2018.\n% Please consider citing the paper if you use this code.\n% ------------------------------------------------------------------------\n% This function produces a struct containing the required options used in \n% scripts/run_sub.m. \n% INPUT:\n% - dataset : name of the dataset, e.g. \"cityscapes\"\n% - split : dataset split, e.g. \"train\", \"val\", \"train_extra\"\n%\n% OUTPUT:\n% - opts: generated options\n%\n% DEMO:\n% - See demo_make_iterative_gt.m\n% ------------------------------------------------------------------------\n\nfunction opts = get_opts(dataset, split)\n% Dataset specific settings\nswitch dataset\n case 'cityscapes'\n switch split\n case 'train'\n opts.list_path = 'lists/train_id.txt';\n opts.annotation_dir = 'gtFine_bboxes/train/panoptic';\n opts.gt_label_dir = 'gtFine/train';\n opts.gt_label_template = '%s_gtFine_labelTrainIds.png';\n case 'train_extra'\n opts.list_path = 'lists/train_extra_id.txt';\n opts.annotation_dir = 'gtCoarse_bboxes/train_extra/panoptic';\n opts.gt_label_dir = 'gtCoarse/train_extra';\n opts.gt_label_template = '%s_gtCoarse_labelTrainIds.png';\n case 'val'\n opts.list_path = 'lists/val_id.txt';\n opts.annotation_dir = 'gtFine_bboxes/val/panoptic';\n opts.gt_label_dir = 'gtFine/val';\n opts.gt_label_template = '%s_gtFine_labelTrainIds.png';\n case 'test'\n error('Cannot post-process inferences on the test set.');\n otherwise\n error('Unknown split option.');\n end\n opts.data_root = 'data/Cityscapes';\n opts.annotation_template = '%s_leftImg8bit.mat';\n opts.objectNames_path = 'objectName19.mat';\n opts.colormap_path = 'colormapcs.mat';\n opts.canvas_size = [1024, 2048];\n opts.ignore_label = 255;\n opts.stuff_classes = 0:10;\n opts.thing_classes = 11:18;\n opts.pred_template = '%s_leftImg8bit.png';\n opts.pred_score_template = '%s_leftImg8bit.mat';\n opts.mcg_and_grabcut_template = '%s_leftImg8bit.png';\n otherwise\n error('Unknown dataset option');\nend\n\n% Job specific settings\nopts.pred_root = 'results';\nopts.score_thresh = 0.99;\nopts.ichk_thresh = 0.5;\nopts.ichk_do_not_alter_existing_things = true;\nopts.schk_crop_size = [400, 500];\n\n% General settings\nopts.pred_dir = 'pred_sem_raw';\nopts.pred_score_dir = 'pred_flat_feat';\nopts.mcg_and_grabcut_dir = fullfile('mcg_and_grabcut', split);\nopts.sem_save_dir = 'pred_sem_clean';\nopts.ins_save_dir = 'pred_ins_clean';\nopts.force_overwrite = true;\nopts.save_sem = true;\nopts.save_ins = true;\nopts.visualise_results = false;\n\n% Select which stages to run\nopts.run_score_thresh = true;\nopts.run_apply_bbox_prior = true;\nopts.run_check_low_iou = true;\nopts.run_check_image_level_tags = true;\nopts.run_merge_with_mcg_and_grabcut = true;\n\n% whether run instance GT generation\nopts.run_ins_box_process = true;\nend"
},
{
"path": "scripts/ins_box_process.m",
"content": "% ------------------------------------------------------------------------ \n% Copyright (C)\n% Torr Vision Group (TVG)\n% University of Oxford - UK\n% \n% Qizhu Li \n% August 2018\n% ------------------------------------------------------------------------ \n% This file is part of the weakly-supervised training method presented in:\n% Qizhu Li*, Anurag Arnab*, Philip H.S. Torr,\n% \"Weakly- and Semi-Supervised Panoptic Segmentation,\"\n% European Conference on Computer Vision (ECCV) 2018.\n% Please consider citing the paper if you use this code.\n% ------------------------------------------------------------------------\n% This function produces instance prediction from semantic prediction \n% using cues from GT bounding boxes. Regions where multiple bounding boxes \n% of the same class overlap is marked with ignore_label in instance pred.\n% INPUT:\n% - sem_label : semantic prediction\n% - annotation : GT boxes, area and class\n% - ignore_label : label to ignore, normally 255\n%\n% OUTPUT:\n% - ins_label: instance prediction\n%\n% DEMO:\n% - See scripts/run_sub.m\n% ------------------------------------------------------------------------\n\nfunction [ins_label, ins_info] = ins_box_process(sem_label, annotation, ignore_label)\n\nins_label = uint8(zeros(size(sem_label)));\nins_info = struct;\n% transfer all ignore region\nins_label(sem_label==ignore_label) = ignore_label;\n\nif isempty(annotation) || isempty(annotation.object)\n return;\nend\n\n% remove repeated bboxes of the same class just to be cautious\nbboxes = reshape(extractfield(annotation.object, 'bbox'), 4, [])';\nclasses = extractfield(annotation.object, 'class')';\n[~, unique_indices, ~] = unique([classes, bboxes], 'rows', 'stable');\n\nins_counter = 0;\n\nfor i = unique_indices'\n \n % thing grp is not an instance\n is_grp = annotation.object(i).is_grp;\n if is_grp\n continue;\n end\n \n ins_counter = ins_counter + 1;\n \n bbox = annotation.object(i).bbox;\n class_id = annotation.object(i).class;\n xmin = bbox(1)+1;\n ymin = bbox(2)+1;\n xmax = bbox(3)+1;\n ymax = bbox(4)+1;\n \n sem_gt_inside_bbox = sem_label(ymin:ymax, xmin:xmax);\n ins_gt_inside_bbox = ins_label(ymin:ymax, xmin:xmax);\n \n % for non-ignore region, if gt_id = class_id && gt_id != 0, then gt_id :-> ignore\n ins_gt_inside_bbox(and(sem_gt_inside_bbox==class_id, ins_gt_inside_bbox~=0)) = ignore_label;\n % for non-ignore region, if gt_id = class_id && gt_id = 0, then gt_id :-> ins_id\n ins_gt_inside_bbox(and(sem_gt_inside_bbox==class_id, ins_gt_inside_bbox==0)) = ins_counter;\n % record info in ins_info\n ins_info(ins_counter).ins_id = ins_counter;\n ins_info(ins_counter).name = annotation.object(i).name;\n ins_info(ins_counter).class = annotation.object(i).class;\n \n ins_label(ymin:ymax, xmin:xmax) = ins_gt_inside_bbox;\nend\n\n% sanity check: all instance labels should be present\nins_gt_labels = setdiff(unique(ins_label(:)), [0, ignore_label]);\nif ~isempty(setdiff(1:ins_counter, ins_gt_labels))\n warning('sanity not preserved');\nend\n\nend"
},
{
"path": "scripts/instanceTrainId_to_dets.m",
"content": "% ------------------------------------------------------------------------ \n% Copyright (C)\n% Torr Vision Group (TVG)\n% University of Oxford - UK\n% \n% Qizhu Li \n% August 2018\n% ------------------------------------------------------------------------ \n% This file is part of the weakly-supervised training method presented in:\n% Qizhu Li*, Anurag Arnab*, Philip H.S. Torr,\n% \"Weakly- and Semi-Supervised Panoptic Segmentation,\"\n% European Conference on Computer Vision (ECCV) 2018.\n% Please consider citing the paper if you use this code.\n% ------------------------------------------------------------------------\n% This function extracts bounding box information from Cityscapes ground\n% truth (*_gtFine_instanceTrainIds.png files). \n% INPUT:\n% - label : Cityscapes ground truth image\n% - is_panoptic: whether to include pseudo-detections for stuff classes\n% (image-wide detections) which are present\n% - incl_grps : whether to include thing groups in output struct\n% - stuff_clases: stuff class ids, e.g. 0:10\n% - thing_classes: thing class ids, e.g. 11:18\n% - objectNames: cell of class names\n% - ignore_label : label to ignore, normally 255\n%\n% OUTPUT:\n% - dets: struct obtaining bounding box information\n%\n% DEMO:\n% - See demo_instanceTrainId_to_dets.m\n% ------------------------------------------------------------------------\n\nfunction dets = instanceTrainId_to_dets(label, is_panoptic, incl_grps, ...\n stuff_classes, thing_classes, objectNames, ignore_label)\n\nunique_ids = unique(label(:));\nunique_ids = sort(unique_ids, 'ascend');\nunique_ids = setxor(unique_ids, ignore_label);\n\n% each entry in unique_ids is an id\n% if id > 1000:\n% it is an instance of a thing, = trainId*1000 + instance_id ...(CASE 1)\n% elseif id <= 1000:\n% if id in 0:1:10 :\n% it is stuff ...(CASE 2)\n% include image-wide det if is_panoptic\n% if id in 11:1:18 :\n% it is a thing group ...(CASE 3)\n% include group-wide det if incl_grps\n\ninstance_counter = 0;\ndets.annotation = [];\ncanvas_dims = size(label);\n\nfor k = 1:numel(unique_ids)\n \n valid = false;\n is_grp = false;\n is_stuff = false;\n id = unique_ids(k);\n \n if is_panoptic && ismember(id, stuff_classes)\n % CASE 2\n valid = true;\n is_stuff = true;\n xmin = num2str(0);\n ymin = num2str(0);\n xmax = num2str(canvas_dims(2) - 1);\n ymax = num2str(canvas_dims(1) - 1);\n class = objectNames{id + 1};\n elseif id > 1000\n % CASE 1\n valid = true;\n trainId = floor(id/1000);\n mask = label == id;\n [y, x] = find(mask);\n xmin = num2str(min(x) - 1);\n ymin = num2str(min(y) - 1);\n xmax = num2str(max(x) - 1);\n ymax = num2str(max(y) - 1);\n class = objectNames{trainId + 1};\n elseif incl_grps && ismember(id, thing_classes)\n % CASE 3\n valid = true;\n is_grp = true;\n mask = label == id;\n [y, x] = find(mask);\n xmin = num2str(min(x) - 1);\n ymin = num2str(min(y) - 1);\n xmax = num2str(max(x) - 1);\n ymax = num2str(max(y) - 1);\n class = objectNames{id + 1};\n end\n \n if valid\n % record class name, and 0-based bbox coordinates\n instance_counter = instance_counter + 1;\n dets.annotation.object(instance_counter).name = class;\n dets.annotation.object(instance_counter).bndbox.xmin = xmin;\n dets.annotation.object(instance_counter).bndbox.ymin = ymin;\n dets.annotation.object(instance_counter).bndbox.xmax = xmax;\n dets.annotation.object(instance_counter).bndbox.ymax = ymax;\n dets.annotation.object(instance_counter).is_grp = num2str(is_grp);\n dets.annotation.object(instance_counter).is_stuff = num2str(is_stuff);\n end\n \nend\n\nend"
},
{
"path": "scripts/load_data.m",
"content": "% ------------------------------------------------------------------------ \n% Copyright (C)\n% Torr Vision Group (TVG)\n% University of Oxford - UK\n% \n% Qizhu Li \n% August 2018\n% ------------------------------------------------------------------------ \n% This file is part of the weakly-supervised training method presented in:\n% Qizhu Li*, Anurag Arnab*, Philip H.S. Torr,\n% \"Weakly- and Semi-Supervised Panoptic Segmentation,\"\n% European Conference on Computer Vision (ECCV) 2018.\n% Please consider citing the paper if you use this code.\n% ------------------------------------------------------------------------\n% This function loads data required by run_sub.m. \n% INPUT:\n% - opts : options as produced by scripts/get_opts.m\n% - k: instructs the function to load data for the k-th entry in \n% opts.list\n%\n% OUTPUT:\n% - results: struct obtaining loaded data\n%\n% DEMO:\n% - See scripts/run_sub.m\n% ------------------------------------------------------------------------\n\nfunction results = load_data(opts, k)\n%% Initialisation\nresults.id = opts.list{k};\n\n%% Load required data\n% pred, pred_scores, gt_bbox_masks, gt_bboxes, gt_label, mandg_pred (MCG&Grabcut)\n[results.pred, results.cmap] = imread(fullfile(opts.pred_root, opts.pred_dir, ...\n sprintf(opts.pred_template, results.id)));\nif opts.run_score_thresh\n temp = load(fullfile(opts.pred_root, opts.pred_score_dir, ...\n sprintf(opts.pred_score_template, results.id)));\n temp_name = fieldnames(temp);\n results.pred_scores = temp.(temp_name{1});\nend\nif opts.run_apply_bbox_prior || opts.run_check_low_iou || opts.run_ins_box_process\n [results.gt_bbox_masks, results.gt_bboxes] = make_bbox_masks(fullfile(opts.data_root, opts.annotation_dir, ...\n sprintf(opts.annotation_template, results.id)), opts.objectNames, opts.canvas_size);\nend\nif opts.run_check_image_level_tags\n city = strtok(results.id, '_');\n results.gt_label = imread(fullfile(opts.data_root, opts.gt_label_dir, city, ...\n sprintf(opts.gt_label_template, results.id)));\nend\nif opts.run_merge_with_mcg_and_grabcut\n [results.mandg_pred, results.mandg_cmap] = imread(fullfile(opts.pred_root, opts.mcg_and_grabcut_dir, ...\n sprintf(opts.mcg_and_grabcut_template, results.id)));\nend\n\nresults.final_pred = results.pred;\n\nend"
},
{
"path": "scripts/make_bbox_masks.m",
"content": "% ------------------------------------------------------------------------ \n% Copyright (C)\n% Torr Vision Group (TVG)\n% University of Oxford - UK\n% \n% Qizhu Li \n% August 2018\n% ------------------------------------------------------------------------ \n% This file is part of the weakly-supervised training method presented in:\n% Qizhu Li*, Anurag Arnab*, Philip H.S. Torr,\n% \"Weakly- and Semi-Supervised Panoptic Segmentation,\"\n% European Conference on Computer Vision (ECCV) 2018.\n% Please consider citing the paper if you use this code.\n% ------------------------------------------------------------------------\n% This function processes the output of instanceTrainId_to_dets.m to make \n% two formats of annotation used by other code. \n% INPUT:\n% - annotation_file_path : file path to the saved output of \n% instanceTrainId_to_dets.m\n% - objectNames: cell of class names\n% - canvas_size: size of original full image\n%\n% OUTPUT:\n% - bbox_masks: class-specific bounding box masks by combining \n% bounding box masks of the same predicted class\n% - bboxes: struct storing bbox location, class, area, whether group\n%\n% DEMO:\n% - See scripts/load_data.m\n% ------------------------------------------------------------------------\n\nfunction [bbox_masks, bboxes] = make_bbox_masks(annotation_file_path, objectNames, canvas_size)\nbbox_masks = cell(length(objectNames), 1);\ntemp = load(annotation_file_path);\nannotation = temp.dets;\nif ~isfield(annotation.annotation, 'object') || isempty(annotation.annotation.object)\n bboxes = [];\n return;\nend\nfor k = 1:length(annotation.annotation.object)\n class = annotation.annotation.object(k).name;\n class_id = find(strcmp(class, objectNames)) - 1;\n bndbox = annotation.annotation.object(k).bndbox;\n xmin = str2double(bndbox.xmin)+1;\n ymin = str2double(bndbox.ymin)+1;\n xmax = str2double(bndbox.xmax)+1;\n ymax = str2double(bndbox.ymax)+1;\n is_grp = logical(str2double(annotation.annotation.object(k).is_grp));\n is_stuff = logical(str2double(annotation.annotation.object(k).is_stuff));\n xmin = max(1, xmin);\n ymin = max(1, ymin);\n xmax = min(canvas_size(2), xmax);\n ymax = min(canvas_size(1), ymax);\n annotation.annotation.object(k).bbox = [xmin, ymin, xmax, ymax] - 1;\n annotation.annotation.object(k).area = (xmax - xmin + 1) * (ymax - ymin + 1);\n annotation.annotation.object(k).class = class_id;\n annotation.annotation.object(k).is_grp = is_grp;\n annotation.annotation.object(k).is_stuff = is_stuff;\n if isempty(bbox_masks{class_id + 1})\n class_mask = false(canvas_size);\n else\n class_mask = bbox_masks{class_id + 1};\n end\n class_mask(ymin:ymax, xmin:xmax) = true;\n bbox_masks{class_id + 1} = class_mask;\nend\nbboxes = annotation.annotation;\nend"
},
{
"path": "scripts/merge_mag_and_pred.m",
"content": "% ------------------------------------------------------------------------ \n% Copyright (C)\n% Torr Vision Group (TVG)\n% University of Oxford - UK\n% \n% Qizhu Li \n% August 2018\n% ------------------------------------------------------------------------ \n% This file is part of the weakly-supervised training method presented in:\n% Qizhu Li*, Anurag Arnab*, Philip H.S. Torr,\n% \"Weakly- and Semi-Supervised Panoptic Segmentation,\"\n% European Conference on Computer Vision (ECCV) 2018.\n% Please consider citing the paper if you use this code.\n% ------------------------------------------------------------------------\n% This function merges network prediction with weak cues from MCG and \n% Grabcut. \n% INPUT:\n% - mag : MCG&Grabcut output\n% - pred: semantic prediction\n% - stuff_clases : stuff class ids, e.g. 0:10\n% - thing_classes: thing class ids, e.g. 11:18\n% - ignore_label : label to ignore, normally 255\n%\n% OUTPUT:\n% - merged_label: the combined label\n%\n% DEMO:\n% - See scripts/clean_label.m\n% ------------------------------------------------------------------------\n\nfunction merged_label = merge_mag_and_pred(mag, pred, stuff_classes, thing_classes, ignore_label)\n\nassert(all(size(mag) == size(pred)), 'Size mismatch');\n\nmag_label_things_mask = ismember(mag, thing_classes);\nmag_label_stuff_mask = ismember(mag, 0);\nmag_label_ignore_mask = ismember(mag, ignore_label);\npred_label_things_mask = ismember(pred, thing_classes);\npred_label_stuff_mask = ismember(pred, stuff_classes);\npred_label_ignore_mask = ismember(pred, ignore_label);\n\nmerged_label = 255*uint8(ones(size(mag)));\n\n%% 1) if m&g label predicts thing class label C1:\n% a. pred predicts thing class C1 => C1\n mask_1a = and(mag_label_things_mask, mag == pred);\n merged_label(mask_1a) = mag(mask_1a); \n% b. pred predicts thing class C2 => C1\n mask_1b = and(and(mag_label_things_mask, pred_label_things_mask), ~mask_1a);\n merged_label(mask_1b) = mag(mask_1b);\n% c. pred predicts background class C3 => C1\n mask_1c = and(mag_label_things_mask, pred_label_stuff_mask);\n merged_label(mask_1c) = mag(mask_1c);\n% d. pred predicts 255 => C1\n mask_1d = and(mag_label_things_mask, pred_label_ignore_mask);\n merged_label(mask_1d) = mag(mask_1d);\n\n%% 2) if m&g label predicts background class label 0:\n% a. pred predicts things class C1 => 255\n mask_2a = and(mag_label_stuff_mask, pred_label_things_mask);\n merged_label(mask_2a) = ignore_label;\n% b. pred predicts background class C2 => C2\n mask_2b = and(mag_label_stuff_mask, pred_label_stuff_mask);\n merged_label(mask_2b) = pred(mask_2b);\n% c. pred predicts 255 => 255\n mask_2c = and(mag_label_stuff_mask, pred_label_ignore_mask);\n merged_label(mask_2c) = ignore_label;\n\n%% 3) if m&g label predicts ignore label 255:\n% a. pred predicts thing class C1 => 255\n mask_3a = and(mag_label_ignore_mask, pred_label_things_mask);\n merged_label(mask_3a) = ignore_label;\n% b. pred predicts background class C2 => C2\n mask_3b = and(mag_label_ignore_mask, pred_label_stuff_mask);\n merged_label(mask_3b) = pred(mask_3b);\n% c. pred predicts 255 => 255\n mask_3c = and(mag_label_ignore_mask, pred_label_ignore_mask);\n merged_label(mask_3c) = ignore_label;\n\nend"
},
{
"path": "scripts/run_sub.m",
"content": "% ------------------------------------------------------------------------ \n% Copyright (C)\n% Torr Vision Group (TVG)\n% University of Oxford - UK\n% \n% Qizhu Li \n% August 2018\n% ------------------------------------------------------------------------ \n% This file is part of the weakly-supervised training method presented in:\n% Qizhu Li*, Anurag Arnab*, Philip H.S. Torr,\n% \"Weakly- and Semi-Supervised Panoptic Segmentation,\"\n% European Conference on Computer Vision (ECCV) 2018.\n% Please consider citing the paper if you use this code.\n% ------------------------------------------------------------------------\n% This function is the main function of iterative GT generation. It \n% processes all entries of the list file found at opts.list_path.\n% INPUT:\n% - opts : options generated by get_opts.m\n%\n% OUTPUT:\n% - opts : options after running the code, for inspection purpose\n% - results: results of the last entry in the list, for inspection purpose\n%\n% DEMO:\n% - See demo_make_iterative_gt.m\n% ------------------------------------------------------------------------\n\nfunction [opts, results] = run_sub(opts)\n% list\nopts.list = importdata(fullfile(opts.data_root, opts.list_path));\n% cmap\ntemp = load(opts.colormap_path);\nopts.cmap = temp.cmap;\n% objectNames\ntemp = load(opts.objectNames_path);\nopts.objectNames = temp.objectNames;\n\n% opts.save_ins is forced to false if opts.run_ins_process = false\nopts.save_ins = opts.save_ins && opts.run_ins_box_process;\n\n% make dirs\nif ~exist(fullfile(opts.pred_root, opts.sem_save_dir), 'dir') && opts.save_sem\n mkdir(fullfile(opts.pred_root, opts.sem_save_dir));\nend\nif ~exist(fullfile(opts.pred_root, opts.ins_save_dir), 'dir') && opts.save_ins\n mkdir(fullfile(opts.pred_root, opts.ins_save_dir));\nend\n\n% init results\nresults = struct;\n\n% main process\nfprintf('[%s] Processing %d image(s)...\\n', char(datetime), length(opts.list));\nfor k = 1:length(opts.list)\n % skip if output exists and not foce overwrite\n if save_results(opts, k)\n continue;\n end\n % load required data\n results = load_data(opts, k);\n % semantic label cleaning\n results = clean_label(opts, results);\n % make instance label\n if opts.run_ins_box_process\n [results.ins_pred, results.ins_info] = ins_box_process(results.final_pred, ...\n results.gt_bboxes, opts.ignore_label);\n end\n % save results\n save_results(opts, k, results);\n \n if mod(k,100) == 0\n fprintf('[%s] Processed %d/%d\\n', char(datetime), k, length(opts.list));\n end\nend\n\nend\n"
},
{
"path": "scripts/save_results.m",
"content": "% ------------------------------------------------------------------------ \n% Copyright (C)\n% Torr Vision Group (TVG)\n% University of Oxford - UK\n% \n% Qizhu Li \n% August 2018\n% ------------------------------------------------------------------------ \n% This file is part of the weakly-supervised training method presented in:\n% Qizhu Li*, Anurag Arnab*, Philip H.S. Torr,\n% \"Weakly- and Semi-Supervised Panoptic Segmentation,\"\n% European Conference on Computer Vision (ECCV) 2018.\n% Please consider citing the paper if you use this code.\n% ------------------------------------------------------------------------\n% This function saves results at save paths instructed by opts. When only\n% 2 arguments are passed in, it checks whether the results already exist,\n% and returns a boolean to indicate the finding.\n% INPUT:\n% - opts : options generated by get_opts.m\n% - k: current iteration number through opts.list\n% - results: struct containing the processed labels\n%\n% OUTPUT:\n% - skip: whether to skip the current iteration based on whether the\n% output already exists, and whether opts.force_overwrite is true\n%\n% DEMO:\n% - See scripts/run_sub.m\n% ------------------------------------------------------------------------\n\nfunction skip = save_results(opts, k, results)\n\npred_sem_save_path = fullfile(opts.pred_root, opts.sem_save_dir, sprintf(opts.pred_template, opts.list{k}));\npred_ins_save_path = fullfile(opts.pred_root, opts.ins_save_dir, sprintf(opts.pred_template, opts.list{k}));\n\nsem_ok = ~opts.save_sem || exist(pred_sem_save_path, 'file');\nins_ok = ~opts.save_ins || exist(pred_ins_save_path, 'file');\n\nif sem_ok && ins_ok && ~opts.force_overwrite\n skip = true;\n return;\nelse\n skip = false;\n if nargin < 3\n return;\n end\nend\n\nif opts.save_sem\n imwrite(results.final_pred, opts.cmap, pred_sem_save_path);\nend\n\nif opts.save_ins\n imwrite(results.ins_pred, opts.cmap, pred_ins_save_path);\nend\n\nend"
},
{
"path": "visualisation/visualise_bboxes.m",
"content": "function vis_im = visualise_bboxes(id, objectNames, cmap, rgb_dir, detection_dir)\n\nfont_size = 25;\n\ncity = strtok(id, '_');\n\nimage_filename = fullfile(rgb_dir, city, [id '_leftImg8bit.png']);\ndetection_filename = fullfile(detection_dir, [id '_leftImg8bit.mat']);\n\nrgb_im = imread(image_filename);\ntemp = load(detection_filename);\ndetections = temp.dets.annotation;\nvis_im = rgb_im;\n\nif ~isfield(detections, 'object') || isempty(detections.object)\n return;\nend\n\nrectangle_position = zeros(length(detections), 4);\ncolour = zeros(length(detections), 3);\ntext_strings = cell(length(detections), 1);\n% we eventually want to display the bboxes differently depending on whether\n% they are a group annotation or not. unfortunately it seems insertShape as\n% it is now doesn't support changes to line style.\nis_grp = zeros(length(detections), 1);\n\nfor k = length(detections.object):-1:1\n name = detections.object(k).name;\n train_id = find(strcmp(objectNames, name)) - 1;\n assert(~isempty(train_id));\n xmin = str2double(detections.object(k).bndbox.xmin) + 1;\n ymin = str2double(detections.object(k).bndbox.ymin) + 1;\n xmax = str2double(detections.object(k).bndbox.xmax) + 1;\n ymax = str2double(detections.object(k).bndbox.ymax) + 1;\n width = xmax - xmin + 1;\n height = ymax - ymin + 1;\n rectangle_position(k, :) = [xmin, ymin, width, height];\n \n colour(k, :) = cmap(train_id + 1,:);\n text_strings{k} = name;\n is_grp(k) = logical(str2double(detections.object(k).is_grp));\n % this is a temporary workaround\n if is_grp(k)\n text_strings{k} = [text_strings{k}, 'group'];\n end\n\nend\n\ntext_position = rectangle_position(:,1:2);\nvis_im = insertShape(vis_im, 'Rectangle', rectangle_position,...\n 'LineWidth', 4, 'Color', colour*255);\n\n% scale font size according to width of bbox for clearer visual\nmax_font_shrink_ratio = 2;\npixel_to_font_size_ratio = 2.6; % for three-letter word\nstep_size = 0.25;\nwidth_threshes = [0, ...\n (pixel_to_font_size_ratio/max_font_shrink_ratio + step_size) : step_size : pixel_to_font_size_ratio,...\n Inf] * font_size;\nfor k = (numel(width_threshes) - 1):(-1):1\n dets_in_range = width_threshes(k) <= rectangle_position(:,3) & ...\n rectangle_position(:,3) < width_threshes(k+1);\n adjusted_font_size = floor(max(width_threshes(k)/pixel_to_font_size_ratio, ...\n font_size/max_font_shrink_ratio));\n if any(dets_in_range)\n vis_im = insertText(vis_im, text_position(dets_in_range, :), text_strings(dets_in_range),...\n 'Font', 'UbuntuMono-R', 'FontSize', adjusted_font_size,...\n 'BoxColor', colour(dets_in_range, :)*255, 'BoxOpacity', 0.5, 'TextColor', 'white', ...\n 'AnchorPoint', 'LeftBottom');\n end\nend\nend\n"
},
{
"path": "visualisation/visualise_results_cam_mandg.m",
"content": "% ------------------------------------------------------------------------ \n% Copyright (C)\n% Torr Vision Group (TVG)\n% University of Oxford - UK\n% \n% Qizhu Li \n% August 2018\n% ------------------------------------------------------------------------ \n% This file is part of the weakly-supervised training method presented in:\n% Qizhu Li*, Anurag Arnab*, Philip H.S. Torr,\n% \"Weakly- and Semi-Supervised Panoptic Segmentation,\"\n% European Conference on Computer Vision (ECCV) 2018.\n% Please consider citing the paper if you use this code.\n% ------------------------------------------------------------------------\n% This function visualises the original prediction, original prediction \n% scores, processed semantic iterative GT, and generated instance \n% iterative GT.\n% INPUT:\n% - opts : options generated by get_opts.m\n% - results : results generated by the preceding code\n%\n% DEMO:\n% - See scripts/run_sub.m\n% ------------------------------------------------------------------------\n\nfunction visualise_results_cam_mandg(opts, results)\n\nif ~opts.visualise_results\n return;\nend\n\nf = figure('units','normalized','outerposition',[0 0 1 1], 'Visible', 'off');\n\nax(1) = subplot(2, 2, 1, 'replace');\nimshow(results.pred, opts.cmap);\nc(1) = colorbar(ax(1));\nc(1).Limits = [0, 19];\nc(1).Ticks = 0.5:1:18.5;\nc(1).TickLabels = opts.objectNames;\nc(1).FontSize = 8;\ntitle(ax(1), '1. CAM prediction');\n\nax(2) = subplot(2, 2, 2, 'replace');\nimshow(results.mandg_pred, results.mandg_cmap);\ntitle(ax(2), '2. MCG&Grabcut Masks');\n\nax(3) = subplot(2, 2, 3, 'replace');\nimshow(results.final_pred, opts.cmap);\nc(3) = colorbar(ax(3));\nc(3).Limits = [0, 19];\nc(3).Ticks = 0.5:1:18.5;\nc(3).TickLabels = opts.objectNames;\nc(3).FontSize = 8;\ntitle(ax(3), '3. Processed semantic GT');\n\nax(4) = subplot(2, 2, 4, 'replace');\nimshow(results.ins_pred, opts.cmap);\nc(4) = colorbar(ax(4));\nnum_instances = numel(results.ins_info);\nc(4).Limits = [1, num_instances + 1];\nc(4).Ticks = 1.5:1:(num_instances+0.5);\nc(4).TickLabels = strcat(strsplit(num2str(extractfield(results.ins_info, 'ins_id'))), ...\n {': '}, extractfield(results.ins_info, 'name'));\nc(4).FontSize = 8;\ntitle(ax(4), '2. Generated instance GT');\n\nf.Visible = 'on';\n\nend"
},
{
"path": "visualisation/visualise_results_iterative_gt.m",
"content": "% ------------------------------------------------------------------------ \n% Copyright (C)\n% Torr Vision Group (TVG)\n% University of Oxford - UK\n% \n% Qizhu Li \n% August 2018\n% ------------------------------------------------------------------------ \n% This file is part of the weakly-supervised training method presented in:\n% Qizhu Li*, Anurag Arnab*, Philip H.S. Torr,\n% \"Weakly- and Semi-Supervised Panoptic Segmentation,\"\n% European Conference on Computer Vision (ECCV) 2018.\n% Please consider citing the paper if you use this code.\n% ------------------------------------------------------------------------\n% This function visualises the original prediction, original prediction \n% scores, processed semantic iterative GT, and generated instance \n% iterative GT.\n% INPUT:\n% - opts : options generated by get_opts.m\n% - results : results generated by the preceding code\n%\n% DEMO:\n% - See scripts/run_sub.m\n% ------------------------------------------------------------------------\n\nfunction visualise_results_iterative_gt(opts, results)\n\nif ~opts.visualise_results\n return;\nend\n\nf = figure('units','normalized','outerposition',[0 0 1 1], 'Visible', 'off');\n\nax(1) = subplot(2, 2, 1, 'replace');\nimshow(results.pred, opts.cmap);\nc(1) = colorbar(ax(1));\nc(1).Limits = [0, 19];\nc(1).Ticks = 0.5:1:18.5;\nc(1).TickLabels = opts.objectNames;\nc(1).FontSize = 8;\ntitle(ax(1), '1. Raw prediction');\n\nax(2) = subplot(2, 2, 2, 'replace');\nimshow(results.pred_scores, []);\nc(2) = colorbar(ax(2));\ntitle(ax(2), '2. Raw prediction score');\n\nax(3) = subplot(2, 2, 3, 'replace');\nimshow(results.final_pred, opts.cmap);\nc(3) = colorbar(ax(3));\nc(3).Limits = [0, 19];\nc(3).Ticks = 0.5:1:18.5;\nc(3).TickLabels = opts.objectNames;\nc(3).FontSize = 8;\ntitle(ax(3), '3. Processed semantic GT');\n\nax(4) = subplot(2, 2, 4, 'replace');\nimshow(results.ins_pred, opts.cmap);\nc(4) = colorbar(ax(4));\nnum_instances = numel(results.ins_info);\nc(4).Limits = [1, num_instances + 1];\nc(4).Ticks = 1.5:1:(num_instances+0.5);\nc(4).TickLabels = strcat(strsplit(num2str(extractfield(results.ins_info, 'ins_id'))), ...\n {': '}, extractfield(results.ins_info, 'name'));\nc(4).FontSize = 8;\ntitle(ax(4), '2. Generated instance GT');\n\nf.Visible = 'on';\n\nend"
}
]