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Repository: qizhuli/Weakly-Supervised-Panoptic-Segmentation
Branch: master
Commit: 80081fc42eff
Files: 32
Total size: 841.0 KB
Directory structure:
gitextract_otvagzvw/
├── .gitattributes
├── .gitignore
├── LICENSE
├── README.md
├── batch_instanceTrainId_to_dets.m
├── data/
│ └── Cityscapes/
│ ├── gtFine/
│ │ └── train/
│ │ └── aachen/
│ │ └── aachen_000000_000019_gtFine_polygons.json
│ └── lists/
│ ├── demo_id.txt
│ ├── test_id.txt
│ ├── train_extra_id.txt
│ ├── train_id.txt
│ └── val_id.txt
├── demo_instanceTrainId_to_dets.m
├── demo_make_iterative_gt.m
├── demo_merge_cam_mandg.m
├── results/
│ └── pred_flat_feat/
│ └── aachen_000000_000019_leftImg8bit.mat
├── scripts/
│ ├── apply_bbox_prior.m
│ ├── check_image_level_tags.m
│ ├── check_low_iou.m
│ ├── clean_label.m
│ ├── get_opts.m
│ ├── ins_box_process.m
│ ├── instanceTrainId_to_dets.m
│ ├── load_data.m
│ ├── make_bbox_masks.m
│ ├── merge_mag_and_pred.m
│ ├── run_sub.m
│ └── save_results.m
├── utils/
│ ├── colormapcs.mat
│ └── objectName19.mat
└── visualisation/
├── visualise_bboxes.m
├── visualise_results_cam_mandg.m
└── visualise_results_iterative_gt.m
================================================
FILE CONTENTS
================================================
================================================
FILE: .gitattributes
================================================
# Auto detect text files and perform LF normalization
* text=auto
================================================
FILE: .gitignore
================================================
*.m~
*.asv
data/Cityscapes/gtFine_bboxes/
results/pred_ins_clean/
results/pred_sem_clean/
results/pred_ins_cam_mandg_merged/
results/pred_sem_cam_mandg_merged/
================================================
FILE: LICENSE
================================================
MIT License
Copyright (c) 2018 Qizhu Li, Anurag Arnab, Philip H.S Torr
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
================================================
# Weakly- and Semi-Supervised Panoptic Segmentation
by [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)
This 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.
For 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.
<sup><sub> \* Equal first authorship </sup></sub>
## Introduction
In 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.
We 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.
## Iterative ground truth generation
For readers' convenience, we will give an outline of the proposed iterative ground truth generation pipeline, and provide demos for some of the key steps.
1. 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.
- 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.
- The CAMs we produced are available for download [here](#downloads-cam).
2. 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.
- 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
1. Download the [official Cityscapes scripts repository](https://github.com/mcordts/cityscapesScripts);
2. Inside the above repository, navigate to `cityscapesscripts/preparation` and run
```sh
python createTrainIdInstanceImgs.py
```
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;
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.
- Please see [here](https://github.com/jponttuset/mcg) for details on MCG.
- We use the [OpenCV implementation](https://docs.opencv.org/3.2.0/d8/d83/tutorial_py_grabcut.html) of Grabcut in our experiments.
- The merged M&G masks we produced are available for download [here](#downloads-mandg).
3. 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:
```matlab
demo_merge_cam_mandg;
```
When post-processing network predictions of images from the Cityscapes `train_extra` split, make sure to use the following settings:
```matlab
opts.run_apply_bbox_prior = false;
opts.run_check_image_level_tags = false;
opts.save_ins = false;
```
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.
- The results from merging CAMs with M&G masks can be downloaded [here](#downloads-cam-mandg-merged).
4. 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.
- 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/`.
5. 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:
```matlab
demo_make_iterative_gt;
```
The generated semantic and instance ground truth labels are saved at `results/pred_sem_clean` and `results/pred_ins_clean` respectively.
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.
6. Repeat step 4 and 5 until training loss no longer reduces.
## Downloads
1. <a id="downloads-crops"></a>Image crops and tags for training multi-class classifier:
- Images
- 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)
- 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)
- val (1.6GB): [Dropbox](https://www.dropbox.com/s/hudd1k4i4zr53qj/leftImg8bit_400x500crops_val.zip?dl=0) or [BaiduYun](https://pan.baidu.com/s/1jSCps4wNg45mbgM0ggM7AQ)
- Ground truth tags
- 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)
- Lists
- train+train_extra+val (827kB): [Dropbox](https://www.dropbox.com/s/8itgdm0nau0rixz/lists.zip?dl=0) or [BaiduYun](https://pan.baidu.com/s/14j9rV3S8599YwYILzEfrCw)
- Semantic labels (provided for convenience; **not** to be used in training)
- 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)
- 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)
- 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)
2. <a id="downloads-cam"></a>CAMs:
- train+train_extra (682MB): [Dropbox](https://www.dropbox.com/s/24p60caieq3skik/cam.zip?dl=0) or [BaiduYun](https://pan.baidu.com/s/1zhgzw0IU7r9YBBBmwwRTzA)
3. <a id="downloads-bboxes"></a>Extracted Cityscapes bounding boxes (.mat format):
- 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)
- train_extra (44.2GB): [Dropbox](https://www.dropbox.com/s/tuv4r44sr5vt15z/gtCoarse_bboxes.zip?dl=0) or [BaiduYun](https://pan.baidu.com/s/1p8bpZ2srcNmHlkCrLnPH2Q)
4. <a id="downloads-mandg"></a>Merged MCG&Grabcut masks:
- 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)
5. <a id="downloads-cam-mandg-merged"></a>CAMs merged with MCG&Grabcut masks:
- 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)
Note 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:
```sh
cat filename.zip.part* > filename.zip
```
The joining operation may take several minutes, depending on file size.
The above does not apply to files downloaded from Dropbox.
## Reference
If you find the code helpful in your research, please cite our paper:
```tex
@InProceedings{Li_2018_ECCV,
author = {Li, Qizhu and
Arnab, Anurag and
Torr, Philip H.S.},
title = {Weakly- and Semi-Supervised Panoptic Segmentation},
booktitle = {The European Conference on Computer Vision (ECCV)},
month = {September},
year = {2018}
}
```
## Questions
Please contact Qizhu Li <qizhu.li@eng.ox.ac.uk> and Anurag Arnab <aarnab@robots.ox.ac.uk> for enquires, issues, and suggestions.
================================================
FILE: batch_instanceTrainId_to_dets.m
================================================
% ------------------------------------------------------------------------
% Copyright (C)
% Torr Vision Group (TVG)
% University of Oxford - UK
%
% Qizhu Li <liqizhu@robots.ox.ac.uk>
% August 2018
% ------------------------------------------------------------------------
% This file is part of the weakly-supervised training method presented in:
% Qizhu Li*, Anurag Arnab*, Philip H.S. Torr,
% "Weakly- and Semi-Supervised Panoptic Segmentation,"
% European Conference on Computer Vision (ECCV) 2018.
% Please consider citing the paper if you use this code.
% ------------------------------------------------------------------------
% This script demos extracting bounding box information from Cityscapes
% instance ground truth (*_gt<Fine,Coarse>_instanceTrainIds.png files) for
% generation of iterative ground truths for weakly-supervised experiments
%
% It is a batch processor. Set split to 'train', 'val', or 'train_extra'
% as desired. Make sure the instance ground truth files are found in
% data/Cityscapes/gt<Fine,Coarse>/<train,val,train_extra>/ respectively.
% If `visualise` is set to true, make sure the original RGB files are
% available in data/Cityscapes/leftImg8bit/<train,val,train_extra>/
% following the standard Cityscapes data folder organisation.
% -----------------------------------------------------------------------
clearvars;
addpath utils
addpath scripts
addpath visualisation
%% Configure
split = 'train'; % train, val or train_extra
% set is_panoptic flag to include image-level stuff class dets
is_panoptic = true;
% set incl_grps flag to include thing groups present in Cityscapes
% annotation
incl_grps = true;
% trainIds of stuff classes
stuff_classes = 0:10;
% trainIds of thing classes
thing_classes = 11:18;
% class names
load objectName19.mat
% ignore label
ignore_label = 255;
% force overwrite
force_overwrite = true;
% visualise
visualise = false;
visualise_save_template = '%s_leftImg8bit.png';
load utils/colormapcs.mat
%% Parse
switch split
case 'train'
instanceTrainId_dir = 'data/Cityscapes/gtFine/train';
instanceTrainId_template = '%s_gtFine_instanceTrainIds.png';
list_path = 'data/Cityscapes/lists/train_id.txt';
save_dir = 'data/Cityscapes/gtFine_bboxes/train/%s'; % panoptic or thing-only as the child folder
save_template = '%s_leftImg8bit.mat';
rgb_dir = 'data/Cityscapes/leftImg8bit/train';
case 'val'
instanceTrainId_dir = 'data/Cityscapes/gtFine/val';
instanceTrainId_template = '%s_gtFine_instanceTrainIds.png';
list_path = 'data/Cityscapes/lists/val_id.txt';
save_dir = 'data/Cityscapes/gtFine_bboxes/val/%s'; % panoptic or thing-only as the child folder
save_template = '%s_leftImg8bit.mat';
rgb_dir = 'data/Cityscapes/leftImg8bit/val';
case 'train_extra'
instanceTrainId_dir = 'data/Cityscapes/gtCoarse/train_extra';
instanceTrainId_template = '%s_gtCoarse_instanceTrainIds.png';
list_path = 'data/Cityscapes/lists/train_extra_id.txt';
save_dir = 'data/Cityscapes/gtCoarse_bboxes/train_extra/%s'; % panoptic or thing-only as the child folder
save_template = '%s_leftImg8bit.mat';
rgb_dir = 'data/Cityscapes/leftImg8bit/train_extra';
otherwise
error('Unrecognised data split.');
end
if is_panoptic
save_dir = sprintf(save_dir, 'panoptic');
else
save_dir = sprintf(save_dir, 'thing-only');
end
if ~exist(save_dir, 'dir')
mkdir(save_dir);
end
list = importdata(list_path);
%% Run the extraction
for k = 1:length(list)
id = list{k};
save_path = fullfile(save_dir, sprintf(save_template, id));
% if the save_path file exists, and we don't require force-overwrite, skip the current file
if ~force_overwrite && exist(save_path, 'file')
continue;
end
city = strtok(id, '_');
label_path = fullfile(instanceTrainId_dir, city, sprintf(instanceTrainId_template, id));
label = imread(label_path);
dets = instanceTrainId_to_dets(label, is_panoptic, incl_grps, ...
stuff_classes, thing_classes, objectNames, ignore_label);
save(fullfile(save_path), 'dets');
if visualise
vis_im = visualise_bboxes(id, objectNames, cmap, rgb_dir, save_dir);
imwrite(vis_im, fullfile(save_dir, sprintf(visualise_save_template, id)));
end
if mod(k, 100) == 0
fprintf('[%s] Processed %d/%d\n', char(datetime), k, length(list));
end
end
================================================
FILE: data/Cityscapes/gtFine/train/aachen/aachen_000000_000019_gtFine_polygons.json
================================================
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"imgWidth": 2048,
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gitextract_otvagzvw/
├── .gitattributes
├── .gitignore
├── LICENSE
├── README.md
├── batch_instanceTrainId_to_dets.m
├── data/
│ └── Cityscapes/
│ ├── gtFine/
│ │ └── train/
│ │ └── aachen/
│ │ └── aachen_000000_000019_gtFine_polygons.json
│ └── lists/
│ ├── demo_id.txt
│ ├── test_id.txt
│ ├── train_extra_id.txt
│ ├── train_id.txt
│ └── val_id.txt
├── demo_instanceTrainId_to_dets.m
├── demo_make_iterative_gt.m
├── demo_merge_cam_mandg.m
├── results/
│ └── pred_flat_feat/
│ └── aachen_000000_000019_leftImg8bit.mat
├── scripts/
│ ├── apply_bbox_prior.m
│ ├── check_image_level_tags.m
│ ├── check_low_iou.m
│ ├── clean_label.m
│ ├── get_opts.m
│ ├── ins_box_process.m
│ ├── instanceTrainId_to_dets.m
│ ├── load_data.m
│ ├── make_bbox_masks.m
│ ├── merge_mag_and_pred.m
│ ├── run_sub.m
│ └── save_results.m
├── utils/
│ ├── colormapcs.mat
│ └── objectName19.mat
└── visualisation/
├── visualise_bboxes.m
├── visualise_results_cam_mandg.m
└── visualise_results_iterative_gt.m
Condensed preview — 32 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (899K chars).
[
{
"path": ".gitattributes",
"chars": 66,
"preview": "# Auto detect text files and perform LF normalization\n* text=auto\n"
},
{
"path": ".gitignore",
"chars": 160,
"preview": "*.m~\n*.asv\ndata/Cityscapes/gtFine_bboxes/\nresults/pred_ins_clean/\nresults/pred_sem_clean/\nresults/pred_ins_cam_mandg_mer"
},
{
"path": "LICENSE",
"chars": 1096,
"preview": "MIT License\n\nCopyright (c) 2018 Qizhu Li, Anurag Arnab, Philip H.S Torr\n\nPermission is hereby granted, free of charge, t"
},
{
"path": "README.md",
"chars": 10370,
"preview": "# Weakly- and Semi-Supervised Panoptic Segmentation\nby [Qizhu Li](http://www.robots.ox.ac.uk/~liqizhu/)\\*, [Anurag Arnab"
},
{
"path": "batch_instanceTrainId_to_dets.m",
"chars": 4477,
"preview": "% ------------------------------------------------------------------------ \n% Copyright (C)\n% Torr Vision Group (TVG)\n"
},
{
"path": "data/Cityscapes/gtFine/train/aachen/aachen_000000_000019_gtFine_polygons.json",
"chars": 208438,
"preview": "{\n \"imgHeight\": 1024, \n \"imgWidth\": 2048, \n \"objects\": [\n {\n \"label\": \"road\", \n \"p"
},
{
"path": "data/Cityscapes/lists/demo_id.txt",
"chars": 20,
"preview": "aachen_000000_000019"
},
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"path": "data/Cityscapes/lists/test_id.txt",
"chars": 32410,
"preview": "berlin_000000_000019\nberlin_000001_000019\nberlin_000002_000019\nberlin_000003_000019\nberlin_000004_000019\nberlin_000005_0"
},
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"path": "data/Cityscapes/lists/train_extra_id.txt",
"chars": 478459,
"preview": "augsburg_000000_000000\naugsburg_000000_000001\naugsburg_000000_000002\naugsburg_000000_000003\naugsburg_000000_000004\naugsb"
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"path": "data/Cityscapes/lists/train_id.txt",
"chars": 66970,
"preview": "aachen_000000_000019\naachen_000001_000019\naachen_000002_000019\naachen_000003_000019\naachen_000004_000019\naachen_000005_0"
},
{
"path": "data/Cityscapes/lists/val_id.txt",
"chars": 11475,
"preview": "frankfurt_000000_000294\nfrankfurt_000000_000576\nfrankfurt_000000_001016\nfrankfurt_000000_001236\nfrankfurt_000000_001751\n"
},
{
"path": "demo_instanceTrainId_to_dets.m",
"chars": 1833,
"preview": "% ------------------------------------------------------------------------ \n% Copyright (C)\n% Torr Vision Group (TVG)\n"
},
{
"path": "demo_make_iterative_gt.m",
"chars": 1858,
"preview": "% ------------------------------------------------------------------------ \n% Copyright (C)\n% Torr Vision Group (TVG)\n"
},
{
"path": "demo_merge_cam_mandg.m",
"chars": 1787,
"preview": "% ------------------------------------------------------------------------ \n% Copyright (C)\n% Torr Vision Group (TVG)\n"
},
{
"path": "scripts/apply_bbox_prior.m",
"chars": 1627,
"preview": "% ------------------------------------------------------------------------ \n% Copyright (C)\n% Torr Vision Group (TVG)\n"
},
{
"path": "scripts/check_image_level_tags.m",
"chars": 2785,
"preview": "% ------------------------------------------------------------------------ \n% Copyright (C)\n% Torr Vision Group (TVG)\n"
},
{
"path": "scripts/check_low_iou.m",
"chars": 3821,
"preview": "% ------------------------------------------------------------------------ \n% Copyright (C)\n% Torr Vision Group (TVG)\n"
},
{
"path": "scripts/clean_label.m",
"chars": 2713,
"preview": "% ------------------------------------------------------------------------ \n% Copyright (C)\n% Torr Vision Group (TVG)\n"
},
{
"path": "scripts/get_opts.m",
"chars": 3567,
"preview": "% ------------------------------------------------------------------------ \n% Copyright (C)\n% Torr Vision Group (TVG)\n"
},
{
"path": "scripts/ins_box_process.m",
"chars": 3083,
"preview": "% ------------------------------------------------------------------------ \n% Copyright (C)\n% Torr Vision Group (TVG)\n"
},
{
"path": "scripts/instanceTrainId_to_dets.m",
"chars": 3838,
"preview": "% ------------------------------------------------------------------------ \n% Copyright (C)\n% Torr Vision Group (TVG)\n"
},
{
"path": "scripts/load_data.m",
"chars": 2335,
"preview": "% ------------------------------------------------------------------------ \n% Copyright (C)\n% Torr Vision Group (TVG)\n"
},
{
"path": "scripts/make_bbox_masks.m",
"chars": 2856,
"preview": "% ------------------------------------------------------------------------ \n% Copyright (C)\n% Torr Vision Group (TVG)\n"
},
{
"path": "scripts/merge_mag_and_pred.m",
"chars": 3278,
"preview": "% ------------------------------------------------------------------------ \n% Copyright (C)\n% Torr Vision Group (TVG)\n"
},
{
"path": "scripts/run_sub.m",
"chars": 2580,
"preview": "% ------------------------------------------------------------------------ \n% Copyright (C)\n% Torr Vision Group (TVG)\n"
},
{
"path": "scripts/save_results.m",
"chars": 1988,
"preview": "% ------------------------------------------------------------------------ \n% Copyright (C)\n% Torr Vision Group (TVG)\n"
},
{
"path": "visualisation/visualise_bboxes.m",
"chars": 2748,
"preview": "function vis_im = visualise_bboxes(id, objectNames, cmap, rgb_dir, detection_dir)\n\nfont_size = 25;\n\ncity = strtok(id, '_"
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{
"path": "visualisation/visualise_results_cam_mandg.m",
"chars": 2254,
"preview": "% ------------------------------------------------------------------------ \n% Copyright (C)\n% Torr Vision Group (TVG)\n"
},
{
"path": "visualisation/visualise_results_iterative_gt.m",
"chars": 2269,
"preview": "% ------------------------------------------------------------------------ \n% Copyright (C)\n% Torr Vision Group (TVG)\n"
}
]
// ... and 3 more files (download for full content)
About this extraction
This page contains the full source code of the qizhuli/Weakly-Supervised-Panoptic-Segmentation GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 32 files (841.0 KB), approximately 300.1k tokens. 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.