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[Project] Medical semantic seg dataset: Kvasir seg (open-mmlab#2677)
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‎projects/medical/2d_image/endoscopy/kvasir_seg/README.md‎

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# Kvasir-Sessile Dataset (Kvasir SEG)
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## Description
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This project supports **`Kvasir-Sessile Dataset (Kvasir SEG) `**, which can be downloaded from [here](https://opendatalab.com/Kvasir-Sessile_dataset).
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## Dataset Overview
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The Kvasir-SEG dataset contains polyp images and their corresponding ground truth from the Kvasir Dataset v2. The resolution of the images contained in Kvasir-SEG varies from 332x487 to 1920x1072 pixels.
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<!-- For a typical model, this section should contain the commands for training and testing. You are also suggested to dump your environment specification to env.yml by `conda env export > env.yml`. -->
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### Information Statistics
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| Dataset Name | Anatomical Region | Task Type | Modality | Num. Classes | Train/Val/Test Images | Train/Val/Test Labeled | Release Date | License |
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| ------------------------------------------------------------- | ----------------- | ------------ | --------- | ------------ | --------------------- | ---------------------- | ------------ | --------------------------------------------------------- |
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| [Kvarsir-SEG](https://opendatalab.com/Kvasir-Sessile_dataset) | abdomen | segmentation | endoscopy | 2 | 196/-/- | yes/-/- | 2020 | [CC-BY 4.0](https://creativecommons.org/licenses/by/4.0/) |
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| Class Name | Num. Train | Pct. Train | Num. Val | Pct. Val | Num. Test | Pct. Test |
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| :--------: | :--------: | :--------: | :------: | :------: | :-------: | :-------: |
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| background | 196 | 92.31 | - | - | - | - |
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| polyp | 196 | 7.69 | - | - | - | - |
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Note:
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- `Pct` means percentage of pixels in this category in all pixels.
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### Visualization
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![kvasir-seg](https://raw.githubusercontent.com/uni-medical/medical-datasets-visualization/main/2d/semantic_seg/endoscopy_images/kvasir_seg/kvasir_seg_dataset.png?raw=true)
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### Dataset Citation
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```
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@inproceedings{jha2020kvasir,
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title={Kvasir-seg: A segmented polyp dataset},
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author={Jha, Debesh and Smedsrud, Pia H and Riegler, Michael A and Halvorsen, P{\aa}l and Lange, Thomas de and Johansen, Dag and Johansen, H{\aa}vard D},
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booktitle={International Conference on Multimedia Modeling},
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pages={451--462},
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year={2020},
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organization={Springer}
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}
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```
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### Prerequisites
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- Python v3.8
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- PyTorch v1.10.0
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- pillow(PIL) v9.3.0
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- scikit-learn(sklearn) v1.2.0
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- [MIM](https://github.com/open-mmlab/mim) v0.3.4
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- [MMCV](https://github.com/open-mmlab/mmcv) v2.0.0rc4
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- [MMEngine](https://github.com/open-mmlab/mmengine) v0.2.0 or higher
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- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation) v1.0.0rc5
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All the commands below rely on the correct configuration of `PYTHONPATH`, which should point to the project's directory so that Python can locate the module files. In `kvasir_seg/` root directory, run the following line to add the current directory to `PYTHONPATH`:
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```shell
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export PYTHONPATH=`pwd`:$PYTHONPATH
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```
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### Dataset Preparing
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- download dataset from [here](https://opendatalab.com/Kvasir-Sessile_dataset) and decompress data to path `'data/'`.
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- run script `"python tools/prepare_dataset.py"` to format data and change folder structure as below.
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- run script `"python ../../tools/split_seg_dataset.py"` to split dataset and generate `train.txt`, `val.txt` and `test.txt`. If the label of official validation set and test set cannot be obtained, we generate `train.txt` and `val.txt` from the training set randomly.
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```none
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mmsegmentation
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├── mmseg
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├── projects
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│ ├── medical
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│ │ ├── 2d_image
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│ │ │ ├── endoscopy
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│ │ │ │ ├── kvasir_seg
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│ │ │ │ │ ├── configs
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│ │ │ │ │ ├── datasets
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│ │ │ │ │ ├── tools
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│ │ │ │ │ ├── data
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│ │ │ │ │ │ ├── train.txt
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│ │ │ │ │ │ ├── val.txt
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│ │ │ │ │ │ ├── images
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│ │ │ │ │ │ │ ├── train
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│ │ │ │ | │ │ │ ├── xxx.png
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│ │ │ │ | │ │ │ ├── ...
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│ │ │ │ | │ │ │ └── xxx.png
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│ │ │ │ │ │ ├── masks
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│ │ │ │ │ │ │ ├── train
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│ │ │ │ | │ │ │ ├── xxx.png
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│ │ │ │ | │ │ │ ├── ...
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│ │ │ │ | │ │ │ └── xxx.png
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```
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### Divided Dataset Information
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***Note: The table information below is divided by ourselves.***
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| Class Name | Num. Train | Pct. Train | Num. Val | Pct. Val | Num. Test | Pct. Test |
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| :--------: | :--------: | :--------: | :------: | :------: | :-------: | :-------: |
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| background | 156 | 92.28 | 40 | 92.41 | - | - |
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| polyp | 156 | 7.72 | 40 | 7.59 | - | - |
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### Training commands
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To train models on a single server with one GPU. (default)
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```shell
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mim train mmseg .configs/${CONFIG_FILE}
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```
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### Testing commands
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To test models on a single server with one GPU. (default)
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```shell
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mim test mmseg ./configs/${CONFIG_FILE} --checkpoint ${CHECKPOINT_PATH}
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```
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<!-- List the results as usually done in other model's README. [Example](https://github.com/open-mmlab/mmsegmentation/tree/dev-1.x/configs/fcn#results-and-models)
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You should claim whether this is based on the pre-trained weights, which are converted from the official release; or it's a reproduced result obtained from retraining the model in this project. -->
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## Checklist
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- [x] Milestone 1: PR-ready, and acceptable to be one of the `projects/`.
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- [x] Finish the code
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- [x] Basic docstrings & proper citation
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- [ ] Test-time correctness
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- [x] A full README
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- [x] Milestone 2: Indicates a successful model implementation.
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- [x] Training-time correctness
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- [ ] Milestone 3: Good to be a part of our core package!
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- [ ] Type hints and docstrings
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- [ ] Unit tests
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- [ ] Code polishing
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- [ ] Metafile.yml
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- [ ] Move your modules into the core package following the codebase's file hierarchy structure.
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- [ ] Refactor your modules into the core package following the codebase's file hierarchy structure.

‎projects/medical/2d_image/endoscopy/kvasir_seg/configs/fcn-unet-s5-d16_unet-{use-sigmoid}_1xb16-0.01-20k_kvasir-seg-512x512.py‎

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_base_ = [
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'mmseg::_base_/models/fcn_unet_s5-d16.py', './kvasir-seg_512x512.py',
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'mmseg::_base_/default_runtime.py',
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'mmseg::_base_/schedules/schedule_20k.py'
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]
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custom_imports = dict(imports='datasets.kvasir-seg_dataset')
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img_scale = (512, 512)
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data_preprocessor = dict(size=img_scale)
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optimizer = dict(lr=0.01)
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optim_wrapper = dict(optimizer=optimizer)
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model = dict(
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data_preprocessor=data_preprocessor,
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decode_head=dict(
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num_classes=2, loss_decode=dict(use_sigmoid=True), out_channels=1),
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auxiliary_head=None,
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test_cfg=dict(mode='whole', _delete_=True))
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vis_backends = None
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visualizer = dict(vis_backends=vis_backends)

‎projects/medical/2d_image/endoscopy/kvasir_seg/configs/fcn-unet-s5-d16_unet_1xb16-0.0001-20k_kvasir-seg-512x512.py‎

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_base_ = [
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'mmseg::_base_/models/fcn_unet_s5-d16.py', './kvasir-seg_512x512.py',
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'mmseg::_base_/default_runtime.py',
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'mmseg::_base_/schedules/schedule_20k.py'
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]
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custom_imports = dict(imports='datasets.kvasir-seg_dataset')
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img_scale = (512, 512)
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data_preprocessor = dict(size=img_scale)
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optimizer = dict(lr=0.0001)
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optim_wrapper = dict(optimizer=optimizer)
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model = dict(
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data_preprocessor=data_preprocessor,
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decode_head=dict(num_classes=2),
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auxiliary_head=None,
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test_cfg=dict(mode='whole', _delete_=True))
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vis_backends = None
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visualizer = dict(vis_backends=vis_backends)

‎projects/medical/2d_image/endoscopy/kvasir_seg/configs/fcn-unet-s5-d16_unet_1xb16-0.001-20k_kvasir-seg-512x512.py‎

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_base_ = [
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'mmseg::_base_/models/fcn_unet_s5-d16.py', './kvasir-seg_512x512.py',
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'mmseg::_base_/default_runtime.py',
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'mmseg::_base_/schedules/schedule_20k.py'
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]
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custom_imports = dict(imports='datasets.kvasir-seg_dataset')
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img_scale = (512, 512)
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data_preprocessor = dict(size=img_scale)
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optimizer = dict(lr=0.001)
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optim_wrapper = dict(optimizer=optimizer)
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model = dict(
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data_preprocessor=data_preprocessor,
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decode_head=dict(num_classes=2),
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auxiliary_head=None,
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test_cfg=dict(mode='whole', _delete_=True))
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vis_backends = None
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visualizer = dict(vis_backends=vis_backends)

‎projects/medical/2d_image/endoscopy/kvasir_seg/configs/fcn-unet-s5-d16_unet_1xb16-0.01-20k_kvasir-seg-512x512.py‎

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_base_ = [
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'mmseg::_base_/models/fcn_unet_s5-d16.py', './kvasir-seg_512x512.py',
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'mmseg::_base_/default_runtime.py',
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'mmseg::_base_/schedules/schedule_20k.py'
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]
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custom_imports = dict(imports='datasets.kvasir-seg_dataset')
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img_scale = (512, 512)
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data_preprocessor = dict(size=img_scale)
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optimizer = dict(lr=0.01)
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optim_wrapper = dict(optimizer=optimizer)
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model = dict(
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data_preprocessor=data_preprocessor,
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decode_head=dict(num_classes=2),
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auxiliary_head=None,
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test_cfg=dict(mode='whole', _delete_=True))
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vis_backends = None
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visualizer = dict(vis_backends=vis_backends)

‎projects/medical/2d_image/endoscopy/kvasir_seg/configs/kvasir-seg_512x512.py‎

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dataset_type = 'KvasirSEGDataset'
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data_root = 'data/'
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img_scale = (512, 512)
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train_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='LoadAnnotations'),
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dict(type='Resize', scale=img_scale, keep_ratio=False),
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dict(type='RandomFlip', prob=0.5),
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dict(type='PhotoMetricDistortion'),
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dict(type='PackSegInputs')
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]
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test_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='Resize', scale=img_scale, keep_ratio=False),
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dict(type='LoadAnnotations'),
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dict(type='PackSegInputs')
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]
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train_dataloader = dict(
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batch_size=16,
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num_workers=4,
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persistent_workers=True,
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sampler=dict(type='InfiniteSampler', shuffle=True),
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dataset=dict(
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type=dataset_type,
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data_root=data_root,
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ann_file='train.txt',
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data_prefix=dict(img_path='images/', seg_map_path='masks/'),
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pipeline=train_pipeline))
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val_dataloader = dict(
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batch_size=1,
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num_workers=4,
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persistent_workers=True,
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sampler=dict(type='DefaultSampler', shuffle=False),
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dataset=dict(
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type=dataset_type,
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data_root=data_root,
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ann_file='val.txt',
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data_prefix=dict(img_path='images/', seg_map_path='masks/'),
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pipeline=test_pipeline))
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test_dataloader = val_dataloader
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val_evaluator = dict(type='IoUMetric', iou_metrics=['mIoU', 'mDice'])
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test_evaluator = dict(type='IoUMetric', iou_metrics=['mIoU', 'mDice'])

‎projects/medical/2d_image/endoscopy/kvasir_seg/datasets/kvasir-seg_dataset.py‎

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from mmseg.datasets import BaseSegDataset
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from mmseg.registry import DATASETS
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@DATASETS.register_module()
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class KvasirSEGDataset(BaseSegDataset):
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"""KvasirSEGDataset dataset.
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In segmentation map annotation for KvasirSEGDataset, 0 stands for
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background, which is included in 2 categories.
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``reduce_zero_label`` is fixed to False. The ``img_suffix`` is
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fixed to '.png' and ``seg_map_suffix`` is fixed to '.png'.
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Args:
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img_suffix (str): Suffix of images. Default: '.png'
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seg_map_suffix (str): Suffix of segmentation maps. Default: '.png'
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reduce_zero_label (bool): Whether to mark label zero as ignored.
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Default to False..
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"""
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METAINFO = dict(classes=('background', 'polyp'))
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def __init__(self,
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img_suffix='.png',
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seg_map_suffix='.png',
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reduce_zero_label=False,
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**kwargs) -> None:
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super().__init__(
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img_suffix=img_suffix,
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seg_map_suffix=seg_map_suffix,
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reduce_zero_label=reduce_zero_label,
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**kwargs)

‎projects/medical/2d_image/endoscopy/kvasir_seg/tools/prepare_dataset.py‎

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import glob
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import os
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import numpy as np
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from PIL import Image
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root_path = 'data/'
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img_suffix = '.jpg'
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seg_map_suffix = '.jpg'
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save_img_suffix = '.png'
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save_seg_map_suffix = '.png'
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tgt_img_dir = os.path.join(root_path, 'images/train/')
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tgt_mask_dir = os.path.join(root_path, 'masks/train/')
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os.system('mkdir -p ' + tgt_img_dir)
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os.system('mkdir -p ' + tgt_mask_dir)
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def filter_suffix_recursive(src_dir, suffix):
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# filter out file names and paths in source directory
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suffix = '.' + suffix if '.' not in suffix else suffix
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file_paths = glob.glob(
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os.path.join(src_dir, '**', '*' + suffix), recursive=True)
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file_names = [_.split('/')[-1] for _ in file_paths]
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return sorted(file_paths), sorted(file_names)
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def convert_label(img, convert_dict):
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arr = np.zeros_like(img, dtype=np.uint8)
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for c, i in convert_dict.items():
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arr[img == c] = i
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return arr
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def convert_pics_into_pngs(src_dir, tgt_dir, suffix, convert='RGB'):
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if not os.path.exists(tgt_dir):
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os.makedirs(tgt_dir)
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src_paths, src_names = filter_suffix_recursive(src_dir, suffix=suffix)
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for i, (src_name, src_path) in enumerate(zip(src_names, src_paths)):
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tgt_name = src_name.replace(suffix, save_img_suffix)
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tgt_path = os.path.join(tgt_dir, tgt_name)
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num = len(src_paths)
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img = np.array(Image.open(src_path))
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if len(img.shape) == 2:
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pil = Image.fromarray(img).convert(convert)
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elif len(img.shape) == 3:
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pil = Image.fromarray(img)
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else:
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raise ValueError('Input image not 2D/3D: ', img.shape)
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pil.save(tgt_path)
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print(f'processed {i+1}/{num}.')
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def convert_label_pics_into_pngs(src_dir,
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tgt_dir,
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suffix,
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convert_dict={
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0: 0,
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255: 1
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}):
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if not os.path.exists(tgt_dir):
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os.makedirs(tgt_dir)
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src_paths, src_names = filter_suffix_recursive(src_dir, suffix=suffix)
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num = len(src_paths)
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for i, (src_name, src_path) in enumerate(zip(src_names, src_paths)):
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tgt_name = src_name.replace(suffix, save_seg_map_suffix)
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tgt_path = os.path.join(tgt_dir, tgt_name)
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img = np.array(Image.open(src_path))
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img = convert_label(img, convert_dict)
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Image.fromarray(img).save(tgt_path)
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print(f'processed {i+1}/{num}.')
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if __name__ == '__main__':
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convert_pics_into_pngs(
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os.path.join(root_path, 'sessile-main-Kvasir-SEG/images'),
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tgt_img_dir,
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suffix=img_suffix)
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convert_label_pics_into_pngs(
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os.path.join(root_path, 'sessile-main-Kvasir-SEG/masks'),
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tgt_mask_dir,
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suffix=seg_map_suffix)

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