[Project] Medical semantic seg dataset: Kvasir seg (#2677)

projects/medical/2d_image/endoscopy/kvasir_seg/README.md

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+# Kvasir-Sessile Dataset (Kvasir SEG)
+
+## Description
+
+This project supports **`Kvasir-Sessile Dataset (Kvasir SEG) `**, which can be downloaded from [here](https://opendatalab.com/Kvasir-Sessile_dataset).
+
+## Dataset Overview
+
+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.
+
+<!-- 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`. -->
+
+### Information Statistics
+
+| Dataset Name                                                  | Anatomical Region | Task Type    | Modality  | Num. Classes | Train/Val/Test Images | Train/Val/Test Labeled | Release Date | License                                                   |
+| ------------------------------------------------------------- | ----------------- | ------------ | --------- | ------------ | --------------------- | ---------------------- | ------------ | --------------------------------------------------------- |
+| [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/) |
+
+| Class Name | Num. Train | Pct. Train | Num. Val | Pct. Val | Num. Test | Pct. Test |
+| :--------: | :--------: | :--------: | :------: | :------: | :-------: | :-------: |
+| background |    196     |   92.31    |    -     |    -     |     -     |     -     |
+|   polyp    |    196     |    7.69    |    -     |    -     |     -     |     -     |
+
+Note:
+
+- `Pct` means percentage of pixels in this category in all pixels.
+
+### Visualization
+
+![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)
+
+### Dataset Citation
+
+```
+@inproceedings{jha2020kvasir,
+	title={Kvasir-seg: A segmented polyp dataset},
+	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},
+	booktitle={International Conference on Multimedia Modeling},
+	pages={451--462},
+	year={2020},
+	organization={Springer}
+ }
+```
+
+### Prerequisites
+
+- Python v3.8
+- PyTorch v1.10.0
+- pillow(PIL) v9.3.0
+- scikit-learn(sklearn) v1.2.0
+- [MIM](https://github.com/open-mmlab/mim) v0.3.4
+- [MMCV](https://github.com/open-mmlab/mmcv) v2.0.0rc4
+- [MMEngine](https://github.com/open-mmlab/mmengine) v0.2.0 or higher
+- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation) v1.0.0rc5
+
+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`:
+
+```shell
+export PYTHONPATH=`pwd`:$PYTHONPATH
+```
+
+### Dataset Preparing
+
+- download dataset from [here](https://opendatalab.com/Kvasir-Sessile_dataset) and decompress data to path `'data/'`.
+- run script `"python tools/prepare_dataset.py"` to format data and change folder structure as below.
+- run script `"python ../../tools/split_seg_dataset.py"` to split dataset and generate `train.txt`, `val.txt` and `test.txt`. If the label of official validation set and test set cannot be obtained, we generate `train.txt` and `val.txt` from the training set randomly.
+
+```none
+  mmsegmentation
+  ├── mmseg
+  ├── projects
+  │   ├── medical
+  │   │   ├── 2d_image
+  │   │   │   ├── endoscopy
+  │   │   │   │   ├── kvasir_seg
+  │   │   │   │   │   ├── configs
+  │   │   │   │   │   ├── datasets
+  │   │   │   │   │   ├── tools
+  │   │   │   │   │   ├── data
+  │   │   │   │   │   │   ├── train.txt
+  │   │   │   │   │   │   ├── val.txt
+  │   │   │   │   │   │   ├── images
+  │   │   │   │   │   │   │   ├── train
+  │   │   │   │   |   │   │   │   ├── xxx.png
+  │   │   │   │   |   │   │   │   ├── ...
+  │   │   │   │   |   │   │   │   └── xxx.png
+  │   │   │   │   │   │   ├── masks
+  │   │   │   │   │   │   │   ├── train
+  │   │   │   │   |   │   │   │   ├── xxx.png
+  │   │   │   │   |   │   │   │   ├── ...
+  │   │   │   │   |   │   │   │   └── xxx.png
+```
+
+### Divided Dataset Information
+
+***Note: The table information below is divided by ourselves.***
+
+| Class Name | Num. Train | Pct. Train | Num. Val | Pct. Val | Num. Test | Pct. Test |
+| :--------: | :--------: | :--------: | :------: | :------: | :-------: | :-------: |
+| background |    156     |   92.28    |    40    |  92.41   |     -     |     -     |
+|   polyp    |    156     |    7.72    |    40    |   7.59   |     -     |     -     |
+
+### Training commands
+
+To train models on a single server with one GPU. (default)
+
+```shell
+mim train mmseg .configs/${CONFIG_FILE}
+```
+
+### Testing commands
+
+To test models on a single server with one GPU. (default)
+
+```shell
+mim test mmseg ./configs/${CONFIG_FILE}  --checkpoint ${CHECKPOINT_PATH}
+```
+
+<!-- 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)
+
+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. -->
+
+## Checklist
+
+- [x] Milestone 1: PR-ready, and acceptable to be one of the `projects/`.
+
+  - [x] Finish the code
+  - [x] Basic docstrings & proper citation
+  - [ ] Test-time correctness
+  - [x] A full README
+
+- [x] Milestone 2: Indicates a successful model implementation.
+
+  - [x] Training-time correctness
+
+- [ ] Milestone 3: Good to be a part of our core package!
+
+  - [ ] Type hints and docstrings
+  - [ ] Unit tests
+  - [ ] Code polishing
+  - [ ] Metafile.yml
+
+- [ ] Move your modules into the core package following the codebase's file hierarchy structure.
+
+- [ ] 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_ = [
+    'mmseg::_base_/models/fcn_unet_s5-d16.py', './kvasir-seg_512x512.py',
+    'mmseg::_base_/default_runtime.py',
+    'mmseg::_base_/schedules/schedule_20k.py'
+]
+custom_imports = dict(imports='datasets.kvasir-seg_dataset')
+img_scale = (512, 512)
+data_preprocessor = dict(size=img_scale)
+optimizer = dict(lr=0.01)
+optim_wrapper = dict(optimizer=optimizer)
+model = dict(
+    data_preprocessor=data_preprocessor,
+    decode_head=dict(
+        num_classes=2, loss_decode=dict(use_sigmoid=True), out_channels=1),
+    auxiliary_head=None,
+    test_cfg=dict(mode='whole', _delete_=True))
+vis_backends = None
+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_ = [
+    'mmseg::_base_/models/fcn_unet_s5-d16.py', './kvasir-seg_512x512.py',
+    'mmseg::_base_/default_runtime.py',
+    'mmseg::_base_/schedules/schedule_20k.py'
+]
+custom_imports = dict(imports='datasets.kvasir-seg_dataset')
+img_scale = (512, 512)
+data_preprocessor = dict(size=img_scale)
+optimizer = dict(lr=0.0001)
+optim_wrapper = dict(optimizer=optimizer)
+model = dict(
+    data_preprocessor=data_preprocessor,
+    decode_head=dict(num_classes=2),
+    auxiliary_head=None,
+    test_cfg=dict(mode='whole', _delete_=True))
+vis_backends = None
+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_ = [
+    'mmseg::_base_/models/fcn_unet_s5-d16.py', './kvasir-seg_512x512.py',
+    'mmseg::_base_/default_runtime.py',
+    'mmseg::_base_/schedules/schedule_20k.py'
+]
+custom_imports = dict(imports='datasets.kvasir-seg_dataset')
+img_scale = (512, 512)
+data_preprocessor = dict(size=img_scale)
+optimizer = dict(lr=0.001)
+optim_wrapper = dict(optimizer=optimizer)
+model = dict(
+    data_preprocessor=data_preprocessor,
+    decode_head=dict(num_classes=2),
+    auxiliary_head=None,
+    test_cfg=dict(mode='whole', _delete_=True))
+vis_backends = None
+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_ = [
+    'mmseg::_base_/models/fcn_unet_s5-d16.py', './kvasir-seg_512x512.py',
+    'mmseg::_base_/default_runtime.py',
+    'mmseg::_base_/schedules/schedule_20k.py'
+]
+custom_imports = dict(imports='datasets.kvasir-seg_dataset')
+img_scale = (512, 512)
+data_preprocessor = dict(size=img_scale)
+optimizer = dict(lr=0.01)
+optim_wrapper = dict(optimizer=optimizer)
+model = dict(
+    data_preprocessor=data_preprocessor,
+    decode_head=dict(num_classes=2),
+    auxiliary_head=None,
+    test_cfg=dict(mode='whole', _delete_=True))
+vis_backends = None
+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'
+data_root = 'data/'
+img_scale = (512, 512)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations'),
+    dict(type='Resize', scale=img_scale, keep_ratio=False),
+    dict(type='RandomFlip', prob=0.5),
+    dict(type='PhotoMetricDistortion'),
+    dict(type='PackSegInputs')
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='Resize', scale=img_scale, keep_ratio=False),
+    dict(type='LoadAnnotations'),
+    dict(type='PackSegInputs')
+]
+train_dataloader = dict(
+    batch_size=16,
+    num_workers=4,
+    persistent_workers=True,
+    sampler=dict(type='InfiniteSampler', shuffle=True),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file='train.txt',
+        data_prefix=dict(img_path='images/', seg_map_path='masks/'),
+        pipeline=train_pipeline))
+val_dataloader = dict(
+    batch_size=1,
+    num_workers=4,
+    persistent_workers=True,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file='val.txt',
+        data_prefix=dict(img_path='images/', seg_map_path='masks/'),
+        pipeline=test_pipeline))
+test_dataloader = val_dataloader
+val_evaluator = dict(type='IoUMetric', iou_metrics=['mIoU', 'mDice'])
+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
+from mmseg.registry import DATASETS
+
+
+@DATASETS.register_module()
+class KvasirSEGDataset(BaseSegDataset):
+    """KvasirSEGDataset dataset.
+
+    In segmentation map annotation for KvasirSEGDataset, 0 stands for
+    background, which is included in 2 categories.
+    ``reduce_zero_label`` is fixed to False. The ``img_suffix`` is
+    fixed to '.png' and ``seg_map_suffix`` is fixed to '.png'.
+    Args:
+        img_suffix (str): Suffix of images. Default: '.png'
+        seg_map_suffix (str): Suffix of segmentation maps. Default: '.png'
+        reduce_zero_label (bool): Whether to mark label zero as ignored.
+            Default to False..
+    """
+    METAINFO = dict(classes=('background', 'polyp'))
+
+    def __init__(self,
+                 img_suffix='.png',
+                 seg_map_suffix='.png',
+                 reduce_zero_label=False,
+                 **kwargs) -> None:
+        super().__init__(
+            img_suffix=img_suffix,
+            seg_map_suffix=seg_map_suffix,
+            reduce_zero_label=reduce_zero_label,
+            **kwargs)

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

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