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Add ISIC dataset
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from torch_em.util.debug import check_loader | ||
from torch_em.data.datasets.medical import get_isic_loader | ||
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ROOT = "/scratch/share/cidas/cca/data/isic" | ||
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def check_isic(): | ||
loader = get_isic_loader( | ||
path=ROOT, | ||
patch_shape=(700, 700), | ||
batch_size=2, | ||
split="test", | ||
download=True, | ||
resize_inputs=True, | ||
) | ||
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check_loader(loader, 8, plt=True, save_path="./isic.png") | ||
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if __name__ == "__main__": | ||
check_isic() |
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import os | ||
from glob import glob | ||
from pathlib import Path | ||
from typing import Union, Tuple | ||
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import torch_em | ||
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from .. import util | ||
from ..light_microscopy.neurips_cell_seg import to_rgb | ||
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URL = { | ||
"images": { | ||
"train": "https://isic-challenge-data.s3.amazonaws.com/2018/ISIC2018_Task1-2_Training_Input.zip", | ||
"val": "https://isic-challenge-data.s3.amazonaws.com/2018/ISIC2018_Task1-2_Validation_Input.zip", | ||
"test": "https://isic-challenge-data.s3.amazonaws.com/2018/ISIC2018_Task1-2_Test_Input.zip", | ||
}, | ||
"gt": { | ||
"train": "https://isic-challenge-data.s3.amazonaws.com/2018/ISIC2018_Task1_Training_GroundTruth.zip", | ||
"val": "https://isic-challenge-data.s3.amazonaws.com/2018/ISIC2018_Task1_Validation_GroundTruth.zip", | ||
"test": "https://isic-challenge-data.s3.amazonaws.com/2018/ISIC2018_Task1_Test_GroundTruth.zip", | ||
}, | ||
} | ||
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CHECKSUM = { | ||
"images": { | ||
"train": "80f98572347a2d7a376227fa9eb2e4f7459d317cb619865b8b9910c81446675f", | ||
"val": "0ea920fcfe512d12a6e620b50b50233c059f67b10146e1479c82be58ff15a797", | ||
"test": "e59ae1f69f4ed16f09db2cb1d76c2a828487b63d28f6ab85997f5616869b127d", | ||
}, | ||
"gt": { | ||
"train": "99f8b2bb3c4d6af483362010715f7e7d5d122d9f6c02cac0e0d15bef77c7604c", | ||
"val": "f6911e9c0a64e6d687dd3ca466ca927dd5e82145cb2163b7a1e5b37d7a716285", | ||
"test": "2e8f6edce454a5bdee52485e39f92bd6eddf357e81f39018d05512175238ef82", | ||
} | ||
} | ||
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def get_isic_data(path, split, download): | ||
os.makedirs(path, exist_ok=True) | ||
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im_url = URL["images"][split] | ||
im_checksum = CHECKSUM["images"][split] | ||
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gt_url = URL["gt"][split] | ||
gt_checksum = CHECKSUM["gt"][split] | ||
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im_zipfile = os.path.split(im_url)[-1] | ||
gt_zipfile = os.path.split(gt_url)[-1] | ||
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imdir = os.path.join(path, Path(im_zipfile).stem) | ||
gtdir = os.path.join(path, Path(gt_zipfile).stem) | ||
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im_zip_path = os.path.join(path, im_zipfile) | ||
gt_zip_path = os.path.join(path, gt_zipfile) | ||
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if os.path.exists(imdir) and os.path.exists(gtdir): | ||
return imdir, gtdir | ||
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# download the images | ||
util.download_source(path=im_zip_path, url=im_url, download=download, checksum=im_checksum) | ||
util.unzip(zip_path=im_zip_path, dst=path, remove=False) | ||
# download the ground-truth | ||
util.download_source(path=gt_zip_path, url=gt_url, download=download, checksum=gt_checksum) | ||
util.unzip(zip_path=gt_zip_path, dst=path, remove=False) | ||
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return imdir, gtdir | ||
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def _get_isic_paths(path, split, download): | ||
image_dir, gt_dir = get_isic_data(path=path, split=split, download=download) | ||
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image_paths = sorted(glob(os.path.join(image_dir, "*.jpg"))) | ||
gt_paths = sorted(glob(os.path.join(gt_dir, "*.png"))) | ||
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return image_paths, gt_paths | ||
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def get_isic_dataset( | ||
path: Union[os.PathLike, str], | ||
patch_shape: Tuple[int, int], | ||
split: str, | ||
download: bool = False, | ||
make_rgb: bool = True, | ||
resize_inputs: bool = False, | ||
**kwargs | ||
): | ||
"""Dataset for the segmentation of skin lesion in dermoscopy images. | ||
This dataset is related to the following publication(s): | ||
- https://doi.org/10.1038/sdata.2018.161 | ||
- https://doi.org/10.48550/arXiv.1710.05006 | ||
- https://doi.org/10.48550/arXiv.1902.03368 | ||
The database is located at https://challenge.isic-archive.com/data/#2018 | ||
Please cite it if you use this dataset for a publication. | ||
""" | ||
assert split in list(URL["images"].keys()), f"{split} is not a valid split." | ||
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image_paths, gt_paths = _get_isic_paths(path=path, split=split, download=download) | ||
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if make_rgb: | ||
kwargs["raw_transform"] = to_rgb | ||
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if resize_inputs: | ||
resize_kwargs = {"patch_shape": patch_shape, "is_rgb": True} | ||
kwargs, patch_shape = util.update_kwargs_for_resize_trafo( | ||
kwargs=kwargs, patch_shape=patch_shape, resize_inputs=resize_inputs, resize_kwargs=resize_kwargs | ||
) | ||
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dataset = torch_em.default_segmentation_dataset( | ||
raw_paths=image_paths, | ||
raw_key=None, | ||
label_paths=gt_paths, | ||
label_key=None, | ||
patch_shape=patch_shape, | ||
is_seg_dataset=False, | ||
**kwargs | ||
) | ||
return dataset | ||
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def get_isic_loader( | ||
path: Union[os.PathLike, str], | ||
patch_shape: Tuple[int, int], | ||
batch_size: int, | ||
split: str, | ||
download: bool = False, | ||
make_rgb: bool = True, | ||
resize_inputs: bool = False, | ||
**kwargs | ||
): | ||
"""Dataloader for the segmentation of skin lesion in dermoscopy images. See `get_isic_dataset` for details. | ||
""" | ||
ds_kwargs, loader_kwargs = util.split_kwargs(torch_em.default_segmentation_dataset, **kwargs) | ||
dataset = get_isic_dataset( | ||
path=path, | ||
patch_shape=patch_shape, | ||
split=split, | ||
download=download, | ||
make_rgb=make_rgb, | ||
resize_inputs=resize_inputs, | ||
**ds_kwargs | ||
) | ||
loader = torch_em.get_data_loader(dataset=dataset, batch_size=batch_size, **loader_kwargs) | ||
return loader |