init

finetune/tools/dataset_converters/hrf.py

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+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import os
+import os.path as osp
+import tempfile
+import zipfile
+
+import mmcv
+from mmengine.utils import mkdir_or_exist
+
+HRF_LEN = 15
+TRAINING_LEN = 5
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description='Convert HRF dataset to mmsegmentation format')
+    parser.add_argument('healthy_path', help='the path of healthy.zip')
+    parser.add_argument(
+        'healthy_manualsegm_path', help='the path of healthy_manualsegm.zip')
+    parser.add_argument('glaucoma_path', help='the path of glaucoma.zip')
+    parser.add_argument(
+        'glaucoma_manualsegm_path', help='the path of glaucoma_manualsegm.zip')
+    parser.add_argument(
+        'diabetic_retinopathy_path',
+        help='the path of diabetic_retinopathy.zip')
+    parser.add_argument(
+        'diabetic_retinopathy_manualsegm_path',
+        help='the path of diabetic_retinopathy_manualsegm.zip')
+    parser.add_argument('--tmp_dir', help='path of the temporary directory')
+    parser.add_argument('-o', '--out_dir', help='output path')
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = parse_args()
+    images_path = [
+        args.healthy_path, args.glaucoma_path, args.diabetic_retinopathy_path
+    ]
+    annotations_path = [
+        args.healthy_manualsegm_path, args.glaucoma_manualsegm_path,
+        args.diabetic_retinopathy_manualsegm_path
+    ]
+    if args.out_dir is None:
+        out_dir = osp.join('data', 'HRF')
+    else:
+        out_dir = args.out_dir
+
+    print('Making directories...')
+    mkdir_or_exist(out_dir)
+    mkdir_or_exist(osp.join(out_dir, 'images'))
+    mkdir_or_exist(osp.join(out_dir, 'images', 'training'))
+    mkdir_or_exist(osp.join(out_dir, 'images', 'validation'))
+    mkdir_or_exist(osp.join(out_dir, 'annotations'))
+    mkdir_or_exist(osp.join(out_dir, 'annotations', 'training'))
+    mkdir_or_exist(osp.join(out_dir, 'annotations', 'validation'))
+
+    print('Generating images...')
+    for now_path in images_path:
+        with tempfile.TemporaryDirectory(dir=args.tmp_dir) as tmp_dir:
+            zip_file = zipfile.ZipFile(now_path)
+            zip_file.extractall(tmp_dir)
+
+            assert len(os.listdir(tmp_dir)) == HRF_LEN, \
+                f'len(os.listdir(tmp_dir)) != {HRF_LEN}'
+
+            for filename in sorted(os.listdir(tmp_dir))[:TRAINING_LEN]:
+                img = mmcv.imread(osp.join(tmp_dir, filename))
+                mmcv.imwrite(
+                    img,
+                    osp.join(out_dir, 'images', 'training',
+                             osp.splitext(filename)[0] + '.png'))
+            for filename in sorted(os.listdir(tmp_dir))[TRAINING_LEN:]:
+                img = mmcv.imread(osp.join(tmp_dir, filename))
+                mmcv.imwrite(
+                    img,
+                    osp.join(out_dir, 'images', 'validation',
+                             osp.splitext(filename)[0] + '.png'))
+
+    print('Generating annotations...')
+    for now_path in annotations_path:
+        with tempfile.TemporaryDirectory(dir=args.tmp_dir) as tmp_dir:
+            zip_file = zipfile.ZipFile(now_path)
+            zip_file.extractall(tmp_dir)
+
+            assert len(os.listdir(tmp_dir)) == HRF_LEN, \
+                f'len(os.listdir(tmp_dir)) != {HRF_LEN}'
+
+            for filename in sorted(os.listdir(tmp_dir))[:TRAINING_LEN]:
+                img = mmcv.imread(osp.join(tmp_dir, filename))
+                # The annotation img should be divided by 128, because some of
+                # the annotation imgs are not standard. We should set a
+                # threshold to convert the nonstandard annotation imgs. The
+                # value divided by 128 is equivalent to '1 if value >= 128
+                # else 0'
+                mmcv.imwrite(
+                    img[:, :, 0] // 128,
+                    osp.join(out_dir, 'annotations', 'training',
+                             osp.splitext(filename)[0] + '.png'))
+            for filename in sorted(os.listdir(tmp_dir))[TRAINING_LEN:]:
+                img = mmcv.imread(osp.join(tmp_dir, filename))
+                mmcv.imwrite(
+                    img[:, :, 0] // 128,
+                    osp.join(out_dir, 'annotations', 'validation',
+                             osp.splitext(filename)[0] + '.png'))
+
+    print('Done!')
+
+
+if __name__ == '__main__':
+    main()