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finetune/tools/dataset_converters/coco_stuff164k.py
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finetune/tools/dataset_converters/coco_stuff164k.py
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# Copyright (c) OpenMMLab. All rights reserved.
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import argparse
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import os.path as osp
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import shutil
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from functools import partial
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from glob import glob
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import numpy as np
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from mmengine.utils import (mkdir_or_exist, track_parallel_progress,
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track_progress)
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from PIL import Image
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COCO_LEN = 123287
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clsID_to_trID = {
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}
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def convert_to_trainID(maskpath, out_mask_dir, is_train):
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mask = np.array(Image.open(maskpath))
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mask_copy = mask.copy()
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for clsID, trID in clsID_to_trID.items():
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mask_copy[mask == clsID] = trID
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seg_filename = osp.join(
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out_mask_dir, 'train2017',
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osp.basename(maskpath).split('.')[0] +
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'_labelTrainIds.png') if is_train else osp.join(
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out_mask_dir, 'val2017',
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osp.basename(maskpath).split('.')[0] + '_labelTrainIds.png')
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Image.fromarray(mask_copy).save(seg_filename, 'PNG')
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def parse_args():
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parser = argparse.ArgumentParser(
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description=\
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'Convert COCO Stuff 164k annotations to mmsegmentation format') # noqa
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parser.add_argument('coco_path', help='coco stuff path')
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parser.add_argument('-o', '--out_dir', help='output path')
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parser.add_argument(
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'--nproc', default=16, type=int, help='number of process')
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args = parser.parse_args()
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return args
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def main():
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args = parse_args()
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coco_path = args.coco_path
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nproc = args.nproc
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out_dir = args.out_dir or coco_path
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out_img_dir = osp.join(out_dir, 'images')
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out_mask_dir = osp.join(out_dir, 'annotations')
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mkdir_or_exist(osp.join(out_mask_dir, 'train2017'))
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mkdir_or_exist(osp.join(out_mask_dir, 'val2017'))
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if out_dir != coco_path:
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shutil.copytree(osp.join(coco_path, 'images'), out_img_dir)
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train_list = glob(osp.join(coco_path, 'annotations', 'train2017', '*.png'))
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train_list = [file for file in train_list if '_labelTrainIds' not in file]
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test_list = glob(osp.join(coco_path, 'annotations', 'val2017', '*.png'))
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test_list = [file for file in test_list if '_labelTrainIds' not in file]
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assert (len(train_list) +
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len(test_list)) == COCO_LEN, 'Wrong length of list {} & {}'.format(
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len(train_list), len(test_list))
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if args.nproc > 1:
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track_parallel_progress(
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partial(
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convert_to_trainID, out_mask_dir=out_mask_dir, is_train=True),
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train_list,
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nproc=nproc)
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track_parallel_progress(
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partial(
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convert_to_trainID, out_mask_dir=out_mask_dir, is_train=False),
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test_list,
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nproc=nproc)
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else:
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track_progress(
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partial(
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convert_to_trainID, out_mask_dir=out_mask_dir, is_train=True),
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train_list)
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track_progress(
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partial(
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convert_to_trainID, out_mask_dir=out_mask_dir, is_train=False),
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test_list)
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print('Done!')
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if __name__ == '__main__':
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main()
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