158 lines
5.7 KiB
Python
158 lines
5.7 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import argparse
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import glob
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import math
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import os
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import os.path as osp
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import tempfile
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import zipfile
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import mmcv
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import numpy as np
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def parse_args():
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parser = argparse.ArgumentParser(
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description='Convert potsdam dataset to mmsegmentation format')
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parser.add_argument('dataset_path', help='potsdam folder path')
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parser.add_argument('--tmp_dir', help='path of the temporary directory')
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parser.add_argument('-o', '--out_dir', help='output path')
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parser.add_argument(
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'--clip_size',
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type=int,
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help='clipped size of image after preparation',
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default=512)
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parser.add_argument(
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'--stride_size',
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type=int,
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help='stride of clipping original images',
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default=256)
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args = parser.parse_args()
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return args
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def clip_big_image(image_path, clip_save_dir, args, to_label=False):
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# Original image of Potsdam dataset is very large, thus pre-processing
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# of them is adopted. Given fixed clip size and stride size to generate
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# clipped image, the intersection of width and height is determined.
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# For example, given one 5120 x 5120 original image, the clip size is
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# 512 and stride size is 256, thus it would generate 20x20 = 400 images
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# whose size are all 512x512.
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image = mmcv.imread(image_path)
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h, w, c = image.shape
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clip_size = args.clip_size
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stride_size = args.stride_size
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num_rows = math.ceil((h - clip_size) / stride_size) if math.ceil(
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(h - clip_size) /
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stride_size) * stride_size + clip_size >= h else math.ceil(
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(h - clip_size) / stride_size) + 1
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num_cols = math.ceil((w - clip_size) / stride_size) if math.ceil(
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(w - clip_size) /
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stride_size) * stride_size + clip_size >= w else math.ceil(
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(w - clip_size) / stride_size) + 1
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x, y = np.meshgrid(np.arange(num_cols + 1), np.arange(num_rows + 1))
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xmin = x * clip_size
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ymin = y * clip_size
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xmin = xmin.ravel()
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ymin = ymin.ravel()
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xmin_offset = np.where(xmin + clip_size > w, w - xmin - clip_size,
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np.zeros_like(xmin))
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ymin_offset = np.where(ymin + clip_size > h, h - ymin - clip_size,
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np.zeros_like(ymin))
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boxes = np.stack([
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xmin + xmin_offset, ymin + ymin_offset,
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np.minimum(xmin + clip_size, w),
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np.minimum(ymin + clip_size, h)
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],
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axis=1)
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if to_label:
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color_map = np.array([[0, 0, 0], [255, 255, 255], [255, 0, 0],
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[255, 255, 0], [0, 255, 0], [0, 255, 255],
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[0, 0, 255]])
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flatten_v = np.matmul(
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image.reshape(-1, c),
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np.array([2, 3, 4]).reshape(3, 1))
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out = np.zeros_like(flatten_v)
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for idx, class_color in enumerate(color_map):
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value_idx = np.matmul(class_color,
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np.array([2, 3, 4]).reshape(3, 1))
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out[flatten_v == value_idx] = idx
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image = out.reshape(h, w)
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for box in boxes:
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start_x, start_y, end_x, end_y = box
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clipped_image = image[start_y:end_y,
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start_x:end_x] if to_label else image[
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start_y:end_y, start_x:end_x, :]
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idx_i, idx_j = osp.basename(image_path).split('_')[2:4]
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mmcv.imwrite(
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clipped_image.astype(np.uint8),
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osp.join(
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clip_save_dir,
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f'{idx_i}_{idx_j}_{start_x}_{start_y}_{end_x}_{end_y}.png'))
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def main():
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args = parse_args()
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splits = {
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'train': [
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'2_10', '2_11', '2_12', '3_10', '3_11', '3_12', '4_10', '4_11',
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'4_12', '5_10', '5_11', '5_12', '6_10', '6_11', '6_12', '6_7',
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'6_8', '6_9', '7_10', '7_11', '7_12', '7_7', '7_8', '7_9'
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],
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'val': [
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'5_15', '6_15', '6_13', '3_13', '4_14', '6_14', '5_14', '2_13',
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'4_15', '2_14', '5_13', '4_13', '3_14', '7_13'
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]
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}
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dataset_path = args.dataset_path
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if args.out_dir is None:
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out_dir = osp.join('data', 'potsdam')
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else:
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out_dir = args.out_dir
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print('Making directories...')
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mmcv.mkdir_or_exist(osp.join(out_dir, 'img_dir', 'train'))
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mmcv.mkdir_or_exist(osp.join(out_dir, 'img_dir', 'val'))
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mmcv.mkdir_or_exist(osp.join(out_dir, 'ann_dir', 'train'))
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mmcv.mkdir_or_exist(osp.join(out_dir, 'ann_dir', 'val'))
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zipp_list = glob.glob(os.path.join(dataset_path, '*.zip'))
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print('Find the data', zipp_list)
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for zipp in zipp_list:
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with tempfile.TemporaryDirectory(dir=args.tmp_dir) as tmp_dir:
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zip_file = zipfile.ZipFile(zipp)
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zip_file.extractall(tmp_dir)
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src_path_list = glob.glob(os.path.join(tmp_dir, '*.tif'))
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if not len(src_path_list):
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sub_tmp_dir = os.path.join(tmp_dir, os.listdir(tmp_dir)[0])
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src_path_list = glob.glob(os.path.join(sub_tmp_dir, '*.tif'))
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prog_bar = mmcv.ProgressBar(len(src_path_list))
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for i, src_path in enumerate(src_path_list):
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idx_i, idx_j = osp.basename(src_path).split('_')[2:4]
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data_type = 'train' if f'{idx_i}_{idx_j}' in splits[
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'train'] else 'val'
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if 'label' in src_path:
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dst_dir = osp.join(out_dir, 'ann_dir', data_type)
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clip_big_image(src_path, dst_dir, args, to_label=True)
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else:
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dst_dir = osp.join(out_dir, 'img_dir', data_type)
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clip_big_image(src_path, dst_dir, args, to_label=False)
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prog_bar.update()
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print('Removing the temporary files...')
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print('Done!')
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if __name__ == '__main__':
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main()
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