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| import argparse
import glob
import math
import os
import os.path as osp
import tempfile
import zipfile
from tqdm import tqdm
from PIL import Image
import numpy as np
def parse_args(arg_list):
parser = argparse.ArgumentParser(
description='Convert potsdam dataset to mmsegmentation format')
parser.add_argument('dataset_path', help='potsdam folder path')
parser.add_argument('--tmp_dir', help='path of the temporary directory')
parser.add_argument('-o', '--out_dir', help='output path')
parser.add_argument(
'--clip_size',
type=int,
help='clipped size of image after preparation',
default=512)
parser.add_argument(
'--stride_size',
type=int,
help='stride of clipping original images',
default=256)
args = parser.parse_args(arg_list)
return args
def clip_big_image(image_path, clip_save_dir, args, to_label=False):
image = Image.open(image_path)
image = np.array(image)
h, w, c = image.shape
clip_size = args.clip_size
stride_size = args.stride_size
num_rows = math.ceil((h - clip_size) / stride_size) if math.ceil(
(h - clip_size) /
stride_size) * stride_size + clip_size >= h else math.ceil(
(h - clip_size) / stride_size) + 1
num_cols = math.ceil((w - clip_size) / stride_size) if math.ceil(
(w - clip_size) /
stride_size) * stride_size + clip_size >= w else math.ceil(
(w - clip_size) / stride_size) + 1
x, y = np.meshgrid(np.arange(num_cols + 1), np.arange(num_rows + 1))
xmin = x * clip_size
ymin = y * clip_size
xmin = xmin.ravel()
ymin = ymin.ravel()
xmin_offset = np.where(xmin + clip_size > w, w - xmin - clip_size,
np.zeros_like(xmin))
ymin_offset = np.where(ymin + clip_size > h, h - ymin - clip_size,
np.zeros_like(ymin))
boxes = np.stack([
xmin + xmin_offset, ymin + ymin_offset,
np.minimum(xmin + clip_size, w),
np.minimum(ymin + clip_size, h)
],
axis=1)
if to_label:
color_map = np.array([[0, 0, 0], [255, 255, 255], [255, 0, 0],
[255, 255, 0], [0, 255, 0], [0, 255, 255],
[0, 0, 255]])
flatten_v = np.matmul(
image.reshape(-1, c),
np.array([2, 3, 4]).reshape(3, 1))
out = np.zeros_like(flatten_v)
for idx, class_color in enumerate(color_map):
value_idx = np.matmul(class_color,
np.array([2, 3, 4]).reshape(3, 1))
out[flatten_v == value_idx] = idx
image = out.reshape(h, w)
for box in boxes:
start_x, start_y, end_x, end_y = box
clipped_image = image[start_y:end_y,
start_x:end_x] if to_label else image[
start_y:end_y, start_x:end_x, :]
idx_i, idx_j = osp.basename(image_path).split('_')[2:4]
clipped_image = Image.fromarray(clipped_image.astype(np.uint8))
clipped_image.save(
fp=osp.join(clip_save_dir, f'{idx_i}_{idx_j}_{start_x}_{start_y}_{end_x}_{end_y}.png'),
format='PNG', compress_level=1
)
def main():
args = parse_args(["D:/Dataset/Potsdam"])
splits = {
'train': [
'2_10', '2_11', '2_12', '3_10', '3_11', '3_12', '4_10', '4_11',
'4_12', '5_10', '5_11', '5_12', '6_10', '6_11', '6_12', '6_7',
'6_8', '6_9', '7_10', '7_11', '7_12', '7_7', '7_8', '7_9'
],
'val': [
'5_15', '6_15', '6_13', '3_13', '4_14', '6_14', '5_14', '2_13',
'4_15', '2_14', '5_13', '4_13', '3_14', '7_13'
]
}
dataset_path = args.dataset_path
if args.out_dir is None:
out_dir = osp.join('data', 'potsdam')
else:
out_dir = args.out_dir
print('Making directories...')
if not osp.exists(osp.join(out_dir, 'img_dir', 'train')):
os.makedirs(osp.join(out_dir, 'img_dir', 'train'))
if not osp.exists(osp.join(out_dir, 'img_dir', 'val')):
os.makedirs(osp.join(out_dir, 'img_dir', 'val'))
if not osp.exists(osp.join(out_dir, 'ann_dir', 'train')):
os.makedirs(osp.join(out_dir, 'ann_dir', 'train'))
if not osp.exists(osp.join(out_dir, 'ann_dir', 'val')):
os.makedirs(osp.join(out_dir, 'ann_dir', 'val'))
zipp_list = glob.glob(os.path.join(dataset_path, '*.zip'))
print('Find the data', zipp_list)
for zipp in zipp_list:
with tempfile.TemporaryDirectory(dir=args.tmp_dir) as tmp_dir:
zip_file = zipfile.ZipFile(zipp)
zip_file.extractall(tmp_dir)
src_path_list = glob.glob(os.path.join(tmp_dir, '*.tif'))
if not len(src_path_list):
sub_tmp_dir = os.path.join(tmp_dir, os.listdir(tmp_dir)[0])
src_path_list = glob.glob(os.path.join(sub_tmp_dir, '*.tif'))
prog_bar = tqdm(src_path_list[:2])
for src_path in prog_bar:
idx_i, idx_j = osp.basename(src_path).split('_')[2:4]
data_type = 'train' if f'{idx_i}_{idx_j}' in splits[
'train'] else 'val'
if 'label' in src_path:
dst_dir = osp.join(out_dir, 'ann_dir', data_type)
clip_big_image(src_path, dst_dir, args, to_label=True)
else:
dst_dir = osp.join(out_dir, 'img_dir', data_type)
clip_big_image(src_path, dst_dir, args, to_label=False)
print('Removing the temporary files...')
print('Done!')
if __name__ == '__main__':
main()
|
