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# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import glob
import os
import os.path as osp
import shutil
import tempfile
import zipfile
import mmcv
import numpy as np
from mmengine.utils import ProgressBar, mkdir_or_exist
from PIL import Image
iSAID_palette = \
{
0: (0, 0, 0),
1: (0, 0, 63),
2: (0, 63, 63),
3: (0, 63, 0),
4: (0, 63, 127),
5: (0, 63, 191),
6: (0, 63, 255),
7: (0, 127, 63),
8: (0, 127, 127),
9: (0, 0, 127),
10: (0, 0, 191),
11: (0, 0, 255),
12: (0, 191, 127),
13: (0, 127, 191),
14: (0, 127, 255),
15: (0, 100, 155)
}
iSAID_invert_palette = {v: k for k, v in iSAID_palette.items()}
def iSAID_convert_from_color(arr_3d, palette=iSAID_invert_palette):
"""RGB-color encoding to grayscale labels."""
arr_2d = np.zeros((arr_3d.shape[0], arr_3d.shape[1]), dtype=np.uint8)
for c, i in palette.items():
m = np.all(arr_3d == np.array(c).reshape(1, 1, 3), axis=2)
arr_2d[m] = i
return arr_2d
def slide_crop_image(src_path, out_dir, mode, patch_H, patch_W, overlap):
img = np.asarray(Image.open(src_path).convert('RGB'))
img_H, img_W, _ = img.shape
if img_H < patch_H and img_W > patch_W:
img = mmcv.impad(img, shape=(patch_H, img_W), pad_val=0)
img_H, img_W, _ = img.shape
elif img_H > patch_H and img_W < patch_W:
img = mmcv.impad(img, shape=(img_H, patch_W), pad_val=0)
img_H, img_W, _ = img.shape
elif img_H < patch_H and img_W < patch_W:
img = mmcv.impad(img, shape=(patch_H, patch_W), pad_val=0)
img_H, img_W, _ = img.shape
for x in range(0, img_W, patch_W - overlap):
for y in range(0, img_H, patch_H - overlap):
x_str = x
x_end = x + patch_W
if x_end > img_W:
diff_x = x_end - img_W
x_str -= diff_x
x_end = img_W
y_str = y
y_end = y + patch_H
if y_end > img_H:
diff_y = y_end - img_H
y_str -= diff_y
y_end = img_H
img_patch = img[y_str:y_end, x_str:x_end, :]
img_patch = Image.fromarray(img_patch.astype(np.uint8))
image = osp.basename(src_path).split('.')[0] + '_' + str(
y_str) + '_' + str(y_end) + '_' + str(x_str) + '_' + str(
x_end) + '.png'
# print(image)
save_path_image = osp.join(out_dir, 'img_dir', mode, str(image))
img_patch.save(save_path_image, format='BMP')
def slide_crop_label(src_path, out_dir, mode, patch_H, patch_W, overlap):
label = mmcv.imread(src_path, channel_order='rgb')
label = iSAID_convert_from_color(label)
img_H, img_W = label.shape
if img_H < patch_H and img_W > patch_W:
label = mmcv.impad(label, shape=(patch_H, img_W), pad_val=255)
img_H = patch_H
elif img_H > patch_H and img_W < patch_W:
label = mmcv.impad(label, shape=(img_H, patch_W), pad_val=255)
img_W = patch_W
elif img_H < patch_H and img_W < patch_W:
label = mmcv.impad(label, shape=(patch_H, patch_W), pad_val=255)
img_H = patch_H
img_W = patch_W
for x in range(0, img_W, patch_W - overlap):
for y in range(0, img_H, patch_H - overlap):
x_str = x
x_end = x + patch_W
if x_end > img_W:
diff_x = x_end - img_W
x_str -= diff_x
x_end = img_W
y_str = y
y_end = y + patch_H
if y_end > img_H:
diff_y = y_end - img_H
y_str -= diff_y
y_end = img_H
lab_patch = label[y_str:y_end, x_str:x_end]
lab_patch = Image.fromarray(lab_patch.astype(np.uint8), mode='P')
image = osp.basename(src_path).split('.')[0].split(
'_')[0] + '_' + str(y_str) + '_' + str(y_end) + '_' + str(
x_str) + '_' + str(x_end) + '_instance_color_RGB' + '.png'
lab_patch.save(osp.join(out_dir, 'ann_dir', mode, str(image)))
def parse_args():
parser = argparse.ArgumentParser(
description='Convert iSAID dataset to mmsegmentation format')
parser.add_argument('dataset_path', help='iSAID 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(
'--patch_width',
default=896,
type=int,
help='Width of the cropped image patch')
parser.add_argument(
'--patch_height',
default=896,
type=int,
help='Height of the cropped image patch')
parser.add_argument(
'--overlap_area', default=384, type=int, help='Overlap area')
args = parser.parse_args()
return args
def main():
args = parse_args()
dataset_path = args.dataset_path
# image patch width and height
patch_H, patch_W = args.patch_width, args.patch_height
overlap = args.overlap_area # overlap area
if args.out_dir is None:
out_dir = osp.join('data', 'iSAID')
else:
out_dir = args.out_dir
print('Making directories...')
mkdir_or_exist(osp.join(out_dir, 'img_dir', 'train'))
mkdir_or_exist(osp.join(out_dir, 'img_dir', 'val'))
mkdir_or_exist(osp.join(out_dir, 'img_dir', 'test'))
mkdir_or_exist(osp.join(out_dir, 'ann_dir', 'train'))
mkdir_or_exist(osp.join(out_dir, 'ann_dir', 'val'))
mkdir_or_exist(osp.join(out_dir, 'ann_dir', 'test'))
assert os.path.exists(os.path.join(dataset_path, 'train')), \
f'train is not in {dataset_path}'
assert os.path.exists(os.path.join(dataset_path, 'val')), \
f'val is not in {dataset_path}'
assert os.path.exists(os.path.join(dataset_path, 'test')), \
f'test is not in {dataset_path}'
with tempfile.TemporaryDirectory(dir=args.tmp_dir) as tmp_dir:
for dataset_mode in ['train', 'val', 'test']:
# for dataset_mode in [ 'test']:
print(f'Extracting {dataset_mode}ing.zip...')
img_zipp_list = glob.glob(
os.path.join(dataset_path, dataset_mode, 'images', '*.zip'))
print('Find the data', img_zipp_list)
for img_zipp in img_zipp_list:
zip_file = zipfile.ZipFile(img_zipp)
zip_file.extractall(os.path.join(tmp_dir, dataset_mode, 'img'))
src_path_list = glob.glob(
os.path.join(tmp_dir, dataset_mode, 'img', 'images', '*.png'))
src_prog_bar = ProgressBar(len(src_path_list))
for i, img_path in enumerate(src_path_list):
if dataset_mode != 'test':
slide_crop_image(img_path, out_dir, dataset_mode, patch_H,
patch_W, overlap)
else:
shutil.move(img_path,
os.path.join(out_dir, 'img_dir', dataset_mode))
src_prog_bar.update()
if dataset_mode != 'test':
label_zipp_list = glob.glob(
os.path.join(dataset_path, dataset_mode, 'Semantic_masks',
'*.zip'))
for label_zipp in label_zipp_list:
zip_file = zipfile.ZipFile(label_zipp)
zip_file.extractall(
os.path.join(tmp_dir, dataset_mode, 'lab'))
lab_path_list = glob.glob(
os.path.join(tmp_dir, dataset_mode, 'lab', 'images',
'*.png'))
lab_prog_bar = ProgressBar(len(lab_path_list))
for i, lab_path in enumerate(lab_path_list):
slide_crop_label(lab_path, out_dir, dataset_mode, patch_H,
patch_W, overlap)
lab_prog_bar.update()
print('Removing the temporary files...')
print('Done!')
if __name__ == '__main__':
main()
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