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# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import glob
import math
import os
import os.path as osp
import tempfile
import zipfile
import mmcv
import numpy as np
from mmengine.utils import ProgressBar, mkdir_or_exist
def parse_args():
parser = argparse.ArgumentParser(
description='Convert vaihingen dataset to mmsegmentation format')
parser.add_argument('dataset_path', help='vaihingen 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()
return args
def clip_big_image(image_path, clip_save_dir, to_label=False):
# Original image of Vaihingen dataset is very large, thus pre-processing
# of them is adopted. Given fixed clip size and stride size to generate
# clipped image, the intersection of width and height is determined.
# For example, given one 5120 x 5120 original image, the clip size is
# 512 and stride size is 256, thus it would generate 20x20 = 400 images
# whose size are all 512x512.
image = mmcv.imread(image_path)
h, w, c = image.shape
cs = args.clip_size
ss = args.stride_size
num_rows = math.ceil((h - cs) / ss) if math.ceil(
(h - cs) / ss) * ss + cs >= h else math.ceil((h - cs) / ss) + 1
num_cols = math.ceil((w - cs) / ss) if math.ceil(
(w - cs) / ss) * ss + cs >= w else math.ceil((w - cs) / ss) + 1
x, y = np.meshgrid(np.arange(num_cols + 1), np.arange(num_rows + 1))
xmin = x * cs
ymin = y * cs
xmin = xmin.ravel()
ymin = ymin.ravel()
xmin_offset = np.where(xmin + cs > w, w - xmin - cs, np.zeros_like(xmin))
ymin_offset = np.where(ymin + cs > h, h - ymin - cs, np.zeros_like(ymin))
boxes = np.stack([
xmin + xmin_offset, ymin + ymin_offset,
np.minimum(xmin + cs, w),
np.minimum(ymin + cs, 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, :]
area_idx = osp.basename(image_path).split('_')[3].strip('.tif')
mmcv.imwrite(
clipped_image.astype(np.uint8),
osp.join(clip_save_dir,
f'{area_idx}_{start_x}_{start_y}_{end_x}_{end_y}.png'))
def main():
splits = {
'train': [
'area1', 'area11', 'area13', 'area15', 'area17', 'area21',
'area23', 'area26', 'area28', 'area3', 'area30', 'area32',
'area34', 'area37', 'area5', 'area7'
],
'val': [
'area6', 'area24', 'area35', 'area16', 'area14', 'area22',
'area10', 'area4', 'area2', 'area20', 'area8', 'area31', 'area33',
'area27', 'area38', 'area12', 'area29'
],
}
dataset_path = args.dataset_path
if args.out_dir is None:
out_dir = osp.join('data', 'vaihingen')
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, 'ann_dir', 'train'))
mkdir_or_exist(osp.join(out_dir, 'ann_dir', 'val'))
zipp_list = glob.glob(os.path.join(dataset_path, '*.zip'))
print('Find the data', zipp_list)
with tempfile.TemporaryDirectory(dir=args.tmp_dir) as tmp_dir:
for zipp in zipp_list:
zip_file = zipfile.ZipFile(zipp)
zip_file.extractall(tmp_dir)
src_path_list = glob.glob(os.path.join(tmp_dir, '*.tif'))
if 'ISPRS_semantic_labeling_Vaihingen' in zipp:
src_path_list = glob.glob(
os.path.join(os.path.join(tmp_dir, 'top'), '*.tif'))
if 'ISPRS_semantic_labeling_Vaihingen_ground_truth_eroded_COMPLETE' in zipp: # noqa
src_path_list = glob.glob(os.path.join(tmp_dir, '*.tif'))
# delete unused area9 ground truth
for area_ann in src_path_list:
if 'area9' in area_ann:
src_path_list.remove(area_ann)
prog_bar = ProgressBar(len(src_path_list))
for i, src_path in enumerate(src_path_list):
area_idx = osp.basename(src_path).split('_')[3].strip('.tif')
data_type = 'train' if area_idx in splits['train'] else 'val'
if 'noBoundary' in src_path:
dst_dir = osp.join(out_dir, 'ann_dir', data_type)
clip_big_image(src_path, dst_dir, to_label=True)
else:
dst_dir = osp.join(out_dir, 'img_dir', data_type)
clip_big_image(src_path, dst_dir, to_label=False)
prog_bar.update()
print('Removing the temporary files...')
print('Done!')
if __name__ == '__main__':
args = parse_args()
main()
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