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import os
import glob
from PIL import Image
help_msg = """
The dataset can be downloaded from https://cityscapes-dataset.com.
Please download the datasets [gtFine_trainvaltest.zip] and [leftImg8bit_trainvaltest.zip] and unzip them.
gtFine contains the semantics segmentations. Use --gtFine_dir to specify the path to the unzipped gtFine_trainvaltest directory.
leftImg8bit contains the dashcam photographs. Use --leftImg8bit_dir to specify the path to the unzipped leftImg8bit_trainvaltest directory.
The processed images will be placed at --output_dir.
Example usage:
python prepare_cityscapes_dataset.py --gtFine_dir ./gtFine/ --leftImg8bit_dir ./leftImg8bit --output_dir ./datasets/cityscapes/
"""
def load_resized_img(path):
return Image.open(path).convert('RGB').resize((256, 256))
def check_matching_pair(segmap_path, photo_path):
segmap_identifier = os.path.basename(segmap_path).replace('_gtFine_color', '')
photo_identifier = os.path.basename(photo_path).replace('_leftImg8bit', '')
assert segmap_identifier == photo_identifier, \
"[%s] and [%s] don't seem to be matching. Aborting." % (segmap_path, photo_path)
def process_cityscapes(gtFine_dir, leftImg8bit_dir, output_dir, phase):
save_phase = 'test' if phase == 'val' else 'train'
savedir = os.path.join(output_dir, save_phase)
os.makedirs(savedir, exist_ok=True)
os.makedirs(savedir + 'A', exist_ok=True)
os.makedirs(savedir + 'B', exist_ok=True)
print("Directory structure prepared at %s" % output_dir)
segmap_expr = os.path.join(gtFine_dir, phase) + "/*/*_color.png"
segmap_paths = glob.glob(segmap_expr)
segmap_paths = sorted(segmap_paths)
photo_expr = os.path.join(leftImg8bit_dir, phase) + "/*/*_leftImg8bit.png"
photo_paths = glob.glob(photo_expr)
photo_paths = sorted(photo_paths)
assert len(segmap_paths) == len(photo_paths), \
"%d images that match [%s], and %d images that match [%s]. Aborting." % (len(segmap_paths), segmap_expr, len(photo_paths), photo_expr)
for i, (segmap_path, photo_path) in enumerate(zip(segmap_paths, photo_paths)):
check_matching_pair(segmap_path, photo_path)
segmap = load_resized_img(segmap_path)
photo = load_resized_img(photo_path)
# data for pix2pix where the two images are placed side-by-side
sidebyside = Image.new('RGB', (512, 256))
sidebyside.paste(segmap, (256, 0))
sidebyside.paste(photo, (0, 0))
savepath = os.path.join(savedir, "%d.jpg" % i)
sidebyside.save(savepath, format='JPEG', subsampling=0, quality=100)
# data for cyclegan where the two images are stored at two distinct directories
savepath = os.path.join(savedir + 'A', "%d_A.jpg" % i)
photo.save(savepath, format='JPEG', subsampling=0, quality=100)
savepath = os.path.join(savedir + 'B', "%d_B.jpg" % i)
segmap.save(savepath, format='JPEG', subsampling=0, quality=100)
if i % (len(segmap_paths) // 10) == 0:
print("%d / %d: last image saved at %s, " % (i, len(segmap_paths), savepath))
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--gtFine_dir', type=str, required=True,
help='Path to the Cityscapes gtFine directory.')
parser.add_argument('--leftImg8bit_dir', type=str, required=True,
help='Path to the Cityscapes leftImg8bit_trainvaltest directory.')
parser.add_argument('--output_dir', type=str, required=True,
default='./datasets/cityscapes',
help='Directory the output images will be written to.')
opt = parser.parse_args()
print(help_msg)
print('Preparing Cityscapes Dataset for val phase')
process_cityscapes(opt.gtFine_dir, opt.leftImg8bit_dir, opt.output_dir, "val")
print('Preparing Cityscapes Dataset for train phase')
process_cityscapes(opt.gtFine_dir, opt.leftImg8bit_dir, opt.output_dir, "train")
print('Done')
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