基于Pytorch的图像风格迁移，高质量毕设源码，小白必看！

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 --gitFine_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')