Misc changes
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@ -20,13 +20,13 @@ def main():
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# CV_IMWRITE_PNG_COMPRESSION from 0 to 9. A higher value means a smaller size and longer
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# compression time. If read raw images during training, use 0 for faster IO speed.
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if mode == 'single':
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opt['input_folder'] = 'F:\\4k6k\\datasets\\fkaw\\images'
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opt['save_folder'] = 'F:\\4k6k\\datasets\\fkaw\\square_images'
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opt['input_folder'] = 'F:\\4k6k\\datasets\\div2k\\DIV2K_train_HR'
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opt['save_folder'] = 'F:\\4k6k\\datasets\\div2k\\tiled1024'
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opt['crop_sz'] = 1024 # the size of each sub-image
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opt['step'] = 880 # step of the sliding crop window
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opt['thres_sz'] = 240 # size threshold
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opt['resize_final_img'] = .5
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opt['only_resize'] = .5
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opt['resize_final_img'] = 1
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opt['only_resize'] = False
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extract_single(opt, split_img)
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elif mode == 'pair':
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GT_folder = '../../datasets/div2k/DIV2K_train_HR'
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@ -164,7 +164,7 @@ def worker(path, opt, split_mode=False, left_img=True):
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crop_img = img[x:x + crop_sz, y:y + crop_sz, :]
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crop_img = np.ascontiguousarray(crop_img)
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# If this fails, change it and the imwrite below to the write extension.
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assert ".jpg" in img_name
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assert ".png" in img_name
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if 'resize_final_img' in opt.keys():
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# Resize too.
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resize_factor = opt['resize_final_img']
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@ -173,7 +173,7 @@ def worker(path, opt, split_mode=False, left_img=True):
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crop_img = cv2.resize(crop_img, dsize, interpolation = cv2.INTER_AREA)
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cv2.imwrite(
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osp.join(opt['save_folder'],
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img_name.replace('.jpg', '_l{:05d}_s{:03d}.png'.format(left, index))), crop_img,
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img_name.replace('.png', '_l{:05d}_s{:03d}.png'.format(left, index))), crop_img,
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[cv2.IMWRITE_PNG_COMPRESSION, opt['compression_level']])
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return 'Processing {:s} ...'.format(img_name)
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@ -33,7 +33,7 @@ def init_dist(backend='nccl', **kwargs):
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def main():
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#### options
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parser = argparse.ArgumentParser()
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parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_div2k_srg3.yml')
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parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../experiments/train_div2k_srg2/train_div2k_srg2_basis.yml')
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parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none',
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help='job launcher')
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parser.add_argument('--local_rank', type=int, default=0)
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