Script updates
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@ -19,9 +19,9 @@ def main():
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# compression time. If read raw images during training, use 0 for faster IO speed.
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opt['dest'] = 'file'
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opt['input_folder'] = ['F:\\4k6k\\datasets\\images\\youtube\\images']
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opt['save_folder'] = 'F:\\4k6k\\datasets\\images\\ge_full_1024'
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opt['imgsize'] = 1024
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opt['input_folder'] = ['F:\\4k6k\\datasets\\ns_images\\imagesets\\imgset4']
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opt['save_folder'] = 'F:\\4k6k\\datasets\\ns_images\\256_unsupervised_new'
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opt['imgsize'] = 256
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#opt['bottom_crop'] = 120
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save_folder = opt['save_folder']
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@ -52,6 +52,7 @@ class TiledDataset(data.Dataset):
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print("Error with ", path)
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return None
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if len(img.shape) == 2:
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print("Skipping due to greyscale")
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return None
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# Perform explicit crops first. These are generally used to get rid of watermarks so we dont even want to
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@ -61,7 +62,8 @@ class TiledDataset(data.Dataset):
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h, w, c = img.shape
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# Uncomment to filter any image that doesnt meet a threshold size.
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if min(h,w) < 1024:
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if min(h,w) < 256:
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print("Skipping due to threshold")
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return None
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# We must convert the image into a square.
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@ -70,13 +72,7 @@ class TiledDataset(data.Dataset):
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img = img[(h - dim) // 2:dim + (h - dim) // 2, (w - dim) // 2:dim + (w - dim) // 2, :]
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img = cv2.resize(img, (self.opt['imgsize'], self.opt['imgsize']), interpolation=cv2.INTER_AREA)
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# I was having some issues with unicode filenames with cv2. Hence using PIL.
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# cv2.imwrite(osp.join(self.opt['save_folder'], basename + ".jpg"), img, [cv2.IMWRITE_JPEG_QUALITY, self.opt['compression_level']])
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img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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img = (img * 255).astype(np.uint8)
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img = Image.fromarray(img)
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img.save(osp.join(self.opt['save_folder'], basename + ".jpg"), "JPEG", quality=self.opt['compression_level'], optimize=True)
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cv2.imwrite(osp.join(self.opt['save_folder'], basename + ".jpg"), img, [cv2.IMWRITE_JPEG_QUALITY, self.opt['compression_level']])
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return None
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def __len__(self):
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@ -13,19 +13,19 @@ import torch
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def main():
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split_img = False
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opt = {}
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opt['n_thread'] = 7
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opt['n_thread'] = 4
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opt['compression_level'] = 90 # JPEG compression quality rating.
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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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opt['dest'] = 'file'
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opt['input_folder'] = 'F:\\4k6k\\datasets\\images\youtube\\images_cook'
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opt['save_folder'] = 'F:\\4k6k\\datasets\\images\\youtube_massive_cook'
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opt['crop_sz'] = [512, 1024, 2048] # the size of each sub-image
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opt['step'] = [256, 512, 1024] # step of the sliding crop window
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opt['input_folder'] = 'F:\\4k6k\\datasets\\ns_images\\imagesets\\images'
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opt['save_folder'] = 'F:\\4k6k\\datasets\\ns_images\\imagesets\\512_with_ref_new'
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opt['crop_sz'] = [1024, 2048] # the size of each sub-image
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opt['step'] = [700, 1200] # step of the sliding crop window
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opt['exclusions'] = [[],[],[]] # image names matching these terms wont be included in the processing.
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opt['thres_sz'] = 128 # size threshold
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opt['resize_final_img'] = [.5, .25, .125]
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opt['thres_sz'] = 256 # size threshold
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opt['resize_final_img'] = [.5, .25]
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opt['only_resize'] = False
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opt['vertical_split'] = False
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opt['input_image_max_size_before_being_halved'] = 5500 # As described, images larger than this dimensional size will be halved before anything else is done.
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@ -245,7 +245,7 @@ class TiledDataset(data.Dataset):
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for exc in exclusions:
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if exc in path:
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excluded = True
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break;
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break
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if excluded:
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continue
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results.extend(self.get_for_scale(img, crop_sz, step, resize_factor, ref_resize_factor))
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@ -262,6 +262,7 @@ def extract_single(opt, writer):
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dataset = TiledDataset(opt)
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dataloader = data.DataLoader(dataset, num_workers=opt['n_thread'], collate_fn=identity)
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tq = tqdm(dataloader)
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i = 0
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for spl_imgs in tq:
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if spl_imgs is None:
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continue
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@ -272,7 +273,8 @@ def extract_single(opt, writer):
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imgs, path = imgs
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if imgs is None or len(imgs) <= 1:
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continue
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path = path + "_" + lbl
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path = f'{path}_{lbl}_{i}'
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i += 1
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ref_id = writer.write_reference_image(imgs[0], path)
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for tile in imgs[1:]:
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writer.write_tile_image(ref_id, tile)
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@ -162,7 +162,8 @@ if __name__ == "__main__":
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torch.backends.cudnn.benchmark = True
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srg_analyze = False
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parser = argparse.ArgumentParser()
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parser.add_argument('-opt', type=str, help='Path to options YAML file.', default='../../options/train_exd_imgsetext_srflow_bigboi_frompsnr.yml')
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parser.add_argument('-opt', type=str, help='Path to options YAML file.', default='../options/train_exd_imgsetext_srflow_bigboi_ganbase.yml')
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#parser.add_argument('-opt', type=str, help='Path to options YAML file.', default='../../options/train_exd_imgsetext_srflow_bigboi_frompsnr.yml')
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opt = option.parse(parser.parse_args().opt, is_train=False)
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opt = option.dict_to_nonedict(opt)
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utils.util.loaded_options = opt
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@ -180,8 +181,8 @@ if __name__ == "__main__":
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gen.eval()
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mode = "feed_through" # temperature | restore | latent_transfer | feed_through
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imgs_to_resample_pattern = "F:\\4k6k\\datasets\\ns_images\\adrianna\\val2\\lr\\*"
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#imgs_to_resample_pattern = "F:\\4k6k\\datasets\\ns_images\\adrianna\\analyze\\analyze_xx\\*"
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#imgs_to_resample_pattern = "F:\\4k6k\\datasets\\ns_images\\adrianna\\val2\\lr\\*"
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imgs_to_resample_pattern = "F:\\4k6k\\datasets\\ns_images\\adrianna\\analyze\\analyze_xx\\*"
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#imgs_to_resample_pattern = "F:\\4k6k\\datasets\\ns_images\\imagesets\\images-half\\*lanette*"
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scale = 2
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resample_factor = 2 # When != 1, the HR image is upsampled by this factor using a bicubic to get the local latents. E.g. set this to '2' to get 2x upsampling.
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