class input: path = DataManager.get_path() if os.path.exists(path + 'test_images/'): path += 'test_images/' else: path += 'train_images/' print("Path to images: ", path) if glob.glob(path+'*.jpeg'): tiff_format = False else: tiff_format = True print("Image tiff-format: ", tiff_format) tiff_level = 1 # only if tiff_format is Ture resize_ratio = 1 # 1 (N x N) or 2 (-> N//2 x N//2) input_shape = (1280, 1280, 3) patch_size = 256 sample_size = 25 preprocess_mode = 'float' objective = 'bce' label_smoothing = 0.0 # only if objective is 'cce'
img_masked = remove_penmarks(img_path) img_masked = Image.fromarray(img_masked) #img_masked.save(img_path, subsampling=0, quality=100) def main(path, marked_images): paths = path + marked_images + '.jpeg' with multiprocessing.Pool() as pool: for c in tqdm.tqdm(pool.imap(save_masked_image, paths), total=len(paths)): pass if __name__ == '__main__': marked_images = np.load('input_/marked_images.npy', allow_pickle=True) path = DataManager.get_path() + 'train_images/' print( f"[Old] images with marks in {path} ({len(marked_images)} of them) " "will be overwritten by the newly generated images (with marks removed)\n" "Are you sure you want to continue? (y/n)") c = input() if c.lower() == 'y' or c.lower() == 'yes': if Config.input.tiff_format: print("Requires images to be .jpeg format") print("Script cancelled") else: main(path, marked_images) else: print("Script cancelled")