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import glob |
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import os |
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import shutil |
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import traceback |
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import hydra |
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from omegaconf import OmegaConf |
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import PIL.Image as Image |
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import numpy as np |
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from joblib import Parallel, delayed |
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from saicinpainting.evaluation.masks.mask import SegmentationMask, propose_random_square_crop |
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from saicinpainting.evaluation.utils import load_yaml, SmallMode |
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from saicinpainting.training.data.masks import MixedMaskGenerator |
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class MakeManyMasksWrapper: |
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def __init__(self, impl, variants_n=2): |
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self.impl = impl |
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self.variants_n = variants_n |
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def get_masks(self, img): |
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img = np.transpose(np.array(img), (2, 0, 1)) |
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return [self.impl(img)[0] for _ in range(self.variants_n)] |
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def process_images(src_images, indir, outdir, config): |
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if config.generator_kind == 'segmentation': |
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mask_generator = SegmentationMask(**config.mask_generator_kwargs) |
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elif config.generator_kind == 'random': |
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mask_generator_kwargs = OmegaConf.to_container(config.mask_generator_kwargs, resolve=True) |
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variants_n = mask_generator_kwargs.pop('variants_n', 2) |
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mask_generator = MakeManyMasksWrapper(MixedMaskGenerator(**mask_generator_kwargs), |
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variants_n=variants_n) |
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else: |
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raise ValueError(f'Unexpected generator kind: {config.generator_kind}') |
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max_tamper_area = config.get('max_tamper_area', 1) |
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for infile in src_images: |
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try: |
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file_relpath = infile[len(indir):] |
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img_outpath = os.path.join(outdir, file_relpath) |
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os.makedirs(os.path.dirname(img_outpath), exist_ok=True) |
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image = Image.open(infile).convert('RGB') |
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if min(image.size) < config.cropping.out_min_size: |
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handle_small_mode = SmallMode(config.cropping.handle_small_mode) |
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if handle_small_mode == SmallMode.DROP: |
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continue |
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elif handle_small_mode == SmallMode.UPSCALE: |
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factor = config.cropping.out_min_size / min(image.size) |
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out_size = (np.array(image.size) * factor).round().astype('uint32') |
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image = image.resize(out_size, resample=Image.BICUBIC) |
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else: |
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factor = config.cropping.out_min_size / min(image.size) |
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out_size = (np.array(image.size) * factor).round().astype('uint32') |
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image = image.resize(out_size, resample=Image.BICUBIC) |
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src_masks = mask_generator.get_masks(image) |
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filtered_image_mask_pairs = [] |
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for cur_mask in src_masks: |
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if config.cropping.out_square_crop: |
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(crop_left, |
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crop_top, |
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crop_right, |
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crop_bottom) = propose_random_square_crop(cur_mask, |
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min_overlap=config.cropping.crop_min_overlap) |
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cur_mask = cur_mask[crop_top:crop_bottom, crop_left:crop_right] |
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cur_image = image.copy().crop((crop_left, crop_top, crop_right, crop_bottom)) |
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else: |
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cur_image = image |
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if len(np.unique(cur_mask)) == 0 or cur_mask.mean() > max_tamper_area: |
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continue |
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filtered_image_mask_pairs.append((cur_image, cur_mask)) |
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mask_indices = np.random.choice(len(filtered_image_mask_pairs), |
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size=min(len(filtered_image_mask_pairs), config.max_masks_per_image), |
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replace=False) |
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mask_basename = os.path.join(outdir, os.path.splitext(file_relpath)[0]) |
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for i, idx in enumerate(mask_indices): |
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cur_image, cur_mask = filtered_image_mask_pairs[idx] |
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cur_basename = mask_basename + f'_crop{i:03d}' |
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Image.fromarray(np.clip(cur_mask * 255, 0, 255).astype('uint8'), |
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mode='L').save(cur_basename + f'_mask{i:03d}.png') |
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cur_image.save(cur_basename + '.png') |
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except KeyboardInterrupt: |
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return |
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except Exception as ex: |
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print(f'Could not make masks for {infile} due to {ex}:\n{traceback.format_exc()}') |
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@hydra.main(config_path='../configs/data_gen/whydra', config_name='random_medium_256.yaml') |
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def main(config: OmegaConf): |
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if not config.indir.endswith('/'): |
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config.indir += '/' |
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os.makedirs(config.outdir, exist_ok=True) |
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in_files = list(glob.glob(os.path.join(config.indir, '**', f'*.{config.location.extension}'), |
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recursive=True)) |
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if config.n_jobs == 0: |
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process_images(in_files, config.indir, config.outdir, config) |
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else: |
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in_files_n = len(in_files) |
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chunk_size = in_files_n // config.n_jobs + (1 if in_files_n % config.n_jobs > 0 else 0) |
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Parallel(n_jobs=config.n_jobs)( |
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delayed(process_images)(in_files[start:start+chunk_size], config.indir, config.outdir, config) |
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for start in range(0, len(in_files), chunk_size) |
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) |
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if __name__ == '__main__': |
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main() |
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