mirror of
https://github.com/YaoFANGUK/video-subtitle-remover.git
synced 2026-02-26 05:34:41 +08:00
103 lines
4.0 KiB
Python
Executable File
103 lines
4.0 KiB
Python
Executable File
#!/usr/bin/env python3
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# Example command:
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# ./bin/predict.py \
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# model.path=<path to checkpoint, prepared by make_checkpoint.py> \
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# indir=<path to input data> \
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# outdir=<where to store predicts>
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import logging
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import os
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import sys
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import traceback
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from saicinpainting.evaluation.utils import move_to_device
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from saicinpainting.evaluation.refinement import refine_predict
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os.environ['OMP_NUM_THREADS'] = '1'
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os.environ['OPENBLAS_NUM_THREADS'] = '1'
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os.environ['MKL_NUM_THREADS'] = '1'
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os.environ['VECLIB_MAXIMUM_THREADS'] = '1'
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os.environ['NUMEXPR_NUM_THREADS'] = '1'
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import cv2
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import hydra
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import numpy as np
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import torch
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import tqdm
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import yaml
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from omegaconf import OmegaConf
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from torch.utils.data._utils.collate import default_collate
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from saicinpainting.training.data.datasets import make_default_val_dataset
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from saicinpainting.training.trainers import load_checkpoint
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from saicinpainting.utils import register_debug_signal_handlers
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LOGGER = logging.getLogger(__name__)
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@hydra.main(config_path='../configs/prediction', config_name='default.yaml')
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def main(predict_config: OmegaConf):
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try:
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register_debug_signal_handlers() # kill -10 <pid> will result in traceback dumped into log
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device = torch.device(predict_config.device)
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train_config_path = os.path.join(predict_config.model.path, 'config.yaml')
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with open(train_config_path, 'r') as f:
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train_config = OmegaConf.create(yaml.safe_load(f))
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train_config.training_model.predict_only = True
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train_config.visualizer.kind = 'noop'
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out_ext = predict_config.get('out_ext', '.png')
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checkpoint_path = os.path.join(predict_config.model.path,
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'models',
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predict_config.model.checkpoint)
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model = load_checkpoint(train_config, checkpoint_path, strict=False, map_location='cpu')
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model.freeze()
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if not predict_config.get('refine', False):
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model.to(device)
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if not predict_config.indir.endswith('/'):
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predict_config.indir += '/'
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dataset = make_default_val_dataset(predict_config.indir, **predict_config.dataset)
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for img_i in tqdm.trange(len(dataset)):
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mask_fname = dataset.mask_filenames[img_i]
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cur_out_fname = os.path.join(
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predict_config.outdir,
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os.path.splitext(mask_fname[len(predict_config.indir):])[0] + out_ext
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)
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os.makedirs(os.path.dirname(cur_out_fname), exist_ok=True)
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batch = default_collate([dataset[img_i]])
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if predict_config.get('refine', False):
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assert 'unpad_to_size' in batch, "Unpadded size is required for the refinement"
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# image unpadding is taken care of in the refiner, so that output image
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# is same size as the input image
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cur_res = refine_predict(batch, model, **predict_config.refiner)
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cur_res = cur_res[0].permute(1,2,0).detach().cpu().numpy()
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else:
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with torch.no_grad():
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batch = move_to_device(batch, device)
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batch['mask'] = (batch['mask'] > 0) * 1
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batch = model(batch)
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cur_res = batch[predict_config.out_key][0].permute(1, 2, 0).detach().cpu().numpy()
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unpad_to_size = batch.get('unpad_to_size', None)
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if unpad_to_size is not None:
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orig_height, orig_width = unpad_to_size
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cur_res = cur_res[:orig_height, :orig_width]
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cur_res = np.clip(cur_res * 255, 0, 255).astype('uint8')
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cur_res = cv2.cvtColor(cur_res, cv2.COLOR_RGB2BGR)
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cv2.imwrite(cur_out_fname, cur_res)
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except KeyboardInterrupt:
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LOGGER.warning('Interrupted by user')
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except Exception as ex:
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LOGGER.critical(f'Prediction failed due to {ex}:\n{traceback.format_exc()}')
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sys.exit(1)
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if __name__ == '__main__':
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main() |