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| # -*- coding: utf-8 -*- | |
| # Author: ximing | |
| # Description: inpaint_util | |
| # Copyright (c) 2023, XiMing Xing. | |
| # License: MIT License | |
| import os | |
| import pathlib | |
| import cv2 | |
| import numpy as np | |
| from omegaconf import OmegaConf | |
| from tqdm import trange | |
| import torch | |
| from torch.utils.data._utils.collate import default_collate | |
| def apply_lama_inpaint(predict_config, device): | |
| # local import | |
| from lama.saicinpainting.evaluation.utils import move_to_device | |
| from lama.saicinpainting.evaluation.refinement import refine_predict | |
| from lama.saicinpainting.training.data.datasets import make_default_val_dataset | |
| from lama.saicinpainting.training.trainers import load_checkpoint | |
| try: | |
| train_config_path = pathlib.Path(predict_config.model.path) / 'config.yaml' | |
| train_config = OmegaConf.load(train_config_path) | |
| train_config.training_model.predict_only = True | |
| train_config.visualizer.kind = 'noop' | |
| out_ext = predict_config.get('out_ext', '.png') | |
| checkpoint_path = os.path.join( | |
| predict_config.model.path, 'models', predict_config.model.checkpoint | |
| ) | |
| model = load_checkpoint(train_config, checkpoint_path, strict=False, map_location='cpu') | |
| model.freeze() | |
| if not predict_config.get('refine', False): | |
| model.to(device) | |
| if not predict_config.indir.endswith('/'): | |
| predict_config.indir += '/' | |
| dataset = make_default_val_dataset(predict_config.indir, **predict_config.dataset) | |
| for img_i in trange(len(dataset)): | |
| mask_fname = dataset.mask_filenames[img_i] | |
| cur_out_fname = os.path.join( | |
| predict_config.outdir, | |
| os.path.splitext(mask_fname[len(predict_config.indir):])[0] + out_ext | |
| ) | |
| os.makedirs(os.path.dirname(cur_out_fname), exist_ok=True) | |
| batch = default_collate([dataset[img_i]]) | |
| if predict_config.get('refine', False): | |
| assert 'unpad_to_size' in batch, "Unpadded size is required for the refinement" | |
| # image unpadding is taken care of in the refiner, so that output image | |
| # is same size as the input image | |
| cur_res = refine_predict(batch, model, **predict_config.refiner) | |
| cur_res = cur_res[0].permute(1, 2, 0).detach().cpu().numpy() | |
| else: | |
| with torch.no_grad(): | |
| batch = move_to_device(batch, device) | |
| batch['mask'] = (batch['mask'] > 0) * 1 | |
| batch = model(batch) | |
| cur_res = batch[predict_config.out_key][0].permute(1, 2, 0).detach().cpu().numpy() | |
| unpad_to_size = batch.get('unpad_to_size', None) | |
| if unpad_to_size is not None: | |
| orig_height, orig_width = unpad_to_size | |
| cur_res = cur_res[:orig_height, :orig_width] | |
| cur_res = np.clip(cur_res * 255, 0, 255).astype('uint8') | |
| cur_res = cv2.cvtColor(cur_res, cv2.COLOR_RGB2BGR) | |
| cv2.imwrite(cur_out_fname, cur_res) | |
| except KeyboardInterrupt: | |
| print('Interrupted by user') | |
| except Exception as ex: | |
| print(f'Prediction failed due to:') | |
| print(f'{ex}') | |
| import sys | |
| sys.exit(1) | |