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| import argparse | |
| import cv2 | |
| import numpy as np | |
| import os | |
| from tqdm import tqdm | |
| import torch | |
| from basicsr.archs.ddcolor_arch import DDColor | |
| import torch.nn.functional as F | |
| class ImageColorizationPipeline(object): | |
| def __init__(self, model_path, input_size=256, model_size='large'): | |
| self.input_size = input_size | |
| if torch.cuda.is_available(): | |
| self.device = torch.device('cuda') | |
| else: | |
| self.device = torch.device('cpu') | |
| if model_size == 'tiny': | |
| self.encoder_name = 'convnext-t' | |
| else: | |
| self.encoder_name = 'convnext-l' | |
| self.decoder_type = "MultiScaleColorDecoder" | |
| if self.decoder_type == 'MultiScaleColorDecoder': | |
| self.model = DDColor( | |
| encoder_name=self.encoder_name, | |
| decoder_name='MultiScaleColorDecoder', | |
| input_size=[self.input_size, self.input_size], | |
| num_output_channels=2, | |
| last_norm='Spectral', | |
| do_normalize=False, | |
| num_queries=100, | |
| num_scales=3, | |
| dec_layers=9, | |
| ).to(self.device) | |
| else: | |
| self.model = DDColor( | |
| encoder_name=self.encoder_name, | |
| decoder_name='SingleColorDecoder', | |
| input_size=[self.input_size, self.input_size], | |
| num_output_channels=2, | |
| last_norm='Spectral', | |
| do_normalize=False, | |
| num_queries=256, | |
| ).to(self.device) | |
| self.model.load_state_dict( | |
| torch.load(model_path, map_location=torch.device('cpu'))['params'], | |
| strict=False) | |
| self.model.eval() | |
| def process(self, img): | |
| self.height, self.width = img.shape[:2] | |
| # print(self.width, self.height) | |
| # if self.width * self.height < 100000: | |
| # self.input_size = 256 | |
| img = (img / 255.0).astype(np.float32) | |
| orig_l = cv2.cvtColor(img, cv2.COLOR_BGR2Lab)[:, :, :1] # (h, w, 1) | |
| # resize rgb image -> lab -> get grey -> rgb | |
| img = cv2.resize(img, (self.input_size, self.input_size)) | |
| img_l = cv2.cvtColor(img, cv2.COLOR_BGR2Lab)[:, :, :1] | |
| img_gray_lab = np.concatenate((img_l, np.zeros_like(img_l), np.zeros_like(img_l)), axis=-1) | |
| img_gray_rgb = cv2.cvtColor(img_gray_lab, cv2.COLOR_LAB2RGB) | |
| tensor_gray_rgb = torch.from_numpy(img_gray_rgb.transpose((2, 0, 1))).float().unsqueeze(0).to(self.device) | |
| output_ab = self.model(tensor_gray_rgb).cpu() # (1, 2, self.height, self.width) | |
| # resize ab -> concat original l -> rgb | |
| output_ab_resize = F.interpolate(output_ab, size=(self.height, self.width))[0].float().numpy().transpose(1, 2, 0) | |
| output_lab = np.concatenate((orig_l, output_ab_resize), axis=-1) | |
| output_bgr = cv2.cvtColor(output_lab, cv2.COLOR_LAB2BGR) | |
| output_img = (output_bgr * 255.0).round().astype(np.uint8) | |
| return output_img | |
| def main(): | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument('--model_path', type=str, default='pretrain/net_g_200000.pth') | |
| parser.add_argument('--input', type=str, default='figure/', help='input test image folder or video path') | |
| parser.add_argument('--output', type=str, default='results', help='output folder or video path') | |
| parser.add_argument('--input_size', type=int, default=512, help='input size for model') | |
| parser.add_argument('--model_size', type=str, default='large', help='ddcolor model size') | |
| args = parser.parse_args() | |
| print(f'Output path: {args.output}') | |
| os.makedirs(args.output, exist_ok=True) | |
| img_list = os.listdir(args.input) | |
| assert len(img_list) > 0 | |
| colorizer = ImageColorizationPipeline(model_path=args.model_path, input_size=args.input_size, model_size=args.model_size) | |
| for name in tqdm(img_list): | |
| img = cv2.imread(os.path.join(args.input, name)) | |
| image_out = colorizer.process(img) | |
| cv2.imwrite(os.path.join(args.output, name), image_out) | |
| if __name__ == '__main__': | |
| main() | |