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| import os | |
| import cv2 | |
| import numpy as np | |
| import torch | |
| import torch.nn.functional as F | |
| import os | |
| import sys | |
| sys.path.append('inpaint') | |
| os.environ['OMP_NUM_THREADS'] = '1' | |
| os.environ['OPENBLAS_NUM_THREADS'] = '1' | |
| os.environ['MKL_NUM_THREADS'] = '1' | |
| os.environ['VECLIB_MAXIMUM_THREADS'] = '1' | |
| os.environ['NUMEXPR_NUM_THREADS'] = '1' | |
| import cv2 | |
| import numpy as np | |
| import torch | |
| import yaml | |
| from omegaconf import OmegaConf | |
| from saicinpainting.training.trainers import load_checkpoint | |
| class IVModel(): | |
| def __init__(self, device=torch.device('cuda:0')): | |
| super(IVModel, self).__init__() | |
| self.device = device | |
| conf_path = 'inpaint/configs/prediction/default.yaml' | |
| predict_config = OmegaConf.load(conf_path) | |
| predict_config.model.path='inpaint/weights/big-lama' | |
| if not os.path.exists(conf_path): | |
| print('未找到配置文件！') | |
| train_config_path = os.path.join(predict_config.model.path, 'config.yaml') | |
| with open(train_config_path, 'r') as f: | |
| train_config = OmegaConf.create(yaml.safe_load(f)) | |
| train_config.training_model.predict_only = True | |
| train_config.visualizer.kind = 'noop' | |
| checkpoint_path = os.path.join(predict_config.model.path, 'models', predict_config.model.checkpoint) | |
| self.model = load_checkpoint(train_config, checkpoint_path, strict=False, map_location='cpu') | |
| self.model.freeze() | |
| self.model.to(device) | |
| self.__first_forward__() | |
| def __first_forward__(self, input_size=(2048, 4096, 3)): | |
| # 调用forward()严格控制最大显存 | |
| print('initialize Inpaint Model...') | |
| _ = self.forward(np.random.rand(*input_size) * 255, None) | |
| print('initialize Complete!') | |
| def __resize_tensor__(self, image, max_size=1024, scale_factor=8): | |
| h, w = image.size()[2:] | |
| if max(h, w) > max_size: | |
| if h < w: | |
| h, w = int(max_size * h / w), max_size | |
| else: | |
| h, w = max_size, int(max_size * w / h) | |
| h = h // scale_factor * scale_factor | |
| w = w // scale_factor * scale_factor | |
| image = F.interpolate(image, (h, w), mode='bicubic') | |
| return image | |
| def input_preprocess_tensor(self, img): | |
| img_t = torch.from_numpy(img.astype(np.float32)) # .to(self.device) | |
| img_t = img_t.permute(2, 0, 1).unsqueeze(0) | |
| img_t = img_t / 255. | |
| img_t_for_net = self.__resize_tensor__(img_t).to(self.device) # 为了控制最大显存容量 | |
| img_t_for_out = self.__resize_tensor__(img_t, max_size=2048).to(self.device) # 为了控制最大显存容量 | |
| return img_t_for_net, img_t_for_out | |
| def forward(self, img, json_data): | |
| _,w,_ = img.shape | |
| mask = img[:,w//2:,0] | |
| kernel = np.ones((4, 4), np.uint8) | |
| mask = cv2.dilate(mask, kernel, iterations=5)[:,:,np.newaxis] | |
| img = img[:,:w//2,::-1] | |
| # print(img.shape,mask.shape) | |
| img_t_for_net, img_t_for_out = self.input_preprocess_tensor(img) | |
| mask_t_for_net, mask_t_for_out = self.input_preprocess_tensor(mask) | |
| h, w = img_t_for_out.shape[2:] | |
| # print(img_t.shape,mask_t.shape) | |
| mask_t_for_out = (mask_t_for_out>0).int() | |
| mask_t_for_net = (mask_t_for_net>0).int() | |
| with torch.no_grad(): | |
| res_t = self.model(dict(image=img_t_for_net, mask=mask_t_for_net))['inpainted'] | |
| res_t = F.interpolate(res_t, (h, w), mode='bicubic') | |
| res_t = img_t_for_out * (1 - mask_t_for_out) + res_t * mask_t_for_out | |
| res_t = torch.clip(res_t * 255, min=0, max=255) | |
| res = res_t.squeeze(0).permute(1, 2, 0).detach().cpu().numpy() | |
| res = res[:, :, (2, 1, 0)].astype(np.uint8) | |
| return res |