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| import torch | |
| import torchvision.transforms as transforms | |
| import cv2 | |
| import numpy as np | |
| from .model import BiSeNet | |
| mask_regions = { | |
| "Background":0, | |
| "Skin":1, | |
| "L-Eyebrow":2, | |
| "R-Eyebrow":3, | |
| "L-Eye":4, | |
| "R-Eye":5, | |
| "Eye-G":6, | |
| "L-Ear":7, | |
| "R-Ear":8, | |
| "Ear-R":9, | |
| "Nose":10, | |
| "Mouth":11, | |
| "U-Lip":12, | |
| "L-Lip":13, | |
| "Neck":14, | |
| "Neck-L":15, | |
| "Cloth":16, | |
| "Hair":17, | |
| "Hat":18 | |
| } | |
| run_with_cuda = False | |
| def init_parser(pth_path, use_cuda=False): | |
| global run_with_cuda | |
| run_with_cuda = use_cuda | |
| n_classes = 19 | |
| net = BiSeNet(n_classes=n_classes) | |
| if run_with_cuda: | |
| net.cuda() | |
| net.load_state_dict(torch.load(pth_path)) | |
| else: | |
| net.load_state_dict(torch.load(pth_path, map_location=torch.device('cpu'))) | |
| net.eval() | |
| return net | |
| def image_to_parsing(img, net): | |
| img = cv2.resize(img, (512, 512)) | |
| img = img[:,:,::-1] | |
| transform = transforms.Compose([ | |
| transforms.ToTensor(), | |
| transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)) | |
| ]) | |
| img = transform(img.copy()) | |
| img = torch.unsqueeze(img, 0) | |
| with torch.no_grad(): | |
| if run_with_cuda: | |
| img = img.cuda() | |
| out = net(img)[0] | |
| parsing = out.squeeze(0).cpu().numpy().argmax(0) | |
| return parsing | |
| def get_mask(parsing, classes): | |
| res = parsing == classes[0] | |
| for val in classes[1:]: | |
| res += parsing == val | |
| return res | |
| def swap_regions(source, target, net, includes=[1,2,3,4,5,10,11,12,13], excludes=[7,8], blur_size=25): | |
| parsing = image_to_parsing(source, net) | |
| if len(includes) == 0: | |
| return source, np.zeros_like(source) | |
| include_mask = get_mask(parsing, includes) | |
| include_mask = np.repeat(np.expand_dims(include_mask.astype('float32'), axis=2), 3, 2) | |
| if len(excludes) > 0: | |
| exclude_mask = get_mask(parsing, excludes) | |
| exclude_mask = np.repeat(np.expand_dims(exclude_mask.astype('float32'), axis=2), 3, 2) | |
| include_mask -= exclude_mask | |
| mask = 1 - cv2.GaussianBlur(include_mask.clip(0,1), (0, 0), blur_size) | |
| result = (1 - mask) * cv2.resize(source, (512, 512)) + mask * cv2.resize(target, (512, 512)) | |
| result = cv2.resize(result.astype("float32"), (source.shape[1], source.shape[0])) | |
| return result, mask.astype('float32') | |
| def mask_regions_to_list(values): | |
| out_ids = [] | |
| for value in values: | |
| if value in mask_regions.keys(): | |
| out_ids.append(mask_regions.get(value)) | |
| return out_ids | |