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| ''' | |
| @paper: GAN Prior Embedded Network for Blind Face Restoration in the Wild (CVPR2021) | |
| @author: yangxy (yangtao9009@gmail.com) | |
| ''' | |
| import os | |
| import cv2 | |
| import torch | |
| import numpy as np | |
| from face_parse.parse_model import ParseNet | |
| import torch.nn.functional as F | |
| from face_parse.model import BiSeNet | |
| import torchvision.transforms as transforms | |
| class FaceParse(object): | |
| def __init__(self, base_dir='./', model='ParseNet-latest', device='cuda', mask_map = [0, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 0, 0, 0, 0, 0, 0]): | |
| self.mfile = os.path.join(base_dir, model+'.pth') | |
| self.size = 512 | |
| self.device = device | |
| ''' | |
| 0: 'background' 1: 'skin' 2: 'nose' | |
| 3: 'eye_g' 4: 'l_eye' 5: 'r_eye' | |
| 6: 'l_brow' 7: 'r_brow' 8: 'l_ear' | |
| 9: 'r_ear' 10: 'mouth' 11: 'u_lip' | |
| 12: 'l_lip' 13: 'hair' 14: 'hat' | |
| 15: 'ear_r' 16: 'neck_l' 17: 'neck' | |
| 18: 'cloth' | |
| ''' | |
| # self.MASK_COLORMAP = [[0, 0, 0], [204, 0, 0], [76, 153, 0], [204, 204, 0], [51, 51, 255], [204, 0, 204], [0, 255, 255], [255, 204, 204], [102, 51, 0], [255, 0, 0], [102, 204, 0], [255, 255, 0], [0, 0, 153], [0, 0, 204], [255, 51, 153], [0, 204, 204], [0, 51, 0], [255, 153, 51], [0, 204, 0]] | |
| #self.#MASK_COLORMAP = [[0, 0, 0], [204, 0, 0], [76, 153, 0], [204, 204, 0], [51, 51, 255], [204, 0, 204], [0, 255, 255], [255, 204, 204], [102, 51, 0], [255, 0, 0], [102, 204, 0], [255, 255, 0], [0, 0, 153], [0, 0, 204], [255, 51, 153], [0, 204, 204], [0, 51, 0], [255, 153, 51], [0, 204, 0]] = [[0, 0, 0], [204, 0, 0], [76, 153, 0], [204, 204, 0], [51, 51, 255], [204, 0, 204], [0, 255, 255], [255, 204, 204], [102, 51, 0], [255, 0, 0], [102, 204, 0], [255, 255, 0], [0, 0, 153], [0, 0, 204], [255, 51, 153], [0, 204, 204], [0, 51, 0], [0, 0, 0], [0, 0, 0]] | |
| # self.MASK_COLORMAP = [0, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 0, 255, 0, 0, 0] | |
| self.MASK_COLORMAP = mask_map | |
| self.load_model() | |
| def load_model(self): | |
| self.faceparse = ParseNet(self.size, self.size, 32, 64, 19, norm_type='bn', relu_type='LeakyReLU', ch_range=[32, 256]) | |
| self.faceparse.load_state_dict(torch.load(self.mfile, map_location=torch.device('cpu'))) | |
| self.faceparse.to(self.device) | |
| self.faceparse.eval() | |
| def process(self, im, masks=[0, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 0, 0, 0, 0, 0, 0]): | |
| im = cv2.resize(im, (self.size, self.size)) | |
| imt = self.img2tensor(im) | |
| with torch.no_grad(): | |
| pred_mask, sr_img_tensor = self.faceparse(imt) # (1, 19, 512, 512) | |
| mask = self.tenor2mask(pred_mask, masks) | |
| return mask | |
| def process_tensor(self, imt): | |
| imt = F.interpolate(imt.flip(1)*2-1, (self.size, self.size)) | |
| pred_mask, sr_img_tensor = self.faceparse(imt) | |
| mask = pred_mask.argmax(dim=1) | |
| for idx, color in enumerate(self.MASK_COLORMAP): | |
| mask = torch.where(mask==idx, color, mask) | |
| #mask = mask.repeat(3, 1, 1).unsqueeze(0) #.cpu().float().numpy() | |
| mask = mask.unsqueeze(0) | |
| return mask | |
| def img2tensor(self, img): | |
| img = img[..., ::-1] # BGR to RGB | |
| img = img / 255. * 2 - 1 | |
| img_tensor = torch.from_numpy(img.transpose(2, 0, 1)).unsqueeze(0).to(self.device) | |
| return img_tensor.float() | |
| def tenor2mask(self, tensor, masks): | |
| if len(tensor.shape) < 4: | |
| tensor = tensor.unsqueeze(0) | |
| if tensor.shape[1] > 1: | |
| tensor = tensor.argmax(dim=1) | |
| tensor = tensor.squeeze(1).data.cpu().numpy() # (1, 512, 512) | |
| color_maps = [] | |
| for t in tensor: | |
| #tmp_img = np.zeros(tensor.shape[1:] + (3,)) | |
| tmp_img = np.zeros(tensor.shape[1:]) | |
| for idx, color in enumerate(masks): | |
| tmp_img[t == idx] = color | |
| color_maps.append(tmp_img.astype(np.uint8)) | |
| return color_maps | |
| class FaceParse_v2(object): | |
| def __init__(self, device='cuda', mask_map = [0, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 0, 0, 0, 0, 0, 0]): | |
| self.mfile = '/apdcephfs/private_quincheng/Expression/face-parsing.PyTorch/res/cp/79999_iter.pth' | |
| self.size = 512 | |
| self.device = device | |
| ''' | |
| 0: 'background' 1: 'skin' 2: 'nose' | |
| 3: 'eye_g' 4: 'l_eye' 5: 'r_eye' | |
| 6: 'l_brow' 7: 'r_brow' 8: 'l_ear' | |
| 9: 'r_ear' 10: 'mouth' 11: 'u_lip' | |
| 12: 'l_lip' 13: 'hair' 14: 'hat' | |
| 15: 'ear_r' 16: 'neck_l' 17: 'neck' | |
| 18: 'cloth' | |
| ''' | |
| # self.MASK_COLORMAP = [[0, 0, 0], [204, 0, 0], [76, 153, 0], [204, 204, 0], [51, 51, 255], [204, 0, 204], [0, 255, 255], [255, 204, 204], [102, 51, 0], [255, 0, 0], [102, 204, 0], [255, 255, 0], [0, 0, 153], [0, 0, 204], [255, 51, 153], [0, 204, 204], [0, 51, 0], [255, 153, 51], [0, 204, 0]] | |
| #self.#MASK_COLORMAP = [[0, 0, 0], [204, 0, 0], [76, 153, 0], [204, 204, 0], [51, 51, 255], [204, 0, 204], [0, 255, 255], [255, 204, 204], [102, 51, 0], [255, 0, 0], [102, 204, 0], [255, 255, 0], [0, 0, 153], [0, 0, 204], [255, 51, 153], [0, 204, 204], [0, 51, 0], [255, 153, 51], [0, 204, 0]] = [[0, 0, 0], [204, 0, 0], [76, 153, 0], [204, 204, 0], [51, 51, 255], [204, 0, 204], [0, 255, 255], [255, 204, 204], [102, 51, 0], [255, 0, 0], [102, 204, 0], [255, 255, 0], [0, 0, 153], [0, 0, 204], [255, 51, 153], [0, 204, 204], [0, 51, 0], [0, 0, 0], [0, 0, 0]] | |
| # self.MASK_COLORMAP = [0, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 0, 255, 0, 0, 0] | |
| self.MASK_COLORMAP = mask_map | |
| self.load_model() | |
| self.to_tensor = transforms.Compose([ | |
| transforms.ToTensor(), | |
| transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)), | |
| ]) | |
| def load_model(self): | |
| self.faceparse = BiSeNet(n_classes=19) | |
| self.faceparse.load_state_dict(torch.load(self.mfile)) | |
| self.faceparse.to(self.device) | |
| self.faceparse.eval() | |
| def process(self, im, masks=[0, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 0, 0, 0, 0, 0, 0]): | |
| im = cv2.resize(im[...,::-1], (self.size, self.size)) | |
| im = self.to_tensor(im) | |
| imt = torch.unsqueeze(im, 0).to(self.device) | |
| with torch.no_grad(): | |
| pred_mask = self.faceparse(imt)[0] | |
| mask = self.tenor2mask(pred_mask, masks) | |
| return mask | |
| # def img2tensor(self, img): | |
| # img = img[..., ::-1] # BGR to RGB | |
| # img = img / 255. * 2 - 1 | |
| # img_tensor = torch.from_numpy(img.transpose(2, 0, 1)).unsqueeze(0).to(self.device) | |
| # return img_tensor.float() | |
| def tenor2mask(self, tensor, masks): | |
| if len(tensor.shape) < 4: | |
| tensor = tensor.unsqueeze(0) | |
| if tensor.shape[1] > 1: | |
| tensor = tensor.argmax(dim=1) | |
| tensor = tensor.squeeze(1).data.cpu().numpy() | |
| color_maps = [] | |
| for t in tensor: | |
| #tmp_img = np.zeros(tensor.shape[1:] + (3,)) | |
| tmp_img = np.zeros(tensor.shape[1:]) | |
| for idx, color in enumerate(masks): | |
| tmp_img[t == idx] = color | |
| color_maps.append(tmp_img.astype(np.uint8)) | |
| return color_maps |