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| import torch | |
| import time | |
| import os | |
| import cv2 | |
| import numpy as np | |
| from PIL import Image | |
| from .model import BiSeNet | |
| import torchvision.transforms as transforms | |
| class FaceParsing(): | |
| def __init__(self): | |
| self.net = self.model_init() | |
| self.preprocess = self.image_preprocess() | |
| def model_init(self, | |
| resnet_path='./models/face-parse-bisent/resnet18-5c106cde.pth', | |
| model_pth='./models/face-parse-bisent/79999_iter.pth'): | |
| net = BiSeNet(resnet_path) | |
| if torch.cuda.is_available(): | |
| net.cuda() | |
| net.load_state_dict(torch.load(model_pth)) | |
| else: | |
| net.load_state_dict(torch.load(model_pth, map_location=torch.device('cpu'))) | |
| net.eval() | |
| return net | |
| def image_preprocess(self): | |
| return transforms.Compose([ | |
| transforms.ToTensor(), | |
| transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)), | |
| ]) | |
| def __call__(self, image, size=(512, 512)): | |
| if isinstance(image, str): | |
| image = Image.open(image) | |
| width, height = image.size | |
| with torch.no_grad(): | |
| image = image.resize(size, Image.BILINEAR) | |
| img = self.preprocess(image) | |
| if torch.cuda.is_available(): | |
| img = torch.unsqueeze(img, 0).cuda() | |
| else: | |
| img = torch.unsqueeze(img, 0) | |
| out = self.net(img)[0] | |
| parsing = out.squeeze(0).cpu().numpy().argmax(0) | |
| parsing[np.where(parsing>13)] = 0 | |
| parsing[np.where(parsing>=1)] = 255 | |
| parsing = Image.fromarray(parsing.astype(np.uint8)) | |
| return parsing | |
| if __name__ == "__main__": | |
| fp = FaceParsing() | |
| segmap = fp('154_small.png') | |
| segmap.save('res.png') | |