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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')
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