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import torch
import torchvision.transforms as transforms
import cv2
import numpy as np
from .model import BiSeNet
def init_parser(pth_path):
n_classes = 19
net = BiSeNet(n_classes=n_classes)
net.cuda()
net.load_state_dict(torch.load(pth_path))
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():
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):
parsing = image_to_parsing(source, net)
face_classes = [1, 11, 12, 13]
mask = get_mask(parsing, face_classes)
mask = np.repeat(np.expand_dims(mask, axis=2), 3, 2)
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
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