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import torch
import torch.nn as nn
import torch.nn.functional as F
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
}
# Borrowed from simswap
# https://github.com/neuralchen/SimSwap/blob/26c84d2901bd56eda4d5e3c5ca6da16e65dc82a6/util/reverse2original.py#L30
class SoftErosion(nn.Module):
def __init__(self, kernel_size=15, threshold=0.6, iterations=1):
super(SoftErosion, self).__init__()
r = kernel_size // 2
self.padding = r
self.iterations = iterations
self.threshold = threshold
# Create kernel
y_indices, x_indices = torch.meshgrid(torch.arange(0., kernel_size), torch.arange(0., kernel_size))
dist = torch.sqrt((x_indices - r) ** 2 + (y_indices - r) ** 2)
kernel = dist.max() - dist
kernel /= kernel.sum()
kernel = kernel.view(1, 1, *kernel.shape)
self.register_buffer('weight', kernel)
def forward(self, x):
x = x.float()
for i in range(self.iterations - 1):
x = torch.min(x, F.conv2d(x, weight=self.weight, groups=x.shape[1], padding=self.padding))
x = F.conv2d(x, weight=self.weight, groups=x.shape[1], padding=self.padding)
mask = x >= self.threshold
x[mask] = 1.0
x[~mask] /= x[~mask].max()
return x, mask
device = "cpu"
def init_parser(pth_path, mode="cpu"):
global device
device = mode
n_classes = 19
net = BiSeNet(n_classes=n_classes)
if device == "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():
img = img.to(device)
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, smooth_mask, includes=[1,2,3,4,5,10,11,12,13], blur=10):
parsing = image_to_parsing(source, net)
if len(includes) == 0:
return source, np.zeros_like(source)
include_mask = get_mask(parsing, includes)
mask = np.repeat(include_mask[:, :, np.newaxis], 3, axis=2).astype("float32")
if smooth_mask is not None:
mask_tensor = torch.from_numpy(mask.copy().transpose((2, 0, 1))).float().to(device)
face_mask_tensor = mask_tensor[0] + mask_tensor[1]
soft_face_mask_tensor, _ = smooth_mask(face_mask_tensor.unsqueeze_(0).unsqueeze_(0))
soft_face_mask_tensor.squeeze_()
mask = np.repeat(soft_face_mask_tensor.cpu().numpy()[:, :, np.newaxis], 3, axis=2)
if blur > 0:
mask = cv2.GaussianBlur(mask, (0, 0), blur)
resized_source = cv2.resize((source/255).astype("float32"), (512, 512))
resized_target = cv2.resize((target/255).astype("float32"), (512, 512))
result = mask * resized_source + (1 - mask) * resized_target
normalized_result = (result - np.min(result)) / (np.max(result) - np.min(result))
result = cv2.resize((result*255).astype("uint8"), (source.shape[1], source.shape[0]))
return result
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
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