537 lines
22 KiB
Python
537 lines
22 KiB
Python
import cv2
|
|
import numpy as np
|
|
import torch
|
|
import torch.nn as nn
|
|
import torch.nn.functional as F
|
|
import torchvision.transforms as transforms
|
|
from PIL import Image
|
|
from skimage import transform
|
|
from .img_utils import np_to_mask
|
|
|
|
# import pydevd_pycharm
|
|
# pydevd_pycharm.settrace('49.7.62.197', port=10090, stdoutToServer=True, stderrToServer=True)
|
|
|
|
def safe_get_box_mask_keypoints(image, retinaface_result, crop_ratio, face_seg, mask_type):
|
|
'''
|
|
Inputs:
|
|
image Input image
|
|
retinaface_result The detection results of retinaface
|
|
crop_ratio The proportion of facial clipping and expansion
|
|
face_seg Facial segmentation model
|
|
mask_type The type of facial segmentation methods, one is loop and the other is skin, and the result of facial segmentation is the skin or frame of the face
|
|
|
|
Outputs:
|
|
retinaface_box After box amplification, the box relative to the original image
|
|
retinaface_keypoints Points relative to the original image
|
|
retinaface_mask_pil Segmentation Results
|
|
'''
|
|
h, w, c = np.shape(image)
|
|
if len(retinaface_result['boxes']) != 0:
|
|
retinaface_boxs = []
|
|
retinaface_keypoints = []
|
|
retinaface_mask_pils = []
|
|
retinaface_masks = []
|
|
for index in range(len(retinaface_result['boxes'])):
|
|
# 获得retinaface的box并做扩充
|
|
retinaface_box = np.array(retinaface_result['boxes'][index])
|
|
face_width = retinaface_box[2] - retinaface_box[0]
|
|
face_height = retinaface_box[3] - retinaface_box[1]
|
|
retinaface_box[0] = np.clip(np.array(retinaface_box[0], np.int32) - face_width * (crop_ratio - 1) / 2, 0, w - 1)
|
|
retinaface_box[1] = np.clip(np.array(retinaface_box[1], np.int32) - face_height * (crop_ratio - 1) / 2, 0, h - 1)
|
|
retinaface_box[2] = np.clip(np.array(retinaface_box[2], np.int32) + face_width * (crop_ratio - 1) / 2, 0, w - 1)
|
|
retinaface_box[3] = np.clip(np.array(retinaface_box[3], np.int32) + face_height * (crop_ratio - 1) / 2, 0, h - 1)
|
|
retinaface_box = np.array(retinaface_box, np.int32)
|
|
retinaface_boxs.append(retinaface_box)
|
|
|
|
# 检测关键点
|
|
retinaface_keypoint = np.reshape(retinaface_result['keypoints'][index], [5, 2])
|
|
retinaface_keypoint = np.array(retinaface_keypoint, np.float32)
|
|
retinaface_keypoints.append(retinaface_keypoint)
|
|
|
|
# mask部分
|
|
retinaface_crop = image.crop(np.int32(retinaface_box))
|
|
retinaface_mask = np.zeros_like(np.array(image, np.uint8))
|
|
if mask_type == "skin":
|
|
retinaface_sub_mask = face_seg(retinaface_crop)
|
|
retinaface_mask[retinaface_box[1]:retinaface_box[3], retinaface_box[0]:retinaface_box[2]] = np.expand_dims(retinaface_sub_mask, -1)
|
|
else:
|
|
retinaface_mask[retinaface_box[1]:retinaface_box[3], retinaface_box[0]:retinaface_box[2]] = 255
|
|
retinaface_masks.append(retinaface_mask)
|
|
retinaface_mask_pil = Image.fromarray(np.uint8(retinaface_mask))
|
|
retinaface_mask_pils.append(retinaface_mask_pil)
|
|
|
|
retinaface_boxs = np.array(retinaface_boxs)
|
|
argindex = np.argsort(retinaface_boxs[:, 0])
|
|
retinaface_boxs = [retinaface_boxs[index] for index in argindex]
|
|
retinaface_keypoints = [retinaface_keypoints[index] for index in argindex]
|
|
retinaface_mask_pils = [retinaface_mask_pils[index] for index in argindex]
|
|
retinaface_mask_np = [retinaface_masks[index] for index in argindex]
|
|
mask_tensor = np_to_mask(retinaface_mask_np[0])
|
|
return retinaface_boxs, retinaface_keypoints, retinaface_mask_pils, mask_tensor
|
|
|
|
else:
|
|
retinaface_box = np.array([])
|
|
retinaface_keypoints = np.array([])
|
|
retinaface_mask = np.zeros_like(np.array(image, np.uint8))
|
|
retinaface_mask_pil = Image.fromarray(np.uint8(retinaface_mask))
|
|
|
|
return retinaface_box, retinaface_keypoints, retinaface_mask_pil, retinaface_mask
|
|
|
|
def crop_and_paste(source_image, source_image_mask, target_image, source_five_point, target_five_point, source_box):
|
|
"""
|
|
Applies a face replacement by cropping and pasting one face onto another image.
|
|
|
|
Args:
|
|
source_image (PIL.Image): The source image containing the face to be pasted.
|
|
source_image_mask (PIL.Image): The mask representing the face in the source image.
|
|
target_image (PIL.Image): The target image where the face will be pasted.
|
|
source_five_point (numpy.ndarray): Five key points of the face in the source image.
|
|
target_five_point (numpy.ndarray): Five key points of the corresponding face in the target image.
|
|
source_box (list): Coordinates of the bounding box around the face in the source image.
|
|
|
|
Returns:
|
|
PIL.Image: The resulting image with the pasted face.
|
|
|
|
Notes:
|
|
The function takes a source image, its corresponding mask, a target image, key points, and the bounding box
|
|
around the face in the source image. It then aligns and pastes the face from the source image onto the
|
|
corresponding location in the target image, taking into account the key points and bounding box.
|
|
"""
|
|
source_five_point = np.reshape(source_five_point, [5, 2]) - np.array(source_box[:2])
|
|
target_five_point = np.reshape(target_five_point, [5, 2])
|
|
|
|
crop_source_image = source_image.crop(np.int32(source_box))
|
|
crop_source_image_mask = source_image_mask.crop(np.int32(source_box))
|
|
source_five_point, target_five_point = np.array(source_five_point), np.array(target_five_point)
|
|
|
|
tform = transform.SimilarityTransform()
|
|
# 程序直接估算出转换矩阵M
|
|
tform.estimate(source_five_point, target_five_point)
|
|
M = tform.params[0:2, :]
|
|
|
|
warped = cv2.warpAffine(np.array(crop_source_image), M, np.shape(target_image)[:2][::-1], borderValue=0.0)
|
|
warped_mask = cv2.warpAffine(np.array(crop_source_image_mask), M, np.shape(target_image)[:2][::-1], borderValue=0.0)
|
|
|
|
mask = np.float32(warped_mask == 0)
|
|
output = mask * np.float32(target_image) + (1 - mask) * np.float32(warped)
|
|
return output
|
|
|
|
def call_face_crop(retinaface_detection, image, crop_ratio, prefix="tmp"):
|
|
# retinaface detect
|
|
retinaface_result = retinaface_detection(image)
|
|
# get mask and keypoints
|
|
retinaface_box, retinaface_keypoints, retinaface_mask_pil, retinaface_mask_tensor = safe_get_box_mask_keypoints(image, retinaface_result, crop_ratio, None, "crop")
|
|
|
|
return retinaface_box, retinaface_keypoints, retinaface_mask_pil, retinaface_mask_tensor
|
|
|
|
def color_transfer(sc, dc):
|
|
"""
|
|
Transfer color distribution from of sc, referred to dc.
|
|
|
|
Args:
|
|
sc (numpy.ndarray): input image to be transfered.
|
|
dc (numpy.ndarray): reference image
|
|
|
|
Returns:
|
|
numpy.ndarray: Transferred color distribution on the sc.
|
|
"""
|
|
|
|
def get_mean_and_std(img):
|
|
x_mean, x_std = cv2.meanStdDev(img)
|
|
x_mean = np.hstack(np.around(x_mean, 2))
|
|
x_std = np.hstack(np.around(x_std, 2))
|
|
return x_mean, x_std
|
|
|
|
sc = cv2.cvtColor(sc, cv2.COLOR_BGR2LAB) # 转换颜色空间为clelab
|
|
s_mean, s_std = get_mean_and_std(sc)
|
|
dc = cv2.cvtColor(dc, cv2.COLOR_BGR2LAB) # 转换颜色空间为clelab
|
|
t_mean, t_std = get_mean_and_std(dc)
|
|
img_n = ((sc - s_mean) * (t_std / s_std)) + t_mean
|
|
np.putmask(img_n, img_n > 255, 255)
|
|
np.putmask(img_n, img_n < 0, 0)
|
|
dst = cv2.cvtColor(cv2.convertScaleAbs(img_n), cv2.COLOR_LAB2BGR)
|
|
return dst
|
|
|
|
def conv3x3(in_planes, out_planes, stride=1):
|
|
"""3x3 convolution with padding"""
|
|
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
|
|
padding=1, bias=False)
|
|
|
|
class BasicBlock(nn.Module):
|
|
def __init__(self, in_chan, out_chan, stride=1):
|
|
super(BasicBlock, self).__init__()
|
|
self.conv1 = conv3x3(in_chan, out_chan, stride)
|
|
self.bn1 = nn.BatchNorm2d(out_chan)
|
|
self.conv2 = conv3x3(out_chan, out_chan)
|
|
self.bn2 = nn.BatchNorm2d(out_chan)
|
|
self.relu = nn.ReLU(inplace=True)
|
|
self.downsample = None
|
|
if in_chan != out_chan or stride != 1:
|
|
self.downsample = nn.Sequential(
|
|
nn.Conv2d(in_chan, out_chan,
|
|
kernel_size=1, stride=stride, bias=False),
|
|
nn.BatchNorm2d(out_chan),
|
|
)
|
|
|
|
def forward(self, x):
|
|
residual = self.conv1(x)
|
|
residual = F.relu(self.bn1(residual))
|
|
residual = self.conv2(residual)
|
|
residual = self.bn2(residual)
|
|
|
|
shortcut = x
|
|
if self.downsample is not None:
|
|
shortcut = self.downsample(x)
|
|
|
|
out = shortcut + residual
|
|
out = self.relu(out)
|
|
return out
|
|
|
|
def create_layer_basic(in_chan, out_chan, bnum, stride=1):
|
|
layers = [BasicBlock(in_chan, out_chan, stride=stride)]
|
|
for i in range(bnum - 1):
|
|
layers.append(BasicBlock(out_chan, out_chan, stride=1))
|
|
return nn.Sequential(*layers)
|
|
|
|
class Resnet18(nn.Module):
|
|
def __init__(self):
|
|
super(Resnet18, self).__init__()
|
|
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
|
|
bias=False)
|
|
self.bn1 = nn.BatchNorm2d(64)
|
|
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
|
|
self.layer1 = create_layer_basic(64, 64, bnum=2, stride=1)
|
|
self.layer2 = create_layer_basic(64, 128, bnum=2, stride=2)
|
|
self.layer3 = create_layer_basic(128, 256, bnum=2, stride=2)
|
|
self.layer4 = create_layer_basic(256, 512, bnum=2, stride=2)
|
|
|
|
def forward(self, x):
|
|
x = self.conv1(x)
|
|
x = F.relu(self.bn1(x))
|
|
x = self.maxpool(x)
|
|
|
|
x = self.layer1(x)
|
|
feat8 = self.layer2(x) # 1/8
|
|
feat16 = self.layer3(feat8) # 1/16
|
|
feat32 = self.layer4(feat16) # 1/32
|
|
return feat8, feat16, feat32
|
|
|
|
def get_params(self):
|
|
wd_params, nowd_params = [], []
|
|
for name, module in self.named_modules():
|
|
if isinstance(module, (nn.Linear, nn.Conv2d)):
|
|
wd_params.append(module.weight)
|
|
if not module.bias is None:
|
|
nowd_params.append(module.bias)
|
|
elif isinstance(module, nn.BatchNorm2d):
|
|
nowd_params += list(module.parameters())
|
|
return wd_params, nowd_params
|
|
|
|
class ConvBNReLU(nn.Module):
|
|
def __init__(self, in_chan, out_chan, ks=3, stride=1, padding=1, *args, **kwargs):
|
|
super(ConvBNReLU, self).__init__()
|
|
self.conv = nn.Conv2d(in_chan,
|
|
out_chan,
|
|
kernel_size=ks,
|
|
stride=stride,
|
|
padding=padding,
|
|
bias=False)
|
|
self.bn = nn.BatchNorm2d(out_chan)
|
|
self.init_weight()
|
|
|
|
def forward(self, x):
|
|
x = self.conv(x)
|
|
x = F.relu(self.bn(x))
|
|
return x
|
|
|
|
def init_weight(self):
|
|
for ly in self.children():
|
|
if isinstance(ly, nn.Conv2d):
|
|
nn.init.kaiming_normal_(ly.weight, a=1)
|
|
if not ly.bias is None: nn.init.constant_(ly.bias, 0)
|
|
|
|
class BiSeNetOutput(nn.Module):
|
|
def __init__(self, in_chan, mid_chan, n_classes, *args, **kwargs):
|
|
super(BiSeNetOutput, self).__init__()
|
|
self.conv = ConvBNReLU(in_chan, mid_chan, ks=3, stride=1, padding=1)
|
|
self.conv_out = nn.Conv2d(mid_chan, n_classes, kernel_size=1, bias=False)
|
|
self.init_weight()
|
|
|
|
def forward(self, x):
|
|
x = self.conv(x)
|
|
x = self.conv_out(x)
|
|
return x
|
|
|
|
def init_weight(self):
|
|
for ly in self.children():
|
|
if isinstance(ly, nn.Conv2d):
|
|
nn.init.kaiming_normal_(ly.weight, a=1)
|
|
if not ly.bias is None: nn.init.constant_(ly.bias, 0)
|
|
|
|
def get_params(self):
|
|
wd_params, nowd_params = [], []
|
|
for name, module in self.named_modules():
|
|
if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d):
|
|
wd_params.append(module.weight)
|
|
if not module.bias is None:
|
|
nowd_params.append(module.bias)
|
|
elif isinstance(module, nn.BatchNorm2d):
|
|
nowd_params += list(module.parameters())
|
|
return wd_params, nowd_params
|
|
|
|
class AttentionRefinementModule(nn.Module):
|
|
def __init__(self, in_chan, out_chan, *args, **kwargs):
|
|
super(AttentionRefinementModule, self).__init__()
|
|
self.conv = ConvBNReLU(in_chan, out_chan, ks=3, stride=1, padding=1)
|
|
self.conv_atten = nn.Conv2d(out_chan, out_chan, kernel_size=1, bias=False)
|
|
self.bn_atten = nn.BatchNorm2d(out_chan)
|
|
self.sigmoid_atten = nn.Sigmoid()
|
|
self.init_weight()
|
|
|
|
def forward(self, x):
|
|
feat = self.conv(x)
|
|
atten = F.avg_pool2d(feat, feat.size()[2:])
|
|
atten = self.conv_atten(atten)
|
|
atten = self.bn_atten(atten)
|
|
atten = self.sigmoid_atten(atten)
|
|
out = torch.mul(feat, atten)
|
|
return out
|
|
|
|
def init_weight(self):
|
|
for ly in self.children():
|
|
if isinstance(ly, nn.Conv2d):
|
|
nn.init.kaiming_normal_(ly.weight, a=1)
|
|
if not ly.bias is None: nn.init.constant_(ly.bias, 0)
|
|
|
|
class ContextPath(nn.Module):
|
|
def __init__(self, *args, **kwargs):
|
|
super(ContextPath, self).__init__()
|
|
self.resnet = Resnet18()
|
|
self.arm16 = AttentionRefinementModule(256, 128)
|
|
self.arm32 = AttentionRefinementModule(512, 128)
|
|
self.conv_head32 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1)
|
|
self.conv_head16 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1)
|
|
self.conv_avg = ConvBNReLU(512, 128, ks=1, stride=1, padding=0)
|
|
|
|
self.init_weight()
|
|
|
|
def forward(self, x):
|
|
H0, W0 = x.size()[2:]
|
|
feat8, feat16, feat32 = self.resnet(x)
|
|
H8, W8 = feat8.size()[2:]
|
|
H16, W16 = feat16.size()[2:]
|
|
H32, W32 = feat32.size()[2:]
|
|
|
|
avg = F.avg_pool2d(feat32, feat32.size()[2:])
|
|
avg = self.conv_avg(avg)
|
|
avg_up = F.interpolate(avg, (H32, W32), mode='nearest')
|
|
|
|
feat32_arm = self.arm32(feat32)
|
|
feat32_sum = feat32_arm + avg_up
|
|
feat32_up = F.interpolate(feat32_sum, (H16, W16), mode='nearest')
|
|
feat32_up = self.conv_head32(feat32_up)
|
|
|
|
feat16_arm = self.arm16(feat16)
|
|
feat16_sum = feat16_arm + feat32_up
|
|
feat16_up = F.interpolate(feat16_sum, (H8, W8), mode='nearest')
|
|
feat16_up = self.conv_head16(feat16_up)
|
|
|
|
return feat8, feat16_up, feat32_up # x8, x8, x16
|
|
|
|
def init_weight(self):
|
|
for ly in self.children():
|
|
if isinstance(ly, nn.Conv2d):
|
|
nn.init.kaiming_normal_(ly.weight, a=1)
|
|
if not ly.bias is None: nn.init.constant_(ly.bias, 0)
|
|
|
|
def get_params(self):
|
|
wd_params, nowd_params = [], []
|
|
for name, module in self.named_modules():
|
|
if isinstance(module, (nn.Linear, nn.Conv2d)):
|
|
wd_params.append(module.weight)
|
|
if not module.bias is None:
|
|
nowd_params.append(module.bias)
|
|
elif isinstance(module, nn.BatchNorm2d):
|
|
nowd_params += list(module.parameters())
|
|
return wd_params, nowd_params
|
|
|
|
### This is not used, since I replace this with the resnet feature with the same size
|
|
class SpatialPath(nn.Module):
|
|
def __init__(self, *args, **kwargs):
|
|
super(SpatialPath, self).__init__()
|
|
self.conv1 = ConvBNReLU(3, 64, ks=7, stride=2, padding=3)
|
|
self.conv2 = ConvBNReLU(64, 64, ks=3, stride=2, padding=1)
|
|
self.conv3 = ConvBNReLU(64, 64, ks=3, stride=2, padding=1)
|
|
self.conv_out = ConvBNReLU(64, 128, ks=1, stride=1, padding=0)
|
|
self.init_weight()
|
|
|
|
def forward(self, x):
|
|
feat = self.conv1(x)
|
|
feat = self.conv2(feat)
|
|
feat = self.conv3(feat)
|
|
feat = self.conv_out(feat)
|
|
return feat
|
|
|
|
def init_weight(self):
|
|
for ly in self.children():
|
|
if isinstance(ly, nn.Conv2d):
|
|
nn.init.kaiming_normal_(ly.weight, a=1)
|
|
if not ly.bias is None: nn.init.constant_(ly.bias, 0)
|
|
|
|
def get_params(self):
|
|
wd_params, nowd_params = [], []
|
|
for name, module in self.named_modules():
|
|
if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d):
|
|
wd_params.append(module.weight)
|
|
if not module.bias is None:
|
|
nowd_params.append(module.bias)
|
|
elif isinstance(module, nn.BatchNorm2d):
|
|
nowd_params += list(module.parameters())
|
|
return wd_params, nowd_params
|
|
|
|
class FeatureFusionModule(nn.Module):
|
|
def __init__(self, in_chan, out_chan, *args, **kwargs):
|
|
super(FeatureFusionModule, self).__init__()
|
|
self.convblk = ConvBNReLU(in_chan, out_chan, ks=1, stride=1, padding=0)
|
|
self.conv1 = nn.Conv2d(out_chan,
|
|
out_chan // 4,
|
|
kernel_size=1,
|
|
stride=1,
|
|
padding=0,
|
|
bias=False)
|
|
self.conv2 = nn.Conv2d(out_chan // 4,
|
|
out_chan,
|
|
kernel_size=1,
|
|
stride=1,
|
|
padding=0,
|
|
bias=False)
|
|
self.relu = nn.ReLU(inplace=True)
|
|
self.sigmoid = nn.Sigmoid()
|
|
self.init_weight()
|
|
|
|
def forward(self, fsp, fcp):
|
|
fcat = torch.cat([fsp, fcp], dim=1)
|
|
feat = self.convblk(fcat)
|
|
atten = F.avg_pool2d(feat, feat.size()[2:])
|
|
atten = self.conv1(atten)
|
|
atten = self.relu(atten)
|
|
atten = self.conv2(atten)
|
|
atten = self.sigmoid(atten)
|
|
feat_atten = torch.mul(feat, atten)
|
|
feat_out = feat_atten + feat
|
|
return feat_out
|
|
|
|
def init_weight(self):
|
|
for ly in self.children():
|
|
if isinstance(ly, nn.Conv2d):
|
|
nn.init.kaiming_normal_(ly.weight, a=1)
|
|
if not ly.bias is None: nn.init.constant_(ly.bias, 0)
|
|
|
|
def get_params(self):
|
|
wd_params, nowd_params = [], []
|
|
for name, module in self.named_modules():
|
|
if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d):
|
|
wd_params.append(module.weight)
|
|
if not module.bias is None:
|
|
nowd_params.append(module.bias)
|
|
elif isinstance(module, nn.BatchNorm2d):
|
|
nowd_params += list(module.parameters())
|
|
return wd_params, nowd_params
|
|
|
|
class BiSeNet(nn.Module):
|
|
def __init__(self, n_classes, *args, **kwargs):
|
|
super(BiSeNet, self).__init__()
|
|
self.cp = ContextPath()
|
|
## here self.sp is deleted
|
|
self.ffm = FeatureFusionModule(256, 256)
|
|
self.conv_out = BiSeNetOutput(256, 256, n_classes)
|
|
self.conv_out16 = BiSeNetOutput(128, 64, n_classes)
|
|
self.conv_out32 = BiSeNetOutput(128, 64, n_classes)
|
|
self.init_weight()
|
|
|
|
def forward(self, x):
|
|
H, W = x.size()[2:]
|
|
feat_res8, feat_cp8, feat_cp16 = self.cp(x) # here return res3b1 feature
|
|
feat_sp = feat_res8 # use res3b1 feature to replace spatial path feature
|
|
feat_fuse = self.ffm(feat_sp, feat_cp8)
|
|
|
|
feat_out = self.conv_out(feat_fuse)
|
|
feat_out16 = self.conv_out16(feat_cp8)
|
|
feat_out32 = self.conv_out32(feat_cp16)
|
|
|
|
feat_out = F.interpolate(feat_out, (H, W), mode='bilinear', align_corners=True)
|
|
feat_out16 = F.interpolate(feat_out16, (H, W), mode='bilinear', align_corners=True)
|
|
feat_out32 = F.interpolate(feat_out32, (H, W), mode='bilinear', align_corners=True)
|
|
return feat_out, feat_out16, feat_out32
|
|
|
|
def init_weight(self):
|
|
for ly in self.children():
|
|
if isinstance(ly, nn.Conv2d):
|
|
nn.init.kaiming_normal_(ly.weight, a=1)
|
|
if not ly.bias is None: nn.init.constant_(ly.bias, 0)
|
|
|
|
def get_params(self):
|
|
wd_params, nowd_params, lr_mul_wd_params, lr_mul_nowd_params = [], [], [], []
|
|
for name, child in self.named_children():
|
|
child_wd_params, child_nowd_params = child.get_params()
|
|
if isinstance(child, FeatureFusionModule) or isinstance(child, BiSeNetOutput):
|
|
lr_mul_wd_params += child_wd_params
|
|
lr_mul_nowd_params += child_nowd_params
|
|
else:
|
|
wd_params += child_wd_params
|
|
nowd_params += child_nowd_params
|
|
return wd_params, nowd_params, lr_mul_wd_params, lr_mul_nowd_params
|
|
|
|
class Face_Skin(object):
|
|
'''
|
|
Inputs:
|
|
image input image.
|
|
Outputs:
|
|
mask output mask.
|
|
'''
|
|
|
|
def __init__(self, model_path) -> None:
|
|
n_classes = 19
|
|
self.model = BiSeNet(n_classes=n_classes)
|
|
self.model.load_state_dict(torch.load(model_path, map_location='cpu'))
|
|
self.model.eval()
|
|
|
|
# transform for input image
|
|
self.trans = transforms.Compose([
|
|
transforms.ToTensor(),
|
|
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
|
|
])
|
|
|
|
def detect(self, image, retinaface_detection, needs_index=[12, 13]):
|
|
# needs_index 12, 13 means seg the lip
|
|
with torch.no_grad():
|
|
total_mask = np.zeros_like(np.uint8(image))
|
|
|
|
# detect image
|
|
retinaface_boxes, _, _, _ = call_face_crop(retinaface_detection, image, 13, prefix="tmp")
|
|
retinaface_box = retinaface_boxes[0]
|
|
|
|
# sub_face for seg skin
|
|
sub_image = image.crop(retinaface_box)
|
|
|
|
image_h, image_w, c = np.shape(np.uint8(sub_image))
|
|
PIL_img = Image.fromarray(np.uint8(sub_image))
|
|
PIL_img = PIL_img.resize((512, 512), Image.BILINEAR)
|
|
|
|
torch_img = self.trans(PIL_img)
|
|
torch_img = torch.unsqueeze(torch_img, 0)
|
|
|
|
out = self.model(torch_img)[0]
|
|
model_mask = out.squeeze(0).cpu().numpy().argmax(0)
|
|
|
|
sub_mask = np.zeros_like(model_mask)
|
|
for index in needs_index:
|
|
sub_mask += np.uint8(model_mask == index)
|
|
|
|
sub_mask = np.clip(sub_mask, 0, 1) * 255
|
|
sub_mask = np.tile(np.expand_dims(cv2.resize(np.uint8(sub_mask), (image_w, image_h)), -1), [1, 1, 3])
|
|
|
|
# detect image
|
|
total_mask[retinaface_box[1]:retinaface_box[3], retinaface_box[0]:retinaface_box[2], :] = sub_mask
|
|
return total_mask
|