File size: 6,717 Bytes
f884940 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 |
import cv2
import numpy as np
# import time
import torch
from torch.nn import functional as F
import torch.nn as nn
def encode_segmentation_rgb(segmentation, no_neck=True):
parse = segmentation
face_part_ids = [1, 2, 3, 4, 5, 6, 10, 12, 13] if no_neck else [1, 2, 3, 4, 5, 6, 7, 8, 10, 12, 13, 14]
mouth_id = 11
# hair_id = 17
face_map = np.zeros([parse.shape[0], parse.shape[1]])
mouth_map = np.zeros([parse.shape[0], parse.shape[1]])
# hair_map = np.zeros([parse.shape[0], parse.shape[1]])
for valid_id in face_part_ids:
valid_index = np.where(parse==valid_id)
face_map[valid_index] = 255
valid_index = np.where(parse==mouth_id)
mouth_map[valid_index] = 255
# valid_index = np.where(parse==hair_id)
# hair_map[valid_index] = 255
#return np.stack([face_map, mouth_map,hair_map], axis=2)
return np.stack([face_map, mouth_map], axis=2)
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
def postprocess(swapped_face, target, target_mask,smooth_mask):
# target_mask = cv2.resize(target_mask, (self.size, self.size))
mask_tensor = torch.from_numpy(target_mask.copy().transpose((2, 0, 1))).float().mul_(1/255.0).cuda()
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_()
soft_face_mask = soft_face_mask_tensor.cpu().numpy()
soft_face_mask = soft_face_mask[:, :, np.newaxis]
result = swapped_face * soft_face_mask + target * (1 - soft_face_mask)
result = result[:,:,::-1]# .astype(np.uint8)
return result
def reverse2wholeimage(b_align_crop_tenor_list,swaped_imgs, mats, crop_size, oriimg, logoclass, save_path = '', \
no_simswaplogo = False,pasring_model =None,norm = None, use_mask = False):
target_image_list = []
img_mask_list = []
if use_mask:
smooth_mask = SoftErosion(kernel_size=17, threshold=0.9, iterations=7).cuda()
else:
pass
# print(len(swaped_imgs))
# print(mats)
# print(len(b_align_crop_tenor_list))
for swaped_img, mat ,source_img in zip(swaped_imgs, mats,b_align_crop_tenor_list):
swaped_img = swaped_img.cpu().detach().numpy().transpose((1, 2, 0))
img_white = np.full((crop_size,crop_size), 255, dtype=float)
# inverse the Affine transformation matrix
mat_rev = np.zeros([2,3])
div1 = mat[0][0]*mat[1][1]-mat[0][1]*mat[1][0]
mat_rev[0][0] = mat[1][1]/div1
mat_rev[0][1] = -mat[0][1]/div1
mat_rev[0][2] = -(mat[0][2]*mat[1][1]-mat[0][1]*mat[1][2])/div1
div2 = mat[0][1]*mat[1][0]-mat[0][0]*mat[1][1]
mat_rev[1][0] = mat[1][0]/div2
mat_rev[1][1] = -mat[0][0]/div2
mat_rev[1][2] = -(mat[0][2]*mat[1][0]-mat[0][0]*mat[1][2])/div2
orisize = (oriimg.shape[1], oriimg.shape[0])
if use_mask:
source_img_norm = norm(source_img)
source_img_512 = F.interpolate(source_img_norm,size=(512,512))
out = pasring_model(source_img_512)[0]
parsing = out.squeeze(0).detach().cpu().numpy().argmax(0)
vis_parsing_anno = parsing.copy().astype(np.uint8)
tgt_mask = encode_segmentation_rgb(vis_parsing_anno)
if tgt_mask.sum() >= 5000:
# face_mask_tensor = tgt_mask[...,0] + tgt_mask[...,1]
target_mask = cv2.resize(tgt_mask, (crop_size, crop_size))
# print(source_img)
target_image_parsing = postprocess(swaped_img, source_img[0].cpu().detach().numpy().transpose((1, 2, 0)), target_mask,smooth_mask)
target_image = cv2.warpAffine(target_image_parsing, mat_rev, orisize)
# target_image_parsing = cv2.warpAffine(swaped_img, mat_rev, orisize)
else:
target_image = cv2.warpAffine(swaped_img, mat_rev, orisize)[..., ::-1]
else:
target_image = cv2.warpAffine(swaped_img, mat_rev, orisize)
# source_image = cv2.warpAffine(source_img, mat_rev, orisize)
img_white = cv2.warpAffine(img_white, mat_rev, orisize)
img_white[img_white>20] =255
img_mask = img_white
# if use_mask:
# kernel = np.ones((40,40),np.uint8)
# img_mask = cv2.erode(img_mask,kernel,iterations = 1)
# else:
kernel = np.ones((40,40),np.uint8)
img_mask = cv2.erode(img_mask,kernel,iterations = 1)
kernel_size = (20, 20)
blur_size = tuple(2*i+1 for i in kernel_size)
img_mask = cv2.GaussianBlur(img_mask, blur_size, 0)
# kernel = np.ones((10,10),np.uint8)
# img_mask = cv2.erode(img_mask,kernel,iterations = 1)
img_mask /= 255
img_mask = np.reshape(img_mask, [img_mask.shape[0],img_mask.shape[1],1])
# pasing mask
# target_image_parsing = postprocess(target_image, source_image, tgt_mask)
if use_mask:
# target_image = np.array(target_image, dtype=np.float) * 255
target_image = np.array(target_image, dtype=float) * 255
else:
target_image = np.array(target_image, dtype=float)[..., ::-1] * 255
img_mask_list.append(img_mask)
target_image_list.append(target_image)
# target_image /= 255
# target_image = 0
img = np.array(oriimg, dtype=float)
for img_mask, target_image in zip(img_mask_list, target_image_list):
img = img_mask * target_image + (1-img_mask) * img
final_img = img.astype(np.uint8)
if not no_simswaplogo:
final_img = logoclass.apply_frames(final_img)
cv2.imwrite(save_path, final_img)
|