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- 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, (224, 224))
- # 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
- else:
- target_image = np.array(target_image, dtype=np.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=np.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)
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