176 lines
6.5 KiB
Python
176 lines
6.5 KiB
Python
import cv2
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import numpy as np
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# import time
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import torch
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from torch.nn import functional as F
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import torch.nn as nn
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def encode_segmentation_rgb(segmentation, no_neck=True):
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parse = segmentation
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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]
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mouth_id = 11
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# hair_id = 17
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face_map = np.zeros([parse.shape[0], parse.shape[1]])
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mouth_map = np.zeros([parse.shape[0], parse.shape[1]])
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# hair_map = np.zeros([parse.shape[0], parse.shape[1]])
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for valid_id in face_part_ids:
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valid_index = np.where(parse==valid_id)
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face_map[valid_index] = 255
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valid_index = np.where(parse==mouth_id)
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mouth_map[valid_index] = 255
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# valid_index = np.where(parse==hair_id)
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# hair_map[valid_index] = 255
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#return np.stack([face_map, mouth_map,hair_map], axis=2)
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return np.stack([face_map, mouth_map], axis=2)
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class SoftErosion(nn.Module):
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def __init__(self, kernel_size=15, threshold=0.6, iterations=1):
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super(SoftErosion, self).__init__()
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r = kernel_size // 2
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self.padding = r
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self.iterations = iterations
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self.threshold = threshold
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# Create kernel
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y_indices, x_indices = torch.meshgrid(torch.arange(0., kernel_size), torch.arange(0., kernel_size))
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dist = torch.sqrt((x_indices - r) ** 2 + (y_indices - r) ** 2)
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kernel = dist.max() - dist
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kernel /= kernel.sum()
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kernel = kernel.view(1, 1, *kernel.shape)
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self.register_buffer('weight', kernel)
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def forward(self, x):
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x = x.float()
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for i in range(self.iterations - 1):
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x = torch.min(x, F.conv2d(x, weight=self.weight, groups=x.shape[1], padding=self.padding))
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x = F.conv2d(x, weight=self.weight, groups=x.shape[1], padding=self.padding)
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mask = x >= self.threshold
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x[mask] = 1.0
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x[~mask] /= x[~mask].max()
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return x, mask
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def postprocess(swapped_face, target, target_mask,smooth_mask):
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# target_mask = cv2.resize(target_mask, (self.size, self.size))
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mask_tensor = torch.from_numpy(target_mask.copy().transpose((2, 0, 1))).float().mul_(1/255.0).cuda()
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face_mask_tensor = mask_tensor[0] + mask_tensor[1]
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soft_face_mask_tensor, _ = smooth_mask(face_mask_tensor.unsqueeze_(0).unsqueeze_(0))
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soft_face_mask_tensor.squeeze_()
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soft_face_mask = soft_face_mask_tensor.cpu().numpy()
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soft_face_mask = soft_face_mask[:, :, np.newaxis]
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result = swapped_face * soft_face_mask + target * (1 - soft_face_mask)
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result = result[:,:,::-1]# .astype(np.uint8)
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return result
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def reverse2wholeimage(b_align_crop_tenor_list,swaped_imgs, mats, crop_size, oriimg, logoclass, save_path = '', \
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no_simswaplogo = False,pasring_model =None,norm = None, use_mask = False):
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target_image_list = []
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img_mask_list = []
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if use_mask:
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smooth_mask = SoftErosion(kernel_size=17, threshold=0.9, iterations=7).cuda()
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else:
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pass
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# print(len(swaped_imgs))
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# print(mats)
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# print(len(b_align_crop_tenor_list))
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for swaped_img, mat ,source_img in zip(swaped_imgs, mats,b_align_crop_tenor_list):
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swaped_img = swaped_img.cpu().detach().numpy().transpose((1, 2, 0))
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img_white = np.full((crop_size,crop_size), 255, dtype=float)
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# inverse the Affine transformation matrix
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mat_rev = np.zeros([2,3])
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div1 = mat[0][0]*mat[1][1]-mat[0][1]*mat[1][0]
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mat_rev[0][0] = mat[1][1]/div1
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mat_rev[0][1] = -mat[0][1]/div1
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mat_rev[0][2] = -(mat[0][2]*mat[1][1]-mat[0][1]*mat[1][2])/div1
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div2 = mat[0][1]*mat[1][0]-mat[0][0]*mat[1][1]
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mat_rev[1][0] = mat[1][0]/div2
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mat_rev[1][1] = -mat[0][0]/div2
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mat_rev[1][2] = -(mat[0][2]*mat[1][0]-mat[0][0]*mat[1][2])/div2
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orisize = (oriimg.shape[1], oriimg.shape[0])
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if use_mask:
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source_img_norm = norm(source_img)
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source_img_512 = F.interpolate(source_img_norm,size=(512,512))
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out = pasring_model(source_img_512)[0]
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parsing = out.squeeze(0).detach().cpu().numpy().argmax(0)
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vis_parsing_anno = parsing.copy().astype(np.uint8)
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tgt_mask = encode_segmentation_rgb(vis_parsing_anno)
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if tgt_mask.sum() >= 5000:
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# face_mask_tensor = tgt_mask[...,0] + tgt_mask[...,1]
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target_mask = cv2.resize(tgt_mask, (crop_size, crop_size))
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# print(source_img)
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target_image_parsing = postprocess(swaped_img, source_img[0].cpu().detach().numpy().transpose((1, 2, 0)), target_mask,smooth_mask)
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target_image = cv2.warpAffine(target_image_parsing, mat_rev, orisize)
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# target_image_parsing = cv2.warpAffine(swaped_img, mat_rev, orisize)
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else:
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target_image = cv2.warpAffine(swaped_img, mat_rev, orisize)[..., ::-1]
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else:
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target_image = cv2.warpAffine(swaped_img, mat_rev, orisize)
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# source_image = cv2.warpAffine(source_img, mat_rev, orisize)
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img_white = cv2.warpAffine(img_white, mat_rev, orisize)
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img_white[img_white>20] =255
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img_mask = img_white
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# if use_mask:
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# kernel = np.ones((40,40),np.uint8)
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# img_mask = cv2.erode(img_mask,kernel,iterations = 1)
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# else:
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kernel = np.ones((40,40),np.uint8)
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img_mask = cv2.erode(img_mask,kernel,iterations = 1)
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kernel_size = (20, 20)
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blur_size = tuple(2*i+1 for i in kernel_size)
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img_mask = cv2.GaussianBlur(img_mask, blur_size, 0)
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# kernel = np.ones((10,10),np.uint8)
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# img_mask = cv2.erode(img_mask,kernel,iterations = 1)
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img_mask /= 255
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img_mask = np.reshape(img_mask, [img_mask.shape[0],img_mask.shape[1],1])
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# pasing mask
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# target_image_parsing = postprocess(target_image, source_image, tgt_mask)
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if use_mask:
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target_image = np.array(target_image, dtype=np.float) * 255
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else:
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target_image = np.array(target_image, dtype=np.float)[..., ::-1] * 255
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img_mask_list.append(img_mask)
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target_image_list.append(target_image)
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# target_image /= 255
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# target_image = 0
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img = np.array(oriimg, dtype=np.float)
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for img_mask, target_image in zip(img_mask_list, target_image_list):
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img = img_mask * target_image + (1-img_mask) * img
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final_img = img.astype(np.uint8)
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if not no_simswaplogo:
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final_img = logoclass.apply_frames(final_img)
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cv2.imwrite(save_path, final_img)
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