import cv2 import numpy as np # import time from util.add_watermark import watermark_image def reverse2wholeimage(swaped_imgs, mats, crop_size, oriimg, logoclass, save_path = '', no_simswaplogo = False): target_image_list = [] img_mask_list = [] for swaped_img, mat in zip(swaped_imgs, mats): 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]) target_image = cv2.warpAffine(swaped_img, mat_rev, orisize) img_white = cv2.warpAffine(img_white, mat_rev, orisize) img_white[img_white>20] =255 img_mask = img_white 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]) 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) # cv2.imwrite('E:\\lny\\SimSwap-main\\output\\img_div.jpg', img * 255) # cv2.imwrite('E:\\lny\\SimSwap-main\\output\\ori_img.jpg', oriimg)