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import cv2import torchimport fractionsimport numpy as npfrom PIL import Imageimport torch.nn.functional as Ffrom torchvision import transformsfrom models.models import create_modelfrom options.test_options import TestOptionsfrom insightface_func.face_detect_crop_single import Face_detect_cropfrom util.videoswap import video_swapimport osdef lcm(a, b): return abs(a * b) / fractions.gcd(a, b) if a and b else 0transformer = transforms.Compose([        transforms.ToTensor(),        #transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])    ])transformer_Arcface = transforms.Compose([        transforms.ToTensor(),        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])    ])# detransformer = transforms.Compose([#         transforms.Normalize([0, 0, 0], [1/0.229, 1/0.224, 1/0.225]),#         transforms.Normalize([-0.485, -0.456, -0.406], [1, 1, 1])#     ])if __name__ == '__main__':    opt = TestOptions().parse()    start_epoch, epoch_iter = 1, 0    crop_size = 224    torch.nn.Module.dump_patches = True    model = create_model(opt)    model.eval()    app = Face_detect_crop(name='antelope', root='./insightface_func/models')    app.prepare(ctx_id= 0, det_thresh=0.6, det_size=(640,640))    pic_a = opt.pic_a_path    # img_a = Image.open(pic_a).convert('RGB')    img_a_whole = cv2.imread(pic_a)    img_a_align_crop, _ = app.get(img_a_whole,crop_size)    img_a_align_crop_pil = Image.fromarray(cv2.cvtColor(img_a_align_crop[0],cv2.COLOR_BGR2RGB))     img_a = transformer_Arcface(img_a_align_crop_pil)    img_id = img_a.view(-1, img_a.shape[0], img_a.shape[1], img_a.shape[2])    # pic_b = opt.pic_b_path    # img_b_whole = cv2.imread(pic_b)    # img_b_align_crop, b_mat = app.get(img_b_whole,crop_size)    # img_b_align_crop_pil = Image.fromarray(cv2.cvtColor(img_b_align_crop,cv2.COLOR_BGR2RGB))     # img_b = transformer(img_b_align_crop_pil)    # img_att = img_b.view(-1, img_b.shape[0], img_b.shape[1], img_b.shape[2])    # convert numpy to tensor    img_id = img_id.cuda()    # img_att = img_att.cuda()    #create latent id    img_id_downsample = F.interpolate(img_id, scale_factor=0.5)    latend_id = model.netArc(img_id_downsample)    latend_id = F.normalize(latend_id, p=2, dim=1)    video_swap(opt.video_path, latend_id, model, app, opt.output_path,temp_results_dir=opt.temp_path,no_simswaplogo=opt.no_simswaplogo)
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