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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_mutil import Face_detect_cropfrom util.videoswap_mutilspecific import video_swapimport osimport globdef 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()    pic_specific = opt.pic_specific_path    start_epoch, epoch_iter = 1, 0    crop_size = 224    mutilsepcific_dir = opt.mutilsepcific_dir    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))    # The specific person to be swapped(source)    source_specific_id_nonorm_list = []    source_path = os.path.join(mutilsepcific_dir,'source','*')    source_specific_images_path = sorted(glob.glob(source_path))    for source_specific_image_path in source_specific_images_path:        specific_person_whole = cv2.imread(source_specific_image_path)        specific_person_align_crop, _ = app.get(specific_person_whole,crop_size)        specific_person_align_crop_pil = Image.fromarray(cv2.cvtColor(specific_person_align_crop[0],cv2.COLOR_BGR2RGB))         specific_person = transformer_Arcface(specific_person_align_crop_pil)        specific_person = specific_person.view(-1, specific_person.shape[0], specific_person.shape[1], specific_person.shape[2])        # convert numpy to tensor        specific_person = specific_person.cuda()        #create latent id        specific_person_downsample = F.interpolate(specific_person, scale_factor=0.5)        specific_person_id_nonorm = model.netArc(specific_person_downsample)        source_specific_id_nonorm_list.append(specific_person_id_nonorm.clone())    # The person who provides id information (list)    target_id_norm_list = []    target_path = os.path.join(mutilsepcific_dir,'target','*')    target_images_path = sorted(glob.glob(target_path))    for target_image_path in target_images_path:        img_a_whole = cv2.imread(target_image_path)        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])        # convert numpy to tensor        img_id = img_id.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)        target_id_norm_list.append(latend_id.clone())    assert len(target_id_norm_list) == len(source_specific_id_nonorm_list), "The number of images in source and target directory must be same !!!"    video_swap(opt.video_path, target_id_norm_list,source_specific_id_nonorm_list, opt.id_thres, \        model, app, opt.output_path,temp_results_dir=opt.temp_path,no_simswaplogo=opt.no_simswaplogo)
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