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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.reverse2original import reverse2wholeimageimport osfrom util.add_watermark import watermark_imageimport torch.nn as nnfrom util.norm import SpecificNormdef lcm(a, b): return abs(a * b) / fractions.gcd(a, b) if a and b else 0transformer_Arcface = transforms.Compose([        transforms.ToTensor(),        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])    ])def _totensor(array):    tensor = torch.from_numpy(array)    img = tensor.transpose(0, 1).transpose(0, 2).contiguous()    return img.float().div(255)def _toarctensor(array):    tensor = torch.from_numpy(array)    img = tensor.transpose(0, 1).transpose(0, 2).contiguous()    return img.float().div(255)if __name__ == '__main__':    opt = TestOptions().parse()    start_epoch, epoch_iter = 1, 0    crop_size = 224    torch.nn.Module.dump_patches = True    logoclass = watermark_image('./simswaplogo/simswaplogo.png')    model = create_model(opt)    model.eval()    mse = torch.nn.MSELoss().cuda()    spNorm =SpecificNorm()    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    pic_specific = opt.pic_specific_path    # The person who provides id information     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])    # 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)    # The specific person to be swapped    specific_person_whole = cv2.imread(pic_specific)    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)    # specific_person_id_norm = F.normalize(specific_person_id_nonorm, p=2, dim=1)    ############## Forward Pass ######################    pic_b = opt.pic_b_path    img_b_whole = cv2.imread(pic_b)    img_b_align_crop_list, b_mat_list = app.get(img_b_whole,crop_size)    # detect_results = None    swap_result_list = []    id_compare_values = []     b_align_crop_tenor_list = []    for b_align_crop in img_b_align_crop_list:        b_align_crop_tenor = _totensor(cv2.cvtColor(b_align_crop,cv2.COLOR_BGR2RGB))[None,...].cuda()        b_align_crop_tenor_arcnorm = spNorm(b_align_crop_tenor)        b_align_crop_tenor_arcnorm_downsample = F.interpolate(b_align_crop_tenor_arcnorm, scale_factor=0.5)        b_align_crop_id_nonorm = model.netArc(b_align_crop_tenor_arcnorm_downsample)        id_compare_values.append(mse(b_align_crop_id_nonorm,specific_person_id_nonorm).detach().cpu().numpy())        b_align_crop_tenor_list.append(b_align_crop_tenor)    id_compare_values_array = np.array(id_compare_values)    min_index = np.argmin(id_compare_values_array)    min_value = id_compare_values_array[min_index]    if min_value < opt.id_thres:        swap_result = model(None, b_align_crop_tenor_list[min_index], latend_id, None, True)[0]        reverse2wholeimage([swap_result], [b_mat_list[min_index]], crop_size, img_b_whole, logoclass, os.path.join(opt.output_path, 'result_whole_swapspecific.jpg'), opt.no_simswaplogo)        print(' ')        print('************ Done ! ************')    else:        print('The person you specified is not found on the picture: {}'.format(pic_b))
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