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- import cv2
 
- import torch
 
- import fractions
 
- import numpy as np
 
- from PIL import Image
 
- import torch.nn.functional as F
 
- from torchvision import transforms
 
- from models.models import create_model
 
- from options.test_options import TestOptions
 
- from insightface_func.face_detect_crop_mutil import Face_detect_crop
 
- from util.reverse2original import reverse2wholeimage
 
- import os
 
- from util.add_watermark import watermark_image
 
- import torch.nn as nn
 
- from util.norm import SpecificNorm
 
- import glob
 
- def lcm(a, b): return abs(a * b) / fractions.gcd(a, b) if a and b else 0
 
- transformer_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
 
-     mutilsepcific_dir = opt.mutilsepcific_dir
 
-     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))
 
-     # 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 !!!"
 
-     ############## 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([])
 
-         for source_specific_id_nonorm_tmp in source_specific_id_nonorm_list:
 
-             id_compare_values[-1].append(mse(b_align_crop_id_nonorm,source_specific_id_nonorm_tmp).detach().cpu().numpy())
 
-         b_align_crop_tenor_list.append(b_align_crop_tenor)
 
-     id_compare_values_array = np.array(id_compare_values).transpose(1,0)
 
-     min_indexs = np.argmin(id_compare_values_array,axis=0)
 
-     min_value = np.min(id_compare_values_array,axis=0)
 
-     swap_result_list = [] 
 
-     swap_result_matrix_list = []
 
-     for tmp_index, min_index in enumerate(min_indexs):
 
-         if min_value[tmp_index] < opt.id_thres:
 
-             swap_result = model(None, b_align_crop_tenor_list[tmp_index], target_id_norm_list[min_index], None, True)[0]
 
-             swap_result_list.append(swap_result)
 
-             swap_result_matrix_list.append(b_mat_list[tmp_index])
 
-         else:
 
-             pass
 
-     if len(swap_result_list) !=0:
 
-     
 
-         reverse2wholeimage(swap_result_list, swap_result_matrix_list, crop_size, img_b_whole, logoclass, os.path.join(opt.output_path, 'result_whole_swap_mutilspecific.jpg'), opt.no_simswaplogo)
 
-         print(' ')
 
-         print('************ Done ! ************')
 
-     
 
-     else:
 
-         print('The people you specified are not found on the picture: {}'.format(pic_b))
 
 
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