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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_multi 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
 
- from parsing_model.model import BiSeNet
 
- 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
 
-     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 opt.use_mask:
 
-         n_classes = 19
 
-         net = BiSeNet(n_classes=n_classes)
 
-         net.cuda()
 
-         save_pth = os.path.join('./parsing_model/checkpoint', '79999_iter.pth')
 
-         net.load_state_dict(torch.load(save_pth))
 
-         net.eval()
 
-     else:
 
-         net =None
 
-     if min_value < opt.id_thres:
 
-         swap_result = model(None, b_align_crop_tenor_list[min_index], latend_id, None, True)[0]
 
-         reverse2wholeimage([b_align_crop_tenor_list[min_index]], [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,pasring_model =net,use_mask=opt.use_mask, norm = spNorm)
 
-         print(' ')
 
-         print('************ Done ! ************')
 
-     else:
 
-         print('The person you specified is not found on the picture: {}'.format(pic_b))
 
 
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