test_wholeimage_swapspecific.py 4.4 KB

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  1. import cv2
  2. import torch
  3. import fractions
  4. import numpy as np
  5. from PIL import Image
  6. import torch.nn.functional as F
  7. from torchvision import transforms
  8. from models.models import create_model
  9. from options.test_options import TestOptions
  10. from insightface_func.face_detect_crop_multi import Face_detect_crop
  11. from util.reverse2original import reverse2wholeimage
  12. import os
  13. from util.add_watermark import watermark_image
  14. import torch.nn as nn
  15. from util.norm import SpecificNorm
  16. def lcm(a, b): return abs(a * b) / fractions.gcd(a, b) if a and b else 0
  17. transformer_Arcface = transforms.Compose([
  18. transforms.ToTensor(),
  19. transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
  20. ])
  21. def _totensor(array):
  22. tensor = torch.from_numpy(array)
  23. img = tensor.transpose(0, 1).transpose(0, 2).contiguous()
  24. return img.float().div(255)
  25. def _toarctensor(array):
  26. tensor = torch.from_numpy(array)
  27. img = tensor.transpose(0, 1).transpose(0, 2).contiguous()
  28. return img.float().div(255)
  29. if __name__ == '__main__':
  30. opt = TestOptions().parse()
  31. start_epoch, epoch_iter = 1, 0
  32. crop_size = 224
  33. torch.nn.Module.dump_patches = True
  34. logoclass = watermark_image('./simswaplogo/simswaplogo.png')
  35. model = create_model(opt)
  36. model.eval()
  37. mse = torch.nn.MSELoss().cuda()
  38. spNorm =SpecificNorm()
  39. app = Face_detect_crop(name='antelope', root='./insightface_func/models')
  40. app.prepare(ctx_id= 0, det_thresh=0.6, det_size=(640,640))
  41. pic_a = opt.pic_a_path
  42. pic_specific = opt.pic_specific_path
  43. # The person who provides id information
  44. img_a_whole = cv2.imread(pic_a)
  45. img_a_align_crop, _ = app.get(img_a_whole,crop_size)
  46. img_a_align_crop_pil = Image.fromarray(cv2.cvtColor(img_a_align_crop[0],cv2.COLOR_BGR2RGB))
  47. img_a = transformer_Arcface(img_a_align_crop_pil)
  48. img_id = img_a.view(-1, img_a.shape[0], img_a.shape[1], img_a.shape[2])
  49. # convert numpy to tensor
  50. img_id = img_id.cuda()
  51. #create latent id
  52. img_id_downsample = F.interpolate(img_id, scale_factor=0.5)
  53. latend_id = model.netArc(img_id_downsample)
  54. latend_id = F.normalize(latend_id, p=2, dim=1)
  55. # The specific person to be swapped
  56. specific_person_whole = cv2.imread(pic_specific)
  57. specific_person_align_crop, _ = app.get(specific_person_whole,crop_size)
  58. specific_person_align_crop_pil = Image.fromarray(cv2.cvtColor(specific_person_align_crop[0],cv2.COLOR_BGR2RGB))
  59. specific_person = transformer_Arcface(specific_person_align_crop_pil)
  60. specific_person = specific_person.view(-1, specific_person.shape[0], specific_person.shape[1], specific_person.shape[2])
  61. # convert numpy to tensor
  62. specific_person = specific_person.cuda()
  63. #create latent id
  64. specific_person_downsample = F.interpolate(specific_person, scale_factor=0.5)
  65. specific_person_id_nonorm = model.netArc(specific_person_downsample)
  66. # specific_person_id_norm = F.normalize(specific_person_id_nonorm, p=2, dim=1)
  67. ############## Forward Pass ######################
  68. pic_b = opt.pic_b_path
  69. img_b_whole = cv2.imread(pic_b)
  70. img_b_align_crop_list, b_mat_list = app.get(img_b_whole,crop_size)
  71. # detect_results = None
  72. swap_result_list = []
  73. id_compare_values = []
  74. b_align_crop_tenor_list = []
  75. for b_align_crop in img_b_align_crop_list:
  76. b_align_crop_tenor = _totensor(cv2.cvtColor(b_align_crop,cv2.COLOR_BGR2RGB))[None,...].cuda()
  77. b_align_crop_tenor_arcnorm = spNorm(b_align_crop_tenor)
  78. b_align_crop_tenor_arcnorm_downsample = F.interpolate(b_align_crop_tenor_arcnorm, scale_factor=0.5)
  79. b_align_crop_id_nonorm = model.netArc(b_align_crop_tenor_arcnorm_downsample)
  80. id_compare_values.append(mse(b_align_crop_id_nonorm,specific_person_id_nonorm).detach().cpu().numpy())
  81. b_align_crop_tenor_list.append(b_align_crop_tenor)
  82. id_compare_values_array = np.array(id_compare_values)
  83. min_index = np.argmin(id_compare_values_array)
  84. min_value = id_compare_values_array[min_index]
  85. if min_value < opt.id_thres:
  86. swap_result = model(None, b_align_crop_tenor_list[min_index], latend_id, None, True)[0]
  87. 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)
  88. print(' ')
  89. print('************ Done ! ************')
  90. else:
  91. print('The person you specified is not found on the picture: {}'.format(pic_b))