|
@@ -0,0 +1,124 @@
|
|
|
|
+
|
|
|
|
+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
|
|
|
|
+
|
|
|
|
+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=0.8, 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))
|