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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
- import glob
- 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
- multisepcific_dir = opt.multisepcific_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))
- with torch.no_grad():
- # The specific person to be swapped(source)
- source_specific_id_nonorm_list = []
- source_path = os.path.join(multisepcific_dir,'SRC_*')
- 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(multisepcific_dir,'DST_*')
- 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 = []
- swap_result_ori_pic_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])
- swap_result_ori_pic_list.append(b_align_crop_tenor_list[tmp_index])
- else:
- pass
- if len(swap_result_list) !=0:
- 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
-
- reverse2wholeimage(swap_result_ori_pic_list, swap_result_list, swap_result_matrix_list, crop_size, img_b_whole, logoclass,\
- os.path.join(opt.output_path, 'result_whole_swap_multispecific.jpg'), opt.no_simswaplogo,pasring_model =net,use_mask=opt.use_mask, norm = spNorm)
- print(' ')
- print('************ Done ! ************')
-
- else:
- print('The people you specified are not found on the picture: {}'.format(pic_b))
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