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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_single import Face_detect_crop
- from util.reverse2original import reverse2wholeimage
- import os
- from util.add_watermark import watermark_image
- 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)
- 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()
- 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():
- pic_a = opt.pic_a_path
- 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)
- ############## 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 = []
- 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()
- swap_result = model(None, b_align_crop_tenor, latend_id, None, True)[0]
- swap_result_list.append(swap_result)
- b_align_crop_tenor_list.append(b_align_crop_tenor)
- 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(b_align_crop_tenor_list, swap_result_list, b_mat_list, crop_size, img_b_whole, logoclass, \
- os.path.join(opt.output_path, 'result_whole_swapsingle.jpg'), opt.no_simswaplogo,pasring_model =net,use_mask=opt.use_mask, norm = spNorm)
- print(' ')
- print('************ Done ! ************')
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