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| ''' | |
| Author: Naiyuan liu | |
| Github: https://github.com/NNNNAI | |
| Date: 2021-11-23 17:03:58 | |
| LastEditors: Naiyuan liu | |
| LastEditTime: 2021-11-24 19:19:26 | |
| Description: | |
| ''' | |
| 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 | |
| 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 = opt.crop_size | |
| torch.nn.Module.dump_patches = True | |
| if crop_size == 512: | |
| opt.which_epoch = 550000 | |
| opt.name = '512' | |
| mode = 'ffhq' | |
| else: | |
| mode = 'None' | |
| 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),mode=mode) | |
| 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, size=(112,112)) | |
| 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_swapmulti.jpg'),opt.no_simswaplogo,pasring_model =net,use_mask=opt.use_mask, norm = spNorm) | |
| print(' ') | |
| print('************ Done ! ************') | |