| ''' |
| 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]) |
|
|
| |
| img_id = img_id.cuda() |
|
|
| |
| 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) |
|
|
|
|
| |
|
|
| 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) |
| |
| 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 ! ************') |
|
|
