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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 19:00:34 | |
| 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.videoswap import video_swap | |
| import os | |
| def lcm(a, b): return abs(a * b) / fractions.gcd(a, b) if a and b else 0 | |
| transformer = transforms.Compose([ | |
| transforms.ToTensor(), | |
| #transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) | |
| ]) | |
| transformer_Arcface = transforms.Compose([ | |
| transforms.ToTensor(), | |
| transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) | |
| ]) | |
| # detransformer = transforms.Compose([ | |
| # transforms.Normalize([0, 0, 0], [1/0.229, 1/0.224, 1/0.225]), | |
| # transforms.Normalize([-0.485, -0.456, -0.406], [1, 1, 1]) | |
| # ]) | |
| 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' | |
| model = create_model(opt) | |
| model.eval() | |
| 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 = Image.open(pic_a).convert('RGB') | |
| 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]) | |
| # pic_b = opt.pic_b_path | |
| # img_b_whole = cv2.imread(pic_b) | |
| # img_b_align_crop, b_mat = app.get(img_b_whole,crop_size) | |
| # img_b_align_crop_pil = Image.fromarray(cv2.cvtColor(img_b_align_crop,cv2.COLOR_BGR2RGB)) | |
| # img_b = transformer(img_b_align_crop_pil) | |
| # img_att = img_b.view(-1, img_b.shape[0], img_b.shape[1], img_b.shape[2]) | |
| # convert numpy to tensor | |
| img_id = img_id.cuda() | |
| # img_att = img_att.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) | |
| video_swap(opt.video_path, latend_id, model, app, opt.output_path,temp_results_dir=opt.temp_path,\ | |
| no_simswaplogo=opt.no_simswaplogo,use_mask=opt.use_mask,crop_size=crop_size) | |