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| import cv2 | |
| import torch | |
| import fractions | |
| 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_multispecific import video_swap | |
| import os | |
| import glob | |
| 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() | |
| pic_specific = opt.pic_specific_path | |
| start_epoch, epoch_iter = 1, 0 | |
| crop_size = opt.crop_size | |
| multisepcific_dir = opt.multisepcific_dir | |
| 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) | |
| # 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)) | |
| with torch.no_grad(): | |
| 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, size=(112,112)) | |
| 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, size=(112,112)) | |
| 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 !!!" | |
| video_swap(opt.video_path, target_id_norm_list,source_specific_id_nonorm_list, opt.id_thres, \ | |
| 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) | |