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_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 = 224 multisepcific_dir = opt.multisepcific_dir torch.nn.Module.dump_patches = True 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)) # 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, scale_factor=0.5) 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, scale_factor=0.5) 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)