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- 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)
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