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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 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 = 224
- 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))
- 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, scale_factor=0.5)
- 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)
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