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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 19:00:38
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_single 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=0.2, 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)
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