111 lines
3.5 KiB
Python
111 lines
3.5 KiB
Python
'''
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Author: Naiyuan liu
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Github: https://github.com/NNNNAI
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Date: 2021-11-23 17:03:58
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LastEditors: Naiyuan liu
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LastEditTime: 2021-11-24 19:19:26
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Description:
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'''
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import cv2
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import torch
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import fractions
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import numpy as np
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from PIL import Image
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import torch.nn.functional as F
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from torchvision import transforms
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from models.models import create_model
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from options.test_options import TestOptions
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from insightface_func.face_detect_crop_multi import Face_detect_crop
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from util.reverse2original import reverse2wholeimage
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import os
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from util.add_watermark import watermark_image
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from util.norm import SpecificNorm
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from parsing_model.model import BiSeNet
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def lcm(a, b): return abs(a * b) / fractions.gcd(a, b) if a and b else 0
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transformer_Arcface = transforms.Compose([
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transforms.ToTensor(),
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transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
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])
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def _totensor(array):
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tensor = torch.from_numpy(array)
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img = tensor.transpose(0, 1).transpose(0, 2).contiguous()
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return img.float().div(255)
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if __name__ == '__main__':
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opt = TestOptions().parse()
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start_epoch, epoch_iter = 1, 0
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crop_size = opt.crop_size
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torch.nn.Module.dump_patches = True
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if crop_size == 512:
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opt.which_epoch = 550000
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opt.name = '512'
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mode = 'ffhq'
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else:
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mode = 'None'
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logoclass = watermark_image('./simswaplogo/simswaplogo.png')
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model = create_model(opt)
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model.eval()
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spNorm =SpecificNorm()
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app = Face_detect_crop(name='antelope', root='./insightface_func/models')
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app.prepare(ctx_id= 0, det_thresh=0.6, det_size=(640,640),mode=mode)
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with torch.no_grad():
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pic_a = opt.pic_a_path
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img_a_whole = cv2.imread(pic_a)
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img_a_align_crop, _ = app.get(img_a_whole,crop_size)
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img_a_align_crop_pil = Image.fromarray(cv2.cvtColor(img_a_align_crop[0],cv2.COLOR_BGR2RGB))
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img_a = transformer_Arcface(img_a_align_crop_pil)
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img_id = img_a.view(-1, img_a.shape[0], img_a.shape[1], img_a.shape[2])
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# convert numpy to tensor
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img_id = img_id.cuda()
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#create latent id
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img_id_downsample = F.interpolate(img_id, size=(112,112))
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latend_id = model.netArc(img_id_downsample)
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latend_id = F.normalize(latend_id, p=2, dim=1)
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############## Forward Pass ######################
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pic_b = opt.pic_b_path
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img_b_whole = cv2.imread(pic_b)
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img_b_align_crop_list, b_mat_list = app.get(img_b_whole,crop_size)
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# detect_results = None
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swap_result_list = []
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b_align_crop_tenor_list = []
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for b_align_crop in img_b_align_crop_list:
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b_align_crop_tenor = _totensor(cv2.cvtColor(b_align_crop,cv2.COLOR_BGR2RGB))[None,...].cuda()
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swap_result = model(None, b_align_crop_tenor, latend_id, None, True)[0]
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swap_result_list.append(swap_result)
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b_align_crop_tenor_list.append(b_align_crop_tenor)
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if opt.use_mask:
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n_classes = 19
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net = BiSeNet(n_classes=n_classes)
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net.cuda()
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save_pth = os.path.join('./parsing_model/checkpoint', '79999_iter.pth')
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net.load_state_dict(torch.load(save_pth))
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net.eval()
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else:
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net =None
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reverse2wholeimage(b_align_crop_tenor_list,swap_result_list, b_mat_list, crop_size, img_b_whole, logoclass, \
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os.path.join(opt.output_path, 'result_whole_swapmulti.jpg'),opt.no_simswaplogo,pasring_model =net,use_mask=opt.use_mask, norm = spNorm)
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print(' ')
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print('************ Done ! ************')
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