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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
- 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
- torch.nn.Module.dump_patches = True
- model = create_model(opt)
- model.eval()
- pic_a = opt.pic_a_path
- img_a = Image.open(pic_a).convert('RGB')
- img_a = transformer_Arcface(img_a)
- 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 = Image.open(pic_b).convert('RGB')
- img_b = transformer(img_b)
- 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 = latend_id.detach().to('cpu')
- latend_id = latend_id/np.linalg.norm(latend_id,axis=1,keepdims=True)
- latend_id = latend_id.to('cuda')
- ############## Forward Pass ######################
- img_fake = model(img_id, img_att, latend_id, latend_id, True)
- for i in range(img_id.shape[0]):
- if i == 0:
- row1 = img_id[i]
- row2 = img_att[i]
- row3 = img_fake[i]
- else:
- row1 = torch.cat([row1, img_id[i]], dim=2)
- row2 = torch.cat([row2, img_att[i]], dim=2)
- row3 = torch.cat([row3, img_fake[i]], dim=2)
- #full = torch.cat([row1, row2, row3], dim=1).detach()
- full = row3.detach()
- full = full.permute(1, 2, 0)
- output = full.to('cpu')
- output = np.array(output)
- output = output[..., ::-1]
- output = output*255
- cv2.imwrite(opt.output_path + 'result.jpg',output)
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