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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()
with torch.no_grad():
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, size=(112,112))
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) |