def __getitem__(self, index, tp=True):
# return 256*256 image
cls = 'A' if index < len(self.cage) else 'B'
img = cv2.resize(cv2.imread(self.real_dir[index]), (256, 256)) / 255.0
img, target_img = random_warp(
random_transform(img, **self.random_transform_args))
img = self.model(
Variable(torch.unsqueeze(self.toTensor(img).float(), 0)), cls)
img = torch.squeeze(img, 0)
target_img = self.toTensor(target_img)
target_img = target_img.float()
return {'LQ': img, 'GT': target_img}
def get_training_data( images, batch_size ):
indices = numpy.random.randint( len(images), size=batch_size )
for i,index in enumerate(indices):
image = images[index]
image = random_transform( image, **random_transform_args )
warped_img, target_img = random_warp( image )
if i == 0:
warped_images = numpy.empty( (batch_size,) + warped_img.shape, warped_img.dtype )
target_images = numpy.empty( (batch_size,) + target_img.shape, warped_img.dtype )
warped_images[i] = warped_img
target_images[i] = target_img
return warped_images, target_images
def get_training_data(images, batch_size):
indices = numpy.random.randint(len(images), size=batch_size)
for i, index in enumerate(indices):
image = images[index]
image = random_transform(image, **random_transform_args)
warped_img, target_img = random_warp(image)
if i == 0:
warped_images = numpy.empty((batch_size,) + warped_img.shape, warped_img.dtype)
target_images = numpy.empty((batch_size,) + target_img.shape, warped_img.dtype)
warped_images[i] = warped_img
target_images[i] = target_img
return warped_images, target_images
def convert_face(croped_face):
resized_face = cv2.resize(croped_face, (256, 256))
normalized_face = resized_face / 255.0
#normalized_face = normalized_face.reshape(1, normalized_face.shape[0], normalized_face.shape[1], normalized_face.shape[2])
warped_img, _ = random_warp(normalized_face)
batch_warped_img = np.expand_dims(warped_img, axis=0)
batch_warped_img = toTensor(batch_warped_img)
batch_warped_img = batch_warped_img.to(device).float()
#print(batch_warped_img.shape, batch_warped_img)
model = Autoencoder().to(device)
checkpoint = torch.load('./checkpoint/autoencoder.t7')
model.load_state_dict(checkpoint['state'])
converted_face = model(batch_warped_img, 'B')
return converted_face
def get_training_data(images, batch_size):
indices = numpy.random.randint(len(images),
size=batch_size) #返回一个随机整型数，范围从低（包括）到高（不包括
for i, index in enumerate(indices):
image = images[index]
image = random_transform(image, **random_transform_args) #进行图片转换，图片增强
warped_img, target_img = random_warp(image) #每一张图片都转换
if i == 0: #两个新的空数组
warped_images = numpy.empty((batch_size, ) + warped_img.shape,
warped_img.dtype)
target_images = numpy.empty((batch_size, ) + target_img.shape,
warped_img.dtype)
warped_images[i] = warped_img
target_images[i] = target_img
return warped_images, target_images
