def tensor2im(image_tensor, imtype=np.uint8, normalize=True):
image_numpy = image_tensor.cpu().float().detach().numpy()
if normalize:
image_numpy = (image_numpy+1)*255.0*0.5
else:
image_numpy = (image_numpy+1)*255.0
image_numpy = np.clip(image_numpy, 0, 255)
blank_image = np.zeros((image_tensor.shape[1],image_tensor.shape[2],image_tensor.shape[0]), np.uint8)
if image_tensor.shape[0] == 3:
blank_image[:,:,0]=image_numpy[2,:,:]
blank_image[:,:,1]=image_numpy[1,:,:]
blank_image[:,:,2]=image_numpy[0,:,:]
else:
blank_image[:,:,:]=image_numpy[:,:,:]
return blank_image
def im2tensor(image_numpy, normalize=True):
if normalize:
image_numpy = (image_numpy/255.0)*2.0-1.0
else:
image_numpy = image_numpy/255.0
image_numpy = np.clip(image_numpy, -1, 1)
blank_image = np.zeros((image_numpy.shape[2],image_numpy.shape[0],image_numpy.shape[1]))
if image_numpy.shape[2] == 3:
blank_image[2,:,:]=image_numpy[:,:,0]
blank_image[1,:,:]=image_numpy[:,:,1]
blank_image[0,:,:]=image_numpy[:,:,2]
else:
blank_image[:,:,:]=image_numpy[:,:,:]
image_tensor = torch.Tensor(blank_image)
return image_tensor
w_size = 1024
h_size = 256
input_label = torch.zeros([input_labe.shape[0],input_labe.shape[1],h_size,w_size], dtype=torch.float32,device=input_labe.device)
for i in range(input_labe.shape[0]):
f_label = input_labe [i,:,:,:]
f_label_img = tensor2im(f_label)
f_label_img = cv2.resize(f_label_img,(w_size,h_size))
input_label[i,:,:,:] = im2tensor(f_label_img, normalize=True)
