"""This module contains simple helper functions """ from __future__ import print_function import torch import numpy as np import os import imageio def tensor2im(input_image, imtype=np.uint8): """"Converts a Tensor array into a numpy image array. Parameters: input_image (tensor) -- the input image tensor array imtype (type) -- the desired type of the converted numpy array """ if not isinstance(input_image, np.ndarray): if isinstance(input_image, torch.Tensor): # get the data from a variable image_tensor = input_image.data else: return input_image image_numpy = image_tensor[0].cpu().float().numpy() # convert it into a numpy array if image_numpy.shape[0] == 1: # grayscale to RGB image_numpy = np.tile(image_numpy, (3, 1, 1)) image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + 1) / 2.0 * 255.0 # post-processing: tranpose and scaling else: # if it is a numpy array, do nothing image_numpy = input_image return image_numpy.astype(imtype) def tensor2array(value_tensor): """Converts a Tensor array into a numpy :param value_tensor: :return: """ if value_tensor.dim() == 3: numpy = value_tensor.view(-1).cpu().float().numpy() else: numpy = value_tensor[0].view(-1).cpu().float().numpy() return numpy def save_image(image_numpy, image_path): """Save a numpy image to the disk Parameters: image_numpy (numpy array) -- input numpy array image_path (str) -- the path of the image """ if image_numpy.shape[2] == 1: image_numpy = image_numpy.reshape(image_numpy.shape[0], image_numpy.shape[1]) imageio.imwrite(image_path, image_numpy) def mkdirs(paths): """create empty directories if they don't exist Parameters: paths (str list) -- a list of directory paths """ if isinstance(paths, list) and not isinstance(paths, str): for path in paths: mkdir(path) else: mkdir(paths) def mkdir(path): """create a single empty directory if it didn't exist Parameters: path (str) -- a single directory path """ if not os.path.exists(path): os.makedirs(path)