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"""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) |