111 lines
3.3 KiB
Python
111 lines
3.3 KiB
Python
"""This module contains simple helper functions """
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from __future__ import print_function
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import torch
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import numpy as np
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from PIL import Image
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import os
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import ntpath
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def save_images(images, img_dir, name):
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"""save images in img_dir, with name
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iamges: torch.float, B*C*H*W
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img_dir: str
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name: list [str]
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"""
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for i, image in enumerate(images):
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print(image.shape)
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image_numpy = tensor2im(image.unsqueeze(0),normalize=False)*255
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basename = os.path.basename(name[i])
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print('name:', basename)
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save_path = os.path.join(img_dir,basename)
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save_image(image_numpy,save_path)
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def save_visuals(visuals,img_dir,name):
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"""
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"""
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name = ntpath.basename(name)
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name = name.split(".")[0]
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print(name)
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# save images to the disk
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for label, image in visuals.items():
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image_numpy = tensor2im(image)
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img_path = os.path.join(img_dir, '%s_%s.png' % (name, label))
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save_image(image_numpy, img_path)
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def tensor2im(input_image, imtype=np.uint8, normalize=True):
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""""Converts a Tensor array into a numpy image array.
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Parameters:
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input_image (tensor) -- the input image tensor array
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imtype (type) -- the desired type of the converted numpy array
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"""
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if not isinstance(input_image, np.ndarray):
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if isinstance(input_image, torch.Tensor): # get the data from a variable
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image_tensor = input_image.data
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else:
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return input_image
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image_numpy = image_tensor[0].cpu().float().numpy() # convert it into a numpy array
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if image_numpy.shape[0] == 1: # grayscale to RGB
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image_numpy = np.tile(image_numpy, (3, 1, 1))
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image_numpy = np.transpose(image_numpy, (1, 2, 0))
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if normalize:
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image_numpy = (image_numpy + 1) / 2.0 * 255.0 # post-processing: tranpose and scaling
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else: # if it is a numpy array, do nothing
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image_numpy = input_image
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return image_numpy.astype(imtype)
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def save_image(image_numpy, image_path):
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"""Save a numpy image to the disk
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Parameters:
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image_numpy (numpy array) -- input numpy array
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image_path (str) -- the path of the image
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"""
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image_pil = Image.fromarray(image_numpy)
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image_pil.save(image_path)
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def print_numpy(x, val=True, shp=False):
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"""Print the mean, min, max, median, std, and size of a numpy array
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Parameters:
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val (bool) -- if print the values of the numpy array
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shp (bool) -- if print the shape of the numpy array
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"""
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x = x.astype(np.float64)
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if shp:
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print('shape,', x.shape)
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if val:
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x = x.flatten()
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print('mean = %3.3f, min = %3.3f, max = %3.3f, median = %3.3f, std=%3.3f' % (
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np.mean(x), np.min(x), np.max(x), np.median(x), np.std(x)))
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def mkdirs(paths):
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"""create empty directories if they don't exist
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Parameters:
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paths (str list) -- a list of directory paths
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"""
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if isinstance(paths, list) and not isinstance(paths, str):
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for path in paths:
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mkdir(path)
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else:
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mkdir(paths)
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def mkdir(path):
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"""create a single empty directory if it didn't exist
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Parameters:
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path (str) -- a single directory path
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"""
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if not os.path.exists(path):
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os.makedirs(path)
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