from __future__ import unicode_literals
from __future__ import print_function
from __future__ import division
from __future__ import absolute_import
import numpy as np
[docs]def whitening(image):
"""Whitening. Normalises image to zero mean and unit variance."""
image = image.astype(np.float32)
mean = np.mean(image)
std = np.std(image)
if std > 0:
ret = (image - mean) / std
else:
ret = image * 0.
return ret
[docs]def normalise_zero_one(image):
"""Image normalisation. Normalises image to fit [0, 1] range."""
image = image.astype(np.float32)
minimum = np.min(image)
maximum = np.max(image)
if maximum > minimum:
ret = (image - minimum) / (maximum - minimum)
else:
ret = image * 0.
return ret
[docs]def normalise_one_one(image):
"""Image normalisation. Normalises image to fit [-1, 1] range."""
ret = normalise_zero_one(image)
ret *= 2.
ret -= 1.
return ret
[docs]def resize_image_with_crop_or_pad(image, img_size=(64, 64, 64), **kwargs):
"""Image resizing. Resizes image by cropping or padding dimension
to fit specified size.
Args:
image (np.ndarray): image to be resized
img_size (list or tuple): new image size
kwargs (): additional arguments to be passed to np.pad
Returns:
np.ndarray: resized image
"""
assert isinstance(image, (np.ndarray, np.generic))
assert (image.ndim - 1 == len(img_size) or image.ndim == len(img_size)), \
'Example size doesnt fit image size'
# Get the image dimensionality
rank = len(img_size)
# Create placeholders for the new shape
from_indices = [[0, image.shape[dim]] for dim in range(rank)]
to_padding = [[0, 0] for dim in range(rank)]
slicer = [slice(None)] * rank
# For each dimensions find whether it is supposed to be cropped or padded
for i in range(rank):
if image.shape[i] < img_size[i]:
to_padding[i][0] = (img_size[i] - image.shape[i]) // 2
to_padding[i][1] = img_size[i] - image.shape[i] - to_padding[i][0]
else:
from_indices[i][0] = int(np.floor((image.shape[i] - img_size[i]) / 2.))
from_indices[i][1] = from_indices[i][0] + img_size[i]
# Create slicer object to crop or leave each dimension
slicer[i] = slice(from_indices[i][0], from_indices[i][1])
# Pad the cropped image to extend the missing dimension
return np.pad(image[slicer], to_padding, **kwargs)