Source code for dltk.io.preprocessing

from __future__ import unicode_literals
from __future__ import print_function
from __future__ import division
from __future__ import absolute_import

import numpy as np

epsilon = 1e-8


[docs]def whitening(image): """Whitening. Normalises image to zero mean and unit variance.""" ret = (image - np.mean(image)) / (np.std(image) + epsilon) return ret
[docs]def normalise_zero_one(image): """Image normalisation. Normalises image to fit [0, 1] range.""" image = image.astype(np.float32) ret = (image - np.min(image)) ret /= (np.max(image) + epsilon) 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)