utils.py

# Authors:
# Christian F. Baumgartner (c.f.baumgartner@gmail.com)
# Lisa M. Koch (lisa.margret.koch@gmail.com)

import nibabel as nib
import numpy as np
import os

def ncc(a,v, zero_norm=True):

    a = a.flatten()
    v = v.flatten()

    if zero_norm:

        a = (a - np.mean(a)) / (np.std(a) * len(a))
        v = (v - np.mean(v)) / np.std(v)

    else:

        a = (a) / (np.std(a) * len(a))
        v = (v) / np.std(v)

    return np.correlate(a,v)


def norm_l2(a,v):

    a = a.flatten()
    v = v.flatten()

    a = (a - np.mean(a)) / (np.std(a) * len(a))
    v = (v - np.mean(v)) / np.std(v)

    return np.mean(np.sqrt(a**2 + v**2))



def all_argmax(arr, axis=None):

    return np.argwhere(arr == np.amax(arr, axis=axis))


def makefolder(folder):
    '''
    Helper function to make a new folder if doesn't exist
    :param folder: path to new folder
    :return: True if folder created, False if folder already exists
    '''
    if not os.path.exists(folder):
        os.makedirs(folder)
        return True
    return False

def load_nii(img_path):

    '''
    Shortcut to load a nifti file
    '''

    nimg = nib.load(img_path)
    return nimg.get_data(), nimg.affine, nimg.header

def save_nii(img_path, data, affine, header):
    '''
    Shortcut to save a nifty file
    '''

    nimg = nib.Nifti1Image(data, affine=affine, header=header)
    nimg.to_filename(img_path)


def create_and_save_nii(data, img_path):

    img = nib.Nifti1Image(data, np.eye(4))
    nib.save(img, img_path)



class Bunch:
    # Useful shortcut for making struct like contructs
    # Example:
    # mystruct = Bunch(a=1, b=2)
    # print(mystruct.a)
    # >>> 1
    def __init__(self, **kwds):
        self.__dict__.update(kwds)



def convert_to_uint8(image):
    image = image - image.min()
    image = 255.0*np.divide(image.astype(np.float32),image.max())
    return image.astype(np.uint8)

def normalise_image(image):
    '''
    make image zero mean and unit standard deviation
    '''

    img_o = np.float32(image.copy())
    m = np.mean(img_o)
    s = np.std(img_o)
    return np.divide((img_o - m), s)


def map_image_to_intensity_range(image, min_o, max_o, percentiles=0):

    # If percentile = 0 uses min and max. Percentile >0 makes normalisation more robust to outliers.

    if image.dtype in [np.uint8, np.uint16, np.uint32]:
        assert min_o >= 0, 'Input image type is uintXX but you selected a negative min_o: %f' % min_o

    if image.dtype == np.uint8:
        assert max_o <= 255, 'Input image type is uint8 but you selected a max_o > 255: %f' % max_o

    min_i = np.percentile(image, 0 + percentiles)
    max_i = np.percentile(image, 100 - percentiles)

    image = (np.divide((image - min_i), max_i - min_i) * (max_o - min_o) + min_o).copy()

    image[image > max_o] = max_o
    image[image < min_o] = min_o

    return image


def normalise_images(X):
    '''
    Helper for making the images zero mean and unit standard deviation i.e. `white`
    '''

    X_white = np.zeros(X.shape, dtype=np.float32)

    for ii in range(X.shape[0]):

        Xc = X[ii,:,:,:]
        mc = Xc.mean()
        sc = Xc.std()

        Xc_white = np.divide((Xc - mc), sc)

        X_white[ii,:,:,:] = Xc_white

    return X_white.astype(np.float32)