How to use the mahotas.thresholding.otsu function in mahotas

# by Willi Richert and Luis Pedro Coelho
# published by PACKT Publishing
#
# It is made available under the MIT License

import numpy as np
import mahotas as mh

# Load our example image:
image = mh.imread('../SimpleImageDataset/building05.jpg')

# Convert to greyscale
image = mh.colors.rgb2gray(image, dtype=np.uint8)

# Compute a threshold value:
thresh = mh.thresholding.otsu(image)
print('Otsu threshold is {0}'.format(thresh))

# Compute the thresholded image
otsubin = (image > thresh)
print('Saving thresholded image (with Otsu threshold) to otsu-threshold.jpeg')
mh.imsave('otsu-threshold.jpeg', otsubin.astype(np.uint8) * 255)

# Execute morphological opening to smooth out the edges
otsubin = mh.open(otsubin, np.ones((15, 15)))
mh.imsave('otsu-closed.jpeg', otsubin.astype(np.uint8) * 255)

# An alternative thresholding method:
thresh = mh.thresholding.rc(image)
print('Ridley-Calvard threshold is {0}'.format(thresh))
print('Saving thresholded image (with Ridley-Calvard threshold) to rc-threshold.jpeg')
mh.imsave('rc-threshold.jpeg', (image > thresh).astype(np.uint8) * 255)

def hog_feature(self, image_orig):
        # print ('CrtFeature!! hog_feature')
        #hog= a_HOG_computation.HOG(orientations=18, pixelsPerCell=(9,9), cellsPerBlock=(3,3), visualise=True, normalize=True) 
        #hog= a_HOG_computation.HOG(orientations=9, pixelsPerCell=(6,6), cellsPerBlock=(1,1), visualise=True, normalize=True)     
        hog = HOG(orientations=9, pixelsPerCell=(9,9), cellsPerBlock=(3,3), visualise=True, normalize=True)
        # Convert the coloured image into grayscale image
        image_resized = Tools.resize(image_orig, width=300)
        image_gray = cv2.cvtColor(image_resized, cv2.COLOR_BGR2GRAY)
        # Do thresholding
        image_thresh = image_gray
        T = mahotas.thresholding.otsu(image_gray) # will find an optimal value of T from the image

        image_thresh[image_thresh>T] = 255 # This goes pixel by pixel if the pixel value of the thresh is greater than the optimal value then the color is white
        image_thresh[image_thresh