BuildingMachineLearningSystemsWithPython/ch10/threshold.py at master · luispedro/BuildingMachineLearningSystemsWithPython · GitHub

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# This code is supporting material for the book

# Building Machine Learning Systems with Python

# 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)

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