Histogram equalization of grayscale images with NumPy

pbu Published at Dev

pbu

How to do histogram equalization for multiple grayscaled images stored in a NumPy array easily?

I have the 96x96 pixel NumPy data in this 4D format:

(1800, 1, 96,96)

Trilarion

Moose's comment which points to this blog entry does the job quite nicely.

For completeness I give an axample here using nicer variable names and a looped execution on 1000 96x96 images which are in a 4D array as in the question. It is fast (1-2 seconds on my computer) and only needs NumPy.

import numpy as np

def image_histogram_equalization(image, number_bins=256):
    # from http://www.janeriksolem.net/2009/06/histogram-equalization-with-python-and.html

    # get image histogram
    image_histogram, bins = np.histogram(image.flatten(), number_bins, density=True)
    cdf = image_histogram.cumsum() # cumulative distribution function
    cdf = 255 * cdf / cdf[-1] # normalize

    # use linear interpolation of cdf to find new pixel values
    image_equalized = np.interp(image.flatten(), bins[:-1], cdf)

    return image_equalized.reshape(image.shape), cdf

if __name__ == '__main__':

    # generate some test data with shape 1000, 1, 96, 96
    data = np.random.rand(1000, 1, 96, 96)

    # loop over them
    data_equalized = np.zeros(data.shape)
    for i in range(data.shape[0]):
        image = data[i, 0, :, :]
        data_equalized[i, 0, :, :] = image_histogram_equalization(image)[0]

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