Histogram equalization of grayscale images with NumPy

Question

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)

Answer 1

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

For completeness, I give an example 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/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]

Answer 2

Very fast and easy way is to use the cumulative distribution function provided by the skimage module. Basically what you do mathematically to proof it.

from skimage import exposure
import numpy as np
def histogram_equalize(img):
    img = rgb2gray(img)
    img_cdf, bin_centers = exposure.cumulative_distribution(img)
    return np.interp(img, bin_centers, img_cdf)