python - Histogram equalization of grayscale images with NumPy

8
votes

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)
3
pythonimage-processingnumpyhistogram
Does this help: web.archive.org/web/20151219221513/http://www.janeriksolem.net/… ? - Martin Thoma
Thanks moose. That helps ofcourse with PIL. Are there any pure numpy solution?? as it could be faster as i am doing it for 10,000 images. PIL and Skimage usually takes a long time. - pbu
@pbu PIL is needed only for reading of the image data in the example. Since you have the data already you don't need it, only NumPy. See the code I have written in the anser. Also many thanks to moose for linking the solution! - Trilarion

3 Answers

17
votes

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]
3
votes

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)
0
votes

As of today janeriksolem's url is broken.

I found however this gist that links the same page and claims to perform histogram equalization without computing the histogram.

The code is:

img_eq = np.sort(img.ravel()).searchsorted(img)