Geek Answers Handbook

Grayscale Image Alignment Bar with NumPy

How to perform histogram alignment for several gray images stored in a NumPy array?

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

(1800, 1, 96,96)
+4
source share
3 answers

The Moose comment that points to this blog post does the job quite nicely.

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

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, normed=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]
+11

- , skimage. , , .

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)
+1

URL- janeriksolem .

, , , .

:

img_eq = np.sort(img.ravel()).searchsorted(img)
0

More articles:


All Articles