Otsu thresholding

import numpy as np
import cv2
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
from skimage.exposure import histogram
 
 
def _validate_image_histogram(image, hist, nbins=None):
    if image is None and hist is None:
        raise Exception("Either image or hist must be provided.")
 
    if hist is not None:
        if isinstance(hist, (tuple, list)):
            counts, bin_centers = hist
        else:
            counts = hist
            bin_centers = np.arange(counts.size)
    else:
        counts, bin_centers = histogram(
            image.ravel(), nbins, source_range='image'
        )
    return counts.astype(float), bin_centers
 
 
def threshold_otsu(image=None, nbins=256, *, hist=None):
 
    if image is not None:
        first_pixel = image.ravel()[0]
        if np.all(image == first_pixel):
            return first_pixel
 
    counts, bin_centers = _validate_image_histogram(image, hist, nbins)
 
    # class probabilities for all possible thresholds
    weight1 = np.cumsum(counts)
    weight2 = np.cumsum(counts[::-1])[::-1]
 
    # class means for all possible thresholds
    mean1 = np.cumsum(counts * bin_centers) / weight1
    mean2 = (np.cumsum((counts * bin_centers)[::-1]) / weight2[::-1])[::-1]
 
    variance12 = weight1[:-1] * weight2[1:] * (mean1[:-1] - mean2[1:]) ** 2
 
    idx = np.argmax(variance12)
    threshold = bin_centers[idx]
 
    return threshold
 
 
if __name__ == '__main__':
 
    imgfile = 'cat-threshimages.jpg'
    try:
        img = cv2.imread(imgfile)
        img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
    except cv2.error:
        print("Add image to args or in the code.")
        exit()
 
    binary_image = img > threshold_otsu(img)
    plt.imshow(binary_image, cmap='gray')
    plt.axis('off')
    plt.show()
 
    mpimg.imsave('CatOtsuThresholding.jpg', binary_image, cmap='gray')