Otsu's method to perform automatic data thresholding

otsu.py

import numpy as np

def otsu(x):
    hist, bin_edges = np.histogram(x, bins=range(min(x), max(x) + 2))
    total = len(x)

    max_, threshold = 0, 0
    sum_total, sum_1, weight_0, weight_1 = 0, 0, 0, 0

    for i in range(len(hist)):
        sum_total += i * hist[i]

    for i in range(len(hist)):
        weight_0 += hist[i]
        if weight_0 == 0:
            continue

        weight_1 = total - weight_0
        if weight_1 == 0:
            break

        sum_1 += i * hist[i]

        mean_0 = sum_1 / weight_0
        mean_1 = (sum_total - sum_1) / weight_1

        between_class_variance = weight_0 * weight_1 * (mean_0 - mean_1) ** 2

        if between_class_variance > max_:
            max_ = between_class_variance
            threshold = i

    return threshold