图像处理（1）Python实现图像最大熵分割

Python实现图像最大熵分割

工具：

• Python
• opencv-python

import numpy as np
import cv2
def segment(img):
    """
    最大熵分割
    :param img:
    :return:
    """
    def calculate_current_entropy(hist, threshold):
        data_hist = hist.copy()
        background_sum = 0.
        target_sum = 0.
        for i in range(256):
            if i < threshold:  # 累积背景
                background_sum += data_hist[i]
            else:  # 累积目标
                target_sum += data_hist[i]
        background_ent = 0.
        target_ent = 0.
        for i in range(256):
            if i < threshold:  # 计算背景熵
                if data_hist[i] == 0:
                    continue
                ratio1 = data_hist[i] / background_sum
                background_ent -= ratio1 * np.log2(ratio1)
            else:
                if data_hist[i] == 0:
                    continue
                ratio2 = data_hist[i] / target_sum
                target_ent -= ratio2 * np.log2(ratio2)
        return target_ent + background_ent

    def max_entropy_segmentation(img):
        channels = [0]
        hist_size = [256]
        prange = [0, 256]
        hist = cv2.calcHist(img, channels, None, hist_size, prange)
        hist = np.reshape(hist, [-1])
        max_ent = 0.
        max_index = 0
        for i in range(256):
            cur_ent = calculate_current_entropy(hist, i)
            if cur_ent > max_ent:
                max_ent = cur_ent
                max_index = i
        ret, th = cv2.threshold(img, max_index, 255, cv2.THRESH_BINARY)
        return th
    img = max_entropy_segmentation(img)
    return img

[1] OpenCV学习笔记（二）之最大熵阈值分割, https://blog.csdn.net/spw_1201/article/details/53510711.