全局阈值分割——直方图法函数返回图片

以下是一个使用全局阈值分割（直方图法）的函数，它将返回阈值处理后的二值图像：

import cv2
import numpy as np

def global_thresholding(image):
    # 将图像转换为灰度图
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    
    # 计算图像直方图
    hist = cv2.calcHist([gray],[0],None,[256],[0,256])
    
    # 计算总像素数
    total_pixels = gray.shape[0] * gray.shape[1]
    
    # 计算每个像素值出现的概率
    probabilities = hist / total_pixels
    
    # 计算累积分布函数
    cdf = np.cumsum(probabilities)
    
    # 找到最小阈值
    min_threshold = 0
    min_value = float('inf')
    for i in range(1, 256):
        w0 = cdf[i]
        w1 = 1 - w0
        if w0 == 0 or w1 == 0:
            continue
        mu0 = np.sum(probabilities[:i] * np.arange(i)) / w0
        mu1 = np.sum(probabilities[i:] * np.arange(i, 256)) / w1
        value = w0 * w1 * (mu0 - mu1) ** 2
        if value < min_value:
            min_threshold = i
            min_value = value
    
    # 应用阈值
    ret, thresh = cv2.threshold(gray, min_threshold, 255, cv2.THRESH_BINARY)
    
    return thresh

使用方法：

# 读取图像文件
image = cv2.imread('image.jpg')

# 应用全局阈值分割
thresh = global_thresholding(image)

# 显示结果图像
cv2.imshow('Thresholded Image', thresh)
cv2.waitKey(0)
cv2.destroyAllWindows()