图像分割-传统算法-阈值分割原理与实践

固定阈值法------直方图双峰法

手动设定一个全局阈值 T。简单、速度快。

缺点：对光照不均、对比度变化敏感。

python 复制代码

img_ori = cv2.imread('./test/test_img.jpg', 0)
width, height = img_ori.shape
cv2.imshow('img', img_ori)
''' 直方图双峰法 '''
img = copy.deepcopy(img_ori)
x, img1 = cv2.threshold(img_ori, 150, 255, cv2.THRESH_BINARY)
x, img2 = cv2.threshold(img_ori, 150, 255, cv2.THRESH_TRUNC)
x, img3 = cv2.threshold(img_ori, 150, 255, cv2.THRESH_TOZERO)
cv2.imshow('BINARY', img1)
cv2.imshow('TRUNC', img2)
cv2.imshow('TOZERO', img3)
cv2.waitKey(0)
cv2.destroyAllWindows()

迭代阈值图像分割

通过对图像的灰度值进行迭代，不断调整阈值使得分割更加准确。

对于大尺寸图像，迭代阈值图像分割的计算速度可能成为其一个缺点。

python 复制代码

img = copy.deepcopy(img_ori)
threshold = 150
delta = 1
while delta > 0:
    foreground = []
    background = []
    for y in range(height):
        for x in range(width):
            if img[x, y] > threshold:
                background.append(img[x, y])
            else:
                foreground.append(img[x, y])
    avr_foreground = np.mean(foreground)
    avr_background = np.mean(background)
    new_threshold = (avr_foreground + avr_background) // 2
    delta = abs(new_threshold - threshold)
    print('delta', delta)
    threshold = new_threshold
for y in range(height):
    for x in range(width):
        if img[x, y] > threshold:
            img[x, y] = 255
        else:
            img[x, y] = 0

cv2.imshow('iter result', img)
cv2.waitKey(0)
cv2.destroyAllWindows()

自适应阈值图像分割

根据像素邻域的灰度分布（如均值、高斯加权和）动态计算阈值。能处理光照不均或渐变图像。

缺点：速度较慢，可能有"块效应"。

python 复制代码

img = copy.deepcopy(img_ori)
block_size = 3
img_thresh = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11, 2)
cv2.imshow('adapt result', img_thresh)

大津法（OTSU）

自动寻找使类间方差最大的阈值。全自动，对双峰直方图效果极佳。

缺点：对非双峰直方图或噪声敏感。

python 复制代码

img = copy.deepcopy(img_ori)
ret, img_thresh = cv2.threshold(img, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
cv2.imshow('OTSU result', img_thresh)
cv2.waitKey(0)
cv2.destroyAllWindows()

全部代码

python 复制代码

import cv2
import numpy as np
import copy

img_ori = cv2.imread('./test/test_img.jpg', 0)
width, height = img_ori.shape

''' 直方图双峰法 '''
img = copy.deepcopy(img_ori)
x, img1 = cv2.threshold(img, 150, 255, cv2.THRESH_BINARY)
x, img2 = cv2.threshold(img, 150, 255, cv2.THRESH_TRUNC)
x, img3 = cv2.threshold(img, 150, 255, cv2.THRESH_TOZERO)
cv2.imshow('img', img_ori)
cv2.imshow('BINARY', img1)
cv2.imshow('TRUNC', img2)
cv2.imshow('TOZERO', img3)
cv2.waitKey(0)
cv2.destroyAllWindows()

''' 迭代阈值图像分割 '''
img = copy.deepcopy(img_ori)
threshold = 150
delta = 1
while delta > 0:
    foreground = []
    background = []
    for y in range(height):
        for x in range(width):
            if img[x, y] > threshold:
                background.append(img[x, y])
            else:
                foreground.append(img[x, y])
    avr_foreground = np.mean(foreground)
    avr_background = np.mean(background)

    new_threshold = (avr_foreground + avr_background) // 2
    delta = abs(new_threshold - threshold)
    print('delta', delta)
    threshold = new_threshold
for y in range(height):
    for x in range(width):
        if img[x, y] > threshold:
            img[x, y] = 255
        else:
            img[x, y] = 0

cv2.imshow('img', img_ori)
cv2.imshow('iter result', img)
cv2.waitKey(0)
cv2.destroyAllWindows()

''' 自适应阈值图像分割 '''
img = copy.deepcopy(img_ori)
block_size = 3
img_thresh = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11, 2)
cv2.imshow('img', img_ori)
cv2.imshow('adapt result', img_thresh)
cv2.waitKey(0)
cv2.destroyAllWindows()

'''  大津法（OTSU） '''
# 大津法阈值分割
img = copy.deepcopy(img_ori)
ret, img_thresh = cv2.threshold(img, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
cv2.imshow('img', img_ori)
cv2.imshow('OTSU result', img_thresh)
cv2.waitKey(0)
cv2.destroyAllWindows()

参考
图像分割参考1
图像分割参考2