python-opencv函数总结之（一）threshold、adaptiveThreshold、Otsu 二值化

图像阈值处理技术

最新推荐文章于 2025-08-07 23:33:35 发布

原创最新推荐文章于 2025-08-07 23:33:35 发布 · 2.8k 阅读

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本文通过使用Python的OpenCV库实现不同类型的阈值处理技术，包括二值化、反二值化、截断等，并展示了如何通过Matplotlib可视化这些处理后的效果。

import cv2
import numpy as np
from matplotlib import pyplot as plt
img = cv2.imread('gradient.png',0)
ret,thresh1 = cv2.threshold(img,127,255,cv2.THRESH_BINARY)
ret,thresh2 = cv2.threshold(img,127,255,cv2.THRESH_BINARY_INV)
ret,thresh3 = cv2.threshold(img,127,255,cv2.THRESH_TRUNC)
ret,thresh4 = cv2.threshold(img,127,255,cv2.THRESH_TOZERO)
ret,thresh5 = cv2.threshold(img,127,255,cv2.THRESH_TOZERO_INV)
titles = ['Original Image','BINARY','BINARY_INV','TRUNC','TOZERO','TOZERO_INV']
images = [img, thresh1, thresh2, thresh3, thresh4, thresh5]
for i in xrange(6):
    plt.subplot(2,3,i+1),plt.imshow(images[i],'gray')
    plt.title(titles[i])
    plt.xticks([]),plt.yticks([])
plt.show()