Python+OpenCV（八）——图像二值化
Python+OpenCV（八）——图像二值化
学习视频：python+opencv3.3视频教学 基础入门
其他学习记录：
Python+OpenCV（一）——基础操作
Python+OpenCV（二）——Numpy模块
Python+OpenCV（三）——色彩空间
Python+OpenCV（四）——像素运算
Python+OpenCV（五）——ROI和泛洪填充
Python+OpenCV（六）——均值/中值/自定义/高斯模糊、高斯噪声处理、高斯双边滤波
Python+OpenCV（七）——直方图及其应用
Python+OpenCV（九）——图像金字塔、图像梯度
Python+OpenCV（十）——Canny边缘提取
Python+OpenCV（十一）——直线检测、圆检测
Python+OpenCV（十二）——轮廓发现
Python+OpenCV（十三）——对象测量
Python+OpenCV（十四）——膨胀与腐蚀
Python+OpenCV（十五）——开闭操作
Python+OpenCV（十六）——顶帽、黑帽
Python+OpenCV（十七）——人脸识别
Python+OpenCV（十八）——数字/字母验证码识别
源码如下：
# -*- coding = utf-8 -*-
# @Time : 2021/8/2 21:20
# @Author : 西兰花
# @File : OpenCV08.py
# @Software : PyCharm
import cv2 as cv # 引入OpenCV模块
import numpy as np
def threshold_demo(image): # 全局阈值二值化
gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
ret, binary = cv.threshold(gray, 127, 255, cv.THRESH_BINARY | cv.THRESH_OTSU)
print("threshold value %s" % ret)
cv.imshow("binary_all", binary)
def local_threshold_demo(image): # 局部阈值二值化
gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
binary = cv.adaptiveThreshold(gray, 255, cv.ADAPTIVE_THRESH_MEAN_C , cv.THRESH_BINARY, 25, 10)
cv.imshow("binary_local", binary)
def custom_threshold_demo(image): # 均值二值化
gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
h, w = gray.shape[:2]
m = np.reshape(gray, [1, w * h])
mean = m.sum() / (w * h)
print("mean : ", mean)
ret, binary = cv.threshold(gray, mean, 255, cv.THRESH_BINARY)
cv.imshow("custom", binary)
def big_image_binary(image): # 对超大图进行二值化处理
print(image.shape)
cw = 256
ch = 256
h, w = image.shape[:2] # 计算图像大小
gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY) # 灰度处理
for row in range(0, h, ch): # 将超大图像分块二值化处理
for col in range(0, w, cw):
roi = gray[row:row + ch, col:col + cw]
dst = cv.adaptiveThreshold(roi, 255, cv.ADAPTIVE_THRESH_MEAN_C, cv.THRESH_BINARY, 127, 20)
# 利用adaptiveThreshold_C消除一些的噪声
gray[row:row + ch, col:col + cw] = dst
print(np.std(dst), np.mean(dst)) # 计算每个分块的均值，标准差
cv.imwrite("C:/Users/Administrator/Pictures/PS/big_image_binary.jpg", gray)
print("------ Hello OpenCV ------")
src = cv.imread("C:/Users/Administrator/Pictures/PS/5.jpg") # 读取图像
# cv.imshow("input image_5.jpg", src) # 显示图像
# threshold_demo(src)
# local_threshold_demo(src)
# custom_threshold_demo(src)
big_image_binary(src)
cv.waitKey(0)
cv.destroyAllWindows() # 销毁/关闭所有窗口
输出结果：
1.全局阈值二值化、局部阈值二值化
2.均值二值化
3.超大图像二值化
