图像二值化 cv2.threshold

图像二值化 cv2.threshold

http://docs.opencv.org/master/d7/d4d/tutorial_py_thresholding.html

（一）简单阈值

import cv2import numpy as npfrom matplotlib import pyplot as pltimg=cv2.imread('1.bmp')# 转灰度图GrayImage=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)# ret,thresh1=cv2.threshold(GrayImage,127,255,cv2.THRESH_BINARY)ret,thresh2=cv2.threshold(GrayImage,127,255,cv2.THRESH_BINARY_INV)ret,thresh3=cv2.threshold(GrayImage,127,255,cv2.THRESH_TRUNC)ret,thresh4=cv2.threshold(GrayImage,127,255,cv2.THRESH_TOZERO)ret,thresh5=cv2.threshold(GrayImage,127,255,cv2.THRESH_TOZERO_INV)titles = ['Gray Image','BINARY','BINARY_INV','TRUNC','TOZERO','TOZERO_INV']images = [GrayImage, 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()

（二）自适应阈值：
自适应的二值化处理不同于固定阈值的处理，每个像素的阈值依赖于其邻近的像素灰度，为了得到(x,y)点的阈值T(x,y)，我们需要进行以下处理。

在此像素周边选取一个bxb的区域，其中了是用户指定的。
计算此bxb区域的加权平均值。OpenCV提供了两种方法计算此加权均值，一种是算术平均法，另一种是高斯加权平均法，后者要计算均值时距离区域中心越近权重越大。我们将得到的加权均值计为WA(x,y)
将上述加权均值与一固定参数相减得到阈值T(x,y)，此固定参数设为param1，则(x,y)点的阈值可以用下式进行计算：
T(x,y)=WA(x,y)-param1

import cv2import numpy as npfrom matplotlib import pyplot as pltimg = cv2.imread('sudoku.png',0)img = cv2.medianBlur(img,5)# 简单ret,th1 = cv2.threshold(img,127,255,cv2.THRESH_BINARY)# 自适应th2 = cv2.adaptiveThreshold(img,255,cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY,11,2)th3 = cv2.adaptiveThreshold(img,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY,11,2)titles = ['Original Image', 'Global Thresholding (v = 127)',            'Adaptive Mean Thresholding', 'Adaptive Gaussian Thresholding']images = [img, th1, th2, th3]for i in xrange(4):    plt.subplot(2,2,i+1),plt.imshow(images[i],'gray')    plt.title(titles[i])    plt.xticks([]),plt.yticks([])plt.show()# cv2.adaptiveThreshold(src, maxValue, adaptiveMethod, thresholdType, blockSize, C[, dst])其中：# src 为输入图像；# maxval 为输出图像的最大值；# adaptiveMethod 设置为cv2.ADAPTIVE_THRESH_MEAN_C表示利用算术均值法，设置为cv2.ADAPTIVE_THRESH_GAUSSIAN_C表示用高斯权重均值法；# thresholdType: 阈值的类型；# blockSize: b的值；# C 为从均值中减去的常数，用于得到阈值；# dst 为目标图像。

（三）Otsu’s二值化

---