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

https://blog.csdn.net/sinat_21258931/article/details/61418681#commentsedit

threshold：固定阈值二值化，

ret, dst = cv2.threshold(src, thresh, maxval, type)

src：输入图，只能输入单通道图像，通常来说为灰度图
dst：输出图
thresh：阈值
maxval：当像素值超过了阈值（或者小于阈值，根据type来决定），所赋予的值
type：二值化操作的类型，包含以下5种类型： cv2.THRESH_BINARY； cv2.THRESH_BINARY_INV； cv2.THRESH_TRUNC； cv2.THRESH_TOZERO；cv2.THRESH_TOZERO_INV

官方文档的示例代码：

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()

结果为：

adaptiveThreshold：自适应阈值二值化

自适应阈值二值化函数根据图片一小块区域的值来计算对应区域的阈值，从而得到也许更为合适的图片。

dst = cv2.adaptiveThreshold(src, maxval, thresh_type, type, Block Size, C)

src：输入图，只能输入单通道图像，通常来说为灰度图
dst：输出图
maxval：当像素值超过了阈值（或者小于阈值，根据type来决定），所赋予的值
thresh_type：阈值的计算方法，包含以下2种类型：cv2.ADAPTIVE_THRESH_MEAN_C； cv2.ADAPTIVE_THRESH_GAUSSIAN_C.
type：二值化操作的类型，与固定阈值函数相同，包含以下5种类型： cv2.THRESH_BINARY； cv2.THRESH_BINARY_INV； cv2.THRESH_TRUNC； cv2.THRESH_TOZERO；cv2.THRESH_TOZERO_INV.
Block Size：图片中分块的大小
C ：阈值计算方法中的常数项

官方文档的示例代码：

import cv2 import numpy as np from matplotlib import pyplot as plt img = cv2.imread('sudoku.png',0) img = cv2.medianBlur(img,5) ret,th1 = cv2.threshold(img,127,255,cv2.THRESH_BINARY) 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()

结果为：

Otsu’s Binarization: 基于直方图的二值化

Otsu’s Binarization是一种基于直方图的二值化方法，它需要和threshold函数配合使用。

4. 把0~255依照顺序多为阈值，重复1-3的步骤，直到得到最小偏移数，其所对应的值即为结果阈值。

官方文档的示例代码：

import cv2 import numpy as np from matplotlib import pyplot as plt img = cv2.imread('noisy2.png',0) # global thresholding ret1,th1 = cv2.threshold(img,127,255,cv2.THRESH_BINARY) # Otsu's thresholding ret2,th2 = cv2.threshold(img,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU) # Otsu's thresholding after Gaussian filtering blur = cv2.GaussianBlur(img,(5,5),0) ret3,th3 = cv2.threshold(blur,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU) # plot all the images and their histograms images = [img, 0, th1,           img, 0, th2,           blur, 0, th3] titles = ['Original Noisy Image','Histogram','Global Thresholding (v=127)',           'Original Noisy Image','Histogram',"Otsu's Thresholding",           'Gaussian filtered Image','Histogram',"Otsu's Thresholding"] for i in xrange(3):     plt.subplot(3,3,i*3+1),plt.imshow(images[i*3],'gray')     plt.title(titles[i*3]), plt.xticks([]), plt.yticks([])     plt.subplot(3,3,i*3+2),plt.hist(images[i*3].ravel(),256)     plt.title(titles[i*3+1]), plt.xticks([]), plt.yticks([])     plt.subplot(3,3,i*3+3),plt.imshow(images[i*3+2],'gray')     plt.title(titles[i*3+2]), plt.xticks([]), plt.yticks([]) plt.show()

结果为：

参考文献：http://docs.opencv.org/3.2.0/d7/d4d/tutorial_py_thresholding.html

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

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二值化

阈值

threshold