前言
opencv-python教程学习系列记录学习python-opencv过程的点滴,本文主要介绍图像阈值/二值化,坚持学习,共同进步。
系列教程参照OpenCV-Python中文教程;
系统环境
系统:win7_x64;
python版本:python3.5.2;
opencv版本:opencv3.3.1;
内容安排
1.知识点介绍;
2.测试代码;
具体内容
1.知识点介绍;
图像的阈值处理一般使得图像的像素值更单一、图像更简单。阈值可以分为全局性质的阈值,也可以分为局部性质的阈值,可以是单阈值的也可以是多阈值的。当然阈值越多是越复杂的。下面将介绍opencv下的三种阈值方法。主要涉及的函数是cv2.threshold , cv2.adaptiveThreshold;
1.1 简单阈值;
简单阈值当然是最简单,选取一个全局阈值,然后把整幅图像分成了非黑即白的二值图像。
使用的函数是cv2.threshold,包括四个参数,第一个是原图像(灰度图像),第二个是进行分类的阈值,第三个是高于(低于)阈值时赋予的新值,第四个是一个方法选择参数,常用的方法有:
• cv2.THRESH_BINARY(黑白二值)
• cv2.THRESH_BINARY_INV(黑白二值反转)
• cv2.THRESH_TRUNC (得到的图像为多像素值)
• cv2.THRESH_TOZERO
• cv2.THRESH_TOZERO_INV
该函数有两个返回值,第一个retVal(得到的分割阈值),第二个就是阈值化后的图像。
ret , thresh5 = cv2.threshold(img,127,255,cv2.THRESH_TOZERO_INV)
1.2 自适应阈值;
自适应阈值可以看成一种局部性的阈值,通过规定一个区域大小,比较这个点与区域大小里面像素点的平均值(或者其他特征)的大小关系确定这个像素点是属于黑或者白(如果是二值情况)。使用的函数为cv2.adaptiveThreshold,该函数有6个参数,分别是原始灰度图像、像素值上限、自适应方法、赋值方法、邻域大小、常数,其中自适应方法包含cv2.ADAPTIVE_THRESH_MEAN_C (邻域均值)和cv2.ADAPTIVE_THRESH_GAUSSIAN_C(邻域加权和)两种,常数表示阈值等于均值或者加权值减去这个常数;
ret , th1 = cv2.threshold(img,127,255,cv2.THRESH_BINARY) # 11为block size,2为C值 th2 = cv2.adaptiveThreshold(img,255,cv2.ADAPTIVE_THRESH_MEAN_C , cv2.THRESH_BINARY,11,2 )
1.3 大津法OTSU;
Otsu方法试图自动找到一个最好的阈值,可以最小化加权的类内方差,并且Otsu非常适合于图像灰度直方图具有双峰的情况,在双峰(bimodal )之间找到一个值作为阈值,对于非双峰图像,可能并不是很好用。
ret2,th2=cv2.threshold(img,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
2.测试代码;
2.1 自适应阈值;
import cv2 import numpy as np from matplotlib import pyplot as plt img = cv2.imread('test.jpg',0) #中值滤波 img = cv2.medianBlur(img,5) ret , th1 = cv2.threshold(img,127,255,cv2.THRESH_BINARY) # 11为block size,2为C值 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 range(4): plt.subplot(2,2,i+1),plt.imshow(images[i],'gray') plt.title(titles[i]) plt.xticks([]),plt.yticks([]) plt.show()
2.2 OTSU方法;
import cv2 import numpy as np from matplotlib import pyplot as plt img = cv2.imread('test.jpg',0) ret1,th1=cv2.threshold(img,127,255,cv2.THRESH_BINARY) ret2,th2=cv2.threshold(img,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU) #(5,5)为高斯核的大小,0为标准差 blur= cv2.GaussianBlur(img,(5,5),0)#高斯滤波平滑 #阀值一定要设为0 ret3,th3=cv2.threshold(blur,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU) images=[img,0,th1, img,0,th2, img,0,th3] titles =['original noisy image','histogram','global thresholding(v=127)', 'original noisy image','histogram',"otsu's thresholding", 'gaussian filtered image','histogram',"otus's thresholding"] #这里使用了pyplot中画直方图的方法,plt.hist要注意的是他的参数是一维数组 #所以这里使用了(numpy)ravel方法,将多维数组转换成一维,也可以使用flatten方法 for i in range(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()
2.3 OTSU的实现;
#opencv官网https://docs.opencv.org/3.3.1/d7/d4d/tutorial_py_thresholding.html import cv2 import numpy as np from matplotlib import pyplot as plt img = cv2.imread('test.jpg',0) blur = cv2.GaussianBlur(img,(5,5),0) # find normalized_histogram, and its cumulative distribution function hist = cv2.calcHist([blur],[0],None,[256],[0,256]) hist_norm = hist.ravel()/hist.max() Q = hist_norm.cumsum() bins = np.arange(256) fn_min = np.inf thresh = -1 for i in range(1,256): p1,p2 = np.hsplit(hist_norm,[i]) # probabilities q1,q2 = Q[i],Q[255]-Q[i] # cum sum of classes b1,b2 = np.hsplit(bins,[i]) # weights # finding means and variances m1,m2 = np.sum(p1*b1)/q1, np.sum(p2*b2)/q2 v1,v2 = np.sum(((b1-m1)**2)*p1)/q1,np.sum(((b2-m2)**2)*p2)/q2 # calculates the minimization function fn = v1*q1 + v2*q2 if fn < fn_min: fn_min = fn thresh = i # find otsu's threshold value with OpenCV function ret, otsu = cv2.threshold(blur,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU) print( "{} {}".format(thresh,ret) )#ret表示得到的分割阈值;
参考
1.图像阈值;
2.CSDN图像阈值;
3.opencv官网;
完