18KW14/2OpenCV入门图像分离（阈值）-一步一步学Python
今天的目标是学习简单阈值，自适应阈值和Otsu’s二值化等，实现图像中不同区域的分离主要利用的函数为cv2.threshold,cv2.adaptiveThreshold第一种，简单阈值
当像素值高于阈值是，我们就给这个像素赋予一个新值，否则就赋予另外一只颜色。这个函数就是cv2.threshold()。def threshold(src合浦天气预报, thresh, maxval, type, dst=None): . @param src input array (multiple-channel, 8-bit or 32-bit floating point). . @param dst output array of the same size and type and the same number of channels as src. . @param thresh threshold value. . @param maxval maximum value to use with the THRESH_BINARY and THRESH_BINARY_INV thresholding types. . @param type thresholding type (see the cv::ThresholdTypes).举个例子，将一副黑白渐变的图片，按简单阈值进行变换，生成不同风格的图片。
import cv2 as cvimport numpy as npimport matplotlib.pyplot as pltimg=cv.imread('pic/whiteblack.jpg',0)ret,thresh1=cv.threshold(img,127,255,cv.THRESH_BINARY)ret,thresh2=cv.threshold(img,127,255,cv.THRESH_BINARY_INV)ret,thresh3=cv.threshold(img,127,255,cv.THRESH_TRUNC)ret,thresh4=cv.threshold(img,127,255,cv.THRESH_TOZERO)ret,thresh5=cv.threshold(img,127,255,cv.THRESH_TOZERO_INV)titles=['Original Image','BINARY','BINARY_INV','TRUNC','TOZERO','TOZERO_INV']images=[img,thresh1,thresh2,thresh3,thresh4,thresh5]for i in range(6): plt.subplot(2,3,i+1),plt.imshow(images[i],'gray') plt.title(titles[i]) plt.xticks([]),plt.yticks([])plt.show()
第二种，自适应阈值
在上面一个例子中，我们使用的是全局阈值，使用同一个数对整幅图像进行整理鼎砥论坛 。但是这种方法并不适应所有的情况，尤其是当同一幅图像上的不同部分具有不同亮度时无锡阿福台。自适应阈值，会根据图像上的每一个小区域计算与其对应的阈值，在不同的区域采用不同的阈值。def adaptiveThreshold(src, maxValue, adaptiveMethod, thresholdType李鸣岩 , blockSize, C, dst=None) . @param blockSize Size of a pixel neighborhood that is used to calculate a threshold value for the pixel: 3, 5, 7, and so on. . @param C Constant subtracted from the mean or weighted mean (see the details below). Normally, it is positive but may be zero or negative as well.我们来看一个例子，就能明白与全局阈值的不同之处。
import cv2 as cvimport numpy as npimport matplotlib.pyplot as pltimg=cv.imread('pic/dave.jpg',0)#这句代码用于过滤图片中的噪点img=cv.medianBlur(img,5)ret,th1=cv.threshold(img,127,255,cv.THRESH_BINARY)th2=cv.adaptiveThreshold(img范世琦女友 ,255,cv.ADAPTIVE_THRESH_MEAN_C,cv.THRESH_BINARY,11,2)th3=cv.adaptiveThreshold(img,255,cv.ADAPTIVE_THRESH_GAUSSIAN_C,cv.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): #subplot(nrows, ncols, plot_number)，如何控制图片排列 plt.subplot(2塞上风论坛 ,2,i+1),plt.imshow(images[i],'gray') plt.title(titles[i]) plt.xticks([]),plt.yticks([])plt.show()

第三种方法，Otsu’s二值化在使用全局阈值的时候，我们随便给定了一个数来做阈值，那么如何知道这个数的好坏呢？对于计算机来说，就是不停的尝试。一般用于双峰对象，简单来说，就是在直方图中存在两个峰值。虽然还是使用cv2.threshold()函数，但是需要传入一个cv2.THRESH_OTSU参数，我们把阈值设为0神关羽 。然后算法会自动计算最优解，然后返回一个retVal。
import cv2 as cvimport numpy as npimport matplotlib.pyplot as pltimg=cv.imread('pic/noisy.jpg'杨宗胜,0)ret1,袁维娅th1=cv.threshold(img,127,255,cv.THRESH_BINARY)ret2,th2=cv.threshold(img,0,255,cv.THRESH_BINARY+cv.THRESH_OTSU)blur=cv.GaussianBlur(img,(5,5),0)ret3,th3=cv.threshold(blur,2,255,cv.THRESH_BINARY+cv.THRESH_OTSU)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 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])巴比伦王妃, plt.xticks([]), plt.yticks([]) plt.subplot(3,3,i*3+3)美女请留步 ,plt.imshow(images[i*3+2],'gray') plt.title(titles[i*3+2]), plt.xticks([]), plt.yticks([])plt.show()