OpenCV笔记-图像处理之阈值化

使用cvThreshold函数阈值化

示例：

#include <stdio.h>

#include <cv.h>

#include <highgui.h>



void sum_rgb( IplImage* src, IplImage* dst )

{

    IplImage* r = cvCreateImage(cvGetSize(src), IPL_DEPTH_8U, 1);

    IplImage* g = cvCreateImage(cvGetSize(src), IPL_DEPTH_8U, 1);

    IplImage* b = cvCreateImage(cvGetSize(src), IPL_DEPTH_8U, 1);



    /*

    *    函数原型：

    *    CVAPI(void)  cvSplit( const CvArr* src, CvArr* dst0, CvArr* dst1,

    *                 CvArr* dst2, CvArr* dst3 );

    *    dst0,dst1,dst2,dst3分别为r,g,b和alpha

    *    复制src各个通道rgb到单通道图像r、g和b

    */

    cvSplit(src, r, g, b, NULL);



    IplImage* s = cvCreateImage(cvGetSize(src), IPL_DEPTH_8U, 1);



    /*

    *    函数原型：

    *    CVAPI(void)  cvAddWeighted( const CvArr* src1, double alpha,

    *                 const CvArr* src2, double beta,

    *                 double gamma, CvArr* dst );

    *    dst(I)=src1(I)*alpha+src2(I)*beta+gamma

    */

    cvAddWeighted( r, 1. / 3., g, 1. / 3., 0.0, s );

    cvAddWeighted( s, 2. / 3., b, 1. / 3., 0.0, s );

    /*

    *    上面最终求解为s(I)=r(I)*2/9+g(I)*2/9+b(I)*1/3

    */



    /*

    *    函数原型：

    *    CVAPI(double)  cvThreshold( const CvArr*  src, CvArr*  dst,

    *                   double  threshold, double  max_value,

    *                   int threshold_type );

    *    图像阈值化

    *    src：输入图像

    *    dst：输出图像

    *    threshold：阈值

    *    max_value：最大值

    *    threshold_type：可选值有：

    *        enum

    *        {

    *            CV_THRESH_BINARY      =0,  // dst(I) = src(I) > threshold ? max_value : 0       

    *            CV_THRESH_BINARY_INV  =1,  // dst(I) = src(I) > threshold ? 0 : max_value       

    *            CV_THRESH_TRUNC       =2,  // dst(I) = src(I) > threshold ? max_value : src(I)   

    *            CV_THRESH_TOZERO      =3,  // dst(I) = src(I) > threshold ? src(I) : 0

    *            CV_THRESH_TOZERO_INV  =4,  // dst(I) = src(I) > threshold ? 0 : src(I)

    *            CV_THRESH_MASK        =7,

    *            CV_THRESH_OTSU        =8  // use Otsu algorithm to choose the optimal threshold value;

    *                                     combine the flag with one of the above CV_THRESH_* values

    *        };

    */

    cvThreshold(s, dst, 100, 100, CV_THRESH_TRUNC);



    cvReleaseImage(&r);

    cvReleaseImage(&g);

    cvReleaseImage(&b);

    cvReleaseImage(&s);

}





int main()

{

    cvNamedWindow( "cvThreshold" );



    IplImage* src = cvLoadImage( "E:\\Res\\Hydrangeas.jpg" );

    IplImage* dst = cvCreateImage( cvGetSize( src ), src->depth, 1 );

    sum_rgb( src, dst );



    cvShowImage( "cvThreshold" , dst);



    cvWaitKey(0);



    cvDestroyWindow( "cvThreshold" );

    cvReleaseImage(&src);

    cvReleaseImage(&dst);

    return 0;

}

 使用自适应阈值AdaptiveThreshold函数：

#include <stdio.h>

#include <cv.h>

#include <highgui.h>





int main()

{

    cvNamedWindow( "cvThreshold" );



    IplImage* src = cvLoadImage( "E:\\Res\\Hydrangeas.jpg" );

    IplImage* gray = cvCreateImage( cvGetSize( src ), src->depth, 1 );

    cvCvtColor( src, gray, CV_BGR2GRAY );



    IplImage* dst = cvCreateImage( cvGetSize( gray ), gray->depth, 1 );

    /*

    *    函数原型：

    *    CVAPI(void)  cvAdaptiveThreshold( const CvArr* src, CvArr* dst, double max_value,

    *                  int adaptive_method CV_DEFAULT(CV_ADAPTIVE_THRESH_MEAN_C),

    *                  int threshold_type CV_DEFAULT(CV_THRESH_BINARY),

    *                  int block_size CV_DEFAULT(3),

    *                  double param1 CV_DEFAULT(5));

    *    src、dst：输入和输出图像

    *    adaptive_method：自适应阈值方法，有以下取值：

    *        enum

    *        {

    *            CV_ADAPTIVE_THRESH_MEAN_C  =0,//区域所有像素平均加权

    *            CV_ADAPTIVE_THRESH_GAUSSIAN_C  =1//根据高斯函数按照区域所有像素离中心距离进行加权计算

    *        };

    *    threshold_type：阈值选取方式

    *    block_size：区域大小

    *    param1：常数

    *    自适应阈值等于区域加权平均减去一个常数param1

    */

    cvAdaptiveThreshold(gray, dst, 255);



    cvShowImage( "cvThreshold" , dst);



    cvWaitKey(0);



    cvDestroyWindow( "cvThreshold" );

    cvReleaseImage(&src);

    cvReleaseImage(&dst);

    return 0;

}