特征提取代码

特征提取代码

分类： OPENCV 2012-09-13 19:23  198人阅读  评论(0)  收藏  举报

dst download float image video null

Ø 颜色直方图提取：

Code:

#include <cv.h>

#include <highgui.h>

#include <iostream>

using namespace std;

int main( int argc, char** argv )

{

IplImage * src= cvLoadImage("E::\\baihe.jpg",1);

IplImage* hsv = cvCreateImage( cvGetSize(src), 8, 3 );

IplImage* h_plane = cvCreateImage( cvGetSize(src), 8, 1 );

IplImage* s_plane = cvCreateImage( cvGetSize(src), 8, 1 );

IplImage* v_plane = cvCreateImage( cvGetSize(src), 8, 1 );

IplImage* planes[] = { h_plane, s_plane };

/** H 分量划分为16个等级，S分量划分为8个等级*/

int h_bins = 16, s_bins = 8;

int hist_size[] = {h_bins, s_bins};

/** H 分量的变化范围*/

float h_ranges[] = { 0, 180 };

/** S 分量的变化范围*/

float s_ranges[] = { 0, 255 };

float* ranges[] = { h_ranges, s_ranges };

/** 输入图像转换到HSV颜色空间*/

cvCvtColor( src, hsv, CV_BGR2HSV );

cvCvtPixToPlane( hsv, h_plane, s_plane, v_plane, 0 );

/** 创建直方图，二维, 每个维度上均分*/

CvHistogram * hist = cvCreateHist( 2, hist_size, CV_HIST_ARRAY, ranges, 1 );

/** 根据H,S两个平面数据统计直方图*/

cvCalcHist( planes, hist, 0, 0 );

/** 获取直方图统计的最大值，用于动态显示直方图*/

float max_value;

cvGetMinMaxHistValue( hist, 0, &max_value, 0, 0 );

/** 设置直方图显示图像*/

int height = 240;

int width = (h_bins*s_bins*6);

IplImage* hist_img = cvCreateImage( cvSize(width,height), 8, 3 );

cvZero( hist_img );

/** 用来进行HSV到RGB颜色转换的临时单位图像*/

IplImage * hsv_color = cvCreateImage(cvSize(1,1),8,3);

IplImage * rgb_color = cvCreateImage(cvSize(1,1),8,3);

int bin_w = width / (h_bins * s_bins);

for(int h = 0; h < h_bins; h++)

{

for(int s = 0; s < s_bins; s++)

{

int i = h*s_bins + s;

/** 获得直方图中的统计次数，计算显示在图像中的高度*/

float bin_val = cvQueryHistValue_2D( hist, h, s );

int intensity = cvRound(bin_val*height/max_value);

/** 获得当前直方图代表的颜色，转换成RGB用于绘制*/

cvSet2D(hsv_color,0,0,cvScalar(h*180.f / h_bins,s*255.f/s_bins,255,0));

cvCvtColor(hsv_color,rgb_color,CV_HSV2BGR);

CvScalar color = cvGet2D(rgb_color,0,0);

cvRectangle( hist_img, cvPoint(i*bin_w,height),

cvPoint((i+1)*bin_w,height - intensity),

color, -1, 8, 0 );

}

}

cvNamedWindow( "Source", 1 );

cvShowImage( "Source", src );

cvNamedWindow( "H-S Histogram", 1 );

cvShowImage( "H-S Histogram", hist_img );

cvWaitKey(0);

}

形状提取

Ø Candy算子对边缘提取：

Code：

#include "cv.h"

#include "cxcore.h"

#include "highgui.h"

int main( int argc, char** argv )

{

    //声明IplImage指针

    IplImage* pImg = NULL;

    IplImage* pCannyImg = NULL;

    //载入图像，强制转化为Gray

pImg = cvLoadImage( "E:\\Download\\test.jpg", 0);

//为canny边缘图像申请空间

pCannyImg = cvCreateImage(cvGetSize(pImg), IPL_DEPTH_8U, 1);

//canny边缘检测

cvCanny(pImg, pCannyImg, 50, 150, 3);

//创建窗口

cvNamedWindow("src", 1);

cvNamedWindow("canny",1);

//显示图像

cvShowImage( "src", pImg );

cvShowImage( "canny", pCannyImg );

//等待按键

cvWaitKey(0);

//销毁窗口

cvDestroyWindow( "src" );

cvDestroyWindow( "canny" );

//释放图像

cvReleaseImage( &pImg );

cvReleaseImage( &pCannyImg );

return 0;

}

角点提取：

Code：

#include <stdio.h>

#include "cv.h"

#include "highgui.h"

#define MAX_CORNERS 100

int main(void)

{

int cornersCount=MAX_CORNERS;//得到的角点数目

CvPoint2D32f corners[MAX_CORNERS];//输出角点集合

IplImage *srcImage = 0,*grayImage = 0,*corners1 = 0,*corners2 = 0;

int i;

CvScalar color = CV_RGB(255,0,0);

cvNamedWindow("image",1);

//Load the image to be processed

srcImage = cvLoadImage("E:\\Download\\1.jpg",1);

grayImage = cvCreateImage(cvGetSize(srcImage),IPL_DEPTH_8U,1);

//copy the source image to copy image after converting the format

//复制并转为灰度图像

cvCvtColor(srcImage,grayImage,CV_BGR2GRAY);

//create empty images os same size as the copied images

//两幅临时位浮点图像，cvGoodFeaturesToTrack会用到

corners1 = cvCreateImage(cvGetSize(srcImage),IPL_DEPTH_32F,1);

corners2 = cvCreateImage(cvGetSize(srcImage),IPL_DEPTH_32F,1);

cvGoodFeaturesToTrack(grayImage,corners1,corners2,corners,&cornersCount,0.05,

30,//角点的最小距离是

0,//整个图像

3,0,0.4);

printf("num corners found: %d\n",cornersCount);

//开始画出每个点

if (cornersCount>0)

{

for (i=0;i<cornersCount;i++)

{

cvCircle(srcImage,cvPoint((int)(corners[i].x),(int)(corners[i].y)),2,color,2,CV_AA,0);

}

}

cvShowImage("image",srcImage);

cvSaveImage("imagedst.png",srcImage);

cvReleaseImage(&srcImage);

cvReleaseImage(&grayImage);

cvReleaseImage(&corners1);

cvReleaseImage(&corners2);

cvWaitKey(0);

return 0;

}

Ø Hough直线提取：

Code：

#include <cv.h>

#include <highgui.h>

#include <math.h>

int main(int argc, char** argv)

{

    IplImage* src = cvLoadImage( "E:\\Download\\2.jpg" , 0 );

    IplImage* dst;

    IplImage* color_dst;

    CvMemStorage* storage = cvCreateMemStorage(0);

    CvSeq* lines = 0;

    int i;

    if( !src )

        return -1;

    dst = cvCreateImage( cvGetSize(src), 8, 1 );

    color_dst = cvCreateImage( cvGetSize(src), 8, 3 );

    cvCanny( src, dst, 50, 200, 3 );

    cvCvtColor( dst, color_dst, CV_GRAY2BGR );

#if 0

    lines = cvHoughLines2( dst, storage, CV_HOUGH_STANDARD, 1, CV_PI/180, 100, 0, 0 );

    for( i = 0; i < MIN(lines->total,100); i++ )

    {

        float* line = (float*)cvGetSeqElem(lines,i);

        float rho = line[0];

        float theta = line[1];

        CvPoint pt1, pt2;

        double a = cos(theta), b = sin(theta);

        double x0 = a*rho, y0 = b*rho;

        pt1.x = cvRound(x0 + 1000*(-b));

        pt1.y = cvRound(y0 + 1000*(a));

        pt2.x = cvRound(x0 - 1000*(-b));

        pt2.y = cvRound(y0 - 1000*(a));

        cvLine( color_dst, pt1, pt2, CV_RGB(255,0,0), 3, CV_AA, 0 );

    }

#else

    lines = cvHoughLines2( dst, storage, CV_HOUGH_PROBABILISTIC, 1, CV_PI/180, 50, 50, 10 );

    for( i = 0; i < lines->total; i++ )

    {

        CvPoint* line = (CvPoint*)cvGetSeqElem(lines,i);

        cvLine( color_dst, line[0], line[1], CV_RGB(255,0,0), 3, CV_AA, 0 );

    }

#endif

    cvNamedWindow( "Source", 1 );

    cvShowImage( "Source", src );

    cvNamedWindow( "Hough", 1 );

    cvShowImage( "Hough", color_dst );

    cvWaitKey(0);

    return 0;

}

Ø Hough圆提取：

Code：

#include <cv.h>

#include <highgui.h>

#include <math.h>

#include <iostream>

using namespace std;

int main(int argc, char** argv)

{

    IplImage* img;

img=cvLoadImage("E:\\Download\\3.jpg", 1);

IplImage* gray = cvCreateImage( cvGetSize(img), 8, 1 );

     CvMemStorage* storage = cvCreateMemStorage(0);

     cvCvtColor( img, gray, CV_BGR2GRAY );

     cvSmooth( gray, gray, CV_GAUSSIAN, 5, 15 );

// smooth it, otherwise a lot of false circles may be detected

CvSeq* circles = cvHoughCircles( gray, storage, CV_HOUGH_GRADIENT, 2, gray->height/4, 200, 100 );

    int i;

     for( i = 0; i < circles->total; i++ )

     {

          float* p = (float*)cvGetSeqElem( circles, i );

          cvCircle( img, cvPoint(cvRound(p[0]),cvRound(p[1])), 3, CV_RGB(0,255,0), -1, 8, 0 );

cvCircle( img, cvPoint(cvRound(p[0]),cvRound(p[1])), cvRound(p[2]), CV_RGB(255,0,0), 3, 8, 0 );

          cout<<"圆心坐标x= "<<cvRound(p[0])<<endl<<"圆心坐标y= "<<cvRound(p[1])<<endl;

          cout<<"半径="<<cvRound(p[2])<<endl;

     }

     cout<<"圆数量="<<circles->total<<endl;

     cvNamedWindow( "circles", 1 );

     cvShowImage( "circles", img );

     cvWaitKey(0);

 

    return 0;

}

Ø Hough矩形提取：

Code：

#include "cv.h"

#include "highgui.h"

#include <stdio.h>

#include <math.h>

#include <string.h>

int thresh = 50;

IplImage* img = 0;

IplImage* img0 = 0;

CvMemStorage* storage = 0;

CvPoint pt[4];const char* wndname = "Square Detection Demo";

double angle( CvPoint* pt1, CvPoint* pt2, CvPoint* pt0 )

{   

double dx1 = pt1->x - pt0->x;

double dy1 = pt1->y - pt0->y; 

double dx2 = pt2->x - pt0->x; 

double dy2 = pt2->y - pt0->y;   

return (dx1*dx2 + dy1*dy2)/sqrt((dx1*dx1 + dy1*dy1)*(dx2*dx2 + dy2*dy2) + 1e-10);

}

CvSeq* findSquares4( IplImage* img, CvMemStorage* storage )

{ 

CvSeq* contours;

int i, c, l, N = 11; 

CvSize sz = cvSize( img->width & -2, img->height & -2 );

IplImage* timg = cvCloneImage( img );

IplImage* gray = cvCreateImage( sz, 8, 1 );

IplImage* pyr = cvCreateImage( cvSize(sz.width/2, sz.height/2), 8, 3 ); 

IplImage* tgray;  

CvSeq* result; 

double s, t; 

CvSeq* squares = cvCreateSeq( 0, sizeof(CvSeq), sizeof(CvPoint), storage );  

cvSetImageROI( timg, cvRect( 0, 0, sz.width, sz.height ));  

// down-scale and upscale the image to filter out the noise

cvPyrDown( timg, pyr, 7 ); 

cvPyrUp( pyr, timg, 7 );  

tgray = cvCreateImage( sz, 8, 1 ); 

// find squares in every color plane of the image

for( c = 0; c < 3; c++ ) 

{      

cvSetImageCOI( timg, c+1 );    

cvCopy( timg, tgray, 0 );          

for( l = 0; l < N; l++ )    

{         

if( l == 0 )    

{              

cvCanny( tgray, gray, 0, thresh, 5 );       

cvDilate( gray, gray, 0, 1 );     

}          

else      

{            

cvThreshold( tgray, gray, (l+1)*255/N, 255, CV_THRESH_BINARY );

}                      

cvFindContours( gray, storage, &contours, sizeof(CvContour),CV_RETR_LIST, CV_CHAIN_APPROX_SIMPLE, cvPoint(0,0) );         

while( contours )   

{             

  result = cvApproxPoly( contours, sizeof(CvContour), storage,CV_POLY_APPROX_DP, cvContourPerimeter(contours)*0.02, 0 );

if( result->total == 4 && fabs(cvContourArea(result,CV_WHOLE_SEQ)) > 1000 &&  cvCheckContourConvexity(result) ) 

{              

s = 0;     

for( i = 0; i < 5; i++ ) 

{                  

if( i >= 2 )          

{              

t = fabs(angle( (CvPoint*)cvGetSeqElem( result, i ),(CvPoint*)cvGetSeqElem( result, i-2 ),(CvPoint*)cvGetSeqElem( result, i-1 )));  

s = s > t ? s : t;    

}        

}                                           

if( s < 0.3 )                     

for( i = 0; i < 4; i++ )             

cvSeqPush( squares,                   

(CvPoint*)cvGetSeqElem( result, i ));    

}                                     

contours = contours->h_next;     

}  

}

}

cvReleaseImage( &gray );  

cvReleaseImage( &pyr ); 

cvReleaseImage( &tgray ); 

cvReleaseImage( &timg );  

return squares;

} 

// the function draws all the squares in the image

void drawSquares( IplImage* img, CvSeq* squares )

{  

CvSeqReader reader;  

IplImage* cpy = cvCloneImage( img );  

int i;      

cvStartReadSeq( squares, &reader, 0 );    

for( i = 0; i < squares->total; i += 4 ) 

{      

CvPoint* rect = pt;   

int count = 4;     

memcpy( pt, reader.ptr, squares->elem_size );

CV_NEXT_SEQ_ELEM( squares->elem_size, reader );

memcpy( pt + 1, reader.ptr, squares->elem_size );    

CV_NEXT_SEQ_ELEM( squares->elem_size, reader );  

memcpy( pt + 2, reader.ptr, squares->elem_size );  

CV_NEXT_SEQ_ELEM( squares->elem_size, reader );    

memcpy( pt + 3, reader.ptr, squares->elem_size ); 

CV_NEXT_SEQ_ELEM( squares->elem_size, reader );        

cvPolyLine( cpy, &rect, &count, 1, 1, CV_RGB(0,255,0), 3, CV_AA, 0 );

}       

cvShowImage( wndname, cpy ); 

cvReleaseImage( &cpy );

}

void on_trackbar( int a )

{  

if( img )   

drawSquares( img, findSquares4( img, storage ) );

}

char* names[] = { "1.jpg", 0 };

int main(int argc, char** argv)

{   

int i, c;

storage = cvCreateMemStorage(0);   

for( i = 0; names[i] != 0; i++ )  

{    

img0 = cvLoadImage( names[i], 1 );  

if( !img0 )   

{       

printf("Couldn't load %s\n", names[i] );   

continue;    

}    

img = cvCloneImage( img0 );      

     cvNamedWindow( wndname, 1 );    

cvCreateTrackbar( "canny thresh", wndname, &thresh, 1000, on_trackbar );    

on_trackbar(0);      

c = cvWaitKey(0);    

cvReleaseImage( &img );   

cvReleaseImage( &img0 );      

cvClearMemStorage( storage );     

if( c == 27 )      

break;  

}      

cvDestroyWindow( wndname );  

return 0;

}

Ø 边缘直方图提取：

Code：

#include "cv.h"

#include "highgui.h"

#include <stdio.h>

#include <ctype.h>

#define PI 3.14

int main()

{

    IplImage *src = 0;  // source imagre

    IplImage *histimg = 0; // histogram image

    CvHistogram *hist = 0; // define multi_demention histogram

    IplImage* canny;

    CvMat* canny_m;

    IplImage* dx; // the sobel x difference

    IplImage* dy; // the sobel y difference

    CvMat* gradient; // value of gradient

    CvMat* gradient_dir; // direction of gradient

    CvMat* dx_m; // format transform to matrix

    CvMat* dy_m;

    CvMat* mask;

    CvSize  size;

    IplImage* gradient_im;

    int i,j;

    float theta;

   

    int hdims = 8;     // 划分HIST的个数，越高越精确

    float hranges_arr[] = {-PI/2,PI/2}; // 直方图的上界和下界

    float* hranges = hranges_arr;

                                                                                                                                                                                                                                                             

    float max_val;  //

    int bin_w;

   

    src=cvLoadImage("E:\\Download\\test.jpg", 0); // force to gray image

       if(src==0) return -1;

   

    cvNamedWindow( "Histogram", 0 );

    //cvNamedWindow( "src", 0);

    size=cvGetSize(src);

    canny=cvCreateImage(cvGetSize(src),8,1);//边缘图像

    dx=cvCreateImage(cvGetSize(src),32,1);//x方向上的差分//此处的数据类型为U 不怕溢出吗？

    dy=cvCreateImage(cvGetSize(src),32,1);

    gradient_im=cvCreateImage(cvGetSize(src),32,1);//梯度图像

    canny_m=cvCreateMat(size.height,size.width,CV_32FC1);//边缘矩阵

    dx_m=cvCreateMat(size.height,size.width,CV_32FC1);

    dy_m=cvCreateMat(size.height,size.width,CV_32FC1);

    gradient=cvCreateMat(size.height,size.width,CV_32FC1);//梯度矩阵

    gradient_dir=cvCreateMat(size.height,size.width,CV_32FC1);//梯度方向矩阵

    mask=cvCreateMat(size.height,size.width,CV_32FC1);//掩码

    cvCanny(src,canny,60,180,3);//边缘检测

    cvConvert(canny,canny_m);//把图像转换为矩阵

    cvSobel(src,dx,1,0,3);// 一阶X方向的图像差分:dx

    cvSobel(src,dy,0,1,3);// 一阶Y方向的图像差分:dy

    cvConvert(dx,dx_m);

    cvConvert(dy,dy_m);

    cvAdd(dx_m,dy_m,gradient); // value of gradient//梯度不是等于根号下x的导数的平方加上y导数的平方吗？

    cvDiv(dx_m,dy_m,gradient_dir); // direction

    for(i=0;i<size.height;i++)

    for(j=0;j<size.width;j++)

    {

      if(cvmGet(canny_m,i,j)!=0 && cvmGet(dx_m,i,j)!=0)//此行是什么意思？只看边缘上的方向？

      {

         theta=cvmGet(gradient_dir,i,j);

         theta=atan(theta);

         cvmSet(gradient_dir,i,j,theta); 

      }

      else

      {

         cvmSet(gradient_dir,i,j,0);

      }

        

    }

   hist = cvCreateHist( 1, &hdims, CV_HIST_ARRAY, &hranges, 1 ); 

// 创建一个指定尺寸的直方图，并返回创建的直方图指针

   histimg = cvCreateImage( cvSize(320,200), 8, 3 ); // 创建一个图像，通道

   cvZero( histimg ); // 清；

   cvConvert(gradient_dir,gradient_im);//把梯度方向矩阵转化为图像

   cvCalcHist( &gradient_im, hist, 0, canny ); // 计算直方图

   cvGetMinMaxHistValue( hist, 0, &max_val, 0, 0 );  // 只找最大值

   cvConvertScale( hist->bins, hist->bins, max_val ? 255. / max_val : 0., 0 );

// 缩放bin 到区间[0,255] ，比例系数

   cvZero( histimg );

   bin_w = histimg->width /16;  // hdims: 条的个数，则bin_w 为条的宽度

   

    // 画直方图

    for( i = 0; i < hdims; i++ )

    {

       double val = ( cvGetReal1D(hist->bins,i)*histimg->height/255 );

// 返回单通道数组的指定元素， 返回直方图第i条的大小，val为histimg中的i条的高度

        CvScalar color = CV_RGB(255,255,0); //(hsv2rgb(i*180.f/hdims);//直方图颜色

        cvRectangle( histimg, cvPoint(100+i*bin_w,histimg->height),cvPoint(100+(i+1)*bin_w,(int)(histimg->height - val)), color, 1, 8, 0 ); // 画直方图——画矩形，左下角，右上角坐标

     }

   

    cvShowImage( "src", src);

    cvShowImage( "Histogram", histimg );

    cvWaitKey(0);

    cvDestroyWindow("src");

    cvDestroyWindow("Histogram");

    cvReleaseImage( &src );

    cvReleaseImage( &histimg );

    cvReleaseHist ( &hist );

   

    return 0;

}

Ø 视频流中边缘检测：

Code：

#include "highgui.h"

#include "cv.h"

#include "stdio.h"

#include <ctype.h>

int main(int argc,char ** argv)

{

       IplImage * laplace = 0;

       IplImage * colorlaplace = 0;

       IplImage * planes[3] = {0,0,0};

       CvCapture *capture = 0;

      //从摄像头读取

       /*if(argc == 1 ||( argc==2 && strlen(argv[1])==1 && isdigit(argv[1][0]) ))

           capture = cvCaptureFromCAM(argc == 2 ? argv[1][0] -'0':0);*/

       //从文件中读取

      /* else if(argc == 2)*/

           capture = cvCaptureFromAVI("1.avi");

       if(!capture)

       {

           fprintf(stderr,"Could not initialize capturing...\n");

           return -1;

       }

       cvNamedWindow("Laplacian",1);

       cvNamedWindow("video",1);

       //循环捕捉，直到用户按键跳出循环体

       for(;;)

       {

            IplImage * frame =0;    //抓起一祯

            frame = cvQueryFrame(capture);

            if(!frame)

                 break;

            if(!laplace)

            {

               //创建图像

               for(int i=0;i<3;i++)

               planes[i] = cvCreateImage(cvSize(frame->width,frame->height),IPL_DEPTH_8U,1);

               laplace = cvCreateImage(cvSize(frame->width,frame->height),IPL_DEPTH_16S,1);

             colorlaplace=cvCreateImage(cvSize(frame->width,frame->height),IPL_DEPTH_8U,3);

            }

             cvCvtPixToPlane(frame,planes[0],planes[1],planes[2],0);

             for(int i=0;i<3;i++)

             {

                //交换，如通道变换

                cvLaplace(planes[i],laplace,3);

                //使用线性变换转换输入函数元素成为无符号整形

                cvConvertScaleAbs(laplace,planes[i],1,0);

              }

            cvCvtPlaneToPix(planes[0],planes[1],planes[2],0,colorlaplace);

            //结构相同（- 顶—左结构,1 - 底—左结构）

            colorlaplace->origin = frame->origin;

           //高斯滤波，平滑图像

           // cvSmooth(colorlaplace, colorlaplace, CV_GAUSSIAN, 1, 0, 0);

           //形态学滤波,闭运算

           cvDilate(colorlaplace, colorlaplace, 0, 1);//膨胀

           cvErode(colorlaplace, colorlaplace, 0, 1);//腐蚀

           cvShowImage("video", frame);

           cvShowImage("Laplacian",colorlaplace);

           if(cvWaitKey(10)>0)

               break;

       }

       cvReleaseCapture(&capture);

       cvDestroyWindow("Laplacian");

       cvDestroyWindow("video");

       return 0;

}

Ø 纹理提取：

Code：

#include <iostream>

#include <math.h>

#include "cv.h"

#include "highgui.h"

int main(int argc, char* argv[])

{

int tmp[8]={0};

int sum=0;int k=0;

IplImage* img,*dst;

img=cvLoadImage("E:\\Download\\2.jpg",0);

CvScalar s;

cvNamedWindow("img",NULL);

cvNamedWindow("dst",NULL);

cvShowImage("img",img);

uchar* data=(uchar*)img->imageData;

int step=img->widthStep;

dst=cvCreateImage(cvSize(img->width,img->height),img->depth,1);

dst->widthStep=img->widthStep;

for(int i=1;i<img->height-1;i++)

for(int j=1;j<img->width-1;j++)

{

if(data[(i-1)*step+j-1]>data[i*step+j]) tmp[0]=1;

else tmp[0]=0;

if(data[i*step+(j-1)]>data[i*step+j]) tmp[1]=1;

else tmp[1]=0;

if(data[(i+1)*step+(j-1)]>data[i*step+j]) tmp[2]=1;

else tmp[2]=0;

if (data[(i+1)*step+j]>data[i*step+j]) tmp[3]=1;

else tmp[3]=0;

if (data[(i+1)*step+(j+1)]>data[i*step+j]) tmp[4]=1;

else tmp[4]=0;

if(data[i*step+(j+1)]>data[i*step+j]) tmp[5]=1;

else tmp[5]=0;

if(data[(i-1)*step+(j+1)]>data[i*step+j]) tmp[6]=1;

else tmp[6]=0;

if(data[(i-1)*step+j]>data[i*step+j]) tmp[7]=1;

else tmp[7]=0;

for(k=0;k<=7;k++)

sum+=abs(tmp[k]-tmp[k+1]);

sum=sum+abs(tmp[7]-tmp[0]);

if (sum<=2)

s.val[0]=(tmp[0]*128+tmp[1]*64+tmp[2]*32+tmp[3]*16+tmp[4]*8+tmp[5]*4+tmp[6]*2+tmp[7]);

else s.val[0]=59;

cvSet2D(dst,i,j,s);

}

cvShowImage("dst",dst);

cvWaitKey(-1);

return 0;

 

opencv 图象角点检测

分类： OPENCV 2012-10-22 21:18  132人阅读  评论(0)  收藏  举报

distance image validation null output c

Code:

[cpp]  view plain copy

1. #include<iostream>2  
2. using namespace cv;  
3. using namespace std;  
4. #include <cv.h>  
5. #include <math.h>  
6.   
7.   
8. //by Huang, Haiqiao 5 Dec. 2009  
9. int main(int argc, char** argv)  
10. {  
11.    cout << "Corner Detection OpenCV!"<<endl;   
12.    char* filename="e://kankan//fish.jpg";  
13.    IplImage* imgRGB = cvLoadImage(filename);   
14.    IplImage* imgRGB2 = cvLoadImage(filename);   
15.    IplImage* imgGrey = cvLoadImage(filename,CV_LOAD_IMAGE_GRAYSCALE);   
16.       
17.    if (imgGrey==NULL){//image validation  
18.       cout << "No valid image input."<<endl;   
19.       char c=getchar();  
20.       return 1;  
21.    }   
22.    int w=imgGrey->width;  
23.    int h=imgGrey->height;  
24.   
25.    IplImage* eig_image = cvCreateImage(cvSize(w, h),IPL_DEPTH_32F, 1);  
26.    IplImage* temp_image = cvCreateImage(cvSize(w, h),IPL_DEPTH_32F, 1);   
27.   
28.    const int MAX_CORNERS = 140;//estimate a corner number  
29.    CvPoint2D32f corners[MAX_CORNERS] = {0};// coordinates of corners  
30.    //CvPoint2D32f* corners = new CvPoint2D32f[ MAX_CORNERS ]; //another method of declaring an array  
31.    int corner_count = MAX_CORNERS;   
32.    double quality_level = 0.1;//threshold for the eigenvalues  
33.    double min_distance = 5;//minimum distance between two corners  
34.    int eig_block_size = 3;//window size  
35.    int use_harris = false;//use 'harris method' or not  
36.   
37.    //----------initial guess by cvGoodFeaturesToTrack---------------  
38.    cvGoodFeaturesToTrack(imgGrey,  
39.             eig_image,   // output                   
40.             temp_image,  
41.             corners,  
42.             &corner_count,  
43.             quality_level,  
44.             min_distance,  
45.             NULL,  
46.             eig_block_size,  
47.             use_harris);  
48.   
49.   
50.    int r=2; //rectangle size  
51.    int lineWidth=1; // rectangle line width  
52.    //-----draw good feature corners on the original RGB image---------  
53.    for (int i=0;i<corner_count;i++){  
54.       cvRectangle(imgRGB2, cvPoint(corners[i].x-r,corners[i].y-r),   
55.             cvPoint(corners[i].x+r,corners[i].y+r), cvScalar(255,0,0),lineWidth);  
56.    }  
57.      
58.    int half_win_size=3;//the window size will be 3+1+3=7  
59.    int iteration=20;  
60.    double epislon=0.1;   
61.    cvFindCornerSubPix(  
62.          imgGrey,  
63.          corners,  
64.          corner_count,  
65.          cvSize(half_win_size,half_win_size),  
66.          cvSize(-1,-1),//no ignoring the neighbours of the center corner  
67.          cvTermCriteria(CV_TERMCRIT_ITER|CV_TERMCRIT_EPS,iteration,epislon)  
68.    );  
69.   
70.    //------draw subpix corners on another original RGB image------------  
71.    for (int i=0;i<corner_count;i++){  
72.       cvRectangle(imgRGB, cvPoint(corners[i].x-r,corners[i].y-r),   
73.             cvPoint(corners[i].x+r,corners[i].y+r), cvScalar(0,0,255),lineWidth);  
74.    }  
75.      
76.    //to display a coordinate of the third corner  
77.    cout<<"x="<<corners[2].x;  
78.    cout<<",y="<<corners[2].y<<endl;  
79.   
80.    cvNamedWindow("cvFindCornerSubPix", CV_WINDOW_AUTOSIZE );  
81.    cvShowImage( "cvFindCornerSubPix", imgRGB );  
82.    cvNamedWindow("cvGoodFeaturesToTrack", CV_WINDOW_AUTOSIZE );  
83.    cvShowImage( "cvGoodFeaturesToTrack", imgRGB2 );  
84.      
85.   
86.    cvWaitKey(0);   
87.    cvReleaseImage(&imgGrey);  
88.    cvReleaseImage(&imgRGB);  
89.    cvReleaseImage(&imgRGB2);  
90.    cvDestroyWindow("cvGoodFeaturesToTrack");  
91.    cvDestroyWindow("cvFindCornerSubPix");  
92.   
93.    //char c=getchar();  
94.    return 0;  
95. }  
96.   
97.   
98.   
99.    

 

result:

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• 上一篇：基于小波变换的图像融合
•  

  opencv 彩色图像直方图

  分类： OPENCV 2012-09-10 21:42  112人阅读  评论(0)  收藏  举报
  dst null float include
  [cpp]  view plain copy

  1.    

  [cpp]  view plain copy

  1.    

  [cpp]  view plain copy

  1. #include <opencv2/opencv.hpp>  
  2. <img style="width: 534px; height: 384px;" alt="" src="https://img-my.youkuaiyun.com/uploads/201209/10/1347284869_6368.jpg" width="534" height="580">  

  [cpp]  view plain copy

  1. #include "cv.h"  
  2. int main()  
  3. {  
  4.     IplImage *src=cvLoadImage("e:\\kankan\\baihe.jpg");  
  5.     IplImage *r=cvCreateImage(cvGetSize(src), 8, 1);  
  6.     IplImage *b=cvCreateImage(cvGetSize(src), 8, 1);  
  7.     IplImage *g=cvCreateImage(cvGetSize(src), 8, 1);  
  8.     cvSplit(src,b, g, r, NULL);  
  9.     IplImage *gray=cvCreateImage(cvGetSize(src),8,1);  
  10.     cvCvtColor(src,gray,CV_BGR2GRAY);  
  11.     int size=256;  
  12.     float range[]={0,255};  
  13.     float *ranges[]={range};  
  14.     CvHistogram *r_hist=cvCreateHist(1, &size, CV_HIST_ARRAY, ranges,1);  
  15.     CvHistogram *g_hist=cvCreateHist(1, &size, CV_HIST_ARRAY, ranges,1);  
  16.     CvHistogram *b_hist=cvCreateHist(1, &size, CV_HIST_ARRAY, ranges,1);  
  17.     CvHistogram *hist=cvCreateHist(1, &size, CV_HIST_ARRAY, ranges,1);  
  18.     cvCalcHist(&r, r_hist, 0, NULL);  
  19.     IplImage *r_dst=cvCreateImage(cvSize(400,300),8,3);  
  20.     cvSet(r_dst,cvScalarAll(255),0);  
  21.     float r_max=0;  
  22.     cvGetMinMaxHistValue(r_hist, NULL, &r_max, NULL, NULL);  
  23.     double r_bin_width=(double)r_dst->width/size;  
  24.     double r_bin_unith=(double)r_dst->height/r_max;  
  25.     for (int i=0; i<size; i++)  
  26.     {  
  27.         CvPoint p0=cvPoint(i*r_bin_width,r_dst->height);  
  28.         CvPoint p1=cvPoint((i+1)*r_bin_width,r_dst->height-cvGetReal1D(r_hist->bins,i)*r_bin_unith);    
  29.         cvRectangle(r_dst, p0, p1, cvScalar(255,0,0),-1,8,0);  
  30.     }  
  31.     cvCalcHist(&g, g_hist, 0, NULL);  
  32.     IplImage* g_dst=cvCreateImage(cvSize(400,300),8,3);    
  33.    cvSet(g_dst,cvScalarAll(255),0);    
  34.     float g_max=0;    
  35.     cvGetMinMaxHistValue(g_hist,NULL,&g_max,NULL,NULL);    
  36.     double g_bin_width=(double)g_dst->width/size;    
  37.     double g_bin_unith=(double)g_dst->height/g_max;    
  38.     for(int i=0;i<size;i++)    
  39.     {    
  40.         CvPoint p0=cvPoint(i*g_bin_width,g_dst->height);    
  41.        CvPoint p1=cvPoint((i+1)*g_bin_width,g_dst->height-cvGetReal1D(g_hist->bins,i)*g_bin_unith);    
  42.         cvRectangle(g_dst,p0,p1,cvScalar(0,255,0),-1,8,0);    
  43.     }    
  44.     cvCalcHist(&b,b_hist,0,NULL);   
  45.   
  46.    IplImage* b_dst=cvCreateImage(cvSize(400,300),8,3);    
  47.     cvSet(b_dst,cvScalarAll(255),0);    
  48.     float b_max=0;    
  49.     cvGetMinMaxHistValue(b_hist,NULL,&b_max,NULL,NULL);    
  50.     double b_bin_width=(double)b_dst->width/size;    
  51.     double b_bin_unith=(double)b_dst->height/b_max;    
  52.    for(int i=0;i<size;i++)    
  53.     {    
  54.         CvPoint p0=cvPoint(i*b_bin_width,b_dst->height);    
  55.         CvPoint p1=cvPoint((i+1)*b_bin_width,b_dst->height-cvGetReal1D(b_hist->bins,i)*b_bin_unith);    
  56.        cvRectangle(b_dst,p0,p1,cvScalar(0,0,255),-1,8,0);    
  57.     }    
  58.     cvCalcHist(&gray,hist,0,NULL);    
  59.     IplImage* gray_dst=cvCreateImage(cvSize(400,300),8,3);    
  60.     cvSet(gray_dst,cvScalarAll(255),0);    
  61.     float max=0;    
  62.     cvGetMinMaxHistValue(hist,NULL,&max,NULL,NULL);    
  63.     double bin_width=(double)gray_dst->width/size;    
  64.     double bin_unith=(double)gray_dst->height/max;    
  65.     for(int i=0;i<size;i++)    
  66.     {    
  67.         CvPoint p0=cvPoint(i*bin_width,gray_dst->height);    
  68.         CvPoint p1=cvPoint((i+1)*bin_width,gray_dst->height-cvGetReal1D(hist->bins,i)*bin_unith);    
  69.        cvRectangle(gray_dst,p0,p1,cvScalar(0),-1,8,0);    
  70.    }    
  71.   
  72.     cvNamedWindow("r");  
  73.     cvShowImage("r",r_dst);  
  74.     cvNamedWindow("g");  
  75.     cvShowImage("g",g_dst);  
  76.     cvNamedWindow("b");  
  77.     cvShowImage("b",b_dst);  
  78.     cvNamedWindow("gray");  
  79.     cvShowImage("gray",gray_dst);  
  80.     cvReleaseImage(&r_dst);    
  81.     cvReleaseImage(&g_dst);    
  82.     cvReleaseImage(&b_dst);    
  83.     cvReleaseImage(&gray_dst);    
  84.     cvWaitKey(0);  
  85.     return 0;  
  86.   
  87. }
  
  

• 图像不变矩

  分类： OPENCV 2012-09-13 19:30  107人阅读  评论(0)  收藏  举报
  math.h
  [cpp]  view plain copy

  1. #include<opencv2/opencv.hpp>  
  2. #include<iostream>  
  3. using namespace std;  
  4. using namespace cv;  
  5. /*===============================================// 
  6.  功能：不变矩匹配 
  7.  时间：3/28/2011 SkySeraph HQU 
  8.  参考： 
  9.  //===============================================*/  
  10.   
  11.  #include "math.h"  
  12.    
  13.  const char* filename ="e:\\kankan\\baihe.jpg";  
  14.  const char* filename2 ="e:\\kankan\\corner.png";  
  15.    
  16.  /*=============================================*/  
  17.  double M[7] = {0}; //HU不变矩  
  18.    
  19.  bool HuMoment(IplImage* img)  
  20.  {  
  21.    
  22.  int bmpWidth = img->width;  
  23.  int bmpHeight = img->height;  
  24.  int bmpStep = img->widthStep;  
  25.  int bmpChannels = img->nChannels;  
  26.  uchar*pBmpBuf = (uchar*)img->imageData;  
  27.    
  28.  double m00=0,m11=0,m20=0,m02=0,m30=0,m03=0,m12=0,m21=0; //中心矩  
  29.  double x0=0,y0=0; //计算中心距时所使用的临时变量（x-x'）  
  30.  double u20=0,u02=0,u11=0,u30=0,u03=0,u12=0,u21=0;//规范化后的中心矩  
  31.  //double M[7]; //HU不变矩  
  32.  double t1=0,t2=0,t3=0,t4=0,t5=0;//临时变量，  
  33.  //double Center_x=0,Center_y=0;//重心  
  34.  int Center_x=0,Center_y=0;//重心  
  35.  int i,j; //循环变量  
  36.    
  37.  // 获得图像的区域重心  
  38.  double s10=0,s01=0,s00=0; //0阶矩和1阶矩 //注：二值图像的0阶矩表示面积  
  39.  for(j=0;j<bmpHeight;j++)//y  
  40.  {  
  41.  for(i=0;i<bmpWidth;i++)//x  
  42.  {  
  43.  s10+=i*pBmpBuf[j*bmpStep+i];  
  44.  s01+=j*pBmpBuf[j*bmpStep+i];  
  45.  s00+=pBmpBuf[j*bmpStep+i];  
  46.  }  
  47.  }  
  48.  Center_x=(int)(s10/s00+0.5);  
  49.  Center_y=(int)(s01/s00+0.5);  
  50.    
  51.  // 计算二阶、三阶矩  
  52.  m00=s00;  
  53.  for(j=0;j<bmpHeight;j++)  
  54.  {  
  55.  for(i=0;i<bmpWidth;i++)//x  
  56.  {  
  57.  x0=(i-Center_x);  
  58.  y0=(j-Center_y);  
  59.  m11+=x0*y0*pBmpBuf[j*bmpStep+i];  
  60.  m20+=x0*x0*pBmpBuf[j*bmpStep+i];  
  61.  m02+=y0*y0*pBmpBuf[j*bmpStep+i];  
  62.  m03+=y0*y0*y0*pBmpBuf[j*bmpStep+i];  
  63.  m30+=x0*x0*x0*pBmpBuf[j*bmpStep+i];  
  64.  m12+=x0*y0*y0*pBmpBuf[j*bmpStep+i];  
  65.  m21+=x0*x0*y0*pBmpBuf[j*bmpStep+i];  
  66.  }  
  67.  }   
  68.    
  69.  // 计算规范化后的中心矩  
  70.  u20=m20/pow(m00,2);  
  71.  u02=m02/pow(m00,2);  
  72.  u11=m11/pow(m00,2);  
  73.  u30=m30/pow(m00,2.5);  
  74.  u03=m03/pow(m00,2.5);  
  75.  u12=m12/pow(m00,2.5);  
  76.  u21=m21/pow(m00,2.5);  
  77.    
  78.  // 计算中间变量。  
  79.  t1=(u20-u02);  
  80.  t2=(u30-3*u12);  
  81.  t3=(3*u21-u03);  
  82.  t4=(u30+u12);  
  83.  t5=(u21+u03);  
  84.    
  85.  // 计算不变矩  
  86.  M[0]=u20+u02;  
  87.  M[1]=t1*t1+4*u11*u11;  
  88.  M[2]=t2*t2+t3*t3;  
  89.  M[3]=t4*t4+t5*t5;  
  90.  M[4]=t2*t4*(t4*t4-3*t5*t5)+t3*t5*(3*t4*t4-t5*t5);  
  91.  M[5]=t1*(t4*t4-t5*t5)+4*u11*t4*t5;  
  92.  M[6]=t3*t4*(t4*t4-3*t5*t5)-t2*t5*(3*t4*t4-t5*t5);  
  93.    
  94.    
  95.  /*cout<<M[0]<<endl;//<<二"<<M[0]<<"三"<<M[0]<<"四"<<M[0]<<"五"<<M[0]<<"六"<<M[0]<<"七"<<M[0]<<endl; 
  96.  cout<<M[1]<<endl; 
  97.  cout<<M[2]<<endl; 
  98.  cout<<M[3]<<endl; 
  99.  cout<<M[4]<<endl; 
  100.  cout<<M[5]<<endl; 
  101.  cout<<M[6]<<endl; 
  102.  cout<<endl;*/  
  103.  return true;  
  104.  }  
  105.    
  106.    
  107.  int main(char argc,char** argv)  
  108.  {  
  109.  int i;  
  110.  double Sa[7] = {0},Ta[7] ={0};  
  111.    
  112.  ///*源图像  
  113.  IplImage*img = cvLoadImage(filename,0);//灰度  
  114.  HuMoment(img);  
  115.  for(i=0;i<7;i++)  
  116.  {  
  117.  Sa[i] = M[i];  
  118.  M[i] =0;  
  119.  }  
  120.  cout<<Sa[0]<<endl;  
  121.  cout<<Sa[1]<<endl;  
  122.  cout<<Sa[2]<<endl;  
  123.  cout<<Sa[3]<<endl;  
  124.  cout<<Sa[4]<<endl;  
  125.  cout<<Sa[5]<<endl;  
  126.  cout<<Sa[6]<<endl;  
  127.  cout<<endl;  
  128.  //*/  
  129.    
  130.    
  131.  ///*模板图  
  132.  IplImage*tpl = cvLoadImage(filename2,0);//灰度  
  133.  HuMoment(tpl);  
  134.  for(i=0;i<7;i++)  
  135.  {  
  136.  Ta[i] = M[i];  
  137.  M[i] =0;  
  138.  }  
  139.  cout<<Ta[0]<<endl;  
  140.  cout<<Ta[1]<<endl;  
  141.  cout<<Ta[2]<<endl;  
  142.  cout<<Ta[3]<<endl;  
  143.  cout<<Ta[4]<<endl;  
  144.  cout<<Ta[5]<<endl;  
  145.  cout<<Ta[6]<<endl;  
  146.  cout<<endl;  
  147.    
  148.    
  149.  // 计算相似度  
  150.  double dbR =0; //相似度  
  151.  double dSigmaST =0;  
  152.  double dSigmaS =0;  
  153.  double dSigmaT =0;  
  154.  double temp =0;   
  155.    
  156.  for(i=0;i<7;i++)  
  157.  {  
  158.  temp = Sa[i]*Ta[i];  
  159.  dSigmaST+=temp;  
  160.  dSigmaS+=pow(Sa[i],2);  
  161.  dSigmaT+=pow(Ta[i],2);  
  162.  }  
  163.  dbR = dSigmaST/(sqrt(dSigmaS)*sqrt(dSigmaT));  
  164.  printf("%lf\n",dbR);  
  165.  //cout<<dbR<<endl;  
  166.    
  167.  cvReleaseImage(&img);  
  168.  cvReleaseImage(&tpl);  
  169.    
  170.  return 0;  
  171.  }