我正在使用带有visual c ++ 2010的openCV 2.44 我想使用网络摄像头识别几个对象，并在找到时打印对象的名称，但是当我与帧比较时，我使用obj1,2,3,4,5（good_matches.size）为帧和图像之间的每次比较获得相同的值（），good_matches1.size（）..具有相同的值） 你能告诉我什么是错的，以及我如何加速算法
#include <opencv2\imgproc\imgproc_c.h>
#include <stdio.h>
#include <math.h>
#include <opencv\highgui.h>
#include <opencv\cv.h>
#include <opencv2/features2d/features2d.hpp>
#include <opencv2/calib3d/calib3d.hpp>
#include <opencv2/core/core.hpp>
#include <opencv2/nonfree/features2d.hpp>
#include <opencv2/legacy/legacy.hpp>
using namespace cv;
#define nimg 5
int main()
{
Mat object = imread( "aa.jpg", CV_LOAD_IMAGE_GRAYSCALE );
Mat object1 = imread( "bb.jpg", CV_LOAD_IMAGE_GRAYSCALE );
Mat object2 = imread( "bb.jpg", CV_LOAD_IMAGE_GRAYSCALE );
Mat object3 = imread( "bb.jpg", CV_LOAD_IMAGE_GRAYSCALE );
Mat object4 = imread( "bb.jpg", CV_LOAD_IMAGE_GRAYSCALE );
/* if( !object.data )
{
std::cout<< "Error reading object " << std::endl;
return -1;
}
*/
//Detect the keypoints using SURF Detector
int minHessian = 500;
SurfFeatureDetector detector( minHessian );
std::vector<KeyPoint> kp_object;
std::vector<KeyPoint> kp_object1;
std::vector<KeyPoint> kp_object2;
std::vector<KeyPoint> kp_object3;
std::vector<KeyPoint> kp_object4;
detector.detect( object, kp_object );
detector.detect( object1, kp_object1 );
detector.detect( object2, kp_object2 );
detector.detect( object3, kp_object3 );
detector.detect( object4, kp_object4 );
//Calculate descriptors (feature vectors)
SurfDescriptorExtractor extractor;
Mat des_object;
Mat des_object1;
Mat des_object2;
Mat des_object3;
Mat des_object4;
extractor.compute( object, kp_object, des_object );
extractor.compute( object1, kp_object1, des_object1 );
extractor.compute( object2, kp_object2, des_object2 );
extractor.compute( object3, kp_object3, des_object3 );
extractor.compute( object4, kp_object4, des_object4 );
FlannBasedMatcher matcher;
VideoCapture cap(1);
namedWindow("Good Matches");
std::vector<Point2f> obj_corners(4);
//Get the corners from the object
obj_corners[0] = cvPoint(0,0);
obj_corners[1] = cvPoint( object.cols, 0 );
obj_corners[2] = cvPoint( object.cols, object.rows );
obj_corners[3] = cvPoint( 0, object.rows );
char key = 'a';
int framecount = 0;
Mat frame;
Mat des_image, img_matches;
char vect[nimg];
char contor;
char ok, ko;
while (1)
{
cap >> frame;
if (framecount < 5)
{
framecount++;
continue;
}
std::vector<KeyPoint> kp_image;
std::vector<vector<DMatch > > matches;
std::vector<vector<DMatch > > matches1;
std::vector<vector<DMatch > > matches2;
std::vector<vector<DMatch > > matches3;
std::vector<vector<DMatch > > matches4;
std::vector<DMatch > good_matches;
std::vector<DMatch > good_matches1;
std::vector<DMatch > good_matches2;
std::vector<DMatch > good_matches3;
std::vector<DMatch > good_matches4;
std::vector<Point2f> obj;
std::vector<Point2f> scene;
std::vector<Point2f> scene_corners(4);
Mat H;
Mat image;
cvtColor(frame, image, CV_RGB2GRAY);
detector.detect( image, kp_image );
extractor.compute( image, kp_image, des_image );
matcher.knnMatch(des_object, des_image, matches, 2);
// printf("d \n");
//////////////////////////////////////////////////////
contor=0;
for(int i = 0; i < min(des_image.rows-1,(int) matches.size()); i++) //THIS LOOP IS SENSITIVE TO SEGFAULTS
{
if((matches[i][0].distance < 0.6*(matches[i][1].distance)) && ((int) matches[i].size()<=2 && (int) matches[i].size()>0))
{
good_matches.push_back(matches[i][0]);
}
}
vect[contor]=good_matches.size();
/////////////////////////////////////////////////////
contor=1;
matcher.knnMatch(des_object, des_image, matches1, 2);
for(int i = 0; i < min(des_image.rows-1,(int) matches1.size()); i++) //THIS LOOP IS SENSITIVE TO SEGFAULTS
{
if((matches1[i][0].distance < 0.6*(matches1[i][1].distance)) && ((int) matches1[i].size()<=2 && (int) matches1[i].size()>0))
{
good_matches1.push_back(matches1[i][0]);
}
}
vect[contor]=good_matches1.size();
/////////////////////////////////////////////////////
contor=2;
matcher.knnMatch(des_object, des_image, matches2, 2);
for(int i = 0; i < min(des_image.rows-1,(int) matches2.size()); i++) //THIS LOOP IS SENSITIVE TO SEGFAULTS
{
if((matches2[i][0].distance < 0.6*(matches2[i][1].distance)) && ((int) matches2[i].size()<=2 && (int) matches2[i].size()>0))
{
good_matches2.push_back(matches2[i][0]);
}
}
vect[contor]=good_matches2.size();
////////////////////////////////////////////////////
contor =3;
matcher.knnMatch(des_object, des_image, matches3, 2);
for(int i = 0; i < min(des_image.rows-1,(int) matches3.size()); i++) //THIS LOOP IS SENSITIVE TO SEGFAULTS
{
if((matches3[i][0].distance < 0.6*(matches3[i][1].distance)) && ((int) matches3[i].size()<=2 && (int) matches3[i].size()>0))
{
good_matches3.push_back(matches[i][0]);
}
}
vect[contor]=good_matches3.size();
//////////////////////////////////////////////////
contor=4;
matcher.knnMatch(des_object, des_image, matches4, 2);
for(int i = 0; i < min(des_image.rows-1,(int) matches4.size()); i++) //THIS LOOP IS SENSITIVE TO SEGFAULTS
{
if((matches4[i][0].distance < 0.6*(matches4[i][1].distance)) && ((int) matches4[i].size()<=2 && (int) matches4[i].size()>0))
{
good_matches4.push_back(matches[i][0]);
}
}
vect[contor]=good_matches4.size();
printf("%d %d %d %d %d \n ",vect[0],vect[1],vect[2],vect[3],vect[4]);
ok=0;
for (contor=1;contor<nimg;contor++)
if (vect[contor]>vect[contor-1])
ok=contor;
for (ko=10;ko>3;ko++)
{
if (ok==0 && vect[ok]>ko)
{printf("obj1 \n");
ko=2;}
else if (ok==1 && vect[ok]>ko)
{printf("obj2 \n");
ko=2;}
else if (ok==2 && vect[ok]>ko)
{printf("obj3 \n");
ko=2;}
else if (ok==3 && vect[ok]>ko)
{printf("obj4 \n");
ko=2;}
else if (ok==4 && vect[ok]>ko)
{printf("obj5 \n");
ko=2;}
}
//Show detected matches
imshow( "Good Matches",frame /*img_matches*/ );
key = waitKey(1);
}
}
答案 0 :(得分：0)
长篇大论，它运行它给我很好的匹配，但只有第一个图像（对象），其他人获得与第一个相同的结果，在此代码的末尾我想要匹配，匹配1,2,3,4为每个图像（对象）提供不同的结果以了解凸轮看到的对象，在vect中为每个图像保留good_matches.size（）（比较）
//////////////////////////////////////////////////////
matcher.knnMatch(des_object, des_image, matches, 2);
contor=0;
for(int i = 0; i < min(des_image.rows-1,(int) matches.size()); i++) //THIS LOOP IS SENSITIVE TO SEGFAULTS
{
if((matches[i][0].distance < 0.6*(matches[i][1].distance)) && ((int) matches[i].size()<=2 && (int) matches[i].size()>0))
{
good_matches.push_back(matches[i][0]);
}
}
vect[contor]=good_matches.size();
/////////////////////////////////////////////////////
contor=1;
matcher.knnMatch(des_object, des_image, matches1, 2);
for(int i = 0; i < min(des_image.rows-1,(int) matches1.size()); i++) //THIS LOOP IS SENSITIVE TO SEGFAULTS
{
if((matches1[i][0].distance < 0.6*(matches1[i][1].distance)) && ((int) matches1[i].size()<=2 && (int) matches1[i].size()>0))
{
good_matches1.push_back(matches1[i][0]);
}
}
vect[contor]=good_matches1.size();
/////////////////////////////////////////////////////
contor=2;
matcher.knnMatch(des_object, des_image, matches2, 2);
for(int i = 0; i < min(des_image.rows-1,(int) matches2.size()); i++) //THIS LOOP IS SENSITIVE TO SEGFAULTS
{
if((matches2[i][0].distance < 0.6*(matches2[i][1].distance)) && ((int) matches2[i].size()<=2 && (int) matches2[i].size()>0))
{
good_matches2.push_back(matches2[i][0]);
}
}
vect[contor]=good_matches2.size();
////////////////////////////////////////////////////
contor =3;
matcher.knnMatch(des_object, des_image, matches3, 2);
for(int i = 0; i < min(des_image.rows-1,(int) matches3.size()); i++) //THIS LOOP IS SENSITIVE TO SEGFAULTS
{
if((matches3[i][0].distance < 0.6*(matches3[i][1].distance)) && ((int) matches3[i].size()<=2 && (int) matches3[i].size()>0))
{
good_matches3.push_back(matches[i][0]);
}
}
vect[contor]=good_matches3.size();
//////////////////////////////////////////////////
contor=4;
matcher.knnMatch(des_object, des_image, matches4, 2);
for(int i = 0; i < min(des_image.rows-1,(int) matches4.size()); i++) //THIS LOOP IS SENSITIVE TO SEGFAULTS
{
if((matches4[i][0].distance < 0.6*(matches4[i][1].distance)) && ((int) matches4[i].size()<=2 && (int) matches4[i].size()>0))
{
good_matches4.push_back(matches[i][0]);
}
}
vect[contor]=good_matches4.size();