我正在尝试使用此代码，以便可以使用彩色帧/图像来实现SURF，然后使用此处的代码Kalman_color_Object_track通过卡尔曼滤波器使用颜色值来跟踪检测到的对象。因此，这些是我打算做的步骤，但我被卡住了，因为这个SURF检测代码不接受/不适用于彩色图像：
- "book1.png"是彩色图像
-
从传入帧中检测到图像周围的矩形后，Mat结构将更改为IplImage，因为的Kalman_Color_Object_Track代码是C++
dest_image=cvCloneImage（&（IplImage）图像）；
mat_frame＝cvCloneImage（&（IplImage）帧）；
-
调用
Kalman_Color_Object_Track( mat_frame,dest_image,30);方法。
问题：（A）如何使此代码工作，以便提取和检测彩色图像的SURF特征？（B） 我不确定Kalman_Color_Object_Track()的函数签名中应该传递什么，以及（C）在对象检测模块中应该调用它的确切位置？
#include <stdio.h>
#include <iostream>
#include "opencv2/core/core.hpp"
#include "opencv2/features2d/features2d.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/calib3d/calib3d.hpp"
using namespace cv;
IplImage *mat_dest_image=0;
IplImage *mat_frame=0;
/* Object Detection and recognition from video*/
int main()
{
Mat object = imread( "book1.png", );
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;
detector.detect( object, kp_object );
//Calculate descriptors (feature vectors)
SurfDescriptorExtractor extractor;
Mat des_object;
extractor.compute( object, kp_object, des_object );
FlannBasedMatcher matcher;
namedWindow("Good Matches");
namedWindow("Tracking");
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;
VideoCapture cap("booksvideo.avi");
for(; ;)
{
Mat frame;
cap >> frame;
imshow("Good Matches", frame);
Mat des_image, img_matches;
std::vector<KeyPoint> kp_image;
std::vector<vector<DMatch > > matches;
std::vector<DMatch > good_matches;
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);
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]);
}
}
//Draw only "good" matches
drawMatches( object, kp_object, image, kp_image, good_matches, img_matches, Scalar::all(-1), Scalar::all(-1), vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );
if (good_matches.size() >= 4)
{
for( int i = 0; i < good_matches.size(); i++ )
{
//Get the keypoints from the good matches
obj.push_back( kp_object[ good_matches[i].queryIdx ].pt );
scene.push_back( kp_image[ good_matches[i].trainIdx ].pt );
}
H = findHomography( obj, scene, CV_RANSAC );
perspectiveTransform( obj_corners, scene_corners, H);
//Draw lines between the corners (the mapped object in the scene image )
line( img_matches, scene_corners[0] + Point2f( object.cols, 0), scene_corners[1] + Point2f( object.cols, 0), Scalar(0, 255, 0), 4 );
line( img_matches, scene_corners[1] + Point2f( object.cols, 0), scene_corners[2] + Point2f( object.cols, 0), Scalar( 0, 255, 0), 4 );
line( img_matches, scene_corners[2] + Point2f( object.cols, 0), scene_corners[3] + Point2f( object.cols, 0), Scalar( 0, 255, 0), 4 );
line( img_matches, scene_corners[3] + Point2f( object.cols, 0), scene_corners[0] + Point2f( object.cols, 0), Scalar( 0, 255, 0), 4 );
mat_dest_image=cvCloneImage(&(IplImage)image);
mat_frame=cvCloneImage(&(IplImage)frame);
Kalman_Color_Object_Track( ); // The tracking method
}
//Show detected matches
imshow( "Good Matches", img_matches );
for( int i = 0; i < good_matches.size(); i++ )
{ printf( "-- Good Match [%d] Keypoint 1: %d -- Keypoint 2: %d n", i, good_matches[i].queryIdx, good_matches[i].trainIdx ); }
waitKey(0);
}
return 0;
}
本文通过独立计算每个通道的梯度直方图来实现彩色图像上的SIFT描述符。也许您可以对SURF功能尝试同样的方法。