OpenCV 使用 FLANN 库实现特征匹配

OpenCV 使用 FLANN 库实现特征匹配

目标

在这篇文章中你将学到：
使用 FlannBasedMatcher 接口来执行快速高效的匹配，用的是 FLANN ( Fast
Approximate Nearest Neighbor Search Library ) 算法

代码

完整代码可从这里 下载

/**
* @file SURF_FlannMatcher
* @brief SURF detector + descriptor + FLANN Matcher
* @author A. Huaman
*/

#include "opencv2/opencv_modules.hpp"
#include <stdio.h>

#ifndef HAVE_OPENCV_NONFREE

int main(int, char**)
{
printf("The sample requires nonfree module that is not available in your OpenCV distribution.\n");
return -1;
}

#else

# include "opencv2/core/core.hpp"
# include "opencv2/features2d/features2d.hpp"
# include "opencv2/highgui/highgui.hpp"
# include "opencv2/nonfree/features2d.hpp"

using namespace cv;

void readme();

/**
* @function main
* @brief Main function
*/
int main( int argc, char** argv )
{
if( argc != 3 )
{ readme(); return -1; }

Mat img_1 = imread( argv[1], CV_LOAD_IMAGE_GRAYSCALE );
Mat img_2 = imread( argv[2], CV_LOAD_IMAGE_GRAYSCALE );

if( !img_1.data || !img_2.data )
{ printf(" --(!) Error reading images \n"); return -1; }

//-- Step 1: Detect the keypoints using SURF Detector
int minHessian = 400;

SurfFeatureDetector detector( minHessian );

std::vector<KeyPoint> keypoints_1, keypoints_2;

detector.detect( img_1, keypoints_1 );
detector.detect( img_2, keypoints_2 );

//-- Step 2: Calculate descriptors (feature vectors)
SurfDescriptorExtractor extractor;

Mat descriptors_1, descriptors_2;

extractor.compute( img_1, keypoints_1, descriptors_1 );
extractor.compute( img_2, keypoints_2, descriptors_2 );

//-- Step 3: Matching descriptor vectors using FLANN matcher
FlannBasedMatcher matcher;
std::vector< DMatch > matches;
matcher.match( descriptors_1, descriptors_2, matches );

double max_dist = 0; double min_dist = 100;

//-- Quick calculation of max and min distances between keypoints
for( int i = 0; i < descriptors_1.rows; i++ )
{ double dist = matches[i].distance;
if( dist < min_dist ) min_dist = dist;
if( dist > max_dist ) max_dist = dist;
}

printf("-- Max dist : %f \n", max_dist );
printf("-- Min dist : %f \n", min_dist );

//-- Draw only "good" matches (i.e. whose distance is less than 2*min_dist,
//-- or a small arbitary value ( 0.02 ) in the event that min_dist is very
//-- small)
//-- PS.- radiusMatch can also be used here.
std::vector< DMatch > good_matches;

for( int i = 0; i < descriptors_1.rows; i++ )
{ if( matches[i].distance <= max(2*min_dist, 0.02) )
{ good_matches.push_back( matches[i]); }
}

//-- Draw only "good" matches
Mat img_matches;
drawMatches( img_1, keypoints_1, img_2, keypoints_2,
good_matches, img_matches, Scalar::all(-1), Scalar::all(-1),
vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );

//-- Show detected matches
imshow( "Good Matches", img_matches );

for( int i = 0; i < (int)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;
}

/**
* @function readme
*/
void readme()
{ printf(" Usage: ./SURF_FlannMatcher <img1> <img2>\n"); }

#endif

结果

这是对首张图片进行特征检测的结果



下面是对关键点进行过滤过程中的控制台输出：



来源：docs.opencv.org [英文]