学习OpenCV——Surf简化版

之前写过一遍关于学习surf算法的blog：http://blog.csdn.net/sangni007/article/details/7482960

但是代码比较麻烦，而且其中还涉及到flann算法（其中的Random KDTree+KNN），虽然能看明白，但是比较费劲，今天在文档中找到一个简化版本：

1.SurfFeatureDetector detector( minHessian );构造surf检测器；

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

2.SurfDescriptorExtractor extractor;提取描述结构

Mat descriptors_1, descriptors_2;

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

3.BruteForceMatcher< L2<float> > matcher;牛逼的匹配结构啊！！！！可以直接暴力测量距离

std::vector< DMatch > matches;

matcher.match( descriptors_1, descriptors_2, matches );

文档：http://opencv.itseez.com/modules/gpu/doc/feature_detection_and_description.html?highlight=bruteforce#gpu::BruteForceMatcher_GPU

PS：OpenCV 你是在太强悍了！！！只有我想不到，木有你办不到的啊！ 我真心跪了！

[cpp] view plain copy

print?

/**

* @file SURF_descriptor

* @brief SURF detector + descritpor + BruteForce Matcher + drawing matches with OpenCV functions

* @author A. Huaman

*/

#include <stdio.h>

#include <iostream>

#include "opencv2/core/core.hpp"

#include "opencv2/features2d/features2d.hpp"

#include "opencv2/highgui/highgui.hpp"

using namespace cv;

using namespace std;

void readme();

/**

* @function main

* @brief Main function

*/

int main( int argc, char** argv )

{

//if( argc != 3 )

//{ return -1; }

Mat img_1 = imread( "D:/src.jpg", CV_LOAD_IMAGE_GRAYSCALE );

Mat img_2 = imread( "D:/Demo.jpg", CV_LOAD_IMAGE_GRAYSCALE );

if( !img_1.data || !img_2.data )

{ return -1; }

//-- Step 1: Detect the keypoints using SURF Detector

int minHessian = 400;

double t=getTickCount();

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 with a brute force matcher

BruteForceMatcher< L2<float> > matcher;

std::vector< DMatch > matches;

matcher.match( descriptors_1, descriptors_2, matches );

t=getTickCount()-t;

t=t*1000/getTickFrequency();

//-- Draw matches

Mat img_matches;

drawMatches( img_1, keypoints_1, img_2, keypoints_2, matches, img_matches );

cout<<"Cost Time:"<<t<<endl;

//-- Show detected matches

imshow("Matches", img_matches );

waitKey(0);

return 0;

}

/**

* @function readme

*/

void readme()

{ std::cout << " Usage: ./SURF_descriptor <img1> <img2>" << std::endl; }



图像中match的keypoints没有经过过滤。导致匹配点过多

文档地址：http://opencv.itseez.com/doc/tutorials/features2d/feature_description/feature_description.html?highlight=description

文档中还有一个版本带定位的和过滤Match的，

：http://opencv.itseez.com/doc/tutorials/features2d/feature_homography/feature_homography.html?highlight=drawmatchesflags

from: http://blog.csdn.net/yangtrees/article/details/7544133