Surf特徵檢測

原文：

對於其原理我還沒看過，只是略知道是特徵點檢測的，最近同學用到需要將檢測到的匹配的特徵點輸出來，這才看了一下函式的介面，如果以後用到了原理，再去研究和學習一下，這裡對**進行一下備份：

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#include

#include "opencv2/core/core.hpp"

#include "opencv2/features2d/features2d.hpp"

#include "opencv2/highgui/highgui.hpp"

#include "opencv2/nonfree/features2d.hpp"

#include

using

namespace

cv;

using

namespace

std;

void

readme();

/** @function main */

intmain(

intargc,

char

** argv )

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 )

//-- step 1: detect the keypoints using surf detector

intminhessian = 400;

surffeaturedetector detector( minhessian );

std::vectorkeypoints_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 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(

inti = 0; i

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 )

//-- ps.- radiusmatch can also be used here.

std::vector good_matches;

for(

inti = 0; i

} cout<

cout<

mat img_matches;

//-- draw only "good" matches

#if 1

drawmatches( img_1, keypoints_1, img_2, keypoints_2,

good_matches, img_matches, scalar::all(-1), scalar::all(-1),

vector

>(), drawmatchesflags::not_draw_single_points );

#endif

#if 0

drawmatches( img_1, keypoints_1, img_2, keypoints_2,

matches, img_matches, scalar::all(-1), scalar::all(-1),

vector

>(), drawmatchesflags::not_draw_single_points );

#endif

//-- show detected matches

imshow( "good matches"

, img_matches );

#if 1

//在原影象上顯示匹配的點

//顏色分別為:0綠色，1黃色，2白色，3紅色，4淺藍色，5藍色，6黑色

string strs=;

scalar colors[7]=;

mat img1_1(img_1.rows,img_1.cols,cv_8uc3);

mat img2_2(img_1.rows,img_1.cols,cv_8uc3);

cvtcolor(img_1,img1_1,cv_gray2bgr);

cvtcolor(img_2,img2_2,cv_gray2bgr);

cout<

for(

intj=0;j

#endif

#if 0

for(

intj=0;j

#endif

imshow("img1_1"

,img1_1);

imshow("img2_2"

,img2_2);

for(

inti = 0; i

waitkey(0);

return

} /** @function readme */

void

readme()

用surf描述子計算每一張的特徵點，然後利用flann演算法將兩張影象的特徵點進行匹配。匹配的資訊儲存在vector型別的容器中，我們通過下表操作符獲取乙個dmatch物件，該物件的queryidx和queryidx用來標識該點在keypoints中的位置。故利用keypoints[matches[i].queryidx]可以訪問到關鍵點（特徵點物件),然後利用特徵點物件的座標成員pt，即得到了特徵點的x和y的座標。我們通過才彩色影象上畫出來標識他們的一一對應，**如下：

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scalar colors[7]=;

mat img1_1(img_1.rows,img_1.cols,cv_8uc3);

mat img2_2(img_1.rows,img_1.cols,cv_8uc3);

cvtcolor(img_1,img1_1,cv_gray2bgr);

cvtcolor(img_2,img2_2,cv_gray2bgr);

for(

intj=0;j

執行**，效果如下:

Surf特徵檢測

特徵檢測演算法 SURF

surf特徵檢測描述和匹配

opencv 33 SURF特徵檢測

Surf特徵檢測

特徵檢測演算法 SURF

surf特徵檢測描述和匹配

opencv 33 SURF特徵檢測

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