偶看新闻绍兴圣苗针纺有限公司:最新版的OpenCV中新增加的ORB特征的使用
来源：百度文库 编辑：偶看新闻 时间：2024/04/30 03:18:01
看到OpenCV2.3.1里面ORB特征提取算法也在里面了，套用给的SURF特征例子程序改为ORB特征一直提示错误，类型不匹配神马的，由于没有找到示例程序，只能自己找答案。
（ORB特征论文：ORB： an efficient alternative to SIFT or SURF.点击下载论文）
经过查找发现：
描述符数据类型有是float的，比如说SIFT，SURF描述符，还有是uchar的，比如说有ORB，BRIEF
对于float 匹配方式有：
FlannBased
BruteForce >
BruteForce >
BruteForce >
对于uchar有：
BruteForce
BruteForce
view plain
BruteForceMatcher< L2 > matcher;//改动的地方
完整代码如下：
view plain
#include
#include "opencv2/core/core.hpp"
#include "opencv2/features2d/features2d.hpp"
#include "opencv2/highgui/highgui.hpp"
#include
#include
using namespace cv;
using namespace std;
int main()
{
Mat img_1 = imread("D:\\image\\img1.jpg");
Mat img_2 = imread("D:\\image\\img2.jpg");
if (!img_1.data || !img_2.data)
{
cout << "error reading images " << endl;
return -1;
}
ORB orb;
vector keyPoints_1, keyPoints_2;
Mat descriptors_1, descriptors_2;
orb(img_1, Mat(), keyPoints_1, descriptors_1);
orb(img_2, Mat(), keyPoints_2, descriptors_2);
BruteForceMatcher matcher;
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( 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 0.6*max_dist )
//-- 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 < 0.6*max_dist )
{
good_matches.push_back( matches[i]);
}
}
Mat img_matches;
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);
imshow( "Match", img_matches);
cvWaitKey();
return 0;
}
另外: SURF SIFT
/*
SIFT sift;
sift(img_1, Mat(), keyPoints_1, descriptors_1);
sift(img_2, Mat(), keyPoints_2, descriptors_2);
BruteForceMatcher > matcher;
*/
/*
SURF surf;
surf(img_1, Mat(), keyPoints_1);
surf(img_2, Mat(), keyPoints_2);
SurfDescriptorExtractor extrator;
extrator.compute(img_1, keyPoints_1, descriptors_1);
extrator.compute(img_2, keyPoints_2, descriptors_2);
BruteForceMatcher > matcher;
*/
效果：
另外一个是寻找目标匹配
在右边的场景图里面寻找左边那幅图的starbucks标志
效果如下：
需要在之前的那个imshow之前加上如下代码即可完成一个简单的功能展示：
view plain
// localize the object
std::vector obj;
std::vector scene;
for (size_t i = 0; i < good_matches.size(); ++i)
{
// get the keypoints from the good matches
obj.push_back(keyPoints_1[ good_matches[i].queryIdx ].pt);
scene.push_back(keyPoints_2[ good_matches[i].trainIdx ].pt);
}
Mat H = findHomography( obj, scene, CV_RANSAC );
// get the corners from the image_1
std::vector obj_corners(4);
obj_corners[0] = cvPoint(0,0);
obj_corners[1] = cvPoint( img_1.cols, 0);
obj_corners[2] = cvPoint( img_1.cols, img_1.rows);
obj_corners[3] = cvPoint( 0, img_1.rows);
std::vector scene_corners(4);
perspectiveTransform( obj_corners, scene_corners, H);
// draw lines between the corners (the mapped object in the scene - image_2)
line( img_matches, scene_corners[0] + Point2f( img_1.cols, 0), scene_corners[1] + Point2f( img_1.cols, 0),Scalar(0,255,0));
line( img_matches, scene_corners[1] + Point2f( img_1.cols, 0), scene_corners[2] + Point2f( img_1.cols, 0),Scalar(0,255,0));
line( img_matches, scene_corners[2] + Point2f( img_1.cols, 0), scene_corners[3] + Point2f( img_1.cols, 0),Scalar(0,255,0));
line( img_matches, scene_corners[3] + Point2f( img_1.cols, 0), scene_corners[0] + Point2f( i
（ORB特征论文：ORB： an efficient alternative to SIFT or SURF.点击下载论文）
经过查找发现：
描述符数据类型有是float的，比如说SIFT，SURF描述符，还有是uchar的，比如说有ORB，BRIEF
对于float 匹配方式有：
FlannBased
BruteForce
BruteForce
BruteForce
对于uchar有：
BruteForce
BruteForce
view plain
BruteForceMatcher< L2
完整代码如下：
view plain
#include
#include "opencv2/core/core.hpp"
#include "opencv2/features2d/features2d.hpp"
#include "opencv2/highgui/highgui.hpp"
#include
#include
using namespace cv;
using namespace std;
int main()
{
Mat img_1 = imread("D:\\image\\img1.jpg");
Mat img_2 = imread("D:\\image\\img2.jpg");
if (!img_1.data || !img_2.data)
{
cout << "error reading images " << endl;
return -1;
}
ORB orb;
vector
Mat descriptors_1, descriptors_2;
orb(img_1, Mat(), keyPoints_1, descriptors_1);
orb(img_2, Mat(), keyPoints_2, descriptors_2);
BruteForceMatcher
vector
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 0.6*max_dist )
//-- 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 < 0.6*max_dist )
{
good_matches.push_back( matches[i]);
}
}
Mat img_matches;
drawMatches(img_1, keyPoints_1, img_2, keyPoints_2,
good_matches, img_matches, Scalar::all(-1), Scalar::all(-1),
vector
imshow( "Match", img_matches);
cvWaitKey();
return 0;
}
另外: SURF SIFT
/*
SIFT sift;
sift(img_1, Mat(), keyPoints_1, descriptors_1);
sift(img_2, Mat(), keyPoints_2, descriptors_2);
BruteForceMatcher
*/
/*
SURF surf;
surf(img_1, Mat(), keyPoints_1);
surf(img_2, Mat(), keyPoints_2);
SurfDescriptorExtractor extrator;
extrator.compute(img_1, keyPoints_1, descriptors_1);
extrator.compute(img_2, keyPoints_2, descriptors_2);
BruteForceMatcher
*/
效果：
另外一个是寻找目标匹配
在右边的场景图里面寻找左边那幅图的starbucks标志
效果如下：
需要在之前的那个imshow之前加上如下代码即可完成一个简单的功能展示：
view plain
// localize the object
std::vector
std::vector
for (size_t i = 0; i < good_matches.size(); ++i)
{
// get the keypoints from the good matches
obj.push_back(keyPoints_1[ good_matches[i].queryIdx ].pt);
scene.push_back(keyPoints_2[ good_matches[i].trainIdx ].pt);
}
Mat H = findHomography( obj, scene, CV_RANSAC );
// get the corners from the image_1
std::vector
obj_corners[0] = cvPoint(0,0);
obj_corners[1] = cvPoint( img_1.cols, 0);
obj_corners[2] = cvPoint( img_1.cols, img_1.rows);
obj_corners[3] = cvPoint( 0, img_1.rows);
std::vector
perspectiveTransform( obj_corners, scene_corners, H);
// draw lines between the corners (the mapped object in the scene - image_2)
line( img_matches, scene_corners[0] + Point2f( img_1.cols, 0), scene_corners[1] + Point2f( img_1.cols, 0),Scalar(0,255,0));
line( img_matches, scene_corners[1] + Point2f( img_1.cols, 0), scene_corners[2] + Point2f( img_1.cols, 0),Scalar(0,255,0));
line( img_matches, scene_corners[2] + Point2f( img_1.cols, 0), scene_corners[3] + Point2f( img_1.cols, 0),Scalar(0,255,0));
line( img_matches, scene_corners[3] + Point2f( img_1.cols, 0), scene_corners[0] + Point2f( i
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