opencv/doc/tutorials/features2d/feature_homography/feature_homography.markdown

Features2D + Homography to find a known object

Goal

In this tutorial you will learn how to:

• Use the function @ref cv::findHomography to find the transform between matched keypoints.
• Use the function @ref cv::perspectiveTransform to map the points.

Theory

Code

This tutorial code's is shown lines below. @code{.cpp} #include <stdio.h> #include #include "opencv2/core.hpp" #include "opencv2/features2d.hpp" #include "opencv2/highgui.hpp" #include "opencv2/calib3d.hpp" #include "opencv2/xfeatures2d.hpp"

using namespace cv; using namespace cv::xfeatures2d;

void readme();

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

Mat img_object = imread( argv[1], IMREAD_GRAYSCALE ); Mat img_scene = imread( argv[2], IMREAD_GRAYSCALE );

if( !img_object.data || !img_scene.data ) { std::cout<< " --(!) Error reading images " << std::endl; return -1; }

//-- Step 1: Detect the keypoints and extract descriptors using SURF int minHessian = 400;

Ptr detector = SURF::create( minHessian );

std::vector keypoints_object, keypoints_scene; Mat descriptors_object, descriptors_scene;

detector->detectAndCompute( img_object, keypoints_object, descriptors_object ); detector->detectAndCompute( img_scene, keypoints_scene, descriptors_scene );

//-- Step 2: Matching descriptor vectors using FLANN matcher FlannBasedMatcher matcher; std::vector< DMatch > matches; matcher.match( descriptors_object, descriptors_scene, matches );

double max_dist = 0; double min_dist = 100;

//-- Quick calculation of max and min distances between keypoints for( int i = 0; i < descriptors_object.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 3*min_dist ) std::vector< DMatch > good_matches;

for( int i = 0; i < descriptors_object.rows; i++ ) { if( matches[i].distance < 3*min_dist ) { good_matches.push_back( matches[i]); } }

Mat img_matches; drawMatches( img_object, keypoints_object, img_scene, keypoints_scene, good_matches, img_matches, Scalar::all(-1), Scalar::all(-1), vector(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );

//-- Localize the object std::vector obj; std::vector scene;

for( int i = 0; i < good_matches.size(); i++ ) { //-- Get the keypoints from the good matches obj.push_back( keypoints_object[ good_matches[i].queryIdx ].pt ); scene.push_back( keypoints_scene[ good_matches[i].trainIdx ].pt ); }

Mat H = findHomography( obj, scene, RANSAC );

//-- Get the corners from the image_1 ( the object to be "detected" ) std::vector obj_corners(4); obj_corners[0] = cvPoint(0,0); obj_corners[1] = cvPoint( img_object.cols, 0 ); obj_corners[2] = cvPoint( img_object.cols, img_object.rows ); obj_corners[3] = cvPoint( 0, img_object.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_object.cols, 0), scene_corners[1] + Point2f( img_object.cols, 0), Scalar(0, 255, 0), 4 ); line( img_matches, scene_corners[1] + Point2f( img_object.cols, 0), scene_corners[2] + Point2f( img_object.cols, 0), Scalar( 0, 255, 0), 4 ); line( img_matches, scene_corners[2] + Point2f( img_object.cols, 0), scene_corners[3] + Point2f( img_object.cols, 0), Scalar( 0, 255, 0), 4 ); line( img_matches, scene_corners[3] + Point2f( img_object.cols, 0), scene_corners[0] + Point2f( img_object.cols, 0), Scalar( 0, 255, 0), 4 );

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

waitKey(0); return 0; }

/* @function readme */ void readme() { std::cout << " Usage: ./SURF_descriptor " << std::endl; } @endcode Explanation

Result

-# And here is the result for the detected object (highlighted in green)

![](images/Feature_Homography_Result.jpg)