--- /dev/null
+.. _feature_homography:
+
+Features2D + Homography to find a known object
+**********************************************
+
+Goal
+=====
+
+In this tutorial you will learn how to:
+
+.. container:: enumeratevisibleitemswithsquare
+
+ * Use the function :find_homography:`findHomography<>` to find the transform between matched keypoints.
+ * Use the function :perspective_transform:`perspectiveTransform<>` to map the points.
+
+
+Theory
+======
+
+Code
+====
+
+This tutorial code's is shown lines below. You can also download it from `here <https://code.ros.org/svn/opencv/trunk/opencv/samples/cpp/tutorial_code/features2D/SURF_Homography.cpp>`_
+
+.. code-block:: cpp
+
+ #include <stdio.h>
+ #include <iostream>
+ #include "opencv2/core/core.hpp"
+ #include "opencv2/features2d/features2d.hpp"
+ #include "opencv2/highgui/highgui.hpp"
+ #include "opencv2/calib3d/calib3d.hpp"
+
+ using namespace cv;
+
+ void readme();
+
+ /** @function main */
+ int main( int argc, char** argv )
+ {
+ if( argc != 3 )
+ { readme(); return -1; }
+
+ Mat img_object = imread( argv[1], CV_LOAD_IMAGE_GRAYSCALE );
+ Mat img_scene = imread( argv[2], CV_LOAD_IMAGE_GRAYSCALE );
+
+ if( !img_object.data || !img_scene.data )
+ { std::cout<< " --(!) Error reading images " << std::endl; return -1; }
+
+ //-- Step 1: Detect the keypoints using SURF Detector
+ int minHessian = 400;
+
+ SurfFeatureDetector detector( minHessian );
+
+ std::vector<KeyPoint> keypoints_object, keypoints_scene;
+
+ detector.detect( img_object, keypoints_object );
+ detector.detect( img_scene, keypoints_scene );
+
+ //-- Step 2: Calculate descriptors (feature vectors)
+ SurfDescriptorExtractor extractor;
+
+ Mat descriptors_object, descriptors_scene;
+
+ extractor.compute( img_object, keypoints_object, descriptors_object );
+ extractor.compute( img_scene, keypoints_scene, descriptors_scene );
+
+ //-- Step 3: 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<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );
+
+ //-- Localize the object
+ std::vector<Point2f> obj;
+ std::vector<Point2f> 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, CV_RANSAC );
+
+ //-- Get the corners from the image_1 ( the object to be "detected" )
+ std::vector<Point2f> 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<Point2f> 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 <img1> <img2>" << std::endl; }
+
+Explanation
+============
+
+Result
+======
+
+
+#. And here is the result for the detected object (highlighted in green)
+
+ .. image:: images/Feature_Homography_Result.jpg
+ :align: center
+ :height: 200pt
+
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 );
+ Mat img_object = imread( argv[1], CV_LOAD_IMAGE_GRAYSCALE );
+ Mat img_scene = imread( argv[2], CV_LOAD_IMAGE_GRAYSCALE );
- if( !img_1.data || !img_2.data )
+ if( !img_object.data || !img_scene.data )
{ std::cout<< " --(!) Error reading images " << std::endl; return -1; }
//-- Step 1: Detect the keypoints using SURF Detector
SurfFeatureDetector detector( minHessian );
- std::vector<KeyPoint> keypoints_1, keypoints_2;
+ std::vector<KeyPoint> keypoints_object, keypoints_scene;
- detector.detect( img_1, keypoints_1 );
- detector.detect( img_2, keypoints_2 );
+ detector.detect( img_object, keypoints_object );
+ detector.detect( img_scene, keypoints_scene );
//-- Step 2: Calculate descriptors (feature vectors)
SurfDescriptorExtractor extractor;
- Mat descriptors_1, descriptors_2;
+ Mat descriptors_object, descriptors_scene;
- extractor.compute( img_1, keypoints_1, descriptors_1 );
- extractor.compute( img_2, keypoints_2, descriptors_2 );
+ extractor.compute( img_object, keypoints_object, descriptors_object );
+ extractor.compute( img_scene, keypoints_scene, descriptors_scene );
//-- Step 3: Matching descriptor vectors using FLANN matcher
FlannBasedMatcher matcher;
std::vector< DMatch > matches;
- matcher.match( descriptors_1, descriptors_2, 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_1.rows; i++ )
+ 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;
//-- 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_1.rows; i++ )
+ 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_1, keypoints_1, img_2, keypoints_2,
+ drawMatches( img_object, keypoints_object, img_scene, keypoints_scene,
good_matches, img_matches, Scalar::all(-1), Scalar::all(-1),
vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );
for( int 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 );
+ 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, CV_RANSAC );
//-- Get the corners from the image_1 ( the object to be "detected" )
- Point2f obj_corners[4] = { cvPoint(0,0), cvPoint( img_1.cols, 0 ), cvPoint( img_1.cols, img_1.rows ), cvPoint( 0, img_1.rows ) };
- Point scene_corners[4];
+ std::vector<Point2f> 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<Point2f> scene_corners(4);
- //-- Map these corners in the scene ( image_2)
- for( int i = 0; i < 4; i++ )
- {
- double x = obj_corners[i].x;
- double y = obj_corners[i].y;
+ perspectiveTransform( obj_corners, scene_corners, H);
- double Z = 1./( H.at<double>(2,0)*x + H.at<double>(2,1)*y + H.at<double>(2,2) );
- double X = ( H.at<double>(0,0)*x + H.at<double>(0,1)*y + H.at<double>(0,2) )*Z;
- double Y = ( H.at<double>(1,0)*x + H.at<double>(1,1)*y + H.at<double>(1,2) )*Z;
- scene_corners[i] = cvPoint( cvRound(X) + img_1.cols, cvRound(Y) );
- }
//-- Draw lines between the corners (the mapped object in the scene - image_2 )
- line( img_matches, scene_corners[0], scene_corners[1], Scalar(0, 255, 0), 2 );
- line( img_matches, scene_corners[1], scene_corners[2], Scalar( 0, 255, 0), 2 );
- line( img_matches, scene_corners[2], scene_corners[3], Scalar( 0, 255, 0), 2 );
- line( img_matches, scene_corners[3], scene_corners[0], Scalar( 0, 255, 0), 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 );
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