Added tutorial for features2d using homography to find a planar object (Based on the well known find_obj.cpp
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Ana Huaman
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@ -366,7 +366,9 @@ extlinks = {'cvt_color': ('http://opencv.willowgarage.com/documentation/cpp/imgp
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'descriptor_extractor': ( 'http://opencv.willowgarage.com/documentation/cpp/features2d_common_interfaces_of_descriptor_extractors.html#descriptorextractor%s', None ),
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'descriptor_extractor_compute' : ( 'http://opencv.willowgarage.com/documentation/cpp/features2d_common_interfaces_of_descriptor_extractors.html#cv-descriptorextractor-compute%s', None ),
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'surf_descriptor_extractor' : ( 'http://opencv.willowgarage.com/documentation/cpp/features2d_common_interfaces_of_descriptor_extractors.html#surfdescriptorextractor%s', None ),
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'draw_matches' : ( 'http://opencv.willowgarage.com/documentation/cpp/features2d_drawing_function_of_keypoints_and_matches.html#cv-drawmatches%s', None )
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'draw_matches' : ( 'http://opencv.willowgarage.com/documentation/cpp/features2d_drawing_function_of_keypoints_and_matches.html#cv-drawmatches%s', None ),
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'find_homography' : ('http://opencv.willowgarage.com/documentation/cpp/calib3d_camera_calibration_and_3d_reconstruction.html?#findHomography%s', None),
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'perspective_transform' : ('http://opencv.willowgarage.com/documentation/cpp/core_operations_on_arrays.html?#perspectiveTransform%s', None )
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}
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@ -0,0 +1,148 @@
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.. _feature_homography:
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Features2D + Homography to find a known object
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**********************************************
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Goal
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=====
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In this tutorial you will learn how to:
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.. container:: enumeratevisibleitemswithsquare
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* Use the function :find_homography:`findHomography<>` to find the transform between matched keypoints.
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* Use the function :perspective_transform:`perspectiveTransform<>` to map the points.
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Theory
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======
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Code
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====
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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>`_
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.. code-block:: cpp
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#include <stdio.h>
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#include <iostream>
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#include "opencv2/core/core.hpp"
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#include "opencv2/features2d/features2d.hpp"
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#include "opencv2/highgui/highgui.hpp"
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#include "opencv2/calib3d/calib3d.hpp"
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using namespace cv;
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void readme();
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/** @function main */
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int main( int argc, char** argv )
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{
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if( argc != 3 )
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{ readme(); return -1; }
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Mat img_object = imread( argv[1], CV_LOAD_IMAGE_GRAYSCALE );
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Mat img_scene = imread( argv[2], CV_LOAD_IMAGE_GRAYSCALE );
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if( !img_object.data || !img_scene.data )
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{ std::cout<< " --(!) Error reading images " << std::endl; return -1; }
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//-- Step 1: Detect the keypoints using SURF Detector
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int minHessian = 400;
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SurfFeatureDetector detector( minHessian );
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std::vector<KeyPoint> keypoints_object, keypoints_scene;
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detector.detect( img_object, keypoints_object );
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detector.detect( img_scene, keypoints_scene );
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//-- Step 2: Calculate descriptors (feature vectors)
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SurfDescriptorExtractor extractor;
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Mat descriptors_object, descriptors_scene;
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extractor.compute( img_object, keypoints_object, descriptors_object );
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extractor.compute( img_scene, keypoints_scene, descriptors_scene );
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//-- Step 3: Matching descriptor vectors using FLANN matcher
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FlannBasedMatcher matcher;
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std::vector< DMatch > matches;
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matcher.match( descriptors_object, descriptors_scene, matches );
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double max_dist = 0; double min_dist = 100;
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//-- Quick calculation of max and min distances between keypoints
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for( int i = 0; i < descriptors_object.rows; i++ )
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{ double dist = matches[i].distance;
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if( dist < min_dist ) min_dist = dist;
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if( dist > max_dist ) max_dist = dist;
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}
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printf("-- Max dist : %f \n", max_dist );
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printf("-- Min dist : %f \n", min_dist );
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//-- Draw only "good" matches (i.e. whose distance is less than 3*min_dist )
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std::vector< DMatch > good_matches;
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for( int i = 0; i < descriptors_object.rows; i++ )
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{ if( matches[i].distance < 3*min_dist )
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{ good_matches.push_back( matches[i]); }
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}
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Mat img_matches;
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drawMatches( img_object, keypoints_object, img_scene, keypoints_scene,
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good_matches, img_matches, Scalar::all(-1), Scalar::all(-1),
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vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );
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//-- Localize the object
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std::vector<Point2f> obj;
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std::vector<Point2f> scene;
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for( int i = 0; i < good_matches.size(); i++ )
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{
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//-- Get the keypoints from the good matches
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obj.push_back( keypoints_object[ good_matches[i].queryIdx ].pt );
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scene.push_back( keypoints_scene[ good_matches[i].trainIdx ].pt );
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}
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Mat H = findHomography( obj, scene, CV_RANSAC );
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//-- Get the corners from the image_1 ( the object to be "detected" )
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std::vector<Point2f> obj_corners(4);
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obj_corners[0] = cvPoint(0,0); obj_corners[1] = cvPoint( img_object.cols, 0 );
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obj_corners[2] = cvPoint( img_object.cols, img_object.rows ); obj_corners[3] = cvPoint( 0, img_object.rows );
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std::vector<Point2f> scene_corners(4);
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perspectiveTransform( obj_corners, scene_corners, H);
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//-- Draw lines between the corners (the mapped object in the scene - image_2 )
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line( img_matches, scene_corners[0] + Point2f( img_object.cols, 0), scene_corners[1] + Point2f( img_object.cols, 0), Scalar(0, 255, 0), 4 );
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line( img_matches, scene_corners[1] + Point2f( img_object.cols, 0), scene_corners[2] + Point2f( img_object.cols, 0), Scalar( 0, 255, 0), 4 );
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line( img_matches, scene_corners[2] + Point2f( img_object.cols, 0), scene_corners[3] + Point2f( img_object.cols, 0), Scalar( 0, 255, 0), 4 );
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line( img_matches, scene_corners[3] + Point2f( img_object.cols, 0), scene_corners[0] + Point2f( img_object.cols, 0), Scalar( 0, 255, 0), 4 );
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//-- Show detected matches
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imshow( "Good Matches & Object detection", img_matches );
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waitKey(0);
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return 0;
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}
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/** @function readme */
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void readme()
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{ std::cout << " Usage: ./SURF_descriptor <img1> <img2>" << std::endl; }
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Explanation
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============
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Result
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======
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#. And here is the result for the detected object (highlighted in green)
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.. image:: images/Feature_Homography_Result.jpg
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:align: center
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:height: 200pt
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@ -140,7 +140,7 @@ Learn about how to use the feature points detectors, descriptors and matching f
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===================== ==============================================
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.. |FeatureFlann| image:: images/Feature_Detection_Tutorial_Cover.jpg
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.. |FeatureFlann| image:: images/Feature_Flann_Matcher_Tutorial_Cover.jpg
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:height: 90pt
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:width: 90pt
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@ -155,11 +155,11 @@ Learn about how to use the feature points detectors, descriptors and matching f
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*Author:* |Author_AnaH|
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In this tutorial, you will use *features2d* to detect interest points.
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In this tutorial, you will use *features2d* and *calib3d* to detect an object in a scene.
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===================== ==============================================
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.. |FeatureHomo| image:: images/Feature_Detection_Tutorial_Cover.jpg
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.. |FeatureHomo| image:: images/Feature_Homography_Tutorial_Cover.jpg
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:height: 90pt
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:width: 90pt
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@ -24,10 +24,10 @@ int main( int argc, char** argv )
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if( argc != 3 )
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{ readme(); return -1; }
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Mat img_1 = imread( argv[1], CV_LOAD_IMAGE_GRAYSCALE );
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Mat img_2 = imread( argv[2], CV_LOAD_IMAGE_GRAYSCALE );
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Mat img_object = imread( argv[1], CV_LOAD_IMAGE_GRAYSCALE );
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Mat img_scene = imread( argv[2], CV_LOAD_IMAGE_GRAYSCALE );
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if( !img_1.data || !img_2.data )
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if( !img_object.data || !img_scene.data )
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{ std::cout<< " --(!) Error reading images " << std::endl; return -1; }
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//-- Step 1: Detect the keypoints using SURF Detector
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@ -35,28 +35,28 @@ int main( int argc, char** argv )
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SurfFeatureDetector detector( minHessian );
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std::vector<KeyPoint> keypoints_1, keypoints_2;
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std::vector<KeyPoint> keypoints_object, keypoints_scene;
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detector.detect( img_1, keypoints_1 );
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detector.detect( img_2, keypoints_2 );
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detector.detect( img_object, keypoints_object );
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detector.detect( img_scene, keypoints_scene );
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//-- Step 2: Calculate descriptors (feature vectors)
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SurfDescriptorExtractor extractor;
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Mat descriptors_1, descriptors_2;
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Mat descriptors_object, descriptors_scene;
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extractor.compute( img_1, keypoints_1, descriptors_1 );
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extractor.compute( img_2, keypoints_2, descriptors_2 );
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extractor.compute( img_object, keypoints_object, descriptors_object );
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extractor.compute( img_scene, keypoints_scene, descriptors_scene );
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//-- Step 3: Matching descriptor vectors using FLANN matcher
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FlannBasedMatcher matcher;
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std::vector< DMatch > matches;
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matcher.match( descriptors_1, descriptors_2, matches );
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matcher.match( descriptors_object, descriptors_scene, matches );
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double max_dist = 0; double min_dist = 100;
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//-- Quick calculation of max and min distances between keypoints
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for( int i = 0; i < descriptors_1.rows; i++ )
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for( int i = 0; i < descriptors_object.rows; i++ )
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{ double dist = matches[i].distance;
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if( dist < min_dist ) min_dist = dist;
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if( dist > max_dist ) max_dist = dist;
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@ -68,13 +68,13 @@ int main( int argc, char** argv )
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//-- Draw only "good" matches (i.e. whose distance is less than 3*min_dist )
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std::vector< DMatch > good_matches;
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for( int i = 0; i < descriptors_1.rows; i++ )
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for( int i = 0; i < descriptors_object.rows; i++ )
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{ if( matches[i].distance < 3*min_dist )
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{ good_matches.push_back( matches[i]); }
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}
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Mat img_matches;
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drawMatches( img_1, keypoints_1, img_2, keypoints_2,
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drawMatches( img_object, keypoints_object, img_scene, keypoints_scene,
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good_matches, img_matches, Scalar::all(-1), Scalar::all(-1),
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vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );
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@ -86,33 +86,26 @@ int main( int argc, char** argv )
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for( int i = 0; i < good_matches.size(); i++ )
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{
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//-- Get the keypoints from the good matches
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obj.push_back( keypoints_1[ good_matches[i].queryIdx ].pt );
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scene.push_back( keypoints_2[ good_matches[i].trainIdx ].pt );
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obj.push_back( keypoints_object[ good_matches[i].queryIdx ].pt );
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scene.push_back( keypoints_scene[ good_matches[i].trainIdx ].pt );
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}
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Mat H = findHomography( obj, scene, CV_RANSAC );
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//-- Get the corners from the image_1 ( the object to be "detected" )
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Point2f obj_corners[4] = { cvPoint(0,0), cvPoint( img_1.cols, 0 ), cvPoint( img_1.cols, img_1.rows ), cvPoint( 0, img_1.rows ) };
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Point scene_corners[4];
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std::vector<Point2f> obj_corners(4);
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obj_corners[0] = cvPoint(0,0); obj_corners[1] = cvPoint( img_object.cols, 0 );
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obj_corners[2] = cvPoint( img_object.cols, img_object.rows ); obj_corners[3] = cvPoint( 0, img_object.rows );
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std::vector<Point2f> scene_corners(4);
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//-- Map these corners in the scene ( image_2)
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for( int i = 0; i < 4; i++ )
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{
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double x = obj_corners[i].x;
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double y = obj_corners[i].y;
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perspectiveTransform( obj_corners, scene_corners, H);
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double Z = 1./( H.at<double>(2,0)*x + H.at<double>(2,1)*y + H.at<double>(2,2) );
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double X = ( H.at<double>(0,0)*x + H.at<double>(0,1)*y + H.at<double>(0,2) )*Z;
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double Y = ( H.at<double>(1,0)*x + H.at<double>(1,1)*y + H.at<double>(1,2) )*Z;
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scene_corners[i] = cvPoint( cvRound(X) + img_1.cols, cvRound(Y) );
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}
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//-- Draw lines between the corners (the mapped object in the scene - image_2 )
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line( img_matches, scene_corners[0], scene_corners[1], Scalar(0, 255, 0), 2 );
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line( img_matches, scene_corners[1], scene_corners[2], Scalar( 0, 255, 0), 2 );
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line( img_matches, scene_corners[2], scene_corners[3], Scalar( 0, 255, 0), 2 );
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line( img_matches, scene_corners[3], scene_corners[0], Scalar( 0, 255, 0), 2 );
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line( img_matches, scene_corners[0] + Point2f( img_object.cols, 0), scene_corners[1] + Point2f( img_object.cols, 0), Scalar(0, 255, 0), 4 );
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line( img_matches, scene_corners[1] + Point2f( img_object.cols, 0), scene_corners[2] + Point2f( img_object.cols, 0), Scalar( 0, 255, 0), 4 );
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line( img_matches, scene_corners[2] + Point2f( img_object.cols, 0), scene_corners[3] + Point2f( img_object.cols, 0), Scalar( 0, 255, 0), 4 );
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line( img_matches, scene_corners[3] + Point2f( img_object.cols, 0), scene_corners[0] + Point2f( img_object.cols, 0), Scalar( 0, 255, 0), 4 );
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//-- Show detected matches
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imshow( "Good Matches & Object detection", img_matches );
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