From: Ana Huaman Date: Fri, 12 Aug 2011 18:04:44 +0000 (+0000) Subject: Added tutorial for features2d using homography to find a planar object (Based on... X-Git-Tag: accepted/2.0/20130307.220821~2043 X-Git-Url: http://review.tizen.org/git/?a=commitdiff_plain;h=f803fc259b20da12f17317645d9a6e9b1af6bdba;p=profile%2Fivi%2Fopencv.git Added tutorial for features2d using homography to find a planar object (Based on the well known find_obj.cpp --- diff --git a/doc/conf.py b/doc/conf.py index 2777095..6219876 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -366,7 +366,9 @@ extlinks = {'cvt_color': ('http://opencv.willowgarage.com/documentation/cpp/imgp 'descriptor_extractor': ( 'http://opencv.willowgarage.com/documentation/cpp/features2d_common_interfaces_of_descriptor_extractors.html#descriptorextractor%s', None ), 'descriptor_extractor_compute' : ( 'http://opencv.willowgarage.com/documentation/cpp/features2d_common_interfaces_of_descriptor_extractors.html#cv-descriptorextractor-compute%s', None ), 'surf_descriptor_extractor' : ( 'http://opencv.willowgarage.com/documentation/cpp/features2d_common_interfaces_of_descriptor_extractors.html#surfdescriptorextractor%s', None ), - 'draw_matches' : ( 'http://opencv.willowgarage.com/documentation/cpp/features2d_drawing_function_of_keypoints_and_matches.html#cv-drawmatches%s', None ) + 'draw_matches' : ( 'http://opencv.willowgarage.com/documentation/cpp/features2d_drawing_function_of_keypoints_and_matches.html#cv-drawmatches%s', None ), + 'find_homography' : ('http://opencv.willowgarage.com/documentation/cpp/calib3d_camera_calibration_and_3d_reconstruction.html?#findHomography%s', None), + 'perspective_transform' : ('http://opencv.willowgarage.com/documentation/cpp/core_operations_on_arrays.html?#perspectiveTransform%s', None ) } diff --git a/doc/tutorials/features2d/feature_homography/feature_homography.rst b/doc/tutorials/features2d/feature_homography/feature_homography.rst new file mode 100644 index 0000000..e04be71 --- /dev/null +++ b/doc/tutorials/features2d/feature_homography/feature_homography.rst @@ -0,0 +1,148 @@ +.. _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 `_ + +.. code-block:: cpp + + #include + #include + #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 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(), 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, CV_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; } + +Explanation +============ + +Result +====== + + +#. And here is the result for the detected object (highlighted in green) + + .. image:: images/Feature_Homography_Result.jpg + :align: center + :height: 200pt + diff --git a/doc/tutorials/features2d/feature_homography/images/Feature_Homography_Result.jpg b/doc/tutorials/features2d/feature_homography/images/Feature_Homography_Result.jpg new file mode 100644 index 0000000..d043a5a Binary files /dev/null and b/doc/tutorials/features2d/feature_homography/images/Feature_Homography_Result.jpg differ diff --git a/doc/tutorials/features2d/table_of_content_features2d/images/Feature_Description_Tutorial_Cover.jpg b/doc/tutorials/features2d/table_of_content_features2d/images/Feature_Description_Tutorial_Cover.jpg index 41dacf5..975caa6 100644 Binary files a/doc/tutorials/features2d/table_of_content_features2d/images/Feature_Description_Tutorial_Cover.jpg and b/doc/tutorials/features2d/table_of_content_features2d/images/Feature_Description_Tutorial_Cover.jpg differ diff --git a/doc/tutorials/features2d/table_of_content_features2d/images/Feature_Homography_Tutorial_Cover.jpg b/doc/tutorials/features2d/table_of_content_features2d/images/Feature_Homography_Tutorial_Cover.jpg new file mode 100644 index 0000000..d509cd9 Binary files /dev/null and b/doc/tutorials/features2d/table_of_content_features2d/images/Feature_Homography_Tutorial_Cover.jpg differ diff --git a/doc/tutorials/features2d/table_of_content_features2d/table_of_content_features2d.rst b/doc/tutorials/features2d/table_of_content_features2d/table_of_content_features2d.rst index ce38b96..e41dee9 100644 --- a/doc/tutorials/features2d/table_of_content_features2d/table_of_content_features2d.rst +++ b/doc/tutorials/features2d/table_of_content_features2d/table_of_content_features2d.rst @@ -140,7 +140,7 @@ Learn about how to use the feature points detectors, descriptors and matching f ===================== ============================================== - .. |FeatureFlann| image:: images/Feature_Detection_Tutorial_Cover.jpg + .. |FeatureFlann| image:: images/Feature_Flann_Matcher_Tutorial_Cover.jpg :height: 90pt :width: 90pt @@ -155,11 +155,11 @@ Learn about how to use the feature points detectors, descriptors and matching f *Author:* |Author_AnaH| - In this tutorial, you will use *features2d* to detect interest points. + In this tutorial, you will use *features2d* and *calib3d* to detect an object in a scene. ===================== ============================================== - .. |FeatureHomo| image:: images/Feature_Detection_Tutorial_Cover.jpg + .. |FeatureHomo| image:: images/Feature_Homography_Tutorial_Cover.jpg :height: 90pt :width: 90pt diff --git a/samples/cpp/tutorial_code/features2D/SURF_Homography.cpp b/samples/cpp/tutorial_code/features2D/SURF_Homography.cpp index 2b87abd..8194a49 100644 --- a/samples/cpp/tutorial_code/features2D/SURF_Homography.cpp +++ b/samples/cpp/tutorial_code/features2D/SURF_Homography.cpp @@ -24,10 +24,10 @@ int main( int argc, char** argv ) 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 @@ -35,28 +35,28 @@ int main( int argc, char** argv ) SurfFeatureDetector detector( minHessian ); - std::vector keypoints_1, keypoints_2; + std::vector 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; @@ -68,13 +68,13 @@ int main( int argc, char** argv ) //-- 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(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS ); @@ -86,33 +86,26 @@ int main( int argc, char** argv ) 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 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); - //-- 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(2,0)*x + H.at(2,1)*y + H.at(2,2) ); - double X = ( H.at(0,0)*x + H.at(0,1)*y + H.at(0,2) )*Z; - double Y = ( H.at(1,0)*x + H.at(1,1)*y + H.at(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 );