Ana Huaman
14 years ago
parent
e6d308ca89
commit
07754b6309
7 changed files with 290 additions and 4 deletions
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.. _feature_description: |
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Feature Description |
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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 :descriptor_extractor:`DescriptorExtractor<>` interface in order to find the feature vector correspondent to the keypoints. Specifically: |
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* Use :surf_descriptor_extractor:`SurfDescriptorExtractor<>` and its function :descriptor_extractor:`compute<>` to perform the required calculations. |
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* Use the function :draw_matches:`drawMatches<>` to draw the detected matches. |
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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_descriptor.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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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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{ 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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if( !img_1.data || !img_2.data ) |
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{ 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_1, keypoints_2; |
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detector.detect( img_1, keypoints_1 ); |
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detector.detect( img_2, keypoints_2 ); |
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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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extractor.compute( img_1, keypoints_1, descriptors_1 ); |
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extractor.compute( img_2, keypoints_2, descriptors_2 ); |
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//-- Step 3: Matching descriptor vectors with a brute force matcher |
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BruteForceMatcher< L2<float> > matcher; |
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std::vector< DMatch > matches; |
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matcher.match( descriptors_1, descriptors_2, matches ); |
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//-- Draw matches |
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Mat img_matches; |
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drawMatches( img_1, keypoints_1, img_2, keypoints_2, matches, img_matches ); |
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//-- Show detected matches |
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imshow("Matches", 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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#. Here is the result after applying the BruteForce matcher between the two original images: |
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.. image:: images/Feature_Description_BruteForce_Result.jpg |
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:align: center |
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:height: 200pt |
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After Width: | Height: | Size: 117 KiB |
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.. _feature_detection: |
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Feature Detection |
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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 :feature_detector:`FeatureDetector<>` interface in order to find interest points. Specifically: |
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* Use the :surf_feature_detector:`SurfFeatureDetector<>` and its function :feature_detector_detect:`detect<>` to perform the detection process |
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* Use the function :draw_keypoints:`drawKeypoints<>` to draw the detected keypoints |
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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_detector.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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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_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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if( !img_1.data || !img_2.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_1, keypoints_2; |
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detector.detect( img_1, keypoints_1 ); |
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detector.detect( img_2, keypoints_2 ); |
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//-- Draw keypoints |
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Mat img_keypoints_1; Mat img_keypoints_2; |
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drawKeypoints( img_1, keypoints_1, img_keypoints_1, Scalar::all(-1), DrawMatchesFlags::DEFAULT ); |
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drawKeypoints( img_2, keypoints_2, img_keypoints_2, Scalar::all(-1), DrawMatchesFlags::DEFAULT ); |
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//-- Show detected (drawn) keypoints |
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imshow("Keypoints 1", img_keypoints_1 ); |
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imshow("Keypoints 2", img_keypoints_2 ); |
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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_detector <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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#. Here is the result of the feature detection applied to the first image: |
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.. image:: images/Feature_Detection_Result_a.jpg |
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:align: center |
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:height: 125pt |
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#. And here is the result for the second image: |
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.. image:: images/Feature_Detection_Result_b.jpg |
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:align: center |
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:height: 200pt |
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After Width: | Height: | Size: 73 KiB |
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After Width: | Height: | Size: 34 KiB |
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