Merge remote-tracking branch 'upstream/3.4' into merge-3.4
Revert "documentation: avoid links to 'master' branch from 3.4 maintenance branch" This reverts commit9ba9358ecb. Revert "documentation: avoid links to 'master' branch from 3.4 maintenance branch (2)" This reverts commitf185802489.
This commit is contained in:
Alexander Alekhin
@@ -10,74 +10,35 @@ In this tutorial you will learn how to:
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to the keypoints. Specifically:
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- Use cv::xfeatures2d::SURF and its function cv::xfeatures2d::SURF::compute to perform the
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required calculations.
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- Use a @ref cv::BFMatcher to match the features vector
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- Use a @ref cv::DescriptorMatcher to match the features vector
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- Use the function @ref cv::drawMatches to draw the detected matches.
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\warning You need the <a href="https://github.com/opencv/opencv_contrib">OpenCV contrib modules</a> to be able to use the SURF features
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(alternatives are ORB, KAZE, ... features).
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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.
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@code{.cpp}
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#include <stdio.h>
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#include <iostream>
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#include "opencv2/core.hpp"
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#include "opencv2/features2d.hpp"
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#include "opencv2/highgui.hpp"
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#include "opencv2/xfeatures2d.hpp"
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@add_toggle_cpp
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This tutorial code's is shown lines below. You can also download it from
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[here](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/features2D/feature_description/SURF_matching_Demo.cpp)
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@include samples/cpp/tutorial_code/features2D/feature_description/SURF_matching_Demo.cpp
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@end_toggle
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using namespace cv;
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using namespace cv::xfeatures2d;
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@add_toggle_java
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This tutorial code's is shown lines below. You can also download it from
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[here](https://github.com/opencv/opencv/tree/master/samples/java/tutorial_code/features2D/feature_description/SURFMatchingDemo.java)
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@include samples/java/tutorial_code/features2D/feature_description/SURFMatchingDemo.java
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@end_toggle
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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], IMREAD_GRAYSCALE );
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Mat img_2 = imread( argv[2], IMREAD_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, compute the descriptors
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int minHessian = 400;
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Ptr<SURF> detector = SURF::create();
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detector->setHessianThreshold(minHessian);
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std::vector<KeyPoint> keypoints_1, keypoints_2;
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Mat descriptors_1, descriptors_2;
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detector->detectAndCompute( img_1, Mat(), keypoints_1, descriptors_1 );
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detector->detectAndCompute( img_2, Mat(), keypoints_2, descriptors_2 );
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//-- Step 2: Matching descriptor vectors with a brute force matcher
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BFMatcher matcher(NORM_L2);
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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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@endcode
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@add_toggle_python
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This tutorial code's is shown lines below. You can also download it from
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[here](https://github.com/opencv/opencv/tree/master/samples/python/tutorial_code/features2D/feature_description/SURF_matching_Demo.py)
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@include samples/python/tutorial_code/features2D/feature_description/SURF_matching_Demo.py
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@end_toggle
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Explanation
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-----------
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@@ -11,67 +11,32 @@ In this tutorial you will learn how to:
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detection process
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- Use the function @ref cv::drawKeypoints to draw the detected keypoints
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\warning You need the <a href="https://github.com/opencv/opencv_contrib">OpenCV contrib modules</a> to be able to use the SURF features
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(alternatives are ORB, KAZE, ... features).
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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.
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@code{.cpp}
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#include <stdio.h>
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#include <iostream>
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#include "opencv2/core.hpp"
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#include "opencv2/features2d.hpp"
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#include "opencv2/xfeatures2d.hpp"
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#include "opencv2/highgui.hpp"
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@add_toggle_cpp
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This tutorial code's is shown lines below. You can also download it from
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[here](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/features2D/feature_detection/SURF_detection_Demo.cpp)
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@include samples/cpp/tutorial_code/features2D/feature_detection/SURF_detection_Demo.cpp
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@end_toggle
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using namespace cv;
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using namespace cv::xfeatures2d;
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@add_toggle_java
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This tutorial code's is shown lines below. You can also download it from
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[here](https://github.com/opencv/opencv/tree/master/samples/java/tutorial_code/features2D/feature_detection/SURFDetectionDemo.java)
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@include samples/java/tutorial_code/features2D/feature_detection/SURFDetectionDemo.java
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@end_toggle
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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], IMREAD_GRAYSCALE );
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Mat img_2 = imread( argv[2], IMREAD_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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Ptr<SURF> detector = SURF::create( 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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@endcode
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@add_toggle_python
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This tutorial code's is shown lines below. You can also download it from
|
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[here](https://github.com/opencv/opencv/tree/master/samples/python/tutorial_code/features2D/feature_detection/SURF_detection_Demo.py)
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@include samples/python/tutorial_code/features2D/feature_detection/SURF_detection_Demo.py
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@end_toggle
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Explanation
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-----------
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@@ -79,10 +44,10 @@ Explanation
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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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-# Here is the result of the feature detection applied to the `box.png` image:
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-# And here is the result for the second image:
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-# And here is the result for the `box_in_scene.png` image:
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@@ -9,114 +9,57 @@ In this tutorial you will learn how to:
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- Use the @ref cv::FlannBasedMatcher interface in order to perform a quick and efficient matching
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by using the @ref flann module
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\warning You need the <a href="https://github.com/opencv/opencv_contrib">OpenCV contrib modules</a> to be able to use the SURF features
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(alternatives are ORB, KAZE, ... features).
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Theory
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------
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Classical feature descriptors (SIFT, SURF, ...) are usually compared and matched using the Euclidean distance (or L2-norm).
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Since SIFT and SURF descriptors represent the histogram of oriented gradient (of the Haar wavelet response for SURF)
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in a neighborhood, alternatives of the Euclidean distance are histogram-based metrics (\f$ \chi^{2} \f$, Earth Mover’s Distance (EMD), ...).
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Arandjelovic et al. proposed in @cite Arandjelovic:2012:TTE:2354409.2355123 to extend to the RootSIFT descriptor:
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> a square root (Hellinger) kernel instead of the standard Euclidean distance to measure the similarity between SIFT descriptors
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> leads to a dramatic performance boost in all stages of the pipeline.
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Binary descriptors (ORB, BRISK, ...) are matched using the <a href="https://en.wikipedia.org/wiki/Hamming_distance">Hamming distance</a>.
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This distance is equivalent to count the number of different elements for binary strings (population count after applying a XOR operation):
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\f[ d_{hamming} \left ( a,b \right ) = \sum_{i=0}^{n-1} \left ( a_i \oplus b_i \right ) \f]
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To filter the matches, Lowe proposed in @cite Lowe:2004:DIF:993451.996342 to use a distance ratio test to try to eliminate false matches.
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The distance ratio between the two nearest matches of a considered keypoint is computed and it is a good match when this value is below
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a thresold. Indeed, this ratio allows helping to discriminate between ambiguous matches (distance ratio between the two nearest neighbors is
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close to one) and well discriminated matches. The figure below from the SIFT paper illustrates the probability that a match is correct
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based on the nearest-neighbor distance ratio test.
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Alternative or additional filterering tests are:
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- cross check test (good match \f$ \left( f_a, f_b \right) \f$ if feature \f$ f_b \f$ is the best match for \f$ f_a \f$ in \f$ I_b \f$
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and feature \f$ f_a \f$ is the best match for \f$ f_b \f$ in \f$ I_a \f$)
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- geometric test (eliminate matches that do not fit to a geometric model, e.g. RANSAC or robust homography for planar objects)
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Code
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----
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This tutorial code's is shown lines below.
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@code{.cpp}
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/*
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* @file SURF_FlannMatcher
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* @brief SURF detector + descriptor + FLANN Matcher
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* @author A. Huaman
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*/
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@add_toggle_cpp
|
||||
This tutorial code's is shown lines below. You can also download it from
|
||||
[here](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/features2D/feature_flann_matcher/SURF_FLANN_matching_Demo.cpp)
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@include samples/cpp/tutorial_code/features2D/feature_flann_matcher/SURF_FLANN_matching_Demo.cpp
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@end_toggle
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#include <stdio.h>
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#include <iostream>
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#include <stdio.h>
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#include <iostream>
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#include "opencv2/core.hpp"
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#include "opencv2/features2d.hpp"
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#include "opencv2/imgcodecs.hpp"
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#include "opencv2/highgui.hpp"
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#include "opencv2/xfeatures2d.hpp"
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@add_toggle_java
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This tutorial code's is shown lines below. You can also download it from
|
||||
[here](https://github.com/opencv/opencv/tree/master/samples/java/tutorial_code/features2D/feature_flann_matcher/SURFFLANNMatchingDemo.java)
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@include samples/java/tutorial_code/features2D/feature_flann_matcher/SURFFLANNMatchingDemo.java
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@end_toggle
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using namespace std;
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using namespace cv;
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using namespace cv::xfeatures2d;
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void readme();
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/*
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* @function main
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* @brief Main function
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*/
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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], IMREAD_GRAYSCALE );
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Mat img_2 = imread( argv[2], IMREAD_GRAYSCALE );
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|
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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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|
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//-- Step 1: Detect the keypoints using SURF Detector, compute the descriptors
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int minHessian = 400;
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Ptr<SURF> detector = SURF::create();
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detector->setHessianThreshold(minHessian);
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std::vector<KeyPoint> keypoints_1, keypoints_2;
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Mat descriptors_1, descriptors_2;
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detector->detectAndCompute( img_1, Mat(), keypoints_1, descriptors_1 );
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detector->detectAndCompute( img_2, Mat(), keypoints_2, descriptors_2 );
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//-- Step 2: 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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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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{ 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 2*min_dist,
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//-- or a small arbitrary value ( 0.02 ) in the event that min_dist is very
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//-- small)
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//-- PS.- radiusMatch can also be used here.
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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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{ if( matches[i].distance <= max(2*min_dist, 0.02) )
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{ good_matches.push_back( matches[i]); }
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}
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//-- Draw only "good" matches
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Mat img_matches;
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drawMatches( img_1, keypoints_1, img_2, keypoints_2,
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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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//-- Show detected matches
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imshow( "Good Matches", img_matches );
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for( int i = 0; i < (int)good_matches.size(); i++ )
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{ printf( "-- Good Match [%d] Keypoint 1: %d -- Keypoint 2: %d \n", i, good_matches[i].queryIdx, good_matches[i].trainIdx ); }
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waitKey(0);
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return 0;
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}
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/*
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* @function readme
|
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*/
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void readme()
|
||||
{ std::cout << " Usage: ./SURF_FlannMatcher <img1> <img2>" << std::endl; }
|
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@endcode
|
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@add_toggle_python
|
||||
This tutorial code's is shown lines below. You can also download it from
|
||||
[here](https://github.com/opencv/opencv/tree/master/samples/python/tutorial_code/features2D/feature_flann_matcher/SURF_FLANN_matching_Demo.py)
|
||||
@include samples/python/tutorial_code/features2D/feature_flann_matcher/SURF_FLANN_matching_Demo.py
|
||||
@end_toggle
|
||||
|
||||
Explanation
|
||||
-----------
|
||||
@@ -124,10 +67,6 @@ Explanation
|
||||
Result
|
||||
------
|
||||
|
||||
-# Here is the result of the feature detection applied to the first image:
|
||||
- Here is the result of the SURF feature matching using the distance ratio test:
|
||||
|
||||
|
||||
|
||||
-# Additionally, we get as console output the keypoints filtered:
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||||
|
||||
|
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|
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|
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BIN
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After Width: | Height: | Size: 53 KiB |
BIN
Binary file not shown.
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After Width: | Height: | Size: 79 KiB |
@@ -9,125 +9,40 @@ 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.
|
||||
|
||||
\warning You need the <a href="https://github.com/opencv/opencv_contrib">OpenCV contrib modules</a> to be able to use the SURF features
|
||||
(alternatives are ORB, KAZE, ... features).
|
||||
|
||||
Theory
|
||||
------
|
||||
|
||||
Code
|
||||
----
|
||||
|
||||
This tutorial code's is shown lines below.
|
||||
@code{.cpp}
|
||||
#include <stdio.h>
|
||||
#include <iostream>
|
||||
#include "opencv2/core.hpp"
|
||||
#include "opencv2/imgproc.hpp"
|
||||
#include "opencv2/features2d.hpp"
|
||||
#include "opencv2/highgui.hpp"
|
||||
#include "opencv2/calib3d.hpp"
|
||||
#include "opencv2/xfeatures2d.hpp"
|
||||
@add_toggle_cpp
|
||||
This tutorial code's is shown lines below. You can also download it from
|
||||
[here](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/features2D/feature_homography/SURF_FLANN_matching_homography_Demo.cpp)
|
||||
@include samples/cpp/tutorial_code/features2D/feature_homography/SURF_FLANN_matching_homography_Demo.cpp
|
||||
@end_toggle
|
||||
|
||||
using namespace cv;
|
||||
using namespace cv::xfeatures2d;
|
||||
@add_toggle_java
|
||||
This tutorial code's is shown lines below. You can also download it from
|
||||
[here](https://github.com/opencv/opencv/tree/master/samples/java/tutorial_code/features2D/feature_homography/SURFFLANNMatchingHomographyDemo.java)
|
||||
@include samples/java/tutorial_code/features2D/feature_homography/SURFFLANNMatchingHomographyDemo.java
|
||||
@end_toggle
|
||||
|
||||
void readme();
|
||||
@add_toggle_python
|
||||
This tutorial code's is shown lines below. You can also download it from
|
||||
[here](https://github.com/opencv/opencv/tree/master/samples/python/tutorial_code/features2D/feature_homography/SURF_FLANN_matching_homography_Demo.py)
|
||||
@include samples/python/tutorial_code/features2D/feature_homography/SURF_FLANN_matching_homography_Demo.py
|
||||
@end_toggle
|
||||
|
||||
/* @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<SURF> detector = SURF::create( minHessian );
|
||||
|
||||
std::vector<KeyPoint> keypoints_object, keypoints_scene;
|
||||
Mat descriptors_object, descriptors_scene;
|
||||
|
||||
detector->detectAndCompute( img_object, Mat(), keypoints_object, descriptors_object );
|
||||
detector->detectAndCompute( img_scene, Mat(), 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),
|
||||
std::vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );
|
||||
|
||||
//-- Localize the object
|
||||
std::vector<Point2f> obj;
|
||||
std::vector<Point2f> scene;
|
||||
|
||||
for( size_t 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<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; }
|
||||
@endcode
|
||||
Explanation
|
||||
-----------
|
||||
|
||||
Result
|
||||
------
|
||||
|
||||
-# And here is the result for the detected object (highlighted in green)
|
||||
- And here is the result for the detected object (highlighted in green). Note that since the homography is estimated with a RANSAC approach,
|
||||
detected false matches will not impact the homography calculation.
|
||||
|
||||
|
||||
|
||||
Binary file not shown.
|
Before Width: | Height: | Size: 90 KiB After Width: | Height: | Size: 88 KiB |
@@ -6,39 +6,51 @@ OpenCV.
|
||||
|
||||
- @subpage tutorial_harris_detector
|
||||
|
||||
*Languages:* C++, Java, Python
|
||||
|
||||
*Compatibility:* \> OpenCV 2.0
|
||||
|
||||
*Author:* Ana Huamán
|
||||
|
||||
Why is it a good idea to track corners? We learn to use the Harris method to detect
|
||||
corners
|
||||
Why is it a good idea to track corners? We learn how to use the Harris method to detect
|
||||
corners.
|
||||
|
||||
- @subpage tutorial_good_features_to_track
|
||||
|
||||
*Languages:* C++, Java, Python
|
||||
|
||||
*Compatibility:* \> OpenCV 2.0
|
||||
|
||||
*Author:* Ana Huamán
|
||||
|
||||
Where we use an improved method to detect corners more accuratelyI
|
||||
Where we use an improved method to detect corners more accurately.
|
||||
|
||||
- @subpage tutorial_generic_corner_detector
|
||||
|
||||
*Languages:* C++, Java, Python
|
||||
|
||||
*Compatibility:* \> OpenCV 2.0
|
||||
|
||||
*Author:* Ana Huamán
|
||||
|
||||
Here you will learn how to use OpenCV functions to make your personalized corner detector!
|
||||
|
||||
- @subpage tutorial_corner_subpixeles
|
||||
*Languages:* C++, Java, Python
|
||||
|
||||
- @subpage tutorial_corner_subpixels
|
||||
|
||||
*Languages:* C++, Java, Python
|
||||
|
||||
*Compatibility:* \> OpenCV 2.0
|
||||
|
||||
*Author:* Ana Huamán
|
||||
|
||||
Is pixel resolution enough? Here we learn a simple method to improve our accuracy.
|
||||
Is pixel resolution enough? Here we learn a simple method to improve our corner location accuracy.
|
||||
|
||||
- @subpage tutorial_feature_detection
|
||||
|
||||
*Languages:* C++, Java, Python
|
||||
|
||||
*Compatibility:* \> OpenCV 2.0
|
||||
|
||||
*Author:* Ana Huamán
|
||||
@@ -47,6 +59,8 @@ OpenCV.
|
||||
|
||||
- @subpage tutorial_feature_description
|
||||
|
||||
*Languages:* C++, Java, Python
|
||||
|
||||
*Compatibility:* \> OpenCV 2.0
|
||||
|
||||
*Author:* Ana Huamán
|
||||
@@ -55,6 +69,8 @@ OpenCV.
|
||||
|
||||
- @subpage tutorial_feature_flann_matcher
|
||||
|
||||
*Languages:* C++, Java, Python
|
||||
|
||||
*Compatibility:* \> OpenCV 2.0
|
||||
|
||||
*Author:* Ana Huamán
|
||||
@@ -63,6 +79,8 @@ OpenCV.
|
||||
|
||||
- @subpage tutorial_feature_homography
|
||||
|
||||
*Languages:* C++, Java, Python
|
||||
|
||||
*Compatibility:* \> OpenCV 2.0
|
||||
|
||||
*Author:* Ana Huamán
|
||||
|
||||
@@ -1,32 +0,0 @@
|
||||
Detecting corners location in subpixeles {#tutorial_corner_subpixeles}
|
||||
========================================
|
||||
|
||||
Goal
|
||||
----
|
||||
|
||||
In this tutorial you will learn how to:
|
||||
|
||||
- Use the OpenCV function @ref cv::cornerSubPix to find more exact corner positions (more exact
|
||||
than integer pixels).
|
||||
|
||||
Theory
|
||||
------
|
||||
|
||||
Code
|
||||
----
|
||||
|
||||
This tutorial code's is shown lines below. You can also download it from
|
||||
[here](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/TrackingMotion/cornerSubPix_Demo.cpp)
|
||||
@include samples/cpp/tutorial_code/TrackingMotion/cornerSubPix_Demo.cpp
|
||||
|
||||
Explanation
|
||||
-----------
|
||||
|
||||
Result
|
||||
------
|
||||
|
||||
|
||||
|
||||
Here is the result:
|
||||
|
||||
|
||||
@@ -0,0 +1,46 @@
|
||||
Detecting corners location in subpixels {#tutorial_corner_subpixels}
|
||||
=======================================
|
||||
|
||||
Goal
|
||||
----
|
||||
|
||||
In this tutorial you will learn how to:
|
||||
|
||||
- Use the OpenCV function @ref cv::cornerSubPix to find more exact corner positions (more exact
|
||||
than integer pixels).
|
||||
|
||||
Theory
|
||||
------
|
||||
|
||||
Code
|
||||
----
|
||||
|
||||
@add_toggle_cpp
|
||||
This tutorial code's is shown lines below. You can also download it from
|
||||
[here](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/TrackingMotion/cornerSubPix_Demo.cpp)
|
||||
@include samples/cpp/tutorial_code/TrackingMotion/cornerSubPix_Demo.cpp
|
||||
@end_toggle
|
||||
|
||||
@add_toggle_java
|
||||
This tutorial code's is shown lines below. You can also download it from
|
||||
[here](https://github.com/opencv/opencv/tree/master/samples/java/tutorial_code/TrackingMotion/corner_subpixels/CornerSubPixDemo.java)
|
||||
@include samples/java/tutorial_code/TrackingMotion/corner_subpixels/CornerSubPixDemo.java
|
||||
@end_toggle
|
||||
|
||||
@add_toggle_python
|
||||
This tutorial code's is shown lines below. You can also download it from
|
||||
[here](https://github.com/opencv/opencv/tree/master/samples/python/tutorial_code/TrackingMotion/corner_subpixels/cornerSubPix_Demo.py)
|
||||
@include samples/python/tutorial_code/TrackingMotion/corner_subpixels/cornerSubPix_Demo.py
|
||||
@end_toggle
|
||||
|
||||
Explanation
|
||||
-----------
|
||||
|
||||
Result
|
||||
------
|
||||
|
||||
|
||||
|
||||
Here is the result:
|
||||
|
||||
|
||||
|
Before Width: | Height: | Size: 6.2 KiB After Width: | Height: | Size: 6.2 KiB |
|
Before Width: | Height: | Size: 15 KiB After Width: | Height: | Size: 15 KiB |
+20
-4
@@ -1,5 +1,5 @@
|
||||
Creating yor own corner detector {#tutorial_generic_corner_detector}
|
||||
================================
|
||||
Creating your own corner detector {#tutorial_generic_corner_detector}
|
||||
=================================
|
||||
|
||||
Goal
|
||||
----
|
||||
@@ -10,7 +10,7 @@ In this tutorial you will learn how to:
|
||||
to determine if a pixel is a corner.
|
||||
- Use the OpenCV function @ref cv::cornerMinEigenVal to find the minimum eigenvalues for corner
|
||||
detection.
|
||||
- To implement our own version of the Harris detector as well as the Shi-Tomasi detector, by using
|
||||
- Implement our own version of the Harris detector as well as the Shi-Tomasi detector, by using
|
||||
the two functions above.
|
||||
|
||||
Theory
|
||||
@@ -19,10 +19,26 @@ Theory
|
||||
Code
|
||||
----
|
||||
|
||||
@add_toggle_cpp
|
||||
This tutorial code's is shown lines below. You can also download it from
|
||||
[here](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/TrackingMotion/cornerDetector_Demo.cpp)
|
||||
|
||||
@include cpp/tutorial_code/TrackingMotion/cornerDetector_Demo.cpp
|
||||
@include samples/cpp/tutorial_code/TrackingMotion/cornerDetector_Demo.cpp
|
||||
@end_toggle
|
||||
|
||||
@add_toggle_java
|
||||
This tutorial code's is shown lines below. You can also download it from
|
||||
[here](https://github.com/opencv/opencv/tree/master/samples/java/tutorial_code/TrackingMotion/generic_corner_detector/CornerDetectorDemo.java)
|
||||
|
||||
@include samples/java/tutorial_code/TrackingMotion/generic_corner_detector/CornerDetectorDemo.java
|
||||
@end_toggle
|
||||
|
||||
@add_toggle_python
|
||||
This tutorial code's is shown lines below. You can also download it from
|
||||
[here](https://github.com/opencv/opencv/tree/master/samples/python/tutorial_code/TrackingMotion/generic_corner_detector/cornerDetector_Demo.py)
|
||||
|
||||
@include samples/python/tutorial_code/TrackingMotion/generic_corner_detector/cornerDetector_Demo.py
|
||||
@end_toggle
|
||||
|
||||
Explanation
|
||||
-----------
|
||||
|
||||
+16
-2
@@ -6,7 +6,7 @@ Goal
|
||||
|
||||
In this tutorial you will learn how to:
|
||||
|
||||
- Use the function @ref cv::goodFeaturesToTrack to detect corners using the Shi-Tomasi method.
|
||||
- Use the function @ref cv::goodFeaturesToTrack to detect corners using the Shi-Tomasi method (@cite Shi94).
|
||||
|
||||
Theory
|
||||
------
|
||||
@@ -14,9 +14,23 @@ Theory
|
||||
Code
|
||||
----
|
||||
|
||||
@add_toggle_cpp
|
||||
This tutorial code's is shown lines below. You can also download it from
|
||||
[here](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/TrackingMotion/goodFeaturesToTrack_Demo.cpp)
|
||||
@include samples/cpp/tutorial_code/TrackingMotion/goodFeaturesToTrack_Demo.cpp
|
||||
@end_toggle
|
||||
|
||||
@add_toggle_java
|
||||
This tutorial code's is shown lines below. You can also download it from
|
||||
[here](https://github.com/opencv/opencv/tree/master/samples/java/tutorial_code/TrackingMotion/good_features_to_track/GoodFeaturesToTrackDemo.java)
|
||||
@include samples/java/tutorial_code/TrackingMotion/good_features_to_track/GoodFeaturesToTrackDemo.java
|
||||
@end_toggle
|
||||
|
||||
@add_toggle_python
|
||||
This tutorial code's is shown lines below. You can also download it from
|
||||
[here](https://github.com/opencv/opencv/tree/master/samples/python/tutorial_code/TrackingMotion/good_features_to_track/goodFeaturesToTrack_Demo.py)
|
||||
@include samples/python/tutorial_code/TrackingMotion/good_features_to_track/goodFeaturesToTrack_Demo.py
|
||||
@end_toggle
|
||||
|
||||
Explanation
|
||||
-----------
|
||||
@@ -24,4 +38,4 @@ Explanation
|
||||
Result
|
||||
------
|
||||
|
||||
|
||||
|
||||
|
||||
BIN
Binary file not shown.
|
Before Width: | Height: | Size: 34 KiB |
BIN
Binary file not shown.
|
Before Width: | Height: | Size: 66 KiB |
BIN
Binary file not shown.
|
After Width: | Height: | Size: 27 KiB |
@@ -118,9 +118,23 @@ In this tutorial we will study the *corner* features, specifically.
|
||||
Code
|
||||
----
|
||||
|
||||
@add_toggle_cpp
|
||||
This tutorial code's is shown lines below. You can also download it from
|
||||
[here](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/TrackingMotion/cornerHarris_Demo.cpp)
|
||||
@include samples/cpp/tutorial_code/TrackingMotion/cornerHarris_Demo.cpp
|
||||
@end_toggle
|
||||
|
||||
@add_toggle_java
|
||||
This tutorial code's is shown lines below. You can also download it from
|
||||
[here](https://github.com/opencv/opencv/tree/master/samples/java/tutorial_code/TrackingMotion/harris_detector/CornerHarrisDemo.java)
|
||||
@include samples/java/tutorial_code/TrackingMotion/harris_detector/CornerHarrisDemo.java
|
||||
@end_toggle
|
||||
|
||||
@add_toggle_python
|
||||
This tutorial code's is shown lines below. You can also download it from
|
||||
[here](https://github.com/opencv/opencv/tree/master/samples/python/tutorial_code/TrackingMotion/harris_detector/cornerHarris_Demo.py)
|
||||
@include samples/python/tutorial_code/TrackingMotion/harris_detector/cornerHarris_Demo.py
|
||||
@end_toggle
|
||||
|
||||
Explanation
|
||||
-----------
|
||||
|
||||
Reference in New Issue
Block a user