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Conflicts: cmake/OpenCVModule.cmake doc/tutorials/calib3d/camera_calibration/camera_calibration.rst doc/tutorials/features2d/feature_detection/feature_detection.rst doc/tutorials/features2d/feature_flann_matcher/feature_flann_matcher.rst doc/tutorials/features2d/feature_homography/feature_homography.rst modules/core/include/opencv2/core/operations.hpp modules/core/src/arithm.cpp modules/gpu/perf/perf_video.cpp modules/imgproc/include/opencv2/imgproc/imgproc.hpp modules/java/generator/gen_java.py modules/java/generator/src/cpp/VideoCapture.cpp modules/nonfree/src/opencl/surf.cl modules/ocl/include/opencv2/ocl/ocl.hpp modules/ocl/perf/perf_haar.cpp modules/ocl/perf/perf_precomp.hpp modules/ocl/src/color.cpp modules/ocl/src/filtering.cpp modules/ocl/test/test_color.cpp modules/ocl/test/test_objdetect.cpp modules/python/src2/cv2.cpp samples/gpu/CMakeLists.txt samples/gpu/super_resolution.cpp
98 lines
2.5 KiB
ReStructuredText
98 lines
2.5 KiB
ReStructuredText
.. _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 <http://code.opencv.org/projects/opencv/repository/revisions/master/raw/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.hpp"
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#include "opencv2/features2d.hpp"
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#include "opencv2/highgui.hpp"
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#include "opencv2/nonfree.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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