2011-08-12 19:01:10 +02:00
.. _feature_detection:
Feature Detection
***** ***** ***** ***
Goal
=====
In this tutorial you will learn how to:
.. container :: enumeratevisibleitemswithsquare
* Use the :feature_detector:`FeatureDetector<>` interface in order to find interest points. Specifically:
* Use the :surf_feature_detector:`SurfFeatureDetector<>` and its function :feature_detector_detect:`detect<>` to perform the detection process
* Use the function :draw_keypoints:`drawKeypoints<>` to draw the detected keypoints
Theory
======
Code
====
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This tutorial code's is shown lines below. You can also download it from `here <http://code.opencv.org/svn/opencv/trunk/opencv/samples/cpp/tutorial_code/features2D/SURF_detector.cpp> `_
2011-08-12 19:01:10 +02:00
.. code-block :: cpp
#include <stdio.h>
#include <iostream>
#include "opencv2/core/core.hpp"
#include "opencv2/features2d/features2d.hpp"
#include "opencv2/highgui/highgui.hpp"
using namespace cv;
void readme();
/** @function main * /
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 );
if( !img_1.data || !img_2.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<KeyPoint> keypoints_1, keypoints_2;
detector.detect( img_1, keypoints_1 );
detector.detect( img_2, keypoints_2 );
//-- Draw keypoints
Mat img_keypoints_1; Mat img_keypoints_2;
drawKeypoints( img_1, keypoints_1, img_keypoints_1, Scalar::all(-1), DrawMatchesFlags::DEFAULT );
drawKeypoints( img_2, keypoints_2, img_keypoints_2, Scalar::all(-1), DrawMatchesFlags::DEFAULT );
//-- Show detected (drawn) keypoints
imshow("Keypoints 1", img_keypoints_1 );
imshow("Keypoints 2", img_keypoints_2 );
waitKey(0);
return 0;
}
/** @function readme * /
void readme()
{ std::cout << " Usage: ./SURF_detector <img1> <img2>" << std::endl; }
Explanation
============
Result
======
#. Here is the result of the feature detection applied to the first image:
.. image :: images/Feature_Detection_Result_a.jpg
:align: center
:height: 125pt
#. And here is the result for the second image:
.. image :: images/Feature_Detection_Result_b.jpg
:align: center
:height: 200pt