Feature Detection — OpenCV 2.4.13.0 documentation

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# Feature Detection

## Goal

In this tutorial you will learn how to:

- Use the FeatureDetector interface in order to find interest points. Specifically:
  - Use the SurfFeatureDetector and its function detect to perform the detection process
  - Use the function drawKeypoints to draw the detected keypoints

## Theory

## Code

This tutorial code’s is shown lines below. You can also download it from here

    #include <stdio.h>
    #include <iostream>
    #include "opencv2/core/core.hpp"
    #include "opencv2/features2d/features2d.hpp"
    #include "opencv2/nonfree/features2d.hpp"
    #include "opencv2/highgui/highgui.hpp"
    #include "opencv2/nonfree/nonfree.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

1. Here is the result of the feature detection applied to the first image:

2. And here is the result for the second image:

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### Navigation

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