Subversion Repositories OpenCV2-Cookbook

/*------------------------------------------------------------------------------------------*\
   This file contains material supporting chapter 8 of the cookbook:  
   Computer Vision Programming using the OpenCV Library.
   by Robert Laganiere, Packt Publishing, 2011.

   This program is free software; permission is hereby granted to use, copy, modify,
   and distribute this source code, or portions thereof, for any purpose, without fee,
   subject to the restriction that the copyright notice may not be removed
   or altered from any source or altered source distribution.
   The software is released on an as-is basis and without any warranties of any kind.
   In particular, the software is not guaranteed to be fault-tolerant or free from failure.
   The author disclaims all warranties with regard to this software, any use,
   and any consequent failure, is purely the responsibility of the user.
 
   Copyright (C) 2010-2011 Robert Laganiere, www.laganiere.name
\*------------------------------------------------------------------------------------------*/

#include <iostream>
#include <vector>
#include <opencv2/core/core.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/features2d/features2d.hpp>

#include "harrisDetector.h"

int main()
{
        // Read input image
        cv::Mat image= cv::imread("../church01.jpg",0);
        if (!image.data)
                return 0;

    // Display the image
        cv::namedWindow("Original Image");
        cv::imshow("Original Image",image);

        // Detect Harris Corners
        cv::Mat cornerStrength;
        cv::cornerHarris(image,cornerStrength,
                          3,     // neighborhood size
                                         3,     // aperture size
                                         0.01); // Harris parameter

   // threshold the corner strengths
        cv::Mat harrisCorners;
        double threshold= 0.0001;
        cv::threshold(cornerStrength,harrisCorners,
                 threshold,255,cv::THRESH_BINARY_INV);

    // Display the corners
        cv::namedWindow("Harris Corner Map");
        cv::imshow("Harris Corner Map",harrisCorners);

        // Create Harris detector instance
        HarrisDetector harris;
    // Compute Harris values
        harris.detect(image);
    // Detect Harris corners
        std::vector<cv::Point> pts;
        harris.getCorners(pts,0.01);
        // Draw Harris corners
        harris.drawOnImage(image,pts);

    // Display the corners
        cv::namedWindow("Harris Corners");
        cv::imshow("Harris Corners",image);

        // Read input image
        image= cv::imread("../church01.jpg",0);

        // Compute good features to track
        std::vector<cv::Point2f> corners;
        cv::goodFeaturesToTrack(image,corners,
                500,    // maximum number of corners to be returned
                0.01,   // quality level
                10);    // minimum allowed distance between points
         
        // for all corners
        std::vector<cv::Point2f>::const_iterator it= corners.begin();
        while (it!=corners.end()) {

                // draw a circle at each corner location
                cv::circle(image,*it,3,cv::Scalar(255,255,255),2);
                ++it;
        }

    // Display the corners
        cv::namedWindow("Good Features to Track");
        cv::imshow("Good Features to Track",image);

        // Read input image
        image= cv::imread("../church01.jpg",0);

        // vector of keypoints
        std::vector<cv::KeyPoint> keypoints;
        // Construction of the Good Feature to Track detector
        cv::GoodFeaturesToTrackDetector gftt(
                500,    // maximum number of corners to be returned
                0.01,   // quality level
                10);    // minimum allowed distance between points
        // point detection using FeatureDetector method
        gftt.detect(image,keypoints);
       
        cv::drawKeypoints(image,                // original image
                keypoints,                                      // vector of keypoints
                image,                                          // the resulting image
                cv::Scalar(255,255,255),        // color of the points
                cv::DrawMatchesFlags::DRAW_OVER_OUTIMG); //drawing flag

    // Display the corners
        cv::namedWindow("Good Features to Track Detector");
        cv::imshow("Good Features to Track Detector",image);

        // Read input image
        image= cv::imread("../church01.jpg",0);

        keypoints.clear();
        cv::FastFeatureDetector fast(40);
        fast.detect(image,keypoints);
       
        cv::drawKeypoints(image,keypoints,image,cv::Scalar(255,255,255),cv::DrawMatchesFlags::DRAW_OVER_OUTIMG);

    // Display the corners
        cv::namedWindow("FAST Features");
        cv::imshow("FAST Features",image);

        // Read input image
        image= cv::imread("../church03.jpg",0);

        keypoints.clear();
        // Construct the SURF feature detector object
        cv::SurfFeatureDetector surf(2500);
        // Detect the SURF features
        surf.detect(image,keypoints);
       
        cv::Mat featureImage;
        cv::drawKeypoints(image,keypoints,featureImage,cv::Scalar(255,255,255),cv::DrawMatchesFlags::DRAW_RICH_KEYPOINTS);

    // Display the corners
        cv::namedWindow("SURF Features");
        cv::imshow("SURF Features",featureImage);

        // Read input image
        image= cv::imread("../church01.jpg",0);

        keypoints.clear();
        // Construct the SURF feature detector object
        cv::SiftFeatureDetector sift(
                0.03,  // feature threshold
                10.);  // threshold to reduce
                   // sensitivity to lines

        // Detect the SURF features
        sift.detect(image,keypoints);
       
        cv::drawKeypoints(image,keypoints,featureImage,cv::Scalar(255,255,255),cv::DrawMatchesFlags::DRAW_RICH_KEYPOINTS);

    // Display the corners
        cv::namedWindow("SIFT Features");
        cv::imshow("SIFT Features",featureImage);

        // Read input image
        image= cv::imread("../church01.jpg",0);

        keypoints.clear();

        cv::MserFeatureDetector mser;
        mser.detect(image,keypoints);
       
        // Draw the keypoints with scale and orientation information
        cv::drawKeypoints(image,                // original image
                keypoints,                                      // vector of keypoints
                featureImage,                           // the resulting image
                cv::Scalar(255,255,255),        // color of the points
                cv::DrawMatchesFlags::DRAW_RICH_KEYPOINTS); //drawing flag

    // Display the corners
        cv::namedWindow("MSER Features");
        cv::imshow("MSER Features",featureImage);

        cv::waitKey();
        return 0;
}