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/*------------------------------------------------------------------------------------------*\
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;
}