首页 > 代码库 > Learning OpenCV Lecture 7 (Detecting and Matching Interest Points)

Learning OpenCV Lecture 7 (Detecting and Matching Interest Points)

2024-07-14 16:15:57   223人阅读

In this chapter, we will cover:

  • Detecting Harris corners
  • Detecting FAST features
  • Detecting the scale-invariant SURF features
  • Describing SURF features

 

Detecting Harris corners

The basic OpenCV function for detecting Harris corners is called cv::cornerHarrisand is straightforward to use. You call it on an input image and the result is an image of floats which gives the corner strength at each pixel location. A threshold is then applied on this output image in order to obtain a set of detected corners. This is accomplished by the following code:

	// 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);	cv::imshow("Original Image", image);	cv::imshow("Harris Corner Map", harrisCorners);

Here is the original image:

 

　

The result is a binary map image shown in the following screenshot which is inverted for better viewing (that is, we used cv::THRESH_BINARY_INVinstead of cv::THRESH_BINARYto get the detected corners in black):

 

The class encapsulates the Harris parameters with their default values and corresponding getter and setter methods (which are not shown here):

#if !defined HARRISDETECTOR#define HARRISDETECTOR#include <core/core.hpp>#include <highgui/highgui.hpp>#include <imgproc/imgproc.hpp>class HarrisDetector {private:	// 32-bit float image of corner strength	cv::Mat cornerStrength;	// 32-bit float image of threshold corners	cv::Mat cornerTh;	// image of local maxima (internal)	cv::Mat localMax;	// size of neighborhood for derivatives smoothing	int neighborhood;	// aperture for gradient computation	int aperture;	// Harris parameter	double k;	// maximum strength for threshold computation	double maxStrength;	// calculated threshold (internal)	double threshold;	// size of neighborhood for non-max supression	int nonMaxSize;	// kernel for non-max supression	cv::Mat kernel;public:	HarrisDetector() : neighborhood(3), aperture(3), 						k(0.01), maxStrength(0.0),						threshold(0.01), nonMaxSize(3) {		// create kernel used in non-max supression		setLocalMaxWindowSize(nonMaxSize);	}	void setLocalMaxWindowSize(int nonMaxSize) {		this->nonMaxSize = nonMaxSize;	}	// Compute Harris corners	void detect(const cv::Mat &image) {		// Harris computation		cv::cornerHarris(image, cornerStrength,			neighborhood,	// neighborhood size			aperture,		// aperture size			k				// Harris parameter			);		// internal threshold computation		double minStrength;	// not used		cv::minMaxLoc(cornerStrength, &minStrength, &maxStrength);		// local maxima detection		cv::Mat dilated;	//temporary image		cv::dilate(cornerStrength, dilated, cv::Mat());		cv::compare(cornerStrength, dilated, localMax, cv::CMP_EQ);	}	// Get the corner map from the comuted Harris values	cv::Mat getCornerMap(double qualityLevel) {		cv::Mat cornerMap;		// thresholding the corner strength		threshold = qualityLevel * maxStrength;		cv::threshold(cornerStrength, cornerTh, threshold, 255, cv::THRESH_BINARY);		// convert to 8-bit image		cornerTh.convertTo(cornerMap, CV_8U);		// non-maxima suppression		cv::bitwise_and(cornerMap, localMax, cornerMap);		return cornerMap;	}	// Get the feature points from the computed Harris value	void getCorners(std::vector<cv::Point> &points, double qualityLevel) {		// Get the corner map		cv::Mat cornerMap = getCornerMap(qualityLevel);		// Get the corners		getCorners(points, cornerMap);	}	// Get the features points from the computed corner map	void getCorners(std::vector<cv::Point> &points, const cv::Mat &cornerMap) {		// Iterate over the pixels to obtain all features		for (int y = 0; y < cornerMap.rows; y++) {			const uchar *rowPtr = cornerMap.ptr<uchar>(y);			for (int x = 0; x < cornerMap.cols; x++) {				// if it is a feature point				if (rowPtr[x]) {					points.push_back(cv::Point(x, y));				}			}		}	}	// Draw circles at feature point locations on an image	void drawOnImage(cv::Mat &image, const std::vector<cv::Point> &points,		cv::Scalar color = cv::Scalar(255, 255, 255),		int radius = 3, int thickness = 2) {		std::vector<cv::Point>::const_iterator it = points.begin();		// for all corners		while (it != points.end()) {			// draw a circle at each corner location			cv::circle(image, *it, radius, color, thickness);			++ it;		}	}};#endif

Using this class, the detection of the Harris points is accomplished as follows:

	// Using HarrisDetector Class	// 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);	cv::imshow("Harris Corners", image);

Which results in the following image:

Additional improvements can be made to the original Harris corner algorithm. This section describes another corner detector found in OpenCV which expands the Harris detector to make its corners more uniformly distributed across the image. As we will see, this operator has an implementation in the new OpenCV 2 common interface for feature detector.

Good features to track:

// Compute good features to trackstd::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

In addition to the quality-level threshold value, and the minimum tolerated distance between interest points, the function also uses a maximum number of points to be returned (this is possible since points are accepted in order of strength). The preceding function call produces the following result:

　　

 

Detecting FAST features

In this recipe, we present another feature point operator. This one has been specifically designed to allow quick detection of interest points in an image. The decision to accept or not to accept a keypoint being based on only a few pixel comparisons.

Using the OpenCV 2 common interface for feature point detection makes the deployment of any feature point detectors easy. The one presented in this recipe is the FAST detector. As the name suggests, it has been designed to be quick to compute. Note that OpenCV also proposes a generic function to draw keypoints on an image:

	// Detection FAST features	image = cv::imread("../church01.jpg");	// vector of keypoints	std::vector<cv::KeyPoint> keypoints;	// Construction of the Fast feature detector object	cv::FastFeatureDetector fast(40); // threshold for detection	// feature point detection	fast.detect(image, keypoints);	// draw keypoints on an image	cv::drawKeypoints(image,		//original image		keypoints,					// vector of keypoints		image,						// the output image		cv::Scalar(255, 255, 255),	// key point color		cv::DrawMatchesFlags::DRAW_OVER_OUTIMG // drawing flag		);	cv::imshow("FAST Features", image);

By specifying the chosen drawing flag, the keypoints are drawn over the output image, thus producing the following result:

 

Detecting the scale-invariant SURF features

The OpenCV implementation of SURF features also use the cv::FeatureDetector interface. Therefore, the detection of these features is similar to what we demonstrated in the previous recipes of this chapter:

 


联系
我们