opencv/modules/features2d/src/dynamic.cpp

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#include "precomp.hpp"
namespace cv {
DynamicDetector::DynamicDetector(int min_features,
		int max_features, int max_iters, const Ptr<AdjusterAdapter>& a) :
	escape_iters_(max_iters), min_features_(min_features), max_features_(
			max_features), adjuster_(a) {
}
void DynamicDetector::detectImpl(const cv::Mat& image, std::vector<
		cv::KeyPoint>& keypoints, const cv::Mat& mask) const {
	//for oscillation testing
	bool down = false;
	bool up = false;

	//flag for whether the correct threshhold has been reached
	bool thresh_good = false;

	//this is bad but adjuster should persist from detection to detection
	AdjusterAdapter& adjuster = const_cast<AdjusterAdapter&> (*adjuster_);

	//break if the desired number hasn't been reached.
	int iter_count = escape_iters_;

	do {
		keypoints.clear();

		//the adjuster takes care of calling the detector with updated parameters
		adjuster.detect(image, keypoints,mask);

		if (int(keypoints.size()) < min_features_) {
			down = true;
			adjuster.tooFew(min_features_, keypoints.size());
		} else if (int(keypoints.size()) > max_features_) {
			up = true;
			adjuster.tooMany(max_features_, keypoints.size());
		} else
			thresh_good = true;
	} while (--iter_count >= 0 && !(down && up) && !thresh_good
			&& adjuster.good());
}

FastAdjuster::FastAdjuster(int init_thresh, bool nonmax) :
	thresh_(init_thresh), nonmax_(nonmax) {
}
void FastAdjuster::detectImpl(const cv::Mat& image,
		std::vector<cv::KeyPoint>& keypoints, const cv::Mat& mask) const {
	FastFeatureDetector(thresh_, nonmax_).detect(image, keypoints, mask);
}
void FastAdjuster::tooFew(int min, int n_detected) {
	//fast is easy to adjust
	thresh_--;
}
void FastAdjuster::tooMany(int max, int n_detected) {
	//fast is easy to adjust
	thresh_++;
}

//return whether or not the threshhold is beyond
//a useful point
bool FastAdjuster::good() const {
	return (thresh_ > 1) && (thresh_ < 200);
}

StarAdjuster::StarAdjuster(double initial_thresh) :
	thresh_(initial_thresh) {
}
void StarAdjuster::detectImpl(const cv::Mat& image,
		std::vector<cv::KeyPoint>& keypoints, const cv::Mat& mask) const {
	StarFeatureDetector detector_tmp(16, thresh_, 10, 8, 3);
	detector_tmp.detect(image, keypoints, mask);
}
void StarAdjuster::tooFew(int min, int n_detected) {
	thresh_ *= 0.9;
	if (thresh_ < 1.1)
		thresh_ = 1.1;
}
void StarAdjuster::tooMany(int max, int n_detected) {
	thresh_ *= 1.1;
}

bool StarAdjuster::good() const {
	return (thresh_ > 2) && (thresh_ < 200);
}

SurfAdjuster::SurfAdjuster() :
	thresh_(400.0) {
}
void SurfAdjuster::detectImpl(const cv::Mat& image,
		std::vector<cv::KeyPoint>& keypoints, const cv::Mat& mask) const {
	SurfFeatureDetector detector_tmp(thresh_);
	detector_tmp.detect(image, keypoints, mask);
}
void SurfAdjuster::tooFew(int min, int n_detected) {
	thresh_ *= 0.9;
	if (thresh_ < 1.1)
		thresh_ = 1.1;
}
void SurfAdjuster::tooMany(int max, int n_detected) {
	thresh_ *= 1.1;
}

//return whether or not the threshhold is beyond
//a useful point
bool SurfAdjuster::good() const {
	return (thresh_ > 2) && (thresh_ < 1000);
}

}