Adding some dynamic feature detectors...
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@ -1448,6 +1448,152 @@ protected:
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int levels;
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int levels;
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};
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};
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/****************************************************************************************\
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* Dynamic Feature Detectors *
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\****************************************************************************************/
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/** \brief an adaptively adjusting detector that iteratively detects until the desired number
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* of features are detected.
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* Beware that this is not thread safe - as the adjustment of parameters breaks the const
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* of the detection routine...
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* /TODO Make this const correct and thread safe
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*/
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template<typename Adjuster>
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class DynamicDetectorAdaptor: public FeatureDetector {
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public:
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/** \param min_features the minimum desired features
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* \param max_features the maximum desired number of features
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* \param max_iters the maximum number of times to try to adjust the feature detector params
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* for the FastAdjuster this can be high, but with Star or Surf this can get time consuming
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* \param a a copy of an Adjuster that will do the detection and parameter adjustment
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*/
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DynamicDetectorAdaptor(int min_features, int max_features,
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int max_iters, const Adjuster& a = Adjuster()) :
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escape_iters_(max_iters), min_features_(min_features), max_features_(
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max_features), adjuster_(a) {
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}
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protected:
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virtual void detectImpl(const cv::Mat& image,
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std::vector<cv::KeyPoint>& keypoints, const cv::Mat& mask =
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cv::Mat()) const {
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//for oscillation testing
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bool down = false;
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bool up = false;
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//flag for whether the correct threshhold has been reached
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bool thresh_good = false;
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//this is bad but adjuster should persist from detection to detection
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Adjuster& adjuster = const_cast<Adjuster&> (adjuster_);
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//break if the desired number hasn't been reached.
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int iter_count = escape_iters_;
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do {
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keypoints.clear();
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//the adjuster takes care of calling the detector with updated parameters
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adjuster.detect(image, mask, keypoints);
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if (int(keypoints.size()) < min_features_) {
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down = true;
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adjuster.tooFew(min_features_, keypoints.size());
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} else if (int(keypoints.size()) > max_features_) {
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up = true;
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adjuster.tooMany(max_features_, keypoints.size());
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} else
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thresh_good = true;
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} while (--iter_count >= 0 && !(down && up) && !thresh_good
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&& adjuster.good());
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}
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private:
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int escape_iters_;
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int min_features_, max_features_;
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Adjuster adjuster_;
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};
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struct FastAdjuster {
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FastAdjuster() :
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thresh_(20) {
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}
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void detect(const Mat& img, const Mat& mask, std::vector<
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KeyPoint>& keypoints) const {
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FastFeatureDetector(thresh_, true).detect(img, keypoints, mask);
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}
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void tooFew(int min, int n_detected) {
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//fast is easy to adjust
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thresh_--;
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}
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void tooMany(int max, int n_detected) {
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//fast is easy to adjust
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thresh_++;
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}
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//return whether or not the threshhold is beyond
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//a useful point
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bool good() const {
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return (thresh_ > 1) && (thresh_ < 200);
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}
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int thresh_;
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};
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struct StarAdjuster {
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StarAdjuster() :
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thresh_(30) {
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}
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void detect(const Mat& img, const Mat& mask, std::vector<
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KeyPoint>& keypoints) const {
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StarFeatureDetector detector_tmp(16, thresh_, 10, 8, 3);
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detector_tmp.detect(img, keypoints, mask);
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}
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void tooFew(int min, int n_detected) {
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thresh_ *= 0.9;
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if (thresh_ < 1.1)
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thresh_ = 1.1;
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}
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void tooMany(int max, int n_detected) {
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thresh_ *= 1.1;
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}
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//return whether or not the threshhold is beyond
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//a useful point
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bool good() const {
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return (thresh_ > 2) && (thresh_ < 200);
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}
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double thresh_;
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};
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struct SurfAdjuster {
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SurfAdjuster() :
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thresh_(400.0) {
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}
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void detect(const Mat& img, const Mat& mask, std::vector<
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KeyPoint>& keypoints) const {
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SurfFeatureDetector detector_tmp(thresh_);
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detector_tmp.detect(img, keypoints, mask);
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}
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void tooFew(int min, int n_detected) {
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thresh_ *= 0.9;
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if (thresh_ < 1.1)
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thresh_ = 1.1;
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}
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void tooMany(int max, int n_detected) {
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thresh_ *= 1.1;
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}
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//return whether or not the threshhold is beyond
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//a useful point
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bool good() const {
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return (thresh_ > 2) && (thresh_ < 1000);
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}
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double thresh_;
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};
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typedef DynamicDetectorAdaptor<FastAdjuster> FASTDynamicDetector;
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typedef DynamicDetectorAdaptor<StarAdjuster> StarDynamicDetector;
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typedef DynamicDetectorAdaptor<SurfAdjuster> SurfDynamicDetector;
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CV_EXPORTS Mat windowedMatchingMask( const vector<KeyPoint>& keypoints1, const vector<KeyPoint>& keypoints2,
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CV_EXPORTS Mat windowedMatchingMask( const vector<KeyPoint>& keypoints1, const vector<KeyPoint>& keypoints2,
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float maxDeltaX, float maxDeltaY );
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float maxDeltaX, float maxDeltaY );
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@ -1717,7 +1863,8 @@ struct CV_EXPORTS L1
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};
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};
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/*
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/*
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* Hamming distance (city block distance) functor
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* Hamming distance functor - counts the bit differences between two strings - useful for the Brief descriptor
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* bit count of A exclusive ored with B
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*/
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*/
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struct CV_EXPORTS HammingLUT
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struct CV_EXPORTS HammingLUT
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{
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{
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@ -526,10 +526,18 @@ Ptr<FeatureDetector> createFeatureDetector( const string& detectorType )
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{
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{
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fd = new FastFeatureDetector();
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fd = new FastFeatureDetector();
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}
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}
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else if( !detectorType.compare( "DynamicFAST" ) )
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{
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fd = new FASTDynamicDetector(400,500,5);
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}
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else if( !detectorType.compare( "STAR" ) )
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else if( !detectorType.compare( "STAR" ) )
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{
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{
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fd = new StarFeatureDetector();
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fd = new StarFeatureDetector();
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}
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}
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else if( !detectorType.compare( "DynamicSTAR" ) )
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{
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fd = new StarDynamicDetector(400,500,5);
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}
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else if( !detectorType.compare( "SIFT" ) )
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else if( !detectorType.compare( "SIFT" ) )
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{
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{
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fd = new SiftFeatureDetector(SIFT::DetectorParams::GET_DEFAULT_THRESHOLD(),
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fd = new SiftFeatureDetector(SIFT::DetectorParams::GET_DEFAULT_THRESHOLD(),
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@ -539,6 +547,10 @@ Ptr<FeatureDetector> createFeatureDetector( const string& detectorType )
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{
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{
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fd = new SurfFeatureDetector();
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fd = new SurfFeatureDetector();
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}
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}
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else if( !detectorType.compare( "DynamicSURF" ) )
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{
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fd = new SurfDynamicDetector(400,500,5);
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}
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else if( !detectorType.compare( "MSER" ) )
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else if( !detectorType.compare( "MSER" ) )
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{
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{
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fd = new MserFeatureDetector();
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fd = new MserFeatureDetector();
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