fixed DynamicAdaptedFeatureDetector

This commit is contained in:
Maria Dimashova 2011-05-11 11:53:53 +00:00
parent 6afd44674f
commit c98c87d545
2 changed files with 119 additions and 88 deletions

View File

@ -1487,20 +1487,22 @@ public:
/** pure virtual interface
*/
virtual ~AdjusterAdapter() {}
/** too few features were detected so, adjust the detector params accordingly
* \param min the minimum number of desired features
* \param n_detected the number previously detected
*/
virtual void tooFew(int min, int n_detected) = 0;
/** too many features were detected so, adjust the detector params accordingly
* \param max the maximum number of desired features
* \param n_detected the number previously detected
*/
virtual void tooMany(int max, int n_detected) = 0;
/** are params maxed out or still valid?
* \return false if the parameters can't be adjusted any more
*/
virtual bool good() const = 0;
/** too few features were detected so, adjust the detector params accordingly
* \param min the minimum number of desired features
* \param n_detected the number previously detected
*/
virtual void tooFew(int min, int n_detected) = 0;
/** too many features were detected so, adjust the detector params accordingly
* \param max the maximum number of desired features
* \param n_detected the number previously detected
*/
virtual void tooMany(int max, int n_detected) = 0;
/** are params maxed out or still valid?
* \return false if the parameters can't be adjusted any more
*/
virtual bool good() const = 0;
virtual Ptr<AdjusterAdapter> clone() const = 0;
static Ptr<AdjusterAdapter> create( const string& detectorType );
};
@ -1521,11 +1523,11 @@ class CV_EXPORTS DynamicAdaptedFeatureDetector: public FeatureDetector
public:
/** \param adjaster an AdjusterAdapter that will do the detection and parameter adjustment
* \param max_features the maximum desired number of features
* \param max_iters the maximum number of times to try to adjust the feature detector params
* for the FastAdjuster this can be high, but with Star or Surf this can get time consuming
* \param min_features the minimum desired features
*/
* \param max_features the maximum desired number of features
* \param max_iters the maximum number of times to try to adjust the feature detector params
* for the FastAdjuster this can be high, but with Star or Surf this can get time consuming
* \param min_features the minimum desired features
*/
DynamicAdaptedFeatureDetector( const Ptr<AdjusterAdapter>& adjaster, int min_features=400, int max_features=500, int max_iters=5 );
virtual bool empty() const;
@ -1534,30 +1536,34 @@ protected:
virtual void detectImpl( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask=Mat() ) const;
private:
int escape_iters_;
int min_features_, max_features_;
Ptr<AdjusterAdapter> adjuster_;
int escape_iters_;
int min_features_, max_features_;
const Ptr<AdjusterAdapter> adjuster_;
};
/**\brief an adjust for the FAST detector. This will basically decrement or increment the
* threshhold by 1
* threshold by 1
*/
class CV_EXPORTS FastAdjuster: public AdjusterAdapter
{
public:
/**\param init_thresh the initial threshhold to start with, default = 20
* \param nonmax whether to use non max or not for fast feature detection
*/
FastAdjuster(int init_thresh = 20, bool nonmax = true);
virtual void tooFew(int min, int n_detected);
virtual void tooMany(int max, int n_detected);
virtual bool good() const;
/**\param init_thresh the initial threshold to start with, default = 20
* \param nonmax whether to use non max or not for fast feature detection
*/
FastAdjuster(int init_thresh = 20, bool nonmax = true);
virtual void tooFew(int min, int n_detected);
virtual void tooMany(int max, int n_detected);
virtual bool good() const;
virtual Ptr<AdjusterAdapter> clone() const;
protected:
virtual void detectImpl( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask=Mat() ) const;
int thresh_;
bool nonmax_;
int thresh_;
bool nonmax_;
int init_thresh_;
};
@ -1567,30 +1573,36 @@ protected:
class CV_EXPORTS StarAdjuster: public AdjusterAdapter
{
public:
StarAdjuster(double initial_thresh = 30.0);
virtual void tooFew(int min, int n_detected);
virtual void tooMany(int max, int n_detected);
virtual bool good() const;
StarAdjuster(double initial_thresh = 30.0);
virtual void tooFew(int min, int n_detected);
virtual void tooMany(int max, int n_detected);
virtual bool good() const;
virtual Ptr<AdjusterAdapter> clone() const;
protected:
virtual void detectImpl( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask=Mat() ) const;
double thresh_;
CvStarDetectorParams params_; //todo use these instead of thresh_
double thresh_, init_thresh_;
CvStarDetectorParams params_; //todo use these instead of thresh_
};
class CV_EXPORTS SurfAdjuster: public AdjusterAdapter
{
public:
SurfAdjuster();
virtual void tooFew(int min, int n_detected);
virtual void tooMany(int max, int n_detected);
virtual bool good() const;
SurfAdjuster( double initial_thresh=400.f );
virtual void tooFew(int min, int n_detected);
virtual void tooMany(int max, int n_detected);
virtual bool good() const;
virtual Ptr<AdjusterAdapter> clone() const;
protected:
virtual void detectImpl( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask=Mat() ) const;
double thresh_;
double thresh_, init_thresh_;
};
CV_EXPORTS Mat windowedMatchingMask( const vector<KeyPoint>& keypoints1, const vector<KeyPoint>& keypoints2,

View File

@ -56,124 +56,143 @@ bool DynamicAdaptedFeatureDetector::empty() const
void DynamicAdaptedFeatureDetector::detectImpl(const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask) const
{
//for oscillation testing
bool down = false;
bool up = false;
//for oscillation testing
bool down = false;
bool up = false;
//flag for whether the correct threshhold has been reached
bool thresh_good = 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_);
Ptr<AdjusterAdapter> adjuster = adjuster_->clone();
//break if the desired number hasn't been reached.
int iter_count = escape_iters_;
//break if the desired number hasn't been reached.
int iter_count = escape_iters_;
do
while( iter_count > 0 && !(down && up) && !thresh_good && adjuster->good() )
{
keypoints.clear();
keypoints.clear();
//the adjuster takes care of calling the detector with updated parameters
adjuster.detect(image, keypoints,mask);
//the adjuster takes care of calling the detector with updated parameters
adjuster->detect(image, keypoints,mask);
if (int(keypoints.size()) < min_features_)
if( int(keypoints.size()) < min_features_ )
{
down = true;
adjuster.tooFew(min_features_, (int)keypoints.size());
down = true;
adjuster->tooFew(min_features_, (int)keypoints.size());
}
else if (int(keypoints.size()) > max_features_)
else if( int(keypoints.size()) > max_features_ )
{
up = true;
adjuster.tooMany(max_features_, (int)keypoints.size());
up = true;
adjuster->tooMany(max_features_, (int)keypoints.size());
}
else
thresh_good = true;
thresh_good = true;
iter_count--;
}
while (--iter_count >= 0 && !(down && up) && !thresh_good && adjuster.good());
}
FastAdjuster::FastAdjuster(int init_thresh, bool nonmax) :
thresh_(init_thresh), nonmax_(nonmax)
thresh_(init_thresh), nonmax_(nonmax), init_thresh_(init_thresh)
{}
void FastAdjuster::detectImpl(const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask) const
{
FastFeatureDetector(thresh_, nonmax_).detect(image, keypoints, mask);
FastFeatureDetector(thresh_, nonmax_).detect(image, keypoints, mask);
}
void FastAdjuster::tooFew(int, int)
{
//fast is easy to adjust
thresh_--;
//fast is easy to adjust
thresh_--;
}
void FastAdjuster::tooMany(int, int)
{
//fast is easy to adjust
thresh_++;
//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);
return (thresh_ > 1) && (thresh_ < 200);
}
Ptr<AdjusterAdapter> FastAdjuster::clone() const
{
Ptr<AdjusterAdapter> cloned_obj = new FastAdjuster( init_thresh_, nonmax_ );
return cloned_obj;
}
StarAdjuster::StarAdjuster(double initial_thresh) :
thresh_(initial_thresh)
thresh_(initial_thresh), init_thresh_(initial_thresh)
{}
void StarAdjuster::detectImpl(const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask) const
{
StarFeatureDetector detector_tmp(16, cvRound(thresh_), 10, 8, 3);
detector_tmp.detect(image, keypoints, mask);
StarFeatureDetector detector_tmp(16, cvRound(thresh_), 10, 8, 3);
detector_tmp.detect(image, keypoints, mask);
}
void StarAdjuster::tooFew(int, int)
{
thresh_ *= 0.9;
if (thresh_ < 1.1)
thresh_ = 1.1;
thresh_ *= 0.9;
if (thresh_ < 1.1)
thresh_ = 1.1;
}
void StarAdjuster::tooMany(int, int)
{
thresh_ *= 1.1;
thresh_ *= 1.1;
}
bool StarAdjuster::good() const
{
return (thresh_ > 2) && (thresh_ < 200);
return (thresh_ > 2) && (thresh_ < 200);
}
SurfAdjuster::SurfAdjuster() :
thresh_(400.0)
Ptr<AdjusterAdapter> StarAdjuster::clone() const
{
Ptr<AdjusterAdapter> cloned_obj = new StarAdjuster( init_thresh_ );
return cloned_obj;
}
SurfAdjuster::SurfAdjuster( double initial_thresh ) :
thresh_(initial_thresh), init_thresh_(initial_thresh)
{}
void SurfAdjuster::detectImpl(const Mat& image, vector<KeyPoint>& keypoints, const cv::Mat& mask) const
{
SurfFeatureDetector detector_tmp(thresh_);
detector_tmp.detect(image, keypoints, mask);
SurfFeatureDetector detector_tmp(thresh_);
detector_tmp.detect(image, keypoints, mask);
}
void SurfAdjuster::tooFew(int, int)
{
thresh_ *= 0.9;
if (thresh_ < 1.1)
thresh_ = 1.1;
thresh_ *= 0.9;
if (thresh_ < 1.1)
thresh_ = 1.1;
}
void SurfAdjuster::tooMany(int, int)
{
thresh_ *= 1.1;
thresh_ *= 1.1;
}
//return whether or not the threshhold is beyond
//a useful point
bool SurfAdjuster::good() const
{
return (thresh_ > 2) && (thresh_ < 1000);
return (thresh_ > 2) && (thresh_ < 1000);
}
Ptr<AdjusterAdapter> SurfAdjuster::clone() const
{
Ptr<AdjusterAdapter> cloned_obj = new SurfAdjuster( init_thresh_ );
return cloned_obj;
}
Ptr<AdjusterAdapter> AdjusterAdapter::create( const string& detectorType )