working on the dynamic detectors documentation.
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Ethan Rublee
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\cvclass{DynamicDetector}
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An adaptively adjusting detector that iteratively detects until the desired number
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of features are found.
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of features are found.
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Adapters can easily be implemented for any detector through the creation of an Adjuster
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object.
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If the detector is persisted, it will "remember" the parameters
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used on the last detection. In this way, the detector may be used for consistent numbers
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of keypoints in a sets of images that are temporally related such as video streams or
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panorama series.
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The DynamicDetector uses another detector such as FAST or SURF to do the dirty work,
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with the help of an AdjusterAdapter.
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After a detection, and an unsatisfactory number of features are detected,
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the AdjusterAdapter will adjust the detection parameters so that the next detection will
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result in more or less features. This is repeated until either the number of desired features are found
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or the parameters are maxed out.
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Adapters can easily be implemented for any detector via the
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AdjusterAdapter interface.
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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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Here is a sample of how to create a DynamicDetector.
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\begin{lstlisting}
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//sample usage:
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//will create a detector that attempts to find 100 - 110 FAST Keypoints, and will at most run
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//FAST feature detection 10 times until that number of keypoints are found
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Ptr<FeatureDetector> detector(new DynamicDetector (100, 110, 10,new FastAdjuster(20,true)));
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//will create a detector that attempts to find
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//100 - 110 FAST Keypoints, and will at most run
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//FAST feature detection 10 times until that
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//number of keypoints are found
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Ptr<FeatureDetector> detector(new DynamicDetector (100, 110, 10,
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new FastAdjuster(20,true)));
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\end{lstlisting}
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class CV_EXPORTS DynamicDetector: public FeatureDetector {
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\begin{lstlisting}
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class DynamicDetector: public FeatureDetector {
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public:
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/**min_features the minimum desired features
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* max_features the maximum desired number of features
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* 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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* a an AdjusterAdapter that will do the detection and parameter adjustment
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*/
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DynamicDetector(int min_features, int max_features, int max_iters,
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const Ptr<AdjusterAdapter>& a);
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...
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};
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\end{lstlisting}
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\end{lstlisting}
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\cvCppFunc{DynamicDetector::DynamicDetector}
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DynamicDetector constructor.
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\cvdefCpp{
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DynamicDetector::DynamicDetector( \par int min\_features, \par int max\_features, \par int max\_iters,
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\par const Ptr<AdjusterAdapter>\& a);
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}
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\begin{description}
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\cvarg{min\_features}{This minimum desired number features.}
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\cvarg{max\_features}{The maximum desired number of features.}
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\cvarg{max\_iters}{The maximum number of times to try to adjust the feature detector parameters. For the \cvCppCross{FastAdjuster} this number can be high,
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but with Star or Surf, many iterations can get time consuming. At each iteration the detector is rerun, so keep this in mind when choosing this value.}
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\cvarg{a}{ An \cvCppCross{AdjusterAdapter} that will do the detection and parameter
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adjustment}
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\end{description}
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\cvclass{AdjusterAdapter}
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A feature detector parameter adjuster interface, this is used by the \cvCppCross{DynamicDetector}
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and is a wrapper for \cvCppCross{FeatureDetecto}r that allow them to be adjusted after a detection.
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@ -36,27 +64,59 @@ See \cvCppCross{FastAdjuster}, \cvCppCross{StarAdjuster}, \cvCppCross{SurfAdjus
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\begin{lstlisting}
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class AdjusterAdapter: public FeatureDetector {
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public:
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/** pure virtual interface
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*/
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virtual ~AdjusterAdapter() {
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}
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/** too few features were detected so, adjust the detector params accordingly
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* \param min the minimum number of desired features
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* \param n_detected the number previously detected
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*/
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virtual void tooFew(int min, int n_detected) = 0;
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/** too many features were detected so, adjust the detector params accordingly
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* \param max the maximum number of desired features
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* \param n_detected the number previously detected
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*/
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virtual void tooMany(int max, int n_detected) = 0;
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/** are params maxed out or still valid?
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* \return false if the parameters can't be adjusted any more
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*/
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virtual bool good() const = 0;
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};
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\end{lstlisting}
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\cvCppFunc{AdjusterAdapter::tooFew}
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\cvdefCpp{
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virtual void tooFew(\par int min, int n\_detected) = 0;
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}
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Too few features were detected so, adjust the detector parameters accordingly - so that the next
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detection detects more features.
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\begin{description}
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\cvarg{min}{This minimum desired number features.}
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\cvarg{n\_detected}{The actual number detected last run.}
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\end{description}
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An example implementation of this is
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\begin{lstlisting}
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void FastAdjuster::tooFew(int min, int n_detected) {
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thresh_--;
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}
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\end{lstlisting}
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\cvCppFunc{AdjusterAdapter::tooMany}
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Too many features were detected so, adjust the detector parameters accordingly - so that the next
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detection detects less features.
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\cvdefCpp{
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virtual void tooMany(int max, int n\_detected) = 0;
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}
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\begin{description}
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\cvarg{max}{This maximum desired number features.}
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\cvarg{n\_detected}{The actual number detected last run.}
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\end{description}
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An example implementation of this is
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\begin{lstlisting}
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void FastAdjuster::tooMany(int min, int n_detected) {
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thresh_++;
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}
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\end{lstlisting}
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\cvCppFunc{AdjusterAdapter::good}
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Are params maxed out or still valid? Returns false if the parameters can't be adjusted any more.
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\cvdefCpp{
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virtual bool good() const = 0;
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}
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An example implementation of this is
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\begin{lstlisting}
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bool FastAdjuster::good() const {
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return (thresh_ > 1) && (thresh_ < 200);
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}
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\end{lstlisting}
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\cvclass{FastAdjuster}
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An \cvCppCross{AdjusterAdapter} for the \cvCppCross{FastFeatureDetector}. This will basically decrement or increment the
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threshhold by 1
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@ -64,9 +124,6 @@ threshhold by 1
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\begin{lstlisting}
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class FastAdjuster FastAdjuster: public AdjusterAdapter {
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public:
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/**\param init_thresh the initial threshhold to start with, default = 20
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* \param nonmax whether to use non max or not for fast feature detection
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*/
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FastAdjuster(int init_thresh = 20, bool nonmax = true);
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...
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};
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@ -84,7 +141,7 @@ class StarAdjuster: public AdjusterAdapter {
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\cvclass{SurfAdjuster}
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An \cvCppCross{AdjusterAdapter} for the \cvCppCross{SurfFeatureDetector}. This adjusts the responseThreshhold of
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An \cvCppCross{AdjusterAdapter} for the \cvCppCross{SurfFeatureDetector}. This adjusts the hessianThreshold of
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SurfFeatureDetector.
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\begin{lstlisting}
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class SurfAdjuster: public SurfAdjuster {
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