class CV_EXPORTS DynamicDetector: public FeatureDetector {
\begin{lstlisting}
class DynamicDetector: public FeatureDetector {
public:
/**min_features the minimum desired features
* max_features the maximum desired number of features
* 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
* a an AdjusterAdapter that will do the detection and parameter adjustment
*/
DynamicDetector(int min_features, int max_features, int max_iters,
const Ptr<AdjusterAdapter>& a);
...
};
\end{lstlisting}
\end{lstlisting}
\cvCppFunc{DynamicDetector::DynamicDetector}
DynamicDetector constructor.
\cvdefCpp{
DynamicDetector::DynamicDetector( \par int min\_features, \par int max\_features, \par int max\_iters,
\par const Ptr<AdjusterAdapter>\& a);
}
\begin{description}
\cvarg{min\_features}{This minimum desired number features.}
\cvarg{max\_features}{The maximum desired number of features.}
\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,
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.}
\cvarg{a}{ An \cvCppCross{AdjusterAdapter} that will do the detection and parameter
adjustment}
\end{description}
\cvclass{AdjusterAdapter}
A feature detector parameter adjuster interface, this is used by the \cvCppCross{DynamicDetector}
and is a wrapper for \cvCppCross{FeatureDetecto}r that allow them to be adjusted after a detection.
...
...
@@ -36,27 +64,59 @@ See \cvCppCross{FastAdjuster}, \cvCppCross{StarAdjuster}, \cvCppCross{SurfAdjus
\begin{lstlisting}
class AdjusterAdapter: public FeatureDetector {
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;
};
\end{lstlisting}
\cvCppFunc{AdjusterAdapter::tooFew}
\cvdefCpp{
virtual void tooFew(\par int min, int n\_detected) = 0;
}
Too few features were detected so, adjust the detector parameters accordingly - so that the next
detection detects more features.
\begin{description}
\cvarg{min}{This minimum desired number features.}
\cvarg{n\_detected}{The actual number detected last run.}
\end{description}
An example implementation of this is
\begin{lstlisting}
void FastAdjuster::tooFew(int min, int n_detected) {
thresh_--;
}
\end{lstlisting}
\cvCppFunc{AdjusterAdapter::tooMany}
Too many features were detected so, adjust the detector parameters accordingly - so that the next
detection detects less features.
\cvdefCpp{
virtual void tooMany(int max, int n\_detected) = 0;
}
\begin{description}
\cvarg{max}{This maximum desired number features.}
\cvarg{n\_detected}{The actual number detected last run.}
\end{description}
An example implementation of this is
\begin{lstlisting}
void FastAdjuster::tooMany(int min, int n_detected) {
thresh_++;
}
\end{lstlisting}
\cvCppFunc{AdjusterAdapter::good}
Are params maxed out or still valid? Returns false if the parameters can't be adjusted any more.
\cvdefCpp{
virtual bool good() const = 0;
}
An example implementation of this is
\begin{lstlisting}
bool FastAdjuster::good() const {
return (thresh_ > 1) && (thresh_ < 200);
}
\end{lstlisting}
\cvclass{FastAdjuster}
An \cvCppCross{AdjusterAdapter} for the \cvCppCross{FastFeatureDetector}. This will basically decrement or increment the
threshhold by 1
...
...
@@ -64,9 +124,6 @@ threshhold by 1
\begin{lstlisting}
class FastAdjuster 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
