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159 lines
5.4 KiB
159 lines
5.4 KiB
\cvclass{DynamicAdaptedFeatureDetector} |
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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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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 DynamicAdaptedFeatureDetector 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 DynamicAdaptedFeatureDetector. |
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\begin{lstlisting} |
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//sample usage: |
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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 DynamicAdaptedFeatureDetector (100, 110, 10, |
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new FastAdjuster(20,true))); |
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\end{lstlisting} |
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\begin{lstlisting} |
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class DynamicAdaptedFeatureDetector: public FeatureDetector |
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{ |
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public: |
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DynamicAdaptedFeatureDetector( const Ptr<AdjusterAdapter>& adjaster, |
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int min_features=400, int max_features=500, int max_iters=5 ); |
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... |
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}; |
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\end{lstlisting} |
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\cvCppFunc{DynamicAdaptedFeatureDetector::DynamicAdaptedFeatureDetector} |
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DynamicAdaptedFeatureDetector constructor. |
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\cvdefCpp{ |
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DynamicAdaptedFeatureDetector::DynamicAdaptedFeatureDetector( |
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\par const Ptr<AdjusterAdapter>\& adjaster, |
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\par int min\_features, \par int max\_features, \par int max\_iters ); |
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} |
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\begin{description} |
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\cvarg{adjaster}{ An \cvCppCross{AdjusterAdapter} that will do the detection and parameter |
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adjustment} |
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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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\end{description} |
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\cvclass{AdjusterAdapter} |
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A feature detector parameter adjuster interface, this is used by the \cvCppCross{DynamicAdaptedFeatureDetector} |
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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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See \cvCppCross{FastAdjuster}, \cvCppCross{StarAdjuster}, \cvCppCross{SurfAdjuster} for concrete implementations. |
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\begin{lstlisting} |
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class AdjusterAdapter: public FeatureDetector |
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{ |
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public: |
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virtual ~AdjusterAdapter() {} |
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virtual void tooFew(int min, int n_detected) = 0; |
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virtual void tooMany(int max, int n_detected) = 0; |
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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(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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{ |
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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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{ |
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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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{ |
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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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\begin{lstlisting} |
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class FastAdjuster FastAdjuster: public AdjusterAdapter |
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{ |
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public: |
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FastAdjuster(int init_thresh = 20, bool nonmax = true); |
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... |
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}; |
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\end{lstlisting} |
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\cvclass{StarAdjuster} |
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An \cvCppCross{AdjusterAdapter} for the \cvCppCross{StarFeatureDetector}. This adjusts the responseThreshhold of |
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StarFeatureDetector. |
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\begin{lstlisting} |
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class StarAdjuster: public AdjusterAdapter |
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{ |
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StarAdjuster(double initial_thresh = 30.0); |
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... |
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}; |
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\end{lstlisting} |
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\cvclass{SurfAdjuster} |
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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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{ |
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SurfAdjuster(); |
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... |
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}; |
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\end{lstlisting}
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