some more doc cleanup
This commit is contained in:
Vadim Pisarevsky
@@ -10,13 +10,13 @@ inherit
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.. index:: KeyPoint
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.. _KeyPoint:
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.. KeyPoint:
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KeyPoint
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--------
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.. c:type:: KeyPoint
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Data structure for salient point detectors. ::
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Data structure for salient point detectors. ::
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class KeyPoint
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{
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@@ -65,7 +65,7 @@ Data structure for salient point detectors. ::
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void write(FileStorage& fs, const string& name, const vector<KeyPoint>& keypoints);
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// reads vector of keypoints from the specified file storage node
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void read(const FileNode& node, CV_OUT vector<KeyPoint>& keypoints);
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..
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.. index:: FeatureDetector
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@@ -97,7 +97,7 @@ Abstract base class for 2D image feature detectors. ::
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protected:
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...
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};
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..
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.. index:: FeatureDetector::detect
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@@ -150,8 +150,7 @@ FeatureDetector::create
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:func:`FeatureDetector`
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.. c:function:: Ptr<FeatureDetector> FeatureDetector::create( const string\& detectorType )
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Feature detector factory that creates of given type with
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default parameters (rather using default constructor).
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Feature detector factory that creates of given type with default parameters (rather using default constructor).
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:param detectorType: Feature detector type.
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@@ -190,7 +189,7 @@ Wrapping class for feature detection using
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protected:
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...
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};
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..
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.. index:: GoodFeaturesToTrackDetector
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@@ -233,7 +232,7 @@ Wrapping class for feature detection using
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protected:
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...
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};
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..
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.. index:: MserFeatureDetector
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@@ -259,7 +258,7 @@ Wrapping class for feature detection using
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protected:
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...
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};
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..
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.. index:: StarFeatureDetector
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@@ -283,7 +282,7 @@ Wrapping class for feature detection using
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protected:
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...
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};
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..
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.. index:: SiftFeatureDetector
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@@ -312,7 +311,7 @@ Wrapping class for feature detection using
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protected:
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...
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};
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..
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.. index:: SurfFeatureDetector
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@@ -335,7 +334,7 @@ Wrapping class for feature detection using
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protected:
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...
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};
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..
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.. index:: GridAdaptedFeatureDetector
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@@ -345,8 +344,7 @@ GridAdaptedFeatureDetector
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--------------------------
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.. c:type:: GridAdaptedFeatureDetector
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Adapts a detector to partition the source image into a grid and detect
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points in each cell. ::
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Adapts a detector to partition the source image into a grid and detect points in each cell. ::
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class GridAdaptedFeatureDetector : public FeatureDetector
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{
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@@ -366,7 +364,7 @@ points in each cell. ::
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protected:
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...
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};
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..
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.. index:: PyramidAdaptedFeatureDetector
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@@ -376,8 +374,7 @@ PyramidAdaptedFeatureDetector
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-----------------------------
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.. c:type:: PyramidAdaptedFeatureDetector
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Adapts a detector to detect points over multiple levels of a Gaussian
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pyramid. Useful for detectors that are not inherently scaled. ::
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Adapts a detector to detect points over multiple levels of a Gaussian pyramid. Useful for detectors that are not inherently scaled. ::
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class PyramidAdaptedFeatureDetector : public FeatureDetector
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{
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@@ -389,18 +386,24 @@ pyramid. Useful for detectors that are not inherently scaled. ::
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protected:
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...
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};
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..
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.. index:: DynamicAdaptedFeatureDetector
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.. _DynamicAdaptedFeatureDetector:
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DynamicAdaptedFeatureDetector
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-----------------------------
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.. c:type:: 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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An adaptively adjusting detector that iteratively detects until the desired number of features are found. ::
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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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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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@@ -429,16 +432,7 @@ Here is a sample of how to create a DynamicAdaptedFeatureDetector. ::
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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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.. ::
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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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..
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.. index:: DynamicAdaptedFeatureDetector::DynamicAdaptedFeatureDetector
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@@ -460,28 +454,26 @@ DynamicAdaptedFeatureDetector::DynamicAdaptedFeatureDetector
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.. index:: AdjusterAdapter
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.. _AdjusterAdapter:
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AdjusterAdapter
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---------------
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.. c:type:: AdjusterAdapter
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A feature detector parameter adjuster interface, this is used by the
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:func:`DynamicAdaptedFeatureDetector` and is a wrapper for
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:func:`FeatureDetecto` r that allow them to be adjusted after a detection.
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A feature detector parameter adjuster interface, this is used by the :func:`DynamicAdaptedFeatureDetector` and is a wrapper for :func:`FeatureDetecto` r that allow them to be adjusted after a detection. ::
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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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See
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:func:`FastAdjuster`,:func:`StarAdjuster`,:func:`SurfAdjuster` for concrete implementations. ::
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:func:`FastAdjuster`,:func:`StarAdjuster`,:func:`SurfAdjuster` for concrete implementations.
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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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..
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.. index:: AdjusterAdapter::tooFew
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@@ -489,8 +481,7 @@ AdjusterAdapter::tooFew
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---------------------------
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.. c:function:: virtual void tooFew(int min, int n_detected) = 0
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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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Too few features were detected so, adjust the detector parameters accordingly - so that the next detection detects more features.
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:param min: This minimum desired number features.
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@@ -502,7 +493,7 @@ An example implementation of this is ::
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{
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thresh_--;
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}
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..
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.. index:: AdjusterAdapter::tooMany
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@@ -510,8 +501,7 @@ AdjusterAdapter::tooMany
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----------------------------
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.. c:function:: virtual void tooMany(int max, int n_detected) = 0
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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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Too many features were detected so, adjust the detector parameters accordingly - so that the next detection detects less features.
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:param max: This maximum desired number features.
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@@ -523,7 +513,7 @@ An example implementation of this is ::
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{
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thresh_++;
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}
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..
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.. index:: AdjusterAdapter::good
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@@ -531,28 +521,504 @@ AdjusterAdapter::good
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-------------------------
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.. c:function:: virtual bool good() const = 0
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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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Are params maxed out or still valid? Returns false if the parameters can't be adjusted any more. An example implementation of this is ::
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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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.. index:: FastAdjuster
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FastAdjuster
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------------
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.. c:type:: FastAdjuster
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:func:`AdjusterAdapter` for the :func:`FastFeatureDetector`. This will basically decrement or increment the threshhold by 1 ::
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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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.. index:: StarAdjuster
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StarAdjuster
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------------
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.. c:type:: StarAdjuster
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:func:`AdjusterAdapter` for the :func:`StarFeatureDetector` . This adjusts the responseThreshhold of StarFeatureDetector. ::
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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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.. index:: SurfAdjuster
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SurfAdjuster
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------------
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.. c:type:: SurfAdjuster
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:func:`AdjusterAdapter` for the :func:`SurfFeatureDetector` . This adjusts the hessianThreshold of SurfFeatureDetector. ::
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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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.. index:: FeatureDetector
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FeatureDetector
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---------------
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.. c:type:: FeatureDetector
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Abstract base class for 2D image feature detectors. ::
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class CV_EXPORTS FeatureDetector
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{
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public:
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virtual ~FeatureDetector();
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void detect( const Mat& image, vector<KeyPoint>& keypoints,
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const Mat& mask=Mat() ) const;
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void detect( const vector<Mat>& images,
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vector<vector<KeyPoint> >& keypoints,
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const vector<Mat>& masks=vector<Mat>() ) const;
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virtual void read(const FileNode&);
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virtual void write(FileStorage&) const;
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static Ptr<FeatureDetector> create( const string& detectorType );
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protected:
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...
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};
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.. index:: FeatureDetector::detect
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FeatureDetector::detect
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---------------------------
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.. c:function:: void FeatureDetector::detect( const Mat\& image, vector<KeyPoint>\& keypoints, const Mat\& mask=Mat() ) const
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Detect keypoints in an image (first variant) or image set (second variant).
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:param image: The image.
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:param keypoints: The detected keypoints.
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:param mask: Mask specifying where to look for keypoints (optional). Must be a char matrix
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with non-zero values in the region of interest.
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.. c:function:: void FeatureDetector::detect( const vector<Mat>\& images, vector<vector<KeyPoint> >\& keypoints, const vector<Mat>\& masks=vector<Mat>() ) const
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* **images** Images set.
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* **keypoints** Collection of keypoints detected in an input images. keypoints[i] is a set of keypoints detected in an images[i].
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* **masks** Masks for each input image specifying where to look for keypoints (optional). masks[i] is a mask for images[i].
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Each element of ``masks`` vector must be a char matrix with non-zero values in the region of interest.
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.. index:: FeatureDetector::read
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FeatureDetector::read
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-------------------------
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.. c:function:: void FeatureDetector::read( const FileNode\& fn )
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Read feature detector object from file node.
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:param fn: File node from which detector will be read.
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.. index:: FeatureDetector::write
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FeatureDetector::write
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--------------------------
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.. c:function:: void FeatureDetector::write( FileStorage\& fs ) const
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Write feature detector object to file storage.
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:param fs: File storage in which detector will be written.
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.. index:: FeatureDetector::create
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FeatureDetector::create
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---------------------------
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:func:`FeatureDetector`
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.. c:function:: Ptr<FeatureDetector> FeatureDetector::create( const string\& detectorType )
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Feature detector factory that creates of given type with default parameters (rather using default constructor).
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:param detectorType: Feature detector type.
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Now the following detector types are supported:
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* ``"FAST"`` -- :func:`FastFeatureDetector`,
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* ``"STAR"`` -- :func:`StarFeatureDetector`,
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* ``"SIFT"`` -- :func:`SiftFeatureDetector`,
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* ``"SURF"`` -- :func:`SurfFeatureDetector`,
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* ``"MSER"`` -- :func:`MserFeatureDetector`,
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* ``"GFTT"`` -- :func:`GfttFeatureDetector`,
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* ``"HARRIS"`` -- :func:`HarrisFeatureDetector` .
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Also combined format is supported: feature detector adapter name ( ``"Grid"`` --
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:func:`GridAdaptedFeatureDetector`,``"Pyramid"`` --
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:func:`PyramidAdaptedFeatureDetector` ) + feature detector name (see above),
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e.g. ``"GridFAST"``,``"PyramidSTAR"`` , etc.
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.. index:: FastFeatureDetector
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FastFeatureDetector
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-------------------
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.. c:type:: FastFeatureDetector
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Wrapping class for feature detection using
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:func:`FAST` method. ::
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class FastFeatureDetector : public FeatureDetector
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{
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public:
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FastFeatureDetector( int threshold=1, bool nonmaxSuppression=true );
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virtual void read( const FileNode& fn );
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virtual void write( FileStorage& fs ) const;
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protected:
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...
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};
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.. index:: GoodFeaturesToTrackDetector
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GoodFeaturesToTrackDetector
|
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---------------------------
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.. c:type:: GoodFeaturesToTrackDetector
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Wrapping class for feature detection using :func:`goodFeaturesToTrack` function. ::
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class GoodFeaturesToTrackDetector : public FeatureDetector
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{
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public:
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class Params
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{
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public:
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Params( int maxCorners=1000, double qualityLevel=0.01,
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double minDistance=1., int blockSize=3,
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bool useHarrisDetector=false, double k=0.04 );
|
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void read( const FileNode& fn );
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void write( FileStorage& fs ) const;
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|
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int maxCorners;
|
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double qualityLevel;
|
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double minDistance;
|
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int blockSize;
|
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bool useHarrisDetector;
|
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double k;
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};
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GoodFeaturesToTrackDetector( const GoodFeaturesToTrackDetector::Params& params=
|
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GoodFeaturesToTrackDetector::Params() );
|
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GoodFeaturesToTrackDetector( int maxCorners, double qualityLevel,
|
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double minDistance, int blockSize=3,
|
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bool useHarrisDetector=false, double k=0.04 );
|
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virtual void read( const FileNode& fn );
|
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virtual void write( FileStorage& fs ) const;
|
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protected:
|
||||
...
|
||||
};
|
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|
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|
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.. index:: MserFeatureDetector
|
||||
|
||||
MserFeatureDetector
|
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-------------------
|
||||
.. c:type:: MserFeatureDetector
|
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|
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Wrapping class for feature detection using :func:`MSER` class. ::
|
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|
||||
class MserFeatureDetector : public FeatureDetector
|
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{
|
||||
public:
|
||||
MserFeatureDetector( CvMSERParams params=cvMSERParams() );
|
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MserFeatureDetector( int delta, int minArea, int maxArea,
|
||||
double maxVariation, double minDiversity,
|
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int maxEvolution, double areaThreshold,
|
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double minMargin, int edgeBlurSize );
|
||||
virtual void read( const FileNode& fn );
|
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virtual void write( FileStorage& fs ) const;
|
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protected:
|
||||
...
|
||||
};
|
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|
||||
|
||||
.. index:: StarFeatureDetector
|
||||
|
||||
StarFeatureDetector
|
||||
-------------------
|
||||
.. c:type:: StarFeatureDetector
|
||||
|
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Wrapping class for feature detection using :func:`StarDetector` class. ::
|
||||
|
||||
class StarFeatureDetector : public FeatureDetector
|
||||
{
|
||||
public:
|
||||
StarFeatureDetector( int maxSize=16, int responseThreshold=30,
|
||||
int lineThresholdProjected = 10,
|
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int lineThresholdBinarized=8, int suppressNonmaxSize=5 );
|
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virtual void read( const FileNode& fn );
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virtual void write( FileStorage& fs ) const;
|
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protected:
|
||||
...
|
||||
};
|
||||
|
||||
|
||||
.. index:: SiftFeatureDetector
|
||||
|
||||
SiftFeatureDetector
|
||||
-------------------
|
||||
.. c:type:: SiftFeatureDetector
|
||||
|
||||
Wrapping class for feature detection using :func:`SIFT` class. ::
|
||||
|
||||
class SiftFeatureDetector : public FeatureDetector
|
||||
{
|
||||
public:
|
||||
SiftFeatureDetector(
|
||||
const SIFT::DetectorParams& detectorParams=SIFT::DetectorParams(),
|
||||
const SIFT::CommonParams& commonParams=SIFT::CommonParams() );
|
||||
SiftFeatureDetector( double threshold, double edgeThreshold,
|
||||
int nOctaves=SIFT::CommonParams::DEFAULT_NOCTAVES,
|
||||
int nOctaveLayers=SIFT::CommonParams::DEFAULT_NOCTAVE_LAYERS,
|
||||
int firstOctave=SIFT::CommonParams::DEFAULT_FIRST_OCTAVE,
|
||||
int angleMode=SIFT::CommonParams::FIRST_ANGLE );
|
||||
virtual void read( const FileNode& fn );
|
||||
virtual void write( FileStorage& fs ) const;
|
||||
protected:
|
||||
...
|
||||
};
|
||||
|
||||
|
||||
.. index:: SurfFeatureDetector
|
||||
|
||||
SurfFeatureDetector
|
||||
-------------------
|
||||
.. c:type:: SurfFeatureDetector
|
||||
|
||||
Wrapping class for feature detection using :func:`SURF` class. ::
|
||||
|
||||
class SurfFeatureDetector : public FeatureDetector
|
||||
{
|
||||
public:
|
||||
SurfFeatureDetector( double hessianThreshold = 400., int octaves = 3,
|
||||
int octaveLayers = 4 );
|
||||
virtual void read( const FileNode& fn );
|
||||
virtual void write( FileStorage& fs ) const;
|
||||
protected:
|
||||
...
|
||||
};
|
||||
|
||||
|
||||
.. index:: GridAdaptedFeatureDetector
|
||||
|
||||
GridAdaptedFeatureDetector
|
||||
--------------------------
|
||||
.. c:type:: GridAdaptedFeatureDetector
|
||||
|
||||
Adapts a detector to partition the source image into a grid and detect points in each cell. ::
|
||||
|
||||
class GridAdaptedFeatureDetector : public FeatureDetector
|
||||
{
|
||||
public:
|
||||
/*
|
||||
* detector Detector that will be adapted.
|
||||
* maxTotalKeypoints Maximum count of keypoints detected on the image.
|
||||
* Only the strongest keypoints will be keeped.
|
||||
* gridRows Grid rows count.
|
||||
* gridCols Grid column count.
|
||||
*/
|
||||
GridAdaptedFeatureDetector( const Ptr<FeatureDetector>& detector,
|
||||
int maxTotalKeypoints, int gridRows=4,
|
||||
int gridCols=4 );
|
||||
virtual void read( const FileNode& fn );
|
||||
virtual void write( FileStorage& fs ) const;
|
||||
protected:
|
||||
...
|
||||
};
|
||||
|
||||
|
||||
.. index:: PyramidAdaptedFeatureDetector
|
||||
|
||||
PyramidAdaptedFeatureDetector
|
||||
-----------------------------
|
||||
.. c:type:: PyramidAdaptedFeatureDetector
|
||||
|
||||
Adapts a detector to detect points over multiple levels of a Gaussian pyramid. Useful for detectors that are not inherently scaled. ::
|
||||
|
||||
class PyramidAdaptedFeatureDetector : public FeatureDetector
|
||||
{
|
||||
public:
|
||||
PyramidAdaptedFeatureDetector( const Ptr<FeatureDetector>& detector,
|
||||
int levels=2 );
|
||||
virtual void read( const FileNode& fn );
|
||||
virtual void write( FileStorage& fs ) const;
|
||||
protected:
|
||||
...
|
||||
};
|
||||
|
||||
|
||||
.. index:: DynamicAdaptedFeatureDetector
|
||||
|
||||
DynamicAdaptedFeatureDetector
|
||||
-----------------------------
|
||||
|
||||
.. c:type:: DynamicAdaptedFeatureDetector
|
||||
|
||||
An adaptively adjusting detector that iteratively detects until the desired number of features are found. ::
|
||||
|
||||
class DynamicAdaptedFeatureDetector: public FeatureDetector
|
||||
{
|
||||
public:
|
||||
DynamicAdaptedFeatureDetector( const Ptr<AdjusterAdapter>& adjaster,
|
||||
int min_features=400, int max_features=500, int max_iters=5 );
|
||||
...
|
||||
};
|
||||
|
||||
|
||||
If the detector is persisted, it will "remember" the parameters
|
||||
used on the last detection. In this way, the detector may be used for consistent numbers
|
||||
of keypoints in a sets of images that are temporally related such as video streams or
|
||||
panorama series.
|
||||
|
||||
The DynamicAdaptedFeatureDetector uses another detector such as FAST or SURF to do the dirty work,
|
||||
with the help of an AdjusterAdapter.
|
||||
After a detection, and an unsatisfactory number of features are detected,
|
||||
the AdjusterAdapter will adjust the detection parameters so that the next detection will
|
||||
result in more or less features. This is repeated until either the number of desired features are found
|
||||
or the parameters are maxed out.
|
||||
|
||||
Adapters can easily be implemented for any detector via the
|
||||
AdjusterAdapter interface.
|
||||
|
||||
Beware that this is not thread safe - as the adjustment of parameters breaks the const
|
||||
of the detection routine...
|
||||
|
||||
Here is a sample of how to create a DynamicAdaptedFeatureDetector. ::
|
||||
|
||||
//sample usage:
|
||||
//will create a detector that attempts to find
|
||||
//100 - 110 FAST Keypoints, and will at most run
|
||||
//FAST feature detection 10 times until that
|
||||
//number of keypoints are found
|
||||
Ptr<FeatureDetector> detector(new DynamicAdaptedFeatureDetector (100, 110, 10,
|
||||
new FastAdjuster(20,true)));
|
||||
|
||||
.. index:: DynamicAdaptedFeatureDetector::DynamicAdaptedFeatureDetector
|
||||
|
||||
DynamicAdaptedFeatureDetector::DynamicAdaptedFeatureDetector
|
||||
----------------------------------------------------------------
|
||||
.. c:function:: DynamicAdaptedFeatureDetector::DynamicAdaptedFeatureDetector( const Ptr<AdjusterAdapter>\& adjaster, int min_features, int max_features, int max_iters )
|
||||
|
||||
DynamicAdaptedFeatureDetector constructor.
|
||||
|
||||
:param adjaster: An :func:`AdjusterAdapter` that will do the detection and parameter
|
||||
adjustment
|
||||
|
||||
:param min_features: This minimum desired number 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 parameters. For the :func:`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.
|
||||
|
||||
.. index:: AdjusterAdapter
|
||||
|
||||
AdjusterAdapter
|
||||
---------------
|
||||
|
||||
.. c:type:: AdjusterAdapter
|
||||
|
||||
A feature detector parameter adjuster interface, this is used by the :func:`DynamicAdaptedFeatureDetector` and is a wrapper for :func:`FeatureDetecto` r that allow them to be adjusted after a detection. ::
|
||||
|
||||
class AdjusterAdapter: public FeatureDetector
|
||||
{
|
||||
public:
|
||||
virtual ~AdjusterAdapter() {}
|
||||
virtual void tooFew(int min, int n_detected) = 0;
|
||||
virtual void tooMany(int max, int n_detected) = 0;
|
||||
virtual bool good() const = 0;
|
||||
};
|
||||
|
||||
See
|
||||
:func:`FastAdjuster`,:func:`StarAdjuster`,:func:`SurfAdjuster` for concrete implementations.
|
||||
|
||||
.. index:: AdjusterAdapter::tooFew
|
||||
|
||||
AdjusterAdapter::tooFew
|
||||
---------------------------
|
||||
.. c:function:: virtual void tooFew(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.
|
||||
|
||||
:param min: This minimum desired number features.
|
||||
|
||||
:param n_detected: The actual number detected last run.
|
||||
|
||||
An example implementation of this is ::
|
||||
|
||||
void FastAdjuster::tooFew(int min, int n_detected)
|
||||
{
|
||||
thresh_--;
|
||||
}
|
||||
|
||||
|
||||
.. index:: AdjusterAdapter::tooMany
|
||||
|
||||
AdjusterAdapter::tooMany
|
||||
----------------------------
|
||||
.. c:function:: virtual void tooMany(int max, int n_detected) = 0
|
||||
|
||||
Too many features were detected so, adjust the detector parameters accordingly - so that the next detection detects less features.
|
||||
|
||||
:param max: This maximum desired number features.
|
||||
|
||||
:param n_detected: The actual number detected last run.
|
||||
|
||||
An example implementation of this is ::
|
||||
|
||||
void FastAdjuster::tooMany(int min, int n_detected)
|
||||
{
|
||||
thresh_++;
|
||||
}
|
||||
|
||||
|
||||
.. index:: AdjusterAdapter::good
|
||||
|
||||
AdjusterAdapter::good
|
||||
-------------------------
|
||||
.. c:function:: virtual bool good() const = 0
|
||||
|
||||
Are params maxed out or still valid? Returns false if the parameters can't be adjusted any more. An example implementation of this is ::
|
||||
|
||||
bool FastAdjuster::good() const
|
||||
{
|
||||
return (thresh_ > 1) && (thresh_ < 200);
|
||||
}
|
||||
..
|
||||
|
||||
|
||||
.. index:: FastAdjuster
|
||||
|
||||
.. _FastAdjuster:
|
||||
|
||||
FastAdjuster
|
||||
------------
|
||||
|
||||
.. c:type:: FastAdjuster
|
||||
|
||||
An
|
||||
:func:`AdjusterAdapter` for the
|
||||
:func:`FastFeatureDetector` . This will basically decrement or increment the
|
||||
threshhold by 1 ::
|
||||
:func:`AdjusterAdapter` for the :func:`FastFeatureDetector`. This will basically decrement or increment the threshhold by 1 ::
|
||||
|
||||
class FastAdjuster FastAdjuster: public AdjusterAdapter
|
||||
{
|
||||
@@ -560,45 +1026,38 @@ threshhold by 1 ::
|
||||
FastAdjuster(int init_thresh = 20, bool nonmax = true);
|
||||
...
|
||||
};
|
||||
..
|
||||
|
||||
|
||||
.. index:: StarAdjuster
|
||||
|
||||
.. _StarAdjuster:
|
||||
|
||||
StarAdjuster
|
||||
------------
|
||||
|
||||
.. c:type:: StarAdjuster
|
||||
|
||||
An
|
||||
:func:`AdjusterAdapter` for the
|
||||
:func:`StarFeatureDetector` . This adjusts the responseThreshhold of
|
||||
StarFeatureDetector. ::
|
||||
:func:`AdjusterAdapter` for the :func:`StarFeatureDetector` . This adjusts the responseThreshhold of StarFeatureDetector. ::
|
||||
|
||||
class StarAdjuster: public AdjusterAdapter
|
||||
{
|
||||
StarAdjuster(double initial_thresh = 30.0);
|
||||
...
|
||||
};
|
||||
..
|
||||
|
||||
|
||||
.. index:: SurfAdjuster
|
||||
|
||||
.. _SurfAdjuster:
|
||||
|
||||
SurfAdjuster
|
||||
------------
|
||||
|
||||
.. c:type:: SurfAdjuster
|
||||
|
||||
An
|
||||
:func:`AdjusterAdapter` for the
|
||||
:func:`SurfFeatureDetector` . This adjusts the hessianThreshold of
|
||||
SurfFeatureDetector. ::
|
||||
:func:`AdjusterAdapter` for the :func:`SurfFeatureDetector` . This adjusts the hessianThreshold of SurfFeatureDetector. ::
|
||||
|
||||
class SurfAdjuster: public SurfAdjuster
|
||||
{
|
||||
SurfAdjuster();
|
||||
...
|
||||
};
|
||||
|
||||
..
|
||||
|
||||
|
||||
Reference in New Issue
Block a user