reviewed ? marks in features2d
This commit is contained in:
Vadim Pisarevsky
@@ -14,7 +14,7 @@ inherit the
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KeyPoint
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--------
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.. c:type:: KeyPoint
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.. cpp:class:: KeyPoint
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Data structure for salient point detectors ::
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@@ -75,7 +75,7 @@ Data structure for salient point detectors ::
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FeatureDetector
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---------------
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.. c:type:: FeatureDetector
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.. cpp:class:: FeatureDetector
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Abstract base class for 2D image feature detectors ::
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@@ -105,7 +105,7 @@ Abstract base class for 2D image feature detectors ::
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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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.. cpp:function:: void FeatureDetector::detect( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask=Mat() ) const
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Detects keypoints in an image (first variant) or image set (second variant).
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@@ -115,7 +115,7 @@ FeatureDetector::detect
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:param mask: Mask specifying where to look for keypoints (optional). It must be a char matrix 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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.. cpp:function:: void FeatureDetector::detect( const vector<Mat>& images, vector<vector<KeyPoint> >& keypoints, const vector<Mat>& masks=vector<Mat>() ) const
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:param images: Image set.
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@@ -127,7 +127,7 @@ FeatureDetector::detect
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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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.. cpp:function:: void FeatureDetector::read( const FileNode& fn )
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Reads a feature detector object from a file node.
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@@ -137,7 +137,7 @@ FeatureDetector::read
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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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.. cpp:function:: void FeatureDetector::write( FileStorage& fs ) const
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Writes a feature detector object to a file storage.
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@@ -147,9 +147,9 @@ FeatureDetector::write
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FeatureDetector::create
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---------------------------
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.. c:function:: Ptr<FeatureDetector> FeatureDetector::create( const string\& detectorType )
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.. cpp:function:: Ptr<FeatureDetector> FeatureDetector::create( const string& detectorType )
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Creates a feature detector of a given type with the default parameters (using the default constructor).??
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Creates a feature detector by its name.
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:param detectorType: Feature detector type.
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@@ -174,7 +174,7 @@ for example: ``"GridFAST"``, ``"PyramidSTAR"`` .
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FastFeatureDetector
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-------------------
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.. c:type:: FastFeatureDetector
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.. cpp:class:: FastFeatureDetector
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Wrapping class for feature detection using the
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:ref:`FAST` method ::
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@@ -196,7 +196,7 @@ Wrapping class for feature detection using the
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GoodFeaturesToTrackDetector
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---------------------------
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.. c:type:: GoodFeaturesToTrackDetector
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.. cpp:class:: GoodFeaturesToTrackDetector
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Wrapping class for feature detection using the
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:ref:`goodFeaturesToTrack` function ::
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@@ -239,7 +239,7 @@ Wrapping class for feature detection using the
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MserFeatureDetector
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-------------------
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.. c:type:: MserFeatureDetector
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.. cpp:class:: MserFeatureDetector
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Wrapping class for feature detection using the
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:ref:`MSER` class ::
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@@ -265,7 +265,7 @@ Wrapping class for feature detection using the
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StarFeatureDetector
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-------------------
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.. c:type:: StarFeatureDetector
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.. cpp:class:: StarFeatureDetector
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Wrapping class for feature detection using the
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:ref:`StarDetector` class ::
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@@ -289,7 +289,7 @@ Wrapping class for feature detection using the
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SiftFeatureDetector
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-------------------
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.. c:type:: SiftFeatureDetector
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.. cpp:class:: SiftFeatureDetector
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Wrapping class for feature detection using the
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:ref:`SIFT` class ::
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@@ -318,7 +318,7 @@ Wrapping class for feature detection using the
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SurfFeatureDetector
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-------------------
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.. c:type:: SurfFeatureDetector
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.. cpp:class:: SurfFeatureDetector
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Wrapping class for feature detection using the
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:ref:`SURF` class ::
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@@ -341,7 +341,7 @@ Wrapping class for feature detection using the
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GridAdaptedFeatureDetector
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--------------------------
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.. c:type:: GridAdaptedFeatureDetector
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.. cpp:class:: GridAdaptedFeatureDetector
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Class adapting a detector to partition the source image into a grid and detect points in each cell ::
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@@ -371,7 +371,7 @@ Class adapting a detector to partition the source image into a grid and detect p
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PyramidAdaptedFeatureDetector
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-----------------------------
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.. c:type:: PyramidAdaptedFeatureDetector
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.. cpp:class:: PyramidAdaptedFeatureDetector
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Class adapting a detector to detect points over multiple levels of a Gaussian pyramid. Consider using this class for detectors that are not inherently scaled. ::
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@@ -392,14 +392,14 @@ Class adapting a detector to detect points over multiple levels of a Gaussian py
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DynamicAdaptedFeatureDetector
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-----------------------------
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.. c:type:: DynamicAdaptedFeatureDetector
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.. cpp:class:: DynamicAdaptedFeatureDetector
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Adaptively adjusting detector that iteratively detects features until the desired number is 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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DynamicAdaptedFeatureDetector( const Ptr<AdjusterAdapter>& adjuster,
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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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@@ -411,7 +411,7 @@ panorama series.
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``DynamicAdaptedFeatureDetector`` uses another detector such as FAST or SURF to do the dirty work,
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with the help of ``AdjusterAdapter`` .
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If the detected number of features is not enough,??
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If the detected number of features is not large enough,
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``AdjusterAdapter`` adjusts the detection parameters so that the next detection
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results in bigger or smaller number of 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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@@ -419,8 +419,7 @@ or the parameters are maxed out.
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Adapters can be easily implemented for any detector via the
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``AdjusterAdapter`` interface.
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Beware that this is not thread-safe since the adjustment of parameters breaks the const??
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of the detection routine.
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Beware that this is not thread-safe since the adjustment of parameters requires modification of the feature detector class instance.
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Example of creating ``DynamicAdaptedFeatureDetector`` : ::
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@@ -438,11 +437,11 @@ Example of creating ``DynamicAdaptedFeatureDetector`` : ::
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DynamicAdaptedFeatureDetector::DynamicAdaptedFeatureDetector
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----------------------------------------------------------------
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.. c:function:: DynamicAdaptedFeatureDetector::DynamicAdaptedFeatureDetector( const Ptr<AdjusterAdapter>\& adjaster, int min_features, int max_features, int max_iters )
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.. cpp:function:: DynamicAdaptedFeatureDetector::DynamicAdaptedFeatureDetector( const Ptr<AdjusterAdapter>& adjuster, int min_features, int max_features, int max_iters )
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``DynamicAdaptedFeatureDetector`` constructor
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The class constructor
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:param adjaster: :ref:`AdjusterAdapter` that detects features and adjusts parameters.??parameter formatting is broken here
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:param adjuster: :ref:`AdjusterAdapter` that detects features and adjusts parameters.
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:param min_features: Minimum desired number of features.
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@@ -455,9 +454,9 @@ DynamicAdaptedFeatureDetector::DynamicAdaptedFeatureDetector
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AdjusterAdapter
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---------------
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.. c:type:: AdjusterAdapter
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.. cpp:class:: AdjusterAdapter
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Class providing an interface for adjusting parameters of a feature detector. This interface is used by :ref:`DynamicAdaptedFeatureDetector` . It is a wrapper for :ref:`FeatureDetector` that enables adjusting parameters after detection.?? ::
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Class providing an interface for adjusting parameters of a feature detector. This interface is used by :ref:`DynamicAdaptedFeatureDetector` . It is a wrapper for :ref:`FeatureDetector` that enables adjusting parameters after feature detection. ::
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class AdjusterAdapter: public FeatureDetector
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{
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@@ -479,9 +478,9 @@ See
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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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.. cpp:function:: void AdjusterAdapter::tooFew(int min, int n_detected)
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Adjusts the detector parameters to detect more features.
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Adjusts the detector parameters to detect more features.
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:param min: Minimum desired number of features.
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@@ -499,7 +498,7 @@ Example: ::
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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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.. cpp:function:: void AdjusterAdapter::tooMany(int max, int n_detected)
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Adjusts the detector parameters to detect less features.
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@@ -519,7 +518,7 @@ Example: ::
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AdjusterAdapter::good
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-------------------------
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.. c:function:: virtual bool good() const = 0
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.. cpp:function:: bool AdjusterAdapter::good() const
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Returns false if the detector parameters cannot be adjusted any more.
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@@ -536,9 +535,9 @@ Example: ::
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FastAdjuster
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------------
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.. c:type:: FastAdjuster
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.. cpp:class:: FastAdjuster
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:ref:`AdjusterAdapter` for :ref:`FastFeatureDetector`. This class decrements or increments the threshhold by 1.?? ::
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:ref:`AdjusterAdapter` for :ref:`FastFeatureDetector`. This class decreases or increases the threshold value by 1 ::
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class FastAdjuster FastAdjuster: public AdjusterAdapter
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{
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@@ -552,7 +551,7 @@ FastAdjuster
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StarAdjuster
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------------
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.. c:type:: StarAdjuster
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.. cpp:class:: StarAdjuster
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:ref:`AdjusterAdapter` for :ref:`StarFeatureDetector` . This class adjusts the ``responseThreshhold`` of ``StarFeatureDetector`` . ::
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@@ -567,7 +566,7 @@ StarAdjuster
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SurfAdjuster
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------------
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.. c:type:: SurfAdjuster
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.. cpp:class:: SurfAdjuster
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:ref:`AdjusterAdapter` for :ref:`SurfFeatureDetector` . This class adjusts the ``hessianThreshold`` of ``SurfFeatureDetector`` . ::
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@@ -581,7 +580,7 @@ SurfAdjuster
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FeatureDetector
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---------------
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.. c:type:: FeatureDetector
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.. cpp:class:: FeatureDetector
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Abstract base class for 2D image feature detectors ::
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@@ -605,462 +604,3 @@ 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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.. 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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Detects keypoints in an image (first variant) or image set (second variant).
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:param image: Image.
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:param keypoints: Detected keypoints.
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:param mask: Mask specifying where to look for keypoints (optional). It 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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:param images: Image set.
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:param keypoints: Collection of keypoints detected in an input image. ``keypoints[i]`` is a set of keypoints detected in ``images[i]`` .
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:param 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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Reads a feature detector object from a file node.
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:param fn: File node from which the detector is 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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Writes a feature detector object to a file storage.
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:param fs: File storage where the detector is written.
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.. index:: FeatureDetector::create
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FeatureDetector::create
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---------------------------
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.. c:function:: Ptr<FeatureDetector> FeatureDetector::create( const string\& detectorType )??
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Creates a feature detector of a given type with the default parameters (using the 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"`` -- :ref:`FastFeatureDetector`
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* ``"STAR"`` -- :ref:`StarFeatureDetector`
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* ``"SIFT"`` -- :ref:`SiftFeatureDetector`
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* ``"SURF"`` -- :ref:`SurfFeatureDetector`
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* ``"MSER"`` -- :ref:`MserFeatureDetector`
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* ``"GFTT"`` -- :ref:`GfttFeatureDetector`
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* ``"HARRIS"`` -- :ref:`HarrisFeatureDetector`
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A combined format is also supported: feature detector adapter name ( ``"Grid"`` --
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:ref:`GridAdaptedFeatureDetector` , ``"Pyramid"`` --
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:ref:`PyramidAdaptedFeatureDetector` ) + feature detector name (see above),
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for example: ``"GridFAST"`` , ``"PyramidSTAR"`` .
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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 the
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:ref:`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 the :ref:`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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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
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MserFeatureDetector
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-------------------
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.. c:type:: MserFeatureDetector
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Wrapping class for feature detection using the :ref:`MSER` class ::
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class MserFeatureDetector : public FeatureDetector
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{
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public:
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MserFeatureDetector( CvMSERParams params=cvMSERParams() );
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MserFeatureDetector( int delta, int minArea, int maxArea,
|
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double maxVariation, double minDiversity,
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int maxEvolution, double areaThreshold,
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double minMargin, int edgeBlurSize );
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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:: StarFeatureDetector
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StarFeatureDetector
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-------------------
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.. c:type:: StarFeatureDetector
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Wrapping class for feature detection using the :ref:`StarDetector` class ::
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class StarFeatureDetector : public FeatureDetector
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{
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public:
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StarFeatureDetector( int maxSize=16, int responseThreshold=30,
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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:
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...
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};
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.. index:: SiftFeatureDetector
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SiftFeatureDetector
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-------------------
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.. c:type:: SiftFeatureDetector
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Wrapping class for feature detection using the :ref:`SIFT` class ::
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class SiftFeatureDetector : public FeatureDetector
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{
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public:
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SiftFeatureDetector(
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const SIFT::DetectorParams& detectorParams=SIFT::DetectorParams(),
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const SIFT::CommonParams& commonParams=SIFT::CommonParams() );
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SiftFeatureDetector( double threshold, double edgeThreshold,
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int nOctaves=SIFT::CommonParams::DEFAULT_NOCTAVES,
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int nOctaveLayers=SIFT::CommonParams::DEFAULT_NOCTAVE_LAYERS,
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int firstOctave=SIFT::CommonParams::DEFAULT_FIRST_OCTAVE,
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int angleMode=SIFT::CommonParams::FIRST_ANGLE );
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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:: SurfFeatureDetector
|
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SurfFeatureDetector
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-------------------
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.. c:type:: SurfFeatureDetector
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Wrapping class for feature detection using the :ref:`SURF` class ::
|
||||
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||||
class SurfFeatureDetector : public FeatureDetector
|
||||
{
|
||||
public:
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SurfFeatureDetector( double hessianThreshold = 400., int octaves = 3,
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int octaveLayers = 4 );
|
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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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...
|
||||
};
|
||||
|
||||
|
||||
.. index:: GridAdaptedFeatureDetector
|
||||
|
||||
GridAdaptedFeatureDetector
|
||||
--------------------------
|
||||
.. c:type:: GridAdaptedFeatureDetector
|
||||
|
||||
Class adapting 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 are kept.
|
||||
* gridRows Grid row 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
|
||||
|
||||
Class adapting a detector to detect points over multiple levels of a Gaussian pyramid. Consider using this class 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
|
||||
|
||||
Adaptively adjusting detector that iteratively detects features until the desired number is 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 "remembers" the parameters
|
||||
used on the last detection. In this case, the detector may be used for consistent numbers
|
||||
of keypoints in a set of images that are temporally related, such as video streams or
|
||||
panorama series.
|
||||
|
||||
``DynamicAdaptedFeatureDetector`` uses another detector such as FAST or SURF to do the dirty work,
|
||||
with the help of ``AdjusterAdapter`` .
|
||||
If the number of detected features is not enough,
|
||||
``AdjusterAdapter`` adjusts the detection parameters so that the next detection
|
||||
results in a bigger or smaller number of 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.
|
||||
|
||||
Example of creating ``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 )
|
||||
|
||||
Provides the ``DynamicAdaptedFeatureDetector`` constructor.??
|
||||
|
||||
:param adjaster: :ref:`AdjusterAdapter` that detects features and adjusts parameters.??formatting issue again
|
||||
|
||||
:param min_features: Minimum desired number of features.
|
||||
|
||||
:param max_features: Maximum desired number of features.
|
||||
|
||||
:param max_iters: Maximum number of times to try adjusting the feature detector parameters. For :ref:`FastAdjuster` , this number can be high, but with ``Star`` or ``Surf`` many iterations can be time-consuming. At each iteration the detector is rerun.
|
||||
|
||||
.. index:: AdjusterAdapter
|
||||
|
||||
AdjusterAdapter
|
||||
---------------
|
||||
|
||||
.. c:type:: AdjusterAdapter
|
||||
|
||||
Class providing an interface for adjusting parameters of a feature detector. This interface is used by :ref:`DynamicAdaptedFeatureDetector` . It is a wrapper for :ref:`FeatureDetector` that enables adjusting parameters after 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
|
||||
:ref:`FastAdjuster`,
|
||||
:ref:`StarAdjuster`,
|
||||
:ref:`SurfAdjuster` for concrete implementations.
|
||||
|
||||
.. index:: AdjusterAdapter::tooFew
|
||||
|
||||
AdjusterAdapter::tooFew
|
||||
---------------------------
|
||||
.. c:function:: virtual void tooFew(int min, int n_detected) = 0
|
||||
|
||||
Adjusts the detector parameters to detect more features.
|
||||
|
||||
:param min: Minimum desired number of features.
|
||||
|
||||
:param n_detected: Number of features detected during the latest run.
|
||||
|
||||
Example: ::
|
||||
|
||||
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
|
||||
|
||||
Adjusts the detector parameters to detect less features.
|
||||
|
||||
:param max: Maximum desired number of features.
|
||||
|
||||
:param n_detected: Number of features detected during the latest run.
|
||||
|
||||
Example: ::
|
||||
|
||||
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 cannot be adjusted any more.
|
||||
|
||||
Example: ::
|
||||
|
||||
bool FastAdjuster::good() const
|
||||
{
|
||||
return (thresh > 1) && (thresh < 200);
|
||||
}
|
||||
|
||||
.. index:: FastAdjuster
|
||||
|
||||
FastAdjuster
|
||||
------------
|
||||
|
||||
.. c:type:: FastAdjuster
|
||||
|
||||
:ref:`AdjusterAdapter` for :ref:`FastFeatureDetector`. This class decrements or increments the threshhld by 1. ::
|
||||
|
||||
class FastAdjuster FastAdjuster: public AdjusterAdapter
|
||||
{
|
||||
public:
|
||||
FastAdjuster(int init_thresh = 20, bool nonmax = true);
|
||||
...
|
||||
};
|
||||
|
||||
|
||||
.. index:: StarAdjuster
|
||||
|
||||
StarAdjuster
|
||||
------------
|
||||
|
||||
.. c:type:: StarAdjuster
|
||||
|
||||
:ref:`AdjusterAdapter` for :ref:`StarFeatureDetector` . This classadjusts the responseThreshhold of StarFeatureDetector. ::
|
||||
|
||||
class StarAdjuster: public AdjusterAdapter
|
||||
{
|
||||
StarAdjuster(double initial_thresh = 30.0);
|
||||
...
|
||||
};
|
||||
|
||||
|
||||
.. index:: SurfAdjuster
|
||||
|
||||
SurfAdjuster
|
||||
------------
|
||||
|
||||
.. c:type:: SurfAdjuster
|
||||
|
||||
:ref:`AdjusterAdapter` for :ref:`SurfFeatureDetector` . This class adjusts the ``hessianThreshold`` of ``SurfFeatureDetector`` . ::
|
||||
|
||||
class SurfAdjuster: public SurfAdjuster
|
||||
{
|
||||
SurfAdjuster();
|
||||
...
|
||||
};
|
||||
|
||||
..
|
||||
|
||||
|
||||
Reference in New Issue
Block a user