Purpose: completed the objdetect chapter
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Elena Fedotova
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@ -9,7 +9,7 @@ FeatureEvaluator
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----------------
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.. c:type:: FeatureEvaluator
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Base class for computing feature values in cascade classifiers. ::
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Base class for computing feature values in cascade classifiers ::
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class CV_EXPORTS FeatureEvaluator
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{
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@ -36,7 +36,7 @@ FeatureEvaluator::read
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--------------------------
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.. c:function:: bool FeatureEvaluator::read(const FileNode\& node)
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Reads parameters of the features from a FileStorage node.
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Reads parameters of features from the ``FileStorage`` node.
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:param node: File node from which the feature parameters are read.
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@ -54,7 +54,7 @@ FeatureEvaluator::getFeatureType
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------------------------------------
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.. c:function:: int FeatureEvaluator::getFeatureType() const
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Returns the feature type (HAAR or LBP for now).
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Returns the feature type (``HAAR`` or ``LBP`` for now).
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.. index:: FeatureEvaluator::setImage
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@ -62,9 +62,9 @@ FeatureEvaluator::setImage
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------------------------------
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.. c:function:: bool FeatureEvaluator::setImage(const Mat\& img, Size origWinSize)
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Sets the image in which to compute the features.
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Sets an image where the features are computed??.
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:param img: Matrix of type ``CV_8UC1`` containing the image in which to compute the features.
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:param img: Matrix of the type ``CV_8UC1`` containing an image where the features are computed.
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:param origWinSize: Size of training images.
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@ -72,12 +72,11 @@ FeatureEvaluator::setImage
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FeatureEvaluator::setWindow
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-------------------------------
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:func:`CascadeClassifier::runAt`
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.. c:function:: bool FeatureEvaluator::setWindow(Point p)
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Sets window in the current image in which the features will be computed (called by ).
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Sets a window in the current image where the features are computed (called by ??).
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:param p: The upper left point of window in which the features will be computed. Size of the window is equal to size of training images.
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:param p: Upper left point of the window where the features are computed. Size of the window is equal to the size of training images.
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.. index:: FeatureEvaluator::calcOrd
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@ -85,11 +84,11 @@ FeatureEvaluator::calcOrd
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-----------------------------
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.. c:function:: double FeatureEvaluator::calcOrd(int featureIdx) const
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Computes value of an ordered (numerical) feature.
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Computes the value of an ordered (numerical) feature.
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:param featureIdx: Index of feature whose value will be computed.
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:param featureIdx: Index of the feature whose value is computed.
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Returns computed value of ordered feature.
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The function returns the computed value of an ordered feature.
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.. index:: FeatureEvaluator::calcCat
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@ -97,11 +96,11 @@ FeatureEvaluator::calcCat
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-----------------------------
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.. c:function:: int FeatureEvaluator::calcCat(int featureIdx) const
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Computes value of a categorical feature.
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Computes the value of a categorical feature.
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:param featureIdx: Index of feature whose value will be computed.
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:param featureIdx: Index of the feature whose value is computed.
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Returns computed label of categorical feature, i.e. value from [0,... (number of categories - 1)].
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The function returns the computed label of a categorical feature, that is, the value from [0,... (number of categories - 1)].
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.. index:: FeatureEvaluator::create
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@ -109,9 +108,9 @@ FeatureEvaluator::create
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----------------------------
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.. c:function:: static Ptr<FeatureEvaluator> FeatureEvaluator::create(int type)
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Constructs feature evaluator.
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Constructs the feature evaluator.
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:param type: Type of features evaluated by cascade (HAAR or LBP for now).
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:param type: Type of features evaluated by cascade (``HAAR`` or ``LBP`` for now).
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.. index:: CascadeClassifier
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@ -121,32 +120,32 @@ CascadeClassifier
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-----------------
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.. c:type:: CascadeClassifier
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The cascade classifier class for object detection. ::
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The cascade classifier class for object detection ::
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class CascadeClassifier
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{
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public:
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// structure for storing tree node
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// structure for storing a tree node
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struct CV_EXPORTS DTreeNode
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{
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int featureIdx; // feature index on which is a split
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int featureIdx; // feature index on which is a split??
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float threshold; // split threshold of ordered features only
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int left; // left child index in the tree nodes array
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int right; // right child index in the tree nodes array
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};
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// structure for storing desision tree
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// structure for storing a decision tree
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struct CV_EXPORTS DTree
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{
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int nodeCount; // nodes count
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};
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// structure for storing cascade stage (BOOST only for now)
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// structure for storing a cascade stage (BOOST only for now)
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struct CV_EXPORTS Stage
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{
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int first; // first tree index in tree array
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int ntrees; // number of trees
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float threshold; // treshold of stage sum
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float threshold; // threshold of stage sum
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};
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enum { BOOST = 0 }; // supported stage types
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@ -196,9 +195,9 @@ CascadeClassifier::CascadeClassifier
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----------------------------------------
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.. c:function:: CascadeClassifier::CascadeClassifier(const string\& filename)
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Loads the classifier from file.
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Loads a classifier from a file.
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:param filename: Name of file from which classifier will be load.
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:param filename: Name of the file from which the classifier is loaded.
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.. index:: CascadeClassifier::empty
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@ -214,9 +213,9 @@ CascadeClassifier::load
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---------------------------
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.. c:function:: bool CascadeClassifier::load(const string\& filename)
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Loads the classifier from file. The previous content is destroyed.
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Loads a classifier from a file. The previous content is destroyed.
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:param filename: Name of file from which classifier will be load. File may contain as old haar classifier (trained by haartraining application) or new cascade classifier (trained traincascade application).
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:param filename: Name of the file from which the classifier is loaded. The file may contain an old HAAR classifier (trained by the haartraining application) or new cascade classifier trained traincascade application.
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.. index:: CascadeClassifier::read
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@ -224,7 +223,7 @@ CascadeClassifier::read
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---------------------------
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.. c:function:: bool CascadeClassifier::read(const FileNode\& node)
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Reads the classifier from a FileStorage node. File may contain a new cascade classifier (trained traincascade application) only.
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Reads a classifier from a FileStorage node. The file may contain a new cascade classifier (trained traincascade application) only.
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.. index:: CascadeClassifier::detectMultiScale
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@ -234,17 +233,17 @@ CascadeClassifier::detectMultiScale
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Detects objects of different sizes in the input image. The detected objects are returned as a list of rectangles.
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:param image: Matrix of type ``CV_8U`` containing the image in which to detect objects.
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:param image: Matrix of the type ``CV_8U`` containing an image where objects are detected.
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:param objects: Vector of rectangles such that each rectangle contains the detected object.
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:param objects: Vector of rectangles where each rectangle contains the detected object.
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:param scaleFactor: Specifies how much the image size is reduced at each image scale.
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:param scaleFactor: Parameter specifying how much the image size is reduced at each image scale.
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:param minNeighbors: Speficifes how many neighbors should each candiate rectangle have to retain it.
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:param minNeighbors: Parameter specifying how many neighbors each candiate rectangle should have to retain it.
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:param flags: This parameter is not used for new cascade and have the same meaning for old cascade as in function cvHaarDetectObjects.
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:param flags: Parameter with the same meaning for an old cascade as in the function ``cvHaarDetectObjects``. It is not used for a new cascade.
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:param minSize: The minimum possible object size. Objects smaller than that are ignored.
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:param minSize: Minimum possible object size. Objects smaller than that are ignored.
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.. index:: CascadeClassifier::setImage
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@ -252,11 +251,11 @@ CascadeClassifier::setImage
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-------------------------------
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.. c:function:: bool CascadeClassifier::setImage( Ptr<FeatureEvaluator>\& feval, const Mat\& image )
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Sets the image for detection (called by detectMultiScale at each image level).
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Sets an image for detection, which is called by ``detectMultiScale`` at each image level.
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:param feval: Pointer to feature evaluator which is used for computing features.
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:param feval: Pointer to the feature evaluator that is used for computing features.
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:param image: Matrix of type ``CV_8UC1`` containing the image in which to compute the features.
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:param image: Matrix of the type ``CV_8UC1`` containing an image where the features are computed.
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.. index:: CascadeClassifier::runAt
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@ -264,15 +263,14 @@ CascadeClassifier::runAt
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----------------------------
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.. c:function:: int CascadeClassifier::runAt( Ptr<FeatureEvaluator>\& feval, Point pt )
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Runs the detector at the specified point (the image that the detector is working with should be set by setImage).
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Runs the detector at the specified point. Use ``setImage`` to set the image that the detector is working with.
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:param feval: Feature evaluator which is used for computing features.
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:param feval: Feature evaluator that is used for computing features.
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:param pt: The upper left point of window in which the features will be computed. Size of the window is equal to size of training images.
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:param pt: Upper left point of the window where the features are computed. Size of the window is equal to the size of training images.
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Returns:
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1 - if cascade classifier detects object in the given location.
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-si - otherwise. si is an index of stage which first predicted that given window is a background image.
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The function returns 1 if the cascade classifier detects an object in the given location.
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Otherwise, it returns ``si``, which is an index of the stage that first predicted that the given window is a background image.??
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.. index:: groupRectangles
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@ -280,14 +278,14 @@ groupRectangles
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-------------------
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.. c:function:: void groupRectangles(vector<Rect>\& rectList, int groupThreshold, double eps=0.2)
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Groups the object candidate rectangles
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Groups the object candidate rectangles.
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:param rectList: The input/output vector of rectangles. On output there will be retained and grouped rectangles
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:param rectList: Input/output vector of rectangles. Output vector includes retained and grouped rectangles.??
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:param groupThreshold: The minimum possible number of rectangles, minus 1, in a group of rectangles to retain it.
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:param groupThreshold: Minimum possible number of rectangles minus 1. The threshold is used in a group of rectangles to retain it.??
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:param eps: The relative difference between sides of the rectangles to merge them into a group
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:param eps: Relative difference between sides of the rectangles to merge them into a group.
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The function is a wrapper for a generic function
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:func:`partition` . It clusters all the input rectangles using the rectangle equivalence criteria, that combines rectangles that have similar sizes and similar locations (the similarity is defined by ``eps`` ). When ``eps=0`` , no clustering is done at all. If
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:math:`\texttt{eps}\rightarrow +\inf` , all the rectangles will be put in one cluster. Then, the small clusters, containing less than or equal to ``groupThreshold`` rectangles, will be rejected. In each other cluster the average rectangle will be computed and put into the output rectangle list.
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The function is a wrapper for the generic function
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:ref:`partition` . It clusters all the input rectangles using the rectangle equivalence criteria that combines rectangles with similar sizes and similar locations (the similarity is defined by ``eps`` ). When ``eps=0`` , no clustering is done at all. If
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:math:`\texttt{eps}\rightarrow +\inf` , all the rectangles are put in one cluster. Then, the small clusters containing less than or equal to ``groupThreshold`` rectangles are rejected. In each other cluster, the average rectangle is computed and put into the output rectangle list.
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