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295 lines
11 KiB
295 lines
11 KiB
Cascade Classification |
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====================== |
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.. highlight:: cpp |
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.. index:: FeatureEvaluator |
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.. _FeatureEvaluator: |
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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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class CV_EXPORTS FeatureEvaluator |
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{ |
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public: |
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enum { HAAR = 0, LBP = 1 }; // supported feature types |
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virtual ~FeatureEvaluator(); // destructor |
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virtual bool read(const FileNode& node); |
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virtual Ptr<FeatureEvaluator> clone() const; |
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virtual int getFeatureType() const; |
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virtual bool setImage(const Mat& img, Size origWinSize); |
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virtual bool setWindow(Point p); |
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virtual double calcOrd(int featureIdx) const; |
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virtual int calcCat(int featureIdx) const; |
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static Ptr<FeatureEvaluator> create(int type); |
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}; |
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.. index:: FeatureEvaluator::read |
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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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:param node: File node from which the feature parameters are read. |
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.. index:: FeatureEvaluator::clone |
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FeatureEvaluator::clone |
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--------------------------- |
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.. c:function:: Ptr<FeatureEvaluator> FeatureEvaluator::clone() const |
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Returns a full copy of the feature evaluator. |
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.. index:: FeatureEvaluator::getFeatureType |
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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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.. index:: FeatureEvaluator::setImage |
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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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:param img: Matrix of type ``CV_8UC1`` containing the image in which to compute the features. |
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:param origWinSize: Size of training images. |
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.. index:: FeatureEvaluator::setWindow |
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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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: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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.. index:: FeatureEvaluator::calcOrd |
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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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:param featureIdx: Index of feature whose value will be computed. |
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Returns computed value of ordered feature. |
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.. index:: FeatureEvaluator::calcCat |
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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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:param featureIdx: Index of feature whose value will be computed. |
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Returns computed label of categorical feature, i.e. value from [0,... (number of categories - 1)]. |
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.. index:: FeatureEvaluator::create |
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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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:param type: Type of features evaluated by cascade (HAAR or LBP for now). |
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.. index:: CascadeClassifier |
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.. _CascadeClassifier: |
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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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class CascadeClassifier |
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{ |
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public: |
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// structure for storing 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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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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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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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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}; |
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enum { BOOST = 0 }; // supported stage types |
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// mode of detection (see parameter flags in function HaarDetectObjects) |
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enum { DO_CANNY_PRUNING = CV_HAAR_DO_CANNY_PRUNING, |
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SCALE_IMAGE = CV_HAAR_SCALE_IMAGE, |
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FIND_BIGGEST_OBJECT = CV_HAAR_FIND_BIGGEST_OBJECT, |
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DO_ROUGH_SEARCH = CV_HAAR_DO_ROUGH_SEARCH }; |
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CascadeClassifier(); // default constructor |
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CascadeClassifier(const string& filename); |
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~CascadeClassifier(); // destructor |
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bool empty() const; |
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bool load(const string& filename); |
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bool read(const FileNode& node); |
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void detectMultiScale( const Mat& image, vector<Rect>& objects, |
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double scaleFactor=1.1, int minNeighbors=3, |
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int flags=0, Size minSize=Size()); |
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bool setImage( Ptr<FeatureEvaluator>&, const Mat& ); |
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int runAt( Ptr<FeatureEvaluator>&, Point ); |
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bool is_stump_based; // true, if the trees are stumps |
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int stageType; // stage type (BOOST only for now) |
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int featureType; // feature type (HAAR or LBP for now) |
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int ncategories; // number of categories (for categorical features only) |
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Size origWinSize; // size of training images |
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vector<Stage> stages; // vector of stages (BOOST for now) |
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vector<DTree> classifiers; // vector of decision trees |
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vector<DTreeNode> nodes; // vector of tree nodes |
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vector<float> leaves; // vector of leaf values |
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vector<int> subsets; // subsets of split by categorical feature |
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Ptr<FeatureEvaluator> feval; // pointer to feature evaluator |
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Ptr<CvHaarClassifierCascade> oldCascade; // pointer to old cascade |
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}; |
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.. index:: CascadeClassifier::CascadeClassifier |
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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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:param filename: Name of file from which classifier will be load. |
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.. index:: CascadeClassifier::empty |
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CascadeClassifier::empty |
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---------------------------- |
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.. c:function:: bool CascadeClassifier::empty() const |
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Checks if the classifier has been loaded or not. |
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.. index:: CascadeClassifier::load |
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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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: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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.. index:: CascadeClassifier::read |
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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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.. index:: CascadeClassifier::detectMultiScale |
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CascadeClassifier::detectMultiScale |
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--------------------------------------- |
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.. c:function:: void CascadeClassifier::detectMultiScale( const Mat\& image, vector<Rect>\& objects, double scaleFactor=1.1, int minNeighbors=3, int flags=0, Size minSize=Size()) |
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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 objects: Vector of rectangles such that 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 minNeighbors: Speficifes how many neighbors should each candiate rectangle 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 minSize: The minimum possible object size. Objects smaller than that are ignored. |
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.. index:: CascadeClassifier::setImage |
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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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:param feval: Pointer to feature evaluator which 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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.. index:: CascadeClassifier::runAt |
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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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:param feval: Feature evaluator which 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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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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.. index:: groupRectangles |
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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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:param rectList: The input/output vector of rectangles. On output there will be 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 eps: The 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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