Cascade Classification

FeatureEvaluator

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FeatureEvaluator

Base class for computing feature values in cascade classifiers.

class CV_EXPORTS FeatureEvaluator
{
public:
    enum { HAAR = 0, LBP = 1 }; // supported feature types
    virtual ~FeatureEvaluator(); // destructor
    virtual bool read(const FileNode& node);
    virtual Ptr<FeatureEvaluator> clone() const;
    virtual int getFeatureType() const;

    virtual bool setImage(const Mat& img, Size origWinSize);
    virtual bool setWindow(Point p);

    virtual double calcOrd(int featureIdx) const;
    virtual int calcCat(int featureIdx) const;

    static Ptr<FeatureEvaluator> create(int type);
};

cv::FeatureEvaluator::read

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bool FeatureEvaluator::read(const FileNode& node)

Reads parameters of the features from a FileStorage node.

Parameters:

• node – File node from which the feature parameters are read.

cv::FeatureEvaluator::clone

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Ptr<FeatureEvaluator> FeatureEvaluator::clone() const

Returns a full copy of the feature evaluator.

cv::FeatureEvaluator::getFeatureType

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int FeatureEvaluator::getFeatureType() const

Returns the feature type (HAAR or LBP for now).

cv::FeatureEvaluator::setImage

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bool FeatureEvaluator::setImage(const Mat& img, Size origWinSize)

Sets the image in which to compute the features.

Parameters:

• img – Matrix of type CV_8UC1 containing the image in which to compute the features.
• origWinSize – Size of training images.

cv::FeatureEvaluator::setWindow

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CascadeClassifier::runAt()

bool FeatureEvaluator::setWindow(Point p)

Sets window in the current image in which the features will be computed (called by ).

Parameters:

• 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.

cv::FeatureEvaluator::calcOrd

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double FeatureEvaluator::calcOrd(int featureIdx) const

Computes value of an ordered (numerical) feature.

Parameters:

• featureIdx – Index of feature whose value will be computed.

Returns computed value of ordered feature.

cv::FeatureEvaluator::calcCat

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int FeatureEvaluator::calcCat(int featureIdx) const

Computes value of a categorical feature.

Parameters:

• featureIdx – Index of feature whose value will be computed.

Returns computed label of categorical feature, i.e. value from [0,... (number of categories - 1)].

cv::FeatureEvaluator::create

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static Ptr<FeatureEvaluator> FeatureEvaluator::create(int type)

Constructs feature evaluator.

Parameters:

• type – Type of features evaluated by cascade (HAAR or LBP for now).

CascadeClassifier

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CascadeClassifier

The cascade classifier class for object detection.

class CascadeClassifier
{
public:
        // structure for storing tree node
    struct CV_EXPORTS DTreeNode
    {
        int featureIdx; // feature index on which is a split
        float threshold; // split threshold of ordered features only
        int left; // left child index in the tree nodes array
        int right; // right child index in the tree nodes array
    };

    // structure for storing desision tree
    struct CV_EXPORTS DTree
    {
        int nodeCount; // nodes count
    };

    // structure for storing cascade stage (BOOST only for now)
    struct CV_EXPORTS Stage
    {
        int first; // first tree index in tree array
        int ntrees; // number of trees
        float threshold; // treshold of stage sum
    };

    enum { BOOST = 0 }; // supported stage types

    // mode of detection (see parameter flags in function HaarDetectObjects)
    enum { DO_CANNY_PRUNING = CV_HAAR_DO_CANNY_PRUNING,
           SCALE_IMAGE = CV_HAAR_SCALE_IMAGE,
           FIND_BIGGEST_OBJECT = CV_HAAR_FIND_BIGGEST_OBJECT,
           DO_ROUGH_SEARCH = CV_HAAR_DO_ROUGH_SEARCH };

    CascadeClassifier(); // default constructor
    CascadeClassifier(const string& filename);
    ~CascadeClassifier(); // destructor

    bool empty() const;
    bool load(const string& filename);
    bool read(const FileNode& node);

    void detectMultiScale( const Mat& image, vector<Rect>& objects,
                           double scaleFactor=1.1, int minNeighbors=3,
                                                   int flags=0, Size minSize=Size());

    bool setImage( Ptr<FeatureEvaluator>&, const Mat& );
    int runAt( Ptr<FeatureEvaluator>&, Point );

    bool is_stump_based; // true, if the trees are stumps

    int stageType; // stage type (BOOST only for now)
    int featureType; // feature type (HAAR or LBP for now)
    int ncategories; // number of categories (for categorical features only)
    Size origWinSize; // size of training images

    vector<Stage> stages; // vector of stages (BOOST for now)
    vector<DTree> classifiers; // vector of decision trees
    vector<DTreeNode> nodes; // vector of tree nodes
    vector<float> leaves; // vector of leaf values
    vector<int> subsets; // subsets of split by categorical feature

    Ptr<FeatureEvaluator> feval; // pointer to feature evaluator
    Ptr<CvHaarClassifierCascade> oldCascade; // pointer to old cascade
};

cv::CascadeClassifier::CascadeClassifier

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CascadeClassifier::CascadeClassifier(const string& filename)

Loads the classifier from file.

Parameters:

• filename – Name of file from which classifier will be load.

cv::CascadeClassifier::empty

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bool CascadeClassifier::empty() const

Checks if the classifier has been loaded or not.

cv::CascadeClassifier::load

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bool CascadeClassifier::load(const string& filename)

Loads the classifier from file. The previous content is destroyed.

Parameters:

• 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).

cv::CascadeClassifier::read

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bool CascadeClassifier::read(const FileNode& node)

Reads the classifier from a FileStorage node. File may contain a new cascade classifier (trained traincascade application) only.

cv::CascadeClassifier::detectMultiScale

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void CascadeClassifier::detectMultiScale(const Mat& image, vector<Rect>& objects, double scaleFactor=1.1, int minNeighbors=3, int flags=0, Size minSize=Size())

Detects objects of different sizes in the input image. The detected objects are returned as a list of rectangles.

Parameters:

• image – Matrix of type CV_8U containing the image in which to detect objects.
• objects – Vector of rectangles such that each rectangle contains the detected object.
• scaleFactor – Specifies how much the image size is reduced at each image scale.
• minNeighbors – Speficifes how many neighbors should each candiate rectangle have to retain it.
• flags – This parameter is not used for new cascade and have the same meaning for old cascade as in function cvHaarDetectObjects.
• minSize – The minimum possible object size. Objects smaller than that are ignored.

cv::CascadeClassifier::setImage

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bool CascadeClassifier::setImage(Ptr<FeatureEvaluator>& feval, const Mat& image)

Sets the image for detection (called by detectMultiScale at each image level).

Parameters:

• feval – Pointer to feature evaluator which is used for computing features.
• image – Matrix of type CV_8UC1 containing the image in which to compute the features.

cv::CascadeClassifier::runAt

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int CascadeClassifier::runAt(Ptr<FeatureEvaluator>& feval, Point pt)

Runs the detector at the specified point (the image that the detector is working with should be set by setImage).

Parameters:

• feval – Feature evaluator which is used for computing features.
• 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.

Returns: 1 - if cascade classifier detects object in the given location. -si - otherwise. si is an index of stage which first predicted that given window is a background image.

cv::groupRectangles

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void groupRectangles(vector<Rect>& rectList, int groupThreshold, double eps=0.2)

Groups the object candidate rectangles

Parameters:

• rectList – The input/output vector of rectangles. On output there will be retained and grouped rectangles
• groupThreshold – The minimum possible number of rectangles, minus 1, in a group of rectangles to retain it.
• eps – The relative difference between sides of the rectangles to merge them into a group

The function is a wrapper for a generic function 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 , 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.