opencv_contrib/modules/xobjdetect/include/opencv2/xobjdetect.hpp

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*/

#ifndef __OPENCV_XOBJDETECT_XOBJDETECT_HPP__
#define __OPENCV_XOBJDETECT_XOBJDETECT_HPP__

#include <opencv2/core.hpp>
#include <vector>
#include <string>

namespace cv
{
namespace xobjdetect
{

/* Compute channel pyramid for acf features

    image — image, for which channels should be computed

    channels — output array for computed channels

*/
void computeChannels(InputArray image, std::vector<Mat>& channels);

class CV_EXPORTS ACFFeatureEvaluator : public Algorithm
{
public:
    /* Set channels for feature evaluation */
    virtual void setChannels(InputArrayOfArrays channels) = 0;

    /* Set window position */
    virtual void setPosition(Size position) = 0;

    virtual void assertChannels() = 0;

    /* Evaluate feature with given index for current channels
        and window position */
    virtual int evaluate(size_t feature_ind) const = 0;

    /* Evaluate all features for current channels and window position

    Returns matrix-column of features
    */
    virtual void evaluateAll(OutputArray feature_values) const = 0;

};

/* Construct evaluator, set features to evaluate */
CV_EXPORTS Ptr<ACFFeatureEvaluator>
createACFFeatureEvaluator(const std::vector<Point3i>& features);

/* Generate acf features

    window_size — size of window in which features should be evaluated

    count — number of features to generate.
    Max number of features is min(count, # possible distinct features)

Returns vector of distinct acf features
*/
std::vector<Point3i>
generateFeatures(Size window_size, int count = INT_MAX);


struct CV_EXPORTS WaldBoostParams
{
    int weak_count;
    float alpha;

    WaldBoostParams(): weak_count(100), alpha(0.02f)
    {}
};



class CV_EXPORTS WaldBoost : public Algorithm
{
public:
    /* Train WaldBoost cascade for given data

        data — matrix of feature values, size M x N, one feature per row

        labels — matrix of sample class labels, size 1 x N. Labels can be from
            {-1, +1}

    Returns feature indices chosen for cascade.
    Feature enumeration starts from 0
    */
    virtual std::vector<int> train(const Mat& /*data*/,
                                   const Mat& /*labels*/) {return std::vector<int>();}

    /* Predict object class given object that can compute object features

       feature_evaluator — object that can compute features by demand

    Returns confidence_value — measure of confidense that object
    is from class +1
    */
    virtual float predict(
        const Ptr<ACFFeatureEvaluator>& /*feature_evaluator*/) const
    {return 0.0f;}

    /* Write WaldBoost to FileStorage */
    virtual void write(FileStorage& /*fs*/) const {}

    /* Read WaldBoost */
    virtual void read(const FileNode& /*node*/) {}
};

void write(FileStorage& fs, String&, const WaldBoost& waldboost);

void read(const FileNode& node, WaldBoost& w,
    const WaldBoost& default_value = WaldBoost());

CV_EXPORTS Ptr<WaldBoost>
createWaldBoost(const WaldBoostParams& params = WaldBoostParams());

struct CV_EXPORTS ICFDetectorParams
{
    int feature_count;
    int weak_count;
    int model_n_rows;
    int model_n_cols;
    int bg_per_image;

    ICFDetectorParams(): feature_count(UINT_MAX), weak_count(100),
        model_n_rows(56), model_n_cols(56), bg_per_image(5)
    {}
};

class CV_EXPORTS ICFDetector
{
public:

    ICFDetector(): waldboost_(), features_() {}

    /* Train detector

        pos_path — path to folder with images of objects

        bg_path — path to folder with background images

        params — parameters for detector training
    */
    void train(const String& pos_path,
               const String& bg_path,
               ICFDetectorParams params = ICFDetectorParams());

    /* Detect object on image

        image — image for detection

        object — output array of bounding boxes

        scaleFactor — scale between layers in detection pyramid

        minSize — min size of objects in pixels

        maxSize — max size of objects in pixels
    */
    void detect(const Mat& image, std::vector<Rect>& objects,
        double scaleFactor, Size minSize, Size maxSize, float threshold);

    /* Write detector to FileStorage */
    void write(FileStorage &fs) const;

    /* Read detector */
    void read(const FileNode &node);

private:
    Ptr<WaldBoost> waldboost_;
    std::vector<Point3i> features_;
    int model_n_rows_;
    int model_n_cols_;
};

CV_EXPORTS void write(FileStorage& fs, String&, const ICFDetector& detector);

CV_EXPORTS void read(const FileNode& node, ICFDetector& d,
    const ICFDetector& default_value = ICFDetector());

} /* namespace xobjdetect */
} /* namespace cv */

#endif /* __OPENCV_XOBJDETECT_XOBJDETECT_HPP__ */