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, OutputArrayOfArrays 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;

    /* 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(0.01f)
    {}
};



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) = 0;

    /* 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 = 0;

};

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;
    double overlap;

    ICFDetectorParams(): feature_count(UINT_MAX), weak_count(100),
        model_n_rows(40), model_n_cols(40), overlap(0.0)
    {}
};

class CV_EXPORTS ICFDetector
{
public:
    /* Train detector

        image_filenames — filenames of images for training

        labelling — vector of object bounding boxes per every image

        params — parameters for detector training
    */
    void train(const std::vector<std::string>& image_filenames,
               const std::vector<std::vector<cv::Rect> >& labelling,
               ICFDetectorParams params = ICFDetectorParams());

    /* Save detector in file, return true on success, false otherwise */
    bool save(const std::string& filename);
};

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

#endif /* __OPENCV_XOBJDETECT_XOBJDETECT_HPP__ */