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

/** @defgroup xobjdetect Extended object detection
*/

namespace cv
{
namespace xobjdetect
{

//! @addtogroup xobjdetect
//! @{

/** @brief Compute channels for integral channel features evaluation

@param image image for which channels should be computed
@param channels output array for computed channels
 */
CV_EXPORTS void computeChannels(InputArray image, std::vector<Mat>& channels);

/** @brief Feature evaluation interface
 */
class CV_EXPORTS FeatureEvaluator : public Algorithm
{
public:
    /** @brief Set channels for feature evaluation

    @param channels array of channels to be set
     */
    virtual void setChannels(InputArrayOfArrays channels) = 0;

    /** @brief Set window position to sample features with shift. By default position is (0, 0).

    @param position position to be set
     */
    virtual void setPosition(Size position) = 0;

    /** @brief Evaluate feature value with given index for current channels and window position.

    @param feature_ind index of feature to be evaluated
     */
    virtual int evaluate(size_t feature_ind) const = 0;

    /** @brief Evaluate all features for current channels and window position.

    @param feature_values matrix-column of evaluated feature values
     */
    virtual void evaluateAll(OutputArray feature_values) const = 0;

    virtual void assertChannels() = 0;
};

/** @brief Construct feature evaluator.

@param features features for evaluation
@param type feature type. Can be "icf" or "acf"
 */
CV_EXPORTS Ptr<FeatureEvaluator>
createFeatureEvaluator(const std::vector<std::vector<int> >& features,
                       const std::string& type);

/** @brief Generate integral features. Returns vector of features.

@param window_size size of window in which features should be evaluated
@param type feature type. Can be "icf" or "acf"
@param count number of features to generate.
@param channel_count number of feature channels
 */
std::vector<std::vector<int> >
generateFeatures(Size window_size, const std::string& type,
                 int count = INT_MAX, int channel_count = 10);

//sort in-place of columns of the input matrix
void sort_columns_without_copy(Mat& m, Mat indices = Mat());

/** @brief Parameters for WaldBoost. weak_count — number of weak learners, alpha — cascade thresholding param.
 */
struct CV_EXPORTS WaldBoostParams
{
    int weak_count;
    float alpha;

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

/** @brief WaldBoost object detector from @cite Sochman05 .
*/
class CV_EXPORTS WaldBoost : public Algorithm
{
public:
    /** @brief Train WaldBoost cascade for given data.

    Returns feature indices chosen for cascade. Feature enumeration starts from 0.
    @param data matrix of feature values, size M x N, one feature per row
    @param labels matrix of samples class labels, size 1 x N. Labels can be from {-1, +1}
    @param use_fast_log
     */
    virtual std::vector<int> train(Mat& data,
                                   const Mat& labels, bool use_fast_log=false) = 0;

    /** @brief Predict objects class given object that can compute object features.

    Returns unnormed confidence value — measure of confidence that object is from class +1.
    @param feature_evaluator object that can compute features by demand
     */
    virtual float predict(
        const Ptr<FeatureEvaluator>& feature_evaluator) const = 0;

    /** @brief Write WaldBoost to FileStorage
    @param fs FileStorage for output
     */
    virtual void write(FileStorage& fs) const = 0;

    /** @brief Write WaldBoost to FileNode

    @param node FileNode for reading
     */
    virtual void read(const FileNode& node) = 0;
};

/** @brief Construct WaldBoost object.
 */
CV_EXPORTS Ptr<WaldBoost>
createWaldBoost(const WaldBoostParams& params = WaldBoostParams());

/** @brief Params for ICFDetector training.
 */
struct CV_EXPORTS ICFDetectorParams
{
    int feature_count;
    int weak_count;
    int model_n_rows;
    int model_n_cols;
    int bg_per_image;
    std::string features_type;
    float alpha;
    bool is_grayscale;
    bool use_fast_log;

    ICFDetectorParams(): feature_count(UINT_MAX), weak_count(100),
        model_n_rows(56), model_n_cols(56), bg_per_image(5), alpha(0.02f), is_grayscale(false), use_fast_log(false)
    {}
};

/** @brief Integral Channel Features from @cite Dollar09 .
*/
class CV_EXPORTS ICFDetector
{
public:

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

    /** @brief Train detector.

    @param pos_filenames path to folder with images of objects (wildcards like /my/path/\*.png are allowed)
    @param bg_filenames path to folder with background images
    @param params parameters for detector training
     */
    void train(const std::vector<String>& pos_filenames,
               const std::vector<String>& bg_filenames,
               ICFDetectorParams params = ICFDetectorParams());

    /** @brief Detect objects on image.
    @param image image for detection
    @param objects output array of bounding boxes
    @param scaleFactor scale between layers in detection pyramid
    @param minSize min size of objects in pixels
    @param maxSize max size of objects in pixels
    @param threshold
    @param slidingStep sliding window step
    @param values output vector with values of positive samples
     */
    void detect(const Mat& image, std::vector<Rect>& objects,
        float scaleFactor, Size minSize, Size maxSize, float threshold, int slidingStep, std::vector<float>& values);
    
    /** @brief Detect objects on image.
    @param img image for detection
    @param objects output array of bounding boxes
    @param minScaleFactor min factor by which the image will be resized
    @param maxScaleFactor max factor by which the image will be resized
    @param factorStep scaling factor is incremented each pyramid layer according to this parameter
    @param threshold
    @param slidingStep sliding window step
    @param values output vector with values of positive samples
     */
    void detect(const Mat& img, std::vector<Rect>& objects, float minScaleFactor, float maxScaleFactor, float factorStep, float threshold, int slidingStep, std::vector<float>& values);

    /** @brief Write detector to FileStorage.
    @param fs FileStorage for output
     */
    void write(FileStorage &fs) const;

    /** @brief Write ICFDetector to FileNode
    @param node FileNode for reading
     */
    void read(const FileNode &node);

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

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__ */