OXIESEC PANEL

Name	Size	Modified	Perms
📁 ..	-	10/28/2024 06:50:42 AM	rwxr-xr-x
📄 deriche_filter.hpp	3.06 KB	05/12/2017 03:45:27 AM	rw-r--r--
📄 disparity_filter.hpp	8.54 KB	05/12/2017 03:45:27 AM	rw-r--r--
📄 edge_filter.hpp	18.65 KB	05/12/2017 03:45:27 AM	rw-r--r--
📄 estimated_covariance.hpp	3.75 KB	05/12/2017 03:45:27 AM	rw-r--r--
📄 fast_hough_transform.hpp	8.06 KB	05/12/2017 03:45:27 AM	rw-r--r--
📄 fast_line_detector.hpp	3.07 KB	05/12/2017 03:45:27 AM	rw-r--r--
📄 lsc.hpp	6.41 KB	05/12/2017 03:45:27 AM	rw-r--r--
📄 paillou_filter.hpp	2.58 KB	05/12/2017 03:45:27 AM	rw-r--r--
📄 seeds.hpp	8.6 KB	05/12/2017 03:45:27 AM	rw-r--r--
📄 segmentation.hpp	14.97 KB	05/12/2017 03:45:27 AM	rw-r--r--
📄 slic.hpp	6.69 KB	05/12/2017 03:45:27 AM	rw-r--r--
📄 sparse_match_interpolator.hpp	5.04 KB	05/12/2017 03:45:27 AM	rw-r--r--
📄 structured_edge_detection.hpp	4.7 KB	05/12/2017 03:45:27 AM	rw-r--r--
📄 weighted_median_filter.hpp	3.79 KB	05/12/2017 03:45:27 AM	rw-r--r--

Editing: structured_edge_detection.hpp

/*M///////////////////////////////////////////////////////////////////////////////////////
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#ifndef __OPENCV_STRUCTURED_EDGE_DETECTION_HPP__
#define __OPENCV_STRUCTURED_EDGE_DETECTION_HPP__
#ifdef __cplusplus

/** @file
@date Jun 17, 2014
@author Yury Gitman
 */

#include <opencv2/core.hpp>

namespace cv
{
namespace ximgproc
{

//! @addtogroup ximgproc_edge
//! @{

/*!
  Helper class for training part of [P. Dollar and C. L. Zitnick. Structured Forests for Fast Edge Detection, 2013].
 */
class CV_EXPORTS_W RFFeatureGetter : public Algorithm
{
public:

/*!
     * This functions extracts feature channels from src.
     * Than StructureEdgeDetection uses this feature space
     * to detect edges.
     *
     * \param src : source image to extract features
     * \param features : output n-channel floating point feature matrix.
     *
     * \param gnrmRad : __rf.options.gradientNormalizationRadius
     * \param gsmthRad : __rf.options.gradientSmoothingRadius
     * \param shrink : __rf.options.shrinkNumber
     * \param outNum : __rf.options.numberOfOutputChannels
     * \param gradNum : __rf.options.numberOfGradientOrientations
     */
    CV_WRAP virtual void getFeatures(const Mat &src, Mat &features,
                                     const int gnrmRad,
                                     const int gsmthRad,
                                     const int shrink,
                                     const int outNum,
                                     const int gradNum) const = 0;
};

CV_EXPORTS_W Ptr<RFFeatureGetter> createRFFeatureGetter();

/** @brief Class implementing edge detection algorithm from @cite Dollar2013 :
 */
class CV_EXPORTS_W StructuredEdgeDetection : public Algorithm
{
public:

/** @brief The function detects edges in src and draw them to dst.

The algorithm underlies this function is much more robust to texture presence, than common
    approaches, e.g. Sobel
    @param src source image (RGB, float, in [0;1]) to detect edges
    @param dst destination image (grayscale, float, in [0;1]) where edges are drawn
    @sa Sobel, Canny
     */
    CV_WRAP virtual void detectEdges(const Mat &src, CV_OUT Mat &dst) const = 0;
};

/*!
* The only constructor
*
* \param model : name of the file where the model is stored
* \param howToGetFeatures : optional object inheriting from RFFeatureGetter.
*                           You need it only if you would like to train your
*                           own forest, pass NULL otherwise
*/
CV_EXPORTS_W Ptr<StructuredEdgeDetection> createStructuredEdgeDetection(const String &model,
    Ptr<const RFFeatureGetter> howToGetFeatures = Ptr<RFFeatureGetter>());

//! @}

}
}
#endif
#endif /* __OPENCV_STRUCTURED_EDGE_DETECTION_HPP__ */