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//M*/
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#ifndef __OPENCV_STRUCTURED_EDGE_DETECTION_HPP__
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#define __OPENCV_STRUCTURED_EDGE_DETECTION_HPP__
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#ifdef __cplusplus
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/** @file
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@date Jun 17, 2014
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@author Yury Gitman
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*/
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#include <opencv2/core.hpp>
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namespace cv
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{
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namespace ximgproc
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{
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//! @addtogroup ximgproc_edge
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//! @{
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/*!
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Helper class for training part of [P. Dollar and C. L. Zitnick. Structured Forests for Fast Edge Detection, 2013].
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*/
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class CV_EXPORTS_W RFFeatureGetter : public Algorithm
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{
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public:
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/*!
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* This functions extracts feature channels from src.
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* Than StructureEdgeDetection uses this feature space
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* to detect edges.
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*
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* \param src : source image to extract features
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* \param features : output n-channel floating point feature matrix.
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*
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* \param gnrmRad : __rf.options.gradientNormalizationRadius
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* \param gsmthRad : __rf.options.gradientSmoothingRadius
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* \param shrink : __rf.options.shrinkNumber
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* \param outNum : __rf.options.numberOfOutputChannels
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* \param gradNum : __rf.options.numberOfGradientOrientations
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*/
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CV_WRAP virtual void getFeatures(const Mat &src, Mat &features,
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const int gnrmRad,
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const int gsmthRad,
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const int shrink,
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const int outNum,
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const int gradNum) const = 0;
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};
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CV_EXPORTS_W Ptr<RFFeatureGetter> createRFFeatureGetter();
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/** @brief Class implementing edge detection algorithm from @cite Dollar2013 :
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*/
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class CV_EXPORTS_W StructuredEdgeDetection : public Algorithm
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{
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public:
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/** @brief The function detects edges in src and draw them to dst.
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The algorithm underlies this function is much more robust to texture presence, than common
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approaches, e.g. Sobel
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@param _src source image (RGB, float, in [0;1]) to detect edges
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@param _dst destination image (grayscale, float, in [0;1]) where edges are drawn
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@sa Sobel, Canny
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*/
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CV_WRAP virtual void detectEdges(cv::InputArray _src, cv::OutputArray _dst) const = 0;
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/** @brief The function computes orientation from edge image.
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@param _src edge image.
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@param _dst orientation image.
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*/
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CV_WRAP virtual void computeOrientation(cv::InputArray _src, cv::OutputArray _dst) const = 0;
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/** @brief The function edgenms in edge image and suppress edges where edge is stronger in orthogonal direction.
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@param edge_image edge image from detectEdges function.
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@param orientation_image orientation image from computeOrientation function.
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@param _dst suppressed image (grayscale, float, in [0;1])
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@param r radius for NMS suppression.
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@param s radius for boundary suppression.
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@param m multiplier for conservative suppression.
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@param isParallel enables/disables parallel computing.
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*/
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CV_WRAP virtual void edgesNms(cv::InputArray edge_image, cv::InputArray orientation_image, cv::OutputArray _dst, int r = 2, int s = 0, float m = 1, bool isParallel = true) const = 0;
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};
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/*!
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* The only constructor
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*
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* \param model : name of the file where the model is stored
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* \param howToGetFeatures : optional object inheriting from RFFeatureGetter.
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* You need it only if you would like to train your
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* own forest, pass NULL otherwise
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*/
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CV_EXPORTS_W Ptr<StructuredEdgeDetection> createStructuredEdgeDetection(const String &model,
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Ptr<const RFFeatureGetter> howToGetFeatures = Ptr<RFFeatureGetter>());
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//! @}
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}
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}
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#endif
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#endif /* __OPENCV_STRUCTURED_EDGE_DETECTION_HPP__ */
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