move soft cascade functionality into dedicated module
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marina.kolpakova
149
modules/softcascade/include/opencv2/softcascade/softcascade.hpp
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149
modules/softcascade/include/opencv2/softcascade/softcascade.hpp
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//M*/
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#ifndef __OPENCV_SOFTCASCADE_HPP__
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#define __OPENCV_SOFTCASCADE_HPP__
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#include "opencv2/core/core.hpp"
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namespace cv {
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class CV_EXPORTS_W ICFPreprocessor
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{
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public:
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CV_WRAP ICFPreprocessor();
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CV_WRAP void apply(cv::InputArray _frame, cv::OutputArray _integrals) const;
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protected:
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enum {BINS = 10};
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};
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// Implementation of soft (stageless) cascaded detector.
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class CV_EXPORTS_W SCascade : public Algorithm
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{
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public:
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// Representation of detectors result.
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struct CV_EXPORTS Detection
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{
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// Default object type.
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enum {PEDESTRIAN = 1};
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// Creates Detection from an object bounding box and confidence.
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// Param b is a bounding box
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// Param c is a confidence that object belongs to class k
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// Param k is an object class
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Detection(const cv::Rect& b, const float c, int k = PEDESTRIAN) : bb(b), confidence(c), kind(k) {}
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cv::Rect bb;
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float confidence;
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int kind;
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};
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// Create channel integrals for Soft Cascade detector.
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class CV_EXPORTS Channels
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{
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public:
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// constrictor form resizing factor.
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// Param shrinkage is a resizing factor. Resize is applied before the computing integral sum
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Channels(const int shrinkage);
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// Appends specified number of HOG first-order features integrals into given vector.
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// Param gray is an input 1-channel gray image.
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// Param integrals is a vector of integrals. Hog-channels will be appended to it.
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// Param bins is a number of hog-bins
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void appendHogBins(const cv::Mat& gray, std::vector<cv::Mat>& integrals, int bins) const;
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// Converts 3-channel BGR input frame in Luv and appends each channel to the integrals.
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// Param frame is an input 3-channel BGR colored image.
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// Param integrals is a vector of integrals. Computed from the frame luv-channels will be appended to it.
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void appendLuvBins(const cv::Mat& frame, std::vector<cv::Mat>& integrals) const;
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private:
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int shrinkage;
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};
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enum { NO_REJECT = 1, DOLLAR = 2, /*PASCAL = 4,*/ DEFAULT = NO_REJECT};
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// An empty cascade will be created.
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// Param minScale is a minimum scale relative to the original size of the image on which cascade will be applied.
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// Param minScale is a maximum scale relative to the original size of the image on which cascade will be applied.
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// Param scales is a number of scales from minScale to maxScale.
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// Param rejCriteria is used for NMS.
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CV_WRAP SCascade(const double minScale = 0.4, const double maxScale = 5., const int scales = 55, const int rejCriteria = 1);
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CV_WRAP virtual ~SCascade();
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cv::AlgorithmInfo* info() const;
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// Load cascade from FileNode.
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// Param fn is a root node for cascade. Should be <cascade>.
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CV_WRAP virtual bool load(const FileNode& fn);
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// Load cascade config.
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CV_WRAP virtual void read(const FileNode& fn);
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// Return the vector of Detection objects.
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// Param image is a frame on which detector will be applied.
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// Param rois is a vector of regions of interest. Only the objects that fall into one of the regions will be returned.
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// Param objects is an output array of Detections
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virtual void detect(InputArray image, InputArray rois, std::vector<Detection>& objects) const;
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// Param rects is an output array of bounding rectangles for detected objects.
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// Param confs is an output array of confidence for detected objects. i-th bounding rectangle corresponds i-th confidence.
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CV_WRAP virtual void detect(InputArray image, InputArray rois, CV_OUT OutputArray rects, CV_OUT OutputArray confs) const;
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private:
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void detectNoRoi(const Mat& image, std::vector<Detection>& objects) const;
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struct Fields;
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Fields* fields;
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double minScale;
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double maxScale;
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int scales;
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int rejCriteria;
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};
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CV_EXPORTS bool initModule_softcascade(void);
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}
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#endif
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