diff options
Diffstat (limited to '2.3-1/thirdparty/raspberrypi/includes/opencv2/objdetect/objdetect.hpp')
| -rw-r--r-- | 2.3-1/thirdparty/raspberrypi/includes/opencv2/objdetect/objdetect.hpp | 1073 |
1 files changed, 0 insertions, 1073 deletions
diff --git a/2.3-1/thirdparty/raspberrypi/includes/opencv2/objdetect/objdetect.hpp b/2.3-1/thirdparty/raspberrypi/includes/opencv2/objdetect/objdetect.hpp deleted file mode 100644 index d5d6f0b2..00000000 --- a/2.3-1/thirdparty/raspberrypi/includes/opencv2/objdetect/objdetect.hpp +++ /dev/null @@ -1,1073 +0,0 @@ -/*M/////////////////////////////////////////////////////////////////////////////////////// -// -// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. -// -// By downloading, copying, installing or using the software you agree to this license. -// If you do not agree to this license, do not download, install, -// copy or use the software. -// -// -// License Agreement -// For Open Source Computer Vision Library -// -// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. -// Copyright (C) 2009, Willow Garage Inc., all rights reserved. -// Third party copyrights are property of their respective owners. -// -// Redistribution and use in source and binary forms, with or without modification, -// are permitted provided that the following conditions are met: -// -// * Redistribution's of source code must retain the above copyright notice, -// this list of conditions and the following disclaimer. -// -// * Redistribution's in binary form must reproduce the above copyright notice, -// this list of conditions and the following disclaimer in the documentation -// and/or other materials provided with the distribution. -// -// * The name of the copyright holders may not be used to endorse or promote products -// derived from this software without specific prior written permission. -// -// This software is provided by the copyright holders and contributors "as is" and -// any express or implied warranties, including, but not limited to, the implied -// warranties of merchantability and fitness for a particular purpose are disclaimed. -// In no event shall the Intel Corporation or contributors be liable for any direct, -// indirect, incidental, special, exemplary, or consequential damages -// (including, but not limited to, procurement of substitute goods or services; -// loss of use, data, or profits; or business interruption) however caused -// and on any theory of liability, whether in contract, strict liability, -// or tort (including negligence or otherwise) arising in any way out of -// the use of this software, even if advised of the possibility of such damage. -// -//M*/ - -#ifndef __OPENCV_OBJDETECT_HPP__ -#define __OPENCV_OBJDETECT_HPP__ - -#include "opencv2/core/core.hpp" - -#ifdef __cplusplus -#include <map> -#include <deque> - -extern "C" { -#endif - -/****************************************************************************************\ -* Haar-like Object Detection functions * -\****************************************************************************************/ - -#define CV_HAAR_MAGIC_VAL 0x42500000 -#define CV_TYPE_NAME_HAAR "opencv-haar-classifier" - -#define CV_IS_HAAR_CLASSIFIER( haar ) \ - ((haar) != NULL && \ - (((const CvHaarClassifierCascade*)(haar))->flags & CV_MAGIC_MASK)==CV_HAAR_MAGIC_VAL) - -#define CV_HAAR_FEATURE_MAX 3 - -typedef struct CvHaarFeature -{ - int tilted; - struct - { - CvRect r; - float weight; - } rect[CV_HAAR_FEATURE_MAX]; -} CvHaarFeature; - -typedef struct CvHaarClassifier -{ - int count; - CvHaarFeature* haar_feature; - float* threshold; - int* left; - int* right; - float* alpha; -} CvHaarClassifier; - -typedef struct CvHaarStageClassifier -{ - int count; - float threshold; - CvHaarClassifier* classifier; - - int next; - int child; - int parent; -} CvHaarStageClassifier; - -typedef struct CvHidHaarClassifierCascade CvHidHaarClassifierCascade; - -typedef struct CvHaarClassifierCascade -{ - int flags; - int count; - CvSize orig_window_size; - CvSize real_window_size; - double scale; - CvHaarStageClassifier* stage_classifier; - CvHidHaarClassifierCascade* hid_cascade; -} CvHaarClassifierCascade; - -typedef struct CvAvgComp -{ - CvRect rect; - int neighbors; -} CvAvgComp; - -/* Loads haar classifier cascade from a directory. - It is obsolete: convert your cascade to xml and use cvLoad instead */ -CVAPI(CvHaarClassifierCascade*) cvLoadHaarClassifierCascade( - const char* directory, CvSize orig_window_size); - -CVAPI(void) cvReleaseHaarClassifierCascade( CvHaarClassifierCascade** cascade ); - -#define CV_HAAR_DO_CANNY_PRUNING 1 -#define CV_HAAR_SCALE_IMAGE 2 -#define CV_HAAR_FIND_BIGGEST_OBJECT 4 -#define CV_HAAR_DO_ROUGH_SEARCH 8 - -//CVAPI(CvSeq*) cvHaarDetectObjectsForROC( const CvArr* image, -// CvHaarClassifierCascade* cascade, CvMemStorage* storage, -// CvSeq** rejectLevels, CvSeq** levelWeightds, -// double scale_factor CV_DEFAULT(1.1), -// int min_neighbors CV_DEFAULT(3), int flags CV_DEFAULT(0), -// CvSize min_size CV_DEFAULT(cvSize(0,0)), CvSize max_size CV_DEFAULT(cvSize(0,0)), -// bool outputRejectLevels = false ); - - -CVAPI(CvSeq*) cvHaarDetectObjects( const CvArr* image, - CvHaarClassifierCascade* cascade, CvMemStorage* storage, - double scale_factor CV_DEFAULT(1.1), - int min_neighbors CV_DEFAULT(3), int flags CV_DEFAULT(0), - CvSize min_size CV_DEFAULT(cvSize(0,0)), CvSize max_size CV_DEFAULT(cvSize(0,0))); - -/* sets images for haar classifier cascade */ -CVAPI(void) cvSetImagesForHaarClassifierCascade( CvHaarClassifierCascade* cascade, - const CvArr* sum, const CvArr* sqsum, - const CvArr* tilted_sum, double scale ); - -/* runs the cascade on the specified window */ -CVAPI(int) cvRunHaarClassifierCascade( const CvHaarClassifierCascade* cascade, - CvPoint pt, int start_stage CV_DEFAULT(0)); - - -/****************************************************************************************\ -* Latent SVM Object Detection functions * -\****************************************************************************************/ - -// DataType: STRUCT position -// Structure describes the position of the filter in the feature pyramid -// l - level in the feature pyramid -// (x, y) - coordinate in level l -typedef struct CvLSVMFilterPosition -{ - int x; - int y; - int l; -} CvLSVMFilterPosition; - -// DataType: STRUCT filterObject -// Description of the filter, which corresponds to the part of the object -// V - ideal (penalty = 0) position of the partial filter -// from the root filter position (V_i in the paper) -// penaltyFunction - vector describes penalty function (d_i in the paper) -// pf[0] * x + pf[1] * y + pf[2] * x^2 + pf[3] * y^2 -// FILTER DESCRIPTION -// Rectangular map (sizeX x sizeY), -// every cell stores feature vector (dimension = p) -// H - matrix of feature vectors -// to set and get feature vectors (i,j) -// used formula H[(j * sizeX + i) * p + k], where -// k - component of feature vector in cell (i, j) -// END OF FILTER DESCRIPTION -typedef struct CvLSVMFilterObject{ - CvLSVMFilterPosition V; - float fineFunction[4]; - int sizeX; - int sizeY; - int numFeatures; - float *H; -} CvLSVMFilterObject; - -// data type: STRUCT CvLatentSvmDetector -// structure contains internal representation of trained Latent SVM detector -// num_filters - total number of filters (root plus part) in model -// num_components - number of components in model -// num_part_filters - array containing number of part filters for each component -// filters - root and part filters for all model components -// b - biases for all model components -// score_threshold - confidence level threshold -typedef struct CvLatentSvmDetector -{ - int num_filters; - int num_components; - int* num_part_filters; - CvLSVMFilterObject** filters; - float* b; - float score_threshold; -} -CvLatentSvmDetector; - -// data type: STRUCT CvObjectDetection -// structure contains the bounding box and confidence level for detected object -// rect - bounding box for a detected object -// score - confidence level -typedef struct CvObjectDetection -{ - CvRect rect; - float score; -} CvObjectDetection; - -//////////////// Object Detection using Latent SVM ////////////// - - -/* -// load trained detector from a file -// -// API -// CvLatentSvmDetector* cvLoadLatentSvmDetector(const char* filename); -// INPUT -// filename - path to the file containing the parameters of - - trained Latent SVM detector -// OUTPUT -// trained Latent SVM detector in internal representation -*/ -CVAPI(CvLatentSvmDetector*) cvLoadLatentSvmDetector(const char* filename); - -/* -// release memory allocated for CvLatentSvmDetector structure -// -// API -// void cvReleaseLatentSvmDetector(CvLatentSvmDetector** detector); -// INPUT -// detector - CvLatentSvmDetector structure to be released -// OUTPUT -*/ -CVAPI(void) cvReleaseLatentSvmDetector(CvLatentSvmDetector** detector); - -/* -// find rectangular regions in the given image that are likely -// to contain objects and corresponding confidence levels -// -// API -// CvSeq* cvLatentSvmDetectObjects(const IplImage* image, -// CvLatentSvmDetector* detector, -// CvMemStorage* storage, -// float overlap_threshold = 0.5f, -// int numThreads = -1); -// INPUT -// image - image to detect objects in -// detector - Latent SVM detector in internal representation -// storage - memory storage to store the resultant sequence -// of the object candidate rectangles -// overlap_threshold - threshold for the non-maximum suppression algorithm - = 0.5f [here will be the reference to original paper] -// OUTPUT -// sequence of detected objects (bounding boxes and confidence levels stored in CvObjectDetection structures) -*/ -CVAPI(CvSeq*) cvLatentSvmDetectObjects(IplImage* image, - CvLatentSvmDetector* detector, - CvMemStorage* storage, - float overlap_threshold CV_DEFAULT(0.5f), - int numThreads CV_DEFAULT(-1)); - -#ifdef __cplusplus -} - -CV_EXPORTS CvSeq* cvHaarDetectObjectsForROC( const CvArr* image, - CvHaarClassifierCascade* cascade, CvMemStorage* storage, - std::vector<int>& rejectLevels, std::vector<double>& levelWeightds, - double scale_factor CV_DEFAULT(1.1), - int min_neighbors CV_DEFAULT(3), int flags CV_DEFAULT(0), - CvSize min_size CV_DEFAULT(cvSize(0,0)), CvSize max_size CV_DEFAULT(cvSize(0,0)), - bool outputRejectLevels = false ); - -namespace cv -{ - -///////////////////////////// Object Detection //////////////////////////// - -/* - * This is a class wrapping up the structure CvLatentSvmDetector and functions working with it. - * The class goals are: - * 1) provide c++ interface; - * 2) make it possible to load and detect more than one class (model) unlike CvLatentSvmDetector. - */ -class CV_EXPORTS LatentSvmDetector -{ -public: - struct CV_EXPORTS ObjectDetection - { - ObjectDetection(); - ObjectDetection( const Rect& rect, float score, int classID=-1 ); - Rect rect; - float score; - int classID; - }; - - LatentSvmDetector(); - LatentSvmDetector( const vector<string>& filenames, const vector<string>& classNames=vector<string>() ); - virtual ~LatentSvmDetector(); - - virtual void clear(); - virtual bool empty() const; - bool load( const vector<string>& filenames, const vector<string>& classNames=vector<string>() ); - - virtual void detect( const Mat& image, - vector<ObjectDetection>& objectDetections, - float overlapThreshold=0.5f, - int numThreads=-1 ); - - const vector<string>& getClassNames() const; - size_t getClassCount() const; - -private: - vector<CvLatentSvmDetector*> detectors; - vector<string> classNames; -}; - -// class for grouping object candidates, detected by Cascade Classifier, HOG etc. -// instance of the class is to be passed to cv::partition (see cxoperations.hpp) -class CV_EXPORTS SimilarRects -{ -public: - SimilarRects(double _eps) : eps(_eps) {} - inline bool operator()(const Rect& r1, const Rect& r2) const - { - double delta = eps*(std::min(r1.width, r2.width) + std::min(r1.height, r2.height))*0.5; - return std::abs(r1.x - r2.x) <= delta && - std::abs(r1.y - r2.y) <= delta && - std::abs(r1.x + r1.width - r2.x - r2.width) <= delta && - std::abs(r1.y + r1.height - r2.y - r2.height) <= delta; - } - double eps; -}; - -CV_EXPORTS void groupRectangles(CV_OUT CV_IN_OUT vector<Rect>& rectList, int groupThreshold, double eps=0.2); -CV_EXPORTS_W void groupRectangles(CV_OUT CV_IN_OUT vector<Rect>& rectList, CV_OUT vector<int>& weights, int groupThreshold, double eps=0.2); -CV_EXPORTS void groupRectangles( vector<Rect>& rectList, int groupThreshold, double eps, vector<int>* weights, vector<double>* levelWeights ); -CV_EXPORTS void groupRectangles(vector<Rect>& rectList, vector<int>& rejectLevels, - vector<double>& levelWeights, int groupThreshold, double eps=0.2); -CV_EXPORTS void groupRectangles_meanshift(vector<Rect>& rectList, vector<double>& foundWeights, vector<double>& foundScales, - double detectThreshold = 0.0, Size winDetSize = Size(64, 128)); - - -class CV_EXPORTS FeatureEvaluator -{ -public: - enum { HAAR = 0, LBP = 1, HOG = 2 }; - virtual ~FeatureEvaluator(); - - virtual bool read(const FileNode& node); - virtual Ptr<FeatureEvaluator> clone() const; - virtual int getFeatureType() const; - - virtual bool setImage(const Mat& img, Size origWinSize); - virtual bool setWindow(Point p); - - virtual double calcOrd(int featureIdx) const; - virtual int calcCat(int featureIdx) const; - - static Ptr<FeatureEvaluator> create(int type); -}; - -template<> CV_EXPORTS void Ptr<CvHaarClassifierCascade>::delete_obj(); - -enum -{ - CASCADE_DO_CANNY_PRUNING=1, - CASCADE_SCALE_IMAGE=2, - CASCADE_FIND_BIGGEST_OBJECT=4, - CASCADE_DO_ROUGH_SEARCH=8 -}; - -class CV_EXPORTS_W CascadeClassifier -{ -public: - CV_WRAP CascadeClassifier(); - CV_WRAP CascadeClassifier( const string& filename ); - virtual ~CascadeClassifier(); - - CV_WRAP virtual bool empty() const; - CV_WRAP bool load( const string& filename ); - virtual bool read( const FileNode& node ); - CV_WRAP virtual void detectMultiScale( const Mat& image, - CV_OUT vector<Rect>& objects, - double scaleFactor=1.1, - int minNeighbors=3, int flags=0, - Size minSize=Size(), - Size maxSize=Size() ); - - CV_WRAP virtual void detectMultiScale( const Mat& image, - CV_OUT vector<Rect>& objects, - vector<int>& rejectLevels, - vector<double>& levelWeights, - double scaleFactor=1.1, - int minNeighbors=3, int flags=0, - Size minSize=Size(), - Size maxSize=Size(), - bool outputRejectLevels=false ); - - - bool isOldFormatCascade() const; - virtual Size getOriginalWindowSize() const; - int getFeatureType() const; - bool setImage( const Mat& ); - -protected: - //virtual bool detectSingleScale( const Mat& image, int stripCount, Size processingRectSize, - // int stripSize, int yStep, double factor, vector<Rect>& candidates ); - - virtual bool detectSingleScale( const Mat& image, int stripCount, Size processingRectSize, - int stripSize, int yStep, double factor, vector<Rect>& candidates, - vector<int>& rejectLevels, vector<double>& levelWeights, bool outputRejectLevels=false); - -protected: - enum { BOOST = 0 }; - enum { DO_CANNY_PRUNING = 1, SCALE_IMAGE = 2, - FIND_BIGGEST_OBJECT = 4, DO_ROUGH_SEARCH = 8 }; - - friend class CascadeClassifierInvoker; - - template<class FEval> - friend int predictOrdered( CascadeClassifier& cascade, Ptr<FeatureEvaluator> &featureEvaluator, double& weight); - - template<class FEval> - friend int predictCategorical( CascadeClassifier& cascade, Ptr<FeatureEvaluator> &featureEvaluator, double& weight); - - template<class FEval> - friend int predictOrderedStump( CascadeClassifier& cascade, Ptr<FeatureEvaluator> &featureEvaluator, double& weight); - - template<class FEval> - friend int predictCategoricalStump( CascadeClassifier& cascade, Ptr<FeatureEvaluator> &featureEvaluator, double& weight); - - bool setImage( Ptr<FeatureEvaluator>& feval, const Mat& image); - virtual int runAt( Ptr<FeatureEvaluator>& feval, Point pt, double& weight ); - - class Data - { - public: - struct CV_EXPORTS DTreeNode - { - int featureIdx; - float threshold; // for ordered features only - int left; - int right; - }; - - struct CV_EXPORTS DTree - { - int nodeCount; - }; - - struct CV_EXPORTS Stage - { - int first; - int ntrees; - float threshold; - }; - - bool read(const FileNode &node); - - bool isStumpBased; - - int stageType; - int featureType; - int ncategories; - Size origWinSize; - - vector<Stage> stages; - vector<DTree> classifiers; - vector<DTreeNode> nodes; - vector<float> leaves; - vector<int> subsets; - }; - - Data data; - Ptr<FeatureEvaluator> featureEvaluator; - Ptr<CvHaarClassifierCascade> oldCascade; - -public: - class CV_EXPORTS MaskGenerator - { - public: - virtual ~MaskGenerator() {} - virtual cv::Mat generateMask(const cv::Mat& src)=0; - virtual void initializeMask(const cv::Mat& /*src*/) {}; - }; - void setMaskGenerator(Ptr<MaskGenerator> maskGenerator); - Ptr<MaskGenerator> getMaskGenerator(); - - void setFaceDetectionMaskGenerator(); - -protected: - Ptr<MaskGenerator> maskGenerator; -}; - - -//////////////// HOG (Histogram-of-Oriented-Gradients) Descriptor and Object Detector ////////////// - -// struct for detection region of interest (ROI) -struct DetectionROI -{ - // scale(size) of the bounding box - double scale; - // set of requrested locations to be evaluated - vector<cv::Point> locations; - // vector that will contain confidence values for each location - vector<double> confidences; -}; - -struct CV_EXPORTS_W HOGDescriptor -{ -public: - enum { L2Hys=0 }; - enum { DEFAULT_NLEVELS=64 }; - - CV_WRAP HOGDescriptor() : winSize(64,128), blockSize(16,16), blockStride(8,8), - cellSize(8,8), nbins(9), derivAperture(1), winSigma(-1), - histogramNormType(HOGDescriptor::L2Hys), L2HysThreshold(0.2), gammaCorrection(true), - nlevels(HOGDescriptor::DEFAULT_NLEVELS) - {} - - CV_WRAP HOGDescriptor(Size _winSize, Size _blockSize, Size _blockStride, - Size _cellSize, int _nbins, int _derivAperture=1, double _winSigma=-1, - int _histogramNormType=HOGDescriptor::L2Hys, - double _L2HysThreshold=0.2, bool _gammaCorrection=false, - int _nlevels=HOGDescriptor::DEFAULT_NLEVELS) - : winSize(_winSize), blockSize(_blockSize), blockStride(_blockStride), cellSize(_cellSize), - nbins(_nbins), derivAperture(_derivAperture), winSigma(_winSigma), - histogramNormType(_histogramNormType), L2HysThreshold(_L2HysThreshold), - gammaCorrection(_gammaCorrection), nlevels(_nlevels) - {} - - CV_WRAP HOGDescriptor(const String& filename) - { - load(filename); - } - - HOGDescriptor(const HOGDescriptor& d) - { - d.copyTo(*this); - } - - virtual ~HOGDescriptor() {} - - CV_WRAP size_t getDescriptorSize() const; - CV_WRAP bool checkDetectorSize() const; - CV_WRAP double getWinSigma() const; - - CV_WRAP virtual void setSVMDetector(InputArray _svmdetector); - - virtual bool read(FileNode& fn); - virtual void write(FileStorage& fs, const String& objname) const; - - CV_WRAP virtual bool load(const String& filename, const String& objname=String()); - CV_WRAP virtual void save(const String& filename, const String& objname=String()) const; - virtual void copyTo(HOGDescriptor& c) const; - - CV_WRAP virtual void compute(const Mat& img, - CV_OUT vector<float>& descriptors, - Size winStride=Size(), Size padding=Size(), - const vector<Point>& locations=vector<Point>()) const; - //with found weights output - CV_WRAP virtual void detect(const Mat& img, CV_OUT vector<Point>& foundLocations, - CV_OUT vector<double>& weights, - double hitThreshold=0, Size winStride=Size(), - Size padding=Size(), - const vector<Point>& searchLocations=vector<Point>()) const; - //without found weights output - virtual void detect(const Mat& img, CV_OUT vector<Point>& foundLocations, - double hitThreshold=0, Size winStride=Size(), - Size padding=Size(), - const vector<Point>& searchLocations=vector<Point>()) const; - //with result weights output - CV_WRAP virtual void detectMultiScale(const Mat& img, CV_OUT vector<Rect>& foundLocations, - CV_OUT vector<double>& foundWeights, double hitThreshold=0, - Size winStride=Size(), Size padding=Size(), double scale=1.05, - double finalThreshold=2.0,bool useMeanshiftGrouping = false) const; - //without found weights output - virtual void detectMultiScale(const Mat& img, CV_OUT vector<Rect>& foundLocations, - double hitThreshold=0, Size winStride=Size(), - Size padding=Size(), double scale=1.05, - double finalThreshold=2.0, bool useMeanshiftGrouping = false) const; - - CV_WRAP virtual void computeGradient(const Mat& img, CV_OUT Mat& grad, CV_OUT Mat& angleOfs, - Size paddingTL=Size(), Size paddingBR=Size()) const; - - CV_WRAP static vector<float> getDefaultPeopleDetector(); - CV_WRAP static vector<float> getDaimlerPeopleDetector(); - - CV_PROP Size winSize; - CV_PROP Size blockSize; - CV_PROP Size blockStride; - CV_PROP Size cellSize; - CV_PROP int nbins; - CV_PROP int derivAperture; - CV_PROP double winSigma; - CV_PROP int histogramNormType; - CV_PROP double L2HysThreshold; - CV_PROP bool gammaCorrection; - CV_PROP vector<float> svmDetector; - CV_PROP int nlevels; - - - // evaluate specified ROI and return confidence value for each location - void detectROI(const cv::Mat& img, const vector<cv::Point> &locations, - CV_OUT std::vector<cv::Point>& foundLocations, CV_OUT std::vector<double>& confidences, - double hitThreshold = 0, cv::Size winStride = Size(), - cv::Size padding = Size()) const; - - // evaluate specified ROI and return confidence value for each location in multiple scales - void detectMultiScaleROI(const cv::Mat& img, - CV_OUT std::vector<cv::Rect>& foundLocations, - std::vector<DetectionROI>& locations, - double hitThreshold = 0, - int groupThreshold = 0) const; - - // read/parse Dalal's alt model file - void readALTModel(std::string modelfile); - void groupRectangles(vector<cv::Rect>& rectList, vector<double>& weights, int groupThreshold, double eps) const; -}; - - -CV_EXPORTS_W void findDataMatrix(InputArray image, - CV_OUT vector<string>& codes, - OutputArray corners=noArray(), - OutputArrayOfArrays dmtx=noArray()); -CV_EXPORTS_W void drawDataMatrixCodes(InputOutputArray image, - const vector<string>& codes, - InputArray corners); -} - -/****************************************************************************************\ -* Datamatrix * -\****************************************************************************************/ - -struct CV_EXPORTS CvDataMatrixCode { - char msg[4]; - CvMat *original; - CvMat *corners; -}; - -CV_EXPORTS std::deque<CvDataMatrixCode> cvFindDataMatrix(CvMat *im); - -/****************************************************************************************\ -* LINE-MOD * -\****************************************************************************************/ - -namespace cv { -namespace linemod { - -using cv::FileNode; -using cv::FileStorage; -using cv::Mat; -using cv::noArray; -using cv::OutputArrayOfArrays; -using cv::Point; -using cv::Ptr; -using cv::Rect; -using cv::Size; - -/// @todo Convert doxy comments to rst - -/** - * \brief Discriminant feature described by its location and label. - */ -struct CV_EXPORTS Feature -{ - int x; ///< x offset - int y; ///< y offset - int label; ///< Quantization - - Feature() : x(0), y(0), label(0) {} - Feature(int x, int y, int label); - - void read(const FileNode& fn); - void write(FileStorage& fs) const; -}; - -inline Feature::Feature(int _x, int _y, int _label) : x(_x), y(_y), label(_label) {} - -struct CV_EXPORTS Template -{ - int width; - int height; - int pyramid_level; - std::vector<Feature> features; - - void read(const FileNode& fn); - void write(FileStorage& fs) const; -}; - -/** - * \brief Represents a modality operating over an image pyramid. - */ -class QuantizedPyramid -{ -public: - // Virtual destructor - virtual ~QuantizedPyramid() {} - - /** - * \brief Compute quantized image at current pyramid level for online detection. - * - * \param[out] dst The destination 8-bit image. For each pixel at most one bit is set, - * representing its classification. - */ - virtual void quantize(Mat& dst) const =0; - - /** - * \brief Extract most discriminant features at current pyramid level to form a new template. - * - * \param[out] templ The new template. - */ - virtual bool extractTemplate(Template& templ) const =0; - - /** - * \brief Go to the next pyramid level. - * - * \todo Allow pyramid scale factor other than 2 - */ - virtual void pyrDown() =0; - -protected: - /// Candidate feature with a score - struct Candidate - { - Candidate(int x, int y, int label, float score); - - /// Sort candidates with high score to the front - bool operator<(const Candidate& rhs) const - { - return score > rhs.score; - } - - Feature f; - float score; - }; - - /** - * \brief Choose candidate features so that they are not bunched together. - * - * \param[in] candidates Candidate features sorted by score. - * \param[out] features Destination vector of selected features. - * \param[in] num_features Number of candidates to select. - * \param[in] distance Hint for desired distance between features. - */ - static void selectScatteredFeatures(const std::vector<Candidate>& candidates, - std::vector<Feature>& features, - size_t num_features, float distance); -}; - -inline QuantizedPyramid::Candidate::Candidate(int x, int y, int label, float _score) : f(x, y, label), score(_score) {} - -/** - * \brief Interface for modalities that plug into the LINE template matching representation. - * - * \todo Max response, to allow optimization of summing (255/MAX) features as uint8 - */ -class CV_EXPORTS Modality -{ -public: - // Virtual destructor - virtual ~Modality() {} - - /** - * \brief Form a quantized image pyramid from a source image. - * - * \param[in] src The source image. Type depends on the modality. - * \param[in] mask Optional mask. If not empty, unmasked pixels are set to zero - * in quantized image and cannot be extracted as features. - */ - Ptr<QuantizedPyramid> process(const Mat& src, - const Mat& mask = Mat()) const - { - return processImpl(src, mask); - } - - virtual std::string name() const =0; - - virtual void read(const FileNode& fn) =0; - virtual void write(FileStorage& fs) const =0; - - /** - * \brief Create modality by name. - * - * The following modality types are supported: - * - "ColorGradient" - * - "DepthNormal" - */ - static Ptr<Modality> create(const std::string& modality_type); - - /** - * \brief Load a modality from file. - */ - static Ptr<Modality> create(const FileNode& fn); - -protected: - // Indirection is because process() has a default parameter. - virtual Ptr<QuantizedPyramid> processImpl(const Mat& src, - const Mat& mask) const =0; -}; - -/** - * \brief Modality that computes quantized gradient orientations from a color image. - */ -class CV_EXPORTS ColorGradient : public Modality -{ -public: - /** - * \brief Default constructor. Uses reasonable default parameter values. - */ - ColorGradient(); - - /** - * \brief Constructor. - * - * \param weak_threshold When quantizing, discard gradients with magnitude less than this. - * \param num_features How many features a template must contain. - * \param strong_threshold Consider as candidate features only gradients whose norms are - * larger than this. - */ - ColorGradient(float weak_threshold, size_t num_features, float strong_threshold); - - virtual std::string name() const; - - virtual void read(const FileNode& fn); - virtual void write(FileStorage& fs) const; - - float weak_threshold; - size_t num_features; - float strong_threshold; - -protected: - virtual Ptr<QuantizedPyramid> processImpl(const Mat& src, - const Mat& mask) const; -}; - -/** - * \brief Modality that computes quantized surface normals from a dense depth map. - */ -class CV_EXPORTS DepthNormal : public Modality -{ -public: - /** - * \brief Default constructor. Uses reasonable default parameter values. - */ - DepthNormal(); - - /** - * \brief Constructor. - * - * \param distance_threshold Ignore pixels beyond this distance. - * \param difference_threshold When computing normals, ignore contributions of pixels whose - * depth difference with the central pixel is above this threshold. - * \param num_features How many features a template must contain. - * \param extract_threshold Consider as candidate feature only if there are no differing - * orientations within a distance of extract_threshold. - */ - DepthNormal(int distance_threshold, int difference_threshold, size_t num_features, - int extract_threshold); - - virtual std::string name() const; - - virtual void read(const FileNode& fn); - virtual void write(FileStorage& fs) const; - - int distance_threshold; - int difference_threshold; - size_t num_features; - int extract_threshold; - -protected: - virtual Ptr<QuantizedPyramid> processImpl(const Mat& src, - const Mat& mask) const; -}; - -/** - * \brief Debug function to colormap a quantized image for viewing. - */ -void colormap(const Mat& quantized, Mat& dst); - -/** - * \brief Represents a successful template match. - */ -struct CV_EXPORTS Match -{ - Match() - { - } - - Match(int x, int y, float similarity, const std::string& class_id, int template_id); - - /// Sort matches with high similarity to the front - bool operator<(const Match& rhs) const - { - // Secondarily sort on template_id for the sake of duplicate removal - if (similarity != rhs.similarity) - return similarity > rhs.similarity; - else - return template_id < rhs.template_id; - } - - bool operator==(const Match& rhs) const - { - return x == rhs.x && y == rhs.y && similarity == rhs.similarity && class_id == rhs.class_id; - } - - int x; - int y; - float similarity; - std::string class_id; - int template_id; -}; - -inline Match::Match(int _x, int _y, float _similarity, const std::string& _class_id, int _template_id) - : x(_x), y(_y), similarity(_similarity), class_id(_class_id), template_id(_template_id) - { - } - -/** - * \brief Object detector using the LINE template matching algorithm with any set of - * modalities. - */ -class CV_EXPORTS Detector -{ -public: - /** - * \brief Empty constructor, initialize with read(). - */ - Detector(); - - /** - * \brief Constructor. - * - * \param modalities Modalities to use (color gradients, depth normals, ...). - * \param T_pyramid Value of the sampling step T at each pyramid level. The - * number of pyramid levels is T_pyramid.size(). - */ - Detector(const std::vector< Ptr<Modality> >& modalities, const std::vector<int>& T_pyramid); - - /** - * \brief Detect objects by template matching. - * - * Matches globally at the lowest pyramid level, then refines locally stepping up the pyramid. - * - * \param sources Source images, one for each modality. - * \param threshold Similarity threshold, a percentage between 0 and 100. - * \param[out] matches Template matches, sorted by similarity score. - * \param class_ids If non-empty, only search for the desired object classes. - * \param[out] quantized_images Optionally return vector<Mat> of quantized images. - * \param masks The masks for consideration during matching. The masks should be CV_8UC1 - * where 255 represents a valid pixel. If non-empty, the vector must be - * the same size as sources. Each element must be - * empty or the same size as its corresponding source. - */ - void match(const std::vector<Mat>& sources, float threshold, std::vector<Match>& matches, - const std::vector<std::string>& class_ids = std::vector<std::string>(), - OutputArrayOfArrays quantized_images = noArray(), - const std::vector<Mat>& masks = std::vector<Mat>()) const; - - /** - * \brief Add new object template. - * - * \param sources Source images, one for each modality. - * \param class_id Object class ID. - * \param object_mask Mask separating object from background. - * \param[out] bounding_box Optionally return bounding box of the extracted features. - * - * \return Template ID, or -1 if failed to extract a valid template. - */ - int addTemplate(const std::vector<Mat>& sources, const std::string& class_id, - const Mat& object_mask, Rect* bounding_box = NULL); - - /** - * \brief Add a new object template computed by external means. - */ - int addSyntheticTemplate(const std::vector<Template>& templates, const std::string& class_id); - - /** - * \brief Get the modalities used by this detector. - * - * You are not permitted to add/remove modalities, but you may dynamic_cast them to - * tweak parameters. - */ - const std::vector< Ptr<Modality> >& getModalities() const { return modalities; } - - /** - * \brief Get sampling step T at pyramid_level. - */ - int getT(int pyramid_level) const { return T_at_level[pyramid_level]; } - - /** - * \brief Get number of pyramid levels used by this detector. - */ - int pyramidLevels() const { return pyramid_levels; } - - /** - * \brief Get the template pyramid identified by template_id. - * - * For example, with 2 modalities (Gradient, Normal) and two pyramid levels - * (L0, L1), the order is (GradientL0, NormalL0, GradientL1, NormalL1). - */ - const std::vector<Template>& getTemplates(const std::string& class_id, int template_id) const; - - int numTemplates() const; - int numTemplates(const std::string& class_id) const; - int numClasses() const { return static_cast<int>(class_templates.size()); } - - std::vector<std::string> classIds() const; - - void read(const FileNode& fn); - void write(FileStorage& fs) const; - - std::string readClass(const FileNode& fn, const std::string &class_id_override = ""); - void writeClass(const std::string& class_id, FileStorage& fs) const; - - void readClasses(const std::vector<std::string>& class_ids, - const std::string& format = "templates_%s.yml.gz"); - void writeClasses(const std::string& format = "templates_%s.yml.gz") const; - -protected: - std::vector< Ptr<Modality> > modalities; - int pyramid_levels; - std::vector<int> T_at_level; - - typedef std::vector<Template> TemplatePyramid; - typedef std::map<std::string, std::vector<TemplatePyramid> > TemplatesMap; - TemplatesMap class_templates; - - typedef std::vector<Mat> LinearMemories; - // Indexed as [pyramid level][modality][quantized label] - typedef std::vector< std::vector<LinearMemories> > LinearMemoryPyramid; - - void matchClass(const LinearMemoryPyramid& lm_pyramid, - const std::vector<Size>& sizes, - float threshold, std::vector<Match>& matches, - const std::string& class_id, - const std::vector<TemplatePyramid>& template_pyramids) const; -}; - -/** - * \brief Factory function for detector using LINE algorithm with color gradients. - * - * Default parameter settings suitable for VGA images. - */ -CV_EXPORTS Ptr<Detector> getDefaultLINE(); - -/** - * \brief Factory function for detector using LINE-MOD algorithm with color gradients - * and depth normals. - * - * Default parameter settings suitable for VGA images. - */ -CV_EXPORTS Ptr<Detector> getDefaultLINEMOD(); - -} // namespace linemod -} // namespace cv - -#endif - -#endif |