/*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.hpp" #ifdef __cplusplus #include #include 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& rejectLevels, std::vector& 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 std::vector& filenames, const std::vector& classNames=std::vector() ); virtual ~LatentSvmDetector(); virtual void clear(); virtual bool empty() const; bool load( const std::vector& filenames, const std::vector& classNames=std::vector() ); virtual void detect( const Mat& image, std::vector& objectDetections, float overlapThreshold=0.5f, int numThreads=-1 ); const std::vector& getClassNames() const; size_t getClassCount() const; private: std::vector detectors; std::vector classNames; }; CV_EXPORTS void groupRectangles(CV_OUT CV_IN_OUT std::vector& rectList, int groupThreshold, double eps=0.2); CV_EXPORTS_W void groupRectangles(CV_OUT CV_IN_OUT std::vector& rectList, CV_OUT std::vector& weights, int groupThreshold, double eps=0.2); CV_EXPORTS void groupRectangles( std::vector& rectList, int groupThreshold, double eps, std::vector* weights, std::vector* levelWeights ); CV_EXPORTS void groupRectangles(std::vector& rectList, std::vector& rejectLevels, std::vector& levelWeights, int groupThreshold, double eps=0.2); CV_EXPORTS void groupRectangles_meanshift(std::vector& rectList, std::vector& foundWeights, std::vector& 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 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 create(int type); }; template<> CV_EXPORTS void Ptr::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 std::string& filename ); virtual ~CascadeClassifier(); CV_WRAP virtual bool empty() const; CV_WRAP bool load( const std::string& filename ); virtual bool read( const FileNode& node ); CV_WRAP virtual void detectMultiScale( const Mat& image, CV_OUT std::vector& 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 std::vector& objects, std::vector& rejectLevels, std::vector& 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, std::vector& candidates ); virtual bool detectSingleScale( const Mat& image, int stripCount, Size processingRectSize, int stripSize, int yStep, double factor, std::vector& candidates, std::vector& rejectLevels, std::vector& 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 friend int predictOrdered( CascadeClassifier& cascade, Ptr &featureEvaluator, double& weight); template friend int predictCategorical( CascadeClassifier& cascade, Ptr &featureEvaluator, double& weight); template friend int predictOrderedStump( CascadeClassifier& cascade, Ptr &featureEvaluator, double& weight); template friend int predictCategoricalStump( CascadeClassifier& cascade, Ptr &featureEvaluator, double& weight); bool setImage( Ptr& feval, const Mat& image); virtual int runAt( Ptr& 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; std::vector stages; std::vector classifiers; std::vector nodes; std::vector leaves; std::vector subsets; }; Data data; Ptr featureEvaluator; Ptr 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); Ptr getMaskGenerator(); void setFaceDetectionMaskGenerator(); protected: Ptr 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 std::vector locations; // vector that will contain confidence values for each location std::vector 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 std::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 std::string& objname) const; CV_WRAP virtual bool load(const std::string& filename, const std::string& objname=std::string()); CV_WRAP virtual void save(const std::string& filename, const std::string& objname=std::string()) const; virtual void copyTo(HOGDescriptor& c) const; CV_WRAP virtual void compute(const Mat& img, CV_OUT std::vector& descriptors, Size winStride=Size(), Size padding=Size(), const std::vector& locations=std::vector()) const; //with found weights output CV_WRAP virtual void detect(const Mat& img, CV_OUT std::vector& foundLocations, CV_OUT std::vector& weights, double hitThreshold=0, Size winStride=Size(), Size padding=Size(), const std::vector& searchLocations=std::vector()) const; //without found weights output virtual void detect(const Mat& img, CV_OUT std::vector& foundLocations, double hitThreshold=0, Size winStride=Size(), Size padding=Size(), const std::vector& searchLocations=std::vector()) const; //with result weights output CV_WRAP virtual void detectMultiScale(const Mat& img, CV_OUT std::vector& foundLocations, CV_OUT std::vector& 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 std::vector& 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 std::vector getDefaultPeopleDetector(); CV_WRAP static std::vector 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 std::vector svmDetector; CV_PROP int nlevels; // evaluate specified ROI and return confidence value for each location virtual void detectROI(const cv::Mat& img, const std::vector &locations, CV_OUT std::vector& foundLocations, CV_OUT std::vector& 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 virtual void detectMultiScaleROI(const cv::Mat& img, CV_OUT std::vector& foundLocations, std::vector& locations, double hitThreshold = 0, int groupThreshold = 0) const; // read/parse Dalal's alt model file void readALTModel(std::string modelfile); }; CV_EXPORTS_W void findDataMatrix(InputArray image, CV_OUT std::vector& codes, OutputArray corners=noArray(), OutputArrayOfArrays dmtx=noArray()); CV_EXPORTS_W void drawDataMatrixCodes(InputOutputArray image, const std::vector& codes, InputArray corners); } /****************************************************************************************\ * Datamatrix * \****************************************************************************************/ struct CV_EXPORTS CvDataMatrixCode { char msg[4]; CvMat *original; CvMat *corners; }; CV_EXPORTS std::deque cvFindDataMatrix(CvMat *im); /****************************************************************************************\ * LINE-MOD * \****************************************************************************************/ namespace cv { namespace linemod { /// @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 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& candidates, std::vector& 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 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 create(const std::string& modality_type); /** * \brief Load a modality from file. */ static Ptr create(const FileNode& fn); protected: // Indirection is because process() has a default parameter. virtual Ptr 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 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 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 >& modalities, const std::vector& 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 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& sources, float threshold, std::vector& matches, const std::vector& class_ids = std::vector(), OutputArrayOfArrays quantized_images = noArray(), const std::vector& masks = std::vector()) 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& 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