Chiebot-Mirror
/
opencv
8
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity
Open Source Computer Vision Library https://opencv.org/
You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
1826 Commits
6 Branches
128 Tags
3.0 GiB
 
 
 
 
 
 
Tag: Branch: Tree: 0f6b8002dc
Branches Tags
${ item.name }
Create tag ${ searchTerm }
Create branch ${ searchTerm }
from '0f6b8002dc'
${ noResults }
opencv/modules/objdetect/include/opencv2/objdetect/objdetect.hpp

711 lines
27 KiB
Raw Blame History

/*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"
#include "opencv2/features2d/features2d.hpp"
#ifdef __cplusplus
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
{
unsigned int x;
unsigned int y;
unsigned 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
// xp - auxillary parameter for internal use
// size of row in feature vectors
// (yp = (int) (p / xp); p = xp * yp)
typedef struct{
CvLSVMFilterPosition V;
float fineFunction[4];
unsigned int sizeX;
unsigned int sizeY;
unsigned int p;
unsigned int xp;
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 ////////////////////////////
CV_EXPORTS_W void groupRectangles(vector<Rect>& rectList, int groupThreshold, double eps=0.2);
CV_EXPORTS_W void groupRectangles(vector<Rect>& rectList, CV_OUT vector<int>& weights, int groupThreshold, double eps=0.2);
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 };
virtual ~FeatureEvaluator();
virtual bool read(const FileNode& node);
virtual Ptr<FeatureEvaluator> clone() const;
virtual int getFeatureType() const;
virtual bool setImage(const Mat&, 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();
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 struct 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>&, const Mat& );
virtual int runAt( Ptr<FeatureEvaluator>&, Point, 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;
};
void CV_EXPORTS_W groupRectangles( vector<Rect>& rectList, int groupThreshold, double eps, vector<int>* weights, vector<double>* levelWeights );
//////////////// HOG (Histogram-of-Oriented-Gradients) Descriptor and Object Detector //////////////
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(const vector<float>& _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,
vector<double>& weights,
double hitThreshold=0, Size winStride=Size(),
Size padding=Size(),
const vector<Point>& searchLocations=vector<Point>()) const;
//without found weights output
CV_WRAP 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,
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
CV_WRAP 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;
static vector<float> getDefaultPeopleDetector();
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;
};
/****************************************************************************************\
* Planar Object Detection *
\****************************************************************************************/
class CV_EXPORTS PlanarObjectDetector
{
public:
PlanarObjectDetector();
PlanarObjectDetector(const FileNode& node);
PlanarObjectDetector(const vector<Mat>& pyr, int _npoints=300,
int _patchSize=FernClassifier::PATCH_SIZE,
int _nstructs=FernClassifier::DEFAULT_STRUCTS,
int _structSize=FernClassifier::DEFAULT_STRUCT_SIZE,
int _nviews=FernClassifier::DEFAULT_VIEWS,
const LDetector& detector=LDetector(),
const PatchGenerator& patchGenerator=PatchGenerator());
virtual ~PlanarObjectDetector();
virtual void train(const vector<Mat>& pyr, int _npoints=300,
int _patchSize=FernClassifier::PATCH_SIZE,
int _nstructs=FernClassifier::DEFAULT_STRUCTS,
int _structSize=FernClassifier::DEFAULT_STRUCT_SIZE,
int _nviews=FernClassifier::DEFAULT_VIEWS,
const LDetector& detector=LDetector(),
const PatchGenerator& patchGenerator=PatchGenerator());
virtual void train(const vector<Mat>& pyr, const vector<KeyPoint>& keypoints,
int _patchSize=FernClassifier::PATCH_SIZE,
int _nstructs=FernClassifier::DEFAULT_STRUCTS,
int _structSize=FernClassifier::DEFAULT_STRUCT_SIZE,
int _nviews=FernClassifier::DEFAULT_VIEWS,
const LDetector& detector=LDetector(),
const PatchGenerator& patchGenerator=PatchGenerator());
Rect getModelROI() const;
vector<KeyPoint> getModelPoints() const;
const LDetector& getDetector() const;
const FernClassifier& getClassifier() const;
void setVerbose(bool verbose);
void read(const FileNode& node);
void write(FileStorage& fs, const String& name=String()) const;
bool operator()(const Mat& image, CV_OUT Mat& H, CV_OUT vector<Point2f>& corners) const;
bool operator()(const vector<Mat>& pyr, const vector<KeyPoint>& keypoints,
CV_OUT Mat& H, CV_OUT vector<Point2f>& corners,
CV_OUT vector<int>* pairs=0) const;
protected:
bool verbose;
Rect modelROI;
vector<KeyPoint> modelPoints;
LDetector ldetector;
FernClassifier fernClassifier;
};
/****************************************************************************************\
* Dominant Orientation Templates *
\****************************************************************************************/
class CV_EXPORTS DOTDetector
{
public:
struct CV_EXPORTS TrainParams
{
enum { BIN_COUNT = 7 };
static double BIN_RANGE() { return 180.0 / BIN_COUNT; }
TrainParams();
TrainParams( const Size& winSize, int regionSize=7, int minMagnitude=60,
int maxStrongestCount=7, int maxNonzeroBits=6,
float minRatio=0.85f );
void read( FileNode& fn );
void write( FileStorage& fs ) const;
void isConsistent() const;
Size winSize;
int regionSize;
int minMagnitude;
int maxStrongestCount;
int maxNonzeroBits;
float minRatio;
};
struct CV_EXPORTS DetectParams
{
DetectParams();
DetectParams( float minRatio, int minRegionSize, int maxRegionSize, int regionSizeStep,
bool isGroup, int groupThreshold=3, double groupEps=0.2f );
void isConsistent( float minTrainRatio=1.f ) const;
float minRatio;
int minRegionSize;
int maxRegionSize;
int regionSizeStep;
bool isGroup;
int groupThreshold;
double groupEps;
};
struct CV_EXPORTS DOTTemplate
{
struct CV_EXPORTS TrainData
{
TrainData();
TrainData( const Mat& maskedImage, const cv::Mat& strongestGradientsMask );
cv::Mat maskedImage;
cv::Mat strongestGradientsMask;
};
DOTTemplate();
DOTTemplate( const cv::Mat& quantizedImage, int objectClassID,
const cv::Mat& maskedImage=cv::Mat(), const cv::Mat& strongestGradientsMask=cv::Mat() );
void addObjectClassID( int objectClassID, const cv::Mat& maskedImage=cv::Mat(), const cv::Mat& strongestGradientsMask=cv::Mat() );
const TrainData* getTrainData( int objectClassID ) const;
static float computeTexturelessRatio( const cv::Mat& quantizedImage );
void read( FileNode& fn );
void write( FileStorage& fs ) const;
cv::Mat quantizedImage;
float texturelessRatio;
std::vector<int> objectClassIDs;
std::vector<TrainData> trainData;
};
DOTDetector();
DOTDetector( const std::string& filename ); // load from xml-file
virtual ~DOTDetector();
void clear();
void read( FileNode& fn );
void write( FileStorage& fs ) const;
void load( const std::string& filename );
void save( const std::string& filename ) const;
void train( const string& baseDirName, const TrainParams& trainParams=TrainParams(), bool isAddImageAndGradientMask=false );
void detectMultiScale( const Mat& image, vector<vector<Rect> >& rects, const DetectParams& detectParams=DetectParams(),
vector<vector<float> >* ratios=0, vector<vector<int> >* dotTemplateIndices=0 ) const;
const vector<DOTTemplate>& getDOTTemplates() const;
const vector<string>& getObjectClassNames() const;
static void groupRectanglesList( std::vector<std::vector<cv::Rect> >& rectList, int groupThreshold, double eps );
protected:
void detectQuantized( const Mat& queryQuantizedImage, float minRatio,
vector<vector<Rect> >& rects,
vector<vector<float> >& ratios,
vector<vector<int> >& dotTemplateIndices ) const;
TrainParams trainParams;
std::vector<std::string> objectClassNames;
std::vector<DOTTemplate> dotTemplates;
};
}
/****************************************************************************************\
* Datamatrix *
\****************************************************************************************/
typedef unsigned char uint8;
class CV_EXPORTS DataMatrixCode {
public:
char msg[4];
CvMat *original;
CvMat *corners;
};
#include <deque>
CV_EXPORTS std::deque<DataMatrixCode> cvFindDataMatrix(CvMat *im);
#endif
#endif