Move C API of opencv_objdetect to separate file
Also move cv::linemod to own header
This commit is contained in:
@@ -7,11 +7,12 @@
|
||||
// copy or use the software.
|
||||
//
|
||||
//
|
||||
// License Agreement
|
||||
// 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.
|
||||
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
|
||||
// Third party copyrights are property of their respective owners.
|
||||
//
|
||||
// Redistribution and use in source and binary forms, with or without modification,
|
||||
@@ -43,248 +44,10 @@
|
||||
#ifndef __OPENCV_OBJDETECT_HPP__
|
||||
#define __OPENCV_OBJDETECT_HPP__
|
||||
|
||||
#ifdef __cplusplus
|
||||
# include "opencv2/core.hpp"
|
||||
#endif
|
||||
#include "opencv2/core/core_c.h"
|
||||
#include "opencv2/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 );
|
||||
typedef struct CvLatentSvmDetector CvLatentSvmDetector;
|
||||
typedef struct CvHaarClassifierCascade CvHaarClassifierCascade;
|
||||
|
||||
namespace cv
|
||||
{
|
||||
@@ -303,24 +66,24 @@ public:
|
||||
struct CV_EXPORTS ObjectDetection
|
||||
{
|
||||
ObjectDetection();
|
||||
ObjectDetection( const Rect& rect, float score, int classID=-1 );
|
||||
ObjectDetection( const Rect& rect, float score, int classID = -1 );
|
||||
Rect rect;
|
||||
float score;
|
||||
int classID;
|
||||
};
|
||||
|
||||
LatentSvmDetector();
|
||||
LatentSvmDetector( const std::vector<String>& filenames, const std::vector<String>& classNames=std::vector<String>() );
|
||||
LatentSvmDetector( const std::vector<String>& filenames, const std::vector<String>& classNames = std::vector<String>() );
|
||||
virtual ~LatentSvmDetector();
|
||||
|
||||
virtual void clear();
|
||||
virtual bool empty() const;
|
||||
bool load( const std::vector<String>& filenames, const std::vector<String>& classNames=std::vector<String>() );
|
||||
bool load( const std::vector<String>& filenames, const std::vector<String>& classNames = std::vector<String>() );
|
||||
|
||||
virtual void detect( const Mat& image,
|
||||
std::vector<ObjectDetection>& objectDetections,
|
||||
float overlapThreshold=0.5f,
|
||||
int numThreads=-1 );
|
||||
float overlapThreshold = 0.5f,
|
||||
int numThreads = -1 );
|
||||
|
||||
const std::vector<String>& getClassNames() const;
|
||||
size_t getClassCount() const;
|
||||
@@ -330,19 +93,22 @@ private:
|
||||
std::vector<String> classNames;
|
||||
};
|
||||
|
||||
CV_EXPORTS void groupRectangles(CV_OUT CV_IN_OUT std::vector<Rect>& rectList, int groupThreshold, double eps=0.2);
|
||||
CV_EXPORTS_W void groupRectangles(CV_OUT CV_IN_OUT std::vector<Rect>& rectList, CV_OUT std::vector<int>& weights, int groupThreshold, double eps=0.2);
|
||||
CV_EXPORTS void groupRectangles( std::vector<Rect>& rectList, int groupThreshold, double eps, std::vector<int>* weights, std::vector<double>* levelWeights );
|
||||
CV_EXPORTS void groupRectangles(std::vector<Rect>& rectList, std::vector<int>& rejectLevels,
|
||||
std::vector<double>& levelWeights, int groupThreshold, double eps=0.2);
|
||||
CV_EXPORTS void groupRectangles_meanshift(std::vector<Rect>& rectList, std::vector<double>& foundWeights, std::vector<double>& foundScales,
|
||||
CV_EXPORTS void groupRectangles(std::vector<Rect>& rectList, int groupThreshold, double eps = 0.2);
|
||||
CV_EXPORTS_W void groupRectangles(CV_IN_OUT std::vector<Rect>& rectList, CV_OUT std::vector<int>& weights, int groupThreshold, double eps = 0.2);
|
||||
CV_EXPORTS void groupRectangles(std::vector<Rect>& rectList, int groupThreshold, double eps, std::vector<int>* weights, std::vector<double>* levelWeights );
|
||||
CV_EXPORTS void groupRectangles(std::vector<Rect>& rectList, std::vector<int>& rejectLevels,
|
||||
std::vector<double>& levelWeights, int groupThreshold, double eps = 0.2);
|
||||
CV_EXPORTS void groupRectangles_meanshift(std::vector<Rect>& rectList, std::vector<double>& foundWeights, std::vector<double>& foundScales,
|
||||
double detectThreshold = 0.0, Size winDetSize = Size(64, 128));
|
||||
|
||||
|
||||
class CV_EXPORTS FeatureEvaluator
|
||||
{
|
||||
public:
|
||||
enum { HAAR = 0, LBP = 1, HOG = 2 };
|
||||
enum { HAAR = 0,
|
||||
LBP = 1,
|
||||
HOG = 2
|
||||
};
|
||||
|
||||
virtual ~FeatureEvaluator();
|
||||
|
||||
virtual bool read(const FileNode& node);
|
||||
@@ -360,13 +126,11 @@ public:
|
||||
|
||||
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
|
||||
};
|
||||
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
|
||||
{
|
||||
@@ -380,20 +144,20 @@ public:
|
||||
virtual bool read( const FileNode& node );
|
||||
CV_WRAP virtual void detectMultiScale( const Mat& image,
|
||||
CV_OUT std::vector<Rect>& objects,
|
||||
double scaleFactor=1.1,
|
||||
int minNeighbors=3, int flags=0,
|
||||
Size minSize=Size(),
|
||||
Size maxSize=Size() );
|
||||
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<Rect>& objects,
|
||||
CV_OUT std::vector<int>& rejectLevels,
|
||||
CV_OUT std::vector<double>& levelWeights,
|
||||
double scaleFactor=1.1,
|
||||
int minNeighbors=3, int flags=0,
|
||||
Size minSize=Size(),
|
||||
Size maxSize=Size(),
|
||||
bool outputRejectLevels=false );
|
||||
double scaleFactor = 1.1,
|
||||
int minNeighbors = 3, int flags = 0,
|
||||
Size minSize = Size(),
|
||||
Size maxSize = Size(),
|
||||
bool outputRejectLevels = false );
|
||||
|
||||
|
||||
bool isOldFormatCascade() const;
|
||||
@@ -402,17 +166,18 @@ public:
|
||||
bool setImage( const Mat& );
|
||||
|
||||
protected:
|
||||
//virtual bool detectSingleScale( const Mat& image, int stripCount, Size processingRectSize,
|
||||
// int stripSize, int yStep, double factor, std::vector<Rect>& candidates );
|
||||
|
||||
virtual bool detectSingleScale( const Mat& image, int stripCount, Size processingRectSize,
|
||||
int stripSize, int yStep, double factor, std::vector<Rect>& candidates,
|
||||
std::vector<int>& rejectLevels, std::vector<double>& levelWeights, bool outputRejectLevels=false);
|
||||
std::vector<int>& rejectLevels, std::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 };
|
||||
enum { BOOST = 0
|
||||
};
|
||||
enum { DO_CANNY_PRUNING = CASCADE_DO_CANNY_PRUNING,
|
||||
SCALE_IMAGE = CASCADE_SCALE_IMAGE,
|
||||
FIND_BIGGEST_OBJECT = CASCADE_FIND_BIGGEST_OBJECT,
|
||||
DO_ROUGH_SEARCH = CASCADE_DO_ROUGH_SEARCH
|
||||
};
|
||||
|
||||
friend class CascadeClassifierInvoker;
|
||||
|
||||
@@ -507,8 +272,10 @@ struct DetectionROI
|
||||
struct CV_EXPORTS_W HOGDescriptor
|
||||
{
|
||||
public:
|
||||
enum { L2Hys=0 };
|
||||
enum { DEFAULT_NLEVELS=64 };
|
||||
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),
|
||||
@@ -548,38 +315,38 @@ public:
|
||||
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;
|
||||
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 std::vector<float>& descriptors,
|
||||
Size winStride=Size(), Size padding=Size(),
|
||||
const std::vector<Point>& locations=std::vector<Point>()) const;
|
||||
Size winStride = Size(), Size padding = Size(),
|
||||
const std::vector<Point>& locations = std::vector<Point>()) const;
|
||||
//with found weights output
|
||||
CV_WRAP virtual void detect(const Mat& img, CV_OUT std::vector<Point>& foundLocations,
|
||||
CV_OUT std::vector<double>& weights,
|
||||
double hitThreshold=0, Size winStride=Size(),
|
||||
Size padding=Size(),
|
||||
const std::vector<Point>& searchLocations=std::vector<Point>()) const;
|
||||
double hitThreshold = 0, Size winStride = Size(),
|
||||
Size padding = Size(),
|
||||
const std::vector<Point>& searchLocations = std::vector<Point>()) const;
|
||||
//without found weights output
|
||||
virtual void detect(const Mat& img, CV_OUT std::vector<Point>& foundLocations,
|
||||
double hitThreshold=0, Size winStride=Size(),
|
||||
Size padding=Size(),
|
||||
double hitThreshold = 0, Size winStride = Size(),
|
||||
Size padding = Size(),
|
||||
const std::vector<Point>& searchLocations=std::vector<Point>()) const;
|
||||
//with result weights output
|
||||
CV_WRAP virtual void detectMultiScale(const Mat& img, CV_OUT std::vector<Rect>& foundLocations,
|
||||
CV_OUT std::vector<double>& foundWeights, double hitThreshold=0,
|
||||
Size winStride=Size(), Size padding=Size(), double scale=1.05,
|
||||
double finalThreshold=2.0,bool useMeanshiftGrouping = false) const;
|
||||
CV_OUT std::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 std::vector<Rect>& foundLocations,
|
||||
double hitThreshold=0, Size winStride=Size(),
|
||||
Size padding=Size(), double scale=1.05,
|
||||
double finalThreshold=2.0, bool useMeanshiftGrouping = false) const;
|
||||
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;
|
||||
Size paddingTL = Size(), Size paddingBR = Size()) const;
|
||||
|
||||
CV_WRAP static std::vector<float> getDefaultPeopleDetector();
|
||||
CV_WRAP static std::vector<float> getDaimlerPeopleDetector();
|
||||
@@ -618,430 +385,14 @@ public:
|
||||
|
||||
CV_EXPORTS_W void findDataMatrix(InputArray image,
|
||||
CV_OUT std::vector<String>& codes,
|
||||
OutputArray corners=noArray(),
|
||||
OutputArrayOfArrays dmtx=noArray());
|
||||
OutputArray corners = noArray(),
|
||||
OutputArrayOfArrays dmtx = noArray());
|
||||
|
||||
CV_EXPORTS_W void drawDataMatrixCodes(InputOutputArray image,
|
||||
const std::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 {
|
||||
|
||||
/// @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 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 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 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 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 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;
|
||||
String class_id;
|
||||
int template_id;
|
||||
};
|
||||
|
||||
inline Match::Match(int _x, int _y, float _similarity, const 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<String>& class_ids = std::vector<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 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 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 String& class_id, int template_id) const;
|
||||
|
||||
int numTemplates() const;
|
||||
int numTemplates(const String& class_id) const;
|
||||
int numClasses() const { return static_cast<int>(class_templates.size()); }
|
||||
|
||||
std::vector<String> classIds() const;
|
||||
|
||||
void read(const FileNode& fn);
|
||||
void write(FileStorage& fs) const;
|
||||
|
||||
String readClass(const FileNode& fn, const String &class_id_override = "");
|
||||
void writeClass(const String& class_id, FileStorage& fs) const;
|
||||
|
||||
void readClasses(const std::vector<String>& class_ids,
|
||||
const String& format = "templates_%s.yml.gz");
|
||||
void writeClasses(const 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<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 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
|
||||
#include "opencv2/objdetect/linemod.hpp"
|
||||
|
||||
#endif
|
||||
|
Reference in New Issue
Block a user