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Move C API of opencv_objdetect to separate file

Browse Source 
Also move cv::linemod to own header
This commit is contained in:
Andrey Kamaev 2013-04-12 12:11:11 +04:00
parent e5a33723fc
commit 5e048d1fa5
32 changed files with 881 additions and 775 deletions
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3
include/opencv/cv.h
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@ -64,11 +64,10 @@
#include "opencv2/imgproc/imgproc_c.h"
#include "opencv2/photo/photo_c.h"
#include "opencv2/video/tracking_c.h"
#include "opencv2/objdetect/objdetect_c.h"
#include "opencv2/legacy.hpp"
#include "opencv2/legacy/compat.hpp"
#include "opencv2/objdetect.hpp"
#if !defined(CV_IMPL)
#define CV_IMPL extern "C"
#endif //CV_IMPL

1
include/opencv/cv.hpp
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@ -55,5 +55,6 @@
#include "opencv2/highgui.hpp"
#include "opencv2/features2d.hpp"
#include "opencv2/calib3d.hpp"
#include "opencv2/objdetect.hpp"
#endif

3
include/opencv/cvaux.h
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@ -50,12 +50,11 @@
#include "opencv2/imgproc/imgproc_c.h"
#include "opencv2/photo/photo_c.h"
#include "opencv2/video/tracking_c.h"
#include "opencv2/objdetect/objdetect_c.h"
#include "opencv2/legacy.hpp"
#include "opencv2/legacy/compat.hpp"
#include "opencv2/legacy/blobtrack.hpp"
#include "opencv2/objdetect.hpp"
#include "opencv2/contrib.hpp"
#endif

2
modules/contrib/include/opencv2/contrib.hpp
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@ -48,6 +48,8 @@
#include "opencv2/features2d.hpp"
#include "opencv2/objdetect.hpp"
#include "opencv2/core/core_c.h"
#include <ostream>
#ifdef __cplusplus

783
modules/objdetect/include/opencv2/objdetect.hpp
View File

@ -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

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/*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.
// 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,
// 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_LINEMOD_HPP__
#define __OPENCV_OBJDETECT_LINEMOD_HPP__
#include "opencv2/core.hpp"
#include <map>
/****************************************************************************************\
* 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 // __OPENCV_OBJDETECT_LINEMOD_HPP__

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/*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.
// 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,
// 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_C_H__
#define __OPENCV_OBJDETECT_C_H__
#include "opencv2/core/core_c.h"
#ifdef __cplusplus
#include <deque>
#include <vector>
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*) 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 = 1.1,
int min_neighbors = 3, int flags = 0,
CvSize min_size = cvSize(0, 0), CvSize max_size = cvSize(0, 0),
bool outputRejectLevels = false );
struct CvDataMatrixCode
{
char msg[4];
CvMat* original;
CvMat* corners;
};
CV_EXPORTS std::deque<CvDataMatrixCode> cvFindDataMatrix(CvMat *im);
#endif
#endif /* __OPENCV_OBJDETECT_C_H__ */

2
modules/objdetect/src/_lsvm_routine.h
View File

@ -1,6 +1,8 @@
#ifndef _LSVM_ROUTINE_H_
#define _LSVM_ROUTINE_H_
#include "opencv2/objdetect/objdetect_c.h"
#include "_lsvm_types.h"
#include "_lsvm_error.h"

1
modules/objdetect/src/_lsvmparser.h
View File

@ -1,5 +1,6 @@
#ifndef LSVM_PARSER
#define LSVM_PARSER
#include "opencv2/objdetect/objdetect_c.h"
#include "_lsvm_types.h"

1
modules/objdetect/src/cascadedetect.cpp
View File

@ -43,6 +43,7 @@
#include <cstdio>
#include "cascadedetect.hpp"
#include "opencv2/objdetect/objdetect_c.h"
#if defined (LOG_CASCADE_STATISTIC)
struct Logger

2
modules/objdetect/src/datamatrix.cpp
View File

@ -1,7 +1,7 @@
#include "precomp.hpp"
#include "opencv2/imgproc/imgproc_c.h"
#include "opencv2/objdetect/objdetect_c.h"
#include <deque>
#include <algorithm>
class Sampler {

1
modules/objdetect/src/haar.cpp
View File

@ -43,6 +43,7 @@
#include "precomp.hpp"
#include "opencv2/imgproc/imgproc_c.h"
#include "opencv2/objdetect/objdetect_c.h"
#include <stdio.h>
#if CV_SSE2

7
modules/objdetect/src/hog.cpp
View File

@ -41,6 +41,7 @@
//M*/
#include "precomp.hpp"
#include "opencv2/core/core_c.h"
#include <cstdio>
#include <iterator>
@ -2862,7 +2863,7 @@ void HOGDescriptor::readALTModel(String modelfile)
String eerr("file not exist");
String efile(__FILE__);
String efunc(__FUNCTION__);
throw Exception(CV_StsError, eerr, efile, efunc, __LINE__);
throw Exception(Error::StsError, eerr, efile, efunc, __LINE__);
}
char version_buffer[10];
if (!fread (&version_buffer,sizeof(char),10,modelfl))
@ -2870,13 +2871,13 @@ void HOGDescriptor::readALTModel(String modelfile)
String eerr("version?");
String efile(__FILE__);
String efunc(__FUNCTION__);
throw Exception(CV_StsError, eerr, efile, efunc, __LINE__);
throw Exception(Error::StsError, eerr, efile, efunc, __LINE__);
}
if(strcmp(version_buffer,"V6.01")) {
String eerr("version doesnot match");
String efile(__FILE__);
String efunc(__FUNCTION__);
throw Exception(CV_StsError, eerr, efile, efunc, __LINE__);
throw Exception(Error::StsError, eerr, efile, efunc, __LINE__);
}
/* read version number */
int version = 0;

1
modules/objdetect/src/latentsvmdetector.cpp
View File

@ -1,5 +1,6 @@
#include "precomp.hpp"
#include "opencv2/imgproc/imgproc_c.h"
#include "opencv2/objdetect/objdetect_c.h"
#include "_lsvmparser.h"
#include "_lsvm_matching.h"

8
modules/objdetect/src/linemod.cpp
View File

@ -66,7 +66,7 @@ static inline int getLabel(int quantized)
case 64: return 6;
case 128: return 7;
default:
CV_Error(CV_StsBadArg, "Invalid value of quantized parameter");
CV_Error(Error::StsBadArg, "Invalid value of quantized parameter");
return -1; //avoid warning
}
}
@ -1398,17 +1398,17 @@ void Detector::match(const std::vector<Mat>& sources, float threshold, std::vect
if (quantized_images.needed())
quantized_images.create(1, static_cast<int>(pyramid_levels * modalities.size()), CV_8U);
assert(sources.size() == modalities.size());
CV_Assert(sources.size() == modalities.size());
// Initialize each modality with our sources
std::vector< Ptr<QuantizedPyramid> > quantizers;
for (int i = 0; i < (int)modalities.size(); ++i){
Mat mask, source;
source = sources[i];
if(!masks.empty()){
assert(masks.size() == modalities.size());
CV_Assert(masks.size() == modalities.size());
mask = masks[i];
}
assert(mask.empty() || mask.size() == source.size());
CV_Assert(mask.empty() || mask.size() == source.size());
quantizers.push_back(modalities[i]->process(source, mask));
}
// pyramid level -> modality -> quantization

1
modules/objdetect/src/matching.cpp
View File

@ -1,4 +1,5 @@
#include "precomp.hpp"
#include "opencv2/objdetect/objdetect_c.h"
#include "_lsvm_matching.h"
#include <stdio.h>

23
modules/objdetect/test/test_cascadeandhog.cpp
View File

@ -41,6 +41,7 @@
#include "test_precomp.hpp"
#include "opencv2/imgproc.hpp"
#include "opencv2/objdetect/objdetect_c.h"
using namespace cv;
using namespace std;
@ -117,7 +118,7 @@ int CV_DetectorTest::prepareData( FileStorage& _fs )
// fn[TOTAL_NO_PAIR_E] >> eps.totalNoPair;
// read detectors
if( fn[DETECTOR_NAMES].node->data.seq != 0 )
if( fn[DETECTOR_NAMES].size() != 0 )
{
FileNodeIterator it = fn[DETECTOR_NAMES].begin();
for( ; it != fn[DETECTOR_NAMES].end(); )
@ -132,7 +133,7 @@ int CV_DetectorTest::prepareData( FileStorage& _fs )
// read images filenames and images
string dataPath = ts->get_data_path();
if( fn[IMAGE_FILENAMES].node->data.seq != 0 )
if( fn[IMAGE_FILENAMES].size() != 0 )
{
for( FileNodeIterator it = fn[IMAGE_FILENAMES].begin(); it != fn[IMAGE_FILENAMES].end(); )
{
@ -210,7 +211,7 @@ void CV_DetectorTest::run( int )
{
char buf[10];
sprintf( buf, "%s%d", "img_", ii );
cvWriteComment( validationFS.fs, buf, 0 );
//cvWriteComment( validationFS.fs, buf, 0 );
validationFS << *it;
}
validationFS << "]"; // IMAGE_FILENAMES
@ -316,7 +317,7 @@ int CV_DetectorTest::validate( int detectorIdx, vector<vector<Rect> >& objects )
string imageIdxStr = buf;
FileNode node = validationFS.getFirstTopLevelNode()[VALIDATION][detectorNames[detectorIdx]][imageIdxStr];
vector<Rect> valRects;
if( node.node->data.seq != 0 )
if( node.size() != 0 )
{
for( FileNodeIterator it2 = node.begin(); it2 != node.end(); )
{
@ -410,12 +411,12 @@ void CV_CascadeDetectorTest::readDetector( const FileNode& fn )
if( flag )
flags.push_back( 0 );
else
flags.push_back( CV_HAAR_SCALE_IMAGE );
flags.push_back( CASCADE_SCALE_IMAGE );
}
void CV_CascadeDetectorTest::writeDetector( FileStorage& fs, int di )
{
int sc = flags[di] & CV_HAAR_SCALE_IMAGE ? 0 : 1;
int sc = flags[di] & CASCADE_SCALE_IMAGE ? 0 : 1;
fs << FILENAME << detectorFilenames[di];
fs << C_SCALE_CASCADE << sc;
}
@ -439,7 +440,7 @@ int CV_CascadeDetectorTest::detectMultiScale_C( const string& filename,
CvMat c_gray = grayImg;
CvSeq* rs = cvHaarDetectObjects(&c_gray, c_cascade, storage, 1.1, 3, flags[di] );
objects.clear();
for( int i = 0; i < rs->total; i++ )
{
@ -494,7 +495,7 @@ CV_HOGDetectorTest::CV_HOGDetectorTest()
void CV_HOGDetectorTest::readDetector( const FileNode& fn )
{
String filename;
if( fn[FILENAME].node->data.seq != 0 )
if( fn[FILENAME].size() != 0 )
fn[FILENAME] >> filename;
detectorFilenames.push_back( filename);
}
@ -1085,7 +1086,7 @@ void HOGDescriptorTester::detect(const Mat& img,
}
const double eps = 0.0;
double diff_norm = norm(Mat(actual_weights) - Mat(weights), CV_L2);
double diff_norm = norm(Mat(actual_weights) - Mat(weights), NORM_L2);
if (diff_norm > eps)
{
ts->printf(cvtest::TS::SUMMARY, "Weights for found locations aren't equal.\n"
@ -1164,7 +1165,7 @@ void HOGDescriptorTester::compute(const Mat& img, vector<float>& descriptors,
std::vector<float> actual_descriptors;
actual_hog->compute(img, actual_descriptors, winStride, padding, locations);
double diff_norm = cv::norm(Mat(actual_descriptors) - Mat(descriptors), CV_L2);
double diff_norm = cv::norm(Mat(actual_descriptors) - Mat(descriptors), NORM_L2);
const double eps = 0.0;
if (diff_norm > eps)
{
@ -1314,7 +1315,7 @@ void HOGDescriptorTester::computeGradient(const Mat& img, Mat& grad, Mat& qangle
const double eps = 0.0;
for (i = 0; i < 2; ++i)
{
double diff_norm = norm(reference_mats[i] - actual_mats[i], CV_L2);
double diff_norm = norm(reference_mats[i] - actual_mats[i], NORM_L2);
if (diff_norm > eps)
{
ts->printf(cvtest::TS::LOG, "%s matrices are not equal\n"

1
modules/objdetect/test/test_latentsvmdetector.cpp
View File

@ -41,6 +41,7 @@
//M*/
#include "test_precomp.hpp"
#include "opencv2/objdetect/objdetect_c.h"
#include <string>
#ifdef HAVE_TBB

4
modules/python/src2/cv2.cpp
View File

@ -18,14 +18,14 @@
#include "opencv2/features2d.hpp"
#include "opencv2/objdetect.hpp"
#include "opencv2/softcascade.hpp"
#include "opencv2/video/tracking.hpp"
#include "opencv2/video/background_segm.hpp"
#include "opencv2/video.hpp"
#include "opencv2/photo.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/highgui/highgui_c.h"
#include "opencv2/photo/photo_c.h"
#include "opencv2/video/tracking_c.h"
#include "opencv2/objdetect/objdetect_c.h"
#include "opencv2/opencv_modules.hpp"

2
modules/superres/src/btv_l1_gpu.cpp
View File

@ -54,7 +54,7 @@ using namespace cv::superres::detail;
Ptr<SuperResolution> cv::superres::createSuperResolution_BTVL1_GPU()
{
CV_Error(CV_StsNotImplemented, "The called functionality is disabled for current build or platform");
CV_Error(Error::StsNotImplemented, "The called functionality is disabled for current build or platform");
return Ptr<SuperResolution>();
}

2
samples/c/convert_cascade.c
View File

@ -1,4 +1,4 @@
#include "opencv2/objdetect/objdetect.hpp"
#include "opencv2/objdetect/objdetect_c.h"
#include "opencv2/highgui/highgui_c.h"
#include <ctype.h>

20
samples/c/facedetect.cpp
View File

@ -216,9 +216,9 @@ void detectAndDraw( Mat& img, CascadeClassifier& cascade,
t = (double)cvGetTickCount();
cascade.detectMultiScale( smallImg, faces,
1.1, 2, 0
//|CV_HAAR_FIND_BIGGEST_OBJECT
//|CV_HAAR_DO_ROUGH_SEARCH
|CV_HAAR_SCALE_IMAGE
//|CASCADE_FIND_BIGGEST_OBJECT
//|CASCADE_DO_ROUGH_SEARCH
|CASCADE_SCALE_IMAGE
,
Size(30, 30) );
if( tryflip )
@ -226,9 +226,9 @@ void detectAndDraw( Mat& img, CascadeClassifier& cascade,
flip(smallImg, smallImg, 1);
cascade.detectMultiScale( smallImg, faces2,
1.1, 2, 0
//|CV_HAAR_FIND_BIGGEST_OBJECT
//|CV_HAAR_DO_ROUGH_SEARCH
|CV_HAAR_SCALE_IMAGE
//|CASCADE_FIND_BIGGEST_OBJECT
//|CASCADE_DO_ROUGH_SEARCH
|CASCADE_SCALE_IMAGE
,
Size(30, 30) );
for( vector<Rect>::const_iterator r = faces2.begin(); r != faces2.end(); r++ )
@ -263,10 +263,10 @@ void detectAndDraw( Mat& img, CascadeClassifier& cascade,
smallImgROI = smallImg(*r);
nestedCascade.detectMultiScale( smallImgROI, nestedObjects,
1.1, 2, 0
//|CV_HAAR_FIND_BIGGEST_OBJECT
//|CV_HAAR_DO_ROUGH_SEARCH
//|CV_HAAR_DO_CANNY_PRUNING
|CV_HAAR_SCALE_IMAGE
//|CASCADE_FIND_BIGGEST_OBJECT
//|CASCADE_DO_ROUGH_SEARCH
//|CASCADE_DO_CANNY_PRUNING
|CASCADE_SCALE_IMAGE
,
Size(30, 30) );
for( vector<Rect>::const_iterator nr = nestedObjects.begin(); nr != nestedObjects.end(); nr++ )

2
samples/c/latentsvmdetect.cpp
View File

@ -1,4 +1,4 @@
#include "opencv2/objdetect.hpp"
#include "opencv2/objdetect/objdetect_c.h"
#include "opencv2/highgui/highgui_c.h"
#include <stdio.h>

20
samples/c/smiledetect.cpp
View File

@ -175,9 +175,9 @@ void detectAndDraw( Mat& img, CascadeClassifier& cascade,
cascade.detectMultiScale( smallImg, faces,
1.1, 2, 0
//|CV_HAAR_FIND_BIGGEST_OBJECT
//|CV_HAAR_DO_ROUGH_SEARCH
|CV_HAAR_SCALE_IMAGE
//|CASCADE_FIND_BIGGEST_OBJECT
//|CASCADE_DO_ROUGH_SEARCH
|CASCADE_SCALE_IMAGE
,
Size(30, 30) );
if( tryflip )
@ -185,9 +185,9 @@ void detectAndDraw( Mat& img, CascadeClassifier& cascade,
flip(smallImg, smallImg, 1);
cascade.detectMultiScale( smallImg, faces2,
1.1, 2, 0
//|CV_HAAR_FIND_BIGGEST_OBJECT
//|CV_HAAR_DO_ROUGH_SEARCH
|CV_HAAR_SCALE_IMAGE
//|CASCADE_FIND_BIGGEST_OBJECT
//|CASCADE_DO_ROUGH_SEARCH
|CASCADE_SCALE_IMAGE
,
Size(30, 30) );
for( vector<Rect>::const_iterator r = faces2.begin(); r != faces2.end(); r++ )
@ -223,10 +223,10 @@ void detectAndDraw( Mat& img, CascadeClassifier& cascade,
smallImgROI = smallImg(*r);
nestedCascade.detectMultiScale( smallImgROI, nestedObjects,
1.1, 0, 0
//|CV_HAAR_FIND_BIGGEST_OBJECT
//|CV_HAAR_DO_ROUGH_SEARCH
//|CV_HAAR_DO_CANNY_PRUNING
|CV_HAAR_SCALE_IMAGE
//|CASCADE_FIND_BIGGEST_OBJECT
//|CASCADE_DO_ROUGH_SEARCH
//|CASCADE_DO_CANNY_PRUNING
|CASCADE_SCALE_IMAGE
,
Size(30, 30) );

2
samples/cpp/dbt_face_detection.cpp
View File

@ -79,7 +79,7 @@ int main(int , char** )
for (size_t i = 0; i < Faces.size(); i++)
{
rectangle(ReferenceFrame, Faces[i], CV_RGB(0,255,0));
rectangle(ReferenceFrame, Faces[i], Scalar(0,255,0));
}
imshow(WindowName, ReferenceFrame);

2
samples/cpp/peopledetect.cpp
View File

@ -141,7 +141,7 @@ int main(int argc, char** argv)
conf << d.confidence;
cv::rectangle(frame, cv::Rect((int)d.x, (int)d.y, (int)d.w, (int)d.h), cv::Scalar(b, 0, 255 - b, 255), 2);
cv::putText(frame, conf.str() , cv::Point((int)d.x + 10, (int)d.y - 5),1, 1.1, cv::Scalar(25, 133, 255, 0), 1, CV_AA);
cv::putText(frame, conf.str() , cv::Point((int)d.x + 10, (int)d.y - 5),1, 1.1, cv::Scalar(25, 133, 255, 0), 1, cv::LINE_AA);
if (wf)
myfile << d.x << "," << d.y << "," << d.w << "," << d.h << "," << d.confidence << "\n";

4
samples/cpp/tutorial_code/objectDetection/objectDetection.cpp
View File

@ -73,7 +73,7 @@ void detectAndDisplay( Mat frame )
cvtColor( frame, frame_gray, COLOR_BGR2GRAY );
equalizeHist( frame_gray, frame_gray );
//-- Detect faces
face_cascade.detectMultiScale( frame_gray, faces, 1.1, 2, 0|CV_HAAR_SCALE_IMAGE, Size(30, 30) );
face_cascade.detectMultiScale( frame_gray, faces, 1.1, 2, 0|CASCADE_SCALE_IMAGE, Size(30, 30) );
for( size_t i = 0; i < faces.size(); i++ )
{
@ -84,7 +84,7 @@ void detectAndDisplay( Mat frame )
std::vector<Rect> eyes;
//-- In each face, detect eyes
eyes_cascade.detectMultiScale( faceROI, eyes, 1.1, 2, 0 |CV_HAAR_SCALE_IMAGE, Size(30, 30) );
eyes_cascade.detectMultiScale( faceROI, eyes, 1.1, 2, 0 |CASCADE_SCALE_IMAGE, Size(30, 30) );
for( size_t j = 0; j < eyes.size(); j++ )
{

2
samples/cpp/tutorial_code/objectDetection/objectDetection2.cpp
View File

@ -82,7 +82,7 @@ void detectAndDisplay( Mat frame )
std::vector<Rect> eyes;
//-- In each face, detect eyes
eyes_cascade.detectMultiScale( faceROI, eyes, 1.1, 2, 0 |CV_HAAR_SCALE_IMAGE, Size(30, 30) );
eyes_cascade.detectMultiScale( faceROI, eyes, 1.1, 2, 0 |CASCADE_SCALE_IMAGE, Size(30, 30) );
if( eyes.size() == 2)
{
//-- Draw the face

4
samples/gpu/cascadeclassifier.cpp
View File

@ -228,8 +228,8 @@ int main(int argc, const char *argv[])
Size minSize = cascade_gpu.getClassifierSize();
cascade_cpu.detectMultiScale(resized_cpu, facesBuf_cpu, 1.2,
(filterRects || findLargestObject) ? 4 : 0,
(findLargestObject ? CV_HAAR_FIND_BIGGEST_OBJECT : 0)
| CV_HAAR_SCALE_IMAGE,
(findLargestObject ? CASCADE_FIND_BIGGEST_OBJECT : 0)
| CASCADE_SCALE_IMAGE,
minSize);
detections_num = (int)facesBuf_cpu.size();
}

2
samples/gpu/hog.cpp
View File

@ -326,7 +326,7 @@ void App::run()
for (size_t i = 0; i < found.size(); i++)
{
Rect r = found[i];
rectangle(img_to_show, r.tl(), r.br(), CV_RGB(0, 255, 0), 3);
rectangle(img_to_show, r.tl(), r.br(), Scalar(0, 255, 0), 3);
}
if (use_gpu)

4
samples/gpu/houghlines.cpp
View File

@ -51,7 +51,7 @@ int main(int argc, const char* argv[])
for (size_t i = 0; i < lines_cpu.size(); ++i)
{
Vec4i l = lines_cpu[i];
line(dst_cpu, Point(l[0], l[1]), Point(l[2], l[3]), Scalar(0, 0, 255), 3, CV_AA);
line(dst_cpu, Point(l[0], l[1]), Point(l[2], l[3]), Scalar(0, 0, 255), 3, LINE_AA);
}
GpuMat d_src(mask);
@ -77,7 +77,7 @@ int main(int argc, const char* argv[])
for (size_t i = 0; i < lines_gpu.size(); ++i)
{
Vec4i l = lines_gpu[i];
line(dst_gpu, Point(l[0], l[1]), Point(l[2], l[3]), Scalar(0, 0, 255), 3, CV_AA);
line(dst_gpu, Point(l[0], l[1]), Point(l[2], l[3]), Scalar(0, 0, 255), 3, LINE_AA);
}
imshow("source", src);

2
samples/gpu/performance/performance.cpp
View File

@ -144,7 +144,7 @@ string abspath(const string& relpath)
}
static int CV_CDECL cvErrorCallback(int /*status*/, const char* /*func_name*/,
static int cvErrorCallback(int /*status*/, const char* /*func_name*/,
const char* err_msg, const char* /*file_name*/,
int /*line*/, void* /*userdata*/)
{