Delphi/Delphi-OpenCV
1
0
Fork 0
mirror of https://github.com/Laex/Delphi-OpenCV.git synced 2024-11-16 16:25:53 +01:00
Code Issues Actions Packages Projects Releases Wiki Activity
13286756ea
Delphi-OpenCV/include/objdetect/objdetect.pas
Laex 7d2ac4512f [+] FaceDetect 
[+] cvGetSubRect

[*] Changed the names of the projects

Signed-off-by: Laex <laex@bk.ru>
2013-03-28 02:28:02 +04:00

1197 lines
42 KiB
ObjectPascal
Raw Blame History

(*
////////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
///
*)
Unit ObjDetect;
interface
Uses core_c, Core.types_c, haar, System.Types, System.Generics.Collections, Xml.XMLDoc;
(*
//****************************************************************************************\
//* Haar-like Object Detection functions *
//****************************************************************************************/
*)
Const
CV_HAAR_MAGIC_VAL = $42500000;
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)
type
THaarFeature = record
r: TCvRect;
weight: Single;
end;
pCvHaarFeature = ^TCvHaarFeature;
TCvHaarFeature = packed record
tilted: Integer;
rect: array [0 .. CV_HAAR_FEATURE_MAX - 1] of THaarFeature;
end;
pCvHaarClassifier = ^TCvHaarClassifier;
TCvHaarClassifier = packed record
count: Integer;
haar_feature: pCvHaarFeature;
threshold: pSingle;
left: pInteger;
right: pInteger;
alpha: pSingle;
end;
pCvHaarStageClassifier = ^TCvHaarStageClassifier;
TCvHaarStageClassifier = packed record
count: Integer;
threshold: Single;
classifier: pCvHaarClassifier;
next: Integer;
child: Integer;
parent: Integer;
end;
// TCvHidHaarClassifierCascade = TCvHidHaarClassifierCascade;
pCvHidHaarClassifierCascade = ^TCvHidHaarClassifierCascade;
pCvHaarClassifierCascade = ^TCvHaarClassifierCascade;
TCvHaarClassifierCascade = packed record
flags: Integer;
count: Integer;
orig_window_size: TCvSize;
real_window_size: TCvSize;
scale: Real;
stage_classifier: pCvHaarStageClassifier;
hid_cascade: pCvHidHaarClassifierCascade;
end;
TCvAvgComp = packed record
rect: TCvRect;
neighbors: Integer;
end;
{
// 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);
}
function cvLoadHaarClassifierCascade(const directory: pCVChar; orig_window_size: TCvSize)
: pCvHaarClassifierCascade; cdecl;
// CVAPI(void) cvReleaseHaarClassifierCascade( CvHaarClassifierCascade** cascade );
procedure cvReleaseHaarClassifierCascade(Var cascade: pCvHaarClassifierCascade); cdecl;
Const
CV_HAAR_DO_CANNY_PRUNING = 1;
CV_HAAR_SCALE_IMAGE = 2;
CV_HAAR_FIND_BIGGEST_OBJECT = 4;
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)));
}
function cvHaarDetectObjects(
{ } const image: pIplImage;
{ } cascade: pCvHaarClassifierCascade;
{ } storage: pCvMemStorage;
{ } scale_factor: double { =1.1 };
{ } min_neighbors: Integer { =3 };
{ } flags: Integer { = 0 };
{ } min_size: TCvSize { =CV_DEFAULT(cvSize(0,0)) };
{ } max_size: TCvSize { =CV_DEFAULT(cvSize(0,0)) } ): pCvSeq; cdecl;
{
/* sets images for haar classifier cascade */
CVAPI(void) cvSetImagesForHaarClassifierCascade( CvHaarClassifierCascade* cascade,
const CvArr* sum, const CvArr* sqsum,
const CvArr* tilted_sum, double scale );
}
procedure cvSetImagesForHaarClassifierCascade(
{ } cascade: pCvHaarClassifierCascade;
{ } const sum: pCvArr;
{ } const sqsum: pCvArr;
{ } const tilted_sum: pCvArr;
{ } scale: double); cdecl;
//
/// * runs the cascade on the specified window */
// CVAPI(int) cvRunHaarClassifierCascade( const CvHaarClassifierCascade* cascade,
// CvPoint pt, int start_stage CV_DEFAULT(0));
//
//
/// ****************************************************************************************\
// * Latent SVM Object Detection functions *
// \****************************************************************************************/
//
/// / DataType: STRUCT position
/// / Structure describes the position of the filter in the feature pyramid
/// / l - level in the feature pyramid
/// / (x, y) - coordinate in level l
// typedef struct CvLSVMFilterPosition
// {
// int x;
// int y;
// int l;
// } CvLSVMFilterPosition;
//
/// / DataType: STRUCT filterObject
/// / Description of the filter, which corresponds to the part of the object
/// / V - ideal (penalty = 0) position of the partial filter
/// / from the root filter position (V_i in the paper)
/// / penaltyFunction - vector describes penalty function (d_i in the paper)
/// / pf[0] * x + pf[1] * y + pf[2] * x^2 + pf[3] * y^2
/// / FILTER DESCRIPTION
/// / Rectangular map (sizeX x sizeY),
/// / every cell stores feature vector (dimension = p)
/// / H - matrix of feature vectors
/// / to set and get feature vectors (i,j)
/// / used formula H[(j * sizeX + i) * p + k], where
/// / k - component of feature vector in cell (i, j)
/// / END OF FILTER DESCRIPTION
// typedef struct CvLSVMFilterObject{
// CvLSVMFilterPosition V;
// float fineFunction[4];
// int sizeX;
// int sizeY;
// int numFeatures;
// float *H;
// } CvLSVMFilterObject;
//
/// / data type: STRUCT CvLatentSvmDetector
/// / structure contains internal representation of trained Latent SVM detector
/// / num_filters - total number of filters (root plus part) in model
/// / num_components - number of components in model
/// / num_part_filters - array containing number of part filters for each component
/// / filters - root and part filters for all model components
/// / b - biases for all model components
/// / score_threshold - confidence level threshold
// typedef struct CvLatentSvmDetector
// {
// int num_filters;
// int num_components;
// int* num_part_filters;
// CvLSVMFilterObject** filters;
// float* b;
// float score_threshold;
// }
// CvLatentSvmDetector;
//
/// / data type: STRUCT CvObjectDetection
/// / structure contains the bounding box and confidence level for detected object
/// / rect - bounding box for a detected object
/// / score - confidence level
// typedef struct CvObjectDetection
// {
// CvRect rect;
// float score;
// } CvObjectDetection;
//
/// /////////////// Object Detection using Latent SVM //////////////
//
//
/// *
/// / load trained detector from a file
/// /
/// / API
/// / CvLatentSvmDetector* cvLoadLatentSvmDetector(const char* filename);
/// / INPUT
/// / filename - path to the file containing the parameters of
// - trained Latent SVM detector
/// / OUTPUT
/// / trained Latent SVM detector in internal representation
// */
// CVAPI(CvLatentSvmDetector*) cvLoadLatentSvmDetector(const char* filename);
//
/// *
/// / release memory allocated for CvLatentSvmDetector structure
/// /
/// / API
/// / void cvReleaseLatentSvmDetector(CvLatentSvmDetector** detector);
/// / INPUT
/// / detector - CvLatentSvmDetector structure to be released
/// / OUTPUT
// */
// CVAPI(void) cvReleaseLatentSvmDetector(CvLatentSvmDetector** detector);
//
/// *
/// / find rectangular regions in the given image that are likely
/// / to contain objects and corresponding confidence levels
/// /
/// / API
/// / CvSeq* cvLatentSvmDetectObjects(const IplImage* image,
/// / CvLatentSvmDetector* detector,
/// / CvMemStorage* storage,
/// / float overlap_threshold = 0.5f,
/// / int numThreads = -1);
/// / INPUT
/// / image - image to detect objects in
/// / detector - Latent SVM detector in internal representation
/// / storage - memory storage to store the resultant sequence
/// / of the object candidate rectangles
/// / overlap_threshold - threshold for the non-maximum suppression algorithm
// = 0.5f [here will be the reference to original paper]
/// / OUTPUT
/// / sequence of detected objects (bounding boxes and confidence levels stored in CvObjectDetection structures)
// */
// CVAPI(CvSeq*) cvLatentSvmDetectObjects(IplImage* image,
// CvLatentSvmDetector* detector,
// CvMemStorage* storage,
// float overlap_threshold CV_DEFAULT(0.5f),
// int numThreads CV_DEFAULT(-1));
//
// #ifdef __cplusplus
// }
//
// CV_EXPORTS CvSeq* cvHaarDetectObjectsForROC( const CvArr* image,
// CvHaarClassifierCascade* cascade, CvMemStorage* storage,
// std::vector<int>& rejectLevels, std::vector<double>& levelWeightds,
// double scale_factor CV_DEFAULT(1.1),
// int min_neighbors CV_DEFAULT(3), int flags CV_DEFAULT(0),
// CvSize min_size CV_DEFAULT(cvSize(0,0)), CvSize max_size CV_DEFAULT(cvSize(0,0)),
// bool outputRejectLevels = false );
//
// namespace cv
// {
//
/// //////////////////////////// Object Detection ////////////////////////////
//
/// *
// * This is a class wrapping up the structure CvLatentSvmDetector and functions working with it.
// * The class goals are:
// * 1) provide c++ interface;
// * 2) make it possible to load and detect more than one class (model) unlike CvLatentSvmDetector.
// */
// class CV_EXPORTS LatentSvmDetector
// {
// public:
// struct CV_EXPORTS ObjectDetection
// {
// ObjectDetection();
// ObjectDetection( const Rect& rect, float score, int classID=-1 );
// Rect rect;
// float score;
// int classID;
// };
//
// LatentSvmDetector();
// LatentSvmDetector( const 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>() );
//
// virtual void detect( const Mat& image,
// std::vector<ObjectDetection>& objectDetections,
// float overlapThreshold=0.5f,
// int numThreads=-1 );
//
// const std::vector<String>& getClassNames() const;
// size_t getClassCount() const;
//
// private:
// std::vector<CvLatentSvmDetector*> detectors;
// 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,
// double detectThreshold = 0.0, Size winDetSize = Size(64, 128));
//
//
// class CV_EXPORTS FeatureEvaluator
// {
// public:
// enum { HAAR = 0, LBP = 1, HOG = 2 };
// virtual ~FeatureEvaluator();
//
// virtual bool read(const FileNode& node);
// virtual Ptr<FeatureEvaluator> clone() const;
// virtual int getFeatureType() const;
//
// virtual bool setImage(const Mat& img, Size origWinSize);
// virtual bool setWindow(Point p);
//
// virtual double calcOrd(int featureIdx) const;
// virtual int calcCat(int featureIdx) const;
//
// static Ptr<FeatureEvaluator> create(int type);
// };
//
// template<> CV_EXPORTS void Ptr<CvHaarClassifierCascade>::delete_obj();
//
// enum
// {
// CASCADE_DO_CANNY_PRUNING=1,
// CASCADE_SCALE_IMAGE=2,
// CASCADE_FIND_BIGGEST_OBJECT=4,
// CASCADE_DO_ROUGH_SEARCH=8
// };
//
Type
TCascadeClassifier = class
public
// CV_WRAP CascadeClassifier();
constructor Create; overload;
// CV_WRAP CascadeClassifier( const String& filename );
constructor Create(const filename: String); overload;
// virtual ~CascadeClassifier();
destructor Destroy; override;
// CV_WRAP virtual bool empty() const;
function Empty: Boolean; virtual;
// CV_WRAP bool load( const String& filename );
function Load(const filename: String): Boolean;
// virtual bool read( const FileNode& node );
function Read(const node: TXMLNode): Boolean; virtual;
// 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() );
procedure detectMultiScale(
{ } const image: pIplImage;
{ } const objects: TList<TcvRect>;
{ } scaleFactor: double { =1.1 };
{ } minNeighbors: Integer { =3 };
{ } flags: Integer { =0 };
{ } minSize: TCvSize { =Size() };
{ } maxSize: TCvSize { =Size() } ); overload;
// CV_WRAP virtual void detectMultiScale( const Mat& image,
// CV_OUT std::vector<Rect>& objects,
// std::vector<int>& rejectLevels,
// std::vector<double>& levelWeights,
// double scaleFactor=1.1,
// int minNeighbors=3, int flags=0,
// Size minSize=Size(),
// Size maxSize=Size(),
// bool outputRejectLevels=false );
procedure detectMultiScale(
{ } const image: pIplImage;
{ } const objects: TList<TcvRect>;
{ } const rejectLevels: TList<Integer>;
{ } const levelWeights: TList<double>;
{ } scaleFactor: double { =1.1 };
{ } minNeighbors: Integer { =3 };
{ } flags: Integer { =0 };
{ } minSize: TCvSize { =Size() };
{ } maxSize: TCvSize { =Size() };
{ } outputRejectLevels: Boolean { =false } ); overload;
// bool isOldFormatCascade() const;
function isOldFormatCascade: Boolean;
// virtual Size getOriginalWindowSize() const;
function getOriginalWindowSize: TCvSize; virtual;
// int getFeatureType() const;
function getFeatureType: Integer;
// bool setImage( const Mat& );
function setImage(const image: pIplImage):Boolean;
protected
// 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);
function detectSingleScale(const image: pIplImage; stripCount: Integer; processingRectSize: TCvSize;
stripSize: Integer; yStep: Integer; factor: double; candidates: TList<TcvRect>; rejectLevels: TList<Integer>;
levelWeights: TList<double>; outputRejectLevels: Boolean = false): Boolean; virtual;
const
BOOST = 0;
DO_CANNY_PRUNING = 1;
SCALE_IMAGE = 2;
FIND_BIGGEST_OBJECT = 4;
DO_ROUGH_SEARCH = 8;
end;
// friend class CascadeClassifierInvoker;
//
// template<class FEval>
// friend int predictOrdered( CascadeClassifier& cascade, Ptr<FeatureEvaluator> &featureEvaluator, double& weight);
//
// template<class FEval>
// friend int predictCategorical( CascadeClassifier& cascade, Ptr<FeatureEvaluator> &featureEvaluator, double& weight);
//
// template<class FEval>
// friend int predictOrderedStump( CascadeClassifier& cascade, Ptr<FeatureEvaluator> &featureEvaluator, double& weight);
//
// template<class FEval>
// friend int predictCategoricalStump( CascadeClassifier& cascade, Ptr<FeatureEvaluator> &featureEvaluator, double& weight);
//
// bool setImage( Ptr<FeatureEvaluator>& feval, const Mat& image);
// virtual int runAt( Ptr<FeatureEvaluator>& feval, Point pt, double& weight );
//
// class Data
// {
// public:
// struct CV_EXPORTS DTreeNode
// {
// int featureIdx;
// float threshold; // for ordered features only
// int left;
// int right;
// };
//
// struct CV_EXPORTS DTree
// {
// int nodeCount;
// };
//
// struct CV_EXPORTS Stage
// {
// int first;
// int ntrees;
// float threshold;
// };
//
// bool read(const FileNode &node);
//
// bool isStumpBased;
//
// int stageType;
// int featureType;
// int ncategories;
// Size origWinSize;
//
// std::vector<Stage> stages;
// std::vector<DTree> classifiers;
// std::vector<DTreeNode> nodes;
// std::vector<float> leaves;
// std::vector<int> subsets;
// };
//
// Data data;
// Ptr<FeatureEvaluator> featureEvaluator;
// Ptr<CvHaarClassifierCascade> oldCascade;
//
// public:
// class CV_EXPORTS MaskGenerator
// {
// public:
// virtual ~MaskGenerator() {}
// virtual cv::Mat generateMask(const cv::Mat& src)=0;
// virtual void initializeMask(const cv::Mat& /*src*/) {};
// };
// void setMaskGenerator(Ptr<MaskGenerator> maskGenerator);
// Ptr<MaskGenerator> getMaskGenerator();
//
// void setFaceDetectionMaskGenerator();
//
// protected:
// Ptr<MaskGenerator> maskGenerator;
// };
//
/// /////////////// HOG (Histogram-of-Oriented-Gradients) Descriptor and Object Detector //////////////
//
/// / struct for detection region of interest (ROI)
// struct DetectionROI
// {
// // scale(size) of the bounding box
// double scale;
// // set of requrested locations to be evaluated
// std::vector<cv::Point> locations;
// // vector that will contain confidence values for each location
// std::vector<double> confidences;
// };
//
// struct CV_EXPORTS_W HOGDescriptor
// {
// public:
// enum { L2Hys=0 };
// enum { DEFAULT_NLEVELS=64 };
//
// CV_WRAP HOGDescriptor() : winSize(64,128), blockSize(16,16), blockStride(8,8),
// cellSize(8,8), nbins(9), derivAperture(1), winSigma(-1),
// histogramNormType(HOGDescriptor::L2Hys), L2HysThreshold(0.2), gammaCorrection(true),
// nlevels(HOGDescriptor::DEFAULT_NLEVELS)
// {}
//
// CV_WRAP HOGDescriptor(Size _winSize, Size _blockSize, Size _blockStride,
// Size _cellSize, int _nbins, int _derivAperture=1, double _winSigma=-1,
// int _histogramNormType=HOGDescriptor::L2Hys,
// double _L2HysThreshold=0.2, bool _gammaCorrection=false,
// int _nlevels=HOGDescriptor::DEFAULT_NLEVELS)
// : winSize(_winSize), blockSize(_blockSize), blockStride(_blockStride), cellSize(_cellSize),
// nbins(_nbins), derivAperture(_derivAperture), winSigma(_winSigma),
// histogramNormType(_histogramNormType), L2HysThreshold(_L2HysThreshold),
// gammaCorrection(_gammaCorrection), nlevels(_nlevels)
// {}
//
// CV_WRAP HOGDescriptor(const String& filename)
// {
// load(filename);
// }
//
// HOGDescriptor(const HOGDescriptor& d)
// {
// d.copyTo(*this);
// }
//
// virtual ~HOGDescriptor() {}
//
// CV_WRAP size_t getDescriptorSize() const;
// CV_WRAP bool checkDetectorSize() const;
// CV_WRAP double getWinSigma() const;
//
// CV_WRAP virtual void setSVMDetector(InputArray _svmdetector);
//
// virtual bool read(FileNode& fn);
// virtual void write(FileStorage& fs, const String& objname) const;
//
// CV_WRAP virtual bool load(const String& filename, const String& objname=String());
// CV_WRAP virtual void save(const String& filename, const String& objname=String()) const;
// virtual void copyTo(HOGDescriptor& c) const;
//
// CV_WRAP virtual void compute(const Mat& img,
// CV_OUT std::vector<float>& descriptors,
// 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;
// //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(),
// 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;
// //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;
//
// CV_WRAP virtual void computeGradient(const Mat& img, CV_OUT Mat& grad, CV_OUT Mat& angleOfs,
// Size paddingTL=Size(), Size paddingBR=Size()) const;
//
// CV_WRAP static std::vector<float> getDefaultPeopleDetector();
// CV_WRAP static std::vector<float> getDaimlerPeopleDetector();
//
// CV_PROP Size winSize;
// CV_PROP Size blockSize;
// CV_PROP Size blockStride;
// CV_PROP Size cellSize;
// CV_PROP int nbins;
// CV_PROP int derivAperture;
// CV_PROP double winSigma;
// CV_PROP int histogramNormType;
// CV_PROP double L2HysThreshold;
// CV_PROP bool gammaCorrection;
// CV_PROP std::vector<float> svmDetector;
// CV_PROP int nlevels;
//
//
// // evaluate specified ROI and return confidence value for each location
// virtual void detectROI(const cv::Mat& img, const std::vector<cv::Point> &locations,
// CV_OUT std::vector<cv::Point>& foundLocations, CV_OUT std::vector<double>& confidences,
// double hitThreshold = 0, cv::Size winStride = Size(),
// cv::Size padding = Size()) const;
//
// // evaluate specified ROI and return confidence value for each location in multiple scales
// virtual void detectMultiScaleROI(const cv::Mat& img,
// CV_OUT std::vector<cv::Rect>& foundLocations,
// std::vector<DetectionROI>& locations,
// double hitThreshold = 0,
// int groupThreshold = 0) const;
//
// // read/parse Dalal's alt model file
// void readALTModel(String modelfile);
// };
//
//
// CV_EXPORTS_W void findDataMatrix(InputArray image,
// CV_OUT std::vector<String>& codes,
// 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
//
// #endif
implementation
uses
LibName;
function cvLoadHaarClassifierCascade; external objdetect_dll;
procedure cvReleaseHaarClassifierCascade; external objdetect_dll;
function cvHaarDetectObjects; external objdetect_dll;
procedure cvSetImagesForHaarClassifierCascade; external objdetect_dll;
{ TCascadeClassifier }
constructor TCascadeClassifier.Create;
begin
end;
constructor TCascadeClassifier.Create(const filename: String);
begin
end;
destructor TCascadeClassifier.Destroy;
begin
inherited;
end;
procedure TCascadeClassifier.detectMultiScale(const image: pIplImage;
const objects: TList<TcvRect>; const rejectLevels: TList<Integer>;
const levelWeights: TList<double>; scaleFactor: double; minNeighbors,
flags: Integer; minSize, maxSize: TCvSize; outputRejectLevels: Boolean);
begin
end;
procedure TCascadeClassifier.detectMultiScale(const image: pIplImage;
const objects: TList<TcvRect>; scaleFactor: double; minNeighbors,
flags: Integer; minSize, maxSize: TCvSize);
begin
end;
function TCascadeClassifier.detectSingleScale(const image: pIplImage;
stripCount: Integer; processingRectSize: TCvSize; stripSize,
yStep: Integer; factor: double; candidates: TList<TcvRect>;
rejectLevels: TList<Integer>; levelWeights: TList<double>;
outputRejectLevels: Boolean): Boolean;
begin
end;
function TCascadeClassifier.Empty: Boolean;
begin
end;
function TCascadeClassifier.getFeatureType: Integer;
begin
end;
function TCascadeClassifier.getOriginalWindowSize: TCvSize;
begin
end;
function TCascadeClassifier.isOldFormatCascade: Boolean;
begin
end;
function TCascadeClassifier.Load(const filename: String): Boolean;
begin
end;
function TCascadeClassifier.Read(const node: TXMLNode): Boolean;
begin
end;
function TCascadeClassifier.setImage(const image: pIplImage): Boolean;
begin
end;
end.
Reference in New Issue View Git Blame Copy Permalink