Delphi-OpenCV/include/objdetect/objdetect_c.pas

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(* /  **************************************************************************************************
  //                                 Project Delphi-OpenCV
  //  **************************************************************************************************
  //  Contributor:
  //  laentir Valetov
  //  email:laex@bk.ru
  //  **************************************************************************************************
  //  You may retrieve the latest version of this file at the GitHub,
  //  located at git://github.com/Laex/Delphi-OpenCV.git
  //  **************************************************************************************************
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  //  The contents of this file are subject to the Mozilla Public License Version 1.1 (the "License");
  //  you may not use this file except in compliance with the License. You may obtain a copy of the
  //  License at http://www.mozilla.org/MPL/
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  //  Software distributed under the License is distributed on an "AS IS" basis, WITHOUT WARRANTY OF
  //  ANY KIND, either express or implied. See the License for the specific language governing rights
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  //  For more information about the LGPL: http://www.gnu.org/copyleft/lesser.html
  //  **************************************************************************************************
  //  Warning: Using Delphi XE3 syntax!
  //  **************************************************************************************************
  //  The Initial Developer of the Original Code:
  //  OpenCV: open source computer vision library
  //  Homepage:    http://opencv.org
  //  Online docs: http://docs.opencv.org
  //  Q&A forum:   http://answers.opencv.org
  //  Dev zone:    http://code.opencv.org
  //  **************************************************************************************************
  //  Original file:
  //  opencv\modules\objdetect\include\opencv2\objdetect_c.h
  //  ************************************************************************************************* *)

{$IFDEF DEBUG}
{$A8,B-,C+,D+,E-,F-,G+,H+,I+,J-,K-,L+,M-,N+,O-,P+,Q+,R+,S-,T-,U-,V+,W+,X+,Y+,Z1}
{$ELSE}
{$A8,B-,C-,D-,E-,F-,G+,H+,I+,J-,K-,L-,M-,N+,O+,P+,Q-,R-,S-,T-,U-,V+,W-,X+,Y-,Z1}
{$ENDIF}
{$WARN SYMBOL_DEPRECATED OFF}
{$WARN SYMBOL_PLATFORM OFF}
{$WARN UNIT_PLATFORM OFF}
{$WARN UNSAFE_TYPE OFF}
{$WARN UNSAFE_CODE OFF}
{$WARN UNSAFE_CAST OFF}
unit objdetect_c;

interface

Uses core_c, Core.types_c;

/// ****************************************************************************************\
// *                         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
type
  pCvLSVMFilterPosition = ^TCvLSVMFilterPosition;

  TCvLSVMFilterPosition = packed record
    x: Integer;
    y: Integer;
    l: Integer;
  end;

  // 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
Type
  pCvLSVMFilterObject = ^TCvLSVMFilterObject;

  TpCvLSVMFilterObject = array [0 .. 1] of pCvLSVMFilterObject;
  ppCvLSVMFilterObject = ^TpCvLSVMFilterObject;

  TCvLSVMFilterObject = packed record
    V: TCvLSVMFilterPosition;
    fineFunction: array [0 .. 3] of single;
    sizeX: Integer;
    sizeY: Integer;
    numFeatures: Integer;
    H: pSingle;
  end;

  // 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
Type
  pCvLatentSvmDetector = ^TCvLatentSvmDetector;

  TCvLatentSvmDetector = packed record
    num_filters: Integer;
    num_components: Integer;
    num_part_filters: pInteger;
    filters: ppCvLSVMFilterObject;
    b: pSingle;
    score_threshold: single;
  end;

  // 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
  pCvObjectDetection = ^TCvObjectDetection;

  TCvObjectDetection = packed record
    rect: TCvRect;
    score: single;
  end;

  /// /////////////// 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);
function cvLoadLatentSvmDetector(const filename: pCVChar): pCvLatentSvmDetector; stdcall;

// release memory allocated for CvLatentSvmDetector structure
//
// API
// void cvReleaseLatentSvmDetector(CvLatentSvmDetector** detector);
// INPUT
// detector             - CvLatentSvmDetector structure to be released
// OUTPUT

// CVAPI(void) cvReleaseLatentSvmDetector(CvLatentSvmDetector** detector);
procedure cvReleaseLatentSvmDetector(Var detector: pCvLatentSvmDetector); stdcall;


// find rectangular regions in the given image that are likely
// to contain objects and corresponding confidence levels
//
// 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));
function cvLatentSvmDetectObjects(image: pIplImage; detector: pCvLatentSvmDetector; storage: pCvMemStorage;
  overlap_threshold: single = 0.5; numThreads: Integer = -1): pCvSeq; stdcall;

// #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__ */

implementation

Uses uLibName;

function cvLatentSvmDetectObjects; external objdetect_dll;
function cvLoadLatentSvmDetector; external objdetect_dll;
procedure cvReleaseLatentSvmDetector; external objdetect_dll;

end.