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1243 lines
43 KiB
ObjectPascal
1243 lines
43 KiB
ObjectPascal
// --------------------------------- OpenCV license.txt ---------------------------
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(* // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage. *)
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(* / **************************************************************************************************
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// Project Delphi-OpenCV
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// **************************************************************************************************
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// Contributor:
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// laentir Valetov
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// email:laex@bk.ru
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// **************************************************************************************************
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// You may retrieve the latest version of this file at the GitHub,
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// located at git://github.com/Laex/Delphi-OpenCV.git
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// **************************************************************************************************
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// License:
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// The contents of this file are subject to the Mozilla Public License Version 1.1 (the "License");
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// you may not use this file except in compliance with the License. You may obtain a copy of the
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// License at http://www.mozilla.org/MPL/
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//
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// Software distributed under the License is distributed on an "AS IS" basis, WITHOUT WARRANTY OF
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// ANY KIND, either express or implied. See the License for the specific language governing rights
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// and limitations under the License.
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//
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// Alternatively, the contents of this file may be used under the terms of the
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// GNU Lesser General Public License (the "LGPL License"), in which case the
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// provisions of the LGPL License are applicable instead of those above.
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// If you wish to allow use of your version of this file only under the terms
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// of the LGPL License and not to allow others to use your version of this file
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// under the MPL, indicate your decision by deleting the provisions above and
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// replace them with the notice and other provisions required by the LGPL
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// License. If you do not delete the provisions above, a recipient may use
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// your version of this file under either the MPL or the LGPL License.
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//
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// For more information about the LGPL: http://www.gnu.org/copyleft/lesser.html
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// **************************************************************************************************
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// Warning: Using Delphi XE3 syntax!
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// **************************************************************************************************
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// The Initial Developer of the Original Code:
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// OpenCV: open source computer vision library
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// Homepage: http://opencv.org
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// Online docs: http://docs.opencv.org
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// Q&A forum: http://answers.opencv.org
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// Dev zone: http://code.opencv.org
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// **************************************************************************************************
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// Original file:
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// opencv\modules\objdetect\include\opencv2\objdetect.hpp
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// ************************************************************************************************* *)
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{$IFDEF DEBUG}
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{$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}
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{$ELSE}
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{$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}
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{$ENDIF}
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{$WARN SYMBOL_DEPRECATED OFF}
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{$WARN SYMBOL_PLATFORM OFF}
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{$WARN UNIT_PLATFORM OFF}
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{$WARN UNSAFE_TYPE OFF}
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{$WARN UNSAFE_CODE OFF}
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{$WARN UNSAFE_CAST OFF}
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Unit ObjDetect;
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interface
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Uses Core.types_c, haar, System.Types, Xml.XMLDoc;
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(*
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//****************************************************************************************\
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//* Haar-like Object Detection functions *
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//****************************************************************************************/
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*)
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Const
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CV_HAAR_MAGIC_VAL = $42500000;
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CV_TYPE_NAME_HAAR = 'opencv-haar-classifier';
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// #define CV_IS_HAAR_CLASSIFIER( haar ) \
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// ((haar) != NULL && \
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// (((const CvHaarClassifierCascade*)(haar))->flags & CV_MAGIC_MASK)==CV_HAAR_MAGIC_VAL)
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type
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THaarFeature = record
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r: TCvRect;
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weight: Single;
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end;
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pCvHaarFeature = ^TCvHaarFeature;
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TCvHaarFeature = packed record
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tilted: Integer;
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rect: array [0 .. CV_HAAR_FEATURE_MAX - 1] of THaarFeature;
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end;
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pCvHaarClassifier = ^TCvHaarClassifier;
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TCvHaarClassifier = packed record
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count: Integer;
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haar_feature: pCvHaarFeature;
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threshold: pSingle;
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left: pInteger;
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right: pInteger;
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alpha: pSingle;
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end;
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pCvHaarStageClassifier = ^TCvHaarStageClassifier;
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TCvHaarStageClassifier = packed record
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count: Integer;
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threshold: Single;
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classifier: pCvHaarClassifier;
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next: Integer;
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child: Integer;
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parent: Integer;
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end;
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// TCvHidHaarClassifierCascade = TCvHidHaarClassifierCascade;
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pCvHidHaarClassifierCascade = ^TCvHidHaarClassifierCascade;
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pCvHaarClassifierCascade = ^TCvHaarClassifierCascade;
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TCvHaarClassifierCascade = packed record
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flags: Integer;
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count: Integer;
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orig_window_size: TCvSize;
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real_window_size: TCvSize;
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scale: Real;
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stage_classifier: pCvHaarStageClassifier;
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hid_cascade: pCvHidHaarClassifierCascade;
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end;
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TCvAvgComp = packed record
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rect: TCvRect;
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neighbors: Integer;
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end;
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{
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// Loads haar classifier cascade from a directory.
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// It is obsolete: convert your cascade to xml and use cvLoad instead
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CVAPI(CvHaarClassifierCascade*) cvLoadHaarClassifierCascade(
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const char* directory,
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CvSize orig_window_size);
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}
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function cvLoadHaarClassifierCascade(const directory: pCVChar; orig_window_size: TCvSize)
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: pCvHaarClassifierCascade; cdecl;
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// CVAPI(void) cvReleaseHaarClassifierCascade( CvHaarClassifierCascade** cascade );
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procedure cvReleaseHaarClassifierCascade(Var cascade: pCvHaarClassifierCascade); cdecl;
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Const
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CV_HAAR_DO_CANNY_PRUNING = 1;
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CV_HAAR_SCALE_IMAGE = 2;
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CV_HAAR_FIND_BIGGEST_OBJECT = 4;
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CV_HAAR_DO_ROUGH_SEARCH = 8;
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// CVAPI(CvSeq*) cvHaarDetectObjectsForROC( const CvArr* image,
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// CvHaarClassifierCascade* cascade, CvMemStorage* storage,
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// CvSeq** rejectLevels, CvSeq** levelWeightds,
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// double scale_factor CV_DEFAULT(1.1),
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// int min_neighbors CV_DEFAULT(3), int flags CV_DEFAULT(0),
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// CvSize min_size CV_DEFAULT(cvSize(0,0)), CvSize max_size CV_DEFAULT(cvSize(0,0)),
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// bool outputRejectLevels = false );
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{
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CVAPI(CvSeq*) cvHaarDetectObjects(
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const CvArr* image,
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CvHaarClassifierCascade* cascade,
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CvMemStorage* storage,
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double scale_factor CV_DEFAULT(1.1),
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int min_neighbors CV_DEFAULT(3),
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int flags CV_DEFAULT(0),
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CvSize min_size CV_DEFAULT(cvSize(0,0)),
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CvSize max_size CV_DEFAULT(cvSize(0,0)));
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}
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function cvHaarDetectObjects(
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{ } const image: pIplImage;
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{ } cascade: pCvHaarClassifierCascade;
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{ } storage: pCvMemStorage;
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{ } scale_factor: double { =1.1 };
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{ } min_neighbors: Integer { =3 };
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{ } flags: Integer { = 0 };
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{ } min_size: TCvSize { =CV_DEFAULT(cvSize(0,0)) };
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{ } max_size: TCvSize { =CV_DEFAULT(cvSize(0,0)) } ): pCvSeq; cdecl;
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{
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/* sets images for haar classifier cascade */
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CVAPI(void) cvSetImagesForHaarClassifierCascade( CvHaarClassifierCascade* cascade,
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const CvArr* sum, const CvArr* sqsum,
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const CvArr* tilted_sum, double scale );
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}
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procedure cvSetImagesForHaarClassifierCascade(
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{ } cascade: pCvHaarClassifierCascade;
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{ } const sum: pCvArr;
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{ } const sqsum: pCvArr;
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{ } const tilted_sum: pCvArr;
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{ } scale: double); cdecl;
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//
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/// * runs the cascade on the specified window */
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// CVAPI(int) cvRunHaarClassifierCascade( const CvHaarClassifierCascade* cascade,
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// CvPoint pt, int start_stage CV_DEFAULT(0));
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//
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//
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/// ****************************************************************************************\
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// * Latent SVM Object Detection functions *
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// \****************************************************************************************/
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//
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/// / DataType: STRUCT position
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/// / Structure describes the position of the filter in the feature pyramid
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/// / l - level in the feature pyramid
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/// / (x, y) - coordinate in level l
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// typedef struct CvLSVMFilterPosition
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// {
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// int x;
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// int y;
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// int l;
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// } CvLSVMFilterPosition;
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//
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/// / DataType: STRUCT filterObject
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/// / Description of the filter, which corresponds to the part of the object
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/// / V - ideal (penalty = 0) position of the partial filter
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/// / from the root filter position (V_i in the paper)
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/// / penaltyFunction - vector describes penalty function (d_i in the paper)
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/// / pf[0] * x + pf[1] * y + pf[2] * x^2 + pf[3] * y^2
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/// / FILTER DESCRIPTION
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/// / Rectangular map (sizeX x sizeY),
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/// / every cell stores feature vector (dimension = p)
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/// / H - matrix of feature vectors
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/// / to set and get feature vectors (i,j)
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/// / used formula H[(j * sizeX + i) * p + k], where
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/// / k - component of feature vector in cell (i, j)
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/// / END OF FILTER DESCRIPTION
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// typedef struct CvLSVMFilterObject{
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// CvLSVMFilterPosition V;
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// float fineFunction[4];
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// int sizeX;
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// int sizeY;
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// int numFeatures;
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// float *H;
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// } CvLSVMFilterObject;
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//
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/// / data type: STRUCT CvLatentSvmDetector
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/// / structure contains internal representation of trained Latent SVM detector
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/// / num_filters - total number of filters (root plus part) in model
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/// / num_components - number of components in model
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/// / num_part_filters - array containing number of part filters for each component
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/// / filters - root and part filters for all model components
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/// / b - biases for all model components
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/// / score_threshold - confidence level threshold
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// typedef struct CvLatentSvmDetector
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// {
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// int num_filters;
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// int num_components;
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// int* num_part_filters;
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// CvLSVMFilterObject** filters;
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// float* b;
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// float score_threshold;
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// }
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// CvLatentSvmDetector;
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//
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/// / data type: STRUCT CvObjectDetection
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/// / structure contains the bounding box and confidence level for detected object
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/// / rect - bounding box for a detected object
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/// / score - confidence level
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// typedef struct CvObjectDetection
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// {
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// CvRect rect;
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// float score;
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// } CvObjectDetection;
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//
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/// /////////////// Object Detection using Latent SVM //////////////
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//
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//
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/// *
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/// / load trained detector from a file
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/// /
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/// / API
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/// / CvLatentSvmDetector* cvLoadLatentSvmDetector(const char* filename);
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/// / INPUT
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/// / filename - path to the file containing the parameters of
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// - trained Latent SVM detector
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/// / OUTPUT
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/// / trained Latent SVM detector in internal representation
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// */
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// CVAPI(CvLatentSvmDetector*) cvLoadLatentSvmDetector(const char* filename);
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//
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/// *
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/// / release memory allocated for CvLatentSvmDetector structure
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/// /
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/// / API
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/// / void cvReleaseLatentSvmDetector(CvLatentSvmDetector** detector);
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/// / INPUT
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/// / detector - CvLatentSvmDetector structure to be released
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/// / OUTPUT
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// */
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// CVAPI(void) cvReleaseLatentSvmDetector(CvLatentSvmDetector** detector);
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//
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/// *
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/// / find rectangular regions in the given image that are likely
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/// / to contain objects and corresponding confidence levels
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/// /
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/// / API
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/// / CvSeq* cvLatentSvmDetectObjects(const IplImage* image,
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/// / CvLatentSvmDetector* detector,
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/// / CvMemStorage* storage,
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/// / float overlap_threshold = 0.5f,
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/// / int numThreads = -1);
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/// / INPUT
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/// / image - image to detect objects in
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/// / detector - Latent SVM detector in internal representation
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/// / storage - memory storage to store the resultant sequence
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/// / of the object candidate rectangles
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/// / overlap_threshold - threshold for the non-maximum suppression algorithm
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// = 0.5f [here will be the reference to original paper]
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/// / OUTPUT
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/// / sequence of detected objects (bounding boxes and confidence levels stored in CvObjectDetection structures)
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// */
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// CVAPI(CvSeq*) cvLatentSvmDetectObjects(IplImage* image,
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// CvLatentSvmDetector* detector,
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// CvMemStorage* storage,
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// float overlap_threshold CV_DEFAULT(0.5f),
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// int numThreads CV_DEFAULT(-1));
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//
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// #ifdef __cplusplus
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// }
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//
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// CV_EXPORTS CvSeq* cvHaarDetectObjectsForROC( const CvArr* image,
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// CvHaarClassifierCascade* cascade, CvMemStorage* storage,
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// std::vector<int>& rejectLevels, std::vector<double>& levelWeightds,
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// double scale_factor CV_DEFAULT(1.1),
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// int min_neighbors CV_DEFAULT(3), int flags CV_DEFAULT(0),
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// CvSize min_size CV_DEFAULT(cvSize(0,0)), CvSize max_size CV_DEFAULT(cvSize(0,0)),
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// bool outputRejectLevels = false );
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//
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// namespace cv
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// {
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//
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/// //////////////////////////// Object Detection ////////////////////////////
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//
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/// *
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// * This is a class wrapping up the structure CvLatentSvmDetector and functions working with it.
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// * The class goals are:
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// * 1) provide c++ interface;
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// * 2) make it possible to load and detect more than one class (model) unlike CvLatentSvmDetector.
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// */
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// class CV_EXPORTS LatentSvmDetector
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// {
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// public:
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// struct CV_EXPORTS ObjectDetection
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// {
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// ObjectDetection();
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// ObjectDetection( const Rect& rect, float score, int classID=-1 );
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// Rect rect;
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// float score;
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// int classID;
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// };
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//
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// LatentSvmDetector();
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// LatentSvmDetector( const std::vector<String>& filenames, const std::vector<String>& classNames=std::vector<String>() );
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// virtual ~LatentSvmDetector();
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//
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// virtual void clear();
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// virtual bool empty() const;
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// bool load( const std::vector<String>& filenames, const std::vector<String>& classNames=std::vector<String>() );
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//
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// virtual void detect( const Mat& image,
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// std::vector<ObjectDetection>& objectDetections,
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// float overlapThreshold=0.5f,
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// int numThreads=-1 );
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//
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// const std::vector<String>& getClassNames() const;
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// size_t getClassCount() const;
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//
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// private:
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// std::vector<CvLatentSvmDetector*> detectors;
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// std::vector<String> classNames;
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// };
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//
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// CV_EXPORTS void groupRectangles(CV_OUT CV_IN_OUT std::vector<Rect>& rectList, int groupThreshold, double eps=0.2);
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// 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);
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// CV_EXPORTS void groupRectangles( std::vector<Rect>& rectList, int groupThreshold, double eps, std::vector<int>* weights, std::vector<double>* levelWeights );
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// CV_EXPORTS void groupRectangles(std::vector<Rect>& rectList, std::vector<int>& rejectLevels,
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// std::vector<double>& levelWeights, int groupThreshold, double eps=0.2);
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// CV_EXPORTS void groupRectangles_meanshift(std::vector<Rect>& rectList, std::vector<double>& foundWeights, std::vector<double>& foundScales,
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// double detectThreshold = 0.0, Size winDetSize = Size(64, 128));
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//
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//
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// class CV_EXPORTS FeatureEvaluator
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// {
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// public:
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// enum { HAAR = 0, LBP = 1, HOG = 2 };
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// virtual ~FeatureEvaluator();
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//
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// virtual bool read(const FileNode& node);
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// virtual Ptr<FeatureEvaluator> clone() const;
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// virtual int getFeatureType() const;
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//
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// virtual bool setImage(const Mat& img, Size origWinSize);
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// virtual bool setWindow(Point p);
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//
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// virtual double calcOrd(int featureIdx) const;
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// virtual int calcCat(int featureIdx) const;
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//
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// static Ptr<FeatureEvaluator> create(int type);
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// };
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//
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// template<> CV_EXPORTS void Ptr<CvHaarClassifierCascade>::delete_obj();
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//
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// enum
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// {
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// CASCADE_DO_CANNY_PRUNING=1,
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// CASCADE_SCALE_IMAGE=2,
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// CASCADE_FIND_BIGGEST_OBJECT=4,
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// CASCADE_DO_ROUGH_SEARCH=8
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// };
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//
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Type
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TCascadeClassifier = class
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public
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// CV_WRAP CascadeClassifier();
|
|
constructor Create; overload;
|
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// CV_WRAP CascadeClassifier( const String& filename );
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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: TArray<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: TArray<TCvRect>;
|
|
{ } const rejectLevels: TArray<Integer>;
|
|
{ } const levelWeights: TArray<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: TArray<TCvRect>; rejectLevels: TArray<Integer>;
|
|
levelWeights: TArray<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
|
|
uLibName;
|
|
|
|
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: TArray<TCvRect>;
|
|
const rejectLevels: TArray<Integer>; const levelWeights: TArray<double>; scaleFactor: double;
|
|
minNeighbors, flags: Integer; minSize, maxSize: TCvSize; outputRejectLevels: Boolean);
|
|
begin
|
|
|
|
end;
|
|
|
|
procedure TCascadeClassifier.detectMultiScale(const image: pIplImage; const objects: TArray<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: TArray<TCvRect>; rejectLevels: TArray<Integer>;
|
|
levelWeights: TArray<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.
|