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Delphi-OpenCV/include/imgproc/imgproc.pas
Laex d1fab15b0f Components!? 
[+] Added demos of creating and using visual and non-visual component using OpenCV (\ component \ OpenCV.dpk)
[+] Added an example of using components
(samples \ Components \ cCameraCapture \ cCameraCapture.dpr)
[*] Minor changes in the modules

Signed-off-by: Laex <laex@bk.ru>
2013-05-30 05:12:44 +04:00

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// --------------------------------- OpenCV license.txt ---------------------------
(* // 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.
//
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// (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. *)
(* / **************************************************************************************************
// 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
// **************************************************************************************************
// License:
// 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/
//
// 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
// and limitations under the License.
//
// Alternatively, the contents of this file may be used under the terms of the
// GNU Lesser General Public License (the "LGPL License"), in which case the
// provisions of the LGPL License are applicable instead of those above.
// If you wish to allow use of your version of this file only under the terms
// of the LGPL License and not to allow others to use your version of this file
// under the MPL, indicate your decision by deleting the provisions above and
// replace them with the notice and other provisions required by the LGPL
// License. If you do not delete the provisions above, a recipient may use
// your version of this file under either the MPL or the LGPL License.
//
// 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\imgproc\include\opencv2\imgproc.hpp
// ************************************************************************************************* *)
{$IFDEF DEBUG}
{$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}
{$WARN UNSAFE_CAST OFF}
{$POINTERMATH ON}
unit imgproc;
interface
Uses Core.types_c, imgproc.types_c, Mat, Core.types;
// {
//
/// /! various border interpolation methods
const
BORDER_REPLICATE = IPL_BORDER_REPLICATE;
BORDER_CONSTANT = IPL_BORDER_CONSTANT;
BORDER_REFLECT = IPL_BORDER_REFLECT;
BORDER_WRAP = IPL_BORDER_WRAP;
BORDER_REFLECT_101 = IPL_BORDER_REFLECT_101;
BORDER_REFLECT101 = BORDER_REFLECT_101;
BORDER_TRANSPARENT = IPL_BORDER_TRANSPARENT;
BORDER_DEFAULT = BORDER_REFLECT_101;
BORDER_ISOLATED = 16;
//
/// /! 1D interpolation function: returns coordinate of the "donor" pixel for the specified location p.
// CV_EXPORTS_W int borderInterpolate( int p, int len, int borderType );
/// *!
// The Base Class for 1D or Row-wise Filters
//
// This is the base class for linear or non-linear filters that process 1D data.
// In particular, such filters are used for the "horizontal" filtering parts in separable filters.
//
// Several functions in OpenCV return Ptr<BaseRowFilter> for the specific types of filters,
// and those pointers can be used directly or within cv::FilterEngine.
// */
// class CV_EXPORTS BaseRowFilter
// {
// public:
// //! the default constructor
// BaseRowFilter();
// //! the destructor
// virtual ~BaseRowFilter();
// //! the filtering operator. Must be overrided in the derived classes. The horizontal border interpolation is done outside of the class.
// virtual void operator()(const uchar* src, uchar* dst,
// int width, int cn) = 0;
// int ksize, anchor;
// };
//
/// *!
// The Base Class for Column-wise Filters
//
// This is the base class for linear or non-linear filters that process columns of 2D arrays.
// Such filters are used for the "vertical" filtering parts in separable filters.
//
// Several functions in OpenCV return Ptr<BaseColumnFilter> for the specific types of filters,
// and those pointers can be used directly or within cv::FilterEngine.
//
// Unlike cv::BaseRowFilter, cv::BaseColumnFilter may have some context information,
// i.e. box filter keeps the sliding sum of elements. To reset the state BaseColumnFilter::reset()
// must be called (e.g. the method is called by cv::FilterEngine)
// */
// class CV_EXPORTS BaseColumnFilter
// {
// public:
// //! the default constructor
// BaseColumnFilter();
// //! the destructor
// virtual ~BaseColumnFilter();
// //! the filtering operator. Must be overrided in the derived classes. The vertical border interpolation is done outside of the class.
// virtual void operator()(const uchar** src, uchar* dst, int dststep,
// int dstcount, int width) = 0;
// //! resets the internal buffers, if any
// virtual void reset();
// int ksize, anchor;
// };
// *!
// The Base Class for Non-Separable 2D Filters.
//
// This is the base class for linear or non-linear 2D filters.
//
// Several functions in OpenCV return Ptr<BaseFilter> for the specific types of filters,
// and those pointers can be used directly or within cv::FilterEngine.
//
// Similar to cv::BaseColumnFilter, the class may have some context information,
// that should be reset using BaseFilter::reset() method before processing the new array.
//
// class CV_EXPORTS BaseFilter
// {
// public:
// //! the default constructor
// BaseFilter();
// //! the destructor
// virtual ~BaseFilter();
// //! the filtering operator. The horizontal and the vertical border interpolation is done outside of the class.
// virtual void operator()(const uchar** src, uchar* dst, int dststep,
// int dstcount, int width, int cn) = 0;
// //! resets the internal buffers, if any
// virtual void reset();
// Size ksize;
// Point anchor;
// };
/// *!
// The Main Class for Image Filtering.
//
// The class can be used to apply an arbitrary filtering operation to an image.
// It contains all the necessary intermediate buffers, it computes extrapolated values
// of the "virtual" pixels outside of the image etc.
// Pointers to the initialized cv::FilterEngine instances
// are returned by various OpenCV functions, such as cv::createSeparableLinearFilter(),
// cv::createLinearFilter(), cv::createGaussianFilter(), cv::createDerivFilter(),
// cv::createBoxFilter() and cv::createMorphologyFilter().
//
// Using the class you can process large images by parts and build complex pipelines
// that include filtering as some of the stages. If all you need is to apply some pre-defined
// filtering operation, you may use cv::filter2D(), cv::erode(), cv::dilate() etc.
// functions that create FilterEngine internally.
//
// Here is the example on how to use the class to implement Laplacian operator, which is the sum of
// second-order derivatives. More complex variant for different types is implemented in cv::Laplacian().
//
// \code
// void laplace_f(const Mat& src, Mat& dst)
// {
// CV_Assert( src.type() == CV_32F );
// // make sure the destination array has the proper size and type
// dst.create(src.size(), src.type());
//
// // get the derivative and smooth kernels for d2I/dx2.
// // for d2I/dy2 we could use the same kernels, just swapped
// Mat kd, ks;
// getSobelKernels( kd, ks, 2, 0, ksize, false, ktype );
//
// // let's process 10 source rows at once
// int DELTA = std::min(10, src.rows);
// Ptr<FilterEngine> Fxx = createSeparableLinearFilter(src.type(),
// dst.type(), kd, ks, Point(-1,-1), 0, borderType, borderType, Scalar() );
// Ptr<FilterEngine> Fyy = createSeparableLinearFilter(src.type(),
// dst.type(), ks, kd, Point(-1,-1), 0, borderType, borderType, Scalar() );
//
// int y = Fxx->start(src), dsty = 0, dy = 0;
// Fyy->start(src);
// const uchar* sptr = src.data + y*src.step;
//
// // allocate the buffers for the spatial image derivatives;
// // the buffers need to have more than DELTA rows, because at the
// // last iteration the output may take max(kd.rows-1,ks.rows-1)
// // rows more than the input.
// Mat Ixx( DELTA + kd.rows - 1, src.cols, dst.type() );
// Mat Iyy( DELTA + kd.rows - 1, src.cols, dst.type() );
//
// // inside the loop we always pass DELTA rows to the filter
// // (note that the "proceed" method takes care of possibe overflow, since
// // it was given the actual image height in the "start" method)
// // on output we can get:
// // * < DELTA rows (the initial buffer accumulation stage)
// // * = DELTA rows (settled state in the middle)
// // * > DELTA rows (then the input image is over, but we generate
// // "virtual" rows using the border mode and filter them)
// // this variable number of output rows is dy.
// // dsty is the current output row.
// // sptr is the pointer to the first input row in the portion to process
// for( ; dsty < dst.rows; sptr += DELTA*src.step, dsty += dy )
// {
// Fxx->proceed( sptr, (int)src.step, DELTA, Ixx.data, (int)Ixx.step );
// dy = Fyy->proceed( sptr, (int)src.step, DELTA, d2y.data, (int)Iyy.step );
// if( dy > 0 )
// {
// Mat dstripe = dst.rowRange(dsty, dsty + dy);
// add(Ixx.rowRange(0, dy), Iyy.rowRange(0, dy), dstripe);
// }
// }
// }
// \endcode
// */
// class CV_EXPORTS FilterEngine
// {
// public:
// //! the default constructor
// FilterEngine();
// //! the full constructor. Either _filter2D or both _rowFilter and _columnFilter must be non-empty.
// FilterEngine(const Ptr<BaseFilter>& _filter2D,
// const Ptr<BaseRowFilter>& _rowFilter,
// const Ptr<BaseColumnFilter>& _columnFilter,
// int srcType, int dstType, int bufType,
// int _rowBorderType=BORDER_REPLICATE,
// int _columnBorderType=-1,
// const Scalar& _borderValue=Scalar());
// //! the destructor
// virtual ~FilterEngine();
// //! reinitializes the engine. The previously assigned filters are released.
// void init(const Ptr<BaseFilter>& _filter2D,
// const Ptr<BaseRowFilter>& _rowFilter,
// const Ptr<BaseColumnFilter>& _columnFilter,
// int srcType, int dstType, int bufType,
// int _rowBorderType=BORDER_REPLICATE, int _columnBorderType=-1,
// const Scalar& _borderValue=Scalar());
// //! starts filtering of the specified ROI of an image of size wholeSize.
// virtual int start(Size wholeSize, Rect roi, int maxBufRows=-1);
// //! starts filtering of the specified ROI of the specified image.
// virtual int start(const Mat& src, const Rect& srcRoi=Rect(0,0,-1,-1),
// bool isolated=false, int maxBufRows=-1);
// //! processes the next srcCount rows of the image.
// virtual int proceed(const uchar* src, int srcStep, int srcCount,
// uchar* dst, int dstStep);
// //! applies filter to the specified ROI of the image. if srcRoi=(0,0,-1,-1), the whole image is filtered.
// virtual void apply( const Mat& src, Mat& dst,
// const Rect& srcRoi=Rect(0,0,-1,-1),
// Point dstOfs=Point(0,0),
// bool isolated=false);
// //! returns true if the filter is separable
// bool isSeparable() const { return (const BaseFilter*)filter2D == 0; }
// //! returns the number
// int remainingInputRows() const;
// int remainingOutputRows() const;
//
// int srcType, dstType, bufType;
// Size ksize;
// Point anchor;
// int maxWidth;
// Size wholeSize;
// Rect roi;
// int dx1, dx2;
// int rowBorderType, columnBorderType;
// std::vector<int> borderTab;
// int borderElemSize;
// std::vector<uchar> ringBuf;
// std::vector<uchar> srcRow;
// std::vector<uchar> constBorderValue;
// std::vector<uchar> constBorderRow;
// int bufStep, startY, startY0, endY, rowCount, dstY;
// std::vector<uchar*> rows;
//
// Ptr<BaseFilter> filter2D;
// Ptr<BaseRowFilter> rowFilter;
// Ptr<BaseColumnFilter> columnFilter;
// };
/// /! type of the kernel
// enum { KERNEL_GENERAL=0, KERNEL_SYMMETRICAL=1, KERNEL_ASYMMETRICAL=2,
// KERNEL_SMOOTH=4, KERNEL_INTEGER=8 };
//
/// /! returns type (one of KERNEL_*) of 1D or 2D kernel specified by its coefficients.
// CV_EXPORTS int getKernelType(InputArray kernel, Point anchor);
//
/// /! returns the primitive row filter with the specified kernel
// CV_EXPORTS Ptr<BaseRowFilter> getLinearRowFilter(int srcType, int bufType,
// InputArray kernel, int anchor,
// int symmetryType);
//
/// /! returns the primitive column filter with the specified kernel
// CV_EXPORTS Ptr<BaseColumnFilter> getLinearColumnFilter(int bufType, int dstType,
// InputArray kernel, int anchor,
// int symmetryType, double delta=0,
// int bits=0);
//
/// /! returns 2D filter with the specified kernel
// CV_EXPORTS Ptr<BaseFilter> getLinearFilter(int srcType, int dstType,
// InputArray kernel,
// Point anchor=Point(-1,-1),
// double delta=0, int bits=0);
// ! returns the separable linear filter engine
// CV_EXPORTS Ptr<FilterEngine> createSeparableLinearFilter(int srcType, int dstType,
// InputArray rowKernel, InputArray columnKernel,
// Point anchor=Point(-1,-1), double delta=0,
// int rowBorderType=BORDER_DEFAULT,
// int columnBorderType=-1,
// const Scalar& borderValue=Scalar());
/// /! returns the non-separable linear filter engine
// CV_EXPORTS Ptr<FilterEngine> createLinearFilter(int srcType, int dstType,
// InputArray kernel, Point _anchor=Point(-1,-1),
// double delta=0, int rowBorderType=BORDER_DEFAULT,
// int columnBorderType=-1, const Scalar& borderValue=Scalar());
//
/// /! returns the Gaussian kernel with the specified parameters
// CV_EXPORTS_W Mat getGaussianKernel( int ksize, double sigma, int ktype=CV_64F );
// ! returns the Gaussian filter engine
// CV_EXPORTS Ptr<FilterEngine> createGaussianFilter( int type, Size ksize,
// double sigma1, double sigma2=0,
// int borderType=BORDER_DEFAULT);
/// /! initializes kernels of the generalized Sobel operator
// CV_EXPORTS_W void getDerivKernels( OutputArray kx, OutputArray ky,
// int dx, int dy, int ksize,
// bool normalize=false, int ktype=CV_32F );
/// /! returns filter engine for the generalized Sobel operator
// CV_EXPORTS Ptr<FilterEngine> createDerivFilter( int srcType, int dstType,
// int dx, int dy, int ksize,
// int borderType=BORDER_DEFAULT );
/// /! returns horizontal 1D box filter
// CV_EXPORTS Ptr<BaseRowFilter> getRowSumFilter(int srcType, int sumType,
// int ksize, int anchor=-1);
/// /! returns vertical 1D box filter
// CV_EXPORTS Ptr<BaseColumnFilter> getColumnSumFilter( int sumType, int dstType,
// int ksize, int anchor=-1,
// double scale=1);
/// /! returns box filter engine
// CV_EXPORTS Ptr<FilterEngine> createBoxFilter( int srcType, int dstType, Size ksize,
// Point anchor=Point(-1,-1),
// bool normalize=true,
// int borderType=BORDER_DEFAULT);
//
/// /! returns the Gabor kernel with the specified parameters
// CV_EXPORTS_W Mat getGaborKernel( Size ksize, double sigma, double theta, double lambd,
// double gamma, double psi=CV_PI*0.5, int ktype=CV_64F );
//
/// /! type of morphological operation
// enum { MORPH_ERODE=CV_MOP_ERODE, MORPH_DILATE=CV_MOP_DILATE,
// MORPH_OPEN=CV_MOP_OPEN, MORPH_CLOSE=CV_MOP_CLOSE,
// MORPH_GRADIENT=CV_MOP_GRADIENT, MORPH_TOPHAT=CV_MOP_TOPHAT,
// MORPH_BLACKHAT=CV_MOP_BLACKHAT };
//
/// /! returns horizontal 1D morphological filter
// CV_EXPORTS Ptr<BaseRowFilter> getMorphologyRowFilter(int op, int type, int ksize, int anchor=-1);
/// /! returns vertical 1D morphological filter
// CV_EXPORTS Ptr<BaseColumnFilter> getMorphologyColumnFilter(int op, int type, int ksize, int anchor=-1);
/// /! returns 2D morphological filter
// CV_EXPORTS Ptr<BaseFilter> getMorphologyFilter(int op, int type, InputArray kernel,
// Point anchor=Point(-1,-1));
//
/// /! returns "magic" border value for erosion and dilation. It is automatically transformed to Scalar::all(-DBL_MAX) for dilation.
// static inline Scalar morphologyDefaultBorderValue() { return Scalar::all(DBL_MAX); }
//
/// /! returns morphological filter engine. Only MORPH_ERODE and MORPH_DILATE are supported.
// CV_EXPORTS Ptr<FilterEngine> createMorphologyFilter(int op, int type, InputArray kernel,
// Point anchor=Point(-1,-1), int rowBorderType=BORDER_CONSTANT,
// int columnBorderType=-1,
// const Scalar& borderValue=morphologyDefaultBorderValue());
const
/// /! shape of the structuring element
// enum { MORPH_RECT=0, MORPH_CROSS=1, MORPH_ELLIPSE=2 };
MORPH_RECT = 0;
MORPH_CROSS = 1;
MORPH_ELLIPSE = 2;
/// /! returns structuring element of the specified shape and size
// CV_EXPORTS_W Mat getStructuringElement(int shape, Size ksize, Point anchor=Point(-1,-1));
function getStructuringElement(shape: Integer; ksize: ISize; anchor: IPoint = nil { =Point(-1,-1) } ): IMat;
// template<> CV_EXPORTS void Ptr<IplConvKernel>::delete_obj();
//
/// /! copies 2D array to a larger destination array with extrapolation of the outer part of src using the specified border mode
// CV_EXPORTS_W void copyMakeBorder( InputArray src, OutputArray dst,
// int top, int bottom, int left, int right,
// int borderType, const Scalar& value=Scalar() );
//
/// /! smooths the image using median filter.
// CV_EXPORTS_W void medianBlur( InputArray src, OutputArray dst, int ksize );
// ! smooths the image using Gaussian filter.
// CV_EXPORTS_W void GaussianBlur( InputArray src,
// OutputArray dst, Size ksize,
// double sigmaX, double sigmaY=0,
// int borderType=BORDER_DEFAULT );
/// /! smooths the image using bilateral filter
// CV_EXPORTS_W void bilateralFilter( InputArray src, OutputArray dst, int d,
// double sigmaColor, double sigmaSpace,
// int borderType=BORDER_DEFAULT );
/// /! smooths the image using the box filter. Each pixel is processed in O(1) time
// CV_EXPORTS_W void boxFilter( InputArray src, OutputArray dst, int ddepth,
// Size ksize, Point anchor=Point(-1,-1),
// bool normalize=true,
// int borderType=BORDER_DEFAULT );
/// /! a synonym for normalized box filter
// CV_EXPORTS_W void blur( InputArray src, OutputArray dst,
// Size ksize, Point anchor=Point(-1,-1),
// int borderType=BORDER_DEFAULT );
//
/// /! applies non-separable 2D linear filter to the image
// CV_EXPORTS_W void filter2D( InputArray src, OutputArray dst, int ddepth,
// InputArray kernel, Point anchor=Point(-1,-1),
// double delta=0, int borderType=BORDER_DEFAULT );
//
/// /! applies separable 2D linear filter to the image
// CV_EXPORTS_W void sepFilter2D( InputArray src, OutputArray dst, int ddepth,
// InputArray kernelX, InputArray kernelY,
// Point anchor=Point(-1,-1),
// double delta=0, int borderType=BORDER_DEFAULT );
//
/// /! applies generalized Sobel operator to the image
// CV_EXPORTS_W void Sobel( InputArray src, OutputArray dst, int ddepth,
// int dx, int dy, int ksize=3,
// double scale=1, double delta=0,
// int borderType=BORDER_DEFAULT );
//
/// /! applies the vertical or horizontal Scharr operator to the image
// CV_EXPORTS_W void Scharr( InputArray src, OutputArray dst, int ddepth,
// int dx, int dy, double scale=1, double delta=0,
// int borderType=BORDER_DEFAULT );
//
/// /! applies Laplacian operator to the image
// CV_EXPORTS_W void Laplacian( InputArray src, OutputArray dst, int ddepth,
// int ksize=1, double scale=1, double delta=0,
// int borderType=BORDER_DEFAULT );
//
/// /! applies Canny edge detector and produces the edge map.
// CV_EXPORTS_W void Canny( InputArray image, OutputArray edges,
// double threshold1, double threshold2,
// int apertureSize=3, bool L2gradient=false );
//
/// /! computes minimum eigen value of 2x2 derivative covariation matrix at each pixel - the cornerness criteria
// CV_EXPORTS_W void cornerMinEigenVal( InputArray src, OutputArray dst,
// int blockSize, int ksize=3,
// int borderType=BORDER_DEFAULT );
//
/// /! computes Harris cornerness criteria at each image pixel
// CV_EXPORTS_W void cornerHarris( InputArray src, OutputArray dst, int blockSize,
// int ksize, double k,
// int borderType=BORDER_DEFAULT );
//
/// / low-level function for computing eigenvalues and eigenvectors of 2x2 matrices
// CV_EXPORTS void eigen2x2( const float* a, float* e, int n );
//
/// /! computes both eigenvalues and the eigenvectors of 2x2 derivative covariation matrix at each pixel. The output is stored as 6-channel matrix.
// CV_EXPORTS_W void cornerEigenValsAndVecs( InputArray src, OutputArray dst,
// int blockSize, int ksize,
// int borderType=BORDER_DEFAULT );
//
/// /! computes another complex cornerness criteria at each pixel
// CV_EXPORTS_W void preCornerDetect( InputArray src, OutputArray dst, int ksize,
// int borderType=BORDER_DEFAULT );
//
/// /! adjusts the corner locations with sub-pixel accuracy to maximize the certain cornerness criteria
// CV_EXPORTS_W void cornerSubPix( InputArray image, InputOutputArray corners,
// Size winSize, Size zeroZone,
// TermCriteria criteria );
//
/// /! finds the strong enough corners where the cornerMinEigenVal() or cornerHarris() report the local maxima
// CV_EXPORTS_W void goodFeaturesToTrack( InputArray image, OutputArray corners,
// int maxCorners, double qualityLevel, double minDistance,
// InputArray mask=noArray(), int blockSize=3,
// bool useHarrisDetector=false, double k=0.04 );
//
/// /! finds lines in the black-n-white image using the standard or pyramid Hough transform
// CV_EXPORTS_W void HoughLines( InputArray image, OutputArray lines,
// double rho, double theta, int threshold,
// double srn=0, double stn=0 );
//
/// /! finds line segments in the black-n-white image using probabalistic Hough transform
// CV_EXPORTS_W void HoughLinesP( InputArray image, OutputArray lines,
// double rho, double theta, int threshold,
// double minLineLength=0, double maxLineGap=0 );
//
/// /! finds circles in the grayscale image using 2+1 gradient Hough transform
// CV_EXPORTS_W void HoughCircles( InputArray image, OutputArray circles,
// int method, double dp, double minDist,
// double param1=100, double param2=100,
// int minRadius=0, int maxRadius=0 );
//
// enum
// {
// GHT_POSITION = 0,
// GHT_SCALE = 1,
// GHT_ROTATION = 2
// };
//
/// /! finds arbitrary template in the grayscale image using Generalized Hough Transform
/// /! Ballard, D.H. (1981). Generalizing the Hough transform to detect arbitrary shapes. Pattern Recognition 13 (2): 111-122.
/// /! Guil, N., González-Linares, J.M. and Zapata, E.L. (1999). Bidimensional shape detection using an invariant approach. Pattern Recognition 32 (6): 1025-1038.
// class CV_EXPORTS GeneralizedHough : public Algorithm
// {
// public:
// static Ptr<GeneralizedHough> create(int method);
//
// virtual ~GeneralizedHough();
//
// //! set template to search
// void setTemplate(InputArray templ, int cannyThreshold = 100, Point templCenter = Point(-1, -1));
// void setTemplate(InputArray edges, InputArray dx, InputArray dy, Point templCenter = Point(-1, -1));
//
// //! find template on image
// void detect(InputArray image, OutputArray positions, OutputArray votes = cv::noArray(), int cannyThreshold = 100);
// void detect(InputArray edges, InputArray dx, InputArray dy, OutputArray positions, OutputArray votes = cv::noArray());
//
// void release();
//
// protected:
// virtual void setTemplateImpl(const Mat& edges, const Mat& dx, const Mat& dy, Point templCenter) = 0;
// virtual void detectImpl(const Mat& edges, const Mat& dx, const Mat& dy, OutputArray positions, OutputArray votes) = 0;
// virtual void releaseImpl() = 0;
//
// private:
// Mat edges_, dx_, dy_;
// };
//
// ! erodes the image (applies the local minimum operator)
// CV_EXPORTS_W void erode( InputArray src, OutputArray dst, InputArray kernel,
// Point anchor=Point(-1,-1), int iterations=1,
// int borderType=BORDER_CONSTANT,
// const Scalar& borderValue=morphologyDefaultBorderValue());
procedure erode(src: IMat; dst: IMat; kernel: IMat; anchor: IPoint { =Point(-1,-1) }; iterations: Integer { =1 };
borderType: Integer { =BORDER_CONSTANT }; const borderValue: IScalar { =morphologyDefaultBorderValue() } );
/// /! dilates the image (applies the local maximum operator)
// CV_EXPORTS_W void dilate( InputArray src, OutputArray dst, InputArray kernel,
// Point anchor=Point(-1,-1), int iterations=1,
// int borderType=BORDER_CONSTANT,
// const Scalar& borderValue=morphologyDefaultBorderValue() );
//
/// /! applies an advanced morphological operation to the image
// CV_EXPORTS_W void morphologyEx( InputArray src, OutputArray dst,
// int op, InputArray kernel,
// Point anchor=Point(-1,-1), int iterations=1,
// int borderType=BORDER_CONSTANT,
// const Scalar& borderValue=morphologyDefaultBorderValue() );
//
/// /! interpolation algorithm
// enum
// {
// INTER_NEAREST=CV_INTER_NN, //!< nearest neighbor interpolation
// INTER_LINEAR=CV_INTER_LINEAR, //!< bilinear interpolation
// INTER_CUBIC=CV_INTER_CUBIC, //!< bicubic interpolation
// INTER_AREA=CV_INTER_AREA, //!< area-based (or super) interpolation
// INTER_LANCZOS4=CV_INTER_LANCZOS4, //!< Lanczos interpolation over 8x8 neighborhood
// INTER_MAX=7,
// WARP_INVERSE_MAP=CV_WARP_INVERSE_MAP
// };
//
/// /! resizes the image
// CV_EXPORTS_W void resize( InputArray src, OutputArray dst,
// Size dsize, double fx=0, double fy=0,
// int interpolation=INTER_LINEAR );
//
/// /! warps the image using affine transformation
// CV_EXPORTS_W void warpAffine( InputArray src, OutputArray dst,
// InputArray M, Size dsize,
// int flags=INTER_LINEAR,
// int borderMode=BORDER_CONSTANT,
// const Scalar& borderValue=Scalar());
//
/// /! warps the image using perspective transformation
// CV_EXPORTS_W void warpPerspective( InputArray src, OutputArray dst,
// InputArray M, Size dsize,
// int flags=INTER_LINEAR,
// int borderMode=BORDER_CONSTANT,
// const Scalar& borderValue=Scalar());
//
// enum
// {
// INTER_BITS=5, INTER_BITS2=INTER_BITS*2,
// INTER_TAB_SIZE=(1<<INTER_BITS),
// INTER_TAB_SIZE2=INTER_TAB_SIZE*INTER_TAB_SIZE
// };
//
/// /! warps the image using the precomputed maps. The maps are stored in either floating-point or integer fixed-point format
// CV_EXPORTS_W void remap( InputArray src, OutputArray dst,
// InputArray map1, InputArray map2,
// int interpolation, int borderMode=BORDER_CONSTANT,
// const Scalar& borderValue=Scalar());
//
/// /! converts maps for remap from floating-point to fixed-point format or backwards
// CV_EXPORTS_W void convertMaps( InputArray map1, InputArray map2,
// OutputArray dstmap1, OutputArray dstmap2,
// int dstmap1type, bool nninterpolation=false );
//
/// /! returns 2x3 affine transformation matrix for the planar rotation.
// CV_EXPORTS_W Mat getRotationMatrix2D( Point2f center, double angle, double scale );
/// /! returns 3x3 perspective transformation for the corresponding 4 point pairs.
// CV_EXPORTS Mat getPerspectiveTransform( const Point2f src[], const Point2f dst[] );
/// /! returns 2x3 affine transformation for the corresponding 3 point pairs.
// CV_EXPORTS Mat getAffineTransform( const Point2f src[], const Point2f dst[] );
/// /! computes 2x3 affine transformation matrix that is inverse to the specified 2x3 affine transformation.
// CV_EXPORTS_W void invertAffineTransform( InputArray M, OutputArray iM );
//
// CV_EXPORTS_W Mat getPerspectiveTransform( InputArray src, InputArray dst );
// CV_EXPORTS_W Mat getAffineTransform( InputArray src, InputArray dst );
//
/// /! extracts rectangle from the image at sub-pixel location
// CV_EXPORTS_W void getRectSubPix( InputArray image, Size patchSize,
// Point2f center, OutputArray patch, int patchType=-1 );
//
/// /! computes the integral image
// CV_EXPORTS_W void integral( InputArray src, OutputArray sum, int sdepth=-1 );
//
/// /! computes the integral image and integral for the squared image
// CV_EXPORTS_AS(integral2) void integral( InputArray src, OutputArray sum,
// OutputArray sqsum, int sdepth=-1 );
/// /! computes the integral image, integral for the squared image and the tilted integral image
// CV_EXPORTS_AS(integral3) void integral( InputArray src, OutputArray sum,
// OutputArray sqsum, OutputArray tilted,
// int sdepth=-1 );
//
/// /! adds image to the accumulator (dst += src). Unlike cv::add, dst and src can have different types.
// CV_EXPORTS_W void accumulate( InputArray src, InputOutputArray dst,
// InputArray mask=noArray() );
/// /! adds squared src image to the accumulator (dst += src*src).
// CV_EXPORTS_W void accumulateSquare( InputArray src, InputOutputArray dst,
// InputArray mask=noArray() );
/// /! adds product of the 2 images to the accumulator (dst += src1*src2).
// CV_EXPORTS_W void accumulateProduct( InputArray src1, InputArray src2,
// InputOutputArray dst, InputArray mask=noArray() );
/// /! updates the running average (dst = dst*(1-alpha) + src*alpha)
// CV_EXPORTS_W void accumulateWeighted( InputArray src, InputOutputArray dst,
// double alpha, InputArray mask=noArray() );
//
/// /! computes PSNR image/video quality metric
// CV_EXPORTS_W double PSNR(InputArray src1, InputArray src2);
//
// CV_EXPORTS_W Point2d phaseCorrelate(InputArray src1, InputArray src2,
// InputArray window = noArray(), CV_OUT double* response=0);
// CV_EXPORTS_W void createHanningWindow(OutputArray dst, Size winSize, int type);
//
/// /! type of the threshold operation
const
THRESH_BINARY = CV_THRESH_BINARY;
THRESH_BINARY_INV = CV_THRESH_BINARY_INV;
THRESH_TRUNC = CV_THRESH_TRUNC;
THRESH_TOZERO = CV_THRESH_TOZERO;
THRESH_TOZERO_INV = CV_THRESH_TOZERO_INV;
THRESH_MASK = CV_THRESH_MASK;
THRESH_OTSU = CV_THRESH_OTSU;
//
/// /! applies fixed threshold to the image
// CV_EXPORTS_W double threshold( InputArray src, OutputArray dst,
// double thresh, double maxval, int type );
//
/// /! adaptive threshold algorithm
// enum { ADAPTIVE_THRESH_MEAN_C=0, ADAPTIVE_THRESH_GAUSSIAN_C=1 };
//
/// /! applies variable (adaptive) threshold to the image
// CV_EXPORTS_W void adaptiveThreshold( InputArray src, OutputArray dst,
// double maxValue, int adaptiveMethod,
// int thresholdType, int blockSize, double C );
//
/// /! smooths and downsamples the image
// CV_EXPORTS_W void pyrDown( InputArray src, OutputArray dst,
// const Size& dstsize=Size(), int borderType=BORDER_DEFAULT );
/// /! upsamples and smoothes the image
// CV_EXPORTS_W void pyrUp( InputArray src, OutputArray dst,
// const Size& dstsize=Size(), int borderType=BORDER_DEFAULT );
//
/// /! builds the gaussian pyramid using pyrDown() as a basic operation
// CV_EXPORTS void buildPyramid( InputArray src, OutputArrayOfArrays dst,
// int maxlevel, int borderType=BORDER_DEFAULT );
//
/// /! corrects lens distortion for the given camera matrix and distortion coefficients
// CV_EXPORTS_W void undistort( InputArray src, OutputArray dst,
// InputArray cameraMatrix,
// InputArray distCoeffs,
// InputArray newCameraMatrix=noArray() );
//
/// /! initializes maps for cv::remap() to correct lens distortion and optionally rectify the image
// CV_EXPORTS_W void initUndistortRectifyMap( InputArray cameraMatrix, InputArray distCoeffs,
// InputArray R, InputArray newCameraMatrix,
// Size size, int m1type, OutputArray map1, OutputArray map2 );
//
// enum
// {
// PROJ_SPHERICAL_ORTHO = 0,
// PROJ_SPHERICAL_EQRECT = 1
// };
//
/// /! initializes maps for cv::remap() for wide-angle
// CV_EXPORTS_W float initWideAngleProjMap( InputArray cameraMatrix, InputArray distCoeffs,
// Size imageSize, int destImageWidth,
// int m1type, OutputArray map1, OutputArray map2,
// int projType=PROJ_SPHERICAL_EQRECT, double alpha=0);
//
/// /! returns the default new camera matrix (by default it is the same as cameraMatrix unless centerPricipalPoint=true)
// CV_EXPORTS_W Mat getDefaultNewCameraMatrix( InputArray cameraMatrix, Size imgsize=Size(),
// bool centerPrincipalPoint=false );
//
/// /! returns points' coordinates after lens distortion correction
// CV_EXPORTS_W void undistortPoints( InputArray src, OutputArray dst,
// InputArray cameraMatrix, InputArray distCoeffs,
// InputArray R=noArray(), InputArray P=noArray());
//
// template<> CV_EXPORTS void Ptr<CvHistogram>::delete_obj();
//
/// /! computes the joint dense histogram for a set of images.
// CV_EXPORTS void calcHist( const Mat* images, int nimages,
// const int* channels, InputArray mask,
// OutputArray hist, int dims, const int* histSize,
// const float** ranges, bool uniform=true, bool accumulate=false );
//
/// /! computes the joint sparse histogram for a set of images.
// CV_EXPORTS void calcHist( const Mat* images, int nimages,
// const int* channels, InputArray mask,
// SparseMat& hist, int dims,
// const int* histSize, const float** ranges,
// bool uniform=true, bool accumulate=false );
//
// CV_EXPORTS_W void calcHist( InputArrayOfArrays images,
// const std::vector<int>& channels,
// InputArray mask, OutputArray hist,
// const std::vector<int>& histSize,
// const std::vector<float>& ranges,
// bool accumulate=false );
//
/// /! computes back projection for the set of images
// CV_EXPORTS void calcBackProject( const Mat* images, int nimages,
// const int* channels, InputArray hist,
// OutputArray backProject, const float** ranges,
// double scale=1, bool uniform=true );
//
/// /! computes back projection for the set of images
// CV_EXPORTS void calcBackProject( const Mat* images, int nimages,
// const int* channels, const SparseMat& hist,
// OutputArray backProject, const float** ranges,
// double scale=1, bool uniform=true );
//
// CV_EXPORTS_W void calcBackProject( InputArrayOfArrays images, const std::vector<int>& channels,
// InputArray hist, OutputArray dst,
// const std::vector<float>& ranges,
// double scale );
//
/// *CV_EXPORTS void calcBackProjectPatch( const Mat* images, int nimages, const int* channels,
// InputArray hist, OutputArray dst, Size patchSize,
// int method, double factor=1 );
//
// CV_EXPORTS_W void calcBackProjectPatch( InputArrayOfArrays images, const std::vector<int>& channels,
// InputArray hist, OutputArray dst, Size patchSize,
// int method, double factor=1 );*/
//
/// /! compares two histograms stored in dense arrays
// CV_EXPORTS_W double compareHist( InputArray H1, InputArray H2, int method );
//
/// /! compares two histograms stored in sparse arrays
// CV_EXPORTS double compareHist( const SparseMat& H1, const SparseMat& H2, int method );
//
/// /! normalizes the grayscale image brightness and contrast by normalizing its histogram
// CV_EXPORTS_W void equalizeHist( InputArray src, OutputArray dst );
//
// class CV_EXPORTS CLAHE : public Algorithm
// {
// public:
// virtual void apply(InputArray src, OutputArray dst) = 0;
//
// virtual void setClipLimit(double clipLimit) = 0;
// virtual double getClipLimit() const = 0;
//
// virtual void setTilesGridSize(Size tileGridSize) = 0;
// virtual Size getTilesGridSize() const = 0;
//
// virtual void collectGarbage() = 0;
// };
// CV_EXPORTS Ptr<CLAHE> createCLAHE(double clipLimit = 40.0, Size tileGridSize = Size(8, 8));
//
// CV_EXPORTS float EMD( InputArray signature1, InputArray signature2,
// int distType, InputArray cost=noArray(),
// float* lowerBound=0, OutputArray flow=noArray() );
//
/// /! segments the image using watershed algorithm
// CV_EXPORTS_W void watershed( InputArray image, InputOutputArray markers );
//
/// /! filters image using meanshift algorithm
// CV_EXPORTS_W void pyrMeanShiftFiltering( InputArray src, OutputArray dst,
// double sp, double sr, int maxLevel=1,
// TermCriteria termcrit=TermCriteria(
// TermCriteria::MAX_ITER+TermCriteria::EPS,5,1) );
//
/// /! class of the pixel in GrabCut algorithm
// enum
// {
// GC_BGD = 0, //!< background
// GC_FGD = 1, //!< foreground
// GC_PR_BGD = 2, //!< most probably background
// GC_PR_FGD = 3 //!< most probably foreground
// };
//
/// /! GrabCut algorithm flags
// enum
// {
// GC_INIT_WITH_RECT = 0,
// GC_INIT_WITH_MASK = 1,
// GC_EVAL = 2
// };
//
/// /! segments the image using GrabCut algorithm
// CV_EXPORTS_W void grabCut( InputArray img, InputOutputArray mask, Rect rect,
// InputOutputArray bgdModel, InputOutputArray fgdModel,
// int iterCount, int mode = GC_EVAL );
//
// enum
// {
// DIST_LABEL_CCOMP = 0,
// DIST_LABEL_PIXEL = 1
// };
//
/// /! builds the discrete Voronoi diagram
// CV_EXPORTS_AS(distanceTransformWithLabels) void distanceTransform( InputArray src, OutputArray dst,
// OutputArray labels, int distanceType, int maskSize,
// int labelType=DIST_LABEL_CCOMP );
//
/// /! computes the distance transform map
// CV_EXPORTS_W void distanceTransform( InputArray src, OutputArray dst,
// int distanceType, int maskSize );
//
// enum { FLOODFILL_FIXED_RANGE = 1 << 16, FLOODFILL_MASK_ONLY = 1 << 17 };
//
/// /! fills the semi-uniform image region starting from the specified seed point
// CV_EXPORTS int floodFill( InputOutputArray image,
// Point seedPoint, Scalar newVal, CV_OUT Rect* rect=0,
// Scalar loDiff=Scalar(), Scalar upDiff=Scalar(),
// int flags=4 );
//
/// /! fills the semi-uniform image region and/or the mask starting from the specified seed point
// CV_EXPORTS_W int floodFill( InputOutputArray image, InputOutputArray mask,
// Point seedPoint, Scalar newVal, CV_OUT Rect* rect=0,
// Scalar loDiff=Scalar(), Scalar upDiff=Scalar(),
// int flags=4 );
//
//
// enum
// {
// COLOR_BGR2BGRA =0,
// COLOR_RGB2RGBA =COLOR_BGR2BGRA,
//
// COLOR_BGRA2BGR =1,
// COLOR_RGBA2RGB =COLOR_BGRA2BGR,
//
// COLOR_BGR2RGBA =2,
// COLOR_RGB2BGRA =COLOR_BGR2RGBA,
//
// COLOR_RGBA2BGR =3,
// COLOR_BGRA2RGB =COLOR_RGBA2BGR,
//
// COLOR_BGR2RGB =4,
// COLOR_RGB2BGR =COLOR_BGR2RGB,
//
// COLOR_BGRA2RGBA =5,
// COLOR_RGBA2BGRA =COLOR_BGRA2RGBA,
//
// COLOR_BGR2GRAY =6,
// COLOR_RGB2GRAY =7,
// COLOR_GRAY2BGR =8,
// COLOR_GRAY2RGB =COLOR_GRAY2BGR,
// COLOR_GRAY2BGRA =9,
// COLOR_GRAY2RGBA =COLOR_GRAY2BGRA,
// COLOR_BGRA2GRAY =10,
// COLOR_RGBA2GRAY =11,
//
// COLOR_BGR2BGR565 =12,
// COLOR_RGB2BGR565 =13,
// COLOR_BGR5652BGR =14,
// COLOR_BGR5652RGB =15,
// COLOR_BGRA2BGR565 =16,
// COLOR_RGBA2BGR565 =17,
// COLOR_BGR5652BGRA =18,
// COLOR_BGR5652RGBA =19,
//
// COLOR_GRAY2BGR565 =20,
// COLOR_BGR5652GRAY =21,
//
// COLOR_BGR2BGR555 =22,
// COLOR_RGB2BGR555 =23,
// COLOR_BGR5552BGR =24,
// COLOR_BGR5552RGB =25,
// COLOR_BGRA2BGR555 =26,
// COLOR_RGBA2BGR555 =27,
// COLOR_BGR5552BGRA =28,
// COLOR_BGR5552RGBA =29,
//
// COLOR_GRAY2BGR555 =30,
// COLOR_BGR5552GRAY =31,
//
// COLOR_BGR2XYZ =32,
// COLOR_RGB2XYZ =33,
// COLOR_XYZ2BGR =34,
// COLOR_XYZ2RGB =35,
//
// COLOR_BGR2YCrCb =36,
// COLOR_RGB2YCrCb =37,
// COLOR_YCrCb2BGR =38,
// COLOR_YCrCb2RGB =39,
//
// COLOR_BGR2HSV =40,
// COLOR_RGB2HSV =41,
//
// COLOR_BGR2Lab =44,
// COLOR_RGB2Lab =45,
//
// COLOR_BayerBG2BGR =46,
// COLOR_BayerGB2BGR =47,
// COLOR_BayerRG2BGR =48,
// COLOR_BayerGR2BGR =49,
//
// COLOR_BayerBG2RGB =COLOR_BayerRG2BGR,
// COLOR_BayerGB2RGB =COLOR_BayerGR2BGR,
// COLOR_BayerRG2RGB =COLOR_BayerBG2BGR,
// COLOR_BayerGR2RGB =COLOR_BayerGB2BGR,
//
// COLOR_BGR2Luv =50,
// COLOR_RGB2Luv =51,
// COLOR_BGR2HLS =52,
// COLOR_RGB2HLS =53,
//
// COLOR_HSV2BGR =54,
// COLOR_HSV2RGB =55,
//
// COLOR_Lab2BGR =56,
// COLOR_Lab2RGB =57,
// COLOR_Luv2BGR =58,
// COLOR_Luv2RGB =59,
// COLOR_HLS2BGR =60,
// COLOR_HLS2RGB =61,
//
// COLOR_BayerBG2BGR_VNG =62,
// COLOR_BayerGB2BGR_VNG =63,
// COLOR_BayerRG2BGR_VNG =64,
// COLOR_BayerGR2BGR_VNG =65,
//
// COLOR_BayerBG2RGB_VNG =COLOR_BayerRG2BGR_VNG,
// COLOR_BayerGB2RGB_VNG =COLOR_BayerGR2BGR_VNG,
// COLOR_BayerRG2RGB_VNG =COLOR_BayerBG2BGR_VNG,
// COLOR_BayerGR2RGB_VNG =COLOR_BayerGB2BGR_VNG,
//
// COLOR_BGR2HSV_FULL = 66,
// COLOR_RGB2HSV_FULL = 67,
// COLOR_BGR2HLS_FULL = 68,
// COLOR_RGB2HLS_FULL = 69,
//
// COLOR_HSV2BGR_FULL = 70,
// COLOR_HSV2RGB_FULL = 71,
// COLOR_HLS2BGR_FULL = 72,
// COLOR_HLS2RGB_FULL = 73,
//
// COLOR_LBGR2Lab = 74,
// COLOR_LRGB2Lab = 75,
// COLOR_LBGR2Luv = 76,
// COLOR_LRGB2Luv = 77,
//
// COLOR_Lab2LBGR = 78,
// COLOR_Lab2LRGB = 79,
// COLOR_Luv2LBGR = 80,
// COLOR_Luv2LRGB = 81,
//
// COLOR_BGR2YUV = 82,
// COLOR_RGB2YUV = 83,
// COLOR_YUV2BGR = 84,
// COLOR_YUV2RGB = 85,
//
// COLOR_BayerBG2GRAY = 86,
// COLOR_BayerGB2GRAY = 87,
// COLOR_BayerRG2GRAY = 88,
// COLOR_BayerGR2GRAY = 89,
//
// //YUV 4:2:0 formats family
// COLOR_YUV2RGB_NV12 = 90,
// COLOR_YUV2BGR_NV12 = 91,
// COLOR_YUV2RGB_NV21 = 92,
// COLOR_YUV2BGR_NV21 = 93,
// COLOR_YUV420sp2RGB = COLOR_YUV2RGB_NV21,
// COLOR_YUV420sp2BGR = COLOR_YUV2BGR_NV21,
//
// COLOR_YUV2RGBA_NV12 = 94,
// COLOR_YUV2BGRA_NV12 = 95,
// COLOR_YUV2RGBA_NV21 = 96,
// COLOR_YUV2BGRA_NV21 = 97,
// COLOR_YUV420sp2RGBA = COLOR_YUV2RGBA_NV21,
// COLOR_YUV420sp2BGRA = COLOR_YUV2BGRA_NV21,
//
// COLOR_YUV2RGB_YV12 = 98,
// COLOR_YUV2BGR_YV12 = 99,
// COLOR_YUV2RGB_IYUV = 100,
// COLOR_YUV2BGR_IYUV = 101,
// COLOR_YUV2RGB_I420 = COLOR_YUV2RGB_IYUV,
// COLOR_YUV2BGR_I420 = COLOR_YUV2BGR_IYUV,
// COLOR_YUV420p2RGB = COLOR_YUV2RGB_YV12,
// COLOR_YUV420p2BGR = COLOR_YUV2BGR_YV12,
//
// COLOR_YUV2RGBA_YV12 = 102,
// COLOR_YUV2BGRA_YV12 = 103,
// COLOR_YUV2RGBA_IYUV = 104,
// COLOR_YUV2BGRA_IYUV = 105,
// COLOR_YUV2RGBA_I420 = COLOR_YUV2RGBA_IYUV,
// COLOR_YUV2BGRA_I420 = COLOR_YUV2BGRA_IYUV,
// COLOR_YUV420p2RGBA = COLOR_YUV2RGBA_YV12,
// COLOR_YUV420p2BGRA = COLOR_YUV2BGRA_YV12,
//
// COLOR_YUV2GRAY_420 = 106,
// COLOR_YUV2GRAY_NV21 = COLOR_YUV2GRAY_420,
// COLOR_YUV2GRAY_NV12 = COLOR_YUV2GRAY_420,
// COLOR_YUV2GRAY_YV12 = COLOR_YUV2GRAY_420,
// COLOR_YUV2GRAY_IYUV = COLOR_YUV2GRAY_420,
// COLOR_YUV2GRAY_I420 = COLOR_YUV2GRAY_420,
// COLOR_YUV420sp2GRAY = COLOR_YUV2GRAY_420,
// COLOR_YUV420p2GRAY = COLOR_YUV2GRAY_420,
//
// //YUV 4:2:2 formats family
// COLOR_YUV2RGB_UYVY = 107,
// COLOR_YUV2BGR_UYVY = 108,
// //COLOR_YUV2RGB_VYUY = 109,
// //COLOR_YUV2BGR_VYUY = 110,
// COLOR_YUV2RGB_Y422 = COLOR_YUV2RGB_UYVY,
// COLOR_YUV2BGR_Y422 = COLOR_YUV2BGR_UYVY,
// COLOR_YUV2RGB_UYNV = COLOR_YUV2RGB_UYVY,
// COLOR_YUV2BGR_UYNV = COLOR_YUV2BGR_UYVY,
//
// COLOR_YUV2RGBA_UYVY = 111,
// COLOR_YUV2BGRA_UYVY = 112,
// //COLOR_YUV2RGBA_VYUY = 113,
// //COLOR_YUV2BGRA_VYUY = 114,
// COLOR_YUV2RGBA_Y422 = COLOR_YUV2RGBA_UYVY,
// COLOR_YUV2BGRA_Y422 = COLOR_YUV2BGRA_UYVY,
// COLOR_YUV2RGBA_UYNV = COLOR_YUV2RGBA_UYVY,
// COLOR_YUV2BGRA_UYNV = COLOR_YUV2BGRA_UYVY,
//
// COLOR_YUV2RGB_YUY2 = 115,
// COLOR_YUV2BGR_YUY2 = 116,
// COLOR_YUV2RGB_YVYU = 117,
// COLOR_YUV2BGR_YVYU = 118,
// COLOR_YUV2RGB_YUYV = COLOR_YUV2RGB_YUY2,
// COLOR_YUV2BGR_YUYV = COLOR_YUV2BGR_YUY2,
// COLOR_YUV2RGB_YUNV = COLOR_YUV2RGB_YUY2,
// COLOR_YUV2BGR_YUNV = COLOR_YUV2BGR_YUY2,
//
// COLOR_YUV2RGBA_YUY2 = 119,
// COLOR_YUV2BGRA_YUY2 = 120,
// COLOR_YUV2RGBA_YVYU = 121,
// COLOR_YUV2BGRA_YVYU = 122,
// COLOR_YUV2RGBA_YUYV = COLOR_YUV2RGBA_YUY2,
// COLOR_YUV2BGRA_YUYV = COLOR_YUV2BGRA_YUY2,
// COLOR_YUV2RGBA_YUNV = COLOR_YUV2RGBA_YUY2,
// COLOR_YUV2BGRA_YUNV = COLOR_YUV2BGRA_YUY2,
//
// COLOR_YUV2GRAY_UYVY = 123,
// COLOR_YUV2GRAY_YUY2 = 124,
// //COLOR_YUV2GRAY_VYUY = COLOR_YUV2GRAY_UYVY,
// COLOR_YUV2GRAY_Y422 = COLOR_YUV2GRAY_UYVY,
// COLOR_YUV2GRAY_UYNV = COLOR_YUV2GRAY_UYVY,
// COLOR_YUV2GRAY_YVYU = COLOR_YUV2GRAY_YUY2,
// COLOR_YUV2GRAY_YUYV = COLOR_YUV2GRAY_YUY2,
// COLOR_YUV2GRAY_YUNV = COLOR_YUV2GRAY_YUY2,
//
// // alpha premultiplication
// COLOR_RGBA2mRGBA = 125,
// COLOR_mRGBA2RGBA = 126,
//
// COLOR_RGB2YUV_I420 = 127,
// COLOR_BGR2YUV_I420 = 128,
// COLOR_RGB2YUV_IYUV = COLOR_RGB2YUV_I420,
// COLOR_BGR2YUV_IYUV = COLOR_BGR2YUV_I420,
//
// COLOR_RGBA2YUV_I420 = 129,
// COLOR_BGRA2YUV_I420 = 130,
// COLOR_RGBA2YUV_IYUV = COLOR_RGBA2YUV_I420,
// COLOR_BGRA2YUV_IYUV = COLOR_BGRA2YUV_I420,
// COLOR_RGB2YUV_YV12 = 131,
// COLOR_BGR2YUV_YV12 = 132,
// COLOR_RGBA2YUV_YV12 = 133,
// COLOR_BGRA2YUV_YV12 = 134,
//
// // Edge-Aware Demosaicing
// COLOR_BayerBG2BGR_EA = 135,
// COLOR_BayerGB2BGR_EA = 136,
// COLOR_BayerRG2BGR_EA = 137,
// COLOR_BayerGR2BGR_EA = 138,
//
// COLOR_BayerBG2RGB_EA = COLOR_BayerRG2BGR_EA,
// COLOR_BayerGB2RGB_EA = COLOR_BayerGR2BGR_EA,
// COLOR_BayerRG2RGB_EA = COLOR_BayerBG2BGR_EA,
// COLOR_BayerGR2RGB_EA = COLOR_BayerGB2BGR_EA,
//
// COLOR_COLORCVT_MAX = 139
// };
//
//
/// /! converts image from one color space to another
// CV_EXPORTS_W void cvtColor( InputArray src, OutputArray dst, int code, int dstCn=0 );
//
/// /! raster image moments
// class CV_EXPORTS_W_MAP Moments
// {
// public:
// //! the default constructor
// Moments();
// //! the full constructor
// Moments(double m00, double m10, double m01, double m20, double m11,
// double m02, double m30, double m21, double m12, double m03 );
// //! the conversion from CvMoments
// Moments( const CvMoments& moments );
// //! the conversion to CvMoments
// operator CvMoments() const;
//
// //! spatial moments
// CV_PROP_RW double m00, m10, m01, m20, m11, m02, m30, m21, m12, m03;
// //! central moments
// CV_PROP_RW double mu20, mu11, mu02, mu30, mu21, mu12, mu03;
// //! central normalized moments
// CV_PROP_RW double nu20, nu11, nu02, nu30, nu21, nu12, nu03;
// };
//
/// /! computes moments of the rasterized shape or a vector of points
// CV_EXPORTS_W Moments moments( InputArray array, bool binaryImage=false );
//
/// /! computes 7 Hu invariants from the moments
// CV_EXPORTS void HuMoments( const Moments& moments, double hu[7] );
// CV_EXPORTS_W void HuMoments( const Moments& m, OutputArray hu );
//
/// /! type of the template matching operation
// enum { TM_SQDIFF=0, TM_SQDIFF_NORMED=1, TM_CCORR=2, TM_CCORR_NORMED=3, TM_CCOEFF=4, TM_CCOEFF_NORMED=5 };
//
/// /! computes the proximity map for the raster template and the image where the template is searched for
// CV_EXPORTS_W void matchTemplate( InputArray image, InputArray templ,
// OutputArray result, int method );
//
// enum { CC_STAT_LEFT=0, CC_STAT_TOP=1, CC_STAT_WIDTH=2, CC_STAT_HEIGHT=3, CC_STAT_AREA=4, CC_STAT_MAX = 5};
//
/// / computes the connected components labeled image of boolean image ``image``
/// / with 4 or 8 way connectivity - returns N, the total
/// / number of labels [0, N-1] where 0 represents the background label.
/// / ltype specifies the output label image type, an important
/// / consideration based on the total number of labels or
/// / alternatively the total number of pixels in the source image.
// CV_EXPORTS_W int connectedComponents(InputArray image, OutputArray labels,
// int connectivity = 8, int ltype=CV_32S);
// CV_EXPORTS_W int connectedComponentsWithStats(InputArray image, OutputArray labels,
// OutputArray stats, OutputArray centroids,
// int connectivity = 8, int ltype=CV_32S);
//
/// /! mode of the contour retrieval algorithm
// enum
// {
// RETR_EXTERNAL=CV_RETR_EXTERNAL, //!< retrieve only the most external (top-level) contours
// RETR_LIST=CV_RETR_LIST, //!< retrieve all the contours without any hierarchical information
// RETR_CCOMP=CV_RETR_CCOMP, //!< retrieve the connected components (that can possibly be nested)
// RETR_TREE=CV_RETR_TREE, //!< retrieve all the contours and the whole hierarchy
// RETR_FLOODFILL=CV_RETR_FLOODFILL
// };
//
/// /! the contour approximation algorithm
// enum
// {
// CHAIN_APPROX_NONE=CV_CHAIN_APPROX_NONE,
// CHAIN_APPROX_SIMPLE=CV_CHAIN_APPROX_SIMPLE,
// CHAIN_APPROX_TC89_L1=CV_CHAIN_APPROX_TC89_L1,
// CHAIN_APPROX_TC89_KCOS=CV_CHAIN_APPROX_TC89_KCOS
// };
//
/// /! retrieves contours and the hierarchical information from black-n-white image.
// CV_EXPORTS_W void findContours( InputOutputArray image, OutputArrayOfArrays contours,
// OutputArray hierarchy, int mode,
// int method, Point offset=Point());
//
/// /! retrieves contours from black-n-white image.
// CV_EXPORTS void findContours( InputOutputArray image, OutputArrayOfArrays contours,
// int mode, int method, Point offset=Point());
//
/// /! approximates contour or a curve using Douglas-Peucker algorithm
// CV_EXPORTS_W void approxPolyDP( InputArray curve,
// OutputArray approxCurve,
// double epsilon, bool closed );
//
/// /! computes the contour perimeter (closed=true) or a curve length
// CV_EXPORTS_W double arcLength( InputArray curve, bool closed );
/// /! computes the bounding rectangle for a contour
// CV_EXPORTS_W Rect boundingRect( InputArray points );
/// /! computes the contour area
// CV_EXPORTS_W double contourArea( InputArray contour, bool oriented=false );
/// /! computes the minimal rotated rectangle for a set of points
// CV_EXPORTS_W RotatedRect minAreaRect( InputArray points );
/// /! computes the minimal enclosing circle for a set of points
// CV_EXPORTS_W void minEnclosingCircle( InputArray points,
// CV_OUT Point2f& center, CV_OUT float& radius );
/// /! matches two contours using one of the available algorithms
// CV_EXPORTS_W double matchShapes( InputArray contour1, InputArray contour2,
// int method, double parameter );
/// /! computes convex hull for a set of 2D points.
// CV_EXPORTS_W void convexHull( InputArray points, OutputArray hull,
// bool clockwise=false, bool returnPoints=true );
/// /! computes the contour convexity defects
// CV_EXPORTS_W void convexityDefects( InputArray contour, InputArray convexhull, OutputArray convexityDefects );
//
/// /! returns true if the contour is convex. Does not support contours with self-intersection
// CV_EXPORTS_W bool isContourConvex( InputArray contour );
//
/// /! finds intersection of two convex polygons
// CV_EXPORTS_W float intersectConvexConvex( InputArray _p1, InputArray _p2,
// OutputArray _p12, bool handleNested=true );
//
/// /! fits ellipse to the set of 2D points
// CV_EXPORTS_W RotatedRect fitEllipse( InputArray points );
//
/// /! fits line to the set of 2D points using M-estimator algorithm
// CV_EXPORTS_W void fitLine( InputArray points, OutputArray line, int distType,
// double param, double reps, double aeps );
/// /! checks if the point is inside the contour. Optionally computes the signed distance from the point to the contour boundary
// CV_EXPORTS_W double pointPolygonTest( InputArray contour, Point2f pt, bool measureDist );
//
//
// class CV_EXPORTS_W Subdiv2D
// {
// public:
// enum
// {
// PTLOC_ERROR = -2,
// PTLOC_OUTSIDE_RECT = -1,
// PTLOC_INSIDE = 0,
// PTLOC_VERTEX = 1,
// PTLOC_ON_EDGE = 2
// };
//
// enum
// {
// NEXT_AROUND_ORG = 0x00,
// NEXT_AROUND_DST = 0x22,
// PREV_AROUND_ORG = 0x11,
// PREV_AROUND_DST = 0x33,
// NEXT_AROUND_LEFT = 0x13,
// NEXT_AROUND_RIGHT = 0x31,
// PREV_AROUND_LEFT = 0x20,
// PREV_AROUND_RIGHT = 0x02
// };
//
// CV_WRAP Subdiv2D();
// CV_WRAP Subdiv2D(Rect rect);
// CV_WRAP void initDelaunay(Rect rect);
//
// CV_WRAP int insert(Point2f pt);
// CV_WRAP void insert(const std::vector<Point2f>& ptvec);
// CV_WRAP int locate(Point2f pt, CV_OUT int& edge, CV_OUT int& vertex);
//
// CV_WRAP int findNearest(Point2f pt, CV_OUT Point2f* nearestPt=0);
// CV_WRAP void getEdgeList(CV_OUT std::vector<Vec4f>& edgeList) const;
// CV_WRAP void getTriangleList(CV_OUT std::vector<Vec6f>& triangleList) const;
// CV_WRAP void getVoronoiFacetList(const std::vector<int>& idx, CV_OUT std::vector<std::vector<Point2f> >& facetList,
// CV_OUT std::vector<Point2f>& facetCenters);
//
// CV_WRAP Point2f getVertex(int vertex, CV_OUT int* firstEdge=0) const;
//
// CV_WRAP int getEdge( int edge, int nextEdgeType ) const;
// CV_WRAP int nextEdge(int edge) const;
// CV_WRAP int rotateEdge(int edge, int rotate) const;
// CV_WRAP int symEdge(int edge) const;
// CV_WRAP int edgeOrg(int edge, CV_OUT Point2f* orgpt=0) const;
// CV_WRAP int edgeDst(int edge, CV_OUT Point2f* dstpt=0) const;
//
// protected:
// int newEdge();
// void deleteEdge(int edge);
// int newPoint(Point2f pt, bool isvirtual, int firstEdge=0);
// void deletePoint(int vtx);
// void setEdgePoints( int edge, int orgPt, int dstPt );
// void splice( int edgeA, int edgeB );
// int connectEdges( int edgeA, int edgeB );
// void swapEdges( int edge );
// int isRightOf(Point2f pt, int edge) const;
// void calcVoronoi();
// void clearVoronoi();
// void checkSubdiv() const;
//
// struct CV_EXPORTS Vertex
// {
// Vertex();
// Vertex(Point2f pt, bool _isvirtual, int _firstEdge=0);
// bool isvirtual() const;
// bool isfree() const;
// int firstEdge;
// int type;
// Point2f pt;
// };
// struct CV_EXPORTS QuadEdge
// {
// QuadEdge();
// QuadEdge(int edgeidx);
// bool isfree() const;
// int next[4];
// int pt[4];
// };
//
// std::vector<Vertex> vtx;
// std::vector<QuadEdge> qedges;
// int freeQEdge;
// int freePoint;
// bool validGeometry;
//
// int recentEdge;
// Point2f topLeft;
// Point2f bottomRight;
// };
//
/// / main function for all demosaicing procceses
// CV_EXPORTS_W void demosaicing(InputArray _src, OutputArray _dst, int code, int dcn = 0);
//
// }
//
// #endif /* __cplusplus */
//
// #endif
//
/// * End of file. */
implementation
Uses uLibName;
// CV_EXPORTS_W void erode( InputArray src, OutputArray dst, InputArray kernel,
// Point anchor=Point(-1,-1), int iterations=1,
// int borderType=BORDER_CONSTANT,
// const Scalar& borderValue=morphologyDefaultBorderValue());
procedure _erode(src: Pointer; dst: Pointer; kernel: Pointer; anchor: Pointer { =Point(-1,-1) };
iterations: Integer { =1 }; borderType: Integer { =BORDER_CONSTANT };
const borderValue: Pointer { =morphologyDefaultBorderValue() } ); cdecl; external imgproc_Dll name 'erode';
procedure erode(src: IMat; dst: IMat; kernel: IMat; anchor: IPoint { =Point(-1,-1) }; iterations: Integer { =1 };
borderType: Integer { =BORDER_CONSTANT }; const borderValue: IScalar { =morphologyDefaultBorderValue() } );
begin
end;
// ! dilates the image (applies the local maximum operator)
// CV_EXPORTS_W void dilate( InputArray src, OutputArray dst, InputArray kernel,
// Point anchor=Point(-1,-1), int iterations=1,
// int borderType=BORDER_CONSTANT,
// const Scalar& borderValue=morphologyDefaultBorderValue() );
// ! applies an advanced morphological operation to the image
// CV_EXPORTS_W void morphologyEx( InputArray src, OutputArray dst,
// int op, InputArray kernel,
// Point anchor=Point(-1,-1), int iterations=1,
// int borderType=BORDER_CONSTANT,
// const Scalar& borderValue=morphologyDefaultBorderValue() );
function _getStructuringElement(shape: Integer; ksize: Pointer; anchor: Pointer): Pointer; cdecl;
external imgproc_Dll name 'getStructuringElement';
// CV_EXPORTS_W Mat getStructuringElement(int shape, Size ksize, Point anchor=Point(-1,-1));
function getStructuringElement(shape: Integer; ksize: ISize; anchor: IPoint { =Point(-1,-1) } ): IMat;
begin
if not Assigned(anchor) then
anchor := Point(-1, -1);
Result := CreateMat(_getStructuringElement(shape, ksize.getSize, anchor.getPoint));
end;
end.
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