/*M/////////////////////////////////////////////////////////////////////////////////////// // // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. // // By downloading, copying, installing or using the software you agree to this license. // If you do not agree to this license, do not download, install, // copy or use the software. // // // License Agreement // For Open Source Computer Vision Library // // Copyright (C) 2000-2008, Intel Corporation, all rights reserved. // Copyright (C) 2009, Willow Garage Inc., all rights reserved. // Third party copyrights are property of their respective owners. // // Redistribution and use in source and binary forms, with or without modification, // are permitted provided that the following conditions are met: // // * Redistribution's of source code must retain the above copyright notice, // this list of conditions and the following disclaimer. // // * Redistribution's in binary form must reproduce the above copyright notice, // this list of conditions and the following disclaimer in the documentation // and/or other materials provided with the distribution. // // * The name of the copyright holders may not be used to endorse or promote products // derived from this software without specific prior written permission. // // This software is provided by the copyright holders and contributors "as is" and // any express or implied warranties, including, but not limited to, the implied // warranties of merchantability and fitness for a particular purpose are disclaimed. // In no event shall the Intel Corporation or contributors be liable for any direct, // indirect, incidental, special, exemplary, or consequential damages // (including, but not limited to, procurement of substitute goods or services; // loss of use, data, or profits; or business interruption) however caused // and on any theory of liability, whether in contract, strict liability, // or tort (including negligence or otherwise) arising in any way out of // the use of this software, even if advised of the possibility of such damage. // //M*/ #ifndef __OPENCV_IMGPROC_HPP__ #define __OPENCV_IMGPROC_HPP__ #include "opencv2/core.hpp" #include "opencv2/imgproc/types_c.h" #ifdef __cplusplus /*! \namespace cv Namespace where all the C++ OpenCV functionality resides */ namespace cv { //! various border interpolation methods enum { 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 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 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 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 Fxx = createSeparableLinearFilter(src.type(), dst.type(), kd, ks, Point(-1,-1), 0, borderType, borderType, Scalar() ); Ptr 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& _filter2D, const Ptr& _rowFilter, const Ptr& _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& _filter2D, const Ptr& _rowFilter, const Ptr& _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 borderTab; int borderElemSize; std::vector ringBuf; std::vector srcRow; std::vector constBorderValue; std::vector constBorderRow; int bufStep, startY, startY0, endY, rowCount, dstY; std::vector rows; Ptr filter2D; Ptr rowFilter; Ptr 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 getLinearRowFilter(int srcType, int bufType, InputArray kernel, int anchor, int symmetryType); //! returns the primitive column filter with the specified kernel CV_EXPORTS Ptr 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 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 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 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 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 createDerivFilter( int srcType, int dstType, int dx, int dy, int ksize, int borderType=BORDER_DEFAULT ); //! returns horizontal 1D box filter CV_EXPORTS Ptr getRowSumFilter(int srcType, int sumType, int ksize, int anchor=-1); //! returns vertical 1D box filter CV_EXPORTS Ptr getColumnSumFilter( int sumType, int dstType, int ksize, int anchor=-1, double scale=1); //! returns box filter engine CV_EXPORTS Ptr 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 getMorphologyRowFilter(int op, int type, int ksize, int anchor=-1); //! returns vertical 1D morphological filter CV_EXPORTS Ptr getMorphologyColumnFilter(int op, int type, int ksize, int anchor=-1); //! returns 2D morphological filter CV_EXPORTS Ptr 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 createMorphologyFilter(int op, int type, InputArray kernel, Point anchor=Point(-1,-1), int rowBorderType=BORDER_CONSTANT, int columnBorderType=-1, const Scalar& borderValue=morphologyDefaultBorderValue()); //! shape of the structuring element enum { 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)); template<> CV_EXPORTS void Ptr::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 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() ); //! 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< CV_EXPORTS void Ptr::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& channels, InputArray mask, OutputArray hist, const std::vector& histSize, const std::vector& 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& channels, InputArray hist, OutputArray dst, const std::vector& 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& 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 ); 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& 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& edgeList) const; CV_WRAP void getTriangleList(CV_OUT std::vector& triangleList) const; CV_WRAP void getVoronoiFacetList(const std::vector& idx, CV_OUT std::vector >& facetList, CV_OUT std::vector& 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 vtx; std::vector 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. */