/*! \file imgproc.hpp
\brief The Image Processing
*/
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#ifndef __OPENCV_IMGPROC_HPP__
#define __OPENCV_IMGPROC_HPP__
#include "opencv2/core/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<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;
vector<int> borderTab;
int borderElemSize;
vector<uchar> ringBuf;
vector<uchar> srcRow;
vector<uchar> constBorderValue;
vector<uchar> constBorderRow;
int bufStep, startY, startY0, endY, rowCount, dstY;
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());
//! 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<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 adaptive bilateral filter
CV_EXPORTS_W void adaptiveBilateralFilter( InputArray src, OutputArray dst, Size ksize,
double sigmaSpace, double maxSigmaColor = 20.0, Point anchor=Point(-1, -1),
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 probabilistic 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() );
//! 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_EXPORTS_W Point2d phaseCorrelateRes(InputArray src1, InputArray src2,
InputArray window, CV_OUT double* response = 0);
CV_EXPORTS_W void createHanningWindow(OutputArray dst, Size winSize, int type);
//! type of the threshold operation
enum { 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 vector<int>& channels,
InputArray mask, OutputArray hist,
const vector<int>& histSize,
const 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 vector<int>& channels,
InputArray hist, OutputArray dst,
const 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 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_W CLAHE : public Algorithm
{
public:
CV_WRAP virtual void apply(InputArray src, OutputArray dst) = 0;
CV_WRAP virtual void setClipLimit(double clipLimit) = 0;
CV_WRAP virtual double getClipLimit() const = 0;
CV_WRAP virtual void setTilesGridSize(Size tileGridSize) = 0;
CV_WRAP virtual Size getTilesGridSize() const = 0;
CV_WRAP virtual void collectGarbage() = 0;
};
CV_EXPORTS_W 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,
COLOR_COLORCVT_MAX = 135
};
//! 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, CV_OUT 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 );
//! 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());
//! draws contours in the image
CV_EXPORTS_W void drawContours( InputOutputArray image, InputArrayOfArrays contours,
int contourIdx, const Scalar& color,
int thickness=1, int lineType=8,
InputArray hierarchy=noArray(),
int maxLevel=INT_MAX, 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 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 vector<Vec4f>& edgeList) const;
CV_WRAP void getTriangleList(CV_OUT vector<Vec6f>& triangleList) const;
CV_WRAP void getVoronoiFacetList(const vector<int>& idx, CV_OUT vector<vector<Point2f> >& facetList,
CV_OUT 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];
};
vector<Vertex> vtx;
vector<QuadEdge> qedges;
int freeQEdge;
int freePoint;
bool validGeometry;
int recentEdge;
Point2f topLeft;
Point2f bottomRight;
};
}
#endif /* __cplusplus */
#endif
/* End of file. */
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