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/*! \file core.hpp
\brief The Core Functionality
*/
/*M///////////////////////////////////////////////////////////////////////////////////////
//
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// copy or use the software.
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//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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//
// Redistribution and use in source and binary forms, with or without modification,
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// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
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#ifndef __OPENCV_CORE_HPP__
#define __OPENCV_CORE_HPP__
#include "opencv2/core/cvdef.h"
#include "opencv2/core/version.hpp"
#ifdef __cplusplus
#include "opencv2/core/base.hpp"
#include "opencv2/core/cvstd.hpp"
#include "opencv2/core/traits.hpp"
#include "opencv2/core/matx.hpp"
#include "opencv2/core/types.hpp"
#endif
#include "opencv2/core/types_c.h"
#ifdef __cplusplus
#ifndef SKIP_INCLUDES
#include <limits.h>
#include <algorithm>
#include <cmath>
#include <cstddef>
#include <complex>
#include <map>
#include <new>
#include <vector>
#include <sstream>
#endif // SKIP_INCLUDES
/*! \namespace cv
Namespace where all the C++ OpenCV functionality resides
*/
namespace cv {
class CV_EXPORTS MatExpr;
class CV_EXPORTS MatOp_Base;
class CV_EXPORTS MatArg;
class CV_EXPORTS MatConstIterator;
template<typename _Tp> class CV_EXPORTS MatCommaInitializer_;
/*!
The standard OpenCV exception class.
Instances of the class are thrown by various functions and methods in the case of critical errors.
*/
class CV_EXPORTS Exception : public std::exception
{
public:
/*!
Default constructor
*/
Exception();
/*!
Full constructor. Normally the constuctor is not called explicitly.
Instead, the macros CV_Error(), CV_Error_() and CV_Assert() are used.
*/
Exception(int _code, const String& _err, const String& _func, const String& _file, int _line);
virtual ~Exception() throw();
/*!
\return the error description and the context as a text string.
*/
virtual const char *what() const throw();
void formatMessage();
String msg; ///< the formatted error message
int code; ///< error code @see CVStatus
String err; ///< error description
String func; ///< function name. Available only when the compiler supports __func__ macro
String file; ///< source file name where the error has occured
int line; ///< line number in the source file where the error has occured
};
//! Signals an error and raises the exception.
/*!
By default the function prints information about the error to stderr,
then it either stops if setBreakOnError() had been called before or raises the exception.
It is possible to alternate error processing by using redirectError().
\param exc the exception raisen.
*/
CV_EXPORTS void error( const Exception& exc );
/*!
Allocates memory buffer
This is specialized OpenCV memory allocation function that returns properly aligned memory buffers.
The usage is identical to malloc(). The allocated buffers must be freed with cv::fastFree().
If there is not enough memory, the function calls cv::error(), which raises an exception.
\param bufSize buffer size in bytes
\return the allocated memory buffer.
*/
CV_EXPORTS void* fastMalloc(size_t bufSize);
/*!
Frees the memory allocated with cv::fastMalloc
This is the corresponding deallocation function for cv::fastMalloc().
When ptr==NULL, the function has no effect.
*/
CV_EXPORTS void fastFree(void* ptr);
template<typename _Tp> static inline _Tp* allocate(size_t n)
{
return new _Tp[n];
}
template<typename _Tp> static inline void deallocate(_Tp* ptr, size_t)
{
delete[] ptr;
}
/*!
The STL-compilant memory Allocator based on cv::fastMalloc() and cv::fastFree()
*/
template<typename _Tp> class CV_EXPORTS Allocator
{
public:
typedef _Tp value_type;
typedef value_type* pointer;
typedef const value_type* const_pointer;
typedef value_type& reference;
typedef const value_type& const_reference;
typedef size_t size_type;
typedef ptrdiff_t difference_type;
template<typename U> class rebind { typedef Allocator<U> other; };
explicit Allocator() {}
~Allocator() {}
explicit Allocator(Allocator const&) {}
template<typename U>
explicit Allocator(Allocator<U> const&) {}
// address
pointer address(reference r) { return &r; }
const_pointer address(const_reference r) { return &r; }
pointer allocate(size_type count, const void* =0)
{ return reinterpret_cast<pointer>(fastMalloc(count * sizeof (_Tp))); }
void deallocate(pointer p, size_type) {fastFree(p); }
size_type max_size() const
{ return max(static_cast<_Tp>(-1)/sizeof(_Tp), 1); }
void construct(pointer p, const _Tp& v) { new(static_cast<void*>(p)) _Tp(v); }
void destroy(pointer p) { p->~_Tp(); }
};
CV_EXPORTS void scalarToRawData(const Scalar& s, void* buf, int type, int unroll_to=0);
//////////////////// generic_type ref-counting pointer class for C/C++ objects ////////////////////////
/*!
Smart pointer to dynamically allocated objects.
This is template pointer-wrapping class that stores the associated reference counter along with the
object pointer. The class is similar to std::smart_ptr<> from the recent addons to the C++ standard,
but is shorter to write :) and self-contained (i.e. does add any dependency on the compiler or an external library).
Basically, you can use "Ptr<MyObjectType> ptr" (or faster "const Ptr<MyObjectType>& ptr" for read-only access)
everywhere instead of "MyObjectType* ptr", where MyObjectType is some C structure or a C++ class.
To make it all work, you need to specialize Ptr<>::delete_obj(), like:
\code
template<> void Ptr<MyObjectType>::delete_obj() { call_destructor_func(obj); }
\endcode
\note{if MyObjectType is a C++ class with a destructor, you do not need to specialize delete_obj(),
since the default implementation calls "delete obj;"}
\note{Another good property of the class is that the operations on the reference counter are atomic,
i.e. it is safe to use the class in multi-threaded applications}
*/
template<typename _Tp> class CV_EXPORTS Ptr
{
public:
//! empty constructor
Ptr();
//! take ownership of the pointer. The associated reference counter is allocated and set to 1
Ptr(_Tp* _obj);
//! calls release()
~Ptr();
//! copy constructor. Copies the members and calls addref()
Ptr(const Ptr& ptr);
template<typename _Tp2> Ptr(const Ptr<_Tp2>& ptr);
//! copy operator. Calls ptr.addref() and release() before copying the members
Ptr& operator = (const Ptr& ptr);
//! increments the reference counter
void addref();
//! decrements the reference counter. If it reaches 0, delete_obj() is called
void release();
//! deletes the object. Override if needed
void delete_obj();
//! returns true iff obj==NULL
bool empty() const;
//! cast pointer to another type
template<typename _Tp2> Ptr<_Tp2> ptr();
template<typename _Tp2> const Ptr<_Tp2> ptr() const;
//! helper operators making "Ptr<T> ptr" use very similar to "T* ptr".
_Tp* operator -> ();
const _Tp* operator -> () const;
operator _Tp* ();
operator const _Tp*() const;
_Tp* obj; //< the object pointer.
int* refcount; //< the associated reference counter
};
template<class T, class U> bool operator==(Ptr<T> const & a, Ptr<U> const & b);
template<class T, class U> bool operator!=(Ptr<T> const & a, Ptr<U> const & b);
//////////////////////// Input/Output Array Arguments /////////////////////////////////
/*!
Proxy datatype for passing Mat's and vector<>'s as input parameters
*/
class CV_EXPORTS _InputArray
{
public:
enum {
KIND_SHIFT = 16,
FIXED_TYPE = 0x8000 << KIND_SHIFT,
FIXED_SIZE = 0x4000 << KIND_SHIFT,
KIND_MASK = ~(FIXED_TYPE|FIXED_SIZE) - (1 << KIND_SHIFT) + 1,
NONE = 0 << KIND_SHIFT,
MAT = 1 << KIND_SHIFT,
MATX = 2 << KIND_SHIFT,
STD_VECTOR = 3 << KIND_SHIFT,
STD_VECTOR_VECTOR = 4 << KIND_SHIFT,
STD_VECTOR_MAT = 5 << KIND_SHIFT,
EXPR = 6 << KIND_SHIFT,
OPENGL_BUFFER = 7 << KIND_SHIFT,
OPENGL_TEXTURE = 8 << KIND_SHIFT,
GPU_MAT = 9 << KIND_SHIFT
};
_InputArray();
_InputArray(const Mat& m);
_InputArray(const MatExpr& expr);
template<typename _Tp> _InputArray(const _Tp* vec, int n);
template<typename _Tp> _InputArray(const std::vector<_Tp>& vec);
template<typename _Tp> _InputArray(const std::vector<std::vector<_Tp> >& vec);
_InputArray(const std::vector<Mat>& vec);
template<typename _Tp> _InputArray(const std::vector<Mat_<_Tp> >& vec);
template<typename _Tp> _InputArray(const Mat_<_Tp>& m);
template<typename _Tp, int m, int n> _InputArray(const Matx<_Tp, m, n>& matx);
_InputArray(const Scalar& s);
_InputArray(const double& val);
_InputArray(const gpu::GpuMat& d_mat);
_InputArray(const ogl::Buffer& buf);
_InputArray(const ogl::Texture2D& tex);
virtual Mat getMat(int i=-1) const;
virtual void getMatVector(std::vector<Mat>& mv) const;
virtual gpu::GpuMat getGpuMat() const;
virtual ogl::Buffer getOGlBuffer() const;
virtual ogl::Texture2D getOGlTexture2D() const;
virtual int kind() const;
virtual Size size(int i=-1) const;
virtual size_t total(int i=-1) const;
virtual int type(int i=-1) const;
virtual int depth(int i=-1) const;
virtual int channels(int i=-1) const;
virtual bool empty() const;
virtual ~_InputArray();
int flags;
void* obj;
Size sz;
};
enum
{
DEPTH_MASK_8U = 1 << CV_8U,
DEPTH_MASK_8S = 1 << CV_8S,
DEPTH_MASK_16U = 1 << CV_16U,
DEPTH_MASK_16S = 1 << CV_16S,
DEPTH_MASK_32S = 1 << CV_32S,
DEPTH_MASK_32F = 1 << CV_32F,
DEPTH_MASK_64F = 1 << CV_64F,
DEPTH_MASK_ALL = (DEPTH_MASK_64F<<1)-1,
DEPTH_MASK_ALL_BUT_8S = DEPTH_MASK_ALL & ~DEPTH_MASK_8S,
DEPTH_MASK_FLT = DEPTH_MASK_32F + DEPTH_MASK_64F
};
/*!
Proxy datatype for passing Mat's and vector<>'s as input parameters
*/
class CV_EXPORTS _OutputArray : public _InputArray
{
public:
_OutputArray();
_OutputArray(Mat& m);
template<typename _Tp> _OutputArray(std::vector<_Tp>& vec);
template<typename _Tp> _OutputArray(std::vector<std::vector<_Tp> >& vec);
_OutputArray(std::vector<Mat>& vec);
template<typename _Tp> _OutputArray(std::vector<Mat_<_Tp> >& vec);
template<typename _Tp> _OutputArray(Mat_<_Tp>& m);
template<typename _Tp, int m, int n> _OutputArray(Matx<_Tp, m, n>& matx);
template<typename _Tp> _OutputArray(_Tp* vec, int n);
_OutputArray(gpu::GpuMat& d_mat);
_OutputArray(ogl::Buffer& buf);
_OutputArray(ogl::Texture2D& tex);
_OutputArray(const Mat& m);
template<typename _Tp> _OutputArray(const std::vector<_Tp>& vec);
template<typename _Tp> _OutputArray(const std::vector<std::vector<_Tp> >& vec);
_OutputArray(const std::vector<Mat>& vec);
template<typename _Tp> _OutputArray(const std::vector<Mat_<_Tp> >& vec);
template<typename _Tp> _OutputArray(const Mat_<_Tp>& m);
template<typename _Tp, int m, int n> _OutputArray(const Matx<_Tp, m, n>& matx);
template<typename _Tp> _OutputArray(const _Tp* vec, int n);
_OutputArray(const gpu::GpuMat& d_mat);
_OutputArray(const ogl::Buffer& buf);
_OutputArray(const ogl::Texture2D& tex);
virtual bool fixedSize() const;
virtual bool fixedType() const;
virtual bool needed() const;
virtual Mat& getMatRef(int i=-1) const;
virtual gpu::GpuMat& getGpuMatRef() const;
virtual ogl::Buffer& getOGlBufferRef() const;
virtual ogl::Texture2D& getOGlTexture2DRef() const;
virtual void create(Size sz, int type, int i=-1, bool allowTransposed=false, int fixedDepthMask=0) const;
virtual void create(int rows, int cols, int type, int i=-1, bool allowTransposed=false, int fixedDepthMask=0) const;
virtual void create(int dims, const int* size, int type, int i=-1, bool allowTransposed=false, int fixedDepthMask=0) const;
virtual void release() const;
virtual void clear() const;
virtual ~_OutputArray();
};
typedef const _InputArray& InputArray;
typedef InputArray InputArrayOfArrays;
typedef const _OutputArray& OutputArray;
typedef OutputArray OutputArrayOfArrays;
typedef OutputArray InputOutputArray;
typedef OutputArray InputOutputArrayOfArrays;
CV_EXPORTS OutputArray noArray();
/////////////////////////////////////// Mat ///////////////////////////////////////////
enum { MAGIC_MASK=0xFFFF0000, TYPE_MASK=0x00000FFF, DEPTH_MASK=7 };
/*!
Custom array allocator
*/
class CV_EXPORTS MatAllocator
{
public:
MatAllocator() {}
virtual ~MatAllocator() {}
virtual void allocate(int dims, const int* sizes, int type, int*& refcount,
uchar*& datastart, uchar*& data, size_t* step) = 0;
virtual void deallocate(int* refcount, uchar* datastart, uchar* data) = 0;
};
/*!
The n-dimensional matrix class.
The class represents an n-dimensional dense numerical array that can act as
a matrix, image, optical flow map, 3-focal tensor etc.
It is very similar to CvMat and CvMatND types from earlier versions of OpenCV,
and similarly to those types, the matrix can be multi-channel. It also fully supports ROI mechanism.
There are many different ways to create cv::Mat object. Here are the some popular ones:
<ul>
<li> using cv::Mat::create(nrows, ncols, type) method or
the similar constructor cv::Mat::Mat(nrows, ncols, type[, fill_value]) constructor.
A new matrix of the specified size and specifed type will be allocated.
"type" has the same meaning as in cvCreateMat function,
e.g. CV_8UC1 means 8-bit single-channel matrix, CV_32FC2 means 2-channel (i.e. complex)
floating-point matrix etc:
\code
// make 7x7 complex matrix filled with 1+3j.
cv::Mat M(7,7,CV_32FC2,Scalar(1,3));
// and now turn M to 100x60 15-channel 8-bit matrix.
// The old content will be deallocated
M.create(100,60,CV_8UC(15));
\endcode
As noted in the introduction of this chapter, Mat::create()
will only allocate a new matrix when the current matrix dimensionality
or type are different from the specified.
<li> by using a copy constructor or assignment operator, where on the right side it can
be a matrix or expression, see below. Again, as noted in the introduction,
matrix assignment is O(1) operation because it only copies the header
and increases the reference counter. cv::Mat::clone() method can be used to get a full
(a.k.a. deep) copy of the matrix when you need it.
<li> by constructing a header for a part of another matrix. It can be a single row, single column,
several rows, several columns, rectangular region in the matrix (called a minor in algebra) or
a diagonal. Such operations are also O(1), because the new header will reference the same data.
You can actually modify a part of the matrix using this feature, e.g.
\code
// add 5-th row, multiplied by 3 to the 3rd row
M.row(3) = M.row(3) + M.row(5)*3;
// now copy 7-th column to the 1-st column
// M.col(1) = M.col(7); // this will not work
Mat M1 = M.col(1);
M.col(7).copyTo(M1);
// create new 320x240 image
cv::Mat img(Size(320,240),CV_8UC3);
// select a roi
cv::Mat roi(img, Rect(10,10,100,100));
// fill the ROI with (0,255,0) (which is green in RGB space);
// the original 320x240 image will be modified
roi = Scalar(0,255,0);
\endcode
Thanks to the additional cv::Mat::datastart and cv::Mat::dataend members, it is possible to
compute the relative sub-matrix position in the main "container" matrix using cv::Mat::locateROI():
\code
Mat A = Mat::eye(10, 10, CV_32S);
// extracts A columns, 1 (inclusive) to 3 (exclusive).
Mat B = A(Range::all(), Range(1, 3));
// extracts B rows, 5 (inclusive) to 9 (exclusive).
// that is, C ~ A(Range(5, 9), Range(1, 3))
Mat C = B(Range(5, 9), Range::all());
Size size; Point ofs;
C.locateROI(size, ofs);
// size will be (width=10,height=10) and the ofs will be (x=1, y=5)
\endcode
As in the case of whole matrices, if you need a deep copy, use cv::Mat::clone() method
of the extracted sub-matrices.
<li> by making a header for user-allocated-data. It can be useful for
<ol>
<li> processing "foreign" data using OpenCV (e.g. when you implement
a DirectShow filter or a processing module for gstreamer etc.), e.g.
\code
void process_video_frame(const unsigned char* pixels,
int width, int height, int step)
{
cv::Mat img(height, width, CV_8UC3, pixels, step);
cv::GaussianBlur(img, img, cv::Size(7,7), 1.5, 1.5);
}
\endcode
<li> for quick initialization of small matrices and/or super-fast element access
\code
double m[3][3] = {{a, b, c}, {d, e, f}, {g, h, i}};
cv::Mat M = cv::Mat(3, 3, CV_64F, m).inv();
\endcode
</ol>
partial yet very common cases of this "user-allocated data" case are conversions
from CvMat and IplImage to cv::Mat. For this purpose there are special constructors
taking pointers to CvMat or IplImage and the optional
flag indicating whether to copy the data or not.
Backward conversion from cv::Mat to CvMat or IplImage is provided via cast operators
cv::Mat::operator CvMat() an cv::Mat::operator IplImage().
The operators do not copy the data.
\code
IplImage* img = cvLoadImage("greatwave.jpg", 1);
Mat mtx(img); // convert IplImage* -> cv::Mat
CvMat oldmat = mtx; // convert cv::Mat -> CvMat
CV_Assert(oldmat.cols == img->width && oldmat.rows == img->height &&
oldmat.data.ptr == (uchar*)img->imageData && oldmat.step == img->widthStep);
\endcode
<li> by using MATLAB-style matrix initializers, cv::Mat::zeros(), cv::Mat::ones(), cv::Mat::eye(), e.g.:
\code
// create a double-precision identity martix and add it to M.
M += Mat::eye(M.rows, M.cols, CV_64F);
\endcode
<li> by using comma-separated initializer:
\code
// create 3x3 double-precision identity matrix
Mat M = (Mat_<double>(3,3) << 1, 0, 0, 0, 1, 0, 0, 0, 1);
\endcode
here we first call constructor of cv::Mat_ class (that we describe further) with the proper matrix,
and then we just put "<<" operator followed by comma-separated values that can be constants,
variables, expressions etc. Also, note the extra parentheses that are needed to avoid compiler errors.
</ul>
Once matrix is created, it will be automatically managed by using reference-counting mechanism
(unless the matrix header is built on top of user-allocated data,
in which case you should handle the data by yourself).
The matrix data will be deallocated when no one points to it;
if you want to release the data pointed by a matrix header before the matrix destructor is called,
use cv::Mat::release().
The next important thing to learn about the matrix class is element access. Here is how the matrix is stored.
The elements are stored in row-major order (row by row). The cv::Mat::data member points to the first element of the first row,
cv::Mat::rows contains the number of matrix rows and cv::Mat::cols - the number of matrix columns. There is yet another member,
cv::Mat::step that is used to actually compute address of a matrix element. cv::Mat::step is needed because the matrix can be
a part of another matrix or because there can some padding space in the end of each row for a proper alignment.
\image html roi.png
Given these parameters, address of the matrix element M_{ij} is computed as following:
addr(M_{ij})=M.data + M.step*i + j*M.elemSize()
if you know the matrix element type, e.g. it is float, then you can use cv::Mat::at() method:
addr(M_{ij})=&M.at<float>(i,j)
(where & is used to convert the reference returned by cv::Mat::at() to a pointer).
if you need to process a whole row of matrix, the most efficient way is to get
the pointer to the row first, and then just use plain C operator []:
\code
// compute sum of positive matrix elements
// (assuming that M is double-precision matrix)
double sum=0;
for(int i = 0; i < M.rows; i++)
{
const double* Mi = M.ptr<double>(i);
for(int j = 0; j < M.cols; j++)
sum += std::max(Mi[j], 0.);
}
\endcode
Some operations, like the above one, do not actually depend on the matrix shape,
they just process elements of a matrix one by one (or elements from multiple matrices
that are sitting in the same place, e.g. matrix addition). Such operations are called
element-wise and it makes sense to check whether all the input/output matrices are continuous,
i.e. have no gaps in the end of each row, and if yes, process them as a single long row:
\code
// compute sum of positive matrix elements, optimized variant
double sum=0;
int cols = M.cols, rows = M.rows;
if(M.isContinuous())
{
cols *= rows;
rows = 1;
}
for(int i = 0; i < rows; i++)
{
const double* Mi = M.ptr<double>(i);
for(int j = 0; j < cols; j++)
sum += std::max(Mi[j], 0.);
}
\endcode
in the case of continuous matrix the outer loop body will be executed just once,
so the overhead will be smaller, which will be especially noticeable in the case of small matrices.
Finally, there are STL-style iterators that are smart enough to skip gaps between successive rows:
\code
// compute sum of positive matrix elements, iterator-based variant
double sum=0;
MatConstIterator_<double> it = M.begin<double>(), it_end = M.end<double>();
for(; it != it_end; ++it)
sum += std::max(*it, 0.);
\endcode
The matrix iterators are random-access iterators, so they can be passed
to any STL algorithm, including std::sort().
*/
class CV_EXPORTS Mat
{
public:
//! default constructor
Mat();
//! constructs 2D matrix of the specified size and type
// (_type is CV_8UC1, CV_64FC3, CV_32SC(12) etc.)
Mat(int rows, int cols, int type);
Mat(Size size, int type);
//! constucts 2D matrix and fills it with the specified value _s.
Mat(int rows, int cols, int type, const Scalar& s);
Mat(Size size, int type, const Scalar& s);
//! constructs n-dimensional matrix
Mat(int ndims, const int* sizes, int type);
Mat(int ndims, const int* sizes, int type, const Scalar& s);
//! copy constructor
Mat(const Mat& m);
//! constructor for matrix headers pointing to user-allocated data
Mat(int rows, int cols, int type, void* data, size_t step=AUTO_STEP);
Mat(Size size, int type, void* data, size_t step=AUTO_STEP);
Mat(int ndims, const int* sizes, int type, void* data, const size_t* steps=0);
//! creates a matrix header for a part of the bigger matrix
Mat(const Mat& m, const Range& rowRange, const Range& colRange=Range::all());
Mat(const Mat& m, const Rect& roi);
Mat(const Mat& m, const Range* ranges);
//! converts old-style CvMat to the new matrix; the data is not copied by default
Mat(const CvMat* m, bool copyData=false);
//! converts old-style CvMatND to the new matrix; the data is not copied by default
Mat(const CvMatND* m, bool copyData=false);
//! converts old-style IplImage to the new matrix; the data is not copied by default
Mat(const IplImage* img, bool copyData=false);
//! builds matrix from std::vector with or without copying the data
template<typename _Tp> explicit Mat(const std::vector<_Tp>& vec, bool copyData=false);
//! builds matrix from cv::Vec; the data is copied by default
template<typename _Tp, int n> explicit Mat(const Vec<_Tp, n>& vec, bool copyData=true);
//! builds matrix from cv::Matx; the data is copied by default
template<typename _Tp, int m, int n> explicit Mat(const Matx<_Tp, m, n>& mtx, bool copyData=true);
//! builds matrix from a 2D point
template<typename _Tp> explicit Mat(const Point_<_Tp>& pt, bool copyData=true);
//! builds matrix from a 3D point
template<typename _Tp> explicit Mat(const Point3_<_Tp>& pt, bool copyData=true);
//! builds matrix from comma initializer
template<typename _Tp> explicit Mat(const MatCommaInitializer_<_Tp>& commaInitializer);
//! download data from GpuMat
explicit Mat(const gpu::GpuMat& m);
//! destructor - calls release()
~Mat();
//! assignment operators
Mat& operator = (const Mat& m);
Mat& operator = (const MatExpr& expr);
//! returns a new matrix header for the specified row
Mat row(int y) const;
//! returns a new matrix header for the specified column
Mat col(int x) const;
//! ... for the specified row span
Mat rowRange(int startrow, int endrow) const;
Mat rowRange(const Range& r) const;
//! ... for the specified column span
Mat colRange(int startcol, int endcol) const;
Mat colRange(const Range& r) const;
//! ... for the specified diagonal
// (d=0 - the main diagonal,
// >0 - a diagonal from the lower half,
// <0 - a diagonal from the upper half)
Mat diag(int d=0) const;
//! constructs a square diagonal matrix which main diagonal is vector "d"
static Mat diag(const Mat& d);
//! returns deep copy of the matrix, i.e. the data is copied
Mat clone() const;
//! copies the matrix content to "m".
// It calls m.create(this->size(), this->type()).
void copyTo( OutputArray m ) const;
//! copies those matrix elements to "m" that are marked with non-zero mask elements.
void copyTo( OutputArray m, InputArray mask ) const;
//! converts matrix to another datatype with optional scalng. See cvConvertScale.
void convertTo( OutputArray m, int rtype, double alpha=1, double beta=0 ) const;
void assignTo( Mat& m, int type=-1 ) const;
//! sets every matrix element to s
Mat& operator = (const Scalar& s);
//! sets some of the matrix elements to s, according to the mask
Mat& setTo(InputArray value, InputArray mask=noArray());
//! creates alternative matrix header for the same data, with different
// number of channels and/or different number of rows. see cvReshape.
Mat reshape(int cn, int rows=0) const;
Mat reshape(int cn, int newndims, const int* newsz) const;
//! matrix transposition by means of matrix expressions
MatExpr t() const;
//! matrix inversion by means of matrix expressions
MatExpr inv(int method=DECOMP_LU) const;
//! per-element matrix multiplication by means of matrix expressions
MatExpr mul(InputArray m, double scale=1) const;
//! computes cross-product of 2 3D vectors
Mat cross(InputArray m) const;
//! computes dot-product
double dot(InputArray m) const;
//! Matlab-style matrix initialization
static MatExpr zeros(int rows, int cols, int type);
static MatExpr zeros(Size size, int type);
static MatExpr zeros(int ndims, const int* sz, int type);
static MatExpr ones(int rows, int cols, int type);
static MatExpr ones(Size size, int type);
static MatExpr ones(int ndims, const int* sz, int type);
static MatExpr eye(int rows, int cols, int type);
static MatExpr eye(Size size, int type);
//! allocates new matrix data unless the matrix already has specified size and type.
// previous data is unreferenced if needed.
void create(int rows, int cols, int type);
void create(Size size, int type);
void create(int ndims, const int* sizes, int type);
//! increases the reference counter; use with care to avoid memleaks
void addref();
//! decreases reference counter;
// deallocates the data when reference counter reaches 0.
void release();
//! deallocates the matrix data
void deallocate();
//! internal use function; properly re-allocates _size, _step arrays
void copySize(const Mat& m);
//! reserves enough space to fit sz hyper-planes
void reserve(size_t sz);
//! resizes matrix to the specified number of hyper-planes
void resize(size_t sz);
//! resizes matrix to the specified number of hyper-planes; initializes the newly added elements
void resize(size_t sz, const Scalar& s);
//! internal function
void push_back_(const void* elem);
//! adds element to the end of 1d matrix (or possibly multiple elements when _Tp=Mat)
template<typename _Tp> void push_back(const _Tp& elem);
template<typename _Tp> void push_back(const Mat_<_Tp>& elem);
void push_back(const Mat& m);
//! removes several hyper-planes from bottom of the matrix
void pop_back(size_t nelems=1);
//! locates matrix header within a parent matrix. See below
void locateROI( Size& wholeSize, Point& ofs ) const;
//! moves/resizes the current matrix ROI inside the parent matrix.
Mat& adjustROI( int dtop, int dbottom, int dleft, int dright );
//! extracts a rectangular sub-matrix
// (this is a generalized form of row, rowRange etc.)
Mat operator()( Range rowRange, Range colRange ) const;
Mat operator()( const Rect& roi ) const;
Mat operator()( const Range* ranges ) const;
//! converts header to CvMat; no data is copied
operator CvMat() const;
//! converts header to CvMatND; no data is copied
operator CvMatND() const;
//! converts header to IplImage; no data is copied
operator IplImage() const;
template<typename _Tp> operator std::vector<_Tp>() const;
template<typename _Tp, int n> operator Vec<_Tp, n>() const;
template<typename _Tp, int m, int n> operator Matx<_Tp, m, n>() const;
//! returns true iff the matrix data is continuous
// (i.e. when there are no gaps between successive rows).
// similar to CV_IS_MAT_CONT(cvmat->type)
bool isContinuous() const;
//! returns true if the matrix is a submatrix of another matrix
bool isSubmatrix() const;
//! returns element size in bytes,
// similar to CV_ELEM_SIZE(cvmat->type)
size_t elemSize() const;
//! returns the size of element channel in bytes.
size_t elemSize1() const;
//! returns element type, similar to CV_MAT_TYPE(cvmat->type)
int type() const;
//! returns element type, similar to CV_MAT_DEPTH(cvmat->type)
int depth() const;
//! returns element type, similar to CV_MAT_CN(cvmat->type)
int channels() const;
//! returns step/elemSize1()
size_t step1(int i=0) const;
//! returns true if matrix data is NULL
bool empty() const;
//! returns the total number of matrix elements
size_t total() const;
//! returns N if the matrix is 1-channel (N x ptdim) or ptdim-channel (1 x N) or (N x 1); negative number otherwise
int checkVector(int elemChannels, int depth=-1, bool requireContinuous=true) const;
//! returns pointer to i0-th submatrix along the dimension #0
uchar* ptr(int i0=0);
const uchar* ptr(int i0=0) const;
//! returns pointer to (i0,i1) submatrix along the dimensions #0 and #1
uchar* ptr(int i0, int i1);
const uchar* ptr(int i0, int i1) const;
//! returns pointer to (i0,i1,i3) submatrix along the dimensions #0, #1, #2
uchar* ptr(int i0, int i1, int i2);
const uchar* ptr(int i0, int i1, int i2) const;
//! returns pointer to the matrix element
uchar* ptr(const int* idx);
//! returns read-only pointer to the matrix element
const uchar* ptr(const int* idx) const;
template<int n> uchar* ptr(const Vec<int, n>& idx);
template<int n> const uchar* ptr(const Vec<int, n>& idx) const;
//! template version of the above method
template<typename _Tp> _Tp* ptr(int i0=0);
template<typename _Tp> const _Tp* ptr(int i0=0) const;
template<typename _Tp> _Tp* ptr(int i0, int i1);
template<typename _Tp> const _Tp* ptr(int i0, int i1) const;
template<typename _Tp> _Tp* ptr(int i0, int i1, int i2);
template<typename _Tp> const _Tp* ptr(int i0, int i1, int i2) const;
template<typename _Tp> _Tp* ptr(const int* idx);
template<typename _Tp> const _Tp* ptr(const int* idx) const;
template<typename _Tp, int n> _Tp* ptr(const Vec<int, n>& idx);
template<typename _Tp, int n> const _Tp* ptr(const Vec<int, n>& idx) const;
//! the same as above, with the pointer dereferencing
template<typename _Tp> _Tp& at(int i0=0);
template<typename _Tp> const _Tp& at(int i0=0) const;
template<typename _Tp> _Tp& at(int i0, int i1);
template<typename _Tp> const _Tp& at(int i0, int i1) const;
template<typename _Tp> _Tp& at(int i0, int i1, int i2);
template<typename _Tp> const _Tp& at(int i0, int i1, int i2) const;
template<typename _Tp> _Tp& at(const int* idx);
template<typename _Tp> const _Tp& at(const int* idx) const;
template<typename _Tp, int n> _Tp& at(const Vec<int, n>& idx);
template<typename _Tp, int n> const _Tp& at(const Vec<int, n>& idx) const;
//! special versions for 2D arrays (especially convenient for referencing image pixels)
template<typename _Tp> _Tp& at(Point pt);
template<typename _Tp> const _Tp& at(Point pt) const;
//! template methods for iteration over matrix elements.
// the iterators take care of skipping gaps in the end of rows (if any)
template<typename _Tp> MatIterator_<_Tp> begin();
template<typename _Tp> MatIterator_<_Tp> end();
template<typename _Tp> MatConstIterator_<_Tp> begin() const;
template<typename _Tp> MatConstIterator_<_Tp> end() const;
enum { MAGIC_VAL=0x42FF0000, AUTO_STEP=0, CONTINUOUS_FLAG=CV_MAT_CONT_FLAG, SUBMATRIX_FLAG=CV_SUBMAT_FLAG };
/*! includes several bit-fields:
- the magic signature
- continuity flag
- depth
- number of channels
*/
int flags;
//! the matrix dimensionality, >= 2
int dims;
//! the number of rows and columns or (-1, -1) when the matrix has more than 2 dimensions
int rows, cols;
//! pointer to the data
uchar* data;
//! pointer to the reference counter;
// when matrix points to user-allocated data, the pointer is NULL
int* refcount;
//! helper fields used in locateROI and adjustROI
uchar* datastart;
uchar* dataend;
uchar* datalimit;
//! custom allocator
MatAllocator* allocator;
struct CV_EXPORTS MSize
{
MSize(int* _p);
Size operator()() const;
const int& operator[](int i) const;
int& operator[](int i);
operator const int*() const;
bool operator == (const MSize& sz) const;
bool operator != (const MSize& sz) const;
int* p;
};
struct CV_EXPORTS MStep
{
MStep();
MStep(size_t s);
const size_t& operator[](int i) const;
size_t& operator[](int i);
operator size_t() const;
MStep& operator = (size_t s);
size_t* p;
size_t buf[2];
protected:
MStep& operator = (const MStep&);
};
MSize size;
MStep step;
protected:
void initEmpty();
};
/*!
Random Number Generator
The class implements RNG using Multiply-with-Carry algorithm
*/
class CV_EXPORTS RNG
{
public:
enum { UNIFORM=0, NORMAL=1 };
RNG();
RNG(uint64 state);
//! updates the state and returns the next 32-bit unsigned integer random number
unsigned next();
operator uchar();
operator schar();
operator ushort();
operator short();
operator unsigned();
//! returns a random integer sampled uniformly from [0, N).
unsigned operator ()(unsigned N);
unsigned operator ()();
operator int();
operator float();
operator double();
//! returns uniformly distributed integer random number from [a,b) range
int uniform(int a, int b);
//! returns uniformly distributed floating-point random number from [a,b) range
float uniform(float a, float b);
//! returns uniformly distributed double-precision floating-point random number from [a,b) range
double uniform(double a, double b);
void fill( InputOutputArray mat, int distType, InputArray a, InputArray b, bool saturateRange=false );
//! returns Gaussian random variate with mean zero.
double gaussian(double sigma);
uint64 state;
};
class CV_EXPORTS RNG_MT19937
{
public:
RNG_MT19937();
RNG_MT19937(unsigned s);
void seed(unsigned s);
unsigned next();
operator int();
operator unsigned();
operator float();
operator double();
unsigned operator ()(unsigned N);
unsigned operator ()();
// returns uniformly distributed integer random number from [a,b) range
int uniform(int a, int b);
// returns uniformly distributed floating-point random number from [a,b) range
float uniform(float a, float b);
// returns uniformly distributed double-precision floating-point random number from [a,b) range
double uniform(double a, double b);
private:
enum PeriodParameters {N = 624, M = 397};
unsigned state[N];
int mti;
};
/*!
Termination criteria in iterative algorithms
*/
class CV_EXPORTS TermCriteria
{
public:
enum
{
COUNT=1, //!< the maximum number of iterations or elements to compute
MAX_ITER=COUNT, //!< ditto
EPS=2 //!< the desired accuracy or change in parameters at which the iterative algorithm stops
};
//! default constructor
TermCriteria();
//! full constructor
TermCriteria(int type, int maxCount, double epsilon);
//! conversion from CvTermCriteria
TermCriteria(const CvTermCriteria& criteria);
//! conversion to CvTermCriteria
operator CvTermCriteria() const;
int type; //!< the type of termination criteria: COUNT, EPS or COUNT + EPS
int maxCount; // the maximum number of iterations/elements
double epsilon; // the desired accuracy
};
typedef void (*BinaryFunc)(const uchar* src1, size_t step1,
const uchar* src2, size_t step2,
uchar* dst, size_t step, Size sz,
void*);
CV_EXPORTS BinaryFunc getConvertFunc(int sdepth, int ddepth);
CV_EXPORTS BinaryFunc getConvertScaleFunc(int sdepth, int ddepth);
CV_EXPORTS BinaryFunc getCopyMaskFunc(size_t esz);
//! swaps two matrices
CV_EXPORTS void swap(Mat& a, Mat& b);
//! extracts Channel of Interest from CvMat or IplImage and makes cv::Mat out of it.
CV_EXPORTS void extractImageCOI(const CvArr* arr, OutputArray coiimg, int coi=-1);
//! inserts single-channel cv::Mat into a multi-channel CvMat or IplImage
CV_EXPORTS void insertImageCOI(InputArray coiimg, CvArr* arr, int coi=-1);
//! adds one matrix to another (dst = src1 + src2)
CV_EXPORTS_W void add(InputArray src1, InputArray src2, OutputArray dst,
InputArray mask=noArray(), int dtype=-1);
//! subtracts one matrix from another (dst = src1 - src2)
CV_EXPORTS_W void subtract(InputArray src1, InputArray src2, OutputArray dst,
InputArray mask=noArray(), int dtype=-1);
//! computes element-wise weighted product of the two arrays (dst = scale*src1*src2)
CV_EXPORTS_W void multiply(InputArray src1, InputArray src2,
OutputArray dst, double scale=1, int dtype=-1);
//! computes element-wise weighted quotient of the two arrays (dst = scale*src1/src2)
CV_EXPORTS_W void divide(InputArray src1, InputArray src2, OutputArray dst,
double scale=1, int dtype=-1);
//! computes element-wise weighted reciprocal of an array (dst = scale/src2)
CV_EXPORTS_W void divide(double scale, InputArray src2,
OutputArray dst, int dtype=-1);
//! adds scaled array to another one (dst = alpha*src1 + src2)
CV_EXPORTS_W void scaleAdd(InputArray src1, double alpha, InputArray src2, OutputArray dst);
//! computes weighted sum of two arrays (dst = alpha*src1 + beta*src2 + gamma)
CV_EXPORTS_W void addWeighted(InputArray src1, double alpha, InputArray src2,
double beta, double gamma, OutputArray dst, int dtype=-1);
//! scales array elements, computes absolute values and converts the results to 8-bit unsigned integers: dst(i)=saturate_cast<uchar>abs(src(i)*alpha+beta)
CV_EXPORTS_W void convertScaleAbs(InputArray src, OutputArray dst,
double alpha=1, double beta=0);
//! transforms array of numbers using a lookup table: dst(i)=lut(src(i))
CV_EXPORTS_W void LUT(InputArray src, InputArray lut, OutputArray dst,
int interpolation=0);
//! computes sum of array elements
CV_EXPORTS_AS(sumElems) Scalar sum(InputArray src);
//! computes the number of nonzero array elements
CV_EXPORTS_W int countNonZero( InputArray src );
//! returns the list of locations of non-zero pixels
CV_EXPORTS_W void findNonZero( InputArray src, OutputArray idx );
//! computes mean value of selected array elements
CV_EXPORTS_W Scalar mean(InputArray src, InputArray mask=noArray());
//! computes mean value and standard deviation of all or selected array elements
CV_EXPORTS_W void meanStdDev(InputArray src, OutputArray mean, OutputArray stddev,
InputArray mask=noArray());
//! computes norm of the selected array part
CV_EXPORTS_W double norm(InputArray src1, int normType=NORM_L2, InputArray mask=noArray());
//! computes norm of selected part of the difference between two arrays
CV_EXPORTS_W double norm(InputArray src1, InputArray src2,
int normType=NORM_L2, InputArray mask=noArray());
//! naive nearest neighbor finder
CV_EXPORTS_W void batchDistance(InputArray src1, InputArray src2,
OutputArray dist, int dtype, OutputArray nidx,
int normType=NORM_L2, int K=0,
InputArray mask=noArray(), int update=0,
bool crosscheck=false);
//! scales and shifts array elements so that either the specified norm (alpha) or the minimum (alpha) and maximum (beta) array values get the specified values
CV_EXPORTS_W void normalize( InputArray src, OutputArray dst, double alpha=1, double beta=0,
int norm_type=NORM_L2, int dtype=-1, InputArray mask=noArray());
//! finds global minimum and maximum array elements and returns their values and their locations
CV_EXPORTS_W void minMaxLoc(InputArray src, CV_OUT double* minVal,
CV_OUT double* maxVal=0, CV_OUT Point* minLoc=0,
CV_OUT Point* maxLoc=0, InputArray mask=noArray());
CV_EXPORTS void minMaxIdx(InputArray src, double* minVal, double* maxVal,
int* minIdx=0, int* maxIdx=0, InputArray mask=noArray());
//! transforms 2D matrix to 1D row or column vector by taking sum, minimum, maximum or mean value over all the rows
CV_EXPORTS_W void reduce(InputArray src, OutputArray dst, int dim, int rtype, int dtype=-1);
//! makes multi-channel array out of several single-channel arrays
CV_EXPORTS void merge(const Mat* mv, size_t count, OutputArray dst);
//! makes multi-channel array out of several single-channel arrays
CV_EXPORTS_W void merge(InputArrayOfArrays mv, OutputArray dst);
//! copies each plane of a multi-channel array to a dedicated array
CV_EXPORTS void split(const Mat& src, Mat* mvbegin);
//! copies each plane of a multi-channel array to a dedicated array
CV_EXPORTS_W void split(InputArray m, OutputArrayOfArrays mv);
//! copies selected channels from the input arrays to the selected channels of the output arrays
CV_EXPORTS void mixChannels(const Mat* src, size_t nsrcs, Mat* dst, size_t ndsts,
const int* fromTo, size_t npairs);
CV_EXPORTS void mixChannels(const std::vector<Mat>& src, std::vector<Mat>& dst,
const int* fromTo, size_t npairs);
CV_EXPORTS_W void mixChannels(InputArrayOfArrays src, InputArrayOfArrays dst,
const std::vector<int>& fromTo);
//! extracts a single channel from src (coi is 0-based index)
CV_EXPORTS_W void extractChannel(InputArray src, OutputArray dst, int coi);
//! inserts a single channel to dst (coi is 0-based index)
CV_EXPORTS_W void insertChannel(InputArray src, InputOutputArray dst, int coi);
//! reverses the order of the rows, columns or both in a matrix
CV_EXPORTS_W void flip(InputArray src, OutputArray dst, int flipCode);
//! replicates the input matrix the specified number of times in the horizontal and/or vertical direction
CV_EXPORTS_W void repeat(InputArray src, int ny, int nx, OutputArray dst);
CV_EXPORTS Mat repeat(const Mat& src, int ny, int nx);
CV_EXPORTS void hconcat(const Mat* src, size_t nsrc, OutputArray dst);
CV_EXPORTS void hconcat(InputArray src1, InputArray src2, OutputArray dst);
CV_EXPORTS_W void hconcat(InputArrayOfArrays src, OutputArray dst);
CV_EXPORTS void vconcat(const Mat* src, size_t nsrc, OutputArray dst);
CV_EXPORTS void vconcat(InputArray src1, InputArray src2, OutputArray dst);
CV_EXPORTS_W void vconcat(InputArrayOfArrays src, OutputArray dst);
//! computes bitwise conjunction of the two arrays (dst = src1 & src2)
CV_EXPORTS_W void bitwise_and(InputArray src1, InputArray src2,
OutputArray dst, InputArray mask=noArray());
//! computes bitwise disjunction of the two arrays (dst = src1 | src2)
CV_EXPORTS_W void bitwise_or(InputArray src1, InputArray src2,
OutputArray dst, InputArray mask=noArray());
//! computes bitwise exclusive-or of the two arrays (dst = src1 ^ src2)
CV_EXPORTS_W void bitwise_xor(InputArray src1, InputArray src2,
OutputArray dst, InputArray mask=noArray());
//! inverts each bit of array (dst = ~src)
CV_EXPORTS_W void bitwise_not(InputArray src, OutputArray dst,
InputArray mask=noArray());
//! computes element-wise absolute difference of two arrays (dst = abs(src1 - src2))
CV_EXPORTS_W void absdiff(InputArray src1, InputArray src2, OutputArray dst);
//! set mask elements for those array elements which are within the element-specific bounding box (dst = lowerb <= src && src < upperb)
CV_EXPORTS_W void inRange(InputArray src, InputArray lowerb,
InputArray upperb, OutputArray dst);
//! compares elements of two arrays (dst = src1 <cmpop> src2)
CV_EXPORTS_W void compare(InputArray src1, InputArray src2, OutputArray dst, int cmpop);
//! computes per-element minimum of two arrays (dst = min(src1, src2))
CV_EXPORTS_W void min(InputArray src1, InputArray src2, OutputArray dst);
//! computes per-element maximum of two arrays (dst = max(src1, src2))
CV_EXPORTS_W void max(InputArray src1, InputArray src2, OutputArray dst);
//! computes per-element minimum of two arrays (dst = min(src1, src2))
CV_EXPORTS void min(const Mat& src1, const Mat& src2, Mat& dst);
//! computes per-element minimum of array and scalar (dst = min(src1, src2))
CV_EXPORTS void min(const Mat& src1, double src2, Mat& dst);
//! computes per-element maximum of two arrays (dst = max(src1, src2))
CV_EXPORTS void max(const Mat& src1, const Mat& src2, Mat& dst);
//! computes per-element maximum of array and scalar (dst = max(src1, src2))
CV_EXPORTS void max(const Mat& src1, double src2, Mat& dst);
//! computes square root of each matrix element (dst = src**0.5)
CV_EXPORTS_W void sqrt(InputArray src, OutputArray dst);
//! raises the input matrix elements to the specified power (b = a**power)
CV_EXPORTS_W void pow(InputArray src, double power, OutputArray dst);
//! computes exponent of each matrix element (dst = e**src)
CV_EXPORTS_W void exp(InputArray src, OutputArray dst);
//! computes natural logarithm of absolute value of each matrix element: dst = log(abs(src))
CV_EXPORTS_W void log(InputArray src, OutputArray dst);
//! computes cube root of the argument
CV_EXPORTS_W float cubeRoot(float val);
//! computes the angle in degrees (0..360) of the vector (x,y)
CV_EXPORTS_W float fastAtan2(float y, float x);
CV_EXPORTS void exp(const float* src, float* dst, int n);
CV_EXPORTS void log(const float* src, float* dst, int n);
CV_EXPORTS void fastAtan2(const float* y, const float* x, float* dst, int n, bool angleInDegrees);
CV_EXPORTS void magnitude(const float* x, const float* y, float* dst, int n);
//! converts polar coordinates to Cartesian
CV_EXPORTS_W void polarToCart(InputArray magnitude, InputArray angle,
OutputArray x, OutputArray y, bool angleInDegrees=false);
//! converts Cartesian coordinates to polar
CV_EXPORTS_W void cartToPolar(InputArray x, InputArray y,
OutputArray magnitude, OutputArray angle,
bool angleInDegrees=false);
//! computes angle (angle(i)) of each (x(i), y(i)) vector
CV_EXPORTS_W void phase(InputArray x, InputArray y, OutputArray angle,
bool angleInDegrees=false);
//! computes magnitude (magnitude(i)) of each (x(i), y(i)) vector
CV_EXPORTS_W void magnitude(InputArray x, InputArray y, OutputArray magnitude);
//! checks that each matrix element is within the specified range.
CV_EXPORTS_W bool checkRange(InputArray a, bool quiet=true, CV_OUT Point* pos=0,
double minVal=-DBL_MAX, double maxVal=DBL_MAX);
//! converts NaN's to the given number
CV_EXPORTS_W void patchNaNs(InputOutputArray a, double val=0);
//! implements generalized matrix product algorithm GEMM from BLAS
CV_EXPORTS_W void gemm(InputArray src1, InputArray src2, double alpha,
InputArray src3, double gamma, OutputArray dst, int flags=0);
//! multiplies matrix by its transposition from the left or from the right
CV_EXPORTS_W void mulTransposed( InputArray src, OutputArray dst, bool aTa,
InputArray delta=noArray(),
double scale=1, int dtype=-1 );
//! transposes the matrix
CV_EXPORTS_W void transpose(InputArray src, OutputArray dst);
//! performs affine transformation of each element of multi-channel input matrix
CV_EXPORTS_W void transform(InputArray src, OutputArray dst, InputArray m );
//! performs perspective transformation of each element of multi-channel input matrix
CV_EXPORTS_W void perspectiveTransform(InputArray src, OutputArray dst, InputArray m );
//! extends the symmetrical matrix from the lower half or from the upper half
CV_EXPORTS_W void completeSymm(InputOutputArray mtx, bool lowerToUpper=false);
//! initializes scaled identity matrix
CV_EXPORTS_W void setIdentity(InputOutputArray mtx, const Scalar& s=Scalar(1));
//! computes determinant of a square matrix
CV_EXPORTS_W double determinant(InputArray mtx);
//! computes trace of a matrix
CV_EXPORTS_W Scalar trace(InputArray mtx);
//! computes inverse or pseudo-inverse matrix
CV_EXPORTS_W double invert(InputArray src, OutputArray dst, int flags=DECOMP_LU);
//! solves linear system or a least-square problem
CV_EXPORTS_W bool solve(InputArray src1, InputArray src2,
OutputArray dst, int flags=DECOMP_LU);
enum
{
SORT_EVERY_ROW=0,
SORT_EVERY_COLUMN=1,
SORT_ASCENDING=0,
SORT_DESCENDING=16
};
//! sorts independently each matrix row or each matrix column
CV_EXPORTS_W void sort(InputArray src, OutputArray dst, int flags);
//! sorts independently each matrix row or each matrix column
CV_EXPORTS_W void sortIdx(InputArray src, OutputArray dst, int flags);
//! finds real roots of a cubic polynomial
CV_EXPORTS_W int solveCubic(InputArray coeffs, OutputArray roots);
//! finds real and complex roots of a polynomial
CV_EXPORTS_W double solvePoly(InputArray coeffs, OutputArray roots, int maxIters=300);
//! finds eigenvalues of a symmetric matrix
CV_EXPORTS bool eigen(InputArray src, OutputArray eigenvalues, int lowindex=-1,
int highindex=-1);
//! finds eigenvalues and eigenvectors of a symmetric matrix
CV_EXPORTS bool eigen(InputArray src, OutputArray eigenvalues,
OutputArray eigenvectors,
int lowindex=-1, int highindex=-1);
CV_EXPORTS_W bool eigen(InputArray src, bool computeEigenvectors,
OutputArray eigenvalues, OutputArray eigenvectors);
enum
{
COVAR_SCRAMBLED=0,
COVAR_NORMAL=1,
COVAR_USE_AVG=2,
COVAR_SCALE=4,
COVAR_ROWS=8,
COVAR_COLS=16
};
//! computes covariation matrix of a set of samples
CV_EXPORTS void calcCovarMatrix( const Mat* samples, int nsamples, Mat& covar, Mat& mean,
int flags, int ctype=CV_64F);
//! computes covariation matrix of a set of samples
CV_EXPORTS_W void calcCovarMatrix( InputArray samples, OutputArray covar,
OutputArray mean, int flags, int ctype=CV_64F);
/*!
Principal Component Analysis
The class PCA is used to compute the special basis for a set of vectors.
The basis will consist of eigenvectors of the covariance matrix computed
from the input set of vectors. After PCA is performed, vectors can be transformed from
the original high-dimensional space to the subspace formed by a few most
prominent eigenvectors (called the principal components),
corresponding to the largest eigenvalues of the covariation matrix.
Thus the dimensionality of the vector and the correlation between the coordinates is reduced.
The following sample is the function that takes two matrices. The first one stores the set
of vectors (a row per vector) that is used to compute PCA, the second one stores another
"test" set of vectors (a row per vector) that are first compressed with PCA,
then reconstructed back and then the reconstruction error norm is computed and printed for each vector.
\code
using namespace cv;
PCA compressPCA(const Mat& pcaset, int maxComponents,
const Mat& testset, Mat& compressed)
{
PCA pca(pcaset, // pass the data
Mat(), // we do not have a pre-computed mean vector,
// so let the PCA engine to compute it
CV_PCA_DATA_AS_ROW, // indicate that the vectors
// are stored as matrix rows
// (use CV_PCA_DATA_AS_COL if the vectors are
// the matrix columns)
maxComponents // specify, how many principal components to retain
);
// if there is no test data, just return the computed basis, ready-to-use
if( !testset.data )
return pca;
CV_Assert( testset.cols == pcaset.cols );
compressed.create(testset.rows, maxComponents, testset.type());
Mat reconstructed;
for( int i = 0; i < testset.rows; i++ )
{
Mat vec = testset.row(i), coeffs = compressed.row(i), reconstructed;
// compress the vector, the result will be stored
// in the i-th row of the output matrix
pca.project(vec, coeffs);
// and then reconstruct it
pca.backProject(coeffs, reconstructed);
// and measure the error
printf("%d. diff = %g\n", i, norm(vec, reconstructed, NORM_L2));
}
return pca;
}
\endcode
*/
class CV_EXPORTS PCA
{
public:
//! default constructor
PCA();
//! the constructor that performs PCA
PCA(InputArray data, InputArray mean, int flags, int maxComponents=0);
PCA(InputArray data, InputArray mean, int flags, double retainedVariance);
//! operator that performs PCA. The previously stored data, if any, is released
PCA& operator()(InputArray data, InputArray mean, int flags, int maxComponents=0);
PCA& operator()(InputArray data, InputArray mean, int flags, double retainedVariance);
//! projects vector from the original space to the principal components subspace
Mat project(InputArray vec) const;
//! projects vector from the original space to the principal components subspace
void project(InputArray vec, OutputArray result) const;
//! reconstructs the original vector from the projection
Mat backProject(InputArray vec) const;
//! reconstructs the original vector from the projection
void backProject(InputArray vec, OutputArray result) const;
Mat eigenvectors; //!< eigenvectors of the covariation matrix
Mat eigenvalues; //!< eigenvalues of the covariation matrix
Mat mean; //!< mean value subtracted before the projection and added after the back projection
};
CV_EXPORTS_W void PCACompute(InputArray data, InputOutputArray mean,
OutputArray eigenvectors, int maxComponents=0);
CV_EXPORTS_W void PCACompute(InputArray data, InputOutputArray mean,
OutputArray eigenvectors, double retainedVariance);
CV_EXPORTS_W void PCAProject(InputArray data, InputArray mean,
InputArray eigenvectors, OutputArray result);
CV_EXPORTS_W void PCABackProject(InputArray data, InputArray mean,
InputArray eigenvectors, OutputArray result);
/*!
Singular Value Decomposition class
The class is used to compute Singular Value Decomposition of a floating-point matrix and then
use it to solve least-square problems, under-determined linear systems, invert matrices,
compute condition numbers etc.
For a bit faster operation you can pass flags=SVD::MODIFY_A|... to modify the decomposed matrix
when it is not necessarily to preserve it. If you want to compute condition number of a matrix
or absolute value of its determinant - you do not need SVD::u or SVD::vt,
so you can pass flags=SVD::NO_UV|... . Another flag SVD::FULL_UV indicates that the full-size SVD::u and SVD::vt
must be computed, which is not necessary most of the time.
*/
class CV_EXPORTS SVD
{
public:
enum { MODIFY_A=1, NO_UV=2, FULL_UV=4 };
//! the default constructor
SVD();
//! the constructor that performs SVD
SVD( InputArray src, int flags=0 );
//! the operator that performs SVD. The previously allocated SVD::u, SVD::w are SVD::vt are released.
SVD& operator ()( InputArray src, int flags=0 );
//! decomposes matrix and stores the results to user-provided matrices
static void compute( InputArray src, OutputArray w,
OutputArray u, OutputArray vt, int flags=0 );
//! computes singular values of a matrix
static void compute( InputArray src, OutputArray w, int flags=0 );
//! performs back substitution
static void backSubst( InputArray w, InputArray u,
InputArray vt, InputArray rhs,
OutputArray dst );
template<typename _Tp, int m, int n, int nm> static void compute( const Matx<_Tp, m, n>& a,
Matx<_Tp, nm, 1>& w, Matx<_Tp, m, nm>& u, Matx<_Tp, n, nm>& vt );
template<typename _Tp, int m, int n, int nm> static void compute( const Matx<_Tp, m, n>& a,
Matx<_Tp, nm, 1>& w );
template<typename _Tp, int m, int n, int nm, int nb> static void backSubst( const Matx<_Tp, nm, 1>& w,
const Matx<_Tp, m, nm>& u, const Matx<_Tp, n, nm>& vt, const Matx<_Tp, m, nb>& rhs, Matx<_Tp, n, nb>& dst );
//! finds dst = arg min_{|dst|=1} |m*dst|
static void solveZ( InputArray src, OutputArray dst );
//! performs back substitution, so that dst is the solution or pseudo-solution of m*dst = rhs, where m is the decomposed matrix
void backSubst( InputArray rhs, OutputArray dst ) const;
Mat u, w, vt;
};
//! computes SVD of src
CV_EXPORTS_W void SVDecomp( InputArray src, OutputArray w, OutputArray u, OutputArray vt, int flags=0 );
//! performs back substitution for the previously computed SVD
CV_EXPORTS_W void SVBackSubst( InputArray w, InputArray u, InputArray vt,
InputArray rhs, OutputArray dst );
//! computes Mahalanobis distance between two vectors: sqrt((v1-v2)'*icovar*(v1-v2)), where icovar is the inverse covariation matrix
CV_EXPORTS_W double Mahalanobis(InputArray v1, InputArray v2, InputArray icovar);
//! a synonym for Mahalanobis
CV_EXPORTS double Mahalonobis(InputArray v1, InputArray v2, InputArray icovar);
//! performs forward or inverse 1D or 2D Discrete Fourier Transformation
CV_EXPORTS_W void dft(InputArray src, OutputArray dst, int flags=0, int nonzeroRows=0);
//! performs inverse 1D or 2D Discrete Fourier Transformation
CV_EXPORTS_W void idft(InputArray src, OutputArray dst, int flags=0, int nonzeroRows=0);
//! performs forward or inverse 1D or 2D Discrete Cosine Transformation
CV_EXPORTS_W void dct(InputArray src, OutputArray dst, int flags=0);
//! performs inverse 1D or 2D Discrete Cosine Transformation
CV_EXPORTS_W void idct(InputArray src, OutputArray dst, int flags=0);
//! computes element-wise product of the two Fourier spectrums. The second spectrum can optionally be conjugated before the multiplication
CV_EXPORTS_W void mulSpectrums(InputArray a, InputArray b, OutputArray c,
int flags, bool conjB=false);
//! computes the minimal vector size vecsize1 >= vecsize so that the dft() of the vector of length vecsize1 can be computed efficiently
CV_EXPORTS_W int getOptimalDFTSize(int vecsize);
/*!
Various k-Means flags
*/
enum
{
KMEANS_RANDOM_CENTERS=0, // Chooses random centers for k-Means initialization
KMEANS_PP_CENTERS=2, // Uses k-Means++ algorithm for initialization
KMEANS_USE_INITIAL_LABELS=1 // Uses the user-provided labels for K-Means initialization
};
//! clusters the input data using k-Means algorithm
CV_EXPORTS_W double kmeans( InputArray data, int K, InputOutputArray bestLabels,
TermCriteria criteria, int attempts,
int flags, OutputArray centers=noArray() );
//! returns the thread-local Random number generator
CV_EXPORTS RNG& theRNG();
//! returns the next unifomly-distributed random number of the specified type
template<typename _Tp> static inline _Tp randu() { return (_Tp)theRNG(); }
//! fills array with uniformly-distributed random numbers from the range [low, high)
CV_EXPORTS_W void randu(InputOutputArray dst, InputArray low, InputArray high);
//! fills array with normally-distributed random numbers with the specified mean and the standard deviation
CV_EXPORTS_W void randn(InputOutputArray dst, InputArray mean, InputArray stddev);
//! shuffles the input array elements
CV_EXPORTS void randShuffle(InputOutputArray dst, double iterFactor=1., RNG* rng=0);
CV_EXPORTS_AS(randShuffle) void randShuffle_(InputOutputArray dst, double iterFactor=1.);
//! draws the line segment (pt1, pt2) in the image
CV_EXPORTS_W void line(CV_IN_OUT Mat& img, Point pt1, Point pt2, const Scalar& color,
int thickness=1, int lineType=8, int shift=0);
//! draws the rectangle outline or a solid rectangle with the opposite corners pt1 and pt2 in the image
CV_EXPORTS_W void rectangle(CV_IN_OUT Mat& img, Point pt1, Point pt2,
const Scalar& color, int thickness=1,
int lineType=8, int shift=0);
//! draws the rectangle outline or a solid rectangle covering rec in the image
CV_EXPORTS void rectangle(CV_IN_OUT Mat& img, Rect rec,
const Scalar& color, int thickness=1,
int lineType=8, int shift=0);
//! draws the circle outline or a solid circle in the image
CV_EXPORTS_W void circle(CV_IN_OUT Mat& img, Point center, int radius,
const Scalar& color, int thickness=1,
int lineType=8, int shift=0);
//! draws an elliptic arc, ellipse sector or a rotated ellipse in the image
CV_EXPORTS_W void ellipse(CV_IN_OUT Mat& img, Point center, Size axes,
double angle, double startAngle, double endAngle,
const Scalar& color, int thickness=1,
int lineType=8, int shift=0);
//! draws a rotated ellipse in the image
CV_EXPORTS_W void ellipse(CV_IN_OUT Mat& img, const RotatedRect& box, const Scalar& color,
int thickness=1, int lineType=8);
//! draws a filled convex polygon in the image
CV_EXPORTS void fillConvexPoly(Mat& img, const Point* pts, int npts,
const Scalar& color, int lineType=8,
int shift=0);
CV_EXPORTS_W void fillConvexPoly(InputOutputArray img, InputArray points,
const Scalar& color, int lineType=8,
int shift=0);
//! fills an area bounded by one or more polygons
CV_EXPORTS void fillPoly(Mat& img, const Point** pts,
const int* npts, int ncontours,
const Scalar& color, int lineType=8, int shift=0,
Point offset=Point() );
CV_EXPORTS_W void fillPoly(InputOutputArray img, InputArrayOfArrays pts,
const Scalar& color, int lineType=8, int shift=0,
Point offset=Point() );
//! draws one or more polygonal curves
CV_EXPORTS void polylines(Mat& img, const Point* const* pts, const int* npts,
int ncontours, bool isClosed, const Scalar& color,
int thickness=1, int lineType=8, int shift=0 );
CV_EXPORTS_W void polylines(InputOutputArray img, InputArrayOfArrays pts,
bool isClosed, const Scalar& color,
int thickness=1, int lineType=8, int shift=0 );
//! 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() );
//! clips the line segment by the rectangle Rect(0, 0, imgSize.width, imgSize.height)
CV_EXPORTS bool clipLine(Size imgSize, CV_IN_OUT Point& pt1, CV_IN_OUT Point& pt2);
//! clips the line segment by the rectangle imgRect
CV_EXPORTS_W bool clipLine(Rect imgRect, CV_OUT CV_IN_OUT Point& pt1, CV_OUT CV_IN_OUT Point& pt2);
/*!
Line iterator class
The class is used to iterate over all the pixels on the raster line
segment connecting two specified points.
*/
class CV_EXPORTS LineIterator
{
public:
//! intializes the iterator
LineIterator( const Mat& img, Point pt1, Point pt2,
int connectivity=8, bool leftToRight=false );
//! returns pointer to the current pixel
uchar* operator *();
//! prefix increment operator (++it). shifts iterator to the next pixel
LineIterator& operator ++();
//! postfix increment operator (it++). shifts iterator to the next pixel
LineIterator operator ++(int);
//! returns coordinates of the current pixel
Point pos() const;
uchar* ptr;
const uchar* ptr0;
int step, elemSize;
int err, count;
int minusDelta, plusDelta;
int minusStep, plusStep;
};
//! converts elliptic arc to a polygonal curve
CV_EXPORTS_W void ellipse2Poly( Point center, Size axes, int angle,
int arcStart, int arcEnd, int delta,
CV_OUT std::vector<Point>& pts );
enum
{
FONT_HERSHEY_SIMPLEX = 0,
FONT_HERSHEY_PLAIN = 1,
FONT_HERSHEY_DUPLEX = 2,
FONT_HERSHEY_COMPLEX = 3,
FONT_HERSHEY_TRIPLEX = 4,
FONT_HERSHEY_COMPLEX_SMALL = 5,
FONT_HERSHEY_SCRIPT_SIMPLEX = 6,
FONT_HERSHEY_SCRIPT_COMPLEX = 7,
FONT_ITALIC = 16
};
//! renders text string in the image
CV_EXPORTS_W void putText( Mat& img, const String& text, Point org,
int fontFace, double fontScale, Scalar color,
int thickness=1, int lineType=8,
bool bottomLeftOrigin=false );
//! returns bounding box of the text string
CV_EXPORTS_W Size getTextSize(const String& text, int fontFace,
double fontScale, int thickness,
CV_OUT int* baseLine);
///////////////////////////////// Mat_<_Tp> ////////////////////////////////////
/*!
Template matrix class derived from Mat
The class Mat_ is a "thin" template wrapper on top of cv::Mat. It does not have any extra data fields,
nor it or cv::Mat have any virtual methods and thus references or pointers to these two classes
can be safely converted one to another. But do it with care, for example:
\code
// create 100x100 8-bit matrix
Mat M(100,100,CV_8U);
// this will compile fine. no any data conversion will be done.
Mat_<float>& M1 = (Mat_<float>&)M;
// the program will likely crash at the statement below
M1(99,99) = 1.f;
\endcode
While cv::Mat is sufficient in most cases, cv::Mat_ can be more convenient if you use a lot of element
access operations and if you know matrix type at compile time.
Note that cv::Mat::at<_Tp>(int y, int x) and cv::Mat_<_Tp>::operator ()(int y, int x) do absolutely the
same thing and run at the same speed, but the latter is certainly shorter:
\code
Mat_<double> M(20,20);
for(int i = 0; i < M.rows; i++)
for(int j = 0; j < M.cols; j++)
M(i,j) = 1./(i+j+1);
Mat E, V;
eigen(M,E,V);
cout << E.at<double>(0,0)/E.at<double>(M.rows-1,0);
\endcode
It is easy to use Mat_ for multi-channel images/matrices - just pass cv::Vec as cv::Mat_ template parameter:
\code
// allocate 320x240 color image and fill it with green (in RGB space)
Mat_<Vec3b> img(240, 320, Vec3b(0,255,0));
// now draw a diagonal white line
for(int i = 0; i < 100; i++)
img(i,i)=Vec3b(255,255,255);
// and now modify the 2nd (red) channel of each pixel
for(int i = 0; i < img.rows; i++)
for(int j = 0; j < img.cols; j++)
img(i,j)[2] ^= (uchar)(i ^ j); // img(y,x)[c] accesses c-th channel of the pixel (x,y)
\endcode
*/
template<typename _Tp> class CV_EXPORTS Mat_ : public Mat
{
public:
typedef _Tp value_type;
typedef typename DataType<_Tp>::channel_type channel_type;
typedef MatIterator_<_Tp> iterator;
typedef MatConstIterator_<_Tp> const_iterator;
//! default constructor
Mat_();
//! equivalent to Mat(_rows, _cols, DataType<_Tp>::type)
Mat_(int _rows, int _cols);
//! constructor that sets each matrix element to specified value
Mat_(int _rows, int _cols, const _Tp& value);
//! equivalent to Mat(_size, DataType<_Tp>::type)
explicit Mat_(Size _size);
//! constructor that sets each matrix element to specified value
Mat_(Size _size, const _Tp& value);
//! n-dim array constructor
Mat_(int _ndims, const int* _sizes);
//! n-dim array constructor that sets each matrix element to specified value
Mat_(int _ndims, const int* _sizes, const _Tp& value);
//! copy/conversion contructor. If m is of different type, it's converted
Mat_(const Mat& m);
//! copy constructor
Mat_(const Mat_& m);
//! constructs a matrix on top of user-allocated data. step is in bytes(!!!), regardless of the type
Mat_(int _rows, int _cols, _Tp* _data, size_t _step=AUTO_STEP);
//! constructs n-dim matrix on top of user-allocated data. steps are in bytes(!!!), regardless of the type
Mat_(int _ndims, const int* _sizes, _Tp* _data, const size_t* _steps=0);
//! selects a submatrix
Mat_(const Mat_& m, const Range& rowRange, const Range& colRange=Range::all());
//! selects a submatrix
Mat_(const Mat_& m, const Rect& roi);
//! selects a submatrix, n-dim version
Mat_(const Mat_& m, const Range* ranges);
//! from a matrix expression
explicit Mat_(const MatExpr& e);
//! makes a matrix out of Vec, std::vector, Point_ or Point3_. The matrix will have a single column
explicit Mat_(const std::vector<_Tp>& vec, bool copyData=false);
template<int n> explicit Mat_(const Vec<typename DataType<_Tp>::channel_type, n>& vec, bool copyData=true);
template<int m, int n> explicit Mat_(const Matx<typename DataType<_Tp>::channel_type, m, n>& mtx, bool copyData=true);
explicit Mat_(const Point_<typename DataType<_Tp>::channel_type>& pt, bool copyData=true);
explicit Mat_(const Point3_<typename DataType<_Tp>::channel_type>& pt, bool copyData=true);
explicit Mat_(const MatCommaInitializer_<_Tp>& commaInitializer);
Mat_& operator = (const Mat& m);
Mat_& operator = (const Mat_& m);
//! set all the elements to s.
Mat_& operator = (const _Tp& s);
//! assign a matrix expression
Mat_& operator = (const MatExpr& e);
//! iterators; they are smart enough to skip gaps in the end of rows
iterator begin();
iterator end();
const_iterator begin() const;
const_iterator end() const;
//! equivalent to Mat::create(_rows, _cols, DataType<_Tp>::type)
void create(int _rows, int _cols);
//! equivalent to Mat::create(_size, DataType<_Tp>::type)
void create(Size _size);
//! equivalent to Mat::create(_ndims, _sizes, DatType<_Tp>::type)
void create(int _ndims, const int* _sizes);
//! cross-product
Mat_ cross(const Mat_& m) const;
//! data type conversion
template<typename T2> operator Mat_<T2>() const;
//! overridden forms of Mat::row() etc.
Mat_ row(int y) const;
Mat_ col(int x) const;
Mat_ diag(int d=0) const;
Mat_ clone() const;
//! overridden forms of Mat::elemSize() etc.
size_t elemSize() const;
size_t elemSize1() const;
int type() const;
int depth() const;
int channels() const;
size_t step1(int i=0) const;
//! returns step()/sizeof(_Tp)
size_t stepT(int i=0) const;
//! overridden forms of Mat::zeros() etc. Data type is omitted, of course
static MatExpr zeros(int rows, int cols);
static MatExpr zeros(Size size);
static MatExpr zeros(int _ndims, const int* _sizes);
static MatExpr ones(int rows, int cols);
static MatExpr ones(Size size);
static MatExpr ones(int _ndims, const int* _sizes);
static MatExpr eye(int rows, int cols);
static MatExpr eye(Size size);
//! some more overriden methods
Mat_& adjustROI( int dtop, int dbottom, int dleft, int dright );
Mat_ operator()( const Range& rowRange, const Range& colRange ) const;
Mat_ operator()( const Rect& roi ) const;
Mat_ operator()( const Range* ranges ) const;
//! more convenient forms of row and element access operators
_Tp* operator [](int y);
const _Tp* operator [](int y) const;
//! returns reference to the specified element
_Tp& operator ()(const int* idx);
//! returns read-only reference to the specified element
const _Tp& operator ()(const int* idx) const;
//! returns reference to the specified element
template<int n> _Tp& operator ()(const Vec<int, n>& idx);
//! returns read-only reference to the specified element
template<int n> const _Tp& operator ()(const Vec<int, n>& idx) const;
//! returns reference to the specified element (1D case)
_Tp& operator ()(int idx0);
//! returns read-only reference to the specified element (1D case)
const _Tp& operator ()(int idx0) const;
//! returns reference to the specified element (2D case)
_Tp& operator ()(int idx0, int idx1);
//! returns read-only reference to the specified element (2D case)
const _Tp& operator ()(int idx0, int idx1) const;
//! returns reference to the specified element (3D case)
_Tp& operator ()(int idx0, int idx1, int idx2);
//! returns read-only reference to the specified element (3D case)
const _Tp& operator ()(int idx0, int idx1, int idx2) const;
_Tp& operator ()(Point pt);
const _Tp& operator ()(Point pt) const;
//! conversion to vector.
operator std::vector<_Tp>() const;
//! conversion to Vec
template<int n> operator Vec<typename DataType<_Tp>::channel_type, n>() const;
//! conversion to Matx
template<int m, int n> operator Matx<typename DataType<_Tp>::channel_type, m, n>() const;
};
typedef Mat_<uchar> Mat1b;
typedef Mat_<Vec2b> Mat2b;
typedef Mat_<Vec3b> Mat3b;
typedef Mat_<Vec4b> Mat4b;
typedef Mat_<short> Mat1s;
typedef Mat_<Vec2s> Mat2s;
typedef Mat_<Vec3s> Mat3s;
typedef Mat_<Vec4s> Mat4s;
typedef Mat_<ushort> Mat1w;
typedef Mat_<Vec2w> Mat2w;
typedef Mat_<Vec3w> Mat3w;
typedef Mat_<Vec4w> Mat4w;
typedef Mat_<int> Mat1i;
typedef Mat_<Vec2i> Mat2i;
typedef Mat_<Vec3i> Mat3i;
typedef Mat_<Vec4i> Mat4i;
typedef Mat_<float> Mat1f;
typedef Mat_<Vec2f> Mat2f;
typedef Mat_<Vec3f> Mat3f;
typedef Mat_<Vec4f> Mat4f;
typedef Mat_<double> Mat1d;
typedef Mat_<Vec2d> Mat2d;
typedef Mat_<Vec3d> Mat3d;
typedef Mat_<Vec4d> Mat4d;
//////////// Iterators & Comma initializers //////////////////
class CV_EXPORTS MatConstIterator
{
public:
typedef uchar* value_type;
typedef ptrdiff_t difference_type;
typedef const uchar** pointer;
typedef uchar* reference;
typedef std::random_access_iterator_tag iterator_category;
//! default constructor
MatConstIterator();
//! constructor that sets the iterator to the beginning of the matrix
MatConstIterator(const Mat* _m);
//! constructor that sets the iterator to the specified element of the matrix
MatConstIterator(const Mat* _m, int _row, int _col=0);
//! constructor that sets the iterator to the specified element of the matrix
MatConstIterator(const Mat* _m, Point _pt);
//! constructor that sets the iterator to the specified element of the matrix
MatConstIterator(const Mat* _m, const int* _idx);
//! copy constructor
MatConstIterator(const MatConstIterator& it);
//! copy operator
MatConstIterator& operator = (const MatConstIterator& it);
//! returns the current matrix element
uchar* operator *() const;
//! returns the i-th matrix element, relative to the current
uchar* operator [](ptrdiff_t i) const;
//! shifts the iterator forward by the specified number of elements
MatConstIterator& operator += (ptrdiff_t ofs);
//! shifts the iterator backward by the specified number of elements
MatConstIterator& operator -= (ptrdiff_t ofs);
//! decrements the iterator
MatConstIterator& operator --();
//! decrements the iterator
MatConstIterator operator --(int);
//! increments the iterator
MatConstIterator& operator ++();
//! increments the iterator
MatConstIterator operator ++(int);
//! returns the current iterator position
Point pos() const;
//! returns the current iterator position
void pos(int* _idx) const;
ptrdiff_t lpos() const;
void seek(ptrdiff_t ofs, bool relative=false);
void seek(const int* _idx, bool relative=false);
const Mat* m;
size_t elemSize;
uchar* ptr;
uchar* sliceStart;
uchar* sliceEnd;
};
/*!
Matrix read-only iterator
*/
template<typename _Tp>
class CV_EXPORTS MatConstIterator_ : public MatConstIterator
{
public:
typedef _Tp value_type;
typedef ptrdiff_t difference_type;
typedef const _Tp* pointer;
typedef const _Tp& reference;
typedef std::random_access_iterator_tag iterator_category;
//! default constructor
MatConstIterator_();
//! constructor that sets the iterator to the beginning of the matrix
MatConstIterator_(const Mat_<_Tp>* _m);
//! constructor that sets the iterator to the specified element of the matrix
MatConstIterator_(const Mat_<_Tp>* _m, int _row, int _col=0);
//! constructor that sets the iterator to the specified element of the matrix
MatConstIterator_(const Mat_<_Tp>* _m, Point _pt);
//! constructor that sets the iterator to the specified element of the matrix
MatConstIterator_(const Mat_<_Tp>* _m, const int* _idx);
//! copy constructor
MatConstIterator_(const MatConstIterator_& it);
//! copy operator
MatConstIterator_& operator = (const MatConstIterator_& it);
//! returns the current matrix element
_Tp operator *() const;
//! returns the i-th matrix element, relative to the current
_Tp operator [](ptrdiff_t i) const;
//! shifts the iterator forward by the specified number of elements
MatConstIterator_& operator += (ptrdiff_t ofs);
//! shifts the iterator backward by the specified number of elements
MatConstIterator_& operator -= (ptrdiff_t ofs);
//! decrements the iterator
MatConstIterator_& operator --();
//! decrements the iterator
MatConstIterator_ operator --(int);
//! increments the iterator
MatConstIterator_& operator ++();
//! increments the iterator
MatConstIterator_ operator ++(int);
//! returns the current iterator position
Point pos() const;
};
/*!
Matrix read-write iterator
*/
template<typename _Tp>
class CV_EXPORTS MatIterator_ : public MatConstIterator_<_Tp>
{
public:
typedef _Tp* pointer;
typedef _Tp& reference;
typedef std::random_access_iterator_tag iterator_category;
//! the default constructor
MatIterator_();
//! constructor that sets the iterator to the beginning of the matrix
MatIterator_(Mat_<_Tp>* _m);
//! constructor that sets the iterator to the specified element of the matrix
MatIterator_(Mat_<_Tp>* _m, int _row, int _col=0);
//! constructor that sets the iterator to the specified element of the matrix
MatIterator_(const Mat_<_Tp>* _m, Point _pt);
//! constructor that sets the iterator to the specified element of the matrix
MatIterator_(const Mat_<_Tp>* _m, const int* _idx);
//! copy constructor
MatIterator_(const MatIterator_& it);
//! copy operator
MatIterator_& operator = (const MatIterator_<_Tp>& it );
//! returns the current matrix element
_Tp& operator *() const;
//! returns the i-th matrix element, relative to the current
_Tp& operator [](ptrdiff_t i) const;
//! shifts the iterator forward by the specified number of elements
MatIterator_& operator += (ptrdiff_t ofs);
//! shifts the iterator backward by the specified number of elements
MatIterator_& operator -= (ptrdiff_t ofs);
//! decrements the iterator
MatIterator_& operator --();
//! decrements the iterator
MatIterator_ operator --(int);
//! increments the iterator
MatIterator_& operator ++();
//! increments the iterator
MatIterator_ operator ++(int);
};
template<typename _Tp> class CV_EXPORTS MatOp_Iter_;
/*!
Comma-separated Matrix Initializer
The class instances are usually not created explicitly.
Instead, they are created on "matrix << firstValue" operator.
The sample below initializes 2x2 rotation matrix:
\code
double angle = 30, a = cos(angle*CV_PI/180), b = sin(angle*CV_PI/180);
Mat R = (Mat_<double>(2,2) << a, -b, b, a);
\endcode
*/
template<typename _Tp> class CV_EXPORTS MatCommaInitializer_
{
public:
//! the constructor, created by "matrix << firstValue" operator, where matrix is cv::Mat
MatCommaInitializer_(Mat_<_Tp>* _m);
//! the operator that takes the next value and put it to the matrix
template<typename T2> MatCommaInitializer_<_Tp>& operator , (T2 v);
//! another form of conversion operator
Mat_<_Tp> operator *() const;
operator Mat_<_Tp>() const;
protected:
MatIterator_<_Tp> it;
};
/////////////////////////// multi-dimensional dense matrix //////////////////////////
/*!
n-Dimensional Dense Matrix Iterator Class.
The class cv::NAryMatIterator is used for iterating over one or more n-dimensional dense arrays (cv::Mat's).
The iterator is completely different from cv::Mat_ and cv::SparseMat_ iterators.
It iterates through the slices (or planes), not the elements, where "slice" is a continuous part of the arrays.
Here is the example on how the iterator can be used to normalize 3D histogram:
\code
void normalizeColorHist(Mat& hist)
{
#if 1
// intialize iterator (the style is different from STL).
// after initialization the iterator will contain
// the number of slices or planes
// the iterator will go through
Mat* arrays[] = { &hist, 0 };
Mat planes[1];
NAryMatIterator it(arrays, planes);
double s = 0;
// iterate through the matrix. on each iteration
// it.planes[i] (of type Mat) will be set to the current plane of
// i-th n-dim matrix passed to the iterator constructor.
for(int p = 0; p < it.nplanes; p++, ++it)
s += sum(it.planes[0])[0];
it = NAryMatIterator(hist);
s = 1./s;
for(int p = 0; p < it.nplanes; p++, ++it)
it.planes[0] *= s;
#elif 1
// this is a shorter implementation of the above
// using built-in operations on Mat
double s = sum(hist)[0];
hist.convertTo(hist, hist.type(), 1./s, 0);
#else
// and this is even shorter one
// (assuming that the histogram elements are non-negative)
normalize(hist, hist, 1, 0, NORM_L1);
#endif
}
\endcode
You can iterate through several matrices simultaneously as long as they have the same geometry
(dimensionality and all the dimension sizes are the same), which is useful for binary
and n-ary operations on such matrices. Just pass those matrices to cv::MatNDIterator.
Then, during the iteration it.planes[0], it.planes[1], ... will
be the slices of the corresponding matrices
*/
class CV_EXPORTS NAryMatIterator
{
public:
//! the default constructor
NAryMatIterator();
//! the full constructor taking arbitrary number of n-dim matrices
NAryMatIterator(const Mat** arrays, uchar** ptrs, int narrays=-1);
//! the full constructor taking arbitrary number of n-dim matrices
NAryMatIterator(const Mat** arrays, Mat* planes, int narrays=-1);
//! the separate iterator initialization method
void init(const Mat** arrays, Mat* planes, uchar** ptrs, int narrays=-1);
//! proceeds to the next plane of every iterated matrix
NAryMatIterator& operator ++();
//! proceeds to the next plane of every iterated matrix (postfix increment operator)
NAryMatIterator operator ++(int);
//! the iterated arrays
const Mat** arrays;
//! the current planes
Mat* planes;
//! data pointers
uchar** ptrs;
//! the number of arrays
int narrays;
//! the number of hyper-planes that the iterator steps through
size_t nplanes;
//! the size of each segment (in elements)
size_t size;
protected:
int iterdepth;
size_t idx;
};
//typedef NAryMatIterator NAryMatNDIterator;
typedef void (*ConvertData)(const void* from, void* to, int cn);
typedef void (*ConvertScaleData)(const void* from, void* to, int cn, double alpha, double beta);
//! returns the function for converting pixels from one data type to another
CV_EXPORTS ConvertData getConvertElem(int fromType, int toType);
//! returns the function for converting pixels from one data type to another with the optional scaling
CV_EXPORTS ConvertScaleData getConvertScaleElem(int fromType, int toType);
/////////////////////////// multi-dimensional sparse matrix //////////////////////////
class SparseMatIterator;
class SparseMatConstIterator;
template<typename _Tp> class SparseMatIterator_;
template<typename _Tp> class SparseMatConstIterator_;
/*!
Sparse matrix class.
The class represents multi-dimensional sparse numerical arrays. Such a sparse array can store elements
of any type that cv::Mat is able to store. "Sparse" means that only non-zero elements
are stored (though, as a result of some operations on a sparse matrix, some of its stored elements
can actually become 0. It's user responsibility to detect such elements and delete them using cv::SparseMat::erase().
The non-zero elements are stored in a hash table that grows when it's filled enough,
so that the search time remains O(1) in average. Elements can be accessed using the following methods:
<ol>
<li>Query operations: cv::SparseMat::ptr() and the higher-level cv::SparseMat::ref(),
cv::SparseMat::value() and cv::SparseMat::find, for example:
\code
const int dims = 5;
int size[] = {10, 10, 10, 10, 10};
SparseMat sparse_mat(dims, size, CV_32F);
for(int i = 0; i < 1000; i++)
{
int idx[dims];
for(int k = 0; k < dims; k++)
idx[k] = rand()%sparse_mat.size(k);
sparse_mat.ref<float>(idx) += 1.f;
}
\endcode
<li>Sparse matrix iterators. Like cv::Mat iterators and unlike cv::Mat iterators, the sparse matrix iterators are STL-style,
that is, the iteration is done as following:
\code
// prints elements of a sparse floating-point matrix and the sum of elements.
SparseMatConstIterator_<float>
it = sparse_mat.begin<float>(),
it_end = sparse_mat.end<float>();
double s = 0;
int dims = sparse_mat.dims();
for(; it != it_end; ++it)
{
// print element indices and the element value
const Node* n = it.node();
printf("(")
for(int i = 0; i < dims; i++)
printf("%3d%c", n->idx[i], i < dims-1 ? ',' : ')');
printf(": %f\n", *it);
s += *it;
}
printf("Element sum is %g\n", s);
\endcode
If you run this loop, you will notice that elements are enumerated
in no any logical order (lexicographical etc.),
they come in the same order as they stored in the hash table, i.e. semi-randomly.
You may collect pointers to the nodes and sort them to get the proper ordering.
Note, however, that pointers to the nodes may become invalid when you add more
elements to the matrix; this is because of possible buffer reallocation.
<li>A combination of the above 2 methods when you need to process 2 or more sparse
matrices simultaneously, e.g. this is how you can compute unnormalized
cross-correlation of the 2 floating-point sparse matrices:
\code
double crossCorr(const SparseMat& a, const SparseMat& b)
{
const SparseMat *_a = &a, *_b = &b;
// if b contains less elements than a,
// it's faster to iterate through b
if(_a->nzcount() > _b->nzcount())
std::swap(_a, _b);
SparseMatConstIterator_<float> it = _a->begin<float>(),
it_end = _a->end<float>();
double ccorr = 0;
for(; it != it_end; ++it)
{
// take the next element from the first matrix
float avalue = *it;
const Node* anode = it.node();
// and try to find element with the same index in the second matrix.
// since the hash value depends only on the element index,
// we reuse hashvalue stored in the node
float bvalue = _b->value<float>(anode->idx,&anode->hashval);
ccorr += avalue*bvalue;
}
return ccorr;
}
\endcode
</ol>
*/
class CV_EXPORTS SparseMat
{
public:
typedef SparseMatIterator iterator;
typedef SparseMatConstIterator const_iterator;
//! the sparse matrix header
struct CV_EXPORTS Hdr
{
Hdr(int _dims, const int* _sizes, int _type);
void clear();
int refcount;
int dims;
int valueOffset;
size_t nodeSize;
size_t nodeCount;
size_t freeList;
std::vector<uchar> pool;
std::vector<size_t> hashtab;
int size[CV_MAX_DIM];
};
//! sparse matrix node - element of a hash table
struct CV_EXPORTS Node
{
//! hash value
size_t hashval;
//! index of the next node in the same hash table entry
size_t next;
//! index of the matrix element
int idx[CV_MAX_DIM];
};
//! default constructor
SparseMat();
//! creates matrix of the specified size and type
SparseMat(int dims, const int* _sizes, int _type);
//! copy constructor
SparseMat(const SparseMat& m);
//! converts dense 2d matrix to the sparse form
/*!
\param m the input matrix
\param try1d if true and m is a single-column matrix (Nx1),
then the sparse matrix will be 1-dimensional.
*/
explicit SparseMat(const Mat& m);
//! converts old-style sparse matrix to the new-style. All the data is copied
SparseMat(const CvSparseMat* m);
//! the destructor
~SparseMat();
//! assignment operator. This is O(1) operation, i.e. no data is copied
SparseMat& operator = (const SparseMat& m);
//! equivalent to the corresponding constructor
SparseMat& operator = (const Mat& m);
//! creates full copy of the matrix
SparseMat clone() const;
//! copies all the data to the destination matrix. All the previous content of m is erased
void copyTo( SparseMat& m ) const;
//! converts sparse matrix to dense matrix.
void copyTo( Mat& m ) const;
//! multiplies all the matrix elements by the specified scale factor alpha and converts the results to the specified data type
void convertTo( SparseMat& m, int rtype, double alpha=1 ) const;
//! converts sparse matrix to dense n-dim matrix with optional type conversion and scaling.
/*!
\param rtype The output matrix data type. When it is =-1, the output array will have the same data type as (*this)
\param alpha The scale factor
\param beta The optional delta added to the scaled values before the conversion
*/
void convertTo( Mat& m, int rtype, double alpha=1, double beta=0 ) const;
// not used now
void assignTo( SparseMat& m, int type=-1 ) const;
//! reallocates sparse matrix.
/*!
If the matrix already had the proper size and type,
it is simply cleared with clear(), otherwise,
the old matrix is released (using release()) and the new one is allocated.
*/
void create(int dims, const int* _sizes, int _type);
//! sets all the sparse matrix elements to 0, which means clearing the hash table.
void clear();
//! manually increments the reference counter to the header.
void addref();
// decrements the header reference counter. When the counter reaches 0, the header and all the underlying data are deallocated.
void release();
//! converts sparse matrix to the old-style representation; all the elements are copied.
operator CvSparseMat*() const;
//! returns the size of each element in bytes (not including the overhead - the space occupied by SparseMat::Node elements)
size_t elemSize() const;
//! returns elemSize()/channels()
size_t elemSize1() const;
//! returns type of sparse matrix elements
int type() const;
//! returns the depth of sparse matrix elements
int depth() const;
//! returns the number of channels
int channels() const;
//! returns the array of sizes, or NULL if the matrix is not allocated
const int* size() const;
//! returns the size of i-th matrix dimension (or 0)
int size(int i) const;
//! returns the matrix dimensionality
int dims() const;
//! returns the number of non-zero elements (=the number of hash table nodes)
size_t nzcount() const;
//! computes the element hash value (1D case)
size_t hash(int i0) const;
//! computes the element hash value (2D case)
size_t hash(int i0, int i1) const;
//! computes the element hash value (3D case)
size_t hash(int i0, int i1, int i2) const;
//! computes the element hash value (nD case)
size_t hash(const int* idx) const;
//@{
/*!
specialized variants for 1D, 2D, 3D cases and the generic_type one for n-D case.
return pointer to the matrix element.
<ul>
<li>if the element is there (it's non-zero), the pointer to it is returned
<li>if it's not there and createMissing=false, NULL pointer is returned
<li>if it's not there and createMissing=true, then the new element
is created and initialized with 0. Pointer to it is returned
<li>if the optional hashval pointer is not NULL, the element hash value is
not computed, but *hashval is taken instead.
</ul>
*/
//! returns pointer to the specified element (1D case)
uchar* ptr(int i0, bool createMissing, size_t* hashval=0);
//! returns pointer to the specified element (2D case)
uchar* ptr(int i0, int i1, bool createMissing, size_t* hashval=0);
//! returns pointer to the specified element (3D case)
uchar* ptr(int i0, int i1, int i2, bool createMissing, size_t* hashval=0);
//! returns pointer to the specified element (nD case)
uchar* ptr(const int* idx, bool createMissing, size_t* hashval=0);
//@}
//@{
/*!
return read-write reference to the specified sparse matrix element.
ref<_Tp>(i0,...[,hashval]) is equivalent to *(_Tp*)ptr(i0,...,true[,hashval]).
The methods always return a valid reference.
If the element did not exist, it is created and initialiazed with 0.
*/
//! returns reference to the specified element (1D case)
template<typename _Tp> _Tp& ref(int i0, size_t* hashval=0);
//! returns reference to the specified element (2D case)
template<typename _Tp> _Tp& ref(int i0, int i1, size_t* hashval=0);
//! returns reference to the specified element (3D case)
template<typename _Tp> _Tp& ref(int i0, int i1, int i2, size_t* hashval=0);
//! returns reference to the specified element (nD case)
template<typename _Tp> _Tp& ref(const int* idx, size_t* hashval=0);
//@}
//@{
/*!
return value of the specified sparse matrix element.
value<_Tp>(i0,...[,hashval]) is equivalent
\code
{ const _Tp* p = find<_Tp>(i0,...[,hashval]); return p ? *p : _Tp(); }
\endcode
That is, if the element did not exist, the methods return 0.
*/
//! returns value of the specified element (1D case)
template<typename _Tp> _Tp value(int i0, size_t* hashval=0) const;
//! returns value of the specified element (2D case)
template<typename _Tp> _Tp value(int i0, int i1, size_t* hashval=0) const;
//! returns value of the specified element (3D case)
template<typename _Tp> _Tp value(int i0, int i1, int i2, size_t* hashval=0) const;
//! returns value of the specified element (nD case)
template<typename _Tp> _Tp value(const int* idx, size_t* hashval=0) const;
//@}
//@{
/*!
Return pointer to the specified sparse matrix element if it exists
find<_Tp>(i0,...[,hashval]) is equivalent to (_const Tp*)ptr(i0,...false[,hashval]).
If the specified element does not exist, the methods return NULL.
*/
//! returns pointer to the specified element (1D case)
template<typename _Tp> const _Tp* find(int i0, size_t* hashval=0) const;
//! returns pointer to the specified element (2D case)
template<typename _Tp> const _Tp* find(int i0, int i1, size_t* hashval=0) const;
//! returns pointer to the specified element (3D case)
template<typename _Tp> const _Tp* find(int i0, int i1, int i2, size_t* hashval=0) const;
//! returns pointer to the specified element (nD case)
template<typename _Tp> const _Tp* find(const int* idx, size_t* hashval=0) const;
//! erases the specified element (2D case)
void erase(int i0, int i1, size_t* hashval=0);
//! erases the specified element (3D case)
void erase(int i0, int i1, int i2, size_t* hashval=0);
//! erases the specified element (nD case)
void erase(const int* idx, size_t* hashval=0);
//@{
/*!
return the sparse matrix iterator pointing to the first sparse matrix element
*/
//! returns the sparse matrix iterator at the matrix beginning
SparseMatIterator begin();
//! returns the sparse matrix iterator at the matrix beginning
template<typename _Tp> SparseMatIterator_<_Tp> begin();
//! returns the read-only sparse matrix iterator at the matrix beginning
SparseMatConstIterator begin() const;
//! returns the read-only sparse matrix iterator at the matrix beginning
template<typename _Tp> SparseMatConstIterator_<_Tp> begin() const;
//@}
/*!
return the sparse matrix iterator pointing to the element following the last sparse matrix element
*/
//! returns the sparse matrix iterator at the matrix end
SparseMatIterator end();
//! returns the read-only sparse matrix iterator at the matrix end
SparseMatConstIterator end() const;
//! returns the typed sparse matrix iterator at the matrix end
template<typename _Tp> SparseMatIterator_<_Tp> end();
//! returns the typed read-only sparse matrix iterator at the matrix end
template<typename _Tp> SparseMatConstIterator_<_Tp> end() const;
//! returns the value stored in the sparse martix node
template<typename _Tp> _Tp& value(Node* n);
//! returns the value stored in the sparse martix node
template<typename _Tp> const _Tp& value(const Node* n) const;
////////////// some internal-use methods ///////////////
Node* node(size_t nidx);
const Node* node(size_t nidx) const;
uchar* newNode(const int* idx, size_t hashval);
void removeNode(size_t hidx, size_t nidx, size_t previdx);
void resizeHashTab(size_t newsize);
enum { MAGIC_VAL=0x42FD0000, MAX_DIM=CV_MAX_DIM, HASH_SCALE=0x5bd1e995, HASH_BIT=0x80000000 };
int flags;
Hdr* hdr;
};
//! finds global minimum and maximum sparse array elements and returns their values and their locations
CV_EXPORTS void minMaxLoc(const SparseMat& a, double* minVal,
double* maxVal, int* minIdx=0, int* maxIdx=0);
//! computes norm of a sparse matrix
CV_EXPORTS double norm( const SparseMat& src, int normType );
//! scales and shifts array elements so that either the specified norm (alpha) or the minimum (alpha) and maximum (beta) array values get the specified values
CV_EXPORTS void normalize( const SparseMat& src, SparseMat& dst, double alpha, int normType );
/*!
Read-Only Sparse Matrix Iterator.
Here is how to use the iterator to compute the sum of floating-point sparse matrix elements:
\code
SparseMatConstIterator it = m.begin(), it_end = m.end();
double s = 0;
CV_Assert( m.type() == CV_32F );
for( ; it != it_end; ++it )
s += it.value<float>();
\endcode
*/
class CV_EXPORTS SparseMatConstIterator
{
public:
//! the default constructor
SparseMatConstIterator();
//! the full constructor setting the iterator to the first sparse matrix element
SparseMatConstIterator(const SparseMat* _m);
//! the copy constructor
SparseMatConstIterator(const SparseMatConstIterator& it);
//! the assignment operator
SparseMatConstIterator& operator = (const SparseMatConstIterator& it);
//! template method returning the current matrix element
template<typename _Tp> const _Tp& value() const;
//! returns the current node of the sparse matrix. it.node->idx is the current element index
const SparseMat::Node* node() const;
//! moves iterator to the previous element
SparseMatConstIterator& operator --();
//! moves iterator to the previous element
SparseMatConstIterator operator --(int);
//! moves iterator to the next element
SparseMatConstIterator& operator ++();
//! moves iterator to the next element
SparseMatConstIterator operator ++(int);
//! moves iterator to the element after the last element
void seekEnd();
const SparseMat* m;
size_t hashidx;
uchar* ptr;
};
/*!
Read-write Sparse Matrix Iterator
The class is similar to cv::SparseMatConstIterator,
but can be used for in-place modification of the matrix elements.
*/
class CV_EXPORTS SparseMatIterator : public SparseMatConstIterator
{
public:
//! the default constructor
SparseMatIterator();
//! the full constructor setting the iterator to the first sparse matrix element
SparseMatIterator(SparseMat* _m);
//! the full constructor setting the iterator to the specified sparse matrix element
SparseMatIterator(SparseMat* _m, const int* idx);
//! the copy constructor
SparseMatIterator(const SparseMatIterator& it);
//! the assignment operator
SparseMatIterator& operator = (const SparseMatIterator& it);
//! returns read-write reference to the current sparse matrix element
template<typename _Tp> _Tp& value() const;
//! returns pointer to the current sparse matrix node. it.node->idx is the index of the current element (do not modify it!)
SparseMat::Node* node() const;
//! moves iterator to the next element
SparseMatIterator& operator ++();
//! moves iterator to the next element
SparseMatIterator operator ++(int);
};
/*!
The Template Sparse Matrix class derived from cv::SparseMat
The class provides slightly more convenient operations for accessing elements.
\code
SparseMat m;
...
SparseMat_<int> m_ = (SparseMat_<int>&)m;
m_.ref(1)++; // equivalent to m.ref<int>(1)++;
m_.ref(2) += m_(3); // equivalent to m.ref<int>(2) += m.value<int>(3);
\endcode
*/
template<typename _Tp> class CV_EXPORTS SparseMat_ : public SparseMat
{
public:
typedef SparseMatIterator_<_Tp> iterator;
typedef SparseMatConstIterator_<_Tp> const_iterator;
//! the default constructor
SparseMat_();
//! the full constructor equivelent to SparseMat(dims, _sizes, DataType<_Tp>::type)
SparseMat_(int dims, const int* _sizes);
//! the copy constructor. If DataType<_Tp>.type != m.type(), the m elements are converted
SparseMat_(const SparseMat& m);
//! the copy constructor. This is O(1) operation - no data is copied
SparseMat_(const SparseMat_& m);
//! converts dense matrix to the sparse form
SparseMat_(const Mat& m);
//! converts the old-style sparse matrix to the C++ class. All the elements are copied
SparseMat_(const CvSparseMat* m);
//! the assignment operator. If DataType<_Tp>.type != m.type(), the m elements are converted
SparseMat_& operator = (const SparseMat& m);
//! the assignment operator. This is O(1) operation - no data is copied
SparseMat_& operator = (const SparseMat_& m);
//! converts dense matrix to the sparse form
SparseMat_& operator = (const Mat& m);
//! makes full copy of the matrix. All the elements are duplicated
SparseMat_ clone() const;
//! equivalent to cv::SparseMat::create(dims, _sizes, DataType<_Tp>::type)
void create(int dims, const int* _sizes);
//! converts sparse matrix to the old-style CvSparseMat. All the elements are copied
operator CvSparseMat*() const;
//! returns type of the matrix elements
int type() const;
//! returns depth of the matrix elements
int depth() const;
//! returns the number of channels in each matrix element
int channels() const;
//! equivalent to SparseMat::ref<_Tp>(i0, hashval)
_Tp& ref(int i0, size_t* hashval=0);
//! equivalent to SparseMat::ref<_Tp>(i0, i1, hashval)
_Tp& ref(int i0, int i1, size_t* hashval=0);
//! equivalent to SparseMat::ref<_Tp>(i0, i1, i2, hashval)
_Tp& ref(int i0, int i1, int i2, size_t* hashval=0);
//! equivalent to SparseMat::ref<_Tp>(idx, hashval)
_Tp& ref(const int* idx, size_t* hashval=0);
//! equivalent to SparseMat::value<_Tp>(i0, hashval)
_Tp operator()(int i0, size_t* hashval=0) const;
//! equivalent to SparseMat::value<_Tp>(i0, i1, hashval)
_Tp operator()(int i0, int i1, size_t* hashval=0) const;
//! equivalent to SparseMat::value<_Tp>(i0, i1, i2, hashval)
_Tp operator()(int i0, int i1, int i2, size_t* hashval=0) const;
//! equivalent to SparseMat::value<_Tp>(idx, hashval)
_Tp operator()(const int* idx, size_t* hashval=0) const;
//! returns sparse matrix iterator pointing to the first sparse matrix element
SparseMatIterator_<_Tp> begin();
//! returns read-only sparse matrix iterator pointing to the first sparse matrix element
SparseMatConstIterator_<_Tp> begin() const;
//! returns sparse matrix iterator pointing to the element following the last sparse matrix element
SparseMatIterator_<_Tp> end();
//! returns read-only sparse matrix iterator pointing to the element following the last sparse matrix element
SparseMatConstIterator_<_Tp> end() const;
};
/*!
Template Read-Only Sparse Matrix Iterator Class.
This is the derived from SparseMatConstIterator class that
introduces more convenient operator *() for accessing the current element.
*/
template<typename _Tp> class CV_EXPORTS SparseMatConstIterator_ : public SparseMatConstIterator
{
public:
typedef std::forward_iterator_tag iterator_category;
//! the default constructor
SparseMatConstIterator_();
//! the full constructor setting the iterator to the first sparse matrix element
SparseMatConstIterator_(const SparseMat_<_Tp>* _m);
//! the copy constructor
SparseMatConstIterator_(const SparseMatConstIterator_& it);
//! the assignment operator
SparseMatConstIterator_& operator = (const SparseMatConstIterator_& it);
//! the element access operator
const _Tp& operator *() const;
//! moves iterator to the next element
SparseMatConstIterator_& operator ++();
//! moves iterator to the next element
SparseMatConstIterator_ operator ++(int);
};
/*!
Template Read-Write Sparse Matrix Iterator Class.
This is the derived from cv::SparseMatConstIterator_ class that
introduces more convenient operator *() for accessing the current element.
*/
template<typename _Tp> class CV_EXPORTS SparseMatIterator_ : public SparseMatConstIterator_<_Tp>
{
public:
typedef std::forward_iterator_tag iterator_category;
//! the default constructor
SparseMatIterator_();
//! the full constructor setting the iterator to the first sparse matrix element
SparseMatIterator_(SparseMat_<_Tp>* _m);
//! the copy constructor
SparseMatIterator_(const SparseMatIterator_& it);
//! the assignment operator
SparseMatIterator_& operator = (const SparseMatIterator_& it);
//! returns the reference to the current element
_Tp& operator *() const;
//! moves the iterator to the next element
SparseMatIterator_& operator ++();
//! moves the iterator to the next element
SparseMatIterator_ operator ++(int);
};
//////////////////// Fast Nearest-Neighbor Search Structure ////////////////////
/*!
Fast Nearest Neighbor Search Class.
The class implements D. Lowe BBF (Best-Bin-First) algorithm for the last
approximate (or accurate) nearest neighbor search in multi-dimensional spaces.
First, a set of vectors is passed to KDTree::KDTree() constructor
or KDTree::build() method, where it is reordered.
Then arbitrary vectors can be passed to KDTree::findNearest() methods, which
find the K nearest neighbors among the vectors from the initial set.
The user can balance between the speed and accuracy of the search by varying Emax
parameter, which is the number of leaves that the algorithm checks.
The larger parameter values yield more accurate results at the expense of lower processing speed.
\code
KDTree T(points, false);
const int K = 3, Emax = INT_MAX;
int idx[K];
float dist[K];
T.findNearest(query_vec, K, Emax, idx, 0, dist);
CV_Assert(dist[0] <= dist[1] && dist[1] <= dist[2]);
\endcode
*/
class CV_EXPORTS_W KDTree
{
public:
/*!
The node of the search tree.
*/
struct Node
{
Node() : idx(-1), left(-1), right(-1), boundary(0.f) {}
Node(int _idx, int _left, int _right, float _boundary)
: idx(_idx), left(_left), right(_right), boundary(_boundary) {}
//! split dimension; >=0 for nodes (dim), < 0 for leaves (index of the point)
int idx;
//! node indices of the left and the right branches
int left, right;
//! go to the left if query_vec[node.idx]<=node.boundary, otherwise go to the right
float boundary;
};
//! the default constructor
CV_WRAP KDTree();
//! the full constructor that builds the search tree
CV_WRAP KDTree(InputArray points, bool copyAndReorderPoints=false);
//! the full constructor that builds the search tree
CV_WRAP KDTree(InputArray points, InputArray _labels,
bool copyAndReorderPoints=false);
//! builds the search tree
CV_WRAP void build(InputArray points, bool copyAndReorderPoints=false);
//! builds the search tree
CV_WRAP void build(InputArray points, InputArray labels,
bool copyAndReorderPoints=false);
//! finds the K nearest neighbors of "vec" while looking at Emax (at most) leaves
CV_WRAP int findNearest(InputArray vec, int K, int Emax,
OutputArray neighborsIdx,
OutputArray neighbors=noArray(),
OutputArray dist=noArray(),
OutputArray labels=noArray()) const;
//! finds all the points from the initial set that belong to the specified box
CV_WRAP void findOrthoRange(InputArray minBounds,
InputArray maxBounds,
OutputArray neighborsIdx,
OutputArray neighbors=noArray(),
OutputArray labels=noArray()) const;
//! returns vectors with the specified indices
CV_WRAP void getPoints(InputArray idx, OutputArray pts,
OutputArray labels=noArray()) const;
//! return a vector with the specified index
const float* getPoint(int ptidx, int* label=0) const;
//! returns the search space dimensionality
CV_WRAP int dims() const;
std::vector<Node> nodes; //!< all the tree nodes
CV_PROP Mat points; //!< all the points. It can be a reordered copy of the input vector set or the original vector set.
CV_PROP std::vector<int> labels; //!< the parallel array of labels.
CV_PROP int maxDepth; //!< maximum depth of the search tree. Do not modify it
CV_PROP_RW int normType; //!< type of the distance (cv::NORM_L1 or cv::NORM_L2) used for search. Initially set to cv::NORM_L2, but you can modify it
};
//////////////////////////////////////// XML & YAML I/O ////////////////////////////////////
class CV_EXPORTS FileNode;
/*!
XML/YAML File Storage Class.
The class describes an object associated with XML or YAML file.
It can be used to store data to such a file or read and decode the data.
The storage is organized as a tree of nested sequences (or lists) and mappings.
Sequence is a heterogenious array, which elements are accessed by indices or sequentially using an iterator.
Mapping is analogue of std::map or C structure, which elements are accessed by names.
The most top level structure is a mapping.
Leaves of the file storage tree are integers, floating-point numbers and text strings.
For example, the following code:
\code
// open file storage for writing. Type of the file is determined from the extension
FileStorage fs("test.yml", FileStorage::WRITE);
fs << "test_int" << 5 << "test_real" << 3.1 << "test_string" << "ABCDEFGH";
fs << "test_mat" << Mat::eye(3,3,CV_32F);
fs << "test_list" << "[" << 0.0000000000001 << 2 << CV_PI << -3435345 << "2-502 2-029 3egegeg" <<
"{:" << "month" << 12 << "day" << 31 << "year" << 1969 << "}" << "]";
fs << "test_map" << "{" << "x" << 1 << "y" << 2 << "width" << 100 << "height" << 200 << "lbp" << "[:";
const uchar arr[] = {0, 1, 1, 0, 1, 1, 0, 1};
fs.writeRaw("u", arr, (int)(sizeof(arr)/sizeof(arr[0])));
fs << "]" << "}";
\endcode
will produce the following file:
\verbatim
%YAML:1.0
test_int: 5
test_real: 3.1000000000000001e+00
test_string: ABCDEFGH
test_mat: !!opencv-matrix
rows: 3
cols: 3
dt: f
data: [ 1., 0., 0., 0., 1., 0., 0., 0., 1. ]
test_list:
- 1.0000000000000000e-13
- 2
- 3.1415926535897931e+00
- -3435345
- "2-502 2-029 3egegeg"
- { month:12, day:31, year:1969 }
test_map:
x: 1
y: 2
width: 100
height: 200
lbp: [ 0, 1, 1, 0, 1, 1, 0, 1 ]
\endverbatim
and to read the file above, the following code can be used:
\code
// open file storage for reading.
// Type of the file is determined from the content, not the extension
FileStorage fs("test.yml", FileStorage::READ);
int test_int = (int)fs["test_int"];
double test_real = (double)fs["test_real"];
String test_string = (String)fs["test_string"];
Mat M;
fs["test_mat"] >> M;
FileNode tl = fs["test_list"];
CV_Assert(tl.type() == FileNode::SEQ && tl.size() == 6);
double tl0 = (double)tl[0];
int tl1 = (int)tl[1];
double tl2 = (double)tl[2];
int tl3 = (int)tl[3];
String tl4 = (String)tl[4];
CV_Assert(tl[5].type() == FileNode::MAP && tl[5].size() == 3);
int month = (int)tl[5]["month"];
int day = (int)tl[5]["day"];
int year = (int)tl[5]["year"];
FileNode tm = fs["test_map"];
int x = (int)tm["x"];
int y = (int)tm["y"];
int width = (int)tm["width"];
int height = (int)tm["height"];
int lbp_val = 0;
FileNodeIterator it = tm["lbp"].begin();
for(int k = 0; k < 8; k++, ++it)
lbp_val |= ((int)*it) << k;
\endcode
*/
class CV_EXPORTS_W FileStorage
{
public:
//! file storage mode
enum
{
READ=0, //! read mode
WRITE=1, //! write mode
APPEND=2, //! append mode
MEMORY=4,
FORMAT_MASK=(7<<3),
FORMAT_AUTO=0,
FORMAT_XML=(1<<3),
FORMAT_YAML=(2<<3)
};
enum
{
UNDEFINED=0,
VALUE_EXPECTED=1,
NAME_EXPECTED=2,
INSIDE_MAP=4
};
//! the default constructor
CV_WRAP FileStorage();
//! the full constructor that opens file storage for reading or writing
CV_WRAP FileStorage(const String& source, int flags, const String& encoding=String());
//! the constructor that takes pointer to the C FileStorage structure
FileStorage(CvFileStorage* fs);
//! the destructor. calls release()
virtual ~FileStorage();
//! opens file storage for reading or writing. The previous storage is closed with release()
CV_WRAP virtual bool open(const String& filename, int flags, const String& encoding=String());
//! returns true if the object is associated with currently opened file.
CV_WRAP virtual bool isOpened() const;
//! closes the file and releases all the memory buffers
CV_WRAP virtual void release();
//! closes the file, releases all the memory buffers and returns the text string
CV_WRAP virtual String releaseAndGetString();
//! returns the first element of the top-level mapping
CV_WRAP FileNode getFirstTopLevelNode() const;
//! returns the top-level mapping. YAML supports multiple streams
CV_WRAP FileNode root(int streamidx=0) const;
//! returns the specified element of the top-level mapping
FileNode operator[](const String& nodename) const;
//! returns the specified element of the top-level mapping
CV_WRAP FileNode operator[](const char* nodename) const;
//! returns pointer to the underlying C FileStorage structure
CvFileStorage* operator *() { return fs; }
//! returns pointer to the underlying C FileStorage structure
const CvFileStorage* operator *() const { return fs; }
//! writes one or more numbers of the specified format to the currently written structure
void writeRaw( const String& fmt, const uchar* vec, size_t len );
//! writes the registered C structure (CvMat, CvMatND, CvSeq). See cvWrite()
void writeObj( const String& name, const void* obj );
//! returns the normalized object name for the specified file name
static String getDefaultObjectName(const String& filename);
Ptr<CvFileStorage> fs; //!< the underlying C FileStorage structure
String elname; //!< the currently written element
std::vector<char> structs; //!< the stack of written structures
int state; //!< the writer state
};
class CV_EXPORTS FileNodeIterator;
/*!
File Storage Node class
The node is used to store each and every element of the file storage opened for reading -
from the primitive objects, such as numbers and text strings, to the complex nodes:
sequences, mappings and the registered objects.
Note that file nodes are only used for navigating file storages opened for reading.
When a file storage is opened for writing, no data is stored in memory after it is written.
*/
class CV_EXPORTS_W_SIMPLE FileNode
{
public:
//! type of the file storage node
enum
{
NONE=0, //!< empty node
INT=1, //!< an integer
REAL=2, //!< floating-point number
FLOAT=REAL, //!< synonym or REAL
STR=3, //!< text string in UTF-8 encoding
STRING=STR, //!< synonym for STR
REF=4, //!< integer of size size_t. Typically used for storing complex dynamic structures where some elements reference the others
SEQ=5, //!< sequence
MAP=6, //!< mapping
TYPE_MASK=7,
FLOW=8, //!< compact representation of a sequence or mapping. Used only by YAML writer
USER=16, //!< a registered object (e.g. a matrix)
EMPTY=32, //!< empty structure (sequence or mapping)
NAMED=64 //!< the node has a name (i.e. it is element of a mapping)
};
//! the default constructor
CV_WRAP FileNode();
//! the full constructor wrapping CvFileNode structure.
FileNode(const CvFileStorage* fs, const CvFileNode* node);
//! the copy constructor
FileNode(const FileNode& node);
//! returns element of a mapping node
FileNode operator[](const String& nodename) const;
//! returns element of a mapping node
CV_WRAP FileNode operator[](const char* nodename) const;
//! returns element of a sequence node
CV_WRAP FileNode operator[](int i) const;
//! returns type of the node
CV_WRAP int type() const;
//! returns true if the node is empty
CV_WRAP bool empty() const;
//! returns true if the node is a "none" object
CV_WRAP bool isNone() const;
//! returns true if the node is a sequence
CV_WRAP bool isSeq() const;
//! returns true if the node is a mapping
CV_WRAP bool isMap() const;
//! returns true if the node is an integer
CV_WRAP bool isInt() const;
//! returns true if the node is a floating-point number
CV_WRAP bool isReal() const;
//! returns true if the node is a text string
CV_WRAP bool isString() const;
//! returns true if the node has a name
CV_WRAP bool isNamed() const;
//! returns the node name or an empty string if the node is nameless
CV_WRAP String name() const;
//! returns the number of elements in the node, if it is a sequence or mapping, or 1 otherwise.
CV_WRAP size_t size() const;
//! returns the node content as an integer. If the node stores floating-point number, it is rounded.
operator int() const;
//! returns the node content as float
operator float() const;
//! returns the node content as double
operator double() const;
//! returns the node content as text string
operator String() const;
#ifndef OPENCV_NOSTL
operator std::string() const;
#endif
//! returns pointer to the underlying file node
CvFileNode* operator *();
//! returns pointer to the underlying file node
const CvFileNode* operator* () const;
//! returns iterator pointing to the first node element
FileNodeIterator begin() const;
//! returns iterator pointing to the element following the last node element
FileNodeIterator end() const;
//! reads node elements to the buffer with the specified format
void readRaw( const String& fmt, uchar* vec, size_t len ) const;
//! reads the registered object and returns pointer to it
void* readObj() const;
// do not use wrapper pointer classes for better efficiency
const CvFileStorage* fs;
const CvFileNode* node;
};
/*!
File Node Iterator
The class is used for iterating sequences (usually) and mappings.
*/
class CV_EXPORTS FileNodeIterator
{
public:
//! the default constructor
FileNodeIterator();
//! the full constructor set to the ofs-th element of the node
FileNodeIterator(const CvFileStorage* fs, const CvFileNode* node, size_t ofs=0);
//! the copy constructor
FileNodeIterator(const FileNodeIterator& it);
//! returns the currently observed element
FileNode operator *() const;
//! accesses the currently observed element methods
FileNode operator ->() const;
//! moves iterator to the next node
FileNodeIterator& operator ++ ();
//! moves iterator to the next node
FileNodeIterator operator ++ (int);
//! moves iterator to the previous node
FileNodeIterator& operator -- ();
//! moves iterator to the previous node
FileNodeIterator operator -- (int);
//! moves iterator forward by the specified offset (possibly negative)
FileNodeIterator& operator += (int ofs);
//! moves iterator backward by the specified offset (possibly negative)
FileNodeIterator& operator -= (int ofs);
//! reads the next maxCount elements (or less, if the sequence/mapping last element occurs earlier) to the buffer with the specified format
FileNodeIterator& readRaw( const String& fmt, uchar* vec,
size_t maxCount=(size_t)INT_MAX );
const CvFileStorage* fs;
const CvFileNode* container;
CvSeqReader reader;
size_t remaining;
};
class CV_EXPORTS Algorithm;
class CV_EXPORTS AlgorithmInfo;
struct CV_EXPORTS AlgorithmInfoData;
template<typename _Tp> struct ParamType {};
/*!
Base class for high-level OpenCV algorithms
*/
class CV_EXPORTS_W Algorithm
{
public:
Algorithm();
virtual ~Algorithm();
String name() const;
template<typename _Tp> typename ParamType<_Tp>::member_type get(const String& name) const;
template<typename _Tp> typename ParamType<_Tp>::member_type get(const char* name) const;
CV_WRAP int getInt(const String& name) const;
CV_WRAP double getDouble(const String& name) const;
CV_WRAP bool getBool(const String& name) const;
CV_WRAP String getString(const String& name) const;
CV_WRAP Mat getMat(const String& name) const;
CV_WRAP std::vector<Mat> getMatVector(const String& name) const;
CV_WRAP Ptr<Algorithm> getAlgorithm(const String& name) const;
void set(const String& name, int value);
void set(const String& name, double value);
void set(const String& name, bool value);
void set(const String& name, const String& value);
void set(const String& name, const Mat& value);
void set(const String& name, const std::vector<Mat>& value);
void set(const String& name, const Ptr<Algorithm>& value);
template<typename _Tp> void set(const String& name, const Ptr<_Tp>& value);
CV_WRAP void setInt(const String& name, int value);
CV_WRAP void setDouble(const String& name, double value);
CV_WRAP void setBool(const String& name, bool value);
CV_WRAP void setString(const String& name, const String& value);
CV_WRAP void setMat(const String& name, const Mat& value);
CV_WRAP void setMatVector(const String& name, const std::vector<Mat>& value);
CV_WRAP void setAlgorithm(const String& name, const Ptr<Algorithm>& value);
template<typename _Tp> void setAlgorithm(const String& name, const Ptr<_Tp>& value);
void set(const char* name, int value);
void set(const char* name, double value);
void set(const char* name, bool value);
void set(const char* name, const String& value);
void set(const char* name, const Mat& value);
void set(const char* name, const std::vector<Mat>& value);
void set(const char* name, const Ptr<Algorithm>& value);
template<typename _Tp> void set(const char* name, const Ptr<_Tp>& value);
void setInt(const char* name, int value);
void setDouble(const char* name, double value);
void setBool(const char* name, bool value);
void setString(const char* name, const String& value);
void setMat(const char* name, const Mat& value);
void setMatVector(const char* name, const std::vector<Mat>& value);
void setAlgorithm(const char* name, const Ptr<Algorithm>& value);
template<typename _Tp> void setAlgorithm(const char* name, const Ptr<_Tp>& value);
CV_WRAP String paramHelp(const String& name) const;
int paramType(const char* name) const;
CV_WRAP int paramType(const String& name) const;
CV_WRAP void getParams(CV_OUT std::vector<String>& names) const;
virtual void write(FileStorage& fs) const;
virtual void read(const FileNode& fn);
typedef Algorithm* (*Constructor)(void);
typedef int (Algorithm::*Getter)() const;
typedef void (Algorithm::*Setter)(int);
CV_WRAP static void getList(CV_OUT std::vector<String>& algorithms);
CV_WRAP static Ptr<Algorithm> _create(const String& name);
template<typename _Tp> static Ptr<_Tp> create(const String& name);
virtual AlgorithmInfo* info() const /* TODO: make it = 0;*/ { return 0; }
};
class CV_EXPORTS AlgorithmInfo
{
public:
friend class Algorithm;
AlgorithmInfo(const String& name, Algorithm::Constructor create);
~AlgorithmInfo();
void get(const Algorithm* algo, const char* name, int argType, void* value) const;
void addParam_(Algorithm& algo, const char* name, int argType,
void* value, bool readOnly,
Algorithm::Getter getter, Algorithm::Setter setter,
const String& help=String());
String paramHelp(const char* name) const;
int paramType(const char* name) const;
void getParams(std::vector<String>& names) const;
void write(const Algorithm* algo, FileStorage& fs) const;
void read(Algorithm* algo, const FileNode& fn) const;
String name() const;
void addParam(Algorithm& algo, const char* name,
int& value, bool readOnly=false,
int (Algorithm::*getter)()=0,
void (Algorithm::*setter)(int)=0,
const String& help=String());
void addParam(Algorithm& algo, const char* name,
bool& value, bool readOnly=false,
int (Algorithm::*getter)()=0,
void (Algorithm::*setter)(int)=0,
const String& help=String());
void addParam(Algorithm& algo, const char* name,
double& value, bool readOnly=false,
double (Algorithm::*getter)()=0,
void (Algorithm::*setter)(double)=0,
const String& help=String());
void addParam(Algorithm& algo, const char* name,
String& value, bool readOnly=false,
String (Algorithm::*getter)()=0,
void (Algorithm::*setter)(const String&)=0,
const String& help=String());
void addParam(Algorithm& algo, const char* name,
Mat& value, bool readOnly=false,
Mat (Algorithm::*getter)()=0,
void (Algorithm::*setter)(const Mat&)=0,
const String& help=String());
void addParam(Algorithm& algo, const char* name,
std::vector<Mat>& value, bool readOnly=false,
std::vector<Mat> (Algorithm::*getter)()=0,
void (Algorithm::*setter)(const std::vector<Mat>&)=0,
const String& help=String());
void addParam(Algorithm& algo, const char* name,
Ptr<Algorithm>& value, bool readOnly=false,
Ptr<Algorithm> (Algorithm::*getter)()=0,
void (Algorithm::*setter)(const Ptr<Algorithm>&)=0,
const String& help=String());
void addParam(Algorithm& algo, const char* name,
float& value, bool readOnly=false,
float (Algorithm::*getter)()=0,
void (Algorithm::*setter)(float)=0,
const String& help=String());
void addParam(Algorithm& algo, const char* name,
unsigned int& value, bool readOnly=false,
unsigned int (Algorithm::*getter)()=0,
void (Algorithm::*setter)(unsigned int)=0,
const String& help=String());
void addParam(Algorithm& algo, const char* name,
uint64& value, bool readOnly=false,
uint64 (Algorithm::*getter)()=0,
void (Algorithm::*setter)(uint64)=0,
const String& help=String());
void addParam(Algorithm& algo, const char* name,
uchar& value, bool readOnly=false,
uchar (Algorithm::*getter)()=0,
void (Algorithm::*setter)(uchar)=0,
const String& help=String());
template<typename _Tp, typename _Base> void addParam(Algorithm& algo, const char* name,
Ptr<_Tp>& value, bool readOnly=false,
Ptr<_Tp> (Algorithm::*getter)()=0,
void (Algorithm::*setter)(const Ptr<_Tp>&)=0,
const String& help=String());
template<typename _Tp> void addParam(Algorithm& algo, const char* name,
Ptr<_Tp>& value, bool readOnly=false,
Ptr<_Tp> (Algorithm::*getter)()=0,
void (Algorithm::*setter)(const Ptr<_Tp>&)=0,
const String& help=String());
protected:
AlgorithmInfoData* data;
void set(Algorithm* algo, const char* name, int argType,
const void* value, bool force=false) const;
};
struct CV_EXPORTS Param
{
enum { INT=0, BOOLEAN=1, REAL=2, STRING=3, MAT=4, MAT_VECTOR=5, ALGORITHM=6, FLOAT=7, UNSIGNED_INT=8, UINT64=9, UCHAR=11 };
Param();
Param(int _type, bool _readonly, int _offset,
Algorithm::Getter _getter=0,
Algorithm::Setter _setter=0,
const String& _help=String());
int type;
int offset;
bool readonly;
Algorithm::Getter getter;
Algorithm::Setter setter;
String help;
};
template<> struct ParamType<bool>
{
typedef bool const_param_type;
typedef bool member_type;
enum { type = Param::BOOLEAN };
};
template<> struct ParamType<int>
{
typedef int const_param_type;
typedef int member_type;
enum { type = Param::INT };
};
template<> struct ParamType<double>
{
typedef double const_param_type;
typedef double member_type;
enum { type = Param::REAL };
};
template<> struct ParamType<String>
{
typedef const String& const_param_type;
typedef String member_type;
enum { type = Param::STRING };
};
template<> struct ParamType<Mat>
{
typedef const Mat& const_param_type;
typedef Mat member_type;
enum { type = Param::MAT };
};
template<> struct ParamType<std::vector<Mat> >
{
typedef const std::vector<Mat>& const_param_type;
typedef std::vector<Mat> member_type;
enum { type = Param::MAT_VECTOR };
};
template<> struct ParamType<Algorithm>
{
typedef const Ptr<Algorithm>& const_param_type;
typedef Ptr<Algorithm> member_type;
enum { type = Param::ALGORITHM };
};
template<> struct ParamType<float>
{
typedef float const_param_type;
typedef float member_type;
enum { type = Param::FLOAT };
};
template<> struct ParamType<unsigned>
{
typedef unsigned const_param_type;
typedef unsigned member_type;
enum { type = Param::UNSIGNED_INT };
};
template<> struct ParamType<uint64>
{
typedef uint64 const_param_type;
typedef uint64 member_type;
enum { type = Param::UINT64 };
};
template<> struct ParamType<uchar>
{
typedef uchar const_param_type;
typedef uchar member_type;
enum { type = Param::UCHAR };
};
} //namespace cv
#include "opencv2/core/operations.hpp"
#include "opencv2/core/mat.hpp"
#include "opencv2/core/cvstd.inl.hpp"
#endif // __cplusplus
#endif /*__OPENCV_CORE_HPP__*/
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