Renesas
openCV library for Renesas RZ/A
Dependents: RZ_A2M_Mbed_samples
Diff: include/opencv2/core/mat.hpp
- Revision:
- 0:0e0631af0305
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/include/opencv2/core/mat.hpp Fri Jan 29 04:53:38 2021 +0000
@@ -0,0 +1,3520 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+// By downloading, copying, installing or using the software you agree to this license.
+// If you do not agree to this license, do not download, install,
+// copy or use the software.
+//
+//
+// License Agreement
+// For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+// * Redistribution's of source code must retain the above copyright notice,
+// this list of conditions and the following disclaimer.
+//
+// * Redistribution's in binary form must reproduce the above copyright notice,
+// this list of conditions and the following disclaimer in the documentation
+// and/or other materials provided with the distribution.
+//
+// * The name of the copyright holders may not be used to endorse or promote products
+// derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef OPENCV_CORE_MAT_HPP
+#define OPENCV_CORE_MAT_HPP
+
+#ifndef __cplusplus
+# error mat.hpp header must be compiled as C++
+#endif
+
+#include "opencv2/core/matx.hpp"
+#include "opencv2/core/types.hpp"
+
+#include "opencv2/core/bufferpool.hpp"
+
+namespace cv
+{
+
+//! @addtogroup core_basic
+//! @{
+
+enum { ACCESS_READ=1<<24, ACCESS_WRITE=1<<25,
+ ACCESS_RW=3<<24, ACCESS_MASK=ACCESS_RW, ACCESS_FAST=1<<26 };
+
+class CV_EXPORTS _OutputArray;
+
+//////////////////////// Input/Output Array Arguments /////////////////////////////////
+
+/** @brief This is the proxy class for passing read-only input arrays into OpenCV functions.
+
+It is defined as:
+@code
+ typedef const _InputArray& InputArray;
+@endcode
+where _InputArray is a class that can be constructed from `Mat`, `Mat_<T>`, `Matx<T, m, n>`,
+`std::vector<T>`, `std::vector<std::vector<T> >` or `std::vector<Mat>`. It can also be constructed
+from a matrix expression.
+
+Since this is mostly implementation-level class, and its interface may change in future versions, we
+do not describe it in details. There are a few key things, though, that should be kept in mind:
+
+- When you see in the reference manual or in OpenCV source code a function that takes
+ InputArray, it means that you can actually pass `Mat`, `Matx`, `vector<T>` etc. (see above the
+ complete list).
+- Optional input arguments: If some of the input arrays may be empty, pass cv::noArray() (or
+ simply cv::Mat() as you probably did before).
+- The class is designed solely for passing parameters. That is, normally you *should not*
+ declare class members, local and global variables of this type.
+- If you want to design your own function or a class method that can operate of arrays of
+ multiple types, you can use InputArray (or OutputArray) for the respective parameters. Inside
+ a function you should use _InputArray::getMat() method to construct a matrix header for the
+ array (without copying data). _InputArray::kind() can be used to distinguish Mat from
+ `vector<>` etc., but normally it is not needed.
+
+Here is how you can use a function that takes InputArray :
+@code
+ std::vector<Point2f> vec;
+ // points or a circle
+ for( int i = 0; i < 30; i++ )
+ vec.push_back(Point2f((float)(100 + 30*cos(i*CV_PI*2/5)),
+ (float)(100 - 30*sin(i*CV_PI*2/5))));
+ cv::transform(vec, vec, cv::Matx23f(0.707, -0.707, 10, 0.707, 0.707, 20));
+@endcode
+That is, we form an STL vector containing points, and apply in-place affine transformation to the
+vector using the 2x3 matrix created inline as `Matx<float, 2, 3>` instance.
+
+Here is how such a function can be implemented (for simplicity, we implement a very specific case of
+it, according to the assertion statement inside) :
+@code
+ void myAffineTransform(InputArray _src, OutputArray _dst, InputArray _m)
+ {
+ // get Mat headers for input arrays. This is O(1) operation,
+ // unless _src and/or _m are matrix expressions.
+ Mat src = _src.getMat(), m = _m.getMat();
+ CV_Assert( src.type() == CV_32FC2 && m.type() == CV_32F && m.size() == Size(3, 2) );
+
+ // [re]create the output array so that it has the proper size and type.
+ // In case of Mat it calls Mat::create, in case of STL vector it calls vector::resize.
+ _dst.create(src.size(), src.type());
+ Mat dst = _dst.getMat();
+
+ for( int i = 0; i < src.rows; i++ )
+ for( int j = 0; j < src.cols; j++ )
+ {
+ Point2f pt = src.at<Point2f>(i, j);
+ dst.at<Point2f>(i, j) = Point2f(m.at<float>(0, 0)*pt.x +
+ m.at<float>(0, 1)*pt.y +
+ m.at<float>(0, 2),
+ m.at<float>(1, 0)*pt.x +
+ m.at<float>(1, 1)*pt.y +
+ m.at<float>(1, 2));
+ }
+ }
+@endcode
+There is another related type, InputArrayOfArrays, which is currently defined as a synonym for
+InputArray:
+@code
+ typedef InputArray InputArrayOfArrays;
+@endcode
+It denotes function arguments that are either vectors of vectors or vectors of matrices. A separate
+synonym is needed to generate Python/Java etc. wrappers properly. At the function implementation
+level their use is similar, but _InputArray::getMat(idx) should be used to get header for the
+idx-th component of the outer vector and _InputArray::size().area() should be used to find the
+number of components (vectors/matrices) of the outer vector.
+ */
+class CV_EXPORTS _InputArray
+{
+public:
+ enum {
+ KIND_SHIFT = 16,
+ FIXED_TYPE = 0x8000 << KIND_SHIFT,
+ FIXED_SIZE = 0x4000 << KIND_SHIFT,
+ KIND_MASK = 31 << KIND_SHIFT,
+
+ 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,
+ CUDA_HOST_MEM = 8 << KIND_SHIFT,
+ CUDA_GPU_MAT = 9 << KIND_SHIFT,
+ UMAT =10 << KIND_SHIFT,
+ STD_VECTOR_UMAT =11 << KIND_SHIFT,
+ STD_BOOL_VECTOR =12 << KIND_SHIFT,
+ STD_VECTOR_CUDA_GPU_MAT = 13 << KIND_SHIFT
+ };
+
+ _InputArray();
+ _InputArray(int _flags, void* _obj);
+ _InputArray(const Mat& m);
+ _InputArray(const MatExpr& expr);
+ _InputArray(const std::vector<Mat>& vec);
+ template<typename _Tp> _InputArray(const Mat_<_Tp>& m);
+ template<typename _Tp> _InputArray(const std::vector<_Tp>& vec);
+ _InputArray(const std::vector<bool>& vec);
+ template<typename _Tp> _InputArray(const std::vector<std::vector<_Tp> >& vec);
+ template<typename _Tp> _InputArray(const std::vector<Mat_<_Tp> >& vec);
+ template<typename _Tp> _InputArray(const _Tp* vec, int n);
+ template<typename _Tp, int m, int n> _InputArray(const Matx<_Tp, m, n>& matx);
+ _InputArray(const double& val);
+ _InputArray(const cuda::GpuMat& d_mat);
+ _InputArray(const std::vector<cuda::GpuMat>& d_mat_array);
+ _InputArray(const ogl::Buffer& buf);
+ _InputArray(const cuda::HostMem& cuda_mem);
+ template<typename _Tp> _InputArray(const cudev::GpuMat_<_Tp>& m);
+ _InputArray(const UMat& um);
+ _InputArray(const std::vector<UMat>& umv);
+
+ Mat getMat(int idx=-1) const;
+ Mat getMat_(int idx=-1) const;
+ UMat getUMat(int idx=-1) const;
+ void getMatVector(std::vector<Mat>& mv) const;
+ void getUMatVector(std::vector<UMat>& umv) const;
+ void getGpuMatVector(std::vector<cuda::GpuMat>& gpumv) const;
+ cuda::GpuMat getGpuMat() const;
+ ogl::Buffer getOGlBuffer() const;
+
+ int getFlags() const;
+ void* getObj() const;
+ Size getSz() const;
+
+ int kind() const;
+ int dims(int i=-1) const;
+ int cols(int i=-1) const;
+ int rows(int i=-1) const;
+ Size size(int i=-1) const;
+ int sizend(int* sz, int i=-1) const;
+ bool sameSize(const _InputArray& arr) const;
+ size_t total(int i=-1) const;
+ int type(int i=-1) const;
+ int depth(int i=-1) const;
+ int channels(int i=-1) const;
+ bool isContinuous(int i=-1) const;
+ bool isSubmatrix(int i=-1) const;
+ bool empty() const;
+ void copyTo(const _OutputArray& arr) const;
+ void copyTo(const _OutputArray& arr, const _InputArray & mask) const;
+ size_t offset(int i=-1) const;
+ size_t step(int i=-1) const;
+ bool isMat() const;
+ bool isUMat() const;
+ bool isMatVector() const;
+ bool isUMatVector() const;
+ bool isMatx() const;
+ bool isVector() const;
+ bool isGpuMatVector() const;
+ ~_InputArray();
+
+protected:
+ int flags;
+ void* obj;
+ Size sz;
+
+ void init(int _flags, const void* _obj);
+ void init(int _flags, const void* _obj, Size _sz);
+};
+
+
+/** @brief This type is very similar to InputArray except that it is used for input/output and output function
+parameters.
+
+Just like with InputArray, OpenCV users should not care about OutputArray, they just pass `Mat`,
+`vector<T>` etc. to the functions. The same limitation as for `InputArray`: *Do not explicitly
+create OutputArray instances* applies here too.
+
+If you want to make your function polymorphic (i.e. accept different arrays as output parameters),
+it is also not very difficult. Take the sample above as the reference. Note that
+_OutputArray::create() needs to be called before _OutputArray::getMat(). This way you guarantee
+that the output array is properly allocated.
+
+Optional output parameters. If you do not need certain output array to be computed and returned to
+you, pass cv::noArray(), just like you would in the case of optional input array. At the
+implementation level, use _OutputArray::needed() to check if certain output array needs to be
+computed or not.
+
+There are several synonyms for OutputArray that are used to assist automatic Python/Java/... wrapper
+generators:
+@code
+ typedef OutputArray OutputArrayOfArrays;
+ typedef OutputArray InputOutputArray;
+ typedef OutputArray InputOutputArrayOfArrays;
+@endcode
+ */
+class CV_EXPORTS _OutputArray : public _InputArray
+{
+public:
+ 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
+ };
+
+ _OutputArray();
+ _OutputArray(int _flags, void* _obj);
+ _OutputArray(Mat& m);
+ _OutputArray(std::vector<Mat>& vec);
+ _OutputArray(cuda::GpuMat& d_mat);
+ _OutputArray(std::vector<cuda::GpuMat>& d_mat);
+ _OutputArray(ogl::Buffer& buf);
+ _OutputArray(cuda::HostMem& cuda_mem);
+ template<typename _Tp> _OutputArray(cudev::GpuMat_<_Tp>& m);
+ template<typename _Tp> _OutputArray(std::vector<_Tp>& vec);
+ _OutputArray(std::vector<bool>& vec);
+ template<typename _Tp> _OutputArray(std::vector<std::vector<_Tp> >& vec);
+ template<typename _Tp> _OutputArray(std::vector<Mat_<_Tp> >& vec);
+ template<typename _Tp> _OutputArray(Mat_<_Tp>& m);
+ template<typename _Tp> _OutputArray(_Tp* vec, int n);
+ template<typename _Tp, int m, int n> _OutputArray(Matx<_Tp, m, n>& matx);
+ _OutputArray(UMat& m);
+ _OutputArray(std::vector<UMat>& vec);
+
+ _OutputArray(const Mat& m);
+ _OutputArray(const std::vector<Mat>& vec);
+ _OutputArray(const cuda::GpuMat& d_mat);
+ _OutputArray(const std::vector<cuda::GpuMat>& d_mat);
+ _OutputArray(const ogl::Buffer& buf);
+ _OutputArray(const cuda::HostMem& cuda_mem);
+ template<typename _Tp> _OutputArray(const cudev::GpuMat_<_Tp>& m);
+ template<typename _Tp> _OutputArray(const std::vector<_Tp>& vec);
+ template<typename _Tp> _OutputArray(const std::vector<std::vector<_Tp> >& vec);
+ template<typename _Tp> _OutputArray(const std::vector<Mat_<_Tp> >& vec);
+ template<typename _Tp> _OutputArray(const Mat_<_Tp>& m);
+ template<typename _Tp> _OutputArray(const _Tp* vec, int n);
+ template<typename _Tp, int m, int n> _OutputArray(const Matx<_Tp, m, n>& matx);
+ _OutputArray(const UMat& m);
+ _OutputArray(const std::vector<UMat>& vec);
+
+ bool fixedSize() const;
+ bool fixedType() const;
+ bool needed() const;
+ Mat& getMatRef(int i=-1) const;
+ UMat& getUMatRef(int i=-1) const;
+ cuda::GpuMat& getGpuMatRef() const;
+ std::vector<cuda::GpuMat>& getGpuMatVecRef() const;
+ ogl::Buffer& getOGlBufferRef() const;
+ cuda::HostMem& getHostMemRef() const;
+ void create(Size sz, int type, int i=-1, bool allowTransposed=false, int fixedDepthMask=0) const;
+ void create(int rows, int cols, int type, int i=-1, bool allowTransposed=false, int fixedDepthMask=0) const;
+ void create(int dims, const int* size, int type, int i=-1, bool allowTransposed=false, int fixedDepthMask=0) const;
+ void createSameSize(const _InputArray& arr, int mtype) const;
+ void release() const;
+ void clear() const;
+ void setTo(const _InputArray& value, const _InputArray & mask = _InputArray()) const;
+
+ void assign(const UMat& u) const;
+ void assign(const Mat& m) const;
+};
+
+
+class CV_EXPORTS _InputOutputArray : public _OutputArray
+{
+public:
+ _InputOutputArray();
+ _InputOutputArray(int _flags, void* _obj);
+ _InputOutputArray(Mat& m);
+ _InputOutputArray(std::vector<Mat>& vec);
+ _InputOutputArray(cuda::GpuMat& d_mat);
+ _InputOutputArray(ogl::Buffer& buf);
+ _InputOutputArray(cuda::HostMem& cuda_mem);
+ template<typename _Tp> _InputOutputArray(cudev::GpuMat_<_Tp>& m);
+ template<typename _Tp> _InputOutputArray(std::vector<_Tp>& vec);
+ _InputOutputArray(std::vector<bool>& vec);
+ template<typename _Tp> _InputOutputArray(std::vector<std::vector<_Tp> >& vec);
+ template<typename _Tp> _InputOutputArray(std::vector<Mat_<_Tp> >& vec);
+ template<typename _Tp> _InputOutputArray(Mat_<_Tp>& m);
+ template<typename _Tp> _InputOutputArray(_Tp* vec, int n);
+ template<typename _Tp, int m, int n> _InputOutputArray(Matx<_Tp, m, n>& matx);
+ _InputOutputArray(UMat& m);
+ _InputOutputArray(std::vector<UMat>& vec);
+
+ _InputOutputArray(const Mat& m);
+ _InputOutputArray(const std::vector<Mat>& vec);
+ _InputOutputArray(const cuda::GpuMat& d_mat);
+ _InputOutputArray(const std::vector<cuda::GpuMat>& d_mat);
+ _InputOutputArray(const ogl::Buffer& buf);
+ _InputOutputArray(const cuda::HostMem& cuda_mem);
+ template<typename _Tp> _InputOutputArray(const cudev::GpuMat_<_Tp>& m);
+ template<typename _Tp> _InputOutputArray(const std::vector<_Tp>& vec);
+ template<typename _Tp> _InputOutputArray(const std::vector<std::vector<_Tp> >& vec);
+ template<typename _Tp> _InputOutputArray(const std::vector<Mat_<_Tp> >& vec);
+ template<typename _Tp> _InputOutputArray(const Mat_<_Tp>& m);
+ template<typename _Tp> _InputOutputArray(const _Tp* vec, int n);
+ template<typename _Tp, int m, int n> _InputOutputArray(const Matx<_Tp, m, n>& matx);
+ _InputOutputArray(const UMat& m);
+ _InputOutputArray(const std::vector<UMat>& vec);
+};
+
+typedef const _InputArray& InputArray;
+typedef InputArray InputArrayOfArrays;
+typedef const _OutputArray& OutputArray;
+typedef OutputArray OutputArrayOfArrays;
+typedef const _InputOutputArray& InputOutputArray;
+typedef InputOutputArray InputOutputArrayOfArrays;
+
+CV_EXPORTS InputOutputArray noArray();
+
+/////////////////////////////////// MatAllocator //////////////////////////////////////
+
+//! Usage flags for allocator
+enum UMatUsageFlags
+{
+ USAGE_DEFAULT = 0,
+
+ // buffer allocation policy is platform and usage specific
+ USAGE_ALLOCATE_HOST_MEMORY = 1 << 0,
+ USAGE_ALLOCATE_DEVICE_MEMORY = 1 << 1,
+ USAGE_ALLOCATE_SHARED_MEMORY = 1 << 2, // It is not equal to: USAGE_ALLOCATE_HOST_MEMORY | USAGE_ALLOCATE_DEVICE_MEMORY
+
+ __UMAT_USAGE_FLAGS_32BIT = 0x7fffffff // Binary compatibility hint
+};
+
+struct CV_EXPORTS UMatData;
+
+/** @brief Custom array allocator
+*/
+class CV_EXPORTS MatAllocator
+{
+public:
+ MatAllocator() {}
+ virtual ~MatAllocator() {}
+
+ // let's comment it off for now to detect and fix all the uses of allocator
+ //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;
+ virtual UMatData* allocate(int dims, const int* sizes, int type,
+ void* data, size_t* step, int flags, UMatUsageFlags usageFlags) const = 0;
+ virtual bool allocate(UMatData* data, int accessflags, UMatUsageFlags usageFlags) const = 0;
+ virtual void deallocate(UMatData* data) const = 0;
+ virtual void map(UMatData* data, int accessflags) const;
+ virtual void unmap(UMatData* data) const;
+ virtual void download(UMatData* data, void* dst, int dims, const size_t sz[],
+ const size_t srcofs[], const size_t srcstep[],
+ const size_t dststep[]) const;
+ virtual void upload(UMatData* data, const void* src, int dims, const size_t sz[],
+ const size_t dstofs[], const size_t dststep[],
+ const size_t srcstep[]) const;
+ virtual void copy(UMatData* srcdata, UMatData* dstdata, int dims, const size_t sz[],
+ const size_t srcofs[], const size_t srcstep[],
+ const size_t dstofs[], const size_t dststep[], bool sync) const;
+
+ // default implementation returns DummyBufferPoolController
+ virtual BufferPoolController* getBufferPoolController(const char* id = NULL) const;
+};
+
+
+//////////////////////////////// MatCommaInitializer //////////////////////////////////
+
+/** @brief 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 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
+ operator Mat_<_Tp>() const;
+protected:
+ MatIterator_<_Tp> it;
+};
+
+
+/////////////////////////////////////// Mat ///////////////////////////////////////////
+
+// note that umatdata might be allocated together
+// with the matrix data, not as a separate object.
+// therefore, it does not have constructor or destructor;
+// it should be explicitly initialized using init().
+struct CV_EXPORTS UMatData
+{
+ enum { COPY_ON_MAP=1, HOST_COPY_OBSOLETE=2,
+ DEVICE_COPY_OBSOLETE=4, TEMP_UMAT=8, TEMP_COPIED_UMAT=24,
+ USER_ALLOCATED=32, DEVICE_MEM_MAPPED=64};
+ UMatData(const MatAllocator* allocator);
+ ~UMatData();
+
+ // provide atomic access to the structure
+ void lock();
+ void unlock();
+
+ bool hostCopyObsolete() const;
+ bool deviceCopyObsolete() const;
+ bool deviceMemMapped() const;
+ bool copyOnMap() const;
+ bool tempUMat() const;
+ bool tempCopiedUMat() const;
+ void markHostCopyObsolete(bool flag);
+ void markDeviceCopyObsolete(bool flag);
+ void markDeviceMemMapped(bool flag);
+
+ const MatAllocator* prevAllocator;
+ const MatAllocator* currAllocator;
+ int urefcount;
+ int refcount;
+ uchar* data;
+ uchar* origdata;
+ size_t size;
+
+ int flags;
+ void* handle;
+ void* userdata;
+ int allocatorFlags_;
+ int mapcount;
+ UMatData* originalUMatData;
+};
+
+
+struct CV_EXPORTS UMatDataAutoLock
+{
+ explicit UMatDataAutoLock(UMatData* u);
+ ~UMatDataAutoLock();
+ UMatData* u;
+};
+
+
+struct CV_EXPORTS MatSize
+{
+ explicit MatSize(int* _p);
+ Size operator()() const;
+ const int& operator[](int i) const;
+ int& operator[](int i);
+ operator const int*() const;
+ bool operator == (const MatSize& sz) const;
+ bool operator != (const MatSize& sz) const;
+
+ int* p;
+};
+
+struct CV_EXPORTS MatStep
+{
+ MatStep();
+ explicit MatStep(size_t s);
+ const size_t& operator[](int i) const;
+ size_t& operator[](int i);
+ operator size_t() const;
+ MatStep& operator = (size_t s);
+
+ size_t* p;
+ size_t buf[2];
+protected:
+ MatStep& operator = (const MatStep&);
+};
+
+/** @example cout_mat.cpp
+An example demonstrating the serial out capabilities of cv::Mat
+*/
+
+ /** @brief n-dimensional dense array class
+
+The class Mat represents an n-dimensional dense numerical single-channel or multi-channel array. It
+can be used to store real or complex-valued vectors and matrices, grayscale or color images, voxel
+volumes, vector fields, point clouds, tensors, histograms (though, very high-dimensional histograms
+may be better stored in a SparseMat ). The data layout of the array `M` is defined by the array
+`M.step[]`, so that the address of element \f$(i_0,...,i_{M.dims-1})\f$, where \f$0\leq i_k<M.size[k]\f$, is
+computed as:
+\f[addr(M_{i_0,...,i_{M.dims-1}}) = M.data + M.step[0]*i_0 + M.step[1]*i_1 + ... + M.step[M.dims-1]*i_{M.dims-1}\f]
+In case of a 2-dimensional array, the above formula is reduced to:
+\f[addr(M_{i,j}) = M.data + M.step[0]*i + M.step[1]*j\f]
+Note that `M.step[i] >= M.step[i+1]` (in fact, `M.step[i] >= M.step[i+1]*M.size[i+1]` ). This means
+that 2-dimensional matrices are stored row-by-row, 3-dimensional matrices are stored plane-by-plane,
+and so on. M.step[M.dims-1] is minimal and always equal to the element size M.elemSize() .
+
+So, the data layout in Mat is fully compatible with CvMat, IplImage, and CvMatND types from OpenCV
+1.x. It is also compatible with the majority of dense array types from the standard toolkits and
+SDKs, such as Numpy (ndarray), Win32 (independent device bitmaps), and others, that is, with any
+array that uses *steps* (or *strides*) to compute the position of a pixel. Due to this
+compatibility, it is possible to make a Mat header for user-allocated data and process it in-place
+using OpenCV functions.
+
+There are many different ways to create a Mat object. The most popular options are listed below:
+
+- Use the create(nrows, ncols, type) method or the similar Mat(nrows, ncols, type[, fillValue])
+constructor. A new array of the specified size and type is allocated. type has the same meaning as
+in the cvCreateMat method. For example, CV_8UC1 means a 8-bit single-channel array, CV_32FC2
+means a 2-channel (complex) floating-point array, and so on.
+@code
+ // make a 7x7 complex matrix filled with 1+3j.
+ Mat M(7,7,CV_32FC2,Scalar(1,3));
+ // and now turn M to a 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 to this chapter, create() allocates only a new array when the shape
+or type of the current array are different from the specified ones.
+
+- Create a multi-dimensional array:
+@code
+ // create a 100x100x100 8-bit array
+ int sz[] = {100, 100, 100};
+ Mat bigCube(3, sz, CV_8U, Scalar::all(0));
+@endcode
+It passes the number of dimensions =1 to the Mat constructor but the created array will be
+2-dimensional with the number of columns set to 1. So, Mat::dims is always \>= 2 (can also be 0
+when the array is empty).
+
+- Use a copy constructor or assignment operator where there can be an array or expression on the
+right side (see below). As noted in the introduction, the array assignment is an O(1) operation
+because it only copies the header and increases the reference counter. The Mat::clone() method can
+be used to get a full (deep) copy of the array when you need it.
+
+- Construct a header for a part of another array. It can be a single row, single column, several
+rows, several columns, rectangular region in the array (called a *minor* in algebra) or a
+diagonal. Such operations are also O(1) because the new header references the same data. You can
+actually modify a part of the array using this feature, for example:
+@code
+ // add the 5-th row, multiplied by 3 to the 3rd row
+ M.row(3) = M.row(3) + M.row(5)*3;
+ // now copy the 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 a new 320x240 image
+ Mat img(Size(320,240),CV_8UC3);
+ // select a ROI
+ 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
+Due to the additional datastart and dataend members, it is possible to compute a relative
+sub-array position in the main *container* array using 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 case of whole matrices, if you need a deep copy, use the `clone()` method of the extracted
+sub-matrices.
+
+- Make a header for user-allocated data. It can be useful to do the following:
+ -# Process "foreign" data using OpenCV (for example, when you implement a DirectShow\* filter or
+ a processing module for gstreamer, and so on). For example:
+ @code
+ void process_video_frame(const unsigned char* pixels,
+ int width, int height, int step)
+ {
+ Mat img(height, width, CV_8UC3, pixels, step);
+ GaussianBlur(img, img, Size(7,7), 1.5, 1.5);
+ }
+ @endcode
+ -# Quickly initialize small matrices and/or get a super-fast element access.
+ @code
+ double m[3][3] = {{a, b, c}, {d, e, f}, {g, h, i}};
+ Mat M = Mat(3, 3, CV_64F, m).inv();
+ @endcode
+ .
+ Partial yet very common cases of this *user-allocated data* case are conversions from CvMat and
+ IplImage to Mat. For this purpose, there is function cv::cvarrToMat taking pointers to CvMat or
+ IplImage and the optional flag indicating whether to copy the data or not.
+ @snippet samples/cpp/image.cpp iplimage
+
+- Use MATLAB-style array initializers, zeros(), ones(), eye(), for example:
+@code
+ // create a double-precision identity martix and add it to M.
+ M += Mat::eye(M.rows, M.cols, CV_64F);
+@endcode
+
+- Use a comma-separated initializer:
+@code
+ // create a 3x3 double-precision identity matrix
+ Mat M = (Mat_<double>(3,3) << 1, 0, 0, 0, 1, 0, 0, 0, 1);
+@endcode
+With this approach, you first call a constructor of the Mat class with the proper parameters, and
+then you just put `<< operator` followed by comma-separated values that can be constants,
+variables, expressions, and so on. Also, note the extra parentheses required to avoid compilation
+errors.
+
+Once the array is created, it is automatically managed via a reference-counting mechanism. If the
+array header is built on top of user-allocated data, you should handle the data by yourself. The
+array data is deallocated when no one points to it. If you want to release the data pointed by a
+array header before the array destructor is called, use Mat::release().
+
+The next important thing to learn about the array class is element access. This manual already
+described how to compute an address of each array element. Normally, you are not required to use the
+formula directly in the code. If you know the array element type (which can be retrieved using the
+method Mat::type() ), you can access the element \f$M_{ij}\f$ of a 2-dimensional array as:
+@code
+ M.at<double>(i,j) += 1.f;
+@endcode
+assuming that `M` is a double-precision floating-point array. There are several variants of the method
+at for a different number of dimensions.
+
+If you need to process a whole row of a 2D array, the most efficient way is to get the pointer to
+the row first, and then just use the plain C operator [] :
+@code
+ // compute sum of positive matrix elements
+ // (assuming that M isa 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 one above, do not actually depend on the array shape. They just process
+elements of an array one by one (or elements from multiple arrays that have the same coordinates,
+for example, array addition). Such operations are called *element-wise*. It makes sense to check
+whether all the input/output arrays are continuous, namely, have no gaps at the end of each row. If
+yes, process them as a long single row:
+@code
+ // compute the 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 case of the continuous matrix, the outer loop body is executed just once. So, the overhead is
+smaller, which is especially noticeable in 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:
+ /**
+ These are various constructors that form a matrix. As noted in the AutomaticAllocation, often
+ the default constructor is enough, and the proper matrix will be allocated by an OpenCV function.
+ The constructed matrix can further be assigned to another matrix or matrix expression or can be
+ allocated with Mat::create . In the former case, the old content is de-referenced.
+ */
+ Mat();
+
+ /** @overload
+ @param rows Number of rows in a 2D array.
+ @param cols Number of columns in a 2D array.
+ @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or
+ CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices.
+ */
+ Mat(int rows, int cols, int type);
+
+ /** @overload
+ @param size 2D array size: Size(cols, rows) . In the Size() constructor, the number of rows and the
+ number of columns go in the reverse order.
+ @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or
+ CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices.
+ */
+ Mat(Size size, int type);
+
+ /** @overload
+ @param rows Number of rows in a 2D array.
+ @param cols Number of columns in a 2D array.
+ @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or
+ CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices.
+ @param s An optional value to initialize each matrix element with. To set all the matrix elements to
+ the particular value after the construction, use the assignment operator
+ Mat::operator=(const Scalar& value) .
+ */
+ Mat(int rows, int cols, int type, const Scalar& s);
+
+ /** @overload
+ @param size 2D array size: Size(cols, rows) . In the Size() constructor, the number of rows and the
+ number of columns go in the reverse order.
+ @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or
+ CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices.
+ @param s An optional value to initialize each matrix element with. To set all the matrix elements to
+ the particular value after the construction, use the assignment operator
+ Mat::operator=(const Scalar& value) .
+ */
+ Mat(Size size, int type, const Scalar& s);
+
+ /** @overload
+ @param ndims Array dimensionality.
+ @param sizes Array of integers specifying an n-dimensional array shape.
+ @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or
+ CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices.
+ */
+ Mat(int ndims, const int* sizes, int type);
+
+ /** @overload
+ @param sizes Array of integers specifying an n-dimensional array shape.
+ @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or
+ CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices.
+ */
+ Mat(const std::vector<int>& sizes, int type);
+
+ /** @overload
+ @param ndims Array dimensionality.
+ @param sizes Array of integers specifying an n-dimensional array shape.
+ @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or
+ CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices.
+ @param s An optional value to initialize each matrix element with. To set all the matrix elements to
+ the particular value after the construction, use the assignment operator
+ Mat::operator=(const Scalar& value) .
+ */
+ Mat(int ndims, const int* sizes, int type, const Scalar& s);
+
+ /** @overload
+ @param sizes Array of integers specifying an n-dimensional array shape.
+ @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or
+ CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices.
+ @param s An optional value to initialize each matrix element with. To set all the matrix elements to
+ the particular value after the construction, use the assignment operator
+ Mat::operator=(const Scalar& value) .
+ */
+ Mat(const std::vector<int>& sizes, int type, const Scalar& s);
+
+
+ /** @overload
+ @param m Array that (as a whole or partly) is assigned to the constructed matrix. No data is copied
+ by these constructors. Instead, the header pointing to m data or its sub-array is constructed and
+ associated with it. The reference counter, if any, is incremented. So, when you modify the matrix
+ formed using such a constructor, you also modify the corresponding elements of m . If you want to
+ have an independent copy of the sub-array, use Mat::clone() .
+ */
+ Mat(const Mat& m);
+
+ /** @overload
+ @param rows Number of rows in a 2D array.
+ @param cols Number of columns in a 2D array.
+ @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or
+ CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices.
+ @param data Pointer to the user data. Matrix constructors that take data and step parameters do not
+ allocate matrix data. Instead, they just initialize the matrix header that points to the specified
+ data, which means that no data is copied. This operation is very efficient and can be used to
+ process external data using OpenCV functions. The external data is not automatically deallocated, so
+ you should take care of it.
+ @param step Number of bytes each matrix row occupies. The value should include the padding bytes at
+ the end of each row, if any. If the parameter is missing (set to AUTO_STEP ), no padding is assumed
+ and the actual step is calculated as cols*elemSize(). See Mat::elemSize.
+ */
+ Mat(int rows, int cols, int type, void* data, size_t step=AUTO_STEP);
+
+ /** @overload
+ @param size 2D array size: Size(cols, rows) . In the Size() constructor, the number of rows and the
+ number of columns go in the reverse order.
+ @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or
+ CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices.
+ @param data Pointer to the user data. Matrix constructors that take data and step parameters do not
+ allocate matrix data. Instead, they just initialize the matrix header that points to the specified
+ data, which means that no data is copied. This operation is very efficient and can be used to
+ process external data using OpenCV functions. The external data is not automatically deallocated, so
+ you should take care of it.
+ @param step Number of bytes each matrix row occupies. The value should include the padding bytes at
+ the end of each row, if any. If the parameter is missing (set to AUTO_STEP ), no padding is assumed
+ and the actual step is calculated as cols*elemSize(). See Mat::elemSize.
+ */
+ Mat(Size size, int type, void* data, size_t step=AUTO_STEP);
+
+ /** @overload
+ @param ndims Array dimensionality.
+ @param sizes Array of integers specifying an n-dimensional array shape.
+ @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or
+ CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices.
+ @param data Pointer to the user data. Matrix constructors that take data and step parameters do not
+ allocate matrix data. Instead, they just initialize the matrix header that points to the specified
+ data, which means that no data is copied. This operation is very efficient and can be used to
+ process external data using OpenCV functions. The external data is not automatically deallocated, so
+ you should take care of it.
+ @param steps Array of ndims-1 steps in case of a multi-dimensional array (the last step is always
+ set to the element size). If not specified, the matrix is assumed to be continuous.
+ */
+ Mat(int ndims, const int* sizes, int type, void* data, const size_t* steps=0);
+
+ /** @overload
+ @param sizes Array of integers specifying an n-dimensional array shape.
+ @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or
+ CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices.
+ @param data Pointer to the user data. Matrix constructors that take data and step parameters do not
+ allocate matrix data. Instead, they just initialize the matrix header that points to the specified
+ data, which means that no data is copied. This operation is very efficient and can be used to
+ process external data using OpenCV functions. The external data is not automatically deallocated, so
+ you should take care of it.
+ @param steps Array of ndims-1 steps in case of a multi-dimensional array (the last step is always
+ set to the element size). If not specified, the matrix is assumed to be continuous.
+ */
+ Mat(const std::vector<int>& sizes, int type, void* data, const size_t* steps=0);
+
+ /** @overload
+ @param m Array that (as a whole or partly) is assigned to the constructed matrix. No data is copied
+ by these constructors. Instead, the header pointing to m data or its sub-array is constructed and
+ associated with it. The reference counter, if any, is incremented. So, when you modify the matrix
+ formed using such a constructor, you also modify the corresponding elements of m . If you want to
+ have an independent copy of the sub-array, use Mat::clone() .
+ @param rowRange Range of the m rows to take. As usual, the range start is inclusive and the range
+ end is exclusive. Use Range::all() to take all the rows.
+ @param colRange Range of the m columns to take. Use Range::all() to take all the columns.
+ */
+ Mat(const Mat& m, const Range& rowRange, const Range& colRange=Range::all());
+
+ /** @overload
+ @param m Array that (as a whole or partly) is assigned to the constructed matrix. No data is copied
+ by these constructors. Instead, the header pointing to m data or its sub-array is constructed and
+ associated with it. The reference counter, if any, is incremented. So, when you modify the matrix
+ formed using such a constructor, you also modify the corresponding elements of m . If you want to
+ have an independent copy of the sub-array, use Mat::clone() .
+ @param roi Region of interest.
+ */
+ Mat(const Mat& m, const Rect& roi);
+
+ /** @overload
+ @param m Array that (as a whole or partly) is assigned to the constructed matrix. No data is copied
+ by these constructors. Instead, the header pointing to m data or its sub-array is constructed and
+ associated with it. The reference counter, if any, is incremented. So, when you modify the matrix
+ formed using such a constructor, you also modify the corresponding elements of m . If you want to
+ have an independent copy of the sub-array, use Mat::clone() .
+ @param ranges Array of selected ranges of m along each dimensionality.
+ */
+ Mat(const Mat& m, const Range* ranges);
+
+ /** @overload
+ @param m Array that (as a whole or partly) is assigned to the constructed matrix. No data is copied
+ by these constructors. Instead, the header pointing to m data or its sub-array is constructed and
+ associated with it. The reference counter, if any, is incremented. So, when you modify the matrix
+ formed using such a constructor, you also modify the corresponding elements of m . If you want to
+ have an independent copy of the sub-array, use Mat::clone() .
+ @param ranges Array of selected ranges of m along each dimensionality.
+ */
+ Mat(const Mat& m, const std::vector<Range>& ranges);
+
+ /** @overload
+ @param vec STL vector whose elements form the matrix. The matrix has a single column and the number
+ of rows equal to the number of vector elements. Type of the matrix matches the type of vector
+ elements. The constructor can handle arbitrary types, for which there is a properly declared
+ DataType . This means that the vector elements must be primitive numbers or uni-type numerical
+ tuples of numbers. Mixed-type structures are not supported. The corresponding constructor is
+ explicit. Since STL vectors are not automatically converted to Mat instances, you should write
+ Mat(vec) explicitly. Unless you copy the data into the matrix ( copyData=true ), no new elements
+ will be added to the vector because it can potentially yield vector data reallocation, and, thus,
+ the matrix data pointer will be invalid.
+ @param copyData Flag to specify whether the underlying data of the STL vector should be copied
+ to (true) or shared with (false) the newly constructed matrix. When the data is copied, the
+ allocated buffer is managed using Mat reference counting mechanism. While the data is shared,
+ the reference counter is NULL, and you should not deallocate the data until the matrix is not
+ destructed.
+ */
+ template<typename _Tp> explicit Mat(const std::vector<_Tp>& vec, bool copyData=false);
+
+ /** @overload
+ */
+ template<typename _Tp, int n> explicit Mat(const Vec<_Tp, n>& vec, bool copyData=true);
+
+ /** @overload
+ */
+ template<typename _Tp, int m, int n> explicit Mat(const Matx<_Tp, m, n>& mtx, bool copyData=true);
+
+ /** @overload
+ */
+ template<typename _Tp> explicit Mat(const Point_<_Tp>& pt, bool copyData=true);
+
+ /** @overload
+ */
+ template<typename _Tp> explicit Mat(const Point3_<_Tp>& pt, bool copyData=true);
+
+ /** @overload
+ */
+ template<typename _Tp> explicit Mat(const MatCommaInitializer_<_Tp>& commaInitializer);
+
+ //! download data from GpuMat
+ explicit Mat(const cuda::GpuMat& m);
+
+ //! destructor - calls release()
+ ~Mat();
+
+ /** @brief assignment operators
+
+ These are available assignment operators. Since they all are very different, make sure to read the
+ operator parameters description.
+ @param m Assigned, right-hand-side matrix. Matrix assignment is an O(1) operation. This means that
+ no data is copied but the data is shared and the reference counter, if any, is incremented. Before
+ assigning new data, the old data is de-referenced via Mat::release .
+ */
+ Mat& operator = (const Mat& m);
+
+ /** @overload
+ @param expr Assigned matrix expression object. As opposite to the first form of the assignment
+ operation, the second form can reuse already allocated matrix if it has the right size and type to
+ fit the matrix expression result. It is automatically handled by the real function that the matrix
+ expressions is expanded to. For example, C=A+B is expanded to add(A, B, C), and add takes care of
+ automatic C reallocation.
+ */
+ Mat& operator = (const MatExpr& expr);
+
+ //! retrieve UMat from Mat
+ UMat getUMat(int accessFlags, UMatUsageFlags usageFlags = USAGE_DEFAULT) const;
+
+ /** @brief Creates a matrix header for the specified matrix row.
+
+ The method makes a new header for the specified matrix row and returns it. This is an O(1)
+ operation, regardless of the matrix size. The underlying data of the new matrix is shared with the
+ original matrix. Here is the example of one of the classical basic matrix processing operations,
+ axpy, used by LU and many other algorithms:
+ @code
+ inline void matrix_axpy(Mat& A, int i, int j, double alpha)
+ {
+ A.row(i) += A.row(j)*alpha;
+ }
+ @endcode
+ @note In the current implementation, the following code does not work as expected:
+ @code
+ Mat A;
+ ...
+ A.row(i) = A.row(j); // will not work
+ @endcode
+ This happens because A.row(i) forms a temporary header that is further assigned to another header.
+ Remember that each of these operations is O(1), that is, no data is copied. Thus, the above
+ assignment is not true if you may have expected the j-th row to be copied to the i-th row. To
+ achieve that, you should either turn this simple assignment into an expression or use the
+ Mat::copyTo method:
+ @code
+ Mat A;
+ ...
+ // works, but looks a bit obscure.
+ A.row(i) = A.row(j) + 0;
+ // this is a bit longer, but the recommended method.
+ A.row(j).copyTo(A.row(i));
+ @endcode
+ @param y A 0-based row index.
+ */
+ Mat row(int y) const;
+
+ /** @brief Creates a matrix header for the specified matrix column.
+
+ The method makes a new header for the specified matrix column and returns it. This is an O(1)
+ operation, regardless of the matrix size. The underlying data of the new matrix is shared with the
+ original matrix. See also the Mat::row description.
+ @param x A 0-based column index.
+ */
+ Mat col(int x) const;
+
+ /** @brief Creates a matrix header for the specified row span.
+
+ The method makes a new header for the specified row span of the matrix. Similarly to Mat::row and
+ Mat::col , this is an O(1) operation.
+ @param startrow An inclusive 0-based start index of the row span.
+ @param endrow An exclusive 0-based ending index of the row span.
+ */
+ Mat rowRange(int startrow, int endrow) const;
+
+ /** @overload
+ @param r Range structure containing both the start and the end indices.
+ */
+ Mat rowRange(const Range& r) const;
+
+ /** @brief Creates a matrix header for the specified column span.
+
+ The method makes a new header for the specified column span of the matrix. Similarly to Mat::row and
+ Mat::col , this is an O(1) operation.
+ @param startcol An inclusive 0-based start index of the column span.
+ @param endcol An exclusive 0-based ending index of the column span.
+ */
+ Mat colRange(int startcol, int endcol) const;
+
+ /** @overload
+ @param r Range structure containing both the start and the end indices.
+ */
+ Mat colRange(const Range& r) const;
+
+ /** @brief Extracts a diagonal from a matrix
+
+ The method makes a new header for the specified matrix diagonal. The new matrix is represented as a
+ single-column matrix. Similarly to Mat::row and Mat::col, this is an O(1) operation.
+ @param d index of the diagonal, with the following values:
+ - `d=0` is the main diagonal.
+ - `d>0` is a diagonal from the lower half. For example, d=1 means the diagonal is set
+ immediately below the main one.
+ - `d<0` is a diagonal from the upper half. For example, d=-1 means the diagonal is set
+ immediately above the main one.
+ */
+ Mat diag(int d=0) const;
+
+ /** @brief creates a diagonal matrix
+
+ The method creates a square diagonal matrix from specified main diagonal.
+ @param d One-dimensional matrix that represents the main diagonal.
+ */
+ static Mat diag(const Mat& d);
+
+ /** @brief Creates a full copy of the array and the underlying data.
+
+ The method creates a full copy of the array. The original step[] is not taken into account. So, the
+ array copy is a continuous array occupying total()*elemSize() bytes.
+ */
+ Mat clone() const;
+
+ /** @brief Copies the matrix to another one.
+
+ The method copies the matrix data to another matrix. Before copying the data, the method invokes :
+ @code
+ m.create(this->size(), this->type());
+ @endcode
+ so that the destination matrix is reallocated if needed. While m.copyTo(m); works flawlessly, the
+ function does not handle the case of a partial overlap between the source and the destination
+ matrices.
+
+ When the operation mask is specified, if the Mat::create call shown above reallocates the matrix,
+ the newly allocated matrix is initialized with all zeros before copying the data.
+ @param m Destination matrix. If it does not have a proper size or type before the operation, it is
+ reallocated.
+ */
+ void copyTo( OutputArray m ) const;
+
+ /** @overload
+ @param m Destination matrix. If it does not have a proper size or type before the operation, it is
+ reallocated.
+ @param mask Operation mask. Its non-zero elements indicate which matrix elements need to be copied.
+ The mask has to be of type CV_8U and can have 1 or multiple channels.
+ */
+ void copyTo( OutputArray m, InputArray mask ) const;
+
+ /** @brief Converts an array to another data type with optional scaling.
+
+ The method converts source pixel values to the target data type. saturate_cast\<\> is applied at
+ the end to avoid possible overflows:
+
+ \f[m(x,y) = saturate \_ cast<rType>( \alpha (*this)(x,y) + \beta )\f]
+ @param m output matrix; if it does not have a proper size or type before the operation, it is
+ reallocated.
+ @param rtype desired output matrix type or, rather, the depth since the number of channels are the
+ same as the input has; if rtype is negative, the output matrix will have the same type as the input.
+ @param alpha optional scale factor.
+ @param beta optional delta added to the scaled values.
+ */
+ void convertTo( OutputArray m, int rtype, double alpha=1, double beta=0 ) const;
+
+ /** @brief Provides a functional form of convertTo.
+
+ This is an internally used method called by the @ref MatrixExpressions engine.
+ @param m Destination array.
+ @param type Desired destination array depth (or -1 if it should be the same as the source type).
+ */
+ void assignTo( Mat& m, int type=-1 ) const;
+
+ /** @brief Sets all or some of the array elements to the specified value.
+ @param s Assigned scalar converted to the actual array type.
+ */
+ Mat& operator = (const Scalar& s);
+
+ /** @brief Sets all or some of the array elements to the specified value.
+
+ This is an advanced variant of the Mat::operator=(const Scalar& s) operator.
+ @param value Assigned scalar converted to the actual array type.
+ @param mask Operation mask of the same size as \*this.
+ */
+ Mat& setTo(InputArray value, InputArray mask=noArray());
+
+ /** @brief Changes the shape and/or the number of channels of a 2D matrix without copying the data.
+
+ The method makes a new matrix header for \*this elements. The new matrix may have a different size
+ and/or different number of channels. Any combination is possible if:
+ - No extra elements are included into the new matrix and no elements are excluded. Consequently,
+ the product rows\*cols\*channels() must stay the same after the transformation.
+ - No data is copied. That is, this is an O(1) operation. Consequently, if you change the number of
+ rows, or the operation changes the indices of elements row in some other way, the matrix must be
+ continuous. See Mat::isContinuous .
+
+ For example, if there is a set of 3D points stored as an STL vector, and you want to represent the
+ points as a 3xN matrix, do the following:
+ @code
+ std::vector<Point3f> vec;
+ ...
+ Mat pointMat = Mat(vec). // convert vector to Mat, O(1) operation
+ reshape(1). // make Nx3 1-channel matrix out of Nx1 3-channel.
+ // Also, an O(1) operation
+ t(); // finally, transpose the Nx3 matrix.
+ // This involves copying all the elements
+ @endcode
+ @param cn New number of channels. If the parameter is 0, the number of channels remains the same.
+ @param rows New number of rows. If the parameter is 0, the number of rows remains the same.
+ */
+ Mat reshape(int cn, int rows=0) const;
+
+ /** @overload */
+ Mat reshape(int cn, int newndims, const int* newsz) const;
+
+ /** @brief Transposes a matrix.
+
+ The method performs matrix transposition by means of matrix expressions. It does not perform the
+ actual transposition but returns a temporary matrix transposition object that can be further used as
+ a part of more complex matrix expressions or can be assigned to a matrix:
+ @code
+ Mat A1 = A + Mat::eye(A.size(), A.type())*lambda;
+ Mat C = A1.t()*A1; // compute (A + lambda*I)^t * (A + lamda*I)
+ @endcode
+ */
+ MatExpr t() const;
+
+ /** @brief Inverses a matrix.
+
+ The method performs a matrix inversion by means of matrix expressions. This means that a temporary
+ matrix inversion object is returned by the method and can be used further as a part of more complex
+ matrix expressions or can be assigned to a matrix.
+ @param method Matrix inversion method. One of cv::DecompTypes
+ */
+ MatExpr inv(int method=DECOMP_LU) const;
+
+ /** @brief Performs an element-wise multiplication or division of the two matrices.
+
+ The method returns a temporary object encoding per-element array multiplication, with optional
+ scale. Note that this is not a matrix multiplication that corresponds to a simpler "\*" operator.
+
+ Example:
+ @code
+ Mat C = A.mul(5/B); // equivalent to divide(A, B, C, 5)
+ @endcode
+ @param m Another array of the same type and the same size as \*this, or a matrix expression.
+ @param scale Optional scale factor.
+ */
+ MatExpr mul(InputArray m, double scale=1) const;
+
+ /** @brief Computes a cross-product of two 3-element vectors.
+
+ The method computes a cross-product of two 3-element vectors. The vectors must be 3-element
+ floating-point vectors of the same shape and size. The result is another 3-element vector of the
+ same shape and type as operands.
+ @param m Another cross-product operand.
+ */
+ Mat cross(InputArray m) const;
+
+ /** @brief Computes a dot-product of two vectors.
+
+ The method computes a dot-product of two matrices. If the matrices are not single-column or
+ single-row vectors, the top-to-bottom left-to-right scan ordering is used to treat them as 1D
+ vectors. The vectors must have the same size and type. If the matrices have more than one channel,
+ the dot products from all the channels are summed together.
+ @param m another dot-product operand.
+ */
+ double dot(InputArray m) const;
+
+ /** @brief Returns a zero array of the specified size and type.
+
+ The method returns a Matlab-style zero array initializer. It can be used to quickly form a constant
+ array as a function parameter, part of a matrix expression, or as a matrix initializer. :
+ @code
+ Mat A;
+ A = Mat::zeros(3, 3, CV_32F);
+ @endcode
+ In the example above, a new matrix is allocated only if A is not a 3x3 floating-point matrix.
+ Otherwise, the existing matrix A is filled with zeros.
+ @param rows Number of rows.
+ @param cols Number of columns.
+ @param type Created matrix type.
+ */
+ static MatExpr zeros(int rows, int cols, int type);
+
+ /** @overload
+ @param size Alternative to the matrix size specification Size(cols, rows) .
+ @param type Created matrix type.
+ */
+ static MatExpr zeros(Size size, int type);
+
+ /** @overload
+ @param ndims Array dimensionality.
+ @param sz Array of integers specifying the array shape.
+ @param type Created matrix type.
+ */
+ static MatExpr zeros(int ndims, const int* sz, int type);
+
+ /** @brief Returns an array of all 1's of the specified size and type.
+
+ The method returns a Matlab-style 1's array initializer, similarly to Mat::zeros. Note that using
+ this method you can initialize an array with an arbitrary value, using the following Matlab idiom:
+ @code
+ Mat A = Mat::ones(100, 100, CV_8U)*3; // make 100x100 matrix filled with 3.
+ @endcode
+ The above operation does not form a 100x100 matrix of 1's and then multiply it by 3. Instead, it
+ just remembers the scale factor (3 in this case) and use it when actually invoking the matrix
+ initializer.
+ @param rows Number of rows.
+ @param cols Number of columns.
+ @param type Created matrix type.
+ */
+ static MatExpr ones(int rows, int cols, int type);
+
+ /** @overload
+ @param size Alternative to the matrix size specification Size(cols, rows) .
+ @param type Created matrix type.
+ */
+ static MatExpr ones(Size size, int type);
+
+ /** @overload
+ @param ndims Array dimensionality.
+ @param sz Array of integers specifying the array shape.
+ @param type Created matrix type.
+ */
+ static MatExpr ones(int ndims, const int* sz, int type);
+
+ /** @brief Returns an identity matrix of the specified size and type.
+
+ The method returns a Matlab-style identity matrix initializer, similarly to Mat::zeros. Similarly to
+ Mat::ones, you can use a scale operation to create a scaled identity matrix efficiently:
+ @code
+ // make a 4x4 diagonal matrix with 0.1's on the diagonal.
+ Mat A = Mat::eye(4, 4, CV_32F)*0.1;
+ @endcode
+ @param rows Number of rows.
+ @param cols Number of columns.
+ @param type Created matrix type.
+ */
+ static MatExpr eye(int rows, int cols, int type);
+
+ /** @overload
+ @param size Alternative matrix size specification as Size(cols, rows) .
+ @param type Created matrix type.
+ */
+ static MatExpr eye(Size size, int type);
+
+ /** @brief Allocates new array data if needed.
+
+ This is one of the key Mat methods. Most new-style OpenCV functions and methods that produce arrays
+ call this method for each output array. The method uses the following algorithm:
+
+ -# If the current array shape and the type match the new ones, return immediately. Otherwise,
+ de-reference the previous data by calling Mat::release.
+ -# Initialize the new header.
+ -# Allocate the new data of total()\*elemSize() bytes.
+ -# Allocate the new, associated with the data, reference counter and set it to 1.
+
+ Such a scheme makes the memory management robust and efficient at the same time and helps avoid
+ extra typing for you. This means that usually there is no need to explicitly allocate output arrays.
+ That is, instead of writing:
+ @code
+ Mat color;
+ ...
+ Mat gray(color.rows, color.cols, color.depth());
+ cvtColor(color, gray, COLOR_BGR2GRAY);
+ @endcode
+ you can simply write:
+ @code
+ Mat color;
+ ...
+ Mat gray;
+ cvtColor(color, gray, COLOR_BGR2GRAY);
+ @endcode
+ because cvtColor, as well as the most of OpenCV functions, calls Mat::create() for the output array
+ internally.
+ @param rows New number of rows.
+ @param cols New number of columns.
+ @param type New matrix type.
+ */
+ void create(int rows, int cols, int type);
+
+ /** @overload
+ @param size Alternative new matrix size specification: Size(cols, rows)
+ @param type New matrix type.
+ */
+ void create(Size size, int type);
+
+ /** @overload
+ @param ndims New array dimensionality.
+ @param sizes Array of integers specifying a new array shape.
+ @param type New matrix type.
+ */
+ void create(int ndims, const int* sizes, int type);
+
+ /** @overload
+ @param sizes Array of integers specifying a new array shape.
+ @param type New matrix type.
+ */
+ void create(const std::vector<int>& sizes, int type);
+
+ /** @brief Increments the reference counter.
+
+ The method increments the reference counter associated with the matrix data. If the matrix header
+ points to an external data set (see Mat::Mat ), the reference counter is NULL, and the method has no
+ effect in this case. Normally, to avoid memory leaks, the method should not be called explicitly. It
+ is called implicitly by the matrix assignment operator. The reference counter increment is an atomic
+ operation on the platforms that support it. Thus, it is safe to operate on the same matrices
+ asynchronously in different threads.
+ */
+ void addref();
+
+ /** @brief Decrements the reference counter and deallocates the matrix if needed.
+
+ The method decrements the reference counter associated with the matrix data. When the reference
+ counter reaches 0, the matrix data is deallocated and the data and the reference counter pointers
+ are set to NULL's. If the matrix header points to an external data set (see Mat::Mat ), the
+ reference counter is NULL, and the method has no effect in this case.
+
+ This method can be called manually to force the matrix data deallocation. But since this method is
+ automatically called in the destructor, or by any other method that changes the data pointer, it is
+ usually not needed. The reference counter decrement and check for 0 is an atomic operation on the
+ platforms that support it. Thus, it is safe to operate on the same matrices asynchronously in
+ different threads.
+ */
+ void release();
+
+ //! deallocates the matrix data
+ void deallocate();
+ //! internal use function; properly re-allocates _size, _step arrays
+ void copySize(const Mat& m);
+
+ /** @brief Reserves space for the certain number of rows.
+
+ The method reserves space for sz rows. If the matrix already has enough space to store sz rows,
+ nothing happens. If the matrix is reallocated, the first Mat::rows rows are preserved. The method
+ emulates the corresponding method of the STL vector class.
+ @param sz Number of rows.
+ */
+ void reserve(size_t sz);
+
+ /** @brief Changes the number of matrix rows.
+
+ The methods change the number of matrix rows. If the matrix is reallocated, the first
+ min(Mat::rows, sz) rows are preserved. The methods emulate the corresponding methods of the STL
+ vector class.
+ @param sz New number of rows.
+ */
+ void resize(size_t sz);
+
+ /** @overload
+ @param sz New number of rows.
+ @param s Value assigned to the newly added elements.
+ */
+ void resize(size_t sz, const Scalar& s);
+
+ //! internal function
+ void push_back_(const void* elem);
+
+ /** @brief Adds elements to the bottom of the matrix.
+
+ The methods add one or more elements to the bottom of the matrix. They emulate the corresponding
+ method of the STL vector class. When elem is Mat , its type and the number of columns must be the
+ same as in the container matrix.
+ @param elem Added element(s).
+ */
+ template<typename _Tp> void push_back(const _Tp& elem);
+
+ /** @overload
+ @param elem Added element(s).
+ */
+ template<typename _Tp> void push_back(const Mat_<_Tp>& elem);
+
+ /** @overload
+ @param m Added line(s).
+ */
+ void push_back(const Mat& m);
+
+ /** @brief Removes elements from the bottom of the matrix.
+
+ The method removes one or more rows from the bottom of the matrix.
+ @param nelems Number of removed rows. If it is greater than the total number of rows, an exception
+ is thrown.
+ */
+ void pop_back(size_t nelems=1);
+
+ /** @brief Locates the matrix header within a parent matrix.
+
+ After you extracted a submatrix from a matrix using Mat::row, Mat::col, Mat::rowRange,
+ Mat::colRange, and others, the resultant submatrix points just to the part of the original big
+ matrix. However, each submatrix contains information (represented by datastart and dataend
+ fields) that helps reconstruct the original matrix size and the position of the extracted
+ submatrix within the original matrix. The method locateROI does exactly that.
+ @param wholeSize Output parameter that contains the size of the whole matrix containing *this*
+ as a part.
+ @param ofs Output parameter that contains an offset of *this* inside the whole matrix.
+ */
+ void locateROI( Size& wholeSize, Point& ofs ) const;
+
+ /** @brief Adjusts a submatrix size and position within the parent matrix.
+
+ The method is complimentary to Mat::locateROI . The typical use of these functions is to determine
+ the submatrix position within the parent matrix and then shift the position somehow. Typically, it
+ can be required for filtering operations when pixels outside of the ROI should be taken into
+ account. When all the method parameters are positive, the ROI needs to grow in all directions by the
+ specified amount, for example:
+ @code
+ A.adjustROI(2, 2, 2, 2);
+ @endcode
+ In this example, the matrix size is increased by 4 elements in each direction. The matrix is shifted
+ by 2 elements to the left and 2 elements up, which brings in all the necessary pixels for the
+ filtering with the 5x5 kernel.
+
+ adjustROI forces the adjusted ROI to be inside of the parent matrix that is boundaries of the
+ adjusted ROI are constrained by boundaries of the parent matrix. For example, if the submatrix A is
+ located in the first row of a parent matrix and you called A.adjustROI(2, 2, 2, 2) then A will not
+ be increased in the upward direction.
+
+ The function is used internally by the OpenCV filtering functions, like filter2D , morphological
+ operations, and so on.
+ @param dtop Shift of the top submatrix boundary upwards.
+ @param dbottom Shift of the bottom submatrix boundary downwards.
+ @param dleft Shift of the left submatrix boundary to the left.
+ @param dright Shift of the right submatrix boundary to the right.
+ @sa copyMakeBorder
+ */
+ Mat& adjustROI( int dtop, int dbottom, int dleft, int dright );
+
+ /** @brief Extracts a rectangular submatrix.
+
+ The operators make a new header for the specified sub-array of \*this . They are the most
+ generalized forms of Mat::row, Mat::col, Mat::rowRange, and Mat::colRange . For example,
+ `A(Range(0, 10), Range::all())` is equivalent to `A.rowRange(0, 10)`. Similarly to all of the above,
+ the operators are O(1) operations, that is, no matrix data is copied.
+ @param rowRange Start and end row of the extracted submatrix. The upper boundary is not included. To
+ select all the rows, use Range::all().
+ @param colRange Start and end column of the extracted submatrix. The upper boundary is not included.
+ To select all the columns, use Range::all().
+ */
+ Mat operator()( Range rowRange, Range colRange ) const;
+
+ /** @overload
+ @param roi Extracted submatrix specified as a rectangle.
+ */
+ Mat operator()( const Rect& roi ) const;
+
+ /** @overload
+ @param ranges Array of selected ranges along each array dimension.
+ */
+ Mat operator()( const Range* ranges ) const;
+
+ /** @overload
+ @param ranges Array of selected ranges along each array dimension.
+ */
+ Mat operator()(const std::vector<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;
+
+ /** @brief Reports whether the matrix is continuous or not.
+
+ The method returns true if the matrix elements are stored continuously without gaps at the end of
+ each row. Otherwise, it returns false. Obviously, 1x1 or 1xN matrices are always continuous.
+ Matrices created with Mat::create are always continuous. But if you extract a part of the matrix
+ using Mat::col, Mat::diag, and so on, or constructed a matrix header for externally allocated data,
+ such matrices may no longer have this property.
+
+ The continuity flag is stored as a bit in the Mat::flags field and is computed automatically when
+ you construct a matrix header. Thus, the continuity check is a very fast operation, though
+ theoretically it could be done as follows:
+ @code
+ // alternative implementation of Mat::isContinuous()
+ bool myCheckMatContinuity(const Mat& m)
+ {
+ //return (m.flags & Mat::CONTINUOUS_FLAG) != 0;
+ return m.rows == 1 || m.step == m.cols*m.elemSize();
+ }
+ @endcode
+ The method is used in quite a few of OpenCV functions. The point is that element-wise operations
+ (such as arithmetic and logical operations, math functions, alpha blending, color space
+ transformations, and others) do not depend on the image geometry. Thus, if all the input and output
+ arrays are continuous, the functions can process them as very long single-row vectors. The example
+ below illustrates how an alpha-blending function can be implemented:
+ @code
+ template<typename T>
+ void alphaBlendRGBA(const Mat& src1, const Mat& src2, Mat& dst)
+ {
+ const float alpha_scale = (float)std::numeric_limits<T>::max(),
+ inv_scale = 1.f/alpha_scale;
+
+ CV_Assert( src1.type() == src2.type() &&
+ src1.type() == CV_MAKETYPE(DataType<T>::depth, 4) &&
+ src1.size() == src2.size());
+ Size size = src1.size();
+ dst.create(size, src1.type());
+
+ // here is the idiom: check the arrays for continuity and,
+ // if this is the case,
+ // treat the arrays as 1D vectors
+ if( src1.isContinuous() && src2.isContinuous() && dst.isContinuous() )
+ {
+ size.width *= size.height;
+ size.height = 1;
+ }
+ size.width *= 4;
+
+ for( int i = 0; i < size.height; i++ )
+ {
+ // when the arrays are continuous,
+ // the outer loop is executed only once
+ const T* ptr1 = src1.ptr<T>(i);
+ const T* ptr2 = src2.ptr<T>(i);
+ T* dptr = dst.ptr<T>(i);
+
+ for( int j = 0; j < size.width; j += 4 )
+ {
+ float alpha = ptr1[j+3]*inv_scale, beta = ptr2[j+3]*inv_scale;
+ dptr[j] = saturate_cast<T>(ptr1[j]*alpha + ptr2[j]*beta);
+ dptr[j+1] = saturate_cast<T>(ptr1[j+1]*alpha + ptr2[j+1]*beta);
+ dptr[j+2] = saturate_cast<T>(ptr1[j+2]*alpha + ptr2[j+2]*beta);
+ dptr[j+3] = saturate_cast<T>((1 - (1-alpha)*(1-beta))*alpha_scale);
+ }
+ }
+ }
+ @endcode
+ This approach, while being very simple, can boost the performance of a simple element-operation by
+ 10-20 percents, especially if the image is rather small and the operation is quite simple.
+
+ Another OpenCV idiom in this function, a call of Mat::create for the destination array, that
+ allocates the destination array unless it already has the proper size and type. And while the newly
+ allocated arrays are always continuous, you still need to check the destination array because
+ Mat::create does not always allocate a new matrix.
+ */
+ bool isContinuous() const;
+
+ //! returns true if the matrix is a submatrix of another matrix
+ bool isSubmatrix() const;
+
+ /** @brief Returns the matrix element size in bytes.
+
+ The method returns the matrix element size in bytes. For example, if the matrix type is CV_16SC3 ,
+ the method returns 3\*sizeof(short) or 6.
+ */
+ size_t elemSize() const;
+
+ /** @brief Returns the size of each matrix element channel in bytes.
+
+ The method returns the matrix element channel size in bytes, that is, it ignores the number of
+ channels. For example, if the matrix type is CV_16SC3 , the method returns sizeof(short) or 2.
+ */
+ size_t elemSize1() const;
+
+ /** @brief Returns the type of a matrix element.
+
+ The method returns a matrix element type. This is an identifier compatible with the CvMat type
+ system, like CV_16SC3 or 16-bit signed 3-channel array, and so on.
+ */
+ int type() const;
+
+ /** @brief Returns the depth of a matrix element.
+
+ The method returns the identifier of the matrix element depth (the type of each individual channel).
+ For example, for a 16-bit signed element array, the method returns CV_16S . A complete list of
+ matrix types contains the following values:
+ - CV_8U - 8-bit unsigned integers ( 0..255 )
+ - CV_8S - 8-bit signed integers ( -128..127 )
+ - CV_16U - 16-bit unsigned integers ( 0..65535 )
+ - CV_16S - 16-bit signed integers ( -32768..32767 )
+ - CV_32S - 32-bit signed integers ( -2147483648..2147483647 )
+ - CV_32F - 32-bit floating-point numbers ( -FLT_MAX..FLT_MAX, INF, NAN )
+ - CV_64F - 64-bit floating-point numbers ( -DBL_MAX..DBL_MAX, INF, NAN )
+ */
+ int depth() const;
+
+ /** @brief Returns the number of matrix channels.
+
+ The method returns the number of matrix channels.
+ */
+ int channels() const;
+
+ /** @brief Returns a normalized step.
+
+ The method returns a matrix step divided by Mat::elemSize1() . It can be useful to quickly access an
+ arbitrary matrix element.
+ */
+ size_t step1(int i=0) const;
+
+ /** @brief Returns true if the array has no elements.
+
+ The method returns true if Mat::total() is 0 or if Mat::data is NULL. Because of pop_back() and
+ resize() methods `M.total() == 0` does not imply that `M.data == NULL`.
+ */
+ bool empty() const;
+
+ /** @brief Returns the total number of array elements.
+
+ The method returns the number of array elements (a number of pixels if the array represents an
+ image).
+ */
+ 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;
+
+ /** @brief Returns a pointer to the specified matrix row.
+
+ The methods return `uchar*` or typed pointer to the specified matrix row. See the sample in
+ Mat::isContinuous to know how to use these methods.
+ @param i0 A 0-based row index.
+ */
+ uchar* ptr(int i0=0);
+ /** @overload */
+ const uchar* ptr(int i0=0) const;
+
+ /** @overload
+ @param row Index along the dimension 0
+ @param col Index along the dimension 1
+ */
+ uchar* ptr(int row, int col);
+ /** @overload
+ @param row Index along the dimension 0
+ @param col Index along the dimension 1
+ */
+ const uchar* ptr(int row, int col) const;
+
+ /** @overload */
+ uchar* ptr(int i0, int i1, int i2);
+ /** @overload */
+ const uchar* ptr(int i0, int i1, int i2) const;
+
+ /** @overload */
+ uchar* ptr(const int* idx);
+ /** @overload */
+ const uchar* ptr(const int* idx) const;
+ /** @overload */
+ template<int n> uchar* ptr(const Vec<int, n>& idx);
+ /** @overload */
+ template<int n> const uchar* ptr(const Vec<int, n>& idx) const;
+
+ /** @overload */
+ template<typename _Tp> _Tp* ptr(int i0=0);
+ /** @overload */
+ template<typename _Tp> const _Tp* ptr(int i0=0) const;
+ /** @overload
+ @param row Index along the dimension 0
+ @param col Index along the dimension 1
+ */
+ template<typename _Tp> _Tp* ptr(int row, int col);
+ /** @overload
+ @param row Index along the dimension 0
+ @param col Index along the dimension 1
+ */
+ template<typename _Tp> const _Tp* ptr(int row, int col) const;
+ /** @overload */
+ template<typename _Tp> _Tp* ptr(int i0, int i1, int i2);
+ /** @overload */
+ template<typename _Tp> const _Tp* ptr(int i0, int i1, int i2) const;
+ /** @overload */
+ template<typename _Tp> _Tp* ptr(const int* idx);
+ /** @overload */
+ template<typename _Tp> const _Tp* ptr(const int* idx) const;
+ /** @overload */
+ template<typename _Tp, int n> _Tp* ptr(const Vec<int, n>& idx);
+ /** @overload */
+ template<typename _Tp, int n> const _Tp* ptr(const Vec<int, n>& idx) const;
+
+ /** @brief Returns a reference to the specified array element.
+
+ The template methods return a reference to the specified array element. For the sake of higher
+ performance, the index range checks are only performed in the Debug configuration.
+
+ Note that the variants with a single index (i) can be used to access elements of single-row or
+ single-column 2-dimensional arrays. That is, if, for example, A is a 1 x N floating-point matrix and
+ B is an M x 1 integer matrix, you can simply write `A.at<float>(k+4)` and `B.at<int>(2*i+1)`
+ instead of `A.at<float>(0,k+4)` and `B.at<int>(2*i+1,0)`, respectively.
+
+ The example below initializes a Hilbert matrix:
+ @code
+ Mat H(100, 100, CV_64F);
+ for(int i = 0; i < H.rows; i++)
+ for(int j = 0; j < H.cols; j++)
+ H.at<double>(i,j)=1./(i+j+1);
+ @endcode
+
+ Keep in mind that the size identifier used in the at operator cannot be chosen at random. It depends
+ on the image from which you are trying to retrieve the data. The table below gives a better insight in this:
+ - If matrix is of type `CV_8U` then use `Mat.at<uchar>(y,x)`.
+ - If matrix is of type `CV_8S` then use `Mat.at<schar>(y,x)`.
+ - If matrix is of type `CV_16U` then use `Mat.at<ushort>(y,x)`.
+ - If matrix is of type `CV_16S` then use `Mat.at<short>(y,x)`.
+ - If matrix is of type `CV_32S` then use `Mat.at<int>(y,x)`.
+ - If matrix is of type `CV_32F` then use `Mat.at<float>(y,x)`.
+ - If matrix is of type `CV_64F` then use `Mat.at<double>(y,x)`.
+
+ @param i0 Index along the dimension 0
+ */
+ template<typename _Tp> _Tp& at(int i0=0);
+ /** @overload
+ @param i0 Index along the dimension 0
+ */
+ template<typename _Tp> const _Tp& at(int i0=0) const;
+ /** @overload
+ @param row Index along the dimension 0
+ @param col Index along the dimension 1
+ */
+ template<typename _Tp> _Tp& at(int row, int col);
+ /** @overload
+ @param row Index along the dimension 0
+ @param col Index along the dimension 1
+ */
+ template<typename _Tp> const _Tp& at(int row, int col) const;
+
+ /** @overload
+ @param i0 Index along the dimension 0
+ @param i1 Index along the dimension 1
+ @param i2 Index along the dimension 2
+ */
+ template<typename _Tp> _Tp& at(int i0, int i1, int i2);
+ /** @overload
+ @param i0 Index along the dimension 0
+ @param i1 Index along the dimension 1
+ @param i2 Index along the dimension 2
+ */
+ template<typename _Tp> const _Tp& at(int i0, int i1, int i2) const;
+
+ /** @overload
+ @param idx Array of Mat::dims indices.
+ */
+ template<typename _Tp> _Tp& at(const int* idx);
+ /** @overload
+ @param idx Array of Mat::dims indices.
+ */
+ template<typename _Tp> const _Tp& at(const int* idx) const;
+
+ /** @overload */
+ template<typename _Tp, int n> _Tp& at(const Vec<int, n>& idx);
+ /** @overload */
+ template<typename _Tp, int n> const _Tp& at(const Vec<int, n>& idx) const;
+
+ /** @overload
+ special versions for 2D arrays (especially convenient for referencing image pixels)
+ @param pt Element position specified as Point(j,i) .
+ */
+ template<typename _Tp> _Tp& at(Point pt);
+ /** @overload
+ special versions for 2D arrays (especially convenient for referencing image pixels)
+ @param pt Element position specified as Point(j,i) .
+ */
+ template<typename _Tp> const _Tp& at(Point pt) const;
+
+ /** @brief Returns the matrix iterator and sets it to the first matrix element.
+
+ The methods return the matrix read-only or read-write iterators. The use of matrix iterators is very
+ similar to the use of bi-directional STL iterators. In the example below, the alpha blending
+ function is rewritten using the matrix iterators:
+ @code
+ template<typename T>
+ void alphaBlendRGBA(const Mat& src1, const Mat& src2, Mat& dst)
+ {
+ typedef Vec<T, 4> VT;
+
+ const float alpha_scale = (float)std::numeric_limits<T>::max(),
+ inv_scale = 1.f/alpha_scale;
+
+ CV_Assert( src1.type() == src2.type() &&
+ src1.type() == DataType<VT>::type &&
+ src1.size() == src2.size());
+ Size size = src1.size();
+ dst.create(size, src1.type());
+
+ MatConstIterator_<VT> it1 = src1.begin<VT>(), it1_end = src1.end<VT>();
+ MatConstIterator_<VT> it2 = src2.begin<VT>();
+ MatIterator_<VT> dst_it = dst.begin<VT>();
+
+ for( ; it1 != it1_end; ++it1, ++it2, ++dst_it )
+ {
+ VT pix1 = *it1, pix2 = *it2;
+ float alpha = pix1[3]*inv_scale, beta = pix2[3]*inv_scale;
+ *dst_it = VT(saturate_cast<T>(pix1[0]*alpha + pix2[0]*beta),
+ saturate_cast<T>(pix1[1]*alpha + pix2[1]*beta),
+ saturate_cast<T>(pix1[2]*alpha + pix2[2]*beta),
+ saturate_cast<T>((1 - (1-alpha)*(1-beta))*alpha_scale));
+ }
+ }
+ @endcode
+ */
+ template<typename _Tp> MatIterator_<_Tp> begin();
+ template<typename _Tp> MatConstIterator_<_Tp> begin() const;
+
+ /** @brief Returns the matrix iterator and sets it to the after-last matrix element.
+
+ The methods return the matrix read-only or read-write iterators, set to the point following the last
+ matrix element.
+ */
+ template<typename _Tp> MatIterator_<_Tp> end();
+ template<typename _Tp> MatConstIterator_<_Tp> end() const;
+
+ /** @brief Runs the given functor over all matrix elements in parallel.
+
+ The operation passed as argument has to be a function pointer, a function object or a lambda(C++11).
+
+ Example 1. All of the operations below put 0xFF the first channel of all matrix elements:
+ @code
+ Mat image(1920, 1080, CV_8UC3);
+ typedef cv::Point3_<uint8_t> Pixel;
+
+ // first. raw pointer access.
+ for (int r = 0; r < image.rows; ++r) {
+ Pixel* ptr = image.ptr<Pixel>(0, r);
+ const Pixel* ptr_end = ptr + image.cols;
+ for (; ptr != ptr_end; ++ptr) {
+ ptr->x = 255;
+ }
+ }
+
+ // Using MatIterator. (Simple but there are a Iterator's overhead)
+ for (Pixel &p : cv::Mat_<Pixel>(image)) {
+ p.x = 255;
+ }
+
+ // Parallel execution with function object.
+ struct Operator {
+ void operator ()(Pixel &pixel, const int * position) {
+ pixel.x = 255;
+ }
+ };
+ image.forEach<Pixel>(Operator());
+
+ // Parallel execution using C++11 lambda.
+ image.forEach<Pixel>([](Pixel &p, const int * position) -> void {
+ p.x = 255;
+ });
+ @endcode
+ Example 2. Using the pixel's position:
+ @code
+ // Creating 3D matrix (255 x 255 x 255) typed uint8_t
+ // and initialize all elements by the value which equals elements position.
+ // i.e. pixels (x,y,z) = (1,2,3) is (b,g,r) = (1,2,3).
+
+ int sizes[] = { 255, 255, 255 };
+ typedef cv::Point3_<uint8_t> Pixel;
+
+ Mat_<Pixel> image = Mat::zeros(3, sizes, CV_8UC3);
+
+ image.forEach<Pixel>([&](Pixel& pixel, const int position[]) -> void {
+ pixel.x = position[0];
+ pixel.y = position[1];
+ pixel.z = position[2];
+ });
+ @endcode
+ */
+ template<typename _Tp, typename Functor> void forEach(const Functor& operation);
+ /** @overload */
+ template<typename _Tp, typename Functor> void forEach(const Functor& operation) const;
+
+#ifdef CV_CXX_MOVE_SEMANTICS
+ Mat(Mat&& m);
+ Mat& operator = (Mat&& m);
+#endif
+
+ enum { MAGIC_VAL = 0x42FF0000, AUTO_STEP = 0, CONTINUOUS_FLAG = CV_MAT_CONT_FLAG, SUBMATRIX_FLAG = CV_SUBMAT_FLAG };
+ enum { MAGIC_MASK = 0xFFFF0000, TYPE_MASK = 0x00000FFF, DEPTH_MASK = 7 };
+
+ /*! 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;
+
+ //! helper fields used in locateROI and adjustROI
+ const uchar* datastart;
+ const uchar* dataend;
+ const uchar* datalimit;
+
+ //! custom allocator
+ MatAllocator* allocator;
+ //! and the standard allocator
+ static MatAllocator* getStdAllocator();
+ static MatAllocator* getDefaultAllocator();
+ static void setDefaultAllocator(MatAllocator* allocator);
+
+ //! interaction with UMat
+ UMatData* u;
+
+ MatSize size;
+ MatStep step;
+
+protected:
+ template<typename _Tp, typename Functor> void forEach_impl(const Functor& operation);
+};
+
+
+///////////////////////////////// Mat_<_Tp> ////////////////////////////////////
+
+/** @brief Template matrix class derived from Mat
+
+@code
+ template<typename _Tp> class Mat_ : public Mat
+ {
+ public:
+ // ... some specific methods
+ // and
+ // no new extra fields
+ };
+@endcode
+The class `Mat_<_Tp>` is a *thin* template wrapper on top of the Mat class. It does not have any
+extra data fields. Nor this class nor Mat has any virtual methods. Thus, references or pointers to
+these two classes can be freely but carefully converted one to another. For example:
+@code
+ // create a 100x100 8-bit matrix
+ Mat M(100,100,CV_8U);
+ // this will be compiled fine. no any data conversion will be done.
+ Mat_<float>& M1 = (Mat_<float>&)M;
+ // the program is likely to crash at the statement below
+ M1(99,99) = 1.f;
+@endcode
+While Mat is sufficient in most cases, Mat_ can be more convenient if you use a lot of element
+access operations and if you know matrix type at the compilation time. Note that
+`Mat::at(int y,int x)` and `Mat_::operator()(int y,int x)` do absolutely the same
+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
+To use Mat_ for multi-channel images/matrices, pass Vec as a Mat_ parameter:
+@code
+ // allocate a 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 scramble 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);
+@endcode
+ */
+template<typename _Tp> class 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);
+ //! selects a submatrix, n-dim version
+ Mat_(const Mat_& m, const std::vector<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;
+
+ //! template methods for for operation over all matrix elements.
+ // the operations take care of skipping gaps in the end of rows (if any)
+ template<typename Functor> void forEach(const Functor& operation);
+ template<typename Functor> void forEach(const Functor& operation) 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;
+ Mat_ operator()(const std::vector<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 row, int col);
+ //! returns read-only reference to the specified element (2D case)
+ const _Tp& operator ()(int row, int col) 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;
+
+#ifdef CV_CXX_MOVE_SEMANTICS
+ Mat_(Mat_&& m);
+ Mat_& operator = (Mat_&& m);
+
+ Mat_(Mat&& m);
+ Mat_& operator = (Mat&& m);
+
+ Mat_(MatExpr&& e);
+#endif
+};
+
+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;
+
+/** @todo document */
+class CV_EXPORTS UMat
+{
+public:
+ //! default constructor
+ UMat(UMatUsageFlags usageFlags = USAGE_DEFAULT);
+ //! constructs 2D matrix of the specified size and type
+ // (_type is CV_8UC1, CV_64FC3, CV_32SC(12) etc.)
+ UMat(int rows, int cols, int type, UMatUsageFlags usageFlags = USAGE_DEFAULT);
+ UMat(Size size, int type, UMatUsageFlags usageFlags = USAGE_DEFAULT);
+ //! constucts 2D matrix and fills it with the specified value _s.
+ UMat(int rows, int cols, int type, const Scalar& s, UMatUsageFlags usageFlags = USAGE_DEFAULT);
+ UMat(Size size, int type, const Scalar& s, UMatUsageFlags usageFlags = USAGE_DEFAULT);
+
+ //! constructs n-dimensional matrix
+ UMat(int ndims, const int* sizes, int type, UMatUsageFlags usageFlags = USAGE_DEFAULT);
+ UMat(int ndims, const int* sizes, int type, const Scalar& s, UMatUsageFlags usageFlags = USAGE_DEFAULT);
+
+ //! copy constructor
+ UMat(const UMat& m);
+
+ //! creates a matrix header for a part of the bigger matrix
+ UMat(const UMat& m, const Range& rowRange, const Range& colRange=Range::all());
+ UMat(const UMat& m, const Rect& roi);
+ UMat(const UMat& m, const Range* ranges);
+ UMat(const UMat& m, const std::vector<Range>& ranges);
+ //! builds matrix from std::vector with or without copying the data
+ template<typename _Tp> explicit UMat(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 UMat(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 UMat(const Matx<_Tp, m, n>& mtx, bool copyData=true);
+ //! builds matrix from a 2D point
+ template<typename _Tp> explicit UMat(const Point_<_Tp>& pt, bool copyData=true);
+ //! builds matrix from a 3D point
+ template<typename _Tp> explicit UMat(const Point3_<_Tp>& pt, bool copyData=true);
+ //! builds matrix from comma initializer
+ template<typename _Tp> explicit UMat(const MatCommaInitializer_<_Tp>& commaInitializer);
+
+ //! destructor - calls release()
+ ~UMat();
+ //! assignment operators
+ UMat& operator = (const UMat& m);
+
+ Mat getMat(int flags) const;
+
+ //! returns a new matrix header for the specified row
+ UMat row(int y) const;
+ //! returns a new matrix header for the specified column
+ UMat col(int x) const;
+ //! ... for the specified row span
+ UMat rowRange(int startrow, int endrow) const;
+ UMat rowRange(const Range& r) const;
+ //! ... for the specified column span
+ UMat colRange(int startcol, int endcol) const;
+ UMat 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)
+ UMat diag(int d=0) const;
+ //! constructs a square diagonal matrix which main diagonal is vector "d"
+ static UMat diag(const UMat& d);
+
+ //! returns deep copy of the matrix, i.e. the data is copied
+ UMat 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( UMat& m, int type=-1 ) const;
+
+ //! sets every matrix element to s
+ UMat& operator = (const Scalar& s);
+ //! sets some of the matrix elements to s, according to the mask
+ UMat& 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.
+ UMat reshape(int cn, int rows=0) const;
+ UMat reshape(int cn, int newndims, const int* newsz) const;
+
+ //! matrix transposition by means of matrix expressions
+ UMat t() const;
+ //! matrix inversion by means of matrix expressions
+ UMat inv(int method=DECOMP_LU) const;
+ //! per-element matrix multiplication by means of matrix expressions
+ UMat mul(InputArray m, double scale=1) const;
+
+ //! computes dot-product
+ double dot(InputArray m) const;
+
+ //! Matlab-style matrix initialization
+ static UMat zeros(int rows, int cols, int type);
+ static UMat zeros(Size size, int type);
+ static UMat zeros(int ndims, const int* sz, int type);
+ static UMat ones(int rows, int cols, int type);
+ static UMat ones(Size size, int type);
+ static UMat ones(int ndims, const int* sz, int type);
+ static UMat eye(int rows, int cols, int type);
+ static UMat 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, UMatUsageFlags usageFlags = USAGE_DEFAULT);
+ void create(Size size, int type, UMatUsageFlags usageFlags = USAGE_DEFAULT);
+ void create(int ndims, const int* sizes, int type, UMatUsageFlags usageFlags = USAGE_DEFAULT);
+ void create(const std::vector<int>& sizes, int type, UMatUsageFlags usageFlags = USAGE_DEFAULT);
+
+ //! 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 UMat& m);
+
+ //! 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.
+ UMat& adjustROI( int dtop, int dbottom, int dleft, int dright );
+ //! extracts a rectangular sub-matrix
+ // (this is a generalized form of row, rowRange etc.)
+ UMat operator()( Range rowRange, Range colRange ) const;
+ UMat operator()( const Rect& roi ) const;
+ UMat operator()( const Range* ranges ) const;
+ UMat operator()(const std::vector<Range>& ranges) 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;
+
+#ifdef CV_CXX_MOVE_SEMANTICS
+ UMat(UMat&& m);
+ UMat& operator = (UMat&& m);
+#endif
+
+ void* handle(int accessFlags) const;
+ void ndoffset(size_t* ofs) const;
+
+ enum { MAGIC_VAL = 0x42FF0000, AUTO_STEP = 0, CONTINUOUS_FLAG = CV_MAT_CONT_FLAG, SUBMATRIX_FLAG = CV_SUBMAT_FLAG };
+ enum { MAGIC_MASK = 0xFFFF0000, TYPE_MASK = 0x00000FFF, DEPTH_MASK = 7 };
+
+ /*! 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;
+
+ //! custom allocator
+ MatAllocator* allocator;
+ UMatUsageFlags usageFlags; // usage flags for allocator
+ //! and the standard allocator
+ static MatAllocator* getStdAllocator();
+
+ // black-box container of UMat data
+ UMatData* u;
+
+ // offset of the submatrix (or 0)
+ size_t offset;
+
+ MatSize size;
+ MatStep step;
+
+protected:
+};
+
+
+/////////////////////////// multi-dimensional sparse matrix //////////////////////////
+
+/** @brief The class SparseMat represents multi-dimensional sparse numerical arrays.
+
+Such a sparse array can store elements of any type that Mat can store. *Sparse* means that only
+non-zero elements are stored (though, as a result of operations on a sparse matrix, some of its
+stored elements can actually become 0. It is up to you to detect such elements and delete them
+using SparseMat::erase ). The non-zero elements are stored in a hash table that grows when it is
+filled so that the search time is O(1) in average (regardless of whether element is there or not).
+Elements can be accessed using the following methods:
+- Query operations (SparseMat::ptr and the higher-level SparseMat::ref, SparseMat::value and
+ SparseMat::find), for example:
+ @code
+ const int dims = 5;
+ int size[5] = {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() % size[k];
+ sparse_mat.ref<float>(idx) += 1.f;
+ }
+ cout << "nnz = " << sparse_mat.nzcount() << endl;
+ @endcode
+- Sparse matrix iterators. They are similar to MatIterator but different from NAryMatIterator.
+ That is, the iteration loop is familiar to STL users:
+ @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 SparseMat::Node* n = it.node();
+ printf("(");
+ for(int i = 0; i < dims; i++)
+ printf("%d%s", n->idx[i], i < dims-1 ? ", " : ")");
+ printf(": %g\n", it.value<float>());
+ s += *it;
+ }
+ printf("Element sum is %g\n", s);
+ @endcode
+ If you run this loop, you will notice that elements are not enumerated in a logical order
+ (lexicographical, and so on). They come in the same order as they are stored in the hash table
+ (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 may happen due to possible buffer reallocation.
+- Combination of the above 2 methods when you need to process 2 or more sparse matrices
+ simultaneously. For example, this is how you can compute unnormalized cross-correlation of the 2
+ floating-point sparse matrices:
+ @code
+ double cross_corr(const SparseMat& a, const SparseMat& b)
+ {
+ const SparseMat *_a = &a, *_b = &b;
+ // if b contains less elements than a,
+ // it is 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 an element with the same index in the second matrix.
+ // since the hash value depends only on the element index,
+ // reuse the hash value stored in the node
+ float bvalue = _b->value<float>(anode->idx,&anode->hashval);
+ ccorr += avalue*bvalue;
+ }
+ return ccorr;
+ }
+ @endcode
+ */
+class CV_EXPORTS SparseMat
+{
+public:
+ typedef SparseMatIterator iterator;
+ typedef SparseMatConstIterator const_iterator;
+
+ enum { MAGIC_VAL=0x42FD0000, MAX_DIM=32, HASH_SCALE=0x5bd1e995, HASH_BIT=0x80000000 };
+
+ //! 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[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[MAX_DIM];
+ };
+
+ /** @brief Various SparseMat constructors.
+ */
+ SparseMat();
+
+ /** @overload
+ @param dims Array dimensionality.
+ @param _sizes Sparce matrix size on all dementions.
+ @param _type Sparse matrix data type.
+ */
+ SparseMat(int dims, const int* _sizes, int _type);
+
+ /** @overload
+ @param m Source matrix for copy constructor. If m is dense matrix (ocvMat) then it will be converted
+ to sparse representation.
+ */
+ SparseMat(const SparseMat& m);
+
+ /** @overload
+ @param m Source matrix for copy constructor. If m is dense matrix (ocvMat) then it will be converted
+ to sparse representation.
+ */
+ explicit SparseMat(const Mat& 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 [out] m - output matrix; if it does not have a proper size or type before the operation,
+ it is reallocated
+ @param [in] rtype – desired output matrix type or, rather, the depth since the number of channels
+ are the same as the input has; if rtype is negative, the output matrix will have the
+ same type as the input.
+ @param [in] alpha – optional scale factor
+ @param [in] beta – optional delta added to the scaled values
+ */
+ 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.
+ - if the element is there (it's non-zero), the pointer to it is returned
+ - if it's not there and createMissing=false, NULL pointer is returned
+ - if it's not there and createMissing=true, then the new element
+ is created and initialized with 0. Pointer to it is returned
+ - if the optional hashval pointer is not NULL, the element hash value is
+ not computed, but *hashval is taken instead.
+ */
+ //! 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 to
+ @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);
+
+ int flags;
+ Hdr* hdr;
+};
+
+
+
+///////////////////////////////// SparseMat_<_Tp> ////////////////////////////////////
+
+/** @brief Template sparse n-dimensional array class derived from SparseMat
+
+SparseMat_ is a thin wrapper on top of SparseMat created in the same way as Mat_ . It simplifies
+notation of some operations:
+@code
+ int sz[] = {10, 20, 30};
+ SparseMat_<double> M(3, sz);
+ ...
+ M.ref(1, 2, 3) = M(4, 5, 6) + M(7, 8, 9);
+@endcode
+ */
+template<typename _Tp> class 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;
+};
+
+
+
+////////////////////////////////// MatConstIterator //////////////////////////////////
+
+class CV_EXPORTS MatConstIterator
+{
+public:
+ typedef uchar* value_type;
+ typedef ptrdiff_t difference_type;
+ typedef const uchar** pointer;
+ typedef uchar* reference;
+
+#ifndef OPENCV_NOSTL
+ typedef std::random_access_iterator_tag iterator_category;
+#endif
+
+ //! 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
+ const uchar* operator *() const;
+ //! returns the i-th matrix element, relative to the current
+ const 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;
+ const uchar* ptr;
+ const uchar* sliceStart;
+ const uchar* sliceEnd;
+};
+
+
+
+////////////////////////////////// MatConstIterator_ /////////////////////////////////
+
+/** @brief Matrix read-only iterator
+ */
+template<typename _Tp>
+class MatConstIterator_ : public MatConstIterator
+{
+public:
+ typedef _Tp value_type;
+ typedef ptrdiff_t difference_type;
+ typedef const _Tp* pointer;
+ typedef const _Tp& reference;
+
+#ifndef OPENCV_NOSTL
+ typedef std::random_access_iterator_tag iterator_category;
+#endif
+
+ //! 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
+ const _Tp& operator *() const;
+ //! returns the i-th matrix element, relative to the current
+ const _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;
+};
+
+
+
+//////////////////////////////////// MatIterator_ ////////////////////////////////////
+
+/** @brief Matrix read-write iterator
+*/
+template<typename _Tp>
+class MatIterator_ : public MatConstIterator_<_Tp>
+{
+public:
+ typedef _Tp* pointer;
+ typedef _Tp& reference;
+
+#ifndef OPENCV_NOSTL
+ typedef std::random_access_iterator_tag iterator_category;
+#endif
+
+ //! 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_(Mat_<_Tp>* _m, Point _pt);
+ //! constructor that sets the iterator to the specified element of the matrix
+ MatIterator_(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);
+};
+
+
+
+/////////////////////////////// SparseMatConstIterator ///////////////////////////////
+
+/** @brief 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;
+};
+
+
+
+////////////////////////////////// SparseMatIterator /////////////////////////////////
+
+/** @brief 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);
+};
+
+
+
+/////////////////////////////// SparseMatConstIterator_ //////////////////////////////
+
+/** @brief 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 SparseMatConstIterator_ : public SparseMatConstIterator
+{
+public:
+
+#ifndef OPENCV_NOSTL
+ typedef std::forward_iterator_tag iterator_category;
+#endif
+
+ //! the default constructor
+ SparseMatConstIterator_();
+ //! the full constructor setting the iterator to the first sparse matrix element
+ SparseMatConstIterator_(const SparseMat_<_Tp>* _m);
+ SparseMatConstIterator_(const SparseMat* _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);
+};
+
+
+
+///////////////////////////////// SparseMatIterator_ /////////////////////////////////
+
+/** @brief 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 SparseMatIterator_ : public SparseMatConstIterator_<_Tp>
+{
+public:
+
+#ifndef OPENCV_NOSTL
+ typedef std::forward_iterator_tag iterator_category;
+#endif
+
+ //! the default constructor
+ SparseMatIterator_();
+ //! the full constructor setting the iterator to the first sparse matrix element
+ SparseMatIterator_(SparseMat_<_Tp>* _m);
+ SparseMatIterator_(SparseMat* _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);
+};
+
+
+
+/////////////////////////////////// NAryMatIterator //////////////////////////////////
+
+/** @brief n-ary multi-dimensional array iterator.
+
+Use the class to implement unary, binary, and, generally, n-ary element-wise operations on
+multi-dimensional arrays. Some of the arguments of an n-ary function may be continuous arrays, some
+may be not. It is possible to use conventional MatIterator 's for each array but incrementing all of
+the iterators after each small operations may be a big overhead. In this case consider using
+NAryMatIterator to iterate through several matrices simultaneously as long as they have the same
+geometry (dimensionality and all the dimension sizes are the same). On each iteration `it.planes[0]`,
+`it.planes[1]`,... will be the slices of the corresponding matrices.
+
+The example below illustrates how you can compute a normalized and threshold 3D color histogram:
+@code
+ void computeNormalizedColorHist(const Mat& image, Mat& hist, int N, double minProb)
+ {
+ const int histSize[] = {N, N, N};
+
+ // make sure that the histogram has a proper size and type
+ hist.create(3, histSize, CV_32F);
+
+ // and clear it
+ hist = Scalar(0);
+
+ // the loop below assumes that the image
+ // is a 8-bit 3-channel. check it.
+ CV_Assert(image.type() == CV_8UC3);
+ MatConstIterator_<Vec3b> it = image.begin<Vec3b>(),
+ it_end = image.end<Vec3b>();
+ for( ; it != it_end; ++it )
+ {
+ const Vec3b& pix = *it;
+ hist.at<float>(pix[0]*N/256, pix[1]*N/256, pix[2]*N/256) += 1.f;
+ }
+
+ minProb *= image.rows*image.cols;
+
+ // initialize iterator (the style is different from STL).
+ // after initialization the iterator will contain
+ // the number of slices or planes the iterator will go through.
+ // it simultaneously increments iterators for several matrices
+ // supplied as a null terminated list of pointers
+ const Mat* arrays[] = {&hist, 0};
+ Mat planes[1];
+ NAryMatIterator itNAry(arrays, planes, 1);
+ double s = 0;
+ // iterate through the matrix. on each iteration
+ // itNAry.planes[i] (of type Mat) will be set to the current plane
+ // of the i-th n-dim matrix passed to the iterator constructor.
+ for(int p = 0; p < itNAry.nplanes; p++, ++itNAry)
+ {
+ threshold(itNAry.planes[0], itNAry.planes[0], minProb, 0, THRESH_TOZERO);
+ s += sum(itNAry.planes[0])[0];
+ }
+
+ s = 1./s;
+ itNAry = NAryMatIterator(arrays, planes, 1);
+ for(int p = 0; p < itNAry.nplanes; p++, ++itNAry)
+ itNAry.planes[0] *= s;
+ }
+@endcode
+ */
+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;
+};
+
+
+
+///////////////////////////////// Matrix Expressions /////////////////////////////////
+
+class CV_EXPORTS MatOp
+{
+public:
+ MatOp();
+ virtual ~MatOp();
+
+ virtual bool elementWise(const MatExpr& expr) const;
+ virtual void assign(const MatExpr& expr, Mat& m, int type=-1) const = 0;
+ virtual void roi(const MatExpr& expr, const Range& rowRange,
+ const Range& colRange, MatExpr& res) const;
+ virtual void diag(const MatExpr& expr, int d, MatExpr& res) const;
+ virtual void augAssignAdd(const MatExpr& expr, Mat& m) const;
+ virtual void augAssignSubtract(const MatExpr& expr, Mat& m) const;
+ virtual void augAssignMultiply(const MatExpr& expr, Mat& m) const;
+ virtual void augAssignDivide(const MatExpr& expr, Mat& m) const;
+ virtual void augAssignAnd(const MatExpr& expr, Mat& m) const;
+ virtual void augAssignOr(const MatExpr& expr, Mat& m) const;
+ virtual void augAssignXor(const MatExpr& expr, Mat& m) const;
+
+ virtual void add(const MatExpr& expr1, const MatExpr& expr2, MatExpr& res) const;
+ virtual void add(const MatExpr& expr1, const Scalar& s, MatExpr& res) const;
+
+ virtual void subtract(const MatExpr& expr1, const MatExpr& expr2, MatExpr& res) const;
+ virtual void subtract(const Scalar& s, const MatExpr& expr, MatExpr& res) const;
+
+ virtual void multiply(const MatExpr& expr1, const MatExpr& expr2, MatExpr& res, double scale=1) const;
+ virtual void multiply(const MatExpr& expr1, double s, MatExpr& res) const;
+
+ virtual void divide(const MatExpr& expr1, const MatExpr& expr2, MatExpr& res, double scale=1) const;
+ virtual void divide(double s, const MatExpr& expr, MatExpr& res) const;
+
+ virtual void abs(const MatExpr& expr, MatExpr& res) const;
+
+ virtual void transpose(const MatExpr& expr, MatExpr& res) const;
+ virtual void matmul(const MatExpr& expr1, const MatExpr& expr2, MatExpr& res) const;
+ virtual void invert(const MatExpr& expr, int method, MatExpr& res) const;
+
+ virtual Size size(const MatExpr& expr) const;
+ virtual int type(const MatExpr& expr) const;
+};
+
+/** @brief Matrix expression representation
+@anchor MatrixExpressions
+This is a list of implemented matrix operations that can be combined in arbitrary complex
+expressions (here A, B stand for matrices ( Mat ), s for a scalar ( Scalar ), alpha for a
+real-valued scalar ( double )):
+- Addition, subtraction, negation: `A+B`, `A-B`, `A+s`, `A-s`, `s+A`, `s-A`, `-A`
+- Scaling: `A*alpha`
+- Per-element multiplication and division: `A.mul(B)`, `A/B`, `alpha/A`
+- Matrix multiplication: `A*B`
+- Transposition: `A.t()` (means A<sup>T</sup>)
+- Matrix inversion and pseudo-inversion, solving linear systems and least-squares problems:
+ `A.inv([method]) (~ A<sup>-1</sup>)`, `A.inv([method])*B (~ X: AX=B)`
+- Comparison: `A cmpop B`, `A cmpop alpha`, `alpha cmpop A`, where *cmpop* is one of
+ `>`, `>=`, `==`, `!=`, `<=`, `<`. The result of comparison is an 8-bit single channel mask whose
+ elements are set to 255 (if the particular element or pair of elements satisfy the condition) or
+ 0.
+- Bitwise logical operations: `A logicop B`, `A logicop s`, `s logicop A`, `~A`, where *logicop* is one of
+ `&`, `|`, `^`.
+- Element-wise minimum and maximum: `min(A, B)`, `min(A, alpha)`, `max(A, B)`, `max(A, alpha)`
+- Element-wise absolute value: `abs(A)`
+- Cross-product, dot-product: `A.cross(B)`, `A.dot(B)`
+- Any function of matrix or matrices and scalars that returns a matrix or a scalar, such as norm,
+ mean, sum, countNonZero, trace, determinant, repeat, and others.
+- Matrix initializers ( Mat::eye(), Mat::zeros(), Mat::ones() ), matrix comma-separated
+ initializers, matrix constructors and operators that extract sub-matrices (see Mat description).
+- Mat_<destination_type>() constructors to cast the result to the proper type.
+@note Comma-separated initializers and probably some other operations may require additional
+explicit Mat() or Mat_<T>() constructor calls to resolve a possible ambiguity.
+
+Here are examples of matrix expressions:
+@code
+ // compute pseudo-inverse of A, equivalent to A.inv(DECOMP_SVD)
+ SVD svd(A);
+ Mat pinvA = svd.vt.t()*Mat::diag(1./svd.w)*svd.u.t();
+
+ // compute the new vector of parameters in the Levenberg-Marquardt algorithm
+ x -= (A.t()*A + lambda*Mat::eye(A.cols,A.cols,A.type())).inv(DECOMP_CHOLESKY)*(A.t()*err);
+
+ // sharpen image using "unsharp mask" algorithm
+ Mat blurred; double sigma = 1, threshold = 5, amount = 1;
+ GaussianBlur(img, blurred, Size(), sigma, sigma);
+ Mat lowContrastMask = abs(img - blurred) < threshold;
+ Mat sharpened = img*(1+amount) + blurred*(-amount);
+ img.copyTo(sharpened, lowContrastMask);
+@endcode
+*/
+class CV_EXPORTS MatExpr
+{
+public:
+ MatExpr();
+ explicit MatExpr(const Mat& m);
+
+ MatExpr(const MatOp* _op, int _flags, const Mat& _a = Mat(), const Mat& _b = Mat(),
+ const Mat& _c = Mat(), double _alpha = 1, double _beta = 1, const Scalar& _s = Scalar());
+
+ operator Mat() const;
+ template<typename _Tp> operator Mat_<_Tp>() const;
+
+ Size size() const;
+ int type() const;
+
+ MatExpr row(int y) const;
+ MatExpr col(int x) const;
+ MatExpr diag(int d = 0) const;
+ MatExpr operator()( const Range& rowRange, const Range& colRange ) const;
+ MatExpr operator()( const Rect& roi ) const;
+
+ MatExpr t() const;
+ MatExpr inv(int method = DECOMP_LU) const;
+ MatExpr mul(const MatExpr& e, double scale=1) const;
+ MatExpr mul(const Mat& m, double scale=1) const;
+
+ Mat cross(const Mat& m) const;
+ double dot(const Mat& m) const;
+
+ const MatOp* op;
+ int flags;
+
+ Mat a, b, c;
+ double alpha, beta;
+ Scalar s;
+};
+
+//! @} core_basic
+
+//! @relates cv::MatExpr
+//! @{
+CV_EXPORTS MatExpr operator + (const Mat& a, const Mat& b);
+CV_EXPORTS MatExpr operator + (const Mat& a, const Scalar& s);
+CV_EXPORTS MatExpr operator + (const Scalar& s, const Mat& a);
+CV_EXPORTS MatExpr operator + (const MatExpr& e, const Mat& m);
+CV_EXPORTS MatExpr operator + (const Mat& m, const MatExpr& e);
+CV_EXPORTS MatExpr operator + (const MatExpr& e, const Scalar& s);
+CV_EXPORTS MatExpr operator + (const Scalar& s, const MatExpr& e);
+CV_EXPORTS MatExpr operator + (const MatExpr& e1, const MatExpr& e2);
+
+CV_EXPORTS MatExpr operator - (const Mat& a, const Mat& b);
+CV_EXPORTS MatExpr operator - (const Mat& a, const Scalar& s);
+CV_EXPORTS MatExpr operator - (const Scalar& s, const Mat& a);
+CV_EXPORTS MatExpr operator - (const MatExpr& e, const Mat& m);
+CV_EXPORTS MatExpr operator - (const Mat& m, const MatExpr& e);
+CV_EXPORTS MatExpr operator - (const MatExpr& e, const Scalar& s);
+CV_EXPORTS MatExpr operator - (const Scalar& s, const MatExpr& e);
+CV_EXPORTS MatExpr operator - (const MatExpr& e1, const MatExpr& e2);
+
+CV_EXPORTS MatExpr operator - (const Mat& m);
+CV_EXPORTS MatExpr operator - (const MatExpr& e);
+
+CV_EXPORTS MatExpr operator * (const Mat& a, const Mat& b);
+CV_EXPORTS MatExpr operator * (const Mat& a, double s);
+CV_EXPORTS MatExpr operator * (double s, const Mat& a);
+CV_EXPORTS MatExpr operator * (const MatExpr& e, const Mat& m);
+CV_EXPORTS MatExpr operator * (const Mat& m, const MatExpr& e);
+CV_EXPORTS MatExpr operator * (const MatExpr& e, double s);
+CV_EXPORTS MatExpr operator * (double s, const MatExpr& e);
+CV_EXPORTS MatExpr operator * (const MatExpr& e1, const MatExpr& e2);
+
+CV_EXPORTS MatExpr operator / (const Mat& a, const Mat& b);
+CV_EXPORTS MatExpr operator / (const Mat& a, double s);
+CV_EXPORTS MatExpr operator / (double s, const Mat& a);
+CV_EXPORTS MatExpr operator / (const MatExpr& e, const Mat& m);
+CV_EXPORTS MatExpr operator / (const Mat& m, const MatExpr& e);
+CV_EXPORTS MatExpr operator / (const MatExpr& e, double s);
+CV_EXPORTS MatExpr operator / (double s, const MatExpr& e);
+CV_EXPORTS MatExpr operator / (const MatExpr& e1, const MatExpr& e2);
+
+CV_EXPORTS MatExpr operator < (const Mat& a, const Mat& b);
+CV_EXPORTS MatExpr operator < (const Mat& a, double s);
+CV_EXPORTS MatExpr operator < (double s, const Mat& a);
+
+CV_EXPORTS MatExpr operator <= (const Mat& a, const Mat& b);
+CV_EXPORTS MatExpr operator <= (const Mat& a, double s);
+CV_EXPORTS MatExpr operator <= (double s, const Mat& a);
+
+CV_EXPORTS MatExpr operator == (const Mat& a, const Mat& b);
+CV_EXPORTS MatExpr operator == (const Mat& a, double s);
+CV_EXPORTS MatExpr operator == (double s, const Mat& a);
+
+CV_EXPORTS MatExpr operator != (const Mat& a, const Mat& b);
+CV_EXPORTS MatExpr operator != (const Mat& a, double s);
+CV_EXPORTS MatExpr operator != (double s, const Mat& a);
+
+CV_EXPORTS MatExpr operator >= (const Mat& a, const Mat& b);
+CV_EXPORTS MatExpr operator >= (const Mat& a, double s);
+CV_EXPORTS MatExpr operator >= (double s, const Mat& a);
+
+CV_EXPORTS MatExpr operator > (const Mat& a, const Mat& b);
+CV_EXPORTS MatExpr operator > (const Mat& a, double s);
+CV_EXPORTS MatExpr operator > (double s, const Mat& a);
+
+CV_EXPORTS MatExpr operator & (const Mat& a, const Mat& b);
+CV_EXPORTS MatExpr operator & (const Mat& a, const Scalar& s);
+CV_EXPORTS MatExpr operator & (const Scalar& s, const Mat& a);
+
+CV_EXPORTS MatExpr operator | (const Mat& a, const Mat& b);
+CV_EXPORTS MatExpr operator | (const Mat& a, const Scalar& s);
+CV_EXPORTS MatExpr operator | (const Scalar& s, const Mat& a);
+
+CV_EXPORTS MatExpr operator ^ (const Mat& a, const Mat& b);
+CV_EXPORTS MatExpr operator ^ (const Mat& a, const Scalar& s);
+CV_EXPORTS MatExpr operator ^ (const Scalar& s, const Mat& a);
+
+CV_EXPORTS MatExpr operator ~(const Mat& m);
+
+CV_EXPORTS MatExpr min(const Mat& a, const Mat& b);
+CV_EXPORTS MatExpr min(const Mat& a, double s);
+CV_EXPORTS MatExpr min(double s, const Mat& a);
+
+CV_EXPORTS MatExpr max(const Mat& a, const Mat& b);
+CV_EXPORTS MatExpr max(const Mat& a, double s);
+CV_EXPORTS MatExpr max(double s, const Mat& a);
+
+/** @brief Calculates an absolute value of each matrix element.
+
+abs is a meta-function that is expanded to one of absdiff or convertScaleAbs forms:
+- C = abs(A-B) is equivalent to `absdiff(A, B, C)`
+- C = abs(A) is equivalent to `absdiff(A, Scalar::all(0), C)`
+- C = `Mat_<Vec<uchar,n> >(abs(A*alpha + beta))` is equivalent to `convertScaleAbs(A, C, alpha,
+beta)`
+
+The output matrix has the same size and the same type as the input one except for the last case,
+where C is depth=CV_8U .
+@param m matrix.
+@sa @ref MatrixExpressions, absdiff, convertScaleAbs
+ */
+CV_EXPORTS MatExpr abs(const Mat& m);
+/** @overload
+@param e matrix expression.
+*/
+CV_EXPORTS MatExpr abs(const MatExpr& e);
+//! @} relates cv::MatExpr
+
+} // cv
+
+#include "opencv2/core/mat.inl.hpp"
+
+#endif // OPENCV_CORE_MAT_HPP