Class cv::Mat#

Comma-separated Matrix Initializer. View details

#include <opencv2/core/mat.hpp>

Collaboration diagram for cv::Mat:

Public Types#

enum cv::Mat {
    cv::MAGIC_VAL = 0x42FF0000,
    cv::AUTO_STEP = 0,
    cv::CONTINUOUS_FLAG = CV_MAT_CONT_FLAG,
    cv::SUBMATRIX_FLAG = CV_SUBMAT_FLAG
}

enum cv::Mat {
    cv::MAGIC_MASK = 0xFFFF0000,
    cv::TYPE_MASK = 0x00000FFF,
    cv::DEPTH_MASK = 7
}

Detailed Description#

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:

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);

Deprecated:

Use constructors with std::initializer_list instead:

 Mat_<int> m1({1, 2, 3, 4}); // 4x1 Mat
 Mat_<uchar> m2({2, 3}, {1, 2, 3, 4, 5, 6}); // 2x3 Mat

 Mat_<double> R({2, 2}, {a, -b, b, a}); // from example
*/
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;
};



// 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 MemoryFlag { 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,
        ASYNC_CLEANUP=128
    };
    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;

    UMatData::MemoryFlag flags;
    void* handle;
    void* userdata;
    int allocatorFlags_;
    int mapcount;
    UMatData* originalUMatData;
    std::shared_ptr<void> allocatorContext;
};
CV_ENUM_FLAGS(UMatData::MemoryFlag)

typedef MatShape MatSize;

struct CV_EXPORTS MatStep
{
    MatStep() CV_NOEXCEPT;
    explicit MatStep(size_t s) CV_NOEXCEPT;
    const size_t& operator[](int i) const CV_NOEXCEPT;
    size_t& operator[](int i) CV_NOEXCEPT;
    operator size_t() const;
    MatStep& operator = (size_t s);
    void clear();

    size_t p[MatShape::MAX_DIMS];
};

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:
```
// create a 100x100x100 8-bit array
int sz[] = {100, 100, 100};
Mat bigCube(3, sz, CV_8U, Scalar::all(0));
```
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:

// 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);

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():

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)

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:

Mat process_video_frame(const unsigned char* pixels,
                        int width, int height, int step)
{
    // wrap input buffer
    Mat img(height, width, CV_8UC3, (unsigned char*)pixels, step);

    Mat result;
    GaussianBlur(img, result, Size(7, 7), 1.5, 1.5);

    return result;
}

Quickly initialize small matrices and/or get a super-fast element access.

double m[3][3] = {{a, b, c}, {d, e, f}, {g, h, i}};
Mat M = Mat(3, 3, CV_64F, m).inv();

Use MATLAB-style array initializers, zeros(), ones(), eye(), for example:

// create a double-precision identity matrix and add it to M.
M += Mat::eye(M.rows, M.cols, CV_64F);

Use a comma-separated initializer:
```
// create a 3x3 double-precision identity matrix
Mat M = (Mat_<double>(3,3) << 1, 0, 0, 0, 1, 0, 0, 0, 1);
```
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 \(M_{ij}\) of a 2-dimensional array as:

M.at<double>(i,j) += 1.f;

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 [] :

// compute sum of positive matrix elements
// (assuming that M is a 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.);
}

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:

// 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.);
}

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:

// 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.);

The matrix iterators are random-access iterators, so they can be passed to any STL algorithm, including std::sort().

Note

Matrix Expressions and arithmetic see MatExpr

Subclassed by cv::Mat_< uchar >, cv::Mat_< float >, cv::Mat_< int >, cv::Mat_< double >, cv::Mat_< unsigned char >, cv::Mat_< _Tp >

Examples: samples/facedetect.cpp, samples/peopledetect.cpp, samples/cpp/connected_components.cpp, samples/cpp/cout_mat.cpp, samples/cpp/demhist.cpp, samples/cpp/floodfill.cpp, samples/cpp/geometry.cpp, samples/cpp/grabcut.cpp, samples/cpp/image_alignment.cpp, samples/cpp/lkdemo.cpp, samples/cpp/pca.cpp, samples/cpp/stitching_detailed.cpp, samples/cpp/tutorial_code/Histograms_Matching/MatchTemplate_Demo.cpp, samples/cpp/tutorial_code/ImgProc/Morphology_1.cpp, samples/cpp/tutorial_code/ImgProc/Morphology_2.cpp, samples/cpp/tutorial_code/ImgProc/Pyramids/Pyramids.cpp, samples/cpp/tutorial_code/ImgProc/Smoothing/Smoothing.cpp, samples/cpp/tutorial_code/ImgProc/basic_drawing/Drawing_1.cpp, samples/cpp/tutorial_code/ImgProc/basic_drawing/Drawing_2.cpp, samples/cpp/tutorial_code/ImgTrans/Sobel_Demo.cpp, samples/cpp/tutorial_code/ImgTrans/copyMakeBorder_demo.cpp, samples/cpp/tutorial_code/ImgTrans/houghcircles.cpp, samples/cpp/tutorial_code/ImgTrans/houghlines.cpp, samples/cpp/tutorial_code/core/file_input_output/file_input_output.cpp, samples/cpp/tutorial_code/core/how_to_scan_images/how_to_scan_images.cpp, samples/cpp/tutorial_code/features/Homography/decompose_homography.cpp, samples/cpp/tutorial_code/features/Homography/homography_from_camera_displacement.cpp, samples/cpp/tutorial_code/features/Homography/pose_from_homography.cpp, samples/cpp/tutorial_code/imgcodecs/animations.cpp, samples/cpp/tutorial_code/ml/introduction_to_pca/introduction_to_pca.cpp, samples/cpp/tutorial_code/photo/non_photorealistic_rendering/npr_demo.cpp, samples/cpp/tutorial_code/photo/seamless_cloning/cloning_demo.cpp, samples/cpp/tutorial_code/videoio/video-write/video-write.cpp, samples/cpp/videowriter.cpp, samples/dnn/classification.cpp, samples/dnn/colorization.cpp, samples/dnn/object_detection.cpp, samples/dnn/openpose.cpp, samples/dnn/segmentation.cpp, and samples/dnn/text_detection.cpp.

Member Enumeration Documentation#

enum Mat

MAGIC_VAL

AUTO_STEP

CONTINUOUS_FLAG

SUBMATRIX_FLAG

enum Mat

MAGIC_MASK

TYPE_MASK

DEPTH_MASK

Constructor & Destructor Documentation#

Mat()#

cv::Mat::Mat()

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()#

cv::Mat::Mat(const cuda::GpuMat & m)

download data from GpuMat

Mat()#

cv::Mat::Mat(const Mat & m)

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

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()#

cv::Mat::Mat(
const Mat & m,
const Range & rowRange,
const Range & colRange = Range::all() )

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

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() .
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.
colRange — Range of the m columns to take. Use Range::all() to take all the columns.

Mat()#

cv::Mat::Mat(
const Mat & m,
const Range * ranges )

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

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() .
ranges — Array of selected ranges of m along each dimensionality.

Mat()#

cv::Mat::Mat(
const Mat & m,
const Rect & roi )

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

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() .
roi — Region of interest.

Mat()#

cv::Mat::Mat(
const Mat & m,
const std::vector< Range > & ranges )

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

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() .
ranges — Array of selected ranges of m along each dimensionality.

Mat()#

template<typename _Tp>
cv::Mat::Mat(const MatCommaInitializer_< _Tp > & commaInitializer)

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Mat()#

cv::Mat::Mat(
const MatShape & shape,
int type )

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

shape — Array shape.
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()#

cv::Mat::Mat(
const MatShape & shape,
int type,
const Scalar & s )

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

shape — Array shape.
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.
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()#

cv::Mat::Mat(
const MatShape & shape,
int type,
void * data,
const size_t * steps = 0 )

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

shape — Array shape.
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.
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.
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()#

template<typename _Tp, int m, int n>
cv::Mat::Mat(
const Matx< _Tp, m, n > & mtx,
bool copyData = true )

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Mat()#

template<typename _Tp>
cv::Mat::Mat(
const Point3_< _Tp > & pt,
bool copyData = true )

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Mat()#

template<typename _Tp>
cv::Mat::Mat(
const Point_< _Tp > & pt,
bool copyData = true )

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Mat()#

template<typename _Tp, size_t _Nm>
cv::Mat::Mat(
const std::array< _Tp, _Nm > & arr,
bool copyData = false )

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Mat()#

template<typename _Tp, typename>
cv::Mat::Mat(const std::initializer_list< _Tp > list)

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Mat()#

template<typename _Tp>
cv::Mat::Mat(
const std::initializer_list< int > sizes,
const std::initializer_list< _Tp > list )

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Mat()#

template<typename _Tp>
cv::Mat::Mat(
const std::vector< _Tp > & vec,
bool copyData = false )

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

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.
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 destructed.

Mat()#

cv::Mat::Mat(
const std::vector< int > & sizes,
int type )

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

sizes — Array of integers specifying an n-dimensional array shape.
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()#

cv::Mat::Mat(
const std::vector< int > & sizes,
int type,
const Scalar & s )

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

sizes — Array of integers specifying an n-dimensional array shape.
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.
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()#

cv::Mat::Mat(
const std::vector< int > & sizes,
int type,
void * data,
const size_t * steps = 0 )

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

sizes — Array of integers specifying an n-dimensional array shape.
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.
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.
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()#

template<typename _Tp, int n>
cv::Mat::Mat(
const Vec< _Tp, n > & vec,
bool copyData = true )

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Mat()#

cv::Mat::Mat(
int ndims,
const int * sizes,
int type )

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

ndims — Array dimensionality.
sizes — Array of integers specifying an n-dimensional array shape.
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()#

cv::Mat::Mat(
int ndims,
const int * sizes,
int type,
const Scalar & s )

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

ndims — Array dimensionality.
sizes — Array of integers specifying an n-dimensional array shape.
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.
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()#

cv::Mat::Mat(
int ndims,
const int * sizes,
int type,
void * data,
const size_t * steps = 0 )

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

ndims — Array dimensionality.
sizes — Array of integers specifying an n-dimensional array shape.
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.
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.
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()#

cv::Mat::Mat(
int rows,
int cols,
int type )

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

rows — Number of rows in a 2D array.
cols — Number of columns in a 2D array.
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()#

cv::Mat::Mat(
int rows,
int cols,
int type,
const Scalar & s )

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

rows — Number of rows in a 2D array.
cols — Number of columns in a 2D array.
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.
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()#

cv::Mat::Mat(
int rows,
int cols,
int type,
void * data,
size_t step = AUTO_STEP )

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

rows — Number of rows in a 2D array.
cols — Number of columns in a 2D array.
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.
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.
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()#

cv::Mat::Mat(Mat && m)

Mat()#

cv::Mat::Mat(
Size size,
int type )

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

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.
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()#

cv::Mat::Mat(
Size size,
int type,
const Scalar & s )

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

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.
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.
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()#

cv::Mat::Mat(
Size size,
int type,
void * data,
size_t step = AUTO_STEP )

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

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.
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.
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.
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()#

cv::Mat::Mat(
std::initializer_list< int > shape,
int type )

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

shape — Array shape.
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()#

cv::Mat::Mat(
std::initializer_list< int > shape,
int type,
const Scalar & s )

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

shape — Array shape.
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.
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()#

cv::Mat::Mat(
std::initializer_list< int > shape,
int type,
void * data,
const size_t * steps = 0 )

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

shape — Array shape.
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.
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.
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()#

cv::Mat::~Mat()

destructor - calls release()

Member Function Documentation#

addref()#

void cv::Mat::addref()

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.

adjustROI()#

Mat & cv::Mat::adjustROI(
int dtop,
int dbottom,
int dleft,
int dright )

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:

A.adjustROI(2, 2, 2, 2);

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.

assignTo()#

void cv::Mat::assignTo(
Mat & m,
int type = -1 )

Provides a functional form of convertTo.

This is an internally used method called by the MatrixExpressions engine.

Parameters

m — Destination array.
type — Desired destination array depth (or -1 if it should be the same as the source type).

at()#

template<typename _Tp>
_Tp & cv::Mat::at(const int * idx)

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

idx — Array of Mat::dims indices.

at()#

template<typename _Tp>
const _Tp & cv::Mat::at(const int * idx)

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

idx — Array of Mat::dims indices.

at()#

template<typename _Tp, int n>
_Tp & cv::Mat::at(const Vec< int, n > & idx)

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

at()#

template<typename _Tp, int n>
const _Tp & cv::Mat::at(const Vec< int, n > & idx)

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

at()#

template<typename _Tp>
_Tp & cv::Mat::at(
int i0,
int i1,
int i2 )

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

i0 — Index along the dimension 0
i1 — Index along the dimension 1
i2 — Index along the dimension 2

at()#

template<typename _Tp>
const _Tp & cv::Mat::at(
int i0,
int i1,
int i2 )

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

i0 — Index along the dimension 0
i1 — Index along the dimension 1
i2 — Index along the dimension 2

at()#

template<typename _Tp>
_Tp & cv::Mat::at(int i0 = 0)

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>(2i+1) instead of A.at<float>(0,k+4) and B.at<int>(2i+1,0), respectively.

The example below initializes a Hilbert matrix:

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);

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).

Parameters

i0 — Index along the dimension 0

at()#

template<typename _Tp>
const _Tp & cv::Mat::at(int i0 = 0)

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

i0 — Index along the dimension 0

at()#

template<typename _Tp>
_Tp & cv::Mat::at(
int row,
int col )

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

row — Index along the dimension 0
col — Index along the dimension 1

at()#

template<typename _Tp>
const _Tp & cv::Mat::at(
int row,
int col )

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

row — Index along the dimension 0
col — Index along the dimension 1

at()#

template<typename _Tp>
_Tp & cv::Mat::at(Point pt)

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts. special versions for 2D arrays (especially convenient for referencing image pixels)

Parameters

pt — Element position specified as Point(j,i) .

at()#

template<typename _Tp>
const _Tp & cv::Mat::at(Point pt)

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts. special versions for 2D arrays (especially convenient for referencing image pixels)

Parameters

pt — Element position specified as Point(j,i) .

begin()#

template<typename _Tp>
MatIterator_< _Tp > cv::Mat::begin()

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:

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() == traits::Type<VT>::value &&
               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));
    }
}

begin()#

template<typename _Tp>
MatConstIterator_< _Tp > cv::Mat::begin()

channels()#

int cv::Mat::channels()

Returns the number of matrix channels.

The method returns the number of matrix channels.

checkVector()#

int cv::Mat::checkVector(
int elemChannels,
int depth = -1,
bool requireContinuous = true )

The following code demonstrates its usage for a 2-d matrix:

    cv::Mat mat(20, 1, CV_32FC2);
    int n = mat.checkVector(2);
    CV_Assert(n == 20); // mat has 20 elements

    mat.create(20, 2, CV_32FC1);
    n = mat.checkVector(1);
    CV_Assert(n == -1); // mat is neither a column nor a row vector

    n = mat.checkVector(2);
    CV_Assert(n == 20); // the 2 columns are considered as 1 element

The following code demonstrates its usage for a 3-d matrix:

    int dims[] = {1, 3, 5}; // 1 plane, every plane has 3 rows and 5 columns
    mat.create(3, dims, CV_32FC1); // for 3-d mat, it MUST have only 1 channel
    n = mat.checkVector(5); // the 5 columns are considered as 1 element
    CV_Assert(n == 3);

    int dims2[] = {3, 1, 5}; // 3 planes, every plane has 1 row and 5 columns
    mat.create(3, dims2, CV_32FC1);
    n = mat.checkVector(5); // the 5 columns are considered as 1 element
    CV_Assert(n == 3);

Parameters

elemChannels — Number of channels or number of columns the matrix should have. For a 2-D matrix, when the matrix has only 1 column, then it should have elemChannels channels; When the matrix has only 1 channel, then it should have elemChannels columns. For a 3-D matrix, it should have only one channel. Furthermore, if the number of planes is not one, then the number of rows within every plane has to be 1; if the number of rows within every plane is not 1, then the number of planes has to be 1.
depth — The depth the matrix should have. Set it to -1 when any depth is fine.
requireContinuous — Set it to true to require the matrix to be continuous

Returns

-1 if the requirement is not satisfied. Otherwise, it returns the number of elements in the matrix. Note that an element may have multiple channels.

clone()#

CV_NODISCARD_STD Mat cv::Mat::clone()

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.

col()#

Mat cv::Mat::col(int x)

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.

Parameters

x — A 0-based column index.

colRange()#

Mat cv::Mat::colRange(const Range & r)

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

r — Range structure containing both the start and the end indices.

colRange()#

Mat cv::Mat::colRange(
int startcol,
int endcol )

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.

Parameters

startcol — An inclusive 0-based start index of the column span.
endcol — An exclusive 0-based ending index of the column span.

convertTo()#

void cv::Mat::convertTo(
OutputArray m,
int rtype,
double alpha = 1,
double beta = 0 )

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:

\[ m(x,y) = saturate \_ cast<rType>( \alpha (*this)(x,y) + \beta ) \]

Parameters

m — output matrix; if it does not have a proper size or type before the operation, it is reallocated.
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.
alpha — optional scale factor.
beta — optional delta added to the scaled values.

copyAt()#

void cv::Mat::copyAt(OutputArray m)

Overwrites the existing matrix.

This method writes existing matrix data, just like copyTo(). But if it does not have a proper size or type before the operation, an exception is thrown. This function is helpful to update ROI in an existing matrix.

If (re)allocation of destination memory is necessary, use copyTo() .

copyAt()#

void cv::Mat::copyAt(
OutputArray m,
InputArray mask )

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

m — Destination matrix. If it does not have a proper size or type before the operation, an exception is thrown.
mask — Operation mask of the same size as *this. 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.

copySize()#

void cv::Mat::copySize(const Mat & m)

internal use function; properly re-allocates _size, _step arrays

copyTo()#

void cv::Mat::copyTo(OutputArray m)

Copies the matrix to another one.

The method copies the matrix data to another matrix. Before copying the data, the method invokes :

m.create(this->size(), this->type());

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.

If (re)allocation of destination memory is not necessary (e.g. updating ROI), use copyAt() .

copyTo()#

void cv::Mat::copyTo(
OutputArray m,
InputArray mask )

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

m — Destination matrix. If it does not have a proper size or type before the operation, it is reallocated.
mask — Operation mask of the same size as *this. Its non-zero elements indicate which matrix elements need to be copied. The mask has to be of type CV_8U, CV_8S or CV_Bool and can have 1 or multiple channels.

create()#

void cv::Mat::create(
const MatShape & shape,
int type )

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

shape — The new shape.
type — New matrix type.

create()#

void cv::Mat::create(
const std::vector< int > & sizes,
int type )

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

sizes — Array of integers specifying a new array shape.
type — New matrix type.

create()#

void cv::Mat::create(
int ndims,
const int * sizes,
int type )

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

ndims — New array dimensionality.
sizes — Array of integers specifying a new array shape.
type — New matrix type.

create()#

void cv::Mat::create(
int rows,
int cols,
int type )

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:

Mat color;
...
Mat gray(color.rows, color.cols, color.depth());
cvtColor(color, gray, COLOR_BGR2GRAY);

you can simply write:

Mat color;
...
Mat gray;
cvtColor(color, gray, COLOR_BGR2GRAY);

because cvtColor, as well as the most of OpenCV functions, calls Mat::create() for the output array internally.

Parameters

rows — New number of rows.
cols — New number of columns.
type — New matrix type.

create()#

void cv::Mat::create(
Size size,
int type )

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

size — Alternative new matrix size specification: Size(cols, rows)
type — New matrix type.

create()#

void cv::Mat::create(
std::initializer_list< int > shape,
int type )

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

shape — The new shape.
type — New matrix type.

createSameSize()#

void cv::Mat::createSameSize(
InputArray arr,
int type )

Creates the matrix of the same size as another array.

The method is similar to _OutputArray::createSameSize(arr, type), but is applied to Mat.

Parameters

arr — The other array.
type — New matrix type.

cross()#

Mat cv::Mat::cross(InputArray m)

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.

Parameters

m — Another cross-product operand.

deallocate()#

void cv::Mat::deallocate()

internal use function, consider to use ‘release’ method instead; deallocates the matrix data

depth()#

int cv::Mat::depth()

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 )

diag()#

Mat cv::Mat::diag(int d = 0)

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.

Parameters

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. For example:

dot()#

double cv::Mat::dot(InputArray m)

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.

Parameters

m — another dot-product operand.

elemSize()#

size_t cv::Mat::elemSize()

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.

elemSize1()#

size_t cv::Mat::elemSize1()

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.

empty()#

bool cv::Mat::empty()

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.

end()#

template<typename _Tp>
MatIterator_< _Tp > cv::Mat::end()

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.

end()#

template<typename _Tp>
MatConstIterator_< _Tp > cv::Mat::end()

fit()#

void cv::Mat::fit(
const MatShape & shape,
int type )

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

shape — The new shape.
type — New matrix type.

fit()#

void cv::Mat::fit(
const std::vector< int > & sizes,
int type )

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

sizes — Array of integers specifying a new array shape.
type — New matrix type.

fit()#

void cv::Mat::fit(
int ndims,
const int * sizes,
int type )

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

ndims — New array dimensionality.
sizes — Array of integers specifying a new array shape.
type — New matrix type.

fit()#

void cv::Mat::fit(
int rows,
int cols,
int type )

Similar to create(rows, cols, type), but only reallocates memory if the existing buffer size is not enough.

Parameters

rows — New number of rows.
cols — New number of columns.
type — New matrix type.

fit()#

void cv::Mat::fit(
Size size,
int type )

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

size — Alternative new matrix size specification: Size(cols, rows)
type — New matrix type.

fit()#

void cv::Mat::fit(
std::initializer_list< int > shape,
int type )

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

shape — The new shape.
type — New matrix type.

fitSameSize()#

void cv::Mat::fitSameSize(
InputArray arr,
int type )

Similar to createSameSize(arr, type), but only reallocates memory if the existing buffer is not enough.

Parameters

arr — The other array.
type — New matrix type.

forEach()#

template<typename _Tp, typename Functor>
void cv::Mat::forEach(const Functor & operation)

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:

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>(r, 0);
    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;
});

Example 2. Using the pixel’s position:

// 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];
});

forEach()#

template<typename _Tp, typename Functor>
void cv::Mat::forEach(const Functor & operation)

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

getUMat()#

UMat cv::Mat::getUMat(
AccessFlag accessFlags,
UMatUsageFlags usageFlags = USAGE_DEFAULT )

retrieve UMat from Mat

inv()#

MatExpr cv::Mat::inv(int method = DECOMP_LU)

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.

Parameters

method — Matrix inversion method. One of cv::DecompTypes

isContinuous()#

bool cv::Mat::isContinuous()

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:

// 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();
}

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:

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(traits::Depth<T>::value, 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);
        }
    }
}

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.

isSubmatrix()#

bool cv::Mat::isSubmatrix()

returns true if the matrix is a submatrix of another matrix

locateROI()#

void cv::Mat::locateROI(
Size & wholeSize,
Point & ofs )

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.

Parameters

wholeSize — Output parameter that contains the size of the whole matrix containing this as a part.
ofs — Output parameter that contains an offset of this inside the whole matrix.

mul()#

MatExpr cv::Mat::mul(
InputArray m,
double scale = 1 )

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:

Mat C = A.mul(5/B); // equivalent to divide(A, B, C, 5)

Parameters

m — Another array of the same type and the same size as *this, or a matrix expression.
scale — Optional scale factor.

operator Matx< _Tp, m, n >()#

template<typename _Tp, int m, int n>
cv::Mat::operator Matx< _Tp, m, n >()

operator std::array< _Tp, _Nm >()#

template<typename _Tp, std::size_t _Nm>
cv::Mat::operator std::array< _Tp, _Nm >()

operator std::vector< _Tp >()#

template<typename _Tp>
cv::Mat::operator std::vector< _Tp >()

operator Vec< _Tp, n >()#

template<typename _Tp, int n>
cv::Mat::operator Vec< _Tp, n >()

operator()()#

Mat cv::Mat::operator()(const Range * ranges)

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

ranges — Array of selected ranges along each array dimension.

operator()()#

Mat cv::Mat::operator()(const Rect & roi)

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

roi — Extracted submatrix specified as a rectangle.

operator()()#

Mat cv::Mat::operator()(const std::vector< Range > & ranges)

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

ranges — Array of selected ranges along each array dimension.

operator()()#

Mat cv::Mat::operator()(
Range rowRange,
Range colRange )

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.

Parameters

rowRange — Start and end row of the extracted submatrix. The upper boundary is not included. To select all the rows, use Range::all().
colRange — Start and end column of the extracted submatrix. The upper boundary is not included. To select all the columns, use Range::all().

operator=()#

Mat & cv::Mat::operator=(const Mat & m)

assignment operators

These are available assignment operators. Since they all are very different, make sure to read the operator parameters description.

Parameters

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 .

operator=()#

Mat & cv::Mat::operator=(const MatExpr & expr)

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

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.

operator=()#

Mat & cv::Mat::operator=(const Scalar & s)

Sets all or some of the array elements to the specified value.

Parameters

s — Assigned scalar converted to the actual array type.

operator=()#

Mat & cv::Mat::operator=(Mat && m)

pop_back()#

void cv::Mat::pop_back(size_t nelems = 1)

Removes elements from the bottom of the matrix.

The method removes one or more rows from the bottom of the matrix.

Parameters

nelems — Number of removed rows. If it is greater than the total number of rows, an exception is thrown.

ptr()#

uchar * cv::Mat::ptr(const int * idx)

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

ptr()#

template<typename _Tp>
_Tp * cv::Mat::ptr(const int * idx)

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

ptr()#

const uchar * cv::Mat::ptr(const int * idx)

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

ptr()#

template<typename _Tp>
const _Tp * cv::Mat::ptr(const int * idx)

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

ptr()#

template<int n>
uchar * cv::Mat::ptr(const Vec< int, n > & idx)

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

ptr()#

template<typename _Tp, int n>
_Tp * cv::Mat::ptr(const Vec< int, n > & idx)

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

ptr()#

template<int n>
const uchar * cv::Mat::ptr(const Vec< int, n > & idx)

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

ptr()#

template<typename _Tp, int n>
const _Tp * cv::Mat::ptr(const Vec< int, n > & idx)

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

ptr()#

uchar * cv::Mat::ptr(
int i0,
int i1,
int i2 )

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

ptr()#

template<typename _Tp>
_Tp * cv::Mat::ptr(
int i0,
int i1,
int i2 )

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

ptr()#

const uchar * cv::Mat::ptr(
int i0,
int i1,
int i2 )

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

ptr()#

template<typename _Tp>
const _Tp * cv::Mat::ptr(
int i0,
int i1,
int i2 )

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

ptr()#

uchar * cv::Mat::ptr(int i0 = 0)

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.

Parameters

i0 — A 0-based row index.

ptr()#

template<typename _Tp>
_Tp * cv::Mat::ptr(int i0 = 0)

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

ptr()#

const uchar * cv::Mat::ptr(int i0 = 0)

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

ptr()#

template<typename _Tp>
const _Tp * cv::Mat::ptr(int i0 = 0)

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

ptr()#

uchar * cv::Mat::ptr(
int row,
int col )

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

row — Index along the dimension 0
col — Index along the dimension 1

ptr()#

template<typename _Tp>
_Tp * cv::Mat::ptr(
int row,
int col )

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

row — Index along the dimension 0
col — Index along the dimension 1

ptr()#

const uchar * cv::Mat::ptr(
int row,
int col )

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

row — Index along the dimension 0
col — Index along the dimension 1

ptr()#

template<typename _Tp>
const _Tp * cv::Mat::ptr(
int row,
int col )

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

row — Index along the dimension 0
col — Index along the dimension 1

push_back()#

template<typename _Tp>
void cv::Mat::push_back(const _Tp & elem)

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.

Parameters

elem — Added element(s).

push_back()#

void cv::Mat::push_back(const Mat & m)

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

m — Added line(s).

push_back()#

template<typename _Tp>
void cv::Mat::push_back(const Mat_< _Tp > & elem)

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

elem — Added element(s).

push_back()#

template<typename _Tp>
void cv::Mat::push_back(const std::vector< _Tp > & elem)

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

elem — Added element(s).

push_back_()#

void cv::Mat::push_back_(const void * elem)

internal function

rbegin()#

template<typename _Tp>
std::reverse_iterator< MatIterator_< _Tp > > cv::Mat::rbegin()

Same as begin() but for inverse traversal.

rbegin()#

template<typename _Tp>
std::reverse_iterator< MatConstIterator_< _Tp > > cv::Mat::rbegin()

reinterpret()#

Mat cv::Mat::reinterpret(int type)

Reset the type of matrix.

The methods reset the data type of matrix. If the new type and the old type of the matrix have the same element size, the current buffer can be reused. The method needs to consider whether the current mat is a submatrix or has any references.

Parameters

type — New data type.

release()#

void cv::Mat::release()

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.

rend()#

template<typename _Tp>
std::reverse_iterator< MatIterator_< _Tp > > cv::Mat::rend()

Same as end() but for inverse traversal.

rend()#

template<typename _Tp>
std::reverse_iterator< MatConstIterator_< _Tp > > cv::Mat::rend()

reserve()#

void cv::Mat::reserve(size_t sz)

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.

Parameters

sz — Number of rows.

reserveBuffer()#

void cv::Mat::reserveBuffer(size_t sz)

Reserves space for the certain number of bytes.

The method reserves space for sz bytes. If the matrix already has enough space to store sz bytes, nothing happens. If matrix has to be reallocated its previous content could be lost.

Parameters

sz — Number of bytes.

reshape()#

Mat cv::Mat::reshape(
int cn,
const MatShape & newshape )

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

cn — New number of channels. If the parameter is 0, the number of channels remains the same.
newshape — New shape in the form of MatShape.

reshape()#

Mat cv::Mat::reshape(
int cn,
const std::vector< int > & newshape )

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

cn — New number of channels. If the parameter is 0, the number of channels remains the same.
newshape — Vector with new matrix size by all dimensions. If some sizes are zero, the original sizes in those dimensions are presumed.

reshape()#

Mat cv::Mat::reshape(
int cn,
int newndims,
const int * newsz )

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

cn — New number of channels. If the parameter is 0, the number of channels remains the same.
newndims — New number of dimensions.
newsz — Array with new matrix size by all dimensions. If some sizes are zero, the original sizes in those dimensions are presumed.

reshape()#

Mat cv::Mat::reshape(
int cn,
int rows = 0 )

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 rowscolschannels() 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:

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

3-channel 2x2 matrix reshaped to 1-channel 4x3 matrix, each column has values from one of original channels:

Mat m(Size(2, 2), CV_8UC3, Scalar(1, 2, 3));
vector<int> new_shape {4, 3};
m = m.reshape(1, new_shape);

or:

Mat m(Size(2, 2), CV_8UC3, Scalar(1, 2, 3));
const int new_shape[] = {4, 3};
m = m.reshape(1, 2, new_shape);

Parameters

cn — New number of channels. If the parameter is 0, the number of channels remains the same.
rows — New number of rows. If the parameter is 0, the number of rows remains the same.

reshape()#

Mat cv::Mat::reshape(
int cn,
std::initializer_list< int > newshape )

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

cn — New number of channels. If the parameter is 0, the number of channels remains the same.
newshape — New shape in the form of initializer list.

resize()#

void cv::Mat::resize(size_t sz)

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.

Parameters

sz — New number of rows.

resize()#

void cv::Mat::resize(
size_t sz,
const Scalar & s )

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

sz — New number of rows.
s — Value assigned to the newly added elements.

row()#

Mat cv::Mat::row(int y)

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:

inline void matrix_axpy(Mat& A, int i, int j, double alpha)
{
    A.row(i) += A.row(j)*alpha;
}

Note

In the current implementation, the following code does not work as expected:

Mat A;
...
A.row(i) = A.row(j); // will not work

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:

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));

Parameters

y — A 0-based row index.

rowRange()#

Mat cv::Mat::rowRange(const Range & r)

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

r — Range structure containing both the start and the end indices.

rowRange()#

Mat cv::Mat::rowRange(
int startrow,
int endrow )

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.

Parameters

startrow — An inclusive 0-based start index of the row span.
endrow — An exclusive 0-based ending index of the row span.

setTo()#

Mat & cv::Mat::setTo(
InputArray value,
InputArray mask = noArray() )

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.

Parameters

value — Assigned scalar converted to the actual array type.
mask — Operation mask of the same size as *this. Its non-zero elements indicate which matrix elements need to be copied. The mask has to be of type CV_8U, CV_8S or CV_Bool and can have 1 or multiple channels.

setZero()#

Mat & cv::Mat::setZero()

Sets all the array elements to 0.

shape()#

MatShape cv::Mat::shape()

Returns the shape.

step1()#

size_t cv::Mat::step1(int i = 0)

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.

t()#

MatExpr cv::Mat::t()

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:

Mat A1 = A + Mat::eye(A.size(), A.type())*lambda;
Mat C = A1.t()*A1; // compute (A + lambda*I)^t * (A + lamda*I)

total()#

size_t cv::Mat::total()

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).

total()#

size_t cv::Mat::total(
int startDim,
int endDim = INT_MAX )

Returns the total number of array elements.

The method returns the number of elements within a certain sub-array slice with startDim <= dim < endDim

type()#

int cv::Mat::type()

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.

updateContinuityFlag()#

void cv::Mat::updateContinuityFlag()

internal use method: updates the continuity flag

diag()#

static CV_NODISCARD_STD Mat cv::Mat::diag(const Mat & d)

creates a diagonal matrix

The method creates a square diagonal matrix from specified main diagonal.

Parameters

d — One-dimensional matrix that represents the main diagonal.

eye()#

static CV_NODISCARD_STD MatExpr cv::Mat::eye(
int rows,
int cols,
int type )

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:

// make a 4x4 diagonal matrix with 0.1's on the diagonal.
Mat A = Mat::eye(4, 4, CV_32F)*0.1;

Note

In case of multi-channels type, identity matrix will be initialized only for the first channel, the others will be set to 0’s

Parameters

rows — Number of rows.
cols — Number of columns.
type — Created matrix type.

eye()#

static CV_NODISCARD_STD MatExpr cv::Mat::eye(
Size size,
int type )

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

size — Alternative matrix size specification as Size(cols, rows) .
type — Created matrix type.

getDefaultAllocator()#

static MatAllocator * cv::Mat::getDefaultAllocator()

getStdAllocator()#

static MatAllocator * cv::Mat::getStdAllocator()

and the standard allocator

ones()#

static CV_NODISCARD_STD MatExpr cv::Mat::ones(
const MatShape & shape,
int type )

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

shape — Array shape.
type — Created matrix type.

ones()#

static CV_NODISCARD_STD MatExpr cv::Mat::ones(
int ndims,
const int * sz,
int type )

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

ndims — Array dimensionality.
sz — Array of integers specifying the array shape.
type — Created matrix type.

ones()#

static CV_NODISCARD_STD MatExpr cv::Mat::ones(
int rows,
int cols,
int type )

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:

Mat A = Mat::ones(100, 100, CV_8U)*3; // make 100x100 matrix filled with 3.

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.

Note

In case of multi-channels type, only the first channel will be initialized with 1’s, the others will be set to 0’s.

Parameters

rows — Number of rows.
cols — Number of columns.
type — Created matrix type.

ones()#

static CV_NODISCARD_STD MatExpr cv::Mat::ones(
Size size,
int type )

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

size — Alternative to the matrix size specification Size(cols, rows) .
type — Created matrix type.

setDefaultAllocator()#

static void cv::Mat::setDefaultAllocator(MatAllocator * allocator)

zeros()#

static CV_NODISCARD_STD MatExpr cv::Mat::zeros(
const MatShape & shape,
int type )

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

shape — Array shape.
type — Created matrix type.

zeros()#

static CV_NODISCARD_STD MatExpr cv::Mat::zeros(
int ndims,
const int * sz,
int type )

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

ndims — Array dimensionality.
sz — Array of integers specifying the array shape.
type — Created matrix type.

zeros()#

static CV_NODISCARD_STD MatExpr cv::Mat::zeros(
int rows,
int cols,
int type )

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:

Mat A;
A = Mat::zeros(3, 3, CV_32F);

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.

Parameters

rows — Number of rows.
cols — Number of columns.
type — Created matrix type.

zeros()#

static CV_NODISCARD_STD MatExpr cv::Mat::zeros(
Size size,
int type )

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

size — Alternative to the matrix size specification Size(cols, rows) .
type — Created matrix type.

Member Data Documentation#

allocator#

MatAllocator * cv::Mat::allocator

custom allocator

cols#

int cv::Mat::cols

data#

uchar * cv::Mat::data

pointer to the data

dataend#

const uchar * cv::Mat::dataend

datalimit#

const uchar * cv::Mat::datalimit

datastart#

const uchar * cv::Mat::datastart

helper fields used in locateROI and adjustROI

dims#

int cv::Mat::dims

the matrix dimensionality, >= 2

dummy#

int cv::Mat::dummy = 153

flags#

int cv::Mat::flags

includes several bit-fields:

the magic signature
continuity flag
depth
number of channels

rows#

int cv::Mat::rows

the number of rows and columns or (-1, -1) when the matrix has more than 2 dimensions

size#

MatSize cv::Mat::size

step#

MatStep cv::Mat::step

u#

UMatData * cv::Mat::u

interaction with UMat

Source file#

The documentation for this class was generated from the following file:

opencv2/core/mat.hpp