# [−][src]Struct opencv::core::Mat

`pub struct Mat { /* fields omitted */ }`

n-dimensional dense array class \anchor CVMat_Details

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 $(i_0,...,i_{M.dims-1})$, where $0\leq i_k, is computed as: $addr(M_{i_0,...,i_{M.dims-1}}) = M.data + M.step[0]*i_0 + M.step[1]*i_1 + ... + M.step[M.dims-1]*i_{M.dims-1}$ In case of a 2-dimensional array, the above formula is reduced to: $addr(M_{i,j}) = M.data + M.step[0]*i + M.step[1]*j$ 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 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.
```   // 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));```

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

## Implementations

### `impl Mat`[src]

#### `pub fn default() -> Result<Mat>`[src]

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.

## 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.

## 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.

## 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) .

## 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) .

## 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.

## 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.

## 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) .

## 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) .

## 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() .

## 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.

## C++ default parameters

• step: AUTO_STEP

## 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.

## C++ default parameters

• step: AUTO_STEP

## 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.

• steps: 0

## 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.

• steps: 0

## 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.

## C++ default parameters

• col_range: Range::all()

## 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.

## 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.

#### `pub fn diag_mat(d: &Mat) -> Result<Mat>`[src]

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.

#### `pub fn zeros(rows: i32, cols: i32, typ: i32) -> Result<MatExpr>`[src]

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.

#### `pub fn zeros_size(size: Size, typ: i32) -> Result<MatExpr>`[src]

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.

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

#### `pub fn zeros_nd(sz: &[i32], typ: i32) -> Result<MatExpr>`[src]

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.

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

#### `pub fn ones(rows: i32, cols: i32, typ: i32) -> Result<MatExpr>`[src]

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.

#### `pub fn ones_size(size: Size, typ: i32) -> Result<MatExpr>`[src]

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.

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

#### `pub fn ones_nd(sz: &[i32], typ: i32) -> Result<MatExpr>`[src]

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.

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

#### `pub fn eye(rows: i32, cols: i32, typ: i32) -> Result<MatExpr>`[src]

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.

#### `pub fn eye_size(size: Size, typ: i32) -> Result<MatExpr>`[src]

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.

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

### `impl Mat`[src]

#### `pub fn from_exact_iter<T: DataType>(    s: impl ExactSizeIterator<Item = T>) -> Result<Self>`[src]

Create new `Mat` from the iterator of known size

## Trait Implementations

### `impl Clone for Mat`[src]

#### `pub fn clone(&self) -> Self`[src]

Calls try_clone() and panics if that fails

### `impl<T: DataType> TryFrom<Mat> for Mat_<T>`[src]

#### `type Error = Error`

The type returned in the event of a conversion error.

## Blanket Implementations

### `impl<T> ToOwned for T where    T: Clone, `[src]

#### `type Owned = T`

The resulting type after obtaining ownership.

### `impl<T, U> TryFrom<U> for T where    U: Into<T>, `[src]

#### `type Error = Infallible`

The type returned in the event of a conversion error.

### `impl<T, U> TryInto<U> for T where    U: TryFrom<T>, `[src]

#### `type Error = <U as TryFrom<T>>::Error`

The type returned in the event of a conversion error.