Struct opencv::core::Mat[−][src]

pub struct Mat { /* fields omitted */ }

Expand description

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 $inline formula$ , where $inline formula$ , is computed as: $block formula$ In case of a 2-dimensional array, the above formula is reduced to: $block formula$ 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 $inline formula$ 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

Struct opencv::core::Mat[−][src]

Implementations

impl Mat

pub fn default() -> Mat

pub unsafe fn new_rows_cols(rows: i32, cols: i32, typ: i32) -> Result<Mat>

pub unsafe fn new_size(size: Size, typ: i32) -> Result<Mat>

pub fn new_rows_cols_with_default( rows: i32, cols: i32, typ: i32, s: Scalar) -> Result<Mat>

pub fn new_size_with_default(size: Size, typ: i32, s: Scalar) -> Result<Mat>

pub unsafe fn new_nd(ndims: i32, sizes: &i32, typ: i32) -> Result<Mat>

pub unsafe fn new_nd_vec(sizes: &Vector<i32>, typ: i32) -> Result<Mat>

pub fn new_nd_with_default(sizes: &[i32], typ: i32, s: Scalar) -> Result<Mat>

pub fn new_nd_vec_with_default( sizes: &Vector<i32>, typ: i32, s: Scalar) -> Result<Mat>

pub fn copy(m: &Mat) -> Result<Mat>

pub unsafe fn new_rows_cols_with_data( rows: i32, cols: i32, typ: i32, data: *mut c_void, step: size_t) -> Result<Mat>

pub unsafe fn new_size_with_data( size: Size, typ: i32, data: *mut c_void, step: size_t) -> Result<Mat>

pub unsafe fn new_nd_with_data( sizes: &[i32], typ: i32, data: *mut c_void, steps: Option<&[size_t]>) -> Result<Mat>

pub unsafe fn new_nd_vec_with_data( sizes: &Vector<i32>, typ: i32, data: *mut c_void, steps: Option<&[size_t]>) -> Result<Mat>

pub fn rowscols(m: &Mat, row_range: &Range, col_range: &Range) -> Result<Mat>

pub fn roi(m: &Mat, roi: Rect) -> Result<Mat>

pub fn ranges(m: &Mat, ranges: &Vector<Range>) -> Result<Mat>

pub fn from_gpumat(m: &GpuMat) -> Result<Mat>

pub fn diag_mat(d: &Mat) -> Result<Mat>

pub fn zeros(rows: i32, cols: i32, typ: i32) -> Result<MatExpr>

pub fn zeros_size(size: Size, typ: i32) -> Result<MatExpr>

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

pub fn ones(rows: i32, cols: i32, typ: i32) -> Result<MatExpr>

pub fn ones_size(size: Size, typ: i32) -> Result<MatExpr>

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

pub fn eye(rows: i32, cols: i32, typ: i32) -> Result<MatExpr>

pub fn eye_size(size: Size, typ: i32) -> Result<MatExpr>

pub fn copy_mut(m: &mut Mat) -> Result<Mat>

impl Mat

pub fn from_exact_iter<T: DataType>( s: impl ExactSizeIterator<Item = T>) -> Result<Self>

pub fn from_slice<T: DataType>(s: &[T]) -> Result<Self>

pub fn from_slice_2d<T: DataType>(s: &[impl AsRef<[T]>]) -> Result<Self>

pub fn try_into_typed<T: DataType>(self) -> Result<Mat_<T>> where Self: Sized,

Trait Implementations

impl Add<&'_ Mat> for Mat

type Output = MatExprResult<MatExpr>

fn add(self, rhs: &Mat) -> Self::Output

impl Add<&'_ Mat> for MatExprResult<Mat>

type Output = MatExprResult<MatExpr>

fn add(self, rhs: &Mat) -> Self::Output

impl Add<&'_ Mat> for &Scalar

type Output = MatExprResult<MatExpr>

fn add(self, rhs: &Mat) -> Self::Output

impl Add<&'_ Mat> for MatExprResult<&Scalar>

type Output = MatExprResult<MatExpr>

fn add(self, rhs: &Mat) -> Self::Output

impl Add<&'_ Mat> for &Mat

type Output = MatExprResult<MatExpr>

fn add(self, rhs: &Mat) -> Self::Output

impl Add<&'_ Mat> for MatExprResult<&Mat>

type Output = MatExprResult<MatExpr>

fn add(self, rhs: &Mat) -> Self::Output

impl Add<&'_ Mat> for MatExpr

type Output = MatExprResult<MatExpr>

fn add(self, rhs: &Mat) -> Self::Output

impl Add<&'_ Mat> for MatExprResult<MatExpr>

type Output = MatExprResult<MatExpr>

fn add(self, rhs: &Mat) -> Self::Output

impl Add<&'_ Mat> for &MatExpr

type Output = MatExprResult<MatExpr>

fn add(self, rhs: &Mat) -> Self::Output

impl Add<&'_ Mat> for MatExprResult<&MatExpr>

type Output = MatExprResult<MatExpr>

fn add(self, rhs: &Mat) -> Self::Output

impl Add<&'_ Mat> for Scalar

type Output = MatExprResult<MatExpr>

fn add(self, rhs: &Mat) -> Self::Output

impl Add<&'_ Mat> for MatExprResult<Scalar>

type Output = MatExprResult<MatExpr>

fn add(self, rhs: &Mat) -> Self::Output

impl Add<&'_ MatExpr> for Mat

type Output = MatExprResult<MatExpr>

fn add(self, rhs: &MatExpr) -> Self::Output

impl Add<&'_ MatExpr> for &Mat

type Output = MatExprResult<MatExpr>

fn add(self, rhs: &MatExpr) -> Self::Output

impl Add<&'_ VecN<f64, 4_usize>> for Mat

pub fn new_rows_cols_with_default(
rows: i32,
cols: i32,
typ: i32,
s: Scalar
) -> Result<Mat>

pub fn new_nd_with_default(sizes: &[i32 ], typ: i32, s: Scalar) -> Result<Mat>

pub fn new_nd_vec_with_default(
sizes: &Vector<i32>,
typ: i32,
s: Scalar
) -> Result<Mat>

pub unsafe fn new_rows_cols_with_data(
rows: i32,
cols: i32,
typ: i32,
data: *mut c_void,
step: size_t
) -> Result<Mat>

pub unsafe fn new_size_with_data(
size: Size,
typ: i32,
data: *mut c_void,
step: size_t
) -> Result<Mat>

pub unsafe fn new_nd_with_data(
sizes: &[i32 ],
typ: i32,
data: *mut c_void,
steps: Option<&[size_t ]>
) -> Result<Mat>

pub unsafe fn new_nd_vec_with_data(
sizes: &Vector<i32>,
typ: i32,
data: *mut c_void,
steps: Option<&[size_t ]>
) -> Result<Mat>

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

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

pub fn from_exact_iter<T: DataType>(
s: impl ExactSizeIterator<Item = T>
) -> Result<Self>

pub fn try_into_typed<T: DataType>(self) -> Result<Mat_<T>> where
Self: Sized,