Mat
import { Mat } from '@banou/opencv-wasm'Use after await initOpenCV(). See the initialization and named imports guide.
Native object: release it with using or delete(). Factories can return null; check before calling methods.
example: samples/cpp/cout_mat.cpp An example demonstrating the serial out capabilities of cv::Mat n-dimensional dense array class
The class Mat represents an n-dimensional dense numerical single-channel or multi-channel array. It
can be used to store real or complex-valued vectors and matrices, grayscale or color images, voxel
volumes, vector fields, point clouds, tensors, histograms (though, very high-dimensional histograms
may be better stored in a SparseMat ). The data layout of the array M is defined by the array
M.step[], so that the address of element (i_0,...,i_{M.dims-1}), where 0\leq i_k<M.size[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
Constructors and members
static new
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.
new(rows: number, cols: number, _2: number, s: Scalar): Mat;6 available overloads
new(): Mat;new(m: Mat): Mat;new(rows: number, cols: number, _2: number): Mat;new(rows: number, cols: number, _2: number, data: number, step: number): Mat;new(size: Size, _1: number): Mat;new(rows: number, cols: number, _2: number, s: Scalar): Mat;mArray 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() .
rowsNumber of rows in a 2D array.
colsNumber of columns in a 2D array.
_2Array 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.
dataPointer 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.
stepNumber 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.
size2D array size: Size(cols, rows) . In the Size() constructor, the number of rows and the number of columns go in the reverse order.
_1Array 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.
sAn 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) .
The Mat result. Release returned native handles with using or delete(), including handles nested in results.
Upstream declaration ↗Upstream declaration 2 ↗Upstream declaration 3 ↗
static eye
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
eye(size: Size, _1: number): Mat;2 available overloads
eye(rows: number, cols: number, _2: number): Mat;eye(size: Size, _1: number): Mat;rowsNumber of rows.
colsNumber of columns.
_2Created matrix type.
sizeAlternative matrix size specification as Size(cols, rows) .
_1Created matrix type.
The Mat result. Release returned native handles with using or delete(), including handles nested in results.
static ones
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.
ones(size: Size, _1: number): Mat;2 available overloads
ones(rows: number, cols: number, _2: number): Mat;ones(size: Size, _1: number): Mat;rowsNumber of rows.
colsNumber of columns.
_2Created matrix type.
sizeAlternative to the matrix size specification Size(cols, rows) .
_1Created matrix type.
The Mat result. Release returned native handles with using or delete(), including handles nested in results.
static zeros
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.
zeros(size: Size, _1: number): Mat;2 available overloads
zeros(rows: number, cols: number, _2: number): Mat;zeros(size: Size, _1: number): Mat;rowsNumber of rows.
colsNumber of columns.
_2Created matrix type.
sizeAlternative to the matrix size specification Size(cols, rows) .
_1Created matrix type.
The Mat result. Release returned native handles with using or delete(), including handles nested in results.
clone
Create another handle to the same native object. This retains the object without copying its pixels or algorithm state; dispose both handles separately.
clone(): this;The this result.
dims
Number of matrix or tensor dimensions. OpenCV 5 preserves one-dimensional tensors and zero-dimensional scalars. Use matSize for the extents.
readonly dims: number;rows
Number of matrix rows. For tensors with more than two dimensions, use matSize instead.
rows: number;cols
Number of matrix columns. For tensors with more than two dimensions, use matSize instead.
cols: number;matSize
Matrix extents in dimension order. For an image these are rows and columns.
readonly matSize: number[];step
Byte strides for each matrix dimension. A region of interest can have a row stride larger than its visible pixel width.
readonly step: number[];data
View matrix pixels as Uint8Array<ArrayBuffer> backed by this instance's WASM memory. Copy before retaining data across memory growth. For non-contiguous regions, use row pointers and strides.
readonly data: Uint8Array<ArrayBuffer>;data8S
View matrix pixels as Int8Array<ArrayBuffer> backed by this instance's WASM memory. Copy before retaining data across memory growth. For non-contiguous regions, use row pointers and strides.
readonly data8S: Int8Array<ArrayBuffer>;data16U
View matrix pixels as Uint16Array<ArrayBuffer> backed by this instance's WASM memory. Copy before retaining data across memory growth. For non-contiguous regions, use row pointers and strides.
readonly data16U: Uint16Array<ArrayBuffer>;data16S
View matrix pixels as Int16Array<ArrayBuffer> backed by this instance's WASM memory. Copy before retaining data across memory growth. For non-contiguous regions, use row pointers and strides.
readonly data16S: Int16Array<ArrayBuffer>;data32S
View matrix pixels as Int32Array<ArrayBuffer> backed by this instance's WASM memory. Copy before retaining data across memory growth. For non-contiguous regions, use row pointers and strides.
readonly data32S: Int32Array<ArrayBuffer>;data32F
View matrix pixels as Float32Array<ArrayBuffer> backed by this instance's WASM memory. Copy before retaining data across memory growth. For non-contiguous regions, use row pointers and strides.
readonly data32F: Float32Array<ArrayBuffer>;data64F
View matrix pixels as Float64Array<ArrayBuffer> backed by this instance's WASM memory. Copy before retaining data across memory growth. For non-contiguous regions, use row pointers and strides.
readonly data64F: Float64Array<ArrayBuffer>;data32U
Borrow an unsigned 32-bit pixel view of WASM memory. Use with CV_32U matrices; copy it before disposing the matrix or growing the heap.
readonly data32U: Uint32Array<ArrayBuffer>;data64S
Borrow an exact signed 64-bit pixel view of WASM memory. Values are bigint. Use with CV_64S matrices; copy it before disposing the matrix or growing the heap.
readonly data64S: BigInt64Array<ArrayBuffer>;data64U
Borrow an exact unsigned 64-bit pixel view of WASM memory. Values are bigint. Use with CV_64U matrices; copy it before disposing the matrix or growing the heap.
readonly data64U: BigUint64Array<ArrayBuffer>;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.
elemSize(): number;The number result.
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.
elemSize1(): number;The number result.
channels
Returns the number of matrix channels.
The method returns the number of matrix channels.
channels(): number;The number result.
convertTo
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 )
convertTo(m: Mat, rtype: number, alpha: number): void;3 available overloads
convertTo(m: Mat, rtype: number, alpha: number, beta: number): void;convertTo(m: Mat, rtype: number): void;convertTo(m: Mat, rtype: number, alpha: number): void;moutput matrix; if it does not have a proper size or type before the operation, it is reallocated.
rtypedesired 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.
alphaoptional scale factor.
betaoptional delta added to the scaled values.
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(): number;The number result.
row
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));
row(y: number): Mat;yA 0-based row index.
The Mat result. Release returned native handles with using or delete(), including handles nested in results.
create
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.
create(size: Size, _1: number): void;2 available overloads
create(rows: number, cols: number, _2: number): void;create(size: Size, _1: number): void;rowsNew number of rows.
colsNew number of columns.
_2New matrix type.
sizeAlternative new matrix size specification: Size(cols, rows)
_1New matrix type.
rowRange
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.
rowRange(r: Range): Mat;startrowAn inclusive 0-based start index of the row span.
endrowAn exclusive 0-based ending index of the row span.
rRange structure containing both the start and the end indices.
The Mat result. Release returned native handles with using or delete(), including handles nested in results.
copyTo
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() .
See: copyAt
copyTo(m: Mat, mask: Mat): void;mDestination matrix. If it does not have a proper size or type before the operation, it is reallocated.
maskOperation 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.
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.
type(): number;The number result.
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`.
empty(): boolean;The boolean result.
colRange
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.
colRange(r: Range): Mat;startcolAn inclusive 0-based start index of the column span.
endcolAn exclusive 0-based ending index of the column span.
rRange structure containing both the start and the end indices.
The Mat result. Release returned native handles with using or delete(), including handles nested in results.
step1
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.
step1(i: number): number;ii argument (number).
The number result.
mat_clone
Copy the matrix and its pixels into independent, continuous storage. Dispose the returned matrix separately. Use clone() only to retain another handle to the same matrix.
mat_clone(): Mat;The Mat result. Release returned native handles with using or delete(), including handles nested in results.
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 )
depth(): number;The number result.
col
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.
col(x: number): Mat;xA 0-based column index.
The Mat result. Release returned native handles with using or delete(), including handles nested in results.
dot
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.
dot(m: Mat): number;manother dot-product operand.
The number result.
mul
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)
mul(m: Mat, scale: number): Mat;mAnother array of the same type and the same size as *this, or a matrix expression.
scaleOptional scale factor.
The Mat result. Release returned native handles with using or delete(), including handles nested in results.
inv
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.
inv(method: number): Mat;methodMatrix inversion method. One of cv::DecompTypes
The Mat result. Release returned native handles with using or delete(), including handles nested in results.
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)
t(): Mat;The Mat result. Release returned native handles with using or delete(), including handles nested in results.
diag
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.
Mat m = (Mat_<int>(3,3) <<
1,2,3,
4,5,6,
7,8,9);
Mat d0 = m.diag(0);
Mat d1 = m.diag(1);
Mat d_1 = m.diag(-1);
The resulting matrices are
d0 =
[1;
5;
9]
d1 =
[2;
6]
d_1 =
[4;
8]
diag(): Mat;dindex of the diagonal, with the following values:
d=0is the main diagonal.d<0is a diagonal from the lower half. For example, d=-1 means the diagonal is set immediately below the main one.d>0is a diagonal from the upper half. For example, d=1 means the diagonal is set immediately above the main one. For example:
The Mat result. Release returned native handles with using or delete(), including handles nested in results.
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.
isContinuous(): boolean;The boolean result.
ptr
View matrix pixels as Uint8Array<ArrayBuffer> backed by this instance's WASM memory. Copy before retaining data across memory growth. For non-contiguous regions, use row pointers and strides.
ptr(row: number, col: number): Uint8Array<ArrayBuffer>;2 available overloads
ptr(i0: number): Uint8Array<ArrayBuffer>;ptr(row: number, col: number): Uint8Array<ArrayBuffer>;i0A 0-based row index.
rowIndex along the dimension 0
colIndex along the dimension 1
The Uint8Array<ArrayBuffer> result.
ucharPtr
View matrix pixels as Uint8Array<ArrayBuffer> backed by this instance's WASM memory. Copy before retaining data across memory growth. For non-contiguous regions, use row pointers and strides.
ucharPtr(_0: number, _1: number): Uint8Array<ArrayBuffer>;2 available overloads
ucharPtr(_0: number): Uint8Array<ArrayBuffer>;ucharPtr(_0: number, _1: number): Uint8Array<ArrayBuffer>;_00 argument (number).
_11 argument (number).
The Uint8Array<ArrayBuffer> result.
charPtr
View matrix pixels as Int8Array<ArrayBuffer> backed by this instance's WASM memory. Copy before retaining data across memory growth. For non-contiguous regions, use row pointers and strides.
charPtr(_0: number, _1: number): Int8Array<ArrayBuffer>;2 available overloads
charPtr(_0: number): Int8Array<ArrayBuffer>;charPtr(_0: number, _1: number): Int8Array<ArrayBuffer>;_00 argument (number).
_11 argument (number).
The Int8Array<ArrayBuffer> result.
shortPtr
View matrix pixels as Int16Array<ArrayBuffer> backed by this instance's WASM memory. Copy before retaining data across memory growth. For non-contiguous regions, use row pointers and strides.
shortPtr(_0: number, _1: number): Int16Array<ArrayBuffer>;2 available overloads
shortPtr(_0: number): Int16Array<ArrayBuffer>;shortPtr(_0: number, _1: number): Int16Array<ArrayBuffer>;_00 argument (number).
_11 argument (number).
The Int16Array<ArrayBuffer> result.
ushortPtr
View matrix pixels as Uint16Array<ArrayBuffer> backed by this instance's WASM memory. Copy before retaining data across memory growth. For non-contiguous regions, use row pointers and strides.
ushortPtr(_0: number, _1: number): Uint16Array<ArrayBuffer>;2 available overloads
ushortPtr(_0: number): Uint16Array<ArrayBuffer>;ushortPtr(_0: number, _1: number): Uint16Array<ArrayBuffer>;_00 argument (number).
_11 argument (number).
The Uint16Array<ArrayBuffer> result.
intPtr
View matrix pixels as Int32Array<ArrayBuffer> backed by this instance's WASM memory. Copy before retaining data across memory growth. For non-contiguous regions, use row pointers and strides.
intPtr(_0: number, _1: number): Int32Array<ArrayBuffer>;2 available overloads
intPtr(_0: number): Int32Array<ArrayBuffer>;intPtr(_0: number, _1: number): Int32Array<ArrayBuffer>;_00 argument (number).
_11 argument (number).
The Int32Array<ArrayBuffer> result.
floatPtr
View matrix pixels as Float32Array<ArrayBuffer> backed by this instance's WASM memory. Copy before retaining data across memory growth. For non-contiguous regions, use row pointers and strides.
floatPtr(_0: number, _1: number): Float32Array<ArrayBuffer>;2 available overloads
floatPtr(_0: number): Float32Array<ArrayBuffer>;floatPtr(_0: number, _1: number): Float32Array<ArrayBuffer>;_00 argument (number).
_11 argument (number).
The Float32Array<ArrayBuffer> result.
doublePtr
View matrix pixels as Float64Array<ArrayBuffer> backed by this instance's WASM memory. Copy before retaining data across memory growth. For non-contiguous regions, use row pointers and strides.
doublePtr(_0: number, _1: number): Float64Array<ArrayBuffer>;2 available overloads
doublePtr(_0: number): Float64Array<ArrayBuffer>;doublePtr(_0: number, _1: number): Float64Array<ArrayBuffer>;_00 argument (number).
_11 argument (number).
The Float64Array<ArrayBuffer> result.
charAt
Read a signed 8-bit scalar at the supplied zero-based matrix indices. The accessor must match the stored element depth. The three-index overload addresses a three-dimensional tensor; it does not select an image channel.
charAt(index0: number, index1: number, index2: number): number;3 available overloads
charAt(index0: number): number;charAt(index0: number, index1: number): number;charAt(index0: number, index1: number, index2: number): number;index0index0 argument (number).
index1index1 argument (number).
index2index2 argument (number).
The number result.
ucharAt
Read a unsigned 8-bit scalar at the supplied zero-based matrix indices. The accessor must match the stored element depth. The three-index overload addresses a three-dimensional tensor; it does not select an image channel.
ucharAt(index0: number, index1: number, index2: number): number;3 available overloads
ucharAt(index0: number): number;ucharAt(index0: number, index1: number): number;ucharAt(index0: number, index1: number, index2: number): number;index0index0 argument (number).
index1index1 argument (number).
index2index2 argument (number).
The number result.
shortAt
Read a signed 16-bit scalar at the supplied zero-based matrix indices. The accessor must match the stored element depth. The three-index overload addresses a three-dimensional tensor; it does not select an image channel.
shortAt(index0: number, index1: number, index2: number): number;3 available overloads
shortAt(index0: number): number;shortAt(index0: number, index1: number): number;shortAt(index0: number, index1: number, index2: number): number;index0index0 argument (number).
index1index1 argument (number).
index2index2 argument (number).
The number result.
ushortAt
Read a unsigned 16-bit scalar at the supplied zero-based matrix indices. The accessor must match the stored element depth. The three-index overload addresses a three-dimensional tensor; it does not select an image channel.
ushortAt(index0: number, index1: number, index2: number): number;3 available overloads
ushortAt(index0: number): number;ushortAt(index0: number, index1: number): number;ushortAt(index0: number, index1: number, index2: number): number;index0index0 argument (number).
index1index1 argument (number).
index2index2 argument (number).
The number result.
intAt
Read a signed 32-bit scalar at the supplied zero-based matrix indices. The accessor must match the stored element depth. The three-index overload addresses a three-dimensional tensor; it does not select an image channel.
intAt(index0: number, index1: number, index2: number): number;3 available overloads
intAt(index0: number): number;intAt(index0: number, index1: number): number;intAt(index0: number, index1: number, index2: number): number;index0index0 argument (number).
index1index1 argument (number).
index2index2 argument (number).
The number result.
floatAt
Read a 32-bit floating-point scalar at the supplied zero-based matrix indices. The accessor must match the stored element depth. The three-index overload addresses a three-dimensional tensor; it does not select an image channel.
floatAt(index0: number, index1: number, index2: number): number;3 available overloads
floatAt(index0: number): number;floatAt(index0: number, index1: number): number;floatAt(index0: number, index1: number, index2: number): number;index0index0 argument (number).
index1index1 argument (number).
index2index2 argument (number).
The number result.
doubleAt
Read a 64-bit floating-point scalar at the supplied zero-based matrix indices. The accessor must match the stored element depth. The three-index overload addresses a three-dimensional tensor; it does not select an image channel.
doubleAt(index0: number, index1: number): number;3 available overloads
doubleAt(index0: number, index1: number, index2: number): number;doubleAt(index0: number): number;doubleAt(index0: number, index1: number): number;index0index0 argument (number).
index1index1 argument (number).
index2index2 argument (number).
The number result.
size
Return the two-dimensional image extent as width and height.
size(): Size;The Size result.
roi
Create an owned matrix header for a rectangular region. Pixel storage is shared with the parent matrix; the result may be non-contiguous.
roi(_0: Rect): Mat;_00 argument (Rect).
The Mat result. Release returned native handles with using or delete(), including handles nested in results.
setTo
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.
setTo(value: Scalar, mask: Mat): void;valueAssigned scalar converted to the actual array type.
maskOperation 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.
These signatures describe this package. Upstream documentation can mention optional backends that are absent from this build. Check runtime compatibility before choosing a backend or file format.