Graph API: Operations on matrices

Detailed Description

Functions
GMat	cv::gapi::absDiff (const GMat &src1, const GMat &src2)
	Calculates the per-element absolute difference between two matrices.
GMat	cv::gapi::absDiffC (const GMat &src, const GScalar &c)
	Calculates absolute value of matrix elements.
GMat	cv::gapi::addWeighted (const GMat &src1, double alpha, const GMat &src2, double beta, double gamma, int ddepth=-1)
	Calculates the weighted sum of two matrices.
GOpaque< int >	cv::gapi::countNonZero (const GMat &src)
	Counts non-zero array elements.
GMat	cv::gapi::inRange (const GMat &src, const GScalar &threshLow, const GScalar &threshUp)
	Applies a range-level threshold to each matrix element.
std::tuple< GMat, GMat >	cv::gapi::integral (const GMat &src, int sdepth=-1, int sqdepth=-1)
	Calculates the integral of an image.
GMat	cv::gapi::max (const GMat &src1, const GMat &src2)
	Calculates per-element maximum of two matrices.
GMat	cv::gapi::min (const GMat &src1, const GMat &src2)
	Calculates per-element minimum of two matrices.
GScalar	cv::gapi::normInf (const GMat &src)
	Calculates the absolute infinite norm of a matrix.
GScalar	cv::gapi::normL1 (const GMat &src)
	Calculates the absolute L1 norm of a matrix.
GScalar	cv::gapi::normL2 (const GMat &src)
	Calculates the absolute L2 norm of a matrix.
GScalar	cv::gapi::sum (const GMat &src)
	Calculates sum of all matrix elements.
std::tuple< GMat, GScalar >	cv::gapi::threshold (const GMat &src, const GScalar &maxval, int type)
GMat	cv::gapi::threshold (const GMat &src, const GScalar &thresh, const GScalar &maxval, int type)
	Applies a fixed-level threshold to each matrix element.

Function Documentation

absDiff()

GMat cv::gapi::absDiff	(	const GMat &	src1,
		const GMat &	src2
	)

Python:
	cv.gapi.absDiff(	src1, src2	) ->	retval

#include <opencv2/gapi/core.hpp>

Calculates the per-element absolute difference between two matrices.

The function absDiff calculates absolute difference between two matrices of the same size and depth:

\[\texttt{dst}(I) = \texttt{saturate} (| \texttt{src1}(I) - \texttt{src2}(I)|)\]

where I is a multi-dimensional index of matrix elements. In case of multi-channel matrices, each channel is processed independently. Output matrix must have the same size and depth as input matrices.

Supported matrix data types are CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1.

Note

Function textual ID is "org.opencv.core.matrixop.absdiff"

Parameters

src1	first input matrix.
src2	second input matrix.

See also

abs

absDiffC()

GMat cv::gapi::absDiffC	(	const GMat &	src,
		const GScalar &	c
	)

Python:
	cv.gapi.absDiffC(	src, c	) ->	retval

#include <opencv2/gapi/core.hpp>

Calculates absolute value of matrix elements.

The function abs calculates absolute difference between matrix elements and given scalar value:

\[\texttt{dst}(I) = \texttt{saturate} (| \texttt{src1}(I) - \texttt{matC}(I)|)\]

where matC is constructed from given scalar c and has the same sizes and depth as input matrix src.

Output matrix must be of the same size and depth as src.

Supported matrix data types are CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1.

Note

Function textual ID is "org.opencv.core.matrixop.absdiffC"

Parameters

src	input matrix.
c	scalar to be subtracted.

See also

min, max

addWeighted()

GMat cv::gapi::addWeighted	(	const GMat &	src1,
		double	alpha,
		const GMat &	src2,
		double	beta,
		double	gamma,
		int	ddepth = -1
	)

Python:
	cv.gapi.addWeighted(	src1, alpha, src2, beta, gamma[, ddepth]	) ->	retval

#include <opencv2/gapi/core.hpp>

Calculates the weighted sum of two matrices.

The function addWeighted calculates the weighted sum of two matrices as follows:

\[\texttt{dst} (I)= \texttt{saturate} ( \texttt{src1} (I)* \texttt{alpha} + \texttt{src2} (I)* \texttt{beta} + \texttt{gamma} )\]

where I is a multi-dimensional index of array elements. In case of multi-channel matrices, each channel is processed independently.

The function can be replaced with a matrix expression:

\[\texttt{dst}(I) = \texttt{alpha} * \texttt{src1}(I) - \texttt{beta} * \texttt{src2}(I) + \texttt{gamma} \]

Supported matrix data types are CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1.

Note

Function textual ID is "org.opencv.core.matrixop.addweighted"

Parameters

src1	first input matrix.
alpha	weight of the first matrix elements.
src2	second input matrix of the same size and channel number as src1.
beta	weight of the second matrix elements.
gamma	scalar added to each sum.
ddepth	optional depth of the output matrix.

See also

add, sub

countNonZero()

GOpaque< int > cv::gapi::countNonZero

(

const GMat &

src

)

Python:
	cv.gapi.countNonZero(	src	) ->	retval

#include <opencv2/gapi/core.hpp>

Counts non-zero array elements.

The function returns the number of non-zero elements in src :

\[\sum _{I: \; \texttt{src} (I) \ne0 } 1\]

Supported matrix data types are CV_8UC1, CV_16UC1, CV_16SC1, CV_32FC1.

Note

Function textual ID is "org.opencv.core.matrixop.countNonZero"

Parameters

src	input single-channel matrix.

See also

mean, min, max

inRange()

GMat cv::gapi::inRange	(	const GMat &	src,
		const GScalar &	threshLow,
		const GScalar &	threshUp
	)

Python:
	cv.gapi.inRange(	src, threshLow, threshUp	) ->	retval

#include <opencv2/gapi/core.hpp>

Applies a range-level threshold to each matrix element.

The function applies range-level thresholding to a single- or multiple-channel matrix. It sets output pixel value to OxFF if the corresponding pixel value of input matrix is in specified range,or 0 otherwise.

Input and output matrices must be CV_8UC1.

Note

Function textual ID is "org.opencv.core.matrixop.inRange"

Parameters

src	input matrix (CV_8UC1).
threshLow	lower boundary value.
threshUp	upper boundary value.

See also

threshold

integral()

std::tuple< GMat, GMat > cv::gapi::integral	(	const GMat &	src,
		int	sdepth = -1,
		int	sqdepth = -1
	)

Python:
	cv.gapi.integral(	src[, sdepth[, sqdepth]]	) ->	retval

#include <opencv2/gapi/core.hpp>

Calculates the integral of an image.

The function calculates one or more integral images for the source image as follows:

\[\texttt{sum} (X,Y) = \sum _{x<X,y<Y} \texttt{image} (x,y)\]

\[\texttt{sqsum} (X,Y) = \sum _{x<X,y<Y} \texttt{image} (x,y)^2\]

The function return integral image as \((W+1)\times (H+1)\) , 32-bit integer or floating-point (32f or 64f) and integral image for squared pixel values; it is \((W+1)\times (H+)\), double-precision floating-point (64f) array.

Note

Function textual ID is "org.opencv.core.matrixop.integral"

Parameters

src	input image.
sdepth	desired depth of the integral and the tilted integral images, CV_32S, CV_32F, or CV_64F.
sqdepth	desired depth of the integral image of squared pixel values, CV_32F or CV_64F.

max()

GMat cv::gapi::max	(	const GMat &	src1,
		const GMat &	src2
	)

Python:
	cv.gapi.max(	src1, src2	) ->	retval

#include <opencv2/gapi/core.hpp>

Calculates per-element maximum of two matrices.

The function max calculates the per-element maximum of two matrices of the same size, number of channels and depth:

\[\texttt{dst} (I)= \max ( \texttt{src1} (I), \texttt{src2} (I))\]

where I is a multi-dimensional index of matrix elements. In case of multi-channel matrices, each channel is processed independently. Output matrix must be of the same size and depth as src1.

Supported matrix data types are CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1.

Note

Function textual ID is "org.opencv.core.matrixop.max"

Parameters

src1	first input matrix.
src2	second input matrix of the same size and depth as src1.

See also

min, compare, cmpEQ, cmpGT, cmpGE

Here is the call graph for this function:

min()

GMat cv::gapi::min	(	const GMat &	src1,
		const GMat &	src2
	)

Python:
	cv.gapi.min(	src1, src2	) ->	retval

#include <opencv2/gapi/core.hpp>

Calculates per-element minimum of two matrices.

The function min calculates the per-element minimum of two matrices of the same size, number of channels and depth:

\[\texttt{dst} (I)= \min ( \texttt{src1} (I), \texttt{src2} (I))\]

where I is a multi-dimensional index of matrix elements. In case of multi-channel matrices, each channel is processed independently. Output matrix must be of the same size and depth as src1.

Supported input matrix data types are CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1.

Note

Function textual ID is "org.opencv.core.matrixop.min"

Parameters

src1	first input matrix.
src2	second input matrix of the same size and depth as src1.

See also

max, cmpEQ, cmpLT, cmpLE

Here is the call graph for this function:

normInf()

GScalar cv::gapi::normInf

(

const GMat &

src

)

Python:
	cv.gapi.normInf(	src	) ->	retval

#include <opencv2/gapi/core.hpp>

Calculates the absolute infinite norm of a matrix.

This version of normInf calculates the absolute infinite norm of src.

As example for one array consider the function \(r(x)= \begin{pmatrix} x \\ 1-x \end{pmatrix}, x \in [-1;1]\). The \( L_{\infty} \) norm for the sample value \(r(-1) = \begin{pmatrix} -1 \\ 2 \end{pmatrix}\) is calculated as follows

\begin{align*} \| r(-1) \|_{L_\infty} &= \max(|-1|,|2|) = 2 \end{align*}

and for \(r(0.5) = \begin{pmatrix} 0.5 \\ 0.5 \end{pmatrix}\) the calculation is

\begin{align*} \| r(0.5) \|_{L_\infty} &= \max(|0.5|,|0.5|) = 0.5. \end{align*}

Supported matrix data types are CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1.

Note

Function textual ID is "org.opencv.core.matrixop.norminf"

Parameters

src	input matrix.

See also

normL1, normL2

normL1()

GScalar cv::gapi::normL1

(

const GMat &

src

)

Python:
	cv.gapi.normL1(	src	) ->	retval

#include <opencv2/gapi/core.hpp>

Calculates the absolute L1 norm of a matrix.

This version of normL1 calculates the absolute L1 norm of src.

As example for one array consider the function \(r(x)= \begin{pmatrix} x \\ 1-x \end{pmatrix}, x \in [-1;1]\). The \( L_{1} \) norm for the sample value \(r(-1) = \begin{pmatrix} -1 \\ 2 \end{pmatrix}\) is calculated as follows

\begin{align*} \| r(-1) \|_{L_1} &= |-1| + |2| = 3 \\ \end{align*}

and for \(r(0.5) = \begin{pmatrix} 0.5 \\ 0.5 \end{pmatrix}\) the calculation is

\begin{align*} \| r(0.5) \|_{L_1} &= |0.5| + |0.5| = 1 \\ \end{align*}

Supported matrix data types are CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1.

Note

Function textual ID is "org.opencv.core.matrixop.norml1"

Parameters

src	input matrix.

See also

normL2, normInf

normL2()

GScalar cv::gapi::normL2

(

const GMat &

src

)

Python:
	cv.gapi.normL2(	src	) ->	retval

#include <opencv2/gapi/core.hpp>

Calculates the absolute L2 norm of a matrix.

This version of normL2 calculates the absolute L2 norm of src.

As example for one array consider the function \(r(x)= \begin{pmatrix} x \\ 1-x \end{pmatrix}, x \in [-1;1]\). The \( L_{2} \) norm for the sample value \(r(-1) = \begin{pmatrix} -1 \\ 2 \end{pmatrix}\) is calculated as follows

\begin{align*} \| r(-1) \|_{L_2} &= \sqrt{(-1)^{2} + (2)^{2}} = \sqrt{5} \\ \end{align*}

and for \(r(0.5) = \begin{pmatrix} 0.5 \\ 0.5 \end{pmatrix}\) the calculation is

\begin{align*} \| r(0.5) \|_{L_2} &= \sqrt{(0.5)^{2} + (0.5)^{2}} = \sqrt{0.5} \\ \end{align*}

Supported matrix data types are CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1.

Note

Function textual ID is "org.opencv.core.matrixop.norml2"

Parameters

src	input matrix.

See also

normL1, normInf

sum()

GScalar cv::gapi::sum

(

const GMat &

src

)

Python:
	cv.gapi.sum(	src	) ->	retval

#include <opencv2/gapi/core.hpp>

Calculates sum of all matrix elements.

The function sum calculates sum of all matrix elements, independently for each channel.

Supported matrix data types are CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1.

Note

Function textual ID is "org.opencv.core.matrixop.sum"

Parameters

src	input matrix.

See also

countNonZero, mean, min, max

threshold() [1/2]

std::tuple< GMat, GScalar > cv::gapi::threshold	(	const GMat &	src,
		const GScalar &	maxval,
		int	type
	)

Python:
	cv.gapi.threshold(	src, thresh, maxval, type	) ->	retval
	cv.gapi.threshold(	src, maxval, type	) ->	retval

#include <opencv2/gapi/core.hpp>

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts. This function applicable for all threshold types except cv::THRESH_OTSU and cv::THRESH_TRIANGLE

Note

Function textual ID is "org.opencv.core.matrixop.thresholdOT"

threshold() [2/2]

GMat cv::gapi::threshold	(	const GMat &	src,
		const GScalar &	thresh,
		const GScalar &	maxval,
		int	type
	)

Python:
	cv.gapi.threshold(	src, thresh, maxval, type	) ->	retval
	cv.gapi.threshold(	src, maxval, type	) ->	retval

#include <opencv2/gapi/core.hpp>

Applies a fixed-level threshold to each matrix element.

The function applies fixed-level thresholding to a single- or multiple-channel matrix. The function is typically used to get a bi-level (binary) image out of a grayscale image ( cmp functions could be also used for this purpose) or for removing a noise, that is, filtering out pixels with too small or too large values. There are several types of thresholding supported by the function. They are determined by type parameter.

Also, the special values cv::THRESH_OTSU or cv::THRESH_TRIANGLE may be combined with one of the above values. In these cases, the function determines the optimal threshold value using the Otsu's or Triangle algorithm and uses it instead of the specified thresh . The function returns the computed threshold value in addititon to thresholded matrix. The Otsu's and Triangle methods are implemented only for 8-bit matrices.

Input image should be single channel only in case of cv::THRESH_OTSU or cv::THRESH_TRIANGLE flags. Output matrix must be of the same size and depth as src.

Note

Function textual ID is "org.opencv.core.matrixop.threshold"

Parameters

src	input matrix (CV_8UC1, CV_8UC3, or CV_32FC1).
thresh	threshold value.
maxval	maximum value to use with the cv::THRESH_BINARY and cv::THRESH_BINARY_INV thresholding types.
type	thresholding type (see the cv::ThresholdTypes).

See also

min, max, cmpGT, cmpLE, cmpGE, cmpLT