OpenCV
4.4.0dev
Open Source Computer Vision

Functions  
GMat  cv::gapi::bilateralFilter (const GMat &src, int d, double sigmaColor, double sigmaSpace, int borderType=BORDER_DEFAULT) 
Applies the bilateral filter to an image. More...  
GMat  cv::gapi::blur (const GMat &src, const Size &ksize, const Point &anchor=Point(1,1), int borderType=BORDER_DEFAULT, const Scalar &borderValue=Scalar(0)) 
Blurs an image using the normalized box filter. More...  
GMat  cv::gapi::boxFilter (const GMat &src, int dtype, const Size &ksize, const Point &anchor=Point(1,1), bool normalize=true, int borderType=BORDER_DEFAULT, const Scalar &borderValue=Scalar(0)) 
Blurs an image using the box filter. More...  
GMat  cv::gapi::Canny (const GMat &image, double threshold1, double threshold2, int apertureSize=3, bool L2gradient=false) 
Finds edges in an image using the Canny algorithm. More...  
GMat  cv::gapi::dilate (const GMat &src, const Mat &kernel, const Point &anchor=Point(1,1), int iterations=1, int borderType=BORDER_CONSTANT, const Scalar &borderValue=morphologyDefaultBorderValue()) 
Dilates an image by using a specific structuring element. More...  
GMat  cv::gapi::dilate3x3 (const GMat &src, int iterations=1, int borderType=BORDER_CONSTANT, const Scalar &borderValue=morphologyDefaultBorderValue()) 
Dilates an image by using 3 by 3 rectangular structuring element. More...  
GMat  cv::gapi::equalizeHist (const GMat &src) 
Equalizes the histogram of a grayscale image. More...  
GMat  cv::gapi::erode (const GMat &src, const Mat &kernel, const Point &anchor=Point(1,1), int iterations=1, int borderType=BORDER_CONSTANT, const Scalar &borderValue=morphologyDefaultBorderValue()) 
Erodes an image by using a specific structuring element. More...  
GMat  cv::gapi::erode3x3 (const GMat &src, int iterations=1, int borderType=BORDER_CONSTANT, const Scalar &borderValue=morphologyDefaultBorderValue()) 
Erodes an image by using 3 by 3 rectangular structuring element. More...  
GMat  cv::gapi::filter2D (const GMat &src, int ddepth, const Mat &kernel, const Point &anchor=Point(1,1), const Scalar &delta=Scalar(0), int borderType=BORDER_DEFAULT, const Scalar &borderValue=Scalar(0)) 
Convolves an image with the kernel. More...  
GMat  cv::gapi::gaussianBlur (const GMat &src, const Size &ksize, double sigmaX, double sigmaY=0, int borderType=BORDER_DEFAULT, const Scalar &borderValue=Scalar(0)) 
Blurs an image using a Gaussian filter. More...  
GArray< Point2f >  cv::gapi::goodFeaturesToTrack (const GMat &image, int maxCorners, double qualityLevel, double minDistance, const Mat &mask=Mat(), int blockSize=3, bool useHarrisDetector=false, double k=0.04) 
Determines strong corners on an image. More...  
GMat  cv::gapi::Laplacian (const GMat &src, int ddepth, int ksize=1, double scale=1, double delta=0, int borderType=BORDER_DEFAULT) 
Calculates the Laplacian of an image. More...  
GMat  cv::gapi::medianBlur (const GMat &src, int ksize) 
Blurs an image using the median filter. More...  
GMat  cv::gapi::sepFilter (const GMat &src, int ddepth, const Mat &kernelX, const Mat &kernelY, const Point &anchor, const Scalar &delta, int borderType=BORDER_DEFAULT, const Scalar &borderValue=Scalar(0)) 
Applies a separable linear filter to a matrix(image). More...  
GMat  cv::gapi::Sobel (const GMat &src, int ddepth, int dx, int dy, int ksize=3, double scale=1, double delta=0, int borderType=BORDER_DEFAULT, const Scalar &borderValue=Scalar(0)) 
Calculates the first, second, third, or mixed image derivatives using an extended Sobel operator. More...  
std::tuple< GMat, GMat >  cv::gapi::SobelXY (const GMat &src, int ddepth, int order, int ksize=3, double scale=1, double delta=0, int borderType=BORDER_DEFAULT, const Scalar &borderValue=Scalar(0)) 
Calculates the first, second, third, or mixed image derivatives using an extended Sobel operator. More...  
GMat cv::gapi::bilateralFilter  (  const GMat &  src, 
int  d,  
double  sigmaColor,  
double  sigmaSpace,  
int  borderType = BORDER_DEFAULT 

) 
#include <opencv2/gapi/imgproc.hpp>
Applies the bilateral filter to an image.
The function applies bilateral filtering to the input image, as described in http://www.dai.ed.ac.uk/CVonline/LOCAL_COPIES/MANDUCHI1/Bilateral_Filtering.html bilateralFilter can reduce unwanted noise very well while keeping edges fairly sharp. However, it is very slow compared to most filters.
Sigma values: For simplicity, you can set the 2 sigma values to be the same. If they are small (< 10), the filter will not have much effect, whereas if they are large (> 150), they will have a very strong effect, making the image look "cartoonish".
Filter size: Large filters (d > 5) are very slow, so it is recommended to use d=5 for realtime applications, and perhaps d=9 for offline applications that need heavy noise filtering.
This filter does not work inplace.
src  Source 8bit or floatingpoint, 1channel or 3channel image. 
d  Diameter of each pixel neighborhood that is used during filtering. If it is nonpositive, it is computed from sigmaSpace. 
sigmaColor  Filter sigma in the color space. A larger value of the parameter means that farther colors within the pixel neighborhood (see sigmaSpace) will be mixed together, resulting in larger areas of semiequal color. 
sigmaSpace  Filter sigma in the coordinate space. A larger value of the parameter means that farther pixels will influence each other as long as their colors are close enough (see sigmaColor ). When d>0, it specifies the neighborhood size regardless of sigmaSpace. Otherwise, d is proportional to sigmaSpace. 
borderType  border mode used to extrapolate pixels outside of the image, see BorderTypes 
GMat cv::gapi::blur  (  const GMat &  src, 
const Size &  ksize,  
const Point &  anchor = Point(1,1) , 

int  borderType = BORDER_DEFAULT , 

const Scalar &  borderValue = Scalar(0) 

) 
#include <opencv2/gapi/imgproc.hpp>
Blurs an image using the normalized box filter.
The function smooths an image using the kernel:
\[\texttt{K} = \frac{1}{\texttt{ksize.width*ksize.height}} \begin{bmatrix} 1 & 1 & 1 & \cdots & 1 & 1 \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \hdotsfor{6} \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \end{bmatrix}\]
The call blur(src, ksize, anchor, borderType)
is equivalent to boxFilter(src, src.type(), ksize, anchor, true, borderType)
.
Supported input matrix data types are CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1. Output image must have the same type, size, and number of channels as the input image.
src  Source image. 
ksize  blurring kernel size. 
anchor  anchor point; default value Point(1,1) means that the anchor is at the kernel center. 
borderType  border mode used to extrapolate pixels outside of the image, see cv::BorderTypes 
borderValue  border value in case of constant border type 
GMat cv::gapi::boxFilter  (  const GMat &  src, 
int  dtype,  
const Size &  ksize,  
const Point &  anchor = Point(1,1) , 

bool  normalize = true , 

int  borderType = BORDER_DEFAULT , 

const Scalar &  borderValue = Scalar(0) 

) 
#include <opencv2/gapi/imgproc.hpp>
Blurs an image using the box filter.
The function smooths an image using the kernel:
\[\texttt{K} = \alpha \begin{bmatrix} 1 & 1 & 1 & \cdots & 1 & 1 \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \hdotsfor{6} \\ 1 & 1 & 1 & \cdots & 1 & 1 \end{bmatrix}\]
where
\[\alpha = \begin{cases} \frac{1}{\texttt{ksize.width*ksize.height}} & \texttt{when } \texttt{normalize=true} \\1 & \texttt{otherwise} \end{cases}\]
Unnormalized box filter is useful for computing various integral characteristics over each pixel neighborhood, such as covariance matrices of image derivatives (used in dense optical flow algorithms, and so on). If you need to compute pixel sums over variablesize windows, use cv::integral.
Supported input matrix data types are CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1. Output image must have the same type, size, and number of channels as the input image.
src  Source image. 
dtype  the output image depth (1 to set the input image data type). 
ksize  blurring kernel size. 
anchor  Anchor position within the kernel. The default value \((1,1)\) means that the anchor is at the kernel center. 
normalize  flag, specifying whether the kernel is normalized by its area or not. 
borderType  Pixel extrapolation method, see cv::BorderTypes 
borderValue  border value in case of constant border type 
GMat cv::gapi::Canny  (  const GMat &  image, 
double  threshold1,  
double  threshold2,  
int  apertureSize = 3 , 

bool  L2gradient = false 

) 
#include <opencv2/gapi/imgproc.hpp>
Finds edges in an image using the Canny algorithm.
The function finds edges in the input image and marks them in the output map edges using the Canny algorithm. The smallest value between threshold1 and threshold2 is used for edge linking. The largest value is used to find initial segments of strong edges. See http://en.wikipedia.org/wiki/Canny_edge_detector
image  8bit input image. 
threshold1  first threshold for the hysteresis procedure. 
threshold2  second threshold for the hysteresis procedure. 
apertureSize  aperture size for the Sobel operator. 
L2gradient  a flag, indicating whether a more accurate \(L_2\) norm \(=\sqrt{(dI/dx)^2 + (dI/dy)^2}\) should be used to calculate the image gradient magnitude ( L2gradient=true ), or whether the default \(L_1\) norm \(=dI/dx+dI/dy\) is enough ( L2gradient=false ). 
GMat cv::gapi::dilate  (  const GMat &  src, 
const Mat &  kernel,  
const Point &  anchor = Point(1,1) , 

int  iterations = 1 , 

int  borderType = BORDER_CONSTANT , 

const Scalar &  borderValue = morphologyDefaultBorderValue() 

) 
#include <opencv2/gapi/imgproc.hpp>
Dilates an image by using a specific structuring element.
The function dilates the source image using the specified structuring element that determines the shape of a pixel neighborhood over which the maximum is taken:
\[\texttt{dst} (x,y) = \max _{(x',y'): \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\]
Dilation can be applied several (iterations) times. In case of multichannel images, each channel is processed independently. Supported input matrix data types are CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, and CV_32FC1. Output image must have the same type, size, and number of channels as the input image.
src  input image. 
kernel  structuring element used for dilation; if elemenat=Mat(), a 3 x 3 rectangular structuring element is used. Kernel can be created using getStructuringElement 
anchor  position of the anchor within the element; default value (1, 1) means that the anchor is at the element center. 
iterations  number of times dilation is applied. 
borderType  pixel extrapolation method, see cv::BorderTypes 
borderValue  border value in case of a constant border 
GMat cv::gapi::dilate3x3  (  const GMat &  src, 
int  iterations = 1 , 

int  borderType = BORDER_CONSTANT , 

const Scalar &  borderValue = morphologyDefaultBorderValue() 

) 
#include <opencv2/gapi/imgproc.hpp>
Dilates an image by using 3 by 3 rectangular structuring element.
The function dilates the source image using the specified structuring element that determines the shape of a pixel neighborhood over which the maximum is taken:
\[\texttt{dst} (x,y) = \max _{(x',y'): \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\]
Dilation can be applied several (iterations) times. In case of multichannel images, each channel is processed independently. Supported input matrix data types are CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, and CV_32FC1. Output image must have the same type, size, and number of channels as the input image.
src  input image. 
iterations  number of times dilation is applied. 
borderType  pixel extrapolation method, see cv::BorderTypes 
borderValue  border value in case of a constant border 
#include <opencv2/gapi/imgproc.hpp>
Equalizes the histogram of a grayscale image.
The function equalizes the histogram of the input image using the following algorithm:
\[H'_i = \sum _{0 \le j < i} H(j)\]
The algorithm normalizes the brightness and increases the contrast of the image.
src  Source 8bit single channel image. 
GMat cv::gapi::erode  (  const GMat &  src, 
const Mat &  kernel,  
const Point &  anchor = Point(1,1) , 

int  iterations = 1 , 

int  borderType = BORDER_CONSTANT , 

const Scalar &  borderValue = morphologyDefaultBorderValue() 

) 
#include <opencv2/gapi/imgproc.hpp>
Erodes an image by using a specific structuring element.
The function erodes the source image using the specified structuring element that determines the shape of a pixel neighborhood over which the minimum is taken:
\[\texttt{dst} (x,y) = \min _{(x',y'): \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\]
Erosion can be applied several (iterations) times. In case of multichannel images, each channel is processed independently. Supported input matrix data types are CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, and CV_32FC1. Output image must have the same type, size, and number of channels as the input image.
src  input image 
kernel  structuring element used for erosion; if element=Mat() , a 3 x 3 rectangular structuring element is used. Kernel can be created using getStructuringElement. 
anchor  position of the anchor within the element; default value (1, 1) means that the anchor is at the element center. 
iterations  number of times erosion is applied. 
borderType  pixel extrapolation method, see cv::BorderTypes 
borderValue  border value in case of a constant border 
GMat cv::gapi::erode3x3  (  const GMat &  src, 
int  iterations = 1 , 

int  borderType = BORDER_CONSTANT , 

const Scalar &  borderValue = morphologyDefaultBorderValue() 

) 
#include <opencv2/gapi/imgproc.hpp>
Erodes an image by using 3 by 3 rectangular structuring element.
The function erodes the source image using the rectangular structuring element with rectangle center as an anchor. Erosion can be applied several (iterations) times. In case of multichannel images, each channel is processed independently. Supported input matrix data types are CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, and CV_32FC1. Output image must have the same type, size, and number of channels as the input image.
src  input image 
iterations  number of times erosion is applied. 
borderType  pixel extrapolation method, see cv::BorderTypes 
borderValue  border value in case of a constant border 
GMat cv::gapi::filter2D  (  const GMat &  src, 
int  ddepth,  
const Mat &  kernel,  
const Point &  anchor = Point(1,1) , 

const Scalar &  delta = Scalar(0) , 

int  borderType = BORDER_DEFAULT , 

const Scalar &  borderValue = Scalar(0) 

) 
#include <opencv2/gapi/imgproc.hpp>
Convolves an image with the kernel.
The function applies an arbitrary linear filter to an image. When the aperture is partially outside the image, the function interpolates outlier pixel values according to the specified border mode.
The function does actually compute correlation, not the convolution:
\[\texttt{dst} (x,y) = \sum _{ \substack{0\leq x' < \texttt{kernel.cols}\\{0\leq y' < \texttt{kernel.rows}}}} \texttt{kernel} (x',y')* \texttt{src} (x+x' \texttt{anchor.x} ,y+y' \texttt{anchor.y} )\]
That is, the kernel is not mirrored around the anchor point. If you need a real convolution, flip the kernel using flip and set the new anchor to (kernel.cols  anchor.x  1, kernel.rows  anchor.y  1)
.
Supported matrix data types are CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1. Output image must have the same size and number of channels an input image.
src  input image. 
ddepth  desired depth of the destination image 
kernel  convolution kernel (or rather a correlation kernel), a singlechannel floating point matrix; if you want to apply different kernels to different channels, split the image into separate color planes using split and process them individually. 
anchor  anchor of the kernel that indicates the relative position of a filtered point within the kernel; the anchor should lie within the kernel; default value (1,1) means that the anchor is at the kernel center. 
delta  optional value added to the filtered pixels before storing them in dst. 
borderType  pixel extrapolation method, see cv::BorderTypes 
borderValue  border value in case of constant border type 
GMat cv::gapi::gaussianBlur  (  const GMat &  src, 
const Size &  ksize,  
double  sigmaX,  
double  sigmaY = 0 , 

int  borderType = BORDER_DEFAULT , 

const Scalar &  borderValue = Scalar(0) 

) 
#include <opencv2/gapi/imgproc.hpp>
Blurs an image using a Gaussian filter.
The function filter2Ds the source image with the specified Gaussian kernel. Output image must have the same type and number of channels an input image.
Supported input matrix data types are CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1. Output image must have the same type, size, and number of channels as the input image.
src  input image; 
ksize  Gaussian kernel size. ksize.width and ksize.height can differ but they both must be positive and odd. Or, they can be zero's and then they are computed from sigma. 
sigmaX  Gaussian kernel standard deviation in X direction. 
sigmaY  Gaussian kernel standard deviation in Y direction; if sigmaY is zero, it is set to be equal to sigmaX, if both sigmas are zeros, they are computed from ksize.width and ksize.height, respectively (see cv::getGaussianKernel for details); to fully control the result regardless of possible future modifications of all this semantics, it is recommended to specify all of ksize, sigmaX, and sigmaY. 
borderType  pixel extrapolation method, see cv::BorderTypes 
borderValue  border value in case of constant border type 
GArray<Point2f> cv::gapi::goodFeaturesToTrack  (  const GMat &  image, 
int  maxCorners,  
double  qualityLevel,  
double  minDistance,  
const Mat &  mask = Mat() , 

int  blockSize = 3 , 

bool  useHarrisDetector = false , 

double  k = 0.04 

) 
#include <opencv2/gapi/imgproc.hpp>
Determines strong corners on an image.
The function finds the most prominent corners in the image or in the specified image region, as described in [212]
The function can be used to initialize a pointbased tracker of an object.
image  Input 8bit or floatingpoint 32bit, singlechannel image. 
maxCorners  Maximum number of corners to return. If there are more corners than are found, the strongest of them is returned. maxCorners <= 0 implies that no limit on the maximum is set and all detected corners are returned. 
qualityLevel  Parameter characterizing the minimal accepted quality of image corners. The parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue (see cornerMinEigenVal ) or the Harris function response (see cornerHarris ). The corners with the quality measure less than the product are rejected. For example, if the best corner has the quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure less than 15 are rejected. 
minDistance  Minimum possible Euclidean distance between the returned corners. 
mask  Optional region of interest. If the image is not empty (it needs to have the type CV_8UC1 and the same size as image ), it specifies the region in which the corners are detected. 
blockSize  Size of an average block for computing a derivative covariation matrix over each pixel neighborhood. See cornerEigenValsAndVecs . 
useHarrisDetector  Parameter indicating whether to use a Harris detector (see cornerHarris) or cornerMinEigenVal. 
k  Free parameter of the Harris detector. 
GMat cv::gapi::Laplacian  (  const GMat &  src, 
int  ddepth,  
int  ksize = 1 , 

double  scale = 1 , 

double  delta = 0 , 

int  borderType = BORDER_DEFAULT 

) 
#include <opencv2/gapi/imgproc.hpp>
Calculates the Laplacian of an image.
The function calculates the Laplacian of the source image by adding up the second x and y derivatives calculated using the Sobel operator:
\[\texttt{dst} = \Delta \texttt{src} = \frac{\partial^2 \texttt{src}}{\partial x^2} + \frac{\partial^2 \texttt{src}}{\partial y^2}\]
This is done when ksize > 1
. When ksize == 1
, the Laplacian is computed by filtering the image with the following \(3 \times 3\) aperture:
\[\vecthreethree {0}{1}{0}{1}{4}{1}{0}{1}{0}\]
src  Source image. 
ddepth  Desired depth of the destination image. 
ksize  Aperture size used to compute the secondderivative filters. See getDerivKernels for details. The size must be positive and odd. 
scale  Optional scale factor for the computed Laplacian values. By default, no scaling is applied. See getDerivKernels for details. 
delta  Optional delta value that is added to the results prior to storing them in dst . 
borderType  Pixel extrapolation method, see BorderTypes. BORDER_WRAP is not supported. 
#include <opencv2/gapi/imgproc.hpp>
Blurs an image using the median filter.
The function smoothes an image using the median filter with the \(\texttt{ksize} \times \texttt{ksize}\) aperture. Each channel of a multichannel image is processed independently. Output image must have the same type, size, and number of channels as the input image.
src  input matrix (image) 
ksize  aperture linear size; it must be odd and greater than 1, for example: 3, 5, 7 ... 
GMat cv::gapi::sepFilter  (  const GMat &  src, 
int  ddepth,  
const Mat &  kernelX,  
const Mat &  kernelY,  
const Point &  anchor,  
const Scalar &  delta,  
int  borderType = BORDER_DEFAULT , 

const Scalar &  borderValue = Scalar(0) 

) 
#include <opencv2/gapi/imgproc.hpp>
Applies a separable linear filter to a matrix(image).
The function applies a separable linear filter to the matrix. That is, first, every row of src is filtered with the 1D kernel kernelX. Then, every column of the result is filtered with the 1D kernel kernelY. The final result is returned.
Supported matrix data types are CV_8UC1, CV_8UC3, CV_16UC1, CV_16SC1, CV_32FC1. Output image must have the same type, size, and number of channels as the input image.
src  Source image. 
ddepth  desired depth of the destination image (the following combinations of src.depth() and ddepth are supported: src.depth() = CV_8U, ddepth = 1/CV_16S/CV_32F/CV_64F src.depth() = CV_16U/CV_16S, ddepth = 1/CV_32F/CV_64F src.depth() = CV_32F, ddepth = 1/CV_32F/CV_64F src.depth() = CV_64F, ddepth = 1/CV_64F 
when ddepth=1, the output image will have the same depth as the source)
kernelX  Coefficients for filtering each row. 
kernelY  Coefficients for filtering each column. 
anchor  Anchor position within the kernel. The default value \((1,1)\) means that the anchor is at the kernel center. 
delta  Value added to the filtered results before storing them. 
borderType  Pixel extrapolation method, see cv::BorderTypes 
borderValue  border value in case of constant border type 
GMat cv::gapi::Sobel  (  const GMat &  src, 
int  ddepth,  
int  dx,  
int  dy,  
int  ksize = 3 , 

double  scale = 1 , 

double  delta = 0 , 

int  borderType = BORDER_DEFAULT , 

const Scalar &  borderValue = Scalar(0) 

) 
#include <opencv2/gapi/imgproc.hpp>
Calculates the first, second, third, or mixed image derivatives using an extended Sobel operator.
In all cases except one, the \(\texttt{ksize} \times \texttt{ksize}\) separable kernel is used to calculate the derivative. When \(\texttt{ksize = 1}\), the \(3 \times 1\) or \(1 \times 3\) kernel is used (that is, no Gaussian smoothing is done). ksize = 1
can only be used for the first or the second x or y derivatives.
There is also the special value ksize = FILTER_SCHARR (1)
that corresponds to the \(3\times3\) Scharr filter that may give more accurate results than the \(3\times3\) Sobel. The Scharr aperture is
\[\vecthreethree{3}{0}{3}{10}{0}{10}{3}{0}{3}\]
for the xderivative, or transposed for the yderivative.
The function calculates an image derivative by convolving the image with the appropriate kernel:
\[\texttt{dst} = \frac{\partial^{xorder+yorder} \texttt{src}}{\partial x^{xorder} \partial y^{yorder}}\]
The Sobel operators combine Gaussian smoothing and differentiation, so the result is more or less resistant to the noise. Most often, the function is called with ( xorder = 1, yorder = 0, ksize = 3) or ( xorder = 0, yorder = 1, ksize = 3) to calculate the first x or y image derivative. The first case corresponds to a kernel of:
\[\vecthreethree{1}{0}{1}{2}{0}{2}{1}{0}{1}\]
The second case corresponds to a kernel of:
\[\vecthreethree{1}{2}{1}{0}{0}{0}{1}{2}{1}\]
src  input image. 
ddepth  output image depth, see combinations; in the case of 8bit input images it will result in truncated derivatives. 
dx  order of the derivative x. 
dy  order of the derivative y. 
ksize  size of the extended Sobel kernel; it must be odd. 
scale  optional scale factor for the computed derivative values; by default, no scaling is applied (see cv::getDerivKernels for details). 
delta  optional delta value that is added to the results prior to storing them in dst. 
borderType  pixel extrapolation method, see cv::BorderTypes 
borderValue  border value in case of constant border type 
std::tuple<GMat, GMat> cv::gapi::SobelXY  (  const GMat &  src, 
int  ddepth,  
int  order,  
int  ksize = 3 , 

double  scale = 1 , 

double  delta = 0 , 

int  borderType = BORDER_DEFAULT , 

const Scalar &  borderValue = Scalar(0) 

) 
#include <opencv2/gapi/imgproc.hpp>
Calculates the first, second, third, or mixed image derivatives using an extended Sobel operator.
In all cases except one, the \(\texttt{ksize} \times \texttt{ksize}\) separable kernel is used to calculate the derivative. When \(\texttt{ksize = 1}\), the \(3 \times 1\) or \(1 \times 3\) kernel is used (that is, no Gaussian smoothing is done). ksize = 1
can only be used for the first or the second x or y derivatives.
There is also the special value ksize = FILTER_SCHARR (1)
that corresponds to the \(3\times3\) Scharr filter that may give more accurate results than the \(3\times3\) Sobel. The Scharr aperture is
\[\vecthreethree{3}{0}{3}{10}{0}{10}{3}{0}{3}\]
for the xderivative, or transposed for the yderivative.
The function calculates an image derivative by convolving the image with the appropriate kernel:
\[\texttt{dst} = \frac{\partial^{xorder+yorder} \texttt{src}}{\partial x^{xorder} \partial y^{yorder}}\]
The Sobel operators combine Gaussian smoothing and differentiation, so the result is more or less resistant to the noise. Most often, the function is called with ( xorder = 1, yorder = 0, ksize = 3) or ( xorder = 0, yorder = 1, ksize = 3) to calculate the first x or y image derivative. The first case corresponds to a kernel of:
\[\vecthreethree{1}{0}{1}{2}{0}{2}{1}{0}{1}\]
The second case corresponds to a kernel of:
\[\vecthreethree{1}{2}{1}{0}{0}{0}{1}{2}{1}\]
src  input image. 
ddepth  output image depth, see combinations; in the case of 8bit input images it will result in truncated derivatives. 
order  order of the derivatives. 
ksize  size of the extended Sobel kernel; it must be odd. 
scale  optional scale factor for the computed derivative values; by default, no scaling is applied (see cv::getDerivKernels for details). 
delta  optional delta value that is added to the results prior to storing them in dst. 
borderType  pixel extrapolation method, see cv::BorderTypes 
borderValue  border value in case of constant border type 