Graph API: Image filters

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...

Detailed Description

Function Documentation

bilateralFilter()

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 real-time applications, and perhaps d=9 for offline applications that need heavy noise filtering.

This filter does not work inplace.

Note

Function textual ID is "org.opencv.imgproc.filters.bilateralfilter"

Parameters

src	Source 8-bit or floating-point, 1-channel or 3-channel image.
d	Diameter of each pixel neighborhood that is used during filtering. If it is non-positive, 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 semi-equal 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

Returns

Destination image of the same size and type as src.

blur()

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.

Note

Rounding to nearest even is procedeed if hardware supports it, if not - to nearest.

Function textual ID is "org.opencv.imgproc.filters.blur"

Parameters

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

See also

boxFilter, bilateralFilter, GaussianBlur, medianBlur

boxFilter()

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 variable-size 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.

Note

Rounding to nearest even is procedeed if hardware supports it, if not - to nearest.

Function textual ID is "org.opencv.imgproc.filters.boxfilter"

Parameters

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

See also

sepFilter, gaussianBlur, medianBlur, integral

Canny()

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

Note

Function textual ID is "org.opencv.imgproc.filters.canny"

Parameters

image	8-bit 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 ).

dilate()

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 multi-channel 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.

Note

Rounding to nearest even is procedeed if hardware supports it, if not - to nearest.

Function textual ID is "org.opencv.imgproc.filters.dilate"

Parameters

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

See also

erode, morphologyEx, getStructuringElement

dilate3x3()

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 multi-channel 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.

Note

Rounding to nearest even is procedeed if hardware supports it, if not - to nearest.

Function textual ID is "org.opencv.imgproc.filters.dilate"

Parameters

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

See also

dilate, erode3x3

equalizeHist()

GMat cv::gapi::equalizeHist

(

const GMat &

src

)

#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:

• Calculate the histogram \(H\) for src .
• Normalize the histogram so that the sum of histogram bins is 255.
• Compute the integral of the histogram:

  \[H'_i = \sum _{0 \le j < i} H(j)\]

• Transform the image using \(H'\) as a look-up table: \(\texttt{dst}(x,y) = H'(\texttt{src}(x,y))\)

The algorithm normalizes the brightness and increases the contrast of the image.

Note

The returned image is of the same size and type as input.

Function textual ID is "org.opencv.imgproc.equalizeHist"

Parameters

src	Source 8-bit single channel image.

erode()

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 multi-channel 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.

Note

Rounding to nearest even is procedeed if hardware supports it, if not - to nearest.

Function textual ID is "org.opencv.imgproc.filters.erode"

Parameters

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

See also

dilate

erode3x3()

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 multi-channel 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.

Note

Rounding to nearest even is procedeed if hardware supports it, if not - to nearest.

Parameters

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

See also

erode, dilate3x3

filter2D()

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.

Note

Rounding to nearest even is procedeed if hardware supports it, if not - to nearest.

Function textual ID is "org.opencv.imgproc.filters.filter2D"

Parameters

src	input image.
ddepth	desired depth of the destination image
kernel	convolution kernel (or rather a correlation kernel), a single-channel 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

See also

sepFilter

gaussianBlur()

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.

Note

Rounding to nearest even is procedeed if hardware supports it, if not - to nearest.

Function textual ID is "org.opencv.imgproc.filters.gaussianBlur"

Parameters

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

See also

sepFilter, boxFilter, medianBlur

goodFeaturesToTrack()

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 [215]

• Function calculates the corner quality measure at every source image pixel using the cornerMinEigenVal or cornerHarris .
• Function performs a non-maximum suppression (the local maximums in 3 x 3 neighborhood are retained).
• The corners with the minimal eigenvalue less than \(\texttt{qualityLevel} \cdot \max_{x,y} qualityMeasureMap(x,y)\) are rejected.
• The remaining corners are sorted by the quality measure in the descending order.
• Function throws away each corner for which there is a stronger corner at a distance less than maxDistance.

The function can be used to initialize a point-based tracker of an object.

Note

If the function is called with different values A and B of the parameter qualityLevel , and A > B, the vector of returned corners with qualityLevel=A will be the prefix of the output vector with qualityLevel=B .

Function textual ID is "org.opencv.imgproc.goodFeaturesToTrack"

Parameters

image	Input 8-bit or floating-point 32-bit, single-channel 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.

Returns

vector of detected corners.

Laplacian()

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:

\[\vecthreethree {0}{1}{0}{1}{-4}{1}{0}{1}{0}\]

Note

Function textual ID is "org.opencv.imgproc.filters.laplacian"

Parameters

src	Source image.
ddepth	Desired depth of the destination image.
ksize	Aperture size used to compute the second-derivative 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.

Returns

Destination image of the same size and the same number of channels as src.

See also

Sobel, Scharr

medianBlur()

GMat cv::gapi::medianBlur	(	const GMat &	src,
		int	ksize
	)

#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 multi-channel image is processed independently. Output image must have the same type, size, and number of channels as the input image.

Note

Rounding to nearest even is procedeed if hardware supports it, if not - to nearest. The median filter uses cv::BORDER_REPLICATE internally to cope with border pixels, see cv::BorderTypes

Function textual ID is "org.opencv.imgproc.filters.medianBlur"

Parameters

src	input matrix (image)
ksize	aperture linear size; it must be odd and greater than 1, for example: 3, 5, 7 ...

See also

boxFilter, gaussianBlur

sepFilter()

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.

Note

In case of floating-point computation, rounding to nearest even is procedeed if hardware supports it (if not - to nearest value).

Function textual ID is "org.opencv.imgproc.filters.sepfilter"

Parameters

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)

Parameters

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

See also

boxFilter, gaussianBlur, medianBlur

Sobel()

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 x-derivative, or transposed for the y-derivative.

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:

\[\vecthreethree{-1}{-2}{-1}{0}{0}{0}{1}{2}{1}\]

Note

Rounding to nearest even is procedeed if hardware supports it, if not - to nearest.

Function textual ID is "org.opencv.imgproc.filters.sobel"

Parameters

src	input image.
ddepth	output image depth, see combinations; in the case of 8-bit 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

See also

filter2D, gaussianBlur, cartToPolar

SobelXY()

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 x-derivative, or transposed for the y-derivative.

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:

\[\vecthreethree{-1}{-2}{-1}{0}{0}{0}{1}{2}{1}\]

Note

First returned matrix correspons to dx derivative while the second one to dy.

Rounding to nearest even is procedeed if hardware supports it, if not - to nearest.

Function textual ID is "org.opencv.imgproc.filters.sobelxy"

Parameters

src	input image.
ddepth	output image depth, see combinations; in the case of 8-bit 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

See also

filter2D, gaussianBlur, cartToPolar