OpenCV  3.3.0-dev
Open Source Computer Vision
Enumerations | Functions
Image Filtering

Enumerations

enum  cv::MorphShapes {
  cv::MORPH_RECT = 0,
  cv::MORPH_CROSS = 1,
  cv::MORPH_ELLIPSE = 2
}
 shape of the structuring element More...
 
enum  cv::MorphTypes {
  cv::MORPH_ERODE = 0,
  cv::MORPH_DILATE = 1,
  cv::MORPH_OPEN = 2,
  cv::MORPH_CLOSE = 3,
  cv::MORPH_GRADIENT = 4,
  cv::MORPH_TOPHAT = 5,
  cv::MORPH_BLACKHAT = 6,
  cv::MORPH_HITMISS = 7
}
 type of morphological operation More...
 

Functions

void cv::bilateralFilter (InputArray src, OutputArray dst, int d, double sigmaColor, double sigmaSpace, int borderType=BORDER_DEFAULT)
 Applies the bilateral filter to an image. More...
 
void cv::blur (InputArray src, OutputArray dst, Size ksize, Point anchor=Point(-1,-1), int borderType=BORDER_DEFAULT)
 Blurs an image using the normalized box filter. More...
 
void cv::boxFilter (InputArray src, OutputArray dst, int ddepth, Size ksize, Point anchor=Point(-1,-1), bool normalize=true, int borderType=BORDER_DEFAULT)
 Blurs an image using the box filter. More...
 
void cv::buildPyramid (InputArray src, OutputArrayOfArrays dst, int maxlevel, int borderType=BORDER_DEFAULT)
 Constructs the Gaussian pyramid for an image. More...
 
void cv::dilate (InputArray src, OutputArray dst, InputArray kernel, 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...
 
void cv::erode (InputArray src, OutputArray dst, InputArray kernel, 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...
 
void cv::filter2D (InputArray src, OutputArray dst, int ddepth, InputArray kernel, Point anchor=Point(-1,-1), double delta=0, int borderType=BORDER_DEFAULT)
 Convolves an image with the kernel. More...
 
void cv::GaussianBlur (InputArray src, OutputArray dst, Size ksize, double sigmaX, double sigmaY=0, int borderType=BORDER_DEFAULT)
 Blurs an image using a Gaussian filter. More...
 
void cv::getDerivKernels (OutputArray kx, OutputArray ky, int dx, int dy, int ksize, bool normalize=false, int ktype=CV_32F)
 Returns filter coefficients for computing spatial image derivatives. More...
 
Mat cv::getGaborKernel (Size ksize, double sigma, double theta, double lambd, double gamma, double psi=CV_PI *0.5, int ktype=CV_64F)
 Returns Gabor filter coefficients. More...
 
Mat cv::getGaussianKernel (int ksize, double sigma, int ktype=CV_64F)
 Returns Gaussian filter coefficients. More...
 
Mat cv::getStructuringElement (int shape, Size ksize, Point anchor=Point(-1,-1))
 Returns a structuring element of the specified size and shape for morphological operations. More...
 
void cv::Laplacian (InputArray src, OutputArray dst, int ddepth, int ksize=1, double scale=1, double delta=0, int borderType=BORDER_DEFAULT)
 Calculates the Laplacian of an image. More...
 
void cv::medianBlur (InputArray src, OutputArray dst, int ksize)
 Blurs an image using the median filter. More...
 
static Scalar cv::morphologyDefaultBorderValue ()
 returns "magic" border value for erosion and dilation. It is automatically transformed to Scalar::all(-DBL_MAX) for dilation. More...
 
void cv::morphologyEx (InputArray src, OutputArray dst, int op, InputArray kernel, Point anchor=Point(-1,-1), int iterations=1, int borderType=BORDER_CONSTANT, const Scalar &borderValue=morphologyDefaultBorderValue())
 Performs advanced morphological transformations. More...
 
void cv::pyrDown (InputArray src, OutputArray dst, const Size &dstsize=Size(), int borderType=BORDER_DEFAULT)
 Blurs an image and downsamples it. More...
 
void cv::pyrMeanShiftFiltering (InputArray src, OutputArray dst, double sp, double sr, int maxLevel=1, TermCriteria termcrit=TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS, 5, 1))
 Performs initial step of meanshift segmentation of an image. More...
 
void cv::pyrUp (InputArray src, OutputArray dst, const Size &dstsize=Size(), int borderType=BORDER_DEFAULT)
 Upsamples an image and then blurs it. More...
 
void cv::Scharr (InputArray src, OutputArray dst, int ddepth, int dx, int dy, double scale=1, double delta=0, int borderType=BORDER_DEFAULT)
 Calculates the first x- or y- image derivative using Scharr operator. More...
 
void cv::sepFilter2D (InputArray src, OutputArray dst, int ddepth, InputArray kernelX, InputArray kernelY, Point anchor=Point(-1,-1), double delta=0, int borderType=BORDER_DEFAULT)
 Applies a separable linear filter to an image. More...
 
void cv::Sobel (InputArray src, OutputArray dst, int ddepth, int dx, int dy, int ksize=3, double scale=1, double delta=0, int borderType=BORDER_DEFAULT)
 Calculates the first, second, third, or mixed image derivatives using an extended Sobel operator. More...
 
void cv::spatialGradient (InputArray src, OutputArray dx, OutputArray dy, int ksize=3, int borderType=BORDER_DEFAULT)
 Calculates the first order image derivative in both x and y using a Sobel operator. More...
 
void cv::sqrBoxFilter (InputArray _src, OutputArray _dst, int ddepth, Size ksize, Point anchor=Point(-1, -1), bool normalize=true, int borderType=BORDER_DEFAULT)
 Calculates the normalized sum of squares of the pixel values overlapping the filter. More...
 

Detailed Description

Functions and classes described in this section are used to perform various linear or non-linear filtering operations on 2D images (represented as Mat's). It means that for each pixel location \((x,y)\) in the source image (normally, rectangular), its neighborhood is considered and used to compute the response. In case of a linear filter, it is a weighted sum of pixel values. In case of morphological operations, it is the minimum or maximum values, and so on. The computed response is stored in the destination image at the same location \((x,y)\). It means that the output image will be of the same size as the input image. Normally, the functions support multi-channel arrays, in which case every channel is processed independently. Therefore, the output image will also have the same number of channels as the input one.

Another common feature of the functions and classes described in this section is that, unlike simple arithmetic functions, they need to extrapolate values of some non-existing pixels. For example, if you want to smooth an image using a Gaussian \(3 \times 3\) filter, then, when processing the left-most pixels in each row, you need pixels to the left of them, that is, outside of the image. You can let these pixels be the same as the left-most image pixels ("replicated border" extrapolation method), or assume that all the non-existing pixels are zeros ("constant border" extrapolation method), and so on. OpenCV enables you to specify the extrapolation method. For details, see cv::BorderTypes

Depth combinations

Input depth (src.depth()) Output depth (ddepth)
CV_8U -1/CV_16S/CV_32F/CV_64F
CV_16U/CV_16S -1/CV_32F/CV_64F
CV_32F -1/CV_32F/CV_64F
CV_64F -1/CV_64F
Note
when ddepth=-1, the output image will have the same depth as the source.

Enumeration Type Documentation

§ MorphShapes

shape of the structuring element

Enumerator
MORPH_RECT 

a rectangular structuring element:

\[E_{ij}=1\]

MORPH_CROSS 

a cross-shaped structuring element:

\[E_{ij} = \fork{1}{if i=\texttt{anchor.y} or j=\texttt{anchor.x}}{0}{otherwise}\]

MORPH_ELLIPSE 

an elliptic structuring element, that is, a filled ellipse inscribed into the rectangle Rect(0, 0, esize.width, 0.esize.height)

§ MorphTypes

type of morphological operation

Enumerator
MORPH_ERODE 

see cv::erode

MORPH_DILATE 

see cv::dilate

MORPH_OPEN 

an opening operation

\[\texttt{dst} = \mathrm{open} ( \texttt{src} , \texttt{element} )= \mathrm{dilate} ( \mathrm{erode} ( \texttt{src} , \texttt{element} ))\]

MORPH_CLOSE 

a closing operation

\[\texttt{dst} = \mathrm{close} ( \texttt{src} , \texttt{element} )= \mathrm{erode} ( \mathrm{dilate} ( \texttt{src} , \texttt{element} ))\]

MORPH_GRADIENT 

a morphological gradient

\[\texttt{dst} = \mathrm{morph\_grad} ( \texttt{src} , \texttt{element} )= \mathrm{dilate} ( \texttt{src} , \texttt{element} )- \mathrm{erode} ( \texttt{src} , \texttt{element} )\]

MORPH_TOPHAT 

"top hat"

\[\texttt{dst} = \mathrm{tophat} ( \texttt{src} , \texttt{element} )= \texttt{src} - \mathrm{open} ( \texttt{src} , \texttt{element} )\]

MORPH_BLACKHAT 

"black hat"

\[\texttt{dst} = \mathrm{blackhat} ( \texttt{src} , \texttt{element} )= \mathrm{close} ( \texttt{src} , \texttt{element} )- \texttt{src}\]

MORPH_HITMISS 

"hit or miss" .- Only supported for CV_8UC1 binary images. A tutorial can be found in the documentation

Function Documentation

§ bilateralFilter()

void cv::bilateralFilter ( InputArray  src,
OutputArray  dst,
int  d,
double  sigmaColor,
double  sigmaSpace,
int  borderType = BORDER_DEFAULT 
)

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.

Parameters
srcSource 8-bit or floating-point, 1-channel or 3-channel image.
dstDestination image of the same size and type as src .
dDiameter of each pixel neighborhood that is used during filtering. If it is non-positive, it is computed from sigmaSpace.
sigmaColorFilter 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.
sigmaSpaceFilter 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.
borderTypeborder mode used to extrapolate pixels outside of the image, see cv::BorderTypes
Examples:
Smoothing.cpp.

§ blur()

void cv::blur ( InputArray  src,
OutputArray  dst,
Size  ksize,
Point  anchor = Point(-1,-1),
int  borderType = BORDER_DEFAULT 
)

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, dst, ksize, anchor, borderType) is equivalent to boxFilter(src, dst, src.type(), anchor, true, borderType).

Parameters
srcinput image; it can have any number of channels, which are processed independently, but the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
dstoutput image of the same size and type as src.
ksizeblurring kernel size.
anchoranchor point; default value Point(-1,-1) means that the anchor is at the kernel center.
borderTypeborder mode used to extrapolate pixels outside of the image, see cv::BorderTypes
See also
boxFilter, bilateralFilter, GaussianBlur, medianBlur
Examples:
edge.cpp, laplace.cpp, and Smoothing.cpp.

§ boxFilter()

void cv::boxFilter ( InputArray  src,
OutputArray  dst,
int  ddepth,
Size  ksize,
Point  anchor = Point(-1,-1),
bool  normalize = true,
int  borderType = BORDER_DEFAULT 
)

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

\[\alpha = \fork{\frac{1}{\texttt{ksize.width*ksize.height}}}{when \texttt{normalize=true}}{1}{otherwise}\]

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.

Parameters
srcinput image.
dstoutput image of the same size and type as src.
ddepththe output image depth (-1 to use src.depth()).
ksizeblurring kernel size.
anchoranchor point; default value Point(-1,-1) means that the anchor is at the kernel center.
normalizeflag, specifying whether the kernel is normalized by its area or not.
borderTypeborder mode used to extrapolate pixels outside of the image, see cv::BorderTypes
See also
blur, bilateralFilter, GaussianBlur, medianBlur, integral

§ buildPyramid()

void cv::buildPyramid ( InputArray  src,
OutputArrayOfArrays  dst,
int  maxlevel,
int  borderType = BORDER_DEFAULT 
)

Constructs the Gaussian pyramid for an image.

The function constructs a vector of images and builds the Gaussian pyramid by recursively applying pyrDown to the previously built pyramid layers, starting from dst[0]==src.

Parameters
srcSource image. Check pyrDown for the list of supported types.
dstDestination vector of maxlevel+1 images of the same type as src. dst[0] will be the same as src. dst[1] is the next pyramid layer, a smoothed and down-sized src, and so on.
maxlevel0-based index of the last (the smallest) pyramid layer. It must be non-negative.
borderTypePixel extrapolation method, see cv::BorderTypes (BORDER_CONSTANT isn't supported)

§ dilate()

void cv::dilate ( InputArray  src,
OutputArray  dst,
InputArray  kernel,
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.

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:

\[\texttt{dst} (x,y) = \max _{(x',y'): \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\]

The function supports the in-place mode. Dilation can be applied several ( iterations ) times. In case of multi-channel images, each channel is processed independently.

Parameters
srcinput image; the number of channels can be arbitrary, but the depth should be one of CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
dstoutput image of the same size and type as src`.
kernelstructuring element used for dilation; if elemenat=Mat(), a 3 x 3 rectangular structuring element is used. Kernel can be created using getStructuringElement
anchorposition of the anchor within the element; default value (-1, -1) means that the anchor is at the element center.
iterationsnumber of times dilation is applied.
borderTypepixel extrapolation method, see cv::BorderTypes
borderValueborder value in case of a constant border
See also
erode, morphologyEx, getStructuringElement
Examples:
morphology2.cpp, Morphology_1.cpp, and segment_objects.cpp.

§ erode()

void cv::erode ( InputArray  src,
OutputArray  dst,
InputArray  kernel,
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.

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:

\[\texttt{dst} (x,y) = \min _{(x',y'): \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\]

The function supports the in-place mode. Erosion can be applied several ( iterations ) times. In case of multi-channel images, each channel is processed independently.

Parameters
srcinput image; the number of channels can be arbitrary, but the depth should be one of CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
dstoutput image of the same size and type as src.
kernelstructuring element used for erosion; if element=Mat(), a 3 x 3 rectangular structuring element is used. Kernel can be created using getStructuringElement.
anchorposition of the anchor within the element; default value (-1, -1) means that the anchor is at the element center.
iterationsnumber of times erosion is applied.
borderTypepixel extrapolation method, see cv::BorderTypes
borderValueborder value in case of a constant border
See also
dilate, morphologyEx, getStructuringElement
Examples:
morphology2.cpp, Morphology_1.cpp, and segment_objects.cpp.

§ filter2D()

void cv::filter2D ( InputArray  src,
OutputArray  dst,
int  ddepth,
InputArray  kernel,
Point  anchor = Point(-1,-1),
double  delta = 0,
int  borderType = BORDER_DEFAULT 
)

Convolves an image with the kernel.

The function applies an arbitrary linear filter to an image. In-place operation is supported. 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:

\[\texttt{dst} (x,y) = \sum _{ \stackrel{0\leq x' < \texttt{kernel.cols},}{0\leq y' < \texttt{kernel.rows}} } \texttt{kernel} (x',y')* \texttt{src} (x+x'- \texttt{anchor.x} ,y+y'- \texttt{anchor.y} )\]

That is, the kernel is not mirrored around the anchor point. If you need a real convolution, flip the kernel using cv::flip and set the new anchor to (kernel.cols - anchor.x - 1, kernel.rows - anchor.y - 1).

The function uses the DFT-based algorithm in case of sufficiently large kernels (~11 x 11 or larger) and the direct algorithm for small kernels.

Parameters
srcinput image.
dstoutput image of the same size and the same number of channels as src.
ddepthdesired depth of the destination image, see combinations
kernelconvolution 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.
anchoranchor 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.
deltaoptional value added to the filtered pixels before storing them in dst.
borderTypepixel extrapolation method, see cv::BorderTypes
See also
sepFilter2D, dft, matchTemplate

§ GaussianBlur()

void cv::GaussianBlur ( InputArray  src,
OutputArray  dst,
Size  ksize,
double  sigmaX,
double  sigmaY = 0,
int  borderType = BORDER_DEFAULT 
)

Blurs an image using a Gaussian filter.

The function convolves the source image with the specified Gaussian kernel. In-place filtering is supported.

Parameters
srcinput image; the image can have any number of channels, which are processed independently, but the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
dstoutput image of the same size and type as src.
ksizeGaussian 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.
sigmaXGaussian kernel standard deviation in X direction.
sigmaYGaussian 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.
borderTypepixel extrapolation method, see cv::BorderTypes
See also
sepFilter2D, filter2D, blur, boxFilter, bilateralFilter, medianBlur
Examples:
laplace.cpp, Smoothing.cpp, and Sobel_Demo.cpp.

§ getDerivKernels()

void cv::getDerivKernels ( OutputArray  kx,
OutputArray  ky,
int  dx,
int  dy,
int  ksize,
bool  normalize = false,
int  ktype = CV_32F 
)

Returns filter coefficients for computing spatial image derivatives.

The function computes and returns the filter coefficients for spatial image derivatives. When ksize=CV_SCHARR, the Scharr \(3 \times 3\) kernels are generated (see cv::Scharr). Otherwise, Sobel kernels are generated (see cv::Sobel). The filters are normally passed to sepFilter2D or to

Parameters
kxOutput matrix of row filter coefficients. It has the type ktype .
kyOutput matrix of column filter coefficients. It has the type ktype .
dxDerivative order in respect of x.
dyDerivative order in respect of y.
ksizeAperture size. It can be CV_SCHARR, 1, 3, 5, or 7.
normalizeFlag indicating whether to normalize (scale down) the filter coefficients or not. Theoretically, the coefficients should have the denominator \(=2^{ksize*2-dx-dy-2}\). If you are going to filter floating-point images, you are likely to use the normalized kernels. But if you compute derivatives of an 8-bit image, store the results in a 16-bit image, and wish to preserve all the fractional bits, you may want to set normalize=false .
ktypeType of filter coefficients. It can be CV_32f or CV_64F .

§ getGaborKernel()

Mat cv::getGaborKernel ( Size  ksize,
double  sigma,
double  theta,
double  lambd,
double  gamma,
double  psi = CV_PI *0.5,
int  ktype = CV_64F 
)

Returns Gabor filter coefficients.

For more details about gabor filter equations and parameters, see: Gabor Filter.

Parameters
ksizeSize of the filter returned.
sigmaStandard deviation of the gaussian envelope.
thetaOrientation of the normal to the parallel stripes of a Gabor function.
lambdWavelength of the sinusoidal factor.
gammaSpatial aspect ratio.
psiPhase offset.
ktypeType of filter coefficients. It can be CV_32F or CV_64F .

§ getGaussianKernel()

Mat cv::getGaussianKernel ( int  ksize,
double  sigma,
int  ktype = CV_64F 
)

Returns Gaussian filter coefficients.

The function computes and returns the \(\texttt{ksize} \times 1\) matrix of Gaussian filter coefficients:

\[G_i= \alpha *e^{-(i-( \texttt{ksize} -1)/2)^2/(2* \texttt{sigma}^2)},\]

where \(i=0..\texttt{ksize}-1\) and \(\alpha\) is the scale factor chosen so that \(\sum_i G_i=1\).

Two of such generated kernels can be passed to sepFilter2D. Those functions automatically recognize smoothing kernels (a symmetrical kernel with sum of weights equal to 1) and handle them accordingly. You may also use the higher-level GaussianBlur.

Parameters
ksizeAperture size. It should be odd ( \(\texttt{ksize} \mod 2 = 1\) ) and positive.
sigmaGaussian standard deviation. If it is non-positive, it is computed from ksize as sigma = 0.3*((ksize-1)*0.5 - 1) + 0.8.
ktypeType of filter coefficients. It can be CV_32F or CV_64F .
See also
sepFilter2D, getDerivKernels, getStructuringElement, GaussianBlur

§ getStructuringElement()

Mat cv::getStructuringElement ( int  shape,
Size  ksize,
Point  anchor = Point(-1,-1) 
)

Returns a structuring element of the specified size and shape for morphological operations.

The function constructs and returns the structuring element that can be further passed to cv::erode, cv::dilate or cv::morphologyEx. But you can also construct an arbitrary binary mask yourself and use it as the structuring element.

Parameters
shapeElement shape that could be one of cv::MorphShapes
ksizeSize of the structuring element.
anchorAnchor position within the element. The default value \((-1, -1)\) means that the anchor is at the center. Note that only the shape of a cross-shaped element depends on the anchor position. In other cases the anchor just regulates how much the result of the morphological operation is shifted.
Examples:
morphology2.cpp, and Morphology_1.cpp.

§ Laplacian()

void cv::Laplacian ( InputArray  src,
OutputArray  dst,
int  ddepth,
int  ksize = 1,
double  scale = 1,
double  delta = 0,
int  borderType = BORDER_DEFAULT 
)

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:

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

Parameters
srcSource image.
dstDestination image of the same size and the same number of channels as src .
ddepthDesired depth of the destination image.
ksizeAperture size used to compute the second-derivative filters. See getDerivKernels for details. The size must be positive and odd.
scaleOptional scale factor for the computed Laplacian values. By default, no scaling is applied. See getDerivKernels for details.
deltaOptional delta value that is added to the results prior to storing them in dst .
borderTypePixel extrapolation method, see cv::BorderTypes
See also
Sobel, Scharr
Examples:
laplace.cpp.

§ medianBlur()

void cv::medianBlur ( InputArray  src,
OutputArray  dst,
int  ksize 
)

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. In-place operation is supported.

Note
The median filter uses BORDER_REPLICATE internally to cope with border pixels, see cv::BorderTypes
Parameters
srcinput 1-, 3-, or 4-channel image; when ksize is 3 or 5, the image depth should be CV_8U, CV_16U, or CV_32F, for larger aperture sizes, it can only be CV_8U.
dstdestination array of the same size and type as src.
ksizeaperture linear size; it must be odd and greater than 1, for example: 3, 5, 7 ...
See also
bilateralFilter, blur, boxFilter, GaussianBlur
Examples:
houghcircles.cpp, laplace.cpp, and Smoothing.cpp.

§ morphologyDefaultBorderValue()

static Scalar cv::morphologyDefaultBorderValue ( )
inlinestatic

returns "magic" border value for erosion and dilation. It is automatically transformed to Scalar::all(-DBL_MAX) for dilation.

§ morphologyEx()

void cv::morphologyEx ( InputArray  src,
OutputArray  dst,
int  op,
InputArray  kernel,
Point  anchor = Point(-1,-1),
int  iterations = 1,
int  borderType = BORDER_CONSTANT,
const Scalar borderValue = morphologyDefaultBorderValue() 
)

Performs advanced morphological transformations.

The function morphologyEx can perform advanced morphological transformations using an erosion and dilation as basic operations.

Any of the operations can be done in-place. In case of multi-channel images, each channel is processed independently.

Parameters
srcSource image. The number of channels can be arbitrary. The depth should be one of CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
dstDestination image of the same size and type as source image.
opType of a morphological operation, see cv::MorphTypes
kernelStructuring element. It can be created using cv::getStructuringElement.
anchorAnchor position with the kernel. Negative values mean that the anchor is at the kernel center.
iterationsNumber of times erosion and dilation are applied.
borderTypePixel extrapolation method, see cv::BorderTypes
borderValueBorder value in case of a constant border. The default value has a special meaning.
See also
dilate, erode, getStructuringElement
Note
The number of iterations is the number of times erosion or dilatation operation will be applied. For instance, an opening operation (MORPH_OPEN) with two iterations is equivalent to apply successively: erode -> erode -> dilate -> dilate (and not erode -> dilate -> erode -> dilate).
Examples:
morphology2.cpp.

§ pyrDown()

void cv::pyrDown ( InputArray  src,
OutputArray  dst,
const Size dstsize = Size(),
int  borderType = BORDER_DEFAULT 
)

Blurs an image and downsamples it.

By default, size of the output image is computed as Size((src.cols+1)/2, (src.rows+1)/2), but in any case, the following conditions should be satisfied:

\[\begin{array}{l} | \texttt{dstsize.width} *2-src.cols| \leq 2 \\ | \texttt{dstsize.height} *2-src.rows| \leq 2 \end{array}\]

The function performs the downsampling step of the Gaussian pyramid construction. First, it convolves the source image with the kernel:

\[\frac{1}{256} \begin{bmatrix} 1 & 4 & 6 & 4 & 1 \\ 4 & 16 & 24 & 16 & 4 \\ 6 & 24 & 36 & 24 & 6 \\ 4 & 16 & 24 & 16 & 4 \\ 1 & 4 & 6 & 4 & 1 \end{bmatrix}\]

Then, it downsamples the image by rejecting even rows and columns.

Parameters
srcinput image.
dstoutput image; it has the specified size and the same type as src.
dstsizesize of the output image.
borderTypePixel extrapolation method, see cv::BorderTypes (BORDER_CONSTANT isn't supported)
Examples:
Pyramids.cpp.

§ pyrMeanShiftFiltering()

void cv::pyrMeanShiftFiltering ( InputArray  src,
OutputArray  dst,
double  sp,
double  sr,
int  maxLevel = 1,
TermCriteria  termcrit = TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS, 5, 1) 
)

Performs initial step of meanshift segmentation of an image.

The function implements the filtering stage of meanshift segmentation, that is, the output of the function is the filtered "posterized" image with color gradients and fine-grain texture flattened. At every pixel (X,Y) of the input image (or down-sized input image, see below) the function executes meanshift iterations, that is, the pixel (X,Y) neighborhood in the joint space-color hyperspace is considered:

\[(x,y): X- \texttt{sp} \le x \le X+ \texttt{sp} , Y- \texttt{sp} \le y \le Y+ \texttt{sp} , ||(R,G,B)-(r,g,b)|| \le \texttt{sr}\]

where (R,G,B) and (r,g,b) are the vectors of color components at (X,Y) and (x,y), respectively (though, the algorithm does not depend on the color space used, so any 3-component color space can be used instead). Over the neighborhood the average spatial value (X',Y') and average color vector (R',G',B') are found and they act as the neighborhood center on the next iteration:

\[(X,Y)~(X',Y'), (R,G,B)~(R',G',B').\]

After the iterations over, the color components of the initial pixel (that is, the pixel from where the iterations started) are set to the final value (average color at the last iteration):

\[I(X,Y) <- (R*,G*,B*)\]

When maxLevel > 0, the gaussian pyramid of maxLevel+1 levels is built, and the above procedure is run on the smallest layer first. After that, the results are propagated to the larger layer and the iterations are run again only on those pixels where the layer colors differ by more than sr from the lower-resolution layer of the pyramid. That makes boundaries of color regions sharper. Note that the results will be actually different from the ones obtained by running the meanshift procedure on the whole original image (i.e. when maxLevel==0).

Parameters
srcThe source 8-bit, 3-channel image.
dstThe destination image of the same format and the same size as the source.
spThe spatial window radius.
srThe color window radius.
maxLevelMaximum level of the pyramid for the segmentation.
termcritTermination criteria: when to stop meanshift iterations.

§ pyrUp()

void cv::pyrUp ( InputArray  src,
OutputArray  dst,
const Size dstsize = Size(),
int  borderType = BORDER_DEFAULT 
)

Upsamples an image and then blurs it.

By default, size of the output image is computed as Size(src.cols\*2, (src.rows\*2), but in any case, the following conditions should be satisfied:

\[\begin{array}{l} | \texttt{dstsize.width} -src.cols*2| \leq ( \texttt{dstsize.width} \mod 2) \\ | \texttt{dstsize.height} -src.rows*2| \leq ( \texttt{dstsize.height} \mod 2) \end{array}\]

The function performs the upsampling step of the Gaussian pyramid construction, though it can actually be used to construct the Laplacian pyramid. First, it upsamples the source image by injecting even zero rows and columns and then convolves the result with the same kernel as in pyrDown multiplied by 4.

Parameters
srcinput image.
dstoutput image. It has the specified size and the same type as src .
dstsizesize of the output image.
borderTypePixel extrapolation method, see cv::BorderTypes (only BORDER_DEFAULT is supported)
Examples:
Pyramids.cpp.

§ Scharr()

void cv::Scharr ( InputArray  src,
OutputArray  dst,
int  ddepth,
int  dx,
int  dy,
double  scale = 1,
double  delta = 0,
int  borderType = BORDER_DEFAULT 
)

Calculates the first x- or y- image derivative using Scharr operator.

The function computes the first x- or y- spatial image derivative using the Scharr operator. The call

\[\texttt{Scharr(src, dst, ddepth, dx, dy, scale, delta, borderType)}\]

is equivalent to

\[\texttt{Sobel(src, dst, ddepth, dx, dy, CV\_SCHARR, scale, delta, borderType)} .\]

Parameters
srcinput image.
dstoutput image of the same size and the same number of channels as src.
ddepthoutput image depth, see combinations
dxorder of the derivative x.
dyorder of the derivative y.
scaleoptional scale factor for the computed derivative values; by default, no scaling is applied (see getDerivKernels for details).
deltaoptional delta value that is added to the results prior to storing them in dst.
borderTypepixel extrapolation method, see cv::BorderTypes
See also
cartToPolar
Examples:
edge.cpp.

§ sepFilter2D()

void cv::sepFilter2D ( InputArray  src,
OutputArray  dst,
int  ddepth,
InputArray  kernelX,
InputArray  kernelY,
Point  anchor = Point(-1,-1),
double  delta = 0,
int  borderType = BORDER_DEFAULT 
)

Applies a separable linear filter to an image.

The function applies a separable linear filter to the image. 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 shifted by delta is stored in dst .

Parameters
srcSource image.
dstDestination image of the same size and the same number of channels as src .
ddepthDestination image depth, see combinations
kernelXCoefficients for filtering each row.
kernelYCoefficients for filtering each column.
anchorAnchor position within the kernel. The default value \((-1,-1)\) means that the anchor is at the kernel center.
deltaValue added to the filtered results before storing them.
borderTypePixel extrapolation method, see cv::BorderTypes
See also
filter2D, Sobel, GaussianBlur, boxFilter, blur

§ Sobel()

void cv::Sobel ( InputArray  src,
OutputArray  dst,
int  ddepth,
int  dx,
int  dy,
int  ksize = 3,
double  scale = 1,
double  delta = 0,
int  borderType = BORDER_DEFAULT 
)

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 = CV_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:

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

Parameters
srcinput image.
dstoutput image of the same size and the same number of channels as src .
ddepthoutput image depth, see combinations; in the case of 8-bit input images it will result in truncated derivatives.
dxorder of the derivative x.
dyorder of the derivative y.
ksizesize of the extended Sobel kernel; it must be 1, 3, 5, or 7.
scaleoptional scale factor for the computed derivative values; by default, no scaling is applied (see cv::getDerivKernels for details).
deltaoptional delta value that is added to the results prior to storing them in dst.
borderTypepixel extrapolation method, see cv::BorderTypes
See also
Scharr, Laplacian, sepFilter2D, filter2D, GaussianBlur, cartToPolar
Examples:
Sobel_Demo.cpp.

§ spatialGradient()

void cv::spatialGradient ( InputArray  src,
OutputArray  dx,
OutputArray  dy,
int  ksize = 3,
int  borderType = BORDER_DEFAULT 
)

Calculates the first order image derivative in both x and y using a Sobel operator.

Equivalent to calling:

Sobel( src, dx, CV_16SC1, 1, 0, 3 );
Sobel( src, dy, CV_16SC1, 0, 1, 3 );
Parameters
srcinput image.
dxoutput image with first-order derivative in x.
dyoutput image with first-order derivative in y.
ksizesize of Sobel kernel. It must be 3.
borderTypepixel extrapolation method, see cv::BorderTypes
See also
Sobel

§ sqrBoxFilter()

void cv::sqrBoxFilter ( InputArray  _src,
OutputArray  _dst,
int  ddepth,
Size  ksize,
Point  anchor = Point(-1, -1),
bool  normalize = true,
int  borderType = BORDER_DEFAULT 
)

Calculates the normalized sum of squares of the pixel values overlapping the filter.

For every pixel \( (x, y) \) in the source image, the function calculates the sum of squares of those neighboring pixel values which overlap the filter placed over the pixel \( (x, y) \).

The unnormalized square box filter can be useful in computing local image statistics such as the the local variance and standard deviation around the neighborhood of a pixel.

Parameters
_srcinput image
_dstoutput image of the same size and type as _src
ddepththe output image depth (-1 to use src.depth())
ksizekernel size
anchorkernel anchor point. The default value of Point(-1, -1) denotes that the anchor is at the kernel center.
normalizeflag, specifying whether the kernel is to be normalized by it's area or not.
borderTypeborder mode used to extrapolate pixels outside of the image, see cv::BorderTypes
See also
boxFilter