funclist.pl

(
['','cubeRoot','@brief Computes the cube root of an argument.

 The function cubeRoot computes \\f$\\sqrt[3]{\\texttt{val}}\\f$. Negative arguments are handled correctly.
 NaN and Inf are not handled. The accuracy approaches the maximum possible accuracy for
 single-precision data.
 @param val A function argument.',0,'float',['float','val','',[]]],
['','fastAtan2','@brief Calculates the angle of a 2D vector in degrees.

 The function fastAtan2 calculates the full-range angle of an input 2D vector. The angle is measured
 in degrees and varies from 0 to 360 degrees. The accuracy is about 0.3 degrees.
 @param x x-coordinate of the vector.
 @param y y-coordinate of the vector.',0,'float',['float','y','',[]],['float','x','',[]]],
['RotatedRect','boundingRect','returns 4 vertices of the rectangle
    @param pts The points array for storing rectangle vertices. The order is bottomLeft, topLeft, topRight, bottomRight.',1,'Rect'],
['KeyPoint','convert','This method converts vector of keypoints to vector of points or the reverse, where each keypoint is
    assigned the same size and the same orientation.

    @param keypoints Keypoints obtained from any feature detection algorithm like SIFT/SURF/ORB
    @param points2f Array of (x,y) coordinates of each keypoint
    @param keypointIndexes Array of indexes of keypoints to be converted to points. (Acts like a mask to
    convert only specified keypoints)',0,'void',['vector_KeyPoint','keypoints','',['/C','/Ref']],['vector_Point2f','points2f','',['/O','/Ref']],['vector_int','keypointIndexes','std::vector<int>()',['/C','/Ref']]],
['KeyPoint','convert','@overload
    @param points2f Array of (x,y) coordinates of each keypoint
    @param keypoints Keypoints obtained from any feature detection algorithm like SIFT/SURF/ORB
    @param size keypoint diameter
    @param response keypoint detector response on the keypoint (that is, strength of the keypoint)
    @param octave pyramid octave in which the keypoint has been detected
    @param class_id object id',0,'void',['vector_Point2f','points2f','',['/C','/Ref']],['vector_KeyPoint','keypoints','',['/O','/Ref']],['float','size','1',[]],['float','response','1',[]],['int','octave','0',[]],['int','class_id','-1',[]]],
['KeyPoint','overlap','This method computes overlap for pair of keypoints. Overlap is the ratio between area of keypoint
    regions\' intersection and area of keypoint regions\' union (considering keypoint region as circle).
    If they don\'t overlap, we get zero. If they coincide at same location with same size, we get 1.
    @param kp1 First keypoint
    @param kp2 Second keypoint',0,'float',['KeyPoint','kp1','',['/C','/Ref']],['KeyPoint','kp2','',['/C','/Ref']]],
['','borderInterpolate','@brief Computes the source location of an extrapolated pixel.

The function computes and returns the coordinate of a donor pixel corresponding to the specified
extrapolated pixel when using the specified extrapolation border mode. For example, if you use
cv::BORDER_WRAP mode in the horizontal direction, cv::BORDER_REFLECT_101 in the vertical direction and
want to compute value of the "virtual" pixel Point(-5, 100) in a floating-point image img , it
looks like:
@code{.cpp}
    float val = img.at<float>(borderInterpolate(100, img.rows, cv::BORDER_REFLECT_101),
                              borderInterpolate(-5, img.cols, cv::BORDER_WRAP));
@endcode
Normally, the function is not called directly. It is used inside filtering functions and also in
copyMakeBorder.
@param p 0-based coordinate of the extrapolated pixel along one of the axes, likely \\<0 or \\>= len
@param len Length of the array along the corresponding axis.
@param borderType Border type, one of the #BorderTypes, except for #BORDER_TRANSPARENT and
#BORDER_ISOLATED . When borderType==#BORDER_CONSTANT , the function always returns -1, regardless
of p and len.

@sa copyMakeBorder',0,'int',['int','p','',[]],['int','len','',[]],['int','borderType','',[]]],
['','copyMakeBorder','@brief Forms a border around an image.

The function copies the source image into the middle of the destination image. The areas to the
left, to the right, above and below the copied source image will be filled with extrapolated
pixels. This is not what filtering functions based on it do (they extrapolate pixels on-fly), but
what other more complex functions, including your own, may do to simplify image boundary handling.

The function supports the mode when src is already in the middle of dst . In this case, the
function does not copy src itself but simply constructs the border, for example:

@code{.cpp}
    // let border be the same in all directions
    int border=2;
    // constructs a larger image to fit both the image and the border
    Mat gray_buf(rgb.rows + border*2, rgb.cols + border*2, rgb.depth());
    // select the middle part of it w/o copying data
    Mat gray(gray_canvas, Rect(border, border, rgb.cols, rgb.rows));
    // convert image from RGB to grayscale
    cvtColor(rgb, gray, COLOR_RGB2GRAY);
    // form a border in-place
    copyMakeBorder(gray, gray_buf, border, border,
                   border, border, BORDER_REPLICATE);
    // now do some custom filtering ...
    ...
@endcode
@note When the source image is a part (ROI) of a bigger image, the function will try to use the
pixels outside of the ROI to form a border. To disable this feature and always do extrapolation, as
if src was not a ROI, use borderType | #BORDER_ISOLATED.

@param src Source image.
@param dst Destination image of the same type as src and the size Size(src.cols+left+right,
src.rows+top+bottom) .
@param top the top pixels
@param bottom the bottom pixels
@param left the left pixels
@param right Parameter specifying how many pixels in each direction from the source image rectangle
to extrapolate. For example, top=1, bottom=1, left=1, right=1 mean that 1 pixel-wide border needs
to be built.
@param borderType Border type. See borderInterpolate for details.
@param value Border value if borderType==BORDER_CONSTANT .

@sa  borderInterpolate',0,'void',['Mat','src','',[]],['Mat','dst','',['/O']],['int','top','',[]],['int','bottom','',[]],['int','left','',[]],['int','right','',[]],['int','borderType','',[]],['Scalar','value','Scalar()',['/C','/Ref']]],
['','add','@brief Calculates the per-element sum of two arrays or an array and a scalar.

The function add calculates:
- Sum of two arrays when both input arrays have the same size and the same number of channels:
\\f[\\texttt{dst}(I) =  \\texttt{saturate} ( \\texttt{src1}(I) +  \\texttt{src2}(I)) \\quad \\texttt{if mask}(I) \\ne0\\f]
- Sum of an array and a scalar when src2 is constructed from Scalar or has the same number of
elements as `src1.channels()`:
\\f[\\texttt{dst}(I) =  \\texttt{saturate} ( \\texttt{src1}(I) +  \\texttt{src2} ) \\quad \\texttt{if mask}(I) \\ne0\\f]
- Sum of a scalar and an array when src1 is constructed from Scalar or has the same number of
elements as `src2.channels()`:
\\f[\\texttt{dst}(I) =  \\texttt{saturate} ( \\texttt{src1} +  \\texttt{src2}(I) ) \\quad \\texttt{if mask}(I) \\ne0\\f]
where `I` is a multi-dimensional index of array elements. In case of multi-channel arrays, each
channel is processed independently.

The first function in the list above can be replaced with matrix expressions:
@code{.cpp}
    dst = src1 + src2;
    dst += src1; // equivalent to add(dst, src1, dst);
@endcode
The input arrays and the output array can all have the same or different depths. For example, you
can add a 16-bit unsigned array to a 8-bit signed array and store the sum as a 32-bit
floating-point array. Depth of the output array is determined by the dtype parameter. In the second
and third cases above, as well as in the first case, when src1.depth() == src2.depth(), dtype can
be set to the default -1. In this case, the output array will have the same depth as the input
array, be it src1, src2 or both.
@note Saturation is not applied when the output array has the depth CV_32S. You may even get
result of an incorrect sign in the case of overflow.
@param src1 first input array or a scalar.
@param src2 second input array or a scalar.
@param dst output array that has the same size and number of channels as the input array(s); the
depth is defined by dtype or src1/src2.
@param mask optional operation mask - 8-bit single channel array, that specifies elements of the
output array to be changed.
@param dtype optional depth of the output array (see the discussion below).
@sa subtract, addWeighted, scaleAdd, Mat::convertTo',0,'void',['Mat','src1','',[]],['Mat','src2','',[]],['Mat','dst','',['/O']],['Mat','mask','Mat()',[]],['int','dtype','-1',[]]],
['','subtract','@brief Calculates the per-element difference between two arrays or array and a scalar.

The function subtract calculates:
- Difference between two arrays, when both input arrays have the same size and the same number of
channels:
    \\f[\\texttt{dst}(I) =  \\texttt{saturate} ( \\texttt{src1}(I) -  \\texttt{src2}(I)) \\quad \\texttt{if mask}(I) \\ne0\\f]
- Difference between an array and a scalar, when src2 is constructed from Scalar or has the same
number of elements as `src1.channels()`:
    \\f[\\texttt{dst}(I) =  \\texttt{saturate} ( \\texttt{src1}(I) -  \\texttt{src2} ) \\quad \\texttt{if mask}(I) \\ne0\\f]
- Difference between a scalar and an array, when src1 is constructed from Scalar or has the same
number of elements as `src2.channels()`:
    \\f[\\texttt{dst}(I) =  \\texttt{saturate} ( \\texttt{src1} -  \\texttt{src2}(I) ) \\quad \\texttt{if mask}(I) \\ne0\\f]
- The reverse difference between a scalar and an array in the case of `SubRS`:
    \\f[\\texttt{dst}(I) =  \\texttt{saturate} ( \\texttt{src2} -  \\texttt{src1}(I) ) \\quad \\texttt{if mask}(I) \\ne0\\f]
where I is a multi-dimensional index of array elements. In case of multi-channel arrays, each
channel is processed independently.

The first function in the list above can be replaced with matrix expressions:
@code{.cpp}
    dst = src1 - src2;
    dst -= src1; // equivalent to subtract(dst, src1, dst);
@endcode
The input arrays and the output array can all have the same or different depths. For example, you
can subtract to 8-bit unsigned arrays and store the difference in a 16-bit signed array. Depth of
the output array is determined by dtype parameter. In the second and third cases above, as well as
in the first case, when src1.depth() == src2.depth(), dtype can be set to the default -1. In this
case the output array will have the same depth as the input array, be it src1, src2 or both.
@note Saturation is not applied when the output array has the depth CV_32S. You may even get
result of an incorrect sign in the case of overflow.
@param src1 first input array or a scalar.
@param src2 second input array or a scalar.
@param dst output array of the same size and the same number of channels as the input array.
@param mask optional operation mask; this is an 8-bit single channel array that specifies elements
of the output array to be changed.
@param dtype optional depth of the output array
@sa  add, addWeighted, scaleAdd, Mat::convertTo',0,'void',['Mat','src1','',[]],['Mat','src2','',[]],['Mat','dst','',['/O']],['Mat','mask','Mat()',[]],['int','dtype','-1',[]]],
['','multiply','@brief Calculates the per-element scaled product of two arrays.

The function multiply calculates the per-element product of two arrays:

\\f[\\texttt{dst} (I)= \\texttt{saturate} ( \\texttt{scale} \\cdot \\texttt{src1} (I)  \\cdot \\texttt{src2} (I))\\f]

There is also a @ref MatrixExpressions -friendly variant of the first function. See Mat::mul .

For a not-per-element matrix product, see gemm .

@note Saturation is not applied when the output array has the depth
CV_32S. You may even get result of an incorrect sign in the case of
overflow.
@param src1 first input array.
@param src2 second input array of the same size and the same type as src1.
@param dst output array of the same size and type as src1.
@param scale optional scale factor.
@param dtype optional depth of the output array
@sa add, subtract, divide, scaleAdd, addWeighted, accumulate, accumulateProduct, accumulateSquare,
Mat::convertTo',0,'void',['Mat','src1','',[]],['Mat','src2','',[]],['Mat','dst','',['/O']],['double','scale','1',[]],['int','dtype','-1',[]]],
['','divide','@brief Performs per-element division of two arrays or a scalar by an array.

The function cv::divide divides one array by another:
\\f[\\texttt{dst(I) = saturate(src1(I)*scale/src2(I))}\\f]
or a scalar by an array when there is no src1 :
\\f[\\texttt{dst(I) = saturate(scale/src2(I))}\\f]

Different channels of multi-channel arrays are processed independently.

For integer types when src2(I) is zero, dst(I) will also be zero.

@note In case of floating point data there is no special defined behavior for zero src2(I) values.
Regular floating-point division is used.
Expect correct IEEE-754 behaviour for floating-point data (with NaN, Inf result values).

@note Saturation is not applied when the output array has the depth CV_32S. You may even get
result of an incorrect sign in the case of overflow.
@param src1 first input array.
@param src2 second input array of the same size and type as src1.
@param scale scalar factor.
@param dst output array of the same size and type as src2.
@param dtype optional depth of the output array; if -1, dst will have depth src2.depth(), but in
case of an array-by-array division, you can only pass -1 when src1.depth()==src2.depth().
@sa  multiply, add, subtract',0,'void',['Mat','src1','',[]],['Mat','src2','',[]],['Mat','dst','',['/O']],['double','scale','1',[]],['int','dtype','-1',[]]],
['','divide','@overload',0,'void',['double','scale','',[]],['Mat','src2','',[]],['Mat','dst','',['/O']],['int','dtype','-1',[]]],
['','scaleAdd','@brief Calculates the sum of a scaled array and another array.

The function scaleAdd is one of the classical primitive linear algebra operations, known as DAXPY
or SAXPY in [BLAS](http://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms). It calculates
the sum of a scaled array and another array:
\\f[\\texttt{dst} (I)= \\texttt{scale} \\cdot \\texttt{src1} (I) +  \\texttt{src2} (I)\\f]
The function can also be emulated with a matrix expression, for example:
@code{.cpp}
    Mat A(3, 3, CV_64F);
    ...
    A.row(0) = A.row(1)*2 + A.row(2);
@endcode
@param src1 first input array.
@param alpha scale factor for the first array.
@param src2 second input array of the same size and type as src1.
@param dst output array of the same size and type as src1.
@sa add, addWeighted, subtract, Mat::dot, Mat::convertTo',0,'void',['Mat','src1','',[]],['double','alpha','',[]],['Mat','src2','',[]],['Mat','dst','',['/O']]],
['','addWeighted','@brief Calculates the weighted sum of two arrays.

The function addWeighted calculates the weighted sum of two arrays as follows:
\\f[\\texttt{dst} (I)= \\texttt{saturate} ( \\texttt{src1} (I)* \\texttt{alpha} +  \\texttt{src2} (I)* \\texttt{beta} +  \\texttt{gamma} )\\f]
where I is a multi-dimensional index of array elements. In case of multi-channel arrays, each
channel is processed independently.
The function can be replaced with a matrix expression:
@code{.cpp}
    dst = src1*alpha + src2*beta + gamma;
@endcode
@note Saturation is not applied when the output array has the depth CV_32S. You may even get
result of an incorrect sign in the case of overflow.
@param src1 first input array.
@param alpha weight of the first array elements.
@param src2 second input array of the same size and channel number as src1.
@param beta weight of the second array elements.
@param gamma scalar added to each sum.
@param dst output array that has the same size and number of channels as the input arrays.
@param dtype optional depth of the output array; when both input arrays have the same depth, dtype
can be set to -1, which will be equivalent to src1.depth().
@sa  add, subtract, scaleAdd, Mat::convertTo',0,'void',['Mat','src1','',[]],['double','alpha','',[]],['Mat','src2','',[]],['double','beta','',[]],['double','gamma','',[]],['Mat','dst','',['/O']],['int','dtype','-1',[]]],
['','convertScaleAbs','@brief Scales, calculates absolute values, and converts the result to 8-bit.

On each element of the input array, the function convertScaleAbs
performs three operations sequentially: scaling, taking an absolute
value, conversion to an unsigned 8-bit type:
\\f[\\texttt{dst} (I)= \\texttt{saturate\\_cast<uchar>} (| \\texttt{src} (I)* \\texttt{alpha} +  \\texttt{beta} |)\\f]
In case of multi-channel arrays, the function processes each channel
independently. When the output is not 8-bit, the operation can be
emulated by calling the Mat::convertTo method (or by using matrix
expressions) and then by calculating an absolute value of the result.
For example:
@code{.cpp}
    Mat_<float> A(30,30);
    randu(A, Scalar(-100), Scalar(100));
    Mat_<float> B = A*5 + 3;
    B = abs(B);
    // Mat_<float> B = abs(A*5+3) will also do the job,
    // but it will allocate a temporary matrix
@endcode
@param src input array.
@param dst output array.
@param alpha optional scale factor.
@param beta optional delta added to the scaled values.
@sa  Mat::convertTo, cv::abs(const Mat&)',0,'void',['Mat','src','',[]],['Mat','dst','',['/O']],['double','alpha','1',[]],['double','beta','0',[]]],
['','convertFp16','@brief Converts an array to half precision floating number.

This function converts FP32 (single precision floating point) from/to FP16 (half precision floating point). CV_16S format is used to represent FP16 data.
There are two use modes (src -> dst): CV_32F -> CV_16S and CV_16S -> CV_32F. The input array has to have type of CV_32F or
CV_16S to represent the bit depth. If the input array is neither of them, the function will raise an error.
The format of half precision floating point is defined in IEEE 754-2008.

@param src input array.
@param dst output array.',0,'void',['Mat','src','',[]],['Mat','dst','',['/O']]],
['','LUT','@brief Performs a look-up table transform of an array.

The function LUT fills the output array with values from the look-up table. Indices of the entries
are taken from the input array. That is, the function processes each element of src as follows:
\\f[\\texttt{dst} (I)  \\leftarrow \\texttt{lut(src(I) + d)}\\f]
where
\\f[d =  \\fork{0}{if \\(\\texttt{src}\\) has depth \\(\\texttt{CV_8U}\\)}{128}{if \\(\\texttt{src}\\) has depth \\(\\texttt{CV_8S}\\)}\\f]
@param src input array of 8-bit elements.
@param lut look-up table of 256 elements; in case of multi-channel input array, the table should
either have a single channel (in this case the same table is used for all channels) or the same
number of channels as in the input array.
@param dst output array of the same size and number of channels as src, and the same depth as lut.
@sa  convertScaleAbs, Mat::convertTo',0,'void',['Mat','src','',[]],['Mat','lut','',[]],['Mat','dst','',['/O']]],
['',['cv::sum','sumElems'],'@brief Calculates the sum of array elements.

The function cv::sum calculates and returns the sum of array elements,
independently for each channel.
@param src input array that must have from 1 to 4 channels.
@sa  countNonZero, mean, meanStdDev, norm, minMaxLoc, reduce',0,'Scalar',['Mat','src','',[]]],
['','countNonZero','@brief Counts non-zero array elements.

The function returns the number of non-zero elements in src :
\\f[\\sum _{I: \\; \\texttt{src} (I) \\ne0 } 1\\f]
@param src single-channel array.
@sa  mean, meanStdDev, norm, minMaxLoc, calcCovarMatrix',0,'int',['Mat','src','',[]]],
['','findNonZero','@brief Returns the list of locations of non-zero pixels

Given a binary matrix (likely returned from an operation such
as threshold(), compare(), >, ==, etc, return all of
the non-zero indices as a cv::Mat or std::vector<cv::Point> (x,y)
For example:
@code{.cpp}
    cv::Mat binaryImage; // input, binary image
    cv::Mat locations;   // output, locations of non-zero pixels
    cv::findNonZero(binaryImage, locations);

    // access pixel coordinates
    Point pnt = locations.at<Point>(i);
@endcode
or
@code{.cpp}
    cv::Mat binaryImage; // input, binary image
    vector<Point> locations;   // output, locations of non-zero pixels
    cv::findNonZero(binaryImage, locations);

    // access pixel coordinates
    Point pnt = locations[i];
@endcode
@param src single-channel array
@param idx the output array, type of cv::Mat or std::vector<Point>, corresponding to non-zero indices in the input',0,'void',['Mat','src','',[]],['Mat','idx','',['/O']]],
['','mean','@brief Calculates an average (mean) of array elements.

The function cv::mean calculates the mean value M of array elements,
independently for each channel, and return it:
\\f[\\begin{array}{l} N =  \\sum _{I: \\; \\texttt{mask} (I) \\ne 0} 1 \\\\ M_c =  \\left ( \\sum _{I: \\; \\texttt{mask} (I) \\ne 0}{ \\texttt{mtx} (I)_c} \\right )/N \\end{array}\\f]
When all the mask elements are 0\'s, the function returns Scalar::all(0)
@param src input array that should have from 1 to 4 channels so that the result can be stored in
Scalar_ .
@param mask optional operation mask.
@sa  countNonZero, meanStdDev, norm, minMaxLoc',0,'Scalar',['Mat','src','',[]],['Mat','mask','Mat()',[]]],
['','meanStdDev','Calculates a mean and standard deviation of array elements.

The function cv::meanStdDev calculates the mean and the standard deviation M
of array elements independently for each channel and returns it via the
output parameters:
\\f[\\begin{array}{l} N =  \\sum _{I, \\texttt{mask} (I)  \\ne 0} 1 \\\\ \\texttt{mean} _c =  \\frac{\\sum_{ I: \\; \\texttt{mask}(I) \\ne 0} \\texttt{src} (I)_c}{N} \\\\ \\texttt{stddev} _c =  \\sqrt{\\frac{\\sum_{ I: \\; \\texttt{mask}(I) \\ne 0} \\left ( \\texttt{src} (I)_c -  \\texttt{mean} _c \\right )^2}{N}} \\end{array}\\f]
When all the mask elements are 0\'s, the function returns
mean=stddev=Scalar::all(0).
@note The calculated standard deviation is only the diagonal of the
complete normalized covariance matrix. If the full matrix is needed, you
can reshape the multi-channel array M x N to the single-channel array
M\\*N x mtx.channels() (only possible when the matrix is continuous) and
then pass the matrix to calcCovarMatrix .
@param src input array that should have from 1 to 4 channels so that the results can be stored in
Scalar_ \'s.
@param mean output parameter: calculated mean value.
@param stddev output parameter: calculated standard deviation.
@param mask optional operation mask.
@sa  countNonZero, mean, norm, minMaxLoc, calcCovarMatrix',0,'void',['Mat','src','',[]],['Mat','mean','',['/O']],['Mat','stddev','',['/O']],['Mat','mask','Mat()',[]]],
['','norm','@brief Calculates the  absolute norm of an array.

This version of #norm calculates the absolute norm of src1. The type of norm to calculate is specified using #NormTypes.

As example for one array consider the function \\f$r(x)= \\begin{pmatrix} x \\\\ 1-x \\end{pmatrix}, x \\in [-1;1]\\f$.
The \\f$ L_{1}, L_{2} \\f$ and \\f$ L_{\\infty} \\f$ norm for the sample value \\f$r(-1) = \\begin{pmatrix} -1 \\\\ 2 \\end{pmatrix}\\f$
is calculated as follows
\\f{align*}
    \\| r(-1) \\|_{L_1} &= |-1| + |2| = 3 \\\\
    \\| r(-1) \\|_{L_2} &= \\sqrt{(-1)^{2} + (2)^{2}} = \\sqrt{5} \\\\
    \\| r(-1) \\|_{L_\\infty} &= \\max(|-1|,|2|) = 2
\\f}
and for \\f$r(0.5) = \\begin{pmatrix} 0.5 \\\\ 0.5 \\end{pmatrix}\\f$ the calculation is
\\f{align*}
    \\| r(0.5) \\|_{L_1} &= |0.5| + |0.5| = 1 \\\\
    \\| r(0.5) \\|_{L_2} &= \\sqrt{(0.5)^{2} + (0.5)^{2}} = \\sqrt{0.5} \\\\
    \\| r(0.5) \\|_{L_\\infty} &= \\max(|0.5|,|0.5|) = 0.5.
\\f}
The following graphic shows all values for the three norm functions \\f$\\| r(x) \\|_{L_1}, \\| r(x) \\|_{L_2}\\f$ and \\f$\\| r(x) \\|_{L_\\infty}\\f$.
It is notable that the \\f$ L_{1} \\f$ norm forms the upper and the \\f$ L_{\\infty} \\f$ norm forms the lower border for the example function \\f$ r(x) \\f$.
![Graphs for the different norm functions from the above example](pics/NormTypes_OneArray_1-2-INF.png)

When the mask parameter is specified and it is not empty, the norm is

If normType is not specified, #NORM_L2 is used.
calculated only over the region specified by the mask.

Multi-channel input arrays are treated as single-channel arrays, that is,
the results for all channels are combined.

Hamming norms can only be calculated with CV_8U depth arrays.

@param src1 first input array.
@param normType type of the norm (see #NormTypes).
@param mask optional operation mask; it must have the same size as src1 and CV_8UC1 type.',0,'double',['Mat','src1','',[]],['int','normType','NORM_L2',[]],['Mat','mask','Mat()',[]]],
['','norm','@brief Calculates an absolute difference norm or a relative difference norm.

This version of cv::norm calculates the absolute difference norm
or the relative difference norm of arrays src1 and src2.
The type of norm to calculate is specified using #NormTypes.

@param src1 first input array.
@param src2 second input array of the same size and the same type as src1.
@param normType type of the norm (see #NormTypes).
@param mask optional operation mask; it must have the same size as src1 and CV_8UC1 type.',0,'double',['Mat','src1','',[]],['Mat','src2','',[]],['int','normType','NORM_L2',[]],['Mat','mask','Mat()',[]]],
['','PSNR','@brief Computes the Peak Signal-to-Noise Ratio (PSNR) image quality metric.

This function calculates the Peak Signal-to-Noise Ratio (PSNR) image quality metric in decibels (dB),
between two input arrays src1 and src2. The arrays must have the same type.

The PSNR is calculated as follows:

\\f[
\\texttt{PSNR} = 10 \\cdot \\log_{10}{\\left( \\frac{R^2}{MSE} \\right) }
\\f]

where R is the maximum integer value of depth (e.g. 255 in the case of CV_8U data)
and MSE is the mean squared error between the two arrays.

@param src1 first input array.
@param src2 second input array of the same size as src1.
@param R the maximum pixel value (255 by default)',0,'double',['Mat','src1','',[]],['Mat','src2','',[]],['double','R','255.',[]]],
['','batchDistance','@brief naive nearest neighbor finder

see http://en.wikipedia.org/wiki/Nearest_neighbor_search
@todo document',0,'void',['Mat','src1','',[]],['Mat','src2','',[]],['Mat','dist','',['/O']],['int','dtype','',[]],['Mat','nidx','',['/O']],['int','normType','NORM_L2',[]],['int','K','0',[]],['Mat','mask','Mat()',[]],['int','update','0',[]],['bool','crosscheck','false',[]]],
['','normalize','@brief Normalizes the norm or value range of an array.

The function cv::normalize normalizes scale and shift the input array elements so that
\\f[\\| \\texttt{dst} \\| _{L_p}= \\texttt{alpha}\\f]
(where p=Inf, 1 or 2) when normType=NORM_INF, NORM_L1, or NORM_L2, respectively; or so that
\\f[\\min _I  \\texttt{dst} (I)= \\texttt{alpha} , \\, \\, \\max _I  \\texttt{dst} (I)= \\texttt{beta}\\f]

when normType=NORM_MINMAX (for dense arrays only). The optional mask specifies a sub-array to be
normalized. This means that the norm or min-n-max are calculated over the sub-array, and then this
sub-array is modified to be normalized. If you want to only use the mask to calculate the norm or
min-max but modify the whole array, you can use norm and Mat::convertTo.

In case of sparse matrices, only the non-zero values are analyzed and transformed. Because of this,
the range transformation for sparse matrices is not allowed since it can shift the zero level.

Possible usage with some positive example data:
@code{.cpp}
    vector<double> positiveData = { 2.0, 8.0, 10.0 };
    vector<double> normalizedData_l1, normalizedData_l2, normalizedData_inf, normalizedData_minmax;

    // Norm to probability (total count)
    // sum(numbers) = 20.0
    // 2.0      0.1     (2.0/20.0)
    // 8.0      0.4     (8.0/20.0)
    // 10.0     0.5     (10.0/20.0)
    normalize(positiveData, normalizedData_l1, 1.0, 0.0, NORM_L1);

    // Norm to unit vector: ||positiveData|| = 1.0
    // 2.0      0.15
    // 8.0      0.62
    // 10.0     0.77
    normalize(positiveData, normalizedData_l2, 1.0, 0.0, NORM_L2);

    // Norm to max element
    // 2.0      0.2     (2.0/10.0)
    // 8.0      0.8     (8.0/10.0)
    // 10.0     1.0     (10.0/10.0)
    normalize(positiveData, normalizedData_inf, 1.0, 0.0, NORM_INF);

    // Norm to range [0.0;1.0]
    // 2.0      0.0     (shift to left border)
    // 8.0      0.75    (6.0/8.0)
    // 10.0     1.0     (shift to right border)
    normalize(positiveData, normalizedData_minmax, 1.0, 0.0, NORM_MINMAX);
@endcode

@param src input array.
@param dst output array of the same size as src .
@param alpha norm value to normalize to or the lower range boundary in case of the range
normalization.
@param beta upper range boundary in case of the range normalization; it is not used for the norm
normalization.
@param norm_type normalization type (see cv::NormTypes).
@param dtype when negative, the output array has the same type as src; otherwise, it has the same
number of channels as src and the depth =CV_MAT_DEPTH(dtype).
@param mask optional operation mask.
@sa norm, Mat::convertTo, SparseMat::convertTo',0,'void',['Mat','src','',[]],['Mat','dst','',['/IO']],['double','alpha','1',[]],['double','beta','0',[]],['int','norm_type','NORM_L2',[]],['int','dtype','-1',[]],['Mat','mask','Mat()',[]]],
['','minMaxLoc','@brief Finds the global minimum and maximum in an array.

The function cv::minMaxLoc finds the minimum and maximum element values and their positions. The
extremums are searched across the whole array or, if mask is not an empty array, in the specified
array region.

The function do not work with multi-channel arrays. If you need to find minimum or maximum
elements across all the channels, use Mat::reshape first to reinterpret the array as
single-channel. Or you may extract the particular channel using either extractImageCOI , or
mixChannels , or split .
@param src input single-channel array.
@param minVal pointer to the returned minimum value; NULL is used if not required.
@param maxVal pointer to the returned maximum value; NULL is used if not required.
@param minLoc pointer to the returned minimum location (in 2D case); NULL is used if not required.
@param maxLoc pointer to the returned maximum location (in 2D case); NULL is used if not required.
@param mask optional mask used to select a sub-array.
@sa max, min, compare, inRange, extractImageCOI, mixChannels, split, Mat::reshape',0,'void',['Mat','src','',[]],['double*','minVal','',['/O']],['double*','maxVal','0',['/O']],['Point*','minLoc','0',['/O']],['Point*','maxLoc','0',['/O']],['Mat','mask','Mat()',[]]],
['','reduce','@brief Reduces a matrix to a vector.

The function #reduce reduces the matrix to a vector by treating the matrix rows/columns as a set of
1D vectors and performing the specified operation on the vectors until a single row/column is
obtained. For example, the function can be used to compute horizontal and vertical projections of a
raster image. In case of #REDUCE_MAX and #REDUCE_MIN , the output image should have the same type as the source one.
In case of #REDUCE_SUM and #REDUCE_AVG , the output may have a larger element bit-depth to preserve accuracy.
And multi-channel arrays are also supported in these two reduction modes.

The following code demonstrates its usage for a single channel matrix.
@snippet snippets/core_reduce.cpp example

And the following code demonstrates its usage for a two-channel matrix.
@snippet snippets/core_reduce.cpp example2

@param src input 2D matrix.
@param dst output vector. Its size and type is defined by dim and dtype parameters.
@param dim dimension index along which the matrix is reduced. 0 means that the matrix is reduced to
a single row. 1 means that the matrix is reduced to a single column.
@param rtype reduction operation that could be one of #ReduceTypes
@param dtype when negative, the output vector will have the same type as the input matrix,
otherwise, its type will be CV_MAKE_TYPE(CV_MAT_DEPTH(dtype), src.channels()).
@sa repeat',0,'void',['Mat','src','',[]],['Mat','dst','',['/O']],['int','dim','',[]],['int','rtype','',[]],['int','dtype','-1',[]]],
['','merge','@overload
@param mv input vector of matrices to be merged; all the matrices in mv must have the same
size and the same depth.
@param dst output array of the same size and the same depth as mv[0]; The number of channels will
be the total number of channels in the matrix array.',0,'void',['vector_Mat','mv','',[]],['Mat','dst','',['/O']]],
['','split','@overload
@param m input multi-channel array.
@param mv output vector of arrays; the arrays themselves are reallocated, if needed.',0,'void',['Mat','m','',[]],['vector_Mat','mv','',['/O']]],
['','mixChannels','@overload
@param src input array or vector of matrices; all of the matrices must have the same size and the
same depth.
@param dst output array or vector of matrices; all the matrices **must be allocated**; their size and
depth must be the same as in src[0].
@param fromTo array of index pairs specifying which channels are copied and where; fromTo[k\\*2] is
a 0-based index of the input channel in src, fromTo[k\\*2+1] is an index of the output channel in
dst; the continuous channel numbering is used: the first input image channels are indexed from 0 to
src[0].channels()-1, the second input image channels are indexed from src[0].channels() to
src[0].channels() + src[1].channels()-1, and so on, the same scheme is used for the output image
channels; as a special case, when fromTo[k\\*2] is negative, the corresponding output channel is
filled with zero .',0,'void',['vector_Mat','src','',[]],['vector_Mat','dst','',['/IO']],['vector_int','fromTo','',['/C','/Ref']]],
['','extractChannel','@brief Extracts a single channel from src (coi is 0-based index)
@param src input array
@param dst output array
@param coi index of channel to extract
@sa mixChannels, split',0,'void',['Mat','src','',[]],['Mat','dst','',['/O']],['int','coi','',[]]],
['','insertChannel','@brief Inserts a single channel to dst (coi is 0-based index)
@param src input array
@param dst output array
@param coi index of channel for insertion
@sa mixChannels, merge',0,'void',['Mat','src','',[]],['Mat','dst','',['/IO']],['int','coi','',[]]],
['','flip','@brief Flips a 2D array around vertical, horizontal, or both axes.

The function cv::flip flips the array in one of three different ways (row
and column indices are 0-based):
\\f[\\texttt{dst} _{ij} =
\\left\\{
\\begin{array}{l l}
\\texttt{src} _{\\texttt{src.rows}-i-1,j} & if\\;  \\texttt{flipCode} = 0 \\\\
\\texttt{src} _{i, \\texttt{src.cols} -j-1} & if\\;  \\texttt{flipCode} > 0 \\\\
\\texttt{src} _{ \\texttt{src.rows} -i-1, \\texttt{src.cols} -j-1} & if\\; \\texttt{flipCode} < 0 \\\\
\\end{array}
\\right.\\f]
The example scenarios of using the function are the following:
*   Vertical flipping of the image (flipCode == 0) to switch between
    top-left and bottom-left image origin. This is a typical operation
    in video processing on Microsoft Windows\\* OS.
*   Horizontal flipping of the image with the subsequent horizontal
    shift and absolute difference calculation to check for a
    vertical-axis symmetry (flipCode \\> 0).
*   Simultaneous horizontal and vertical flipping of the image with
    the subsequent shift and absolute difference calculation to check
    for a central symmetry (flipCode \\< 0).
*   Reversing the order of point arrays (flipCode \\> 0 or
    flipCode == 0).
@param src input array.
@param dst output array of the same size and type as src.
@param flipCode a flag to specify how to flip the array; 0 means
flipping around the x-axis and positive value (for example, 1) means
flipping around y-axis. Negative value (for example, -1) means flipping
around both axes.
@sa transpose , repeat , completeSymm',0,'void',['Mat','src','',[]],['Mat','dst','',['/O']],['int','flipCode','',[]]],
['','rotate','@brief Rotates a 2D array in multiples of 90 degrees.
The function cv::rotate rotates the array in one of three different ways:
*   Rotate by 90 degrees clockwise (rotateCode = ROTATE_90_CLOCKWISE).
*   Rotate by 180 degrees clockwise (rotateCode = ROTATE_180).
*   Rotate by 270 degrees clockwise (rotateCode = ROTATE_90_COUNTERCLOCKWISE).
@param src input array.
@param dst output array of the same type as src.  The size is the same with ROTATE_180,
and the rows and cols are switched for ROTATE_90_CLOCKWISE and ROTATE_90_COUNTERCLOCKWISE.
@param rotateCode an enum to specify how to rotate the array; see the enum #RotateFlags
@sa transpose , repeat , completeSymm, flip, RotateFlags',0,'void',['Mat','src','',[]],['Mat','dst','',['/O']],['int','rotateCode','',[]]],
['','repeat','@brief Fills the output array with repeated copies of the input array.

The function cv::repeat duplicates the input array one or more times along each of the two axes:
\\f[\\texttt{dst} _{ij}= \\texttt{src} _{i\\mod src.rows, \\; j\\mod src.cols }\\f]
The second variant of the function is more convenient to use with @ref MatrixExpressions.
@param src input array to replicate.
@param ny Flag to specify how many times the `src` is repeated along the
vertical axis.
@param nx Flag to specify how many times the `src` is repeated along the
horizontal axis.
@param dst output array of the same type as `src`.
@sa cv::reduce',0,'void',['Mat','src','',[]],['int','ny','',[]],['int','nx','',[]],['Mat','dst','',['/O']]],
['','hconcat','@overload
 @code{.cpp}
    std::vector<cv::Mat> matrices = { cv::Mat(4, 1, CV_8UC1, cv::Scalar(1)),
                                      cv::Mat(4, 1, CV_8UC1, cv::Scalar(2)),
                                      cv::Mat(4, 1, CV_8UC1, cv::Scalar(3)),};

    cv::Mat out;
    cv::hconcat( matrices, out );
    //out:
    //[1, 2, 3;
    // 1, 2, 3;
    // 1, 2, 3;
    // 1, 2, 3]
 @endcode
 @param src input array or vector of matrices. all of the matrices must have the same number of rows and the same depth.
 @param dst output array. It has the same number of rows and depth as the src, and the sum of cols of the src.
same depth.',0,'void',['vector_Mat','src','',[]],['Mat','dst','',['/O']]],
['','vconcat','@overload
 @code{.cpp}
    std::vector<cv::Mat> matrices = { cv::Mat(1, 4, CV_8UC1, cv::Scalar(1)),
                                      cv::Mat(1, 4, CV_8UC1, cv::Scalar(2)),
                                      cv::Mat(1, 4, CV_8UC1, cv::Scalar(3)),};

    cv::Mat out;
    cv::vconcat( matrices, out );
    //out:
    //[1,   1,   1,   1;
    // 2,   2,   2,   2;
    // 3,   3,   3,   3]
 @endcode
 @param src input array or vector of matrices. all of the matrices must have the same number of cols and the same depth
 @param dst output array. It has the same number of cols and depth as the src, and the sum of rows of the src.
same depth.',0,'void',['vector_Mat','src','',[]],['Mat','dst','',['/O']]],
['','bitwise_and','@brief computes bitwise conjunction of the two arrays (dst = src1 & src2)
Calculates the per-element bit-wise conjunction of two arrays or an
array and a scalar.

The function cv::bitwise_and calculates the per-element bit-wise logical conjunction for:
*   Two arrays when src1 and src2 have the same size:
    \\f[\\texttt{dst} (I) =  \\texttt{src1} (I)  \\wedge \\texttt{src2} (I) \\quad \\texttt{if mask} (I) \\ne0\\f]
*   An array and a scalar when src2 is constructed from Scalar or has
    the same number of elements as `src1.channels()`:
    \\f[\\texttt{dst} (I) =  \\texttt{src1} (I)  \\wedge \\texttt{src2} \\quad \\texttt{if mask} (I) \\ne0\\f]
*   A scalar and an array when src1 is constructed from Scalar or has
    the same number of elements as `src2.channels()`:
    \\f[\\texttt{dst} (I) =  \\texttt{src1}  \\wedge \\texttt{src2} (I) \\quad \\texttt{if mask} (I) \\ne0\\f]
In case of floating-point arrays, their machine-specific bit
representations (usually IEEE754-compliant) are used for the operation.
In case of multi-channel arrays, each channel is processed
independently. In the second and third cases above, the scalar is first
converted to the array type.
@param src1 first input array or a scalar.
@param src2 second input array or a scalar.
@param dst output array that has the same size and type as the input
arrays.
@param mask optional operation mask, 8-bit single channel array, that
specifies elements of the output array to be changed.',0,'void',['Mat','src1','',[]],['Mat','src2','',[]],['Mat','dst','',['/O']],['Mat','mask','Mat()',[]]],
['','bitwise_or','@brief Calculates the per-element bit-wise disjunction of two arrays or an
array and a scalar.

The function cv::bitwise_or calculates the per-element bit-wise logical disjunction for:
*   Two arrays when src1 and src2 have the same size:
    \\f[\\texttt{dst} (I) =  \\texttt{src1} (I)  \\vee \\texttt{src2} (I) \\quad \\texttt{if mask} (I) \\ne0\\f]
*   An array and a scalar when src2 is constructed from Scalar or has
    the same number of elements as `src1.channels()`:
    \\f[\\texttt{dst} (I) =  \\texttt{src1} (I)  \\vee \\texttt{src2} \\quad \\texttt{if mask} (I) \\ne0\\f]
*   A scalar and an array when src1 is constructed from Scalar or has
    the same number of elements as `src2.channels()`:
    \\f[\\texttt{dst} (I) =  \\texttt{src1}  \\vee \\texttt{src2} (I) \\quad \\texttt{if mask} (I) \\ne0\\f]
In case of floating-point arrays, their machine-specific bit
representations (usually IEEE754-compliant) are used for the operation.
In case of multi-channel arrays, each channel is processed
independently. In the second and third cases above, the scalar is first
converted to the array type.
@param src1 first input array or a scalar.
@param src2 second input array or a scalar.
@param dst output array that has the same size and type as the input
arrays.
@param mask optional operation mask, 8-bit single channel array, that
specifies elements of the output array to be changed.',0,'void',['Mat','src1','',[]],['Mat','src2','',[]],['Mat','dst','',['/O']],['Mat','mask','Mat()',[]]],
['','bitwise_xor','@brief Calculates the per-element bit-wise "exclusive or" operation on two
arrays or an array and a scalar.

The function cv::bitwise_xor calculates the per-element bit-wise logical "exclusive-or"
operation for:
*   Two arrays when src1 and src2 have the same size:
    \\f[\\texttt{dst} (I) =  \\texttt{src1} (I)  \\oplus \\texttt{src2} (I) \\quad \\texttt{if mask} (I) \\ne0\\f]
*   An array and a scalar when src2 is constructed from Scalar or has
    the same number of elements as `src1.channels()`:
    \\f[\\texttt{dst} (I) =  \\texttt{src1} (I)  \\oplus \\texttt{src2} \\quad \\texttt{if mask} (I) \\ne0\\f]
*   A scalar and an array when src1 is constructed from Scalar or has
    the same number of elements as `src2.channels()`:
    \\f[\\texttt{dst} (I) =  \\texttt{src1}  \\oplus \\texttt{src2} (I) \\quad \\texttt{if mask} (I) \\ne0\\f]
In case of floating-point arrays, their machine-specific bit
representations (usually IEEE754-compliant) are used for the operation.
In case of multi-channel arrays, each channel is processed
independently. In the 2nd and 3rd cases above, the scalar is first
converted to the array type.
@param src1 first input array or a scalar.
@param src2 second input array or a scalar.
@param dst output array that has the same size and type as the input
arrays.
@param mask optional operation mask, 8-bit single channel array, that
specifies elements of the output array to be changed.',0,'void',['Mat','src1','',[]],['Mat','src2','',[]],['Mat','dst','',['/O']],['Mat','mask','Mat()',[]]],
['','bitwise_not','@brief  Inverts every bit of an array.

The function cv::bitwise_not calculates per-element bit-wise inversion of the input
array:
\\f[\\texttt{dst} (I) =  \\neg \\texttt{src} (I)\\f]
In case of a floating-point input array, its machine-specific bit
representation (usually IEEE754-compliant) is used for the operation. In
case of multi-channel arrays, each channel is processed independently.
@param src input array.
@param dst output array that has the same size and type as the input
array.
@param mask optional operation mask, 8-bit single channel array, that
specifies elements of the output array to be changed.',0,'void',['Mat','src','',[]],['Mat','dst','',['/O']],['Mat','mask','Mat()',[]]],
['','absdiff','@brief Calculates the per-element absolute difference between two arrays or between an array and a scalar.

The function cv::absdiff calculates:
*   Absolute difference between two arrays when they have the same
    size and type:
    \\f[\\texttt{dst}(I) =  \\texttt{saturate} (| \\texttt{src1}(I) -  \\texttt{src2}(I)|)\\f]
*   Absolute difference between an array and a scalar when the second
    array is constructed from Scalar or has as many elements as the
    number of channels in `src1`:
    \\f[\\texttt{dst}(I) =  \\texttt{saturate} (| \\texttt{src1}(I) -  \\texttt{src2} |)\\f]
*   Absolute difference between a scalar and an array when the first
    array is constructed from Scalar or has as many elements as the
    number of channels in `src2`:
    \\f[\\texttt{dst}(I) =  \\texttt{saturate} (| \\texttt{src1} -  \\texttt{src2}(I) |)\\f]
    where I is a multi-dimensional index of array elements. In case of
    multi-channel arrays, each channel is processed independently.
@note Saturation is not applied when the arrays have the depth CV_32S.
You may even get a negative value in the case of overflow.
@param src1 first input array or a scalar.
@param src2 second input array or a scalar.
@param dst output array that has the same size and type as input arrays.
@sa cv::abs(const Mat&)',0,'void',['Mat','src1','',[]],['Mat','src2','',[]],['Mat','dst','',['/O']]],
['','copyTo','@brief  This is an overloaded member function, provided for convenience (python)
Copies the matrix to another one.
When the operation mask is specified, if the Mat::create call shown above reallocates the matrix, the newly allocated matrix is initialized with all zeros before copying the data.
@param src source matrix.
@param dst Destination matrix. If it does not have a proper size or type before the operation, it is
reallocated.
@param mask Operation mask of the same size as \\*this. Its non-zero elements indicate which matrix
elements need to be copied. The mask has to be of type CV_8U and can have 1 or multiple channels.',0,'void',['Mat','src','',[]],['Mat','dst','',['/O']],['Mat','mask','',[]]],
['','inRange','@brief  Checks if array elements lie between the elements of two other arrays.

The function checks the range as follows:
-   For every element of a single-channel input array:
    \\f[\\texttt{dst} (I)= \\texttt{lowerb} (I)_0  \\leq \\texttt{src} (I)_0 \\leq  \\texttt{upperb} (I)_0\\f]
-   For two-channel arrays:
    \\f[\\texttt{dst} (I)= \\texttt{lowerb} (I)_0  \\leq \\texttt{src} (I)_0 \\leq  \\texttt{upperb} (I)_0  \\land \\texttt{lowerb} (I)_1  \\leq \\texttt{src} (I)_1 \\leq  \\texttt{upperb} (I)_1\\f]
-   and so forth.

That is, dst (I) is set to 255 (all 1 -bits) if src (I) is within the
specified 1D, 2D, 3D, ... box and 0 otherwise.

When the lower and/or upper boundary parameters are scalars, the indexes
(I) at lowerb and upperb in the above formulas should be omitted.
@param src first input array.
@param lowerb inclusive lower boundary array or a scalar.
@param upperb inclusive upper boundary array or a scalar.
@param dst output array of the same size as src and CV_8U type.',0,'void',['Mat','src','',[]],['Mat','lowerb','',[]],['Mat','upperb','',[]],['Mat','dst','',['/O']]],
['','compare','@brief Performs the per-element comparison of two arrays or an array and scalar value.

The function compares:
*   Elements of two arrays when src1 and src2 have the same size:
    \\f[\\texttt{dst} (I) =  \\texttt{src1} (I)  \\,\\texttt{cmpop}\\, \\texttt{src2} (I)\\f]
*   Elements of src1 with a scalar src2 when src2 is constructed from
    Scalar or has a single element:
    \\f[\\texttt{dst} (I) =  \\texttt{src1}(I) \\,\\texttt{cmpop}\\,  \\texttt{src2}\\f]
*   src1 with elements of src2 when src1 is constructed from Scalar or
    has a single element:
    \\f[\\texttt{dst} (I) =  \\texttt{src1}  \\,\\texttt{cmpop}\\, \\texttt{src2} (I)\\f]
When the comparison result is true, the corresponding element of output
array is set to 255. The comparison operations can be replaced with the
equivalent matrix expressions:
@code{.cpp}
    Mat dst1 = src1 >= src2;
    Mat dst2 = src1 < 8;
    ...
@endcode
@param src1 first input array or a scalar; when it is an array, it must have a single channel.
@param src2 second input array or a scalar; when it is an array, it must have a single channel.
@param dst output array of type ref CV_8U that has the same size and the same number of channels as
    the input arrays.
@param cmpop a flag, that specifies correspondence between the arrays (cv::CmpTypes)
@sa checkRange, min, max, threshold',0,'void',['Mat','src1','',[]],['Mat','src2','',[]],['Mat','dst','',['/O']],['int','cmpop','',[]]],
['','min','@brief Calculates per-element minimum of two arrays or an array and a scalar.

The function cv::min calculates the per-element minimum of two arrays:
\\f[\\texttt{dst} (I)= \\min ( \\texttt{src1} (I), \\texttt{src2} (I))\\f]
or array and a scalar:
\\f[\\texttt{dst} (I)= \\min ( \\texttt{src1} (I), \\texttt{value} )\\f]
@param src1 first input array.
@param src2 second input array of the same size and type as src1.
@param dst output array of the same size and type as src1.
@sa max, compare, inRange, minMaxLoc',0,'void',['Mat','src1','',[]],['Mat','src2','',[]],['Mat','dst','',['/O']]],
['','max','@brief Calculates per-element maximum of two arrays or an array and a scalar.

The function cv::max calculates the per-element maximum of two arrays:
\\f[\\texttt{dst} (I)= \\max ( \\texttt{src1} (I), \\texttt{src2} (I))\\f]
or array and a scalar:
\\f[\\texttt{dst} (I)= \\max ( \\texttt{src1} (I), \\texttt{value} )\\f]
@param src1 first input array.
@param src2 second input array of the same size and type as src1 .
@param dst output array of the same size and type as src1.
@sa  min, compare, inRange, minMaxLoc, @ref MatrixExpressions',0,'void',['Mat','src1','',[]],['Mat','src2','',[]],['Mat','dst','',['/O']]],
['','sqrt','@brief Calculates a square root of array elements.

The function cv::sqrt calculates a square root of each input array element.
In case of multi-channel arrays, each channel is processed
independently. The accuracy is approximately the same as of the built-in
std::sqrt .
@param src input floating-point array.
@param dst output array of the same size and type as src.',0,'void',['Mat','src','',[]],['Mat','dst','',['/O']]],
['','pow','@brief Raises every array element to a power.

The function cv::pow raises every element of the input array to power :
\\f[\\texttt{dst} (I) =  \\fork{\\texttt{src}(I)^{power}}{if \\(\\texttt{power}\\) is integer}{|\\texttt{src}(I)|^{power}}{otherwise}\\f]

So, for a non-integer power exponent, the absolute values of input array
elements are used. However, it is possible to get true values for
negative values using some extra operations. In the example below,
computing the 5th root of array src shows:
@code{.cpp}
    Mat mask = src < 0;
    pow(src, 1./5, dst);
    subtract(Scalar::all(0), dst, dst, mask);
@endcode
For some values of power, such as integer values, 0.5 and -0.5,
specialized faster algorithms are used.

Special values (NaN, Inf) are not handled.
@param src input array.
@param power exponent of power.
@param dst output array of the same size and type as src.
@sa sqrt, exp, log, cartToPolar, polarToCart',0,'void',['Mat','src','',[]],['double','power','',[]],['Mat','dst','',['/O']]],
['','exp','@brief Calculates the exponent of every array element.

The function cv::exp calculates the exponent of every element of the input
array:
\\f[\\texttt{dst} [I] = e^{ src(I) }\\f]

The maximum relative error is about 7e-6 for single-precision input and
less than 1e-10 for double-precision input. Currently, the function
converts denormalized values to zeros on output. Special values (NaN,
Inf) are not handled.
@param src input array.
@param dst output array of the same size and type as src.
@sa log , cartToPolar , polarToCart , phase , pow , sqrt , magnitude',0,'void',['Mat','src','',[]],['Mat','dst','',['/O']]],
['','log','@brief Calculates the natural logarithm of every array element.

The function cv::log calculates the natural logarithm of every element of the input array:
\\f[\\texttt{dst} (I) =  \\log (\\texttt{src}(I)) \\f]

Output on zero, negative and special (NaN, Inf) values is undefined.

@param src input array.
@param dst output array of the same size and type as src .
@sa exp, cartToPolar, polarToCart, phase, pow, sqrt, magnitude',0,'void',['Mat','src','',[]],['Mat','dst','',['/O']]],
['','polarToCart','@brief Calculates x and y coordinates of 2D vectors from their magnitude and angle.

The function cv::polarToCart calculates the Cartesian coordinates of each 2D
vector represented by the corresponding elements of magnitude and angle:
\\f[\\begin{array}{l} \\texttt{x} (I) =  \\texttt{magnitude} (I) \\cos ( \\texttt{angle} (I)) \\\\ \\texttt{y} (I) =  \\texttt{magnitude} (I) \\sin ( \\texttt{angle} (I)) \\\\ \\end{array}\\f]

The relative accuracy of the estimated coordinates is about 1e-6.
@param magnitude input floating-point array of magnitudes of 2D vectors;
it can be an empty matrix (=Mat()), in this case, the function assumes
that all the magnitudes are =1; if it is not empty, it must have the
same size and type as angle.
@param angle input floating-point array of angles of 2D vectors.
@param x output array of x-coordinates of 2D vectors; it has the same
size and type as angle.
@param y output array of y-coordinates of 2D vectors; it has the same
size and type as angle.
@param angleInDegrees when true, the input angles are measured in
degrees, otherwise, they are measured in radians.
@sa cartToPolar, magnitude, phase, exp, log, pow, sqrt',0,'void',['Mat','magnitude','',[]],['Mat','angle','',[]],['Mat','x','',['/O']],['Mat','y','',['/O']],['bool','angleInDegrees','false',[]]],
['','cartToPolar','@brief Calculates the magnitude and angle of 2D vectors.

The function cv::cartToPolar calculates either the magnitude, angle, or both
for every 2D vector (x(I),y(I)):
\\f[\\begin{array}{l} \\texttt{magnitude} (I)= \\sqrt{\\texttt{x}(I)^2+\\texttt{y}(I)^2} , \\\\ \\texttt{angle} (I)= \\texttt{atan2} ( \\texttt{y} (I), \\texttt{x} (I))[ \\cdot180 / \\pi ] \\end{array}\\f]

The angles are calculated with accuracy about 0.3 degrees. For the point
(0,0), the angle is set to 0.
@param x array of x-coordinates; this must be a single-precision or
double-precision floating-point array.
@param y array of y-coordinates, that must have the same size and same type as x.
@param magnitude output array of magnitudes of the same size and type as x.
@param angle output array of angles that has the same size and type as
x; the angles are measured in radians (from 0 to 2\\*Pi) or in degrees (0 to 360 degrees).
@param angleInDegrees a flag, indicating whether the angles are measured
in radians (which is by default), or in degrees.
@sa Sobel, Scharr',0,'void',['Mat','x','',[]],['Mat','y','',[]],['Mat','magnitude','',['/O']],['Mat','angle','',['/O']],['bool','angleInDegrees','false',[]]],
['','phase','@brief Calculates the rotation angle of 2D vectors.

The function cv::phase calculates the rotation angle of each 2D vector that
is formed from the corresponding elements of x and y :
\\f[\\texttt{angle} (I) =  \\texttt{atan2} ( \\texttt{y} (I), \\texttt{x} (I))\\f]

The angle estimation accuracy is about 0.3 degrees. When x(I)=y(I)=0 ,
the corresponding angle(I) is set to 0.
@param x input floating-point array of x-coordinates of 2D vectors.
@param y input array of y-coordinates of 2D vectors; it must have the
same size and the same type as x.
@param angle output array of vector angles; it has the same size and
same type as x .
@param angleInDegrees when true, the function calculates the angle in
degrees, otherwise, they are measured in radians.',0,'void',['Mat','x','',[]],['Mat','y','',[]],['Mat','angle','',['/O']],['bool','angleInDegrees','false',[]]],
['','magnitude','@brief Calculates the magnitude of 2D vectors.

The function cv::magnitude calculates the magnitude of 2D vectors formed
from the corresponding elements of x and y arrays:
\\f[\\texttt{dst} (I) =  \\sqrt{\\texttt{x}(I)^2 + \\texttt{y}(I)^2}\\f]
@param x floating-point array of x-coordinates of the vectors.
@param y floating-point array of y-coordinates of the vectors; it must
have the same size as x.
@param magnitude output array of the same size and type as x.
@sa cartToPolar, polarToCart, phase, sqrt',0,'void',['Mat','x','',[]],['Mat','y','',[]],['Mat','magnitude','',['/O']]],
['','checkRange','@brief Checks every element of an input array for invalid values.

The function cv::checkRange checks that every array element is neither NaN nor infinite. When minVal \\>
-DBL_MAX and maxVal \\< DBL_MAX, the function also checks that each value is between minVal and
maxVal. In case of multi-channel arrays, each channel is processed independently. If some values
are out of range, position of the first outlier is stored in pos (when pos != NULL). Then, the
function either returns false (when quiet=true) or throws an exception.
@param a input array.
@param quiet a flag, indicating whether the functions quietly return false when the array elements
are out of range or they throw an exception.
@param pos optional output parameter, when not NULL, must be a pointer to array of src.dims
elements.
@param minVal inclusive lower boundary of valid values range.
@param maxVal exclusive upper boundary of valid values range.',0,'bool',['Mat','a','',[]],['bool','quiet','true',[]],['Point*','pos','0',['/O']],['double','minVal','-DBL_MAX',[]],['double','maxVal','DBL_MAX',[]]],
['','patchNaNs','@brief converts NaNs to the given number
@param a input/output matrix (CV_32F type).
@param val value to convert the NaNs',0,'void',['Mat','a','',['/IO']],['double','val','0',[]]],
['','gemm','@brief Performs generalized matrix multiplication.

The function cv::gemm performs generalized matrix multiplication similar to the
gemm functions in BLAS level 3. For example,
`gemm(src1, src2, alpha, src3, beta, dst, GEMM_1_T + GEMM_3_T)`
corresponds to
\\f[\\texttt{dst} =  \\texttt{alpha} \\cdot \\texttt{src1} ^T  \\cdot \\texttt{src2} +  \\texttt{beta} \\cdot \\texttt{src3} ^T\\f]

In case of complex (two-channel) data, performed a complex matrix
multiplication.

The function can be replaced with a matrix expression. For example, the
above call can be replaced with:
@code{.cpp}
    dst = alpha*src1.t()*src2 + beta*src3.t();
@endcode
@param src1 first multiplied input matrix that could be real(CV_32FC1,
CV_64FC1) or complex(CV_32FC2, CV_64FC2).
@param src2 second multiplied input matrix of the same type as src1.
@param alpha weight of the matrix product.
@param src3 third optional delta matrix added to the matrix product; it
should have the same type as src1 and src2.
@param beta weight of src3.
@param dst output matrix; it has the proper size and the same type as
input matrices.
@param flags operation flags (cv::GemmFlags)
@sa mulTransposed , transform',0,'void',['Mat','src1','',[]],['Mat','src2','',[]],['double','alpha','',[]],['Mat','src3','',[]],['double','beta','',[]],['Mat','dst','',['/O']],['int','flags','0',[]]],
['','mulTransposed','@brief Calculates the product of a matrix and its transposition.

The function cv::mulTransposed calculates the product of src and its
transposition:
\\f[\\texttt{dst} = \\texttt{scale} ( \\texttt{src} - \\texttt{delta} )^T ( \\texttt{src} - \\texttt{delta} )\\f]
if aTa=true , and
\\f[\\texttt{dst} = \\texttt{scale} ( \\texttt{src} - \\texttt{delta} ) ( \\texttt{src} - \\texttt{delta} )^T\\f]
otherwise. The function is used to calculate the covariance matrix. With
zero delta, it can be used as a faster substitute for general matrix
product A\\*B when B=A\'
@param src input single-channel matrix. Note that unlike gemm, the
function can multiply not only floating-point matrices.
@param dst output square matrix.
@param aTa Flag specifying the multiplication ordering. See the
description below.
@param delta Optional delta matrix subtracted from src before the
multiplication. When the matrix is empty ( delta=noArray() ), it is
assumed to be zero, that is, nothing is subtracted. If it has the same
size as src , it is simply subtracted. Otherwise, it is "repeated" (see
repeat ) to cover the full src and then subtracted. Type of the delta
matrix, when it is not empty, must be the same as the type of created
output matrix. See the dtype parameter description below.
@param scale Optional scale factor for the matrix product.
@param dtype Optional type of the output matrix. When it is negative,
the output matrix will have the same type as src . Otherwise, it will be
type=CV_MAT_DEPTH(dtype) that should be either CV_32F or CV_64F .
@sa calcCovarMatrix, gemm, repeat, reduce',0,'void',['Mat','src','',[]],['Mat','dst','',['/O']],['bool','aTa','',[]],['Mat','delta','Mat()',[]],['double','scale','1',[]],['int','dtype','-1',[]]],
['','transpose','@brief Transposes a matrix.

The function cv::transpose transposes the matrix src :
\\f[\\texttt{dst} (i,j) =  \\texttt{src} (j,i)\\f]
@note No complex conjugation is done in case of a complex matrix. It
should be done separately if needed.
@param src input array.
@param dst output array of the same type as src.',0,'void',['Mat','src','',[]],['Mat','dst','',['/O']]],
['','transform','@brief Performs the matrix transformation of every array element.

The function cv::transform performs the matrix transformation of every
element of the array src and stores the results in dst :
\\f[\\texttt{dst} (I) =  \\texttt{m} \\cdot \\texttt{src} (I)\\f]
(when m.cols=src.channels() ), or
\\f[\\texttt{dst} (I) =  \\texttt{m} \\cdot [ \\texttt{src} (I); 1]\\f]
(when m.cols=src.channels()+1 )

Every element of the N -channel array src is interpreted as N -element
vector that is transformed using the M x N or M x (N+1) matrix m to
M-element vector - the corresponding element of the output array dst .

The function may be used for geometrical transformation of
N -dimensional points, arbitrary linear color space transformation (such
as various kinds of RGB to YUV transforms), shuffling the image
channels, and so forth.
@param src input array that must have as many channels (1 to 4) as
m.cols or m.cols-1.
@param dst output array of the same size and depth as src; it has as
many channels as m.rows.
@param m transformation 2x2 or 2x3 floating-point matrix.
@sa perspectiveTransform, getAffineTransform, estimateAffine2D, warpAffine, warpPerspective',0,'void',['Mat','src','',[]],['Mat','dst','',['/O']],['Mat','m','',[]]],
['','perspectiveTransform','@brief Performs the perspective matrix transformation of vectors.

The function cv::perspectiveTransform transforms every element of src by
treating it as a 2D or 3D vector, in the following way:
\\f[(x, y, z)  \\rightarrow (x\'/w, y\'/w, z\'/w)\\f]
where
\\f[(x\', y\', z\', w\') =  \\texttt{mat} \\cdot \\begin{bmatrix} x & y & z & 1  \\end{bmatrix}\\f]
and
\\f[w =  \\fork{w\'}{if \\(w\' \\ne 0\\)}{\\infty}{otherwise}\\f]

Here a 3D vector transformation is shown. In case of a 2D vector
transformation, the z component is omitted.

@note The function transforms a sparse set of 2D or 3D vectors. If you
want to transform an image using perspective transformation, use
warpPerspective . If you have an inverse problem, that is, you want to
compute the most probable perspective transformation out of several
pairs of corresponding points, you can use getPerspectiveTransform or
findHomography .
@param src input two-channel or three-channel floating-point array; each
element is a 2D/3D vector to be transformed.
@param dst output array of the same size and type as src.
@param m 3x3 or 4x4 floating-point transformation matrix.
@sa  transform, warpPerspective, getPerspectiveTransform, findHomography',0,'void',['Mat','src','',[]],['Mat','dst','',['/O']],['Mat','m','',[]]],
['','completeSymm','@brief Copies the lower or the upper half of a square matrix to its another half.

The function cv::completeSymm copies the lower or the upper half of a square matrix to
its another half. The matrix diagonal remains unchanged:
 - \\f$\\texttt{m}_{ij}=\\texttt{m}_{ji}\\f$ for \\f$i > j\\f$ if
    lowerToUpper=false
 - \\f$\\texttt{m}_{ij}=\\texttt{m}_{ji}\\f$ for \\f$i < j\\f$ if
    lowerToUpper=true

@param m input-output floating-point square matrix.
@param lowerToUpper operation flag; if true, the lower half is copied to
the upper half. Otherwise, the upper half is copied to the lower half.
@sa flip, transpose',0,'void',['Mat','m','',['/IO']],['bool','lowerToUpper','false',[]]],
['','setIdentity','@brief Initializes a scaled identity matrix.

The function cv::setIdentity initializes a scaled identity matrix:
\\f[\\texttt{mtx} (i,j)= \\fork{\\texttt{value}}{ if \\(i=j\\)}{0}{otherwise}\\f]

The function can also be emulated using the matrix initializers and the
matrix expressions:
@code
    Mat A = Mat::eye(4, 3, CV_32F)*5;
    // A will be set to [[5, 0, 0], [0, 5, 0], [0, 0, 5], [0, 0, 0]]
@endcode
@param mtx matrix to initialize (not necessarily square).
@param s value to assign to diagonal elements.
@sa Mat::zeros, Mat::ones, Mat::setTo, Mat::operator=',0,'void',['Mat','mtx','',['/IO']],['Scalar','s','Scalar(1)',['/C','/Ref']]],
['','determinant','@brief Returns the determinant of a square floating-point matrix.

The function cv::determinant calculates and returns the determinant of the
specified matrix. For small matrices ( mtx.cols=mtx.rows\\<=3 ), the
direct method is used. For larger matrices, the function uses LU
factorization with partial pivoting.

For symmetric positively-determined matrices, it is also possible to use
eigen decomposition to calculate the determinant.
@param mtx input matrix that must have CV_32FC1 or CV_64FC1 type and
square size.
@sa trace, invert, solve, eigen, @ref MatrixExpressions',0,'double',['Mat','mtx','',[]]],
['','trace','@brief Returns the trace of a matrix.

The function cv::trace returns the sum of the diagonal elements of the
matrix mtx .
\\f[\\mathrm{tr} ( \\texttt{mtx} ) =  \\sum _i  \\texttt{mtx} (i,i)\\f]
@param mtx input matrix.',0,'Scalar',['Mat','mtx','',[]]],
['','invert','@brief Finds the inverse or pseudo-inverse of a matrix.

The function cv::invert inverts the matrix src and stores the result in dst
. When the matrix src is singular or non-square, the function calculates
the pseudo-inverse matrix (the dst matrix) so that norm(src\\*dst - I) is
minimal, where I is an identity matrix.

In case of the #DECOMP_LU method, the function returns non-zero value if
the inverse has been successfully calculated and 0 if src is singular.

In case of the #DECOMP_SVD method, the function returns the inverse
condition number of src (the ratio of the smallest singular value to the
largest singular value) and 0 if src is singular. The SVD method
calculates a pseudo-inverse matrix if src is singular.

Similarly to #DECOMP_LU, the method #DECOMP_CHOLESKY works only with
non-singular square matrices that should also be symmetrical and
positively defined. In this case, the function stores the inverted
matrix in dst and returns non-zero. Otherwise, it returns 0.

@param src input floating-point M x N matrix.
@param dst output matrix of N x M size and the same type as src.
@param flags inversion method (cv::DecompTypes)
@sa solve, SVD',0,'double',['Mat','src','',[]],['Mat','dst','',['/O']],['int','flags','DECOMP_LU',[]]],
['','solve','@brief Solves one or more linear systems or least-squares problems.

The function cv::solve solves a linear system or least-squares problem (the
latter is possible with SVD or QR methods, or by specifying the flag
#DECOMP_NORMAL ):
\\f[\\texttt{dst} =  \\arg \\min _X \\| \\texttt{src1} \\cdot \\texttt{X} -  \\texttt{src2} \\|\\f]

If #DECOMP_LU or #DECOMP_CHOLESKY method is used, the function returns 1
if src1 (or \\f$\\texttt{src1}^T\\texttt{src1}\\f$ ) is non-singular. Otherwise,
it returns 0. In the latter case, dst is not valid. Other methods find a
pseudo-solution in case of a singular left-hand side part.

@note If you want to find a unity-norm solution of an under-defined
singular system \\f$\\texttt{src1}\\cdot\\texttt{dst}=0\\f$ , the function solve
will not do the work. Use SVD::solveZ instead.

@param src1 input matrix on the left-hand side of the system.
@param src2 input matrix on the right-hand side of the system.
@param dst output solution.
@param flags solution (matrix inversion) method (#DecompTypes)
@sa invert, SVD, eigen',0,'bool',['Mat','src1','',[]],['Mat','src2','',[]],['Mat','dst','',['/O']],['int','flags','DECOMP_LU',[]]],
['','sort','@brief Sorts each row or each column of a matrix.

The function cv::sort sorts each matrix row or each matrix column in
ascending or descending order. So you should pass two operation flags to
get desired behaviour. If you want to sort matrix rows or columns
lexicographically, you can use STL std::sort generic function with the
proper comparison predicate.

@param src input single-channel array.
@param dst output array of the same size and type as src.
@param flags operation flags, a combination of #SortFlags
@sa sortIdx, randShuffle',0,'void',['Mat','src','',[]],['Mat','dst','',['/O']],['int','flags','',[]]],
['','sortIdx','@brief Sorts each row or each column of a matrix.

The function cv::sortIdx sorts each matrix row or each matrix column in the
ascending or descending order. So you should pass two operation flags to
get desired behaviour. Instead of reordering the elements themselves, it
stores the indices of sorted elements in the output array. For example:
@code
    Mat A = Mat::eye(3,3,CV_32F), B;
    sortIdx(A, B, SORT_EVERY_ROW + SORT_ASCENDING);
    // B will probably contain
    // (because of equal elements in A some permutations are possible):
    // [[1, 2, 0], [0, 2, 1], [0, 1, 2]]
@endcode
@param src input single-channel array.
@param dst output integer array of the same size as src.
@param flags operation flags that could be a combination of cv::SortFlags
@sa sort, randShuffle',0,'void',['Mat','src','',[]],['Mat','dst','',['/O']],['int','flags','',[]]],
['','solveCubic','@brief Finds the real roots of a cubic equation.

The function solveCubic finds the real roots of a cubic equation:
-   if coeffs is a 4-element vector:
\\f[\\texttt{coeffs} [0] x^3 +  \\texttt{coeffs} [1] x^2 +  \\texttt{coeffs} [2] x +  \\texttt{coeffs} [3] = 0\\f]
-   if coeffs is a 3-element vector:
\\f[x^3 +  \\texttt{coeffs} [0] x^2 +  \\texttt{coeffs} [1] x +  \\texttt{coeffs} [2] = 0\\f]

The roots are stored in the roots array.
@param coeffs equation coefficients, an array of 3 or 4 elements.
@param roots output array of real roots that has 1 or 3 elements.
@return number of real roots. It can be 0, 1 or 2.',0,'int',['Mat','coeffs','',[]],['Mat','roots','',['/O']]],
['','solvePoly','@brief Finds the real or complex roots of a polynomial equation.

The function cv::solvePoly finds real and complex roots of a polynomial equation:
\\f[\\texttt{coeffs} [n] x^{n} +  \\texttt{coeffs} [n-1] x^{n-1} + ... +  \\texttt{coeffs} [1] x +  \\texttt{coeffs} [0] = 0\\f]
@param coeffs array of polynomial coefficients.
@param roots output (complex) array of roots.
@param maxIters maximum number of iterations the algorithm does.',0,'double',['Mat','coeffs','',[]],['Mat','roots','',['/O']],['int','maxIters','300',[]]],
['','eigen','@brief Calculates eigenvalues and eigenvectors of a symmetric matrix.

The function cv::eigen calculates just eigenvalues, or eigenvalues and eigenvectors of the symmetric
matrix src:
@code
    src*eigenvectors.row(i).t() = eigenvalues.at<srcType>(i)*eigenvectors.row(i).t()
@endcode

@note Use cv::eigenNonSymmetric for calculation of real eigenvalues and eigenvectors of non-symmetric matrix.

@param src input matrix that must have CV_32FC1 or CV_64FC1 type, square size and be symmetrical
(src ^T^ == src).
@param eigenvalues output vector of eigenvalues of the same type as src; the eigenvalues are stored
in the descending order.
@param eigenvectors output matrix of eigenvectors; it has the same size and type as src; the
eigenvectors are stored as subsequent matrix rows, in the same order as the corresponding
eigenvalues.
@sa eigenNonSymmetric, completeSymm , PCA',0,'bool',['Mat','src','',[]],['Mat','eigenvalues','',['/O']],['Mat','eigenvectors','Mat()',['/O']]],
['','eigenNonSymmetric','@brief Calculates eigenvalues and eigenvectors of a non-symmetric matrix (real eigenvalues only).

@note Assumes real eigenvalues.

The function calculates eigenvalues and eigenvectors (optional) of the square matrix src:
@code
    src*eigenvectors.row(i).t() = eigenvalues.at<srcType>(i)*eigenvectors.row(i).t()
@endcode

@param src input matrix (CV_32FC1 or CV_64FC1 type).
@param eigenvalues output vector of eigenvalues (type is the same type as src).
@param eigenvectors output matrix of eigenvectors (type is the same type as src). The eigenvectors are stored as subsequent matrix rows, in the same order as the corresponding eigenvalues.
@sa eigen',0,'void',['Mat','src','',[]],['Mat','eigenvalues','',['/O']],['Mat','eigenvectors','',['/O']]],
['','calcCovarMatrix','@overload
@note use #COVAR_ROWS or #COVAR_COLS flag
@param samples samples stored as rows/columns of a single matrix.
@param covar output covariance matrix of the type ctype and square size.
@param mean input or output (depending on the flags) array as the average value of the input vectors.
@param flags operation flags as a combination of #CovarFlags
@param ctype type of the matrixl; it equals \'CV_64F\' by default.',0,'void',['Mat','samples','',[]],['Mat','covar','',['/O']],['Mat','mean','',['/IO']],['int','flags','',[]],['int','ctype','CV_64F',[]]],
['','PCACompute','wrap PCA::operator()',0,'void',['Mat','data','',[]],['Mat','mean','',['/IO']],['Mat','eigenvectors','',['/O']],['int','maxComponents','0',[]]],
['','PCACompute','wrap PCA::operator() and add eigenvalues output parameter',0,'void',['Mat','data','',[]],['Mat','mean','',['/IO']],['Mat','eigenvectors','',['/O']],['Mat','eigenvalues','',['/O']],['int','maxComponents','0',[]]],
['','PCACompute','wrap PCA::operator()',0,'void',['Mat','data','',[]],['Mat','mean','',['/IO']],['Mat','eigenvectors','',['/O']],['double','retainedVariance','',[]]],
['','PCACompute','wrap PCA::operator() and add eigenvalues output parameter',0,'void',['Mat','data','',[]],['Mat','mean','',['/IO']],['Mat','eigenvectors','',['/O']],['Mat','eigenvalues','',['/O']],['double','retainedVariance','',[]]],
['','PCAProject','wrap PCA::project',0,'void',['Mat','data','',[]],['Mat','mean','',[]],['Mat','eigenvectors','',[]],['Mat','result','',['/O']]],
['','PCABackProject','wrap PCA::backProject',0,'void',['Mat','data','',[]],['Mat','mean','',[]],['Mat','eigenvectors','',[]],['Mat','result','',['/O']]],
['','SVDecomp','wrap SVD::compute',0,'void',['Mat','src','',[]],['Mat','w','',['/O']],['Mat','u','',['/O']],['Mat','vt','',['/O']],['int','flags','0',[]]],
['','SVBackSubst','wrap SVD::backSubst',0,'void',['Mat','w','',[]],['Mat','u','',[]],['Mat','vt','',[]],['Mat','rhs','',[]],['Mat','dst','',['/O']]],
['','Mahalanobis','@brief Calculates the Mahalanobis distance between two vectors.

The function cv::Mahalanobis calculates and returns the weighted distance between two vectors:
\\f[d( \\texttt{vec1} , \\texttt{vec2} )= \\sqrt{\\sum_{i,j}{\\texttt{icovar(i,j)}\\cdot(\\texttt{vec1}(I)-\\texttt{vec2}(I))\\cdot(\\texttt{vec1(j)}-\\texttt{vec2(j)})} }\\f]
The covariance matrix may be calculated using the #calcCovarMatrix function and then inverted using
the invert function (preferably using the #DECOMP_SVD method, as the most accurate).
@param v1 first 1D input vector.
@param v2 second 1D input vector.
@param icovar inverse covariance matrix.',0,'double',['Mat','v1','',[]],['Mat','v2','',[]],['Mat','icovar','',[]]],
['','dft','@brief Performs a forward or inverse Discrete Fourier transform of a 1D or 2D floating-point array.

The function cv::dft performs one of the following:
-   Forward the Fourier transform of a 1D vector of N elements:
    \\f[Y = F^{(N)}  \\cdot X,\\f]
    where \\f$F^{(N)}_{jk}=\\exp(-2\\pi i j k/N)\\f$ and \\f$i=\\sqrt{-1}\\f$
-   Inverse the Fourier transform of a 1D vector of N elements:
    \\f[\\begin{array}{l} X\'=  \\left (F^{(N)} \\right )^{-1}  \\cdot Y =  \\left (F^{(N)} \\right )^*  \\cdot y  \\\\ X = (1/N)  \\cdot X, \\end{array}\\f]
    where \\f$F^*=\\left(\\textrm{Re}(F^{(N)})-\\textrm{Im}(F^{(N)})\\right)^T\\f$
-   Forward the 2D Fourier transform of a M x N matrix:
    \\f[Y = F^{(M)}  \\cdot X  \\cdot F^{(N)}\\f]
-   Inverse the 2D Fourier transform of a M x N matrix:
    \\f[\\begin{array}{l} X\'=  \\left (F^{(M)} \\right )^*  \\cdot Y  \\cdot \\left (F^{(N)} \\right )^* \\\\ X =  \\frac{1}{M \\cdot N} \\cdot X\' \\end{array}\\f]

In case of real (single-channel) data, the output spectrum of the forward Fourier transform or input
spectrum of the inverse Fourier transform can be represented in a packed format called *CCS*
(complex-conjugate-symmetrical). It was borrowed from IPL (Intel\\* Image Processing Library). Here
is how 2D *CCS* spectrum looks:
\\f[\\begin{bmatrix} Re Y_{0,0} & Re Y_{0,1} & Im Y_{0,1} & Re Y_{0,2} & Im Y_{0,2} &  \\cdots & Re Y_{0,N/2-1} & Im Y_{0,N/2-1} & Re Y_{0,N/2}  \\\\ Re Y_{1,0} & Re Y_{1,1} & Im Y_{1,1} & Re Y_{1,2} & Im Y_{1,2} &  \\cdots & Re Y_{1,N/2-1} & Im Y_{1,N/2-1} & Re Y_{1,N/2}  \\\\ Im Y_{1,0} & Re Y_{2,1} & Im Y_{2,1} & Re Y_{2,2} & Im Y_{2,2} &  \\cdots & Re Y_{2,N/2-1} & Im Y_{2,N/2-1} & Im Y_{1,N/2}  \\\\ \\hdotsfor{9} \\\\ Re Y_{M/2-1,0} &  Re Y_{M-3,1}  & Im Y_{M-3,1} &  \\hdotsfor{3} & Re Y_{M-3,N/2-1} & Im Y_{M-3,N/2-1}& Re Y_{M/2-1,N/2}  \\\\ Im Y_{M/2-1,0} &  Re Y_{M-2,1}  & Im Y_{M-2,1} &  \\hdotsfor{3} & Re Y_{M-2,N/2-1} & Im Y_{M-2,N/2-1}& Im Y_{M/2-1,N/2}  \\\\ Re Y_{M/2,0}  &  Re Y_{M-1,1} &  Im Y_{M-1,1} &  \\hdotsfor{3} & Re Y_{M-1,N/2-1} & Im Y_{M-1,N/2-1}& Re Y_{M/2,N/2} \\end{bmatrix}\\f]

In case of 1D transform of a real vector, the output looks like the first row of the matrix above.

So, the function chooses an operation mode depending on the flags and size of the input array:
-   If #DFT_ROWS is set or the input array has a single row or single column, the function
    performs a 1D forward or inverse transform of each row of a matrix when #DFT_ROWS is set.
    Otherwise, it performs a 2D transform.
-   If the input array is real and #DFT_INVERSE is not set, the function performs a forward 1D or
    2D transform:
    -   When #DFT_COMPLEX_OUTPUT is set, the output is a complex matrix of the same size as
        input.
    -   When #DFT_COMPLEX_OUTPUT is not set, the output is a real matrix of the same size as
        input. In case of 2D transform, it uses the packed format as shown above. In case of a
        single 1D transform, it looks like the first row of the matrix above. In case of
        multiple 1D transforms (when using the #DFT_ROWS flag), each row of the output matrix
        looks like the first row of the matrix above.
-   If the input array is complex and either #DFT_INVERSE or #DFT_REAL_OUTPUT are not set, the
    output is a complex array of the same size as input. The function performs a forward or
    inverse 1D or 2D transform of the whole input array or each row of the input array
    independently, depending on the flags DFT_INVERSE and DFT_ROWS.
-   When #DFT_INVERSE is set and the input array is real, or it is complex but #DFT_REAL_OUTPUT
    is set, the output is a real array of the same size as input. The function performs a 1D or 2D
    inverse transformation of the whole input array or each individual row, depending on the flags
    #DFT_INVERSE and #DFT_ROWS.

If #DFT_SCALE is set, the scaling is done after the transformation.

Unlike dct , the function supports arrays of arbitrary size. But only those arrays are processed
efficiently, whose sizes can be factorized in a product of small prime numbers (2, 3, and 5 in the
current implementation). Such an efficient DFT size can be calculated using the getOptimalDFTSize
method.

The sample below illustrates how to calculate a DFT-based convolution of two 2D real arrays:
@code
    void convolveDFT(InputArray A, InputArray B, OutputArray C)
    {
        // reallocate the output array if needed
        C.create(abs(A.rows - B.rows)+1, abs(A.cols - B.cols)+1, A.type());
        Size dftSize;
        // calculate the size of DFT transform
        dftSize.width = getOptimalDFTSize(A.cols + B.cols - 1);
        dftSize.height = getOptimalDFTSize(A.rows + B.rows - 1);

        // allocate temporary buffers and initialize them with 0\'s
        Mat tempA(dftSize, A.type(), Scalar::all(0));
        Mat tempB(dftSize, B.type(), Scalar::all(0));

        // copy A and B to the top-left corners of tempA and tempB, respectively
        Mat roiA(tempA, Rect(0,0,A.cols,A.rows));
        A.copyTo(roiA);
        Mat roiB(tempB, Rect(0,0,B.cols,B.rows));
        B.copyTo(roiB);

        // now transform the padded A & B in-place;
        // use "nonzeroRows" hint for faster processing
        dft(tempA, tempA, 0, A.rows);
        dft(tempB, tempB, 0, B.rows);

        // multiply the spectrums;
        // the function handles packed spectrum representations well
        mulSpectrums(tempA, tempB, tempA);

        // transform the product back from the frequency domain.
        // Even though all the result rows will be non-zero,
        // you need only the first C.rows of them, and thus you
        // pass nonzeroRows == C.rows
        dft(tempA, tempA, DFT_INVERSE + DFT_SCALE, C.rows);

        // now copy the result back to C.
        tempA(Rect(0, 0, C.cols, C.rows)).copyTo(C);

        // all the temporary buffers will be deallocated automatically
    }
@endcode
To optimize this sample, consider the following approaches:
-   Since nonzeroRows != 0 is passed to the forward transform calls and since A and B are copied to
    the top-left corners of tempA and tempB, respectively, it is not necessary to clear the whole
    tempA and tempB. It is only necessary to clear the tempA.cols - A.cols ( tempB.cols - B.cols)
    rightmost columns of the matrices.
-   This DFT-based convolution does not have to be applied to the whole big arrays, especially if B
    is significantly smaller than A or vice versa. Instead, you can calculate convolution by parts.
    To do this, you need to split the output array C into multiple tiles. For each tile, estimate
    which parts of A and B are required to calculate convolution in this tile. If the tiles in C are
    too small, the speed will decrease a lot because of repeated work. In the ultimate case, when
    each tile in C is a single pixel, the algorithm becomes equivalent to the naive convolution
    algorithm. If the tiles are too big, the temporary arrays tempA and tempB become too big and
    there is also a slowdown because of bad cache locality. So, there is an optimal tile size
    somewhere in the middle.
-   If different tiles in C can be calculated in parallel and, thus, the convolution is done by
    parts, the loop can be threaded.

All of the above improvements have been implemented in #matchTemplate and #filter2D . Therefore, by
using them, you can get the performance even better than with the above theoretically optimal
implementation. Though, those two functions actually calculate cross-correlation, not convolution,
so you need to "flip" the second convolution operand B vertically and horizontally using flip .
@note
-   An example using the discrete fourier transform can be found at
    opencv_source_code/samples/cpp/dft.cpp
-   (Python) An example using the dft functionality to perform Wiener deconvolution can be found
    at opencv_source/samples/python/deconvolution.py
-   (Python) An example rearranging the quadrants of a Fourier image can be found at
    opencv_source/samples/python/dft.py
@param src input array that could be real or complex.
@param dst output array whose size and type depends on the flags .
@param flags transformation flags, representing a combination of the #DftFlags
@param nonzeroRows when the parameter is not zero, the function assumes that only the first
nonzeroRows rows of the input array (#DFT_INVERSE is not set) or only the first nonzeroRows of the
output array (#DFT_INVERSE is set) contain non-zeros, thus, the function can handle the rest of the
rows more efficiently and save some time; this technique is very useful for calculating array
cross-correlation or convolution using DFT.
@sa dct , getOptimalDFTSize , mulSpectrums, filter2D , matchTemplate , flip , cartToPolar ,
magnitude , phase',0,'void',['Mat','src','',[]],['Mat','dst','',['/O']],['int','flags','0',[]],['int','nonzeroRows','0',[]]],
['','idft','@brief Calculates the inverse Discrete Fourier Transform of a 1D or 2D array.

idft(src, dst, flags) is equivalent to dft(src, dst, flags | #DFT_INVERSE) .
@note None of dft and idft scales the result by default. So, you should pass #DFT_SCALE to one of
dft or idft explicitly to make these transforms mutually inverse.
@sa dft, dct, idct, mulSpectrums, getOptimalDFTSize
@param src input floating-point real or complex array.
@param dst output array whose size and type depend on the flags.
@param flags operation flags (see dft and #DftFlags).
@param nonzeroRows number of dst rows to process; the rest of the rows have undefined content (see
the convolution sample in dft description.',0,'void',['Mat','src','',[]],['Mat','dst','',['/O']],['int','flags','0',[]],['int','nonzeroRows','0',[]]],
['','dct','@brief Performs a forward or inverse discrete Cosine transform of 1D or 2D array.

The function cv::dct performs a forward or inverse discrete Cosine transform (DCT) of a 1D or 2D
floating-point array:
-   Forward Cosine transform of a 1D vector of N elements:
    \\f[Y = C^{(N)}  \\cdot X\\f]
    where
    \\f[C^{(N)}_{jk}= \\sqrt{\\alpha_j/N} \\cos \\left ( \\frac{\\pi(2k+1)j}{2N} \\right )\\f]
    and
    \\f$\\alpha_0=1\\f$, \\f$\\alpha_j=2\\f$ for *j \\> 0*.
-   Inverse Cosine transform of a 1D vector of N elements:
    \\f[X =  \\left (C^{(N)} \\right )^{-1}  \\cdot Y =  \\left (C^{(N)} \\right )^T  \\cdot Y\\f]
    (since \\f$C^{(N)}\\f$ is an orthogonal matrix, \\f$C^{(N)} \\cdot \\left(C^{(N)}\\right)^T = I\\f$ )
-   Forward 2D Cosine transform of M x N matrix:
    \\f[Y = C^{(N)}  \\cdot X  \\cdot \\left (C^{(N)} \\right )^T\\f]
-   Inverse 2D Cosine transform of M x N matrix:
    \\f[X =  \\left (C^{(N)} \\right )^T  \\cdot X  \\cdot C^{(N)}\\f]

The function chooses the mode of operation by looking at the flags and size of the input array:
-   If (flags & #DCT_INVERSE) == 0 , the function does a forward 1D or 2D transform. Otherwise, it
    is an inverse 1D or 2D transform.
-   If (flags & #DCT_ROWS) != 0 , the function performs a 1D transform of each row.
-   If the array is a single column or a single row, the function performs a 1D transform.
-   If none of the above is true, the function performs a 2D transform.

@note Currently dct supports even-size arrays (2, 4, 6 ...). For data analysis and approximation, you
can pad the array when necessary.
Also, the function performance depends very much, and not monotonically, on the array size (see
getOptimalDFTSize ). In the current implementation DCT of a vector of size N is calculated via DFT
of a vector of size N/2 . Thus, the optimal DCT size N1 \\>= N can be calculated as:
@code
    size_t getOptimalDCTSize(size_t N) { return 2*getOptimalDFTSize((N+1)/2); }
    N1 = getOptimalDCTSize(N);
@endcode
@param src input floating-point array.
@param dst output array of the same size and type as src .
@param flags transformation flags as a combination of cv::DftFlags (DCT_*)
@sa dft , getOptimalDFTSize , idct',0,'void',['Mat','src','',[]],['Mat','dst','',['/O']],['int','flags','0',[]]],
['','idct','@brief Calculates the inverse Discrete Cosine Transform of a 1D or 2D array.

idct(src, dst, flags) is equivalent to dct(src, dst, flags | DCT_INVERSE).
@param src input floating-point single-channel array.
@param dst output array of the same size and type as src.
@param flags operation flags.
@sa  dct, dft, idft, getOptimalDFTSize',0,'void',['Mat','src','',[]],['Mat','dst','',['/O']],['int','flags','0',[]]],
['','mulSpectrums','@brief Performs the per-element multiplication of two Fourier spectrums.

The function cv::mulSpectrums performs the per-element multiplication of the two CCS-packed or complex
matrices that are results of a real or complex Fourier transform.

The function, together with dft and idft , may be used to calculate convolution (pass conjB=false )
or correlation (pass conjB=true ) of two arrays rapidly. When the arrays are complex, they are
simply multiplied (per element) with an optional conjugation of the second-array elements. When the
arrays are real, they are assumed to be CCS-packed (see dft for details).
@param a first input array.
@param b second input array of the same size and type as src1 .
@param c output array of the same size and type as src1 .
@param flags operation flags; currently, the only supported flag is cv::DFT_ROWS, which indicates that
each row of src1 and src2 is an independent 1D Fourier spectrum. If you do not want to use this flag, then simply add a `0` as value.
@param conjB optional flag that conjugates the second input array before the multiplication (true)
or not (false).',0,'void',['Mat','a','',[]],['Mat','b','',[]],['Mat','c','',['/O']],['int','flags','',[]],['bool','conjB','false',[]]],
['','getOptimalDFTSize','@brief Returns the optimal DFT size for a given vector size.

DFT performance is not a monotonic function of a vector size. Therefore, when you calculate
convolution of two arrays or perform the spectral analysis of an array, it usually makes sense to
pad the input data with zeros to get a bit larger array that can be transformed much faster than the
original one. Arrays whose size is a power-of-two (2, 4, 8, 16, 32, ...) are the fastest to process.
Though, the arrays whose size is a product of 2\'s, 3\'s, and 5\'s (for example, 300 = 5\\*5\\*3\\*2\\*2)
are also processed quite efficiently.

The function cv::getOptimalDFTSize returns the minimum number N that is greater than or equal to vecsize
so that the DFT of a vector of size N can be processed efficiently. In the current implementation N
= 2 ^p^ \\* 3 ^q^ \\* 5 ^r^ for some integer p, q, r.

The function returns a negative number if vecsize is too large (very close to INT_MAX ).

While the function cannot be used directly to estimate the optimal vector size for DCT transform
(since the current DCT implementation supports only even-size vectors), it can be easily processed
as getOptimalDFTSize((vecsize+1)/2)\\*2.
@param vecsize vector size.
@sa dft , dct , idft , idct , mulSpectrums',0,'int',['int','vecsize','',[]]],
['','setRNGSeed','@brief Sets state of default random number generator.

The function cv::setRNGSeed sets state of default random number generator to custom value.
@param seed new state for default random number generator
@sa RNG, randu, randn',0,'void',['int','seed','',[]]],
['','randu','@brief Generates a single uniformly-distributed random number or an array of random numbers.

Non-template variant of the function fills the matrix dst with uniformly-distributed
random numbers from the specified range:
\\f[\\texttt{low} _c  \\leq \\texttt{dst} (I)_c <  \\texttt{high} _c\\f]
@param dst output array of random numbers; the array must be pre-allocated.
@param low inclusive lower boundary of the generated random numbers.
@param high exclusive upper boundary of the generated random numbers.
@sa RNG, randn, theRNG',0,'void',['Mat','dst','',['/IO']],['Mat','low','',[]],['Mat','high','',[]]],
['','randn','@brief Fills the array with normally distributed random numbers.

The function cv::randn fills the matrix dst with normally distributed random numbers with the specified
mean vector and the standard deviation matrix. The generated random numbers are clipped to fit the
value range of the output array data type.
@param dst output array of random numbers; the array must be pre-allocated and have 1 to 4 channels.
@param mean mean value (expectation) of the generated random numbers.
@param stddev standard deviation of the generated random numbers; it can be either a vector (in
which case a diagonal standard deviation matrix is assumed) or a square matrix.
@sa RNG, randu',0,'void',['Mat','dst','',['/IO']],['Mat','mean','',[]],['Mat','stddev','',[]]],
['','randShuffle','@brief Shuffles the array elements randomly.

The function cv::randShuffle shuffles the specified 1D array by randomly choosing pairs of elements and
swapping them. The number of such swap operations will be dst.rows\\*dst.cols\\*iterFactor .
@param dst input/output numerical 1D array.
@param iterFactor scale factor that determines the number of random swap operations (see the details
below).
@param rng optional random number generator used for shuffling; if it is zero, theRNG () is used
instead.
@sa RNG, sort',0,'void',['Mat','dst','',['/IO']],['double','iterFactor','1.',[]],['RNG*','rng','0',[]]],
['RNG','fill','@brief Fills arrays with random numbers.

    @param mat 2D or N-dimensional matrix; currently matrices with more than
    4 channels are not supported by the methods, use Mat::reshape as a
    possible workaround.
    @param distType distribution type, RNG::UNIFORM or RNG::NORMAL.
    @param a first distribution parameter; in case of the uniform
    distribution, this is an inclusive lower boundary, in case of the normal
    distribution, this is a mean value.
    @param b second distribution parameter; in case of the uniform
    distribution, this is a non-inclusive upper boundary, in case of the
    normal distribution, this is a standard deviation (diagonal of the
    standard deviation matrix or the full standard deviation matrix).
    @param saturateRange pre-saturation flag; for uniform distribution only;
    if true, the method will first convert a and b to the acceptable value
    range (according to the mat datatype) and then will generate uniformly
    distributed random numbers within the range [saturate(a), saturate(b)),
    if saturateRange=false, the method will generate uniformly distributed
    random numbers in the original range [a, b) and then will saturate them,
    it means, for example, that
    <tt>theRNG().fill(mat_8u, RNG::UNIFORM, -DBL_MAX, DBL_MAX)</tt> will likely
    produce array mostly filled with 0\'s and 255\'s, since the range (0, 255)
    is significantly smaller than [-DBL_MAX, DBL_MAX).

    Each of the methods fills the matrix with the random values from the
    specified distribution. As the new numbers are generated, the RNG state
    is updated accordingly. In case of multiple-channel images, every
    channel is filled independently, which means that RNG cannot generate
    samples from the multi-dimensional Gaussian distribution with
    non-diagonal covariance matrix directly. To do that, the method
    generates samples from multi-dimensional standard Gaussian distribution
    with zero mean and identity covariation matrix, and then transforms them
    using transform to get samples from the specified Gaussian distribution.',1,'void',['Mat','mat','',['/IO']],['int','distType','',[]],['Mat','a','',[]],['Mat','b','',[]],['bool','saturateRange','false',[]]],
['','kmeans','@brief Finds centers of clusters and groups input samples around the clusters.

The function kmeans implements a k-means algorithm that finds the centers of cluster_count clusters
and groups the input samples around the clusters. As an output, \\f$\\texttt{bestLabels}_i\\f$ contains a
0-based cluster index for the sample stored in the \\f$i^{th}\\f$ row of the samples matrix.

@note
-   (Python) An example on K-means clustering can be found at
    opencv_source_code/samples/python/kmeans.py
@param data Data for clustering. An array of N-Dimensional points with float coordinates is needed.
Examples of this array can be:
-   Mat points(count, 2, CV_32F);
-   Mat points(count, 1, CV_32FC2);
-   Mat points(1, count, CV_32FC2);
-   std::vector\\<cv::Point2f\\> points(sampleCount);
@param K Number of clusters to split the set by.
@param bestLabels Input/output integer array that stores the cluster indices for every sample.
@param criteria The algorithm termination criteria, that is, the maximum number of iterations and/or
the desired accuracy. The accuracy is specified as criteria.epsilon. As soon as each of the cluster
centers moves by less than criteria.epsilon on some iteration, the algorithm stops.
@param attempts Flag to specify the number of times the algorithm is executed using different
initial labellings. The algorithm returns the labels that yield the best compactness (see the last
function parameter).
@param flags Flag that can take values of cv::KmeansFlags
@param centers Output matrix of the cluster centers, one row per each cluster center.
@return The function returns the compactness measure that is computed as
\\f[\\sum _i  \\| \\texttt{samples} _i -  \\texttt{centers} _{ \\texttt{labels} _i} \\| ^2\\f]
after every attempt. The best (minimum) value is chosen and the corresponding labels and the
compactness value are returned by the function. Basically, you can use only the core of the
function, set the number of attempts to 1, initialize labels each time using a custom algorithm,
pass them with the ( flags = #KMEANS_USE_INITIAL_LABELS ) flag, and then choose the best
(most-compact) clustering.',0,'double',['Mat','data','',[]],['int','K','',[]],['Mat','bestLabels','',['/IO']],['TermCriteria','criteria','',[]],['int','attempts','',[]],['int','flags','',[]],['Mat','centers','Mat()',['/O']]],
['Algorithm','clear','@brief Clears the algorithm state',1,'void'],
['Algorithm','write','@brief simplified API for language bindings
    * @overload',1,'void',['Ptr_FileStorage','fs','',['/C','/Ref']],['String','name','String()',['/C','/Ref']]],
['Algorithm','read','@brief Reads algorithm parameters from a file storage',1,'void',['FileNode','fn','',['/C','/Ref']]],
['Algorithm','empty','@brief Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read',1,'bool'],
['Algorithm','save','Saves the algorithm to a file.
In order to make this method work, the derived class must implement Algorithm::write(FileStorage& fs).',1,'void',['String','filename','',['/C','/Ref']]],
['Algorithm','getDefaultName','Returns the algorithm string identifier.
This string is used as top level xml/yml node tag when the object is saved to a file or string.',1,'String'],
['FileStorage','open','@brief Opens a file.

     See description of parameters in FileStorage::FileStorage. The method calls FileStorage::release
     before opening the file.
     @param filename Name of the file to open or the text string to read the data from.
     Extension of the file (.xml, .yml/.yaml or .json) determines its format (XML, YAML or JSON
     respectively). Also you can append .gz to work with compressed files, for example myHugeMatrix.xml.gz. If both
     FileStorage::WRITE and FileStorage::MEMORY flags are specified, source is used just to specify
     the output file format (e.g. mydata.xml, .yml etc.). A file name can also contain parameters.
     You can use this format, "*?base64" (e.g. "file.json?base64" (case sensitive)), as an alternative to
     FileStorage::BASE64 flag.
     @param flags Mode of operation. One of FileStorage::Mode
     @param encoding Encoding of the file. Note that UTF-16 XML encoding is not supported currently and
     you should use 8-bit encoding instead of it.',1,'bool',['String','filename','',['/C','/Ref']],['int','flags','',[]],['String','encoding','String()',['/C','/Ref']]],
['FileStorage','isOpened','@brief Checks whether the file is opened.

     @returns true if the object is associated with the current file and false otherwise. It is a
     good practice to call this method after you tried to open a file.',1,'bool'],
['FileStorage','release','@brief Closes the file and releases all the memory buffers.

     Call this method after all I/O operations with the storage are finished.',1,'void'],
['FileStorage','releaseAndGetString','@brief Closes the file and releases all the memory buffers.

     Call this method after all I/O operations with the storage are finished. If the storage was
     opened for writing data and FileStorage::WRITE was specified',1,'String'],
['FileStorage','getFirstTopLevelNode','@brief Returns the first element of the top-level mapping.
     @returns The first element of the top-level mapping.',1,'FileNode'],
['FileStorage','root','@brief Returns the top-level mapping
     @param streamidx Zero-based index of the stream. In most cases there is only one stream in the file.
     However, YAML supports multiple streams and so there can be several.
     @returns The top-level mapping.',1,'FileNode',['int','streamidx','0',[]]],
['FileStorage',['operator[]','getNode'],'@overload',1,'FileNode',['c_string','nodename','',['/C']]],
['FileStorage','write','* @brief Simplified writing API to use with bindings.
     * @param name Name of the written object. When writing to sequences (a.k.a. "arrays"), pass an empty string.
     * @param val Value of the written object.',1,'void',['String','name','',['/C','/Ref']],['int','val','',[]]],
['FileStorage','write','',1,'void',['String','name','',['/C','/Ref']],['double','val','',[]]],
['FileStorage','write','',1,'void',['String','name','',['/C','/Ref']],['String','val','',['/C','/Ref']]],
['FileStorage','write','',1,'void',['String','name','',['/C','/Ref']],['Mat','val','',['/C','/Ref']]],
['FileStorage','write','',1,'void',['String','name','',['/C','/Ref']],['vector_String','val','',['/C','/Ref']]],
['FileStorage','writeComment','@brief Writes a comment.

     The function writes a comment into file storage. The comments are skipped when the storage is read.
     @param comment The written comment, single-line or multi-line
     @param append If true, the function tries to put the comment at the end of current line.
     Else if the comment is multi-line, or if it does not fit at the end of the current
     line, the comment starts a new line.',1,'void',['String','comment','',['/C','/Ref']],['bool','append','false',[]]],
['FileStorage','startWriteStruct','@brief Starts to write a nested structure (sequence or a mapping).
    @param name name of the structure. When writing to sequences (a.k.a. "arrays"), pass an empty string.
    @param flags type of the structure (FileNode::MAP or FileNode::SEQ (both with optional FileNode::FLOW)).
    @param typeName optional name of the type you store. The effect of setting this depends on the storage format.
    I.e. if the format has a specification for storing type information, this parameter is used.',1,'void',['String','name','',['/C','/Ref']],['int','flags','',[]],['String','typeName','String()',['/C','/Ref']]],
['FileStorage','endWriteStruct','@brief Finishes writing nested structure (should pair startWriteStruct())',1,'void'],
['FileStorage','getFormat','@brief Returns the current format.
     * @returns The current format, see FileStorage::Mode',1,'int'],
['FileNode',['operator[]','getNode'],'@overload
     @param nodename Name of an element in the mapping node.',1,'FileNode',['c_string','nodename','',['/C']]],
['FileNode',['operator[]','at'],'@overload
     @param i Index of an element in the sequence node.',1,'FileNode',['int','i','',[]]],
['FileNode','keys','@brief Returns keys of a mapping node.
     @returns Keys of a mapping node.',1,'vector_String'],
['FileNode','type','@brief Returns type of the node.
     @returns Type of the node. See FileNode::Type',1,'int'],
['FileNode','empty','',1,'bool'],
['FileNode','isNone','',1,'bool'],
['FileNode','isSeq','',1,'bool'],
['FileNode','isMap','',1,'bool'],
['FileNode','isInt','',1,'bool'],
['FileNode','isReal','',1,'bool'],
['FileNode','isString','',1,'bool'],
['FileNode','isNamed','',1,'bool'],
['FileNode','name','',1,'string'],
['FileNode','size','',1,'size_t'],
['FileNode','rawSize','',1,'size_t'],
['FileNode','real','Internal method used when reading FileStorage.
     Sets the type (int, real or string) and value of the previously created node.',1,'double'],
['FileNode','string','',1,'string'],
['FileNode','mat','',1,'Mat'],
);