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Merge pull request #25356 from Kumataro:fix25345
core: doc: add note for countNonZero, hasNonZero and findNonZero #25356 Close #25345 ### Pull Request Readiness Checklist See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [x] I agree to contribute to the project under Apache 2 License. - [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [x] The PR is proposed to the proper branch - [x] There is a reference to the original bug report and related work - [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [ ] The feature is well documented and sample code can be built with the project CMake
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@ -248,7 +248,7 @@ CV_EXPORTS void swap( UMat& a, UMat& b );
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The function computes and returns the coordinate of a donor pixel corresponding to the specified
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extrapolated pixel when using the specified extrapolation border mode. For example, if you use
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cv::BORDER_WRAP mode in the horizontal direction, cv::BORDER_REFLECT_101 in the vertical direction and
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want to compute value of the "virtual" pixel Point(-5, 100) in a floating-point image img , it
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want to compute value of the "virtual" pixel Point(-5, 100) in a floating-point image img, it
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looks like:
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@code{.cpp}
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float val = img.at<float>(borderInterpolate(100, img.rows, cv::BORDER_REFLECT_101),
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@ -259,7 +259,7 @@ copyMakeBorder.
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@param p 0-based coordinate of the extrapolated pixel along one of the axes, likely \<0 or \>= len
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@param len Length of the array along the corresponding axis.
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@param borderType Border type, one of the #BorderTypes, except for #BORDER_TRANSPARENT and
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#BORDER_ISOLATED . When borderType==#BORDER_CONSTANT , the function always returns -1, regardless
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#BORDER_ISOLATED. When borderType==#BORDER_CONSTANT, the function always returns -1, regardless
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of p and len.
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@sa copyMakeBorder
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@ -585,8 +585,18 @@ CV_EXPORTS_AS(sumElems) Scalar sum(InputArray src);
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/** @brief Checks for the presence of at least one non-zero array element.
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The function returns whether there are non-zero elements in src
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The function do not work with multi-channel arrays. If you need to check non-zero array
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elements across all the channels, use Mat::reshape first to reinterpret the array as
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single-channel. Or you may extract the particular channel using either extractImageCOI, or
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mixChannels, or split.
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@note
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- If the location of non-zero array elements is important, @ref findNonZero is helpful.
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- If the count of non-zero array elements is important, @ref countNonZero is helpful.
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@param src single-channel array.
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@sa mean, meanStdDev, norm, minMaxLoc, calcCovarMatrix
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@sa findNonZero, countNonZero
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*/
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CV_EXPORTS_W bool hasNonZero( InputArray src );
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@ -594,8 +604,18 @@ CV_EXPORTS_W bool hasNonZero( InputArray src );
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The function returns the number of non-zero elements in src :
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\f[\sum _{I: \; \texttt{src} (I) \ne0 } 1\f]
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The function do not work with multi-channel arrays. If you need to count non-zero array
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elements across all the channels, use Mat::reshape first to reinterpret the array as
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single-channel. Or you may extract the particular channel using either extractImageCOI, or
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mixChannels, or split.
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@note
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- If only whether there are non-zero elements is important, @ref hasNonZero is helpful.
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- If the location of non-zero array elements is important, @ref findNonZero is helpful.
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@param src single-channel array.
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@sa mean, meanStdDev, norm, minMaxLoc, calcCovarMatrix
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@sa findNonZero, hasNonZero
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*/
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CV_EXPORTS_W int countNonZero( InputArray src );
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@ -622,8 +642,18 @@ or
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// access pixel coordinates
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Point pnt = locations[i];
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@endcode
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The function do not work with multi-channel arrays. If you need to find non-zero
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elements across all the channels, use Mat::reshape first to reinterpret the array as
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single-channel. Or you may extract the particular channel using either extractImageCOI, or
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mixChannels, or split.
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@note
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- If only count of non-zero array elements is important, @ref countNonZero is helpful.
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- If only whether there are non-zero elements is important, @ref hasNonZero is helpful.
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@param src single-channel array
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@param idx the output array, type of cv::Mat or std::vector<Point>, corresponding to non-zero indices in the input
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@sa countNonZero, hasNonZero
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*/
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CV_EXPORTS_W void findNonZero( InputArray src, OutputArray idx );
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@ -830,8 +860,8 @@ array region.
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The function do not work with multi-channel arrays. If you need to find minimum or maximum
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elements across all the channels, use Mat::reshape first to reinterpret the array as
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single-channel. Or you may extract the particular channel using either extractImageCOI , or
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mixChannels , or split .
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single-channel. Or you may extract the particular channel using either extractImageCOI, or
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mixChannels, or split.
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@param src input single-channel array.
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@param minVal pointer to the returned minimum value; NULL is used if not required.
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@param maxVal pointer to the returned maximum value; NULL is used if not required.
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@ -884,8 +914,8 @@ The function cv::minMaxIdx finds the minimum and maximum element values and thei
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extremums are searched across the whole array or, if mask is not an empty array, in the specified
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array region. The function does not work with multi-channel arrays. If you need to find minimum or
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maximum elements across all the channels, use Mat::reshape first to reinterpret the array as
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single-channel. Or you may extract the particular channel using either extractImageCOI , or
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mixChannels , or split . In case of a sparse matrix, the minimum is found among non-zero elements
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single-channel. Or you may extract the particular channel using either extractImageCOI, or
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mixChannels, or split. In case of a sparse matrix, the minimum is found among non-zero elements
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only.
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@note When minIdx is not NULL, it must have at least 2 elements (as well as maxIdx), even if src is
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a single-row or single-column matrix. In OpenCV (following MATLAB) each array has at least 2
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@ -921,8 +951,8 @@ CV_EXPORTS void minMaxLoc(const SparseMat& a, double* minVal,
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The function #reduce reduces the matrix to a vector by treating the matrix rows/columns as a set of
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1D vectors and performing the specified operation on the vectors until a single row/column is
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obtained. For example, the function can be used to compute horizontal and vertical projections of a
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raster image. In case of #REDUCE_MAX and #REDUCE_MIN , the output image should have the same type as the source one.
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In case of #REDUCE_SUM, #REDUCE_SUM2 and #REDUCE_AVG , the output may have a larger element bit-depth to preserve accuracy.
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raster image. In case of #REDUCE_MAX and #REDUCE_MIN, the output image should have the same type as the source one.
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In case of #REDUCE_SUM, #REDUCE_SUM2 and #REDUCE_AVG, the output may have a larger element bit-depth to preserve accuracy.
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And multi-channel arrays are also supported in these two reduction modes.
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The following code demonstrates its usage for a single channel matrix.
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@ -976,7 +1006,7 @@ CV_EXPORTS_W void merge(InputArrayOfArrays mv, OutputArray dst);
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The function cv::split splits a multi-channel array into separate single-channel arrays:
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\f[\texttt{mv} [c](I) = \texttt{src} (I)_c\f]
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If you need to extract a single channel or do some other sophisticated channel permutation, use
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mixChannels .
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mixChannels.
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The following example demonstrates how to split a 3-channel matrix into 3 single channel matrices.
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@snippet snippets/core_split.cpp example
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@ -1117,7 +1147,7 @@ The example scenarios of using the function are the following:
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flipping around the x-axis and positive value (for example, 1) means
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flipping around y-axis. Negative value (for example, -1) means flipping
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around both axes.
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@sa transpose , repeat , completeSymm
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@sa transpose, repeat, completeSymm
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*/
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CV_EXPORTS_W void flip(InputArray src, OutputArray dst, int flipCode);
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@ -1149,7 +1179,7 @@ The function cv::rotate rotates the array in one of three different ways:
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@param dst output array of the same type as src. The size is the same with ROTATE_180,
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and the rows and cols are switched for ROTATE_90_CLOCKWISE and ROTATE_90_COUNTERCLOCKWISE.
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@param rotateCode an enum to specify how to rotate the array; see the enum #RotateFlags
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@sa transpose , repeat , completeSymm, flip, RotateFlags
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@sa transpose, repeat, completeSymm, flip, RotateFlags
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*/
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CV_EXPORTS_W void rotate(InputArray src, OutputArray dst, int rotateCode);
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@ -1583,7 +1613,7 @@ converts denormalized values to zeros on output. Special values (NaN,
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Inf) are not handled.
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@param src input array.
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@param dst output array of the same size and type as src.
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@sa log , cartToPolar , polarToCart , phase , pow , sqrt , magnitude
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@sa log, cartToPolar, polarToCart, phase, pow, sqrt, magnitude
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*/
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CV_EXPORTS_W void exp(InputArray src, OutputArray dst);
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@ -1727,7 +1757,7 @@ should have the same type as src1 and src2.
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@param dst output matrix; it has the proper size and the same type as
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input matrices.
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@param flags operation flags (cv::GemmFlags)
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@sa mulTransposed , transform
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@sa mulTransposed, transform
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*/
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CV_EXPORTS_W void gemm(InputArray src1, InputArray src2, double alpha,
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InputArray src3, double beta, OutputArray dst, int flags = 0);
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@ -1737,7 +1767,7 @@ CV_EXPORTS_W void gemm(InputArray src1, InputArray src2, double alpha,
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The function cv::mulTransposed calculates the product of src and its
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transposition:
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\f[\texttt{dst} = \texttt{scale} ( \texttt{src} - \texttt{delta} )^T ( \texttt{src} - \texttt{delta} )\f]
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if aTa=true , and
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if aTa=true, and
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\f[\texttt{dst} = \texttt{scale} ( \texttt{src} - \texttt{delta} ) ( \texttt{src} - \texttt{delta} )^T\f]
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otherwise. The function is used to calculate the covariance matrix. With
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zero delta, it can be used as a faster substitute for general matrix
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@ -1750,7 +1780,7 @@ description below.
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@param delta Optional delta matrix subtracted from src before the
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multiplication. When the matrix is empty ( delta=noArray() ), it is
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assumed to be zero, that is, nothing is subtracted. If it has the same
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size as src , it is simply subtracted. Otherwise, it is "repeated" (see
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size as src, it is simply subtracted. Otherwise, it is "repeated" (see
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repeat ) to cover the full src and then subtracted. Type of the delta
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matrix, when it is not empty, must be the same as the type of created
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output matrix. See the dtype parameter description below.
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@ -2024,7 +2054,7 @@ in the descending order.
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@param eigenvectors output matrix of eigenvectors; it has the same size and type as src; the
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eigenvectors are stored as subsequent matrix rows, in the same order as the corresponding
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eigenvalues.
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@sa eigenNonSymmetric, completeSymm , PCA
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@sa eigenNonSymmetric, completeSymm, PCA
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*/
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CV_EXPORTS_W bool eigen(InputArray src, OutputArray eigenvalues,
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OutputArray eigenvectors = noArray());
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@ -2164,7 +2194,7 @@ So, the function chooses an operation mode depending on the flags and size of th
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If #DFT_SCALE is set, the scaling is done after the transformation.
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Unlike dct , the function supports arrays of arbitrary size. But only those arrays are processed
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Unlike dct, the function supports arrays of arbitrary size. But only those arrays are processed
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efficiently, whose sizes can be factorized in a product of small prime numbers (2, 3, and 5 in the
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current implementation). Such an efficient DFT size can be calculated using the getOptimalDFTSize
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method.
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@ -2247,8 +2277,8 @@ nonzeroRows rows of the input array (#DFT_INVERSE is not set) or only the first
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output array (#DFT_INVERSE is set) contain non-zeros, thus, the function can handle the rest of the
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rows more efficiently and save some time; this technique is very useful for calculating array
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cross-correlation or convolution using DFT.
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@sa dct , getOptimalDFTSize , mulSpectrums, filter2D , matchTemplate , flip , cartToPolar ,
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magnitude , phase
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@sa dct, getOptimalDFTSize, mulSpectrums, filter2D, matchTemplate, flip, cartToPolar,
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magnitude, phase
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*/
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CV_EXPORTS_W void dft(InputArray src, OutputArray dst, int flags = 0, int nonzeroRows = 0);
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@ -2285,9 +2315,9 @@ floating-point array:
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\f[X = \left (C^{(N)} \right )^T \cdot X \cdot C^{(N)}\f]
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The function chooses the mode of operation by looking at the flags and size of the input array:
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- If (flags & #DCT_INVERSE) == 0 , the function does a forward 1D or 2D transform. Otherwise, it
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- If (flags & #DCT_INVERSE) == 0, the function does a forward 1D or 2D transform. Otherwise, it
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is an inverse 1D or 2D transform.
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- If (flags & #DCT_ROWS) != 0 , the function performs a 1D transform of each row.
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- If (flags & #DCT_ROWS) != 0, the function performs a 1D transform of each row.
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- If the array is a single column or a single row, the function performs a 1D transform.
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- If none of the above is true, the function performs a 2D transform.
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@ -2303,7 +2333,7 @@ of a vector of size N/2 . Thus, the optimal DCT size N1 \>= N can be calculated
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@param src input floating-point array.
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@param dst output array of the same size and type as src .
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@param flags transformation flags as a combination of cv::DftFlags (DCT_*)
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@sa dft , getOptimalDFTSize , idct
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@sa dft, getOptimalDFTSize, idct
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*/
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CV_EXPORTS_W void dct(InputArray src, OutputArray dst, int flags = 0);
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@ -2322,7 +2352,7 @@ CV_EXPORTS_W void idct(InputArray src, OutputArray dst, int flags = 0);
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The function cv::mulSpectrums performs the per-element multiplication of the two CCS-packed or complex
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matrices that are results of a real or complex Fourier transform.
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The function, together with dft and idft , may be used to calculate convolution (pass conjB=false )
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The function, together with dft and idft, may be used to calculate convolution (pass conjB=false )
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or correlation (pass conjB=true ) of two arrays rapidly. When the arrays are complex, they are
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simply multiplied (per element) with an optional conjugation of the second-array elements. When the
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arrays are real, they are assumed to be CCS-packed (see dft for details).
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@ -2356,7 +2386,7 @@ While the function cannot be used directly to estimate the optimal vector size f
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(since the current DCT implementation supports only even-size vectors), it can be easily processed
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as getOptimalDFTSize((vecsize+1)/2)\*2.
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@param vecsize vector size.
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@sa dft , dct , idft , idct , mulSpectrums
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@sa dft, dct, idft, idct, mulSpectrums
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*/
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CV_EXPORTS_W int getOptimalDFTSize(int vecsize);
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@ -2908,7 +2938,7 @@ public:
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The methods transform the state using the MWC algorithm and return the
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next random number. The first form is equivalent to RNG::next . The
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second form returns the random number modulo N , which means that the
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second form returns the random number modulo N, which means that the
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result is in the range [0, N) .
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*/
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unsigned operator ()();
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