Matrix Reductions

cuda::norm

Returns the norm of a matrix (or difference of two matrices).

C++: double cuda::norm(InputArray src1, int normType)

C++: double cuda::norm(InputArray src1, int normType, GpuMat& buf)

C++: double cuda::norm(InputArray src1, int normType, InputArray mask, GpuMat& buf)

C++: double cuda::norm(InputArray src1, InputArray src2, int normType=NORM_L2)

Parameters:

• src1 – Source matrix. Any matrices except 64F are supported.
• src2 – Second source matrix (if any) with the same size and type as src1.
• normType – Norm type. NORM_L1 , NORM_L2 , and NORM_INF are supported for now.
• mask – optional operation mask; it must have the same size as src1 and CV_8UC1 type.
• buf – Optional buffer to avoid extra memory allocations. It is resized automatically.

See also

norm()

cuda::sum

Returns the sum of matrix elements.

C++: Scalar cuda::sum(InputArray src)

C++: Scalar cuda::sum(InputArray src, GpuMat& buf)

C++: Scalar cuda::sum(InputArray src, InputArray mask, GpuMat& buf)

Parameters:

• src – Source image of any depth except for CV_64F .
• mask – optional operation mask; it must have the same size as src1 and CV_8UC1 type.
• buf – Optional buffer to avoid extra memory allocations. It is resized automatically.

See also

sum()

cuda::absSum

Returns the sum of absolute values for matrix elements.

C++: Scalar cuda::absSum(InputArray src)

C++: Scalar cuda::absSum(InputArray src, GpuMat& buf)

C++: Scalar cuda::absSum(InputArray src, InputArray mask, GpuMat& buf)

Parameters:

• src – Source image of any depth except for CV_64F .
• mask – optional operation mask; it must have the same size as src1 and CV_8UC1 type.
• buf – Optional buffer to avoid extra memory allocations. It is resized automatically.

cuda::sqrSum

Returns the squared sum of matrix elements.

C++: Scalar cuda::sqrSum(InputArray src)

C++: Scalar cuda::sqrSum(InputArray src, GpuMat& buf)

C++: Scalar cuda::sqrSum(InputArray src, InputArray mask, GpuMat& buf)

Parameters:

• src – Source image of any depth except for CV_64F .
• mask – optional operation mask; it must have the same size as src1 and CV_8UC1 type.
• buf – Optional buffer to avoid extra memory allocations. It is resized automatically.

cuda::minMax

Finds global minimum and maximum matrix elements and returns their values.

C++: void cuda::minMax(InputArray src, double* minVal, double* maxVal=0, InputArray mask=noArray())

C++: void cuda::minMax(InputArray src, double* minVal, double* maxVal, InputArray mask, GpuMat& buf)

Parameters:

• src – Single-channel source image.
• minVal – Pointer to the returned minimum value. Use NULL if not required.
• maxVal – Pointer to the returned maximum value. Use NULL if not required.
• mask – Optional mask to select a sub-matrix.
• buf – Optional buffer to avoid extra memory allocations. It is resized automatically.

The function does not work with CV_64F images on GPUs with the compute capability < 1.3.

See also

minMaxLoc()

cuda::minMaxLoc

Finds global minimum and maximum matrix elements and returns their values with locations.

C++: void cuda::minMaxLoc(InputArray src, double* minVal, double* maxVal=0, Point* minLoc=0, Point* maxLoc=0, InputArray mask=noArray())

C++: void cuda::minMaxLoc(InputArray src, double* minVal, double* maxVal, Point* minLoc, Point* maxLoc, InputArray mask, GpuMat& valbuf, GpuMat& locbuf)

Parameters:

• src – Single-channel source image.
• minVal – Pointer to the returned minimum value. Use NULL if not required.
• maxVal – Pointer to the returned maximum value. Use NULL if not required.
• minLoc – Pointer to the returned minimum location. Use NULL if not required.
• maxLoc – Pointer to the returned maximum location. Use NULL if not required.
• mask – Optional mask to select a sub-matrix.
• valbuf – Optional values buffer to avoid extra memory allocations. It is resized automatically.
• locbuf – Optional locations buffer to avoid extra memory allocations. It is resized automatically.

The function does not work with CV_64F images on GPU with the compute capability < 1.3.

See also

minMaxLoc()

cuda::countNonZero

Counts non-zero matrix elements.

C++: int cuda::countNonZero(InputArray src)

C++: int cuda::countNonZero(InputArray src, GpuMat& buf)

Parameters:

• src – Single-channel source image.
• buf – Optional buffer to avoid extra memory allocations. It is resized automatically.

The function does not work with CV_64F images on GPUs with the compute capability < 1.3.

See also

countNonZero()

cuda::reduce

Reduces a matrix to a vector.

C++: void cuda::reduce(InputArray mtx, OutputArray vec, int dim, int reduceOp, int dtype=-1, Stream& stream=Stream::Null())

Parameters:

• mtx – Source 2D matrix.
• vec – Destination vector. Its size and type is defined by dim and dtype parameters.
• 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.
• reduceOp –

  Reduction operation that could be one of the following:

  • CV_REDUCE_SUM The output is the sum of all rows/columns of the matrix.
  • CV_REDUCE_AVG The output is the mean vector of all rows/columns of the matrix.
  • CV_REDUCE_MAX The output is the maximum (column/row-wise) of all rows/columns of the matrix.
  • CV_REDUCE_MIN The output is the minimum (column/row-wise) of all rows/columns of the matrix.
• dtype – When it is negative, the destination vector will have the same type as the source matrix. Otherwise, its type will be CV_MAKE_TYPE(CV_MAT_DEPTH(dtype), mtx.channels()) .
• stream – Stream for the asynchronous version.

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 CV_REDUCE_SUM and CV_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.

See also

reduce()

cuda::meanStdDev

Computes a mean value and a standard deviation of matrix elements.

C++: void cuda::meanStdDev(InputArray mtx, Scalar& mean, Scalar& stddev)

C++: void cuda::meanStdDev(InputArray mtx, Scalar& mean, Scalar& stddev, GpuMat& buf)

Parameters:

• mtx – Source matrix. CV_8UC1 matrices are supported for now.
• mean – Mean value.
• stddev – Standard deviation value.
• buf – Optional buffer to avoid extra memory allocations. It is resized automatically.

See also

meanStdDev()

cuda::rectStdDev

Computes a standard deviation of integral images.

C++: void cuda::rectStdDev(InputArray src, InputArray sqr, OutputArray dst, Rect rect, Stream& stream=Stream::Null())

Parameters:

• src – Source image. Only the CV_32SC1 type is supported.
• sqr – Squared source image. Only the CV_32FC1 type is supported.
• dst – Destination image with the same type and size as src .
• rect – Rectangular window.
• stream – Stream for the asynchronous version.

cuda::normalize

Normalizes the norm or value range of an array.

C++: void cuda::normalize(InputArray src, OutputArray dst, double alpha=1, double beta=0, int norm_type=NORM_L2, int dtype=-1, InputArray mask=noArray())

C++: void cuda::normalize(InputArray src, OutputArray dst, double alpha, double beta, int norm_type, int dtype, InputArray mask, GpuMat& norm_buf, GpuMat& cvt_buf)

Parameters:

• src – Input array.
• dst – Output array of the same size as src .
• alpha – Norm value to normalize to or the lower range boundary in case of the range normalization.
• beta – Upper range boundary in case of the range normalization; it is not used for the norm normalization.
• normType – Normalization type ( NORM_MINMAX , NORM_L2 , NORM_L1 or NORM_INF ).
• 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).
• mask – Optional operation mask.
• norm_buf – Optional buffer to avoid extra memory allocations. It is resized automatically.
• cvt_buf – Optional buffer to avoid extra memory allocations. It is resized automatically.

See also

normalize()

cuda::integral

Computes an integral image.

C++: void cuda::integral(InputArray src, OutputArray sum, Stream& stream=Stream::Null())

C++: void cuda::integral(InputArray src, OutputArray sum, GpuMat& buffer, Stream& stream=Stream::Null())

Parameters:

• src – Source image. Only CV_8UC1 images are supported for now.
• sum – Integral image containing 32-bit unsigned integer values packed into CV_32SC1 .
• buffer – Optional buffer to avoid extra memory allocations. It is resized automatically.
• stream – Stream for the asynchronous version.

See also

integral()

cuda::sqrIntegral

Computes a squared integral image.

C++: void cuda::sqrIntegral(InputArray src, OutputArray sqsum, Stream& stream=Stream::Null())

C++: void cuda::sqrIntegral(InputArray src, OutputArray sqsum, GpuMat& buf, Stream& stream=Stream::Null())

Parameters:

• src – Source image. Only CV_8UC1 images are supported for now.
• sqsum – Squared integral image containing 64-bit unsigned integer values packed into CV_64FC1 .
• buf – Optional buffer to avoid extra memory allocations. It is resized automatically.
• stream – Stream for the asynchronous version.