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# Image Processing

## gpu::meanShiftFiltering

Performs mean-shift filtering for each point of the source image.

C++:`` void `gpu::``meanShiftFiltering`(const GpuMat& src, GpuMat& dst, int sp, int sr, TermCriteria criteria=TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 5, 1), Stream& stream=Stream::Null() )

  Parameters: | src – Source image. Only `CV_8UC4` images are supported for now. dst – Destination image containing the color of mapped points. It has the same size and type as `src` . sp – Spatial window radius. sr – Color window radius. criteria – Termination criteria. See `TermCriteria`.

It maps each point of the source image into another point. As a result, you have a new color and new position of each point.

## gpu::meanShiftProc

Performs a mean-shift procedure and stores information about processed points (their colors and positions) in two images.

C++:`` void `gpu::``meanShiftProc`(const GpuMat& src, GpuMat& dstr, GpuMat& dstsp, int sp, int sr, TermCriteria criteria=TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 5, 1), Stream& stream=Stream::Null() )

  Parameters: | src – Source image. Only `CV_8UC4` images are supported for now. dstr – Destination image containing the color of mapped points. The size and type is the same as `src` . dstsp – Destination image containing the position of mapped points. The size is the same as `src` size. The type is `CV_16SC2` . sp – Spatial window radius. sr – Color window radius. criteria – Termination criteria. See `TermCriteria`.

See also

`gpu::meanShiftFiltering()`

## gpu::meanShiftSegmentation

Performs a mean-shift segmentation of the source image and eliminates small segments.

C++:`` void `gpu::``meanShiftSegmentation`(const GpuMat& src, Mat& dst, int sp, int sr, int minsize, TermCriteria criteria=TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 5, 1))

  Parameters: | src – Source image. Only `CV_8UC4` images are supported for now. dst – Segmented image with the same size and type as `src` . sp – Spatial window radius. sr – Color window radius. minsize – Minimum segment size. Smaller segments are merged. criteria – Termination criteria. See `TermCriteria`.

## gpu::integral

Computes an integral image.

C++:`` void `gpu::``integral`(const GpuMat& src, GpuMat& sum, 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` . stream – Stream for the asynchronous version.

See also

`integral()`

## gpu::sqrIntegral

Computes a squared integral image.

C++:`` void `gpu::``sqrIntegral`(const GpuMat& src, GpuMat& sqsum, 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` . stream – Stream for the asynchronous version.

## gpu::columnSum

Computes a vertical (column) sum.

C++:`` void `gpu::``columnSum`(const GpuMat& src, GpuMat& sum)

  Parameters: | src – Source image. Only `CV_32FC1` images are supported for now. sum – Destination image of the `CV_32FC1` type.

## gpu::cornerHarris

Computes the Harris cornerness criteria at each image pixel.

C++:`` void `gpu::``cornerHarris`(const GpuMat& src, GpuMat& dst, int blockSize, int ksize, double k, int borderType=BORDER_REFLECT101)

  Parameters: | src – Source image. Only `CV_8UC1` and `CV_32FC1` images are supported for now. dst – Destination image containing cornerness values. It has the same size as `src` and `CV_32FC1` type. blockSize – Neighborhood size. ksize – Aperture parameter for the Sobel operator. k – Harris detector free parameter. borderType – Pixel extrapolation method. Only `BORDER_REFLECT101` and `BORDER_REPLICATE` are supported for now.

See also

`cornerHarris()`

## gpu::cornerMinEigenVal

Computes the minimum eigen value of a 2x2 derivative covariation matrix at each pixel (the cornerness criteria).

C++:`` void `gpu::``cornerMinEigenVal`(const GpuMat& src, GpuMat& dst, int blockSize, int ksize, int borderType=BORDER_REFLECT101)

C++:`` void `gpu::``cornerMinEigenVal`(const GpuMat& src, GpuMat& dst, GpuMat& Dx, GpuMat& Dy, int blockSize, int ksize, int borderType=BORDER_REFLECT101)

C++:`` void `gpu::``cornerMinEigenVal`(const GpuMat& src, GpuMat& dst, GpuMat& Dx, GpuMat& Dy, GpuMat& buf, int blockSize, int ksize, int borderType=BORDER_REFLECT101, Stream& stream=Stream::Null())

  Parameters: | src – Source image. Only `CV_8UC1` and `CV_32FC1` images are supported for now. dst – Destination image containing cornerness values. The size is the same. The type is `CV_32FC1` . blockSize – Neighborhood size. ksize – Aperture parameter for the Sobel operator. borderType – Pixel extrapolation method. Only `BORDER_REFLECT101` and `BORDER_REPLICATE` are supported for now.

See also

`cornerMinEigenVal()`

## gpu::mulSpectrums

Performs a per-element multiplication of two Fourier spectrums.

C++:`` void `gpu::``mulSpectrums`(const GpuMat& a, const GpuMat& b, GpuMat& c, int flags, bool conjB=false, Stream& stream=Stream::Null() )

  Parameters: | a – First spectrum. b – Second spectrum with the same size and type as `a` . c – Destination spectrum. flags – Mock parameter used for CPU/GPU interfaces similarity. conjB – Optional flag to specify if the second spectrum needs to be conjugated before the multiplication.

  Only full (not packed) `CV_32FC2` complex spectrums in the interleaved format are supported for now.

See also

`mulSpectrums()`

## gpu::mulAndScaleSpectrums

Performs a per-element multiplication of two Fourier spectrums and scales the result.

C++:`` void `gpu::``mulAndScaleSpectrums`(const GpuMat& a, const GpuMat& b, GpuMat& c, int flags, float scale, bool conjB=false, Stream& stream=Stream::Null() )

  Parameters: | a – First spectrum. b – Second spectrum with the same size and type as `a` . c – Destination spectrum. flags – Mock parameter used for CPU/GPU interfaces similarity. scale – Scale constant. conjB – Optional flag to specify if the second spectrum needs to be conjugated before the multiplication.

  Only full (not packed) `CV_32FC2` complex spectrums in the interleaved format are supported for now.

See also

`mulSpectrums()`

## gpu::dft

Performs a forward or inverse discrete Fourier transform (1D or 2D) of the floating point matrix.

C++:`` void `gpu::``dft`(const GpuMat& src, GpuMat& dst, Size dft_size, int flags=0, Stream& stream=Stream::Null() )

  Parameters: | src – Source matrix (real or complex). dst – Destination matrix (real or complex). dft_size – Size of a discrete Fourier transform. flags – Optional flags: DFT_ROWS transforms each individual row of the source matrix. DFT_SCALE scales the result: divide it by the number of elements in the transform (obtained from `dft_size` ). DFT_INVERSE inverts DFT. Use for complex-complex cases (real-complex and complex-real cases are always forward and inverse, respectively). DFT_REAL_OUTPUT specifies the output as real. The source matrix is the result of real-complex transform, so the destination matrix must be real.

Use to handle real matrices ( `CV32FC1` ) and complex matrices in the interleaved format ( `CV32FC2` ).

The source matrix should be continuous, otherwise reallocation and data copying is performed. The function chooses an operation mode depending on the flags, size, and channel count of the source matrix:

> - If the source matrix is complex and the output is not specified as real, the destination matrix is complex and has the `dft_size` size and `CV_32FC2` type. The destination matrix contains a full result of the DFT (forward or inverse).
> - If the source matrix is complex and the output is specified as real, the function assumes that its input is the result of the forward transform (see the next item). The destination matrix has the `dft_size` size and `CV_32FC1` type. It contains the result of the inverse DFT.
> - If the source matrix is real (its type is `CV_32FC1` ), forward DFT is performed. The result of the DFT is packed into complex ( `CV_32FC2` ) matrix. So, the width of the destination matrix is `dft_size.width / 2 + 1` . But if the source is a single column, the height is reduced instead of the width.

See also

`dft()`

## gpu::ConvolveBuf

struct `gpu::``ConvolveBuf`

Class providing a memory buffer for `gpu::convolve()` function, plus it allows to adjust some specific parameters.

    struct CV_EXPORTS ConvolveBuf
    {
        Size result_size;
        Size block_size;
        Size user_block_size;
        Size dft_size;
        int spect_len;

        GpuMat image_spect, templ_spect, result_spect;
        GpuMat image_block, templ_block, result_data;

        void create(Size image_size, Size templ_size);
        static Size estimateBlockSize(Size result_size, Size templ_size);
    };

You can use field user_block_size to set specific block size for `gpu::convolve()` function. If you leave its default value Size(0,0) then automatic estimation of block size will be used (which is optimized for speed). By varying user_block_size you can reduce memory requirements at the cost of speed.

## gpu::ConvolveBuf::create

C++:`` `gpu::ConvolveBuf::``create`(Size image_size, Size templ_size)

Constructs a buffer for `gpu::convolve()` function with respective arguments.

## gpu::convolve

Computes a convolution (or cross-correlation) of two images.

C++:`` void `gpu::``convolve`(const GpuMat& image, const GpuMat& templ, GpuMat& result, bool ccorr=false)

C++:`` void `gpu::``convolve`(const GpuMat& image, const GpuMat& templ, GpuMat& result, bool ccorr, ConvolveBuf& buf, Stream& stream=Stream::Null() )

  Parameters: | image – Source image. Only `CV_32FC1` images are supported for now. templ – Template image. The size is not greater than the `image` size. The type is the same as `image` . result – Result image. If `image` is W x H and `templ` is w x h, then `result` must be W-w+1 x H-h+1. ccorr – Flags to evaluate cross-correlation instead of convolution. buf – Optional buffer to avoid extra memory allocations and to adjust some specific parameters. See `gpu::ConvolveBuf`. stream – Stream for the asynchronous version.

See also

`gpu::filter2D()`

## gpu::MatchTemplateBuf

struct `gpu::``MatchTemplateBuf`

Class providing memory buffers for `gpu::matchTemplate()` function, plus it allows to adjust some specific parameters.

    struct CV_EXPORTS MatchTemplateBuf
    {
        Size user_block_size;
        GpuMat imagef, templf;
        std::vector<GpuMat> images;
        std::vector<GpuMat> image_sums;
        std::vector<GpuMat> image_sqsums;
    };

You can use field user_block_size to set specific block size for `gpu::matchTemplate()` function. If you leave its default value Size(0,0) then automatic estimation of block size will be used (which is optimized for speed). By varying user_block_size you can reduce memory requirements at the cost of speed.

## gpu::matchTemplate

Computes a proximity map for a raster template and an image where the template is searched for.

C++:`` void `gpu::``matchTemplate`(const GpuMat& image, const GpuMat& templ, GpuMat& result, int method, Stream& stream=Stream::Null())

C++:`` void `gpu::``matchTemplate`(const GpuMat& image, const GpuMat& templ, GpuMat& result, int method, MatchTemplateBuf& buf, Stream& stream=Stream::Null())

  Parameters: | image – Source image. `CV_32F` and `CV_8U` depth images (1..4 channels) are supported for now. templ – Template image with the size and type the same as `image` . result – Map containing comparison results ( `CV_32FC1` ). If `image` is W x H and `templ` is w x h, then `result` must be W-w+1 x H-h+1. method – Specifies the way to compare the template with the image. buf – Optional buffer to avoid extra memory allocations and to adjust some specific parameters. See `gpu::MatchTemplateBuf`. stream – Stream for the asynchronous version.

  The following methods are supported for the `CV_8U` depth images for now:

  - `CV_TM_SQDIFF`
  - `CV_TM_SQDIFF_NORMED`
  - `CV_TM_CCORR`
  - `CV_TM_CCORR_NORMED`
  - `CV_TM_CCOEFF`
  - `CV_TM_CCOEFF_NORMED`

  The following methods are supported for the `CV_32F` images for now:

  - `CV_TM_SQDIFF`
  - `CV_TM_CCORR`

See also

`matchTemplate()`

## gpu::remap

Applies a generic geometrical transformation to an image.

C++:`` void `gpu::``remap`(const GpuMat& src, GpuMat& dst, const GpuMat& xmap, const GpuMat& ymap, int interpolation, int borderMode=BORDER_CONSTANT, Scalar borderValue=Scalar(), Stream& stream=Stream::Null() )

  Parameters: | src – Source image. dst – Destination image with the size the same as `xmap` and the type the same as `src` . xmap – X values. Only `CV_32FC1` type is supported. ymap – Y values. Only `CV_32FC1` type is supported. interpolation – Interpolation method (see `resize()` ). `INTER_NEAREST` , `INTER_LINEAR` and `INTER_CUBIC` are supported for now. borderMode – Pixel extrapolation method (see `borderInterpolate()` ). `BORDER_REFLECT101` , `BORDER_REPLICATE` , `BORDER_CONSTANT` , `BORDER_REFLECT` and `BORDER_WRAP` are supported for now. borderValue – Value used in case of a constant border. By default, it is 0. stream – Stream for the asynchronous version.

The function transforms the source image using the specified map:

Values of pixels with non-integer coordinates are computed using the bilinear interpolation.

See also

`remap()`

## gpu::cvtColor

Converts an image from one color space to another.

C++:`` void `gpu::``cvtColor`(const GpuMat& src, GpuMat& dst, int code, int dcn=0, Stream& stream=Stream::Null())

  Parameters: | src – Source image with `CV_8U` , `CV_16U` , or `CV_32F` depth and 1, 3, or 4 channels. dst – Destination image with the same size and depth as `src` . code – Color space conversion code. For details, see `cvtColor()` . Conversion to/from Luv and Bayer color spaces is not supported. dcn – Number of channels in the destination image. If the parameter is 0, the number of the channels is derived automatically from `src` and the `code` . stream – Stream for the asynchronous version.

3-channel color spaces (like `HSV`, `XYZ`, and so on) can be stored in a 4-channel image for better performance.

See also

`cvtColor()`

## gpu::swapChannels

Exchanges the color channels of an image in-place.

C++:`` void `gpu::``swapChannels`(GpuMat& image, const int dstOrder[4], Stream& stream=Stream::Null())

  Parameters: | image – Source image. Supports only `CV_8UC4` type. dstOrder – Integer array describing how channel values are permutated. The n-th entry of the array contains the number of the channel that is stored in the n-th channel of the output image. E.g. Given an RGBA image, aDstOrder = [3,2,1,0] converts this to ABGR channel order. stream – Stream for the asynchronous version.

The methods support arbitrary permutations of the original channels, including replication.

## gpu::threshold

Applies a fixed-level threshold to each array element.

C++:`` double `gpu::``threshold`(const GpuMat& src, GpuMat& dst, double thresh, double maxval, int type, Stream& stream=Stream::Null())

  Parameters: | src – Source array (single-channel). dst – Destination array with the same size and type as `src` . thresh – Threshold value. maxval – Maximum value to use with `THRESH_BINARY` and `THRESH_BINARY_INV` threshold types. type – Threshold type. For details, see `threshold()` . The `THRESH_OTSU` threshold type is not supported. stream – Stream for the asynchronous version.

See also

`threshold()`

## gpu::resize

Resizes an image.

C++:`` void `gpu::``resize`(const GpuMat& src, GpuMat& dst, Size dsize, double fx=0, double fy=0, int interpolation=INTER_LINEAR, Stream& stream=Stream::Null())

  Parameters: | src – Source image. dst – Destination image with the same type as `src` . The size is `dsize` (when it is non-zero) or the size is computed from `src.size()` , `fx` , and `fy` . dsize – Destination image size. If it is zero, it is computed as: Either `dsize` or both `fx` and `fy` must be non-zero. fx – Scale factor along the horizontal axis. If it is zero, it is computed as: fy – Scale factor along the vertical axis. If it is zero, it is computed as: interpolation – Interpolation method. `INTER_NEAREST` , `INTER_LINEAR` and `INTER_CUBIC` are supported for now. stream – Stream for the asynchronous version.

See also

`resize()`

## gpu::warpAffine

Applies an affine transformation to an image.

C++:`` void `gpu::``warpAffine`(const GpuMat& src, GpuMat& dst, const Mat& M, Size dsize, int flags=INTER_LINEAR, int borderMode=BORDER_CONSTANT, Scalar borderValue=Scalar(), Stream& stream=Stream::Null() )

  Parameters: | src – Source image. `CV_8U` , `CV_16U` , `CV_32S` , or `CV_32F` depth and 1, 3, or 4 channels are supported. dst – Destination image with the same type as `src` . The size is `dsize` . M – 2x3 transformation matrix. dsize – Size of the destination image. flags – Combination of interpolation methods (see `resize()`) and the optional flag `WARP_INVERSE_MAP` specifying that `M` is an inverse transformation ( `dst=>src` ). Only `INTER_NEAREST` , `INTER_LINEAR` , and `INTER_CUBIC` interpolation methods are supported. stream – Stream for the asynchronous version.

See also

`warpAffine()`

## gpu::buildWarpAffineMaps

Builds transformation maps for affine transformation.

C++:`` void `gpu::``buildWarpAffineMaps`(const Mat& M, bool inverse, Size dsize, GpuMat& xmap, GpuMat& ymap, Stream& stream=Stream::Null())

  Parameters: | M – 2x3 transformation matrix. inverse – Flag specifying that `M` is an inverse transformation ( `dst=>src` ). dsize – Size of the destination image. xmap – X values with `CV_32FC1` type. ymap – Y values with `CV_32FC1` type. stream – Stream for the asynchronous version.

See also

`gpu::warpAffine()` , `gpu::remap()`

## gpu::warpPerspective

Applies a perspective transformation to an image.

C++:`` void `gpu::``warpPerspective`(const GpuMat& src, GpuMat& dst, const Mat& M, Size dsize, int flags=INTER_LINEAR, int borderMode=BORDER_CONSTANT, Scalar borderValue=Scalar(), Stream& stream=Stream::Null() )

  Parameters: | src – Source image. `CV_8U` , `CV_16U` , `CV_32S` , or `CV_32F` depth and 1, 3, or 4 channels are supported. dst – Destination image with the same type as `src` . The size is `dsize` . M – 3x3 transformation matrix. dsize – Size of the destination image. flags – Combination of interpolation methods (see `resize()` ) and the optional flag `WARP_INVERSE_MAP` specifying that `M` is the inverse transformation ( `dst => src` ). Only `INTER_NEAREST` , `INTER_LINEAR` , and `INTER_CUBIC` interpolation methods are supported. stream – Stream for the asynchronous version.

See also

`warpPerspective()`

## gpu::buildWarpPerspectiveMaps

Builds transformation maps for perspective transformation.

C++:`` void `gpu::``buildWarpAffineMaps`(const Mat& M, bool inverse, Size dsize, GpuMat& xmap, GpuMat& ymap, Stream& stream=Stream::Null())

  Parameters: | M – 3x3 transformation matrix. inverse – Flag specifying that `M` is an inverse transformation ( `dst=>src` ). dsize – Size of the destination image. xmap – X values with `CV_32FC1` type. ymap – Y values with `CV_32FC1` type. stream – Stream for the asynchronous version.

See also

`gpu::warpPerspective()` , `gpu::remap()`

## gpu::rotate

Rotates an image around the origin (0,0) and then shifts it.

C++:`` void `gpu::``rotate`(const GpuMat& src, GpuMat& dst, Size dsize, double angle, double xShift=0, double yShift=0, int interpolation=INTER_LINEAR, Stream& stream=Stream::Null())

  Parameters: | src – Source image. Supports 1, 3 or 4 channels images with `CV_8U` , `CV_16U` or `CV_32F` depth. dst – Destination image with the same type as `src` . The size is `dsize` . dsize – Size of the destination image. angle – Angle of rotation in degrees. xShift – Shift along the horizontal axis. yShift – Shift along the vertical axis. interpolation – Interpolation method. Only `INTER_NEAREST` , `INTER_LINEAR` , and `INTER_CUBIC` are supported. stream – Stream for the asynchronous version.

See also

`gpu::warpAffine()`

## gpu::copyMakeBorder

Forms a border around an image.

C++:`` void `gpu::``copyMakeBorder`(const GpuMat& src, GpuMat& dst, int top, int bottom, int left, int right, int borderType, const Scalar& value=Scalar(), Stream& stream=Stream::Null())

  Parameters: | src – Source image. `CV_8UC1` , `CV_8UC4` , `CV_32SC1` , and `CV_32FC1` types are supported. dst – Destination image with the same type as `src`. The size is `Size(src.cols+left+right, src.rows+top+bottom)` . top – bottom – left – right – Number of 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. borderType – Border type. See `borderInterpolate()` for details. `BORDER_REFLECT101` , `BORDER_REPLICATE` , `BORDER_CONSTANT` , `BORDER_REFLECT` and `BORDER_WRAP` are supported for now. value – Border value. stream – Stream for the asynchronous version.

See also

`copyMakeBorder()`

## gpu::rectStdDev

Computes a standard deviation of integral images.

C++:`` void `gpu::``rectStdDev`(const GpuMat& src, const GpuMat& sqr, GpuMat& dst, const 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.

## gpu::evenLevels

Computes levels with even distribution.

C++:`` void `gpu::``evenLevels`(GpuMat& levels, int nLevels, int lowerLevel, int upperLevel)

  Parameters: | levels – Destination array. `levels` has 1 row, `nLevels` columns, and the `CV_32SC1` type. nLevels – Number of computed levels. `nLevels` must be at least 2. lowerLevel – Lower boundary value of the lowest level. upperLevel – Upper boundary value of the greatest level.

## gpu::histEven

Calculates a histogram with evenly distributed bins.

C++:`` void `gpu::``histEven`(const GpuMat& src, GpuMat& hist, int histSize, int lowerLevel, int upperLevel, Stream& stream=Stream::Null())

C++:`` void `gpu::``histEven`(const GpuMat& src, GpuMat& hist, GpuMat& buf, int histSize, int lowerLevel, int upperLevel, Stream& stream=Stream::Null())

C++:`` void `gpu::``histEven`(const GpuMat& src, GpuMat hist[4], int histSize[4], int lowerLevel[4], int upperLevel[4], Stream& stream=Stream::Null() )

C++:`` void `gpu::``histEven`(const GpuMat& src, GpuMat hist[4], GpuMat& buf, int histSize[4], int lowerLevel[4], int upperLevel[4], Stream& stream=Stream::Null() )

  Parameters: | src – Source image. `CV_8U`, `CV_16U`, or `CV_16S` depth and 1 or 4 channels are supported. For a four-channel image, all channels are processed separately. hist – Destination histogram with one row, `histSize` columns, and the `CV_32S` type. histSize – Size of the histogram. lowerLevel – Lower boundary of lowest-level bin. upperLevel – Upper boundary of highest-level bin. buf – Optional buffer to avoid extra memory allocations (for many calls with the same sizes). stream – Stream for the asynchronous version.

## gpu::histRange

Calculates a histogram with bins determined by the `levels` array.

C++:`` void `gpu::``histRange`(const GpuMat& src, GpuMat& hist, const GpuMat& levels, Stream& stream=Stream::Null())

C++:`` void `gpu::``histRange`(const GpuMat& src, GpuMat& hist, const GpuMat& levels, GpuMat& buf, Stream& stream=Stream::Null())

  Parameters: | src – Source image. `CV_8U` , `CV_16U` , or `CV_16S` depth and 1 or 4 channels are supported. For a four-channel image, all channels are processed separately. hist – Destination histogram with one row, `(levels.cols-1)` columns, and the `CV_32SC1` type. levels – Number of levels in the histogram. buf – Optional buffer to avoid extra memory allocations (for many calls with the same sizes). stream – Stream for the asynchronous version.

## gpu::calcHist

Calculates histogram for one channel 8-bit image.

C++:`` void `gpu::``calcHist`(const GpuMat& src, GpuMat& hist, Stream& stream=Stream::Null())

C++:`` void `gpu::``calcHist`(const GpuMat& src, GpuMat& hist, GpuMat& buf, Stream& stream=Stream::Null())

  Parameters: | src – Source image. hist – Destination histogram with one row, 256 columns, and the `CV_32SC1` type. buf – Optional buffer to avoid extra memory allocations (for many calls with the same sizes). stream – Stream for the asynchronous version.

## gpu::equalizeHist

Equalizes the histogram of a grayscale image.

C++:`` void `gpu::``equalizeHist`(const GpuMat& src, GpuMat& dst, Stream& stream=Stream::Null())

C++:`` void `gpu::``equalizeHist`(const GpuMat& src, GpuMat& dst, GpuMat& hist, Stream& stream=Stream::Null())

C++:`` void `gpu::``equalizeHist`(const GpuMat& src, GpuMat& dst, GpuMat& hist, GpuMat& buf, Stream& stream=Stream::Null())

  Parameters: | src – Source image. dst – Destination image. hist – Destination histogram with one row, 256 columns, and the `CV_32SC1` type. buf – Optional buffer to avoid extra memory allocations (for many calls with the same sizes). stream – Stream for the asynchronous version.

See also

`equalizeHist()`

## gpu::buildWarpPlaneMaps

Builds plane warping maps.

C++:`` void `gpu::``buildWarpPlaneMaps`(Size src_size, Rect dst_roi, const Mat& K, const Mat& R, const Mat& T, float scale, GpuMat& map_x, GpuMat& map_y, Stream& stream=Stream::Null() )

  Parameters: | stream – Stream for the asynchronous version.

## gpu::buildWarpCylindricalMaps

Builds cylindrical warping maps.

C++:`` void `gpu::``buildWarpCylindricalMaps`(Size src_size, Rect dst_roi, const Mat& K, const Mat& R, float scale, GpuMat& map_x, GpuMat& map_y, Stream& stream=Stream::Null() )

  Parameters: | stream – Stream for the asynchronous version.

## gpu::buildWarpSphericalMaps

Builds spherical warping maps.

C++:`` void `gpu::``buildWarpSphericalMaps`(Size src_size, Rect dst_roi, const Mat& K, const Mat& R, float scale, GpuMat& map_x, GpuMat& map_y, Stream& stream=Stream::Null() )

  Parameters: | stream – Stream for the asynchronous version.

## gpu::pyrDown

Smoothes an image and downsamples it.

C++:`` void `gpu::``pyrDown`(const GpuMat& src, GpuMat& dst, Stream& stream=Stream::Null())

  Parameters: | src – Source image. dst – Destination image. Will have `Size((src.cols+1)/2, (src.rows+1)/2)` size and the same type as `src` . stream – Stream for the asynchronous version.

See also

`pyrDown()`

## gpu::pyrUp

Upsamples an image and then smoothes it.

C++:`` void `gpu::``pyrUp`(const GpuMat& src, GpuMat& dst, Stream& stream=Stream::Null())

  Parameters: | src – Source image. dst – Destination image. Will have `Size(src.cols*2, src.rows*2)` size and the same type as `src` . stream – Stream for the asynchronous version.

See also

`pyrUp()`

## gpu::blendLinear

Performs linear blending of two images.

C++:`` void `gpu::``blendLinear`(const GpuMat& img1, const GpuMat& img2, const GpuMat& weights1, const GpuMat& weights2, GpuMat& result, Stream& stream=Stream::Null())

  Parameters: | img1 – First image. Supports only `CV_8U` and `CV_32F` depth. img2 – Second image. Must have the same size and the same type as `img1` . weights1 – Weights for first image. Must have tha same size as `img1` . Supports only `CV_32F` type. weights2 – Weights for second image. Must have tha same size as `img2` . Supports only `CV_32F` type. result – Destination image. stream – Stream for the asynchronous version.

## gpu::bilateralFilter

Performs bilateral filtering of passed image

C++:`` void `gpu::``bilateralFilter`(const GpuMat& src, GpuMat& dst, int kernel_size, float sigma_color, float sigma_spatial, int borderMode=BORDER_DEFAULT, Stream& stream=Stream::Null() )

  Parameters: | src – Source image. Supports only (channles != 2 && depth() != CV_8S && depth() != CV_32S && depth() != CV_64F). dst – Destination imagwe. kernel_size – Kernel window size. sigma_color – Filter sigma in the color space. sigma_spatial – Filter sigma in the coordinate space. borderMode – Border type. See `borderInterpolate()` for details. `BORDER_REFLECT101` , `BORDER_REPLICATE` , `BORDER_CONSTANT` , `BORDER_REFLECT` and `BORDER_WRAP` are supported for now. stream – Stream for the asynchronous version.

See also

`bilateralFilter()`,

## gpu::nonLocalMeans

Performs pure non local means denoising without any simplification, and thus it is not fast.

C++:`` void `gpu::``nonLocalMeans`(const GpuMat& src, GpuMat& dst, float h, int search_window=21, int block_size=7, int borderMode=BORDER_DEFAULT, Stream& s=Stream::Null())

  Parameters: | src – Source image. Supports only CV_8UC1, CV_8UC2 and CV_8UC3. dst – Destination image. h – Filter sigma regulating filter strength for color. search_window – Size of search window. block_size – Size of block used for computing weights. borderMode – Border type. See `borderInterpolate()` for details. `BORDER_REFLECT101` , `BORDER_REPLICATE` , `BORDER_CONSTANT` , `BORDER_REFLECT` and `BORDER_WRAP` are supported for now. stream – Stream for the asynchronous version.

See also

`fastNlMeansDenoising()`

## gpu::FastNonLocalMeansDenoising

class `gpu::``FastNonLocalMeansDenoising`

      class FastNonLocalMeansDenoising
      {
      public:
          //! Simple method, recommended for grayscale images (though it supports multichannel images)
          void simpleMethod(const GpuMat& src, GpuMat& dst, float h, int search_window = 21, int block_size = 7, Stream& s = Stream::Null())
          //! Processes luminance and color components separatelly
          void labMethod(const GpuMat& src, GpuMat& dst, float h_luminance, float h_color, int search_window = 21, int block_size = 7, Stream& s = Stream::Null())
      };

The class implements fast approximate Non Local Means Denoising algorithm.

## gpu::FastNonLocalMeansDenoising::simpleMethod()

Perform image denoising using Non-local Means Denoising algorithm http://www.ipol.im/pub/algo/bcm_non_local_means_denoising with several computational optimizations. Noise expected to be a gaussian white noise

C++:`` void `gpu::FastNonLocalMeansDenoising::``simpleMethod`(const GpuMat& src, GpuMat& dst, float h, int search_window=21, int block_size=7, Stream& s=Stream::Null())

  Parameters: | src – Input 8-bit 1-channel, 2-channel or 3-channel image. dst – Output image with the same size and type as `src` . h – Parameter regulating filter strength. Big h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise search_window – Size in pixels of the window that is used to compute weighted average for given pixel. Should be odd. Affect performance linearly: greater search_window - greater denoising time. Recommended value 21 pixels block_size – Size in pixels of the template patch that is used to compute weights. Should be odd. Recommended value 7 pixels stream – Stream for the asynchronous invocations.

This function expected to be applied to grayscale images. For colored images look at `FastNonLocalMeansDenoising::labMethod`.

See also

`fastNlMeansDenoising()`

## gpu::FastNonLocalMeansDenoising::labMethod()

Modification of `FastNonLocalMeansDenoising::simpleMethod` for color images

C++:`` void `gpu::FastNonLocalMeansDenoising::``labMethod`(const GpuMat& src, GpuMat& dst, float h_luminance, float h_color, int search_window=21, int block_size=7, Stream& s=Stream::Null())

  Parameters: | src – Input 8-bit 3-channel image. dst – Output image with the same size and type as `src` . h_luminance – Parameter regulating filter strength. Big h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise float – The same as h but for color components. For most images value equals 10 will be enought to remove colored noise and do not distort colors search_window – Size in pixels of the window that is used to compute weighted average for given pixel. Should be odd. Affect performance linearly: greater search_window - greater denoising time. Recommended value 21 pixels block_size – Size in pixels of the template patch that is used to compute weights. Should be odd. Recommended value 7 pixels stream – Stream for the asynchronous invocations.

The function converts image to CIELAB colorspace and then separately denoise L and AB components with given h parameters using `FastNonLocalMeansDenoising::simpleMethod` function.

See also

`fastNlMeansDenoisingColored()`

## gpu::alphaComp

Composites two images using alpha opacity values contained in each image.

C++:`` void `gpu::``alphaComp`(const GpuMat& img1, const GpuMat& img2, GpuMat& dst, int alpha_op, Stream& stream=Stream::Null())

  Parameters: | img1 – First image. Supports `CV_8UC4` , `CV_16UC4` , `CV_32SC4` and `CV_32FC4` types. img2 – Second image. Must have the same size and the same type as `img1` . dst – Destination image. alpha_op – Flag specifying the alpha-blending operation: ALPHA_OVER ALPHA_IN ALPHA_OUT ALPHA_ATOP ALPHA_XOR ALPHA_PLUS ALPHA_OVER_PREMUL ALPHA_IN_PREMUL ALPHA_OUT_PREMUL ALPHA_ATOP_PREMUL ALPHA_XOR_PREMUL ALPHA_PLUS_PREMUL ALPHA_PREMUL stream – Stream for the asynchronous version.

## gpu::Canny

Finds edges in an image using the [Canny86] algorithm.

C++:`` void `gpu::``Canny`(const GpuMat& image, GpuMat& edges, double low_thresh, double high_thresh, int apperture_size=3, bool L2gradient=false)

C++:`` void `gpu::``Canny`(const GpuMat& image, CannyBuf& buf, GpuMat& edges, double low_thresh, double high_thresh, int apperture_size=3, bool L2gradient=false)

C++:`` void `gpu::``Canny`(const GpuMat& dx, const GpuMat& dy, GpuMat& edges, double low_thresh, double high_thresh, bool L2gradient=false)

C++:`` void `gpu::``Canny`(const GpuMat& dx, const GpuMat& dy, CannyBuf& buf, GpuMat& edges, double low_thresh, double high_thresh, bool L2gradient=false)

  Parameters: | image – Single-channel 8-bit input image. dx – First derivative of image in the vertical direction. Support only `CV_32S` type. dy – First derivative of image in the horizontal direction. Support only `CV_32S` type. edges – Output edge map. It has the same size and type as `image` . low_thresh – First threshold for the hysteresis procedure. high_thresh – Second threshold for the hysteresis procedure. apperture_size – Aperture size for the `Sobel()` operator. L2gradient – Flag indicating whether a more accurate norm should be used to compute the image gradient magnitude ( `L2gradient=true` ), or a faster default norm is enough ( `L2gradient=false` ). buf – Optional buffer to avoid extra memory allocations (for many calls with the same sizes).

See also

`Canny()`

## gpu::HoughLines

Finds lines in a binary image using the classical Hough transform.

C++:`` void `gpu::``HoughLines`(const GpuMat& src, GpuMat& lines, float rho, float theta, int threshold, bool doSort=false, int maxLines=4096)

C++:`` void `gpu::``HoughLines`(const GpuMat& src, GpuMat& lines, HoughLinesBuf& buf, float rho, float theta, int threshold, bool doSort=false, int maxLines=4096)

  Parameters: | src – 8-bit, single-channel binary source image. lines – Output vector of lines. Each line is represented by a two-element vector . is the distance from the coordinate origin (top-left corner of the image). is the line rotation angle in radians ( ). rho – Distance resolution of the accumulator in pixels. theta – Angle resolution of the accumulator in radians. threshold – Accumulator threshold parameter. Only those lines are returned that get enough votes ( ). doSort – Performs lines sort by votes. maxLines – Maximum number of output lines. buf – Optional buffer to avoid extra memory allocations (for many calls with the same sizes).

See also

`HoughLines()`

## gpu::HoughLinesDownload

Downloads results from `gpu::HoughLines()` to host memory.

C++:`` void `gpu::``HoughLinesDownload`(const GpuMat& d_lines, OutputArray h_lines, OutputArray h_votes=noArray())

  Parameters: | d_lines – Result of `gpu::HoughLines()` . h_lines – Output host array. h_votes – Optional output array for line’s votes.

See also

`gpu::HoughLines()`

## gpu::HoughCircles

Finds circles in a grayscale image using the Hough transform.

C++:`` void `gpu::``HoughCircles`(const GpuMat& src, GpuMat& circles, int method, float dp, float minDist, int cannyThreshold, int votesThreshold, int minRadius, int maxRadius, int maxCircles=4096)

C++:`` void `gpu::``HoughCircles`(const GpuMat& src, GpuMat& circles, HoughCirclesBuf& buf, int method, float dp, float minDist, int cannyThreshold, int votesThreshold, int minRadius, int maxRadius, int maxCircles=4096)

  Parameters: | src – 8-bit, single-channel grayscale input image. circles – Output vector of found circles. Each vector is encoded as a 3-element floating-point vector . method – Detection method to use. Currently, the only implemented method is `CV_HOUGH_GRADIENT` , which is basically 21HT , described in [Yuen90]. dp – Inverse ratio of the accumulator resolution to the image resolution. For example, if `dp=1` , the accumulator has the same resolution as the input image. If `dp=2` , the accumulator has half as big width and height. minDist – Minimum distance between the centers of the detected circles. If the parameter is too small, multiple neighbor circles may be falsely detected in addition to a true one. If it is too large, some circles may be missed. cannyThreshold – The higher threshold of the two passed to the `gpu::Canny()` edge detector (the lower one is twice smaller). votesThreshold – The accumulator threshold for the circle centers at the detection stage. The smaller it is, the more false circles may be detected. minRadius – Minimum circle radius. maxRadius – Maximum circle radius. maxCircles – Maximum number of output circles. buf – Optional buffer to avoid extra memory allocations (for many calls with the same sizes).

See also

`HoughCircles()`

## gpu::HoughCirclesDownload

Downloads results from `gpu::HoughCircles()` to host memory.

C++:`` void `gpu::``HoughCirclesDownload`(const GpuMat& d_circles, OutputArray h_circles)

  Parameters: | d_circles – Result of `gpu::HoughCircles()` . h_circles – Output host array.

See also

`gpu::HoughCircles()`

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