View Source Evision.CUDA (Evision v0.1.38)
Summary
Functions
Variant 1:
Computes an absolute value of each matrix element.
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Computes an absolute value of each matrix element.
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Computes per-element absolute difference of two matrices (or of a matrix and scalar).
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Computes per-element absolute difference of two matrices (or of a matrix and scalar).
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Returns the sum of absolute values for matrix elements.
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Returns the sum of absolute values for matrix elements.
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Computes a matrix-matrix or matrix-scalar sum.
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Computes a matrix-matrix or matrix-scalar sum.
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Computes the weighted sum of two arrays.
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Computes the weighted sum of two arrays.
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Composites two images using alpha opacity values contained in each image.
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Composites two images using alpha opacity values contained in each image.
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Performs bilateral filtering of passed image
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Performs bilateral filtering of passed image
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Performs linear blending of two images.
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Performs linear blending of two images.
buildWarpAffineMaps
buildWarpAffineMaps
buildWarpPerspectiveMaps
buildWarpPerspectiveMaps
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calcAbsSum
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calcAbsSum
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Calculates histogram for one channel 8-bit image.
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Calculates histogram for one channel 8-bit image confined in given mask.
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Calculates histogram for one channel 8-bit image confined in given mask.
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calcNorm
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calcNorm
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calcNormDiff
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calcNormDiff
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calcSqrSum
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calcSqrSum
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calcSum
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calcSum
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Converts Cartesian coordinates into polar.
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Converts Cartesian coordinates into polar.
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Compares elements of two matrices (or of a matrix and scalar).
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Compares elements of two matrices (or of a matrix and scalar).
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connectedComponents
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connectedComponents
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Computes the Connected Components Labeled image of a binary image.
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Computes the Connected Components Labeled image of a binary image.
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Forms a border around an image.
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Forms a border around an image.
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countNonZero
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countNonZero
Creates MOG2 Background Subtractor
Creates MOG2 Background Subtractor
Creates mixture-of-gaussian background subtractor
Creates mixture-of-gaussian background subtractor
Creates a normalized 2D box filter.
Creates a normalized 2D box filter.
Creates the maximum filter.
Creates the maximum filter.
Creates the minimum filter.
Creates the minimum filter.
Creates implementation for cuda::CannyEdgeDetector .
Creates implementation for cuda::CannyEdgeDetector .
Creates implementation for cuda::CLAHE .
Creates implementation for cuda::CLAHE .
Creates a vertical 1D box filter.
Creates a vertical 1D box filter.
Creates a continuous matrix.
Creates a continuous matrix.
Creates implementation for cuda::Convolution .
Creates implementation for cuda::Convolution .
Creates a generalized Deriv operator.
Creates a generalized Deriv operator.
Creates implementation for cuda::DFT.
Creates DisparityBilateralFilter object.
Creates DisparityBilateralFilter object.
Creates a Gaussian filter.
Creates a Gaussian filter.
Creates implementation for generalized hough transform from @cite Ballard1981 .
Creates implementation for generalized hough transform from @cite Guil1999 .
Creates implementation for cuda::CornersDetector .
Creates implementation for cuda::CornersDetector .
createGpuMatFromCudaMemory
Variant 1:
Bindings overload to create a GpuMat from existing GPU memory.
Bindings overload to create a GpuMat from existing GPU memory.
Creates implementation for Harris cornerness criteria.
Creates implementation for Harris cornerness criteria.
Creates implementation for cuda::HoughCirclesDetector .
Creates implementation for cuda::HoughCirclesDetector .
Creates implementation for cuda::HoughLinesDetector .
Creates implementation for cuda::HoughLinesDetector .
Creates implementation for cuda::HoughSegmentDetector .
Creates implementation for cuda::HoughSegmentDetector .
Creates a Laplacian operator.
Creates a Laplacian operator.
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Creates a non-separable linear 2D filter.
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Creates a non-separable linear 2D filter.
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Creates implementation for cuda::LookUpTable .
Performs median filtering for each point of the source image.
Performs median filtering for each point of the source image.
Creates implementation for the minimum eigen value of a 2x2 derivative covariation matrix (the cornerness criteria).
Creates implementation for the minimum eigen value of a 2x2 derivative covariation matrix (the cornerness criteria).
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Creates a 2D morphological filter.
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Creates a 2D morphological filter.
Creates a horizontal 1D box filter.
Creates a horizontal 1D box filter.
Creates a vertical or horizontal Scharr operator.
Creates a vertical or horizontal Scharr operator.
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Creates a separable linear filter.
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Creates a separable linear filter.
Creates a Sobel operator.
Creates a Sobel operator.
Creates StereoBeliefPropagation object.
Creates StereoBeliefPropagation object.
Creates StereoBM object.
Creates StereoBM object.
Creates StereoConstantSpaceBP object.
Creates StereoConstantSpaceBP object.
Creates StereoSGM object.
Creates StereoSGM object.
Creates implementation for cuda::TemplateMatching .
Creates implementation for cuda::TemplateMatching .
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Converts an image from one color space to another.
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Converts an image from one color space to another.
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Converts an image from Bayer pattern to RGB or grayscale.
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Converts an image from Bayer pattern to RGB or grayscale.
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Performs a forward or inverse discrete Fourier transform (1D or 2D) of the floating point matrix.
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Performs a forward or inverse discrete Fourier transform (1D or 2D) of the floating point matrix.
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Computes a matrix-matrix or matrix-scalar division.
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Computes a matrix-matrix or matrix-scalar division.
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Colors a disparity image.
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Colors a disparity image.
Ensures that the size of a matrix is big enough and the matrix has a proper type.
Ensures that the size of a matrix is big enough and the matrix has a proper type.
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Equalizes the histogram of a grayscale image.
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Equalizes the histogram of a grayscale image.
Computes levels with even distribution.
Computes levels with even distribution.
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Computes an exponent of each matrix element.
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Computes an exponent of each matrix element.
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
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
Modification of fastNlMeansDenoising function for colored images
Modification of fastNlMeansDenoising function for colored images
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findMinMax
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findMinMax
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findMinMaxLoc
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findMinMaxLoc
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Flips a 2D matrix around vertical, horizontal, or both axes.
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Flips a 2D matrix around vertical, horizontal, or both axes.
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Routines for correcting image color gamma.
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Routines for correcting image color gamma.
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Performs generalized matrix multiplication.
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Performs generalized matrix multiplication.
Returns the number of installed CUDA-enabled devices.
Returns the current device index set by cuda::setDevice or initialized by default.
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Calculates a histogram with evenly distributed bins.
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Calculates a histogram with evenly distributed bins.
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histEven
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Calculates a histogram with bins determined by the levels array.
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Calculates a histogram with bins determined by the levels array.
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histRange
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Checks if array elements lie between two scalars.
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Checks if array elements lie between two scalars.
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Computes an integral image.
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Computes an integral image.
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Computes a natural logarithm of absolute value of each matrix element.
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Computes a natural logarithm of absolute value of each matrix element.
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Performs pixel by pixel right left of an image by a constant value.
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Performs pixel by pixel right left of an image by a constant value.
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Computes magnitudes of complex matrix elements.
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magnitude
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magnitude
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Computes squared magnitudes of complex matrix elements.
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magnitudeSqr
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magnitudeSqr
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Computes the per-element maximum of two matrices (or a matrix and a scalar).
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Computes the per-element maximum of two matrices (or a matrix and a scalar).
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Performs mean-shift filtering for each point of the source image.
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Performs mean-shift filtering for each point of the source image.
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Performs a mean-shift procedure and stores information about processed points (their colors and positions) in two images.
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Performs a mean-shift procedure and stores information about processed points (their colors and positions) in two images.
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Performs a mean-shift segmentation of the source image and eliminates small segments.
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Performs a mean-shift segmentation of the source image and eliminates small segments.
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meanStdDev
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meanStdDev
merge
merge
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Computes the per-element minimum of two matrices (or a matrix and a scalar).
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Computes the per-element minimum of two matrices (or a matrix and a scalar).
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Finds global minimum and maximum matrix elements and returns their values.
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Finds global minimum and maximum matrix elements and returns their values.
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Finds global minimum and maximum matrix elements and returns their values with locations.
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Finds global minimum and maximum matrix elements and returns their values with locations.
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Calculates all of the moments up to the 3rd order of a rasterized shape.
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Calculates all of the moments up to the 3rd order of a rasterized shape.
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Performs a per-element multiplication of two Fourier spectrums and scales the result.
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Performs a per-element multiplication of two Fourier spectrums and scales the result.
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Performs a per-element multiplication of two Fourier spectrums.
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Performs a per-element multiplication of two Fourier spectrums.
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Computes a matrix-matrix or matrix-scalar per-element product.
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Computes a matrix-matrix or matrix-scalar per-element product.
Performs pure non local means denoising without any simplification, and thus it is not fast.
Performs pure non local means denoising without any simplification, and thus it is not fast.
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Returns the difference of two matrices.
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Returns the difference of two matrices.
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Normalizes the norm or value range of an array.
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Normalizes the norm or value range of an array.
Returns the number of image moments less than or equal to the largest image moments \a order.
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Computes polar angles of complex matrix elements.
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Computes polar angles of complex matrix elements.
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Converts polar coordinates into Cartesian.
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Converts polar coordinates into Cartesian.
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Raises every matrix element to a power.
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Raises every matrix element to a power.
printCudaDeviceInfo
printShortCudaDeviceInfo
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Smoothes an image and downsamples it.
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Smoothes an image and downsamples it.
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Upsamples an image and then smoothes it.
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Upsamples an image and then smoothes it.
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Computes a standard deviation of integral images.
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Computes a standard deviation of integral images.
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Reduces a matrix to a vector.
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Reduces a matrix to a vector.
Page-locks the memory of matrix and maps it for the device(s).
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Applies a generic geometrical transformation to an image.
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Applies a generic geometrical transformation to an image.
Reprojects a disparity image to 3D space.
Reprojects a disparity image to 3D space.
Explicitly destroys and cleans up all resources associated with the current device in the current process.
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Resizes an image.
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Resizes an image.
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Rotates an image around the origin (0,0) and then shifts it.
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Rotates an image around the origin (0,0) and then shifts it.
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Performs pixel by pixel right shift of an image by a constant value.
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Performs pixel by pixel right shift of an image by a constant value.
setBufferPoolConfig
setBufferPoolUsage
Sets a device and initializes it for the current thread.
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Calculates all of the spatial moments up to the 3rd order of a rasterized shape.
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Calculates all of the spatial moments up to the 3rd order of a rasterized shape.
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split
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split
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Computes a square value of each matrix element.
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Computes a square value of each matrix element.
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Computes a squared integral image.
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Computes a squared integral image.
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Returns the squared sum of matrix elements.
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Returns the squared sum of matrix elements.
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Computes a square root of each matrix element.
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Computes a square root of each matrix element.
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Computes a matrix-matrix or matrix-scalar difference.
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Computes a matrix-matrix or matrix-scalar difference.
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Returns the sum of matrix elements.
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Returns the sum of matrix elements.
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Applies a fixed-level threshold to each array element.
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Applies a fixed-level threshold to each array element.
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Transposes a matrix.
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Transposes a matrix.
Unmaps the memory of matrix and makes it pageable again.
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warpAffine
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warpAffine
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warpPerspective
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warpPerspective
Bindings overload to create a Stream object from the address stored in an existing CUDA Runtime API stream pointer (cudaStream_t).
Types
@type t() :: %Evision.CUDA{ref: reference()}
Type that represents an CUDA struct.
ref.
reference()The underlying erlang resource variable.
Functions
@spec abs(Evision.Mat.maybe_mat_in()) :: Evision.Mat.t() | {:error, String.t()}
@spec abs(Evision.CUDA.GpuMat.t()) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Computes an absolute value of each matrix element.
Positional Arguments
src:
Evision.Mat.t().Source matrix.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Destination matrix with the same size and type as src .
@sa abs
Python prototype (for reference only):
abs(src[, dst[, stream]]) -> dstVariant 2:
Computes an absolute value of each matrix element.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Source matrix.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Destination matrix with the same size and type as src .
@sa abs
Python prototype (for reference only):
abs(src[, dst[, stream]]) -> dst@spec abs(Evision.Mat.maybe_mat_in(), [{atom(), term()}, ...] | nil) :: Evision.Mat.t() | {:error, String.t()}
@spec abs(Evision.CUDA.GpuMat.t(), [{atom(), term()}, ...] | nil) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Computes an absolute value of each matrix element.
Positional Arguments
src:
Evision.Mat.t().Source matrix.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Destination matrix with the same size and type as src .
@sa abs
Python prototype (for reference only):
abs(src[, dst[, stream]]) -> dstVariant 2:
Computes an absolute value of each matrix element.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Source matrix.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Destination matrix with the same size and type as src .
@sa abs
Python prototype (for reference only):
abs(src[, dst[, stream]]) -> dst@spec absdiff(Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in()) :: Evision.Mat.t() | {:error, String.t()}
@spec absdiff(Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t()) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Computes per-element absolute difference of two matrices (or of a matrix and scalar).
Positional Arguments
src1:
Evision.Mat.t().First source matrix or scalar.
src2:
Evision.Mat.t().Second source matrix or scalar.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Destination matrix that has the same size and type as the input array(s).
@sa absdiff
Python prototype (for reference only):
absdiff(src1, src2[, dst[, stream]]) -> dstVariant 2:
Computes per-element absolute difference of two matrices (or of a matrix and scalar).
Positional Arguments
src1:
Evision.CUDA.GpuMat.t().First source matrix or scalar.
src2:
Evision.CUDA.GpuMat.t().Second source matrix or scalar.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Destination matrix that has the same size and type as the input array(s).
@sa absdiff
Python prototype (for reference only):
absdiff(src1, src2[, dst[, stream]]) -> dst@spec absdiff( Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in(), [{atom(), term()}, ...] | nil ) :: Evision.Mat.t() | {:error, String.t()}
@spec absdiff( Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t(), [{atom(), term()}, ...] | nil ) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Computes per-element absolute difference of two matrices (or of a matrix and scalar).
Positional Arguments
src1:
Evision.Mat.t().First source matrix or scalar.
src2:
Evision.Mat.t().Second source matrix or scalar.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Destination matrix that has the same size and type as the input array(s).
@sa absdiff
Python prototype (for reference only):
absdiff(src1, src2[, dst[, stream]]) -> dstVariant 2:
Computes per-element absolute difference of two matrices (or of a matrix and scalar).
Positional Arguments
src1:
Evision.CUDA.GpuMat.t().First source matrix or scalar.
src2:
Evision.CUDA.GpuMat.t().Second source matrix or scalar.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Destination matrix that has the same size and type as the input array(s).
@sa absdiff
Python prototype (for reference only):
absdiff(src1, src2[, dst[, stream]]) -> dst@spec absSum(Evision.Mat.maybe_mat_in()) :: {number()} | {number(), number()} | {number(), number(), number()} | {number(), number(), number(), number()} | {:error, String.t()}
@spec absSum(Evision.CUDA.GpuMat.t()) :: {number()} | {number(), number()} | {number(), number(), number()} | {number(), number(), number(), number()} | {:error, String.t()}
Variant 1:
Returns the sum of absolute values for matrix elements.
Positional Arguments
src:
Evision.Mat.t().Source image of any depth except for CV_64F .
Keyword Arguments
mask:
Evision.Mat.t().optional operation mask; it must have the same size as src1 and CV_8UC1 type.
Return
- retval:
Scalar
Python prototype (for reference only):
absSum(src[, mask]) -> retvalVariant 2:
Returns the sum of absolute values for matrix elements.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Source image of any depth except for CV_64F .
Keyword Arguments
mask:
Evision.CUDA.GpuMat.t().optional operation mask; it must have the same size as src1 and CV_8UC1 type.
Return
- retval:
Scalar
Python prototype (for reference only):
absSum(src[, mask]) -> retval@spec absSum(Evision.Mat.maybe_mat_in(), [{atom(), term()}, ...] | nil) :: {number()} | {number(), number()} | {number(), number(), number()} | {number(), number(), number(), number()} | {:error, String.t()}
@spec absSum(Evision.CUDA.GpuMat.t(), [{atom(), term()}, ...] | nil) :: {number()} | {number(), number()} | {number(), number(), number()} | {number(), number(), number(), number()} | {:error, String.t()}
Variant 1:
Returns the sum of absolute values for matrix elements.
Positional Arguments
src:
Evision.Mat.t().Source image of any depth except for CV_64F .
Keyword Arguments
mask:
Evision.Mat.t().optional operation mask; it must have the same size as src1 and CV_8UC1 type.
Return
- retval:
Scalar
Python prototype (for reference only):
absSum(src[, mask]) -> retvalVariant 2:
Returns the sum of absolute values for matrix elements.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Source image of any depth except for CV_64F .
Keyword Arguments
mask:
Evision.CUDA.GpuMat.t().optional operation mask; it must have the same size as src1 and CV_8UC1 type.
Return
- retval:
Scalar
Python prototype (for reference only):
absSum(src[, mask]) -> retval@spec add(Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in()) :: Evision.Mat.t() | {:error, String.t()}
@spec add(Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t()) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Computes a matrix-matrix or matrix-scalar sum.
Positional Arguments
src1:
Evision.Mat.t().First source matrix or scalar.
src2:
Evision.Mat.t().Second source matrix or scalar. Matrix should have the same size and type as src1 .
Keyword Arguments
mask:
Evision.Mat.t().Optional operation mask, 8-bit single channel array, that specifies elements of the destination array to be changed. The mask can be used only with single channel images.
dtype:
int.Optional depth of the output array.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Destination matrix that has the same size and number of channels as the input array(s). The depth is defined by dtype or src1 depth.
@sa add
Python prototype (for reference only):
add(src1, src2[, dst[, mask[, dtype[, stream]]]]) -> dstVariant 2:
Computes a matrix-matrix or matrix-scalar sum.
Positional Arguments
src1:
Evision.CUDA.GpuMat.t().First source matrix or scalar.
src2:
Evision.CUDA.GpuMat.t().Second source matrix or scalar. Matrix should have the same size and type as src1 .
Keyword Arguments
mask:
Evision.CUDA.GpuMat.t().Optional operation mask, 8-bit single channel array, that specifies elements of the destination array to be changed. The mask can be used only with single channel images.
dtype:
int.Optional depth of the output array.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Destination matrix that has the same size and number of channels as the input array(s). The depth is defined by dtype or src1 depth.
@sa add
Python prototype (for reference only):
add(src1, src2[, dst[, mask[, dtype[, stream]]]]) -> dst@spec add( Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in(), [{atom(), term()}, ...] | nil ) :: Evision.Mat.t() | {:error, String.t()}
@spec add( Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t(), [{atom(), term()}, ...] | nil ) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Computes a matrix-matrix or matrix-scalar sum.
Positional Arguments
src1:
Evision.Mat.t().First source matrix or scalar.
src2:
Evision.Mat.t().Second source matrix or scalar. Matrix should have the same size and type as src1 .
Keyword Arguments
mask:
Evision.Mat.t().Optional operation mask, 8-bit single channel array, that specifies elements of the destination array to be changed. The mask can be used only with single channel images.
dtype:
int.Optional depth of the output array.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Destination matrix that has the same size and number of channels as the input array(s). The depth is defined by dtype or src1 depth.
@sa add
Python prototype (for reference only):
add(src1, src2[, dst[, mask[, dtype[, stream]]]]) -> dstVariant 2:
Computes a matrix-matrix or matrix-scalar sum.
Positional Arguments
src1:
Evision.CUDA.GpuMat.t().First source matrix or scalar.
src2:
Evision.CUDA.GpuMat.t().Second source matrix or scalar. Matrix should have the same size and type as src1 .
Keyword Arguments
mask:
Evision.CUDA.GpuMat.t().Optional operation mask, 8-bit single channel array, that specifies elements of the destination array to be changed. The mask can be used only with single channel images.
dtype:
int.Optional depth of the output array.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Destination matrix that has the same size and number of channels as the input array(s). The depth is defined by dtype or src1 depth.
@sa add
Python prototype (for reference only):
add(src1, src2[, dst[, mask[, dtype[, stream]]]]) -> dst@spec addWeighted( Evision.Mat.maybe_mat_in(), number(), Evision.Mat.maybe_mat_in(), number(), number() ) :: Evision.Mat.t() | {:error, String.t()}
@spec addWeighted( Evision.CUDA.GpuMat.t(), number(), Evision.CUDA.GpuMat.t(), number(), number() ) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Computes the weighted sum of two arrays.
Positional Arguments
src1:
Evision.Mat.t().First source array.
alpha:
double.Weight for the first array elements.
src2:
Evision.Mat.t().Second source array of the same size and channel number as src1 .
beta:
double.Weight for the second array elements.
gamma:
double.Scalar added to each sum.
Keyword Arguments
dtype:
int.Optional depth of the destination array. When both input arrays have the same depth, dtype can be set to -1, which will be equivalent to src1.depth().
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Destination array that has the same size and number of channels as the input 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. @sa addWeighted
Python prototype (for reference only):
addWeighted(src1, alpha, src2, beta, gamma[, dst[, dtype[, stream]]]) -> dstVariant 2:
Computes the weighted sum of two arrays.
Positional Arguments
src1:
Evision.CUDA.GpuMat.t().First source array.
alpha:
double.Weight for the first array elements.
src2:
Evision.CUDA.GpuMat.t().Second source array of the same size and channel number as src1 .
beta:
double.Weight for the second array elements.
gamma:
double.Scalar added to each sum.
Keyword Arguments
dtype:
int.Optional depth of the destination array. When both input arrays have the same depth, dtype can be set to -1, which will be equivalent to src1.depth().
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Destination array that has the same size and number of channels as the input 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. @sa addWeighted
Python prototype (for reference only):
addWeighted(src1, alpha, src2, beta, gamma[, dst[, dtype[, stream]]]) -> dst@spec addWeighted( Evision.Mat.maybe_mat_in(), number(), Evision.Mat.maybe_mat_in(), number(), number(), [{atom(), term()}, ...] | nil ) :: Evision.Mat.t() | {:error, String.t()}
@spec addWeighted( Evision.CUDA.GpuMat.t(), number(), Evision.CUDA.GpuMat.t(), number(), number(), [{atom(), term()}, ...] | nil ) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Computes the weighted sum of two arrays.
Positional Arguments
src1:
Evision.Mat.t().First source array.
alpha:
double.Weight for the first array elements.
src2:
Evision.Mat.t().Second source array of the same size and channel number as src1 .
beta:
double.Weight for the second array elements.
gamma:
double.Scalar added to each sum.
Keyword Arguments
dtype:
int.Optional depth of the destination array. When both input arrays have the same depth, dtype can be set to -1, which will be equivalent to src1.depth().
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Destination array that has the same size and number of channels as the input 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. @sa addWeighted
Python prototype (for reference only):
addWeighted(src1, alpha, src2, beta, gamma[, dst[, dtype[, stream]]]) -> dstVariant 2:
Computes the weighted sum of two arrays.
Positional Arguments
src1:
Evision.CUDA.GpuMat.t().First source array.
alpha:
double.Weight for the first array elements.
src2:
Evision.CUDA.GpuMat.t().Second source array of the same size and channel number as src1 .
beta:
double.Weight for the second array elements.
gamma:
double.Scalar added to each sum.
Keyword Arguments
dtype:
int.Optional depth of the destination array. When both input arrays have the same depth, dtype can be set to -1, which will be equivalent to src1.depth().
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Destination array that has the same size and number of channels as the input 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. @sa addWeighted
Python prototype (for reference only):
addWeighted(src1, alpha, src2, beta, gamma[, dst[, dtype[, stream]]]) -> dst@spec alphaComp(Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in(), integer()) :: Evision.Mat.t() | {:error, String.t()}
@spec alphaComp(Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t(), integer()) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Composites two images using alpha opacity values contained in each image.
Positional Arguments
img1:
Evision.Mat.t().First image. Supports CV_8UC4 , CV_16UC4 , CV_32SC4 and CV_32FC4 types.
img2:
Evision.Mat.t().Second image. Must have the same size and the same type as img1 .
alpha_op:
int.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
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Destination image.
Note:
- An example demonstrating the use of alphaComp can be found at opencv_source_code/samples/gpu/alpha_comp.cpp
Python prototype (for reference only):
alphaComp(img1, img2, alpha_op[, dst[, stream]]) -> dstVariant 2:
Composites two images using alpha opacity values contained in each image.
Positional Arguments
img1:
Evision.CUDA.GpuMat.t().First image. Supports CV_8UC4 , CV_16UC4 , CV_32SC4 and CV_32FC4 types.
img2:
Evision.CUDA.GpuMat.t().Second image. Must have the same size and the same type as img1 .
alpha_op:
int.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
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Destination image.
Note:
- An example demonstrating the use of alphaComp can be found at opencv_source_code/samples/gpu/alpha_comp.cpp
Python prototype (for reference only):
alphaComp(img1, img2, alpha_op[, dst[, stream]]) -> dst@spec alphaComp( Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in(), integer(), [{atom(), term()}, ...] | nil ) :: Evision.Mat.t() | {:error, String.t()}
@spec alphaComp( Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t(), integer(), [{atom(), term()}, ...] | nil ) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Composites two images using alpha opacity values contained in each image.
Positional Arguments
img1:
Evision.Mat.t().First image. Supports CV_8UC4 , CV_16UC4 , CV_32SC4 and CV_32FC4 types.
img2:
Evision.Mat.t().Second image. Must have the same size and the same type as img1 .
alpha_op:
int.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
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Destination image.
Note:
- An example demonstrating the use of alphaComp can be found at opencv_source_code/samples/gpu/alpha_comp.cpp
Python prototype (for reference only):
alphaComp(img1, img2, alpha_op[, dst[, stream]]) -> dstVariant 2:
Composites two images using alpha opacity values contained in each image.
Positional Arguments
img1:
Evision.CUDA.GpuMat.t().First image. Supports CV_8UC4 , CV_16UC4 , CV_32SC4 and CV_32FC4 types.
img2:
Evision.CUDA.GpuMat.t().Second image. Must have the same size and the same type as img1 .
alpha_op:
int.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
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Destination image.
Note:
- An example demonstrating the use of alphaComp can be found at opencv_source_code/samples/gpu/alpha_comp.cpp
Python prototype (for reference only):
alphaComp(img1, img2, alpha_op[, dst[, stream]]) -> dst@spec bilateralFilter(Evision.Mat.maybe_mat_in(), integer(), number(), number()) :: Evision.Mat.t() | {:error, String.t()}
@spec bilateralFilter(Evision.CUDA.GpuMat.t(), integer(), number(), number()) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Performs bilateral filtering of passed image
Positional Arguments
src:
Evision.Mat.t().Source image. Supports only (channels != 2 && depth() != CV_8S && depth() != CV_32S && depth() != CV_64F).
kernel_size:
int.Kernel window size.
sigma_color:
float.Filter sigma in the color space.
sigma_spatial:
float.Filter sigma in the coordinate space.
Keyword Arguments
borderMode:
int.Border type. See borderInterpolate for details. BORDER_REFLECT101 , BORDER_REPLICATE , BORDER_CONSTANT , BORDER_REFLECT and BORDER_WRAP are supported for now.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Destination imagwe.
@sa bilateralFilter
Python prototype (for reference only):
bilateralFilter(src, kernel_size, sigma_color, sigma_spatial[, dst[, borderMode[, stream]]]) -> dstVariant 2:
Performs bilateral filtering of passed image
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Source image. Supports only (channels != 2 && depth() != CV_8S && depth() != CV_32S && depth() != CV_64F).
kernel_size:
int.Kernel window size.
sigma_color:
float.Filter sigma in the color space.
sigma_spatial:
float.Filter sigma in the coordinate space.
Keyword Arguments
borderMode:
int.Border type. See borderInterpolate for details. BORDER_REFLECT101 , BORDER_REPLICATE , BORDER_CONSTANT , BORDER_REFLECT and BORDER_WRAP are supported for now.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Destination imagwe.
@sa bilateralFilter
Python prototype (for reference only):
bilateralFilter(src, kernel_size, sigma_color, sigma_spatial[, dst[, borderMode[, stream]]]) -> dstbilateralFilter(src, kernel_size, sigma_color, sigma_spatial, opts)
View Source@spec bilateralFilter( Evision.Mat.maybe_mat_in(), integer(), number(), number(), [{atom(), term()}, ...] | nil ) :: Evision.Mat.t() | {:error, String.t()}
@spec bilateralFilter( Evision.CUDA.GpuMat.t(), integer(), number(), number(), [{atom(), term()}, ...] | nil ) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Performs bilateral filtering of passed image
Positional Arguments
src:
Evision.Mat.t().Source image. Supports only (channels != 2 && depth() != CV_8S && depth() != CV_32S && depth() != CV_64F).
kernel_size:
int.Kernel window size.
sigma_color:
float.Filter sigma in the color space.
sigma_spatial:
float.Filter sigma in the coordinate space.
Keyword Arguments
borderMode:
int.Border type. See borderInterpolate for details. BORDER_REFLECT101 , BORDER_REPLICATE , BORDER_CONSTANT , BORDER_REFLECT and BORDER_WRAP are supported for now.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Destination imagwe.
@sa bilateralFilter
Python prototype (for reference only):
bilateralFilter(src, kernel_size, sigma_color, sigma_spatial[, dst[, borderMode[, stream]]]) -> dstVariant 2:
Performs bilateral filtering of passed image
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Source image. Supports only (channels != 2 && depth() != CV_8S && depth() != CV_32S && depth() != CV_64F).
kernel_size:
int.Kernel window size.
sigma_color:
float.Filter sigma in the color space.
sigma_spatial:
float.Filter sigma in the coordinate space.
Keyword Arguments
borderMode:
int.Border type. See borderInterpolate for details. BORDER_REFLECT101 , BORDER_REPLICATE , BORDER_CONSTANT , BORDER_REFLECT and BORDER_WRAP are supported for now.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Destination imagwe.
@sa bilateralFilter
Python prototype (for reference only):
bilateralFilter(src, kernel_size, sigma_color, sigma_spatial[, dst[, borderMode[, stream]]]) -> dst@spec blendLinear( Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in() ) :: Evision.Mat.t() | {:error, String.t()}
@spec blendLinear( Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t() ) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Performs linear blending of two images.
Positional Arguments
img1:
Evision.Mat.t().First image. Supports only CV_8U and CV_32F depth.
img2:
Evision.Mat.t().Second image. Must have the same size and the same type as img1 .
weights1:
Evision.Mat.t().Weights for first image. Must have tha same size as img1 . Supports only CV_32F type.
weights2:
Evision.Mat.t().Weights for second image. Must have tha same size as img2 . Supports only CV_32F type.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
result:
Evision.Mat.t().Destination image.
Python prototype (for reference only):
blendLinear(img1, img2, weights1, weights2[, result[, stream]]) -> resultVariant 2:
Performs linear blending of two images.
Positional Arguments
img1:
Evision.CUDA.GpuMat.t().First image. Supports only CV_8U and CV_32F depth.
img2:
Evision.CUDA.GpuMat.t().Second image. Must have the same size and the same type as img1 .
weights1:
Evision.CUDA.GpuMat.t().Weights for first image. Must have tha same size as img1 . Supports only CV_32F type.
weights2:
Evision.CUDA.GpuMat.t().Weights for second image. Must have tha same size as img2 . Supports only CV_32F type.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
result:
Evision.CUDA.GpuMat.t().Destination image.
Python prototype (for reference only):
blendLinear(img1, img2, weights1, weights2[, result[, stream]]) -> result@spec blendLinear( Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in(), [{atom(), term()}, ...] | nil ) :: Evision.Mat.t() | {:error, String.t()}
@spec blendLinear( Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t(), [{atom(), term()}, ...] | nil ) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Performs linear blending of two images.
Positional Arguments
img1:
Evision.Mat.t().First image. Supports only CV_8U and CV_32F depth.
img2:
Evision.Mat.t().Second image. Must have the same size and the same type as img1 .
weights1:
Evision.Mat.t().Weights for first image. Must have tha same size as img1 . Supports only CV_32F type.
weights2:
Evision.Mat.t().Weights for second image. Must have tha same size as img2 . Supports only CV_32F type.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
result:
Evision.Mat.t().Destination image.
Python prototype (for reference only):
blendLinear(img1, img2, weights1, weights2[, result[, stream]]) -> resultVariant 2:
Performs linear blending of two images.
Positional Arguments
img1:
Evision.CUDA.GpuMat.t().First image. Supports only CV_8U and CV_32F depth.
img2:
Evision.CUDA.GpuMat.t().Second image. Must have the same size and the same type as img1 .
weights1:
Evision.CUDA.GpuMat.t().Weights for first image. Must have tha same size as img1 . Supports only CV_32F type.
weights2:
Evision.CUDA.GpuMat.t().Weights for second image. Must have tha same size as img2 . Supports only CV_32F type.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
result:
Evision.CUDA.GpuMat.t().Destination image.
Python prototype (for reference only):
blendLinear(img1, img2, weights1, weights2[, result[, stream]]) -> result@spec buildWarpAffineMaps(Evision.Mat.maybe_mat_in(), boolean(), {number(), number()}) :: {Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t()} | {:error, String.t()}
buildWarpAffineMaps
Positional Arguments
- m:
Evision.Mat.t() - inverse:
bool - dsize:
Size
Keyword Arguments
- stream:
Evision.CUDA.Stream.t().
Return
- xmap:
Evision.CUDA.GpuMat.t(). - ymap:
Evision.CUDA.GpuMat.t().
Python prototype (for reference only):
buildWarpAffineMaps(M, inverse, dsize[, xmap[, ymap[, stream]]]) -> xmap, ymap@spec buildWarpAffineMaps( Evision.Mat.maybe_mat_in(), boolean(), {number(), number()}, [{atom(), term()}, ...] | nil ) :: {Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t()} | {:error, String.t()}
buildWarpAffineMaps
Positional Arguments
- m:
Evision.Mat.t() - inverse:
bool - dsize:
Size
Keyword Arguments
- stream:
Evision.CUDA.Stream.t().
Return
- xmap:
Evision.CUDA.GpuMat.t(). - ymap:
Evision.CUDA.GpuMat.t().
Python prototype (for reference only):
buildWarpAffineMaps(M, inverse, dsize[, xmap[, ymap[, stream]]]) -> xmap, ymap@spec buildWarpPerspectiveMaps( Evision.Mat.maybe_mat_in(), boolean(), {number(), number()} ) :: {Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t()} | {:error, String.t()}
buildWarpPerspectiveMaps
Positional Arguments
- m:
Evision.Mat.t() - inverse:
bool - dsize:
Size
Keyword Arguments
- stream:
Evision.CUDA.Stream.t().
Return
- xmap:
Evision.CUDA.GpuMat.t(). - ymap:
Evision.CUDA.GpuMat.t().
Python prototype (for reference only):
buildWarpPerspectiveMaps(M, inverse, dsize[, xmap[, ymap[, stream]]]) -> xmap, ymap@spec buildWarpPerspectiveMaps( Evision.Mat.maybe_mat_in(), boolean(), {number(), number()}, [{atom(), term()}, ...] | nil ) :: {Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t()} | {:error, String.t()}
buildWarpPerspectiveMaps
Positional Arguments
- m:
Evision.Mat.t() - inverse:
bool - dsize:
Size
Keyword Arguments
- stream:
Evision.CUDA.Stream.t().
Return
- xmap:
Evision.CUDA.GpuMat.t(). - ymap:
Evision.CUDA.GpuMat.t().
Python prototype (for reference only):
buildWarpPerspectiveMaps(M, inverse, dsize[, xmap[, ymap[, stream]]]) -> xmap, ymap@spec calcAbsSum(Evision.Mat.maybe_mat_in()) :: Evision.Mat.t() | {:error, String.t()}
@spec calcAbsSum(Evision.CUDA.GpuMat.t()) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
calcAbsSum
Positional Arguments
- src:
Evision.Mat.t()
Keyword Arguments
- mask:
Evision.Mat.t(). - stream:
Evision.CUDA.Stream.t().
Return
- dst:
Evision.Mat.t().
Has overloading in C++
Python prototype (for reference only):
calcAbsSum(src[, dst[, mask[, stream]]]) -> dstVariant 2:
calcAbsSum
Positional Arguments
- src:
Evision.CUDA.GpuMat.t()
Keyword Arguments
- mask:
Evision.CUDA.GpuMat.t(). - stream:
Evision.CUDA.Stream.t().
Return
- dst:
Evision.CUDA.GpuMat.t().
Has overloading in C++
Python prototype (for reference only):
calcAbsSum(src[, dst[, mask[, stream]]]) -> dst@spec calcAbsSum(Evision.Mat.maybe_mat_in(), [{atom(), term()}, ...] | nil) :: Evision.Mat.t() | {:error, String.t()}
@spec calcAbsSum(Evision.CUDA.GpuMat.t(), [{atom(), term()}, ...] | nil) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
calcAbsSum
Positional Arguments
- src:
Evision.Mat.t()
Keyword Arguments
- mask:
Evision.Mat.t(). - stream:
Evision.CUDA.Stream.t().
Return
- dst:
Evision.Mat.t().
Has overloading in C++
Python prototype (for reference only):
calcAbsSum(src[, dst[, mask[, stream]]]) -> dstVariant 2:
calcAbsSum
Positional Arguments
- src:
Evision.CUDA.GpuMat.t()
Keyword Arguments
- mask:
Evision.CUDA.GpuMat.t(). - stream:
Evision.CUDA.Stream.t().
Return
- dst:
Evision.CUDA.GpuMat.t().
Has overloading in C++
Python prototype (for reference only):
calcAbsSum(src[, dst[, mask[, stream]]]) -> dst@spec calcHist(Evision.Mat.maybe_mat_in()) :: Evision.Mat.t() | {:error, String.t()}
@spec calcHist(Evision.CUDA.GpuMat.t()) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Calculates histogram for one channel 8-bit image.
Positional Arguments
src:
Evision.Mat.t().Source image with CV_8UC1 type.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
hist:
Evision.Mat.t().Destination histogram with one row, 256 columns, and the CV_32SC1 type.
Python prototype (for reference only):
calcHist(src[, hist[, stream]]) -> histVariant 2:
Calculates histogram for one channel 8-bit image.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Source image with CV_8UC1 type.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
hist:
Evision.CUDA.GpuMat.t().Destination histogram with one row, 256 columns, and the CV_32SC1 type.
Python prototype (for reference only):
calcHist(src[, hist[, stream]]) -> hist@spec calcHist(Evision.Mat.maybe_mat_in(), [{atom(), term()}, ...] | nil) :: Evision.Mat.t() | {:error, String.t()}
@spec calcHist(Evision.CUDA.GpuMat.t(), [{atom(), term()}, ...] | nil) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
@spec calcHist(Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in()) :: Evision.Mat.t() | {:error, String.t()}
@spec calcHist(Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t()) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Calculates histogram for one channel 8-bit image confined in given mask.
Positional Arguments
src:
Evision.Mat.t().Source image with CV_8UC1 type.
mask:
Evision.Mat.t().A mask image same size as src and of type CV_8UC1.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
hist:
Evision.Mat.t().Destination histogram with one row, 256 columns, and the CV_32SC1 type.
Python prototype (for reference only):
calcHist(src, mask[, hist[, stream]]) -> histVariant 2:
Calculates histogram for one channel 8-bit image confined in given mask.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Source image with CV_8UC1 type.
mask:
Evision.CUDA.GpuMat.t().A mask image same size as src and of type CV_8UC1.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
hist:
Evision.CUDA.GpuMat.t().Destination histogram with one row, 256 columns, and the CV_32SC1 type.
Python prototype (for reference only):
calcHist(src, mask[, hist[, stream]]) -> histVariant 3:
Calculates histogram for one channel 8-bit image.
Positional Arguments
src:
Evision.Mat.t().Source image with CV_8UC1 type.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
hist:
Evision.Mat.t().Destination histogram with one row, 256 columns, and the CV_32SC1 type.
Python prototype (for reference only):
calcHist(src[, hist[, stream]]) -> histVariant 4:
Calculates histogram for one channel 8-bit image.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Source image with CV_8UC1 type.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
hist:
Evision.CUDA.GpuMat.t().Destination histogram with one row, 256 columns, and the CV_32SC1 type.
Python prototype (for reference only):
calcHist(src[, hist[, stream]]) -> hist@spec calcHist( Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in(), [{atom(), term()}, ...] | nil ) :: Evision.Mat.t() | {:error, String.t()}
@spec calcHist( Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t(), [{atom(), term()}, ...] | nil ) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Calculates histogram for one channel 8-bit image confined in given mask.
Positional Arguments
src:
Evision.Mat.t().Source image with CV_8UC1 type.
mask:
Evision.Mat.t().A mask image same size as src and of type CV_8UC1.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
hist:
Evision.Mat.t().Destination histogram with one row, 256 columns, and the CV_32SC1 type.
Python prototype (for reference only):
calcHist(src, mask[, hist[, stream]]) -> histVariant 2:
Calculates histogram for one channel 8-bit image confined in given mask.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Source image with CV_8UC1 type.
mask:
Evision.CUDA.GpuMat.t().A mask image same size as src and of type CV_8UC1.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
hist:
Evision.CUDA.GpuMat.t().Destination histogram with one row, 256 columns, and the CV_32SC1 type.
Python prototype (for reference only):
calcHist(src, mask[, hist[, stream]]) -> hist@spec calcNorm(Evision.Mat.maybe_mat_in(), integer()) :: Evision.Mat.t() | {:error, String.t()}
@spec calcNorm(Evision.CUDA.GpuMat.t(), integer()) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
calcNorm
Positional Arguments
- src:
Evision.Mat.t() - normType:
int
Keyword Arguments
- mask:
Evision.Mat.t(). - stream:
Evision.CUDA.Stream.t().
Return
- dst:
Evision.Mat.t().
Has overloading in C++
Python prototype (for reference only):
calcNorm(src, normType[, dst[, mask[, stream]]]) -> dstVariant 2:
calcNorm
Positional Arguments
- src:
Evision.CUDA.GpuMat.t() - normType:
int
Keyword Arguments
- mask:
Evision.CUDA.GpuMat.t(). - stream:
Evision.CUDA.Stream.t().
Return
- dst:
Evision.CUDA.GpuMat.t().
Has overloading in C++
Python prototype (for reference only):
calcNorm(src, normType[, dst[, mask[, stream]]]) -> dst@spec calcNorm(Evision.Mat.maybe_mat_in(), integer(), [{atom(), term()}, ...] | nil) :: Evision.Mat.t() | {:error, String.t()}
@spec calcNorm(Evision.CUDA.GpuMat.t(), integer(), [{atom(), term()}, ...] | nil) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
calcNorm
Positional Arguments
- src:
Evision.Mat.t() - normType:
int
Keyword Arguments
- mask:
Evision.Mat.t(). - stream:
Evision.CUDA.Stream.t().
Return
- dst:
Evision.Mat.t().
Has overloading in C++
Python prototype (for reference only):
calcNorm(src, normType[, dst[, mask[, stream]]]) -> dstVariant 2:
calcNorm
Positional Arguments
- src:
Evision.CUDA.GpuMat.t() - normType:
int
Keyword Arguments
- mask:
Evision.CUDA.GpuMat.t(). - stream:
Evision.CUDA.Stream.t().
Return
- dst:
Evision.CUDA.GpuMat.t().
Has overloading in C++
Python prototype (for reference only):
calcNorm(src, normType[, dst[, mask[, stream]]]) -> dst@spec calcNormDiff(Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in()) :: Evision.Mat.t() | {:error, String.t()}
@spec calcNormDiff(Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t()) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
calcNormDiff
Positional Arguments
- src1:
Evision.Mat.t() - src2:
Evision.Mat.t()
Keyword Arguments
- normType:
int. - stream:
Evision.CUDA.Stream.t().
Return
- dst:
Evision.Mat.t().
Has overloading in C++
Python prototype (for reference only):
calcNormDiff(src1, src2[, dst[, normType[, stream]]]) -> dstVariant 2:
calcNormDiff
Positional Arguments
- src1:
Evision.CUDA.GpuMat.t() - src2:
Evision.CUDA.GpuMat.t()
Keyword Arguments
- normType:
int. - stream:
Evision.CUDA.Stream.t().
Return
- dst:
Evision.CUDA.GpuMat.t().
Has overloading in C++
Python prototype (for reference only):
calcNormDiff(src1, src2[, dst[, normType[, stream]]]) -> dst@spec calcNormDiff( Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in(), [{atom(), term()}, ...] | nil ) :: Evision.Mat.t() | {:error, String.t()}
@spec calcNormDiff( Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t(), [{atom(), term()}, ...] | nil ) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
calcNormDiff
Positional Arguments
- src1:
Evision.Mat.t() - src2:
Evision.Mat.t()
Keyword Arguments
- normType:
int. - stream:
Evision.CUDA.Stream.t().
Return
- dst:
Evision.Mat.t().
Has overloading in C++
Python prototype (for reference only):
calcNormDiff(src1, src2[, dst[, normType[, stream]]]) -> dstVariant 2:
calcNormDiff
Positional Arguments
- src1:
Evision.CUDA.GpuMat.t() - src2:
Evision.CUDA.GpuMat.t()
Keyword Arguments
- normType:
int. - stream:
Evision.CUDA.Stream.t().
Return
- dst:
Evision.CUDA.GpuMat.t().
Has overloading in C++
Python prototype (for reference only):
calcNormDiff(src1, src2[, dst[, normType[, stream]]]) -> dst@spec calcSqrSum(Evision.Mat.maybe_mat_in()) :: Evision.Mat.t() | {:error, String.t()}
@spec calcSqrSum(Evision.CUDA.GpuMat.t()) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
calcSqrSum
Positional Arguments
- src:
Evision.Mat.t()
Keyword Arguments
- mask:
Evision.Mat.t(). - stream:
Evision.CUDA.Stream.t().
Return
- dst:
Evision.Mat.t().
Has overloading in C++
Python prototype (for reference only):
calcSqrSum(src[, dst[, mask[, stream]]]) -> dstVariant 2:
calcSqrSum
Positional Arguments
- src:
Evision.CUDA.GpuMat.t()
Keyword Arguments
- mask:
Evision.CUDA.GpuMat.t(). - stream:
Evision.CUDA.Stream.t().
Return
- dst:
Evision.CUDA.GpuMat.t().
Has overloading in C++
Python prototype (for reference only):
calcSqrSum(src[, dst[, mask[, stream]]]) -> dst@spec calcSqrSum(Evision.Mat.maybe_mat_in(), [{atom(), term()}, ...] | nil) :: Evision.Mat.t() | {:error, String.t()}
@spec calcSqrSum(Evision.CUDA.GpuMat.t(), [{atom(), term()}, ...] | nil) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
calcSqrSum
Positional Arguments
- src:
Evision.Mat.t()
Keyword Arguments
- mask:
Evision.Mat.t(). - stream:
Evision.CUDA.Stream.t().
Return
- dst:
Evision.Mat.t().
Has overloading in C++
Python prototype (for reference only):
calcSqrSum(src[, dst[, mask[, stream]]]) -> dstVariant 2:
calcSqrSum
Positional Arguments
- src:
Evision.CUDA.GpuMat.t()
Keyword Arguments
- mask:
Evision.CUDA.GpuMat.t(). - stream:
Evision.CUDA.Stream.t().
Return
- dst:
Evision.CUDA.GpuMat.t().
Has overloading in C++
Python prototype (for reference only):
calcSqrSum(src[, dst[, mask[, stream]]]) -> dst@spec calcSum(Evision.Mat.maybe_mat_in()) :: Evision.Mat.t() | {:error, String.t()}
@spec calcSum(Evision.CUDA.GpuMat.t()) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
calcSum
Positional Arguments
- src:
Evision.Mat.t()
Keyword Arguments
- mask:
Evision.Mat.t(). - stream:
Evision.CUDA.Stream.t().
Return
- dst:
Evision.Mat.t().
Has overloading in C++
Python prototype (for reference only):
calcSum(src[, dst[, mask[, stream]]]) -> dstVariant 2:
calcSum
Positional Arguments
- src:
Evision.CUDA.GpuMat.t()
Keyword Arguments
- mask:
Evision.CUDA.GpuMat.t(). - stream:
Evision.CUDA.Stream.t().
Return
- dst:
Evision.CUDA.GpuMat.t().
Has overloading in C++
Python prototype (for reference only):
calcSum(src[, dst[, mask[, stream]]]) -> dst@spec calcSum(Evision.Mat.maybe_mat_in(), [{atom(), term()}, ...] | nil) :: Evision.Mat.t() | {:error, String.t()}
@spec calcSum(Evision.CUDA.GpuMat.t(), [{atom(), term()}, ...] | nil) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
calcSum
Positional Arguments
- src:
Evision.Mat.t()
Keyword Arguments
- mask:
Evision.Mat.t(). - stream:
Evision.CUDA.Stream.t().
Return
- dst:
Evision.Mat.t().
Has overloading in C++
Python prototype (for reference only):
calcSum(src[, dst[, mask[, stream]]]) -> dstVariant 2:
calcSum
Positional Arguments
- src:
Evision.CUDA.GpuMat.t()
Keyword Arguments
- mask:
Evision.CUDA.GpuMat.t(). - stream:
Evision.CUDA.Stream.t().
Return
- dst:
Evision.CUDA.GpuMat.t().
Has overloading in C++
Python prototype (for reference only):
calcSum(src[, dst[, mask[, stream]]]) -> dst@spec cartToPolar(Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in()) :: {Evision.Mat.t(), Evision.Mat.t()} | {:error, String.t()}
@spec cartToPolar(Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t()) :: {Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t()} | {:error, String.t()}
Variant 1:
Converts Cartesian coordinates into polar.
Positional Arguments
x:
Evision.Mat.t().Source matrix containing real components ( CV_32FC1 ).
y:
Evision.Mat.t().Source matrix containing imaginary components ( CV_32FC1 ).
Keyword Arguments
angleInDegrees:
bool.Flag for angles that must be evaluated in degrees.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
magnitude:
Evision.Mat.t().Destination matrix of float magnitudes ( CV_32FC1 ).
angle:
Evision.Mat.t().Destination matrix of angles ( CV_32FC1 ).
@sa cartToPolar
Python prototype (for reference only):
cartToPolar(x, y[, magnitude[, angle[, angleInDegrees[, stream]]]]) -> magnitude, angleVariant 2:
Converts Cartesian coordinates into polar.
Positional Arguments
x:
Evision.CUDA.GpuMat.t().Source matrix containing real components ( CV_32FC1 ).
y:
Evision.CUDA.GpuMat.t().Source matrix containing imaginary components ( CV_32FC1 ).
Keyword Arguments
angleInDegrees:
bool.Flag for angles that must be evaluated in degrees.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
magnitude:
Evision.CUDA.GpuMat.t().Destination matrix of float magnitudes ( CV_32FC1 ).
angle:
Evision.CUDA.GpuMat.t().Destination matrix of angles ( CV_32FC1 ).
@sa cartToPolar
Python prototype (for reference only):
cartToPolar(x, y[, magnitude[, angle[, angleInDegrees[, stream]]]]) -> magnitude, angle@spec cartToPolar( Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in(), [{atom(), term()}, ...] | nil ) :: {Evision.Mat.t(), Evision.Mat.t()} | {:error, String.t()}
@spec cartToPolar( Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t(), [{atom(), term()}, ...] | nil ) :: {Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t()} | {:error, String.t()}
Variant 1:
Converts Cartesian coordinates into polar.
Positional Arguments
x:
Evision.Mat.t().Source matrix containing real components ( CV_32FC1 ).
y:
Evision.Mat.t().Source matrix containing imaginary components ( CV_32FC1 ).
Keyword Arguments
angleInDegrees:
bool.Flag for angles that must be evaluated in degrees.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
magnitude:
Evision.Mat.t().Destination matrix of float magnitudes ( CV_32FC1 ).
angle:
Evision.Mat.t().Destination matrix of angles ( CV_32FC1 ).
@sa cartToPolar
Python prototype (for reference only):
cartToPolar(x, y[, magnitude[, angle[, angleInDegrees[, stream]]]]) -> magnitude, angleVariant 2:
Converts Cartesian coordinates into polar.
Positional Arguments
x:
Evision.CUDA.GpuMat.t().Source matrix containing real components ( CV_32FC1 ).
y:
Evision.CUDA.GpuMat.t().Source matrix containing imaginary components ( CV_32FC1 ).
Keyword Arguments
angleInDegrees:
bool.Flag for angles that must be evaluated in degrees.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
magnitude:
Evision.CUDA.GpuMat.t().Destination matrix of float magnitudes ( CV_32FC1 ).
angle:
Evision.CUDA.GpuMat.t().Destination matrix of angles ( CV_32FC1 ).
@sa cartToPolar
Python prototype (for reference only):
cartToPolar(x, y[, magnitude[, angle[, angleInDegrees[, stream]]]]) -> magnitude, angle@spec compare(Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in(), integer()) :: Evision.Mat.t() | {:error, String.t()}
@spec compare(Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t(), integer()) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Compares elements of two matrices (or of a matrix and scalar).
Positional Arguments
src1:
Evision.Mat.t().First source matrix or scalar.
src2:
Evision.Mat.t().Second source matrix or scalar.
cmpop:
int.Flag specifying the relation between the elements to be checked:
- CMP_EQ: a(.) == b(.)
- CMP_GT: a(.) > b(.)
- CMP_GE: a(.) >= b(.)
- CMP_LT: a(.) \< b(.)
- CMP_LE: a(.) \<= b(.)
- CMP_NE: a(.) != b(.)
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Destination matrix that has the same size as the input array(s) and type CV_8U.
@sa compare
Python prototype (for reference only):
compare(src1, src2, cmpop[, dst[, stream]]) -> dstVariant 2:
Compares elements of two matrices (or of a matrix and scalar).
Positional Arguments
src1:
Evision.CUDA.GpuMat.t().First source matrix or scalar.
src2:
Evision.CUDA.GpuMat.t().Second source matrix or scalar.
cmpop:
int.Flag specifying the relation between the elements to be checked:
- CMP_EQ: a(.) == b(.)
- CMP_GT: a(.) > b(.)
- CMP_GE: a(.) >= b(.)
- CMP_LT: a(.) \< b(.)
- CMP_LE: a(.) \<= b(.)
- CMP_NE: a(.) != b(.)
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Destination matrix that has the same size as the input array(s) and type CV_8U.
@sa compare
Python prototype (for reference only):
compare(src1, src2, cmpop[, dst[, stream]]) -> dst@spec compare( Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in(), integer(), [{atom(), term()}, ...] | nil ) :: Evision.Mat.t() | {:error, String.t()}
@spec compare( Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t(), integer(), [{atom(), term()}, ...] | nil ) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Compares elements of two matrices (or of a matrix and scalar).
Positional Arguments
src1:
Evision.Mat.t().First source matrix or scalar.
src2:
Evision.Mat.t().Second source matrix or scalar.
cmpop:
int.Flag specifying the relation between the elements to be checked:
- CMP_EQ: a(.) == b(.)
- CMP_GT: a(.) > b(.)
- CMP_GE: a(.) >= b(.)
- CMP_LT: a(.) \< b(.)
- CMP_LE: a(.) \<= b(.)
- CMP_NE: a(.) != b(.)
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Destination matrix that has the same size as the input array(s) and type CV_8U.
@sa compare
Python prototype (for reference only):
compare(src1, src2, cmpop[, dst[, stream]]) -> dstVariant 2:
Compares elements of two matrices (or of a matrix and scalar).
Positional Arguments
src1:
Evision.CUDA.GpuMat.t().First source matrix or scalar.
src2:
Evision.CUDA.GpuMat.t().Second source matrix or scalar.
cmpop:
int.Flag specifying the relation between the elements to be checked:
- CMP_EQ: a(.) == b(.)
- CMP_GT: a(.) > b(.)
- CMP_GE: a(.) >= b(.)
- CMP_LT: a(.) \< b(.)
- CMP_LE: a(.) \<= b(.)
- CMP_NE: a(.) != b(.)
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Destination matrix that has the same size as the input array(s) and type CV_8U.
@sa compare
Python prototype (for reference only):
compare(src1, src2, cmpop[, dst[, stream]]) -> dst@spec connectedComponents(Evision.Mat.maybe_mat_in()) :: Evision.Mat.t() | {:error, String.t()}
@spec connectedComponents(Evision.CUDA.GpuMat.t()) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
connectedComponents
Positional Arguments
image:
Evision.Mat.t().The 8-bit single-channel image to be labeled.
Keyword Arguments
connectivity:
int.Connectivity to use for the labeling procedure. 8 for 8-way connectivity is supported.
ltype:
int.Output image label type. Currently CV_32S is supported.
Return
labels:
Evision.Mat.t().Destination labeled image.
Has overloading in C++
Python prototype (for reference only):
connectedComponents(image[, labels[, connectivity[, ltype]]]) -> labelsVariant 2:
connectedComponents
Positional Arguments
image:
Evision.CUDA.GpuMat.t().The 8-bit single-channel image to be labeled.
Keyword Arguments
connectivity:
int.Connectivity to use for the labeling procedure. 8 for 8-way connectivity is supported.
ltype:
int.Output image label type. Currently CV_32S is supported.
Return
labels:
Evision.CUDA.GpuMat.t().Destination labeled image.
Has overloading in C++
Python prototype (for reference only):
connectedComponents(image[, labels[, connectivity[, ltype]]]) -> labels@spec connectedComponents(Evision.Mat.maybe_mat_in(), [{atom(), term()}, ...] | nil) :: Evision.Mat.t() | {:error, String.t()}
@spec connectedComponents(Evision.CUDA.GpuMat.t(), [{atom(), term()}, ...] | nil) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
connectedComponents
Positional Arguments
image:
Evision.Mat.t().The 8-bit single-channel image to be labeled.
Keyword Arguments
connectivity:
int.Connectivity to use for the labeling procedure. 8 for 8-way connectivity is supported.
ltype:
int.Output image label type. Currently CV_32S is supported.
Return
labels:
Evision.Mat.t().Destination labeled image.
Has overloading in C++
Python prototype (for reference only):
connectedComponents(image[, labels[, connectivity[, ltype]]]) -> labelsVariant 2:
connectedComponents
Positional Arguments
image:
Evision.CUDA.GpuMat.t().The 8-bit single-channel image to be labeled.
Keyword Arguments
connectivity:
int.Connectivity to use for the labeling procedure. 8 for 8-way connectivity is supported.
ltype:
int.Output image label type. Currently CV_32S is supported.
Return
labels:
Evision.CUDA.GpuMat.t().Destination labeled image.
Has overloading in C++
Python prototype (for reference only):
connectedComponents(image[, labels[, connectivity[, ltype]]]) -> labelsconnectedComponentsWithAlgorithm(image, connectivity, ltype, ccltype)
View Source@spec connectedComponentsWithAlgorithm( Evision.Mat.maybe_mat_in(), integer(), integer(), integer() ) :: Evision.Mat.t() | {:error, String.t()}
@spec connectedComponentsWithAlgorithm( Evision.CUDA.GpuMat.t(), integer(), integer(), integer() ) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Computes the Connected Components Labeled image of a binary image.
Positional Arguments
image:
Evision.Mat.t().The 8-bit single-channel image to be labeled.
connectivity:
int.Connectivity to use for the labeling procedure. 8 for 8-way connectivity is supported.
ltype:
int.Output image label type. Currently CV_32S is supported.
ccltype:
cuda_ConnectedComponentsAlgorithmsTypes.Connected components algorithm type (see the #ConnectedComponentsAlgorithmsTypes).
Return
labels:
Evision.Mat.t().Destination labeled image.
The function takes as input a binary image and performs Connected Components Labeling. The output is an image where each Connected Component is assigned a unique label (integer value). ltype specifies the output label image type, an important consideration based on the total number of labels or alternatively the total number of pixels in the source image. ccltype specifies the connected components labeling algorithm to use, currently BKE @cite Allegretti2019 is supported, see the #ConnectedComponentsAlgorithmsTypes for details. Note that labels in the output are not required to be sequential.
Note: A sample program demonstrating Connected Components Labeling in CUDA can be found at\n opencv_contrib_source_code/modules/cudaimgproc/samples/connected_components.cpp
Python prototype (for reference only):
connectedComponentsWithAlgorithm(image, connectivity, ltype, ccltype[, labels]) -> labelsVariant 2:
Computes the Connected Components Labeled image of a binary image.
Positional Arguments
image:
Evision.CUDA.GpuMat.t().The 8-bit single-channel image to be labeled.
connectivity:
int.Connectivity to use for the labeling procedure. 8 for 8-way connectivity is supported.
ltype:
int.Output image label type. Currently CV_32S is supported.
ccltype:
cuda_ConnectedComponentsAlgorithmsTypes.Connected components algorithm type (see the #ConnectedComponentsAlgorithmsTypes).
Return
labels:
Evision.CUDA.GpuMat.t().Destination labeled image.
The function takes as input a binary image and performs Connected Components Labeling. The output is an image where each Connected Component is assigned a unique label (integer value). ltype specifies the output label image type, an important consideration based on the total number of labels or alternatively the total number of pixels in the source image. ccltype specifies the connected components labeling algorithm to use, currently BKE @cite Allegretti2019 is supported, see the #ConnectedComponentsAlgorithmsTypes for details. Note that labels in the output are not required to be sequential.
Note: A sample program demonstrating Connected Components Labeling in CUDA can be found at\n opencv_contrib_source_code/modules/cudaimgproc/samples/connected_components.cpp
Python prototype (for reference only):
connectedComponentsWithAlgorithm(image, connectivity, ltype, ccltype[, labels]) -> labelsconnectedComponentsWithAlgorithm(image, connectivity, ltype, ccltype, opts)
View Source@spec connectedComponentsWithAlgorithm( Evision.Mat.maybe_mat_in(), integer(), integer(), integer(), [{atom(), term()}, ...] | nil ) :: Evision.Mat.t() | {:error, String.t()}
@spec connectedComponentsWithAlgorithm( Evision.CUDA.GpuMat.t(), integer(), integer(), integer(), [{atom(), term()}, ...] | nil ) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Computes the Connected Components Labeled image of a binary image.
Positional Arguments
image:
Evision.Mat.t().The 8-bit single-channel image to be labeled.
connectivity:
int.Connectivity to use for the labeling procedure. 8 for 8-way connectivity is supported.
ltype:
int.Output image label type. Currently CV_32S is supported.
ccltype:
cuda_ConnectedComponentsAlgorithmsTypes.Connected components algorithm type (see the #ConnectedComponentsAlgorithmsTypes).
Return
labels:
Evision.Mat.t().Destination labeled image.
The function takes as input a binary image and performs Connected Components Labeling. The output is an image where each Connected Component is assigned a unique label (integer value). ltype specifies the output label image type, an important consideration based on the total number of labels or alternatively the total number of pixels in the source image. ccltype specifies the connected components labeling algorithm to use, currently BKE @cite Allegretti2019 is supported, see the #ConnectedComponentsAlgorithmsTypes for details. Note that labels in the output are not required to be sequential.
Note: A sample program demonstrating Connected Components Labeling in CUDA can be found at\n opencv_contrib_source_code/modules/cudaimgproc/samples/connected_components.cpp
Python prototype (for reference only):
connectedComponentsWithAlgorithm(image, connectivity, ltype, ccltype[, labels]) -> labelsVariant 2:
Computes the Connected Components Labeled image of a binary image.
Positional Arguments
image:
Evision.CUDA.GpuMat.t().The 8-bit single-channel image to be labeled.
connectivity:
int.Connectivity to use for the labeling procedure. 8 for 8-way connectivity is supported.
ltype:
int.Output image label type. Currently CV_32S is supported.
ccltype:
cuda_ConnectedComponentsAlgorithmsTypes.Connected components algorithm type (see the #ConnectedComponentsAlgorithmsTypes).
Return
labels:
Evision.CUDA.GpuMat.t().Destination labeled image.
The function takes as input a binary image and performs Connected Components Labeling. The output is an image where each Connected Component is assigned a unique label (integer value). ltype specifies the output label image type, an important consideration based on the total number of labels or alternatively the total number of pixels in the source image. ccltype specifies the connected components labeling algorithm to use, currently BKE @cite Allegretti2019 is supported, see the #ConnectedComponentsAlgorithmsTypes for details. Note that labels in the output are not required to be sequential.
Note: A sample program demonstrating Connected Components Labeling in CUDA can be found at\n opencv_contrib_source_code/modules/cudaimgproc/samples/connected_components.cpp
Python prototype (for reference only):
connectedComponentsWithAlgorithm(image, connectivity, ltype, ccltype[, labels]) -> labels@spec copyMakeBorder( Evision.Mat.maybe_mat_in(), integer(), integer(), integer(), integer(), integer() ) :: Evision.Mat.t() | {:error, String.t()}
@spec copyMakeBorder( Evision.CUDA.GpuMat.t(), integer(), integer(), integer(), integer(), integer() ) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Forms a border around an image.
Positional Arguments
src:
Evision.Mat.t().Source image. CV_8UC1 , CV_8UC4 , CV_32SC1 , and CV_32FC1 types are supported.
top:
int.Number of top pixels
bottom:
int.Number of bottom pixels
left:
int.Number of left pixels
right:
int.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:
int.Border type. See borderInterpolate for details. BORDER_REFLECT101 , BORDER_REPLICATE , BORDER_CONSTANT , BORDER_REFLECT and BORDER_WRAP are supported for now.
Keyword Arguments
value:
Scalar.Border value.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Destination image with the same type as src. The size is Size(src.cols+left+right, src.rows+top+bottom) .
Python prototype (for reference only):
copyMakeBorder(src, top, bottom, left, right, borderType[, dst[, value[, stream]]]) -> dstVariant 2:
Forms a border around an image.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Source image. CV_8UC1 , CV_8UC4 , CV_32SC1 , and CV_32FC1 types are supported.
top:
int.Number of top pixels
bottom:
int.Number of bottom pixels
left:
int.Number of left pixels
right:
int.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:
int.Border type. See borderInterpolate for details. BORDER_REFLECT101 , BORDER_REPLICATE , BORDER_CONSTANT , BORDER_REFLECT and BORDER_WRAP are supported for now.
Keyword Arguments
value:
Scalar.Border value.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Destination image with the same type as src. The size is Size(src.cols+left+right, src.rows+top+bottom) .
Python prototype (for reference only):
copyMakeBorder(src, top, bottom, left, right, borderType[, dst[, value[, stream]]]) -> dst@spec copyMakeBorder( Evision.Mat.maybe_mat_in(), integer(), integer(), integer(), integer(), integer(), [{atom(), term()}, ...] | nil ) :: Evision.Mat.t() | {:error, String.t()}
@spec copyMakeBorder( Evision.CUDA.GpuMat.t(), integer(), integer(), integer(), integer(), integer(), [{atom(), term()}, ...] | nil ) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Forms a border around an image.
Positional Arguments
src:
Evision.Mat.t().Source image. CV_8UC1 , CV_8UC4 , CV_32SC1 , and CV_32FC1 types are supported.
top:
int.Number of top pixels
bottom:
int.Number of bottom pixels
left:
int.Number of left pixels
right:
int.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:
int.Border type. See borderInterpolate for details. BORDER_REFLECT101 , BORDER_REPLICATE , BORDER_CONSTANT , BORDER_REFLECT and BORDER_WRAP are supported for now.
Keyword Arguments
value:
Scalar.Border value.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Destination image with the same type as src. The size is Size(src.cols+left+right, src.rows+top+bottom) .
Python prototype (for reference only):
copyMakeBorder(src, top, bottom, left, right, borderType[, dst[, value[, stream]]]) -> dstVariant 2:
Forms a border around an image.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Source image. CV_8UC1 , CV_8UC4 , CV_32SC1 , and CV_32FC1 types are supported.
top:
int.Number of top pixels
bottom:
int.Number of bottom pixels
left:
int.Number of left pixels
right:
int.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:
int.Border type. See borderInterpolate for details. BORDER_REFLECT101 , BORDER_REPLICATE , BORDER_CONSTANT , BORDER_REFLECT and BORDER_WRAP are supported for now.
Keyword Arguments
value:
Scalar.Border value.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Destination image with the same type as src. The size is Size(src.cols+left+right, src.rows+top+bottom) .
Python prototype (for reference only):
copyMakeBorder(src, top, bottom, left, right, borderType[, dst[, value[, stream]]]) -> dst@spec countNonZero(Evision.Mat.maybe_mat_in()) :: Evision.Mat.t() | {:error, String.t()}
@spec countNonZero(Evision.CUDA.GpuMat.t()) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
countNonZero
Positional Arguments
- src:
Evision.Mat.t()
Keyword Arguments
- stream:
Evision.CUDA.Stream.t().
Return
- dst:
Evision.Mat.t().
Has overloading in C++
Python prototype (for reference only):
countNonZero(src[, dst[, stream]]) -> dstVariant 2:
countNonZero
Positional Arguments
- src:
Evision.CUDA.GpuMat.t()
Keyword Arguments
- stream:
Evision.CUDA.Stream.t().
Return
- dst:
Evision.CUDA.GpuMat.t().
Has overloading in C++
Python prototype (for reference only):
countNonZero(src[, dst[, stream]]) -> dst@spec countNonZero(Evision.Mat.maybe_mat_in(), [{atom(), term()}, ...] | nil) :: Evision.Mat.t() | {:error, String.t()}
@spec countNonZero(Evision.CUDA.GpuMat.t(), [{atom(), term()}, ...] | nil) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
countNonZero
Positional Arguments
- src:
Evision.Mat.t()
Keyword Arguments
- stream:
Evision.CUDA.Stream.t().
Return
- dst:
Evision.Mat.t().
Has overloading in C++
Python prototype (for reference only):
countNonZero(src[, dst[, stream]]) -> dstVariant 2:
countNonZero
Positional Arguments
- src:
Evision.CUDA.GpuMat.t()
Keyword Arguments
- stream:
Evision.CUDA.Stream.t().
Return
- dst:
Evision.CUDA.GpuMat.t().
Has overloading in C++
Python prototype (for reference only):
countNonZero(src[, dst[, stream]]) -> dst@spec createBackgroundSubtractorMOG2() :: Evision.CUDA.BackgroundSubtractorMOG2.t() | {:error, String.t()}
Creates MOG2 Background Subtractor
Keyword Arguments
history:
int.Length of the history.
varThreshold:
double.Threshold on the squared Mahalanobis distance between the pixel and the model to decide whether a pixel is well described by the background model. This parameter does not affect the background update.
detectShadows:
bool.If true, the algorithm will detect shadows and mark them. It decreases the speed a bit, so if you do not need this feature, set the parameter to false.
Return
- retval:
Evision.CUDA.BackgroundSubtractorMOG2.t()
Python prototype (for reference only):
createBackgroundSubtractorMOG2([, history[, varThreshold[, detectShadows]]]) -> retval@spec createBackgroundSubtractorMOG2([{atom(), term()}, ...] | nil) :: Evision.CUDA.BackgroundSubtractorMOG2.t() | {:error, String.t()}
Creates MOG2 Background Subtractor
Keyword Arguments
history:
int.Length of the history.
varThreshold:
double.Threshold on the squared Mahalanobis distance between the pixel and the model to decide whether a pixel is well described by the background model. This parameter does not affect the background update.
detectShadows:
bool.If true, the algorithm will detect shadows and mark them. It decreases the speed a bit, so if you do not need this feature, set the parameter to false.
Return
- retval:
Evision.CUDA.BackgroundSubtractorMOG2.t()
Python prototype (for reference only):
createBackgroundSubtractorMOG2([, history[, varThreshold[, detectShadows]]]) -> retval@spec createBackgroundSubtractorMOG() :: Evision.CUDA.BackgroundSubtractorMOG.t() | {:error, String.t()}
Creates mixture-of-gaussian background subtractor
Keyword Arguments
history:
int.Length of the history.
nmixtures:
int.Number of Gaussian mixtures.
backgroundRatio:
double.Background ratio.
noiseSigma:
double.Noise strength (standard deviation of the brightness or each color channel). 0 means some automatic value.
Return
- retval:
Evision.CUDA.BackgroundSubtractorMOG.t()
Python prototype (for reference only):
createBackgroundSubtractorMOG([, history[, nmixtures[, backgroundRatio[, noiseSigma]]]]) -> retval@spec createBackgroundSubtractorMOG([{atom(), term()}, ...] | nil) :: Evision.CUDA.BackgroundSubtractorMOG.t() | {:error, String.t()}
Creates mixture-of-gaussian background subtractor
Keyword Arguments
history:
int.Length of the history.
nmixtures:
int.Number of Gaussian mixtures.
backgroundRatio:
double.Background ratio.
noiseSigma:
double.Noise strength (standard deviation of the brightness or each color channel). 0 means some automatic value.
Return
- retval:
Evision.CUDA.BackgroundSubtractorMOG.t()
Python prototype (for reference only):
createBackgroundSubtractorMOG([, history[, nmixtures[, backgroundRatio[, noiseSigma]]]]) -> retval@spec createBoxFilter(integer(), integer(), {number(), number()}) :: Evision.CUDA.Filter.t() | {:error, String.t()}
Creates a normalized 2D box filter.
Positional Arguments
srcType:
int.Input image type. Only CV_8UC1, CV_8UC4 and CV_32FC1 are supported for now.
dstType:
int.Output image type. Only the same type as src is supported for now.
ksize:
Size.Kernel size.
Keyword Arguments
anchor:
Point.Anchor point. The default value Point(-1, -1) means that the anchor is at the kernel center.
borderMode:
int.Pixel extrapolation method. For details, see borderInterpolate .
borderVal:
Scalar.Default border value.
Return
- retval:
Filter
@sa boxFilter
Python prototype (for reference only):
createBoxFilter(srcType, dstType, ksize[, anchor[, borderMode[, borderVal]]]) -> retval@spec createBoxFilter( integer(), integer(), {number(), number()}, [{atom(), term()}, ...] | nil ) :: Evision.CUDA.Filter.t() | {:error, String.t()}
Creates a normalized 2D box filter.
Positional Arguments
srcType:
int.Input image type. Only CV_8UC1, CV_8UC4 and CV_32FC1 are supported for now.
dstType:
int.Output image type. Only the same type as src is supported for now.
ksize:
Size.Kernel size.
Keyword Arguments
anchor:
Point.Anchor point. The default value Point(-1, -1) means that the anchor is at the kernel center.
borderMode:
int.Pixel extrapolation method. For details, see borderInterpolate .
borderVal:
Scalar.Default border value.
Return
- retval:
Filter
@sa boxFilter
Python prototype (for reference only):
createBoxFilter(srcType, dstType, ksize[, anchor[, borderMode[, borderVal]]]) -> retval@spec createBoxMaxFilter( integer(), {number(), number()} ) :: Evision.CUDA.Filter.t() | {:error, String.t()}
Creates the maximum filter.
Positional Arguments
srcType:
int.Input/output image type. Only CV_8UC1 and CV_8UC4 are supported.
ksize:
Size.Kernel size.
Keyword Arguments
anchor:
Point.Anchor point. The default value (-1) means that the anchor is at the kernel center.
borderMode:
int.Pixel extrapolation method. For details, see borderInterpolate .
borderVal:
Scalar.Default border value.
Return
- retval:
Filter
Python prototype (for reference only):
createBoxMaxFilter(srcType, ksize[, anchor[, borderMode[, borderVal]]]) -> retval@spec createBoxMaxFilter( integer(), {number(), number()}, [{atom(), term()}, ...] | nil ) :: Evision.CUDA.Filter.t() | {:error, String.t()}
Creates the maximum filter.
Positional Arguments
srcType:
int.Input/output image type. Only CV_8UC1 and CV_8UC4 are supported.
ksize:
Size.Kernel size.
Keyword Arguments
anchor:
Point.Anchor point. The default value (-1) means that the anchor is at the kernel center.
borderMode:
int.Pixel extrapolation method. For details, see borderInterpolate .
borderVal:
Scalar.Default border value.
Return
- retval:
Filter
Python prototype (for reference only):
createBoxMaxFilter(srcType, ksize[, anchor[, borderMode[, borderVal]]]) -> retval@spec createBoxMinFilter( integer(), {number(), number()} ) :: Evision.CUDA.Filter.t() | {:error, String.t()}
Creates the minimum filter.
Positional Arguments
srcType:
int.Input/output image type. Only CV_8UC1 and CV_8UC4 are supported.
ksize:
Size.Kernel size.
Keyword Arguments
anchor:
Point.Anchor point. The default value (-1) means that the anchor is at the kernel center.
borderMode:
int.Pixel extrapolation method. For details, see borderInterpolate .
borderVal:
Scalar.Default border value.
Return
- retval:
Filter
Python prototype (for reference only):
createBoxMinFilter(srcType, ksize[, anchor[, borderMode[, borderVal]]]) -> retval@spec createBoxMinFilter( integer(), {number(), number()}, [{atom(), term()}, ...] | nil ) :: Evision.CUDA.Filter.t() | {:error, String.t()}
Creates the minimum filter.
Positional Arguments
srcType:
int.Input/output image type. Only CV_8UC1 and CV_8UC4 are supported.
ksize:
Size.Kernel size.
Keyword Arguments
anchor:
Point.Anchor point. The default value (-1) means that the anchor is at the kernel center.
borderMode:
int.Pixel extrapolation method. For details, see borderInterpolate .
borderVal:
Scalar.Default border value.
Return
- retval:
Filter
Python prototype (for reference only):
createBoxMinFilter(srcType, ksize[, anchor[, borderMode[, borderVal]]]) -> retval@spec createCannyEdgeDetector(number(), number()) :: Evision.CUDA.CannyEdgeDetector.t() | {:error, String.t()}
Creates implementation for cuda::CannyEdgeDetector .
Positional Arguments
low_thresh:
double.First threshold for the hysteresis procedure.
high_thresh:
double.Second threshold for the hysteresis procedure.
Keyword Arguments
apperture_size:
int.Aperture size for the Sobel operator.
l2gradient:
bool.Flag indicating whether a more accurate \f$L_2\f$ norm \f$=\sqrt{(dI/dx)^2 + (dI/dy)^2}\f$ should be used to compute the image gradient magnitude ( L2gradient=true ), or a faster default \f$L_1\f$ norm \f$=|dI/dx|+|dI/dy|\f$ is enough ( L2gradient=false ).
Return
- retval:
CannyEdgeDetector
Python prototype (for reference only):
createCannyEdgeDetector(low_thresh, high_thresh[, apperture_size[, L2gradient]]) -> retval@spec createCannyEdgeDetector(number(), number(), [{atom(), term()}, ...] | nil) :: Evision.CUDA.CannyEdgeDetector.t() | {:error, String.t()}
Creates implementation for cuda::CannyEdgeDetector .
Positional Arguments
low_thresh:
double.First threshold for the hysteresis procedure.
high_thresh:
double.Second threshold for the hysteresis procedure.
Keyword Arguments
apperture_size:
int.Aperture size for the Sobel operator.
l2gradient:
bool.Flag indicating whether a more accurate \f$L_2\f$ norm \f$=\sqrt{(dI/dx)^2 + (dI/dy)^2}\f$ should be used to compute the image gradient magnitude ( L2gradient=true ), or a faster default \f$L_1\f$ norm \f$=|dI/dx|+|dI/dy|\f$ is enough ( L2gradient=false ).
Return
- retval:
CannyEdgeDetector
Python prototype (for reference only):
createCannyEdgeDetector(low_thresh, high_thresh[, apperture_size[, L2gradient]]) -> retval@spec createCLAHE() :: Evision.CUDA.CLAHE.t() | {:error, String.t()}
Creates implementation for cuda::CLAHE .
Keyword Arguments
clipLimit:
double.Threshold for contrast limiting.
tileGridSize:
Size.Size of grid for histogram equalization. Input image will be divided into equally sized rectangular tiles. tileGridSize defines the number of tiles in row and column.
Return
- retval:
Evision.CUDA.CLAHE.t()
Python prototype (for reference only):
createCLAHE([, clipLimit[, tileGridSize]]) -> retval@spec createCLAHE([{atom(), term()}, ...] | nil) :: Evision.CUDA.CLAHE.t() | {:error, String.t()}
Creates implementation for cuda::CLAHE .
Keyword Arguments
clipLimit:
double.Threshold for contrast limiting.
tileGridSize:
Size.Size of grid for histogram equalization. Input image will be divided into equally sized rectangular tiles. tileGridSize defines the number of tiles in row and column.
Return
- retval:
Evision.CUDA.CLAHE.t()
Python prototype (for reference only):
createCLAHE([, clipLimit[, tileGridSize]]) -> retval@spec createColumnSumFilter(integer(), integer(), integer()) :: Evision.CUDA.Filter.t() | {:error, String.t()}
Creates a vertical 1D box filter.
Positional Arguments
srcType:
int.Input image type. Only CV_8UC1 type is supported for now.
dstType:
int.Output image type. Only CV_32FC1 type is supported for now.
ksize:
int.Kernel size.
Keyword Arguments
anchor:
int.Anchor point. The default value (-1) means that the anchor is at the kernel center.
borderMode:
int.Pixel extrapolation method. For details, see borderInterpolate .
borderVal:
Scalar.Default border value.
Return
- retval:
Filter
Python prototype (for reference only):
createColumnSumFilter(srcType, dstType, ksize[, anchor[, borderMode[, borderVal]]]) -> retval@spec createColumnSumFilter( integer(), integer(), integer(), [{atom(), term()}, ...] | nil ) :: Evision.CUDA.Filter.t() | {:error, String.t()}
Creates a vertical 1D box filter.
Positional Arguments
srcType:
int.Input image type. Only CV_8UC1 type is supported for now.
dstType:
int.Output image type. Only CV_32FC1 type is supported for now.
ksize:
int.Kernel size.
Keyword Arguments
anchor:
int.Anchor point. The default value (-1) means that the anchor is at the kernel center.
borderMode:
int.Pixel extrapolation method. For details, see borderInterpolate .
borderVal:
Scalar.Default border value.
Return
- retval:
Filter
Python prototype (for reference only):
createColumnSumFilter(srcType, dstType, ksize[, anchor[, borderMode[, borderVal]]]) -> retval@spec createContinuous(integer(), integer(), integer()) :: Evision.Mat.t() | {:error, String.t()}
Creates a continuous matrix.
Positional Arguments
rows:
int.Row count.
cols:
int.Column count.
type:
int.Type of the matrix.
Return
arr:
Evision.Mat.t().Destination matrix. This parameter changes only if it has a proper type and area ( \f$\texttt{rows} \times \texttt{cols}\f$ ).
Matrix is called continuous if its elements are stored continuously, that is, without gaps at the end of each row.
Python prototype (for reference only):
createContinuous(rows, cols, type[, arr]) -> arr@spec createContinuous(integer(), integer(), integer(), [{atom(), term()}, ...] | nil) :: Evision.Mat.t() | {:error, String.t()}
Creates a continuous matrix.
Positional Arguments
rows:
int.Row count.
cols:
int.Column count.
type:
int.Type of the matrix.
Return
arr:
Evision.Mat.t().Destination matrix. This parameter changes only if it has a proper type and area ( \f$\texttt{rows} \times \texttt{cols}\f$ ).
Matrix is called continuous if its elements are stored continuously, that is, without gaps at the end of each row.
Python prototype (for reference only):
createContinuous(rows, cols, type[, arr]) -> arr@spec createConvolution() :: Evision.CUDA.Convolution.t() | {:error, String.t()}
Creates implementation for cuda::Convolution .
Keyword Arguments
user_block_size:
Size.Block size. If you leave 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.
Return
- retval:
Convolution
Python prototype (for reference only):
createConvolution([, user_block_size]) -> retval@spec createConvolution([{atom(), term()}, ...] | nil) :: Evision.CUDA.Convolution.t() | {:error, String.t()}
Creates implementation for cuda::Convolution .
Keyword Arguments
user_block_size:
Size.Block size. If you leave 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.
Return
- retval:
Convolution
Python prototype (for reference only):
createConvolution([, user_block_size]) -> retval@spec createDerivFilter(integer(), integer(), integer(), integer(), integer()) :: Evision.CUDA.Filter.t() | {:error, String.t()}
Creates a generalized Deriv operator.
Positional Arguments
srcType:
int.Source image type.
dstType:
int.Destination array type.
dx:
int.Derivative order in respect of x.
dy:
int.Derivative order in respect of y.
ksize:
int.Aperture size. See getDerivKernels for details.
Keyword Arguments
normalize:
bool.Flag indicating whether to normalize (scale down) the filter coefficients or not. See getDerivKernels for details.
scale:
double.Optional scale factor for the computed derivative values. By default, no scaling is applied. For details, see getDerivKernels .
rowBorderMode:
int.Pixel extrapolation method in the vertical direction. For details, see borderInterpolate.
columnBorderMode:
int.Pixel extrapolation method in the horizontal direction.
Return
- retval:
Filter
Python prototype (for reference only):
createDerivFilter(srcType, dstType, dx, dy, ksize[, normalize[, scale[, rowBorderMode[, columnBorderMode]]]]) -> retval@spec createDerivFilter( integer(), integer(), integer(), integer(), integer(), [{atom(), term()}, ...] | nil ) :: Evision.CUDA.Filter.t() | {:error, String.t()}
Creates a generalized Deriv operator.
Positional Arguments
srcType:
int.Source image type.
dstType:
int.Destination array type.
dx:
int.Derivative order in respect of x.
dy:
int.Derivative order in respect of y.
ksize:
int.Aperture size. See getDerivKernels for details.
Keyword Arguments
normalize:
bool.Flag indicating whether to normalize (scale down) the filter coefficients or not. See getDerivKernels for details.
scale:
double.Optional scale factor for the computed derivative values. By default, no scaling is applied. For details, see getDerivKernels .
rowBorderMode:
int.Pixel extrapolation method in the vertical direction. For details, see borderInterpolate.
columnBorderMode:
int.Pixel extrapolation method in the horizontal direction.
Return
- retval:
Filter
Python prototype (for reference only):
createDerivFilter(srcType, dstType, dx, dy, ksize[, normalize[, scale[, rowBorderMode[, columnBorderMode]]]]) -> retval@spec createDFT( {number(), number()}, integer() ) :: Evision.CUDA.DFT.t() | {:error, String.t()}
Creates implementation for cuda::DFT.
Positional Arguments
dft_size:
Size.The image size.
flags:
int.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_COMPLEX_INPUT Specifies that inputs will be complex with 2 channels.
- 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.
Return
- retval:
DFT
Python prototype (for reference only):
createDFT(dft_size, flags) -> retval@spec createDisparityBilateralFilter() :: Evision.CUDA.DisparityBilateralFilter.t() | {:error, String.t()}
Creates DisparityBilateralFilter object.
Keyword Arguments
ndisp:
int.Number of disparities.
radius:
int.Filter radius.
iters:
int.Number of iterations.
Return
- retval:
Evision.CUDA.DisparityBilateralFilter.t()
Python prototype (for reference only):
createDisparityBilateralFilter([, ndisp[, radius[, iters]]]) -> retval@spec createDisparityBilateralFilter([{atom(), term()}, ...] | nil) :: Evision.CUDA.DisparityBilateralFilter.t() | {:error, String.t()}
Creates DisparityBilateralFilter object.
Keyword Arguments
ndisp:
int.Number of disparities.
radius:
int.Filter radius.
iters:
int.Number of iterations.
Return
- retval:
Evision.CUDA.DisparityBilateralFilter.t()
Python prototype (for reference only):
createDisparityBilateralFilter([, ndisp[, radius[, iters]]]) -> retval@spec createGaussianFilter(integer(), integer(), {number(), number()}, number()) :: Evision.CUDA.Filter.t() | {:error, String.t()}
Creates a Gaussian filter.
Positional Arguments
srcType:
int.Source image type.
dstType:
int.Destination array type.
ksize:
Size.Aperture size. See getGaussianKernel for details.
sigma1:
double.Gaussian sigma in the horizontal direction. See getGaussianKernel for details.
Keyword Arguments
sigma2:
double.Gaussian sigma in the vertical direction. If 0, then \f$\texttt{sigma2}\leftarrow\texttt{sigma1}\f$ .
rowBorderMode:
int.Pixel extrapolation method in the vertical direction. For details, see borderInterpolate.
columnBorderMode:
int.Pixel extrapolation method in the horizontal direction.
Return
- retval:
Filter
@sa GaussianBlur
Python prototype (for reference only):
createGaussianFilter(srcType, dstType, ksize, sigma1[, sigma2[, rowBorderMode[, columnBorderMode]]]) -> retval@spec createGaussianFilter( integer(), integer(), {number(), number()}, number(), [{atom(), term()}, ...] | nil ) :: Evision.CUDA.Filter.t() | {:error, String.t()}
Creates a Gaussian filter.
Positional Arguments
srcType:
int.Source image type.
dstType:
int.Destination array type.
ksize:
Size.Aperture size. See getGaussianKernel for details.
sigma1:
double.Gaussian sigma in the horizontal direction. See getGaussianKernel for details.
Keyword Arguments
sigma2:
double.Gaussian sigma in the vertical direction. If 0, then \f$\texttt{sigma2}\leftarrow\texttt{sigma1}\f$ .
rowBorderMode:
int.Pixel extrapolation method in the vertical direction. For details, see borderInterpolate.
columnBorderMode:
int.Pixel extrapolation method in the horizontal direction.
Return
- retval:
Filter
@sa GaussianBlur
Python prototype (for reference only):
createGaussianFilter(srcType, dstType, ksize, sigma1[, sigma2[, rowBorderMode[, columnBorderMode]]]) -> retval@spec createGeneralizedHoughBallard() :: Evision.GeneralizedHoughBallard.t() | {:error, String.t()}
Creates implementation for generalized hough transform from @cite Ballard1981 .
Return
- retval:
Evision.GeneralizedHoughBallard.t()
Python prototype (for reference only):
createGeneralizedHoughBallard() -> retval@spec createGeneralizedHoughGuil() :: Evision.GeneralizedHoughGuil.t() | {:error, String.t()}
Creates implementation for generalized hough transform from @cite Guil1999 .
Return
- retval:
Evision.GeneralizedHoughGuil.t()
Python prototype (for reference only):
createGeneralizedHoughGuil() -> retval@spec createGoodFeaturesToTrackDetector(integer()) :: Evision.CUDA.CornersDetector.t() | {:error, String.t()}
Creates implementation for cuda::CornersDetector .
Positional Arguments
srcType:
int.Input source type. Only CV_8UC1 and CV_32FC1 are supported for now.
Keyword Arguments
maxCorners:
int.Maximum number of corners to return. If there are more corners than are found, the strongest of them is returned.
qualityLevel:
double.Parameter characterizing the minimal accepted quality of image corners. The parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue (see cornerMinEigenVal ) or the Harris function response (see cornerHarris ). The corners with the quality measure less than the product are rejected. For example, if the best corner has the quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure less than 15 are rejected.
minDistance:
double.Minimum possible Euclidean distance between the returned corners.
blockSize:
int.Size of an average block for computing a derivative covariation matrix over each pixel neighborhood. See cornerEigenValsAndVecs .
useHarrisDetector:
bool.Parameter indicating whether to use a Harris detector (see cornerHarris) or cornerMinEigenVal.
harrisK:
double.Free parameter of the Harris detector.
Return
- retval:
CornersDetector
Python prototype (for reference only):
createGoodFeaturesToTrackDetector(srcType[, maxCorners[, qualityLevel[, minDistance[, blockSize[, useHarrisDetector[, harrisK]]]]]]) -> retval@spec createGoodFeaturesToTrackDetector(integer(), [{atom(), term()}, ...] | nil) :: Evision.CUDA.CornersDetector.t() | {:error, String.t()}
Creates implementation for cuda::CornersDetector .
Positional Arguments
srcType:
int.Input source type. Only CV_8UC1 and CV_32FC1 are supported for now.
Keyword Arguments
maxCorners:
int.Maximum number of corners to return. If there are more corners than are found, the strongest of them is returned.
qualityLevel:
double.Parameter characterizing the minimal accepted quality of image corners. The parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue (see cornerMinEigenVal ) or the Harris function response (see cornerHarris ). The corners with the quality measure less than the product are rejected. For example, if the best corner has the quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure less than 15 are rejected.
minDistance:
double.Minimum possible Euclidean distance between the returned corners.
blockSize:
int.Size of an average block for computing a derivative covariation matrix over each pixel neighborhood. See cornerEigenValsAndVecs .
useHarrisDetector:
bool.Parameter indicating whether to use a Harris detector (see cornerHarris) or cornerMinEigenVal.
harrisK:
double.Free parameter of the Harris detector.
Return
- retval:
CornersDetector
Python prototype (for reference only):
createGoodFeaturesToTrackDetector(srcType[, maxCorners[, qualityLevel[, minDistance[, blockSize[, useHarrisDetector[, harrisK]]]]]]) -> retval@spec createGpuMatFromCudaMemory({number(), number()}, integer(), integer()) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
createGpuMatFromCudaMemory
Positional Arguments
size:
Size.2D array size: Size(cols, rows). In the Size() constructor, the number of rows and the number of columns go in the reverse order.
type:
int.Type of the matrix.
cudaMemoryAddress:
size_t.Address of the allocated GPU memory on the device. This does not allocate matrix data. Instead, it just initializes the matrix header that points to the specified \a cudaMemoryAddress, which means that no data is copied. This operation is very efficient and can be used to process external data using OpenCV functions. The external data is not automatically deallocated, so you should take care of it.
Keyword Arguments
step:
size_t.Number of bytes each matrix row occupies. The value should include the padding bytes at the end of each row, if any. If the parameter is missing (set to Mat::AUTO_STEP ), no padding is assumed and the actual step is calculated as cols*elemSize(). See GpuMat::elemSize.
Return
- retval:
Evision.CUDA.GpuMat.t()
Has overloading in C++
Note: Overload for generation of bindings only, not exported or intended for use internally from C++.
Python prototype (for reference only):
createGpuMatFromCudaMemory(size, type, cudaMemoryAddress[, step]) -> retval@spec createGpuMatFromCudaMemory( {number(), number()}, integer(), integer(), [{atom(), term()}, ...] | nil ) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
@spec createGpuMatFromCudaMemory(integer(), integer(), integer(), integer()) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Bindings overload to create a GpuMat from existing GPU memory.
Positional Arguments
rows:
int.Row count.
cols:
int.Column count.
type:
int.Type of the matrix.
cudaMemoryAddress:
size_t.Address of the allocated GPU memory on the device. This does not allocate matrix data. Instead, it just initializes the matrix header that points to the specified \a cudaMemoryAddress, which means that no data is copied. This operation is very efficient and can be used to process external data using OpenCV functions. The external data is not automatically deallocated, so you should take care of it.
Keyword Arguments
step:
size_t.Number of bytes each matrix row occupies. The value should include the padding bytes at the end of each row, if any. If the parameter is missing (set to Mat::AUTO_STEP ), no padding is assumed and the actual step is calculated as cols*elemSize(). See GpuMat::elemSize.
Return
- retval:
Evision.CUDA.GpuMat.t()
Note: Overload for generation of bindings only, not exported or intended for use internally from C++.
Python prototype (for reference only):
createGpuMatFromCudaMemory(rows, cols, type, cudaMemoryAddress[, step]) -> retvalVariant 2:
createGpuMatFromCudaMemory
Positional Arguments
size:
Size.2D array size: Size(cols, rows). In the Size() constructor, the number of rows and the number of columns go in the reverse order.
type:
int.Type of the matrix.
cudaMemoryAddress:
size_t.Address of the allocated GPU memory on the device. This does not allocate matrix data. Instead, it just initializes the matrix header that points to the specified \a cudaMemoryAddress, which means that no data is copied. This operation is very efficient and can be used to process external data using OpenCV functions. The external data is not automatically deallocated, so you should take care of it.
Keyword Arguments
step:
size_t.Number of bytes each matrix row occupies. The value should include the padding bytes at the end of each row, if any. If the parameter is missing (set to Mat::AUTO_STEP ), no padding is assumed and the actual step is calculated as cols*elemSize(). See GpuMat::elemSize.
Return
- retval:
Evision.CUDA.GpuMat.t()
Has overloading in C++
Note: Overload for generation of bindings only, not exported or intended for use internally from C++.
Python prototype (for reference only):
createGpuMatFromCudaMemory(size, type, cudaMemoryAddress[, step]) -> retvalcreateGpuMatFromCudaMemory(rows, cols, type, cudaMemoryAddress, opts)
View Source@spec createGpuMatFromCudaMemory( integer(), integer(), integer(), integer(), [{atom(), term()}, ...] | nil ) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Bindings overload to create a GpuMat from existing GPU memory.
Positional Arguments
rows:
int.Row count.
cols:
int.Column count.
type:
int.Type of the matrix.
cudaMemoryAddress:
size_t.Address of the allocated GPU memory on the device. This does not allocate matrix data. Instead, it just initializes the matrix header that points to the specified \a cudaMemoryAddress, which means that no data is copied. This operation is very efficient and can be used to process external data using OpenCV functions. The external data is not automatically deallocated, so you should take care of it.
Keyword Arguments
step:
size_t.Number of bytes each matrix row occupies. The value should include the padding bytes at the end of each row, if any. If the parameter is missing (set to Mat::AUTO_STEP ), no padding is assumed and the actual step is calculated as cols*elemSize(). See GpuMat::elemSize.
Return
- retval:
Evision.CUDA.GpuMat.t()
Note: Overload for generation of bindings only, not exported or intended for use internally from C++.
Python prototype (for reference only):
createGpuMatFromCudaMemory(rows, cols, type, cudaMemoryAddress[, step]) -> retval@spec createHarrisCorner(integer(), integer(), integer(), number()) :: Evision.CUDA.CornernessCriteria.t() | {:error, String.t()}
Creates implementation for Harris cornerness criteria.
Positional Arguments
srcType:
int.Input source type. Only CV_8UC1 and CV_32FC1 are supported for now.
blockSize:
int.Neighborhood size.
ksize:
int.Aperture parameter for the Sobel operator.
k:
double.Harris detector free parameter.
Keyword Arguments
borderType:
int.Pixel extrapolation method. Only BORDER_REFLECT101 and BORDER_REPLICATE are supported for now.
Return
- retval:
CornernessCriteria
@sa cornerHarris
Python prototype (for reference only):
createHarrisCorner(srcType, blockSize, ksize, k[, borderType]) -> retval@spec createHarrisCorner( integer(), integer(), integer(), number(), [{atom(), term()}, ...] | nil ) :: Evision.CUDA.CornernessCriteria.t() | {:error, String.t()}
Creates implementation for Harris cornerness criteria.
Positional Arguments
srcType:
int.Input source type. Only CV_8UC1 and CV_32FC1 are supported for now.
blockSize:
int.Neighborhood size.
ksize:
int.Aperture parameter for the Sobel operator.
k:
double.Harris detector free parameter.
Keyword Arguments
borderType:
int.Pixel extrapolation method. Only BORDER_REFLECT101 and BORDER_REPLICATE are supported for now.
Return
- retval:
CornernessCriteria
@sa cornerHarris
Python prototype (for reference only):
createHarrisCorner(srcType, blockSize, ksize, k[, borderType]) -> retvalcreateHoughCirclesDetector(dp, minDist, cannyThreshold, votesThreshold, minRadius, maxRadius)
View Source@spec createHoughCirclesDetector( number(), number(), integer(), integer(), integer(), integer() ) :: Evision.CUDA.HoughCirclesDetector.t() | {:error, String.t()}
Creates implementation for cuda::HoughCirclesDetector .
Positional Arguments
dp:
float.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:
float.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:
int.The higher threshold of the two passed to Canny edge detector (the lower one is twice smaller).
votesThreshold:
int.The accumulator threshold for the circle centers at the detection stage. The smaller it is, the more false circles may be detected.
minRadius:
int.Minimum circle radius.
maxRadius:
int.Maximum circle radius.
Keyword Arguments
maxCircles:
int.Maximum number of output circles.
Return
- retval:
HoughCirclesDetector
Python prototype (for reference only):
createHoughCirclesDetector(dp, minDist, cannyThreshold, votesThreshold, minRadius, maxRadius[, maxCircles]) -> retvalcreateHoughCirclesDetector(dp, minDist, cannyThreshold, votesThreshold, minRadius, maxRadius, opts)
View Source@spec createHoughCirclesDetector( number(), number(), integer(), integer(), integer(), integer(), [{atom(), term()}, ...] | nil ) :: Evision.CUDA.HoughCirclesDetector.t() | {:error, String.t()}
Creates implementation for cuda::HoughCirclesDetector .
Positional Arguments
dp:
float.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:
float.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:
int.The higher threshold of the two passed to Canny edge detector (the lower one is twice smaller).
votesThreshold:
int.The accumulator threshold for the circle centers at the detection stage. The smaller it is, the more false circles may be detected.
minRadius:
int.Minimum circle radius.
maxRadius:
int.Maximum circle radius.
Keyword Arguments
maxCircles:
int.Maximum number of output circles.
Return
- retval:
HoughCirclesDetector
Python prototype (for reference only):
createHoughCirclesDetector(dp, minDist, cannyThreshold, votesThreshold, minRadius, maxRadius[, maxCircles]) -> retval@spec createHoughLinesDetector(number(), number(), integer()) :: Evision.CUDA.HoughLinesDetector.t() | {:error, String.t()}
Creates implementation for cuda::HoughLinesDetector .
Positional Arguments
rho:
float.Distance resolution of the accumulator in pixels.
theta:
float.Angle resolution of the accumulator in radians.
threshold:
int.Accumulator threshold parameter. Only those lines are returned that get enough votes ( \f$>\texttt{threshold}\f$ ).
Keyword Arguments
doSort:
bool.Performs lines sort by votes.
maxLines:
int.Maximum number of output lines.
Return
- retval:
HoughLinesDetector
Python prototype (for reference only):
createHoughLinesDetector(rho, theta, threshold[, doSort[, maxLines]]) -> retval@spec createHoughLinesDetector( number(), number(), integer(), [{atom(), term()}, ...] | nil ) :: Evision.CUDA.HoughLinesDetector.t() | {:error, String.t()}
Creates implementation for cuda::HoughLinesDetector .
Positional Arguments
rho:
float.Distance resolution of the accumulator in pixels.
theta:
float.Angle resolution of the accumulator in radians.
threshold:
int.Accumulator threshold parameter. Only those lines are returned that get enough votes ( \f$>\texttt{threshold}\f$ ).
Keyword Arguments
doSort:
bool.Performs lines sort by votes.
maxLines:
int.Maximum number of output lines.
Return
- retval:
HoughLinesDetector
Python prototype (for reference only):
createHoughLinesDetector(rho, theta, threshold[, doSort[, maxLines]]) -> retval@spec createHoughSegmentDetector(number(), number(), integer(), integer()) :: Evision.CUDA.HoughSegmentDetector.t() | {:error, String.t()}
Creates implementation for cuda::HoughSegmentDetector .
Positional Arguments
rho:
float.Distance resolution of the accumulator in pixels.
theta:
float.Angle resolution of the accumulator in radians.
minLineLength:
int.Minimum line length. Line segments shorter than that are rejected.
maxLineGap:
int.Maximum allowed gap between points on the same line to link them.
Keyword Arguments
maxLines:
int.Maximum number of output lines.
threshold:
int.%Accumulator threshold parameter. Only those lines are returned that get enough votes ( \f$>\texttt{threshold}\f$ ).
Return
- retval:
HoughSegmentDetector
Python prototype (for reference only):
createHoughSegmentDetector(rho, theta, minLineLength, maxLineGap[, maxLines[, threshold]]) -> retvalcreateHoughSegmentDetector(rho, theta, minLineLength, maxLineGap, opts)
View Source@spec createHoughSegmentDetector( number(), number(), integer(), integer(), [{atom(), term()}, ...] | nil ) :: Evision.CUDA.HoughSegmentDetector.t() | {:error, String.t()}
Creates implementation for cuda::HoughSegmentDetector .
Positional Arguments
rho:
float.Distance resolution of the accumulator in pixels.
theta:
float.Angle resolution of the accumulator in radians.
minLineLength:
int.Minimum line length. Line segments shorter than that are rejected.
maxLineGap:
int.Maximum allowed gap between points on the same line to link them.
Keyword Arguments
maxLines:
int.Maximum number of output lines.
threshold:
int.%Accumulator threshold parameter. Only those lines are returned that get enough votes ( \f$>\texttt{threshold}\f$ ).
Return
- retval:
HoughSegmentDetector
Python prototype (for reference only):
createHoughSegmentDetector(rho, theta, minLineLength, maxLineGap[, maxLines[, threshold]]) -> retval@spec createLaplacianFilter(integer(), integer()) :: Evision.CUDA.Filter.t() | {:error, String.t()}
Creates a Laplacian operator.
Positional Arguments
srcType:
int.Input image type. Supports CV_8U , CV_16U and CV_32F one and four channel image.
dstType:
int.Output image type. Only the same type as src is supported for now.
Keyword Arguments
ksize:
int.Aperture size used to compute the second-derivative filters (see getDerivKernels). It must be positive and odd. Only ksize = 1 and ksize = 3 are supported.
scale:
double.Optional scale factor for the computed Laplacian values. By default, no scaling is applied (see getDerivKernels ).
borderMode:
int.Pixel extrapolation method. For details, see borderInterpolate .
borderVal:
Scalar.Default border value.
Return
- retval:
Filter
@sa Laplacian
Python prototype (for reference only):
createLaplacianFilter(srcType, dstType[, ksize[, scale[, borderMode[, borderVal]]]]) -> retval@spec createLaplacianFilter(integer(), integer(), [{atom(), term()}, ...] | nil) :: Evision.CUDA.Filter.t() | {:error, String.t()}
Creates a Laplacian operator.
Positional Arguments
srcType:
int.Input image type. Supports CV_8U , CV_16U and CV_32F one and four channel image.
dstType:
int.Output image type. Only the same type as src is supported for now.
Keyword Arguments
ksize:
int.Aperture size used to compute the second-derivative filters (see getDerivKernels). It must be positive and odd. Only ksize = 1 and ksize = 3 are supported.
scale:
double.Optional scale factor for the computed Laplacian values. By default, no scaling is applied (see getDerivKernels ).
borderMode:
int.Pixel extrapolation method. For details, see borderInterpolate .
borderVal:
Scalar.Default border value.
Return
- retval:
Filter
@sa Laplacian
Python prototype (for reference only):
createLaplacianFilter(srcType, dstType[, ksize[, scale[, borderMode[, borderVal]]]]) -> retval@spec createLinearFilter(integer(), integer(), Evision.Mat.maybe_mat_in()) :: Evision.CUDA.Filter.t() | {:error, String.t()}
@spec createLinearFilter(integer(), integer(), Evision.CUDA.GpuMat.t()) :: Evision.CUDA.Filter.t() | {:error, String.t()}
Variant 1:
Creates a non-separable linear 2D filter.
Positional Arguments
srcType:
int.Input image type. Supports CV_8U , CV_16U and CV_32F one and four channel image.
dstType:
int.Output image type. Only the same type as src is supported for now.
kernel:
Evision.Mat.t().2D array of filter coefficients.
Keyword Arguments
anchor:
Point.Anchor point. The default value Point(-1, -1) means that the anchor is at the kernel center.
borderMode:
int.Pixel extrapolation method. For details, see borderInterpolate .
borderVal:
Scalar.Default border value.
Return
- retval:
Filter
@sa filter2D
Python prototype (for reference only):
createLinearFilter(srcType, dstType, kernel[, anchor[, borderMode[, borderVal]]]) -> retvalVariant 2:
Creates a non-separable linear 2D filter.
Positional Arguments
srcType:
int.Input image type. Supports CV_8U , CV_16U and CV_32F one and four channel image.
dstType:
int.Output image type. Only the same type as src is supported for now.
kernel:
Evision.CUDA.GpuMat.t().2D array of filter coefficients.
Keyword Arguments
anchor:
Point.Anchor point. The default value Point(-1, -1) means that the anchor is at the kernel center.
borderMode:
int.Pixel extrapolation method. For details, see borderInterpolate .
borderVal:
Scalar.Default border value.
Return
- retval:
Filter
@sa filter2D
Python prototype (for reference only):
createLinearFilter(srcType, dstType, kernel[, anchor[, borderMode[, borderVal]]]) -> retval@spec createLinearFilter( integer(), integer(), Evision.Mat.maybe_mat_in(), [{atom(), term()}, ...] | nil ) :: Evision.CUDA.Filter.t() | {:error, String.t()}
@spec createLinearFilter( integer(), integer(), Evision.CUDA.GpuMat.t(), [{atom(), term()}, ...] | nil ) :: Evision.CUDA.Filter.t() | {:error, String.t()}
Variant 1:
Creates a non-separable linear 2D filter.
Positional Arguments
srcType:
int.Input image type. Supports CV_8U , CV_16U and CV_32F one and four channel image.
dstType:
int.Output image type. Only the same type as src is supported for now.
kernel:
Evision.Mat.t().2D array of filter coefficients.
Keyword Arguments
anchor:
Point.Anchor point. The default value Point(-1, -1) means that the anchor is at the kernel center.
borderMode:
int.Pixel extrapolation method. For details, see borderInterpolate .
borderVal:
Scalar.Default border value.
Return
- retval:
Filter
@sa filter2D
Python prototype (for reference only):
createLinearFilter(srcType, dstType, kernel[, anchor[, borderMode[, borderVal]]]) -> retvalVariant 2:
Creates a non-separable linear 2D filter.
Positional Arguments
srcType:
int.Input image type. Supports CV_8U , CV_16U and CV_32F one and four channel image.
dstType:
int.Output image type. Only the same type as src is supported for now.
kernel:
Evision.CUDA.GpuMat.t().2D array of filter coefficients.
Keyword Arguments
anchor:
Point.Anchor point. The default value Point(-1, -1) means that the anchor is at the kernel center.
borderMode:
int.Pixel extrapolation method. For details, see borderInterpolate .
borderVal:
Scalar.Default border value.
Return
- retval:
Filter
@sa filter2D
Python prototype (for reference only):
createLinearFilter(srcType, dstType, kernel[, anchor[, borderMode[, borderVal]]]) -> retval@spec createLookUpTable(Evision.Mat.maybe_mat_in()) :: Evision.CUDA.LookUpTable.t() | {:error, String.t()}
@spec createLookUpTable(Evision.CUDA.GpuMat.t()) :: Evision.CUDA.LookUpTable.t() | {:error, String.t()}
Variant 1:
Creates implementation for cuda::LookUpTable .
Positional Arguments
lut:
Evision.Mat.t().Look-up table of 256 elements. It is a continuous CV_8U matrix.
Return
- retval:
LookUpTable
Python prototype (for reference only):
createLookUpTable(lut) -> retvalVariant 2:
Creates implementation for cuda::LookUpTable .
Positional Arguments
lut:
Evision.CUDA.GpuMat.t().Look-up table of 256 elements. It is a continuous CV_8U matrix.
Return
- retval:
LookUpTable
Python prototype (for reference only):
createLookUpTable(lut) -> retval@spec createMedianFilter(integer(), integer()) :: Evision.CUDA.Filter.t() | {:error, String.t()}
Performs median filtering for each point of the source image.
Positional Arguments
srcType:
int.type of of source image. Only CV_8UC1 images are supported for now.
windowSize:
int.Size of the kernerl used for the filtering. Uses a (windowSize x windowSize) filter.
Keyword Arguments
partition:
int.Specifies the parallel granularity of the workload. This parameter should be used GPU experts when optimizing performance.
Return
- retval:
Filter
Outputs an image that has been filtered using a median-filtering formulation. Details on this algorithm can be found in: Green, O., 2017. "Efficient scalable median filtering using histogram-based operations", IEEE Transactions on Image Processing, 27(5), pp.2217-2228.
Python prototype (for reference only):
createMedianFilter(srcType, windowSize[, partition]) -> retval@spec createMedianFilter(integer(), integer(), [{atom(), term()}, ...] | nil) :: Evision.CUDA.Filter.t() | {:error, String.t()}
Performs median filtering for each point of the source image.
Positional Arguments
srcType:
int.type of of source image. Only CV_8UC1 images are supported for now.
windowSize:
int.Size of the kernerl used for the filtering. Uses a (windowSize x windowSize) filter.
Keyword Arguments
partition:
int.Specifies the parallel granularity of the workload. This parameter should be used GPU experts when optimizing performance.
Return
- retval:
Filter
Outputs an image that has been filtered using a median-filtering formulation. Details on this algorithm can be found in: Green, O., 2017. "Efficient scalable median filtering using histogram-based operations", IEEE Transactions on Image Processing, 27(5), pp.2217-2228.
Python prototype (for reference only):
createMedianFilter(srcType, windowSize[, partition]) -> retval@spec createMinEigenValCorner(integer(), integer(), integer()) :: Evision.CUDA.CornernessCriteria.t() | {:error, String.t()}
Creates implementation for the minimum eigen value of a 2x2 derivative covariation matrix (the cornerness criteria).
Positional Arguments
srcType:
int.Input source type. Only CV_8UC1 and CV_32FC1 are supported for now.
blockSize:
int.Neighborhood size.
ksize:
int.Aperture parameter for the Sobel operator.
Keyword Arguments
borderType:
int.Pixel extrapolation method. Only BORDER_REFLECT101 and BORDER_REPLICATE are supported for now.
Return
- retval:
CornernessCriteria
@sa cornerMinEigenVal
Python prototype (for reference only):
createMinEigenValCorner(srcType, blockSize, ksize[, borderType]) -> retval@spec createMinEigenValCorner( integer(), integer(), integer(), [{atom(), term()}, ...] | nil ) :: Evision.CUDA.CornernessCriteria.t() | {:error, String.t()}
Creates implementation for the minimum eigen value of a 2x2 derivative covariation matrix (the cornerness criteria).
Positional Arguments
srcType:
int.Input source type. Only CV_8UC1 and CV_32FC1 are supported for now.
blockSize:
int.Neighborhood size.
ksize:
int.Aperture parameter for the Sobel operator.
Keyword Arguments
borderType:
int.Pixel extrapolation method. Only BORDER_REFLECT101 and BORDER_REPLICATE are supported for now.
Return
- retval:
CornernessCriteria
@sa cornerMinEigenVal
Python prototype (for reference only):
createMinEigenValCorner(srcType, blockSize, ksize[, borderType]) -> retval@spec createMorphologyFilter(integer(), integer(), Evision.Mat.maybe_mat_in()) :: Evision.CUDA.Filter.t() | {:error, String.t()}
@spec createMorphologyFilter(integer(), integer(), Evision.CUDA.GpuMat.t()) :: Evision.CUDA.Filter.t() | {:error, String.t()}
Variant 1:
Creates a 2D morphological filter.
Positional Arguments
op:
int.Type of morphological operation. The following types are possible:
- MORPH_ERODE erode
- MORPH_DILATE dilate
- MORPH_OPEN opening
- MORPH_CLOSE closing
- MORPH_GRADIENT morphological gradient
- MORPH_TOPHAT "top hat"
- MORPH_BLACKHAT "black hat"
srcType:
int.Input/output image type. Only CV_8UC1, CV_8UC4, CV_32FC1 and CV_32FC4 are supported.
kernel:
Evision.Mat.t().2D 8-bit structuring element for the morphological operation.
Keyword Arguments
anchor:
Point.Anchor position within the structuring element. Negative values mean that the anchor is at the center.
iterations:
int.Number of times erosion and dilation to be applied.
Return
- retval:
Filter
@sa morphologyEx
Python prototype (for reference only):
createMorphologyFilter(op, srcType, kernel[, anchor[, iterations]]) -> retvalVariant 2:
Creates a 2D morphological filter.
Positional Arguments
op:
int.Type of morphological operation. The following types are possible:
- MORPH_ERODE erode
- MORPH_DILATE dilate
- MORPH_OPEN opening
- MORPH_CLOSE closing
- MORPH_GRADIENT morphological gradient
- MORPH_TOPHAT "top hat"
- MORPH_BLACKHAT "black hat"
srcType:
int.Input/output image type. Only CV_8UC1, CV_8UC4, CV_32FC1 and CV_32FC4 are supported.
kernel:
Evision.CUDA.GpuMat.t().2D 8-bit structuring element for the morphological operation.
Keyword Arguments
anchor:
Point.Anchor position within the structuring element. Negative values mean that the anchor is at the center.
iterations:
int.Number of times erosion and dilation to be applied.
Return
- retval:
Filter
@sa morphologyEx
Python prototype (for reference only):
createMorphologyFilter(op, srcType, kernel[, anchor[, iterations]]) -> retval@spec createMorphologyFilter( integer(), integer(), Evision.Mat.maybe_mat_in(), [{atom(), term()}, ...] | nil ) :: Evision.CUDA.Filter.t() | {:error, String.t()}
@spec createMorphologyFilter( integer(), integer(), Evision.CUDA.GpuMat.t(), [{atom(), term()}, ...] | nil ) :: Evision.CUDA.Filter.t() | {:error, String.t()}
Variant 1:
Creates a 2D morphological filter.
Positional Arguments
op:
int.Type of morphological operation. The following types are possible:
- MORPH_ERODE erode
- MORPH_DILATE dilate
- MORPH_OPEN opening
- MORPH_CLOSE closing
- MORPH_GRADIENT morphological gradient
- MORPH_TOPHAT "top hat"
- MORPH_BLACKHAT "black hat"
srcType:
int.Input/output image type. Only CV_8UC1, CV_8UC4, CV_32FC1 and CV_32FC4 are supported.
kernel:
Evision.Mat.t().2D 8-bit structuring element for the morphological operation.
Keyword Arguments
anchor:
Point.Anchor position within the structuring element. Negative values mean that the anchor is at the center.
iterations:
int.Number of times erosion and dilation to be applied.
Return
- retval:
Filter
@sa morphologyEx
Python prototype (for reference only):
createMorphologyFilter(op, srcType, kernel[, anchor[, iterations]]) -> retvalVariant 2:
Creates a 2D morphological filter.
Positional Arguments
op:
int.Type of morphological operation. The following types are possible:
- MORPH_ERODE erode
- MORPH_DILATE dilate
- MORPH_OPEN opening
- MORPH_CLOSE closing
- MORPH_GRADIENT morphological gradient
- MORPH_TOPHAT "top hat"
- MORPH_BLACKHAT "black hat"
srcType:
int.Input/output image type. Only CV_8UC1, CV_8UC4, CV_32FC1 and CV_32FC4 are supported.
kernel:
Evision.CUDA.GpuMat.t().2D 8-bit structuring element for the morphological operation.
Keyword Arguments
anchor:
Point.Anchor position within the structuring element. Negative values mean that the anchor is at the center.
iterations:
int.Number of times erosion and dilation to be applied.
Return
- retval:
Filter
@sa morphologyEx
Python prototype (for reference only):
createMorphologyFilter(op, srcType, kernel[, anchor[, iterations]]) -> retval@spec createRowSumFilter(integer(), integer(), integer()) :: Evision.CUDA.Filter.t() | {:error, String.t()}
Creates a horizontal 1D box filter.
Positional Arguments
srcType:
int.Input image type. Only CV_8UC1 type is supported for now.
dstType:
int.Output image type. Only CV_32FC1 type is supported for now.
ksize:
int.Kernel size.
Keyword Arguments
anchor:
int.Anchor point. The default value (-1) means that the anchor is at the kernel center.
borderMode:
int.Pixel extrapolation method. For details, see borderInterpolate .
borderVal:
Scalar.Default border value.
Return
- retval:
Filter
Python prototype (for reference only):
createRowSumFilter(srcType, dstType, ksize[, anchor[, borderMode[, borderVal]]]) -> retval@spec createRowSumFilter( integer(), integer(), integer(), [{atom(), term()}, ...] | nil ) :: Evision.CUDA.Filter.t() | {:error, String.t()}
Creates a horizontal 1D box filter.
Positional Arguments
srcType:
int.Input image type. Only CV_8UC1 type is supported for now.
dstType:
int.Output image type. Only CV_32FC1 type is supported for now.
ksize:
int.Kernel size.
Keyword Arguments
anchor:
int.Anchor point. The default value (-1) means that the anchor is at the kernel center.
borderMode:
int.Pixel extrapolation method. For details, see borderInterpolate .
borderVal:
Scalar.Default border value.
Return
- retval:
Filter
Python prototype (for reference only):
createRowSumFilter(srcType, dstType, ksize[, anchor[, borderMode[, borderVal]]]) -> retval@spec createScharrFilter(integer(), integer(), integer(), integer()) :: Evision.CUDA.Filter.t() | {:error, String.t()}
Creates a vertical or horizontal Scharr operator.
Positional Arguments
srcType:
int.Source image type.
dstType:
int.Destination array type.
dx:
int.Order of the derivative x.
dy:
int.Order of the derivative y.
Keyword Arguments
scale:
double.Optional scale factor for the computed derivative values. By default, no scaling is applied. See getDerivKernels for details.
rowBorderMode:
int.Pixel extrapolation method in the vertical direction. For details, see borderInterpolate.
columnBorderMode:
int.Pixel extrapolation method in the horizontal direction.
Return
- retval:
Filter
@sa Scharr
Python prototype (for reference only):
createScharrFilter(srcType, dstType, dx, dy[, scale[, rowBorderMode[, columnBorderMode]]]) -> retval@spec createScharrFilter( integer(), integer(), integer(), integer(), [{atom(), term()}, ...] | nil ) :: Evision.CUDA.Filter.t() | {:error, String.t()}
Creates a vertical or horizontal Scharr operator.
Positional Arguments
srcType:
int.Source image type.
dstType:
int.Destination array type.
dx:
int.Order of the derivative x.
dy:
int.Order of the derivative y.
Keyword Arguments
scale:
double.Optional scale factor for the computed derivative values. By default, no scaling is applied. See getDerivKernels for details.
rowBorderMode:
int.Pixel extrapolation method in the vertical direction. For details, see borderInterpolate.
columnBorderMode:
int.Pixel extrapolation method in the horizontal direction.
Return
- retval:
Filter
@sa Scharr
Python prototype (for reference only):
createScharrFilter(srcType, dstType, dx, dy[, scale[, rowBorderMode[, columnBorderMode]]]) -> retvalcreateSeparableLinearFilter(srcType, dstType, rowKernel, columnKernel)
View Source@spec createSeparableLinearFilter( integer(), integer(), Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in() ) :: Evision.CUDA.Filter.t() | {:error, String.t()}
@spec createSeparableLinearFilter( integer(), integer(), Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t() ) :: Evision.CUDA.Filter.t() | {:error, String.t()}
Variant 1:
Creates a separable linear filter.
Positional Arguments
srcType:
int.Source array type.
dstType:
int.Destination array type.
rowKernel:
Evision.Mat.t().Horizontal filter coefficients. Support kernels with size \<= 32 .
columnKernel:
Evision.Mat.t().Vertical filter coefficients. Support kernels with size \<= 32 .
Keyword Arguments
anchor:
Point.Anchor position within the kernel. Negative values mean that anchor is positioned at the aperture center.
rowBorderMode:
int.Pixel extrapolation method in the vertical direction For details, see borderInterpolate.
columnBorderMode:
int.Pixel extrapolation method in the horizontal direction.
Return
- retval:
Filter
@sa sepFilter2D
Python prototype (for reference only):
createSeparableLinearFilter(srcType, dstType, rowKernel, columnKernel[, anchor[, rowBorderMode[, columnBorderMode]]]) -> retvalVariant 2:
Creates a separable linear filter.
Positional Arguments
srcType:
int.Source array type.
dstType:
int.Destination array type.
rowKernel:
Evision.CUDA.GpuMat.t().Horizontal filter coefficients. Support kernels with size \<= 32 .
columnKernel:
Evision.CUDA.GpuMat.t().Vertical filter coefficients. Support kernels with size \<= 32 .
Keyword Arguments
anchor:
Point.Anchor position within the kernel. Negative values mean that anchor is positioned at the aperture center.
rowBorderMode:
int.Pixel extrapolation method in the vertical direction For details, see borderInterpolate.
columnBorderMode:
int.Pixel extrapolation method in the horizontal direction.
Return
- retval:
Filter
@sa sepFilter2D
Python prototype (for reference only):
createSeparableLinearFilter(srcType, dstType, rowKernel, columnKernel[, anchor[, rowBorderMode[, columnBorderMode]]]) -> retvalcreateSeparableLinearFilter(srcType, dstType, rowKernel, columnKernel, opts)
View Source@spec createSeparableLinearFilter( integer(), integer(), Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in(), [{atom(), term()}, ...] | nil ) :: Evision.CUDA.Filter.t() | {:error, String.t()}
@spec createSeparableLinearFilter( integer(), integer(), Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t(), [{atom(), term()}, ...] | nil ) :: Evision.CUDA.Filter.t() | {:error, String.t()}
Variant 1:
Creates a separable linear filter.
Positional Arguments
srcType:
int.Source array type.
dstType:
int.Destination array type.
rowKernel:
Evision.Mat.t().Horizontal filter coefficients. Support kernels with size \<= 32 .
columnKernel:
Evision.Mat.t().Vertical filter coefficients. Support kernels with size \<= 32 .
Keyword Arguments
anchor:
Point.Anchor position within the kernel. Negative values mean that anchor is positioned at the aperture center.
rowBorderMode:
int.Pixel extrapolation method in the vertical direction For details, see borderInterpolate.
columnBorderMode:
int.Pixel extrapolation method in the horizontal direction.
Return
- retval:
Filter
@sa sepFilter2D
Python prototype (for reference only):
createSeparableLinearFilter(srcType, dstType, rowKernel, columnKernel[, anchor[, rowBorderMode[, columnBorderMode]]]) -> retvalVariant 2:
Creates a separable linear filter.
Positional Arguments
srcType:
int.Source array type.
dstType:
int.Destination array type.
rowKernel:
Evision.CUDA.GpuMat.t().Horizontal filter coefficients. Support kernels with size \<= 32 .
columnKernel:
Evision.CUDA.GpuMat.t().Vertical filter coefficients. Support kernels with size \<= 32 .
Keyword Arguments
anchor:
Point.Anchor position within the kernel. Negative values mean that anchor is positioned at the aperture center.
rowBorderMode:
int.Pixel extrapolation method in the vertical direction For details, see borderInterpolate.
columnBorderMode:
int.Pixel extrapolation method in the horizontal direction.
Return
- retval:
Filter
@sa sepFilter2D
Python prototype (for reference only):
createSeparableLinearFilter(srcType, dstType, rowKernel, columnKernel[, anchor[, rowBorderMode[, columnBorderMode]]]) -> retval@spec createSobelFilter(integer(), integer(), integer(), integer()) :: Evision.CUDA.Filter.t() | {:error, String.t()}
Creates a Sobel operator.
Positional Arguments
srcType:
int.Source image type.
dstType:
int.Destination array type.
dx:
int.Derivative order in respect of x.
dy:
int.Derivative order in respect of y.
Keyword Arguments
ksize:
int.Size of the extended Sobel kernel. Possible values are 1, 3, 5 or 7.
scale:
double.Optional scale factor for the computed derivative values. By default, no scaling is applied. For details, see getDerivKernels .
rowBorderMode:
int.Pixel extrapolation method in the vertical direction. For details, see borderInterpolate.
columnBorderMode:
int.Pixel extrapolation method in the horizontal direction.
Return
- retval:
Filter
@sa Sobel
Python prototype (for reference only):
createSobelFilter(srcType, dstType, dx, dy[, ksize[, scale[, rowBorderMode[, columnBorderMode]]]]) -> retval@spec createSobelFilter( integer(), integer(), integer(), integer(), [{atom(), term()}, ...] | nil ) :: Evision.CUDA.Filter.t() | {:error, String.t()}
Creates a Sobel operator.
Positional Arguments
srcType:
int.Source image type.
dstType:
int.Destination array type.
dx:
int.Derivative order in respect of x.
dy:
int.Derivative order in respect of y.
Keyword Arguments
ksize:
int.Size of the extended Sobel kernel. Possible values are 1, 3, 5 or 7.
scale:
double.Optional scale factor for the computed derivative values. By default, no scaling is applied. For details, see getDerivKernels .
rowBorderMode:
int.Pixel extrapolation method in the vertical direction. For details, see borderInterpolate.
columnBorderMode:
int.Pixel extrapolation method in the horizontal direction.
Return
- retval:
Filter
@sa Sobel
Python prototype (for reference only):
createSobelFilter(srcType, dstType, dx, dy[, ksize[, scale[, rowBorderMode[, columnBorderMode]]]]) -> retval@spec createStereoBeliefPropagation() :: Evision.CUDA.StereoBeliefPropagation.t() | {:error, String.t()}
Creates StereoBeliefPropagation object.
Keyword Arguments
ndisp:
int.Number of disparities.
iters:
int.Number of BP iterations on each level.
levels:
int.Number of levels.
msg_type:
int.Type for messages. CV_16SC1 and CV_32FC1 types are supported.
Return
- retval:
Evision.CUDA.StereoBeliefPropagation.t()
Python prototype (for reference only):
createStereoBeliefPropagation([, ndisp[, iters[, levels[, msg_type]]]]) -> retval@spec createStereoBeliefPropagation([{atom(), term()}, ...] | nil) :: Evision.CUDA.StereoBeliefPropagation.t() | {:error, String.t()}
Creates StereoBeliefPropagation object.
Keyword Arguments
ndisp:
int.Number of disparities.
iters:
int.Number of BP iterations on each level.
levels:
int.Number of levels.
msg_type:
int.Type for messages. CV_16SC1 and CV_32FC1 types are supported.
Return
- retval:
Evision.CUDA.StereoBeliefPropagation.t()
Python prototype (for reference only):
createStereoBeliefPropagation([, ndisp[, iters[, levels[, msg_type]]]]) -> retval@spec createStereoBM() :: Evision.CUDA.StereoBM.t() | {:error, String.t()}
Creates StereoBM object.
Keyword Arguments
numDisparities:
int.the disparity search range. For each pixel algorithm will find the best disparity from 0 (default minimum disparity) to numDisparities. The search range can then be shifted by changing the minimum disparity.
blockSize:
int.the linear size of the blocks compared by the algorithm. The size should be odd (as the block is centered at the current pixel). Larger block size implies smoother, though less accurate disparity map. Smaller block size gives more detailed disparity map, but there is higher chance for algorithm to find a wrong correspondence.
Return
- retval:
Evision.CUDA.StereoBM.t()
Python prototype (for reference only):
createStereoBM([, numDisparities[, blockSize]]) -> retval@spec createStereoBM([{atom(), term()}, ...] | nil) :: Evision.CUDA.StereoBM.t() | {:error, String.t()}
Creates StereoBM object.
Keyword Arguments
numDisparities:
int.the disparity search range. For each pixel algorithm will find the best disparity from 0 (default minimum disparity) to numDisparities. The search range can then be shifted by changing the minimum disparity.
blockSize:
int.the linear size of the blocks compared by the algorithm. The size should be odd (as the block is centered at the current pixel). Larger block size implies smoother, though less accurate disparity map. Smaller block size gives more detailed disparity map, but there is higher chance for algorithm to find a wrong correspondence.
Return
- retval:
Evision.CUDA.StereoBM.t()
Python prototype (for reference only):
createStereoBM([, numDisparities[, blockSize]]) -> retval@spec createStereoConstantSpaceBP() :: Evision.CUDA.StereoConstantSpaceBP.t() | {:error, String.t()}
Creates StereoConstantSpaceBP object.
Keyword Arguments
ndisp:
int.Number of disparities.
iters:
int.Number of BP iterations on each level.
levels:
int.Number of levels.
nr_plane:
int.Number of disparity levels on the first level.
msg_type:
int.Type for messages. CV_16SC1 and CV_32FC1 types are supported.
Return
- retval:
Evision.CUDA.StereoConstantSpaceBP.t()
Python prototype (for reference only):
createStereoConstantSpaceBP([, ndisp[, iters[, levels[, nr_plane[, msg_type]]]]]) -> retval@spec createStereoConstantSpaceBP([{atom(), term()}, ...] | nil) :: Evision.CUDA.StereoConstantSpaceBP.t() | {:error, String.t()}
Creates StereoConstantSpaceBP object.
Keyword Arguments
ndisp:
int.Number of disparities.
iters:
int.Number of BP iterations on each level.
levels:
int.Number of levels.
nr_plane:
int.Number of disparity levels on the first level.
msg_type:
int.Type for messages. CV_16SC1 and CV_32FC1 types are supported.
Return
- retval:
Evision.CUDA.StereoConstantSpaceBP.t()
Python prototype (for reference only):
createStereoConstantSpaceBP([, ndisp[, iters[, levels[, nr_plane[, msg_type]]]]]) -> retval@spec createStereoSGM() :: Evision.CUDA.StereoSGM.t() | {:error, String.t()}
Creates StereoSGM object.
Keyword Arguments
minDisparity:
int.Minimum possible disparity value. Normally, it is zero but sometimes rectification algorithms can shift images, so this parameter needs to be adjusted accordingly.
numDisparities:
int.Maximum disparity minus minimum disparity. The value must be 64, 128 or 256.
p1:
int.The first parameter controlling the disparity smoothness.This parameter is used for the case of slanted surfaces (not fronto parallel).
p2:
int.The second parameter controlling the disparity smoothness.This parameter is used for "solving" the depth discontinuities problem.
uniquenessRatio:
int.Margin in percentage by which the best (minimum) computed cost function value should "win" the second best value to consider the found match correct. Normally, a value within the 5-15 range is good enough.
mode:
int.Set it to StereoSGM::MODE_HH to run the full-scale two-pass dynamic programming algorithm. It will consume O(W*H*numDisparities) bytes. By default, it is set to StereoSGM::MODE_HH4.
Return
- retval:
Evision.CUDA.StereoSGM.t()
Python prototype (for reference only):
createStereoSGM([, minDisparity[, numDisparities[, P1[, P2[, uniquenessRatio[, mode]]]]]]) -> retval@spec createStereoSGM([{atom(), term()}, ...] | nil) :: Evision.CUDA.StereoSGM.t() | {:error, String.t()}
Creates StereoSGM object.
Keyword Arguments
minDisparity:
int.Minimum possible disparity value. Normally, it is zero but sometimes rectification algorithms can shift images, so this parameter needs to be adjusted accordingly.
numDisparities:
int.Maximum disparity minus minimum disparity. The value must be 64, 128 or 256.
p1:
int.The first parameter controlling the disparity smoothness.This parameter is used for the case of slanted surfaces (not fronto parallel).
p2:
int.The second parameter controlling the disparity smoothness.This parameter is used for "solving" the depth discontinuities problem.
uniquenessRatio:
int.Margin in percentage by which the best (minimum) computed cost function value should "win" the second best value to consider the found match correct. Normally, a value within the 5-15 range is good enough.
mode:
int.Set it to StereoSGM::MODE_HH to run the full-scale two-pass dynamic programming algorithm. It will consume O(W*H*numDisparities) bytes. By default, it is set to StereoSGM::MODE_HH4.
Return
- retval:
Evision.CUDA.StereoSGM.t()
Python prototype (for reference only):
createStereoSGM([, minDisparity[, numDisparities[, P1[, P2[, uniquenessRatio[, mode]]]]]]) -> retval@spec createTemplateMatching(integer(), integer()) :: Evision.CUDA.TemplateMatching.t() | {:error, String.t()}
Creates implementation for cuda::TemplateMatching .
Positional Arguments
srcType:
int.Input source type. CV_32F and CV_8U depth images (1..4 channels) are supported for now.
method:
int.Specifies the way to compare the template with the image.
Keyword Arguments
user_block_size:
Size.You can use field user_block_size to set specific block size. 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.
Return
- retval:
TemplateMatching
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
@sa matchTemplate
Python prototype (for reference only):
createTemplateMatching(srcType, method[, user_block_size]) -> retval@spec createTemplateMatching(integer(), integer(), [{atom(), term()}, ...] | nil) :: Evision.CUDA.TemplateMatching.t() | {:error, String.t()}
Creates implementation for cuda::TemplateMatching .
Positional Arguments
srcType:
int.Input source type. CV_32F and CV_8U depth images (1..4 channels) are supported for now.
method:
int.Specifies the way to compare the template with the image.
Keyword Arguments
user_block_size:
Size.You can use field user_block_size to set specific block size. 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.
Return
- retval:
TemplateMatching
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
@sa matchTemplate
Python prototype (for reference only):
createTemplateMatching(srcType, method[, user_block_size]) -> retval@spec cvtColor(Evision.Mat.maybe_mat_in(), integer()) :: Evision.Mat.t() | {:error, String.t()}
@spec cvtColor(Evision.CUDA.GpuMat.t(), integer()) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Converts an image from one color space to another.
Positional Arguments
src:
Evision.Mat.t().Source image with CV_8U , CV_16U , or CV_32F depth and 1, 3, or 4 channels.
code:
int.Color space conversion code. For details, see cvtColor .
Keyword Arguments
dcn:
int.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:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Destination image.
3-channel color spaces (like HSV, XYZ, and so on) can be stored in a 4-channel image for better performance. @sa cvtColor
Python prototype (for reference only):
cvtColor(src, code[, dst[, dcn[, stream]]]) -> dstVariant 2:
Converts an image from one color space to another.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Source image with CV_8U , CV_16U , or CV_32F depth and 1, 3, or 4 channels.
code:
int.Color space conversion code. For details, see cvtColor .
Keyword Arguments
dcn:
int.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:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Destination image.
3-channel color spaces (like HSV, XYZ, and so on) can be stored in a 4-channel image for better performance. @sa cvtColor
Python prototype (for reference only):
cvtColor(src, code[, dst[, dcn[, stream]]]) -> dst@spec cvtColor(Evision.Mat.maybe_mat_in(), integer(), [{atom(), term()}, ...] | nil) :: Evision.Mat.t() | {:error, String.t()}
@spec cvtColor(Evision.CUDA.GpuMat.t(), integer(), [{atom(), term()}, ...] | nil) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Converts an image from one color space to another.
Positional Arguments
src:
Evision.Mat.t().Source image with CV_8U , CV_16U , or CV_32F depth and 1, 3, or 4 channels.
code:
int.Color space conversion code. For details, see cvtColor .
Keyword Arguments
dcn:
int.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:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Destination image.
3-channel color spaces (like HSV, XYZ, and so on) can be stored in a 4-channel image for better performance. @sa cvtColor
Python prototype (for reference only):
cvtColor(src, code[, dst[, dcn[, stream]]]) -> dstVariant 2:
Converts an image from one color space to another.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Source image with CV_8U , CV_16U , or CV_32F depth and 1, 3, or 4 channels.
code:
int.Color space conversion code. For details, see cvtColor .
Keyword Arguments
dcn:
int.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:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Destination image.
3-channel color spaces (like HSV, XYZ, and so on) can be stored in a 4-channel image for better performance. @sa cvtColor
Python prototype (for reference only):
cvtColor(src, code[, dst[, dcn[, stream]]]) -> dst@spec demosaicing(Evision.Mat.maybe_mat_in(), integer()) :: Evision.Mat.t() | {:error, String.t()}
@spec demosaicing(Evision.CUDA.GpuMat.t(), integer()) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Converts an image from Bayer pattern to RGB or grayscale.
Positional Arguments
src:
Evision.Mat.t().Source image (8-bit or 16-bit single channel).
code:
int.Color space conversion code (see the description below).
Keyword Arguments
dcn:
int.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:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Destination image.
The function can do the following transformations:
- Demosaicing using bilinear interpolation
- COLOR_BayerBG2GRAY , COLOR_BayerGB2GRAY , COLOR_BayerRG2GRAY , COLOR_BayerGR2GRAY
- COLOR_BayerBG2BGR , COLOR_BayerGB2BGR , COLOR_BayerRG2BGR , COLOR_BayerGR2BGR
- Demosaicing using Malvar-He-Cutler algorithm (@cite MHT2011)
- COLOR_BayerBG2GRAY_MHT , COLOR_BayerGB2GRAY_MHT , COLOR_BayerRG2GRAY_MHT , COLOR_BayerGR2GRAY_MHT
- COLOR_BayerBG2BGR_MHT , COLOR_BayerGB2BGR_MHT , COLOR_BayerRG2BGR_MHT , COLOR_BayerGR2BGR_MHT @sa cvtColor
Python prototype (for reference only):
demosaicing(src, code[, dst[, dcn[, stream]]]) -> dstVariant 2:
Converts an image from Bayer pattern to RGB or grayscale.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Source image (8-bit or 16-bit single channel).
code:
int.Color space conversion code (see the description below).
Keyword Arguments
dcn:
int.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:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Destination image.
The function can do the following transformations:
- Demosaicing using bilinear interpolation
- COLOR_BayerBG2GRAY , COLOR_BayerGB2GRAY , COLOR_BayerRG2GRAY , COLOR_BayerGR2GRAY
- COLOR_BayerBG2BGR , COLOR_BayerGB2BGR , COLOR_BayerRG2BGR , COLOR_BayerGR2BGR
- Demosaicing using Malvar-He-Cutler algorithm (@cite MHT2011)
- COLOR_BayerBG2GRAY_MHT , COLOR_BayerGB2GRAY_MHT , COLOR_BayerRG2GRAY_MHT , COLOR_BayerGR2GRAY_MHT
- COLOR_BayerBG2BGR_MHT , COLOR_BayerGB2BGR_MHT , COLOR_BayerRG2BGR_MHT , COLOR_BayerGR2BGR_MHT @sa cvtColor
Python prototype (for reference only):
demosaicing(src, code[, dst[, dcn[, stream]]]) -> dst@spec demosaicing( Evision.Mat.maybe_mat_in(), integer(), [{atom(), term()}, ...] | nil ) :: Evision.Mat.t() | {:error, String.t()}
@spec demosaicing(Evision.CUDA.GpuMat.t(), integer(), [{atom(), term()}, ...] | nil) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Converts an image from Bayer pattern to RGB or grayscale.
Positional Arguments
src:
Evision.Mat.t().Source image (8-bit or 16-bit single channel).
code:
int.Color space conversion code (see the description below).
Keyword Arguments
dcn:
int.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:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Destination image.
The function can do the following transformations:
- Demosaicing using bilinear interpolation
- COLOR_BayerBG2GRAY , COLOR_BayerGB2GRAY , COLOR_BayerRG2GRAY , COLOR_BayerGR2GRAY
- COLOR_BayerBG2BGR , COLOR_BayerGB2BGR , COLOR_BayerRG2BGR , COLOR_BayerGR2BGR
- Demosaicing using Malvar-He-Cutler algorithm (@cite MHT2011)
- COLOR_BayerBG2GRAY_MHT , COLOR_BayerGB2GRAY_MHT , COLOR_BayerRG2GRAY_MHT , COLOR_BayerGR2GRAY_MHT
- COLOR_BayerBG2BGR_MHT , COLOR_BayerGB2BGR_MHT , COLOR_BayerRG2BGR_MHT , COLOR_BayerGR2BGR_MHT @sa cvtColor
Python prototype (for reference only):
demosaicing(src, code[, dst[, dcn[, stream]]]) -> dstVariant 2:
Converts an image from Bayer pattern to RGB or grayscale.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Source image (8-bit or 16-bit single channel).
code:
int.Color space conversion code (see the description below).
Keyword Arguments
dcn:
int.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:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Destination image.
The function can do the following transformations:
- Demosaicing using bilinear interpolation
- COLOR_BayerBG2GRAY , COLOR_BayerGB2GRAY , COLOR_BayerRG2GRAY , COLOR_BayerGR2GRAY
- COLOR_BayerBG2BGR , COLOR_BayerGB2BGR , COLOR_BayerRG2BGR , COLOR_BayerGR2BGR
- Demosaicing using Malvar-He-Cutler algorithm (@cite MHT2011)
- COLOR_BayerBG2GRAY_MHT , COLOR_BayerGB2GRAY_MHT , COLOR_BayerRG2GRAY_MHT , COLOR_BayerGR2GRAY_MHT
- COLOR_BayerBG2BGR_MHT , COLOR_BayerGB2BGR_MHT , COLOR_BayerRG2BGR_MHT , COLOR_BayerGR2BGR_MHT @sa cvtColor
Python prototype (for reference only):
demosaicing(src, code[, dst[, dcn[, stream]]]) -> dst@spec dft( Evision.Mat.maybe_mat_in(), {number(), number()} ) :: Evision.Mat.t() | {:error, String.t()}
@spec dft( Evision.CUDA.GpuMat.t(), {number(), number()} ) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Performs a forward or inverse discrete Fourier transform (1D or 2D) of the floating point matrix.
Positional Arguments
src:
Evision.Mat.t().Source matrix (real or complex).
dft_size:
Size.Size of a discrete Fourier transform.
Keyword Arguments
flags:
int.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_COMPLEX_INPUT Specifies that input is complex input with 2 channels.
- 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.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Destination matrix (real or complex).
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.
@sa dft
Python prototype (for reference only):
dft(src, dft_size[, dst[, flags[, stream]]]) -> dstVariant 2:
Performs a forward or inverse discrete Fourier transform (1D or 2D) of the floating point matrix.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Source matrix (real or complex).
dft_size:
Size.Size of a discrete Fourier transform.
Keyword Arguments
flags:
int.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_COMPLEX_INPUT Specifies that input is complex input with 2 channels.
- 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.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Destination matrix (real or complex).
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.
@sa dft
Python prototype (for reference only):
dft(src, dft_size[, dst[, flags[, stream]]]) -> dst@spec dft( Evision.Mat.maybe_mat_in(), {number(), number()}, [{atom(), term()}, ...] | nil ) :: Evision.Mat.t() | {:error, String.t()}
@spec dft( Evision.CUDA.GpuMat.t(), {number(), number()}, [{atom(), term()}, ...] | nil ) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Performs a forward or inverse discrete Fourier transform (1D or 2D) of the floating point matrix.
Positional Arguments
src:
Evision.Mat.t().Source matrix (real or complex).
dft_size:
Size.Size of a discrete Fourier transform.
Keyword Arguments
flags:
int.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_COMPLEX_INPUT Specifies that input is complex input with 2 channels.
- 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.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Destination matrix (real or complex).
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.
@sa dft
Python prototype (for reference only):
dft(src, dft_size[, dst[, flags[, stream]]]) -> dstVariant 2:
Performs a forward or inverse discrete Fourier transform (1D or 2D) of the floating point matrix.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Source matrix (real or complex).
dft_size:
Size.Size of a discrete Fourier transform.
Keyword Arguments
flags:
int.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_COMPLEX_INPUT Specifies that input is complex input with 2 channels.
- 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.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Destination matrix (real or complex).
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.
@sa dft
Python prototype (for reference only):
dft(src, dft_size[, dst[, flags[, stream]]]) -> dst@spec divide(Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in()) :: Evision.Mat.t() | {:error, String.t()}
@spec divide(Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t()) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Computes a matrix-matrix or matrix-scalar division.
Positional Arguments
src1:
Evision.Mat.t().First source matrix or a scalar.
src2:
Evision.Mat.t().Second source matrix or scalar.
Keyword Arguments
scale:
double.Optional scale factor.
dtype:
int.Optional depth of the output array.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Destination matrix that has the same size and number of channels as the input array(s). The depth is defined by dtype or src1 depth.
This function, in contrast to divide, uses a round-down rounding mode. @sa divide
Python prototype (for reference only):
divide(src1, src2[, dst[, scale[, dtype[, stream]]]]) -> dstVariant 2:
Computes a matrix-matrix or matrix-scalar division.
Positional Arguments
src1:
Evision.CUDA.GpuMat.t().First source matrix or a scalar.
src2:
Evision.CUDA.GpuMat.t().Second source matrix or scalar.
Keyword Arguments
scale:
double.Optional scale factor.
dtype:
int.Optional depth of the output array.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Destination matrix that has the same size and number of channels as the input array(s). The depth is defined by dtype or src1 depth.
This function, in contrast to divide, uses a round-down rounding mode. @sa divide
Python prototype (for reference only):
divide(src1, src2[, dst[, scale[, dtype[, stream]]]]) -> dst@spec divide( Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in(), [{atom(), term()}, ...] | nil ) :: Evision.Mat.t() | {:error, String.t()}
@spec divide( Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t(), [{atom(), term()}, ...] | nil ) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Computes a matrix-matrix or matrix-scalar division.
Positional Arguments
src1:
Evision.Mat.t().First source matrix or a scalar.
src2:
Evision.Mat.t().Second source matrix or scalar.
Keyword Arguments
scale:
double.Optional scale factor.
dtype:
int.Optional depth of the output array.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Destination matrix that has the same size and number of channels as the input array(s). The depth is defined by dtype or src1 depth.
This function, in contrast to divide, uses a round-down rounding mode. @sa divide
Python prototype (for reference only):
divide(src1, src2[, dst[, scale[, dtype[, stream]]]]) -> dstVariant 2:
Computes a matrix-matrix or matrix-scalar division.
Positional Arguments
src1:
Evision.CUDA.GpuMat.t().First source matrix or a scalar.
src2:
Evision.CUDA.GpuMat.t().Second source matrix or scalar.
Keyword Arguments
scale:
double.Optional scale factor.
dtype:
int.Optional depth of the output array.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Destination matrix that has the same size and number of channels as the input array(s). The depth is defined by dtype or src1 depth.
This function, in contrast to divide, uses a round-down rounding mode. @sa divide
Python prototype (for reference only):
divide(src1, src2[, dst[, scale[, dtype[, stream]]]]) -> dst@spec drawColorDisp(Evision.Mat.maybe_mat_in(), integer()) :: Evision.Mat.t() | {:error, String.t()}
@spec drawColorDisp(Evision.CUDA.GpuMat.t(), integer()) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Colors a disparity image.
Positional Arguments
src_disp:
Evision.Mat.t().Input single-channel 8-bit unsigned, 16-bit signed, 32-bit signed or 32-bit floating-point disparity image. If 16-bit signed format is used, the values are assumed to have no fractional bits.
ndisp:
int.Number of disparities.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst_disp:
Evision.Mat.t().Output disparity image. It has the same size as src_disp. The type is CV_8UC4 in BGRA format (alpha = 255).
This function draws a colored disparity map by converting disparity values from [0..ndisp) interval first to HSV color space (where different disparity values correspond to different hues) and then converting the pixels to RGB for visualization.
Python prototype (for reference only):
drawColorDisp(src_disp, ndisp[, dst_disp[, stream]]) -> dst_dispVariant 2:
Colors a disparity image.
Positional Arguments
src_disp:
Evision.CUDA.GpuMat.t().Input single-channel 8-bit unsigned, 16-bit signed, 32-bit signed or 32-bit floating-point disparity image. If 16-bit signed format is used, the values are assumed to have no fractional bits.
ndisp:
int.Number of disparities.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst_disp:
Evision.CUDA.GpuMat.t().Output disparity image. It has the same size as src_disp. The type is CV_8UC4 in BGRA format (alpha = 255).
This function draws a colored disparity map by converting disparity values from [0..ndisp) interval first to HSV color space (where different disparity values correspond to different hues) and then converting the pixels to RGB for visualization.
Python prototype (for reference only):
drawColorDisp(src_disp, ndisp[, dst_disp[, stream]]) -> dst_disp@spec drawColorDisp( Evision.Mat.maybe_mat_in(), integer(), [{atom(), term()}, ...] | nil ) :: Evision.Mat.t() | {:error, String.t()}
@spec drawColorDisp(Evision.CUDA.GpuMat.t(), integer(), [{atom(), term()}, ...] | nil) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Colors a disparity image.
Positional Arguments
src_disp:
Evision.Mat.t().Input single-channel 8-bit unsigned, 16-bit signed, 32-bit signed or 32-bit floating-point disparity image. If 16-bit signed format is used, the values are assumed to have no fractional bits.
ndisp:
int.Number of disparities.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst_disp:
Evision.Mat.t().Output disparity image. It has the same size as src_disp. The type is CV_8UC4 in BGRA format (alpha = 255).
This function draws a colored disparity map by converting disparity values from [0..ndisp) interval first to HSV color space (where different disparity values correspond to different hues) and then converting the pixels to RGB for visualization.
Python prototype (for reference only):
drawColorDisp(src_disp, ndisp[, dst_disp[, stream]]) -> dst_dispVariant 2:
Colors a disparity image.
Positional Arguments
src_disp:
Evision.CUDA.GpuMat.t().Input single-channel 8-bit unsigned, 16-bit signed, 32-bit signed or 32-bit floating-point disparity image. If 16-bit signed format is used, the values are assumed to have no fractional bits.
ndisp:
int.Number of disparities.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst_disp:
Evision.CUDA.GpuMat.t().Output disparity image. It has the same size as src_disp. The type is CV_8UC4 in BGRA format (alpha = 255).
This function draws a colored disparity map by converting disparity values from [0..ndisp) interval first to HSV color space (where different disparity values correspond to different hues) and then converting the pixels to RGB for visualization.
Python prototype (for reference only):
drawColorDisp(src_disp, ndisp[, dst_disp[, stream]]) -> dst_disp@spec ensureSizeIsEnough(integer(), integer(), integer()) :: Evision.Mat.t() | {:error, String.t()}
Ensures that the size of a matrix is big enough and the matrix has a proper type.
Positional Arguments
rows:
int.Minimum desired number of rows.
cols:
int.Minimum desired number of columns.
type:
int.Desired matrix type.
Return
arr:
Evision.Mat.t().Destination matrix.
The function does not reallocate memory if the matrix has proper attributes already.
Python prototype (for reference only):
ensureSizeIsEnough(rows, cols, type[, arr]) -> arr@spec ensureSizeIsEnough( integer(), integer(), integer(), [{atom(), term()}, ...] | nil ) :: Evision.Mat.t() | {:error, String.t()}
Ensures that the size of a matrix is big enough and the matrix has a proper type.
Positional Arguments
rows:
int.Minimum desired number of rows.
cols:
int.Minimum desired number of columns.
type:
int.Desired matrix type.
Return
arr:
Evision.Mat.t().Destination matrix.
The function does not reallocate memory if the matrix has proper attributes already.
Python prototype (for reference only):
ensureSizeIsEnough(rows, cols, type[, arr]) -> arr@spec equalizeHist(Evision.Mat.maybe_mat_in()) :: Evision.Mat.t() | {:error, String.t()}
@spec equalizeHist(Evision.CUDA.GpuMat.t()) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Equalizes the histogram of a grayscale image.
Positional Arguments
src:
Evision.Mat.t().Source image with CV_8UC1 type.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Destination image.
@sa equalizeHist
Python prototype (for reference only):
equalizeHist(src[, dst[, stream]]) -> dstVariant 2:
Equalizes the histogram of a grayscale image.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Source image with CV_8UC1 type.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Destination image.
@sa equalizeHist
Python prototype (for reference only):
equalizeHist(src[, dst[, stream]]) -> dst@spec equalizeHist(Evision.Mat.maybe_mat_in(), [{atom(), term()}, ...] | nil) :: Evision.Mat.t() | {:error, String.t()}
@spec equalizeHist(Evision.CUDA.GpuMat.t(), [{atom(), term()}, ...] | nil) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Equalizes the histogram of a grayscale image.
Positional Arguments
src:
Evision.Mat.t().Source image with CV_8UC1 type.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Destination image.
@sa equalizeHist
Python prototype (for reference only):
equalizeHist(src[, dst[, stream]]) -> dstVariant 2:
Equalizes the histogram of a grayscale image.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Source image with CV_8UC1 type.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Destination image.
@sa equalizeHist
Python prototype (for reference only):
equalizeHist(src[, dst[, stream]]) -> dst@spec evenLevels(integer(), integer(), integer()) :: Evision.Mat.t() | {:error, String.t()}
Computes levels with even distribution.
Positional Arguments
nLevels:
int.Number of computed levels. nLevels must be at least 2.
lowerLevel:
int.Lower boundary value of the lowest level.
upperLevel:
int.Upper boundary value of the greatest level.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
levels:
Evision.Mat.t().Destination array. levels has 1 row, nLevels columns, and the CV_32SC1 type.
Python prototype (for reference only):
evenLevels(nLevels, lowerLevel, upperLevel[, levels[, stream]]) -> levels@spec evenLevels(integer(), integer(), integer(), [{atom(), term()}, ...] | nil) :: Evision.Mat.t() | {:error, String.t()}
Computes levels with even distribution.
Positional Arguments
nLevels:
int.Number of computed levels. nLevels must be at least 2.
lowerLevel:
int.Lower boundary value of the lowest level.
upperLevel:
int.Upper boundary value of the greatest level.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
levels:
Evision.Mat.t().Destination array. levels has 1 row, nLevels columns, and the CV_32SC1 type.
Python prototype (for reference only):
evenLevels(nLevels, lowerLevel, upperLevel[, levels[, stream]]) -> levels@spec exp(Evision.Mat.maybe_mat_in()) :: Evision.Mat.t() | {:error, String.t()}
@spec exp(Evision.CUDA.GpuMat.t()) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Computes an exponent of each matrix element.
Positional Arguments
src:
Evision.Mat.t().Source matrix.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Destination matrix with the same size and type as src .
@sa exp
Python prototype (for reference only):
exp(src[, dst[, stream]]) -> dstVariant 2:
Computes an exponent of each matrix element.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Source matrix.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Destination matrix with the same size and type as src .
@sa exp
Python prototype (for reference only):
exp(src[, dst[, stream]]) -> dst@spec exp(Evision.Mat.maybe_mat_in(), [{atom(), term()}, ...] | nil) :: Evision.Mat.t() | {:error, String.t()}
@spec exp(Evision.CUDA.GpuMat.t(), [{atom(), term()}, ...] | nil) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Computes an exponent of each matrix element.
Positional Arguments
src:
Evision.Mat.t().Source matrix.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Destination matrix with the same size and type as src .
@sa exp
Python prototype (for reference only):
exp(src[, dst[, stream]]) -> dstVariant 2:
Computes an exponent of each matrix element.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Source matrix.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Destination matrix with the same size and type as src .
@sa exp
Python prototype (for reference only):
exp(src[, dst[, stream]]) -> dst@spec fastNlMeansDenoising(Evision.CUDA.GpuMat.t(), number()) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
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
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Input 8-bit 1-channel, 2-channel or 3-channel image.
h:
float.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
Keyword Arguments
search_window:
int.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:
int.Size in pixels of the template patch that is used to compute weights. Should be odd. Recommended value 7 pixels
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous invocations.
Return
dst:
Evision.CUDA.GpuMat.t().Output image with the same size and type as src .
This function expected to be applied to grayscale images. For colored images look at FastNonLocalMeansDenoising::labMethod. @sa fastNlMeansDenoising
Python prototype (for reference only):
fastNlMeansDenoising(src, h[, dst[, search_window[, block_size[, stream]]]]) -> dst@spec fastNlMeansDenoising( Evision.CUDA.GpuMat.t(), number(), [{atom(), term()}, ...] | nil ) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
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
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Input 8-bit 1-channel, 2-channel or 3-channel image.
h:
float.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
Keyword Arguments
search_window:
int.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:
int.Size in pixels of the template patch that is used to compute weights. Should be odd. Recommended value 7 pixels
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous invocations.
Return
dst:
Evision.CUDA.GpuMat.t().Output image with the same size and type as src .
This function expected to be applied to grayscale images. For colored images look at FastNonLocalMeansDenoising::labMethod. @sa fastNlMeansDenoising
Python prototype (for reference only):
fastNlMeansDenoising(src, h[, dst[, search_window[, block_size[, stream]]]]) -> dst@spec fastNlMeansDenoisingColored(Evision.CUDA.GpuMat.t(), number(), number()) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Modification of fastNlMeansDenoising function for colored images
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Input 8-bit 3-channel image.
h_luminance:
float.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
photo_render:
float.float The same as h but for color components. For most images value equals 10 will be enough to remove colored noise and do not distort colors
Keyword Arguments
search_window:
int.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:
int.Size in pixels of the template patch that is used to compute weights. Should be odd. Recommended value 7 pixels
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous invocations.
Return
dst:
Evision.CUDA.GpuMat.t().Output image with the same size and type as src .
The function converts image to CIELAB colorspace and then separately denoise L and AB components with given h parameters using FastNonLocalMeansDenoising::simpleMethod function. @sa fastNlMeansDenoisingColored
Python prototype (for reference only):
fastNlMeansDenoisingColored(src, h_luminance, photo_render[, dst[, search_window[, block_size[, stream]]]]) -> dst@spec fastNlMeansDenoisingColored( Evision.CUDA.GpuMat.t(), number(), number(), [{atom(), term()}, ...] | nil ) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Modification of fastNlMeansDenoising function for colored images
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Input 8-bit 3-channel image.
h_luminance:
float.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
photo_render:
float.float The same as h but for color components. For most images value equals 10 will be enough to remove colored noise and do not distort colors
Keyword Arguments
search_window:
int.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:
int.Size in pixels of the template patch that is used to compute weights. Should be odd. Recommended value 7 pixels
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous invocations.
Return
dst:
Evision.CUDA.GpuMat.t().Output image with the same size and type as src .
The function converts image to CIELAB colorspace and then separately denoise L and AB components with given h parameters using FastNonLocalMeansDenoising::simpleMethod function. @sa fastNlMeansDenoisingColored
Python prototype (for reference only):
fastNlMeansDenoisingColored(src, h_luminance, photo_render[, dst[, search_window[, block_size[, stream]]]]) -> dst@spec findMinMax(Evision.Mat.maybe_mat_in()) :: Evision.Mat.t() | {:error, String.t()}
@spec findMinMax(Evision.CUDA.GpuMat.t()) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
findMinMax
Positional Arguments
- src:
Evision.Mat.t()
Keyword Arguments
- mask:
Evision.Mat.t(). - stream:
Evision.CUDA.Stream.t().
Return
- dst:
Evision.Mat.t().
Has overloading in C++
Python prototype (for reference only):
findMinMax(src[, dst[, mask[, stream]]]) -> dstVariant 2:
findMinMax
Positional Arguments
- src:
Evision.CUDA.GpuMat.t()
Keyword Arguments
- mask:
Evision.CUDA.GpuMat.t(). - stream:
Evision.CUDA.Stream.t().
Return
- dst:
Evision.CUDA.GpuMat.t().
Has overloading in C++
Python prototype (for reference only):
findMinMax(src[, dst[, mask[, stream]]]) -> dst@spec findMinMax(Evision.Mat.maybe_mat_in(), [{atom(), term()}, ...] | nil) :: Evision.Mat.t() | {:error, String.t()}
@spec findMinMax(Evision.CUDA.GpuMat.t(), [{atom(), term()}, ...] | nil) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
findMinMax
Positional Arguments
- src:
Evision.Mat.t()
Keyword Arguments
- mask:
Evision.Mat.t(). - stream:
Evision.CUDA.Stream.t().
Return
- dst:
Evision.Mat.t().
Has overloading in C++
Python prototype (for reference only):
findMinMax(src[, dst[, mask[, stream]]]) -> dstVariant 2:
findMinMax
Positional Arguments
- src:
Evision.CUDA.GpuMat.t()
Keyword Arguments
- mask:
Evision.CUDA.GpuMat.t(). - stream:
Evision.CUDA.Stream.t().
Return
- dst:
Evision.CUDA.GpuMat.t().
Has overloading in C++
Python prototype (for reference only):
findMinMax(src[, dst[, mask[, stream]]]) -> dst@spec findMinMaxLoc(Evision.Mat.maybe_mat_in()) :: {Evision.Mat.t(), Evision.Mat.t()} | {:error, String.t()}
@spec findMinMaxLoc(Evision.CUDA.GpuMat.t()) :: {Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t()} | {:error, String.t()}
Variant 1:
findMinMaxLoc
Positional Arguments
- src:
Evision.Mat.t()
Keyword Arguments
- mask:
Evision.Mat.t(). - stream:
Evision.CUDA.Stream.t().
Return
- minMaxVals:
Evision.Mat.t(). - loc:
Evision.Mat.t().
Has overloading in C++
Python prototype (for reference only):
findMinMaxLoc(src[, minMaxVals[, loc[, mask[, stream]]]]) -> minMaxVals, locVariant 2:
findMinMaxLoc
Positional Arguments
- src:
Evision.CUDA.GpuMat.t()
Keyword Arguments
- mask:
Evision.CUDA.GpuMat.t(). - stream:
Evision.CUDA.Stream.t().
Return
- minMaxVals:
Evision.CUDA.GpuMat.t(). - loc:
Evision.CUDA.GpuMat.t().
Has overloading in C++
Python prototype (for reference only):
findMinMaxLoc(src[, minMaxVals[, loc[, mask[, stream]]]]) -> minMaxVals, loc@spec findMinMaxLoc(Evision.Mat.maybe_mat_in(), [{atom(), term()}, ...] | nil) :: {Evision.Mat.t(), Evision.Mat.t()} | {:error, String.t()}
@spec findMinMaxLoc(Evision.CUDA.GpuMat.t(), [{atom(), term()}, ...] | nil) :: {Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t()} | {:error, String.t()}
Variant 1:
findMinMaxLoc
Positional Arguments
- src:
Evision.Mat.t()
Keyword Arguments
- mask:
Evision.Mat.t(). - stream:
Evision.CUDA.Stream.t().
Return
- minMaxVals:
Evision.Mat.t(). - loc:
Evision.Mat.t().
Has overloading in C++
Python prototype (for reference only):
findMinMaxLoc(src[, minMaxVals[, loc[, mask[, stream]]]]) -> minMaxVals, locVariant 2:
findMinMaxLoc
Positional Arguments
- src:
Evision.CUDA.GpuMat.t()
Keyword Arguments
- mask:
Evision.CUDA.GpuMat.t(). - stream:
Evision.CUDA.Stream.t().
Return
- minMaxVals:
Evision.CUDA.GpuMat.t(). - loc:
Evision.CUDA.GpuMat.t().
Has overloading in C++
Python prototype (for reference only):
findMinMaxLoc(src[, minMaxVals[, loc[, mask[, stream]]]]) -> minMaxVals, loc@spec flip(Evision.Mat.maybe_mat_in(), integer()) :: Evision.Mat.t() | {:error, String.t()}
@spec flip(Evision.CUDA.GpuMat.t(), integer()) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Flips a 2D matrix around vertical, horizontal, or both axes.
Positional Arguments
src:
Evision.Mat.t().Source matrix. Supports 1, 3 and 4 channels images with CV_8U, CV_16U, CV_32S or CV_32F depth.
flipCode:
int.Flip mode for the source:
- 0 Flips around x-axis.
- > 0 Flips around y-axis.
- \< 0 Flips around both axes.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Destination matrix.
@sa flip
Python prototype (for reference only):
flip(src, flipCode[, dst[, stream]]) -> dstVariant 2:
Flips a 2D matrix around vertical, horizontal, or both axes.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Source matrix. Supports 1, 3 and 4 channels images with CV_8U, CV_16U, CV_32S or CV_32F depth.
flipCode:
int.Flip mode for the source:
- 0 Flips around x-axis.
- > 0 Flips around y-axis.
- \< 0 Flips around both axes.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Destination matrix.
@sa flip
Python prototype (for reference only):
flip(src, flipCode[, dst[, stream]]) -> dst@spec flip(Evision.Mat.maybe_mat_in(), integer(), [{atom(), term()}, ...] | nil) :: Evision.Mat.t() | {:error, String.t()}
@spec flip(Evision.CUDA.GpuMat.t(), integer(), [{atom(), term()}, ...] | nil) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Flips a 2D matrix around vertical, horizontal, or both axes.
Positional Arguments
src:
Evision.Mat.t().Source matrix. Supports 1, 3 and 4 channels images with CV_8U, CV_16U, CV_32S or CV_32F depth.
flipCode:
int.Flip mode for the source:
- 0 Flips around x-axis.
- > 0 Flips around y-axis.
- \< 0 Flips around both axes.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Destination matrix.
@sa flip
Python prototype (for reference only):
flip(src, flipCode[, dst[, stream]]) -> dstVariant 2:
Flips a 2D matrix around vertical, horizontal, or both axes.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Source matrix. Supports 1, 3 and 4 channels images with CV_8U, CV_16U, CV_32S or CV_32F depth.
flipCode:
int.Flip mode for the source:
- 0 Flips around x-axis.
- > 0 Flips around y-axis.
- \< 0 Flips around both axes.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Destination matrix.
@sa flip
Python prototype (for reference only):
flip(src, flipCode[, dst[, stream]]) -> dst@spec gammaCorrection(Evision.Mat.maybe_mat_in()) :: Evision.Mat.t() | {:error, String.t()}
@spec gammaCorrection(Evision.CUDA.GpuMat.t()) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Routines for correcting image color gamma.
Positional Arguments
src:
Evision.Mat.t().Source image (3- or 4-channel 8 bit).
Keyword Arguments
forward:
bool.true for forward gamma correction or false for inverse gamma correction.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Destination image.
Python prototype (for reference only):
gammaCorrection(src[, dst[, forward[, stream]]]) -> dstVariant 2:
Routines for correcting image color gamma.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Source image (3- or 4-channel 8 bit).
Keyword Arguments
forward:
bool.true for forward gamma correction or false for inverse gamma correction.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Destination image.
Python prototype (for reference only):
gammaCorrection(src[, dst[, forward[, stream]]]) -> dst@spec gammaCorrection(Evision.Mat.maybe_mat_in(), [{atom(), term()}, ...] | nil) :: Evision.Mat.t() | {:error, String.t()}
@spec gammaCorrection(Evision.CUDA.GpuMat.t(), [{atom(), term()}, ...] | nil) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Routines for correcting image color gamma.
Positional Arguments
src:
Evision.Mat.t().Source image (3- or 4-channel 8 bit).
Keyword Arguments
forward:
bool.true for forward gamma correction or false for inverse gamma correction.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Destination image.
Python prototype (for reference only):
gammaCorrection(src[, dst[, forward[, stream]]]) -> dstVariant 2:
Routines for correcting image color gamma.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Source image (3- or 4-channel 8 bit).
Keyword Arguments
forward:
bool.true for forward gamma correction or false for inverse gamma correction.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Destination image.
Python prototype (for reference only):
gammaCorrection(src[, dst[, forward[, stream]]]) -> dst@spec gemm( Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in(), number(), Evision.Mat.maybe_mat_in(), number() ) :: Evision.Mat.t() | {:error, String.t()}
@spec gemm( Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t(), number(), Evision.CUDA.GpuMat.t(), number() ) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Performs generalized matrix multiplication.
Positional Arguments
src1:
Evision.Mat.t().First multiplied input matrix that should have CV_32FC1 , CV_64FC1 , CV_32FC2 , or CV_64FC2 type.
src2:
Evision.Mat.t().Second multiplied input matrix of the same type as src1 .
alpha:
double.Weight of the matrix product.
src3:
Evision.Mat.t().Third optional delta matrix added to the matrix product. It should have the same type as src1 and src2 .
beta:
double.Weight of src3 .
Keyword Arguments
flags:
int.Operation flags:
- GEMM_1_T transpose src1
- GEMM_2_T transpose src2
- GEMM_3_T transpose src3
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Destination matrix. It has the proper size and the same type as input matrices.
The function performs generalized matrix multiplication similar to the gemm functions in BLAS level
- 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] Note: Transposition operation doesn't support CV_64FC2 input type. @sa gemm
Python prototype (for reference only):
gemm(src1, src2, alpha, src3, beta[, dst[, flags[, stream]]]) -> dstVariant 2:
Performs generalized matrix multiplication.
Positional Arguments
src1:
Evision.CUDA.GpuMat.t().First multiplied input matrix that should have CV_32FC1 , CV_64FC1 , CV_32FC2 , or CV_64FC2 type.
src2:
Evision.CUDA.GpuMat.t().Second multiplied input matrix of the same type as src1 .
alpha:
double.Weight of the matrix product.
src3:
Evision.CUDA.GpuMat.t().Third optional delta matrix added to the matrix product. It should have the same type as src1 and src2 .
beta:
double.Weight of src3 .
Keyword Arguments
flags:
int.Operation flags:
- GEMM_1_T transpose src1
- GEMM_2_T transpose src2
- GEMM_3_T transpose src3
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Destination matrix. It has the proper size and the same type as input matrices.
The function performs generalized matrix multiplication similar to the gemm functions in BLAS level
- 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] Note: Transposition operation doesn't support CV_64FC2 input type. @sa gemm
Python prototype (for reference only):
gemm(src1, src2, alpha, src3, beta[, dst[, flags[, stream]]]) -> dst@spec gemm( Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in(), number(), Evision.Mat.maybe_mat_in(), number(), [{atom(), term()}, ...] | nil ) :: Evision.Mat.t() | {:error, String.t()}
@spec gemm( Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t(), number(), Evision.CUDA.GpuMat.t(), number(), [{atom(), term()}, ...] | nil ) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Performs generalized matrix multiplication.
Positional Arguments
src1:
Evision.Mat.t().First multiplied input matrix that should have CV_32FC1 , CV_64FC1 , CV_32FC2 , or CV_64FC2 type.
src2:
Evision.Mat.t().Second multiplied input matrix of the same type as src1 .
alpha:
double.Weight of the matrix product.
src3:
Evision.Mat.t().Third optional delta matrix added to the matrix product. It should have the same type as src1 and src2 .
beta:
double.Weight of src3 .
Keyword Arguments
flags:
int.Operation flags:
- GEMM_1_T transpose src1
- GEMM_2_T transpose src2
- GEMM_3_T transpose src3
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Destination matrix. It has the proper size and the same type as input matrices.
The function performs generalized matrix multiplication similar to the gemm functions in BLAS level
- 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] Note: Transposition operation doesn't support CV_64FC2 input type. @sa gemm
Python prototype (for reference only):
gemm(src1, src2, alpha, src3, beta[, dst[, flags[, stream]]]) -> dstVariant 2:
Performs generalized matrix multiplication.
Positional Arguments
src1:
Evision.CUDA.GpuMat.t().First multiplied input matrix that should have CV_32FC1 , CV_64FC1 , CV_32FC2 , or CV_64FC2 type.
src2:
Evision.CUDA.GpuMat.t().Second multiplied input matrix of the same type as src1 .
alpha:
double.Weight of the matrix product.
src3:
Evision.CUDA.GpuMat.t().Third optional delta matrix added to the matrix product. It should have the same type as src1 and src2 .
beta:
double.Weight of src3 .
Keyword Arguments
flags:
int.Operation flags:
- GEMM_1_T transpose src1
- GEMM_2_T transpose src2
- GEMM_3_T transpose src3
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Destination matrix. It has the proper size and the same type as input matrices.
The function performs generalized matrix multiplication similar to the gemm functions in BLAS level
- 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] Note: Transposition operation doesn't support CV_64FC2 input type. @sa gemm
Python prototype (for reference only):
gemm(src1, src2, alpha, src3, beta[, dst[, flags[, stream]]]) -> dstReturns the number of installed CUDA-enabled devices.
Return
- retval:
int
Use this function before any other CUDA functions calls. If OpenCV is compiled without CUDA support, this function returns 0. If the CUDA driver is not installed, or is incompatible, this function returns -1.
Python prototype (for reference only):
getCudaEnabledDeviceCount() -> retvalReturns the current device index set by cuda::setDevice or initialized by default.
Return
- retval:
int
Python prototype (for reference only):
getDevice() -> retval@spec histEven(Evision.Mat.maybe_mat_in(), integer(), integer(), integer()) :: Evision.Mat.t() | {:error, String.t()}
@spec histEven(Evision.CUDA.GpuMat.t(), integer(), integer(), integer()) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Calculates a histogram with evenly distributed bins.
Positional Arguments
src:
Evision.Mat.t().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.
histSize:
int.Size of the histogram.
lowerLevel:
int.Lower boundary of lowest-level bin.
upperLevel:
int.Upper boundary of highest-level bin.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
hist:
Evision.Mat.t().Destination histogram with one row, histSize columns, and the CV_32S type.
Python prototype (for reference only):
histEven(src, histSize, lowerLevel, upperLevel[, hist[, stream]]) -> histVariant 2:
Calculates a histogram with evenly distributed bins.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().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.
histSize:
int.Size of the histogram.
lowerLevel:
int.Lower boundary of lowest-level bin.
upperLevel:
int.Upper boundary of highest-level bin.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
hist:
Evision.CUDA.GpuMat.t().Destination histogram with one row, histSize columns, and the CV_32S type.
Python prototype (for reference only):
histEven(src, histSize, lowerLevel, upperLevel[, hist[, stream]]) -> hist@spec histEven( Evision.Mat.maybe_mat_in(), integer(), integer(), integer(), [{atom(), term()}, ...] | nil ) :: Evision.Mat.t() | {:error, String.t()}
@spec histEven( Evision.CUDA.GpuMat.t(), integer(), integer(), integer(), [{atom(), term()}, ...] | nil ) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
@spec histEven( Evision.Mat.maybe_mat_in(), Evision.CUDA.GpuMat.t(), integer(), integer(), integer() ) :: :ok | {:error, String.t()}
@spec histEven( Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t(), integer(), integer(), integer() ) :: :ok | {:error, String.t()}
Variant 1:
Calculates a histogram with evenly distributed bins.
Positional Arguments
src:
Evision.Mat.t().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.
histSize:
int.Size of the histogram.
lowerLevel:
int.Lower boundary of lowest-level bin.
upperLevel:
int.Upper boundary of highest-level bin.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
hist:
Evision.Mat.t().Destination histogram with one row, histSize columns, and the CV_32S type.
Python prototype (for reference only):
histEven(src, histSize, lowerLevel, upperLevel[, hist[, stream]]) -> histVariant 2:
Calculates a histogram with evenly distributed bins.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().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.
histSize:
int.Size of the histogram.
lowerLevel:
int.Lower boundary of lowest-level bin.
upperLevel:
int.Upper boundary of highest-level bin.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
hist:
Evision.CUDA.GpuMat.t().Destination histogram with one row, histSize columns, and the CV_32S type.
Python prototype (for reference only):
histEven(src, histSize, lowerLevel, upperLevel[, hist[, stream]]) -> histVariant 3:
histEven
Positional Arguments
- src:
Evision.Mat.t() - hist:
GpuMat* - histSize:
int* - lowerLevel:
int* - upperLevel:
int*
Keyword Arguments
- stream:
Evision.CUDA.Stream.t().
Has overloading in C++
Python prototype (for reference only):
histEven(src, hist, histSize, lowerLevel, upperLevel[, stream]) -> NoneVariant 4:
histEven
Positional Arguments
- src:
Evision.CUDA.GpuMat.t() - hist:
GpuMat* - histSize:
int* - lowerLevel:
int* - upperLevel:
int*
Keyword Arguments
- stream:
Evision.CUDA.Stream.t().
Has overloading in C++
Python prototype (for reference only):
histEven(src, hist, histSize, lowerLevel, upperLevel[, stream]) -> None@spec histEven( Evision.Mat.maybe_mat_in(), Evision.CUDA.GpuMat.t(), integer(), integer(), integer(), [{atom(), term()}, ...] | nil ) :: :ok | {:error, String.t()}
@spec histEven( Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t(), integer(), integer(), integer(), [{atom(), term()}, ...] | nil ) :: :ok | {:error, String.t()}
Variant 1:
histEven
Positional Arguments
- src:
Evision.Mat.t() - hist:
GpuMat* - histSize:
int* - lowerLevel:
int* - upperLevel:
int*
Keyword Arguments
- stream:
Evision.CUDA.Stream.t().
Has overloading in C++
Python prototype (for reference only):
histEven(src, hist, histSize, lowerLevel, upperLevel[, stream]) -> NoneVariant 2:
histEven
Positional Arguments
- src:
Evision.CUDA.GpuMat.t() - hist:
GpuMat* - histSize:
int* - lowerLevel:
int* - upperLevel:
int*
Keyword Arguments
- stream:
Evision.CUDA.Stream.t().
Has overloading in C++
Python prototype (for reference only):
histEven(src, hist, histSize, lowerLevel, upperLevel[, stream]) -> None@spec histRange(Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in()) :: Evision.Mat.t() | {:error, String.t()}
@spec histRange(Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t()) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Calculates a histogram with bins determined by the levels array.
Positional Arguments
src:
Evision.Mat.t().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.
levels:
Evision.Mat.t().Number of levels in the histogram.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
hist:
Evision.Mat.t().Destination histogram with one row, (levels.cols-1) columns, and the CV_32SC1 type.
Python prototype (for reference only):
histRange(src, levels[, hist[, stream]]) -> histVariant 2:
Calculates a histogram with bins determined by the levels array.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().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.
levels:
Evision.CUDA.GpuMat.t().Number of levels in the histogram.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
hist:
Evision.CUDA.GpuMat.t().Destination histogram with one row, (levels.cols-1) columns, and the CV_32SC1 type.
Python prototype (for reference only):
histRange(src, levels[, hist[, stream]]) -> hist@spec histRange( Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in(), [{atom(), term()}, ...] | nil ) :: Evision.Mat.t() | {:error, String.t()}
@spec histRange( Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t(), [{atom(), term()}, ...] | nil ) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
@spec histRange( Evision.Mat.maybe_mat_in(), Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t() ) :: :ok | {:error, String.t()}
@spec histRange( Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t() ) :: :ok | {:error, String.t()}
Variant 1:
Calculates a histogram with bins determined by the levels array.
Positional Arguments
src:
Evision.Mat.t().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.
levels:
Evision.Mat.t().Number of levels in the histogram.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
hist:
Evision.Mat.t().Destination histogram with one row, (levels.cols-1) columns, and the CV_32SC1 type.
Python prototype (for reference only):
histRange(src, levels[, hist[, stream]]) -> histVariant 2:
Calculates a histogram with bins determined by the levels array.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().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.
levels:
Evision.CUDA.GpuMat.t().Number of levels in the histogram.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
hist:
Evision.CUDA.GpuMat.t().Destination histogram with one row, (levels.cols-1) columns, and the CV_32SC1 type.
Python prototype (for reference only):
histRange(src, levels[, hist[, stream]]) -> histVariant 3:
histRange
Positional Arguments
- src:
Evision.Mat.t() - hist:
GpuMat* - levels:
GpuMat*
Keyword Arguments
- stream:
Evision.CUDA.Stream.t().
Has overloading in C++
Python prototype (for reference only):
histRange(src, hist, levels[, stream]) -> NoneVariant 4:
histRange
Positional Arguments
- src:
Evision.CUDA.GpuMat.t() - hist:
GpuMat* - levels:
GpuMat*
Keyword Arguments
- stream:
Evision.CUDA.Stream.t().
Has overloading in C++
Python prototype (for reference only):
histRange(src, hist, levels[, stream]) -> None@spec histRange( Evision.Mat.maybe_mat_in(), Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t(), [{atom(), term()}, ...] | nil ) :: :ok | {:error, String.t()}
@spec histRange( Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t(), [{atom(), term()}, ...] | nil ) :: :ok | {:error, String.t()}
Variant 1:
histRange
Positional Arguments
- src:
Evision.Mat.t() - hist:
GpuMat* - levels:
GpuMat*
Keyword Arguments
- stream:
Evision.CUDA.Stream.t().
Has overloading in C++
Python prototype (for reference only):
histRange(src, hist, levels[, stream]) -> NoneVariant 2:
histRange
Positional Arguments
- src:
Evision.CUDA.GpuMat.t() - hist:
GpuMat* - levels:
GpuMat*
Keyword Arguments
- stream:
Evision.CUDA.Stream.t().
Has overloading in C++
Python prototype (for reference only):
histRange(src, hist, levels[, stream]) -> None@spec inRange( Evision.Mat.maybe_mat_in(), {number()} | {number(), number()} | {number(), number(), number()} | {number(), number(), number(), number()}, {number()} | {number(), number()} | {number(), number(), number()} | {number(), number(), number(), number()} ) :: Evision.Mat.t() | {:error, String.t()}
@spec inRange( Evision.CUDA.GpuMat.t(), {number()} | {number(), number()} | {number(), number(), number()} | {number(), number(), number(), number()}, {number()} | {number(), number()} | {number(), number(), number()} | {number(), number(), number(), number()} ) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Checks if array elements lie between two scalars.
Positional Arguments
src:
Evision.Mat.t().first input array.
lowerb:
Scalar.inclusive lower boundary cv::Scalar.
upperb:
Scalar.inclusive upper boundary cv::Scalar.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().output array of the same size as src and CV_8U type.
The function checks the range as follows:
For every element of a single-channel input array: \f[\texttt{dst} (I)= \texttt{lowerb}_0 \leq \texttt{src} (I)_0 \leq \texttt{upperb}_0\f]
For two-channel arrays: \f[\texttt{dst} (I)= \texttt{lowerb}_0 \leq \texttt{src} (I)_0 \leq \texttt{upperb}_0 \land \texttt{lowerb}_1 \leq \texttt{src} (I)_1 \leq \texttt{upperb}_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. Note that unlike the CPU inRange, this does NOT accept an array for lowerb or upperb, only a cv::Scalar.
@sa cv::inRange
Python prototype (for reference only):
inRange(src, lowerb, upperb[, dst[, stream]]) -> dstVariant 2:
Checks if array elements lie between two scalars.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().first input array.
lowerb:
Scalar.inclusive lower boundary cv::Scalar.
upperb:
Scalar.inclusive upper boundary cv::Scalar.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().output array of the same size as src and CV_8U type.
The function checks the range as follows:
For every element of a single-channel input array: \f[\texttt{dst} (I)= \texttt{lowerb}_0 \leq \texttt{src} (I)_0 \leq \texttt{upperb}_0\f]
For two-channel arrays: \f[\texttt{dst} (I)= \texttt{lowerb}_0 \leq \texttt{src} (I)_0 \leq \texttt{upperb}_0 \land \texttt{lowerb}_1 \leq \texttt{src} (I)_1 \leq \texttt{upperb}_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. Note that unlike the CPU inRange, this does NOT accept an array for lowerb or upperb, only a cv::Scalar.
@sa cv::inRange
Python prototype (for reference only):
inRange(src, lowerb, upperb[, dst[, stream]]) -> dst@spec inRange( Evision.Mat.maybe_mat_in(), {number()} | {number(), number()} | {number(), number(), number()} | {number(), number(), number(), number()}, {number()} | {number(), number()} | {number(), number(), number()} | {number(), number(), number(), number()}, [{atom(), term()}, ...] | nil ) :: Evision.Mat.t() | {:error, String.t()}
@spec inRange( Evision.CUDA.GpuMat.t(), {number()} | {number(), number()} | {number(), number(), number()} | {number(), number(), number(), number()}, {number()} | {number(), number()} | {number(), number(), number()} | {number(), number(), number(), number()}, [{atom(), term()}, ...] | nil ) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Checks if array elements lie between two scalars.
Positional Arguments
src:
Evision.Mat.t().first input array.
lowerb:
Scalar.inclusive lower boundary cv::Scalar.
upperb:
Scalar.inclusive upper boundary cv::Scalar.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().output array of the same size as src and CV_8U type.
The function checks the range as follows:
For every element of a single-channel input array: \f[\texttt{dst} (I)= \texttt{lowerb}_0 \leq \texttt{src} (I)_0 \leq \texttt{upperb}_0\f]
For two-channel arrays: \f[\texttt{dst} (I)= \texttt{lowerb}_0 \leq \texttt{src} (I)_0 \leq \texttt{upperb}_0 \land \texttt{lowerb}_1 \leq \texttt{src} (I)_1 \leq \texttt{upperb}_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. Note that unlike the CPU inRange, this does NOT accept an array for lowerb or upperb, only a cv::Scalar.
@sa cv::inRange
Python prototype (for reference only):
inRange(src, lowerb, upperb[, dst[, stream]]) -> dstVariant 2:
Checks if array elements lie between two scalars.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().first input array.
lowerb:
Scalar.inclusive lower boundary cv::Scalar.
upperb:
Scalar.inclusive upper boundary cv::Scalar.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().output array of the same size as src and CV_8U type.
The function checks the range as follows:
For every element of a single-channel input array: \f[\texttt{dst} (I)= \texttt{lowerb}_0 \leq \texttt{src} (I)_0 \leq \texttt{upperb}_0\f]
For two-channel arrays: \f[\texttt{dst} (I)= \texttt{lowerb}_0 \leq \texttt{src} (I)_0 \leq \texttt{upperb}_0 \land \texttt{lowerb}_1 \leq \texttt{src} (I)_1 \leq \texttt{upperb}_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. Note that unlike the CPU inRange, this does NOT accept an array for lowerb or upperb, only a cv::Scalar.
@sa cv::inRange
Python prototype (for reference only):
inRange(src, lowerb, upperb[, dst[, stream]]) -> dst@spec integral(Evision.Mat.maybe_mat_in()) :: Evision.Mat.t() | {:error, String.t()}
@spec integral(Evision.CUDA.GpuMat.t()) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Computes an integral image.
Positional Arguments
src:
Evision.Mat.t().Source image. Only CV_8UC1 images are supported for now.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
sum:
Evision.Mat.t().Integral image containing 32-bit unsigned integer values packed into CV_32SC1 .
@sa integral
Python prototype (for reference only):
integral(src[, sum[, stream]]) -> sumVariant 2:
Computes an integral image.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Source image. Only CV_8UC1 images are supported for now.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
sum:
Evision.CUDA.GpuMat.t().Integral image containing 32-bit unsigned integer values packed into CV_32SC1 .
@sa integral
Python prototype (for reference only):
integral(src[, sum[, stream]]) -> sum@spec integral(Evision.Mat.maybe_mat_in(), [{atom(), term()}, ...] | nil) :: Evision.Mat.t() | {:error, String.t()}
@spec integral(Evision.CUDA.GpuMat.t(), [{atom(), term()}, ...] | nil) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Computes an integral image.
Positional Arguments
src:
Evision.Mat.t().Source image. Only CV_8UC1 images are supported for now.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
sum:
Evision.Mat.t().Integral image containing 32-bit unsigned integer values packed into CV_32SC1 .
@sa integral
Python prototype (for reference only):
integral(src[, sum[, stream]]) -> sumVariant 2:
Computes an integral image.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Source image. Only CV_8UC1 images are supported for now.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
sum:
Evision.CUDA.GpuMat.t().Integral image containing 32-bit unsigned integer values packed into CV_32SC1 .
@sa integral
Python prototype (for reference only):
integral(src[, sum[, stream]]) -> sum@spec log(Evision.Mat.maybe_mat_in()) :: Evision.Mat.t() | {:error, String.t()}
@spec log(Evision.CUDA.GpuMat.t()) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Computes a natural logarithm of absolute value of each matrix element.
Positional Arguments
src:
Evision.Mat.t().Source matrix.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Destination matrix with the same size and type as src .
@sa log
Python prototype (for reference only):
log(src[, dst[, stream]]) -> dstVariant 2:
Computes a natural logarithm of absolute value of each matrix element.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Source matrix.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Destination matrix with the same size and type as src .
@sa log
Python prototype (for reference only):
log(src[, dst[, stream]]) -> dst@spec log(Evision.Mat.maybe_mat_in(), [{atom(), term()}, ...] | nil) :: Evision.Mat.t() | {:error, String.t()}
@spec log(Evision.CUDA.GpuMat.t(), [{atom(), term()}, ...] | nil) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Computes a natural logarithm of absolute value of each matrix element.
Positional Arguments
src:
Evision.Mat.t().Source matrix.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Destination matrix with the same size and type as src .
@sa log
Python prototype (for reference only):
log(src[, dst[, stream]]) -> dstVariant 2:
Computes a natural logarithm of absolute value of each matrix element.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Source matrix.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Destination matrix with the same size and type as src .
@sa log
Python prototype (for reference only):
log(src[, dst[, stream]]) -> dst@spec lshift( Evision.Mat.maybe_mat_in(), {number()} | {number(), number()} | {number(), number(), number()} | {number(), number(), number(), number()} ) :: Evision.Mat.t() | {:error, String.t()}
@spec lshift( Evision.CUDA.GpuMat.t(), {number()} | {number(), number()} | {number(), number(), number()} | {number(), number(), number(), number()} ) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Performs pixel by pixel right left of an image by a constant value.
Positional Arguments
src:
Evision.Mat.t().Source matrix. Supports 1, 3 and 4 channels images with CV_8U , CV_16U or CV_32S depth.
val:
Scalar.Constant values, one per channel.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Destination matrix with the same size and type as src .
Python prototype (for reference only):
lshift(src, val[, dst[, stream]]) -> dstVariant 2:
Performs pixel by pixel right left of an image by a constant value.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Source matrix. Supports 1, 3 and 4 channels images with CV_8U , CV_16U or CV_32S depth.
val:
Scalar.Constant values, one per channel.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Destination matrix with the same size and type as src .
Python prototype (for reference only):
lshift(src, val[, dst[, stream]]) -> dst@spec lshift( Evision.Mat.maybe_mat_in(), {number()} | {number(), number()} | {number(), number(), number()} | {number(), number(), number(), number()}, [{atom(), term()}, ...] | nil ) :: Evision.Mat.t() | {:error, String.t()}
@spec lshift( Evision.CUDA.GpuMat.t(), {number()} | {number(), number()} | {number(), number(), number()} | {number(), number(), number(), number()}, [{atom(), term()}, ...] | nil ) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Performs pixel by pixel right left of an image by a constant value.
Positional Arguments
src:
Evision.Mat.t().Source matrix. Supports 1, 3 and 4 channels images with CV_8U , CV_16U or CV_32S depth.
val:
Scalar.Constant values, one per channel.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Destination matrix with the same size and type as src .
Python prototype (for reference only):
lshift(src, val[, dst[, stream]]) -> dstVariant 2:
Performs pixel by pixel right left of an image by a constant value.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Source matrix. Supports 1, 3 and 4 channels images with CV_8U , CV_16U or CV_32S depth.
val:
Scalar.Constant values, one per channel.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Destination matrix with the same size and type as src .
Python prototype (for reference only):
lshift(src, val[, dst[, stream]]) -> dst@spec magnitude(Evision.Mat.maybe_mat_in()) :: Evision.Mat.t() | {:error, String.t()}
@spec magnitude(Evision.CUDA.GpuMat.t()) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Computes magnitudes of complex matrix elements.
Positional Arguments
xy:
Evision.Mat.t().Source complex matrix in the interleaved format ( CV_32FC2 ).
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
magnitude:
Evision.Mat.t().Destination matrix of float magnitudes ( CV_32FC1 ).
@sa magnitude
Python prototype (for reference only):
magnitude(xy[, magnitude[, stream]]) -> magnitudeVariant 2:
Computes magnitudes of complex matrix elements.
Positional Arguments
xy:
Evision.CUDA.GpuMat.t().Source complex matrix in the interleaved format ( CV_32FC2 ).
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
magnitude:
Evision.CUDA.GpuMat.t().Destination matrix of float magnitudes ( CV_32FC1 ).
@sa magnitude
Python prototype (for reference only):
magnitude(xy[, magnitude[, stream]]) -> magnitude@spec magnitude(Evision.Mat.maybe_mat_in(), [{atom(), term()}, ...] | nil) :: Evision.Mat.t() | {:error, String.t()}
@spec magnitude(Evision.CUDA.GpuMat.t(), [{atom(), term()}, ...] | nil) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
@spec magnitude(Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in()) :: Evision.Mat.t() | {:error, String.t()}
@spec magnitude(Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t()) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
magnitude
Positional Arguments
x:
Evision.Mat.t().Source matrix containing real components ( CV_32FC1 ).
y:
Evision.Mat.t().Source matrix containing imaginary components ( CV_32FC1 ).
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
magnitude:
Evision.Mat.t().Destination matrix of float magnitudes ( CV_32FC1 ).
Has overloading in C++
computes magnitude of each (x(i), y(i)) vector supports only floating-point source
Python prototype (for reference only):
magnitude(x, y[, magnitude[, stream]]) -> magnitudeVariant 2:
magnitude
Positional Arguments
x:
Evision.CUDA.GpuMat.t().Source matrix containing real components ( CV_32FC1 ).
y:
Evision.CUDA.GpuMat.t().Source matrix containing imaginary components ( CV_32FC1 ).
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
magnitude:
Evision.CUDA.GpuMat.t().Destination matrix of float magnitudes ( CV_32FC1 ).
Has overloading in C++
computes magnitude of each (x(i), y(i)) vector supports only floating-point source
Python prototype (for reference only):
magnitude(x, y[, magnitude[, stream]]) -> magnitudeVariant 3:
Computes magnitudes of complex matrix elements.
Positional Arguments
xy:
Evision.Mat.t().Source complex matrix in the interleaved format ( CV_32FC2 ).
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
magnitude:
Evision.Mat.t().Destination matrix of float magnitudes ( CV_32FC1 ).
@sa magnitude
Python prototype (for reference only):
magnitude(xy[, magnitude[, stream]]) -> magnitudeVariant 4:
Computes magnitudes of complex matrix elements.
Positional Arguments
xy:
Evision.CUDA.GpuMat.t().Source complex matrix in the interleaved format ( CV_32FC2 ).
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
magnitude:
Evision.CUDA.GpuMat.t().Destination matrix of float magnitudes ( CV_32FC1 ).
@sa magnitude
Python prototype (for reference only):
magnitude(xy[, magnitude[, stream]]) -> magnitude@spec magnitude( Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in(), [{atom(), term()}, ...] | nil ) :: Evision.Mat.t() | {:error, String.t()}
@spec magnitude( Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t(), [{atom(), term()}, ...] | nil ) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
magnitude
Positional Arguments
x:
Evision.Mat.t().Source matrix containing real components ( CV_32FC1 ).
y:
Evision.Mat.t().Source matrix containing imaginary components ( CV_32FC1 ).
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
magnitude:
Evision.Mat.t().Destination matrix of float magnitudes ( CV_32FC1 ).
Has overloading in C++
computes magnitude of each (x(i), y(i)) vector supports only floating-point source
Python prototype (for reference only):
magnitude(x, y[, magnitude[, stream]]) -> magnitudeVariant 2:
magnitude
Positional Arguments
x:
Evision.CUDA.GpuMat.t().Source matrix containing real components ( CV_32FC1 ).
y:
Evision.CUDA.GpuMat.t().Source matrix containing imaginary components ( CV_32FC1 ).
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
magnitude:
Evision.CUDA.GpuMat.t().Destination matrix of float magnitudes ( CV_32FC1 ).
Has overloading in C++
computes magnitude of each (x(i), y(i)) vector supports only floating-point source
Python prototype (for reference only):
magnitude(x, y[, magnitude[, stream]]) -> magnitude@spec magnitudeSqr(Evision.Mat.maybe_mat_in()) :: Evision.Mat.t() | {:error, String.t()}
@spec magnitudeSqr(Evision.CUDA.GpuMat.t()) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Computes squared magnitudes of complex matrix elements.
Positional Arguments
xy:
Evision.Mat.t().Source complex matrix in the interleaved format ( CV_32FC2 ).
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
magnitude:
Evision.Mat.t().Destination matrix of float magnitude squares ( CV_32FC1 ).
Python prototype (for reference only):
magnitudeSqr(xy[, magnitude[, stream]]) -> magnitudeVariant 2:
Computes squared magnitudes of complex matrix elements.
Positional Arguments
xy:
Evision.CUDA.GpuMat.t().Source complex matrix in the interleaved format ( CV_32FC2 ).
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
magnitude:
Evision.CUDA.GpuMat.t().Destination matrix of float magnitude squares ( CV_32FC1 ).
Python prototype (for reference only):
magnitudeSqr(xy[, magnitude[, stream]]) -> magnitude@spec magnitudeSqr(Evision.Mat.maybe_mat_in(), [{atom(), term()}, ...] | nil) :: Evision.Mat.t() | {:error, String.t()}
@spec magnitudeSqr(Evision.CUDA.GpuMat.t(), [{atom(), term()}, ...] | nil) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
@spec magnitudeSqr(Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in()) :: Evision.Mat.t() | {:error, String.t()}
@spec magnitudeSqr(Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t()) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
magnitudeSqr
Positional Arguments
x:
Evision.Mat.t().Source matrix containing real components ( CV_32FC1 ).
y:
Evision.Mat.t().Source matrix containing imaginary components ( CV_32FC1 ).
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
magnitude:
Evision.Mat.t().Destination matrix of float magnitude squares ( CV_32FC1 ).
Has overloading in C++
computes squared magnitude of each (x(i), y(i)) vector supports only floating-point source
Python prototype (for reference only):
magnitudeSqr(x, y[, magnitude[, stream]]) -> magnitudeVariant 2:
magnitudeSqr
Positional Arguments
x:
Evision.CUDA.GpuMat.t().Source matrix containing real components ( CV_32FC1 ).
y:
Evision.CUDA.GpuMat.t().Source matrix containing imaginary components ( CV_32FC1 ).
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
magnitude:
Evision.CUDA.GpuMat.t().Destination matrix of float magnitude squares ( CV_32FC1 ).
Has overloading in C++
computes squared magnitude of each (x(i), y(i)) vector supports only floating-point source
Python prototype (for reference only):
magnitudeSqr(x, y[, magnitude[, stream]]) -> magnitudeVariant 3:
Computes squared magnitudes of complex matrix elements.
Positional Arguments
xy:
Evision.Mat.t().Source complex matrix in the interleaved format ( CV_32FC2 ).
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
magnitude:
Evision.Mat.t().Destination matrix of float magnitude squares ( CV_32FC1 ).
Python prototype (for reference only):
magnitudeSqr(xy[, magnitude[, stream]]) -> magnitudeVariant 4:
Computes squared magnitudes of complex matrix elements.
Positional Arguments
xy:
Evision.CUDA.GpuMat.t().Source complex matrix in the interleaved format ( CV_32FC2 ).
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
magnitude:
Evision.CUDA.GpuMat.t().Destination matrix of float magnitude squares ( CV_32FC1 ).
Python prototype (for reference only):
magnitudeSqr(xy[, magnitude[, stream]]) -> magnitude@spec magnitudeSqr( Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in(), [{atom(), term()}, ...] | nil ) :: Evision.Mat.t() | {:error, String.t()}
@spec magnitudeSqr( Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t(), [{atom(), term()}, ...] | nil ) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
magnitudeSqr
Positional Arguments
x:
Evision.Mat.t().Source matrix containing real components ( CV_32FC1 ).
y:
Evision.Mat.t().Source matrix containing imaginary components ( CV_32FC1 ).
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
magnitude:
Evision.Mat.t().Destination matrix of float magnitude squares ( CV_32FC1 ).
Has overloading in C++
computes squared magnitude of each (x(i), y(i)) vector supports only floating-point source
Python prototype (for reference only):
magnitudeSqr(x, y[, magnitude[, stream]]) -> magnitudeVariant 2:
magnitudeSqr
Positional Arguments
x:
Evision.CUDA.GpuMat.t().Source matrix containing real components ( CV_32FC1 ).
y:
Evision.CUDA.GpuMat.t().Source matrix containing imaginary components ( CV_32FC1 ).
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
magnitude:
Evision.CUDA.GpuMat.t().Destination matrix of float magnitude squares ( CV_32FC1 ).
Has overloading in C++
computes squared magnitude of each (x(i), y(i)) vector supports only floating-point source
Python prototype (for reference only):
magnitudeSqr(x, y[, magnitude[, stream]]) -> magnitude@spec max(Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in()) :: Evision.Mat.t() | {:error, String.t()}
@spec max(Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t()) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Computes the per-element maximum of two matrices (or a matrix and a scalar).
Positional Arguments
src1:
Evision.Mat.t().First source matrix or scalar.
src2:
Evision.Mat.t().Second source matrix or scalar.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Destination matrix that has the same size and type as the input array(s).
@sa max
Python prototype (for reference only):
max(src1, src2[, dst[, stream]]) -> dstVariant 2:
Computes the per-element maximum of two matrices (or a matrix and a scalar).
Positional Arguments
src1:
Evision.CUDA.GpuMat.t().First source matrix or scalar.
src2:
Evision.CUDA.GpuMat.t().Second source matrix or scalar.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Destination matrix that has the same size and type as the input array(s).
@sa max
Python prototype (for reference only):
max(src1, src2[, dst[, stream]]) -> dst@spec max( Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in(), [{atom(), term()}, ...] | nil ) :: Evision.Mat.t() | {:error, String.t()}
@spec max( Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t(), [{atom(), term()}, ...] | nil ) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Computes the per-element maximum of two matrices (or a matrix and a scalar).
Positional Arguments
src1:
Evision.Mat.t().First source matrix or scalar.
src2:
Evision.Mat.t().Second source matrix or scalar.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Destination matrix that has the same size and type as the input array(s).
@sa max
Python prototype (for reference only):
max(src1, src2[, dst[, stream]]) -> dstVariant 2:
Computes the per-element maximum of two matrices (or a matrix and a scalar).
Positional Arguments
src1:
Evision.CUDA.GpuMat.t().First source matrix or scalar.
src2:
Evision.CUDA.GpuMat.t().Second source matrix or scalar.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Destination matrix that has the same size and type as the input array(s).
@sa max
Python prototype (for reference only):
max(src1, src2[, dst[, stream]]) -> dst@spec meanShiftFiltering(Evision.Mat.maybe_mat_in(), integer(), integer()) :: Evision.Mat.t() | {:error, String.t()}
@spec meanShiftFiltering(Evision.CUDA.GpuMat.t(), integer(), integer()) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Performs mean-shift filtering for each point of the source image.
Positional Arguments
src:
Evision.Mat.t().Source image. Only CV_8UC4 images are supported for now.
sp:
int.Spatial window radius.
sr:
int.Color window radius.
Keyword Arguments
criteria:
TermCriteria.Termination criteria. See TermCriteria.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Destination image containing the color of mapped points. It has the same size and type as src .
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.
Python prototype (for reference only):
meanShiftFiltering(src, sp, sr[, dst[, criteria[, stream]]]) -> dstVariant 2:
Performs mean-shift filtering for each point of the source image.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Source image. Only CV_8UC4 images are supported for now.
sp:
int.Spatial window radius.
sr:
int.Color window radius.
Keyword Arguments
criteria:
TermCriteria.Termination criteria. See TermCriteria.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Destination image containing the color of mapped points. It has the same size and type as src .
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.
Python prototype (for reference only):
meanShiftFiltering(src, sp, sr[, dst[, criteria[, stream]]]) -> dst@spec meanShiftFiltering( Evision.Mat.maybe_mat_in(), integer(), integer(), [{atom(), term()}, ...] | nil ) :: Evision.Mat.t() | {:error, String.t()}
@spec meanShiftFiltering( Evision.CUDA.GpuMat.t(), integer(), integer(), [{atom(), term()}, ...] | nil ) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Performs mean-shift filtering for each point of the source image.
Positional Arguments
src:
Evision.Mat.t().Source image. Only CV_8UC4 images are supported for now.
sp:
int.Spatial window radius.
sr:
int.Color window radius.
Keyword Arguments
criteria:
TermCriteria.Termination criteria. See TermCriteria.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Destination image containing the color of mapped points. It has the same size and type as src .
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.
Python prototype (for reference only):
meanShiftFiltering(src, sp, sr[, dst[, criteria[, stream]]]) -> dstVariant 2:
Performs mean-shift filtering for each point of the source image.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Source image. Only CV_8UC4 images are supported for now.
sp:
int.Spatial window radius.
sr:
int.Color window radius.
Keyword Arguments
criteria:
TermCriteria.Termination criteria. See TermCriteria.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Destination image containing the color of mapped points. It has the same size and type as src .
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.
Python prototype (for reference only):
meanShiftFiltering(src, sp, sr[, dst[, criteria[, stream]]]) -> dst@spec meanShiftProc(Evision.Mat.maybe_mat_in(), integer(), integer()) :: {Evision.Mat.t(), Evision.Mat.t()} | {:error, String.t()}
@spec meanShiftProc(Evision.CUDA.GpuMat.t(), integer(), integer()) :: {Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t()} | {:error, String.t()}
Variant 1:
Performs a mean-shift procedure and stores information about processed points (their colors and positions) in two images.
Positional Arguments
src:
Evision.Mat.t().Source image. Only CV_8UC4 images are supported for now.
sp:
int.Spatial window radius.
sr:
int.Color window radius.
Keyword Arguments
criteria:
TermCriteria.Termination criteria. See TermCriteria.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dstr:
Evision.Mat.t().Destination image containing the color of mapped points. The size and type is the same as src .
dstsp:
Evision.Mat.t().Destination image containing the position of mapped points. The size is the same as src size. The type is CV_16SC2 .
@sa cuda::meanShiftFiltering
Python prototype (for reference only):
meanShiftProc(src, sp, sr[, dstr[, dstsp[, criteria[, stream]]]]) -> dstr, dstspVariant 2:
Performs a mean-shift procedure and stores information about processed points (their colors and positions) in two images.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Source image. Only CV_8UC4 images are supported for now.
sp:
int.Spatial window radius.
sr:
int.Color window radius.
Keyword Arguments
criteria:
TermCriteria.Termination criteria. See TermCriteria.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dstr:
Evision.CUDA.GpuMat.t().Destination image containing the color of mapped points. The size and type is the same as src .
dstsp:
Evision.CUDA.GpuMat.t().Destination image containing the position of mapped points. The size is the same as src size. The type is CV_16SC2 .
@sa cuda::meanShiftFiltering
Python prototype (for reference only):
meanShiftProc(src, sp, sr[, dstr[, dstsp[, criteria[, stream]]]]) -> dstr, dstsp@spec meanShiftProc( Evision.Mat.maybe_mat_in(), integer(), integer(), [{atom(), term()}, ...] | nil ) :: {Evision.Mat.t(), Evision.Mat.t()} | {:error, String.t()}
@spec meanShiftProc( Evision.CUDA.GpuMat.t(), integer(), integer(), [{atom(), term()}, ...] | nil ) :: {Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t()} | {:error, String.t()}
Variant 1:
Performs a mean-shift procedure and stores information about processed points (their colors and positions) in two images.
Positional Arguments
src:
Evision.Mat.t().Source image. Only CV_8UC4 images are supported for now.
sp:
int.Spatial window radius.
sr:
int.Color window radius.
Keyword Arguments
criteria:
TermCriteria.Termination criteria. See TermCriteria.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dstr:
Evision.Mat.t().Destination image containing the color of mapped points. The size and type is the same as src .
dstsp:
Evision.Mat.t().Destination image containing the position of mapped points. The size is the same as src size. The type is CV_16SC2 .
@sa cuda::meanShiftFiltering
Python prototype (for reference only):
meanShiftProc(src, sp, sr[, dstr[, dstsp[, criteria[, stream]]]]) -> dstr, dstspVariant 2:
Performs a mean-shift procedure and stores information about processed points (their colors and positions) in two images.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Source image. Only CV_8UC4 images are supported for now.
sp:
int.Spatial window radius.
sr:
int.Color window radius.
Keyword Arguments
criteria:
TermCriteria.Termination criteria. See TermCriteria.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dstr:
Evision.CUDA.GpuMat.t().Destination image containing the color of mapped points. The size and type is the same as src .
dstsp:
Evision.CUDA.GpuMat.t().Destination image containing the position of mapped points. The size is the same as src size. The type is CV_16SC2 .
@sa cuda::meanShiftFiltering
Python prototype (for reference only):
meanShiftProc(src, sp, sr[, dstr[, dstsp[, criteria[, stream]]]]) -> dstr, dstsp@spec meanShiftSegmentation( Evision.Mat.maybe_mat_in(), integer(), integer(), integer() ) :: Evision.Mat.t() | {:error, String.t()}
@spec meanShiftSegmentation(Evision.CUDA.GpuMat.t(), integer(), integer(), integer()) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Performs a mean-shift segmentation of the source image and eliminates small segments.
Positional Arguments
src:
Evision.Mat.t().Source image. Only CV_8UC4 images are supported for now.
sp:
int.Spatial window radius.
sr:
int.Color window radius.
minsize:
int.Minimum segment size. Smaller segments are merged.
Keyword Arguments
criteria:
TermCriteria.Termination criteria. See TermCriteria.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Segmented image with the same size and type as src (host or gpu memory).
Python prototype (for reference only):
meanShiftSegmentation(src, sp, sr, minsize[, dst[, criteria[, stream]]]) -> dstVariant 2:
Performs a mean-shift segmentation of the source image and eliminates small segments.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Source image. Only CV_8UC4 images are supported for now.
sp:
int.Spatial window radius.
sr:
int.Color window radius.
minsize:
int.Minimum segment size. Smaller segments are merged.
Keyword Arguments
criteria:
TermCriteria.Termination criteria. See TermCriteria.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Segmented image with the same size and type as src (host or gpu memory).
Python prototype (for reference only):
meanShiftSegmentation(src, sp, sr, minsize[, dst[, criteria[, stream]]]) -> dst@spec meanShiftSegmentation( Evision.Mat.maybe_mat_in(), integer(), integer(), integer(), [{atom(), term()}, ...] | nil ) :: Evision.Mat.t() | {:error, String.t()}
@spec meanShiftSegmentation( Evision.CUDA.GpuMat.t(), integer(), integer(), integer(), [{atom(), term()}, ...] | nil ) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Performs a mean-shift segmentation of the source image and eliminates small segments.
Positional Arguments
src:
Evision.Mat.t().Source image. Only CV_8UC4 images are supported for now.
sp:
int.Spatial window radius.
sr:
int.Color window radius.
minsize:
int.Minimum segment size. Smaller segments are merged.
Keyword Arguments
criteria:
TermCriteria.Termination criteria. See TermCriteria.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Segmented image with the same size and type as src (host or gpu memory).
Python prototype (for reference only):
meanShiftSegmentation(src, sp, sr, minsize[, dst[, criteria[, stream]]]) -> dstVariant 2:
Performs a mean-shift segmentation of the source image and eliminates small segments.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Source image. Only CV_8UC4 images are supported for now.
sp:
int.Spatial window radius.
sr:
int.Color window radius.
minsize:
int.Minimum segment size. Smaller segments are merged.
Keyword Arguments
criteria:
TermCriteria.Termination criteria. See TermCriteria.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Segmented image with the same size and type as src (host or gpu memory).
Python prototype (for reference only):
meanShiftSegmentation(src, sp, sr, minsize[, dst[, criteria[, stream]]]) -> dst@spec meanStdDev(Evision.Mat.maybe_mat_in()) :: {{number()} | {number(), number()} | {number(), number(), number()} | {number(), number(), number(), number()}, {number()} | {number(), number()} | {number(), number(), number()} | {number(), number(), number(), number()}} | {:error, String.t()}
@spec meanStdDev(Evision.CUDA.GpuMat.t()) :: {{number()} | {number(), number()} | {number(), number(), number()} | {number(), number(), number(), number()}, {number()} | {number(), number()} | {number(), number(), number()} | {number(), number(), number(), number()}} | {:error, String.t()}
Variant 1:
meanStdDev
Positional Arguments
mtx:
Evision.Mat.t().Source matrix. CV_8UC1 and CV_32FC1 matrices are supported for now.
Return
mean:
Scalar.Mean value.
stddev:
Scalar.Standard deviation value.
Has overloading in C++
Python prototype (for reference only):
meanStdDev(mtx) -> mean, stddevVariant 2:
meanStdDev
Positional Arguments
mtx:
Evision.CUDA.GpuMat.t().Source matrix. CV_8UC1 and CV_32FC1 matrices are supported for now.
Return
mean:
Scalar.Mean value.
stddev:
Scalar.Standard deviation value.
Has overloading in C++
Python prototype (for reference only):
meanStdDev(mtx) -> mean, stddev@spec meanStdDev(Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in()) :: {{number()} | {number(), number()} | {number(), number(), number()} | {number(), number(), number(), number()}, {number()} | {number(), number()} | {number(), number(), number()} | {number(), number(), number(), number()}} | {:error, String.t()}
@spec meanStdDev(Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t()) :: {{number()} | {number(), number()} | {number(), number(), number()} | {number(), number(), number(), number()}, {number()} | {number(), number()} | {number(), number(), number()} | {number(), number(), number(), number()}} | {:error, String.t()}
Variant 1:
meanStdDev
Positional Arguments
src:
Evision.Mat.t().Source matrix. CV_8UC1 and CV_32FC1 matrices are supported for now.
mask:
Evision.Mat.t().Operation mask.
Return
mean:
Scalar.Mean value.
stddev:
Scalar.Standard deviation value.
Has overloading in C++
Python prototype (for reference only):
meanStdDev(src, mask) -> mean, stddevVariant 2:
meanStdDev
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Source matrix. CV_8UC1 and CV_32FC1 matrices are supported for now.
mask:
Evision.CUDA.GpuMat.t().Operation mask.
Return
mean:
Scalar.Mean value.
stddev:
Scalar.Standard deviation value.
Has overloading in C++
Python prototype (for reference only):
meanStdDev(src, mask) -> mean, stddev@spec merge([Evision.CUDA.GpuMat.t()]) :: Evision.Mat.t() | {:error, String.t()}
merge
Positional Arguments
- src:
[Evision.CUDA.GpuMat]
Keyword Arguments
- stream:
Evision.CUDA.Stream.t().
Return
- dst:
Evision.Mat.t().
Has overloading in C++
Python prototype (for reference only):
merge(src[, dst[, stream]]) -> dst@spec merge([Evision.CUDA.GpuMat.t()], [{atom(), term()}, ...] | nil) :: Evision.Mat.t() | {:error, String.t()}
merge
Positional Arguments
- src:
[Evision.CUDA.GpuMat]
Keyword Arguments
- stream:
Evision.CUDA.Stream.t().
Return
- dst:
Evision.Mat.t().
Has overloading in C++
Python prototype (for reference only):
merge(src[, dst[, stream]]) -> dst@spec min(Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in()) :: Evision.Mat.t() | {:error, String.t()}
@spec min(Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t()) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Computes the per-element minimum of two matrices (or a matrix and a scalar).
Positional Arguments
src1:
Evision.Mat.t().First source matrix or scalar.
src2:
Evision.Mat.t().Second source matrix or scalar.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Destination matrix that has the same size and type as the input array(s).
@sa min
Python prototype (for reference only):
min(src1, src2[, dst[, stream]]) -> dstVariant 2:
Computes the per-element minimum of two matrices (or a matrix and a scalar).
Positional Arguments
src1:
Evision.CUDA.GpuMat.t().First source matrix or scalar.
src2:
Evision.CUDA.GpuMat.t().Second source matrix or scalar.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Destination matrix that has the same size and type as the input array(s).
@sa min
Python prototype (for reference only):
min(src1, src2[, dst[, stream]]) -> dst@spec min( Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in(), [{atom(), term()}, ...] | nil ) :: Evision.Mat.t() | {:error, String.t()}
@spec min( Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t(), [{atom(), term()}, ...] | nil ) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Computes the per-element minimum of two matrices (or a matrix and a scalar).
Positional Arguments
src1:
Evision.Mat.t().First source matrix or scalar.
src2:
Evision.Mat.t().Second source matrix or scalar.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Destination matrix that has the same size and type as the input array(s).
@sa min
Python prototype (for reference only):
min(src1, src2[, dst[, stream]]) -> dstVariant 2:
Computes the per-element minimum of two matrices (or a matrix and a scalar).
Positional Arguments
src1:
Evision.CUDA.GpuMat.t().First source matrix or scalar.
src2:
Evision.CUDA.GpuMat.t().Second source matrix or scalar.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Destination matrix that has the same size and type as the input array(s).
@sa min
Python prototype (for reference only):
min(src1, src2[, dst[, stream]]) -> dst@spec minMax(Evision.Mat.maybe_mat_in()) :: {number(), number()} | {:error, String.t()}
@spec minMax(Evision.CUDA.GpuMat.t()) :: {number(), number()} | {:error, String.t()}
Variant 1:
Finds global minimum and maximum matrix elements and returns their values.
Positional Arguments
src:
Evision.Mat.t().Single-channel source image.
Keyword Arguments
mask:
Evision.Mat.t().Optional mask to select a sub-matrix.
Return
minVal:
double*.Pointer to the returned minimum value. Use NULL if not required.
maxVal:
double*.Pointer to the returned maximum value. Use NULL if not required.
The function does not work with CV_64F images on GPUs with the compute capability \< 1.3. @sa minMaxLoc
Python prototype (for reference only):
minMax(src[, mask]) -> minVal, maxValVariant 2:
Finds global minimum and maximum matrix elements and returns their values.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Single-channel source image.
Keyword Arguments
mask:
Evision.CUDA.GpuMat.t().Optional mask to select a sub-matrix.
Return
minVal:
double*.Pointer to the returned minimum value. Use NULL if not required.
maxVal:
double*.Pointer to the returned maximum value. Use NULL if not required.
The function does not work with CV_64F images on GPUs with the compute capability \< 1.3. @sa minMaxLoc
Python prototype (for reference only):
minMax(src[, mask]) -> minVal, maxVal@spec minMax(Evision.Mat.maybe_mat_in(), [{atom(), term()}, ...] | nil) :: {number(), number()} | {:error, String.t()}
@spec minMax(Evision.CUDA.GpuMat.t(), [{atom(), term()}, ...] | nil) :: {number(), number()} | {:error, String.t()}
Variant 1:
Finds global minimum and maximum matrix elements and returns their values.
Positional Arguments
src:
Evision.Mat.t().Single-channel source image.
Keyword Arguments
mask:
Evision.Mat.t().Optional mask to select a sub-matrix.
Return
minVal:
double*.Pointer to the returned minimum value. Use NULL if not required.
maxVal:
double*.Pointer to the returned maximum value. Use NULL if not required.
The function does not work with CV_64F images on GPUs with the compute capability \< 1.3. @sa minMaxLoc
Python prototype (for reference only):
minMax(src[, mask]) -> minVal, maxValVariant 2:
Finds global minimum and maximum matrix elements and returns their values.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Single-channel source image.
Keyword Arguments
mask:
Evision.CUDA.GpuMat.t().Optional mask to select a sub-matrix.
Return
minVal:
double*.Pointer to the returned minimum value. Use NULL if not required.
maxVal:
double*.Pointer to the returned maximum value. Use NULL if not required.
The function does not work with CV_64F images on GPUs with the compute capability \< 1.3. @sa minMaxLoc
Python prototype (for reference only):
minMax(src[, mask]) -> minVal, maxVal@spec minMaxLoc(Evision.Mat.maybe_mat_in()) :: {number(), number(), {number(), number()}, {number(), number()}} | {:error, String.t()}
@spec minMaxLoc(Evision.CUDA.GpuMat.t()) :: {number(), number(), {number(), number()}, {number(), number()}} | {:error, String.t()}
Variant 1:
Finds global minimum and maximum matrix elements and returns their values with locations.
Positional Arguments
src:
Evision.Mat.t().Single-channel source image.
Keyword Arguments
mask:
Evision.Mat.t().Optional mask to select a sub-matrix.
Return
minVal:
double*.Pointer to the returned minimum value. Use NULL if not required.
maxVal:
double*.Pointer to the returned maximum value. Use NULL if not required.
minLoc:
Point*.Pointer to the returned minimum location. Use NULL if not required.
maxLoc:
Point*.Pointer to the returned maximum location. Use NULL if not required.
The function does not work with CV_64F images on GPU with the compute capability \< 1.3. @sa minMaxLoc
Python prototype (for reference only):
minMaxLoc(src[, mask]) -> minVal, maxVal, minLoc, maxLocVariant 2:
Finds global minimum and maximum matrix elements and returns their values with locations.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Single-channel source image.
Keyword Arguments
mask:
Evision.CUDA.GpuMat.t().Optional mask to select a sub-matrix.
Return
minVal:
double*.Pointer to the returned minimum value. Use NULL if not required.
maxVal:
double*.Pointer to the returned maximum value. Use NULL if not required.
minLoc:
Point*.Pointer to the returned minimum location. Use NULL if not required.
maxLoc:
Point*.Pointer to the returned maximum location. Use NULL if not required.
The function does not work with CV_64F images on GPU with the compute capability \< 1.3. @sa minMaxLoc
Python prototype (for reference only):
minMaxLoc(src[, mask]) -> minVal, maxVal, minLoc, maxLoc@spec minMaxLoc(Evision.Mat.maybe_mat_in(), [{atom(), term()}, ...] | nil) :: {number(), number(), {number(), number()}, {number(), number()}} | {:error, String.t()}
@spec minMaxLoc(Evision.CUDA.GpuMat.t(), [{atom(), term()}, ...] | nil) :: {number(), number(), {number(), number()}, {number(), number()}} | {:error, String.t()}
Variant 1:
Finds global minimum and maximum matrix elements and returns their values with locations.
Positional Arguments
src:
Evision.Mat.t().Single-channel source image.
Keyword Arguments
mask:
Evision.Mat.t().Optional mask to select a sub-matrix.
Return
minVal:
double*.Pointer to the returned minimum value. Use NULL if not required.
maxVal:
double*.Pointer to the returned maximum value. Use NULL if not required.
minLoc:
Point*.Pointer to the returned minimum location. Use NULL if not required.
maxLoc:
Point*.Pointer to the returned maximum location. Use NULL if not required.
The function does not work with CV_64F images on GPU with the compute capability \< 1.3. @sa minMaxLoc
Python prototype (for reference only):
minMaxLoc(src[, mask]) -> minVal, maxVal, minLoc, maxLocVariant 2:
Finds global minimum and maximum matrix elements and returns their values with locations.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Single-channel source image.
Keyword Arguments
mask:
Evision.CUDA.GpuMat.t().Optional mask to select a sub-matrix.
Return
minVal:
double*.Pointer to the returned minimum value. Use NULL if not required.
maxVal:
double*.Pointer to the returned maximum value. Use NULL if not required.
minLoc:
Point*.Pointer to the returned minimum location. Use NULL if not required.
maxLoc:
Point*.Pointer to the returned maximum location. Use NULL if not required.
The function does not work with CV_64F images on GPU with the compute capability \< 1.3. @sa minMaxLoc
Python prototype (for reference only):
minMaxLoc(src[, mask]) -> minVal, maxVal, minLoc, maxLoc@spec moments(Evision.Mat.maybe_mat_in()) :: Evision.CUDA.Moments.t() | {:error, String.t()}
@spec moments(Evision.CUDA.GpuMat.t()) :: Evision.CUDA.Moments.t() | {:error, String.t()}
Variant 1:
Calculates all of the moments up to the 3rd order of a rasterized shape.
Positional Arguments
src:
Evision.Mat.t().Raster image (single-channel 2D array).
Keyword Arguments
binaryImage:
bool.If it is true, all non-zero image pixels are treated as 1's.
order:
MomentsOrder.Order of largest moments to calculate with lower order moments requiring less computation.
momentsType:
int.Precision to use when calculating moments. Available types are
CV_32FandCV_64Fwith the performance ofCV_32Fan order of magnitude greater thanCV_64F. If the image is small the accuracy fromCV_32Fcan be equal or very close toCV_64F.
Return
- retval:
Moments
The function computes moments, up to the 3rd order, of a rasterized shape. The results are returned in the structure cv::Moments.
Note: For maximum performance use the asynchronous version cuda::spatialMoments() as this version interally allocates and deallocates both GpuMat and HostMem to respectively perform the calculation on the device and download the result to the host. The costly HostMem allocation cannot be avoided however the GpuMat device allocation can be by using BufferPool, e.g.
setBufferPoolUsage(true);
setBufferPoolConfig(getDevice(), numMoments(order) * ((momentsType == CV_64F) ? sizeof(double) : sizeof(float)), 1);see the \a CUDA_TEST_P(Moments, Accuracy) test inside opencv_contrib_source_code/modules/cudaimgproc/test/test_moments.cpp for an example. @returns cv::Moments. @sa cuda::spatialMoments
Python prototype (for reference only):
moments(src[, binaryImage[, order[, momentsType]]]) -> retvalVariant 2:
Calculates all of the moments up to the 3rd order of a rasterized shape.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Raster image (single-channel 2D array).
Keyword Arguments
binaryImage:
bool.If it is true, all non-zero image pixels are treated as 1's.
order:
MomentsOrder.Order of largest moments to calculate with lower order moments requiring less computation.
momentsType:
int.Precision to use when calculating moments. Available types are
CV_32FandCV_64Fwith the performance ofCV_32Fan order of magnitude greater thanCV_64F. If the image is small the accuracy fromCV_32Fcan be equal or very close toCV_64F.
Return
- retval:
Moments
The function computes moments, up to the 3rd order, of a rasterized shape. The results are returned in the structure cv::Moments.
Note: For maximum performance use the asynchronous version cuda::spatialMoments() as this version interally allocates and deallocates both GpuMat and HostMem to respectively perform the calculation on the device and download the result to the host. The costly HostMem allocation cannot be avoided however the GpuMat device allocation can be by using BufferPool, e.g.
setBufferPoolUsage(true);
setBufferPoolConfig(getDevice(), numMoments(order) * ((momentsType == CV_64F) ? sizeof(double) : sizeof(float)), 1);see the \a CUDA_TEST_P(Moments, Accuracy) test inside opencv_contrib_source_code/modules/cudaimgproc/test/test_moments.cpp for an example. @returns cv::Moments. @sa cuda::spatialMoments
Python prototype (for reference only):
moments(src[, binaryImage[, order[, momentsType]]]) -> retval@spec moments(Evision.Mat.maybe_mat_in(), [{atom(), term()}, ...] | nil) :: Evision.CUDA.Moments.t() | {:error, String.t()}
@spec moments(Evision.CUDA.GpuMat.t(), [{atom(), term()}, ...] | nil) :: Evision.CUDA.Moments.t() | {:error, String.t()}
Variant 1:
Calculates all of the moments up to the 3rd order of a rasterized shape.
Positional Arguments
src:
Evision.Mat.t().Raster image (single-channel 2D array).
Keyword Arguments
binaryImage:
bool.If it is true, all non-zero image pixels are treated as 1's.
order:
MomentsOrder.Order of largest moments to calculate with lower order moments requiring less computation.
momentsType:
int.Precision to use when calculating moments. Available types are
CV_32FandCV_64Fwith the performance ofCV_32Fan order of magnitude greater thanCV_64F. If the image is small the accuracy fromCV_32Fcan be equal or very close toCV_64F.
Return
- retval:
Moments
The function computes moments, up to the 3rd order, of a rasterized shape. The results are returned in the structure cv::Moments.
Note: For maximum performance use the asynchronous version cuda::spatialMoments() as this version interally allocates and deallocates both GpuMat and HostMem to respectively perform the calculation on the device and download the result to the host. The costly HostMem allocation cannot be avoided however the GpuMat device allocation can be by using BufferPool, e.g.
setBufferPoolUsage(true);
setBufferPoolConfig(getDevice(), numMoments(order) * ((momentsType == CV_64F) ? sizeof(double) : sizeof(float)), 1);see the \a CUDA_TEST_P(Moments, Accuracy) test inside opencv_contrib_source_code/modules/cudaimgproc/test/test_moments.cpp for an example. @returns cv::Moments. @sa cuda::spatialMoments
Python prototype (for reference only):
moments(src[, binaryImage[, order[, momentsType]]]) -> retvalVariant 2:
Calculates all of the moments up to the 3rd order of a rasterized shape.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Raster image (single-channel 2D array).
Keyword Arguments
binaryImage:
bool.If it is true, all non-zero image pixels are treated as 1's.
order:
MomentsOrder.Order of largest moments to calculate with lower order moments requiring less computation.
momentsType:
int.Precision to use when calculating moments. Available types are
CV_32FandCV_64Fwith the performance ofCV_32Fan order of magnitude greater thanCV_64F. If the image is small the accuracy fromCV_32Fcan be equal or very close toCV_64F.
Return
- retval:
Moments
The function computes moments, up to the 3rd order, of a rasterized shape. The results are returned in the structure cv::Moments.
Note: For maximum performance use the asynchronous version cuda::spatialMoments() as this version interally allocates and deallocates both GpuMat and HostMem to respectively perform the calculation on the device and download the result to the host. The costly HostMem allocation cannot be avoided however the GpuMat device allocation can be by using BufferPool, e.g.
setBufferPoolUsage(true);
setBufferPoolConfig(getDevice(), numMoments(order) * ((momentsType == CV_64F) ? sizeof(double) : sizeof(float)), 1);see the \a CUDA_TEST_P(Moments, Accuracy) test inside opencv_contrib_source_code/modules/cudaimgproc/test/test_moments.cpp for an example. @returns cv::Moments. @sa cuda::spatialMoments
Python prototype (for reference only):
moments(src[, binaryImage[, order[, momentsType]]]) -> retval@spec mulAndScaleSpectrums( Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in(), integer(), number() ) :: Evision.Mat.t() | {:error, String.t()}
@spec mulAndScaleSpectrums( Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t(), integer(), number() ) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Performs a per-element multiplication of two Fourier spectrums and scales the result.
Positional Arguments
src1:
Evision.Mat.t().First spectrum.
src2:
Evision.Mat.t().Second spectrum with the same size and type as a .
flags:
int.Mock parameter used for CPU/CUDA interfaces similarity, simply add a
0value.scale:
float.Scale constant.
Keyword Arguments
conjB:
bool.Optional flag to specify if the second spectrum needs to be conjugated before the multiplication.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Destination spectrum.
Only full (not packed) CV_32FC2 complex spectrums in the interleaved format are supported for now. @sa mulSpectrums
Python prototype (for reference only):
mulAndScaleSpectrums(src1, src2, flags, scale[, dst[, conjB[, stream]]]) -> dstVariant 2:
Performs a per-element multiplication of two Fourier spectrums and scales the result.
Positional Arguments
src1:
Evision.CUDA.GpuMat.t().First spectrum.
src2:
Evision.CUDA.GpuMat.t().Second spectrum with the same size and type as a .
flags:
int.Mock parameter used for CPU/CUDA interfaces similarity, simply add a
0value.scale:
float.Scale constant.
Keyword Arguments
conjB:
bool.Optional flag to specify if the second spectrum needs to be conjugated before the multiplication.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Destination spectrum.
Only full (not packed) CV_32FC2 complex spectrums in the interleaved format are supported for now. @sa mulSpectrums
Python prototype (for reference only):
mulAndScaleSpectrums(src1, src2, flags, scale[, dst[, conjB[, stream]]]) -> dst@spec mulAndScaleSpectrums( Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in(), integer(), number(), [{atom(), term()}, ...] | nil ) :: Evision.Mat.t() | {:error, String.t()}
@spec mulAndScaleSpectrums( Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t(), integer(), number(), [{atom(), term()}, ...] | nil ) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Performs a per-element multiplication of two Fourier spectrums and scales the result.
Positional Arguments
src1:
Evision.Mat.t().First spectrum.
src2:
Evision.Mat.t().Second spectrum with the same size and type as a .
flags:
int.Mock parameter used for CPU/CUDA interfaces similarity, simply add a
0value.scale:
float.Scale constant.
Keyword Arguments
conjB:
bool.Optional flag to specify if the second spectrum needs to be conjugated before the multiplication.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Destination spectrum.
Only full (not packed) CV_32FC2 complex spectrums in the interleaved format are supported for now. @sa mulSpectrums
Python prototype (for reference only):
mulAndScaleSpectrums(src1, src2, flags, scale[, dst[, conjB[, stream]]]) -> dstVariant 2:
Performs a per-element multiplication of two Fourier spectrums and scales the result.
Positional Arguments
src1:
Evision.CUDA.GpuMat.t().First spectrum.
src2:
Evision.CUDA.GpuMat.t().Second spectrum with the same size and type as a .
flags:
int.Mock parameter used for CPU/CUDA interfaces similarity, simply add a
0value.scale:
float.Scale constant.
Keyword Arguments
conjB:
bool.Optional flag to specify if the second spectrum needs to be conjugated before the multiplication.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Destination spectrum.
Only full (not packed) CV_32FC2 complex spectrums in the interleaved format are supported for now. @sa mulSpectrums
Python prototype (for reference only):
mulAndScaleSpectrums(src1, src2, flags, scale[, dst[, conjB[, stream]]]) -> dst@spec mulSpectrums(Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in(), integer()) :: Evision.Mat.t() | {:error, String.t()}
@spec mulSpectrums(Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t(), integer()) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Performs a per-element multiplication of two Fourier spectrums.
Positional Arguments
src1:
Evision.Mat.t().First spectrum.
src2:
Evision.Mat.t().Second spectrum with the same size and type as a .
flags:
int.Mock parameter used for CPU/CUDA interfaces similarity.
Keyword Arguments
conjB:
bool.Optional flag to specify if the second spectrum needs to be conjugated before the multiplication.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Destination spectrum.
Only full (not packed) CV_32FC2 complex spectrums in the interleaved format are supported for now. @sa mulSpectrums
Python prototype (for reference only):
mulSpectrums(src1, src2, flags[, dst[, conjB[, stream]]]) -> dstVariant 2:
Performs a per-element multiplication of two Fourier spectrums.
Positional Arguments
src1:
Evision.CUDA.GpuMat.t().First spectrum.
src2:
Evision.CUDA.GpuMat.t().Second spectrum with the same size and type as a .
flags:
int.Mock parameter used for CPU/CUDA interfaces similarity.
Keyword Arguments
conjB:
bool.Optional flag to specify if the second spectrum needs to be conjugated before the multiplication.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Destination spectrum.
Only full (not packed) CV_32FC2 complex spectrums in the interleaved format are supported for now. @sa mulSpectrums
Python prototype (for reference only):
mulSpectrums(src1, src2, flags[, dst[, conjB[, stream]]]) -> dst@spec mulSpectrums( Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in(), integer(), [{atom(), term()}, ...] | nil ) :: Evision.Mat.t() | {:error, String.t()}
@spec mulSpectrums( Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t(), integer(), [{atom(), term()}, ...] | nil ) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Performs a per-element multiplication of two Fourier spectrums.
Positional Arguments
src1:
Evision.Mat.t().First spectrum.
src2:
Evision.Mat.t().Second spectrum with the same size and type as a .
flags:
int.Mock parameter used for CPU/CUDA interfaces similarity.
Keyword Arguments
conjB:
bool.Optional flag to specify if the second spectrum needs to be conjugated before the multiplication.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Destination spectrum.
Only full (not packed) CV_32FC2 complex spectrums in the interleaved format are supported for now. @sa mulSpectrums
Python prototype (for reference only):
mulSpectrums(src1, src2, flags[, dst[, conjB[, stream]]]) -> dstVariant 2:
Performs a per-element multiplication of two Fourier spectrums.
Positional Arguments
src1:
Evision.CUDA.GpuMat.t().First spectrum.
src2:
Evision.CUDA.GpuMat.t().Second spectrum with the same size and type as a .
flags:
int.Mock parameter used for CPU/CUDA interfaces similarity.
Keyword Arguments
conjB:
bool.Optional flag to specify if the second spectrum needs to be conjugated before the multiplication.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Destination spectrum.
Only full (not packed) CV_32FC2 complex spectrums in the interleaved format are supported for now. @sa mulSpectrums
Python prototype (for reference only):
mulSpectrums(src1, src2, flags[, dst[, conjB[, stream]]]) -> dst@spec multiply(Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in()) :: Evision.Mat.t() | {:error, String.t()}
@spec multiply(Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t()) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Computes a matrix-matrix or matrix-scalar per-element product.
Positional Arguments
src1:
Evision.Mat.t().First source matrix or scalar.
src2:
Evision.Mat.t().Second source matrix or scalar.
Keyword Arguments
scale:
double.Optional scale factor.
dtype:
int.Optional depth of the output array.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Destination matrix that has the same size and number of channels as the input array(s). The depth is defined by dtype or src1 depth.
@sa multiply
Python prototype (for reference only):
multiply(src1, src2[, dst[, scale[, dtype[, stream]]]]) -> dstVariant 2:
Computes a matrix-matrix or matrix-scalar per-element product.
Positional Arguments
src1:
Evision.CUDA.GpuMat.t().First source matrix or scalar.
src2:
Evision.CUDA.GpuMat.t().Second source matrix or scalar.
Keyword Arguments
scale:
double.Optional scale factor.
dtype:
int.Optional depth of the output array.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Destination matrix that has the same size and number of channels as the input array(s). The depth is defined by dtype or src1 depth.
@sa multiply
Python prototype (for reference only):
multiply(src1, src2[, dst[, scale[, dtype[, stream]]]]) -> dst@spec multiply( Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in(), [{atom(), term()}, ...] | nil ) :: Evision.Mat.t() | {:error, String.t()}
@spec multiply( Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t(), [{atom(), term()}, ...] | nil ) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Computes a matrix-matrix or matrix-scalar per-element product.
Positional Arguments
src1:
Evision.Mat.t().First source matrix or scalar.
src2:
Evision.Mat.t().Second source matrix or scalar.
Keyword Arguments
scale:
double.Optional scale factor.
dtype:
int.Optional depth of the output array.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Destination matrix that has the same size and number of channels as the input array(s). The depth is defined by dtype or src1 depth.
@sa multiply
Python prototype (for reference only):
multiply(src1, src2[, dst[, scale[, dtype[, stream]]]]) -> dstVariant 2:
Computes a matrix-matrix or matrix-scalar per-element product.
Positional Arguments
src1:
Evision.CUDA.GpuMat.t().First source matrix or scalar.
src2:
Evision.CUDA.GpuMat.t().Second source matrix or scalar.
Keyword Arguments
scale:
double.Optional scale factor.
dtype:
int.Optional depth of the output array.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Destination matrix that has the same size and number of channels as the input array(s). The depth is defined by dtype or src1 depth.
@sa multiply
Python prototype (for reference only):
multiply(src1, src2[, dst[, scale[, dtype[, stream]]]]) -> dst@spec nonLocalMeans(Evision.CUDA.GpuMat.t(), number()) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Performs pure non local means denoising without any simplification, and thus it is not fast.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Source image. Supports only CV_8UC1, CV_8UC2 and CV_8UC3.
h:
float.Filter sigma regulating filter strength for color.
Keyword Arguments
search_window:
int.Size of search window.
block_size:
int.Size of block used for computing weights.
borderMode:
int.Border type. See borderInterpolate for details. BORDER_REFLECT101 , BORDER_REPLICATE , BORDER_CONSTANT , BORDER_REFLECT and BORDER_WRAP are supported for now.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Destination image.
@sa fastNlMeansDenoising
Python prototype (for reference only):
nonLocalMeans(src, h[, dst[, search_window[, block_size[, borderMode[, stream]]]]]) -> dst@spec nonLocalMeans(Evision.CUDA.GpuMat.t(), number(), [{atom(), term()}, ...] | nil) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Performs pure non local means denoising without any simplification, and thus it is not fast.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Source image. Supports only CV_8UC1, CV_8UC2 and CV_8UC3.
h:
float.Filter sigma regulating filter strength for color.
Keyword Arguments
search_window:
int.Size of search window.
block_size:
int.Size of block used for computing weights.
borderMode:
int.Border type. See borderInterpolate for details. BORDER_REFLECT101 , BORDER_REPLICATE , BORDER_CONSTANT , BORDER_REFLECT and BORDER_WRAP are supported for now.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Destination image.
@sa fastNlMeansDenoising
Python prototype (for reference only):
nonLocalMeans(src, h[, dst[, search_window[, block_size[, borderMode[, stream]]]]]) -> dst@spec norm(Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in()) :: number() | {:error, String.t()}
@spec norm(Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t()) :: number() | {:error, String.t()}
@spec norm(Evision.Mat.maybe_mat_in(), integer()) :: number() | {:error, String.t()}
@spec norm(Evision.CUDA.GpuMat.t(), integer()) :: number() | {:error, String.t()}
Variant 1:
Returns the difference of two matrices.
Positional Arguments
src1:
Evision.Mat.t().Source matrix. Any matrices except 64F are supported.
src2:
Evision.Mat.t().Second source matrix (if any) with the same size and type as src1.
Keyword Arguments
normType:
int.Norm type. NORM_L1 , NORM_L2 , and NORM_INF are supported for now.
Return
- retval:
double
@sa norm
Python prototype (for reference only):
norm(src1, src2[, normType]) -> retvalVariant 2:
Returns the difference of two matrices.
Positional Arguments
src1:
Evision.CUDA.GpuMat.t().Source matrix. Any matrices except 64F are supported.
src2:
Evision.CUDA.GpuMat.t().Second source matrix (if any) with the same size and type as src1.
Keyword Arguments
normType:
int.Norm type. NORM_L1 , NORM_L2 , and NORM_INF are supported for now.
Return
- retval:
double
@sa norm
Python prototype (for reference only):
norm(src1, src2[, normType]) -> retvalVariant 3:
Returns the norm of a matrix (or difference of two matrices).
Positional Arguments
src1:
Evision.Mat.t().Source matrix. Any matrices except 64F are supported.
normType:
int.Norm type. NORM_L1 , NORM_L2 , and NORM_INF are supported for now.
Keyword Arguments
mask:
Evision.Mat.t().optional operation mask; it must have the same size as src1 and CV_8UC1 type.
Return
- retval:
double
@sa norm
Python prototype (for reference only):
norm(src1, normType[, mask]) -> retvalVariant 4:
Returns the norm of a matrix (or difference of two matrices).
Positional Arguments
src1:
Evision.CUDA.GpuMat.t().Source matrix. Any matrices except 64F are supported.
normType:
int.Norm type. NORM_L1 , NORM_L2 , and NORM_INF are supported for now.
Keyword Arguments
mask:
Evision.CUDA.GpuMat.t().optional operation mask; it must have the same size as src1 and CV_8UC1 type.
Return
- retval:
double
@sa norm
Python prototype (for reference only):
norm(src1, normType[, mask]) -> retval@spec norm( Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in(), [{atom(), term()}, ...] | nil ) :: number() | {:error, String.t()}
@spec norm( Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t(), [{atom(), term()}, ...] | nil ) :: number() | {:error, String.t()}
@spec norm(Evision.Mat.maybe_mat_in(), integer(), [{atom(), term()}, ...] | nil) :: number() | {:error, String.t()}
@spec norm(Evision.CUDA.GpuMat.t(), integer(), [{atom(), term()}, ...] | nil) :: number() | {:error, String.t()}
Variant 1:
Returns the difference of two matrices.
Positional Arguments
src1:
Evision.Mat.t().Source matrix. Any matrices except 64F are supported.
src2:
Evision.Mat.t().Second source matrix (if any) with the same size and type as src1.
Keyword Arguments
normType:
int.Norm type. NORM_L1 , NORM_L2 , and NORM_INF are supported for now.
Return
- retval:
double
@sa norm
Python prototype (for reference only):
norm(src1, src2[, normType]) -> retvalVariant 2:
Returns the difference of two matrices.
Positional Arguments
src1:
Evision.CUDA.GpuMat.t().Source matrix. Any matrices except 64F are supported.
src2:
Evision.CUDA.GpuMat.t().Second source matrix (if any) with the same size and type as src1.
Keyword Arguments
normType:
int.Norm type. NORM_L1 , NORM_L2 , and NORM_INF are supported for now.
Return
- retval:
double
@sa norm
Python prototype (for reference only):
norm(src1, src2[, normType]) -> retvalVariant 3:
Returns the norm of a matrix (or difference of two matrices).
Positional Arguments
src1:
Evision.Mat.t().Source matrix. Any matrices except 64F are supported.
normType:
int.Norm type. NORM_L1 , NORM_L2 , and NORM_INF are supported for now.
Keyword Arguments
mask:
Evision.Mat.t().optional operation mask; it must have the same size as src1 and CV_8UC1 type.
Return
- retval:
double
@sa norm
Python prototype (for reference only):
norm(src1, normType[, mask]) -> retvalVariant 4:
Returns the norm of a matrix (or difference of two matrices).
Positional Arguments
src1:
Evision.CUDA.GpuMat.t().Source matrix. Any matrices except 64F are supported.
normType:
int.Norm type. NORM_L1 , NORM_L2 , and NORM_INF are supported for now.
Keyword Arguments
mask:
Evision.CUDA.GpuMat.t().optional operation mask; it must have the same size as src1 and CV_8UC1 type.
Return
- retval:
double
@sa norm
Python prototype (for reference only):
norm(src1, normType[, mask]) -> retval@spec normalize(Evision.Mat.maybe_mat_in(), number(), number(), integer(), integer()) :: Evision.Mat.t() | {:error, String.t()}
@spec normalize(Evision.CUDA.GpuMat.t(), number(), number(), integer(), integer()) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Normalizes the norm or value range of an array.
Positional Arguments
src:
Evision.Mat.t().Input array.
alpha:
double.Norm value to normalize to or the lower range boundary in case of the range normalization.
beta:
double.Upper range boundary in case of the range normalization; it is not used for the norm normalization.
norm_type:
int.Normalization type ( NORM_MINMAX , NORM_L2 , NORM_L1 or NORM_INF ).
dtype:
int.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).
Keyword Arguments
mask:
Evision.Mat.t().Optional operation mask.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Output array of the same size as src .
@sa normalize
Python prototype (for reference only):
normalize(src, alpha, beta, norm_type, dtype[, dst[, mask[, stream]]]) -> dstVariant 2:
Normalizes the norm or value range of an array.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Input array.
alpha:
double.Norm value to normalize to or the lower range boundary in case of the range normalization.
beta:
double.Upper range boundary in case of the range normalization; it is not used for the norm normalization.
norm_type:
int.Normalization type ( NORM_MINMAX , NORM_L2 , NORM_L1 or NORM_INF ).
dtype:
int.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).
Keyword Arguments
mask:
Evision.CUDA.GpuMat.t().Optional operation mask.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Output array of the same size as src .
@sa normalize
Python prototype (for reference only):
normalize(src, alpha, beta, norm_type, dtype[, dst[, mask[, stream]]]) -> dst@spec normalize( Evision.Mat.maybe_mat_in(), number(), number(), integer(), integer(), [{atom(), term()}, ...] | nil ) :: Evision.Mat.t() | {:error, String.t()}
@spec normalize( Evision.CUDA.GpuMat.t(), number(), number(), integer(), integer(), [{atom(), term()}, ...] | nil ) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Normalizes the norm or value range of an array.
Positional Arguments
src:
Evision.Mat.t().Input array.
alpha:
double.Norm value to normalize to or the lower range boundary in case of the range normalization.
beta:
double.Upper range boundary in case of the range normalization; it is not used for the norm normalization.
norm_type:
int.Normalization type ( NORM_MINMAX , NORM_L2 , NORM_L1 or NORM_INF ).
dtype:
int.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).
Keyword Arguments
mask:
Evision.Mat.t().Optional operation mask.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Output array of the same size as src .
@sa normalize
Python prototype (for reference only):
normalize(src, alpha, beta, norm_type, dtype[, dst[, mask[, stream]]]) -> dstVariant 2:
Normalizes the norm or value range of an array.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Input array.
alpha:
double.Norm value to normalize to or the lower range boundary in case of the range normalization.
beta:
double.Upper range boundary in case of the range normalization; it is not used for the norm normalization.
norm_type:
int.Normalization type ( NORM_MINMAX , NORM_L2 , NORM_L1 or NORM_INF ).
dtype:
int.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).
Keyword Arguments
mask:
Evision.CUDA.GpuMat.t().Optional operation mask.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Output array of the same size as src .
@sa normalize
Python prototype (for reference only):
normalize(src, alpha, beta, norm_type, dtype[, dst[, mask[, stream]]]) -> dstReturns the number of image moments less than or equal to the largest image moments \a order.
Positional Arguments
order:
MomentsOrder.Order of largest moments to calculate with lower order moments requiring less computation.
Return
- retval:
int
@returns number of image moments. @sa cuda::moments, cuda::spatialMoments, cuda::MomentsOrder
Python prototype (for reference only):
numMoments(order) -> retval@spec phase(Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in()) :: Evision.Mat.t() | {:error, String.t()}
@spec phase(Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t()) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Computes polar angles of complex matrix elements.
Positional Arguments
x:
Evision.Mat.t().Source matrix containing real components ( CV_32FC1 ).
y:
Evision.Mat.t().Source matrix containing imaginary components ( CV_32FC1 ).
Keyword Arguments
angleInDegrees:
bool.Flag for angles that must be evaluated in degrees.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
angle:
Evision.Mat.t().Destination matrix of angles ( CV_32FC1 ).
@sa phase
Python prototype (for reference only):
phase(x, y[, angle[, angleInDegrees[, stream]]]) -> angleVariant 2:
Computes polar angles of complex matrix elements.
Positional Arguments
x:
Evision.CUDA.GpuMat.t().Source matrix containing real components ( CV_32FC1 ).
y:
Evision.CUDA.GpuMat.t().Source matrix containing imaginary components ( CV_32FC1 ).
Keyword Arguments
angleInDegrees:
bool.Flag for angles that must be evaluated in degrees.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
angle:
Evision.CUDA.GpuMat.t().Destination matrix of angles ( CV_32FC1 ).
@sa phase
Python prototype (for reference only):
phase(x, y[, angle[, angleInDegrees[, stream]]]) -> angle@spec phase( Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in(), [{atom(), term()}, ...] | nil ) :: Evision.Mat.t() | {:error, String.t()}
@spec phase( Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t(), [{atom(), term()}, ...] | nil ) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Computes polar angles of complex matrix elements.
Positional Arguments
x:
Evision.Mat.t().Source matrix containing real components ( CV_32FC1 ).
y:
Evision.Mat.t().Source matrix containing imaginary components ( CV_32FC1 ).
Keyword Arguments
angleInDegrees:
bool.Flag for angles that must be evaluated in degrees.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
angle:
Evision.Mat.t().Destination matrix of angles ( CV_32FC1 ).
@sa phase
Python prototype (for reference only):
phase(x, y[, angle[, angleInDegrees[, stream]]]) -> angleVariant 2:
Computes polar angles of complex matrix elements.
Positional Arguments
x:
Evision.CUDA.GpuMat.t().Source matrix containing real components ( CV_32FC1 ).
y:
Evision.CUDA.GpuMat.t().Source matrix containing imaginary components ( CV_32FC1 ).
Keyword Arguments
angleInDegrees:
bool.Flag for angles that must be evaluated in degrees.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
angle:
Evision.CUDA.GpuMat.t().Destination matrix of angles ( CV_32FC1 ).
@sa phase
Python prototype (for reference only):
phase(x, y[, angle[, angleInDegrees[, stream]]]) -> angle@spec polarToCart(Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in()) :: {Evision.Mat.t(), Evision.Mat.t()} | {:error, String.t()}
@spec polarToCart(Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t()) :: {Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t()} | {:error, String.t()}
Variant 1:
Converts polar coordinates into Cartesian.
Positional Arguments
magnitude:
Evision.Mat.t().Source matrix containing magnitudes ( CV_32FC1 or CV_64FC1 ).
angle:
Evision.Mat.t().Source matrix containing angles ( same type as magnitude ).
Keyword Arguments
angleInDegrees:
bool.Flag that indicates angles in degrees.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
x:
Evision.Mat.t().Destination matrix of real components ( same type as magnitude ).
y:
Evision.Mat.t().Destination matrix of imaginary components ( same type as magnitude ).
Python prototype (for reference only):
polarToCart(magnitude, angle[, x[, y[, angleInDegrees[, stream]]]]) -> x, yVariant 2:
Converts polar coordinates into Cartesian.
Positional Arguments
magnitude:
Evision.CUDA.GpuMat.t().Source matrix containing magnitudes ( CV_32FC1 or CV_64FC1 ).
angle:
Evision.CUDA.GpuMat.t().Source matrix containing angles ( same type as magnitude ).
Keyword Arguments
angleInDegrees:
bool.Flag that indicates angles in degrees.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
x:
Evision.CUDA.GpuMat.t().Destination matrix of real components ( same type as magnitude ).
y:
Evision.CUDA.GpuMat.t().Destination matrix of imaginary components ( same type as magnitude ).
Python prototype (for reference only):
polarToCart(magnitude, angle[, x[, y[, angleInDegrees[, stream]]]]) -> x, y@spec polarToCart( Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in(), [{atom(), term()}, ...] | nil ) :: {Evision.Mat.t(), Evision.Mat.t()} | {:error, String.t()}
@spec polarToCart( Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t(), [{atom(), term()}, ...] | nil ) :: {Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t()} | {:error, String.t()}
Variant 1:
Converts polar coordinates into Cartesian.
Positional Arguments
magnitude:
Evision.Mat.t().Source matrix containing magnitudes ( CV_32FC1 or CV_64FC1 ).
angle:
Evision.Mat.t().Source matrix containing angles ( same type as magnitude ).
Keyword Arguments
angleInDegrees:
bool.Flag that indicates angles in degrees.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
x:
Evision.Mat.t().Destination matrix of real components ( same type as magnitude ).
y:
Evision.Mat.t().Destination matrix of imaginary components ( same type as magnitude ).
Python prototype (for reference only):
polarToCart(magnitude, angle[, x[, y[, angleInDegrees[, stream]]]]) -> x, yVariant 2:
Converts polar coordinates into Cartesian.
Positional Arguments
magnitude:
Evision.CUDA.GpuMat.t().Source matrix containing magnitudes ( CV_32FC1 or CV_64FC1 ).
angle:
Evision.CUDA.GpuMat.t().Source matrix containing angles ( same type as magnitude ).
Keyword Arguments
angleInDegrees:
bool.Flag that indicates angles in degrees.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
x:
Evision.CUDA.GpuMat.t().Destination matrix of real components ( same type as magnitude ).
y:
Evision.CUDA.GpuMat.t().Destination matrix of imaginary components ( same type as magnitude ).
Python prototype (for reference only):
polarToCart(magnitude, angle[, x[, y[, angleInDegrees[, stream]]]]) -> x, y@spec pow(Evision.Mat.maybe_mat_in(), number()) :: Evision.Mat.t() | {:error, String.t()}
@spec pow(Evision.CUDA.GpuMat.t(), number()) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Raises every matrix element to a power.
Positional Arguments
src:
Evision.Mat.t().Source matrix.
power:
double.Exponent of power.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Destination matrix with the same size and type as src .
The function pow raises every element of the input matrix to power : \f[\texttt{dst} (I) = \fork{\texttt{src}(I)^power}{if \texttt{power} is integer}{|\texttt{src}(I)|^power}{otherwise}\f] @sa pow
Python prototype (for reference only):
pow(src, power[, dst[, stream]]) -> dstVariant 2:
Raises every matrix element to a power.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Source matrix.
power:
double.Exponent of power.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Destination matrix with the same size and type as src .
The function pow raises every element of the input matrix to power : \f[\texttt{dst} (I) = \fork{\texttt{src}(I)^power}{if \texttt{power} is integer}{|\texttt{src}(I)|^power}{otherwise}\f] @sa pow
Python prototype (for reference only):
pow(src, power[, dst[, stream]]) -> dst@spec pow(Evision.Mat.maybe_mat_in(), number(), [{atom(), term()}, ...] | nil) :: Evision.Mat.t() | {:error, String.t()}
@spec pow(Evision.CUDA.GpuMat.t(), number(), [{atom(), term()}, ...] | nil) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Raises every matrix element to a power.
Positional Arguments
src:
Evision.Mat.t().Source matrix.
power:
double.Exponent of power.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Destination matrix with the same size and type as src .
The function pow raises every element of the input matrix to power : \f[\texttt{dst} (I) = \fork{\texttt{src}(I)^power}{if \texttt{power} is integer}{|\texttt{src}(I)|^power}{otherwise}\f] @sa pow
Python prototype (for reference only):
pow(src, power[, dst[, stream]]) -> dstVariant 2:
Raises every matrix element to a power.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Source matrix.
power:
double.Exponent of power.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Destination matrix with the same size and type as src .
The function pow raises every element of the input matrix to power : \f[\texttt{dst} (I) = \fork{\texttt{src}(I)^power}{if \texttt{power} is integer}{|\texttt{src}(I)|^power}{otherwise}\f] @sa pow
Python prototype (for reference only):
pow(src, power[, dst[, stream]]) -> dstprintCudaDeviceInfo
Positional Arguments
- device:
int
Python prototype (for reference only):
printCudaDeviceInfo(device) -> NoneprintShortCudaDeviceInfo
Positional Arguments
- device:
int
Python prototype (for reference only):
printShortCudaDeviceInfo(device) -> None@spec pyrDown(Evision.Mat.maybe_mat_in()) :: Evision.Mat.t() | {:error, String.t()}
@spec pyrDown(Evision.CUDA.GpuMat.t()) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Smoothes an image and downsamples it.
Positional Arguments
src:
Evision.Mat.t().Source image.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Destination image. Will have Size((src.cols+1)/2, (src.rows+1)/2) size and the same type as src .
@sa pyrDown
Python prototype (for reference only):
pyrDown(src[, dst[, stream]]) -> dstVariant 2:
Smoothes an image and downsamples it.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Source image.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Destination image. Will have Size((src.cols+1)/2, (src.rows+1)/2) size and the same type as src .
@sa pyrDown
Python prototype (for reference only):
pyrDown(src[, dst[, stream]]) -> dst@spec pyrDown(Evision.Mat.maybe_mat_in(), [{atom(), term()}, ...] | nil) :: Evision.Mat.t() | {:error, String.t()}
@spec pyrDown(Evision.CUDA.GpuMat.t(), [{atom(), term()}, ...] | nil) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Smoothes an image and downsamples it.
Positional Arguments
src:
Evision.Mat.t().Source image.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Destination image. Will have Size((src.cols+1)/2, (src.rows+1)/2) size and the same type as src .
@sa pyrDown
Python prototype (for reference only):
pyrDown(src[, dst[, stream]]) -> dstVariant 2:
Smoothes an image and downsamples it.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Source image.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Destination image. Will have Size((src.cols+1)/2, (src.rows+1)/2) size and the same type as src .
@sa pyrDown
Python prototype (for reference only):
pyrDown(src[, dst[, stream]]) -> dst@spec pyrUp(Evision.Mat.maybe_mat_in()) :: Evision.Mat.t() | {:error, String.t()}
@spec pyrUp(Evision.CUDA.GpuMat.t()) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Upsamples an image and then smoothes it.
Positional Arguments
src:
Evision.Mat.t().Source image.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Destination image. Will have Size(src.cols*2, src.rows*2) size and the same type as src .
Python prototype (for reference only):
pyrUp(src[, dst[, stream]]) -> dstVariant 2:
Upsamples an image and then smoothes it.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Source image.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Destination image. Will have Size(src.cols*2, src.rows*2) size and the same type as src .
Python prototype (for reference only):
pyrUp(src[, dst[, stream]]) -> dst@spec pyrUp(Evision.Mat.maybe_mat_in(), [{atom(), term()}, ...] | nil) :: Evision.Mat.t() | {:error, String.t()}
@spec pyrUp(Evision.CUDA.GpuMat.t(), [{atom(), term()}, ...] | nil) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Upsamples an image and then smoothes it.
Positional Arguments
src:
Evision.Mat.t().Source image.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Destination image. Will have Size(src.cols*2, src.rows*2) size and the same type as src .
Python prototype (for reference only):
pyrUp(src[, dst[, stream]]) -> dstVariant 2:
Upsamples an image and then smoothes it.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Source image.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Destination image. Will have Size(src.cols*2, src.rows*2) size and the same type as src .
Python prototype (for reference only):
pyrUp(src[, dst[, stream]]) -> dst@spec rectStdDev( Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in(), {number(), number(), number(), number()} ) :: Evision.Mat.t() | {:error, String.t()}
@spec rectStdDev( Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t(), {number(), number(), number(), number()} ) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Computes a standard deviation of integral images.
Positional Arguments
src:
Evision.Mat.t().Source image. Only the CV_32SC1 type is supported.
sqr:
Evision.Mat.t().Squared source image. Only the CV_32FC1 type is supported.
rect:
Rect.Rectangular window.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Destination image with the same type and size as src.
Python prototype (for reference only):
rectStdDev(src, sqr, rect[, dst[, stream]]) -> dstVariant 2:
Computes a standard deviation of integral images.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Source image. Only the CV_32SC1 type is supported.
sqr:
Evision.CUDA.GpuMat.t().Squared source image. Only the CV_32FC1 type is supported.
rect:
Rect.Rectangular window.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Destination image with the same type and size as src.
Python prototype (for reference only):
rectStdDev(src, sqr, rect[, dst[, stream]]) -> dst@spec rectStdDev( Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in(), {number(), number(), number(), number()}, [{atom(), term()}, ...] | nil ) :: Evision.Mat.t() | {:error, String.t()}
@spec rectStdDev( Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t(), {number(), number(), number(), number()}, [{atom(), term()}, ...] | nil ) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Computes a standard deviation of integral images.
Positional Arguments
src:
Evision.Mat.t().Source image. Only the CV_32SC1 type is supported.
sqr:
Evision.Mat.t().Squared source image. Only the CV_32FC1 type is supported.
rect:
Rect.Rectangular window.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Destination image with the same type and size as src.
Python prototype (for reference only):
rectStdDev(src, sqr, rect[, dst[, stream]]) -> dstVariant 2:
Computes a standard deviation of integral images.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Source image. Only the CV_32SC1 type is supported.
sqr:
Evision.CUDA.GpuMat.t().Squared source image. Only the CV_32FC1 type is supported.
rect:
Rect.Rectangular window.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Destination image with the same type and size as src.
Python prototype (for reference only):
rectStdDev(src, sqr, rect[, dst[, stream]]) -> dst@spec reduce(Evision.Mat.maybe_mat_in(), integer(), integer()) :: Evision.Mat.t() | {:error, String.t()}
@spec reduce(Evision.CUDA.GpuMat.t(), integer(), integer()) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Reduces a matrix to a vector.
Positional Arguments
mtx:
Evision.Mat.t().Source 2D matrix.
dim:
int.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:
int.Reduction operation that could be one of the following:
- REDUCE_SUM The output is the sum of all rows/columns of the matrix.
- REDUCE_AVG The output is the mean vector of all rows/columns of the matrix.
- REDUCE_MAX The output is the maximum (column/row-wise) of all rows/columns of the matrix.
- REDUCE_MIN The output is the minimum (column/row-wise) of all rows/columns of the matrix.
Keyword Arguments
dtype:
int.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:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
vec:
Evision.Mat.t().Destination vector. Its size and type is defined by dim and dtype parameters.
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_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. @sa reduce
Python prototype (for reference only):
reduce(mtx, dim, reduceOp[, vec[, dtype[, stream]]]) -> vecVariant 2:
Reduces a matrix to a vector.
Positional Arguments
mtx:
Evision.CUDA.GpuMat.t().Source 2D matrix.
dim:
int.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:
int.Reduction operation that could be one of the following:
- REDUCE_SUM The output is the sum of all rows/columns of the matrix.
- REDUCE_AVG The output is the mean vector of all rows/columns of the matrix.
- REDUCE_MAX The output is the maximum (column/row-wise) of all rows/columns of the matrix.
- REDUCE_MIN The output is the minimum (column/row-wise) of all rows/columns of the matrix.
Keyword Arguments
dtype:
int.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:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
vec:
Evision.CUDA.GpuMat.t().Destination vector. Its size and type is defined by dim and dtype parameters.
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_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. @sa reduce
Python prototype (for reference only):
reduce(mtx, dim, reduceOp[, vec[, dtype[, stream]]]) -> vec@spec reduce( Evision.Mat.maybe_mat_in(), integer(), integer(), [{atom(), term()}, ...] | nil ) :: Evision.Mat.t() | {:error, String.t()}
@spec reduce( Evision.CUDA.GpuMat.t(), integer(), integer(), [{atom(), term()}, ...] | nil ) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Reduces a matrix to a vector.
Positional Arguments
mtx:
Evision.Mat.t().Source 2D matrix.
dim:
int.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:
int.Reduction operation that could be one of the following:
- REDUCE_SUM The output is the sum of all rows/columns of the matrix.
- REDUCE_AVG The output is the mean vector of all rows/columns of the matrix.
- REDUCE_MAX The output is the maximum (column/row-wise) of all rows/columns of the matrix.
- REDUCE_MIN The output is the minimum (column/row-wise) of all rows/columns of the matrix.
Keyword Arguments
dtype:
int.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:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
vec:
Evision.Mat.t().Destination vector. Its size and type is defined by dim and dtype parameters.
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_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. @sa reduce
Python prototype (for reference only):
reduce(mtx, dim, reduceOp[, vec[, dtype[, stream]]]) -> vecVariant 2:
Reduces a matrix to a vector.
Positional Arguments
mtx:
Evision.CUDA.GpuMat.t().Source 2D matrix.
dim:
int.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:
int.Reduction operation that could be one of the following:
- REDUCE_SUM The output is the sum of all rows/columns of the matrix.
- REDUCE_AVG The output is the mean vector of all rows/columns of the matrix.
- REDUCE_MAX The output is the maximum (column/row-wise) of all rows/columns of the matrix.
- REDUCE_MIN The output is the minimum (column/row-wise) of all rows/columns of the matrix.
Keyword Arguments
dtype:
int.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:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
vec:
Evision.CUDA.GpuMat.t().Destination vector. Its size and type is defined by dim and dtype parameters.
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_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. @sa reduce
Python prototype (for reference only):
reduce(mtx, dim, reduceOp[, vec[, dtype[, stream]]]) -> vec@spec registerPageLocked(Evision.Mat.maybe_mat_in()) :: :ok | {:error, String.t()}
Page-locks the memory of matrix and maps it for the device(s).
Positional Arguments
m:
Evision.Mat.t().Input matrix.
Python prototype (for reference only):
registerPageLocked(m) -> None@spec remap( Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in(), integer() ) :: Evision.Mat.t() | {:error, String.t()}
@spec remap( Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t(), integer() ) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Applies a generic geometrical transformation to an image.
Positional Arguments
src:
Evision.Mat.t().Source image.
xmap:
Evision.Mat.t().X values. Only CV_32FC1 type is supported.
ymap:
Evision.Mat.t().Y values. Only CV_32FC1 type is supported.
interpolation:
int.Interpolation method (see resize ). INTER_NEAREST , INTER_LINEAR and INTER_CUBIC are supported for now.
Keyword Arguments
borderMode:
int.Pixel extrapolation method (see borderInterpolate ). BORDER_REFLECT101 , BORDER_REPLICATE , BORDER_CONSTANT , BORDER_REFLECT and BORDER_WRAP are supported for now.
borderValue:
Scalar.Value used in case of a constant border. By default, it is 0.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Destination image with the size the same as xmap and the type the same as src .
The function transforms the source image using the specified map: \f[\texttt{dst} (x,y) = \texttt{src} (xmap(x,y), ymap(x,y))\f] Values of pixels with non-integer coordinates are computed using the bilinear interpolation. @sa remap
Python prototype (for reference only):
remap(src, xmap, ymap, interpolation[, dst[, borderMode[, borderValue[, stream]]]]) -> dstVariant 2:
Applies a generic geometrical transformation to an image.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Source image.
xmap:
Evision.CUDA.GpuMat.t().X values. Only CV_32FC1 type is supported.
ymap:
Evision.CUDA.GpuMat.t().Y values. Only CV_32FC1 type is supported.
interpolation:
int.Interpolation method (see resize ). INTER_NEAREST , INTER_LINEAR and INTER_CUBIC are supported for now.
Keyword Arguments
borderMode:
int.Pixel extrapolation method (see borderInterpolate ). BORDER_REFLECT101 , BORDER_REPLICATE , BORDER_CONSTANT , BORDER_REFLECT and BORDER_WRAP are supported for now.
borderValue:
Scalar.Value used in case of a constant border. By default, it is 0.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Destination image with the size the same as xmap and the type the same as src .
The function transforms the source image using the specified map: \f[\texttt{dst} (x,y) = \texttt{src} (xmap(x,y), ymap(x,y))\f] Values of pixels with non-integer coordinates are computed using the bilinear interpolation. @sa remap
Python prototype (for reference only):
remap(src, xmap, ymap, interpolation[, dst[, borderMode[, borderValue[, stream]]]]) -> dst@spec remap( Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in(), integer(), [{atom(), term()}, ...] | nil ) :: Evision.Mat.t() | {:error, String.t()}
@spec remap( Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t(), integer(), [{atom(), term()}, ...] | nil ) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Applies a generic geometrical transformation to an image.
Positional Arguments
src:
Evision.Mat.t().Source image.
xmap:
Evision.Mat.t().X values. Only CV_32FC1 type is supported.
ymap:
Evision.Mat.t().Y values. Only CV_32FC1 type is supported.
interpolation:
int.Interpolation method (see resize ). INTER_NEAREST , INTER_LINEAR and INTER_CUBIC are supported for now.
Keyword Arguments
borderMode:
int.Pixel extrapolation method (see borderInterpolate ). BORDER_REFLECT101 , BORDER_REPLICATE , BORDER_CONSTANT , BORDER_REFLECT and BORDER_WRAP are supported for now.
borderValue:
Scalar.Value used in case of a constant border. By default, it is 0.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Destination image with the size the same as xmap and the type the same as src .
The function transforms the source image using the specified map: \f[\texttt{dst} (x,y) = \texttt{src} (xmap(x,y), ymap(x,y))\f] Values of pixels with non-integer coordinates are computed using the bilinear interpolation. @sa remap
Python prototype (for reference only):
remap(src, xmap, ymap, interpolation[, dst[, borderMode[, borderValue[, stream]]]]) -> dstVariant 2:
Applies a generic geometrical transformation to an image.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Source image.
xmap:
Evision.CUDA.GpuMat.t().X values. Only CV_32FC1 type is supported.
ymap:
Evision.CUDA.GpuMat.t().Y values. Only CV_32FC1 type is supported.
interpolation:
int.Interpolation method (see resize ). INTER_NEAREST , INTER_LINEAR and INTER_CUBIC are supported for now.
Keyword Arguments
borderMode:
int.Pixel extrapolation method (see borderInterpolate ). BORDER_REFLECT101 , BORDER_REPLICATE , BORDER_CONSTANT , BORDER_REFLECT and BORDER_WRAP are supported for now.
borderValue:
Scalar.Value used in case of a constant border. By default, it is 0.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Destination image with the size the same as xmap and the type the same as src .
The function transforms the source image using the specified map: \f[\texttt{dst} (x,y) = \texttt{src} (xmap(x,y), ymap(x,y))\f] Values of pixels with non-integer coordinates are computed using the bilinear interpolation. @sa remap
Python prototype (for reference only):
remap(src, xmap, ymap, interpolation[, dst[, borderMode[, borderValue[, stream]]]]) -> dst@spec reprojectImageTo3D(Evision.CUDA.GpuMat.t(), Evision.Mat.maybe_mat_in()) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Reprojects a disparity image to 3D space.
Positional Arguments
disp:
Evision.CUDA.GpuMat.t().Input single-channel 8-bit unsigned, 16-bit signed, 32-bit signed or 32-bit floating-point disparity image. If 16-bit signed format is used, the values are assumed to have no fractional bits.
q:
Evision.Mat.t().\f$4 \times 4\f$ perspective transformation matrix that can be obtained via stereoRectify .
Keyword Arguments
dst_cn:
int.The number of channels for output image. Can be 3 or 4.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
xyzw:
Evision.CUDA.GpuMat.t().Output 3- or 4-channel floating-point image of the same size as disp . Each element of xyzw(x,y) contains 3D coordinates (x,y,z) or (x,y,z,1) of the point (x,y) , computed from the disparity map.
@sa reprojectImageTo3D
Python prototype (for reference only):
reprojectImageTo3D(disp, Q[, xyzw[, dst_cn[, stream]]]) -> xyzw@spec reprojectImageTo3D( Evision.CUDA.GpuMat.t(), Evision.Mat.maybe_mat_in(), [{atom(), term()}, ...] | nil ) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Reprojects a disparity image to 3D space.
Positional Arguments
disp:
Evision.CUDA.GpuMat.t().Input single-channel 8-bit unsigned, 16-bit signed, 32-bit signed or 32-bit floating-point disparity image. If 16-bit signed format is used, the values are assumed to have no fractional bits.
q:
Evision.Mat.t().\f$4 \times 4\f$ perspective transformation matrix that can be obtained via stereoRectify .
Keyword Arguments
dst_cn:
int.The number of channels for output image. Can be 3 or 4.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
xyzw:
Evision.CUDA.GpuMat.t().Output 3- or 4-channel floating-point image of the same size as disp . Each element of xyzw(x,y) contains 3D coordinates (x,y,z) or (x,y,z,1) of the point (x,y) , computed from the disparity map.
@sa reprojectImageTo3D
Python prototype (for reference only):
reprojectImageTo3D(disp, Q[, xyzw[, dst_cn[, stream]]]) -> xyzw@spec resetDevice() :: :ok | {:error, String.t()}
Explicitly destroys and cleans up all resources associated with the current device in the current process.
Any subsequent API call to this device will reinitialize the device.
Python prototype (for reference only):
resetDevice() -> None@spec resize( Evision.Mat.maybe_mat_in(), {number(), number()} ) :: Evision.Mat.t() | {:error, String.t()}
@spec resize( Evision.CUDA.GpuMat.t(), {number(), number()} ) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Resizes an image.
Positional Arguments
src:
Evision.Mat.t().Source image.
dsize:
Size.Destination image size. If it is zero, it is computed as: \f[\texttt{dsize = Size(round(fxsrc.cols), round(fysrc.rows))}\f] Either dsize or both fx and fy must be non-zero.
Keyword Arguments
fx:
double.Scale factor along the horizontal axis. If it is zero, it is computed as: \f[\texttt{(double)dsize.width/src.cols}\f]
fy:
double.Scale factor along the vertical axis. If it is zero, it is computed as: \f[\texttt{(double)dsize.height/src.rows}\f]
interpolation:
int.Interpolation method. INTER_NEAREST , INTER_LINEAR and INTER_CUBIC are supported for now.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().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 .
@sa resize
Python prototype (for reference only):
resize(src, dsize[, dst[, fx[, fy[, interpolation[, stream]]]]]) -> dstVariant 2:
Resizes an image.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Source image.
dsize:
Size.Destination image size. If it is zero, it is computed as: \f[\texttt{dsize = Size(round(fxsrc.cols), round(fysrc.rows))}\f] Either dsize or both fx and fy must be non-zero.
Keyword Arguments
fx:
double.Scale factor along the horizontal axis. If it is zero, it is computed as: \f[\texttt{(double)dsize.width/src.cols}\f]
fy:
double.Scale factor along the vertical axis. If it is zero, it is computed as: \f[\texttt{(double)dsize.height/src.rows}\f]
interpolation:
int.Interpolation method. INTER_NEAREST , INTER_LINEAR and INTER_CUBIC are supported for now.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().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 .
@sa resize
Python prototype (for reference only):
resize(src, dsize[, dst[, fx[, fy[, interpolation[, stream]]]]]) -> dst@spec resize( Evision.Mat.maybe_mat_in(), {number(), number()}, [{atom(), term()}, ...] | nil ) :: Evision.Mat.t() | {:error, String.t()}
@spec resize( Evision.CUDA.GpuMat.t(), {number(), number()}, [{atom(), term()}, ...] | nil ) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Resizes an image.
Positional Arguments
src:
Evision.Mat.t().Source image.
dsize:
Size.Destination image size. If it is zero, it is computed as: \f[\texttt{dsize = Size(round(fxsrc.cols), round(fysrc.rows))}\f] Either dsize or both fx and fy must be non-zero.
Keyword Arguments
fx:
double.Scale factor along the horizontal axis. If it is zero, it is computed as: \f[\texttt{(double)dsize.width/src.cols}\f]
fy:
double.Scale factor along the vertical axis. If it is zero, it is computed as: \f[\texttt{(double)dsize.height/src.rows}\f]
interpolation:
int.Interpolation method. INTER_NEAREST , INTER_LINEAR and INTER_CUBIC are supported for now.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().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 .
@sa resize
Python prototype (for reference only):
resize(src, dsize[, dst[, fx[, fy[, interpolation[, stream]]]]]) -> dstVariant 2:
Resizes an image.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Source image.
dsize:
Size.Destination image size. If it is zero, it is computed as: \f[\texttt{dsize = Size(round(fxsrc.cols), round(fysrc.rows))}\f] Either dsize or both fx and fy must be non-zero.
Keyword Arguments
fx:
double.Scale factor along the horizontal axis. If it is zero, it is computed as: \f[\texttt{(double)dsize.width/src.cols}\f]
fy:
double.Scale factor along the vertical axis. If it is zero, it is computed as: \f[\texttt{(double)dsize.height/src.rows}\f]
interpolation:
int.Interpolation method. INTER_NEAREST , INTER_LINEAR and INTER_CUBIC are supported for now.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().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 .
@sa resize
Python prototype (for reference only):
resize(src, dsize[, dst[, fx[, fy[, interpolation[, stream]]]]]) -> dst@spec rotate(Evision.Mat.maybe_mat_in(), {number(), number()}, number()) :: Evision.Mat.t() | {:error, String.t()}
@spec rotate(Evision.CUDA.GpuMat.t(), {number(), number()}, number()) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Rotates an image around the origin (0,0) and then shifts it.
Positional Arguments
src:
Evision.Mat.t().Source image. Supports 1, 3 or 4 channels images with CV_8U , CV_16U or CV_32F depth.
dsize:
Size.Size of the destination image.
angle:
double.Angle of rotation in degrees.
Keyword Arguments
xShift:
double.Shift along the horizontal axis.
yShift:
double.Shift along the vertical axis.
interpolation:
int.Interpolation method. Only INTER_NEAREST , INTER_LINEAR , and INTER_CUBIC are supported.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Destination image with the same type as src . The size is dsize .
@sa cuda::warpAffine
Python prototype (for reference only):
rotate(src, dsize, angle[, dst[, xShift[, yShift[, interpolation[, stream]]]]]) -> dstVariant 2:
Rotates an image around the origin (0,0) and then shifts it.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Source image. Supports 1, 3 or 4 channels images with CV_8U , CV_16U or CV_32F depth.
dsize:
Size.Size of the destination image.
angle:
double.Angle of rotation in degrees.
Keyword Arguments
xShift:
double.Shift along the horizontal axis.
yShift:
double.Shift along the vertical axis.
interpolation:
int.Interpolation method. Only INTER_NEAREST , INTER_LINEAR , and INTER_CUBIC are supported.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Destination image with the same type as src . The size is dsize .
@sa cuda::warpAffine
Python prototype (for reference only):
rotate(src, dsize, angle[, dst[, xShift[, yShift[, interpolation[, stream]]]]]) -> dst@spec rotate( Evision.Mat.maybe_mat_in(), {number(), number()}, number(), [{atom(), term()}, ...] | nil ) :: Evision.Mat.t() | {:error, String.t()}
@spec rotate( Evision.CUDA.GpuMat.t(), {number(), number()}, number(), [{atom(), term()}, ...] | nil ) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Rotates an image around the origin (0,0) and then shifts it.
Positional Arguments
src:
Evision.Mat.t().Source image. Supports 1, 3 or 4 channels images with CV_8U , CV_16U or CV_32F depth.
dsize:
Size.Size of the destination image.
angle:
double.Angle of rotation in degrees.
Keyword Arguments
xShift:
double.Shift along the horizontal axis.
yShift:
double.Shift along the vertical axis.
interpolation:
int.Interpolation method. Only INTER_NEAREST , INTER_LINEAR , and INTER_CUBIC are supported.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Destination image with the same type as src . The size is dsize .
@sa cuda::warpAffine
Python prototype (for reference only):
rotate(src, dsize, angle[, dst[, xShift[, yShift[, interpolation[, stream]]]]]) -> dstVariant 2:
Rotates an image around the origin (0,0) and then shifts it.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Source image. Supports 1, 3 or 4 channels images with CV_8U , CV_16U or CV_32F depth.
dsize:
Size.Size of the destination image.
angle:
double.Angle of rotation in degrees.
Keyword Arguments
xShift:
double.Shift along the horizontal axis.
yShift:
double.Shift along the vertical axis.
interpolation:
int.Interpolation method. Only INTER_NEAREST , INTER_LINEAR , and INTER_CUBIC are supported.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Destination image with the same type as src . The size is dsize .
@sa cuda::warpAffine
Python prototype (for reference only):
rotate(src, dsize, angle[, dst[, xShift[, yShift[, interpolation[, stream]]]]]) -> dst@spec rshift( Evision.Mat.maybe_mat_in(), {number()} | {number(), number()} | {number(), number(), number()} | {number(), number(), number(), number()} ) :: Evision.Mat.t() | {:error, String.t()}
@spec rshift( Evision.CUDA.GpuMat.t(), {number()} | {number(), number()} | {number(), number(), number()} | {number(), number(), number(), number()} ) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Performs pixel by pixel right shift of an image by a constant value.
Positional Arguments
src:
Evision.Mat.t().Source matrix. Supports 1, 3 and 4 channels images with integers elements.
val:
Scalar.Constant values, one per channel.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Destination matrix with the same size and type as src .
Python prototype (for reference only):
rshift(src, val[, dst[, stream]]) -> dstVariant 2:
Performs pixel by pixel right shift of an image by a constant value.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Source matrix. Supports 1, 3 and 4 channels images with integers elements.
val:
Scalar.Constant values, one per channel.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Destination matrix with the same size and type as src .
Python prototype (for reference only):
rshift(src, val[, dst[, stream]]) -> dst@spec rshift( Evision.Mat.maybe_mat_in(), {number()} | {number(), number()} | {number(), number(), number()} | {number(), number(), number(), number()}, [{atom(), term()}, ...] | nil ) :: Evision.Mat.t() | {:error, String.t()}
@spec rshift( Evision.CUDA.GpuMat.t(), {number()} | {number(), number()} | {number(), number(), number()} | {number(), number(), number(), number()}, [{atom(), term()}, ...] | nil ) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Performs pixel by pixel right shift of an image by a constant value.
Positional Arguments
src:
Evision.Mat.t().Source matrix. Supports 1, 3 and 4 channels images with integers elements.
val:
Scalar.Constant values, one per channel.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Destination matrix with the same size and type as src .
Python prototype (for reference only):
rshift(src, val[, dst[, stream]]) -> dstVariant 2:
Performs pixel by pixel right shift of an image by a constant value.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Source matrix. Supports 1, 3 and 4 channels images with integers elements.
val:
Scalar.Constant values, one per channel.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Destination matrix with the same size and type as src .
Python prototype (for reference only):
rshift(src, val[, dst[, stream]]) -> dstsetBufferPoolConfig
Positional Arguments
- deviceId:
int - stackSize:
size_t - stackCount:
int
Python prototype (for reference only):
setBufferPoolConfig(deviceId, stackSize, stackCount) -> NonesetBufferPoolUsage
Positional Arguments
- on:
bool
Python prototype (for reference only):
setBufferPoolUsage(on) -> NoneSets a device and initializes it for the current thread.
Positional Arguments
device:
int.System index of a CUDA device starting with 0.
If the call of this function is omitted, a default device is initialized at the fist CUDA usage.
Python prototype (for reference only):
setDevice(device) -> None@spec spatialMoments(Evision.Mat.maybe_mat_in()) :: Evision.Mat.t() | {:error, String.t()}
@spec spatialMoments(Evision.CUDA.GpuMat.t()) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Calculates all of the spatial moments up to the 3rd order of a rasterized shape.
Positional Arguments
src:
Evision.Mat.t().Raster image (single-channel 2D array).
Keyword Arguments
binaryImage:
bool.If it is true, all non-zero image pixels are treated as 1's.
order:
MomentsOrder.Order of largest moments to calculate with lower order moments requiring less computation.
momentsType:
int.Precision to use when calculating moments. Available types are
CV_32FandCV_64Fwith the performance ofCV_32Fan order of magnitude greater thanCV_64F. If the image is small the accuracy fromCV_32Fcan be equal or very close toCV_64F.stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
- moments:
Evision.Mat.t().
Asynchronous version of cuda::moments() which only calculates the spatial (not centralized or normalized) moments, up to the 3rd order, of a rasterized shape. Each moment is returned as a column entry in the 1D \a moments array.
Note: For maximum performance pre-allocate a 1D GpuMat for \a moments of the correct type and size large enough to store the all the image moments of up to the desired \a order. e.g. With \a order === MomentsOrder::SECOND_ORDER_MOMENTS and \a momentsType == CV_32F \a moments can be allocated as
GpuMat momentsDevice(1,numMoments(MomentsOrder::SECOND_ORDER_MOMENTS),CV_32F)The central and normalized moments can easily be calculated on the host by downloading the \a moments array and using the cv::Moments constructor. e.g.
HostMem momentsHostMem(1, numMoments(MomentsOrder::SECOND_ORDER_MOMENTS), CV_32F);
momentsDevice.download(momentsHostMem, stream);
stream.waitForCompletion();
Mat momentsMat = momentsHostMem.createMatHeader();
cv::Moments cvMoments(momentsMat.at<float>(0), momentsMat.at<float>(1), momentsMat.at<float>(2), momentsMat.at<float>(3), momentsMat.at<float>(4), momentsMat.at<float>(5), momentsMat.at<float>(6), momentsMat.at<float>(7), momentsMat.at<float>(8), momentsMat.at<float>(9));see the \a CUDA_TEST_P(Moments, Async) test inside opencv_contrib_source_code/modules/cudaimgproc/test/test_moments.cpp for an example. @returns cv::Moments. @sa cuda::moments
Python prototype (for reference only):
spatialMoments(src[, moments[, binaryImage[, order[, momentsType[, stream]]]]]) -> momentsVariant 2:
Calculates all of the spatial moments up to the 3rd order of a rasterized shape.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Raster image (single-channel 2D array).
Keyword Arguments
binaryImage:
bool.If it is true, all non-zero image pixels are treated as 1's.
order:
MomentsOrder.Order of largest moments to calculate with lower order moments requiring less computation.
momentsType:
int.Precision to use when calculating moments. Available types are
CV_32FandCV_64Fwith the performance ofCV_32Fan order of magnitude greater thanCV_64F. If the image is small the accuracy fromCV_32Fcan be equal or very close toCV_64F.stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
- moments:
Evision.CUDA.GpuMat.t().
Asynchronous version of cuda::moments() which only calculates the spatial (not centralized or normalized) moments, up to the 3rd order, of a rasterized shape. Each moment is returned as a column entry in the 1D \a moments array.
Note: For maximum performance pre-allocate a 1D GpuMat for \a moments of the correct type and size large enough to store the all the image moments of up to the desired \a order. e.g. With \a order === MomentsOrder::SECOND_ORDER_MOMENTS and \a momentsType == CV_32F \a moments can be allocated as
GpuMat momentsDevice(1,numMoments(MomentsOrder::SECOND_ORDER_MOMENTS),CV_32F)The central and normalized moments can easily be calculated on the host by downloading the \a moments array and using the cv::Moments constructor. e.g.
HostMem momentsHostMem(1, numMoments(MomentsOrder::SECOND_ORDER_MOMENTS), CV_32F);
momentsDevice.download(momentsHostMem, stream);
stream.waitForCompletion();
Mat momentsMat = momentsHostMem.createMatHeader();
cv::Moments cvMoments(momentsMat.at<float>(0), momentsMat.at<float>(1), momentsMat.at<float>(2), momentsMat.at<float>(3), momentsMat.at<float>(4), momentsMat.at<float>(5), momentsMat.at<float>(6), momentsMat.at<float>(7), momentsMat.at<float>(8), momentsMat.at<float>(9));see the \a CUDA_TEST_P(Moments, Async) test inside opencv_contrib_source_code/modules/cudaimgproc/test/test_moments.cpp for an example. @returns cv::Moments. @sa cuda::moments
Python prototype (for reference only):
spatialMoments(src[, moments[, binaryImage[, order[, momentsType[, stream]]]]]) -> moments@spec spatialMoments(Evision.Mat.maybe_mat_in(), [{atom(), term()}, ...] | nil) :: Evision.Mat.t() | {:error, String.t()}
@spec spatialMoments(Evision.CUDA.GpuMat.t(), [{atom(), term()}, ...] | nil) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Calculates all of the spatial moments up to the 3rd order of a rasterized shape.
Positional Arguments
src:
Evision.Mat.t().Raster image (single-channel 2D array).
Keyword Arguments
binaryImage:
bool.If it is true, all non-zero image pixels are treated as 1's.
order:
MomentsOrder.Order of largest moments to calculate with lower order moments requiring less computation.
momentsType:
int.Precision to use when calculating moments. Available types are
CV_32FandCV_64Fwith the performance ofCV_32Fan order of magnitude greater thanCV_64F. If the image is small the accuracy fromCV_32Fcan be equal or very close toCV_64F.stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
- moments:
Evision.Mat.t().
Asynchronous version of cuda::moments() which only calculates the spatial (not centralized or normalized) moments, up to the 3rd order, of a rasterized shape. Each moment is returned as a column entry in the 1D \a moments array.
Note: For maximum performance pre-allocate a 1D GpuMat for \a moments of the correct type and size large enough to store the all the image moments of up to the desired \a order. e.g. With \a order === MomentsOrder::SECOND_ORDER_MOMENTS and \a momentsType == CV_32F \a moments can be allocated as
GpuMat momentsDevice(1,numMoments(MomentsOrder::SECOND_ORDER_MOMENTS),CV_32F)The central and normalized moments can easily be calculated on the host by downloading the \a moments array and using the cv::Moments constructor. e.g.
HostMem momentsHostMem(1, numMoments(MomentsOrder::SECOND_ORDER_MOMENTS), CV_32F);
momentsDevice.download(momentsHostMem, stream);
stream.waitForCompletion();
Mat momentsMat = momentsHostMem.createMatHeader();
cv::Moments cvMoments(momentsMat.at<float>(0), momentsMat.at<float>(1), momentsMat.at<float>(2), momentsMat.at<float>(3), momentsMat.at<float>(4), momentsMat.at<float>(5), momentsMat.at<float>(6), momentsMat.at<float>(7), momentsMat.at<float>(8), momentsMat.at<float>(9));see the \a CUDA_TEST_P(Moments, Async) test inside opencv_contrib_source_code/modules/cudaimgproc/test/test_moments.cpp for an example. @returns cv::Moments. @sa cuda::moments
Python prototype (for reference only):
spatialMoments(src[, moments[, binaryImage[, order[, momentsType[, stream]]]]]) -> momentsVariant 2:
Calculates all of the spatial moments up to the 3rd order of a rasterized shape.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Raster image (single-channel 2D array).
Keyword Arguments
binaryImage:
bool.If it is true, all non-zero image pixels are treated as 1's.
order:
MomentsOrder.Order of largest moments to calculate with lower order moments requiring less computation.
momentsType:
int.Precision to use when calculating moments. Available types are
CV_32FandCV_64Fwith the performance ofCV_32Fan order of magnitude greater thanCV_64F. If the image is small the accuracy fromCV_32Fcan be equal or very close toCV_64F.stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
- moments:
Evision.CUDA.GpuMat.t().
Asynchronous version of cuda::moments() which only calculates the spatial (not centralized or normalized) moments, up to the 3rd order, of a rasterized shape. Each moment is returned as a column entry in the 1D \a moments array.
Note: For maximum performance pre-allocate a 1D GpuMat for \a moments of the correct type and size large enough to store the all the image moments of up to the desired \a order. e.g. With \a order === MomentsOrder::SECOND_ORDER_MOMENTS and \a momentsType == CV_32F \a moments can be allocated as
GpuMat momentsDevice(1,numMoments(MomentsOrder::SECOND_ORDER_MOMENTS),CV_32F)The central and normalized moments can easily be calculated on the host by downloading the \a moments array and using the cv::Moments constructor. e.g.
HostMem momentsHostMem(1, numMoments(MomentsOrder::SECOND_ORDER_MOMENTS), CV_32F);
momentsDevice.download(momentsHostMem, stream);
stream.waitForCompletion();
Mat momentsMat = momentsHostMem.createMatHeader();
cv::Moments cvMoments(momentsMat.at<float>(0), momentsMat.at<float>(1), momentsMat.at<float>(2), momentsMat.at<float>(3), momentsMat.at<float>(4), momentsMat.at<float>(5), momentsMat.at<float>(6), momentsMat.at<float>(7), momentsMat.at<float>(8), momentsMat.at<float>(9));see the \a CUDA_TEST_P(Moments, Async) test inside opencv_contrib_source_code/modules/cudaimgproc/test/test_moments.cpp for an example. @returns cv::Moments. @sa cuda::moments
Python prototype (for reference only):
spatialMoments(src[, moments[, binaryImage[, order[, momentsType[, stream]]]]]) -> moments@spec split(Evision.Mat.maybe_mat_in()) :: [Evision.CUDA.GpuMat.t()] | {:error, String.t()}
@spec split(Evision.CUDA.GpuMat.t()) :: [Evision.CUDA.GpuMat.t()] | {:error, String.t()}
Variant 1:
split
Positional Arguments
- src:
Evision.Mat.t()
Keyword Arguments
- stream:
Evision.CUDA.Stream.t().
Return
- dst:
[Evision.CUDA.GpuMat].
Has overloading in C++
Python prototype (for reference only):
split(src[, dst[, stream]]) -> dstVariant 2:
split
Positional Arguments
- src:
Evision.CUDA.GpuMat.t()
Keyword Arguments
- stream:
Evision.CUDA.Stream.t().
Return
- dst:
[Evision.CUDA.GpuMat].
Has overloading in C++
Python prototype (for reference only):
split(src[, dst[, stream]]) -> dst@spec split(Evision.Mat.maybe_mat_in(), [{atom(), term()}, ...] | nil) :: [Evision.CUDA.GpuMat.t()] | {:error, String.t()}
@spec split(Evision.CUDA.GpuMat.t(), [{atom(), term()}, ...] | nil) :: [Evision.CUDA.GpuMat.t()] | {:error, String.t()}
Variant 1:
split
Positional Arguments
- src:
Evision.Mat.t()
Keyword Arguments
- stream:
Evision.CUDA.Stream.t().
Return
- dst:
[Evision.CUDA.GpuMat].
Has overloading in C++
Python prototype (for reference only):
split(src[, dst[, stream]]) -> dstVariant 2:
split
Positional Arguments
- src:
Evision.CUDA.GpuMat.t()
Keyword Arguments
- stream:
Evision.CUDA.Stream.t().
Return
- dst:
[Evision.CUDA.GpuMat].
Has overloading in C++
Python prototype (for reference only):
split(src[, dst[, stream]]) -> dst@spec sqr(Evision.Mat.maybe_mat_in()) :: Evision.Mat.t() | {:error, String.t()}
@spec sqr(Evision.CUDA.GpuMat.t()) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Computes a square value of each matrix element.
Positional Arguments
src:
Evision.Mat.t().Source matrix.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Destination matrix with the same size and type as src .
Python prototype (for reference only):
sqr(src[, dst[, stream]]) -> dstVariant 2:
Computes a square value of each matrix element.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Source matrix.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Destination matrix with the same size and type as src .
Python prototype (for reference only):
sqr(src[, dst[, stream]]) -> dst@spec sqr(Evision.Mat.maybe_mat_in(), [{atom(), term()}, ...] | nil) :: Evision.Mat.t() | {:error, String.t()}
@spec sqr(Evision.CUDA.GpuMat.t(), [{atom(), term()}, ...] | nil) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Computes a square value of each matrix element.
Positional Arguments
src:
Evision.Mat.t().Source matrix.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Destination matrix with the same size and type as src .
Python prototype (for reference only):
sqr(src[, dst[, stream]]) -> dstVariant 2:
Computes a square value of each matrix element.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Source matrix.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Destination matrix with the same size and type as src .
Python prototype (for reference only):
sqr(src[, dst[, stream]]) -> dst@spec sqrIntegral(Evision.Mat.maybe_mat_in()) :: Evision.Mat.t() | {:error, String.t()}
@spec sqrIntegral(Evision.CUDA.GpuMat.t()) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Computes a squared integral image.
Positional Arguments
src:
Evision.Mat.t().Source image. Only CV_8UC1 images are supported for now.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
sqsum:
Evision.Mat.t().Squared integral image containing 64-bit unsigned integer values packed into CV_64FC1 .
Python prototype (for reference only):
sqrIntegral(src[, sqsum[, stream]]) -> sqsumVariant 2:
Computes a squared integral image.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Source image. Only CV_8UC1 images are supported for now.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
sqsum:
Evision.CUDA.GpuMat.t().Squared integral image containing 64-bit unsigned integer values packed into CV_64FC1 .
Python prototype (for reference only):
sqrIntegral(src[, sqsum[, stream]]) -> sqsum@spec sqrIntegral(Evision.Mat.maybe_mat_in(), [{atom(), term()}, ...] | nil) :: Evision.Mat.t() | {:error, String.t()}
@spec sqrIntegral(Evision.CUDA.GpuMat.t(), [{atom(), term()}, ...] | nil) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Computes a squared integral image.
Positional Arguments
src:
Evision.Mat.t().Source image. Only CV_8UC1 images are supported for now.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
sqsum:
Evision.Mat.t().Squared integral image containing 64-bit unsigned integer values packed into CV_64FC1 .
Python prototype (for reference only):
sqrIntegral(src[, sqsum[, stream]]) -> sqsumVariant 2:
Computes a squared integral image.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Source image. Only CV_8UC1 images are supported for now.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
sqsum:
Evision.CUDA.GpuMat.t().Squared integral image containing 64-bit unsigned integer values packed into CV_64FC1 .
Python prototype (for reference only):
sqrIntegral(src[, sqsum[, stream]]) -> sqsum@spec sqrSum(Evision.Mat.maybe_mat_in()) :: {number()} | {number(), number()} | {number(), number(), number()} | {number(), number(), number(), number()} | {:error, String.t()}
@spec sqrSum(Evision.CUDA.GpuMat.t()) :: {number()} | {number(), number()} | {number(), number(), number()} | {number(), number(), number(), number()} | {:error, String.t()}
Variant 1:
Returns the squared sum of matrix elements.
Positional Arguments
src:
Evision.Mat.t().Source image of any depth except for CV_64F .
Keyword Arguments
mask:
Evision.Mat.t().optional operation mask; it must have the same size as src1 and CV_8UC1 type.
Return
- retval:
Scalar
Python prototype (for reference only):
sqrSum(src[, mask]) -> retvalVariant 2:
Returns the squared sum of matrix elements.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Source image of any depth except for CV_64F .
Keyword Arguments
mask:
Evision.CUDA.GpuMat.t().optional operation mask; it must have the same size as src1 and CV_8UC1 type.
Return
- retval:
Scalar
Python prototype (for reference only):
sqrSum(src[, mask]) -> retval@spec sqrSum(Evision.Mat.maybe_mat_in(), [{atom(), term()}, ...] | nil) :: {number()} | {number(), number()} | {number(), number(), number()} | {number(), number(), number(), number()} | {:error, String.t()}
@spec sqrSum(Evision.CUDA.GpuMat.t(), [{atom(), term()}, ...] | nil) :: {number()} | {number(), number()} | {number(), number(), number()} | {number(), number(), number(), number()} | {:error, String.t()}
Variant 1:
Returns the squared sum of matrix elements.
Positional Arguments
src:
Evision.Mat.t().Source image of any depth except for CV_64F .
Keyword Arguments
mask:
Evision.Mat.t().optional operation mask; it must have the same size as src1 and CV_8UC1 type.
Return
- retval:
Scalar
Python prototype (for reference only):
sqrSum(src[, mask]) -> retvalVariant 2:
Returns the squared sum of matrix elements.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Source image of any depth except for CV_64F .
Keyword Arguments
mask:
Evision.CUDA.GpuMat.t().optional operation mask; it must have the same size as src1 and CV_8UC1 type.
Return
- retval:
Scalar
Python prototype (for reference only):
sqrSum(src[, mask]) -> retval@spec sqrt(Evision.Mat.maybe_mat_in()) :: Evision.Mat.t() | {:error, String.t()}
@spec sqrt(Evision.CUDA.GpuMat.t()) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Computes a square root of each matrix element.
Positional Arguments
src:
Evision.Mat.t().Source matrix.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Destination matrix with the same size and type as src .
@sa sqrt
Python prototype (for reference only):
sqrt(src[, dst[, stream]]) -> dstVariant 2:
Computes a square root of each matrix element.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Source matrix.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Destination matrix with the same size and type as src .
@sa sqrt
Python prototype (for reference only):
sqrt(src[, dst[, stream]]) -> dst@spec sqrt(Evision.Mat.maybe_mat_in(), [{atom(), term()}, ...] | nil) :: Evision.Mat.t() | {:error, String.t()}
@spec sqrt(Evision.CUDA.GpuMat.t(), [{atom(), term()}, ...] | nil) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Computes a square root of each matrix element.
Positional Arguments
src:
Evision.Mat.t().Source matrix.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Destination matrix with the same size and type as src .
@sa sqrt
Python prototype (for reference only):
sqrt(src[, dst[, stream]]) -> dstVariant 2:
Computes a square root of each matrix element.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Source matrix.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Destination matrix with the same size and type as src .
@sa sqrt
Python prototype (for reference only):
sqrt(src[, dst[, stream]]) -> dst@spec subtract(Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in()) :: Evision.Mat.t() | {:error, String.t()}
@spec subtract(Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t()) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Computes a matrix-matrix or matrix-scalar difference.
Positional Arguments
src1:
Evision.Mat.t().First source matrix or scalar.
src2:
Evision.Mat.t().Second source matrix or scalar. Matrix should have the same size and type as src1 .
Keyword Arguments
mask:
Evision.Mat.t().Optional operation mask, 8-bit single channel array, that specifies elements of the destination array to be changed. The mask can be used only with single channel images.
dtype:
int.Optional depth of the output array.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Destination matrix that has the same size and number of channels as the input array(s). The depth is defined by dtype or src1 depth.
@sa subtract
Python prototype (for reference only):
subtract(src1, src2[, dst[, mask[, dtype[, stream]]]]) -> dstVariant 2:
Computes a matrix-matrix or matrix-scalar difference.
Positional Arguments
src1:
Evision.CUDA.GpuMat.t().First source matrix or scalar.
src2:
Evision.CUDA.GpuMat.t().Second source matrix or scalar. Matrix should have the same size and type as src1 .
Keyword Arguments
mask:
Evision.CUDA.GpuMat.t().Optional operation mask, 8-bit single channel array, that specifies elements of the destination array to be changed. The mask can be used only with single channel images.
dtype:
int.Optional depth of the output array.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Destination matrix that has the same size and number of channels as the input array(s). The depth is defined by dtype or src1 depth.
@sa subtract
Python prototype (for reference only):
subtract(src1, src2[, dst[, mask[, dtype[, stream]]]]) -> dst@spec subtract( Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in(), [{atom(), term()}, ...] | nil ) :: Evision.Mat.t() | {:error, String.t()}
@spec subtract( Evision.CUDA.GpuMat.t(), Evision.CUDA.GpuMat.t(), [{atom(), term()}, ...] | nil ) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Computes a matrix-matrix or matrix-scalar difference.
Positional Arguments
src1:
Evision.Mat.t().First source matrix or scalar.
src2:
Evision.Mat.t().Second source matrix or scalar. Matrix should have the same size and type as src1 .
Keyword Arguments
mask:
Evision.Mat.t().Optional operation mask, 8-bit single channel array, that specifies elements of the destination array to be changed. The mask can be used only with single channel images.
dtype:
int.Optional depth of the output array.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Destination matrix that has the same size and number of channels as the input array(s). The depth is defined by dtype or src1 depth.
@sa subtract
Python prototype (for reference only):
subtract(src1, src2[, dst[, mask[, dtype[, stream]]]]) -> dstVariant 2:
Computes a matrix-matrix or matrix-scalar difference.
Positional Arguments
src1:
Evision.CUDA.GpuMat.t().First source matrix or scalar.
src2:
Evision.CUDA.GpuMat.t().Second source matrix or scalar. Matrix should have the same size and type as src1 .
Keyword Arguments
mask:
Evision.CUDA.GpuMat.t().Optional operation mask, 8-bit single channel array, that specifies elements of the destination array to be changed. The mask can be used only with single channel images.
dtype:
int.Optional depth of the output array.
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Destination matrix that has the same size and number of channels as the input array(s). The depth is defined by dtype or src1 depth.
@sa subtract
Python prototype (for reference only):
subtract(src1, src2[, dst[, mask[, dtype[, stream]]]]) -> dst@spec sum(Evision.Mat.maybe_mat_in()) :: {number()} | {number(), number()} | {number(), number(), number()} | {number(), number(), number(), number()} | {:error, String.t()}
@spec sum(Evision.CUDA.GpuMat.t()) :: {number()} | {number(), number()} | {number(), number(), number()} | {number(), number(), number(), number()} | {:error, String.t()}
Variant 1:
Returns the sum of matrix elements.
Positional Arguments
src:
Evision.Mat.t().Source image of any depth except for CV_64F .
Keyword Arguments
mask:
Evision.Mat.t().optional operation mask; it must have the same size as src1 and CV_8UC1 type.
Return
- retval:
Scalar
@sa sum
Python prototype (for reference only):
sum(src[, mask]) -> retvalVariant 2:
Returns the sum of matrix elements.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Source image of any depth except for CV_64F .
Keyword Arguments
mask:
Evision.CUDA.GpuMat.t().optional operation mask; it must have the same size as src1 and CV_8UC1 type.
Return
- retval:
Scalar
@sa sum
Python prototype (for reference only):
sum(src[, mask]) -> retval@spec sum(Evision.Mat.maybe_mat_in(), [{atom(), term()}, ...] | nil) :: {number()} | {number(), number()} | {number(), number(), number()} | {number(), number(), number(), number()} | {:error, String.t()}
@spec sum(Evision.CUDA.GpuMat.t(), [{atom(), term()}, ...] | nil) :: {number()} | {number(), number()} | {number(), number(), number()} | {number(), number(), number(), number()} | {:error, String.t()}
Variant 1:
Returns the sum of matrix elements.
Positional Arguments
src:
Evision.Mat.t().Source image of any depth except for CV_64F .
Keyword Arguments
mask:
Evision.Mat.t().optional operation mask; it must have the same size as src1 and CV_8UC1 type.
Return
- retval:
Scalar
@sa sum
Python prototype (for reference only):
sum(src[, mask]) -> retvalVariant 2:
Returns the sum of matrix elements.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Source image of any depth except for CV_64F .
Keyword Arguments
mask:
Evision.CUDA.GpuMat.t().optional operation mask; it must have the same size as src1 and CV_8UC1 type.
Return
- retval:
Scalar
@sa sum
Python prototype (for reference only):
sum(src[, mask]) -> retval@spec threshold(Evision.Mat.maybe_mat_in(), number(), number(), integer()) :: {number(), Evision.Mat.t()} | {:error, String.t()}
@spec threshold(Evision.CUDA.GpuMat.t(), number(), number(), integer()) :: {number(), Evision.CUDA.GpuMat.t()} | {:error, String.t()}
Variant 1:
Applies a fixed-level threshold to each array element.
Positional Arguments
src:
Evision.Mat.t().Source array (single-channel).
thresh:
double.Threshold value.
maxval:
double.Maximum value to use with THRESH_BINARY and THRESH_BINARY_INV threshold types.
type:
int.Threshold type. For details, see threshold . The THRESH_OTSU and THRESH_TRIANGLE threshold types are not supported.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
retval:
doubledst:
Evision.Mat.t().Destination array with the same size and type as src .
@sa threshold
Python prototype (for reference only):
threshold(src, thresh, maxval, type[, dst[, stream]]) -> retval, dstVariant 2:
Applies a fixed-level threshold to each array element.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Source array (single-channel).
thresh:
double.Threshold value.
maxval:
double.Maximum value to use with THRESH_BINARY and THRESH_BINARY_INV threshold types.
type:
int.Threshold type. For details, see threshold . The THRESH_OTSU and THRESH_TRIANGLE threshold types are not supported.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
retval:
doubledst:
Evision.CUDA.GpuMat.t().Destination array with the same size and type as src .
@sa threshold
Python prototype (for reference only):
threshold(src, thresh, maxval, type[, dst[, stream]]) -> retval, dst@spec threshold( Evision.Mat.maybe_mat_in(), number(), number(), integer(), [{atom(), term()}, ...] | nil ) :: {number(), Evision.Mat.t()} | {:error, String.t()}
@spec threshold( Evision.CUDA.GpuMat.t(), number(), number(), integer(), [{atom(), term()}, ...] | nil ) :: {number(), Evision.CUDA.GpuMat.t()} | {:error, String.t()}
Variant 1:
Applies a fixed-level threshold to each array element.
Positional Arguments
src:
Evision.Mat.t().Source array (single-channel).
thresh:
double.Threshold value.
maxval:
double.Maximum value to use with THRESH_BINARY and THRESH_BINARY_INV threshold types.
type:
int.Threshold type. For details, see threshold . The THRESH_OTSU and THRESH_TRIANGLE threshold types are not supported.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
retval:
doubledst:
Evision.Mat.t().Destination array with the same size and type as src .
@sa threshold
Python prototype (for reference only):
threshold(src, thresh, maxval, type[, dst[, stream]]) -> retval, dstVariant 2:
Applies a fixed-level threshold to each array element.
Positional Arguments
src:
Evision.CUDA.GpuMat.t().Source array (single-channel).
thresh:
double.Threshold value.
maxval:
double.Maximum value to use with THRESH_BINARY and THRESH_BINARY_INV threshold types.
type:
int.Threshold type. For details, see threshold . The THRESH_OTSU and THRESH_TRIANGLE threshold types are not supported.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
retval:
doubledst:
Evision.CUDA.GpuMat.t().Destination array with the same size and type as src .
@sa threshold
Python prototype (for reference only):
threshold(src, thresh, maxval, type[, dst[, stream]]) -> retval, dst@spec transpose(Evision.Mat.maybe_mat_in()) :: Evision.Mat.t() | {:error, String.t()}
@spec transpose(Evision.CUDA.GpuMat.t()) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Transposes a matrix.
Positional Arguments
src1:
Evision.Mat.t().Source matrix. 1-, 4-, 8-byte element sizes are supported for now.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Destination matrix.
@sa transpose
Python prototype (for reference only):
transpose(src1[, dst[, stream]]) -> dstVariant 2:
Transposes a matrix.
Positional Arguments
src1:
Evision.CUDA.GpuMat.t().Source matrix. 1-, 4-, 8-byte element sizes are supported for now.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Destination matrix.
@sa transpose
Python prototype (for reference only):
transpose(src1[, dst[, stream]]) -> dst@spec transpose(Evision.Mat.maybe_mat_in(), [{atom(), term()}, ...] | nil) :: Evision.Mat.t() | {:error, String.t()}
@spec transpose(Evision.CUDA.GpuMat.t(), [{atom(), term()}, ...] | nil) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
Transposes a matrix.
Positional Arguments
src1:
Evision.Mat.t().Source matrix. 1-, 4-, 8-byte element sizes are supported for now.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.Mat.t().Destination matrix.
@sa transpose
Python prototype (for reference only):
transpose(src1[, dst[, stream]]) -> dstVariant 2:
Transposes a matrix.
Positional Arguments
src1:
Evision.CUDA.GpuMat.t().Source matrix. 1-, 4-, 8-byte element sizes are supported for now.
Keyword Arguments
stream:
Evision.CUDA.Stream.t().Stream for the asynchronous version.
Return
dst:
Evision.CUDA.GpuMat.t().Destination matrix.
@sa transpose
Python prototype (for reference only):
transpose(src1[, dst[, stream]]) -> dst@spec unregisterPageLocked(Evision.Mat.maybe_mat_in()) :: :ok | {:error, String.t()}
Unmaps the memory of matrix and makes it pageable again.
Positional Arguments
m:
Evision.Mat.t().Input matrix.
Python prototype (for reference only):
unregisterPageLocked(m) -> None@spec warpAffine( Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in(), {number(), number()} ) :: Evision.Mat.t() | {:error, String.t()}
@spec warpAffine( Evision.CUDA.GpuMat.t(), Evision.Mat.maybe_mat_in(), {number(), number()} ) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
warpAffine
Positional Arguments
- src:
Evision.Mat.t() - m:
Evision.Mat.t() - dsize:
Size
Keyword Arguments
- flags:
int. - borderMode:
int. - borderValue:
Scalar. - stream:
Evision.CUDA.Stream.t().
Return
- dst:
Evision.Mat.t().
Python prototype (for reference only):
warpAffine(src, M, dsize[, dst[, flags[, borderMode[, borderValue[, stream]]]]]) -> dstVariant 2:
warpAffine
Positional Arguments
- src:
Evision.CUDA.GpuMat.t() - m:
Evision.Mat.t() - dsize:
Size
Keyword Arguments
- flags:
int. - borderMode:
int. - borderValue:
Scalar. - stream:
Evision.CUDA.Stream.t().
Return
- dst:
Evision.CUDA.GpuMat.t().
Python prototype (for reference only):
warpAffine(src, M, dsize[, dst[, flags[, borderMode[, borderValue[, stream]]]]]) -> dst@spec warpAffine( Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in(), {number(), number()}, [{atom(), term()}, ...] | nil ) :: Evision.Mat.t() | {:error, String.t()}
@spec warpAffine( Evision.CUDA.GpuMat.t(), Evision.Mat.maybe_mat_in(), {number(), number()}, [{atom(), term()}, ...] | nil ) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
warpAffine
Positional Arguments
- src:
Evision.Mat.t() - m:
Evision.Mat.t() - dsize:
Size
Keyword Arguments
- flags:
int. - borderMode:
int. - borderValue:
Scalar. - stream:
Evision.CUDA.Stream.t().
Return
- dst:
Evision.Mat.t().
Python prototype (for reference only):
warpAffine(src, M, dsize[, dst[, flags[, borderMode[, borderValue[, stream]]]]]) -> dstVariant 2:
warpAffine
Positional Arguments
- src:
Evision.CUDA.GpuMat.t() - m:
Evision.Mat.t() - dsize:
Size
Keyword Arguments
- flags:
int. - borderMode:
int. - borderValue:
Scalar. - stream:
Evision.CUDA.Stream.t().
Return
- dst:
Evision.CUDA.GpuMat.t().
Python prototype (for reference only):
warpAffine(src, M, dsize[, dst[, flags[, borderMode[, borderValue[, stream]]]]]) -> dst@spec warpPerspective( Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in(), {number(), number()} ) :: Evision.Mat.t() | {:error, String.t()}
@spec warpPerspective( Evision.CUDA.GpuMat.t(), Evision.Mat.maybe_mat_in(), {number(), number()} ) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
warpPerspective
Positional Arguments
- src:
Evision.Mat.t() - m:
Evision.Mat.t() - dsize:
Size
Keyword Arguments
- flags:
int. - borderMode:
int. - borderValue:
Scalar. - stream:
Evision.CUDA.Stream.t().
Return
- dst:
Evision.Mat.t().
Python prototype (for reference only):
warpPerspective(src, M, dsize[, dst[, flags[, borderMode[, borderValue[, stream]]]]]) -> dstVariant 2:
warpPerspective
Positional Arguments
- src:
Evision.CUDA.GpuMat.t() - m:
Evision.Mat.t() - dsize:
Size
Keyword Arguments
- flags:
int. - borderMode:
int. - borderValue:
Scalar. - stream:
Evision.CUDA.Stream.t().
Return
- dst:
Evision.CUDA.GpuMat.t().
Python prototype (for reference only):
warpPerspective(src, M, dsize[, dst[, flags[, borderMode[, borderValue[, stream]]]]]) -> dst@spec warpPerspective( Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in(), {number(), number()}, [{atom(), term()}, ...] | nil ) :: Evision.Mat.t() | {:error, String.t()}
@spec warpPerspective( Evision.CUDA.GpuMat.t(), Evision.Mat.maybe_mat_in(), {number(), number()}, [{atom(), term()}, ...] | nil ) :: Evision.CUDA.GpuMat.t() | {:error, String.t()}
Variant 1:
warpPerspective
Positional Arguments
- src:
Evision.Mat.t() - m:
Evision.Mat.t() - dsize:
Size
Keyword Arguments
- flags:
int. - borderMode:
int. - borderValue:
Scalar. - stream:
Evision.CUDA.Stream.t().
Return
- dst:
Evision.Mat.t().
Python prototype (for reference only):
warpPerspective(src, M, dsize[, dst[, flags[, borderMode[, borderValue[, stream]]]]]) -> dstVariant 2:
warpPerspective
Positional Arguments
- src:
Evision.CUDA.GpuMat.t() - m:
Evision.Mat.t() - dsize:
Size
Keyword Arguments
- flags:
int. - borderMode:
int. - borderValue:
Scalar. - stream:
Evision.CUDA.Stream.t().
Return
- dst:
Evision.CUDA.GpuMat.t().
Python prototype (for reference only):
warpPerspective(src, M, dsize[, dst[, flags[, borderMode[, borderValue[, stream]]]]]) -> dst@spec wrapStream(integer()) :: Evision.CUDA.Stream.t() | {:error, String.t()}
Bindings overload to create a Stream object from the address stored in an existing CUDA Runtime API stream pointer (cudaStream_t).
Positional Arguments
cudaStreamMemoryAddress:
size_t.Memory address stored in a CUDA Runtime API stream pointer (cudaStream_t). The created Stream object does not perform any allocation or deallocation and simply wraps existing raw CUDA Runtime API stream pointer.
Return
- retval:
Evision.CUDA.Stream.t()
Note: Overload for generation of bindings only, not exported or intended for use internally from C++.
Python prototype (for reference only):
wrapStream(cudaStreamMemoryAddress) -> retval