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Superclass: vtkImageSpatialAlgorithm vtkImageAnisotropicDiffusion2D diffuses a 2d image iteratively. The neighborhood of the diffusion is determined by the instance flags. If "Edges" is on the 4 edge connected voxels are included, and if "Corners" is on, the 4 corner connected voxels are included. "DiffusionFactor" determines how far a pixel value moves toward its neighbors, and is insensitive to the number of neighbors chosen. The diffusion is anisotropic because it only occurs when a gradient measure is below "GradientThreshold". Two gradient measures exist and are toggled by the "GradientMagnitudeThreshold" flag. When "GradientMagnitudeThreshold" is on, the magnitude of the gradient, computed by central differences, above "DiffusionThreshold" a voxel is not modified. The alternative measure examines each neighbor independently. The gradient between the voxel and the neighbor must be below the "DiffusionThreshold" for diffusion to occur with THAT neighbor. @sa vtkImageAnisotropicDiffusion3D IsTypeOfV.IsTypeOf(string) -> int C++: static vtkTypeBool IsTypeOf(const char *type) Return 1 if this class type is the same type of (or a subclass of) the named class. Returns 0 otherwise. This method works in combination with vtkTypeMacro found in vtkSetGet.h. IsAV.IsA(string) -> int C++: vtkTypeBool IsA(const char *type) override; Return 1 if this class is the same type of (or a subclass of) the named class. Returns 0 otherwise. This method works in combination with vtkTypeMacro found in vtkSetGet.h. SafeDownCastV.SafeDownCast(vtkObjectBase) -> vtkImageAnisotropicDiffusion2D C++: static vtkImageAnisotropicDiffusion2D *SafeDownCast( vtkObjectBase *o) NewInstanceV.NewInstance() -> vtkImageAnisotropicDiffusion2D C++: vtkImageAnisotropicDiffusion2D *NewInstance() SetNumberOfIterationsV.SetNumberOfIterations(int) C++: void SetNumberOfIterations(int num) This method sets the number of interations which also affects the input neighborhood needed to compute one output pixel. Each iterations requires an extra pixel layer on the neighborhood. This is only relavent when you are trying to stream or are requesting a sub extent of the "wholeExtent". GetNumberOfIterationsV.GetNumberOfIterations() -> int C++: virtual int GetNumberOfIterations() Get the number of iterations. SetDiffusionThresholdV.SetDiffusionThreshold(float) C++: virtual void SetDiffusionThreshold(double _arg) Set/Get the difference threshold that stops diffusion. when the difference between two pixel is greater than this threshold, the pixels are not diffused. This causes diffusion to avoid sharp edges. If the GradientMagnitudeThreshold is set, then gradient magnitude is used for comparison instead of pixel differences. GetDiffusionThresholdV.GetDiffusionThreshold() -> float C++: virtual double GetDiffusionThreshold() Set/Get the difference threshold that stops diffusion. when the difference between two pixel is greater than this threshold, the pixels are not diffused. This causes diffusion to avoid sharp edges. If the GradientMagnitudeThreshold is set, then gradient magnitude is used for comparison instead of pixel differences. SetDiffusionFactorV.SetDiffusionFactor(float) C++: virtual void SetDiffusionFactor(double _arg) The diffusion factor specifies how much neighboring pixels effect each other. No diffusion occurs with a factor of 0, and a diffusion factor of 1 causes the pixel to become the average of all its neighbors. GetDiffusionFactorV.GetDiffusionFactor() -> float C++: virtual double GetDiffusionFactor() The diffusion factor specifies how much neighboring pixels effect each other. No diffusion occurs with a factor of 0, and a diffusion factor of 1 causes the pixel to become the average of all its neighbors. SetFacesV.SetFaces(int) C++: virtual void SetFaces(int _arg) Choose neighbors to diffuse (6 faces, 12 edges, 8 corners). GetFacesV.GetFaces() -> int C++: virtual int GetFaces() Choose neighbors to diffuse (6 faces, 12 edges, 8 corners). FacesOnV.FacesOn() C++: virtual void FacesOn() Choose neighbors to diffuse (6 faces, 12 edges, 8 corners). FacesOffV.FacesOff() C++: virtual void FacesOff() Choose neighbors to diffuse (6 faces, 12 edges, 8 corners). SetEdgesV.SetEdges(int) C++: virtual void SetEdges(int _arg) Choose neighbors to diffuse (6 faces, 12 edges, 8 corners). GetEdgesV.GetEdges() -> int C++: virtual int GetEdges() Choose neighbors to diffuse (6 faces, 12 edges, 8 corners). EdgesOnV.EdgesOn() C++: virtual void EdgesOn() Choose neighbors to diffuse (6 faces, 12 edges, 8 corners). EdgesOffV.EdgesOff() C++: virtual void EdgesOff() Choose neighbors to diffuse (6 faces, 12 edges, 8 corners). SetCornersV.SetCorners(int) C++: virtual void SetCorners(int _arg) Choose neighbors to diffuse (6 faces, 12 edges, 8 corners). GetCornersV.GetCorners() -> int C++: virtual int GetCorners() Choose neighbors to diffuse (6 faces, 12 edges, 8 corners). CornersOnV.CornersOn() C++: virtual void CornersOn() Choose neighbors to diffuse (6 faces, 12 edges, 8 corners). CornersOffV.CornersOff() C++: virtual void CornersOff() Choose neighbors to diffuse (6 faces, 12 edges, 8 corners). SetGradientMagnitudeThresholdV.SetGradientMagnitudeThreshold(int) C++: virtual void SetGradientMagnitudeThreshold(int _arg) Switch between gradient magnitude threshold and pixel gradient threshold. GetGradientMagnitudeThresholdV.GetGradientMagnitudeThreshold() -> int C++: virtual int GetGradientMagnitudeThreshold() Switch between gradient magnitude threshold and pixel gradient threshold. GradientMagnitudeThresholdOnV.GradientMagnitudeThresholdOn() C++: virtual void GradientMagnitudeThresholdOn() Switch between gradient magnitude threshold and pixel gradient threshold. GradientMagnitudeThresholdOffV.GradientMagnitudeThresholdOff() C++: virtual void GradientMagnitudeThresholdOff() Switch between gradient magnitude threshold and pixel gradient threshold. vtkImageSpatialAlgorithmvtkThreadedImageAlgorithmvtkImageAlgorithmvtkAlgorithmvtkObjectvtkObjectBasevtkImageAnisotropicDiffusion3DvtkImagingGeneralPython.vtkImageAnisotropicDiffusion3DvtkImageAnisotropicDiffusion3D - edge preserving smoothing. Superclass: vtkImageSpatialAlgorithm vtkImageAnisotropicDiffusion3D diffuses an volume iteratively. The neighborhood of the diffusion is determined by the instance flags. if "Faces" is on, the 6 voxels adjoined by faces are included in the neighborhood. If "Edges" is on the 12 edge connected voxels are included, and if "Corners" is on, the 8 corner connected voxels are included. "DiffusionFactor" determines how far a pixel value moves toward its neighbors, and is insensitive to the number of neighbors chosen. The diffusion is anisotropic because it only occurs when a gradient measure is below "GradientThreshold". Two gradient measures exist and are toggled by the "GradientMagnitudeThreshold" flag. When "GradientMagnitudeThreshold" is on, the magnitude of the gradient, computed by central differences, above "DiffusionThreshold" a voxel is not modified. The alternative measure examines each neighbor independently. The gradient between the voxel and the neighbor must be below the "DiffusionThreshold" for diffusion to occur with THAT neighbor. @sa vtkImageAnisotropicDiffusion2D V.SafeDownCast(vtkObjectBase) -> vtkImageAnisotropicDiffusion3D C++: static vtkImageAnisotropicDiffusion3D *SafeDownCast( vtkObjectBase *o) V.NewInstance() -> vtkImageAnisotropicDiffusion3D C++: vtkImageAnisotropicDiffusion3D *NewInstance() V.SetDiffusionFactor(float) C++: virtual void SetDiffusionFactor(double _arg) Set/Get the difference factor V.GetDiffusionFactor() -> float C++: virtual double GetDiffusionFactor() Set/Get the difference factor vtkImageCheckerboardvtkImagingGeneralPython.vtkImageCheckerboardvtkImageCheckerboard - show two images at once using a checkboard pattern Superclass: vtkThreadedImageAlgorithm vtkImageCheckerboard displays two images as one using a checkerboard pattern. This filter can be used to compare two images. The checkerboard pattern is controlled by the NumberOfDivisions ivar. This controls the number of checkerboard divisions in the whole extent of the image. V.SafeDownCast(vtkObjectBase) -> vtkImageCheckerboard C++: static vtkImageCheckerboard *SafeDownCast(vtkObjectBase *o) V.NewInstance() -> vtkImageCheckerboard C++: vtkImageCheckerboard *NewInstance() SetNumberOfDivisionsV.SetNumberOfDivisions(int, int, int) C++: void SetNumberOfDivisions(int, int, int) V.SetNumberOfDivisions((int, int, int)) C++: void SetNumberOfDivisions(int a[3]) GetNumberOfDivisionsV.GetNumberOfDivisions() -> (int, int, int) C++: int *GetNumberOfDivisions() Set/Get the number of divisions along each axis. SetInput1DataV.SetInput1Data(vtkDataObject) C++: virtual void SetInput1Data(vtkDataObject *in) Set the two inputs to this filter SetInput2DataV.SetInput2Data(vtkDataObject) C++: virtual void SetInput2Data(vtkDataObject *in) vtkDataObjectvtkImageCityBlockDistancevtkImagingGeneralPython.vtkImageCityBlockDistancevtkImageCityBlockDistance - 1,2 or 3D distance map. Superclass: vtkImageDecomposeFilter vtkImageCityBlockDistance creates a distance map using the city block (Manhatten) distance measure. The input is a mask. Zero values are considered boundaries. The output pixel is the minimum of the input pixel and the distance to a boundary (or neighbor value + 1 unit). distance values are calculated in pixels. The filter works by taking 6 passes (for 3d distance map): 2 along each axis (forward and backward). Each pass keeps a running minimum distance. For some reason, I preserve the sign if the distance. If the input mask is initially negative, the output distances will be negative. Distances maps can have inside (negative regions) and outsides (positive regions). V.IsTypeOf(string) -> int C++: static vtkTypeBool IsTypeOf(const char *type) Construct an instance of vtkImageDecomposeFilter filter with default dimensionality 3. V.IsA(string) -> int C++: vtkTypeBool IsA(const char *type) override; Construct an instance of vtkImageDecomposeFilter filter with default dimensionality 3. V.SafeDownCast(vtkObjectBase) -> vtkImageCityBlockDistance C++: static vtkImageCityBlockDistance *SafeDownCast( vtkObjectBase *o) Construct an instance of vtkImageDecomposeFilter filter with default dimensionality 3. V.NewInstance() -> vtkImageCityBlockDistance C++: vtkImageCityBlockDistance *NewInstance() Construct an instance of vtkImageDecomposeFilter filter with default dimensionality 3. vtkImageDecomposeFiltervtkImageIterateFiltervtkImageConvolvevtkImagingGeneralPython.vtkImageConvolvevtkImageConvolve - Convolution of an image with a kernel. Superclass: vtkThreadedImageAlgorithm vtkImageConvolve convolves the image with a 3D NxNxN kernel or a 2D NxN kernal. The output image is cropped to the same size as the input. V.IsTypeOf(string) -> int C++: static vtkTypeBool IsTypeOf(const char *type) Construct an instance of vtkImageConvolve filter. V.IsA(string) -> int C++: vtkTypeBool IsA(const char *type) override; Construct an instance of vtkImageConvolve filter. V.SafeDownCast(vtkObjectBase) -> vtkImageConvolve C++: static vtkImageConvolve *SafeDownCast(vtkObjectBase *o) Construct an instance of vtkImageConvolve filter. V.NewInstance() -> vtkImageConvolve C++: vtkImageConvolve *NewInstance() Construct an instance of vtkImageConvolve filter. GetKernelSizeV.GetKernelSize() -> (int, int, int) C++: int *GetKernelSize() SetKernel3x3V.SetKernel3x3((float, float, float, float, float, float, float, float, float)) C++: void SetKernel3x3(const double kernel[9]) Set the kernel to be a given 3x3 or 5x5 or 7x7 kernel. SetKernel5x5V.SetKernel5x5((float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float)) C++: void SetKernel5x5(const double kernel[25]) Set the kernel to be a given 3x3 or 5x5 or 7x7 kernel. SetKernel7x7V.SetKernel7x7((float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float)) C++: void SetKernel7x7(const double kernel[49]) GetKernel3x3V.GetKernel3x3() -> (float, ...) C++: double *GetKernel3x3() V.GetKernel3x3([float, float, float, float, float, float, float, float, float]) C++: void GetKernel3x3(double kernel[9]) Return an array that contains the kernel. GetKernel5x5V.GetKernel5x5() -> (float, ...) C++: double *GetKernel5x5() V.GetKernel5x5([float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float]) C++: void GetKernel5x5(double kernel[25]) Return an array that contains the kernel. GetKernel7x7V.GetKernel7x7() -> (float, ...) C++: double *GetKernel7x7() V.GetKernel7x7([float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float]) C++: void GetKernel7x7(double kernel[49]) SetKernel3x3x3V.SetKernel3x3x3((float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float)) C++: void SetKernel3x3x3(const double kernel[27]) Set the kernel to be a 3x3x3 or 5x5x5 or 7x7x7 kernel. SetKernel5x5x5V.SetKernel5x5x5((float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float)) C++: void SetKernel5x5x5(const double kernel[125]) SetKernel7x7x7 GetKernel3x3x3V.GetKernel3x3x3() -> (float, ...) C++: double *GetKernel3x3x3() V.GetKernel3x3x3([float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float]) C++: void GetKernel3x3x3(double kernel[27]) Return an array that contains the kernel GetKernel5x5x5V.GetKernel5x5x5() -> (float, ...) C++: double *GetKernel5x5x5() V.GetKernel5x5x5([float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float, float]) C++: void GetKernel5x5x5(double kernel[125]) GetKernel7x7x7V.GetKernel7x7x7() -> (float, ...) C++: double *GetKernel7x7x7() p_voidvtkImageCorrelationvtkImagingGeneralPython.vtkImageCorrelationvtkImageCorrelation - Correlation imageof the two inputs. Superclass: vtkThreadedImageAlgorithm vtkImageCorrelation finds the correlation between two data sets. SetDimensionality determines whether the Correlation will be 3D, 2D or 1D. The default is a 2D Correlation. The Output type will be double. The output size will match the size of the first input. The second input is considered the correlation kernel. V.SafeDownCast(vtkObjectBase) -> vtkImageCorrelation C++: static vtkImageCorrelation *SafeDownCast(vtkObjectBase *o) V.NewInstance() -> vtkImageCorrelation C++: vtkImageCorrelation *NewInstance() SetDimensionalityV.SetDimensionality(int) C++: virtual void SetDimensionality(int _arg) Determines how the input is interpreted (set of 2d slices ...). The default is 2. GetDimensionalityMinValueV.GetDimensionalityMinValue() -> int C++: virtual int GetDimensionalityMinValue() Determines how the input is interpreted (set of 2d slices ...). The default is 2. GetDimensionalityMaxValueV.GetDimensionalityMaxValue() -> int C++: virtual int GetDimensionalityMaxValue() Determines how the input is interpreted (set of 2d slices ...). The default is 2. GetDimensionalityV.GetDimensionality() -> int C++: virtual int GetDimensionality() Determines how the input is interpreted (set of 2d slices ...). The default is 2. V.SetInput1Data(vtkDataObject) C++: virtual void SetInput1Data(vtkDataObject *in) Set the input image. V.SetInput2Data(vtkDataObject) C++: virtual void SetInput2Data(vtkDataObject *in) Set the correlation kernel. vtkImageEuclideanDistanceVTK_EDT_SAITO_CACHEDVTK_EDT_SAITOvtkImagingGeneralPython.vtkImageEuclideanDistancevtkImageEuclideanDistance - computes 3D Euclidean DT Superclass: vtkImageDecomposeFilter vtkImageEuclideanDistance implements the Euclidean DT using Saito's algorithm. The distance map produced contains the square of the Euclidean distance values. The algorithm has a o(n^(D+1)) complexity over nxnx...xn images in D dimensions. It is very efficient on relatively small images. Cuisenaire's algorithms should be used instead if n >> 500. These are not implemented yet. For the special case of images where the slice-size is a multiple of 2^N with a large N (typically for 256x256 slices), Saito's algorithm encounters a lot of cache conflicts during the 3rd iteration which can slow it very significantly. In that case, one should use ::SetAlgorithmToSaitoCached() instead for better performance. References: T. Saito and J.I. Toriwaki. New algorithms for Euclidean distance transformations of an n-dimensional digitised picture with applications. Pattern Recognition, 27(11). pp. 1551--1565, 1994. O. Cuisenaire. Distance Transformation: fast algorithms and applications to medical image processing. PhD Thesis, Universite catholique de Louvain, October 1999. http://ltswww.epfl.ch/~cuisenai/papers/oc_thesis.pdf V.SafeDownCast(vtkObjectBase) -> vtkImageEuclideanDistance C++: static vtkImageEuclideanDistance *SafeDownCast( vtkObjectBase *o) Construct an instance of vtkImageDecomposeFilter filter with default dimensionality 3. V.NewInstance() -> vtkImageEuclideanDistance C++: vtkImageEuclideanDistance *NewInstance() Construct an instance of vtkImageDecomposeFilter filter with default dimensionality 3. SetInitializeV.SetInitialize(int) C++: virtual void SetInitialize(int _arg) Used to set all non-zero voxels to MaximumDistance before starting the distance transformation. Setting Initialize off keeps the current value in the input image as starting point. This allows to superimpose several distance maps. GetInitializeV.GetInitialize() -> int C++: virtual int GetInitialize() Used to set all non-zero voxels to MaximumDistance before starting the distance transformation. Setting Initialize off keeps the current value in the input image as starting point. This allows to superimpose several distance maps. InitializeOnV.InitializeOn() C++: virtual void InitializeOn() Used to set all non-zero voxels to MaximumDistance before starting the distance transformation. Setting Initialize off keeps the current value in the input image as starting point. This allows to superimpose several distance maps. InitializeOffV.InitializeOff() C++: virtual void InitializeOff() Used to set all non-zero voxels to MaximumDistance before starting the distance transformation. Setting Initialize off keeps the current value in the input image as starting point. This allows to superimpose several distance maps. SetConsiderAnisotropyV.SetConsiderAnisotropy(int) C++: virtual void SetConsiderAnisotropy(int _arg) Used to define whether Spacing should be used in the computation of the distances GetConsiderAnisotropyV.GetConsiderAnisotropy() -> int C++: virtual int GetConsiderAnisotropy() Used to define whether Spacing should be used in the computation of the distances ConsiderAnisotropyOnV.ConsiderAnisotropyOn() C++: virtual void ConsiderAnisotropyOn() Used to define whether Spacing should be used in the computation of the distances ConsiderAnisotropyOffV.ConsiderAnisotropyOff() C++: virtual void ConsiderAnisotropyOff() Used to define whether Spacing should be used in the computation of the distances SetMaximumDistanceV.SetMaximumDistance(float) C++: virtual void SetMaximumDistance(double _arg) Any distance bigger than this->MaximumDistance will not ne computed but set to this->MaximumDistance instead. GetMaximumDistanceV.GetMaximumDistance() -> float C++: virtual double GetMaximumDistance() Any distance bigger than this->MaximumDistance will not ne computed but set to this->MaximumDistance instead. SetAlgorithmV.SetAlgorithm(int) C++: virtual void SetAlgorithm(int _arg) Selects a Euclidean DT algorithm. 1. Saito 2. Saito-cached More algorithms will be added later on. GetAlgorithmV.GetAlgorithm() -> int C++: virtual int GetAlgorithm() Selects a Euclidean DT algorithm. 1. Saito 2. Saito-cached More algorithms will be added later on. SetAlgorithmToSaitoV.SetAlgorithmToSaito() C++: void SetAlgorithmToSaito() Selects a Euclidean DT algorithm. 1. Saito 2. Saito-cached More algorithms will be added later on. SetAlgorithmToSaitoCachedV.SetAlgorithmToSaitoCached() C++: void SetAlgorithmToSaitoCached() Selects a Euclidean DT algorithm. 1. Saito 2. Saito-cached More algorithms will be added later on. vtkImageEuclideanToPolarvtkImagingGeneralPython.vtkImageEuclideanToPolarvtkImageEuclideanToPolar - Converts 2D Euclidean coordinates to polar. Superclass: vtkThreadedImageAlgorithm For each pixel with vector components x,y, this filter outputs theta in component0, and radius in component1. V.SafeDownCast(vtkObjectBase) -> vtkImageEuclideanToPolar C++: static vtkImageEuclideanToPolar *SafeDownCast( vtkObjectBase *o) V.NewInstance() -> vtkImageEuclideanToPolar C++: vtkImageEuclideanToPolar *NewInstance() SetThetaMaximumV.SetThetaMaximum(float) C++: virtual void SetThetaMaximum(double _arg) Theta is an angle. Maximum specifies when it maps back to 0. ThetaMaximum defaults to 255 instead of 2PI, because unsigned char is expected as input. The output type must be the same as input type. GetThetaMaximumV.GetThetaMaximum() -> float C++: virtual double GetThetaMaximum() Theta is an angle. Maximum specifies when it maps back to 0. ThetaMaximum defaults to 255 instead of 2PI, because unsigned char is expected as input. The output type must be the same as input type. vtkImageGaussianSmoothvtkImagingGeneralPython.vtkImageGaussianSmoothvtkImageGaussianSmooth - Performs a gaussian convolution. Superclass: vtkThreadedImageAlgorithm vtkImageGaussianSmooth implements a convolution of the input image with a gaussian. Supports from one to three dimensional convolutions. V.SafeDownCast(vtkObjectBase) -> vtkImageGaussianSmooth C++: static vtkImageGaussianSmooth *SafeDownCast(vtkObjectBase *o) V.NewInstance() -> vtkImageGaussianSmooth C++: vtkImageGaussianSmooth *NewInstance() SetStandardDeviationsV.SetStandardDeviations(float, float, float) C++: void SetStandardDeviations(double, double, double) V.SetStandardDeviations((float, float, float)) C++: void SetStandardDeviations(double a[3]) V.SetStandardDeviations(float, float) C++: void SetStandardDeviations(double a, double b) SetStandardDeviationV.SetStandardDeviation(float) C++: void SetStandardDeviation(double std) V.SetStandardDeviation(float, float) C++: void SetStandardDeviation(double a, double b) V.SetStandardDeviation(float, float, float) C++: void SetStandardDeviation(double a, double b, double c) Sets/Gets the Standard deviation of the gaussian in pixel units. GetStandardDeviationsV.GetStandardDeviations() -> (float, float, float) C++: double *GetStandardDeviations() SetRadiusFactorsV.SetRadiusFactors(float, float, float) C++: void SetRadiusFactors(double, double, double) V.SetRadiusFactors((float, float, float)) C++: void SetRadiusFactors(double a[3]) V.SetRadiusFactors(float, float) C++: void SetRadiusFactors(double f, double f2) SetRadiusFactorV.SetRadiusFactor(float) C++: void SetRadiusFactor(double f) Sets/Gets the Radius Factors of the gaussian (no unit). The radius factors determine how far out the gaussian kernel will go before being clamped to zero. GetRadiusFactorsV.GetRadiusFactors() -> (float, float, float) C++: double *GetRadiusFactors() V.SetDimensionality(int) C++: virtual void SetDimensionality(int _arg) Set/Get the dimensionality of this filter. This determines whether a one, two, or three dimensional gaussian is performed. V.GetDimensionality() -> int C++: virtual int GetDimensionality() Set/Get the dimensionality of this filter. This determines whether a one, two, or three dimensional gaussian is performed. vtkImageGradientvtkImagingGeneralPython.vtkImageGradientvtkImageGradient - Computes the gradient vector. Superclass: vtkThreadedImageAlgorithm vtkImageGradient computes the gradient vector of an image. The vector results are stored as scalar components. The Dimensionality determines whether to perform a 2d or 3d gradient. The default is two dimensional XY gradient. OutputScalarType is always double. Gradient is computed using central differences. V.SafeDownCast(vtkObjectBase) -> vtkImageGradient C++: static vtkImageGradient *SafeDownCast(vtkObjectBase *o) V.NewInstance() -> vtkImageGradient C++: vtkImageGradient *NewInstance() V.SetDimensionality(int) C++: virtual void SetDimensionality(int _arg) Determines how the input is interpreted (set of 2d slices ...) V.GetDimensionalityMinValue() -> int C++: virtual int GetDimensionalityMinValue() Determines how the input is interpreted (set of 2d slices ...) V.GetDimensionalityMaxValue() -> int C++: virtual int GetDimensionalityMaxValue() Determines how the input is interpreted (set of 2d slices ...) V.GetDimensionality() -> int C++: virtual int GetDimensionality() Determines how the input is interpreted (set of 2d slices ...) SetHandleBoundariesV.SetHandleBoundaries(int) C++: virtual void SetHandleBoundaries(int _arg) Get/Set whether to handle boundaries. If enabled, boundary pixels are treated as duplicated so that central differencing works for the boundary pixels. If disabled, the output whole extent of the image is reduced by one pixel. GetHandleBoundariesV.GetHandleBoundaries() -> int C++: virtual int GetHandleBoundaries() Get/Set whether to handle boundaries. If enabled, boundary pixels are treated as duplicated so that central differencing works for the boundary pixels. If disabled, the output whole extent of the image is reduced by one pixel. HandleBoundariesOnV.HandleBoundariesOn() C++: virtual void HandleBoundariesOn() Get/Set whether to handle boundaries. If enabled, boundary pixels are treated as duplicated so that central differencing works for the boundary pixels. If disabled, the output whole extent of the image is reduced by one pixel. HandleBoundariesOffV.HandleBoundariesOff() C++: virtual void HandleBoundariesOff() Get/Set whether to handle boundaries. If enabled, boundary pixels are treated as duplicated so that central differencing works for the boundary pixels. If disabled, the output whole extent of the image is reduced by one pixel. vtkImageGradientMagnitudevtkImagingGeneralPython.vtkImageGradientMagnitudevtkImageGradientMagnitude - Computes magnitude of the gradient. Superclass: vtkThreadedImageAlgorithm vtkImageGradientMagnitude computes the gradient magnitude of an image. Setting the dimensionality determines whether the gradient is computed on 2D images, or 3D volumes. The default is two dimensional XY images. @sa vtkImageGradient vtkImageMagnitude V.SafeDownCast(vtkObjectBase) -> vtkImageGradientMagnitude C++: static vtkImageGradientMagnitude *SafeDownCast( vtkObjectBase *o) V.NewInstance() -> vtkImageGradientMagnitude C++: vtkImageGradientMagnitude *NewInstance() V.SetHandleBoundaries(int) C++: virtual void SetHandleBoundaries(int _arg) If "HandleBoundariesOn" then boundary pixels are duplicated So central differences can get values. V.GetHandleBoundaries() -> int C++: virtual int GetHandleBoundaries() If "HandleBoundariesOn" then boundary pixels are duplicated So central differences can get values. V.HandleBoundariesOn() C++: virtual void HandleBoundariesOn() If "HandleBoundariesOn" then boundary pixels are duplicated So central differences can get values. V.HandleBoundariesOff() C++: virtual void HandleBoundariesOff() If "HandleBoundariesOn" then boundary pixels are duplicated So central differences can get values. vtkImageHybridMedian2DvtkImagingGeneralPython.vtkImageHybridMedian2DvtkImageHybridMedian2D - Median filter that preserves lines and corners. Superclass: vtkImageSpatialAlgorithm vtkImageHybridMedian2D is a median filter that preserves thin lines and corners. It operates on a 5x5 pixel neighborhood. It computes two values initially: the median of the + neighbors and the median of the x neighbors. It then computes the median of these two values plus the center pixel. This result of this second median is the output pixel value. V.SafeDownCast(vtkObjectBase) -> vtkImageHybridMedian2D C++: static vtkImageHybridMedian2D *SafeDownCast(vtkObjectBase *o) V.NewInstance() -> vtkImageHybridMedian2D C++: vtkImageHybridMedian2D *NewInstance() vtkImageLaplacianvtkImagingGeneralPython.vtkImageLaplacianvtkImageLaplacian - Computes divergence of gradient. Superclass: vtkThreadedImageAlgorithm vtkImageLaplacian computes the Laplacian (like a second derivative) of a scalar image. The operation is the same as taking the divergence after a gradient. Boundaries are handled, so the input is the same as the output. Dimensionality determines how the input regions are interpreted. (images, or volumes). The Dimensionality defaults to two. V.SafeDownCast(vtkObjectBase) -> vtkImageLaplacian C++: static vtkImageLaplacian *SafeDownCast(vtkObjectBase *o) V.NewInstance() -> vtkImageLaplacian C++: vtkImageLaplacian *NewInstance() vtkImageMedian3DvtkImagingGeneralPython.vtkImageMedian3DvtkImageMedian3D - Median Filter Superclass: vtkImageSpatialAlgorithm vtkImageMedian3D a Median filter that replaces each pixel with the median value from a rectangular neighborhood around that pixel. Neighborhoods can be no more than 3 dimensional. Setting one axis of the neighborhood kernelSize to 1 changes the filter into a 2D median. V.SafeDownCast(vtkObjectBase) -> vtkImageMedian3D C++: static vtkImageMedian3D *SafeDownCast(vtkObjectBase *o) V.NewInstance() -> vtkImageMedian3D C++: vtkImageMedian3D *NewInstance() SetKernelSizeV.SetKernelSize(int, int, int) C++: void SetKernelSize(int size0, int size1, int size2) This method sets the size of the neighborhood. It also sets the default middle of the neighborhood GetNumberOfElementsV.GetNumberOfElements() -> int C++: virtual int GetNumberOfElements() Return the number of elements in the median mask vtkImageNormalizevtkImagingGeneralPython.vtkImageNormalizevtkImageNormalize - Normalizes that scalar components for each point. Superclass: vtkThreadedImageAlgorithm For each point, vtkImageNormalize normalizes the vector defined by the scalar components. If the magnitude of this vector is zero, the output vector is zero also. V.SafeDownCast(vtkObjectBase) -> vtkImageNormalize C++: static vtkImageNormalize *SafeDownCast(vtkObjectBase *o) V.NewInstance() -> vtkImageNormalize C++: vtkImageNormalize *NewInstance() vtkImageRange3DvtkImagingGeneralPython.vtkImageRange3DvtkImageRange3D - Max - min of a circular neighborhood. Superclass: vtkImageSpatialAlgorithm vtkImageRange3D replaces a pixel with the maximum minus minimum over an ellipsoidal neighborhood. If KernelSize of an axis is 1, no processing is done on that axis. V.SafeDownCast(vtkObjectBase) -> vtkImageRange3D C++: static vtkImageRange3D *SafeDownCast(vtkObjectBase *o) V.NewInstance() -> vtkImageRange3D C++: vtkImageRange3D *NewInstance() V.SetKernelSize(int, int, int) C++: void SetKernelSize(int size0, int size1, int size2) This method sets the size of the neighborhood. It also sets the default middle of the neighborhood and computes the elliptical foot print. vtkImageSeparableConvolutionvtkImagingGeneralPython.vtkImageSeparableConvolutionvtkImageSeparableConvolution - 3 1D convolutions on an image Superclass: vtkImageDecomposeFilter vtkImageSeparableConvolution performs a convolution along the X, Y, and Z axes of an image, based on the three different 1D convolution kernels. The kernels must be of odd size, and are considered to be centered at (int)((kernelsize - 1) / 2.0 ). If a kernel is nullptr, that dimension is skipped. This filter is designed to efficiently convolve separable filters that can be decomposed into 1 or more 1D convolutions. It also handles arbitrarly large kernel sizes, and uses edge replication to handle boundaries. V.SafeDownCast(vtkObjectBase) -> vtkImageSeparableConvolution C++: static vtkImageSeparableConvolution *SafeDownCast( vtkObjectBase *o) Construct an instance of vtkImageDecomposeFilter filter with default dimensionality 3. V.NewInstance() -> vtkImageSeparableConvolution C++: vtkImageSeparableConvolution *NewInstance() Construct an instance of vtkImageDecomposeFilter filter with default dimensionality 3. SetXKernelV.SetXKernel(vtkFloatArray) C++: virtual void SetXKernel(vtkFloatArray *) GetXKernelV.GetXKernel() -> vtkFloatArray C++: virtual vtkFloatArray *GetXKernel() SetYKernelV.SetYKernel(vtkFloatArray) C++: virtual void SetYKernel(vtkFloatArray *) GetYKernelV.GetYKernel() -> vtkFloatArray C++: virtual vtkFloatArray *GetYKernel() SetZKernelV.SetZKernel(vtkFloatArray) C++: virtual void SetZKernel(vtkFloatArray *) GetZKernelV.GetZKernel() -> vtkFloatArray C++: virtual vtkFloatArray *GetZKernel() GetMTimeV.GetMTime() -> int C++: vtkMTimeType GetMTime() override; Overload standard modified time function. If kernel arrays are modified, then this object is modified as well. vtkFloatArrayvtkImageSobel2DvtkImagingGeneralPython.vtkImageSobel2DvtkImageSobel2D - Computes a vector field using sobel functions. Superclass: vtkImageSpatialAlgorithm vtkImageSobel2D computes a vector field from a scalar field by using Sobel functions. The number of vector components is 2 because the input is an image. Output is always doubles. V.SafeDownCast(vtkObjectBase) -> vtkImageSobel2D C++: static vtkImageSobel2D *SafeDownCast(vtkObjectBase *o) V.NewInstance() -> vtkImageSobel2D C++: vtkImageSobel2D *NewInstance() vtkImageSobel3DvtkImagingGeneralPython.vtkImageSobel3DvtkImageSobel3D - Computes a vector field using sobel functions. Superclass: vtkImageSpatialAlgorithm vtkImageSobel3D computes a vector field from a scalar field by using Sobel functions. The number of vector components is 3 because the input is a volume. Output is always doubles. A little creative liberty was used to extend the 2D sobel kernels into 3D. V.SafeDownCast(vtkObjectBase) -> vtkImageSobel3D C++: static vtkImageSobel3D *SafeDownCast(vtkObjectBase *o) V.NewInstance() -> vtkImageSobel3D C++: vtkImageSobel3D *NewInstance() vtkImagingGeneralPython.vtkImageSpatialAlgorithmvtkImageSpatialAlgorithm - Filters that operate on pixel neighborhoods. Superclass: vtkThreadedImageAlgorithm vtkImageSpatialAlgorithm is a super class for filters that operate on an input neighborhood for each output pixel. It handles even sized neighborhoods, but their can be a half pixel shift associated with processing. This superclass has some logic for handling boundaries. It can split regions into boundary and non-boundary pieces and call different execute methods. V.SafeDownCast(vtkObjectBase) -> vtkImageSpatialAlgorithm C++: static vtkImageSpatialAlgorithm *SafeDownCast( vtkObjectBase *o) V.NewInstance() -> vtkImageSpatialAlgorithm C++: vtkImageSpatialAlgorithm *NewInstance() GetKernelMiddleV.GetKernelMiddle() -> (int, int, int) C++: int *GetKernelMiddle() vtkImageVariance3DvtkImagingGeneralPython.vtkImageVariance3DvtkImageVariance3D - Variance in a neighborhood. Superclass: vtkImageSpatialAlgorithm vtkImageVariance3D replaces each pixel with a measurement of pixel variance in a elliptical neighborhood centered on that pixel. The value computed is not exactly the variance. The difference between the neighbor values and center value is computed and squared for each neighbor. These values are summed and divided by the total number of neighbors to produce the output value. V.SafeDownCast(vtkObjectBase) -> vtkImageVariance3D C++: static vtkImageVariance3D *SafeDownCast(vtkObjectBase *o) V.NewInstance() -> vtkImageVariance3D C++: vtkImageVariance3D *NewInstance() V.SetKernelSize(int, int, int) C++: void SetKernelSize(int size0, int size1, int size2) This method sets the size of the neighborhood. It also sets the default middle of the neighborhood and computes the Elliptical foot print. vtkSimpleImageFilterExamplevtkImagingGeneralPython.vtkSimpleImageFilterExamplevtkSimpleImageFilterExample - Simple example of an image-image filter. Superclass: vtkSimpleImageToImageFilter This is an example of a simple image-image filter. It copies it's input to it's output (point by point). It shows how templates can be used to support various data types. @sa vtkSimpleImageToImageFilter V.SafeDownCast(vtkObjectBase) -> vtkSimpleImageFilterExample C++: static vtkSimpleImageFilterExample *SafeDownCast( vtkObjectBase *o) V.NewInstance() -> vtkSimpleImageFilterExample C++: vtkSimpleImageFilterExample *NewInstance() vtkSimpleImageToImageFiltervtkImageSlabvtkImagingGeneralPython.vtkImageSlabvtkImageSlab - combine image slices to form a slab image Superclass: vtkThreadedImageAlgorithm vtkImageSlab will combine all of the slices of an image to create a single slice. The slices can be combined with the following operations: averaging, summation, minimum, maximum. If you require an arbitrary angle of projection, you can use vtkImageReslice.@par Thanks: Thanks to David Gobbi for contributing this class to VTK. V.SafeDownCast(vtkObjectBase) -> vtkImageSlab C++: static vtkImageSlab *SafeDownCast(vtkObjectBase *o) V.NewInstance() -> vtkImageSlab C++: vtkImageSlab *NewInstance() SetOrientationV.SetOrientation(int) C++: virtual void SetOrientation(int _arg) Set the slice direction: zero for x, 1 for y, 2 for z. The default is the Z direction. GetOrientationMinValueV.GetOrientationMinValue() -> int C++: virtual int GetOrientationMinValue() Set the slice direction: zero for x, 1 for y, 2 for z. The default is the Z direction. GetOrientationMaxValueV.GetOrientationMaxValue() -> int C++: virtual int GetOrientationMaxValue() Set the slice direction: zero for x, 1 for y, 2 for z. The default is the Z direction. SetOrientationToXV.SetOrientationToX() C++: void SetOrientationToX() Set the slice direction: zero for x, 1 for y, 2 for z. The default is the Z direction. SetOrientationToYV.SetOrientationToY() C++: void SetOrientationToY() Set the slice direction: zero for x, 1 for y, 2 for z. The default is the Z direction. SetOrientationToZV.SetOrientationToZ() C++: void SetOrientationToZ() Set the slice direction: zero for x, 1 for y, 2 for z. The default is the Z direction. GetOrientationV.GetOrientation() -> int C++: virtual int GetOrientation() Set the slice direction: zero for x, 1 for y, 2 for z. The default is the Z direction. SetSliceRangeV.SetSliceRange(int, int) C++: void SetSliceRange(int, int) V.SetSliceRange((int, int)) C++: void SetSliceRange(int a[2]) GetSliceRangeV.GetSliceRange() -> (int, int) C++: int *GetSliceRange() SetOperationV.SetOperation(int) C++: virtual void SetOperation(int _arg) Set the operation to use when combining slices. The choices are "Mean", "Sum", "Min", "Max". The default is "Mean". GetOperationMinValueV.GetOperationMinValue() -> int C++: virtual int GetOperationMinValue() Set the operation to use when combining slices. The choices are "Mean", "Sum", "Min", "Max". The default is "Mean". GetOperationMaxValueV.GetOperationMaxValue() -> int C++: virtual int GetOperationMaxValue() Set the operation to use when combining slices. The choices are "Mean", "Sum", "Min", "Max". The default is "Mean". SetOperationToMinV.SetOperationToMin() C++: void SetOperationToMin() Set the operation to use when combining slices. The choices are "Mean", "Sum", "Min", "Max". The default is "Mean". SetOperationToMaxV.SetOperationToMax() C++: void SetOperationToMax() Set the operation to use when combining slices. The choices are "Mean", "Sum", "Min", "Max". The default is "Mean". SetOperationToMeanV.SetOperationToMean() C++: void SetOperationToMean() Set the operation to use when combining slices. The choices are "Mean", "Sum", "Min", "Max". The default is "Mean". SetOperationToSumV.SetOperationToSum() C++: void SetOperationToSum() Set the operation to use when combining slices. The choices are "Mean", "Sum", "Min", "Max". The default is "Mean". GetOperationV.GetOperation() -> int C++: virtual int GetOperation() Set the operation to use when combining slices. The choices are "Mean", "Sum", "Min", "Max". The default is "Mean". GetOperationAsStringV.GetOperationAsString() -> string C++: const char *GetOperationAsString() Set the operation to use when combining slices. The choices are "Mean", "Sum", "Min", "Max". The default is "Mean". SetTrapezoidIntegrationV.SetTrapezoidIntegration(int) C++: virtual void SetTrapezoidIntegration(int _arg) Use trapezoid integration for slab computation. This weighs the first and last slices by half when doing sum and mean, as compared to the default midpoint integration that weighs all slices equally. It is off by default. TrapezoidIntegrationOnV.TrapezoidIntegrationOn() C++: virtual void TrapezoidIntegrationOn() Use trapezoid integration for slab computation. This weighs the first and last slices by half when doing sum and mean, as compared to the default midpoint integration that weighs all slices equally. It is off by default. TrapezoidIntegrationOffV.TrapezoidIntegrationOff() C++: virtual void TrapezoidIntegrationOff() Use trapezoid integration for slab computation. This weighs the first and last slices by half when doing sum and mean, as compared to the default midpoint integration that weighs all slices equally. It is off by default. GetTrapezoidIntegrationV.GetTrapezoidIntegration() -> int C++: virtual int GetTrapezoidIntegration() Use trapezoid integration for slab computation. This weighs the first and last slices by half when doing sum and mean, as compared to the default midpoint integration that weighs all slices equally. It is off by default. SetMultiSliceOutputV.SetMultiSliceOutput(int) C++: virtual void SetMultiSliceOutput(int _arg) Turn on multi-slice output. Each slice of the output will be a projection through the specified range of input slices, e.g. if the SliceRange is [0,3] then slice 'i' of the output will be a projection through slices 'i' through '3+i' of the input. This flag is off by default. MultiSliceOutputOnV.MultiSliceOutputOn() C++: virtual void MultiSliceOutputOn() Turn on multi-slice output. Each slice of the output will be a projection through the specified range of input slices, e.g. if the SliceRange is [0,3] then slice 'i' of the output will be a projection through slices 'i' through '3+i' of the input. This flag is off by default. MultiSliceOutputOffV.MultiSliceOutputOff() C++: virtual void MultiSliceOutputOff() Turn on multi-slice output. Each slice of the output will be a projection through the specified range of input slices, e.g. if the SliceRange is [0,3] then slice 'i' of the output will be a projection through slices 'i' through '3+i' of the input. This flag is off by default. GetMultiSliceOutputV.GetMultiSliceOutput() -> int C++: virtual int GetMultiSliceOutput() Turn on multi-slice output. Each slice of the output will be a projection through the specified range of input slices, e.g. if the SliceRange is [0,3] then slice 'i' of the output will be a projection through slices 'i' through '3+i' of the input. This flag is off by default. SetOutputScalarTypeToFloatV.SetOutputScalarTypeToFloat() C++: void SetOutputScalarTypeToFloat() Set the output scalar type to float or double, to avoid potential overflow when doing a summation operation. The default is to use the scalar type of the input data, and clamp the output to the range of the input scalar type. SetOutputScalarTypeToDoubleV.SetOutputScalarTypeToDouble() C++: void SetOutputScalarTypeToDouble() Set the output scalar type to float or double, to avoid potential overflow when doing a summation operation. The default is to use the scalar type of the input data, and clamp the output to the range of the input scalar type. SetOutputScalarTypeToInputScalarTypeV.SetOutputScalarTypeToInputScalarType() C++: void SetOutputScalarTypeToInputScalarType() Set the output scalar type to float or double, to avoid potential overflow when doing a summation operation. The default is to use the scalar type of the input data, and clamp the output to the range of the input scalar type. GetOutputScalarTypeV.GetOutputScalarType() -> int C++: virtual int GetOutputScalarType() Set the output scalar type to float or double, to avoid potential overflow when doing a summation operation. The default is to use the scalar type of the input data, and clamp the output to the range of the input scalar type. vtkImageSlabReslicevtkImagingGeneralPython.vtkImageSlabReslicevtkImageSlabReslice - Thick slab reformat through data. Superclass: vtkImageReslice This class derives from vtkImageResliceBase. Much like vtkImageReslice, it reslices the data. It is multi-threaded. It takes a three dimensional image as input and produces a two dimensional thick MPR along some direction. The class reslices the thick slab using a blending function. Supported blending functions are Minimum Intensity blend through the slab, maximum intensity blend and a Mean (average) intensity of values across the slab. The user can adjust the thickness of the slab by using the method SetSlabThickness. The distance between sample points used for blending across the thickness of the slab is controlled by the method SetSlabResolution. These two methods determine the number of slices used across the slab for blending, which is computed as {(2 x (int)(0.5 x SlabThickness/SlabResolution)) + 1}. This value may be queried via GetNumBlendSamplePoints() and is always >= 1. Much like vtkImageReslice, the reslice axes direction cosines may be set via the methods SetResliceAxes or SetResliceAxesDirectionCosines. The output spacing is controlled by SetOutputSpacing and the output origin is controlled by SetOutputOrigin. The default value to be set on pixels that lie outside the volume when reformatting is controlled by SetBackgroundColor or SetBackgroundLevel. The SetResliceAxesOrigin() method can also be used to provide an (x,y,z) point that the slice will pass through. @sa vtkImageReslice V.SafeDownCast(vtkObjectBase) -> vtkImageSlabReslice C++: static vtkImageSlabReslice *SafeDownCast(vtkObjectBase *o) V.NewInstance() -> vtkImageSlabReslice C++: vtkImageSlabReslice *NewInstance() SetBlendModeV.SetBlendMode(int) C++: virtual void SetBlendMode(int _arg) Set/Get the blend mode. Default is MIP (ie Max) GetBlendModeV.GetBlendMode() -> int C++: virtual int GetBlendMode() Set/Get the blend mode. Default is MIP (ie Max) SetBlendModeToMinV.SetBlendModeToMin() C++: void SetBlendModeToMin() Set/Get the blend mode. Default is MIP (ie Max) SetBlendModeToMaxV.SetBlendModeToMax() C++: void SetBlendModeToMax() Set/Get the blend mode. Default is MIP (ie Max) SetBlendModeToMeanV.SetBlendModeToMean() C++: void SetBlendModeToMean() Set/Get the blend mode. Default is MIP (ie Max) GetNumBlendSamplePointsV.GetNumBlendSamplePoints() -> int C++: virtual int GetNumBlendSamplePoints() Number of sample points used across the slab cross-section. If equal to 1, this ends up being a thin reslice through the data a.k.a. vtkImageReslice SetSlabThicknessV.SetSlabThickness(float) C++: virtual void SetSlabThickness(double _arg) SlabThickness of slab in world coords. SlabThickness must be non-zero and positive. GetSlabThicknessV.GetSlabThickness() -> float C++: virtual double GetSlabThickness() SlabThickness of slab in world coords. SlabThickness must be non-zero and positive. SetSlabResolutionV.SetSlabResolution(float) C++: virtual void SetSlabResolution(double _arg) Spacing between slabs in world units. (Number of Slices, ie samples to blend is computed from SlabThickness and SlabResolution). GetSlabResolutionV.GetSlabResolution() -> float C++: virtual double GetSlabResolution() Spacing between slabs in world units. 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