This module contains various functions for 3D image processing and analysis.
| convolve(input[, weights, mode, cval, ...]) | 3D convolution filter. |
| sobel(input[, output, mode, cval]) | Sobel gradient-magnitude filter. |
| discrete_gaussian(input[, sigma, errtol]) | Discrete-Gaussian image convolution. |
| discrete_gaussian_kernel([sigma, errtol, dim]) | Discrete-Gaussian convolution kernel. |
| discrete_gaussian_gradient_kernel([axis, ...]) | Discrete-Gaussian convolution kernel. |
| discrete_gaussian_mean_stdd(input[, sigma, ...]) | Discrete-Gaussian weighted mean and weighted standard-deviation image calculation. |
| discrete_gaussian_gradient_magnitude(input) | Discrete-Gaussian gradient magnitude (equavalent to Discrete Gaussian blur followed by gradient magnitude). |
| mean_filter(input[, se, mode, cval, stride, ...]) | Neighbourhood mean filter. |
| median_filter(input[, se, mode, cval, ...]) | Neighbourhood median filter. |
| stdd_filter(input[, se, mode, cval, stride, ...]) | Neighbourhood standard deviation filter. |
| mad_filter(input[, se, mode, cval, stride, ...]) | Neighbourhood median absolute deviation filter. |
| bilateral_filter(input, intensity_sigma[, ...]) | 3D bilateral filter. |
| se([structure, dim]) | Returns a StructuringElement object for the specified structure array. |
| sphere_se([radius, dim]) | Returns a SphereStructuringElement object for the specified radius. |
| order_se([order, dim]) | Returns a OrderStructuringElement object for the specified neighbourhood order. |
| box_se([shape, dim]) | Returns a BoxStructuringElement object for the specified box shape. |
| StructuringElement((object)arg1) | Structuring element for neighbourhood filters. |
| SphereStructuringElement((object)arg1) | Spherical structuring element for neighbourhood filters. |
| OrderStructuringElement((object)arg1) | Neighbourhood-order (spherical) structuring element for neighbourhood filters. |
| BoxStructuringElement((object)arg1) | Box/rectangular structuring element for neighbourhood filters. |
| resample(input[, interptype, factor, voxsz, ...]) | Re-sample mango.Dds object on new point-grid. |
| gaussian_downsample(input[, sigma, ...]) | Downsample an image (mango.Dds) by blurring with a Discrete Gaussian kernel and resampling on a sparse grid. |
| InterpolationType | Interpolation types/identifiers NEAREST_NEIGHBOUR, LINEAR,... |
| affine_transform(input, matrix[, shift, ...]) | Applies an affine transformation to an image. |
| rotate(input, angle[, axis, offset, ...]) | Applies a rotation transformation to an image. |
| histogramdd(inputList, bins[, rootRank]) | Returns tuple pair (H,edges) of multi-dimensional histogram counts and bin edges. |
| centre_of_mass(( (object)input) -> object :) | Calculates the centre of mass of the elements of the mango.Dds array. |
| moment_of_inertia(( (object)input [, ...) | Calculates the centre of mass and the principal moment of inertia tensor (about centre of mass) of the elements of the mango.Dds array. |
| intensity_spherical_histogram(input, sphhist) | Populates bins of the specified spherical histogram with intensity counts from the input mango.Dds image. |
| distance_spherical_histogram(input, sphhist) | Populates bins of the specified spherical histogram with distance counts from the input mango.Dds image. |
| intensity_mult_distance_spherical_histogram(...) | Populates bins of the specified spherical histogram with intensity times distance counts from the input mango.Dds image. |
| label(input, val[, connectivity, dosort]) | Generates image of labeled connected components. |
| eliminate_labels_by_size(input[, minsz, ...]) | Eliminates labeled objects (clusters) according to size (number of voxels). |
| convex_hull_2d(input[, axis, inputmsk, ...]) | Calculates 2D convex hull of non-masked voxels in each slice of an image. |
| convex_hull_3d(input[, inputmsk, outhull, ...]) | Calculates 3D convex hull of non-masked voxels of an image. |
| distance_transform_edt(input[, val]) | Returns Euclidean distance transform image. |
| max_covering_radius(input[, maxdist, filecache]) | Calculates the maximal covering radius transform from the input Euclidean distance transform image. |
| crop(input[, offset, shape]) | Crops an input image to a new origin and shape. |
| auto_crop(input[, mask]) | Crops an image to smallest possible bounding box containing all non-masked voxels. |
| subset(input[, offset, shape]) | Crops an input image to a new origin and shape. |
| subsample(input[, step, start, stop]) | Sub-sample/slice a mango.Dds object. |
| gather_slice(input[, axis, index, rank]) | Gathers a 2D slice of a 3D mango.Dds array to a single process. |
| SphericalHistogram((object)arg1 [, ...) | Triangular histogram bins on a sphere (requires mango compiled with CGAL support, i.e. |
| spherical_histogram([sphere_c, sphere_r, ...]) | Factory function for creating SphericalHistogram object. |