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# Multi-dimensional image processing (`scipy.ndimage`)

Functions for multi-dimensional image processing.

## Filters `scipy.ndimage.filters`

`convolve` (input, weights[, output, mode, cval, ...]) | Multi-dimensional convolution.
`convolve1d` (input, weights[, axis, output, mode, ...]) | Calculate a one-dimensional convolution along the given axis.
`correlate` (input, weights[, output, mode, cval, ...]) | Multi-dimensional correlation.
`correlate1d` (input, weights[, axis, output, mode, ...]) | Calculate a one-dimensional correlation along the given axis.
`gaussian_filter` (input, sigma[, order, output, mode, ...]) | Multi-dimensional Gaussian filter.
`gaussian_filter1d` (input, sigma[, axis, order, output, ...]) | One-dimensional Gaussian filter.
`gaussian_gradient_magnitude` (input, sigma[, output, mode, cval]) | Calculate a multidimensional gradient magnitude using gaussian derivatives.
`gaussian_laplace` (input, sigma[, output, mode, cval]) | Calculate a multidimensional laplace filter using gaussian second derivatives.
`generic_filter` (input, function[, size, footprint, ...]) | Calculates a multi-dimensional filter using the given function.
`generic_filter1d` (input, function, filter_size[, axis, output, mode, ...]) | Calculate a one-dimensional filter along the given axis.
`generic_gradient_magnitude` (input, derivative[, output, mode, cval, ...]) | Calculate a gradient magnitude using the provided function for the gradient.
`generic_laplace` (input, derivative2[, output, mode, cval, ...]) | Calculate a multidimensional laplace filter using the provided second derivative function.
`laplace` (input[, output, mode, cval]) | Calculate a multidimensional laplace filter using an estimation for the second derivative based on differences.
`maximum_filter` (input[, size, footprint, ...]) | Calculates a multi-dimensional maximum filter.
`maximum_filter1d` (input, size[, axis, output, mode, ...]) | Calculate a one-dimensional maximum filter along the given axis.
`median_filter` (input[, size, footprint, ...]) | Calculates a multi-dimensional median filter.
`minimum_filter` (input[, size, footprint, ...]) | Calculates a multi-dimensional minimum filter.
`minimum_filter1d` (input, size[, axis, output, mode, ...]) | Calculate a one-dimensional minimum filter along the given axis.
`percentile_filter` (input, percentile[, size, footprint, ...]) | Calculates a multi-dimensional percentile filter.
`prewitt` (input[, axis, output, mode, ...]) | Calculate a Prewitt filter.
`rank_filter` (input, rank[, size, footprint, ...]) | Calculates a multi-dimensional rank filter.
`sobel` (input[, axis, output, mode, ...]) | Calculate a Sobel filter.
`uniform_filter` (input[, size, output, mode, ...]) | Multi-dimensional uniform filter.
`uniform_filter1d` (input, size[, axis, output, mode, ...]) | Calculate a one-dimensional uniform filter along the given axis.

## Fourier filters `scipy.ndimage.fourier`

`fourier_ellipsoid` (input, size[, n, axis, output]) | Multi-dimensional ellipsoid fourier filter.
`fourier_gaussian` (input, sigma[, n, axis, output]) | Multi-dimensional Gaussian fourier filter.
`fourier_shift` (input, shift[, n, axis, output]) | Multi-dimensional fourier shift filter.
`fourier_uniform` (input, size[, n, axis, output]) | Multi-dimensional Uniform fourier filter.

## Interpolation `scipy.ndimage.interpolation`

`affine_transform` (input, matrix[, offset, output_shape, ...]) | Apply an affine transformation.
`geometric_transform` (input, mapping[, output_shape, output_type, ...]) | Apply an arbritrary geometric transform.
`map_coordinates` (input, coordinates[, output_type, output, ...]) | Map the input array to new coordinates by interpolation.
`rotate` (input, angle[, axes, 0), reshape, ...]) | Rotate an array.
`shift` (input, shift[, output_type, output, ...]) | Shift an array.
`spline_filter` (input[, order, output, output_type]) | Multi-dimensional spline filter.
`spline_filter1d` (input[, order, axis, output, ...]) | Calculates a one-dimensional spline filter along the given axis.
`zoom` (input, zoom[, output_type, output, ...]) | Zoom an array.

## Measurements `scipy.ndimage.measurements`

`center_of_mass` (input[, labels, index]) | Calculate the center of mass of of the array.
`extrema` (input[, labels, index]) | Calculate the minimum, the maximum and their positions of the values of the array.
`find_objects` (input[, max_label]) | Find objects in a labeled array.
`histogram` (input, min, max, bins[, labels, index]) | Calculate a histogram of of the array.
`label` (input[, structure, output]) | Label an array of objects.
`maximum` (input[, labels, index]) | Return the maximum input value.
`maximum_position` (input[, labels, index]) | Find the position of the maximum of the values of the array.
`mean` (input[, labels, index]) | Calculate the mean of the values of the array.
`minimum` (input[, labels, index]) | Calculate the minimum of the values of the array.
`minimum_position` (input[, labels, index]) | Find the position of the minimum of the values of the array.
`standard_deviation` (input[, labels, index]) | Calculate the standard deviation of the values of the array.
`sum` (input[, labels, index]) | Calculate the sum of the values of the array.
`variance` (input[, labels, index]) | Calculate the variance of the values of the array.
`watershed_ift` (input, markers[, structure, output]) | Apply watershed from markers using a iterative forest transform algorithm.

## Morphology `scipy.ndimage.morphology`

`binary_closing` (input[, structure, iterations, ...]) | Multi-dimensional binary closing with the given structure.
`binary_dilation` (input[, structure, iterations, ...]) | Multi-dimensional binary dilation with the given structure.
`binary_erosion` (input[, structure, iterations, ...]) | Multi-dimensional binary erosion with the given structure.
`binary_fill_holes` (input[, structure, output, ...]) | Fill the holes in binary objects.
`binary_hit_or_miss` (input[, structure1, structure2, ...]) | Multi-dimensional binary hit-or-miss transform.
`binary_opening` (input[, structure, iterations, ...]) | Multi-dimensional binary opening with the given structure.
`binary_propagation` (input[, structure, mask, ...]) | Multi-dimensional binary propagation with the given structure.
`black_tophat` (input[, size, footprint, ...]) | Multi-dimensional black tophat filter.
`distance_transform_bf` (input[, metric, sampling, ...]) | Distance transform function by a brute force algorithm.
`distance_transform_cdt` (input[, metric, return_distances, ...]) | Distance transform for chamfer type of transforms.
`distance_transform_edt` (input[, sampling, return_distances, ...]) | Exact euclidean distance transform.
`generate_binary_structure` (rank, connectivity) | Generate a binary structure for binary morphological operations.
`grey_closing` (input[, size, footprint, ...]) | Multi-dimensional grey valued closing.
`grey_dilation` (input[, size, footprint, ...]) | Calculate a grey values dilation.
`grey_erosion` (input[, size, footprint, ...]) | Calculate a grey values erosion.
`grey_opening` (input[, size, footprint, ...]) | Multi-dimensional grey valued opening.
`iterate_structure` (structure, iterations[, origin]) | Iterate a structure by dilating it with itself.
`morphological_gradient` (input[, size, footprint, ...]) | Multi-dimensional morphological gradient.
`morphological_laplace` (input[, size, footprint, ...]) | Multi-dimensional morphological laplace.
`white_tophat` (input[, size, footprint, ...]) | Multi-dimensional white tophat filter.

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