### Table Of Contents

- Multi-dimensional image processing (`scipy.ndimage`)
  - Filters `scipy.ndimage.filters`
  - Fourier filters `scipy.ndimage.fourier`
  - Interpolation `scipy.ndimage.interpolation`
  - Measurements `scipy.ndimage.measurements`
  - Morphology `scipy.ndimage.morphology`
  - Utility

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

This package contains various functions for multi-dimensional image processing.

## Filters `scipy.ndimage.filters`

`convolve`(input, weights[, output, mode, ...]) | Multidimensional convolution.
`convolve1d`(input, weights[, axis, output, ...]) | Calculate a one-dimensional convolution along the given axis.
`correlate`(input, weights[, output, mode, ...]) | Multi-dimensional correlation.
`correlate1d`(input, weights[, axis, output, ...]) | Calculate a one-dimensional correlation along the given axis.
`gaussian_filter`(input, sigma[, order, ...]) | Multidimensional Gaussian filter.
`gaussian_filter1d`(input, sigma[, axis, ...]) | One-dimensional Gaussian filter.
`gaussian_gradient_magnitude`(input, sigma[, ...]) | Multidimensional gradient magnitude using Gaussian derivatives.
`gaussian_laplace`(input, sigma[, output, ...]) | Multidimensional Laplace filter using gaussian second derivatives.
`generic_filter`(input, function[, size, ...]) | Calculates a multi-dimensional filter using the given function.
`generic_filter1d`(input, function, filter_size) | Calculate a one-dimensional filter along the given axis.
`generic_gradient_magnitude`(input, derivative) | Gradient magnitude using a provided gradient function.
`generic_laplace`(input, derivative2[, ...]) | N-dimensional Laplace filter using a provided second derivative function :Parameters: input : array_like Input array to filter.
`laplace`(input[, output, mode, cval]) | N-dimensional Laplace filter based on approximate second derivatives.
`maximum_filter`(input[, size, footprint, ...]) | Calculates a multi-dimensional maximum filter.
`maximum_filter1d`(input, size[, axis, ...]) | Calculate a one-dimensional maximum filter along the given axis.
`median_filter`(input[, size, footprint, ...]) | Calculates a multidimensional median filter.
`minimum_filter`(input[, size, footprint, ...]) | Calculates a multi-dimensional minimum filter.
`minimum_filter1d`(input, size[, axis, ...]) | Calculate a one-dimensional minimum filter along the given axis.
`percentile_filter`(input, percentile[, size, ...]) | Calculates a multi-dimensional percentile filter.
`prewitt`(input[, axis, output, mode, cval]) | Calculate a Prewitt filter.
`rank_filter`(input, rank[, size, footprint, ...]) | Calculates a multi-dimensional rank filter.
`sobel`(input[, axis, output, mode, cval]) | Calculate a Sobel filter.
`uniform_filter`(input[, size, output, mode, ...]) | Multi-dimensional uniform filter.
`uniform_filter1d`(input, size[, axis, ...]) | 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, ...]) | Apply an affine transformation.
`geometric_transform`(input, mapping[, ...]) | Apply an arbritrary geometric transform.
`map_coordinates`(input, coordinates[, ...]) | Map the input array to new coordinates by interpolation.
`rotate`(input, angle[, axes, reshape, ...]) | Rotate an array.
`shift`(input, shift[, output, order, mode, ...]) | Shift an array.
`spline_filter`(input[, order, output]) | Multi-dimensional spline filter.
`spline_filter1d`(input[, order, axis, output]) | Calculates a one-dimensional spline filter along the given axis.
`zoom`(input, zoom[, output, order, mode, ...]) | Zoom an array.

## Measurements `scipy.ndimage.measurements`

`center_of_mass`(input[, labels, index]) | Calculate the center of mass of the values of an array at labels.
`extrema`(input[, labels, index]) | Calculate the minimums and maximums of the values of an array at labels, along with their positions.
`find_objects`(input[, max_label]) | Find objects in a labeled array.
`histogram`(input, min, max, bins[, labels, index]) | Calculate the histogram of the values of an array, optionally at labels.
`label`(input[, structure, output]) | Label features in an array.
`labeled_comprehension`(input, labels, index, ...) | Roughly equivalent to [func(input[labels == i]) for i in index].
`maximum`(input[, labels, index]) | Calculate the maximum of the values of an array over labeled regions.
`maximum_position`(input[, labels, index]) | Find the positions of the maximums of the values of an array at labels.
`mean`(input[, labels, index]) | Calculate the mean of the values of an array at labels.
`minimum`(input[, labels, index]) | Calculate the minimum of the values of an array over labeled regions.
`minimum_position`(input[, labels, index]) | Find the positions of the minimums of the values of an array at labels.
`standard_deviation`(input[, labels, index]) | Calculate the standard deviation of the values of an n-D image array, optionally at specified sub-regions.
`sum`(input[, labels, index]) | Calculate the sum of the values of the array.
`variance`(input[, labels, index]) | Calculate the variance of the values of an n-D image array, optionally at specified sub-regions.
`watershed_ift`(input, markers[, structure, ...]) | Apply watershed from markers using image foresting transform algorithm.

## Morphology `scipy.ndimage.morphology`

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

## Utility

`imread`(fname[, flatten, mode]) | Read an image from a file as an array.