# 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, ...]) Multi-dimensional 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, ...]) Multi-dimensional Gaussian filter. gaussian_filter1d(input, sigma[, axis, ...]) One-dimensional Gaussian filter. gaussian_gradient_magnitude(input, sigma[, ...]) Calculate a multidimensional gradient magnitude using gaussian derivatives. gaussian_laplace(input, sigma[, output, ...]) Calculate a 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) Calculate a gradient magnitude using the provided function for the gradient. generic_laplace(input, derivative2[, ...]) 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, ...]) 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, ...]) 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. 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, 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 watershed_ift(input, markers[, structure, ...]) Apply watershed from markers using a iterative forest 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]) Load an image from file.

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