SciPy

Multi-dimensional image processing (scipy.ndimage)

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

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, …])

Calculate 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.

laplace(input[, output, mode, cval])

N-dimensional Laplace filter based on approximate second derivatives.

maximum_filter(input[, size, footprint, …])

Calculate 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, …])

Calculate a multidimensional median filter.

minimum_filter(input[, size, footprint, …])

Calculate 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, …])

Calculate a multi-dimensional percentile filter.

prewitt(input[, axis, output, mode, cval])

Calculate a Prewitt filter.

rank_filter(input, rank[, size, footprint, …])

Calculate 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

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

affine_transform(input, matrix[, offset, …])

Apply an affine transformation.

geometric_transform(input, mapping[, …])

Apply an arbitrary 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, mode])

Multi-dimensional spline filter.

spline_filter1d(input[, order, axis, …])

Calculate a one-dimensional spline filter along the given axis.

zoom(input, zoom[, output, order, mode, …])

Zoom an array.

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.

median(input[, labels, index])

Calculate the median of the values of an array over labeled regions.

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

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(\*args, \*\*kwds)

imread is deprecated! imread is deprecated in SciPy 1.0.0.