# Multidimensional image processing (`cupyx.scipy.ndimage`)¶

## Filters¶

 `convolve`(input, weights[, output, mode, …]) Multi-dimensional convolution. `convolve1d`(input, weights[, axis, output, …]) One-dimensional convolution. `correlate`(input, weights[, output, mode, …]) Multi-dimensional correlate. `correlate1d`(input, weights[, axis, output, …]) One-dimensional correlate. `gaussian_filter`(input, sigma[, order, …]) Multi-dimensional Gaussian filter. `gaussian_filter1d`(input, sigma[, axis, …]) One-dimensional Gaussian filter along the given axis. `gaussian_gradient_magnitude`(input, sigma[, …]) Multi-dimensional gradient magnitude using Gaussian derivatives. `gaussian_laplace`(input, sigma[, output, …]) Multi-dimensional Laplace filter using Gaussian second derivatives. `generic_filter`(input, function[, size, …]) Compute a multi-dimensional filter using the provided raw kernel or reduction kernel. `generic_filter1d`(input, function, filter_size) Compute a 1D filter along the given axis using the provided raw kernel. `generic_gradient_magnitude`(input, derivative) Multi-dimensional gradient magnitude filter using a provided derivative function. `generic_laplace`(input, derivative2[, …]) Multi-dimensional Laplace filter using a provided second derivative function. `laplace`(input[, output, mode, cval]) Multi-dimensional Laplace filter based on approximate second derivatives. `maximum_filter`(input[, size, footprint, …]) Multi-dimensional maximum filter. `maximum_filter1d`(input, size[, axis, …]) Compute the maximum filter along a single axis. `median_filter`(input[, size, footprint, …]) Multi-dimensional median filter. `minimum_filter`(input[, size, footprint, …]) Multi-dimensional minimum filter. `minimum_filter1d`(input, size[, axis, …]) Compute the minimum filter along a single axis. `percentile_filter`(input, percentile[, size, …]) Multi-dimensional percentile filter. `prewitt`(input[, axis, output, mode, cval]) Compute a Prewitt filter along the given axis. `rank_filter`(input, rank[, size, footprint, …]) Multi-dimensional rank filter. `sobel`(input[, axis, output, mode, cval]) Compute a Sobel filter along the given axis. `uniform_filter`(input[, size, output, mode, …]) Multi-dimensional uniform filter. `uniform_filter1d`(input, size[, axis, …]) One-dimensional uniform filter along the given axis.

## Fourier filters¶

 `fourier_ellipsoid`(input, size[, n, axis, output]) Multidimensional ellipsoid Fourier filter. `fourier_gaussian`(input, sigma[, n, axis, output]) Multidimensional Gaussian shift filter. `fourier_shift`(input, shift[, n, axis, output]) Multidimensional Fourier shift filter. `fourier_uniform`(input, size[, n, axis, output]) Multidimensional uniform shift filter.

## Interpolation¶

 `affine_transform`(input, matrix[, offset, …]) Apply an affine transformation. `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]) Multidimensional spline filter. `spline_filter1d`(input[, order, axis, …]) Calculate a 1-D 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. `histogram`(input, min, max, bins[, labels, index]) Calculate the histogram of the values of an array, optionally at labels. `label`(input[, structure, output]) Labels features in an array. `labeled_comprehension`(input, labels, index, …) Array resulting from applying `func` to each labeled region. `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]) Calculates the mean of the values of an n-D image array, optionally `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]) Calculates the standard deviation of the values of an n-D image array, optionally at specified sub-regions. `sum_labels`(input[, labels, index]) Calculates the sum of the values of an n-D image array, optionally `variance`(input[, labels, index]) Calculates the variance of the values of an n-D image array, optionally at specified sub-regions.

## Morphology¶

 `binary_closing`(input[, structure, …]) Multidimensional binary closing with the given structuring element. `binary_dilation`(input[, structure, …]) Multidimensional binary dilation with the given structuring element. `binary_erosion`(input[, structure, …]) Multidimensional binary erosion with a given structuring element. `binary_fill_holes`(input[, structure, …]) Fill the holes in binary objects. `binary_hit_or_miss`(input[, structure1, …]) Multidimensional binary hit-or-miss transform. `binary_opening`(input[, structure, …]) Multidimensional binary opening with the given structuring element. `binary_propagation`(input[, structure, mask, …]) Multidimensional binary propagation with the given structuring element. `black_tophat`(input[, size, footprint, …]) Multidimensional black tophat filter. `generate_binary_structure`(rank, connectivity) Generate a binary structure for binary morphological operations. `grey_closing`(input[, size, footprint, …]) Calculates a multi-dimensional greyscale closing. `grey_dilation`(input[, size, footprint, …]) Calculates a greyscale dilation. `grey_erosion`(input[, size, footprint, …]) Calculates a greyscale erosion. `grey_opening`(input[, size, footprint, …]) Calculates a multi-dimensional greyscale opening. `iterate_structure`(structure, iterations[, …]) Iterate a structure by dilating it with itself. `morphological_gradient`(input[, size, …]) Multidimensional morphological gradient. `morphological_laplace`(input[, size, …]) Multidimensional morphological laplace. `white_tophat`(input[, size, footprint, …]) Multidimensional white tophat filter.

## OpenCV mode¶

`cupyx.scipy.ndimage` supports additional mode, `opencv`. If it is given, the function performs like cv2.warpAffine or cv2.resize. Example:

```import cupyx.scipy.ndimage
import cupy as cp
import cv2