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 |
| 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 an 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. |