pyvista.ImageDataFilters

class ImageDataFilters(*args, **kwargs)

An internal class to manage filters/algorithms for uniform grid datasets.

Methods

ImageDataFilters.cells_to_points([scalars, ...])	Re-mesh image data from a cell-based to a point-based representation.
ImageDataFilters.close([kernel_size, ...])	Perform morphological closing on continuous or binary data.
ImageDataFilters.concatenate(images[, axis, ...])	Combine multiple images into one.
ImageDataFilters.contour_labeled([n_labels, ...])	Generate labeled contours from 3D label maps.
ImageDataFilters.contour_labels([...])	Generate surface contours from 3D image label maps.
ImageDataFilters.crop(*[, factor, margin, ...])	Crop this image to remove points at its boundaries.
ImageDataFilters.dilate([kernel_size, ...])	Morphologically dilate grayscale or binary data.
ImageDataFilters.erode([kernel_size, ...])	Morphologically erode grayscale or binary data.
ImageDataFilters.extract_subset(voi[, rate, ...])	Select piece (e.g., volume of interest).
ImageDataFilters.fft([output_scalars_name, ...])	Apply a fast Fourier transform (FFT) to the active scalars.
ImageDataFilters.gaussian_smooth([...])	Smooth the data with a Gaussian kernel.
ImageDataFilters.high_pass(x_cutoff, ...[, ...])	Perform a Butterworth high pass filter in the frequency domain.
ImageDataFilters.image_dilate_erode([...])	Dilates one value and erodes another.
ImageDataFilters.image_threshold(threshold)	Apply a threshold to scalar values in a uniform grid.
ImageDataFilters.label_connectivity(*[, ...])	Find and label connected regions in a ImageData.
ImageDataFilters.low_pass(x_cutoff, ...[, ...])	Perform a Butterworth low pass filter in the frequency domain.
ImageDataFilters.median_smooth([...])	Smooth data using a median filter.
ImageDataFilters.open([kernel_size, ...])	Perform morphological opening on continuous or binary data.
ImageDataFilters.pad_image([pad_value, ...])	Enlarge an image by padding its boundaries with new points.
ImageDataFilters.points_to_cells([scalars, ...])	Re-mesh image data from a point-based to a cell-based representation.
ImageDataFilters.resample([sample_rate, ...])	Resample the image to modify its dimensions and spacing.
ImageDataFilters.rfft([output_scalars_name, ...])	Apply a reverse fast Fourier transform (RFFT) to the active scalars.
ImageDataFilters.select_values([values, ...])	Select values of interest and fill the rest with a constant.
ImageDataFilters.slice_index([i, j, k, ...])	Extract a subset using IJK indices.

Attributes

ImageDataFilters.points