Collection of functions for altering images based on structural properties#

This module contains a variety of functions for altering images based on the structural characteristics, such as pore sizes. A definition of a filter is a function that returns an image the shape as the original image, but with altered values.

filters.apply_chords(im[, spacing, axis, ...])

Adds chords to the void space in the specified direction.

filters.apply_chords_3D(im[, spacing, ...])

Adds chords to the void space in all three principle directions.

filters.apply_padded(im, pad_width, func[, ...])

Applies padding to an image before sending to func, then extracts the result corresponding to the original image shape.

filters.chunked_func(func[, overlap, divs, ...])

Performs the specfied operation "chunk-wise" in parallel using dask.

filters.distance_transform_lin(im[, axis, mode])

Replaces each void voxel with the linear distance to the nearest solid voxel along the specified axis.

filters.fftmorphology(im, strel[, mode])

Perform morphological operations on binary images using fft approach for improved performance

filters.fill_blind_pores(im[, conn, surface])

Fills all blind pores that are isolated from the main void space.

filters.find_disconnected_voxels(im[, conn, ...])

Identifies all voxels that are not connected to the edge of the image.


Label points in a distance transform that are closer to image boundary than solid

filters.find_peaks(dt[, r_max, strel, ...])

Finds local maxima in the distance transform

filters.find_trapped_regions(seq[, outlets, ...])

Find the trapped regions given an invasion sequence image

filters.flood(im, labels[, mode])

Floods/fills each region in an image with a single value based on the specific values in that region.

filters.flood_func(im, func[, labels])

Flood each isolated region in an image with a constant value calculated by the given function.

filters.hold_peaks(im[, axis, ascending])

Replaces each voxel with the highest value along the given axis.


This function has been moved to the simulations module, please use that.


This function has been moved to the simulations module, please use that.



filters.local_thickness(im[, sizes, mode, divs])

For each voxel, this function calculates the radius of the largest sphere that both engulfs the voxel and fits entirely within the foreground.

filters.nl_means_layered(im[, cores, axis, ...])

Apply the non-local means filter to each 2D layer of a stack in parallel.

filters.nphase_border(im[, include_diagonals])

Identifies the voxels in regions that border N other regions.

filters.pc_to_satn(pc, im)

Converts an image of capillary entry pressures to saturation values

filters.porosimetry(im[, sizes, inlets, ...])

Performs a porosimetry simulution on an image.

filters.prune_branches(skel[, ...])

Remove all dangling ends or tails of a skeleton


Any peaks that are broad or elongated are replaced with a single voxel that is located at the center of mass of the original voxels.


Replace each voxel with the size of the region to which it belongs

filters.satn_to_seq(satn[, im])

Converts an image of nonwetting phase saturations to invasion sequence values

filters.seq_to_satn(seq[, im])

Converts an image of invasion sequence values to saturation values.

filters.size_to_satn(size[, im, bins])

Converts an image of invasion size values into saturations.

filters.size_to_seq(size[, im, bins])

Converts an image of invasion size values into sequence values

filters.snow_partitioning(im[, dt, r_max, ...])

Partition the void space into pore regions using a marker-based watershed algorithm

filters.snow_partitioning_n(im[, r_max, ...])

This function partitions an imaging oontain an arbitrary number of phases into regions using a marker-based watershed segmentation.

filters.snow_partitioning_parallel(im[, ...])

Performs SNOW algorithm in parallel (or serial) to reduce time (or memory usage) by geomertirc domain decomposition of large images.

filters.trim_disconnected_blobs(im, inlets)

Removes foreground voxels not connected to specified inlets.

filters.trim_extrema(im, h[, mode])

Trims local extrema in greyscale values by a specified amount.

filters.trim_floating_solid(im[, conn, surface])

Removes all solid that that is not attached to main solid structure.

filters.trim_nearby_peaks(peaks, dt[, f])

Removes peaks that are nearer to another peak than to solid

filters.trim_nonpercolating_paths(im, ...[, ...])

Remove all nonpercolating paths between specified locations

filters.trim_saddle_points(peaks, dt[, maxiter])

Removes peaks that were mistakenly identified because they lied on a saddle or ridge in the distance transform that was not actually a true local peak.

filters.trim_small_clusters(im[, size])

Remove isolated voxels or clusters of a given size or smaller