pyvista.DataSetFilters.voxelize_binary_mask

DataSetFilters.voxelize_binary_mask(

*,

background_value: int | float = 0,

foreground_value: int | float = 1,

reference_volume: pyvista.ImageData | None = None,

dimensions: VectorLike[int] | None = None,

spacing: float | VectorLike[float] | None = None,

rounding_func: Callable[[VectorLike[float]], VectorLike[int]] | None = None,

cell_length_percentile: float | None = None,

cell_length_sample_size: int | None = None,

progress_bar: bool = False,

)

Voxelize mesh as a binary ImageData mask.

The binary mask is a point data array where points inside and outside of the input surface are labelled with foreground_value and background_value, respectively.

This filter implements vtkPolyDataToImageStencil. This algorithm operates as follows:

• The algorithm iterates through the z-slice of the reference_volume.

• For each slice, it cuts the input PolyData surface to create 2D polylines at that z position. It attempts to close any open polylines.

• For each x position along the polylines, the corresponding y positions are determined.

• For each slice, the grid points are labelled as foreground or background based on their xy coordinates.

The voxelization can be controlled in several ways:

1. Specify the output geometry using a reference_volume.

2. Specify the spacing explicitly.

3. Specify the dimensions explicitly.

4. Specify the cell_length_percentile. The spacing is estimated from the surface’s cells using the specified percentile.

Use reference_volume for full control of the output mask’s geometry. For all other options, the geometry is implicitly defined such that the generated mask fits the bounds of the input surface.

If no inputs are provided, cell_length_percentile=0.1 (10th percentile) is used by default to estimate the spacing. On systems with VTK < 9.2, the default spacing is set to 1/100 of the input mesh’s length.

Added in version 0.45.0.

Note

For best results, ensure the input surface is a closed surface. The surface is considered closed if it has zero n_open_edges.

Note

This filter returns voxels represented as point data, not VOXEL cells. This differs from voxelize() and voxelize_volume() which return meshes with voxel cells. See Image Data Representations for examples demonstrating the difference.

Note

This filter does not discard internal surfaces, due, for instance, to intersecting meshes. Instead, the intersection will be considered as background which may produce unexpected results. See Examples.

Parameters:

background_valueint, default: 0

Background value of the generated mask.

foreground_valueint, default: 1

Foreground value of the generated mask.

reference_volumepyvista.ImageData, optional

Volume to use as a reference. The output will have the same dimensions, origin, spacing, and direction_matrix as the reference.

dimensionsVectorLike[int], optional

Dimensions of the generated mask image. Set this value to control the dimensions explicitly. If unset, the dimensions are defined implicitly through other parameter. See summary and examples for details.

spacingVectorLike[float], optional

Approximate spacing to use for the generated mask image. Set this value to control the spacing explicitly. If unset, the spacing is defined implicitly through other parameters. See summary and examples for details.

rounding_funcCallable[VectorLike[float], VectorLike[int]], optional

Control how the dimensions are rounded to integers based on the provided or calculated spacing. Should accept a length-3 vector containing the dimension values along the three directions and return a length-3 vector. numpy.round() is used by default.

Rounding the dimensions implies rounding the actual spacing.

Has no effect if reference_volume or dimensions are specified.

cell_length_percentilefloat, optional

Cell length percentage p to use for computing the default spacing. Default is 0.1 (10th percentile) and must be between 0 and 1. The p-th percentile is computed from the cumulative distribution function (CDF) of lengths which are representative of the cell length scales present in the input. The CDF is computed by:

1. Triangulating the input cells.

2. Sampling a subset of up to cell_length_sample_size cells.

3. Computing the distance between two random points in each cell.

4. Inserting the distance into an ordered set to create the CDF.

Has no effect if dimension or reference_volume are specified.

Note

This option is only available for VTK 9.2 or greater.

cell_length_sample_sizeint, optional

Number of samples to use for the cumulative distribution function (CDF) when using the cell_length_percentile option. 100 000 samples are used by default.

progress_barbool, default: False

Display a progress bar to indicate progress.

Returns:

pyvista.ImageData

Generated binary mask with a 'mask' point data array. The data array has dtype numpy.uint8 if the foreground and background values are unsigned and less than 256.

See also

voxelize

Similar function that returns a UnstructuredGrid of VOXEL cells.

voxelize_volume

Similar function that returns a RectilinearGrid with cell data.

pyvista.ImageDataFilters.contour_labels

Filter that generates surface contours from labeled image data. Can be loosely considered as an inverse of this filter.

pyvista.ImageDataFilters.points_to_cells

Convert voxels represented as points to VOXEL cells.

pyvista.ImageData

Class used to build custom reference_volume.

Examples

Generate a binary mask from a coarse mesh.

>>> import numpy as np
>>> import pyvista as pv
>>> from pyvista import examples
>>> poly = examples.download_bunny_coarse()
>>> mask = poly.voxelize_binary_mask()

The mask is stored as ImageData with point data scalars (zeros for background, ones for foreground).

>>> mask
ImageData (...)
  N Cells:      7056
  N Points:     8228
  X Bounds:     -1.245e-01, 1.731e-01
  Y Bounds:     -1.135e-01, 1.807e-01
  Z Bounds:     -1.359e-01, 9.140e-02
  Dimensions:   22, 22, 17
  Spacing:      1.417e-02, 1.401e-02, 1.421e-02
  N Arrays:     1

>>> np.unique(mask.point_data['mask'])
pyvista_ndarray([0, 1], dtype=uint8)

To visualize it as voxel cells, use points_to_cells(), then use threshold() to extract the foreground.

We also plot the voxel cells in blue and the input poly data in green for comparison.

>>> def mask_and_polydata_plotter(mask, poly):
...     voxel_cells = mask.points_to_cells().threshold(0.5)
...
...     plot = pv.Plotter()
...     _ = plot.add_mesh(voxel_cells, color='blue')
...     _ = plot.add_mesh(poly, color='lime')
...     plot.camera_position = 'xy'
...     return plot

>>> plot = mask_and_polydata_plotter(mask, poly)
>>> plot.show()

Static Scene

Interactive Scene

The spacing of the mask image is automatically adjusted to match the density of the input.

Repeat the previous example with a finer mesh.

>>> poly = examples.download_bunny()
>>> mask = poly.voxelize_binary_mask()
>>> plot = mask_and_polydata_plotter(mask, poly)
>>> plot.show()

Static Scene

Interactive Scene

Control the spacing manually instead. Here, a very coarse spacing is used.

>>> mask = poly.voxelize_binary_mask(spacing=(0.01, 0.04, 0.02))
>>> plot = mask_and_polydata_plotter(mask, poly)
>>> plot.show()

Static Scene

Interactive Scene

Note that the spacing is only approximate. Check the mask’s actual spacing.

>>> mask.spacing
(0.009731187485158443, 0.03858340159058571, 0.020112216472625732)

The actual values may be greater or less than the specified values. Use rounding_func=np.floor to force all values to be greater.

>>> mask = poly.voxelize_binary_mask(
...     spacing=(0.01, 0.04, 0.02), rounding_func=np.floor
... )
>>> mask.spacing
(0.01037993331750234, 0.05144453545411428, 0.020112216472625732)

Set the dimensions instead of the spacing.

>>> mask = poly.voxelize_binary_mask(dimensions=(10, 20, 30))
>>> plot = mask_and_polydata_plotter(mask, poly)
>>> plot.show()

Static Scene

Interactive Scene

>>> mask.dimensions
(10, 20, 30)

Create a mask using a reference volume. First generate polydata from an existing mask.

>>> volume = examples.load_frog_tissues()
>>> poly = volume.contour_labels()

Now create the mask from the polydata using the volume as a reference.

>>> mask = poly.voxelize_binary_mask(reference_volume=volume)
>>> plot = mask_and_polydata_plotter(mask, poly)
>>> plot.show()

Static Scene

Interactive Scene

Visualize the effect of internal surfaces.

>>> mesh = pv.Cylinder() + pv.Cylinder((0, 0.75, 0))
>>> binary_mask = mesh.voxelize_binary_mask(
...     dimensions=(1, 100, 50)
... ).points_to_cells()
>>> plot = pv.Plotter()
>>> _ = plot.add_mesh(binary_mask)
>>> _ = plot.add_mesh(mesh.slice(), color='red')
>>> plot.show(cpos='yz')

Static Scene

Interactive Scene

Note how the intersection is excluded from the mask. To include the voxels delimited by internal surfaces in the foreground, the internal surfaces should be removed, for instance by applying a boolean union. Note that this operation in unreliable in VTK but may be performed with external tools such as vtkbool.

Alternatively, the intersecting parts of the mesh can be processed sequentially.

>>> cylinder_1 = pv.Cylinder()
>>> cylinder_2 = pv.Cylinder((0, 0.75, 0))

>>> reference_volume = pv.ImageData(
...     dimensions=(1, 100, 50),
...     spacing=(1, 0.0175, 0.02),
...     origin=(0, -0.5 + 0.0175 / 2, -0.5 + 0.02 / 2),
... )

>>> binary_mask_1 = cylinder_1.voxelize_binary_mask(
...     reference_volume=reference_volume
... ).points_to_cells()
>>> binary_mask_2 = cylinder_2.voxelize_binary_mask(
...     reference_volume=reference_volume
... ).points_to_cells()

>>> binary_mask_1['mask'] = binary_mask_1['mask'] | binary_mask_2['mask']

>>> plot = pv.Plotter()
>>> _ = plot.add_mesh(binary_mask_1)
>>> _ = plot.add_mesh(cylinder_1.slice(), color='red')
>>> _ = plot.add_mesh(cylinder_2.slice(), color='red')
>>> plot.show(cpos='yz')

Static Scene

Interactive Scene

When multiple internal surfaces are nested, they are successively treated as interfaces between background and foreground.

>>> mesh = pv.Tube(radius=2) + pv.Tube(radius=3) + pv.Tube(radius=4)
>>> binary_mask = mesh.voxelize_binary_mask(
...     dimensions=(1, 50, 50)
... ).points_to_cells()
>>> plot = pv.Plotter()
>>> _ = plot.add_mesh(binary_mask)
>>> _ = plot.add_mesh(mesh.slice(), color='red')
>>> plot.show(cpos='yz')

Static Scene

Interactive Scene