vedo

A python module for scientific analysis of 3D data based on VTK and Numpy

marcomusy
2052
266
Python

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Your friendly python module
for scientific analysis and visualization of 3d objects.

💾 Installation

pip install vedo
additional installation details [click to expand]
  • To install the latest dev version of vedo:
pip install -U git+https://github.com/marcomusy/vedo.git
  • To install from the conda-forge channel:
conda install -c conda-forge vedo

📙 Documentation

The webpage of the library with documentation is available here.

📌 Need help? Have a question, or wish to ask for a missing feature?
Do not hesitate to ask any questions on the image.sc forum
or by opening a github issue.

🎨 Features

The library includes a large set of working examples
for a wide range of functionalities

working with polygonal meshes and point clouds [click to expand]
  • Import meshes from VTK format, STL, Wavefront OBJ, 3DS, Dolfin-XML, Neutral, GMSH, OFF, PCD (PointCloud),
  • Export meshes as ASCII or binary to VTK, STL, OBJ, PLY … formats.
  • Analysis tools like Moving Least Squares, mesh morphing and more…
  • Tools to visualize and edit meshes (cutting a mesh with another mesh, slicing, normalizing, moving vertex positions, etc…).
  • Split mesh based on surface connectivity. Extract the largest connected area.
  • Calculate areas, volumes, center of mass, average sizes etc.
  • Calculate vertex and face normals, curvatures, feature edges. Fill mesh holes.
  • Subdivide faces of a mesh, increasing the number of vertex points. Mesh simplification.
  • Coloring and thresholding of meshes based on associated scalar or vectorial data.
  • Point-surface operations: find nearest points, determine if a point lies inside or outside of a mesh.
  • Create primitive shapes: spheres, arrows, cubes, torus, ellipsoids…
  • Generate glyphs (associate a mesh to every vertex of a source mesh).
  • Create animations easily by just setting the position of the displayed objects in the 3D scene. Add trailing lines and shadows to moving objects is supported.
  • Straightforward support for multiple sync-ed or independent renderers in the same window.
  • Registration (alignment) of meshes with different techniques.
  • Mesh smoothing.
  • Delaunay triangulation in 2D and 3D.
  • Generate meshes by joining nearby lines in space.
  • Find the closest path from one point to another, traveling along the edges of a mesh.
  • Find the intersection of a mesh with lines, planes or other meshes.
  • Interpolate scalar and vectorial fields with Radial Basis Functions and Thin Plate Splines.
  • Add sliders and buttons to interact with the scene and the individual objects.
  • Visualization of tensors.
  • Analysis of Point Clouds
  • Moving Least Squares smoothing of 2D, 3D and 4D clouds
  • Fit lines, planes, spheres and ellipsoids in space
  • Identify outliers in a distribution of points
  • Decimate a cloud to a uniform distribution.
working with volumetric data and tetrahedral meshes
  • Import data from VTK format volumetric TIFF stacks, DICOM, SLC, MHD and more
  • Import 2D images as PNG, JPEG, BMP
  • Isosurfacing of volumes
  • Composite and maximum projection volumetric rendering
  • Generate volumetric signed-distance data from an input surface mesh
  • Probe volumes with lines and planes
  • Generate stream-lines and stream-tubes from vectorial fields
  • Slice and crop volumes
  • Support for other volumetric structures (structured and grid data)
plotting and histogramming in 2D and 3D
  • Polygonal 3D text rendering with Latex-like syntax and unicode characters, with 30 different fonts.
  • Fully customizable axis styles
  • donut plots and pie charts
  • Scatter plots in 2D and 3D
  • Surface function plotting
  • 1D customizable histograms
  • 2D hexagonal histograms
  • Polar plots, spherical plots and histogramming
  • Draw latex-formatted formulas in the rendering window.
  • Quiver, violin, whisker and stream-line plots
  • Graphical markers analogous to matplotlib
integration with other libraries
  • Integration with the Qt5 framework.
  • Support for FEniCS/Dolfin platform for visualization of PDE/FEM solutions.
  • Interoperability with the trimesh, pyvista and pymeshlab libraries.
  • Export 3D scenes and embed them into a web page.
  • Embed 3D scenes in jupyter notebooks with K3D (can export an interactive 3D-snapshot page here).

⌨ Command Line Interface

Visualize a polygonal mesh or a volume from a terminal window simply with:

vedo https://vedo.embl.es/examples/data/embryo.tif
volumetric files (slc, tiff, DICOM...) can be visualized in different modes [click to expand]
Volume 3D slicing
vedo --slicer embryo.slc		 Ray-casting
vedo -g		 2D slicing
vedo --slicer2d
slicer isohead viz_slicer

Type vedo -h for the complete list of options.

🐾 Gallery

vedo currently includes 300+ working examples and notebooks.

Run any of the built-in examples. In a terminal type: vedo -r warp2

Check out the example galleries organized by subject here:

✏ Contributing

Any contributions are greatly appreciated!
If you have a suggestion that would make this better, please fork the repo and create a pull request.
You can also simply open an issue with the tag “enhancement”.

📜 References

Scientific publications leveraging vedo:

  • X. Diego et al.:
    “Key features of Turing systems are determined purely by network topology”,
    Phys. Rev. X 8, 021071,
    DOI.
  • M. Musy, K. Flaherty et al.:
    “A Quantitative Method for Staging Mouse Limb Embryos based on Limb Morphometry”,
    Development (2018) 145 (7): dev154856,
    DOI.
  • F. Claudi, A. L. Tyson, T. Branco, “Brainrender. A python based software for visualisation
    of neuroanatomical and morphological data.”    ,
    eLife 2021;10:e65751,
    DOI.
  • J. S. Bennett, D. Sijacki,
    “Resolving shocks and filaments in galaxy formation simulations: effects on gas properties and
    star formation in the circumgalactic medium”    ,
    Monthly Notices of the Royal Astronomical Society, Volume 499, Issue 1,
    DOI.
  • J.D.P. Deshapriya et al.,
    “Spectral analysis of craters on (101955) Bennu”.
    Icarus 2020,
    DOI.
  • A. Pollack et al.,
    “Stochastic inversion of gravity, magnetic, tracer, lithology, and fault data
    for geologically realistic structural models: Patua Geothermal Field case study”    ,
    Geothermics, Volume 95, September 2021,
    DOI.
  • X. Lu et al.,
    “3D electromagnetic modeling of graphitic faults in the Athabasca
    Basin using a finite-volume time-domain approach with unstructured grids”    ,
    Geophysics,
    DOI.
  • M. Deepa Maheshvare et al.,
    “A Graph-Based Framework for Multiscale Modeling of Physiological Transport”,
    Front. Netw. Physiol. 1:802881,
    DOI.
  • F. Claudi, T. Branco,
    “Differential geometry methods for constructing manifold-targeted recurrent neural networks”,
    bioRxiv 2021.10.07.463479,
    DOI.
  • J. Klatzow, G. Dalmasso, N. Martínez-Abadías, J. Sharpe, V. Uhlmann,
    “µMatch: 3D shape correspondence for microscopy data”,
    Front. Comput. Sci., 15 February 2022.
    DOI
  • G. Dalmasso et al., “4D reconstruction of murine developmental trajectories using spherical harmonics”,
    Developmental Cell 57, 1–11 September 2022,
    DOI.
  • D.J.E Waibel et al., “Capturing Shape Information with Multi-scale Topological Loss Terms for 3D Reconstruction”,
    Lecture Notes in Computer Science, vol 13434. Springer, Cham.
    DOI.
  • N. Lamb et al., “DeepJoin: Learning a Joint Occupancy, Signed Distance, and Normal Field Function for Shape Repair”,
    ACM Transactions on Graphics (TOG), vol 41, 6, 2022.
    DOI
  • J. Cotterell et al., “Cell 3D Positioning by Optical encoding (C3PO) and its application to spatial transcriptomics”, bioRxiv 2024.03.12.584578;
    DOI

Have you found this software useful for your research? Star ✨ the project and cite it as:

M. Musy et al.,
“vedo, a python module for scientific analysis and visualization of 3D objects and point clouds”,
Zenodo, 2021, doi: 10.5281/zenodo.7019968.

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