Multilabel marching cubes and simplification of volumetric data.
Project description
zmesh: Multi-Label Marching Cubes & Mesh Simplification
from zmesh import Mesher labels = ... # some dense volumetric labeled image mesher = Mesher( (4,4,40) ) # anisotropy of image mesher.mesh(labels) # initial marching cubes pass meshes = [] for obj_id in mesher.ids(): meshes.append( mesher.get_mesh( obj_id, normals=False, # whether to calculate normals or not # tries to reduce triangles by this factor # 0 disables simplification simplification_factor=100, # Max tolerable error in physical distance max_simplification_error=8 ) ) mesher.erase(obj_id) # delete high res mesh mesher.clear() # clear memory retained by mesher mesh = meshes[0] mesh = mesher.simplify( mesh, # same as simplification_factor in get_mesh reduction_factor=100, # same as max_simplification_error in get_mesh max_error=40, compute_normals=False, # whether to also compute face normals ) # apply simplifier to a pre-existing mesh mesh.vertices mesh.faces mesh.normals mesh.triangles() # compute triangles from vertices and faces # Extremely common obj format with open('iconic_doge.obj', 'wb') as f: f,write(mesh.to_obj()) # Common binary format with open('iconic_doge.ply', 'wb') as f: f,write(mesh.to_ply()) # Neuroglancer Precomputed format with open('10001001:0', 'wb') as f: f.write(mesh.to_precomputed())
Installation
If binaries are available for your system:
pip install zmesh
Requires a C++ compiler
sudo apt-get install python3-dev libboost-all-dev pip install zmesh --no-binary :all:
Performance Tuning
- The mesher will consume about double memory in 64 bit mode if the size of the object exceeds <511, 1023, 511> on the x, y, or z axes. This is due to a limitation of the 32-bit format. It might be possible to get x to 1023 as well.
- Input labels are converted to uint32 or uint64. Use one of these data types to avoid a copy.
- The mesher processes in C order.
Related Projects
- zi_lib - zmesh makes heavy use of Aleks' C++ library.
- Igneous - Visualization of connectomics data using cloud computing.
Credits
Thanks to Aleks Zlateski for creating and sharing this beautiful mesher.
Later changes by Will Silversmith, Nico Kemnitz, and Jingpeng Wu.
References
- W. Lorensen and H. Cline. "Marching Cubes: A High Resolution 3D Surface Construction Algorithm". pp 163-169. Computer Graphics, Volume 21, Number 4, July 1987.
- TK Quadratic Edge Collapse Paper
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