## zmesh: Multi-Label Marching Cubes & Mesh Simplification
[](https://github.com/seung-lab/zmesh/actions/workflows/test.yml) [](https://badge.fury.io/py/zmesh)
```python
from zmesh import Mesher
labels = ... # some dense volumetric labeled image
mesher = Mesher( (4,4,40) ) # anisotropy of image
# initial marching cubes pass
# close controls whether meshes touching
# the image boundary are left open or closed
mesher.mesh(labels, close=False)
meshes = []
for obj_id in mesher.ids():
meshes.append(
mesher.get(
obj_id,
normals=False, # whether to calculate normals or not
# tries to reduce triangles by this factor
# 0 disables simplification
reduction_factor=100,
# Max tolerable error in physical distance
# note: if max_error is not set, the max error
# will be set equivalent to one voxel along the
# smallest dimension.
max_error=8,
# whether meshes should be centered in the voxel
# on (0,0,0) [False] or (0.5,0.5,0.5) [True]
voxel_centered=False,
)
)
mesher.erase(obj_id) # delete high res mesh
mesher.clear() # clear memory retained by mesher
mesh = meshes[0]
mesh = mesher.simplify(
mesh,
# same as reduction_factor in get
reduction_factor=100,
# same as max_error in get
max_error=40,
compute_normals=False, # whether to also compute face normals
) # apply simplifier to a pre-existing mesh
# compute normals without simplifying
mesh = mesher.compute_normals(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())
```
Note: As of the latest version, `mesher.get_mesh` has been deprecated in favor of `mesher.get` which fixes a long standing bug where you needed to transpose your data in order to get a mesh in the correct orientation.
## Installation
If binaries are not available for your system, ensure you have a C++ compiler installed.
```bash
pip install zmesh
```
## Performance Tuning & Notes
- The mesher will consume about double memory in 64 bit mode if the size of the
object exceeds <1023, 1023, 511> on the x, y, or z axes. This is due to a limitation
of the 32-bit format.
- The mesher is ambidextrous, it can handle C or Fortran order arrays.
- The maximum vertex range supported `.simplify` after converting to voxel space is 2<sup>20</sup> (appx. 1M) due to the packed 64-bit vertex format.
## Related Projects
- [zi_lib](https://github.com/zlateski/zi_lib) - zmesh makes heavy use of Aleks' C++ library.
- [Igneous](https://github.com/seung-lab/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
1. 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. ([link](https://people.eecs.berkeley.edu/~jrs/meshpapers/LorensenCline.pdf))
2. M. Garland and P. Heckbert. "Surface simplification using quadric error metrics". SIGGRAPH '97: Proceedings of the 24th annual conference on Computer graphics and interactive techniques. Pages 209–216. August 1997. doi: 10.1145/258734.258849 ([link](https://mgarland.org/files/papers/quadrics.pdf))
3. H. Hoppe. "New Quadric Metric for Simplifying Meshes with Appearance Attributes". IEEE Visualization 1999 Conference. pp. 59-66. doi: 10.1109/VISUAL.1999.809869 ([link](http://hhoppe.com/newqem.pdf))
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"description": "## zmesh: Multi-Label Marching Cubes & Mesh Simplification\n[](https://github.com/seung-lab/zmesh/actions/workflows/test.yml) [](https://badge.fury.io/py/zmesh) \n\n```python\nfrom zmesh import Mesher\n\nlabels = ... # some dense volumetric labeled image\nmesher = Mesher( (4,4,40) ) # anisotropy of image\n\n# initial marching cubes pass\n# close controls whether meshes touching\n# the image boundary are left open or closed\nmesher.mesh(labels, close=False) \n\nmeshes = []\nfor obj_id in mesher.ids():\n meshes.append(\n mesher.get(\n obj_id, \n normals=False, # whether to calculate normals or not\n\n # tries to reduce triangles by this factor\n # 0 disables simplification\n reduction_factor=100, \n\n # Max tolerable error in physical distance\n # note: if max_error is not set, the max error\n # will be set equivalent to one voxel along the \n # smallest dimension.\n max_error=8,\n # whether meshes should be centered in the voxel\n # on (0,0,0) [False] or (0.5,0.5,0.5) [True]\n voxel_centered=False, \n )\n )\n mesher.erase(obj_id) # delete high res mesh\n\nmesher.clear() # clear memory retained by mesher\n\nmesh = meshes[0]\nmesh = mesher.simplify(\n mesh, \n # same as reduction_factor in get\n reduction_factor=100, \n # same as max_error in get\n max_error=40, \n compute_normals=False, # whether to also compute face normals\n) # apply simplifier to a pre-existing mesh\n\n# compute normals without simplifying\nmesh = mesher.compute_normals(mesh) \n\nmesh.vertices\nmesh.faces \nmesh.normals\nmesh.triangles() # compute triangles from vertices and faces\n\n# Extremely common obj format\nwith open('iconic_doge.obj', 'wb') as f:\n f.write(mesh.to_obj())\n\n# Common binary format\nwith open('iconic_doge.ply', 'wb') as f:\n f.write(mesh.to_ply())\n\n# Neuroglancer Precomputed format\nwith open('10001001:0', 'wb') as f:\n f.write(mesh.to_precomputed())\n```\n\nNote: As of the latest version, `mesher.get_mesh` has been deprecated in favor of `mesher.get` which fixes a long standing bug where you needed to transpose your data in order to get a mesh in the correct orientation.\n\n## Installation \n\nIf binaries are not available for your system, ensure you have a C++ compiler installed.\n\n```bash\npip install zmesh\n```\n\n## Performance Tuning & Notes\n\n- The mesher will consume about double memory in 64 bit mode if the size of the \nobject exceeds <1023, 1023, 511> on the x, y, or z axes. This is due to a limitation \nof the 32-bit format. \n- The mesher is ambidextrous, it can handle C or Fortran order arrays.\n- The maximum vertex range supported `.simplify` after converting to voxel space is 2<sup>20</sup> (appx. 1M) due to the packed 64-bit vertex format.\n\n## Related Projects \n\n- [zi_lib](https://github.com/zlateski/zi_lib) - zmesh makes heavy use of Aleks' C++ library.\n- [Igneous](https://github.com/seung-lab/igneous) - Visualization of connectomics data using cloud computing.\n\n## Credits\n\nThanks to Aleks Zlateski for creating and sharing this beautiful mesher. \n\nLater changes by Will Silversmith, Nico Kemnitz, and Jingpeng Wu. \n\n## References \n\n1. 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. ([link](https://people.eecs.berkeley.edu/~jrs/meshpapers/LorensenCline.pdf)) \n2. M. Garland and P. Heckbert. \"Surface simplification using quadric error metrics\". SIGGRAPH '97: Proceedings of the 24th annual conference on Computer graphics and interactive techniques. Pages 209\u2013216. August 1997. doi: 10.1145/258734.258849 ([link](https://mgarland.org/files/papers/quadrics.pdf)) \n3. H. Hoppe. \"New Quadric Metric for Simplifying Meshes with Appearance Attributes\". IEEE Visualization 1999 Conference. pp. 59-66. doi: 10.1109/VISUAL.1999.809869 ([link](http://hhoppe.com/newqem.pdf))\n\n",
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