Hi Thiago,
I also noticed a strong memory consumption and cpu usage with big STL files
(1e6 faces is actually a *huge* number of faces).
The thing is : why do you want to load these STL files? If it is only for
visualization, may be OCC/pythonOCC is not the best tool for that need (you
talk about MeshLab, which I don't know).
Now, if you want to do 3D modeling over your STL file (like add a chamfer on
an edge, drill a hole, slice the shape for manufacturing etc.) then you
*need* a 3D modeling library and pythonOCC can help you. But, in that case,
the STL file needs to be mapped to the OCC internal data model
(TopoDS_Shapes, *Edges, Vertices*) and that's why the loading requires so
much memory/cpu usage. So I don't know if you face a bug : OCC should be
compared to another modeling tool, and that's a benchmark I didn't make.
Best Regards,
Thomas
2010/6/24 Thiago Franco Moraes <totonixs...@gmail.com>
> Hi all,
>
> I've been trying to read STL files. With small STL files it reads
> normally. With not so big STL files (like the one I've just tried to
> read that have 51 MB, binary, 534678 vertices, 1069782 faces) it can't
> read, the CPU usage is about 100% and memory grows up from a few MB to
> 4 GB then I have to kill my script. Using Meshlab the memory usage is
> about 270 MB. I've tried to use STLImporter class and StlAPI class.
> Bellow the code I used:
>
> from OCC.Utils.DataExchange import STL
> reader = STL.STLImporter('file.stl')
> reader.ReadFile()
>
> Ah, I'm using the packages from Ubuntu 10.04 64 bits, the last version
> in PPA repository [1].
>
> I have done something wrong on trying to read that file? Is it a bug?
> If necessary I can upload the STL in some place.
>
> Thanks!
>
> [1] - https://launchpad.net/~cae-team/+archive/ppa
>
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