On Thu, Jun 24, 2010 at 10:28 AM, Jelle Feringa <jelleferi...@gmail.com> wrote:
> Hi Thiango,
>
> That is a large difference.
> I see you aren't building -nurbs- faces from the STL data, so that difference
> in memory consumption is strange.
> Have you tried various .stl files?
>
> The difference in performance can perhaps be partially explained on a
> functional basis. PythonOCC deals with CAD, essentially higher order nurbs
> geometry, while meshlab is highly optimized for massive polygon data. So, in
> that sense it is not unexpected to see a performance difference ( albeit not
> so large ).
>
> PythonOCC can be most useful for say, fitting nurbs through your STL
> pointcloud.
> However, for dealing exclusively with polygonal data, Meshlab is the way to
> go.
>
> Best,
>
> -jelle
Hi Jelle,
Yes, I tried. Using a simple STL from blender it opens, but from
InVesalius it doesn't because the STL created are very complex, they
are based on CT or RMI exams.
Thanks!
> On Jun 24, 2010, at 3:13 PM, Thiago Franco Moraes wrote:
>
>> 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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