pcc_geo_cnn
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mauriceqch
./mesh_to_pc.py ../data/ModelNet40 ../data/ModelNet40_pc_64 --vg_size 64 gives error
I've downloaded the ModelNet40 and extracted it on ../data/ModelNet40.
After executing ./mesh_to_pc.py ../data/ModelNet40 ../data/ModelNet40_pc_64 --vg_size 64 , I got the error:
File "/opt/conda/Anaconda3/lib/python3.7/multiprocessing/pool.py", line 121, in worker
result = (True, func(*args, **kwds))
File "./mesh_to_pc.py", line 39, in process
pc = pc_mesh.get_sample("mesh_random", n=args.n_samples, as_PyntCloud=True)
File "/opt/conda/Anaconda3/lib/python3.7/site-packages/pyntcloud-0.1.2-py3.7.egg/pyntcloud/core_class.py", line 534, in get_sample
sample = sampler.compute()
File "/opt/conda/Anaconda3/lib/python3.7/site-packages/pyntcloud-0.1.2-py3.7.egg/pyntcloud/samplers/mesh.py", line 67, in compute
np.arange(len(areas)), size=self.n, p=probabilities)
File "mtrand.pyx", line 793, in numpy.random.mtrand.RandomState.choice
ValueError: probabilities contain NaN```
The full error message:
./mesh_to_pc.py ../data/ModelNet40 ../data/ModelNet40_pc_64 --vg_size 64
2019-10-25 08:46:30.595 INFO mesh_to_pc -
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "./mesh_to_pc.py", line 86, in
Hi Pedro,
It seems that there is an overflow when computing areas to obtain the sampling probabilities.
To help you with your issue, can you give me more information about your configuration (OS, package versions) and where you downloaded the dataset. It would also help if you can rerun the script with the parallel processing disabled (by removing the call to pool.imap). That way, you will be able to run the script with pdb and pinpoint the exact point cloud causing the issue.
cone/train/cone_0117.off curtain/train/curtain_0066.off
These two meshes causing this issue. The value of the vertice is too large, causing the overflow during sampling.
This is surprising as I did not have this issue in my environment. One possible cause could be the version of numpy.
Could you provide the output of the following ?
import numpy as np
a = np.zeros((2,2))
print(a.dtype)
It should output float64 on recent numpy versions.
A possible solution would be to patch the pyntcloud sampler to avoid this overflow issue. Replace
probabilities = areas / np.sum(areas)
by
max_area = np.max(areas)
areas = areas / max_area
probabilities = areas / np.sum(areas)
(areas / max_area) / np.sum(areas / max_area) should avoid the overflow issue and be equal to areas / np.sum(areas).
The output is float64 as expected.
The replacement didn't work, maybe the problem is in numpy.linalg.
BTW, what's your numpy version? I have experience with and without this problem. But I don't really remember what version of numpy I have used.
I am using numpy 1.17.4 and I have reproduced the issue. I issued a fix in commit e0862279ae0bf5fce9190403f9419a7e3b58910a.
Essentially, you can add the following lines right after pc_mesh = PyntCloud.from_file(ori_path) to fix the overflow issue:
mesh = pc_mesh.mesh
pc_mesh.points = pc_mesh.points.astype('float64', copy=False)
pc_mesh.mesh = mesh
This enforces the use of float64 in PyntCloud to avoid overflow.
We first backup the mesh and re-set it later as the points setter removes the mesh.
Hi guys,
import numpy as np
a = np.zeros((2,2))
print(a.dtype)
is float64 as expected.
np.__version__ is 1.18.1.
I got the same error.
Hi @pedrogarciafreitas,
Have you tried the patched version of the repository ?
I am using numpy 1.17.4 and I have reproduced the issue. I issued a fix in commit e086227.
Essentially, you can add the following lines right after
pc_mesh = PyntCloud.from_file(ori_path)to fix the overflow issue:mesh = pc_mesh.mesh pc_mesh.points = pc_mesh.points.astype('float64', copy=False) pc_mesh.mesh = meshThis enforces the use of float64 in PyntCloud to avoid overflow. We first backup the mesh and re-set it later as the
pointssetter removes the mesh.
This works. Thanks for your reply.