neka-nat/cupoch

cupoch.cupoch.device_vector_vector3f can not convert to numpy array?

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longwoo commented

Hello,

Your Cupoch is really a useful tools.

when I run the example clustering.py , it gives me
Traceback (most recent call last): File "my_clustering.py", line 36, in <module> print("%s has %d points" % (pcl_name, np.asarray(pcl.points).shape[0])) IndexError: tuple index out of range

It seems that cupoch.cupoch.device_vector_vector3f can not convert to numpy array?

By the way my Os is Jectson Pack 4.2 with ubuntu18.04, and Cupoch is build from source with python2.7 binding.

Could you give me some advice?

Thank you.

neka-nat commented

Hi @longwoo ,

Thank you for reporting.
Please try the conversion in the following way.

np.asarray(pcd.points.cpu())

And DBSCAN clustering may not work. I am currently debugging.

  • 👍1
longwoo commented

@neka-nat
Thank you for your quick answer. It solves my problem.

And I want to make sure that pcd.crop is not support OrientedBoundingBox type yet? Since it gives me

    pc_ground = pcd.crop(box_ground)
TypeError: crop(): incompatible function arguments. The following argument types are supported:
    1. (self: cupoch.cupoch.geometry.PointCloud, bounding_box: cupoch::geometry::AxisAlignedBoundingBox) -> cupoch.cupoch.geometry.PointCloud

Invoked with: geometry::PointCloud with 139003 points., geometry::OrientedBoundingBox

So I need to use AxisAlignedBoundingBox as input instead of OrientedBoundingBox.

--------------------------------------------------------------------------------------------------------------------

now I use

box_ground = x3d.geometry.OrientedBoundingBox(np.array([0.0, height_max_temp, 0.0]), np.eye(3), np.array([4.5, 0.2, max_depth]))
pc_ground = pcd.crop(box_ground.get_axis_aligned_bounding_box())

It works!

neka-nat commented

I added crop function that takes OrientedBoundingBox.
You can use it by fetching the latest "master" branch and compiling it.

  • 👍1
pbdahzou commented

Hi @longwoo ,

Thank you for reporting. Please try the conversion in the following way.

np.asarray(pcd.points.cpu())

And DBSCAN clustering may not work. I am currently debugging.

Can DBSCAN clustering work correctly ?

pbdahzou commented

I run the clustering.py and get the following results:
fragment has 196133 points
[2023-06-21 21:12:18.968] [debug] Precompute Neighbours
Precompute Neighbours[========================================] 100%
fragment has 196133 clusters