This is a small python binding to the pointcloud library. Currently, the following parts of the API are wrapped (all methods operate on PointXYZ) point types
- I/O and integration; saving and loading PCD files
- registration (ICP, GICP, ICP_NL)
The code tries to follow the Point Cloud API, and also provides helper function for interacting with NumPy. For example (from tests/test.py)
import pcl import numpy as np p = pcl.PointCloud(np.array([[1, 2, 3], [3, 4, 5]], dtype=np.float32)) seg = p.make_segmenter() seg.set_model_type(pcl.SACMODEL_PLANE) seg.set_method_type(pcl.SAC_RANSAC) indices, model = seg.segment()
or, for smoothing
import pcl p = pcl.load("C/table_scene_lms400.pcd") fil = p.make_statistical_outlier_filter() fil.set_mean_k (50) fil.set_std_dev_mul_thresh (1.0) fil.filter().to_file("inliers.pcd")
Point clouds can be viewed as NumPy arrays, so modifying them is possible using all the familiar NumPy functionality:
import numpy as np import pcl p = pcl.PointCloud(10) # "empty" point cloud a = np.asarray(p) # NumPy view on the cloud a[:] = 0 # fill with zeros print(p) # prints (0.0, 0.0, 0.0) a[:, 0] = 1 # set x coordinates to 1 print(p) # prints (1.0, 0.0, 0.0)
This work was supported by Strawlab.
This release has been tested on Linux Mint 17 with
- Python 2.7.6
- pcl 1.7.2
- Cython 0.21.2
and CentOS 6.5 with
- Python 2.6.6
- pcl 1.6.0
- Cython 0.21
A note about types
Point Cloud is a heavily templated API, and consequently mapping this into Python using Cython is challenging.
It is written in Cython, and implements enough hard bits of the API (from Cythons perspective, i.e the template/smart_ptr bits) to provide a foundation for someone wishing to carry on.
.. autosummary:: pcl.PointCloud pcl.Segmentation pcl.SegmentationNormal pcl.StatisticalOutlierRemovalFilter pcl.MovingLeastSquares pcl.PassThroughFilter pcl.VoxelGridFilter
.. automodule:: pcl :members: :undoc-members: