You want to mess around with your point cloud data without writing C++ and waiting hours for the template-heavy PCL code to compile.
You tried to get some of the Python bindings for PCL to compile and just gave up.
How does it work?
It parses the PCD header and loads the data (whether in
binary_compressed format) as a
Numpy structured array. It creates an
instance of the
class, containing the point cloud data as
some convenience functions for I/O and metadata access.
See the comments in
pypcd.py for some info on the point cloud
import pypcd # also can read from file handles. pc = pypcd.PointCloud.from_path('foo.pcd') # pc.pc_data has the data as a structured array # pc.fields, pc.count, etc have the metadata # center the x field pc.pc_data['x'] -= pc.pc_data['x'].mean() # save as binary compressed pc.save_pcd('bar.pcd', compression='binary_compressed')
How to install
pip install pypcd
That's it! You may want to install optional dependencies such as pandas.
You can also clone this repo and use setup.py.
git clone https://github.com/dimatura/pypcd
Note that downloading data assets will require git-lfs.
Using with ROS
You can also use this library with ROS
sensor_msgs, but it is not a dependency.
You don't need to install this package with catkin -- using pip should be fine --
but if you want to it is possible:
# you need to do this manually in this case pip install python-lzf cd your_workspace/src git clone https://github.com/dimatura/pypcd mv setup_ros.py setup.py catkin build pypcd source ../devel/setup.bash
Then you can do something like this:
import pypcd import rospy from sensor_msgs.msg import PointCloud2 def cb(msg): pc = PointCloud.from_msg(msg) pc.save('foo.pcd', compression='binary_compressed') # maybe manipulate your pointcloud pc.pc_data['x'] *= -1 outmsg = pc.to_msg() # you'll probably need to set the header outmsg.header = msg.header pub.publish(outmsg) # ... sub = rospy.Subscriber('incloud', PointCloud2) pub = rospy.Publisher('outcloud', PointCloud2, cb) rospy.init('pypcd_node') rospy.spin()
Is it beautiful, production-ready code?
What else can it do?
There's a bunch of functionality accumulated over time, much of it hackish and untested. In no particular order,
binary_compresseddata. The latter requires the
- Decode and encode RGB into a single
float32number. If you don't know what I'm talking about consider yourself lucky.
- Point clouds to pandas dataframes. This in particular is quite useful, since pandas is pretty powerful and makes various operations such as merging point clouds or manipulating values easy. Conceptually, data frames are a good match to the point cloud format, since many point clouds in reality have heterogeneous data types - e.g. x, y and z are float fields but label is an int.
- Convert to and from ROS PointCloud2
Requires the ROS
sensor_msgspackage with Python bindings installed. This functionality uses code developed by Jon Binney under the BSD license, included as
What can't it do?
There's no synchronization between the metadata fields in
and the data in
pc_data. If you change the shape of
without updating the metadata fields you'll run into trouble.
I've only used it for unorganized point cloud data
(in PCD conventions,
height=1), not organized
data like what you get from RGBD.
However, some things may still work.
While padding and fields with count larger
than 1 seem to work, this is a somewhat
ad-hoc aspect of the PCD format, so be careful.
If you want to be safe, you're probably better off
using neither -- just name each component
of your field something like
It also can't run on Python 3, yet, but there's a PR to fix this that might get pulled in the near future.
ASCII is slow and takes up a lot of space, not to
mention possibly inaccurate if you're not careful
with how you format your floats.
I found a bug / I added a feature / I made your code cleaner
Thanks! You can submit a pull request. But honestly, I'm not too good at keeping up with my github :(
- Better API for various operations.
- Clean up, get rid of cruft.
- Add a cli for common use cases like file type conversion.
- Better support for structured point clouds, with tests.
- Better testing.
- Better docs. More examples.
- More testing of padding
- Improve handling of multicount fields
- Better support for rgb nonsense
- Export to ply?
- Figure out if it's acceptable to use "pointcloud" as a single word.
- Package data assets in pypi?
The code for compressed point cloud data was informed by looking at Matlab PCL.
@wkentaro for some minor changes.
I used cookiecutter to help with the packaging.
The code in
numpy_pc2.py was developed by Jon Binney under
the BSD license for ROS.
I want to congratulate you / insult you
My email is
Copyright (C) 2015-2017 Daniel Maturana