• Description
• Installation
• Usage
  • Importing pypcd4
  • Loading PCD File
  • Converting to NumPy Array
  • Converting to ROS Message
  • Concatenating PointClouds
  • Filtering PointClouds
  • Saving Your Work
• Contributing
• License

pypcd4 is a modern reimagining of the original pypcd library, offering enhanced capabilities and performance for working with Point Cloud Data (PCD) files.

This library builds upon the foundation laid by the original pypcd while incorporating modern Python3 syntax and methodologies to provide a more efficient and user-friendly experience.

To get started with pypcd4, install it using pip:

pip install pypcd4

Let’s walk through some examples of how you can use pypcd4:

First, import the PointCloud class from pypcd4:

from pypcd4 import PointCloud

If you have a .pcd file, you can read it into a PointCloud object:

pc: PointCloud = PointCloud.from_path("point_cloud.pcd")

pc.fields
# ('x', 'y', 'z', 'intensity')

You can convert a PointCloud to a NumPy array:

array: np.ndarray = pc.numpy()

array.shape
# (1000, 4)

You can also specify the fields you want to include in the conversion:

array: np.ndarray = pc.numpy(("x", "y", "z"))

array.shape
# (1000, 3)

And you can convert a NumPy array back to a PointCloud. The method you use depends on the fields in your array:

# If the array has x, y, z, and intensity fields,
pc = PointCloud.from_xyzi_points(array)

# Or if the array has x, y, z, and label fields,
pc = PointCloud.from_xyzl_points(array, label_type=np.uint32)

If you can’t find your preferred point type in the pre-defined conversion methods, you can create your own:

fields = ("x", "y", "z", "intensity", "new_field")
types = (np.float32, np.float32, np.float32, np.float32, np.float64)

pc = PointCloud.from_points(array, fields, types)

You can convert a ROS PointCloud2 Message to a PointCloud and vice versa:

def callback(in_msg: sensor_msgs.msg.PointCloud2):
    # Convert ROS PointCloud2 Message to a PointCloud
    pc = PointCloud.from_msg(in_msg)

    pc.fields
    # ("x", "y", "z", "intensity", "ring", "time")

    # Convert PointCloud to ROS PointCloud2 Message with the input message header
    out_msg = pc.to_msg(in_msg.header)

    publisher.publish(out_msg)

The pypcd4 supports concatenating two PointCloud objects together using the + operator. This can be very useful when you want to merge two point clouds into one.

Here's how you can use it:

pc1: PointCloud = PointCloud.from_path("xyzi1.pcd")
pc2: PointCloud = PointCloud.from_path("xyzi2.pcd")

# Concatenate two PointClouds
pc3: PointCloud = pc1 + pc2

Please note that the two PointCloud objects must have the same fields and types. If they don’t, a ValueError will be raised.

The pypcd4 library provides a convenient way to filter a PointCloud using a subscript.

You can use a slice to access a range of points in the point cloud. Here’s an example:

# Create a point cloud with random points
pc = PointCloud.from_xyz_points(np.random.rand(10, 3))

# Access points using a slice
subset = pc[3:8]

In this case, subset will be a new PointCloud object containing only the points from index 3 to 7.

You can use a boolean mask to access points that satisfy certain conditions. Here’s an example:

# Create a point cloud with random points
pc = PointCloud.from_xyz_points(np.random.rand(10000, 3))

# Create a boolean mask
mask = (pc.pc_data["x"] > 0.5) & (pc.pc_data["y"] < 0.5)

# Access points using the mask
subset = pc[mask]

In this case, subset will be a new PointCloud object containing only the points where the x-coordinate is greater than 0.5 and the y-coordinate is less than 0.5.

You can use a field name or a sequence of field names to access specific fields in the point cloud. Here’s an example:

# Create a point cloud with random points
pc = PointCloud.from_xyz_points(np.random.rand(100, 3))

# Access specific fields
subset = pc[("x", "y")]

In this case, subset will be a new PointCloud object containing only the x and y coordinates of the points. The z-coordinate will not be included.

Finally, you can save your PointCloud as a .pcd file:

pc.save("nice_point_cloud.pcd")

We are always looking for contributors. If you are interested in contributing, please run the lint and test before submitting a pull request:

Just run the following command:

rye sync
rye run lint

Install the testing dependencies by the following command:

pip install mypy pytest ruff

Then run the following command:

ruff check --fix src
ruff format src
mypy src
pytest

Make sure all lints and tests pass before submitting a pull request.

The library was rewritten and does not borrow any code from the original pypcd library. Since it was heavily inspired by the original author's work, we extend his original BSD 3-Clause License and include his Copyright notice.