Pypcd4 1.1.0

Description:

pypcd4 1.1.0

pypcd4

Table of Contents

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

Description
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.
Installation
To get started with pypcd4, install it using pip:
pip install pypcd4

Usage
Let’s walk through some examples of how you can use pypcd4:
Getting Started
First, import the PointCloud class from pypcd4:
from pypcd4 import PointCloud

Working with .pcd Files
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')

Converting Between PointCloud and NumPy Array
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)

Creating Custom Conversion Methods
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)

Working with ROS PointCloud2 Messages
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)

Concatenating Two PointClouds
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.
Filtering a PointCloud
The pypcd4 library provides a convenient way to filter a PointCloud using a subscript.
Using a Slice
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.
Using a Boolean Mask
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.
Using Field Names
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.
Saving Your Work
Finally, you can save your PointCloud as a .pcd file:
pc.save("nice_point_cloud.pcd")

Contributing
We are always looking for contributors. If you are interested in contributing,
please run the lint and test before submitting a pull request:
Using Rye (Recommended)
Just run the following command:
rye sync
rye run lint

Using pip
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.
License
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.