pypcd4 0.4.9

Read and write PCL .pcd files in python

pypcd4

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

As of v0.4.0, you can convert a ROS PointCloud2 Message to a PointCloud:

def callback(msg):
    pc = PointCloud.from_msg(msg)

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

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 mask. Here's an example of how you can use it:

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

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

# Apply the mask to the PointCloud
filtered_pc = pc[mask]

# The filtered PointCloud only includes the points that match the mask
print(filtered_pc.numpy())

Please note that the mask must be a 1-dimensional array. If it’s not, a ValueError will be raised.

Saving Your Work

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

pc.save("nice_point_cloud.pcd")

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.