Point Cloud Fetch

In this guide, a secure connection to a Qb2 device is established in order to fetch a point cloud frame, extract its point cloud data, and display it in a 2D plot.

Please follow the Python Client Library Guide to install the Python package first.

Fetching point cloud frames from Qb2

First a single point cloud frame, then a stream of frames is requested using the gRPC PointCloud service from the blickfeld_qb2.core_processing.services namespace. The code snippets below provide examples of how to connect to a Qb2 using the hostname qb2-xxxxxxxxx, and of how to fetch point cloud frames from it.

Getting a single point cloud frame

The following example shows how to fetch a single point cloud frame using the get method.

import blickfeld_qb2

# Open a secure connection to Qb2
with blickfeld_qb2.Channel(fqdn_or_ip="qb2-xxxxxxxxx") as channel:
    service = blickfeld_qb2.core_processing.services.PointCloud(channel)

    frame = service.get().frame

    # Print the frame ID
    print("Received frame with ID:", frame.id)

Continuously streaming point cloud frames

The following example shows how to continuously stream point cloud frames from a Qb2 device.

import blickfeld_qb2

# Open a secure connection to Qb2
with blickfeld_qb2.Channel(fqdn_or_ip="qb2-xxxxxxxxx") as channel:
    service = blickfeld_qb2.core_processing.services.PointCloud(channel)

    # Request a point cloud stream from Qb2
    for i, response in enumerate(service.stream()):

        # Extract a point cloud frame from the response
        frame = response.frame

        # Print the frame ID
        print("Received frame with ID:", frame.id)

        # Get 3 frames and stop
        if i == 2:
            break

Getting the data from a point cloud frame

In the code snippet below, the Cartesian coordinates xyz for each point in the received frame are extracted. These values are then used to calculate the azimuth and elevation vectors.

import numpy as np

# Get the x,y,z coordinates of the points in the frame
xyz = frame.binary.cartesian

# Calculate the azimuth and elevation
distance = np.sqrt(xyz[:,0] ** 2 + xyz[:,1] ** 2 + xyz[:,2] ** 2)
azimuth = np.arctan(xyz[:,0] / xyz[:,1])
elevation = np.arcsin(xyz[:,2] / distance)

Plotting a point cloud frame

Finally, azimuth, elevation, and photon_count vectors are used to plot the desired field of view from the point cloud frame as shown in the code snippet below.

import matplotlib.pyplot as plt

index = np.argwhere((azimuth > np.deg2rad(20)) & (azimuth < np.deg2rad(30)) & (elevation > np.deg2rad(5)))
plt.scatter(azimuth[index], elevation[index], c=frame.binary.photon_count[index], s=0.1)
plt.xlabel("Azimuth [rad]")
plt.ylabel("Elevation [rad]")
plt.show()

The plot result is shown in the figure below:

Figure 1. Point Cloud Frame