Developer’s Quick Start with the Ouster Python SDK

This quickstart guide will walk you through visualizing Ouster sensor data quickly with Python code you write yourself. It assumes that you have followed the steps in Python Installation to install the Ouster Python SDK.

Using this Guide

You’ll want to start an interactive Python session and keep it open through the sections, as we’ll be reusing variables created in earlier parts while explaining what we’re doing as we go.

To get started, open a new console/Powershell window and start a python interpreter:

$ python3

Throughout this guide we will indicate console commands with $ and python interpreter commands with >>>, just as we have above.

If you’d like to start by working with sample data, continue to the next section. If you’d prefer to start by capturing data from a sensor, you can skip to Using an Ouster Sensor below.

Using Sample Data

You can download sample data from the sensor documentation by clicking through the Ouster Data App links and using the Download button. After download, you should have two files, a .pcap file and a .json file.

We will use SAMPLE_DATA_PCAP_PATH to refer to this pcap and SAMPLE_DATA_JSON_PATH to this json in the following. You may find it convenient to assign the paths appropriately in your console.

The downloaded pcap file contains lidar and imu packets captured from the network. You can read more about the IMU Data Format and Lidar Data Format in the Ouster Sensor Documentation. The JSON file contains metadata queried from the sensor TCP interface necessary for interpreting the packet data.

In your open python session, save the two paths to variables:

>>> pcap_path = '<SAMPLE_DATA_PCAP_PATH>'
>>> metadata_path = '<SAMPLE_DATA_JSON_PATH>'

Note

Starting with ouster-sdk v0.11.0, most of core sdk objects have been moved from the ouster namespace into the ouster.sdk namespace.

Using the ScanSource interface

In this example we are going to demonstrate the use of the ScanSource API.

Using the open_source method

The ScanSource API introduces a new method name open_source which allows users to handle different source types using the same API. Current supported source types are live sensor, pcap file, or osf file. For example, opening the sample pcap file can be accomplished as follows:

>>> pcap_path = '<SAMPLE_DATA_PCAP_PATH>'
>>> metadata_path = '<SAMPLE_DATA_JSON_PATH>'
>>> from ouster.sdk import open_source
>>> source = open_source(pcap_path, meta=[metadata_path])

The source object returned by open_source provides access to LidarScan objects, regardless of whether the source data comes from a sensor, pcap, or osf file.

Notice here that rather than we try to load and parse the metadata ourselves we only need to pass to metadata to the method through meta parameter and the method will take care of loading it and associating it with the source object. The meta parameter however is optional and can be omitted. When the meta parameter is not set explicity the open_source method will attempt to locate the metadata automatically for us and we can reduce the call to:

However if metadata file is not in the same folder as the pcap and don’t have a shared name prefix the method will fail.

Note

Another optional but important parameter for the open_source method is sensor_idx. This parameter is set to zero by default, which should always be the case unless the pcap file that you are using (or osf or any LidarScan storage) contains scans from more than one sensor, in this case, users can set the sensor_idx to a any value between zero and sensors_count -1 to access and manipulate scans from a specific sensor by the order they appear in the file. Alternatively, if users set the value of sensor_idx to -1 then open_source will return a slightly differnt interface from ScanSource which is the MultiScanSource this interface and as the name suggests allows users to work with sensor data collected from multiple sensors at the same time.

The main different between the MultiScanSource and the ScanSource is the expected return of some of the object methods. For example, when creating an iterator for a ScanSource object, the user would get a single LidarScan object per iteration. Iterating over the contents of a MultiScanSource object always yields a list of LidarScan(s) per iteration corresponding to the number of sensors stored in the pcap file or whatever source type is being used. This is true even when the pcap file contains data for a single sensor.

On the other hand, if the user wants to open an osf file or access the a live sensor, all that changes is url of the source. For example, to interact with a live sensor the user can execute the following snippet:

>>> sensor_url = '<SENSOR-HOSTNAME-OR-IP>'
>>> from ouster.sdk import open_source
>>> source = open_source(sensor_url)

Obtaining sensor metadata

Every ScanSource holds a reference to the sensor metadata, which has crucial information that is important when when processing the invidivual scans. Continuing the example, a user this can access the metadata through the metadata property of a ScanSource object:

>>> print(source.metadata)

Iterating over Scans

Once we have successfully obtain a handle to the ScanSource we can iterate over LidarScan objects stored in the pcap file and manipulate each one individually. For example, let’s say we want to print the frame id of the first 10 scans. We can achieve that using:

>>> ctr = 0
>>> source_iter = iter(source)
>>> for scan in source_iter:
...     print(scan.frame_id)
...     ctr += 1
...     if ctr == 10:
...         break

As we noted earlier, if we set sensor_idx=-1 when invoking open_source method, the method will construct a MultiScanSource, which always addresses a group of sensors. Thus, when iterating over the source the user receives a collated set of scans from the addressed sensors per iteration. The MultiScanSource examines the timestamp of every scan from every sensor and returns a list of scans that fit within the same time window as single batch. The size of the batch is fixed corresponding to how many sensors contained in the pcap or osf file. However, the collation could yield a null value if one or more of the sensors didn’t produce a LidarScan object that fits within the time frame of current batch or iteration. Thus, depending on the operation at hand it is crticial to check if we got a valid LidarScan object when examining the iteration output of a MultiScanSource. If we are to perform the same example as above when source is a handle to MultiScanSource and display the frame_id of LidarScan objects the belongs to the same batch on the same line the code needs to updated to the following:

>>> ctr = 0
>>> source_iter = iter(source)
>>> for scans in source_iter:
...     for scan in scans:    # source_iter here returns a list of scans
...         if scan:          # check if invidiual scan object is valid
...             print(scan.frame_id, end=', ')
...     print()   # new line for next batch
...     ctr += 1
...     if ctr == 10:
...         break

Note that when iterating over a MultiScanSource object, it always a list of scans, even when the underlying scan source has only a single sensor. In this case, the iterator will yield a list with a single element per iteration.

Using indexing and slicing capabilities of a ScanSource

One of the most prominent new features of the ScanSource API, (besides being able to address multi sensors), is the ability to use indexing and slicing when accessing the stored scans within the LidarScan source. Currently, this capability is only supported for indexable sources. That is to say, the functionality we are discussing can only be used when accessing a pcap or an osf file with indexing turned on. To turn on indexing simply add the index flag and set it True when opening a pcap or osf file:

>>> pcap_path = '<SAMPLE_DATA_PCAP_PATH>'
>>> from ouster.sdk import open_source
>>> source = open_source(pcap_path, index=True)

Note

We omitted the meta parameter since it can be populated automatically as we explained earlier.

Depending on the file size and the underlying file format there can be some delay before the file is fully indexed (OSF file take much less time than pcap file to index). A progress bar will appear to indicate progress of the indexing.

Once the index is built up, then we can start using utilizing and interact with the ScanSource object to access scans in the same manner we are dealing with a python list that holds reference to LidarScan objects.

For example to access the 10th LidarScan and print its frame id, we can do the following:

>>> print(source[10].frame_id)

Similarly we can access the last LidarScan object and print its frame_id using:

>>> print(source[-1].frame_id)

Alternatively we can instead request a range of scans using the python slice operator. For example, to request the first 10 scans from a ScanSource and print their frame ids, we can do the following:

>>> for scan in source[0:10]:
...     print(scan.frame_id)

Note we don’t need to add any break here since the operation source[0:10] will only yield the first 10 LidarScan(s).

To print frame_id of the last 10 LidarScans we do:

>>> for scan in source[-11:-1]:
...     print(scan.frame_id)

Finally, as you would expect from a typical slice operation, you can also using negative step and also use a reversed iteration as shown in the following example:

>>> for scan in source[0:10:2]:     # prints the frame_id of every second scan of the first 10 scans
...     print(scan.frame_id)

>>> for scan in source[10:0:-1]:     # prints the frame_id of every scan of the first 10 scans in reverse
...     print(scan.frame_id)

Slicing operator as a ScanSource

In ouster-sdk 0.11.0 release, the slice [::] operator used to return a list of LidarScan objects (or list of lists for the MultiScanSource case). However, starting with ouster-sdk 0.12.0 the ScanSource slice operator [::] returns a ScanSource scoped to the indicated slice range. This means that the users can pass the object returned by the slice operator [::] to any function or code that expects a ScanSource object (or MultiScanSource). The following snippet shows few examples to demonstrate this capability:

>>> # ... continuing with the `source` object from the previous example
>>> source2 = source[5:10]
>>> print("source2 length:", len(source2))   # This should print 5 since source2 is scoped to the range [5, 10)
>>> print(source2[0].frame_id)   # This is equivalent to calling `print(source[5].frame_id)`
>>> print(source2[4].frame_id)   # This is equivalent to calling `print(source[9].frame_id)`
>>> # invoking source2[10].frame_id would result in an `out of range exception`` since source2 is scoped to 5 frames
>>> source_iter = iter(source2)  # Use `source2` as an iterator similar to the main `source`
>>> for scan in source_iter:
...     print(scan.frame_id)
>>> # it is possible to sub slice, meaning take the result of a previous slice operation and slice it
>>> # Thus, the following statement is valid
>>> source3 = source2[2:4] # this would yield the same frames as `source3 = source[7:9]`
>>> print("source3 length:", len(source3))   # Should print 2

Using the client API

The client API provides PacketSource implementations, as well as access to methods for configuring a sensor or reading metadata.

As such, you can use it instead of the ScanSource API if you prefer to work with individual packets rather than lidar scans.

Reading packets from a pcap file

Because our sample pcap file contains the UDP packet stream but not the sensor metadata, we load the sensor information from metadata_path first, using the client module:

>>> from ouster.sdk import client
>>> with open(metadata_path, 'r') as f:
...     info = client.SensorInfo(f.read())

Now that we’ve parsed the metadata file into a SensorInfo, we can use it to read our captured UDP data by instantiating pcap.Pcap. This class acts as a PacketSource and can be used in many of the same contexts as a real sensor.

>>> from ouster.sdk import pcap
>>> source = pcap.Pcap(pcap_path, info)

To visualize data from this pcap file, proceed to Visualizations in 3D examples.

Using an Ouster Sensor

If you have access to sensor hardware, you can start reading data by instantiating a PacketSource that listens for a UDP data stream on a local socket.

Note

Connecting to an Ouster sensor is covered in the Networking Guide section of the Ouster Sensor Documentation.

In the following, <SENSOR_HOSTNAME> should be substituted for the actual hostname or IP of your sensor.

To make sure everything is connected, open a separate console window and try pinging the sensor. You should see some output like:

$ ping -c1 <SENSOR_HOSTNAME>
PING <SENSOR_HOSTNAME> (192.0.2.42) 56(84) bytes of data.
64 bytes from <SENSOR_HOSTNAME> (192.0.2.42): icmp_seq=1 ttl=64 time=0.217 ms

Next, you’ll need to configure the sensor with the config parameters using the client module.

In your open python session, set hostname as <SENSOR_HOSTNAME>:

>>> hostname = '<SENSOR_HOSTNAME>'

Now configure the client:

>>> from ouster.sdk import client
>>> config = client.SensorConfig()
>>> config.udp_port_lidar = 7502
>>> config.udp_port_imu = 7503
>>> config.operating_mode = client.OperatingMode.OPERATING_NORMAL
>>> client.set_config(hostname, config, persist=True, udp_dest_auto = True)

Just like with the sample data, you can create a PacketSource from the sensor:

>>> source = client.Sensor(hostname, 7502, 7503)
>>> info = source.metadata

Now we have a source from our sensor! You’re ready to record, visualize to visualize data from your sensor, proceed to Visualizations in 3D examples. Or you can check out other things you can do with a source in the Python Examples & Concepts.

Next Steps

Now that you know the basics, you can check out our annotated examples for a more detailed look at how to work with our data.

Here are a few things you might be interested in: