ROS2 bag tools

This package is built and tested with ROS2 galactic.

This package adds verb extensions to the ros2bag cli.

Verb	Usage
cut	cut time slice by wall time or duration offset
drop	drop X out of every Y messages of a topic
export	export data to other formats
extract	extract topics by name
merge	merge multiple bags into one
plot	plot message data to a new window
reframe	change frame_id on messages with headers
rename	change name of a topic
restamp	for all messages with headers, change the bag timestamp to their header stamp
replace	replace messages of a specific topic with message data specified in a yaml file
summary	print summary on data to stdout
video	show or write video of image data

You can check detailed usage information with ros2 bag $VERB --help.

Each command writes a new output bag on disk. If you need to chain multiple commands together, you can use ros2 bag process to process all messages in-memory and write only one output bag. To use it, create a config file that contains one line per command, with the same arguments as the cli interface.

For example create a file named process.config containing those lines:

cut --duration 10
extract -t /image_rect
restamp

and run ros2 bag process -c process.config in.bag -o out.bag to crop to the first 10 seconds, keep only the /image_rect topic and restamp the remaining messages in one go.

Python API

The verbs are built on top of utilities in rosbag2_tools.

You can iterate message data using BagView.

from rosbag2_tools.bag_view import BagView
reader = SequentialReader()
storage_options = StorageOptions(uri='test/range.bag', storage_id='sqlite3')
converter_options = ConverterOptions(
    input_serialization_format='cdr',
    output_serialization_format='cdr')
reader.open(storage_options, converter_options)
for _, range_msg, _ in BagView(reader):
    print('r', range_msg.range)

Or read topics as pandas data frames.

from rosbag2_tools.data_frame import read_data_frames
reader = SequentialReader()
storage_options = StorageOptions(uri='test/range.bag', storage_id='sqlite3')
converter_options = ConverterOptions(
    input_serialization_format='cdr',
    output_serialization_format='cdr')
reader.open(storage_options, converter_options)
dfs = read_data_frames(BagView(reader))
dfs['/range'].summary()

Current limitations

• Tools do not operate in-place, they all create new output bags, potentially doubling the required disk space
• The time filters used in the cut verb truncate timestamps to the microsecond, due to the precision loss of the pybind11-conversion of C++ chrono time objects to python3 datetime objects. Thus, filters are not sufficiently precise to handle timestamp deltas below 1000ns.