https://github.com/raymondcm/topic_store
ROS package used for serialising common ROS messages to a database or filesystem
https://github.com/raymondcm/topic_store
mongodb ros storage
Last synced: about 1 month ago
JSON representation
ROS package used for serialising common ROS messages to a database or filesystem
- Host: GitHub
- URL: https://github.com/raymondcm/topic_store
- Owner: RaymondCM
- License: gpl-3.0
- Created: 2020-04-21T14:13:47.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2023-03-22T13:49:23.000Z (about 3 years ago)
- Last Synced: 2025-09-20T18:21:52.065Z (9 months ago)
- Topics: mongodb, ros, storage
- Language: Python
- Homepage:
- Size: 410 KB
- Stars: 6
- Watchers: 2
- Forks: 12
- Open Issues: 7
-
Metadata Files:
- Readme: docs/README.md
- Changelog: CHANGELOG.rst
- Citation: CITATION.cff
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README
# Topic Store
[](https://pypi.org/project/topic-store/)
Topic Store is a ROS package for storing ROS messages to a database or filesystem.
Unlike ROS bags Topic Store adds flexibility by serialising all messages into a data hierarchy that's easily
searchable with database queries and allows for remote storage.
You can also use Topic Store as a standalone python package to read and write data without a ROS installation!
# Installation
### ROS
```bash
# From source
cd catkin_ws/src
git clone https://github.com/RaymondKirk/topic_store
catkin build topic_store
# From apt
sudo apt install ros-melodic-topic-store # you need to add the L-CAS ros source
```
### Installing without ROS
You can install topic_store as an independent python2/3 package without a working ROS installation.
```bash
# From source
git clone https://github.com/RaymondKirk/topic_store
cd topic_store/src
pip install --extra-index-url https://rospypi.github.io/simple/ -e .
# From PyPi
pip install --extra-index-url https://rospypi.github.io/simple/ topic-store
```
To install other dependencies i.e. `ros_numpy` you can run the following
```bash
pip install --extra-index-url https://rospypi.github.io/simple/ ros_numpy
```
# Usage
[](https://asciinema.org/a/Cq9i3a41fzuULw52tRLkHvBQS)
Create a scenario file by following the documentation in [scenarios](https://github.com/RaymondKirk/topic_store/wiki/Scenarios) or by following the example file
[default_config.yaml](../scenarios/default_config.yaml). The example below will save a history of all messages sent to the ros log topic.
```yaml
# Save to /path/to/your/scenario/file.yaml. This collection behaviour will save your log history.
context: "save_ros_logs"
storage:
"method": "filesystem"
"location": "default"
data: {
"ros_msg": "/rosout",
}
collection:
method: "event"
watch_topic: "/rosout"
```
If ```storage.method``` is database ensure that your database is accessible at the host/port in the config file found at
```storage.config```. To launch the default database ```roslaunch topic_store start_database.launch```, the default
database creates/uses a Mongo 4.2 server instance in the default ```${HOME}/.ros/topic_store/database```
folder exposed on ```localhost:65530``` (defined in ```storage.config``` of the scenario file).
Docker is a requirement to use a database backend to avoid conflicts with system mongo. A utility script is provided in
`scripts/install_docker.sh` if you do not have it installed.
Launch your data collection scenario!
```
roslaunch topic_store run_scenario.launch scenario_file:="/path/to/your/scenario/file.yaml"
```
# Examples
## Database data
The below example shows how to load and use files stored in a database.
```python
import topic_store as ts
# Read data
storage = ts.load("/path/to/scenario/file/containing/db/connection/info.yaml")
for item in storage:
print("As Python Dict", item.dict) # or item["key"]
print("As ROS Msgs", item.msgs) # or item("key")
# Write data
storage.insert_one({"important_data": "topic store is great!"})
```
## Filesystem data
The below example shows how to load and use `.topic_store` files, saved from when scenarios are ran with the
`storage_method="filesystem"` option.
```python
import topic_store as ts
# Read data
storage = ts.load("/path/to/file.topic_store")
for item in storage:
print("As Python Dict", item.dict) # or item["key"]
print("As ROS Msgs", item.msgs) # or item("key")
# Write data
storage.insert_one({"important_data": "topic store is great!"})
```
## Launch a database
When launching a data collection scenario where ```storage.method==database``` you must also launch the database or
ensure it's already running at the uri defined in the host/port parameters of the config file located at ```storage.config```.
To launch a database prior to running a data collection scenario.
```bash
roslaunch topic_store start_database.launch scenario_file:="/path/to/your/scenario/file.yaml"
```
## Convert to ROS bags
Filesystem `.topic_store` files and database collections can be converted to ROS bags.
```bash
# Convert filesystem files to rosbags
rosrun topic_store convert.py -i input.topic_store -o output.bag
# Convert database collections to rosbags
# Pass scenario file containing connection and collection info as the input
rosrun topic_store convert.py -i scenario_config.yaml -o output.bag
```
## Convert between Filesystem and Database
Filesystem `.topic_store` files can be migrated to a MongoDB database. Pass the scenario file that contains the database
connection information as the output file.
```bash
rosrun topic_store convert.py -i input.topic_store -o scenario_config.yaml
```
Database collections can be converted to a filesystem `.topic_store` file. Pass the scenario file that contains the database
connection information as the input file.
```bash
rosrun topic_store convert.py -i scenario_config.yaml -o output.topic_store
```
## Export from Mongodb query into rosbag/filesystem
Example call:
* With typical mongodb URI for SSL and authentication
* On database `ff_rasberry` and collection `2020_riseholme_framos_cameras`
* Specify database connection URI (`-i`) with db name `authSource=database`
* Including a query (`-q`) for a specific document
* Add projection to return only sub-documents (`-p`)
```bash
convert.py -i "mongodb://USER:PASS@HOST:PORT/?authSource=ff_rasberry&tls=true&tlsAllowInvalidCertificates=true" -c 2020_riseholme_framos_cameras -q '{"_id":"ObjectId(5f115ee6af915351df739757)"}' -p '{"cameras.top.color":1, "robot": 1}' -o out.bag
```
## Storing high bandwidth image data
To store a lot of image data you will be limited to the IO of your machine.
[Topic Compression](https://github.com/RaymondKirk/topic_compression) offers a compression solution to increase capture performance.
```bash
# Install the ROS package
cd catkin_ws/src
git clone https://github.com/RaymondKirk/topic_compression
catkin build topic_compression
# Compression/Decompression is chosen automatically so just pass the input topic name and optionally the out topic name
rosrun topic_compression run in:=/camera/colour/image_raw # out=/camera/colour/image_raw/compressed
rosrun topic_compression run in:=/camera/depth/image_raw # out=/camera/depth/image_raw/compressed
```