Scripts to read and dump data from the rosbag format used in the udacity self-driving dataset(s).
The scripts were setup to run within a Docker container so that I can extract the data from the rosbag format without needing to install ROS on my system. The docker container is built from the ROS kinetic-perception image. I've added python modules and the latest Tensorflow on top of that.
I'm currently running and testing this code on Ubuntu 16.04. No other platform has been tried.
Checkout this code and run in place. I have not pushed docker container to hub.
Build the docker container manually or using ./build.sh
Run one of the run scripts for dumping to images + csv or Tensorflow sharded records files.
./run-bagdump.sh -i [local dir with dataset.bag] -o [local output dir] -- [args to pass to python script]
For example, if your dataset bag is in /data/dataset.bag and you'd like the output in /output:
./run-bagdump.sh -i /data -o /output
The same as above, but you want to convert to png instead of jpg:
./run-bagdump.sh -i /data -o /output -- -f png
Same basic arguments as for bagdump above. There are some additional arguments of note to pass to the python script.
The default arguments write all cameras into the same sharded stream along with latest steering entry. To write images to three separate streams, one for each camera, add an -s (or --separate) argument.
i.e.
./run-bag2tf.sh -i /data -o /output -- --separate
The paths the python script operates on are relative to the docker container, your local paths are intended to be passed by mapping to docker volumes. Keep this in mind if you try to change the outdir or bagfile arguments to the python script from the defaults.
Some issues with the readtf.py reading test script. The TF queue is not closing after one pass through.