Welcome to Flowty

Flowty (pronounced floʊtiː) is the swiss army knife of computing optical flow.

Flow methods

The following OpenCV optical flow methods are implemented:


The following methods aren’t implemented, but are on the roadmap to implement next.


Flowty is packaged as a docker container to save you the hassle of having to build an accelerated version of OpenCV linked with FFmpeg by hand.


You will need the following software installed on your host:

To check these prerequisites are satified, run

$ docker run --runtime=nvidia --rm nvidia/cuda:10.1-base nvidia-smi

It should print the output of nvidia-smi

Invoking flowty

The container will run flowty by default, so you can provide arguments like you would if you were running flowty installed natively on your host.

For example, to compute TV-L1 optical flow from the video /absolute/path/to/video_dir/video.mp4 and save the flow u, v components as separate JPEGs in /absolute/path/to/video_dir/flow/{axis}/, run the following command:

# CPU only version
$ docker run --rm -it \
    --mount "type=bind,source=/absolute/path/to/video_dir,target=/data" \
    willprice/flowty \
    tvl1 "/data/video.mp4" "/data/flow/{axis}/frame_{index:05d}.jpg"

# GPU accelerated version
$ docker run --runtime=nvidia --rm -it \
    --mount "type=bind,source=/absolute/path/to/video_dir,target=/data" \
    willprice/flowty \
    tvl1 "/data/video.mp4" "/data/flow/{axis}/frame_{index:05d}.jpg" --cuda

Check out Input/Output Formats to find out more about supported formats.


As docker isn’t heavily used in the computer vision community we’ll break the above example command down piece by piece to explain what’s going on.

First, an explanation of what Docker is: Docker is a container platform, it allows you to build and run containers, which are a little like light-weight VMs. A container contains an entire OS and all the dependencies of the application you’d like to run. It doesn’t share any storage with the host by default, so if you want to access files on your computer from inside the container you need to mount the directory into the container, this allows you to read and write to a host directory from within the container.

  • docker run is used to run an instance of a container, this is the equivalent of launching a VM, but is much quicker.
  • --rm indicates that we want to remove the container after usage (to prevent it taking up space)
  • -it is short for --interactive and --tty. --interactive hooks up STDIN from your console to the container, allowing you to Ctrl-C to kill the operation, --tty allocates a pseudo-TTY and pipes this to STDOUT so that you can see the log messages printed by the tool, if this wasn’t present no output from the container would be printed.
  • --runtime=nvidia is used to switch out the docker container backend to NVIDIA’s version which injects the necessary hooks to run CUDA programs. It is only necessary to specify this if you are using the CUDA accelerated routines (i.e. you pass the --cuda arg to flowty)
  • --mount type=bind,source=/absolute/path/to/video_dir,dest=/data is the specification to docker that allows you to access a host directory within the container, this is called a bind mount, hence the type=bind specification. We mount the directory /absolute/path/to/video_dir on the host to /data within the container. Now anytime the container writes or reads anything from /data it will actually read from /absolute/path/to/video_dir/.