This repository contains supplementary code for reproducing results in the paper
Output of rendered notebooks are found at https://computationalphysiology.github.io/automatic-motion-estimation/README.html.
The code is heavily based on the library mps-motion
. Please check out the documentation in that library for more details.
If you want to run the examples without installing anything you can launch an interactive environment with Binder, by clicking the following button
To run the notebooks locally, you first need to install the necessary requirements. There are two options. Either, you can create a virtual environment with python
python3 -m venv venv
and activate it. On Unix (MacOSX and Linux) you do
. \venv/bin/activate
and on Windows you do
.\venv\Scripts\activate
Next you install the dependencies
python3 -m pip install -r requirements.txt
We also provide two different docker images for you. In order to start a container you can use the docker run command. For example the command
docker run --rm -v $(pwd):/home/shared -w /home/shared -ti ghcr.io/computationalphysiology/automatic-motion-estimation:latest
will run the latest version and share your current working directory with the container. The source code of the repository is located at /repo
in the docker container.
To run the notebooks, one can use ghcr.io/computationalphysiology/automatic-motion-estimation-lab:latest
, i.e
docker run -ti -p 8888:8888 --rm ghcr.io/computationalphysiology/automatic-motion-estimation-lab:latest
to run interactively with Jupyter lab in browser.
If you use this code in your research, please cite the following paper
@article{10.1088/2057-1976/ad7268,
author={Finsberg, Henrik Nicolay Topnes and Charwat, Verena and Healy, Kevin E and Wall, Samuel},
title={Automatic motion estimation with applications to hiPSC-CMs},
journal={Biomedical Physics & Engineering Express},
url={http://iopscience.iop.org/article/10.1088/2057-1976/ad7268},
year={2024},
}