.. image:: https://readthedocs.org/projects/lensless/badge/?version=latest :target: http://lensless.readthedocs.io/en/latest/ :alt: Documentation Status
.. image:: https://joss.theoj.org/papers/10.21105/joss.04747/status.svg :target: https://doi.org/10.21105/joss.04747 :alt: DOI
.. image:: https://static.pepy.tech/badge/lensless :target: https://www.pepy.tech/projects/lensless :alt: Downloads
.. image:: https://colab.research.google.com/assets/colab-badge.svg :target: https://lensless.readthedocs.io/en/latest/examples.html :alt: notebooks
.. image:: https://huggingface.co/datasets/huggingface/badges/resolve/main/powered-by-huggingface-dark.svg :target: https://huggingface.co/bezzam :alt: huggingface
.. image:: https://github.com/LCAV/LenslessPiCam/raw/main/scripts/recon/example.png :alt: Lensless imaging example :align: center
This toolkit has everything you need to perform imaging with a lensless camera.
The sensor in most examples is the Raspberry Pi HQ <https://www.raspberrypi.com/products/raspberry-pi-high-quality-camera>,
camera sensor as it is low cost (around 50 USD) and has a high resolution (12 MP).
The lensless encoder/mask used in most examples is either a piece of tape or a low-cost LCD <https://www.adafruit.com/product/358>.
As modularity is a key feature of this toolkit, we try to support different sensors and/or lensless encoders.
The toolkit includes:
link <https://lensless.readthedocs.io/en/latest/building.html>__).link <https://lensless.readthedocs.io/en/latest/measurement.html>__).Hugging Face <https://huggingface.co/bezzam>__ integration (slides <https://docs.google.com/presentation/d/18h7jTcp20jeoiF8dJIEcc7wHgjpgFgVxZ_bJ04W55lg/edit?usp=sharing> on uploading a dataset to Hugging Face with this script <https://github.com/LCAV/LenslessPiCam/blob/main/scripts/data/upload_dataset_huggingface.py>).Reconstruction algorithms <https://lensless.readthedocs.io/en/latest/reconstruction.html>__ (e.g. FISTA, ADMM, unrolled algorithms, trainable inversion, , multi-Wiener deconvolution network, pre- and post-processors).Training script <https://github.com/LCAV/LenslessPiCam/blob/main/scripts/recon/train_learning_based.py>__ for learning-based reconstruction.Pre-trained models <https://github.com/LCAV/LenslessPiCam/blob/main/lensless/recon/model_dict.py> that can be loaded from Hugging Face <https://huggingface.co/bezzam>, for example in this script <https://github.com/LCAV/LenslessPiCam/blob/main/scripts/recon/diffusercam_mirflickr.py>__.design <https://lensless.readthedocs.io/en/latest/mask.html> and fabrication <https://lensless.readthedocs.io/en/latest/fabrication.html> tools.Simulation tools <https://lensless.readthedocs.io/en/latest/simulation.html>__.Evalutions tools <https://lensless.readthedocs.io/en/latest/evaluation.html>__ (e.g. PSNR, LPIPS, SSIM, visualizations).Demo <https://lensless.readthedocs.io/en/latest/demo.html#telegram-demo>__ that can be run on Telegram!Please refer to the documentation <http://lensless.readthedocs.io> for more details,
while an overview of example notebooks can be found here <https://lensless.readthedocs.io/en/latest/examples.html>.
We've also written a few Medium articles to guide users through the process
of building the camera, measuring data with it, and reconstruction.
They are all laid out in this post <https://medium.com/@bezzam/a-complete-lensless-imaging-tutorial-hardware-software-and-algorithms-8873fa81a660>__.
If you are just interested in using the reconstruction algorithms and
plotting / evaluation tools you can install the package via pip:
.. code:: bash
pip install lensless
For plotting, you may also need to install
Tk <https://stackoverflow.com/questions/5459444/tkinter-python-may-not-be-configured-for-tk>__.
For performing measurements, the expected workflow is to have a local
computer which interfaces remotely with a Raspberry Pi equipped with
the HQ camera sensor (or V2 sensor). Instructions on building the camera
can be found here <https://lensless.readthedocs.io/en/latest/building.html>__.
The software from this repository has to be installed on both your
local machine and the Raspberry Pi. Note that we highly recommend using
Python 3.9, as some Python library versions may not be available with
earlier versions of Python. Moreover, its end-of-life <https://endoflife.date/python>__
is Oct 2025.
Below are commands that worked for our configuration (Ubuntu 21.04), but there are certainly other ways to download a repository and install the library locally.
.. code:: bash
git clone git@github.com:LCAV/LenslessPiCam.git cd LenslessPiCam
conda create -n lensless python=3.11 conda activate lensless
python3.11 -m venv lensless_env source lensless_env/bin/activate
pip install -e .
pip install -r recon_requirements.txt
python scripts/recon/admm.py
python scripts/recon/admm.py -cn pytorch
Note (25-04-2023): for using the :py:class:~lensless.recon.apgd.APGD reconstruction method based on Pycsou
(now Pyxu <https://github.com/matthieumeo/pyxu>__), a specific commit has
to be installed (as there was no release at the time of implementation):
.. code:: bash
pip install git+https://github.com/matthieumeo/pycsou.git@38e9929c29509d350a7ff12c514e2880fdc99d6e
If PyTorch is installed, you will need to be sure to have PyTorch 2.0 or higher,
as Pycsou is not compatible with earlier versions of PyTorch. Moreover,
Pycsou requires Python within
[3.9, 3.11) <https://github.com/matthieumeo/pycsou/blob/v2-dev/setup.cfg#L28>__.
Moreover, numba (requirement for Pycsou V2) may require an older version of NumPy:
.. code:: bash
pip install numpy==1.23.5
After flashing your Raspberry Pi with SSH enabled <https://medium.com/@bezzam/setting-up-a-raspberry-pi-without-a-monitor-headless-9a3c2337f329>,
you need to set it up for passwordless access <https://medium.com/@bezzam/headless-and-passwordless-interfacing-with-a-raspberry-pi-ssh-453dd75154c3>.
Do not set a password for your SSH key pair, as this will not work with the
provided scripts.
On the Raspberry Pi, you can then run the following commands (from the home
directory):
.. code:: bash
sudo apt-get install -y libimage-exiftool-perl libatlas-base-dev \ python3-numpy python3-scipy python3-opencv sudo pip3 install -U virtualenv
git clone git@github.com:LCAV/LenslessPiCam.git
cd LenslessPiCam virtualenv --system-site-packages -p python3 lensless_env source lensless_env/bin/activate pip install --no-deps -e . pip install -r rpi_requirements.txt
(lensless_env) python scripts/measure/on_device_capture.py
You may still need to manually install numpy and/or scipy with pip in case libraries (e.g. libopenblas.so.0) cannot be detected.
The idea of building a lensless camera from a Raspberry Pi and a piece of tape comes from Prof. Laura Waller's group at UC Berkeley. So a huge kudos to them for the idea and making tools/code/data available! Below is some of the work that has inspired this toolkit:
Build your own DiffuserCam tutorial <https://waller-lab.github.io/DiffuserCam/tutorial>__.DiffuserCam Lensless MIR Flickr dataset <https://waller-lab.github.io/LenslessLearning/dataset.html>_ [1]. A few students at EPFL have also contributed to this project:
If you use these tools in your own research, please cite the following:
::
@article{Bezzam2023, doi = {10.21105/joss.04747}, url = {https://doi.org/10.21105/joss.04747}, year = {2023}, publisher = {The Open Journal}, volume = {8}, number = {86}, pages = {4747}, author = {Eric Bezzam and Sepand Kashani and Martin Vetterli and Matthieu Simeoni}, title = {LenslessPiCam: A Hardware and Software Platform for Lensless Computational Imaging with a Raspberry Pi}, journal = {Journal of Open Source Software} }
.. [1] Monakhova, K., Yurtsever, J., Kuo, G., Antipa, N., Yanny, K., & Waller, L. (2019). Learned reconstructions for practical mask-based lensless imaging. Optics express, 27(20), 28075-28090.