LenslessPiCam
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Lensless imaging toolkit. Complete tutorial: https://go.epfl.ch/lenslesspicam
============= LenslessPiCam
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A Hardware and Software Toolkit for Lensless Computational Imaging with a Raspberry Pi
.. 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. We make use of a low-cost implementation of DiffuserCam [1]_,
where we use a piece of tape instead of the lens and the
Raspberry Pi HQ camera sensor <https://www.raspberrypi.com/products/raspberry-pi-high-quality-camera>
__
(the V2 sensor <https://www.raspberrypi.com/products/camera-module-v2/>
__
is also supported). Similar principles and methods can be used for a
different lensless encoder and a different sensor.
If you are interested in exploring reconstruction algorithms without building the camera, that is entirely possible! The provided reconstruction algorithms can be used with the provided data or simulated data.
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>
__.
Setup
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.
Local machine setup
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
download from GitHub
git clone [email protected]:LCAV/LenslessPiCam.git cd LenslessPiCam
create virtual environment (as of Oct 4 2023, rawpy is not compatible with Python 3.12)
-- using conda
conda create -n lensless python=3.11 conda activate lensless
-- OR venv
python3.11 -m venv lensless_env source lensless_env/bin/activate
install package
pip install -e .
extra dependencies for local machine for plotting/reconstruction
pip install -r recon_requirements.txt
(optional) try reconstruction on local machine
python scripts/recon/admm.py
(optional) try reconstruction on local machine with GPU
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
Raspberry Pi setup
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
dependencies
sudo apt-get install -y libimage-exiftool-perl libatlas-base-dev
python3-numpy python3-scipy python3-opencv
sudo pip3 install -U virtualenv
download from GitHub
git clone [email protected]:LCAV/LenslessPiCam.git
install in virtual environment
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
test on-device camera capture (after setting up the camera)
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.
Acknowledgements
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>
__ [2]_.
A few students at EPFL have also contributed to this project:
- Julien Sahli: support and extension of algorithms for 3D.
- Yohann Perron: unrolled algorithms for reconstruction.
Citing this work
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} }
References
.. [1] Antipa, N., Kuo, G., Heckel, R., Mildenhall, B., Bostan, E., Ng, R., & Waller, L. (2018). DiffuserCam: lensless single-exposure 3D imaging. Optica, 5(1), 1-9.
.. [2] 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.