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| ============= | ||
| LenslessPiCam | ||
| ============= | ||
| Ahmed Elalamy (324610), Seif Hamed (312081), Ghita Tagemouati (330383) | ||
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| .. image:: https://readthedocs.org/projects/lensless/badge/?version=latest | ||
| :target: http://lensless.readthedocs.io/en/latest/ | ||
| :alt: Documentation Status | ||
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| .. image:: https://joss.theoj.org/papers/10.21105/joss.04747/status.svg | ||
| :target: https://doi.org/10.21105/joss.04747 | ||
| :alt: DOI | ||
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| .. image:: https://static.pepy.tech/badge/lensless | ||
| :target: https://www.pepy.tech/projects/lensless | ||
| :alt: Downloads | ||
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| *A Hardware and Software Toolkit for Lensless Computational Imaging with a Raspberry Pi* | ||
| ----------------------------------------------------------------------------------------- | ||
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| .. image:: https://github.com/LCAV/LenslessPiCam/raw/main/scripts/recon/example.png | ||
| :alt: Lensless imaging example | ||
| :align: center | ||
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| 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. | ||
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| *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. | ||
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| 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>`__. | ||
| Our work is mainly done in the trainable_mask.py and mask.py files. | ||
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| Setup | ||
| ----- | ||
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| If you are just interested in using the reconstruction algorithms and | ||
| plotting / evaluation tools you can install the package via ``pip``: | ||
| First, install the lensless package | ||
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| .. 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>`__. | ||
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| 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>`__. | ||
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| 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. | ||
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| *Local machine setup* | ||
| ===================== | ||
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@@ -92,101 +42,8 @@ install the library locally. | |
| # extra dependencies for local machine for plotting/reconstruction | ||
| pip install -r recon_requirements.txt | ||
| pip install -r mask_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): | ||
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| .. 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>`__. | ||
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| Moreover, ``numba`` (requirement for Pycsou V2) may require an older version of NumPy: | ||
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| .. code:: bash | ||
| pip install numpy==1.23.5 | ||
| *Raspberry Pi setup* | ||
| ==================== | ||
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| 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. | ||
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| On the Raspberry Pi, you can then run the following commands (from the ``home`` | ||
| directory): | ||
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| .. 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 | ||
| Acknowledgements | ||
| ---------------- | ||
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| 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: | ||
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| * `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]_. | ||
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| A few students at EPFL have also contributed to this project: | ||
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| * Julien Sahli: support and extension of algorithms for 3D. | ||
| * Yohann Perron: unrolled algorithms for reconstruction. | ||
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| Citing this work | ||
| ---------------- | ||
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| If you use these tools in your own research, please cite the following: | ||
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| :: | ||
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| @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} | ||
| } | ||
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| References | ||
| ---------- | ||
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| .. [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. | ||
| # training with the height varying mask | ||
| python scripts/recon/train_unrolled.py -cn train_heightvarying | ||
| .. [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. | ||