diff --git a/.gitignore b/.gitignore index cb70e669..190c1e14 100644 --- a/.gitignore +++ b/.gitignore @@ -165,3 +165,5 @@ dmypy.json # Pyre type checker .pyre/ +# ignore all generated outputs +lenslesswebapp/outputs/ diff --git a/README.rst b/README.rst index b23abf57..cc9364da 100644 --- a/README.rst +++ b/README.rst @@ -1,326 +1,143 @@ -============= -LenslessPiCam -============= +Lensless Imaging WebApp +======================== -.. image:: https://img.shields.io/badge/GitHub-100000?style=for-the-badge&logo=github&logoColor=white - :target: https://github.com/LCAV/LenslessPiCam - :alt: GitHub page +This web application provides an interactive interface for capturing, calibrating, +and reconstructing lensless images using the Lensless Imaging Development Kit. +It is designed to run on a Raspberry Pi and accessed through a browser on the same network. +No software installation is required on the host machine. -.. image:: https://readthedocs.org/projects/lensless/badge/?version=latest - :target: http://lensless.readthedocs.io/en/latest/ - :alt: Documentation Status +This module is located in the ``lenslesswebapp/`` directory of the main project. -.. image:: https://joss.theoj.org/papers/10.21105/joss.04747/status.svg - :target: https://doi.org/10.21105/joss.04747 - :alt: DOI +Features +-------- -.. image:: https://static.pepy.tech/badge/lensless - :target: https://www.pepy.tech/projects/lensless - :alt: Downloads +- Full lensless imaging pipeline: PSF capture, autocorrelatio plotting, object capture, and image reconstruction +- Optional auto exposure and RGB gain correction +- Remote capture and reconstruction +- Headless access via browser using preconfigured Raspberry Pi image +- No installation required on client machines -.. image:: https://colab.research.google.com/assets/colab-badge.svg - :target: https://lensless.readthedocs.io/en/latest/examples.html - :alt: notebooks -.. image:: https://img.shields.io/badge/Google_Slides-yellow - :target: https://docs.google.com/presentation/d/1PcNhMfjATSwcpbHUMrmc88ciQmheBJ7alz8hel8xnGU/edit?usp=sharing - :alt: slides +Project Structure +----------------- -.. image:: https://huggingface.co/datasets/huggingface/badges/resolve/main/powered-by-huggingface-dark.svg - :target: https://huggingface.co/bezzam - :alt: huggingface - - -*A Hardware and Software Toolkit for Lensless Computational Imaging* --------------------------------------------------------------------- - -.. 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 `__, -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 `__. -As **modularity** is a key feature of this toolkit, we try to support different sensors and/or lensless encoders. - -The toolkit includes: - -* Training scripts/configuration for various learnable, physics-informed reconstruction approaches, as shown `here `__. -* Camera assembly tutorials (`link `__). -* Measurement scripts (`link `__). -* Dataset preparation and loading tools, with `Hugging Face `__ integration (`slides `__ on uploading a dataset to Hugging Face with `this script `__). -* `Reconstruction algorithms `__ (e.g. FISTA, ADMM, unrolled algorithms, trainable inversion, , multi-Wiener deconvolution network, pre- and post-processors). -* `Pre-trained models `__ that can be loaded from `Hugging Face `__, for example in `this script `__. -* Mask `design `__ and `fabrication `__ tools. -* `Simulation tools `__. -* `Evalutions tools `__ (e.g. PSNR, LPIPS, SSIM, visualizations). -* `Demo `__ that can be run on Telegram! - -Please refer to the `documentation `__ for more details, -while an overview of example notebooks can be found `here `__. - -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 `__. - -Collection of lensless imaging research ---------------------------------------- - -The following works have been implemented in the toolkit: - -Reconstruction algorithms: - -* ADMM with total variation regularization and 3D support (`source code `__, `usage `__). [1]_ -* Unrolled ADMM (`source code `__, `usage `__). [2]_ -* Unrolled ADMM with compensation branch (`source code `__, `usage `__). [3]_ -* Trainable inversion from Flatnet (`source code `__, `usage `__). [4]_ -* Multi-Wiener deconvolution network (`source code `__, `usage `__). [5]_ -* SVDeconvNet (for learning multi-PSF deconvolution) from PhoCoLens (`source code `__, `usage `__). [6]_ -* Incorporating pre-processor (`source code `__). [7]_ -* Accounting for external illumination(`source code 1 `__, `source code 2 `__, `usage `__). [8]_ - -Camera / mask design: - -* Fresnel zone aperture mask pattern (`source code `__). [9]_ -* Coded aperture mask pattern (`source code `__). [10]_ -* Near-field Phase Retrieval for designing a high-contrast phase mask (`source code `__). [11]_ -* LCD-based camera, i.e. DigiCam (`source code `__). [7]_ - -Datasets (hosted on Hugging Face and downloaded via their API): - -* DiffuserCam Lensless MIR Flickr dataset (copy on `Hugging Face `__). [2]_ -* TapeCam MIR Flickr (`Hugging Face `__). [7]_ -* DigiCam MIR Flickr (`Hugging Face `__). [7]_ -* DigiCam MIR Flickr with multiple mask patterns (`Hugging Face `__). [7]_ -* DigiCam CelebA (`Hugging Face `__). [7]_ -* MultiFocal mask MIR Flickr under external illumination (`Hugging Face `__). [8]_ Mask fabricated by [12]_ - - -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 `__. - - -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 `__. - -The software from this repository has to be installed on **both** your -local machine and the Raspberry Pi. Note that we recommend using -Python 3.11, as some Python library versions may not be available with -earlier versions of Python. Moreover, its `end-of-life `__ -is Oct 2027. - -*Local machine setup* -===================== - -Below are commands that worked for our configuration (Ubuntu 22.04.5 LTS), -but there are certainly other ways to download a repository and -install the library locally. - -Note that ``(lensless)`` is a convention to indicate that the virtual -environment is activated. After activating your virtual environment, you only -have to copy the command after ``(lensless)``. - -.. code:: bash +:: - # download from GitHub - git clone git@github.com:LCAV/LenslessPiCam.git - cd LenslessPiCam + lensless-web-app/ + ├── client/ # Vite-based frontend (React + Tailwind) + ├── server/ # Node.js backend (image processing, control) + ├── public/ # Static assets + ├── captures/ # PSFs and image captures + ├── README.rst # This file - # 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 +Getting Started +--------------- - # install package - (lensless) pip install -e . +1. Flash and Boot Raspberry Pi - # extra dependencies for local machine for plotting/reconstruction - (lensless) pip install -r recon_requirements.txt +- Use the provided `.img` file to flash your SD card. +- Insert the SD card into the Raspberry Pi and power it up. +- Ensure the Pi is connected to the same local network as your browser device. - # pre-commit hooks for code formatting - (lensless) pip install pre-commit black - (lensless) pre-commit install +2. Run the Application - # (optional) try reconstruction on local machine - (lensless) python scripts/recon/admm.py +Frontend (client) +~~~~~~~~~~~~~~~~~ - # (optional) try reconstruction on local machine with GPU - (lensless) python scripts/recon/admm.py -cn pytorch +Start the frontend development server: +.. code-block:: bash -Note (25-04-2023): for using the :py:class:`~lensless.recon.apgd.APGD` reconstruction method based on Pycsou -(now `Pyxu `__), a specific commit has -to be installed (as there was no release at the time of implementation): + cd lenslesswebapp/ + npm install + npm run dev -.. code:: bash +To keep the frontend running in the background using `screen`: - pip install git+https://github.com/matthieumeo/pycsou.git@38e9929c29509d350a7ff12c514e2880fdc99d6e +.. code-block:: bash -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) `__. + screen -S client + npm run dev -Moreover, ``numba`` (requirement for Pycsou V2) may require an older version of NumPy: +To detach: press `Ctrl + A`, then `D` +To reattach later: `screen -r client` -.. code:: bash +Backend (server) +~~~~~~~~~~~~~~~~ - pip install numpy==1.23.5 +Start the backend server: -*Raspberry Pi setup* -==================== +.. code-block:: bash -After `flashing your Raspberry Pi with SSH enabled `__, -you need to set it up for `passwordless access `__. -Do not set a password for your SSH key pair, as this will not work with the -provided scripts. + cd lenslesswebapp + node server/index.cjs -On the Raspberry Pi, you can then run the following commands (from the ``home`` -directory): +To keep the backend running in the background using `screen`: -.. code:: bash +.. code-block:: bash - # dependencies - sudo apt-get install -y libimage-exiftool-perl libatlas-base-dev \ - python3-numpy python3-scipy python3-opencv - sudo pip3 install -U virtualenv + screen -S server + node server/index.cjs - # download from GitHub - git clone git@github.com:LCAV/LenslessPiCam.git +To detach: press `Ctrl + A`, then `D` +To reattach later: `screen -r server` - # 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 +Module Features +--------------- - # test on-device camera capture (after setting up the camera) - (lensless_env) python scripts/measure/on_device_capture.py +PSF Capture Block +~~~~~~~~~~~~~~~~~ -You may still need to manually install ``numpy`` and/or ``scipy`` with ``pip`` in case libraries (e.g. ``libopenblas.so.0``) cannot be detected. - +This block handles the acquisition and calibration of the Point Spread Function using a known white point light source. -Acknowledgements ----------------- +Acquisition Options +^^^^^^^^^^^^^^^^^^^ -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: +- Capture a new PSF using the camera interface and plot its autocorrelation +- Load a previously saved PSF from disk with its autocorrelation +- Download a zip of the 2 pictures -* `Build your own DiffuserCam tutorial `__. -* `DiffuserCam Lensless MIR Flickr dataset `__ [2]_. +Exposure and Color Correction +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -A few students at EPFL have also contributed to this project: +- Automatic exposure tuning (`auto_exp_psf`) +- Automatic red and blue gain calibration -* Julien Sahli: support and extension of algorithms for 3D. -* Yohann Perron: unrolled algorithms for reconstruction. -* Aaron Fargeon: mask designs. -* Rein Bentdal and David Karoubi: mask fabrication with 3D printing. -* Stefan Peters: imaging under external illumination. +Visualization Tools +^^^^^^^^^^^^^^^^^^^ -We also thank the Swiss National Science Foundation for funding this project through the `Open Research Data (ORD) program `__. +- Autocorrelation plot generated to assess sharpness and quality +- Useful for visually validating PSF quality and compare with others -Citing this work ----------------- +Image Capture & Reconstruction Block +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -If you use this toolkit in your own research, please cite the following: +This block allows you to capture scene images and reconstruct them using a selected PSF. -:: +Image Input Options +^^^^^^^^^^^^^^^^^^^ - @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} - } +- Take a photo directly on your device +- Upload an image from your device +- Upload an image from your phone via the `/phone` page ( PS : to take selfies from your phone and retrieve them to reconstruct them on your computer) +Reconstruction Workflow +^^^^^^^^^^^^^^^^^^^^^^^ -The following papers have contributed new approaches to the field of lensless imaging: +- Select a PSF (from all the captured ones) +- Reconstruct the captured image using that PSF using ADMM or Gradient descent with a selected number of iterations -* Introducing pre-processor component as part of modular reconstruction (`IEEE Transactions on Computational Imaging `__ and `IEEE International Conference on Image Processing (ICIP) 2024 `__): +Display and Feedback +^^^^^^^^^^^^^^^^^^^^ -:: +- View the raw lensless image and its reconstruction side-by-side +- Allows inspection of reconstruction quality in real time and can change number of iterations and the algorithm of reconstruction for comparison - @ARTICLE{10908470, - author={Bezzam, Eric and Perron, Yohann and Vetterli, Martin}, - journal={IEEE Transactions on Computational Imaging}, - title={Towards Robust and Generalizable Lensless Imaging With Modular Learned Reconstruction}, - year={2025}, - volume={11}, - number={}, - pages={213-227}, - keywords={Training;Wiener filters;Computational modeling;Transfer learning;Computer architecture;Cameras;Transformers;Software;Software measurement;Image reconstruction;Lensless imaging;modularity;robustness;generalizability;programmable mask;transfer learning}, - doi={10.1109/TCI.2025.3539448} - } - - @INPROCEEDINGS{10647433, - author={Perron, Yohann and Bezzam, Eric and Vetterli, Martin}, - booktitle={2024 IEEE International Conference on Image Processing (ICIP)}, - title={A Modular and Robust Physics-Based Approach for Lensless Image Reconstruction}, - year={2024}, - volume={}, - number={}, - pages={3979-3985}, - keywords={Training;Multiplexing;Pipelines;Noise;Cameras;Robustness;Reproducibility of results;Lensless imaging;modular reconstruction;end-to-end optimization}, - doi={10.1109/ICIP51287.2024.10647433} - } - - -* Lensless imaging under external illumination (`IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2025 `__): -:: +Author +------ - @INPROCEEDINGS{10888030, - author={Bezzam, Eric and Peters, Stefan and Vetterli, Martin}, - booktitle={ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, - title={Let There Be Light: Robust Lensless Imaging Under External Illumination With Deep Learning}, - year={2025}, - volume={}, - number={}, - pages={1-5}, - keywords={Source separation;Noise;Lighting;Interference;Reconstruction algorithms;Cameras;Optics;Speech processing;Image reconstruction;Standards;lensless imaging;ambient lighting;external illumination;background subtraction;learned reconstruction}, - doi={10.1109/ICASSP49660.2025.10888030} - } - -References ----------- - -.. [1] Antipa, N., Kuo, G., Heckel, R., Mildenhall, B., Bostan, E., Ng, R., & Waller, L. (2017). 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. -.. [3] Zeng, T., & Lam, E. Y. (2021). Robust reconstruction with deep learning to handle model mismatch in lensless imaging. IEEE Transactions on Computational Imaging, 7, 1080-1092. -.. [4] Khan, S. S., Sundar, V., Boominathan, V., Veeraraghavan, A., & Mitra, K. (2020). Flatnet: Towards photorealistic scene reconstruction from lensless measurements. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(4), 1934-1948. -.. [5] Li, Y., Li, Z., Chen, K., Guo, Y., & Rao, C. (2023). MWDNs: reconstruction in multi-scale feature spaces for lensless imaging. Optics Express, 31(23), 39088-39101. -.. [6] Cai, X., You, Z., Zhang, H., Gu, J., Liu, W., & Xue, T. (2024). Phocolens: Photorealistic and consistent reconstruction in lensless imaging. Advances in Neural Information Processing Systems, 37, 12219-12242. -.. [7] Bezzam, E., Perron, Y., & Vetterli, M. (2025). Towards Robust and Generalizable Lensless Imaging with Modular Learned Reconstruction. IEEE Transactions on Computational Imaging. -.. [8] Bezzam, E., Peters, S., & Vetterli, M. (2024). Let there be light: Robust lensless imaging under external illumination with deep learning. IEEE International Conference on Acoustics, Speech and Signal Processing. -.. [9] Wu, J., Zhang, H., Zhang, W., Jin, G., Cao, L., & Barbastathis, G. (2020). Single-shot lensless imaging with fresnel zone aperture and incoherent illumination. Light: Science & Applications, 9(1), 53. -.. [10] Asif, M. S., Ayremlou, A., Sankaranarayanan, A., Veeraraghavan, A., & Baraniuk, R. G. (2016). Flatcam: Thin, lensless cameras using coded aperture and computation. IEEE Transactions on Computational Imaging, 3(3), 384-397. -.. [11] Boominathan, V., Adams, J. K., Robinson, J. T., & Veeraraghavan, A. (2020). Phlatcam: Designed phase-mask based thin lensless camera. IEEE transactions on pattern analysis and machine intelligence, 42(7), 1618-1629. -.. [12] Lee, K. C., Bae, J., Baek, N., Jung, J., Park, W., & Lee, S. A. (2023). Design and single-shot fabrication of lensless cameras with arbitrary point spread functions. Optica, 10(1), 72-80. +Imane Raihane +Semester Project, Spring 2025 +Supervisor: Eric Bezzam (LCAV – EPFL) diff --git a/configs/capture.yaml b/configs/capture.yaml index 18b6c030..680edca5 100644 --- a/configs/capture.yaml +++ b/configs/capture.yaml @@ -16,4 +16,6 @@ down: null res: null nbits_out: 8 awb_gains: null +auto_exp_psf: false +auto_exp_img: false # awb_gains: [1.9, 1.2] # red, blue \ No newline at end of file diff --git a/lensless/utils/io.py b/lensless/utils/io.py index 5596befd..43e07963 100644 --- a/lensless/utils/io.py +++ b/lensless/utils/io.py @@ -166,9 +166,6 @@ def load_image( if flip_lr: img = np.fliplr(img) - if verbose: - print_image_info(img) - if bg is not None: # if bg is float vector, turn into int-valued vector @@ -204,6 +201,9 @@ def load_image( dtype = original_dtype img = img.astype(dtype) + if verbose: + print_image_info(img) + return img @@ -292,7 +292,7 @@ def load_psf( else: psf = load_image( fp, - verbose=verbose, + verbose=False, flip=flip, flip_ud=flip_ud, flip_lr=flip_lr, @@ -377,6 +377,9 @@ def load_psf( else: psf = psf.astype(original_dtype) + if verbose: + print_image_info(psf) + if return_bg: return psf, bg else: diff --git a/lensless/utils/plot.py b/lensless/utils/plot.py index d3537f6c..8c910c2e 100644 --- a/lensless/utils/plot.py +++ b/lensless/utils/plot.py @@ -244,6 +244,7 @@ def plot_cross_section( width = 2 * np.abs(first_crossing) ax.axvline(x=-first_crossing, c="k", linestyle="--") ax.axvline(x=+first_crossing, c="k", linestyle="--") + print(f"{plot_db_drop}dB width : {width} pixels") ax.set_title("Cross-section") ax.set_xlabel(f"-{plot_db_drop}dB width = {width}") diff --git a/lenslesswebapp/.gitignore b/lenslesswebapp/.gitignore new file mode 100644 index 00000000..74bb975e --- /dev/null +++ b/lenslesswebapp/.gitignore @@ -0,0 +1,27 @@ +# Logs +logs +*.log +npm-debug.log* +yarn-debug.log* +yarn-error.log* +pnpm-debug.log* +lerna-debug.log* + +node_modules +dist +dist-ssr +*.local + +# Editor directories and files +.vscode/* +!.vscode/extensions.json +.idea +.DS_Store +*.suo +*.ntvs* +*.njsproj +*.sln +*.sw? +!public/logo.png +lenslesswebapp/outputs/ + diff --git a/lenslesswebapp/README.md b/lenslesswebapp/README.md new file mode 100644 index 00000000..da984443 --- /dev/null +++ b/lenslesswebapp/README.md @@ -0,0 +1,54 @@ +# React + TypeScript + Vite + +This template provides a minimal setup to get React working in Vite with HMR and some ESLint rules. + +Currently, two official plugins are available: + +- [@vitejs/plugin-react](https://github.com/vitejs/vite-plugin-react/blob/main/packages/plugin-react) uses [Babel](https://babeljs.io/) for Fast Refresh +- [@vitejs/plugin-react-swc](https://github.com/vitejs/vite-plugin-react/blob/main/packages/plugin-react-swc) uses [SWC](https://swc.rs/) for Fast Refresh + +## Expanding the ESLint configuration + +If you are developing a production application, we recommend updating the configuration to enable type-aware lint rules: + +```js +export default tseslint.config({ + extends: [ + // Remove ...tseslint.configs.recommended and replace with this + ...tseslint.configs.recommendedTypeChecked, + // Alternatively, use this for stricter rules + ...tseslint.configs.strictTypeChecked, + // Optionally, add this for stylistic rules + ...tseslint.configs.stylisticTypeChecked, + ], + languageOptions: { + // other options... + parserOptions: { + project: ['./tsconfig.node.json', './tsconfig.app.json'], + tsconfigRootDir: import.meta.dirname, + }, + }, +}) +``` + +You can also install [eslint-plugin-react-x](https://github.com/Rel1cx/eslint-react/tree/main/packages/plugins/eslint-plugin-react-x) and [eslint-plugin-react-dom](https://github.com/Rel1cx/eslint-react/tree/main/packages/plugins/eslint-plugin-react-dom) for React-specific lint rules: + +```js +// eslint.config.js +import reactX from 'eslint-plugin-react-x' +import reactDom from 'eslint-plugin-react-dom' + +export default tseslint.config({ + plugins: { + // Add the react-x and react-dom plugins + 'react-x': reactX, + 'react-dom': reactDom, + }, + rules: { + // other rules... + // Enable its recommended typescript rules + ...reactX.configs['recommended-typescript'].rules, + ...reactDom.configs.recommended.rules, + }, +}) +``` diff --git a/lenslesswebapp/certs/cert.pem b/lenslesswebapp/certs/cert.pem new file mode 100644 index 00000000..3487c375 --- /dev/null +++ b/lenslesswebapp/certs/cert.pem @@ -0,0 +1,21 @@ +-----BEGIN CERTIFICATE----- +MIIDazCCAlOgAwIBAgIUAmpNnEumFYfvt5NTep9I3OCY77EwDQYJKoZIhvcNAQEL +BQAwRTELMAkGA1UEBhMCQVUxEzARBgNVBAgMClNvbWUtU3RhdGUxITAfBgNVBAoM +GEludGVybmV0IFdpZGdpdHMgUHR5IEx0ZDAeFw0yNTA1MjYyMDQxMTNaFw0yNjA1 +MjYyMDQxMTNaMEUxCzAJBgNVBAYTAkFVMRMwEQYDVQQIDApTb21lLVN0YXRlMSEw +HwYDVQQKDBhJbnRlcm5ldCBXaWRnaXRzIFB0eSBMdGQwggEiMA0GCSqGSIb3DQEB +AQUAA4IBDwAwggEKAoIBAQC7OtwdIEVMKhFahfN1e3TSQ9gES6wKwtK+dZhFghHG +5Sss1kMCV6zEZwklJ0fRbLO1v0r6dRI4oa/Db+N7VLyWqQ9pwg6pXm5sH50Vy/Bq +fx6+GqZJD+IyaRLuozZthIJ6kRFDtnpq4XRoyaKfR2LflEcwaoPoC4uz/+wuUNWQ +X8omD9hTrsaPRmv2EXhDIDt/qRFK/B9HKaXDhb+f26RH0BNBn2DR2fWWf7hhdzJI +Hhcjseld9FOx0aQwck/9W9TQyq6tJWv6u/YhclWluRbhJeEsRXOhPInn4nvLT8RS +Jxsmlrgseel3xCd8I1bDBqkgNJk9mXRenN63FmG1WQ23AgMBAAGjUzBRMB0GA1Ud +DgQWBBQwkkpU1nzXUjkTJ8JEhQUEF+BfYTAfBgNVHSMEGDAWgBQwkkpU1nzXUjkT +J8JEhQUEF+BfYTAPBgNVHRMBAf8EBTADAQH/MA0GCSqGSIb3DQEBCwUAA4IBAQB2 +jS76qgb0/IT8NkCCH5Rz1fvCadkP3wsPhpU1hs7SQymmyWEra+M+SsyZ2jEotERg +l/wjeY0RY2YQpBOqTZ/HM0kdsz/JnMs1LciQmlVoU8Po7KiJ8m+6+WducoDYqFks +CXoQDXObfDHaRJDEcgqpPqLXMkRNZMGajB4Axp2Osbjxx2aNVqSCXDRPcnrbXaed +BWVDwDXMsGCOp0oBfldTbxSRiU3IaOaIpPzJJz2ML87hpQbO4SuXjA2gI6AOdCbc +7gcVn1HmmP0oL9EsILgqlOww8J0bEUr2AJIz9Av86Jy4iE24Q9FYDDXKApNYnsKS +lcQAnY/DCgySeDrbPwTg +-----END CERTIFICATE----- diff --git a/lenslesswebapp/certs/key.pem b/lenslesswebapp/certs/key.pem new file mode 100644 index 00000000..f911b156 --- /dev/null +++ b/lenslesswebapp/certs/key.pem @@ -0,0 +1,28 @@ +-----BEGIN PRIVATE KEY----- +MIIEvgIBADANBgkqhkiG9w0BAQEFAASCBKgwggSkAgEAAoIBAQC7OtwdIEVMKhFa 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'eslint-plugin-react-refresh' +import tseslint from 'typescript-eslint' + +export default tseslint.config( + { ignores: ['dist'] }, + { + extends: [js.configs.recommended, ...tseslint.configs.recommended], + files: ['**/*.{ts,tsx}'], + languageOptions: { + ecmaVersion: 2020, + globals: globals.browser, + }, + plugins: { + 'react-hooks': reactHooks, + 'react-refresh': reactRefresh, + }, + rules: { + ...reactHooks.configs.recommended.rules, + 'react-refresh/only-export-components': [ + 'warn', + { allowConstantExport: true }, + ], + }, + }, +) diff --git a/lenslesswebapp/index.html b/lenslesswebapp/index.html new file mode 100644 index 00000000..636950c9 --- /dev/null +++ b/lenslesswebapp/index.html @@ -0,0 +1,13 @@ + + + + + + + Lensless PiCam Kit + + +
+ + + diff --git a/lenslesswebapp/package-lock.json b/lenslesswebapp/package-lock.json new file mode 100644 index 00000000..21909733 --- /dev/null +++ b/lenslesswebapp/package-lock.json @@ -0,0 +1,7401 @@ +{ + "name": "lenslesswebapp", + "version": "0.0.0", + "lockfileVersion": 3, + "requires": true, + "packages": { + "": { + "name": "lenslesswebapp", + "version": "0.0.0", + "dependencies": { + "archiver": "^7.0.1", + "cors": "^2.8.5", + "express": "^5.1.0", + "jimp": "^0.16.1", + "multer": "^2.0.1", + "react": "^19.1.0", + "react-dom": "^19.1.0", + "react-router-dom": "^7.6.0" + }, + "devDependencies": { + "@eslint/js": "^9.25.0", + "@tailwindcss/postcss": "^4.1.7", + "@types/react": "^19.1.2", + "@types/react-dom": "^19.1.2", + "@vitejs/plugin-react": "^4.4.1", + "autoprefixer": "^10.4.21", + "eslint": "^9.25.0", + "eslint-plugin-react-hooks": "^5.2.0", + "eslint-plugin-react-refresh": "^0.4.19", + "globals": "^16.0.0", + "postcss": "^8.5.3", + "tailwindcss": "^3.4.17", + "tsx": "^4.19.4", + "typescript": "~5.8.3", + "typescript-eslint": "^8.30.1", + "vite": "^6.3.5" + } + }, + "node_modules/@alloc/quick-lru": { + "version": "5.2.0", + "resolved": "https://registry.npmjs.org/@alloc/quick-lru/-/quick-lru-5.2.0.tgz", + "integrity": "sha512-UrcABB+4bUrFABwbluTIBErXwvbsU/V7TZWfmbgJfbkwiBuziS9gxdODUyuiecfdGQ85jglMW6juS3+z5TsKLw==", + "dev": true, + "license": "MIT", + "engines": { + "node": ">=10" + }, + "funding": { + "url": "https://github.com/sponsors/sindresorhus" + } + }, + "node_modules/@ampproject/remapping": { + "version": "2.3.0", + "resolved": "https://registry.npmjs.org/@ampproject/remapping/-/remapping-2.3.0.tgz", + "integrity": "sha512-30iZtAPgz+LTIYoeivqYo853f02jBYSd5uGnGpkFV0M3xOt9aN73erkgYAmZU43x4VfqcnLxW9Kpg3R5LC4YYw==", + "dev": true, + "license": "Apache-2.0", + "dependencies": { + "@jridgewell/gen-mapping": "^0.3.5", + "@jridgewell/trace-mapping": "^0.3.24" + }, + "engines": { + "node": ">=6.0.0" + } + }, + "node_modules/@babel/code-frame": { + 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00000000..b3b6b0a8 --- /dev/null +++ b/lenslesswebapp/package.json @@ -0,0 +1,40 @@ +{ + "name": "lenslesswebapp", + "private": true, + "version": "0.0.0", + "type": "module", + "scripts": { + "dev": "vite", + "build": "tsc -b && vite build", + "lint": "eslint .", + "preview": "vite preview" + }, + "dependencies": { + "archiver": "^7.0.1", + "cors": "^2.8.5", + "express": "^5.1.0", + "jimp": "^0.16.1", + "multer": "^2.0.1", + "react": "^19.1.0", + "react-dom": "^19.1.0", + "react-router-dom": "^7.6.0" + }, + "devDependencies": { + "@eslint/js": "^9.25.0", + "@tailwindcss/postcss": "^4.1.7", + "@types/react": "^19.1.2", + "@types/react-dom": "^19.1.2", + "@vitejs/plugin-react": "^4.4.1", + "autoprefixer": "^10.4.21", + "eslint": "^9.25.0", + "eslint-plugin-react-hooks": "^5.2.0", + "eslint-plugin-react-refresh": "^0.4.19", + "globals": "^16.0.0", + "postcss": "^8.5.3", + "tailwindcss": "^3.4.17", + "tsx": "^4.19.4", + "typescript": "~5.8.3", + "typescript-eslint": "^8.30.1", + "vite": "^6.3.5" + } +} diff --git a/lenslesswebapp/postcss.config.js b/lenslesswebapp/postcss.config.js new file mode 100644 index 00000000..2e7af2b7 --- /dev/null +++ b/lenslesswebapp/postcss.config.js @@ -0,0 +1,6 @@ +export default { + plugins: { + tailwindcss: {}, + autoprefixer: {}, + }, +} diff --git a/lenslesswebapp/public/logo.png b/lenslesswebapp/public/logo.png new file mode 100644 index 00000000..36320455 Binary files /dev/null and b/lenslesswebapp/public/logo.png differ diff --git a/lenslesswebapp/public/signal-lines.svg b/lenslesswebapp/public/signal-lines.svg new file mode 100644 index 00000000..5d16c226 --- /dev/null +++ b/lenslesswebapp/public/signal-lines.svg @@ -0,0 +1,11 @@ + + + + + + + + + + + \ No newline at end of file diff --git a/lenslesswebapp/public/vite.svg b/lenslesswebapp/public/vite.svg new file mode 100644 index 00000000..e7b8dfb1 --- /dev/null +++ b/lenslesswebapp/public/vite.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/lenslesswebapp/server/index.cjs b/lenslesswebapp/server/index.cjs new file mode 100644 index 00000000..55fe28bd --- /dev/null +++ b/lenslesswebapp/server/index.cjs @@ -0,0 +1,715 @@ +const express = require('express'); +const cors = require('cors'); +const { spawn } = require('child_process'); +const path = require('path'); +const fs = require('fs'); +const https = require('https'); +const multer = require('multer'); +const upload = multer({ dest: '/tmp' }); // Dossier temporaire +const Jimp = require('jimp'); + + +const app = express(); +const PORT = 5000; + +app.use(cors({ + origin: '*', + methods: ['GET', 'POST'], +})); +app.use(express.json()); + +// Helper function to execute a shell command with real-time output +function runCommandLive(command, args, cwd, extractHydraDir = false) { + console.log(`\n[COMMAND] ${command} ${args.join(' ')}\n`); + let output = ''; + return new Promise((resolve, reject) => { + const proc = spawn(command, args, { cwd, shell: true }); + + proc.stdout.on('data', (data) => { + process.stdout.write(`[stdout] ${data}`); + output += data.toString(); + }); + + proc.stderr.on('data', (data) => { + process.stderr.write(`[stderr] ${data}`); + }); + + proc.on('close', (code) => { + if (code === 0) { + if (extractHydraDir) { + // Recherche du chemin Hydra uniquement si demandé + const match = output.match(/Files saved to : (.*)/); + const hydraOutputDir = match && match[1] ? match[1].trim() : null; + resolve(hydraOutputDir); + } else { + resolve(); // classique : rien à retourner + } + } else { + reject(new Error(`Command failed with exit code ${code}`)); + } + }); + }); +} + +app.post('/run-demo', async (req, res) => { + try { + console.log("[SERVER] /run-demo endpoint hit"); + + const repoRoot = path.resolve(__dirname) + + const { psfName } = req.body; + const baseDir = '/home/pi3/LenslessPiCam-Imane/Psf'; + const userDir = path.join(baseDir, psfName); + + // Create directory if it does not exist + if (!fs.existsSync(userDir)) { + fs.mkdirSync(userDir, { recursive: true }); + } +; + + + // 1. Run on-device capture + await runCommandLive( + '/home/pi3/LenslessPiCam/lensless_env/bin/python', + [ + 'scripts/measure/on_device_capture.py', + 'sensor=rpi_hq', + 'bayer=True', + 'fn=capture', + 'exp=0.15', + 'iso=100', + 'config_pause=2', + 'sensor_mode=0', + 'nbits_out=12', + 'legacy=True', + 'rgb=False', + 'gray=False', + 'sixteen=True', + 'auto_exp_psf=true' + ], + '/home/pi3/LenslessPiCam-Imane' + ); + + await runCommandLive('mv', [ + 'capture.png', + `Psf/${psfName}/raw_data.png` + ], '/home/pi3/LenslessPiCam-Imane'); + console.log("File moved"); + + // 2. Run color correction & autocorrelation + await runCommandLive( + '/home/pi3/LenslessPiCam/lensless_env/bin/python', + [ + 'scripts/measure/analyze_image.py', + '--fp', `../LenslessPiCam-Imane/Psf/${psfName}/raw_data.png`, + '--bayer', + '--gamma', '2.2', + '--rg', '2.0', + '--bg', '1.2', + '--lensless', + '--down', '2', + '--save', `Psf/${psfName}/psf_rgb.png`, + '--save_auto' + ], + '/home/pi3/LenslessPiCam-Imane' + ); + + + // 5. Read images and respond + console.log('PSF PATH:', path.resolve(__dirname, '../../Psf', psfName, 'psf_rgb.png')); + const psfBuffer = fs.readFileSync(path.resolve(__dirname, '../../Psf', psfName, 'psf_rgb_8bit.png')); + const autocorrBuffer = fs.readFileSync(path.resolve(__dirname, '../../Psf', psfName, 'autocorrelation.png')); + res.json({ + psf: psfBuffer.toString('base64'), + autocorr: autocorrBuffer.toString('base64') + }); + + } catch (err) { + console.error("[SERVER] Error in /run-demo", err); + res.status(500).send({ error: 'Demo failed to run', details: err.toString() }); + } +}); + + app.post('/run-full-imaging', upload.single('image'), async (req, res) => { + try { + const psfChosen = req.body.psfChosen; + const iterations = parseInt(req.body.iterations || '10', 10); + const algorithm = req.body.algorithm || "ADMM"; + const useAutoExposure = req.body.useAutoExposure === 'true'; + const manualExposure = parseFloat(req.body.manualExposure || '0.04'); + const imagePath = req.file.path; + + console.log(`[DEBUG] useAutoExposure: ${useAutoExposure}, manualExposure: ${manualExposure}s`); + + + // 1. Nom de dossier basé sur timestamp (très simple et lisible) + const captureId = req.body.captureName || Date.now().toString(); + const captureDir = `/home/pi3/LenslessPiCam-Imane/Captures/${captureId}`; + console.log(`[1] Création du dossier de capture : ${captureDir}`); + + // 2. Crée le dossier de la capture + if (!fs.existsSync(captureDir)) { + fs.mkdirSync(captureDir, { recursive: true }); + console.log(`[1] Dossier créé : ${captureDir}`); + } else { + console.log(`[1] Dossier déjà existant : ${captureDir}`); + } + + // 3. Copie l'image uploadée/taken dans ce dossier + console.log(`[2] Copie de l'image uploadée (${imagePath}) dans ${captureDir}/uploaded.png`); + await runCommandLive('cp', [ + imagePath, + `${captureDir}/uploaded.png` + ], '/home/pi3/LenslessPiCam-Imane'); + console.log(`[2] Copie terminée.`); + + // 4. Affiche l'image sur l'écran distant + console.log(`[3] Affichage de l'image sur l'écran distant (remote_display.py)`); + await runCommandLive( + '/home/pi3/LenslessPiCam/lensless_env/bin/python', + [ + 'scripts/measure/remote_display.py', + 'rpi.username=pi3', + 'rpi.hostname=128.179.187.191', + `fp=${captureDir}/uploaded.png`, + "'display.image_res=[900,900]'", + "'display.vshift=-25'" + ], + '/home/pi3/LenslessPiCam-Imane' + ); + console.log(`[3] Image affichée.`); + + // 5. Capture la photo via la caméra du Pi + console.log(`[4] Capture via la caméra du Pi (on_device_capture.py)`); + const baseArgs = [ + 'scripts/measure/on_device_capture.py', + 'sensor=rpi_hq', + 'bayer=True', + 'fn=capture', + ...(useAutoExposure ? ['exp=0.04', 'auto_exp_img=true'] : [`exp=${manualExposure}`]), + 'iso=100', + 'config_pause=2', + 'sensor_mode=0', + 'nbits_out=12', + 'legacy=True', + 'rgb=False', + 'gray=False', + 'sixteen=True' + ]; + + if (useAutoExposure) { + baseArgs.push('auto_exp_img=true');} // ← très important + + await runCommandLive( + '/home/pi3/LenslessPiCam/lensless_env/bin/python', + baseArgs, + '/home/pi3/LenslessPiCam-Imane' + ); + console.log(`[4] Capture terminée.`); + + // 6. Déplace la capture (raw) dans le dossier + console.log(`[5] Déplacement de la capture (raw_data.png)`); + await runCommandLive('mv', [ + 'capture.png', + `${captureDir}/raw_data.png` + ], '/home/pi3/LenslessPiCam-Imane'); + console.log(`[5] Déplacement terminé.`); + + // 7. Color-correct + const colorCorrectedPath = `${captureDir}/data_rgb.png`; + console.log(`[6] Correction couleur (analyze_image.py)`); + await runCommandLive( + '/home/pi3/LenslessPiCam/lensless_env/bin/python', + [ + 'scripts/measure/analyze_image.py', + '--fp', `${captureDir}/raw_data.png`, + '--bayer', + '--gamma', '2.2', + '--rg', '2.0', + '--bg', '1.2', + '--save', colorCorrectedPath + ], + '/home/pi3/LenslessPiCam-Imane' + ); + console.log(`[6] Correction couleur terminée.`); + + // 8. Reconstruction ADMM + const reconPath = `${captureDir}/reconstruction.png`; + const psfPath = `/home/pi3/LenslessPiCam-Imane/Psf/${psfChosen}/psf_rgb.png`; + console.log(`[7] Reconstruction ADMM avec PSF : ${psfPath}`); + let reconScript, reconArgs; + if (algorithm === "Gradient Descent") { + reconScript = 'scripts/recon/gradient_descent.py'; + reconArgs = [ + `input.data=${colorCorrectedPath}`, + `input.psf=${psfPath}`, + `gradient_descent.n_iter=${iterations}` + ]; + } else { + reconScript = 'scripts/recon/admm.py'; + reconArgs = [ + `input.data=${colorCorrectedPath}`, + `input.psf=${psfPath}`, + `admm.n_iter=${iterations}`, + `preprocess.downsample=2`, + `display.plot=true` + ]; + } + + const hydraOutputDir = await runCommandLive( + '/home/pi3/LenslessPiCam/lensless_env/bin/python', + [reconScript, ...reconArgs], + '/home/pi3/LenslessPiCam-Imane', + true + ); + + console.log(`[7] Reconstruction ADMM terminée.`); + if (hydraOutputDir) { + const reconSrc = path.join(hydraOutputDir, `${iterations}.png`); + await runCommandLive('mv', [reconSrc, reconPath], '/home/pi3/LenslessPiCam-Imane'); + + // 🧠 Smart cropping logic starts here + const Jimp = require('jimp'); + const image = await Jimp.read(reconPath); + + // Step 1: Remove white matplotlib border + const whiteMargin = 100; + if (image.bitmap.width > 2 * whiteMargin && image.bitmap.height > 2 * whiteMargin) { + image.crop( + whiteMargin, + whiteMargin, + image.bitmap.width - 2 * whiteMargin, + image.bitmap.height - 2 * whiteMargin + ); + } else { + console.warn(`⚠️ Skipping white border crop — image too small (${image.bitmap.width}x${image.bitmap.height})`); + } + + // Step 2: Histogram-based black crop + const w = image.bitmap.width; + const h = image.bitmap.height; + const brightnessThreshold = 30; + const pixelThreshold = 1; + + let top = 0, bottom = h - 1; + let left = 0, right = w - 1; + + for (let y = 0; y < h; y++) { + let count = 0; + for (let x = 0; x < w; x++) { + const idx = image.getPixelIndex(x, y); + const r = image.bitmap.data[idx + 0]; + const g = image.bitmap.data[idx + 1]; + const b = image.bitmap.data[idx + 2]; + const brightness = (r + g + b) / 3; + if (brightness > brightnessThreshold) count++; + } + if (count > pixelThreshold) { + top = y; + break; + } + } + + for (let y = h - 1; y >= 0; y--) { + let count = 0; + for (let x = 0; x < w; x++) { + const idx = image.getPixelIndex(x, y); + const r = image.bitmap.data[idx + 0]; + const g = image.bitmap.data[idx + 1]; + const b = image.bitmap.data[idx + 2]; + const brightness = (r + g + b) / 3; + if (brightness > brightnessThreshold) count++; + } + if (count > pixelThreshold) { + bottom = y; + break; + } + } + + for (let x = 0; x < w; x++) { + let count = 0; + for (let y = 0; y < h; y++) { + const idx = image.getPixelIndex(x, y); + const r = image.bitmap.data[idx + 0]; + const g = image.bitmap.data[idx + 1]; + const b = image.bitmap.data[idx + 2]; + const brightness = (r + g + b) / 3; + if (brightness > brightnessThreshold) count++; + } + if (count > pixelThreshold) { + left = x; + break; + } + } + + for (let x = w - 1; x >= 0; x--) { + let count = 0; + for (let y = 0; y < h; y++) { + const idx = image.getPixelIndex(x, y); + const r = image.bitmap.data[idx + 0]; + const g = image.bitmap.data[idx + 1]; + const b = image.bitmap.data[idx + 2]; + const brightness = (r + g + b) / 3; + if (brightness > brightnessThreshold) count++; + } + if (count > pixelThreshold) { + right = x; + break; + } + } + + const cropWidth = right - left + 1; + const cropHeight = bottom - top + 1; + + if (cropWidth > 0 && cropHeight > 0) { + image.crop(left, top, cropWidth, cropHeight); + await image.writeAsync(reconPath); + console.log(`✅ Smart-cropped to: ${cropWidth}x${cropHeight}`); + } else { + console.warn(`⚠️ Skipping: invalid crop area.`); + } + + } else { + throw new Error('No Hydra output directory detected!'); + } + // 9. Lis les images pour le frontend + console.log(`[8] Lecture des images pour le frontend`); + const imgCapture = fs.readFileSync(path.join(captureDir, 'data_rgb_8bit.png')); + const imgRecon = fs.readFileSync(reconPath); + console.log(`[8] Envoi des résultats au frontend 🎉`); + + res.json({ + imgCapture: imgCapture.toString('base64'), + imgRecon: imgRecon.toString('base64'), + captureId: captureId + }); + + } catch (err) { + console.error("[SERVER] Error in /run-full-imaging", err); + res.status(500).send({ error: 'Imaging workflow failed', details: err.toString() }); + } + }); + + app.post('/upload-photo', upload.single('photo'), async (req, res) => { + try { + const photoPath = path.join('/home/pi3/LenslessPiCam-Imane/PhoneUploads', 'latest.jpg'); + await runCommandLive('mv', [req.file.path, photoPath], '.'); + console.log(`[BACKEND] Received and stored photo at: ${photoPath}`); + res.send({ status: 'ok' }); + } catch (err) { + console.error('[SERVER] Error in /upload-photo:', err); + res.status(500).send({ error: 'Failed to upload photo' }); + } + }); + + app.get('/latest-photo', (req, res) => { + const filePath = '/home/pi3/LenslessPiCam-Imane/PhoneUploads/latest.jpg'; + if (!fs.existsSync(filePath)) { + return res.status(404).send("No photo uploaded yet."); + } + res.sendFile(filePath); + }); + + + app.post('/rerun-reconstruction', async (req, res) => { + const { captureId, psfName, iterations, algorithm = "ADMM" } = req.body; + + if (!captureId || !psfName || !iterations) { + return res.status(400).json({ error: "Missing parameters" }); + } + + const captureDir = path.join('/home/pi3/LenslessPiCam-Imane/Captures', captureId); + const psfPath = path.join('/home/pi3/LenslessPiCam-Imane/Psf', psfName, 'psf_rgb.png'); + const colorCorrectedPath = path.join(captureDir, 'data_rgb.png'); + const reconPath = path.join(captureDir, 'reconstruction.png'); + + try { + if (!fs.existsSync(colorCorrectedPath) || !fs.existsSync(psfPath)) { + return res.status(404).json({ error: "Required files not found" }); + } + + let reconScript, reconArgs; + if (algorithm === "Gradient Descent") { + reconScript = 'scripts/recon/gradient_descent.py'; + reconArgs = [ + `input.data=${colorCorrectedPath}`, + `input.psf=${psfPath}`, + `gradient_descent.n_iter=${iterations}` + ]; + } else { + reconScript = 'scripts/recon/admm.py'; + reconArgs = [ + `input.data=${colorCorrectedPath}`, + `input.psf=${psfPath}`, + `admm.n_iter=${iterations}`, + `preprocess.downsample=2`, + `display.plot=true` + ]; + } + + const hydraOutputDir = await runCommandLive( + '/home/pi3/LenslessPiCam/lensless_env/bin/python', + [reconScript, ...reconArgs], + '/home/pi3/LenslessPiCam-Imane', + true + ); + + + + if (!hydraOutputDir) { + return res.status(500).json({ error: "No Hydra output directory found" }); + } + + const newReconPath = path.join(hydraOutputDir, `${iterations}.png`); + await runCommandLive('mv', [newReconPath, reconPath], '/home/pi3/LenslessPiCam-Imane'); + + // 🧠 Smart cropping logic starts here + const Jimp = require('jimp'); + const image = await Jimp.read(reconPath); + + // Step 1: Remove white matplotlib border + const whiteMargin = 100; + if (image.bitmap.width > 2 * whiteMargin && image.bitmap.height > 2 * whiteMargin) { + image.crop( + whiteMargin, + whiteMargin, + image.bitmap.width - 2 * whiteMargin, + image.bitmap.height - 2 * whiteMargin + ); + } else { + console.warn(`⚠️ Skipping white border crop — image too small (${image.bitmap.width}x${image.bitmap.height})`); + } + + // Step 2: Histogram-based black crop + const w = image.bitmap.width; + const h = image.bitmap.height; + const brightnessThreshold = 25; + const pixelThreshold = 1; + + let top = 0, bottom = h - 1; + let left = 0, right = w - 1; + + for (let y = 0; y < h; y++) { + let count = 0; + for (let x = 0; x < w; x++) { + const idx = image.getPixelIndex(x, y); + const r = image.bitmap.data[idx + 0]; + const g = image.bitmap.data[idx + 1]; + const b = image.bitmap.data[idx + 2]; + const brightness = (r + g + b) / 3; + if (brightness > brightnessThreshold) count++; + } + if (count > pixelThreshold) { + top = y; + break; + } + } + + for (let y = h - 1; y >= 0; y--) { + let count = 0; + for (let x = 0; x < w; x++) { + const idx = image.getPixelIndex(x, y); + const r = image.bitmap.data[idx + 0]; + const g = image.bitmap.data[idx + 1]; + const b = image.bitmap.data[idx + 2]; + const brightness = (r + g + b) / 3; + if (brightness > brightnessThreshold) count++; + } + if (count > pixelThreshold) { + bottom = y; + break; + } + } + + for (let x = 0; x < w; x++) { + let count = 0; + for (let y = 0; y < h; y++) { + const idx = image.getPixelIndex(x, y); + const r = image.bitmap.data[idx + 0]; + const g = image.bitmap.data[idx + 1]; + const b = image.bitmap.data[idx + 2]; + const brightness = (r + g + b) / 3; + if (brightness > brightnessThreshold) count++; + } + if (count > pixelThreshold) { + left = x; + break; + } + } + + for (let x = w - 1; x >= 0; x--) { + let count = 0; + for (let y = 0; y < h; y++) { + const idx = image.getPixelIndex(x, y); + const r = image.bitmap.data[idx + 0]; + const g = image.bitmap.data[idx + 1]; + const b = image.bitmap.data[idx + 2]; + const brightness = (r + g + b) / 3; + if (brightness > brightnessThreshold) count++; + } + if (count > pixelThreshold) { + right = x; + break; + } + } + + const cropWidth = right - left + 1; + const cropHeight = bottom - top + 1; + + if (cropWidth > 0 && cropHeight > 0) { + image.crop(left, top, cropWidth, cropHeight); + await image.writeAsync(reconPath); + console.log(`✅ Smart-cropped to: ${cropWidth}x${cropHeight}`); + } else { + console.warn(`⚠️ Skipping: invalid crop area.`); + } + + + + const reconBuffer = fs.readFileSync(reconPath); + res.json({ recon: reconBuffer.toString('base64') }); + + } catch (err) { + console.error("[SERVER] Error in /rerun-reconstruction", err); + res.status(500).send({ error: "Reconstruction failed", details: err.toString() }); + } + }); + + + // Endpoint to list available PSFs (directories inside /Psf) + app.get('/list-psfs', (req, res) => { + const baseDir = '/home/pi3/LenslessPiCam-Imane/Psf'; + fs.readdir(baseDir, { withFileTypes: true }, (err, files) => { + if (err) { + console.error("[SERVER] Error listing PSFs:", err); + return res.status(500).json({ error: 'Could not read PSF directory' }); + } + // Filter for directories only + const psfList = files.filter(f => f.isDirectory()).map(f => f.name); + res.json({ psfs: psfList }); + }); + }); + + app.get('/load-psf/:psfname', (req, res) => { + const psfName = req.params.psfname; + const psfDir = path.join('/home/pi3/LenslessPiCam-Imane/Psf', psfName); + + const psfPath = path.join(psfDir, 'psf_rgb.png'); + const autocorrPath = path.join(psfDir, 'autocorrelation.png'); + + if (!fs.existsSync(psfPath) || !fs.existsSync(autocorrPath)) { + return res.status(404).json({ error: 'PSF or autocorrelation image not found' }); + } + + const psfBuffer = fs.readFileSync(path.join(psfDir, 'psf_rgb_8bit.png')); + const autocorrBuffer = fs.readFileSync(autocorrPath); + + res.json({ + psf: psfBuffer.toString('base64'), + autocorr: autocorrBuffer.toString('base64'), + }); + }); + + app.get('/list-captures', (req, res) => { + const baseDir = '/home/pi3/LenslessPiCam-Imane/Captures'; + fs.readdir(baseDir, { withFileTypes: true }, (err, files) => { + if (err) { + console.error("[SERVER] Error listing captures:", err); + return res.status(500).json({ error: 'Could not read Captures directory' }); + } + const captureList = files.filter(f => f.isDirectory()).map(f => f.name); + res.json({ captures: captureList }); + }); + }) + + app.get('/load-capture/:captureId', (req, res) => { + const { captureId } = req.params; + const captureDir = path.join('/home/pi3/LenslessPiCam-Imane/Captures', captureId); + let psfName = 'PSF_1mm'; // ⬅️ fallback default + + const rawPath = path.join(captureDir, 'data_rgb_8bit.png'); + const reconPath = path.join(captureDir, 'reconstruction.png'); + const uploadedPath = path.join(captureDir, 'uploaded.png'); + + if (!fs.existsSync(rawPath) || !fs.existsSync(reconPath)) { + return res.status(404).json({ error: 'Files not found in capture folder' }); + } + + const imgCapture = fs.readFileSync(rawPath); + const imgRecon = fs.readFileSync(reconPath); + let imgUpload = null; + if (fs.existsSync(uploadedPath)) { + imgUpload = fs.readFileSync(uploadedPath); + } + res.json({ + imgCapture: imgCapture.toString('base64'), + imgRecon: imgRecon.toString('base64'), + imgUpload: imgUpload ? imgUpload.toString('base64') : null,psfName + + }); + }); + + + + const archiver = require('archiver'); // ← if not yet added at the top + + app.get('/download-psf-zip/:psfname', (req, res) => { + const psfName = req.params.psfname; + console.log(`[BACKEND] Requested PSF ZIP: ${psfName}`); + const psfDir = path.join('/home/pi3/LenslessPiCam-Imane/Psf', psfName); + + if (!fs.existsSync(psfDir)) { + return res.status(404).send('PSF not found'); + } + + const zipName = `${psfName}_psf.zip`; + res.setHeader('Content-Disposition', `attachment; filename=${zipName}`); + res.setHeader('Content-Type', 'application/zip'); + + const archive = archiver('zip', { zlib: { level: 9 } }); + archive.pipe(res); + + ['psf_rgb_8bit.png', 'autocorrelation.png'].forEach(file => { + const fullPath = path.join(psfDir, file); + if (fs.existsSync(fullPath)) { + archive.file(fullPath, { name: file }); + } + }); + + archive.finalize(); + }); + + app.get('/download-capture-zip/:captureId', (req, res) => { + const captureId = req.params.captureId; + const captureDir = path.join('/home/pi3/LenslessPiCam-Imane/Captures', captureId); + + if (!fs.existsSync(captureDir)) { + return res.status(404).send("Capture folder not found."); + } + + const zipName = `capture_${captureId}.zip`; + res.setHeader('Content-Disposition', `attachment; filename=${zipName}`); + res.setHeader('Content-Type', 'application/zip'); + + const archive = archiver('zip', { zlib: { level: 9 } }); + archive.pipe(res); + + ['data_rgb_8bit.png', 'reconstruction.png'].forEach(file => { + const filePath = path.join(captureDir, file); + if (fs.existsSync(filePath)) { + archive.file(filePath, { name: file }); + } + }); + + archive.finalize(); +}); + + + +// HTTPS server +const key = fs.readFileSync(path.join(__dirname, '../certs/key.pem')); +const cert = fs.readFileSync(path.join(__dirname, '../certs/cert.pem')); + +https.createServer({ key, cert }, app).listen(PORT, '0.0.0.0', () => { + console.log(`Demo backend running on https://128.179.187.191:${PORT}`); +}); \ No newline at end of file diff --git a/lenslesswebapp/src/App.css b/lenslesswebapp/src/App.css new file mode 100644 index 00000000..e69de29b diff --git a/lenslesswebapp/src/App.tsx b/lenslesswebapp/src/App.tsx new file mode 100644 index 00000000..fcab17b0 --- /dev/null +++ b/lenslesswebapp/src/App.tsx @@ -0,0 +1,231 @@ +import { Link } from "react-router-dom"; +import { useEffect, useState } from "react"; + +export default function App() { + const [loaded, setLoaded] = useState(false); + const [showArticles, setShowArticles] = useState(false); + + useEffect(() => { + setTimeout(() => setLoaded(true), 100); + }, []); + + return ( +
+ {/* Navbar */} + + + {/* Hero Section */} +
+
+ Lensless PiCam Logo +
+
+

+ Lensless PiCam Kit +

+

+ All-in-One Starter Kit for Computational Imaging +

+ + Try our Online Demo + +
+
+ + {/* Why Lensless Section */} +
+

+ Why Lensless? +

+
+
+

+ Reduced size, weight & cost +

+

+ Lensless designs eliminate bulky optical components, making systems lighter, smaller and cheaper. +

+
+
+

+ Visual privacy +

+

+ Raw images captured are unintelligible to humans, offering native privacy by design. +

+
+
+

+ Compressive imaging +

+

+ Captures and reconstructs only what’s necessary, reducing data and enabling smarter sensing. +

+
+
+
+ + {/* About Section */} +
+

About the Project

+

+ The Lensless PiCam Kit is an affordable, modular development platform for lensless imaging. + It includes 3D-printable hardware, open-source software, and tools for integrating machine learning into the imaging pipeline. +

+
+ + {/* Documentation Section */} +
+

Documentation

+
+ {/* Medium Articles Section */} +
+

Medium Articles

+

+ Explore our insights and development stories on Medium. +

+ + {showArticles && ( + + )} +
+ + {/* Other Documentation Cards */} +
+

Software & Firmware

+

Arduino and Python code examples hosted on GitHub.

+ Browse GitHub +
+
+

Full Online Documentation

+

Setup, imaging, firmware flashing, ML integration, and deployment.

+ View Full Docs +
+
+
+ + {/* Product Section */} +
+

Our Product

+

+ Prototype kit for lensless imaging. Fixed price:{" "} + $100 +

+ + Discover Our Product + +
+ + {/* Footer */} + + + {/* Floating Animation */} + +
+ ); +} diff --git a/lenslesswebapp/src/DemoPage.tsx b/lenslesswebapp/src/DemoPage.tsx new file mode 100644 index 00000000..8e0cdb7a --- /dev/null +++ b/lenslesswebapp/src/DemoPage.tsx @@ -0,0 +1,884 @@ +import { useState, useRef, useEffect } from "react"; +import { Link } from "react-router-dom"; + + +const downloadImagingZip = async (captureId) => { + try { + const response = await fetch(`https://128.179.187.191:5000/download-capture-zip/${captureId}`); + if (!response.ok) throw new Error("Failed to fetch ZIP"); + + const blob = await response.blob(); + const link = document.createElement('a'); + link.href = URL.createObjectURL(blob); + link.download = `capture_${captureId}.zip`; + document.body.appendChild(link); + link.click(); + document.body.removeChild(link); + URL.revokeObjectURL(link.href); // cleanup + } catch (err) { + console.error("Imaging ZIP download failed:", err); + alert("Download failed"); + } +}; + + + + +export default function Demo() { + const [selectedFile, setSelectedFile] = useState(null); + const [imagePreview, setImagePreview] = useState(null); + const [showPSFModal, setShowPSFModal] = useState(false); + const [showImagingModal, setShowImagingModal] = useState(false); + const [showCamera, setShowCamera] = useState(false); + const [psfName, setPsfName] = useState(""); + const [algorithm, setAlgorithm] = useState("ADMM"); + const [iterations, setIterations] = useState(10); + const [psfList, setPsfList] = useState([]); + const [selectedPsf, setSelectedPsf] = useState(""); + const [rawLenslessImage, setRawLenslessImage] = useState(null); + const [reconImage, setReconImage] = useState(null); + const [enlargedImage, setEnlargedImage] = useState(null); + const [psfStatus, setPsfStatus] = useState(""); + const [imagingStatus, setImagingStatus] = useState(""); + const [showPsfDropdown, setShowPsfDropdown] = useState(false); + const [showIterationModal, setShowIterationModal] = useState(false); + const [newIterations, setNewIterations] = useState(iterations); + const [useAutoExposure, setUseAutoExposure] = useState(true); + const [manualExposure, setManualExposure] = useState(1); // default to 1s + const [showAlgorithmModal, setShowAlgorithmModal] = useState(false); + const [newAlgorithm, setNewAlgorithm] = useState(algorithm); + const [captureName, setCaptureName] = useState(""); + const [captureList, setCaptureList] = useState([]); + const [selectedCapture, setSelectedCapture] = useState(""); + const [showCaptureDropdown, setShowCaptureDropdown] = useState(false); // ✅ add this + + + + + + const fileInputRef = useRef(null); + const videoRef = useRef(null); + const canvasRef = useRef(null); + const streamRef = useRef(null); + + const [psfImage, setPsfImage] = useState(null); + const [autocorrImage, setAutocorrImage] = useState(null); + const handleLoadCapture = async (id) => { + try { + const res = await fetch(`https://128.179.187.191:5000/load-capture/${id}`); + if (!res.ok) throw new Error("Failed to load capture"); + const data = await res.json(); + setRawLenslessImage(`data:image/png;base64,${data.imgCapture}`); + setReconImage(`data:image/png;base64,${data.imgRecon}`); + sessionStorage.setItem("latestCaptureId", id); // set for download & rerun + if (data.psfName) { + setSelectedPsf(data.psfName); + sessionStorage.setItem("selectedPsf", data.psfName); + } + + if (data.imgUpload) { + const uploadUrl = `data:image/png;base64,${data.imgUpload}`; + setImagePreview(uploadUrl); // 🔍 for visual preview + + // Also keep File object in case user reruns reconstruction + const blob = await (await fetch(uploadUrl)).blob(); + const file = new File([blob], "loaded.png", { type: "image/png" }); + setSelectedFile(file); // ✅ Makes sure the backend can rerun with this + } + } catch (err) { + setImagingStatus("❌ Load failed."); + console.error(err); + } + }; + useEffect(() => { + if (showCamera && videoRef.current && streamRef.current) { + videoRef.current.srcObject = streamRef.current; + videoRef.current.play(); + console.log("Video is now playing via useEffect."); + } + }, [showCamera]); + + useEffect(() => { + fetch('https://128.179.187.191:5000/list-psfs') + .then(res => res.json()) + .then(data => setPsfList(data.psfs || [])) + .catch(err => { + setPsfList([]); + console.error('Could not load PSF list:', err); + }); +}, []); + + useEffect(() => { + fetch('https://128.179.187.191:5000/list-captures') + .then(res => res.json()) + .then(data => setCaptureList(data.captures || [])) + .catch(err => { + setCaptureList([]); + console.error('Could not load capture list:', err); + }); + }, []); + + + const handleFileChange = (e) => { + const file = e.target.files[0]; + setSelectedFile(file); + if (file) { + const reader = new FileReader(); + reader.onloadend = () => { + setImagePreview(reader.result); + }; + reader.readAsDataURL(file); + } + }; + + const handleCapture = () => { + setShowPSFModal(true); + }; + + const handleTakePicture = () => { + setShowImagingModal(true); + }; + + const handleRunCapture = () => { + setShowPSFModal(false); + + setPsfStatus("Capturing PSF image..."); + + fetch('https://128.179.187.191:5000/run-demo', { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ psfName: psfName.trim() }), + }) + .then((res) => { + setPsfStatus("Color correcting & computing autocorrelation..."); + return res.json(); + }) + .then((data) => { + setPsfImage(`data:image/png;base64,${data.psf}`); + setAutocorrImage(`data:image/png;base64,${data.autocorr}`); + setPsfStatus("Done."); + setTimeout(() => setPsfStatus(""), 3000); // nettoie après 3s + }) + .catch((err) => { + setPsfStatus("Error during PSF capture."); + console.error('Demo run failed:', err); + }); + }; + + + + + const handleRunImaging = async () => { + if (!selectedFile) { + alert("Please upload or capture an image!"); + return; + } + if (!selectedPsf) { + alert("Please select a PSF!"); + return; + } + + const formData = new FormData(); + formData.append('iterations', iterations); + formData.append('psfChosen', selectedPsf); + formData.append('image', selectedFile); + formData.append('useAutoExposure', useAutoExposure.toString()); + formData.append('manualExposure', manualExposure.toString()); + formData.append('algorithm', algorithm); + formData.append('captureName', captureName.trim()); + try { + setShowImagingModal(false); + setImagingStatus("Uploading and displaying image..."); + const response = await fetch('https://128.179.187.191:5000/run-full-imaging', { + method: 'POST', + body: formData, + }); + + if (!response.ok) throw new Error("Request failed"); + setImagingStatus("Capturing from camera, please wait..."); + const data = await response.json(); + setRawLenslessImage(`data:image/png;base64,${data.imgCapture}`); + setReconImage(`data:image/png;base64,${data.imgRecon}`); + sessionStorage.setItem("latestCaptureId", data.captureId); // ✅ Store capture ID + setImagingStatus("✅ Imaging complete."); + } catch (err) { + setImagingStatus("❌ Imaging failed."); + console.error(err); + } + }; + + const handleRerunReconstruction = async () => { + const captureId = sessionStorage.getItem("latestCaptureId"); + if (!captureId || !selectedPsf) { + alert("Missing capture ID or PSF."); + return; + } + setShowIterationModal(false); + try { + setImagingStatus(`Rerunning with ${newIterations} iterations...`); + + const res = await fetch("https://128.179.187.191:5000/rerun-reconstruction", { + method: "POST", + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ + captureId, + psfName: selectedPsf, + iterations: newIterations, + algorithm + }) + }); + + if (!res.ok) throw new Error("Reconstruction failed"); + + const data = await res.json(); + setReconImage(`data:image/png;base64,${data.recon}`); + setImagingStatus("✅ Reconstruction updated."); + setIterations(newIterations); // update main iteration value + setShowIterationModal(false); + } catch (err) { + setImagingStatus("❌ Reconstruction failed."); + console.error(err); + } + }; + + + + + const triggerUpload = () => { + fileInputRef.current.click(); + }; + + const triggerCamera = async () => { + console.log("Requesting webcam access..."); + try { + const stream = await navigator.mediaDevices.getUserMedia({ video: true }); + console.log("Webcam stream obtained:", stream); + streamRef.current = stream; + setShowCamera(true); + } catch (err) { + console.error("Error accessing webcam: ", err); + } + }; + + const takeSnapshot = () => { + const video = videoRef.current; + const canvas = canvasRef.current; + if (!video || !canvas) { + console.error("Video or canvas not available for snapshot."); + return; + } + const context = canvas.getContext("2d"); + canvas.width = video.videoWidth; + canvas.height = video.videoHeight; + context.drawImage(video, 0, 0, canvas.width, canvas.height); + const dataURL = canvas.toDataURL("image/png"); + setImagePreview(dataURL); + setShowCamera(false); + + // ✅ Convert dataURL to File and set selectedFile + fetch(dataURL) + .then(res => res.blob()) + .then(blob => { + const file = new File([blob], "captured.png", { type: "image/png" }); + setSelectedFile(file); + }); + + // ✅ Stop the webcam + if (video.srcObject) { + video.srcObject.getTracks().forEach((track) => track.stop()); + } + }; + + const handleRetrieveFromBackend = async () => { + try { + const res = await fetch('https://128.179.187.191:5000/latest-photo'); + if (!res.ok) throw new Error("Failed to retrieve photo"); + + const blob = await res.blob(); + const file = new File([blob], "phone_capture.png", { type: "image/png" }); + setSelectedFile(file); + + const reader = new FileReader(); + reader.onloadend = () => { + setImagePreview(reader.result); + }; + reader.readAsDataURL(file); + + setShowCamera(false); // just in case camera was active + } catch (err) { + alert("Could not retrieve selfie from phone."); + console.error(err); + } + }; + + + + + + return ( +
+ + +
+

Lensless Imaging Demo

+ +
+

PSF Calibration

+

+ {psfStatus && ( +

{psfStatus}

+ )} + + Link to tutorial for setting / placing PSF box +

+
+ + + + {showPsfDropdown && ( + + )} +
+
+ {/* PSF */} +
+ {!psfImage ? ( +
+ PSF +
+ ) : ( + PSF setEnlargedImage(psfImage)} + style={{ + cursor: 'zoom-in', + maxWidth: 500, + maxHeight: 500, + width: "100%", + borderRadius: "1.5rem", + boxShadow: "0 4px 24px #0008", + background: "transparent", + border: "2px solid #00ff99", + padding: "0.75rem" + }} + /> + )} +
+ + {/* Autocorrelation */} +
+ {!autocorrImage ? ( +
+ Autocorrelation +
+ ) : ( + Autocorrelation setEnlargedImage(autocorrImage)} + style={{ + cursor: 'zoom-in', + maxWidth: 500, + maxHeight: 500, + width: "100%", + borderRadius: "1.5rem", + boxShadow: "0 4px 24px #0008", + background: "transparent", + border: "2px solid #00ff99", + padding: "0.75rem" + }} + /> + )} +
+
+ +
+ +
+

Lensless Imaging

+ {imagingStatus && ( +

+ {imagingStatus} +

+ )} +
+ + + + + + {showCaptureDropdown && ( + + )} +
+ +
+ {/* Raw Lensless */} +
+ {!rawLenslessImage ? ( +
+ Raw Lensless +
+ ) : ( + Raw Lensless setEnlargedImage(rawLenslessImage)} + style={{ + cursor: 'zoom-in', + maxWidth: 500, + maxHeight: 500, + width: "100%", + borderRadius: "1.5rem", + boxShadow: "0 4px 24px #0008", + background: "transparent", + border: "2px solid #00ff99", + padding: "0.75rem" + }} + /> + )} +
+ + {/* Reconstruction */} +
+ {!reconImage ? ( +
+ Reconstruction +
+ ) : ( + Reconstruction setEnlargedImage(reconImage)} + style={{ + cursor: 'zoom-in', + maxWidth: 500, + maxHeight: 500, + width: "100%", + borderRadius: "1.5rem", + boxShadow: "0 4px 24px #0008", + background: "transparent", + border: "2px solid #00ff99", + padding: "0.75rem" + }} + /> + )} +
+
+ {rawLenslessImage && reconImage && ( +
+ + + +
+ )} + + + {imagePreview && ( +
+

Image Preview:

+ Preview +
+ )} +
+
+ + {showPSFModal && ( +
+
+

Name your PSF

+

+ Please include diameter, e.g. psf_1mm +

+ setPsfName(e.target.value)} placeholder="Enter PSF name" className="w-full px-4 py-2 rounded text-black mb-4" /> +
+ + +
+
+
+ )} + + {showAlgorithmModal && ( +
+
+

Change Algorithm

+
+ + +
+
+ + +
+
+
+ )} + + {showImagingModal && ( +
+
+

Taking a Picture

+
+ + + {!selectedPsf &&

Please select a PSF above.

} +
+ + +
+ + + +
+

Algorithm parameters

+
+ + +
+ + {!useAutoExposure && ( +
+ + setManualExposure(Number(e.target.value))} + className="w-full px-3 py-1 rounded text-black" + /> +
+ )} +
+ + +
+ + setIterations(Number(e.target.value))} className="w-full" /> +
+ + setCaptureName(e.target.value)} + placeholder="Enter folder name" + className="w-full px-4 py-2 rounded text-black" + /> +
+ + +
+
+ )} + + {showIterationModal && ( +
+
+

Change Iterations

+ setNewIterations(Number(e.target.value))} + className="w-full px-4 py-2 mb-4 rounded text-black" + /> +
+ + +
+
+
+ )} + + + {showCamera && ( +
+
+
+
+ )} + {enlargedImage && ( +
setEnlargedImage(null)} + > +
e.stopPropagation()} + > + + Enlarged +
+
+ )} + +
+ ); +} diff --git a/lenslesswebapp/src/PhoneCapturePage.tsx b/lenslesswebapp/src/PhoneCapturePage.tsx new file mode 100644 index 00000000..285d37fd --- /dev/null +++ b/lenslesswebapp/src/PhoneCapturePage.tsx @@ -0,0 +1,104 @@ +// src/PhoneCapturePage.tsx +import { useEffect, useRef, useState } from 'react'; +import { useNavigate } from 'react-router-dom'; + +export default function PhoneCapturePage() { + const videoRef = useRef(null); + const canvasRef = useRef(null); + const streamRef = useRef(null); + const [status, setStatus] = useState(''); + const navigate = useNavigate(); + + useEffect(() => { + async function initCamera() { + try { + const stream = await navigator.mediaDevices.getUserMedia({ video: true }); + streamRef.current = stream; + if (videoRef.current) { + videoRef.current.srcObject = stream; + videoRef.current.onloadedmetadata = () => { + videoRef.current?.play().catch(err => { + console.error("Video play() failed:", err); + }); + }; + } + + } catch (err) { + console.error("Camera access failed", err); + setStatus("Camera access denied."); + } + } + initCamera(); + + return () => { + if (streamRef.current) { + streamRef.current.getTracks().forEach((track) => track.stop()); + } + }; + }, []); + + const handleCaptureAndUpload = async () => { + const video = videoRef.current; + const canvas = canvasRef.current; + + if (!video || !canvas) return; + + const ctx = canvas.getContext('2d'); + canvas.width = video.videoWidth; + canvas.height = video.videoHeight; + ctx?.drawImage(video, 0, 0, canvas.width, canvas.height); + + const dataUrl = canvas.toDataURL('image/png'); + const blob = await (await fetch(dataUrl)).blob(); + const file = new File([blob], 'selfie.png', { type: 'image/png' }); + + const formData = new FormData(); + formData.append('photo', file); + + try { + setStatus("Uploading..."); + const res = await fetch('https://128.179.187.191:5000/upload-photo', { + method: 'POST', + body: formData, + }); + if (!res.ok) throw new Error("Upload failed"); + setStatus("✅ Uploaded successfully"); + setTimeout(() => navigate('/demo'), 2000); + } catch (err) { + console.error(err); + setStatus("❌ Upload failed"); + } + }; + + return ( +
+

Take a Selfie for Lensless Imaging

+
+ ); +} diff --git a/lenslesswebapp/src/ProductPage.tsx b/lenslesswebapp/src/ProductPage.tsx new file mode 100644 index 00000000..f2f4d965 --- /dev/null +++ b/lenslesswebapp/src/ProductPage.tsx @@ -0,0 +1,94 @@ +import { Link } from "react-router-dom"; + +export default function ProductPage() { + return ( +
+ + {/* Navbar */} + + + {/* Header */} +
+

+ Lensless PiCam Kit +

+

Modular, Affordable, and Open-Source

+
+ + {/* Content */} +
+ {/* Images */} +
+
Main Image
+
Thumb 1
+
Thumb 2
+
Thumb 3
+
+ + {/* Product Info */} +
+

$100.00

+

Available Now

+ +

+ The Lensless PiCam Kit includes all the hardware you need to begin experimenting with computational imaging: + a custom PCB, image sensor, microcontroller connectors, and full documentation. Compatible with Raspberry Pi and Arduino, + and designed to support real-time capture and machine learning processing. +

+ +
    +
  • Open-source and educational
    • +
  • Modular and extensible
    • +
  • Includes pre-trained models and firmware
    • +
  • Designed by LCAV – EPFL
    • +
+
+
+ + {/* Footer */} + +
+ ); +} diff --git a/lenslesswebapp/src/assets/react.svg b/lenslesswebapp/src/assets/react.svg new file mode 100644 index 00000000..6c87de9b --- /dev/null +++ b/lenslesswebapp/src/assets/react.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/lenslesswebapp/src/index.css b/lenslesswebapp/src/index.css new file mode 100644 index 00000000..b5c61c95 --- /dev/null +++ b/lenslesswebapp/src/index.css @@ -0,0 +1,3 @@ +@tailwind base; +@tailwind components; +@tailwind utilities; diff --git a/lenslesswebapp/src/main.tsx b/lenslesswebapp/src/main.tsx new file mode 100644 index 00000000..854af990 --- /dev/null +++ b/lenslesswebapp/src/main.tsx @@ -0,0 +1,22 @@ +import React from 'react' +import ReactDOM from 'react-dom/client' +import { BrowserRouter, Routes, Route } from 'react-router-dom' +import App from './App.tsx' +import ProductPage from './ProductPage.tsx' +import DemoPage from './DemoPage.tsx' +import PhoneCapturePage from './PhoneCapturePage.tsx'; + +import './index.css' + +ReactDOM.createRoot(document.getElementById('root')!).render( + + + + } /> + } /> + } /> + } /> + + + +) \ No newline at end of file diff --git a/lenslesswebapp/src/vite-env.d.ts b/lenslesswebapp/src/vite-env.d.ts new file mode 100644 index 00000000..11f02fe2 --- /dev/null +++ b/lenslesswebapp/src/vite-env.d.ts @@ -0,0 +1 @@ +/// diff --git a/lenslesswebapp/tailwind.config.js b/lenslesswebapp/tailwind.config.js new file mode 100644 index 00000000..d8f08734 --- /dev/null +++ b/lenslesswebapp/tailwind.config.js @@ -0,0 +1,7 @@ +export default { + content: ["./index.html", "./src/**/*.{js,ts,jsx,tsx}"], + theme: { + extend: {}, + }, + plugins: [], +} diff --git a/lenslesswebapp/tsconfig.app.json b/lenslesswebapp/tsconfig.app.json new file mode 100644 index 00000000..c9ccbd4c --- /dev/null +++ b/lenslesswebapp/tsconfig.app.json @@ -0,0 +1,27 @@ +{ + "compilerOptions": { + "tsBuildInfoFile": "./node_modules/.tmp/tsconfig.app.tsbuildinfo", + "target": "ES2020", + "useDefineForClassFields": true, + "lib": ["ES2020", "DOM", "DOM.Iterable"], + "module": "ESNext", + "skipLibCheck": true, + + /* Bundler mode */ + "moduleResolution": "bundler", + "allowImportingTsExtensions": true, + "verbatimModuleSyntax": true, + "moduleDetection": "force", + "noEmit": true, + "jsx": "react-jsx", + + /* Linting */ + "strict": true, + "noUnusedLocals": true, + "noUnusedParameters": true, + "erasableSyntaxOnly": true, + "noFallthroughCasesInSwitch": true, + "noUncheckedSideEffectImports": true + }, + "include": ["src"] +} diff --git a/lenslesswebapp/tsconfig.json b/lenslesswebapp/tsconfig.json new file mode 100644 index 00000000..1ffef600 --- /dev/null +++ b/lenslesswebapp/tsconfig.json @@ -0,0 +1,7 @@ +{ + "files": [], + "references": [ + { "path": "./tsconfig.app.json" }, + { "path": "./tsconfig.node.json" } + ] +} diff --git a/lenslesswebapp/tsconfig.node.json b/lenslesswebapp/tsconfig.node.json new file mode 100644 index 00000000..9728af2d --- /dev/null +++ b/lenslesswebapp/tsconfig.node.json @@ -0,0 +1,25 @@ +{ + "compilerOptions": { + "tsBuildInfoFile": "./node_modules/.tmp/tsconfig.node.tsbuildinfo", + "target": "ES2022", + "lib": ["ES2023"], + "module": "ESNext", + "skipLibCheck": true, + + /* Bundler mode */ + "moduleResolution": "bundler", + "allowImportingTsExtensions": true, + "verbatimModuleSyntax": true, + "moduleDetection": "force", + "noEmit": true, + + /* Linting */ + "strict": true, + "noUnusedLocals": true, + "noUnusedParameters": true, + "erasableSyntaxOnly": true, + "noFallthroughCasesInSwitch": true, + "noUncheckedSideEffectImports": true + }, + "include": ["vite.config.ts"] +} diff --git a/lenslesswebapp/vite.config.ts b/lenslesswebapp/vite.config.ts new file mode 100644 index 00000000..03e4b92e --- /dev/null +++ b/lenslesswebapp/vite.config.ts @@ -0,0 +1,14 @@ +import { defineConfig } from 'vite'; +import fs from 'fs'; +import path from 'path'; + +export default defineConfig({ + server: { + https: { + key: fs.readFileSync(path.resolve(__dirname, 'certs/key.pem')), + cert: fs.readFileSync(path.resolve(__dirname, 'certs/cert.pem')), + }, + host: true, + port: 5173, + }, +}); diff --git a/scripts/measure/analyze_image.py b/scripts/measure/analyze_image.py index 7496c4ab..9d3eaaf5 100644 --- a/scripts/measure/analyze_image.py +++ b/scripts/measure/analyze_image.py @@ -100,18 +100,38 @@ type=str, help="File name to save color correct bayer as RGB.", ) +@click.option( + "--save_auto", + is_flag=True, + help="Save autocorrelation instead of pop-up window.", +) @click.option( "--nbits", default=None, type=int, help="Number of bits for output. Only used for Bayer data", ) +@click.option( + "--down", + default=1, + type=int, + help="Factor by which to downsample.", +) @click.option( "--back", type=str, help="File path for background image, e.g. for screen.", ) -def analyze_image(fp, gamma, width, bayer, lens, lensless, bg, rg, plot_width, save, nbits, back): +@click.option( + "--gain_search", + type=int, + default=0, + help="Number of auto-gain iterations to run (0 = disable).", +) + +def analyze_image( + fp, gamma, width, bayer, lens, lensless, bg, rg, plot_width, save, save_auto, nbits, down, back, gain_search +): assert fp is not None, "Must pass file path." # initialize plotting axis @@ -121,6 +141,13 @@ def analyze_image(fp, gamma, width, bayer, lens, lensless, bg, rg, plot_width, s else: fig_gray, ax_gray = plt.subplots(ncols=2, nrows=1, num="Grayscale", figsize=(15, 5)) + + if bayer and gain_search > 0 : + return auto_gain_locally( + fp, gamma, width, bayer, lens, lensless, + plot_width, save, save_auto, nbits, down, back, + rg, bg, gain_search + ) # load PSF/image if lensless: img = load_psf( @@ -131,6 +158,7 @@ def analyze_image(fp, gamma, width, bayer, lens, lensless, bg, rg, plot_width, s red_gain=rg, nbits_out=nbits, return_float=False, + downsample=down, )[0] else: img = load_image( @@ -141,10 +169,10 @@ def analyze_image(fp, gamma, width, bayer, lens, lensless, bg, rg, plot_width, s red_gain=rg, nbits_out=nbits, back=back, + downsample=down, ) if nbits is None: nbits = int(np.ceil(np.log2(img.max()))) - # plot RGB and grayscale ax = plot_image(img, gamma=gamma, normalize=True, ax=ax_rgb[0]) ax.set_title("RGB") @@ -167,8 +195,9 @@ def analyze_image(fp, gamma, width, bayer, lens, lensless, bg, rg, plot_width, s plot_cross_section( img_grey, color="gray", plot_db_drop=width, ax=ax_gray[2], plot_width=plot_width ) - _, ax_cross = plt.subplots(ncols=3, nrows=1, num="RGB widths", figsize=(15, 5)) + fig_auto, ax_cross = plt.subplots(ncols=3, nrows=1, num="RGB widths", figsize=(15, 5)) for i, c in enumerate(["r", "g", "b"]): + print(f"-- {c} channel") ax, _ = plot_cross_section( img[:, :, i], color=c, @@ -181,18 +210,18 @@ def analyze_image(fp, gamma, width, bayer, lens, lensless, bg, rg, plot_width, s ax.set_ylabel("") elif lensless: - # plot autocorrelations and width # -- grey - _, ax_auto = plt.subplots(ncols=4, nrows=2, num="Autocorrelations", figsize=(15, 5)) + fig_auto, ax_auto = plt.subplots(ncols=4, nrows=2, num="Autocorrelations", figsize=(15, 5)) _, autocorr_grey = plot_autocorr2d(img_grey, ax=ax_auto[0][0]) + print("-- grayscale") plot_cross_section( autocorr_grey, color="gray", plot_db_drop=width, ax=ax_auto[1][0], plot_width=plot_width ) # -- rgb for i, c in enumerate(["r", "g", "b"]): _, autocorr_c = plot_autocorr2d(img[:, :, i], ax=ax_auto[0][i + 1]) - + print(f"-- {c} channel") ax, _ = plot_cross_section( autocorr_c, color=c, @@ -214,8 +243,75 @@ def analyze_image(fp, gamma, width, bayer, lens, lensless, bg, rg, plot_width, s save_image(img, save_8bit, normalize=True) print(f"\n8bit version saved to: {save_8bit}") - plt.show() + if save_auto: + auto_fp = os.path.join(os.path.dirname(fp), "autocorrelation.png") + fig_auto.savefig(auto_fp) + print(f"\nAutocorrelation saved to: {auto_fp}") + else: + plt.show() + +def fit_gains_by_quantiles(img, nbits=12): + """ + Fit RG/BG so that R and B match G over a dense set of mid-tail percentiles, + with a triangular weighting that emphasizes the shoulder (around 80%). + """ + # 10 percentiles from 0.65 to 0.95 + ps = np.linspace(0.65, 0.95, 10) + perc = (100 * ps).tolist() + + # compute percentiles for each channel + qs = {c: np.percentile(img[...,c], perc) for c in (0,1,2)} + qR, qG, qB = qs[0], qs[1], qs[2] + + # build triangular weights (peak at index 5, i.e. ~80%) + w = np.arange(1, 11) + w = np.minimum(w, w[::-1]).astype(float) # [1,2,3,4,5,5,4,3,2,1] + + wB = w.copy() + wB[-2:] *= 0.5 + + # least-squares with weights: gR = (w·qG·qR)/(w·qR·qR) + numR = np.dot(w * qG, qR) + denR = np.dot(w * qR, qR) + 1e-12 + numB = np.dot(w * qG, qB) + denB = np.dot(w * qB, qB) + 1e-12 + + gR = np.clip(numR/denR, 0.5, 2.5) + gB = np.clip( (numB/denB) * 0.8, 0.5, 2.5 ) + + # clamp to sensible bounds + return gR, gB + +def auto_gain_locally(fp, gamma, width, bayer, lens, lensless, + plot_width, save, save_auto, _nbits, down, back, + rg, bg, gain_search): + """ + One‐shot quantile‐fitting auto‐gain: + • Loads image once with unity gains (or supplied). + • Computes gR,gB by fitting 3 quantiles. + • Invokes final analysis with these gains. + """ + img0 = load_image( + fp, verbose=False, bayer=True, + red_gain=rg or 1.0, blue_gain=bg or 1.0, + nbits_out=12, return_float=False, + downsample=down, back=back + ) + + # fit via many quantiles + gR, gB = fit_gains_by_quantiles(img0) + + click.echo(f"[AutoGain] fitted gains → rg={gR:.3f}, bg={gB:.3f}") + # final one-shot + analyze_image.callback( + fp=fp, gamma=gamma, width=width, + bayer=bayer, lens=lens, lensless=lensless, + bg=gB, rg=gR, plot_width=plot_width, + save=save, save_auto=save_auto, + nbits=12, down=down, back=back, + gain_search=0 + ) if __name__ == "__main__": analyze_image() diff --git a/scripts/measure/on_device_capture.py b/scripts/measure/on_device_capture.py index 00c8c470..039c2a51 100644 --- a/scripts/measure/on_device_capture.py +++ b/scripts/measure/on_device_capture.py @@ -37,7 +37,8 @@ from lensless.hardware.sensor import SensorOptions, sensor_dict, SensorParam from fractions import Fraction import time - +import copy +from lensless.utils.io import load_image SENSOR_MODES = [ "off", @@ -53,7 +54,6 @@ ] -@hydra.main(version_base=None, config_path="../../configs", config_name="capture") def capture(config): sensor = config.sensor @@ -183,78 +183,80 @@ def capture(config): if bayer: camera = picamerax.PiCamera(framerate=1 / exp, sensor_mode=sensor_mode, resolution=res) - - # camera settings, as little processing as possible - camera.iso = iso - camera.shutter_speed = int(exp * 1e6) - camera.exposure_mode = "off" - camera.drc_strength = "off" - camera.image_denoise = False - camera.image_effect = "none" - camera.still_stats = False - - sleep(config_pause) - awb_gains = camera.awb_gains - camera.awb_mode = "off" - camera.awb_gains = awb_gains - - print("Resolution : {}".format(camera.resolution)) - print("Shutter speed : {}".format(camera.shutter_speed)) - print("ISO : {}".format(camera.iso)) - print("Frame rate : {}".format(camera.framerate)) - print("Sensor mode : {}".format(SENSOR_MODES[sensor_mode])) - # keep this as it needs to be parsed from remote script! - red_gain = float(awb_gains[0]) - blue_gain = float(awb_gains[1]) - print("Red gain : {}".format(red_gain)) - print("Blue gain : {}".format(blue_gain)) - - # capture data - stream = picamerax.array.PiBayerArray(camera) - camera.capture(stream, "jpeg", bayer=True) - - # get bayer data - if sixteen: - output = np.sum(stream.array, axis=2).astype(np.uint16) - else: - output = (np.sum(stream.array, axis=2) >> 2).astype(np.uint8) - - # returning non-bayer data - if rgb or gray: + try: + # camera settings, as little processing as possible + camera.iso = iso + camera.shutter_speed = int(exp * 1e6) + camera.exposure_mode = "off" + camera.drc_strength = "off" + camera.image_denoise = False + camera.image_effect = "none" + camera.still_stats = False + + sleep(config_pause) + awb_gains = camera.awb_gains + camera.awb_mode = "off" + camera.awb_gains = awb_gains + + print("Resolution : {}".format(camera.resolution)) + print("Shutter speed : {}".format(camera.shutter_speed)) + print("ISO : {}".format(camera.iso)) + print("Frame rate : {}".format(camera.framerate)) + print("Sensor mode : {}".format(SENSOR_MODES[sensor_mode])) + # keep this as it needs to be parsed from remote script! + red_gain = float(awb_gains[0]) + blue_gain = float(awb_gains[1]) + print("Red gain : {}".format(red_gain)) + print("Blue gain : {}".format(blue_gain)) + + # capture data + stream = picamerax.array.PiBayerArray(camera) + camera.capture(stream, "jpeg", bayer=True) + + # get bayer data if sixteen: - n_bits = 12 # assuming Raspberry Pi HQ + output = np.sum(stream.array, axis=2).astype(np.uint16) else: - n_bits = 8 - - if config.awb_gains is not None: - red_gain = config.awb_gains[0] - blue_gain = config.awb_gains[1] - - output_rgb = bayer2rgb_cc( - output, - nbits=n_bits, - blue_gain=blue_gain, - red_gain=red_gain, - black_level=RPI_HQ_CAMERA_BLACK_LEVEL, - ccm=RPI_HQ_CAMERA_CCM_MATRIX, - nbits_out=nbits_out, - ) - - if down: - output_rgb = resize( - output_rgb[None, ...], 1 / down, interpolation=cv2.INTER_CUBIC - )[0] - - # need OpenCV to save 16-bit RGB image - if gray: - output_gray = rgb2gray(output_rgb[None, ...]) - output_gray = output_gray.astype(output_rgb.dtype).squeeze() - cv2.imwrite(fn, output_gray) + output = (np.sum(stream.array, axis=2) >> 2).astype(np.uint8) + + # returning non-bayer data + if rgb or gray: + if sixteen: + n_bits = 12 # assuming Raspberry Pi HQ + else: + n_bits = 8 + + if config.awb_gains is not None: + red_gain = config.awb_gains[0] + blue_gain = config.awb_gains[1] + + output_rgb = bayer2rgb_cc( + output, + nbits=n_bits, + blue_gain=blue_gain, + red_gain=red_gain, + black_level=RPI_HQ_CAMERA_BLACK_LEVEL, + ccm=RPI_HQ_CAMERA_CCM_MATRIX, + nbits_out=nbits_out, + ) + + if down: + output_rgb = resize( + output_rgb[None, ...], 1 / down, interpolation=cv2.INTER_CUBIC + )[0] + + # need OpenCV to save 16-bit RGB image + if gray: + output_gray = rgb2gray(output_rgb[None, ...]) + output_gray = output_gray.astype(output_rgb.dtype).squeeze() + cv2.imwrite(fn, output_gray) + else: + cv2.imwrite(fn, cv2.cvtColor(output_rgb, cv2.COLOR_RGB2BGR)) else: - cv2.imwrite(fn, cv2.cvtColor(output_rgb, cv2.COLOR_RGB2BGR)) - else: - img = Image.fromarray(output) - img.save(fn) + img = Image.fromarray(output) + img.save(fn) + finally: + camera.close() else: @@ -271,31 +273,176 @@ def capture(config): # -- now set up camera with desired settings camera = PiCamera(framerate=1 / exp, sensor_mode=sensor_mode, resolution=tuple(res)) + try: + # Wait for the automatic gain control to settle + time.sleep(config.config_pause) - # Wait for the automatic gain control to settle - time.sleep(config.config_pause) + if config.awb_gains is not None: + assert len(config.awb_gains) == 2 + g = (Fraction(config.awb_gains[0]), Fraction(config.awb_gains[1])) + g = tuple(g) + camera.awb_mode = "off" + camera.awb_gains = g + time.sleep(0.1) + + print("Capturing at resolution: ", res) + print("AWB gains: ", float(camera.awb_gains[0]), float(camera.awb_gains[1])) + + try: + camera.resolution = tuple(res) + camera.capture(fn) + except ValueError: + raise ValueError( + "Out of resources! Use bayer for higher resolution, or increase `gpu_mem` in `/boot/config.txt`." + ) + finally: + camera.close() + - if config.awb_gains is not None: - assert len(config.awb_gains) == 2 - g = (Fraction(config.awb_gains[0]), Fraction(config.awb_gains[1])) - g = tuple(g) - camera.awb_mode = "off" - camera.awb_gains = g - time.sleep(0.1) + print("Image saved to : {}".format(fn)) - print("Capturing at resolution: ", res) - print("AWB gains: ", float(camera.awb_gains[0]), float(camera.awb_gains[1])) +def auto_expose_psf_locally(config): + import copy + import numpy as np - try: - camera.resolution = tuple(res) - camera.capture(fn) - except ValueError: - raise ValueError( - "Out of resources! Use bayer for higher resolution, or increase `gpu_mem` in `/boot/config.txt`." - ) + max_iter = 10 + exp = config.exp + tested_exposures = [] + + min_exp = sensor_dict[config.sensor][SensorParam.MIN_EXPOSURE] + max_exp = sensor_dict[config.sensor][SensorParam.MAX_EXPOSURE] + + i = 0 + while i < max_iter: + print(f"\\n[Auto Exposure] Attempt {i+1} | exp = {exp:.6f}s") + tested_exposures.append(exp) + + # Clone config and update exposure + config_local = copy.deepcopy(config) + config_local.exp = exp + + # Capture image + capture(config_local) + fn = config.fn + ".png" + img = load_image(fn, verbose=False, bayer=config.bayer, + blue_gain=None, red_gain=None, nbits_out=config.nbits_out) + + if img is None: + print(f" Failed to load image from {fn}") + break + + # Histogram analysis + max_val = img.max() + hist, _ = np.histogram(img, bins=range(4097)) + val_4095 = hist[4095] + val_3000_4000 = next((hist[v] for v in range(3000, 4000) if hist[v] > 0), 0) + + print(f" Max: {max_val} | hist[4095]: {val_4095} | first non-zero hist[3000–4000]: {val_3000_4000}") + + # --- Stopping condition --- + if max_val == 4095 and val_4095 <= 3 * val_3000_4000: + print(f" Good exposure at exp = {exp:.6f}s") + break + + # --- Stop if at min exposure and still saturated --- + if exp <= min_exp and max_val == 4095: + print(f" Reached minimum exposure ({exp:.6f}s) and still saturated. Stopping.") + break + + # --- Fine-tuning adjustment --- + if max_val == 4095 and val_4095 <= 20: + print("Fine-tuning: small saturation, slightly reducing exposure") + exp *= 0.95 + # --- General adjustment --- + elif max_val < 4095: + exp *= 1.4 + else: + exp *= 0.8 + + # Clamp to sensor limits + exp = min(max(exp, min_exp), max_exp) + + i += 1 + + print(f"Final exposure used: {exp:.6f}s") + +def auto_expose_image_locally(config): + import copy + import numpy as np + + max_iter = 10 + exp = config.exp + tested_exposures = [] + + min_exp = sensor_dict[config.sensor][SensorParam.MIN_EXPOSURE] + max_exp = sensor_dict[config.sensor][SensorParam.MAX_EXPOSURE] + + i = 0 + while i < max_iter: + print(f"\n[Auto Exposure - Image] Attempt {i+1} | exp = {exp:.6f}s") + tested_exposures.append(exp) + + config_local = copy.deepcopy(config) + config_local.exp = exp + + capture(config_local) + fn = config.fn + ".png" + img = load_image(fn, verbose=False, bayer=config.bayer, + blue_gain=None, red_gain=None, nbits_out=config.nbits_out) + + if img is None: + print(f" Failed to load image from {fn}") + break + + max_val = img.max() + hist, _ = np.histogram(img, bins=range(4097)) + print(f" Max: {max_val} | hist[4095]: {hist[4095]}") + + # Condition 1: max between 3900–4080 + if 3900 <= max_val <= 4080: + print(f" Accepted exposure at exp = {exp:.6f}s (max in range)") + break + + # Condition 2: max == 4095 and contrast is good + if max_val == 4095: + for v in range(1000, 2001): + if hist[v] > 0 and hist[4095] <= 3 * hist[v]: + print(f" Accepted: hist[4095] = {hist[4095]} <= 3 × hist[{v}] = {hist[v]}") + break + else: + # condition not satisfied → decrease exposure + print(" Max = 4095 but low contrast → decreasing exposure") + exp *= 0.85 + exp = max(exp, min_exp) + i += 1 + continue + break + + # 🔼 Increase if too dark + if max_val < 3900: + print(" Too dark → increasing exposure") + exp *= 1.4 + else: + print(" Too bright → decreasing exposure") + exp *= 0.8 + + # Clamp to bounds + exp = min(max(exp, min_exp), max_exp) + i += 1 + + print(f"[Auto Exposure - Image] Final exposure used: {exp:.6f}s") - print("Image saved to : {}".format(fn)) +@hydra.main(version_base=None, config_path="../../configs", config_name="capture") +def main(config): + if getattr(config, "auto_exp_psf", False): + auto_expose_psf_locally(config) + elif getattr(config, "auto_exp_img", False): + auto_expose_image_locally(config) + else: + capture(config) + if __name__ == "__main__": - capture() + main() + diff --git a/scripts/measure/remote_capture.py b/scripts/measure/remote_capture.py index a38f722a..a9b0b60c 100644 --- a/scripts/measure/remote_capture.py +++ b/scripts/measure/remote_capture.py @@ -263,4 +263,4 @@ def liveview(config): if __name__ == "__main__": - liveview() + liveview() \ No newline at end of file