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‎README.md

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# Single-Pixel Acquisition Software (SPAS) - Python version
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# Single-Pixel Acquisition Software (SPAS)
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A python toolbox for acquisition of images based on the single-pixel framework.
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It has been tested using a Digital Light Processor [DLP7000](https://www.vialux.de/en/hi-speed-v-modules.html) from ViALUX GmbH and a Spectrometer [AvaSpec-ULS2048CL-EVO](https://www.avantes.com/products/spectrometers/starline/avaspec-uls2048cl-evo/) from Avantes, but may as well work for similar equipment with a few minor changes.
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SPAS is python package designed for single-pixel acquisition.
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## Installation (Windows only)
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SPAS has been tested for controlling a [DLP7000](https://www.vialux.de/en/hi-speed-v-modules.html) Spatial Light Modulator and an [AvaSpec-ULS2048CL-EVO](https://www.avantes.com/products/spectrometers/starline/avaspec-uls2048cl-evo/) spectrometer. It should work as well for for similar equipment with a few changes.
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1. Create a new environment (tested under conda)
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SPAS is a companion package to the [SPyRiT](https://github.com/openspyrit/spyrit) package.
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```powershell
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conda create --name my_spas_env
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conda activate my_spas_env
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conda install -c anaconda pip
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```
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2. Install the [SPyRiT](https://github.com/openspyrit/spyrit) package (tested with version 1.0.0). Typically
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# Installation
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The SPAS package can be installed on Linux, MacOs and Windows. However, it will be fully functional on Windows only due to DLL dependencies required for harware control.
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```powershell
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pip install requests torch==1.8.0+cpu torchvision==0.9.0+cpu -f https://download.pytorch.org/whl/torch_stable.html
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pip install spyrit==1.0.0
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```
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We recommend using a virtual environment.
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2. Clone the SPAS repository
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* Clone the SPAS repository
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```powershell
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git clone [email protected]:openspyrit/spas.git
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pip install -e .
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```
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3. Add DLLs
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* Add DLLs (optional, for instrumentation control only)
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The following dynamic-link libraries (DLLs) are required
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The following dynamic-link libraries (DLLs) were required to control our instrumentation
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* `avaspecx64.dll` provided by your Avantes distributor
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* `alpV42.dll` available [here](https://www.vialux.de/en/hi-speed-download.html) by installing the entire ALP4 library
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* `avaspecx64.dll` provided by your Avantes distributor
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* `alpV42.dll` available [here](https://www.vialux.de/en/hi-speed-download.html) by installing the entire ALP4 library
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They should be placed inside the `lib` folder
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4. The typical directory structure is
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* The DLLs should be placed inside the `lib` folder. The typical directory structure is
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```
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├───lib
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│ ├───Cov_64x64.npy
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```
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# API Documentation
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https://spas.readthedocs.io/
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# Contributors (alphabetical order)
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* Thomas Baudier
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* Nicolas Ducros - [Website](https://www.creatis.insa-lyon.fr/~ducros/WebPage/index.html)
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* Laurent Mahieu Williame
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# How to cite?
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When using SPAS in scientific publications, please cite the following paper:
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* G. Beneti-Martin, L Mahieu-Williame, T Baudier, N Ducros, "OpenSpyrit: an Ecosystem for Reproducible Single-Pixel Hyperspectral Imaging," Optics Express, Vol. 31, No. 10, (2023). https://doi.org/10.1364/OE.483937.
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# License
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This project is licensed under the LGPL-3.0 license - see the [LICENSE.md](LICENSE.md) file for details
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# Getting started
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## Preparation (just once)
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### 1. Creating Walsh-Hadamard patterns
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plot_color(rec_bin, wavelengths_bin)
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```
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* Measurements are saved in the disk (fully sampled)
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* Measurements are saved on the disk (fully sampled)
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Reconstruct the measurements saved as `../meas/my_first_measurement`.
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```
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## Reconstruction with a Neural Network
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* Measurements are on the disk (fully-sampled here, works too with `pattern_compression=.25`)
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* We consider an existing acquisition that was saved on the disk in the `../meas/` folder
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Read the data:
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``` python
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_, acquisition_parameters, _, _ = read_metadata('../meas/my_first_measurement' + '_metadata.json')
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```
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* We have access to a trained network:
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* We consider that we have access to a trained network and the covariance matrix associated to it.
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An example network can be downloaded [here](https://www.creatis.insa-lyon.fr/~ducros/spyritexamples/2021_ISTE/NET_c0mp_N0_50.0_sig_0.0_Denoi_N_64_M_1024_epo_40_lr_0.001_sss_20_sdr_0.2_bs_256_reg_1e-07.pth) , which you can save in `./models/`. It allows to reconstruction images from only 1024 hadamard coefficients (i.e., 2048 raw measurements):
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An example network can be downloaded [here](https://pilot-warehouse.creatis.insa-lyon.fr/#collection/6140ba6929e3fc10d47dbe3e/folder/622b5ea843258e76eab21740). It allows the reconstruction of a 128 x 128 image from only 4096 Hadamard coefficients (i.e., 8192 raw measurements) that correspond to a full acquisition at a 64 x 64 resolution. Its associated covariance matrix can be downloaded [here](https://pilot-warehouse.creatis.insa-lyon.fr/#collection/6140ba6929e3fc10d47dbe3e/folder/63d7f3620386da2747641e1b).
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``` python
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from spas import ReconstructionParameters, setup_reconstruction
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network_params = ReconstructionParameters(
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img_size=64,
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CR=1024,
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denoise=True,
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epochs=40,
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learning_rate=1e-3,
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step_size=20,
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gamma=0.2,
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batch_size=256,
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regularization=1e-7,
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N0=50.0,
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sig=0.0,
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arch_name='c0mp',)
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cov_path = '../stats/Cov_64x64.npy'
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mean_path = '../stats/Average_64x64.npy'
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model_root = '../models/'
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import spyrit.misc.walsh_hadamard as wh
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H = wh.walsh2_matrix(64)/64
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model, device = setup_reconstruction(cov_path, mean_path, H, model_root, network_params)
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network_param = ReconstructionParameters(
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# Reconstruction network
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M = 64*64, # Number of measurements
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img_size = 128, # Image size
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arch = 'dc-net', # Main architecture
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denoi = 'unet', # Image domain denoiser
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subs = 'rect', # Subsampling scheme
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# Training
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data = 'imagenet', # Training database
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N0 = 10, # Intensity (max of ph./pixel)
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# Optimisation (from train2.py)
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num_epochs = 30, # Number of training epochs
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learning_rate = 0.001, # Learning Rate
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step_size = 10, # Scheduler Step Size
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gamma = 0.5, # Scheduler Decrease Rate
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batch_size = 256, # Size of the training batch
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regularization = 1e-7 # Regularisation Parameter
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)
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cov_path = '../stat/Cov_8_128x128.npy'
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model_folder = '../model/'
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model, device = setup_reconstruction(cov_path, model_folder, network_param)
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```
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Load noise calibration parameters (provided with the data or computed using tools in `/noise-calibration`)
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Load noise calibration parameters (provided with the data or computed using tools in `/noise-calibration`). :warning: Noise parameters are not used anymore in the current implementation of `spas`.
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from spas import load_noise
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noise = load_noise('../noise-calibration/fit_model.npz')
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Bin before reconstruction and plot
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Bin the spectral measurements (here, 4 bins between 530 nm and 730 nm)
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``` python
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from spas import spectral_binning
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meas_bin, wavelengths_bin, _, noise_bin = spectral_binning(meas.T, acquisition_parameters.wavelengths, 530, 730, 8, noise)
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meas_bin, wavelengths_bin, _ = spectral_binning(meas.T, wavelengths, 530, 730, 4)
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```
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Reorder and subsample the spectral measurements
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``` python
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from spas.reconstruction_nn import reorder_subsample
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meas_bin_2 = reorder_subsample(meas_bin, acquisition_param, network_param)
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```
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Reconstruct the spectral images
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``` python
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from spas import reconstruct
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rec = reconstruct(model, device, meas_bin_2)
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```
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Reconstruction and plot
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Plot the spectral images
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``` python
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from spas import reconstruct, plot_color
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rec = reconstruct(model, device, meas_bin[0:8192//4,:], 1, noise_bin)
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from spas import plot_color
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plot_color(rec, wavelengths_bin)
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```

‎license.md

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GNU LESSER GENERAL PUBLIC LICENSE
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Version 3, 29 June 2007
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Copyright (C) 2007 Free Software Foundation, Inc. <http://fsf.org/>
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Everyone is permitted to copy and distribute verbatim copies
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of this license document, but changing it is not allowed.
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This version of the GNU Lesser General Public License incorporates
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the terms and conditions of version 3 of the GNU General Public
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License, supplemented by the additional permissions listed below.
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0. Additional Definitions.
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As used herein, "this License" refers to version 3 of the GNU Lesser
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