English · العربية · Español · Français · 日本語 · 한국어 · Tiếng Việt · 中文 (简体) · 中文（繁體） · Deutsch · Русский

Lazeal OptiX

🌐 Multilingual status: i18n/ is present and reserved for language-specific README files. Linked localized documents are planned/in-progress.

✨ At a Glance

Focus	Location
Core workflow	notebooks/
Environment spec	notebooks/reconstruction/lensless.yaml
Component notes	camera/, light_source/, reconstruction/, three_axis_cnc/
Entry docs	i18n/README.*.md

Prototype for Individual Use (left) and Institutional Use (right)

Overview

Lazeal OptiX is a research/prototype project for lensless imaging workflows in healthcare-adjacent diagnostics. The repository is currently notebook-centric and experimental, and is intended to make advanced diagnostic approaches more accessible in constrained settings.

Core ideas include:

• lensless image reconstruction,
• light source localization,
• multiple-image matching and alignment.

The repository is primarily maintained through Jupyter notebooks under notebooks/, with module-specific context stored in dedicated directories.

Repository Status Snapshot

Area	Current Status
Project maturity	Research prototype
Primary execution model	Jupyter notebook workflows
Main experiment domains	Reconstruction, light source localization, multiple-image matching
Packaging/CI at root	Not currently declared
Multilingual docs	i18n/ directory scaffold exists

Features

1. Advanced Microscopy Concepts: advanced optics and image capture patterns for detailed analysis.
2. Biochemical / Diagnostic Context: experimental workflows aimed at health-indicator detection.
3. Home-friendly Direction: designed for accessible use and practical deployment.
4. Laptop-first Experience: notebooks provide the primary execution path.
5. Lensless Reconstruction Utilities: computational pipelines for high-resolution reconstruction.
6. Light Source Localization Tools: experiments on source localization and geometry calibration.
7. Multiple Image Matching: SIFT-based matching, chaining, and alignment utilities.

Project Structure

lazealoptix/
├── README.md
├── prototype_individual.jpg
├── prototype_institute.png
├── figs/
│   ├── banner.svg|png
│   ├── logo.svg|png
│   └── logo-w-text.svg|png
├── camera/
│   └── README.md
├── light_source/
│   └── README.md
├── reconstruction/
│   └── README.md
├── three_axis_cnc/
│   └── README.md
├── notebooks/
│   ├── light_source_location/
│   │   ├── light_source_location_estimator_v1.4.ipynb
│   │   ├── light_source_location_estimator_varied_heights_v1.1.4.ipynb
│   │   └── light_source_location_estimator_varied_heights_v1.1.7.ipynb
│   ├── multiple_match/
│   │   ├── multiple_all_combination_v2.ipynb
│   │   ├── multiple_match.cpp
│   │   ├── multiple_match_centeralized_v1.6.ipynb
│   │   └── multiple_match_chain_v1.5.ipynb
│   └── reconstruction/
│       ├── dataset_prep.ipynb
│       ├── lensless.yaml
│       └── lensless-dropout-one-led-mahuichong.ipynb
└── i18n/

Module Notes

• camera/: scripts/resources related to camera usage for high-resolution sample capture.
• light_source/: scripts/resources for light source control and optimization.
• reconstruction/: scripts/resources for computational reconstruction.
• three_axis_cnc/: scripts/resources for three-axis CNC positioning/control.
• notebooks/: primary technical workspace for experiments and methods.

Notebooks

The notebooks directory contains Jupyter notebooks that document the core experimental methods. These notebooks provide code, visualizations, and method notes for each area.

light_source_location

Contains notebooks related to estimation of light source locations. These methods support source geometry calibration and reconstruction fidelity.

multiple_match

Contains notebooks and scripts for image/pattern matching and alignment to support robust registration workflows.

reconstruction

Contains notebooks related to reconstruction from captured images, including preprocessing and experiment scripts.

Prerequisites

• OS: Linux/macOS recommended for current Conda and OpenCV workflows.
• Python: environment targets Python 3.7.
• Conda: needed to reproduce the documented lensless environment.
• Jupyter Notebook/Lab.
• Optional C++ toolchain for multiple_match.cpp:
  • g++ with C++17 support.
  • OpenCV 4.x with contrib modules (opencv2/xfeatures2d.hpp / SIFT).

Installation

1) Clone

git clone https://github.com/lachlanchen/lazealoptix.git
cd lazealoptix

2) Create notebook environment

conda env create -f notebooks/reconstruction/lensless.yaml
conda activate lensless

3) Start Jupyter

jupyter notebook

Usage

This repository is primarily used by opening notebooks and running cells in documented order.

Reconstruction track

• Open notebooks/reconstruction/dataset_prep.ipynb for dataset preparation.
• Open notebooks/reconstruction/lensless-dropout-one-led-mahuichong.ipynb for reconstruction/training experiments.

Light source localization track

• Open notebooks under notebooks/light_source_location/.

Multiple match track

• Open notebooks under notebooks/multiple_match/.
• Optional utility: notebooks/multiple_match/multiple_match.cpp.

Configuration

Conda environment

Primary environment specification:

• notebooks/reconstruction/lensless.yaml

Notable dependencies include:

• python=3.7
• pytorch=1.9.0
• pyro-ppl
• opencv-adjacent computer vision workflow dependencies in notebooks

Data and paths

• Assumption: datasets are local and are not centrally declared at repository root.
• Assumption: the C++ matching utility expects an all/ directory (relative to its execution path) containing grayscale-readable images.

If your local setup differs, update notebook path cells and the C++ input directory accordingly.

Examples

Run the matching utility

cd notebooks/multiple_match
g++ -std=c++17 multiple_match.cpp -o multiple_match `pkg-config --cflags --libs opencv4`
./multiple_match

Expected behavior:

• Reads images from all/
• Computes chained SIFT-based matches across images
• Writes an output image like result_<timestamp>.png

Launch a specific notebook

conda activate lensless
jupyter notebook notebooks/reconstruction/dataset_prep.ipynb

Development Notes

• No root-level packaging manifest (pyproject.toml, requirements.txt, setup.py) or CI/test harness is currently present.
• Work is experiment-first; notebooks are the source-of-truth for current algorithms.
• camera/, light_source/, reconstruction/, and three_axis_cnc/ contain component-level descriptions and are good extension points for runbooks.
• i18n/ is prepped for language-specific documentation.

Troubleshooting

• Conda solve issues: update Conda, verify channel ordering, and retry environment creation.
• Kernel mismatch in notebooks: confirm Jupyter is using the lensless environment.
• OpenCV/SIFT compile errors: install OpenCV contrib modules and validate opencv2/xfeatures2d.hpp availability.
• Notebook file-not-found errors: verify expected datasets and notebook-relative paths.
• Matcher reads no images: ensure notebooks/multiple_match/all/ exists with valid image files.

Roadmap

• Expand module-level runbooks in camera/, light_source/, reconstruction/, and three_axis_cnc/.
• Document dataset contracts and provide reproducible sample-data references.
• Add script wrappers for major notebook pipelines.
• Add validation checks for reconstruction and matching outputs.
• Complete multilingual README files under i18n/.

Getting Involved

We welcome collaboration and contributions.

• Open an issue for discussion.
• Submit a pull request for scoped documentation or experimental changes.
• Contact maintainers for hardware and protocol-level changes before large refactors.

Contributing

1. Fork the repository.
2. Create a feature branch.
3. Keep changes scoped and documented (especially for notebooks).
4. Open a pull request with motivation, method, and any validation notes.

❤️ Support

Donate	PayPal	Stripe

License

No license file is currently present in the repository root.

Assumption/Action needed: add a LICENSE file and update this section with the exact SPDX identifier.

Contact

For further inquiries or collaboration interests, please reach out at contact@lazealoptix.com.