Structured light project

This project is an attempt to create an easy-to-understand and flexible-to-use framework for implementing the Fringe Projection Profilometry (FPP) method in the Python.

Implementation

Ability to use any camera (modules with webcams through OpenCV and Baumer cameras through NeoAPI are implemented in the project)
The Phaseshift Projection Profilometry (PSP) method with sinusoidal fringes is implemented to obtain phase fields
Projection pattern generation supports an arbitrary number of phase shifts and an arbitrary number of periods
A hierarchical approach is used to unwrap the phase fields
Implemented automatic detection of the fringe projection area on the images (ROI)
A simple gamma correction method for projected images is implemented
Flexible adjustment of the experiment and hardware parameters with the help of config files

How to use

Install depedicies

pip install opencv-contrib-python numpy scipy matplotlib

Setting the parameters of the experiment and the hardware in the file config.py
Launch main module

python main.py

In the script examples/test_plate_phasogrammetry.py there is an example of processing the results of the experiment to determine the shape of the surface of a granite slab using the phasogrammetric approach. To date, the measurement accuracy of about 60 µm has been achieved.

References

The following sources were used to implement the algorithms

Zuo C. et al. Phase shifting algorithms for fringe projection profilometry: A review // Optics and Lasers in Engineering. 2018. Vol. 109. P. 23-59.

Zuo C. et al. Temporal phase unwrapping algorithms for fringe projectionprofilometry: A comparative review // Optics and Lasers in Engineering. 2016. Vol. 85. P. 84-103.

Feng S. et al. Calibration of fringe projection profilometry: A comparative review // Optics and Lasers in Engineering. 2021. Vol. 143. P. 106622.

Zhong K. et al. Pre-calibration-free 3D shape measurement method based on fringe projection // Optics Express. 2016. Vol. 24. №. 13. P. 14196-14207.

Authors

Anton Poroykov, Ph.D., associated professor

Nikita Sivov, graduate student

Acknowledgements

The research was carried out at the expense of the grant Russian Science Foundation No. 22-21-00550 (https://rscf.ru/project/22-21-00550/).

helleb0re/structured-light-python

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