Free-LSPIV: A simple image velocimetry tool


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Created: Feb 06, 2020 at 1:20 p.m.
Last updated: Aug 13, 2021 at 7:27 p.m.
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Abstract

This is an image velocimetry tool based on LSPIV methodology. This tool is able of applying image velocimetry directly on videos. It runs on MATLAB (compatible also with Octave version ≥ 5.1).

QUICK START

- Start MATLAB/Octave and go into the folder of the normxcorr2_mex file.
- _Notes.m gives commands to perform (do not run it, copy paste only).
- Slightly different commands for MATLAB/Octave, see comments in _Notes.m.

REFERENCES

Rozos, E., Dimitriadis, P., Mazi, K., Lykoudis, S. and Koussis, A., 2020. Application of a Simple Image Velocimetry Algorithm in hydrometry.

Rozos, E., Dimitriadis, P., Mazi, K., Lykoudis, S. and Koussis, A., 2020. On the Uncertainty of the Image Velocimetry Method Parameters. Hydrology, 7(3), p.65.

Rozos E, Mazi K, Koussis AD. Probabilistic Evaluation and Filtering of Image Velocimetry Measurements. Water. 2021; 13(16):2206. https://doi.org/10.3390/w13162206

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How to Cite

Rozos, E. (2021). Free-LSPIV: A simple image velocimetry tool, HydroShare, http://www.hydroshare.org/resource/e713f959ad564adf8acacf1687250ea0

This resource is shared under the Creative Commons Attribution CC BY.

 http://creativecommons.org/licenses/by/4.0/
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Comments

Evangelos Rozos 1 year ago

A new version has been uploaded. It employs a third party normxcorr2 to achieve much better performance. Hopefully, it will run without requiring any compilation. If not, the file 'normxcorr2_mex.cpp' inside 'lib' folder should be compiled (see comments in this file).

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William T 1 year ago

Hi Evangelos, thanks for the update it now works perfectly. Your code indeed is much faster (1 minute for your code to run the example, 5 minutes for PIVlab to analyze the same region of interest). However, PIVlab can only use quadratic interrogation areas, therefore it needs to do about 5 times more cross-correlations.
The results look pretty different however:
https://drive.google.com/drive/folders/140A13-c30b5Pgjck_og0mmxUDs-C4cU8?usp=sharing
Note that I did not calibrate or transform the input images.
Cheers,
William

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Evangelos Rozos 1 year ago

Thank you for your reply. Regarding the difference, you cannot compare the results of these two tools unless you register properly the frames in both tools. For the camera angle of the Nedon case study, you need to define the real coordinates of 4 pixels.
Besides this, significant differences are expected anyway not only between different image velocimetry tools, but also even when the same tool is employed with different parameters (see Fig. 7 and Fig. 8 of Pearce et al., 2020). For this reason, we suggest employing Monte Carlo simulations for both estimating the uncertainty and obtaining a better estimation of the cross-section velocity profile (Rozos et al., 2020).

References
Pearce, S.; Ljubiˇci´c, R.; Peña-Haro, S.; Perks, M.T.; Tauro, F.; Pizarro, A.; Sasso, S.F.D.; Strelnikova, D.; Grimaldi, S.; Maddock, I.; et al. An evaluation of image velocimetry techniques under low flow conditions and high seeding densities using unmanned aerial systems. Remote. Sens. 2020, 12, 232.
Rozos, E.; Dimitriadis, P.; Mazi, K.; Lykoudis, S.; Koussis, A. On the Uncertainty of the Image Velocimetry Method Parameters. Hydrology 2020, 7, 65.

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Evangelos Rozos 3 months, 2 weeks ago

A new version has been uploaded that includes the projects of the Rozos et al. (2021) publication.

The videos are not include in the file, they must be downloaded (see _Notes.txt files).

Rozos E, Mazi K, Koussis AD. Probabilistic Evaluation and Filtering of Image Velocimetry Measurements. Water. 2021; 13(16):2206. https://doi.org/10.3390/w13162206

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