PIVlab v2.50 with parallel processing and camera / laser control released! And: New tutorial videos.

At least for me, this is a great update as processing speed is improved quite a bit, and I can now capture image data and control our laser directly in PIVlab. Sooooo much easier to do experiments for our customers...! With some additional hardware, PIVlab can control a laser and a camera directly. Hardware is available at OPTOLUTION The new PIV acquisition panel - very comfortable and simple. PIVlab-SimpleSync with wireless USB dongle. I also recorded three new tutorial videos for PIVlab: PIVlab tutorial, part 1/3: Quickstart PIVlab tutorial, part 2/3: Pre-processing, analysis and data validation PIVlab tutorial, part 3/3: Data exploration and data export

PIVlab will soon capture images!

Soon, PIVlab will be more than just a PIV post-processing tool! I am currently implementing some additional features, so that PIVlab can control our laser and the camera directly. I am doing this, because we are using PIVlab for commercial services, and I want to have a PIV setup that is really handy in practice. I also developed a small synchronizer (see picture) that controls our Quantel Evergreen 200 PIV laser and synchronizes it with the ILA5150 PIV.Nano double-image camera. And this synchronizer (which is wireless by the way) is also controlled directly from PIVlab. Everything is as simple as it can get: Press a single button to start camera and laser, images will be saved to your hard disk and directly loaded into PIVlab. Awesome! If you want to have this too for your research, you'll need the PIVlab-SimpleSync ( ), the ILA5150 PIV.Nano camera, and a double-puls laser (e.g. Quantel EVG00070). The PIVlab-SimpleSync wireless synchronizer controls Lamp 1&2, Q

Most downloaded MATLAB toolbox on official Mathworks FileExchange!

 The amount of active PIVlab users is increasing constantly, and has now made PIVlab the most downloaded toolbox on FileExchange. And among the top 4 Toolboxes, PIVlab is the only one that is not made by a Mathworks employee. Hooray! By the way: I have added some useful features to PIVlab, and I'll record another PIV video tutorial that introduces these additions soon.

Comparison of OpenPIV and PIVlab

Since quite a while, I benefit from discussions with the main developer of OpenPIV (Prof. Alex Liberzon  from Tel Aviv University) and other people that contribute to OpenPIV. It is almost a bit like a cooperation, as we share thoughts on different topics, and Alex is really into practical and theoretical PIV application. I think we have a very friendly rivalry in developing useful PIV software. The results are available here: You will notice that they are very noisy. But we intentionally decided to make the analysis very challenging for our software. The final interrogation area is e.g. only 6*6 pixels, and no smoothing is allowed. I don't really see a difference between OpenPIV and PIVlab, which is probably a good sign. It might be interesting to include commercial software too in this comparison in the future. By the way: Happy New Year to all PIVlab users (and of course all OpenPIV users too ;-D). The year 2020 has been

PIVlab update v2.38

I made two updates to PIVlab in the last weeks, the latest release is 2.38. The following things were added: Video import functionality: PIVlab can directly work with video files now. Videos are not converted, but frames are directly accessed from the video stream. Vorticity direction was inverted (positive is now ccw) *.png files can be imported now The correlation coefficient is available in the derived parameters panel. It might be used as a measure for the quality of an analysis. I tested several different things, including correlation peak height vs. mean signal strength and ratio of first to second peak. I might make these available later, when I am played around with the data a bit more. Added the PIVlab Quickviewer, an additional GUI that helps to quickly tweak the setup of your experiment while you are recording your images. Image histograms are automatically optimized to get a better display. This can be disabled in "Modify plot appearance" The correlation coefficie

PIVlab is now on GitHub!

Since quite a while, Matlab File Exchange offers the opportunity to link to a GitHub repository. So I just created a PIVlab repository on GitHub, and changed the Matlab File Exchange site accordingly. I hope that this works out smoothly, and everyone benefits from this change! Here is the link to GitHub: And here is the modified File Exchange site:

Please support PIVlab!

If you like PIVlab, please donate for a beer here: E4slJx

PIVlab Update v2.36

Ensemble correlation: Performance upgrade and bug fix (with old matlab versions) Saving huge PIVlab sessions (> 2GB) now works more reliable Enhanced compatibility with old Matlab releases Updated uipickfiles to latest version PIVlab more reliable remembers last folder and last import settings (PIVlab v2.35 contained a bug, so it was replaced with 2.36 instantly)

Vortex street analysis in 4k and 60 fps

This is some data that I recorded quite a while ago, but I just discovered it on a backup HDD. The output data of PIVlab was edited and rendered in high resolution. Click here to watch it in full resolution

PIVlab Update 2.31

PIVlab 2.31 is released: New features: Ensemble correlation with all the features of the existing algorithm (muti pass window deformation, repeated correlation, disabling self correlation, etc). Information here and here . Fixes: Quick acces toggle button status fixed Bug in a visibility property fixed Opening Shortcut PDF throws a more appropriate error message when it fails

The benefit of ensemble correlation in PIVlab v2.30

PIVlab v2.30 features 'multipass window deformation ensemble correlation'. This type of correlation has been introduced by Santiago et al.  in 1998. It is especially helpful in micron-resolution particle image velocimetry (micro-PIV, µPIV), as it can deal with very low seeding densities (see below). Ensemble correlation PIV should be used if you want to analyze a steady flow that has a low seeding density. You need to record a large number of images to have the full benefit of ensemble correlation in PIVlab. The yellow lines show the interrogation areas (IAs). The white dots are particles, and you can see that there are hardly any particles in the IAs. The general rule of thumb for PIV is that there should be 5 - 15 particles per IA. This is clearly not the case here. Standard PIV algorithms will fail. In order to get a better result, you would have to increase the size of the IAs dramatically. With ensemble correlation, PIVlab analyzes a series of sparsely seede

Soon: Ensemble correlation!

I am currently working on the implementation of ensemble correlation. This kind of correlation averages the correlation matrices of a frame series before searching for a peak. Using it makes sense, when there a only a few particles visible in the flow. This is often the case in micro-piv, and I hope that there will be many users that benefit from this feature. The changes to the GUI are finished, and I have a simple 1-pass ensemble correlation running. But I want to have the full potential of multi-pass window deformation algorithms with ensemble correlation. Implementation therefore takes a bit more time as there are some changes that challenge my brain. Additionally, most of the programming currently happens single handed because of my third daughter... Slow & cozy programming... 

PIVlab update 2.2

New features / enhancements: New and enhanced correlation algorithm (linear correlation instead of circular correlation). Details here. Correlation quality dropdown menu: Normal: Circular correlation, linear window deformation Better: Circular correlation, spline window deformation High: Linear correlation, spline window deformation Extreme: Linear correlation, spline window deformation, repeated correlation Suggestion for selecting an appropriate interrogation area size. Works by trying to count the amount of texture / particles and calculating the maximum displacement. PIVlab will try to suggest settings so that every window contains sufficient texture / particles and the displacement is not too large. Quick acces toolbar for the most important PIVlab functions. Will not be displayed if there is not enough space in PIVlab's program window. Automatic histogram limiting and stretching works now seperately for both images of a frame. This accounts better for unevenly ill

Evaluation of the new PIVlab (v2.2) settings

Recently, an interesting discussion , initiated by Theo, popped up in the PIVlab forum. Alex, the developer from OpenPIV also joined and gave valuable comments. The discussion was about whether circular correlation or linear correlation would be more suitable for processing PIV images (read the details in the link given above if you're interested). PIVlab uses circular correlation since the beginning. Now, I also tried linear correlation, and the results were pretty good: Linear correlation decreases bias and random (RMS) error, and it also enhances the robustness (less wrong vectors in bad image data). However, the processing time increases by a factor of 2.5 too. As there are too many settings in PIVlab (window deformation interpolator, repeated correlation, correlation type) that are difficult to understand for a standard user, I decided to remove these settings. In the latest release (coming in a few days), there is instead a drop-down box that looks like this: The user

My talk on the MATLAB EXPO 2019 in Munich (July, 2nd)

I will be giving a talk on PIVlab on the "MATLAB EXPO 2019 Deutschland 2. Juli | München". The link to the agenda is here: Maybe this would be a nice chance to meet...?

Update PIVlab 2.02

New derivative: Color-coded vector direction Changes: New derivative: Vector direction (use HSV colormap for best display performance) Save and load PIVlab masks, also load masks from previously saved session files Mask transparency can be set, can also be used to make the mask invisible (100% transparent) Tool tips display help and information for all buttons Keyboard shortcuts enables the user to work more efficiently List with all keyboard shortcuts can be accessed from the help menu Fixed a zoom bug When smoothing data, some extra information on what actually happens is now displayed in Matlabs command window Load external mask(s) improved Removed some unnecessary data in *.mat file export