This week we created a basic user guide for GRIME2. Our next step is to work through some bugs we encountered along the way. More documentation to follow, including image timestamp handling and a how-to for using command line tools.
The guide can be found with the GRIME2 Software information on the main website. Or follow https://gaugecam.org/wp-content/uploads/2024/11/GRIME2_v0040-1.pdf.
🚨 Updates 🚨
Featured Resource (article, database, etc.)
PhD student John Stranzl has been digging into The Color of Rivers (Gardner et al. 2021). This work is based on satellite remote sensing but is interesting to read with ground-based cameras in mind. The list of citing literature is also worth a look.
Gardner, J. R., Yang, X., Topp, S. N., Ross, M. R. V., Altenau, E. H., & Pavelsky, T. M. (2021). The Color of Rivers. Geophysical Research Letters, 48(1), e2020GL088946. https://doi.org/10.1029/2020GL088946
Featured Photo Information
Silhouettes of three scientists and a trusty Platte Basin Timelapse camera on a tea-colored Sandhills stream.
Credit: Troy Gilmore
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Conference Sessions focused on image-based research
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Feature Photo Information
The attached figures are composite images composed of pixel columns from 800 time-lapse images captured midday at an urban pond during 2020-2023. One composite was created using the center pixels from each image, showing ice, algae and vegetation. The other composite shows only vegetation from the far-right pixel columns of each image. Camera movement can easily be detected with these visualizations. Original images courtesy of Aaron Mittelstet and Platte Basin Timelapse. Visualization concept inspired in part by Andrew Richardson (PhenoCam).
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This release makes the creation of CLI calls much easier. The ROI’s and other parameters you select in the GUI can be used to create CLI parameters and output them as test to the textbox below the main image. There are two “Create command line” buttons: one on the Calibration tab and one on the Find Line tab.
https://github.com/gaugecam-dev/GRIME2/releases/tag/v0.3.0.8
Planning ecological and/or hydrological research project using trail cams? If so, you might be wondering about which camera and mounting system to use. We have some ideas. But first, here are some helpful references from groups that have many years of experiences with camera traps and ecohydrological monitoring:
Learn from Andrew Richardson’s account of PhenoCam’s history and lessons learned from operating a large scientific camera network: https://doi.org/10.1016/j.agrformet.2023.109751
Get inspired by Platte Basin Timelapse’ artistic time-lapse camera network, oriented toward conservation storytelling in support of science: https://plattebasintimelapse.com/timelapses/
Explore streams and rivers on the United States Geological Survey’s HIVIS site: https://apps.usgs.gov/hivis/
Honestly, the groups above have more experience installing time-lapse cameras than we do. That said, we have been learning and are happy to share the approach we are now using at stream monitoring sites like the Kearney Outdoor Learning Area (KOLA).
The Camera:
Our current preference is the Reconyx Hyperfire 2 Professional camera.
Why “Professional”? These cameras are $60 more than the standard Hyperfire 2. Based on the Reconyx comparison tool, here are key differences.
The Security Enclosure:
A good lock and security enclosure are important for most sites. But we also like the Reconyx security enclosure for another reason: image stability. Minimizing camera movement is one of the most important considerations for effective monitoring! Of course, a security enclosure does not guarantee a perfectly stable camera. But we like the way the enclosure can be mounted in a permanent position and the camera can be removed for maintenance and placed back in the security enclosure without large translational or rotational shifts in the field of view. We have used other cameras and mounting systems where the camera and/or mount has to be loosened or removed when swapping the SD card and/or changing batteries. When we re-attach the camera and/or mount, it’s a guessing game as to whether we’ve returned the camera to a position that captures even a similar field of view.
Other Accessories:
Things we think you should NOT do:
Pretty please, do not just stick a t-post in the ground and attach a camera. You will get a lot of camera movement and it will make life more difficult when you want to process your images.
Do not just strap a camera on a tree. If you are using a tree and can’t use screws or lag bolts, then securely attach an enclosure (directly, or via swivel mount that is strapped to the tree). If you just strap the camera to a tree and then have to remove the strap and camera each time you swap an SD card and/or batteries, you will get a lot of camera movement and it will make life more difficult when you want to process your images.
In conclusion, we think the Reconyx camera is a good choice for our research projects. It is a relatively expensive option and much cheaper cameras might acquire imagery that is suitable for your work. We’d be happy to hear if there are other options that have worked well for you. When it’s all said and done, the best advice we can offer is to create a stable mounting system that minimizes changes in the field of view. Otherwise, you will get a lot of camera movement and it will make life more difficult when you want to process your images!
We have a new GRIME2 release. It is a bug-fix release that allows the program to run a little more quickly and use less disk space when the octagon target is used. We were creating unneeded debug information and images that have been removed.
Download and use the following installer to replace the previous software:
https://github.com/gaugecam-dev/GRIME2/releases/tag/v0.3.0.4-beta
https://github.com/gaugecam-dev/GRIME2/releases/tag/v0.3.0.5-beta
This post describes the first testing of a mini-octagon calibration target for measuring water level with a camera and machine vision algorithms.
The original GaugeCam “bow-tie” calibration target was about three feet wide and four feet tall. This target yielded excellent calibration and precise water level readings. However, the size of the target is obtrusive in images and prohibitive at some sites.
The next generation calibration target, the “octagon target,” is approximately two feet wide. The benefits of the octagon are that (1) the target footprint is much smaller, and (2) the calibration target remains above the water line, so a calibration can be performed for every image. Calibrating each image is more robust because it accounts for camera movement, which is inevitable. The large octagon target performs on par with the original bow-tie calibration target, as shown in Ken Chapman’s dissertation.
Our goal with the mini-octagon is to reduce the target background to the minimal size required for robust calibration and water level measurement. The current size is larger than a traditional staff gauge but reasonable size for installation in many environments. Below you can see our field fabrication of the first mini-octagon, using a sheet of Coroplast, spray paint, and octagon stencil.
Initial tests show that our algorithms can detect the vertices of the mini-octagon in low-light conditions and under IR illumination.
Featured Photo
Updates
Software Information
What is GRIME?
GRIME (GaugeCam Remote Imagery Manager Educational) is open-source commercial-friendly software (Apache 2.0) that enables ecohydrological research using ground-based time-lapse imagery. The first GRIME software for measuring water level with cameras was developed in François Birgand’s lab at North Carolina State University.
What are GRIME2 and GRIME-AI?
GRIME2 and GRIME-AI are the two desktop applications developed by Ken Chapman and John Stranzl, respectively.
Who is involved in the GRIME Lab?
See the growing list at the bottom of our home page: https://gaugecam.org/
We collaborate closely with Mary Harner at University of Nebraska at Kearney: https://witnessingwatersheds.com/
Building on the successful WaterFront Software and KOLA Data Portal projects, we embarked on another student-led adventure in the Fall 2023 semester! Professor Elizabeth LĂłpez Ramos connected the GRIME Lab team with an excellent student team at TecnolĂłgico de Monterrey (ITESM). These students led the Data Fusion Project.
The Data Fusion Project is a first step toward integrating data fusion features in the GRIME-AI user interface. And the ITESM team dived DEEP into the software development life cycle on this one! As “clients” the GRIME Lab team had multiple meetings and filled out an extensive questionnaire. This made us really think through the requirements we desired. The ITESM team extensively documented this process, which is a major benefit to everyone going forward. Below are some screenshots from the ITESM team’s final presentation.
The ITESM did a great job of working across campuses and completing a lot of behind-the-scenes work required to finish this project. Their project can be found on GitHub.
Overall, we are grateful for the opportunity to work with the ITESM Team. They were very professional and worked hard to create a viable product!
Many thanks to:
This video shows steps and time required for data download and image analysis of over 5,000 images from a USGS HIVIS site on the Elkhorn River in Nebraska. The process includes setting regions of interest (ROIs) and extraction of color and other scalar image features suitable for machine learning applications. This work was done on a laptop computer running GRIME-AI v0.0.3.8c-003.
PROCESSES COMPLETED:
• Data selection
• Imagery download
• Stage and discharge data download
• Image processing
• Image feature dataset created
• Ready for data fusion, then ML modeling
LAPTOP SPECIFICATIONS:
Intel i7-9850H @ 2.60GHz 2.59GHz
32 GB RAM
NVIDIA GeForce GTX 1650
Home fiber internet connection over Wifi
TIME REQUIRED:
The overall process took 1:04 hours, including all download and processing time. Extrapolating, this suggests about 4:15 hours required to download and process one year’s worth of imagery when working in my home office.