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!
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.
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.
This post builds on our recent update about GRIME-AI capabilities. The previous post (and video) described features in GRIME-AI that are reasonably stable (although subject to occasional changes in APIs for public data sources). The description below is our current roadmap to a full GRIME-AI suite of tools for using imagery in ecohydrological studies. Please contact us if you see major gaps or are interested in helping us test the software as new features are developed!
The following features are implemented or planned for GRIME-AI:
You will notice asterisks that indicate *planned future functionality (timeframe = months to years) and **functionality under development (timeframe = weeks to months). All other features are developed, but subject to additional user testing as we work toward a stable public release. GRIME-AI is being developed open source commercial friendly (Apache 2.0).
All of the above are being developed under the MESH development philosophy:
John Stranzl has been continuously adding features to GRIME-AI, which is open-source software for acquiring data and processing imagery from ground-based cameras.
Here’s a quick update on the current capabilities of GRIME-A v0.0.3.3. This video features:
When the GaugeCam project started back in 2009, we documented much of the work through blog posts.
We’re excited to start blogging again in 2022, as we take GaugeCam in new directions. The photoshopped image below shows a site we’re planning on instrumenting in 2022.
