How to Get Started with pyOpenRiverCam (pyorc)

Are you interested in performing image-based river flow analysis with ease? Look no further! The pyOpenRiverCam (or pyorc) library is your go-to open-source solution designed for exciting tasks like Large-scale Particle Image Velocimetry (LSPIV). This guide aims to walk you through the steps needed to successfully install and utilize the pyorc library for your river flow analyses.

Understanding pyorc

Think of pyorc as a friendly toolkit for river scientists, akin to a fisherman’s tackle box. Each tool in this box serves a specific purpose, enabling you to analyze the flow of water efficiently. Just as a fisherman needs various tools to catch different types of fish, as a researcher, you need pyorc to perform various computations such as reading frames, estimating velocities, and calculating discharges in rivers through simple code.

Current Capabilities of pyorc

• Reads frames and reprojects them onto a surface.
• Estimates velocimetry at user-defined resolution.
• Calculates discharge over provided cross-sections.
• Plots velocimetry results in three different perspectives: camera, geographical, and orthoprojected.

With future updates, you can expect even more functionalities, enabling complex analyses under less favorable conditions.

Installation Procedure

To get started with pyorc, we recommend using Miniconda or Anaconda. These environments facilitate a smooth installation process. Below, you will find two options to install on your system depending on your preference.

1. Installation for Direct Use

If you want to add pyorc to an existing Python installation or virtual environment, follow these steps:

• Activate the environment where you want to install pyorc:

conda activate name-of-your-environment

• Next, install pyorc with all its dependencies:

conda install -c conda-forge pyopenrivercam

• If you use mamba as a package manager:

mamba install pyopenrivercam

2. Installation from the Latest Code Base

If you want the latest non-released version, install pyorc from the code base:

• Clone the repository:

git clone https://github.com/localdevices/pyorc.git

• Move into the cloned directory:

cd pyorc

• Setup a virtual environment and install dependencies:

conda env create -f envspyorc-dev.yml

• Activate the environment:

conda activate pyorc-dev

• Finally, install pyorc:

pip install .

Using pyorc

The pyorc library provides an intuitive API for processing your river data. Though a command-line interface is in development, you will be equipped enough to start your analyses right away with the current API.

Troubleshooting

If you encounter issues during installation or usage, consider the following:

• Ensure that your environment is set up correctly and that all dependencies are fulfilled.
• Check for compatibility with the videos you are processing; they must work with OpenCV and have proper metadata.
• Refer to the latest documentation available here.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Final Thoughts

This guide serves as a launchpad into the world of river flow analysis using pyorc. By following these instructions, you are well on your way to unveiling insights hidden in the flow of rivers. At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.