This repository includes tutorials and examples for processing MicaSense RedEdge images into usable information using the python programming language. The intended audience is researchers and developers with some software development experience that want to do their own image processing. While a number of commercial tools fully support processing RedEdge data into reflectance maps, there are a number of reasons to process your own data, including controlling the entire radiometric workflow (for academic or publication reasons), pre-processing images to be used in a non-radiometric photogrammetry suite, or processing single sets of 5 images without building a larger map.
First, git clone this repository, as it has all of the code and examples you'll need.
To do that you'll need git
Once you have git installed and the repository cloned, you are ready to start with the first tutorial. Check out the setup tutorial which will walk through installing and checking the necessary tools to run the remaining tutorials.
- MicaSense Image Processing Setup
- MicaSense Image Processing Tutorial #1 (basic radiometic corrections)
- MicaSense Image Processing Tutorial #2 (library introduction)
- MicaSense Image Processing Tutorial #3 (basic DLS processing)
- Image Class Examples
- Capture Class Examples
- ImageSet Examples
- Capture Alignment and Analysis Examples
In addition to the tutorials, we've created library code that shows some common transformations, usages, and applications of RedEdge imagery. In general, these are intended for developers that are familiar with installing and managing python packages and third party software. The purpose of this code is readability and clarity to help others develop processing workflows, therefore performance may not be optimal.
While this code is similar to an installable python library (and we may support the pip install process in the future) the main purpose of this library is one of documentation and education. For this reason, we expect most users to be looking at the source code for understanding or improvement, and because of this you will currently need to run your notebooks from the directory you git cloned it into.
The code in these tutorials consists of two parts. First, the tutorials generally end in .ipynb and are the Jupyter notebooks that were used to create the web page tutorials linked above. You can run this code by opening a terminal/iTerm (linux/mac) or Anaconda Command Prompt (Windows), navigating to the folder you cloned the git repository into, and running
jupyter notebook .That command should open a web browser window showing the set of files and folder in the repository. Click the ...Setup.ipynb notebook to get started.
Second, a set of helper utilities is available in the micasense folder that can be used both with these tutorials as well as separtely.
Find a problem with the tutorial? Please create an issue on github.
Want to correct an issue or expand library functionality? Fork the repository, make your fix, and submit a pull request on github.
Have a question? Double-check that you're able to run the setup notebook successfully, and then check the MicaSense Knowledgebase before contacting support.
Tests for many library functions are included in the tests diretory. Install the pytest module through your package manager (e.g. pip install pytest) and then tests can be run from the main directory using the command:
pytest testsData used by the tests is included in the data folder.
To generate the HTML pages after updating the jupyter notebooks, run the following command in the repository directory:
jupyter nbconvert --to html --ExecutePreprocessor.timeout=None --output-dir docs --execute *.ipynbThe MIT License (MIT)
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