This repository includes tutorials and examples for processing MicaSense RedEdge and Altum images into usable information using the Python programming language. RedEdge images captured with firmware 2.1.0 (released June 2017) or newer are required. Altum images captured with all firmware versions are supported. Dual-camera (10-band) capture are also included.
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 images without building a larger map.
A working knowledge of running Python software on your system and using the command line are both very helpful. We've worked hard to make these tutorials straightforward to run and understand, but the target audience is someone that's looking to learn more about how to process their own imagery and write software to perform more powerful analysis.
You can start today even if you don't have your own RedEdge or Altum. We provide example images, including full flight datasets.
For a user of RedEdge or Altum that wants a turnkey processing solution, this repository probably is not the best place to start. Instead, consider one of the MicaSense processing partners who provide turnkey software for processing and analysis.
First, check out the setup tutorial which will walk you through installing and checking the necessary tools to run the remaining tutorials.
Next, click here to view the tutorial articles. The set of example notebooks and their outputs can be viewed in your browser without downloading anything or running any code.
For a quick start, make sure you have git, git-lfs, and Anaconda installed.
And then:
git clone https://github.com/micasense/imageprocessing
cd imageprocessing
conda env create -f micasense_conda_env.yml # or pip install .
conda activate micasense
jupyter notebook .
In addition to the tutorials, we've created library code that shows some common transformations, usages, and applications of RedEdge and Altum 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 supports the python setup.py install process) 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, so they will run the notebooks from the directory that the library was 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/macOS) 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.
Note that some of the hyperlinks in the notebooks may give you a 404 Not Found error. This is because the links are setup to allow the list of files above to be accessed on the github.io site. When running the notebooks, use your jupyter "home" tab to open the different notebooks.
Find a problem with the tutorial? Please look through the existing issues (open and closed) and if it's new, 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? Please double-check that you're able to run the setup notebook successfully, and resolve any issues with that first. If you're pulling newer code, it may be necessary in some cases to delete and re-create your micasense conda environment to make sure you have all of the expected packages.
This code is a community effort and is not supported by MicaSense support. Please don't reach out to MicaSense support for issues with this codebase; instead, work through the above troubleshooting steps and then create an issue on github.
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:
pytestTest execution can be relatively slow (2-3 minutes) as there is a lot of image processing occuring in some of the tests, and quite a bit of re-used IO. To speed up tests, install the pytest-xdist plugin using conda or pip and achieve a significant speed up by running tests in parallel.
pytest -n autoData 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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