![]() When running the notebooks, use your jupyter "home" tab to open the different notebooks.įind a problem with the tutorial? Please look through the existing issues (open and closed) and if it's new, (). This is because the links are setup to allow the list of files above to be accessed on the github.io site. Note that some of the hyperlinks in the notebooks may give you a 404 Not Found error. Second, a set of helper utilities is available in the `micasense` folder that can be used both with these tutorials as well as separtely. Click the `.Setup.ipynb` notebook to get started. That command should open a web browser window showing the set of files and folder in the repository. ![]() 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 First, the tutorials generally end in `.ipynb` and are the Jupyter notebooks that were used to create the web page tutorials linked above. The code in these tutorials consists of two parts. 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 clone`d it into. 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. The purpose of this code is readability and clarity to help others develop processing workflows, therefore performance may not be optimal. In general, these are intended for developers that are familiar with installing and managing python packages and third party software. In addition to the tutorials, we've created library code that shows some common transformations, usages, and applications of RedEdge and Altum imagery. The set of example notebooks and their outputs can be viewed in your browser without downloading anything or running any code.įor a quick start, make sure you have (), (), and () installed.Ĭonda env create -f micasense_conda_env.yml # or pip install. Instead, consider one of the MicaSense processing partners who provide turnkey software for processing and analysis.įirst, () which will walk you through installing and checking the necessary tools to run the remaining tutorials. We provide example images, including full flight datasets.įor a user of RedEdge or Altum that wants a turnkey processing solution, this repository probably is not the best place to start. You can start today even if you don't have your own RedEdge or Altum. 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. 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.Ī working knowledge of running Python software on your system and using the command line are both very helpful. ![]() The intended audience is researchers and developers with some software development experience that want to do their own image processing. Dual-camera (10-band) capture are also included. Altum images captured with all firmware versions are supported. RedEdge images captured with firmware 2.1.0 (released June 2017) or newer are required. This repository includes tutorials and examples for processing MicaSense RedEdge and Altum images into usable information using the Python programming language. ![]() # MicaSense RedEdge and Altum Image Processing Tutorials
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