Automated sparse control network generation to support photogrammetric control of planetary image data.
- Documentation: https://autocnet.readthedocs.org.
We suggest using Anaconda Python to install Autocnet within a virtual environment. These steps will walk you through the process.
- [Download](https://www.continuum.io/downloads) and install the Python 3.x Miniconda installer. Respond
Yes
when prompted to add conda to your BASH profile.
- (Optional) We like to sequester applications in their own environments to avoid any dependency conflicts. To do this:
conda create -n <your_environment_name> python=3 && source activate <your_environment_name>
- Bring up a command line and add three channels to your conda config (
~/condarc
):
conda config --add channels conda-forge
conda condig --add channels jlaura
conda config --add channels menpo
- Finally, install autocnet:
conda install -c jlaura autocnet-dev