Installation Instructions¶
Dependencies¶
The perception module depends on the Berkeley AutoLab’s autolab_core module, which can be installed using the instructions here. The module primarily wraps OpenCV version >= 2.11 which can be installed using pip.
Furthermore, the perception module optionally depends on Tensorflow and pylibfreenect2 for Convolutional Neural Networks and Kinect2 sensor usage, repectively. Install these according to their website’s instructions if their functionality is required.
Installing our repo using pip will attempt to install these automatically.
Any other dependencies will be installed automatically when perception is installed with pip.
Cloning the Repository¶
You can clone or download our source code from Github.
$ git clone git@github.com:BerkeleyAutomation/perception.git
Installation¶
To install perception in your current Python environment, simply change directories into the perception repository and run
$ pip install -e .
or
$ pip install -r requirements.txt
Alternatively, you can run
$ pip install /path/to/perception
to install perception from anywhere.
Testing¶
To test your installation, run
$ python setup.py test
We highly recommend testing before using the module.
Building Documentation¶
Building perception’s documentation requires a few extra dependencies – specifically, sphinx and a few plugins.
To install the dependencies required, simply run
$ pip install -r docs_requirements.txt
Then, go to the docs directory and run make
with the appropriate target.
For example,
$ cd docs/
$ make html
will generate a set of web pages. Any documentation files generated in this manner can be found in docs/build.
Deploying Documentation¶
To deploy documentation to the Github Pages site for the repository, simply push any changes to the documentation source to master and then run
$ . gh_deploy.sh
from the docs folder. This script will automatically checkout the
gh-pages
branch, build the documentation from source, and push it
to Github.