Here is a listing of that code: from import FERModel from pkg_resources import resource_filename target_emotions = model = FERModel ( target_emotions, verbose = True ) print ( 'Predicting on happy image.' ) model. The best place to start is the FERModel example. If you choose to use the installed package, you can find the examples directory by starting in the virtual environment directory you created and typing: cd lib/python3.6/site-packages/EmoPy/examples
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You may either download the example directory to a location of your choice on your machine, or find the example directory included in the installation.
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You can find example code to run each of the current neural net classes in examples.
Now you're ready to go! Running the examples
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Install the remaining dependencies using pip. Once the virtual environment is activated, you may install EmoPy using pip install EmoPyĬlone the directory and open it in your terminal. You'll know it has been deactivated when the prefix (venv) disappears.) Installation From PyPi (To deactivate the virtual environment run deactivate in the command line. Your terminal command line should now be prefixed with (venv). To activate, run from the same directory: source venv/bin/activate Where the second venv is the name of your virtual environment. sudo pip install virtualenvĬreate and activate the virtual environment. The next step is to set up a virtual environment using virtualenv. GraphViz is required for visualisation functions.
If you do not have Homebrew installed run this command to install: /usr/bin/ruby -e "$(curl -fsSL )" Let us know how you get on, and we will try to support you and share you results. If you would like to use EmoPy on another OS, please convert these instructions to match your target environment. Python is compatible with multiple operating systems. Please note that this is not the most current version of Python, but the TensorFlow package doesn't work with Python 3.7 yet, so EmoPy cannot run with Python 3.7. You can install Python 3.6.6 from the Python website. We tested EmoPy using Python 3.6.6 on OSX. Environment SetupĮmoPy runs using Python 3.6, theoretically on any Python-compatible OS. Some good example public datasets are the Extended Cohn-Kanade and FER+. Predictions ideally perform well on a diversity of datasets, illumination conditions, and subsets of the standard 7 emotion labels (happiness, anger, fear, surprise, disgust, sadness, calm/neutral) seen in FER research. The sample datasets we provide will not yield good results due to their small size, but they serve as a great way to get started. Try out the system using your own dataset or a small dataset we have provided in the examples/image_data subdirectory. Also, an overview of the different neural nets included in this project is included below. The EmoPy documentation contains detailed information on the classes and their interactions. Each of these subclasses implements a different neural net architecture using the Keras framework with Tensorflow backend, allowing you to experiment and see which one performs best for your needs. The fermodel.py module uses pretrained models for FER prediction, making it the easiest entry point to get a trained model up and running quickly.Įach of the modules contains one class, except for neuralnets.py, which has one interface and four subclasses. OverviewĮmoPy includes several modules that are plugged together to build a trained FER prediction model. We aim to expand our development community, and we are open to suggestions and contributions.
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