Added a new file wrapper.py. This file receives an mp4 file as input and returns in which class it belongs to.
In the train.py file removed the one-hot encoding of the labeled data and added the saving of the trained model.
In the _analyzevisual.py added saving feature extraction in .npy format (not only in .csv).
How to QA:
Run the script python3 wrapper.py -f dataset/trump.mp4 -m SVM where -f option is the name of the file and -m the saved model of the training algorithm. The script must return the class that the file belongs.
What this PR does:
Added a new file wrapper.py. This file receives an mp4 file as input and returns in which class it belongs to.
In the train.py file removed the one-hot encoding of the labeled data and added the saving of the trained model.
In the _analyzevisual.py added saving feature extraction in .npy format (not only in .csv).
How to QA:
Run the script
python3 wrapper.py -f dataset/trump.mp4 -m SVM
where -f option is the name of the file and -m the saved model of the training algorithm. The script must return the class that the file belongs.