Putting it all together

In order to run our app, we will need to execute the main function routine (in chapter7.py) that loads the pre-trained cascade classifier and the pre-trained multi-layer perceptron, and applies them to each frame of the webcam live stream.

However, this time, instead of collecting more training samples, we will select the radio button that says Test. This will trigger an EVT_RADIOBUTTON event, which binds to FaceLayout._on_testing, disabling all training-related buttons in the GUI and switching the app to the testing mode. In this mode, the pre-trained MLP classifier is applied to every frame of the live stream, trying to predict the current facial expression.

As promised earlier, we now return to FaceLayout._process_frame:

def _process_frame(self, frame):
    """ detects face, predicts face label in testing mode """

Unchanged from what we discussed earlier, the method begins by detecting faces in a downscaled grayscale version of the current frame:

    success, frame...