def recognize_gesture(self, source):
     # calculating the optical flow of unknown gesture
     # and reside to match with training data's
     source_flow = measure(source, f=optical_flow_magnitude())
     source_flow = cv2.resize(source_flow, (180, 320)).flatten()
     # getting the prediction after passing the unknown data
     # to SVM
     i = int(self.model.predict(source_flow))
     return GESTURES[i]
    def recognize_gesture(self, source):
        # Calculate the optical flow magnitute of unknown gesture
        source_flow = measure(source, f=optical_flow_magnitude())
        # resize to compare
        source_flow = cv2.resize(source_flow, (180, 320))

        avg_diffs = {}
        for gesture in GESTURES:
            # compare the unknown optical flow with data
            diffs = [simple_difference(source_flow, flow)
                     for flow in self.known_gestures[gesture]]
            avg_diffs[gesture] = np.mean(diffs)

        print avg_diffs
        # return the smallest difference -> the predicted gesture
        return min(GESTURES, key=lambda x: avg_diffs[x])
def _process(gesture, idx, f):
    source = 'videos/{0}{1}_blackbackground.mov'.format(gesture, idx)
    print '\t', source
    data = measure(source, f)
    data = cv2.resize(data, (180, 320))
    return data.flatten()