def run_example(*args, **kwargs):
# Create the SerialDataReader
sdr = SerialDataReader(kwargs['port'], expected_axis=6, verbose=False)
# Create the SampleManager
manager = StreamSampleManager(step=20, window=20)
# Attach the manager
sdr.attach_manager(manager)
# Create a threshold middleware
middleware = GradientThresholdMiddleware(verbose=False,
threshold=40,
sample_group_delay=5,
group=True)
# Attach the middleware
manager.attach_receiver(middleware)
# Create a classifier
classifier = SVMClassifier(model_path=args[0])
# Load the model
classifier.load()
# Print classifier info
classifier.print_info()
# Create a ClassifierPredictor
predictor = ClassifierPredictor(classifier)
# Filter the samples that are too short or too long
lfmiddleware = LengthThresholdMiddleware(verbose=True,
min_len=180,
max_len=600)
middleware.attach_receiver(lfmiddleware)
# Attach the classifier predictor
lfmiddleware.attach_receiver(predictor)
# Create a CallbackManager
callback_mg = CallbackManager(verbose=True)
# Attach the callback manager
predictor.attach_callback_manager(callback_mg)
# Attach the callbacks
callback_mg.attach_callback("knock", receive_gesture)
callback_mg.attach_callback("doubleknock", receive_gesture)
# Open the serial connection
sdr.open()
print("Opened!")
# Start the main loop
sdr.mainloop()
def receive_character(number):
pyautogui.doubleClick(155, 69)
if number != "1":
pyautogui.typewrite(number)
else:
pyautogui.keyDown('backspace')
pyautogui.keyUp('backspace')
# Create the SerialDataReader
sdr = SerialDataReader(PORT, expected_axis=6, verbose=False)
# Create the SampleManager
manager = StreamSampleManager()
# Attach the manager
sdr.attach_manager(manager)
# Create a classifier
classifier = SVMClassifier(model_path=MODEL_PATH)
# Load the model
classifier.load()
# Print classifier info
classifier.print_info()
# Create a ClassifierPredictor
predictor = ClassifierPredictor(classifier)
# Attach the classifier predictor
manager.attach_receiver(predictor)
# Create a CallbackManager
callback_mg = CallbackManager(verbose=True)
# Attach the callback manager
predictor.attach_callback_manager(callback_mg)
# Bind all the numbers
callback_mg.attach_callback(" ", receive_character)
for c in ["1", "2", "3", "4"]:
callback_mg.attach_callback(c, receive_character)
# Open the serial connection
sdr.open()
print("Opened!")
# Start the main loop
sdr.mainloop()
sdr = SerialDataReader(PORT, expected_axis=6, verbose=False) # Create the SampleManager manager = DiscreteSampleManager() # Attach the manager sdr.attach_manager(manager) # Create a classifier classifier = SVMClassifier(model_path=MODEL_PATH) # Load the model classifier.load() # Print classifier info classifier.print_info() # Create a ClassifierPredictor predictor = ClassifierPredictor(classifier) # Attach the classifier predictor manager.attach_receiver(predictor) # Create a CallbackManager callback_mg = CallbackManager(verbose=True) # Attach the callback manager predictor.attach_callback_manager(callback_mg) # Bind all the numbers for c in ["1","2","3","4","Bow;ing","Push","Pull","Bye-Bye","Drinking"]: