def run_example(*args, **kwargs):
# Create the SerialDataReader
sdr = SerialDataReader(kwargs['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=args[0])
# Load the model
classifier.load()
# 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)
# Open the serial connection
sdr.open()
print("Opened!")
# Start the main loop
sdr.mainloop()
def run_example(*args, **kwargs):
# Create the SerialDataReader
sdr = SerialDataReader(kwargs['port'], expected_axis=6, verbose=False)
# Create the SampleManager
manager = StreamSampleManager(window=10, step=5)
# Attach the manager
sdr.attach_manager(manager)
# Create the VerboseMiddleware that prints the received sample
middleware = VerboseMiddleware(verbose=False)
# Attach the middleware
manager.attach_receiver(middleware)
# Create a predictor
predictor = HighestAxisPredictor(absolute_values=True)
# Attach the predictor
middleware.attach_receiver(predictor)
# Create a callback manager
callback_mg = CallbackManager(verbose=True)
# Attach the callback manager
predictor.attach_callback_manager(callback_mg)
# Open the serial connection
sdr.open()
print("Opened!")
# Start the main loop
sdr.mainloop()
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()
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"]:
callback_mg.attach_callback(c, receive_character)
# Open the serial connection
sdr.open()
print("Opened!")
# Start the main loop
sdr.mainloop()
# 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=False, 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=False)
# Attach the callback manager
predictor.attach_callback_manager(callback_mg)
# Attach the callbacks
callback_mg.attach_callback("tap", receive_gesture)
callback_mg.attach_callback("doubletap", receive_gesture)
callback_mg.attach_callback("left", receive_gesture)
callback_mg.attach_callback("right", receive_gesture)
callback_mg.attach_callback("tapclockwise", receive_gesture)
callback_mg.attach_callback("tapanticlockwise", receive_gesture)
callback_mg.attach_callback("pull", receive_gesture)
callback_mg.attach_callback("push", receive_gesture)
# Open the serial connection