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(step=20, window=20)
# Attach the manager
sdr.attach_manager(manager)
# Create a threshold middleware
middleware = GradientThresholdMiddleware(verbose=True, threshold=10, sample_group_delay=5, group=True)
# Attach the middleware
manager.attach_receiver(middleware)
# Also plot the sample
plotter_mid = PlotterMiddleware()
middleware.attach_receiver(plotter_mid)
# 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()
def record_new_samples_piezo(port, gesture_id, target_dir, threshold=5):
"""
Used to record new samples for the specified gesture_id using a StreamSampleManager and a GradientThresholdMiddleware
"""
print("RECORDING NEW SAMPLES")
# Create the target directory if it doesn't exists
if not os.path.exists(target_dir):
os.makedirs(target_dir)
print("SERIAL PORT:", port)
print("TARGET DIRECTORY:", target_dir)
print("GESTURE ID:", gesture_id)
print("THRESHOLD:", threshold)
# Create the SerialDataReader
sdr = SerialDataReader(port, expected_axis=1, baud_rate=74880, verbose=False)
# Create the SampleManager
manager = StreamSampleManager(step=10, window=10)
# Attach the manager
sdr.attach_manager(manager)
# Create a threshold middleware
middleware = GradientThresholdMiddleware(verbose=False, threshold=threshold, sample_group_delay=20, group=True)
# Attach the middleware
manager.attach_receiver(middleware)
# Also plot the sample
plotter_mid = PlotterMiddleware()
middleware.attach_receiver(plotter_mid)
# Create a FileGestureRecorder
recorder = FileGestureRecorder(target_dir=target_dir, forced_gesture_id=gesture_id, verbose=True)
# Attach the recorder to the manager
middleware.attach_receiver(recorder)
# Open the serial connection
print("Opening serial port...")
sdr.open()
print("Opened!")
print("To exit the loop, press Ctrl+C.")
# Start the main loop
sdr.mainloop()
def record_new_samples(port, gesture_id, target_dir, expected_axis):
"""
Used to record new samples for the specified gesture_id
"""
print("RECORDING NEW SAMPLES")
# Create the target directory if it doesn't exists
if not os.path.exists(target_dir):
os.makedirs(target_dir)
gestures = []
# If the gesture id is made of multiple gestures separated by a comma, extract them
if ',' in gesture_id:
gestures = map(lambda x: x.strip(), gesture_id.split(","))
else:
gestures = [gesture_id]
print("SERIAL PORT:", port)
print("TARGET DIRECTORY:", target_dir)
print("GESTURE ID:", gesture_id)
# Create the SerialDataReader
sdr = SerialDataReader(port, expected_axis=expected_axis, verbose=False)
# Create the SampleManager
manager = DiscreteSampleManager()
# Attach the manager
sdr.attach_manager(manager)
# Create the gesture chooser
gesture_chooser = RandomGestureChooser(gestures)
# Create a FileGestureRecorder
recorder = FileGestureRecorder(target_dir=target_dir, forced_gesture_id=gesture_chooser, verbose=True)
# Attach the recorder to the manager
manager.attach_receiver(recorder)
# Open the serial connection
print("Opening serial port...")
sdr.open()
print("Opened!")
print("To exit the loop, press Ctrl+C.")
# Start the main loop
sdr.mainloop()
def run_example(*args, **kwargs):
# Create the SerialDataReader
sdr = SerialDataReader(kwargs['port'], expected_axis=6, verbose=False)
# Create a simple SampleManager that only prints the received data and signals
manager = VerboseTestSampleManager()
# Attach the manager
sdr.attach_manager(manager)
# 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 = DiscreteSampleManager()
# Attach the manager
sdr.attach_manager(manager)
# Create the VerboseMiddleware that prints the received sample
middleware = PlotterMiddleware()
# Attach the middleware
manager.attach_receiver(middleware)
# Open the serial connection
sdr.open()
print("Opened!")
# Start the main loop
sdr.mainloop()
from pygarl.base import CallbackManager
from pygarl.classifiers import SVMClassifier
from pygarl.mocks import VerboseMiddleware
from pygarl.data_readers import SerialDataReader
from pygarl.predictors import ClassifierPredictor
from pygarl.sample_managers import DiscreteSampleManager
MODEL_PATH="/home/dhwani/Desktop/gesture-keyboard/model.svm"
PORT="/dev/rfcomm0"
def receive_character(number):
print(number)
# Create the SerialDataReader
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()
def record_new_samples_stream(port, gesture_id, target_dir, expected_axis, threshold=50):
"""
Used to record new samples for the specified gesture_id using a StreamSampleManager and a GradientThresholdMiddleware
"""
print("RECORDING NEW SAMPLES")
gestures = []
# Create the target directory if it doesn't exists
if not os.path.exists(target_dir):
os.makedirs(target_dir)
# If the gesture id is made of multiple gestures separated by a comma, extract them
if ',' in gesture_id:
gestures = map(lambda x: x.strip(), gesture_id.split(","))
else:
gestures = [gesture_id]
print("SERIAL PORT:", port)
print("TARGET DIRECTORY:", target_dir)
print("GESTURE ID:", gesture_id)
print("THRESHOLD:", threshold)
# Create the SerialDataReader
sdr = SerialDataReader(port, expected_axis=expected_axis, 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=threshold, sample_group_delay=5, group=True)
# Attach the middleware
manager.attach_receiver(middleware)
lfmiddleware = LengthThresholdMiddleware(verbose=True, min_len=180, max_len=600)
middleware.attach_receiver(lfmiddleware)
# Also plot the sample
plotter_mid = PlotterMiddleware()
middleware.attach_receiver(plotter_mid)
# Create the gesture chooser
gesture_chooser = RandomGestureChooser(gestures)
# Create a FileGestureRecorder
recorder = FileGestureRecorder(target_dir=target_dir, forced_gesture_id=gesture_chooser, verbose=True)
# Attach the recorder to the manager
lfmiddleware.attach_receiver(recorder)
# Open the serial connection
print("Opening serial port...")
sdr.open()
print("Opened!")
print("To exit the loop, press Ctrl+C.")
# Start the main loop
sdr.mainloop()
from pygarl.classifiers import SVMClassifier, MLPClassifier
from pygarl.middlewares import GradientThresholdMiddleware, LengthThresholdMiddleware
from pygarl.mocks import VerboseMiddleware
from pygarl.data_readers import SerialDataReader
from pygarl.predictors import ClassifierPredictor
from pygarl.sample_managers import DiscreteSampleManager, StreamSampleManager
print("LOADED")
sys.stdout.flush()
def receive_gesture(gesture):
print("GESTURE", gesture)
sys.stdout.flush()
# Create the SerialDataReader
sdr = SerialDataReader(sys.argv[1], 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=sys.argv[2])