def __init__(self):
# Retrieve all settings
self.settings = Settings()
# Get the context and initialise it
self.context = Context()
self.context.init()
# Create the depth generator to get the depth map of the scene
self.depth = DepthGenerator()
self.depth.create(self.context)
self.depth.set_resolution_preset(RES_VGA)
self.depth.fps = 30
# Create the user generator to detect skeletons
self.user = UserGenerator()
self.user.create(self.context)
# Initialise the skeleton tracking
skeleton.init(self.user)
# Start generating
self.context.start_generating_all()
# Create a new dataset item
self.data = LiveDataset()
self.data.hand = self.settings.BOTH_HAND
# Update the frame
Timer(0.001, self.updateImage, ()).start()
def __init__(self): # Retrieve all settings self.settings = Settings() # Get the context and initialise it self.context = Context() self.context.init() # Create the depth generator to get the depth map of the scene self.depth = DepthGenerator() self.depth.create(self.context) self.depth.set_resolution_preset(RES_VGA) self.depth.fps = 30 # Create the user generator to detect skeletons self.user = UserGenerator() self.user.create(self.context) # Initialise the skeleton tracking skeleton.init(self.user) # Start generating self.context.start_generating_all() # Create a new dataset item self.data = LiveDataset() self.data.hand = self.settings.BOTH_HAND # Update the frame Timer(0.001, self.updateImage, ()).start()
def __init__(self):
super(LiveGui, self).__init__()
self.setWindowTitle("Pointing Gesture Recognition - Live")
# Retrieve all settings
self.settings = Settings()
# Get the context and initialise it
self.context = Context()
self.context.init()
# Create the depth generator to get the depth map of the scene
self.depth = DepthGenerator()
self.depth.create(self.context)
self.depth.set_resolution_preset(RES_VGA)
self.depth.fps = 30
# Create the user generator to detect skeletons
self.user = UserGenerator()
self.user.create(self.context)
# Initialise the skeleton tracking
skeleton.init(self.user)
# Start generating
self.context.start_generating_all()
print "Starting to detect users.."
# Create a new dataset item
self.data = LiveDataset()
# Create the global layout
self.layout = QtWidgets.QVBoxLayout(self)
# Create custom widgets to hold sensor's images
self.depthImage = SensorWidget()
self.depthImage.setGeometry(10, 10, 640, 480)
# Add these custom widgets to the global layout
self.layout.addWidget(self.depthImage)
# Set the default result text
self.resultLabel = QtWidgets.QLabel()
self.resultLabel.setText("No")
# Create the acquisition form elements
self.createAcquisitionForm()
# Create and launch a timer to update the images
self.timerScreen = QtCore.QTimer()
self.timerScreen.setInterval(30)
self.timerScreen.setSingleShot(True)
self.timerScreen.timeout.connect(self.updateImage)
self.timerScreen.start()
def test_init(self):
path = os.path.join(os.getcwd(), '.skeleton.json')
self.assertFalse(os.path.exists(path))
skeleton.init()
self.assertTrue(os.path.exists(path))
os.unlink(path)
def __init__(self):
super(LiveGui, self).__init__()
self.setWindowTitle("Pointing Gesture Recognition - Live")
# Retrieve all settings
self.settings = Settings()
# Get the context and initialise it
self.context = Context()
self.context.init()
# Create the depth generator to get the depth map of the scene
self.depth = DepthGenerator()
self.depth.create(self.context)
self.depth.set_resolution_preset(RES_VGA)
self.depth.fps = 30
# Create the user generator to detect skeletons
self.user = UserGenerator()
self.user.create(self.context)
# Initialise the skeleton tracking
skeleton.init(self.user)
# Start generating
self.context.start_generating_all()
print "Starting to detect users.."
# Create a new dataset item
self.data = LiveDataset()
# Create the global layout
self.layout = QtWidgets.QVBoxLayout(self)
# Create custom widgets to hold sensor's images
self.depthImage = SensorWidget()
self.depthImage.setGeometry(10, 10, 640, 480)
# Add these custom widgets to the global layout
self.layout.addWidget(self.depthImage)
# Set the default result text
self.resultLabel = QtWidgets.QLabel()
self.resultLabel.setText("No")
# Create the acquisition form elements
self.createAcquisitionForm()
# Create and launch a timer to update the images
self.timerScreen = QtCore.QTimer()
self.timerScreen.setInterval(30)
self.timerScreen.setSingleShot(True)
self.timerScreen.timeout.connect(self.updateImage)
self.timerScreen.start()
def __init__(self):
super(DatasetGui, self).__init__()
self.setWindowTitle("Pointing Gesture Recognition - Dataset recording")
# Retrieve all settings
self.settings = Settings()
# Load sounds
self.countdownSound = QtMultimedia.QSound(
self.settings.getResourceFolder() + "countdown.wav")
self.countdownEndedSound = QtMultimedia.QSound(
self.settings.getResourceFolder() + "countdown-ended.wav")
# Get the context and initialise it
self.context = Context()
self.context.init()
# Create the depth generator to get the depth map of the scene
self.depth = DepthGenerator()
self.depth.create(self.context)
self.depth.set_resolution_preset(RES_VGA)
self.depth.fps = 30
# Create the image generator to get an RGB image of the scene
self.image = ImageGenerator()
self.image.create(self.context)
self.image.set_resolution_preset(RES_VGA)
self.image.fps = 30
# Create the user generator to detect skeletons
self.user = UserGenerator()
self.user.create(self.context)
# Initialise the skeleton tracking
skeleton.init(self.user)
# Start generating
self.context.start_generating_all()
print "Starting to detect users.."
# Create a new dataset item
self.data = Dataset()
# Create a timer for an eventual countdown before recording the data
self.countdownTimer = QtCore.QTimer()
self.countdownRemaining = 10
self.countdownTimer.setInterval(1000)
self.countdownTimer.setSingleShot(True)
self.countdownTimer.timeout.connect(self.recordCountdown)
# Create a timer to eventually record data for a heat map
self.heatmapRunning = False
self.heatmapTimer = QtCore.QTimer()
self.heatmapTimer.setInterval(10)
self.heatmapTimer.setSingleShot(True)
self.heatmapTimer.timeout.connect(self.recordHeatmap)
# Create the global layout
self.layout = QtWidgets.QVBoxLayout(self)
# Create custom widgets to hold sensor's images
self.depthImage = SensorWidget()
self.depthImage.setGeometry(10, 10, 640, 480)
# Add these custom widgets to the global layout
self.layout.addWidget(self.depthImage)
# Hold the label indicating the number of dataset taken
self.numberLabel = QtWidgets.QLabel()
self.updateDatasetNumberLabel()
# Create the acquisition form elements
self.createAcquisitionForm()
# Register a dialog window to prompt the target position
self.dialogWindow = DatasetDialog(self)
# Allow to save the data when the right distance is reached
self.recordIfReady = False
# Create and launch a timer to update the images
self.timerScreen = QtCore.QTimer()
self.timerScreen.setInterval(30)
self.timerScreen.setSingleShot(True)
self.timerScreen.timeout.connect(self.updateImage)
self.timerScreen.start()
def __init__(self):
super(DatasetGui, self).__init__()
self.setWindowTitle("Pointing Gesture Recognition - Dataset recording")
# Retrieve all settings
self.settings = Settings()
# Load sounds
self.countdownSound = QtMultimedia.QSound(self.settings.getResourceFolder()+"countdown.wav")
self.countdownEndedSound = QtMultimedia.QSound(self.settings.getResourceFolder()+"countdown-ended.wav")
# Get the context and initialise it
self.context = Context()
self.context.init()
# Create the depth generator to get the depth map of the scene
self.depth = DepthGenerator()
self.depth.create(self.context)
self.depth.set_resolution_preset(RES_VGA)
self.depth.fps = 30
# Create the image generator to get an RGB image of the scene
self.image = ImageGenerator()
self.image.create(self.context)
self.image.set_resolution_preset(RES_VGA)
self.image.fps = 30
# Create the user generator to detect skeletons
self.user = UserGenerator()
self.user.create(self.context)
# Initialise the skeleton tracking
skeleton.init(self.user)
# Start generating
self.context.start_generating_all()
print "Starting to detect users.."
# Create a new dataset item
self.data = Dataset()
# Create a timer for an eventual countdown before recording the data
self.countdownTimer = QtCore.QTimer()
self.countdownRemaining = 10
self.countdownTimer.setInterval(1000)
self.countdownTimer.setSingleShot(True)
self.countdownTimer.timeout.connect(self.recordCountdown)
# Create a timer to eventually record data for a heat map
self.heatmapRunning = False
self.heatmapTimer = QtCore.QTimer()
self.heatmapTimer.setInterval(10)
self.heatmapTimer.setSingleShot(True)
self.heatmapTimer.timeout.connect(self.recordHeatmap)
# Create the global layout
self.layout = QtWidgets.QVBoxLayout(self)
# Create custom widgets to hold sensor's images
self.depthImage = SensorWidget()
self.depthImage.setGeometry(10, 10, 640, 480)
# Add these custom widgets to the global layout
self.layout.addWidget(self.depthImage)
# Hold the label indicating the number of dataset taken
self.numberLabel = QtWidgets.QLabel()
self.updateDatasetNumberLabel()
# Create the acquisition form elements
self.createAcquisitionForm()
# Register a dialog window to prompt the target position
self.dialogWindow = DatasetDialog(self)
# Allow to save the data when the right distance is reached
self.recordIfReady = False
# Create and launch a timer to update the images
self.timerScreen = QtCore.QTimer()
self.timerScreen.setInterval(30)
self.timerScreen.setSingleShot(True)
self.timerScreen.timeout.connect(self.updateImage)
self.timerScreen.start()
