def createInitialDatasets():
print "Creating data sets"
t0 = time.time()
datasetDict = {}
dataFile = h5py.File(dataFileName, 'r')
mapCoord['x1'] = len(dataFile['bed'][:][0])
mapCoord['y1'] = len(dataFile['bed'][:])
mapCoord['proj_x1'] = dataFile['x'][:][-1]
mapCoord['proj_y1'] = dataFile['y'][:][-1]
surfaceX = Dataset('surfaceGradX')
surfaceY = Dataset('surfaceGradY')
datasetDict['surfaceGradX'] = surfaceX
datasetDict['surfaceGradY'] = surfaceY
velocity = Dataset('velocity')
datasetDict['velocity'] = velocity
smb = Dataset('smb')
datasetDict['smb'] = smb
bed = Dataset('bed')
datasetDict['bed'] = bed
surface = Dataset('surface')
datasetDict['surface'] = surface
thickness = Dataset('thickness')
datasetDict['thickness'] = thickness
t2m = Dataset('t2m')
datasetDict['t2m'] = t2m
datasetDict['x'] = Dataset('x')
datasetDict['y'] = Dataset('y')
dataFile.close()
print "Loaded all data sets in ", time.time() - t0, " seconds"
return datasetDict
def test3():
d = sentences()
literals = []
literals.append([d['not_a'], d['c']])
literals.append([d['not_b'], d['c']])
literals.append([d['a'], d['b']])
clauses = create_clauses(literals)
not_observable_fact = [d['not_c']]
not_observable_clause = Clause(not_observable_fact)
clauses.append(not_observable_clause)
dataset = Dataset(clauses)
return dataset
def createInitialDataSets():
print "Creating data sets"
t0 = time.time()
datasetDict = {}
"""
Read in dimensions of bed
at point of comment x = 1670, y = 2991
read in last values of x and y and map to the object map
"""
dataFile = h5py.File(dataFileName, 'r')
map['x1'] = len(dataFile['bed'][:][0])
map['y1'] = len(dataFile['bed'][:])
map['proj_x1'] = dataFile['x'][:][-1]
map['proj_y1'] = dataFile['y'][:][-1]
velocity = Dataset('velocity', greenPlotPen)
datasetDict['velocity'] = velocity
smb = Dataset('smb', redPlotPen)
datasetDict['smb'] = smb
bed = Dataset('bed', bluePlotPen)
datasetDict['bed'] = bed
surface = Dataset('surface', greyPlotPen)
datasetDict['surface'] = surface
thickness = Dataset('thickness', orangePlotPen)
datasetDict['thickness'] = thickness
t2m = Dataset('t2m', tealPlotPen)
datasetDict['t2m'] = t2m
dataFile.close()
print "Loaded all data sets in ", time.time() - t0, " seconds"
return datasetDict
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()
class DatasetGui(QtWidgets.QWidget):
utils = Utils()
featureExtractor = FeatureExtractor()
bpn = BPNHandler(True)
accuracy = accuracy.Accuracy()
# Constructor of the DatasetGui class
#
# @param None
# @return None
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()
# Update the depth image displayed within the main window
#
# @param None
# @return None
def updateImage(self):
# Update to next frame
self.context.wait_and_update_all()
# Extract informations of each tracked user
self.data = skeleton.track(self.user, self.depth, self.data)
# Get the whole depth map
self.data.depth_map = np.asarray(
self.depth.get_tuple_depth_map()).reshape(480, 640)
# Create the frame from the raw depth map string and convert it to RGB
frame = np.fromstring(self.depth.get_raw_depth_map_8(),
np.uint8).reshape(480, 640)
frame = cv2.cvtColor(frame, cv2.COLOR_GRAY2RGB)
# Get the RGB image of the scene
self.data.image = np.fromstring(self.image.get_raw_image_map_bgr(),
dtype=np.uint8).reshape(480, 640, 3)
# Will be used to specify the depth of the current hand wished
currentDepth, showCurrentDepth = 0, ""
if len(self.user.users) > 0 and len(self.data.skeleton["head"]) > 0:
# Highlight the head
ui.drawPoint(frame, self.data.skeleton["head"][0],
self.data.skeleton["head"][1], 5)
# Display lines from elbows to the respective hands
ui.drawElbowLine(frame, self.data.skeleton["elbow"]["left"],
self.data.skeleton["hand"]["left"])
ui.drawElbowLine(frame, self.data.skeleton["elbow"]["right"],
self.data.skeleton["hand"]["right"])
# Get the pixel's depth from the coordinates of the hands
leftPixel = self.utils.getDepthFromMap(
self.data.depth_map, self.data.skeleton["hand"]["left"])
rightPixel = self.utils.getDepthFromMap(
self.data.depth_map, self.data.skeleton["hand"]["right"])
if self.data.hand == self.settings.LEFT_HAND:
currentDepth = leftPixel
elif self.data.hand == self.settings.RIGHT_HAND:
currentDepth = rightPixel
# Get the shift of the boundaries around both hands
leftShift = self.utils.getHandBoundShift(leftPixel)
rightShift = self.utils.getHandBoundShift(rightPixel)
# Display a rectangle around both hands
ui.drawHandBoundaries(frame, self.data.skeleton["hand"]["left"],
leftShift, (50, 100, 255))
ui.drawHandBoundaries(frame, self.data.skeleton["hand"]["right"],
rightShift, (200, 70, 30))
# Record the current data if the user is ready
if self.recordIfReady:
cv2.putText(frame, str(self.data.getWishedDistance()), (470, 60),
cv2.FONT_HERSHEY_SIMPLEX, 2, (252, 63, 253), 5)
if self.data.getWishedDistance(
) >= int(currentDepth) - 10 and self.data.getWishedDistance(
) <= int(currentDepth) + 10:
self.record([])
self.recordIfReady = False
else:
if int(currentDepth) < self.data.getWishedDistance():
showCurrentDepth = str(currentDepth) + " +"
else:
showCurrentDepth = str(currentDepth) + " -"
else:
showCurrentDepth = str(currentDepth)
cv2.putText(frame, showCurrentDepth, (5, 60), cv2.FONT_HERSHEY_SIMPLEX,
2, (50, 100, 255), 5)
# Update the frame
self.depthImage.setPixmap(ui.convertOpenCVFrameToQPixmap(frame))
self.timerScreen.start()
# Update the label indicating the number of dataset elements saved so far for the current type
#
# @param None
# @return None
def updateDatasetNumberLabel(self):
if self.data.type == Dataset.TYPE_POSITIVE:
self.numberLabel.setText("Dataset #%d" %
(self.utils.getFileNumberInFolder(
self.settings.getPositiveFolder())))
elif self.data.type == Dataset.TYPE_NEGATIVE:
self.numberLabel.setText("Dataset #%d" %
(self.utils.getFileNumberInFolder(
self.settings.getNegativeFolder())))
elif self.data.type == Dataset.TYPE_ACCURACY:
self.numberLabel.setText("Dataset #%d" %
(self.utils.getFileNumberInFolder(
self.settings.getAccuracyFolder())))
else:
self.numberLabel.setText("Dataset #%d" %
(self.utils.getFileNumberInFolder(
self.settings.getDatasetFolder())))
# Record the actual informations
#
# @param obj Initiator of the event
# @return None
def record(self, obj):
# If the user collects data to check accuracy, prompts additional informations
if self.data.type == Dataset.TYPE_ACCURACY:
self.saveForTarget()
# If the user collects data for a heat map, let's do it
elif self.data.type == Dataset.TYPE_HEATMAP:
# The same button will be used to stop recording
if not self.heatmapRunning:
self.startRecordHeatmap()
else:
self.stopRecordHeatmap()
else:
# Directly save the dataset and update the label number
self.data.save()
self.countdownEndedSound.play()
self.updateDatasetNumberLabel()
# Handle a countdown as a mean to record the informations with a delay
#
# @param None
# @return None
def recordCountdown(self):
# Decrease the countdown and check if it needs to continue
self.countdownRemaining -= 1
if self.countdownRemaining <= 0:
# Re-initialise the timer and record the data
self.countdownTimer.stop()
self.countdownButton.setText("Saving..")
self.countdownRemaining = 10
self.record([])
else:
self.countdownTimer.start()
self.countdownSound.play()
# Display the actual reminaining
self.countdownButton.setText("Save in %ds" % (self.countdownRemaining))
# Record a heatmap representation of the informations by successive captures
#
# @param None
# @return None
def recordHeatmap(self):
if self.data.hand == self.settings.NO_HAND:
print "Unable to record as no hand is selected"
return False
if len(self.user.users) > 0 and len(self.data.skeleton["head"]) > 0:
# Input the data into the feature extractor
result = self.bpn.check(
self.featureExtractor.getFeatures(self.data))
# Add the depth of the finger tip
point = self.featureExtractor.fingerTip[result[1]]
point.append(self.utils.getDepthFromMap(self.data.depth_map,
point))
# Verify that informations are correct
if point[0] != 0 and point[1] != 0 and point[2] != 0:
# Add the result of the neural network
point.append(result[0])
self.heatmap.append(point)
self.countdownSound.play()
# Loop timer
self.heatmapTimer.start()
# Start the recording of the heatmap
#
# @param None
# @return None
def startRecordHeatmap(self):
self.saveButton.setText("Stop recording")
self.heatmapRunning = True
self.heatmapTimer.start()
# Stop the recording of the heatmap
#
# @param None
# @return None
def stopRecordHeatmap(self):
self.heatmapTimer.stop()
self.heatmapRunning = False
self.countdownEndedSound.play()
self.saveButton.setText("Record")
self.accuracy.showHeatmap(self.heatmap, "front")
self.heatmap = []
# Raise a flag to record the informations when the chosen distance will be met
#
# @param None
# @return None
def startRecordWhenReady(self):
self.recordIfReady = True
# Hold the current informations to indicate the position of the target thanks to the dialog window
#
# @param None
# @return None
def saveForTarget(self):
# Freeze the data
self.timerScreen.stop()
self.countdownEndedSound.play()
# Translate the depth values to a frame and set it in the dialog window
frame = np.fromstring(self.depth.get_raw_depth_map_8(),
np.uint8).reshape(480, 640)
frame = cv2.cvtColor(frame, cv2.COLOR_GRAY2RGB)
self.dialogWindow.setFrame(frame)
# Prompt the position of the target
self.dialogWindow.exec_()
# Toggle the type of dataset chosen
#
# @param value Identifier of the new type of dataset
# @return None
def toggleType(self, value):
self.data.toggleType(value)
if value == self.data.TYPE_HEATMAP:
self.saveButton.setText("Record")
self.countdownButton.setText("Record in %ds" %
(self.countdownRemaining))
self.readyButton.setEnabled(False)
# Create an array to hold all points
self.heatmap = []
else:
self.updateDatasetNumberLabel()
if hasattr(self, 'saveButton'):
self.saveButton.setText("Save")
self.countdownButton.setText("Save in %ds" %
(self.countdownRemaining))
self.readyButton.setEnabled(True)
# Create the acquisition form of the main window
#
# @param None
# @return None
def createAcquisitionForm(self):
globalLayout = QtWidgets.QHBoxLayout()
vlayout = QtWidgets.QVBoxLayout()
# Drop down menu of the distance to record the informations when the pointing hand meet the corresponding value
hlayout = QtWidgets.QHBoxLayout()
label = QtWidgets.QLabel("Distance")
label.setFixedWidth(100)
comboBox = QtWidgets.QComboBox()
comboBox.currentIndexChanged.connect(self.data.toggleDistance)
comboBox.setFixedWidth(200)
comboBox.addItem("550")
comboBox.addItem("750")
comboBox.addItem("1000")
comboBox.addItem("1250")
comboBox.addItem("1500")
comboBox.addItem("1750")
comboBox.addItem("2000")
comboBox.setCurrentIndex(0)
hlayout.addWidget(label)
hlayout.addWidget(comboBox)
vlayout.addLayout(hlayout)
# Drop down menu to select the type of hand of the dataset
hlayout = QtWidgets.QHBoxLayout()
label = QtWidgets.QLabel("Pointing hand")
label.setFixedWidth(100)
comboBox = QtWidgets.QComboBox()
comboBox.currentIndexChanged.connect(self.data.toggleHand)
comboBox.setFixedWidth(200)
comboBox.addItem("Left")
comboBox.addItem("Right")
comboBox.addItem("None")
comboBox.setCurrentIndex(0)
hlayout.addWidget(label)
hlayout.addWidget(comboBox)
vlayout.addLayout(hlayout)
# Drop down menu of the dataset type
hlayout = QtWidgets.QHBoxLayout()
label = QtWidgets.QLabel("Type")
label.setFixedWidth(100)
comboBox = QtWidgets.QComboBox()
comboBox.currentIndexChanged.connect(self.toggleType)
comboBox.setFixedWidth(200)
comboBox.addItem("Positive")
comboBox.addItem("Negative")
comboBox.addItem("Accuracy")
comboBox.addItem("Heat map")
comboBox.setCurrentIndex(0)
hlayout.addWidget(label)
hlayout.addWidget(comboBox)
vlayout.addLayout(hlayout)
globalLayout.addLayout(vlayout)
vlayout = QtWidgets.QVBoxLayout()
self.numberLabel.setAlignment(QtCore.Qt.AlignCenter)
vlayout.addWidget(self.numberLabel)
# Action buttons to record the way that suits the most
hLayout = QtWidgets.QHBoxLayout()
self.readyButton = QtWidgets.QPushButton(
'Save when ready', clicked=self.startRecordWhenReady)
self.saveButton = QtWidgets.QPushButton('Save', clicked=self.record)
hLayout.addWidget(self.readyButton)
vlayout.addLayout(hLayout)
item_layout = QtWidgets.QHBoxLayout()
self.countdownButton = QtWidgets.QPushButton(
"Save in %ds" % (self.countdownRemaining),
clicked=self.countdownTimer.start)
self.saveButton = QtWidgets.QPushButton('Save', clicked=self.record)
item_layout.addWidget(self.countdownButton)
item_layout.addWidget(self.saveButton)
vlayout.addLayout(item_layout)
globalLayout.addLayout(vlayout)
self.layout.addLayout(globalLayout)
from classes import Dataset
from classes import Classifier
from classes import Classifier_random
from classes import Generation
from classes import First_Generation
import time
import pickle
t = time.time()
data_1 = '\\flowers\\daisy'
data_2 = '\\flowers\\tulip'
data = Dataset(data_1, data_2)
#with open('test_dataset.pickle', 'rb') as handle:
# data = pickle.load(handle)
print('---Data Loaded(' + str(round(time.time() - t, 2)) + 's)---\n')
t = time.time()
g = First_Generation(
500) #The number of individuals in all the generations is the same
g.evaluate(data)
print(f'Génération 1 ({round(time.time()-t,2)}s)')
print(g)
for i in range(2, 100):
t = time.time()
g = g.make_new_gen(150, mutation_rate=0.005)
g.evaluate(data)
print(f'\nGénération {i} ({round(time.time()-t,2)}s)')
print(g)
def remove_duplicate_clauses(dataset):
new_clauses = list(set(dataset.clauses))
new_dataset = Dataset(new_clauses)
return new_dataset
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()
class DatasetGui(QtWidgets.QWidget):
utils = Utils()
featureExtractor = FeatureExtractor()
bpn = BPNHandler(True)
accuracy = accuracy.Accuracy()
# Constructor of the DatasetGui class
#
# @param None
# @return None
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()
# Update the depth image displayed within the main window
#
# @param None
# @return None
def updateImage(self):
# Update to next frame
self.context.wait_and_update_all()
# Extract informations of each tracked user
self.data = skeleton.track(self.user, self.depth, self.data)
# Get the whole depth map
self.data.depth_map = np.asarray(self.depth.get_tuple_depth_map()).reshape(480, 640)
# Create the frame from the raw depth map string and convert it to RGB
frame = np.fromstring(self.depth.get_raw_depth_map_8(), np.uint8).reshape(480, 640)
frame = cv2.cvtColor(frame, cv2.COLOR_GRAY2RGB)
# Get the RGB image of the scene
self.data.image = np.fromstring(self.image.get_raw_image_map_bgr(), dtype=np.uint8).reshape(480, 640, 3)
# Will be used to specify the depth of the current hand wished
currentDepth, showCurrentDepth = 0, ""
if len(self.user.users) > 0 and len(self.data.skeleton["head"]) > 0:
# Highlight the head
ui.drawPoint(frame, self.data.skeleton["head"][0], self.data.skeleton["head"][1], 5)
# Display lines from elbows to the respective hands
ui.drawElbowLine(frame, self.data.skeleton["elbow"]["left"], self.data.skeleton["hand"]["left"])
ui.drawElbowLine(frame, self.data.skeleton["elbow"]["right"], self.data.skeleton["hand"]["right"])
# Get the pixel's depth from the coordinates of the hands
leftPixel = self.utils.getDepthFromMap(self.data.depth_map, self.data.skeleton["hand"]["left"])
rightPixel = self.utils.getDepthFromMap(self.data.depth_map, self.data.skeleton["hand"]["right"])
if self.data.hand == self.settings.LEFT_HAND:
currentDepth = leftPixel
elif self.data.hand == self.settings.RIGHT_HAND:
currentDepth = rightPixel
# Get the shift of the boundaries around both hands
leftShift = self.utils.getHandBoundShift(leftPixel)
rightShift = self.utils.getHandBoundShift(rightPixel)
# Display a rectangle around both hands
ui.drawHandBoundaries(frame, self.data.skeleton["hand"]["left"], leftShift, (50, 100, 255))
ui.drawHandBoundaries(frame, self.data.skeleton["hand"]["right"], rightShift, (200, 70, 30))
# Record the current data if the user is ready
if self.recordIfReady:
cv2.putText(frame, str(self.data.getWishedDistance()), (470, 60), cv2.FONT_HERSHEY_SIMPLEX, 2, (252, 63, 253), 5)
if self.data.getWishedDistance()>=int(currentDepth)-10 and self.data.getWishedDistance()<=int(currentDepth)+10:
self.record([])
self.recordIfReady = False
else:
if int(currentDepth)<self.data.getWishedDistance():
showCurrentDepth = str(currentDepth)+" +"
else:
showCurrentDepth = str(currentDepth)+" -"
else:
showCurrentDepth = str(currentDepth)
cv2.putText(frame, showCurrentDepth, (5, 60), cv2.FONT_HERSHEY_SIMPLEX, 2, (50, 100, 255), 5)
# Update the frame
self.depthImage.setPixmap(ui.convertOpenCVFrameToQPixmap(frame))
self.timerScreen.start()
# Update the label indicating the number of dataset elements saved so far for the current type
#
# @param None
# @return None
def updateDatasetNumberLabel(self):
if self.data.type == Dataset.TYPE_POSITIVE:
self.numberLabel.setText("Dataset #%d" % (self.utils.getFileNumberInFolder(self.settings.getPositiveFolder())))
elif self.data.type == Dataset.TYPE_NEGATIVE:
self.numberLabel.setText("Dataset #%d" % (self.utils.getFileNumberInFolder(self.settings.getNegativeFolder())))
elif self.data.type == Dataset.TYPE_ACCURACY:
self.numberLabel.setText("Dataset #%d" % (self.utils.getFileNumberInFolder(self.settings.getAccuracyFolder())))
else:
self.numberLabel.setText("Dataset #%d" % (self.utils.getFileNumberInFolder(self.settings.getDatasetFolder())))
# Record the actual informations
#
# @param obj Initiator of the event
# @return None
def record(self, obj):
# If the user collects data to check accuracy, prompts additional informations
if self.data.type == Dataset.TYPE_ACCURACY:
self.saveForTarget()
# If the user collects data for a heat map, let's do it
elif self.data.type == Dataset.TYPE_HEATMAP:
# The same button will be used to stop recording
if not self.heatmapRunning:
self.startRecordHeatmap()
else:
self.stopRecordHeatmap()
else:
# Directly save the dataset and update the label number
self.data.save()
self.countdownEndedSound.play()
self.updateDatasetNumberLabel()
# Handle a countdown as a mean to record the informations with a delay
#
# @param None
# @return None
def recordCountdown(self):
# Decrease the countdown and check if it needs to continue
self.countdownRemaining -= 1
if self.countdownRemaining <= 0:
# Re-initialise the timer and record the data
self.countdownTimer.stop()
self.countdownButton.setText("Saving..")
self.countdownRemaining = 10
self.record([])
else:
self.countdownTimer.start()
self.countdownSound.play()
# Display the actual reminaining
self.countdownButton.setText("Save in %ds"%(self.countdownRemaining))
# Record a heatmap representation of the informations by successive captures
#
# @param None
# @return None
def recordHeatmap(self):
if self.data.hand == self.settings.NO_HAND:
print "Unable to record as no hand is selected"
return False
if len(self.user.users) > 0 and len(self.data.skeleton["head"]) > 0:
# Input the data into the feature extractor
result = self.bpn.check(self.featureExtractor.getFeatures(self.data))
# Add the depth of the finger tip
point = self.featureExtractor.fingerTip[result[1]]
point.append(self.utils.getDepthFromMap(self.data.depth_map, point))
# Verify that informations are correct
if point[0]!=0 and point[1]!=0 and point[2]!=0:
# Add the result of the neural network
point.append(result[0])
self.heatmap.append(point)
self.countdownSound.play()
# Loop timer
self.heatmapTimer.start()
# Start the recording of the heatmap
#
# @param None
# @return None
def startRecordHeatmap(self):
self.saveButton.setText("Stop recording")
self.heatmapRunning = True
self.heatmapTimer.start()
# Stop the recording of the heatmap
#
# @param None
# @return None
def stopRecordHeatmap(self):
self.heatmapTimer.stop()
self.heatmapRunning = False
self.countdownEndedSound.play()
self.saveButton.setText("Record")
self.accuracy.showHeatmap(self.heatmap, "front")
self.heatmap = []
# Raise a flag to record the informations when the chosen distance will be met
#
# @param None
# @return None
def startRecordWhenReady(self):
self.recordIfReady = True
# Hold the current informations to indicate the position of the target thanks to the dialog window
#
# @param None
# @return None
def saveForTarget(self):
# Freeze the data
self.timerScreen.stop()
self.countdownEndedSound.play()
# Translate the depth values to a frame and set it in the dialog window
frame = np.fromstring(self.depth.get_raw_depth_map_8(), np.uint8).reshape(480, 640)
frame = cv2.cvtColor(frame, cv2.COLOR_GRAY2RGB)
self.dialogWindow.setFrame(frame)
# Prompt the position of the target
self.dialogWindow.exec_()
# Toggle the type of dataset chosen
#
# @param value Identifier of the new type of dataset
# @return None
def toggleType(self, value):
self.data.toggleType(value)
if value == self.data.TYPE_HEATMAP:
self.saveButton.setText("Record")
self.countdownButton.setText("Record in %ds"%(self.countdownRemaining))
self.readyButton.setEnabled(False)
# Create an array to hold all points
self.heatmap = []
else:
self.updateDatasetNumberLabel()
if hasattr(self, 'saveButton'):
self.saveButton.setText("Save")
self.countdownButton.setText("Save in %ds"%(self.countdownRemaining))
self.readyButton.setEnabled(True)
# Create the acquisition form of the main window
#
# @param None
# @return None
def createAcquisitionForm(self):
globalLayout = QtWidgets.QHBoxLayout()
vlayout = QtWidgets.QVBoxLayout()
# Drop down menu of the distance to record the informations when the pointing hand meet the corresponding value
hlayout = QtWidgets.QHBoxLayout()
label = QtWidgets.QLabel("Distance")
label.setFixedWidth(100)
comboBox = QtWidgets.QComboBox()
comboBox.currentIndexChanged.connect(self.data.toggleDistance)
comboBox.setFixedWidth(200)
comboBox.addItem("550")
comboBox.addItem("750")
comboBox.addItem("1000")
comboBox.addItem("1250")
comboBox.addItem("1500")
comboBox.addItem("1750")
comboBox.addItem("2000")
comboBox.setCurrentIndex(0)
hlayout.addWidget(label)
hlayout.addWidget(comboBox)
vlayout.addLayout(hlayout)
# Drop down menu to select the type of hand of the dataset
hlayout = QtWidgets.QHBoxLayout()
label = QtWidgets.QLabel("Pointing hand")
label.setFixedWidth(100)
comboBox = QtWidgets.QComboBox()
comboBox.currentIndexChanged.connect(self.data.toggleHand)
comboBox.setFixedWidth(200)
comboBox.addItem("Left")
comboBox.addItem("Right")
comboBox.addItem("None")
comboBox.setCurrentIndex(0)
hlayout.addWidget(label)
hlayout.addWidget(comboBox)
vlayout.addLayout(hlayout)
# Drop down menu of the dataset type
hlayout = QtWidgets.QHBoxLayout()
label = QtWidgets.QLabel("Type")
label.setFixedWidth(100)
comboBox = QtWidgets.QComboBox()
comboBox.currentIndexChanged.connect(self.toggleType)
comboBox.setFixedWidth(200)
comboBox.addItem("Positive")
comboBox.addItem("Negative")
comboBox.addItem("Accuracy")
comboBox.addItem("Heat map")
comboBox.setCurrentIndex(0)
hlayout.addWidget(label)
hlayout.addWidget(comboBox)
vlayout.addLayout(hlayout)
globalLayout.addLayout(vlayout)
vlayout = QtWidgets.QVBoxLayout()
self.numberLabel.setAlignment(QtCore.Qt.AlignCenter)
vlayout.addWidget(self.numberLabel)
# Action buttons to record the way that suits the most
hLayout = QtWidgets.QHBoxLayout()
self.readyButton = QtWidgets.QPushButton('Save when ready', clicked=self.startRecordWhenReady)
self.saveButton = QtWidgets.QPushButton('Save', clicked=self.record)
hLayout.addWidget(self.readyButton)
vlayout.addLayout(hLayout)
item_layout = QtWidgets.QHBoxLayout()
self.countdownButton = QtWidgets.QPushButton("Save in %ds"%(self.countdownRemaining), clicked=self.countdownTimer.start)
self.saveButton = QtWidgets.QPushButton('Save', clicked=self.record)
item_layout.addWidget(self.countdownButton)
item_layout.addWidget(self.saveButton)
vlayout.addLayout(item_layout)
globalLayout.addLayout(vlayout)
self.layout.addLayout(globalLayout)
if not os.path.isdir(name_exp + "/models"): os.mkdir(name_exp + "/models")
#CUDA
use_cuda = torch.cuda.is_available()
device = torch.device("cuda:0" if use_cuda else "cpu")
torch.backends.cudnn.benchmark = True
params = {'batch_size': 64, 'shuffle': True, 'num_workers': 6}
#Dataset
partition = create_partition_dict(list_utt_id, [])
spk_id_att_labels = txt_2_dict(att_labels_txt)
labels = create_labels_dict(list_utt_id, [], spk_id_att_labels)
#Generators
training_set = Dataset(partition['train'], labels, data_file, prob_file)
generator = data.DataLoader(training_set, **params)
# Layer dimension
input_dim = 512
latent_dim = 128
input_dim_discrim = latent_dim
hidden_dim_discrim = 128
model_ae = Autoencoder(input_dim, latent_dim)
optimizer_ae = torch.optim.SGD(model_ae.parameters(),
lr=0.0001,
momentum=0.9)
model_ae.to(device)
