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()
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.astype(np.uint8), cv2.COLOR_GRAY2RGB)
# 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"])
# Get the shift of the boundaries around both hands
leftShift = self.utils.getHandBoundShift(leftPixel)
rightShift = self.utils.getHandBoundShift(rightPixel)
if self.data.hand == self.settings.LEFT_HAND:
currentDepth = leftPixel
# Display a rectangle around the current hand
#ui.drawHandBoundaries(frame, self.data.skeleton["hand"]["left"], leftShift, (200, 70, 30))
elif self.data.hand == self.settings.RIGHT_HAND:
currentDepth = rightPixel
# Display a rectangle around the current hand
#ui.drawHandBoundaries(frame, self.data.skeleton["hand"]["right"], rightShift, (200, 70, 30))
#else:
# Display a rectangle around both hands
#ui.drawHandBoundaries(frame, self.data.skeleton["hand"]["left"], leftShift, (200, 70, 30))
#ui.drawHandBoundaries(frame, self.data.skeleton["hand"]["right"], rightShift, (200, 70, 30))
# Test the data against the neural network if possible
if self.data.hand != self.settings.NO_HAND:
result = self.bpn.check(self.featureExtractor.getFeatures(self.data))
self.resultLabel.setText(str(result[0]))
# Highlight the finger tip
if result[0] != False:
ui.drawPoint(frame, self.featureExtractor.fingerTip[result[1]][0], self.featureExtractor.fingerTip[result[1]][1], 5)
# Highlight the eye
ui.drawPoint(frame, self.featureExtractor.eyePosition[result[1]][0], self.featureExtractor.eyePosition[result[1]][1], 5)
# Line of sight
ui.drawElbowLine(frame, self.featureExtractor.eyePosition[result[1]], self.featureExtractor.fingerTip[result[1]])
# Indicate orientation
cv2.putText(frame, self.featureExtractor.orientation[result[1]], (5, 60), cv2.FONT_HERSHEY_SIMPLEX, 2, (50, 100, 255), 5)
# Update the frame
self.depthImage.setPixmap(ui.convertOpenCVFrameToQPixmap(frame))
self.timerScreen.start()
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()
def updateImage(self, frame): self.depthImage.setPixmap(ui.convertOpenCVFrameToQPixmap(frame))
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.astype(np.uint8), cv2.COLOR_GRAY2RGB)
# 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"])
# Get the shift of the boundaries around both hands
leftShift = self.utils.getHandBoundShift(leftPixel)
rightShift = self.utils.getHandBoundShift(rightPixel)
if self.data.hand == self.settings.LEFT_HAND:
currentDepth = leftPixel
# Display a rectangle around the current hand
#ui.drawHandBoundaries(frame, self.data.skeleton["hand"]["left"], leftShift, (200, 70, 30))
elif self.data.hand == self.settings.RIGHT_HAND:
currentDepth = rightPixel
# Display a rectangle around the current hand
#ui.drawHandBoundaries(frame, self.data.skeleton["hand"]["right"], rightShift, (200, 70, 30))
#else:
# Display a rectangle around both hands
#ui.drawHandBoundaries(frame, self.data.skeleton["hand"]["left"], leftShift, (200, 70, 30))
#ui.drawHandBoundaries(frame, self.data.skeleton["hand"]["right"], rightShift, (200, 70, 30))
# Test the data against the neural network if possible
if self.data.hand != self.settings.NO_HAND:
result = self.bpn.check(
self.featureExtractor.getFeatures(self.data))
self.resultLabel.setText(str(result[0]))
# Highlight the finger tip
if result[0] != False:
ui.drawPoint(frame,
self.featureExtractor.fingerTip[result[1]][0],
self.featureExtractor.fingerTip[result[1]][1],
5)
# Highlight the eye
ui.drawPoint(
frame, self.featureExtractor.eyePosition[result[1]][0],
self.featureExtractor.eyePosition[result[1]][1], 5)
# Line of sight
ui.drawElbowLine(
frame, self.featureExtractor.eyePosition[result[1]],
self.featureExtractor.fingerTip[result[1]])
# Indicate orientation
cv2.putText(frame,
self.featureExtractor.orientation[result[1]],
(5, 60), cv2.FONT_HERSHEY_SIMPLEX, 2,
(50, 100, 255), 5)
# Update the frame
self.depthImage.setPixmap(ui.convertOpenCVFrameToQPixmap(frame))
self.timerScreen.start()
