def main(responses):
pub = rospy.Publisher('tocabi/emotion', Int64, queue_size=10)
rospy.init_node('Emotion')
prev_speaking_flag = -1
prev_action = 1
cur_action = 1
D = Decision()
for response in responses:
if not response.results:
continue
result = response.results[0]
cur_flag = 0 if result.is_final == False else 1
if prev_speaking_flag != cur_flag and not result.is_final:
# TODO: Modulize Action Part
IS_SPEAKING = True
cur_action = D.decide(IS_SPEAKING, "")
print("Started Speaking!")
if cur_action != prev_action:
prev_action = cur_action
prev_speaking_flag = cur_flag
pub.publish(cur_action)
if not result.alternatives:
continue
transcript = result.alternatives[0].transcript
if result.is_final:
IS_SPEAKING = False
# TODO: Modulize Action Part
cur_action = D.decide(IS_SPEAKING, transcript)
print "Finished speaking. Recognized sentence: ", "'",transcript,"'"
print "Classified Emotion Index: ", cur_action,"\n"
if cur_action != prev_action:
prev_action = cur_action
prev_speaking_flag = -1
pub.publish(cur_action)
# Exit recognition if any of the transcribed phrases could be one of ["exit", "quit"]
if re.search(r"\b(exit|quit)\b", transcript, re.I):
print("Exiting..")
break
def main(responses):
pub = rospy.Publisher('tocabi/emotion', Int64, queue_size=10)
overlay_control_pub = rospy.Publisher('overlay_command',
String,
queue_size=5)
pose_calibration_pub = rospy.Publisher(
'/tocabi/dg/avatar/pose_calibration_flag', Int8, queue_size=5)
retargeting_sync_pub = rospy.Publisher('/tocabi/dg/upperbodymodecommand',
Float32,
queue_size=5)
rospy.init_node('Emotion')
prev_speaking_flag = -1
prev_action = 1
cur_action = 1
D = Decision()
for response in responses:
if not response.results:
continue
result = response.results[0]
cur_flag = 0 if result.is_final == False else 1
if prev_speaking_flag != cur_flag and not result.is_final:
# TODO: Modulize Action Part
IS_SPEAKING = True
cur_action = D.decide(IS_SPEAKING, "")
print("Started Speaking!")
if cur_action != prev_action:
prev_action = cur_action
prev_speaking_flag = cur_flag
pub.publish(cur_action)
if not result.alternatives:
continue
transcript = result.alternatives[0].transcript.lower()
if result.is_final:
if "close" in transcript:
overlay_control_pub.publish("close")
if "open" in transcript:
overlay_control_pub.publish("open")
if "right" in transcript:
overlay_control_pub.publish("right")
if "left" in transcript:
overlay_control_pub.publish("left")
if "up" in transcript:
overlay_control_pub.publish("up")
if "down" in transcript:
overlay_control_pub.publish("down")
if "front" in transcript:
overlay_control_pub.publish("front")
if "back" in transcript:
overlay_control_pub.publish("back")
if "opacity" in transcript:
overlay_control_pub.publish(transcript)
if "1" in transcript:
pose_calibration_pub.publish(1)
elif "2" in transcript:
pose_calibration_pub.publish(2)
elif "3" in transcript:
pose_calibration_pub.publish(3)
elif "4" in transcript:
pose_calibration_pub.publish(4)
elif "5" in transcript:
pose_calibration_pub.publish(5)
if "stop" in transcript:
retargeting_sync_pub.publish(3)
IS_SPEAKING = False
# TODO: Modulize Action Part
cur_action = D.decide(IS_SPEAKING, transcript)
print "Finished speaking. Recognized sentence: ", "'", transcript, "'"
print "Classified Emotion Index: ", cur_action, "\n"
if cur_action != prev_action:
prev_action = cur_action
prev_speaking_flag = -1
pub.publish(cur_action)
# Exit recognition if any of the transcribed phrases could be one of ["exit", "quit"]
if re.search(r"\b(exit|quit)\b", transcript, re.I):
print("Exiting..")
break