def ImageProcessor(self):
"""
Process each frame
> detect left and right hand-gestures
> perform mapping to {left token, right token}s
> use Finite state machine to generate full instruction
see more details in the paper: ieeexplore.ieee.org/document/8543168
"""
# get the tokens (and the bounding boxes for vizualization)
left_token, left_box, right_token, right_box, success_ = self.gest_rec.Get_gest(self.original, self.use_single_hand)
print ("Hand gestures detection success: {2}. token: ({0}, {1})".format(right_token, left_token, success_))
if success_:
# ROBO_GEST mode
if self.robo_gest_mode:
# reverse left and right since camera(left, right) == person(right, left)
# then pass it to generate instruction
get_token, done_ = self.ins.decode(right_token, left_token)
print (get_token, done_)
if done_:
print
print ("*** Decoded Instruction: {0}".format(get_token))
print
# For Menue Selection only
if self.menue_mode:
#Notifies aquamenu that the gesture is waiting.
msg = String()
msg.data = 'WAITING'
self.stat_pub.publish(msg)
men_ins_, men_done_ = self.men_sel.decode(right_token, left_token)
#print(men_ins_, men_done_)
if men_done_:
#Notifies aquamenu that the gesture is done.
msg.data = 'DONE'
self.stat_pub.publish(msg)
print
print ("Decoded Instruction: {0}".format(men_ins_))
print
men_tok = men_ins_.split(' ')
if (len(men_tok)>0 and men_tok[1] in self.menue_map.keys()):
menue_selected = self.menue_map[men_tok[1]]
msg = Tags()
tag = Tag()
tag.id = menue_selected
msg.tags = [tag]
self.tags_pub.publish(msg)
print ('***** Menue selected :: {0}'.format(menue_selected))
print
if (self.bench_test or self.publish_image):
localised_objs = [(left_token, left_box), (right_token, right_box)]
self.draw_boxes_and_labels(localised_objs)
if self.bench_test:
self.showFrame(self.original, 'test_viz')
if self.publish_image:
msg_frame = CvBridge().cv2_to_imgmsg(self.original, encoding="bgr8")
self.ProcessedRaw.publish(msg_frame)
def ImageProcessor(self):
"""
Process each frame
> detect left and right hand-gestures
> perform mapping to {left token, right token}s
> use Finite state machine to generate full instruction
see more details in the paper: ieeexplore.ieee.org/document/8543168
"""
# get the tokens (and the bounding boxes for vizualization)
left_token, left_box, right_token, right_box, success_ = self.gest_rec.Get_gest(
self.original, self.use_single_hand)
print("Hand gestures detection success: {2}. token: ({0}, {1})".format(
right_token, left_token, success_))
if success_:
# ROBO_GEST mode
if self.robo_gest_mode:
# reverse left and right since camera(left, right) == person(right, left)
# then pass it to generate instruction
get_token, done_ = self.ins.decode(right_token, left_token)
print(get_token, done_)
if done_:
print
print("*** Decoded Instruction: {0}".format(get_token))
print
# For Menue Selection only
if self.menue_mode:
men_ins_, men_done_ = self.men_sel.decode(
right_token, left_token)
#print(men_ins_, men_done_)
if men_done_:
print
print("Decoded Instruction: {0}".format(men_ins_))
print
men_tok = men_ins_.split(' ')
if (len(men_tok) > 0
and men_tok[1] in self.menue_map.keys()):
menue_selected = self.menue_map[men_tok[1]]
msg = Tags()
tag = Tag()
tag.id = menue_selected
msg.tags = [tag]
self.tags_pub.publish(msg)
print('***** Menue selected :: {0}'.format(
menue_selected))
print
if self.bench_test:
self.showFrame(self.original, 'test_viz')
if self.publish_image:
if left_box != None:
output_img = cv2.rectangle(self.original,
(left_box[0], left_box[2]),
(left_box[1], left_box[3]),
(255, 0, 0), 2)
else:
output_img = self.original
msg_frame = CvBridge().cv2_to_imgmsg(output_img, encoding="bgr8")
self.ProcessedRaw.publish(msg_frame)
