class TLDetector(object):
def __init__(self):
rospy.init_node('tl_detector')
self.pose = None
self.waypoints = None
self.waypoints_2d = None
self.waypoint_tree = None
self.camera_image = None
self.lights = []
self.light_classifier = TLClassifier()
# Image object from the image_color topic needs to be converted to numpy array for Tensorflow
# We are going to send "bgr8" layers to tl_classifier.
# http://library.isr.ist.utl.pt/docs/roswiki/cv_bridge(2f)Tutorials(2f)ConvertingBetweenROSImagesAndOpenCVImagesPython.html
self.cv_bridge = CvBridge()
sub1 = rospy.Subscriber('/current_pose', PoseStamped, self.pose_cb)
sub2 = rospy.Subscriber('/base_waypoints', Lane, self.waypoints_cb)
'''
/vehicle/traffic_lights provides you with the location of the traffic light in 3D map space and
helps you acquire an accurate ground truth data source for the traffic light
classifier by sending the current color state of all traffic lights in the
simulator. When testing on the vehicle, the color state will not be available. You'll need to
rely on the position of the light and the camera image to predict it.
'''
sub3 = rospy.Subscriber('/vehicle/traffic_lights', TrafficLightArray,
self.traffic_cb)
sub6 = rospy.Subscriber('/image_color', Image, self.image_cb)
config_string = rospy.get_param("/traffic_light_config")
self.config = yaml.load(config_string)
self.upcoming_red_light_pub = rospy.Publisher('/traffic_waypoint',
Int32,
queue_size=1)
self.tl_detector_ready = rospy.Publisher('/tl_detector_ready',
Bool,
queue_size=1,
latch=True)
self.bridge = CvBridge()
self.listener = tf.TransformListener()
self.state = TrafficLight.UNKNOWN
self.last_state = TrafficLight.UNKNOWN
self.last_wp = -1
self.state_count = 0
rospy.spin()
def pose_cb(self, msg):
self.pose = msg
def waypoints_cb(self, waypoints):
self.waypoints = waypoints
if not self.waypoints_2d:
self.waypoints_2d = [[
waypoint.pose.pose.position.x, waypoint.pose.pose.position.y
] for waypoint in waypoints.waypoints]
self.waypoint_tree = KDTree(self.waypoints_2d)
def traffic_cb(self, msg):
self.lights = msg.lights
def image_cb(self, msg):
"""Identifies red lights in the incoming camera image and publishes the index
of the waypoint closest to the red light's stop line to /traffic_waypoint
Args:
msg (Image): image from car-mounted camera
"""
self.has_image = True
self.camera_image = msg
self.cv_image = self.cv_bridge.imgmsg_to_cv2(msg, "bgr8")
'''
Publish upcoming red lights at camera frequency.
Each predicted state has to occur `STATE_COUNT_THRESHOLD` number
of times till we start using it. Otherwise the previous stable state is
used.
'''
img_crop = self.light_classifier.detect_traffic_light(self.cv_image)
if len(img_crop) == 0:
state = TrafficLight.UNKNOWN
else:
img_crop_msg = self.cv_bridge.cv2_to_imgmsg(img_crop,
encoding="bgr8")
state = self.light_classifier.get_classification(img_crop)
self.update_state(state)
self.tl_detector_ready.publish(Bool(True))
def update_state(self, state):
light_wp, true_state = self.process_traffic_lights()
if self.state != state:
self.state_count = 0
self.state = state
elif self.state_count >= STATE_COUNT_THRESHOLD:
self.last_state = self.state
light_wp = light_wp if state == TrafficLight.RED else -1
self.last_wp = light_wp
self.upcoming_red_light_pub.publish(Int32(light_wp))
else:
self.upcoming_red_light_pub.publish(Int32(self.last_wp))
self.state_count += 1
def get_closest_waypoint(self, x, y):
"""Identifies the closest path waypoint to the given position
https://en.wikipedia.org/wiki/Closest_pair_of_points_problem
Args:
pose (Pose): position to match a waypoint to
Returns:
int: index of the closest waypoint in self.waypoints
"""
closest_idx = self.waypoint_tree.query([x, y], 1)[1]
return closest_idx
def get_light_state(self, light):
"""Determines the current color of the traffic light
Args:
light (TrafficLight): light to classify
Returns:
int: ID of traffic light color (specified in styx_msgs/TrafficLight)
"""
#if(not self.has_image):
# self.prev_light_loc = None
# return False
#cv_image = self.bridge.imgmsg_to_cv2(self.camera_image, "bgr8")
##Get classification
#return self.light_classifier.get_classification(cv_image)
return self.last_state
def process_traffic_lights(self):
"""Finds closest visible traffic light, if one exists, and determines its
location and color
Returns:
int: index of waypoint closes to the upcoming stop line for a traffic light (-1 if none exists)
int: ID of traffic light color (specified in styx_msgs/TrafficLight)
"""
closest_light = None
line_wp_idx = None
# List of positions that correspond to the line to stop in front of for a given intersection
stop_line_positions = self.config['stop_line_positions']
if (self.pose and self.waypoint_tree):
car_wp_idx = self.get_closest_waypoint(self.pose.pose.position.x,
self.pose.pose.position.y)
#TODO find the closest visible traffic light (if one exists)
diff = len(self.waypoints.waypoints)
for i, light in enumerate(self.lights):
# Get stop line waypoint index
line = stop_line_positions[i]
temp_wp_idx = self.get_closest_waypoint(line[0], line[1])
# Find closest stop line waypoint index
d = temp_wp_idx - car_wp_idx
if d >= 0 and d < diff:
diff = d
closest_light = light
line_wp_idx = temp_wp_idx
if closest_light:
state = self.get_light_state(closest_light)
return line_wp_idx, state
return -1, TrafficLight.UNKNOWN
class TLDetector(object):
def __init__(self):
rospy.init_node('tl_detector')
self.pose = None
self.waypoints = None
self.waypoints_2d = None
self.waypoint_tree = None
self.camera_image = None
self.lights = []
rospy.Subscriber('/current_pose', PoseStamped, self.pose_cb)
rospy.Subscriber('/base_waypoints', Lane, self.waypoints_cb)
'''
/vehicle/traffic_lights provides you with the location of the traffic light in 3D map space and
helps you acquire an accurate ground truth data source for the traffic light
classifier by sending the current color state of all traffic lights in the
simulator. When testing on the vehicle, the color state will not be available. You'll need to
rely on the position of the light and the camera image to predict it.
'''
rospy.Subscriber('/vehicle/traffic_lights', TrafficLightArray,
self.traffic_cb)
rospy.Subscriber('/image_color',
Image,
self.image_cb,
queue_size=1,
buff_size=2 * 52428800)
config_string = rospy.get_param("/traffic_light_config")
self.config = yaml.load(config_string)
self.upcoming_red_light_pub = rospy.Publisher('/traffic_waypoint',
Int32,
queue_size=1)
self.bridge = CvBridge()
self.light_classifier = TLClassifier()
self.listener = tf.TransformListener()
self.state = TrafficLight.UNKNOWN
self.last_state = TrafficLight.UNKNOWN
self.last_wp = -1
self.state_count = 0
self.image_counter = 0
rospy.spin()
def pose_cb(self, msg):
self.pose = msg
def waypoints_cb(self, waypoints):
self.waypoints = waypoints
if not self.waypoints_2d:
self.waypoints_2d = [[
waypoint.pose.pose.position.x, waypoint.pose.pose.position.y
] for waypoint in waypoints.waypoints]
self.waypoint_tree = KDTree(self.waypoints_2d)
def traffic_cb(self, msg):
self.lights = msg.lights
def image_cb(self, msg):
"""Identifies red lights in the incoming camera image and publishes the index
of the waypoint closest to the red light's stop line to /traffic_waypoint
Args:
msg (Image): image from car-mounted camera
"""
if self.image_counter == 3:
self.has_image = True
self.camera_image = msg
light_wp, state = self.process_traffic_lights()
'''
Publish upcoming red lights at camera frequency.
Each predicted state has to occur `STATE_COUNT_THRESHOLD` number
of times till we start using it. Otherwise the previous stable state is
used.
'''
if self.state != state:
self.state_count = 0
self.state = state
elif self.state_count >= STATE_COUNT_THRESHOLD:
self.last_state = self.state
light_wp = light_wp if state == TrafficLight.RED else -1
self.last_wp = light_wp
self.upcoming_red_light_pub.publish(Int32(light_wp))
else:
self.upcoming_red_light_pub.publish(Int32(self.last_wp))
self.state_count += 1
self.image_counter = 0
else:
self.image_counter += 1
def get_closest_waypoint(self, x, y):
"""Identifies the closest path waypoint to the given position
https://en.wikipedia.org/wiki/Closest_pair_of_points_problem
Args:
pose (Pose): position to match a waypoint to
Returns:
int: index of the closest waypoint in self.waypoints
"""
closest_idx = self.waypoint_tree.query([x, y], 1)[1]
return closest_idx
def get_light_state(self, light):
"""Determines the current color of the traffic light
Args:
light (TrafficLight): light to classify
Returns:
int: ID of traffic light color (specified in styx_msgs/TrafficLight)
"""
predicted_state = None
if (not self.has_image):
# self.prev_light_loc = None
return TrafficLight.UNKNOWN
cv_image = self.bridge.imgmsg_to_cv2(self.camera_image, "bgr8")
box, score = self.light_classifier.detect_traffic_light(cv_image)
if box is not None and score is not None:
roi = cv_image[box[0]:box[2], box[1]:box[3]]
resize = (32, 32)
img = cv2.resize(roi, resize, interpolation=cv2.INTER_AREA)
img = np.array([img])
predicted_state = self.light_classifier.get_classification(img)
return predicted_state
def process_traffic_lights(self):
"""Finds closest visible traffic light, if one exists, and determines its
location and color
Returns:
int: index of waypoint closes to the upcoming stop line for a traffic light (-1 if none exists)
int: ID of traffic light color (specified in styx_msgs/TrafficLight)
"""
closest_light = None
line_wp_idx = None
# List of positions that correspond to the line to stop in front of for a given intersection
stop_line_positions = self.config['stop_line_positions']
if (self.pose):
car_wp_idx = self.get_closest_waypoint(self.pose.pose.position.x,
self.pose.pose.position.y)
# find the closest visible traffic light (if one exists)
diff = len(self.waypoints.waypoints)
for i, light in enumerate(self.lights):
# get stop line waypoint index
line = stop_line_positions[i]
temp_wp_idx = self.get_closest_waypoint(line[0], line[1])
# find closest stop line waypoint index
d = temp_wp_idx - car_wp_idx
if d >= 0 and d < diff:
diff = d
closest_light = light
line_wp_idx = temp_wp_idx
if closest_light:
state = self.get_light_state(closest_light)
return line_wp_idx, state
return -1, TrafficLight.UNKNOWN
