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