class TLDetector(object):
def __init__(self):
rospy.init_node('tl_detector')
self.pose = None
self.waypoints = None
self.camera_image = None
self.lights = []
self.waypoints_2D = None
self.waypoint_TREE = None
self.sim_testing = False
self.gamma_correction = False
self.count = 0
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', GlobalTrafficLightMessage, queue_size=1)
self.bridge = CvBridge()
self.light_classifier = TLClassifier(0.01, 0.5, False)
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 _pass_threshold(self):
if self.state == TrafficLight.YELLOW:
return self.state_count >= STATE_COUNT_THRESHOLD - 1
return self.state_count >= STATE_COUNT_THRESHOLD
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
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
if self.state != state:
self.state_count = 0
self.state = state
elif self._pass_threshold():
self.last_wp = light_wp
msg = self._prepare_result_msg(state, light_wp)
#rospy.loginfo(msg)
self.upcoming_red_light_pub.publish(msg)
else:
msg = self._prepare_result_msg(self.state, self.last_wp)
rospy.loginfo(msg)
self.upcoming_red_light_pub.publish(msg)
self.state_count += 1
def _prepare_result_msg(self, tl_state, tl_stop_waypoint):
tl_result = GlobalTrafficLightMessage()
tl_result.state = tl_state
tl_result.waypoint = tl_stop_waypoint
return tl_result
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
"""
#TODO implement
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 self.light_classifier is None: # Stop if classifer is not yet ready
return TrafficLight.RED
if (not self.has_image):
self.prev_light_loc = None
return False
cv_image = self.bridge.imgmsg_to_cv2(self.camera_image, "bgr8")
processed_img = cv_image[0:600, 0:800] # was [20:400, 0:800]
#Convert image to RGB format
processed_img = cv2.cvtColor(processed_img, cv2.COLOR_BGR2RGB)
#Get classification
#initialize light_state to unknown by default
light_state = TrafficLight.UNKNOWN
light_state_via_msg = None
img_full_np = self.light_classifier.load_image_into_numpy_array(
processed_img)
unknown = False
b, conf, cls_idx = self.light_classifier.get_localization_classification(
img_full_np)
if np.array_equal(b, np.zeros(4)):
print('unknown')
unknown = True
else:
#light_state = cls_idx
if cls_idx == 1.0:
print('Green', b)
light_state = TrafficLight.GREEN
elif cls_idx == 2.0:
print('Red', b)
light_state = TrafficLight.RED
elif cls_idx == 3.0:
print('Yellow', b)
light_state = TrafficLight.YELLOW
elif cls_idx == 4.0:
print('Unknown', b)
light_state = TrafficLight.UNKNOWN
else:
print('Really Unknown! Didn\'t process image well', b)
light_state = TrafficLight.UNKNOWN
self.count = self.count + 1
#filename = "site_image_" + str(self.count) + str(light_state) + ".jpg"
#cv2.imwrite(filename, cv_image)
return light_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)
#TODO find the closest visible traffic light (if one exists)
diff = len(self.waypoints.waypoints)
isclosestTFavilable = false
for i, light in enumerate(self.lights):
line = stop_line_positions[i]
temp_wp_idx = self.get_closest_waypoint(line[0], line[1])
d = temp_wp_idx - car_wp_idx
if d >= 0 and d < diff:
rospy.loginfo(d)
diff = d
isclosestTFavilable = True
line_wp_idx = temp_wp_idx
if d < 67:
closest_light = light
if closest_light and closest_light is not None:
rospy.loginfo("Inside classifer")
state = self.get_light_state(closest_light)
return line_wp_idx, state
if isclosestTFavilable:
return line_wp_idx, TrafficLight.UNKNOWN
else:
return -1, TrafficLight.UNKNOWN
class TLDetector(object):
def __init__(self):
rospy.init_node('tl_detector')
self.sim_testing = bool(rospy.get_param("~sim_testing", True))
threshold = rospy.get_param('~threshold', 0.3)
hw_ratio = rospy.get_param('~hw_ratio', 1.5)
self.gamma_correction = bool(
rospy.get_param("~gamma_correction", False))
self.pose = None
self.waypoints = None
self.camera_image = None
self.lights = []
config_string = rospy.get_param("/traffic_light_config")
self.config = yaml.load(config_string)
self.bridge = CvBridge()
self.light_classifier = None
self.light_classifier = TLClassifier(threshold, hw_ratio,
self.sim_testing)
self.listener = tf.TransformListener()
img_full_np = self.light_classifier.load_image_into_numpy_array(
np.zeros((800, 600, 3)))
#prime the pump. Don't need to do it for get_localization_classification.
self.light_classifier.get_localization(img_full_np) # "prime the pump"
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,
queue_size=1,
buff_size=2 * 52428800) # extended
#we don't use this publisher anymore. We publish the status of all traffic lights. Not just the red ones.
self.upcoming_red_light_pub = rospy.Publisher('/traffic_waypoint',
Int32,
queue_size=1)
self.upcoming_traffic_light_pub = rospy.Publisher(
'/all_traffic_waypoint', TLStatus, queue_size=1)
self.state = None
self.last_state = None
self.last_wp = -1
self.state_count = 0
self.count = 0 #processed images count
self.total_classification = 0
self.tp_classification = 0.0
print("TL Detection ... initialization complete")
rospy.spin()
def pose_cb(self, msg):
self.pose = msg
def waypoints_cb(self, waypoints):
self.waypoints = waypoints.waypoints
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
# rospy.loginfo('Image seq %s', msg.header.seq)
# img_time = msg.header.stamp
# age = rospy.Time.now() - img_time
# if age.secs > 0.2:
# rospy.loginfo('Image too old. Age %s', age.secs)
# light_wp, state = -1, TrafficLight.UNKNOWN
# else:
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 = state
#light_wp = light_wp if state == TrafficLight.RED else -1
self.last_wp = light_wp
tl_status_msg = TLStatus()
tl_status_msg.header.frame_id = '/world'
tl_status_msg.header.stamp = rospy.Time.now()
tl_status_msg.waypoint = light_wp
tl_status_msg.state = state
# rospy.loginfo("Light Wp is %s and state is %s", light_wp, self.state)
self.upcoming_traffic_light_pub.publish(tl_status_msg)
else:
# we keep publishing the last state and wp until it gets confirmed.
tl_status_msg = TLStatus()
tl_status_msg.header.frame_id = '/world'
tl_status_msg.header.stamp = rospy.Time.now()
tl_status_msg.waypoint = self.last_wp
if self.last_state is not None:
tl_status_msg.state = self.last_state
else:
# Do not start moving until first light state is obtained
# Fix for starting issues
tl_status_msg.state = TrafficLight.RED
#rospy.loginfo("Light Wp is %s and state is %s:", self.last_wp, self.last_state)
self.upcoming_traffic_light_pub.publish(tl_status_msg)
self.state_count += 1
def get_closest_waypoint(self, pose):
"""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
"""
if self.waypoints is None:
return
#TODO implement
min_dist = 10000
min_loc = None
pos_x = pose.position.x
pos_y = pose.position.y
# check all the waypoints to see which one is the closest to our current position
for i, waypoint in enumerate(self.waypoints):
wp_x = waypoint.pose.pose.position.x
wp_y = waypoint.pose.pose.position.y
dist = math.sqrt((pos_x - wp_x)**2 + (pos_y - wp_y)**2)
if (dist < min_dist): #we found a closer wp
min_loc = i # we store the index of the closest waypoint
min_dist = dist # we save the distance of the closest waypoint
# returns the index of the closest waypoint
return min_loc
def find_stop_line(self, closest_light_wp):
"""Find waypoint for stop line associated to traffic light
Args:
closest_light_wp: closest waypoint to the light
Returns:
closest_stop_line_wp: waypoint closest to the sop line before the light
"""
stop_line_positions = self.config['stop_line_positions']
closest_light_stop_wp = None
#search the stop line waypoint which is the closest to closest_light_wp
min_dist = 10000
for light_stop_position in stop_line_positions:
#convert 2D position into Pose format to get closest waypoint
light_stop_pose = Pose()
light_stop_pose.position.x = light_stop_position[0]
light_stop_pose.position.y = light_stop_position[1]
light_stop_wp = self.get_closest_waypoint(light_stop_pose)
dist = abs(closest_light_wp - light_stop_wp)
if dist < min_dist:
min_dist = dist
closest_light_stop_wp = light_stop_wp #note that it can be a bit ahead of the stop line
return closest_light_stop_wp
def project_to_image_plane(self, point_in_world):
"""Project point from 3D world coordinates to 2D camera image location
Args:
point_in_world (Point): 3D location of a point in the world
Returns:
x (int): x coordinate of target point in image
y (int): y coordinate of target point in image
"""
fx = self.config['camera_info']['focal_length_x']
fy = self.config['camera_info']['focal_length_y']
image_width = self.config['camera_info']['image_width']
image_height = self.config['camera_info']['image_height']
#print("Image Size:", image_width, image_height)
cx = image_width / 2
cy = image_height / 2
# get transform between pose of camera and world frame
trans = None
rot = None
x = 0
y = 0
try:
now = rospy.Time.now()
self.listener.waitForTransform("/base_link", "/world", now,
rospy.Duration(1.0))
(trans,
rot) = self.listener.lookupTransform("/base_link", "/world", now)
except (tf.Exception, tf.LookupException, tf.ConnectivityException):
rospy.logerr("Failed to find camera to map transform")
return None, None
if (trans and rot):
rpy = tf.transformations.euler_from_quaternion(rot)
yaw = rpy[2]
(ptx, pty,
ptz) = (point_in_world.position.x, point_in_world.position.y,
point_in_world.position.z)
#rotation
point_to_cam = (ptx * math.cos(yaw) - pty * math.sin(yaw),
ptx * math.sin(yaw) + pty * math.cos(yaw), ptz)
#translation
point_to_cam = [sum(x) for x in zip(point_to_cam, trans)]
#project to plan
x = -fx * point_to_cam[1] / point_to_cam[0]
y = -fy * point_to_cam[2] / point_to_cam[0]
x = int(x + cx)
y = int(y + cy)
return (x, y)
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 self.light_classifier is None: # Stop if classifer is not yet ready
return TrafficLight.RED
if (not self.has_image):
self.prev_light_loc = None
return False
cv_image = self.bridge.imgmsg_to_cv2(self.camera_image, "bgr8")
#TODO use light location to zoom in on traffic light in image
#Prepare image for classification
if self.sim_testing: #we cut 50 pixels left and right of the image and the bottom 100 pixels
width, height, _ = cv_image.shape
x_start = int(width * 0.10)
x_end = int(width * 0.90)
y_start = 0
y_end = int(height * 0.85)
processed_img = cv_image[y_start:y_end, x_start:x_end]
else: # real-case testing. Reduce image size to avoid light reflections on hood.
processed_img = cv_image[0:600, 0:800] # was [20:400, 0:800]
#Convert image to RGB format
processed_img = cv2.cvtColor(processed_img, cv2.COLOR_BGR2RGB)
#Get classification
#initialize light_state to unknown by default
light_state = TrafficLight.UNKNOWN
light_state_via_msg = None
#get the ground truth traffic light states through the traffic light messages
for tl in self.lights:
dist = math.sqrt((tl.pose.pose.position.x - light.position.x)**2 +
(tl.pose.pose.position.y - light.position.y)**2)
if (dist < 50): #means we found the light close to the stop line
light_state_via_msg = tl.state
break #no need to parse other lights once light was found
#detect traffic light position (box) in image
#convert image to np array
img_full_np = self.light_classifier.load_image_into_numpy_array(
processed_img)
#apply gamma correction to site testing if parameter set in launch file.
if (self.gamma_correction == True):
img_full_np = adjust_gamma(img_full_np, 0.4)
# if simulator, we apply detection and classification separately
unknown = False
if self.sim_testing:
# find traffic light in image.
b = self.light_classifier.get_localization(img_full_np)
print(b)
# If there is no detection or low-confidence detection
if np.array_equal(b, np.zeros(4)):
print('unknown')
unknown = True
else: #we can use the classifier to classify the state of the traffic light
img_np = cv2.resize(processed_img[b[0]:b[2], b[1]:b[3]],
(32, 32))
self.light_classifier.get_classification(img_np)
light_state = self.light_classifier.signal_status
else:
print("Get in Localization-Classification")
b, conf, cls_idx = self.light_classifier.get_localization_classification(
img_full_np, visual=False)
print("Get out of Localization-Classification")
if np.array_equal(b, np.zeros(4)):
print('unknown')
unknown = True
else:
#light_state = cls_idx
if cls_idx == 1.0:
print('Green', b)
light_state = TrafficLight.GREEN
elif cls_idx == 2.0:
print('Red', b)
light_state = TrafficLight.RED
elif cls_idx == 3.0:
print('Yellow', b)
light_state = TrafficLight.YELLOW
elif cls_idx == 4.0:
print('Unknown', b)
light_state = TrafficLight.UNKNOWN
else:
print('Really Unknown! Didn\'t process image well', b)
light_state = TrafficLight.UNKNOWN
#check prediction against ground truth
if self.sim_testing:
rospy.loginfo("Upcoming light %s, True state: %s", light_state,
light_state_via_msg)
#compare detected state against ground truth for (simulator only)
if not unknown:
self.count = self.count + 1
filename = "sim_image_" + str(self.count)
if (light_state == light_state_via_msg):
self.tp_classification = self.tp_classification + 1
#filename = filename + "_good_" + str(light_state) + ".jpg"
else:
filename = filename + "_bad_" + str(light_state) + ".jpg"
#cv2.imwrite(filename, cv_image)
self.total_classification = self.total_classification + 1
accuracy = (self.tp_classification /
self.total_classification) * 100
if self.count % 20 == 0:
rospy.loginfo("Classification accuracy: %s", accuracy)
else: #site testing
self.count = self.count + 1
#filename = "site_image_" + str(self.count) + str(light_state) + ".jpg"
#cv2.imwrite(filename, cv_image)
return light_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)
"""
light = None
closest_light_wp = None
closest_light_stop_wp = None
dist_to_light = 10000 #initialize to high value
stop_line_positions = self.config['stop_line_positions']
if (self.pose):
car_position = self.get_closest_waypoint(self.pose.pose)
#rospy.loginfo("Car position (at Wp Index): %s", car_position)
else:
return -1, TrafficLight.UNKNOWN
for light_stop_position in stop_line_positions:
light_stop_pose = Pose()
light_stop_pose.position.x = light_stop_position[0]
light_stop_pose.position.y = light_stop_position[1]
light_stop_wp = self.get_closest_waypoint(
light_stop_pose) #get the wp closest to each light_position
if light_stop_wp >= car_position: #it found a waypoint close to the traffic light and ahead of the car
if closest_light_stop_wp is None: #check if this is the first light we process
closest_light_stop_wp = light_stop_wp
light = light_stop_pose
elif light_stop_wp < closest_light_stop_wp:
closest_light_stop_wp = light_stop_wp #if we have a closer light_wp ahead of the car position, we allocate closer value
light = light_stop_pose
if ((car_position is not None)
and (closest_light_stop_wp is not None)):
dist_to_light = abs(car_position - closest_light_stop_wp)
#rospy.loginfo("Closest light position (in Wp index): %s", closest_light_stop_wp)
if light and dist_to_light < 100: #we check the status of the traffic light if it's within 200 waypoints distance
state = self.get_light_state(light)
#closest_stop_line_wp = self.find_stop_line(closest_light_wp)
return closest_light_stop_wp, state
#self.waypoints = None
return -1, TrafficLight.UNKNOWN
