def check_regions_and_pairs():
"""
Make sure the tracking regions and camera pairs are well defined.
"""
regions_dict = file_tools.read_tracking_2d_regions()
camera_pairs_dict = file_tools.read_tracking_2d_camera_pairs()
region_tools.check_regions_and_camera_pairs(regions_dict, camera_pairs_dict)
def stitched_image_labelers_ready():
"""
Checks to see if the stitched image labelers are ready. They are considered
ready if a image_stitched_labeled topic is being published for every
tracking region.
"""
regions_dict = file_tools.read_tracking_2d_regions()
number_of_regions = len(regions_dict)
number_of_labeled = len(find_topics_w_ending('image_stitched_labeled'))
if number_of_regions == number_of_labeled:
return True
else:
return False
def stitched_images_ready():
"""
Check to see if the stitched images topics are ready. There should be one
per region.
"""
regions_dict = file_tools.read_tracking_2d_regions()
num_regions = len(regions_dict)
num_stitched = get_number_of_stitched_image_topics()
num_seq_and_stitched = get_number_of_seq_and_image_stitched_topics()
if num_regions == num_stitched and num_regions == num_seq_and_stitched:
return True
else:
return False
def three_point_tracker_synchronizers_ready():
"""
Checks to see if the three point tracker synchronizers are ready. They are
considered to be ready if the tracking_pts and image_tracking_pts topics are
being published for every tracking region.
"""
regions_dict = file_tools.read_tracking_2d_regions()
num_regions = len(regions_dict)
num_tracking_pts = sum([len(find_region_tracking_pts_topics(r)) for r in regions_dict])
num_image_tracking_pts = sum([len(find_region_image_tracking_pts_topics(r)) for r in regions_dict])
if num_regions == num_tracking_pts and num_regions == num_image_tracking_pts:
return True
else:
return False
def __init__(self):
self.regions_dict = file_tools.read_tracking_2d_regions()
self.camera_pairs_dict = file_tools.read_tracking_2d_camera_pairs()
# Create dictionaries describing tranformation setup
self._create_camera_to_region_dict()
self._create_tracking_plane_to_region_dict()
self._create_region_to_anchor_plane_dict()
self._create_tracking_to_anchor_plane_dict()
self._create_camera_to_image_size_dict()
self._get_stitching_params()
# Create transformation dictionaries
self._create_tracking_plane_to_transform_dict()
self._create_camera_to_transform_dict()
def three_point_trackers_ready():
"""
Checks to see if the three point trackers are ready. They are considered ready
if for every camera in a tracking
"""
regions_dict = file_tools.read_tracking_2d_regions()
camera_set = set()
for region, camera_list in regions_dict.iteritems():
camera_set.update(camera_list)
num_cameras = len(camera_set)
num_tracking_pts = len(find_camera_tracking_pts_topics())
num_image_tracking_pts =len(find_camera_image_tracking_pts_topics())
if num_cameras == num_tracking_pts and num_cameras == num_image_tracking_pts:
return True
else:
return False
def setup_redis_db():
# Create db and add empty camera assignment
db = redis.Redis('localhost', db=config.redis_db)
# Add regions dictionary
regions_dict = file_tools.read_tracking_2d_regions()
redis_tools.set_dict(db, 'regions_dict', regions_dict)
# Add extra video dictionary
extra_video_dict = file_tools.read_logging_extra_video()
redis_tools.set_dict(db, 'extra_video_dict', extra_video_dict)
# Add mjpeg info dictionary
mjpeg_info_dict = mjpeg_servers.get_mjpeg_info_dict()
redis_tools.set_dict(db, 'mjpeg_info_dict', mjpeg_info_dict)
# Add external network interface
machine_def = mct_introspection.get_machine_def()
ip_iface_ext = iface_tools.get_ip_addr(
machine_def['mct_master']['iface-ext'])
redis_tools.set_str(db, 'ip_iface_ext', ip_iface_ext)
# Set operating mode
redis_tools.set_str(db, 'current_mode', 'preview')
## Get frame rates
frame_rate_dict = file_tools.read_frame_rates()
redis_tools.set_dict(db, 'frame_rate_dict', frame_rate_dict)
frame_rate = frame_rate_dict['tracking_2d']
# Get logging directory - add base path to directory, and create if it doesn't exist
logging_params_dict = file_tools.read_logging_params()
logging_params_dict['directory'] = os.path.join(
os.environ['HOME'], logging_params_dict['directory'])
if not os.path.exists(logging_params_dict['directory']):
os.mkdir(logging_params_dict['directory'])
redis_tools.set_dict(db, 'logging_params_dict', logging_params_dict)
# Get lighting params
mightex_params_dict = file_tools.read_mightex_params()
lighting_params_dict = mightex_to_lighting_params(mightex_params_dict)
redis_tools.set_dict(db, 'lighting_params_dict', lighting_params_dict)
return db
def __init__(self, region, max_seq_age=200):
self.lock = threading.Lock()
self.region = region
regions_dict = file_tools.read_tracking_2d_regions()
self.camera_list = regions_dict[region]
self.camera_list.sort()
self.create_camera_to_tracking_dict()
self.latest_seq = None
self.max_seq_age = max_seq_age
self.tracking_pts_pool = {}
self.tracking_pts_roi_size = rospy.get_param(
'/three_point_tracker_params/roi_size', (150, 150))
# Color and font for tracking points image
self.magenta = (255, 255, 0)
self.cv_text_font = cv.InitFont(cv.CV_FONT_HERSHEY_TRIPLEX,
0.6,
0.6,
thickness=0)
# Font for PIL tracking info image
self.info_image_size = (180, 100)
self.font = PILImageFont.truetype(
"/usr/share/fonts/truetype/ubuntu-font-family/Ubuntu-B.ttf", 16)
# Get transforms from cameras to tracking and stitched image planes
self.tf2d = transform_2d.Transform2d()
self.bridge = CvBridge()
self.ready = False
rospy.init_node('three_point_tracker_synchronizer',
log_level=rospy.DEBUG)
# Subscribe to raw tracking pts topics
self.tracking_pts_sub = {}
for camera, topic in self.camera_to_tracking.iteritems():
handler = functools.partial(self.tracking_pts_handler, camera)
self.tracking_pts_sub[camera] = rospy.Subscriber(
topic, ThreePointTrackerRaw, handler)
# Create publishers
self.tracking_pts_pub = rospy.Publisher('tracking_pts',
ThreePointTracker)
self.image_tracking_pts = None
self.image_tracking_pts_pub = rospy.Publisher('image_tracking_pts',
Image)
self.image_tracking_info_pub = rospy.Publisher('image_tracking_info',
Image)
# Setup rand sync service
rospy.wait_for_service('/get_rand_sync_signal')
self.rand_sync_srv = rospy.ServiceProxy('/get_rand_sync_signal',
GetRandSyncSignal)
# Setup reset service - needs to be called anytime the camera trigger is
# stopped - before it is restarted. Empties buffers of images and sequences.
self.reset_srv = rospy.Service('reset_tracker_synchronizer', Empty,
self.reset_handler)
self.ready = True
from mct_frame_drop_corrector import frame_drop_corrector_master
from mct_frame_drop_corrector import frame_drop_corrector
from mct_image_stitcher import image_stitcher_master
from mct_tracking_2d import three_point_tracker_master
from mct_tracking_2d import stitched_image_labeler_master
from mct_avi_writer import avi_writer_master
from mct_logging import tracking_pts_logger_master
from mct_rand_sync import reset_rand_sync
# Startup parameters
ready_poll_dt = 0.2 # Sleep time for polling loop when waiting for node or topic to start
camera_startup_t = 10.0 # Sleep interval for waiting for camera processes to startup
buffer_flush_t = 2.0 # Sleep interval for flushing buffers
# Get Startup configuration information
regions_dict = file_tools.read_tracking_2d_regions()
extra_video_dict = file_tools.read_logging_extra_video()
camera_pairs_dict = file_tools.read_tracking_2d_camera_pairs()
region_tools.check_regions_and_camera_pairs(regions_dict, camera_pairs_dict)
def debug_print(*args, **kwarg):
debug = kwarg.pop('debug')
if debug:
print(*args, **kwarg)
def tracking_2d_node_startup(debug=False):
# Stop camera triggers
debug_print(' * stopping camera triggers ... ', end='', debug=debug)
def __init__(
self,
region,
topic_end='image_rect_skip',
topic_type='sensor_msgs/Image',
max_seq_age=150,
max_stamp_age=1.5
):
self.topic_end = topic_end
self.topic_type = topic_type
self.max_seq_age = max_seq_age
self.max_stamp_age = max_stamp_age
self.warp_flags = cv.CV_INTER_LINEAR
self.stitching_params = file_tools.read_tracking_2d_stitching_params()
rospy.init_node('image_stitcher')
self.ready = False
self.is_first_write = True
self.stitched_image = None
self.seq_to_images = {}
self.stamp_to_seq_pool= {}
self.image_waiting_pool = {}
# Information on the latest sequence received and stitched
self.seq_newest = None
self.stamp_newest = None
self.lock = threading.Lock()
self.bridge = CvBridge()
# Get image and camera information
regions_dict = file_tools.read_tracking_2d_regions()
self.region = region
self.camera_list = regions_dict[region]
self.create_camera_to_image_dict()
self.create_camera_to_info_dict()
# Get transforms from cameras to stitched image and stitched image size
self.tf2d = transform_2d.Transform2d()
self.create_tf_data()
self.get_stitched_image_size()
# Subscribe to topics based on incoming topic type.
if self.topic_type == 'sensor_msgs/Image':
# Create pool dictionaries for incomming data.
for camera in self.camera_list:
self.stamp_to_seq_pool[camera] = {}
self.image_waiting_pool[camera] = {}
# Subscribe to camera info topics
self.info_sub = {}
for camera, topic in self.camera_to_info.iteritems():
info_handler = functools.partial(self.info_handler, camera)
self.info_sub[camera] = rospy.Subscriber(topic, CameraInfo, info_handler)
# Subscribe to rectified image topics
self.image_sub = {}
for camera, topic in self.camera_to_image.iteritems():
image_handler = functools.partial(self.image_handler, camera)
self.image_sub[camera] = rospy.Subscriber(topic, Image, image_handler)
elif self.topic_type == 'mct_msg_and_srv/SeqAndImage':
# Subscribe to SeqAndImage topics
self.seq_and_image_sub = {}
for camera, topic in self.camera_to_image.iteritems():
seq_and_image_handler = functools.partial(self.seq_and_image_handler, camera)
self.seq_and_image_sub[camera] = rospy.Subscriber(topic, SeqAndImage, seq_and_image_handler)
else:
err_msg = 'unable to handle topic type: {0}'.format(self.topic_type)
raise ValueError, err_msg
# Stitched image publisher and seq publisher
self.image_pub = rospy.Publisher('image_stitched', Image)
self.image_and_seq_pub = rospy.Publisher('seq_and_image_stitched', SeqAndImage)
self.seq_pub = rospy.Publisher('image_stitched/seq', UInt32)
self.stamp_pub = rospy.Publisher('image_stitched/stamp_info', StampInfo)
self.processing_dt_pub = rospy.Publisher('image_stitched/processing_dt', ProcessingInfo)
# Setup reset service - needs to be called anytime the camera trigger is
# stopped - before it is restarted. Empties buffers of images and sequences.
self.reset_srv = rospy.Service('reset_image_stitcher', Empty, self.handle_reset_srv)
self.ready = True