def go(self):
vehicle_name = dtu.get_current_robot_name()
# noinspection PyUnresolvedReferences
output = self.options.output
if output is None:
output = "out/pipeline" # + dtu.get_md5(self.options.image)[:6]
self.info(f"No --output given, using {output}")
# noinspection PyUnresolvedReferences
opt_image = self.options.image
if opt_image is not None:
image_filename = opt_image
if image_filename.startswith("http"):
image_filename = dtu.get_file_from_url(image_filename)
bgr = dtu.bgr_from_jpg_fn(image_filename)
else:
self.info("Validating using the ROS image stream...")
import rospy
from sensor_msgs.msg import CompressedImage
topic_name = os.path.join("/", vehicle_name,
"camera_node/image/compressed")
self.info("Let's wait for an image. Say cheese!")
# Dummy to get ROS message
rospy.init_node("single_image")
try:
img_msg = cast(
CompressedImage,
rospy.wait_for_message(topic_name,
CompressedImage,
timeout=10))
self.info("Image captured")
except rospy.ROSException as e:
self.info(
f"\n\n\nDidn't get any message: {e}\n MAKE SURE YOU USE DT SHELL COMMANDS OF VERSION "
"4.1.9 OR HIGHER!\n\n\n")
raise
bgr = dtu.bgr_from_rgb(dru.rgb_from_ros(img_msg))
self.info(f"Picture taken: {str(bgr.shape)} ")
dtu.DuckietownConstants.show_timeit_benchmarks = True
res, _stats = run_pipeline(bgr)
self.info("Resizing images..")
res = dtu.resize_small_images(res)
self.info("Writing images..")
dtu.write_bgr_images_as_jpgs(res, output)
def look_at(
log,
output: str,
anti_instagram: str,
line_detector: str,
image_prep: str,
lane_filter: str,
all_details: bool,
) -> None:
filename = get_local_bag_file(log)
bag = rosbag.Bag(filename)
vehicle_name = dbu.which_robot(bag)
dtu.logger.info(f"Vehicle name: {vehicle_name}")
brp = dbu.BagReadProxy(bag)
rcg = get_robot_camera_geometry_from_log(brp)
topic = dbu.get_image_topic(bag)
res = dbu.d8n_read_all_images_from_bag(bag, topic, max_images=1)
image_cv = res[0]["rgb"]
# dtu.logger.debug(dtu.describe_value(image_cv))
image_cv_bgr = dtu.bgr_from_rgb(image_cv)
dtu.DuckietownConstants.show_timeit_benchmarks = True
res, _stats = run_pipeline(
image_cv_bgr,
gpg=rcg.gpg,
rectifier=rcg.rectifier,
anti_instagram_name=anti_instagram,
line_detector_name=line_detector,
image_prep_name=image_prep,
lane_filter_name=lane_filter,
all_details=all_details,
)
res = dtu.resize_small_images(res)
dtu.write_bgr_images_as_jpgs(res, output)
def test_synthetic(
actual_map_name: str,
template,
robot_name: str,
line_detector_name: str,
image_prep_name: str,
lane_filter_name: str,
pose_or_location,
outd: str,
seed: int = 42,
) -> Tuple[object, object]:
np.random.seed(seed)
db = get_easy_algo_db()
actual_map = db.create_instance(FAMILY_SEGMAPS, actual_map_name)
# first, load calibration for robot
easy_algo_db = get_easy_algo_db()
dtu.logger.debug(f"looking for localization template {template!r}")
localization_template = easy_algo_db.create_instance(
FAMILY_LOC_TEMPLATES, template)
rcg = get_robot_camera_geometry(robot_name)
if pose_or_location.shape == (3, 3): # SE(2)
pose = pose_or_location
location = localization_template.coords_from_pose(pose)
else:
location = pose_or_location
pose = localization_template.pose_from_coords(location)
simulation_data = simulate_image(actual_map,
pose,
gpg=rcg.gpg,
rectifier=rcg.rectifier,
blur_sigma=0.3)
image = simulation_data.distorted_synthetic_bgr
# anti_instagram_name='identity' # skip
anti_instagram_name = "baseline"
all_details = False
res, stats = run_pipeline(
image,
gpg=rcg.gpg,
rectifier=rcg.rectifier,
line_detector_name=line_detector_name,
image_prep_name=image_prep_name,
lane_filter_name=lane_filter_name,
anti_instagram_name=anti_instagram_name,
all_details=all_details,
ground_truth=pose,
actual_map=actual_map,
)
error = np.empty_like(location)
for k in error.dtype.fields:
error[k] = stats["estimate"][k] - location[k]
stats["error"] = error
res = dtu.resize_small_images(res)
dtu.write_bgr_images_as_jpgs(res, outd, extra_string=outd.split("/")[-1])
return res, stats