def jobs_videos(context, log, name, outd, only_camera):
filename = get_local_bag_file(log)
bag = rosbag.Bag(filename)
main_camera_topic = dbu.get_image_topic(bag)
min_messages = 5 # need at least 5 frames to make a video
topics = [
_ for _, __ in dbu.d8n_get_all_images_topic_bag(
bag, min_messages=min_messages)
]
bag.close()
only_camera_fn = outd + "-video.mp4"
for topic in topics:
stop_at = min_messages + 2
actual_count, count, _stopped_early = dbu.count_messages_in_slice(
filename, topic, log.t0, log.t1, stop_at=stop_at)
assert count >= min_messages
if actual_count < min_messages:
msg = f"There are only {actual_count} (out of {count}) in the slice [{log.t0}, {log.t1}]"
msg += f"\n topic: {topic}"
continue
d = topic.replace("/", "_")
if d.startswith("_"):
d = d[1:]
if only_camera:
if topic != main_camera_topic:
continue
out = only_camera_fn
j = context.comp(
dbu.d8n_make_video_from_bag,
filename,
topic,
out,
t0=log.t0,
t1=log.t1,
job_id=f"{name}-{topic}",
)
else:
out = os.path.join(outd, name + "-" + d + ".mp4")
j = context.comp(dbu.d8n_make_video_from_bag,
filename,
topic,
out,
job_id=f"{name}-{topic}")
# create link
if topic == main_camera_topic:
context.comp(link, j, out, only_camera_fn)
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 work(log, outd, max_images, only_camera, write_frames):
filename = get_local_bag_file(log)
t0 = log.t0
t1 = log.t1
MIN_HEIGHT = 480
# noinspection PyUnboundLocalVariable
bag = rosbag.Bag(filename)
main = dbu.get_image_topic(bag)
topics = [_ for _, __ in dbu.d8n_get_all_images_topic_bag(bag)]
bag.close()
dtu.logger.debug(f"{filename} - topics: {topics}")
for topic in topics:
if only_camera and topic != main:
continue
try:
bag = rosbag.Bag(filename)
except:
msg = f"Cannot read Bag file {filename}"
dtu.logger.error(msg)
raise
bag_proxy = dbu.BagReadProxy(bag, t0, t1)
res = dbu.d8n_read_all_images_from_bag(bag_proxy,
topic,
max_images=max_images,
use_relative_time=True)
bag.close()
d = topic.replace("/", "_")
if d.startswith("_"):
d = d[1:]
d0 = os.path.join(outd, d)
images_with_label = []
for i in range(len(res)):
rgb = res[i]["rgb"]
H, _W = rgb.shape[:2]
if H < MIN_HEIGHT:
zoom = int(np.ceil(MIN_HEIGHT / H))
rgb = dtu.zoom_image(rgb, zoom)
timestamp = res[i]["timestamp"]
s = f"t = {timestamp - t0:.2f}"
with_label = dtu.add_header_to_rgb(rgb, s)
images_with_label.append(with_label)
if write_frames:
for i, rgb in enumerate(images_with_label):
rgb = res[i]["rgb"]
fn = os.path.join(d0, f"image-{i:05d}" + ".jpg")
dtu.write_bgr_as_jpg(dtu.bgr_from_rgb(rgb), fn)
grid = dtu.make_images_grid(
images_with_label,
pad=4,
bgcolor=dtu.ColorConstants.RGB_DUCKIETOWN_YELLOW)
s = log.log_name
grid = dtu.add_header_to_rgb(grid, s, max_height=32)
if (topic != main) or len(topics) > 1:
fn = d0 + ".jpg"
dtu.write_rgb_as_jpg(grid, fn)
if topic == main:
fn = outd + ".thumbnails.jpg"
dtu.write_rgb_as_jpg(grid, fn)
def process_log(
self,
bag_in: dbu.BagReadProxy,
prefix_in: str,
bag_out,
prefix_out: str,
utils: ProcessorUtilsInterface,
):
log_name = utils.get_log().log_name
vehicle_name = dbu.which_robot(bag_in)
logger.info(f"Vehicle name: {vehicle_name}")
topic = dbu.get_image_topic(bag_in)
rcg = get_robot_camera_geometry_from_log(bag_in)
bgcolor = dtu.ColorConstants.BGR_DUCKIETOWN_YELLOW
sequence = bag_in.read_messages_plus(topics=[topic])
for _i, mp in enumerate(sequence):
bgr = dtu.bgr_from_rgb(dru.rgb_from_ros(mp.msg))
res, stats = run_pipeline(
bgr,
gpg=rcg.gpg,
rectifier=rcg.rectifier,
line_detector_name=self.line_detector,
image_prep_name=self.image_prep,
lane_filter_name=self.lane_filter,
anti_instagram_name=self.anti_instagram,
all_details=self.all_details,
)
rect = (480, 640) if not self.all_details else (240, 320)
res = dtu.resize_images_to_fit_in_rect(res, rect, bgcolor=bgcolor)
msg = (
f"abs: {mp.time_absolute} window: {mp.time_window} from log: "
f"{mp.time_from_physical_log_start}")
dtu.logger.info(msg)
headers = [
f"Robot: {vehicle_name} log: {log_name} time: {mp.time_from_physical_log_start:.2f} s",
f"Algorithms | color correction: {self.anti_instagram} | preparation: {self.image_prep} | "
f"detector: {self.line_detector} | filter: {self.lane_filter}",
]
res = dtu.write_bgr_images_as_jpgs(res,
dirname=None,
bgcolor=bgcolor)
cv_image = res["all"]
for head in reversed(headers):
max_height = 35
cv_image = dtu.add_header_to_bgr(cv_image,
head,
max_height=max_height)
otopic = "all"
omsg = dru.d8_compressed_image_from_cv_image(
cv_image, same_timestamp_as=mp.msg)
t = rospy.Time.from_sec(mp.time_absolute)
dtu.logger.info(f"written {otopic!r} at t = {t.to_sec()}")
bag_out.write(prefix_out + "/" + otopic, omsg, t=t)
for name, value in list(stats.items()):
utils.write_stat(prefix_out + "/" + name, value, t=t)