def look_at(log, output, anti_instagram, line_detector, image_prep,
lane_filter, all_details):
filename = get_local_bag_file(log)
bag = rosbag.Bag(filename)
vehicle_name = dtu.which_robot(bag)
dtu.logger.info('Vehicle name: %s' % vehicle_name)
gp = GroundProjection(vehicle_name)
topic = dtu.get_image_topic(bag)
res = dtu.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,
gp=gp,
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 jobs_videos(context, log, name, outd, only_camera):
filename = get_local_bag_file(log)
bag = rosbag.Bag(filename)
main_camera_topic = dtu.get_image_topic(bag)
min_messages = 5 # need at least 5 frames to make a video
topics = [
_ for _, __ in dtu.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 = \
count_messages_in_slice(filename, topic, log.t0, log.t1, stop_at=stop_at)
assert count >= min_messages
if actual_count < min_messages:
msg = 'There are only %d (out of %d) in the slice [%s, %s]' % (
actual_count, count, log.t0, log.t1)
msg += '\n topic: %s' % 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(dtu.d8n_make_video_from_bag,
filename,
topic,
out,
t0=log.t0,
t1=log.t1,
job_id='%s-%s' % (name, topic))
else:
out = os.path.join(outd, name + '-' + d + '.mp4')
j = context.comp(dtu.d8n_make_video_from_bag,
filename,
topic,
out,
job_id='%s-%s' % (name, topic))
# create link
if topic == main_camera_topic:
context.comp(link, j, out, only_camera_fn)
def run(self, cis):
assert isinstance(cis, ChallengeInterfaceSolution)
params = cis.get_challenge_parameters()
print('parameters: %s' % params)
to_process = params['to_process']
for basename in to_process:
filename = cis.get_challenge_file(basename)
print('processing %s' % filename)
bag = rosbag.Bag(filename)
topic = dtu.get_image_topic(bag)
fn = os.path.join(cis.get_tmp_dir(), 'tmp.mp4')
print('creating %s' % fn)
dtu.d8n_make_video_from_bag(filename, topic, fn)
cis.set_solution_output_file(basename + '.mp4', fn,
'processed video')
cis.set_solution_output_dict({'processed': to_process})
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 = dtu.get_image_topic(bag)
topics = [_ for _, __ in dtu.d8n_get_all_images_topic_bag(bag)]
bag.close()
dtu.logger.debug('%s - topics: %s' % (filename, topics))
for topic in topics:
if only_camera and topic != main:
continue
try:
bag = rosbag.Bag(filename)
except:
msg = 'Cannot read Bag file %s' % filename
dtu.logger.error(msg)
raise
bag_proxy = dtu.BagReadProxy(bag, t0, t1)
res = dtu.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 = 't = %.2f' % (timestamp - t0)
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, ('image-%05d' % i) + '.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, prefix_in, bag_out, prefix_out,
utils): #@UnusedVariable
log_name = utils.get_log().log_name
vehicle_name = dtu.which_robot(bag_in)
dtu.logger.info('Vehicle name: %s' % vehicle_name)
gp = GroundProjection(vehicle_name)
topic = dtu.get_image_topic(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(dtu.rgb_from_ros(mp.msg))
res, stats = run_pipeline(bgr,
gp=gp,
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)
print('abs: %s window: %s fron log: %s' %
(mp.time_absolute, mp.time_window,
mp.time_from_physical_log_start))
headers = [
"Robot: %s log: %s time: %.2f s" %
(vehicle_name, log_name, mp.time_from_physical_log_start),
"Algorithms | color correction: %s | preparation: %s | detector: %s | filter: %s"
% (
self.anti_instagram,
self.image_prep,
self.line_detector,
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 = dtu.d8_compressed_image_from_cv_image(
cv_image, same_timestamp_as=mp.msg)
t = rospy.Time.from_sec(mp.time_absolute) # @UndefinedVariable
print('written %r at t = %s' % (otopic, t.to_sec()))
bag_out.write(prefix_out + '/' + otopic, omsg, t=t)
for name, value in stats.items():
utils.write_stat(prefix_out + '/' + name, value, t=t)