def set_path(self, path):
"""Set the path for the corrector"""
# The path is expected to be non-empty, so we no longer check for it
self.path_raw = get_raw_folder(path)
self.path_processed = get_processed_folder(path)
self.path_config = get_config_folder(path)
self.mission = Mission(self.path_raw / "mission.yaml")
self.user_specified_image_list = None # To be overwritten on parse/process
self.user_specified_image_list_parse = None
self.user_specified_image_list_process = None
self.camera_name = self.camera.name
# Find the camera topic corresponding to the name
for camera in self.mission.image.cameras:
if camera.name == self.camera_name and camera.topic is not None:
self.camera.topic = camera.topic
break
self._type = self.camera.type
# Load camera configuration
image_properties = self.camera.image_properties
self.image_height = image_properties[0]
self.image_width = image_properties[1]
self.image_channels = image_properties[2]
def parse_NOC_nmea(mission, vehicle, category, ftype, outpath):
# parser meta data
sensor_string = "autosub"
category = category
output_format = ftype
if category == Category.USBL:
filepath = mission.usbl.filepath
timezone = mission.usbl.timezone
beacon_id = mission.usbl.label
timeoffset = mission.usbl.timeoffset
timezone_offset = read_timezone(timezone)
latitude_reference = mission.origin.latitude
longitude_reference = mission.origin.longitude
usbl = Usbl(
mission.usbl.std_factor,
mission.usbl.std_offset,
latitude_reference,
longitude_reference,
)
usbl.sensor_string = sensor_string
path = get_raw_folder(outpath / ".." / filepath)
file_list = get_file_list(path)
data_list = []
for file in file_list:
with file.open("r", errors="ignore") as nmea_file:
for line in nmea_file.readlines():
parts = line.split("\t")
if len(parts) < 2:
continue
msg = pynmea2.parse(parts[1])
if int(msg.ref_station_id) != beacon_id:
continue
date_str = line.split(" ")[0]
hour_str = str(parts[1]).split(",")[1]
yyyy = int(date_str[6:10])
mm = int(date_str[3:5])
dd = int(date_str[0:2])
hour = int(hour_str[0:2])
mins = int(hour_str[2:4])
secs = int(hour_str[4:6])
msec = int(hour_str[7:10])
epoch_time = date_time_to_epoch(yyyy, mm, dd, hour, mins,
secs, timezone_offset)
epoch_timestamp = epoch_time + msec / 1000 + timeoffset
msg.timestamp = epoch_timestamp
usbl.from_nmea(msg)
data = usbl.export(output_format)
data_list.append(data)
return data_list
def parse_NOC_polpred(mission, vehicle, category, ftype, outpath):
# parser meta data
sensor_string = "autosub"
category = category
output_format = ftype
if category == Category.TIDE:
filepath = mission.tide.filepath
timezone = mission.tide.timezone
timeoffset = mission.tide.timeoffset
timezone_offset = read_timezone(timezone)
tide = Tide(mission.tide.std_offset)
tide.sensor_string = sensor_string
path = get_raw_folder(outpath / ".." / filepath)
file_list = get_file_list(path)
data_list = []
Console.info("... parsing NOC tide data")
# Data format sample
# Date Time Level Speed Direc'n
# m m/s deg
# 6/ 9/2019 00:00 0.74 0.14 51
# 6/ 9/2019 01:00 0.58 0.15 56
for file in file_list:
with file.open("r", errors="ignore") as tide_file:
for line in tide_file.readlines()[6:]:
# we have to skip the first 5 rows of the file
dd = int(line[0:2])
mm = int(line[3:5])
yyyy = int(line[6:10])
hour = int(line[12:14])
mins = int(line[15:17])
secs = 0 # current models only provide resolution in minutes
msec = 0
epoch_time = date_time_to_epoch(yyyy, mm, dd, hour, mins,
secs, timezone_offset)
epoch_timestamp = epoch_time + msec / 1000 + timeoffset
tide.epoch_timestamp = epoch_timestamp
tide.height = float(line[22:28])
tide.height_std = tide.height * tide.height_std_factor
data = tide.export(output_format)
data_list.append(data)
return data_list
def parse(self):
# parse phins data
Console.info("... parsing phins standard data")
data_list = []
path = get_raw_folder(self.outpath / ".." / self.filepath /
self.filename)
with path.open("r", encoding="utf-8", errors="ignore") as filein:
for complete_line in filein.readlines():
line_and_md5 = complete_line.strip().split("*")
line = line_and_md5[0].strip().split(",")
if not self.line_is_valid(line, line_and_md5):
continue
header = line[1]
data = self.process_line(header, line)
if data is not None:
data_list.append(data)
return data_list
def __init__(self, filepath, force_overwite=False, overwrite_all=False):
filepath = Path(filepath).resolve()
self.filepath = get_raw_folder(filepath)
self.fo = force_overwite
self.foa = overwrite_all
if self.foa:
self.fo = True
self.configuration_path = (get_config_folder(self.filepath.parent) /
"calibration")
calibration_config_file = self.configuration_path / "calibration.yaml"
if calibration_config_file.exists():
Console.info("Loading existing calibration.yaml at {}".format(
calibration_config_file))
else:
root = Path(__file__).parents[1]
default_file = root / "auv_cal/default_yaml" / "default_calibration.yaml"
Console.warn("Cannot find {}, generating default from {}".format(
calibration_config_file, default_file))
# save localisation yaml to processed directory
default_file.copy(calibration_config_file)
Console.warn("Edit the file at: \n\t" +
str(calibration_config_file))
Console.warn(
"Try to use relative paths to the calibration datasets")
Console.quit(
"Modify the file calibration.yaml and run this code again.")
with calibration_config_file.open("r") as stream:
self.calibration_config = yaml.safe_load(stream)
# Create the calibration folder at the same level as the dives
self.output_path = get_processed_folder(
self.filepath.parent) / "calibration"
if not self.output_path.exists():
self.output_path.mkdir(parents=True)
if not self.configuration_path.exists():
self.configuration_path.mkdir(parents=True)
def parse_seaxerocks_images(mission, vehicle, category, ftype, outpath):
data_list = []
if ftype == "acfr":
data_list = ""
# parser meta data
class_string = "measurement"
frame_string = "body"
category_stereo = "image"
category_laser = "laser"
sensor_string = "seaxerocks_3"
timezone = mission.image.timezone
timeoffset = mission.image.timeoffset
camera1_filepath = mission.image.cameras[0].path
camera2_filepath = mission.image.cameras[1].path
camera3_filepath = mission.image.cameras[2].path
camera1_label = mission.image.cameras[0].name
camera2_label = mission.image.cameras[1].name
camera3_label = mission.image.cameras[2].name
epoch_timestamp_stereo = []
epoch_timestamp_laser = []
epoch_timestamp_camera1 = []
epoch_timestamp_camera2 = []
epoch_timestamp_camera3 = []
stereo_index = []
laser_index = []
camera1_index = []
camera2_index = []
camera3_index = []
camera1_filename = []
camera2_filename = []
camera3_filename = []
camera1_serial = list(camera1_label)
camera2_serial = list(camera2_label)
camera3_serial = list(camera3_label)
for i in range(1, len(camera1_label)):
if camera1_label[i] == "/":
camera1_serial[i] = "_"
for i in range(1, len(camera2_label)):
if camera2_label[i] == "/":
camera2_serial[i] = "_"
for i in range(1, len(camera3_label)):
if camera3_label[i] == "/":
camera3_serial[i] = "_"
camera1_serial = "".join(camera1_serial)
camera2_serial = "".join(camera2_serial)
camera3_serial = "".join(camera3_serial)
i = 0
# read in timezone
# TODO change ALL timezones to integers
if isinstance(timezone, str):
if timezone == "utc" or timezone == "UTC":
timezone_offset = 0
elif timezone == "jst" or timezone == "JST":
timezone_offset = 9
else:
try:
timezone_offset = float(timezone)
except ValueError:
print(
"Error: timezone",
timezone,
"in mission.cfg not recognised, \
please enter value from UTC in hours",
)
return
# convert to seconds from utc
# timeoffset = -timezone_offset*60*60 + timeoffset
Console.info(" Parsing " + sensor_string + " images...")
cam1_path = get_raw_folder(outpath / ".." / camera1_filepath / "..")
cam1_filetime = cam1_path / "FileTime.csv"
with cam1_filetime.open("r", encoding="utf-8", errors="ignore") as filein:
for line in filein.readlines():
stereo_index_timestamps = line.strip().split(",")
index_string = stereo_index_timestamps[0]
date_string = stereo_index_timestamps[1]
time_string = stereo_index_timestamps[2]
ms_time_string = stereo_index_timestamps[3]
# read in date
if date_string != "date": # ignore header
stereo_index.append(index_string)
if len(date_string) != 8:
Console.warn(
"Date string ({}) in FileTime.csv file has "
"unexpected length. Expected length: 8.".format(
date_string))
yyyy = int(date_string[0:4])
mm = int(date_string[4:6])
dd = int(date_string[6:8])
# read in time
if len(time_string) != 6:
Console.warn(
"Time string ({}) in FileTime.csv file has "
"unexpected length. Expected length: 6.".format(
time_string))
hour = int(time_string[0:2])
mins = int(time_string[2:4])
secs = int(time_string[4:6])
msec = int(ms_time_string[0:3])
epoch_time = date_time_to_epoch(yyyy, mm, dd, hour, mins, secs,
timezone_offset)
epoch_timestamp_stereo.append(
float(epoch_time + msec / 1000 + timeoffset))
camera1_list = ["{}.raw".format(i) for i in stereo_index]
camera2_list = ["{}.raw".format(i) for i in stereo_index]
for i in range(len(camera1_list)):
camera1_image = camera1_list[i].split(".")
camera2_image = camera2_list[i].split(".")
camera1_index.append(camera1_image[0])
camera2_index.append(camera2_image[0])
j = 0
for i in range(len(camera1_list)):
# find corresponding timestamp even if some images are deletec
if camera1_index[i] == stereo_index[j]:
epoch_timestamp_camera1.append(epoch_timestamp_stereo[j])
epoch_timestamp_camera2.append(epoch_timestamp_stereo[j])
date = epoch_to_day(epoch_timestamp_stereo[0])
if ftype == "acfr":
camera1_filename.append("sx3_" + date[2:4] + date[5:7] +
date[8:10] + "_image" +
str(camera1_index[i]) + "_FC.png")
camera2_filename.append("sx3_" + date[2:4] + date[5:7] +
date[8:10] + "_image" +
str(camera2_index[i]) + "_AC.png")
j = j + 1
elif stereo_index[j] > camera1_index[i]:
j = j + 1
else:
j = j - 1
if ftype == "oplab":
camera1_filename = [line for line in camera1_list]
camera2_filename = [line for line in camera2_list]
for i in range(len(camera1_list)):
if ftype == "acfr":
data = ("VIS: " + str(float(epoch_timestamp_camera1[i])) + " [" +
str(float(epoch_timestamp_camera1[i])) + "] " +
str(camera1_filename[i]) + " exp: 0\n")
data_list += data
data = ("VIS: " + str(float(epoch_timestamp_camera2[i])) + " [" +
str(float(epoch_timestamp_camera2[i])) + "] " +
str(camera2_filename[i]) + " exp: 0\n")
data_list += data
if ftype == "oplab":
data = {
"epoch_timestamp":
float(epoch_timestamp_camera1[i]),
"class":
class_string,
"sensor":
sensor_string,
"frame":
frame_string,
"category":
category_stereo,
"camera1": [{
"epoch_timestamp":
float(epoch_timestamp_camera1[i]),
"serial":
camera1_serial,
"filename":
str(camera1_filepath + "/" + camera1_filename[i]),
}],
"camera2": [{
"epoch_timestamp":
float(epoch_timestamp_camera2[i]),
"serial":
camera2_serial,
"filename":
str(camera2_filepath + "/" + camera2_filename[i]),
}],
}
data_list.append(data)
cam3_path = get_raw_folder(outpath / ".." / camera3_filepath)
cam3_filetime = cam3_path / "FileTime.csv"
with cam3_filetime.open("r", encoding="utf-8", errors="ignore") as filein:
for line in filein.readlines():
laser_index_timestamps = line.strip().split(",")
if len(laser_index_timestamps) < 4:
Console.warn("The laser FileTime.csv is apparently corrupt...")
continue
index_string = laser_index_timestamps[0]
date_string = laser_index_timestamps[1]
time_string = laser_index_timestamps[2]
ms_time_string = laser_index_timestamps[3]
# read in date
if date_string != "date": # ignore header
laser_index.append(index_string)
yyyy = int(date_string[0:4])
mm = int(date_string[4:6])
dd = int(date_string[6:8])
# read in time
hour = int(time_string[0:2])
mins = int(time_string[2:4])
secs = int(time_string[4:6])
msec = int(ms_time_string[0:3])
epoch_time = date_time_to_epoch(yyyy, mm, dd, hour, mins, secs,
timezone_offset)
epoch_timestamp_laser.append(
float(epoch_time + msec / 1000 + timeoffset))
# try use pandas for all parsers, should be faster
camera3_list = ["{}".format(i) for i in laser_index]
# The LM165 images are saved either as jpg or as tif, and are split into
# subfolders either at every 1000 or every 10000 images. Find out which
# convention is used in current dataset by looking at the files.
if len(camera3_list) > 0:
s, extension = determine_extension_and_images_per_folder(
cam3_path, camera3_list, camera3_label)
for i in range(len(camera3_list)):
camera3_filename.append("{}/image{}.{}".format(
camera3_list[i][s:(s + 3)],
camera3_list[i],
extension, # noqa: E203
))
camera3_index.append(camera3_list[i])
j = 0
# original comment: find corresponding timestamp even if some images are
# deleted
for i in range(len(camera3_filename)):
if camera3_index[i] == laser_index[j]:
epoch_timestamp_camera3.append(epoch_timestamp_laser[j])
j = j + 1
# Jin: incomplete? it means that laser data is missing for this image
# file, so no epoch_timestamp data, and do what when this happens?
elif laser_index[j] > camera3_index[i]:
j = j + 1
else:
# Jin: incomplete and possibly wrong? it means that this laser
# data is extra, with no accompanying image file, so it should be
# j+1 till index match?
j = j - 1
if ftype == "oplab":
data = {
"epoch_timestamp": float(epoch_timestamp_camera3[i]),
"class": class_string,
"sensor": sensor_string,
"frame": frame_string,
"category": category_laser,
"serial": camera3_serial,
"filename": camera3_filepath + "/" + str(camera3_filename[i]),
}
data_list.append(data)
Console.info(" ...done parsing " + sensor_string + " images.")
return data_list
def parse_rdi(mission, vehicle, category, ftype, outpath):
# parser meta data
sensor_string = "rdi"
output_format = ftype
filename = ""
filepath = ""
timeoffset_s = 0
# RDI format only has two digits for year,
# extract the other two from dive folder
# name
prepend_year = outpath.parents[0].name[0:2]
# autosub std models
velocity_std_factor = mission.velocity.std_factor
velocity_std_offset = mission.velocity.std_offset
orientation_std_offset = mission.orientation.std_offset
altitude_std_factor = mission.altitude.std_factor
headingoffset = vehicle.dvl.yaw
bv = BodyVelocity(velocity_std_factor, velocity_std_offset, headingoffset)
ot = Orientation(headingoffset, orientation_std_offset)
al = Altitude(altitude_std_factor)
bv.sensor_string = sensor_string
ot.sensor_string = sensor_string
al.sensor_string = sensor_string
if category == Category.VELOCITY:
Console.info("... parsing RDI velocity")
filename = mission.velocity.filename
filepath = mission.velocity.filepath
timeoffset_s = mission.velocity.timeoffset_s
elif category == Category.ORIENTATION:
Console.info("... parsing RDI orientation")
filename = mission.orientation.filename
filepath = mission.orientation.filepath
timeoffset_s = mission.orientation.timeoffset_s
elif category == Category.ALTITUDE:
Console.info("... parsing RDI altitude")
filename = mission.altitude.filename
timeoffset_s = mission.altitude.timeoffset_s
filepath = mission.altitude.filepath
logfile = get_raw_folder(outpath / ".." / filepath / filename)
data_list = []
altitude_valid = False
with logfile.open("r", errors="ignore") as rdi_file:
for line in rdi_file.readlines():
# Lines start with TS, BI, BS, BE, BD, SA
parts = line.split(",")
if parts[0] == ":SA" and len(parts) == 4:
ot.roll = math.radians(float(parts[2]))
ot.pitch = math.radians(float(parts[1]))
ot.yaw = math.radians(float(parts[3]))
elif parts[0] == ":TS" and len(parts) == 7:
# Every time a new TimeStamp is received, send the previous
# data packet
if category == Category.VELOCITY:
data = bv.export(output_format)
if data is not None:
data_list.append(data)
if category == Category.ORIENTATION:
data = ot.export(output_format)
if data is not None:
data_list.append(data)
if category == Category.ALTITUDE:
data = al.export(output_format)
if data is not None:
data_list.append(data)
date = parts[1]
year = int(prepend_year + date[0:2])
month = int(date[2:4])
day = int(date[4:6])
hour = int(date[6:8])
minute = int(date[8:10])
second = int(date[10:12])
millisecond = float(date[12:14]) * 1e-2
date = datetime(
int(year),
int(month),
int(day),
int(hour),
int(minute),
int(second),
)
stamp = (float(calendar.timegm(date.timetuple())) +
millisecond + timeoffset_s)
bv.epoch_timestamp = stamp
bv.epoch_timestamp_dvl = stamp
ot.epoch_timestamp = stamp
al.epoch_timestamp = stamp
al.altitude_timestamp = stamp
al.sound_velocity = float(parts[5])
altitude_valid = False
elif parts[0] == ":BI" and len(parts) == 6:
status = parts[5].strip()
if status == "A":
altitude_valid = True
x = float(parts[1]) * 0.001
y = float(parts[2]) * 0.001
bv.x_velocity = x * math.cos(headingoffset) - y * math.sin(
headingoffset)
bv.y_velocity = x * math.sin(headingoffset) + y * math.cos(
headingoffset)
bv.z_velocity = float(parts[3]) * 0.001
bv.x_velocity_std = float(parts[4]) * 0.001
bv.y_velocity_std = float(parts[4]) * 0.001
bv.z_velocity_std = float(parts[4]) * 0.001
elif parts[0] == ":BD" and len(parts) == 6 and altitude_valid:
al.altitude = float(parts[4])
al.altitude_std = altitude_std_factor * al.altitude
# print(data_list)
return data_list
def parse_ntnu_dvl(mission, vehicle, category, output_format, outpath):
# Get your data from a file using mission paths, for example
filepath = mission.velocity.filepath
log_file_path = get_raw_folder(outpath / ".." / filepath)
category_str = None
if category == Category.VELOCITY:
category_str = "velocity"
elif category == Category.ALTITUDE:
category_str = "altitude"
velocity_std_factor = mission.velocity.std_factor
velocity_std_offset = mission.velocity.std_offset
heading_offset = vehicle.dvl.yaw
body_velocity = BodyVelocity(
velocity_std_factor,
velocity_std_offset,
heading_offset,
)
altitude = Altitude(altitude_std_factor=mission.altitude.std_factor)
data_list = []
num_entries = 0
num_valid_entries = 0
# For each log file
for log_file in log_file_path.glob("*.log"):
# Open the log file
df = pd.read_csv(log_file,
sep="\t",
skiprows=(0, 1),
names=header_list)
if category == Category.VELOCITY:
Console.info("... parsing velocity")
# For each row in the file
for index, row in df.iterrows():
body_velocity.from_ntnu_dvl(str(log_file.name), row)
num_entries += 1
if body_velocity.valid():
# DVL provides -32 when no bottom lock
data = body_velocity.export(output_format)
num_valid_entries += 1
data_list.append(data)
elif category == Category.ALTITUDE:
Console.info("... parsing altitude")
# For each row in the file
for index, row in df.iterrows():
num_entries += 1
altitude.from_ntnu_dvl(str(log_file.name), row)
if altitude.valid():
num_valid_entries += 1
data = altitude.export(output_format)
data_list.append(data)
elif category == Category.ORIENTATION:
Console.quit("NTNU DVL parser has no ORIENTATION available")
elif category == Category.DEPTH:
Console.quit("NTNU DVL parser has no DEPTH available")
elif category == Category.USBL:
Console.quit("NTNU DVL parser has no USBL available")
else:
Console.quit("NTNU DVL parser has no category", category,
"available")
Console.info(
"... parsed",
num_entries,
"entries, of which",
num_valid_entries,
"are valid for category",
category_str,
)
return data_list
def parse_gaps(mission, vehicle, category, ftype, outpath):
Console.info(" Parsing GAPS data...")
# parser meta data
class_string = "measurement"
sensor_string = "gaps"
frame_string = "inertial"
timezone = mission.usbl.timezone
timeoffset = mission.usbl.timeoffset
filepath = mission.usbl.filepath
usbl_id = mission.usbl.label
latitude_reference = mission.origin.latitude
longitude_reference = mission.origin.longitude
# define headers used in phins
header_absolute = "$PTSAG" # '<< $PTSAG' #georeferenced strings
header_heading = "$HEHDT" # '<< $HEHDT'
# gaps std models
distance_std_factor = mission.usbl.std_factor
distance_std_offset = mission.usbl.std_offset
broken_packet_flag = False
# read in timezone
timezone_offset = read_timezone(timezone)
# convert to seconds from utc
# timeoffset = -timezone_offset*60*60 + timeoffset
# determine file paths
path = (outpath / ".." / filepath).absolute()
filepath = get_raw_folder(path)
all_list = os.listdir(str(filepath))
gaps_list = [line for line in all_list if ".dat" in line]
Console.info(" " + str(len(gaps_list)) + " GAPS file(s) found")
# extract data from files
data_list = []
if ftype == "acfr":
data_list = ""
for i in range(len(gaps_list)):
path_gaps = filepath / gaps_list[i]
with path_gaps.open("r", errors="ignore") as gaps:
# initialise flag
flag_got_time = 0
for line in gaps.readlines():
line_split = line.strip().split("*")
line_split_no_checksum = line_split[0].strip().split(",")
broken_packet_flag = False
# print(line_split_no_checksum)
# keep on upating ship position to find the prior interpolation
# value of ship coordinates
# line_split_no_checksum[0] == header_absolute:
if header_absolute in line_split_no_checksum[0]:
# start with a ship coordinate
if line_split_no_checksum[6] == str(
usbl_id) and flag_got_time == 2:
if (line_split_no_checksum[11] == "F"
and line_split_no_checksum[13] == "1"):
# read in date
yyyy = int(line_split_no_checksum[5])
mm = int(line_split_no_checksum[4])
dd = int(line_split_no_checksum[3])
# read in time
time_string = str(line_split_no_checksum[2])
try:
hour = int(time_string[0:2])
mins = int(time_string[2:4])
secs = int(time_string[4:6])
msec = int(time_string[7:10])
except ValueError:
broken_packet_flag = True
pass
if secs >= 60:
mins += 1
secs = 0
broken_packet_flag = True
if mins >= 60:
hour += 1
mins = 0
broken_packet_flag = True
if hour >= 24:
dd += 1
hour = 0
broken_packet_flag = True
epoch_time = date_time_to_epoch(
yyyy, mm, dd, hour, mins, secs,
timezone_offset)
# dt_obj = datetime(yyyy,mm,dd,hour,mins,secs)
# time_tuple = dt_obj.timetuple()
# epoch_time = time.mktime(time_tuple)
epoch_timestamp = epoch_time + msec / 1000 + timeoffset
# get position
latitude_negative_flag = False
longitude_negative_flag = False
latitude_string = line_split_no_checksum[7]
latitude_degrees = int(latitude_string[0:2])
latitude_minutes = float(latitude_string[2:10])
if line_split_no_checksum[8] == "S":
latitude_negative_flag = True
longitude_string = line_split_no_checksum[9]
longitude_degrees = int(longitude_string[0:3])
longitude_minutes = float(longitude_string[3:11])
if line_split_no_checksum[10] == "W":
longitude_negative_flag = True
depth = float(line_split_no_checksum[12])
latitude = latitude_degrees + latitude_minutes / 60.0
longitude = longitude_degrees + longitude_minutes / 60.0
if latitude_negative_flag:
latitude = -latitude
if longitude_negative_flag:
longitude = -longitude
# flag raised to proceed
flag_got_time = 3
else:
flag_got_time = 0
if line_split_no_checksum[6] == "0":
if flag_got_time < 3:
# read in date
yyyy = int(line_split_no_checksum[5])
mm = int(line_split_no_checksum[4])
dd = int(line_split_no_checksum[3])
# print(yyyy,mm,dd)
# read in time
time_string = str(line_split_no_checksum[2])
# print(time_string)
hour = int(time_string[0:2])
mins = int(time_string[2:4])
secs = int(time_string[4:6])
try:
msec = int(time_string[7:10])
except ValueError:
broken_packet_flag = True
pass
if secs >= 60:
mins += 1
secs = 0
broken_packet_flag = True
if mins >= 60:
hour += 1
mins = 0
broken_packet_flag = True
if hour >= 24:
dd += 1
hour = 0
broken_packet_flag = True
epoch_time = date_time_to_epoch(
yyyy, mm, dd, hour, mins, secs,
timezone_offset)
# dt_obj = datetime(yyyy,mm,dd,hour,mins,secs)
# time_tuple = dt_obj.timetuple()
# epoch_time = time.mktime(time_tuple)
epoch_timestamp_ship_prior = (epoch_time +
msec / 1000 +
timeoffset)
# get position
latitude_string = line_split_no_checksum[7]
latitude_degrees_ship_prior = int(
latitude_string[0:2])
latitude_minutes_ship_prior = float(
latitude_string[2:10])
latitude_prior = (
latitude_degrees_ship_prior +
latitude_minutes_ship_prior / 60.0)
if line_split_no_checksum[8] == "S":
latitude_prior = -latitude_prior
longitude_string = line_split_no_checksum[9]
longitude_degrees_ship_prior = int(
longitude_string[0:3])
longitude_minutes_ship_prior = float(
longitude_string[3:11])
longitude_prior = (
longitude_degrees_ship_prior +
longitude_minutes_ship_prior / 60.0)
if line_split_no_checksum[10] == "W":
longitude_prior = -longitude_prior
# flag raised to proceed
if flag_got_time < 2:
flag_got_time = flag_got_time + 1
elif flag_got_time >= 3:
if line_split_no_checksum[6] == "0":
# read in date
yyyy = int(line_split_no_checksum[5])
mm = int(line_split_no_checksum[4])
dd = int(line_split_no_checksum[3])
# read in time
time_string = str(line_split_no_checksum[2])
hour = int(time_string[0:2])
mins = int(time_string[2:4])
secs = int(time_string[4:6])
msec = int(time_string[7:10])
# calculate epoch time
epoch_time = date_time_to_epoch(
yyyy,
mm,
dd,
hour,
mins,
secs,
timezone_offset,
)
# dt_obj = datetime(yyyy,mm,dd,hour,mins,secs)
# time_tuple = dt_obj.timetuple()
# epoch_time = time.mktime(time_tuple)
epoch_timestamp_ship_posterior = (epoch_time +
msec / 1000 +
timeoffset)
# get position
latitude_string = line_split_no_checksum[7]
latitude_degrees_ship_posterior = int(
latitude_string[0:2])
latitude_minutes_ship_posterior = float(
latitude_string[2:10])
latitude_posterior = (
latitude_degrees_ship_posterior +
latitude_minutes_ship_posterior / 60.0)
if line_split_no_checksum[8] == "S":
latitude_posterior = -latitude_posterior
longitude_string = line_split_no_checksum[9]
longitude_degrees_ship_posterior = int(
longitude_string[0:3])
longitude_minutes_ship_posterior = float(
longitude_string[3:11])
longitude_posterior = (
longitude_degrees_ship_posterior +
longitude_minutes_ship_posterior / 60.0)
if line_split_no_checksum[10] == "W":
longitude_posterior = -longitude_posterior
# flag raised to proceed
flag_got_time = flag_got_time + 1
# line_split_no_checksum[0] == header_heading:
if header_heading in line_split_no_checksum[0]:
if flag_got_time < 3:
heading_ship_prior = float(line_split_no_checksum[1])
if flag_got_time < 2:
flag_got_time = flag_got_time + 1
else:
heading_ship_posterior = float(
line_split_no_checksum[1])
flag_got_time = flag_got_time + 1
if flag_got_time >= 5:
# interpolate for the ships location and heading
inter_time = (epoch_timestamp - epoch_timestamp_ship_prior
) / (epoch_timestamp_ship_posterior -
epoch_timestamp_ship_prior)
longitude_ship = (inter_time *
(longitude_posterior - longitude_prior) +
longitude_prior)
latitude_ship = (inter_time *
(latitude_posterior - latitude_prior) +
latitude_prior)
heading_ship = (
inter_time *
(heading_ship_posterior - heading_ship_prior) +
heading_ship_prior)
while heading_ship > 360:
heading_ship = heading_ship - 360
while heading_ship < 0:
heading_ship = heading_ship + 360
lateral_distance, bearing = latlon_to_metres(
latitude, longitude, latitude_ship, longitude_ship)
# determine range to input to uncertainty model
distance = math.sqrt(lateral_distance * lateral_distance +
depth * depth)
distance_std = distance_std_factor * distance + distance_std_offset
# determine uncertainty in terms of latitude and longitude
latitude_offset, longitude_offset = metres_to_latlon(
abs(latitude),
abs(longitude),
distance_std,
distance_std,
)
latitude_std = abs(abs(latitude) - latitude_offset)
longitude_std = abs(abs(longitude) - longitude_offset)
# calculate in metres from reference
lateral_distance_ship, bearing_ship = latlon_to_metres(
latitude_ship,
longitude_ship,
latitude_reference,
longitude_reference,
)
eastings_ship = (math.sin(bearing_ship * math.pi / 180.0) *
lateral_distance_ship)
northings_ship = (
math.cos(bearing_ship * math.pi / 180.0) *
lateral_distance_ship)
lateral_distance_target, bearing_target = latlon_to_metres(
latitude,
longitude,
latitude_reference,
longitude_reference,
)
eastings_target = (
math.sin(bearing_target * math.pi / 180.0) *
lateral_distance_target)
northings_target = (
math.cos(bearing_target * math.pi / 180.0) *
lateral_distance_target)
if not broken_packet_flag:
if ftype == "oplab" and category == Category.USBL:
data = {
"epoch_timestamp":
float(epoch_timestamp),
"class":
class_string,
"sensor":
sensor_string,
"frame":
frame_string,
"category":
category,
"data_ship": [
{
"latitude": float(latitude_ship),
"longitude": float(longitude_ship),
},
{
"northings": float(northings_ship),
"eastings": float(eastings_ship),
},
{
"heading": float(heading_ship)
},
],
"data_target": [
{
"latitude": float(latitude),
"latitude_std": float(latitude_std),
},
{
"longitude": float(longitude),
"longitude_std": float(longitude_std),
},
{
"northings": float(northings_target),
"northings_std": float(distance_std),
},
{
"eastings": float(eastings_target),
"eastings_std": float(distance_std),
},
{
"depth": float(depth),
"depth_std": float(distance_std),
},
{
"distance_to_ship": float(distance)
},
],
}
data_list.append(data)
elif ftype == "oplab" and category == Category.DEPTH:
data = {
"epoch_timestamp":
float(epoch_timestamp),
"epoch_timestamp_depth":
float(epoch_timestamp),
"class":
class_string,
"sensor":
sensor_string,
"frame":
"inertial",
"category":
Category.DEPTH,
"data": [{
"depth": float(depth),
"depth_std": float(distance_std),
}],
}
data_list.append(data)
if ftype == "acfr":
data = (
"SSBL_FIX: " + str(float(epoch_timestamp)) +
" ship_x: " + str(float(northings_ship)) +
" ship_y: " + str(float(eastings_ship)) +
" target_x: " + str(float(northings_target)) +
" target_y: " + str(float(eastings_target)) +
" target_z: " + str(float(depth)) +
" target_hr: " + str(float(lateral_distance)) +
" target_sr: " + str(float(distance)) +
" target_bearing: " + str(float(bearing)) +
"\n")
data_list += data
else:
Console.warn("Badly formatted packet (GAPS TIME)")
Console.warn(line)
# print(hour,mins,secs)
# reset flag
flag_got_time = 0
Console.info(" ...done parsing GAPS data.")
return data_list
def laser_imp(self, c0, c1):
main_camera_name = c1["name"]
calibration_file = self.output_path / ("laser_calibration_top_" +
main_camera_name + ".yaml")
calibration_file_b = self.output_path / ("laser_calibration_bottom_" +
main_camera_name + ".yaml")
Console.info("Looking for a calibration file at " +
str(calibration_file))
if calibration_file.exists() and not self.fo:
Console.quit(
"The laser planes from cameras " + c1["name"] + " and " +
c0["name"] +
" have already been calibrated. If you want to overwite the " +
"calibration, use the -F flag.")
Console.info(
"The laser planes are not calibrated, running laser calibration..."
)
# Check if the stereo pair has already been calibrated
stereo_calibration_file = self.output_path / str("stereo_" +
c1["name"] + "_" +
c0["name"] + ".yaml")
if not stereo_calibration_file.exists():
Console.warn("Could not find a stereo calibration file " +
str(stereo_calibration_file) + "...")
self.stereo()
non_laser_cam_name = c0["name"]
non_laser_cam_filepath = get_raw_folder(self.filepath) / str(
c0["laser_calibration"]["path"])
non_laser_cam_extension = str(c0["laser_calibration"]["glob_pattern"])
laser_cam_name = c1["name"]
laser_cam_filepath = get_raw_folder(self.filepath) / str(
c1["laser_calibration"]["path"])
laser_cam_extension = str(c1["laser_calibration"]["glob_pattern"])
if not non_laser_cam_filepath.exists():
non_laser_cam_filepath = get_processed_folder(
non_laser_cam_filepath)
if not non_laser_cam_filepath.exists():
Console.quit(
"Could not find stereo image folder, neither in raw nor " +
"in processed folder (" + str(non_laser_cam_filepath) +
").")
if not laser_cam_filepath.exists():
laser_cam_filepath = get_processed_folder(laser_cam_filepath)
if not laser_cam_filepath.exists():
Console.quit(
"Could not find stereo image folder, neither in raw nor " +
"in processed folder (" + str(laser_cam_filepath) + ").")
non_laser_cam_filepath = non_laser_cam_filepath.resolve()
laser_cam_filepath = laser_cam_filepath.resolve()
Console.info("Reading stereo images of laser line from " +
str(non_laser_cam_filepath) + " and " +
str(laser_cam_filepath))
if "skip_first" not in self.calibration_config:
self.calibration_config["skip_first"] = 0
calibrate_laser(
laser_cam_name,
laser_cam_filepath,
laser_cam_extension,
non_laser_cam_name,
non_laser_cam_filepath,
non_laser_cam_extension,
stereo_calibration_file,
self.calibration_config["laser_calibration"],
calibration_file,
calibration_file_b,
self.calibration_config["skip_first"],
self.fo,
self.foa,
)
def __init__(self, navigation_file, image_path, reference_lat,
reference_lon):
navigation_file = get_raw_folder(navigation_file)
image_path = Path(image_path)
image_path = get_raw_folder(image_path)
# extract data from files
df = pd.read_csv(navigation_file, skipinitialspace=True)
date = list(df["Date "])
timestr = list(df["Time"])
roll = list(df["Roll"])
pitch = list(df["Pitch"])
heading = list(df["Heading"])
depth = list(df["Pressure"])
altitude = list(df["Altitude"])
lon = list(df["Hybis Long"])
lat = list(df["Hybis Lat"])
Console.info("Found " + str(len(df)) + " navigation records!")
hybis_vec = []
for i in range(len(df)):
if len(lon[i]) < 11:
continue
p = HyBisPos(
roll[i],
pitch[i],
heading[i],
depth[i],
altitude[i],
lon[i],
lat[i],
date[i],
timestr[i],
)
hybis_vec.append(p)
for i in range(len(hybis_vec) - 1):
if hybis_vec[i].altitude == 0:
hybis_vec[i].altitude = interpolate(
hybis_vec[i].epoch_timestamp,
hybis_vec[i - 1].epoch_timestamp,
hybis_vec[i + 1].epoch_timestamp,
hybis_vec[i - 1].altitude,
hybis_vec[i + 1].altitude,
)
if hybis_vec[i].depth == 0:
hybis_vec[i].depth = interpolate(
hybis_vec[i].epoch_timestamp,
hybis_vec[i - 1].epoch_timestamp,
hybis_vec[i + 1].epoch_timestamp,
hybis_vec[i - 1].depth,
hybis_vec[i + 1].depth,
)
if reference_lon == 0 or reference_lat == 0:
i = 0
while hybis_vec[i].latitude == 0:
i += 1
latitude_ref = hybis_vec[i].latitude
longitude_ref = hybis_vec[i].longitude
else:
latitude_ref = reference_lat
longitude_ref = reference_lon
self.data = [
"image_number,",
"northing [m],",
"easting [m],",
"depth [m],",
"roll [deg],",
"pitch [deg],",
"heading [deg],",
"altitude [m],",
"timestamp,",
"latitude [deg],",
"longitude [deg],",
"relative_path\n",
]
image_list = sorted(os.listdir(str(image_path)))
Console.info("Found " + str(len(image_list)) + " images!")
Console.info("Interpolating...")
for k, filename in enumerate(image_list):
modification_time = os.stat(str(image_path) + "/" +
filename).st_mtime
filename = str(image_path) + "/" + filename
i = 0
while (i < len(hybis_vec) - 2
and hybis_vec[i].epoch_timestamp < modification_time):
i += 1
latitude = interpolate(
modification_time,
hybis_vec[i].epoch_timestamp,
hybis_vec[i + 1].epoch_timestamp,
hybis_vec[i].latitude,
hybis_vec[i + 1].latitude,
)
longitude = interpolate(
modification_time,
hybis_vec[i].epoch_timestamp,
hybis_vec[i + 1].epoch_timestamp,
hybis_vec[i].longitude,
hybis_vec[i + 1].longitude,
)
depth = interpolate(
modification_time,
hybis_vec[i].epoch_timestamp,
hybis_vec[i + 1].epoch_timestamp,
hybis_vec[i].depth,
hybis_vec[i + 1].depth,
)
roll = interpolate(
modification_time,
hybis_vec[i].epoch_timestamp,
hybis_vec[i + 1].epoch_timestamp,
hybis_vec[i].roll,
hybis_vec[i + 1].roll,
)
pitch = interpolate(
modification_time,
hybis_vec[i].epoch_timestamp,
hybis_vec[i + 1].epoch_timestamp,
hybis_vec[i].pitch,
hybis_vec[i + 1].pitch,
)
heading = interpolate(
modification_time,
hybis_vec[i].epoch_timestamp,
hybis_vec[i + 1].epoch_timestamp,
hybis_vec[i].heading,
hybis_vec[i + 1].heading,
)
altitude = interpolate(
modification_time,
hybis_vec[i].epoch_timestamp,
hybis_vec[i + 1].epoch_timestamp,
hybis_vec[i].altitude,
hybis_vec[i + 1].altitude,
)
lateral_distance, bearing = latlon_to_metres(
latitude, longitude, latitude_ref, longitude_ref)
eastings = math.sin(bearing * math.pi / 180.0) * lateral_distance
northings = math.cos(bearing * math.pi / 180.0) * lateral_distance
msg = (str(k) + "," + str(northings) + "," + str(eastings) + "," +
str(depth) + "," + str(roll) + "," + str(pitch) + "," +
str(heading) + "," + str(altitude) + "," +
str(modification_time) + "," + str(latitude) + "," +
str(longitude) + "," + str(filename) + "\n")
self.data.append(msg)
self.start_epoch = hybis_vec[0].epoch_timestamp
self.finish_epoch = hybis_vec[-1].epoch_timestamp
def load_configuration_and_camera_system(path, suffix=None):
"""Generate correct_config and camera system objects from input config
yaml files
Parameters
-----------
path : Path
User provided Path of source images
"""
Console.warn("Parsing multipaths with suffix:", suffix)
# resolve paths to raw, processed and config folders
path_raw_folder = get_raw_folder(path)
path_config_folder = get_config_folder(path)
# resolve path to mission.yaml
path_mission = path_raw_folder / "mission.yaml"
# find mission and correct_images yaml files
if path_mission.exists():
Console.info("File mission.yaml found at", path_mission)
else:
Console.quit("File mission.yaml file not found at", path_raw_folder)
# load mission parameters
mission = Mission(path_mission)
# resolve path to camera.yaml file
camera_yaml_raw_path = path_raw_folder / "camera.yaml"
camera_yaml_config_path = path_config_folder / "camera.yaml"
camera_yaml_path = None
default_file_path_correct_config = None
if not camera_yaml_raw_path.exists(
) and not camera_yaml_config_path.exists():
Console.info(
"Not found camera.yaml file in /raw folder...Using default ",
"camera.yaml file...",
)
# find out default yaml paths
root = Path(__file__).resolve().parents[1]
acfr_std_camera_file = "auv_nav/default_yaml/ts1/SSK17-01/camera.yaml"
sx3_camera_file = "auv_nav/default_yaml/ae2000/YK17-23C/camera.yaml"
biocam_camera_file = "auv_nav/default_yaml/as6/DY109/camera.yaml"
biocam4000_15c_camera_file = "auv_nav/default_yaml/alr/jc220/camera.yaml"
hybis_camera_file = "auv_nav/default_yaml/hybis/camera.yaml"
ntnu_camera_file = "auv_nav/default_yaml/ntnu_stereo/tautra21/camera.yaml"
rosbag_camera_file = "auv_nav/default_yaml/rosbag/grassmap/camera.yaml"
acfr_std_correct_config_file = (
"correct_images/default_yaml/acfr/correct_images.yaml")
sx3_std_correct_config_file = (
"correct_images/default_yaml/sx3/correct_images.yaml")
biocam_std_correct_config_file = (
"correct_images/default_yaml/biocam/correct_images.yaml")
biocam4000_15c_std_correct_config_file = (
"correct_images/default_yaml/biocam4000_15c/correct_images.yaml")
hybis_std_correct_config_file = (
"correct_images/default_yaml/hybis/correct_images.yaml")
ntnu_std_correct_config_file = (
"correct_images/default_yaml/ntnu_stereo/correct_images.yaml")
rosbag_std_correct_config_file = (
"correct_images/default_yaml/rosbag/correct_images.yaml")
Console.info("Image format:", mission.image.format)
if mission.image.format == "acfr_standard":
camera_yaml_path = root / acfr_std_camera_file
default_file_path_correct_config = root / acfr_std_correct_config_file
elif mission.image.format == "seaxerocks_3":
camera_yaml_path = root / sx3_camera_file
default_file_path_correct_config = root / sx3_std_correct_config_file
elif mission.image.format == "biocam":
camera_yaml_path = root / biocam_camera_file
default_file_path_correct_config = root / biocam_std_correct_config_file
elif mission.image.format == "biocam4000_15c":
camera_yaml_path = root / biocam4000_15c_camera_file
default_file_path_correct_config = (
root / biocam4000_15c_std_correct_config_file)
elif mission.image.format == "hybis":
camera_yaml_path = root / hybis_camera_file
default_file_path_correct_config = root / hybis_std_correct_config_file
elif mission.image.format == "ntnu_stereo":
camera_yaml_path = root / ntnu_camera_file
default_file_path_correct_config = root / ntnu_std_correct_config_file
elif mission.image.format == "rosbag":
camera_yaml_path = root / rosbag_camera_file
default_file_path_correct_config = root / rosbag_std_correct_config_file
camera_yaml_path.copy(camera_yaml_config_path)
Console.info(
"Copied camera.yaml file to config folder. Please edit it.")
Console.info("The file is located at", camera_yaml_config_path)
else:
Console.quit(
"Image system in camera.yaml does not match with mission.yaml",
"Provide correct camera.yaml in /raw folder... ",
)
elif camera_yaml_raw_path.exists(
) and not camera_yaml_config_path.exists():
Console.info("Found camera.yaml file in /raw folder")
camera_yaml_path = camera_yaml_raw_path
elif not camera_yaml_raw_path.exists() and camera_yaml_config_path.exists(
):
Console.info("Found camera.yaml file in /config folder")
camera_yaml_path = camera_yaml_config_path
elif camera_yaml_raw_path.exists() and camera_yaml_config_path.exists():
Console.info("Found camera.yaml both in /raw and in /config folder")
Console.info("Using camera.yaml from /config folder")
camera_yaml_path = camera_yaml_config_path
else:
Console.quit(
"rosbag image type requires a camera.yaml file in /config folder",
"Please provide camera.yaml file in /config folder",
)
# instantiate the camera system and setup cameras from mission and
# config files / auv_nav
camera_system = CameraSystem(camera_yaml_path, path_raw_folder)
if camera_system.camera_system != mission.image.format:
Console.quit(
"Image system in camera.yaml does not match with mission.yaml",
"Provide correct camera.yaml in /raw folder",
)
# check for correct_config yaml path
path_correct_images = None
if suffix == "" or suffix is None:
path_correct_images = path_config_folder / "correct_images.yaml"
else:
path_correct_images = path_config_folder / ("correct_images_" +
suffix + ".yaml")
if path_correct_images.exists():
Console.info(
"Configuration file correct_images.yaml file found at",
path_correct_images,
)
else:
default_file_path_correct_config.copy(path_correct_images)
Console.warn(
"Configuration file not found, copying a default one at",
path_correct_images,
)
# load parameters from correct_config.yaml
correct_config = CorrectConfig(path_correct_images)
return correct_config, camera_system
def parse_alr(mission, vehicle, category, ftype, outpath):
# parser meta data
sensor_string = "alr"
category = category
output_format = ftype
filename = mission.velocity.filename
filepath = mission.velocity.filepath
# ALR std models
depth_std_factor = mission.depth.std_factor
velocity_std_factor = mission.velocity.std_factor
velocity_std_offset = mission.velocity.std_offset
orientation_std_offset = mission.orientation.std_offset
altitude_std_factor = mission.altitude.std_factor
headingoffset = vehicle.dvl.yaw
body_velocity = BodyVelocity(
velocity_std_factor, velocity_std_offset, headingoffset
)
orientation = Orientation(headingoffset, orientation_std_offset)
depth = Depth(depth_std_factor)
altitude = Altitude(altitude_std_factor)
body_velocity.sensor_string = sensor_string
orientation.sensor_string = sensor_string
depth.sensor_string = sensor_string
altitude.sensor_string = sensor_string
path = get_raw_folder(outpath / ".." / filepath / filename)
alr_log = loadmat(str(path))
mission_data = alr_log["MissionData"]
data_list = []
if category == Category.VELOCITY:
Console.info("Parsing alr velocity...")
previous_timestamp = 0
for i in range(len(mission_data["epoch_timestamp"])):
if mission_data["DVL_down_BT_is_good"][i] == 1:
vx = mission_data["DVL_down_BT_x"][i]
vy = mission_data["DVL_down_BT_y"][i]
vz = mission_data["DVL_down_BT_z"][i]
# vx, vy, vz should not be NaN on lines with bottom lock,
# but check to be on the safe side:
if not isnan(vx) and not isnan(vy) and not isnan(vz):
t = mission_data["epoch_timestamp"][i]
body_velocity.from_alr(t, vx, vy, vz)
data = body_velocity.export(output_format)
if body_velocity.epoch_timestamp > previous_timestamp:
data_list.append(data)
else:
data_list[-1] = data
previous_timestamp = body_velocity.epoch_timestamp
Console.info("...done parsing alr velocity")
if category == Category.ORIENTATION:
Console.info("Parsing alr orientation...")
previous_timestamp = 0
for i in range(len(mission_data["epoch_timestamp"])):
roll = mission_data["AUV_roll"][i]
pitch = mission_data["AUV_pitch"][i]
yaw = mission_data["AUV_heading"][i]
if not isnan(roll) and not isnan(pitch) and not isnan(yaw):
t = mission_data["epoch_timestamp"][i]
orientation.from_alr(t, roll, pitch, yaw)
data = orientation.export(output_format)
if orientation.epoch_timestamp > previous_timestamp:
data_list.append(data)
else:
data_list[-1] = data
previous_timestamp = orientation.epoch_timestamp
Console.info("...done parsing alr orientation")
if category == Category.DEPTH:
Console.info("Parsing alr depth...")
previous_timestamp = 0
for i in range(len(mission_data["epoch_timestamp"])):
d = mission_data["AUV_depth"][i]
if not isnan(d):
t = mission_data["epoch_timestamp"][i]
depth.from_alr(t, d)
data = depth.export(output_format)
if depth.epoch_timestamp > previous_timestamp:
data_list.append(data)
else:
data_list[-1] = data
previous_timestamp = depth.epoch_timestamp
Console.info("...done parsing alr depth")
if category == Category.ALTITUDE:
Console.info("Parsing alr altitude...")
previous_timestamp = 0
for i in range(len(mission_data["epoch_timestamp"])):
if mission_data["DVL_down_BT_is_good"][i] == 1:
a = mission_data["AUV_altitude"][i]
# The altitude should not be NaN on lines with bottom lock,
# but check to be on the safe side:
if not isnan(a):
t = mission_data["epoch_timestamp"][i]
altitude.from_alr(t, a)
data = altitude.export(output_format)
if altitude.epoch_timestamp > previous_timestamp:
data_list.append(data)
else:
data_list[-1] = data
previous_timestamp = altitude.epoch_timestamp
Console.info("...done parsing alr altitude")
return data_list
def parse_eiva_navipac(mission, vehicle, category, output_format, outpath):
# Get your data from a file using mission paths, for example
# Get your data from a file using mission paths, for example
filepath = None
category_str = None
if category == Category.ORIENTATION:
category_str = "orientation"
filepath = mission.orientation.filepath
elif category == Category.DEPTH:
category_str = "depth"
filepath = mission.depth.filepath
elif category == Category.USBL:
category_str = "usbl"
filepath = mission.usbl.filepath
log_file_path = get_raw_folder(outpath / ".." / filepath)
depth_std_factor = mission.depth.std_factor
orientation_std_offset = mission.orientation.std_offset
heading_offset = vehicle.ins.yaw
usbl_id = mission.usbl.label
latitude_reference = float(mission.origin.latitude)
longitude_reference = float(mission.origin.longitude)
orientation = Orientation(heading_offset, orientation_std_offset)
depth = Depth(depth_std_factor)
usbl = Usbl(
mission.usbl.std_factor,
mission.usbl.std_offset,
latitude_reference,
longitude_reference,
)
data_list = []
num_entries = 0
num_valid_entries = 0
# For each log file
for log_file in log_file_path.glob("*_G.NPD"):
# Open the log file
with log_file.open("r", encoding="utf-8", errors="ignore") as filein:
if category == Category.ORIENTATION:
Console.info("... parsing orientation")
# For each line in the file
for line in filein.readlines():
if line.startswith("R132 4") and "$PRDID" in line:
orientation.from_eiva_navipac(line)
num_entries += 1
if orientation.roll is None:
continue
if orientation.valid():
num_valid_entries += 1
data = orientation.export(output_format)
data_list.append(data)
elif category == Category.DEPTH:
Console.info("... parsing depth")
for line in filein.readlines():
if line[0:14] == "D " + str(usbl_id) + " 19 1":
depth.from_eiva_navipac(line)
num_entries += 1
if depth.valid():
num_valid_entries += 1
data = depth.export(output_format)
data_list.append(data)
elif category == Category.USBL:
Console.info("... parsing USBL")
for line in filein.readlines():
if line.startswith("P D") and int(line[6:10]) == usbl_id:
num_entries += 1
usbl.from_eiva_navipac(line)
if usbl.valid():
num_valid_entries += 1
data = usbl.export(output_format)
data_list.append(data)
elif category == Category.ALTITUDE:
Console.quit("EIVA Navipac parser has no ALTITUDE available")
elif category == Category.VELOCITY:
Console.quit("EIVA Navipac parser has no VELOCITY available")
else:
Console.quit("EIVA Navipac parser has no category", category,
"available")
Console.info(
"Processed",
num_entries,
"entries , of which",
num_valid_entries,
"are valid for category",
category_str,
)
return data_list
def parse_single(filepath, force_overwrite):
# initiate data and processing flags
filepath = Path(filepath).resolve()
filepath = get_raw_folder(filepath)
if not force_overwrite:
existing_files = check_output_files_exist(
get_processed_folder(filepath))
if existing_files:
msg = ("It looks like this dataset has already been parsed.\n" +
"The following file(s) already exist:\n" + existing_files +
"If you would like auv_nav to overwrite existing file," +
" rerun it with the flag -F.\n" +
"Example: auv_nav parse -F PATH")
Console.warn(msg)
Console.warn("Dataset skipped")
return
ftype = "oplab"
# load mission.yaml config file
mission_file = filepath / "mission.yaml"
vehicle_file = filepath / "vehicle.yaml"
mission_file = get_raw_folder(mission_file)
vehicle_file = get_raw_folder(vehicle_file)
Console.info("Loading mission.yaml at", mission_file)
mission = Mission(mission_file)
Console.info("Loading vehicle.yaml at", vehicle_file)
vehicle = Vehicle(vehicle_file)
# copy mission.yaml and vehicle.yaml to processed folder for process step
mission_processed = get_processed_folder(mission_file)
vehicle_processed = get_processed_folder(vehicle_file)
# Write mission with metadata (username, date and hostname)
mission.write(mission_processed)
# mission_file.copy(mission_processed)
vehicle.write(vehicle_processed)
# check for recognised formats and create nav file
outpath = get_processed_folder(filepath)
outpath = outpath / "nav"
filename = "nav_standard.json"
# make file path if not exist
if not outpath.is_dir():
try:
outpath.mkdir()
except Exception as e:
print("Warning:", e)
Console.info("Loading raw data...")
if multiprocessing.cpu_count() < 4:
cpu_to_use = 1
else:
cpu_to_use = multiprocessing.cpu_count() - 2
try:
pool = multiprocessing.Pool(cpu_to_use)
except AttributeError as e:
print(
"Error: ",
e,
"\n===============\nThis error is known to \
happen when running the code more than once from the same \
console in Spyder. Please run the code from a new console \
to prevent this error from happening. You may close the \
current console.\n==============",
)
pool_list = []
# read in, parse data and write data
if not mission.image.empty():
if mission.image.format == "acfr_standard" or mission.image.format == "unagi":
pool_list.append(
pool.apply_async(
parse_acfr_images,
[mission, vehicle, "images", ftype, outpath],
))
elif mission.image.format == "seaxerocks_3":
pool_list.append(
pool.apply_async(
parse_seaxerocks_images,
[mission, vehicle, "images", ftype, outpath],
))
elif (mission.image.format == "biocam"
or mission.image.format == "biocam4000_15c"):
pool_list.append(
pool.apply_async(
parse_biocam_images,
[mission, vehicle, "images", ftype, outpath],
))
elif mission.image.format == "ntnu_stereo":
pool_list.append(
pool.apply_async(
parse_ntnu_stereo_images,
[mission, vehicle, "images", ftype, outpath],
))
elif mission.image.format == "rosbag_extracted_images":
pool_list.append(
pool.apply_async(
parse_rosbag_extracted_images,
[mission, vehicle, "images", ftype, outpath],
))
elif mission.image.format == "rosbag":
pool_list.append(
pool.apply_async(
parse_rosbag,
[mission, vehicle, "images", ftype, outpath],
))
else:
Console.quit("Mission image format", mission.image.format,
"not supported.")
if not mission.usbl.empty():
if mission.usbl.format == "gaps":
pool_list.append(
pool.apply_async(parse_gaps,
[mission, vehicle, "usbl", ftype, outpath]))
elif mission.usbl.format == "usbl_dump":
pool_list.append(
pool.apply_async(parse_usbl_dump,
[mission, vehicle, "usbl", ftype, outpath]))
elif mission.usbl.format == "NOC_nmea":
pool_list.append(
pool.apply_async(parse_NOC_nmea,
[mission, vehicle, "usbl", ftype, outpath]))
elif mission.usbl.format == "eiva_navipac":
pool_list.append(
pool.apply_async(
parse_eiva_navipac,
[mission, vehicle, "usbl", ftype, outpath],
))
elif mission.usbl.format == "rosbag":
pool_list.append(
pool.apply_async(
parse_rosbag,
[mission, vehicle, "usbl", ftype, outpath],
))
else:
Console.quit("Mission usbl format", mission.usbl.format,
"not supported.")
if not mission.velocity.empty():
if mission.velocity.format == "phins":
pool_list.append(
pool.apply_async(
parse_phins,
[mission, vehicle, "velocity", ftype, outpath]))
elif mission.velocity.format == "ae2000":
pool_list.append(
pool.apply_async(
parse_ae2000,
[mission, vehicle, "velocity", ftype, outpath],
))
elif mission.velocity.format == "alr":
pool_list.append(
pool.apply_async(
parse_alr,
[mission, vehicle, "velocity", ftype, outpath],
))
elif mission.velocity.format == "autosub":
pool_list.append(
pool.apply_async(
parse_autosub,
[mission, vehicle, "velocity", ftype, outpath],
))
elif mission.velocity.format == "rdi":
pool_list.append(
pool.apply_async(
parse_rdi, [mission, vehicle, "velocity", ftype, outpath]))
elif mission.velocity.format == "ntnu_dvl":
pool_list.append(
pool.apply_async(
parse_ntnu_dvl,
[mission, vehicle, "velocity", ftype, outpath],
))
elif mission.usbl.format == "rosbag":
pool_list.append(
pool.apply_async(
parse_rosbag,
[mission, vehicle, "velocity", ftype, outpath],
))
else:
Console.quit(
"Mission velocity format",
mission.velocity.format,
"not supported.",
)
if not mission.orientation.empty():
if mission.orientation.format == "phins":
pool_list.append(
pool.apply_async(
parse_phins,
[mission, vehicle, "orientation", ftype, outpath],
))
elif mission.orientation.format == "ae2000":
pool_list.append(
pool.apply_async(
parse_ae2000,
[mission, vehicle, "orientation", ftype, outpath],
))
elif mission.orientation.format == "alr":
pool_list.append(
pool.apply_async(
parse_alr,
[mission, vehicle, "orientation", ftype, outpath],
))
elif mission.orientation.format == "autosub":
pool_list.append(
pool.apply_async(
parse_autosub,
[mission, vehicle, "orientation", ftype, outpath],
))
elif mission.orientation.format == "rdi":
pool_list.append(
pool.apply_async(
parse_rdi,
[mission, vehicle, "orientation", ftype, outpath],
))
elif mission.orientation.format == "eiva_navipac":
pool_list.append(
pool.apply_async(
parse_eiva_navipac,
[mission, vehicle, "orientation", ftype, outpath],
))
elif mission.usbl.format == "rosbag":
pool_list.append(
pool.apply_async(
parse_rosbag,
[mission, vehicle, "orientation", ftype, outpath],
))
else:
Console.quit(
"Mission orientation format",
mission.orientation.format,
"not supported.",
)
if not mission.depth.empty():
if mission.depth.format == "phins":
pool_list.append(
pool.apply_async(parse_phins,
[mission, vehicle, "depth", ftype, outpath]))
elif mission.depth.format == "ae2000":
pool_list.append(
pool.apply_async(parse_ae2000,
[mission, vehicle, "depth", ftype, outpath]))
elif mission.depth.format == "alr":
pool_list.append(
pool.apply_async(parse_alr,
[mission, vehicle, "depth", ftype, outpath]))
elif mission.depth.format == "autosub":
pool_list.append(
pool.apply_async(parse_autosub,
[mission, vehicle, "depth", ftype, outpath]))
elif mission.depth.format == "gaps":
pool_list.append(
pool.apply_async(parse_gaps,
[mission, vehicle, "depth", ftype, outpath]))
elif mission.depth.format == "eiva_navipac":
pool_list.append(
pool.apply_async(
parse_eiva_navipac,
[mission, vehicle, "depth", ftype, outpath],
))
elif mission.usbl.format == "rosbag":
pool_list.append(
pool.apply_async(
parse_rosbag,
[mission, vehicle, "depth", ftype, outpath],
))
else:
Console.quit("Mission depth format", mission.depth.format,
"not supported.")
if not mission.altitude.empty():
if mission.altitude.format == "phins":
pool_list.append(
pool.apply_async(
parse_phins,
[mission, vehicle, "altitude", ftype, outpath]))
elif mission.altitude.format == "ae2000":
pool_list.append(
pool.apply_async(
parse_ae2000,
[mission, vehicle, "altitude", ftype, outpath],
))
elif mission.altitude.format == "alr":
pool_list.append(
pool.apply_async(
parse_alr,
[mission, vehicle, "altitude", ftype, outpath],
))
elif mission.altitude.format == "autosub":
pool_list.append(
pool.apply_async(
parse_autosub,
[mission, vehicle, "altitude", ftype, outpath],
))
elif mission.altitude.format == "rdi":
pool_list.append(
pool.apply_async(
parse_rdi, [mission, vehicle, "altitude", ftype, outpath]))
elif mission.altitude.format == "ntnu_dvl":
pool_list.append(
pool.apply_async(
parse_ntnu_dvl,
[mission, vehicle, "altitude", ftype, outpath],
))
elif mission.usbl.format == "rosbag":
pool_list.append(
pool.apply_async(
parse_rosbag,
[mission, vehicle, "altitude", ftype, outpath],
))
else:
Console.quit(
"Mission altitude format",
mission.altitude.format,
"not supported.",
)
if not mission.tide.empty():
if mission.tide.format == "NOC_polpred":
tide_list = parse_NOC_polpred(mission, vehicle, "tide", ftype,
outpath)
else:
Console.quit("Mission tide format", mission.tide.format,
"not supported.")
else:
tide_list = None
pool.close()
pool.join()
Console.info("...done loading raw data.")
Console.info("Compile data list...")
data_list = [[{
"epoch_timestamp":
0.0,
"class":
"origin",
"category":
"origin",
"data": [{
"latitude": mission.origin.latitude,
"longitude": mission.origin.longitude,
"crs": mission.origin.crs,
"date": mission.origin.date,
}],
}]]
# Set advance True/False flag so not comparing for every i in pool_list
if mission.image.format == "biocam":
correcting_timestamps = True
else:
correcting_timestamps = False
for i in pool_list:
results = i.get()
# If current retrieved data is DEPTH
# and if TIDE data is available
if len(results) < 1:
continue
if results[0] is None:
Console.warn(
"Some results are empty. Please check whether this is correct or not"
)
continue
if correcting_timestamps:
if results[0]["category"] == "image":
Console.info("Correction of BioCam cpu timestamps...")
results = correct_timestamps(results)
if (results[0]["category"] == Category.DEPTH
or results[0]["category"] == Category.USBL):
if not mission.tide.empty():
# proceed to tidal correction
Console.info("Tidal correction of depth vector...")
# Offset depth to acknowledge for tides
j = 0
for k in range(len(results)):
while (j < len(tide_list)
and tide_list[j]["epoch_timestamp"] <
results[k]["epoch_timestamp"]):
j = j + 1
if j >= 1:
_result = interpolate(
results[k]["epoch_timestamp"],
tide_list[j - 1]["epoch_timestamp"],
tide_list[j]["epoch_timestamp"],
tide_list[j - 1]["data"][0]["height"],
tide_list[j]["data"][0]["height"],
)
if results[0]["category"] == Category.DEPTH:
results[k]["data"][0]["depth"] = (
results[k]["data"][0]["depth"] - _result)
elif results[0]["category"] == Category.USBL:
results[k]["data_target"][4]["depth"] = (
results[k]["data_target"][4]["depth"] -
_result)
data_list.append(results)
Console.info("...done compiling data list.")
Console.info("Writing to output file...")
data_list_temp = []
for i in data_list:
data_list_temp += i
# create file (overwrite if exists)
nav_file = outpath / filename
with nav_file.open("w") as fileout:
json.dump(data_list_temp, fileout, indent=2)
fileout.close()
Console.info("...done writing to output file.")
del data_list_temp
del data_list
# interlace the data based on timestamps
Console.info("Interlacing data...")
parse_interlacer(outpath, filename)
Console.info("...done interlacing data. Output saved to",
outpath / filename)
plot_parse_data(outpath, ftype)
Console.info("Complete parse data")
def parse_autosub(mission, vehicle, category, ftype, outpath):
# parser meta data
sensor_string = "autosub"
category = category
output_format = ftype
filename = mission.velocity.filename
filepath = mission.velocity.filepath
# autosub std models
depth_std_factor = mission.depth.std_factor
velocity_std_factor = mission.velocity.std_factor
velocity_std_offset = mission.velocity.std_offset
orientation_std_offset = mission.orientation.std_offset
altitude_std_factor = mission.altitude.std_factor
headingoffset = vehicle.dvl.yaw
body_velocity = BodyVelocity(velocity_std_factor, velocity_std_offset,
headingoffset)
orientation = Orientation(headingoffset, orientation_std_offset)
depth = Depth(depth_std_factor)
altitude = Altitude(altitude_std_factor)
body_velocity.sensor_string = sensor_string
orientation.sensor_string = sensor_string
depth.sensor_string = sensor_string
altitude.sensor_string = sensor_string
path = get_raw_folder(outpath / ".." / filepath / filename)
autosub_log = loadmat(str(path))
autosub_adcp = autosub_log["missionData"]["ADCPbin00"]
autosub_ins = autosub_log["missionData"]["INSAttitude"]
autosub_dep_ctl = autosub_log["missionData"]["DepCtlNode"]
autosub_adcp_log1 = autosub_log["missionData"]["ADCPLog_1"]
data_list = []
if category == Category.VELOCITY:
Console.info("... parsing autosub velocity")
previous_timestamp = 0
for i in range(len(autosub_adcp["eTime"])):
body_velocity.from_autosub(autosub_adcp, i)
data = body_velocity.export(output_format)
if body_velocity.epoch_timestamp > previous_timestamp:
data_list.append(data)
else:
data_list[-1] = data
previous_timestamp = body_velocity.epoch_timestamp
if category == Category.ORIENTATION:
Console.info("... parsing autosub orientation")
previous_timestamp = 0
for i in range(len(autosub_ins["eTime"])):
orientation.from_autosub(autosub_ins, i)
data = orientation.export(output_format)
if orientation.epoch_timestamp > previous_timestamp:
data_list.append(data)
else:
data_list[-1] = data
previous_timestamp = orientation.epoch_timestamp
if category == Category.DEPTH:
Console.info("... parsing autosub depth")
previous_timestamp = 0
for i in range(len(autosub_dep_ctl["eTime"])):
depth.from_autosub(autosub_dep_ctl, i)
data = depth.export(output_format)
if depth.epoch_timestamp > previous_timestamp:
data_list.append(data)
else:
data_list[-1] = data
previous_timestamp = depth.epoch_timestamp
if category == Category.ALTITUDE:
Console.info("... parsing autosub altitude")
previous_timestamp = 0
for i in range(len(autosub_adcp_log1["eTime"])):
altitude.from_autosub(autosub_adcp_log1, i)
data = altitude.export(output_format)
if altitude.epoch_timestamp > previous_timestamp:
data_list.append(data)
else:
data_list[-1] = data
previous_timestamp = altitude.epoch_timestamp
return data_list
def parse_usbl_dump(mission, vehicle, category, ftype, outpath):
# parser meta data
class_string = "measurement"
sensor_string = "jamstec_usbl"
frame_string = "inertial"
# gaps std models
distance_std_factor = mission.usbl.std_factor
distance_std_offset = mission.usbl.std_offset
timezone = mission.usbl.timezone
timeoffset = mission.usbl.timeoffset
filepath = mission.usbl.filepath
filename = mission.usbl.filename
label = mission.usbl.label
filepath = get_raw_folder(outpath / ".." / filepath)
latitude_reference = mission.origin.latitude
longitude_reference = mission.origin.longitude
# read in timezone
timezone_offset = read_timezone(timezone)
# convert to seconds from utc
# timeoffset = -timezone_offset*60*60 + timeoffset
# extract data from files
Console.info("... parsing usbl dump")
data_list = []
if ftype == "acfr":
data_list = ""
usbl_file = filepath / filename
with usbl_file.open("r", encoding="utf-8", errors="ignore") as filein:
for line in filein.readlines():
line_split = line.strip().split(",")
if line_split[2] == label:
date = line_split[0].split("-")
# read in date
yyyy = int(date[0])
mm = int(date[1])
dd = int(date[2])
timestamp = line_split[1].split(":")
# read in time
hour = int(timestamp[0])
mins = int(timestamp[1])
secs = int(timestamp[2])
msec = 0
epoch_time = date_time_to_epoch(
yyyy, mm, dd, hour, mins, secs, timezone_offset
)
# dt_obj = datetime(yyyy,mm,dd,hour,mins,secs)
# time_tuple = dt_obj.timetuple()
# epoch_time = time.mktime(time_tuple)
epoch_timestamp = epoch_time + msec / 1000 + timeoffset
if line_split[6] != "":
# get position
latitude_full = line_split[6].split(" ")
latitude_list = latitude_full[0].split("-")
latitude_degrees = int(latitude_list[0])
latitude_minutes = float(latitude_list[1])
if latitude_full[1] != "N":
latitude_degrees = latitude_degrees * -1 # southern hemisphere
latitude = latitude_degrees + latitude_minutes / 60
longitude_full = line_split[7].split(" ")
longitude_list = longitude_full[0].split("-")
longitude_degrees = int(longitude_list[0])
longitude_minutes = float(longitude_list[1])
if longitude_full[1] != "E":
longitude_degrees = (
longitude_degrees * -1
) # southern hemisphere
longitude = longitude_degrees + longitude_minutes / 60
depth_full = line_split[8].split("=")
depth = float(depth_full[1])
distance_full = line_split[11].split("=")
distance = float(distance_full[1])
distance_std = distance_std_factor * distance + distance_std_offset
lateral_distance_full = line_split[9].split("=")
lateral_distance = float(lateral_distance_full[1])
bearing_full = line_split[10].split("=")
bearing = float(bearing_full[1])
# determine uncertainty in terms of latitude and longitude
latitude_offset, longitude_offset = metres_to_latlon(
latitude, longitude, distance_std, distance_std
)
latitude_std = latitude - latitude_offset
longitude_std = longitude - longitude_offset
# calculate in metres from reference
eastings_ship = 0
northings_ship = 0
latitude_ship = 0
longitude_ship = 0
heading_ship = 0
lateral_distance_target, bearing_target = latlon_to_metres(
latitude, longitude, latitude_reference, longitude_reference,
)
eastings_target = (
math.sin(bearing_target * math.pi / 180.0)
* lateral_distance_target
)
northings_target = (
math.cos(bearing_target * math.pi / 180.0)
* lateral_distance_target
)
if ftype == "oplab":
data = {
"epoch_timestamp": float(epoch_timestamp),
"class": class_string,
"sensor": sensor_string,
"frame": frame_string,
"category": category,
"data_ship": [
{
"latitude": float(latitude_ship),
"longitude": float(longitude_ship),
},
{
"northings": float(northings_ship),
"eastings": float(eastings_ship),
},
{"heading": float(heading_ship)},
],
"data_target": [
{
"latitude": float(latitude),
"latitude_std": float(latitude_std),
},
{
"longitude": float(longitude),
"longitude_std": float(longitude_std),
},
{
"northings": float(northings_target),
"northings_std": float(distance_std),
},
{
"eastings": float(eastings_target),
"eastings_std": float(distance_std),
},
{
"depth": float(depth),
"depth_std": float(distance_std),
},
{"distance_to_ship": float(distance)},
],
}
data_list.append(data)
if ftype == "acfr":
data = (
"SSBL_FIX: "
+ str(float(epoch_timestamp))
+ " ship_x: "
+ str(float(northings_ship))
+ " ship_y: "
+ str(float(eastings_ship))
+ " target_x: "
+ str(float(northings_target))
+ " target_y: "
+ str(float(eastings_target))
+ " target_z: "
+ str(float(depth))
+ " target_hr: "
+ str(float(lateral_distance))
+ " target_sr: "
+ str(float(distance))
+ " target_bearing: "
+ str(float(bearing))
+ "\n"
)
data_list += data
return data_list
def parse_acfr_images(mission, vehicle, category, ftype, outpath):
# parser meta data
class_string = "measurement"
frame_string = "body"
category = "image"
sensor_string = "acfr_standard"
timezone = mission.image.timezone
timeoffset = mission.image.timeoffset
filepath = mission.image.cameras[0].path
camera1_label = mission.image.cameras[0].name
camera2_label = mission.image.cameras[1].name
# read in timezone
if isinstance(timezone, str):
if timezone == "utc" or timezone == "UTC":
timezone_offset = 0
elif timezone == "jst" or timezone == "JST":
timezone_offset = 9
else:
try:
timezone_offset = float(timezone)
except ValueError:
print(
"Error: timezone",
timezone,
"in mission.cfg not recognised, please enter value from UTC",
"in hours",
)
return
# convert to seconds from utc
# timeoffset = -timezone_offset*60*60 + timeoffset
Console.info("... parsing " + sensor_string + " images")
# determine file paths
filepath = get_raw_folder(outpath / ".." / filepath)
all_list = os.listdir(str(filepath))
camera1_filename = [
line for line in all_list
if camera1_label in line and ".txt" not in line and "._" not in line
]
camera2_filename = [
line for line in all_list
if camera2_label in line and ".txt" not in line and "._" not in line
]
data_list = []
if ftype == "acfr":
data_list = ""
for i in range(len(camera1_filename)):
epoch_timestamp = acfr_timestamp_from_filename(camera1_filename[i],
timezone_offset,
timeoffset)
epoch_timestamp_camera1.append(str(epoch_timestamp))
for i in range(len(camera2_filename)):
epoch_timestamp = acfr_timestamp_from_filename(camera2_filename[i],
timezone_offset,
timeoffset)
epoch_timestamp_camera2.append(str(epoch_timestamp))
for i in range(len(camera1_filename)):
# print(epoch_timestamp_camera1[i])
values = []
for j in range(len(camera2_filename)):
# print(epoch_timestamp_camera2[j])
values.append(
abs(
float(epoch_timestamp_camera1[i]) -
float(epoch_timestamp_camera2[j])))
(sync_difference, sync_pair) = min(
(v, k) for k, v in enumerate(values))
if sync_difference > tolerance:
# Skip the pair
continue
if ftype == "oplab":
data = {
"epoch_timestamp":
float(epoch_timestamp_camera1[i]),
"class":
class_string,
"sensor":
sensor_string,
"frame":
frame_string,
"category":
category,
"camera1": [{
"epoch_timestamp":
float(epoch_timestamp_camera1[i]),
"filename":
str(filepath) + "/" + str(camera1_filename[i]),
}],
"camera2": [{
"epoch_timestamp":
float(epoch_timestamp_camera2[sync_pair]),
"filename":
str(filepath) + "/" + str(camera2_filename[sync_pair]),
}],
}
data_list.append(data)
if ftype == "acfr":
data = ("VIS: " + str(float(epoch_timestamp_camera1[i])) + " [" +
str(float(epoch_timestamp_camera1[i])) + "] " +
str(camera1_filename[i]) + " exp: 0\n")
# fileout.write(data)
data_list += data
data = ("VIS: " + str(float(epoch_timestamp_camera2[sync_pair])) +
" [" + str(float(epoch_timestamp_camera2[sync_pair])) +
"] " + str(camera2_filename[sync_pair]) + " exp: 0\n")
# fileout.write(data)
data_list += data
return data_list
def parse_biocam_images(mission, vehicle, category, ftype, outpath):
# parser meta data
class_string = "measurement"
frame_string = "body"
category = "image"
sensor_string = "biocam"
timezone = mission.image.timezone
timezone_offset = 0
timeoffset = mission.image.timeoffset
filepath = mission.image.cameras[0].path
camera1_label = mission.image.cameras[0].name
camera2_label = mission.image.cameras[1].name
# read in timezone
if isinstance(timezone, str):
if timezone == "utc" or timezone == "UTC":
timezone_offset = 0
elif timezone == "jst" or timezone == "JST":
timezone_offset = 9
else:
try:
timezone_offset = float(timezone)
except ValueError:
print(
"Error: timezone",
timezone,
"in mission.cfg not recognised, please enter value from UTC",
"in hours",
)
return
# convert to seconds from utc
# timeoffset = -timezone_offset*60*60 + timeoffset
Console.info("... parsing " + sensor_string + " images")
# determine file paths
base_path = get_raw_folder(outpath / ".." / filepath)
filepath1 = base_path / str(camera1_label + "_strobe/*.*")
filepath1b = base_path / str(camera1_label + "_laser/**/*.*")
filepath2 = base_path / str(camera2_label + "_strobe/*.*")
filepath2b = base_path / str(camera2_label + "_laser/**/*.*")
camera1_list = glob.glob(str(filepath1))
camera1_list.extend(glob.glob(str(filepath1b), recursive=True))
camera2_list = glob.glob(str(filepath2))
camera2_list.extend(glob.glob(str(filepath2b), recursive=True))
camera1_filename = [
line for line in camera1_list
if ".txt" not in line and "._" not in line
]
camera2_filename = [
line for line in camera2_list
if ".txt" not in line and ".jpg" not in line
]
camera3_filename = [line for line in camera2_list if ".jpg" in line]
dive_folder = get_raw_folder(outpath / "..")
camera1_relfilename = pathlist_relativeto(camera1_filename, dive_folder)
camera2_relfilename = pathlist_relativeto(camera2_filename, dive_folder)
camera3_relfilename = pathlist_relativeto(camera3_filename, dive_folder)
Console.info("Found " + str(
len(camera2_filename) + len(camera1_filename) +
len(camera3_filename)) + " BioCam images!")
data_list = []
if ftype == "acfr":
data_list = ""
for i in range(len(camera1_filename)):
t1, tc1 = biocam_timestamp_from_filename(
Path(camera1_filename[i]).name, timezone_offset, timeoffset)
stamp_pc1.append(str(t1))
stamp_cam1.append(str(tc1))
for i in range(len(camera2_filename)):
t2, tc2 = biocam_timestamp_from_filename(
Path(camera2_filename[i]).name, timezone_offset, timeoffset)
stamp_pc2.append(str(t2))
stamp_cam2.append(str(tc2))
for i in range(len(camera1_filename)):
values = []
for j in range(len(camera2_filename)):
values.append(abs(float(stamp_pc1[i]) - float(stamp_pc2[j])))
(sync_difference, sync_pair) = min(
(v, k) for k, v in enumerate(values))
if sync_difference < tolerance:
if ftype == "oplab":
data = {
"epoch_timestamp":
float(stamp_pc1[i]),
"class":
class_string,
"sensor":
sensor_string,
"frame":
frame_string,
"category":
category,
"camera1": [{
"epoch_timestamp": float(stamp_pc1[i]),
# Duplicate for timestamp prediction purposes
"epoch_timestamp_cpu": float(stamp_pc1[i]),
"epoch_timestamp_cam": float(stamp_cam1[i]),
"filename": str(camera1_relfilename[i]),
}],
"camera2": [{
"epoch_timestamp":
float(stamp_pc2[sync_pair]),
# Duplicate for timestamp prediction purposes
"epoch_timestamp_cpu":
float(stamp_pc2[sync_pair]),
"epoch_timestamp_cam":
float(stamp_cam2[sync_pair]),
"filename":
str(camera2_relfilename[sync_pair]),
}],
}
data_list.append(data)
if ftype == "acfr":
data = ("VIS: " + str(float(stamp_pc1[i])) + " [" +
str(float(stamp_pc1[i])) + "] " +
str(camera1_relfilename[i]) + " exp: 0\n")
# fileout.write(data)
data_list += data
data = ("VIS: " + str(float(stamp_pc2[sync_pair])) + " [" +
str(float(stamp_pc2[sync_pair])) + "] " +
str(camera2_relfilename[sync_pair]) + " exp: 0\n")
# fileout.write(data)
data_list += data
for i in range(len(camera3_filename)):
t3, tc3 = biocam_timestamp_from_filename(
Path(camera3_filename[i]).name, timezone_offset, timeoffset)
data = {
"epoch_timestamp":
float(t3),
"class":
class_string,
"sensor":
sensor_string,
"frame":
frame_string,
"category":
"laser",
"camera3": [{
"epoch_timestamp": float(t3),
# Duplicate for timestamp prediction purposes
"epoch_timestamp_cpu": float(t3),
"epoch_timestamp_cam": float(tc3),
"filename": str(camera3_relfilename[i]),
}],
}
data_list.append(data)
return data_list
