def parse_or_load_xtf(path):
if os.path.exists("xtf_cache.cereal"):
xtf_pings = xtf_data.xtf_sss_ping.read_data("xtf_cache.cereal")
else:
xtf_pings = xtf_data.xtf_sss_ping.parse_folder(path)
#nav_entries = csv_data.csv_nav_entry.parse_file(csv_path)
xtf_data.write_data(xtf_pings, "xtf_cache.cereal")
return xtf_pings
def match_or_load_xtf(xtf_path, csv_path):
if os.path.exists("matched_cache.cereal"):
xtf_pings = xtf_data.xtf_sss_ping.read_data("matched_cache.cereal")
else:
xtf_pings = utils.parse_or_load_xtf(xtf_path)
nav_entries = utils.parse_or_load_csv(csv_path)
xtf_pings = csv_data.convert_matched_entries(xtf_pings, nav_entries)
xtf_data.write_data(xtf_pings, "matched_cache.cereal")
return xtf_pings
#------------------------------------------------------------------------------
#SAVE XTF DATA FROM SELECTED REGION
#Read xtf data
xtf_data = xtf_data.xtf_sss_ping.read_data(xtf_file)
#Pick out data points from selected region
xtf_region_data = []
for ping in xtf_data:
pos = ping.pos_
if pos[0] > low_x and pos[0] < high_x and pos[1] > low_y and pos[
1] < high_y:
xtf_region_data.append(ping)
#Save data points from selected region
xtf_data.write_data(xtf_region_data, save_path_xtf + outputfile_xtf)
#------------------------------------------------------------------------------
#SAVE MULTIBEAM DATA FROM SELECTED REGION
#Read multibeam data
mbes_data = std_data.mbes_ping.read_data(mbes_file)
#Pick out data points from selected region
mbes_region_data = []
for ping in mbes_data:
pos = ping.pos_
if pos[0] > low_x and pos[0] < high_x and pos[1] > low_y and pos[
1] < high_y:
mbes_region_data.append(ping)
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
''' cut some unuseful pings in each layer '''
xtf_file = "/home/chs/Desktop/Sonar/Data/xtf_ping/xtf_pings_low.cereal" # sidescan data
xtf_pings = xtf_data.xtf_sss_ping.read_data(xtf_file)
choosen_pings = []
for ping in xtf_pings:
if 1560597112120 < ping.time_stamp_ < 1560598940700:
choosen_pings.append(ping)
''' Show moving curve of vechile in xtf '''
x_xtf = []
y_xtf = []
z_xtf = []
for ping in choosen_pings:
x_xtf.append(ping.pos_[0])
y_xtf.append(ping.pos_[1])
z_xtf.append(ping.pos_[2])
ax = plt.subplot(projection='3d')
ax.scatter(x_xtf, y_xtf, z_xtf, s=2, c='r', marker='.')
ax.set_zlabel('Z')
ax.set_ylabel('Y')
ax.set_xlabel('X')
plt.show()
xtf_data.write_data(
choosen_pings,
"/home/chs/Desktop/Sonar/Data/xtf_ping/xtf_pings_low_1.cereal")
print('Num of pings: %i' % len(choosen_pings))
from auvlib.data_tools import xtf_data
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
xtf_file = "/home/chs/Desktop/Sonar/Data/xtf_ping/xtf_pings_mid.cereal" # sidescan data
new_xtf_pings = xtf_data.xtf_sss_ping.read_data(xtf_file)
xtf_file = "/home/chs/Desktop/Sonar/Data/xtf_ping/xtf_pings_mid_old.cereal" # sidescan data
old_xtf_pings = xtf_data.xtf_sss_ping.read_data(xtf_file)
for ping in old_xtf_pings:
if new_xtf_pings[0].time_stamp_ == ping.time_stamp_:
delta_pos = new_xtf_pings[0].pos_ - ping.pos_
print("Delta pos =")
print(delta_pos)
''' Delta pos = [845.22058245 82.73684846 0. ]'''
''' Note: this step has been added to divide data '''
std_pings_moved = []
for ping in new_xtf_pings:
new_pos = ping.pos_ + np.array([845.22058245, 82.73684846, 0.])
ping.pos_ = new_pos
std_pings_moved.append(ping)
xtf_data.write_data(
std_pings_moved,
"/home/chs/Desktop/Sonar/Data/xtf_ping/xtf_pings_mid_new_1.cereal")
print("???")
def saver(choosen_pings, i):
choosen_pings =move_base(choosen_pings)
xtf_data.write_data(choosen_pings, "/home/chs/Desktop/Sonar/Data/xtf_ping/xtf_pings_%d.cereal" % i)
# print('Num of pings: %i' % len(choosen_pings))
return choosen_pings[0].time_stamp_