def process_dji(logfile):
'''Processes the provided DJI logfile as a CVS and outputs the maximum distance
from home.
param: logfile Path to the DJI csv log file
return: max_rng Maximum range from start point, or None if bad file
'''
if os.path.getsize(logfile) == 0:
return
log = dji_extract.DJILog(logfile)
try:
positions_unfiltered = log.extract_6dof()
except KeyError:
badlogs = open('badlogs.txt', 'a')
badlogs.write('%s\n' % (logfile))
badlogs.close()
return
positions = []
for i in range(len(positions_unfiltered)):
if positions_unfiltered[i, 0] != 0:
utm_coord = utm.from_latlon(positions_unfiltered[i, 2],
positions_unfiltered[i, 1])
positions.append(
[utm_coord[0], utm_coord[1], positions_unfiltered[i, 2]])
if len(positions) == 0:
return
positions = np.array(positions)
distances = np.zeros(len(positions))
for i in range(len(distances)):
distances[i] = np.linalg.norm(positions[i, :] - positions[0, :])
distance = np.max(distances)
return distance
def get_extents(logfile):
'''Plots the extents of the specified logfile'''
if os.path.getsize(logfile) == 0:
return
if os.path.splitext(logfile)[1].lower() == '.bin':
logstruct = arducopter_extract.ArduLog(logfile)
try:
positions_unfiltered = logstruct.extract_6dof3()[:,(5, 6, 4)] #x, y, -z
except KeyError:
badlogs = open('badlogs.txt', 'a')
badlogs.write('%s\n' % (logfile))
badlogs.close()
return
elif os.path.splitext(logfile)[1].lower() == '.csv':
logstruct = dji_extract.DJILog(logfile)
positions_unfiltered = logstruct.extract_6dof()
if positions_unfiltered is None:
return
positions_unfiltered = np.array([[position[2], position[1], position[3]] for position in positions_unfiltered])
positions = []
utm_zone = 'Z'
utm_zonenum = 0
for i in range(len(positions_unfiltered)):
if positions_unfiltered[i, 0] != 0 and positions_unfiltered[i, 1] != 0:
utm_coord = utm.from_latlon(positions_unfiltered[i, 0], positions_unfiltered[i, 1])
positions.append([utm_coord[0], utm_coord[1], positions_unfiltered[i, 2]])
utm_zonenum = utm_coord[2]
utm_zone = utm_coord[3]
if len(positions) == 0:
return
np_positions = np.array(positions)
min_coords = np.amin(np_positions, 0)
max_coords = np.amax(np_positions, 0)
coord_range = max_coords - min_coords
scale = 0.1
pos_map = np.zeros((int(coord_range[0] * scale) + 1, int(coord_range[1] * scale) + 1), dtype=np.uint8)
mTw = np.array([[scale, 0, -scale * min_coords[0]],
[0, scale, -scale * min_coords[1]],
[0, 0, 1]])
world_pos = np.vstack((np_positions[:,0:2].transpose(), np.ones(np_positions.shape[0])))
map_pos = np.floor(np.matmul(mTw, world_pos))
for i in range(map_pos.shape[1] - 1):
cv2.line(pos_map, (int(map_pos[0, i]), int(map_pos[1, i])), (int(map_pos[0, i+1]), int(map_pos[1, i+1])), 255)
contours, hierarchy = cv2.findContours(pos_map, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
rect = cv2.boundingRect(contours[0])
box = np.array([[rect[0], rect[0], rect[0] + rect[2], rect[0] + rect[2]],
[rect[1], rect[1] + rect[3], rect[1], rect[1] + rect[3]],
[1, 1, 1, 1]])
wTm = invert_tf(mTw)
box_world = np.matmul(wTm, box)
extents = []
for i in range(box_world.shape[1]):
ll_coord = utm.to_latlon(box_world[0, i], box_world[1, i], utm_zonenum, utm_zone)
extents.append(ll_coord)
return extents
def extract_takeoffs_dji(logfile):
'''Processes the given DJI logfile for the aircraft type and number of takeoffs
:returns acft Aircraft type as string as DJI
tofs Number of takeoffs
'''
log_struct = dji_extract.DJILog(logfile)
acft_str = 'DJI'
tofs = log_struct.get_takeoffs()
return acft_str, tofs
def process_dji(logfile, output_file):
'''Processes the provided DJI logfile as a CVS and outputs the distance flown
to the specified output file.
Appends the following line to the output file:
`'$s:%.0f\n' % (logfile, distance)`
param: logfile Path to the DJI csv log file
param: output_file Path to the common output file
'''
if os.path.getsize(logfile) == 0:
return
log = dji_extract.DJILog(logfile)
try:
positions_unfiltered = log.extract_6dof()
except KeyError:
badlogs = open('badlogs.txt', 'a')
badlogs.write('%s\n' % (logfile))
badlogs.close()
return
positions = []
for i in range(len(positions_unfiltered)):
if positions_unfiltered[i, 0] != 0:
utm_coord = utm.from_latlon(positions_unfiltered[i, 2],
positions_unfiltered[i, 1])
positions.append(
[utm_coord[0], utm_coord[1], positions_unfiltered[i, 2]])
if len(positions) == 0:
return
positions = np.array(positions)
distances = np.zeros(len(positions) - 1)
for i in range(len(distances)):
distances[i] = np.linalg.norm(positions[i, :] - positions[i + 1, :])
distance = np.sum(distances)
with open(output_file, 'a') as file:
file.write('%s:%.0f\n' % (logfile, distance))
def plot_djipath(log, output_filename, overwrite=False):
'''Plots the specified DJI log as an ESRI shapefile at the specified
output path.
param: log DJI binary log file path
param: output_filename Output path
'''
if os.path.getsize(log) == 0:
return
if os.path.isfile(
"%s.shp" %
(os.path.splitext(output_filename)[0])) and not overwrite:
return
logstruct = dji_extract.DJILog(log)
try:
positions_unfiltered = logstruct.extract_6dof()
except KeyError:
badlogs = open('badlogs.txt', 'a')
badlogs.write('%s\n' % (logfile))
badlogs.close()
return
positions = []
for i in range(len(positions_unfiltered)):
if positions_unfiltered[i, 0] != 0:
positions.append(positions_unfiltered[i, (2, 1, 3)])
if len(positions) == 0:
return
coords = [list(position[0:2]) for position in positions]
alts = [position[2] for position in positions]
writer = shapefile.Writer(output_filename)
writer.field('log', 'C')
writer.line([coords])
writer.record(log)
writer.close()
proj = open("%s.prj" % (os.path.splitext(output_filename)[0]), 'w')
epsg1 = 'GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433]]'
proj.write(epsg1)
proj.close()