def show_output(self):
"""
运行对应算法，返回深度值矩阵，将图片保存到根目录；再将图片加载到深度图帆布中；所有功能用一个函数搞定
:return: None
"""
self.depth = get_depth(self.tool_depth_cbb1.get(), self.input_path)
self.line.get_depth(self.depth) # 将深度矩阵传入Line类
plt.imsave('pred.jpg', self.depth) # 保存深度图片
self.show_image('pred.jpg', self.work_output_cv) # 将生成图加载到深度图帆布中
self.result = True # 深度图已生成，可以保存
return
def read(humfile, sonpath, cs2cs_args, c, draft, doplot, t, bedpick, flip_lr,
model, calc_bearing, filt_bearing, chunk): #cog = 1,
'''
Read a .DAT and associated set of .SON files recorded by a Humminbird(R)
instrument.
Parse the data into a set of memory mapped files that will
subsequently be used by the other functions of the PyHum module.
Export time-series data and metadata in other formats.
Create a kml file for visualising boat track
Syntax
----------
[] = PyHum.read(humfile, sonpath, cs2cs_args, c, draft, doplot, t, bedpick, flip_lr, chunksize, model, calc_bearing, filt_bearing, chunk)
Parameters
------------
humfile : str
path to the .DAT file
sonpath : str
path where the *.SON files are
cs2cs_args : int, *optional* [Default="epsg:26949"]
arguments to create coordinates in a projected coordinate system
this argument gets given to pyproj to turn wgs84 (lat/lon) coordinates
into any projection supported by the proj.4 libraries
c : float, *optional* [Default=1450.0]
speed of sound in water (m/s). Defaults to a value of freshwater
draft : float, *optional* [Default=0.3]
draft from water surface to transducer face (m)
doplot : float, *optional* [Default=1]
if 1, plots will be made
t : float, *optional* [Default=0.108]
length of transducer array (m).
Default value is that of the 998 series Humminbird(R)
bedpick : int, *optional* [Default=1]
if 1, bedpicking with be carried out automatically
if 0, user will be prompted to pick the bed location on screen
flip_lr : int, *optional* [Default=0]
if 1, port and starboard scans will be flipped
(for situations where the transducer is flipped 180 degrees)
model: int, *optional* [Default=998]
A 3 or 4 number code indicating the model number
Examples: 998, 997, 1198, 1199
calc_bearing : float, *optional* [Default=0]
if 1, bearing will be calculated from coordinates
filt_bearing : float, *optional* [Default=0]
if 1, bearing will be filtered
chunk : str, *optional* [Default='d100' (distance, 100 m)]
letter, followed by a number.
There are the following letter options:
'd' - parse chunks based on distance, then number which is distance in m
'p' - parse chunks based on number of pings, then number which is number of pings
'h' - parse chunks based on change in heading, then number which is the change in heading in degrees
'1' - process just 1 chunk
Returns
---------
sonpath+base+'_data_port.dat': memory-mapped file
contains the raw echogram from the port side
sidescan sonar (where present)
sonpath+base+'_data_port.dat': memory-mapped file
contains the raw echogram from the starboard side
sidescan sonar (where present)
sonpath+base+'_data_dwnhi.dat': memory-mapped file
contains the raw echogram from the high-frequency
echosounder (where present)
sonpath+base+'_data_dwnlow.dat': memory-mapped file
contains the raw echogram from the low-frequency
echosounder (where present)
sonpath+base+"trackline.kml": google-earth kml file
contains the trackline of the vessel during data
acquisition
sonpath+base+'rawdat.csv': comma separated value file
contains time-series data. columns corresponding to
longitude
latitude
easting (m)
northing (m)
depth to bed (m)
alongtrack cumulative distance (m)
vessel heading (deg.)
sonpath+base+'meta.mat': .mat file
matlab format file containing a dictionary object
holding metadata information. Fields are:
e : ndarray, easting (m)
n : ndarray, northing (m)
es : ndarray, low-pass filtered easting (m)
ns : ndarray, low-pass filtered northing (m)
lat : ndarray, latitude
lon : ndarray, longitude
shape_port : tuple, shape of port scans in memory mapped file
shape_star : tuple, shape of starboard scans in memory mapped file
shape_hi : tuple, shape of high-freq. scans in memory mapped file
shape_low : tuple, shape of low-freq. scans in memory mapped file
dep_m : ndarray, depth to bed (m)
dist_m : ndarray, distance along track (m)
heading : ndarray, heading of vessel (deg. N)
pix_m: float, size of 1 pixel in across-track dimension (m)
bed : ndarray, depth to bed (m)
c : float, speed of sound in water (m/s)
t : length of sidescan transducer array (m)
spd : ndarray, vessel speed (m/s)
time_s : ndarray, time elapsed (s)
caltime : ndarray, unix epoch time (s)
'''
# prompt user to supply file if no input file given
if not humfile:
print('An input file is required!!!!!!')
Tk().withdraw(
) # we don't want a full GUI, so keep the root window from appearing
humfile = askopenfilename(filetypes=[("DAT files", "*.DAT")])
# prompt user to supply directory if no input sonpath is given
if not sonpath:
print('A *.SON directory is required!!!!!!')
Tk().withdraw(
) # we don't want a full GUI, so keep the root window from appearing
sonpath = askdirectory()
# print given arguments to screen and convert data type where necessary
if humfile:
print('Input file is %s' % (humfile))
if sonpath:
print('Son files are in %s' % (sonpath))
if cs2cs_args:
print('cs2cs arguments are %s' % (cs2cs_args))
if draft:
draft = float(draft)
print('Draft: %s' % (str(draft)))
if c:
c = float(c)
print('Celerity of sound: %s m/s' % (str(c)))
if doplot:
doplot = int(doplot)
if doplot == 0:
print("Plots will not be made")
if flip_lr:
flip_lr = int(flip_lr)
if flip_lr == 1:
print("Port and starboard will be flipped")
if t:
t = np.asarray(t, float)
print('Transducer length is %s m' % (str(t)))
if bedpick:
bedpick = np.asarray(bedpick, int)
if bedpick == 1:
print('Bed picking is auto')
elif bedpick == 0:
print('Bed picking is manual')
else:
print('User will be prompted per chunk about bed picking method')
if chunk:
chunk = str(chunk)
if chunk[0] == 'd':
chunkmode = 1
chunkval = int(chunk[1:])
print('Chunks based on distance of %s m' % (str(chunkval)))
elif chunk[0] == 'p':
chunkmode = 2
chunkval = int(chunk[1:])
print('Chunks based on %s pings' % (str(chunkval)))
elif chunk[0] == 'h':
chunkmode = 3
chunkval = int(chunk[1:])
print('Chunks based on heading devation of %s degrees' %
(str(chunkval)))
elif chunk[0] == '1':
chunkmode = 4
chunkval = 1
print('Only 1 chunk will be produced')
else:
print(
"Chunk mode not understood - should be 'd', 'p', or 'h' - using defaults"
)
chunkmode = 1
chunkval = 100
print('Chunks based on distance of %s m' % (str(chunkval)))
if model:
try:
model = int(model)
print("Data is from the %s series" % (str(model)))
except:
if model == 'onix':
model = 0
print("Data is from the ONIX series")
elif model == 'helix':
model = 1
print("Data is from the HELIX series")
elif model == 'mega':
model = 2
print("Data is from the MEGA series")
# if cog:
# cog = int(cog)
# if cog==1:
# print "Heading based on course-over-ground"
if calc_bearing:
calc_bearing = int(calc_bearing)
if calc_bearing == 1:
print("Bearing will be calculated from coordinates")
if filt_bearing:
filt_bearing = int(filt_bearing)
if filt_bearing == 1:
print("Bearing will be filtered")
## for debugging
#humfile = r"test.DAT"; sonpath = "test_data"
#cs2cs_args = "epsg:26949"; doplot = 1; draft = 0
#c=1450; bedpick=1; fliplr=1; chunk = 'd100'
#model=998; cog=1; calc_bearing=0; filt_bearing=0
#if model==2:
# f = 1000
#else:
f = 455
try:
print(
"Checking the epsg code you have chosen for compatibility with Basemap ... "
)
from mpl_toolkits.basemap import Basemap
m = Basemap(projection='merc',
epsg=cs2cs_args.split(':')[1],
resolution='i',
llcrnrlon=10,
llcrnrlat=10,
urcrnrlon=30,
urcrnrlat=30)
del m
print("... epsg code compatible")
except (ValueError):
print(
"Error: the epsg code you have chosen is not compatible with Basemap"
)
print(
"please choose a different epsg code (http://spatialreference.org/)"
)
print("program will now close")
sys.exit()
# start timer
if os.name == 'posix': # true if linux/mac or cygwin on windows
start = time.time()
else: # windows
start = time.clock()
# if son path name supplied has no separator at end, put one on
if sonpath[-1] != os.sep:
sonpath = sonpath + os.sep
# get the SON files from this directory
sonfiles = glob.glob(sonpath + '*.SON')
if not sonfiles:
sonfiles = glob.glob(os.getcwd() + os.sep + sonpath + '*.SON')
base = humfile.split('.DAT') # get base of file name for output
base = base[0].split(os.sep)[-1]
# remove underscores, negatives and spaces from basename
base = humutils.strip_base(base)
print("WARNING: Because files have to be read in byte by byte,")
print("this could take a very long time ...")
#reading each sonfile in parallel should be faster ...
try:
o = Parallel(n_jobs=np.min([len(sonfiles), cpu_count()]), verbose=0)(
delayed(getscans)(sonfiles[k], humfile, c, model, cs2cs_args)
for k in range(len(sonfiles)))
X, Y, A, B = zip(*o)
for k in range(len(Y)):
if Y[k] == 'sidescan_port':
dat = A[k] #data.gethumdat()
metadat = B[k] #data.getmetadata()
if flip_lr == 0:
data_port = X[k].astype('int16')
else:
data_star = X[k].astype('int16')
elif Y[k] == 'sidescan_starboard':
if flip_lr == 0:
data_star = X[k].astype('int16')
else:
data_port = X[k].astype('int16')
elif Y[k] == 'down_lowfreq':
data_dwnlow = X[k].astype('int16')
elif Y[k] == 'down_highfreq':
data_dwnhi = X[k].astype('int16')
elif Y[k] == 'down_vhighfreq': #hopefully this only applies to mega systems
data_dwnhi = X[k].astype('int16')
del X, Y, A, B, o
old_pyread = 0
if 'data_port' not in locals():
data_port = ''
print("portside scan not available")
if 'data_star' not in locals():
data_star = ''
print("starboardside scan not available")
if 'data_dwnhi' not in locals():
data_dwnlow = ''
print("high-frq. downward scan not available")
if 'data_dwnlow' not in locals():
data_dwnlow = ''
print("low-frq. downward scan not available")
except: # revert back to older version if paralleleised version fails
print(
"something went wrong with the parallelised version of pyread ...")
try:
import pyread
except:
from . import pyread
data = pyread.pyread(sonfiles, humfile, c, model, cs2cs_args)
dat = data.gethumdat()
metadat = data.getmetadata()
old_pyread = 1
nrec = len(metadat['n'])
metadat['instr_heading'] = metadat['heading'][:nrec]
#metadat['heading'] = humutils.get_bearing(calc_bearing, filt_bearing, cog, metadat['lat'], metadat['lon'], metadat['instr_heading'])
try:
es = humutils.runningMeanFast(metadat['e'][:nrec],
len(metadat['e'][:nrec]) / 100)
ns = humutils.runningMeanFast(metadat['n'][:nrec],
len(metadat['n'][:nrec]) / 100)
except:
es = metadat['e'][:nrec]
ns = metadat['n'][:nrec]
metadat['es'] = es
metadat['ns'] = ns
try:
trans = pyproj.Proj(init=cs2cs_args)
except:
trans = pyproj.Proj(cs2cs_args.lstrip(), inverse=True)
lon, lat = trans(es, ns, inverse=True)
metadat['lon'] = lon
metadat['lat'] = lat
metadat['heading'] = humutils.get_bearing(calc_bearing, filt_bearing,
metadat['lat'], metadat['lon'],
metadat['instr_heading']) #cog
dist_m = humutils.get_dist(lat, lon)
metadat['dist_m'] = dist_m
if calc_bearing == 1: # recalculate speed, m/s
ds = np.gradient(np.squeeze(metadat['time_s']))
dx = np.gradient(np.squeeze(metadat['dist_m']))
metadat['spd'] = dx[:nrec] / ds[:nrec]
# theta at 3dB in the horizontal
theta3dB = np.arcsin(c / (t * (f * 1000)))
#resolution of 1 sidescan pixel to nadir
ft = (np.pi / 2) * (1 / theta3dB) #/ (f/455)
dep_m = humutils.get_depth(metadat['dep_m'][:nrec])
if old_pyread == 1: #older pyread version
# port scan
try:
if flip_lr == 0:
data_port = data.getportscans().astype('int16')
else:
data_port = data.getstarscans().astype('int16')
except:
data_port = ''
print("portside scan not available")
if data_port != '':
Zt, ind_port = makechunks_scan(chunkmode, chunkval, metadat, data_port,
0)
del data_port
## create memory mapped file for Z
shape_port = io.set_mmap_data(sonpath, base, '_data_port.dat', 'int16',
Zt)
##we are only going to access the portion of memory required
port_fp = io.get_mmap_data(sonpath, base, '_data_port.dat', 'int16',
shape_port)
if old_pyread == 1: #older pyread version
# starboard scan
try:
if flip_lr == 0:
data_star = data.getstarscans().astype('int16')
else:
data_star = data.getportscans().astype('int16')
except:
data_star = ''
print("starboardside scan not available")
if data_star != '':
Zt, ind_star = makechunks_scan(chunkmode, chunkval, metadat, data_star,
1)
del data_star
# create memory mapped file for Z
shape_star = io.set_mmap_data(sonpath, base, '_data_star.dat', 'int16',
Zt)
star_fp = io.get_mmap_data(sonpath, base, '_data_star.dat', 'int16',
shape_star)
if 'star_fp' in locals() and 'port_fp' in locals():
# check that port and starboard are same size
# and trim if not
if np.shape(star_fp) != np.shape(port_fp):
print(
"port and starboard scans are different sizes ... rectifying")
if np.shape(port_fp[0])[1] > np.shape(star_fp[0])[1]:
tmp = port_fp.copy()
tmp2 = np.empty_like(star_fp)
for k in range(len(tmp)):
tmp2[k] = tmp[k][:, :np.shape(star_fp[k])[1]]
del tmp
# create memory mapped file for Z
shape_port = io.set_mmap_data(sonpath, base, '_data_port2.dat',
'int16', tmp2)
#shape_star = shape_port.copy()
shape_star = tuple(np.asarray(shape_port).copy())
##we are only going to access the portion of memory required
port_fp = io.get_mmap_data(sonpath, base, '_data_port2.dat',
'int16', shape_port)
ind_port = list(ind_port)
ind_port[-1] = np.shape(star_fp[0])[1]
ind_port = tuple(ind_port)
elif np.shape(port_fp[0])[1] < np.shape(star_fp[0])[1]:
tmp = star_fp.copy()
tmp2 = np.empty_like(port_fp)
for k in range(len(tmp)):
tmp2[k] = tmp[k][:, :np.shape(port_fp[k])[1]]
del tmp
# create memory mapped file for Z
shape_port = io.set_mmap_data(sonpath, base, '_data_star2.dat',
'int16', tmp2)
#shape_star = shape_port.copy()
shape_star = tuple(np.asarray(shape_port).copy())
#we are only going to access the portion of memory required
star_fp = io.get_mmap_data(sonpath, base, '_data_star2.dat',
'int16', shape_star)
ind_star = list(ind_star)
ind_star[-1] = np.shape(port_fp[0])[1]
ind_star = tuple(ind_star)
if old_pyread == 1: #older pyread version
# low-freq. sonar
try:
data_dwnlow = data.getlowscans().astype('int16')
except:
data_dwnlow = ''
print("low-freq. scan not available")
if data_dwnlow != '':
Zt, ind_low = makechunks_scan(chunkmode, chunkval, metadat,
data_dwnlow, 2)
del data_dwnlow
# create memory mapped file for Z
shape_low = io.set_mmap_data(sonpath, base, '_data_dwnlow.dat',
'int16', Zt)
##we are only going to access the portion of memory required
dwnlow_fp = io.get_mmap_data(sonpath, base, '_data_dwnlow.dat',
'int16', shape_low)
if old_pyread == 1: #older pyread version
# hi-freq. sonar
try:
data_dwnhi = data.gethiscans().astype('int16')
except:
data_dwnhi = ''
print("high-freq. scan not available")
if data_dwnhi != '':
Zt, ind_hi = makechunks_scan(chunkmode, chunkval, metadat, data_dwnhi,
3)
del data_dwnhi
# create memory mapped file for Z
shape_hi = io.set_mmap_data(sonpath, base, '_data_dwnhi.dat', 'int16',
Zt)
dwnhi_fp = io.get_mmap_data(sonpath, base, '_data_dwnhi.dat', 'int16',
shape_hi)
if 'dwnhi_fp' in locals() and 'dwnlow_fp' in locals():
# check that low and high are same size
# and trim if not
if (np.shape(dwnhi_fp) != np.shape(dwnlow_fp)) and (chunkmode != 4):
print("dwnhi and dwnlow are different sizes ... rectifying")
if np.shape(dwnhi_fp[0])[1] > np.shape(dwnlow_fp[0])[1]:
tmp = dwnhi_fp.copy()
tmp2 = np.empty_like(dwnlow_fp)
for k in range(len(tmp)):
tmp2[k] = tmp[k][:, :np.shape(dwnlow_fp[k])[1]]
del tmp
# create memory mapped file for Z
shape_low = io.set_mmap_data(sonpath, base, '_data_dwnhi2.dat',
'int16', tmp2)
#shape_hi = shape_low.copy()
shape_hi = tuple(np.asarray(shape_low).copy())
##we are only going to access the portion of memory required
dwnhi_fp = io.get_mmap_data(sonpath, base, '_data_dwnhi2.dat',
'int16', shape_hi)
ind_hi = list(ind_hi)
ind_hi[-1] = np.shape(dwnlow_fp[0])[1]
ind_hi = tuple(ind_hi)
elif np.shape(dwnhi_fp[0])[1] < np.shape(dwnlow_fp[0])[1]:
tmp = dwnlow_fp.copy()
tmp2 = np.empty_like(dwnhi_fp)
for k in range(len(tmp)):
tmp2[k] = tmp[k][:, :np.shape(dwnhi_fp[k])[1]]
del tmp
# create memory mapped file for Z
shape_low = io.set_mmap_data(sonpath, base,
'_data_dwnlow2.dat', 'int16',
tmp2)
#shape_hi = shape_low.copy()
shape_hi = tuple(np.asarray(shape_low).copy())
##we are only going to access the portion of memory required
dwnlow_fp = io.get_mmap_data(sonpath, base,
'_data_dwnlow2.dat', 'int16',
shape_low)
ind_low = list(ind_low)
ind_low[-1] = np.shape(dwnhi_fp[0])[1]
ind_low = tuple(ind_low)
if old_pyread == 1: #older pyread version
del data
if ('shape_port' in locals()) and (chunkmode != 4):
metadat['shape_port'] = shape_port
nrec = metadat['shape_port'][0] * metadat['shape_port'][2]
elif ('shape_port' in locals()) and (chunkmode == 4):
metadat['shape_port'] = shape_port
nrec = metadat['shape_port'][1]
else:
metadat['shape_port'] = ''
if ('shape_star' in locals()) and (chunkmode != 4):
metadat['shape_star'] = shape_star
nrec = metadat['shape_star'][0] * metadat['shape_star'][2]
elif ('shape_star' in locals()) and (chunkmode == 4):
metadat['shape_star'] = shape_star
nrec = metadat['shape_star'][1]
else:
metadat['shape_star'] = ''
if ('shape_hi' in locals()) and (chunkmode != 4):
metadat['shape_hi'] = shape_hi
#nrec = metadat['shape_hi'][0] * metadat['shape_hi'][2] * 2
elif ('shape_hi' in locals()) and (chunkmode == 4):
metadat['shape_hi'] = shape_hi
else:
metadat['shape_hi'] = ''
if ('shape_low' in locals()) and (chunkmode != 4):
metadat['shape_low'] = shape_low
#nrec = metadat['shape_low'][0] * metadat['shape_low'][2] * 2
elif ('shape_low' in locals()) and (chunkmode == 4):
metadat['shape_low'] = shape_low
else:
metadat['shape_low'] = ''
#make kml boat trackline
humutils.make_trackline(lon, lat, sonpath, base)
if 'port_fp' in locals() and 'star_fp' in locals():
#if not os.path.isfile(os.path.normpath(os.path.join(sonpath,base+'meta.mat'))):
if 2 > 1:
if bedpick == 1: # auto
x, bed = humutils.auto_bedpick(ft, dep_m, chunkmode, port_fp,
c)
if len(dist_m) < len(bed):
dist_m = np.append(
dist_m, dist_m[-1] * np.ones(len(bed) - len(dist_m)))
if doplot == 1:
if chunkmode != 4:
for k in range(len(star_fp)):
plot_2bedpicks(
port_fp[k], star_fp[k],
bed[ind_port[-1] * k:ind_port[-1] * (k + 1)],
dist_m[ind_port[-1] * k:ind_port[-1] *
(k + 1)],
x[ind_port[-1] * k:ind_port[-1] * (k + 1)], ft,
shape_port, sonpath, k, chunkmode)
else:
plot_2bedpicks(port_fp, star_fp, bed, dist_m, x, ft,
shape_port, sonpath, 0, chunkmode)
# 'real' bed is estimated to be the minimum of the two
bed = np.min(np.vstack((bed[:nrec], np.squeeze(x[:nrec]))),
axis=0)
bed = humutils.runningMeanFast(bed, 3)
elif bedpick > 1: # user prompt
x, bed = humutils.auto_bedpick(ft, dep_m, chunkmode, port_fp,
c)
if len(dist_m) < len(bed):
dist_m = np.append(
dist_m, dist_m[-1] * np.ones(len(bed) - len(dist_m)))
# 'real' bed is estimated to be the minimum of the two
bed = np.min(np.vstack((bed[:nrec], np.squeeze(x[:nrec]))),
axis=0)
bed = humutils.runningMeanFast(bed, 3)
# manually intervene
fig = plt.figure()
ax = plt.gca()
if chunkmode != 4:
im = ax.imshow(np.hstack(port_fp),
cmap='gray',
origin='upper')
else:
im = ax.imshow(port_fp, cmap='gray', origin='upper')
plt.plot(bed, 'r')
plt.axis('normal')
plt.axis('tight')
pts1 = plt.ginput(
n=300,
timeout=30) # it will wait for 200 clicks or 60 seconds
x1 = map(lambda x: x[0],
pts1) # map applies the function passed as
y1 = map(lambda x: x[1],
pts1) # first parameter to each element of pts
plt.close()
del fig
if x1 != []: # if x1 is not empty
tree = KDTree(zip(np.arange(1, len(bed)), bed))
try:
dist, inds = tree.query(zip(x1, y1),
k=100,
eps=5,
n_jobs=-1)
except:
dist, inds = tree.query(zip(x1, y1), k=100, eps=5)
b = np.interp(inds, x1, y1)
bed2 = bed.copy()
bed2[inds] = b
bed = bed2
if doplot == 1:
if chunkmode != 4:
for k in range(len(star_fp)):
plot_2bedpicks(
port_fp[k], star_fp[k],
bed[ind_port[-1] * k:ind_port[-1] * (k + 1)],
dist_m[ind_port[-1] * k:ind_port[-1] *
(k + 1)],
x[ind_port[-1] * k:ind_port[-1] * (k + 1)], ft,
shape_port, sonpath, k, chunkmode)
else:
plot_2bedpicks(port_fp, star_fp, bed, dist_m, x, ft,
shape_port, sonpath, 0, chunkmode)
else: #manual
beds = []
if chunkmode != 4:
for k in range(len(port_fp)):
raw_input(
"Bed picking " + str(k + 1) + " of " +
str(len(port_fp)) +
", are you ready? 30 seconds. Press Enter to continue..."
)
bed = {}
fig = plt.figure()
ax = plt.gca()
im = ax.imshow(port_fp[k], cmap='gray', origin='upper')
pts1 = plt.ginput(
n=300, timeout=30
) # it will wait for 200 clicks or 60 seconds
x1 = map(lambda x: x[0],
pts1) # map applies the function passed as
y1 = map(
lambda x: x[1],
pts1) # first parameter to each element of pts
bed = np.interp(np.r_[:ind_port[-1]], x1, y1)
plt.close()
del fig
beds.append(bed)
extent = np.shape(port_fp[k])[0]
bed = np.asarray(np.hstack(beds), 'float')
else:
raw_input(
"Bed picking - are you ready? 30 seconds. Press Enter to continue..."
)
bed = {}
fig = plt.figure()
ax = plt.gca()
im = ax.imshow(port_fp, cmap='gray', origin='upper')
pts1 = plt.ginput(
n=300, timeout=30
) # it will wait for 200 clicks or 60 seconds
x1 = map(lambda x: x[0],
pts1) # map applies the function passed as
y1 = map(lambda x: x[1],
pts1) # first parameter to each element of pts
bed = np.interp(np.r_[:ind_port[-1]], x1, y1)
plt.close()
del fig
beds.append(bed)
extent = np.shape(port_fp)[1]
bed = np.asarray(np.hstack(beds), 'float')
# now revise the depth in metres
dep_m = (1 / ft) * bed
if doplot == 1:
if chunkmode != 4:
for k in range(len(star_fp)):
plot_bedpick(
port_fp[k], star_fp[k], (1 / ft) *
bed[ind_port[-1] * k:ind_port[-1] * (k + 1)],
dist_m[ind_port[-1] * k:ind_port[-1] * (k + 1)],
ft, shape_port, sonpath, k, chunkmode)
else:
plot_bedpick(port_fp, star_fp, (1 / ft) * bed, dist_m, ft,
shape_port, sonpath, 0, chunkmode)
metadat['bed'] = bed[:nrec]
else:
metadat['bed'] = dep_m[:nrec] * ft
metadat['heading'] = metadat['heading'][:nrec]
metadat['lon'] = lon[:nrec]
metadat['lat'] = lat[:nrec]
metadat['dist_m'] = dist_m[:nrec]
metadat['dep_m'] = dep_m[:nrec]
metadat['pix_m'] = 1 / ft
metadat['bed'] = metadat['bed'][:nrec]
metadat['c'] = c
metadat['t'] = t
if model == 2:
metadat['f'] = f * 2
else:
metadat['f'] = f
metadat['spd'] = metadat['spd'][:nrec]
metadat['time_s'] = metadat['time_s'][:nrec]
metadat['e'] = metadat['e'][:nrec]
metadat['n'] = metadat['n'][:nrec]
metadat['es'] = metadat['es'][:nrec]
metadat['ns'] = metadat['ns'][:nrec]
try:
metadat['caltime'] = metadat['caltime'][:nrec]
except:
metadat['caltime'] = metadat['caltime']
savemat(os.path.normpath(os.path.join(sonpath, base + 'meta.mat')),
metadat,
oned_as='row')
f = open(os.path.normpath(os.path.join(sonpath, base + 'rawdat.csv')),
'wt')
writer = csv.writer(f)
writer.writerow(
('longitude', 'latitude', 'easting', 'northing', 'depth (m)',
'distance (m)', 'instr. heading (deg)', 'heading (deg.)'))
for i in range(0, nrec):
writer.writerow(
(float(lon[i]), float(lat[i]), float(es[i]), float(ns[i]),
float(dep_m[i]), float(dist_m[i]),
float(metadat['instr_heading'][i]), float(metadat['heading'][i])))
f.close()
del lat, lon, dep_m #, dist_m
if doplot == 1:
plot_pos(sonpath, metadat, es, ns)
if 'dwnlow_fp' in locals():
plot_dwnlow(dwnlow_fp, chunkmode, sonpath)
if 'dwnhi_fp' in locals():
plot_dwnhi(dwnhi_fp, chunkmode, sonpath)
if os.name == 'posix': # true if linux/mac
elapsed = (time.time() - start)
else: # windows
elapsed = (time.clock() - start)
print("Processing took " + str(elapsed) + "seconds to analyse")
print("Done!")
print("===================================================")
# ----------------------------------------------------------------------------
# | | 200-epoch | Orig Paper| 200-epoch | Orig Paper| sec/epoch
# Model | n | ResNet v1 | ResNet v1 | ResNet v2 | ResNet v2 | GTX1080Ti
# |v1(v2)| %Accuracy | %Accuracy | %Accuracy | %Accuracy | v1 (v2)
# ----------------------------------------------------------------------------
# ResNet20 | 3 (2)| 92.16 | 91.25 | ----- | ----- | 35 (---)
# ResNet32 | 5(NA)| 92.46 | 92.49 | NA | NA | 50 ( NA)
# ResNet44 | 7(NA)| 92.50 | 92.83 | NA | NA | 70 ( NA)
# ResNet56 | 9 (6)| 92.71 | 93.03 | 93.01 | NA | 90 (100)
# ResNet110 |18(12)| 92.65 | 93.39+-.16| 93.15 | 93.63 | 165(180)
# ResNet164 |27(18)| ----- | 94.07 | ----- | 94.54 | ---(---)
# ResNet1001| (111)| ----- | 92.39 | ----- | 95.08+-.14| ---(---)
# ---------------------------------------------------------------------------
# Model name, depth and version
depth = get_depth(version=args.version,
model_depth_param=args.model_depth_param)
model_type = 'ResNet%dv%d' % (depth, args.version)
# Subtracting pixel mean improves accuracy
subtract_pixel_mean = True
# Data
train_data, test_data, input_shape = get_data(subtract_pixel_mean,
args.num_classes)
# Score trained model.
model = get_model(version=args.version,
input_shape=input_shape,
depth=depth)
train(model, model_type, train_data['x'], train_data['y'], test_data['x'],
test_data['y'], args.data_augmentation, args.batch_size, args.epochs)
scores = model.evaluate(test_data['x'], test_data['y'], verbose=1)
GRASS_TERRAIN = "grass"
CONCRETE_TERRAIN = "concrete"
if __name__ == "__main__":
if not os.path.exists(os.path.join(CORPUS_PATH)):
os.mkdir(CORPUS_PATH)
if not os.path.exists(os.path.join(CORPUS_PATH, CONCRETE_TERRAIN)):
os.mkdir(os.path.join(CORPUS_PATH, CONCRETE_TERRAIN))
while 1:
# get a frame from RGB camera
frame = get_video()
# get a frame from depth sensor
depth = get_depth()
cv2.imwrite(
os.path.join(CORPUS_PATH, CONCRETE_TERRAIN,
"{}.png".format(uuid.uuid4())), frame)
img = frame
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# display RGB image
cv2.imshow('RGB image', frame)
# display depth image
cv2.imshow('Depth image', depth)
# quit program when 'esc' key is pressed
k = cv2.waitKey(5) & 0xFF