def detect_saccades(orientations, timestep):
orientations = orientations[~numpy.isnan(orientations)]
unwrappedOrientations = numpy.unwrap(orientations, 180)
SMOOTH_TIME = 1
smoothWindow = int(round(SMOOTH_TIME / timestep))
smoothedOrientations = tools.smooth(unwrappedOrientations, smoothWindow)
angSpds = abs(numpy.diff(smoothedOrientations)) / timestep
MIN_PEAK_SPD = 30
sacInds = tools.local_minima(-angSpds) & (angSpds > MIN_PEAK_SPD)
sacSpds = angSpds[sacInds]
MIN_SAC_SPD = 5
inSac = angSpds > MIN_SAC_SPD
startInds = pylab.find(numpy.diff(inSac.view(dtype=int8)) == 1)
stopInds = pylab.find(numpy.diff(inSac.view(dtype=int8)) == -1)
if stopInds[-1] < startInds[-1]:
l = stopInds.tolist()
l.append(len(inSac) - 1)
stopInds = numpy.array(l)
if startInds[0] > stopInds[0]:
l = startInds.tolist()
l.insert(0, 0)
startInds = numpy.array(l)
angDisp = numpy.zeros(len(angSpds))
for i, startInd in enumerate(startInds):
stopInd = stopInds[i]
angDisp[startInd:stopInd] = smoothedOrientations[stopInd] - smoothedOrientations[startInd]
sacAmps = angDisp[sacInds]
return sacAmps
def mapper(self, _, line):
""" The mapper loads a track and yields its ramp factor """
t = track.load_track(line)
segments = t['segments']
duration = t['duration']
xdata = []
ydata = []
for i in xrange(len(segments)):
seg = segments[i]
sloudness = seg['loudness_max']
sstart = seg['start'] + seg['loudness_max_time']
xdata.append( sstart )
ydata.append( sloudness )
if duration > 20:
idata = tools.interpolate(xdata, ydata, int(duration) * 10)
smooth = tools.smooth(idata, 20)
samp = tools.sample(smooth, self.SIZE)
ndata = tools.rnormalize(samp, -60, 0)
if self.DUMP:
for i, (x, y) in enumerate(zip(self.MATCH, ndata)):
print i, x, y
if self.VECTOR:
yield (t['artist_name'], t['title'], t['track_id']), ndata
else:
distance = tools.distance(self.MATCH, ndata)
yield (t['artist_name'], t['title'], t['track_id']), distance
def mapper(self, _, line):
""" The mapper loads a track and yields its ramp factor """
t = track.load_track(line)
segments = t['segments']
duration = t['duration']
xdata = []
ydata = []
for i in xrange(len(segments)):
seg = segments[i]
sloudness = seg['loudness_max']
sstart = seg['start'] + seg['loudness_max_time']
xdata.append(sstart)
ydata.append(sloudness)
if duration > 20:
idata = tools.interpolate(xdata, ydata, int(duration) * 10)
smooth = tools.smooth(idata, 20)
samp = tools.sample(smooth, self.SIZE)
ndata = tools.rnormalize(samp, -60, 0)
if self.DUMP:
for i, (x, y) in enumerate(zip(self.MATCH, ndata)):
print i, x, y
if self.VECTOR:
yield (t['artist_name'], t['title'], t['track_id']), ndata
else:
distance = tools.distance(self.MATCH, ndata)
yield (t['artist_name'], t['title'], t['track_id']), distance
def get_normals(clines, lat, lon, ktop, kbot):
# Smooth coastline
window_len = 21
clines2 = np.zeros((clines.shape[0], clines.shape[1]))
for k in range(clines.shape[0]):
tmp = tools.smooth(clines[k, :], window_len, "flat")
clines2[k, :] = 1 * tmp[np.int64(window_len / 2 -
1):np.int64(clines.shape[1] +
window_len / 2 - 1)]
tangent = np.ones((clines.shape[0], clines.shape[1], 2))
normal = np.ones((clines.shape[0], clines.shape[1], 2))
for j in range(0, clines.shape[1] - 1):
# the tangent component points northward
# the normal component points towards the coast!!!
tangent[:, j, 0] = 9900 # ty
tangent[:, j, 1] = 10855 * (-clines2[:, j + 1] + clines2[:, j]) # tx
normal[:, j, 0] = -1 * tangent[:, j, 1] # ny
normal[:, j, 1] = 1 * tangent[:, j, 0] # nx
#tangent[:,0,0] = 1*tangent[:,1,0]
#tangent[:,0,1] = 1*tangent[:,1,1]
tangent[:, clines.shape[1] - 1, 0] = 1 * tangent[:, clines.shape[1] - 2, 0]
tangent[:, clines.shape[1] - 1, 1] = 1 * tangent[:, clines.shape[1] - 2, 1]
#normal[:,0,0] = 1*normal[:,1,0]
#normal[:,0,1] = 1*normal[:,1,1]
normal[:, clines.shape[1] - 1, 0] = 1 * normal[:, clines.shape[1] - 2, 0]
normal[:, clines.shape[1] - 1, 1] = 1 * normal[:, clines.shape[1] - 2, 1]
#tangent_ = tangent.copy()
# Normalize Vectors
normal_ = np.zeros((normal.shape))
tangent_ = np.zeros((tangent.shape))
for j in range(clines.shape[1]):
normal_[:, j, 0] = 1 * normal[:, j, 0] / np.sqrt(
normal[:, j, 1] * normal[:, j, 1] +
normal[:, j, 0] * normal[:, j, 0])
normal_[:, j, 1] = 1 * normal[:, j, 1] / np.sqrt(
normal[:, j, 1] * normal[:, j, 1] +
normal[:, j, 0] * normal[:, j, 0])
tangent_[:, j, 0] = 1 * tangent[:, j, 0] / np.sqrt(
tangent[:, j, 1] * tangent[:, j, 1] +
tangent[:, j, 0] * tangent[:, j, 0])
tangent_[:, j, 1] = 1 * tangent[:, j, 1] / np.sqrt(
tangent[:, j, 1] * tangent[:, j, 1] +
tangent[:, j, 0] * tangent[:, j, 0])
return tangent_, normal_
def detect_saccades0(orientations, timestep):
orientations = orientations[~numpy.isnan(orientations)]
unwrappedOrientations = numpy.unwrap(orientations, 180)
SMOOTH_TIME = 0.5
smoothWindow = int(round(SMOOTH_TIME / timestep))
smoothedOrientations = tools.smooth(unwrappedOrientations, smoothWindow)
angSpds = abs(numpy.diff(smoothedOrientations)) / timestep
sacInds = tools.local_minima(-angSpds) & (angSpds > 10)
saccades = angSpds[sacInds]
# sacs = tools.zigzag(smoothedOrientations)
# sacInds = abs(sacs) > 10
# saccades = sacs[sacInds]
return saccades
def new_change_times(sky):
"""record new change times
arguments:
sky sky dict
"""
pylab.figure()
pylab.plot(sky['times'],tools.smooth(sky['pixelMeans'],100))
pylab.draw()
pylab.title('click on points, center click when done')
pylab.hold('on')
pts = pylab.ginput(0,timeout=0)
changeTimes = [pt[0] for pt in pts]
y = [pt[1] for pt in pts]
directions = []
for cT in changeTimes:
directions.append(input('direction after rotation at '+time.ctime(cT)+' '))
sky['changeTimes'] = changeTimes
sky['directions'] = directions
return sky
if cline_x[y] >= tmp:
u_x.append(cline_x[y])
u_y.append(cline_y[y])
for y in range(301,851):
u_x.append(cline_x[y])
u_y.append(cline_y[y])
u_p = interp1d(u_y,u_x, kind = 'cubic',bounds_error = False, fill_value=0.0)
for y in range(len(cline_x)):
q_u[y] = u_p(cline_y[y])
window_len = 71
tmp = tools.smooth(q_u,window_len,"hanning")
smooth_x= tmp[window_len/2-1:cline_x.shape[0]+window_len/2-1]
smooth_x = np.around(smooth_x)
smooth_x = np.int64(smooth_x) # Now contains the x-positions of the coastline as integer
clines = np.zeros((60,851)) # 60 clines, because the first 20 are not considered
clines[0,:] = smooth_x
## NOW go on finding the other coastline, starting from smooth_x
coastlines = np.zeros((60,var.shape[0],var.shape[1]))
coastlines_ = np.zeros((60,var.shape[0],var.shape[1]))
for k in range(21,80):
#for k in range(20,80):
var = vke[k,:,:].copy()
def get_normals_sophisticated(clines, lat, lon):
clines_lat_ = np.zeros((60, clines.shape[1]))
clines_lon_ = np.zeros((60, clines.shape[1]))
clines_lat = np.zeros((60, clines.shape[1]))
clines_lon = np.zeros((60, clines.shape[1]))
for k in range(clines.shape[0]):
for j in range(clines.shape[1]):
clines_lat_[k, j] = lat[j, np.int64(clines[k, j])]
clines_lon_[k, j] = lon[j, np.int64(clines[k, j])]
# Smooth coastline
window_len = 31
for k in range(clines.shape[0]):
tmp = tools.smooth(clines_lat_[k, :], window_len, "flat")
clines_lat[k, :] = tmp[np.int64(window_len / 2 -
1):np.int64(clines_lat_.shape[1] +
window_len / 2 - 1)]
tmp = tools.smooth(clines_lon_[k, :], window_len, "flat")
clines_lon[k, :] = tmp[np.int64(window_len / 2 -
1):np.int64(clines_lon_.shape[1] +
window_len / 2 - 1)]
tangent = np.ones((60, clines.shape[1], 2))
normal = np.ones((60, clines.shape[1], 2))
for j in range(1, clines.shape[1] - 1):
tangent[:, j, 0] = -clines_lat[:, j + 1] + clines_lat[:, j] # ty
tangent[:, j, 1] = -clines_lon[:, j + 1] + clines_lon[:, j] # tx
normal[:, j, 0] = -tangent[:, j, 1] # ny
normal[:, j, 1] = tangent[:, j, 0] # nx
tangent[:, 0, 0] = tangent[:, 1, 0]
tangent[:, 0, 1] = tangent[:, 1, 1]
tangent[:, clines.shape[1] - 1, 0] = tangent[:, clines.shape[1] - 2, 0]
tangent[:, clines.shape[1] - 1, 1] = tangent[:, clines.shape[1] - 2, 1]
normal[:, 0, 0] = normal[:, 1, 0]
normal[:, 0, 1] = normal[:, 1, 1]
normal[:, clines.shape[1] - 1, 0] = normal[:, clines.shape[1] - 2, 0]
normal[:, clines.shape[1] - 1, 1] = normal[:, clines.shape[1] - 2, 1]
# Normalize Vectors
normal_ = np.zeros((normal.shape))
tangent_ = np.zeros((tangent.shape))
for j in range(clines.shape[1]):
normal_[:, j, 0] = 1 * normal[:, j, 0] / np.sqrt(
normal[:, j, 1] * normal[:, j, 1] +
normal[:, j, 0] * normal[:, j, 0])
normal_[:, j, 1] = 1 * normal[:, j, 1] / np.sqrt(
normal[:, j, 1] * normal[:, j, 1] +
normal[:, j, 0] * normal[:, j, 0])
tangent_[:, j, 0] = 1 * tangent[:, j, 0] / np.sqrt(
tangent[:, j, 1] * tangent[:, j, 1] +
tangent[:, j, 0] * tangent[:, j, 0])
tangent_[:, j, 1] = 1 * tangent[:, j, 1] / np.sqrt(
tangent[:, j, 1] * tangent[:, j, 1] +
tangent[:, j, 0] * tangent[:, j, 0])
return tangent_, normal_
def get_normals_dd(lat, lon, ktop, kbot, uko2, vke2):
maxpos = np.zeros((kbot - ktop, uko2.shape[1]))
tmp = vke2**2 + uko2**2
for k in range(0, 18):
ik = k + ktop
for j in range(uko2.shape[1]):
tmp1 = tmp[ik, j, :].max()
tmp2 = np.where(tmp[ik, j, :] == tmp1)[0]
maxpos[k, j] = 1 * tmp2[0]
for k in range(18, kbot - ktop):
maxpos[k, :] = 1 * maxpos[17, :]
clines = 1 * maxpos
# Smooth coastline
window_len = 5
clines2 = np.zeros((clines.shape[0], clines.shape[1]))
for k in range(clines.shape[0]):
tmp = tools.smooth(clines[k, :], window_len, "flat")
clines2[k, :] = 1 * tmp[np.int64(window_len / 2 -
1):np.int64(clines.shape[1] +
window_len / 2 - 1)]
#clines2 = 1*clines
tangent = np.ones((kbot - ktop, clines.shape[1], 2))
normal = np.ones((kbot - ktop, clines.shape[1], 2))
for j in range(0, clines.shape[1] - 1):
# the tangent component points northward
# the normal component points towards the coast!!!
tangent[:, j, 0] = 9900 # ty
tangent[:, j, 1] = 10855 * (-clines2[:, j + 1] + clines2[:, j]) # tx
normal[:, j, 0] = -1 * tangent[:, j, 1] # ny
normal[:, j, 1] = 1 * tangent[:, j, 0] # nx
#tangent[:,0,0] = 1*tangent[:,1,0]
#tangent[:,0,1] = 1*tangent[:,1,1]
tangent[:, clines.shape[1] - 1, 0] = 1 * tangent[:, clines.shape[1] - 2, 0]
tangent[:, clines.shape[1] - 1, 1] = 1 * tangent[:, clines.shape[1] - 2, 1]
#normal[:,0,0] = 1*normal[:,1,0]
#normal[:,0,1] = 1*normal[:,1,1]
normal[:, clines.shape[1] - 1, 0] = 1 * normal[:, clines.shape[1] - 2, 0]
normal[:, clines.shape[1] - 1, 1] = 1 * normal[:, clines.shape[1] - 2, 1]
#tangent_ = tangent.copy()
# Normalize Vectors
normal_ = np.zeros((normal.shape))
tangent_ = np.zeros((tangent.shape))
for j in range(clines.shape[1]):
normal_[:, j, 0] = 1 * normal[:, j, 0] / np.sqrt(
normal[:, j, 1] * normal[:, j, 1] +
normal[:, j, 0] * normal[:, j, 0])
normal_[:, j, 1] = 1 * normal[:, j, 1] / np.sqrt(
normal[:, j, 1] * normal[:, j, 1] +
normal[:, j, 0] * normal[:, j, 0])
tangent_[:, j, 0] = 1 * tangent[:, j, 0] / np.sqrt(
tangent[:, j, 1] * tangent[:, j, 1] +
tangent[:, j, 0] * tangent[:, j, 0])
tangent_[:, j, 1] = 1 * tangent[:, j, 1] / np.sqrt(
tangent[:, j, 1] * tangent[:, j, 1] +
tangent[:, j, 0] * tangent[:, j, 0])
return tangent_, normal_
import tools
data_dir = "/home/cs4li/Dev/end_to_end_visual_odometry/results/trajectory_results_1/"
losses_to_overlay = [
(data_dir + "run_results_trajectory_results_1-tag-se3_losses.csv", {"linewidth": 1.0, "color": "r"}, "Training Loss"),
(data_dir + "run_results_trajectory_results_1-tag-se3_losses_val.csv", {"linewidth": 1.0, "color": "b"}, "Validation Loss")
]
plt.figure(1)
loss_to_draw = losses_to_overlay[0]
data = np.loadtxt(loss_to_draw[0], skiprows=1, delimiter=",")
x = data[:, 1] / 797
y = data[:, 2]
y = tools.smooth(y, window_len=2000, window="hanning")
plt.plot(x, y[0: len(x)], **loss_to_draw[1], label=loss_to_draw[2])
loss_to_draw = losses_to_overlay[1]
data = np.loadtxt(loss_to_draw[0], skiprows=1, delimiter=",")
x = data[:, 1] / 24
y = data[:, 2]
y = tools.smooth(y, window_len=100, window="hanning")
plt.plot(x, y[0: len(x)], **loss_to_draw[1], label=loss_to_draw[2])
plt.xlabel("epochs")
plt.ylabel("SE(3) losses")
plt.ylim(0, 0.1)
plt.xlim(0, 151)
plt.title("SE(3) Training and Validation Losses")
plt.legend()
def analyze_directory(dirName):
"""pixel mean analysis
arguments:
dirName directory path to analyze
example:
analyze_directory('/home/cardini/data/fly07/')
"""
filenames = os.listdir(dirName)
skyFilenames = [f for f in filenames if f[:3] == 'sky' and f[-3:] == 'fmf']
skyFilenames.sort(compare_file_times)
annotationFileExists = False
for f, filename in enumerate(filenames):
if filename[-4:] == '.txt':
annotationFileExists = True
annotationFilename = filename
elif filename[:4] == 'aSky' and filename[-3:] == 'pkl': # not sure which will choose if >1 pkl file
inPklFile = open(os.path.join(dirName,filename), 'rb')
sky = pickle.load(inPklFile)
inPklFile.close()
break
else:
sky = {}
if annotationFileExists:
changeTimes, directions, stopStartTimes = read_annotation_file(os.path.join(dirName,annotationFilename))
sky['dirName'], sky['fileName'] = dirName, filename
sky['stopStartTimes']=stopStartTimes
sky['changeTimes']=changeTimes
sky['directions']=directions
elif skyFilenames==[]:
print 'no sky filenames'
flyFilenames = [f for f in filenames if f[:3] == 'fly' and f[-3:] == 'fmf']
flyFilenames.sort(compare_file_times)
for f, filename in enumerate(flyFilenames):
fmf = FMF.FlyMovie(os.path.join(dirName,filename))
timestamps = fmf.get_all_timestamps()
sky['dirName'], sky['fileName'] = dirName, filename
sky['times'] = timestamps
sky['changeTimes'] = []
sky['directions'] = []
else:
for f, filename in enumerate(skyFilenames):
pixelStats = get_pixel_stats(os.path.join(dirName,filename),1)
pylab.figure()
pylab.plot(pixelStats.t,tools.smooth(pixelStats.m,100))
pylab.draw()
pylab.title('click on points, center click when done')
pylab.hold('on')
pylab.plot(pixelStats.t,tools.smooth(pixelStats.s,100))
pts = pylab.ginput(0,timeout=0)
changeTimes = [pt[0] for pt in pts]
y = [pt[1] for pt in pts]
directions = []
for cT in changeTimes:
directions.append(input('direction after rotation at '+time.ctime(cT)+' '))
sky['dirName'], sky['fileName'] = dirName,filename
sky['pixelMeans'] = pixelStats.m
sky['pixelStds'] = pixelStats.s
sky['times'] = pixelStats.t
sky['changeTimes'] = changeTimes
sky['directions'] = directions
date_time = time.strftime("%Y%m%d_%H%M%S")
pklFilename = 'aSky'+ date_time +'.pkl'
sky['pklFileName'] = pklFilename
outPklFile = open(os.path.join(dirName,pklFilename), 'wb')
pickle.dump(sky, outPklFile)
outPklFile.close()
return sky
