def __init__(self,
log,
pipeline,
sigma,
outdir,
tracks_filename,
interval=None,
min_led_distance=0,
weight_others=0,
detect_smooth_sigma=1.0,
detect_neighbors=10,
write_png=False):
# get configuration
blink_detect_config = get_blink_config(log)
# create trackers
frequencies = blink_detect_config.get_frequencies()
self.trackers = []
for f in frequencies:
others = list(frequencies)
others.remove(f)
tracker = TrackerFixedFreq(f,
others=others,
others_weight=weight_others,
sigma=sigma,
interval=interval)
self.trackers.append(tracker)
# start track log
# tracks_filename = os.path.splitext(log)[0] + '.%s.tracks' % suffix
self.tracklog = AERTrackLogWriter(tracks_filename)
params = dict(frequencies=frequencies,
sigma=sigma,
weight_others=weight_others,
interval=interval)
self.tracklog.write_comment('params: %s' % params)
# open files
if False:
raw_sequence = aer_load_log_generic(log)
self.transitions = aer_pipeline_transitions1(
raw_sequence, pipeline)
else:
self.transitions = aer_pipeline_transitions1_all(log, pipeline)
# save params needed later
self.detect_smooth_sigma = detect_smooth_sigma
self.detect_neighbors = detect_neighbors
self.min_led_distance = int(min_led_distance)
self.write_png = write_png
self.outdir = outdir
def __init__(self, log, pipeline, sigma, outdir, tracks_filename,
interval=None,
min_led_distance=0, weight_others=0,
detect_smooth_sigma=1.0,
detect_neighbors=10, write_png=False):
# get configuration
blink_detect_config = get_blink_config(log)
# create trackers
frequencies = blink_detect_config.get_frequencies()
self.trackers = []
for f in frequencies:
others = list(frequencies)
others.remove(f)
tracker = TrackerFixedFreq(f, others=others,
others_weight=weight_others,
sigma=sigma, interval=interval)
self.trackers.append(tracker)
# start track log
# tracks_filename = os.path.splitext(log)[0] + '.%s.tracks' % suffix
self.tracklog = AERTrackLogWriter(tracks_filename)
params = dict(frequencies=frequencies,
sigma=sigma, weight_others=weight_others,
interval=interval)
self.tracklog.write_comment('params: %s' % params)
# open files
if False:
raw_sequence = aer_load_log_generic(log)
self.transitions = aer_pipeline_transitions1(raw_sequence, pipeline)
else:
self.transitions = aer_pipeline_transitions1_all(log, pipeline)
# save params needed later
self.detect_smooth_sigma = detect_smooth_sigma
self.detect_neighbors = detect_neighbors
self.min_led_distance = int(min_led_distance)
self.write_png = write_png
self.outdir = outdir
class MultipleDetector(object):
def __init__(self, log, pipeline, sigma, outdir, tracks_filename,
interval=None,
min_led_distance=0, weight_others=0,
detect_smooth_sigma=1.0,
detect_neighbors=10, write_png=False):
# get configuration
blink_detect_config = get_blink_config(log)
# create trackers
frequencies = blink_detect_config.get_frequencies()
self.trackers = []
for f in frequencies:
others = list(frequencies)
others.remove(f)
tracker = TrackerFixedFreq(f, others=others,
others_weight=weight_others,
sigma=sigma, interval=interval)
self.trackers.append(tracker)
# start track log
# tracks_filename = os.path.splitext(log)[0] + '.%s.tracks' % suffix
self.tracklog = AERTrackLogWriter(tracks_filename)
params = dict(frequencies=frequencies,
sigma=sigma, weight_others=weight_others,
interval=interval)
self.tracklog.write_comment('params: %s' % params)
# open files
if False:
raw_sequence = aer_load_log_generic(log)
self.transitions = aer_pipeline_transitions1(raw_sequence, pipeline)
else:
self.transitions = aer_pipeline_transitions1_all(log, pipeline)
# save params needed later
self.detect_smooth_sigma = detect_smooth_sigma
self.detect_neighbors = detect_neighbors
self.min_led_distance = int(min_led_distance)
self.write_png = write_png
self.outdir = outdir
def go(self):
for f in self.transitions:
# check if this event makes any of those trigger
triggered = []
for tracker in self.trackers:
res = tracker.integrate(f)
if res is not None:
triggered.append(tracker)
# Check that all trackers have produced a frame
# if so, start two-phase estimation
if triggered and all([t.has_frame() for t in self.trackers]):
self.two_phase_estimation(f['timestamp'], triggered)
self.tracklog.done()
def two_phase_estimation(self, timestamp, triggered):
# first get the current spots
first = []
for t in self.trackers:
td = peak_detect(t.get_accum(),
sigma=self.detect_smooth_sigma,
neighbors=self.detect_neighbors)
first.append(dict(tracker=t, detection=td))
# now let's order by quality
first.sort(key=lambda x: (-x['detection']['quality']))
# one by one
second = []
occupied = []
while first:
f = first.pop(0)
f_accum = remove_occupied(accum=f['tracker'].get_accum(),
occupied=occupied, distance=self.min_led_distance)
f_d = peak_detect(f_accum, sigma=self.detect_smooth_sigma,
neighbors=self.detect_neighbors)
if f_d is None:
logger.debug('No track for %s' % f['tracker'])
else:
c = f_d['centroid']
occupied.append((int(c[0]), int(c[1])))
second.append(dict(tracker=f['tracker'], detection=f_d))
d = lambda a, b: np.linalg.norm(np.array(a, 'float') - np.array(b))
distances = [d(p1, p2) for p1, p2 in itertools.combinations(occupied, 2)]
if distances:
min_distance = np.min(distances)
assert min_distance >= self.min_led_distance
for s in second:
if s['tracker'] in triggered:
triggered.remove(s['tracker'])
self.output(timestamp, s['tracker'], s['detection'])
# ones that are invalid
for t in triggered:
if s['tracker'] == t:
s['detection']['centroid'] = (5, 5)
self.output(timestamp, s['tracker'], s['detection'])
def output(self, timestamp, tracker, detection):
if self.write_png:
rcl_detect_write_png(tracker, detection, timestamp, self.outdir)
else:
pass
# print('%14s %s' % (timestamp, tracker.freq))
center = detection['centroid']
quality = detection['centroid_nevents']
id_track = int(tracker.freq)
self.tracklog.write(timestamp=timestamp, id_track=id_track,
x=center[0], y=center[1], quality=quality)
class MultipleDetector(object):
def __init__(self,
log,
pipeline,
sigma,
outdir,
tracks_filename,
interval=None,
min_led_distance=0,
weight_others=0,
detect_smooth_sigma=1.0,
detect_neighbors=10,
write_png=False):
# get configuration
blink_detect_config = get_blink_config(log)
# create trackers
frequencies = blink_detect_config.get_frequencies()
self.trackers = []
for f in frequencies:
others = list(frequencies)
others.remove(f)
tracker = TrackerFixedFreq(f,
others=others,
others_weight=weight_others,
sigma=sigma,
interval=interval)
self.trackers.append(tracker)
# start track log
# tracks_filename = os.path.splitext(log)[0] + '.%s.tracks' % suffix
self.tracklog = AERTrackLogWriter(tracks_filename)
params = dict(frequencies=frequencies,
sigma=sigma,
weight_others=weight_others,
interval=interval)
self.tracklog.write_comment('params: %s' % params)
# open files
if False:
raw_sequence = aer_load_log_generic(log)
self.transitions = aer_pipeline_transitions1(
raw_sequence, pipeline)
else:
self.transitions = aer_pipeline_transitions1_all(log, pipeline)
# save params needed later
self.detect_smooth_sigma = detect_smooth_sigma
self.detect_neighbors = detect_neighbors
self.min_led_distance = int(min_led_distance)
self.write_png = write_png
self.outdir = outdir
def go(self):
for f in self.transitions:
# check if this event makes any of those trigger
triggered = []
for tracker in self.trackers:
res = tracker.integrate(f)
if res is not None:
triggered.append(tracker)
# Check that all trackers have produced a frame
# if so, start two-phase estimation
if triggered and all([t.has_frame() for t in self.trackers]):
self.two_phase_estimation(f['timestamp'], triggered)
self.tracklog.done()
def two_phase_estimation(self, timestamp, triggered):
# first get the current spots
first = []
for t in self.trackers:
td = peak_detect(t.get_accum(),
sigma=self.detect_smooth_sigma,
neighbors=self.detect_neighbors)
first.append(dict(tracker=t, detection=td))
# now let's order by quality
first.sort(key=lambda x: (-x['detection']['quality']))
# one by one
second = []
occupied = []
while first:
f = first.pop(0)
f_accum = remove_occupied(accum=f['tracker'].get_accum(),
occupied=occupied,
distance=self.min_led_distance)
f_d = peak_detect(f_accum,
sigma=self.detect_smooth_sigma,
neighbors=self.detect_neighbors)
if f_d is None:
logger.debug('No track for %s' % f['tracker'])
else:
c = f_d['centroid']
occupied.append((int(c[0]), int(c[1])))
second.append(dict(tracker=f['tracker'], detection=f_d))
d = lambda a, b: np.linalg.norm(np.array(a, 'float') - np.array(b))
distances = [
d(p1, p2) for p1, p2 in itertools.combinations(occupied, 2)
]
if distances:
min_distance = np.min(distances)
assert min_distance >= self.min_led_distance
for s in second:
if s['tracker'] in triggered:
triggered.remove(s['tracker'])
self.output(timestamp, s['tracker'], s['detection'])
# ones that are invalid
for t in triggered:
if s['tracker'] == t:
s['detection']['centroid'] = (5, 5)
self.output(timestamp, s['tracker'], s['detection'])
def output(self, timestamp, tracker, detection):
if self.write_png:
rcl_detect_write_png(tracker, detection, timestamp, self.outdir)
else:
pass
# print('%14s %s' % (timestamp, tracker.freq))
center = detection['centroid']
quality = detection['centroid_nevents']
id_track = int(tracker.freq)
self.tracklog.write(timestamp=timestamp,
id_track=id_track,
x=center[0],
y=center[1],
quality=quality)