def __call__(self):
self.timeholder = TimeHolder(self.position, self._all_channel_regions,
None, self.meta_data, self.settings,
self._frames, self.plate_id,
**self._hdf_options)
ca = CellAnalyzer(timeholder=self.timeholder,
position=self.position,
create_images=True,
binning_factor=1,
detect_objects=self.settings.get(
'Processing', 'objectdetection'))
n_images = self._analyze(ca)
def __call__(self):
hdf5_fname = join(self._hdf5_dir, '%s.ch5' % self.position)
self.timeholder = TimeHolder(self.position, self._all_channel_regions,
hdf5_fname, self.meta_data, self.settings,
self._frames, self.plate_id,
**self._hdf_options)
stopwatch = StopWatch(start=True)
ca = CellAnalyzer(timeholder=self.timeholder,
position=self.position,
create_images=True,
binning_factor=1,
detect_objects=self.settings.get(
'Processing', 'objectdetection'))
#self.setup_classifiers()
#self.export_features = self.define_exp_features()
self._analyze(ca)
return ca
def __call__(self):
self.timeholder = TimeHolder(self.position,
self._all_channel_regions,
self.datafile,
self.meta_data,
self.settings,
self._frames,
self.plate_id,
well=None,
site=None,
**self._hdf_options)
stopwatch = StopWatch(start=True)
ca = CellAnalyzer(timeholder=self.timeholder,
position=self.position,
create_images=True,
binning_factor=1,
detect_objects=self.settings.get(
'Processing', 'objectdetection'))
self._analyze(ca)
return ca
def __call__(self):
# include hdf5 file name in hdf5_options
# perhaps timeholder might be a good place to read out the options
# file does not have to exist to proceed
hdf5_fname = join(self._hdf5_dir, '%s.ch5' % self.position)
self.timeholder = TimeHolder(self.position, self._all_channel_regions,
hdf5_fname,
self.meta_data, self.settings,
self._frames,
self.plate_id,
**self._hdf_options)
self.settings.set_section('Tracking')
self.setup_classifiers()
# setup tracker
if self.settings('Processing', 'tracking'):
tropts = (self.settings('Tracking', 'tracking_maxobjectdistance'),
self.settings('Tracking', 'tracking_maxsplitobjects'),
self.settings('Tracking', 'tracking_maxtrackinggap'))
self._tracker = Tracker(*tropts)
stopwatch = StopWatch(start=True)
ca = CellAnalyzer(timeholder=self.timeholder,
position = self.position,
create_images = True,
binning_factor = 1,
detect_objects = self.settings('Processing',
'objectdetection'))
self.export_features = self.define_exp_features()
n_images = self._analyze(ca)
if n_images > 0:
# invoke event selection
if self.settings('Processing', 'eventselection') and \
self.settings('Processing', 'tracking'):
evchannel = self.settings('EventSelection', 'eventchannel')
region = self.classifiers[evchannel].regions
if self.settings('EventSelection', 'unsupervised_event_selection'):
graph = self._tracker.graph
elif evchannel != PrimaryChannel.NAME or \
region != self.settings("Tracking", "region"):
graph = self._tracker.clone_graph(self.timeholder,
evchannel,
region)
else:
graph = self._tracker.graph
self._tes = self.setup_eventselection(graph)
self.logger.debug("--- visitor start")
self._tes.find_events()
self.logger.debug("--- visitor ok")
if self.is_aborted():
return 0 # number of processed images
# save all the data of the position, no aborts from here on
# want all processed data saved
if self.settings('Output', 'export_object_counts') and \
self.settings('EventSelection', 'supervised_event_selection'):
# no object counts in case of unsupervised event selection
self.export_object_counts()
if self.settings('Output', 'export_object_details'):
self.export_object_details()
if self.settings('Output', 'export_file_names'):
self.export_image_names()
if self.settings('Processing', 'tracking'):
self.export_tracks_hdf5()
self.update_status({'text': 'export events...'})
if self.settings('Output', 'hdf5_include_events'):
self.export_events_hdf5()
if self.settings('Output', "export_events"):
if self.settings('Processing', 'eventselection'):
self.export_events()
if self.settings('EventSelection', 'unsupervised_event_selection'):
self.export_tc3()
if self.settings('Output', 'export_track_data'):
self.export_full_tracks()
if self.settings('Output', 'export_tracking_as_dot'):
self.export_graphviz(channel_name =PrimaryChannel.NAME,\
region_name =self._all_channel_regions[PrimaryChannel.NAME][PrimaryChannel.NAME])
self.export_classlabels()
self.update_status({'text': 'export events...',
'max': 1,
'progress': 1})
# remove all features from all channels to free memory
# for the generation of gallery images
self.timeholder.purge_features()
if self.settings.get('Output', 'events_export_gallery_images') and \
self.settings.get('Processing', 'eventselection'):
self.export_gallery_images()
try:
intval = stopwatch.stop()/n_images*1000
except ZeroDivisionError:
pass
else:
self.logger.info(" - %d image sets analyzed, %3d ms per image set" %
(n_images, intval))
self.touch_finished()
self.clear()
return n_images
def __call__(self):
thread = QThread.currentThread()
well, site = self._posinfo()
self.timeholder = TimeHolder(self.position, self._all_channel_regions,
self.datafile, self.meta_data,
self.settings, self._frames,
self.plate_id, well, site,
**self._hdf_options)
self.settings.set_section('Tracking')
self.setup_classifiers()
# setup tracker
if self.settings('Processing', 'tracking'):
tropts = (self.settings('Tracking', 'tracking_maxobjectdistance'),
self.settings('Tracking', 'tracking_maxsplitobjects'),
self.settings('Tracking', 'tracking_maxtrackinggap'))
self._tracker = Tracker(*tropts)
stopwatch = StopWatch(start=True)
ca = CellAnalyzer(timeholder=self.timeholder,
position=self.position,
create_images=True,
binning_factor=1,
detect_objects=self.settings('Processing',
'objectdetection'))
self.export_features = self.define_exp_features()
self._analyze(ca)
# invoke event selection
if self.settings('Processing', 'eventselection') and \
self.settings('Processing', 'tracking'):
evchannel = self.settings('EventSelection', 'eventchannel')
region = self.classifiers[evchannel].regions
if evchannel != PrimaryChannel.NAME or region != self.settings(
"Tracking", "region"):
graph = self._tracker.clone_graph(self.timeholder, evchannel,
region)
else:
graph = self._tracker.graph
self._tes = self.setup_eventselection(graph)
self.logger.info("Event detection")
self._tes.find_events()
if self.isAborted():
return 0 # number of processed images
# save all the data of the position, no aborts from here on
# want all processed data saved
if self.settings('Processing', 'tracking'):
self.statusUpdate(text="Saving Tracking Data to cellh5...")
self.save_tracks()
if self.settings('Output', 'hdf5_include_events') and \
self.settings('Processing', "eventselection"):
self.statusUpdate(text="Saving Event Data to cellh5...")
self.save_events()
self.save_classification()
self.timeholder.purge()
try:
n = len(self._frames)
intval = stopwatch.stop() / n * 1000
except ZeroDivisionError:
pass
else:
self.logger.info("%d images analyzed, %3d ms per image set" %
(n, intval))
self.clear()
if isfile(self.datafile):
with Ch5File(self.datafile, mode="r+") as ch5:
ch5.savePlateLayout(self.layout, self.plate_id)