def __init__(self, dataset, train_dataset, cls, dets, detname, rtype,
args):
'''
Also pass in the region-type as rtype and the according arguments as args
'''
ExternalDetector.__init__(self, dataset, train_dataset, cls, dets,
detname)
self.region_model = RegionModel(rtype, args)
def compute_score(self, image, region_id, oracle=False):
"""
Return the 0/1 decision of whether the cls of this detector is present in
the image, given the detections table for a given region id.
If oracle=True, returns the correct answer (look up the ground truth).
"""
img_ind = self.dataset.get_img_ind(image)
dets = self.dets.filter_on_column('img_ind',img_ind,omit=True)
self.filter_dets_for_reg_id(image, dets, region_id) # At this position choose only the detections that fall into the desired region
return ExternalDetector.compute_score(self, image, oracle, dets) # Now call the regular external detector with just those detections
def detect(self, image, region_id, astable=False):
"""
Return the detections that match that image index in cached dets for a
specific region in the image that is determined by the region_id.
Must return in the same format as the Detector superclass, so we have to
delete a column.
"""
img_ind = self.dataset.get_img_ind(image)
dets = self.dets.filter_on_column('img_ind',img_ind,omit=True) # This function already creates a copy
self.filter_dets_for_reg_id(image, dets, region_id) # At this position choose only the detections that fall into the desired region
return ExternalDetector.detect(self, image, astable, dets) # Now call the regular external detector with just those detections
def compute_score(self, image, region_id, oracle=False):
"""
Return the 0/1 decision of whether the cls of this detector is present in
the image, given the detections table for a given region id.
If oracle=True, returns the correct answer (look up the ground truth).
"""
img_ind = self.dataset.get_img_ind(image)
dets = self.dets.filter_on_column('img_ind', img_ind, omit=True)
self.filter_dets_for_reg_id(
image, dets, region_id
) # At this position choose only the detections that fall into the desired region
return ExternalDetector.compute_score(
self, image, oracle, dets
) # Now call the regular external detector with just those detections
def init_actions(self, train=False):
"""
Return list of actions, which involves initializing the detectors.
If train==True, the external detectors will load trainset detections.
"""
dataset = self.dataset
if train:
dataset = self.train_dataset
actions = []
for detector in self.detectors:
# synthetic perfect detector
if detector == 'perfect':
for cls in dataset.classes:
det = PerfectDetector(dataset, self.train_dataset, cls)
actions.append(ImageAction('%s_%s' % (detector, cls), det))
# GIST classifier
elif detector == 'gist':
# Make a single action that runs all the GIST classifiers
dets = []
for cls in self.dataset.classes:
gist_table = np.load(
config.get_gist_dict_filename(self.dataset))
dets.append(
GistClassifier(cls, self.train_dataset, gist_table,
self.dataset))
gist_all = GistForAllClasses(dets)
actions.append(ImageAction('gist', gist_all))
# real detectors, with pre-cached detections
elif detector in ['dpm', 'csc_default', 'csc_half']:
# load the dets from cache file and parcel out to classes
all_dets = self.load_ext_detections(dataset, detector)
for cls in dataset.classes:
cls_ind = dataset.get_ind(cls)
all_dets_for_cls = all_dets.filter_on_column('cls_ind',
cls_ind,
omit=True)
det = ExternalDetector(dataset, self.train_dataset, cls,
all_dets_for_cls, detector)
actions.append(ImageAction('%s_%s' % (detector, cls), det))
else:
raise RuntimeError("Unknown mode in detectors: %s" %
self.detectors)
return actions
def detect(self, image, region_id, astable=False):
"""
Return the detections that match that image index in cached dets for a
specific region in the image that is determined by the region_id.
Must return in the same format as the Detector superclass, so we have to
delete a column.
"""
img_ind = self.dataset.get_img_ind(image)
dets = self.dets.filter_on_column(
'img_ind', img_ind,
omit=True) # This function already creates a copy
self.filter_dets_for_reg_id(
image, dets, region_id
) # At this position choose only the detections that fall into the desired region
return ExternalDetector.detect(
self, image, astable, dets
) # Now call the regular external detector with just those detections
def __init__(self, dataset, train_dataset, cls, dets, detname, rtype, args): ''' Also pass in the region-type as rtype and the according arguments as args ''' ExternalDetector.__init__(self, dataset, train_dataset, cls, dets, detname) self.region_model = RegionModel(rtype, args)
