def get_detection_scores_slow(self,E,template,bgd_length,mean_background,
edge_feature_row_breaks,
edge_orientations,
abst_threshold=-np.inf *np.ones(8),
spread_length=3):
template_height,template_length = template.shape
num_detections = E.shape[1]-template_length+1
E_background, estimated_background_idx = self._get_E_background(E,num_detections,bgd_length, mean_background,
edge_feature_row_breaks,
edge_orientations,
abst_threshold=abst_threshold,
spread_length=spread_length)
Ps = np.zeros(num_detections)
Cs = np.zeros(num_detections)
for frame_idx in xrange(num_detections):
E_segment = E[:,frame_idx:frame_idx+template_length].copy()
esp.threshold_edgemap(E_segment,.30,edge_feature_row_breaks,abst_threshold=abst_threshold)
esp.spread_edgemap(E_segment,edge_feature_row_breaks,edge_orientations,spread_length=spread_length)
Ps[frame_idx],Cs[frame_idx] = tt.score_template_background_section(template,E_background[:,frame_idx],E_segment)
return Ps,Cs
def __init__(self,base_object, coarse_factor=2,
coarse_template_threshold = .5,
bg = None):
self.coarse_factor = coarse_factor
if base_object.__class__ is list:
# we assume that all objects have the same
# length so we are comparing apples to apples
template_height = base_object[0].shape[0]
template_length = max([t.shape[1] for t in base_object])
self.type = "collection"
self.window = np.array((template_height,template_length))
self.template = base_object
self.bg = bg
self.pad_template = lambda tmplt, bg: np.hstack((tmplt,
np.tile(
bg.reshape(self.window[0],1),
(1,self.window[1]-tmplt.shape[1]))))
self.score = lambda E_window,bg:\
max([sum(tt.score_template_background_section(
self.pad_template(tmplt,bg),
bg,E_window)) for tmplt in self.template])
self.score_register = lambda E_window,bg:\
max([sum(tt.score_template_background_section(
_stretch_template(tmplt,E_window.shape[0],E_window.shape[1]),
bg,E_window)) for tmplt in self.template])
self.score_no_bg = lambda E_window:\
max([sum(tt.score_template_background_section(
self.pad_template(tmplt,self.bg),
self.bg,
E_window)) for tmplt in self.template])
self.coarse_template = [get_coarse_segment(
tmplt,
coarse_type="avg",
coarse_factor = self.coarse_factor) for tmplt in self.template]
self.coarse_length = self.coarse_template[0].shape[1]
self.coarse_template_mask = [T >.7 for T in self.coarse_template]
self.coarse_score_like = lambda E_window,bg:\
max([sum(tt.score_template_background_section(T,
bg,
get_coarse_segment(E_window,
coarse_type='max',
coarse_factor=self.coarse_factor))) for T in self.coarse_template])
self.coarse_score_like_no_bg = lambda E_window:\
max([sum(tt.score_template_background_section(T,
self.bg,
get_coarse_segment(E_window,
coarse_type='max',
coarse_factor=self.coarse_factor))) for T in self.coarse_template])
self.coarse_score_count = lambda E_window:\
max([np.sum(get_coarse_segment(E_window,
coarse_type='max',
coarse_factor=self.coarse_factor)[T_mask]) for T_mask in self.coarse_template_mask])
if base_object.__class__ == np.ndarray:
# this means that we just have a template which will be a
# 2-d ndarray, our function assumes this
template_height, template_length = base_object.shape
self.type = "template"
self.window = np.array((template_height,template_length))
self.template = base_object
self.bg = bg
self.score = lambda E_window,bg:\
sum(tt.score_template_background_section(self.template,
bg,E_window))
# just create a uniform background with .4 as the edge frequency
self.score_register = lambda E_window,bg:\
sum(tt.score_template_background_section(
_stretch_template(self.template,E_window.shape[0],E_window.shape[1]),
bg,E_window))
self.score_no_bg = lambda E_window:\
sum(tt.score_template_background_section(self.template,
self.bg,
E_window))
self.coarse_template = get_coarse_segment(self.template,
coarse_type="avg",
coarse_factor = self.coarse_factor)
self.coarse_length = self.coarse_template.shape[1]
self.coarse_template_mask = self.coarse_template > .7
self.coarse_score_like = lambda E_window,bg:\
sum(tt.score_template_background_section(self.coarse_template,
bg,
get_coarse_segment(E_window,
coarse_type='max',
coarse_factor=self.coarse_factor)))
self.coarse_score_like_no_bg = lambda E_window:\
sum(tt.score_template_background_section(self.coarse_template,
self.bg,
get_coarse_segment(E_window,
coarse_type='max',
coarse_factor=self.coarse_factor)))
self.coarse_score_count = lambda E_window:\
np.sum(get_coarse_segment(E_window,
coarse_type='max',
coarse_factor=self.coarse_factor)[self.coarse_template_mask])
elif base_object.__class__ == pm.TwoPartModel:
template_height, template_length = base_object.bg.shape[0],base_object.length_range[1]
if bg is not None:
self.bg = bg
else:
self.bg = .4 * np.ones(template_height)
self.type = "TwoPartModel"
self.window = np.array((template_height,template_length))
self.template = base_object
self._score_sub_no_bg = lambda E_window,t_id:\
sum(tt.score_template_background_section(self.template.def_templates[t_id],
self.template.bg,E_window))
self.score_no_bg = lambda E_window:\
max([ self._score_sub_no_bg(E_window,t_id) for t_id in xrange(self.template.def_templates.shape[0])])
# just create a uniform background with .4 as the edge frequency
self.coarse_template = get_coarse_segment(self.template.base_template,
coarse_type="avg",
coarse_factor = self.coarse_factor)
self.coarse_length = self.coarse_template.shape[1]
self.coarse_template_mask = self.coarse_template > .7
self.coarse_score_like_no_bg = lambda E_window:\
sum(tt.score_template_background_section(self.coarse_template,
self.bg,
get_coarse_segment(E_window,
coarse_type='max',
coarse_factor=self.coarse_factor)[:,:self.coarse_length]))
self.coarse_score_count = lambda E_window:\
np.sum(get_coarse_segment(E_window,
coarse_type='max',
coarse_factor=self.coarse_factor)[:,:self.coarse_length][self.coarse_template_mask])
def score_pad(E_window,bg):
diff = E_window.shape[1] - self.window[1]
if diff >= 0:
return max([self.score(E_window[:,
loc:loc+self.window[1]],bg) for loc in np.arange(diff+1,dtype=int)])
else: # diff < 0
# padd E_window with background
return self.score( np.hstack((E_window,
np.tile(bg,(-diff,1)).T)),
bg)
def score_def_template(self,hyp_segment,def_template,bgd):
P,C = tt.score_template_background_section(def_template,bgd,hyp_segment)
return P+C
