def match(self, input_frame):
"Return a score for how well the template matches an input frame"
image_x_sums = self.convert_frame(input_frame)
toffset, tdiff = compare_segments(image_x_sums, self.template,
self.shift_match,
image_x_sums.shape[0])
return tdiff * len(image_x_sums)
def match(self, input_frame):
"Return a score for how well the template matches an input frame"
image_x_sums = self.convert_frame(input_frame)
toffset, tdiff = compare_segments(image_x_sums,
self.template,
self.shift_match,
image_x_sums.shape[0])
return tdiff * len(image_x_sums)
def match(self, input_frame, x, y, th):
vt_id = 0 # this is the return value
sub_im = input_frame[self.match_y_range, self.match_x_range]
# normalised intensity sums
image_x_sums = sum(sub_im, 0)
image_x_sums = image_x_sums / sum(image_x_sums)
min_offset = np.ones((len(self.templates), 1))
min_diff = np.ones((len(self.templates), 1));
for k in range(0, len(self.templates)):
vt = self.templates[k]
vt.template_decay -= self.global_decay;
if vt.template_decay < 0.0:
vt.template_decay = 0.0
(min_offset[k], min_diff[k]) = compare_segments(image_x_sums,
vt.template,
self.shift_match,
image_x_sums.shape[0])
if len(self.templates) == 0:
diff = np.inf
diff_id = None
else:
diff = min(min_diff)
diff_id = argmin(min_diff)
# if this intensity template doesn't match any of the existing templates
# then create a new template
if (diff * image_x_sums.shape[1]) > self.match_threshold or (len(self.templates) == 0):
vt_id = len(self.templates)
template = image_x_sums
new_template = VisualTemplate( template,
vt_id, x, y, th,
self.template_decay)
self.templates.append(new_template)
else:
vt_id = diff_id
self.templates[vt_id].template_decay += self.template_decay;
if self.prev_vt_id != self.vt_id:
self.templates[vt_id].first = False
# self.vt_history.append(vt_id)
self.curr_vt_id = vt_id
return vt_id