def __get_movement_zones(self, f):
boxes = []
if len(self.bg_frames) >= self.bg_history / 2:
boxes = scan(f, self.expansion_step)
if self.group_boxes:
boxes = optimize_bounding_boxes(boxes)
self.detection_boxed = self.detection.copy()
for b in boxes:
cv2.rectangle(self.detection_boxed, (b[0], b[1]), (b[2], b[3]), 250, 1)
orig_boxes = []
for b in boxes:
orig_boxes.append(numba_scale_box(b, self.pixel_compression_ratio))
self.boxes = orig_boxes
return orig_boxes
def __get_movement_zones(self, f):
boxes = []
# wait until the bg gets established to decrease the level of initial unstable noise
if len(self.bg_frames) >= self.bg_history / 2:
boxes = scan(f, self.expansion_step)
if self.group_boxes:
boxes = optimize_bounding_boxes(boxes)
self.detection_boxed = self.detection.copy()
for b in boxes:
cv2.rectangle(self.detection_boxed, (b[0], b[1]), (b[2], b[3]), 250, 1)
orig_boxes = []
for b in boxes:
orig_boxes.append(numba_scale_box(b, self.pixel_compression_ratio))
self.boxes = orig_boxes
return orig_boxes