def depth_callback(self, dev, data, timestamp):
# add the data to our running average
self.running_avg.add(data)
if self.mode == 'uncalibrated':
self.frame_count += 1
if self.frame_count == self.dsum_buffer.size():
self.set_mode('nomotion')
else:
mean_array = self.running_avg.mean()
# difference from sum of buffer
dsum = mean_array.sum()
self.dsum_buffer.add(dsum)
delta = self.dsum_buffer.std_delta(dsum)
# frame will count as a motion frame
if delta > self.change_threshold:
self.motion_frames += 1
if (self.motion_frames == self.min_report_event and
self.mode == 'nomotion'):
self.set_mode('motion')
alert = Alert.objects.create(event_type='motion')
self._snapshot = alert.id
else:
# don't let motion_frames drop below 0
self.motion_frames = max(self.motion_frames-1, 0)
if self.motion_frames == 0 and self.mode == 'motion':
self.set_mode('nomotion')
# could log how long the event was and its intensity here
if self.debug:
cv.ShowImage('Depth', simplify_cv(mean_array.astype(numpy.uint16)))
cv.WaitKey(1)
def video_callback(self, dev, data, timestamp):
if self.nightvision:
cv_data = simplify_cv(data)
else:
cv_data = video_cv(data)
if self.debug:
cv.ShowImage('Video', cv_data)
if (self._last_img + self.snapshot_secs < time.time() or
self._snapshot):
cv.SaveImage('babby-current.jpg', cv_data)
k = boto.s3.key.Key(self.s3bucket)
if self._snapshot:
k.key = '/babby/snapshot-%s.jpg' % self._snapshot
self._snapshot = False
else:
k.key = '/babby/current.jpg'
k.set_contents_from_filename('babby-current.jpg')
k.set_acl('public-read')
self._last_img = time.time()