def waitUntilListen(self):
self.logger.debug(
"waitUntilListen function is now monitoring the ENVIRON listen variable"
)
self.interrupted = False
while True:
self.logger.debug("waitUntilListen - listen variable is %s" %
str(self.ENVIRON['listen']))
if busyCheck(self.ENVIRON, self.logger) == False:
self.passiveListen()
time.sleep(1)
def startListenningActively(self):
#Lee AutoLevel - display the current average noise level
self.logger.debug("Current avg_noise is %s" %
self.ENVIRON["avg_noise"])
if busyCheck(self.ENVIRON, self.logger) == True:
self.logger.debug("KEYWORD DETECTED. But we are busy so ignore it")
else:
# set system to indicate things are busy
busyOn(self.ENVIRON, self.logger)
self.logger.debug("KEYWORD DETECTED. Beginning active listen ")
self.activeListen()
#go back to passive listening once ENVIRON["listen"] indicates it is OK
self.waitUntilListen()
def detectionEvent(self, lastAlert, camera):
# Check lastAlert to see if we need to trigger a new Alert for the motion
curDTime = datetime.datetime.today()
self.logger.debug("Motion detected at %s " % curDTime)
diff = curDTime - lastAlert
if (diff.seconds) < self.delay:
self.logger.debug(
"Motion delay has not expired. %s seconds remaining." %
str(self.delay - diff.seconds))
else:
lastAlert = curDTime
self.logger.debug(
"Motion detected at %s and motion delay has expired" %
curDTime)
# Check listen loop to ensure brain is not busy with an event
#if self.ENVIRON["listen"] == True:
if busyCheck(self.ENVIRON, self.logger) == False:
command = None
# if in security camera mode then capture video
#----------------------------------------------
if self.ENVIRON["security"] == True:
self.logger.debug(
"Security mode is enabled so trigger security alert")
self.securityWarn(camera)
# else check whether we should begin a chat loop
#----------------------------------------------
else:
self.logger.debug("Checking if we should trigger a chat")
diff = 0
# only trigger a chat if we have a delay and a chat ID
if self.chatDelay > 0 and len(self.motionChat) > 0:
diff = curDTime - self.lastChat
if diff > datetime.timedelta(minutes=self.chatDelay):
self.lastChat = curDTime
command = 'CHATBOT:%s' % self.motionChat
# set system to indicate things are busy
busyOn(self.ENVIRON, self.logger)
self.logger.debug("Posting %s to Queue" % command)
self.SENSORQ.put(['brain', command])
# pre-fetch the security chat text to regenerate file
self.createFile()
return lastAlert
def detectMotion(self):
self.logger.debug("Starting to detect Motion")
# define feed from camera
camera = cv2.VideoCapture(0)
time.sleep(1)
# initialize variables used by the motion sensing
firstFrame = None
lastAlert = datetime.datetime.today()
frames = 0
# loop over the frames of the video feed and detect motion
while True:
# if we are busy processing a job then skip motion until we are done
#if self.ENVIRON["listen"] == False:
if busyCheck(self.ENVIRON, self.logger) == True:
continue
# grab the current frame and initialize the occupied/unoccupied text
self.logger.debug("Getting another frame. ENVIRON listen = %s" %
self.ENVIRON["listen"])
(grabbed, frame) = camera.read()
frames += 1
# resize the frame, convert it to grayscale, and blur it
frame = imutils.resize(frame, width=500)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (21, 21), 0)
# if the first frame is None, initialize it
if firstFrame is None:
firstFrame = gray
continue
# compute the absolute difference between the current frame and first frame
frameDelta = cv2.absdiff(firstFrame, gray)
thresh = cv2.threshold(frameDelta, 25, 255, cv2.THRESH_BINARY)[1]
# Update the reference frame
firstFrame = gray
# dilate the thresholded image to fill in holes, then find contours on thresholded image
thresh = cv2.dilate(thresh, None, iterations=2)
#open CV seems to change the number of params returned
try:
(img, cnts, _) = cv2.findContours(thresh.copy(),
cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
except:
(cnts, _) = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
# loop over the contours
for c in cnts:
# if the contour is too small, ignore it
if cv2.contourArea(c) < self.min_area:
continue
#motion must be detected, see if we need to trigger a new Alert for the motion
lastAlert = self.detectionEvent(lastAlert, camera)
# check the ENVIRON when frame count reaches check point
if frames > self.framesCheck:
self.logger.debug(
"Checking to see if we should stop detecting motion")
frames = 0
if not self.ENVIRON["motion"]:
self.logger.debug("Time to stop detecting motion")
# cleanup the camera quit function
camera.release()
break
def interrupt_callback(self):
if busyCheck(self.ENVIRON, self.logger) == True:
self.interrupted = True
return self.interrupted
def detectWithCam(self):
self.logger.debug("Starting to detect Motion")
#variables for TensorFlow human detection
model_path = os.path.join(self.TOPDIR, "client/objectDetect/ssd_mobilenet_v1_coco_2017_11_17/frozen_inference_graph.pb")
detector = detectorAPI(self.ENVIRON, path_to_ckpt=model_path)
# define feed from camera
camera = cv2.VideoCapture(0)
time.sleep(1)
# initialize variables used by the motion sensing
firstFrame = None
lastAlert = datetime.datetime.today()
frames = 0
# loop over the frames of the video feed and detect motion
while True:
# if we are busy processing a job then skip motion until we are done
#if self.ENVIRON["listen"] == False:
if busyCheck(self.ENVIRON, self.logger) == True:
continue
# grab the current frame and initialize the occupied/unoccupied text
self.logger.debug("Getting another frame. ENVIRON listen = %s" % self.ENVIRON["listen"])
(grabbed, frame) = camera.read()
frames += 1
# resize the frame, convert it to grayscale, and blur it
frame = imutils.resize(frame, width=500)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (21, 21), 0)
# if the first frame is None, initialize it
if firstFrame is None:
firstFrame = gray
continue
# compute the absolute difference between the current frame and first frame
frameDelta = cv2.absdiff(firstFrame, gray)
thresh = cv2.threshold(frameDelta, 25, 255, cv2.THRESH_BINARY)[1]
# Update the reference frame
firstFrame = gray
# dilate the thresholded image to fill in holes, then find contours on thresholded image
thresh = cv2.dilate(thresh, None, iterations=2)
(cnts, _) = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# loop over the contours
for c in cnts:
# if the contour is too small, ignore it
if cv2.contourArea(c) < self.min_area:
continue
#motion detected, see if a person was detected by objectDetect
#------------------------------------------------------------
objDict = detector.objectCount(frame)
if 'person' in objDict:
lastAlert = self.detectionEvent(lastAlert, camera)
# check the ENVIRON when frame count reaches check point
if frames > self.framesCheck:
self.logger.debug("Checking to see if we should stop detecting motion")
frames = 0
if not self.ENVIRON["motion"]:
self.logger.debug("Time to stop detecting motion")
# cleanup the camera quit function
camera.release()
break