# check to see if the number of frames with consistent motion is
# high enough
if motionCounter >= conf["min_motion_frames"]:
# check to see if dropbox sohuld be used
if conf["use_dropbox"]:
# write the image to temporary file
t = TempImage()
cv2.imwrite(t.path, frame)
# upload the image to Dropbox and cleanup the tempory image
print("[UPLOAD] {}".format(ts))
path = "/{base_path}/{timestamp}.jpg".format(
base_path=conf["dropbox_base_path"], timestamp=ts)
client.files_upload(open(t.path, "rb").read(), path)
t.cleanup()
# update the last uploaded timestamp and reset the motion
# counter
lastUploaded = timestamp
motionCounter = 0
# otherwise, the room is not occupied
else:
motionCounter = 0
# check to see if the frames should be displayed to screen
if conf["show_video"]:
def main():
log.basicConfig(format="[ %(levelname)s ] %(message)s",
level=log.INFO,
stream=sys.stdout)
args = build_argparser().parse_args()
log.info("Loading Inference Engine")
ie = IECore()
log.info("Initializing plugin for {} device...".format(args.device))
plugin = IEPlugin(device=args.device, plugin_dirs=args.plugin_dir)
model_xml = args.model #'face-detection-retail-0004.xml'
model_bin = os.path.splitext(model_xml)[0] + ".bin"
log.info("Loading network files:\n\t{}\n\t{}".format(model_xml, model_bin))
net = ie.read_network(model=model_xml, weights=model_bin)
log.info("Device info:")
versions = ie.get_versions(args.device)
print("{}{}".format(" " * 8, args.device))
print("{}MKLDNNPlugin version ......... {}.{}".format(
" " * 8, versions[args.device].major, versions[args.device].minor))
print("{}Build ........... {}".format(" " * 8,
versions[args.device].build_number))
input_blob = next(iter(net.inputs))
out_blob = next(iter(net.outputs))
log.info("Loading IR to the plugin...")
exec_net = plugin.load(network=net, num_requests=2)
if args.labels:
with open(args.labels, 'r') as f:
labels_map = [x.strip() for x in f]
else:
labels_map = None
n, c, h, w = net.inputs[input_blob].shape
del net
#s3client = boto3.resource('s3')
expiresIn = 5 * 24 * 3600 #expires recorded voice after 5 days
cur_request_id = 0
next_request_id = 1
render_time = 0
last_epoch = time.time()
cap = cv2.VideoCapture(0)
cap.set(3, 640)
cap.set(4, 480)
ret, frame = cap.read()
log.info("To close the application, press 'CTRL+C'")
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
initial_w = cap.get(3)
initial_h = cap.get(4)
# Main sync point:
# in the truly Async mode we start the NEXT infer request, while waiting for the CURRENT to complete
# in the regular mode we start the CURRENT request and immediately wait for it's completion
inf_start = time.time()
in_frame = cv2.resize(frame, (w, h))
in_frame = in_frame.transpose(
(2, 0, 1)) # Change data layout from HWC to CHW
in_frame = in_frame.reshape((n, c, h, w))
exec_net.start_async(request_id=cur_request_id,
inputs={input_blob: in_frame})
if exec_net.requests[cur_request_id].wait(-1) == 0:
inf_end = time.time()
det_time = inf_end - inf_start
# Parse detection results of the current request
res = exec_net.requests[cur_request_id].outputs[out_blob]
#log.info("Processing output blobs")
#print(res)
for obj in res[0][0]:
# Draw only objects when probability more than specified threshold
if obj[2] > args.prob_threshold:
xmin = int(obj[3] * initial_w)
ymin = int(obj[4] * initial_h)
xmax = int(obj[5] * initial_w)
ymax = int(obj[6] * initial_h)
class_id = int(obj[1])
# Draw box and label\class_id
#color = (min(class_id * 12.5, 255), min(class_id * 7, 255), min(class_id * 5, 255))
color = (232, 35, 244)
cv2.rectangle(frame, (xmin, ymin), (xmax, ymax), color, 2)
det_label = labels_map[class_id] if labels_map else str(
class_id)
cv2.putText(
frame,
det_label + ' ' + str(round(obj[2] * 100, 1)) + ' %',
(xmin, ymin - 7), cv2.FONT_HERSHEY_COMPLEX, 0.6, color,
1)
diff = (time.time() - last_epoch)
log.info("Object detected {:.1f} s".format(
round(obj[2] * 100, 1)))
if diff > 60:
last_epoch = time.time()
t = TempImage()
cv2.imwrite(t.path, frame)
#s3client.meta.client.upload_file(t.path, args.bucket, t.key )
#log.info("upload image to {}".format(signedUrl))
play_mp3('alert.wav')
t.cleanup()
inf_time_message = "Inference time: {:.3f} ms".format(det_time *
1000)
render_time_message = "OpenCV rendering time: {:.3f} ms".format(
render_time * 1000)
cv2.putText(frame, inf_time_message, (15, 15),
cv2.FONT_HERSHEY_COMPLEX, 0.5, (10, 10, 200), 1)
cv2.putText(frame, render_time_message, (15, 30),
cv2.FONT_HERSHEY_COMPLEX, 0.5, (10, 10, 200), 1)
render_start = time.time()
cv2.imshow("Detection Results", frame)
render_end = time.time()
render_time = render_end - render_start
key = cv2.waitKey(1)
if key == 27:
break
cv2.destroyAllWindows()
cap.release()
def check_for_objects():
global last_epoch
global last_upload
global motionCounter
while True:
try:
_, found_obj,frame = video_camera.get_object(object_classifier)
if found_obj and (time.time() - last_epoch) > conf["min_motion_window"]:
motionCounter += 1
print("$$$ object found with motion counter ", motionCounter)
print("min_motion_frames",conf["min_motion_frames"])
print("last upload was x seconds ago ",(time.time() - last_upload))
print("min upload interval ",conf["upload_interval"])
last_epoch = time.time()
if motionCounter >= conf["min_motion_frames"] and (time.time() - last_upload) > conf["upload_interval"] :
print("$$$ upload")
last_upload = time.time()
motionCounter = 0
t = TempImage()
cv2.imwrite(t.path, frame)
s3client.meta.client.upload_file(t.path, conf["s3bucket_name"], t.key )
signedUrl = s3client.meta.client.generate_presigned_url('get_object', Params = {'Bucket': conf["s3bucket_name"], 'Key': t.key}, ExpiresIn = expiresIn)
faceId=None
faceName="Visitor"
matched = False
if conf["use_rekognition"]== True:
time.sleep(1)
rekodata ={}
rekodata["key"]=t.key
matched,piFace = search_face(rekodata)
if matched == True:
faceId = piFace['faceId']
faceName = piFace['faceName']
slack_data = {
'attachments': [
{
'color': "#36a64f",
'pretext': faceName+ " at the front door",
'title': "Home Surveillance",
'title_link': "https://master.d5yvordr34qco.amplifyapp.com",
'image_url': signedUrl
}
]
}
requests.post(conf["slack_incoming_webhook"], data=json.dumps(slack_data),headers={'Content-Type': 'application/json'})
persistNotification({
"id": str(uuid.uuid4()),
"createdOn": datetime.now().isoformat(),
"bucket": conf["s3bucket_name"],
"signedUrl":signedUrl,
"key":t.key,
"faceId": faceId,
"faceName":faceName
})
t.cleanup()
# prompt visitor to leave voice message
polly.speak("Hello "+faceName+".... Please leave your brief message after the beep....")
recordGuestVoice()
else:
print("$$$ dont upload")
except:
print ("Error sending email: ", sys.exc_info()[0])
counter = 0
ts = timestamp #.strftime("%A %d %B %Y %I:%M:%S%p")
#if (timestamp - lastUploaded).seconds >= conf["min_upload_seconds"]:
# check to see if dropbox sohuld be used
if conf["use_dropbox"]:
# write the image to temporary file
t = TempImage()
cv2.imwrite(t.path, frame)
# upload the image to Dropbox and cleanup the tempory image
print "[UPLOAD] {}".format(ts)
path = "{base_path}/{timestamp}.jpg".format(
base_path=conf["dropbox_base_path"], timestamp=ts)
client.put_file(path, open(t.path, "rb"))
t.cleanup()
# update the last uploaded timestamp and reset the motion
# counter
#lastUploaded = timestamp
timestamp = timestamp + 1
'''# So basically we set the conditional to have a limited range of blurDetectionThresh
# We specify it to be 2 times the threshValue to save it.
st = datetime.datetime.fromtimestamp(ts).strftime('_TS:%Y-%m-%d::%H:%M:%S')
# Get a timestamp for the photo.
# Perform encoding and decoding for safe practice & application of use for programs.
imgCmpr.encodeImage(frame); # Encoding method.
# Decode
imgCmpr.decodeImage(frame);
def surveillance_loop(conf):
global terminate
global cloud_storage_bucket
# check to see if cloud upload should be used
# initialize the camera and grab a reference to the raw camera capture
camera = cv2.VideoCapture(0)
camera.set(3, conf["resolution"][0])
camera.set(4, conf["resolution"][1])
camera.set(5, conf["fps"])
time.sleep(0.25)
# allow the camera to warmup, then initialize the average frame, last
# uploaded timestamp, and frame motion counter
print("[INFO] warming up...")
time.sleep(conf["camera_warmup_time"])
avg = None
lastUploaded = datetime.datetime.now()
motionCounter = 0
# capture frames from the camera
while (terminate == False):
# grab the raw NumPy array representing the image and initialize
# the timestamp and occupied/unoccupied text
(grabbed, frame) = camera.read()
if not grabbed:
break
timestamp = datetime.datetime.now()
text = "Unoccupied"
# resize the frame, convert it to grayscale, and blur it
frame = imutils.resize(frame, width=500)
frame = cv2.flip(frame, -1)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (21, 21), 0)
# if the average frame is None, initialize it
if avg is None:
print("[INFO] starting background model...")
avg = gray.copy().astype("float")
#rawCapture.truncate(0)
continue
# accumulate the weighted average between the current frame and
# previous frames, then compute the difference between the current
# frame and running average
cv2.accumulateWeighted(gray, avg, 0.5)
frameDelta = cv2.absdiff(gray, cv2.convertScaleAbs(avg))
# threshold the delta image, dilate the thresholded image to fill
# in holes, then find contours on thresholded image
thresh = cv2.threshold(frameDelta, conf["delta_thresh"], 255,
cv2.THRESH_BINARY)[1]
thresh = cv2.dilate(thresh, None, iterations=2)
cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if imutils.is_cv2() else cnts[1]
# loop over the contours
for c in cnts:
# if the contour is too small, ignore it
if cv2.contourArea(c) < conf["min_area"]:
continue
# compute the bounding box for the contour, draw it on the frame,
# and update the text
(x, y, w, h) = cv2.boundingRect(c)
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
text = "Occupied"
# draw the text and timestamp on the frame
ts = timestamp.strftime("%A %d %B %Y %I:%M:%S%p")
cv2.putText(frame, "Room Status: {}".format(text), (10, 20),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
cv2.putText(frame, ts, (10, frame.shape[0] - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.35, (0, 0, 255), 1)
# check to see if the room is occupied
if text == "Occupied":
setColor(colors['red']
) # Set light to "RED" to indicate that motion detected
# check to see if enough time has passed between uploads
if (timestamp -
lastUploaded).seconds >= conf["min_upload_seconds"]:
# increment the motion counter
motionCounter += 1
# check to see if the number of frames with consistent motion is
# high enough
if motionCounter >= conf["min_motion_frames"]:
# check to see if cloud upload sohuld be used
if conf["use_cloud"]:
# write the image to temporary file
t = TempImage()
cv2.imwrite(t.path, frame)
# upload the image to cloud storage and cleanup the tempory image
print("[UPLOAD] {}".format(ts))
k = cloud_storage_bucket.new_key(ts)
k.set_contents_from_filename(t.path)
t.cleanup()
# update the last uploaded timestamp and reset the motion
# counter
lastUploaded = timestamp
motionCounter = 0
# otherwise, the room is not occupied
else:
motionCounter = 0
setColor(
colors['green']
) # set light to "GREEN" to indicate no motion being detected.
# check to see if the frames should be displayed to screen
if conf["show_video"]:
# display the security feed
cv2.imshow("Security Feed", frame)
key = cv2.waitKey(1) & 0xFF
# if the `q` key is pressed, break from the lop
if key == ord("q"):
terminate = True
if conf["show_video"]: cv2.destroyAllWindows()
camera.release()