def recognition():
global count
# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-t",
"--target",
type=str,
required=True,
choices=["myriad", "cpu"],
help="target processor for object detection")
ap.add_argument("-m",
"--mode",
type=str,
required=True,
choices=["horizontal", "vertical"],
help="direction in which people will be moving")
ap.add_argument("-c",
"--conf",
required=True,
help="Path to the input configuration file")
ap.add_argument("-i",
"--input",
type=str,
help="path to optional input video file")
ap.add_argument("-o",
"--output",
type=str,
help="path to optional output video file")
args = vars(ap.parse_args())
# load the configuration file
conf = Conf(args["conf"])
# initialize the list of class labels MobileNet SSD detects
CLASSES = [
"background", "aeroplane", "bicycle", "bird", "boat", "bottle", "bus",
"car", "cat", "chair", "cow", "diningtable", "dog", "horse",
"motorbike", "person", "pottedplant", "sheep", "sofa", "train",
"tvmonitor"
]
# load our serialized model from disk
print("[INFO] loading model...")
net = cv2.dnn.readNetFromCaffe(conf["prototxt_path"], conf["model_path"])
# check if the target processor is myriad, if so, then set the
# preferable target to myriad
if args["target"] == "myriad":
net.setPreferableTarget(cv2.dnn.DNN_TARGET_MYRIAD)
# otherwise, the target processor is CPU
else:
# set the preferable target processor to CPU and preferable
# backend to OpenCV
net.setPreferableTarget(cv2.dnn.DNN_TARGET_CPU)
net.setPreferableBackend(cv2.dnn.DNN_BACKEND_OPENCV)
# if a video path was not supplied, grab a reference to the webcam
if not args.get("input", False):
print("[INFO] starting video stream...")
vs = VideoStream(src=0).start()
#vs = VideoStream(usePiCamera=True).start()
time.sleep(2.0)
# otherwise, grab a reference to the video file
else:
print("[INFO] opening video file...")
vs = cv2.VideoCapture(args["input"])
# initialize the video writer process (we'll instantiate later if
# need be) along with the frame dimensions
writerProcess = None
W = None
H = None
# instantiate our centroid tracker, then initialize a list to store
# each of our dlib correlation trackers, followed by a dictionary to
# map each unique object ID to a trackable object
ct = CentroidTracker(maxDisappeared=20, maxDistance=30)
trackers = []
trackableObjects = {}
# initialize the direction info variable (used to store information
# such as up/down or left/right people count) and a variable to store
# the the total number of frames processed thus far
directionInfo = None
totalFrames = 0
# start the frames per second throughput estimator
fps = FPS().start()
# loop over frames from the video stream
while True:
# grab the next frame and handle if we are reading from either
# VideoCapture or VideoStream
frame = vs.read()
frame = frame[1] if args.get("input", False) else frame
# if we are viewing a video and we did not grab a frame then we
# have reached the end of the video
if args["input"] is not None and frame is None:
break
# convert the frame from BGR to RGB for dlib
rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
# check to see if the frame dimensions are not set
if W is None or H is None:
# set the frame dimensions and instantiate our direction
# counter
(H, W) = frame.shape[:2]
dc = DirectionCounter(args["mode"], H, W)
# begin writing the video to disk if required
if args["output"] is not None and writerProcess is None:
# set the value of the write flag (used to communicate when
# to stop the process)
writeVideo = Value('i', 1)
# initialize a frame queue and start the video writer
frameQueue = Queue()
writerProcess = Process(target=write_video,
args=(args["output"], writeVideo,
frameQueue, W, H))
writerProcess.start()
# initialize the current status along with our list of bounding
# box rectangles returned by either (1) our object detector or
# (2) the correlation trackers
status = "Waiting"
rects = []
# check to see if we should run a more computationally expensive
# object detection method to aid our tracker
if totalFrames % conf["skip_frames"] == 0:
# set the status and initialize our new set of object
# trackers
status = "Detecting"
trackers = []
# convert the frame to a blob and pass the blob through the
# network and obtain the detections
blob = cv2.dnn.blobFromImage(frame,
size=(300, 300),
ddepth=cv2.CV_8U)
net.setInput(blob,
scalefactor=1.0 / 127.5,
mean=[127.5, 127.5, 127.5])
detections = net.forward()
# loop over the detections
for i in np.arange(0, detections.shape[2]):
# extract the confidence (i.e., probability) associated
# with the prediction
confidence = detections[0, 0, i, 2]
# filter out weak detections by requiring a minimum
# confidence
if confidence > conf["confidence"]:
# extract the index of the class label from the
# detections list
idx = int(detections[0, 0, i, 1])
# if the class label is not a person, ignore it
if CLASSES[idx] != "person":
continue
# compute the (x, y)-coordinates of the bounding box
# for the object
box = detections[0, 0, i, 3:7] * np.array([W, H, W, H])
(startX, startY, endX, endY) = box.astype("int")
# construct a dlib rectangle object from the bounding
# box coordinates and then start the dlib correlation
# tracker
tracker = dlib.correlation_tracker()
rect = dlib.rectangle(startX, startY, endX, endY)
tracker.start_track(rgb, rect)
# add the tracker to our list of trackers so we can
# utilize it during skip frames
trackers.append(tracker)
# otherwise, we should utilize our object *trackers* rather than
# object *detectors* to obtain a higher frame processing
# throughput
else:
# loop over the trackers
for tracker in trackers:
# set the status of our system to be 'tracking' rather
# than 'waiting' or 'detecting'
status = "Tracking"
# update the tracker and grab the updated position
tracker.update(rgb)
pos = tracker.get_position()
# unpack the position object
startX = int(pos.left())
startY = int(pos.top())
endX = int(pos.right())
endY = int(pos.bottom())
# add the bounding box coordinates to the rectangles list
rects.append((startX, startY, endX, endY))
# check if the direction is *vertical*
if args["mode"] == "vertical":
# draw a horizontal line in the center of the frame -- once an
# object crosses this line we will determine whether they were
# moving 'up' or 'down'
cv2.line(frame, (0, H // 2), (W, H // 2), (0, 255, 255), 2)
# otherwise, the direction is *horizontal*
else:
# draw a vertical line in the center of the frame -- once an
# object crosses this line we will determine whether they were
# moving 'left' or 'right'
cv2.line(frame, (W // 2, 0), (W // 2, H), (0, 255, 255), 2)
# use the centroid tracker to associate the (1) old object
# centroids with (2) the newly computed object centroids
objects = ct.update(rects)
# loop over the tracked objects
for (objectID, centroid) in objects.items():
# grab the trackable object via its object ID
to = trackableObjects.get(objectID, None)
# create a new trackable object if needed
if to is None:
to = TrackableObject(objectID, centroid,
datetime.datetime.now())
cursor.execute(
'INSERT INTO trackhistory (track_time,track_date) VALUES (%s,%s)',
(to.dt, to.dt))
#urllib.request.urlopen("https://api.thingspeak.com/update?api_key="+API+"&field1=0"+str(1))
count += 1
# otherwise, there is a trackable object so we can utilize it
# to determine direction
else:
# find the direction and update the list of centroids
dc.find_direction(to, centroid)
to.centroids.append(centroid)
# check to see if the object has been counted or not
if not to.counted:
# find the direction of motion of the people
directionInfo = dc.count_object(to, centroid)
# store the trackable object in our dictionary
trackableObjects[objectID] = to
# draw both the ID of the object and the centroid of the
# object on the output frame
text = "ID {}".format(objectID)
color = (0, 255, 0) if to.counted else (0, 0, 255)
cv2.putText(frame, text, (centroid[0] - 10, centroid[1] - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
cv2.circle(frame, (centroid[0], centroid[1]), 4, color, -1)
# check if there is any direction info available
if directionInfo is not None:
# construct a list of information as a combination of
# direction info and status info
info = directionInfo + [("Status", status)]
# otherwise, there is no direction info available yet
else:
# construct a list of information as status info since we
# don't have any direction info available yet
info = [("Status", status)]
# loop over the info tuples and draw them on our frame
for (i, (k, v)) in enumerate(info):
text = "{}: {}".format(k, v)
cv2.putText(frame, text, (10, H - ((i * 20) + 20)),
cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2)
# put frame into the shared queue for video writing
if writerProcess is not None:
frameQueue.put(frame)
# show the output frame
cv2.imshow("Frame", frame)
key = cv2.waitKey(1) & 0xFF
# if the `q` key was pressed, break from the loop
if key == ord("q"):
break
# increment the total number of frames processed thus far and
# then update the FPS counter
totalFrames += 1
fps.update()
# stop the timer and display FPS information
fps.stop()
print("[INFO] elapsed time: {:.2f}".format(fps.elapsed()))
print("[INFO] approx. FPS: {:.2f}".format(fps.fps()))
import matplotlib.pyplot as plt
# terminate the video writer process
if writerProcess is not None:
writeVideo.value = 0
writerProcess.join()
# if we are not using a video file, stop the camera video stream
if not args.get("input", False):
vs.stop()
# otherwise, release the video file pointer
else:
vs.release()
# close any open windows
cv2.destroyAllWindows()
count = -1
cv2.namedWindow("set_points")
cv2.setMouseCallback("set_points", set_points, [args["mode"]])
else:
start = True
directionInfo = None
diffPt = None
while True:
frame = vs.read()
frame = frame[1] if args.get("input", False) else frame
if args["input"] is not None and frame is None:
break
if start:
if W is None or H is None:
fps = FPS().start()
(H, W) = frame.shape[:2]
dc = DirectionCounter(args["mode"], W - conf["x_offset"],
H - conf["y_offset"])
ct.direction = args["mode"]
if diffPt is not None:
ct.diffPt = diffPt
if args["output"] is not None and writerProcess is None:
writeVideo = Value('i', 1)
frameQueue = Queue()
writerProcess = Process(target=write_video,
args=(args["output"], writeVideo,
frameQueue, W, H))
writerProcess.start()
rects = []
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (5, 5), 0)
mask = mog.apply(gray)
dilation = cv2.dilate(mask, dKernel, iterations=2)
while True:
# grab the next frame and handle if we are reading from either
# VideoCapture or VideoStream
frame = vs.read()
frame = frame[1] if args.get("input", False) else frame
# if we are viewing a video and we did not grab a frame then we
# have reached the end of the video
if args["input"] is not None and frame is None:
break
# set the frame dimensions and instantiate direction counter
# object if required
if W is None or H is None:
(H, W) = frame.shape[:2]
dc = DirectionCounter(args["mode"], H, W)
# begin writing the video to disk if required
if args["output"] is not None and writerProcess is None:
# set the value of the write flag (used to communicate when
# to stop the process)
writeVideo = Value('i', 1)
# initialize a shared queue for the exhcange frames,
# initialize a process, and start the process
frameQueue = Queue()
writerProcess = Process(target=write_video,
args=(args["output"], writeVideo, frameQueue,
W, H))
writerProcess.start()
ssl_context.load_cert_chain(args.cert_file, args.key_file)
else:
ssl_context = None
#initialize the MQTT Client
# http://www.steves-internet-guide.com/client-objects-python-mqtt/
mqtt.Client.connected_flag=False #create flag in class
mqttClient = mqtt.Client(mqtt_client_name) #create new instance
mqttClient.on_connect=mqtt_on_connect #bind call back function
mqttClient.on_publish = mqtt_on_publish #assign function to callback
mqttClient.on_disconnect=mqtt_on_disconnect #bind call back function
mqttClient.connect(mqtt_broker_address,mqtt_port) #establish connection
mqttClient.loop_start() #start the loop
# initialize camera; start grabbing frames
webcam = WebcamVideoStream().start()
# initialize the
trafficDetector = detector().start()
# instantiate our direction counter
directionMode = "horizontal" # "vertical"
direction_Counter = DirectionCounter(directionMode, imageHeigth, imageWidth)
app = web.Application()
app.on_shutdown.append(on_shutdown)
app.router.add_get("/", index)
app.router.add_get("/client.js", javascript)
app.router.add_post("/offer", offer)
web.run_app(app, host=args.host, port=args.port, ssl_context=ssl_context)
def video_feed_counter(conf, mode, input, output, url, camera):
# construct the argument parser and parse the arguments
# load the configuration file
conf = Conf(conf)
count = 0
# initialize the MOG foreground background subtractor object
# mog = cv2.bgsegm.createBackgroundSubtractorMOG()
mog = cv2.createBackgroundSubtractorMOG2()
# initialize and define the dilation kernel
dKernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
# initialize the video writer process
writerProcess = None
# initialize the frame dimensions (we'll set them as soon as we read
# the first frame from the video)
W = None
H = None
# instantiate our centroid tracker and initialize a dictionary to
# map each unique object ID to a trackable object
ct = CentroidTracker(conf["max_disappeared"], conf["max_distance"])
trackableObjects = {}
# if a video path was not supplied, grab a reference to the webcam
# if not args.get("input", False):
# if input:
# print("[INFO] starting video stream...")
# # vs = VideoStream(src=0).start()
# vs = VideoStream(usePiCamera=True).start()
# time.sleep(2.0)
# otherwise, grab a reference to the video file
# else:
print("[INFO] opening video file...")
vs = cv2.VideoCapture(url, cv2.CAP_FFMPEG)
# vs = cv2.VideoCapture(args["input"])
# check if the user wants to use the difference flag feature
if conf["diff_flag"]:
# initialize the start counting flag and mouse click callback
start = False
cv2.namedWindow("set_points")
cv2.setMouseCallback("set_points", set_points, [mode])
# otherwise, the user does not want to use it
else:
# set the start flag as true indicating to start traffic counting
start = True
# initialize the direction info variable (used to store information
# such as up/down or left/right vehicle count) and the difference
# point (used to differentiate between left and right lanes)
directionInfo = None
diffPt = None
fps = FPS().start()
# print('fbs')
# loop over frames from the video stream
while (vs.isOpened()):
# grab the next frame and handle if we are reading from either
# VideoCapture or VideoStream
# frame = vs.read()
ret, frame = vs.read() # import image
# if not ret:
# frame = cv2.VideoCapture(url)
# continue
# if ret:
# frame = cv2.VideoCapture(url)
# continue
# if we are viewing a video and we did not grab a frame then we
# have reached the end of the video
if input is not None and frame is None:
break
#print("frame in while")
# check if the start flag is set, if so, we will start traffic
# counting
if start:
# if the frame dimensions are empty, grab the frame
# dimensions, instantiate the direction counter, and set the
# centroid tracker direction
if W is None or H is None:
# start the frames per second throughput estimator
#fps = FPS().start()
(H, W) = frame.shape[:2]
dc = DirectionCounter(mode, W - conf["x_offset"],
H - conf["y_offset"])
ct.direction = mode
# check if the difference point is set, if it is, then
# set it in the centroid tracker object
if diffPt is not None:
ct.diffPt = diffPt
# begin writing the video to disk if required
if output is not None and writerProcess is None:
# set the value of the write flag (used to communicate when
# to stop the process)
writeVideo = Value('i', 1)
# initialize a shared queue for the exhcange frames,
# initialize a process, and start the process
frameQueue = Queue()
writerProcess = Process(target=write_video,
args=(output, writeVideo, frameQueue,
W, H))
writerProcess.start()
# initialize a list to store the bounding box rectangles
# returned by background subtraction model
rects = []
# convert the frame to grayscale image and then blur it
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (5, 5), 0)
# apply the MOG background subtraction model which returns
# a mask
mask = mog.apply(gray)
# apply dilation
dilation = cv2.dilate(mask, dKernel, iterations=2)
# find contours in the mask
cnts = cv2.findContours(dilation.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)
# loop over each contour
for c in cnts:
# if the contour area is less than the minimum area
# required then ignore the object
if cv2.contourArea(c) < conf["min_area"]:
continue
# get the (x, y)-coordinates of the contour, along with
# height and width
(x, y, w, h) = cv2.boundingRect(c)
# check if direction is *vertical and the vehicle is
# further away from the line, if so then, no need to
# detect it
if mode == "vertical" and y < conf["limit"]:
continue
# otherwise, check if direction is horizontal and the
# vehicle is further away from the line, if so then,
# no need to detect it
elif mode == "horizontal" and x > conf["limit"]:
continue
# add the bounding box coordinates to the rectangles list
rects.append((x, y, x + w, y + h))
# check if the direction is vertical
if mode == "vertical":
# draw a horizontal line in the frame -- once an object
# crosses this line we will determine whether they were
# moving 'up' or 'down'
cv2.line(frame, (0, H - conf["y_offset"]),
(W, H - conf["y_offset"]), (0, 255, 255), 2)
# check if a difference point has been set, if so, draw
# a line diving the two lanes
if diffPt is not None:
cv2.line(frame, (diffPt, 0), (diffPt, H), (255, 0, 0), 2)
# otherwise, the direction is horizontal
else:
# draw a vertical line in the frame -- once an object
# crosses this line we will determine whether they were
# moving 'left' or 'right'
# print('ddds')
cv2.line(frame, (W - conf["x_offset"], 0),
(W - conf["x_offset"], H), (0, 255, 255), 2)
# check if a difference point has been set, if so, draw a
# line dividing the two lanes
if diffPt is not None:
cv2.line(frame, (0, diffPt), (W, diffPt), (255, 0, 0), 2)
# use the centroid tracker to associate the (1) old object
# centroids with (2) the newly computed object centroids
objects = ct.update(rects)
# loop over the tracked objects
for (objectID, centroid) in objects.items():
# check to see if a trackable object exists for the
# current object ID and initialize the color
to = trackableObjects.get(objectID, None)
color = (0, 0, 255)
# create a new trackable object if needed
if to is None:
to = TrackableObject(objectID, centroid)
# otherwise, there is a trackable object so we can
# utilize it to determine direction
else:
# find the direction and update the list of centroids
dc.find_direction(to, centroid)
to.centroids.append(centroid)
# check to see if the object has been counted or not
if not to.counted:
# find the direction of motion of the vehicles
directionInfo = dc.count_object(to, centroid, camera)
# otherwise, the object has been counted and set the
# color to green indicate it has been counted
else:
color = (0, 255, 0)
# store the trackable object in our dictionary
trackableObjects[objectID] = to
# draw both the ID of the object and the centroid of the
# object on the output frame
text = "ID {}".format(objectID)
cv2.putText(frame, text, (centroid[0] - 10, centroid[1] - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
cv2.circle(frame, (centroid[0], centroid[1]), 4, color, -1)
# extract the traffic counts and write/draw them
if directionInfo is not None:
for (i, (k, v)) in enumerate(directionInfo):
text = "{}: {}".format(k, v)
cv2.putText(frame, text, (10, ((i * 20) + 20)),
cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2)
# put frame into the shared queue for video writing
if writerProcess is not None:
frameQueue.put(frame)
# show the output frame
# cv2.imshow("Frame", frame)
frames = cv2.imencode('.jpg', frame)[1].tobytes()
yield (b'--frame\r\n'
b'Content-Type: image/jpeg\r\n\r\n' + frames + b'\r\n')
key = cv2.waitKey(1) & 0xFF
# if the `q` key was pressed, break from the loop
if key == ord("q"):
break
# update the FPS counter
fps.update()
# otherwise, the user has to select a difference point
else:
# show the output frame
# cv2.imshow("set_points", frame)
frames = cv2.imencode('.jpg', frame)[1].tobytes()
yield (b'--frame\r\n'
b'Content-Type: image/jpeg\r\n\r\n' + frames + b'\r\n')
key = cv2.waitKey(1) & 0xFF
# if the `s` key was pressed, start traffic counting
if key == ord("s"):
# begin counting and eliminate the informational window
start = True
cv2.destroyWindow("set_points")
# stop the timer and display FPS information
fps.stop()
print("[INFO] elapsed time: {:.2f}".format(fps.elapsed()))
print("[INFO] approx. FPS: {:.2f}".format(fps.fps()))
# terminate the video writer process
if writerProcess is not None:
writeVideo.value = 0
writerProcess.join()
# if we are not using a video file, stop the camera video stream
# if not args.get("input", False):
# vs.stop()
# otherwise, release the video file pointer
else:
vs.release()
# close any open windows
cv2.destroyAllWindows()