from mylib.centroidtracker import CentroidTracker from mylib.trackableobject import TrackableObject from imutils.video import VideoStream from imutils.video import FPS from mylib.mailer import Mailer from mylib import config, thread import time, schedule, csv import numpy as np import argparse, imutils import time, dlib, cv2, datetime import threading from itertools import zip_longest from flask import Response from flask import Flask from flask import jsonify from flask_cors import CORS, cross_origin import subprocess t0 = time.time() outputFrame = None people_count = 0 lock = threading.Lock() # initialize a flask object app = Flask(__name__) cors = CORS(app) app.config['CORS_HEADERS'] = 'Content-Type' args = None @app.route("/counter") @cross_origin() def counter(): # return the rendered template global people_count return jsonify([ 1000, 980, 700, 500, 670, 567, people_count ]) @app.route("/video") @cross_origin() def video_feed(): # return the response generated along with the specific media # type (mime type) return Response(generate(), mimetype = "multipart/x-mixed-replace; boundary=frame") def open_ffmpeg_stream_process(height, width): # args = ( # "ffmpeg -re -stream_loop -1 -f rawvideo -pix_fmt " # "rgb24 -s 1920x1080 -i pipe:0 -pix_fmt yuv420p " # "-f rtsp rtsp://172.23.90.205:8554/stream" # ).split() args = ( "ffmpeg -re -stream_loop -1 -f rawvideo -vcodec rawvideo -pix_fmt " "rgb24 -s " + str(width) + "x" + str(height) + " -i pipe:0 -pix_fmt nv12 " "-c:v libx264 -preset fast -crf 22 -bf 0 " "-f rtsp -rtsp_transport tcp rtsp://172.17.2.39/counter" ).split() return subprocess.Popen(args, stdin=subprocess.PIPE) def PeopleCounter(): global outputFrame, lock, people_count # construct the argument parse and parse the arguments # ap = argparse.ArgumentParser() # ap.add_argument("-p", "--prototxt", required=False, # help="path to Caffe 'deploy' prototxt file") # ap.add_argument("-m", "--model", required=True, # help="path to Caffe pre-trained model") # 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") # # confidence default 0.4 # ap.add_argument("-c", "--confidence", type=float, default=0.4, # help="minimum probability to filter weak detections") # ap.add_argument("-s", "--skip-frames", type=int, default=30, # help="# of skip frames between detections") # args = vars(ap.parse_args()) # initialize the list of class labels MobileNet SSD was trained to # detect 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 net = cv2.dnn.readNetFromCaffe(args["prototxt"], args["model"]) # if a video path was not supplied, grab a reference to the ip camera if not args.get("input", False): print("[INFO] Starting the live stream..") vs = VideoStream(config.url).start() time.sleep(2.0) # otherwise, grab a reference to the video file else: print("[INFO] Starting the video..") vs = cv2.VideoCapture(args["input"]) # initialize the video writer (we'll instantiate later if need be) writer = 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, then initialize a list to store # each of our dlib correlation trackers, followed by a dictionary to # map each unique object ID to a TrackableObject ct = CentroidTracker(maxDisappeared=40, maxDistance=50) trackers = [] trackableObjects = {} # initialize the total number of frames processed thus far, along # with the total number of objects that have moved either up or down totalFrames = 0 totalDown = 0 totalUp = 0 x = [] empty=[] empty1=[] # start the frames per second throughput estimator fps = FPS().start() if config.Thread: vs = thread.ThreadingClass(config.url) ffmpeg_process = open_ffmpeg_stream_process(1920, 1080) # 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 # resize the frame to have a maximum width of 500 pixels (the # less data we have, the faster we can process it), then convert # the frame from BGR to RGB for dlib frame = imutils.resize(frame, width = 500) rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) # if the frame dimensions are empty, set them if W is None or H is None: (H, W) = frame.shape[:2] # if we are supposed to be writing a video to disk, initialize # the writer if args["output"] is not None and writer is None: fourcc = cv2.VideoWriter_fourcc(*"mp4v") writer = cv2.VideoWriter(args["output"], fourcc, 30, (W, H), True) # 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 % args["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, 0.007843, (W, H), 127.5) net.setInput(blob) 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 > args["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)) # 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, 0, 0), 3) cv2.putText(frame, "-Prediction border - Entrance-", (10, H - ((i * 20) + 200)), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 1) # 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 to = trackableObjects.get(objectID, None) # if there is no existing trackable object, create one if to is None: to = TrackableObject(objectID, centroid) # otherwise, there is a trackable object so we can utilize it # to determine direction else: # the difference between the y-coordinate of the *current* # centroid and the mean of *previous* centroids will tell # us in which direction the object is moving (negative for # 'up' and positive for 'down') y = [c[1] for c in to.centroids] direction = centroid[1] - np.mean(y) to.centroids.append(centroid) # check to see if the object has been counted or not if not to.counted: # if the direction is negative (indicating the object # is moving up) AND the centroid is above the center # line, count the object if direction < 0 and centroid[1] < H // 2: totalUp += 1 empty.append(totalUp) to.counted = True # if the direction is positive (indicating the object # is moving down) AND the centroid is below the # center line, count the object elif direction > 0 and centroid[1] > H // 2: totalDown += 1 empty1.append(totalDown) #print(empty1[-1]) # if the people limit exceeds over threshold, send an email alert if sum(x) >= config.Threshold: cv2.putText(frame, "-ALERT: People limit exceeded-", (10, frame.shape[0] - 80), cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 255), 2) if config.ALERT: print("[INFO] Sending email alert..") Mailer().send(config.MAIL) print("[INFO] Alert sent") to.counted = True x = [] # compute the sum of total people inside x.append(len(empty1)-len(empty)) #print("Total people inside:", x) # 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, (255, 255, 255), 2) cv2.circle(frame, (centroid[0], centroid[1]), 4, (255, 255, 255), -1) people_count = totalDown # construct a tuple of information we will be displaying on the info = [ ("Exit", totalUp), ("Enter", totalDown), ("Status", status), ] info2 = [ ("Total people inside", x), ] # Display the output 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, 0), 2) for (i, (k, v)) in enumerate(info2): text = "{}: {}".format(k, v) cv2.putText(frame, text, (265, H - ((i * 20) + 60)), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 2) # Initiate a simple log to save data at end of the day if config.Log: datetimee = [datetime.datetime.now()] d = [datetimee, empty1, empty, x] export_data = zip_longest(*d, fillvalue = '') with open('Log.csv', 'w', newline='') as myfile: wr = csv.writer(myfile, quoting=csv.QUOTE_ALL) wr.writerow(("End Time", "In", "Out", "Total Inside")) wr.writerows(export_data) # check to see if we should write the frame to disk if writer is not None: writer.write(frame) # show the output frame with lock: ffmpeg_process.stdin.write(frame.astype(np.uint8).tobytes()) # outputFrame = frame.copy() # cv2.imshow("Real-Time Monitoring/Analysis Window", 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() if config.Timer: # Automatic timer to stop the live stream. Set to 8 hours (28800s). t1 = time.time() num_seconds=(t1-t0) if num_seconds > 28800: break # 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())) # # 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() # issue 15 if config.Thread: vs.release() # close any open windows cv2.destroyAllWindows() def generate(): # grab global references to the output frame and lock variables global outputFrame, lock # loop over frames from the output stream while True: # wait until the lock is acquired with lock: # check if the output frame is available, otherwise skip # the iteration of the loop if outputFrame is None: continue # encode the frame in JPEG format (flag, encodedImage) = cv2.imencode(".jpg", outputFrame) # ensure the frame was successfully encoded if not flag: continue # yield the output frame in the byte format yield(b'--frame\r\n' b'Content-Type: image/jpeg\r\n\r\n' + bytearray(encodedImage) + b'\r\n') # check to see if this is the main thread of execution if __name__ == '__main__': # construct the argument parser and parse command line arguments ap = argparse.ArgumentParser() ap.add_argument("-t", "--ip", type=str, default="0.0.0.0", help="ip address of the device") ap.add_argument("-u", "--port", type=int, default=8081, help="ephemeral port number of the server (1024 to 65535)") ap.add_argument("-p", "--prototxt", required=False, help="path to Caffe 'deploy' prototxt file") ap.add_argument("-m", "--model", required=True, help="path to Caffe pre-trained model") 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") # confidence default 0.4 ap.add_argument("-c", "--confidence", type=float, default=0.4, help="minimum probability to filter weak detections") ap.add_argument("-s", "--skip-frames", type=int, default=30, help="# of skip frames between detections") args = vars(ap.parse_args()) # start a thread that will perform motion detection t = threading.Thread(target=PeopleCounter) t.daemon = True t.start() # start the flask app app.run(host=args["ip"], port=args["port"], debug=False, threaded=True, use_reloader=False)