import threading from datetime import datetime from oldcare.track.centroidtracker import CentroidTracker from oldcare.track.trackableobject import TrackableObject from imutils.video import FPS import numpy as np from Post import post import imutils import argparse import time import dlib import cv2 # 全局变量 # prototxt_file_path = 'data/data_opencv/MobileNetSSD_deploy.prototxt' # # Contains the Caffe deep learning model files. # # We’ll be using a MobileNet Single Shot Detector (SSD), # # “Single Shot Detectors for object detection”. # model_file_path = 'data/data_opencv/MobileNetSSD_deploy.caffemodel' # 物体识别模型能识别的物体（21种） CLASSES = ["background", "aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"] # 加载物体识别模型 # net = cv2.dnn.readNetFromCaffe(prototxt_file_path, model_file_path) class Intrusion_Detection(): def __init__(self, net): self.net = net # initialize the frame dimensions (we'll set them as soon as we read # the first frame from the video) self.W = None self.H = None self.pre = datetime.now() # 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 self.ct = CentroidTracker(maxDisappeared=40, maxDistance=50) self.trackers = [] self.trackableObjects = {} # initialize the total number of frames processed thus far, along # with the total number of objects that have moved either up or down self.totalFrames = 0 self.totalDown = 0 self.totalUp = 0 # start the frames per second throughput estimator self.fps = FPS().start() # loop over frames from the video stream def process(self, frame): # grab the next frame and handle if we are reading from either # VideoCapture or VideoStream rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) # if the frame dimensions are empty, set them if self.W is None or self.H is None: (self.H, self.W) = frame.shape[:2] # initialize the current status along with our list of bounding # box rectangles returned by either (1) our object detector or # (14) 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 self.totalFrames % 20 == 0: # set the status and initialize our new set of object trackers status = "Detecting" self.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, (self.W, self.H), 127.5) self.net.setInput(blob) detections = self.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 > 0.5: # 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([self.W, self.H, self.W, self.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 self.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 self.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()) # draw a rectangle around the people cv2.rectangle(frame, (startX, startY), (endX, endY), (0, 255, 0), 2) # 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 // 14), (W, H // 14), (0, 255, 255), 14) # use the centroid tracker to associate the (1) old object # centroids with (14) the newly computed object centroids objects = self.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 = self.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] < self.H // 2: self.totalUp += 1 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] > self.H // 2: self.totalDown += 1 to.counted = True current_time = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time())) event_desc = '有人闯入禁止区域!!!' event_location = '院子' print('[EVENT] %s, 院子, 有人闯入禁止区域!!!' % (current_time)) time_snap = datetime.now() cv2.imwrite('intrusion' + str(time_snap).replace(':', '') + '.jpg', frame) if (datetime.now() - self.pre).total_seconds() > 5: t = threading.Thread( target=post(event=4, imagePath='intrusion' + str(time_snap).replace(':', '') + '.jpg')) t.setDaemon(False) t.start() self.pre = datetime.now() # todo insert into database # command = '%s inserting.py --event_desc %s--event_type4 - -event_location % s' % \ # (python_path, event_desc, event_location) # p = subprocess.Popen(command, shell=True) # store the trackable obj ect in our dictionary self.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, (0, 255, 0), 2) cv2.circle(frame, (centroid[0], centroid[1]), 4, (0, 255, 0), -1) # construct a tuple of information we will be displaying on the # frame info = [ # ("Up", totalUp), # ("Down", totalDown), ("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, self.H - ((i * 20) + 20)), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2) # show the output frame frame = cv2.resize(frame, (640, 480)) # increment the total number of frames processed thus far and # then update the FPS counter self.totalFrames += 1 return frame