from oldcare.track.centroidtracker import CentroidTracker from oldcare.track.trackableobject import TrackableObject from imutils.video import FPS import numpy as np 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' skip_frames = 30 # of skip frames between detections # 超参数 # minimum probability to filter weak detections minimum_confidence = 0.80 # 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 # 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 # (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 self.totalFrames % 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, (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 > minimum_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([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 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 // 2), (W, H // 2), (0, 255, 255), 2) # use the centroid tracker to associate the (1) old object # centroids with (2) 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)) cv2.imwrite('intrusion.jpg', frame) # 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