def push_frame():
# current_time = time.strftime('%Y-%m-%d %H:%M:%S',
# time.localtime(time.time()))
# print('[INFO] %s 禁止区域检测程序启动了.' % (current_time))
# 传入参数
# 全局变量
prototxt_file_path = 'models/mobilenet_ssd/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 = 'models/mobilenet_ssd/MobileNetSSD_deploy.caffemodel'
output_fence_path = 'supervision/fence'
skip_frames = 10 # of skip frames between detections
# your python path
python_path = '/root/anaconda3/envs/tensorflow/bin/python'
# 超参数
# minimum probability to filter weak detections
minimum_confidence = 0.5
# 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"
]
# if a video path was not supplied, grab a reference to the webcam
# 加载物体识别模型
print("[INFO] loading model...")
net = cv2.dnn.readNetFromCaffe(prototxt_file_path, model_file_path)
# 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
# 推流函数
accum_time = 0
curr_fps = 0
fps = "FPS: ??"
# prev_time = time()
# 防止多线程时 command 未被设置
while True:
print('command lenth', len(command))
if len(command) > 0:
# 管道配置，其中用到管道
p = sp.Popen(command, stdin=sp.PIPE)
break
while True:
if frame_queue.empty() != True:
#从队列中取出图片
image = frame_queue.get()
frame = imutils.resize(image, width=640, height=480) # 压缩，加快识别速度
rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# if the frame dimensions are empty, set them
if W is None or H is None:
(H, W) = image.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 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(image, 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 > 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([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())
# draw a rectangle around the people
cv2.rectangle(image, (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(image, (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 = 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
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
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(
os.path.join(
output_fence_path, 'snapshot_%s.jpg' %
(time.strftime('%Y%m%d_%H%M%S'))),
image) # snapshot
pic_path = os.path.join(
output_fence_path, 'snapshot_%s.jpg' %
(time.strftime('%Y%m%d_%H%M%S')))
# insert into database
command1 = '%s inserting.py --event_desc %s --event_type 4 --event_location %s --pic_path %s' % (
python_path, event_desc, event_location,
pic_path)
p2 = subprocess.Popen(command1, shell=True)
# 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(image, text, (centroid[0] - 10, centroid[1] - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
cv2.circle(image, (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(image, text, (10, H - ((i * 20) + 20)),
cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2)
# show the output frame
# image = cv2.cvtColor(np.asarray(image), cv2.COLOR_RGB2BGR)
p.stdin.write(image.tostring())
cv2.imshow("Prohibited Area", image)
k = cv2.waitKey(1) & 0xff
if k == 27:
break
# increment the total number of frames processed thus far and
# then update the FPS counter
totalFrames += 1
print("[INFO] opening video file...")
vs = cv2.VideoCapture(input_video)
# 加载物体识别模型
print("[INFO] loading model...")
net = cv2.dnn.readNetFromCaffe(prototxt_file_path, model_file_path)
# 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
# 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
class fence:
# 得到当前时间
fall_timing = 0 # 计时开始
fall_start_time = 0 # 开始时间
fall_limit_time = 2
current_time = time.strftime('%Y-%m-%d %H:%M:%S',
time.localtime(time.time()))
print('[INFO] %s 禁止区域检测程序启动了.' % (current_time))
flag = 1
# 传入参数
ap = argparse.ArgumentParser()
ap.add_argument("-f", "--filename", required=False, default='', help="")
args = vars(ap.parse_args())
# 全局变量
prototxt_file_path = 'models/mobilenet_ssd/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 = 'models/mobilenet_ssd/MobileNetSSD_deploy.caffemodel'
output_fence_path = 'supervision/fence'
input_video = args['filename']
skip_frames = 30 # of skip frames between detections
# your python path
python_path = '/home/reed/anaconda3/envs/tensorflow/bin/python'
# 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"
]
# if a video path was not supplied, grab a reference to the webcam
if not input_video:
print("[INFO] starting video stream...")
vs = cv2.VideoCapture('rtmp://39.100.106.24:1935/stream/pupils_trace')
time.sleep(2)
else:
print("[INFO] opening video file...")
vs = cv2.VideoCapture(input_video)
# 加载物体识别模型
print("[INFO] loading model...")
net = cv2.dnn.readNetFromCaffe(prototxt_file_path, model_file_path)
# 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
# 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
ret, frame = vs.read()
vs.grab()
vs.grab()
vs.grab()
vs.grab()
vs.grab()
# if we are viewing a video and we did not grab a frame then we
# have reached the end of the video
if input_video and not ret:
break
if not input_video:
frame = cv2.flip(frame, 1)
# 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]
# 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 % 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 > 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([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())
# 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 = 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 fall_timing == 0: # just start timing
fall_timing = 1
fall_start_time = time.time()
else: # alredy started timing
fall_end_time = time.time()
difference = fall_end_time - fall_start_time
current_time = time.strftime('%Y-%m-%d %H:%M:%S',
time.localtime(time.time()))
if difference < fall_limit_time:
print('[INFO] %s, 院子, people仅出现 %.1f 秒. 忽略.' %
(current_time, difference))
else: # strangers appear
if (flag == 1):
current_time = time.strftime(
'%Y-%m-%d %H:%M:%S', time.localtime(time.time()))
audioplayer.play_audio(
os.path.join('audios/off-limits-area.mp3'))
print('[EVENT] %s, 院子, 有人闯入禁止区域!!!' % (current_time))
img_name = 'snapshot_%s.jpg' % (
time.strftime('%Y%m%d_%H%M%S'))
cv2.imwrite(os.path.join(output_fence_path, img_name),
frame)
path = output_fence_path + "/" + img_name
myimg = open(path, 'rb')
img_data = myimg.read()
db = pymysql.connect(host='123.57.246.9',
user='******',
password='******',
port=3306,
db='oldcare',
charset='utf8')
cursor = db.cursor()
try:
cursor.execute(
"insert into event_info(event_date,event_type,event_desc,event_location,image) values(now(),4,'有人闯入禁止区域!!!','院子', %s)",
(img_data))
print('Successful')
db.commit()
except:
print('Failed')
db.rollback()
cursor.close()
db.close()
flag = 0
# if there is no existing trackable object, create one
if to is None:
to = TrackableObject(objectID, centroid)
flag = 1
# 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
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
to.counted = True
# 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, (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, H - ((i * 20) + 20)),
cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2)
# show the output frame
cv2.imshow("Prohibited Area", frame)
k = cv2.waitKey(1) & 0xff
if k == 27:
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())) # 14.19
print("[INFO] approx. FPS: {:.2f}".format(fps.fps())) # 90.43
# close any open windows
vs.release()
cv2.destroyAllWindows()