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refactored threading

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Sai Subhakar T 2020-09-30 14:58:58 +02:00
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32
README.md
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@ -1,7 +1,7 @@
# People-Counting-in-Real-Time # People-Counting-in-Real-Time
People Counting in Real-Time using live video stream/IP camera in OpenCV. People Counting in Real-Time using live video stream/IP camera in OpenCV.
> This repo is an improvement/modification to https://www.pyimagesearch.com/2018/08/13/opencv-people-counter/ > This is an improvement/modification to https://www.pyimagesearch.com/2018/08/13/opencv-people-counter/
> Refer to added [Features](#features). Also, added support for an IP camera. > Refer to added [Features](#features). Also, added support for an IP camera.
@ -13,6 +13,7 @@ People Counting in Real-Time using live video stream/IP camera in OpenCV.
- The primary aim is to use the project as a business perspective, ready to scale. - The primary aim is to use the project as a business perspective, ready to scale.
- Use case: counting the number of people in the stores/buildings/shopping malls etc., in real-time. - Use case: counting the number of people in the stores/buildings/shopping malls etc., in real-time.
- Sending an alert to the staff if the people are way over the limit. - Sending an alert to the staff if the people are way over the limit.
- Automating features and optimising the real-time stream for better performance.
- Acts as a measure towards footfall analysis and in a way to tackle COVID-19. - Acts as a measure towards footfall analysis and in a way to tackle COVID-19.
--- ---
@ -48,12 +49,11 @@ pip install -r requirements.txt
python run.py --prototxt mobilenet_ssd/MobileNetSSD_deploy.prototxt --model mobilenet_ssd/MobileNetSSD_deploy.caffemodel --input videos/example_01.mp4 python run.py --prototxt mobilenet_ssd/MobileNetSSD_deploy.prototxt --model mobilenet_ssd/MobileNetSSD_deploy.caffemodel --input videos/example_01.mp4
``` ```
> To run inference on an IP camera: > To run inference on an IP camera:
- Setup your camera url in 'run.py': - Setup your camera url in 'mylib/config.py':
``` ```
# the following is an ip camera url example # Enter the ip camera url (e.g., url = 'http://191.138.0.100:8040/video')
# just enter your camera url and it should work url = ''
url = 'http://191.138.0.100:8040/video'
vs = VideoStream(url).start()
``` ```
- Then run with the command: - Then run with the command:
``` ```
@ -61,26 +61,24 @@ python run.py --prototxt mobilenet_ssd/MobileNetSSD_deploy.prototxt --model mobi
``` ```
## Features ## Features
The following are some of the added features. Note: You can easily on/off them in the config. options (mylib>config.py): The following are the added features. Note: You can easily on/off them in the config. options (mylib/config.py):
<img src="https://imgur.com/9hw1NP0.png" width=500> <img src="https://imgur.com/Lr8mdUW.png" width=500>
***1. Real-Time alert:*** ***1. Real-Time alert:***
- If selected, we send an email alert in real-time. Use case: If the total number of people (say 30) exceeded in a store/building, we simply alert the staff. - If selected, we send an email alert in real-time. Use case: If the total number of people (say 30) exceeded in a store/building, we simply alert the staff.
- This is pretty useful considering the COVID-19 scenario. - This is pretty useful considering the COVID-19 scenario.
<img src="https://imgur.com/35Yf1SR.png" width=400> <img src="https://imgur.com/35Yf1SR.png" width=350>
- Note: To setup the sender email, please refer the instructions inside 'mylib/mailer.py'. Setup receiver email at the start of 'run.py'. - Note: To setup the sender email, please refer the instructions inside 'mylib/mailer.py'. Setup receiver email in the config.
***2. Threading:*** ***2. Threading:***
- Multi-Threading is implemented in 'Thread.py'. If you ever see a lag/delay in your real-time stream, consider running it. - Multi-Threading is implemented in 'mylib/thread.py'. If you ever see a lag/delay in your real-time stream, consider using it.
- Threaing removes OpenCV's internal buffer (which stores the frames yet to be processed) and thus reduces the lag. - Threaing removes OpenCV's internal buffer (which stores the frames yet to be processed) and thus reduces the lag/increases fps.
- It is most preferred for complex real-time applications. Use the command: - It is most suitable for solid performance on complex real-time applications. To use threading:
``` ``` set Thread = True in config. ```
python thread.py --prototxt mobilenet_ssd/MobileNetSSD_deploy.prototxt --model mobilenet_ssd/MobileNetSSD_deploy.caffemodel
```
***3. Scheduler:*** ***3. Scheduler:***
@ -106,7 +104,7 @@ if Timer:
break break
``` ```
***4. Simple log:*** ***5. Simple log:***
- Logs all data at end of the day. - Logs all data at end of the day.
- Useful for footfall analysis. - Useful for footfall analysis.
<img src="https://imgur.com/CV2nCjx.png" width=400> <img src="https://imgur.com/CV2nCjx.png" width=400>

28
Run.py
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@ -3,7 +3,7 @@ from mylib.trackableobject import TrackableObject
from imutils.video import VideoStream from imutils.video import VideoStream
from imutils.video import FPS from imutils.video import FPS
from mylib.mailer import Mailer from mylib.mailer import Mailer
from mylib import config from mylib import config, thread
import time, schedule, csv import time, schedule, csv
import numpy as np import numpy as np
import argparse, imutils import argparse, imutils
@ -12,7 +12,6 @@ from itertools import zip_longest
t0 = time.time() t0 = time.time()
def run(): def run():
# construct the argument parse and parse the arguments # construct the argument parse and parse the arguments
@ -45,14 +44,12 @@ def run():
# if a video path was not supplied, grab a reference to the ip camera # if a video path was not supplied, grab a reference to the ip camera
if not args.get("input", False): if not args.get("input", False):
print("[INFO] Starting the live stream..") print("[INFO] Starting the live stream..")
# the following is an ip camera url example vs = VideoStream(config.url).start()
# just enter your camera url and it should work
url = 'http://191.138.0.100:8040/video'
vs = VideoStream(url).start()
time.sleep(2.0) time.sleep(2.0)
# otherwise, grab a reference to the video file # otherwise, grab a reference to the video file
else: else:
print("[INFO] Starting the video..")
vs = cv2.VideoCapture(args["input"]) vs = cv2.VideoCapture(args["input"])
# initialize the video writer (we'll instantiate later if need be) # initialize the video writer (we'll instantiate later if need be)
@ -82,6 +79,9 @@ def run():
# start the frames per second throughput estimator # start the frames per second throughput estimator
fps = FPS().start() fps = FPS().start()
if config.Thread:
vs = thread.ThreadingClass(config.url)
# loop over frames from the video stream # loop over frames from the video stream
while True: while True:
# grab the next frame and handle if we are reading from either # grab the next frame and handle if we are reading from either
@ -97,7 +97,7 @@ def run():
# resize the frame to have a maximum width of 500 pixels (the # resize the frame to have a maximum width of 500 pixels (the
# less data we have, the faster we can process it), then convert # less data we have, the faster we can process it), then convert
# the frame from BGR to RGB for dlib # the frame from BGR to RGB for dlib
frame = imutils.resize(frame, width=500) frame = imutils.resize(frame, width = 500)
rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
# if the frame dimensions are empty, set them # if the frame dimensions are empty, set them
@ -319,13 +319,13 @@ def run():
print("[INFO] approx. FPS: {:.2f}".format(fps.fps())) print("[INFO] approx. FPS: {:.2f}".format(fps.fps()))
# if we are not using a video file, stop the camera video stream # # if we are not using a video file, stop the camera video stream
if not args.get("input", False): # if not args.get("input", False):
vs.stop() # vs.stop()
#
# otherwise, release the video file pointer # # otherwise, release the video file pointer
else: # else:
vs.release() # vs.release()
# close any open windows # close any open windows
cv2.destroyAllWindows() cv2.destroyAllWindows()

339
Thread.py
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@ -1,339 +0,0 @@
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
import time, schedule, csv
import numpy as np
import argparse, imutils, queue, threading
import time, dlib, cv2, datetime
from itertools import zip_longest
t0 = time.time()
# 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"])
class VideoCapture:
# initiate threading
def __init__(self, name):
self.cap = cv2.VideoCapture(name)
self.q = queue.Queue()
t = threading.Thread(target=self._reader)
t.daemon = True
t.start()
# read frames as soon as they are available
# this approach removes OpenCV's internal buffer and reduces the frame lag
def _reader(self):
while True:
ret, frame = self.cap.read()
if not ret:
break
if not self.q.empty():
try:
self.q.get_nowait()
except queue.Empty:
pass
self.q.put(frame)
def read(self):
return self.q.get()
# 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()
print("[INFO] Starting the live stream..")
url = 'http://192.134.0.102:8290/video'
cap = VideoCapture(url)
# 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 = cap.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(*"MJPG")
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])
x = []
# compute the sum of total people inside
x.append(len(empty1)-len(empty))
#print("Total people inside:", x)
# Optimise number below: 10, 50, 100, etc., indicate the max. people inside limit
# if the limit exceeds, send an email alert
people_limit = 10
if sum(x) == people_limit:
if config.ALERT:
print("[INFO] Sending email alert..")
Mailer().send(config.MAIL)
print("[INFO] Alert sent")
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, (255, 255, 255), 2)
cv2.circle(frame, (centroid[0], centroid[1]), 4, (255, 255, 255), -1)
# 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)
# show the output frame
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()))
# close any open windows
cv2.destroyAllWindows()

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mylib/config.py
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@ -4,9 +4,13 @@
# Enter mail below to receive real-time email alerts # Enter mail below to receive real-time email alerts
# e.g., 'email@gmail.com' # e.g., 'email@gmail.com'
MAIL = '' MAIL = ''
# Enter the ip camera url (e.g., url = 'http://191.138.0.100:8040/video')
url = ''
# ON/OFF for mail feature. Enter True to turn on the email alert feature. # ON/OFF for mail feature. Enter True to turn on the email alert feature.
ALERT = False ALERT = False
# Threading ON/OFF
Thread = False
# Simple log to log the counting data # Simple log to log the counting data
Log = False Log = False

28
mylib/thread.py Normal file
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@ -0,0 +1,28 @@
import cv2, threading, queue
class ThreadingClass:
# initiate threading class
def __init__(self, name):
self.cap = cv2.VideoCapture(name)
# define an empty queue and thread
self.q = queue.Queue()
t = threading.Thread(target=self._reader)
t.daemon = True
t.start()
# read the frames as soon as they are available
# this approach removes OpenCV's internal buffer and reduces the frame lag
def _reader(self):
while True:
ret, frame = self.cap.read() # read the frames and ---
if not ret:
break
if not self.q.empty():
try:
self.q.get_nowait()
except queue.Empty:
pass
self.q.put(frame) # --- store them in a queue (instead of the buffer)
def read(self):
return self.q.get() # fetch frames from the queue one by one