Person Counting System Using Opencv

Person Counting System Using Opencv and Python:

In this Person Counting System using Opencv and Python project, we are using one raspberry pi and one usb camera for this project.

Project Description:

People counting system can be implemented in various domains such as libraries, schools, airports, malls. People observation and counting is of interest in many commercial and non-commercial scenarios. The number of people entering and leaving shops, the occupancy of office buildings or the passenger count of commuter trains provide useful information to shop merchants and marketers, security officials or train operators. People counter is a device used to count the number of pedestrians walking through a door or corridor. Most of the time, this system is used at the entrance of a building so that the total number of visitors can be recorded.

Hardware Used:

You only need two hardware here:

  1) Raspberry Pi:

 This is the latest version of raspberry pi. In this we have inbuilt Bluetooth and wi-fi, unlike previously we have to use Wi-Fi dongle in one of its usb port. There are total 40 pins in RPI3. Of the 40 pins, 26 are GPIO pins and the others are power or ground pins (plus two ID EEPROM pins.) There are 4 USB Port and 1 Ethernet slot, one HDMI port, 1 audio output port and 1 micro usb port and also many other things you can see the diagram on right side. And also we have one micro sd card slot wherein we have to installed the recommended Operating system on micro sd card. There are two ways to interact with your raspberry pi. Either you can interact directly through HDMI port by connecting HDMI to VGA cable, and keyboard and mouse or else you can interact from any system through SSH (Secure Shell). (For example in windows you can interact from putty ssh.) Figure is given below.

2) USB Cameras

USB Cameras are imaging cameras that use USB 2.0 or USB 3.0 technology to transfer image data. USB Cameras are designed to easily interface with dedicated computer systems by using the same USB technology that is found on most computers. The accessibility of USB technology in computer systems as well as the 480 Mb/s transfer rate of USB 2.0 makes USB Cameras ideal for many imaging applications. An increasing selection of USB 3.0 Cameras is also available with data transfer rates of up to 5 Gb/s.

Installation steps for Python OpenCv on Raspberry Pi:

1) sudo apt-get update

2) sudo apt-get upgrade

3) sudo apt-get install build-essential

4) sudo apt-get install cmake git libgtk2.0-dev pkg-config libavcodec-dev libavformat-dev libswscale-dev

5)sudo apt-get install python-dev python-numpy libtbb2 libtbb-dev libjpeg-dev libpng-dev libtiff-dev libjasper-dev libdc1394-22-dev

6) sudo apt-get install python-opencv

7) sudo apt-get install python-matplotlib

Code of Project:

import argparse

import datetime

import imutils

import math

import cv2

import numpy as np

width = 800

textIn = 0

textOut = 0

def testIntersectionIn(x, y):

    res = -450 * x + 400 * y + 157500

    if((res >= -550) and  (res < 550)):

        print (str(res))

        return True

    return False

def testIntersectionOut(x, y):

    res = -450 * x + 400 * y + 180000

    if ((res >= -550) and (res <= 550)):

        print (str(res))

        return True

    return False

camera = cv2.VideoCapture(0)

firstFrame = None

while True:

    (grabbed, frame) = camera.read()

    text = "Unoccupied"

    if not grabbed:

        break

    frame = imutils.resize(frame, width=width)

    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    gray = cv2.GaussianBlur(gray, (21, 21), 0)

    if firstFrame is None:

        firstFrame = gray

        continue

    frameDelta = cv2.absdiff(firstFrame, gray)

    thresh = cv2.threshold(frameDelta, 25, 255, cv2.THRESH_BINARY)[1]

    thresh = cv2.dilate(thresh, None, iterations=2)

    _, cnts, _ = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

    for c in cnts:

        if cv2.contourArea(c) < 12000:

            continue

        (x, y, w, h) = cv2.boundingRect(c)

        cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)

        cv2.line(frame, (width / 2, 0), (width, 450), (250, 0, 1), 2) #blue line

        cv2.line(frame, (width / 2 - 50, 0), (width - 50, 450), (0, 0, 255), 2)#red line

        rectagleCenterPont = ((x + x + w) /2, (y + y + h) /2)

        cv2.circle(frame, rectagleCenterPont, 1, (0, 0, 255), 5)

        if(testIntersectionIn((x + x + w) / 2, (y + y + h) / 2)):

            textIn += 1

        if(testIntersectionOut((x + x + w) / 2, (y + y + h) / 2)):

            textOut += 1

    if cv2.waitKey(1) & 0xFF == ord('q'):

        break

    cv2.putText(frame, "In: {}".format(str(textIn)), (10, 50),

                cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)

    cv2.putText(frame, "Out: {}".format(str(textOut)), (10, 70),

                cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)

    cv2.putText(frame, datetime.datetime.now().strftime("%A %d %B %Y %I:%M:%S%p"),

                (10, frame.shape[0] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.35, (0, 0, 255), 1)

    cv2.imshow("Security Feed", frame)

camera.release()

cv2.destroyAllWindows()