Person_Counting.py

import argparse
import datetime
import imutils
import math
import cv2
import numpy as np

width = 800
textIn = 0
textOut = 0
total = textIn

# set the capacity of the hall here we have set this to 1 by default
THRESHOLD = 1

# function to check that the person have moved outside
def testIntersectionOut(x, y):
    res = -450 * x + 400 * y + 157500
    if ((res >= -500) and (res < 550)):
        print(str(res))
        return True
    return False

# function to check that the person have moved inside
def testIntersectionIn(x, y):
    res = -450 * x + 400 * y + 180000
    if ((res >= -500) and (res <= 550)):
        print(str(res))
        return True
    return False


if __name__ == "__main__":
    
    # set the test video path here
    # download the test video from https://drive.google.com/file/d/1935x6wqD0qjPM3marxC-bihZFLafZBi3/view?usp=sharing
    PATH_TO_STREAM = "test2.mp4"
    
    # initialize the stream
    camera = cv2.VideoCapture(PATH_TO_STREAM)
    
    # initialize the first frame to None by default
    firstFrame = None

    # loop over the frames of the video
    while True:
      
        # grab the current frame and initialize the occupied/unoccupied
        # text
        # read the frame from the stream
        (grabbed, frame) = camera.read()
        text = "Unoccupied"

        # if the frame could not be grabbed, then we have reached the end
        # of the video
        if not grabbed:
            break
        
        cv2.putText(frame, "IN".format(str(textIn)), (30, 200),
                     cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255,0,0), 2)
        cv2.putText(frame, "OUT".format(str(textIn)), (700, 50),
                     cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255,0,0), 2)
        
        # resize the frame, convert it to grayscale, and blur it applying gaussian blur with kernel size of 21 x 21
        frame = imutils.resize(frame, width=width)
        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        gray = cv2.GaussianBlur(gray, (21, 21), 0)

        # if the first frame is None, initialize it
        if firstFrame is None:
            firstFrame = gray
            continue

        # compute the absolute difference between the current frame and
        # first frame
        frameDelta = cv2.absdiff(firstFrame, gray)
        thresh = cv2.threshold(frameDelta, 25, 255, cv2.THRESH_BINARY)[1]
        # dilate the thresholded image twice to fill in holes, then find contours
        # on thresholded image
        thresh = cv2.dilate(thresh, None, iterations=2)
       
        cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[0]
        
        # loop over the contours
        for c in cnts:
            
            # if the contour is too small, ignore it
            if cv2.contourArea(c) < 12000:
                continue
            
            # compute the bounding box for the contour, draw it on the frame,
            # and update the text
            (x, y, w, h) = cv2.boundingRect(c)
            cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)

            # draw lines to denote  the boundary that saprate IN and OUT
            cv2.line(frame, (width // 2, 0), (width, 450), (250, 0, 1), 2)  # blue line OUT
            cv2.line(frame, (width // 2 - 50, 0), (width - 50, 450), (0, 0, 255), 2)  # red line IN
            
            # draw the center point of the contour
            rectagleCenterPont = ((x + x + w) // 2, (y + y + h) // 2)
            cv2.circle(frame, rectagleCenterPont, 1, (0, 0, 255), 5)
            
            # check if is the person is leaving then decrement the total counter and increment textOut counter
            if (testIntersectionOut((x + x + w) // 2, (y + y + h) // 2)):
                textOut += 1
                total -= 1
                
            # check if is the person is leaving then increment the total counter and increment textIn counter
            if (testIntersectionIn((x + x + w) // 2, (y + y + h) // 2)):
                textIn += 1
                total += 1
                
            # check if thresold limit is reached and add the respective labels
            if total >= THRESHOLD:
                cv2.putText(frame, "Hall is: {}".format(str("Full")), (10, 120),
                        cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
            if total < THRESHOLD:
                cv2.putText(frame, "Hall is: {}".format(str("Open")), (10, 120),
                            cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
                cv2.putText(frame, "Total In: {}".format(str(textIn)), (10, 50),
                            cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
                cv2.putText(frame, "Total Out: {}".format(str(textOut)), (10, 70),
                            cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
                cv2.putText(frame, "Current In: {}".format(str(total)), (10, 100),
                            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("Frame",frame)
                
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break
        
    # cleanup the camera and close any open windows
    camera.release()
    cv2.destroyAllWindows()