car_detect.py

#import libraries of python opencv
import cv2
import numpy as np
from firebase import firebase

firebase = firebase.FirebaseApplication('https://alternate-f167e.firebaseio.com/', None)


#create VideoCapture object and read from video file
cap = cv2.VideoCapture(0)
#use trained cars XML classifiers
car_cascade = cv2.CascadeClassifier('cars.xml')
count=0

weirdint=0

#read until video is completed
while True:
    #capture frame by frame
    ret, frame = cap.read()
    #convert video into gray scale of each frames
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    #detect cars in the video
    cars = car_cascade.detectMultiScale(gray, 1.1, 3)

    #to draw arectangle in each cars
    for (x,y,w,h) in cars:
        cv2.rectangle(frame,(x,y),(x+w,y+h),(0,255,0),2)

    #display the resulting frame
    cv2.imshow('video', frame)
    count=len(cars)
    print(count)
    weirdint=weirdint+1
    if weirdint%20==0 :
        data = {"cars": count}
        result= firebase.put('/p2',"cars", count)
        print result
    #press Q on keyboard to exit
    if cv2.waitKey(25) & 0xFF == ord('q'):
        break
#release the videocapture object
cap.release()
#close all the frames
cv2.destroyAllWindows()