Detection moving object

#!/usr/bin/env python
# -*- coding: utf-8 -*-

# necessary imports
import numpy as np  # for maths operations
import cv2          # OpenCV for Python ver. 2.4.10
import datetime     # for time stamp

# OpenCV can retieve any video source like
# USB cam - cv2.VideoCapture(0) - where param is no. of USB device
# Video - cv2.VideoCapture("video.avi") - where param is path to video
# Video Stream - cv2.VideoCapture("http://192.168.0.101/stream.mpeg") - where param is path to stream
cap = cv2.VideoCapture("video.avi")

# taking first frame as a background (background can be retaken later)
# params are history, number of Gaussian mixtures and baground ratio
# parameters were chosen experimentally
bg = cv2.BackgroundSubtractorMOG(3, 100, 0.8)

# number of founded objects
prev_contours = -1

# main loop of program
while(1):
    # read frame from source
    # ret can be True or False, frame is readed image if ret is True
    ret, frame = cap.read()

    # loop for operations if image was readed correctly
    if ret == True:
        # subtracting background from frame, it will return binary image with:
        # white pixel it was diffrent on background and frame
        # black if pixel was the same on both frames
        fgmask = bg.apply(frame)

        # kernel is matrix full of ones, first param determinate size of it
        kernel_open = np.ones((25,25),np.uint8)
        kernel_close = np.ones((3,3),np.uint8)

        # morphology opening is erosion followed by dilatation
        # removing noise in binary image
        fgmask_open = cv2.morphologyEx(fgmask, cv2.MORPH_OPEN, kernel_close)

        # morphology closing is dilatation followed by eresion (oposite for opening)
        # usefull to close small holes inside foreground objects, or small black points in the object
        fgmask_open_close = cv2.morphologyEx(fgmask_open, cv2.MORPH_CLOSE, kernel_open)

        # fiding the contours of objects in binary image
        # params are source image, contour retrieval mode and contour approximation method
        contours, hierarchy = cv2.findContours(fgmask_open_close,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)

        # drawing the contours if founded
        if len(contours) > 0:
            # loop for each founded contours
            for i in range(len(contours)):
                cnt = contours[i]
                x,y,w,h = cv2.boundingRect(cnt)
                # drawing rectangle on founded object
                cv2.rectangle(frame,(x,y),(x+w,y+h),(0,0,255),2)

        # generate time stamp if there is new amount of moving objects in frames
        if len(contours)!=prev_contours:
            time_stamp = str(datetime.datetime.now().strftime("%H:%M:%S.%f"))[:-3]
            print time_stamp + ' - contours: ' + str(len(contours))
            prev_contours = len(contours)

        # show frame with image of each operation taken
        cv2.imshow('masked',fgmask) # image with subtracted background
        cv2.imshow('after_open',fgmask_open) # image after open
        cv2.imshow('after_close',fgmask_open_close) # image after close
        cv2.imshow('original',frame) # original image with contours on objects

        # keyboard key support
        k = cv2.waitKey(30) & 0xff
        # ESC to close
        if k == 27:
            break
        #n to take new background image
        if k == 110:
            bg = cv2.BackgroundSubtractorMOG()
    # close aplication if there is no readed image
    else:
        cap.release()
        cv2.destroyAllWindows()
        break

# close aplication after finishing main loop
cap.release()
cv2.destroyAllWindows()