# How to Perform Motion Detection Using Python
# Importing the Pandas libraries
import pandas as panda
# Importing the OpenCV libraries
import cv2
# Importing the time module
import time
# Importing the datetime function of the datetime module
from datetime import datetime
# Assigning our initial state in the form of variable initialState as None for initial frames
initialState = None
# List of all the tracks when there is any detected of motion in the frames
motionTrackList= [ None, None ]
# A new list 'time' for storing the time when movement detected
motionTime = []
# Initialising DataFrame variable 'dataFrame' using pandas libraries panda with Initial and Final column
dataFrame = panda.DataFrame(columns = ["Initial", "Final"])
# starting the webCam to capture the video using cv2 module
video = cv2.VideoCapture(0)
# using infinite loop to capture the frames from the video
while True:
# Reading each image or frame from the video using read function
check, cur_frame = video.read()
# Defining 'motion' variable equal to zero as initial frame
var_motion = 0
# From colour images creating a gray frame
gray_image = cv2.cvtColor(cur_frame, cv2.COLOR_BGR2GRAY)
# To find the changes creating a GaussianBlur from the gray scale image
gray_frame = cv2.GaussianBlur(gray_image, (21, 21), 0)
# For the first iteration checking the condition
# we will assign grayFrame to initalState if is none
if initialState is None:
initialState = gray_frame
continue
# Calculation of difference between static or initial and gray frame we created
differ_frame = cv2.absdiff(initialState, gray_frame)
# the change between static or initial background and current gray frame are highlighted
thresh_frame = cv2.threshold(differ_frame, 30, 255, cv2.THRESH_BINARY)[1]
thresh_frame = cv2.dilate(thresh_frame, None, iterations = 2)
# For the moving object in the frame finding the coutours
cont,_ = cv2.findContours(thresh_frame.copy(),
cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
for cur in cont:
if cv2.contourArea(cur) < 10000:
continue
var_motion = 1
(cur_x, cur_y,cur_w, cur_h) = cv2.boundingRect(cur)
# To create a rectangle of green color around the moving object
cv2.rectangle(cur_frame, (cur_x, cur_y), (cur_x + cur_w, cur_y + cur_h), (0, 255, 0), 3)
# from the frame adding the motion status
motionTrackList.append(var_motion)
motionTrackList = motionTrackList[-2:]
# Adding the Start time of the motion
if motionTrackList[-1] == 1 and motionTrackList[-2] == 0:
motionTime.append(datetime.now())
# Adding the End time of the motion
if motionTrackList[-1] == 0 and motionTrackList[-2] == 1:
motionTime.append(datetime.now())
# In the gray scale displaying the captured image
cv2.imshow("The image captured in the Gray Frame is shown below: ", gray_frame)
# To display the difference between inital static frame and the current frame
cv2.imshow("Difference between the inital static frame and the current frame: ", differ_frame)
# To display on the frame screen the black and white images from the video
cv2.imshow("Threshold Frame created from the PC or Laptop Webcam is: ", thresh_frame)
# Through the colour frame displaying the contour of the object
cv2.imshow("From the PC or Laptop webcam, this is one example of the Colour Frame:", cur_frame)
# Creating a key to wait
wait_key = cv2.waitKey(1)
# With the help of the 'm' key ending the whole process of our system
if wait_key == ord('m'):
# adding the motion variable value to motiontime list when something is moving on the screen
if var_motion == 1:
motionTime.append(datetime.now())
break
# At last we are adding the time of motion or var_motion inside the data frame
for a in range(0, len(motionTime), 2):
dataFrame = dataFrame.append({"Initial" : time[a], "Final" : motionTime[a + 1]}, ignore_index = True)
# To record all the movements, creating a CSV file
dataFrame.to_csv("EachMovement.csv")
# Releasing the video
video.release()
# Now, Closing or destroying all the open windows with the help of openCV
cv2.destroyAllWindows()