WebCam Motion Detector in Python - GeeksforGeeks

10 Nov, 2021
This python program will allow you to detect motion and also store the time interval of the motion.
Requirement: 
Python3OpenCV(libraries)Pandas(libraries)
Python3
OpenCV(libraries)
Pandas(libraries)
Install Requirements : Install Python3, install Pandas and OpenCV libraries.
Main Logic : Videos can be treated as stack of pictures called frames. Here I am comparing different frames(pictures) to the first frame which should be static(No movements initially). We compare two images by comparing the intensity value of each pixels. In python we can do it easily as you can see in following code:
Python3
# Python program to implement# Webcam Motion Detector # importing OpenCV, time and Pandas libraryimport cv2, time, pandas# importing datetime class from datetime libraryfrom datetime import datetime # Assigning our static_back to Nonestatic_back = None # List when any moving object appearmotion_list = [ None, None ] # Time of movementtime = [] # Initializing DataFrame, one column is start# time and other column is end timedf = pandas.DataFrame(columns = ["Start", "End"]) # Capturing videovideo = cv2.VideoCapture(0) # Infinite while loop to treat stack of image as videowhile True:    # Reading frame(image) from video    check, frame = video.read()     # Initializing motion = 0(no motion)    motion = 0     # Converting color image to gray_scale image    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)     # Converting gray scale image to GaussianBlur    # so that change can be find easily    gray = cv2.GaussianBlur(gray, (21, 21), 0)     # In first iteration we assign the value    # of static_back to our first frame    if static_back is None:        static_back = gray        continue     # Difference between static background    # and current frame(which is GaussianBlur)    diff_frame = cv2.absdiff(static_back, gray)     # If change in between static background and    # current frame is greater than 30 it will show white color(255)    thresh_frame = cv2.threshold(diff_frame, 30, 255, cv2.THRESH_BINARY)[1]    thresh_frame = cv2.dilate(thresh_frame, None, iterations = 2)     # Finding contour of moving object    cnts,_ = cv2.findContours(thresh_frame.copy(),                       cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)     for contour in cnts:        if cv2.contourArea(contour) < 10000:            continue        motion = 1         (x, y, w, h) = cv2.boundingRect(contour)        # making green rectangle around the moving object        cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 3)     # Appending status of motion    motion_list.append(motion)     motion_list = motion_list[-2:]     # Appending Start time of motion    if motion_list[-1] == 1 and motion_list[-2] == 0:        time.append(datetime.now())     # Appending End time of motion    if motion_list[-1] == 0 and motion_list[-2] == 1:        time.append(datetime.now())     # Displaying image in gray_scale    cv2.imshow("Gray Frame", gray)     # Displaying the difference in currentframe to    # the staticframe(very first_frame)    cv2.imshow("Difference Frame", diff_frame)     # Displaying the black and white image in which if    # intensity difference greater than 30 it will appear white    cv2.imshow("Threshold Frame", thresh_frame)     # Displaying color frame with contour of motion of object    cv2.imshow("Color Frame", frame)     key = cv2.waitKey(1)    # if q entered whole process will stop    if key == ord('q'):        # if something is movingthen it append the end time of movement        if motion == 1:            time.append(datetime.now())        break # Appending time of motion in DataFramefor i in range(0, len(time), 2):    df = df.append({"Start":time[i], "End":time[i + 1]}, ignore_index = True) # Creating a CSV file in which time of movements will be saveddf.to_csv("Time_of_movements.csv") video.release() # Destroying all the windowscv2.destroyAllWindows()
Analysis of all windows After running the code there 4 new window will appear on screen. Let’s analyze it one by one: 
1. Gray Frame : In Gray frame the image is a bit blur and in grayscale we did so because, In gray pictures there is only one intensity value whereas in RGB(Red, Green and Blue) image there are three intensity values. So it would be easy to calculate the intensity difference in grayscale.  
2. Difference Frame : Difference frame shows the difference of intensities of first frame to the current frame. 
3. Threshold Frame : If the intensity difference for a particular pixel is more than 30(in my case) then that pixel will be white and if the difference is less than 30 that pixel will be black 
4. Color Frame : In this frame, you can see the color images in color frame along with green contour around the moving objects 
Time Record of movements
The Time_of_movements file will be stored in the folder where your code file is stored. This file will be in csv extension. In this file the start time of motion and the end time of motion will be recorded. As you can see in picture:  
Video Demonstration 
 
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