python_motion_detector.py

# Pyhton program to implement  
# WebCam Motion Detector 
  
# importing OpenCV, time and Pandas library 
import cv2, time, pandas 
# importing datetime class from datetime library 
from datetime import datetime 
  
# Assigning our static_back to None 
static_back = None
  
# List when any moving object appear 
motion_list = [ None, None ] 
  
# Time of movement 
time = [] 
  
# Initializing DataFrame, one column is start  
# time and other column is end time 
df = pandas.DataFrame(columns = ["Start", "End"]) 
  
# Capturing video 
video = cv2.VideoCapture(0) 
  
# Infinite while loop to treat stack of image as video 
while 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 arround 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 
    # intencity 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 DataFrame 
for 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 saved 
df.to_csv("Time_of_movements.csv") 
  
video.release() 
  
# Destroying all the windows 
cv2.destroyAllWindows()