Using Raspberry Pi camera (NOIR) and the power of Python and OpenCV TO BUILD A HOME SURVEILLANCE CAMERA – PART 3 OF 4 – USING OpenCV motion detection capability to trigger video recording, no need PIR sensor

from picamera.array import PiRGBArray # Generates a 3D RGB array
from picamera import PiCamera # Provides a Python interface for the RPi Camera Module
import time 
from time import sleep
import datetime
import cv2 
import numpy as np 
 
# Initialize the camera
camera = PiCamera()

camera.rotation = -90
 
# Set the camera resolution
camera.resolution = (640, 480)
#camera.resolution = (1280, 720)
#camera.resolution = (1640, 922)
#camera.resolution = (3280, 2464)
 
# Set the number of frames per second
#Resolution and frame rate affect FOV

#camera.framerate = 30
#camera.framerate = 60
camera.framerate = 30

#Generates a 3D RGB array from camera object(source) 
#and put into raw_capture
raw_capture = PiRGBArray(camera, size=(640, 480))
#raw_capture = PiRGBArray(camera, size=(1280, 720))
#raw_capture = PiRGBArray(camera, size=(1640, 922))
#raw_capture = PiRGBArray(camera, size=(3280, 2464))
 
# Create the background object
# Parameters set to default values if leave blank
# MOG2 comes with detectShadows parameter, MOG does not.
#back_sub = cv2.createBackgroundSubtractorMOG2(history=150,
  #varThreshold=25, detectShadows=True)
back_sub = cv2.createBackgroundSubtractorMOG2(history=25,
  varThreshold=20, detectShadows=True)
 
# Wait a certain number of seconds to allow the camera time to warmup
time.sleep(0.1)

# Create kernel for morphological operation. You can tweak
# the dimensions of the kernel.
# e.g. instead of 20, 20, you can try 30, 30
# This is like a template running through fg_mask and smoothen the edges
kernel = np.ones((20,20),np.uint8)
 
# Capture frames continuously from the camera
for frame in camera.capture_continuous(raw_capture, format="bgr", use_video_port=True):
     
    # Grab the raw NumPy array representing the image
    image = frame.array
 
    # Get the foreground mask
    fg_mask = back_sub.apply(image)
     
    # Perform morphological operation (function cv2.morphologyEx aka Closing), 
	# which is a combination of Dilation followed by Erosion.
	# Closing closes any small holes inside fg_mask, or remove any small black points in 
	# fg_mask. Closing is opposite of Opening (Erosion followed by Dilation)
    fg_mask = cv2.morphologyEx(fg_mask,cv2.MORPH_CLOSE,kernel)

# Remove the unnessary details (salt n pepper noise),we need the outline only
    fg_mask = cv2.medianBlur(fg_mask,5)
       
     # If a pixel is less than 127, it is considered black (background)
	# and set to 0, otherwise, it is white (foreground). Set to 255.
    # Modify the number after fg_mask as you see fit.
	#4th parameter is one of the type of threshold which is : if less than
	# 127 set to black else set to white
# ret usually in front is not neccessary as we do not require a return.
    _, fg_mask = cv2.threshold(fg_mask, 127, 255, cv2.THRESH_BINARY)
 
    # Find the contours of the object inside the binary image
    contours, hierarchy = cv2.findContours(fg_mask,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)[-2:]
    areas = [cv2.contourArea(c) for c in contours]
 
    # If there are no countours
    if len(areas) < 1:
 
      # Display the resulting frame
      cv2.imshow('Frame',image)

#Record.....
    #while w > 500 and h > 500:    #doesn't work
    while h > 200:
        date = datetime.datetime.now().strftime("%d-%m-%Y_%H-%M-%S")
        camera.start_recording('/mnt/mydisk/'+date+'.h264')
        sleep(5)
        camera.stop_recording()
        break

 
      # Wait for keyPress for 1 millisecond
      key = cv2.waitKey(1) & 0xFF

# Clear the stream in preparation for the next frame
      raw_capture.truncate(0)
     
      # If "q" is pressed on the keyboard,
      # exit this loop
      if key == ord("q"):
        break
     
      # Go to the top of the for loop
      continue
 
    else:
         
      # Find the largest moving object in the image
      max_index = np.argmax(areas)
       
    # Draw the bounding box
    cnt = contours[max_index]
    x,y,w,h = cv2.boundingRect(cnt)
    cv2.rectangle(image,(x,y),(x+w,y+h),(0,255,0),2)
break
       
    # Wait for keyPress for 1 millisecond
    key = cv2.waitKey(1) & 0xFF
 
    # Clear the stream in preparation for the next frame
    raw_capture.truncate(0)
     
    # If "q" is pressed on the keyboard,
    # exit this loop
    if key == ord("q"):
      break
 
# Close down windows
cv2.destroyAllWindows()