Background Averaging (Background Subtraction) in Python+OpenCV

backgroundAveraging.py

import numpy as np
import cv2


class BackGroundSubtractor:
	# When constructing background subtractor, we
	# take in two arguments:
	# 1) alpha: The background learning factor, its value should
	# be between 0 and 1. The higher the value, the more quickly
	# your program learns the changes in the background. Therefore, 
	# for a static background use a lower value, like 0.001. But if 
	# your background has moving trees and stuff, use a higher value,
	# maybe start with 0.01.
	# 2) firstFrame: This is the first frame from the video/webcam.
	def __init__(self,alpha,firstFrame):
		self.alpha  = alpha
		self.backGroundModel = firstFrame

	def getForeground(self,frame):
		# apply the background averaging formula:
		# NEW_BACKGROUND = CURRENT_FRAME * ALPHA + OLD_BACKGROUND * (1 - APLHA)
		self.backGroundModel =  frame * self.alpha + self.backGroundModel * (1 - self.alpha)

		# after the previous operation, the dtype of
		# self.backGroundModel will be changed to a float type
		# therefore we do not pass it to cv2.absdiff directly,
		# instead we acquire a copy of it in the uint8 dtype
		# and pass that to absdiff.

		return cv2.absdiff(self.backGroundModel.astype(np.uint8),frame)

cam = cv2.VideoCapture(0)

# Just a simple function to perform
# some filtering before any further processing.
def denoise(frame):
    frame = cv2.medianBlur(frame,5)
    frame = cv2.GaussianBlur(frame,(5,5),0)
    
    return frame

ret,frame = cam.read()
if ret is True:
	backSubtractor = BackGroundSubtractor(0.01,denoise(frame))
	run = True
else:
	run = False

while(run):
	# Read a frame from the camera
	ret,frame = cam.read()

	# If the frame was properly read.
	if ret is True:
		# Show the filtered image
		cv2.imshow('input',denoise(frame))

		# get the foreground
		foreGround = backSubtractor.getForeground(denoise(frame))

		# Apply thresholding on the background and display the resulting mask
		ret, mask = cv2.threshold(foreGround, 15, 255, cv2.THRESH_BINARY)

		# Note: The mask is displayed as a RGB image, you can
		# display a grayscale image by converting 'foreGround' to
		# a grayscale before applying the threshold.
		cv2.imshow('mask',mask)

		key = cv2.waitKey(10) & 0xFF
	else:
		break

	if key == 27:
		break

cam.release()
cv2.destroyAllWindows()

Works great on Raspberry pi too (py2cv3)

whats the difference between this and cv2.createBackgroundSubtractorMOG2()

@elhoussinetalab I think the difference is you can set the learning rate for static objects too, which you cant in cv2.createBackgroundSubtractorMOG2()