Skip to content

Instantly share code, notes, and snippets.

Last active February 12, 2023 03:47
Show Gist options
  • Save TheSalarKhan/7c3d01ad13b0e7e5985a to your computer and use it in GitHub Desktop.
Save TheSalarKhan/7c3d01ad13b0e7e5985a to your computer and use it in GitHub Desktop.
Background Averaging (Background Subtraction) in Python+OpenCV
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:
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 =
if ret is True:
backSubtractor = BackGroundSubtractor(0.01,denoise(frame))
run = True
run = False
# Read a frame from the camera
ret,frame =
# If the frame was properly read.
if ret is True:
# Show the filtered image
# 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.
key = cv2.waitKey(10) & 0xFF
if key == 27:
Copy link

mrlunk commented Jun 4, 2017

Works great on Raspberry pi too (py2cv3)

Copy link

whats the difference between this and cv2.createBackgroundSubtractorMOG2()

Copy link

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

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment