Background subtraction is any technique that allows an image's foreground to be extracted for further processing Many applications do not need to know everything about the evolution of movement in a video sequence, but only require the information of changes in the scene, because an image's regions of interest are objects(humans, cars, text, etc) in its foreground. After the stage of image processing(which may include image denoising , post-processing like morphology, etc.) object localization is required which may make use of this technique So let's create this most valuable project with python so easily. Modules: OpenCV Numpy Code: import numpy as np import cv2 cap = cv2 . VideoCapture ( 'walking.avi' ) # Initlaize background subtractor foreground_background = cv2 . createBackgroundSubtractorMOG2 () while True : ret , frame = cap .read() # Apply background subtractor to get our foreground mask foreground_mask ...
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