motiondetector.py

import cv2

class MotionDetector:
    def __init__(self, accumWeight=0.7):
        #store the accumulated weight factor
        self.accumWeight = accumWeight

        #initialize the background model
        self.bg = None

    def update(self, image):
        # if the background model is None, initialize it
        if self.bg is None:
            self.bg = image.copy().astype("float")
            return

        # update the background model by accumulating the weighted average
        cv2.accumulateWeighted(image, self.bg, self.accumWeight)

    def detect(self, image, tVal=25):
        # compute the absolute difference between the background model and the image
        # passed in, then threshold the delta image
        kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (7, 7))
        delta = cv2.absdiff(self.bg.astype("uint8"), image)
        # thresh = cv2.adaptiveThreshold(delta, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, 25, 15)
        # thresh = cv2.threshold(delta, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
        thresh = cv2.threshold(delta, tVal, 255, cv2.THRESH_BINARY)[1]
        thresh = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)

        # find contours in the thresholded image
        (_, cnts, _) = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

        # if no contours were found, return None
        if len(cnts) == 0:
            return None

        # otherwise, return a tuple of the thresholded image along with the contour area
        return (thresh, max(cnts, key=cv2.contourArea))