import numpy as np
import imutils
import cv2


class SingleMotionDetector:
    def __init__(self, accumWeight=0.5):
        # 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
        delta = cv2.absdiff(self.bg.astype('uint8'), image)
        thresh = cv2.threshold(delta, tVal, 255, cv2.THRESH_BINARY)[1]
        # perform a series of erosions and dilations to remove small blobs
        thresh = cv2.erode(thresh, None, iterations=2)
        thresh = cv2.dilate(thresh, None, iterations=2)

        # find contours in the thresholded image and initialize
        # the minimum and maximum bounding box regions for motion
        cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
        cnts = imutils.grab_contours(cnts)
        (minX, minY) = (np.inf, np.inf)
        (maxX, maxY) = (-np.inf, -np.inf)

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

        # otherwise, loop over the contours
        for c in cnts:
            # compute the bounding box of the contour and use it to
            # update the minimum and maximum bounding box regions
            (x, y, w, h) = cv2.boundingRect(c)
            (minX, minY) = (min(minX, x), min(minY, y))
            (maxX, maxY) = (max(maxX, x + w), max(maxY, y + h))
        # otherwise, return a tuple of the thresholded image along with bounding box
        return (thresh, (minX, minY, maxX, maxY))