52 lines
2.0 KiB
Python
52 lines
2.0 KiB
Python
import numpy as np
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import imutils
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import cv2
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class SingleMotionDetector:
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def __init__(self, accumWeight=0.5):
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# store the accumulated weight factor
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self.accumWeight = accumWeight
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# initialize the background model
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self.bg = None
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def update(self, image):
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# if the background model is None, initialize it
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if self.bg is None:
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self.bg = image.copy().astype('float')
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return
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# update the background model by accumulating the weighted
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# average
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cv2.accumulateWeighted(image, self.bg, self.accumWeight)
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def detect(self, image, tVal=25):
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# compute the absolute difference between the background model
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# and the image passed in, then threshold the delta image
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delta = cv2.absdiff(self.bg.astype('uint8'), image)
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thresh = cv2.threshold(delta, tVal, 255, cv2.THRESH_BINARY)[1]
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# perform a series of erosions and dilations to remove small blobs
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thresh = cv2.erode(thresh, None, iterations=2)
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thresh = cv2.dilate(thresh, None, iterations=2)
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# find contours in the thresholded image and initialize
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# the minimum and maximum bounding box regions for motion
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cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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cnts = imutils.grab_contours(cnts)
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(minX, minY) = (np.inf, np.inf)
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(maxX, maxY) = (-np.inf, -np.inf)
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# if no contours were found, return None
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if len(cnts) == 0:
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return None
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# otherwise, loop over the contours
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for c in cnts:
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# compute the bounding box of the contour and use it to
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# update the minimum and maximum bounding box regions
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(x, y, w, h) = cv2.boundingRect(c)
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(minX, minY) = (min(minX, x), min(minY, y))
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(maxX, maxY) = (max(maxX, x + w), max(maxY, y + h))
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# otherwise, return a tuple of the thresholded image along with bounding box
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return (thresh, (minX, minY, maxX, maxY))
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