freeRAM func

projects/Python/OpenCV_to_HTML/motion_detection.py

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+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))