text: import cv2
  import imutils
  import numpy as np
  
  class MotionDetector():
      def __init__(self, frame_shape, mask, resize_factor=4):
          self.resize_factor = resize_factor
          self.motion_frame_size = (int(frame_shape[0]/resize_factor), int(frame_shape[1]/resize_factor))
          self.avg_frame = np.zeros(self.motion_frame_size, np.float)
          self.avg_delta = np.zeros(self.motion_frame_size, np.float)
          self.motion_frame_count = 0
          self.frame_counter = 0
          resized_mask = cv2.resize(mask, dsize=(self.motion_frame_size[1], self.motion_frame_size[0]), interpolation=cv2.INTER_LINEAR)
          self.mask = np.where(resized_mask==[0])
  
      def detect(self, frame):
          motion_boxes = []
  
          # resize frame
          resized_frame = cv2.resize(frame, dsize=(self.motion_frame_size[1], self.motion_frame_size[0]), interpolation=cv2.INTER_LINEAR)
  
          # convert to grayscale
          gray = cv2.cvtColor(resized_frame, cv2.COLOR_BGR2GRAY)
  
          # mask frame
          gray[self.mask] = [255]
  
          # it takes ~30 frames to establish a baseline
          # dont bother looking for motion
          if self.frame_counter < 30:
              self.frame_counter += 1
          else:
              # compare to average
              frameDelta = cv2.absdiff(gray, cv2.convertScaleAbs(self.avg_frame))
  
              # compute the average delta over the past few frames
              # the alpha value can be modified to configure how sensitive the motion detection is.
              # higher values mean the current frame impacts the delta a lot, and a single raindrop may
              # register as motion, too low and a fast moving person wont be detected as motion
              # this also assumes that a person is in the same location across more than a single frame
              cv2.accumulateWeighted(frameDelta, self.avg_delta, 0.2)
  
              # compute the threshold image for the current frame
              current_thresh = cv2.threshold(frameDelta, 25, 255, cv2.THRESH_BINARY)[1]
  
              # black out everything in the avg_delta where there isnt motion in the current frame
              avg_delta_image = cv2.convertScaleAbs(self.avg_delta)
              avg_delta_image[np.where(current_thresh==[0])] = [0]
  
              # then look for deltas above the threshold, but only in areas where there is a delta
              # in the current frame. this prevents deltas from previous frames from being included
              thresh = cv2.threshold(avg_delta_image, 25, 255, cv2.THRESH_BINARY)[1]
  
              # dilate the thresholded image to fill in holes, then find contours
              # on thresholded image
              thresh = cv2.dilate(thresh, None, iterations=2)
              cnts = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
              cnts = imutils.grab_contours(cnts)
  
              # loop over the contours
              for c in cnts:
                  # if the contour is big enough, count it as motion
                  contour_area = cv2.contourArea(c)
                  if contour_area > 100:
                      x, y, w, h = cv2.boundingRect(c)
                      motion_boxes.append((x*self.resize_factor, y*self.resize_factor, (x+w)*self.resize_factor, (y+h)*self.resize_factor))
          
          if len(motion_boxes) > 0:
              self.motion_frame_count += 1
              # TODO: this really depends on FPS
              if self.motion_frame_count >= 10:
                  # only average in the current frame if the difference persists for at least 3 frames
                  cv2.accumulateWeighted(gray, self.avg_frame, 0.2)
          else:
              # when no motion, just keep averaging the frames together
              cv2.accumulateWeighted(gray, self.avg_frame, 0.2)
              self.motion_frame_count = 0
  
          return motion_boxes