Motion-Detection in Python on Raspberry Pi

#!/usr/bin/python

# Simple motion-detection using the picamera library.
# Runs at up to 10 fps depending on resolution, etc.
# This is demonstration / proof-of-concept code.
# It probably isn't useful in a real application.
# Nov. 30 2013 J.Beale

from __future__ import print_function
import io, os, time, datetime, picamera, cv2
import numpy as np

#width = 32
#height = 16 # minimum size that works ?
width =144
height = 96
frames = 0
first_frame = True
frac = 0.05  # fraction to update long-term average on each pass
a_thresh = 16.0  # amplitude change detection threshold for any pixel
pc_thresh = 20   # number of pixels required to exceed threshold
avgmax = 3     # long-term average of maximum-pixel-change-value
tfactor = 2  # threshold above max.average diff per frame for motion detect
picHoldoff = 1.0  # minimum interval (seconds) between saving images
fupdate = 10000   # report debug data every this many frames
#fupdate = 10   # report debug data every this many frames
logfile = "/home/pi/pics/cam-log.csv"

np.set_printoptions(precision=2)
f = open(logfile, 'a')
f.write ("# cam log v0.1 Nov.28 2013 JPB\n")
outbuf = "# Start: " +  str(datetime.datetime.now()) + "\n"
f.write (outbuf)
f.flush()

daytime = datetime.datetime.now().strftime("%y%m%d-%H_%M_%S.%f")
daytime = daytime[:-3]  # remove last three digits (xxx microseconds)
print ("# Start at %s" % str(datetime.datetime.now()))

stream = io.BytesIO()
with picamera.PiCamera() as camera:
    camera.resolution = (width, height)
    camera.start_preview()
    time.sleep(2)
    start = time.time()
    while True:
      camera.capture(stream, format='jpeg', use_video_port=True)
#      time.sleep(0.15)  # sometimes delay needed to avoid crashes (!?)
      stream.seek(0)
      data = np.fromstring(stream.getvalue(), dtype=np.uint8)
      image = cv2.imdecode(data, 1)
      image = image.astype(np.float32)
      (h,w,cols) = image.shape
      (xc,yc) = (h/2,w/2)
      frames = frames + 1
      if (frames % fupdate) == 0:
        print ("%s,  %03d max = %5.3f, avg = %5.3f" % (str(datetime.datetime.now()),frames,max,avgmax))
#       print (avgcol); print (avgdiff)
#       print "%02d center: %s (BGR)" % (frames,image[xc,yc])
      if first_frame == False:
        newcol = image[:,:,1] # full image, green only
        avgcol = (avgcol * (1.0-frac)) + (newcol * frac)
        diff = newcol - avgcol  # matrix of difference-from-average pixel values
        diff = abs(diff)   # take absolute value of difference
        avgdiff = ((1 - frac)*avgdiff) + (frac * diff)  # long-term average difference
        a_thresh = tfactor*avgmax  # adaptive amplitude-of-change threshold
        condition = diff > a_thresh
        changedPixels = np.extract(condition,diff)
        countPixels = changedPixels.size
        max = np.amax(diff)     # find the biggest (single-pixel) change
        avgmax = ((1 - frac)*avgmax) + (max * frac)
        if countPixels > pc_thresh:  # notable change of enough pixels => motion!
          now = time.time()
          interval = now - start
          start = now
          daytime = datetime.datetime.now().strftime("%y%m%d-%H_%M_%S.%f")
          daytime = daytime[:-3]  # remove last three digits (xxx microseconds)
          daytime = daytime + "_" + str(countPixels)
          tstr = ("%s,  %04.1f, %6.3f, %03d\n" % (daytime,max,interval,countPixels))
          print (tstr, end='')
          f.write(tstr)
          f.flush()
          if (interval > picHoldoff):  # don't write images more quickly than picHoldoff interval
            imgName = daytime + ".jpg"
            cv2.imwrite(imgName, image)  # save as an image
      else:         # very first frame
        avgcol = image[:,:,1]  # green channel, set the initial average to first frame
        avgdiff = avgcol / 20.0 # obviously a coarse estimate
        first_frame = False