Motions sensitive imaging using a Raspberry Pi

One of the interesting things one can do, in this case using a Raspberry Pi and an infrared camera is to take motion-activatedpictures. It's possible to do video too and stills but for this example it's motion-sensitive images.

The GIF shown is made of 20 separate images as a hand was moved in front of the camera. This is definitely not a video. The code to do this is shown below and was written by somebody from MIT.

The original code has been modified to display on the screen, the filename used. Just to avoid confusion, the images are stored in a desktop folder called "NAS". 

This does show rather the limitations of the Raspberry Pi in that between each photo it really does need 2 -3 seconds to read the brightness of a scene. Having said that, the Pi is an excellent little computer that can be used to develop proof of concept applications.

Something to have fun with.

#! /usr/bin/env python3

#
#The MIT License (MIT)
#Copyright (c) 2014 Ron Ostafichuk
#
#Permission is hereby granted, free of charge, to any person obtaining a copy
#of this software and associated documentation files (the "Software"), to deal
#in the Software without restriction, including without limitation the rights
#to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
#copies of the Software, and to permit persons to whom the Software is
#furnished to do so, subject to the following conditions:
#
#The above copyright notice and this permission notice shall be included in all
#copies or substantial portions of the Software.
#
#THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
#IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
#FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
#AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
#LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
#OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
#SOFTWARE.


import io
import os
import time
import picamera
import numpy as np


widthH = 2592 # too slow for motion check, only use for the save sequence 
heightH = 1944
width = 1440 # use lower resolution for motion check
height = 1080


threshold = 30 # how much must the color value (0-255) change to be considered a change
minPixelsChanged = width * height * 2 / 100 # % change  # how many pixels must change to begin a save sequence
print("minPixelsChanged=",minPixelsChanged) # debug


print ('Creating in-memory stream')
stream = io.BytesIO()
step = 1  # use this to toggle where the image gets saved
numImages = 1 # count number of images processed
captureCount = 0 # flag used to begin a sequence capture


# function to generate a sequence of 20 filenames for the picamera capture_sequence command to use
def filenames():
    frame = 0
    fileName = time.strftime("NAS/%Y%m%d/%Y%m%d-%H%M%S-",time.localtime())
    while frame < 20:
        yield '%s%02d.jpg' % (fileName, frame)
        frame += 1


# begin monitoring
with picamera.PiCamera() as camera:
    time.sleep(1) # let camera warm up
    try:
        while threshold > 0:
            camera.resolution = (1440,1080) # use a smaller resolution for higher speed compare


            print ('Capture ' , numImages)
            if step == 1:
                stream.seek(0)
                camera.capture(stream, 'rgba',True) # use video port for high speed
                data1 = np.fromstring(stream.getvalue(), dtype=np.uint8)
                step = 2
            else:
                stream.seek(0)
                camera.capture(stream, 'rgba',True)
                data2 = np.fromstring(stream.getvalue(), dtype=np.uint8)
                step = 1
            numImages = numImages + 1


            if numImages > 4:  # ignore first few images because if the camera is not quite ready it will register as motion right away
                # look for motion unless we are in save mode
                if captureCount <= 0:
                    print("Compare")
                    # not capturing, test for motion (very simplistic, but works good enough for my purposes)
                    data3 = np.abs(data1 - data2)  # get difference between 2 successive images
                    numTriggers = np.count_nonzero(data3 > threshold) / 4 / threshold #there are 4 times the number of pixels due to rgba


                    print("Trigger cnt=",numTriggers)


                    if numTriggers > minPixelsChanged:
                        captureCount = 1 # capture ? sequences in a row
                        # make sure directory exists for today
                        d = time.strftime("NAS/%Y%m%d") #unfortunately this saves as UTC time instead of local, will fix it later sorry
                        if not os.path.exists(d):
                            os.makedirs(d)


                if captureCount > 0:
            # in capture mode, save an image in hi res
                    camera.resolution = (widthH,heightH)
                    dtFileStr = time.strftime("NAS/%Y%m%d/%Y%m%d-%H%M%S-00.jpg",time.localtime()) # once again, UTC time instead of local time
                    print("Saving sequence ",dtFileStr)
                    # save full resolution images to the NAS
                    camera.capture_sequence(filenames(),'jpeg',use_video_port=True, quality=92)
                    captureCount = captureCount-1
                    print(filenames())


    finally:
        camera.close()
        print ('Program Terminated')