Monitoring Freezer Temperature

25 Sep 2016 22:11

For some time we've had suspicions the freezer in our apartment doesn't work correctly. Recently my wife reached to it to get some ice cubes but found water in the form instead. That clearly meant the temperature was over the freezing point and stayed there for some time. She put a thermometer on the fridge, put the sensor into the freezer and started watching the readings. The temperature reached around -20°C and stayed there, fluctuating between -15°C and -20°C.

The webcam reader

I decided, it's time to record the temperature, so I brought the laptop, adjusted the screen angle, set the brightness to max and took a shot from the webcam to see if the thermometer is in the view of the camera:

mplayer tv:// -vo png -frames 3

This takes 3 frames from the webcam and saves them as PNG files: 00000001.png, 00000002.png, and 00000003.png. If you wonder why I need 3 frames, the first one is always under- or overexposed and out of focus, the second is usually OK and the third is almost always good (as for a webcam), so long story short, I'm giving some time to the camera to auto-adjust its settings.

Here's a script that does just a bit more than that:

#!/bin/bash

set -e

export DISPLAY=:0

curdate=`date +%x-%X | sed 's/[\.:]//g'`
outpng=/home/quake/git/thermonitor/out/"$curdate".png
tmppng=/home/quake/git/thermonitor/tmp/00000005.png

mkdir -p /home/quake/git/thermonitor/out/ /home/quake/git/thermonitor/tmp
cd /home/quake/git/thermonitor/tmp

# White background
ristretto ../white.png &

# Wake screen
xte "mousemove 100 100"
sleep 0.2
xte "mousemove 99 99"

# Dump screen
mplayer tv:// -vo png -frames 5 -noconsolecontrols
cp "$tmppng" "$outpng"
echo "$outpng"

kill $!

It's very hacky, but here are the big parts:

  • set -e makes the script stop if any of the commands in it fail
  • export DISPLAY=:0 selects the default X display, so I can run the command from an SSH shell
  • Then we have some hard-coded paths to tmp and out directories
  • ristretto ./white.png & starts a image-viewer and shows a white.png file which is a big all-white file. This makes the screen display enough white, so the LCD thermometer is properly lit
  • I use xte to move the mouse around: this blocks the automatic screen dimming, so the screen continues to be 100% bright
  • Then we have the mplayer command from before, just changed to 5 frames (just to be sure) and -noconsolecontrols. mplayer kind of hangs when you start it without a proper terminal on stdin, unless you pass this option
  • Then I copy the 5th frame to the output file, which has the current date and the time in its path
  • Finally I close the ristretto process I opened before

I run this command in a while loop like that:

while sleep 28 ; do  ./dup.sh ; done

./dup.sh takes around 2 seconds, so I have about 2 photos a minute.

I put that to a screen session.

Now in the out directory I serve the files using Python HTTP server:

python -m SimpleHTTPServer

This starts an HTTP server on port 8000 and serves all the files and creates an index of them when requesting /.

The processing

How easier to copy the HTTP-exposed files than using a plain-old wget command?

wget -r http://dell:8000/

This creates directory dell:8000 and downloads all the PNG files to it.

Once you have that directory you may later want to update it with only the newer files:

rm dell\:8000/index.html
wget -nc -r http://dell:8000/

The -nc switch makes wget ignore files that are already there. We explicitly remove the index.html so it downloads a new list (that includes newer files).

I wanted to make a program that gets a photo of the 7-segment display and reads it producing a number that along with the date could be used to graph the temperature over time.

First, let's use ImageMagick to:

  • crop the picture to only the interesting part: the OUT reading
  • shear the picture to make the LCD segments vertical and horizontal, not skewed
  • bump contrast/gamma to remove the noise
  • auto-adjust the visual properties of the picture so it's more consistent across different times (like night vs day)
  • make it black and white — turned out it works best if I only use the Green channel, R and B turned out more noisy than G.
  • resize to as small size as needed — for easier and faster processing

Here's the actual command:

convert $file_in -gamma 1.5 -auto-level -brightness-contrast 35x80 -shear -14x0 -crop 260x70+320+227 -channel Green -separate -resize 40x20 $file_out

Input image:

input.png

Output image:

output.png

Zoomed in:

output-scaled.png

You can see the bottom segment is not really visible, but that's OK, all the digits can be properly read without that segment being visible, so we'll only consider the 6 segments that we can easily read.

Now comes the meat, the Python program reading the file and returning the temperature:

#!/usr/bin/env python
#encoding: utf-8
 
from PIL import Image
 
# Points that hold each segment:
# d1 is the first digit
d1_sA = [(12,1), (13,1), (14,1), (15,1)]
d1_sB = [(18,2), (18,3), (18,4), (18,5)]
d1_sC = [(18,8), (18,9)]
d1_sE = [(11,8), (12,9), (11,9), (12,8)]
# Don't need this, as we only ever have 1 and 2 in the first place
d1_sF = []
d1_sG = [(13,7), (14,7), (15,7), (16,7)]
 
# d2 is the second digit
d2_sA = [(24,1), (25,1), (26,1), (27,1)]
d2_sB = [(29,2), (29,3), (29,4), (29,5)]
d2_sC = [(29,8), (29,9)]
d2_sE = [(22,8), (22,9)]
d2_sF = [(22,2), (22,3), (22,4), (22,5)]
d2_sG = [(24,6), (25,6), (26,6), (24,7), (25,7), (26,7)]
 
# d3 is the small digit (first after the decimal point)
d3_sA = [(35,4), (36,4), (37,4)]
d3_sB = [(38,5), (38,6), (38,7)]
d3_sC = [(38,9)]
d3_sE = [(34,9)]
d3_sF = [(34,5), (34,6), (34,7)]
d3_sG = [(35,8), (36,8)]
 
# Now the tricky part, for each segment I define a threshold below which I consider it "lit"
# 0 means completely black, 255 is white.
# Because of uneven lighting, for each segment (and digit, but we ignore that) the value is different.
tA = 200
tB = 170
tC = 120
tE = 115
tF = 140
tG = 170
 
# A threshold for the "-" sign
tSIGN = 200
# All of those were obviously updated on the go to match the files
 
# A nice debugging function that prints which segments the code considers lit
# Also if you wondered what A, B, C, E, F, G meant, here's the schematic:
 
def print_digit(segs):
    # Only print this if there's a second argument to the script passed
    if len(sys.argv) == 2:
        return
    print '''
                                                                                   {A}{A}
                                                                                   {A}{A}
                                                                                {F}      {B}
                                                                                {F}      {B}
                                                                                {F}      {B}
                                                                                   {G}{G}
                                                                                   {G}{G}
                                                                                {E}      {C}
                                                                                {E}      {C}
                                                                                {E}      {C}
'''.format(
    A='###' if 'A' in segs else '   ',
    B='###' if 'B' in segs else '   ',
    C='###' if 'C' in segs else '   ',
    E='###' if 'E' in segs else '   ',
    F='###' if 'F' in segs else '   ',
    G='###' if 'G' in segs else '   ',
    )
 
# This doesn't do anything spectacular, just passes the coordinates for each of the segments and the average value of the first 9 pixels of the image: (0,0) - (3,3)
 
def read_digit(im, pointsA, pointsB, pointsC, pointsE, pointsF, pointsG, avg9px):
     segs = ''
     segs += 'A' if read_segment(im, pointsA, tA-avg9px) else ''
     segs += 'B' if read_segment(im, pointsB, tB-avg9px) else ''
     segs += 'C' if read_segment(im, pointsC, tC-avg9px) else ''
     segs += 'E' if read_segment(im, pointsE, tE-avg9px) else ''
     segs += 'F' if read_segment(im, pointsF, tF-avg9px) else ''
     segs += 'G' if read_segment(im, pointsG, tG-avg9px) else ''
 
     print_digit(segs)
 
# A list of all digits and their representation on the 7-segment display:
 
     if segs == 'ABCEF':
         return 0
     if segs == 'BC':
         return 1
     if segs == 'ABEG':
         return 2
     if segs == 'ABCG':
         return 3
     if segs == 'BCFG':
         return 4
     if segs == 'ACFG':
         return 5
     if segs == 'ACEFG':
         return 6
     if segs == 'ABC':
         return 7
     if segs == 'ABCEFG':
         return 8
     if segs == 'ABCFG':
         return 9
 
# A special case for the first digit, it doesn't display "0", just doesn't display any segments
     if segs == '':
         return 0
 
# The function that takes the PIL image object, gets a list of (x,y) coordinates
# and checks if the average value of them is smaller than the threshold passed: segment "on"
# For 0 points passed it returns False: segment "off"
def read_segment(im, points, threshold=128):
    val = 0
    for point in points:
        val += im.getpixel(point)
    return val < threshold * len(points)
 
# Nothing interesting in here, just for printing the date from the file name
def get_date(file_name):
    time_str = file_name.split('-')[-1].replace('.png', '')
    d1, d2, m1, m2, y1, y2, y3, y4 = file_name.split('/')[-1].split('-')[0]
    return '{}{}/{}{}/{}{}{}{} '.format(m1, m2, d1, d2, y1, y2, y3, y4) + '{}{}:{}{}:{}{}'.format(*list(time_str))
 
# A bunch of imports in the middle of the file
# Don't do that at home ;-)
import sys
import subprocess
 
# This will be for example: pngs/dell\:8000/24092016-101055.png
file_in = sys.argv[1]
 
# And this: processed-pngs/dell\:8000/24092016-101055.png
file_out = 'processed-' + file_in
 
# Calling the ImageMagick as discussed in the article
subprocess.check_call(['convert', file_in, '-gamma', '1.5', '-auto-level', '-brightness-contrast', '35x80', '-shear', '-14x0', '-crop', '260x70+320+227', '-channel', 'Green', '-separate', '-resize', '40x20', file_out])
 
# Reading what it created
im=Image.open(file_out)
 
# Now a thing I added at some point later.
# Because of different lighting throughout the day and because the ImageMagick command
# above was not good compensating for it (in spite of auto-level and high contrast)
# some of the images were darker than the others. In most images (the ideal scenario for the code)
# the first 9 pixels of the image (0,0) to (3,3) were just white (or very close), but in those darker
# images the whole image was darker and I used the first 9 pixels to detect how much darker
first9px = im.getpixel((0,0)) + im.getpixel((0,1)) + im.getpixel((0,2)) \
         + im.getpixel((1,0)) + im.getpixel((1,1)) + im.getpixel((1,2)) \
         + im.getpixel((2,0)) + im.getpixel((2,1)) + im.getpixel((2,2))
 
# This is the compensation, for most of the images it's 0 or very few, but for darker images, it's more
avg9px = 255-first9px/9
 
num = '{}{}{}.{}'.format(
    '-' if read_segment(im, [(2,6), (3,6), (4,6)], tSIGN-avg9px) else '+',
    read_digit(im, d1_sA, d1_sB, d1_sC, d1_sE, d1_sF, d1_sG, avg9px),
    read_digit(im, d2_sA, d2_sB, d2_sC, d2_sE, d2_sF, d2_sG, avg9px),
    read_digit(im, d3_sA, d3_sB, d3_sC, d3_sE, d3_sF, d3_sG, avg9px),
)
 
if 'None' not in num:
    print get_date(file_in) + '\t' + num
 
# If there's a second argument to the script passed, show the original image for comparison
if len(sys.argv) > 2:
    Image.open(file_in).show()

Even though this script is so simple, after tweaking the thresholds, most of the files were recognized correctly, those that weren't had one or more non-recognized digits, so they were easy to filter out. For over 1-day worth of images, only 2 or 3 minutes had a missing reading.

I loaded the data to LibreOffice and generated this pretty graph:

graph.jpg

Timelapse video

Another approach visualizing the data was to create a video.

The plan:

  • Annotate the images with the recorded time
  • Compose the video from single frames, putting 60 frames per a second of the resulting video

60 frames a second with roughly 2 frames captured a minutes means a day of recording is compressed to:

2 frames a minute * 60 minutes an hour * 24 hours a day = 2880 frames
2880 frames / 60 frames a second = 48 seconds

This makes is "viewable". 60 FPS (versus 30 FPS at higher speed) means you can pause at any time and read the crisp time and temperature.

Here's the annotate part:

#!/usr/bin/env python
 
import sys
import subprocess
 
path_in = sys.argv[1]
path_out = sys.argv[2]
date, time = path_in.replace('.png', '').split('/')[-1].split('-')
 
D1, D2, M1, M2, Y1, Y2, Y3, Y4 = date
h1, h2, m1, m2, s1, s2 = time
 
label = '{}{}/{}{}/{}{}{}{} {}{}:{}{}:{}{}\\n'.format(M1, M2, D1, D2, Y1, Y2, Y3, Y4, h1, h2, m1, m2, s1, s2)
 
subprocess.check_call([
    'convert', path_in,
    '-gravity', 'south',
    '-pointsize', '45',
    '-font', 'FreeMono',
    '-annotate', '0', label,
    '-fill', 'black',
    path_out
    ])

Here's the result:

annotated.png

Running this in a loop, 16 images at a time:

#!/bin/bash

i=0
for file_in in dell*/*.png; do
  file_out="`printf "labeled/%06d.png" $i`"
  echo ./add-labels.py "$file_in" "$file_out"
  i=$((i+1))
done | parallel -j 16

In addition to annotatig the image it also names them as 000001.png, 000002.png etc, which makes it easy for avconv to convert them to a video:

avconv -fflags +genpts -r 60 -i labeled/%06d.png -r 60 temperature.mkv

And here's the video:

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