The MagPi - July 2018

raspberrypi.org/magpi July 2018 35

Michael used the free

tier of Amazon’s object

storage service S3

(aws.amazon.com/s3),

which allows 2000 images

to be uploaded each month

TRADING CARD SCANNER/ORGANISER Projects

>STEP-01

Insert the cards

Load your cards into the LEGO device and run a

script on the Raspberry Pi to move the rear servos

and allow a single card to get into position. A front

wheel stops the other cards slipping.

>STEP-02

Snap an image

Once the card is in the scanning area, a Raspberry

Pi Camera Module – carefully angled and sitting

on a stack of bricks mere inches away – captures

the top part of the card. Stop the script.

>STEP-03

Performing the action

Drag and drop the scanned card files into Amazon

S3’s online interface via the Pi. Run another script to

get Rekognition to analyse the images and look up

the market price.

TRADING TIME

FOR CREATIVITY

Building it, however, was largely a
process of trial and error.
“I had a cheap card shuffler
lying around,” recalls Michael.
“After taking it apart, I found the
simple cog system powered by a
DC motor that moves the hammer
and pushes one card forward into a
slot, creating a new stack of cards.
This would be the inspiration for
the servo and wheels needed to
accomplish the automation.”

Box of bricks
Michael turned to LEGO for
the main structure due to its
versatility. It allowed him to easily

build, modify, and tear down his
project. He placed servos in the
back of the build and had them
spin continuously. Carefully
positioned LEGO tyres then move
forward in a cog-like setup to get
the cards into position.
Once the device was built and
he was happy, Michael could then
start coding, which he says was
the easiest part of the build. He
used Python 2.7 to program a
script to power both the servos
and take a picture via a Raspberry
Pi Camera Module. He then wrote

another script to send the images

to Amazon’s cloud computing web

service S3, for storage and to tackle

image processing.

“I originally tried Tesseract

and OpenCV for optical character

recognition, but I spent a lot

of time trying to get so many

variables perfect and I couldn’t get

consistency,” he explains. To fix

this, he turned to Amazon’s deep

learning-based image analyser,

Rekognition (magpi.cc/sfLJLE),

which extracts text and indexes a

collection. “It works well. I did not

have to worry about getting the

angle just right, making sure the

lighting was perfect, or performing

any machine learning – it worked

despite all of those factors.”

Apart from issues deciphering

some fonts, it performed well: 619

of the 920 cards scanned perfectly

and he was able to feed the data

through TCGPlayer.com’s price

data API to determine the value of

each card and, therefore, his MTG

collection. “I had about $275 worth

of commons, uncommons, and

rares,” he says, pleased as punch.

“And through trial and error, I also

learned a lot along the way.”

The idea was to make light

work of organising and valuing

even the largest of collections