Coolometer

A device, inspired by Futurama, to measure your coolness with a "real" AI.

Components

This are the components we used. Alternatives might work also

• Raspberry PI Model 3 B+
• Raspberry PI Camera
• Micro Servo SG90
• MAX 4218CNG LED Matrix
• L9110 Motor Driver
• DC Motor 3V 16500 RPM 0.35A 130 Type
• Pizzo Speaker
• 3.5 mm Headphone Jack
• LY591-4 speaker
• PAM8403 Mini Amplifier
• Button
• 5V 2.1 A PowerBank for Power supply

Wiring

Raspberry PI Model 3 B+ has enough ground Pins for all parts. Only one 5V pin has to be shared by the LED Matrix and the Servo motor.

Micro Servo SG90

One 5V shared with MAX 4218CNG LED Matrix
Data on GPIO 17 / PIN 11

MAX 4218CNG LED Matrix

One 5V shared with MAX 4218CNG LED Matrix
DIN on GPIO 10 / PIN 19
CS on GPIO 8 / PIN 24
CLK on GPIO 11 / PIN 23

L9110 Motor Driver

VCC on 3.3
IA1 on GPIO 26 / PIN 37
IB1 on GPIO 20 / PIN 38

Pizzo Speaker

VCC on 3.3
GND on GPIO 36 / PIN 16

Headphone Jack / Amplifier

The Headphone Jack uses the Audio Out from the Raspberry PI.
Solder the longest PIN of the Jack to the "I" PIN of the Amplifier Board Solder the second longest PIN longest PIN of the Jack to the "R" PIN of the Amplifier Board. We only use one Channel here.

Solder the R OUT PINs the the speaker.

Use one 5V Pin from the Raspberry PI to power the Amplifier Board

Button

VCC on 3.3
GND to GPIO 18 / PIN 12

Camera

The camera is connected on the Raspberry Camera slot.

Install

Set up a Raspberry PI with Raspberry Pi OS Lite Enable SSH and connect via SSH to the Raspberry Update System ( sudo apt full-upgrade) Run sudo raspi-config:

• ADVANCED -> Resize File System
• SYSTEM -> AUDIO -> Headphone Jack
• Interface -> Camera -> ON
• Interface -> SPI -> ON

Set System volume with

sudo amixer -q -M sset Headphone 100%

Install depenendices:

sudo apt-get install python3-pip python3-venv python3-pygame 
libfreetype6-dev libjpeg-dev libopenjp2-7 libtiff5 libjpeg-dev libtiff5-dev libjasper-dev 
libpng12-dev libavcodec-dev libavformat-dev libswscale-dev 
libv4l-dev libxvidcore-dev libx264-dev qt4-dev-tools libatlas-base-dev 
libilmbase-dev libopenexr-dev libgstreamer1.0-dev libgirepository1.0-dev libcairo2-dev

Install Python dependencies:

python3 -m pip install --user --upgrade pip
python3 -m pip install --user virtualenv
python3 -m venv env
source env/bin/activate
pip3 install https://dl.google.com/coral/python/tflite_runtime-2.1.0.post1-cp37-cp37m-linux_armv7l.whl
pip install -r requirements.txt

Create Autostart:

sudo nano /etc/rc.local 

add:

sudo sh /home/pi/CoolOMeter/run.sh &

Now Reboot the system. When booting is finished, you will hear 2 short noises from the speaker. You Coolometer should now work.

3D Print and Lasercut files

The Files are Available in the CAD-Zeichnungen folder.

Usage

After you power your raspberry, it can take about 40 seconds to boot. When the device is ready, the pizzo will make noise for a short time and the clockhand will go to zero.

Now press the button.

The clockhand will go to 50% and the camera starts to search for a human face.

If the face is detected the clockhand will go to your coolness value and the pizzo will make noise according to the coolness. If your coolness is 90% or higher, the thumb will come out and you will hear an "OH Yeah"

If your press the button again you will start from the beginning.

HAVE FUN :)

Train your own tensorflow lite model

The files contains our trained tensorflow lite model. The train data are our little secret. If you are not happy with it, here is a short description how to make your own model.

On your host machine with Linux, Python3 and pip:

pip3 install tensorflow
pip3 install tensorflow-hub[make_image_classifier]

Create a folder structure like the following one. And put picture into the cool / lame folder. Only JPG Files are allowed. Base Folder

• coolometer (Folder)
  • cool (Folder)
    • img.jpg ...
  • lame (Folder)
    • img.jpg ..
• model (Folder)

In the Base folder call this command:

make_image_classifier   
--image_dir coolometer   
--tfhub_module https://tfhub.dev/google/tf2-preview/mobilenet_v2/feature_vector/4 
--image_size 224 
--saved_model_dir model/coolModel 
--batch_size 20  
--labels_output_file class_labels.txt 
--tflite_output_file tf_model_file.tflite

You can use other models if you exchange the tfhub_module link to something else from the tensorflow hub . Make sure you adjuste the image_size to your choosen model.
In your base folder should now be a class_labels.txt and a tf_model_file.tflite file. Copy this to your Raspberry into the Coolometer folder.

If you have a new model, make sure to share it with us by open a pull request with your model data.

Credits

Big thanks to the futurama creators comming up with this great Idea. The OhYeah sound is from here: https://www.youtube.com/watch?v=c4c-egnkdLI