Difference between revisions of "UBISS2024"

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== Task 3: ML on Arduino Nano Connect RP2040 ==
 
== Task 3: ML on Arduino Nano Connect RP2040 ==
 
We will use https://github.com/eloquentarduino/everywhereml to detect the same gestures as in 2.2. For this, install everywhereml:
 
We will use https://github.com/eloquentarduino/everywhereml to detect the same gestures as in 2.2. For this, install everywhereml:
 +
<syntaxhighlight lang="Bash">
 +
pip3 install -U everywhere
 +
</syntaxhighlight>
  
−
<syntaxhighlight lang="Bash">
+
Using everywhereml we can train a model on a more powerful machine for deployment on a microcontroller. See https://eloquentarduino.com/posts/micropython-machine-learning for example for such a training process. Assuming that our ML model is trained and stored in variable clf then we can save the model to a file using
−
pip3 install -U everywhereml
+
<syntaxhighlight lang="python">
 +
clf.to_micropython_file("MyModel.py")
 +
</syntaxhighlight>
 +
 
 +
The MyModel.py file can then be saved and called directly on the microcontroller. To run the model on the microcontroller, assume your data is stored in x and you trained a RandomForestClassifier. Then you can predict via the following code snippet
 +
<syntaxhighlight lang="python">
 +
import MyModel
 +
clf = MyModel.RandomForestClassifier()
 +
clf.predict(x)
 
</syntaxhighlight>
 
</syntaxhighlight>
  

Revision as of 09:40, 10 June 2024

Link Page

https://www.sketching-with-hardware.org/wiki/UBISS2024-Links

Tasks

Project 0: connect a Arduino Nano ESP32 board

solution Project 0: LED Blinking

 1 # Blinky example
 2 
 3 import time
 4 from machine import Pin
 5 
 6 # This is the only LED pin available on the Nano RP2040,
 7 # other than the RGB LED connected to Nano WiFi module.
 8 led = Pin(6, Pin.OUT)
 9 
10 while (True):
11    led.on()
12    time.sleep_ms(250)
13    led.off()
14    time.sleep_ms(200)

solution Project 0: Control external RGB

 1 # RGB example
 2 
 3 import time
 4 from machine import Pin
 5 
 6 # RGB LED connected to Nano WiFi module.
 7 ledG = Pin(2, Pin.OUT)
 8 ledR = Pin(3, Pin.OUT)
 9 ledB = Pin(4, Pin.OUT)
10 print("start")
11 
12 while (True):
13     print("*")
14     ledG.on()
15     ledR.off()
16     ledB.off()
17     time.sleep_ms(250)
18     ledG.off()
19     ledR.on()
20     ledB.off()
21     time.sleep_ms(250)
22     ledG.off()
23     ledR.off()
24     ledB.on()
25     time.sleep_ms(250)

Project 1: read Acceleration from Arduino Nano ESP32 board

solution Project 1: Read Accelerometer and Gyro

 1 import time
 2 from lsm6dsox import LSM6DSOX
 3 
 4 from machine import Pin, I2C
 5 lsm = LSM6DSOX(I2C(0, scl=Pin(13), sda=Pin(12)))
 6 
 7 while (True):
 8     accel_data = lsm.accel()
 9     print('Accelerometer: x:{:>8.3f} y:{:>8.3f} z:{:>8.3f}'.format(*accel_data))
10     gyro_data = lsm.gyro()
11     print('Gyroscope:     x:{:>8.3f} y:{:>8.3f} z:{:>8.3f}'.format(*gyro_data))
12     print("")
13     time.sleep_ms(100)


solution Project 2: Read analog values - Code Example Arduino Nano Connect RP2040

A0 is the analog input with 16 bit resolution. It reads the analog value every second and print it to the console- 

 1 #Example usage for Arduino Nano
 2 from machine import Pin, ADC
 3 from time import sleep
 4 
 5 analogPin = ADC(Pin(26))
 6 
 7 while True:
 8   analogVal16 = analogPin.read_u16()
 9   print(analogVal16)
10   sleep(1)

Project 2: Jupyter Notebook


Task 2.1: is it moved?

  • read acceleration and gyro
  • calculate the differences between values
  • show an ouput when it is move
  • create a file on the device that logs, when it is moved

Task 2.2: it was turned upside down?

  • read acceleration and gyro
  • make a rule based "AI" that records
    • it was put upside down
    • it was turned 360
    • it was moved "quickly"

Task 3: ML on Arduino Nano Connect RP2040

We will use https://github.com/eloquentarduino/everywhereml to detect the same gestures as in 2.2. For this, install everywhereml: 

pip3 install -U everywhere

Using everywhereml we can train a model on a more powerful machine for deployment on a microcontroller. See https://eloquentarduino.com/posts/micropython-machine-learning for example for such a training process. Assuming that our ML model is trained and stored in variable clf then we can save the model to a file using 

clf.to_micropython_file("MyModel.py")

The MyModel.py file can then be saved and called directly on the microcontroller. To run the model on the microcontroller, assume your data is stored in x and you trained a RandomForestClassifier. Then you can predict via the following code snippet 

import MyModel
clf = MyModel.RandomForestClassifier()
clf.predict(x)

Task 4: connect both boards to WIFI

  • connect both boards to WIFI using Tutorial_Network
  • use the Arduino Nano ESP32 as output (showing a color)
  • use the Arduino Nano Connect RP2040 as input (recognize with rules 3 gestures)

Links

See the full list of links: UBISS2024-Links

Local Links

https://ubicomp.net/sw/db1/var2db.php? http://localhost:8888/notebooks/ArduinoNanoRP2040_v01.ipynb http://localhost:8888/doc/tree/create-ML-model01.ipynb

Reading

Required Reading before the course

Recommended Reading before the course

Random Commands

pip install micropython-lsm6dsox

picotool.exe load -x C:\Users\ru42qak\AppData\Roaming\OpenMV\openmvide\firmware\ARDUINO_NANO_RP2040_CONNECT\firmware.bin

pip install jupyterlab

pip install everywhereml

python -m pip install jupyter

git clone https://github.com/goatchurchprime/jupyter_micropython_kernel.git

pip install -e jupyter_micropython_kernel

python -m notebook

python -m jupyter kernelspec list


C:\Users\ru42qak\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\LocalCache\local-packages\Python311\site-packages\jupyterlab>pip install -e jupyter_micropython_kernel

C:\Users\ru42qak\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\LocalCache\local-packages\Python311\site-packages\jupyterlab>python -m notebook

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