DIsplay to Nicla Vision

Arduino Boards
1

Hello, how to connect a display to nicla vision. I have a project running in OpenMV IDE, basically it’s a machine learning model for classifying tomatoes. My project is running without problems, but I want to connect a display to see the same thing that the openmv ide camera shows me.

I was watching this tutorial (https://youtu.be/21gAfCXcHSk) and they are using an LCD Display but I’m not sure if it is compatible with arduino nicla vision.

2

Yes, you can use a display with the system.

LCD Shield – OpenMV

You need to hook the SPI pins to these: openmv/src/omv/boards/ARDUINO_NICLA_VISION/omv_boardconfig.h at master · openmv/openmv (github.com)

You can get the Nicla Pinout here: ABX00051-full-pinout.pdf (arduino.cc)

And the LCD shield pinout here: https://openmv.io/cdn/shop/files/lcd-pinout_2048x.png?v=1613516978

There’s 6 data pins and power/ground you have to hookup. Then you can use the SPI Disiplay module to send images to the screen.

4

See the LCD Examples. Here’s the class here: class SPIDisplay – SPI Display Driver — MicroPython 1.23 documentation (openmv.io)

Note, the pins mentioned in the class aren’t the same the Nicla uses. Please follow my links above for that.

6

Hi, I don’t know how the LCD shield you show above works. Please provide the datasheet for it.

8

Hi, so, that display looks like the same display we use for the OpenMV Cam normally. However, there are strap pins on these displays which change the read-out order and etc. Since you are not using the hardware we sell I can only provide limited help. If you need to modify the driver you’ll need to do it yourself in Python. That said, other customers have done this and it’s not impossible.

Anyway, as described in this post: DIsplay to Nicla Vision - #2 by kwagyeman… you have the wiring correct as far as I can tell. It’s not clear that the A0 pin is equal to the RS pin and that SDA is equal to MOSI. Assuming this is right then it should work.

LED being set to 3.3V should light the display.

Anyway, run the LCD shield example on the Nicla and you should get some sort of image. The bytes may be reversed, if so, then you can turn on a flag to fix that. class SPIDisplay – SPI Display Driver — MicroPython 1.23 documentation

10

firmware.zip (1.0 MB)

Sorry, it’s not enabled by default. Here’s a firmware snapshot with it enabled.

1 Like
12 
import sensor, time, ml, uos, gc, display

sensor.reset()                         # Reset and initialize the sensor.
sensor.set_pixformat(sensor.RGB565)    # Set pixel format to RGB565 (or GRAYSCALE)
sensor.set_framesize(sensor.QVGA)      # Set frame size to QVGA (320x240)
sensor.set_windowing((160, 160))       # Set 160x160 window.
sensor.skip_frames(time=2000)          # Let the camera adjust.

net = None
labels = None

lcd = display.SPIDisplay()

try:
    # load the model, alloc the model file on the heap if we have at least 64K free after loading
    net = ml.Model("trained.tflite", load_to_fb=uos.stat('trained.tflite')[6] > (gc.mem_free() - (64*1024)))
except Exception as e:
    print(e)
    raise Exception('Failed to load "trained.tflite", did you copy the .tflite and labels.txt file onto the mass-storage device? (' + str(e) + ')')

try:
    labels = [line.rstrip('\n') for line in open("labels.txt")]
except Exception as e:
    raise Exception('Failed to load "labels.txt", did you copy the .tflite and labels.txt file onto the mass-storage device? (' + str(e) + ')')

clock = time.clock()
while(True):
    clock.tick()

    img = sensor.snapshot()
    predictions = net.predict([img])[0].flatten().tolist()
    predictions_list = list(zip(labels, predictions))

    # Encontrar la clase con el porcentaje más alto
    top_class = max(predictions_list, key=lambda x: x[1])
    top_label, top_score = top_class

    # Mostrar la clase clasificada en el buffer de OpenMV
    #img.draw_string(0, 0, f"{top_label}: {top_score:.2f}", color=(255, 0, 0), scale=1)
    # Mostrar la clase clasificada en el buffer de OpenMV
    img.draw_string(0, 10, f"{top_label}", color=(255, 0, 0), scale=0.9)         # Nombre de la clase
    img.draw_string(0, 20, f"{top_score:.2f}", color=(255, 0, 0), scale=1)     # Porcentaje en la línea de abajo

    # Imprimir resultados en la consola
    for label, score in predictions_list:
        print(f"{label} = {score:.2f}")
    print(f"Clasificación: {top_label} = {top_score:.2f}")
    print(clock.fps(), "fps")
    lcd.write(img)
15

Hi, I’d recommend adding a physical switch to just remove battery or USB-C power. The RT1062 has an on off button. The Nicla does not.