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  • how do i add confidence values to boxes?

Hi someone made me a quick piece of code that switches a relay when a item is detected i would like to see the confidence value on screen so i can see how well my model is working. i didn't know if this in the main.py or models .json file?
Cheers
Adam

edit
i believe he used parts of the people counter demo. The host is a raspberry pi and I have trained my own model. So far I have just been adjusting the confidence value in the .json file so can tune it but would like to see the actual value on screen which would help. Hopefully someone can point me in the right Direction

    Luxonis-Lukasz nice one thank you for this!! Will have a look at it today and see if I can get it working
    Cheers
    Adam

      hi i thought i'd post the main.py file code

      import logging
      import threading
      import argparse
      import ctypes
      import json
      import time
      import os
      from pathlib import Path

      import cv2
      from modules import Solenoid
      from depthai_utils import DepthAI
      from multiprocessing import Process, Manager
      parser = argparse.ArgumentParser()
      parser.add_argument('-m', '--model', default='poa', choices=list(os.listdir('./models')))
      args = parser.parse_args()

      debug = True

      if name == "main":

      Start threading

      p = Process(target=store, args=(shared_results, ))

      p.daemon = True

      p.start()

      # Initialize the model
d = DepthAI(model_name=args.model)

# Initialize the solenoid object
sol = Solenoid(12)

for frame, detections in d.run():
    if len(detections) >= 1:
         
         print ("Weed found")
         sol.run(True)
        
    else:
        sol.run(False)

    if debug:
        cv2.putText(frame, "Find Poa", (20, 20), cv2.FONT_HERSHEY_TRIPLEX, 0.9, 255)
        img_h = frame.shape[0]
        img_w = frame.shape[1]
        for detection in detections:
            left, top = int(detection.x_min * img_w), int(detection.y_min * img_h)
            right, bottom = int(detection.x_max * img_w), int(detection.y_max * img_h)
            cv2.rectangle(frame, (left, top), (right, bottom), (0, 0,255), 5)
        cv2.imshow('Result', frame)
        
        if cv2.waitKey(1) == ord('q'):
            cv2.destroyAllWindows()
            break
del d