So in general there are no problems, the script works well and does what it is supposed to do, I would just like to ask you for advice on what I can improve in the code and what other function I can implement.

Below you will find the py code, I used yolo for training and coco.names for labels:

  `import cv2

  import numpy as np
  from concurrent.futures import ThreadPoolExecutor

  net = cv2.dnn.readNet("yolov3.weights", "yolov3.cfg")
  classes = []
  with open("coco.names", "r") as f:
  classes = [line.strip() for line in f.readlines()]

  layer_names = net.getLayerNames()
  output_layers = [layer_names[i - 1] for i in net.getUnconnectedOutLayers()]

  cap = cv2.VideoCapture(0)
  min_confidence = 0.5
  max_detections = 10
  detections_count = 0

  input_width = 416
  input_height = 416

  def process_detection(frame, detection, detected_objects):
  global detections_count

scores = detection[5:]
class_id = np.argmax(scores)
confidence = scores[class_id]


if confidence > min_confidence:
  
    center_x = int(detection[0] * width)
    center_y = int(detection[1] * height)
    w = int(detection[2] * width)
    h = int(detection[3] * height)
    x = int(center_x - w / 2)
    y = int(center_y - h / 2)

 detected_objects.append({"class_id": class_id, "confidence": confidence, "x": x, "y": y, "w": w, "h": h})

    detections_count += 1
    if detections_count >= max_detections:
        return True

return False

 while True:

ret, frame = cap.read()
height, width, channels = frame.shape

   detected_objects = []


blob = cv2.dnn.blobFromImage(frame, 0.00392, (input_width, input_height), (0, 0, 0), True, crop=False)
net.setInput(blob)
outs = net.forward(output_layers)


with ThreadPoolExecutor() as executor:
    futures = [executor.submit(process_detection, frame, detection, detected_objects) for detection in outs[0]]
    for future in futures:
        if future.result():
            break


for obj in detected_objects:
     cv2.rectangle(frame, (obj["x"], obj["y"]), (obj["x"] + obj["w"], obj["y"] + obj["h"]),  (0,255, 0), 2)
    cv2.putText(frame, classes[obj["class_id"]], (obj["x"], obj["y"] - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)


cv2.imshow("Object Detection", frame)


if cv2.waitKey(1) & 0xFF == ord('q'):
    break
   cap.release()
   cv2.destroyAllWindows()`

I would like to be able to implement new features, and if possible understand how to improve the code and what to optimize.