How to print the class name from yolo object detection?

Question

I am new to using the YOLOv4 for a real-time object detection application for a project.

I followed the steps from the darknet website. And for the real-time detection using a webcam, I ran this code below

import cv2
import numpy as np
import time

# Load the YOLO model
net = cv2.dnn.readNet("./solarpanel/cfg/weights/yolov4-tiny-custom_last.weights",
                      "./solarpanel/cfg/yolov4-tiny-custom.cfg")
classes = []
with open("./solarpanel/cfg/obj.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()]
colors = np.random.uniform(0, 255, size=(len(classes), 3))

cap=None
font=None
starting_time = None
frame_id = 0
def loadWebCam():
    global cap, font, starting_time, frame_id
    # Load webcam
    cap = cv2.VideoCapture(0)
    font = cv2.FONT_HERSHEY_SIMPLEX
    starting_time = time.time()
    frame_id = 0
    

def unLoadWebCam():
    global cap
    cap.release()
    cv2.destroyAllWindows()


def checkcellfailure():
    global cap, font, starting_time, frame_id
    loadWebCam()
    class_ids = []
    confidences = []
    boxes = []
    while True:
        # Read webcam
        _, frame = cap.read()
        frame_id += 1
        height, width, channels = frame.shape

        # Detecting objects
        blob = cv2.dnn.blobFromImage(frame,
                                    0.00392, (416, 416), (0, 0, 0),
                                    True,
                                    crop=False)
        net.setInput(blob)
        outs = net.forward(output_layers)

        # Visualising data
        class_ids = []
        confidences = []
        boxes = []
        for out in outs:
            for detection in out:
                scores = detection[5:]
                class_id = np.argmax(scores)
                confidence = scores[class_id]
                if confidence > 0.1:
                    # Object detected
                    center_x = int(detection[0] * width)
                    center_y = int(detection[1] * height)
                    w = int(detection[2] * width)
                    h = int(detection[3] * height)

                    # Rectangle coordinates
                    x = int(center_x - w / 2)
                    y = int(center_y - h / 2)

                    boxes.append([x, y, w, h])
                    confidences.append(float(confidence))
                    class_ids.append(class_id)

        indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.8, 0.3)

        for i in range(len(boxes)):
            if i in indexes:
                x, y, w, h = boxes[i]
                label = str(classes[class_ids[i]])
                confidence = confidences[i]
                color = colors[class_ids[i]]
                cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2)
                cv2.putText(frame, label + " " + str(round(confidence, 2)),
                            (x, y + 30), font, 3, color, 3)

        elapsed_time = time.time() - starting_time
        fps = frame_id / elapsed_time
        cv2.putText(frame, "FPS: " + str(round(fps, 2)), (40, 670), font, .7,
                    (0, 255, 255), 1)
        cv2.putText(frame, "press [esc] to exit", (40, 690), font, .45,
                    (0, 255, 255), 1)
        cv2.imshow("Image", frame)
        key = cv2.waitKey(1)
       # print(confidences)
        found=False
        for conf in confidences:
            if conf > 0.9:
                found=True
        if key == 27 or found:
            print("cellfailure detected")
            #print(" Global Location: %s" % vehicle.location.global_frame)
            #print(" Global Location (relative altitude): %s" % vehicle.location.global_relative_frame)
            #print(" Local Location: %s" % vehicle.location.local_frame)
            #print("[button pressed] ///// [esc].")
            #print("[feedback] ///// Videocapturing succesfully stopped")
            break
    #unLoadWebCam()
    return confidences

This opens up a new camera window that detects the classes and closes the webcam.(wherein classes I have 2 classes) but I need only specified classes as output.

Could anyone suggest me the right way of code to print the specified class as output and to print the location of the drone?