IndexError: invalid index to scalar variable while testing

Question

This is the code used in testing. I have been getting the error shown bellow. Can someone please tell me how to solve it? This is a custom trained model. I have done it for images, but while trying for Videos I am getting this error. There are 177 images in my dataset.

The error message points to the following line:

confidence_threshold = output_data_2[0][i]

    import numpy as np
    import tensorflow as tf
    import cv2
    import time
    print(tf.__version__)
    
    Model_Path = "/content/android.tflite"
    Video_path = "/content/gdrive/MyDrive/CAPSTONE/VID_20220426_164917.mp4"
    
    interpreter = tf.lite.Interpreter(model_path=Model_Path)
    interpreter.allocate_tensors()
    
    input_details = interpreter.get_input_details()
    output_details = interpreter.get_output_details()
    
    class_names = ['Car', 'ambulance', 'firebrigade']
    
    cap = cv2.VideoCapture(Video_path)
    ok, frame_image = cap.read()
    original_image_height, original_image_width, _ = frame_image.shape
    thickness = original_image_height // 500  
    fontsize = original_image_height / 1500
    print(thickness)
    print(fontsize)
    
    while True:
        ok, frame_image = cap.read()
        if not ok:
            break
    
        model_interpreter_start_time = time.time()
        resize_img = cv2.resize(frame_image, (320, 320), interpolation=cv2.INTER_CUBIC)
        reshape_image = resize_img.reshape(320, 320, 3)
        image_np_expanded = np.expand_dims(reshape_image, axis=0)
        image_np_expanded = image_np_expanded.astype('uint8')  # float32
    
        interpreter.set_tensor(input_details[0]['index'], image_np_expanded) 
        interpreter.invoke()
    
        output_data = interpreter.get_tensor(output_details[0]['index'])
        output_data_1 = interpreter.get_tensor(output_details[1]['index']) 
        output_data_2 = interpreter.get_tensor(output_details[2]['index'])
        output_data_3 = interpreter.get_tensor(output_details[3]['index'])  
        each_interpreter_time = time.time() - model_interpreter_start_time
    
        for i in range(len(output_data_1[0])):
            confidence_threshold = output_data_2[0][i] //Error in this line
            if confidence_threshold > 0.3:
                label = "{}: {:.2f}% ".format(class_names[int(output_data_1[0][i])], output_data_2[0][i] * 100) 
                label2 = "inference time : {:.3f}s" .format(each_interpreter_time)
                left_up_corner = (int(output_data[0][i][1]*original_image_width), int(output_data[0][i][0]*original_image_height))
                left_up_corner_higher = (int(output_data[0][i][1]*original_image_width), int(output_data[0][i][0]*original_image_height)-20)
                right_down_corner = (int(output_data[0][i][3]*original_image_width), int(output_data[0][i][2]*original_image_height))
                cv2.rectangle(frame_image, left_up_corner_higher, right_down_corner, (0, 255, 0), thickness)
                cv2.putText(frame_image, label, left_up_corner_higher, cv2.FONT_HERSHEY_DUPLEX, fontsize, (255, 255, 255), thickness=thickness)
                cv2.putText(frame_image, label2, (30, 30), cv2.FONT_HERSHEY_DUPLEX, fontsize, (255, 255, 255), thickness=thickness)
        cv2.namedWindow('detect_result', cv2.WINDOW_NORMAL)
        # cv2.resizeWindow('detect_result', 800, 600)
        cv2.imshow("detect_result", frame_image)
    
        key = cv2.waitKey(10) & 0xFF
        if key == ord("q"):
            break
        elif key == 32:
            cv2.waitKey(0)
            continue
    cap.release()
    cv2.destroyAllWindows()
    
    
    
    
    2.8.0
    2
    0.72
    ---------------------------------------------------------------------------
    IndexError                                Traceback (most recent call last)
    <ipython-input-25-15b378ffd621> in <module>()
         45 
         46     for i in range(len(output_data_1[0])):
    ---> 47         confidence_threshold = output_data_2[0][i]
         48         if confidence_threshold > 0.3:
         49             label = "{}: {:.2f}% ".format(class_names[int(output_data_1[0][i])], output_data_2[0][i] * 100)
    
    IndexError: invalid index to scalar variable.