我无法理解这是什么类型的错误。我已经写了一个python脚本，它接受用户的图像输入并运行情感检测。下面是代码：

import numpy as np
import os
import sys
import tensorflow as tf
import json
from PIL import Image

sys.path.append("..")
from object_detection.utils import ops as utils_ops

from utils import label_map_util
from utils import visualization_utils as vis_util

def Image_tensorflow(xa,ya):
    PATH_TO_FROZEN_GRAPH = 'frozen_inference_graph.pb'
    PATH_TO_LABELS = 'object-detection.pbtxt'
    NUM_CLASSES = 4

    detection_graph = tf.Graph()
    with detection_graph.as_default():
        od_graph_def = tf.GraphDef()
        with tf.gfile.GFile(PATH_TO_FROZEN_GRAPH, 'rb') as fid:
            serialized_graph = fid.read()
            od_graph_def.ParseFromString(serialized_graph)
            tf.import_graph_def(od_graph_def, name='')

    label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
    categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES,
                                                                use_display_name=True)
    category_index = label_map_util.create_category_index(categories)

    def load_image_into_numpy_array(image):
        (im_width, im_height) = image.size
        return np.array(image.getdata()).reshape(
            (im_height, im_width, 3)).astype(np.uint8)

    def image_url(xa, ya):
        file_path = 'images/'
        file_name = ya
        image = xa
        f = open((file_path + str(file_name) + ".json"), "w")
        f.close
        return_dict = {'image': image, 'file': f};
        return return_dict

    get_image_data = image_url(xa,ya)
    image_path= get_image_data['image']

    IMAGE_SIZE = (12, 8)

    def run_inference_for_single_image(image, graph):
        with graph.as_default():
            with tf.Session() as sess:
                # Get handles to input and output tensors
                ops = tf.get_default_graph().get_operations()
                all_tensor_names = {output.name for op in ops for output in op.outputs}
                tensor_dict = {}
                for key in [
                    'num_detections', 'detection_boxes', 'detection_scores',
                    'detection_classes', 'detection_masks'
                ]:
                    tensor_name = key + ':0'
                    if tensor_name in all_tensor_names:
                        tensor_dict[key] = tf.get_default_graph().get_tensor_by_name(
                            tensor_name)
                if 'detection_masks' in tensor_dict:
                    # The following processing is only for single image
                    detection_boxes = tf.squeeze(tensor_dict['detection_boxes'], [0])
                    detection_masks = tf.squeeze(tensor_dict['detection_masks'], [0])
                    # Reframe is required to translate mask from box coordinates to image coordinates and fit the image size.
                    real_num_detection = tf.cast(tensor_dict['num_detections'][0], tf.int32)
                    detection_boxes = tf.slice(detection_boxes, [0, 0], [real_num_detection, -1])
                    detection_masks = tf.slice(detection_masks, [0, 0, 0], [real_num_detection, -1, -1])
                    detection_masks_reframed = utils_ops.reframe_box_masks_to_image_masks(
                        detection_masks, detection_boxes, image.shape[0], image.shape[1])
                    detection_masks_reframed = tf.cast(
                        tf.greater(detection_masks_reframed, 0.5), tf.uint8)
                    tensor_dict['detection_masks'] = tf.expand_dims(
                        detection_masks_reframed, 0)
                image_tensor = tf.get_default_graph().get_tensor_by_name('image_tensor:0')

                output_dict = sess.run(tensor_dict,
                                       feed_dict={image_tensor: np.expand_dims(image, 0)})

                output_dict['num_detections'] = int(output_dict['num_detections'][0])
                output_dict['detection_classes'] = output_dict[
                    'detection_classes'][0].astype(np.uint8)
                output_dict['detection_boxes'] = output_dict['detection_boxes'][0]
                output_dict['detection_scores'] = output_dict['detection_scores'][0]
                if 'detection_masks' in output_dict:
                    output_dict['detection_masks'] = output_dict['detection_masks'][0]
        return output_dict

    for img in xa:
        image = Image.open(img)
    image_np = load_image_into_numpy_array(image)
    image_np_expanded = np.expand_dims(image_np, axis=0)
    # Actual detection.
    output_dict = run_inference_for_single_image(image_np, detection_graph)
    # Visualization of the results of a detection.
    vis_util.visualize_boxes_and_labels_on_image_array(
        image_np,
        output_dict['detection_boxes'],
        output_dict['detection_classes'],
        output_dict['detection_scores'],
        category_index,
        instance_masks=output_dict.get('detection_masks'),
        use_normalized_coordinates=True,
        line_thickness=8)

    # get_image_data = image_url(sys.argv[1],sys.argv[2])
    # image_file = get_image_data['image']

    # pass values

    import cv2 as cv

    image_file = image_path
    img = cv.imread('image_file')
    i = 0
    j = 0
    limiter = 0.3

    while (i < 100):
        if (output_dict['detection_scores'][i] > limiter):
            j = j + 1
        i = i + 1

    # In[17]:

    # store the pass values in lists
    i = 0
    detection_classes = []
    detection_boxes = [[]] * j
    detection_scores = []
    while (i < j):
        detection_classes.append(output_dict['detection_classes'][i])
        detection_scores.append(output_dict['detection_scores'][i])
        detection_boxes[i].append(output_dict['detection_boxes'][i])
        i = i + 1

    list1 = []
    for items in detection_classes:
        if items == 1:
            list1.append("Angry")
        elif items == 2:
            list1.append("Sad")
        elif items == 3:
            list1.append("Neutral")
        elif items == 4:
            list1.append("Happy")

    final_dict = {'DETECTION': list1}

    file_to_write_to = get_image_data['file'].name
    file_to_write_to = str(file_to_write_to)
    text_file = open(file_to_write_to, "w")
    text_file.write(json.dumps(final_dict))
    text_file.close()
    final_path = "images/" + str(ya) + "_annotated" + ".jpg"

    # draw bounding boxes
    img = cv.imread('xa')
    i = 0
    for item in detection_classes:
        width, height = image.size
        ymin = int(detection_boxes[0][i][0] * height)
        xmin = int(detection_boxes[0][i][1] * width)
        ymax = int(detection_boxes[0][i][2] * height)
        xmax = int(detection_boxes[0][i][3] * width)
        font = cv.FONT_HERSHEY_SIMPLEX
        panel_colour = (182, 182, 42)
        bumper_colour = (241, 239, 236)
        damage_colour = (0, 255, 0)
        text_colour = (255, 255, 255)
        bumper_text = (0, 0, 0)
        buffer = int(5 * width / 1000)
        if (detection_classes[i] == 1):
            img = cv.rectangle(img, (xmin, ymin), (xmax, ymax), panel_colour, int(2 * (height / 600)))
            cv.rectangle(img, (xmin, (ymin + (buffer * 8))), (xmax, ymin), panel_colour, -1)
            cv.putText(img, 'angry', (xmin, (ymin + (buffer * 6))), font, 0.8 * (height / 500), text_colour,
                       int(2 * (height / 400)), cv.LINE_AA)
        elif (detection_classes[i] == 2):
            img = cv.rectangle(img, (xmin, ymin), (xmax, ymax), panel_colour, int(2 * (height / 600)))
            cv.rectangle(img, (xmin, (ymin + (buffer * 8))), (xmax, ymin), panel_colour, -1)
            cv.putText(img, 'sad', (xmin, (ymin + (buffer * 6))), font, 0.8 * (height / 500), text_colour,
                       int(2 * (height / 400)), cv.LINE_AA)
        elif (detection_classes[i] == 3):
            img = cv.rectangle(img, (xmin, ymin), (xmax, ymax), bumper_colour, int(2 * (height / 600)))
            cv.rectangle(img, (xmin, (ymin + (buffer * 8))), (xmax, ymin), bumper_colour, -1)
            cv.putText(img, 'neutral', (xmin, (ymin + (buffer * 6))), font, 0.8 * (height / 500), bumper_text,
                       int(2 * (height / 400)), cv.LINE_AA)
        elif (detection_classes[i] == 4):
            img = cv.rectangle(img, (xmin, ymin), (xmax, ymax), panel_colour, int(2 * (height / 600)))
            cv.rectangle(img, (xmin, (ymin + (buffer * 8))), (xmax, ymin), panel_colour, -1)
            cv.putText(img, 'happy', (xmin, (ymin + (buffer * 6))), font, 0.8 * (height / 500), text_colour,
                       int(2 * (height / 400)), cv.LINE_AA)
        i = i + 1

    final_path = "/home/mayureshk/PycharmProjects/ImageDetection/venv/models/research/object_detection/images/" + str(ya) + "_annotated" + ".jpg"
    cv.imwrite(final_path, img)

这个问题困扰了我两天，我无法自己解决它。需要OpenCVMaven的帮助。我到底做错了什么？