如何通过仅过滤掉其他类别而不进行重新训练将ssd mobilenet v1可可对象检测变成人工检测？

如何解决如何通过仅过滤掉其他类别而不进行重新训练将ssd mobilenet v1可可对象检测变成人工检测？

我想将此对象检测模型转换为人体检测模型。为此，我只需要使用人员类并为其他类过滤掉框，而无需重新训练整个模型。如果没有显示检测框，则在后台检测到其他类也可以。有人可以帮我找出为实现此目的而对该代码进行哪些更改？

import numpy as np
import os
import six.moves.urllib as urllib
import sys
import tarfile
import tensorflow as tf
import zipfile
 
from collections import defaultdict
from io import StringIO
from matplotlib import pyplot as plt
from PIL import Image
 
import cv2
cap = cv2.VideoCapture(0)

# cap = cv2.VideoCapture("video.mp4")
 
sys.path.append("..")
 
from utils import label_map_util
 
from utils import visualization_utils as vis_util
 
MODEL_NAME = 'ssd_mobilenet_v1_coco_11_06_2017'
MODEL_FILE = MODEL_NAME + '.tar.gz'
DOWNLOAD_BASE = 'http://download.tensorflow.org/models/object_detection/'
 
# Path to frozen detection graph. This is the actual model that is used for the object detection.
PATH_TO_CKPT = MODEL_NAME + '/frozen_inference_graph.pb'
 
# List of the strings that is used to add correct label for each box.
PATH_TO_LABELS = os.path.join('data','mscoco_label_map.pbtxt')
 
NUM_CLASSES = 90
 
opener = urllib.request.URLopener()
opener.retrieve(DOWNLOAD_BASE + MODEL_FILE,MODEL_FILE)
tar_file = tarfile.open(MODEL_FILE)
for file in tar_file.getmembers():
  file_name = os.path.basename(file.name)
  if 'frozen_inference_graph.pb' in file_name:
    tar_file.extract(file,os.getcwd())
 
detection_graph = tf.Graph()
with detection_graph.as_default():
  od_graph_def = tf.compat.v1.GraphDef()
  with tf.compat.v2.io.gfile.GFile(PATH_TO_CKPT,'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)
 
with detection_graph.as_default():
  with tf.compat.v1.Session(graph=detection_graph) as sess:
    while True:
      ret,image_np = cap.read()
      # Expand dimensions since the model expects images to have shape: [1,None,3]
      image_np_expanded = np.expand_dims(image_np,axis=0)
      image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
      # Each box represents a part of the image where a particular object was detected.
      boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
      # Each score represent how level of confidence for each of the objects.
      # Score is shown on the result image,together with the class label.
      scores = detection_graph.get_tensor_by_name('detection_scores:0')
      classes = detection_graph.get_tensor_by_name('detection_classes:0')
      num_detections = detection_graph.get_tensor_by_name('num_detections:0')
      # Actual detection.
      (boxes,scores,classes,num_detections) = sess.run(
        [boxes,num_detections],feed_dict={image_tensor: image_np_expanded})
      # Visualization of the results of a detection.
      vis_util.visualize_boxes_and_labels_on_image_array(
          image_np,np.squeeze(boxes),np.squeeze(classes).astype(np.int32),np.squeeze(scores),category_index,use_normalized_coordinates=True,line_thickness=8)
   
      cv2.imshow('object detection',cv2.resize(image_np,(800,600)))
      if cv2.waitKey(25) & 0xFF == ord('q'):
        cv2.destroyAllWindows()
        break