code: def visualize_detection(
      pred: pd.DataFrame,
      detection_classes: List[str],
      conf_threshold: float,
      visualization_result_dir: str,
  ):
      """
      Visualize detection results for one image, and save to visualization_result_dir
  
      Parameters
      ----------
      pred
          Detection results as in pd.DataFrame format
      detection_classes
          All classes for detection
      conf_threshold
          Bounding box confidence threshold to filter unwanted detections
      visualization_result_dir
          Directory to save the visualization results
      Returns
      -------
      an List of np.ndarray of visualized images
      """
      try:
          import cv2
      except:
          raise ImportError(
              "No module named: cv2. Please install cv2 by 'pip install opencv-python'"
          )
  
      if not os.path.exists(visualization_result_dir):
          os.makedirs(visualization_result_dir, exist_ok=True)
  
      classname2idx = {
          classname: i for i, classname in enumerate(detection_classes)
      }
      idx2classname = {
          i: classname for i, classname in enumerate(detection_classes)
      }
  
      visualized_images = []
      for i in range(len(pred)):
          image_path = pred.iloc[i]["image"]
          image_pred = pred.iloc[i]["bboxes"]
          im = cv2.imread(image_path)
          tlwhs = []
          obj_ids = []
          conf_scores = []
          for data in image_pred:
              if data["score"] > conf_threshold:
                  obj_ids.append(classname2idx[data["class"]])
                  tlwhs.append(bbox_xyxy_to_xywh(data["bbox"]))
                  conf_scores.append(data["score"])
          visualized_im = plot_detections(
              im,
              tlwhs,
              obj_ids,
              idx2classname,
              conf_threshold,
              scores=conf_scores,
          )
          visualized_images.append(visualized_im)
          imgname = os.path.basename(image_path)
          cv2.imwrite(
              os.path.join(visualization_result_dir, imgname),
              visualized_im,
          )
      logger.info(
          "Saved visualizations to {}".format(visualization_result_dir)
      )
      return visualized_images
  
imports: import json
  import logging
  import os
  import warnings
  from pathlib import Path
  from typing import Dict, Iterable, List, Optional, Union
  import defusedxml.ElementTree
  import numpy
  import pandas
  import PIL
  from torchmetrics.detection.mean_ap import MeanAveragePrecision
  from ..constants import BBOX, LABEL, MAP, MAP_50, MAP_75, MAP_LARGE, MAP_MEDIUM, MAP_SMALL, MAR_1, MAR_10, MAR_100, MAR_LARGE, MAR_MEDIUM, MAR_SMALL, MEAN_AVERAGE_PRECISION
  from .download import download, is_url
label_desc: Use image node_id 30 for calling a global function with example usage: visualize_detection(pred, detection_classes, conf_threshold, visualization_result_dir) and returns: visualized_images