class_docstr: 24 keypoints 3D dataset for pose estimation. 
  
  each item is a list of images
  
  The dataset loads raw features and apply specified transforms
  to return a dict containing the image tensors and other information.
  
  Args:
      dataset_dir (str): Root path to the dataset.
      image_dir (str): Path to a directory where images are held.
class_docstr_tok: ["24","keypoints","3D","dataset","for","pose","estimation",".","each","item","is","a","list","of","images","The","dataset","loads","raw","features","and","apply","specified","transforms","to","return","a","dict","containing","the","image","tensors","and","other","information",".","Args",":","dataset_dir","(","str",")",":","Root","path","to","the","dataset",".","image_dir","(","str",")",":","Path","to","a","directory","where","images","are","held","."]
code: def __init__(
      self,
      dataset_dir,  # 数据集根目录
      image_dir,  # 图像文件夹
      p3d_dir,  # 3D关键点文件夹
      json_path,
      img_size,  # 图像resize大小
      num_frames,  # 帧序列长度
      anno_path=None,
  ):
      self.dataset_dir = dataset_dir
      self.image_dir = image_dir
      self.p3d_dir = p3d_dir
      self.json_path = json_path
      self.img_size = img_size
      self.num_frames = num_frames
      self.anno_path = anno_path
  
      (
          self.data_labels,
          self.mf_inds,
      ) = self._generate_multi_frames_list()
  
imports: import os
  import cv2
  import numpy
  import json
  import copy
  import pycocotools
  from pycocotools.coco import COCO
  from .dataset import DetDataset
  from ppdet.core.workspace import register, serializable
  from paddle.io import Dataset
label_desc: Use image node_id 1 to create a new Keypoint3DMultiFramesDataset object from inherited base classes: Dataset with example: obj = Keypoint3DMultiFramesDataset(dataset_dir, image_dir, p3d_dir, json_path, img_size, num_frames, anno_path)