class_docstr: Pre-processor for Ava Actions Dataset stored as image frames -
      `<https://research.google.com/ava/download.html>_`
  This class handles the parsing of all the necessary
  csv files containing frame paths and frame labels.
class_docstr_tok: ["Pre-processor","for","Ava","Actions","Dataset","stored","as","image","frames","-","`","<","https",":","\/\/research.google.com\/ava\/download.html",">","_","`","This","class","handles","the","parsing","of","all","the","necessary","csv","files","containing","frame","paths","and","frame","labels","."]
docstr: Parses AVA per frame labels .csv file.
  Args:
      frame_labels_file (str): Path to the file containing labels
          per key frame. Acceptible file formats are,
          Type 1:
              <original_vido_id, frame_time_stamp, bbox_x_1, bbox_y_1, ...
              bbox_x_2, bbox_y_2, action_lable, detection_iou>
          Type 2:
              <original_vido_id, frame_time_stamp, bbox_x_1, bbox_y_1, ...
              bbox_x_2, bbox_y_2, action_lable, person_label>
      video_name_to_idx (dict): Dictionary mapping video names to indices.
      allowed_class_ids (set): A set of integer unique class (bbox label)
          id's that are allowed in the dataset. If not set, all class id's
          are allowed in the bbox labels.
  Returns:
      (dict): A dictionary of dictionary containing labels per each keyframe
          in each video. Here, the label for each keyframe is again a dict
          of the form,
          {
              'labels': a list of bounding boxes
              'boxes':a list of action lables for the bounding box
              'extra_info': ist of extra information cotaining either
                  detections iou's or person id's depending on the
                  csv format.
          }
docstr_tok: ["Parses","AVA","per","frame","labels",".csv","file",".","Args",":","frame_labels_file","(","str",")",":","Path","to","the","file","containing","labels","per","key","frame",".","Acceptible","file","formats","are",",","Type","1",":","<","original_vido_id",",","frame_time_stamp",",","bbox_x_1",",","bbox_y_1",",","...","bbox_x_2",",","bbox_y_2",",","action_lable",",","detection_iou",">","Type","2",":","<","original_vido_id",",","frame_time_stamp",",","bbox_x_1",",","bbox_y_1",",","...","bbox_x_2",",","bbox_y_2",",","action_lable",",","person_label",">","video_name_to_idx","(","dict",")",":","Dictionary","mapping","video","names","to","indices",".","allowed_class_ids","(","set",")",":","A","set","of","integer","unique","class","(","bbox","label",")","id","'s","that","are","allowed","in","the","dataset",".","If","not","set",",","all","class","id's","are","allowed","in","the","bbox","labels",".","Returns",":","(","dict",")",":","A","dictionary","of","dictionary","containing","labels","per","each","keyframe","in","each","video",".","Here",",","the","label","for","each","keyframe","is","again","a","dict","of","the","form",",","{","'labels","'",":","a","list","of","bounding","boxes","'boxes","'",":","a","list","of","action","lables","for","the","bounding","box","'extra_info","'",":","ist","of","extra","information","cotaining","either","detections","iou","'s","or","person","id","'s","depending","on","the","csv","format.","}"]
code: def load_and_parse_labels_csv(
      frame_labels_file: str,
      video_name_to_idx: dict,
      allowed_class_ids: Optional[Set] = None,
  ):
      """
      Parses AVA per frame labels .csv file.
      Args:
          frame_labels_file (str): Path to the file containing labels
              per key frame. Acceptible file formats are,
              Type 1:
                  <original_vido_id, frame_time_stamp, bbox_x_1, bbox_y_1, ...
                  bbox_x_2, bbox_y_2, action_lable, detection_iou>
              Type 2:
                  <original_vido_id, frame_time_stamp, bbox_x_1, bbox_y_1, ...
                  bbox_x_2, bbox_y_2, action_lable, person_label>
          video_name_to_idx (dict): Dictionary mapping video names to indices.
          allowed_class_ids (set): A set of integer unique class (bbox label)
              id's that are allowed in the dataset. If not set, all class id's
              are allowed in the bbox labels.
      Returns:
          (dict): A dictionary of dictionary containing labels per each keyframe
              in each video. Here, the label for each keyframe is again a dict
              of the form,
              {
                  'labels': a list of bounding boxes
                  'boxes':a list of action lables for the bounding box
                  'extra_info': ist of extra information cotaining either
                      detections iou's or person id's depending on the
                      csv format.
              }
      """
      labels_dict = {}
      with g_pathmgr.open(frame_labels_file, "r") as f:
          for line in f:
              row = line.strip().split(",")
  
              video_name = row[0]
              video_idx = video_name_to_idx[video_name]
  
              frame_sec = float(row[1])
              if (
                  frame_sec
                  > AvaLabeledVideoFramePaths.AVA_VALID_FRAMES[-1]
                  or frame_sec
                  < AvaLabeledVideoFramePaths.AVA_VALID_FRAMES[0]
              ):
                  continue
  
              # Since frame labels in video start from 0 not at 900 secs
              frame_sec = (
                  frame_sec
                  - AvaLabeledVideoFramePaths.AVA_VIDEO_START_SEC
              )
  
              # Box with format [x1, y1, x2, y2] with a range of [0, 1] as float.
              bbox = list(map(float, row[2:6]))
  
              # Label
              label = -1 if row[6] == "" else int(row[6])
              # Continue if the current label is not in allowed labels.
              if (allowed_class_ids is not None) and (
                  label not in allowed_class_ids
              ):
                  continue
  
              # Both id's and iou's are treated as float
              extra_info = float(row[7])
  
              if video_idx not in labels_dict:
                  labels_dict[video_idx] = {}
  
              if frame_sec not in labels_dict[video_idx]:
                  labels_dict[video_idx][frame_sec] = defaultdict(list)
  
              labels_dict[video_idx][frame_sec]["boxes"].append(bbox)
              labels_dict[video_idx][frame_sec]["labels"].append(label)
              labels_dict[video_idx][frame_sec]["extra_info"].append(
                  extra_info
              )
      return labels_dict
  
file_path: pytorchvideo/pytorchvideo/data/ava.py
imports: from __future__ import annotations
  import os
  from collections import defaultdict
  from typing import Any, Callable, Dict, Optional, Set, Tuple, Type
  import torch
  from iopath.common.file_io import g_pathmgr
  from pytorchvideo.data.clip_sampling import ClipInfo, ClipSampler
  from pytorchvideo.data.labeled_video_dataset import LabeledVideoDataset
label_desc: Use image node_id 3 for calling the AvaLabeledVideoFramePaths obj's underlying member method code with example usage: obj.load_and_parse_labels_csv(frame_labels_file, video_name_to_idx, allowed_class_ids) and returns: labels_dict