class_docstr: Get Norfair detections from MOTChallenge text files containing detections
class_docstr_tok: ["Get","Norfair","detections","from","MOTChallenge","text","files","containing","detections"]
code: def __init__(self, input_path, information_file=None):
      self.frame_number = 1
  
      # Get detecions matrix data with rows corresponding to:
      # frame, id, bb_left, bb_top, bb_right, bb_down, conf, x, y, z
      detections_path = os.path.join(input_path, "det/det.txt")
  
      self.matrix_detections = np.loadtxt(
          detections_path, dtype="f", delimiter=","
      )
      row_order = np.argsort(self.matrix_detections[:, 0])
      self.matrix_detections = self.matrix_detections[row_order]
      # Coordinates refer to box corners
      self.matrix_detections[:, 4] = (
          self.matrix_detections[:, 2] + self.matrix_detections[:, 4]
      )
      self.matrix_detections[:, 5] = (
          self.matrix_detections[:, 3] + self.matrix_detections[:, 5]
      )
  
      if information_file is None:
          seqinfo_path = os.path.join(input_path, "seqinfo.ini")
          information_file = InformationFile(file_path=seqinfo_path)
      self.length = information_file.search(variable_name="seqLength")
  
      self.sorted_by_frame = []
      for frame_number in range(1, self.length + 1):
          self.sorted_by_frame.append(
              self.get_dets_from_frame(frame_number)
          )
  
imports: import os
  import numpy
  from rich import print
  from rich.progress import track
  from norfair import Detection
  from collections import OrderedDict
label_desc: Use image node_id 1 to create a new DetectionFileParser object  with example: obj = DetectionFileParser(input_path, information_file)