code: def predict_directory(self, path, **kwargs):
      image_file_names = list()
  
      for root, directories, files in os.walk(path):
          if root == path:
              for file in files:
                  if file.endswith(".png"):
                      image_file_names.append(file)
  
      if not os.path.exists("datasets/predicted"):
          os.mkdir("datasets/predicted")
  
      for i, image_file_name in enumerate(image_file_names):
          image_path = f"{path}/{image_file_name}"
          save_path = f"datasets/predicted/{str(i + 1).zfill(6)}.png"
  
          luminoth_predict(self.model, image_path, save_path=save_path)
  
      # Attempt to make a video if FFMPEG is in PATH
      try:
          subprocess.call(
              shlex.split(
                  "ffmpeg -framerate 10 -i datasets/predicted/%06d.png  -c:v libx264 -r 30 -pix_fmt yuv420p datasets/predicted/predicted.mp4"
              )
          )
      except Exception:
          pass
  
imports: import os
  import yaml
  import json
  import subprocess
  import shlex
  import time
  import tensorflow
  from luminoth.tools.dataset.readers.object_detection import ObjectDetectionReader
  from luminoth.tools.dataset.writers.object_detection_writer import ObjectDetectionWriter
  from luminoth.tools.checkpoint import get_checkpoint_config
  from luminoth.utils.dataset import read_xml, read_image
  from luminoth.utils.config import get_config
  from luminoth.utils.predicting import PredictorNetwork
  from luminoth.train import run
  from luminoth.predict import predict_image
  from serpent.machine_learning.object_recognition.object_recognizer import ObjectRecognizer
  from serpent.utilities import SerpentError
label_desc: Use image node_id 4 for calling the LuminothObjectRecognizer obj's underlying member method code with example usage: obj.predict_directory(path) without return types