text: import os
  from tqdm import tqdm
  import multiprocessing as mp
  
  from pipeline.capture_images import CaptureImages
  from pipeline.capture_image import CaptureImage
  from pipeline.predict import Predict
  from pipeline.async_predict import AsyncPredict
  from pipeline.separate_background import SeparateBackground
  from pipeline.annotate_image import AnnotateImage
  from pipeline.save_image import SaveImage
  from pipeline.utils import detectron
  
  
  def parse_args():
      import argparse
  
      # Parse command line arguments
      ap = argparse.ArgumentParser(description="Detectron2 image processing pipeline")
      ap.add_argument(
          "-i", "--input", required=True, help="path to input image file or directory"
      )
      ap.add_argument(
          "-o",
          "--output",
          default="output",
          help="path to output directory (default: output)",
      )
      ap.add_argument("-p", "--progress", action="store_true", help="display progress")
      ap.add_argument(
          "-sb", "--separate-background", action="store_true", help="separate background"
      )
  
      # Detectron settings
      ap.add_argument(
          "--config-file",
          default="configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml",
          help="path to config file (default: configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml)",
      )
      ap.add_argument(
          "--config-opts",
          default=[],
          nargs=argparse.REMAINDER,
          help="modify model config options using the command-line",
      )
      ap.add_argument("--weights-file", default=None, help="path to model weights file")
      ap.add_argument(
          "--confidence-threshold",
          type=float,
          default=0.5,
          help="minimum score for instance predictions to be shown (default: 0.5)",
      )
  
      # Mutliprocessing settings
      ap.add_argument("--gpus", type=int, default=1, help="number of GPUs (default: 1)")
      ap.add_argument("--cpus", type=int, default=0, help="number of CPUs (default: 1)")
      ap.add_argument(
          "--queue-size", type=int, default=3, help="queue size per process (default: 3)"
      )
      ap.add_argument(
          "--single-process",
          action="store_true",
          help="force the pipeline to run in a single process",
      )
  
      return ap.parse_args()
  
  
  def main(args):
      # Create output directory if needed
      os.makedirs(args.output, exist_ok=True)
  
      # Create pipeline steps
      capture_images = (
          CaptureImages(args.input)
          if os.path.isdir(args.input)
          else CaptureImage(args.input)
      )
  
      cfg = detectron.setup_cfg(
          config_file=args.config_file,
          weights_file=args.weights_file,
          config_opts=args.config_opts,
          confidence_threshold=args.confidence_threshold,
          cpu=False if args.gpus > 0 else True,
      )
      if not args.single_process:
          mp.set_start_method("spawn", force=True)
          predict = AsyncPredict(
              cfg,
              num_gpus=args.gpus,
              num_cpus=args.cpus,
              queue_size=args.queue_size,
              ordered=False,
          )
      else:
          predict = Predict(cfg)
  
      if args.separate_background:
          separate_background = SeparateBackground("vis_image")
          annotate_image = None
      else:
          separate_background = None
          metadata_name = cfg.DATASETS.TEST[0] if len(cfg.DATASETS.TEST) else "__unused"
          annotate_image = AnnotateImage("vis_image", metadata_name)
  
      save_image = SaveImage("vis_image", args.output)
  
      # Create image processing pipeline
      pipeline = (
          capture_images | predict | separate_background | annotate_image | save_image
      )
  
      # Iterate through pipeline
      try:
          for _ in tqdm(pipeline, disable=not args.progress):
              pass
      except StopIteration:
          return
      except KeyboardInterrupt:
          return
      finally:
          # Pipeline cleanup
          if isinstance(predict, AsyncPredict):
              predict.cleanup()
  
  
  if __name__ == "__main__":
      args = parse_args()
      main(args)
  