69 lines
2.7 KiB
Python
Executable File
69 lines
2.7 KiB
Python
Executable File
import argparse
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import sys
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from pathlib import Path
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FILE = Path(__file__).resolve()
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ROOT = FILE.parents[2]
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if str(ROOT) not in sys.path:
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sys.path.append(str(ROOT))
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from tools.pdcl_inference.run_batch_two_roi_infer import (
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add_inference_args,
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build_two_roi_inference_context_from_args,
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)
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from tools.pdcl_inference.two_roi_inference import (
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DEFAULT_VISUALIZATION_ROOT,
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run_case_inference,
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run_video_case_inference,
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)
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def parse_args() -> argparse.Namespace:
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parser = argparse.ArgumentParser(description="Run two-ROI Detect3D inference on one exported PDCL case or image directory.")
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parser.add_argument("--case-dir", type=str, default="", help="Case directory containing images/ and calib/L2_calib/camera4.json")
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parser.add_argument("--video-case-dir", type=str, default="", help="Video case path or camera4.bin path with nearby camera4.json")
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parser.add_argument("--video-stride", type=int, default=1, help="Read every Nth frame from camera4.bin inputs")
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parser.add_argument("--images-dir", type=str, default="", help="Input image directory when --case-dir is not used")
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parser.add_argument("--calib-file", type=str, default="", help="Input camera4.json path when --case-dir is not used")
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parser.add_argument("--glob", type=str, default="*.png", help="Image glob inside the case images directory")
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parser.add_argument("--max-images", type=int, default=0, help="Maximum number of images/frames to process; 0 means all")
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parser.add_argument(
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"--output-dir",
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type=str,
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default=str(DEFAULT_VISUALIZATION_ROOT),
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help="Visualization output directory or root",
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)
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add_inference_args(parser)
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return parser.parse_args()
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def main() -> None:
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args = parse_args()
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context = build_two_roi_inference_context_from_args(args)
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output_dir = Path(args.output_dir)
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output_root = output_dir if output_dir != DEFAULT_VISUALIZATION_ROOT else DEFAULT_VISUALIZATION_ROOT
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if args.video_case_dir:
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result = run_video_case_inference(
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context=context,
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video_case_dir=args.video_case_dir,
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output_dir=output_root,
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max_images=args.max_images,
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video_stride=args.video_stride,
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)
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else:
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result = run_case_inference(
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context=context,
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case_dir=args.case_dir,
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images_dir=args.images_dir,
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calib_file=args.calib_file,
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output_dir=output_root,
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glob_pattern=args.glob,
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max_images=args.max_images,
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)
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print(f"Saved {result['num_frames']} visualizations to {result['output_dir']}")
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print(f"Predictions JSON: {result['predictions_path']}")
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if __name__ == "__main__":
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main()
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