Files
yolov26_3d/tools/pdcl_inference/run_two_roi_infer.py
2026-06-24 09:35:46 +08:00

69 lines
2.7 KiB
Python
Executable File

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