| 1 | """Run the trained ball detector on one frame and save an annotated image.""" |
| 2 | from __future__ import annotations |
| 3 | |
| 4 | import argparse |
| 5 | from pathlib import Path |
| 6 | |
| 7 | import cv2 |
| 8 | from rich.console import Console |
| 9 | |
| 10 | ROOT = Path(__file__).resolve().parents[1] |
| 11 | MODEL = ROOT / "configs" / "models" / "mlb26_ball.pt" |
| 12 | console = Console() |
| 13 | |
| 14 | def main() -> int: |
| 15 | ap = argparse.ArgumentParser(description="Infer MLB ball model on one image.") |
| 16 | ap.add_argument("image", type=Path) |
| 17 | ap.add_argument("--model", type=Path, default=MODEL) |
| 18 | ap.add_argument("--conf", type=float, default=0.25) |
| 19 | ap.add_argument("--out", type=Path, default=ROOT / "logs" / "yolo_infer.png") |
| 20 | args = ap.parse_args() |
| 21 | |
| 22 | from ultralytics import YOLO |
| 23 | |
| 24 | model = YOLO(str(args.model)) |
| 25 | results = model.predict(source=str(args.image), conf=args.conf, imgsz=640, verbose=False) |
| 26 | annotated = results[0].plot() |
| 27 | args.out.parent.mkdir(parents=True, exist_ok=True) |
| 28 | cv2.imwrite(str(args.out), annotated) |
| 29 | console.print(f"[green]Saved annotated output -> {args.out}[/green]") |
| 30 | for box in results[0].boxes: |
| 31 | xyxy = [float(x) for x in box.xyxy[0]] |
| 32 | conf = float(box.conf[0]) |
| 33 | console.print(f"ball conf={conf:.3f} xyxy={xyxy}") |
| 34 | return 0 |
| 35 | |
| 36 | if __name__ == "__main__": |
| 37 | raise SystemExit(main()) |