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import argparse
import json
import sys
from io import BytesIO
from pathlib import Path
from typing import Any, Dict, List
from PIL import Image
from ultralytics import YOLO
import numpy as np
def load_image(frame: Any, base_dir: Path) -> Image.Image:
if isinstance(frame, (bytes, bytearray, memoryview)):
return Image.open(BytesIO(frame)).convert("RGB")
path = Path(str(frame))
if not path.is_absolute():
path = (Path.cwd() / path).resolve()
if not path.exists():
candidate = (base_dir / str(frame)).resolve()
if candidate.exists():
path = candidate
return Image.open(path).convert("RGB")
def load_model(*_args: Any, **_kwargs: Any):
base_dir = Path(__file__).resolve().parent
model_path = base_dir / "yolo12l-person-seg.pt"
if not model_path.exists():
return None
return YOLO(str(model_path))
def run_model(model, frame: "np.ndarray") -> List[Dict[str, Any]]:
image = Image.fromarray(frame)
results = model(image)
detections: List[Dict[str, Any]] = []
result = results[0]
names = result.names or model.names
name_overrides = {"item": "person"}
for det_idx, box in enumerate(result.boxes):
xyxy = box.xyxy[0].tolist()
class_id = int(box.cls[0].item())
class_name = names.get(class_id, str(class_id))
class_name = name_overrides.get(class_name, class_name)
detections.append(
{
"frame_idx": 0,
"class": class_name,
"bbox": [float(x) for x in xyxy],
"score": float(box.conf[0].item()),
"track_id": f"f0-d{det_idx}",
}
)
return detections
def build_parser() -> argparse.ArgumentParser:
parser = argparse.ArgumentParser(description="Run YOLO12l person segmentation.")
parser.add_argument(
"--stdin-raw",
action="store_true",
default=True,
help="Read raw image bytes from stdin.",
)
return parser
if __name__ == "__main__":
args = build_parser().parse_args()
base_dir = Path(__file__).resolve().parent
model = load_model()
if model is None:
print("[]")
sys.exit(0)
try:
image = load_image(sys.stdin.buffer.read(), base_dir)
except Exception:
print("[]")
sys.exit(0)
frame = np.array(image)
output = run_model(model, frame)
print(json.dumps(output))
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