Delete main.py

main.py

@@ -1,99 +0,0 @@
-from __future__ import annotations
-
-import json
-import os
-import sys
-from pathlib import Path
-from typing import Any
-
-import cv2
-
-from miner import Miner
-
-
-CLASS_NAMES = ['football', 'player', 'pitch']
-MODEL_TYPE = 'ultralytics-yolo'
-
-
-def _to_dict(value: Any) -> dict[str, Any]:
-    if isinstance(value, dict):
-        return value
-    if hasattr(value, "model_dump") and callable(value.model_dump):
-        dumped = value.model_dump()
-        if isinstance(dumped, dict):
-            return dumped
-    if hasattr(value, "__dict__"):
-        return dict(value.__dict__)
-    return {}
-
-
-def _extract_boxes(frame_result: Any) -> list[Any]:
-    frame = _to_dict(frame_result)
-    boxes = frame.get("boxes", [])
-    if isinstance(boxes, list):
-        return boxes
-    return []
-
-
-def _to_detection(box: Any) -> dict[str, Any]:
-    payload = _to_dict(box)
-    cls_id = int(payload.get("cls_id", 0))
-    x1 = float(payload.get("x1", 0.0))
-    y1 = float(payload.get("y1", 0.0))
-    x2 = float(payload.get("x2", 0.0))
-    y2 = float(payload.get("y2", 0.0))
-    width = max(0.0, x2 - x1)
-    height = max(0.0, y2 - y1)
-    return {
-        "x": x1 + width / 2.0,
-        "y": y1 + height / 2.0,
-        "width": width,
-        "height": height,
-        "confidence": float(payload.get("conf", 0.0)),
-        "class_id": cls_id,
-        "class": CLASS_NAMES[cls_id] if 0 <= cls_id < len(CLASS_NAMES) else str(cls_id),
-    }
-
-
-def load_model(onnx_path: str | None = None, data_dir: str | None = None):
-    del onnx_path
-    repo_dir = Path(data_dir) if data_dir else Path(__file__).resolve().parent
-    miner = Miner(repo_dir)
-    return {
-        "miner": miner,
-        "model_type": MODEL_TYPE,
-        "class_names": CLASS_NAMES,
-    }
-
-
-def run_model(model: Any, image: Any = None, onnx_path: str | None = None, data_dir: str | None = None):
-    del onnx_path
-    if image is None:
-        image = model
-        model = load_model(data_dir=data_dir)
-    miner = model["miner"]
-    results = miner.predict_batch([image], offset=0, n_keypoints=0)
-    if not results:
-        return [[]]
-    frame_boxes = _extract_boxes(results[0])
-    detections = [_to_detection(box) for box in frame_boxes]
-    return [detections]
-
-
-def main() -> None:
-    if len(sys.argv) < 2:
-        print("Usage: main.py <image_path>", file=sys.stderr)
-        raise SystemExit(1)
-    image_path = sys.argv[1]
-    image = cv2.imread(image_path, cv2.IMREAD_COLOR)
-    if image is None:
-        print(f"Could not read image: {image_path}", file=sys.stderr)
-        raise SystemExit(1)
-    data_dir = os.path.dirname(os.path.abspath(__file__))
-    model = load_model(data_dir=data_dir)
-    output = run_model(model, image)
-    print(json.dumps(output, indent=2))
-
-
-if __name__ == "__main__":
-    main()