subnet_bridge: copy winning miner repo into library

main.py

@@ -51,6 +51,48 @@ def _extract_boxes(frame_result: Any) -> list[Any]:
     return []
 
 
+def _extract_polygons(frame_result: Any) -> list[Any]:
+    frame = _to_dict(frame_result)
+    polygons = frame.get("polygons", [])
+    if isinstance(polygons, list):
+        return polygons
+    polygon = frame.get("polygon", [])
+    if isinstance(polygon, list):
+        return [polygon]
+    return []
+
+
+def _normalize_polygon_item(polygon: Any) -> list[Any]:
+    payload = _to_dict(polygon)
+    points = payload.get("polygon")
+    if isinstance(points, list) and points:
+        return points
+    points = payload.get("points")
+    if isinstance(points, list) and points:
+        return points
+    if isinstance(polygon, list):
+        return polygon
+    return []
+
+
+def _bbox_from_polygon(polygon: Any) -> tuple[float, float, float, float] | None:
+    if not isinstance(polygon, list) or not polygon:
+        return None
+    xs: list[float] = []
+    ys: list[float] = []
+    for point in polygon:
+        if not isinstance(point, (list, tuple)) or len(point) < 2:
+            continue
+        try:
+            xs.append(float(point[0]))
+            ys.append(float(point[1]))
+        except (TypeError, ValueError):
+            continue
+    if not xs or not ys:
+        return None
+    return min(xs), min(ys), max(xs), max(ys)
+
+
 def _resolve_runtime_class_names(miner: Any) -> list[str]:
     value = getattr(miner, "class_names", None)
     if isinstance(value, (list, tuple)):
@@ -63,13 +105,25 @@ def _resolve_runtime_class_names(miner: Any) -> list[str]:
 def _to_detection(box: Any, class_names: list[str]) -> 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))
+    polygon = payload.get("polygon")
+    if not (isinstance(polygon, list) and polygon):
+        polygons = payload.get("polygons")
+        if isinstance(polygons, list) and polygons:
+            polygon = polygons[0]
+    if not (isinstance(polygon, list) and polygon):
+        points = payload.get("points")
+        if isinstance(points, list) and points:
+            polygon = points
+
+    bbox = _bbox_from_polygon(polygon) if polygon is not None else None
+    x1 = float(payload.get("x1", bbox[0] if bbox else 0.0))
+    y1 = float(payload.get("y1", bbox[1] if bbox else 0.0))
+    x2 = float(payload.get("x2", bbox[2] if bbox else 0.0))
+    y2 = float(payload.get("y2", bbox[3] if bbox else 0.0))
     width = max(0.0, x2 - x1)
     height = max(0.0, y2 - y1)
-    return {
+    detection = {
+        "bbox": [x1, y1, x2, y2],
         "x": x1 + width / 2.0,
         "y": y1 + height / 2.0,
         "width": width,
@@ -78,6 +132,18 @@ def _to_detection(box: Any, class_names: list[str]) -> dict[str, Any]:
         "class_id": cls_id,
         "class": class_names[cls_id] if 0 <= cls_id < len(class_names) else str(cls_id),
     }
+    if isinstance(polygon, list) and polygon:
+        detection["polygon"] = polygon
+    polygons = payload.get("polygons")
+    if isinstance(polygons, list) and polygons:
+        detection["polygons"] = polygons
+        if "polygon" not in detection:
+            detection["polygon"] = polygons[0]
+    if "polygon" not in detection:
+        points = payload.get("points")
+        if isinstance(points, list) and points:
+            detection["polygon"] = points
+    return detection
 
 
 def _normalize_image_for_miner(image: Any) -> Any:
@@ -164,7 +230,25 @@ def run_model(model: Any, image: Any = None, onnx_path: str | None = None, data_
     if not results:
         return [[]]
     frame_boxes = _extract_boxes(results[0])
-    detections = [_to_detection(box, class_names) for box in frame_boxes]
+    frame_polygons = _extract_polygons(results[0])
+    detections = []
+    for idx, box in enumerate(frame_boxes):
+        detection = _to_detection(box, class_names)
+        box_polygon = _normalize_polygon_item(box)
+        if box_polygon:
+            detection["polygons"] = [box_polygon]
+        if idx < len(frame_polygons):
+            polygon = _normalize_polygon_item(frame_polygons[idx])
+            if polygon:
+                detection["polygon"] = polygon
+                detection["polygons"] = [polygon]
+        detections.append(detection)
+        if not detections and frame_polygons:
+            for idx, polygon in enumerate(frame_polygons):
+                normalized_polygon = _normalize_polygon_item(polygon)
+                detection = _to_detection({"polygon": normalized_polygon}, class_names)
+                detection["polygons"] = [normalized_polygon]
+                detections.append(detection)
     return [detections]