subnet_bridge: copy winning miner repo into library

README.md

@@ -4,19 +4,12 @@ tags:
 - model:ultralytics-yolo
 - subnet:winner
 - object:person
+
 manako:
   source: winner_fetch
   manifest_element_name: manak0/Detect-Person
   winner_repo_id: beketov027/ScoreVision2
   winner_revision: a45e30524d27706f624880d0910246d9c63f21c1
-  score:
-    size_mb: 9.850342
-    performance_map50: 0.7215138820273345
-    E: 0.07324759709128216
-  last_benchmark:
-    type: synthetic_fixed
-    ran_at: '2026-10-19T16:31:25.140691Z'
-    result_path: benchmark/synthetic/f2b3a0a0-3c7a-40c2-82e7-86005ced7e92.json
 ---
 
 # Detect-Person-winner

benchmark/synthetic/f2b3a0a0-3c7a-40c2-82e7-86005ced7e92.json

@@ -1,147 +0,0 @@
-{
-  "meta": {
-    "element_id": "manako/Detect-Person-winner",
-    "run_id": "f2b3a0a0-3c7a-40c2-82e7-86005ced7e92",
-    "benchmark_type": "synthetic_fixed",
-    "saved_at": "2026-10-19T16:31:25.140691Z",
-    "result_path": "benchmark/synthetic/f2b3a0a0-3c7a-40c2-82e7-86005ced7e92.json",
-    "split_ref": "private_final"
-  },
-  "results": {
-    "overall_iou": 0.8069316190218553,
-    "per_class_iou": {
-      "person": 0.8069316190218553
-    },
-    "map_50": 0.7215138820273345,
-    "map_50_90": 0.6054387100686015,
-    "precision": 0.9402942024805307,
-    "recall": 0.7314336997980705,
-    "per_class_map_50": {
-      "person": 0.7215138820273345
-    },
-    "per_class_map_50_90": {
-      "person": 0.6054387100686015
-    },
-    "per_class_precision": {
-      "person": 0.9402942024805307
-    },
-    "per_class_recall": {
-      "person": 0.7314336997980705
-    },
-    "adaptive_map_50": 0.7286559727737668,
-    "ffpi": 1.945054945054945,
-    "false_positive_score": 0.8054945054945055,
-    "foundry_score": 0.7593913858620623,
-    "composite_score": 0.7593913858620623,
-    "scoring_policy": "subnet-style-composite-v1",
-    "matched_images": 91,
-    "gt_count": 4457,
-    "pred_count": 3467,
-    "classes": [
-      "person"
-    ],
-    "evaluated_element_revision": null,
-    "dataset_id": "bdf4d5b4-7dec-4b72-8747-ab005ca92b3b",
-    "dataset_version_id": "f2b3a0a0-3c7a-40c2-82e7-86005ced7e92",
-    "run_id": "f2b3a0a0-3c7a-40c2-82e7-86005ced7e92",
-    "split_ref": "private_final",
-    "selected_frame_count": 91,
-    "selected_frame_indices": [
-      5,
-      11,
-      16,
-      20,
-      24,
-      26,
-      28,
-      32,
-      41,
-      49,
-      55,
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-      63,
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-      118,
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-      121,
-      129,
-      130,
-      138,
-      139,
-      146,
-      151,
-      156,
-      173,
-      175,
-      183,
-      184,
-      185,
-      187,
-      193,
-      197,
-      200,
-      201,
-      205,
-      206,
-      218,
-      222,
-      223,
-      226,
-      227,
-      234,
-      235,
-      241,
-      242,
-      244,
-      247,
-      249,
-      250,
-      251,
-      257,
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-      262,
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-      267,
-      282,
-      296,
-      302,
-      307,
-      314,
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-      331,
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-      341,
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-      363,
-      364,
-      367,
-      370,
-      374,
-      377,
-      382,
-      383,
-      387,
-      399
-    ],
-    "evaluated_version_ids": [
-      "f2b3a0a0-3c7a-40c2-82e7-86005ced7e92"
-    ],
-    "skipped_missing_pairs": 0
-  }
-}

benchmark/synthetic/index.json

@@ -1,16 +0,0 @@
-[
-  {
-    "run_id": "f2b3a0a0-3c7a-40c2-82e7-86005ced7e92",
-    "saved_at": "2026-10-19T16:31:25.140691Z",
-    "result_path": "benchmark/synthetic/f2b3a0a0-3c7a-40c2-82e7-86005ced7e92.json",
-    "overall_iou": 0.8069316190218553,
-    "map_50": 0.7215138820273345,
-    "map_50_90": 0.6054387100686015,
-    "precision": 0.9402942024805307,
-    "recall": 0.7314336997980705,
-    "matched_images": 91,
-    "gt_count": 4457,
-    "pred_count": 3467,
-    "split_ref": "private_final"
-  }
-]

benchmark/synthetic/latest.json

@@ -1,147 +0,0 @@
-{
-  "meta": {
-    "element_id": "manako/Detect-Person-winner",
-    "run_id": "f2b3a0a0-3c7a-40c2-82e7-86005ced7e92",
-    "benchmark_type": "synthetic_fixed",
-    "saved_at": "2026-10-19T16:31:25.140691Z",
-    "result_path": "benchmark/synthetic/f2b3a0a0-3c7a-40c2-82e7-86005ced7e92.json",
-    "split_ref": "private_final"
-  },
-  "results": {
-    "overall_iou": 0.8069316190218553,
-    "per_class_iou": {
-      "person": 0.8069316190218553
-    },
-    "map_50": 0.7215138820273345,
-    "map_50_90": 0.6054387100686015,
-    "precision": 0.9402942024805307,
-    "recall": 0.7314336997980705,
-    "per_class_map_50": {
-      "person": 0.7215138820273345
-    },
-    "per_class_map_50_90": {
-      "person": 0.6054387100686015
-    },
-    "per_class_precision": {
-      "person": 0.9402942024805307
-    },
-    "per_class_recall": {
-      "person": 0.7314336997980705
-    },
-    "adaptive_map_50": 0.7286559727737668,
-    "ffpi": 1.945054945054945,
-    "false_positive_score": 0.8054945054945055,
-    "foundry_score": 0.7593913858620623,
-    "composite_score": 0.7593913858620623,
-    "scoring_policy": "subnet-style-composite-v1",
-    "matched_images": 91,
-    "gt_count": 4457,
-    "pred_count": 3467,
-    "classes": [
-      "person"
-    ],
-    "evaluated_element_revision": null,
-    "dataset_id": "bdf4d5b4-7dec-4b72-8747-ab005ca92b3b",
-    "dataset_version_id": "f2b3a0a0-3c7a-40c2-82e7-86005ced7e92",
-    "run_id": "f2b3a0a0-3c7a-40c2-82e7-86005ced7e92",
-    "split_ref": "private_final",
-    "selected_frame_count": 91,
-    "selected_frame_indices": [
-      5,
-      11,
-      16,
-      20,
-      24,
-      26,
-      28,
-      32,
-      41,
-      49,
-      55,
-      59,
-      63,
-      67,
-      70,
-      73,
-      79,
-      80,
-      81,
-      84,
-      86,
-      95,
-      103,
-      104,
-      110,
-      116,
-      118,
-      120,
-      121,
-      129,
-      130,
-      138,
-      139,
-      146,
-      151,
-      156,
-      173,
-      175,
-      183,
-      184,
-      185,
-      187,
-      193,
-      197,
-      200,
-      201,
-      205,
-      206,
-      218,
-      222,
-      223,
-      226,
-      227,
-      234,
-      235,
-      241,
-      242,
-      244,
-      247,
-      249,
-      250,
-      251,
-      257,
-      259,
-      262,
-      266,
-      267,
-      282,
-      296,
-      302,
-      307,
-      314,
-      315,
-      331,
-      333,
-      341,
-      345,
-      346,
-      354,
-      357,
-      361,
-      363,
-      364,
-      367,
-      370,
-      374,
-      377,
-      382,
-      383,
-      387,
-      399
-    ],
-    "evaluated_version_ids": [
-      "f2b3a0a0-3c7a-40c2-82e7-86005ced7e92"
-    ],
-    "skipped_missing_pairs": 0
-  }
-}

main.py

@@ -146,32 +146,16 @@ def _to_detection(box: Any, class_names: list[str]) -> dict[str, Any]:
     return detection
 
 
-def _normalize_image_for_miner(image: Any) -> Any:
-    if image is None or hasattr(image, "shape"):
-        return image
-    if isinstance(image, (bytes, bytearray, memoryview)):
-        try:
-            buffer = np.frombuffer(bytes(image), dtype=np.uint8)
-            decoded = cv2.imdecode(buffer, cv2.IMREAD_COLOR)
-            if decoded is not None:
-                return decoded
-        except Exception:
-            return image
-    if hasattr(image, "convert") and callable(image.convert):
-        try:
-            rgb = image.convert("RGB")
-            array = np.array(rgb)
-            if getattr(array, "ndim", 0) == 3 and array.shape[-1] == 3:
-                return cv2.cvtColor(array, cv2.COLOR_RGB2BGR)
-            return array
-        except Exception:
-            return image
-    try:
-        array = np.asarray(image)
-        if getattr(array, "shape", None):
-            return array
-    except Exception:
-        return image
+def _require_bgr_frame(image: Any) -> Any:
+    # Same frame the turbovision validator hands predict_batch: cv2.imdecode(...,
+    # IMREAD_COLOR) -> BGR uint8 HWC with 3 channels. Anything else is a harness
+    # bug and must fail loudly rather than be coerced.
+    if not isinstance(image, np.ndarray):
+        raise TypeError(f"frame must be a numpy.ndarray (BGR uint8 HWC), got {type(image).__name__}")
+    if image.dtype != np.uint8:
+        raise TypeError(f"frame dtype must be uint8, got {image.dtype}")
+    if image.ndim != 3 or image.shape[2] != 3:
+        raise TypeError(f"frame must be HWC with 3 channels (BGR), got shape {image.shape}")
     return image
 
 
@@ -187,34 +171,14 @@ def load_model(onnx_path: str | None = None, data_dir: str | None = None):
     }
 
 
-def _candidate_keypoint_counts(miner: Any) -> list[int]:
-    counts: list[int] = [0]
-    for attr in ("n_keypoints", "num_keypoints", "keypoint_count", "num_joints"):
-        value = getattr(miner, attr, None)
-        if isinstance(value, int) and value > 0:
-            counts.append(value)
-    counts.append(32)
-
-    seen: set[int] = set()
-    ordered: list[int] = []
-    for count in counts:
-        if count in seen:
-            continue
-        seen.add(count)
-        ordered.append(count)
-    return ordered
+# Validator chute: predict_batch(batch_images=..., offset=0, n_keypoints=32)
+# (turbovision chute_template :376-380; scorevision/utils/challenges.py:412).
+VALIDATOR_N_KEYPOINTS = 32
 
 
-def _predict_batch_with_fallbacks(miner: Any, image: Any) -> list[Any]:
-    normalized_image = _normalize_image_for_miner(image)
-    errors: list[str] = []
-    for n_keypoints in _candidate_keypoint_counts(miner):
-        try:
-            return miner.predict_batch([normalized_image], offset=0, n_keypoints=n_keypoints)
-        except Exception as exc:
-            errors.append(f"n_keypoints={n_keypoints} -> {exc}")
-            continue
-    raise RuntimeError("predict_batch failed for all keypoint candidates: " + " | ".join(errors))
+def _predict_frame(miner: Any, image: Any) -> list[Any]:
+    frame = _require_bgr_frame(image)
+    return miner.predict_batch(batch_images=[frame], offset=0, n_keypoints=VALIDATOR_N_KEYPOINTS)
 
 
 def run_model(model: Any, image: Any = None, onnx_path: str | None = None, data_dir: str | None = None):
@@ -226,7 +190,7 @@ def run_model(model: Any, image: Any = None, onnx_path: str | None = None, data_
     class_names = model.get("class_names")
     if not isinstance(class_names, list):
         class_names = list(CLASS_NAMES)
-    results = _predict_batch_with_fallbacks(miner, image)
+    results = _predict_frame(miner, image)
     if not results:
         return [[]]
     frame_boxes = _extract_boxes(results[0])

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "miner-element-adapter"
 version = "0.1.0"
-requires-python = ">=3.11"
+requires-python = ">=3.12"
 
 dependencies = [
   "numpy>=1.23",