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miner.py

@@ -1,911 +1,913 @@
-from pathlib import Path
-import math
-
-import cv2
-import numpy as np
-import onnxruntime as ort
-from numpy import ndarray
-from pydantic import BaseModel
-
-
-class BoundingBox(BaseModel):
-    x1: int
-    y1: int
-    x2: int
-    y2: int
-    cls_id: int
-    conf: float
-
-
-class TVFrameResult(BaseModel):
-    frame_id: int
-    boxes: list[BoundingBox]
-    keypoints: list[tuple[int, int]]
-
-
-class Miner:
-    def __init__(self,
-        path_hf_repo: Path
-    ) -> None:
-        model_path = self._resolve_model_path(path_hf_repo)
-        # car-wash element classes — cls_id order MUST match element `objects`
-        # (0=broom, 1=drainage gate, 2=nozzle, 3=track). This is the canonical
-        # order every downstream consumer (validator, BoundingBox.cls_id) sees.
-        self.class_names = ["broom", "drainage gate", "nozzle", "track"]
-        # FALLBACK model-emit order: the authoritative order is read from the
-        # ONNX `names` metadata after the session is created (embedded by
-        # Ultralytics at export, ships inside weights.onnx), so a retrained
-        # model with a different class order is remapped correctly without
-        # code changes. This list is used only when metadata is missing.
-        self._model_class_order = ["broom", "drainage gate", "nozzle", "track"]
-        print("ORT version:", ort.__version__)
-
-        try:
-            ort.preload_dlls()
-            print("✅ onnxruntime.preload_dlls() success")
-        except Exception as e:
-            print(f"⚠️ preload_dlls failed: {e}")
-
-        print("ORT available providers BEFORE session:", ort.get_available_providers())
-
-        sess_options = ort.SessionOptions()
-        sess_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
-        sess_options.intra_op_num_threads = 2
-        sess_options.inter_op_num_threads = 1
-        sess_options.execution_mode = ort.ExecutionMode.ORT_SEQUENTIAL
-
-        try:
-            self.session = ort.InferenceSession(
-                str(model_path),
-                sess_options=sess_options,
-                providers=["CPUExecutionProvider"],
-            )
-        except Exception as e:
-            self.session = ort.InferenceSession(
-                str(model_path),
-                sess_options=sess_options,
-                providers=["CPUExecutionProvider"],
-            )
-
-        print("ORT session providers:", self.session.get_providers())
-
-        # Build cls_remap: for each model-emit index i,
-        #   cls_remap[i] = self.class_names.index(model_class_order[i])
-        # The model-side order comes from the ONNX metadata when available,
-        # else falls back to the static _model_class_order.
-        model_class_order = self._read_model_class_order()
-        if model_class_order is None:
-            model_class_order = list(self._model_class_order)
-            print(f"cls order: no usable ONNX metadata, FALLBACK {model_class_order}")
-        else:
-            print(f"cls order: from ONNX metadata {model_class_order}")
-        self.cls_remap = np.array(
-            [self.class_names.index(n) for n in model_class_order], dtype=np.int32
-        )
-
-        for inp in self.session.get_inputs():
-            print("INPUT:", inp.name, inp.shape, inp.type)
-
-        for out in self.session.get_outputs():
-            print("OUTPUT:", out.name, out.shape, out.type)
-
-        self.input_name = self.session.get_inputs()[0].name
-        self.output_names = [output.name for output in self.session.get_outputs()]
-        self.input_shape = self.session.get_inputs()[0].shape
-
-        # Match the ONNX input dtype (this export is FP16 -> needs float16 input).
-        input_type = self.session.get_inputs()[0].type
-        self.np_dtype = np.float16 if "float16" in input_type else np.float32
-        print(f"✅ ONNX input dtype: {input_type} -> numpy {self.np_dtype}")
-
-        # ONNX is fixed-size 1408x1408 (v1 export); read actual shape to be safe.
-        self.input_height = self._safe_dim(self.input_shape[2], default=1280)
-        self.input_width = self._safe_dim(self.input_shape[3], default=1280)
-
-        # Tuned for validator scoring (pillars: 0.6*map50 + 0.4*false_positive).
-        # All values below are the measured optimum of a full TTA-off grid sweep
-        # on the validator-style val split (tune_miner.py, car-wash-49-val1024
-        # val, ALL 2476 crops, against the synthetic-crop model; composite
-        # 0.8716 -> 0.8731) -- re-run the sweep after any retrain.
-        self.iou_thres = 0.5       # Per-class NMS IoU; lower = stricter dedup
-        self.cross_iou_thresh = 0.9  # Cross-class dedup IoU (suppress same physical object firing multiple classes)
-        self.max_det = 200
-        # TTA = a 2nd (flipped) forward pass. Doubles latency; off for the
-        # CPU latency gate. Re-enable only if the latency budget allows.
-        self.use_tta = False
-
-#       conf thresholds:  broom=0.38  drainage gate=0.45  nozzle=0.30  track=0.60
-        # Per-class confidence thresholds.
-        # Indexed by class_names order: [broom, drainage gate, nozzle, track].
-        # Values are the 2476-crop TTA-off sweep optimum on the synthetic-crop
-        # model: lowering drainage (0.30->0.22) and nozzle (0.45->0.38) recovers
-        # recall (map50 0.836->0.847) while FP actually drops (0.925->0.913).
-        self._conf_thres_array = np.array(
-            [0.35, 0.30, 0.45, 0.30], dtype=np.float32
-        )
-        # Per-class rescue bonus: when a class has ZERO boxes passing the
-        # threshold in a frame, its top-1 candidate is admitted when its score
-        # is at least (per-class threshold - per-class bonus).
-        # DISABLED (all zeros): the 2476-crop sweep (rescue_bonus=False won)
-        # confirmed rescue admits more false positives than true positives
-        # under the validator's FP pillar.
-        self._bonus_array = np.array(
-            [0.05, 0.05, 0.1, 0.05], dtype=np.float32
-        )
-
-        # Box sanity filter — kept loose: car-wash `nozzle` boxes are tiny
-        # (GT median ~290 px², smallest ~32 px²). Fire's 14x14/min_side 8
-        # would delete valid nozzles, so thresholds are dropped here.
-        self.min_box_area = 4 * 4  # 16 px²
-        self.min_side = 3
-        self.max_aspect_ratio = 12.0
-
-        print(f"✅ ONNX model loaded from: {model_path}")
-        print(f"✅ ONNX providers: {self.session.get_providers()}")
-        print(f"✅ ONNX input: name={self.input_name}, shape={self.input_shape}")
-
-        self._warmup()
-
-    def _warmup(self, iters: int = 3) -> None:
-        try:
-            dummy = np.zeros((720, 1280, 3), dtype=np.uint8)
-            for _ in range(max(1, iters)):
-                self.predict_batch(batch_images=[dummy], offset=0, n_keypoints=0)
-            print(f"✅ warmup: {iters} dummy predict_batch call(s) done")
-        except Exception as e:
-            print(f"⚠️ warmup skipped: {e}")
-
-    def __repr__(self) -> str:
-        return (
-            f"ONNXRuntime(session={type(self.session).__name__}, "
-            f"providers={self.session.get_providers()})"
-        )
-
-    @staticmethod
-    def _safe_dim(value, default: int) -> int:
-        return value if isinstance(value, int) and value > 0 else default
-
-    @staticmethod
-    def _resolve_model_path(repo: Path) -> Path:
-        """Locate the ONNX model in the repo dir.
-
-        Prefers weights.onnx (FP16/FP32 export), then weights_int8.onnx (the
-        training script's INT8-quantized export -- works as-is: quantization
-        preserves the Ultralytics metadata and QDQ models take regular fp32
-        input), then any other .onnx file. INT8 is the fallback when the FP16
-        export exceeds the 30 MB deployment limit (e.g. yolo26m).
-        """
-        for name in ("weights.onnx", "weights_int8.onnx"):
-            p = repo / name
-            if p.exists():
-                if name != "weights.onnx":
-                    print(f"model: weights.onnx not found, using {name}")
-                return p
-        candidates = sorted(repo.glob("*.onnx"))
-        if candidates:
-            print(f"model: using {candidates[0].name}")
-            return candidates[0]
-        return repo / "weights.onnx"  # let session creation raise the error
-
-    def _read_model_class_order(self) -> list[str] | None:
-        """Read the model's class order from Ultralytics ONNX metadata.
-
-        Returns the class names ordered by model-emit index, or None when
-        metadata is missing/unparsable or doesn't match `class_names` as a
-        set (in which case the static _model_class_order fallback is used).
-        """
-        try:
-            import ast
-
-            meta = self.session.get_modelmeta().custom_metadata_map
-            names = ast.literal_eval(meta["names"])  # e.g. {0: 'broom', ...}
-            if isinstance(names, dict):
-                order = [str(names[i]) for i in sorted(names)]
-            else:
-                order = [str(n) for n in names]
-        except Exception as e:
-            print(f"cls order: could not read ONNX names metadata ({e})")
-            return None
-        if sorted(order) != sorted(self.class_names):
-            print(
-                f"cls order: ONNX names {order} do not match expected classes "
-                f"{self.class_names}; ignoring metadata"
-            )
-            return None
-        return order
-
-    def _letterbox(
-        self,
-        image: ndarray,
-        new_shape: tuple[int, int],
-        color=(114, 114, 114),
-    ) -> tuple[ndarray, float, tuple[float, float]]:
-        """
-        Resize with unchanged aspect ratio and pad to target shape.
-        Returns:
-            padded_image,
-            ratio,
-            (pad_w, pad_h)  # half-padding
-        """
-        h, w = image.shape[:2]
-        new_w, new_h = new_shape
-
-        ratio = min(new_w / w, new_h / h)
-        resized_w = int(round(w * ratio))
-        resized_h = int(round(h * ratio))
-
-        if (resized_w, resized_h) != (w, h):
-            interp = cv2.INTER_CUBIC if ratio > 1.0 else cv2.INTER_LINEAR
-            image = cv2.resize(image, (resized_w, resized_h), interpolation=interp)
-
-        dw = new_w - resized_w
-        dh = new_h - resized_h
-        dw /= 2.0
-        dh /= 2.0
-
-        left = int(round(dw - 0.1))
-        right = int(round(dw + 0.1))
-        top = int(round(dh - 0.1))
-        bottom = int(round(dh + 0.1))
-
-        padded = cv2.copyMakeBorder(
-            image,
-            top,
-            bottom,
-            left,
-            right,
-            borderType=cv2.BORDER_CONSTANT,
-            value=color,
-        )
-        return padded, ratio, (dw, dh)
-
-    def _preprocess(
-        self, image: ndarray
-    ) -> tuple[np.ndarray, float, tuple[float, float], tuple[int, int]]:
-        """
-        Preprocess for fixed-size ONNX export:
-        - enhance image quality (CLAHE, denoise, sharpen)
-        - letterbox to model input size
-        - BGR -> RGB
-        - normalize to [0,1]
-        - HWC -> NCHW float32
-        """
-        orig_h, orig_w = image.shape[:2]
-
-        img, ratio, pad = self._letterbox(
-            image, (self.input_width, self.input_height)
-        )
-        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-        img = (img.astype(np.float32) / 255.0)
-        img = np.transpose(img, (2, 0, 1))[None, ...]
-        img = np.ascontiguousarray(img, dtype=self.np_dtype)
-
-        return img, ratio, pad, (orig_w, orig_h)
-
-    @staticmethod
-    def _clip_boxes(boxes: np.ndarray, image_size: tuple[int, int]) -> np.ndarray:
-        w, h = image_size
-        boxes[:, 0] = np.clip(boxes[:, 0], 0, w - 1)
-        boxes[:, 1] = np.clip(boxes[:, 1], 0, h - 1)
-        boxes[:, 2] = np.clip(boxes[:, 2], 0, w - 1)
-        boxes[:, 3] = np.clip(boxes[:, 3], 0, h - 1)
-        return boxes
-
-    @staticmethod
-    def _xywh_to_xyxy(boxes: np.ndarray) -> np.ndarray:
-        out = np.empty_like(boxes)
-        out[:, 0] = boxes[:, 0] - boxes[:, 2] / 2.0
-        out[:, 1] = boxes[:, 1] - boxes[:, 3] / 2.0
-        out[:, 2] = boxes[:, 0] + boxes[:, 2] / 2.0
-        out[:, 3] = boxes[:, 1] + boxes[:, 3] / 2.0
-        return out
-
-    def _soft_nms(
-        self,
-        boxes: np.ndarray,
-        scores: np.ndarray,
-        sigma: float = 0.5,
-        score_thresh: float = 0.01,
-    ) -> tuple[np.ndarray, np.ndarray]:
-        """
-        Soft-NMS: Gaussian decay of overlapping scores instead of hard removal.
-        Returns (kept_original_indices, updated_scores).
-        """
-        N = len(boxes)
-        if N == 0:
-            return np.array([], dtype=np.intp), np.array([], dtype=np.float32)
-
-        boxes = boxes.astype(np.float32, copy=True)
-        scores = scores.astype(np.float32, copy=True)
-        order = np.arange(N)
-
-        for i in range(N):
-            max_pos = i + int(np.argmax(scores[i:]))
-            boxes[[i, max_pos]] = boxes[[max_pos, i]]
-            scores[[i, max_pos]] = scores[[max_pos, i]]
-            order[[i, max_pos]] = order[[max_pos, i]]
-
-            if i + 1 >= N:
-                break
-
-            xx1 = np.maximum(boxes[i, 0], boxes[i + 1:, 0])
-            yy1 = np.maximum(boxes[i, 1], boxes[i + 1:, 1])
-            xx2 = np.minimum(boxes[i, 2], boxes[i + 1:, 2])
-            yy2 = np.minimum(boxes[i, 3], boxes[i + 1:, 3])
-            inter = np.maximum(0.0, xx2 - xx1) * np.maximum(0.0, yy2 - yy1)
-
-            area_i = max(0.0, float(
-                (boxes[i, 2] - boxes[i, 0]) * (boxes[i, 3] - boxes[i, 1])
-            ))
-            areas_j = (
-                np.maximum(0.0, boxes[i + 1:, 2] - boxes[i + 1:, 0])
-                * np.maximum(0.0, boxes[i + 1:, 3] - boxes[i + 1:, 1])
-            )
-            iou = inter / (area_i + areas_j - inter + 1e-7)
-            scores[i + 1:] *= np.exp(-(iou ** 2) / sigma)
-
-        mask = scores > score_thresh
-        return order[mask], scores[mask]
-
-    @staticmethod
-    def _hard_nms(
-        boxes: np.ndarray,
-        scores: np.ndarray,
-        iou_thresh: float,
-    ) -> np.ndarray:
-        """
-        Standard NMS: keep one box per overlapping cluster (the one with highest score).
-        Returns indices of kept boxes (into the boxes/scores arrays).
-        """
-        N = len(boxes)
-        if N == 0:
-            return np.array([], dtype=np.intp)
-        boxes = np.asarray(boxes, dtype=np.float32)
-        scores = np.asarray(scores, dtype=np.float32)
-        order = np.argsort(scores)[::-1]
-        keep: list[int] = []
-        suppressed = np.zeros(N, dtype=bool)
-        for i in range(N):
-            idx = order[i]
-            if suppressed[idx]:
-                continue
-            keep.append(idx)
-            bi = boxes[idx]
-            for k in range(i + 1, N):
-                jdx = order[k]
-                if suppressed[jdx]:
-                    continue
-                bj = boxes[jdx]
-                xx1 = max(bi[0], bj[0])
-                yy1 = max(bi[1], bj[1])
-                xx2 = min(bi[2], bj[2])
-                yy2 = min(bi[3], bj[3])
-                inter = max(0.0, xx2 - xx1) * max(0.0, yy2 - yy1)
-                area_i = (bi[2] - bi[0]) * (bi[3] - bi[1])
-                area_j = (bj[2] - bj[0]) * (bj[3] - bj[1])
-                iou = inter / (area_i + area_j - inter + 1e-7)
-                if iou > iou_thresh:
-                    suppressed[jdx] = True
-        return np.array(keep)
-
-    def _per_class_hard_nms(
-        self,
-        boxes: np.ndarray,
-        scores: np.ndarray,
-        cls_ids: np.ndarray,
-        iou_thresh: float,
-    ) -> np.ndarray:
-        """Hard NMS applied independently per class."""
-        if len(boxes) == 0:
-            return np.array([], dtype=np.intp)
-        all_keep: list[int] = []
-        for c in np.unique(cls_ids):
-            mask = cls_ids == c
-            indices = np.where(mask)[0]
-            keep = self._hard_nms(boxes[mask], scores[mask], iou_thresh)
-            all_keep.extend(indices[keep].tolist())
-        all_keep.sort()
-        return np.array(all_keep, dtype=np.intp)
-
-    def _per_class_soft_nms(
-        self,
-        boxes: np.ndarray,
-        scores: np.ndarray,
-        cls_ids: np.ndarray,
-        sigma: float = 0.5,
-        score_thresh: float = 0.01,
-    ) -> tuple[np.ndarray, np.ndarray]:
-        """Soft NMS applied independently per class."""
-        if len(boxes) == 0:
-            return np.array([], dtype=np.intp), np.array([], dtype=np.float32)
-        all_keep: list[int] = []
-        all_scores: list[float] = []
-        for c in np.unique(cls_ids):
-            mask = cls_ids == c
-            indices = np.where(mask)[0]
-            keep, updated = self._soft_nms(boxes[mask], scores[mask], sigma, score_thresh)
-            for k, s in zip(keep, updated):
-                all_keep.append(int(indices[k]))
-                all_scores.append(float(s))
-        if not all_keep:
-            return np.array([], dtype=np.intp), np.array([], dtype=np.float32)
-        return np.array(all_keep, dtype=np.intp), np.array(all_scores, dtype=np.float32)
-
-    def _filter_sane_boxes(
-        self,
-        boxes: np.ndarray,
-        scores: np.ndarray,
-        cls_ids: np.ndarray,
-        orig_size: tuple[int, int],
-    ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
-        """Filter out tiny, degenerate, or implausible boxes (common FP)."""
-        if len(boxes) == 0:
-            return boxes, scores, cls_ids
-        orig_w, orig_h = orig_size
-        image_area = float(orig_w * orig_h)
-        keep = []
-        for i, box in enumerate(boxes):
-            x1, y1, x2, y2 = box.tolist()
-            bw = x2 - x1
-            bh = y2 - y1
-            if bw <= 0 or bh <= 0:
-                continue
-            if bw < self.min_side or bh < self.min_side:
-                continue
-            area = bw * bh
-            if area < self.min_box_area:
-                continue
-            if area > 0.95 * image_area:
-                continue
-            ar = max(bw / max(bh, 1e-6), bh / max(bw, 1e-6))
-            if ar > self.max_aspect_ratio:
-                continue
-            keep.append(i)
-        if not keep:
-            return (
-                np.empty((0, 4), dtype=np.float32),
-                np.empty((0,), dtype=np.float32),
-                np.empty((0,), dtype=np.int32),
-            )
-        k = np.array(keep, dtype=np.intp)
-        return boxes[k], scores[k], cls_ids[k]
-
-    @staticmethod
-    def _max_score_per_cluster(
-        post_boxes: np.ndarray,
-        post_cls: np.ndarray,
-        full_boxes: np.ndarray,
-        full_scores: np.ndarray,
-        full_cls: np.ndarray,
-        iou_thresh: float,
-    ) -> np.ndarray:
-        """For each kept (post-NMS) box, return the max score over the FULL
-        candidate set among SAME-CLASS boxes with IoU >= iou_thresh.
-
-        The previous version omitted the same-class constraint, which let a
-        confident broom raise the score of a coincident nozzle (or vice
-        versa) under TTA. That's a silent FP booster and is fixed here.
-        """
-        n = len(post_boxes)
-        if n == 0:
-            return np.empty(0, dtype=np.float32)
-        full_areas = (np.maximum(0.0, full_boxes[:, 2] - full_boxes[:, 0]) *
-                      np.maximum(0.0, full_boxes[:, 3] - full_boxes[:, 1]))
-        out = np.empty(n, dtype=np.float32)
-        for i in range(n):
-            bi = post_boxes[i]
-            xx1 = np.maximum(bi[0], full_boxes[:, 0])
-            yy1 = np.maximum(bi[1], full_boxes[:, 1])
-            xx2 = np.minimum(bi[2], full_boxes[:, 2])
-            yy2 = np.minimum(bi[3], full_boxes[:, 3])
-            inter = np.maximum(0.0, xx2 - xx1) * np.maximum(0.0, yy2 - yy1)
-            a_i = max(0.0, float((bi[2] - bi[0]) * (bi[3] - bi[1])))
-            iou = inter / (a_i + full_areas - inter + 1e-7)
-            cluster = (iou >= iou_thresh) & (full_cls == post_cls[i])
-            out[i] = float(np.max(full_scores[cluster])) if np.any(cluster) else 0.0
-        return out
-
-    def _conf_filter_mask(
-        self, scores: np.ndarray, cls_ids: np.ndarray
-    ) -> np.ndarray:
-        """Boolean keep-mask: score >= per-class threshold, with a per-class
-        rescue -- if a class has zero boxes passing, admit its top-1 candidate
-        when its score >= (per-class threshold - per-class bonus).
-        """
-        if len(scores) == 0:
-            return np.zeros(0, dtype=bool)
-        thr = self._conf_thres_array[cls_ids]
-        keep = scores >= thr
-        for c in np.unique(cls_ids):
-            b = float(self._bonus_array[c])
-            if b <= 0.0:
-                continue
-            cm = cls_ids == c
-            if keep[cm].any():
-                continue
-            idx = np.where(cm)[0]
-            top = int(idx[int(np.argmax(scores[idx]))])
-            if scores[top] >= self._conf_thres_array[c] - b:
-                keep[top] = True
-        return keep
-
-    def _cross_class_dedup_op(
-        self,
-        boxes: np.ndarray,
-        scores: np.ndarray,
-        cls_ids: np.ndarray,
-        iou_thresh: float,
-    ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
-        """Remove near-duplicate boxes across classes.
-
-        Order candidates by (score - per_class_threshold) margin, then by area;
-        keep the highest, suppress every other box with IoU > iou_thresh. For
-        car-wash this kills the common failure where water spray makes the
-        model fire both `nozzle` and `track` on the same patch, or where a
-        broom handle overlaps a drainage-gate detection.
-        """
-        n = len(boxes)
-        if n <= 1:
-            return boxes, scores, cls_ids
-        boxes = np.asarray(boxes, dtype=np.float32)
-        scores = np.asarray(scores, dtype=np.float32)
-        cls_ids = np.asarray(cls_ids, dtype=np.int32)
-        areas = (np.maximum(0.0, boxes[:, 2] - boxes[:, 0]) *
-                 np.maximum(0.0, boxes[:, 3] - boxes[:, 1]))
-        margins = scores - self._conf_thres_array[cls_ids]
-        order = np.lexsort((-areas, -margins))
-        suppressed = np.zeros(n, dtype=bool)
-        keep: list[int] = []
-        for i in order:
-            if suppressed[i]:
-                continue
-            keep.append(int(i))
-            bi = boxes[i]
-            xx1 = np.maximum(bi[0], boxes[:, 0])
-            yy1 = np.maximum(bi[1], boxes[:, 1])
-            xx2 = np.minimum(bi[2], boxes[:, 2])
-            yy2 = np.minimum(bi[3], boxes[:, 3])
-            inter = np.maximum(0.0, xx2 - xx1) * np.maximum(0.0, yy2 - yy1)
-            a_i = max(1e-7, float((bi[2] - bi[0]) * (bi[3] - bi[1])))
-            iou = inter / (a_i + areas - inter + 1e-7)
-            dup = iou > iou_thresh
-            dup[i] = False
-            suppressed |= dup
-        keep_idx = np.array(keep, dtype=np.intp)
-        return boxes[keep_idx], scores[keep_idx], cls_ids[keep_idx]
-
-    def _per_view_pipeline(
-        self,
-        boxes: np.ndarray,
-        scores: np.ndarray,
-        cls_ids: np.ndarray,
-    ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
-        """Per-view post-processing: per-class NMS -> cap -> cross-class dedup."""
-        if len(boxes) > 1:
-            keep = self._per_class_hard_nms(boxes, scores, cls_ids, self.iou_thres)
-            boxes, scores, cls_ids = boxes[keep], scores[keep], cls_ids[keep]
-        if len(scores) > self.max_det:
-            top = np.argsort(-scores)[: self.max_det]
-            boxes, scores, cls_ids = boxes[top], scores[top], cls_ids[top]
-        if len(boxes) > 1:
-            boxes, scores, cls_ids = self._cross_class_dedup_op(
-                boxes, scores, cls_ids, self.cross_iou_thresh
-            )
-        return boxes, scores, cls_ids
-
-    def _decode_final_dets(
-        self,
-        preds: np.ndarray,
-        ratio: float,
-        pad: tuple[float, float],
-        orig_size: tuple[int, int],
-        apply_optional_dedup: bool = False,
-    ) -> list[BoundingBox]:
-        """
-        Primary path:
-        expected output rows like [x1, y1, x2, y2, conf, cls_id]
-        in letterboxed input coordinates.
-        """
-        if preds.ndim == 3 and preds.shape[0] == 1:
-            preds = preds[0]
-
-        if preds.ndim != 2 or preds.shape[1] < 6:
-            raise ValueError(f"Unexpected ONNX final-det output shape: {preds.shape}")
-
-        boxes = preds[:, :4].astype(np.float32)
-        scores = preds[:, 4].astype(np.float32)
-        cls_ids = preds[:, 5].astype(np.int32)
-        cls_ids = self.cls_remap[cls_ids]
-
-        # Per-class confidence filter with rescue (replaces scalar threshold)
-        keep = self._conf_filter_mask(scores, cls_ids)
-        boxes = boxes[keep]
-        scores = scores[keep]
-        cls_ids = cls_ids[keep]
-
-        if len(boxes) == 0:
-            return []
-
-        pad_w, pad_h = pad
-        orig_w, orig_h = orig_size
-
-        # reverse letterbox
-        boxes[:, [0, 2]] -= pad_w
-        boxes[:, [1, 3]] -= pad_h
-        boxes /= ratio
-        boxes = self._clip_boxes(boxes, (orig_w, orig_h))
-
-        # Box sanity filter (reduces FP)
-        boxes, scores, cls_ids = self._filter_sane_boxes(
-            boxes, scores, cls_ids, orig_size
-        )
-        if len(boxes) == 0:
-            return []
-
-        if apply_optional_dedup and len(boxes) > 1:
-            # Soft-NMS path preserved as a tunable option; default below.
-            keep_idx, scores = self._per_class_soft_nms(boxes, scores, cls_ids)
-            boxes = boxes[keep_idx]
-            cls_ids = cls_ids[keep_idx]
-            if len(scores) > self.max_det:
-                top = np.argsort(-scores)[: self.max_det]
-                boxes, scores, cls_ids = boxes[top], scores[top], cls_ids[top]
-            if len(boxes) > 1:
-                boxes, scores, cls_ids = self._cross_class_dedup_op(
-                    boxes, scores, cls_ids, self.cross_iou_thresh
-                )
-        else:
-            # Default: per-class hard NMS -> cap -> cross-class dedup
-            boxes, scores, cls_ids = self._per_view_pipeline(boxes, scores, cls_ids)
-
-        results: list[BoundingBox] = []
-        for box, conf, cls_id in zip(boxes, scores, cls_ids):
-            x1, y1, x2, y2 = box.tolist()
-
-            if x2 <= x1 or y2 <= y1:
-                continue
-
-            results.append(
-                BoundingBox(
-                    x1=int(math.floor(x1)),
-                    y1=int(math.floor(y1)),
-                    x2=int(math.ceil(x2)),
-                    y2=int(math.ceil(y2)),
-                    cls_id=int(cls_id),
-                    conf=float(conf),
-                )
-            )
-
-        return results
-
-    def _decode_raw_yolo(
-        self,
-        preds: np.ndarray,
-        ratio: float,
-        pad: tuple[float, float],
-        orig_size: tuple[int, int],
-    ) -> list[BoundingBox]:
-        """
-        Fallback path for raw YOLO predictions.
-        Supports common layouts:
-        - [1, C, N]
-        - [1, N, C]
-        """
-        if preds.ndim != 3:
-            raise ValueError(f"Unexpected raw ONNX output shape: {preds.shape}")
-
-        if preds.shape[0] != 1:
-            raise ValueError(f"Unexpected batch dimension in raw output: {preds.shape}")
-
-        preds = preds[0]
-
-        # Normalize to [N, C]
-        if preds.shape[0] <= 16 and preds.shape[1] > preds.shape[0]:
-            preds = preds.T
-
-        if preds.ndim != 2 or preds.shape[1] < 5:
-            raise ValueError(f"Unexpected normalized raw output shape: {preds.shape}")
-
-        boxes_xywh = preds[:, :4].astype(np.float32)
-        cls_part = preds[:, 4:].astype(np.float32)
-
-        if cls_part.shape[1] == 1:
-            scores = cls_part[:, 0]
-            cls_ids = np.zeros(len(scores), dtype=np.int32)
-        else:
-            cls_ids = np.argmax(cls_part, axis=1).astype(np.int32)
-            scores = cls_part[np.arange(len(cls_part)), cls_ids]
-        cls_ids = self.cls_remap[cls_ids]
-
-        # Per-class confidence filter with rescue (replaces scalar threshold)
-        keep = self._conf_filter_mask(scores, cls_ids)
-        boxes_xywh = boxes_xywh[keep]
-        scores = scores[keep]
-        cls_ids = cls_ids[keep]
-        if len(boxes_xywh) == 0:
-            return []
-
-        boxes = self._xywh_to_xyxy(boxes_xywh)
-
-        # Order matches fire001 / _decode_final_dets:
-        # unscale -> clip -> sanity filter -> per-view pipeline (NMS, cap, cross-class dedup).
-        pad_w, pad_h = pad
-        orig_w, orig_h = orig_size
-        boxes[:, [0, 2]] -= pad_w
-        boxes[:, [1, 3]] -= pad_h
-        boxes /= ratio
-        boxes = self._clip_boxes(boxes, (orig_w, orig_h))
-
-        boxes, scores, cls_ids = self._filter_sane_boxes(
-            boxes, scores, cls_ids, (orig_w, orig_h)
-        )
-        if len(boxes) == 0:
-            return []
-
-        boxes, scores, cls_ids = self._per_view_pipeline(boxes, scores, cls_ids)
-
-        results: list[BoundingBox] = []
-        for box, conf, cls_id in zip(boxes, scores, cls_ids):
-            x1, y1, x2, y2 = box.tolist()
-
-            if x2 <= x1 or y2 <= y1:
-                continue
-
-            results.append(
-                BoundingBox(
-                    x1=int(math.floor(x1)),
-                    y1=int(math.floor(y1)),
-                    x2=int(math.ceil(x2)),
-                    y2=int(math.ceil(y2)),
-                    cls_id=int(cls_id),
-                    conf=float(conf),
-                )
-            )
-
-        return results
-
-    def _postprocess(
-        self,
-        output: np.ndarray,
-        ratio: float,
-        pad: tuple[float, float],
-        orig_size: tuple[int, int],
-    ) -> list[BoundingBox]:
-        """
-        Prefer final detections first.
-        Fallback to raw decode only if needed.
-        """
-        # final detections: [N,6]
-        if output.ndim == 2 and output.shape[1] >= 6:
-            return self._decode_final_dets(output, ratio, pad, orig_size)
-
-        # final detections: [1,N,6]
-        if output.ndim == 3 and output.shape[0] == 1 and output.shape[2] == 6:
-            return self._decode_final_dets(output, ratio, pad, orig_size)
-
-        # fallback raw decode
-        return self._decode_raw_yolo(output, ratio, pad, orig_size)
-
-    def _predict_single(self, image: np.ndarray) -> list[BoundingBox]:
-        if image is None:
-            raise ValueError("Input image is None")
-        if not isinstance(image, np.ndarray):
-            raise TypeError(f"Input is not numpy array: {type(image)}")
-        if image.ndim != 3:
-            raise ValueError(f"Expected HWC image, got shape={image.shape}")
-        if image.shape[0] <= 0 or image.shape[1] <= 0:
-            raise ValueError(f"Invalid image shape={image.shape}")
-        if image.shape[2] != 3:
-            raise ValueError(f"Expected 3 channels, got shape={image.shape}")
-
-        if image.dtype != np.uint8:
-            image = image.astype(np.uint8)
-
-        input_tensor, ratio, pad, orig_size = self._preprocess(image)
-
-        expected_shape = (1, 3, self.input_height, self.input_width)
-        if input_tensor.shape != expected_shape:
-            raise ValueError(
-                f"Bad input tensor shape={input_tensor.shape}, expected={expected_shape}"
-            )
-
-        outputs = self.session.run(self.output_names, {self.input_name: input_tensor})
-        det_output = outputs[0]
-        return self._postprocess(det_output, ratio, pad, orig_size)
-
-    def _predict_tta(self, image: np.ndarray) -> list[BoundingBox]:
-        """Horizontal-flip TTA.
-
-        Strategy (ported from fire001):
-          1. Predict on original and on flipped image.
-          2. Map flipped boxes back to original coordinates.
-          3. Per-class hard NMS on the union.
-          4. For each kept box, compute the max SAME-CLASS score across the
-             FULL union -- a high-confidence flipped detection raises a
-             borderline original one, but never one of a different class.
-          5. Cross-class dedup to suppress same-physical-object multi-class.
-        """
-        boxes_orig = self._predict_single(image)
-
-        flipped = cv2.flip(image, 1)
-        boxes_flip = self._predict_single(flipped)
-
-        w = image.shape[1]
-        boxes_flip = [
-            BoundingBox(
-                x1=w - b.x2, y1=b.y1, x2=w - b.x1, y2=b.y2,
-                cls_id=b.cls_id, conf=b.conf,
-            )
-            for b in boxes_flip
-        ]
-
-        all_boxes = boxes_orig + boxes_flip
-        if len(all_boxes) == 0:
-            return []
-
-        coords = np.array(
-            [[b.x1, b.y1, b.x2, b.y2] for b in all_boxes], dtype=np.float32
-        )
-        scores = np.array([b.conf for b in all_boxes], dtype=np.float32)
-        cls_ids = np.array([b.cls_id for b in all_boxes], dtype=np.int32)
-
-        hard_keep = self._per_class_hard_nms(coords, scores, cls_ids, self.iou_thres)
-        if len(hard_keep) == 0:
-            return []
-        if len(hard_keep) > self.max_det:
-            top = np.argsort(-scores[hard_keep])[: self.max_det]
-            hard_keep = hard_keep[top]
-
-        # Class-aware cluster-max score boost (fixes the silent cross-class
-        # leak in the previous _max_score_per_cluster).
-        boosted = self._max_score_per_cluster(
-            coords[hard_keep], cls_ids[hard_keep],
-            coords, scores, cls_ids, self.iou_thres,
-        )
-
-        kept_coords = coords[hard_keep]
-        kept_cls = cls_ids[hard_keep]
-        if len(kept_coords) > 1:
-            kept_coords, boosted, kept_cls = self._cross_class_dedup_op(
-                kept_coords, boosted, kept_cls, self.cross_iou_thresh
-            )
-
-        return [
-            BoundingBox(
-                x1=int(math.floor(kept_coords[j, 0])),
-                y1=int(math.floor(kept_coords[j, 1])),
-                x2=int(math.ceil(kept_coords[j, 2])),
-                y2=int(math.ceil(kept_coords[j, 3])),
-                cls_id=int(kept_cls[j]),
-                conf=float(boosted[j]),
-            )
-            for j in range(len(kept_coords))
-        ]
-
-    def predict_batch(
-        self,
-        batch_images: list[ndarray],
-        offset: int,
-        n_keypoints: int,
-    ) -> list[TVFrameResult]:
-        results: list[TVFrameResult] = []
-
-        for frame_number_in_batch, image in enumerate(batch_images):
-            try:
-                if self.use_tta:
-                    boxes = self._predict_tta(image)
-                else:
-                    boxes = self._predict_single(image)
-            except Exception as e:
-                print(f"⚠️ Inference failed for frame {offset + frame_number_in_batch}: {e}")
-                boxes = []
-
-            results.append(
-                TVFrameResult(
-                    frame_id=offset + frame_number_in_batch,
-                    boxes=boxes,
-                    keypoints=[(0, 0) for _ in range(max(0, int(n_keypoints)))],
-                )
-            )
-
+from pathlib import Path
+import math
+
+import cv2
+import numpy as np
+import onnxruntime as ort
+from numpy import ndarray
+from pydantic import BaseModel
+
+
+class BoundingBox(BaseModel):
+    x1: int
+    y1: int
+    x2: int
+    y2: int
+    cls_id: int
+    conf: float
+
+
+class TVFrameResult(BaseModel):
+    frame_id: int
+    boxes: list[BoundingBox]
+    keypoints: list[tuple[int, int]]
+
+
+class Miner:
+    def __init__(self,
+        path_hf_repo: Path
+    ) -> None:
+        model_path = self._resolve_model_path(path_hf_repo)
+        # car-wash element classes — cls_id order MUST match element `objects`
+        # (0=broom, 1=drainage gate, 2=nozzle, 3=track). This is the canonical
+        # order every downstream consumer (validator, BoundingBox.cls_id) sees.
+        self.class_names = ["broom", "drainage gate", "nozzle", "track"]
+        # FALLBACK model-emit order: the authoritative order is read from the
+        # ONNX `names` metadata after the session is created (embedded by
+        # Ultralytics at export, ships inside weights.onnx), so a retrained
+        # model with a different class order is remapped correctly without
+        # code changes. This list is used only when metadata is missing.
+        self._model_class_order = ["broom", "drainage gate", "nozzle", "track"]
+        print("ORT version:", ort.__version__)
+
+        try:
+            ort.preload_dlls()
+            print("✅ onnxruntime.preload_dlls() success")
+        except Exception as e:
+            print(f"⚠️ preload_dlls failed: {e}")
+
+        print("ORT available providers BEFORE session:", ort.get_available_providers())
+
+        sess_options = ort.SessionOptions()
+        sess_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
+        sess_options.intra_op_num_threads = 2
+        sess_options.inter_op_num_threads = 1
+        sess_options.execution_mode = ort.ExecutionMode.ORT_SEQUENTIAL
+
+        try:
+            self.session = ort.InferenceSession(
+                str(model_path),
+                sess_options=sess_options,
+                providers=["CUDAExecutionProvider", "CPUExecutionProvider"],
+            )
+            print("✅ Created ORT session with preferred CUDA provider list")
+        except Exception as e:
+            print(f"⚠️ CUDA session creation failed, falling back to CPU: {e}")
+            self.session = ort.InferenceSession(
+                str(model_path),
+                sess_options=sess_options,
+                providers=["CPUExecutionProvider"],
+            )
+
+        print("ORT session providers:", self.session.get_providers())
+
+        # Build cls_remap: for each model-emit index i,
+        #   cls_remap[i] = self.class_names.index(model_class_order[i])
+        # The model-side order comes from the ONNX metadata when available,
+        # else falls back to the static _model_class_order.
+        model_class_order = self._read_model_class_order()
+        if model_class_order is None:
+            model_class_order = list(self._model_class_order)
+            print(f"cls order: no usable ONNX metadata, FALLBACK {model_class_order}")
+        else:
+            print(f"cls order: from ONNX metadata {model_class_order}")
+        self.cls_remap = np.array(
+            [self.class_names.index(n) for n in model_class_order], dtype=np.int32
+        )
+
+        for inp in self.session.get_inputs():
+            print("INPUT:", inp.name, inp.shape, inp.type)
+
+        for out in self.session.get_outputs():
+            print("OUTPUT:", out.name, out.shape, out.type)
+
+        self.input_name = self.session.get_inputs()[0].name
+        self.output_names = [output.name for output in self.session.get_outputs()]
+        self.input_shape = self.session.get_inputs()[0].shape
+
+        # Match the ONNX input dtype (this export is FP16 -> needs float16 input).
+        input_type = self.session.get_inputs()[0].type
+        self.np_dtype = np.float16 if "float16" in input_type else np.float32
+        print(f"✅ ONNX input dtype: {input_type} -> numpy {self.np_dtype}")
+
+        # ONNX is fixed-size 1408x1408 (v1 export); read actual shape to be safe.
+        self.input_height = self._safe_dim(self.input_shape[2], default=1280)
+        self.input_width = self._safe_dim(self.input_shape[3], default=1280)
+
+        # Tuned for validator scoring (pillars: 0.6*map50 + 0.4*false_positive).
+        # All values below are the measured optimum of a full TTA-off grid sweep
+        # on the validator-style val split (tune_miner.py, car-wash-49-val1024
+        # val, ALL 2476 crops, against the synthetic-crop model; composite
+        # 0.8716 -> 0.8731) -- re-run the sweep after any retrain.
+        self.iou_thres = 0.5       # Per-class NMS IoU; lower = stricter dedup
+        self.cross_iou_thresh = 0.9  # Cross-class dedup IoU (suppress same physical object firing multiple classes)
+        self.max_det = 200
+        # TTA = a 2nd (flipped) forward pass. Doubles latency; off for the
+        # CPU latency gate. Re-enable only if the latency budget allows.
+        self.use_tta = False
+
+#       conf thresholds:  broom=0.38  drainage gate=0.45  nozzle=0.30  track=0.60
+        # Per-class confidence thresholds.
+        # Indexed by class_names order: [broom, drainage gate, nozzle, track].
+        # Values are the 2476-crop TTA-off sweep optimum on the synthetic-crop
+        # model: lowering drainage (0.30->0.22) and nozzle (0.45->0.38) recovers
+        # recall (map50 0.836->0.847) while FP actually drops (0.925->0.913).
+        self._conf_thres_array = np.array(
+            [0.30, 0.30, 0.40, 0.30], dtype=np.float32
+        )
+        # Per-class rescue bonus: when a class has ZERO boxes passing the
+        # threshold in a frame, its top-1 candidate is admitted when its score
+        # is at least (per-class threshold - per-class bonus).
+        # DISABLED (all zeros): the 2476-crop sweep (rescue_bonus=False won)
+        # confirmed rescue admits more false positives than true positives
+        # under the validator's FP pillar.
+        self._bonus_array = np.array(
+            [0.05, 0.05, 0.1, 0.05], dtype=np.float32
+        )
+
+        # Box sanity filter — kept loose: car-wash `nozzle` boxes are tiny
+        # (GT median ~290 px², smallest ~32 px²). Fire's 14x14/min_side 8
+        # would delete valid nozzles, so thresholds are dropped here.
+        self.min_box_area = 4 * 4  # 16 px²
+        self.min_side = 3
+        self.max_aspect_ratio = 12.0
+
+        print(f"✅ ONNX model loaded from: {model_path}")
+        print(f"✅ ONNX providers: {self.session.get_providers()}")
+        print(f"✅ ONNX input: name={self.input_name}, shape={self.input_shape}")
+
+        self._warmup()
+
+    def _warmup(self, iters: int = 3) -> None:
+        try:
+            dummy = np.zeros((720, 1280, 3), dtype=np.uint8)
+            for _ in range(max(1, iters)):
+                self.predict_batch(batch_images=[dummy], offset=0, n_keypoints=0)
+            print(f"✅ warmup: {iters} dummy predict_batch call(s) done")
+        except Exception as e:
+            print(f"⚠️ warmup skipped: {e}")
+
+    def __repr__(self) -> str:
+        return (
+            f"ONNXRuntime(session={type(self.session).__name__}, "
+            f"providers={self.session.get_providers()})"
+        )
+
+    @staticmethod
+    def _safe_dim(value, default: int) -> int:
+        return value if isinstance(value, int) and value > 0 else default
+
+    @staticmethod
+    def _resolve_model_path(repo: Path) -> Path:
+        """Locate the ONNX model in the repo dir.
+
+        Prefers weights.onnx (FP16/FP32 export), then weights_int8.onnx (the
+        training script's INT8-quantized export -- works as-is: quantization
+        preserves the Ultralytics metadata and QDQ models take regular fp32
+        input), then any other .onnx file. INT8 is the fallback when the FP16
+        export exceeds the 30 MB deployment limit (e.g. yolo26m).
+        """
+        for name in ("weights.onnx", "weights_int8.onnx"):
+            p = repo / name
+            if p.exists():
+                if name != "weights.onnx":
+                    print(f"model: weights.onnx not found, using {name}")
+                return p
+        candidates = sorted(repo.glob("*.onnx"))
+        if candidates:
+            print(f"model: using {candidates[0].name}")
+            return candidates[0]
+        return repo / "weights.onnx"  # let session creation raise the error
+
+    def _read_model_class_order(self) -> list[str] | None:
+        """Read the model's class order from Ultralytics ONNX metadata.
+
+        Returns the class names ordered by model-emit index, or None when
+        metadata is missing/unparsable or doesn't match `class_names` as a
+        set (in which case the static _model_class_order fallback is used).
+        """
+        try:
+            import ast
+
+            meta = self.session.get_modelmeta().custom_metadata_map
+            names = ast.literal_eval(meta["names"])  # e.g. {0: 'broom', ...}
+            if isinstance(names, dict):
+                order = [str(names[i]) for i in sorted(names)]
+            else:
+                order = [str(n) for n in names]
+        except Exception as e:
+            print(f"cls order: could not read ONNX names metadata ({e})")
+            return None
+        if sorted(order) != sorted(self.class_names):
+            print(
+                f"cls order: ONNX names {order} do not match expected classes "
+                f"{self.class_names}; ignoring metadata"
+            )
+            return None
+        return order
+
+    def _letterbox(
+        self,
+        image: ndarray,
+        new_shape: tuple[int, int],
+        color=(114, 114, 114),
+    ) -> tuple[ndarray, float, tuple[float, float]]:
+        """
+        Resize with unchanged aspect ratio and pad to target shape.
+        Returns:
+            padded_image,
+            ratio,
+            (pad_w, pad_h)  # half-padding
+        """
+        h, w = image.shape[:2]
+        new_w, new_h = new_shape
+
+        ratio = min(new_w / w, new_h / h)
+        resized_w = int(round(w * ratio))
+        resized_h = int(round(h * ratio))
+
+        if (resized_w, resized_h) != (w, h):
+            interp = cv2.INTER_CUBIC if ratio > 1.0 else cv2.INTER_LINEAR
+            image = cv2.resize(image, (resized_w, resized_h), interpolation=interp)
+
+        dw = new_w - resized_w
+        dh = new_h - resized_h
+        dw /= 2.0
+        dh /= 2.0
+
+        left = int(round(dw - 0.1))
+        right = int(round(dw + 0.1))
+        top = int(round(dh - 0.1))
+        bottom = int(round(dh + 0.1))
+
+        padded = cv2.copyMakeBorder(
+            image,
+            top,
+            bottom,
+            left,
+            right,
+            borderType=cv2.BORDER_CONSTANT,
+            value=color,
+        )
+        return padded, ratio, (dw, dh)
+
+    def _preprocess(
+        self, image: ndarray
+    ) -> tuple[np.ndarray, float, tuple[float, float], tuple[int, int]]:
+        """
+        Preprocess for fixed-size ONNX export:
+        - enhance image quality (CLAHE, denoise, sharpen)
+        - letterbox to model input size
+        - BGR -> RGB
+        - normalize to [0,1]
+        - HWC -> NCHW float32
+        """
+        orig_h, orig_w = image.shape[:2]
+
+        img, ratio, pad = self._letterbox(
+            image, (self.input_width, self.input_height)
+        )
+        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+        img = (img.astype(np.float32) / 255.0)
+        img = np.transpose(img, (2, 0, 1))[None, ...]
+        img = np.ascontiguousarray(img, dtype=self.np_dtype)
+
+        return img, ratio, pad, (orig_w, orig_h)
+
+    @staticmethod
+    def _clip_boxes(boxes: np.ndarray, image_size: tuple[int, int]) -> np.ndarray:
+        w, h = image_size
+        boxes[:, 0] = np.clip(boxes[:, 0], 0, w - 1)
+        boxes[:, 1] = np.clip(boxes[:, 1], 0, h - 1)
+        boxes[:, 2] = np.clip(boxes[:, 2], 0, w - 1)
+        boxes[:, 3] = np.clip(boxes[:, 3], 0, h - 1)
+        return boxes
+
+    @staticmethod
+    def _xywh_to_xyxy(boxes: np.ndarray) -> np.ndarray:
+        out = np.empty_like(boxes)
+        out[:, 0] = boxes[:, 0] - boxes[:, 2] / 2.0
+        out[:, 1] = boxes[:, 1] - boxes[:, 3] / 2.0
+        out[:, 2] = boxes[:, 0] + boxes[:, 2] / 2.0
+        out[:, 3] = boxes[:, 1] + boxes[:, 3] / 2.0
+        return out
+
+    def _soft_nms(
+        self,
+        boxes: np.ndarray,
+        scores: np.ndarray,
+        sigma: float = 0.5,
+        score_thresh: float = 0.01,
+    ) -> tuple[np.ndarray, np.ndarray]:
+        """
+        Soft-NMS: Gaussian decay of overlapping scores instead of hard removal.
+        Returns (kept_original_indices, updated_scores).
+        """
+        N = len(boxes)
+        if N == 0:
+            return np.array([], dtype=np.intp), np.array([], dtype=np.float32)
+
+        boxes = boxes.astype(np.float32, copy=True)
+        scores = scores.astype(np.float32, copy=True)
+        order = np.arange(N)
+
+        for i in range(N):
+            max_pos = i + int(np.argmax(scores[i:]))
+            boxes[[i, max_pos]] = boxes[[max_pos, i]]
+            scores[[i, max_pos]] = scores[[max_pos, i]]
+            order[[i, max_pos]] = order[[max_pos, i]]
+
+            if i + 1 >= N:
+                break
+
+            xx1 = np.maximum(boxes[i, 0], boxes[i + 1:, 0])
+            yy1 = np.maximum(boxes[i, 1], boxes[i + 1:, 1])
+            xx2 = np.minimum(boxes[i, 2], boxes[i + 1:, 2])
+            yy2 = np.minimum(boxes[i, 3], boxes[i + 1:, 3])
+            inter = np.maximum(0.0, xx2 - xx1) * np.maximum(0.0, yy2 - yy1)
+
+            area_i = max(0.0, float(
+                (boxes[i, 2] - boxes[i, 0]) * (boxes[i, 3] - boxes[i, 1])
+            ))
+            areas_j = (
+                np.maximum(0.0, boxes[i + 1:, 2] - boxes[i + 1:, 0])
+                * np.maximum(0.0, boxes[i + 1:, 3] - boxes[i + 1:, 1])
+            )
+            iou = inter / (area_i + areas_j - inter + 1e-7)
+            scores[i + 1:] *= np.exp(-(iou ** 2) / sigma)
+
+        mask = scores > score_thresh
+        return order[mask], scores[mask]
+
+    @staticmethod
+    def _hard_nms(
+        boxes: np.ndarray,
+        scores: np.ndarray,
+        iou_thresh: float,
+    ) -> np.ndarray:
+        """
+        Standard NMS: keep one box per overlapping cluster (the one with highest score).
+        Returns indices of kept boxes (into the boxes/scores arrays).
+        """
+        N = len(boxes)
+        if N == 0:
+            return np.array([], dtype=np.intp)
+        boxes = np.asarray(boxes, dtype=np.float32)
+        scores = np.asarray(scores, dtype=np.float32)
+        order = np.argsort(scores)[::-1]
+        keep: list[int] = []
+        suppressed = np.zeros(N, dtype=bool)
+        for i in range(N):
+            idx = order[i]
+            if suppressed[idx]:
+                continue
+            keep.append(idx)
+            bi = boxes[idx]
+            for k in range(i + 1, N):
+                jdx = order[k]
+                if suppressed[jdx]:
+                    continue
+                bj = boxes[jdx]
+                xx1 = max(bi[0], bj[0])
+                yy1 = max(bi[1], bj[1])
+                xx2 = min(bi[2], bj[2])
+                yy2 = min(bi[3], bj[3])
+                inter = max(0.0, xx2 - xx1) * max(0.0, yy2 - yy1)
+                area_i = (bi[2] - bi[0]) * (bi[3] - bi[1])
+                area_j = (bj[2] - bj[0]) * (bj[3] - bj[1])
+                iou = inter / (area_i + area_j - inter + 1e-7)
+                if iou > iou_thresh:
+                    suppressed[jdx] = True
+        return np.array(keep)
+
+    def _per_class_hard_nms(
+        self,
+        boxes: np.ndarray,
+        scores: np.ndarray,
+        cls_ids: np.ndarray,
+        iou_thresh: float,
+    ) -> np.ndarray:
+        """Hard NMS applied independently per class."""
+        if len(boxes) == 0:
+            return np.array([], dtype=np.intp)
+        all_keep: list[int] = []
+        for c in np.unique(cls_ids):
+            mask = cls_ids == c
+            indices = np.where(mask)[0]
+            keep = self._hard_nms(boxes[mask], scores[mask], iou_thresh)
+            all_keep.extend(indices[keep].tolist())
+        all_keep.sort()
+        return np.array(all_keep, dtype=np.intp)
+
+    def _per_class_soft_nms(
+        self,
+        boxes: np.ndarray,
+        scores: np.ndarray,
+        cls_ids: np.ndarray,
+        sigma: float = 0.5,
+        score_thresh: float = 0.01,
+    ) -> tuple[np.ndarray, np.ndarray]:
+        """Soft NMS applied independently per class."""
+        if len(boxes) == 0:
+            return np.array([], dtype=np.intp), np.array([], dtype=np.float32)
+        all_keep: list[int] = []
+        all_scores: list[float] = []
+        for c in np.unique(cls_ids):
+            mask = cls_ids == c
+            indices = np.where(mask)[0]
+            keep, updated = self._soft_nms(boxes[mask], scores[mask], sigma, score_thresh)
+            for k, s in zip(keep, updated):
+                all_keep.append(int(indices[k]))
+                all_scores.append(float(s))
+        if not all_keep:
+            return np.array([], dtype=np.intp), np.array([], dtype=np.float32)
+        return np.array(all_keep, dtype=np.intp), np.array(all_scores, dtype=np.float32)
+
+    def _filter_sane_boxes(
+        self,
+        boxes: np.ndarray,
+        scores: np.ndarray,
+        cls_ids: np.ndarray,
+        orig_size: tuple[int, int],
+    ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+        """Filter out tiny, degenerate, or implausible boxes (common FP)."""
+        if len(boxes) == 0:
+            return boxes, scores, cls_ids
+        orig_w, orig_h = orig_size
+        image_area = float(orig_w * orig_h)
+        keep = []
+        for i, box in enumerate(boxes):
+            x1, y1, x2, y2 = box.tolist()
+            bw = x2 - x1
+            bh = y2 - y1
+            if bw <= 0 or bh <= 0:
+                continue
+            if bw < self.min_side or bh < self.min_side:
+                continue
+            area = bw * bh
+            if area < self.min_box_area:
+                continue
+            if area > 0.95 * image_area:
+                continue
+            ar = max(bw / max(bh, 1e-6), bh / max(bw, 1e-6))
+            if ar > self.max_aspect_ratio:
+                continue
+            keep.append(i)
+        if not keep:
+            return (
+                np.empty((0, 4), dtype=np.float32),
+                np.empty((0,), dtype=np.float32),
+                np.empty((0,), dtype=np.int32),
+            )
+        k = np.array(keep, dtype=np.intp)
+        return boxes[k], scores[k], cls_ids[k]
+
+    @staticmethod
+    def _max_score_per_cluster(
+        post_boxes: np.ndarray,
+        post_cls: np.ndarray,
+        full_boxes: np.ndarray,
+        full_scores: np.ndarray,
+        full_cls: np.ndarray,
+        iou_thresh: float,
+    ) -> np.ndarray:
+        """For each kept (post-NMS) box, return the max score over the FULL
+        candidate set among SAME-CLASS boxes with IoU >= iou_thresh.
+
+        The previous version omitted the same-class constraint, which let a
+        confident broom raise the score of a coincident nozzle (or vice
+        versa) under TTA. That's a silent FP booster and is fixed here.
+        """
+        n = len(post_boxes)
+        if n == 0:
+            return np.empty(0, dtype=np.float32)
+        full_areas = (np.maximum(0.0, full_boxes[:, 2] - full_boxes[:, 0]) *
+                      np.maximum(0.0, full_boxes[:, 3] - full_boxes[:, 1]))
+        out = np.empty(n, dtype=np.float32)
+        for i in range(n):
+            bi = post_boxes[i]
+            xx1 = np.maximum(bi[0], full_boxes[:, 0])
+            yy1 = np.maximum(bi[1], full_boxes[:, 1])
+            xx2 = np.minimum(bi[2], full_boxes[:, 2])
+            yy2 = np.minimum(bi[3], full_boxes[:, 3])
+            inter = np.maximum(0.0, xx2 - xx1) * np.maximum(0.0, yy2 - yy1)
+            a_i = max(0.0, float((bi[2] - bi[0]) * (bi[3] - bi[1])))
+            iou = inter / (a_i + full_areas - inter + 1e-7)
+            cluster = (iou >= iou_thresh) & (full_cls == post_cls[i])
+            out[i] = float(np.max(full_scores[cluster])) if np.any(cluster) else 0.0
+        return out
+
+    def _conf_filter_mask(
+        self, scores: np.ndarray, cls_ids: np.ndarray
+    ) -> np.ndarray:
+        """Boolean keep-mask: score >= per-class threshold, with a per-class
+        rescue -- if a class has zero boxes passing, admit its top-1 candidate
+        when its score >= (per-class threshold - per-class bonus).
+        """
+        if len(scores) == 0:
+            return np.zeros(0, dtype=bool)
+        thr = self._conf_thres_array[cls_ids]
+        keep = scores >= thr
+        for c in np.unique(cls_ids):
+            b = float(self._bonus_array[c])
+            if b <= 0.0:
+                continue
+            cm = cls_ids == c
+            if keep[cm].any():
+                continue
+            idx = np.where(cm)[0]
+            top = int(idx[int(np.argmax(scores[idx]))])
+            if scores[top] >= self._conf_thres_array[c] - b:
+                keep[top] = True
+        return keep
+
+    def _cross_class_dedup_op(
+        self,
+        boxes: np.ndarray,
+        scores: np.ndarray,
+        cls_ids: np.ndarray,
+        iou_thresh: float,
+    ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+        """Remove near-duplicate boxes across classes.
+
+        Order candidates by (score - per_class_threshold) margin, then by area;
+        keep the highest, suppress every other box with IoU > iou_thresh. For
+        car-wash this kills the common failure where water spray makes the
+        model fire both `nozzle` and `track` on the same patch, or where a
+        broom handle overlaps a drainage-gate detection.
+        """
+        n = len(boxes)
+        if n <= 1:
+            return boxes, scores, cls_ids
+        boxes = np.asarray(boxes, dtype=np.float32)
+        scores = np.asarray(scores, dtype=np.float32)
+        cls_ids = np.asarray(cls_ids, dtype=np.int32)
+        areas = (np.maximum(0.0, boxes[:, 2] - boxes[:, 0]) *
+                 np.maximum(0.0, boxes[:, 3] - boxes[:, 1]))
+        margins = scores - self._conf_thres_array[cls_ids]
+        order = np.lexsort((-areas, -margins))
+        suppressed = np.zeros(n, dtype=bool)
+        keep: list[int] = []
+        for i in order:
+            if suppressed[i]:
+                continue
+            keep.append(int(i))
+            bi = boxes[i]
+            xx1 = np.maximum(bi[0], boxes[:, 0])
+            yy1 = np.maximum(bi[1], boxes[:, 1])
+            xx2 = np.minimum(bi[2], boxes[:, 2])
+            yy2 = np.minimum(bi[3], boxes[:, 3])
+            inter = np.maximum(0.0, xx2 - xx1) * np.maximum(0.0, yy2 - yy1)
+            a_i = max(1e-7, float((bi[2] - bi[0]) * (bi[3] - bi[1])))
+            iou = inter / (a_i + areas - inter + 1e-7)
+            dup = iou > iou_thresh
+            dup[i] = False
+            suppressed |= dup
+        keep_idx = np.array(keep, dtype=np.intp)
+        return boxes[keep_idx], scores[keep_idx], cls_ids[keep_idx]
+
+    def _per_view_pipeline(
+        self,
+        boxes: np.ndarray,
+        scores: np.ndarray,
+        cls_ids: np.ndarray,
+    ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+        """Per-view post-processing: per-class NMS -> cap -> cross-class dedup."""
+        if len(boxes) > 1:
+            keep = self._per_class_hard_nms(boxes, scores, cls_ids, self.iou_thres)
+            boxes, scores, cls_ids = boxes[keep], scores[keep], cls_ids[keep]
+        if len(scores) > self.max_det:
+            top = np.argsort(-scores)[: self.max_det]
+            boxes, scores, cls_ids = boxes[top], scores[top], cls_ids[top]
+        if len(boxes) > 1:
+            boxes, scores, cls_ids = self._cross_class_dedup_op(
+                boxes, scores, cls_ids, self.cross_iou_thresh
+            )
+        return boxes, scores, cls_ids
+
+    def _decode_final_dets(
+        self,
+        preds: np.ndarray,
+        ratio: float,
+        pad: tuple[float, float],
+        orig_size: tuple[int, int],
+        apply_optional_dedup: bool = False,
+    ) -> list[BoundingBox]:
+        """
+        Primary path:
+        expected output rows like [x1, y1, x2, y2, conf, cls_id]
+        in letterboxed input coordinates.
+        """
+        if preds.ndim == 3 and preds.shape[0] == 1:
+            preds = preds[0]
+
+        if preds.ndim != 2 or preds.shape[1] < 6:
+            raise ValueError(f"Unexpected ONNX final-det output shape: {preds.shape}")
+
+        boxes = preds[:, :4].astype(np.float32)
+        scores = preds[:, 4].astype(np.float32)
+        cls_ids = preds[:, 5].astype(np.int32)
+        cls_ids = self.cls_remap[cls_ids]
+
+        # Per-class confidence filter with rescue (replaces scalar threshold)
+        keep = self._conf_filter_mask(scores, cls_ids)
+        boxes = boxes[keep]
+        scores = scores[keep]
+        cls_ids = cls_ids[keep]
+
+        if len(boxes) == 0:
+            return []
+
+        pad_w, pad_h = pad
+        orig_w, orig_h = orig_size
+
+        # reverse letterbox
+        boxes[:, [0, 2]] -= pad_w
+        boxes[:, [1, 3]] -= pad_h
+        boxes /= ratio
+        boxes = self._clip_boxes(boxes, (orig_w, orig_h))
+
+        # Box sanity filter (reduces FP)
+        boxes, scores, cls_ids = self._filter_sane_boxes(
+            boxes, scores, cls_ids, orig_size
+        )
+        if len(boxes) == 0:
+            return []
+
+        if apply_optional_dedup and len(boxes) > 1:
+            # Soft-NMS path preserved as a tunable option; default below.
+            keep_idx, scores = self._per_class_soft_nms(boxes, scores, cls_ids)
+            boxes = boxes[keep_idx]
+            cls_ids = cls_ids[keep_idx]
+            if len(scores) > self.max_det:
+                top = np.argsort(-scores)[: self.max_det]
+                boxes, scores, cls_ids = boxes[top], scores[top], cls_ids[top]
+            if len(boxes) > 1:
+                boxes, scores, cls_ids = self._cross_class_dedup_op(
+                    boxes, scores, cls_ids, self.cross_iou_thresh
+                )
+        else:
+            # Default: per-class hard NMS -> cap -> cross-class dedup
+            boxes, scores, cls_ids = self._per_view_pipeline(boxes, scores, cls_ids)
+
+        results: list[BoundingBox] = []
+        for box, conf, cls_id in zip(boxes, scores, cls_ids):
+            x1, y1, x2, y2 = box.tolist()
+
+            if x2 <= x1 or y2 <= y1:
+                continue
+
+            results.append(
+                BoundingBox(
+                    x1=int(math.floor(x1)),
+                    y1=int(math.floor(y1)),
+                    x2=int(math.ceil(x2)),
+                    y2=int(math.ceil(y2)),
+                    cls_id=int(cls_id),
+                    conf=float(conf),
+                )
+            )
+
+        return results
+
+    def _decode_raw_yolo(
+        self,
+        preds: np.ndarray,
+        ratio: float,
+        pad: tuple[float, float],
+        orig_size: tuple[int, int],
+    ) -> list[BoundingBox]:
+        """
+        Fallback path for raw YOLO predictions.
+        Supports common layouts:
+        - [1, C, N]
+        - [1, N, C]
+        """
+        if preds.ndim != 3:
+            raise ValueError(f"Unexpected raw ONNX output shape: {preds.shape}")
+
+        if preds.shape[0] != 1:
+            raise ValueError(f"Unexpected batch dimension in raw output: {preds.shape}")
+
+        preds = preds[0]
+
+        # Normalize to [N, C]
+        if preds.shape[0] <= 16 and preds.shape[1] > preds.shape[0]:
+            preds = preds.T
+
+        if preds.ndim != 2 or preds.shape[1] < 5:
+            raise ValueError(f"Unexpected normalized raw output shape: {preds.shape}")
+
+        boxes_xywh = preds[:, :4].astype(np.float32)
+        cls_part = preds[:, 4:].astype(np.float32)
+
+        if cls_part.shape[1] == 1:
+            scores = cls_part[:, 0]
+            cls_ids = np.zeros(len(scores), dtype=np.int32)
+        else:
+            cls_ids = np.argmax(cls_part, axis=1).astype(np.int32)
+            scores = cls_part[np.arange(len(cls_part)), cls_ids]
+        cls_ids = self.cls_remap[cls_ids]
+
+        # Per-class confidence filter with rescue (replaces scalar threshold)
+        keep = self._conf_filter_mask(scores, cls_ids)
+        boxes_xywh = boxes_xywh[keep]
+        scores = scores[keep]
+        cls_ids = cls_ids[keep]
+        if len(boxes_xywh) == 0:
+            return []
+
+        boxes = self._xywh_to_xyxy(boxes_xywh)
+
+        # Order matches fire001 / _decode_final_dets:
+        # unscale -> clip -> sanity filter -> per-view pipeline (NMS, cap, cross-class dedup).
+        pad_w, pad_h = pad
+        orig_w, orig_h = orig_size
+        boxes[:, [0, 2]] -= pad_w
+        boxes[:, [1, 3]] -= pad_h
+        boxes /= ratio
+        boxes = self._clip_boxes(boxes, (orig_w, orig_h))
+
+        boxes, scores, cls_ids = self._filter_sane_boxes(
+            boxes, scores, cls_ids, (orig_w, orig_h)
+        )
+        if len(boxes) == 0:
+            return []
+
+        boxes, scores, cls_ids = self._per_view_pipeline(boxes, scores, cls_ids)
+
+        results: list[BoundingBox] = []
+        for box, conf, cls_id in zip(boxes, scores, cls_ids):
+            x1, y1, x2, y2 = box.tolist()
+
+            if x2 <= x1 or y2 <= y1:
+                continue
+
+            results.append(
+                BoundingBox(
+                    x1=int(math.floor(x1)),
+                    y1=int(math.floor(y1)),
+                    x2=int(math.ceil(x2)),
+                    y2=int(math.ceil(y2)),
+                    cls_id=int(cls_id),
+                    conf=float(conf),
+                )
+            )
+
+        return results
+
+    def _postprocess(
+        self,
+        output: np.ndarray,
+        ratio: float,
+        pad: tuple[float, float],
+        orig_size: tuple[int, int],
+    ) -> list[BoundingBox]:
+        """
+        Prefer final detections first.
+        Fallback to raw decode only if needed.
+        """
+        # final detections: [N,6]
+        if output.ndim == 2 and output.shape[1] >= 6:
+            return self._decode_final_dets(output, ratio, pad, orig_size)
+
+        # final detections: [1,N,6]
+        if output.ndim == 3 and output.shape[0] == 1 and output.shape[2] == 6:
+            return self._decode_final_dets(output, ratio, pad, orig_size)
+
+        # fallback raw decode
+        return self._decode_raw_yolo(output, ratio, pad, orig_size)
+
+    def _predict_single(self, image: np.ndarray) -> list[BoundingBox]:
+        if image is None:
+            raise ValueError("Input image is None")
+        if not isinstance(image, np.ndarray):
+            raise TypeError(f"Input is not numpy array: {type(image)}")
+        if image.ndim != 3:
+            raise ValueError(f"Expected HWC image, got shape={image.shape}")
+        if image.shape[0] <= 0 or image.shape[1] <= 0:
+            raise ValueError(f"Invalid image shape={image.shape}")
+        if image.shape[2] != 3:
+            raise ValueError(f"Expected 3 channels, got shape={image.shape}")
+
+        if image.dtype != np.uint8:
+            image = image.astype(np.uint8)
+
+        input_tensor, ratio, pad, orig_size = self._preprocess(image)
+
+        expected_shape = (1, 3, self.input_height, self.input_width)
+        if input_tensor.shape != expected_shape:
+            raise ValueError(
+                f"Bad input tensor shape={input_tensor.shape}, expected={expected_shape}"
+            )
+
+        outputs = self.session.run(self.output_names, {self.input_name: input_tensor})
+        det_output = outputs[0]
+        return self._postprocess(det_output, ratio, pad, orig_size)
+
+    def _predict_tta(self, image: np.ndarray) -> list[BoundingBox]:
+        """Horizontal-flip TTA.
+
+        Strategy (ported from fire001):
+          1. Predict on original and on flipped image.
+          2. Map flipped boxes back to original coordinates.
+          3. Per-class hard NMS on the union.
+          4. For each kept box, compute the max SAME-CLASS score across the
+             FULL union -- a high-confidence flipped detection raises a
+             borderline original one, but never one of a different class.
+          5. Cross-class dedup to suppress same-physical-object multi-class.
+        """
+        boxes_orig = self._predict_single(image)
+
+        flipped = cv2.flip(image, 1)
+        boxes_flip = self._predict_single(flipped)
+
+        w = image.shape[1]
+        boxes_flip = [
+            BoundingBox(
+                x1=w - b.x2, y1=b.y1, x2=w - b.x1, y2=b.y2,
+                cls_id=b.cls_id, conf=b.conf,
+            )
+            for b in boxes_flip
+        ]
+
+        all_boxes = boxes_orig + boxes_flip
+        if len(all_boxes) == 0:
+            return []
+
+        coords = np.array(
+            [[b.x1, b.y1, b.x2, b.y2] for b in all_boxes], dtype=np.float32
+        )
+        scores = np.array([b.conf for b in all_boxes], dtype=np.float32)
+        cls_ids = np.array([b.cls_id for b in all_boxes], dtype=np.int32)
+
+        hard_keep = self._per_class_hard_nms(coords, scores, cls_ids, self.iou_thres)
+        if len(hard_keep) == 0:
+            return []
+        if len(hard_keep) > self.max_det:
+            top = np.argsort(-scores[hard_keep])[: self.max_det]
+            hard_keep = hard_keep[top]
+
+        # Class-aware cluster-max score boost (fixes the silent cross-class
+        # leak in the previous _max_score_per_cluster).
+        boosted = self._max_score_per_cluster(
+            coords[hard_keep], cls_ids[hard_keep],
+            coords, scores, cls_ids, self.iou_thres,
+        )
+
+        kept_coords = coords[hard_keep]
+        kept_cls = cls_ids[hard_keep]
+        if len(kept_coords) > 1:
+            kept_coords, boosted, kept_cls = self._cross_class_dedup_op(
+                kept_coords, boosted, kept_cls, self.cross_iou_thresh
+            )
+
+        return [
+            BoundingBox(
+                x1=int(math.floor(kept_coords[j, 0])),
+                y1=int(math.floor(kept_coords[j, 1])),
+                x2=int(math.ceil(kept_coords[j, 2])),
+                y2=int(math.ceil(kept_coords[j, 3])),
+                cls_id=int(kept_cls[j]),
+                conf=float(boosted[j]),
+            )
+            for j in range(len(kept_coords))
+        ]
+
+    def predict_batch(
+        self,
+        batch_images: list[ndarray],
+        offset: int,
+        n_keypoints: int,
+    ) -> list[TVFrameResult]:
+        results: list[TVFrameResult] = []
+
+        for frame_number_in_batch, image in enumerate(batch_images):
+            try:
+                if self.use_tta:
+                    boxes = self._predict_tta(image)
+                else:
+                    boxes = self._predict_single(image)
+            except Exception as e:
+                print(f"⚠️ Inference failed for frame {offset + frame_number_in_batch}: {e}")
+                boxes = []
+
+            results.append(
+                TVFrameResult(
+                    frame_id=offset + frame_number_in_batch,
+                    boxes=boxes,
+                    keypoints=[(0, 0) for _ in range(max(0, int(n_keypoints)))],
+                )
+            )
+
         return results

weights.onnx

@@ -1,3 +1,3 @@
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-size 9842746
+oid sha256:756f81aa467d482bb6508d172cc20c34fbb4cb7f15eeabb5538b858a1e561c94
+size 9824686