miner.py

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:
    """ONNX Runtime miner with per-class candidates, TTA fusion, and temporal rescue."""

    class_names = ["cup", "bottle", "can"]
    model_class_names = ["cup", "bottle", "can"]
    _model_to_competition_cls = np.array([0, 1, 2], dtype=np.int32)
    input_size = 1280
    iou_thres = 0.3
    cross_iou_thresh = 0.65
    min_side = 8.0
    min_box_area = 100.0
    max_aspect_ratio = 10.0
    max_det = 300
    _conf_thres_array = np.array([0.60, 0.45, 0.50], dtype=np.float32)
    _candidate_conf_thres_array = np.array([0.20, 0.30, 0.30], dtype=np.float32)
    _tta_conf_thres_array = np.array([0.52, 0.37, 0.42], dtype=np.float32)
    _temporal_conf_thres_array = np.array([0.54, 0.39, 0.44], dtype=np.float32)
    _tta_confirmed_views = 1
    temporal_iou_thresh = 0.25
    track_iou_thresh = 0.35
    track_keep_frames = 2
    track_min_conf = np.array([0.50, 0.35, 0.40], dtype=np.float32)
    sparse_candidate_count = 8
    crowded_candidate_count = 28
    crowded_area_ratio = 0.030
    sparse_relax = 0.04
    crowded_raise = 0.04

    def __init__(self, path_hf_repo: Path) -> None:
        model_path = path_hf_repo / "weights.onnx"
        print("ORT version:", ort.__version__)

        try:
            ort.preload_dlls()
            print("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

        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())

        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
        self.input_dtype = self._input_dtype(self.session.get_inputs()[0].type)

        self.input_height = self._safe_dim(self.input_shape[2], default=self.input_size)
        self.input_width = self._safe_dim(self.input_shape[3], default=self.input_size)
        self._tracks: list[dict] = []
        self._last_track_frame_id: int | None = None

        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}")
        print(f"ONNX input dtype: {self.input_dtype}")

    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 _input_dtype(input_type: str) -> np.dtype:
        if input_type == "tensor(float16)":
            return np.dtype(np.float16)
        return np.dtype(np.float32)

    @classmethod
    def _to_competition_cls(cls, cls_ids: np.ndarray) -> np.ndarray:
        if len(cls_ids) == 0:
            return cls_ids.astype(np.int32)
        valid = (cls_ids >= 0) & (cls_ids < len(cls._model_to_competition_cls))
        remapped = np.full_like(cls_ids, fill_value=-1, dtype=np.int32)
        remapped[valid] = cls._model_to_competition_cls[cls_ids[valid]]
        return remapped

    def _letterbox(self, image: ndarray, new_shape: tuple[int, int],

                   color=(114, 114, 114)

                   ) -> tuple[ndarray, float, tuple[float, float]]:
        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) / 2.0
        dh = (new_h - resized_h) / 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]]:
        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(self.input_dtype) / self.input_dtype.type(255.0)
        img = np.transpose(img, (2, 0, 1))[None, ...]
        img = np.ascontiguousarray(img, dtype=self.input_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

    @staticmethod
    def _hard_nms(boxes: np.ndarray, scores: np.ndarray,

                  iou_thresh: float) -> np.ndarray:
        n = len(boxes)
        if n == 0:
            return np.array([], dtype=np.intp)
        order = np.argsort(-scores)
        keep: list[int] = []
        while len(order) > 0:
            i = int(order[0])
            keep.append(i)
            if len(order) == 1:
                break
            rest = order[1:]
            xx1 = np.maximum(boxes[i, 0], boxes[rest, 0])
            yy1 = np.maximum(boxes[i, 1], boxes[rest, 1])
            xx2 = np.minimum(boxes[i, 2], boxes[rest, 2])
            yy2 = np.minimum(boxes[i, 3], boxes[rest, 3])
            inter = np.maximum(0.0, xx2 - xx1) * np.maximum(0.0, yy2 - yy1)
            a_i = (max(0.0, boxes[i, 2] - boxes[i, 0]) *
                   max(0.0, boxes[i, 3] - boxes[i, 1]))
            a_r = (np.maximum(0.0, boxes[rest, 2] - boxes[rest, 0]) *
                   np.maximum(0.0, boxes[rest, 3] - boxes[rest, 1]))
            iou = inter / (a_i + a_r - inter + 1e-7)
            order = rest[iou <= iou_thresh]
        return np.array(keep, dtype=np.intp)

    def _per_class_hard_nms(self, boxes: np.ndarray, scores: np.ndarray,

                            cls_ids: np.ndarray, iou_thresh: float

                            ) -> np.ndarray:
        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 _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]:
        keep_idx = self._cross_class_dedup_keep_indices(
            boxes, scores, cls_ids, iou_thresh
        )
        return boxes[keep_idx], scores[keep_idx], cls_ids[keep_idx]

    def _cross_class_dedup_keep_indices(self, boxes: np.ndarray,

                                        scores: np.ndarray,

                                        cls_ids: np.ndarray,

                                        iou_thresh: float) -> np.ndarray:
        n = len(boxes)
        if n <= 1:
            return np.arange(n, dtype=np.intp)
        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
        return np.array(keep, dtype=np.intp)

    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]:
        if len(boxes) == 0:
            return boxes, scores, cls_ids
        orig_w, orig_h = orig_size
        image_area = float(orig_w * orig_h)
        bw = np.maximum(0.0, boxes[:, 2] - boxes[:, 0])
        bh = np.maximum(0.0, boxes[:, 3] - boxes[:, 1])
        area = bw * bh
        ar = np.where(
            (bw > 0) & (bh > 0),
            np.maximum(bw / np.maximum(bh, 1e-6), bh / np.maximum(bw, 1e-6)),
            np.inf,
        )
        keep = (
            (bw >= self.min_side) & (bh >= self.min_side) &
            (area >= self.min_box_area) &
            (area <= 0.95 * image_area) &
            (ar <= self.max_aspect_ratio)
        )
        return boxes[keep], scores[keep], cls_ids[keep]

    def _max_score_per_cluster(self, 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:
        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 _view_support_per_cluster(self, post_boxes: np.ndarray,

                                  post_cls: np.ndarray,

                                  full_boxes: np.ndarray,

                                  full_cls: np.ndarray,

                                  full_view_ids: np.ndarray,

                                  iou_thresh: float) -> np.ndarray:
        n = len(post_boxes)
        if n == 0:
            return np.empty(0, dtype=np.int32)
        full_areas = (np.maximum(0.0, full_boxes[:, 2] - full_boxes[:, 0]) *
                      np.maximum(0.0, full_boxes[:, 3] - full_boxes[:, 1]))
        out = np.ones(n, dtype=np.int32)
        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])
            if np.any(cluster):
                out[i] = int(len(np.unique(full_view_ids[cluster])))
        return out

    def _conf_filter_mask(self, scores: np.ndarray,

                          cls_ids: np.ndarray) -> np.ndarray:
        """Keep low-score candidates; final acceptance happens after evidence fusion."""
        if len(scores) == 0:
            return np.zeros(0, dtype=bool)
        return scores >= self._candidate_conf_thres_array[cls_ids]

    def _scene_adjustment(self, boxes: list[BoundingBox],

                          image_shape: tuple[int, int, int] | None) -> float:
        if not boxes or image_shape is None:
            return 0.0
        h, w = image_shape[:2]
        image_area = max(1.0, float(w * h))
        total_box_area = sum(
            max(0, box.x2 - box.x1) * max(0, box.y2 - box.y1)
            for box in boxes
        )
        area_ratio = float(total_box_area) / image_area
        if len(boxes) >= self.crowded_candidate_count or area_ratio >= self.crowded_area_ratio:
            return self.crowded_raise
        if len(boxes) <= self.sparse_candidate_count and area_ratio < self.crowded_area_ratio * 0.5:
            return -self.sparse_relax
        return 0.0

    def _adaptive_thresholds(self, cls_ids: np.ndarray,

                             boxes: list[BoundingBox],

                             image_shape: tuple[int, int, int] | None

                             ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
        adjustment = self._scene_adjustment(boxes, image_shape)
        auto = np.clip(
            self._conf_thres_array[cls_ids] + adjustment,
            self._candidate_conf_thres_array[cls_ids] + 0.05,
            0.95,
        )
        tta = np.clip(
            self._tta_conf_thres_array[cls_ids] + adjustment,
            self._candidate_conf_thres_array[cls_ids],
            auto,
        )
        temporal = np.clip(
            self._temporal_conf_thres_array[cls_ids] + adjustment,
            self._candidate_conf_thres_array[cls_ids],
            auto,
        )
        return auto, tta, temporal

    def _candidate_accept_mask(self, boxes: list[BoundingBox],

                               view_support: np.ndarray,

                               image_shape: tuple[int, int, int] | None

                               ) -> np.ndarray:
        _, scores, cls_ids = self._boxes_to_arrays(boxes)
        if len(scores) == 0:
            return np.zeros(0, dtype=bool)
        auto_thres, tta_thres, _ = self._adaptive_thresholds(
            cls_ids, boxes, image_shape
        )
        auto = scores >= auto_thres
        tta_confirmed = (
            (scores >= tta_thres) &
            (view_support >= self._tta_confirmed_views)
        )
        return auto | tta_confirmed

    @staticmethod
    def _boxes_to_arrays(boxes: list[BoundingBox]

                         ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
        if not boxes:
            return (
                np.empty((0, 4), dtype=np.float32),
                np.empty(0, dtype=np.float32),
                np.empty(0, dtype=np.int32),
            )
        coords = np.array(
            [[b.x1, b.y1, b.x2, b.y2] for b in boxes], dtype=np.float32
        )
        scores = np.array([b.conf for b in boxes], dtype=np.float32)
        cls_ids = np.array([b.cls_id for b in boxes], dtype=np.int32)
        return coords, scores, cls_ids

    @staticmethod
    def _image_shape(image: np.ndarray | None) -> tuple[int, int, int] | None:
        if isinstance(image, np.ndarray) and image.ndim == 3:
            return image.shape
        return None

    @staticmethod
    def _single_box_iou(box: BoundingBox, boxes: np.ndarray) -> np.ndarray:
        if len(boxes) == 0:
            return np.empty(0, dtype=np.float32)
        xx1 = np.maximum(float(box.x1), boxes[:, 0])
        yy1 = np.maximum(float(box.y1), boxes[:, 1])
        xx2 = np.minimum(float(box.x2), boxes[:, 2])
        yy2 = np.minimum(float(box.y2), boxes[:, 3])
        inter = np.maximum(0.0, xx2 - xx1) * np.maximum(0.0, yy2 - yy1)
        a_i = max(0.0, float((box.x2 - box.x1) * (box.y2 - box.y1)))
        areas = (np.maximum(0.0, boxes[:, 2] - boxes[:, 0]) *
                 np.maximum(0.0, boxes[:, 3] - boxes[:, 1]))
        return inter / (a_i + areas - inter + 1e-7)

    def _per_view_pipeline(self, boxes: np.ndarray, scores: np.ndarray,

                           cls_ids: np.ndarray, orig_size: tuple[int, int]

                           ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
        boxes, scores, cls_ids = self._filter_sane_boxes(
            boxes, scores, cls_ids, orig_size
        )
        if len(boxes) == 0:
            return boxes, scores, cls_ids
        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]) -> list[BoundingBox]:
        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 = self._to_competition_cls(preds[:, 5].astype(np.int32))
        valid_cls = cls_ids >= 0
        boxes = boxes[valid_cls]
        scores = scores[valid_cls]
        cls_ids = cls_ids[valid_cls]

        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
        boxes[:, [0, 2]] -= pad_w
        boxes[:, [1, 3]] -= pad_h
        boxes /= ratio
        boxes = self._clip_boxes(boxes, orig_size)

        boxes, scores, cls_ids = self._per_view_pipeline(
            boxes, scores, cls_ids, orig_size
        )
        return self._build_results(boxes, scores, cls_ids)

    def _decode_raw_yolo(self, preds: np.ndarray, ratio: float,

                         pad: tuple[float, float],

                         orig_size: tuple[int, int]) -> list[BoundingBox]:
        if preds.ndim != 3 or preds.shape[0] != 1:
            raise ValueError(f"Unexpected raw ONNX output shape: {preds.shape}")
        preds = preds[0]
        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 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._to_competition_cls(cls_ids)
        valid_cls = cls_ids >= 0
        boxes_xywh = boxes_xywh[valid_cls]
        scores = scores[valid_cls]
        cls_ids = cls_ids[valid_cls]

        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)

        pad_w, pad_h = pad
        boxes[:, [0, 2]] -= pad_w
        boxes[:, [1, 3]] -= pad_h
        boxes /= ratio
        boxes = self._clip_boxes(boxes, orig_size)

        boxes, scores, cls_ids = self._per_view_pipeline(
            boxes, scores, cls_ids, orig_size
        )
        return self._build_results(boxes, scores, cls_ids)

    @staticmethod
    def _build_results(boxes: np.ndarray, scores: np.ndarray,

                       cls_ids: np.ndarray) -> list[BoundingBox]:
        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]:
        if output.ndim == 2 and output.shape[1] >= 6:
            return self._decode_final_dets(output, ratio, pad, orig_size)
        if output.ndim == 3 and output.shape[0] == 1 and output.shape[2] == 6:
            return self._decode_final_dets(output, ratio, pad, orig_size)
        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[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 = (1, 3, self.input_height, self.input_width)
        if input_tensor.shape != expected:
            raise ValueError(
                f"Bad input tensor shape={input_tensor.shape}, expected={expected}"
            )

        outputs = self.session.run(self.output_names, {self.input_name: input_tensor})
        return self._postprocess(outputs[0], ratio, pad, orig_size)

    def _predict_tta_candidates(self, image: np.ndarray

                                ) -> tuple[list[BoundingBox], np.ndarray]:
        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 not all_boxes:
            return [], np.empty(0, dtype=np.int32)

        coords, scores, cls_ids = self._boxes_to_arrays(all_boxes)
        view_ids = np.array(
            [0] * len(boxes_orig) + [1] * len(boxes_flip), dtype=np.int32
        )

        hard_keep = self._per_class_hard_nms(coords, scores, cls_ids, self.iou_thres)
        if len(hard_keep) == 0:
            return [], np.empty(0, dtype=np.int32)
        if len(hard_keep) > self.max_det:
            top = np.argsort(-scores[hard_keep])[: self.max_det]
            hard_keep = hard_keep[top]
        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]
        view_support = self._view_support_per_cluster(
            kept_coords, kept_cls, coords, cls_ids, view_ids, self.iou_thres,
        )
        if len(kept_coords) > 1:
            dedup_keep = self._cross_class_dedup_keep_indices(
                kept_coords, boosted, kept_cls, self.cross_iou_thresh
            )
            kept_coords = kept_coords[dedup_keep]
            boosted = boosted[dedup_keep]
            kept_cls = kept_cls[dedup_keep]
            view_support = view_support[dedup_keep]

        boxes = [
            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))
        ]
        return boxes, view_support

    def _filter_by_evidence(self, boxes: list[BoundingBox],

                            view_support: np.ndarray,

                            image_shape: tuple[int, int, int] | None

                            ) -> list[BoundingBox]:
        keep = self._candidate_accept_mask(boxes, view_support, image_shape)
        return [box for box, ok in zip(boxes, keep) if bool(ok)]

    def _predict_tta(self, image: np.ndarray) -> list[BoundingBox]:
        boxes, view_support = self._predict_tta_candidates(image)
        return self._filter_by_evidence(boxes, view_support, image.shape)

    def _has_temporal_support(self, frame_idx: int, box: BoundingBox,

                              candidate_boxes: list[list[BoundingBox]],

                              initial_keep: list[np.ndarray]) -> bool:
        neighbor_indices = [
            idx for idx in (frame_idx - 1, frame_idx + 1)
            if 0 <= idx < len(candidate_boxes)
        ]
        for idx in neighbor_indices:
            coords, _, cls_ids = self._boxes_to_arrays(candidate_boxes[idx])
            same_cls = cls_ids == box.cls_id
            if not np.any(same_cls):
                continue
            accepted = same_cls & initial_keep[idx]
            if np.any(accepted):
                if np.max(self._single_box_iou(box, coords[accepted])) >= self.temporal_iou_thresh:
                    return True

        two_sided_candidate_support = []
        for idx in (frame_idx - 1, frame_idx + 1):
            if not 0 <= idx < len(candidate_boxes):
                two_sided_candidate_support.append(False)
                continue
            coords, scores, cls_ids = self._boxes_to_arrays(candidate_boxes[idx])
            same_cls = cls_ids == box.cls_id
            if not np.any(same_cls):
                two_sided_candidate_support.append(False)
                continue
            score_ok = scores >= self._temporal_conf_thres_array[cls_ids]
            neighbor_ok = same_cls & score_ok
            supported = (
                np.any(neighbor_ok) and
                np.max(self._single_box_iou(box, coords[neighbor_ok])) >= self.temporal_iou_thresh
            )
            two_sided_candidate_support.append(bool(supported))
        return all(two_sided_candidate_support)

    def _reset_tracks_if_needed(self, frame_id: int) -> None:
        if self._last_track_frame_id is None:
            self._last_track_frame_id = frame_id - 1
            return
        if frame_id <= self._last_track_frame_id:
            self._tracks = []
        self._last_track_frame_id = frame_id

    def _track_supported(self, box: BoundingBox, frame_id: int) -> bool:
        best_iou = 0.0
        for track in self._tracks:
            if int(track["cls_id"]) != box.cls_id:
                continue
            age = frame_id - int(track["frame_id"])
            if age < 1 or age > self.track_keep_frames:
                continue
            iou = self._single_box_iou(box, track["coords"])[0]
            best_iou = max(best_iou, float(iou))
        return best_iou >= self.track_iou_thresh

    def _update_tracks(self, boxes: list[BoundingBox], frame_id: int) -> None:
        fresh_tracks = []
        for track in self._tracks:
            if frame_id - int(track["frame_id"]) <= self.track_keep_frames:
                fresh_tracks.append(track)
        for box in boxes:
            coords = np.array(
                [[box.x1, box.y1, box.x2, box.y2]], dtype=np.float32
            )
            updated = False
            for track in fresh_tracks:
                if int(track["cls_id"]) != box.cls_id:
                    continue
                iou = self._single_box_iou(box, track["coords"])[0]
                if iou >= self.track_iou_thresh:
                    track["coords"] = coords
                    track["frame_id"] = frame_id
                    track["conf"] = box.conf
                    updated = True
                    break
            if not updated:
                fresh_tracks.append(
                    {
                        "coords": coords,
                        "cls_id": box.cls_id,
                        "conf": box.conf,
                        "frame_id": frame_id,
                    }
                )
        self._tracks = fresh_tracks

    def predict_batch(self, batch_images: list[ndarray], offset: int,

                      n_keypoints: int) -> list[TVFrameResult]:
        candidate_boxes: list[list[BoundingBox]] = []
        view_supports: list[np.ndarray] = []
        image_shapes = [self._image_shape(image) for image in batch_images]
        results: list[TVFrameResult] = []
        for frame_number_in_batch, image in enumerate(batch_images):
            try:
                boxes, view_support = self._predict_tta_candidates(image)
            except Exception as e:
                print(f"Inference failed for frame {offset + frame_number_in_batch}: {e}")
                boxes = []
                view_support = np.empty(0, dtype=np.int32)
            candidate_boxes.append(boxes)
            view_supports.append(view_support)

        initial_keep: list[np.ndarray] = []
        for boxes, view_support, image_shape in zip(
            candidate_boxes, view_supports, image_shapes
        ):
            initial_keep.append(
                self._candidate_accept_mask(boxes, view_support, image_shape)
            )

        for frame_number_in_batch, boxes in enumerate(candidate_boxes):
            frame_id = offset + frame_number_in_batch
            self._reset_tracks_if_needed(frame_id)
            keep = initial_keep[frame_number_in_batch].copy()
            _, scores, cls_ids = self._boxes_to_arrays(boxes)
            _, _, temporal_thres = self._adaptive_thresholds(
                cls_ids, boxes, image_shapes[frame_number_in_batch]
            )
            temporal_ready = scores >= temporal_thres
            track_ready = scores >= self.track_min_conf[cls_ids]
            for i, box in enumerate(boxes):
                if keep[i]:
                    continue
                has_neighbor_support = (
                    bool(temporal_ready[i]) and
                    self._has_temporal_support(
                        frame_number_in_batch, box, candidate_boxes, initial_keep
                    )
                )
                has_track_support = (
                    bool(track_ready[i]) and self._track_supported(box, frame_id)
                )
                if has_neighbor_support or has_track_support:
                    keep[i] = True
            boxes = [box for box, ok in zip(boxes, keep) if bool(ok)]
            self._update_tracks(boxes, frame_id)
            results.append(
                TVFrameResult(
                    frame_id=frame_id,
                    boxes=boxes,
                    keypoints=[(0, 0) for _ in range(max(0, int(n_keypoints)))],
                )
            )
        return results

# if __name__ == "__main__":

# # predict batch with local images for testing
#     import json
#     from time import time

#     test_images = [
#         cv2.imread(str(p)) for p in sorted(Path("test_images").glob("*.jpg"))
#     ]
#     miner = Miner(Path("hf_repo"))
#     start_time = time()
#     results = miner.predict_batch(test_images, offset=0, n_keypoints=0)
#     end_time = time()
#     print(f"Predicted {len(test_images)} images in {end_time - start_time:.2f} seconds")
#     print(json.dumps([r.dict() for r in results], indent=2))