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:
    class_names = ['fire', 'smoke', 'fire extinguisher']
    _model_class_order = ["fire", "fire extinguisher", "smoke"]
    iou_thres = 0.55
    cross_iou_thresh = 0.8
    max_det = 30
    _conf_thres_array = np.array([0.22, 0.30, 0.30], dtype=np.float32)
    _bonus_array = np.array([0.05, 0.05, 0.05], dtype=np.float32)
    min_box_area = 0
    min_side = 8
    max_aspect_ratio = 8.0
    smoke_merge_overlap = 0.8
    fire_merge_overlap = 0.9
    fire_suppress_overlap = 0.88
    smoke_raw_refine_overlap = 0.9
    smoke_ext_shrink = 0.95
    fire_expand = 1.05
    fire_color_filter_max_conf = 0.45
    fire_ext_color_filter_max_conf = 0.0
    color_filter_min_saturation = 0.06
    use_edge_filter = False
    edge_filter_max_conf = 0.0
    edge_tol = 2.0
    use_tta_view_filter = False
    tta_view_filter_max_conf = 0.0
    tta_view_iou_thresh = 0.5

    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('✅ 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())
        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
        self.input_height = self._safe_dim(self.input_shape[2], default=1280)
        self.input_width = self._safe_dim(self.input_shape[3], default=1280)
        self.use_tta = False
        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('per-class conf: ' + ', '.join((f'{n}={t:.3f}' for n, t in zip(self.class_names, self._conf_thres_array.tolist()))))
        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__}, providers={self.session.get_providers()})'

    @staticmethod
    def _safe_dim(value, default: int) -> int:
        return value if isinstance(value, int) and value > 0 else default

    def _read_model_class_order(self) -> list[str] | None:
        try:
            import ast
            meta = self.session.get_modelmeta().custom_metadata_map
            names = ast.literal_eval(meta['names'])
            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 {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]]:
        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))
        blob = cv2.dnn.blobFromImage(img, scalefactor=1.0 / 255.0, swapRB=True)
        return (blob, 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-07)
            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]:
        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-07, float((bi[2] - bi[0]) * (bi[3] - bi[1])))
            iou = inter / (a_i + areas - inter + 1e-07)
            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 _merge_class_boxes(self, boxes: np.ndarray, scores: np.ndarray, cls_ids: np.ndarray, target_cls: int, overlap: float) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
        if overlap > 1.0:
            return (boxes, scores, cls_ids)
        idx = np.where(cls_ids == target_cls)[0]
        if len(idx) <= 1:
            return (boxes, scores, cls_ids)
        sb = boxes[idx].astype(np.float32).tolist()
        ss = scores[idx].astype(np.float32).tolist()
        merged_any = True
        while merged_any and len(sb) > 1:
            merged_any = False
            for i in range(len(sb)):
                for j in range(i + 1, len(sb)):
                    a, b = (sb[i], sb[j])
                    ix1 = max(a[0], b[0])
                    iy1 = max(a[1], b[1])
                    ix2 = min(a[2], b[2])
                    iy2 = min(a[3], b[3])
                    inter = max(0.0, ix2 - ix1) * max(0.0, iy2 - iy1)
                    area_a = max(0.0, a[2] - a[0]) * max(0.0, a[3] - a[1])
                    area_b = max(0.0, b[2] - b[0]) * max(0.0, b[3] - b[1])
                    smaller = min(area_a, area_b)
                    if inter / (smaller + 1e-07) >= overlap:
                        sb[i] = [min(a[0], b[0]), min(a[1], b[1]), max(a[2], b[2]), max(a[3], b[3])]
                        ss[i] = max(ss[i], ss[j])
                        del sb[j]
                        del ss[j]
                        merged_any = True
                        break
                if merged_any:
                    break
        other = cls_ids != target_cls
        new_boxes = np.concatenate([boxes[other].astype(np.float32), np.array(sb, dtype=np.float32).reshape(-1, 4)])
        new_scores = np.concatenate([scores[other].astype(np.float32), np.array(ss, dtype=np.float32)])
        new_cls = np.concatenate([cls_ids[other].astype(np.int32), np.full(len(sb), target_cls, dtype=np.int32)])
        return (new_boxes, new_scores, new_cls)

    def _suppress_contained_lower_conf(self, boxes: np.ndarray, scores: np.ndarray, cls_ids: np.ndarray, target_cls: int, overlap: float) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
        if overlap > 1.0:
            return (boxes, scores, cls_ids)
        idx = np.where(cls_ids == target_cls)[0]
        if len(idx) <= 1:
            return (boxes, scores, cls_ids)
        order = idx[np.argsort(-scores[idx])]
        remove: set[int] = set()
        for a in range(len(order)):
            i = int(order[a])
            if i in remove:
                continue
            bi = boxes[i]
            area_i = max(1e-07, float((bi[2] - bi[0]) * (bi[3] - bi[1])))
            for b in range(a + 1, len(order)):
                j = int(order[b])
                if j in remove:
                    continue
                bj = boxes[j]
                ix1 = max(bi[0], bj[0])
                iy1 = max(bi[1], bj[1])
                ix2 = min(bi[2], bj[2])
                iy2 = min(bi[3], bj[3])
                inter = max(0.0, ix2 - ix1) * max(0.0, iy2 - iy1)
                if inter <= 0.0:
                    continue
                area_j = max(1e-07, float((bj[2] - bj[0]) * (bj[3] - bj[1])))
                if inter / (min(area_i, area_j) + 1e-07) >= overlap:
                    remove.add(j)
        if not remove:
            return (boxes, scores, cls_ids)
        keep = np.array([k not in remove for k in range(len(boxes))], dtype=bool)
        return (boxes[keep], scores[keep], cls_ids[keep])

    def _merge_same_class_boxes(self, boxes: np.ndarray, scores: np.ndarray, cls_ids: np.ndarray) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
        boxes, scores, cls_ids = self._merge_class_boxes(boxes, scores, cls_ids, self.class_names.index('smoke'), self.smoke_merge_overlap)
        boxes, scores, cls_ids = self._merge_class_boxes(boxes, scores, cls_ids, self.class_names.index('fire'), self.fire_merge_overlap)
        boxes, scores, cls_ids = self._suppress_contained_lower_conf(boxes, scores, cls_ids, self.class_names.index('fire'), self.fire_suppress_overlap)
        return (boxes, scores, cls_ids)

    def _merge_smoke_boxes(self, boxes: np.ndarray, scores: np.ndarray, cls_ids: np.ndarray) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
        return self._merge_same_class_boxes(boxes, scores, cls_ids)

    @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:
        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-07)
            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:
        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 _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)
        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-06), bh / max(bw, 1e-06))
            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])

    def _per_view_pipeline(self, boxes: np.ndarray, scores: np.ndarray, cls_ids: np.ndarray) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
        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)
        if len(boxes) > 1:
            boxes, scores, cls_ids = self._merge_same_class_boxes(boxes, scores, cls_ids)
        return (boxes, scores, cls_ids)

    @staticmethod
    def _roi_for_box(image: np.ndarray, box: BoundingBox) -> np.ndarray | None:
        h, w = image.shape[:2]
        x1 = max(0, int(math.floor(box.x1)))
        y1 = max(0, int(math.floor(box.y1)))
        x2 = min(w, int(math.ceil(box.x2)))
        y2 = min(h, int(math.ceil(box.y2)))
        if x2 <= x1 or y2 <= y1:
            return None
        roi = image[y1:y2, x1:x2]
        return roi if roi.size else None

    def _roi_is_near_grayscale(self, roi: np.ndarray) -> bool:
        mx = roi.max(axis=2).astype(np.float32)
        mn = roi.min(axis=2).astype(np.float32)
        sat = (mx - mn) / (mx + 1e-06)
        return float(sat.mean()) < self.color_filter_min_saturation

    @staticmethod
    def _passes_fire_color(roi: np.ndarray) -> bool:
        blue = roi[:, :, 0].astype(np.float32)
        green = roi[:, :, 1].astype(np.float32)
        red = roi[:, :, 2].astype(np.float32)
        mean_r = float(np.mean(red))
        max_rgb = float(max(np.max(red), np.max(green), np.max(blue)))
        bright_frac = float(np.mean(np.max(roi, axis=2) >= 150))
        if max_rgb >= 200.0 and bright_frac >= 0.01:
            return True
        warm = (red > green + 10.0) & (red > blue + 10.0)
        warm_frac = float(np.mean(warm))
        r_minus_g = mean_r - float(np.mean(green))
        if warm_frac >= 0.05 and (max_rgb >= 120.0 or mean_r >= 120.0 or warm_frac >= 0.15):
            return True
        if bright_frac >= 0.12 and r_minus_g >= 2.0:
            return True
        return False

    @staticmethod
    def _passes_fire_ext_red_color(roi: np.ndarray) -> bool:
        blue = roi[:, :, 0].astype(np.float32)
        green = roi[:, :, 1].astype(np.float32)
        red = roi[:, :, 2].astype(np.float32)
        red_dom = float(np.mean((red > green + 10.0) & (red > blue + 10.0)))
        if red_dom >= 0.03:
            return True
        if float(np.mean(red)) - float(np.mean(green)) >= 0.0 and float(np.mean(red)) >= 50.0:
            return True
        return False

    def _remove_edge_low_conf(self, results: list[BoundingBox], orig_size: tuple[int, int]) -> list[BoundingBox]:
        if not self.use_edge_filter or self.edge_filter_max_conf <= 0.0 or (not results):
            return results
        w, h = orig_size
        tol = self.edge_tol
        out: list[BoundingBox] = []
        for b in results:
            on_edge = b.x1 <= tol or b.y1 <= tol or b.x2 >= w - 1 - tol or (b.y2 >= h - 1 - tol)
            if on_edge and b.conf <= self.edge_filter_max_conf:
                continue
            out.append(b)
        return out

    def _views_corroborated(self, post_boxes: np.ndarray, post_cls: np.ndarray, full_boxes: np.ndarray, full_cls: np.ndarray, full_views: np.ndarray, iou_thresh: float) -> np.ndarray:
        n = len(post_boxes)
        if n == 0:
            return np.zeros(0, dtype=bool)
        full_areas = np.maximum(0.0, full_boxes[:, 2] - full_boxes[:, 0]) * np.maximum(0.0, full_boxes[:, 3] - full_boxes[:, 1])
        out = np.zeros(n, dtype=bool)
        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-07)
            mask = (iou >= iou_thresh) & (full_cls == post_cls[i])
            if np.any(mask):
                out[i] = len(np.unique(full_views[mask])) >= 2
        return out

    def _filter_low_conf_by_color(self, image: np.ndarray, results: list[BoundingBox]) -> list[BoundingBox]:
        if not results:
            return results
        cls_fire = self.class_names.index('fire')
        cls_ext = self.class_names.index('fire extinguisher')
        out: list[BoundingBox] = []
        for box in results:
            check_fire = box.cls_id == cls_fire and box.conf <= self.fire_color_filter_max_conf
            check_ext = box.cls_id == cls_ext and box.conf <= self.fire_ext_color_filter_max_conf
            if not check_fire and (not check_ext):
                out.append(box)
                continue
            roi = self._roi_for_box(image, box)
            if roi is None or self._roi_is_near_grayscale(roi):
                out.append(box)
                continue
            if check_fire and (not self._passes_fire_color(roi)):
                continue
            if check_ext and (not self._passes_fire_ext_red_color(roi)):
                continue
            out.append(box)
        return out

    @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

    @staticmethod
    def _empty_raw() -> tuple[np.ndarray, np.ndarray, np.ndarray]:
        return (np.empty((0, 4), dtype=np.float32), np.empty((0,), dtype=np.float32), np.empty((0,), dtype=np.int32))

    @staticmethod
    def _iomin(a: np.ndarray, b: np.ndarray) -> float:
        ix1 = max(float(a[0]), float(b[0]))
        iy1 = max(float(a[1]), float(b[1]))
        ix2 = min(float(a[2]), float(b[2]))
        iy2 = min(float(a[3]), float(b[3]))
        inter = max(0.0, ix2 - ix1) * max(0.0, iy2 - iy1)
        area_a = max(0.0, float(a[2] - a[0]) * float(a[3] - a[1]))
        area_b = max(0.0, float(b[2] - b[0]) * float(b[3] - b[1]))
        smaller = min(area_a, area_b)
        return inter / (smaller + 1e-07)

    def _refine_smoke_from_raw(self, finals: list[BoundingBox], raw_boxes: np.ndarray, raw_scores: np.ndarray, raw_cls: np.ndarray) -> list[BoundingBox]:
        del raw_scores
        if not finals or len(raw_boxes) == 0:
            return finals
        smoke_id = self.class_names.index('smoke')
        raw_smoke = raw_cls == smoke_id
        if not np.any(raw_smoke):
            return finals
        cand_boxes = raw_boxes[raw_smoke]
        out: list[BoundingBox] = []
        thr = float(self.smoke_raw_refine_overlap)
        for b in finals:
            if b.cls_id != smoke_id:
                out.append(b)
                continue
            final_xyxy = np.array([b.x1, b.y1, b.x2, b.y2], dtype=np.float32)
            best_idx = -1
            best_area = None
            for i, rb in enumerate(cand_boxes):
                if self._iomin(final_xyxy, rb) < thr:
                    continue
                area = max(0.0, float(rb[2] - rb[0]) * float(rb[3] - rb[1]))
                if best_area is None or area < best_area:
                    best_area = area
                    best_idx = i
            if best_idx < 0:
                out.append(b)
                continue
            rb = cand_boxes[best_idx]
            out.append(BoundingBox(x1=int(math.floor(rb[0])), y1=int(math.floor(rb[1])), x2=int(math.ceil(rb[2])), y2=int(math.ceil(rb[3])), cls_id=b.cls_id, conf=b.conf))
        return out

    def _rescale_class_boxes(self, finals: list[BoundingBox], orig_size: tuple[int, int]) -> list[BoundingBox]:
        if not finals:
            return finals
        img_w, img_h = orig_size
        fire_id = self.class_names.index('fire')
        smoke_id = self.class_names.index('smoke')
        ext_id = self.class_names.index('fire extinguisher')
        out: list[BoundingBox] = []
        for b in finals:
            x1, y1, x2, y2 = (float(b.x1), float(b.y1), float(b.x2), float(b.y2))
            w = max(0.0, x2 - x1)
            h = max(0.0, y2 - y1)
            if w <= 0.0 or h <= 0.0:
                continue
            if b.cls_id in (smoke_id, ext_id):
                scale = float(self.smoke_ext_shrink)
                nw, nh = (w * scale, h * scale)
                cx = 0.5 * (x1 + x2)
                nx1 = cx - 0.5 * nw
                nx2 = cx + 0.5 * nw
                ny2 = y2
                ny1 = ny2 - nh
            elif b.cls_id == fire_id:
                scale = float(self.fire_expand)
                nw, nh = (w * scale, h * scale)
                cx = 0.5 * (x1 + x2)
                cy = 0.5 * (y1 + y2)
                nx1 = cx - 0.5 * nw
                nx2 = cx + 0.5 * nw
                ny1 = cy - 0.5 * nh
                ny2 = cy + 0.5 * nh
            else:
                out.append(b)
                continue
            nx1 = max(0.0, min(float(img_w), nx1))
            nx2 = max(0.0, min(float(img_w), nx2))
            ny1 = max(0.0, min(float(img_h), ny1))
            ny2 = max(0.0, min(float(img_h), ny2))
            if nx2 <= nx1 or ny2 <= ny1:
                continue
            out.append(BoundingBox(x1=int(math.floor(nx1)), y1=int(math.floor(ny1)), x2=int(math.ceil(nx2)), y2=int(math.ceil(ny2)), cls_id=b.cls_id, conf=b.conf))
        return out

    def _apply_extra_post(self, finals: list[BoundingBox], raw_boxes: np.ndarray, raw_scores: np.ndarray, raw_cls: np.ndarray, orig_size: tuple[int, int]) -> list[BoundingBox]:
        finals = self._refine_smoke_from_raw(finals, raw_boxes, raw_scores, raw_cls)
        return self._rescale_class_boxes(finals, orig_size)

    def _candidates_final_dets(self, preds: np.ndarray, ratio: float, pad: tuple[float, float], orig_size: tuple[int, int]) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
        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]
        keep = self._conf_filter_mask(scores, cls_ids)
        boxes = boxes[keep]
        scores = scores[keep]
        cls_ids = cls_ids[keep]
        if len(boxes) == 0:
            return self._empty_raw()
        pad_w, pad_h = pad
        boxes[:, [0, 2]] -= pad_w
        boxes[:, [1, 3]] -= pad_h
        boxes /= ratio
        boxes = self._clip_boxes(boxes, orig_size)
        return (boxes, scores, cls_ids)

    def _candidates_raw_yolo(self, preds: np.ndarray, ratio: float, pad: tuple[float, float], orig_size: tuple[int, int]) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
        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.cls_remap[cls_ids]
        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 self._empty_raw()
        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)
        return (boxes, scores, cls_ids)

    def _pipeline_from_candidates(self, boxes: np.ndarray, scores: np.ndarray, cls_ids: np.ndarray, orig_size: tuple[int, int]) -> list[BoundingBox]:
        if len(boxes) == 0:
            return []
        boxes, scores, cls_ids = self._filter_sane_boxes(boxes, scores, cls_ids, orig_size)
        if len(boxes) == 0:
            return []
        boxes, scores, cls_ids = self._per_view_pipeline(boxes, scores, cls_ids)
        return self._build_results(boxes, scores, cls_ids)

    def _decode_final_dets(self, preds: np.ndarray, ratio: float, pad: tuple[float, float], orig_size: tuple[int, int]) -> tuple[list[BoundingBox], tuple[np.ndarray, np.ndarray, np.ndarray]]:
        raw = self._candidates_final_dets(preds, ratio, pad, orig_size)
        return (self._pipeline_from_candidates(*raw, orig_size), raw)

    def _decode_raw_yolo(self, preds: np.ndarray, ratio: float, pad: tuple[float, float], orig_size: tuple[int, int]) -> tuple[list[BoundingBox], tuple[np.ndarray, np.ndarray, np.ndarray]]:
        raw = self._candidates_raw_yolo(preds, ratio, pad, orig_size)
        return (self._pipeline_from_candidates(*raw, orig_size), raw)

    def _postprocess(self, output: np.ndarray, ratio: float, pad: tuple[float, float], orig_size: tuple[int, int]) -> tuple[list[BoundingBox], tuple[np.ndarray, np.ndarray, np.ndarray]]:
        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) -> tuple[list[BoundingBox], tuple[np.ndarray, np.ndarray, np.ndarray]]:
        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 = (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(self, image: np.ndarray) -> tuple[list[BoundingBox], tuple[np.ndarray, np.ndarray, np.ndarray]]:
        boxes_orig, raw_orig = self._predict_single(image)
        flipped = cv2.flip(image, 1)
        boxes_flip, raw_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]
        raw_boxes_o, raw_scores_o, raw_cls_o = raw_orig
        raw_boxes_f, raw_scores_f, raw_cls_f = raw_flip
        if len(raw_boxes_f) > 0:
            mapped_f = raw_boxes_f.copy()
            mapped_f[:, 0] = w - raw_boxes_f[:, 2]
            mapped_f[:, 2] = w - raw_boxes_f[:, 0]
            mapped_f[:, 1] = raw_boxes_f[:, 1]
            mapped_f[:, 3] = raw_boxes_f[:, 3]
        else:
            mapped_f = raw_boxes_f
        if len(raw_boxes_o) == 0 and len(mapped_f) == 0:
            raw_all = self._empty_raw()
        elif len(raw_boxes_o) == 0:
            raw_all = (mapped_f, raw_scores_f, raw_cls_f)
        elif len(mapped_f) == 0:
            raw_all = (raw_boxes_o, raw_scores_o, raw_cls_o)
        else:
            raw_all = (np.concatenate([raw_boxes_o, mapped_f], axis=0), np.concatenate([raw_scores_o, raw_scores_f], axis=0), np.concatenate([raw_cls_o, raw_cls_f], axis=0))
        all_boxes = boxes_orig + boxes_flip
        if not all_boxes:
            return ([], raw_all)
        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)
        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 ([], raw_all)
        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]
        if self.use_tta_view_filter and self.tta_view_filter_max_conf > 0.0 and (len(kept_coords) > 0):
            corrob = self._views_corroborated(kept_coords, kept_cls, coords, cls_ids, view_ids, self.tta_view_iou_thresh)
            keep = ~((boosted <= self.tta_view_filter_max_conf) & ~corrob)
            kept_coords = kept_coords[keep]
            boosted = boosted[keep]
            kept_cls = kept_cls[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)
        if len(kept_coords) > 1:
            kept_coords, boosted, kept_cls = self._merge_same_class_boxes(kept_coords, boosted, kept_cls)
        finals = [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 (finals, raw_all)

    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, raw = self._predict_tta(image)
                else:
                    boxes, raw = self._predict_single(image)
                if isinstance(image, np.ndarray) and image.ndim == 3:
                    boxes = self._filter_low_conf_by_color(image, boxes)
                    boxes = self._remove_edge_low_conf(boxes, (image.shape[1], image.shape[0]))
                    boxes = self._apply_extra_post(boxes, raw[0], raw[1], raw[2], (image.shape[1], image.shape[0]))
                else:
                    boxes = self._apply_extra_post(boxes, raw[0], raw[1], raw[2], (0, 0))
            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