Detect-fire miner

chute_config.yml

@@ -0,0 +1,23 @@
+Image:
+  from_base: parachutes/python:3.12
+  run_command:
+    - pip install --upgrade setuptools wheel
+    # CPU onnxruntime pinned to the conformity checker's version (Dockerfile.latency_conformity):
+    # identical kernels => identical boxes between the live chute and the offline FAIL_OUTPUT check.
+    - pip install 'numpy>=1.23,<3' 'onnxruntime==1.26.0' 'opencv-python-headless>=4.8' 'pillow>=9.5' 'huggingface_hub>=0.24' 'pydantic>=2.0' 'pyyaml>=6.0' 'aiohttp>=3.9'
+    - pip install torch==2.8.0 torchvision==0.23.0 --index-url https://download.pytorch.org/whl/cu128
+
+NodeSelector:
+  gpu_count: 1
+  min_vram_gb_per_gpu: 16
+  max_hourly_price_per_gpu: 2
+  include:
+    - pro_6000
+
+Chute:
+  timeout_seconds: 900
+  concurrency: 4
+  max_instances: 5
+  scaling_threshold: 0.5
+  shutdown_after_seconds: 288000
+  tee: true

miner.py

@@ -0,0 +1,790 @@
+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', 'smoke', 'fire extinguisher']
+    iou_thres = 0.2
+    cross_iou_thresh = 1.01
+    max_det = 300
+    _conf_thres_array = np.array([0.14, 0.16, 0.1], dtype=np.float32)
+    _bonus_array = np.array([0.06, 0.02, 0], dtype=np.float32)
+    _bonus_classes = tuple((int(c) for c in np.nonzero(_bonus_array > 0.0)[0]))
+    min_box_area = 100
+    min_side = 8
+    max_aspect_ratio = 8
+    smoke_merge_overlap = 0.6
+    fire_merge_overlap = 0.5
+    fire_suppress_overlap = 0.9
+    smoke_raw_refine_overlap = 1.9
+    smoke_ext_shrink = 0.98
+    fire_expand = 1
+    use_edge_filter = True
+    edge_filter_max_conf = 0.3
+    edge_tol = 8
+    fire_color_filter_max_conf = 0.0
+    fire_ext_color_filter_max_conf = 0.0
+    color_filter_min_saturation = 0.06
+    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=["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._blob = np.empty((1, 3, self.input_height, self.input_width), dtype=np.float32)
+        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))
+        np.multiply(img.transpose(2, 0, 1)[::-1], 1.0 / 255.0, out=self._blob[0])
+        return (self._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)
+        if n == 1:
+            return np.zeros(1, dtype=np.intp)
+        areas = np.maximum(0.0, boxes[:, 2] - boxes[:, 0]) * np.maximum(0.0, boxes[:, 3] - boxes[:, 1])
+        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:]
+            bx = boxes[rest]
+            xx1 = np.maximum(boxes[i, 0], bx[:, 0])
+            yy1 = np.maximum(boxes[i, 1], bx[:, 1])
+            xx2 = np.minimum(boxes[i, 2], bx[:, 2])
+            yy2 = np.minimum(boxes[i, 3], bx[:, 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])
+            iou = inter / (a_i + areas[rest] - 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):
+            indices = np.where(cls_ids == c)[0]
+            if len(indices) == 1:
+                all_keep.append(int(indices[0]))
+                continue
+            keep = self._hard_nms(boxes[indices], scores[indices], 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))
+        if iou_thresh >= 1.0:
+            return (boxes[order], scores[order], cls_ids[order])
+        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 self._bonus_classes:
+            b = float(self._bonus_array[c])
+            idx = np.where(cls_ids == c)[0]
+            if len(idx) == 0:
+                continue
+            if keep[idx].any():
+                continue
+            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
+        thr = float(self.smoke_raw_refine_overlap)
+        if thr > 1.0:
+            return self._nms_final_boxes(list(finals))
+        cand_boxes = raw_boxes[raw_smoke]
+        out: list[BoundingBox] = []
+        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 self._nms_final_boxes(out)
+
+    def _nms_final_boxes(self, out: list[BoundingBox]) -> list[BoundingBox]:
+        if len(out) <= 1:
+            return out
+        boxes = np.array([[b.x1, b.y1, b.x2, b.y2] for b in out], dtype=np.float32)
+        scores = np.array([b.conf for b in out], dtype=np.float32)
+        cls_ids = np.array([b.cls_id for b in out], dtype=np.int32)
+        keep = self._per_class_hard_nms(boxes, scores, cls_ids, self.iou_thres)
+        return [out[int(i)] for i in keep]
+
+    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

weights.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c5b2e7c0bd4c1def3e715fdfa4c033a722f04605282d0ad5c91e840f7318fbc9
+size 9734899