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miner.py

@@ -1,13 +1,11 @@
 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
@@ -16,151 +14,127 @@ class BoundingBox(BaseModel):
     cls_id: int
     conf: float
 
-
 class TVFrameResult(BaseModel):
     frame_id: int
     boxes: list[BoundingBox]
     keypoints: list[tuple[int, int]]
 
-
 class Miner:
-    """
-    YOLO (NMS-free) ONNX miner for road-sign detection. One forward pass per
-    frame — no TTA.
-
-    Class: single 'road sign'.
-
-    Pipeline per frame: preprocess -> ONNX -> decode -> conf threshold (+ rescue
-    bonus) -> un-letterbox -> sanity filter -> per-class NMS -> cross-class dedup
-    -> same-class cluster score boost -> results.
-
-    Speed: the detection pipeline runs exactly ONCE per frame (the old redundant
-    second NMS/dedup pass is gone), and the cluster boost is a single vectorized
-    IoU matrix, so cost stays flat as the number of detected objects grows.
-    """
-
-    class_names = ['road sign']
-    input_size = 1536
-    cross_iou_thresh = 0.8
-    max_det = 300
-    # NMS is O(n^2); if a frame yields a huge candidate list, keep the top-K by
-    # score before NMS. Set high enough never to touch real detections.
-    pre_nms_topk = 1000
-    #overlap_suppress_threshold = 0.85
-
-    # Per-class confidence threshold
-    _conf_thres_array = np.array([0.32], dtype=np.float32)
-
-    # Per-class IoU threshold for same-class NMS
-    _iou_thres_array = np.array([0.8], dtype=np.float32)
-
-    # Per-class rescue bonus
-    _bonus_array = np.array([0.2], dtype=np.float32)
-
-    # Per-class minimum box area (index 0 = road sign)
-    _min_box_area_array = np.array([9.0], dtype=np.float32)
+    class_names = ['fire', 'smoke', 'fire extinguisher']
+    _model_class_order = ['fire', 'smoke', 'fire extinguisher']
+    iou_thres = 0.5
+    max_det = 30
+    _conf_thres_array = np.array([0.10, 0.10, 0.20], dtype=np.float32)
+    _bonus_array = np.array([0.05, 0.05, 0.05], dtype=np.float32)
+    min_box_area = 100
+    min_side = 8
+    max_aspect_ratio = 8.0
+    smoke_merge_overlap = 0.35
+    fire_suppress_overlap = 0.9
+    ext_scale = 0.95
+    fire_expand = 1.01
+    fire_color_filter_max_conf = 0.45
+    color_filter_min_saturation = 0.06
 
     def __init__(self, path_hf_repo: Path) -> None:
-        self.path_hf_repo = path_hf_repo
-
-        print("ORT version:", ort.__version__)
-
+        model_path = path_hf_repo / 'weights.onnx'
+        print('ORT version:', ort.__version__)
         try:
             ort.preload_dlls()
-            print("preload_dlls success")
+            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())
-
+            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
-
-        self.session = ort.InferenceSession(
-            str(path_hf_repo / "weights.onnx"),
-            sess_options=sess_options,
-            providers=["CUDAExecutionProvider", "CPUExecutionProvider"],
-        )
-        print("Created ORT session with preferred CUDA provider list")
-        print("ORT session providers:", self.session.get_providers())
-
+        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
-        input_shape = self.session.get_inputs()[0].shape
-
-        self.input_h = self._safe_dim(input_shape[2], default=self.input_size)
-        self.input_w = self._safe_dim(input_shape[3], default=self.input_size)
-
-        # Same-class cluster score boost (raises overlapping same-class survivors
-        # to their cluster max). Part of the current tuned behaviour; for a single
-        # class it is nearly a no-op. Set False to disable.
-        self.use_cluster_boost = True
-        self._avg_iou = float(np.mean(self._iou_thres_array))
-
+        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)
+        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:
+    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")
+            print(f'✅ warmup: {iters} dummy predict_batch call(s) done')
         except Exception as e:
-            print(f"warmup skipped: {e}")
+            print(f'⚠️ warmup skipped: {e}')
 
     def __repr__(self) -> str:
-        return f"road sign Miner classes={len(self.class_names)}"
+        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
 
-    # ─── Preprocessing ────────────────────────────────────────────
-
-    def _letterbox(
-        self, image: ndarray, new_shape: tuple[int, int],
-        color: tuple[int, int, int] = (114, 114, 114),
-    ) -> tuple[ndarray, float, float, float]:
+    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]
-        target_w, target_h = new_shape
-
-        r = min(target_w / orig_w, target_h / orig_h)
-        new_unpad_w = int(round(orig_w * r))
-        new_unpad_h = int(round(orig_h * r))
-
-        resized = cv2.resize(image, (new_unpad_w, new_unpad_h), interpolation=cv2.INTER_LINEAR)
-
-        dw = target_w - new_unpad_w
-        dh = target_h - new_unpad_h
-        pad_w = dw / 2.0
-        pad_h = dh / 2.0
-
-        left = int(round(pad_w - 0.1))
-        right = int(round(pad_w + 0.1))
-        top = int(round(pad_h - 0.1))
-        bottom = int(round(pad_h + 0.1))
-
-        out = cv2.copyMakeBorder(
-            resized, top, bottom, left, right,
-            cv2.BORDER_CONSTANT, value=color,
-        )
-        return out, r, pad_w, pad_h
-
-    def _preprocess(self, image_bgr: ndarray) -> tuple[np.ndarray, dict]:
-        orig_h, orig_w = image_bgr.shape[:2]
-        rgb = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2RGB)
-        img, ratio, pad_w, pad_h = self._letterbox(rgb, (self.input_w, self.input_h))
-        x = img.astype(np.float32) / 255.0
-        x = np.transpose(x, (2, 0, 1))[None, ...]
-        x = np.ascontiguousarray(x)
-        return x, {
-            "orig_h": orig_h,
-            "orig_w": orig_w,
-            "ratio": ratio,
-            "pad_w": pad_w,
-            "pad_h": pad_h,
-        }
-
-    # ─── Vectorized box operations ───────────────────────────────
+        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:
@@ -172,134 +146,126 @@ class Miner:
         return boxes
 
     @staticmethod
-    def _hard_nms(boxes: np.ndarray, scores: np.ndarray,
-                  iou_thresh: float) -> np.ndarray:
-        """Vectorized greedy NMS. Areas precomputed once. Returns indices to keep."""
+    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)
-        x1, y1 = boxes[:, 0], boxes[:, 1]
-        x2, y2 = boxes[:, 2], boxes[:, 3]
-        areas = np.maximum(0.0, x2 - x1) * np.maximum(0.0, y2 - y1)
         order = np.argsort(-scores)
-        keep = []
-        while order.size > 0:
+        keep: list[int] = []
+        while len(order) > 0:
             i = int(order[0])
             keep.append(i)
-            if order.size == 1:
+            if len(order) == 1:
                 break
             rest = order[1:]
-            xx1 = np.maximum(x1[i], x1[rest])
-            yy1 = np.maximum(y1[i], y1[rest])
-            xx2 = np.minimum(x2[i], x2[rest])
-            yy2 = np.minimum(y2[i], y2[rest])
+            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)
-            iou = inter / (areas[i] + areas[rest] - inter + 1e-7)
+            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) -> np.ndarray:
-        """Per-class NMS using per-class IoU thresholds."""
+    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 = []
+        all_keep: list[int] = []
         for c in np.unique(cls_ids):
             mask = cls_ids == c
             indices = np.where(mask)[0]
-            cls_iou = float(self._iou_thres_array[c])  # per-class IoU threshold
-            keep = self._hard_nms(boxes[mask], scores[mask], cls_iou)
+            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
+    def _order_by_margin(self, boxes: np.ndarray, scores: np.ndarray, cls_ids: np.ndarray) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
         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]))
+        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 = []
-        for i in order:
-            if suppressed[i]:
+        return (boxes[order], scores[order], cls_ids[order])
+
+    def _merge_smoke_boxes(self, boxes: np.ndarray, scores: np.ndarray, cls_ids: np.ndarray) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+        target_cls = self.class_names.index('smoke')
+        overlap = self.smoke_merge_overlap
+        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_fire(self, boxes: np.ndarray, scores: np.ndarray, cls_ids: np.ndarray) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+        target_cls = self.class_names.index('fire')
+        overlap = self.fire_suppress_overlap
+        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
-            keep.append(int(i))
             bi = boxes[i]
-            xx1 = np.maximum(bi[0], boxes[:, 0])
-            yy1 = np.maximum(bi[1], boxes[:, 1])
-            xx2 = np.minimum(bi[2], boxes[:, 2])
-            yy2 = np.minimum(bi[3], boxes[:, 3])
-            inter = np.maximum(0.0, xx2 - xx1) * np.maximum(0.0, yy2 - yy1)
-            a_i = max(1e-7, float((bi[2] - bi[0]) * (bi[3] - bi[1])))
-            iou = inter / (a_i + areas - inter + 1e-7)
-            dup = iou > iou_thresh
-            dup[i] = False
-            suppressed |= dup
-        keep_idx = np.array(keep, dtype=np.intp)
-        return boxes[keep_idx], scores[keep_idx], cls_ids[keep_idx]
-
-    def _filter_sane_boxes(self, boxes: np.ndarray, scores: np.ndarray,
-                       cls_ids: np.ndarray, orig_size: tuple[int, int]
-                       ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
-        """Filter by per-class min area and max area ratio."""
-        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
-
-        class_min_area = self._min_box_area_array[cls_ids]
-
-        keep = (
-            (area >= class_min_area) &
-            (area <= 0.95 * image_area)
-        )
-        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:
-        """For each kept box, confidence = max score in its SAME-CLASS IoU cluster.
-        Vectorized: single (n_post x n_full) IoU matrix, no per-box Python loop."""
-        n = len(post_boxes)
-        if n == 0:
-            return np.empty(0, dtype=np.float32)
-        m = len(full_boxes)
-        if m == 0:
-            return np.zeros(n, dtype=np.float32)
-        pa = (np.maximum(0.0, post_boxes[:, 2] - post_boxes[:, 0]) *
-              np.maximum(0.0, post_boxes[:, 3] - post_boxes[:, 1]))
-        fa = (np.maximum(0.0, full_boxes[:, 2] - full_boxes[:, 0]) *
-              np.maximum(0.0, full_boxes[:, 3] - full_boxes[:, 1]))
-        xx1 = np.maximum(post_boxes[:, 0][:, None], full_boxes[:, 0][None, :])
-        yy1 = np.maximum(post_boxes[:, 1][:, None], full_boxes[:, 1][None, :])
-        xx2 = np.minimum(post_boxes[:, 2][:, None], full_boxes[:, 2][None, :])
-        yy2 = np.minimum(post_boxes[:, 3][:, None], full_boxes[:, 3][None, :])
-        inter = np.maximum(0.0, xx2 - xx1) * np.maximum(0.0, yy2 - yy1)
-        iou = inter / (pa[:, None] + fa[None, :] - inter + 1e-7)
-        mask = (iou >= iou_thresh) & (post_cls[:, None] == full_cls[None, :])
-        tiled = np.where(mask, full_scores[None, :], -np.inf)
-        out = tiled.max(axis=1)
-        out[~np.isfinite(out)] = 0.0
-        return out.astype(np.float32)
-
-    def _conf_filter_mask(self, scores: np.ndarray,
-                          cls_ids: np.ndarray) -> np.ndarray:
-        """Per-class threshold with rescue bonus for missed classes."""
+            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_smoke_boxes(boxes, scores, cls_ids)
+        return self._suppress_contained_fire(boxes, scores, cls_ids)
+
+    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]
@@ -317,145 +283,231 @@ class Miner:
                 keep[top] = True
         return keep
 
-    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]:
-        """Sanity filter -> (top-k cap) -> per-class NMS -> cap -> cross-class dedup."""
-        boxes, scores, cls_ids = self._filter_sane_boxes(
-            boxes, scores, cls_ids, orig_size
-        )
+    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
-        if len(scores) > self.pre_nms_topk:
-            top = np.argpartition(-scores, self.pre_nms_topk)[: self.pre_nms_topk]
-            boxes, scores, cls_ids = boxes[top], scores[top], cls_ids[top]
+            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)
-            boxes, scores, cls_ids = boxes[keep], scores[keep], cls_ids[keep]
+            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]
+            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._order_by_margin(boxes, scores, cls_ids)
         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
-
-    # ─── Decoding ─────────────────────────────────────────────────
-
-    def _decode_yolo_output(self, preds: np.ndarray, ratio: float,
-                           pad: tuple[float, float],
-                           orig_size: tuple[int, int]
-                           ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
-        """Decode NMS-free output [1,N,6]=(x1,y1,x2,y2,conf,cls) -> conf filter ->
-        un-letterbox -> sanity+NMS+dedup. Returns arrays in ORIGINAL coords."""
-        empty = (np.empty((0, 4), np.float32), np.empty(0, np.float32),
-                 np.empty(0, np.int32))
+            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
+
+    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')
+        out: list[BoundingBox] = []
+        for box in results:
+            if box.cls_id != cls_fire or box.conf > self.fire_color_filter_max_conf:
+                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 not self._passes_fire_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))
+
+    def _renms_when_raw_smoke(self, finals: list[BoundingBox], raw_boxes: np.ndarray, raw_cls: np.ndarray) -> list[BoundingBox]:
+        if not finals or len(raw_boxes) == 0 or len(finals) <= 1:
+            return finals
+        if not np.any(raw_cls == self.class_names.index('smoke')):
+            return finals
+        boxes = np.array([[b.x1, b.y1, b.x2, b.y2] for b in finals], dtype=np.float32)
+        scores = np.array([b.conf for b in finals], dtype=np.float32)
+        cls_ids = np.array([b.cls_id for b in finals], dtype=np.int32)
+        keep = self._per_class_hard_nms(boxes, scores, cls_ids, self.iou_thres)
+        return [finals[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')
+        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 == ext_id:
+                scale = float(self.ext_scale)
+                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_cls: np.ndarray, orig_size: tuple[int, int]) -> list[BoundingBox]:
+        finals = self._renms_when_raw_smoke(finals, raw_boxes, raw_cls)
+        return self._rescale_class_boxes(finals, orig_size)
+
+    def _postprocess(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]]:
         if preds.ndim == 3 and preds.shape[0] == 1:
             preds = preds[0]
         if preds.ndim != 2 or preds.shape[1] < 6:
-            print(f"Warning: Unexpected output shape: {preds.shape}")
-            return empty
-
+            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)
-
-        n_cls = len(self.class_names)
-        valid = (cls_ids >= 0) & (cls_ids < n_cls)
-        boxes, scores, cls_ids = boxes[valid], scores[valid], cls_ids[valid]
-        if len(boxes) == 0:
-            return empty
-
+        cls_ids = self.cls_remap[preds[:, 5].astype(np.int32)]
         keep = self._conf_filter_mask(scores, cls_ids)
-        boxes, scores, cls_ids = boxes[keep], scores[keep], cls_ids[keep]
+        boxes = boxes[keep]
+        scores = scores[keep]
+        cls_ids = cls_ids[keep]
         if len(boxes) == 0:
-            return empty
-
+            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 self._per_view_pipeline(boxes, scores, cls_ids, orig_size)
-
-    @staticmethod
-    def _build_results(boxes: np.ndarray, scores: np.ndarray,
-                       cls_ids: np.ndarray,
-                       orig_size: tuple[int, int]) -> list[BoundingBox]:
-        results = []
-        orig_w, orig_h = orig_size
-        for box, conf, cls_id in zip(boxes, scores, cls_ids):
-            x1, y1, x2, y2 = box.tolist() if hasattr(box, "tolist") else box
-            if x2 <= x1 or y2 <= y1:
-                continue
-            results.append(
-                BoundingBox(
-                    x1=max(0, min(orig_w, int(math.floor(x1)))),
-                    y1=max(0, min(orig_h, int(math.floor(y1)))),
-                    x2=max(0, min(orig_w, int(math.ceil(x2)))),
-                    y2=max(0, min(orig_h, int(math.ceil(y2)))),
-                    cls_id=int(cls_id),
-                    conf=float(max(0.0, min(1.0, conf))),
-                )
-            )
-        return results
-
-    # ─── Inference (single view, no TTA) ──────────────────────────
-
-    def _predict_single(self, image_bgr: np.ndarray
-                        ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
-        """One forward pass -> decoded (boxes, scores, cls_ids) in original coords."""
-        if image_bgr is None or not isinstance(image_bgr, np.ndarray):
-            raise ValueError("Invalid image input")
-        if image_bgr.dtype != np.uint8:
-            image_bgr = image_bgr.astype(np.uint8)
-
-        inp, meta = self._preprocess(image_bgr)
-        outputs = self.session.run(None, {self.input_name: inp})
-
-        ratio = float(meta["ratio"])
-        pad = (float(meta["pad_w"]), float(meta["pad_h"]))
-        orig_size = (int(meta["orig_w"]), int(meta["orig_h"]))
-
-        return self._decode_yolo_output(outputs[0], ratio, pad, orig_size)
-
-    def _infer_single(self, image_bgr: ndarray) -> list[BoundingBox]:
-        orig_h, orig_w = image_bgr.shape[:2]
-        orig_size = (orig_w, orig_h)
-
-        boxes, scores, cls_ids = self._predict_single(image_bgr)
+        raw = (boxes, scores, cls_ids)
+        boxes, scores, cls_ids = self._filter_sane_boxes(boxes, scores, cls_ids, orig_size)
         if len(boxes) == 0:
-            return []
-
-        if self.use_cluster_boost and len(boxes) > 1:
-            scores = self._max_score_per_cluster(
-                boxes, cls_ids, boxes, scores, cls_ids, self._avg_iou)
-
-        return self._build_results(boxes, scores, cls_ids, orig_size)
-
-    # ─── Public API ───────────────────────────────────────────────
-
-    def predict_batch(
-        self,
-        batch_images: list[ndarray],
-        offset: int,
-        n_keypoints: int,
-    ) -> list[TVFrameResult]:
+            return ([], raw)
+        boxes, scores, cls_ids = self._per_view_pipeline(boxes, scores, cls_ids)
+        return (self._build_results(boxes, scores, cls_ids), raw)
+
+    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_batch(self, batch_images: list[ndarray], offset: int, n_keypoints: int) -> list[TVFrameResult]:
         results: list[TVFrameResult] = []
-        for idx, image in enumerate(batch_images):
+        for frame_number_in_batch, image in enumerate(batch_images):
             try:
-                boxes = self._infer_single(image)
+                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._apply_extra_post(boxes, raw[0], raw[2], (image.shape[1], image.shape[0]))
+                else:
+                    boxes = self._apply_extra_post(boxes, raw[0], raw[2], (0, 0))
             except Exception as e:
-                print(f"Inference failed for frame {offset + idx}: {e}")
+                print(f'⚠️ Inference failed for frame {offset + frame_number_in_batch}: {e}')
                 boxes = []
-            keypoints = [(0, 0) for _ in range(max(0, int(n_keypoints)))]
-            results.append(
-                TVFrameResult(
-                    frame_id=offset + idx,
-                    boxes=boxes,
-                    keypoints=keypoints,
-                )
-            )
-        return results
+            results.append(TVFrameResult(frame_id=offset + frame_number_in_batch, boxes=boxes, keypoints=[(0, 0) for _ in range(max(0, int(n_keypoints)))]))
+        return results

weights.onnx

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:9ad0e8fc7e38bd4c5f5c8f4cfece75dcc2fa6a41c2cf71e4cba9672c87b98687
-size 9757809
+oid sha256:f6a645e1257d008bd69e5589a8bad49155040175af30a4bdc03e97cd1b19fa8d
+size 9842101