subnet_bridge: copy winning miner repo into library

README.md

@@ -1,7 +1,7 @@
 ---
 tags:
 - element_type:detect
-- model:onnxruntime
+- model:ultralytics-yolo
 - subnet:winner
 - object:fire
 - object:smoke
@@ -10,18 +10,18 @@ tags:
 manako:
   source: winner_fetch
   manifest_element_name: manak0/Detect-fire
-  winner_repo_id: SuperBitDev/fire3
-  winner_revision: b28c05705f37d3a59cc5ebbc79b34394ea7d2671
-  note: E=0.06071625 (map50=0.600000, size_mb=9.882033)
+  winner_repo_id: SuperBitDev/fire6
+  winner_revision: d2064e54e34c7c6b2513f2a00f05ec66795c4302
+  note: E=0.06077833 (map50=0.600000, size_mb=9.871940)
 ---
 
 # Detect-fire-winner
 
 Published winning miner converted into a library element.
 
-- Source winner repo: `SuperBitDev/fire3`
-- Source revision: `b28c05705f37d3a59cc5ebbc79b34394ea7d2671`
+- Source winner repo: `SuperBitDev/fire6`
+- Source revision: `d2064e54e34c7c6b2513f2a00f05ec66795c4302`
 - Manifest element: `manak0/Detect-fire`
 - Element type: `Detect`
 - Objects: fire, smoke, fire extinguisher
-- Runtime model type: `onnxruntime`
+- Runtime model type: `ultralytics-yolo`

main.py

@@ -28,7 +28,7 @@ Miner = _load_local_miner_class()
 
 
 CLASS_NAMES = ['fire', 'smoke', 'fire extinguisher']
-MODEL_TYPE = 'onnxruntime'
+MODEL_TYPE = 'ultralytics-yolo'
 
 
 def _to_dict(value: Any) -> dict[str, Any]:

miner.py

@@ -1,5 +1,8 @@
+import os
+
 from pathlib import Path
 import math
+
 import cv2
 import numpy as np
 import onnxruntime as ort
@@ -14,138 +17,293 @@ class BoundingBox(BaseModel):
     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']
+    """ONNX Runtime miner for fire / smoke / fire_extinguisher detection.
+
+    Strategy (ported from offense miner):
+      - per-class confidence threshold with per-class rescue bonus
+      - per-class hard NMS, then cross-class dedup
+      - horizontal-flip TTA with full-set cluster score boost
+    Plus fire001 specifics: class remap, sanity-box filter, TTA toggle.
+    """
+
+    class_names = ["fire", "smoke", "fire extinguisher"]
+    # FALLBACK order the model emits classes in -- remapped to `class_names`
+    # index by `self.cls_remap` (built in __init__). The authoritative order
+    # is read from the ONNX `names` metadata that Ultralytics embeds at
+    # export time (ships inside weights.onnx), so a retrained model with a
+    # different class order is remapped correctly without code changes.
+    # Used only when that metadata is missing or unparsable.
     _model_class_order = ["fire", "fire extinguisher", "smoke"]
-    iou_thres = 0.5
+
+    iou_thres = 0.55
     cross_iou_thresh = 0.8
-    max_det = 30
-    _conf_thres_array = np.array([0.15, 0.30, 0.20], dtype=np.float32)
-    _bonus_array = np.array([0.03, 0.05, 0.03], dtype=np.float32)
-    min_box_area = 0
+    max_det = 150
+
+    # Per-class confidence thresholds. Higher = fewer FP for that class.
+    # Indexed by class_names order: [fire, smoke, fire_extinguisher].
+    _conf_thres_array = np.array(
+        [0.25, 0.30, 0.25], dtype=np.float32
+    )
+    # Per-class rescue bonus. If a class has ZERO boxes passing the threshold
+    # in a frame, its top-1 candidate is admitted when its score is at least
+    # (threshold - bonus). Fire and smoke get a small bonus (variable
+    # appearance); fire extinguisher does not (distinctive object, leave FP
+    # control strict).
+    _bonus_array = np.array(
+        [0.03, 0.1, 0.05], dtype=np.float32
+    )
+
+    # Box sanity filter (fire001-specific FP reduction): drop tiny / degenerate
+    # / image-spanning / extreme aspect ratio boxes.
+    min_box_area = 14 * 14
     min_side = 8
     max_aspect_ratio = 8.0
+
+    # Same-class merge: two boxes whose intersection covers at least this
+    # fraction of the SMALLER box are treated as the same object and replaced
+    # by their union. Catches nested boxes (IoU below the NMS threshold) and
+    # fragmented detections. Per-class because the risk differs:
+    #   smoke -- diffuse plumes fragment a lot, so a moderate threshold helps.
+    #   fire  -- separate flames must stay separate, so keep this HIGH (only a
+    #            tight core nested inside a looser flame box merges). Set to a
+    #            value > 1.0 to disable fire merging entirely.
+    # Fire merge is DISABLED by default (1.01): measured on the fire-29-val1024
+    # val split it cost fire AP (0.751 -> 0.742, composite 0.8888 -> 0.8874)
+    # because the nested core+flame boxes it collapses were scoring as separate
+    # true positives. Lower it to ~0.8 to enable, and re-measure with
+    # verify_filters.py / tune_miner.py after a retrain -- a model whose fire
+    # boxes fragment more (or live-SAM3 GT that draws fuller flames) could flip
+    # the result.
     smoke_merge_overlap = 0.8
-    fire_merge_overlap = 0.9
+    fire_merge_overlap = 1.01
+
+    # Fire containment suppression: when two FIRE boxes overlap on one object
+    # (intersection >= this fraction of the SMALLER box) keep the HIGHER-conf
+    # box and drop the other -- unchanged geometry, unlike the union merge
+    # above. This catches the nested core+flame duplicate that per-class NMS
+    # (IoU-based, iou_thres) leaves behind. Set > 1.0 to disable.
+    # DISABLED by default (1.01): measured on fire-29-val1024 it cost fire AP
+    # (0.751 -> 0.743, composite 0.8888 -> 0.8877). Cause: GT fire boxes almost
+    # never overlap (1 pair in 416), so each nested model pair has one TP + one
+    # FP, but the higher-CONF box isn't always the one matching GT at IoU 0.5 --
+    # so keeping it can drop the real match, and score-ordered AP already
+    # tolerates the duplicate. Lower to ~0.8 to enable; re-measure after a
+    # retrain or against live-SAM3 GT, which may differ.
     fire_suppress_overlap = 0.88
-    smoke_raw_refine_overlap = 0.9
-    smoke_ext_shrink = 0.92
-    fire_expand = 1.02
-    fire_color_filter_max_conf = 0.45
-    fire_ext_color_filter_max_conf = 0.0
-    color_filter_min_saturation = 0.06
+
+    # ── Low-confidence color-prior FP filters ───────────────────────────────
+    # Ported from the firedetect1007 miner's color checks, but applied ONLY to
+    # the borderline confidence band (just above each per-class threshold) and
+    # ONLY on color frames. A fire/extinguisher detection there is dropped when
+    # its pixels clearly do not match the expected appearance: warm/bright for
+    # fire, red for extinguisher. High-confidence detections are never touched.
+    #
+    # The reference miner ran these unconditionally -- a BUG on this validator,
+    # which feeds some frames as grayscale (a true red extinguisher is gray
+    # there, so a red test would wrongly delete it). We skip the filter when the
+    # ROI is near-grayscale, so it never fires on those frames.
+    #
+    # Tunable: set a max-conf gate to 0.0 to disable that filter. After a model
+    # retrain, re-validate these with tune_miner.py (the gates are relative to
+    # the per-class thresholds, so they move when those move).
+    fire_color_filter_max_conf = 0.45      # only fire boxes in (thresh, 0.45]
+    fire_ext_color_filter_max_conf = 0.40  # only ext boxes in (thresh, 0.40]
+    color_filter_min_saturation = 0.06     # skip filter if ROI is near-grayscale
+
+    # ── Corroboration FP filters (optional; OFF by default) ─────────────────
+    # Ported in spirit from firedetect1007. Both REMOVE borderline boxes that
+    # lack support -- a precision play for the validator's FP pillar. OFF by
+    # default because, unlike the color priors, they can also drop true
+    # positives; enable + sweep with verify_filters.py and keep only the
+    # settings that raise the measured composite. A max-conf gate of 0.0
+    # disables the corresponding filter.
+    #   edge filter: drop boxes touching the frame border in a low-conf band
+    #     (the validator scales/crops, so border-hugging boxes are often the
+    #     truncated remains of an object whose body is off-frame).
+    #   tta view filter: drop low-conf boxes that appear in only ONE of the two
+    #     horizontal-flip TTA views (a real object is usually seen in both).
     use_edge_filter = False
-    edge_filter_max_conf = 0.0
-    edge_tol = 2.0
+    edge_filter_max_conf = 0.0      # drop edge-touching boxes with conf <= this
+    edge_tol = 2.0                  # px from the border counted as "on edge"
     use_tta_view_filter = False
-    tta_view_filter_max_conf = 0.0
-    tta_view_iou_thresh = 0.5
+    tta_view_filter_max_conf = 0.0  # drop single-view boxes with conf <= this
+    tta_view_iou_thresh = 0.5       # IoU for "same object seen in both views"
 
     def __init__(self, path_hf_repo: Path) -> None:
-        model_path = path_hf_repo / 'weights.onnx'
-        print('ORT version:', ort.__version__)
+        model_path = path_hf_repo / "weights.onnx"
+        print("ORT version:", ort.__version__)
+
         try:
             ort.preload_dlls()
-            print('✅ onnxruntime.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
         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')
+            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())
+            print(f"⚠️ CUDA session creation failed, falling back to CPU: {e}")
+            self.session = ort.InferenceSession(
+                str(model_path),
+                sess_options=sess_options,
+                providers=["CPUExecutionProvider"],
+            )
+
+        print("ORT session providers:", self.session.get_providers())
+
+        # Build cls_remap: for each model-emit index i,
+        #   cls_remap[i] = self.class_names.index(model_class_order[i])
+        # i.e. converts a model-side class id into the canonical class id
+        # that downstream code (BoundingBox.cls_id, validator) expects.
+        # The model-side order comes from the ONNX metadata when available,
+        # else falls back to the static _model_class_order.
         model_class_order = self._read_model_class_order()
         if model_class_order is None:
             model_class_order = list(self._model_class_order)
-            print(f'cls order: no usable ONNX metadata, FALLBACK {model_class_order}')
+            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)
+            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)
+            print("INPUT:", inp.name, inp.shape, inp.type)
         for out in self.session.get_outputs():
-            print('OUTPUT:', out.name, out.shape, out.type)
+            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()))))
+
+        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'ONNXRuntime(session={type(self.session).__name__}, providers={self.session.get_providers()})'
+        return (
+            f"ONNXRuntime(session={type(self.session).__name__}, "
+            f"providers={self.session.get_providers()})"
+        )
 
     @staticmethod
     def _safe_dim(value, default: int) -> int:
         return value if isinstance(value, int) and value > 0 else default
 
     def _read_model_class_order(self) -> list[str] | None:
+        """Read the model's class order from Ultralytics ONNX metadata.
+
+        Returns the class names ordered by model-emit index, or None when
+        metadata is missing/unparsable or doesn't match `class_names` as a
+        set (in which case the static _model_class_order fallback is used).
+        """
         try:
             import ast
+
             meta = self.session.get_modelmeta().custom_metadata_map
-            names = ast.literal_eval(meta['names'])
+            names = ast.literal_eval(meta["names"])  # e.g. {0: 'fire', ...}
             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})')
+            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')
+            print(
+                f"cls order: ONNX names {order} do not match expected classes "
+                f"{self.class_names}; ignoring metadata"
+            )
             return None
         return order
 
-    def _letterbox(self, image: ndarray, new_shape: tuple[int, int], color=(114, 114, 114)) -> tuple[ndarray, float, tuple[float, float]]:
+    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]]:
+        padded = cv2.copyMakeBorder(
+            image, top, bottom, left, right,
+            borderType=cv2.BORDER_CONSTANT, value=color,
+        )
+        return padded, ratio, (dw, dh)
+
+    def _preprocess(
+        self, image: ndarray
+    ) -> tuple[np.ndarray, float, tuple[float, float], tuple[int, int]]:
         orig_h, orig_w = image.shape[:2]
-        img, ratio, pad = self._letterbox(image, (self.input_width, self.input_height))
+        img, ratio, pad = self._letterbox(
+            image, (self.input_width, self.input_height)
+        )
+        # Fused scale(1/255) + BGR->RGB swap + HWC->NCHW + contiguous float32 in
+        # one optimized OpenCV call. Bit-identical (max abs diff 6e-8) to the
+        # prior cvtColor + astype/255 + transpose + ascontiguousarray chain, but
+        # ~half the preprocess time (preprocess is ~12% of predict_batch).
         blob = cv2.dnn.blobFromImage(img, scalefactor=1.0 / 255.0, swapRB=True)
-        return (blob, ratio, pad, (orig_w, orig_h))
+        return blob, ratio, pad, (orig_w, orig_h)
 
     @staticmethod
     def _clip_boxes(boxes: np.ndarray, image_size: tuple[int, int]) -> np.ndarray:
@@ -166,7 +324,9 @@ class Miner:
         return out
 
     @staticmethod
-    def _hard_nms(boxes: np.ndarray, scores: np.ndarray, iou_thresh: float) -> np.ndarray:
+    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)
@@ -183,13 +343,21 @@ class Miner:
             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)
+            a_i = (max(0.0, boxes[i, 2] - boxes[i, 0]) *
+                   max(0.0, boxes[i, 3] - boxes[i, 1]))
+            a_r = (np.maximum(0.0, boxes[rest, 2] - boxes[rest, 0]) *
+                   np.maximum(0.0, boxes[rest, 3] - boxes[rest, 1]))
+            iou = inter / (a_i + a_r - inter + 1e-7)
             order = rest[iou <= iou_thresh]
         return np.array(keep, dtype=np.intp)
 
-    def _per_class_hard_nms(self, boxes: np.ndarray, scores: np.ndarray, cls_ids: np.ndarray, iou_thresh: float) -> np.ndarray:
+    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] = []
@@ -201,14 +369,28 @@ class Miner:
         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]:
+    def _cross_class_dedup_op(
+        self,
+        boxes: np.ndarray,
+        scores: np.ndarray,
+        cls_ids: np.ndarray,
+        iou_thresh: float,
+    ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+        """Remove near-duplicate boxes across classes.
+
+        Order candidates by (score - per_class_threshold) margin, then by area;
+        keep the highest, suppress every other box with IoU > iou_thresh.
+        This suppresses the case where the same physical object is detected
+        as multiple classes (e.g. fire vs smoke on the same flames).
+        """
         n = len(boxes)
         if n <= 1:
-            return (boxes, scores, cls_ids)
+            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])
+        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)
@@ -223,20 +405,37 @@ class Miner:
             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)
+            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])
+        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]:
+        """Merge overlapping detections of ONE class into single boxes.
 
-    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]:
+        Two same-class boxes whose intersection covers >= `overlap` of the
+        SMALLER box are treated as one object and replaced by their union with
+        the max confidence of the pair. Repeats until no pair merges, so chains
+        of fragments collapse. `overlap` is intersection-over-minimum-area, so
+        only nested / heavily-overlapping boxes merge -- two spatially separate
+        objects (low mutual overlap) are never fused. `overlap > 1.0` disables.
+        """
         if overlap > 1.0:
-            return (boxes, scores, cls_ids)
+            return boxes, scores, cls_ids
         idx = np.where(cls_ids == target_cls)[0]
         if len(idx) <= 1:
-            return (boxes, scores, cls_ids)
+            return boxes, scores, cls_ids
+
         sb = boxes[idx].astype(np.float32).tolist()
         ss = scores[idx].astype(np.float32).tolist()
         merged_any = True
@@ -244,7 +443,7 @@ class Miner:
             merged_any = False
             for i in range(len(sb)):
                 for j in range(i + 1, len(sb)):
-                    a, b = (sb[i], sb[j])
+                    a, b = sb[i], sb[j]
                     ix1 = max(a[0], b[0])
                     iy1 = max(a[1], b[1])
                     ix2 = min(a[2], b[2])
@@ -253,8 +452,11 @@ class Miner:
                     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])]
+                    if inter / (smaller + 1e-7) >= 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]
@@ -262,61 +464,126 @@ class Miner:
                         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)
+        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]:
+    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]:
+        """For one class, when two boxes overlap (intersection >= `overlap` of
+        the smaller box) keep the higher-confidence box and drop the other.
+        Geometry is never changed -- only the redundant lower-conf box is
+        removed. `overlap > 1.0` disables."""
         if overlap > 1.0:
-            return (boxes, scores, cls_ids)
+            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])]
+            return boxes, scores, cls_ids
+
+        order = idx[np.argsort(-scores[idx])]  # highest confidence first
         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])))
+            area_i = max(1e-7, 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])
+                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)
+                area_j = max(1e-7, float((bj[2] - bj[0]) * (bj[3] - bj[1])))
+                if inter / (min(area_i, area_j) + 1e-7) >= overlap:
+                    remove.add(j)  # j is the lower-confidence box (order desc)
         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])
+            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_same_class_boxes(
+        self,
+        boxes: np.ndarray,
+        scores: np.ndarray,
+        cls_ids: np.ndarray,
+    ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+        """Resolve nested / fragmented same-object detections, per class.
 
-    def _merge_smoke_boxes(self, boxes: np.ndarray, scores: np.ndarray, cls_ids: np.ndarray) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+        Smoke: diffuse plumes fragment into nested boxes NMS can't collapse, so
+        they are UNION-merged (smoke_merge_overlap).
+        Fire: a tight hot-core box and a looser flame box are the same flame;
+        keep the HIGHER-confidence one and drop the other (fire_suppress_overlap),
+        which leaves geometry intact. The union-merge variant (fire_merge_overlap)
+        is also available but measured worse, so it is disabled by default.
+        """
+        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
+
+    # Back-compat alias (older callers / tune_miner referenced this name).
+    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:
+    def _max_score_per_cluster(
+        post_boxes: np.ndarray,
+        post_cls: np.ndarray,
+        full_boxes: np.ndarray,
+        full_scores: np.ndarray,
+        full_cls: np.ndarray,
+        iou_thresh: float,
+    ) -> np.ndarray:
+        """For each kept (post-NMS) box, return the max score over the FULL
+        candidate set among same-class boxes with IoU >= iou_thresh.
+
+        Used after horizontal-flip TTA: a high-confidence flipped detection
+        can raise the score of the corresponding original detection.
+        """
         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])
+        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]
@@ -326,12 +593,17 @@ class Miner:
             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)
+            iou = inter / (a_i + full_areas - inter + 1e-7)
             cluster = (iou >= iou_thresh) & (full_cls == post_cls[i])
             out[i] = float(np.max(full_scores[cluster])) if np.any(cluster) else 0.0
         return out
 
-    def _conf_filter_mask(self, scores: np.ndarray, cls_ids: np.ndarray) -> np.ndarray:
+    def _conf_filter_mask(
+        self, scores: np.ndarray, cls_ids: np.ndarray
+    ) -> np.ndarray:
+        """Boolean keep-mask: score >= per-class threshold, with a per-class
+        rescue -- if a class has zero boxes passing, admit its top-1 candidate
+        when its score >= (per-class threshold - per-class bonus)."""
         if len(scores) == 0:
             return np.zeros(0, dtype=bool)
         thr = self._conf_thres_array[cls_ids]
@@ -349,9 +621,16 @@ class Miner:
                 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]:
+    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]:
+        """Drop tiny / degenerate / image-spanning / extreme-AR boxes (FP)."""
         if len(boxes) == 0:
-            return (boxes, scores, cls_ids)
+            return boxes, scores, cls_ids
         orig_w, orig_h = orig_size
         image_area = float(orig_w * orig_h)
         keep = []
@@ -368,30 +647,43 @@ class Miner:
                 continue
             if area > 0.95 * image_area:
                 continue
-            ar = max(bw / max(bh, 1e-06), bh / max(bw, 1e-06))
+            ar = max(bw / max(bh, 1e-6), bh / max(bw, 1e-6))
             if ar > self.max_aspect_ratio:
                 continue
             keep.append(i)
         if not keep:
-            return (np.empty((0, 4), dtype=np.float32), np.empty((0,), dtype=np.float32), np.empty((0,), dtype=np.int32))
+            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])
+        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]:
+    def _per_view_pipeline(
+        self,
+        boxes: np.ndarray,
+        scores: np.ndarray,
+        cls_ids: np.ndarray,
+    ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+        """Per-view post-processing pipeline: per-class NMS -> cap -> cross-class dedup -> smoke merge."""
         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])
+            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._cross_class_dedup_op(boxes, scores, cls_ids, self.cross_iou_thresh)
+            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)
+        return boxes, scores, cls_ids
 
     @staticmethod
     def _roi_for_box(image: np.ndarray, box: BoundingBox) -> np.ndarray | None:
+        """Clip a BoundingBox to the image and return its BGR pixel ROI."""
         h, w = image.shape[:2]
         x1 = max(0, int(math.floor(box.x1)))
         y1 = max(0, int(math.floor(box.y1)))
@@ -403,25 +695,33 @@ class Miner:
         return roi if roi.size else None
 
     def _roi_is_near_grayscale(self, roi: np.ndarray) -> bool:
+        """True if the ROI carries almost no color (validator grayscale frame).
+        On such ROIs the color priors are skipped so they can't delete valid
+        red/warm objects that have been stripped of color."""
         mx = roi.max(axis=2).astype(np.float32)
         mn = roi.min(axis=2).astype(np.float32)
-        sat = (mx - mn) / (mx + 1e-06)
+        sat = (mx - mn) / (mx + 1e-6)
         return float(sat.mean()) < self.color_filter_min_saturation
 
     @staticmethod
     def _passes_fire_color(roi: np.ndarray) -> bool:
+        """Fire is warm and/or has a bright hotspot. ROI is BGR."""
         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))
+        # A bright hotspot is fire-like even with little hue (also covers the
+        # near-white core of an intense flame).
         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):
+        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
@@ -429,34 +729,60 @@ class Miner:
 
     @staticmethod
     def _passes_fire_ext_red_color(roi: np.ndarray) -> bool:
+        """Fire extinguishers are red. ROI is BGR. Lenient: only clearly
+        cool/green/blue or very dark regions fail."""
         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:
+        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):
+    def _remove_edge_low_conf(
+        self, results: list[BoundingBox], orig_size: tuple[int, int]
+    ) -> list[BoundingBox]:
+        """Drop border-hugging boxes in the low-confidence band."""
+        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)
+            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:
+    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:
+        """For each post-NMS box, True if same-class detections from >= 2
+        distinct TTA views overlap it (IoU >= iou_thresh) in the full union."""
         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])
+        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]
@@ -466,170 +792,123 @@ class Miner:
             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)
+            iou = inter / (a_i + full_areas - inter + 1e-7)
             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]:
+    def _filter_low_conf_by_color(
+        self, image: np.ndarray, results: list[BoundingBox]
+    ) -> list[BoundingBox]:
+        """Drop borderline fire / extinguisher detections whose pixels clearly
+        contradict the class's expected color. No-op on near-grayscale ROIs and
+        on detections above the per-class color-filter conf gate."""
         if not results:
             return results
-        cls_fire = self.class_names.index('fire')
-        cls_ext = self.class_names.index('fire extinguisher')
+        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):
+            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)):
+            if check_fire and not self._passes_fire_color(roi):
                 continue
-            if check_ext and (not self._passes_fire_ext_red_color(roi)):
+            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]:
+    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)))
+            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]:
+    def _decode_final_dets(
+        self,
+        preds: np.ndarray,
+        ratio: float,
+        pad: tuple[float, float],
+        orig_size: tuple[int, int],
+    ) -> list[BoundingBox]:
+        """Final-detection output path: rows shaped [x1, y1, x2, y2, conf, cls_id]."""
         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}')
+            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()
+            return []
+
         pad_w, pad_h = pad
         boxes[:, [0, 2]] -= pad_w
         boxes[:, [1, 3]] -= pad_h
         boxes /= ratio
         boxes = self._clip_boxes(boxes, orig_size)
-        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]:
+        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_raw_yolo(
+        self,
+        preds: np.ndarray,
+        ratio: float,
+        pad: tuple[float, float],
+        orig_size: tuple[int, int],
+    ) -> list[BoundingBox]:
+        """Fallback raw-YOLO output path: per-anchor class logits."""
         if preds.ndim != 3 or preds.shape[0] != 1:
-            raise ValueError(f'Unexpected raw ONNX output shape: {preds.shape}')
+            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}')
+            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:
@@ -639,133 +918,189 @@ class Miner:
             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()
+            return []
         boxes = self._xywh_to_xyxy(boxes_xywh)
+
         pad_w, pad_h = pad
         boxes[:, [0, 2]] -= pad_w
         boxes[:, [1, 3]] -= pad_h
         boxes /= ratio
         boxes = self._clip_boxes(boxes, orig_size)
-        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)
+        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]]:
+    def _postprocess(
+        self,
+        output: np.ndarray,
+        ratio: float,
+        pad: tuple[float, float],
+        orig_size: tuple[int, int],
+    ) -> list[BoundingBox]:
         if output.ndim == 2 and output.shape[1] >= 6:
             return self._decode_final_dets(output, ratio, pad, orig_size)
-        if output.ndim == 3 and output.shape[0] == 1 and (output.shape[2] == 6):
+        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]]:
+    def _predict_single(self, image: np.ndarray) -> list[BoundingBox]:
         if image is None:
-            raise ValueError('Input 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)}')
+            raise TypeError(f"Input is not numpy array: {type(image)}")
         if image.ndim != 3:
-            raise ValueError(f'Expected HWC image, got shape={image.shape}')
+            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}')
+            raise ValueError(f"Invalid image shape={image.shape}")
         if image.shape[2] != 3:
-            raise ValueError(f'Expected 3 channels, got shape={image.shape}')
+            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}')
+            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)
+    def _predict_tta(self, image: np.ndarray) -> list[BoundingBox]:
+        """Horizontal-flip TTA.
+
+        Strategy:
+          1. Predict on original and on flipped image.
+          2. Map flipped boxes back to original coordinates.
+          3. Per-class hard NMS on the union.
+          4. For each kept box, compute the max same-class score across the
+             FULL union (not just the post-NMS subset) -- this lets a high-
+             confidence flipped detection raise a borderline original one.
+          5. Cross-class dedup to suppress same-physical-object multi-class.
+          6. Smoke merge: overlapping / nested smoke boxes collapse into
+             their union (one box per smoke object).
+        """
+        boxes_orig = self._predict_single(image)
         flipped = cv2.flip(image, 1)
-        boxes_flip, raw_flip = self._predict_single(flipped)
+        boxes_flip = self._predict_single(flipped)
         w = image.shape[1]
-        boxes_flip = [BoundingBox(x1=w - b.x2, y1=b.y1, x2=w - b.x1, y2=b.y2, cls_id=b.cls_id, conf=b.conf) for b in boxes_flip]
-        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))
+        boxes_flip = [
+            BoundingBox(
+                x1=w - b.x2, y1=b.y1, x2=w - b.x1, y2=b.y2,
+                cls_id=b.cls_id, conf=b.conf,
+            )
+            for b in boxes_flip
+        ]
         all_boxes = boxes_orig + boxes_flip
         if not all_boxes:
-            return ([], raw_all)
-        coords = np.array([[b.x1, b.y1, b.x2, b.y2] for b in all_boxes], dtype=np.float32)
+            return []
+
+        coords = np.array(
+            [[b.x1, b.y1, b.x2, b.y2] for b in all_boxes], dtype=np.float32
+        )
         scores = np.array([b.conf for b in all_boxes], dtype=np.float32)
         cls_ids = np.array([b.cls_id for b in all_boxes], dtype=np.int32)
-        view_ids = np.array([0] * len(boxes_orig) + [1] * len(boxes_flip), dtype=np.int32)
+        # view_id 0 = original, 1 = horizontal flip (mapped back to orig coords)
+        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)
+            return []
         if len(hard_keep) > self.max_det:
-            top = np.argsort(-scores[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)
+
+        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)
+
+        # Optional: drop low-conf detections seen in only one TTA view.
+        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)
+            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)
+            kept_coords, boosted, kept_cls = self._merge_same_class_boxes(
+                kept_coords, boosted, kept_cls
+            )
 
-    def predict_batch(self, batch_images: list[ndarray], offset: int, n_keypoints: int) -> list[TVFrameResult]:
+        return [
+            BoundingBox(
+                x1=int(math.floor(kept_coords[j, 0])),
+                y1=int(math.floor(kept_coords[j, 1])),
+                x2=int(math.ceil(kept_coords[j, 2])),
+                y2=int(math.ceil(kept_coords[j, 3])),
+                cls_id=int(kept_cls[j]),
+                conf=float(boosted[j]),
+            )
+            for j in range(len(kept_coords))
+        ]
+
+    def predict_batch(
+        self,
+        batch_images: list[ndarray],
+        offset: int,
+        n_keypoints: int,
+    ) -> list[TVFrameResult]:
         results: list[TVFrameResult] = []
         for frame_number_in_batch, image in enumerate(batch_images):
             try:
                 if self.use_tta:
-                    boxes, raw = self._predict_tta(image)
+                    boxes = self._predict_tta(image)
                 else:
-                    boxes, raw = self._predict_single(image)
+                    boxes = self._predict_single(image)
+                # Color-prior + edge FP filters on the merged result, in
+                # original-image coords. Single insertion point so they run once
+                # per frame for both the TTA and non-TTA paths.
                 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))
+                    boxes = self._remove_edge_low_conf(
+                        boxes, (image.shape[1], image.shape[0])
+                    )
             except Exception as e:
-                print(f'⚠️ Inference failed for frame {offset + frame_number_in_batch}: {e}')
+                print(
+                    f"⚠️ Inference failed for frame "
+                    f"{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)))]))
+            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:adf874cfbef9f8dab160dea0ed705b96b809cabd339e90890cddea6041716dc3
-size 9842050
+oid sha256:0a157aa22ed68bb11e35546cd2ef2b3502d20ed5e2468c068544ad3c85298375
+size 9823990