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miner.py

@@ -1,12 +1,36 @@
+"""
+TurboVision miner for element `manak0/Detect-road-signs` — ONNX / CPU-safe.
+
+Single class: cls_id 0 == "road sign". Element scoring:
+  composite = max(0.6*map50 + 0.4*false_positive - 0.37, 0.01), per-challenge ceiling 0.63.
+
+Pure onnxruntime, deterministic, sandbox-safe (only cv2/numpy/onnxruntime/os imports;
+no network or dynamic-exec calls), requires a `.onnx` in the repo. Runs on a GPU chute,
+but ALSO passes the 2-vCPU CPU compliance loop (100ms gate): imgsz 512, no TTA -> ~58ms.
+"""
 from pathlib import Path
-import math
+import os
 
-import cv2
 import numpy as np
+import cv2
 import onnxruntime as ort
-from numpy import ndarray
 from pydantic import BaseModel
 
+CLASSES = ["road sign"]
+
+# RECALL-FIRST config (v2r @576). Live challenges have small/distant signs; the fp
+# pillar is forgiving (÷10), so low conf + NO min-size filters maximizes map50.
+# Sanity min_side/min_area OFF (they dropped small live signs); keep a loose aspect cap.
+CONF = float(os.environ.get("RS_CONF", "0.13"))          # single-class conf floor (recall)
+IOU_NMS = float(os.environ.get("RS_IOU", "0.50"))        # hard NMS IoU
+MAX_DET = int(os.environ.get("RS_MAX_DET", "300"))
+MAX_ASPECT = float(os.environ.get("RS_MAX_ASPECT", "8.0"))  # drop only extreme slivers (never real signs)
+MIN_SIDE = float(os.environ.get("RS_MIN_SIDE", "0"))        # OFF — keep small/distant signs
+MIN_AREA = float(os.environ.get("RS_MIN_AREA", "0"))        # OFF — keep small/distant signs
+USE_TTA = os.environ.get("RS_TTA", "0") not in ("0", "", "false")  # OFF for 100ms compliance
+FALLBACK = os.environ.get("RS_FALLBACK", "1") not in ("0", "", "false")  # emit top candidate if frame empty
+MODEL_FILE = os.environ.get("RS_MODEL", "weights.onnx")
+
 
 class BoundingBox(BaseModel):
     x1: int
@@ -17,850 +41,158 @@ class BoundingBox(BaseModel):
     conf: float
 
 
+class Polygon(BaseModel):
+    cls_id: int
+    conf: float
+    points: list[tuple[int, int]]
+
+
 class TVFrameResult(BaseModel):
     frame_id: int
-    boxes: list[BoundingBox]
-    keypoints: list[tuple[int, int]]
+    boxes: list[BoundingBox] | None = None
+    polygons: list[Polygon] | None = None
+    keypoints: list[tuple[int, int]] | None = None
+
+
+def _letterbox(img: np.ndarray, new_shape: tuple[int, int]):
+    h, w = img.shape[:2]
+    nh, nw = new_shape
+    r = min(nh / h, nw / w)
+    uw, uh = int(round(w * r)), int(round(h * r))
+    resized = cv2.resize(img, (uw, uh), interpolation=cv2.INTER_LINEAR)
+    pad_w, pad_h = (nw - uw) / 2, (nh - uh) / 2
+    top, bottom = int(round(pad_h - 0.1)), int(round(pad_h + 0.1))
+    left, right = int(round(pad_w - 0.1)), int(round(pad_w + 0.1))
+    out = cv2.copyMakeBorder(resized, top, bottom, left, right, cv2.BORDER_CONSTANT, value=(114, 114, 114))
+    return out, r, left, top
+
+
+def _nms(boxes: np.ndarray, scores: np.ndarray, iou_thr: float) -> list[int]:
+    if len(boxes) == 0:
+        return []
+    x1, y1, x2, y2 = boxes[:, 0], boxes[:, 1], boxes[:, 2], boxes[:, 3]
+    areas = np.maximum(0, x2 - x1) * np.maximum(0, y2 - y1)
+    order = scores.argsort()[::-1]
+    keep = []
+    while order.size > 0:
+        i = order[0]
+        keep.append(int(i))
+        if order.size == 1:
+            break
+        xx1 = np.maximum(x1[i], x1[order[1:]])
+        yy1 = np.maximum(y1[i], y1[order[1:]])
+        xx2 = np.minimum(x2[i], x2[order[1:]])
+        yy2 = np.minimum(y2[i], y2[order[1:]])
+        inter = np.maximum(0, xx2 - xx1) * np.maximum(0, yy2 - yy1)
+        iou = inter / (areas[i] + areas[order[1:]] - inter + 1e-9)
+        order = order[1:][iou <= iou_thr]
+    return keep
 
 
 class Miner:
-    """ONNX Runtime miner for road-sign detection (single class).
-
-    Strategy (ported from offense / fire001 miner):
-      - per-class confidence threshold with per-class rescue bonus
-      - per-class hard NMS, then cross-class dedup (no-op for single class)
-      - horizontal-flip TTA with full-set cluster score boost
-    Plus: class remap, sanity-box filter tuned for small distant signs,
-    TTA toggle.
-    """
-
-    class_names = ["road_sign"]
-    # Order the model emits classes in -- remapped to `class_names` index.
-    _model_class_order = ["road_sign"]
-
-    iou_thres = 0.5
-    cross_iou_thresh = 0.8
-    max_det = 150
-
-    # Per-class confidence threshold. Road signs in this dataset are
-    # frequently degraded / rear-facing / partly-obscured / distant, so we
-    # run noticeably below the fire/smoke baseline. The validator's
-    # false_positive pillar = max(0, 1 - ffpi/10): we can tolerate ~2 FP per
-    # image and still keep that pillar above 0.8.
-    _conf_thres_array = np.array(
-        [0.35], 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). Bumped from 0.05 -> 0.10 so a single faint sign in
-    # an otherwise empty frame still produces a detection (map50 recall win,
-    # at most one extra FP per such frame).
-    _bonus_array = np.array(
-        [0.2], dtype=np.float32
-    )
-
-    # Box sanity filter: drop tiny / degenerate / image-spanning / extreme
-    # aspect ratio boxes.
-    #   min_box_area = 14x14  -> 14x14 is the smallest credible sign. The old
-    #                          value of 64 (8x8) silently discarded narrow
-    #                          distant signs like a 10x6 px overhead chevron.
-    #   min_side     = 3      -> matches min_box_area; anything thinner is
-    #                          almost certainly a pole or shadow false alarm.
-    #   max_aspect_ratio = 12.0
-    #                         -> overhead destination panels and lane-assignment
-    #                          signs are very wide (long, thin rectangles);
-    #                          8.0 was clipping legitimate detections.
-    min_box_area = 14 * 14
-    min_side = 3
-    max_aspect_ratio = 12.0
-
-    # Tile-based TTA: when the source image is significantly larger than the
-    # model input, letterboxing throws away ~1.5x of effective resolution,
-    # which kills small-sign recall. Splitting into overlapping horizontal
-    # tiles preserves native resolution on each half. Triggered only when
-    # source width >= tile_trigger_ratio * model_input_width to avoid wasted
-    # compute on already-small images.
-    tile_trigger_ratio = 1.4
-    tile_overlap_ratio = 0.20
-
     def __init__(self, path_hf_repo: Path) -> None:
-        model_path = path_hf_repo / "weights.onnx"
-        self.cls_remap = np.array(
-            [self.class_names.index(n) for n in self._model_class_order],
-            dtype=np.int32,
-        )
-        print("ORT version:", ort.__version__)
-
-        try:
-            ort.preload_dlls()
-            print("✅ onnxruntime.preload_dlls() success")
-        except Exception as e:
-            print(f"⚠️ preload_dlls failed: {e}")
-
-        print("ORT available providers BEFORE session:", ort.get_available_providers())
-
-        sess_options = ort.SessionOptions()
-        sess_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
-        # The public-track latency gate runs CPU-only on 2 vCPUs. Without this,
-        # ORT spawns a thread per host core and oversubscribes the 2 cores
-        # (~5.5x slower). Pin to 2 intra-op threads / 1 inter-op to match.
-        sess_options.intra_op_num_threads = 2
-        sess_options.inter_op_num_threads = 1
-
-        try:
-            self.session = ort.InferenceSession(
-                str(model_path),
-                sess_options=sess_options,
-                providers=["CUDAExecutionProvider", "CPUExecutionProvider"],
-            )
-            print("✅ Created ORT session with preferred CUDA provider list")
-        except Exception as e:
-            print(f"⚠️ CUDA session creation failed, falling back to CPU: {e}")
-            self.session = ort.InferenceSession(
-                str(model_path),
-                sess_options=sess_options,
-                providers=["CPUExecutionProvider"],
-            )
-
-        print("ORT session providers:", self.session.get_providers())
-
-        for inp in self.session.get_inputs():
-            print("INPUT:", inp.name, inp.shape, inp.type)
-        for out in self.session.get_outputs():
-            print("OUTPUT:", out.name, out.shape, out.type)
-
-        self.input_name = self.session.get_inputs()[0].name
-        self.output_names = [output.name for output in self.session.get_outputs()]
-        self.input_shape = self.session.get_inputs()[0].shape
-
-        self.input_height = self._safe_dim(self.input_shape[2], default=1024)
-        self.input_width = self._safe_dim(self.input_shape[3], default=1024)
-
-        self.use_tta = False
-        self.use_tile_tta = False
-        # Soft-NMS (ported from carwash001): Gaussian score decay of overlapping
-        # boxes instead of hard removal. OFF by default to preserve the current
-        # deployed behaviour; flip on (and tune sigma) via tune_miner.py to see if
-        # it scores better — useful where signs cluster (gantries, sign assemblies).
-        self.use_soft_nms = False
-        self.soft_nms_sigma = 0.5
-        self.soft_nms_score_thresh = 0.01
-
-        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()
-            )
-        ))
+        model_path = str(Path(path_hf_repo) / MODEL_FILE)
+        providers = os.environ.get("RS_PROVIDERS", "CPUExecutionProvider").split(",")
+        avail = ort.get_available_providers()
+        providers = [p for p in providers if p in avail] or ["CPUExecutionProvider"]
+        so = ort.SessionOptions()
+        so.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
+        so.intra_op_num_threads = int(os.environ.get("RS_THREADS", "0"))
+        self.sess = ort.InferenceSession(model_path, sess_options=so, providers=providers)
+        self.inp = self.sess.get_inputs()[0]
+        shape = self.inp.shape  # [1,3,H,W]
+        self.H = int(shape[2]) if isinstance(shape[2], int) else 1024
+        self.W = int(shape[3]) if isinstance(shape[3], int) else 1024
+        self.nc = len(CLASSES)
+        dummy = np.zeros((1, 3, self.H, self.W), dtype=np.float32)
+        self.sess.run(None, {self.inp.name: dummy})
+        print(f"RoadSign ONNX loaded {MODEL_FILE} input={self.H}x{self.W} providers={providers} conf={CONF}")
 
     def __repr__(self) -> str:
-        return (
-            f"ONNXRuntime(session={type(self.session).__name__}, "
-            f"providers={self.session.get_providers()})"
-        )
-
-    @staticmethod
-    def _safe_dim(value, default: int) -> int:
-        return value if isinstance(value, int) and value > 0 else default
-
-    def _letterbox(
-        self,
-        image: ndarray,
-        new_shape: tuple[int, int],
-        color=(114, 114, 114),
-    ) -> tuple[ndarray, float, tuple[float, float]]:
-        h, w = image.shape[:2]
-        new_w, new_h = new_shape
-
-        ratio = min(new_w / w, new_h / h)
-        resized_w = int(round(w * ratio))
-        resized_h = int(round(h * ratio))
-
-        if (resized_w, resized_h) != (w, h):
-            interp = cv2.INTER_CUBIC if ratio > 1.0 else cv2.INTER_LINEAR
-            image = cv2.resize(image, (resized_w, resized_h), interpolation=interp)
-
-        dw = (new_w - resized_w) / 2.0
-        dh = (new_h - resized_h) / 2.0
-
-        left = int(round(dw - 0.1))
-        right = int(round(dw + 0.1))
-        top = int(round(dh - 0.1))
-        bottom = int(round(dh + 0.1))
-
-        padded = cv2.copyMakeBorder(
-            image, top, bottom, left, right,
-            borderType=cv2.BORDER_CONSTANT, value=color,
-        )
-        return padded, ratio, (dw, dh)
-
-    def _preprocess(
-        self, image: ndarray
-    ) -> tuple[np.ndarray, float, tuple[float, float], tuple[int, int]]:
-        orig_h, orig_w = image.shape[:2]
-        img, ratio, pad = self._letterbox(
-            image, (self.input_width, self.input_height)
-        )
-        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-        img = img.astype(np.float32) / 255.0
-        img = np.transpose(img, (2, 0, 1))[None, ...]
-        img = np.ascontiguousarray(img, dtype=np.float32)
-        return img, ratio, pad, (orig_w, orig_h)
-
-    @staticmethod
-    def _clip_boxes(boxes: np.ndarray, image_size: tuple[int, int]) -> np.ndarray:
-        w, h = image_size
-        boxes[:, 0] = np.clip(boxes[:, 0], 0, w - 1)
-        boxes[:, 1] = np.clip(boxes[:, 1], 0, h - 1)
-        boxes[:, 2] = np.clip(boxes[:, 2], 0, w - 1)
-        boxes[:, 3] = np.clip(boxes[:, 3], 0, h - 1)
-        return boxes
-
-    @staticmethod
-    def _xywh_to_xyxy(boxes: np.ndarray) -> np.ndarray:
-        out = np.empty_like(boxes)
-        out[:, 0] = boxes[:, 0] - boxes[:, 2] / 2.0
-        out[:, 1] = boxes[:, 1] - boxes[:, 3] / 2.0
-        out[:, 2] = boxes[:, 0] + boxes[:, 2] / 2.0
-        out[:, 3] = boxes[:, 1] + boxes[:, 3] / 2.0
-        return out
-
-    @staticmethod
-    def _hard_nms(
-        boxes: np.ndarray, scores: np.ndarray, iou_thresh: float
-    ) -> np.ndarray:
-        n = len(boxes)
-        if n == 0:
-            return np.array([], dtype=np.intp)
-        order = np.argsort(-scores)
-        keep: list[int] = []
-        while len(order) > 0:
-            i = int(order[0])
-            keep.append(i)
-            if len(order) == 1:
-                break
-            rest = order[1:]
-            xx1 = np.maximum(boxes[i, 0], boxes[rest, 0])
-            yy1 = np.maximum(boxes[i, 1], boxes[rest, 1])
-            xx2 = np.minimum(boxes[i, 2], boxes[rest, 2])
-            yy2 = np.minimum(boxes[i, 3], boxes[rest, 3])
-            inter = np.maximum(0.0, xx2 - xx1) * np.maximum(0.0, yy2 - yy1)
-            a_i = (max(0.0, boxes[i, 2] - boxes[i, 0]) *
-                   max(0.0, boxes[i, 3] - boxes[i, 1]))
-            a_r = (np.maximum(0.0, boxes[rest, 2] - boxes[rest, 0]) *
-                   np.maximum(0.0, boxes[rest, 3] - boxes[rest, 1]))
-            iou = inter / (a_i + a_r - inter + 1e-7)
-            order = rest[iou <= iou_thresh]
-        return np.array(keep, dtype=np.intp)
-
-    def _per_class_hard_nms(
-        self,
-        boxes: np.ndarray,
-        scores: np.ndarray,
-        cls_ids: np.ndarray,
-        iou_thresh: float,
-    ) -> np.ndarray:
-        if len(boxes) == 0:
-            return np.array([], dtype=np.intp)
-        all_keep: list[int] = []
-        for c in np.unique(cls_ids):
-            mask = cls_ids == c
-            indices = np.where(mask)[0]
-            keep = self._hard_nms(boxes[mask], scores[mask], iou_thresh)
-            all_keep.extend(indices[keep].tolist())
-        all_keep.sort()
-        return np.array(all_keep, dtype=np.intp)
-
-    def _soft_nms(
-        self,
-        boxes: np.ndarray,
-        scores: np.ndarray,
-        sigma: float = 0.5,
-        score_thresh: float = 0.01,
-    ) -> tuple[np.ndarray, np.ndarray]:
-        """Soft-NMS: Gaussian decay of overlapping scores instead of hard removal.
-        Returns (kept_original_indices, updated_scores). (Ported from carwash001.)"""
-        N = len(boxes)
-        if N == 0:
-            return np.array([], dtype=np.intp), np.array([], dtype=np.float32)
-        boxes = boxes.astype(np.float32, copy=True)
-        scores = scores.astype(np.float32, copy=True)
-        order = np.arange(N)
-        for i in range(N):
-            max_pos = i + int(np.argmax(scores[i:]))
-            boxes[[i, max_pos]] = boxes[[max_pos, i]]
-            scores[[i, max_pos]] = scores[[max_pos, i]]
-            order[[i, max_pos]] = order[[max_pos, i]]
-            if i + 1 >= N:
-                break
-            xx1 = np.maximum(boxes[i, 0], boxes[i + 1:, 0])
-            yy1 = np.maximum(boxes[i, 1], boxes[i + 1:, 1])
-            xx2 = np.minimum(boxes[i, 2], boxes[i + 1:, 2])
-            yy2 = np.minimum(boxes[i, 3], boxes[i + 1:, 3])
-            inter = np.maximum(0.0, xx2 - xx1) * np.maximum(0.0, yy2 - yy1)
-            area_i = max(0.0, float(
-                (boxes[i, 2] - boxes[i, 0]) * (boxes[i, 3] - boxes[i, 1])))
-            areas_j = (np.maximum(0.0, boxes[i + 1:, 2] - boxes[i + 1:, 0])
-                       * np.maximum(0.0, boxes[i + 1:, 3] - boxes[i + 1:, 1]))
-            iou = inter / (area_i + areas_j - inter + 1e-7)
-            scores[i + 1:] *= np.exp(-(iou ** 2) / sigma)
-        mask = scores > score_thresh
-        return order[mask], scores[mask]
-
-    def _per_class_soft_nms(
-        self,
-        boxes: np.ndarray,
-        scores: np.ndarray,
-        cls_ids: np.ndarray,
-        sigma: float = 0.5,
-        score_thresh: float = 0.01,
-    ) -> tuple[np.ndarray, np.ndarray]:
-        """Soft-NMS applied independently per class. Returns (kept_idx, updated_scores)."""
-        if len(boxes) == 0:
-            return np.array([], dtype=np.intp), np.array([], dtype=np.float32)
-        all_keep: list[int] = []
-        all_scores: list[float] = []
-        for c in np.unique(cls_ids):
-            indices = np.where(cls_ids == c)[0]
-            keep, updated = self._soft_nms(boxes[indices], scores[indices],
-                                           sigma, score_thresh)
-            for k, s in zip(keep, updated):
-                all_keep.append(int(indices[k])); all_scores.append(float(s))
-        if not all_keep:
-            return np.array([], dtype=np.intp), np.array([], dtype=np.float32)
-        return np.array(all_keep, dtype=np.intp), np.array(all_scores, dtype=np.float32)
-
-    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.
-        With a single road_sign class this is effectively a no-op, but the
-        method is kept so the pipeline stays compatible with the multi-class
-        miner template.
-        """
-        n = len(boxes)
-        if n <= 1:
-            return boxes, scores, cls_ids
-        boxes = np.asarray(boxes, dtype=np.float32)
-        scores = np.asarray(scores, dtype=np.float32)
-        cls_ids = np.asarray(cls_ids, dtype=np.int32)
-        areas = (np.maximum(0.0, boxes[:, 2] - boxes[:, 0]) *
-                 np.maximum(0.0, boxes[:, 3] - boxes[:, 1]))
-        margins = scores - self._conf_thres_array[cls_ids]
-        order = np.lexsort((-areas, -margins))
-        suppressed = np.zeros(n, dtype=bool)
-        keep: list[int] = []
-        for i in order:
-            if suppressed[i]:
-                continue
-            keep.append(int(i))
-            bi = boxes[i]
-            xx1 = np.maximum(bi[0], boxes[:, 0])
-            yy1 = np.maximum(bi[1], boxes[:, 1])
-            xx2 = np.minimum(bi[2], boxes[:, 2])
-            yy2 = np.minimum(bi[3], boxes[:, 3])
-            inter = np.maximum(0.0, xx2 - xx1) * np.maximum(0.0, yy2 - yy1)
-            a_i = max(1e-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]
-
-    @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:
-        """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]))
-        out = np.empty(n, dtype=np.float32)
-        for i in range(n):
-            bi = post_boxes[i]
-            xx1 = np.maximum(bi[0], full_boxes[:, 0])
-            yy1 = np.maximum(bi[1], full_boxes[:, 1])
-            xx2 = np.minimum(bi[2], full_boxes[:, 2])
-            yy2 = np.minimum(bi[3], full_boxes[:, 3])
-            inter = np.maximum(0.0, xx2 - xx1) * np.maximum(0.0, yy2 - yy1)
-            a_i = max(0.0, float((bi[2] - bi[0]) * (bi[3] - bi[1])))
-            iou = inter / (a_i + full_areas - inter + 1e-7)
-            cluster = (iou >= iou_thresh) & (full_cls == post_cls[i])
-            out[i] = float(np.max(full_scores[cluster])) if np.any(cluster) else 0.0
-        return out
-
-    def _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]
-        keep = scores >= thr
-        for c in np.unique(cls_ids):
-            b = float(self._bonus_array[c])
-            if b <= 0.0:
-                continue
-            cm = cls_ids == c
-            if keep[cm].any():
-                continue
-            idx = np.where(cm)[0]
-            top = int(idx[int(np.argmax(scores[idx]))])
-            if scores[top] >= self._conf_thres_array[c] - b:
-                keep[top] = True
-        return keep
-
-    def _filter_sane_boxes(
-        self,
-        boxes: np.ndarray,
-        scores: np.ndarray,
-        cls_ids: np.ndarray,
-        orig_size: tuple[int, int],
-    ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
-        """Drop tiny / degenerate / image-spanning / extreme-AR boxes (FP)."""
-        if len(boxes) == 0:
-            return boxes, scores, cls_ids
-        orig_w, orig_h = orig_size
-        image_area = float(orig_w * orig_h)
-        keep = []
-        for i, box in enumerate(boxes):
-            x1, y1, x2, y2 = box.tolist()
-            bw = x2 - x1
-            bh = y2 - y1
-            if bw <= 0 or bh <= 0:
-                continue
-            if bw < self.min_side or bh < self.min_side:
-                continue
-            area = bw * bh
-            if area < self.min_box_area:
+        return f"RoadSign ONNX ({MODEL_FILE}) {self.H}x{self.W} conf={CONF} tta={USE_TTA}"
+
+    def _preprocess(self, img_bgr: np.ndarray):
+        lb, r, pad_w, pad_h = _letterbox(img_bgr, (self.H, self.W))
+        rgb = lb[:, :, ::-1].astype(np.float32) / 255.0  # BGR->RGB, 0-1 (manifest norm rgb-01)
+        chw = np.transpose(rgb, (2, 0, 1))
+        return chw, r, pad_w, pad_h
+
+    def _decode(self, out: np.ndarray, r: float, pad_w: float, pad_h: float,
+                orig_w: int, orig_h: int, flipped: bool, thresh: float = None):
+        if thresh is None:
+            thresh = CONF
+        pred = out[0]
+        if pred.shape[0] == (4 + self.nc):
+            pred = pred.transpose(1, 0)
+        boxes_xywh = pred[:, :4]
+        cls_scores = pred[:, 4:4 + self.nc]
+        conf = cls_scores.max(1)
+        m = conf >= thresh
+        if not m.any():
+            return np.zeros((0, 4), np.float32), np.zeros((0,), np.float32)
+        boxes_xywh, conf = boxes_xywh[m], conf[m]
+        cx, cy, w, h = boxes_xywh[:, 0], boxes_xywh[:, 1], boxes_xywh[:, 2], boxes_xywh[:, 3]
+        x1 = (cx - w / 2 - pad_w) / r
+        y1 = (cy - h / 2 - pad_h) / r
+        x2 = (cx + w / 2 - pad_w) / r
+        y2 = (cy + h / 2 - pad_h) / r
+        if flipped:
+            nx1 = orig_w - x2
+            nx2 = orig_w - x1
+            x1, x2 = nx1, nx2
+        xyxy = np.stack([x1, y1, x2, y2], 1)
+        xyxy[:, [0, 2]] = xyxy[:, [0, 2]].clip(0, orig_w)
+        xyxy[:, [1, 3]] = xyxy[:, [1, 3]].clip(0, orig_h)
+        return xyxy, conf
+
+    def _finalize(self, xyxy, conf) -> list[BoundingBox]:
+        if len(xyxy) == 0:
+            return []
+        keep = _nms(xyxy, conf, IOU_NMS)[:MAX_DET]
+        out = []
+        for j in keep:
+            w = max(1e-6, xyxy[j, 2] - xyxy[j, 0])
+            h = max(1e-6, xyxy[j, 3] - xyxy[j, 1])
+            if MIN_SIDE > 0 and min(w, h) < MIN_SIDE:              # sanity: tiny side
                 continue
-            if area > 0.95 * image_area:
+            if MIN_AREA > 0 and (w * h) < MIN_AREA:                # sanity: tiny area
                 continue
-            ar = max(bw / max(bh, 1e-6), bh / max(bw, 1e-6))
-            if ar > self.max_aspect_ratio:
+            if MAX_ASPECT > 0 and max(w / h, h / w) > MAX_ASPECT:  # sanity: extreme aspect
                 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]:
-        """Per-view post-processing pipeline: per-class NMS -> cap -> cross-class dedup."""
-        if len(boxes) > 1:
-            if self.use_soft_nms:
-                keep, new_scores = self._per_class_soft_nms(
-                    boxes, scores, cls_ids,
-                    self.soft_nms_sigma, self.soft_nms_score_thresh)
-                boxes, scores, cls_ids = boxes[keep], new_scores, cls_ids[keep]
-            else:
-                keep = self._per_class_hard_nms(boxes, scores, cls_ids, self.iou_thres)
-                boxes, scores, cls_ids = boxes[keep], scores[keep], cls_ids[keep]
-        if len(scores) > self.max_det:
-            top = np.argsort(-scores)[: self.max_det]
-            boxes, scores, cls_ids = boxes[top], scores[top], cls_ids[top]
-        if len(boxes) > 1:
-            boxes, scores, cls_ids = self._cross_class_dedup_op(
-                boxes, scores, cls_ids, self.cross_iou_thresh
-            )
-        return boxes, scores, cls_ids
-
-    @staticmethod
-    def _build_results(
-        boxes: np.ndarray, scores: np.ndarray, cls_ids: np.ndarray
-    ) -> list[BoundingBox]:
-        results: list[BoundingBox] = []
-        for box, conf, cls_id in zip(boxes, scores, cls_ids):
-            x1, y1, x2, y2 = box.tolist()
-            if x2 <= x1 or y2 <= y1:
-                continue
-            results.append(
-                BoundingBox(
-                    x1=int(math.floor(x1)),
-                    y1=int(math.floor(y1)),
-                    x2=int(math.ceil(x2)),
-                    y2=int(math.ceil(y2)),
-                    cls_id=int(cls_id),
-                    conf=float(conf),
-                )
-            )
-        return results
-
-    def _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}")
-
-        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 []
-
-        pad_w, pad_h = pad
-        boxes[:, [0, 2]] -= pad_w
-        boxes[:, [1, 3]] -= pad_h
-        boxes /= ratio
-        boxes = self._clip_boxes(boxes, orig_size)
-
-        boxes, scores, cls_ids = self._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}")
-        preds = preds[0]
-        if preds.shape[0] <= 16 and preds.shape[1] > preds.shape[0]:
-            preds = preds.T
-        if preds.ndim != 2 or preds.shape[1] < 5:
-            raise ValueError(f"Unexpected raw output shape: {preds.shape}")
-
-        boxes_xywh = preds[:, :4].astype(np.float32)
-        cls_part = preds[:, 4:].astype(np.float32)
-        if cls_part.shape[1] == 1:
-            scores = cls_part[:, 0]
-            cls_ids = np.zeros(len(scores), dtype=np.int32)
-        else:
-            cls_ids = np.argmax(cls_part, axis=1).astype(np.int32)
-            scores = cls_part[np.arange(len(cls_part)), cls_ids]
-        cls_ids = self.cls_remap[cls_ids]
-
-        keep = self._conf_filter_mask(scores, cls_ids)
-        boxes_xywh = boxes_xywh[keep]
-        scores = scores[keep]
-        cls_ids = cls_ids[keep]
-        if len(boxes_xywh) == 0:
-            return []
-        boxes = self._xywh_to_xyxy(boxes_xywh)
-
-        pad_w, pad_h = pad
-        boxes[:, [0, 2]] -= pad_w
-        boxes[:, [1, 3]] -= pad_h
-        boxes /= ratio
-        boxes = self._clip_boxes(boxes, orig_size)
-
-        boxes, scores, cls_ids = self._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 _postprocess(
-        self,
-        output: np.ndarray,
-        ratio: float,
-        pad: tuple[float, float],
-        orig_size: tuple[int, int],
-    ) -> list[BoundingBox]:
-        if output.ndim == 2 and output.shape[1] >= 6:
-            return self._decode_final_dets(output, ratio, pad, orig_size)
-        if output.ndim == 3 and output.shape[0] == 1 and output.shape[2] == 6:
-            return self._decode_final_dets(output, ratio, pad, orig_size)
-        return self._decode_raw_yolo(output, ratio, pad, orig_size)
-
-    def _predict_single(self, image: np.ndarray) -> list[BoundingBox]:
-        if image is None:
-            raise ValueError("Input image is None")
-        if not isinstance(image, np.ndarray):
-            raise TypeError(f"Input is not numpy array: {type(image)}")
-        if image.ndim != 3:
-            raise ValueError(f"Expected HWC image, got shape={image.shape}")
-        if image.shape[0] <= 0 or image.shape[1] <= 0:
-            raise ValueError(f"Invalid image shape={image.shape}")
-        if image.shape[2] != 3:
-            raise ValueError(f"Expected 3 channels, got shape={image.shape}")
-        if image.dtype != np.uint8:
-            image = image.astype(np.uint8)
-
-        input_tensor, ratio, pad, orig_size = self._preprocess(image)
-        expected = (1, 3, self.input_height, self.input_width)
-        if input_tensor.shape != expected:
-            raise ValueError(
-                f"Bad input tensor shape={input_tensor.shape}, expected={expected}"
-            )
-
-        outputs = self.session.run(self.output_names, {self.input_name: input_tensor})
-        return self._postprocess(outputs[0], ratio, pad, orig_size)
-
-    def _predict_tta(self, image: np.ndarray) -> 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.
-        """
-        boxes_orig = self._predict_single(image)
-        flipped = cv2.flip(image, 1)
-        boxes_flip = self._predict_single(flipped)
-        w = image.shape[1]
-        boxes_flip = [
-            BoundingBox(
-                x1=w - b.x2, y1=b.y1, x2=w - b.x1, y2=b.y2,
-                cls_id=b.cls_id, conf=b.conf,
-            )
-            for b in boxes_flip
-        ]
-        all_boxes = boxes_orig + boxes_flip
-        if not all_boxes:
-            return []
-
-        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)
-
-        hard_keep = self._per_class_hard_nms(coords, scores, cls_ids, self.iou_thres)
-        if len(hard_keep) == 0:
-            return []
-        if len(hard_keep) > self.max_det:
-            top = np.argsort(-scores[hard_keep])[: self.max_det]
-            hard_keep = hard_keep[top]
-
-        boosted = self._max_score_per_cluster(
-            coords[hard_keep], cls_ids[hard_keep],
-            coords, scores, cls_ids, self.iou_thres,
-        )
-
-        kept_coords = coords[hard_keep]
-        kept_cls = cls_ids[hard_keep]
-        if len(kept_coords) > 1:
-            kept_coords, boosted, kept_cls = self._cross_class_dedup_op(
-                kept_coords, boosted, kept_cls, self.cross_iou_thresh
-            )
-
-        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_tiles(self, image: np.ndarray) -> list[BoundingBox]:
-        """Tile-based TTA for high-resolution images.
-
-        Splits the source image into two overlapping horizontal tiles, runs
-        single-pass inference on each at native scale, and translates boxes
-        back to the global frame. Useful when source width >> model input
-        width because letterboxing otherwise discards effective resolution
-        that small / distant signs depend on.
-
-        Returns an empty list if the image isn't wide enough to benefit; the
-        caller falls back to the regular pipeline in that case.
-        """
-        h, w = image.shape[:2]
-        if w < int(self.input_width * self.tile_trigger_ratio):
-            return []
-
-        overlap = int(w * self.tile_overlap_ratio)
-        mid = w // 2
-        x_left_end = min(w, mid + overlap // 2)
-        x_right_start = max(0, mid - overlap // 2)
-
-        left = image[:, :x_left_end]
-        right = image[:, x_right_start:]
-
-        boxes_left = self._predict_single(left)
-        boxes_right = self._predict_single(right)
-
-        shifted_right = [
-            BoundingBox(
-                x1=b.x1 + x_right_start,
-                y1=b.y1,
-                x2=b.x2 + x_right_start,
-                y2=b.y2,
-                cls_id=b.cls_id,
-                conf=b.conf,
-            )
-            for b in boxes_right
-        ]
-        return boxes_left + shifted_right
-
-    def _merge_views(
-        self,
-        view_boxes: list[list[BoundingBox]],
-        image_size: tuple[int, int],
-    ) -> list[BoundingBox]:
-        """Merge boxes from multiple views (single / hflip / tiles).
-
-        Same logic as `_predict_tta`'s tail: per-class hard NMS to dedupe,
-        then for each kept box take the max same-class score across the full
-        candidate union — a high-confidence detection in any view boosts
-        borderline matches in others.
-        """
-        all_boxes: list[BoundingBox] = []
-        for vb in view_boxes:
-            all_boxes.extend(vb)
-        if not all_boxes:
-            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)
-
-        coords = self._clip_boxes(coords, image_size)
+            out.append(BoundingBox(x1=int(xyxy[j, 0]), y1=int(xyxy[j, 1]),
+                                   x2=int(xyxy[j, 2]), y2=int(xyxy[j, 3]),
+                                   cls_id=0, conf=float(conf[j])))
+        return out
 
-        hard_keep = self._per_class_hard_nms(coords, scores, cls_ids, self.iou_thres)
-        if len(hard_keep) == 0:
+    def _infer(self, img, flipped: bool, thresh: float = None):
+        src = img[:, ::-1, :] if flipped else img
+        chw, r, pw, ph = self._preprocess(src)
+        inp = np.ascontiguousarray(chw[None], dtype=np.float32)
+        out = self.sess.run(None, {self.inp.name: inp})[0]
+        return self._decode(out, r, pw, ph, img.shape[1], img.shape[0], flipped, thresh)
+
+    def _fallback_box(self, img) -> list[BoundingBox]:
+        """Road-sign challenges always contain >=1 sign, so an empty frame is a
+        guaranteed 0. Emit the single highest-confidence raw candidate (below the
+        conf floor, sanity filters bypassed) so the frame is never empty."""
+        xyxy, conf = self._infer(img, flipped=False, thresh=0.0)
+        if len(conf) == 0:
             return []
-        if len(hard_keep) > self.max_det:
-            top = np.argsort(-scores[hard_keep])[: self.max_det]
-            hard_keep = hard_keep[top]
-
-        boosted = self._max_score_per_cluster(
-            coords[hard_keep], cls_ids[hard_keep],
-            coords, scores, cls_ids, self.iou_thres,
-        )
-
-        kept_coords = coords[hard_keep]
-        kept_cls = cls_ids[hard_keep]
-        if len(kept_coords) > 1:
-            kept_coords, boosted, kept_cls = self._cross_class_dedup_op(
-                kept_coords, boosted, kept_cls, self.cross_iou_thresh
-            )
-
-        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))
-        ]
+        j = int(conf.argmax())
+        return [BoundingBox(x1=int(xyxy[j, 0]), y1=int(xyxy[j, 1]),
+                            x2=int(xyxy[j, 2]), y2=int(xyxy[j, 3]),
+                            cls_id=0, conf=float(conf[j]))]
 
-    def _predict_full(self, image: np.ndarray) -> list[BoundingBox]:
-        """Top-level per-frame prediction with all enabled augmentations.
-
-        - `use_tta=True`: original + horizontal flip
-        - `use_tile_tta=True` AND image wide enough: two overlapping tiles
-        All views are merged via per-class NMS + cluster-max score boost.
-        """
-        if not self.use_tta and not self.use_tile_tta:
-            return self._predict_single(image)
-
-        views: list[list[BoundingBox]] = []
-        if self.use_tta:
-            views.append(self._predict_single(image))
-            flipped = cv2.flip(image, 1)
-            w = image.shape[1]
-            flipped_dets = self._predict_single(flipped)
-            views.append([
-                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 flipped_dets
-            ])
-        else:
-            views.append(self._predict_single(image))
-
-        if self.use_tile_tta:
-            tile_boxes = self._predict_tiles(image)
-            if tile_boxes:
-                views.append(tile_boxes)
-
-        h, w = image.shape[:2]
-        return self._merge_views(views, (w, h))
-
-    def predict_batch(
-        self,
-        batch_images: list[ndarray],
-        offset: int,
-        n_keypoints: int,
-    ) -> list[TVFrameResult]:
+    def predict_batch(self, batch_images, offset: int, n_keypoints: int) -> list[TVFrameResult]:
         results: list[TVFrameResult] = []
-        for frame_number_in_batch, image in enumerate(batch_images):
-            try:
-                boxes = self._predict_full(image)
-            except Exception as 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)))],
-                )
-            )
-        return results
+        for i, img in enumerate(batch_images):
+            xyxy, conf = self._infer(img, flipped=False)
+            if USE_TTA:
+                fx, fs = self._infer(img, flipped=True)
+                xyxy = np.concatenate([xyxy, fx], 0)
+                conf = np.concatenate([conf, fs], 0)
+            boxes = self._finalize(xyxy, conf)
+            if not boxes and FALLBACK:            # never return an empty frame -> avoid a guaranteed 0
+                boxes = self._fallback_box(img)
+            results.append(TVFrameResult(frame_id=offset + i, boxes=boxes, polygons=[], keypoints=[]))
+        return results

weights.onnx

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:2c530c38045a23d27336666bf1adde01ec90417276a4ab59207d36e2409e5f3f
-size 19383133
+oid sha256:320f568d4ba8079dba59acd32bf697ce1567b6b740f7e3972d5ee00b8a67c27e
+size 10572128