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chute_config.yml

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+Image:
+  from_base: parachutes/python:3.12
+  run_command:
+    - pip install --upgrade setuptools wheel
+    - pip install 'numpy>=1.23' 'onnxruntime-gpu>=1.16' 'opencv-python>=4.7' 'pillow>=9.5' 'huggingface_hub>=0.19.4' 'pydantic>=2.0' 'pyyaml>=6.0' 'aiohttp>=3.9'
+    - pip install torch==2.8.0 torchvision==0.23.0 torchaudio==2.8.0 --index-url https://download.pytorch.org/whl/cu128
+
+NodeSelector:
+  gpu_count: 1
+  min_vram_gb_per_gpu: 16
+  include:
+    - pro_6000
+
+Chute:
+  timeout_seconds: 900
+  concurrency: 4
+  max_instances: 5
+  scaling_threshold: 0.5
+  shutdown_after_seconds: 288000
+  tee: true

miner.py

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+from pathlib import Path
+import math
+from typing import Any
+
+import cv2
+import numpy as np
+import onnxruntime as ort
+from numpy import ndarray
+from pydantic import BaseModel
+
+# Profile 002-001: same implementation as miner_script/002/miner.py (crime
+# classes, defaults, TTA merge, and ONNX sweep-cache helpers). Fork here only
+# when you need a frozen hyperparameter variant.
+
+
+class BoundingBox(BaseModel):
+    x1: int
+    y1: int
+    x2: int
+    y2: int
+    cls_id: int
+    conf: float
+
+
+class TVFrameResult(BaseModel):
+    frame_id: int
+    boxes: list[BoundingBox]
+    keypoints: list[tuple[int, int]]
+
+
+class Miner:
+    def __init__(self, path_hf_repo: Path) -> None:
+        model_path = path_hf_repo / "weights.onnx"
+
+        # Canonical class indices (validator / PGT / manifest); match dataset.yaml names order.
+        # Crime element (evaluations_7/dataset.yaml):
+        #   balaclava=0, hoodie=1, glove=2, bat=3, spray paint=4, graffiti=5
+        self.class_names = ['balaclava', 'hoodie', 'glove', 'bat', 'spray paint', 'graffiti']
+        # ONNX class index order from training export (Ultralytics names 0..5 in dataset.yaml).
+        model_class_order = ['balaclava', 'bat', 'glove', 'graffiti', 'hoodie', 'spray paint']
+        self._train_cls_to_canonical = np.array(
+            [self.class_names.index(n) for n in 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
+
+        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=1280)
+        self.input_width = self._safe_dim(self.input_shape[3], default=1280)
+
+        # ---------- Scoring-oriented thresholds (crime: balaclava / hoodie / glove /
+        #                                           bat / spray paint / graffiti) ----------
+        # Crime classes mix wearables (balaclava, hoodie), small handhelds
+        # (glove, spray paint), long handhelds (bat) and large markings
+        # (graffiti). Confidence peaks vary widely so we keep a moderate
+        # floor and lean on TTA consensus for the soft tail.
+        self.conf_thres = 0.15
+
+        # Above this on orig view, accept directly. Below it, require TTA agreement.
+        self.conf_high = 0.32
+
+        # Standard NMS IoU; balaclavas / hoodies on the same person can
+        # overlap heavily but they're different classes so the per-class
+        # NMS handles that — 0.50 stays a safe default.
+        self.iou_thres = 0.35
+
+        # Balaclavas / hoodies barely shift between orig and h-flipped views,
+        # but graffiti and bats can have asymmetric extents so a moderate
+        # IoU gate keeps the consensus rule from getting too strict.
+        self.tta_match_iou = 0.15
+
+        self.max_det = 150
+        self.use_tta = True
+
+        # Box sanity filters tuned for crime:
+        # - smallest classes (glove, spray paint) can shrink to ~10 px on
+        #   the short side in wide shots — keep min thresholds modest
+        # - aspect ratio: bats are long/thin (h/w ratio up to ~6) and
+        #   graffiti can be very wide; allow up to 8.0
+        # - a single graffiti tag can fill most of a wall, so allow up to
+        #   ~95% image-area for one box
+        self.min_box_area = 10 * 10
+        self.min_w = 6
+        self.min_h = 6
+        self.max_aspect_ratio = 8.0
+        self.max_box_area_ratio = 0.95
+
+        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}")
+
+    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 _remap_train_cls_ids(self, cls_ids: np.ndarray) -> np.ndarray:
+        idx = np.clip(cls_ids.astype(np.int64, copy=False), 0, len(self._train_cls_to_canonical) - 1)
+        return self._train_cls_to_canonical[idx]
+
+    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
+        dh = new_h - resized_h
+        dw /= 2.0
+        dh /= 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:
+        if len(boxes) == 0:
+            return np.array([], dtype=np.intp)
+
+        boxes = np.asarray(boxes, dtype=np.float32)
+        scores = np.asarray(scores, dtype=np.float32)
+        order = np.argsort(scores)[::-1]
+        keep = []
+
+        while len(order) > 0:
+            i = 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)
+
+            area_i = np.maximum(0.0, (boxes[i, 2] - boxes[i, 0])) * np.maximum(0.0, (boxes[i, 3] - boxes[i, 1]))
+            area_r = np.maximum(0.0, (boxes[rest, 2] - boxes[rest, 0])) * np.maximum(0.0, (boxes[rest, 3] - boxes[rest, 1]))
+
+            iou = inter / (area_i + area_r - inter + 1e-7)
+            order = rest[iou <= iou_thresh]
+
+        return np.array(keep, dtype=np.intp)
+
+    @classmethod
+    def _nms_per_class(
+        cls,
+        boxes: np.ndarray,
+        scores: np.ndarray,
+        cls_ids: np.ndarray,
+        iou_thresh: float,
+        max_det: int,
+    ) -> np.ndarray:
+        """NMS within each class so overlapping predictions of different classes are not merged away."""
+        if len(boxes) == 0:
+            return np.array([], dtype=np.intp)
+        keep_all: list[int] = []
+        for c in np.unique(cls_ids):
+            idxs = np.nonzero(cls_ids == c)[0]
+            if len(idxs) == 0:
+                continue
+            local_keep = cls._hard_nms(boxes[idxs], scores[idxs], iou_thresh)
+            keep_all.extend(idxs[local_keep].tolist())
+        keep_all = np.array(keep_all, dtype=np.intp)
+        order = np.argsort(scores[keep_all])[::-1]
+        return keep_all[order[:max_det]]
+
+    @staticmethod
+    def _box_iou_one_to_many(box: np.ndarray, boxes: np.ndarray) -> np.ndarray:
+        xx1 = np.maximum(box[0], boxes[:, 0])
+        yy1 = np.maximum(box[1], boxes[:, 1])
+        xx2 = np.minimum(box[2], boxes[:, 2])
+        yy2 = np.minimum(box[3], boxes[:, 3])
+
+        inter = np.maximum(0.0, xx2 - xx1) * np.maximum(0.0, yy2 - yy1)
+
+        area_a = max(0.0, (box[2] - box[0]) * (box[3] - box[1]))
+        area_b = np.maximum(0.0, boxes[:, 2] - boxes[:, 0]) * np.maximum(0.0, boxes[:, 3] - boxes[:, 1])
+
+        return inter / (area_a + area_b - inter + 1e-7)
+
+    def _filter_sane_boxes(
+        self,
+        boxes: np.ndarray,
+        scores: np.ndarray,
+        cls_ids: np.ndarray,
+        orig_size: tuple[int, int],
+    ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+        if len(boxes) == 0:
+            return boxes, scores, cls_ids
+
+        orig_w, orig_h = orig_size
+        image_area = float(orig_w * orig_h)
+
+        keep = []
+        for i, box in enumerate(boxes):
+            x1, y1, x2, y2 = box.tolist()
+            bw = x2 - x1
+            bh = y2 - y1
+
+            if bw <= 0 or bh <= 0:
+                continue
+            if bw < self.min_w or bh < self.min_h:
+                continue
+
+            area = bw * bh
+            if area < self.min_box_area:
+                continue
+            if area > self.max_box_area_ratio * image_area:
+                continue
+
+            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),
+            )
+
+        keep = np.array(keep, dtype=np.intp)
+        return boxes[keep], scores[keep], cls_ids[keep]
+
+    def _decode_final_dets(
+        self,
+        preds: np.ndarray,
+        ratio: float,
+        pad: tuple[float, float],
+        orig_size: tuple[int, int],
+    ) -> list[BoundingBox]:
+        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 = self._remap_train_cls_ids(preds[:, 5].astype(np.int32))
+        # Multi-class crime: balaclava / hoodie / glove / bat / spray paint / graffiti
+        # (see self.class_names).
+
+        # candidate threshold
+        keep = scores >= self.conf_thres
+        boxes = boxes[keep]
+        scores = scores[keep]
+        cls_ids = cls_ids[keep]
+
+        if len(boxes) == 0:
+            return []
+
+        pad_w, pad_h = pad
+        orig_w, orig_h = orig_size
+
+        boxes[:, [0, 2]] -= pad_w
+        boxes[:, [1, 3]] -= pad_h
+        boxes /= ratio
+        boxes = self._clip_boxes(boxes, (orig_w, orig_h))
+
+        boxes, scores, cls_ids = self._filter_sane_boxes(boxes, scores, cls_ids, orig_size)
+        if len(boxes) == 0:
+            return []
+
+        keep_idx = self._nms_per_class(
+            boxes, scores, cls_ids, self.iou_thres, self.max_det
+        )
+
+        boxes = boxes[keep_idx]
+        scores = scores[keep_idx]
+        cls_ids = cls_ids[keep_idx]
+
+        return [
+            BoundingBox(
+                x1=int(math.floor(box[0])),
+                y1=int(math.floor(box[1])),
+                x2=int(math.ceil(box[2])),
+                y2=int(math.ceil(box[3])),
+                cls_id=int(cls_id),
+                conf=float(conf),
+            )
+            for box, conf, cls_id in zip(boxes, scores, cls_ids)
+            if box[2] > box[0] and box[3] > box[1]
+        ]
+
+    def _decode_raw_yolo(
+        self,
+        preds: np.ndarray,
+        ratio: float,
+        pad: tuple[float, float],
+        orig_size: tuple[int, int],
+    ) -> list[BoundingBox]:
+        if preds.ndim != 3:
+            raise ValueError(f"Unexpected raw ONNX output shape: {preds.shape}")
+        if preds.shape[0] != 1:
+            raise ValueError(f"Unexpected batch dimension in raw output: {preds.shape}")
+
+        preds = preds[0]
+
+        # Normalize to [N, C]
+        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 normalized raw output shape: {preds.shape}")
+
+        boxes_xywh = preds[:, :4].astype(np.float32)
+        tail = preds[:, 4:].astype(np.float32)
+
+        # Supports:
+        # [x,y,w,h,score]                 single-class
+        # [x,y,w,h,obj,cls]               YOLO standard single-class
+        # [x,y,w,h,obj,cls1,cls2,...]     multi-class
+        if tail.shape[1] == 1:
+            scores = tail[:, 0]
+            cls_ids = np.zeros(len(scores), dtype=np.int32)
+        elif tail.shape[1] == 2:
+            obj = tail[:, 0]
+            cls_prob = tail[:, 1]
+            scores = obj * cls_prob
+            cls_ids = np.zeros(len(scores), dtype=np.int32)
+        else:
+            obj = tail[:, 0]
+            class_probs = tail[:, 1:]
+            cls_ids = np.argmax(class_probs, axis=1).astype(np.int32)
+            cls_scores = class_probs[np.arange(len(class_probs)), cls_ids]
+            scores = obj * cls_scores
+
+        cls_ids = self._remap_train_cls_ids(cls_ids)
+
+        keep = scores >= self.conf_thres
+        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
+        orig_w, orig_h = orig_size
+
+        boxes[:, [0, 2]] -= pad_w
+        boxes[:, [1, 3]] -= pad_h
+        boxes /= ratio
+        boxes = self._clip_boxes(boxes, (orig_w, orig_h))
+
+        boxes, scores, cls_ids = self._filter_sane_boxes(boxes, scores, cls_ids, orig_size)
+        if len(boxes) == 0:
+            return []
+
+        keep_idx = self._nms_per_class(
+            boxes, scores, cls_ids, self.iou_thres, self.max_det
+        )
+
+        boxes = boxes[keep_idx]
+        scores = scores[keep_idx]
+        cls_ids = cls_ids[keep_idx]
+
+        return [
+            BoundingBox(
+                x1=int(math.floor(box[0])),
+                y1=int(math.floor(box[1])),
+                x2=int(math.ceil(box[2])),
+                y2=int(math.ceil(box[3])),
+                cls_id=int(cls_id),
+                conf=float(conf),
+            )
+            for box, conf, cls_id in zip(boxes, scores, cls_ids)
+            if box[2] > box[0] and box[3] > box[1]
+        ]
+
+    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_shape = (1, 3, self.input_height, self.input_width)
+        if input_tensor.shape != expected_shape:
+            raise ValueError(
+                f"Bad input tensor shape={input_tensor.shape}, expected={expected_shape}"
+            )
+
+        outputs = self.session.run(self.output_names, {self.input_name: input_tensor})
+        det_output = outputs[0]
+        return self._postprocess(det_output, ratio, pad, orig_size)
+
+    def _merge_tta_consensus(
+        self,
+        boxes_orig: list[BoundingBox],
+        boxes_flip: list[BoundingBox],
+        *,
+        conf_high: float | None = None,
+        tta_match_iou: float | None = None,
+        iou_thres: float | None = None,
+    ) -> list[BoundingBox]:
+        """
+        Keep:
+        - any box with conf >= conf_high
+        - low/medium-conf boxes only if confirmed across TTA views
+        Then run final hard NMS.
+        All thresholds default to the instance attributes when not supplied,
+        so the non-sweep path can call this without args. The sweep path
+        passes explicit values to avoid mutating shared state across
+        parameter combinations (and to be safe under any future concurrency).
+        """
+        ch = float(conf_high) if conf_high is not None else float(self.conf_high)
+        tm = float(tta_match_iou) if tta_match_iou is not None else float(self.tta_match_iou)
+        ih = float(iou_thres) if iou_thres is not None else float(self.iou_thres)
+
+        if not boxes_orig and not boxes_flip:
+            return []
+
+        coords_o = np.array([[b.x1, b.y1, b.x2, b.y2] for b in boxes_orig], dtype=np.float32) if boxes_orig else np.empty((0, 4), dtype=np.float32)
+        scores_o = np.array([b.conf for b in boxes_orig], dtype=np.float32) if boxes_orig else np.empty((0,), dtype=np.float32)
+        cls_o = np.array([b.cls_id for b in boxes_orig], dtype=np.int32) if boxes_orig else np.empty((0,), dtype=np.int32)
+
+        coords_f = np.array([[b.x1, b.y1, b.x2, b.y2] for b in boxes_flip], dtype=np.float32) if boxes_flip else np.empty((0, 4), dtype=np.float32)
+        scores_f = np.array([b.conf for b in boxes_flip], dtype=np.float32) if boxes_flip else np.empty((0,), dtype=np.float32)
+        cls_f = np.array([b.cls_id for b in boxes_flip], dtype=np.int32) if boxes_flip else np.empty((0,), dtype=np.int32)
+
+        accepted_boxes = []
+        accepted_scores = []
+        accepted_cls = []
+
+        # Original view candidates
+        for i in range(len(coords_o)):
+            score = scores_o[i]
+            if score >= ch:
+                accepted_boxes.append(coords_o[i])
+                accepted_scores.append(score)
+                accepted_cls.append(int(cls_o[i]))
+            elif len(coords_f) > 0:
+                ious = self._box_iou_one_to_many(coords_o[i], coords_f)
+                j = int(np.argmax(ious))
+                if ious[j] >= tm:
+                    fused_score = max(score, scores_f[j])
+                    accepted_boxes.append(coords_o[i])
+                    accepted_scores.append(fused_score)
+                    accepted_cls.append(int(cls_o[i]))
+
+        # Flipped-view high-confidence boxes that original missed
+        for i in range(len(coords_f)):
+            score = scores_f[i]
+            if score < ch:
+                continue
+
+            if len(coords_o) == 0:
+                accepted_boxes.append(coords_f[i])
+                accepted_scores.append(score)
+                accepted_cls.append(int(cls_f[i]))
+                continue
+
+            ious = self._box_iou_one_to_many(coords_f[i], coords_o)
+            if np.max(ious) < tm:
+                accepted_boxes.append(coords_f[i])
+                accepted_scores.append(score)
+                accepted_cls.append(int(cls_f[i]))
+
+        if not accepted_boxes:
+            return []
+
+        boxes = np.array(accepted_boxes, dtype=np.float32)
+        scores = np.array(accepted_scores, dtype=np.float32)
+        cls_ids = np.array(accepted_cls, dtype=np.int32)
+
+        keep = self._nms_per_class(boxes, scores, cls_ids, ih, self.max_det)
+
+        out = []
+        for idx in keep:
+            x1, y1, x2, y2 = boxes[idx].tolist()
+            out.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_ids[idx]),
+                    conf=float(scores[idx]),
+                )
+            )
+        return out
+
+    def _predict_tta(self, image: np.ndarray) -> list[BoundingBox]:
+        boxes_orig = self._predict_single(image)
+
+        flipped = cv2.flip(image, 1)
+        boxes_flip_raw = 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
+        ]
+
+        return self._merge_tta_consensus(boxes_orig, boxes_flip)
+
+    # --- Fast sweep: two ONNX runs per image, then CPU-only threshold / NMS / TTA merge ---
+    # Must be <= the smallest conf_thres any sweep will try, otherwise the sweep silently
+    # caps the effective threshold and reported "best" params won't reproduce in non-sweep.
+    SWEEP_CONF_FLOOR = 0.0
+
+    def _final_dets_to_arrays_no_nms(
+        self,
+        preds: np.ndarray,
+        ratio: float,
+        pad: tuple[float, float],
+        orig_size: tuple[int, int],
+        score_floor: float,
+    ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+        if preds.ndim == 3 and preds.shape[0] == 1:
+            preds = preds[0]
+        if preds.ndim != 2 or preds.shape[1] < 6:
+            raise ValueError(f"Unexpected ONNX final-det output shape: {preds.shape}")
+        boxes = preds[:, :4].astype(np.float32)
+        scores = preds[:, 4].astype(np.float32)
+        cls_ids = self._remap_train_cls_ids(preds[:, 5].astype(np.int32))
+        keep = scores >= float(score_floor)
+        boxes = boxes[keep]
+        scores = scores[keep]
+        cls_ids = cls_ids[keep]
+        if len(boxes) == 0:
+            return (
+                np.empty((0, 4), dtype=np.float32),
+                np.empty((0,), dtype=np.float32),
+                np.empty((0,), dtype=np.int32),
+            )
+        pad_w, pad_h = pad
+        orig_w, orig_h = orig_size
+        boxes[:, [0, 2]] -= pad_w
+        boxes[:, [1, 3]] -= pad_h
+        boxes /= ratio
+        boxes = self._clip_boxes(boxes, (orig_w, orig_h))
+        return self._filter_sane_boxes(boxes, scores, cls_ids, orig_size)
+
+    def _raw_yolo_to_arrays_no_nms(
+        self,
+        preds: np.ndarray,
+        ratio: float,
+        pad: tuple[float, float],
+        orig_size: tuple[int, int],
+        score_floor: float,
+    ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+        if preds.ndim != 3:
+            raise ValueError(f"Unexpected raw ONNX output shape: {preds.shape}")
+        if preds.shape[0] != 1:
+            raise ValueError(f"Unexpected batch dimension in raw output: {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 normalized raw output shape: {preds.shape}")
+        boxes_xywh = preds[:, :4].astype(np.float32)
+        tail = preds[:, 4:].astype(np.float32)
+        if tail.shape[1] == 1:
+            scores = tail[:, 0]
+            cls_ids = np.zeros(len(scores), dtype=np.int32)
+        elif tail.shape[1] == 2:
+            obj = tail[:, 0]
+            cls_prob = tail[:, 1]
+            scores = obj * cls_prob
+            cls_ids = np.zeros(len(scores), dtype=np.int32)
+        else:
+            obj = tail[:, 0]
+            class_probs = tail[:, 1:]
+            cls_ids = np.argmax(class_probs, axis=1).astype(np.int32)
+            cls_scores = class_probs[np.arange(len(class_probs)), cls_ids]
+            scores = obj * cls_scores
+        cls_ids = self._remap_train_cls_ids(cls_ids)
+        keep = scores >= float(score_floor)
+        boxes_xywh = boxes_xywh[keep]
+        scores = scores[keep]
+        cls_ids = cls_ids[keep]
+        if len(boxes_xywh) == 0:
+            return (
+                np.empty((0, 4), dtype=np.float32),
+                np.empty((0,), dtype=np.float32),
+                np.empty((0,), dtype=np.int32),
+            )
+        boxes = self._xywh_to_xyxy(boxes_xywh)
+        pad_w, pad_h = pad
+        orig_w, orig_h = orig_size
+        boxes[:, [0, 2]] -= pad_w
+        boxes[:, [1, 3]] -= pad_h
+        boxes /= ratio
+        boxes = self._clip_boxes(boxes, (orig_w, orig_h))
+        return self._filter_sane_boxes(boxes, scores, cls_ids, orig_size)
+
+    def _postprocess_to_arrays_no_nms(
+        self,
+        output: np.ndarray,
+        ratio: float,
+        pad: tuple[float, float],
+        orig_size: tuple[int, int],
+        score_floor: float,
+    ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+        if output.ndim == 2 and output.shape[1] >= 6:
+            return self._final_dets_to_arrays_no_nms(output, ratio, pad, orig_size, score_floor)
+        if output.ndim == 3 and output.shape[0] == 1 and output.shape[2] >= 6:
+            return self._final_dets_to_arrays_no_nms(output, ratio, pad, orig_size, score_floor)
+        return self._raw_yolo_to_arrays_no_nms(output, ratio, pad, orig_size, score_floor)
+
+    @staticmethod
+    def _horizontal_flip_boxes_xyxy(boxes: np.ndarray, w: int) -> np.ndarray:
+        if len(boxes) == 0:
+            return boxes
+        out = boxes.astype(np.float32, copy=True)
+        x1 = out[:, 0].copy()
+        x2 = out[:, 2].copy()
+        out[:, 0] = w - x2
+        out[:, 2] = w - x1
+        return out
+
+    def build_vehicle_sweep_cache(self, image_bgr: np.ndarray) -> dict[str, Any]:
+        """Two ONNX forwards (TTA views); candidates kept at score >= SWEEP_CONF_FLOOR."""
+        if image_bgr.dtype != np.uint8:
+            image_bgr = image_bgr.astype(np.uint8)
+        h, w = int(image_bgr.shape[0]), int(image_bgr.shape[1])
+        input_tensor, ratio, pad, orig_size = self._preprocess(image_bgr)
+        outputs = self.session.run(self.output_names, {self.input_name: input_tensor})
+        bo, so, co = self._postprocess_to_arrays_no_nms(
+            outputs[0], ratio, pad, orig_size, self.SWEEP_CONF_FLOOR
+        )
+        flipped = cv2.flip(image_bgr, 1)
+        input_tensor_f, ratio_f, pad_f, orig_size_f = self._preprocess(flipped)
+        outputs_f = self.session.run(self.output_names, {self.input_name: input_tensor_f})
+        bf, sf, cf = self._postprocess_to_arrays_no_nms(
+            outputs_f[0], ratio_f, pad_f, orig_size_f, self.SWEEP_CONF_FLOOR
+        )
+        bf = self._horizontal_flip_boxes_xyxy(bf, w)
+        return {"orig": (bo, so, co), "flip": (bf, sf, cf), "image_shape": (h, w)}
+
+    def _arrays_to_boxes_after_conf_nms(
+        self,
+        boxes: np.ndarray,
+        scores: np.ndarray,
+        cls_ids: np.ndarray,
+        conf_thres: float,
+        iou_thres: float,
+    ) -> list[BoundingBox]:
+        if len(boxes) == 0:
+            return []
+        m = scores >= float(conf_thres)
+        if not np.any(m):
+            return []
+        boxes = boxes[m]
+        scores = scores[m]
+        cls_ids = cls_ids[m]
+        keep_idx = self._nms_per_class(boxes, scores, cls_ids, float(iou_thres), self.max_det)
+        out: list[BoundingBox] = []
+        for i in keep_idx:
+            box = boxes[i]
+            x1, y1, x2, y2 = float(box[0]), float(box[1]), float(box[2]), float(box[3])
+            if x2 <= x1 or y2 <= y1:
+                continue
+            out.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_ids[i]),
+                    conf=float(scores[i]),
+                )
+            )
+        return out
+
+    def predict_vehicle_from_sweep_cache(
+        self,
+        cache: dict[str, Any],
+        *,
+        conf_thres: float,
+        iou_thres: float,
+        conf_high: float | None = None,
+        tta_match_iou: float | None = None,
+    ) -> list[BoundingBox]:
+        bo, so, co = cache["orig"]
+        bf, sf, cf = cache["flip"]
+        boxes_orig = self._arrays_to_boxes_after_conf_nms(bo, so, co, conf_thres, iou_thres)
+        # Match predict_batch: single-view path when TTA is off (sweep cache must not force merge).
+        if not getattr(self, "use_tta", True):
+            return boxes_orig
+
+        boxes_flip = self._arrays_to_boxes_after_conf_nms(bf, sf, cf, conf_thres, iou_thres)
+        # Pass swept thresholds explicitly to avoid mutating self.* (race-free
+        # under any future concurrent sweep harness; also robust to early returns).
+        return self._merge_tta_consensus(
+            boxes_orig,
+            boxes_flip,
+            conf_high=conf_high,
+            tta_match_iou=tta_match_iou,
+            iou_thres=iou_thres,
+        )
+
+    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 = self._predict_tta(image)
+                else:
+                    boxes = self._predict_single(image)
+            except Exception as e:
+                print(f"⚠️ Inference failed for frame {offset + frame_number_in_batch}: {e}")
+                boxes = []
+
+            results.append(
+                TVFrameResult(
+                    frame_id=offset + frame_number_in_batch,
+                    boxes=boxes,
+                    keypoints=[(0, 0) for _ in range(max(0, int(n_keypoints)))],
+                )
+            )
+
+        return results

weights.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:71009e2a8381b151383d0f6d1c87b2d0af7911664a267a30e3e7160143a83f94
+size 19157269