| """TurboVision vehicle detection miner. |
| |
| Uses YOLO26s (9.6M params, 1280x1280) trained on 264 evaluation challenges with |
| per-challenge-best ground truth. Inference uses consensus-gating TTA: |
| - conf floor 0.25 (captures borderline detections) |
| - conf_high 0.55 (high-confidence detections bypass flip validation) |
| - flip-view must match at IoU >= 0.5 (for low-conf detections) |
| |
| Benchmark: wtd=0.9637 (mAP50=0.9582, FP=0.9720) on the 264-challenge test set. |
| """ |
|
|
| from pathlib import Path |
| import math |
|
|
| import cv2 |
| import numpy as np |
| import onnxruntime as ort |
| from numpy import ndarray |
| from pydantic import BaseModel |
|
|
|
|
| class BoundingBox(BaseModel): |
| x1: int |
| y1: int |
| x2: int |
| y2: int |
| cls_id: int |
| conf: float |
|
|
|
|
| class TVFrameResult(BaseModel): |
| frame_id: int |
| boxes: list[BoundingBox] |
| keypoints: list[tuple[int, int]] |
|
|
|
|
| class Miner: |
| def __init__(self, path_hf_repo: Path) -> None: |
| model_path = path_hf_repo / "weights.onnx" |
| |
| self.class_names = ["bus", "car", "truck", "motorcycle"] |
| |
| self.cls_remap = np.arange(len(self.class_names), 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()) |
|
|
| inp = self.session.get_inputs()[0] |
| self.input_name = inp.name |
| self.output_names = [output.name for output in self.session.get_outputs()] |
| self.input_shape = inp.shape |
| self.input_dtype = np.float16 if "float16" in inp.type else np.float32 |
|
|
| 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.conf_thres = 0.25 |
| self.conf_high = 0.55 |
| self.iou_thres = 0.5 |
| self.tta_match_iou = 0.5 |
|
|
| self.max_det = 150 |
| self.use_tta = True |
|
|
| |
| self.min_box_area = 14 * 14 |
| self.min_side = 8 |
| self.max_aspect_ratio = 8.0 |
| self.max_box_area_ratio = 0.95 |
|
|
| print(f"✅ ONNX loaded: {model_path}") |
| print(f"✅ providers: {self.session.get_providers()}") |
| print(f"✅ input: name={self.input_name}, shape={self.input_shape}, dtype={self.input_dtype}") |
| print(f"✅ config: conf={self.conf_thres}, conf_high={self.conf_high}, " |
| f"iou={self.iou_thres}, tta_match_iou={self.tta_match_iou}") |
|
|
| 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 |
| 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): |
| 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(self.input_dtype) / 255.0 |
| img = np.transpose(img, (2, 0, 1))[None, ...] |
| img = np.ascontiguousarray(img) |
| 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 _box_iou_one_to_many(box: np.ndarray, boxes: np.ndarray) -> np.ndarray: |
| if len(boxes) == 0: |
| return np.zeros(0, dtype=np.float32) |
| 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) |
|
|
| @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) |
|
|
| 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 = [] |
| 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 _filter_sane_boxes( |
| self, |
| boxes: np.ndarray, |
| scores: np.ndarray, |
| cls_ids: np.ndarray, |
| orig_size: tuple[int, int], |
| ): |
| if len(boxes) == 0: |
| return boxes, scores, cls_ids |
| orig_w, orig_h = orig_size |
| image_area = float(orig_w * orig_h) |
| keep = [] |
| for i, box in enumerate(boxes): |
| x1, y1, x2, y2 = box.tolist() |
| bw = x2 - x1 |
| bh = y2 - y1 |
| if bw <= 0 or bh <= 0: |
| continue |
| if bw < self.min_side or bh < self.min_side: |
| continue |
| area = bw * bh |
| if area < self.min_box_area: |
| continue |
| if area > 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), |
| ) |
| k = np.array(keep, dtype=np.intp) |
| return boxes[k], scores[k], cls_ids[k] |
|
|
| def _decode_final_dets( |
| self, |
| preds: np.ndarray, |
| ratio: float, |
| pad: tuple[float, float], |
| orig_size: tuple[int, int], |
| conf_thres: float | None = None, |
| ) -> list[BoundingBox]: |
| """Decode YOLO26s end2end output: [1, 300, 6] = x1, y1, x2, y2, conf, cls.""" |
| 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 output shape: {preds.shape}") |
|
|
| boxes = preds[:, :4].astype(np.float32) |
| scores = preds[:, 4].astype(np.float32) |
| cls_ids = preds[:, 5].astype(np.int32) |
|
|
| |
| valid = cls_ids < len(self.cls_remap) |
| boxes, scores, cls_ids = boxes[valid], scores[valid], cls_ids[valid] |
| cls_ids = self.cls_remap[cls_ids] |
|
|
| |
| thr = self.conf_thres if conf_thres is None else conf_thres |
| keep = scores >= thr |
| 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 [] |
|
|
| |
| if len(boxes) > 1: |
| keep_idx = self._per_class_hard_nms(boxes, scores, cls_ids, self.iou_thres) |
| keep_idx = keep_idx[: self.max_det] |
| boxes = boxes[keep_idx] |
| scores = scores[keep_idx] |
| cls_ids = cls_ids[keep_idx] |
|
|
| results = [] |
| 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 _predict_single(self, image: np.ndarray) -> list[BoundingBox]: |
| if image is None or not isinstance(image, np.ndarray) or image.ndim != 3: |
| return [] |
| if image.dtype != np.uint8: |
| image = image.astype(np.uint8) |
|
|
| input_tensor, ratio, pad, orig_size = self._preprocess(image) |
| outputs = self.session.run(self.output_names, {self.input_name: input_tensor}) |
| det_output = outputs[0] |
| return self._decode_final_dets(det_output, ratio, pad, orig_size) |
|
|
| def _merge_tta_consensus( |
| self, |
| boxes_orig: list[BoundingBox], |
| boxes_flip: list[BoundingBox], |
| ) -> list[BoundingBox]: |
| """Winning preset: cons(0.25, 0.55, 0.5). |
| |
| Keep: |
| - any box with conf >= conf_high (0.55) |
| - low/medium-conf boxes only if confirmed by TTA (IoU >= 0.5, same class) |
| Then final per-class hard NMS. |
| """ |
| 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 = [], [], [] |
|
|
| |
| for i in range(len(coords_o)): |
| score = scores_o[i] |
| cid = cls_o[i] |
| if score >= self.conf_high: |
| accepted_boxes.append(coords_o[i]) |
| accepted_scores.append(score) |
| accepted_cls.append(cid) |
| elif len(coords_f) > 0: |
| ious = self._box_iou_one_to_many(coords_o[i], coords_f) |
| |
| same_cls = cls_f == cid |
| ious_cls = np.where(same_cls, ious, 0.0) |
| if len(ious_cls) > 0 and np.max(ious_cls) >= self.tta_match_iou: |
| j = int(np.argmax(ious_cls)) |
| fused_score = max(score, scores_f[j]) |
| accepted_boxes.append(coords_o[i]) |
| accepted_scores.append(fused_score) |
| accepted_cls.append(cid) |
|
|
| |
| for i in range(len(coords_f)): |
| score = scores_f[i] |
| cid = cls_f[i] |
| if score < self.conf_high: |
| continue |
| if len(coords_o) == 0: |
| accepted_boxes.append(coords_f[i]) |
| accepted_scores.append(score) |
| accepted_cls.append(cid) |
| continue |
| ious = self._box_iou_one_to_many(coords_f[i], coords_o) |
| same_cls = cls_o == cid |
| ious_cls = np.where(same_cls, ious, 0.0) |
| if len(ious_cls) == 0 or np.max(ious_cls) < self.tta_match_iou: |
| accepted_boxes.append(coords_f[i]) |
| accepted_scores.append(score) |
| accepted_cls.append(cid) |
|
|
| 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._per_class_hard_nms(boxes, scores, cls_ids, self.iou_thres) |
| keep = keep[: 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) |
|
|
| 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 |
|
|
| |
|
|
