| 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: |
| """ |
| YOLO (NMS-free) ONNX miner for road-sign detection. One forward pass per |
| frame — no TTA. |
| |
| Class: single 'road sign'. |
| |
| Pipeline per frame: preprocess -> ONNX -> decode -> conf threshold (+ rescue |
| bonus) -> un-letterbox -> sanity filter -> per-class NMS -> cross-class dedup |
| -> same-class cluster score boost -> results. |
| |
| Speed: the detection pipeline runs exactly ONCE per frame (the old redundant |
| second NMS/dedup pass is gone), and the cluster boost is a single vectorized |
| IoU matrix, so cost stays flat as the number of detected objects grows. |
| """ |
|
|
| class_names = ['road sign'] |
| input_size = 1536 |
| cross_iou_thresh = 0.8 |
| max_det = 300 |
| |
| |
| pre_nms_topk = 1000 |
| |
|
|
| |
| _conf_thres_array = np.array([0.31], dtype=np.float32) |
|
|
| |
| _iou_thres_array = np.array([0.85], dtype=np.float32) |
|
|
| |
| _bonus_array = np.array([0.2], dtype=np.float32) |
|
|
| |
| _min_box_area_array = np.array([9.0], dtype=np.float32) |
|
|
| def __init__(self, path_hf_repo: Path) -> None: |
| self.path_hf_repo = path_hf_repo |
|
|
| print("ORT version:", ort.__version__) |
|
|
| try: |
| ort.preload_dlls() |
| print("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 |
|
|
| self.session = ort.InferenceSession( |
| str(path_hf_repo / "weights.onnx"), |
| sess_options=sess_options, |
| providers=["CUDAExecutionProvider", "CPUExecutionProvider"], |
| ) |
| print("Created ORT session with preferred CUDA provider list") |
| print("ORT session providers:", self.session.get_providers()) |
|
|
| self.input_name = self.session.get_inputs()[0].name |
| input_shape = self.session.get_inputs()[0].shape |
|
|
| self.input_h = self._safe_dim(input_shape[2], default=self.input_size) |
| self.input_w = self._safe_dim(input_shape[3], default=self.input_size) |
|
|
| |
| |
| |
| self.use_cluster_boost = True |
| self._avg_iou = float(np.mean(self._iou_thres_array)) |
|
|
| self._warmup() |
|
|
| def _warmup(self, iters: int = 3) -> None: |
| try: |
| dummy = np.zeros((720, 1280, 3), dtype=np.uint8) |
| for _ in range(max(1, iters)): |
| self.predict_batch(batch_images=[dummy], offset=0, n_keypoints=0) |
| print(f"warmup: {iters} dummy predict_batch call(s) done") |
| except Exception as e: |
| print(f"warmup skipped: {e}") |
|
|
| def __repr__(self) -> str: |
| return f"road sign Miner classes={len(self.class_names)}" |
|
|
| @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: tuple[int, int, int] = (114, 114, 114), |
| ) -> tuple[ndarray, float, float, float]: |
| orig_h, orig_w = image.shape[:2] |
| target_w, target_h = new_shape |
|
|
| r = min(target_w / orig_w, target_h / orig_h) |
| new_unpad_w = int(round(orig_w * r)) |
| new_unpad_h = int(round(orig_h * r)) |
|
|
| resized = cv2.resize(image, (new_unpad_w, new_unpad_h), interpolation=cv2.INTER_LINEAR) |
|
|
| dw = target_w - new_unpad_w |
| dh = target_h - new_unpad_h |
| pad_w = dw / 2.0 |
| pad_h = dh / 2.0 |
|
|
| left = int(round(pad_w - 0.1)) |
| right = int(round(pad_w + 0.1)) |
| top = int(round(pad_h - 0.1)) |
| bottom = int(round(pad_h + 0.1)) |
|
|
| out = cv2.copyMakeBorder( |
| resized, top, bottom, left, right, |
| cv2.BORDER_CONSTANT, value=color, |
| ) |
| return out, r, pad_w, pad_h |
|
|
| def _preprocess(self, image_bgr: ndarray) -> tuple[np.ndarray, dict]: |
| orig_h, orig_w = image_bgr.shape[:2] |
| rgb = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2RGB) |
| img, ratio, pad_w, pad_h = self._letterbox(rgb, (self.input_w, self.input_h)) |
| x = img.astype(np.float32) / 255.0 |
| x = np.transpose(x, (2, 0, 1))[None, ...] |
| x = np.ascontiguousarray(x) |
| return x, { |
| "orig_h": orig_h, |
| "orig_w": orig_w, |
| "ratio": ratio, |
| "pad_w": pad_w, |
| "pad_h": pad_h, |
| } |
|
|
| |
|
|
| @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 _hard_nms(boxes: np.ndarray, scores: np.ndarray, |
| iou_thresh: float) -> np.ndarray: |
| """Vectorized greedy NMS. Areas precomputed once. Returns indices to keep.""" |
| n = len(boxes) |
| if n == 0: |
| return np.array([], dtype=np.intp) |
| x1, y1 = boxes[:, 0], boxes[:, 1] |
| x2, y2 = boxes[:, 2], boxes[:, 3] |
| areas = np.maximum(0.0, x2 - x1) * np.maximum(0.0, y2 - y1) |
| order = np.argsort(-scores) |
| keep = [] |
| while order.size > 0: |
| i = int(order[0]) |
| keep.append(i) |
| if order.size == 1: |
| break |
| rest = order[1:] |
| xx1 = np.maximum(x1[i], x1[rest]) |
| yy1 = np.maximum(y1[i], y1[rest]) |
| xx2 = np.minimum(x2[i], x2[rest]) |
| yy2 = np.minimum(y2[i], y2[rest]) |
| inter = np.maximum(0.0, xx2 - xx1) * np.maximum(0.0, yy2 - yy1) |
| iou = inter / (areas[i] + areas[rest] - 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) -> np.ndarray: |
| """Per-class NMS using per-class IoU thresholds.""" |
| 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] |
| cls_iou = float(self._iou_thres_array[c]) |
| keep = self._hard_nms(boxes[mask], scores[mask], cls_iou) |
| all_keep.extend(indices[keep].tolist()) |
| all_keep.sort() |
| return np.array(all_keep, dtype=np.intp) |
|
|
| def _cross_class_dedup_op(self, boxes: np.ndarray, scores: np.ndarray, |
| cls_ids: np.ndarray, iou_thresh: float |
| ) -> tuple[np.ndarray, np.ndarray, np.ndarray]: |
| n = len(boxes) |
| if n <= 1: |
| return boxes, scores, cls_ids |
| 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 = [] |
| 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] |
|
|
| def _filter_sane_boxes(self, boxes: np.ndarray, scores: np.ndarray, |
| cls_ids: np.ndarray, orig_size: tuple[int, int] |
| ) -> tuple[np.ndarray, np.ndarray, np.ndarray]: |
| """Filter by per-class min area and max area ratio.""" |
| if len(boxes) == 0: |
| return boxes, scores, cls_ids |
|
|
| orig_w, orig_h = orig_size |
| image_area = float(orig_w * orig_h) |
| bw = np.maximum(0.0, boxes[:, 2] - boxes[:, 0]) |
| bh = np.maximum(0.0, boxes[:, 3] - boxes[:, 1]) |
| area = bw * bh |
|
|
| class_min_area = self._min_box_area_array[cls_ids] |
|
|
| keep = ( |
| (area >= class_min_area) & |
| (area <= 0.95 * image_area) |
| ) |
| return boxes[keep], scores[keep], cls_ids[keep] |
|
|
| def _max_score_per_cluster(self, post_boxes: np.ndarray, |
| post_cls: np.ndarray, |
| full_boxes: np.ndarray, |
| full_scores: np.ndarray, |
| full_cls: np.ndarray, |
| iou_thresh: float) -> np.ndarray: |
| """For each kept box, confidence = max score in its SAME-CLASS IoU cluster. |
| Vectorized: single (n_post x n_full) IoU matrix, no per-box Python loop.""" |
| n = len(post_boxes) |
| if n == 0: |
| return np.empty(0, dtype=np.float32) |
| m = len(full_boxes) |
| if m == 0: |
| return np.zeros(n, dtype=np.float32) |
| pa = (np.maximum(0.0, post_boxes[:, 2] - post_boxes[:, 0]) * |
| np.maximum(0.0, post_boxes[:, 3] - post_boxes[:, 1])) |
| fa = (np.maximum(0.0, full_boxes[:, 2] - full_boxes[:, 0]) * |
| np.maximum(0.0, full_boxes[:, 3] - full_boxes[:, 1])) |
| xx1 = np.maximum(post_boxes[:, 0][:, None], full_boxes[:, 0][None, :]) |
| yy1 = np.maximum(post_boxes[:, 1][:, None], full_boxes[:, 1][None, :]) |
| xx2 = np.minimum(post_boxes[:, 2][:, None], full_boxes[:, 2][None, :]) |
| yy2 = np.minimum(post_boxes[:, 3][:, None], full_boxes[:, 3][None, :]) |
| inter = np.maximum(0.0, xx2 - xx1) * np.maximum(0.0, yy2 - yy1) |
| iou = inter / (pa[:, None] + fa[None, :] - inter + 1e-7) |
| mask = (iou >= iou_thresh) & (post_cls[:, None] == full_cls[None, :]) |
| tiled = np.where(mask, full_scores[None, :], -np.inf) |
| out = tiled.max(axis=1) |
| out[~np.isfinite(out)] = 0.0 |
| return out.astype(np.float32) |
|
|
| def _conf_filter_mask(self, scores: np.ndarray, |
| cls_ids: np.ndarray) -> np.ndarray: |
| """Per-class threshold with rescue bonus for missed classes.""" |
| 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 _per_view_pipeline(self, boxes: np.ndarray, scores: np.ndarray, |
| cls_ids: np.ndarray, orig_size: tuple[int, int] |
| ) -> tuple[np.ndarray, np.ndarray, np.ndarray]: |
| """Sanity filter -> (top-k cap) -> per-class NMS -> cap -> cross-class dedup.""" |
| boxes, scores, cls_ids = self._filter_sane_boxes( |
| boxes, scores, cls_ids, orig_size |
| ) |
| if len(boxes) == 0: |
| return boxes, scores, cls_ids |
| if len(scores) > self.pre_nms_topk: |
| top = np.argpartition(-scores, self.pre_nms_topk)[: self.pre_nms_topk] |
| boxes, scores, cls_ids = boxes[top], scores[top], cls_ids[top] |
| if len(boxes) > 1: |
| keep = self._per_class_hard_nms(boxes, scores, cls_ids) |
| boxes, scores, cls_ids = boxes[keep], scores[keep], cls_ids[keep] |
| 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 |
|
|
| |
|
|
| def _decode_yolo_output(self, preds: np.ndarray, ratio: float, |
| pad: tuple[float, float], |
| orig_size: tuple[int, int] |
| ) -> tuple[np.ndarray, np.ndarray, np.ndarray]: |
| """Decode NMS-free output [1,N,6]=(x1,y1,x2,y2,conf,cls) -> conf filter -> |
| un-letterbox -> sanity+NMS+dedup. Returns arrays in ORIGINAL coords.""" |
| empty = (np.empty((0, 4), np.float32), np.empty(0, np.float32), |
| np.empty(0, np.int32)) |
| if preds.ndim == 3 and preds.shape[0] == 1: |
| preds = preds[0] |
| if preds.ndim != 2 or preds.shape[1] < 6: |
| print(f"Warning: Unexpected output shape: {preds.shape}") |
| return empty |
|
|
| boxes = preds[:, :4].astype(np.float32) |
| scores = preds[:, 4].astype(np.float32) |
| cls_ids = preds[:, 5].astype(np.int32) |
|
|
| n_cls = len(self.class_names) |
| valid = (cls_ids >= 0) & (cls_ids < n_cls) |
| boxes, scores, cls_ids = boxes[valid], scores[valid], cls_ids[valid] |
| if len(boxes) == 0: |
| return empty |
|
|
| keep = self._conf_filter_mask(scores, cls_ids) |
| boxes, scores, cls_ids = boxes[keep], scores[keep], cls_ids[keep] |
| if len(boxes) == 0: |
| return empty |
|
|
| pad_w, pad_h = pad |
| boxes[:, [0, 2]] -= pad_w |
| boxes[:, [1, 3]] -= pad_h |
| boxes /= ratio |
| boxes = self._clip_boxes(boxes, orig_size) |
|
|
| return self._per_view_pipeline(boxes, scores, cls_ids, orig_size) |
|
|
| @staticmethod |
| def _build_results(boxes: np.ndarray, scores: np.ndarray, |
| cls_ids: np.ndarray, |
| orig_size: tuple[int, int]) -> list[BoundingBox]: |
| results = [] |
| orig_w, orig_h = orig_size |
| for box, conf, cls_id in zip(boxes, scores, cls_ids): |
| x1, y1, x2, y2 = box.tolist() if hasattr(box, "tolist") else box |
| if x2 <= x1 or y2 <= y1: |
| continue |
| results.append( |
| BoundingBox( |
| x1=max(0, min(orig_w, int(math.floor(x1)))), |
| y1=max(0, min(orig_h, int(math.floor(y1)))), |
| x2=max(0, min(orig_w, int(math.ceil(x2)))), |
| y2=max(0, min(orig_h, int(math.ceil(y2)))), |
| cls_id=int(cls_id), |
| conf=float(max(0.0, min(1.0, conf))), |
| ) |
| ) |
| return results |
|
|
| |
|
|
| def _predict_single(self, image_bgr: np.ndarray |
| ) -> tuple[np.ndarray, np.ndarray, np.ndarray]: |
| """One forward pass -> decoded (boxes, scores, cls_ids) in original coords.""" |
| if image_bgr is None or not isinstance(image_bgr, np.ndarray): |
| raise ValueError("Invalid image input") |
| if image_bgr.dtype != np.uint8: |
| image_bgr = image_bgr.astype(np.uint8) |
|
|
| inp, meta = self._preprocess(image_bgr) |
| outputs = self.session.run(None, {self.input_name: inp}) |
|
|
| ratio = float(meta["ratio"]) |
| pad = (float(meta["pad_w"]), float(meta["pad_h"])) |
| orig_size = (int(meta["orig_w"]), int(meta["orig_h"])) |
|
|
| return self._decode_yolo_output(outputs[0], ratio, pad, orig_size) |
|
|
| def _infer_single(self, image_bgr: ndarray) -> list[BoundingBox]: |
| orig_h, orig_w = image_bgr.shape[:2] |
| orig_size = (orig_w, orig_h) |
|
|
| boxes, scores, cls_ids = self._predict_single(image_bgr) |
| if len(boxes) == 0: |
| return [] |
|
|
| if self.use_cluster_boost and len(boxes) > 1: |
| scores = self._max_score_per_cluster( |
| boxes, cls_ids, boxes, scores, cls_ids, self._avg_iou) |
|
|
| return self._build_results(boxes, scores, cls_ids, orig_size) |
|
|
| |
|
|
| def predict_batch( |
| self, |
| batch_images: list[ndarray], |
| offset: int, |
| n_keypoints: int, |
| ) -> list[TVFrameResult]: |
| results: list[TVFrameResult] = [] |
| for idx, image in enumerate(batch_images): |
| try: |
| boxes = self._infer_single(image) |
| except Exception as e: |
| print(f"Inference failed for frame {offset + idx}: {e}") |
| boxes = [] |
| keypoints = [(0, 0) for _ in range(max(0, int(n_keypoints)))] |
| results.append( |
| TVFrameResult( |
| frame_id=offset + idx, |
| boxes=boxes, |
| keypoints=keypoints, |
| ) |
| ) |
| return results |
