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: """ONNX Runtime miner. Hard per-class NMS + cross-class dedup + flip TTA.""" class_names = ["cup", "bottle", "can"] input_size = 1280 iou_thres = 0.3 cross_iou_thresh = 0.6 max_det = 150 _conf_thres_array = np.array([0.45, 0.35, 0.45], dtype=np.float32) def __init__(self, path_hf_repo: Path) -> None: model_path = path_hf_repo / "weights.onnx" 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 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=self.input_size) self.input_width = self._safe_dim(self.input_shape[3], default=self.input_size) print(f"ONNX model loaded from: {model_path}") 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()))) 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) @staticmethod def _cross_class_dedup_op(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])) order = np.lexsort((-scores, -areas)) 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, full_boxes: np.ndarray, full_scores: np.ndarray, iou_thresh: float) -> np.ndarray: 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 out[i] = float(np.max(full_scores[cluster])) if np.any(cluster) else 0.0 return out def _per_view_pipeline(self, boxes: np.ndarray, scores: np.ndarray, cls_ids: np.ndarray ) -> tuple[np.ndarray, np.ndarray, np.ndarray]: if len(boxes) > 1: keep = self._per_class_hard_nms(boxes, scores, cls_ids, self.iou_thres) boxes, scores, cls_ids = boxes[keep], scores[keep], cls_ids[keep] 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_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 = preds[:, 5].astype(np.int32) keep = scores >= self._conf_thres_array[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._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]: 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] keep = scores >= self._conf_thres_array[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._per_view_pipeline(boxes, scores, cls_ids) return self._build_results(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 _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[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]: 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 [] hard_keep = hard_keep[: self.max_det] boosted = self._max_score_per_cluster( coords[hard_keep], coords, scores, self.iou_thres ) return [ BoundingBox( x1=all_boxes[i].x1, y1=all_boxes[i].y1, x2=all_boxes[i].x2, y2=all_boxes[i].y2, cls_id=all_boxes[i].cls_id, conf=float(boosted[j]), ) for j, i in enumerate(hard_keep) ] 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: boxes = self._predict_tta(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