| |
| 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 global NMS + sanity filter + dedup + flip TTA, with per-class rescue bonus.""" |
|
|
| class_names = ["cup", "bottle", "can"] |
| input_size = 1280 |
| iou_thres = 0.4 |
| cross_iou_thresh = 0.7 |
| min_side = 8.0 |
| min_box_area = 100.0 |
| max_aspect_ratio = 10.0 |
| max_det = 300 |
| _conf_thres_array = np.array([0.6, 0.45, 0.5], dtype=np.float32) |
| _bonus_array = np.array([0.0, 0.0, 0.2], 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 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 _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 _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: 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] |
|
|
| 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) |
| bw = np.maximum(0.0, boxes[:, 2] - boxes[:, 0]) |
| bh = np.maximum(0.0, boxes[:, 3] - boxes[:, 1]) |
| area = bw * bh |
| ar = np.where( |
| (bw > 0) & (bh > 0), |
| np.maximum(bw / np.maximum(bh, 1e-6), bh / np.maximum(bw, 1e-6)), |
| np.inf, |
| ) |
| keep = ( |
| (bw >= self.min_side) & (bh >= self.min_side) & |
| (area >= self.min_box_area) & |
| (area <= 0.95 * image_area) & |
| (ar <= self.max_aspect_ratio) |
| ) |
| 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: |
| 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 _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]: |
| 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(boxes) > 1: |
| keep = self._hard_nms(boxes, scores, 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 = 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._per_view_pipeline( |
| boxes, scores, cls_ids, orig_size |
| ) |
| 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 = 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._per_view_pipeline( |
| boxes, scores, cls_ids, orig_size |
| ) |
| 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 [] |
| 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_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 |
|
|
