| """TurboVision beverage detection miner — score-beverage-v3. |
| |
| YOLO11s @ 1280x1280, 3-class beverage detection (bottle/can/cup), |
| ONNX with end-to-end NMS baked in (output [1, 300, 6] = x1, y1, x2, y2, conf, cls). |
| |
| Inference pipeline (v3): |
| 1) Primary forward pass on the full image. |
| 2) Hflip TTA: forward on horizontally-flipped image, transform boxes back. |
| 3) Per-class hard-NMS to merge primary + flip outputs. |
| 4) Cross-class IoU dedup (suppresses same physical object getting two class labels). |
| 5) Consensus-confidence boost: when both views agree on a cluster, take the max |
| score so true-positives rank higher in the validator's PR curve. |
| 6) Sanity filter (min size, aspect ratio). |
| """ |
|
|
| 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" |
|
|
| cn_path = model_path.with_name("class_names.txt") |
| if cn_path.is_file(): |
| self.class_names = [ |
| ln.strip() |
| for ln in cn_path.read_text(encoding="utf-8").splitlines() |
| if ln.strip() and not ln.strip().startswith("#") |
| ] |
| else: |
| self.class_names = ["cup", "bottle", "can"] |
| 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 = [o.name for o 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 = 0.20 |
| self.iou_thres = 0.5 |
| self.cross_iou_thresh = 0.7 |
| self.max_det = 300 |
| self.use_tta = True |
|
|
| |
| self.min_box_area = 6 * 6 |
| self.min_side = 4 |
| 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"✅ classes: {self.class_names}") |
| print(f"✅ config: conf={self.conf_thres}, iou={self.iou_thres}, " |
| f"cross_iou={self.cross_iou_thresh}, TTA={self.use_tta}") |
|
|
| 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): |
| 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 |
|
|
| 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] |
|
|
| @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) |
| boxes = np.asarray(boxes, dtype=np.float32) |
| scores = np.asarray(scores, dtype=np.float32) |
| order = np.argsort(scores)[::-1] |
| keep: list[int] = [] |
| suppressed = np.zeros(N, dtype=bool) |
| for i in range(N): |
| idx = order[i] |
| if suppressed[idx]: |
| continue |
| keep.append(int(idx)) |
| bi = boxes[idx] |
| for k in range(i + 1, N): |
| jdx = order[k] |
| if suppressed[jdx]: |
| continue |
| bj = boxes[jdx] |
| xx1 = max(bi[0], bj[0]) |
| yy1 = max(bi[1], bj[1]) |
| xx2 = min(bi[2], bj[2]) |
| yy2 = min(bi[3], bj[3]) |
| inter = max(0.0, xx2 - xx1) * max(0.0, yy2 - yy1) |
| area_i = (bi[2] - bi[0]) * (bi[3] - bi[1]) |
| area_j = (bj[2] - bj[0]) * (bj[3] - bj[1]) |
| iou = inter / (area_i + area_j - inter + 1e-7) |
| if iou > iou_thresh: |
| suppressed[jdx] = True |
| 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( |
| 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) |
| area_i = max(1e-7, float((bi[2] - bi[0]) * (bi[3] - bi[1]))) |
| union = area_i + areas - inter + 1e-7 |
| iou = inter / union |
| 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( |
| coords: np.ndarray, |
| scores: np.ndarray, |
| keep_indices: np.ndarray, |
| iou_thresh: float, |
| ) -> np.ndarray: |
| n_keep = len(keep_indices) |
| if n_keep == 0: |
| return np.array([], dtype=np.float32) |
| coords = np.asarray(coords, dtype=np.float32) |
| scores = np.asarray(scores, dtype=np.float32) |
| out = np.empty(n_keep, dtype=np.float32) |
| for i in range(n_keep): |
| idx = keep_indices[i] |
| bi = coords[idx] |
| xx1 = np.maximum(bi[0], coords[:, 0]) |
| yy1 = np.maximum(bi[1], coords[:, 1]) |
| xx2 = np.minimum(bi[2], coords[:, 2]) |
| yy2 = np.minimum(bi[3], coords[:, 3]) |
| inter = np.maximum(0.0, xx2 - xx1) * np.maximum(0.0, yy2 - yy1) |
| area_i = (bi[2] - bi[0]) * (bi[3] - bi[1]) |
| areas_j = (coords[:, 2] - coords[:, 0]) * (coords[:, 3] - coords[:, 1]) |
| iou = inter / (area_i + areas_j - inter + 1e-7) |
| in_cluster = iou >= iou_thresh |
| out[i] = float(np.max(scores[in_cluster])) |
| return out |
|
|
| def _decode_raw_dets( |
| self, |
| preds: np.ndarray, |
| ratio: float, |
| pad: tuple[float, float], |
| orig_size: tuple[int, int], |
| ) -> tuple[np.ndarray, np.ndarray, np.ndarray]: |
| """Decode end2end NMS output and return (boxes, scores, cls_ids) |
| in original image coordinates, after conf-threshold + remap + letterbox-reverse + sanity.""" |
| 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 >= 0) & (cls_ids < len(self.cls_remap)) |
| boxes, scores, cls_ids = boxes[valid], scores[valid], cls_ids[valid] |
| cls_ids = self.cls_remap[cls_ids] |
|
|
| keep = scores >= self.conf_thres |
| 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)) |
|
|
| boxes, scores, cls_ids = self._filter_sane_boxes(boxes, scores, cls_ids, orig_size) |
| return boxes, scores, cls_ids |
|
|
| def _forward( |
| self, image: np.ndarray |
| ) -> tuple[np.ndarray, np.ndarray, np.ndarray]: |
| x, ratio, pad, orig_size = self._preprocess(image) |
| out = self.session.run(self.output_names, {self.input_name: x})[0] |
| return self._decode_raw_dets(out, ratio, pad, orig_size) |
|
|
| def _predict_single(self, image: np.ndarray) -> list[BoundingBox]: |
| boxes, scores, cls_ids = self._forward(image) |
| if len(boxes) == 0: |
| return [] |
| return self._build_results(boxes, scores, cls_ids) |
|
|
| def _predict_tta(self, image: np.ndarray) -> list[BoundingBox]: |
| """Hflip TTA: merge primary + flipped via per-class hard-NMS, |
| then cross-class dedup, with consensus-confidence boost.""" |
| ow = image.shape[1] |
| b1, s1, c1 = self._forward(image) |
|
|
| flipped = cv2.flip(image, 1) |
| b2, s2, c2 = self._forward(flipped) |
| if len(b2): |
| x1f = ow - b2[:, 2] |
| x2f = ow - b2[:, 0] |
| b2 = np.stack([x1f, b2[:, 1], x2f, b2[:, 3]], axis=1) |
|
|
| if len(b1) == 0 and len(b2) == 0: |
| return [] |
|
|
| boxes = np.concatenate([b1, b2], axis=0) if len(b2) else b1 |
| scores = np.concatenate([s1, s2], axis=0) if len(b2) else s1 |
| cls_ids = np.concatenate([c1, c2], axis=0) if len(b2) else c1 |
|
|
| keep = self._per_class_hard_nms(boxes, scores, cls_ids, self.iou_thres) |
| if len(keep) == 0: |
| return [] |
| keep = keep[: self.max_det] |
|
|
| |
| boosted = self._max_score_per_cluster(boxes, scores, keep, self.iou_thres) |
|
|
| boxes = boxes[keep] |
| cls_ids = cls_ids[keep] |
| scores = boosted |
|
|
| boxes, scores, cls_ids = self._cross_class_dedup( |
| boxes, scores, cls_ids, self.cross_iou_thresh |
| ) |
| if len(boxes) == 0: |
| return [] |
|
|
| return self._build_results(boxes, scores, cls_ids) |
|
|
| def _build_results( |
| self, 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 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): |
| if image is None or not isinstance(image, np.ndarray) or image.ndim != 3: |
| results.append( |
| TVFrameResult( |
| frame_id=offset + frame_number_in_batch, |
| boxes=[], |
| keypoints=[(0, 0) for _ in range(max(0, int(n_keypoints)))], |
| ) |
| ) |
| continue |
| if image.dtype != np.uint8: |
| image = image.astype(np.uint8) |
| 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 |
|
|
