| import os |
| import math |
| from pathlib import Path |
| 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] |
| polygons: list | None = None |
| keypoints: list[tuple[int, int]] |
|
|
| class Miner: |
|
|
| def __init__(self, path_hf_repo: Path) -> None: |
| cv2.setNumThreads(1) |
| model_path = self._resolve_model_path(path_hf_repo) |
| self.class_names = ['car', 'bus', 'truck', 'motorcycle'] |
| self._model_class_order = ['car', 'bus', 'truck', 'motorcycle'] |
| print('ORT version:', ort.__version__) |
| print('ORT available providers:', ort.get_available_providers()) |
| self._model_path = str(model_path) |
| self.session = self._open_session(self._model_path) |
| print('ORT session providers:', self.session.get_providers()) |
| self.sink_name = 'tuktuk' |
| self.sink_margin = 0.05 |
| self.sink_index = None |
| self.vehicle_cols = None |
| model_class_order = self._read_model_class_order() |
| if model_class_order is None: |
| model_class_order = list(self._model_class_order) |
| print(f'cls order: no usable ONNX metadata, FALLBACK {model_class_order}') |
| else: |
| print(f'cls order: from ONNX metadata {model_class_order}') |
| if self.sink_name in model_class_order: |
| self.sink_index = model_class_order.index(self.sink_name) |
| self.vehicle_cols = np.array([model_class_order.index(n) for n in self.class_names], dtype=np.int32) |
| print(f'cls order: drop {self.sink_name} col {self.sink_index} when score + {self.sink_margin} >= vehicle') |
| self.cls_remap = np.full(len(model_class_order), -1, dtype=np.int32) |
| for i, name in enumerate(model_class_order): |
| if name in self.class_names: |
| self.cls_remap[i] = self.class_names.index(name) |
| 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 = [o.name for o in self.session.get_outputs()] |
| self.input_shape = self.session.get_inputs()[0].shape |
| input_type = self.session.get_inputs()[0].type |
| self.np_dtype = np.float16 if 'float16' in input_type else np.float32 |
| print(f'ONNX input dtype: {input_type} -> numpy {self.np_dtype}') |
| self.input_height = self._safe_dim(self.input_shape[2], default=704) |
| self.input_width = self._safe_dim(self.input_shape[3], default=704) |
| self._iou_thres_array = np.array([0.37, 0.4, 0.38, 0.35], dtype=np.float32) |
| self.cross_iou_thresh = 0.7 |
| self.max_det = 300 |
| self.pre_nms_topk = 1000 |
| self.use_tta = False |
| self.use_cluster_boost = True |
| self.use_soft_nms = False |
| self.soft_nms_sigma = 0.5 |
| self._conf_thres_array = np.array([0.35, 0.2, 0.5, 0.45], dtype=np.float32) |
| self._extra_conf_thres_array = np.array([0.25, 0.2, 0.2, 0.2], dtype=np.float32) |
| self._bonus_array = np.array([0, 0, 0.15, 0], dtype=np.float32) |
| self._min_box_area_array = np.array([64, 12, 12, 12], dtype=np.float32) |
| self.min_side = 3 |
| self.max_aspect_ratio = 20 |
| self.max_area_frac = 0.95 |
| self.square_pad_frac = 0 |
| self.dual_label_pair = ('bus', 'truck') |
| self.dual_label_below = 0 |
| self._avg_iou = float(np.mean(self._iou_thres_array)) |
| print(f'ONNX model loaded from: {model_path}') |
| print(f'ONNX input: name={self.input_name}, shape={self.input_shape}') |
| self._warmup() |
|
|
| def _session_options(self) -> ort.SessionOptions: |
| options = ort.SessionOptions() |
| options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL |
| options.intra_op_num_threads = 2 |
| options.inter_op_num_threads = 1 |
| options.execution_mode = ort.ExecutionMode.ORT_SEQUENTIAL |
| return options |
|
|
| def _open_session(self, model_path: str) -> ort.InferenceSession: |
| |
| |
| |
| use_cuda = os.environ.get('MINER_USE_CUDA', '') == '1' and os.environ.get('MINER_FORCE_CPU', '') != '1' |
| if use_cuda and 'CUDAExecutionProvider' in ort.get_available_providers(): |
| try: |
| session = ort.InferenceSession(model_path, sess_options=self._session_options(), providers=['CUDAExecutionProvider', 'CPUExecutionProvider']) |
| self._probe_session(session) |
| print('ORT session: CUDA probe ok') |
| return session |
| except Exception as e: |
| print(f'CUDA session unusable, using CPU: {e}') |
| session = ort.InferenceSession(model_path, sess_options=self._session_options(), providers=['CPUExecutionProvider']) |
| self._probe_session(session) |
| print('ORT session: CPU probe ok') |
| return session |
|
|
| @staticmethod |
| def _probe_session(session: ort.InferenceSession) -> None: |
| inp = session.get_inputs()[0] |
| dims = list(inp.shape) |
| if len(dims) != 4: |
| shape = [1, 3, 704, 704] |
| else: |
| shape = [] |
| for axis, dim in enumerate(dims): |
| if isinstance(dim, int) and dim > 0: |
| shape.append(dim) |
| elif axis == 1: |
| shape.append(3) |
| elif axis == 0: |
| shape.append(1) |
| else: |
| shape.append(704) |
| dtype = np.float16 if 'float16' in inp.type else np.float32 |
| session.run(None, {inp.name: np.zeros(shape, dtype=dtype)}) |
|
|
| def _run(self, tensor: np.ndarray) -> list: |
| try: |
| return self.session.run(self.output_names, {self.input_name: tensor}) |
| except Exception as e: |
| if self.session.get_providers() == ['CPUExecutionProvider']: |
| raise |
| print(f'inference on {self.session.get_providers()} failed, switching to CPU: {e}') |
| self.session = self._open_session_cpu() |
| return self.session.run(self.output_names, {self.input_name: tensor}) |
|
|
| def _open_session_cpu(self) -> ort.InferenceSession: |
| session = ort.InferenceSession(self._model_path, sess_options=self._session_options(), providers=['CPUExecutionProvider']) |
| self._probe_session(session) |
| return session |
|
|
| def _warmup(self, iters: int=1) -> None: |
| dummy = np.zeros((1024, 1024, 3), dtype=np.uint8) |
| for _ in range(max(1, iters)): |
| self.predict_batch(batch_images=[dummy], offset=0, n_keypoints=0) |
| print(f'warmup: {max(1, iters)} dummy predict_batch call(s) done') |
|
|
| def __repr__(self) -> str: |
| return f'PetrolMiner(classes={len(self.class_names)}, providers={self.session.get_providers()})' |
|
|
| @staticmethod |
| def _safe_dim(value, default: int) -> int: |
| return value if isinstance(value, int) and value > 0 else default |
|
|
| @staticmethod |
| def _resolve_model_path(repo: Path) -> Path: |
| for name in ('weights.onnx', 'weights_int8.onnx'): |
| p = repo / name |
| if p.exists(): |
| if name != 'weights.onnx': |
| print(f'model: weights.onnx not found, using {name}') |
| return p |
| candidates = sorted(repo.glob('*.onnx')) |
| if candidates: |
| print(f'model: using {candidates[0].name}') |
| return candidates[0] |
| return repo / 'weights.onnx' |
|
|
| def _read_model_class_order(self) -> list[str] | None: |
| try: |
| import ast |
| meta = self.session.get_modelmeta().custom_metadata_map |
| names = ast.literal_eval(meta['names']) |
| order = [str(names[i]) for i in sorted(names)] if isinstance(names, dict) else [str(n) for n in names] |
| except Exception as e: |
| print(f'cls order: could not read ONNX names metadata ({e})') |
| return None |
| lower = [n.lower() for n in order] |
| vehicles = [n for n in lower if n != self.sink_name] |
| if sorted(vehicles) != sorted(self.class_names): |
| print(f'cls order: ONNX names {order} != expected {self.class_names}; ignoring') |
| return None |
| return lower |
|
|
| 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) |
| rw, rh = (int(round(w * ratio)), int(round(h * ratio))) |
| if (rw, rh) != (w, h): |
| interp = cv2.INTER_CUBIC if ratio > 1.0 else cv2.INTER_LINEAR |
| image = cv2.resize(image, (rw, rh), interpolation=interp) |
| dw = (new_w - rw) / 2.0 |
| dh = (new_h - rh) / 2.0 |
| left, right = (int(round(dw - 0.1)), int(round(dw + 0.1))) |
| top, bottom = (int(round(dh - 0.1)), int(round(dh + 0.1))) |
| padded = cv2.copyMakeBorder(image, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) |
| if padded.shape[0] != new_h or padded.shape[1] != new_w: |
| padded = cv2.resize(padded, (new_w, new_h), interpolation=cv2.INTER_LINEAR) |
| return (padded, ratio, (dw, dh)) |
|
|
| def _preprocess(self, image: ndarray) -> tuple[np.ndarray, dict]: |
| orig_h, orig_w = image.shape[:2] |
| extra_left = extra_right = 0 |
| if self.square_pad_frac > 0.0 and orig_w == orig_h: |
| target_w = int(orig_w * (1.0 + self.square_pad_frac)) |
| if target_w > orig_w: |
| total = target_w - orig_w |
| extra_left = total // 2 |
| extra_right = total - extra_left |
| image = cv2.copyMakeBorder(image, 0, 0, extra_left, extra_right, cv2.BORDER_CONSTANT, value=(114, 114, 114)) |
| 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=self.np_dtype) |
| return (img, {'ratio': ratio, 'pad': pad, 'orig_size': (orig_w, orig_h), 'extra_left': extra_left, 'extra_right': extra_right}) |
|
|
| @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) |
| x1, y1, x2, y2 = (boxes[:, 0], boxes[:, 1], 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-07) |
| order = rest[iou <= iou_thresh] |
| return np.array(keep, dtype=np.intp) |
|
|
| @staticmethod |
| def _soft_nms(boxes: np.ndarray, scores: np.ndarray, sigma: float) -> tuple[np.ndarray, np.ndarray]: |
| n = len(boxes) |
| if n == 0: |
| return (np.array([], dtype=np.intp), np.array([], dtype=np.float32)) |
| b = boxes.astype(np.float32, copy=True) |
| sc = scores.astype(np.float32, copy=True) |
| order = np.arange(n) |
| for i in range(n): |
| mp = i + int(np.argmax(sc[i:])) |
| b[[i, mp]] = b[[mp, i]] |
| sc[[i, mp]] = sc[[mp, i]] |
| order[[i, mp]] = order[[mp, i]] |
| if i + 1 >= n: |
| break |
| xx1 = np.maximum(b[i, 0], b[i + 1:, 0]) |
| yy1 = np.maximum(b[i, 1], b[i + 1:, 1]) |
| xx2 = np.minimum(b[i, 2], b[i + 1:, 2]) |
| yy2 = np.minimum(b[i, 3], b[i + 1:, 3]) |
| inter = np.maximum(0.0, xx2 - xx1) * np.maximum(0.0, yy2 - yy1) |
| ai = max(0.0, float((b[i, 2] - b[i, 0]) * (b[i, 3] - b[i, 1]))) |
| aj = np.maximum(0.0, b[i + 1:, 2] - b[i + 1:, 0]) * np.maximum(0.0, b[i + 1:, 3] - b[i + 1:, 1]) |
| iou = inter / (ai + aj - inter + 1e-07) |
| sc[i + 1:] *= np.exp(-iou ** 2 / sigma) |
| return (order, sc) |
|
|
| def _per_class_soft_nms(self, boxes: np.ndarray, scores: np.ndarray, cls_ids: np.ndarray) -> tuple[np.ndarray, np.ndarray]: |
| keep_idx: list[int] = [] |
| keep_sc: list[float] = [] |
| for c in np.unique(cls_ids): |
| idx = np.where(cls_ids == c)[0] |
| order, dec = self._soft_nms(boxes[idx], scores[idx], self.soft_nms_sigma) |
| thr = float(self._conf_thres_array[c]) |
| for k, s_dec in zip(order, dec): |
| if s_dec >= thr: |
| keep_idx.append(int(idx[k])) |
| keep_sc.append(float(s_dec)) |
| return (np.array(keep_idx, dtype=np.intp), np.array(keep_sc, dtype=np.float32)) |
|
|
| def _per_class_hard_nms(self, boxes: np.ndarray, scores: np.ndarray, cls_ids: np.ndarray) -> np.ndarray: |
| if len(boxes) == 0: |
| return np.array([], dtype=np.intp) |
| all_keep: list[int] = [] |
| for c in np.unique(cls_ids): |
| idx = np.where(cls_ids == c)[0] |
| cls_iou = float(self._iou_thres_array[c]) |
| keep = self._hard_nms(boxes[idx], scores[idx], cls_iou) |
| all_keep.extend(idx[keep].tolist()) |
| all_keep.sort() |
| return np.array(all_keep, dtype=np.intp) |
|
|
| @staticmethod |
| def _max_score_per_cluster(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) |
| 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-07) |
| 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, extra_left: int) -> np.ndarray: |
| if len(scores) == 0: |
| return np.zeros(0, dtype=bool) |
| thr_arr = self._extra_conf_thres_array if extra_left > 0 else self._conf_thres_array |
| keep = scores >= thr_arr[cls_ids] |
| 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] >= float(self._conf_thres_array[c]) - b: |
| keep[top] = True |
| return keep |
|
|
| 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.maximum(bw / np.maximum(bh, 1e-06), bh / np.maximum(bw, 1e-06)) |
| keep = (bw >= self.min_side) & (bh >= self.min_side) & (area >= self._min_box_area_array[cls_ids]) & (area <= self.max_area_frac * image_area) & (ar <= self.max_aspect_ratio) |
| return (boxes[keep], scores[keep], cls_ids[keep]) |
|
|
| 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-07, float((bi[2] - bi[0]) * (bi[3] - bi[1]))) |
| iou = inter / (a_i + areas - inter + 1e-07) |
| 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 _apply_dual_label(self, boxes: np.ndarray, scores: np.ndarray, cls_ids: np.ndarray) -> tuple[np.ndarray, np.ndarray, np.ndarray]: |
| if self.dual_label_below <= 0.0 or len(boxes) == 0: |
| return (boxes, scores, cls_ids) |
| a = self.class_names.index(self.dual_label_pair[0]) |
| b = self.class_names.index(self.dual_label_pair[1]) |
| sib = np.full(len(self.class_names), -1, dtype=np.int32) |
| sib[a], sib[b] = (b, a) |
| mask = (sib[cls_ids] >= 0) & (scores < self.dual_label_below) |
| if not mask.any(): |
| return (boxes, scores, cls_ids) |
| boxes = np.concatenate([boxes, boxes[mask]], axis=0) |
| scores = np.concatenate([scores, scores[mask]], axis=0) |
| cls_ids = np.concatenate([cls_ids, sib[cls_ids[mask]]], axis=0) |
| return (boxes, scores, cls_ids) |
|
|
| 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(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: |
| if self.use_soft_nms: |
| keep, scores = self._per_class_soft_nms(boxes, scores, cls_ids) |
| boxes, cls_ids = (boxes[keep], cls_ids[keep]) |
| else: |
| 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 _unmap_and_finish(self, boxes: np.ndarray, scores: np.ndarray, cls_ids: np.ndarray, meta: dict) -> tuple[np.ndarray, np.ndarray, np.ndarray]: |
| pad_w, pad_h = meta['pad'] |
| ratio = meta['ratio'] |
| orig_w, orig_h = meta['orig_size'] |
| extra_left, extra_right = (meta['extra_left'], meta['extra_right']) |
| boxes[:, [0, 2]] -= pad_w |
| boxes[:, [1, 3]] -= pad_h |
| boxes /= ratio |
| if extra_left: |
| boxes[:, [0, 2]] -= extra_left |
| if extra_left or extra_right: |
| cx = (boxes[:, 0] + boxes[:, 2]) * 0.5 |
| inside = (cx >= 0) & (cx <= orig_w) |
| boxes, scores, cls_ids = (boxes[inside], scores[inside], cls_ids[inside]) |
| if len(boxes) == 0: |
| return (boxes, scores, cls_ids) |
| boxes = self._clip_boxes(boxes, (orig_w, orig_h)) |
| return self._per_view_pipeline(boxes, scores, cls_ids, (orig_w, orig_h)) |
|
|
| def _decode_final_dets(self, preds: np.ndarray, meta: dict) -> tuple[np.ndarray, np.ndarray, np.ndarray]: |
| 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: |
| raise ValueError(f'Unexpected final-det output shape: {preds.shape}') |
| boxes = preds[:, :4].astype(np.float32) |
| scores = preds[:, 4].astype(np.float32) |
| raw_cls = preds[:, 5].astype(np.int32) |
| raw_cls = np.clip(raw_cls, 0, len(self.cls_remap) - 1) |
| cls_ids = self.cls_remap[raw_cls] |
| known = cls_ids >= 0 |
| boxes, scores, cls_ids = (boxes[known], scores[known], cls_ids[known]) |
| keep = self._conf_filter_mask(scores, cls_ids, meta['extra_left']) |
| boxes, scores, cls_ids = (boxes[keep], scores[keep], cls_ids[keep]) |
| if len(boxes) == 0: |
| return empty |
| return self._unmap_and_finish(boxes, scores, cls_ids, meta) |
|
|
| def _decode_raw_yolo(self, preds: np.ndarray, meta: dict) -> tuple[np.ndarray, np.ndarray, np.ndarray]: |
| empty = (np.empty((0, 4), np.float32), np.empty(0, np.float32), np.empty(0, np.int32)) |
| if preds.ndim != 3 or preds.shape[0] != 1: |
| raise ValueError(f'Unexpected raw 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 normalized raw 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) |
| elif self.vehicle_cols is not None and cls_part.shape[1] > int(self.vehicle_cols.max()): |
| veh = cls_part[:, self.vehicle_cols] |
| cls_ids = np.argmax(veh, axis=1).astype(np.int32) |
| scores = veh[np.arange(len(veh)), cls_ids] |
| if self.sink_index is not None and self.sink_index < cls_part.shape[1]: |
| keep_veh = cls_part[:, self.sink_index] + self.sink_margin < scores |
| boxes_xywh, scores, cls_ids = (boxes_xywh[keep_veh], scores[keep_veh], cls_ids[keep_veh]) |
| else: |
| cls_ids = np.argmax(cls_part, axis=1).astype(np.int32) |
| scores = cls_part[np.arange(len(cls_part)), cls_ids] |
| cls_ids = self.cls_remap[np.clip(cls_ids, 0, len(self.cls_remap) - 1)] |
| keep_known = cls_ids >= 0 |
| boxes_xywh, scores, cls_ids = (boxes_xywh[keep_known], scores[keep_known], cls_ids[keep_known]) |
| keep = self._conf_filter_mask(scores, cls_ids, meta['extra_left']) |
| boxes_xywh, scores, cls_ids = (boxes_xywh[keep], scores[keep], cls_ids[keep]) |
| if len(boxes_xywh) == 0: |
| return empty |
| boxes = self._xywh_to_xyxy(boxes_xywh) |
| return self._unmap_and_finish(boxes, scores, cls_ids, meta) |
|
|
| def _decode(self, output: np.ndarray, meta: dict) -> tuple[np.ndarray, np.ndarray, np.ndarray]: |
| if output.ndim == 2 and output.shape[1] >= 6: |
| return self._decode_final_dets(output, meta) |
| if output.ndim == 3 and output.shape[0] == 1 and (output.shape[2] == 6): |
| return self._decode_final_dets(output, meta) |
| return self._decode_raw_yolo(output, meta) |
|
|
| def _predict_single_arrays(self, image: np.ndarray) -> tuple[np.ndarray, np.ndarray, np.ndarray, dict]: |
| if not isinstance(image, np.ndarray) or image.ndim != 3 or image.shape[2] != 3: |
| raise ValueError(f"Expected HWC BGR image, got {type(image)} {getattr(image, 'shape', None)}") |
| if image.dtype != np.uint8: |
| image = image.astype(np.uint8) |
| inp, meta = self._preprocess(image) |
| outputs = self._run(inp) |
| boxes, scores, cls_ids = self._decode(outputs[0], meta) |
| return (boxes, scores, cls_ids, meta) |
|
|
| @staticmethod |
| def _to_boxes(boxes: np.ndarray, scores: np.ndarray, cls_ids: np.ndarray, orig_size: tuple[int, int]) -> list[BoundingBox]: |
| results: list[BoundingBox] = [] |
| orig_w, orig_h = orig_size |
| 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=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 _infer_single(self, image: np.ndarray) -> list[BoundingBox]: |
| boxes, scores, cls_ids, meta = self._predict_single_arrays(image) |
| 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) |
| boxes, scores, cls_ids = self._apply_dual_label(boxes, scores, cls_ids) |
| return self._to_boxes(boxes, scores, cls_ids, meta['orig_size']) |
|
|
| def _infer_tta(self, image: np.ndarray) -> list[BoundingBox]: |
| b0, s0, c0, meta = self._predict_single_arrays(image) |
| w = image.shape[1] |
| bf, sf, cf, _ = self._predict_single_arrays(cv2.flip(image, 1)) |
| if len(bf): |
| bf = bf.copy() |
| bf[:, [0, 2]] = w - bf[:, [2, 0]] |
| coords = np.concatenate([b0, bf], axis=0) if len(bf) else b0 |
| scores = np.concatenate([s0, sf], axis=0) if len(sf) else s0 |
| cls_ids = np.concatenate([c0, cf], axis=0) if len(cf) else c0 |
| if len(coords) == 0: |
| return [] |
| keep = self._per_class_hard_nms(coords, scores, cls_ids) |
| if len(keep) == 0: |
| return [] |
| if len(keep) > self.max_det: |
| keep = keep[np.argsort(-scores[keep])[:self.max_det]] |
| boosted = self._max_score_per_cluster(coords[keep], cls_ids[keep], coords, scores, cls_ids, self._avg_iou) |
| kb, kc = (coords[keep], cls_ids[keep]) |
| if len(kb) > 1: |
| kb, boosted, kc = self._cross_class_dedup_op(kb, boosted, kc, self.cross_iou_thresh) |
| kb, boosted, kc = self._apply_dual_label(kb, boosted, kc) |
| return self._to_boxes(kb, boosted, kc, meta['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): |
| boxes = self._infer_tta(image) if self.use_tta else self._infer_single(image) |
| keypoints = [(0, 0) for _ in range(max(0, int(n_keypoints)))] |
| results.append(TVFrameResult(frame_id=offset + idx, boxes=boxes, polygons=[], keypoints=keypoints)) |
| return results |
