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"""C08 outputs: the result classes of the 13 bundles and their ``POST /predict`` JSON (BUNDLE_CONVENTIONS.md 7.4).
Every class has ``to_dicts()`` (one plain dict per detection / pose / class) and ``to_dict(output_format)`` (the
whole response body: ``model``, ``frame_id``, the payload, ``meta``, ``timing_ms``). The Python API returns these
objects and the server returns ``to_dict(...)`` of the same object, so both agree bit for bit.
================== ================================= ==================================================
class families payload
================== ================================= ==================================================
``Detections3D`` CenterPoint, TransFusion, BEVFusion, ``detections`` [label, label_id, score, center, size,
PointPainting, StreamPETR, BEVDet, yaw, velocity], ``num_detections``; rows sorted by
BEVFormer, PTv3-det descending score at construction
``Detections2D`` YOLOX ``detections`` [label, label_id, score, box_xyxy] +
``extras`` (e.g. ``{"semseg": Mask2D}``)
``Segmentation3D`` FRNet, PTv3-seg ``labels`` (npz uint8/uint16 [N]), ``scores``,
``class_names``, ``class_counts``
``Mask2D`` SceneSeg (YOLOX semseg extra) ``mask`` (png or npz), ``class_names``, ``class_counts``
``Trajectory`` Diffusion Planner ``trajectory`` [[x, y, yaw, ...] x T], ``columns``,
``turn_indicator``, ``predicted_agents``
================== ================================= ==================================================
"""
from __future__ import annotations
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Sequence
import numpy as np
from .io import encode_array, encode_png, to_jsonable
__all__ = ["Detections3D", "Detections2D", "Segmentation3D", "Mask2D", "Trajectory", "label_name"]
def label_name(labels: Sequence[str], label_id: int) -> str:
"""``labels[label_id]``, or the id as text when it is out of range."""
return labels[label_id] if 0 <= label_id < len(labels) else str(label_id)
def _envelope(model: str, frame_id: str, meta: dict, timing_ms: dict) -> Dict[str, Any]:
return {"model": model, "frame_id": frame_id, "meta": to_jsonable(meta),
"timing_ms": {k: round(float(v), 3) for k, v in timing_ms.items()}}
def _sorted_by_score(scores: np.ndarray) -> np.ndarray:
return np.argsort(-scores, kind="stable")
@dataclass
class Detections3D:
"""3D boxes in ``frame_id`` (Autoware ``base_link``: x forward, y left, z up; metres, radians).
``boxes`` float32 ``[N, 7]`` = x, y, z (box centre), length, width, height, yaw; ``scores`` ``[N]``;
``label_ids`` int32 ``[N]`` (index into ``labels``); ``velocities`` ``[N, 2]`` (vx, vy) m/s or None.
Rows are sorted by descending score (stable) at construction."""
boxes: np.ndarray
scores: np.ndarray
label_ids: np.ndarray
velocities: Optional[np.ndarray] = None
labels: Sequence[str] = ()
model: str = ""
frame_id: str = "base_link"
timing_ms: dict = field(default_factory=dict)
meta: dict = field(default_factory=dict)
def __post_init__(self) -> None:
self.boxes = np.asarray(self.boxes, np.float32).reshape(-1, 7)
self.scores = np.asarray(self.scores, np.float32).reshape(-1)
self.label_ids = np.asarray(self.label_ids, np.int32).reshape(-1)
n = len(self.scores)
if len(self.boxes) != n or len(self.label_ids) != n:
raise ValueError(f"boxes {self.boxes.shape}, scores {self.scores.shape}, labels {self.label_ids.shape}")
if self.velocities is not None:
self.velocities = np.asarray(self.velocities, np.float32).reshape(n, 2)
order = _sorted_by_score(self.scores)
self.boxes, self.scores, self.label_ids = self.boxes[order], self.scores[order], self.label_ids[order]
if self.velocities is not None:
self.velocities = self.velocities[order]
def __len__(self) -> int:
return int(self.scores.shape[0])
@property
def label_names(self) -> List[str]:
return [label_name(self.labels, i) for i in self.label_ids.tolist()]
def to_dicts(self) -> List[Dict[str, Any]]:
out = []
for i, name in enumerate(self.label_names):
x, y, z, length, width, height, yaw = (float(v) for v in self.boxes[i])
d = {"label": name, "label_id": int(self.label_ids[i]), "score": round(float(self.scores[i]), 4),
"center": [round(x, 3), round(y, 3), round(z, 3)],
"size": [round(length, 3), round(width, 3), round(height, 3)], "yaw": round(yaw, 4)}
if self.velocities is not None:
d["velocity"] = [round(float(v), 3) for v in self.velocities[i]]
out.append(d)
return out
def to_dict(self, output_format: str = "json") -> Dict[str, Any]:
body = _envelope(self.model, self.frame_id, self.meta, self.timing_ms)
body.update(num_detections=len(self), detections=self.to_dicts())
if output_format == "npz": # lossless arrays for programmatic clients
body["arrays"] = {"boxes": encode_array(self.boxes, key="boxes"),
"scores": encode_array(self.scores, key="scores"),
"label_ids": encode_array(self.label_ids, key="label_ids")}
if self.velocities is not None:
body["arrays"]["velocities"] = encode_array(self.velocities, key="velocities")
return body
@dataclass
class Mask2D:
"""A per-pixel class map (H, W) (uint8 for <= 256 classes) in the source image's pixel grid."""
mask: np.ndarray
class_names: Sequence[str] = ()
model: str = ""
frame_id: str = "camera"
encoding: str = "png"
timing_ms: dict = field(default_factory=dict)
meta: dict = field(default_factory=dict)
def __post_init__(self) -> None:
self.mask = np.asarray(self.mask)
if self.mask.ndim != 2:
raise ValueError(f"mask must be (H, W), got {self.mask.shape}")
if self.encoding not in ("png", "npz"):
raise ValueError("encoding must be 'png' or 'npz'")
def class_counts(self) -> Dict[str, int]:
ids, counts = np.unique(self.mask, return_counts=True)
return {label_name(self.class_names, int(i)): int(c) for i, c in zip(ids, counts)}
def to_dicts(self) -> List[Dict[str, Any]]:
ids, counts = np.unique(self.mask, return_counts=True)
return [{"label": label_name(self.class_names, int(i)), "label_id": int(i), "pixels": int(c)}
for i, c in zip(ids, counts)]
def payload(self, output_format: str = "json") -> Dict[str, Any]:
"""The encoded mask alone (also used when the mask is an extra of another output)."""
if self.encoding == "png" and output_format != "npz" and self.mask.dtype == np.uint8:
return encode_png(self.mask, key="mask")
return encode_array(self.mask, key="mask")
def to_dict(self, output_format: str = "json") -> Dict[str, Any]:
body = _envelope(self.model, self.frame_id, self.meta, self.timing_ms)
body.update(mask=self.payload(output_format), class_names=list(self.class_names),
class_counts=self.class_counts())
return body
@dataclass
class Detections2D:
"""2-D boxes in original image pixels: ``boxes_xyxy`` float32 ``[N, 4]``, ``scores``, ``label_ids``.
``extras`` holds companion outputs encoded into the body by name (``{"semseg": Mask2D(...)}``).
Rows are sorted by descending score (stable) at construction."""
boxes_xyxy: np.ndarray
scores: np.ndarray
label_ids: np.ndarray
labels: Sequence[str] = ()
extras: Dict[str, Any] = field(default_factory=dict)
model: str = ""
frame_id: str = "camera"
timing_ms: dict = field(default_factory=dict)
meta: dict = field(default_factory=dict)
def __post_init__(self) -> None:
self.boxes_xyxy = np.asarray(self.boxes_xyxy, np.float32).reshape(-1, 4)
self.scores = np.asarray(self.scores, np.float32).reshape(-1)
self.label_ids = np.asarray(self.label_ids, np.int32).reshape(-1)
if not (len(self.boxes_xyxy) == len(self.scores) == len(self.label_ids)):
raise ValueError("boxes_xyxy, scores and label_ids lengths differ")
order = _sorted_by_score(self.scores)
self.boxes_xyxy, self.scores, self.label_ids = self.boxes_xyxy[order], self.scores[order], self.label_ids[order]
def __len__(self) -> int:
return int(self.scores.shape[0])
def to_dicts(self) -> List[Dict[str, Any]]:
return [{"label": label_name(self.labels, int(self.label_ids[i])), "label_id": int(self.label_ids[i]),
"score": round(float(self.scores[i]), 4),
"box_xyxy": [round(float(v), 2) for v in self.boxes_xyxy[i]]} for i in range(len(self))]
def to_dict(self, output_format: str = "json") -> Dict[str, Any]:
body = _envelope(self.model, self.frame_id, self.meta, self.timing_ms)
body.update(num_detections=len(self), detections=self.to_dicts())
for name, extra in self.extras.items():
if hasattr(extra, "payload"):
body[name] = extra.payload(output_format)
elif isinstance(extra, np.ndarray):
body[name] = encode_array(extra, key=name)
else:
body[name] = to_jsonable(extra)
if output_format == "npz":
body["arrays"] = {"boxes_xyxy": encode_array(self.boxes_xyxy, key="boxes_xyxy"),
"scores": encode_array(self.scores, key="scores"),
"label_ids": encode_array(self.label_ids, key="label_ids")}
return body
@dataclass
class Segmentation3D:
"""Per-point classes, in input point order after NaN removal (stated in SERVING.md). ``label_ids`` ``[N]``;
``scores`` optional ``[N]`` (winning-class probability) or ``[N, C]``."""
label_ids: np.ndarray
class_names: Sequence[str] = ()
scores: Optional[np.ndarray] = None
model: str = ""
frame_id: str = "base_link"
timing_ms: dict = field(default_factory=dict)
meta: dict = field(default_factory=dict)
def __post_init__(self) -> None:
ids = np.asarray(self.label_ids).reshape(-1)
if ids.size and ids.min() < 0:
raise ValueError("label ids must be >= 0")
self.label_ids = ids.astype(np.uint8 if (not ids.size or ids.max() < 256) else np.uint16)
if self.scores is not None:
self.scores = np.asarray(self.scores, np.float32)
if self.scores.shape[0] != ids.shape[0]:
raise ValueError("scores and label_ids lengths differ")
def __len__(self) -> int:
return int(self.label_ids.shape[0])
def class_counts(self) -> Dict[str, int]:
counts = np.bincount(self.label_ids.astype(np.int64), minlength=len(self.class_names))
return {label_name(self.class_names, i): int(c) for i, c in enumerate(counts) if c or i < len(self.class_names)}
def to_dicts(self) -> List[Dict[str, Any]]:
counts = np.bincount(self.label_ids.astype(np.int64), minlength=len(self.class_names))
return [{"label": label_name(self.class_names, i), "label_id": i, "points": int(c)}
for i, c in enumerate(counts)]
def to_dict(self, output_format: str = "json") -> Dict[str, Any]:
body = _envelope(self.model, self.frame_id, self.meta, self.timing_ms)
body.update(num_points=len(self), labels=encode_array(self.label_ids, key="labels"),
class_names=list(self.class_names), class_counts=self.class_counts())
if self.scores is not None:
body["scores"] = encode_array(self.scores, key="scores")
return body
@dataclass
class Trajectory:
"""A planned trajectory ``poses`` ``[T, D]`` whose columns are named by ``columns`` (default x, y, yaw) in
``frame_id``; optional ``turn_indicator`` and ``predicted_agents`` ``[A, T, D']``."""
poses: np.ndarray
columns: Sequence[str] = ("x", "y", "yaw")
turn_indicator: Any = None
predicted_agents: Optional[np.ndarray] = None
model: str = ""
frame_id: str = "base_link"
timing_ms: dict = field(default_factory=dict)
meta: dict = field(default_factory=dict)
def __post_init__(self) -> None:
self.poses = np.asarray(self.poses, np.float32)
if self.poses.ndim != 2 or self.poses.shape[1] != len(self.columns):
raise ValueError(f"poses {self.poses.shape} do not match columns {list(self.columns)}")
if self.predicted_agents is not None:
self.predicted_agents = np.asarray(self.predicted_agents, np.float32)
def __len__(self) -> int:
return int(self.poses.shape[0])
def to_dicts(self) -> List[Dict[str, Any]]:
return [{c: round(float(v), 4) for c, v in zip(self.columns, row)} for row in self.poses]
def to_dict(self, output_format: str = "json") -> Dict[str, Any]:
body = _envelope(self.model, self.frame_id, self.meta, self.timing_ms)
body.update(num_poses=len(self), columns=list(self.columns),
trajectory=[[round(float(v), 4) for v in row] for row in self.poses],
turn_indicator=to_jsonable(self.turn_indicator))
if self.predicted_agents is not None:
body["predicted_agents"] = encode_array(self.predicted_agents, key="predicted_agents")
if output_format == "npz":
body["arrays"] = {"poses": encode_array(self.poses, key="poses")}
return body
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