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f1c3ff2 75b7ba3 f1c3ff2 75b7ba3 f1c3ff2 75b7ba3 f1c3ff2 75b7ba3 f1c3ff2 75b7ba3 f1c3ff2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 | """Normalize Audi A2D2 samples into UnifiedSequence."""
from __future__ import annotations
import json
from pathlib import Path
from typing import Any, Dict, List
import numpy as np
from sensorflow.adapters.base import VendorAdapter
from sensorflow.adapters.stub_images import stub_camera_path, write_stub_camera_png
from sensorflow.schemas.taxonomy_axes import assign_taxonomy_axes
from sensorflow.schemas.unified_frame import (
CameraView,
EgoPose,
FusedFrame,
GroundTruthObject,
LidarData,
UnifiedSequence,
)
# Audi A2D2 class names used in the public dataset catalog.
A2D2_CLASSES = (
"car",
"pedestrian",
"truck",
"bicycle",
"traffic_sign",
"signal",
)
def _default_a2d2_frames() -> List[Dict[str, Any]]:
"""Built-in shard when A2D2 HDF5/PNG sources are unavailable."""
return [
{
"frame_id": "a2d2_0000",
"timestamp_us": 0,
"lidar_points": 2200,
"ego_speed_kmh": 52.0,
"image_path": "https://images.unsplash.com/photo-1449965408869-eaa3f722e40d?w=800",
"labels": [
{"instance_id": "a2d2_1", "class_name": "car",
"center_x": 12.0, "center_y": 1.5, "center_z": 0.6,
"length": 4.4, "width": 1.8, "height": 1.5, "heading": 0.05},
{"instance_id": "a2d2_2", "class_name": "pedestrian",
"center_x": 8.0, "center_y": -2.0, "center_z": 0.9,
"length": 0.6, "width": 0.5, "height": 1.7, "heading": -0.3},
{"instance_id": "a2d2_3", "class_name": "traffic_sign",
"center_x": 18.0, "center_y": 3.0, "center_z": 1.8,
"length": 0.3, "width": 0.8, "height": 1.2, "heading": 0.0},
],
},
{
"frame_id": "a2d2_0001",
"timestamp_us": 100_000,
"lidar_points": 2150,
"ego_speed_kmh": 51.2,
"image_path": "https://images.unsplash.com/photo-1492144534655-ae79c964c9d7?w=800",
"labels": [
{"instance_id": "a2d2_1", "class_name": "car",
"center_x": 11.5, "center_y": 1.4, "center_z": 0.6,
"length": 4.4, "width": 1.8, "height": 1.5, "heading": 0.04},
{"instance_id": "a2d2_2", "class_name": "pedestrian",
"center_x": 7.6, "center_y": -1.8, "center_z": 0.9,
"length": 0.6, "width": 0.5, "height": 1.7, "heading": -0.2},
{"instance_id": "a2d2_4", "class_name": "bicycle",
"center_x": 14.0, "center_y": -3.0, "center_z": 0.7,
"length": 1.6, "width": 0.5, "height": 1.2, "heading": 1.1},
],
},
{
"frame_id": "a2d2_0002",
"timestamp_us": 200_000,
"lidar_points": 2300,
"ego_speed_kmh": 49.8,
"image_path": "https://images.unsplash.com/photo-1503376780353-7e6692767b70?w=800",
"labels": [
{"instance_id": "a2d2_1", "class_name": "car",
"center_x": 11.0, "center_y": 1.3, "center_z": 0.6,
"length": 4.4, "width": 1.8, "height": 1.5, "heading": 0.02},
{"instance_id": "a2d2_5", "class_name": "truck",
"center_x": 22.0, "center_y": 0.5, "center_z": 1.2,
"length": 8.5, "width": 2.5, "height": 3.2, "heading": 0.0},
{"instance_id": "a2d2_6", "class_name": "signal",
"center_x": 16.0, "center_y": 2.5, "center_z": 3.0,
"length": 0.4, "width": 0.4, "height": 1.0, "heading": 0.0},
],
},
]
def _write_synthetic_lidar(path: Path, num_points: int, labels: List[Dict]) -> None:
path.parent.mkdir(parents=True, exist_ok=True)
points = []
for lbl in labels:
cx, cy, cz = lbl["center_x"], lbl["center_y"], lbl["center_z"]
for _ in range(max(20, num_points // max(len(labels), 1) // 10)):
points.append([
cx + np.random.randn() * 0.2,
cy + np.random.randn() * 0.2,
cz + np.random.randn() * 0.1,
])
if not points:
points = np.random.randn(num_points, 3).astype(np.float32)
else:
points = np.array(points, dtype=np.float32)
points.tofile(path)
DEFAULT_A2D2_SAMPLES = {
"ingolstadt": {
"dataset": "audi/a2d2",
"frames": _default_a2d2_frames(),
},
}
class A2D2Adapter(VendorAdapter):
"""Audi A2D2 adapter — stub frames when HDF5/PNG lake is unavailable."""
def load(self, source: Dict[str, Any], sequence_id: str) -> UnifiedSequence:
from sensorflow.adapters.vendor_media import (
frames_from_media_root,
media_available,
resolve_vendor_root,
)
source = source or {}
root = resolve_vendor_root(source, "source_path", "root", "path")
if root is not None and media_available(root):
frames = frames_from_media_root(
sequence_id=sequence_id,
vendor="a2d2",
root=root,
max_frames=source.get("max_frames"),
)
return UnifiedSequence(
sequence_id=sequence_id,
vendor="a2d2",
frames=frames,
taxonomy_manifest={
"source_dataset": source.get("dataset", "audi/a2d2"),
"source_path": str(root.resolve()),
"demo_stub": False,
"total_frames": len(frames),
},
)
shard_dir = Path("runs/pipeline/a2d2_shards")
shard_path = shard_dir / f"{source.get('shard_id', 'default')}.json"
if shard_path.exists():
with open(shard_path) as f:
frames_data = json.load(f)
elif source.get("frames"):
frames_data = source["frames"]
else:
frames_data = DEFAULT_A2D2_SAMPLES["ingolstadt"]["frames"]
shard_dir.mkdir(parents=True, exist_ok=True)
with open(shard_path, "w") as f:
json.dump(frames_data, f, indent=2)
frames: List[FusedFrame] = []
lidar_dir = Path("runs/pipeline") / sequence_id / "lidar"
for shard in frames_data:
frame_id = shard["frame_id"]
lidar_path = lidar_dir / f"{frame_id}_lidar.bin"
labels = shard.get("labels", [])
num_points = shard.get("lidar_points", 2048)
_write_synthetic_lidar(lidar_path, num_points, labels)
gt = []
speed = shard.get("ego_speed_kmh", 50.0)
for lbl in labels:
class_name = lbl.get("class_name", "car")
if class_name not in A2D2_CLASSES:
class_name = "car"
axes = assign_taxonomy_axes(class_name, speed_kmh=speed)
gt.append(GroundTruthObject(
instance_id=lbl["instance_id"],
class_name=class_name,
confidence=1.0,
bbox_3d=[
lbl["center_x"], lbl["center_y"], lbl["center_z"],
lbl["length"], lbl["width"], lbl["height"], lbl["heading"],
],
taxonomy_axes=axes,
))
image_path = shard.get("image_path") or ""
if not image_path or str(image_path).startswith("http"):
cam_file = stub_camera_path(sequence_id, frame_id, "front")
write_stub_camera_png(
cam_file,
seed=sum(ord(c) for c in frame_id) % 991,
label=f"a2d2:{frame_id}",
)
image_path = str(cam_file)
frames.append(FusedFrame(
frame_id=frame_id,
timestamp_us=shard.get("timestamp_us", 0),
lidar=LidarData(path=str(lidar_path), num_points=num_points),
cameras={"front": CameraView(image_path=image_path)},
ego_pose=EgoPose(speed_kmh=speed),
ground_truth=gt,
))
demo_stub = bool(source.get("demo_stub", True)) if not source.get("frames") else bool(
source.get("demo_stub", False)
)
if shard_path.exists() and not source.get("frames") and len(frames_data) <= 3:
demo_stub = True
return UnifiedSequence(
sequence_id=sequence_id,
vendor="a2d2",
frames=frames,
taxonomy_manifest={
"source_dataset": source.get("dataset", "audi/a2d2"),
"shard": str(shard_path),
"demo_stub": demo_stub,
"stub_note": (
"Built-in Audi A2D2 sample (~3 frames), not a full AV video lake. "
"Set a2d2_root to a real A2D2 PNG/HDF5 export folder when available."
) if demo_stub else None,
"total_frames": len(frames),
},
)
@classmethod
def load_from_file(cls, path: Path, sequence_id: str) -> UnifiedSequence:
with open(path) as f:
return cls().load(json.load(f), sequence_id)
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