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729628a 6342806 729628a 17fbf45 729628a 17fbf45 729628a 6342806 729628a | 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 | """Automated 2D/3D perception: SAM masks + LiDAR lifting."""
from __future__ import annotations
import json
import zlib
from pathlib import Path
from typing import Dict, List, Optional, Tuple
import numpy as np
from sensorflow.schemas.taxonomy_axes import assign_taxonomy_axes
from sensorflow.schemas.unified_frame import FusedFrame, Object3D, UnifiedSequence
class PerceptionAutomator:
"""SAM-based 2D masks lifted to 3D bounding box proposals."""
def __init__(
self,
sam_checkpoint: str = "models/sam_vit_b.pth",
device: str = "cpu",
use_sam: bool = True,
):
self.sam_checkpoint = sam_checkpoint
self.device = device
self.use_sam = use_sam
self._sam = None
self._mask_generator = None
def _init_sam(self):
if self._sam is not None:
return
if not self.use_sam:
return
if not Path(self.sam_checkpoint).exists():
raise FileNotFoundError(
f"SAM checkpoint not found: {self.sam_checkpoint}. "
"Download segment-anything ViT-B weights or set use_sam=False."
)
try:
from segment_anything import SamAutomaticMaskGenerator, sam_model_registry
self._sam = sam_model_registry["vit_b"](checkpoint=self.sam_checkpoint)
self._sam.to(self.device)
self._mask_generator = SamAutomaticMaskGenerator(self._sam)
except ImportError as e:
raise FileNotFoundError(
"segment_anything is not installed; pip install segment-anything or set use_sam=False"
) from e
def run_sequence(self, sequence: UnifiedSequence, output_dir: Path) -> Dict[str, List[Object3D]]:
output_dir.mkdir(parents=True, exist_ok=True)
all_proposals: Dict[str, List[Object3D]] = {}
for frame in sequence.frames:
proposals = self.run_frame(frame)
all_proposals[frame.frame_id] = proposals
out_path = output_dir / f"{frame.frame_id}.json"
with open(out_path, "w") as f:
json.dump([p.model_dump() for p in proposals], f, indent=2)
return all_proposals
def run_frame(self, frame: FusedFrame) -> List[Object3D]:
camera_name = "front" if "front" in frame.cameras else next(iter(frame.cameras), None)
if camera_name is None:
return self._proposals_from_gt(frame)
camera = frame.cameras[camera_name]
masks = self._sam_masks(camera.image_path)
if not masks:
masks = self._fallback_masks(frame)
points = self._load_lidar(frame.lidar.path)
proposals = self._lift_masks_to_3d(points, masks, frame)
return self._assign_taxonomy(proposals, frame)
def _sam_masks(self, image_path: str) -> List[Dict]:
self._init_sam()
if not self.use_sam or not self._mask_generator:
return []
try:
import cv2
if image_path.startswith("http"):
return []
img = cv2.imread(image_path)
if img is None:
return []
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
return self._mask_generator.generate(img)
except Exception:
return []
def _fallback_masks(self, frame: FusedFrame) -> List[Dict]:
"""Generate masks from GT 2D annotations or synthetic grid."""
masks = []
for i, gt in enumerate(frame.ground_truth):
masks.append({
"segmentation": None,
"bbox": self._bbox3d_to_2d(gt.bbox_3d),
"area": 1000,
"predicted_iou": gt.confidence,
"stability_score": gt.confidence,
"mask_id": f"mask_{i}",
})
if not masks:
masks.append({
"segmentation": None,
"bbox": [100, 100, 200, 200],
"area": 10000,
"predicted_iou": 0.8,
"stability_score": 0.8,
"mask_id": "mask_default",
})
return masks
def _bbox3d_to_2d(self, bbox_3d: List[float]) -> List[float]:
x, y = bbox_3d[0], bbox_3d[1]
l, w = bbox_3d[3], bbox_3d[4]
cx = int(320 + x / 0.05)
cy = int(240 - y / 0.05)
bw = int(l / 0.05)
bh = int(w / 0.05)
return [cx - bw // 2, cy - bh // 2, bw, bh]
def _load_lidar(self, path: Optional[str]) -> np.ndarray:
if path and Path(path).exists():
pts = np.fromfile(path, dtype=np.float32)
if len(pts) % 3 == 0:
return pts.reshape(-1, 3)
# SYNTHETIC_FALLBACK: no LiDAR file available. The point cloud is
# simulated, seeded deterministically from the requested path so that
# repeated runs over the same input produce identical proposals.
seed = zlib.crc32((path or "missing").encode("utf-8")) & 0xFFFFFFFF
rng = np.random.default_rng(seed)
return (rng.standard_normal((500, 3)) * 2).astype(np.float32)
def _lift_masks_to_3d(
self,
points: np.ndarray,
masks: List[Dict],
frame: FusedFrame,
) -> List[Object3D]:
proposals = []
for mask in masks:
bbox_2d = mask.get("bbox", [0, 0, 100, 100])
x1, y1, w, h = bbox_2d
x2, y2 = x1 + w, y1 + h
cx_img = (x1 + x2) / 2
cy_img = (y1 + y2) / 2
x_world = (cx_img - 320) * 0.05
y_world = (240 - cy_img) * 0.05
nearby = points[
(np.abs(points[:, 0] - x_world) < 3) &
(np.abs(points[:, 1] - y_world) < 3)
]
if len(nearby) < 3:
nearby = np.array([[x_world, y_world, 0.5]])
center = nearby.mean(axis=0)
if len(nearby) >= 3:
cov = np.cov(nearby[:, :2].T)
eigvals, eigvecs = np.linalg.eigh(cov)
yaw = float(np.arctan2(eigvecs[1, -1], eigvecs[0, -1]))
l = float(np.sqrt(max(eigvals[1], 0.1)) * 4) if len(eigvals) > 1 else 2.0
w = float(np.sqrt(max(eigvals[0], 0.1)) * 4) if len(eigvals) > 0 else 1.0
else:
yaw = 0.0
l, w = 2.0, 1.0
conf = mask.get("predicted_iou", mask.get("stability_score", 0.5))
proposals.append(Object3D(
bbox_3d=[float(center[0]), float(center[1]), float(center[2]),
max(l, 0.5), max(w, 0.3), 1.5, yaw],
class_name="unknown",
confidence=float(conf),
sam_mask_ref=mask.get("mask_id", ""),
))
return proposals
def _assign_taxonomy(self, proposals: List[Object3D], frame: FusedFrame) -> List[Object3D]:
speed = frame.ego_pose.speed_kmh
for i, prop in enumerate(proposals):
if i < len(frame.ground_truth):
prop.class_name = frame.ground_truth[i].class_name
prop.taxonomy_axes = assign_taxonomy_axes(prop.class_name, speed_kmh=speed)
return proposals
def _proposals_from_gt(self, frame: FusedFrame) -> List[Object3D]:
return [
Object3D(
bbox_3d=gt.bbox_3d,
class_name=gt.class_name,
confidence=gt.confidence,
taxonomy_axes=gt.taxonomy_axes,
)
for gt in frame.ground_truth
]
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