You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
270 lines
14 KiB
270 lines
14 KiB
import os
|
|
import sys
|
|
import numpy as np
|
|
import tqdm
|
|
from pathlib import Path
|
|
import json
|
|
import base64
|
|
import argparse
|
|
from mcap.writer import Writer
|
|
|
|
# Add project root and ISOLATE paths
|
|
project_root = Path(__file__).parent.parent
|
|
sys.path.append(str(project_root))
|
|
sys.path.append(str(project_root / 'scripts' / 'ISOLATE'))
|
|
|
|
try:
|
|
from scripts.ISOLATE.model_wrapper import ShenronRadarModel
|
|
except ImportError as e:
|
|
print(f"Error: Failed to import ShenronRadarModel. Ensure scripts/ISOLATE/model_wrapper.py exists. ({e})")
|
|
sys.exit(1)
|
|
|
|
# Official Foxglove JSON Schemas
|
|
FOXGLOVE_POSE_SCHEMA = {
|
|
"$schema": "https://json-schema.org/draft/2020-12/schema",
|
|
"$id": "foxglove.Pose",
|
|
"title": "foxglove.Pose",
|
|
"type": "object",
|
|
"properties": {
|
|
"position": {"type": "object", "properties": {"x": {"type": "number"}, "y": {"type": "number"}, "z": {"type": "number"}}},
|
|
"orientation": {"type": "object", "properties": {"x": {"type": "number"}, "y": {"type": "number"}, "z": {"type": "number"}, "w": {"type": "number"}}}
|
|
}
|
|
}
|
|
FOXGLOVE_IMAGE_SCHEMA = {
|
|
"$schema": "https://json-schema.org/draft/2020-12/schema",
|
|
"$id": "foxglove.CompressedImage",
|
|
"title": "foxglove.CompressedImage",
|
|
"type": "object",
|
|
"properties": {
|
|
"timestamp": {"type": "object", "properties": {"sec": {"type": "integer"}, "nsec": {"type": "integer"}}},
|
|
"frame_id": {"type": "string"},
|
|
"data": {"type": "string", "contentEncoding": "base64"},
|
|
"format": {"type": "string"}
|
|
}
|
|
}
|
|
FOXGLOVE_PCL_SCHEMA = {
|
|
"$schema": "https://json-schema.org/draft/2020-12/schema",
|
|
"$id": "foxglove.PointCloud",
|
|
"title": "foxglove.PointCloud",
|
|
"type": "object",
|
|
"properties": {
|
|
"timestamp": {"type": "object", "properties": {"sec": {"type": "integer"}, "nsec": {"type": "integer"}}},
|
|
"frame_id": {"type": "string"},
|
|
"pose": FOXGLOVE_POSE_SCHEMA,
|
|
"point_stride": {"type": "integer"},
|
|
"fields": {
|
|
"type": "array",
|
|
"items": {"type": "object", "properties": {"name": {"type": "string"}, "offset": {"type": "integer"}, "type": {"type": "integer"}}}
|
|
},
|
|
"data": {"type": "string", "contentEncoding": "base64"}
|
|
}
|
|
}
|
|
|
|
def load_frames(folder_path):
|
|
with open(os.path.join(folder_path, "frames.jsonl")) as f:
|
|
for line in f:
|
|
yield json.loads(line)
|
|
|
|
def run_testbench(iter_name):
|
|
# Setup directories
|
|
debug_dir = project_root / 'Shenron_debug'
|
|
logs_dir = debug_dir / 'logs'
|
|
iter_dir = debug_dir / 'iterations' / iter_name
|
|
|
|
if not logs_dir.exists():
|
|
print(f"[ERROR] Required base folder {logs_dir} not found!")
|
|
return
|
|
|
|
lidar_dir = logs_dir / 'lidar'
|
|
if not lidar_dir.exists():
|
|
print(f"[ERROR] Required base folder {lidar_dir} not found!")
|
|
return
|
|
|
|
iter_dir.mkdir(parents=True, exist_ok=True)
|
|
radar_types = ['awrl1432', 'radarbook', 'ti_cascade']
|
|
|
|
print(f"\n======================================")
|
|
print(f"SHENRON TESTBENCH ITERATION: {iter_name}")
|
|
print(f"======================================")
|
|
|
|
# 1. GENERATE SYNTHETIC DATA
|
|
print("\n[Stage 1]: Processing Physics models...")
|
|
models = {}
|
|
for r_type in radar_types:
|
|
try:
|
|
print(f" -> Initializing {r_type} engine...")
|
|
models[r_type] = ShenronRadarModel(radar_type=r_type)
|
|
(iter_dir / r_type).mkdir(exist_ok=True)
|
|
except Exception as e:
|
|
print(f" -> [WARNING] Failed to init {r_type}: {e}")
|
|
|
|
lidar_files = sorted(list(lidar_dir.glob("*.npy")))
|
|
for lidar_file in tqdm.tqdm(lidar_files, desc=" Simulating Radars", unit="frame"):
|
|
data = np.load(lidar_file)
|
|
# Pad to [x, y, z, intensity, cos_inc_angle, obj, tag] if needed
|
|
if data.shape[1] == 6:
|
|
padded_data = np.zeros((data.shape[0], 7), dtype=np.float32)
|
|
padded_data[:, 0:3] = data[:, 0:3]
|
|
padded_data[:, 4:7] = data[:, 3:6]
|
|
data = padded_data
|
|
|
|
for r_type, model in models.items():
|
|
try:
|
|
rich_pcd = model.process(data)
|
|
out_path = iter_dir / r_type / lidar_file.name
|
|
np.save(out_path, rich_pcd)
|
|
except Exception as e:
|
|
print(f"[ERROR] Frame {lidar_file.name} failed for {r_type}: {e}")
|
|
|
|
# 2. GENERATE MCAP
|
|
print("\n[Stage 2]: Weaving MCAP Comparison Package...")
|
|
output_mcap = iter_dir / f"{iter_name}.mcap"
|
|
|
|
with open(output_mcap, "wb") as f:
|
|
writer = Writer(f)
|
|
writer.start(profile="foxglove")
|
|
|
|
# Register Schemas
|
|
pose_schema_id = writer.register_schema(name="foxglove.Pose", encoding="jsonschema", data=json.dumps(FOXGLOVE_POSE_SCHEMA).encode())
|
|
camera_schema_id = writer.register_schema(name="foxglove.CompressedImage", encoding="jsonschema", data=json.dumps(FOXGLOVE_IMAGE_SCHEMA).encode())
|
|
lidar_schema_id = writer.register_schema(name="foxglove.PointCloud", encoding="jsonschema", data=json.dumps(FOXGLOVE_PCL_SCHEMA).encode())
|
|
|
|
# Register Channels
|
|
channels = {
|
|
'camera': writer.register_channel(topic="/camera", message_encoding="json", schema_id=camera_schema_id),
|
|
'camera_tpp': writer.register_channel(topic="/camera_tpp", message_encoding="json", schema_id=camera_schema_id),
|
|
'lidar': writer.register_channel(topic="/lidar", message_encoding="json", schema_id=lidar_schema_id),
|
|
'native_radar': writer.register_channel(topic="/radar/native", message_encoding="json", schema_id=lidar_schema_id),
|
|
'ego_pose': writer.register_channel(topic="/ego_pose", message_encoding="json", schema_id=pose_schema_id)
|
|
}
|
|
|
|
shenron_channels = {}
|
|
for r_type in radar_types:
|
|
shenron_channels[r_type] = writer.register_channel(topic=f"/radar/{r_type}", message_encoding="json", schema_id=lidar_schema_id)
|
|
|
|
try:
|
|
frames_gen = load_frames(logs_dir)
|
|
frames = list(frames_gen)
|
|
except Exception as e:
|
|
print(f"[ERROR] Could not load frames.jsonl from {logs_dir}: {e}")
|
|
return
|
|
|
|
for frame in tqdm.tqdm(frames, desc=" Packaging Frames", unit="frame"):
|
|
ts_ns = int(frame["timestamp"] * 1e9)
|
|
ts_sec = ts_ns // 1_000_000_000
|
|
ts_nsec = ts_ns % 1_000_000_000
|
|
|
|
raw_pose = frame["ego_pose"]
|
|
x, y, z = raw_pose["x"], -raw_pose["y"], raw_pose["z"]
|
|
yaw_rad = -np.radians(raw_pose.get("yaw", 0))
|
|
|
|
ego_world_pose = {"position": {"x": x, "y": y, "z": z}, "orientation": {"x": 0.0, "y": 0.0, "z": float(np.sin(yaw_rad/2)), "w": float(np.cos(yaw_rad/2))}}
|
|
identity_pose = {"position": {"x": 0.0, "y": 0.0, "z": 0.0}, "orientation": {"x": 0.0, "y": 0.0, "z": 0.0, "w": 1.0}}
|
|
|
|
# POSE
|
|
writer.add_message(channels['ego_pose'], log_time=ts_ns, data=json.dumps(ego_world_pose).encode(), publish_time=ts_ns)
|
|
|
|
# CAMERA
|
|
cam_path = logs_dir / "camera" / frame["camera"]
|
|
if cam_path.exists():
|
|
with open(cam_path, "rb") as img_f: img_bytes = img_f.read()
|
|
cam_msg = {"timestamp": {"sec": ts_sec, "nsec": ts_nsec}, "frame_id": "ego_vehicle", "format": "png", "data": base64.b64encode(img_bytes).decode("ascii")}
|
|
writer.add_message(channels['camera'], log_time=ts_ns, data=json.dumps(cam_msg).encode(), publish_time=ts_ns)
|
|
|
|
# CAMERA TPP
|
|
if "camera_tpp" in frame:
|
|
cam_tpp_path = logs_dir / "camera_tpp" / frame["camera_tpp"]
|
|
if cam_tpp_path.exists():
|
|
with open(cam_tpp_path, "rb") as img_f: img_bytes = img_f.read()
|
|
cam_msg = {"timestamp": {"sec": ts_sec, "nsec": ts_nsec}, "frame_id": "ego_vehicle", "format": "png", "data": base64.b64encode(img_bytes).decode("ascii")}
|
|
writer.add_message(channels['camera_tpp'], log_time=ts_ns, data=json.dumps(cam_msg).encode(), publish_time=ts_ns)
|
|
|
|
# LIDAR
|
|
lidar_p = logs_dir / "lidar" / frame["lidar"]
|
|
if lidar_p.exists():
|
|
points = np.load(lidar_p)
|
|
# Robustness: Handle 6-column (Old) vs 7-column (Modern with Velocity) data
|
|
if points.shape[1] == 6:
|
|
# Pad to 7-column [x, y, z, vel, cos, obj, tag]
|
|
# Note: obj and tag columns [4,5] are actually uint32 bitstreams
|
|
padded = np.zeros((points.shape[0], 7), dtype=np.float32)
|
|
padded[:, 0:3] = points[:, 0:3]
|
|
padded[:, 4] = points[:, 3] # cos (pure float)
|
|
padded[:, 5] = points[:, 4].view(np.uint32).astype(np.float32) # real object id
|
|
padded[:, 6] = points[:, 5].view(np.uint32).astype(np.float32) # real semantic tag
|
|
ros_points = padded
|
|
else:
|
|
ros_points = points.copy().astype(np.float32)
|
|
# For newer 7-col data: [x,y,z,vel,cos,obj,tag]
|
|
# We still need to fix the obj/tag bits at [5,6]
|
|
ros_points[:, 5] = ros_points[:, 5].view(np.uint32).astype(np.float32)
|
|
ros_points[:, 6] = ros_points[:, 6].view(np.uint32).astype(np.float32)
|
|
|
|
ros_points[:, 1] = -ros_points[:, 1] # RHS conversion
|
|
|
|
# MOUNT OFFSET: LiDAR is on the roof (Z=2.5)
|
|
lidar_pose = {"position": {"x": 0.0, "y": 0.0, "z": 2.5}, "orientation": {"x": 0.0, "y": 0.0, "z": 0.0, "w": 1.0}}
|
|
|
|
lidar_msg = {
|
|
"timestamp": {"sec": ts_sec, "nsec": ts_nsec}, "frame_id": "ego_vehicle", "pose": lidar_pose, "point_stride": 28,
|
|
"fields": [
|
|
{"name":"x","offset":0,"type":7},
|
|
{"name":"y","offset":4,"type":7},
|
|
{"name":"z","offset":8,"type":7},
|
|
{"name":"velocity","offset":12,"type":7},
|
|
{"name":"cos_inc_angle","offset":16,"type":7},
|
|
{"name":"object_id","offset":20,"type":7},
|
|
{"name":"semantic_tag","offset":24,"type":7}
|
|
],
|
|
"data": base64.b64encode(ros_points.tobytes()).decode("ascii")
|
|
}
|
|
writer.add_message(channels['lidar'], log_time=ts_ns, data=json.dumps(lidar_msg).encode(), publish_time=ts_ns)
|
|
|
|
# NATIVE RADAR
|
|
radar_p = logs_dir / "radar" / frame["radar"]
|
|
if radar_p.exists():
|
|
r_data = np.load(radar_p)
|
|
if r_data.size > 0:
|
|
dist, az, alt, vel = r_data[:, 0], -r_data[:, 1], r_data[:, 2], r_data[:, 3]
|
|
xr, yr, zr = dist*np.cos(az)*np.cos(alt), dist*np.sin(az)*np.cos(alt), dist*np.sin(alt)
|
|
radar_points = np.stack([xr, yr, zr, vel], axis=1).astype(np.float32)
|
|
|
|
# MOUNT OFFSET: Native Radar is on the front bumper (X=2.0, Z=1.0)
|
|
radar_pose = {"position": {"x": 2.0, "y": 0.0, "z": 1.0}, "orientation": {"x": 0.0, "y": 0.0, "z": 0.0, "w": 1.0}}
|
|
|
|
radar_msg = {
|
|
"timestamp": {"sec": ts_sec, "nsec": ts_nsec}, "frame_id": "ego_vehicle", "pose": radar_pose, "point_stride": 16,
|
|
"fields": [{"name":"x","offset":0,"type":7}, {"name":"y","offset":4,"type":7}, {"name":"z","offset":8,"type":7}, {"name":"velocity","offset":12,"type":7}],
|
|
"data": base64.b64encode(radar_points.tobytes()).decode("ascii")
|
|
}
|
|
writer.add_message(channels['native_radar'], log_time=ts_ns, data=json.dumps(radar_msg).encode(), publish_time=ts_ns)
|
|
|
|
# SHENRON RADARS
|
|
shenron_fname = f"frame_{int(frame['frame_id']):06d}.npy"
|
|
for r_type in radar_types:
|
|
s_path = iter_dir / r_type / shenron_fname
|
|
if s_path.exists():
|
|
s_data = np.load(s_path)
|
|
if s_data.size > 0:
|
|
ros_shenron = s_data.copy().astype(np.float32)
|
|
ros_shenron[:, 1] = -ros_shenron[:, 1] # Negate Y for ROS
|
|
# MOUNT OFFSET: Shenron Radars use the same pose as native (X=2.0, Z=1.0)
|
|
shenron_pose = {"position": {"x": 2.0, "y": 0.0, "z": 1.0}, "orientation": {"x": 0.0, "y": 0.0, "z": 0.0, "w": 1.0}}
|
|
|
|
shenron_msg = {
|
|
"timestamp": {"sec": ts_sec, "nsec": ts_nsec}, "frame_id": "ego_vehicle", "pose": shenron_pose, "point_stride": 20,
|
|
"fields": [{"name":"x","offset":0,"type":7}, {"name":"y","offset":4,"type":7}, {"name":"z","offset":8,"type":7}, {"name":"velocity","offset":12,"type":7}, {"name":"magnitude","offset":16,"type":7}],
|
|
"data": base64.b64encode(ros_shenron.tobytes()).decode("ascii")
|
|
}
|
|
writer.add_message(shenron_channels[r_type], log_time=ts_ns, data=json.dumps(shenron_msg).encode(), publish_time=ts_ns)
|
|
|
|
writer.finish()
|
|
print(f"\n[SUCCESS] Iteration packaged to: {output_mcap}")
|
|
print(f"File size: {os.path.getsize(output_mcap)/1024/1024:.2f} MB")
|
|
|
|
if __name__ == "__main__":
|
|
parser = argparse.ArgumentParser(description="Shenron Physics Iteration Testbench")
|
|
parser.add_argument("--iter", required=True, help="Name of the current debug iteration (e.g., 01_baseline)")
|
|
args = parser.parse_args()
|
|
|
|
run_testbench(args.iter)
|