Spaces:
Running
on
Zero
Running
on
Zero
File size: 3,398 Bytes
98a8f68 161b8e5 289635e 161b8e5 c065381 161b8e5 c065381 161b8e5 44f6fd9 289635e 98a8f68 289635e c065381 161b8e5 98a8f68 161b8e5 98a8f68 161b8e5 c065381 98a8f68 289635e c065381 98a8f68 c065381 289635e 98a8f68 289635e 98a8f68 289635e 98a8f68 289635e 98a8f68 289635e c065381 161b8e5 98a8f68 44f6fd9 98a8f68 c065381 98a8f68 |
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 |
import tempfile
import os
import spaces
import numpy as np
import torch
import torch.nn.functional as F
from evo.tools.file_interface import read_kitti_poses_file
from pathlib import Path
import rerun as rr
from typing import Optional, Dict
from visualization.logger import SimulationLogger
from scipy.spatial.transform import Rotation
def load_trajectory_data(traj_file: str, char_file: str) -> Dict:
trajectory = read_kitti_poses_file(traj_file)
matrix_trajectory = torch.from_numpy(
np.array(trajectory.poses_se3)).to(torch.float32)
char_feature = torch.from_numpy(np.load(char_file)).to(torch.float32)
return {
"traj_filename": Path(traj_file).name,
"char_filename": Path(char_file).name,
"char_feat": char_feature,
"matrix_trajectory": matrix_trajectory
}
class ETLogger(SimulationLogger):
def __init__(self):
super().__init__()
rr.init("et_visualization")
rr.log("world", rr.ViewCoordinates.RIGHT_HAND_Y_UP, timeless=True)
self.K = np.array([
[500, 0, 320],
[0, 500, 240],
[0, 0, 1]
])
def log_trajectory(self, trajectory: np.ndarray):
positions = trajectory[:, :3, 3]
rr.log(
"world/trajectory/points",
rr.Points3D(
positions,
colors=np.full((len(positions), 4), [0.0, 0.8, 0.8, 1.0])
),
timeless=True
)
if len(positions) > 1:
lines = np.stack([positions[:-1], positions[1:]], axis=1)
rr.log(
"world/trajectory/line",
rr.LineStrips3D(
lines,
colors=[(0.0, 0.8, 0.8, 1.0)]
),
timeless=True
)
for k in range(len(trajectory)):
rr.set_time_sequence("frame_idx", k)
translation = trajectory[k, :3, 3]
rotation_q = Rotation.from_matrix(
trajectory[k, :3, :3]).as_quat()
rr.log(
f"world/camera",
rr.Transform3D(
translation=translation,
rotation=rr.Quaternion(xyzw=rotation_q),
),
)
rr.log(
f"world/camera/image",
rr.Pinhole(
image_from_camera=self.K,
width=640,
height=480,
),
)
def log_character(self, char_feature: np.ndarray):
rr.log(
"world/character",
rr.Points3D(
char_feature.reshape(-1, 3),
colors=np.full(
(char_feature.reshape(-1, 3).shape[0], 4), [0.8, 0.2, 0.2, 1.0])
),
timeless=True
)
@spaces.GPU
def visualize_et_data(traj_file: str, char_file: str) -> Optional[str]:
try:
data = load_trajectory_data(traj_file, char_file)
temp_dir = tempfile.mkdtemp()
rrd_path = os.path.join(temp_dir, "et_visualization.rrd")
logger = ETLogger()
logger.log_trajectory(data["matrix_trajectory"].numpy())
logger.log_character(data["char_feat"].numpy())
rr.save(rrd_path)
return rrd_path
except Exception as e:
print(f"Error visualizing E.T. data: {str(e)}")
return None
|