Spaces:
Sleeping
Sleeping
File size: 2,718 Bytes
3167cfe ba85228 ffbf761 247f6df ba85228 cc5f32c ba85228 247f6df ba85228 247f6df ba85228 3167cfe 247f6df ba85228 |
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 |
import gradio as gr
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
from visualization.visualizer import visualize_simulation
def load_trajectory_data(traj_file, char_file, num_cams=30):
trajectory = read_kitti_poses_file(traj_file)
matrix_trajectory = torch.from_numpy(np.array(trajectory.poses_se3)).to(torch.float32)
raw_trans = torch.clone(matrix_trajectory[:, :3, 3])
raw_rot = matrix_trajectory[:, :3, :3]
rot6d = raw_rot[:, :, :2].permute(0, 2, 1).reshape(-1, 6)
trajectory_feature = torch.hstack([rot6d, raw_trans]).permute(1, 0)
padded_trajectory_feature = F.pad(
trajectory_feature,
(0, num_cams - trajectory_feature.shape[1])
)
padding_mask = torch.ones((num_cams))
padding_mask[trajectory_feature.shape[1]:] = 0
char_feature = torch.from_numpy(np.load(char_file)).to(torch.float32)
padding_size = num_cams - char_feature.shape[0]
padded_char_feature = F.pad(char_feature, (0, 0, 0, padding_size)).permute(1, 0)
return {
"traj_filename": Path(traj_file).name,
"char_filename": Path(char_file).name,
"traj_feat": padded_trajectory_feature,
"char_feat": padded_char_feature,
"padding_mask": padding_mask,
"raw_matrix_trajectory": matrix_trajectory
}
def create_trajectory_interface():
"""Create Gradio interface for loading trajectory data"""
def process_files(traj_file, char_file):
try:
result = load_trajectory_data(traj_file.name, char_file.name)
# Convert tensors to numpy for display
info = {
"Trajectory filename": result["traj_filename"],
"Character filename": result["char_filename"],
"Trajectory shape": result["traj_feat"].shape,
"Character shape": result["char_feat"].shape,
"Valid frames": int(result["padding_mask"].sum().item())
}
return str(info)
except Exception as e:
return f"Error processing files: {str(e)}"
interface = gr.Interface(
fn=process_files,
inputs=[
gr.File(label="Trajectory File (.txt)"),
gr.File(label="Character File (.npy)")
],
outputs=gr.Textbox(label="Results"),
title="Trajectory Data Loader",
description="Upload trajectory (.txt) and character (.npy) files to load and process them."
)
return interface
if __name__ == "__main__":
interface = create_trajectory_interface()
interface.launch() |