try to load et data

app.py

@@ -1,55 +1,77 @@
 import gradio as gr
-from gradio_rerun import Rerun
-from data.loader import load_simulation_data
+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 update_simulation_dropdown(file):
-    simulations, descriptions = load_simulation_data(file)
-    return gr.Dropdown(
-        choices=descriptions if descriptions else [],
-        value=None,
-        allow_custom_value=False
+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)}"
 
-def create_app():
-    with gr.Blocks() as demo:
-        gr.Markdown("""
-        # Camera Simulation Visualizer
-        Upload a JSON file containing camera simulation data and select a simulation to visualize.
-        """)
-
-        with gr.Row():
-            file_input = gr.File(
-                label="Upload Simulation JSON",
-                file_types=[".json"]
-            )
-            simulation_dropdown = gr.Dropdown(
-                label="Select Simulation",
-                choices=[],
-                type="index",
-                scale=2
-            )
-
-        with gr.Row():
-            viewer = Rerun(streaming=False)
-
-        file_input.change(
-            update_simulation_dropdown,
-            inputs=[file_input],
-            outputs=[simulation_dropdown]
-        )
-
-        simulation_dropdown.change(
-            visualize_simulation,
-            inputs=[file_input, simulation_dropdown],
-            outputs=[viewer]
-        )
-
-    return demo
-
+    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__":
-    demo = create_app()
-    demo.queue().launch(share=False)
+    interface = create_trajectory_interface()
+    interface.launch()

requirements.txt

@@ -1,2 +1,3 @@
 gradio_rerun
-scipy==1.14.1
+scipy==1.14.1
+evo==1.30.3