initial commit

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README.md

@@ -0,0 +1,27 @@
+---
+title: FingerNet Demo
+emoji: 🖐️
+colorFrom: pink
+colorTo: orange
+sdk: gradio
+sdk_version: 5.49.1
+app_file: app.py
+pinned: false
+license: bsd-3-clause
+---
+
+# FingerNet Demo
+
+This is an interactive demo for FingerNet, a deep learning model for estimating force and shape of soft robotic fingers. The demo allows users to manipulate the motion of the finger as the model inputs and visualize the predicted force and shape outputs.
+
+In this demo, we provide two models: FingerNet and FingerNet-Surf, corresponding to different finger designs. More details about the models can be found in the [FingerNet](https://huggingface.co/asRobotics/fingernet) and [FingerNet-Surf](https://huggingface.co/asRobotics/fingernet-surf) model cards.
+
+## Features
+
+- Select between different FingerNet models (FingerNet and FingerNet-Surf).
+- Adjust finger motion parameters using sliders.
+- Visualize predicted force and shape in real-time.
+
+## License
+
+FingerNet is licensed under the BSD-3-Clause License.

app.py

@@ -0,0 +1,188 @@
+#!/usr/bin/env python
+
+"""
+FingerNet Demo App
+"""
+
+import numpy as np
+import trimesh
+import pandas as pd
+import tempfile
+import gradio as gr
+import matplotlib as mpl
+from huggingface_hub import hf_hub_download
+import onnxruntime as ort
+
+model_dict = {
+    "FingerNet": "fingernet",
+    "FingerNet-Surf": "fingernet-surf",
+}
+
+vertices_dict = {}
+faces_dict = {}
+def_index_dict = {}
+for name in model_dict.keys():
+    vertices_dict[name] = np.loadtxt(
+        f"assets/{name.lower().replace('net', '')}/surf_coor.txt", delimiter=","
+    )
+    faces_dict[name] = (
+        np.loadtxt(
+            f"assets/{name.lower().replace('net', '')}/surf_tri.txt",
+            delimiter=",",
+            dtype=int,
+        )
+        - 1
+    )
+    def_index_dict[name] = np.loadtxt(
+        f"assets/{name.lower().replace('net', '')}/def_node.txt", dtype=int
+    )
+colormap = mpl.colormaps.get_cmap("viridis")
+cmin = 0.0
+cmax = 12.0
+
+def load_model(model_name="fingernet"):
+    repo_id = f"asRobotics/{model_name}"
+    model_filename = "model.onnx"
+    model_path = hf_hub_download(repo_id=repo_id, filename=model_filename)
+    return ort.InferenceSession(model_path)
+
+ort_dict = {}
+for name, path in model_dict.items():
+    try:
+        ort_dict[name] = load_model(name)
+    except Exception as e:
+        print(f"Warning: Could not load model '{name}' from '{path}'. Error: {e}")
+
+
+def make_double_sided(mesh: trimesh.Trimesh) -> trimesh.Trimesh:
+    faces_front = mesh.faces.copy()
+    vertices = mesh.vertices.copy()
+    faces_back = np.fliplr(faces_front) + len(vertices)
+    vertices_double = np.vstack([vertices, vertices])
+    faces_double = np.vstack([faces_front, faces_back])
+
+    if hasattr(mesh.visual, "vertex_colors") and mesh.visual.vertex_colors is not None:
+        colors = mesh.visual.vertex_colors.copy()
+        colors_double = np.vstack([colors, colors])
+    else:
+        colors_double = None
+
+    mesh_double = trimesh.Trimesh(
+        vertices=vertices_double,
+        faces=faces_double,
+        vertex_colors=colors_double,
+        process=False,
+    )
+
+    return mesh_double
+
+
+def predict_from_motion(dx, dy, dz, rx, ry, rz, model_name):
+    motion = np.array(
+        [dx, dy, dz, rx / 180.0 * np.pi, ry / 180.0 * np.pi, rz / 180.0 * np.pi],
+        dtype=np.float32,
+    ).reshape(1, -1)
+    assert motion.shape[1] == 6, "Motion must be 6D (x,y,z,rx,ry,rz)"
+
+    outputs = ort_dict[model_name].run(None, {"motion": motion})
+    force = np.array(outputs[0]).flatten()
+    nodes = np.array(outputs[1]).reshape(-1, 3)
+
+    df = pd.DataFrame(
+        force.reshape(1, -1),
+        columns=["Fx (N)", "Fy (N)", "Fz (N)", "Tx (Nmm)", "Ty (Nmm)", "Tz (Nmm)"],
+    )
+    df = df.style.format("{:.3f}")
+
+    delta_vertices = np.zeros_like(vertices_dict[model_name])
+    delta_vertices[def_index_dict[model_name] - 1] += nodes.reshape(-1, 3)
+    new_vertices = vertices_dict[model_name] + delta_vertices
+
+    rot_x_neg90 = np.array(
+        [
+            [1, 0, 0],
+            [0, 0, 1],
+            [0, -1, 0],
+        ]
+    )
+    new_vertices = new_vertices @ rot_x_neg90.T
+
+    norm_dist = np.clip(np.linalg.norm(delta_vertices, axis=1), cmin, cmax)
+    colors = (colormap((norm_dist - cmin) / (cmax - cmin)) * 255).astype(np.uint8)
+
+    mesh = trimesh.Trimesh(
+        vertices=new_vertices,
+        faces=faces_dict[model_name],
+        vertex_colors=colors,
+        process=False,
+    )
+    mesh = make_double_sided(mesh)
+
+    tmp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".glb")
+    mesh.export(tmp_file.name, file_type="glb")
+
+    return df, tmp_file.name
+
+
+with gr.Blocks(title="FingerNet Demo") as demo:
+    gr.Markdown("# 🖐️ FingerNet Demo\n\n")
+
+    with gr.Row():
+        with gr.Column():
+            gr.Markdown("## Model Selection")
+            model = gr.Dropdown(
+                choices=list(model_dict.keys()),
+                value=list(model_dict.keys())[0],
+                label="Select Model",
+                show_label=False,
+            )
+
+            gr.Markdown("## Motion Inputs")
+            gr.Markdown("### Translation (mm)")
+            dx = gr.Slider(-10, 10, 0, step=0.1, label="Dx")
+            dy = gr.Slider(-10, 10, 0, step=0.1, label="Dy")
+            dz = gr.Slider(-3, 3, 0, step=0.1, label="Dz")
+            gr.Markdown("### Rotation (degrees)")
+            rx = gr.Slider(-30, 30, 0, step=0.5, label="Rx")
+            ry = gr.Slider(-30, 30, 0, step=0.5, label="Ry")
+            rz = gr.Slider(-30, 30, 0, step=0.5, label="Rz")
+
+        with gr.Column():
+            gr.Markdown("## Force Outputs")
+            output_force = gr.DataFrame(
+                headers=[
+                    "Fx (N)",
+                    "Fy (N)",
+                    "Fz (N)",
+                    "Tx (Nmm)",
+                    "Ty (Nmm)",
+                    "Tz (Nmm)",
+                ],
+                label="Predicted Force",
+                show_label=False,
+                datatype=["number"] * 6,
+                row_count=(1, "fixed"),
+                interactive=False,
+                column_widths=["16%"] * 6,
+            )
+
+            gr.Markdown("## Mesh Outputs")
+            height = 500
+            output_mesh = gr.Model3D(
+                label="Mesh",
+                show_label=False,
+                height=height,
+                camera_position=[45, 60, 150],
+            )
+
+    inputs = [dx, dy, dz, rx, ry, rz, model]
+    for inp in inputs:
+        inp.change(
+            fn=predict_from_motion, inputs=inputs, outputs=[output_force, output_mesh]
+        )
+
+    demo.load(
+        fn=predict_from_motion, inputs=inputs, outputs=[output_force, output_mesh]
+    )
+
+demo.launch(server_name="127.0.0.1", server_port=7860, share=False)

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requirements.txt

@@ -0,0 +1,8 @@
+gradio>=4.44
+huggingface_hub
+onnxruntime
+numpy
+pandas
+matplotlib
+trimesh
+scipy