Upload 4 files

app.py

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+from pytorch3d.io import load_obj
+from pytorch3d.structures import Meshes
+import torch
+import gradio as gr
+import plotly.graph_objects as go
+
+device = torch.device("cpu")
+model = torch.jit.load('model_scripted.pt').to(device)
+
+def normalize_vertices(verts):
+    # Normalize verts
+    center = verts.mean(0)
+    verts = verts - center
+    scale = max(verts.abs().max(0)[0])
+    return verts / scale
+
+
+def plot_3d_results(verts, faces, uv_seam_edge_indices):
+    # Convert vertices to NumPy for easier manipulation
+    verts_np = verts.cpu().numpy()
+    faces_np = faces.verts_idx.cpu().numpy()
+
+    # Prepare the vertex coordinates for the Mesh3d plot
+    x, y, z = verts_np[:, 0], verts_np[:, 1], verts_np[:, 2]
+    i, j, k = faces_np[:, 0], faces_np[:, 1], faces_np[:, 2]
+
+    # Create the 3D mesh plot
+    mesh = go.Mesh3d(x=x, y=y, z=z, i=i, j=j, k=k, color='lightblue', opacity=0.50, name='Mesh')
+
+    # Prepare lines for the predicted edges
+    edge_x, edge_y, edge_z = [], [], []
+    for edge in uv_seam_edge_indices:
+        x0, y0, z0 = verts_np[edge[0]]
+        x1, y1, z1 = verts_np[edge[1]]
+        edge_x.extend([x0, x1, None])
+        edge_y.extend([y0, y1, None])
+        edge_z.extend([z0, z1, None])
+
+    # Create a trace for edges
+    edges_trace = go.Scatter3d(x=edge_x, y=edge_y, z=edge_z, mode='lines', line=dict(color='red', width=2),
+                               name='Predicted Edges')
+
+    # Create a figure and add the mesh and edges
+    fig = go.Figure(data=[mesh, edges_trace])
+    fig.update_layout(scene=dict(
+        xaxis=dict(nticks=4, backgroundcolor="rgb(200, 200, 230)", gridcolor="white", showbackground=True,
+                   zerolinecolor="white"),
+        yaxis=dict(nticks=4, backgroundcolor="rgb(230, 200,230)", gridcolor="white", showbackground=True,
+                   zerolinecolor="white"),
+        zaxis=dict(nticks=4, backgroundcolor="rgb(230, 230,200)", gridcolor="white", showbackground=True,
+                   zerolinecolor="white"), camera=dict(up=dict(x=0, y=1, z=0), eye=dict(x=1.25, y=1.25, z=1.25))),
+        title_text='Predicted Edges')
+
+    # return the figure
+    return fig
+
+
+def generate_prediction(file_input, treshold_value=0.5):
+    verts, faces, aux = load_obj(file_input)
+    verts = normalize_vertices(verts)
+    mesh = Meshes(verts=[verts.to(device)], faces=[faces.verts_idx.to(device)])
+
+    model.eval()
+
+    test_verts = mesh.verts_packed().to(device)
+    test_edges = mesh.edges_packed().to(device)
+
+    with torch.no_grad():
+        test_outputs_logits = model(test_verts, test_edges).to(device)
+        test_outputs = torch.sigmoid(test_outputs_logits).to(device)
+        test_predictions = (test_outputs > treshold_value).int().cpu()
+        uv_seam_edges_mask = test_predictions.cpu().squeeze() == 1
+        uv_seam_edges = test_edges[uv_seam_edges_mask].cpu().tolist()
+
+    # Return the HTML content generated by plot_3d_results
+    return plot_3d_results(verts, faces, uv_seam_edges)
+
+
+def run_gradio():
+    with gr.Blocks() as demo:
+        gr.Label("Proof of concept demo. Predict UV seams on a 3D sphere meshes.")
+
+        with gr.Row():
+            model3d_input = gr.File(label="Sphere Prototype Model", value="sphere.obj")
+            with gr.Column():
+                model3d_output = gr.Plot()
+                treshold_value = gr.Slider(minimum=0, maximum=1, value=0.5, label="Threshold")
+
+        button = gr.Button("Predict")
+        button.click(generate_prediction, inputs=[model3d_input, treshold_value], outputs=model3d_output)
+
+    demo.launch()
+
+
+run_gradio()

model_scripted.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5475053a74a596b223abc768bf8d926247bf94f826c1ed41b469f593a11828c8
+size 255324

requirements.txt

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+torch
+pytorch3d
+plotly

sphere.obj

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+# This file uses centimeters as units for non-parametric coordinates.
+
+mtllib sphere.mtl
+g default
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