normalization fix

app.py

@@ -6,11 +6,18 @@ import trimesh
 device = torch.device("cpu")
 model = torch.jit.load('model_scripted.pt').to(device)
 
+
 def normalize_vertices(verts):
-    # Normalize verts
-    center = verts.mean(0)
+    # Center the vertices
+    center = verts.mean(dim=0)
     verts = verts - center
-    scale = max(verts.abs().max(0)[0])
+
+    # Find the maximum absolute value for each axis to scale them independently
+    scale = verts.abs().max(dim=0)[0]  # This finds the max in each dimension independently
+
+    # Scale the vertices so that in each dimension, the furthest point is exactly at 1 or -1
+    # We avoid division by zero by ensuring scale values are at least a very small number
+    scale = torch.where(scale == 0, torch.ones_like(scale), scale)  # Prevent division by zero
     return verts / scale
 
 def plot_3d_results(verts, faces, uv_seam_edge_indices):
@@ -54,14 +61,17 @@ def plot_3d_results(verts, faces, uv_seam_edge_indices):
 
 
 def generate_prediction(file_input, treshold_value=0.5):
-    # Load the triangle mesh
+    # Load and triangulate the mesh
     mesh = trimesh.load_mesh(file_input)
 
+
     # For production, we should use a faster method to preprocess the mesh!
 
     # Convert vertices to a PyTorch tensor
     vertices = torch.tensor(mesh.vertices, dtype=torch.float32)
 
+    vertices = normalize_vertices(vertices)
+
     # Initialize containers for unique vertices and mapping
     unique_vertices = []
     vertex_mapping = {}
@@ -111,13 +121,13 @@ def generate_prediction(file_input, treshold_value=0.5):
 
 def run_gradio():
     with gr.Blocks() as demo:
-        gr.Label("Proof of concept demo. Predict UV seams on a triangulated 3D sphere mesh.")
+        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")
+            model3d_input = gr.File(label="Sphere Prototype Model", value="randomSphere_001.obj")
             with gr.Column():
                 model3d_output = gr.Plot()
-                treshold_value = gr.Slider(minimum=0, maximum=1, value=0.5, label="Threshold")
+                treshold_value = gr.Slider(minimum=0, maximum=1, value=0.6, label="Threshold")
 
         button = gr.Button("Predict")
         button.click(generate_prediction, inputs=[model3d_input, treshold_value], outputs=model3d_output)

model_scripted.pt

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:6f0d5dcd806540ba8061e3d70ed17eac539e560ae0932f04679f706898126588
+oid sha256:e582a3e912ec5af5f860b751c514d8f9c75a33dd11466bde858c1745bf6e632c
 size 255324

randomSphere_001.obj

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+
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