error version

app.py

@@ -183,6 +183,43 @@ dpt_processor = None
 def process_image(image, model_choice="GLPN (Recommended)", visualization_type="mesh"):
     """Main processing pipeline - simplified from first version"""
     
+    def _generate_quality_assessment(metrics):
+        """Generate quality assessment based on metrics"""
+        assessment = []
+        
+        # Check outlier removal
+        outlier_pct = (metrics['outliers_removed'] / metrics['initial_points']) * 100
+        if outlier_pct < 5:
+            assessment.append("✅ Very clean depth estimation (low noise)")
+        elif outlier_pct < 15:
+            assessment.append("✅ Good depth quality (normal noise level)")
+        else:
+            assessment.append("⚠️ High noise in depth estimation")
+        
+        # Check manifold properties
+        if metrics['is_edge_manifold'] and metrics['is_vertex_manifold']:
+            assessment.append("✅ Excellent topology - mesh is well-formed")
+        elif metrics['is_vertex_manifold']:
+            assessment.append("⚠️ Good local topology but has some edge issues")
+        else:
+            assessment.append("⚠️ Topology issues present - may need cleanup")
+        
+        # Check watertight
+        if metrics['is_watertight']:
+            assessment.append("✅ Watertight mesh - ready for 3D printing!")
+        else:
+            assessment.append("ℹ️ Not watertight - use MeshLab's 'Close Holes' for 3D printing")
+        
+        # Check complexity
+        if metrics['triangles'] > 1000000:
+            assessment.append("ℹ️ Very detailed mesh - may be slow in some software")
+        elif metrics['triangles'] > 500000:
+            assessment.append("✅ High detail mesh - good quality")
+        else:
+            assessment.append("✅ Moderate detail - good balance of quality and performance")
+        
+        return "\n".join(f"- {item}" for item in assessment)
+    
     if image is None:
         return None, None, None, "Please upload an image first.", None
     
@@ -727,6 +764,12 @@ with gr.Blocks(title="Advanced 3D Reconstruction", theme=gr.themes.Soft()) as de
             - https://www.creators3d.com/online-viewer
             - Just drag and drop your OBJ/PLY file
             
+            ### For 3D Printing:
+            1. Use the `mesh.stl` file
+            2. Check metrics: Look for "Watertight: ✓"
+            3. Import into your slicer (Cura, PrusaSlicer, etc.)
+            4. Scale to desired size
+            5. Slice and print!
             
             ## Tips for Best Results
             
@@ -745,6 +788,7 @@ with gr.Blocks(title="Advanced 3D Reconstruction", theme=gr.themes.Soft()) as de
             - Extreme close-ups or very distant scenes
             - Heavy shadows or very dark images
             - Transparent objects
+            - Outdoor scenes with sky (models trained on indoor data)
             
             ## Understanding the Metrics
             
@@ -823,6 +867,10 @@ with gr.Blocks(title="Advanced 3D Reconstruction", theme=gr.themes.Soft()) as de
         # ========== CITATION TAB ==========
         with gr.Tab("📄 Citation & Credits"):
             gr.Markdown("""
+            ## How to Cite
+            
+            If you use this application in your research, please cite the relevant papers:
+            
             ### For GLPN Model:
             ```bibtex
             @inproceedings{kim2022global,
@@ -851,18 +899,32 @@ with gr.Blocks(title="Advanced 3D Reconstruction", theme=gr.themes.Soft()) as de
             - **PyTorch**: Deep learning framework
             - **Plotly**: Interactive 3D visualization
             - **Gradio**: Web interface
+            
+            ## License & Usage
+            
+            - **Code**: MIT License
+            - **Generated 3D Models**: You own the outputs
+            
+            ## Contact & Support
+            
+            For issues, questions, or suggestions, use the Community tab on Hugging Face.
             """)
     
     # ========== FOOTER ==========
     gr.Markdown("""
     ---
     
+    ### 🎓 PhD Application Demo
+    
     This application demonstrates comprehensive understanding of:
     - Computer vision and deep learning
     - 3D geometry and reconstruction
     - Software engineering best practices
     - Research methodology and evaluation
     
+    **Developed for academic research purposes**
+    
+    *Version 1.0*
     """)
 
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