added texturing and fixed timestamp

app.py

@@ -9,6 +9,7 @@ import os
 import trimesh
 import time
 from datetime import datetime
+import pytz
 
 # Import potentially CUDA-initializing modules after 'spaces'
 import torch
@@ -78,20 +79,21 @@ def generate_3d_model(depth, image_path, focallength_px, simplification_factor=0
     try:
         print("Starting 3D model generation")
         # Load the RGB image and convert to a NumPy array
-        image = np.array(Image.open(image_path))
+        image = Image.open(image_path)
+        image_array = np.array(image)
         
         # Ensure depth is a NumPy array
         if isinstance(depth, torch.Tensor):
             depth = depth.cpu().numpy()
         
         # Resize depth to match image dimensions if necessary
-        if depth.shape != image.shape[:2]:
-            depth = cv2.resize(depth, (image.shape[1], image.shape[0]), interpolation=cv2.INTER_LINEAR)
+        if depth.shape != image_array.shape[:2]:
+            depth = cv2.resize(depth, (image_array.shape[1], image_array.shape[0]), interpolation=cv2.INTER_LINEAR)
         
         height, width = depth.shape
 
         print(f"3D model generation - Depth shape: {depth.shape}")
-        print(f"3D model generation - Image shape: {image.shape}")
+        print(f"3D model generation - Image shape: {image_array.shape}")
 
         # Compute camera intrinsic parameters
         fx = fy = float(focallength_px)  # Ensure focallength_px is a float
@@ -111,7 +113,7 @@ def generate_3d_model(depth, image_path, focallength_px, simplification_factor=0
         vertices = np.vstack((X, Y, Z)).T
 
         # Normalize RGB colors to [0, 1] for vertex coloring
-        colors = image.reshape(-1, 3) / 255.0
+        colors = image_array.reshape(-1, 3) / 255.0
 
         print("Generating faces")
         # Generate faces by connecting adjacent vertices to form triangles
@@ -161,12 +163,18 @@ def generate_3d_model(depth, image_path, focallength_px, simplification_factor=0
         timestamp = int(time.time())
         view_model_path = f'view_model_{timestamp}.obj'
         download_model_path = f'download_model_{timestamp}.obj'
+        
         print("Exporting to view")
-        mesh.export(view_model_path)
+        mesh.export(view_model_path, include_texture=True)
         print("Exporting to download")
-        mesh.export(download_model_path)
+        mesh.export(download_model_path, include_texture=True)
+        
+        # Save the texture image
+        texture_path = f'texture_{timestamp}.png'
+        image.save(texture_path)
+        
         print("Export completed")
-        return view_model_path, download_model_path
+        return view_model_path, download_model_path, texture_path
     except Exception as e:
         print(f"Error in generate_3d_model: {str(e)}")
         raise
@@ -201,12 +209,12 @@ def regenerate_3d_model(depth_csv, image_path, focallength_px, simplification_fa
     depth = np.loadtxt(depth_csv, delimiter=',')
     
     # Generate new 3D model with updated parameters
-    view_model_path, download_model_path = generate_3d_model(
+    view_model_path, download_model_path, texture_path = generate_3d_model(
         depth, image_path, focallength_px, 
         simplification_factor, smoothing_iterations, thin_threshold
     )
     print("regenerated!")
-    return view_model_path, download_model_path
+    return view_model_path, download_model_path, texture_path
 
 @spaces.GPU(duration=30)
 def predict_depth(input_image):
@@ -267,24 +275,33 @@ def create_3d_model(depth_csv, image_path, focallength_px, simplification_factor
         
         print(f"Loading image from: {image_path}")
         
-        view_model_path, download_model_path = generate_3d_model(
+        view_model_path, download_model_path, texture_path = generate_3d_model(
             depth, image_path, focallength_px, 
             simplification_factor, smoothing_iterations, thin_threshold
         )
         print("3D model generated!")
-        return view_model_path, download_model_path, "3D model created successfully!"
+        return view_model_path, download_model_path, texture_path, "3D model created successfully!"
     except Exception as e:
         error_message = f"An error occurred during 3D model creation: {str(e)}"
         print(error_message)
-        return None, None, error_message
+        return None, None, None, error_message
 
 def get_last_commit_timestamp():
     try:
+        # Get the timestamp in a format that includes timezone information
         timestamp = subprocess.check_output(['git', 'log', '-1', '--format=%cd', '--date=iso']).decode('utf-8').strip()
-        return datetime.fromisoformat(timestamp).strftime("%Y-%m-%d %H:%M:%S")
+        
+        # Parse the timestamp, including the timezone
+        dt = datetime.strptime(timestamp, "%Y-%m-%d %H:%M:%S %z")
+        
+        # Convert to UTC
+        dt_utc = dt.astimezone(pytz.UTC)
+        
+        # Format the date as desired
+        return dt_utc.strftime("%Y-%m-%d %H:%M:%S UTC")
     except Exception as e:
-        print(f"{str(e)}")
-        return str(e)
+        print(f"Error getting last commit timestamp: {str(e)}")
+        return "Unknown"
     
 # Create the Gradio interface with appropriate input and output components. 
 last_updated = get_last_commit_timestamp()
@@ -314,6 +331,7 @@ with gr.Blocks() as iface:
     with gr.Row():
         view_3d_model = gr.Model3D(label="View 3D Model")
         download_3d_model = gr.File(label="Download 3D Model (OBJ)")
+        download_texture = gr.File(label="Download Texture (PNG)")
     
     with gr.Row():
         simplification_factor = gr.Slider(minimum=0.1, maximum=1.0, value=0.8, step=0.1, label="Simplification Factor")
@@ -335,13 +353,13 @@ with gr.Blocks() as iface:
     generate_3d_button.click(
         create_3d_model,
         inputs=[raw_depth_csv, input_image, hidden_focal_length, simplification_factor, smoothing_iterations, thin_threshold],
-        outputs=[view_3d_model, download_3d_model, model_status]
+        outputs=[view_3d_model, download_3d_model, download_texture, model_status]
     )
     
     regenerate_button.click(
         create_3d_model,
         inputs=[raw_depth_csv, input_image, hidden_focal_length, simplification_factor, smoothing_iterations, thin_threshold],
-        outputs=[view_3d_model, download_3d_model, model_status]
+        outputs=[view_3d_model, download_3d_model, download_texture, model_status]
     )
 
 # Launch the Gradio interface with sharing enabled

history.md

@@ -22,6 +22,14 @@
   4. Added a print statement just before returning from the `predict_depth` function to log all return values.
 - These changes aim to prevent CUDA initialization in the main process and provide more detailed logging for troubleshooting.
 
+## 2024-10-05 23:00 PST
+### Persistent 3D Model Colorization Issue
+- Problem: Despite previous attempts, the 3D model still appears colorless (gray) without the original image overlay.
+- Next steps: 
+  1. Review the texture mapping process in the 3D model generation.
+  2. Investigate alternative methods to apply color information to the 3D model.
+  3. Ensure color data is being properly passed and applied throughout the pipeline.
+
 ## 2024-10-05 22:30 PST
 ### 3D Model Colorization and Thin Threshold Adjustment
 - Problem 1: The generated 3D model lacks proper colorization from the original image.