Update app.py

app.py

@@ -19,7 +19,7 @@ parameters_to_prune = [
 prune.global_unstructured(
     parameters_to_prune,
     pruning_method=prune.L1Unstructured,
-    amount=0.4,  # Prune 40% of weights
+    amount=0.2,  # Prune 20% of weights
 )
 
 for module, _ in parameters_to_prune:
@@ -41,15 +41,25 @@ def preprocess_image(image):
     image = torch.from_numpy(image).permute(2, 0, 1).unsqueeze(0).float().to(device)
     return image / 255.0
 
-def plot_depth_map(depth_map):
-    fig = plt.figure(figsize=(16, 9))  # Set figure size to 16:9
+def plot_depth_map(depth_map, original_image):
+    fig = plt.figure(figsize=(16, 9))
     ax = fig.add_subplot(111, projection='3d')
     x, y = np.meshgrid(range(depth_map.shape[1]), range(depth_map.shape[0]))
-    ax.plot_surface(x, y, depth_map, cmap='viridis')
-    ax.view_init(azim=180, elev=0)  # Rotate the view forward and clockwise
+    
+    # Resize original image to match depth map dimensions
+    original_image_resized = cv2.resize(original_image, (depth_map.shape[1], depth_map.shape[0]))
+    colors = original_image_resized.reshape(depth_map.shape[0], depth_map.shape[1], 3) / 255.0
+    
+    ax.plot_surface(x, y, depth_map, facecolors=colors, shade=False)
     ax.set_zlim(0, 1)
+    plt.axis('off')
     plt.close(fig)
-    return fig
+    
+    fig.canvas.draw()
+    img = np.frombuffer(fig.canvas.tostring_rgb(), dtype=np.uint8)
+    img = img.reshape(fig.canvas.get_width_height()[::-1] + (3,))
+    
+    return img
 
 @torch.inference_mode()
 def process_frame(image):
@@ -62,15 +72,11 @@ def process_frame(image):
     # Normalize depth map
     depth_map = (depth_map - depth_map.min()) / (depth_map.max() - depth_map.min())
     
-    # Create 3D plot
-    fig = plot_depth_map(depth_map)
-    
-    # Convert plot to image
-    fig.canvas.draw()
-    img = np.frombuffer(fig.canvas.tostring_rgb(), dtype=np.uint8)
-    img = img.reshape(fig.canvas.get_width_height()[::-1] + (3,))
+    # Convert BGR to RGB if necessary
+    if image.shape[2] == 3:  # Check if it's a color image
+        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
     
-    return img
+    return plot_depth_map(depth_map, image)
 
 interface = gr.Interface(
     fn=process_frame,