Update app.py

app.py

@@ -48,58 +48,28 @@ def stitch_rgbd_videos(
             rgb_full = full_frames[i]  # Full-resolution RGB frame.
             depth_frame = depths[i]  # Already in uint8 format
             
-            # Handle the case where depth is already in a 3-channel format
-            if len(depth_frame.shape) == 3 and depth_frame.shape[2] == 3:
-                # The depth is already a color or grayscale image with 3 channels
-                if grayscale:
-                    if convert_from_color:
-                        # Convert to grayscale if it's a color image
-                        depth_gray = cv2.cvtColor(depth_frame, cv2.COLOR_RGB2GRAY)
-                        depth_vis = np.stack([depth_gray] * 3, axis=-1)
-                    else:
-                        # Assume it's already the right format
-                        depth_vis = depth_frame
+            if grayscale:
+                if convert_from_color:
+                    # Convert to grayscale if it's a color image
+                    depth_gray = cv2.cvtColor(depth_frame, cv2.COLOR_RGB2GRAY)
+                    depth_vis = np.stack([depth_gray] * 3, axis=-1)
                 else:
-                    if depth_frame.max() > 0:  # Ensure we have valid depth data
-                        # Use the inferno colormap if requested 
-                        cmap = matplotlib.colormaps.get_cmap("inferno")
-                        # Convert to single channel first
-                        depth_gray = cv2.cvtColor(depth_frame, cv2.COLOR_RGB2GRAY)
-                        # Normalize to 0-1 range for colormap
-                        depth_norm = depth_gray / 255.0
-                        # Apply colormap
-                        depth_vis = (cmap(depth_norm)[..., :3] * 255).astype(np.uint8)
-                    else:
-                        # If zero depth, just use the original
-                        depth_vis = depth_frame
+                    # Assume it's already the right format
+                    depth_vis = depth_frame
             else:
-                # Process as in original code (single channel depth)
-                if d_max == d_min:
-                    d_max = d_min + 1
-                
-                # Normalize the depth frame to the range [0, 255]
-                depth_norm = np.clip((depth_frame - d_min) / (d_max - d_min) * 255, 0, 255).astype(np.uint8)
-                
-                # Ensure depth_norm is 2D (remove singleton dimensions if present)
-                if depth_norm.ndim == 3:
-                    depth_norm = np.squeeze(depth_norm)
-                    
-                # Generate depth visualization:
-                if grayscale:
-                    if convert_from_color:
-                        # First, generate a color depth image using the inferno colormap,
-                        # then convert that color image to grayscale.
-                        cmap = matplotlib.colormaps.get_cmap("inferno")
-                        depth_color = (cmap(depth_norm / 255.0)[..., :3] * 255).astype(np.uint8)
-                        depth_gray = cv2.cvtColor(depth_color, cv2.COLOR_RGB2GRAY)
-                        depth_vis = np.stack([depth_gray] * 3, axis=-1)
-                    else:
-                        # Directly generate a grayscale image from the normalized depth values.
-                        depth_vis = np.stack([depth_norm] * 3, axis=-1)
-                else:
-                    # Generate a color depth image using the inferno colormap.
+                if depth_frame.max() > 0:  # Ensure we have valid depth data
+                    # Use the inferno colormap if requested 
                     cmap = matplotlib.colormaps.get_cmap("inferno")
-                    depth_vis = (cmap(depth_norm / 255.0)[..., :3] * 255).astype(np.uint8)
+                    # Convert to single channel first
+                    depth_gray = cv2.cvtColor(depth_frame, cv2.COLOR_RGB2GRAY)
+                    # Normalize to 0-1 range for colormap
+                    depth_norm = depth_gray / 255.0
+                    # Apply colormap
+                    depth_vis = (cmap(depth_norm)[..., :3] * 255).astype(np.uint8)
+                else:
+                    # If zero depth, just use the original
+                    depth_vis = depth_frame
+        
             
             # Apply Gaussian blur if requested
             if blur > 0: