Update app.py

app.py

@@ -33,11 +33,26 @@ def stitch_rgbd_videos(
     
     stitched_video_path = None
     if stitch:
+        # Ensure target_fps is valid (positive) or use original fps
+        safe_target_fps = max(1, target_fps) if target_fps > 0 else -1
+        
         # For stitching: read the original video in full resolution (without downscaling).
-        full_frames, target_fps = read_video_frames(processed_video, max_len, target_fps, max_res=-1)
+        full_frames, original_fps = read_video_frames(processed_video, max_len, target_fps, max_res=-1)
         depths, _ = read_video_frames(depth_vis_video, max_len, target_fps, max_res=-1)
+
+        # Use original_fps if target_fps wasn't specified
+        if target_fps <= 0:
+            target_fps = original_fps
         
-        print(f"Depth frame shape: {depths[0].shape}, dtype: {depths[0].dtype}, min: {depths[0].min()}, max: {depths[0].max()}")
+        print(f"Video fps: {original_fps}, target fps: {target_fps}")
+        print(f"Depth frame shape: {depths[0].shape if len(depths) > 0 else 'No frames'}, "
+              f"dtype: {depths[0].dtype if len(depths) > 0 else 'N/A'}, "
+              f"min: {depths.min() if len(depths) > 0 else 'N/A'}, "
+              f"max: {depths.max() if len(depths) > 0 else 'N/A'}")
+
+        if len(depths) == 0 or len(full_frames) == 0:
+            print("Error: No frames to process in either RGB or depth video")
+            return None
         
         # For each frame, create a visual depth image from the inferenced depths.
         d_min, d_max = np.min(depths), np.max(depths)