Optimize WLED processing pipeline and add FPS metrics

- Add numpy-based DDP pixel packing (send_pixels_numpy) and fast send
  path (send_pixels_fast) eliminating per-pixel Python loops
- Move ProcessedLiveStream filter processing to background thread so
  get_latest_frame() returns pre-computed cached result instantly
- Vectorize map_border_to_leds for average interpolation using cumulative
  sums instead of 934 individual np.mean calls (~16ms -> <1ms)
- Batch all CPU work into single asyncio.to_thread call per frame
- Fix FPS calculation to measure frame-to-frame interval (was measuring
  processing time only, reporting inflated values)
- Add Potential FPS metric showing theoretical max without throttling
- Add FPS label to WLED target card properties
- Add fps_potential field to TargetProcessingState API schema

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

server/src/wled_controller/api/schemas/picture_targets.py

@@ -123,6 +123,7 @@ class TargetProcessingState(BaseModel):
     device_id: Optional[str] = Field(None, description="Device ID")
     processing: bool = Field(description="Whether processing is active")
     fps_actual: Optional[float] = Field(None, description="Actual FPS achieved")
+    fps_potential: Optional[float] = Field(None, description="Potential FPS (processing speed without throttle)")
     fps_target: int = Field(default=0, description="Target FPS")
     display_index: int = Field(default=0, description="Current display index")
     last_update: Optional[datetime] = Field(None, description="Last successful update")