Add EntitySelect/IconSelect UI improvements across modals

- Portal IconSelect popups to document.body with position:fixed to prevent
  clipping by modal overflow-y:auto
- Replace custom scene selectors in automation editor with EntitySelect
  command-palette pickers (main scene + fallback scene)
- Add IconSelect grid for automation deactivation mode (none/revert/fallback)
- Add IconSelect grid for automation condition type and match type
- Replace mapped zone source dropdowns with EntitySelect pickers
- Replace scene target selector with EntityPalette.pick() pattern
- Remove effect palette preview bar from CSS editor
- Remove sensitivity badge from audio color strip source cards
- Clean up unused scene-selector CSS and scene-target-add-row CSS
- Add locale keys for all new UI elements across en/ru/zh

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

server/src/wled_controller/core/capture/calibration.py

@@ -1,7 +1,7 @@
 """Calibration system for mapping screen pixels to LED positions."""
 
-from dataclasses import dataclass
-from typing import Dict, List, Literal, Tuple
+from dataclasses import dataclass, field
+from typing import Dict, List, Literal, Optional, Set, Tuple
 
 import numpy as np
 
@@ -51,6 +51,19 @@ class CalibrationSegment:
     reverse: bool = False
 
 
+@dataclass
+class CalibrationLine:
+    """One LED line in advanced calibration — references one picture source edge."""
+
+    picture_source_id: str
+    edge: Literal["top", "right", "bottom", "left"]
+    led_count: int
+    span_start: float = 0.0
+    span_end: float = 1.0
+    reverse: bool = False
+    border_width: int = 10
+
+
 @dataclass
 class CalibrationConfig:
     """Complete calibration configuration.
@@ -59,8 +72,14 @@ class CalibrationConfig:
     are derived at runtime via the `segments` property.
     """
 
-    layout: Literal["clockwise", "counterclockwise"]
-    start_position: Literal["top_left", "top_right", "bottom_left", "bottom_right"]
+    # Mode: "simple" = 4-edge model (backward compat), "advanced" = generic line list
+    mode: Literal["simple", "advanced"] = "simple"
+    # Advanced mode: ordered list of CalibrationLine objects (ignored in simple mode)
+    lines: List[CalibrationLine] = field(default_factory=list)
+
+    # Simple mode fields (also used as defaults for CalibrationConfig constructor)
+    layout: Literal["clockwise", "counterclockwise"] = "clockwise"
+    start_position: Literal["top_left", "top_right", "bottom_left", "bottom_right"] = "bottom_left"
     offset: int = 0
     leds_top: int = 0
     leds_right: int = 0
@@ -135,6 +154,20 @@ class CalibrationConfig:
         Raises:
             ValueError: If configuration is invalid
         """
+        if self.mode == "advanced":
+            if not self.lines:
+                raise ValueError("Advanced calibration must have at least one line")
+            for i, line in enumerate(self.lines):
+                if line.led_count <= 0:
+                    raise ValueError(f"Line {i}: LED count must be positive, got {line.led_count}")
+                if not (0.0 <= line.span_start <= 1.0) or not (0.0 <= line.span_end <= 1.0):
+                    raise ValueError(f"Line {i}: span must be in [0.0, 1.0]")
+                if line.span_end <= line.span_start:
+                    raise ValueError(f"Line {i}: span_end must be greater than span_start")
+                if line.border_width < 1:
+                    raise ValueError(f"Line {i}: border_width must be at least 1")
+            return True
+
         total = self.get_total_leds()
         if total <= 0:
             raise ValueError("Calibration must have at least one LED")
@@ -154,9 +187,26 @@ class CalibrationConfig:
         return True
 
     def get_total_leds(self) -> int:
-        """Get total number of LEDs across all edges."""
+        """Get total number of LEDs across all edges/lines."""
+        if self.mode == "advanced":
+            return sum(line.led_count for line in self.lines)
         return self.leds_top + self.leds_right + self.leds_bottom + self.leds_left
 
+    def get_required_picture_source_ids(self) -> List[str]:
+        """Get deduplicated list of picture source IDs referenced by lines.
+
+        Returns empty list for simple mode (the stream provides the source).
+        """
+        if self.mode != "advanced":
+            return []
+        seen: Set[str] = set()
+        result: List[str] = []
+        for line in self.lines:
+            if line.picture_source_id not in seen:
+                seen.add(line.picture_source_id)
+                result.append(line.picture_source_id)
+        return result
+
     def get_segment_for_edge(self, edge: str) -> CalibrationSegment | None:
         """Get segment configuration for a specific edge."""
         for seg in self.segments:
@@ -408,6 +458,216 @@ class PixelMapper:
         return led_colors
 
 
+class AdvancedPixelMapper:
+    """Maps multi-source screen pixels to LED colors for advanced calibration.
+
+    Each CalibrationLine references a picture source and an edge, with its own
+    span and border_width. Frames from multiple sources are passed in as a dict.
+    """
+
+    def __init__(
+        self,
+        calibration: CalibrationConfig,
+        interpolation_mode: Literal["average", "median", "dominant"] = "average",
+    ):
+        self.calibration = calibration
+        self.interpolation_mode = interpolation_mode
+        calibration.validate()
+
+        if interpolation_mode == "average":
+            self._calc_color = calculate_average_color
+        elif interpolation_mode == "median":
+            self._calc_color = calculate_median_color
+        elif interpolation_mode == "dominant":
+            self._calc_color = calculate_dominant_color
+        else:
+            raise ValueError(f"Invalid interpolation mode: {interpolation_mode}")
+
+        total_leds = calibration.get_total_leds()
+        self._total_leds = total_leds
+        self._led_buf = np.zeros((total_leds, 3), dtype=np.uint8)
+        self._use_fast_avg = interpolation_mode == "average"
+
+        # Build segment-like metadata from lines (led_start for each line)
+        offset = calibration.offset % total_leds if total_leds > 0 else 0
+        self._line_indices: List[np.ndarray] = []
+        led_start = 0
+        for line in calibration.lines:
+            indices = np.arange(led_start, led_start + line.led_count)
+            if line.reverse:
+                indices = indices[::-1]
+            if offset > 0:
+                indices = (indices + offset) % total_leds
+            self._line_indices.append(indices)
+            led_start += line.led_count
+
+        # Skip arrays (same logic as PixelMapper)
+        skip_start = calibration.skip_leds_start
+        skip_end = calibration.skip_leds_end
+        self._skip_start = skip_start
+        self._skip_end = skip_end
+        self._active_count = max(0, total_leds - skip_start - skip_end)
+        if 0 < self._active_count < total_leds:
+            self._skip_src = np.linspace(0, total_leds - 1, self._active_count)
+            self._skip_x = np.arange(total_leds, dtype=np.float64)
+            self._skip_float = np.empty((total_leds, 3), dtype=np.float64)
+            self._skip_resampled = np.empty((self._active_count, 3), dtype=np.uint8)
+        else:
+            self._skip_src = self._skip_x = self._skip_float = self._skip_resampled = None
+
+        # Per-line edge cache (keyed by line index to avoid collision)
+        self._edge_cache: Dict[int, tuple] = {}
+
+        logger.info(
+            f"Initialized advanced pixel mapper with {total_leds} LEDs, "
+            f"{len(calibration.lines)} lines, {interpolation_mode} interpolation"
+        )
+
+    @staticmethod
+    def _extract_edge_strip(
+        frame: np.ndarray, edge: str, border_width: int,
+        span_start: float, span_end: float,
+    ) -> np.ndarray:
+        """Extract a border strip from a frame for the given edge and span."""
+        h, w = frame.shape[:2]
+        bw = min(border_width, h // 4, w // 4)
+
+        if edge == "top":
+            strip = frame[:bw, :, :]
+        elif edge == "bottom":
+            strip = frame[-bw:, :, :]
+        elif edge == "right":
+            strip = frame[:, -bw:, :]
+        else:  # left
+            strip = frame[:, :bw, :]
+
+        # Apply span
+        if span_start > 0.0 or span_end < 1.0:
+            if edge in ("top", "bottom"):
+                total_w = strip.shape[1]
+                s, e = int(span_start * total_w), int(span_end * total_w)
+                strip = strip[:, s:e, :]
+            else:
+                total_h = strip.shape[0]
+                s, e = int(span_start * total_h), int(span_end * total_h)
+                strip = strip[s:e, :, :]
+
+        return strip
+
+    def _map_edge_average(
+        self, edge_pixels: np.ndarray, edge_name: str, led_count: int,
+        cache_key: int,
+    ) -> np.ndarray:
+        """Vectorized average-color mapping (same algo as PixelMapper)."""
+        if edge_name in ("top", "bottom"):
+            axis = 0
+            edge_len = edge_pixels.shape[1]
+        else:
+            axis = 1
+            edge_len = edge_pixels.shape[0]
+
+        cache = self._edge_cache.get(cache_key)
+        if cache is None or cache[0] != edge_len:
+            step = edge_len / led_count
+            boundaries = (np.arange(led_count + 1, dtype=np.float64) * step).astype(np.int64)
+            boundaries[1:] = np.maximum(boundaries[1:], boundaries[:-1] + 1)
+            np.minimum(boundaries, edge_len, out=boundaries)
+            starts = boundaries[:-1]
+            ends = boundaries[1:]
+            lengths = (ends - starts).reshape(-1, 1).astype(np.float64)
+            cumsum_buf = np.empty((edge_len + 1, 3), dtype=np.float64)
+            edge_1d_buf = np.empty((edge_len, 3), dtype=np.float64)
+            cache = (edge_len, starts, ends, lengths, cumsum_buf, edge_1d_buf)
+            self._edge_cache[cache_key] = cache
+
+        _, starts, ends, lengths, cumsum_buf, edge_1d_buf = cache
+        np.mean(edge_pixels, axis=axis, out=edge_1d_buf)
+        cumsum_buf[0] = 0
+        np.cumsum(edge_1d_buf, axis=0, out=cumsum_buf[1:])
+        segment_sums = cumsum_buf[ends] - cumsum_buf[starts]
+        return np.clip(segment_sums / lengths, 0, 255).astype(np.uint8)
+
+    def _map_edge_fallback(
+        self, edge_pixels: np.ndarray, edge_name: str, led_count: int,
+    ) -> np.ndarray:
+        """Per-LED color mapping for median/dominant modes."""
+        if edge_name in ("top", "bottom"):
+            edge_len = edge_pixels.shape[1]
+        else:
+            edge_len = edge_pixels.shape[0]
+
+        step = edge_len / led_count
+        result = np.empty((led_count, 3), dtype=np.uint8)
+        for i in range(led_count):
+            start = int(i * step)
+            end = max(start + 1, int((i + 1) * step))
+            end = min(end, edge_len)
+            if edge_name in ("top", "bottom"):
+                segment = edge_pixels[:, start:end, :]
+            else:
+                segment = edge_pixels[start:end, :, :]
+            result[i] = self._calc_color(segment)
+        return result
+
+    def map_lines_to_leds(self, frames: Dict[str, np.ndarray]) -> np.ndarray:
+        """Map multi-source frames to LED colors using calibration lines.
+
+        Args:
+            frames: Dict mapping picture_source_id to captured frame (H, W, 3) uint8
+
+        Returns:
+            numpy array of shape (total_leds, 3), dtype uint8
+        """
+        led_array = self._led_buf
+        led_array[:] = 0
+
+        for i, line in enumerate(self.calibration.lines):
+            frame = frames.get(line.picture_source_id)
+            if frame is None:
+                continue
+
+            edge_pixels = self._extract_edge_strip(
+                frame, line.edge, line.border_width,
+                line.span_start, line.span_end,
+            )
+
+            if self._use_fast_avg:
+                colors = self._map_edge_average(
+                    edge_pixels, line.edge, line.led_count, cache_key=i,
+                )
+            else:
+                colors = self._map_edge_fallback(
+                    edge_pixels, line.edge, line.led_count,
+                )
+
+            led_array[self._line_indices[i]] = colors
+
+        # Phase 3: Physical skip (same as PixelMapper)
+        if self._skip_src is not None:
+            np.copyto(self._skip_float, led_array, casting='unsafe')
+            for ch in range(3):
+                self._skip_resampled[:, ch] = np.round(
+                    np.interp(self._skip_src, self._skip_x, self._skip_float[:, ch])
+                ).astype(np.uint8)
+            led_array[:] = 0
+            end_idx = self._total_leds - self._skip_end
+            led_array[self._skip_start:end_idx] = self._skip_resampled
+        elif self._active_count <= 0:
+            led_array[:] = 0
+
+        return led_array
+
+
+def create_pixel_mapper(
+    calibration: CalibrationConfig,
+    interpolation_mode: str = "average",
+):
+    """Factory: create the right mapper for the calibration mode."""
+    if calibration.mode == "advanced":
+        return AdvancedPixelMapper(calibration, interpolation_mode)
+    return PixelMapper(calibration, interpolation_mode)
+
+
 def create_default_calibration(led_count: int) -> CalibrationConfig:
     """Create a default calibration for a rectangular screen.
 
@@ -464,7 +724,35 @@ def calibration_from_dict(data: dict) -> CalibrationConfig:
         ValueError: If data is invalid
     """
     try:
+        mode = data.get("mode", "simple")
+
+        if mode == "advanced":
+            lines_data = data.get("lines", [])
+            lines = [
+                CalibrationLine(
+                    picture_source_id=ld["picture_source_id"],
+                    edge=ld["edge"],
+                    led_count=ld["led_count"],
+                    span_start=ld.get("span_start", 0.0),
+                    span_end=ld.get("span_end", 1.0),
+                    reverse=ld.get("reverse", False),
+                    border_width=ld.get("border_width", 10),
+                )
+                for ld in lines_data
+            ]
+            config = CalibrationConfig(
+                mode="advanced",
+                lines=lines,
+                offset=data.get("offset", 0),
+                skip_leds_start=data.get("skip_leds_start", 0),
+                skip_leds_end=data.get("skip_leds_end", 0),
+            )
+            config.validate()
+            return config
+
+        # Simple mode (backward compat — missing "mode" key defaults here)
         config = CalibrationConfig(
+            mode="simple",
             layout=data["layout"],
             start_position=data["start_position"],
             offset=data.get("offset", 0),
@@ -505,7 +793,33 @@ def calibration_to_dict(config: CalibrationConfig) -> dict:
     Returns:
         Dictionary representation
     """
+    if config.mode == "advanced":
+        result: dict = {
+            "mode": "advanced",
+            "lines": [
+                {
+                    "picture_source_id": line.picture_source_id,
+                    "edge": line.edge,
+                    "led_count": line.led_count,
+                    "span_start": line.span_start,
+                    "span_end": line.span_end,
+                    "reverse": line.reverse,
+                    "border_width": line.border_width,
+                }
+                for line in config.lines
+            ],
+        }
+        if config.offset != 0:
+            result["offset"] = config.offset
+        if config.skip_leds_start > 0:
+            result["skip_leds_start"] = config.skip_leds_start
+        if config.skip_leds_end > 0:
+            result["skip_leds_end"] = config.skip_leds_end
+        return result
+
+    # Simple mode
     result = {
+        "mode": "simple",
         "layout": config.layout,
         "start_position": config.start_position,
         "offset": config.offset,