refactor(mask): [PKG-MASK-04] migrate strict types and preserve public consumers

packages/artplayer-plugin-danmuku-mask/src/sdk.ts

@@ -0,0 +1,51 @@
+import type { BodySegmenter } from '@tensorflow-models/body-segmentation'
+import type { Active, MaskConfig, SegmenterConfig } from './types'
+import * as bodySegmentation from '@tensorflow-models/body-segmentation'
+import * as tf from '@tensorflow/tfjs-core'
+import '@tensorflow/tfjs-backend-webgl'
+import '@tensorflow/tfjs-backend-cpu'
+
+export async function loadSegmenter(config: MaskConfig, active: Active): Promise<BodySegmenter | null> {
+  try {
+    await tf.setBackend('webgl')
+  }
+  catch (error) {
+    if (!active())
+      return null
+    // Keep the historical message property read, including non-Error rejection behavior.
+    console.warn('WebGL backend not available, falling back to CPU', (error as { message: unknown }).message)
+    await tf.setBackend('cpu')
+  }
+  if (!active())
+    return null
+  try {
+    // Validate the full legacy object without dropping keys absent from the SDK config union.
+    return await bodySegmentation.createSegmenter(bodySegmentation.SupportedModels.MediaPipeSelfieSegmentation, {
+      runtime: 'mediapipe',
+      modelType: 'general',
+      solutionPath: config.solutionPath,
+      modelSelection: config.modelSelection,
+      smoothSegmentation: config.smoothSegmentation,
+      minDetectionConfidence: config.minDetectionConfidence,
+      minTrackingConfidence: config.minTrackingConfidence,
+      selfieMode: config.selfieMode,
+    } satisfies SegmenterConfig as SegmenterConfig)
+  }
+  catch (error) {
+    if (active())
+      console.error('Error initializing segmenter:', error)
+    return null
+  }
+}
+
+export async function releaseSegmenter(segmenter: BodySegmenter): Promise<void> {
+  try {
+    await segmenter.dispose()
+  }
+  catch (error) {
+    console.warn('Failed to dispose danmuku mask segmenter:', error)
+  }
+}
+
+export const toBinaryMask = (...args: Parameters<typeof bodySegmentation.toBinaryMask>) => bodySegmentation.toBinaryMask(...args)
+export const drawMask = (...args: Parameters<typeof bodySegmentation.drawMask>) => bodySegmentation.drawMask(...args)