FaceDetector faceDetector = new FaceDetector();
 var faceImage0 = new Bitmap(dirPath).ToFaceImage();
 var infos0 = faceDetector.Detect(faceImage0);
 FaceLandmarker faceMark = new FaceLandmarker();
 var points0 = faceMark.Mark(faceImage0, infos0[0]);
 FaceRecognizer faceRecognizer = new FaceRecognizer();
 float[] fromData = faceRecognizer.Extract(faceImage0, points0);
 float[] faceData = model.FaceVector;
 return faceRecognizer.Compare(fromData, faceData);

async getSingleFace(s) {
    var data = this.faceImgPath;
    ar = await dds.dynamic.api.exectuce({ method: "post", pluginName: "Face", actionName: "GetSingleFace", actionData: (data) })
    console.log('人臉信息', ar)
    this.faceKeys = ar.Data;
},
async compareFace(cb) {
    var data = { fileName: this.faceImgPath, targetKeys: this.faceKeys };
    ar = await dds.dynamic.api.exectuce({ method: "post", pluginName: "Face", actionName: "Compare", actionData: JSON.stringify(data) })
    console.log('人臉比較', ar)
    this.resultInfo = "相似度：" + ar.Data + ""
    if (ar.Data < 0.8) {
        cb && cb()
    } else {
        this.startVideo = false;
    }
},

    async function updateResults() {
      if (!isFaceDetectionModelLoaded()) {
        return
      }
      const inputImgEl = $('#inputImg').get(0)
      const options = getFaceDetectorOptions()
      const results = await faceapi.detectAllFaces(inputImgEl, options)
      const canvas = $('#overlay').get(0)
      faceapi.matchDimensions(canvas, inputImgEl)
      faceapi.draw.drawDetections(canvas, faceapi.resizeResults(results, inputImgEl))
    }
    async function run() {
      // 加載人臉檢測模型
      await changeFaceDetector(SSD_MOBILENETV1)
      // 開始處理圖片
      updateResults()
    }