//加载训练好的模型（weight，bias）
// ageGenderNet 识别性别和年龄
// faceExpressionNet 识别表情,开心，沮丧，普通
// faceLandmark68Net 识别脸部特征用于mobilenet算法
// faceLandmark68TinyNet 识别脸部特征用于tiny算法
// faceRecognitionNet 识别人脸
// ssdMobilenetv1 google开源AI算法除库包含分类和线性回归
// tinyFaceDetector 比Google的mobilenet更轻量级，速度更快一点
// mtcnn  多任务CNN算法，一开浏览器就卡死
// tinyYolov2 识别身体轮廓的算法，不知道怎么用
      Promise.all([
        faceapi.nets.faceRecognitionNet.loadFromUri(‘https://raw.githubusercontent.com/justadudewhohacks/face-api.js/master/weights‘),
        faceapi.nets.faceLandmark68Net.loadFromUri(‘https://raw.githubusercontent.com/justadudewhohacks/face-api.js/master/weights‘),
        faceapi.nets.faceLandmark68TinyNet.loadFromUri(‘https://raw.githubusercontent.com/justadudewhohacks/face-api.js/master/weights‘),
        faceapi.nets.ssdMobilenetv1.loadFromUri(‘https://raw.githubusercontent.com/justadudewhohacks/face-api.js/master/weights‘),
        faceapi.nets.tinyFaceDetector.loadFromUri(‘https://raw.githubusercontent.com/justadudewhohacks/face-api.js/master/weights‘),
        faceapi.nets.mtcnn.loadFromUri(‘https://raw.githubusercontent.com/justadudewhohacks/face-api.js/master/weights‘),
        //faceapi.nets.tinyYolov.loadFromUri(‘https://raw.githubusercontent.com/justadudewhohacks/face-api.js/master/weights‘)
      ]).then(async () => {})

SsdMobilenetv1Options
export interface ISsdMobilenetv1Options {
  // minimum confidence threshold
  // default: 0.5
  minConfidence?: number

  // maximum number of faces to return
  // default: 100
  maxResults?: number
}

// example
const options = new faceapi.SsdMobilenetv1Options({ minConfidence: 0.8 })
TinyFaceDetectorOptions
export interface ITinyFaceDetectorOptions {
  // size at which image is processed, the smaller the faster,
  // but less precise in detecting smaller faces, must be divisible
  // by 32, common sizes are 128, 160, 224, 320, 416, 512, 608,
  // for face tracking via webcam I would recommend using smaller sizes,
  // e.g. 128, 160, for detecting smaller faces use larger sizes, e.g. 512, 608
  // default: 416
  inputSize?: number

  // minimum confidence threshold
  // default: 0.5
  scoreThreshold?: number
}

// example
const options = new faceapi.TinyFaceDetectorOptions({ inputSize: 320 })
MtcnnOptions
export interface IMtcnnOptions {
  // minimum face size to expect, the higher the faster processing will be,
  // but smaller faces won‘t be detected
  // default: 20
  minFaceSize?: number

  // the score threshold values used to filter the bounding
  // boxes of stage 1, 2 and 3
  // default: [0.6, 0.7, 0.7]
  scoreThresholds?: number[]

  // scale factor used to calculate the scale steps of the image
  // pyramid used in stage 1
  // default: 0.709
  scaleFactor?: number

  // number of scaled versions of the input image passed through the CNN
  // of the first stage, lower numbers will result in lower inference time,
  // but will also be less accurate
  // default: 10
  maxNumScales?: number

  // instead of specifying scaleFactor and maxNumScales you can also
  // set the scaleSteps manually
  scaleSteps?: number[]
}

// example
const options = new faceapi.MtcnnOptions({ minFaceSize: 100, scaleFactor: 0.8 })

// all faces
await faceapi.detectAllFaces(input)
await faceapi.detectAllFaces(input).withFaceExpressions()
await faceapi.detectAllFaces(input).withFaceLandmarks()
await faceapi.detectAllFaces(input).withFaceLandmarks().withFaceExpressions()
await faceapi.detectAllFaces(input).withFaceLandmarks().withFaceExpressions().withFaceDescriptors()
await faceapi.detectAllFaces(input).withFaceLandmarks().withAgeAndGender().withFaceDescriptors()
await faceapi.detectAllFaces(input).withFaceLandmarks().withFaceExpressions().withAgeAndGender().withFaceDescriptors()

// single face
await faceapi.detectSingleFace(input)
await faceapi.detectSingleFace(input).withFaceExpressions()
await faceapi.detectSingleFace(input).withFaceLandmarks()
await faceapi.detectSingleFace(input).withFaceLandmarks().withFaceExpressions()
await faceapi.detectSingleFace(input).withFaceLandmarks().withFaceExpressions().withFaceDescriptor()
await faceapi.detectSingleFace(input).withFaceLandmarks().withAgeAndGender().withFaceDescriptor()
await faceapi.detectSingleFace(input).withFaceLandmarks().withFaceExpressions().withAgeAndGender().withFaceDescriptor()