0

此代码可以通过您上传的图像检测人（它适用于vscode中的 liveServer），我从https://github.com/WebDevSimplified/Face-Recognition-JavaScript下载了它

它真的很好用！但是……我想要别的东西

我想用我的网络摄像头视频替换输入，所以这将是一个实时识别系统......我有一个可以帮助的链接：事实上这是我想要的结果：https://www.shorturl。 at/dstHI仅使用此 html 文件而不使用 nodejs 这也可以提供帮助：https ://github.com/justadudewhohacks/face-api.js#models-face-recognition

<html lang="en">
<head>
  <meta charset="UTF-8">
  <meta name="viewport" content="width=device-width, initial-scale=1.0">
  <meta http-equiv="X-UA-Compatible" content="ie=edge">

  <script defer src="face-api.min.js"></script>
  <script defer src="script.js"></script>

  <title>Face Recognition</title>
  <style>
    body {
      margin: 0;
      padding: 0;
      width: 100vw;
      height: 100vh;
      display: flex;
      justify-content: center;
      align-items: center;
      flex-direction: column
    }

    canvas {
      position: absolute;
      top: 0;
      left: 0;
    }
  </style>
</head>
<body>


    <input type="file" id="imageUpload">


</body>
</html>

script.js：_

const imageUpload = document.getElementById('imageUpload')

Promise.all([
  faceapi.nets.faceRecognitionNet.loadFromUri('/models'),
  faceapi.nets.faceLandmark68Net.loadFromUri('/models'),
  faceapi.nets.ssdMobilenetv1.loadFromUri('/models')
]).then(start)

async function start() {
  const container = document.createElement('div')
  container.style.position = 'relative'
  document.body.append(container)
  const labeledFaceDescriptors = await loadLabeledImages()
  const faceMatcher = new faceapi.FaceMatcher(labeledFaceDescriptors, 0.6)
  let image
  let canvas
  document.body.append('Loaded')
  imageUpload.addEventListener('change', async () => {
    if (image) image.remove()
    if (canvas) canvas.remove()
    image = await faceapi.bufferToImage(imageUpload.files[0])
    container.append(image)
    canvas = faceapi.createCanvasFromMedia(image)
    container.append(canvas)
    const displaySize = { width: image.width, height: image.height }
    faceapi.matchDimensions(canvas, displaySize)
    const detections = await faceapi.detectAllFaces(image).withFaceLandmarks().withFaceDescriptors()
    const resizedDetections = faceapi.resizeResults(detections, displaySize)
    const results = resizedDetections.map(d => faceMatcher.findBestMatch(d.descriptor))
    results.forEach((result, i) => {
      const box = resizedDetections[i].detection.box
      const drawBox = new faceapi.draw.DrawBox(box, { label: result.toString() })
      drawBox.draw(canvas)
    })
  })
}

function loadLabeledImages() {
  const labels = ['Black Widow', 'Captain America', 'Captain Marvel', 'Hawkeye', 'Jim Rhodes', 'Thor', 'Tony Stark']
  return Promise.all(
    labels.map(async label => {
      const descriptions = []
      for (let i = 1; i <= 2; i++) {
        const img = await faceapi.fetchImage(`https://raw.githubusercontent.com/WebDevSimplified/Face-Recognition-JavaScript/master/labeled_images/${label}/${i}.jpg`)
        const detections = await faceapi.detectSingleFace(img).withFaceLandmarks().withFaceDescriptor()
        descriptions.push(detections.descriptor)
      }

      return new faceapi.LabeledFaceDescriptors(label, descriptions)
    })
  )
}
4

0 回答 0