前端人脸识别功能实现代码
时间: 2023-08-24 15:03:53 浏览: 49
前端实现人脸识别需要借助浏览器提供的WebRTC技术,结合一些JavaScript库来完成。以下是一个基于Face-API.js库实现前端人脸识别的示例代码:
```html
<!DOCTYPE html>
<html>
<head>
<title>前端人脸识别示例</title>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-core"></script>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-converter"></script>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow-models/face-landmarks-detection"></script>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow-models/face-landmarks-detection"></script>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow-models/face-api"></script>
</head>
<body>
<h1>前端人脸识别示例</h1>
<video id="video" width="640" height="480"></video>
<canvas id="canvas" width="640" height="480"></canvas>
<script>
// 获取视频元素
const video = document.getElementById('video');
// 获取画布元素
const canvas = document.getElementById('canvas');
const context = canvas.getContext('2d');
// 获取人脸检测模型
Promise.all([
faceapi.nets.tinyFaceDetector.loadFromUri('/models'),
faceapi.nets.faceLandmark68Net.loadFromUri('/models'),
faceapi.nets.faceRecognitionNet.loadFromUri('/models'),
faceapi.nets.faceExpressionNet.loadFromUri('/models')
]).then(startVideo)
// 开始捕获视频
function startVideo() {
navigator.getUserMedia(
{ video: {} },
stream => video.srcObject = stream,
err => console.error(err)
)
}
// 检测人脸
video.addEventListener('play', () => {
const displaySize = { width: video.width, height: video.height }
faceapi.matchDimensions(canvas, displaySize)
setInterval(async () => {
const detections = await faceapi.detectAllFaces(video, new faceapi.TinyFaceDetectorOptions()).withFaceLandmarks().withFaceExpressions()
const resizedDetections = faceapi.resizeResults(detections, displaySize)
context.clearRect(0, 0, canvas.width, canvas.height)
faceapi.draw.drawDetections(canvas, resizedDetections)
faceapi.draw.drawFaceLandmarks(canvas, resizedDetections)
faceapi.draw.drawFaceExpressions(canvas, resizedDetections)
}, 100)
})
</script>
</body>
</html>
```
这段代码使用了Face-API.js库来实现人脸检测和面部特征点识别。首先,通过`Promise.all`函数加载了所需的模型,然后使用`navigator.getUserMedia`函数获取用户摄像头的视频流并将其显示在`video`元素中。接着,使用`setInterval`函数定时检测视频帧中的人脸,并使用`faceapi.draw`函数在画布上绘制人脸检测结果。最后,将画布的内容显示在页面上。
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