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vue实现人脸识别

2026-01-17 15:30:43VUE

Vue 实现人脸识别的方法

在 Vue 项目中实现人脸识别功能，通常需要结合第三方库或 API。以下是几种常见的实现方式：

使用 TensorFlow.js

TensorFlow.js 是一个 JavaScript 机器学习库，可以在浏览器中运行。结合人脸识别模型（如 Face-api.js），可以实现在 Vue 中的人脸检测。

安装依赖：

npm install face-api.js

示例代码：

<template>
  <div>
    <video ref="video" width="600" height="450" autoplay></video>
    <canvas ref="canvas" width="600" height="450"></canvas>
  </div>
</template>

<script>
import * as faceapi from 'face-api.js';

export default {
  mounted() {
    this.loadModels();
    this.startVideo();
  },
  methods: {
    async loadModels() {
      await faceapi.nets.tinyFaceDetector.loadFromUri('/models');
      await faceapi.nets.faceLandmark68Net.loadFromUri('/models');
    },
    async startVideo() {
      const video = this.$refs.video;
      navigator.mediaDevices.getUserMedia({ video: true })
        .then(stream => {
          video.srcObject = stream;
          this.detectFaces();
        });
    },
    async detectFaces() {
      const video = this.$refs.video;
      const canvas = this.$refs.canvas;
      const displaySize = { width: video.width, height: video.height };

      setInterval(async () => {
        const detections = await faceapi.detectAllFaces(video, 
          new faceapi.TinyFaceDetectorOptions()).withFaceLandmarks();

        faceapi.matchDimensions(canvas, displaySize);
        const resizedDetections = faceapi.resizeResults(detections, displaySize);
        faceapi.draw.drawDetections(canvas, resizedDetections);
        faceapi.draw.drawFaceLandmarks(canvas, resizedDetections);
      }, 100);
    }
  }
};
</script>

使用百度 AI 开放平台

百度 AI 提供人脸识别 API，可以通过 HTTP 请求调用。

安装 axios：

npm install axios

示例代码：

<template>
  <div>
    <input type="file" @change="handleFileUpload" accept="image/*">
    <img :src="imageUrl" v-if="imageUrl" width="300">
  </div>
</template>

<script>
import axios from 'axios';

export default {
  data() {
    return {
      imageUrl: ''
    };
  },
  methods: {
    handleFileUpload(event) {
      const file = event.target.files[0];
      this.imageUrl = URL.createObjectURL(file);
      this.detectFace(file);
    },
    async detectFace(file) {
      const formData = new FormData();
      formData.append('image', file);

      try {
        const response = await axios.post(
          'https://aip.baidubce.com/rest/2.0/face/v3/detect',
          formData,
          {
            headers: {
              'Content-Type': 'multipart/form-data',
              'Authorization': 'Bearer YOUR_ACCESS_TOKEN'
            }
          }
        );
        console.log('识别结果:', response.data);
      } catch (error) {
        console.error('识别失败:', error);
      }
    }
  }
};
</script>

使用 OpenCV.js

OpenCV.js 是 OpenCV 的 JavaScript 版本，可以在浏览器中进行图像处理和人脸检测。

引入 OpenCV.js：

<script async src="https://docs.opencv.org/4.5.5/opencv.js" onload="onOpenCvReady();"></script>

示例代码：

<template>
  <div>
    <video ref="video" width="600" height="450" autoplay></video>
    <canvas ref="canvas" width="600" height="450"></canvas>
  </div>
</template>

<script>
export default {
  mounted() {
    this.startVideo();
  },
  methods: {
    startVideo() {
      const video = this.$refs.video;
      navigator.mediaDevices.getUserMedia({ video: true })
        .then(stream => {
          video.srcObject = stream;
          this.processVideo();
        });
    },
    processVideo() {
      const video = this.$refs.video;
      const canvas = this.$refs.canvas;
      const ctx = canvas.getContext('2d');

      function processFrame() {
        ctx.drawImage(video, 0, 0, canvas.width, canvas.height);
        const src = cv.imread(canvas);
        const gray = new cv.Mat();
        cv.cvtColor(src, gray, cv.COLOR_RGBA2GRAY);

        const faces = new cv.RectVector();
        const faceCascade = new cv.CascadeClassifier();
        faceCascade.load('haarcascade_frontalface_default.xml');

        faceCascade.detectMultiScale(gray, faces);
        for (let i = 0; i < faces.size(); ++i) {
          const face = faces.get(i);
          const point1 = new cv.Point(face.x, face.y);
          const point2 = new cv.Point(face.x + face.width, face.y + face.height);
          cv.rectangle(src, point1, point2, [255, 0, 0, 255]);
        }

        cv.imshow(canvas, src);
        requestAnimationFrame(processFrame);
      }

      setTimeout(processFrame, 0);
    }
  }
};
</script>