Question

）因此，让它开始吧。我想实现下一个想法：我想使用webrtc（交换视频和音频数据）与其他计算机上的其他用户建立联系，然后重新确定自己的情绪。因此，在这个项目中，我使用node-webrtc addon（这里是examples）。因此，我已经下载了示例并测试了视频合成示例，并且一切正常。 Here is result of testing

下一部分是我要识别面部表情。对于此任务，我使用face-api.js。我已经测试过此nice video。我不会附上照片，因为现在我正在使用ubuntu，但在Windows上对其进行了测试，只是相信我也可以正常工作。因此，现在是将两个模块结合在一起的时候了。

作为主项目，我使用node-webrtc示例，所有后续解释将围绕该模块进行。因此，要运行结果，您应该将weights文件夹从face-api复制到node-webrtc / examples / video-compositing文件夹中，然后仅替换下面的代码，而不是node-webrtc / example / video-compositing / server.js。

'use strict';

require('@tensorflow/tfjs-node');
const tf = require('@tensorflow/tfjs');
const nodeFetch = require('node-fetch');
const fapi = require('face-api.js');
const path = require('path');
const { createCanvas, createImageData } = require('canvas');
const { RTCVideoSink, RTCVideoSource, i420ToRgba, rgbaToI420 } = require('wrtc').nonstandard;


fapi.env.monkeyPatch({ fetch: nodeFetch });
const MODELS_URL = path.join(__dirname, '/weights');

const width = 640;
const height = 480;

Promise.all([
  fapi.nets.tinyFaceDetector.loadFromDisk(MODELS_URL),
  fapi.nets.faceLandmark68Net.loadFromDisk(MODELS_URL),
  fapi.nets.faceRecognitionNet.loadFromDisk(MODELS_URL),
  fapi.nets.faceExpressionNet.loadFromDisk(MODELS_URL)
]);

function beforeOffer(peerConnection) {
  const source = new RTCVideoSource();
  const track = source.createTrack();
  const transceiver = peerConnection.addTransceiver(track);
  const sink = new RTCVideoSink(transceiver.receiver.track);

  let lastFrame = null;

  function onFrame({ frame }) {
    lastFrame = frame;
  }

  sink.addEventListener('frame', onFrame);

  // TODO(mroberts): Is pixelFormat really necessary?
  const canvas = createCanvas(width, height);
  const context = canvas.getContext('2d', { pixelFormat: 'RGBA24' });
  context.fillStyle = 'white';
  context.fillRect(0, 0, width, height);

  let emotion = '';
  const interval = setInterval(() => {
    if (lastFrame) {
      const lastFrameCanvas = createCanvas(lastFrame.width,  lastFrame.height);
      const lastFrameContext = lastFrameCanvas.getContext('2d', { pixelFormat: 'RGBA24' });

      const rgba = new Uint8ClampedArray(lastFrame.width *  lastFrame.height * 4);
      const rgbaFrame = createImageData(rgba, lastFrame.width, lastFrame.height);
      i420ToRgba(lastFrame, rgbaFrame);

      lastFrameContext.putImageData(rgbaFrame, 0, 0);
      context.drawImage(lastFrameCanvas, 0, 0);

      const emotionsArr = { 0: 'neutral', 1: 'happy', 2: 'sad', 3: 'angry', 4: 'fearful', 5: 'disgusted', 6: 'surprised' };

      async function detectEmotion() {
        let frameTensor3D = tf.browser.fromPixels(lastFrameCanvas)
        let face = await fapi.detectSingleFace(frameTensor3D, new fapi.TinyFaceDetectorOptions()).withFaceExpressions();
        //console.log(face);
        function getEmotion(face) {
          try {
            let mostLikelyEmotion = emotionsArr[0];
            let predictionArruracy =  face.expressions[emotionsArr[0]];

            for (let i = 0; i < Object.keys(face.expressions).length; i++) {
              if (face.expressions[emotionsArr[i]] > predictionArruracy && face.expressions[emotionsArr[i]] < 1 ){
                mostLikelyEmotion = emotionsArr[i];
                predictionArruracy = face.expressions[emotionsArr[i]];
              }
            }

            return mostLikelyEmotion;
          }
          catch (e){
            return '';
          }
        }
        let emot = getEmotion(face);
        return emot;
      }


      detectEmotion().then(function(res) {
        emotion = res;
      });

    } else {
      context.fillStyle = 'rgba(255, 255, 255, 0.025)';
      context.fillRect(0, 0, width, height);
    }

    if (emotion != ''){
      context.font = '60px Sans-serif';
      context.strokeStyle = 'black';
      context.lineWidth = 1;
      context.fillStyle = `rgba(${Math.round(255)}, ${Math.round(255)}, ${Math.round(255)}, 1)`;
      context.textAlign = 'center';
      context.save();
      context.translate(width / 2, height);
      context.strokeText(emotion, 0, 0);
      context.fillText(emotion, 0, 0);
      context.restore();
    }


    const rgbaFrame = context.getImageData(0, 0, width, height);
    const i420Frame = {
      width,
      height,
      data: new Uint8ClampedArray(1.5 * width * height)
    };
    rgbaToI420(rgbaFrame, i420Frame);
    source.onFrame(i420Frame);
  });

  const { close } = peerConnection;
  peerConnection.close = function() {
    clearInterval(interval);
    sink.stop();
    track.stop();
    return close.apply(this, arguments);
  };
}

module.exports = { beforeOffer };

这是results1，result2和result3，它们都可以正常工作））...好吧，不，在2-3分钟后，我的计算机停止执行任何操作，我什至无法移动鼠标，然后在终端中出现错误“ Killed”。我读到有关此错误here的信息，因为我只更改了项目中的一个脚本，所以我怀疑代码中的某个地方存在数据泄漏，并且随着时间的流逝，我的RAM越来越满。有人可以帮我解决这个问题吗？为什么程序以杀死进程结束？如果有人要自己测试，我将保留json包以轻松安装所有要求。

{
  "name": "node-webrtc-examples",
  "version": "0.1.0",
  "description": "This project presents a few example applications using node-webrtc.",
  "private": true,
  "main": "index.js",
  "scripts": {
    "lint": "eslint index.js examples lib test",
    "start": "node index.js",
    "test": "npm run test:unit && npm run test:integration",
    "test:unit": "tape 'test/unit/**/*.js'",
    "test:integration": "tape 'test/integration/**/*.js'"
  },
  "keywords": [
    "Web",
    "Audio"
  ],
  "author": "Mark Andrus Roberts <markandrusroberts@gmail.com>",
  "license": "BSD-3-Clause",
  "dependencies": {
    "@tensorflow/tfjs": "^1.2.9",
    "@tensorflow/tfjs-core": "^1.2.9",
    "@tensorflow/tfjs-node": "^1.2.9",
    "Scope": "github:kevincennis/Scope",
    "body-parser": "^1.18.3",
    "browserify-middleware": "^8.1.1",
    "canvas": "^2.6.0",
    "color-space": "^1.16.0",
    "express": "^4.16.4",
    "face-api.js": "^0.21.0",
    "node-fetch": "^2.3.0",
    "uuid": "^3.3.2",
    "wrtc": "^0.4.1"
  },
  "devDependencies": {
    "eslint": "^5.15.1",
    "tape": "^4.10.0"
  }
}

如果您遇到诸如“ someFunction不是函数”之类的错误或类似之类的错误，可能是因为您需要安装@ tensorflow / tfjs-core，tfjs和tfjs-node 1.2.9版本。像npm一样，我@ tensorflow / tfjs-core @ 1.2.9。对于所有3个程序包。感谢您的回答和理解））

Answer 1

在这一年中，我使用faceapi.js和tensorflow.js，我测试了您的代码及其正常运行，但是在不到一分钟的时间内将我的RAM增加到了2GB，这会导致内存泄漏，使用Tensor时应该释放记忆？你好吗？

但是您应该在节点to inspect memory leak 中使用--inspect arg

仅通话：

  frameTensor3D.dispose();

我重构了您的代码并与您分享，希望对您有所帮助

    "use strict";

require("@tensorflow/tfjs-node");
const tf = require("@tensorflow/tfjs");
const nodeFetch = require("node-fetch");
const fapi = require("face-api.js");
const path = require("path");
const { createCanvas, createImageData } = require("canvas");
const {
  RTCVideoSink,
  RTCVideoSource,
  i420ToRgba,
  rgbaToI420
} = require("wrtc").nonstandard;

fapi.env.monkeyPatch({ fetch: nodeFetch });
const MODELS_URL = path.join(__dirname, "/weights");

const width = 640;
const height = 480;

Promise.all([
  fapi.nets.tinyFaceDetector.loadFromDisk(MODELS_URL),
  fapi.nets.faceLandmark68Net.loadFromDisk(MODELS_URL),
  fapi.nets.faceRecognitionNet.loadFromDisk(MODELS_URL),
  fapi.nets.faceExpressionNet.loadFromDisk(MODELS_URL)
]);

function beforeOffer(peerConnection) {
  const source = new RTCVideoSource();
  const track = source.createTrack();
  const transceiver = peerConnection.addTransceiver(track);
  const sink = new RTCVideoSink(transceiver.receiver.track);

  let lastFrame = null;

  function onFrame({ frame }) {
    lastFrame = frame;
  }

  sink.addEventListener("frame", onFrame);

  // TODO(mroberts): Is pixelFormat really necessary?
  const canvas = createCanvas(width, height);
  const context = canvas.getContext("2d", { pixelFormat: "RGBA24" });
  context.fillStyle = "white";
  context.fillRect(0, 0, width, height);
  const emotionsArr = {
    0: "neutral",
    1: "happy",
    2: "sad",
    3: "angry",
    4: "fearful",
    5: "disgusted",
    6: "surprised"
  };
  async function detectEmotion(lastFrameCanvas) {
    const frameTensor3D = tf.browser.fromPixels(lastFrameCanvas);
    const face = await fapi
      .detectSingleFace(
        frameTensor3D,
        new fapi.TinyFaceDetectorOptions({ inputSize: 160 })
      )
      .withFaceExpressions();
    //console.log(face);
    const emo = getEmotion(face);
    frameTensor3D.dispose();
    return emo;
  }
  function getEmotion(face) {
    try {
      let mostLikelyEmotion = emotionsArr[0];
      let predictionArruracy = face.expressions[emotionsArr[0]];

      for (let i = 0; i < Object.keys(face.expressions).length; i++) {
        if (
          face.expressions[emotionsArr[i]] > predictionArruracy &&
          face.expressions[emotionsArr[i]] < 1
        ) {
          mostLikelyEmotion = emotionsArr[i];
          predictionArruracy = face.expressions[emotionsArr[i]];
        }
      }
      //console.log(mostLikelyEmotion);
      return mostLikelyEmotion;
    } catch (e) {
      return "";
    }
  }
  let emotion = "";
  const interval = setInterval(() => {
    if (lastFrame) {
      const lastFrameCanvas = createCanvas(lastFrame.width, lastFrame.height);
      const lastFrameContext = lastFrameCanvas.getContext("2d", {
        pixelFormat: "RGBA24"
      });

      const rgba = new Uint8ClampedArray(
        lastFrame.width * lastFrame.height * 4
      );
      const rgbaFrame = createImageData(
        rgba,
        lastFrame.width,
        lastFrame.height
      );
      i420ToRgba(lastFrame, rgbaFrame);

      lastFrameContext.putImageData(rgbaFrame, 0, 0);
      context.drawImage(lastFrameCanvas, 0, 0);

      detectEmotion(lastFrameCanvas).then(function(res) {
        emotion = res;
      });
    } else {
      context.fillStyle = "rgba(255, 255, 255, 0.025)";
      context.fillRect(0, 0, width, height);
    }

    if (emotion != "") {
      context.font = "60px Sans-serif";
      context.strokeStyle = "black";
      context.lineWidth = 1;
      context.fillStyle = `rgba(${Math.round(255)}, ${Math.round(
        255
      )}, ${Math.round(255)}, 1)`;
      context.textAlign = "center";
      context.save();
      context.translate(width / 2, height);
      context.strokeText(emotion, 0, 0);
      context.fillText(emotion, 0, 0);
      context.restore();
    }

    const rgbaFrame = context.getImageData(0, 0, width, height);
    const i420Frame = {
      width,
      height,
      data: new Uint8ClampedArray(1.5 * width * height)
    };
    rgbaToI420(rgbaFrame, i420Frame);
    source.onFrame(i420Frame);
  });

  const { close } = peerConnection;
  peerConnection.close = function() {
    clearInterval(interval);
    sink.stop();
    track.stop();
    return close.apply(this, arguments);
  };
}

module.exports = { beforeOffer };

对不起，我的英语，祝你好运

需要推荐代码，因为它会杀死我的程序

1 个答案: