SpringBoot+SeetaFace6搭建人脸识别平台

前言

最近多个项目需要接入人脸识别功能，之前的方案是使用百度云api集成，但是后续部分项目是内网部署及使用，考虑到接入复杂程度及收费等多种因素，决定参考开源方案自己搭建，保证服务的稳定性与可靠性

项目地址：https://gitee.com/code2roc/fastface

设计

经过检索对别多个方案后，使用了基于seetaface6+springboot的方式进行搭建，能够无缝接入应用

seetaface6是中科视拓最新开源的商业正式版本，包含人脸识别的基本能力：人脸检测、关键点定位、人脸识别，同时增加了活体检测、质量评估、年龄性别估计

官网地址：https://github.com/SeetaFace6Open/index

使用对接的sdk是tracy100大神的封装，支持 jdk8-jdk14，支持windows和Linux，无需考虑部署问题，直接使用jar包实现业务即可，内部同时封装了bean对象spring能够开箱即用

官网地址：https://github.com/tracy100/seetaface6SDK

系统目标实现人脸注册，人脸比对，人脸查找基础功能即可

实现

引用jar包

        <dependency>
            <groupId>com.seeta.sdk</groupId>
            <artifactId>seeta-sdk-platform</artifactId>
            <scope>system</scope>
            <version>1.2.1</version>
            <systemPath>${project.basedir}/lib/seetaface.jar</systemPath>
        </dependency>

bean对象注册

FaceDetectorProxy为人脸检测bean，能够检测图像中是否有人脸

FaceRecognizerProxy为人脸比对bean，能够比对两张人脸的相似度

FaceLandmarkerProxy为人脸关键点bean，能够检测人脸的关键点，支持5个点和68个点

@Configuration
public class FaceConfig {
    @Value("${face.modelPath}")
    private String modelPath;

    @Bean
    public FaceDetectorProxy faceDetector() throws FileNotFoundException {
        SeetaConfSetting detectorPoolSetting = new SeetaConfSetting(
                new SeetaModelSetting(0, new String[]{modelPath + File.separator + "face_detector.csta"}, SeetaDevice.SEETA_DEVICE_CPU));
        FaceDetectorProxy faceDetectorProxy = new FaceDetectorProxy(detectorPoolSetting);
        return faceDetectorProxy;
    }

    @Bean
    public FaceRecognizerProxy faceRecognizer() throws FileNotFoundException {
        SeetaConfSetting detectorPoolSetting = new SeetaConfSetting(
                new SeetaModelSetting(0, new String[]{modelPath + File.separator + "face_recognizer.csta"}, SeetaDevice.SEETA_DEVICE_CPU));
        FaceRecognizerProxy faceRecognizerProxy = new FaceRecognizerProxy(detectorPoolSetting);
        return faceRecognizerProxy;
    }

    @Bean
    public FaceLandmarkerProxy faceLandmarker() throws FileNotFoundException {
        SeetaConfSetting detectorPoolSetting = new SeetaConfSetting(
                new SeetaModelSetting(0, new String[]{modelPath + File.separator + "face_landmarker_pts5.csta"}, SeetaDevice.SEETA_DEVICE_CPU));
        FaceLandmarkerProxy faceLandmarkerProxy = new FaceLandmarkerProxy(detectorPoolSetting);
        return faceLandmarkerProxy;
    }
}

在使用相关bean对象时，需要进行library的本地注册，指定cpu还是gpu模式

LoadNativeCore.LOAD_NATIVE(SeetaDevice.SEETA_DEVICE_CPU)

人脸检测

    public FaceEnum.CheckImageFaceStatus getFace(BufferedImage image) throws Exception {
        SeetaImageData imageData = SeetafaceUtil.toSeetaImageData(image);
        SeetaRect[] detects = faceDetectorProxy.detect(imageData);
        if (detects.length == 0) {
            return FaceEnum.CheckImageFaceStatus.NoFace;
        } else if (detects.length == 1) {
            return FaceEnum.CheckImageFaceStatus.OneFace;
        } else {
            return FaceEnum.CheckImageFaceStatus.MoreFace;
        }
    }

人脸比对

    public FaceEnum.CompareImageFaceStatus compareFace(BufferedImage source, BufferedImage compare) throws Exception {
        float[] sourceFeature = extract(source);
        float[] compareFeature = extract(compare);
        if (sourceFeature != null && compareFeature != null) {
            float calculateSimilarity = faceRecognizerProxy.calculateSimilarity(sourceFeature, compareFeature);
            System.out.printf("相似度:%f\n", calculateSimilarity);
            if (calculateSimilarity >= CHECK_SIM) {
                return FaceEnum.CompareImageFaceStatus.Same;
            } else {
                return FaceEnum.CompareImageFaceStatus.Different;
            }
        } else {
            return FaceEnum.CompareImageFaceStatus.LostFace;
        }
    }

人脸关键点

    private float[] extract(BufferedImage image) throws Exception {
        SeetaImageData imageData = SeetafaceUtil.toSeetaImageData(image);
        SeetaRect[] detects = faceDetectorProxy.detect(imageData);
        if (detects.length > 0) {
            SeetaPointF[] pointFS = faceLandmarkerProxy.mark(imageData, detects[0]);
            float[] features = faceRecognizerProxy.extract(imageData, pointFS);
            return features;
        }
        return null;
    }

人脸数据库

• 注册

    public long registFace(BufferedImage image) throws Exception {
        long result = -1;
        SeetaImageData imageData = SeetafaceUtil.toSeetaImageData(image);
        SeetaRect[] detects = faceDetectorProxy.detect(imageData);
        if (detects.length > 0) {
            SeetaPointF[] pointFS = faceLandmarkerProxy.mark(imageData, detects[0]);
            result = faceDatabase.Register(imageData, pointFS);
            faceDatabase.Save(dataBasePath);
        }
        return result;
    }

• 查找

    public long queryFace(BufferedImage image) throws Exception {
        long result = -1;
        SeetaImageData imageData = SeetafaceUtil.toSeetaImageData(image);
        SeetaRect[] detects = faceDetectorProxy.detect(imageData);
        if (detects.length > 0) {
            SeetaPointF[] pointFS = faceLandmarkerProxy.mark(imageData, detects[0]);
            long[] index = new long[1];
            float[] sim = new float[1];
            result = faceDatabase.QueryTop(imageData, pointFS, 1, index, sim);
            if (result > 0) {
                float similarity = sim[0];
                if (similarity >= CHECK_SIM) {
                    result = index[0];
                } else {
                    result = -1;
                }
            }
        }
        return result;
    }

• 删除

    public long deleteFace(long index) throws Exception {
        long result = faceDatabase.Delete(index);
        faceDatabase.Save(dataBasePath);
        return result;
    }

拓展

集成了face-api.js，实现简单的张张嘴，摇摇头活体检测，精确度不是很高，作为一个参考选项

官网地址：https://github.com/justadudewhohacks/face-api.js

加载模型

        Promise.all([
            faceapi.loadFaceDetectionModel('models'),
            faceapi.loadFaceLandmarkModel('models')
        ]).then(startAnalysis);

    function startAnalysis() {
        console.log('模型加载成功！');
        var canvas1 = faceapi.createCanvasFromMedia(document.getElementById('showImg'))
        faceapi.detectSingleFace(canvas1).then((detection) => {
            if (detection) {
                faceapi.detectFaceLandmarks(canvas1).then((landmarks) => {
                    console.log('模型预热调用成功！');
                })
            }
        })

    }

打开摄像头

	<video id="video" muted playsinline></video>
    function AnalysisFaceOnline() {
        var videoElement = document.getElementById('video');
        // 检查浏览器是否支持getUserMedia API
        if (navigator.mediaDevices.getUserMedia) {
            navigator.mediaDevices.getUserMedia({ video: { facingMode: "user" } }) // 请求视频流
                .then(function(stream) {
                    videoElement.srcObject = stream; // 将视频流设置到<video>元素
                    videoElement.play();
                })
                .catch(function(err) {
                    console.error("获取摄像头错误：", err); // 处理错误
                });
        } else {
            console.error("您的浏览器不支持getUserMedia API");
        }
    }

捕捉帧计算关键点

function vedioCatchInit() {
        video.addEventListener('play', function() {
            function captureFrame() {
                if (!video.paused && !video.ended) {
                    // 设置canvas的尺寸与视频帧相同
                    canvas.width = 200;
                    canvas.height = 300;
                    // 绘制当前视频帧到canvas
                    context.drawImage(video, 0, 0, canvas.width, canvas.height);
                    // 将canvas内容转换为data URL
                    //outputImage.src = canvas.toDataURL('image/png');
                    // 可以在这里添加代码将data URL发送到服务器或进行其他处理
                    faceapi.detectSingleFace(canvas).then((detection) => {
                        if (detection) {
                            faceapi.detectFaceLandmarks(canvas).then((landmarks) => {
                               
     
                            })
                        } else {
                            console.log("no face")
                        }
                    })
                    // 递归调用以持续捕获帧
                    setTimeout(captureFrame, 100); // 每500毫秒捕获一次
                }
            }
            captureFrame(); // 开始捕获帧
        });
    }