安卓CameraX基于虹软人脸识别程序开发

一、前言

需求：根据镜头内看到的人脸，获取其在系统中注册的用户信息

设计方案：

①注册：用户通过前端系统自拍图片发送给后端注册。后端对图片进行人脸特征数据分析，将特征数据保存到用户数据库。

②识别：用户正对设备镜头，通过前端识别到人脸时，对人脸分析获取特征数据，将特征数据发送给后端获取用户信息。后端比对特征数据，将相似度0.9以上中最高的用户返回。

准备：虹软注册账号获取APP_ID和SDK_KEY（付费版还要一个Active_Key）和下载SDK

二、初始SDK

要使用SDK，得先把sdk放到项目里，然后首次需要激活SDK，每次使用SDK需要初始化

1、先切换到Project视图，将下载的SDK 放到对应位置。然后在build.gradle中引入一下

2、到MainActive中先激活SDK，实际生产中不要每次都去激活，记录首次取激活就好了

protected void onCreate(Bundle savedInstanceState) {super.onCreate(savedInstanceState);setContentView(R.layout.activity_main);toActiveFaceSDK(); // 激活
}
public void toActiveFaceSDK(){int status=FaceEngine.activeOnline(this, 你的APP_ID, 你的SDK_KEY);if(status == ErrorInfo.MOK || status == ErrorInfo.MERR_ASF_ALREADY_ACTIVATED){Toast.makeText(this, "激活成功", Toast.LENGTH_LONG).show();initEngine();//激活成功那就去初始化SDK，准备使用}else if(status == ErrorInfo.MERR_ASF_VERSION_NOT_SUPPORT){Toast.makeText(this, "安卓版本不支持人脸识别", Toast.LENGTH_LONG).show();}else{Toast.makeText(this, "激活失败，状态码：" + status, Toast.LENGTH_LONG).show();}
}

3、初始化SDK，准备使用，每次页面onDestroy的时候记得对应卸载SDK

public void initEngine(){ // 初始化sdkfaceObj = new FaceEngine();DetectFaceOrientPriority ftOrient = DetectFaceOrientPriority.ASF_OP_ALL_OUT; // 默认是对拿到的图像进行全角度检测
//  DetectFaceOrientPriority ftOrient = DetectFaceOrientPriority.ASF_OP_270_ONLY; // 270afCode = faceObj.init(that.getApplicationContext(),DetectMode.ASF_DETECT_MODE_VIDEO,ftOrient,16, 3,faceObj.ASF_FACE_DETECT | faceObj.ASF_AGE |faceObj.ASF_FACE3DANGLE|faceObj.ASF_GENDER | faceObj.ASF_LIVENESS| faceObj.ASF_FACE_RECOGNITION);if (afCode != ErrorInfo.MOK) {if(afCode == ErrorInfo.MERR_ASF_NOT_ACTIVATED){Toast.makeText(this, "设备SDK未激活", Toast.LENGTH_LONG).show();}else{Toast.makeText(this, "初始化失败"+afCode, Toast.LENGTH_LONG).show();       }}
}
public void unInitEngine() { //卸载SDKif (afCode == 0) {afCode = faceObj.unInit();}
}

三、配置相机

安卓Camera API到目前为止已有三代，ArcFace官方教程给的是Camera 1，但我在AS中引入的时候，会划删除线，提示Out并建议使用Camera2替代。于是乎，我使用Camera2去尝试替换官方的一代写法，虽然勉强成功，但是说实在的各种适配问题层出不穷，里面做适配写的代码占了一大半。So 最终我使用了CameraX，就这个预览自适应，就已经天下无敌了！推荐《为什么使用CameraX》。

1、CameraX需要在build.gradle中引入几个包，如下：

    def camerax_version = "1.1.0-alpha08"implementation("androidx.camera:camera-core:${camerax_version}")implementation("androidx.camera:camera-camera2:${camerax_version}")implementation("androidx.camera:camera-lifecycle:${camerax_version}")implementation("androidx.camera:camera-view:1.0.0-alpha25")implementation("androidx.camera:camera-extensions:1.0.0-alpha25")

2、创建预览View

页面定义变量public int rational;private Size mImageReaderSize;private FaceEngine faceObj; //sdk对象private int afCode = -1; // sdk状态private ProcessCameraProvider cameraProvider  = null; // cameraX对象private Preview mPreviewBuild = null; //cameraX的previewprivate PreviewView viewFinder = null; //  X的自带预览viewprivate byte[] nv21 = null; // 实时nv21图像流protected void onCreate()(RelativeLayout layout=that.findViewById(R.id.camerax_preview);viewFinder =new PreviewView(that);//我是动态创建了个Preview，你也可以在页面直接写上layout.addView(viewFinder);Point screenSize = new Point(); // 获取屏幕总宽高        getWindowManager().getDefaultDisplay().getSize(screenSize);//getRealSize就会包括状态栏rational = aspectRatio(screenSize.x, screenSize.y);startCameraX();
}private double RATIO_4_3_VALUE = 4.0 / 3.0;private double RATIO_16_9_VALUE = 16.0 / 9.0;private int aspectRatio(int width, int height) {double previewRatio = Math.max(width, height) * 1.00 / Math.min(width, height);if (Math.abs(previewRatio - RATIO_4_3_VALUE) <= Math.abs(previewRatio - RATIO_16_9_VALUE)) {return AspectRatio.RATIO_4_3;}return AspectRatio.RATIO_16_9;}

3、初始化相机

    private void startCameraX() {ListenableFuture<ProcessCameraProvider> providerFuture = ProcessCameraProvider.getInstance(that);providerFuture.addListener(() -> {try { // 检测CameraProvider可用性cameraProvider = providerFuture.get();} catch (ExecutionException | InterruptedException e) {Toast.makeText(this, "相机X不可用", Toast.LENGTH_LONG).show();e.printStackTrace();return;}CameraSelector cameraSelector = new CameraSelector.Builder().requireLensFacing(CameraSelector.LENS_FACING_FRONT
//                    CameraSelector.LENS_FACING_BACK).build();//绑定预览mPreviewBuild = new Preview.Builder()
//                 .setTargetRotation(Surface.ROTATION_180)//设置预览旋转角度.build();mPreviewBuild.setSurfaceProvider(viewFinder.getSurfaceProvider()); // 绑定显示// 图像分析，监听试试获取的图像流ImageAnalysis imageAnalysis = new ImageAnalysis.Builder().setTargetAspectRatio(rational).setBackpressureStrategy(ImageAnalysis.STRATEGY_KEEP_ONLY_LATEST)
//阻塞模式：ImageAnalysis.STRATEGY_BLOCK_PRODUCER  （在此模式下，执行器会依序从相应相机接收帧；这意味着，如果 analyze() 方法所用的时间超过单帧在当前帧速率下的延迟时间，所接收的帧便可能不再是最新的帧，因为在该方法返回之前，新帧会被阻止进入流水线）
//非阻塞模式： ImageAnalysis.STRATEGY_KEEP_ONLY_LATEST （在此模式下，执行程序在调用 analyze() 方法时会从相机接收最新的可用帧。如果此方法所用的时间超过单帧在当前帧速率下的延迟时间，它可能会跳过某些帧，以便 analyze() 在下一次接收数据时获取相机流水线中的最新可用帧）.build();imageAnalysis.setAnalyzer(ContextCompat.getMainExecutor(this),new FaceAnalyze.MyAnalyzer() //绑定给我的图片分析类);// 绑定生命周期前先解绑cameraProvider.unbindAll();// 绑定Camera mCameraX = cameraProvider.bindToLifecycle((LifecycleOwner) this,cameraSelector,imageAnalysis,mPreviewBuild);}, ContextCompat.getMainExecutor(this));}

4、图片分析类，ImageUtil里的代码最后给出

    /*** 实时预览 Analyzer处理*/private class MyAnalyzer implements ImageAnalysis.Analyzer {private byte[] y;private byte[] u;private byte[] v;private ReentrantLock lock = new ReentrantLock();private Object mImageReaderLock = 1;//1 available,0 unAvailableprivate long lastDrawTime = 0;private int timerSpace = 300; // 识别间隔@Overridepublic void analyze(@NonNull ImageProxy imageProxy) {@SuppressLint("UnsafeOptInUsageError") Image mediaImage = imageProxy.getImage();
//            int rotationDegrees = imageProxy.getImageInfo().getRotationDegrees();if(mediaImage != null){synchronized (mImageReaderLock) {/*识别频率Start 和状态*/long start = System.currentTimeMillis();if (start - lastDrawTime < timerSpace || !mImageReaderLock.equals(1)) {mediaImage.close();imageProxy.close();return;}lastDrawTime = System.currentTimeMillis();/*识别频率End*///判断YUV类型，我们申请的格式类型是YUV_420_888if (ImageFormat.YUV_420_888 == mediaImage.getFormat()) {Image.Plane[] planes = mediaImage.getPlanes();if (mImageReaderSize == null) {mImageReaderSize = new Size(planes[0].getRowStride(), mediaImage.getHeight());}lock.lock();if (y == null) {y = new byte[planes[0].getBuffer().limit() - planes[0].getBuffer().position()];u = new byte[planes[1].getBuffer().limit() - planes[1].getBuffer().position()];v = new byte[planes[2].getBuffer().limit() - planes[2].getBuffer().position()];}//从planes中分别获取y、u、v 变量数据if (mediaImage.getPlanes()[0].getBuffer().remaining() == y.length) {planes[0].getBuffer().get(y);planes[1].getBuffer().get(u);planes[2].getBuffer().get(v);if (nv21 == null) {nv21 = new byte[planes[0].getRowStride() * mediaImage.getHeight() * 3 / 2];}if (nv21 != null && (nv21.length != planes[0].getRowStride() * mediaImage.getHeight() * 3 / 2)) {return;}// 回传数据是YUV422if (y.length / u.length == 2) {ImageUtil.yuv422ToYuv420sp(y, u, v, nv21, planes[0].getRowStride(), mediaImage.getHeight());}// 回传数据是YUV420else if (y.length / u.length == 4) {nv21 = ImageUtil.yuv420ToNv21(mediaImage);}//调用Arcsoft算法,获取人脸特征getFaceInfo(nv21);}lock.unlock();}}}//一定要关闭mediaImage.close();imageProxy.close();}}

5、进行人脸特征分析

    /*** 获取人脸特征*/private int lastFaceID=-1;private void getFaceInfo(byte[] nv21) {List<FaceInfo> faceInfoList = new ArrayList<FaceInfo>();FaceFeature faceFeature = new FaceFeature();//第一步是送数据给arcsoft sdk，检查是否有人脸信息。int code = faceObj.detectFaces(nv21, mImageReaderSize.getWidth(),mImageReaderSize.getHeight(),faceObj.CP_PAF_NV21, faceInfoList);//数据检查正常，并且含有人脸信息，则进行下一步的人脸识别。if (code == ErrorInfo.MOK && faceInfoList.size() > 0) {int newFaceID = faceInfoList.get(0).getFaceId();// 每次不离镜头的同一人脸，其ID均相同if (lastFaceID == newFaceID) { // 相同脸则returnLog.i("人脸ID相同", "旧" + lastFaceID);// + "、新" + newFaceIDreturn;}lastFaceID = newFaceID;
//            long start = System.currentTimeMillis();// 特征数据分析部分机型会卡code = faceObj.extractFaceFeature(nv21, mImageReaderSize.getWidth(), mImageReaderSize.getHeight(),faceObj.CP_PAF_NV21, faceInfoList.get(0), faceFeature);if (code != ErrorInfo.MOK) {return;}byte[] featureData=faceFeature.getFeatureData();//最终的特征数据！！！post(featureData); //把数据给后端去查信息
//            Log.e(TAG, "特征长度"+faceFeature.getFeatureData().length);//            long end = System.currentTimeMillis();
//            Log.e(TAG+lastFaceID, "耗时: "+(end-start)+"ms特征数据---: "+featureData.length);}}

四、ImageUtil代码

public class ImageUtil {/*** 将Y:U:V == 4:2:2的数据转换为nv21** @param y      Y 数据* @param u      U 数据* @param v      V 数据* @param nv21   生成的nv21，需要预先分配内存* @param stride 步长* @param height 图像高度*/public static void yuv422ToYuv420sp(byte[] y, byte[] u, byte[] v, byte[] nv21, int stride, int height) {System.arraycopy(y, 0, nv21, 0, y.length);// 注意，若length值为 y.length * 3 / 2 会有数组越界的风险，需使用真实数据长度计算int length = y.length + u.length / 2 + v.length / 2;int uIndex = 0, vIndex = 0;for (int i = stride * height; i < length; i += 2) {nv21[i] = v[vIndex];nv21[i + 1] = u[uIndex];vIndex += 2;uIndex += 2;}}/*** 将Y:U:V == 4:1:1的数据转换为nv21** @param y      Y 数据* @param u      U 数据* @param v      V 数据* @param nv21   生成的nv21，需要预先分配内存* @param stride 步长* @param height 图像高度*/public static void yuv420ToYuv420sp(byte[] y, byte[] u, byte[] v, byte[] nv21, int stride, int height) {System.arraycopy(y, 0, nv21, 0, y.length);// 注意，若length值为 y.length * 3 / 2 会有数组越界的风险，需使用真实数据长度计算int length = y.length + u.length + v.length;int uIndex = 0, vIndex = 0;for (int i = stride * height; i < length; i++) {nv21[i] = v[vIndex++];nv21[i + 1] = u[uIndex++];}}public static byte[] yuv420ToNv21(Image image) {Image.Plane[] planes = image.getPlanes();ByteBuffer yBuffer = planes[0].getBuffer();ByteBuffer uBuffer = planes[1].getBuffer();ByteBuffer vBuffer = planes[2].getBuffer();int ySize = yBuffer.remaining();int uSize = uBuffer.remaining();int vSize = vBuffer.remaining();int size = image.getWidth() * image.getHeight();byte[] nv21 = new byte[size * 3 / 2];yBuffer.get(nv21, 0, ySize);vBuffer.get(nv21, ySize, vSize);byte[] u = new byte[uSize];uBuffer.get(u);//每隔开一位替换V，达到VU交替int pos = ySize + 1;for (int i = 0; i < uSize; i++) {if (i % 2 == 0) {nv21[pos] = u[i];pos += 2;}}return nv21;}public static Bitmap nv21ToBitmap(byte[] nv21,int w, int h) {YuvImage image = new YuvImage(nv21, ImageFormat.NV21, w, h, null);ByteArrayOutputStream stream = new ByteArrayOutputStream();image.compressToJpeg(new Rect(0, 0, w, h), 80, stream);Bitmap newBitmap = BitmapFactory.decodeByteArray(stream.toByteArray(), 0, stream.size());try{stream.close();}catch (Exception e){}return newBitmap;}
}

That's all

Thanks !!!

附：

人脸路程中的坑《人脸识别坑》

二进制的特征数据怎么发给后台，参考我的另外一篇《安卓form-data形式上传二进制》