android 人脸识别demo

Android 人脸识别 demo 可以使用 Google 的 Mobile Vision API 来实现。这是一个免费的、开源的库，可以帮助开发人员在应用程序中检测人脸、识别人脸、扫描条形码和二维码。使用这个 API 需要在 Android Studio 中导入库并在代码中调用相应的方法。具体的实现方法可以参考 Google 的官方文档和示例代码。

相关问题

OpenCV android 人脸识别demo

### OpenCV Android 人脸识别 Demo 示例代码

为了在 Android 平台上使用 OpenCV 实现人脸识别功能，可以参考如下示例代码。此代码展示了如何加载预训练的人脸检测模型并应用到摄像头捕获的图像帧中。

#### 准备工作
确保已按照说明将 OpenCV SDK 正确集成至项目中[^1]。对于较新版本的 OpenCV (如 opencv4.6.0)，同样需遵循相应的配置指南来设置开发环境[^2]。

#### Java 类 `FaceDetectionActivity.java`

package org.opencv.samples.facedetect;

import android.os.Bundle;
import androidx.appcompat.app.AppCompatActivity;
import org.opencv.android.BaseLoaderCallback;
import org.opencv.android.CameraBridgeViewBase.CvCameraViewFrame;
import org.opencv.android.LoaderCallbackInterface;
import org.opencv.android.OpenCVLoader;
import org.opencv.core.Mat;
import org.opencv.core.MatOfRect;
import org.opencv.core.Point;
import org.opencv.core.Rect;
import org.opencv.core.Scalar;
import org.opencv.imgproc.Imgproc;
import org.opencv.objdetect.CascadeClassifier;

public class FaceDetectionActivity extends AppCompatActivity implements CameraBridgeViewBase.CvCameraViewListener2 {

    private static final String TAG = "OCVSample::Activity";
    
    private CascadeClassifier mJavaDetector; // 使用级联分类器进行人脸检测
    
    private Mat mRgba, mGray;
    private BaseLoaderCallback mLoaderCallback = new BaseLoaderCallback(this) {
        @Override
        public void onManagerConnected(int status) {
            switch (status) {
                case LoaderCallbackInterface.SUCCESS: {
                    try {
                        InputStream is = getResources().openRawResource(R.raw.lbpcascade_frontalface);
                        File cascadeDir = getDir("cascade", Context.MODE_PRIVATE);
                        File mCascadeFile = new File(cascadeDir, "lbpcascade_frontalface.xml");
                        FileOutputStream os = new FileOutputStream(mCascadeFile);

                        byte[] buffer = new byte[4096];
                        int bytesRead;
                        while ((bytesRead = is.read(buffer)) != -1) {
                            os.write(buffer, 0, bytesRead);
                        }
                        is.close();
                        os.close();

                        mJavaDetector = new CascadeClassifier(mCascadeFile.getAbsolutePath());
                        if (mJavaDetector.empty()) {
                            Log.e(TAG, "Failed to load cascade classifier");
                        } else {
                            Log.i(TAG, "Loaded cascade classifier from " + mCascadeFile.getAbsolutePath());
                        }

                        cascadeDir.delete();

                    } catch (IOException e) {
                        e.printStackTrace();
                    }
                    
                    mOpenCvCameraView.enableView(); // 启动相机视图
                    
                }
                break;
                default: {
                    super.onManagerConnected(status);
                }
                break;
            }
        }
    };

    private CameraBridgeViewBase mOpenCvCameraView;

    @Override
    protected void onCreate(Bundle savedInstanceState) {
        super.onCreate(savedInstanceState);
        setContentView(R.layout.activity_face_detection);

        mOpenCvCameraView = findViewById(R.id.fd_activity_surface_view);
        mOpenCvCameraView.setCvCameraViewListener(this);        
    }

    @Override
    public void onResume() {
        super.onResume();
        if (!OpenCVLoader.initDebug()) { // 初始化OpenCV库
            Log.d(TAG, "Internal OpenCV library not found.");
            OpenCVLoader.initAsync(OpenCVLoader.OPENCV_VERSION_3_2_0, this, mLoaderCallback);
        } else {
            Log.d(TAG, "OpenCV library found inside package. Using it!");
            mLoaderCallback.onManagerConnected(LoaderCallbackInterface.SUCCESS);
        }
    }

    @Override
    public void onPause() {
        super.onPause();
        if (mOpenCvCameraView != null)
            mOpenCvCameraView.disableView();
    }

    @Override
    public void onDestroy() {
        super.onDestroy();
        if (mJavaDetector != null) {
            mJavaDetector.release();
            mJavaDetector = null;
        }
    }

    @Override
    public void onCameraViewStarted(int width, int height) {
        mGray = new Mat();
        mRgba = new Mat();
    }

    @Override
    public void onCameraViewStopped() {
        mGray.release();
        mRgba.release();
    }

    @Override
    public Mat onCameraFrame(CvCameraViewFrame inputFrame) {
        mRgba = inputFrame.rgba();
        mGray = inputFrame.gray();

        if (mJavaDetector != null){
            MatOfRect faces = new MatOfRect();
            
            mJavaDetector.detectMultiScale(mGray, faces); // 执行人脸检测
            
            Rect[] faceArray = faces.toArray();
            for (int i = 0; i < faceArray.length; i++) {
                Imgproc.rectangle(mRgba, 
                                  new Point(faceArray[i].x, faceArray[i].y), 
                                  new Point(faceArray[i].x + faceArray[i].width, faceArray[i].y + faceArray[i].height),
                                  new Scalar(0, 255, 0)); // 绘制矩形框标记人脸位置
            }
        }
        
        return mRgba;
    }
}

上述代码实现了基本的人脸识别流程，在每一帧视频流上执行实时检测，并通过绘制绿色方框标注出所有人脸的位置。