人脸检测

这里的人脸检测并非人脸识别，但是却可以识别出是否有人，当有人时候，你可以将帧图进行人脸识别（这里推荐Face++的sdk）,当然我写的demo中没有加入人脸识别，有兴趣的朋友可以追加。face++

android自带的人脸检测

这里我们用到了人脸检测类为 FaceDetector.这个类提供了强大的人脸检测功能，可以方便我们进行人脸的侦测，因此我们使用他来进行动态的人脸检测，实现原理，其实也挺简单，主要是通过Carmen的回调PreviewCallback 在其中对帧图进行操作，并通过FaceDetector来检测该帧图中是否有人脸。当然如果你想在surfaceview中绘制人脸的范围，可以将画布与其绑定，画完再解绑。

第一步

我们首先来定义一个surfaceview 盖在我们Carmen使用的surfaceview上 进行对人脸范围的绘制

public class FindFaceView extends SurfaceView implements SurfaceHolder.Callback { private SurfaceHolder holder; private int mWidth; private int mHeight; private float eyesDistance; public FindFaceView(Context context, AttributeSet attrs) { super(context, attrs); holder = getHolder(); holder.addCallback(this); holder.setFormat(PixelFormat.TRANSPARENT); this.setZOrderOnTop(true); } @Override public void surfaceChanged(SurfaceHolder holder, int format, int width, int height) { mWidth = width; mHeight = height; } @Override public void surfaceCreated(SurfaceHolder holder) { } @Override public void surfaceDestroyed(SurfaceHolder holder) { } public void drawRect(FaceDetector.Face[] faces, int numberOfFaceDetected) { Canvas canvas = holder.lockCanvas(); if (canvas != null) { Paint clipPaint = new Paint(); clipPaint.setAntiAlias(true); clipPaint.setStyle(Paint.Style.STROKE); clipPaint .setXfermode(new PorterDuffXfermode(PorterDuff.Mode.CLEAR)); canvas.drawPaint(clipPaint); canvas.drawColor(getResources().getColor(color.transparent)); Paint paint = new Paint(); paint.setAntiAlias(true); paint.setColor(Color.GREEN); paint.setStyle(Style.STROKE); paint.setStrokeWidth(5.0f); for (int i = 0; i < numberOfFaceDetected; i++) { Face face = faces[i]; PointF midPoint = new PointF(); // 获得两眼之间的中间点 face.getMidPoint(midPoint); // 获得两眼之间的距离 eyesDistance = face.eyesDistance(); // 换算出预览图片和屏幕显示区域的比例参数 float scale_x = mWidth / 500; float scale_y = mHeight / 600; Log.e("eyesDistance=", eyesDistance + ""); Log.e("midPoint.x=", midPoint.x + ""); Log.e("midPoint.y=", midPoint.y + ""); // 因为拍摄的相片跟实际显示的图像是镜像关系，所以在图片上获取的两眼中间点跟手机上显示的是相反方向 canvas.drawRect((int) (240 - midPoint.x - eyesDistance) * scale_x, (int) (midPoint.y * scale_y), (int) (240 - midPoint.x + eyesDistance) * scale_x, (int) (midPoint.y + 3 * eyesDistance) * scale_y, paint); } holder.unlockCanvasAndPost(canvas); } }}

重要的地方

1. holder = getHolder();获取surfaceholder与我们要绘制人脸范围的画布进行绑定Canvas canvas = holder.lockCanvas();这样我们就可以愉快的进行绘制了，当然前提是我们要拿到人脸的坐标位置。

2. 还有重要的一点，就是要让我们用来盖在Carema上的Surfaceview可以同名，并且设置起在视图树的层级为最高。

holder.setFormat(PixelFormat.TRANSPARENT); this.setZOrderOnTop(true);

第二步

就是我们对人脸进行检测了，当然前提是我们要获得帧图

public class FaceRecognitionDemoActivity extends Activity implements OnClickListener { private SurfaceView preview; private Camera camera; private Camera.Parameters parameters; private int orientionOfCamera;// 前置摄像头的安装角度 private int faceNumber;// 识别的人脸数 private FaceDetector.Face[] faces; private FindFaceView mFindFaceView; private ImageView iv_photo; private Button bt_camera; TextView mTV; /** * Called when the activity is first created. */ @Override public void onCreate(Bundle savedInstanceState) { super.onCreate(savedInstanceState); setContentView(R.layout.main); } @Override protected void onStart() { super.onStart(); iv_photo = (ImageView) findViewById(R.id.iv_photo); bt_camera = (Button) findViewById(R.id.bt_camera); mTV = (TextView) findViewById(R.id.show_count); bt_camera.setOnClickListener(this); mFindFaceView = (FindFaceView) findViewById(R.id.my_preview); preview = (SurfaceView) findViewById(R.id.preview); // 设置缓冲类型（必不可少） preview.getHolder().setType(SurfaceHolder.SURFACE_TYPE_PUSH_BUFFERS); // 设置surface的分辨率 preview.getHolder().setFixedSize(176, 144); // 设置屏幕常亮（必不可少） preview.getHolder().setKeepScreenOn(true); preview.getHolder().addCallback(new SurfaceCallback()); } private final class MyPictureCallback implements PictureCallback { @Override public void onPictureTaken(byte[] data, Camera camera) { try { Bitmap bitmap = BitmapFactory.decodeByteArray(data, 0, data.length); Matrix matrix = new Matrix(); matrix.setRotate(-90); Bitmap bmp = Bitmap.createBitmap(bitmap, 0, 0, bitmap .getWidth(), bitmap.getHeight(), matrix, true); bitmap.recycle(); iv_photo.setImageBitmap(bmp); camera.startPreview(); } catch (Exception e) { e.printStackTrace(); } } } private final class SurfaceCallback implements Callback { @Override public void surfaceChanged(SurfaceHolder holder, int format, int width, int height) { if (camera != null) { parameters = camera.getParameters(); parameters.setPictureFormat(PixelFormat.JPEG); // 设置预览区域的大小 parameters.setPreviewSize(width, height); // 设置每秒钟预览帧数 parameters.setPreviewFrameRate(20); // 设置预览图片的大小 parameters.setPictureSize(width, height); parameters.setJpegQuality(80); } } @Override public void surfaceCreated(SurfaceHolder holder) { int cameraCount = 0; Camera.CameraInfo cameraInfo = new Camera.CameraInfo(); cameraCount = Camera.getNumberOfCameras(); //设置相机的参数 for (int i = 0; i < cameraCount; i++) { Camera.getCameraInfo(i, cameraInfo); if (cameraInfo.facing == Camera.CameraInfo.CAMERA_FACING_FRONT) { try { camera = Camera.open(i); camera.setPreviewDisplay(holder); setCameraDisplayOrientation(i, camera); //最重要的设置 帧图的回调 camera.setPreviewCallback(new MyPreviewCallback()); camera.startPreview(); } catch (Exception e) { e.printStackTrace(); } } } } @Override public void surfaceDestroyed(SurfaceHolder holder) { //记得释放，避免OOM和占用 if (camera != null) { camera.setPreviewCallback(null); camera.stopPreview(); camera.release(); camera = null; } } } private class MyPreviewCallback implements PreviewCallback { @Override public void onPreviewFrame(byte[] data, Camera camera) { //这里需要注意，回调出来的data不是我们直接意义上的RGB图 而是YUV图，因此我们需要 //将YUV转化为bitmap再进行相应的人脸检测，同时注意必须使用RGB_565，才能进行人脸检测，其余无效 Camera.Size size = camera.getParameters().getPreviewSize(); YuvImage yuvImage = new YuvImage(data, ImageFormat.NV21, size.width, size.height, null); ByteArrayOutputStream baos = new ByteArrayOutputStream(); yuvImage.compressToJpeg(new Rect(0, 0, size.width, size.height), 80, baos); byte[] byteArray = baos.toByteArray(); detectionFaces(byteArray); } } /** * 检测人脸 * * @param data 预览的图像数据 */ private void detectionFaces(byte[] data) { BitmapFactory.Options options = new BitmapFactory.Options(); Bitmap bitmap1 = BitmapFactory.decodeByteArray(data, 0, data.length, options); int width = bitmap1.getWidth(); int height = bitmap1.getHeight(); Matrix matrix = new Matrix(); Bitmap bitmap2 = null; FaceDetector detector = null; //设置各个角度的相机，这样我们的检测效果才是最好 switch (orientionOfCamera) { case 0: //初始化人脸检测（下同） detector = new FaceDetector(width, height, 10); matrix.postRotate(0.0f, width / 2, height / 2); // 以指定的宽度和高度创建一张可变的bitmap（图片格式必须是RGB_565，不然检测不到人脸） bitmap2 = Bitmap.createBitmap(width, height, Bitmap.Config.RGB_565); break; case 90: detector = new FaceDetector(height, width, 1); matrix.postRotate(-270.0f, height / 2, width / 2); bitmap2 = Bitmap.createBitmap(height, width, Bitmap.Config.RGB_565); break; case 180: detector = new FaceDetector(width, height, 1); matrix.postRotate(-180.0f, width / 2, height / 2); bitmap2 = Bitmap.createBitmap(width, height, Bitmap.Config.RGB_565); break; case 270: detector = new FaceDetector(height, width, 1); matrix.postRotate(-90.0f, height / 2, width / 2); bitmap2 = Bitmap.createBitmap(height, width, Bitmap.Config.RGB_565); break; } //设置支持的面数（最大支持检测多少人的脸 ，可以根据需要调整，不过需要与findFaces中的参数数值相同，否则会抛出异常） faces = new FaceDetector.Face[10]; Paint paint = new Paint(); paint.setDither(true); Canvas canvas = new Canvas(); canvas.setBitmap(bitmap2); canvas.setMatrix(matrix); // 将bitmap1画到bitmap2上（这里的偏移参数根据实际情况可能要修改） canvas.drawBitmap(bitmap1, 0, 0, paint); //这里通过向findFaces中传递帧图转化后的bitmap和最大检测的人脸数face，返回检测后的人脸数 faceNumber = detector.findFaces(bitmap2, faces); mTV.setText("facnumber----" + faceNumber); mTV.setTextColor(Color.RED); //这里就是我们的人脸识别，绘制识别后的人脸区域的类 if (faceNumber != 0) { mFindFaceView.setVisibility(View.VISIBLE); mFindFaceView.drawRect(faces, faceNumber); } else { mFindFaceView.setVisibility(View.GONE); } bitmap2.recycle(); bitmap1.recycle(); } /** * 设置相机的显示方向（这里必须这么设置，不然检测不到人脸） * * @param cameraId 相机ID(0是后置摄像头，1是前置摄像头） * @param camera 相机对象 */ private void setCameraDisplayOrientation(int cameraId, Camera camera) { Camera.CameraInfo info = new Camera.CameraInfo(); Camera.getCameraInfo(cameraId, info); int rotation = getWindowManager().getDefaultDisplay().getRotation(); int degree = 0; switch (rotation) { case Surface.ROTATION_0: degree = 0; break; case Surface.ROTATION_90: degree = 90; break; case Surface.ROTATION_180: degree = 180; break; case Surface.ROTATION_270: degree = 270; break; } orientionOfCamera = info.orientation; int result; if (info.facing == Camera.CameraInfo.CAMERA_FACING_FRONT) { result = (info.orientation + degree) % 360; result = (360 - result) % 360; } else { result = (info.orientation - degree + 360) % 360; } camera.setDisplayOrientation(result); } @Override public void onClick(View v) { switch (v.getId()) { case R.id.bt_camera: if (camera != null) { try { camera.takePicture(null, null, new MyPictureCallback()); } catch (Exception e) { e.printStackTrace(); } } break; } }}

到这里我们的人脸识别就已经大功告成。demo地址

如果您想了解更多关于人脸识别方面的只是，先去关注并了解OpenCV。

以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持。