Android NDK开发：SeetaFace2实现人脸检测

目录

效果展示

代码解释

这里的SeetaFace2库的引入请查看：Android NDK开发：SeetaFace2人脸识别算法简介
这里进行人脸检测所需要的图像模式为BGR，所以我们需要将原本的RGBA图像转为BGR图像，好在SeetaFace2案例中给出了转换的方法，这里我封装成了一个工具类：

public class ConvertUtil {
    /**
     * 转换生成SeetaImageData
     * @param bitmap
     * @return
     */
    public static SeetaImageData ConvertToSeetaImageData(Bitmap bitmap) {
        Bitmap bmp_src = bitmap.copy(Bitmap.Config.ARGB_8888, true); // true is RGBA
        //SeetaImageData大小与原图像一致，但是通道数为3个通道即BGR
        SeetaImageData imageData = new SeetaImageData(bmp_src.getWidth(), bmp_src.getHeight(), 3);
        imageData.data = getPixelsBGR(bmp_src);
        return imageData;
    }

    /**
     * 提取图像中的BGR像素
     * @param image
     * @return
     */
    public static byte[] getPixelsBGR(Bitmap image) {
        // calculate how many bytes our image consists of
        int bytes = image.getByteCount();

        ByteBuffer buffer = ByteBuffer.allocate(bytes); // Create a new buffer
        image.copyPixelsToBuffer(buffer); // Move the byte data to the buffer

        byte[] temp = buffer.array(); // Get the underlying array containing the data.

        byte[] pixels = new byte[(temp.length/4) * 3]; // Allocate for BGR

        // Copy pixels into place
        for (int i = 0; i < temp.length/4; i++) {

            pixels[i * 3] = temp[i * 4 + 2];        //B
            pixels[i * 3 + 1] = temp[i * 4 + 1];    //G
            pixels[i * 3 + 2] = temp[i * 4 ];       //R

        }

        return pixels;
    }
}

下面是Activity中的代码：

public class FaceDetectorActivity extends AppCompatActivity {
    private Button mBt;
    private ImageView mImg;
    private FaceDetector2 faceDetector;
    private SeetaRect[] faceRects;
    private Bitmap bitmap;
    private SeetaImageData seetaImageData;

    @Override
    protected void onCreate(Bundle savedInstanceState) {
        super.onCreate(savedInstanceState);
        setContentView(R.layout.activity_facedetector);
        //将模型拷贝到SD卡中
        //FileUtil.CopyAssets(this,"SeetaFaceDetector2.0.ats",Environment.getExternalStorageDirectory()+ File.separator+"SeetaFaceDetector2.0.ats");
        initView();
        initFace();
        mBt.setOnClickListener(new View.OnClickListener() {
            @Override
            public void onClick(View v) {
                //如果图像中检测出人脸了再进行绘制
                if(faceRects.length>0){
                    //这里必须进行copy否则修改不了
                    Bitmap copy = bitmap.copy(Bitmap.Config.ARGB_8888, true);
                    //利用Bitmap创建Canvas，为了在图像上绘制人脸区域
                    Canvas canvas = new Canvas(copy);
                    Paint paint = new Paint(Paint.ANTI_ALIAS_FLAG);
                    paint.setStyle(Paint.Style.STROKE);
                    paint.setStrokeWidth(3);
                    //绘制出所有的检测出来的人脸的区域
                    for(int i = 0 ; i < faceRects.length ; i++){
                        paint.setColor(Color.BLUE);
                        SeetaRect faceRect = faceRects[i];
                        Rect rect = new Rect(faceRect.x,faceRect.y,faceRect.x+faceRect.width,faceRect.y+faceRect.height);
                        canvas.drawRect(rect,paint);
                    }
                    mImg.setImageBitmap(copy);
                }
            }
        });
    }

    /**
     * 初始化人脸检测器
     */
    private void initFace() {
        //初始化检测器（参数是模型在SD卡的位置）
        faceDetector = new FaceDetector2(Environment.getExternalStorageDirectory()+ File.separator+"seetaface"+File.separator+"SeetaFaceDetector2.0.ats");
        bitmap = BitmapFactory.decodeResource(getResources(), R.drawable.heying);
        //利用SeetaFace2提供的转换方法获取SeetaRect（人脸识别结果）
        seetaImageData = ConvertUtil.ConvertToSeetaImageData(bitmap);
        faceRects = faceDetector.Detect(seetaImageData);
    }

    private void initView() {
        mBt = findViewById(R.id.bt_face);
        mImg = findViewById(R.id.img);
    }

    @Override
    protected void onDestroy() {
        super.onDestroy();
        faceDetector.dispose();
    }
}

案例源码

https://github.com/myml666/Seetaface2