Android人脸识别的初步学习(1)

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2401_84132381

1364人浏览 · 2024-04-08 11:42:06

2401_84132381 · 2024-04-08 11:42:06 发布

try {

Thread.sleep(100);

} catch (InterruptedException e) {

e.printStackTrace();

}

//创建字节数组大小由拍照传来的图片尺寸决定

byte[] data = new byte[mBitmap.getWidth() * mBitmap.getHeight() * 3 / 2];

try {

//将bitmap转换成nv21，结果保存到data数组中

ImageConverter convert = new ImageConverter();

convert.initial(mBitmap.getWidth(), mBitmap.getHeight(), ImageConverter.CP_PAF_NV21);

if (convert.convert(mBitmap, data)) {

Log.d(TAG, “convert ok!”);

}

convert.destroy();

} catch (Exception e) {

e.printStackTrace();

}

第二步：

AFD_FSDKFace是人脸识别的结果，定义如下:

public class AFD_FSDKFace {

android.graphics.Rect mRect;

int mDegree;

}

调用AFD_FSDK_StillImageFaceDetection返回检测到的人脸信息

//创建FD人脸检测引擎

AFD_FSDKEngine engine = new AFD_FSDKEngine();

AFD_FSDKVersion version = new AFD_FSDKVersion();

List<AFD_FSDKFace> result = new ArrayList<AFD_FSDKFace>(); //注册结果？人脸探测结果

//初始化引擎

AFD_FSDKError err = engine.AFD_FSDK_InitialFaceEngine(

FaceDB.appid, FaceDB.fd_key, AFD_FSDKEngine.AFD_OPF_0_HIGHER_EXT, 16, 300);

//错误码

Log.d(TAG, "AFD_FSDK_InitialFaceEngine = " + err.getCode());

if (err.getCode() != AFD_FSDKError.MOK) {

//引擎初始化失败

Message reg = Message.obtain();

reg.what = MSG_CODE;

reg.arg1 = MSG_EVENT_FD_ERROR;

reg.arg2 = err.getCode();

mUIHandler.sendMessage(reg);

}

err = engine.AFD_FSDK_GetVersion(version);

Log.d(TAG, “AFD_FSDK_GetVersion =” + version.toString() + ", " + err.getCode());

//FD人脸探测，转化的nv21数据数组，传入图片的宽度、高度、NV21、探测结果

err = engine.AFD_FSDK_StillImageFaceDetection(data, mBitmap.getWidth(), mBitmap.getHeight(), AFD_FSDKEngine.CP_PAF_NV21, result);

Log.d(TAG, “AFD_FSDK_StillImageFaceDetection =” + err.getCode() + “<” + result.size());

至此我们就获得了一张图片中的全部人脸数据了，他们都被保存在result这个List列表中。

第三步：

使用 FR 人脸识别引擎识别该位置人脸中的特征信息。

if (!result.isEmpty()) {

//探测结果不为空-存在人脸 FR 人脸识别

AFR_FSDKVersion version1 = new AFR_FSDKVersion();

AFR_FSDKEngine engine1 = new AFR_FSDKEngine();

AFR_FSDKFace result1 = new AFR_FSDKFace();

AFR_FSDKError error1 = engine1.AFR_FSDK_InitialEngine(FaceDB.appid, FaceDB.fr_key);

Log.d(“com.arcsoft”, "AFR_FSDK_InitialEngine = " + error1.getCode());

if (error1.getCode() != AFD_FSDKError.MOK) {

//人脸识别引擎初始化失败

Message reg = Message.obtain();

reg.what = MSG_CODE;

reg.arg1 = MSG_EVENT_FR_ERROR;

reg.arg2 = error1.getCode();

mUIHandler.sendMessage(reg);

}

error1 = engine1.AFR_FSDK_GetVersion(version1);

Log.d(“com.arcsoft”, “FR=” + version.toString() + “,” + error1.getCode()); //(210, 178 - 478, 446), degree = 1　780, 2208 - 1942, 3370

//提取人脸识别特征

error1 = engine1.AFR_FSDK_ExtractFRFeature(data, mBitmap.getWidth(), mBitmap.getHeight(), AFR_FSDKEngine.CP_PAF_NV21, new Rect(result.get(0).getRect()), result.get(0).getDegree(), result1);

Log.d(“com.arcsoft”, “Face=” + result1.getFeatureData()[0] + “,” + result1.getFeatureData()[1] + “,” + result1.getFeatureData()[2] + “,” + error1.getCode());

if(error1.getCode() == error1.MOK) {

//提取出了特征

mAFR_FSDKFace = result1.clone();

int width = result.get(0).getRect().width();

int height = result.get(0).getRect().height();

Bitmap face_bitmap = Bitmap.createBitmap(width, height, Bitmap.Config.RGB_565);

Canvas face_canvas = new Canvas(face_bitmap);

face_canvas.drawBitmap(mBitmap, result.get(0).getRect(), new Rect(0, 0, width, height), null);

Message reg = Message.obtain();

reg.what = MSG_CODE;

reg.arg1 = MSG_EVENT_REG;

reg.obj = face_bitmap;

mUIHandler.sendMessage(reg);

} else {

//没有提取出特征

Message reg = Message.obtain();

reg.what = MSG_CODE;

reg.arg1 = MSG_EVENT_NO_FEATURE;

mUIHandler.sendMessage(reg);

}

error1 = engine1.AFR_FSDK_UninitialEngine();

Log.d(“com.arcsoft”, "AFR_FSDK_UninitialEngine : " + error1.getCode());

} else {

//人脸识别解决为空，不存在人脸

Message reg = Message.obtain();

reg.what = MSG_CODE;

reg.arg1 = MSG_EVENT_NO_FACE;

mUIHandler.sendMessage(reg);

}

err = engine.AFD_FSDK_UninitialFaceEngine();

Log.d(TAG, “AFD_FSDK_UninitialFaceEngine =” + err.getCode());

}

});

第四步：

到此我们已经获得了整个人脸注册流程中所需要的几个关键值了：

人脸位置 Rect 及该 Rect 的 Bitmap；

人脸特征信息实例 mAFR_FSDKFace；

检测到了人脸，我们可以输入相应的描述信息，加入到人脸库中。

public void addFace(String name, AFR_FSDKFace face) {

try {

//check if already registered.

boolean add = true;

for (FaceRegist frface : mRegister) {

if (frface.mName.equals(name)) {

frface.mFaceList.add(face);

add = false;

break;

}

if (add) { // not registered.

FaceRegist frface = new FaceRegist(name);

frface.mFaceList.add(face);

mRegister.add(frface);

}

//清空原有txt文件

if (saveInfo()) {

//update all names

//把当前内存里的新数据全部重新添加一次

FileOutputStream fs = new FileOutputStream(mDBPath + “/face.txt”, true);

ExtOutputStream bos = new ExtOutputStream(fs);

for (FaceRegist frface : mRegister) {

bos.writeString(frface.mName);

}

bos.close();

fs.close();

//save new feature

fs = new FileOutputStream(mDBPath + “/” + name + “.data”, true);

bos = new ExtOutputStream(fs);

bos.writeBytes(face.getFeatureData());

bos.close();

fs.close();

}

} catch (FileNotFoundException e) {

e.printStackTrace();

} catch (IOException e) {

e.printStackTrace();

}

销毁人脸检测引擎

err = engine.AFD_FSDK_UninitialFaceEngine();

Log.d(TAG, “AFD_FSDK_UninitialFaceEngine =” + err.getCode());

3. 实现人脸识别

初始化相机：引擎需要的图像格式是NV21的，所以需要将摄像头中的图像格式预设置为NV21

@Override

public Camera setupCamera() {

// TODO Auto-generated method stub

mCamera = Camera.open(mCameraID);

try {

Camera.Parameters parameters = mCamera.getParameters();

parameters.setPreviewSize(mWidth, mHeight);

parameters.setPreviewFormat(mFormat);

for( Camera.Size size : parameters.getSupportedPreviewSizes()) {

Log.d(TAG, “SIZE:” + size.width + “x” + size.height);

}

for( Integer format : parameters.getSupportedPreviewFormats()) {

Log.d(TAG, “FORMAT:” + format);

}

List<int[]> fps = parameters.getSupportedPreviewFpsRange();

for(int[] count : fps) {

Log.d(TAG, “T:”);

for (int data : count) {

Log.d(TAG, “V=” + data);

}

//parameters.setPreviewFpsRange(15000, 30000);

//parameters.setExposureCompensation(parameters.getMaxExposureCompensation());

//parameters.setWhiteBalance(Camera.Parameters.WHITE_BALANCE_AUTO);

//parameters.setAntibanding(Camera.Parameters.ANTIBANDING_AUTO);

//parmeters.setFocusMode(Camera.Parameters.FOCUS_MODE_AUTO);

//parameters.setSceneMode(Camera.Parameters.SCENE_MODE_AUTO);

//parameters.setColorEffect(Camera.Parameters.EFFECT_NONE);

mCamera.setParameters(parameters);

} catch (Exception e) {

e.printStackTrace();

}

if (mCamera != null) {

mWidth = mCamera.getParameters().getPreviewSize().width;

mHeight = mCamera.getParameters().getPreviewSize().height;

}

return mCamera;

}

初始化人脸检测引擎（FT）

Log.d(TAG, “AFT_FSDK_InitialFaceEngine =” + err.getCode());

err = engine.AFT_FSDK_GetVersion(version);

Log.d(TAG, “AFT_FSDK_GetVersion:” + version.toString() + “,” + err.getCode());

摄像头开始预览时，在摄像头的预览事件处理函数中，先调用FT的人脸识函数函数，然后再调用FR中的人脸信息特征提取数函数。

AFT_FSDKError err = engine.AFT_FSDK_FaceFeatureDetect(data, width, height, AFT_FSDKEngine.CP_PAF_NV21, result);

AFR_FSDKError error = engine.AFR_FSDK_ExtractFRFeature(mImageNV21, mWidth, mHeight, AFR_FSDKEngine.CP_PAF_NV21,mAFT_FSDKFace.getRect(), mAFT_FSDKFace.getDegree(), result);

这里面的result中保存了人脸特征信息，与其他信息进行对比，当score的特征信息大于0.6时，我们就可以认为匹配到了人脸。

AFR_FSDKMatching score = new AFR_FSDKMatching();

float max = 0.0f;

String name = null;

for (FaceDB.FaceRegist fr : mResgist) {

for (AFR_FSDKFace face : fr.mFaceList) {

error = engine.AFR_FSDK_FacePairMatching(result, face, score);

Log.d(TAG, “Score:” + score.getScore() + “, AFR_FSDK_FacePairMatching=” + error.getCode());

if (max < score.getScore()) {

max = score.getScore();

name = fr.mName;

}