前言
继续前面 MTCNN移植安卓并检测视频中人脸 ，已经检测到人脸，那么需要对所检测的人脸和本地的人脸数据做比对，此时采用的是基于Facenet模型，它的逻辑和实现原理 可以看
之前一篇文章是通过python介绍，访问 基于facenet做人脸比对
介绍
下面是将Facenet移植到Android上来使用
- 1、首先新建一个Facenet类
package com.cayden.face.facenet;
import android.content.res.AssetManager;
import android.graphics.Bitmap;
import android.graphics.Matrix;
import android.os.Environment;
import android.util.Log;
import org.tensorflow.contrib.android.TensorFlowInferenceInterface;
import java.io.File;
import java.io.FileInputStream;
/*
import android.graphics.Bitmap;
import android.os.Trace;
import android.util.Log;
import java.io.BufferedReader;
import java.io.IOException;
import java.io.InputStreamReader;
import java.util.ArrayList;
import java.util.Comparator;
import java.util.List;
import java.util.PriorityQueue;
import java.util.Vector;
*/
/**功能：人脸转换为512维特征向量
*/
public class Facenet{
private static final String MODEL_FILE = "file:///android_asset/20180402-114759.pb";
private static final String INPUT_NAME = "input:0";
private static final String OUTPUT_NAME = "embeddings:0";
private static final String PHASE_NAME = "phase_train:0";
private static final String[] outputNames = new String[] {OUTPUT_NAME};
//输入图片大小.(图片非此大小，会rescale)
private static final int INPUT_SIZE=160;
private float[] floatValues; //保存input的值
private int[] intValues; //像素值
private AssetManager assetManager;
private TensorFlowInferenceInterface inferenceInterface;
private static class SingletonInstance {
private static final Facenet INSTANCE = new Facenet();
}
public static Facenet getInstance() {
return SingletonInstance.INSTANCE;
}
private Facenet() {
loadModel();
floatValues=new float[INPUT_SIZE*INPUT_SIZE*3];
intValues = new int[INPUT_SIZE * INPUT_SIZE];
}
private boolean loadModel(){
//AssetManager
try {
String file= Environment.getExternalStorageDirectory().getAbsolutePath()+"/20180402-114759.pb";
FileInputStream fileInputStream=new FileInputStream(new File(file));
inferenceInterface = new TensorFlowInferenceInterface(fileInputStream);
Log.d("Facenet","[*]load model success");
fileInputStream.close();
}catch(Exception e){
Log.e("Facenet","[*]load model failed"+e);
return false;
}
return true;
}
//Bitmap to floatValues
private int normalizeImage(final Bitmap _bitmap){
// (0) bitmap缩放到INPUT_SIZE*INPUT_SIZE
float scale_width=((float)INPUT_SIZE)/_bitmap.getWidth();
float scale_height=((float)INPUT_SIZE)/_bitmap.getHeight();
Matrix matrix = new Matrix();
matrix.postScale(scale_width,scale_height);
Bitmap bitmap = Bitmap.createBitmap(_bitmap,0,0,_bitmap.getWidth(),_bitmap.getHeight(),matrix,true);
//Log.d("Facenet","[*]bitmap size:"+bitmap.getHeight()+"x"+bitmap.getWidth());
// (1) 将像素映射到[-1,1]区间内
float imageMean=127.5f;
float imageStd=128;
bitmap.getPixels(intValues,0,bitmap.getWidth(),0,0,bitmap.getWidth(),bitmap.getHeight());
for (int i=0;i<intValues.length;i++){
final int val=intValues[i];
floatValues[i * 3 + 0] = (((val >> 16) & 0xFF) - imageMean) / imageStd;
floatValues[i * 3 + 1] = (((val >> 8) & 0xFF) - imageMean) / imageStd;
floatValues[i * 3 + 2] = ((val & 0xFF) - imageMean) / imageStd;
}
//Log.d("Facenet","[*]normalizeImage");
//Log.d("Facenet","[*]normalizeImage"+intValues.length);
return 0;
}
public FaceFeature recognizeImage(final Bitmap bitmap){
//Log.d("Facenet","[*]recognizeImage");
//(0)图片预处理，normailize
normalizeImage(bitmap);
//(1)Feed
try {
inferenceInterface.feed(INPUT_NAME, floatValues, 1, INPUT_SIZE, INPUT_SIZE, 3);
boolean [] phase=new boolean[1];
// phase[0]=false;
inferenceInterface.feed(PHASE_NAME,phase);
}catch (Exception e){
Log.e("Facenet","[*] feed Error\n"+e);
return null;
}
//(2)run
// Log.d("Facenet","[*]Feed:"+INPUT_NAME);
try {
inferenceInterface.run(outputNames, false);
}catch (Exception e){
Log.e("Facenet","[*] run error\n"+e);
return null;
}
//(3)fetch
FaceFeature faceFeature=new FaceFeature();
float[] outputs=faceFeature.getFeature();
try {
inferenceInterface.fetch(OUTPUT_NAME, outputs);
}catch (Exception e){
Log.e("Facenet","[*] fetch error\n"+e);
return null;
}
return faceFeature;
}
}
- 2、计算特征向量的欧式距离
人脸特征(512维特征值)
package com.cayden.face.facenet;
/**
* Created by caydencui on 2018/9/6.
* 人脸特征(512维特征值)
* 相似度取特征向量之间的欧式距离.
*/
public class FaceFeature {
public static final int DIMS=512;
private float fea[];
public FaceFeature(){
fea=new float[DIMS];
}
public float[] getFeature(){
return fea;
}
public void setFea(float[] fea) {
this.fea = fea;
}
//比较当前特征和另一个特征之间的相似度
public double compare(FaceFeature ff){
double dist=0;
for (int i=0;i<DIMS;i++)
dist+=(fea[i]-ff.fea[i])*(fea[i]-ff.fea[i]);
dist= Math.sqrt(dist);
return dist;
}
}
- 3、对检测的人脸进行特征值提取 并和已存在本地的特征值做比对
mNxpRtsp = new Rtsp(this, new RtspCallbackInterface() {
@Override
public void decodeOutputBuffer(int frameLen, byte[] bytes, long width, long height) {
Log.d(TAG, "Get frameLen :" + frameLen + " width :" + width + " height :" + height);
if (frameLen == 0) return;
if (iw == 0) {
iw = (int) width;
ih = (int) height;
int surface_w = mVideoTexture.getWidth();
int surface_h = mVideoTexture.getHeight();
scale_bit = (float) surface_h / ih;
ViewGroup.LayoutParams params = draw_view.getLayoutParams();
params.width = surface_w;
params.height = surface_w;
DLog.d("scale_bit:" + scale_bit + ",surface_w:" + surface_w + ",surface_h:" + surface_h + ",iw:" + iw + ",ih:" + ih);
draw_view.requestLayout();
}
// if (isSave) {
// mPictureSave(bytes);
// isSave = false;
// }
/**
* 将bytes转为bitmap
*/
synchronized (lock) {
Bitmap bitmap = null;
byte[] NV21 = new byte[bytes.length];
NV12ToNV21(bytes, NV21, iw, ih);
NV21ToBitmap nv21ToBitmap = new NV21ToBitmap(MainActivity.this);
bitmap = nv21ToBitmap.nv21ToBitmap(NV21, iw, ih);
if (isSave) {//保存图片
ImageUtils.saveImg(bitmap);
isSave = false;
}
Vector<Box> boxes = mtcnn.detectFaces(bitmap, 20);
drawAnim(boxes, draw_view, scale_bit, 1, "");
if (boxes.size()==0) return ;
for (int i=0;i<boxes.size();i++) Utils.drawBox(bitmap,boxes.get(i),1+bitmap.getWidth()/500 );
Log.i("Main","[*]boxNum："+boxes.size());
Box box=boxes.get(0);
Rect rect1=box.transform2Rect();
//MTCNN检测到的人脸框，再上下左右扩展margin个像素点，再放入facenet中。
int margin=20; //20这个值是facenet中设置的。自己应该可以调整。
Utils.rectExtend(bitmap,rect1,margin);
//要比较的两个人脸，加厚Rect
Utils.drawRect(bitmap,rect1,1+bitmap.getWidth()/100 );
//(2)裁剪出人脸(只取第一张)
final Bitmap face1=Utils.crop(bitmap,rect1);
FaceFeature ff1=facenet.recognizeImage(face1);
if(null==ff1||ff1.getFeature().length<0)return;
List<FaceDB.FaceRegist> mResgist =FaceDB.getInstance().mRegister;
double tempScore=-1;
for(int i=0;i<mResgist.size();i++){
FaceDB.FaceRegist faceRegist= mResgist.get(i);
double temp= ff1.compare(faceRegist.faceFeature);
/**
* 找出值最小的
*/
if(tempScore==-1){
tempScore=temp;//第一次直接赋值
foundName=faceRegist.mName;
}else{
if(tempScore>temp){
foundName=faceRegist.mName;
tempScore=temp;
}
}
Log.d(TAG,">>>>>>>>>>temp="+temp+",tempScore="+tempScore+",foundName："+foundName);
}
cmp=tempScore;
runOnUiThread(new Runnable() {
@Override
public void run() {
tv_result.setText(String.format("名字:%s 相似度 : %.4f", foundName,cmp) );
}
});
}
}
});
-
4、比对结果如图所示
比对结果
这里的伐值可以设置为0.8 ，但是官网给出的是1.1 ，可以理解为低于伐值表示是同一个人！
项目源码
https://github.com/cayden/facesample
本项目主要基于vlc来播放流媒体视频
主要包含以下内容
- 1、使用已经编译的libvlc来播放流媒体视频
- 2、使用MTCNN进行人脸识别并标记人脸
- 3、保存标记的人脸图片
- 4、使用FACENET进行人脸比对
- 未完待续...
v1.0.3
- 1, 通过FACENET获取脸部特征数据
- 2, 人脸比对，找出相似度最高的人
v1.0.2
- 1, 通过MTCNN检测人脸
- 2, 对人脸进行标记
v1.0.1
- 1, 获取返回的视频流数据
- 2, 将数据nv12转换为nv21,并保存图片
v1.0.0
- 1, added libvlc
- 2, support for playing rtsp video stream
感谢大家的阅读，也希望能转发并关注我的公众号
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