MTCNN移植安卓并检测视频中人脸
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继续此前的文章，使用vlc播放了rtsp流媒体视频后，想检测视频中的人脸，之前采用了opencv但是遇到低头、抬头和侧脸时候，效果就不太好。所以本篇介绍如何使用mtcnn来检测视频中的人脸。
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大致流程：
一、Tensorflow 模型固化
将PNet、ONet、RNet 网络参数.npy固化成.pb格式，方便java载入， 固化后的文件在assets中，文件名mtcnn_freezed_model.pb。
二、引入android tensorflow lite 库
只需在build.gradle(module)最后添加以下几行语句即可。
dependencies
implementation fileTree(include: ['*.jar'], dir: 'libs')
implementation 'com.android.support:appcompat-v7:27.1.1'
implementation 'com.android.support.constraint:constraint-layout:1.1.3'
testImplementation 'junit:junit:4.12'
androidTestImplementation 'com.android.support.test:runner:1.0.2'
androidTestImplementation 'com.android.support.test.espresso:espresso-core:3.0.2'
compile(name: 'libvlc-3.0.0', ext: 'aar')
implementation files('libs/androidutils.jar')
compile 'org.tensorflow:tensorflow-android:+'
implementation files('libs/libutils.jar')
三、新建MTCNN类
该类包含加载模型文件，并检测bitmap中的人脸
package com.cayden.face.facenet;
import android.content.res.AssetManager;
import android.graphics.Bitmap;
import android.graphics.Matrix;
import android.graphics.Point;
import android.util.Log;
import com.cayden.face.FaceApplication;
import org.tensorflow.contrib.android.TensorFlowInferenceInterface;
import java.util.Vector;
import static java.lang.Math.max;
import static java.lang.Math.min;
/**
* Created by caydencui on 2018/9/6.
*/
public class MTCNN
//参数
private float factor=0.709f;
private float PNetThreshold=0.6f;
private float RNetThreshold=0.7f;
private float ONetThreshold=0.7f;
//MODEL PATH
private static final String MODEL_FILE = "file:///android_asset/mtcnn_freezed_model.pb";
//tensor name
private static final String PNetInName ="pnet/input:0";
private static final String[] PNetOutName =new String[]"pnet/prob1:0","pnet/conv4-2/BiasAdd:0";
private static final String RNetInName ="rnet/input:0";
private static final String[] RNetOutName =new String[] "rnet/prob1:0","rnet/conv5-2/conv5-2:0",;
private static final String ONetInName ="onet/input:0";
private static final String[] ONetOutName =new String[] "onet/prob1:0","onet/conv6-2/conv6-2:0","onet/conv6-3/conv6-3:0";
private static class SingletonInstance
private static final MTCNN INSTANCE = new MTCNN();
public static MTCNN getInstance()
return SingletonInstance.INSTANCE;
//安卓相关
public long lastProcessTime; //最后一张图片处理的时间ms
private static final String TAG="MTCNN";
private AssetManager assetManager;
private TensorFlowInferenceInterface inferenceInterface;
private MTCNN()
assetManager= FaceApplication.getMyApplication().getAssets();
loadModel();
private boolean loadModel()
//AssetManager
try
inferenceInterface = new TensorFlowInferenceInterface(assetManager, MODEL_FILE);
Log.d("MTCNN","[*]load model success");
catch(Exception e)
Log.e("MTCNN","[*]load model failed"+e);
return false;
return true;
//读取Bitmap像素值，预处理(-127.5 /128)，转化为一维数组返回
private float[] normalizeImage(Bitmap bitmap)
int w=bitmap.getWidth();
int h=bitmap.getHeight();
float[] floatValues=new float[w*h*3];
int[] intValues=new int[w*h];
bitmap.getPixels(intValues,0,bitmap.getWidth(),0,0,bitmap.getWidth(),bitmap.getHeight());
float imageMean=127.5f;
float imageStd=128;
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;
return floatValues;
/*
检测人脸,minSize是最小的人脸像素值
*/
private Bitmap bitmapResize(Bitmap bm, float scale)
int width = bm.getWidth();
int height = bm.getHeight();
// CREATE A MATRIX FOR THE MANIPULATION。matrix指定图片仿射变换参数
Matrix matrix = new Matrix();
// RESIZE THE BIT MAP
matrix.postScale(scale, scale);
Bitmap resizedBitmap = Bitmap.createBitmap(
bm, 0, 0, width, height, matrix, true);
return resizedBitmap;
//输入前要翻转，输出也要翻转
private int PNetForward(Bitmap bitmap, float [][]PNetOutProb, float[][][]PNetOutBias)
int w=bitmap.getWidth();
int h=bitmap.getHeight();
float[] PNetIn=normalizeImage(bitmap);
Utils.flip_diag(PNetIn,h,w,3); //沿着对角线翻转
inferenceInterface.feed(PNetInName,PNetIn,1,w,h,3);
inferenceInterface.run(PNetOutName,false);
int PNetOutSizeW=(int) Math.ceil(w*0.5-5);
int PNetOutSizeH=(int) Math.ceil(h*0.5-5);
float[] PNetOutP=new float[PNetOutSizeW*PNetOutSizeH*2];
float[] PNetOutB=new float[PNetOutSizeW*PNetOutSizeH*4];
inferenceInterface.fetch(PNetOutName[0],PNetOutP);
inferenceInterface.fetch(PNetOutName[1],PNetOutB);
//【写法一】先翻转，后转为2/3维数组
Utils.flip_diag(PNetOutP,PNetOutSizeW,PNetOutSizeH,2);
Utils.flip_diag(PNetOutB,PNetOutSizeW,PNetOutSizeH,4);
Utils.expand(PNetOutB,PNetOutBias);
Utils.expandProb(PNetOutP,PNetOutProb);
/*
*【写法二】这个比较快，快了3ms。意义不大，用上面的方法比较直观
for (int y=0;y<PNetOutSizeH;y++)
for (int x=0;x<PNetOutSizeW;x++)
int idx=PNetOutSizeH*x+y;
PNetOutProb[y][x]=PNetOutP[idx*2+1];
for(int i=0;i<4;i++)
PNetOutBias[y][x][i]=PNetOutB[idx*4+i];
*/
return 0;
//Non-Maximum Suppression
//nms，不符合条件的deleted设置为true
private void nms(Vector<Box> boxes, float threshold, String method)
//NMS.两两比对
//int delete_cnt=0;
for(int i=0;i<boxes.size();i++)
Box box = boxes.get(i);
if (!box.deleted)
//score<0表示当前矩形框被删除
for (int j = i + 1; j < boxes.size(); j++)
Box box2=boxes.get(j);
if (!box2.deleted)
int x1 = max(box.box[0], box2.box[0]);
int y1 = max(box.box[1], box2.box[1]);
int x2 = min(box.box[2], box2.box[2]);
int y2 = min(box.box[3], box2.box[3]);
if (x2 < x1 || y2 < y1) continue;
int areaIoU = (x2 - x1 + 1) * (y2 - y1 + 1);
float iou=0f;
if (method.equals("Union"))
iou = 1.0f*areaIoU / (box.area() + box2.area() - areaIoU);
else if (method.equals("Min"))
iou= 1.0f*areaIoU / (min(box.area(),box2.area()));
if (iou >= threshold) //删除prob小的那个框
if (box.score>box2.score)
box2.deleted=true;
else
box.deleted=true;
//delete_cnt++;
//Log.i(TAG,"[*]sum:"+boxes.size()+" delete:"+delete_cnt);
private int generateBoxes(float[][] prob,float[][][]bias,float scale,float threshold,Vector<Box> boxes)
int h=prob.length;
int w=prob[0].length;
//Log.i(TAG,"[*]height:"+prob.length+" width:"+prob[0].length);
for (int y=0;y<h;y++)
for (int x=0;x<w;x++)
float score=prob[y][x];
//only accept prob >threadshold(0.6 here)
if (score>PNetThreshold)
Box box=new Box();
//score
box.score=score;
//box
box.box[0]= Math.round(x*2/scale);
box.box[1]= Math.round(y*2/scale);
box.box[2]= Math.round((x*2+11)/scale);
box.box[3]= Math.round((y*2+11)/scale);
//bbr
for(int i=0;i<4;i++)
box.bbr[i]=bias[y][x][i];
//add
boxes.addElement(box);
return 0;
private void BoundingBoxReggression(Vector<Box> boxes)
for (int i=0;i<boxes.size();i++)
boxes.get(i).calibrate();
//Pnet + Bounding Box Regression + Non-Maximum Regression
/* NMS执行完后，才执行Regression
* (1) For each scale , use NMS with threshold=0.5
* (2) For all candidates , use NMS with threshold=0.7
* (3) Calibrate Bounding Box
* 注意：CNN输入图片最上面一行，坐标为[0..width,0]。所以Bitmap需要对折后再跑网络;网络输出同理.
*/
private Vector<Box> PNet(Bitmap bitmap, int minSize)
int whMin=min(bitmap.getWidth(),bitmap.getHeight());
float currentFaceSize=minSize; //currentFaceSize=minSize/(factor^k) k=0,1,2... until excced whMin
Vector<Box> totalBoxes=new Vector<Box>();
//【1】Image Paramid and Feed to Pnet
while (currentFaceSize<=whMin)
float scale=12.0f/currentFaceSize;
//(1)Image Resize
Bitmap bm=bitmapResize(bitmap,scale);
int w=bm.getWidth();
int h=bm.getHeight();
//(2)RUN CNN
int PNetOutSizeW=(int)(Math.ceil(w*0.5-5)+0.5);
int PNetOutSizeH=(int)(Math.ceil(h*0.5-5)+0.5);
float[][] PNetOutProb=new float[PNetOutSizeH][PNetOutSizeW];;
float[][][] PNetOutBias=new float[PNetOutSizeH][PNetOutSizeW][4];
PNetForward(bm,PNetOutProb,PNetOutBias);
//(3)数据解析
Vector<Box> curBoxes=new Vector<Box>();
generateBoxes(PNetOutProb,PNetOutBias,scale,PNetThreshold,curBoxes);
//Log.i(TAG,"[*]CNN Output Box number:"+curBoxes.size()+" Scale:"+scale);
//(4)nms 0.5
nms(curBoxes,0.5f,"Union");
//(5)add to totalBoxes
for (int i=0;i<curBoxes.size();i++)
if (!curBoxes.get(i).deleted)
totalBoxes.addElement(curBoxes.get(i));
//Face Size等比递增
currentFaceSize/=factor;
//NMS 0.7
nms(totalBoxes,0.7f,"Union");
//BBR
BoundingBoxReggression(totalBoxes);
return Utils.updateBoxes(totalBoxes);
//截取box中指定的矩形框(越界要处理)，并resize到size*size大小，返回数据存放到data中。
public Bitmap tmp_bm;
private void crop_and_resize(Bitmap bitmap, Box box, int size, float[] data)
//(2)crop and resize
Matrix matrix = new Matrix();
float scale=1.0f*size/box.width();
matrix.postScale(scale, scale);
Bitmap croped= Bitmap.createBitmap(bitmap, box.left(),box.top(),box.width(), box.height(),matrix,true);
//(3)save
int[] pixels_buf=new int[size*size];
croped.getPixels(pixels_buf,0,croped.getWidth(),0,0,croped.getWidth(),croped.getHeight());
float imageMean=127.5f;
float imageStd=128;
for (int i=0;i<pixels_buf.length;i++)
final int val=pixels_buf[i];
data[i * 3 + 0] = (((val >> 16) & 0xFF) - imageMean) / imageStd;
data[i * 3 + 1] = (((val >> 8) & 0xFF) - imageMean) / imageStd;
data[i * 3 + 2] = ((val & 0xFF) - imageMean) / imageStd;
/*
* RNET跑神经网络，将score和bias写入boxes
*/
private void RNetForward(float[] RNetIn,Vector<Box> boxes)
int num=RNetIn.length/24/24/3;
//feed & run
inferenceInterface.feed(RNetInName,RNetIn,num,24,24,3);
inferenceInterface.run(RNetOutName,false);
//fetch
float[] RNetP=new float[num*2];
float[] RNetB=new float[num*4];
inferenceInterface.fetch(RNetOutName[0],RNetP);
inferenceInterface.fetch(RNetOutName[1],RNetB);
//转换
for (int i=0;i<num;i++)
boxes.get(i).score = RNetP[i * 2 + 1];
for (int j=0;j<4;j++)
boxes.get(i).bbr[j]=RNetB[i*4+j];
//Refine Net
private Vector<Box> RNet(Bitmap bitmap, Vector<Box> boxes)
//RNet Input Init
int num=boxes.size();
float[] RNetIn=new float[num*24*24*3];
float[] curCrop=new float[24*24*3];
int RNetInIdx=0;
for (int i=0;i<num;i++)
crop_and_resize(bitmap,boxes.get(i),24,curCrop);
Utils.flip_diag(curCrop,24,24,3);
//Log.i(TAG,"[*]Pixels values:"+curCrop[0]+" "+curCrop[1]);
for (int j=0;j<curCrop.length;j++) RNetIn[RNetInIdx++]= curCrop[j];
//Run RNet
RNetForward(RNetIn,boxes);
//RNetThreshold
for (int i=0;i<num;i++)
if (boxes.get(i).score<RNetThreshold)
boxes.get(i).deleted=true;
//Nms
nms(boxes,0.7f,"Union");
BoundingBoxReggression(boxes);
return Utils.updateBoxes(boxes);
/*
* ONet跑神经网络，将score和bias写入boxes
*/
private void ONetForward(float[] ONetIn,Vector<Box> boxes)
int num=ONetIn.length/48/48/3;
//feed & run
inferenceInterface.feed(ONetInName,ONetIn,num,48,48,3);
inferenceInterface.run(ONetOutName,false);
//fetch
float[] ONetP=new float[num*2]; //prob
float[] ONetB=new float[num*4]; //bias
float[] ONetL=new float[num*10]; //landmark
inferenceInterface.fetch(ONetOutName[0],ONetP);
inferenceInterface.fetch(ONetOutName[1],ONetB);
inferenceInterface.fetch(ONetOutName[2],ONetL);
//转换
for (int i=0;i<num;i++)
//prob
boxes.get(i).score = ONetP[i * 2 + 1];
//bias
for (int j=0;j<4;j++)
boxes.get(i).bbr[j]=ONetB[i*4+j];
//landmark
for (int j=0;j<5;j++)
int x=boxes.get(i).left()+(int) (ONetL[i * 10 + j]*boxes.get(i).width());
int y= boxes.get(i).top()+(int) (ONetL[i * 10 + j +5]*boxes.get(i).height());
boxes.get(i).landmark[j] = new Point(x,y);
//Log.i(TAG,"[*] landmarkd "+x+ " "+y);
//ONet
private Vector<Box> ONet(Bitmap bitmap, Vector<Box> boxes)
//ONet Input Init
int num=boxes.size();
float[] ONetIn=new float[num*48*48*3];
float[] curCrop=new float[48*48*3];
int ONetInIdx=0;
for (int i=0;i<num;i++)
crop_and_resize(bitmap,boxes.get(i),48,curCrop);
Utils.flip_diag(curCrop,48,48,3);
for (int j=0;j<curCrop.length;j++) ONetIn[ONetInIdx++]= curCrop[j];
//Run ONet
ONetForward(ONetIn,boxes);
//ONetThreshold
for (int i=0;i<num;i++)
if (boxes.get(i).score<ONetThreshold)
boxes.get(i).deleted=true;
BoundingBoxReggression(boxes);
//Nms
nms(boxes,0.7f,"Min");
return Utils.updateBoxes(boxes);
private void square_limit(Vector<Box> boxes, int w, int h)
//square
for (int i=0;i<boxes.size();i++)
boxes.get(i).toSquareShape();
boxes.get(i).limit_square(w,h);
/*
* 参数：
* bitmap:要处理的图片
* minFaceSize:最小的人脸像素值.(此值越大，检测越快)
* 返回：
* 人脸框
*/
public Vector<Box> detectFaces(Bitmap bitmap, int minFaceSize)
long t_start = System.currentTimeMillis();
//【1】PNet generate candidate boxes
Vector<Box> boxes=PNet(bitmap,minFaceSize);
square_limit(boxes,bitmap.getWidth(),bitmap.getHeight());
//【2】RNet
boxes=RNet(bitmap,boxes);
square_limit(boxes,bitmap.getWidth(),bitmap.getHeight());
//【3】ONet
boxes=ONet(bitmap,boxes);
//return
Log.i(TAG,"[*]Mtcnn Detection Time:"+(System.currentTimeMillis()-t_start));
lastProcessTime=(System.currentTimeMillis()-t_start);
return boxes;
四、视频流数据处理
由于vlc播放的流媒体视频格式是nv12，需要将其转为nv21，并保存为bitmap
- 1、首先给出nv12转为nv21方法
private void NV12ToNV21(byte[] nv12, byte[] nv21, int width, int height)
if (nv21 == null || nv12 == null) return;
int framesize = width * height;
int i = 0, j = 0;
//System.arraycopy(nv21, test, nv12, test, framesize);
for (i = 0; i < framesize; i++)
nv21[i] = nv12[i];
for (j = 0; j < framesize / 2; j += 2)
nv21[framesize + j] = nv12[j + framesize + 1];
for (j = 0; j < framesize / 2; j += 2)
nv21[framesize + j + 1] = nv12[j + framesize];
- 2、然后给出nv21高效率转换为bitmap的方法
可以查看此前的一篇文章 nv21高效率转换为bitmap - 3、调用mtcnn的人脸检测方法
Vector boxes = mtcnn.detectFaces(bitmap, 20);
- 4、根据返回的数据 ，标记人脸
protected void drawAnim(Vector<Box> faces, SurfaceView outputView, float scale_bit, int cameraId, String fps)
Paint paint = new Paint();
Canvas canvas = ((SurfaceView) outputView).getHolder().lockCanvas();
if (canvas != null)
try
int viewH = outputView.getHeight();
int viewW = outputView.getWidth();
// DLog.d("viewW:"+viewW+",viewH:"+viewH);
canvas.drawColor(0, PorterDuff.Mode.CLEAR);
if (faces == null || faces.size() == 0) return;
for (int i = 0; i < faces.size(); i++)
paint.setColor(Color.BLUE);
int size = DisplayUtil.dip2px(this, 3);
paint.setStrokeWidth(size);
paint.setStyle(Paint.Style.STROKE);
Box box = faces.get(i);
float[] rect = box.transform2float();
float x1 = rect[0] * scale_bit;
float y1 = rect[1] * scale_bit;
float rect_width = rect[2] * 0.5F;
RectF rectf = new RectF(x1, y1, x1 + rect_width, y1 + rect_width);
canvas.drawRect(rectf, paint);
catch (Exception e)
e.printStackTrace();
finally
((SurfaceView) outputView).getHolder().unlockCanvasAndPost(canvas);
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…
v1.0.2
- 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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