MTCNN移植java_MTCNN移植安卓并检测视频中人脸

小鱼儿 2022-11-07 14:44 204阅读 0赞

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。

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> 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;yboxes, float threshold, String method)\{

//NMS.两两比对

//int delete\_cnt=0;

for(int i=0;i= 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,Vectorboxes)\{

int h=prob.length;

int w=prob\[0\].length;

//Log.i(TAG,"\[\*\]height:"+prob.length+" width:"+prob\[0\].length);

for (int y=0;ythreadshold(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(Vectorboxes)\{

for (int i=0;iPNet(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

VectortotalBoxes=new Vector();

//【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

VectorcurBoxes=new Vector();

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> 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,Vectorboxes)\{

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;iRNet(Bitmap bitmap, Vectorboxes)\{

//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;iboxes)\{

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;iONet(Bitmap bitmap, Vectorboxes)\{

//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;iboxes, int w, int h)\{

//square

for (int i=0;idetectFaces(Bitmap bitmap, int minFaceSize) \{

long t\_start = System.currentTimeMillis();

//【1】PNet generate candidate boxes

Vectorboxes=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

Vectorboxes = mtcnn.detectFaces(bitmap, 20);

4、根据返回的数据 ，标记人脸

protected void drawAnim(Vectorfaces, 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

我的公众号

MTCNN移植java_MTCNN移植安卓并检测视频中人脸

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