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<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bitmap3、调用mtcnn的人脸检测方法
Vector<Box> 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);
}
}
}
作者：一杯茶一本书
链接：https://www.jianshu.com/p/1787ccde11d5
随时随地看视频
