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背景
参见LinkSVP例子之一背静介绍。
LinkSVP简介
参见LinkSVP例子之一LinkSVP
介绍。
FaceDetect人脸检测与追踪示例
该示例程序演示了如何综合使用LinkIVE、NCNN、IVE进行一个人脸检测与追踪的功能开发。
准备工作
- 参照用户手册搭建开发环境、编译3531D工程、配置网络启动参数。
- 将带有HDMI输出功能的设备(如摄像机、笔记本、机顶盒等)接入评估板的HDMI-A接口
- 将评估板的HDMI-OUT接显示器(支持1080P即可,程序默认输出1080P60)。
- 上电,进入
/root/demo
目录 - 运行
FaceDetect
程序
运行结果
完整工程
完整工程参见:https://gitee.com/LinkPi/LinkSVP/tree/master/FaceDetect
主要源代码
main.cpp
#include <QCoreApplication>
#include "Link.h"
#include "FaceDetect.h"
int main(int argc, char *argv[])
{
QCoreApplication a(argc, argv);
Link::init();
LinkObject *vi=Link::create("InputVi");
QVariantMap dataVi;
dataVi["interface"]="HDMI-A";
vi->start(dataVi);
FaceDetect *FD=new FaceDetect();
QVariantMap dataFD;
dataFD["framerate"]=5;
FD->start(dataFD);
LinkObject *vo=Link::create("OutputVo");
QVariantMap dataVo;
dataVo["type"]="hdmi";
vo->start(dataVo);
vi->linkV(FD)->linkV(vo);
return a.exec();
}
MTCNN.cpp
#include "MTCNN.h"
bool cmpScore(orderScore lsh, orderScore rsh){
if(lsh.score<rsh.score)
return true;
else
return false;
}
MTCNN::MTCNN(QObject *parent) : QObject(parent)
{
Pnet.load_param("model/det1-opt.param");
Pnet.load_model("model/det1-opt.bin");
Rnet.load_param("model/det2-opt.param");
Rnet.load_model("model/det2-opt.bin");
Onet.load_param("model/det3-opt.param");
Onet.load_model("model/det3-opt.bin");
}
void MTCNN::detect(IVEMem &rgb)
{
mem=rgb;
QMetaObject::invokeMethod(this,"doDetect",Qt::QueuedConnection);
}
void MTCNN::generateBbox(ncnn::Mat score, ncnn::Mat location, std::vector<Bbox>& boundingBox_, std::vector<orderScore>& bboxScore_, float scale){
int stride = 2;
int cellsize = 12;
int count = 0;
//score p
float *p = score.channel(1);
float *plocal = location.channel(0);
Bbox bbox;
orderScore order;
for(int row=0;row<score.h;row++){
for(int col=0;col<score.w;col++){
if(*p>threshold[0]){
bbox.score = *p;
order.score = *p;
order.oriOrder = count;
bbox.x1 = round((stride*col+1)/scale);
bbox.y1 = round((stride*row+1)/scale);
bbox.x2 = round((stride*col+1+cellsize)/scale);
bbox.y2 = round((stride*row+1+cellsize)/scale);
bbox.exist = true;
bbox.area = (bbox.x2 - bbox.x1)*(bbox.y2 - bbox.y1);
for(int channel=0;channel<4;channel++)
bbox.regreCoord[channel]=location.channel(channel)[0];
boundingBox_.push_back(bbox);
bboxScore_.push_back(order);
count++;
}
p++;
plocal++;
}
}
}
void MTCNN::nms(std::vector<Bbox> &boundingBox_, std::vector<orderScore> &bboxScore_, const float overlap_threshold, string modelname){
if(boundingBox_.empty()){
return;
}
std::vector<int> heros;
//sort the score
sort(bboxScore_.begin(), bboxScore_.end(), cmpScore);
int order = 0;
float IOU = 0;
float maxX = 0;
float maxY = 0;
float minX = 0;
float minY = 0;
while(bboxScore_.size()>0){
order = bboxScore_.back().oriOrder;
bboxScore_.pop_back();
if(order<0)continue;
if(boundingBox_.at(order).exist == false) continue;
heros.push_back(order);
boundingBox_.at(order).exist = false;//delete it
for(int num=0;num<boundingBox_.size();num++){
if(boundingBox_.at(num).exist){
//the iou
maxX = (boundingBox_.at(num).x1>boundingBox_.at(order).x1)?boundingBox_.at(num).x1:boundingBox_.at(order).x1;
maxY = (boundingBox_.at(num).y1>boundingBox_.at(order).y1)?boundingBox_.at(num).y1:boundingBox_.at(order).y1;
minX = (boundingBox_.at(num).x2<boundingBox_.at(order).x2)?boundingBox_.at(num).x2:boundingBox_.at(order).x2;
minY = (boundingBox_.at(num).y2<boundingBox_.at(order).y2)?boundingBox_.at(num).y2:boundingBox_.at(order).y2;
//maxX1 and maxY1 reuse
maxX = ((minX-maxX+1)>0)?(minX-maxX+1):0;
maxY = ((minY-maxY+1)>0)?(minY-maxY+1):0;
//IOU reuse for the area of two bbox
IOU = maxX * maxY;
if(!modelname.compare("Union"))
IOU = IOU/(boundingBox_.at(num).area + boundingBox_.at(order).area - IOU);
else if(!modelname.compare("Min")){
IOU = IOU/((boundingBox_.at(num).area<boundingBox_.at(order).area)?boundingBox_.at(num).area:boundingBox_.at(order).area);
}
if(IOU>overlap_threshold){
boundingBox_.at(num).exist=false;
for(vector<orderScore>::iterator it=bboxScore_.begin(); it!=bboxScore_.end();it++){
if((*it).oriOrder == num) {
(*it).oriOrder = -1;
break;
}
}
}
}
}
}
for(int i=0;i<heros.size();i++)
boundingBox_.at(heros.at(i)).exist = true;
}
void MTCNN::refineAndSquareBbox(vector<Bbox> &vecBbox, const int &height, const int &width){
if(vecBbox.empty()){
cout<<"Bbox is empty!!"<<endl;
return;
}
float bbw=0, bbh=0, maxSide=0;
float h = 0, w = 0;
float x1=0, y1=0, x2=0, y2=0;
for(vector<Bbox>::iterator it=vecBbox.begin(); it!=vecBbox.end();it++){
if((*it).exist){
bbw = (*it).x2 - (*it).x1 + 1;
bbh = (*it).y2 - (*it).y1 + 1;
x1 = (*it).x1 + (*it).regreCoord[0]*bbw;
y1 = (*it).y1 + (*it).regreCoord[1]*bbh;
x2 = (*it).x2 + (*it).regreCoord[2]*bbw;
y2 = (*it).y2 + (*it).regreCoord[3]*bbh;
w = x2 - x1 + 1;
h = y2 - y1 + 1;
maxSide = (h>w)?h:w;
x1 = x1 + w*0.5 - maxSide*0.5;
y1 = y1 + h*0.5 - maxSide*0.5;
(*it).x2 = round(x1 + maxSide - 1);
(*it).y2 = round(y1 + maxSide - 1);
(*it).x1 = round(x1);
(*it).y1 = round(y1);
//boundary check
if((*it).x1<0)(*it).x1=0;
if((*it).y1<0)(*it).y1=0;
if((*it).x2>width)(*it).x2 = width - 1;
if((*it).y2>height)(*it).y2 = height - 1;
it->area = (it->x2 - it->x1)*(it->y2 - it->y1);
}
}
}
void MTCNN::doDetect()
{
std::vector<Bbox> finalBbox;
ncnn::Mat ncnn_img = ncnn::Mat::from_pixels((uchar*)mem.data(), ncnn::Mat::PIXEL_BGR2RGB, 1920,1080);
int cc=0;
QVariantList list;
QRect rt;
detect_net(ncnn_img, finalBbox);
for (vector<Bbox>::iterator it = finalBbox.begin(); it != finalBbox.end(); it++){
if ((*it).exist)
{
cc++;
rt=QRect((*it).x1, (*it).y1, ((*it).x2-(*it).x1), ((*it).y2-(*it).y1));
list.append(rt);
}
}
emit this->detectDone(list);
}
void MTCNN::detect_net(ncnn::Mat& img_, std::vector<Bbox>& finalBbox_){
firstBbox_.clear();
firstOrderScore_.clear();
secondBbox_.clear();
secondBboxScore_.clear();
thirdBbox_.clear();
thirdBboxScore_.clear();
img = img_;
img_w = img.w;
img_h = img.h;
img.substract_mean_normalize(mean_vals, norm_vals);
float minl = img_w<img_h?img_w:img_h;
int MIN_DET_SIZE = 12;
int minsize = img_h/11;//90
float m = (float)MIN_DET_SIZE/minsize;
minl *= m;
float factor = 0.5;//0.709
int factor_count = 0;
vector<float> scales_;
while(minl>MIN_DET_SIZE){
if(factor_count>0)m = m*factor;
scales_.push_back(m);
minl *= factor;
factor_count++;
}
orderScore order;
int count = 0;
for (size_t i = 0; i < scales_.size(); i++) {
int hs = (int)ceil(img_h*scales_[i]);
int ws = (int)ceil(img_w*scales_[i]);
ncnn::Mat in;
resize_bilinear(img_, in, ws, hs);
ncnn::Extractor ex = Pnet.create_extractor();
ex.set_num_threads(2);
ex.set_light_mode(true);
ex.input("data", in);
ncnn::Mat score_, location_;
ex.extract("prob1", score_);
ex.extract("conv4-2", location_);
std::vector<Bbox> boundingBox_;
std::vector<orderScore> bboxScore_;
generateBbox(score_, location_, boundingBox_, bboxScore_, scales_[i]);
nms(boundingBox_, bboxScore_, nms_threshold[0]);
for(vector<Bbox>::iterator it=boundingBox_.begin(); it!=boundingBox_.end();it++){
if((*it).exist){
firstBbox_.push_back(*it);
order.score = (*it).score;
order.oriOrder = count;
firstOrderScore_.push_back(order);
count++;
}
}
bboxScore_.clear();
boundingBox_.clear();
}
//the first stage's nms
if(count<1)return;
nms(firstBbox_, firstOrderScore_, nms_threshold[0]);
refineAndSquareBbox(firstBbox_, img_h, img_w);
//second stage
count = 0;
for(vector<Bbox>::iterator it=firstBbox_.begin(); it!=firstBbox_.end();it++){
if((*it).exist){
ncnn::Mat tempIm;
copy_cut_border(img, tempIm, (*it).y1, img_h-(*it).y2, (*it).x1, img_w-(*it).x2);
ncnn::Mat in;
resize_bilinear(tempIm, in, 24, 24);
ncnn::Extractor ex = Rnet.create_extractor();
ex.set_num_threads(2);
ex.set_light_mode(true);
ex.input("data", in);
ncnn::Mat score, bbox;
ex.extract("prob1", score);
ex.extract("conv5-2", bbox);
if((score[1])>threshold[1]){
for(int channel=0;channel<4;channel++)
it->regreCoord[channel]=bbox[channel];
it->area = (it->x2 - it->x1)*(it->y2 - it->y1);
it->score = score[1];
secondBbox_.push_back(*it);
order.score = it->score;
order.oriOrder = count++;
secondBboxScore_.push_back(order);
}
else{
(*it).exist=false;
}
}
}
if(count<1)return;
nms(secondBbox_, secondBboxScore_, nms_threshold[1]);
refineAndSquareBbox(secondBbox_, img_h, img_w);
//third stage
count = 0;
for(vector<Bbox>::iterator it=secondBbox_.begin(); it!=secondBbox_.end();it++){
if((*it).exist){
ncnn::Mat tempIm;
copy_cut_border(img, tempIm, (*it).y1, img_h-(*it).y2, (*it).x1, img_w-(*it).x2);
ncnn::Mat in;
resize_bilinear(tempIm, in, 48, 48);
ncnn::Extractor ex = Onet.create_extractor();
ex.set_num_threads(2);
ex.set_light_mode(true);
ex.input("data", in);
ncnn::Mat score, bbox, keyPoint;
ex.extract("prob1", score);
ex.extract("conv6-2", bbox);
ex.extract("conv6-3", keyPoint);
if(score[1]>threshold[2]){
for(int channel=0;channel<4;channel++)
it->regreCoord[channel]=bbox[channel];
it->area = (it->x2 - it->x1)*(it->y2 - it->y1);
it->score = score[1];
for(int num=0;num<5;num++){
(it->ppoint)[num] = it->x1 + (it->x2 - it->x1)*keyPoint[num];
(it->ppoint)[num+5] = it->y1 + (it->y2 - it->y1)*keyPoint[num+5];
}
thirdBbox_.push_back(*it);
order.score = it->score;
order.oriOrder = count++;
thirdBboxScore_.push_back(order);
}
else
(*it).exist=false;
}
}
if(count<1)return;
refineAndSquareBbox(thirdBbox_, img_h, img_w);
nms(thirdBbox_, thirdBboxScore_, nms_threshold[2], "Min");
finalBbox_ = thirdBbox_;
}
FaceDetect.cpp
#include "FaceDetect.h"
FaceDetect::FaceDetect(QObject *parent) : LinkFrame(parent)
{
data["framerate"]=5;
data["width"]=1920;
data["height"]=1080;
mem["rgb"]=IVEMem(1920,1080,3,true);
mem["rect"]=IVEMem(1920,1080,1,true);
mem["mat"]=IVEMem(1920/8,1080/8,1,true);
mem["mat2"]=IVEMem(1920/8,1080/8,1,true);
trackers=NULL;
connect(&detector,SIGNAL(detectDone(QVariantList)),this,SLOT(onDetect(QVariantList)));
}
void FaceDetect::oneFrame()
{
int scale=8;
static int ccc=-1;
ccc=(ccc+1)%5;
if(ccc==0)
{
IVE_CSC_CTRL_S ctrlCSC;
ctrlCSC.enMode=IVE_CSC_MODE_PIC_BT709_YUV2RGB;
HI_S32 ret=HI_MPI_IVE_CSC(&handle, mem["in"].toImage(IVE_IMAGE_TYPE_YUV420SP), mem["rgb"].toImage(IVE_IMAGE_TYPE_U8C3_PACKAGE), &ctrlCSC, HI_TRUE);
if(ret!=HI_SUCCESS)
{
qDebug("HI_MPI_IVE_CSC failed %#x",ret);
}
copySmall(mem["in"],mem["mat"]);
wait();
detector.detect(mem["rgb"]);
}
{
if(trackers==NULL)
return;
copySmall(mem["in"],mem["mat2"]);
wait();
mat2=Mat(1080/scale, 1920/scale, CV_8UC1, (void*)mem["mat2"].data());
struct timeval tpstart,tpend;
gettimeofday(&tpstart,NULL);
trackers->update(mat2);
gettimeofday(&tpend,NULL);
copy(mem["in"],mem["rect"]);
IVEMem UVin=mem["in"].toUV();
IVEMem UVout=mem["out"].toUV();
copy(UVin,UVout);
wait();
matout=Mat(1080, 1920, CV_8UC1, (void*)mem["rect"].data());
QVariantList ret;
for(int i=0;i<trackers->getObjects().size();i++)
{
Rect rt1=trackers->getObjects()[i];
Rect rt2;
rt2.x=rt1.x*scale;//-0.33*rt1.width*scale;
rt2.y=rt1.y*scale;//-0.33*rt1.height*scale;
rt2.width=rt1.width*scale;//+0.66*rt1.width*scale;
rt2.height=rt1.height*scale;//+0.66*rt1.height*scale;
rectangle( matout, rt2, Scalar( 255, 0, 0 ), 4, 1 );
QVariantMap rect;
rect["x"]=(double)rt2.x/1920.0;
rect["y"]=(double)rt2.y/1080.0;
rect["w"]=(double)rt2.width/1920.0;
rect["h"]=(double)rt2.height/1080.0;
ret.push_back(rect);
}
emit this->newEvent("FD",ret);
mem["rect"].flush();
copy(mem["rect"],mem["out"]);
wait();
}
}
void FaceDetect::drawRect(QRect rect)
{
char *data=mem["rect"].data();
for(int i=0;i<5;i++)
{
memset(data+1920*(rect.top()+i)+rect.left(),255,rect.width());
memset(data+1920*(rect.bottom()-i)+rect.left(),255,rect.width());
}
for(int i=0;i<rect.height();i++)
{
memset(data+1920*(rect.top()+i)+rect.left(),255,5);
memset(data+1920*(rect.top()+i)+rect.right()-5,255,5);
}
}
void FaceDetect::onDetect(QVariantList list)
{
int scale=8;
if(trackers!=NULL)
{
delete trackers;
trackers=NULL;
}
wait();
mat=Mat(1080/scale, 1920/scale, CV_8UC1, (void*)mem["mat"].data());
trackers=new MultiTracker();
std::vector<Ptr<Tracker> > algorithms;
vector<Rect2d> objects;
for(int i=0;i<list.count();i++)
{
Rect rt;
QRect rect=list[i].toRect();
rt.x=rect.x()/scale;//+0.2*rect.width()/scale;
rt.y=rect.y()/scale;//+0.2*rect.height()/scale;
rt.width=rect.width()/scale;//-0.4*rect.width()/scale;
rt.height=rect.height()/scale;//-0.4*rect.height()/scale;
objects.push_back(rt);
algorithms.push_back(TrackerKCF::create());
}
trackers->add(algorithms,mat,objects);
}