测试和使用了虹软的人脸API在QT5环境下设计了一个简单的人脸识别软件，实现了对人脸的跟踪和人脸识别。摄像头的控制以及图像格式的转换使用了Opencv，图像显示使用的是QT5的Qimage控件。下面是详细介绍
1基本流程
（1）加载存储的参考图像数据和图像标签，这里简单的使用图像的名字作为标签
（2）使用虹软人脸识别API计算参考图像的人脸位置数据并存储
（3）使用opencv VideoCapture 类采集摄像头图像数据
（2）采集的图像数据送入虹软人脸识别API 计算人脸位置，并和参考人脸数据计算相似距离，返回最相似的人脸标签
2 Visual Studio 下构建Qt工程
（1）工程目录如下图所示：
在这里插入图片描述
其中QtGuiApplication1.ui是界面文件，Header File文件夹中的amcomdef.h
ammem.h arcsoft_fsdk_face_detection.h arcsoft_fsdk_face_recognition.h
asvloffscreen.h merror.h 是从虹软库中拷贝的头文件未做任何修改
FaceDiscern.h 和FaceDiscern.cpp是自定义的一个人脸识别类
（2）工程属性配置
点击工程属性->连接器->输入中出了QT5的库文件，添加opencv_world340d.lib
在这里插入图片描述
点击工程属性-》VC++目录添加OpenCV的头文件和库文件的路径，其中包含目录添加opencv的头文件路径，库目录添加opencv的dll路径，如下图
在这里插入图片描述
2工程类文件详解
（1）QtGuiApplication1 ui类的源文件如下所示，其中Mat2QImage函数将opencv采集的图像数据转化为QImage支 持 的数据格式, VideoCapture 是Opencv用来操作摄像头的类，QImage用来显示采集的图像数据
#pragma once
#include <QtWidgets/QMainWindow>
#include "ui_QtGuiApplication1.h"
#include "qmessagebox.h"
#include "opencv2/core/core.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include <iostream>
#include "qtimer.h"
#include "FaceDiscern.h"
#include "qrect.h"
#include "qpainter.h"
using namespace cv;
using namespace std;
class QtGuiApplication1 : public QMainWindow
{
Q_OBJECT
public:
QtGuiApplication1(QWidget *parent = Q_NULLPTR);
~QtGuiApplication1();
QImage Mat2QImage(cv::Mat cvImg); //图像格式转换
QTimer *timer;
Mat frame; //摄像头直接获得的数据
FaceDiscern *facediscern; //人脸识别类
private:
Ui::QtGuiApplication1Class ui;
VideoCapture capture; //采集摄像头的数据
QImage qImg; //展示图像的控件
//---槽函数 用作事件触发
public slots :
void openVideo();
void stopVideo();
void nextFrame();
};
（2）QtGuiApplication1.cpp
#include "QtGuiApplication1.h"
QtGuiApplication1::QtGuiApplication1(QWidget *parent)
: QMainWindow(parent)
{
ui.setupUi(this);
ui.image->setScaledContents(true); //fit video to label area
facediscern = new FaceDiscern("F:\\trainimages");//加载参考图像数据和标签
facediscern->Train();//计算参考数据图像数据的人脸位置等
}
QtGuiApplication1::~QtGuiApplication1()
{
if (capture.isOpened())
capture.release();
delete(timer);
}
void QtGuiApplication1::openVideo()
{
if (capture.isOpened())
capture.release(); //decide if capture is already opened; if so,close it
capture.open(0); //open the default camera
if (capture.isOpened())
{
double rate = capture.get(CV_CAP_PROP_FPS);
capture >> frame; //获得摄像头图像数据
if (!frame.empty())
{
QImage image = Mat2QImage(frame); //将摄像头的图像数据转换为QImage支持的格式
this->ui.image->setPixmap(QPixmap::fromImage(image));
timer = new QTimer(this); //循环获得摄像头数据
connect(timer, SIGNAL(timeout()), this, SLOT(nextFrame()));
timer->start(40);
}
}
}
void QtGuiApplication1::stopVideo()
{
if (capture.isOpened())
{
capture.release();
}
}
//循环获得摄像头数据
void QtGuiApplication1::nextFrame()
{
capture >> frame;
double rate = capture.get(CV_CAP_PROP_FPS);
if (!frame.empty())
{
QImage image = Mat2QImage(frame);
//通过人脸检测API获得人脸的位置并在Qimage上显示人脸框
QRect rect;
//RecognizeFace识别人脸的位置并计算人脸所属的标签
string result = facediscern->RecognizeFace(&frame, rect);
static QTextCodec *codecForCStrings;
QString strQ = QString::fromLocal8Bit(result.c_str());
QString s1 = strQ;//这是在qlabel中显示中文的办法
this->ui.result->setText(s1); //在控件上显示人脸所属的标签
QPainter painter(&image);
// 设置画笔颜色
painter.setPen(QColor(255, 0, 0));
painter.drawRect(rect);//绘制人脸的框
this->ui.image->setPixmap(QPixmap::fromImage(image));
}
}
//将opencv 的cv::Mat 格式图像转换为QImage图像
QImage QtGuiApplication1::Mat2QImage(cv::Mat cvImg)
{
if (cvImg.channels() == 3) //3 channels color image
{
cv::cvtColor(cvImg, cvImg, CV_BGR2RGB); //BGR 转为 RGB
qImg = QImage((const unsigned char*)(cvImg.data),
cvImg.cols, cvImg.rows,
cvImg.cols*cvImg.channels(),
QImage::Format_RGB888);
}
else if (cvImg.channels() == 1) //grayscale image
{
qImg = QImage((const unsigned char*)(cvImg.data),
cvImg.cols, cvImg.rows,
cvImg.cols*cvImg.channels(),
QImage::Format_Indexed8);
}
else
{
qImg = QImage((const unsigned char*)(cvImg.data),
cvImg.cols, cvImg.rows,
cvImg.cols*cvImg.channels(),
QImage::Format_RGB888);
}
return qImg;
}
(3) FaceDiscern.h
FaceDiscern 是人脸识别的主类 执行了人脸位置检测和人脸相似度计算等功能
#pragma once
#include <stdio.h>
#include <stdlib.h>
#include <stdint.h>
#include <Windows.h>
#include <iostream>
#include <vector>
#include <string>
#include <io.h>
#include <map>
#include "arcsoft_fsdk_face_recognition.h"
#include "merror.h"
#include "arcsoft_fsdk_face_detection.h"
#include "opencv2/core/core.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "qrect.h"
//动态载入人脸识别的API库 libarcsoft_fsdk_face_detection是人脸检测库
//libarcsoft_fsdk_face_recognition.lib是人脸识别库
#pragma comment(lib,"libarcsoft_fsdk_face_detection.lib")
#pragma comment(lib,"./libarcsoft_fsdk_face_recognition.lib")
using namespace cv;
#define WORKBUF_SIZE (40*1024*1024)
class FaceDiscern
{
public:
FaceDiscern(std::string _trainpath);
~FaceDiscern();
//将cv::Mat格式的图像转换为Bitmap
void ConvertMatToBitmap(cv::Mat *img, uint8_t **imageData, int *pWidth, int *pHeight);
void getFiles(std::string path, std::vector<std::string>& files, std::vector<std::string> &ownname);
void Train();
bool readBmp24(const char* path, uint8_t **imageData, int *pWidth, int *pHeight);
std::string RecognizeFace(cv::Mat *img, QRect &rect);
//APPID是从网站上注册的免费使用id
char APPID[45] = "9aEAsHDYzzzWapX9rH9BZHhdBz8CPTfws4WuF5xdmgnf";
char SDKKey[45] = "61MrwdsfKaMT8cm41uKPQBdCm4rKMLSELtJqs12p7WoV"; //SDKKey
char DETECTIONKKey[45] = "61MrwdsfKaMT8cm41uKPQBci7TocqKmAASGS7infomre";
std::string trainpath = "F:\\trainimages";
MRESULT nRet ;
MHandle hEngine ;
MInt32 nScale ;
MInt32 nMaxFace ;
MByte *pWorkMem;
std::vector<std::string> trainfullfiles;//完整路径名
std::vector<std::string> trainnamefiles;
std::string *labels;
std::map<std::string, std::string> dicfilenametoname;
/* 初始化引擎和变量 */
MRESULT detectionnRet;
MHandle hdetectionEngine;
MInt32 ndetetionScale;
MInt32 ndetectionMaxFace ;
MByte *pdetectionWorkMem;
int trainCount = 0;
LPAFR_FSDK_FACEMODEL *trainfaceModels;
AFR_FSDK_FACEMODEL dectfaceModels;
};
（4）FaceDiscern.cpp
#include "FaceDiscern.h"
FaceDiscern::FaceDiscern(std::string _trainpath)
{
nRet = MERR_UNKNOWN;
hEngine = nullptr;
nScale = 16;
nMaxFace = 10;
pWorkMem = (MByte *)malloc(WORKBUF_SIZE);
/* 初始化引擎和变量 */
detectionnRet = MERR_UNKNOWN;
hdetectionEngine = nullptr;
ndetetionScale = 16;
ndetectionMaxFace = 10;
pdetectionWorkMem = (MByte *)malloc(WORKBUF_SIZE);
dicfilenametoname.insert(std::pair<std::string, std::string>("bingbing.bmp", "冰冰女神"));
dicfilenametoname.insert(std::pair<std::string, std::string>("fangfang.bmp", "村里有个姑娘叫小芳"));
dicfilenametoname.insert(std::pair<std::string, std::string>("feifei.bmp", "刘亦菲"));
dicfilenametoname.insert(std::pair<std::string, std::string>("huihui.bmp", "冷工"));
dicfilenametoname.insert(std::pair<std::string, std::string>("shishi.bmp", "诗诗妹妹"));
dicfilenametoname.insert(std::pair<std::string, std::string>("xiaxia.bmp", "天上掉下个林妹妹"));
dicfilenametoname.insert(std::pair<std::string, std::string>("xudasong.bmp", "松哥"));
dicfilenametoname.insert(std::pair<std::string, std::string>("likunpeng.bmp", "李工"));
dicfilenametoname.insert(std::pair<std::string, std::string>("gaojianjun.bmp", "高建军"));
dicfilenametoname.insert(std::pair<std::string, std::string>("liuzhen.bmp", "小鲜肉振哥"));
dicfilenametoname.insert(std::pair<std::string, std::string>("liting.bmp", "女王婷姐"));
dicfilenametoname.insert(std::pair<std::string, std::string>("wangxuetao.bmp", "雪涛"));
dicfilenametoname.insert(std::pair<std::string, std::string>("guowei.bmp", "郭大侠"));
dicfilenametoname.insert(std::pair<std::string, std::string>("mingxin.bmp", "宝宝鸣新"));
this->trainpath = _trainpath;
}
FaceDiscern::~FaceDiscern()
{
/* 释放引擎和内存 */
detectionnRet = AFD_FSDK_UninitialFaceEngine(hdetectionEngine);
if (detectionnRet != MOK)
{
fprintf(stderr, "UninitialFaceEngine failed , errorcode is %d \n", detectionnRet);
}
free(pdetectionWorkMem);
for (int i = 0; i < trainCount; i++)
{
if (trainfaceModels[i]->pbFeature != NULL)
free(trainfaceModels[i]->pbFeature);
}
nRet = AFR_FSDK_UninitialEngine(hEngine);
if (nRet != MOK)
{
fprintf(stderr, "UninitialFaceEngine failed , errorcode is %d \n", nRet);
}
}
//加载所有的参考图像和图像名字作为参考库
void FaceDiscern::getFiles(std::string path, std::vector<std::string>& files, std::vector<std::string> &ownname)
{
/*files存储文件的路径及名称(eg. C:\Users\WUQP\Desktop\test_devided\data1.txt)
4 ownname只存储文件的名称(eg. data1.txt)*/
//文件句柄
long long hFile = 0;
//文件信息
struct _finddata_t fileinfo;
std::string p;
if ((hFile = _findfirst(p.assign(path).append("\\*").c_str(), &fileinfo)) != -1)
{
do
{
//如果是目录,迭代之
//如果不是,加入列表
if ((fileinfo.attrib & _A_SUBDIR))
{ /*
if(strcmp(fileinfo.name,".") != 0 && strcmp(fileinfo.name,"..") != 0)
getFiles( p.assign(path).append("\\").append(fileinfo.name), files, ownname ); */
}
else
{
files.push_back(p.assign(path).append("\\").append(fileinfo.name));
ownname.push_back(fileinfo.name);
}
} while (_findnext(hFile, &fileinfo) == 0);
_findclose(hFile);
}
}
//将cv::Mat转换为Bitmap
void FaceDiscern::ConvertMatToBitmap(cv::Mat *img, uint8_t **imageData, int *pWidth, int *pHeight)
{
//======建立位图信息 ===========
int width, height, depth, channel;
width = img->cols;
height = img->rows;
depth = img->depth();
channel = img->channels();
*pWidth = width; //图像宽。高
*pHeight = height;
int linebyte = width * channel;
*imageData = (uint8_t *)malloc(linebyte * (*pHeight));
for (int i = 0; i<height; i++) {
for (int j = 0; j<width; j++) {
*((*imageData) + i * width*channel + j * channel) = (*img).at<Vec3b>(i, j)[2];// (uint8_t)(*(img + i * width*channel + j * width + 2));
*((*imageData) + i * width*channel + j * channel + 1) = (*img).at<Vec3b>(i, j)[1];
*((*imageData) + i * width*channel + j * channel + 2) = (*img).at<Vec3b>(i, j)[0];
} // end of line
}
}
//从文件中读取图像并转化为bitmap
bool FaceDiscern::readBmp24(const char* path, uint8_t **imageData, int *pWidth, int *pHeight)
{
if (path == NULL || imageData == NULL || pWidth == NULL || pHeight == NULL)
{
return false;
}
FILE *fp = fopen(path, "rb");
if (fp == NULL)
{
return false;
}
fseek(fp, sizeof(BITMAPFILEHEADER), 0);
BITMAPINFOHEADER head;
fread(&head, sizeof(BITMAPINFOHEADER), 1, fp);
*pWidth = head.biWidth;
*pHeight = head.biHeight;
int biBitCount = head.biBitCount;
if (24 == biBitCount)
{
int lineByte = ((*pWidth) * biBitCount / 8 + 3) / 4 * 4;
*imageData = (uint8_t *)malloc(lineByte * (*pHeight));
uint8_t * data = (uint8_t *)malloc(lineByte * (*pHeight));
fseek(fp, 54, SEEK_SET);
fread(data, 1, lineByte * (*pHeight), fp);
for (int i = 0; i < *pHeight; i++)
{
for (int j = 0; j < *pWidth; j++)
{
memcpy((*imageData) + i * (*pWidth) * 3 + j * 3, data + (((*pHeight) - 1) - i) * lineByte + j * 3, 3);
}
}
free(data);
}
else
{
fclose(fp);
return false;
}
fclose(fp);
return true;
}
//加载所有的参考数据
void FaceDiscern::Train()
{
if (pWorkMem == nullptr)
{
return;
}
nRet = AFR_FSDK_InitialEngine(APPID, SDKKey, pWorkMem, WORKBUF_SIZE, &hEngine); //初始化引擎
if (nRet != MOK)
{
return;
}
getFiles(trainpath, trainfullfiles, trainnamefiles);
//生成训练数据 特征集合
if (trainfullfiles.size() > 0)
{
//参考图像数据的人脸特征和标签的存储
trainfaceModels = new LPAFR_FSDK_FACEMODEL[trainfullfiles.size()];
labels = new std::string[trainfullfiles.size()];
}
else
{
return ;
}
for (int i = 0; i < trainfullfiles.size(); i++)
{
std::string filename = trainfullfiles[i];
/* 读取第一张静态图片信息，并保存到ASVLOFFSCREEN结构体 （以ASVL_PAF_RGB24_B8G8R8格式为例） */
ASVLOFFSCREEN offInput = { 0 };
offInput.u32PixelArrayFormat = ASVL_PAF_RGB24_B8G8R8;
offInput.ppu8Plane[0] = nullptr;
const char * path = filename.c_str();
readBmp24(path, (uint8_t**)&offInput.ppu8Plane[0], &offInput.i32Width, &offInput.i32Height);
if (!offInput.ppu8Plane[0])
{
fprintf(stderr, "fail to ReadBmp(%s)\n", path);
AFR_FSDK_UninitialEngine(hEngine);
free(pWorkMem);
continue ;
}
offInput.pi32Pitch[0] = offInput.i32Width * 3;
AFR_FSDK_FACEMODEL *faceModels = new AFR_FSDK_FACEMODEL();
{
AFR_FSDK_FACEINPUT faceInput;
//第一张人脸信息通过face detection\face tracking获得
faceInput.lOrient = AFR_FSDK_FOC_0;//人脸方向
//人脸框位置
faceInput.rcFace.left = 0;
faceInput.rcFace.top = 0;
faceInput.rcFace.right = offInput.i32Width - 2;;
faceInput.rcFace.bottom = offInput.i32Height - 2;;
//提取第一张人脸特征
AFR_FSDK_FACEMODEL LocalFaceModels = { 0 };
nRet = AFR_FSDK_ExtractFRFeature(hEngine, &offInput, &faceInput, &LocalFaceModels);
if (nRet != MOK)
{
fprintf(stderr, "fail to Extract 1st FR Feature, error code: %d\n", nRet);
}
/* 拷贝人脸特征结果 */
faceModels->lFeatureSize = LocalFaceModels.lFeatureSize;
faceModels->pbFeature = (MByte*)malloc(faceModels->lFeatureSize);
memcpy(faceModels->pbFeature, LocalFaceModels.pbFeature, faceModels->lFeatureSize);
}
trainfaceModels[i] = faceModels;
labels[i] = trainnamefiles[i];
trainCount++;
}
if (pdetectionWorkMem == nullptr)
{
return;
}
//人脸检测engine
detectionnRet = AFD_FSDK_InitialFaceEngine(APPID, DETECTIONKKey, pdetectionWorkMem, WORKBUF_SIZE, &hdetectionEngine, AFD_FSDK_OPF_0_HIGHER_EXT, ndetetionScale, ndetectionMaxFace);
if (detectionnRet != MOK)
{
return;
}
}
//简单的通过距离相似计算出最相似的参考图像
std::string FaceDiscern::RecognizeFace(cv::Mat *img, QRect &rect)
{
/* 读取静态图片信息，并保存到ASVLOFFSCREEN结构体 （以ASVL_PAF_RGB24_B8G8R8格式为例） */
/* 人脸检测 */
ASVLOFFSCREEN offInput = { 0 };
offInput.u32PixelArrayFormat = ASVL_PAF_RGB24_B8G8R8;
offInput.ppu8Plane[0] = nullptr;
ConvertMatToBitmap(img, (uint8_t**)&offInput.ppu8Plane[0], &offInput.i32Width, &offInput.i32Height);
if (!offInput.ppu8Plane[0])
{
return "";
}
offInput.pi32Pitch[0] = offInput.i32Width * 3;
LPAFD_FSDK_FACERES FaceRes = nullptr;
detectionnRet = AFD_FSDK_StillImageFaceDetection(hdetectionEngine, &offInput, &FaceRes);
void *imgptr = offInput.ppu8Plane[0];
////识别人脸信息
AFR_FSDK_FACEINPUT faceInput;
faceInput.lOrient = AFR_FSDK_FOC_0;//人脸方向 //人脸框位置
faceInput.rcFace.left =FaceRes->rcFace[0].left;
faceInput.rcFace.top = FaceRes->rcFace[0].top;
faceInput.rcFace.right = FaceRes->rcFace[0].right;
faceInput.rcFace.bottom = FaceRes->rcFace[0].bottom;
rect.setLeft(FaceRes->rcFace[0].left);
rect.setTop(FaceRes->rcFace[0].top);
rect.setRight(FaceRes->rcFace[0].right);
rect.setBottom(FaceRes->rcFace[0].bottom);
//提取人脸特征
nRet = AFR_FSDK_ExtractFRFeature(hEngine, &offInput, &faceInput, &dectfaceModels);
free(imgptr);
if (nRet != MOK)
{
return "";
}
float maxscore = -1.0;
int index = -1;
for (int i = 0; i < trainCount; i++)
{
MFloat fSimilScore = 0.0f;
nRet = AFR_FSDK_FacePairMatching(hEngine, &dectfaceModels, trainfaceModels[i], &fSimilScore);
if (fSimilScore > maxscore)
{
maxscore = fSimilScore;
index = i;
}
}
if (index != -1)
{
double num = maxscore * 100.0;
std::string str;
char ctr[10];
_gcvt(num, 6, ctr);
str = ctr;
std::string nameresult = labels[index];
if (dicfilenametoname.find(nameresult) != dicfilenametoname.end())
{
nameresult = dicfilenametoname[nameresult];
}
return nameresult + "," + str;
}
//释放
if(dectfaceModels.lFeatureSize>0)
free(dectfaceModels.pbFeature);
return "";
}
(3) 界面展示
在这里插入图片描述
最后是SDK下载地址 https://ai.arcsoft.com.cn/ucenter/user/reg?utm_source=csdn1&utm_medium=referral
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