seetaface6教程:封装人脸识别,人脸检测,,眼睛检测,状态,特征.....

seetaface是中科视扩开发的一个项目,目前,seetaface2是开源的,可以自己下载编译,我没有具体用过,所以不是很清楚,今年3月底,seetaface6出来了,它并不开源,但是可以免费做商用,而且其中的功能还是不错的,而且它不依赖于其他任何库,但是它对于opencv还是挺支持的,这个项目的主要功能包括
1,人脸检测
2,关键点提取
3,人脸识别
4,眼睛状态
5,/活体检测
6,人脸追踪
7,状态评估

我把它的大部分功能都封装成了一个类,大家需要注意我封装文件中所对应的模型文件路径,我把它都放在同目录的model下

这个项目我感觉是非常好用的,我用的是vs2017搭配的环境,至于配置方法,可以参考vs2017搭配opencv环境,百度一搜就有,

这个封装非常精炼,可以说涵盖了它大部分主要功能.话不多说,代码奉上

ps:你看不懂封装代码没关系,尽管我已经在里面写了很多注释,我在问着末尾写了测试代码,是针对封装代码的测试,通俗易懂

ps2:一定要注意封装代码的模型路径否则会出错

#pragma once
#include<opencv2/opencv.hpp>
#include<memory>
#include<iostream>#include<seeta/FaceDetector.h>//人脸检测器
#include<seeta/FaceLandmarker.h>//关键点提取
#include<seeta/Common/Struct.h>
#include<seeta/FaceRecognizer.h>//人脸识别
#include<seeta/EyeStateDetector.h>//眼睛状态
#include<seeta/FaceAntiSpoofing.h>//活体检测
#include<seeta/FaceTracker.h>//人脸追踪#include<seeta/QualityOfBrightness.h>//亮度评估,判断人脸亮度值是都正常,过量或过暗评价LOW,0,1,2
#include<seeta/QualityOfClarity.h>//清晰度评估
#include<seeta/QualityOfIntegrity.h>//人脸完整度评估,判断人脸是否完全进入摄像头
#include<seeta/QualityOfPose.h>//姿态评估,通过人脸5点坐标值来判断姿态是否为正面。
#include<seeta/QualityOfResolution.h>//分辨率评估
#include<seeta/QualityStructure.h>//===============================补充
namespace seeta {class QualityOfNoMask : public QualityRule {public:QualityOfNoMask() {m_marker = std::make_shared<seeta::FaceLandmarker>(ModelSetting("./model/face_landmarker_mask_pts5.csta"));}QualityResult check(const SeetaImageData &image, const SeetaRect &face, const SeetaPointF *points, int32_t N) override {auto mask_points = m_marker->mark_v2(image, face);int mask_count = 0;for (auto point : mask_points) {if (point.mask) mask_count++;}QualityResult result;if (mask_count > 0) {return { QualityLevel::LOW, 1 - float(mask_count) / mask_points.size() };}else {return { QualityLevel::HIGH, 1 };}}private:std::shared_ptr<seeta::FaceLandmarker> m_marker;};
}/*
使用任何一个模型都一定要初始化,否则它会为一个空的检测器
*/
namespace seeta_pacakge {//========/============================================
#define  Brightness 0
#define  Clarity 1
#define  Integrity 2
#define  Pose 3
#define Resolution 4
#define NoMask 5//======================================================================================================存放检测器地址seeta::FaceLandmarker *facelandmarker;seeta::FaceDetector *facedector;seeta::FaceRecognizer *facerecognizer;seeta::EyeStateDetector *eyestatedector;seeta::FaceAntiSpoofing *faceantspoofing;seeta::FaceTracker *facetracker;seeta::QualityRule *qualityrelu;//======================================================================================================const char* FaceDector_path = "./model/face_detector.csta";//人脸检测模型const char* FaceLandmarker_path = "./model/face_landmarker_pts5.csta";//五点检测模型const char* FaceRecognizer_path = "./model/face_recognizer.csta";//人脸特征匹配和对比模型const char* EyeStateDetector_path = "./model/eye_state.csta";//眼睛状态检测模型const char* fasfirst_path = "./model/fas_first.csta";//局部活体const char* fassecond_path = "./model/fas_second.csta";//全局活体const char* face_landmarker_mask_pts5_path = "./model/face_landmarker_mask_pts5.ctsa";//===============================================================================================================人脸识别块=void facerecognizer_init(){seeta::ModelSetting setting;setting.append(FaceRecognizer_path);facerecognizer = new seeta::FaceRecognizer(setting);}//获取图片中特征float* extract_feature(const SeetaImageData& simg, std::vector<SeetaPointF> faces){if (facerecognizer == NULL){std::cout << "dont init facerecongizer";throw 1;}SeetaPointF points[5];for (int i = 0; i < 5; i++){points[i] = faces.at(i);//   std::cout << points[i].x << "," << points[i].y << std::endl;}float* feature = new float[facerecognizer->GetExtractFeatureSize()];facerecognizer->Extract(simg, points, feature);return feature;}
//比较两个特征判断是否相似float compare(float* feature1, float* feature2){return facerecognizer->CalculateSimilarity(feature1, feature2);}//====================================================================================人脸特征点提取块===================void facelandmarker_init(){seeta::ModelSetting setting;setting.append(FaceLandmarker_path);facelandmarker = new seeta::FaceLandmarker(setting);}//提取图像中人脸的特征点std::vector<SeetaPointF> mark(const SeetaImageData& image, const SeetaRect& face){if (facelandmarker == NULL){std::cout << "dont init facelandmarker";throw 1;}return facelandmarker->mark(image, face);//这里检测到的5点坐标循序依次为，左眼中心、右眼中心、鼻尖、左嘴角和右嘴角。}//==================================================================================================人脸检测(base)======void facedector_init(){seeta::ModelSetting setting;setting.append(FaceDector_path);facedector = new seeta::FaceDetector(setting);}//检测人脸并放到数组中SeetaFaceInfoArray detect(const SeetaImageData &image){if (facedector == NULL){std::cout << "dont init facedector";throw 1;}return facedector->detect(image);}
//按人脸大小排列人脸数组void sort(SeetaFaceInfoArray face_sfia){int m = face_sfia.size;std::vector<SeetaFaceInfo> faces(m);for (int i = 0; i < face_sfia.size; i++){faces.at(i) = face_sfia.data[i];}std::partial_sort(faces.begin(), faces.begin() + 1, faces.end(), [](SeetaFaceInfo a, SeetaFaceInfo b) {return a.pos.width > b.pos.width;});for (int i = 0; i < face_sfia.size; i++){face_sfia.data[i] = faces.at(i);}}//==========================================================眼部特征提取const char *EYE_STATE_STR[] = { "close", "open", "random", "unknown" };void eyestatedector_init(){seeta::ModelSetting setting;setting.append(EyeStateDetector_path);eyestatedector = new seeta::EyeStateDetector(setting);}int* eye_state(const SeetaImageData &img, const std::vector<SeetaPointF> &points){if (eyestatedector == NULL){std::cout << "eyestatedector dont init";throw 1;}seeta::EyeStateDetector::EYE_STATE left_eye, right_eye;int status[2];eyestatedector->Detect(img, points.data(), left_eye, right_eye);status[0] = left_eye;status[1] = right_eye;return status;}//==============================活体检测==================================================================const char *SPOOF_STATE_STR[] = { "real face","spoof face","unknown","judging" };//0局部//1全局//2局部+全局void faceantspoofing_init(int version = 0){seeta::ModelSetting setting;switch (version){case 0:setting.append(fasfirst_path);break;case 1:setting.append(fassecond_path);break;case 2:setting.append(fasfirst_path);setting.append(fassecond_path);break;default:std::cout << "version input error";throw 2;}faceantspoofing = new seeta::FaceAntiSpoofing(setting);}int predict(const SeetaImageData &image,const SeetaRect &face,std::vector<SeetaPointF> v_points,int way = 0)//如果是0为单帧识别,1为多帧识别{if (faceantspoofing == NULL){std::cout << "faceantspoofing dont init";throw 1;}SeetaPointF points[5];for (int i = 0; i < 5; i++){points[i] = v_points.at(i);}int status;switch (way){case 0:status = faceantspoofing->Predict(image, face, points);break;case 1:status = faceantspoofing->PredictVideo(image, face, points);break;default:std::cout << "way input error";throw 2;}auto status1 = faceantspoofing->PredictVideo(image, face, points);switch (status1) {case seeta::FaceAntiSpoofing::REAL:std::cout << "真实人脸" << std::endl; break;case seeta::FaceAntiSpoofing::SPOOF:std::cout << "攻击人脸" << std::endl; break;case seeta::FaceAntiSpoofing::FUZZY:std::cout << "无法判断" << std::endl; break;case seeta::FaceAntiSpoofing::DETECTING:std::cout << "正在检测" << std::endl; break;}return status;}void reset_video() {faceantspoofing->ResetVideo();}void set_frame(int32_t number){faceantspoofing->SetVideoFrameCount(number);//默认是10;}//=======================================================================================================SeetaImageData mat_to_seetaImageData(cv::Mat& m){SeetaImageData* sid = new SeetaImageData();sid->data = m.data;sid->channels = m.channels();sid->height = m.rows;sid->width = m.cols;return *sid;}//=======================================人脸追踪=====================================void facetracker_init(int width, int height){seeta::ModelSetting setting;setting.append(FaceDector_path);facetracker = new seeta::FaceTracker(setting, width, height);}std::vector<SeetaTrackingFaceInfo> tracker(const SeetaImageData &image){if (facetracker == NULL){std::cout << "facetracker dont init";throw 1;}SeetaTrackingFaceInfoArray cfaces = facetracker->Track(image);std::vector<SeetaTrackingFaceInfo> faces(cfaces.data, cfaces.data + cfaces.size);return faces;}//========================质量评估==================================================void qualityrelu_init(int choose){switch (choose){case Brightness: qualityrelu = new seeta::QualityOfBrightness();//亮度case Clarity: qualityrelu = new seeta::QualityOfClarity();//清晰度case Integrity:qualityrelu = new seeta::QualityOfIntegrity();//完整度case Pose:qualityrelu = new seeta::QualityOfPose();//姿态case Resolution: qualityrelu = new seeta::QualityOfResolution();//分辨率case NoMask:qualityrelu = new seeta::QualityOfNoMask();}}const char *level_string[] = { "LOW", "MEDIUM", "HIGH" };int plot_quality(const SeetaImageData &image,const SeetaRect &face,const std::vector < SeetaPointF>& points){seeta::QualityResult result = qualityrelu->check(image, face, points.data(), int(points.size()));return result.level;}
}

当然,这个包装库我写的时候也一一测试了功能了,测试代码奉上

#include"seeta_own.h"
#include<Windows.h>using namespace std;
using namespace cv;
using namespace seeta_pacakge;
//打开E:/source/test.jpg检测人脸并显示
//打开E:/source/test2.jpg检测到人脸后与test.jpg的人脸进行匹配
//最后会打印这两张脸相似度,注意,如果一张照片有多张脸,这里设置的是只提取最大的一张脸
void test1()
{Mat m = imread("E:/source/test.jpg");facedector_init();facelandmarker_init();facerecognizer_init();SeetaImageData sid = mat_to_seetaImageData(m);SeetaFaceInfoArray sfia = detect(sid);sort(sfia);if (sfia.data <= 0){cout << "未检测到脸";system("pause");}vector<SeetaPointF> spf = mark(sid, sfia.data[0].pos);rectangle(m, Rect(sfia.data[0].pos.x, sfia.data[0].pos.y, sfia.data[0].pos.width, sfia.data[0].pos.height), Scalar(0, 0, 255));for (int i = 0; i < 5; i++){circle(m, Point(spf.at(i).x, spf.at(i).y), 5, Scalar(0, 255, 0));}imshow("1", m);float* feature = extract_feature(sid, spf);Mat m2 = imread("E:/source/test2.jpg");SeetaImageData sid2 = mat_to_seetaImageData(m2);SeetaFaceInfoArray sfia2 = detect(sid2);sort(sfia2);vector<SeetaPointF> spf2 = mark(sid2, sfia2.data[0].pos);float* feature2 = extract_feature(sid2, spf2);cout << "大小" << facerecognizer->GetExtractFeatureSize();std::cout << "相似度" << facerecognizer->CalculateSimilarity(feature, feature2) << std::endl;for (int j = 0; j < 2048; j++)std::cout << j << "...." << feature2[j] << std::endl;float* f = new float[1024];for (int o = 0; o < 1024; o++)f[o] = feature[o];std::cout << "2相似度" << compare(f, feature2);waitKey(0);
}
//打开摄像头,检测眼睛状态并显示以及打印
void test2()
{facedector_init();facelandmarker_init();eyestatedector_init();VideoCapture videocapture(0);Mat mat;while (videocapture.isOpened()){videocapture.read(mat);flip(mat, mat,1);SeetaImageData sid=mat_to_seetaImageData(mat);SeetaFaceInfoArray sfia=detect(sid);sort(sfia);vector<SeetaPointF> spf  =mark(sid, sfia.data[0].pos);int* eye=eye_state(sid, spf);cout << "左眼:" << eye[0] << EYE_STATE_STR[eye[0]]<< "   "<< "右眼:" << eye[1] << EYE_STATE_STR[eye[1]]<<std::endl;imshow("1", mat);waitKey(1);}
}
//读取test.jpg,打开摄像头,分析摄像头的人脸与test.jpg是否相似,并在控制台打印结果
void test3()
{facedector_init();facelandmarker_init();eyestatedector_init();facerecognizer_init();Mat ori = imread("E:/source/test2.jpg");SeetaImageData sid_ori = mat_to_seetaImageData(ori);SeetaFaceInfoArray sfia_ori = detect(sid_ori);sort(sfia_ori);vector<SeetaPointF> spf2=mark(sid_ori, sfia_ori.data[0].pos);float* ori_ss = extract_feature(sid_ori, spf2);VideoCapture videocapture(0);Mat mat;while (videocapture.isOpened()){videocapture.read(mat);flip(mat, mat, 1);SeetaImageData sid = mat_to_seetaImageData(mat);SeetaFaceInfoArray sfia = detect(sid);sort(sfia);vector<SeetaPointF> spf = mark(sid, sfia.data[0].pos);float* curr=extract_feature(sid, spf);std::cout << compare(curr, ori_ss) << std::endl;imshow("1", mat);waitKey(1);}
}//下面的自己看吧,活体检测,状态检测等等void test4()
{facedector_init();facelandmarker_init();eyestatedector_init();facerecognizer_init();faceantspoofing_init();VideoCapture videocapture(0);Mat mat;while (videocapture.isOpened()){videocapture.read(mat);flip(mat, mat, 1);SeetaImageData sid = mat_to_seetaImageData(mat);SeetaFaceInfoArray sfia = detect(sid);sort(sfia);vector<SeetaPointF> spf = mark(sid, sfia.data[0].pos);int status=predict(sid, sfia.data[0].pos, spf,1);cout << status << SPOOF_STATE_STR[status] << std::endl;imshow("1", mat);waitKey(1);}}
void test5()
{facedector_init();facelandmarker_init();eyestatedector_init();facerecognizer_init();faceantspoofing_init();VideoCapture videocapture(0);facetracker_init(videocapture.get(CAP_PROP_FRAME_WIDTH),videocapture.get(CAP_PROP_FRAME_HEIGHT));Mat mat;while (videocapture.isOpened()){videocapture.read(mat);flip(mat, mat, 1);SeetaImageData sid = mat_to_seetaImageData(mat);auto tra=tracker(sid);for (int i = 0; i < tra.size(); i++){SeetaTrackingFaceInfo id=tra.at(i);rectangle(mat, Rect(id.pos.x, id.pos.y, id.pos.width, id.pos.height), Scalar(255, 0, 0));putText(mat, to_string(id.PID), Point(id.pos.x, id.pos.y), FONT_HERSHEY_SIMPLEX, 1, Scalar(255, 23, 0), 4, 8);}imshow("1", mat);waitKey(1);}
}
void test6()
{facedector_init();facelandmarker_init();eyestatedector_init();facerecognizer_init();faceantspoofing_init();qualityrelu_init(NoMask);VideoCapture videocapture(0);Mat mat;while (videocapture.isOpened()){videocapture.read(mat);flip(mat, mat, 1);SeetaImageData sid = mat_to_seetaImageData(mat);SeetaFaceInfoArray sfia = detect(sid);sort(sfia);vector<SeetaPointF> spf = mark(sid, sfia.data[0].pos);imshow("1", mat);waitKey(1);int status=plot_quality(sid, sfia.data[0].pos, spf);std::cout << level_string[status] << std::endl;}
}
int main()
{test6();
}

如果有什么不对的地方可以给我留言

封装是没有问题的,我已经多次使用这个封装做过项目,如果遇到其他问题也可以给我留言

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