SeetaFaceEngine代码总结

人脸检测

#include <stdlib.h>
#include <iostream>
#include <string>
#include "opencv2\highgui.hpp" //GUI模块
#include "opencv2\imgproc.hpp" //图像处理模块
#include "opencv2\imgcodecs.hpp" //图像读写模块
#include "opencv2\core.hpp" //核心模块
#include "face_detection.h" //SeetaFaceEngine 人脸检测模块
#pragma comment(lib,"opencv_highgui330.lib")
#pragma comment(lib,"opencv_imgproc330.lib")
#pragma comment(lib,"opencv_imgcodecs330.lib")
#pragma comment(lib,"opencv_core330.lib")
#pragma comment(lib,"FaceDetection.lib")

using namespace std;
using namespace cv;
using namespace seeta;

int main()
{
    //加载模型
    seeta::FaceDetection detector("model/seeta_fd_frontal_v1.0.bin");
    //设置参数
    detector.SetMinFaceSize(40);//设置检测器最小尺寸，默认为20。
    detector.SetScoreThresh(2.f);//设置检测阈值
    detector.SetImagePyramidScaleFactor(0.8f);//设置图像金字塔缩放系数
    detector.SetWindowStep(4, 4);//设置滑动窗口步长
    //读取待检测图像并转换为灰度图像。
    cv::Mat img = cv::imread("1.jpg", cv::IMREAD_UNCHANGED);
    cv::Mat img_gray;
    if (img.channels() != 1)
    {
        cv::cvtColor(img, img_gray, cv::COLOR_BGR2GRAY);
    }
    else
    {
        img_gray = img;
    }
    //初始化 seeta::ImageData  图像数据结构体
    seeta::ImageData img_data;
    img_data.data = img_gray.data;
    img_data.width = img_gray.cols;
    img_data.height = img_gray.rows;
    img_data.num_channels = 1;
    //开始检测人脸并获取运行时间
    long t0 = cv::getTickCount();
    std::vector<seeta::FaceInfo> faces = detector.Detect(img_data);
    long t1 = cv::getTickCount();
    double secs = (t1 - t0) / cv::getTickFrequency();
    //获取搜寻到人脸的数量并绘制矩形框
    cv::Rect face_rect;
    int32_t num_face = static_cast<int32_t>(faces.size());
    for (int32_t i = 0; i < num_face; i++) {
        face_rect.x = faces[i].bbox.x;
        face_rect.y = faces[i].bbox.y;
        face_rect.width = faces[i].bbox.width;
        face_rect.height = faces[i].bbox.height;

        cv::rectangle(img, face_rect, CV_RGB(0, 255, 0), 2, 8, 0);
    }
    //显示最后结果图像
    cv::namedWindow("Win7", cv::WINDOW_AUTOSIZE);
    cv::imshow("Win7", img);
    cv::waitKey(0);
    cv::destroyAllWindows();
    system("pause");
    return 0;
}

人脸校正

#include <stdlib.h>
#include <iostream>
#include <string>
#include "opencv2\highgui.hpp" //GUI模块
#include "opencv2\imgproc.hpp" //图像处理模块
#include "opencv2\imgcodecs.hpp" //图像读写模块
#include "opencv2\core.hpp" //核心模块
#include "face_detection.h" //SeetaFaceEngine           人脸检测模块
#include "face_alignment.h" //SeetaFaceAlignment        人脸校准模块

#pragma comment(lib,"opencv_highgui330.lib")
#pragma comment(lib,"opencv_imgproc330.lib")
#pragma comment(lib,"opencv_imgcodecs330.lib")
#pragma comment(lib,"opencv_core330.lib")
#pragma comment(lib,"FaceDetection.lib")
#pragma comment(lib,"SeetaFaceAlignment.lib")

using namespace std;
using namespace cv;
using namespace seeta;


int main()
{
    //加载模型
    seeta::FaceDetection detector("model/seeta_fd_frontal_v1.0.bin");
    seeta::FaceAlignment point_detector("model/seeta_fa_v1.1.bin");

    //设置参数
    detector.SetMinFaceSize(40);//设置检测器最小尺寸，默认为20。
    detector.SetScoreThresh(2.f);//设置检测阈值
    detector.SetImagePyramidScaleFactor(0.8f);//设置图像金字塔缩放系数
    detector.SetWindowStep(4, 4);//设置滑动窗口步长
    //读取待检测图像并转换为灰度图像。
    cv::Mat img = cv::imread("000.jpg", cv::IMREAD_UNCHANGED);
    cv::Mat img_gray;
    if (img.channels() != 1)
    {
        cv::cvtColor(img, img_gray, cv::COLOR_BGR2GRAY);
    }
    else
    {
        img_gray = img;
    }
    //初始化 seeta::ImageData  图像数据结构体
    seeta::ImageData img_data;
    img_data.data = img_gray.data;
    img_data.width = img_gray.cols;
    img_data.height = img_gray.rows;
    img_data.num_channels = 1;
    //开始检测人脸并获取运行时间
    long t0 = cv::getTickCount();
    std::vector<seeta::FaceInfo> faces = detector.Detect(img_data);
    long t1 = cv::getTickCount();
    double secs = (t1 - t0) / cv::getTickFrequency();
    //检测5个特征点
    int pts_num = 5;
    seeta::FacialLandmark points[5];
    //获取搜寻到人脸的数量并绘制矩形框
    cv::Rect face_rect;
    int32_t num_face = static_cast<int32_t>(faces.size());
    for (int32_t i = 0; i < num_face; ++i) {
        face_rect.x = faces[i].bbox.x;
        face_rect.y = faces[i].bbox.y;
        face_rect.width = faces[i].bbox.width;
        face_rect.height = faces[i].bbox.height;
        cv::rectangle(img, face_rect, CV_RGB(0, 255, 0), 2, 8, 0);
        //开始查找特征点
        point_detector.PointDetectLandmarks(img_data, faces[i], points);
        //绘制
        //cv::rectangle(img, cvPoint(faces[0].bbox.x, faces[0].bbox.y), cvPoint(faces[0].bbox.x + faces[0].bbox.width - 1, faces[0].bbox.y + faces[0].bbox.height - 1), CV_RGB(255, 0, 0), 0, 0, 0);
        for (int j = 0; j < pts_num; ++j)
        {
            cv::circle(img, cvPoint(points[j].x, points[j].y), 2, CV_RGB(0, 255, 0), CV_FILLED);
        }
    }
    cv::resize(img, img, cv::Size(img.cols * 1, img.rows * 1));
    //显示最后结果图像
    cv::namedWindow("Win7", cv::WINDOW_AUTOSIZE);
    cv::imshow("Win7", img);
    cv::waitKey(0);
    cv::destroyAllWindows();
    system("pause");
    return 0;
}

人脸识别

#include <stdlib.h>
#include <iostream>
#include <string>
#include "opencv2\highgui.hpp" //GUI模块
#include "opencv2\imgproc.hpp" //图像处理模块
#include "opencv2\imgcodecs.hpp" //图像读写模块
#include "opencv2\core.hpp" //核心模块
#include "face_detection.h" //SeetaFaceEngine           人脸检测模块
#include "face_alignment.h" //SeetaFaceAlignment        人脸校准模块
#include "face_identification.h" //FaceIdentification   人脸识别模块
#pragma comment(lib,"opencv_highgui330.lib")
#pragma comment(lib,"opencv_imgproc330.lib")
#pragma comment(lib,"opencv_imgcodecs330.lib")
#pragma comment(lib,"opencv_core330.lib")
#pragma comment(lib,"FaceDetection.lib")
#pragma comment(lib,"SeetaFaceAlignment.lib")
#pragma comment(lib,"FaceIdentification.lib")
using namespace std;
using namespace cv;
using namespace seeta;


int main()
{
    //加载模型
    seeta::FaceDetection detector("model/seeta_fd_frontal_v1.0.bin");
    seeta::FaceAlignment point_detector("model/seeta_fa_v1.1.bin");
    seeta::FaceIdentification face_recognizer("model/seeta_fr_v1.0.bin");
    //设置参数
    detector.SetMinFaceSize(40);//设置检测器最小尺寸，默认为20。
    detector.SetScoreThresh(2.f);//设置检测阈值
    detector.SetImagePyramidScaleFactor(0.8f);//设置图像金字塔缩放系数
    detector.SetWindowStep(4, 4);//设置滑动窗口步长
    //读取待检测图像并转换为灰度图像。
    cv::Mat img = cv::imread("Dilraba3.jpg", cv::IMREAD_UNCHANGED);
    //保存彩色图像样本用于裁剪
    cv::Mat _img = img.clone();
    ImageData src_img_data(_img.cols, _img.rows, _img.channels());
    src_img_data.data = _img.data;
    cv::Mat img_gray;
    if (img.channels() != 1)
    {
        cv::cvtColor(img, img_gray, cv::COLOR_BGR2GRAY);
    }
    else
    {
        img_gray = img;
    }
    //初始化 seeta::ImageData  图像数据结构体
    seeta::ImageData img_data;
    img_data.data = img_gray.data;
    img_data.width = img_gray.cols;
    img_data.height = img_gray.rows;
    img_data.num_channels = 1;
    //开始检测人脸并获取运行时间
    long t0 = cv::getTickCount();
    std::vector<seeta::FaceInfo> faces = detector.Detect(img_data);
    long t1 = cv::getTickCount();
    double secs = (t1 - t0) / cv::getTickFrequency();
    //检测5个特征点
    int pts_num = 5;
    seeta::FacialLandmark points[5];
    //获取搜寻到人脸的数量并绘制矩形框
    cv::Rect face_rect;
    int32_t num_face = static_cast<int32_t>(faces.size());
    for (int32_t i = 0; i < num_face; ++i) {
        face_rect.x = faces[i].bbox.x;
        face_rect.y = faces[i].bbox.y;
        face_rect.width = faces[i].bbox.width;
        face_rect.height = faces[i].bbox.height;
        cv::rectangle(img, face_rect, CV_RGB(0, 255, 0), 2, 8, 0);
        //开始查找特征点
        point_detector.PointDetectLandmarks(img_data, faces[i], points);
        //绘制
        //cv::rectangle(img, cvPoint(faces[0].bbox.x, faces[0].bbox.y), cvPoint(faces[0].bbox.x + faces[0].bbox.width - 1, faces[0].bbox.y + faces[0].bbox.height - 1), CV_RGB(255, 0, 0), 0, 0, 0);
        for (int j = 0; j < pts_num; ++j)
        {
            cv::circle(img, cvPoint(points[j].x, points[j].y), 2, CV_RGB(0, 255, 0), CV_FILLED);
        }
    }
    //创建一个用来存储根据5个特征点裁剪后的图像
    cv::Mat dst_img(face_recognizer.crop_height(), face_recognizer.crop_width(), CV_8UC(face_recognizer.crop_channels()));
    ImageData dst_img_data(dst_img.cols, dst_img.rows, dst_img.channels());
    dst_img_data.data = dst_img.data;
    //裁剪图像并显示 256x256
    face_recognizer.CropFace(src_img_data, points, dst_img_data);
    cv::imwrite("Dilraba3Crop.jpg", dst_img);
    cv::imshow("Crop Face", dst_img);

    //判定特征维度是否等于2048
    int feat_size = face_recognizer.feature_size();
    if (feat_size != 2048)
    {
        return 1;
    }
    //读取待测图片并大小归一化
    cv::Mat src_img = cv::imread("Dilraba1Crop.jpg", cv::IMREAD_COLOR);
    cv::imshow("Probe Face", src_img);
    cv::resize(src_img, src_img, cv::Size(face_recognizer.crop_height(), face_recognizer.crop_width()));
    //
    ImageData _src_img_data(src_img.cols, src_img.rows, src_img.channels());
    _src_img_data.data = src_img.data;
    //获取脸部特征，注意获取的特征维度必须等于模型的特征维度
    float* feats = new  float[face_recognizer.feature_size()];
    float probe_fea[2048];
    //FacialLandmark pt5[5] = { points[0],points[1],points[2],points[3],points[4] };
    face_recognizer.ExtractFeature(dst_img_data, feats);
    face_recognizer.ExtractFeature(_src_img_data, probe_fea);
    //face_recognizer.ExtractFeatureWithCrop(_src_img_data, pt5, feats);
    //计算相似度
    float sim = face_recognizer.CalcSimilarity(feats, probe_fea);
    std::cout << sim << std::endl;
    //图像比较小，不好查看，就把图像放大，太大就缩小
    cv::resize(img, img, cv::Size(img.cols * 1, img.rows * 1));
    //显示最后结果图像
    cv::namedWindow("Win7", cv::WINDOW_AUTOSIZE);
    cv::imshow("Win7", img);
    cv::waitKey(0);
    cv::destroyAllWindows();
    system("pause");
    delete[] feats;
    return 0;
}