OpenCV + CPP 系列（圩八）人脸识别经典算法（实时检测识别）

文章目录

• ​​制作数据集​​
• ​​训练并人脸识别​​

本文以

OpenCV + CPP 系列（圩七）人脸识别经典算法 为基础，实现简单的人脸识别系统。

若需要实现商业可行的算法，使用深度学习的人脸检测技术，人脸特征提取与存储技术，海量特征向量检索技术。
实现流程：

1. 视频（摄像头）获取图像
2. 人脸检测技术截取目标区域
3. 使用PCA特征训练模型
4. 使用摄像头获取人脸进行预测识别

头文件 ​​FaceRecognize.h​​：

#pragma once
#include <opencv2/opencv.hpp>
#include <opencv2/face.hpp>
#include <iostream>
#include <fstream>

using namespace cv;
using namespace std;


class FaceRecognize {
public:
  void video2image(const char* face_path, const char* model_path);
  void video_face_recognize(const char* csv_path, const char* model_path);
};

主函数​​main.cpp​​

#include "FaceRecognize.h"


int main(int argc, char**argv) {
  const char* csv_path = "D:/opencv-c++/orl_faces/images_info.txt";
  const char* face_path = "D:/opencv-c++/our_faces";
  const char* face_xml_path = "D:/opencv_build/install/etc/haarcascades/haarcascade_frontalface_alt_tree.xml";

  FaceRecognize face_recognize;
  //face_recognize.video2image(face_path, face_xml_path);
  face_recognize.video_face_recognize(csv_path, face_xml_path);

  waitKey(0);
  destroyAllWindows();
  return 0;
}

制作数据集

void FaceRecognize::video2image(const char* face_path, const char* model_path) {
  CascadeClassifier faceDetector;
  VideoCapture capture(0);
  if (!capture.isOpened()|| !faceDetector.load(model_path)) {
    cout << "相机打开失败，或者人脸检测模型加载失败，退出！" << endl;
    exit(-1);
  }
  int height = capture.get(cv::CAP_PROP_FRAME_HEIGHT);
  int width = capture.get(cv::CAP_PROP_FRAME_WIDTH);
  int FPS = capture.get(cv::CAP_PROP_FPS);
  printf("height:%d \t width:%d \t FPS:%d\n", height, width, FPS);
  vector<Rect> face_rects;
  Mat frame, gray_img, dist_img;
  int count = 0;
  while (capture.read(frame))
  {
    cv::cvtColor(frame, gray_img, COLOR_BGR2GRAY);
    cv::equalizeHist(gray_img, gray_img);
    faceDetector.detectMultiScale(gray_img, face_rects, 1.1, 2, 0, Size(30, 30), Size(380, 400));
    for (size_t i = 0; i < face_rects.size(); i++) {
      if (count % 3 == 0) {
        resize(frame(face_rects[i]), dist_img, Size(100, 100));
        cv::imwrite(face_path + format("/face_%d.png", count), dist_img);
      }
      rectangle(frame, face_rects[i], Scalar(0, 0, 255), 2, 8, 0);
    }
    count++;
    imshow("frame", frame);
    if (waitKey(FPS) == 27) {
      break;
    }
  }
  capture.release();
}

训练并人脸识别

void load_image_sample(const char* csv_path, vector<Mat>& images, vector<int>& labels) {
  ifstream file(csv_path, ifstream::in);
  if (!file) {
    cout << "could not read files." << endl;
    exit(-1);
  }
  string line, imgPath, classLabel;
  while (getline(file, line)) {
    stringstream lines(line);
    getline(lines, imgPath, '\t');
    getline(lines, classLabel);
    if (!imgPath.empty() && !classLabel.empty()) {
      images.push_back(imread(imgPath, 0));
      labels.push_back(atoi(classLabel.c_str()));
    }
  }
}

void FaceRecognize::video_face_recognize(const char* csv_path, const char* face_xml_path) {
  vector<Mat> images;
  vector<int> labels;
  images.reserve(400);
  labels.reserve(400);
  load_image_sample(csv_path, images, labels);
  if (images.size() < 1 || labels.size() < 1) {
    printf("invalid image path...\n");
    exit(-1);
  }

  int height = images[0].rows;
  int width = images[0].cols;
  printf("查看第一个样本信息：height : %d, width : %d\n", height, width);

  // 弹出最后最后一个样本作为测试数据
  Mat testImage = images[images.size() - 1];
  int testLable = labels[labels.size() - 1];
  images.pop_back();
  labels.pop_back();

  //训练 预测
  Ptr<cv::face::BasicFaceRecognizer> model = cv::face::EigenFaceRecognizer::create();
  model->train(images, labels);
  int predictLabel = model->predict(testImage);
  printf("actual label : %d, predict label :  %d\n", testLable, predictLabel);

  CascadeClassifier faceDetector;
  VideoCapture capture(0);
  if (!capture.isOpened() || !faceDetector.load(face_xml_path)) {
    cout << "相机打开失败，或者人脸检测模型加载失败，退出！" << endl;
    exit(-1);
  }
  int cap_height = capture.get(cv::CAP_PROP_FRAME_HEIGHT);
  int cap_width = capture.get(cv::CAP_PROP_FRAME_WIDTH);
  int FPS = capture.get(cv::CAP_PROP_FPS);
  printf("cap_height:%d \t cap_width:%d \t FPS:%d\n", cap_height, cap_width, FPS);
  vector<Rect> face_rects;
  Mat frame, gray_img;
  while (capture.read(frame))
  {
    cv::cvtColor(frame, gray_img, COLOR_BGR2GRAY);
    cv::equalizeHist(gray_img, gray_img);
    faceDetector.detectMultiScale(gray_img, face_rects, 1.1, 2, 0, Size(30, 30), Size(380, 400));
    for (size_t i = 0; i < face_rects.size(); i++) {
      Mat roi = frame(face_rects[i]);
      cvtColor(roi, gray_img, COLOR_BGR2GRAY);
      resize(gray_img, testImage, testImage.size());
      int label = model->predict(testImage);
      rectangle(frame, face_rects[i], Scalar(255, 0, 0), 2, 8, 0);
      putText(frame, format("i'm %s", (label == 19 ? "me" : "Unknow")), face_rects[i].tl(), FONT_HERSHEY_PLAIN, 1.0, Scalar(0, 0, 255), 2, 8);
    }
    imshow("frame", frame);
    if (waitKey(FPS) == 27) {
      break;
    }
  }
  capture.release();
}