opencv学习－建立人脸识别分类器

在sample/cpp下面有一个文件叫做facerec_demo.cpp,还有一个文件叫做facerec_at_t.txt

首先我们要去
AT&T人脸库下载400张图片，分别时40个人，每个人有10张照片

下载完成后，随便放在哪里，但是注意要修改facerec_at_t.txt文件里面的路径，下面是我的文件路径

/home/myname/Desktop/opencv-2.4.7/orl_faces/s13/2.pgm;12

/home/myname/Desktop/opencv-2.4.7/orl_faces/s13/7.pgm;12

/home/myname/Desktop/opencv-2.4.7/orl_faces/s13/6.pgm;12

;后面的12代表类别，如果你要创建自己和同学的人脸库，记住要给不同的人赋予不同的label

然后我们看看facerec_demo.cpp,先贴上源代码

#include "opencv2/core/core.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/contrib/contrib.hpp"

#include <iostream>
#include <fstream>
#include <sstream>

using namespace cv;
using namespace std;

static void read_csv(const string& filename, vector<Mat>& images, vector<int>& labels, char separator = ';') {
    std::ifstream file(filename.c_str(), ifstream::in);
    if (!file) {
        string error_message = "No valid input file was given, please check the given filename.";
        CV_Error(CV_StsBadArg, error_message);
    }
    string line, path, classlabel;
    while (getline(file, line)) {
        stringstream liness(line);
        getline(liness, path, separator);
        getline(liness, classlabel);
        if(!path.empty() && !classlabel.empty()) {
            images.push_back(imread(path, 0));
            labels.push_back(atoi(classlabel.c_str()));
        }
    }
}

int main(int argc, const char *argv[]) {
    // Check for valid command line arguments, print usage
    // if no arguments were given.
    if (argc != 2) {
        cout << "usage: " << argv[0] << " <csv.ext>" << endl;
        exit(1);
    }
    // Get the path to your CSV.
    string fn_csv = string(argv[1]);
    // These vectors hold the images and corresponding labels.
    vector<Mat> images;
    vector<int> labels;
    // Read in the data. This can fail if no valid
    // input filename is given.
    try {
        read_csv(fn_csv, images, labels);
    } catch (cv::Exception& e) {
        cerr << "Error opening file \"" << fn_csv << "\". Reason: " << e.msg << endl;
        // nothing more we can do
        exit(1);
    }
    // Quit if there are not enough images for this demo.
    if(images.size() <= 1) {
        string error_message = "This demo needs at least 2 images to work. Please add more images to your data set!";
        CV_Error(CV_StsError, error_message);
    }
    // Get the height from the first image. We'll need this
    // later in code to reshape the images to their original
    // size:
    int height = images[0].rows;
    //作为我的唯一test图片
    Mat testSample = images[images.size() - 1];
    int testLabel = labels[labels.size() - 1];
    images.pop_back();
    labels.pop_back();

    Ptr<FaceRecognizer> model = createEigenFaceRecognizer();
    model->train(images, labels);
    // The following line predicts the label of a given
    // test image:
    int predictedLabel = model->predict(testSample);
    //
    // To get the confidence of a prediction call the model with:
    //
    //      int predictedLabel = -1;
    //      double confidence = 0.0;
    //      model->predict(testSample, predictedLabel, confidence);
    //
    string result_message = format("Predicted class = %d / Actual class = %d.", predictedLabel, testLabel);
    cout << result_message << endl;
    waitKey(0);

    return 0;
}

这段代码做的事情是这样的，读取400张图片，将399张用于训练，再用最后一张去做识别

代码很短，也很容易看懂

下面是我的输出

Predicted class = 37 / Actual class = 37.

输出表示识别正确

注释中说了这是用PCA实现的，下次得好好看看论文仔细研究算法了！

有了这个，你也可以快速建立你的人脸识别分类器了！