openCV学习笔记（十七） —— 人脸识别 —— 程序 —— LDA线性判别分析FisherFace

谁践踏了优雅 2021-09-27 06:24 387阅读 0赞

# 流程 #

![70][]

# PCA和LDA的对比 #

![70 1][]

# 程序  #

/*
     * Copyright (c) 2011. Philipp Wagner <bytefish[at]gmx[dot]de>.
     * Released to public domain under terms of the BSD Simplified license.
     *
     * Redistribution and use in source and binary forms, with or without
     * modification, are permitted provided that the following conditions are met:
     *   * Redistributions of source code must retain the above copyright
     *     notice, this list of conditions and the following disclaimer.
     *   * Redistributions in binary form must reproduce the above copyright
     *     notice, this list of conditions and the following disclaimer in the
     *     documentation and/or other materials provided with the distribution.
     *   * Neither the name of the organization nor the names of its contributors
     *     may be used to endorse or promote products derived from this software
     *     without specific prior written permission.
     *
     *   See <http://www.opensource.org/licenses/bsd-license>
     */
    
    #include "opencv2/core.hpp"
    #include "opencv2/face.hpp"
    #include "opencv2/highgui.hpp"
    #include "opencv2/imgproc.hpp"
    
    #include <iostream>
    #include <fstream>
    #include <sstream>
    
    using namespace cv;
    using namespace cv::face;
    using namespace std;
    
    static Mat norm_0_255(InputArray _src) {
        Mat src = _src.getMat();
        // Create and return normalized image:
        Mat dst;
        switch(src.channels()) {
        case 1:
            cv::normalize(_src, dst, 0, 255, NORM_MINMAX, CV_8UC1);
            break;
        case 3:
            cv::normalize(_src, dst, 0, 255, NORM_MINMAX, CV_8UC3);
            break;
        default:
            src.copyTo(dst);
            break;
        }
        return dst;
    }
    
    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(Error::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> <output_folder> " << endl;
            exit(1);
        }
        string output_folder = ".";
        if (argc == 3) {
            output_folder = string(argv[2]);
        }
        // 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(Error::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;
        // The following lines simply get the last images from
        // your dataset and remove it from the vector. This is
        // done, so that the training data (which we learn the
        // cv::BasicFaceRecognizer on) and the test data we test
        // the model with, do not overlap.
        Mat testSample = images[images.size() - 1];
        int testLabel = labels[labels.size() - 1];
        images.pop_back();
        labels.pop_back();
        // The following lines create an Fisherfaces model for
        // face recognition and train it with the images and
        // labels read from the given CSV file.
        // If you just want to keep 10 Fisherfaces, then call
        // the factory method like this:
        //
        //      FisherFaceRecognizer::create(10);
        //
        // However it is not useful to discard Fisherfaces! Please
        // always try to use _all_ available Fisherfaces for
        // classification.
        //
        // If you want to create a FaceRecognizer with a
        // confidence threshold (e.g. 123.0) and use _all_
        // Fisherfaces, then call it with:
        //
        //      FisherFaceRecognizer::create(0, 123.0);
        //
        Ptr<FisherFaceRecognizer> model = FisherFaceRecognizer::create();
        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;
        // Here is how to get the eigenvalues of this Eigenfaces model:
        Mat eigenvalues = model->getEigenValues();
        // And we can do the same to display the Eigenvectors (read Eigenfaces):
        Mat W = model->getEigenVectors();
        // Get the sample mean from the training data
        Mat mean = model->getMean();
        // Display or save:
        if(argc == 2) {
            imshow("mean", norm_0_255(mean.reshape(1, images[0].rows)));
        } else {
            imwrite(format("%s/mean.png", output_folder.c_str()), norm_0_255(mean.reshape(1, images[0].rows)));
        }
        // Display or save the first, at most 16 Fisherfaces:
        for (int i = 0; i < min(16, W.cols); i++) {
            string msg = format("Eigenvalue #%d = %.5f", i, eigenvalues.at<double>(i));
            cout << msg << endl;
            // get eigenvector #i
            Mat ev = W.col(i).clone();
            // Reshape to original size & normalize to [0...255] for imshow.
            Mat grayscale = norm_0_255(ev.reshape(1, height));
            // Show the image & apply a Bone colormap for better sensing.
            Mat cgrayscale;
            applyColorMap(grayscale, cgrayscale, COLORMAP_BONE);
            // Display or save:
            if(argc == 2) {
                imshow(format("fisherface_%d", i), cgrayscale);
            } else {
                imwrite(format("%s/fisherface_%d.png", output_folder.c_str(), i), norm_0_255(cgrayscale));
            }
        }
        // Display or save the image reconstruction at some predefined steps:
        for(int num_component = 0; num_component < min(16, W.cols); num_component++) {
            // Slice the Fisherface from the model:
            Mat ev = W.col(num_component);
            Mat projection = LDA::subspaceProject(ev, mean, images[0].reshape(1,1));
            Mat reconstruction = LDA::subspaceReconstruct(ev, mean, projection);
            // Normalize the result:
            reconstruction = norm_0_255(reconstruction.reshape(1, images[0].rows));
            // Display or save:
            if(argc == 2) {
                imshow(format("fisherface_reconstruction_%d", num_component), reconstruction);
            } else {
                imwrite(format("%s/fisherface_reconstruction_%d.png", output_folder.c_str(), num_component), reconstruction);
            }
        }
        // Display if we are not writing to an output folder:
        if(argc == 2) {
            waitKey(0);
        }
        return 0;
    }

该程序来源于facerec\_fisherfaces.cpp，位于opencv\_contrib\\modules\\face\\samples

[70]: /images/20210923/46027a05fe2b475595f0f86014f08552.png
[70 1]: /images/20210923/563cd4ab164c408ea8f84d8efd976c29.png