Added the cv::FaceRecognizer documentation (API, Face Recognition Guide, Tutorials).
This commit is contained in:
Philipp Wagner
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modules/contrib/doc/facerec/src/facerec_save_load.cpp
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modules/contrib/doc/facerec/src/facerec_save_load.cpp
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/*
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* Copyright (c) 2011. Philipp Wagner <bytefish[at]gmx[dot]de>.
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* Released to public domain under terms of the BSD Simplified license.
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions are met:
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* * Redistributions of source code must retain the above copyright
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* notice, this list of conditions and the following disclaimer.
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* * Redistributions in binary form must reproduce the above copyright
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* notice, this list of conditions and the following disclaimer in the
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* documentation and/or other materials provided with the distribution.
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* * Neither the name of the organization nor the names of its contributors
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* may be used to endorse or promote products derived from this software
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* without specific prior written permission.
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*
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* See <http://www.opensource.org/licenses/bsd-license>
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*/
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#include "opencv2/contrib/contrib.hpp"
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#include "opencv2/core/core.hpp"
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#include "opencv2/highgui/highgui.hpp"
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#include <iostream>
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#include <fstream>
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#include <sstream>
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using namespace cv;
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using namespace std;
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static Mat norm_0_255(InputArray _src) {
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Mat src = _src.getMat();
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// Create and return normalized image:
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Mat dst;
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switch(src.channels()) {
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case 1:
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cv::normalize(_src, dst, 0, 255, NORM_MINMAX, CV_8UC1);
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break;
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case 3:
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cv::normalize(_src, dst, 0, 255, NORM_MINMAX, CV_8UC3);
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break;
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default:
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src.copyTo(dst);
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break;
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}
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return dst;
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}
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static void read_csv(const string& filename, vector<Mat>& images, vector<int>& labels, char separator = ';') {
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std::ifstream file(filename.c_str(), ifstream::in);
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if (!file) {
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string error_message = "No valid input file was given, please check the given filename.";
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CV_Error(CV_StsBadArg, error_message);
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}
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string line, path, classlabel;
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while (getline(file, line)) {
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stringstream liness(line);
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getline(liness, path, separator);
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getline(liness, classlabel);
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if(!path.empty() && !classlabel.empty()) {
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images.push_back(imread(path, 0));
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labels.push_back(atoi(classlabel.c_str()));
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}
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}
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}
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int main(int argc, const char *argv[]) {
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// Check for valid command line arguments, print usage
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// if no arguments were given.
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if (argc < 2) {
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cout << "usage: " << argv[0] << " <csv.ext> <output_folder> " << endl;
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exit(1);
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}
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string output_folder;
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if (argc == 3) {
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output_folder = string(argv[2]);
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}
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// Get the path to your CSV.
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string fn_csv = string(argv[1]);
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// These vectors hold the images and corresponding labels.
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vector<Mat> images;
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vector<int> labels;
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// Read in the data. This can fail if no valid
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// input filename is given.
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try {
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read_csv(fn_csv, images, labels);
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} catch (cv::Exception& e) {
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cerr << "Error opening file \"" << fn_csv << "\". Reason: " << e.msg << endl;
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// nothing more we can do
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exit(1);
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}
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// Quit if there are not enough images for this demo.
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if(images.size() <= 1) {
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string error_message = "This demo needs at least 2 images to work. Please add more images to your data set!";
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CV_Error(CV_StsError, error_message);
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}
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// Get the height from the first image. We'll need this
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// later in code to reshape the images to their original
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// size:
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int height = images[0].rows;
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// The following lines simply get the last images from
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// your dataset and remove it from the vector. This is
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// done, so that the training data (which we learn the
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// cv::FaceRecognizer on) and the test data we test
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// the model with, do not overlap.
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Mat testSample = images[images.size() - 1];
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int testLabel = labels[labels.size() - 1];
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images.pop_back();
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labels.pop_back();
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// The following lines create an Eigenfaces model for
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// face recognition and train it with the images and
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// labels read from the given CSV file.
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// This here is a full PCA, if you just want to keep
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// 10 principal components (read Eigenfaces), then call
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// the factory method like this:
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//
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// cv::createEigenFaceRecognizer(10);
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//
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// If you want to create a FaceRecognizer with a
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// confidence threshold (e.g. 123.0), call it with:
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//
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// cv::createEigenFaceRecognizer(10, 123.0);
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//
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// If you want to use _all_ Eigenfaces and have a threshold,
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// then call the method like this:
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//
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// cv::createEigenFaceRecognizer(0, 123.0);
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//
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Ptr<FaceRecognizer> model0 = createEigenFaceRecognizer();
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model0->train(images, labels);
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// save the model to eigenfaces_at.yaml
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model0->save("eigenfaces_at.yml");
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//
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//
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// Now create a new Eigenfaces Recognizer
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//
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Ptr<FaceRecognizer> model1 = createEigenFaceRecognizer();
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model1->load("eigenfaces_at.yml");
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// The following line predicts the label of a given
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// test image:
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int predictedLabel = model1->predict(testSample);
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//
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// To get the confidence of a prediction call the model with:
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//
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// int predictedLabel = -1;
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// double confidence = 0.0;
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// model->predict(testSample, predictedLabel, confidence);
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//
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string result_message = format("Predicted class = %d / Actual class = %d.", predictedLabel, testLabel);
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cout << result_message << endl;
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// Here is how to get the eigenvalues of this Eigenfaces model:
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Mat eigenvalues = model1->getMat("eigenvalues");
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// And we can do the same to display the Eigenvectors (read Eigenfaces):
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Mat W = model1->getMat("eigenvectors");
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// Get the sample mean from the training data
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Mat mean = model1->getMat("mean");
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// Display or save:
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if(argc == 2) {
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imshow("mean", norm_0_255(mean.reshape(1, images[0].rows)));
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} else {
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imwrite(format("%s/mean.png", output_folder.c_str()), norm_0_255(mean.reshape(1, images[0].rows)));
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}
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// Display or save the Eigenfaces:
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for (int i = 0; i < min(10, W.cols); i++) {
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string msg = format("Eigenvalue #%d = %.5f", i, eigenvalues.at<double>(i));
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cout << msg << endl;
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// get eigenvector #i
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Mat ev = W.col(i).clone();
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// Reshape to original size & normalize to [0...255] for imshow.
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Mat grayscale = norm_0_255(ev.reshape(1, height));
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// Show the image & apply a Jet colormap for better sensing.
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Mat cgrayscale;
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applyColorMap(grayscale, cgrayscale, COLORMAP_JET);
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// Display or save:
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if(argc == 2) {
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imshow(format("eigenface_%d", i), cgrayscale);
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} else {
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imwrite(format("%s/eigenface_%d.png", output_folder.c_str(), i), norm_0_255(cgrayscale));
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}
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}
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// Display or save the image reconstruction at some predefined steps:
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for(int num_components = 10; num_components < 300; num_components+=15) {
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// slice the eigenvectors from the model
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Mat evs = Mat(W, Range::all(), Range(0, num_components));
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Mat projection = subspaceProject(evs, mean, images[0].reshape(1,1));
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Mat reconstruction = subspaceReconstruct(evs, mean, projection);
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// Normalize the result:
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reconstruction = norm_0_255(reconstruction.reshape(1, images[0].rows));
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// Display or save:
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if(argc == 2) {
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imshow(format("eigenface_reconstruction_%d", num_components), reconstruction);
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} else {
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imwrite(format("%s/eigenface_reconstruction_%d.png", output_folder.c_str(), num_components), reconstruction);
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}
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}
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// Display if we are not writing to an output folder:
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if(argc == 2) {
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waitKey(0);
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}
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return 0;
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}
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