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187 lines
8.1 KiB
C++
187 lines
8.1 KiB
C++
/*
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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/core/core.hpp"
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#include "opencv2/highgui/highgui.hpp"
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#include "opencv2/contrib/contrib.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 void read_csv(const string& filename, vector<Mat>& images, vector<int>& labels, std::map<int, string>& labelsInfo, char separator = ';') {
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ifstream csv(filename.c_str());
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if (!csv) CV_Error(CV_StsBadArg, "No valid input file was given, please check the given filename.");
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string line, path, classlabel, info;
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while (getline(csv, line)) {
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stringstream liness(line);
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path.clear(); classlabel.clear(); info.clear();
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getline(liness, path, separator);
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getline(liness, classlabel, separator);
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getline(liness, info, separator);
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if(!path.empty() && !classlabel.empty()) {
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cout << "Processing " << path << endl;
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int label = atoi(classlabel.c_str());
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if(!info.empty())
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labelsInfo.insert(std::make_pair(label, info));
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// 'path' can be file, dir or wildcard path
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String root(path.c_str());
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vector<String> files;
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glob(root, files, true);
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for(vector<String>::const_iterator f = files.begin(); f != files.end(); ++f) {
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cout << "\t" << *f << endl;
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Mat img = imread(*f, CV_LOAD_IMAGE_GRAYSCALE);
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static int w=-1, h=-1;
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static bool showSmallSizeWarning = true;
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if(w>0 && h>0 && (w!=img.cols || h!=img.rows)) cout << "\t* Warning: images should be of the same size!" << endl;
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if(showSmallSizeWarning && (img.cols<50 || img.rows<50)) {
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cout << "* Warning: for better results images should be not smaller than 50x50!" << endl;
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showSmallSizeWarning = false;
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}
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images.push_back(img);
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labels.push_back(label);
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}
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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 && argc != 3) {
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cout << "Usage: " << argv[0] << " <csv> [arg2]\n"
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<< "\t<csv> - path to config file in CSV format\n"
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<< "\targ2 - if the 2nd argument is provided (with any value) "
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<< "the advanced stuff is run and shown to console.\n"
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<< "The CSV config file consists of the following lines:\n"
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<< "<path>;<label>[;<comment>]\n"
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<< "\t<path> - file, dir or wildcard path\n"
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<< "\t<label> - non-negative integer person label\n"
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<< "\t<comment> - optional comment string (e.g. person name)"
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<< endl;
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exit(1);
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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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std::map<int, string> labelsInfo;
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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, labelsInfo);
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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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// 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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// confidennce threshold, call it with:
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//
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// cv::createEigenFaceRecognizer(10, 123.0);
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//
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Ptr<FaceRecognizer> model = createEigenFaceRecognizer();
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model->setLabelsInfo(labelsInfo);
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model->train(images, labels);
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string saveModelPath = "face-rec-model.txt";
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cout << "Saving the trained model to " << saveModelPath << endl;
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model->save(saveModelPath);
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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 = model->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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if( (predictedLabel == testLabel) && !model->getLabelInfo(predictedLabel).empty() )
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cout << format("%d-th label's info: %s", predictedLabel, model->getLabelInfo(predictedLabel).c_str()) << endl;
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// advanced stuff
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if(argc>2) {
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// Sometimes you'll need to get/set internal model data,
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// which isn't exposed by the public cv::FaceRecognizer.
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// Since each cv::FaceRecognizer is derived from a
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// cv::Algorithm, you can query the data.
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//
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// First we'll use it to set the threshold of the FaceRecognizer
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// to 0.0 without retraining the model. This can be useful if
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// you are evaluating the model:
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//
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model->set("threshold", 0.0);
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// Now the threshold of this model is set to 0.0. A prediction
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// now returns -1, as it's impossible to have a distance below
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// it
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predictedLabel = model->predict(testSample);
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cout << "Predicted class = " << predictedLabel << endl;
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// Here is how to get the eigenvalues of this Eigenfaces model:
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Mat eigenvalues = model->getMat("eigenvalues");
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// And we can do the same to display the Eigenvectors (read Eigenfaces):
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Mat W = model->getMat("eigenvectors");
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// From this we will display the (at most) first 10 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;
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normalize(ev.reshape(1), grayscale, 0, 255, NORM_MINMAX, CV_8UC1);
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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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imshow(format("%d", i), cgrayscale);
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}
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waitKey(0);
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}
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return 0;
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}
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