/* * 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/highgui.hpp" #include "opencv2/imgproc.hpp" #include "opencv2/face.hpp" #include "opencv2/core/utility.hpp" #include <iostream> #include <fstream> #include <sstream> #include <map> using namespace cv; using namespace cv::face; using namespace std; static void read_csv(const string& filename, vector<Mat>& images, vector<int>& labels, std::map<int, string>& labelsInfo, char separator = ';') { ifstream csv(filename.c_str()); if (!csv) CV_Error(Error::StsBadArg, "No valid input file was given, please check the given filename."); string line, path, classlabel, info; while (getline(csv, line)) { stringstream liness(line); path.clear(); classlabel.clear(); info.clear(); getline(liness, path, separator); getline(liness, classlabel, separator); getline(liness, info, separator); if(!path.empty() && !classlabel.empty()) { cout << "Processing " << path << endl; int label = atoi(classlabel.c_str()); if(!info.empty()) labelsInfo.insert(std::make_pair(label, info)); // 'path' can be file, dir or wildcard path String root(path.c_str()); vector<String> files; glob(root, files, true); for(vector<String>::const_iterator f = files.begin(); f != files.end(); ++f) { cout << "\t" << *f << endl; Mat img = imread(*f, IMREAD_GRAYSCALE); static int w=-1, h=-1; static bool showSmallSizeWarning = true; if(w>0 && h>0 && (w!=img.cols || h!=img.rows)) cout << "\t* Warning: images should be of the same size!" << endl; if(showSmallSizeWarning && (img.cols<50 || img.rows<50)) { cout << "* Warning: for better results images should be not smaller than 50x50!" << endl; showSmallSizeWarning = false; } images.push_back(img); labels.push_back(label); } } } } int main(int argc, const char *argv[]) { // Check for valid command line arguments, print usage // if no arguments were given. if (argc != 2 && argc != 3) { cout << "Usage: " << argv[0] << " <csv> [arg2]\n" << "\t<csv> - path to config file in CSV format\n" << "\targ2 - if the 2nd argument is provided (with any value) " << "the advanced stuff is run and shown to console.\n" << "The CSV config file consists of the following lines:\n" << "<path>;<label>[;<comment>]\n" << "\t<path> - file, dir or wildcard path\n" << "\t<label> - non-negative integer person label\n" << "\t<comment> - optional comment string (e.g. person name)" << 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; std::map<int, string> labelsInfo; // Read in the data. This can fail if no valid // input filename is given. try { read_csv(fn_csv, images, labels, labelsInfo); } catch (const 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); } // 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::FaceRecognizer on) and the test data we test // the model with, do not overlap. Mat testSample = images[images.size() - 1]; int nlabels = (int)labels.size(); int testLabel = labels[nlabels-1]; images.pop_back(); labels.pop_back(); // The following lines create an Eigenfaces model for // face recognition and train it with the images and // labels read from the given CSV file. // This here is a full PCA, if you just want to keep // 10 principal components (read Eigenfaces), then call // the factory method like this: // // EigenFaceRecognizer::create(10); // // If you want to create a FaceRecognizer with a // confidennce threshold, call it with: // // EigenFaceRecognizer::create(10, 123.0); // Ptr<EigenFaceRecognizer> model = EigenFaceRecognizer::create(); for( int i = 0; i < nlabels; i++ ) model->setLabelInfo(i, labelsInfo[i]); model->train(images, labels); string saveModelPath = "face-rec-model.txt"; cout << "Saving the trained model to " << saveModelPath << endl; model->save(saveModelPath); // 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; if( (predictedLabel == testLabel) && !model->getLabelInfo(predictedLabel).empty() ) cout << format("%d-th label's info: %s", predictedLabel, model->getLabelInfo(predictedLabel).c_str()) << endl; // advanced stuff if(argc>2) { // Sometimes you'll need to get/set internal model data, // which isn't exposed by the public cv::FaceRecognizer. // Since each cv::FaceRecognizer is derived from a // cv::Algorithm, you can query the data. // // First we'll use it to set the threshold of the FaceRecognizer // to 0.0 without retraining the model. This can be useful if // you are evaluating the model: // model->setThreshold(0.0); // Now the threshold of this model is set to 0.0. A prediction // now returns -1, as it's impossible to have a distance below // it predictedLabel = model->predict(testSample); cout << "Predicted class = " << predictedLabel << 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(); // From this we will display the (at most) first 10 Eigenfaces: for (int i = 0; i < min(10, 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; normalize(ev.reshape(1), grayscale, 0, 255, NORM_MINMAX, CV_8UC1); // Show the image & apply a Jet colormap for better sensing. Mat cgrayscale; applyColorMap(grayscale, cgrayscale, COLORMAP_JET); imshow(format("%d", i), cgrayscale); } waitKey(0); } return 0; }