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/*
* 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 (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:
//
// cv::createEigenFaceRecognizer(10);
//
// If you want to create a FaceRecognizer with a
// confidennce threshold, call it with:
//
// cv::createEigenFaceRecognizer(10, 123.0);
//
Ptr<BasicFaceRecognizer> model = createEigenFaceRecognizer();
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;
}