103 lines
3.5 KiB
C++
103 lines
3.5 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/opencv.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 toGrayscale(InputArray _src) {
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Mat src = _src.getMat();
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// only allow one channel
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if(src.channels() != 1)
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CV_Error(CV_StsBadArg, "Only Matrices with one channel are supported");
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// create and return normalized image
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Mat dst;
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cv::normalize(_src, dst, 0, 255, NORM_MINMAX, CV_8UC1);
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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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throw std::exception();
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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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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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int main(int argc, const char *argv[]) {
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// check for command line arguments
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if (argc != 2) {
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cout << "usage: " << argv[0] << " <csv.ext>" << endl;
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exit(1);
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}
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// path to your CSV
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string fn_csv = string(argv[1]);
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// 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
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try {
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read_csv(fn_csv, images, labels);
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} catch (exception&) {
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cerr << "Error opening file \"" << fn_csv << "\"." << endl;
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exit(1);
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}
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// get width and height
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//int width = images[0].cols;
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int height = images[0].rows;
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// get test instances
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Mat testSample = images[images.size() - 1];
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int testLabel = labels[labels.size() - 1];
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// ... and delete last element
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images.pop_back();
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labels.pop_back();
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// build the Fisherfaces model
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Ptr<FaceRecognizer> model = createFisherFaceRecognizer();
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model->train(images, labels);
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// test model
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int predicted = model->predict(testSample);
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cout << "predicted class = " << predicted << endl;
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cout << "actual class = " << testLabel << endl;
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// get the eigenvectors
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Mat W = model->eigenvectors();
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// show first 10 fisherfaces
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for (int i = 0; i < min(10, W.cols); i++) {
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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 AND normalize between [0...255]
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Mat grayscale = toGrayscale(ev.reshape(1, height));
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// show image (with Jet colormap)
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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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return 0;
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}
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