Fisherfaces 算法的具体实现源码

  1 /*
  2  * Copyright (c) 2011. Philipp Wagner <bytefish[at]gmx[dot]de>.
  3  * Released to public domain under terms of the BSD Simplified license.
  4  *
  5  * Redistribution and use in source and binary forms, with or without
  6  * modification, are permitted provided that the following conditions are met:
  7  *   * Redistributions of source code must retain the above copyright
  8  *     notice, this list of conditions and the following disclaimer.
  9  *   * Redistributions in binary form must reproduce the above copyright
 10  *     notice, this list of conditions and the following disclaimer in the
 11  *     documentation and/or other materials provided with the distribution.
 12  *   * Neither the name of the organization nor the names of its contributors
 13  *     may be used to endorse or promote products derived from this software
 14  *     without specific prior written permission.
 15  *
 16  *   See <http://www.opensource.org/licenses/bsd-license>
 17  */
 18 
 19 #include "opencv2/core/core.hpp"
 20 #include "opencv2/contrib/contrib.hpp"
 21 #include "opencv2/highgui/highgui.hpp"
 22 
 23 #include <iostream>
 24 #include <fstream>
 25 #include <sstream>
 26 
 27 using namespace cv;
 28 using namespace std;
 29 
 30 static Mat norm_0_255(InputArray _src) {
 31     Mat src = _src.getMat();
 32     // Create and return normalized image:
 33     Mat dst;
 34     switch(src.channels()) {
 35     case 1:
 36         cv::normalize(_src, dst, 0, 255, NORM_MINMAX, CV_8UC1);
 37         break;
 38     case 3:
 39         cv::normalize(_src, dst, 0, 255, NORM_MINMAX, CV_8UC3);
 40         break;
 41     default:
 42         src.copyTo(dst);
 43         break;
 44     }
 45     return dst;
 46 }
 47 
 48 static void read_csv(const string& filename, vector<Mat>& images, vector<int>& labels, char separator = ‘;‘) {
 49     std::ifstream file(filename.c_str(), ifstream::in);
 50     if (!file) {
 51         string error_message = "No valid input file was given, please check the given filename.";
 52         CV_Error(CV_StsBadArg, error_message);
 53     }
 54     string line, path, classlabel;
 55     while (getline(file, line)) {
 56         stringstream liness(line);
 57         getline(liness, path, separator);
 58         getline(liness, classlabel);
 59         if(!path.empty() && !classlabel.empty()) {
 60             images.push_back(imread(path, 0));
 61             labels.push_back(atoi(classlabel.c_str()));
 62         }
 63     }
 64 }
 65 
 66 int main(int argc, const char *argv[]) {
 67     // Check for valid command line arguments, print usage
 68     // if no arguments were given.
 69     if (argc < 2) {
 70         cout << "usage: " << argv[0] << " <csv.ext> <output_folder> " << endl;
 71         exit(1);
 72     }
 73     string output_folder = ".";
 74     if (argc == 3) {
 75         output_folder = string(argv[2]);
 76     }
 77     // Get the path to your CSV.
 78     string fn_csv = string(argv[1]);
 79     // These vectors hold the images and corresponding labels.
 80     vector<Mat> images;
 81     vector<int> labels;
 82     // Read in the data. This can fail if no valid
 83     // input filename is given.
 84     try {
 85         read_csv(fn_csv, images, labels);
 86     } catch (cv::Exception& e) {
 87         cerr << "Error opening file \"" << fn_csv << "\". Reason: " << e.msg << endl;
 88         // nothing more we can do
 89         exit(1);
 90     }
 91     // Quit if there are not enough images for this demo.
 92     if(images.size() <= 1) {
 93         string error_message = "This demo needs at least 2 images to work. Please add more images to your data set!";
 94         CV_Error(CV_StsError, error_message);
 95     }
 96     // Get the height from the first image. We‘ll need this
 97     // later in code to reshape the images to their original
 98     // size:
 99     int height = images[0].rows;
100     // The following lines simply get the last images from
101     // your dataset and remove it from the vector. This is
102     // done, so that the training data (which we learn the
103     // cv::FaceRecognizer on) and the test data we test
104     // the model with, do not overlap.
105     Mat testSample = images[images.size() - 1];
106     int testLabel = labels[labels.size() - 1];
107     images.pop_back();
108     labels.pop_back();
109     // The following lines create an Fisherfaces model for
110     // face recognition and train it with the images and
111     // labels read from the given CSV file.
112     // If you just want to keep 10 Fisherfaces, then call
113     // the factory method like this:
114     //
115     //      cv::createFisherFaceRecognizer(10);
116     //
117     // However it is not useful to discard Fisherfaces! Please
118     // always try to use _all_ available Fisherfaces for
119     // classification.
120     //
121     // If you want to create a FaceRecognizer with a
122     // confidence threshold (e.g. 123.0) and use _all_
123     // Fisherfaces, then call it with:
124     //
125     //      cv::createFisherFaceRecognizer(0, 123.0);
126     //
127     Ptr<FaceRecognizer> model = createFisherFaceRecognizer();
128     model->train(images, labels);
129     // The following line predicts the label of a given
130     // test image:
131     int predictedLabel = model->predict(testSample);
132     //
133     // To get the confidence of a prediction call the model with:
134     //
135     //      int predictedLabel = -1;
136     //      double confidence = 0.0;
137     //      model->predict(testSample, predictedLabel, confidence);
138     //
139     string result_message = format("Predicted class = %d / Actual class = %d.", predictedLabel, testLabel);
140     cout << result_message << endl;
141     // Here is how to get the eigenvalues of this Eigenfaces model:
142     Mat eigenvalues = model->getMat("eigenvalues");
143     // And we can do the same to display the Eigenvectors (read Eigenfaces):
144     Mat W = model->getMat("eigenvectors");
145     // Get the sample mean from the training data
146     Mat mean = model->getMat("mean");
147     // Display or save:
148     if(argc == 2) {
149         imshow("mean", norm_0_255(mean.reshape(1, images[0].rows)));
150     } else {
151         imwrite(format("%s/mean.png", output_folder.c_str()), norm_0_255(mean.reshape(1, images[0].rows)));
152     }
153     // Display or save the first, at most 16 Fisherfaces:
154     for (int i = 0; i < min(16, W.cols); i++) {
155         string msg = format("Eigenvalue #%d = %.5f", i, eigenvalues.at<double>(i));
156         cout << msg << endl;
157         // get eigenvector #i
158         Mat ev = W.col(i).clone();
159         // Reshape to original size & normalize to [0...255] for imshow.
160         Mat grayscale = norm_0_255(ev.reshape(1, height));
161         // Show the image & apply a Bone colormap for better sensing.
162         Mat cgrayscale;
163         applyColorMap(grayscale, cgrayscale, COLORMAP_BONE);
164         // Display or save:
165         if(argc == 2) {
166             imshow(format("fisherface_%d", i), cgrayscale);
167         } else {
168             imwrite(format("%s/fisherface_%d.png", output_folder.c_str(), i), norm_0_255(cgrayscale));
169         }
170     }
171     // Display or save the image reconstruction at some predefined steps:
172     for(int num_component = 0; num_component < min(16, W.cols); num_component++) {
173         // Slice the Fisherface from the model:
174         Mat ev = W.col(num_component);
175         Mat projection = subspaceProject(ev, mean, images[0].reshape(1,1));
176         Mat reconstruction = subspaceReconstruct(ev, mean, projection);
177         // Normalize the result:
178         reconstruction = norm_0_255(reconstruction.reshape(1, images[0].rows));
179         // Display or save:
180         if(argc == 2) {
181             imshow(format("fisherface_reconstruction_%d", num_component), reconstruction);
182         } else {
183             imwrite(format("%s/fisherface_reconstruction_%d.png", output_folder.c_str(), num_component), reconstruction);
184         }
185     }
186     // Display if we are not writing to an output folder:
187     if(argc == 2) {
188         waitKey(0);
189     }
190     return 0;
191 }