OpenCV人脸识别Eigen算法源码分析

  1 #include "opencv2/core/core.hpp"
  2 #include "opencv2/highgui/highgui.hpp"
  3 #include "opencv2/contrib/contrib.hpp"
  4 
  5 #define CV_VERSION_ID       CVAUX_STR(CV_MAJOR_VERSION) CVAUX_STR(CV_MINOR_VERSION) CVAUX_STR(CV_SUBMINOR_VERSION)
  6 
  7 #ifdef _DEBUG
  8 #define cvLIB(name) "opencv_" name CV_VERSION_ID "d"
  9 #else
 10 #define cvLIB(name) "opencv_" name CV_VERSION_ID
 11 #endif
 12 
 13 #pragma comment( lib, cvLIB("core") )
 14 #pragma comment( lib, cvLIB("imgproc") )
 15 #pragma comment( lib, cvLIB("highgui") )
 16 #pragma comment( lib, cvLIB("flann") )
 17 #pragma comment( lib, cvLIB("features2d") )
 18 #pragma comment( lib, cvLIB("calib3d") )
 19 #pragma comment( lib, cvLIB("gpu") )
 20 #pragma comment( lib, cvLIB("legacy") )
 21 #pragma comment( lib, cvLIB("ml") )
 22 #pragma comment( lib, cvLIB("objdetect") )
 23 #pragma comment( lib, cvLIB("ts") )
 24 #pragma comment( lib, cvLIB("video") )
 25 #pragma comment( lib, cvLIB("contrib") )
 26 #pragma comment( lib, cvLIB("nonfree") )
 27 
 28 #include <iostream>
 29 #include <fstream>
 30 #include <sstream>
 31 
 32 using namespace cv;
 33 using namespace std;
 34 
 35 static Mat toGrayscale(InputArray _src) {
 36     Mat src = _src.getMat();
 37     // only allow one channel
 38     if(src.channels() != 1) {
 39         CV_Error(CV_StsBadArg, "Only Matrices with one channel are supported");
 40     }
 41     // create and return normalized image
 42     Mat dst;
 43     cv::normalize(_src, dst, 0, 255, NORM_MINMAX, CV_8UC1);
 44     return dst;
 45 }
 46 
 47 static void read_csv(const string& filename, vector<Mat>& images, vector<int>& labels, char separator = ‘;‘) {
 48     std::ifstream file(filename.c_str(), ifstream::in);
 49     if (!file) {
 50         string error_message = "No valid input file was given, please check the given filename.";
 51         CV_Error(CV_StsBadArg, error_message);
 52     }
 53     string line, path, classlabel;
 54     while (getline(file, line)) {
 55         stringstream liness(line);
 56         getline(liness, path, separator);
 57         getline(liness, classlabel);
 58         if(!path.empty() && !classlabel.empty()) {
 59             images.push_back(imread(path, 0));
 60             labels.push_back(atoi(classlabel.c_str()));
 61         }
 62     }
 63 }
 64 
 65 int main(int argc, const char *argv[]) {
 66     // Check for valid command line arguments, print usage
 67     // if no arguments were given.
 68     if (argc != 2) {
 69         cout << "usage: " << argv[0] << " <csv.ext>" << endl;
 70         exit(1);
 71     }
 72 
 73     // Get the path to your CSV.
 74     string fn_csv = string(argv[1]);
 75     // These vectors hold the images and corresponding labels.
 76     vector<Mat> images;
 77     vector<int> labels;
 78     // Read in the data. This can fail if no valid
 79     // input filename is given.
 80     try {
 81         read_csv(fn_csv, images, labels);
 82     } catch (cv::Exception& e) {
 83         cerr << "Error opening file \"" << fn_csv << "\". Reason: " << e.msg << endl;
 84         // nothing more we can do
 85         exit(1);
 86     }
 87     // Quit if there are not enough images for this demo.
 88     if(images.size() <= 1) {
 89         string error_message = "This demo needs at least 2 images to work. Please add more images to your data set!";
 90         CV_Error(CV_StsError, error_message);
 91     }
 92     // Get the height from the first image. We‘ll need this
 93     // later in code to reshape the images to their original
 94     // size:
 95     int height = images[0].rows;
 96     // The following lines simply get the last images from
 97     // your dataset and remove it from the vector. This is
 98     // done, so that the training data (which we learn the
 99     // cv::FaceRecognizer on) and the test data we test
100     // the model with, do not overlap.
101     Mat testSample = images[images.size() - 1];
102     int testLabel = labels[labels.size() - 1];
103     images.pop_back();
104     labels.pop_back();
105     // The following lines create an Eigenfaces model for
106     // face recognition and train it with the images and
107     // labels read from the given CSV file.
108     // This here is a full PCA, if you just want to keep
109     // 10 principal components (read Eigenfaces), then call
110     // the factory method like this:
111     //
112     //      cv::createEigenFaceRecognizer(10);
113     //
114     // If you want to create a FaceRecognizer with a
115     // confidennce threshold, call it with:
116     //
117     //      cv::createEigenFaceRecognizer(10, 123.0);
118     //
119     Ptr<FaceRecognizer> model = createEigenFaceRecognizer();
120     model->train(images, labels);
121     // The following line predicts the label of a given
122     // test image:
123     int predictedLabel = model->predict(testSample);
124     //
125     // To get the confidence of a prediction call the model with:
126     //
127     //      int predictedLabel = -1;
128     //      double confidence = 0.0;
129     //      model->predict(testSample, predictedLabel, confidence);
130     //
131     string result_message = format("Predicted class = %d / Actual class = %d.", predictedLabel, testLabel);
132     cout << result_message << endl;
133     // Sometimes you‘ll need to get/set internal model data,
134     // which isn‘t exposed by the public cv::FaceRecognizer.
135     // Since each cv::FaceRecognizer is derived from a
136     // cv::Algorithm, you can query the data.
137     //
138     // First we‘ll use it to set the threshold of the FaceRecognizer
139     // to 0.0 without retraining the model. This can be useful if
140     // you are evaluating the model:
141     //
142     model->set("threshold", 0.0);
143     // Now the threshold of this model is set to 0.0. A prediction
144     // now returns -1, as it‘s impossible to have a distance below
145     // it
146     predictedLabel = model->predict(testSample);
147     cout << "Predicted class = " << predictedLabel << endl;
148     // Here is how to get the eigenvalues of this Eigenfaces model:
149     Mat eigenvalues = model->getMat("eigenvalues");
150     // And we can do the same to display the Eigenvectors (read Eigenfaces):
151     Mat W = model->getMat("eigenvectors");
152     // From this we will display the (at most) first 10 Eigenfaces:
153     for (int i = 0; i < min(10, W.cols); i++) {
154         string msg = format("Eigenvalue #%d = %.5f", i, eigenvalues.at<double>(i));
155         cout << msg << endl;
156         // get eigenvector #i
157         Mat ev = W.col(i).clone();
158         // Reshape to original size & normalize to [0...255] for imshow.
159         Mat grayscale = toGrayscale(ev.reshape(1, height));
160         // Show the image & apply a Jet colormap for better sensing.
161         Mat cgrayscale;
162         applyColorMap(grayscale, cgrayscale, COLORMAP_JET);
163         imshow(format("%d", i), cgrayscale);
164     }
165     waitKey(0);
166 
167     return 0;
168 }