Got basic face recognition working

FacialRec.cpp

@@ -85,7 +85,7 @@ int main() {
 					load_image_grayscale = load_image.clone();
 					cvtColor(load_image, load_image_grayscale, CV_BGR2GRAY);
 					images.push_back(load_image_grayscale);
-					labels.push_back(0);
+					labels.push_back(j);
 					i++;
 				}
 
@@ -100,51 +100,28 @@ int main() {
 			//Check that images were read correctly
 			cout << "Images read: " << images.size() << endl;
 
-			int counter = 0;
-			for (Mat test_image : images) {
-				string window_name = format("test_image_%d", counter);
-				namedWindow(window_name, CV_WINDOW_AUTOSIZE);
-				imshow(window_name, test_image);
-				cout << "showing image " << counter << endl;
-				counter++;
-			}
+			//Display all images read
+			// int counter = 0;
+			// for (Mat test_image : images) {
+			// 	string window_name = format("test_image_%d", counter);
+			// 	namedWindow(window_name, CV_WINDOW_AUTOSIZE);
+			// 	imshow(window_name, test_image);
+			// 	cout << "showing image " << counter << endl;
+			// 	counter++;
+			// }
 
 			//Convert to grayscale
 			picture_grayscale = picture.clone();
 			cvtColor(picture, picture_grayscale, CV_BGR2GRAY);
 
-			// generate eigenface
-			labels.push_back(0);
-			images.push_back(picture_grayscale);
-			
-			Ptr<FaceRecognizer> model = createEigenFaceRecognizer();
+			// generate eigenfaces
+			Ptr<FaceRecognizer> model = createLBPHFaceRecognizer();
 			model->train(images, labels);
 			cout << "Face Recognizer created" << endl;
+			model->save("FaceRecognizer");
 			int predictedLabel = model->predict(picture_grayscale);
 			cout << "Predicted label: " << predictedLabel << endl;
 
-			// //Display mean
-			// Mat eigenvalues = model->getMat("eigenvalues");
-		 //    // And we can do the same to display the Eigenvectors (read Eigenfaces):
-		 //    Mat W = model->getMat("eigenvectors");
-		 //    // Get the sample mean from the training data
-		 //    Mat mean = model->getMat("mean");
-		 //    imshow("mean", mean);
-		 //    // Display or save the 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 = ev;
-		 //        // Show the image & apply a Jet colormap for better sensing.
-		 //        Mat cgrayscale;
-		 //        applyColorMap(grayscale, cgrayscale, COLORMAP_JET);
-		 //        // Display or save:
-		 //        imshow(format("eigenface_%d", i), cgrayscale);
-		 //    }
-
 			while (true) {
 				Mat picture_with_text = picture.clone();
 				putText(picture_with_text, "Press 's' to save", Point2f(375,100), FONT_HERSHEY_SIMPLEX, 2.0,  Scalar(255,0,0,0), 3);

lbph.cpp

+/*
+ * 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/core.hpp"
+#include "opencv2/contrib/contrib.hpp"
+#include "opencv2/highgui/highgui.hpp"
+
+#include <iostream>
+#include <fstream>
+#include <sstream>
+
+using namespace cv;
+using namespace std;
+
+static void read_csv(const string& filename, vector<Mat>& images, vector<int>& labels, char separator = ';') {
+    std::ifstream file(filename.c_str(), ifstream::in);
+    if (!file) {
+        string error_message = "No valid input file was given, please check the given filename.";
+        CV_Error(CV_StsBadArg, error_message);
+    }
+    string line, path, classlabel;
+    while (getline(file, line)) {
+        stringstream liness(line);
+        getline(liness, path, separator);
+        getline(liness, classlabel);
+        if(!path.empty() && !classlabel.empty()) {
+            images.push_back(imread(path, 0));
+            labels.push_back(atoi(classlabel.c_str()));
+        }
+    }
+}
+
+int main(int argc, const char *argv[]) {
+    // Check for valid command line arguments, print usage
+    // if no arguments were given.
+    if (argc != 2) {
+        cout << "usage: " << argv[0] << " <csv.ext>" << 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;
+    // Read in the data. This can fail if no valid
+    // input filename is given.
+    try {
+        read_csv(fn_csv, images, labels);
+    } 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(CV_StsError, error_message);
+    }
+    // Get the height from the first image. We'll need this
+    // later in code to reshape the images to their original
+    // size:
+    int height = images[0].rows;
+    // 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 testLabel = labels[labels.size() - 1];
+    images.pop_back();
+    labels.pop_back();
+    // The following lines create an LBPH model for
+    // face recognition and train it with the images and
+    // labels read from the given CSV file.
+    //
+    // The LBPHFaceRecognizer uses Extended Local Binary Patterns
+    // (it's probably configurable with other operators at a later
+    // point), and has the following default values
+    //
+    //      radius = 1
+    //      neighbors = 8
+    //      grid_x = 8
+    //      grid_y = 8
+    //
+    // So if you want a LBPH FaceRecognizer using a radius of
+    // 2 and 16 neighbors, call the factory method with:
+    //
+    //      cv::createLBPHFaceRecognizer(2, 16);
+    //
+    // And if you want a threshold (e.g. 123.0) call it with its default values:
+    //
+    //      cv::createLBPHFaceRecognizer(1,8,8,8,123.0)
+    //
+    Ptr<FaceRecognizer> model = createLBPHFaceRecognizer();
+    model->train(images, labels);
+    // 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;
+    // 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->set("threshold", 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;
+    // Show some informations about the model, as there's no cool
+    // Model data to display as in Eigenfaces/Fisherfaces.
+    // Due to efficiency reasons the LBP images are not stored
+    // within the model:
+    cout << "Model Information:" << endl;
+    string model_info = format("\tLBPH(radius=%i, neighbors=%i, grid_x=%i, grid_y=%i, threshold=%.2f)",
+            model->getInt("radius"),
+            model->getInt("neighbors"),
+            model->getInt("grid_x"),
+            model->getInt("grid_y"),
+            model->getDouble("threshold"));
+    cout << model_info << endl;
+    // We could get the histograms for example:
+    vector<Mat> histograms = model->getMatVector("histograms");
+    // But should I really visualize it? Probably the length is interesting:
+    cout << "Size of the histograms: " << histograms[0].total() << endl;
+    return 0;
+}
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