Several exceptions added to the available FaceRecognizer classes and helper methods, so wrong input data is reported to the user. facerec_demo.cpp updated to latest cv::Algorithm changes and commented.

samples/cpp/facerec_demo.cpp

@@ -16,7 +16,9 @@
  *   See <http://www.opensource.org/licenses/bsd-license>
  */
 
-#include "opencv2/opencv.hpp"
+#include "opencv2/core/core.hpp"
+#include "opencv2/highgui/highgui.hpp"
+#include "opencv2/contrib/contrib.hpp"
 
 #include <iostream>
 #include <fstream>
@@ -38,65 +40,102 @@ static Mat toGrayscale(InputArray _src) {
 
 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)
-        throw std::exception();
+    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);
-        images.push_back(imread(path, 0));
-        labels.push_back(atoi(classlabel.c_str()));
+        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 command line arguments
+    // 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);
     }
-    // path to your CSV
+    // Get the path to your CSV.
     string fn_csv = string(argv[1]);
-    // images and corresponding labels
+    // These vectors hold the images and corresponding labels.
     vector<Mat> images;
     vector<int> labels;
-    // read in the data
+    // Read in the data. This can fail if no valid
+    // input filename is given.
     try {
         read_csv(fn_csv, images, labels);
-    } catch (exception&) {
-        cerr << "Error opening file \"" << fn_csv << "\"." << endl;
+    } catch (cv::Exception& e) {
+        cerr << "Error opening file \"" << fn_csv << "\". Reason: " << e.msg << endl;
+        // nothing more we can do
         exit(1);
     }
-    // get width and height
-    //int width = images[0].cols;
+    // 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;
-    // get test instances
+    // 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];
-    // ... and delete last element
     images.pop_back();
     labels.pop_back();
-    // build the Fisherfaces model
-    Ptr<FaceRecognizer> model = createFisherFaceRecognizer();
+    // 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);
+    Ptr<FaceRecognizer> model = createEigenFaceRecognizer();
     model->train(images, labels);
-    // test model
+    // The following line predicts the label of a given
+    // test image. In this example no thresholding is
+    // done.
     int predicted = model->predict(testSample);
-    cout << "predicted class = " << predicted << endl;
-    cout << "actual class = " << testLabel << endl;
-    // get the eigenvectors
-    Mat W = model->eigenvectors();
-    // show first 10 fisherfaces
+    // Show the prediction and actual class of the given
+    // sample:
+    string result_message = format("Predicted class=%d / Actual class=%d.", predicted, testLabel);
+    cout << result_message << endl;
+    // Sometimes you'll need to get some 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.
+	//
+	// Here is how to get the eigenvalues of this Eigenfaces model:
+    Mat eigenvalues = model->getMat("eigenvalues");
+    // And we can do the same to display the Eigenvectors ("Eigenfaces"):
+    Mat W = model->getMat("eigenvectors");
+    // 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 AND normalize between [0...255]
+        // Reshape to original size & normalize to [0...255] for imshow.
         Mat grayscale = toGrayscale(ev.reshape(1, height));
-        // show image (with Jet colormap)
+        // 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;
 }