Several exceptions added to the available FaceRecognizer classes and helper…

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.

modules/contrib/include/opencv2/contrib/contrib.hpp

@@ -942,8 +942,6 @@ namespace cv
         // Deserializes this object from a given cv::FileStorage.
         virtual void load(const FileStorage& fs) = 0;
 
-        // Returns eigenvectors (if any)
-        virtual Mat eigenvectors() const { return Mat(); }
     };
 
     CV_EXPORTS Ptr<FaceRecognizer> createEigenFaceRecognizer(int num_components = 0);

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;
 }