Exceptions now go into CV_Error. Added thresholding to the FaceRecognizer and…

Exceptions now go into CV_Error. Added thresholding to the FaceRecognizer and updated the demo accordingly.

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

@@ -930,6 +930,9 @@ namespace cv
         // Gets a prediction from a FaceRecognizer.
         virtual int predict(InputArray src) const = 0;
 
+        // Predicts the label and confidence for a given sample.
+        virtual void predict(InputArray src, int &label, double &dist) const = 0;
+
         // Serializes this object to a given filename.
         virtual void save(const string& filename) const;
 
@@ -944,10 +947,10 @@ namespace cv
 
     };
 
-    CV_EXPORTS Ptr<FaceRecognizer> createEigenFaceRecognizer(int num_components = 0);
-    CV_EXPORTS Ptr<FaceRecognizer> createFisherFaceRecognizer(int num_components = 0);
+    CV_EXPORTS Ptr<FaceRecognizer> createEigenFaceRecognizer(int num_components = 0, double threshold = DBL_MAX);
+    CV_EXPORTS Ptr<FaceRecognizer> createFisherFaceRecognizer(int num_components = 0, double threshold = DBL_MAX);
     CV_EXPORTS Ptr<FaceRecognizer> createLBPHFaceRecognizer(int radius=1, int neighbors=8,
-                                                            int grid_x=8, int grid_y=8);
+                                                            int grid_x=8, int grid_y=8, double threshold = DBL_MAX);
 
     enum
     {

modules/contrib/src/lda.cpp

@@ -48,7 +48,7 @@ static Mat argsort(InputArray _src, bool ascending=true)
     Mat src = _src.getMat();
     if (src.rows != 1 && src.cols != 1) {
     	string error_message = "Wrong shape of input matrix! Expected a matrix with one row or column.";
-    	error(cv::Exception(CV_StsBadArg, error_message, "argsort", __FILE__, __LINE__));
+    	CV_Error(CV_StsBadArg, error_message);
     }
     int flags = CV_SORT_EVERY_ROW+(ascending ? CV_SORT_ASCENDING : CV_SORT_DESCENDING);
     Mat sorted_indices;
@@ -60,7 +60,7 @@ static Mat asRowMatrix(InputArrayOfArrays src, int rtype, double alpha=1, double
     // make sure the input data is a vector of matrices or vector of vector
     if(src.kind() != _InputArray::STD_VECTOR_MAT && src.kind() != _InputArray::STD_VECTOR_VECTOR) {
     	string error_message = "The data is expected as InputArray::STD_VECTOR_MAT (a std::vector<Mat>) or _InputArray::STD_VECTOR_VECTOR (a std::vector< vector<...> >).";
-        error(cv::Exception(CV_StsBadArg, error_message, "asRowMatrix", __FILE__, __LINE__));
+        CV_Error(CV_StsBadArg, error_message);
     }
     // number of samples
     size_t n = src.total();
@@ -76,7 +76,7 @@ static Mat asRowMatrix(InputArrayOfArrays src, int rtype, double alpha=1, double
         // make sure data can be reshaped, throw exception if not!
         if(src.getMat(i).total() != d) {
             string error_message = format("Wrong number of elements in matrix #%d! Expected %d was %d.", i, d, src.getMat(i).total());
-            error(cv::Exception(CV_StsBadArg, error_message, "cv::asRowMatrix", __FILE__, __LINE__));
+            CV_Error(CV_StsBadArg, error_message);
         }
         // get a hold of the current row
         Mat xi = data.row(i);
@@ -91,8 +91,9 @@ static Mat asRowMatrix(InputArrayOfArrays src, int rtype, double alpha=1, double
 }
 
 static void sortMatrixColumnsByIndices(InputArray _src, InputArray _indices, OutputArray _dst) {
-    if(_indices.getMat().type() != CV_32SC1)
+    if(_indices.getMat().type() != CV_32SC1) {
         CV_Error(CV_StsUnsupportedFormat, "cv::sortColumnsByIndices only works on integer indices!");
+    }
     Mat src = _src.getMat();
     vector<int> indices = _indices.getMat();
     _dst.create(src.rows, src.cols, src.type());
@@ -183,12 +184,12 @@ Mat subspaceProject(InputArray _W, InputArray _mean, InputArray _src) {
     // make sure the data has the correct shape
     if(W.rows != d) {
         string error_message = format("Wrong shapes for given matrices. Was size(src) = (%d,%d), size(W) = (%d,%d).", src.rows, src.cols, W.rows, W.cols);
-        error(cv::Exception(CV_StsBadArg, error_message, "cv::subspace::project", __FILE__, __LINE__));
+        CV_Error(CV_StsBadArg, error_message);
     }
     // make sure mean is correct if not empty
     if(!mean.empty() && (mean.total() != (size_t) d)) {
         string error_message = format("Wrong mean shape for the given data matrix. Expected %d, but was %d.", d, mean.total());
-        error(cv::Exception(CV_StsBadArg, error_message, "cv::subspace::project", __FILE__, __LINE__));
+        CV_Error(CV_StsBadArg, error_message);
     }
     // create temporary matrices
     Mat X, Y;
@@ -221,12 +222,12 @@ Mat subspaceReconstruct(InputArray _W, InputArray _mean, InputArray _src)
     // make sure the data has the correct shape
     if(W.cols != d) {
         string error_message = format("Wrong shapes for given matrices. Was size(src) = (%d,%d), size(W) = (%d,%d).", src.rows, src.cols, W.rows, W.cols);
-        error(cv::Exception(CV_StsBadArg, error_message, "cv::subspaceReconstruct", __FILE__, __LINE__));
+        CV_Error(CV_StsBadArg, error_message);
     }
     // make sure mean is correct if not empty
     if(!mean.empty() && (mean.total() != (size_t) W.rows)) {
         string error_message = format("Wrong mean shape for the given eigenvector matrix. Expected %d, but was %d.", W.cols, mean.total());
-        error(cv::Exception(CV_StsBadArg, error_message, "cv::subspaceReconstruct", __FILE__, __LINE__));
+        CV_Error(CV_StsBadArg, error_message);
     }
     // initalize temporary matrices
     Mat X, Y;
@@ -999,12 +1000,12 @@ void LDA::lda(InputArray _src, InputArray _lbls) {
     // want to separate from each other then?
     if(C == 1) {
         string error_message = "At least two classes are needed to perform a LDA. Reason: Only one class was given!";
-        error(cv::Exception(CV_StsBadArg, error_message, "cv::LDA::lda", __FILE__, __LINE__));
+        CV_Error(CV_StsBadArg, error_message);
     }
     // throw error if less labels, than samples
     if (labels.size() != static_cast<size_t>(N)) {
         string error_message = format("The number of samples must equal the number of labels. Given %d labels, %d samples. ", labels.size(), N);
-        error(cv::Exception(CV_StsBadArg, error_message, "LDA::lda", __FILE__, __LINE__));
+        CV_Error(CV_StsBadArg, error_message);
     }
     // warn if within-classes scatter matrix becomes singular
     if (N < D) {
@@ -1087,7 +1088,7 @@ void LDA::compute(InputArray _src, InputArray _lbls) {
         break;
     default:
     	string error_message= format("InputArray Datatype %d is not supported.", _src.kind());
-    	error(cv::Exception(CV_StsBadArg, error_message, "LDA::compute", __FILE__, __LINE__));
+        CV_Error(CV_StsBadArg, error_message);
         break;
     }
 }

samples/cpp/facerec_demo.cpp

@@ -41,7 +41,7 @@ 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) {
-    	string error_message = "No valid input file was given, please check the given filename.";
+        string error_message = "No valid input file was given, please check the given filename.";
         CV_Error(CV_StsBadArg, error_message);
     }
     string line, path, classlabel;
@@ -58,7 +58,7 @@ static void read_csv(const string& filename, vector<Mat>& images, vector<int>& l
 
 int main(int argc, const char *argv[]) {
     // Check for valid command line arguments, print usage
-	// if no arguments were given.
+    // if no arguments were given.
     if (argc != 2) {
         cout << "usage: " << argv[0] << " <csv.ext>" << endl;
         exit(1);
@@ -79,8 +79,8 @@ int main(int argc, const char *argv[]) {
     }
     // 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);
+        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
@@ -102,30 +102,51 @@ int main(int argc, const char *argv[]) {
     // 10 principal components (read Eigenfaces), then call
     // the factory method like this:
     //
-    //		cv::createEigenFaceRecognizer(10);
+    //      cv::createEigenFaceRecognizer(10);
+    //
+    // If you want to create a FaceRecognizer with a
+    // confidennce threshold, call it with:
+    //
+    //      cv::createEigenFaceRecognizer(10, 123.0);
+    //
     Ptr<FaceRecognizer> model = createEigenFaceRecognizer();
     model->train(images, labels);
     // The following line predicts the label of a given
-    // test image. In this example no thresholding is
-    // done.
-    int predicted = model->predict(testSample);
-    // Show the prediction and actual class of the given
-    // sample:
-    string result_message = format("Predicted class=%d / Actual class=%d.", predicted, testLabel);
+    // test image:
+    int predictedLabel = model->predict(testSample);
+    //
+    // To get the confidence of a prediction call it with:
+    //
+    // 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 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:
+    // 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
+    // without retraining the model:
+    //
+    model->set("threshold", 0.0);
+    // Now the threshold is of this model is 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;
+    // Now 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"):
+    // And we can do the same to display the Eigenvectors (read 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;
+        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.