From b3c61ee0fe75a61450e15dd28c9cce93ff5554ad Mon Sep 17 00:00:00 2001
From: Philipp Wagner <no@email>
Date: Sun, 10 Jun 2012 22:42:20 +0000
Subject: [PATCH] Minor grammatical correction in comments.

---
 modules/contrib/src/facerec.cpp | 17 ++++++++---------
 samples/cpp/facerec_demo.cpp    | 15 ++++++++-------
 2 files changed, 16 insertions(+), 16 deletions(-)

diff --git a/modules/contrib/src/facerec.cpp b/modules/contrib/src/facerec.cpp
index 1bf7f9b708..632fe0154d 100644
--- a/modules/contrib/src/facerec.cpp
+++ b/modules/contrib/src/facerec.cpp
@@ -53,7 +53,7 @@ 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(Exception(CV_StsBadArg, error_message, "asRowMatrix", __FILE__, __LINE__));
+        CV_Error(CV_StsBadArg, error_message);
     }
     // number of samples
     size_t n = src.total();
@@ -69,7 +69,7 @@ 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(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);
@@ -125,8 +125,7 @@ public:
     // corresponding labels in labels. num_components will be kept for
     // classification.
     Eigenfaces(InputArray src, InputArray labels,
-            int num_components = 0,
-            double threshold = DBL_MAX) :
+            int num_components = 0, double threshold = DBL_MAX) :
         _num_components(num_components),
         _threshold(threshold) {
         train(src, labels);
@@ -178,10 +177,8 @@ public:
     // Initializes and computes a Fisherfaces model with images in src and
     // corresponding labels in labels. num_components will be kept for
     // classification.
-    Fisherfaces(InputArray src,
-            InputArray labels,
-            int num_components = 0,
-            double threshold = DBL_MAX) :
+    Fisherfaces(InputArray src, InputArray labels,
+            int num_components = 0, double threshold = DBL_MAX) :
         _num_components(num_components),
         _threshold(threshold) {
         train(src, labels);
@@ -235,7 +232,9 @@ public:
     //
     // radius, neighbors are used in the local binary patterns creation.
     // grid_x, grid_y control the grid size of the spatial histograms.
-    LBPH(int radius=1, int neighbors=8, int grid_x=8, int grid_y=8, double threshold = DBL_MAX) :
+    LBPH(int radius=1, int neighbors=8,
+            int grid_x=8, int grid_y=8,
+            double threshold = DBL_MAX) :
         _grid_x(grid_x),
         _grid_y(grid_y),
         _radius(radius),
diff --git a/samples/cpp/facerec_demo.cpp b/samples/cpp/facerec_demo.cpp
index d252ba1eec..a06a119fcd 100644
--- a/samples/cpp/facerec_demo.cpp
+++ b/samples/cpp/facerec_demo.cpp
@@ -30,8 +30,9 @@ using namespace std;
 static Mat toGrayscale(InputArray _src) {
     Mat src = _src.getMat();
     // only allow one channel
-    if(src.channels() != 1)
+    if(src.channels() != 1) {
         CV_Error(CV_StsBadArg, "Only Matrices with one channel are supported");
+    }
     // create and return normalized image
     Mat dst;
     cv::normalize(_src, dst, 0, 255, NORM_MINMAX, CV_8UC1);
@@ -130,16 +131,16 @@ int main(int argc, const char *argv[]) {
     // cv::Algorithm, you can query the data.
     //
     // First we'll use it to set the threshold of the FaceRecognizer
-    // without retraining the model:
+    // 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 is of this model is 0.0. A prediction
-    // now returns -1, as it's impossible to have a distance
-    // below it
-    //
+    // 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;
-    // Now here is how to get the eigenvalues of this Eigenfaces model:
+    // 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 (read Eigenfaces):
     Mat W = model->getMat("eigenvectors");