Warning fixes continued

modules/contrib/src/facerec.cpp

@@ -22,7 +22,7 @@ namespace cv
 {
 
 using std::set;
-    
+
 // Reads a sequence from a FileNode::SEQ with type _Tp into a result vector.
 template<typename _Tp>
 inline void readFileNodeList(const FileNode& fn, vector<_Tp>& result) {
@@ -48,7 +48,7 @@ inline void writeFileNodeList(FileStorage& fs, const string& name,
     }
     fs << "]";
 }
-    
+
 static Mat asRowMatrix(InputArrayOfArrays src, int rtype, double alpha=1, double beta=0)
 {
     // number of samples
@@ -67,7 +67,7 @@ static Mat asRowMatrix(InputArrayOfArrays src, int rtype, double alpha=1, double
     }
     return data;
 }
-    
+
 // Removes duplicate elements in a given vector.
 template<typename _Tp>
 inline vector<_Tp> remove_dups(const vector<_Tp>& src) {
@@ -82,7 +82,7 @@ inline vector<_Tp> remove_dups(const vector<_Tp>& src) {
     return elems;
 }
 
-    
+
 // Turk, M., and Pentland, A. "Eigenfaces for recognition.". Journal of
 // Cognitive Neuroscience 3 (1991), 71–86.
 class Eigenfaces : public FaceRecognizer
@@ -124,10 +124,10 @@ public:
 
     // See FaceRecognizer::save.
     void save(FileStorage& fs) const;
-    
+
     AlgorithmInfo* info() const;
 };
-    
+
 // Belhumeur, P. N., Hespanha, J., and Kriegman, D. "Eigenfaces vs. Fisher-
 // faces: Recognition using class specific linear projection.". IEEE
 // Transactions on Pattern Analysis and Machine Intelligence 19, 7 (1997),
@@ -208,11 +208,11 @@ 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) :
-        _grid_x(grid_x),
-        _grid_y(grid_y),
-        _radius(radius),
-        _neighbors(neighbors) {}
+    LBPH(int radius_=1, int neighbors_=8, int grid_x_=8, int grid_y_=8) :
+        _grid_x(grid_x_),
+        _grid_y(grid_y_),
+        _radius(radius_),
+        _neighbors(neighbors_) {}
 
     // Initializes and computes this LBPH Model. The current implementation is
     // rather fixed as it uses the Extended Local Binary Patterns per default.
@@ -221,12 +221,12 @@ public:
     // (grid_x=8), (grid_y=8) controls the grid size of the spatial histograms.
     LBPH(InputArray src,
             InputArray labels,
-            int radius=1, int neighbors=8,
-            int grid_x=8, int grid_y=8) :
-                _grid_x(grid_x),
-                _grid_y(grid_y),
-                _radius(radius),
-                _neighbors(neighbors) {
+            int radius_=1, int neighbors_=8,
+            int grid_x_=8, int grid_y_=8) :
+                _grid_x(grid_x_),
+                _grid_y(grid_y_),
+                _radius(radius_),
+                _neighbors(neighbors_) {
         train(src, labels);
     }
 
@@ -359,9 +359,9 @@ void Fisherfaces::train(InputArray src, InputArray _lbls) {
     // get data
     Mat labels = _lbls.getMat();
     Mat data = asRowMatrix(src, CV_64FC1);
-    
+
     CV_Assert( labels.type() == CV_32S && (labels.cols == 1 || labels.rows == 1));
-    
+
     // dimensionality
     int N = data.rows; // number of samples
     //int D = data.cols; // dimension of samples
@@ -369,7 +369,7 @@ void Fisherfaces::train(InputArray src, InputArray _lbls) {
     if(labels.total() != (size_t)N)
         CV_Error(CV_StsUnsupportedFormat, "Labels must be given as integer (CV_32SC1).");
     // compute the Fisherfaces
-    
+
     vector<int> ll;
     labels.copyTo(ll);
     int C = (int)remove_dups(ll).size(); // number of unique classes
@@ -570,7 +570,7 @@ static Mat histc(InputArray _src, int minVal, int maxVal, bool normed)
     return Mat();
 }
 
-    
+
 static Mat spatial_histogram(InputArray _src, int numPatterns,
                              int grid_x, int grid_y, bool normed)
 {
@@ -610,7 +610,7 @@ static Mat elbp(InputArray src, int radius, int neighbors) {
     elbp(src, dst, radius, neighbors);
     return dst;
 }
-    
+
 void LBPH::load(const FileStorage& fs) {
     fs["radius"] >> _radius;
     fs["neighbors"] >> _neighbors;
@@ -684,24 +684,24 @@ int LBPH::predict(InputArray _src) const {
     }
     return minClass;
 }
-    
-    
+
+
 Ptr<FaceRecognizer> createEigenFaceRecognizer(int num_components)
 {
     return new Eigenfaces(num_components);
 }
-    
+
 Ptr<FaceRecognizer> createFisherFaceRecognizer(int num_components)
 {
     return new Fisherfaces(num_components);
 }
-    
+
 Ptr<FaceRecognizer> createLBPHFaceRecognizer(int radius, int neighbors,
                                              int grid_x, int grid_y)
 {
     return new LBPH(radius, neighbors, grid_x, grid_y);
 }
-    
+
 CV_INIT_ALGORITHM(Eigenfaces, "FaceRecognizer.Eigenfaces",
                   obj.info()->addParam(obj, "ncomponents", obj._num_components);
                   obj.info()->addParam(obj, "projections", obj._projections, true);
@@ -716,8 +716,8 @@ CV_INIT_ALGORITHM(Fisherfaces, "FaceRecognizer.Fisherfaces",
                   obj.info()->addParam(obj, "labels", obj._labels, true);
                   obj.info()->addParam(obj, "eigenvectors", obj._eigenvectors, true);
                   obj.info()->addParam(obj, "eigenvalues", obj._eigenvalues, true);
-                  obj.info()->addParam(obj, "mean", obj._mean, true));    
-    
+                  obj.info()->addParam(obj, "mean", obj._mean, true));
+
 CV_INIT_ALGORITHM(LBPH, "FaceRecognizer.LBPH",
                   obj.info()->addParam(obj, "radius", obj._radius);
                   obj.info()->addParam(obj, "neighbors", obj._neighbors);
@@ -725,7 +725,7 @@ CV_INIT_ALGORITHM(LBPH, "FaceRecognizer.LBPH",
                   obj.info()->addParam(obj, "grid_y", obj._grid_y);
                   obj.info()->addParam(obj, "histograms", obj._histograms, true);
                   obj.info()->addParam(obj, "labels", obj._labels, true));
-    
+
 bool initModule_contrib()
 {
     Ptr<Algorithm> efaces = createEigenfaces(), ffaces = createFisherfaces(), lbph = createLBPH();