Deleted trailing whitespaces

modules/dnn/include/opencv2/dnn.hpp

@@ -8,6 +8,4 @@
 #include <opencv2/dnn/dnn.hpp>
 
 
-#endif /* __OPENCV_DNN_HPP__ */
-
-
+#endif /* __OPENCV_DNN_HPP__ */

modules/dnn/include/opencv2/dnn/dnn.hpp

@@ -109,7 +109,7 @@ namespace dnn
         virtual String getInputName(int inputNum);
         virtual String getOutputName(int outputNum);
     };
-    
+
     //containers for String and int
     typedef DictValue LayerId;
     typedef DictValue BlobId;
@@ -124,7 +124,7 @@ namespace dnn
         int addLayer(const String &name, const String &type, LayerParams &params);
         int getLayerId(LayerId layer);
         void deleteLayer(LayerId layer);
-        
+
         //each output of each layer can be labeled by unique string label (as in Caffe)
         //if label not specified then %layer_name%.%layer_output_id% can be used
         void setOutputNames(LayerId layer, const std::vector<String> &outputNames);
@@ -145,7 +145,7 @@ namespace dnn
 
         void setBlob(BlobId outputName, const Blob &blob);
         Blob getBlob(BlobId outputName);
-        
+
         void setParam(LayerId layer, int numParam, const Blob &blob);
         void getParam(LayerId layer, int numParam);
 

modules/dnn/include/opencv2/dnn/dnn.inl.hpp

@@ -44,13 +44,13 @@ namespace dnn
     {
         return Size(cols(), rows());
     }
-    
+
     inline int Blob::channels() const
     {
         CV_DbgAssert(m.dims >= 3);
         return m.size[m.dims-3];
     }
-    
+
     inline int Blob::num() const
     {
         CV_DbgAssert(m.dims == 4);

modules/dnn/samples/alexnet.cpp

@@ -61,7 +61,7 @@ int main(void)
 
     Blob probBlob = net.getBlob("prob");
     ClassProb bc = getMaxClass(probBlob);
-    
+
     initClassesNames();
     std::string className = (bc.first < (int)CLASES_NAMES.size()) ? CLASES_NAMES[bc.first] : "unnamed";
 
@@ -71,6 +71,6 @@ int main(void)
     std::cout << " \"" + className << "\"";
     std::cout <<  std::endl;
     std::cout << "Prob: " << bc.second * 100 << "%" << std::endl;
-    
+
     return 0;
 }

modules/dnn/src/caffe/glog_emulator.hpp

@@ -27,12 +27,12 @@ class GLogWrapper
 
 public:
 
-    GLogWrapper(const char *_file, const char *_func, int _line, 
-                const char *_type, 
+    GLogWrapper(const char *_file, const char *_func, int _line,
+                const char *_type,
                 const char *_cond_str = NULL, bool _cond_status = true
                ) :
-               stream(selectStream(_type)), 
-               file(_file), func(_func), line(_line), 
+               stream(selectStream(_type)),
+               file(_file), func(_func), line(_line),
                type(_type), cond_str(_cond_str), cond_staus(_cond_status) {}
 
     template<typename T>

modules/dnn/src/caffe_importer.cpp

@@ -26,7 +26,7 @@ namespace
     {
         caffe::NetParameter net;
         caffe::NetParameter netBinary;
- 
+
     public:
 
         CaffeImporter(const char *pototxt, const char *caffeModel)
@@ -69,7 +69,7 @@ namespace
             const std::string &name = field->name();
 
             std::cout << field->type_name() << " " << name << ":";
-            
+
             #define GET_FIRST(Type) (isRepeated ? msgRefl->GetRepeated##Type(msg, field, 0) : msgRefl->Get##Type(msg, field))
 
             switch (type)
@@ -97,7 +97,7 @@ namespace
                 break;
             }
 
-            std::cout << std::endl; 
+            std::cout << std::endl;
         }
 
         void extractLayerParams(const Message &msg, cv::dnn::LayerParams &params)
@@ -109,7 +109,7 @@ namespace
             {
                 const FieldDescriptor *fd = msgDesc->field(fieldId);
 
-                bool hasData =  fd->is_required() || 
+                bool hasData =  fd->is_required() ||
                                (fd->is_optional() && (msgRefl->HasField(msg, fd) /*|| fd->has_default_value()*/)) ||
                                (fd->is_repeated() && msgRefl->FieldSize(msg, fd) > 0);
 
@@ -119,7 +119,7 @@ namespace
                 if (fd->cpp_type() == FieldDescriptor::CPPTYPE_MESSAGE)
                 {
                     if (fd->is_repeated()) //Extract only first item!
-                        extractLayerParams(msgRefl->GetRepeatedMessage(msg, fd, 0), params); 
+                        extractLayerParams(msgRefl->GetRepeatedMessage(msg, fd, 0), params);
                     else
                         extractLayerParams(msgRefl->GetMessage(msg, fd), params);
                 }
@@ -218,7 +218,7 @@ namespace
 
                 extractLayerParams(layer, layerParams);
                 extractBinaryLayerParms(layer, layerParams);
-                
+
                 int id = dstNet.addLayer(name, type, layerParams);
                 dstNet.setOutputNames(id, tops);
 

modules/dnn/src/dnn.cpp

@@ -131,7 +131,7 @@ struct LayerOutId
     String name;
 
     LayerOutId() {}
-    LayerOutId(int layerId, int outputId, const String &outputName = String()) 
+    LayerOutId(int layerId, int outputId, const String &outputName = String())
         : lid(layerId), oid(outputId), name(outputName) {}
 };
 
@@ -323,7 +323,7 @@ struct Net::Impl
     void computeNetOutputs()
     {
         netOutputs.clear();
-        
+
         MapIdToLayerData::iterator it;
         for (it = layers.begin(); it != layers.end(); it++)
         {
@@ -346,7 +346,7 @@ struct Net::Impl
         //already allocated
         if (ld.flag)
             return;
-        
+
         //allocate parents
         for (set<int>::iterator i = ld.inputLayersId.begin(); i != ld.inputLayersId.end(); i++)
             allocateLayer(*i);
@@ -461,7 +461,7 @@ int Net::addLayer(const String &name, const String &type, LayerParams &params)
 
 void Net::connect(BlobId input, BlobId output)
 {
-    
+
 }
 
 void Net::setOutputNames(LayerId layer, const std::vector<String> &outputNames)

modules/dnn/src/layers/convolution_layer.cpp

@@ -86,7 +86,7 @@ namespace dnn
             biasOnesMat = Mat::ones(1, outH * outW, CV_32F);
         }
     }
-    
+
     template <typename Dtype>
     void im2col_cpu(const Dtype* data_im, const int channels,
         const int height, const int width, const int kernel_h, const int kernel_w,

modules/dnn/src/layers/lrn_layer.cpp

@@ -24,7 +24,7 @@ namespace dnn
         void spatialNormalization(Blob &src, Blob &dst);
 
     public:
-        
+
         LRNLayer(LayerParams &params);
         void allocate(const std::vector<Blob*> &inputs, std::vector<Blob> &outputs);
         void forward(std::vector<Blob*> &inputs, std::vector<Blob> &outputs);
@@ -101,7 +101,7 @@ namespace dnn
 
             accum.convertTo(accum, accum.type(), alpha/channels, 1);
             cv::pow(accum, beta, accum);
-            
+
             for (int cn = channels - 1; cn >= 0; cn--)
             {
                 cv::divide(srcBlob.getMat(n, cn), accum, dstBlob.getMat(n, cn));

modules/dnn/src/layers/pooling_layer.cpp

@@ -10,7 +10,7 @@ namespace dnn
 {
     class PoolingLayer : public Layer
     {
-        enum 
+        enum
         {
             MAX,
             AVE,

modules/dnn/src/layers/softmax_layer.cpp

@@ -32,7 +32,7 @@ namespace dnn
     void SoftMaxLayer::allocate(const std::vector<Blob*> &inputs, std::vector<Blob> &outputs)
     {
         CV_Assert(inputs.size() == 1);
-        
+
         Vec4i shape = inputs[0]->shape();
         outputs.resize(1);
         outputs[0].create(shape);
@@ -91,7 +91,7 @@ namespace dnn
         {
             size_t srcOffset = outerDim * outerStep;
             size_t bufOffset = outerDim * cnStep;
-            
+
             //sum exp along axis
             for (size_t i = 0; i < innerSize; i++)
                 bufPtr[bufOffset + i] = 0.f;