Fixed several issues found by static analysis

modules/dnn/src/dnn.cpp

@@ -317,9 +317,9 @@ private:
 
 struct LayerData
 {
-    LayerData() {}
+    LayerData() : id(-1), flag(0) {}
     LayerData(int _id, const String &_name, const String &_type, LayerParams &_params)
-        : id(_id), name(_name), type(_type), params(_params)
+        : id(_id), name(_name), type(_type), params(_params), flag(0)
     {
         //add logging info
         params.name = name;

modules/dnn/src/layers/convolution_layer.cpp

@@ -287,7 +287,10 @@ public:
         bool is1x1_;
         bool useAVX2;
 
-        ParallelConv() {}
+        ParallelConv()
+            : input_(0), weights_(0), output_(0), ngroups_(0), nstripes_(0),
+              is1x1_(false), useAVX2(false)
+        {}
 
         static void run( const Mat& input, Mat& output, const Mat& weights,
                          const std::vector<float>& biasvec,
@@ -921,7 +924,11 @@ public:
         int nstripes;
         bool is1x1;
 
-        Col2ImInvoker() {}
+        Col2ImInvoker()
+            : data_col(0), biasvec(0), channels(0), height(0), width(0),
+              kernel_h(0), kernel_w(0), pad_h(0), pad_w(0), stride_h(0), stride_w(0), data_im(0),
+              height_col(0), width_col(0), nstripes(0), is1x1(0)
+        {}
 
         static void run(const float* data_col,
                         int channels, int height, int width,

modules/dnn/src/layers/elementwise_layers.cpp

@@ -105,7 +105,7 @@ public:
         }
     };
 
-    ElementWiseLayer(const Func &f=Func()) { func = f; }
+    ElementWiseLayer(const Func &f=Func()) : run_parallel(false) { func = f; }
 
     virtual bool supportBackend(int backendId)
     {

modules/dnn/src/layers/permute_layer.cpp

@@ -83,10 +83,10 @@ public:
     }
 
     PermuteLayerImpl(const LayerParams &params)
+        : _count(0), _needsPermute(false), _numAxes(0)
     {
         if (!params.has("order"))
         {
-            _needsPermute = false;
             return;
         }
 

modules/dnn/src/layers/prior_box_layer.cpp

@@ -160,6 +160,7 @@ public:
     }
 
     PriorBoxLayerImpl(const LayerParams &params)
+        : _boxWidth(0), _boxHeight(0)
     {
         setParamsFrom(params);
         _minSize = getParameter<unsigned>(params, "min_size");

modules/dnn/src/layers/recurrent_layers.cpp

@@ -94,6 +94,7 @@ class LSTMLayerImpl : public LSTMLayer
 public:
 
     LSTMLayerImpl(const LayerParams& params)
+        : numTimeStamps(0), numSamples(0)
     {
         setParamsFrom(params);
         type = "LSTM";
@@ -307,6 +308,7 @@ class RNNLayerImpl : public RNNLayer
 public:
 
     RNNLayerImpl(const LayerParams& params)
+        : numX(0), numH(0), numO(0), numSamples(0), numTimestamps(0), numSamplesTotal(0), dtype(0)
     {
         setParamsFrom(params);
         type = "RNN";

modules/dnn/src/torch/THDiskFile.cpp

@@ -556,6 +556,8 @@ static long THDiskFile_readString(THFile *self, const char *format, char **str_)
         total += TBRS_BSZ;
         p = (char*)THRealloc(p, total);
       }
+      if (p == NULL)
+        THError("read error: failed to allocate buffer");
       if (fgets(p+pos, total-pos, dfself->handle) == NULL) /* eof? */
       {
         if(pos == 0L)

modules/dnn/src/torch/torch_importer.cpp

@@ -876,7 +876,7 @@ struct TorchImporter : public ::cv::dnn::Importer
                 return mergeId;
             }
             else if (module->thName == "ConcatTable") {
-                int newId, splitId;
+                int newId = -1, splitId;
                 LayerParams splitParams;
 
                 splitId = net.addLayer(generateLayerName("torchSplit"), "Split", splitParams);