Changed Blob::ptr and Blob::offset methods

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

@@ -67,6 +67,7 @@ namespace dnn
 
         Mat& getMatRef();
         const Mat& getMatRef() const;
+        //TODO: add UMat get methods
         Mat getMat(int n, int cn);
 
         //shape getters
@@ -98,13 +99,13 @@ namespace dnn
         */
         int canonicalAxis(int axis) const;
 
-        /** @brief returns real shape of the blob
+        /** @brief returns shape of the blob
         */
         BlobShape shape() const;
 
         bool equalShape(const Blob &other) const;
 
-        //shape getters, oriented for 4-dim Blobs processing
+        //shape getters for 4-dim Blobs processing
         int cols() const;
         int rows() const;
         int channels() const;
@@ -113,11 +114,14 @@ namespace dnn
         Vec4i shape4() const;
 
         //CPU data pointer functions
-        int offset(int n = 0, int cn = 0, int row = 0, int col = 0) const;
+        template<int n>
-        uchar *ptrRaw(int n = 0, int cn = 0, int row = 0, int col = 0);
+        size_t offset(const Vec<int, n> &pos) const;
-        float *ptrf(int n = 0, int cn = 0, int row = 0, int col = 0);
+        size_t offset(int n = 0, int cn = 0, int row = 0, int col = 0) const;
+        uchar *ptr(int n = 0, int cn = 0, int row = 0, int col = 0);
         template<typename TFloat>
         TFloat *ptr(int n = 0, int cn = 0, int row = 0, int col = 0);
+        float *ptrf(int n = 0, int cn = 0, int row = 0, int col = 0);
+        //TODO: add const ptr methods
 
         /** @brief share data with other blob and returns *this
         @returns *this

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

@@ -158,10 +158,25 @@ inline size_t Blob::total(int startAxis, int endAxis) const
     return size;
 }
 
-inline int Blob::offset(int n, int cn, int row, int col) const
+
+template<int n>
+inline size_t Blob::offset(const Vec<int, n> &pos) const
+{
+    size_t ofs = 0;
+    int i;
+    for (i = 0; i < std::min(n, dims()); i++)
+    {
+        CV_DbgAssert(pos[i] >= 0 && pos[i] < size(i));
+        ofs = ofs * (size_t)size(i) + pos[i];
+    }
+    for (; i < dims(); i++)
+        ofs *= (size_t)size(i);
+    return ofs;
+}
+
+inline size_t Blob::offset(int n, int cn, int row, int col) const
 {
-    CV_DbgAssert(0 <= n && n < num() && 0 <= cn && cn < channels() && 0 <= row && row < rows() && 0 <= col && col < cols());
+    return offset(Vec4i(n, cn, row, col));
-    return ((n*channels() + cn)*rows() + row)*cols() + col;
 }
 
 inline float *Blob::ptrf(int n, int cn, int row, int col)
@@ -170,7 +185,7 @@ inline float *Blob::ptrf(int n, int cn, int row, int col)
     return (float*)m.data + offset(n, cn, row, col);
 }
 
-inline uchar *Blob::ptrRaw(int n, int cn, int row, int col)
+inline uchar *Blob::ptr(int n, int cn, int row, int col)
 {
     return m.data + m.elemSize() * offset(n, cn, row, col);
 }
@@ -179,7 +194,7 @@ template<typename TFloat>
 inline TFloat* Blob::ptr(int n, int cn, int row, int col)
 {
     CV_Assert(type() == cv::DataDepth<TFloat>::value);
-    return (TFloat*) ptrRaw(n, cn, row, col);
+    return (TFloat*) ptr(n, cn, row, col);
 }
 
 inline BlobShape Blob::shape() const
@@ -212,7 +227,7 @@ inline const Mat& Blob::getMatRef() const
 
 inline Mat Blob::getMat(int n, int cn)
 {
-    return Mat(rows(), cols(), m.type(), this->ptrRaw(n, cn));
+    return Mat(rows(), cols(), m.type(), this->ptr(n, cn));
 }
 
 inline int Blob::cols() const

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

@@ -79,7 +79,7 @@ namespace dnn
         Ptr<Impl> impl;
     };
 
-    class CV_EXPORTS Importer
+    class Importer
     {
     public:
 

modules/dnn/src/blob.cpp

@@ -88,7 +88,7 @@ namespace dnn
 
         std::vector<Mat> wrapBuf(dstShape[-3]);
         int elemSize = (int)m.elemSize();
-        uchar *ptr = this->ptrRaw();
+        uchar *ptr = this->ptr();
         for (size_t i = 0; i < inMats.size(); i++)
         {
             Mat inMat = inMats[i];

modules/dnn/src/layers/convolution_layer.cpp

@@ -86,8 +86,8 @@ namespace dnn
                 {
                     im2col(inpBlob, n, g);
 
-                    Mat kerMat(outGroupCn, ksize, wgtBlob.type(), wgtBlob.ptrRaw(g*outGroupCn));
+                    Mat kerMat(outGroupCn, ksize, wgtBlob.type(), wgtBlob.ptr(g*outGroupCn));
-                    Mat dstMat(outGroupCn, outH*outW, outBlob.type(), outBlob.ptrRaw(n, g*outGroupCn));
+                    Mat dstMat(outGroupCn, outH*outW, outBlob.type(), outBlob.ptr(n, g*outGroupCn));
 
                     cv::gemm(kerMat, colMat, 1, noArray(), 0, dstMat);
 
@@ -104,7 +104,7 @@ namespace dnn
 
     void ConvolutionLayer::im2col(Blob &inpBlob, int imNum, int cnGroup)
     {
-        uchar *srcPtr = inpBlob.ptrRaw(imNum, cnGroup*inpGroupCn);
+        uchar *srcPtr = inpBlob.ptr(imNum, cnGroup*inpGroupCn);
 
         if (is1x1())
         {
@@ -170,13 +170,13 @@ namespace dnn
             {
                 for (int g = 0; g < group; g++)
                 {
-                    Mat dstMat(inpGroupCn, inpH*inpW, decnBlob.type(), decnBlob.ptrRaw(n, g*inpGroupCn));
+                    Mat dstMat(inpGroupCn, inpH*inpW, decnBlob.type(), decnBlob.ptr(n, g*inpGroupCn));
 
                     if (is1x1())
                         colMat = dstMat;
 
-                    Mat convMat(outGroupCn, outH*outW, convBlob.type(), convBlob.ptrRaw(n, g*outGroupCn));
+                    Mat convMat(outGroupCn, outH*outW, convBlob.type(), convBlob.ptr(n, g*outGroupCn));
-                    Mat wghtMat(outGroupCn, ksize, wghtBlob.type(), wghtBlob.ptrRaw(g*outGroupCn));
+                    Mat wghtMat(outGroupCn, ksize, wghtBlob.type(), wghtBlob.ptr(g*outGroupCn));
                     cv::gemm(wghtMat, convMat, 1, noArray(), 0, colMat, GEMM_1_T);
 
                     col2im(dstMat);

modules/dnn/src/layers/elementwise_layers.hpp

@@ -33,7 +33,7 @@ using std::pow;
         {
             for (size_t i = 0; i < inputs.size(); i++)
             {
-                CV_Assert(inputs[i]->ptrRaw() == outputs[i].ptrRaw() && inputs[i]->type() == outputs[i].type());
+                CV_Assert(inputs[i]->ptr() == outputs[i].ptr() && inputs[i]->type() == outputs[i].type());
 
                 size_t size = outputs[i].total();
 

modules/dnn/src/layers/lrn_layer.cpp

@@ -59,7 +59,7 @@ namespace dnn
 
     void LRNLayer::channelNoramlization(Blob &srcBlob, Blob &dstBlob)
     {
-        CV_DbgAssert(srcBlob.ptrRaw() != dstBlob.ptrRaw());
+        CV_DbgAssert(srcBlob.ptr() != dstBlob.ptr());
 
         int num = srcBlob.num();
         int channels = srcBlob.channels();