Made the default input layer automatically normalize unsigned char pixel values

to the range [0,1].

dlib/dnn/input.h

@@ -58,6 +58,8 @@ namespace dlib
             // initialize data to the right size to contain the stuff in the iterator range.
             data.set_size(std::distance(ibegin,iend), pixel_traits<T>::num, nr, nc);
 
+            typedef typename pixel_traits<T>::basic_pixel_type bptype;
+
             const size_t offset = nr*nc;
             auto ptr = data.host();
             for (auto i = ibegin; i != iend; ++i)
@@ -70,6 +72,9 @@ namespace dlib
                         auto p = ptr++;
                         for (long j = 0; j < temp.size(); ++j)
                         {
+                            if (is_same_type<bptype,unsigned char>::value)
+                                *p = temp(j)/256.0;
+                            else
                                 *p = temp(j);
                             p += offset;
                         }
@@ -130,6 +135,7 @@ namespace dlib
             
             // initialize data to the right size to contain the stuff in the iterator range.
             data.set_size(std::distance(ibegin,iend), pixel_traits<T>::num, nr, nc);
+            typedef typename pixel_traits<T>::basic_pixel_type bptype;
 
             const size_t offset = nr*nc;
             auto ptr = data.host();
@@ -143,6 +149,9 @@ namespace dlib
                         auto p = ptr++;
                         for (long j = 0; j < temp.size(); ++j)
                         {
+                            if (is_same_type<bptype,unsigned char>::value)
+                                *p = temp(j)/256.0;
+                            else
                                 *p = temp(j);
                             p += offset;
                         }

dlib/dnn/input_abstract.h

@@ -135,6 +135,10 @@ namespace dlib
                   For example, a matrix<float,3,3> would turn into a tensor with 3 rows, 3
                   columns, and k()==1.  Or a matrix<rgb_pixel,4,5> would turn into a tensor
                   with 4 rows, 5 columns, and k()==3 (since rgb_pixels have 3 channels).
+                - If the input data contains pixels of type unsigned char, rgb_pixel, or
+                  other pixel types with a basic_pixel_type of unsigned char then each
+                  value written to the output tensor is first divided by 256.0 so that the
+                  resulting outputs are all in the range [0,1].
         !*/
     };