Updates gapi tutorial using normalize kernel

Changes doc, images and sample code itself

doc/tutorials/gapi/anisotropic_segmentation/porting_anisotropic_segmentation.markdown

@@ -106,9 +106,7 @@ like this:
 
 Note that this code slightly changes from the original one: forming up
 the resulting image is also a part of the pipeline (done with
-cv::gapi::addWeighted). Normalization of orientation and coherency
-images is still done by traditional OpenCV (using cv::normalize) as
-G-API doesn't provide such kernel at the moment.
+cv::gapi::addWeighted).
 
 Result of this G-API pipeline bit-exact matches the original one
 (given the same input image):
@@ -211,7 +209,7 @@ algorithm versions:
     ==6117==
 
 Once done, we can inspect the collected profiles with
-[Massif Visualizer](@https://github.com/KDE/massif-visualizer)
+[Massif Visualizer](https://github.com/KDE/massif-visualizer)
 (installed in the above step).
 
 Below is the visualized memory profile of the original OpenCV version
@@ -231,7 +229,7 @@ Now let's have a look on the profile of G-API version:
 Once G-API computation is created and its execution starts, G-API
 allocates all required memory at once and then the memory profile
 remains flat until the termination of the program. Massif reports us
-peak memory consumption of 10.6 MiB.
+peak memory consumption of 11.4 MiB.
 
 A reader may ask a right question at this point -- is G-API that bad?
 What is the reason in using it than?
@@ -367,9 +365,9 @@ Fluid backend. Now it looks like this:
 
 ![Memory profile: G-API/Fluid port of Anisotropic Image Segmentation sample](pics/massif_export_gapi_fluid.png)
 
-Now the tool reports 3.8MiB -- and we just changed a few lines in our
-code, without modifying the graph itself! It is a ~2.8X improvement of
-the previous G-API result, and 2X improvement of the original OpenCV
+Now the tool reports 4.7MiB -- and we just changed a few lines in our
+code, without modifying the graph itself! It is a ~2.4X improvement of
+the previous G-API result, and ~1.6X improvement of the original OpenCV
 version.
 
 Let's also examine how the internal representation of the graph now

samples/cpp/tutorial_code/gapi/porting_anisotropic_image_segmentation/porting_anisotropic_image_segmentation_gapi.cpp

@@ -45,8 +45,12 @@ int main()
     cv::GMat imgBin = imgCoherencyBin & imgOrientationBin;
     cv::GMat out = cv::gapi::addWeighted(in, 0.5, imgBin, 0.5, 0.0);
 
+    // Normalize extra outputs
+    cv::GMat imgCoherencyNorm = cv::gapi::normalize(imgCoherency, 0, 255, cv::NORM_MINMAX);
+    cv::GMat imgOrientationNorm = cv::gapi::normalize(imgOrientation, 0, 255, cv::NORM_MINMAX);
+
     // Capture the graph into object segm
-    cv::GComputation segm(cv::GIn(in), cv::GOut(out, imgCoherency, imgOrientation));
+    cv::GComputation segm(cv::GIn(in), cv::GOut(out, imgCoherencyNorm, imgOrientationNorm));
 
     // Define cv::Mats for output data
     cv::Mat imgOut, imgOutCoherency, imgOutOrientation;
@@ -54,10 +58,6 @@ int main()
     // Run the graph
     segm.apply(cv::gin(imgIn), cv::gout(imgOut, imgOutCoherency, imgOutOrientation));
 
-    // Normalize extra outputs (out of the graph)
-    cv::normalize(imgOutCoherency, imgOutCoherency, 0, 255, cv::NORM_MINMAX);
-    cv::normalize(imgOutOrientation, imgOutOrientation, 0, 255, cv::NORM_MINMAX);
-
     cv::imwrite("result.jpg", imgOut);
     cv::imwrite("Coherency.jpg", imgOutCoherency);
     cv::imwrite("Orientation.jpg", imgOutOrientation);

samples/cpp/tutorial_code/gapi/porting_anisotropic_image_segmentation/porting_anisotropic_image_segmentation_gapi_fluid.cpp

@@ -50,8 +50,12 @@ int main()
     auto imgBin = imgCoherencyBin & imgOrientationBin;
     cv::GMat out = cv::gapi::addWeighted(in, 0.5, imgBin, 0.5, 0.0);
 
+    // Normalize extra outputs
+    cv::GMat imgCoherencyNorm = cv::gapi::normalize(imgCoherency, 0, 255, cv::NORM_MINMAX);
+    cv::GMat imgOrientationNorm = cv::gapi::normalize(imgOrientation, 0, 255, cv::NORM_MINMAX);
+
     // Capture the graph into object segm
-    cv::GComputation segm(cv::GIn(in), cv::GOut(out, imgCoherency, imgOrientation));
+    cv::GComputation segm(cv::GIn(in), cv::GOut(out, imgCoherencyNorm, imgOrientationNorm));
 
     // Define cv::Mats for output data
     cv::Mat imgOut, imgOutCoherency, imgOutOrientation;
@@ -75,10 +79,6 @@ int main()
     //! [kernel_pkg_use]
     //! [kernel_pkg_proper]
 
-    // Normalize extra outputs (out of the graph)
-    cv::normalize(imgOutCoherency, imgOutCoherency, 0, 255, cv::NORM_MINMAX);
-    cv::normalize(imgOutOrientation, imgOutOrientation, 0, 255, cv::NORM_MINMAX);
-
     cv::imwrite("result.jpg", imgOut);
     cv::imwrite("Coherency.jpg", imgOutCoherency);
     cv::imwrite("Orientation.jpg", imgOutOrientation);