dnn: move module from opencv_contrib

https://github.com/opencv/opencv_contrib/tree/e6f63c7a38ca40c5dc33e38736e3027e3528d6cb/modules/dnn

modules/dnn/tutorials/tutorial_dnn_googlenet.markdown

@@ -0,0 +1,66 @@
+Load Caffe framework models  {#tutorial_dnn_googlenet}
+===========================
+
+Introduction
+------------
+
+In this tutorial you will learn how to use opencv_dnn module for image classification by using
+GoogLeNet trained network from [Caffe model zoo](http://caffe.berkeleyvision.org/model_zoo.html).
+
+We will demonstrate results of this example on the following picture.
+![Buran space shuttle](images/space_shuttle.jpg)
+
+Source Code
+-----------
+
+We will be using snippets from the example application, that can be downloaded [here](https://github.com/ludv1x/opencv_contrib/blob/master/modules/dnn/samples/caffe_googlenet.cpp).
+
+@include dnn/samples/caffe_googlenet.cpp
+
+Explanation
+-----------
+
+-# Firstly, download GoogLeNet model files:
+   [bvlc_googlenet.prototxt  ](https://raw.githubusercontent.com/ludv1x/opencv_contrib/master/modules/dnn/samples/bvlc_googlenet.prototxt) and
+   [bvlc_googlenet.caffemodel](http://dl.caffe.berkeleyvision.org/bvlc_googlenet.caffemodel)
+
+   Also you need file with names of [ILSVRC2012](http://image-net.org/challenges/LSVRC/2012/browse-synsets) classes:
+   [synset_words.txt](https://raw.githubusercontent.com/ludv1x/opencv_contrib/master/modules/dnn/samples/synset_words.txt).
+
+   Put these files into working dir of this program example.
+
+-# Read and initialize network using path to .prototxt and .caffemodel files
+   @snippet dnn/samples/caffe_googlenet.cpp Read and initialize network
+
+-# Check that network was read successfully
+   @snippet dnn/samples/caffe_googlenet.cpp Check that network was read successfully
+
+-# Read input image and convert to the blob, acceptable by GoogleNet
+   @snippet dnn/samples/caffe_googlenet.cpp Prepare blob
+   Firstly, we resize the image and change its channel sequence order.
+
+   Now image is actually a 3-dimensional array with 224x224x3 shape.
+
+   Next, we convert the image to 4-dimensional blob (so-called batch) with 1x3x224x224 shape by using special cv::dnn::blobFromImages constructor.
+
+-# Pass the blob to the network
+   @snippet dnn/samples/caffe_googlenet.cpp Set input blob
+   In bvlc_googlenet.prototxt the network input blob named as "data", therefore this blob labeled as ".data" in opencv_dnn API.
+
+   Other blobs labeled as "name_of_layer.name_of_layer_output".
+
+-# Make forward pass
+   @snippet dnn/samples/caffe_googlenet.cpp Make forward pass
+   During the forward pass output of each network layer is computed, but in this example we need output from "prob" layer only.
+
+-# Determine the best class
+   @snippet dnn/samples/caffe_googlenet.cpp Gather output
+   We put the output of "prob" layer, which contain probabilities for each of 1000 ILSVRC2012 image classes, to the `prob` blob.
+   And find the index of element with maximal value in this one. This index correspond to the class of the image.
+
+-# Print results
+   @snippet dnn/samples/caffe_googlenet.cpp Print results
+   For our image we get:
+> Best class: #812 'space shuttle'
+>
+> Probability: 99.6378%