Update tutorials. A new cv::dnn::readNet function

doc/tutorials/dnn/dnn_googlenet/dnn_googlenet.markdown

@@ -13,50 +13,53 @@ We will demonstrate results of this example on the following picture.
 Source Code
 -----------
 
-We will be using snippets from the example application, that can be downloaded [here](https://github.com/opencv/opencv/blob/master/samples/dnn/caffe_googlenet.cpp).
+We will be using snippets from the example application, that can be downloaded [here](https://github.com/opencv/opencv/blob/master/samples/dnn/classification.cpp).
 
-@include dnn/caffe_googlenet.cpp
+@include dnn/classification.cpp
 
 Explanation
 -----------
 
 -# Firstly, download GoogLeNet model files:
-   [bvlc_googlenet.prototxt  ](https://raw.githubusercontent.com/opencv/opencv/master/samples/data/dnn/bvlc_googlenet.prototxt) and
+   [bvlc_googlenet.prototxt  ](https://github.com/opencv/opencv_extra/blob/master/testdata/dnn/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/opencv/opencv/master/samples/data/dnn/synset_words.txt).
+   [classification_classes_ILSVRC2012.txt](https://github.com/opencv/opencv/tree/master/samples/dnn/classification_classes_ILSVRC2012.txt).
 
    Put these files into working dir of this program example.
 
 -# Read and initialize network using path to .prototxt and .caffemodel files
-   @snippet dnn/caffe_googlenet.cpp Read and initialize network
+   @snippet dnn/classification.cpp Read and initialize network
 
--# Check that network was read successfully
-   @snippet dnn/caffe_googlenet.cpp Check that network was read successfully
+   You can skip an argument `framework` if one of the files `model` or `config` has an
+   extension `.caffemodel` or `.prototxt`.
+   This way function cv::dnn::readNet can automatically detects a model's format.
 
 -# Read input image and convert to the blob, acceptable by GoogleNet
-   @snippet dnn/caffe_googlenet.cpp Prepare blob
-   We convert the image to a 4-dimensional blob (so-called batch) with 1x3x224x224 shape after applying necessary pre-processing like resizing and mean subtraction using cv::dnn::blobFromImage constructor.
+   @snippet dnn/classification.cpp Open a video file or an image file or a camera stream
+
+   cv::VideoCapture can load both images and videos.
+
+   @snippet dnn/classification.cpp Create a 4D blob from a frame
+   We convert the image to a 4-dimensional blob (so-called batch) with `1x3x224x224` shape
+   after applying necessary pre-processing like resizing and mean subtraction
+   `(-104, -117, -123)` for each blue, green and red channels correspondingly using cv::dnn::blobFromImage function.
 
 -# Pass the blob to the network
-   @snippet dnn/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".
+   @snippet dnn/classification.cpp Set input blob
 
 -# Make forward pass
-   @snippet dnn/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.
+   @snippet dnn/classification.cpp Make forward pass
+   During the forward pass output of each network layer is computed, but in this example we need output from the last layer only.
 
 -# Determine the best class
-   @snippet dnn/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.
+   @snippet dnn/classification.cpp Get a class with a highest score
+   We put the output of network, 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 corresponds to the class of the image.
 
--# Print results
-   @snippet dnn/caffe_googlenet.cpp Print results
-   For our image we get:
-> Best class: #812 'space shuttle'
->
-> Probability: 99.6378%
+-# Run an example from command line
+   @code
+   ./example_dnn_classification --model=bvlc_googlenet.caffemodel --config=bvlc_googlenet.prototxt --width=224 --height=224 --classes=classification_classes_ILSVRC2012.txt --input=space_shuttle.jpg --mean="104 117 123"
+   @endcode
+   For our image we get prediction of class `space shuttle` with more than 99% sureness.

doc/tutorials/dnn/dnn_halide/dnn_halide.markdown

@@ -74,46 +74,7 @@ When you build OpenCV add the following configuration flags:
 
 - `HALIDE_ROOT_DIR` - path to Halide build directory
 
-## Sample
-
-@include dnn/squeezenet_halide.cpp
-
-## Explanation
-Download Caffe model from SqueezeNet repository: [train_val.prototxt](https://github.com/DeepScale/SqueezeNet/blob/master/SqueezeNet_v1.1/train_val.prototxt) and [squeezenet_v1.1.caffemodel](https://github.com/DeepScale/SqueezeNet/blob/master/SqueezeNet_v1.1/squeezenet_v1.1.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/opencv/opencv/master/samples/data/dnn/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/squeezenet_halide.cpp Read and initialize network
-
--# Check that network was read successfully
-@snippet dnn/squeezenet_halide.cpp Check that network was read successfully
-
--# Read input image and convert to the 4-dimensional blob, acceptable by SqueezeNet v1.1
-@snippet dnn/squeezenet_halide.cpp Prepare blob
-
--# Pass the blob to the network
-@snippet dnn/squeezenet_halide.cpp Set input blob
-
--# Enable Halide backend for layers where it is implemented
-@snippet dnn/squeezenet_halide.cpp Enable Halide backend
-
--# Make forward pass
-@snippet dnn/squeezenet_halide.cpp Make forward pass
-Remember that the first forward pass after initialization require quite more
-time that the next ones. It's because of runtime compilation of Halide pipelines
-at the first invocation.
-
--# Determine the best class
-@snippet dnn/squeezenet_halide.cpp Determine the best class
-
--# Print results
-@snippet dnn/squeezenet_halide.cpp Print results
-For our image we get:
-
-> Best class: #812 'space shuttle'
->
-> Probability: 97.9812%
+## Set Halide as a preferable backend
+@code
+net.setPreferableBackend(DNN_BACKEND_HALIDE);
+@endcode