init - 初始化项目

doc/tutorials/dnn/dnn_googlenet/dnn_googlenet.markdown

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+Load Caffe framework models  {#tutorial_dnn_googlenet}
+===========================
+
+@tableofcontents
+
+@next_tutorial{tutorial_dnn_halide}
+
+|    |    |
+| -: | :- |
+| Original author | Vitaliy Lyudvichenko |
+| Compatibility | OpenCV >= 3.3 |
+
+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/opencv/opencv/blob/master/samples/dnn/classification.cpp).
+
+@include dnn/classification.cpp
+
+Explanation
+-----------
+
+-# Firstly, download GoogLeNet model files:
+   [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:
+   [classification_classes_ILSVRC2012.txt](https://github.com/opencv/opencv/blob/master/samples/data/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/classification.cpp Read and initialize network
+
+   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/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/classification.cpp Set input blob
+
+-# Make forward pass
+   @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/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.
+
+-# 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.