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