opencv/doc/tutorials/dnn/dnn_googlenet/dnn_googlenet.markdown

Load Caffe framework models

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.

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.

@include dnn/classification.cpp

Explanation

-# Firstly, download GoogLeNet model files: bvlc_googlenet.prototxt and bvlc_googlenet.caffemodel

Also you need file with names of ILSVRC2012 classes: 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.