2.9 KiB
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/samples/caffe_googlenet.cpp
Explanation
-# Firstly, download GoogLeNet model files: bvlc_googlenet.prototxt and bvlc_googlenet.caffemodel
Also you need file with names of ILSVRC2012 classes: 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%