struct CV_EXPORTS ImageFeatures

{

int img_idx;

Size img_size;

std::vector keypoints;

Mat descriptors;

};

Extracting Features

In this tutorial, we will extract features using a pre-trained model with the included C++ utility. Note that we recommend using the Python interface for this task, as for example in the filter
 visualization example.

Follow instructions for installing
 Caffe and run scripts/download_model_binary.py
 models/bvlc_reference_caffenet from caffe root directory. If you need detailed information about the tools below, please consult their source code, in which additional documentation is usually provided.

Select data to run on

We’ll make a temporary folder to store things into.

mkdir examples/_temp



Generate a list of the files to process. We’re going to use the images that ship with caffe.

find `pwd`/examples/images -type f -exec echo {} \; > examples/_temp/temp.txt



The ImageDataLayer we’ll use expects
 labels after each filenames, so let’s add a 0 to the end of each line

sed "s/$/ 0/" examples/_temp/temp.txt > examples/_temp/file_list.txt



Define the Feature Extraction Network Architecture

In practice, subtracting the mean image from a dataset significantly improves classification accuracies. Download the mean image of the ILSVRC dataset.

./data/ilsvrc12/get_ilsvrc_aux.sh



We will use data/ilsvrc212/imagenet_mean.binaryproto in
 the network definition prototxt.

Let’s copy and modify the network definition. We’ll be using the ImageDataLayer,
 which will load and resize images for us.

cp examples/feature_extraction/imagenet_val.prototxt examples/_temp



Extract Features

Now everything necessary is in place.

./build/tools/extract_features.bin models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel examples/_temp/imagenet_val.prototxt fc7 examples/_temp/features 10 leveldb



The name of feature blob that you extract is fc7,
 which represents the highest level feature of the reference model. We can use any other layer, as well, such as conv5 or pool3.

The last parameter above is the number of data mini-batches.

The features are stored to LevelDB examples/_temp/features,
 ready for access by some other code.

If you meet with the error “Check failed: status.ok() Failed to open leveldb examples/_temp/features”, it is because the directory examples/_temp/features has been created the last time you run the command. Remove it and run again.

rm -rf examples/_temp/features/



If you’d like to use the Python wrapper for extracting features, check out the filter
 visualization notebook.

Clean Up

Let’s remove the temporary directory now.

rm -r examples/_temp

Features
Features detection/Description
From handcrafted to deep local features. G. Csurka, C. R. Dance, M. Humenberger. 2018.




Project
Detection
Description




AKAZE
x
MSURF/MLDB


DART
x
x


KAZE
x
MSURF/MLDB


LIOP/MIOP

x


LIFT (machine learning)
x
x


MROGH

x


SIFT
x
x


SURF
x
x


SFOP
x



…

Python小白，学习时候用到bs4解析网站，报错

bs4.FeatureNotFound: Couldn't find a tree builder with the features you requested: lxml. Do you need to install a parser library?

几经周折才知道是bs4调用了python自带的html解析器，我用的mac，默认安装的是python2，所以内置的解释器也是捆绑在python2上，而我学习的时候又自己安装了python3，开发环境也是python3的，貌似是没有html解释器，所以会报错。

问题找到了，那么怎么解决呢？对，在python3也装一个html解析器就好了，那么怎么安装呢？查阅资料获悉：一般pip和pip2对应的是python2.x，pip3对应的是python3.x的版本，python2和python3的模块是独立的，不能混用，混用会出问题。所以命令行通过python3的pip：pip3 安装解析器：

$ pip3 install lxml

3.8M，稍等片刻即可

再次运行项目，完美解决，特此记录

说的直白点就是你的开发环境下下载的包里不存在你需要的lxml，比如说你用的是py2，但是把包装到了py3的包路径下，当然就找不到了，反之亦然。那么就检查一下当前的开发环境是用的Python版本和对应的Python版本下的Packages目录下有没有你需要的包，如果没有就安装一个。就是这么简单。

$ pip3(如果是py2就用pip) show lxml

如果已经安装了就会显示包的位置及信息，没有则会报错，执行上一步安装操作即可