#flask-image-retrieval keywords : CNN, feature exact, image retrieval, caffe

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Python flask web service image retrieval project based https://github.com/kevinlin311tw/caffe-cvprw15 Matlab version.

Introduction

Flask image retrieval is an image retrieval framework based on deep convolutional networks implemented by python , it mainly includes image feature extract part (to form the feature library )and feature retrieval part( search the image accoding to similarity). This code has been tested on ubuntu 14.04 LTS and python 2.7.

##Install Caffe The Caffe version in this README is from anaconda, and it is configured to a no-gpu version. However, it has also been tested on GPU version.

###Install anaconda Download the anaconda from https://www.continuum.io/downloads, select the python2.7 version. run to install anaconda

$ ./Anaconda.sh

When install anaconda, many dependencies like below will be installed automatically:

• flask: Alightweight web application framework, this project is based on this web framework.
• pandas: A data analysis package for Python
• qt ,numpy,matplotlib and so on
• simplejson, flask, pillow, scikit-image

###Install dependencies

 $ conda install  -c conda-forge caffe

Use command below to install several dependencies automatically:

• opencv: open source computer vision library
• lmdb, leveldb,snappy:IO libraries (leveldb requires snappy)
• openBLAS: basic linear algebra subprograms, as the ackend of matrix and vector computations
• Boost : c++ extension library
• protobuf, glog, gflags and so on ###Configure Caffe Configure the build by copying and modifying the example Makefile.config for your setup. Uncomment the relevant lines accoding to your anaconda python. Uncomment to build without GPU support

CPU_ONLY := 1

If you're using OpenCV 3, configure as below

OPENCV_VERSION := 3
OPENCV_INCLUDE := /Your/Anaconda/Path/pkgs/opencv3-3.1.0-py27_0/include
OPENCV_LIB := /Your/Anaconda/Path/pkgs/opencv3-3.1.0-py27_0/lib

BLAS choice: atlas for ATLAS (default) , mkl for MKL, open for OpenBlas

BLAS := open 
BLAS_INCLUDE := /Your/Anaconda/Path/pkgs/openblas-0.2.19/include
BLAS_LIB := /Your/Anaconda/Path/pkgs/openblas-0.2.19/lib

PYTHON_VERSION := 2.7
ANACONDA_HOME := /Your/Anaconda/Path
PYTHON_INCLUDE := $(ANACONDA_HOME))/include \
              `$(ANACONDA_HOME)/include/python2.7 \
               `$(ANACONDA_HOME)/lib/python2.7/site-packages/numpy/core/include \
PYTHON_LIB := $(ANACONDA_HOME)/lib

###Build caffe Go to https://github.com/BVLC/caffe, download zip archive and unpack it. Or clone the source code. Enter caffe-home/python directory to install Python packages:

for req in $(cat requirements.txt); do conda install $req;

Then enter caffe-home/python directory:

$ make all 
$ make runtest
$ make pycaffe

Verify the installation by running python -c "import caffe;print caffe.__version__"

Build your own feature library

• You can download deploy file from vgg_train_val.prototxt and
• Download your own dataset and train your model, it will generate filename.caffemodelfile.
• In imagesearch dictionary, there is a feature_extract.py file . You can set as below :

deployPrototxt = '/Your/Deploy/Path/XXX.prototxt';
modelFile = '/Your/Model/Path/XXX.caffemodel';
meanFile = 'db/XXX_mean.npy';
imageListFile = '/Your/Image/Path/images_total.txt';
imageBasePath = '/Your/Image/Path';
image_cateFile='/Your/Image/Path/image_cate.txt';

• Run feature_extract.py and generate feature library file feature_db.h5.

##Run the demo

• Run feature_extract.py to extract feature. the feature library will be saved in hdf5 format.
• Run app.py to make a link with browser.
• Open a browser, enter YourIpAdder:5000 in the address bar.
• Load your image and then it will show retrieval result automatically.