image_retrieval_binary_hash_code

Image Retrieval based on Binary Hash Code and CNN

Image Retrieval realizing cvprw15 paper，including Face image and other image

Created by zhleternity

Introduction:

This repo is based on the cvprw15 paper about image retrieval(proposed by Kevin Lin - cvprw15) for CBIR tasks.it is about the using of binary hash code with image retrieval.

Goals:

• The extraction of binary hash codes & deep features
• Fast indexing of both binary hash codes & deepfeatures
• Fast computing of similarity (distances) based on features & binary codes extrated
• Easy request for similar images (in database)
• Sorted visualization of results

Datasets && Downloads:

In the repo, you can test on Webface dataset and foods25 dataset directly. If you wanna to download the above mentioned datasets and model,please have a look on Running this demo section to downlowd what you wanted. But also,you can train your own dataset and other datasets from scratch,and then do the testing.Please have a look on Training From Scratch section to do the training work.

Hardwares/Softwares:

OS: Ubuntu 16.04 64 bit
GPU: Nvidia RTX 2070
Cuda 9.2
CuDNN 7.5.0
Python 2.7.12
OpenCV 3.2.0

Prerequisites:

1. Caffe(https://github.com/BVLC/caffe)
2. Python's packages (requirements.txt),install command is: $ pip install -r requirements.txt
3. OpenCV((http://www.pyimagesearch.com/2016/10/24/ubuntu-16-04-how-to-install-opencv/)

Running this demo:

1. Download this repository:

   $ git clone --recursive https://github.com/zhleternity/image_retrieval_binary_hash_code.git
   $ cd image-retrieval-lsh
   

   ├── corefuncs #main functions about fetaure extraction,indexing,distances,and results showing
   ├── data  #dataset and extracted feature file(.hdf5)
   ├── examples #model and prototxt
   ├── results #output results
   |── indexing.py #extract features,and build the index
   ├── retrieve.py #do the image retrieval
   ├── init_path.py #init caffe path and other config
   ├── README.md
   └── requirements.txt

2. Download datasets and model:

For the foods25 dataset,it has already exists in this repo,so you need not downlowd. For the Webface dataset, you need download by yourself. Get the data and models required:

• Download the data and uncompress in 'image_retrieval_binary_hash_code/data'

  • foods25: Google Drive

  • Webface: http://www.cbsr.ia.ac.cn/english/CASIA-WebFace-Database.html

• Download the models：

  For foods25,uncompress in 'image_retrieval_binary_hash_code/examples/'

  • foods25: Google Drive

  For webface,download the model and put it in 'image_retrieval_binary_hash_code/examples/facescrub/models/'

  • Webface: https://pan.baidu.com/s/1ecbDJgp48IvRBrU4RHTFKA Extract Code：p5l3

3. Extract features && Build the index:

$ python indexing.py -u <use GPU or not,0 or 1> -d <path to your dataset> -o <your output data-db name>

4. Searching:

$ python retrieve.py -u <use GPU or not,0 or 1> -f <your output data-db name> -d <path to your dataset> -q <your query image>

Experiments:

1. Feature extraction & indexing of a sample dataset (foods25) with the following commands:

   $ python indexing -u 1 -d data/foods25/imgs -o data/foods25/foods25.hdf5
   

   In my repo, the foods25.hdf5 have already exists,so you can skip thie step.

The output of this command is stored as 'data/foods25/foods25.hdf5'.

2. Search for similar images:

   $ python retrieve.py -u 1 -f data/foods25/foods25.hdf5 -d data/foods25/imgs -q data/foods25/imgs/taro/28581814.jpg
   

The output of this command is stored in 'results/'

Matters need attention

When you run the above Experiments section,the model and prototxt path in indexing.py && retrieve.py should be corresponding to your dataset.

Please see th following screenshot:

Show results

Query:

Results:

Training From Scratch

Now,we begin to train the demo from scratch.And then you can test the retrieval on your trained model.

• Split your data

$ cd data/facescrub

$ mkdir train && mkdir val

$ python split_dataset.py

Before this op,you will see your images is in train && val

• Convert txt

$ touch train.txt && touch val.txt

$ python generate_txt.py

This operation will yeild two non-empty txt profile:train.txt && val.txt

• Convert data into LMDB or LevelDB

$ sh create_imagenet.sh

This operation will yeild two leveldb profile:facescrub_train_leveldb && facescrub_val_leveldb

Attention that:you may need do some change on the path,please change it to correspond to your directory path and dataset.

• Train

$ cd ../../examples/facescrub

Please replace the data source path to your leveldb path in train_face_48.prototxt and test_face_48.prototxt .Also change the path in solver_face_48.prototxt.

$ /xxx/xxx/caffe/build/tools/caffe train -solver solver_face_48.prototxt -gpu 1

All this steps are easy for human who are familiar to Caffe，hence issues above mentioned are brief.

Contact

Please pull issues to me directly,i will return you on time.