Deep-Object-Search-With-Hash

A Short Description Of Project

This repo contains an object search mechanism with hashing techniques using deep features of available pretrained object detection models. It can detect multiple objects from a given video then read features of those objects from a chosen feature extractor. Afterwards those features are transformed into hash codes using LSH(Locality Sensitive Hashing) technique and indexed into a python “Dictionary”.Finally for a given video it takes objects from the video as a query image and displays the close matched images which features have been previously indexed with hash codes.

Demo Video Link

click here

Acknowledgements

To learn more about LSH(Locality Sensitive Hashing)

visit:

https://github.com/kayzhu/LSHash

and

https://github.com/pixelogik/NearPy

Environment Setup:

Installation instructions

_Run the commands in a terminal or command-prompt.

• Install Python 3.6 or >3.6 for your operating system, if it does not already exist.

• For Mac

• For Windows

• For Ubuntu/Debian

sudo apt-get install python3.6

Check if the correct version of Python (3.6) is installed.

python --version

Make sure your terminal is at the root of the project i.e. where 'README.md' is located.

• Get virtualenv.

pip install virtualenv

• Create a virtual environment named .env using python 3.6 and activate the environment.

# command for gnu/linux systems
virtualenv -p $(which python3.6) .env

source .env/bin/activate

• If any error occurs to install a virtual environment you can see this link

• Install python dependencies from requirements.txt.

 pip install -r requirements.txt

How to run

After installing all the required libraries run the following commands in the terminal. The details of required input parameters have been described in the following section.

• Step 1: First run object detection script from terminal using following command

python object_detection_YOLO.py --video_dir <video file path>

Example :

python object_detection_YOLO.py --video_dir VideoFileDirectory

This will create a directory named “temp” in your project directory and under this directory, subdirectories will also be created according to object class or category. Object class wise cropped images will be saved into those subdirectories.

Currently 7 object classes are available. You can use other object classes by changing the parameter name and value.

Person
Bicycle
Car
Bike
Bus
truck 
Chair

• Step 2 : After the completion of step 1 , run the the following command from terminal

Example :

python start.py --range 350 --hash_length 48 --type discrete --function pca --n_of_HPT 5 --n_of_NN 20  --DSF 16 --QOC person --RNF True --TVD VideoFileDirectory

Options for feature extraction from pretrained models will appear on terminal.

Currently 3 pretrained models are available:

Resnet-50
VGG-19
MobileNet-SSD

For the first time select Resnet-50 and hit Enter Button then you will see features of object images saved in “temp/person” being extracted and indexed.

After indexing all features a video(path given in the input arguments) will start to play and press 'Q' to pause the video you will see another gallery window showing Query Image and corresponding Query Results. Then again press 'Q' to continue.

Description of Input arguments

range :

Number of images to be read from directory

hash_length :

Length of hash key to be generated from features

type :

Type of hash keys, currently "Binary and Discrete" hash types are available

functions :

Type of hash functions

--n_of_HPT :
Number of hash function per table (Python dictionary object)

--n_of_NN
Number of nearest neighbour for NN search

--RNF
Read New Features (RNF) from object image directory

--DSF
Downsampling Factor

--QOC
Query Object Class

--TVD
Test Video Directory/Path

--OID
Object Image Directory

--OFD
Object Features Directory

Contacts

1. Md. Faisal Ahmed Siddiqi (ahmedfaisal.fa21@gmail.com)

2. Anis-Ul-Isalm Rafid (au.i.rafid15@gmail.com) linkedin Profile