information-retrieval's Introduction

Image similarity search using deep learning

This project demonstrates, how we can make use of deep learning to do state-of-the-art image similarity search. I have used tensorflow and some publicly available datasets.

Results

Environment

==compiler==: python 3.11

==packages==: Flask PyQt5 numpy tensorflow Flask-HTTPAuth scipy imageio matplotlib sklearn

==editor==: Visual Studio Code

==programming language==: html( css, javascript ), python

How to run

Download imagenet folder, extraxt and keep it in server directory
Download datasets for footwares, apparels keep them inside a directory under upload folder. Final folder strcture will be as below

├─.idea
│  └─inspectionProfiles
├─.vscode
├─assets
└─server
    ├─database
    │  ├─dataset
    │  └─tags
    ├─imagenet
    ├─images
    ├─static
    │  ├─images
    │  └─result
    ├─templates
    ├─uploads
    └─__pycache__

Run image vectorizer which passes each data through an inception-v3 model and collects the bottleneck layer vectors and stores in disc. Edit dataset paths accordingly indide the image_vectorizer.py

  python server/image_vectorizer.py

This will generate two files namely, image_list.pickle and saved_features.txt. Keep them inside lib folder where search.py script is available.

Start the server by running rest-server.py. This project uses flask based REST implementation for UI

  python server/rest-server.py

Once the server starts up, access the url 127.0.0.1:5000 to get the UI. Now upload any file and see 9 similar images. You can change the value of K from 9 to any values, but dont foreget to update the html file accordingly for displaying.

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