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https://github.com/linggarm/fruit-images-search-engine
A Content Based Image Retrieval (CBIR) application of Information Retrieval with fruit images
https://github.com/linggarm/fruit-images-search-engine
artificial-intelligence cbir computer-vision content-based-image-retrieval css flask html information-retrieval javascript machine-learning numpy pillow python search-engine tensorflow
Last synced: 7 days ago
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A Content Based Image Retrieval (CBIR) application of Information Retrieval with fruit images
- Host: GitHub
- URL: https://github.com/linggarm/fruit-images-search-engine
- Owner: LinggarM
- License: mit
- Created: 2021-07-10T16:20:00.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2023-11-05T11:44:19.000Z (about 1 year ago)
- Last Synced: 2023-11-05T21:31:06.013Z (about 1 year ago)
- Topics: artificial-intelligence, cbir, computer-vision, content-based-image-retrieval, css, flask, html, information-retrieval, javascript, machine-learning, numpy, pillow, python, search-engine, tensorflow
- Language: JavaScript
- Homepage:
- Size: 13 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Fruit-Images-Search-Engine
Content-Based Image Retrieval (CBIR) Applications of Information Retrieval with Fruit Images.
## About the Project
This is a web-based search engine program of Information Retrieval or **Content-Based Image Retrieval (CBIR)** application to search the most similar fruit images based on an uploaded image.
The result will also show the **Euclidean distance** between the uploaded image (query) and the resulting images.
This program is created with Flask using Python programming language.
Files informations:
* **app.py**: flask server
* **feature_extractor.py**: a Feature Extractor module to extract image features using VGG16 architecture, either training images or query image
* **feature_extraction.py**: run this file to extract features of new training images. Copy and paste your new training images into "static/img" folder, run **feature_extraction.py**, and the features will be saved in "static/feature" folder
## Screenshots
### Dashboard
### Upload an image as a query (You can use any files from "/Testing Images" folder)
### Search results (The number is the distance from query to each search result)
## Live Demo
Web App **Fruit Images Search Engine**: [http://xxx.xxx](http://xxx.xxx)
## Technology Used
* HTML
* CSS
* Javascript
* Python
* Numpy
* Tensorflow
* Pillow
* Flask
## Installation
1. Clone this repo
```sh
git clone https://github.com/LinggarM/Fruit-Images-Search-Engine
```
2. Open the repo folder you have cloned in your PC
3. Create a virtual environment
```sh
python -m venv myenv
```
4. Activate the virtual environment
```sh
myenv/Scripts/activate or "myenv/Scripts/activate" (Windows)
myenv/bin/activate (Linux)
```
5. Install the requirements/ dependencies
```sh
pip install -r requirements.txt
```
## Usage (Tutorials)
1. Put all of the fruit image datasets that you want to add in the "static/img" folder
2. Open CMD in Repository Folder
3. Do the feature extraction to the image datasets by executing this command :
```
python feature_extraction.py
```
4. The features of new image datasets would be saved on "static/feature" folder
4. Run the web app by executing this command :
```
python app.py
```
or :
```
run Flask
```
3. Open the given URL
```
http://127.0.0.1:5000/
```
4. Upload any fruit image files as the query image, but make sure the fruit exists in the database in order to get a good result. You can use images in "Testing Images" folder
## Contributors
* [Linggar Maretva Cendani](https://github.com/LinggarM) - [[email protected]](mailto:[email protected])
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details
## Acknowledgments
* [Colorlib](https://colorlib.com/) for HTML templates
* [https://github.com/matsui528/sis](https://github.com/matsui528/sis) as the source code for this project