{"id":19626689,"url":"https://github.com/linggarm/fruit-images-search-engine","last_synced_at":"2026-04-12T13:09:00.712Z","repository":{"id":145513687,"uuid":"384740826","full_name":"LinggarM/Fruit-Images-Search-Engine","owner":"LinggarM","description":"A Content Based Image Retrieval (CBIR) application of Information Retrieval with fruit images","archived":false,"fork":false,"pushed_at":"2023-11-05T11:44:19.000Z","size":13649,"stargazers_count":0,"open_issues_count":0,"forks_count":1,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-01-09T12:43:07.826Z","etag":null,"topics":["artificial-intelligence","cbir","computer-vision","content-based-image-retrieval","css","flask","html","information-retrieval","javascript","machine-learning","numpy","pillow","python","search-engine","tensorflow"],"latest_commit_sha":null,"homepage":"","language":"JavaScript","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/LinggarM.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2021-07-10T16:20:00.000Z","updated_at":"2023-11-05T13:17:06.000Z","dependencies_parsed_at":null,"dependency_job_id":"f170a75d-ae71-452a-bfa8-79d3f1a1ef79","html_url":"https://github.com/LinggarM/Fruit-Images-Search-Engine","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/LinggarM%2FFruit-Images-Search-Engine","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/LinggarM%2FFruit-Images-Search-Engine/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/LinggarM%2FFruit-Images-Search-Engine/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/LinggarM%2FFruit-Images-Search-Engine/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/LinggarM","download_url":"https://codeload.github.com/LinggarM/Fruit-Images-Search-Engine/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":240926398,"owners_count":19879737,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["artificial-intelligence","cbir","computer-vision","content-based-image-retrieval","css","flask","html","information-retrieval","javascript","machine-learning","numpy","pillow","python","search-engine","tensorflow"],"created_at":"2024-11-11T11:47:20.764Z","updated_at":"2026-04-12T13:09:00.665Z","avatar_url":"https://github.com/LinggarM.png","language":"JavaScript","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Fruit-Images-Search-Engine\n\u003cb\u003eContent-Based Image Retrieval (CBIR)\u003c/b\u003e Applications of \u003cb\u003eInformation Retrieval\u003c/b\u003e with Fruit Images.\n\n## About the Project\n\nThis 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.\n\nThe result will also show the **Euclidean distance** between the uploaded image (query) and the resulting images.\n\nThis program is created with Flask using Python programming language.\n\nFiles informations:\n* **app.py**: flask server\n* **feature_extractor.py**: a Feature Extractor module to extract image features using VGG16 architecture, either training images or query image\n* **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\n\n## Screenshots\n\n### Dashboard\n![Screenshots/1.%20Dashboard.png](Screenshots/1.%20Dashboard.png)\n\n### Upload an image as a query (You can use any files from \"/Testing Images\" folder)\n![Screenshots/2.%20Upload%20a%20File.png](Screenshots/2.%20Upload%20a%20File.png)\n\n### Search results (The number is the distance from query to each search result)\n![Screenshots/3.%20Search%20Result.png](Screenshots/3.%20Search%20Result.png)\n\n## Live Demo\nWeb App **Fruit Images Search Engine**: [http://xxx.xxx](http://xxx.xxx)\n\n## Technology Used\n* HTML\n* CSS\n* Javascript\n* Python\n* Numpy\n* Tensorflow\n* Pillow\n* Flask\n\n## Installation\n\n1. Clone this repo\n   ```sh\n   git clone https://github.com/LinggarM/Fruit-Images-Search-Engine\n   ```\n2. Open the repo folder you have cloned in your PC\n3. Create a virtual environment\n   ```sh\n   python -m venv myenv\n   ```\n4. Activate the virtual environment\n   ```sh\n   myenv/Scripts/activate or \"myenv/Scripts/activate\" (Windows)\n   myenv/bin/activate (Linux)\n   ```\n5. Install the requirements/ dependencies\n   ```sh\n   pip install -r requirements.txt\n   ```\n\n## Usage (Tutorials)\n\n1. Put all of the fruit image datasets that you want to add in the \"static/img\" folder\n2. Open CMD in Repository Folder\n3. Do the feature extraction to the image datasets by executing this command :\n  ```\n  python feature_extraction.py\n  ```\n4. The features of new image datasets would be saved on \"static/feature\" folder\n4. Run the web app by executing this command :\n   ```\n   python app.py\n   ```\n   or :\n   ```\n   run Flask\n   ```\n3. Open the given URL\n   ```\n   http://127.0.0.1:5000/\n   ```\n4. 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\n\n## Contributors\n* [Linggar Maretva Cendani](https://github.com/LinggarM) - [linggarmc@gmail.com](mailto:linggarmc@gmail.com)\n\n## License\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details\n\n## Acknowledgments\n* [Colorlib](https://colorlib.com/) for HTML templates\n* [https://github.com/matsui528/sis](https://github.com/matsui528/sis) as the source code for this project\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flinggarm%2Ffruit-images-search-engine","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Flinggarm%2Ffruit-images-search-engine","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flinggarm%2Ffruit-images-search-engine/lists"}