{"id":29550895,"url":"https://github.com/swedeshnamishra/image_classification","last_synced_at":"2026-04-11T03:05:23.244Z","repository":{"id":303973439,"uuid":"1017358032","full_name":"SwedeshnaMishra/Image_Classification","owner":"SwedeshnaMishra","description":"An end-to-end image classification project for sports celebrities using machine learning, OpenCV, wavelet transform, and Flask for model deployment with a browser-based UI.","archived":false,"fork":false,"pushed_at":"2025-07-10T13:55:39.000Z","size":80501,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-07-18T06:30:21.236Z","etag":null,"topics":["computer-vision","css","data-science","flask","html","javascript","jupyter-notebook","machine-learning","opencv","python","wavelet-transform"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/SwedeshnaMishra.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"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,"zenodo":null}},"created_at":"2025-07-10T12:13:11.000Z","updated_at":"2025-07-10T15:02:30.000Z","dependencies_parsed_at":"2025-07-18T05:09:04.072Z","dependency_job_id":null,"html_url":"https://github.com/SwedeshnaMishra/Image_Classification","commit_stats":null,"previous_names":["swedeshnamishra/image_classification"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/SwedeshnaMishra/Image_Classification","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SwedeshnaMishra%2FImage_Classification","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SwedeshnaMishra%2FImage_Classification/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SwedeshnaMishra%2FImage_Classification/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SwedeshnaMishra%2FImage_Classification/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/SwedeshnaMishra","download_url":"https://codeload.github.com/SwedeshnaMishra/Image_Classification/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SwedeshnaMishra%2FImage_Classification/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":279076961,"owners_count":26098231,"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","status":"online","status_checked_at":"2025-10-15T02:00:07.814Z","response_time":56,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"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":["computer-vision","css","data-science","flask","html","javascript","jupyter-notebook","machine-learning","opencv","python","wavelet-transform"],"created_at":"2025-07-18T04:01:23.998Z","updated_at":"2025-10-15T11:44:17.605Z","avatar_url":"https://github.com/SwedeshnaMishra.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# 🧠 AI-Powered Sports Celebrity Image Classification\n\nThis is an end-to-end **Machine Learning/Data Science** project that classifies images of sports celebrities using image processing and supervised learning techniques. The system uses **Python**, **Flask**, and a browser-based interface to allow users to upload images and get predictions via a trained model.\n\n---\n\n## 🚀 Project Overview\n\nThis project demonstrates how to build a complete machine learning pipeline for image classification, including:\n\n- Data preprocessing using OpenCV and wavelet transformation\n- Model training using `scikit-learn`\n- Model deployment using `Flask`\n- User interface with HTML, CSS, and JavaScript\n- Local image prediction using webcam or file upload\n\n---\n\n## 📁 Folder Structure\n\n```\nImage_Classification/\n│\n├── model/\n│   ├── sports_celebrity_classification.ipynb   # Model building and training notebook\n│   ├── opencv/haarcascades/                    # Face detection models\n│   ├── test_images/                            # Sample test images\n│   └── requirements.txt                        # Python dependencies\n│\n├── server/\n│   ├── artifacts/                              # Saved model and class dictionaries\n│   ├── opencv/haarcascades/                    # Haarcascade XML files for face detection\n│   ├── test_images/                            # Test images\n│   ├── server.py                               # Flask backend API\n│   ├── util.py                                 # Helper functions (wavelet, preprocessing, etc.)\n│   └── wavelet.py                              # Custom wavelet transform code\n│\n├── UI/\n│   ├── app.html                                # Web UI page\n│   ├── app.css                                 # Styling\n│   ├── app.js                                  # JS logic\n│   ├── dropzone.min.css                        # Dropzone drag-and-drop support\n│   └── dropzone.min.js                         # Dropzone logic\n│\n└── image dataset/                              # Dataset of sports celebrity images\n```\n\n---\n\n\n## 🛠️ Technologies Used\n\n### 🧮 Core Libraries:\n- **Python**\n- **NumPy** \u0026 **OpenCV** – for image preprocessing\n- **Matplotlib** \u0026 **Seaborn** – for data visualization\n- **scikit-learn** – for model training and evaluation\n\n### 💻 Development:\n- **Jupyter Notebook**\n- **Visual Studio Code**\n- **PyCharm**\n\n### 🌐 Deployment:\n- **Flask** – to serve the ML model as an HTTP API\n- **HTML/CSS/JavaScript** – to build the user interface\n\n---\n\n## ✅ Features\n\n- Real-time image classification of sports celebrities\n- Automatic face detection using Haarcascade classifiers\n- Wavelet transform-based feature extraction\n- Interactive and modern drag-and-drop interface\n- Flask-powered lightweight backend API\n\n---\n\n## 🔧 Installation \u0026 Running the Project\n\n### 1. Clone the Repository\n\n```bash\ngit clone https://github.com/SwedeshnaMishra/Image_Classification.git\ncd Image_Classification\n```\n\n### 2. Create and Activate Virtual Environment (Optional but Recommended)\n\n```bash\npython -m venv venv\nsource venv/bin/activate\n```\n\n### 3. Install Required Dependencies\n\n```bash\npip install -r model/requirements.txt\n```\n\n### 4. Run the Flask Server\n\n```bash\ncd server\npython server.py\n```\n\n### 5. Open the UI\n\nOpen `UI/app.html` in your browser to interact with the system.\n\n---\n\n## 📷 Sample Workflow\n- User uploads an image using the drag-and-drop UI.\n- Flask server detects face using Haarcascade from OpenCV.\n- Feature extraction is performed using wavelet transform.\n- Trained model predicts the celebrity class.\n- Prediction is returned and shown in the frontend.\n\n---\n\n📌 Celebrities Included\n- Lionel Messi\n- Maria Sharapova\n- Roger Federer\n- Serena Williams\n- Virat Kohli\n\n---\n\n## For Contributing\nIf you want to contribute to this project, please follow these steps:\n- `Fork` the repository.\n- Create a new branch `(git checkout -b feature/your-feature-name)`.\n- Make your changes and commit them `(git commit -m 'Add some feature')`.\n- Push to the branch `(git push origin feature/your-feature-name)`.\n- Open a pull request.\n\n---\n\n## Project Maintainer\n**Github:** [Swedeshna Mishra](https://github.com/SwedeshnaMishra)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fswedeshnamishra%2Fimage_classification","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fswedeshnamishra%2Fimage_classification","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fswedeshnamishra%2Fimage_classification/lists"}