{"id":25501716,"url":"https://github.com/singhxtushar/automark","last_synced_at":"2026-04-29T00:05:10.973Z","repository":{"id":239290733,"uuid":"799114912","full_name":"SINGHxTUSHAR/AutoMark","owner":"SINGHxTUSHAR","description":"This is a Smart Attendance System designed using a pre-trained model called Haar-Cascade Classifier, OpenCV and other various Dependencies to mark the attendance in a smarter way and saves the lecture 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license](https://img.shields.io/github/license/SINGHxTUSHAR/AutoMark.svg)](https://github.com/SINGHxTUSHAR/AutoMark/blob/master/LICENSE)\n[![GitHub contributors](https://img.shields.io/github/contributors/SINGHxTUSHAR/AutoMark.svg)](https://GitHub.com/SINGHxTUSHAR/AutoMark/graphs/contributors/)\n[![GitHub issues](https://img.shields.io/github/issues/SINGHxTUSHAR/AutoMark.svg)](https://GitHub.com/SINGHxTUSHAR/AutoMark/issues/)\n[![GitHub pull-requests](https://img.shields.io/github/issues-pr/SINGHxTUSHAR/AutoMark.svg)](https://GitHub.com/SINGHxTUSHAR/AutoMark/pulls/)\n[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com)\n\n\n[![GitHub watchers](https://img.shields.io/github/watchers/SINGHxTUSHAR/AutoMark.svg?style=social\u0026label=Watch\u0026maxAge=2592000)](https://GitHub.com/SINGHxTUSHAR/AutoMark/watchers/)\n[![GitHub forks](https://img.shields.io/github/forks/SINGHxTUSHAR/AutoMark.svg?style=social\u0026label=Fork\u0026maxAge=2592000)](https://GitHub.com/SINGHxTUSHAR/AutoMark/network/)\n[![GitHub stars](https://img.shields.io/github/stars/SINGHxTUSHAR/AutoMark.svg?style=social\u0026label=Star\u0026maxAge=2592000)](https://GitHub.com/SINGHxTUSHAR/AutoMark/stargazers/)\n\n[![Open in Visual Studio Code](https://img.shields.io/static/v1?logo=visualstudiocode\u0026label=\u0026message=Open%20in%20Visual%20Studio%20Code\u0026labelColor=2c2c32\u0026color=007acc\u0026logoColor=007acc)](https://open.vscode.dev/SINGHxTUSHAR/AutoMark)\n\n# AutoMark 🙋🏻‍♂️📋:\nAutoMark is a smart Attendance system that uses the OpenCV and Haar-Cascade Classifier to mark the attendance of the students.\n![Designer (1)](https://github.com/SINGHxTUSHAR/AutoMark/assets/113624520/b6778527-0c5e-4eff-b816-80ac06baaa04)\n\nThis project implements a smart attendance system for students using OpenCV (Open Source Computer Vision Library) and a pre-trained Haar cascade classifier. Here's a breakdown of the functionalities:\n### `System Components:`\n* Camera: Captures real-time video feed of the students.\n* OpenCV Libraries: Used for image processing tasks like frame grabbing, face detection, and drawing bounding boxes.\n* Haar Cascade Classifier: A pre-trained model that efficiently detects frontal human faces within the video frames.\n* Student Database: Stores student information like IDs, names, and potentially facial images (optional for enhanced recognition).\n* Attendance Marking System: Logs attendance data, typically with timestamps and student IDs. This can be a simple text file, a spreadsheet, or integrated with existing attendance management software.\n\n### `Working Process:`\n* Real-time Video Capture: The system continuously captures video frames from the camera.\n* Face Detection: OpenCV utilizes the Haar cascade classifier to identify and locate faces within each frame.\n* Student Recognition (Optional): If the system stores student facial data, additional algorithms (not necessarily Haar cascade) might be used to recognize specific students within the detected faces.\n* Attendance Marking: Based on detected faces (and potentially recognized students), the system marks attendance in the database. This might involve recording timestamps and student IDs (or names).\n* Visualization (Optional): The system can display the video feed with bounding boxes around the detected faces for real-time monitoring purposes.\n\n### `Benefits:`\n* Automated Attendance: Eliminates the need for manual attendance checks, saving time and reducing errors.\n* Scalability: The system can handle multiple students simultaneously.\n* Reduced Contact (Optional): If facial recognition is implemented, students might not need to physically interact with a device to register attendance.\n* Cost-effective: Leverages open-source libraries like OpenCV, making it a budget-friendly solution.\n\n### `Limitations:`\n* Haar cascade limitations: The pre-trained model might struggle with angled faces, poor lighting conditions, or occlusions (e.g., hats, masks).\n* Privacy Concerns: Storing student facial data raises privacy considerations that need to be addressed with proper security measures and user consent.\n\n\n## Commands ✍️:\n* This will be used to add face to dataset and face will be recorded via webcam.\n```bash\npython Add_faces.py\n```\n* Record attendance of added face , press 'o' to record attendance and it will create csv file corresponding to date present , name and timestamp will be recorded.\n```bash\npython test.py\n```\n* Attendance will be displayed here and can be downloaded in csv format.\n```bash\nstreamlit run app.py\n```\n\n## Output 📁:\nFor more output images visit: \u003ca href=\"https://github.com/SINGHxTUSHAR/AutoMark/tree/main/Output\"\u003e Link \u003c/a\u003e\n![out_1](https://github.com/SINGHxTUSHAR/AutoMark/assets/113624520/d433ea2c-dfad-47cd-a149-0d497ff4c87f)\n![out_2](https://github.com/SINGHxTUSHAR/AutoMark/assets/113624520/05a13127-9cb0-46b7-868a-9de3e8edc325)\n![out_6](https://github.com/SINGHxTUSHAR/AutoMark/assets/113624520/51cf88ca-0315-46fd-959e-5d3cd3130eb1)\n\n\n## Reference 🧧:\n* Classifier \u003ca href=\"https://docs.opencv.org/3.4/db/d28/tutorial_cascade_classifier.html\"\u003e Documentation \u003c/a\u003e\n* Streamlit \u003ca href=\"https://docs.streamlit.io/\"\u003e Documentation \u003c/a\u003e\n* Research Paper \u003ca href=\"https://ieeexplore.ieee.org/document/8776934\"\u003e Documentation \u003c/a\u003e\n\n\n## Requirements💻 :\n\nEnsure you have the following dependencies installed:\n\n- Python (version 3.9.x || 3.12.x)\n- IDE: VS-CODE or collab\n- Virtual-environment(venv)\n- Other dependencies (refer to the requirements.txt)\n\nYou can install the required Python packages using:\n\n```bash\npip install -r requirements.txt\n```\n\n## Setup 💿:\n\n- Clone the repository:\n```bash\ngit clone https://github.com/SINGHxTUSHAR/AutoMark.git\ncd AutoMark\n```\n- Create a virtual environment (optional but recommended):\n```bash\npython -m venv venv\n```\n- Activate the virtual environment:\n  - On Windows:\n   ```bash\n   venv\\Scripts\\activate\n   ```\n  - On macOS/Linux:\n  ```bash\n  source venv/bin/activate\n  ```\n\n## Contributing 📌:\nIf you'd like to contribute to this project, please follow the standard GitHub fork and pull request process. Contributions, issues, and feature requests are welcome!\n\n## Suggestion 🚀: \nIf you have any suggestions for me related to this project, feel free to contact me at tusharsinghrawat.delhi@gmail.com or \u003ca href=\"https://www.linkedin.com/in/singhxtushar/\"\u003eLinkedIn\u003c/a\u003e.\n\n## License 📝:\nThis project is licensed under the \u003ca href=\"https://github.com/SINGHxTUSHAR/AutoMark/blob/main/LICENSE\"\u003eMIT License\u003c/a\u003e - see the LICENSE file for details.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsinghxtushar%2Fautomark","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsinghxtushar%2Fautomark","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsinghxtushar%2Fautomark/lists"}