https://github.com/surajsanap/indshield_intro
Coming Soon
https://github.com/surajsanap/indshield_intro
ai googlegemini opencv python pytorch tensorflow yolov8
Last synced: about 2 months ago
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Coming Soon
- Host: GitHub
- URL: https://github.com/surajsanap/indshield_intro
- Owner: SurajSanap
- Created: 2025-04-03T13:13:32.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-04-06T17:39:07.000Z (about 1 year ago)
- Last Synced: 2025-04-09T16:49:18.328Z (about 1 year ago)
- Topics: ai, googlegemini, opencv, python, pytorch, tensorflow, yolov8
- Homepage:
- Size: 14.6 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# IndShield: A Cutting-Edge Web App for Enhanced Industrial Safety
**IndShield** is a web application designed to revolutionize industrial safety protocols by leveraging advanced technologies. It empowers organizations to create a safer and more secure work environment for personnel and equipment.
**Key Features:**
* **Motion Amplification:** Identifies subtle equipment movements often invisible to the naked eye, enabling proactive maintenance and preventing unexpected breakdowns.
* **Emergency Alert System:** Allows individuals to signal for help by making a specific gesture (L pose) in front of a camera, ensuring prompt assistance in emergency situations.
* **Restricted Zone Enforcement:** Utilizes live CCTV feeds and object detection to identify unauthorized access to restricted areas, facilitating timely intervention and enforcing safety protocols.
* **Fire and Safety Gear Detection:** Employs machine learning algorithms to identify safety gear worn by personnel and detect potential fire risks in real-time, upholding the highest standards of safety and compliance.
**Benefits:**
* **Early detection of potential issues** minimizes downtime and prevents costly emergency repairs.
* **Enhanced maintenance** allows for targeted repairs and optimization of equipment performance.
* **Reduced risk of accidents and injuries** by ensuring adherence to safety protocols.
* **Improved response times** to emergencies through the use of the alert system.
## File Structure
* **`_pycache_`:** This folder is automatically generated by Python and stores compiled bytecode for faster program execution. You don't need to modify its contents.
* **`app.py`:** This is the core Python script containing the application logic. It defines routes, handles user interactions, and interacts with other components.
* **`instance`:** This folder is typically used for storing runtime data or configuration specific to a particular instance of the application.
* **`models`:** This folder contains yolov8 models for detection.
* **`requirements.txt`:** This text file lists the external Python libraries required for the application to run. Dependencies like Flask, SQLAlchemy, OpenCV, and YOLOv8 are likely specified here.
* **`static`:** This folder stores static assets used by the application, such as images, CSS stylesheets, and JavaScript code. These files are typically served directly by the web server without involving server-side processing.
* **`templates`:** This folder contains HTML templates used to dynamically generate web pages. The application likely uses a templating engine like Jinja2 to render these templates with data retrieved from the database or user input.
## Working
* **Backend:** Flask, a lightweight web framework in Python, is likely used to handle user requests, interact with the database, and manage application logic.
* **Database:** SQLAlchemy, an object-relational mapper (ORM), simplifies interaction with the database by providing an object-oriented interface.
* **Machine Learning:** YOLOv8, a real-time object detection model, is likely employed for features like restricted zone enforcement, fire detection, and potentially gear detection.
* **MediaPipe:** This open-source framework might be used for pose estimation in the emergency alert system.
* **OpenCV:** This open-source computer vision library is likely used for image and video processing tasks like frame capturing, object detection bounding boxes, and motion amplification.
Running the application typically involves:
python -m venv .env
.env/Scripts/activate
1. Installing required dependencies using `pip install -r requirements.txt`.
2. Setting up the database (likely SQLite in this case) and creating the necessary tables as defined by the models.
3. Running the application using `python app.py`.
Once running, users can access the web interface and leverage the provided functionalities to enhance industrial safety.