Trismfeinym
Trismfeinym: Real-Time Object Detection using YOLO and OpenCV
TRISMFEINYM
Welcome to Trismfeinym! This project implements real-time object detection using the YOLO (You Only Look Once) model with OpenCV and Flask. Achieve high accuracy for various object detection tasks using state-of-the-art YOLO technology.
🚀 Features
- Real-time object detection with YOLO
- High accuracy with the latest YOLO model
- Efficient video frame processing using OpenCV
- Web-based UI using Flask
- Customizable detection classes
🛠️ Installation
Follow these steps to set up the project on your local machine:
Prerequisites
Make sure you have Python 3.9 or above installed.
Step 1: Clone the Repository
git clone https://github.com/Sk16er/Trismfeinym.git
cd Trismfeinym
Step 2: Create a Virtual Environment
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
Step 3: Install Dependencies
pip install -r requirements.txt
Step 4: Download YOLO Model
Ensure you have the YOLOv8 model from Ultralytics. You can download it using:
pip install ultralytics
🚦 Usage
Run the Application
python app.py
!https://www.loom.com/share/6a0009887f0f45b9bd65df0dd4d3411c (this is the video)
Access the App
Navigate to http://localhost:5000 in your browser.
Upload and Detect
- Upload an image or provide a video feed.
- Perform real-time object detection.
- Visualize detected objects with bounding boxes and labels.
⚙️ Configuration
You can adjust parameters like model type, confidence threshold, and object classes by modifying config.py.
Example Configuration:
MODEL_PATH = 'yolov8n.pt' # Path to YOLO model
CONFIDENCE_THRESHOLD = 0.5
🧪 Testing
You can run tests to ensure everything is working correctly:
pytest
🛎️ Troubleshooting
- Ensure your camera is accessible and drivers are installed.
- Verify all dependencies are installed using
pip freeze. - If the model isn’t loading, confirm the path in
config.py. - to install the commands here is the commands.
pip install Flask opencv-python-headless torch ultralytics
📝 License
This project is licensed under the MIT License. See the LICENSE file for details.
🙌 Acknowledgements
- YOLO by Ultralytics
- OpenCV
- Flask for the web application
☑️ Important links
📧 Contact
For any inquiries or issues, please open an issue on the GitHub repo or contact me at [shushankpawar664@gmail.com].