CAMERA_PROJECT-WITH_QUEUE_TREADING_DATABASE

This project demonstrates a multithreaded image processing pipeline that utilizes the YOLOv10n model for object detection in videos. The processed data is saved to a structured SQLite database for further analysis.

Features

• YOLOv10n Integration: Object detection using the YOLOv10n model.
• Multithreaded Processing: Processes video frames concurrently using threading.
• Database Management: Saves object detection results into SQLite, organized by object classes.
• Error Handling: Includes robust mechanisms for managing errors and logging.

Prerequisites

To run this project, ensure you have the following installed:

• Python 3.8+
• Required Python packages (install with pip install -r requirements.txt):
  • OpenCV
  • SQLite3
  • Colorama
  • Ultralytics (for YOLO)

File Structure

• 1_MY_PROJECT_CAMERA_QUEUE_TREADING_DATABASE.py: Main entry point of the application.
• MODULE_READ_PROCESS.py: Contains the logic for reading video frames and processing them using the YOLOv10n model.
• MODULE_SAVA_ENTERE.py: Handles database interactions, including saving processed data.
• requirements.txt: Lists all the dependencies for the project.

How to Use

1. Clone the repository:

   git clone https://github.com/Amin-moniry-pr7/CAMERA_PROJECT-WITH_QUEUE_TREADING_DATABASE.git
   cd CAMERA_PROJECT-WITH_QUEUE_TREADING_DATABASE

2. Install dependencies:

   pip install -r requirements.txt

3. Add your video file: Place the video file (VL_VIDEO.mp4) in the project directory.

4. Run the main script:

   python 1_MY_PROJECT_CAMERA_QUEUE_TREADING_DATABASE.py

5. Enter the desired number of threads when prompted.

Output

• Processed Images: Saved in a folder named ⚠️_AMIN_FRAME_⚠️, categorized by detected classes.
• Database Records: Saved in FRAME_PROCESSING.db with tables created for each detected class.

Highlights

• Frame Processing: Video frames are extracted and passed through YOLOv10n for object detection.
• Dynamic Table Creation: A new table is created for each unique detected class in the database.
• Scalable Threads: Users can choose the number of threads for concurrent processing.

Example Output

• Processed frame counts and class-specific images are saved dynamically.
• SQLite database contains detailed logs and image paths.

Future Improvements

• Add a graphical user interface (GUI).
• Support for real-time video streams.
• Extend functionality for additional model integration.

License

This project is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License.

Contact

For any queries, please contact:

• Amin Moniry: GitHub Profile