AutomationIMG
AutomationIMG is a Python-based tool for automated image preprocessing and object detection, featuring an intuitive graphical user interface. It simplifies the process of organizing and analyzing image datasets for machine learning and computer vision tasks.
🚀 Features
-
Preprocessing Pipeline
- Automated image organization
- Support for various directory structures
- Batch processing capabilities
-
Object Detection
- Single object detection using Canny edge detection
- Bounding box visualization
- Quality assessment of detections
-
User Interface
- Intuitive GUI built with PyQt5
- Real-time progress tracking
- Easy folder selection and processing
📋 Prerequisites
Before you begin, ensure you have the following installed:
- Python 3.7 or higher
- pip (Python package installer)
💻 Installation
- Clone the repository:
git clone https://github.com/akshitharsola/AutomationIMG.git
cd AutomationIMG
- Install the package:
pip install -e .
📖 Usage
Directory Structure
The tool supports the following directory structure for input data:
Main_Folder/
└── Apple/
└── image1.jpg
└── image2.jpg
└── Orange/
└── image1.jpg
└── image2.jpg
Running the Tool
- Start the application:
# Simply run
automationIMG
- Using the GUI:
- Click "Input Folder" to select your image directory
- Choose "Output Folder" for processed results
- Run "Preprocess Dataset" to organize images
- Use "Single Object Detection" for detection and analysis
Output Structure
The tool generates the following output structure:
Output_Folder/
└── processed/
└── images
└── annotations.json
└── edge_detection/
└── detection results
└── bounding_boxes/
└── visualizations
🗑️ Uninstallation
You have several ways to uninstall AutomationIMG:
-
Using the GUI
- Launch the application:
automationimg - Click the "Uninstall Tool" button
- Follow the prompts
- Launch the application:
-
Using the uninstall module
python -m automationimg.uninstall
- Manual uninstallation
pip uninstall automationimg -y
Note: After uninstallation, any remaining files in the installation directory can be safely deleted manually.
🤝 Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
- Fork the repository
- Create your feature branch (
git checkout -b feature/AmazingFeature) - Commit your changes (
git commit -m 'Add some AmazingFeature') - Push to the branch (
git push origin feature/AmazingFeature) - Open a Pull Request
📄 License
This project is licensed under the GNU General Public License v3.0 - see the LICENSE file for details.
👤 Author
Akshit Harsola
- Email: harsolaakshit@gmail.com
- GitHub: @akshitharsola
🙏 Acknowledgments
- Thanks to all contributors who help improve this tool
- Special thanks to the open-source community for their valuable tools and libraries
📞 Support
If you encounter any problems or have suggestions, please open an issue.
Metadata
Release files for automationimg 0.1.0
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Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| automationimg-0.1.0.tar.gz | 4.0 kB | Without provenance | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| automationimg-0.1.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 7.7 kB
Release files / automationimg-0.1.0.tar.gz
| Download URL | automationimg-0.1.0.tar.gz |
|---|---|
| Size | 4.0 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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No |
| Uploaded via |
twine/5.1.1 CPython/3.9.20
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Release files / automationimg-0.1.0-py3-none-any.whl
| Download URL | automationimg-0.1.0-py3-none-any.whl |
|---|---|
| Size | 3.7 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/5.1.1 CPython/3.9.20
|
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