ImageCleaner
A Unified Python Library for Automated Image Dataset Cleaning and Validation for Machine Learning Applications.
Overview
ImageCleaner is an open-source Python library designed to automate image dataset cleaning and validation before machine learning and computer vision training.
The library helps researchers, students, and developers improve dataset quality by detecting common image issues such as duplicate images, corrupted files, blur, low resolution, improper brightness, and unsupported formats.
Features
- Duplicate Image Detection
- Corrupted Image Detection
- Blur Detection
- Resolution Validation
- Brightness Analysis
- Image Format Validation
- Dataset Quality Score
- CSV Report Generation
- JSON Report Generation
- Modular Python API
- Machine Learning Ready Output
Project Structure
SmartClean-AI/
│
├── imagecleaner/
├── datasets/
├── docs/
├── examples/
├── logs/
├── reports/
├── tests/
│
├── README.md
├── LICENSE
├── pyproject.toml
├── requirements.txt
└── .gitignore
Installation
Clone the repository
git clone https://github.com/<your-username>/SmartClean-AI.git
Move into the project
cd SmartClean-AI
Create virtual environment
py -3.13 -m venv .venv
Activate
Windows
.venv\Scripts\activate
Install dependencies
pip install -r requirements.txt
Basic Usage
from imagecleaner import ImageCleaner
cleaner = ImageCleaner("datasets")
cleaner.clean()
Current Modules
- Dataset Loader
- Image Validator
- Duplicate Detector
- Blur Detector
- Resolution Detector
- Brightness Detector
- Report Generator
- Quality Score
Development Roadmap
Foundation Edition (v1.0)
- Core Library
- Image Validation
- Duplicate Detection
- Blur Detection
- Reporting
Enhanced Edition (v2.0)
- Better Architecture
- Configurable Rules
- Improved Reports
- Dataset Quality Metrics
Professional Edition (v3.0)
- CLI
- Unit Testing
- GitHub Actions
- API Documentation
- PyPI Release
Technology Stack
- Python 3.13
- OpenCV
- Pillow
- NumPy
- pandas
- imagehash
- pytest
- Ruff
- Black
License
MIT License
Author
Sumit Palekar
Creator and Maintainer of SmartImageClean
- Email:sumitpalekar101@gmail.com
1 maintainer
Metadata
1 maintainer
Release files for smartimageclean 0.1.2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| smartimageclean-0.1.2.tar.gz | 36.1 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| smartimageclean-0.1.2-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 79.8 kB
Release files / smartimageclean-0.1.2.tar.gz
| Download URL | smartimageclean-0.1.2.tar.gz |
|---|---|
| Size | 36.1 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
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No |
| Uploaded via |
twine/7.0.0 CPython/3.13.15
|
Release files / smartimageclean-0.1.2-py3-none-any.whl
| Download URL | smartimageclean-0.1.2-py3-none-any.whl |
|---|---|
| Size | 43.6 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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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/7.0.0 CPython/3.13.15
|
