Metadata-Version: 2.1
Name: histeqkit
Version: 0.0.5
Summary: package for histogram equalization algorithms and image accuracy measures.
Home-page: https://github.com/pypa/sampleproject
Author: Aditya Raj
Author-email: aditya2134.ar@gmail.com
Project-URL: Bug Tracker, https://github.com/pypa/sampleproject/issues
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE

# HistEQKit

HistEQKit is a Python package that provides a collection of histogram equalization algorithms and image accuracy measures for image enhancement and analysis tasks.

## Installation

You can install HistEQKit using pip:

```shell
pip install -i https://test.pypi.org/simple/ histeqkit
```

HistEQKit requires Python 3.6 or higher.

Usage

To use HistEQKit, import the desired histogram equalization algorithms from the package,
 along with OpenCV for image processing. Here's an example:
```shell
from histeqkit import algorithms
import cv2

# Load an image
input_image = cv2.imread("/path/to/input_image.png", cv2.IMREAD_GRAYSCALE)

# Apply histogram equalization using an algorithm
ie = algorithms.IE(input_image)
output_image = ie.INVERT()

# Save the output image
cv2.imwrite('output.jpg', output_image)
```
Make sure to replace "/path/to/input_image.png" with the path to your actual input image.

Algorithms

HistEQKit currently provides the following histogram equalization algorithms:

algorithms.IE: Inverts the intensity values of the input image.
Note: This is just an example algorithm. HistEQKit can include other algorithms as well.

Contributing

Contributions to HistEQKit are welcome! If you have any bug fixes, enhancements, or new algorithms/measures to add, please open an issue or submit a pull request on GitHub.

License

This project is licensed under the MIT License. See the LICENSE file for more details.


Feel free to customize the content further to provide additional details about the algorithms, accuracy measures, or any other relevant information about your package.
