fezrs 0.4.107

Feature Extraction and Zoning for Remote Sensing (FEZrs)

FEZrs

FEZrs is an advanced Python library developed by FEZtool for remote sensing applications. It provides a set of powerful tools for image processing, feature extraction, and analysis of geospatial data.

Features

✅ Apply various image filtering techniques (Gaussian, Laplacian, Sobel, Median, Mean)
✅ Contrast enhancement and edge detection
✅ Support for geospatial raster data (TIFF)
✅ Designed for remote sensing and satellite imagery analysis
✅ Easy integration with FastAPI for web-based processing

Here’s a more polished and professional version of your Installation section, suitable for documentation or a README.md:

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📦 Installation

You can install FEZrs using your preferred Python package manager:

Using pip (PyPI)

pip install fezrs

Using conda (Anaconda)

conda install -c FEZtool fezrs

Using mamba (optional, faster conda alternative)

mamba install FEZtool::fezrs

Note: The mamba command requires Mamba to be installed. If it's not installed, use the conda command instead.

Usage

Example of applying a Gaussian filter to an image:

from fezrs import EqualizeRGBCalculator

equalize = EqualizeRGBCalculator(
    blue_path="path/to/your/image_band.tif",
    green_path="path/to/your/image_band.tif",
    red_path="path/to/your/image_band.tif",
)

equalize.chart_export(output_path="./your/export/path")
equalize.execute(output_path="./your/export/path")

Modules

• KMeansCalculator
• GuassianCalculator
• LaplacianCalculator
• MeanCalculator
• MedianCalculator
• SobelCalculator
• GLCMCalculator
• HSVCalculator
• IRHSVCalculator
• AdaptiveCalculator
• AdaptiveRGBCalculator
• EqualizeCalculator
• EqualizeRGBCalculator
• FloatCalculator
• GammaCalculator
• GammaRGBCalculator
• LogAdjustCalculator
• OriginalCalculator
• OriginalRGBCalculator
• SigmoidAdjustCalculator
• PCACalculator
• AFVICalculator
• BICalculator
• NDVICalculator
• NDWICalculator
• SAVICalculator
• UICalculator
• SpectralProfileCalculator

Contributing

We welcome contributions! To contribute:

1. Fork the repository
2. Create a new branch (git checkout -b feature-name)
3. Commit your changes (git commit -m "Add new feature")
4. Push to your branch (git push origin feature-name)
5. Open a Pull Request

Acknowledgment

Special thanks to Chakad Cafe for the coffee that kept us fueled during development! ☕

License

This project is licensed under the Apache-2.0 license.