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0.1.1

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adrishyam

A Python package for image dehazing using Dark Channel Prior

Published12 Apr 2025

Direct dependencies4

Transitive dependenciesNot calculated

README

Version 0.1.1 documentation

Adrishyam

A Python package for image dehazing using Dark Channel Prior algorithm.

Installation

pip install adrishyam

Usage

from adrishyam import dehaze_image

# Basic usage
dehaze_image(
    input_path="path/to/hazy/image.jpg",
    output_dir="path/to/output/directory"
)

# Advanced usage with custom parameters
dehaze_image(
    input_path="path/to/hazy/image.jpg",
    output_dir="path/to/output/directory",
    t_min=0.1,  # Minimum transmission value (default: 0.1)
    patch_size=15,  # Size of the local patch (default: 15)
    omega=0.95,  # Dehazing strength (default: 0.95)
    radius=60,  # Filter radius for guided filter (default: 60)
    eps=0.01,  # Regularization parameter (default: 0.01)
    show_results=False  # Whether to display results (default: False)
)

Output

The package will create the following files in the output directory:

original.png: Original hazy image
dark_channel.png: Dark channel of the image
transmission.png: Estimated transmission map
refined_transmission.png: Refined transmission map
dehazed.png: Final dehazed image
result.png: Combined visualization of all steps

License

MIT License

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Declared dependencies 4

Native requirements

4 direct targetsNot calculated transitive count

Package / target	Requirement	Relationship
numpy Environment { "extras": "", "marker": null, "requires_python": ">=3.8", "evaluation": "NOT_EVALUATED" }	`numpy>=1.24.0`	runtime
pillow Environment { "extras": "", "marker": null, "requires_python": ">=3.8", "evaluation": "NOT_EVALUATED" }	`pillow>=10.0.0`	runtime
matplotlib Environment { "extras": "", "marker": null, "requires_python": ">=3.8", "evaluation": "NOT_EVALUATED" }	`matplotlib>=3.7.0`	runtime
scipy Environment { "extras": "", "marker": null, "requires_python": ">=3.8", "evaluation": "NOT_EVALUATED" }	`scipy>=1.10.0`	runtime