noises 1.0.2

A package for generation Procedural Noises in Image form.

Version 1.0.2

• File Structure: Introduced an organized file structure to improve project maintainability and ease of navigation. This includes the integration of the README.md file from version 1.0.0 for consistency and reference.
• Update Checking System: Implemented an update checking system to facilitate the detection of new releases and ensure users are informed about the latest improvements and fixes. This feature enhances the library's usability by keeping users up-to-date with minimal effort.

Noises by EscapedShadows

A comprehensive library for generating various types of image noise.

Overview

Noises is a free and open-source Python library designed for generating a range of image noise types. Supported (or planned) types include:

• Gaussian Noise: Random values distributed according to a Gaussian distribution.
• Perlin Noise: Smooth, natural-looking noise commonly used for textures and procedural content.
• Simplex Noise: An enhanced version of Perlin noise offering improved performance and visual quality.
• White Noise: Random noise with equal intensity across all frequencies.
• Pink Noise: Noise with a frequency spectrum that decreases with increasing frequency.
• Speckle Noise: Grainy noise affecting images and signals.
• Poisson Noise: Noise following a Poisson distribution, particularly useful in low-light conditions.
• Uniform Noise: Noise with values uniformly distributed over a specified range.
• Brownian Noise: Also known as red noise, with power density inversely proportional to frequency.
• Bernoulli Noise: Noise generated using Bernoulli processes, often utilized in simulations.
• Quantization Noise: Arising from rounding during the digitization process.
• Rayleigh Noise: Noise based on the Rayleigh distribution, used in radar and signal processing.
• Cauchy Noise: Noise with heavy tails, used in specialized statistical models.
• Gamma Noise: Noise based on the Gamma distribution, useful for statistical simulations.
• Exponential Noise: Noise following an Exponential distribution, applied in various modeling scenarios.
• Log-Normal Noise: Noise based on the Log-Normal distribution, modeling multiplicative processes.
• Chi-Squared Noise: Noise modeled after the Chi-Squared distribution, used in statistical tests.
• Weibull Noise: Noise based on the Weibull distribution, used in reliability engineering.
• Kurtosis Noise: Noise with specific kurtosis properties for modeling heavy-tailed distributions.
• Laplace Noise: Noise based on the Laplace distribution, applied in diverse contexts.
• Blue Noise: Noise with increasing power density at higher frequencies.
• Violet Noise: Noise with power density increasing with the square of the frequency.
• Poisson-Gaussian Noise: A combination of Poisson and Gaussian noise, suitable for modeling complex environments.
• Impulse Noise: Sudden changes in pixel values, simulating digital corruption.

Why Noises?

The primary advantage of Noises is its lightweight, dependency-free nature. For optimal performance, it is recommended to use it alongside requests and Pillow.

Background

During a search for inspiration on PyPI, I found that "noises" was available. I decided to take it on and have been developing it since.

Notes

Please be aware that updates may not be frequent. As I am still in school and have other personal commitments, this README is a preliminary version and will be updated as necessary.