pixrr 1.0.0

A lightweight image processing toolkit for Python

Pixrr : A lightweight image processing toolkit for python

pixrr is a lightweight, beginner-friendly image processing library built for fast experimentation and teaching. It focuses on simplicity, clean function names, and easy-to-understand code, making it useful both for quick image tasks and for pedagogical environments such as introductory image processing courses.

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Features (Planned & Implemented)

Core Utilities

• Convert color images to grayscale and binary
• Basic I/O helpers (read, write, display)
• Plot and analyze histograms
• Image thresholding
• Image Cropping and Rotation (planned)

Filtering & Enhancement

• Convolution using masks
• Convolution using FFT (planned)
• Contrast enhancement
• Histogram equalization
• Smoothing filters (Gaussian, median, weighted median)
• Sharpening and Laplacian operators

Edge and Gradient Detection

• Prewitt and Sobel (horizontal & vertical)
• Second-order derivatives
• Canny edge detector (planned)

Segmentation & Classification

• Otsu thresholding
• K-means clustering for image segmentation
• Mean-Shift clustering (planned)
• EM/Bayesian pixel classification (planned)

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Project Roadmap

Stage	Goal
Part 1	Basic conversions, contour extraction, histogram tools
Part 2	Convolution, enhancement techniques, gradient operators
Part 3	Automatic segmentation & clustering
Part 4	Advanced edge detection and EM classification

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Installation (when it’s live)

pip install pixrr

(Currently under development. Not on PyPI yet.)

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Usage Example

import pixrr as pix

# Example 
img = pix.handle_image("peda_img/test_images/test2.png")
grey = pix.convert_to_gray(img)
# Applying gaussian
pix.show_image(pix.gaussian_smoothing(grey, kernel_size=3))

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Contribution

This library is early-stage but open to improvements, bug reports, and algorithm implementations. Ideal for students learning image processing or developers wanting simple utilities without heavy dependencies.

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