pixeloe

library0.1.4pypypiunverified

PixelOE is a Python library for detail-oriented image pixelization based on a contrast-aware outline expansion algorithm. It's designed to create pixel art-style images while preserving key details. The current version is 0.1.4, and the library appears to be actively maintained with releases as features and improvements are developed.

ai-ml data

pip install pixeloe

github ↗package ↗

INSTALL

IMPORT

SIG · PIXELOE

pixeloe

ai-mlpythonv0.1.4

Install

75.9s avg

Import

—

Disk

5094MB

Pass rate

4/ 10

Env Coverage4 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v0.1.4 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

glibc

py 3.10

✕ build_error

✓ 80.75s

py 3.11

✕ build_error

✓ 77.3s

py 3.12

✕ build_error

✓ 73.05s

py 3.13

✕ build_error

✓ 72.5s

py 3.9

✕ build_error

5094MB installed

● package 5094MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

pixelize

✓ from pixeloe_exp import pixelize

✗ from pixeloe import pixelize

This quickstart loads an image (a dummy one created for the example), applies the `pixelize` function with common parameters, and saves the resulting pixelized image. The `pixelize` function takes a `PIL.Image` object as input and returns a new `PIL.Image` object.

from PIL import Image import pixeloe import os # Create a dummy image for demonstration img_path = "example_input.png" Image.new('RGB', (100, 100), color = 'red').save(img_path) # Load the image img = Image.open(img_path).convert("RGB") # Pixelize the image with specified parameters # pixels: size of pixelization blocks # contrast: contrast awareness level # outline: whether to include outlines # levels: number of color levels to quantize to out_img = pixeloe.pixelize(img, pixels=8, contrast=250, outline=True, levels=8) # Save the output image output_path = "pixelized_output.png" out_img.save(output_path) print(f"Original image saved to {img_path}") print(f"Pixelized image saved to {output_path}") # Clean up dummy image os.remove(img_path)

Debug

Known issues

gotchaPixelOE requires Python 3.10 or newer. Installing on older Python versions will fail or result in dependency issues.

fix

Ensure your Python environment is 3.10 or higher. Use `python --version` to check.

affects: <0.1.0

gotchaThe `pixelize` function exclusively expects a `PIL.Image` object as input. Passing NumPy arrays (common for OpenCV) or other image formats will lead to `AttributeError` or `TypeError`.

fix

Convert your image to a PIL Image first. E.g., for an OpenCV (NumPy) image: `pil_img = Image.fromarray(cv2.cvtColor(np_img, cv2.COLOR_BGR2RGB))`.

affects: All

gotchaProcessing very large images (e.g., several megapixels) can be slow and consume significant memory due to the detailed analysis performed by the algorithm. Consider downscaling images if performance is critical.

fix

For performance-critical applications, pre-process large images by resizing them to a smaller resolution using `PIL.Image.resize()` before passing them to `pixeloe.pixelize()`.

affects: All

Upgrade

Version history

0.1.4latest on PyPI · released Feb 16, 2025

Audit

Dependencies

numpyrequiredCore numerical operations for image processing.

opencv-pythonrequiredComputer vision functionalities.

scikit-imagerequiredImage processing algorithms.

scipyrequiredScientific computing, potentially for image filtering or transformations.

PillowrequiredImage loading, saving, and basic manipulation.

tqdmrequiredProgress bar for long-running operations.

Agent activity

16 hits · last 30 days

node

Resources

githubhttps://github.com/KohakuBlueleaf/PixelOE ↗

packagehttps://pypi.org/project/pixeloe/ ↗