{"categories":[{"label":"Graphics","url":"https://skillfed.io/packages/category/multimedia-graphics/2"}],"enrichment":{"capability":"Converts standard images into pixel art using contrast-aware outline expansion and intelligent downsampling, without requiring neural networks or AI models.","skillfed_tags":["image-processing","pixel-art","gpu-optional"],"use_cases":["Convert photographs into retro-style pixel art for game assets or artistic projects without training neural networks.","Batch process image collections via CLI to generate consistent pixel art with configurable detail levels and color palettes.","Integrate pixelization into ComfyUI workflows as a post-processing or artistic effect node alongside other image operations.","Preserve fine details in downsampled images for thumbnail generation or low-resolution display while maintaining visual clarity.","Apply outline expansion alone to improve existing pixelization methods, including neural network-based approaches."],"what_it_does":"PixelOE is a Python library that transforms photographs and high-resolution images into pixel art using classical image processing rather than machine learning. It works by first expanding the outlines of important visual features based on local contrast, then intelligently downsampling the result to a target resolution. The library offers both a legacy numpy-based API and a modern PyTorch implementation; the torch version supports GPU acceleration and can run CPU-only if needed.\n\nThe package provides three interfaces: a Python API for direct integration, a command-line tool for batch processing, and a ComfyUI custom node for integration into node-based workflows. It includes multiple downsampling modes (center, contrast, k-centroid, bicubic, nearest) and optional color palette quantization for classic pixel art aesthetics. The contrast-aware approach preserves fine details and sharp edges that naive downsampling would lose.","worth_installing":"Yes, with conditions. Install if you need deterministic, parameter-tunable pixel art generation without AI overhead and can accept the five-dependency footprint (especially torch). The active maintenance, permissive license, and no known vulnerabilities support adoption. Skip if you want a lightweight solution or prefer neural network-based pixelization; the high install friction and torch dependency are real costs for simpler use cases."},"id":"pixeloe","links":{"html":"https://skillfed.io/packages/pixeloe","md":"https://skillfed.io/packages/pixeloe.md","pypi":"https://pypi.org/project/pixeloe/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2025-02-16","license_spdx":null,"license_treatment":"permissive","name":"pixeloe","python_support":"supports_current","summary":"Detail-Oriented Pixelization based on Contrast-Aware Outline Expansion."},"popularity":{"monthly_downloads":96866,"position":13191,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.1.4"}