repixelizer/README.md

Repixelizer

Repixelizer is a Python tool for images that are doing a pixel art impression instead of actually respecting the grid.

It is aimed at the annoying middle ground: sprites, emblems, and logos that look locally pixelated but fall apart the second you ask them to commit to one lattice like grown-ups.

Instead of pretending this is a resize problem, Repixelizer treats it as lattice inference plus phase-field reconstruction: infer the ruler, scout the edges, give every output cell a tiny shove vector, nudge those shoves until the cells settle into quieter paint while staying in order, then sample once and go home.

GUI First

The web GUI is the main way to use this thing unless you enjoy raw flags for sport.

It gives you:

• drag-and-drop input
• live phase-field feedback while lattice search, rerank, and solving run
• synchronized input/output inspection zoom for close comparison
• a built-in output editor for single-pixel cleanup with eyedropper and pencil tools

Run it from the repo checkout:

.\scripts\run_gui.ps1

If you want to bypass the PowerShell wrapper and call Python directly:

.\.venv\Scripts\python scripts\run_gui.py

If you want to support the official hosted deployment and help us scale the poor little server when people start leaning on it, join the GameCult Patreon. At $10 per subscriber, the math gets a lot less tragic.

Example

This sheet carries two cases: the ugly fake-pixel badge and a dense higher-resolution landscape. The badge row shows the core salvage story. The landscape row shows a different failure mode: auto lattice inference still undershoots that source, so the phase-field panel is the tracked fixed 512x512 result next to the same cheap Lanczos baseline.

Each row carries its own inset, so the tiny details have nowhere to hide.

How It Works

Repixelizer does one thing:

source image -> lattice inference -> edge scout -> fixed lattice centers -> projected displacement-field optimization -> nearest source sample -> cleanup / diagnostics

In plainer language:

1. infer the target lattice size and phase
2. build one edge scout map over the source
3. nail down fixed lattice centers
4. run the phase-field solve by optimizing one (dx, dy) shove vector per output cell
5. sample the source once from the final displaced cell positions
6. clean up the discrete result, optionally fit a palette, and write diagnostics

That is the machine described in docs/lean-optimizer-algorithm-map.md. Comparison mode just runs that same phase-field result next to the baselines.

Current Status

This repo is past the "pile of hopeful heuristics" stage and into "real machine, still experimental."

What exists now:

• a web GUI with drag-and-drop, live diagnostics, comparison tools, and pixel cleanup
• lattice inference with CUDA support
• the phase-field reconstruction engine in src/repixelizer/phase_field.py
• automatic diagnostics, comparisons, and benchmark runs
• a tuning harness for offline parameter sweeps
• metrics that finally care about visible structure instead of only pleasing the lattice accountant
• source_structure is reported alongside source_fidelity, because the old metric was happily calling better-looking images worse
• the tracked sword-tip blemish on the AI badge has its own focused fixture in tests/fixtures/real/ai-badge-tip-focus.json

Current weak spots:

• phase-field still needs better along-stroke versus across-stroke behavior near tapered contours
• lattice selection is still low-confidence on some ugly generated inputs, so pinned-size iteration remains an important workflow

Quickstart

python -m venv .venv
.venv\Scripts\python -m pip install -e .[dev]

Launch the GUI:

.\scripts\run_gui.ps1

Run the optimizer from the CLI:

repixelize input.png --out output.png
repixelize input.png --out output.png --diagnostics-dir diagnostics --device auto
repixelize input.png --out output.png --target-width 126 --target-height 126 --phase-x 0.0 --phase-y -0.2 --device cuda

Run the optimizer plus baselines:

repixelize compare input.png --out output.png --diagnostics-dir diagnostics

Corpus And Benchmarks

Prepare a local Creative Commons corpus:

repixelize prepare-corpus --corpus-dir examples/corpus

Run the round-trip benchmark:

repixelize benchmark --corpus-dir examples/corpus --out-dir artifacts/benchmark

Useful benchmark flags:

• --profile soft or --profile crisp
• --case <name> repeated for a focused slice
• --limit <n> for the first n matching cases
• --infer-size to test automatic size inference
• --keep-existing if you explicitly want to preserve an old output directory

By default the benchmark clears its output directory before each run so the artifacts folder stays readable.

Tuning

Repixelizer includes a black-box tuning loop for longer offline searches over solver weights:

repixelize tune --corpus-dir examples/corpus --out-dir artifacts/tuning --profile soft --limit 8

This is intentionally a search-based tuner, not gradient descent. The objective depends on discrete argmins, thresholding, and benchmark comparisons, so it is better treated as a repeatable black-box optimization problem.

Repository Hygiene

This repo is set up to be safe for local experimentation:

• generated outputs under artifacts/ are ignored
• local corpus payloads under examples/corpus/originals/ are ignored
• archived raw sprite sheets under examples/corpus/source-sheets/ are ignored
• generated corpus metadata files are ignored

That keeps benchmark assets, attribution exports, and tuning runs from polluting upstream history while still keeping the code, docs, and corpus layout tracked.

Repo Layout

• src/repixelizer: core package
• tests: focused regression tests
• docs/spec.md: product and technical spec
• docs/implementation-plan.md: working roadmap
• docs/lean-optimizer-algorithm-map.md: map of the live displacement-field optimizer
• examples/corpus/README.md: local corpus layout and attribution workflow

Core modules:

• inference.py: target size and phase inference
• phase_field.py: default displacement-field optimizer
• metrics.py: fidelity, adjacency, and motif metrics
• benchmark.py: corpus benchmark runner
• tuning.py: black-box hyperparameter search harness
• synthetic.py: facsimile generation for tests and benchmarks

Validation

Run the focused test suite with:

.venv\Scripts\python -m pytest -q

Regenerating README Assets

The README images are generated from repo-tracked fixtures, not from random artifacts left lying around. The dense landscape row reuses the tracked fixed 512x512 output fixture because auto lattice inference still undershoots that case:

.venv\Scripts\python scripts\generate_readme_previews.py --out-sheet docs\readme-assets\badge-example-sheet.png --out-guard-crop docs\readme-assets\guard-right-crop-comparison.png --scratch-dir artifacts\readme-build --device auto

That regenerates the main README sheet, the standalone sword-guard closeup strip, and scratch outputs under artifacts/readme-build/ so you can inspect the actual low-res results used to build the docs sample.

Diagnostic Closeups

For docs or regression notes, you can generate reproducible source/output closeups from output-grid cell coordinates with:

.venv\Scripts\python scripts\render_focus_crop.py --input tests\fixtures\real\ai-badge-cleaned.png --out docs\readme-assets\guard-right-crop-source-final.png --cell-bbox 73 20 107 44 --panels source,final --scale 16 --steps 48 --device cpu

If you want the full internal state sheet instead of the docs-facing comparison:

.venv\Scripts\python scripts\render_focus_crop.py --input tests\fixtures\real\ai-badge-cleaned.png --out docs\readme-assets\guard-right-crop-states.png --cell-bbox 73 20 107 44 --panels source,initial,final --scale 16 --steps 48 --device cpu

Notes:

• --cell-bbox is in output-grid coordinates, not source-image pixels.
• The script maps that region back to the matching source crop automatically.
• --device cpu is the polite choice for docs generation if you do not want to stress the GPU for a tiny crop.

Notes

• The local corpus is optional; the codebase is still usable without it.
• The benchmark and tuning commands are designed for iterative local work, not for shipping generated artifacts in git.
• Palette enforcement is optional and can be left off for unconstrained recovery runs.