# Repixelizer

Repixelizer is a standalone Python CLI 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 local structure preservation: infer the implied grid, choose real output cells, and snap them back onto a coherent pixel lattice while preserving the source's local adjacency patterns.

## Examples

Repixelizer was built to rescue fake pixel art, but it can also be used to generate pixel art directly from non-pixel source art when the shapes are clean and the local structure is doing something useful.

Rows show the source art, a plain Lanczos downscale to the same target size, and the repixelized result. The bottom row also carries a sword-guard picture-in-picture, because that tiny region is one of the more honest tests of whether the tool is preserving local cell adjacency instead of just getting the vibes approximately correct.

![Repixelizer example comparison](docs/readme-assets/badge-example-sheet.png)

That is the two-headed pitch in one image: force non-pixel art onto a coherent grid, or take AI pixel art that only respects the grid locally and make it commit.

## Current Status

This is already a serious experimental baseline, not a toy prototype:

- CUDA-backed inference and solver paths are wired up
- a shared source-lattice reference now drives solver, metrics, and reranking
- round-trip corpus benchmarking is built in
- soft and crisp corruption profiles are supported
- diagnostics and baseline comparisons are written automatically, including per-stage source-fidelity in `run.json`
- a black-box tuning harness exists for longer offline parameter sweeps

The current pipeline is especially strong on benchmarked sprite and emblem inputs where local fake-pixel texture is meaningful but the global grid is inconsistent.

Recent adjacency-focused status:

- the cleaned AI badge regression now beats naive resize on the repo's source-lattice consistency metric under the selected lattice
- the snap-to-refine handoff is now source-first rather than dominated by the softened representative lattice
- low-confidence phase reranking now uses a soft size penalty instead of a hard size-jump reject
- an experimental `tile-graph` reconstruction mode now exists behind `--reconstruction-mode tile-graph`

Current tile-graph status:

- it now keeps candidates strictly local to each output coord and only uses literal source-pixel colors, so it no longer invents averaged patch colors or borrow labels from distant regions
- tile-graph prep is now an edge-only path with no cluster analysis, no hybrid geometry sidecar, and no tile-graph-specific source-reference portrait
- extraction now runs at full source resolution; the old `source_region_stride` subsampling path is gone
- candidate selection is now source-owned: extracted tiles are ranked by their own area, coverage, and edge signal instead of by resemblance to `sharp_rgba` / `edge_rgba`
- occupied foreground cells no longer get papered over with injected `sharp_pixel` or `edge_pixel` fallbacks; if extraction cannot produce a real tile there, the build fails instead of lying
- the adjacency term is now learned from extracted tiles themselves, not from sampled one-cell-away RGBA deltas
- tile extraction no longer walks components with seed queues and one-cell windows; it now directly reduces `(component_id, output_cell)` overlaps into candidate shards
- the deep-dive map in `docs/tile-graph-algorithm-map.md` now reflects the living machine directly instead of documenting the dead portrait/stride/delta path
- the extraction stage still has an explicit empty-cell overlap fill pass, so output cells that actually contain opaque source pixels do not silently lose their extracted region bucket
- the latest full-emblem probe under `artifacts/full-emblem-tile-graph-atomic-v3-cuda/` lands at `0.0224` source-fidelity, beating the earlier full-CUDA tile-graph baseline at `0.0283`
- tile-graph now falls back to its initial assignment when the propagation loop would make source-lattice fidelity worse, which is currently important for preserving sharp internal contour cells
- it still beats naive resize on the repo's synthetic thin-feature regression and now does so without letting the final propagation step blur past the initial placement
- source-region connected-components now have a device-side Torch path, so the expensive labeling step no longer depends on Python flood fill
- profiling the older selected badge candidate showed the main bottleneck was model construction rather than the solver loop: `build_tile_graph_model(...)` took about `142.3s`, and about `131.2s` of that was `_extract_source_region_tiles(...)`
- after the first pruning pass, tile-graph no longer spends iteration time on pipeline rerank probes at all; it now runs the chosen or pinned lattice directly
- tile-graph now also skips cluster analysis entirely and no longer uses sharp/edge portrait anchors in unary scoring; the path keeps only edge scouting plus direct per-cell source summaries
- after the reduce-by-key rewrite, the large-fixture bottleneck moved: extraction is much cheaper, and the main cold-build cost is now connected-component labeling / label compression rather than per-component window cutting
- the pipeline now has a direct-control path for iteration: `--target-width` / `--target-height` plus optional `--phase-x` / `--phase-y` let you run an exact lattice without paying the full lattice search, and `--skip-phase-rerank` lets you keep the pipeline from second-guessing that choice
- repeated fixed-lattice `tile-graph` runs in the same Python process now reuse the expensive model build; on the cleaned badge at pinned `126x126` / phase `(0.0, -0.2)`, the first CUDA run took about `10.2s` and the second cached rerun took about `2.1s`
- the latest reduce-by-key pinned badge probe under `artifacts/tile-graph-reducebykey-v1-badge-126/` finishes in about `16.1s` cold and `6.0s` warm on CUDA, versus the previous full-resolution cut at about `96.9s` cold and `5.4s` warm
- that speed pass is a real throughput win but a small quality regression: the same pinned badge case moves from `0.1814` to `0.2036` source-fidelity
- the optimizer now has its own deep-dive map in `docs/optimizer-algorithm-map.md`, and the first cut from that map removed the old UV-optimizer name plus the continuous k-means cluster-boundary scout; the continuous path is now a fixed-lattice, source-first discrete chooser guided by real edge evidence
- optimizer preparation is now split into a private `_OptimizerPrep` bundle, so the continuous path has a visible seam between "build the map" and "choose the pixels"
- refine is now source-first too: the representative portrait still stabilizes snap, but refine and final structure choice no longer consult it, and the pinned badge smoke improved slightly from `0.07535` to `0.07485`
- the next relax cut also paid off: removing the old relax-mode bonus while keeping the core relax stage improved the same pinned badge smoke again from `0.07485` to `0.07451`
- after the first pruning pass, tile-graph no longer participates in pipeline phase-rerank probes and no longer carries hybrid geometry priors through its unary cost; the path is now one lattice-conditioned candidate generator plus one local discrete solver
- this fixes the core design mismatch that had allowed repeated distant labels to create big same-color patches and opaque black background blocks
- on the current `24x24` emblem smoke case, an end-to-end `tile-graph` run dropped from about `2.57s` on CPU to `0.61s` on CUDA on this machine
- the older hard-edge-only candidate widening pass under `artifacts/full-emblem-tile-graph-hard-edge-v2-cuda/` remains a useful negative result: more edge choices alone sharpen some cells locally but still regress full-emblem source-fidelity (`0.0377`)

## Quickstart

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

Run the optimizer:

```powershell
repixelize input.png --out output.png
repixelize input.png --out output.png --diagnostics-dir diagnostics --device auto
repixelize input.png --out output.png --reconstruction-mode tile-graph --diagnostics-dir diagnostics --device cpu
repixelize input.png --out output.png --reconstruction-mode tile-graph --diagnostics-dir diagnostics --device cuda
repixelize input.png --out output.png --reconstruction-mode tile-graph --target-width 126 --target-height 126 --phase-x 0.0 --phase-y -0.2 --device cuda
```

Run the optimizer plus baselines:

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

## Corpus And Benchmarks

Prepare a local Creative Commons corpus:

```powershell
repixelize prepare-corpus --corpus-dir examples/corpus
```

Run the round-trip benchmark:

```powershell
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:

```powershell
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/tile-graph-algorithm-map.md`: detailed tile-graph dataflow and failure-point map
- `docs/optimizer-algorithm-map.md`: detailed optimizer dataflow and contradiction map
- `examples/corpus/README.md`: local corpus layout and attribution workflow

Core modules:

- `inference.py`: target size and phase inference
- `continuous.py`: source-aware lattice snapping and local discrete refinement
- `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:

```powershell
.venv\Scripts\python -m pytest -q
```

## Regenerating README Assets

The README comparison sheet is generated from repo-tracked fixtures, not from random artifacts left lying around:

```powershell
.venv\Scripts\python scripts\generate_readme_previews.py --vector-input tests\fixtures\real\ai-badge-vector.png --ai-input tests\fixtures\real\ai-badge-cleaned.png --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 cpu
```

That regenerates the 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:

```powershell
.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:

```powershell
.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,snap,relaxed,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.