113 lines
4.8 KiB
Python
113 lines
4.8 KiB
Python
from __future__ import annotations
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from pathlib import Path
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import numpy as np
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from repixelizer.baselines import naive_resize_baseline
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from repixelizer.metrics import coherence_breakdown, foreground_reconstruction_error
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from repixelizer.pipeline import _select_phase_candidate, run_pipeline
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from repixelizer.io import nearest_resize
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from repixelizer.synthetic import fake_pixelize, make_emblem, make_sprite
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from repixelizer.types import InferenceCandidate, InferenceResult
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def test_pipeline_writes_output_and_diagnostics(tmp_path: Path) -> None:
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source = make_emblem(32, 32)
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fake = fake_pixelize(source, upscale=10, phase_x=0.15, phase_y=0.25, blur_radius=0.5)
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input_path = tmp_path / "input.png"
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output_path = tmp_path / "output.png"
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diagnostics_dir = tmp_path / "diagnostics"
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from repixelizer.io import save_rgba
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save_rgba(input_path, fake)
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result = run_pipeline(input_path, output_path, diagnostics_dir=diagnostics_dir, steps=24)
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assert output_path.exists()
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assert (diagnostics_dir / "run.json").exists()
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assert (diagnostics_dir / "output-preview.png").exists()
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assert result.output_rgba.shape[0] == result.inference.target_height
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assert result.output_rgba.shape[1] == result.inference.target_width
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def test_pipeline_beats_naive_on_roundtrip_fidelity_for_synthetic_emblem(tmp_path: Path) -> None:
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source = make_emblem(32, 32)
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fake = fake_pixelize(source, upscale=12, phase_x=0.2, phase_y=0.35, blur_radius=0.65)
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input_path = tmp_path / "input.png"
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output_path = tmp_path / "output.png"
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from repixelizer.io import save_rgba
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save_rgba(input_path, fake)
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result = run_pipeline(input_path, output_path, steps=32)
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naive = naive_resize_baseline(fake, width=result.inference.target_width, height=result.inference.target_height)
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optimized_preview = nearest_resize(result.output_rgba, width=source.shape[1], height=source.shape[0])
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naive_preview = nearest_resize(naive, width=source.shape[1], height=source.shape[0])
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optimized_error = foreground_reconstruction_error(optimized_preview, source)
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naive_error = foreground_reconstruction_error(naive_preview, source)
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assert optimized_error <= naive_error
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def test_pipeline_preserves_transparency_for_sprite(tmp_path: Path) -> None:
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source = make_sprite(24, 24)
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fake = fake_pixelize(source, upscale=9, phase_x=0.1, phase_y=0.22, blur_radius=0.45)
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input_path = tmp_path / "input.png"
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output_path = tmp_path / "output.png"
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from repixelizer.io import save_rgba
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save_rgba(input_path, fake)
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result = run_pipeline(input_path, output_path, steps=20)
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alpha = result.output_rgba[..., 3]
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assert alpha.min() < 0.05
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assert alpha.max() > 0.9
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partial = alpha[(alpha > 0.05) & (alpha < 0.95)]
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assert partial.size == 0
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def test_pipeline_can_emit_initialized_output_without_optimizer_steps(tmp_path: Path) -> None:
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source = make_emblem(24, 24)
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fake = fake_pixelize(source, upscale=10, phase_x=0.15, phase_y=0.2, blur_radius=0.6, warp_strength=0.2, warp_detail=5)
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input_path = tmp_path / "input.png"
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output_path = tmp_path / "output.png"
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from repixelizer.io import save_rgba
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save_rgba(input_path, fake)
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result = run_pipeline(input_path, output_path, steps=0)
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assert output_path.exists()
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assert result.output_rgba.shape[0] == result.inference.target_height
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assert result.output_rgba.shape[1] == result.inference.target_width
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def test_phase_rerank_can_override_low_confidence_inference_pick(monkeypatch) -> None:
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source = np.zeros((2, 2, 4), dtype=np.float32)
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source[0, 0] = np.asarray([1.0, 0.0, 0.0, 1.0], dtype=np.float32)
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candidate_a = InferenceCandidate(target_width=2, target_height=2, phase_x=-0.4, phase_y=0.2, score=0.91, breakdown={})
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candidate_b = InferenceCandidate(target_width=2, target_height=2, phase_x=0.4, phase_y=0.2, score=0.909, breakdown={})
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inference = InferenceResult(
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target_width=2,
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target_height=2,
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phase_x=candidate_a.phase_x,
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phase_y=candidate_a.phase_y,
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confidence=0.0,
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top_candidates=[candidate_a, candidate_b],
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)
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outputs = {
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(candidate_a.phase_x, candidate_a.phase_y): np.ones_like(source),
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(candidate_b.phase_x, candidate_b.phase_y): source.copy(),
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}
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class DummyArtifacts:
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def __init__(self, rgba: np.ndarray) -> None:
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self.target_rgba = rgba
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def fake_optimize_uv_field(source_rgba, inference, analysis, steps, seed, device, solver_params=None):
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assert steps == 0
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return DummyArtifacts(outputs[(inference.phase_x, inference.phase_y)])
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monkeypatch.setattr("repixelizer.pipeline.optimize_uv_field", fake_optimize_uv_field)
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selected = _select_phase_candidate(source, inference, analysis=object(), seed=7, device="cpu")
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assert selected.phase_x == candidate_b.phase_x
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assert selected.phase_y == candidate_b.phase_y
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