262 lines
11 KiB
Python
262 lines
11 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(
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source,
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upscale=12,
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phase_x=0.2,
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phase_y=0.35,
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blur_radius=0.65,
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warp_strength=0.28,
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warp_detail=6,
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seed=5,
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)
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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_strip_checkerboard_background(tmp_path: Path) -> None:
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source = make_sprite(24, 24)
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checker = np.ones_like(source)
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checker[..., 3] = 1.0
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for y in range(checker.shape[0]):
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for x in range(checker.shape[1]):
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tile = 0.97 if ((x // 4) + (y // 4)) % 2 == 0 else 0.91
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checker[y, x, :3] = tile
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alpha = source[..., 3:4]
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opaque_input = checker.copy()
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opaque_input[..., :3] = source[..., :3] * alpha + checker[..., :3] * (1.0 - alpha)
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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, opaque_input)
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result = run_pipeline(input_path, output_path, target_size=24, steps=0, strip_background=True)
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assert result.source_rgba[..., 3].min() < 0.05
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assert result.output_rgba[..., 3].min() < 0.05
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assert result.output_rgba[..., 3].max() > 0.9
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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=0.1,
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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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def test_phase_rerank_can_override_to_better_size_candidate(monkeypatch) -> None:
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source = np.zeros((16, 16, 4), dtype=np.float32)
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candidate_a = InferenceCandidate(target_width=16, target_height=16, phase_x=0.0, phase_y=0.0, score=0.91, breakdown={})
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candidate_b = InferenceCandidate(target_width=18, target_height=18, phase_x=0.0, phase_y=0.0, score=0.88, breakdown={})
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inference = InferenceResult(
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target_width=16,
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target_height=16,
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phase_x=0.0,
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phase_y=0.0,
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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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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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rgba = np.zeros((inference.target_height, inference.target_width, 4), dtype=np.float32)
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return DummyArtifacts(rgba)
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def fake_support(source_rgba, output_rgba, *, target_width, target_height, phase_x, phase_y):
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if target_width == 16:
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return {"score": 0.10}
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return {"score": 0.01}
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monkeypatch.setattr("repixelizer.pipeline.optimize_uv_field", fake_optimize_uv_field)
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monkeypatch.setattr("repixelizer.pipeline.source_lattice_consistency_breakdown", fake_support)
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selected = _select_phase_candidate(source, inference, analysis=object(), seed=7, device="cpu")
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assert selected.target_width == candidate_b.target_width
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assert selected.target_height == candidate_b.target_height
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def test_phase_rerank_rejects_large_size_jump(monkeypatch) -> None:
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source = np.zeros((16, 16, 4), dtype=np.float32)
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candidate_a = InferenceCandidate(target_width=16, target_height=16, phase_x=0.0, phase_y=0.0, score=0.91, breakdown={})
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candidate_b = InferenceCandidate(target_width=24, target_height=24, phase_x=0.0, phase_y=0.0, score=0.88, breakdown={})
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inference = InferenceResult(
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target_width=16,
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target_height=16,
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phase_x=0.0,
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phase_y=0.0,
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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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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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rgba = np.zeros((inference.target_height, inference.target_width, 4), dtype=np.float32)
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return DummyArtifacts(rgba)
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def fake_support(source_rgba, output_rgba, *, target_width, target_height, phase_x, phase_y):
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if target_width == 16:
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return {"score": 0.10}
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return {"score": 0.01}
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monkeypatch.setattr("repixelizer.pipeline.optimize_uv_field", fake_optimize_uv_field)
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monkeypatch.setattr("repixelizer.pipeline.source_lattice_consistency_breakdown", fake_support)
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selected = _select_phase_candidate(source, inference, analysis=object(), seed=7, device="cpu")
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assert selected.target_width == candidate_a.target_width
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assert selected.target_height == candidate_a.target_height
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def test_phase_rerank_can_prefer_better_line_metrics(monkeypatch) -> None:
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source = np.zeros((4, 4, 4), dtype=np.float32)
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candidate_a = InferenceCandidate(target_width=2, target_height=2, phase_x=-0.2, phase_y=0.0, score=0.91, breakdown={})
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candidate_b = InferenceCandidate(target_width=2, target_height=2, phase_x=0.2, phase_y=0.0, 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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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_support(source_rgba, output_rgba, *, target_width, target_height, phase_x, phase_y):
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return {"score": 0.02}
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def fake_edge_position(preview, source_rgba):
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return 0.05
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def fake_wobble(preview, source_rgba):
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return 1.2 if np.mean(preview) < 0.01 else 0.8
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def fake_concentration(rgba):
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return 0.15 if np.mean(rgba) < 0.01 else 0.28
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outputs = {
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candidate_a.phase_x: np.zeros((2, 2, 4), dtype=np.float32),
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candidate_b.phase_x: np.ones((2, 2, 4), dtype=np.float32),
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}
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def fake_optimize_uv_field(source_rgba, inference, analysis, steps, seed, device, solver_params=None):
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return DummyArtifacts(outputs[inference.phase_x])
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monkeypatch.setattr("repixelizer.pipeline.optimize_uv_field", fake_optimize_uv_field)
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monkeypatch.setattr("repixelizer.pipeline.source_lattice_consistency_breakdown", fake_support)
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monkeypatch.setattr("repixelizer.pipeline.foreground_edge_position_error", fake_edge_position)
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monkeypatch.setattr("repixelizer.pipeline.foreground_stroke_wobble_error", fake_wobble)
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monkeypatch.setattr("repixelizer.pipeline.foreground_edge_concentration", fake_concentration)
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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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