225 lines
8.9 KiB
Python
225 lines
8.9 KiB
Python
from __future__ import annotations
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import importlib.util
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import io
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from pathlib import Path
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import numpy as np
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from PIL import Image
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from starlette.datastructures import UploadFile
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import repixelizer.inference as inference_module
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import repixelizer.pipeline as pipeline_module
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from repixelizer.pipeline import run_pipeline_rgba
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from repixelizer.synthetic import fake_pixelize, make_emblem
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from repixelizer.params import SolverHyperParams
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from repixelizer.types import InferenceCandidate, InferenceResult, PhaseFieldSourceAnalysis, SolverArtifacts
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def test_run_pipeline_rgba_emits_observer_events_for_gui() -> None:
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source = make_emblem(20, 20)
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fake = fake_pixelize(source, upscale=8, phase_x=0.15, phase_y=0.2, blur_radius=0.45, seed=3)
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events: list[str] = []
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def observer(event: str, payload) -> None:
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del payload
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events.append(event)
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run_pipeline_rgba(
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fake,
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target_width=20,
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target_height=20,
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phase_x=0.0,
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phase_y=0.0,
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steps=2,
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device="cpu",
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enable_phase_rerank=False,
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observer=observer,
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)
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assert events[0] == "source_loaded"
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assert events.count("stage_started") == 6
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assert events.index("inference_candidates_ready") < events.index("analysis_completed")
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assert events.index("analysis_completed") < events.index("phase_selection_completed")
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assert events.index("phase_selection_completed") < events.index("phase_field_prepared")
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assert "phase_field_prepared" in events
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assert "phase_field_initial" in events
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assert events.count("phase_field_step") == 2
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assert events[-4:] == ["cleanup_completed", "stage_started", "palette_completed", "pipeline_completed"]
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def test_repo_gui_runner_dispatches_to_gui_main(monkeypatch) -> None:
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repo_root = Path(__file__).resolve().parents[1]
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script_path = repo_root / "scripts" / "run_gui.py"
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spec = importlib.util.spec_from_file_location("repixelizer_run_gui_script", script_path)
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assert spec is not None
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assert spec.loader is not None
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module = importlib.util.module_from_spec(spec)
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spec.loader.exec_module(module)
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called = {}
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def fake_gui_main(*, host: str, port: int, reload: bool) -> int:
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called["host"] = host
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called["port"] = port
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called["reload"] = reload
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return 0
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monkeypatch.setattr("repixelizer.gui.main", fake_gui_main)
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exit_code = module.main(["--host", "127.0.0.1", "--port", "8123", "--reload"])
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assert exit_code == 0
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assert called == {"host": "127.0.0.1", "port": 8123, "reload": True}
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def test_create_job_upload_field_validates_without_forward_ref_errors() -> None:
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from repixelizer.gui import create_app
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image = Image.new("RGBA", (1, 1), (12, 34, 56, 255))
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buffer = io.BytesIO()
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image.save(buffer, format="PNG")
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app = create_app()
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route = next(route for route in app.routes if getattr(route, "path", None) == "/api/jobs")
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image_field = next(field for field in route.dependant.body_params if field.name == "image")
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upload = UploadFile(filename="tiny.png", file=io.BytesIO(buffer.getvalue()))
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value, errors = image_field.validate(upload, {}, loc=("body", "image"))
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assert errors == []
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assert value.filename == "tiny.png"
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def test_infer_lattice_emits_search_progress_events(monkeypatch) -> None:
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class _FakeCuda:
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@staticmethod
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def is_available() -> bool:
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return False
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class _FakeTorch:
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cuda = _FakeCuda()
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monkeypatch.setattr(inference_module, "_require_torch", lambda: (_FakeTorch(), object()))
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monkeypatch.setattr(inference_module, "_estimate_lattice_spacing", lambda rgba: ((None, 0.0), (None, 0.0)))
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monkeypatch.setattr(inference_module, "_hint_target_sizes_from_spacing", lambda width, height, spacing_x, spacing_y: [])
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monkeypatch.setattr(inference_module, "_estimate_lattice_prior_details", lambda rgba: (4.0, 4.0, 0.5))
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monkeypatch.setattr(
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inference_module,
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"_candidate_dims",
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lambda width, height, target_size, *, hinted_sizes=None: [(10, 8), (12, 10)],
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)
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def fake_score_phase_group(
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rgba,
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*,
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target_width,
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target_height,
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prior_cell_x,
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prior_cell_y,
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prior_reliability,
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phase_x_values,
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phase_y_values,
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device,
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):
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del rgba, prior_cell_x, prior_cell_y, prior_reliability, phase_x_values, phase_y_values, device
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return [
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InferenceCandidate(
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target_width=target_width,
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target_height=target_height,
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phase_x=0.0,
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phase_y=0.0,
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score=0.9 if target_width == 12 else 0.6,
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breakdown={},
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)
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]
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monkeypatch.setattr(inference_module, "_score_phase_group", fake_score_phase_group)
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monkeypatch.setattr(inference_module, "_top_candidates_by_size", lambda candidates, limit: candidates[:limit])
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monkeypatch.setattr(
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inference_module,
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"_rerank_size_candidates_with_source_evidence",
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lambda rgba, candidates: sorted(candidates, key=lambda candidate: candidate.score, reverse=True),
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)
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events: list[tuple[str, dict[str, object]]] = []
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def observer(event: str, payload: dict[str, object]) -> None:
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events.append((event, payload))
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result = inference_module.infer_lattice(np.zeros((16, 20, 4), dtype=np.uint8), observer=observer)
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assert result.target_width == 12
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assert [event for event, _payload in events] == [
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"lattice_search_started",
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"lattice_search_progress",
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"lattice_search_progress",
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]
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assert events[0][1]["candidate_count"] == 2
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assert events[1][1]["completed_candidates"] == 1
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assert events[2][1]["completed_candidates"] == 2
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assert events[2][1]["target_width"] == 12
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def test_phase_rerank_emits_candidate_progress_events(monkeypatch) -> None:
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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=[
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InferenceCandidate(target_width=16, target_height=16, phase_x=0.0, phase_y=0.0, score=0.75, breakdown={}),
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InferenceCandidate(target_width=18, target_height=18, phase_x=0.1, phase_y=-0.1, score=0.73, breakdown={}),
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],
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)
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analysis = PhaseFieldSourceAnalysis(edge_map=np.zeros((4, 4), dtype=np.float32))
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def fake_run_reconstruction(source, *, observer=None, inference, **kwargs):
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del source, kwargs
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if observer is not None:
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observer("phase_field_initial", {"step": 0, "total_steps": 2})
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observer("phase_field_step", {"step": 1, "total_steps": 2, "loss": 0.25})
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observer("phase_field_step", {"step": 2, "total_steps": 2, "loss": 0.125})
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target_rgba = np.zeros((inference.target_height, inference.target_width, 4), dtype=np.uint8)
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artifacts = SolverArtifacts(
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target_rgba=target_rgba,
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uv_field=np.zeros((inference.target_height, inference.target_width, 2), dtype=np.float32),
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guidance_strength=np.zeros((inference.target_height, inference.target_width), dtype=np.float32),
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initial_rgba=target_rgba.copy(),
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loss_history=[0.25, 0.125],
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)
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return artifacts, {}
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monkeypatch.setattr(pipeline_module, "_run_reconstruction", fake_run_reconstruction)
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monkeypatch.setattr(pipeline_module, "source_lattice_consistency_breakdown", lambda *args, **kwargs: {"score": 0.8})
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monkeypatch.setattr(
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pipeline_module,
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"nearest_resize",
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lambda rgba, width, height: np.zeros((height, width, 4), dtype=np.uint8),
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)
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monkeypatch.setattr(pipeline_module, "foreground_edge_position_error", lambda preview, source: 0.12)
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monkeypatch.setattr(pipeline_module, "foreground_stroke_wobble_error", lambda preview, source: 0.08)
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monkeypatch.setattr(pipeline_module, "foreground_edge_concentration", lambda rgba: 0.9)
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events: list[tuple[str, dict[str, object]]] = []
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def observer(event: str, payload: dict[str, object]) -> None:
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events.append((event, payload))
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pipeline_module._select_phase_candidate_with_reconstruction(
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np.zeros((16, 16, 4), dtype=np.uint8),
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inference,
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analysis=analysis,
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steps=2,
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seed=1,
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device="cpu",
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solver_params=SolverHyperParams(phase_rerank_preview_steps=2, phase_rerank_confidence_threshold=1.0),
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observer=observer,
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)
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event_names = [event for event, _payload in events]
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assert event_names[0] == "phase_rerank_started"
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assert event_names.count("phase_rerank_candidate_started") == 2
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assert event_names.count("phase_rerank_candidate_step") == 6
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assert event_names.count("phase_rerank_candidate_completed") == 2
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first_step_payload = next(payload for event, payload in events if event == "phase_rerank_candidate_step")
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assert first_step_payload["candidate_index"] == 1
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assert first_step_payload["total_steps"] == 2
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