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105 lines
4.1 KiB
Python
105 lines
4.1 KiB
Python
"""Tests for the SynthID pixel-carrier probe.
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The probe's logic is validated synthetically: a fixed carrier injected into
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uniform fills must correlate strongly, random noise must not, and simulated
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removal (dropping the carrier) must collapse the correlation. No real SynthID
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fills or GPU are needed.
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"""
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from __future__ import annotations
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import sys
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from pathlib import Path
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import numpy as np
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from click.testing import CliRunner
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from PIL import Image
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# scripts/ is not an installed package; add it to the path for import.
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sys.path.insert(0, str(Path(__file__).resolve().parent.parent / "scripts"))
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import synthid_pixel_probe as probe
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_SHAPE = (64, 64)
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def _fixed_carrier(seed: int = 7) -> np.ndarray:
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"""A reproducible 2-D carrier pattern (the stand-in for the SynthID signal)."""
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rng = np.random.default_rng(seed)
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return rng.standard_normal(_SHAPE)
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def _fill_with_carrier(base: float, pattern: np.ndarray, noise_seed: int, noise: float = 0.05) -> np.ndarray:
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"""A solid fill at brightness ``base`` carrying ``pattern`` plus light noise."""
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rng = np.random.default_rng(noise_seed)
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return base + pattern + noise * rng.standard_normal(_SHAPE)
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class TestCarrier:
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def test_flat_image_has_zero_carrier(self):
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# A perfectly uniform fill (std 0, like the synthetic refs) carries nothing.
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flat = np.zeros(_SHAPE)
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assert not np.any(probe.carrier(flat))
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def test_carrier_is_unit_norm(self):
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c = probe.carrier(_fixed_carrier() + 100.0)
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assert np.isclose(np.linalg.norm(c), 1.0)
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def test_ncc_self_is_one(self):
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c = probe.carrier(_fixed_carrier())
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assert np.isclose(probe.ncc(c, c), 1.0)
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def test_ncc_mismatched_shape_is_zero(self):
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a = probe.carrier(np.random.default_rng(1).standard_normal((8, 8)))
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b = probe.carrier(np.random.default_rng(2).standard_normal((16, 16)))
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assert probe.ncc(a, b) == 0.0
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class TestConsistency:
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def test_shared_carrier_correlates_high(self):
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pattern = _fixed_carrier()
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carriers = [probe.carrier(_fill_with_carrier(b, pattern, s)) for s, b in enumerate((10, 60, 120, 200))]
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assert probe.mean_pairwise_ncc(carriers) > 0.8
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def test_random_fills_near_zero(self):
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rng = np.random.default_rng(0)
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carriers = [probe.carrier(rng.standard_normal(_SHAPE)) for _ in range(5)]
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assert abs(probe.mean_pairwise_ncc(carriers)) < 0.2
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def test_random_baseline_near_zero(self):
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assert abs(probe.random_baseline(_SHAPE, 6)) < 0.2
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class TestRemoval:
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def test_removed_carrier_collapses_correlation(self):
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pattern = _fixed_carrier()
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pos = [probe.carrier(_fill_with_carrier(b, pattern, s)) for s, b in enumerate((20, 90, 160))]
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tmpl = probe.template(pos)
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# "Cleaned" fills keep the base + noise but lose the shared pattern.
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rng = np.random.default_rng(99)
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cleaned = [probe.carrier(b + 0.05 * rng.standard_normal(_SHAPE)) for b in (20, 90, 160)]
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pos_corr = float(np.mean([probe.ncc(c, tmpl) for c in pos]))
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cleaned_corr = float(np.mean([probe.ncc(c, tmpl) for c in cleaned]))
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assert pos_corr > 0.8
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assert cleaned_corr < 0.2
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class TestCli:
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def _solid_fill_png(self, tmp_path: Path, name: str, base: int, pattern: np.ndarray, seed: int) -> Path:
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arr = np.clip(_fill_with_carrier(base, pattern, seed), 0, 255).astype(np.uint8)
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path = tmp_path / name
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Image.fromarray(np.stack([arr] * 3, axis=2)).save(path)
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return path
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def test_consistency_command_runs(self, tmp_path: Path):
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pattern = _fixed_carrier()
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paths = [str(self._solid_fill_png(tmp_path, f"f{i}.png", b, pattern, i)) for i, b in enumerate((40, 120, 200))]
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result = CliRunner().invoke(probe.cli, ["consistency", *paths])
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assert result.exit_code == 0, result.output
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assert "mean pairwise NCC" in result.output
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def test_consistency_needs_two_images(self, tmp_path: Path):
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path = str(self._solid_fill_png(tmp_path, "only.png", 100, _fixed_carrier(), 0))
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result = CliRunner().invoke(probe.cli, ["consistency", path])
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assert result.exit_code != 0
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