182 lines
6.7 KiB
Python
182 lines
6.7 KiB
Python
from __future__ import annotations
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import numpy as np
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from repixelizer.metrics import (
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exact_match_ratio,
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foreground_adjacency_error,
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foreground_edge_concentration,
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foreground_edge_position_error,
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foreground_edge_support_breakdown,
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foreground_exact_match_ratio,
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foreground_motif_error,
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foreground_reconstruction_error,
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foreground_stroke_wobble_error,
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reconstruction_error,
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source_lattice_consistency_breakdown,
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source_structure_breakdown,
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)
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def test_reconstruction_error_ignores_hidden_rgb_in_transparent_pixels() -> None:
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transparent_a = np.zeros((4, 4, 4), dtype=np.float32)
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transparent_b = transparent_a.copy()
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transparent_b[..., 0] = 1.0
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assert reconstruction_error(transparent_a, transparent_b) == 0.0
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assert exact_match_ratio(transparent_a, transparent_b) == 1.0
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def test_foreground_metrics_penalize_visible_sprite_errors_and_leaks() -> None:
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original = np.zeros((4, 4, 4), dtype=np.float32)
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original[1, 1] = np.asarray([1.0, 0.0, 0.0, 1.0], dtype=np.float32)
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hidden_rgb = original.copy()
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hidden_rgb[0, 0, :3] = np.asarray([0.2, 0.8, 0.4], dtype=np.float32)
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assert foreground_reconstruction_error(original, hidden_rgb) == 0.0
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assert foreground_exact_match_ratio(original, hidden_rgb) == 1.0
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wrong_foreground = original.copy()
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wrong_foreground[1, 1] = np.asarray([0.0, 1.0, 0.0, 1.0], dtype=np.float32)
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assert foreground_reconstruction_error(original, wrong_foreground) > 0.0
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assert foreground_exact_match_ratio(original, wrong_foreground) < 1.0
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leaked_foreground = original.copy()
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leaked_foreground[0, 0] = np.asarray([1.0, 1.0, 1.0, 1.0], dtype=np.float32)
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assert foreground_reconstruction_error(original, leaked_foreground) > 0.0
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def test_texture_metrics_track_adjacency_and_local_motifs() -> None:
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original = np.zeros((4, 4, 4), dtype=np.float32)
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original[1:3, 1:3] = np.asarray(
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[
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[[1.0, 0.0, 0.0, 1.0], [0.0, 0.0, 1.0, 1.0]],
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[[0.0, 0.0, 1.0, 1.0], [1.0, 0.0, 0.0, 1.0]],
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],
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dtype=np.float32,
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)
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same_colors_wrong_structure = original.copy()
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same_colors_wrong_structure[1:3, 1:3] = np.asarray(
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[
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[[1.0, 0.0, 0.0, 1.0], [1.0, 0.0, 0.0, 1.0]],
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[[0.0, 0.0, 1.0, 1.0], [0.0, 0.0, 1.0, 1.0]],
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],
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dtype=np.float32,
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)
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assert foreground_adjacency_error(original, original) == 0.0
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assert foreground_motif_error(original, original) == 0.0
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assert foreground_adjacency_error(original, same_colors_wrong_structure) > 0.0
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assert foreground_motif_error(original, same_colors_wrong_structure) > 0.0
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def test_edge_position_metric_prefers_crisp_shift_to_blurry_smear() -> None:
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original = np.zeros((7, 7, 4), dtype=np.float32)
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original[1:6, 3] = np.asarray([1.0, 1.0, 1.0, 1.0], dtype=np.float32)
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shifted = np.zeros_like(original)
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shifted[1:6, 4] = np.asarray([1.0, 1.0, 1.0, 1.0], dtype=np.float32)
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blurred = np.zeros_like(original)
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blurred[1:6, 2] = np.asarray([0.4, 0.4, 0.4, 1.0], dtype=np.float32)
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blurred[1:6, 3] = np.asarray([0.7, 0.7, 0.7, 1.0], dtype=np.float32)
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blurred[1:6, 4] = np.asarray([0.4, 0.4, 0.4, 1.0], dtype=np.float32)
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assert foreground_edge_position_error(original, original) == 0.0
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assert foreground_edge_position_error(original, shifted) > 0.0
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assert foreground_edge_position_error(original, blurred) > foreground_edge_position_error(original, shifted)
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def test_edge_concentration_prefers_crisp_edges_to_blurry_smear() -> None:
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shifted = np.zeros((7, 7, 4), dtype=np.float32)
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shifted[1:6, 4] = np.asarray([1.0, 1.0, 1.0, 1.0], dtype=np.float32)
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blurred = np.zeros_like(shifted)
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blurred[1:6, 2] = np.asarray([0.4, 0.4, 0.4, 1.0], dtype=np.float32)
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blurred[1:6, 3] = np.asarray([0.7, 0.7, 0.7, 1.0], dtype=np.float32)
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blurred[1:6, 4] = np.asarray([0.4, 0.4, 0.4, 1.0], dtype=np.float32)
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assert foreground_edge_concentration(shifted) > foreground_edge_concentration(blurred)
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def test_stroke_wobble_metric_penalizes_local_line_jitter() -> None:
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original = np.zeros((9, 9, 4), dtype=np.float32)
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original[2:7, 4] = np.asarray([1.0, 1.0, 1.0, 1.0], dtype=np.float32)
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stable = original.copy()
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wobbly = np.zeros_like(original)
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wobbly[2:4, 4] = np.asarray([1.0, 1.0, 1.0, 1.0], dtype=np.float32)
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wobbly[4:7, 5] = np.asarray([1.0, 1.0, 1.0, 1.0], dtype=np.float32)
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assert foreground_stroke_wobble_error(original, stable) == 0.0
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assert foreground_stroke_wobble_error(original, wobbly) > 0.0
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def test_edge_support_breakdown_rewards_nearby_edge_recall() -> None:
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source = np.zeros((9, 9, 4), dtype=np.float32)
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source[2:7, 4] = np.asarray([1.0, 1.0, 1.0, 1.0], dtype=np.float32)
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stable = source.copy()
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shifted = np.zeros_like(source)
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shifted[2:7, 5] = np.asarray([1.0, 1.0, 1.0, 1.0], dtype=np.float32)
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missing = np.zeros_like(source)
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stable_support = foreground_edge_support_breakdown(stable, source)
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shifted_support = foreground_edge_support_breakdown(shifted, source)
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missing_support = foreground_edge_support_breakdown(missing, source)
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assert stable_support["f1"] >= shifted_support["f1"] > missing_support["f1"]
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def test_source_structure_breakdown_prefers_exact_and_supported_structure() -> None:
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source = np.zeros((9, 9, 4), dtype=np.float32)
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source[2:7, 4] = np.asarray([1.0, 1.0, 1.0, 1.0], dtype=np.float32)
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exact = source.copy()
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shifted = np.zeros_like(source)
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shifted[2:7, 5] = np.asarray([1.0, 1.0, 1.0, 1.0], dtype=np.float32)
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blurred = np.zeros_like(source)
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blurred[2:7, 3] = np.asarray([0.4, 0.4, 0.4, 1.0], dtype=np.float32)
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blurred[2:7, 4] = np.asarray([0.7, 0.7, 0.7, 1.0], dtype=np.float32)
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blurred[2:7, 5] = np.asarray([0.4, 0.4, 0.4, 1.0], dtype=np.float32)
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exact_score = source_structure_breakdown(source, exact)["score"]
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shifted_score = source_structure_breakdown(source, shifted)["score"]
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blurred_score = source_structure_breakdown(source, blurred)["score"]
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assert exact_score < shifted_score
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assert exact_score < blurred_score
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def test_source_lattice_consistency_prefers_matching_cell_structure() -> None:
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lowres = np.asarray(
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[
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[[1.0, 0.0, 0.0, 1.0], [0.0, 0.0, 1.0, 1.0]],
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[[0.0, 1.0, 0.0, 1.0], [1.0, 1.0, 0.0, 1.0]],
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],
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dtype=np.float32,
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)
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source = np.repeat(np.repeat(lowres, 4, axis=0), 4, axis=1)
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good = lowres.copy()
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bad = lowres[:, ::-1].copy()
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good_score = source_lattice_consistency_breakdown(
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source,
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good,
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target_width=2,
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target_height=2,
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)["score"]
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bad_score = source_lattice_consistency_breakdown(
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source,
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bad,
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target_width=2,
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target_height=2,
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)["score"]
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assert good_score < bad_score
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