mirror of
https://github.com/wiltodelta/remove-ai-watermarks.git
synced 2026-06-05 02:28:00 +02:00
Victor Kuznetsov
175609b60a
detect_watermark's size-weighted global NCC search lets a larger, mediocre match (e.g. a bright collar in a portrait) outrank a small, near-perfect sparkle in the bottom-right corner, so a faint sparkle on a busy background scored below threshold and the image read as clean -- the regression from widening the search window 256px->512px between v0.7.2 and v0.8.8. Add _corner_promote: a bottom-right-corner raw-NCC pass that overrides the global pick when the corner holds a match with raw NCC >= 0.85 that beats it. It only ever replaces a lower-fidelity pick (cannot weaken an existing detection) and keeps the wider window for variant margins. The corner side is relative-clamped (0.20 of the short side, [96, 384]) so it stays a true corner at every scale: a fixed 256px covers ~70% of a small portrait, where a real photo raw-matches the star at ~0.81; relative tightening drops that to ~0.69. The 0.85 gate sits between the worst real-photo corner match (~0.78) and a genuine faint sparkle (~0.93): zero false positives across native + downscaled negatives, headshot rescued from below-threshold to 0.71. Factor the shared multi-scale matchTemplate loop into _scan_scales. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
352 lines
15 KiB
Python
352 lines
15 KiB
Python
"""Tests for the Gemini visible-watermark engine."""
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from __future__ import annotations
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import cv2
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import numpy as np
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import pytest
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from remove_ai_watermarks.gemini_engine import (
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DetectionResult,
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GeminiEngine,
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WatermarkPosition,
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WatermarkSize,
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_calculate_alpha_map,
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detect_sparkle_confidence,
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get_watermark_config,
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get_watermark_size,
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)
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# ── WatermarkSize / config helpers ──────────────────────────────────
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class TestWatermarkConfig:
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"""Tests for watermark size detection and position calculation."""
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def test_small_image_gets_small_watermark(self):
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assert get_watermark_size(800, 600) == WatermarkSize.SMALL
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def test_large_image_gets_large_watermark(self):
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assert get_watermark_size(1920, 1080) == WatermarkSize.LARGE
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def test_boundary_image_stays_small(self):
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"""Exactly 1024x1024 should be SMALL (rule: > 1024 for LARGE)."""
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assert get_watermark_size(1024, 1024) == WatermarkSize.SMALL
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def test_one_dimension_small(self):
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"""Only one dimension > 1024 → still SMALL."""
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assert get_watermark_size(2000, 500) == WatermarkSize.SMALL
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def test_config_small_returns_correct_values(self):
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config = get_watermark_config(800, 600)
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assert config.margin_right == 32
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assert config.margin_bottom == 32
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assert config.logo_size == 48
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def test_config_large_returns_correct_values(self):
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config = get_watermark_config(1920, 1080)
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assert config.margin_right == 64
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assert config.margin_bottom == 64
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assert config.logo_size == 96
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def test_position_calculation(self):
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pos = WatermarkPosition(margin_right=32, margin_bottom=32, logo_size=48)
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x, y = pos.get_position(800, 600)
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assert x == 800 - 32 - 48 # 720
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assert y == 600 - 32 - 48 # 520
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# ── Alpha map ───────────────────────────────────────────────────────
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class TestAlphaMap:
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"""Tests for alpha map calculation."""
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def test_pure_black_gives_zero_alpha(self):
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black = np.zeros((10, 10, 3), dtype=np.uint8)
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alpha = _calculate_alpha_map(black)
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assert alpha.shape == (10, 10)
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np.testing.assert_array_equal(alpha, 0.0)
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def test_pure_white_gives_one_alpha(self):
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white = np.full((10, 10, 3), 255, dtype=np.uint8)
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alpha = _calculate_alpha_map(white)
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np.testing.assert_allclose(alpha, 1.0)
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def test_grayscale_input(self):
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gray = np.full((10, 10), 128, dtype=np.uint8)
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alpha = _calculate_alpha_map(gray)
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np.testing.assert_allclose(alpha, 128 / 255.0)
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def test_max_channel_used(self):
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"""Alpha should use max(R, G, B)."""
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img = np.zeros((1, 1, 3), dtype=np.uint8)
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img[0, 0] = [50, 200, 100] # BGR
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alpha = _calculate_alpha_map(img)
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assert pytest.approx(alpha[0, 0], rel=1e-3) == 200 / 255.0
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# ── GeminiEngine ────────────────────────────────────────────────────
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class TestGeminiEngine:
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"""Tests for the GeminiEngine class."""
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@pytest.fixture(autouse=True)
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def _setup_engine(self):
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self.engine = GeminiEngine()
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def test_engine_loads_alpha_maps(self):
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small = self.engine.get_alpha_map(WatermarkSize.SMALL)
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large = self.engine.get_alpha_map(WatermarkSize.LARGE)
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assert small.shape == (48, 48)
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assert large.shape == (96, 96)
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def test_remove_watermark_returns_same_shape(self, tmp_image_path):
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image = cv2.imread(str(tmp_image_path), cv2.IMREAD_COLOR)
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result = self.engine.remove_watermark(image)
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assert result.shape == image.shape
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assert result.dtype == np.uint8
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def test_remove_watermark_does_not_modify_input(self, tmp_image_path):
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image = cv2.imread(str(tmp_image_path), cv2.IMREAD_COLOR)
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original = image.copy()
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self.engine.remove_watermark(image)
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np.testing.assert_array_equal(image, original)
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def test_remove_watermark_large_image(self, tmp_large_image_path):
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image = cv2.imread(str(tmp_large_image_path), cv2.IMREAD_COLOR)
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result = self.engine.remove_watermark(image)
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assert result.shape == image.shape
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def test_remove_watermark_custom_region(self, tmp_image_path):
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image = cv2.imread(str(tmp_image_path), cv2.IMREAD_COLOR)
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result = self.engine.remove_watermark_custom(image, (10, 10, 48, 48))
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assert result.shape == image.shape
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def test_remove_watermark_custom_large_region(self, tmp_image_path):
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image = cv2.imread(str(tmp_image_path), cv2.IMREAD_COLOR)
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result = self.engine.remove_watermark_custom(image, (10, 10, 96, 96))
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assert result.shape == image.shape
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def test_remove_watermark_custom_arbitrary_region(self, tmp_image_path):
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image = cv2.imread(str(tmp_image_path), cv2.IMREAD_COLOR)
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result = self.engine.remove_watermark_custom(image, (5, 5, 60, 60))
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assert result.shape == image.shape
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def test_force_size(self, tmp_image_path):
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image = cv2.imread(str(tmp_image_path), cv2.IMREAD_COLOR)
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result = self.engine.remove_watermark(image, force_size=WatermarkSize.LARGE)
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assert result.shape == image.shape
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# ── Detection ───────────────────────────────────────────────────────
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class TestDetection:
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"""Tests for watermark detection."""
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@pytest.fixture(autouse=True)
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def _setup_engine(self):
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self.engine = GeminiEngine()
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def test_detect_returns_result_object(self, tmp_image_path):
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image = cv2.imread(str(tmp_image_path), cv2.IMREAD_COLOR)
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result = self.engine.detect_watermark(image)
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assert isinstance(result, DetectionResult)
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assert 0.0 <= result.confidence <= 1.0
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def test_detect_empty_image_returns_no_detection(self):
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empty = np.zeros((0, 0, 3), dtype=np.uint8)
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result = self.engine.detect_watermark(empty)
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assert not result.detected
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assert result.confidence == 0.0
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def test_detect_none_image_returns_no_detection(self):
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result = self.engine.detect_watermark(None)
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assert not result.detected
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def test_detect_random_image_low_confidence(self, tmp_image_path):
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"""Random noise should not look like a watermark."""
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image = cv2.imread(str(tmp_image_path), cv2.IMREAD_COLOR)
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result = self.engine.detect_watermark(image)
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# Random image may or may not be detected; confidence should be meaningful
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assert isinstance(result.spatial_score, float)
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assert isinstance(result.gradient_score, float)
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class TestDetectSparkleConfidence:
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"""File-level entry point used by identify.py."""
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def test_returns_float_in_range_for_real_image(self, tmp_image_path):
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conf = detect_sparkle_confidence(tmp_image_path)
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assert conf is not None
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assert 0.0 <= conf <= 1.0
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def test_returns_none_for_unreadable_file(self, tmp_path):
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# cv2.imread returns None for a non-image; the helper must not raise.
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bogus = tmp_path / "not_an_image.png"
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bogus.write_bytes(b"this is not a PNG")
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assert detect_sparkle_confidence(bogus) is None
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# ── Inpainting ──────────────────────────────────────────────────────
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class TestInpainting:
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"""Tests for residual inpainting."""
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@pytest.fixture(autouse=True)
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def _setup_engine(self):
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self.engine = GeminiEngine()
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def test_inpaint_ns(self, tmp_image_path):
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image = cv2.imread(str(tmp_image_path), cv2.IMREAD_COLOR)
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result = self.engine.inpaint_residual(image, (150, 150, 48, 48), method="ns")
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assert result.shape == image.shape
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def test_inpaint_telea(self, tmp_image_path):
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image = cv2.imread(str(tmp_image_path), cv2.IMREAD_COLOR)
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result = self.engine.inpaint_residual(image, (150, 150, 48, 48), method="telea")
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assert result.shape == image.shape
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def test_inpaint_gaussian(self, tmp_image_path):
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image = cv2.imread(str(tmp_image_path), cv2.IMREAD_COLOR)
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result = self.engine.inpaint_residual(image, (150, 150, 48, 48), method="gaussian")
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assert result.shape == image.shape
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def test_inpaint_zero_strength(self, tmp_image_path):
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image = cv2.imread(str(tmp_image_path), cv2.IMREAD_COLOR)
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result = self.engine.inpaint_residual(image, (150, 150, 48, 48), strength=0.0)
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np.testing.assert_array_equal(result, image)
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def test_inpaint_tiny_region_returns_unchanged(self, tmp_image_path):
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image = cv2.imread(str(tmp_image_path), cv2.IMREAD_COLOR)
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result = self.engine.inpaint_residual(image, (10, 10, 2, 2))
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np.testing.assert_array_equal(result, image)
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def test_inpaint_does_not_modify_input(self, tmp_image_path):
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image = cv2.imread(str(tmp_image_path), cv2.IMREAD_COLOR)
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original = image.copy()
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self.engine.inpaint_residual(image, (150, 150, 48, 48))
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np.testing.assert_array_equal(image, original)
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class TestOverSubtractionGuard:
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"""Issue #30: reverse-alpha must not turn the sparkle into a black pit.
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On a dark background the captured alpha over-estimates the real sparkle opacity,
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so the fixed-alpha reverse blend over-subtracts and drives the footprint to black.
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The engine detects this and inpaints the footprint instead.
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"""
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# Composite the mark at ~60% of the captured opacity: the engine's alpha maxes at
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# ~0.51, real dark-background sparkles sit nearer ~0.31, so 0.6x reproduces the
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# capture-over-estimates-reality mismatch that triggers the bug.
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_REALISTIC_ALPHA_SCALE = 0.6
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@pytest.fixture(autouse=True)
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def _setup_engine(self):
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self.engine = GeminiEngine()
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def _composite_sparkle(self, bg_value: int, size: int = 1400, alpha_scale: float = _REALISTIC_ALPHA_SCALE):
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"""Build a flat BGR image of ``bg_value`` with the sparkle composited in.
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The mark is composited at a LOWER effective opacity than the engine's captured
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alpha map (``alpha_scale`` < 1), reproducing the real-world mismatch behind
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issue #30: the captured alpha (~0.51) over-estimates a real sparkle whose
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effective opacity is lower, so the fixed-alpha reverse blend over-subtracts.
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Placed at the configured large-image position so the detector locates it.
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"""
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img = np.full((size, size, 3), bg_value, dtype=np.float32)
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config = get_watermark_config(size, size)
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x, y = config.get_position(size, size)
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alpha = self.engine.get_alpha_map(WatermarkSize.LARGE)
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ah, aw = alpha.shape[:2]
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a = (alpha * alpha_scale)[:, :, None]
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roi = img[y : y + ah, x : x + aw]
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img[y : y + ah, x : x + aw] = a * 255.0 + (1.0 - a) * roi
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return np.clip(img, 0, 255).astype(np.uint8), (x, y, aw, ah)
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def test_dark_background_does_not_leave_black_pit(self):
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image, (x, y, w, h) = self._composite_sparkle(bg_value=60)
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out = self.engine.remove_watermark(image)
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footprint = out[y : y + h, x : x + w]
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# The recovered footprint must read like the dark background, not a black hole.
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assert footprint.min() > 25, f"black pit: min={footprint.min()}"
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assert abs(float(footprint.mean()) - 60.0) < 25.0
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def test_bright_background_keeps_reverse_alpha(self):
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"""A bright background does not over-subtract, so reverse-alpha is used."""
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bright, pos = self._composite_sparkle(bg_value=230)
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alpha = self.engine.get_interpolated_alpha(pos[2])
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assert self.engine._reverse_alpha_oversubtracts(bright, alpha, (pos[0], pos[1])) is False
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dark, dpos = self._composite_sparkle(bg_value=60)
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dalpha = self.engine.get_interpolated_alpha(dpos[2])
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assert self.engine._reverse_alpha_oversubtracts(dark, dalpha, (dpos[0], dpos[1])) is True
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class TestCornerPromotion:
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"""Issue #36: a small sparkle in the corner must not be lost to a larger decoy.
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The size weight that suppresses tiny-patch false positives also lets a larger,
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mediocre match elsewhere outrank a small, near-perfect sparkle in the corner --
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so a faint sparkle on a busy background (e.g. a portrait whose bright collar
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out-scores it) reads as clean. The corner-promotion override rescues it.
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"""
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_W, _H = 400, 520
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_CORNER = (_W - 40 - 20, _H - 40 - 20, 20) # bottom-right small sparkle (x, y, scale)
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_DECOY = (15, 210, 92) # large decoy: inside the search window, left of the corner
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@pytest.fixture(autouse=True)
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def _setup_engine(self):
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self.engine = GeminiEngine()
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def _paste(self, img: np.ndarray, scale: int, x: int, y: int, alpha_scale: float) -> None:
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tmpl = cv2.resize(self.engine._alpha_large, (scale, scale), interpolation=cv2.INTER_AREA)
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a = (tmpl * alpha_scale)[:, :, None]
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roi = img[y : y + scale, x : x + scale]
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img[y : y + scale, x : x + scale] = a * 255.0 + (1.0 - a) * roi
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def _scene(self, bg_value: int = 40) -> np.ndarray:
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"""Dark scene with a large decoy on the left and a small sparkle in the corner.
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Without the corner-promotion fix the global, size-weighted search locks onto
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the larger decoy; with it the small corner sparkle wins.
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"""
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img = np.full((self._H, self._W, 3), bg_value, dtype=np.float32)
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self._paste(img, self._DECOY[2], self._DECOY[0], self._DECOY[1], 0.55)
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self._paste(img, self._CORNER[2], self._CORNER[0], self._CORNER[1], 0.55)
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return np.clip(img, 0, 255).astype(np.uint8)
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def _in_bottom_right(self, region: tuple[int, int, int, int]) -> bool:
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x, y = region[0], region[1]
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return x >= self._W * 0.6 and y >= self._H * 0.6
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def test_small_corner_sparkle_is_detected_and_localized(self):
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det = self.engine.detect_watermark(self._scene())
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assert det.detected
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# Must localize to the planted corner sparkle, not the larger left-side decoy.
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assert self._in_bottom_right(det.region), f"localized to decoy, not corner: {det.region}"
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assert abs(det.region[0] - self._CORNER[0]) < 16
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assert abs(det.region[1] - self._CORNER[1]) < 16
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def test_promotion_is_what_rescues_it(self, monkeypatch):
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"""Guard the mechanism: disabling the override mislocalizes to the decoy.
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Proves the scene genuinely needs the override (so the localization test above
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is not a fluke): with the gate set unreachable the larger decoy wins.
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"""
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scene = self._scene()
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assert self._in_bottom_right(self.engine.detect_watermark(scene).region)
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monkeypatch.setattr(GeminiEngine, "_CORNER_PROMOTE_NCC", 2.0)
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assert not self._in_bottom_right(self.engine.detect_watermark(scene).region), (
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"decoy expected to win without the override"
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)
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def test_no_promotion_on_clean_flat_image(self):
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"""A flat image with no sparkle yields no corner match to promote."""
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flat = np.full((self._H, self._W, 3), 40, dtype=np.uint8)
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assert self.engine._corner_promote(flat, -1.0) is None
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