mirror of
https://github.com/wiltodelta/remove-ai-watermarks.git
synced 2026-05-21 04:06:48 +02:00
test-user
e5d8970add
- CLI with visible, invisible, all, metadata, and batch commands - Gemini watermark removal via reverse alpha blending - Invisible watermark removal via diffusion regeneration (SynthID, TreeRing) - AI metadata stripping (EXIF, PNG text, C2PA) - Face protection (YOLO/Haar) and analog humanizer - 137 tests covering all CLI modes and core engines - Ruff and Pyright clean
217 lines
8.6 KiB
Python
217 lines
8.6 KiB
Python
"""Tests for the Gemini visible-watermark engine."""
|
||
|
||
from __future__ import annotations
|
||
|
||
import cv2
|
||
import numpy as np
|
||
import pytest
|
||
|
||
from remove_ai_watermarks.gemini_engine import (
|
||
DetectionResult,
|
||
GeminiEngine,
|
||
WatermarkPosition,
|
||
WatermarkSize,
|
||
_calculate_alpha_map,
|
||
get_watermark_config,
|
||
get_watermark_size,
|
||
)
|
||
|
||
# ── WatermarkSize / config helpers ──────────────────────────────────
|
||
|
||
|
||
class TestWatermarkConfig:
|
||
"""Tests for watermark size detection and position calculation."""
|
||
|
||
def test_small_image_gets_small_watermark(self):
|
||
assert get_watermark_size(800, 600) == WatermarkSize.SMALL
|
||
|
||
def test_large_image_gets_large_watermark(self):
|
||
assert get_watermark_size(1920, 1080) == WatermarkSize.LARGE
|
||
|
||
def test_boundary_image_stays_small(self):
|
||
"""Exactly 1024×1024 should be SMALL (rule: > 1024 for LARGE)."""
|
||
assert get_watermark_size(1024, 1024) == WatermarkSize.SMALL
|
||
|
||
def test_one_dimension_small(self):
|
||
"""Only one dimension > 1024 → still SMALL."""
|
||
assert get_watermark_size(2000, 500) == WatermarkSize.SMALL
|
||
|
||
def test_config_small_returns_correct_values(self):
|
||
config = get_watermark_config(800, 600)
|
||
assert config.margin_right == 32
|
||
assert config.margin_bottom == 32
|
||
assert config.logo_size == 48
|
||
|
||
def test_config_large_returns_correct_values(self):
|
||
config = get_watermark_config(1920, 1080)
|
||
assert config.margin_right == 64
|
||
assert config.margin_bottom == 64
|
||
assert config.logo_size == 96
|
||
|
||
def test_position_calculation(self):
|
||
pos = WatermarkPosition(margin_right=32, margin_bottom=32, logo_size=48)
|
||
x, y = pos.get_position(800, 600)
|
||
assert x == 800 - 32 - 48 # 720
|
||
assert y == 600 - 32 - 48 # 520
|
||
|
||
|
||
# ── Alpha map ───────────────────────────────────────────────────────
|
||
|
||
|
||
class TestAlphaMap:
|
||
"""Tests for alpha map calculation."""
|
||
|
||
def test_pure_black_gives_zero_alpha(self):
|
||
black = np.zeros((10, 10, 3), dtype=np.uint8)
|
||
alpha = _calculate_alpha_map(black)
|
||
assert alpha.shape == (10, 10)
|
||
np.testing.assert_array_equal(alpha, 0.0)
|
||
|
||
def test_pure_white_gives_one_alpha(self):
|
||
white = np.full((10, 10, 3), 255, dtype=np.uint8)
|
||
alpha = _calculate_alpha_map(white)
|
||
np.testing.assert_allclose(alpha, 1.0)
|
||
|
||
def test_grayscale_input(self):
|
||
gray = np.full((10, 10), 128, dtype=np.uint8)
|
||
alpha = _calculate_alpha_map(gray)
|
||
np.testing.assert_allclose(alpha, 128 / 255.0)
|
||
|
||
def test_max_channel_used(self):
|
||
"""Alpha should use max(R, G, B)."""
|
||
img = np.zeros((1, 1, 3), dtype=np.uint8)
|
||
img[0, 0] = [50, 200, 100] # BGR
|
||
alpha = _calculate_alpha_map(img)
|
||
assert pytest.approx(alpha[0, 0], rel=1e-3) == 200 / 255.0
|
||
|
||
|
||
# ── GeminiEngine ────────────────────────────────────────────────────
|
||
|
||
|
||
class TestGeminiEngine:
|
||
"""Tests for the GeminiEngine class."""
|
||
|
||
@pytest.fixture(autouse=True)
|
||
def _setup_engine(self):
|
||
self.engine = GeminiEngine()
|
||
|
||
def test_engine_loads_alpha_maps(self):
|
||
small = self.engine.get_alpha_map(WatermarkSize.SMALL)
|
||
large = self.engine.get_alpha_map(WatermarkSize.LARGE)
|
||
assert small.shape == (48, 48)
|
||
assert large.shape == (96, 96)
|
||
|
||
def test_remove_watermark_returns_same_shape(self, tmp_image_path):
|
||
image = cv2.imread(str(tmp_image_path), cv2.IMREAD_COLOR)
|
||
result = self.engine.remove_watermark(image)
|
||
assert result.shape == image.shape
|
||
assert result.dtype == np.uint8
|
||
|
||
def test_remove_watermark_does_not_modify_input(self, tmp_image_path):
|
||
image = cv2.imread(str(tmp_image_path), cv2.IMREAD_COLOR)
|
||
original = image.copy()
|
||
self.engine.remove_watermark(image)
|
||
np.testing.assert_array_equal(image, original)
|
||
|
||
def test_remove_watermark_large_image(self, tmp_large_image_path):
|
||
image = cv2.imread(str(tmp_large_image_path), cv2.IMREAD_COLOR)
|
||
result = self.engine.remove_watermark(image)
|
||
assert result.shape == image.shape
|
||
|
||
def test_remove_watermark_custom_region(self, tmp_image_path):
|
||
image = cv2.imread(str(tmp_image_path), cv2.IMREAD_COLOR)
|
||
result = self.engine.remove_watermark_custom(image, (10, 10, 48, 48))
|
||
assert result.shape == image.shape
|
||
|
||
def test_remove_watermark_custom_large_region(self, tmp_image_path):
|
||
image = cv2.imread(str(tmp_image_path), cv2.IMREAD_COLOR)
|
||
result = self.engine.remove_watermark_custom(image, (10, 10, 96, 96))
|
||
assert result.shape == image.shape
|
||
|
||
def test_remove_watermark_custom_arbitrary_region(self, tmp_image_path):
|
||
image = cv2.imread(str(tmp_image_path), cv2.IMREAD_COLOR)
|
||
result = self.engine.remove_watermark_custom(image, (5, 5, 60, 60))
|
||
assert result.shape == image.shape
|
||
|
||
def test_force_size(self, tmp_image_path):
|
||
image = cv2.imread(str(tmp_image_path), cv2.IMREAD_COLOR)
|
||
result = self.engine.remove_watermark(image, force_size=WatermarkSize.LARGE)
|
||
assert result.shape == image.shape
|
||
|
||
|
||
# ── Detection ───────────────────────────────────────────────────────
|
||
|
||
|
||
class TestDetection:
|
||
"""Tests for watermark detection."""
|
||
|
||
@pytest.fixture(autouse=True)
|
||
def _setup_engine(self):
|
||
self.engine = GeminiEngine()
|
||
|
||
def test_detect_returns_result_object(self, tmp_image_path):
|
||
image = cv2.imread(str(tmp_image_path), cv2.IMREAD_COLOR)
|
||
result = self.engine.detect_watermark(image)
|
||
assert isinstance(result, DetectionResult)
|
||
assert 0.0 <= result.confidence <= 1.0
|
||
|
||
def test_detect_empty_image_returns_no_detection(self):
|
||
empty = np.zeros((0, 0, 3), dtype=np.uint8)
|
||
result = self.engine.detect_watermark(empty)
|
||
assert not result.detected
|
||
assert result.confidence == 0.0
|
||
|
||
def test_detect_none_image_returns_no_detection(self):
|
||
result = self.engine.detect_watermark(None)
|
||
assert not result.detected
|
||
|
||
def test_detect_random_image_low_confidence(self, tmp_image_path):
|
||
"""Random noise should not look like a watermark."""
|
||
image = cv2.imread(str(tmp_image_path), cv2.IMREAD_COLOR)
|
||
result = self.engine.detect_watermark(image)
|
||
# Random image may or may not be detected; confidence should be meaningful
|
||
assert isinstance(result.spatial_score, float)
|
||
assert isinstance(result.gradient_score, float)
|
||
|
||
|
||
# ── Inpainting ──────────────────────────────────────────────────────
|
||
|
||
|
||
class TestInpainting:
|
||
"""Tests for residual inpainting."""
|
||
|
||
@pytest.fixture(autouse=True)
|
||
def _setup_engine(self):
|
||
self.engine = GeminiEngine()
|
||
|
||
def test_inpaint_ns(self, tmp_image_path):
|
||
image = cv2.imread(str(tmp_image_path), cv2.IMREAD_COLOR)
|
||
result = self.engine.inpaint_residual(image, (150, 150, 48, 48), method="ns")
|
||
assert result.shape == image.shape
|
||
|
||
def test_inpaint_telea(self, tmp_image_path):
|
||
image = cv2.imread(str(tmp_image_path), cv2.IMREAD_COLOR)
|
||
result = self.engine.inpaint_residual(image, (150, 150, 48, 48), method="telea")
|
||
assert result.shape == image.shape
|
||
|
||
def test_inpaint_gaussian(self, tmp_image_path):
|
||
image = cv2.imread(str(tmp_image_path), cv2.IMREAD_COLOR)
|
||
result = self.engine.inpaint_residual(image, (150, 150, 48, 48), method="gaussian")
|
||
assert result.shape == image.shape
|
||
|
||
def test_inpaint_zero_strength(self, tmp_image_path):
|
||
image = cv2.imread(str(tmp_image_path), cv2.IMREAD_COLOR)
|
||
result = self.engine.inpaint_residual(image, (150, 150, 48, 48), strength=0.0)
|
||
np.testing.assert_array_equal(result, image)
|
||
|
||
def test_inpaint_tiny_region_returns_unchanged(self, tmp_image_path):
|
||
image = cv2.imread(str(tmp_image_path), cv2.IMREAD_COLOR)
|
||
result = self.engine.inpaint_residual(image, (10, 10, 2, 2))
|
||
np.testing.assert_array_equal(result, image)
|
||
|
||
def test_inpaint_does_not_modify_input(self, tmp_image_path):
|
||
image = cv2.imread(str(tmp_image_path), cv2.IMREAD_COLOR)
|
||
original = image.copy()
|
||
self.engine.inpaint_residual(image, (150, 150, 48, 48))
|
||
np.testing.assert_array_equal(image, original)
|