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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
53 lines
1.6 KiB
Python
53 lines
1.6 KiB
Python
import numpy as np
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from remove_ai_watermarks.humanizer import apply_analog_humanizer
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def test_humanizer_does_not_modify_original_if_disabled():
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img = np.zeros((100, 100, 3), dtype=np.uint8)
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img[50, 50] = [100, 150, 200]
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org_img = img.copy()
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# grain=0, shift=0 means disabled essentially. But wait, apply_analog_humanizer currently applies chromatic shift even if grain=0.
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result = apply_analog_humanizer(img, grain_intensity=0.0, chromatic_shift=0)
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assert np.array_equal(result, org_img)
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def test_chromatic_shift():
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# Only green channel is centered, red/blue should shift.
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img = np.zeros((5, 5, 3), dtype=np.uint8)
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img[2, 2] = [255, 255, 255] # B, G, R
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# shift=1
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result = apply_analog_humanizer(img, grain_intensity=0.0, chromatic_shift=1)
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# G (index 1) stays at [2,2]
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assert result[2, 2, 1] == 255
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# B (index 0) shifted right (+1 axis 1) -> [2, 3]
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assert result[2, 3, 0] == 255
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# R (index 2) shifted left (-1 axis 1) -> [2, 1]
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assert result[2, 1, 2] == 255
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def test_grain_intensity():
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# Gray image
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img = np.full((100, 100, 3), 128, dtype=np.uint8)
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# Add strong noise
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result = apply_analog_humanizer(img, grain_intensity=10.0, chromatic_shift=0)
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# Image should no longer be purely 128
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unique_vals = np.unique(result)
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assert len(unique_vals) > 5
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# Mean should roughly be 128
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assert 126 < np.mean(result) < 130
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def test_invalid_shape():
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# Missing color channel
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img = np.zeros((100, 100), dtype=np.uint8)
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img[0, 0] = 50
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result = apply_analog_humanizer(img)
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assert np.array_equal(img, result)
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