wehub-resource-sync
6f330990b5
Tests / lint (push) Has been cancelled
Tests / test macos-latest py3.10 (push) Has been cancelled
Tests / test macos-latest py3.12 (push) Has been cancelled
Tests / test ubuntu-latest py3.10 (push) Has been cancelled
Tests / test ubuntu-latest py3.12 (push) Has been cancelled
Tests / test windows-latest py3.10 (push) Has been cancelled
Tests / test windows-latest py3.12 (push) Has been cancelled
80 lines
2.5 KiB
Python
80 lines
2.5 KiB
Python
"""Shared fixtures for remove-ai-watermarks test suite."""
|
|
|
|
from __future__ import annotations
|
|
|
|
from pathlib import Path
|
|
|
|
import cv2
|
|
import numpy as np
|
|
import pytest
|
|
from PIL import Image
|
|
from PIL.PngImagePlugin import PngInfo
|
|
|
|
CORPUS_NEG_DIR = Path(__file__).resolve().parent.parent / "data" / "synthid_corpus" / "images" / "neg"
|
|
|
|
|
|
@pytest.fixture
|
|
def clean_photo() -> Path:
|
|
"""A verified-negative real photo from the corpus neg/ set.
|
|
|
|
Used by the "non-AI image" assertions (no SynthID, verdict unknown). These
|
|
are real photos with no AI provenance, the ground truth for "must not false-
|
|
positive". Skips if the corpus is not checked out.
|
|
"""
|
|
files = sorted(CORPUS_NEG_DIR.glob("*")) if CORPUS_NEG_DIR.exists() else []
|
|
if not files:
|
|
pytest.skip("no corpus neg/ images present")
|
|
return files[0]
|
|
|
|
|
|
@pytest.fixture
|
|
def tmp_image_path(tmp_path: Path) -> Path:
|
|
"""Create a minimal 200x200 test PNG image and return its path."""
|
|
img = np.random.randint(0, 255, (200, 200, 3), dtype=np.uint8)
|
|
path = tmp_path / "test_image.png"
|
|
cv2.imwrite(str(path), img)
|
|
return path
|
|
|
|
|
|
@pytest.fixture
|
|
def tmp_large_image_path(tmp_path: Path) -> Path:
|
|
"""Create a 1200x1200 test PNG image (triggers large watermark branch)."""
|
|
img = np.random.randint(0, 255, (1200, 1200, 3), dtype=np.uint8)
|
|
path = tmp_path / "test_large.png"
|
|
cv2.imwrite(str(path), img)
|
|
return path
|
|
|
|
|
|
@pytest.fixture
|
|
def tmp_jpeg_path(tmp_path: Path) -> Path:
|
|
"""Create a minimal JPEG test image."""
|
|
img = np.random.randint(0, 255, (100, 100, 3), dtype=np.uint8)
|
|
path = tmp_path / "test_image.jpg"
|
|
cv2.imwrite(str(path), img)
|
|
return path
|
|
|
|
|
|
@pytest.fixture
|
|
def tmp_png_with_ai_metadata(tmp_path: Path) -> Path:
|
|
"""Create a PNG with AI-related metadata keys."""
|
|
img = Image.new("RGB", (64, 64), color=(128, 128, 128))
|
|
pnginfo = PngInfo()
|
|
pnginfo.add_text("parameters", "Steps: 20, Sampler: Euler, CFG scale: 7")
|
|
pnginfo.add_text("prompt", "a beautiful landscape")
|
|
pnginfo.add_text("Author", "Test Author")
|
|
path = tmp_path / "ai_metadata.png"
|
|
img.save(path, pnginfo=pnginfo)
|
|
return path
|
|
|
|
|
|
@pytest.fixture
|
|
def tmp_clean_png(tmp_path: Path) -> Path:
|
|
"""Create a PNG with no AI metadata."""
|
|
img = Image.new("RGB", (64, 64), color=(200, 100, 50))
|
|
pnginfo = PngInfo()
|
|
pnginfo.add_text("Author", "Human Artist")
|
|
pnginfo.add_text("Title", "Test Artwork")
|
|
path = tmp_path / "clean.png"
|
|
img.save(path, pnginfo=pnginfo)
|
|
return path
|