import pytest
import os
import shutil
import cv2
import numpy as np
from pathlib import Path
import sys
# Ensure photographi packages are in path
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '../../photo-quality-analyzer')))
@pytest.fixture(scope="session")
def test_assets_dir(tmp_path_factory):
"""
Creates a temporary directory with synthetic test assets
that persist for the entire test session.
"""
assets_dir = tmp_path_factory.mktemp("assets")
# 1. Create a "perfect" sharp image
img_sharp = np.zeros((512, 512, 3), dtype=np.uint8)
# Add a checkerboard pattern for sharpness detection
for y in range(0, 512, 64):
for x in range(0, 512, 64):
if (x // 64 + y // 64) % 2 == 0:
cv2.rectangle(img_sharp, (x, y), (x+64, y+64), (200, 200, 200), -1)
cv2.putText(img_sharp, "SHARP TARGET", (50, 256), cv2.FONT_HERSHEY_SIMPLEX, 1.5, (255, 255, 255), 2)
cv2.imwrite(str(assets_dir / "sharp.jpg"), img_sharp)
# 2. Create a VERY blurry image
img_blur = cv2.GaussianBlur(img_sharp, (91, 91), 0)
cv2.imwrite(str(assets_dir / "blur.jpg"), img_blur)
# 3. Create a dark/underexposed image
img_dark = (img_sharp * 0.1).astype(np.uint8)
cv2.imwrite(str(assets_dir / "dark.jpg"), img_dark)
# 4. Create a noisy image
noise = np.random.normal(0, 20, (512, 512, 3)).astype(np.uint8)
img_noise = cv2.add(img_sharp, noise)
cv2.imwrite(str(assets_dir / "noise.jpg"), img_noise)
return assets_dir
@pytest.fixture
def mcp_server():
"""
Returns the imported server module for direct function testing.
"""
import server
return server
