feat(cherry-tomato): 新增图片混淆/解混淆插件（小番茄）
Gilbert 曲线像素重排，可与网页版互解；`混淆` / `解混淆` 命令 （回复图片或图文同条消息发送），附 tests/test_cherry_tomato.py Co-Authored-By: Claude Code <noreply@anthropic.com>
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"""小番茄图片混淆 —— 纯算法测试。
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utils/obfuscation.py 无 nonebot 依赖，用 importlib 按文件路径裸加载
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（先例 test_rate_limit.py），避免拉起 NoneBot 运行时。
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核心断言：
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- `_gilbert_positions` 与参考实现（逐点 append 版 gilbert2d，原样抄录）逐元素
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一致 —— 这是「与网页版互解」的关键（曲线必须同一个）；
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- 位置序列恒为 0..total-1 的排列；
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- encrypt / decrypt 互为逆操作（数组级 + PIL 级），偏移公式与参考一致。
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"""
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from __future__ import annotations
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import importlib.util
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import io
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import math
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import random
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import sys
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from pathlib import Path
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import numpy as np
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import pytest
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from PIL import Image
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_MODULE_PATH = (
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Path(__file__).resolve().parents[1]
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/ "hexi"
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/ "plugins"
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/ "nonebot_plugin_cherry_tomato"
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/ "utils"
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/ "obfuscation.py"
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)
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@pytest.fixture(scope="module")
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def obf():
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"""以独立模块名加载 obfuscation.py，避免触发插件包 __init__ 的依赖链。"""
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spec = importlib.util.spec_from_file_location(
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"hexi_cherry_tomato_obfuscation_test", _MODULE_PATH
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)
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assert spec is not None and spec.loader is not None
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module = importlib.util.module_from_spec(spec)
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sys.modules[spec.name] = module
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spec.loader.exec_module(module)
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yield module
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sys.modules.pop(spec.name, None)
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# ─────────────────────────────────────────────────────────────
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# 参考实现（Cherry-tomato-image-obfuscation 逐点 append 版），
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# 原样抄录，仅作对照，不执行于生产路径
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# ─────────────────────────────────────────────────────────────
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def _naive_gilbert2d(width: int, height: int) -> list[tuple[int, int]]:
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coordinates: list[tuple[int, int]] = []
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if width >= height:
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_naive_generate2d(0, 0, width, 0, 0, height, coordinates)
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else:
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_naive_generate2d(0, 0, 0, height, width, 0, coordinates)
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return coordinates
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def _naive_sign(x):
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if x > 0:
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return 1
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elif x < 0:
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return -1
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return 0
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def _naive_generate2d(x, y, ax, ay, bx, by, coordinates):
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w = abs(ax + ay)
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h = abs(bx + by)
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dax = _naive_sign(ax)
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day = _naive_sign(ay)
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dbx = _naive_sign(bx)
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dby = _naive_sign(by)
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if h == 1:
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for _ in range(w):
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coordinates.append((x, y))
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x += dax
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y += day
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return
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if w == 1:
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for _ in range(h):
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coordinates.append((x, y))
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x += dbx
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y += dby
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return
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ax2 = ax // 2
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ay2 = ay // 2
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bx2 = bx // 2
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by2 = by // 2
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w2 = abs(ax2 + ay2)
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h2 = abs(bx2 + by2)
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if 2 * w > 3 * h:
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if (w2 % 2) and (w > 2):
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ax2 += dax
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ay2 += day
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_naive_generate2d(x, y, ax2, ay2, bx, by, coordinates)
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_naive_generate2d(x + ax2, y + ay2, ax - ax2, ay - ay2, bx, by, coordinates)
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else:
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if (h2 % 2) and (h > 2):
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bx2 += dbx
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by2 += dby
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_naive_generate2d(x, y, bx2, by2, ax2, ay2, coordinates)
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_naive_generate2d(x + bx2, y + by2, ax, ay, bx - bx2, by - by2, coordinates)
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_naive_generate2d(
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x + (ax - dax) + (bx2 - dbx),
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y + (ay - day) + (by2 - dby),
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-bx2,
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-by2,
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-(ax - ax2),
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-(ay - ay2),
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coordinates,
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)
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def _naive_positions(width: int, height: int) -> list[int]:
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return [x + y * width for x, y in _naive_gilbert2d(width, height)]
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def _build_sizes() -> list[tuple[int, int]]:
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sizes: list[tuple[int, int]] = [
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(1, 1),
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(1, 2),
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(2, 1),
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(1, 3),
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(3, 1),
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(2, 2),
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(3, 3),
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(7, 5),
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(5, 7),
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(1, 33),
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(33, 1),
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(1, 400),
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(400, 1),
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(128, 96),
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(200, 50),
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(50, 200),
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(333, 37),
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(256, 256),
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]
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primes = (2, 3, 5, 13, 31, 61, 79)
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sizes += [(p, q) for p in primes for q in primes]
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rng = random.Random(20261010)
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sizes += [(rng.randint(1, 80), rng.randint(1, 80)) for _ in range(220)]
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seen: set[tuple[int, int]] = set()
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unique: list[tuple[int, int]] = []
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for size in sizes:
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if size not in seen:
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seen.add(size)
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unique.append(size)
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return unique
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_SIZES = _build_sizes()
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@pytest.mark.parametrize("size", _SIZES, ids=[f"{w}x{h}" for w, h in _SIZES])
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def test_positions_match_reference(obf, size):
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"""任何尺寸下位置序列都与参考逐点实现完全一致（互解的前提）。"""
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width, height = size
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pos = obf._gilbert_positions(width, height)
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assert pos.dtype == np.int32
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assert pos.tolist() == _naive_positions(width, height)
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@pytest.mark.parametrize("size", _SIZES, ids=[f"{w}x{h}" for w, h in _SIZES])
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def test_positions_is_permutation(obf, size):
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"""位置序列必须是 0..total-1 的排列（不重不漏）。"""
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width, height = size
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pos = obf._gilbert_positions(width, height)
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assert sorted(pos.tolist()) == list(range(width * height))
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def test_gilbert_offset_formula(obf):
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"""黄金比例偏移与参考/JS 公式一致。"""
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for total in (1, 2, 3, 6, 10, 1920 * 1080, 8_000_000):
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assert obf.gilbert_offset(total) == round((math.sqrt(5) - 1) / 2 * total)
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assert obf.gilbert_offset(10) == 6 # 0.618...*10 = 6.18 → 6（与 JS 一致）
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@pytest.mark.parametrize("size", [(3, 2), (17, 5), (40, 33), (64, 64)])
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def test_encrypt_decrypt_array_roundtrip(obf, size):
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"""数组级 encrypt∘decrypt 恒等；encrypt 不改输入、确实重排了像素。"""
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width, height = size
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rng = np.random.default_rng(20261010)
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src = rng.integers(0, 256, size=(width * height, 3), dtype=np.uint8)
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original = src.copy()
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enc = obf.encrypt_pixels(src, width, height)
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assert np.array_equal(src, original) # 输入未被改写
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assert not np.array_equal(enc, src) # 偏移非 0，随机图必被重排
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dec = obf.decrypt_pixels(enc, width, height)
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assert np.array_equal(dec, src)
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@pytest.mark.parametrize("size", [(3, 2), (13, 9), (40, 33)])
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def test_encrypt_decrypt_pil_roundtrip(obf, size):
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"""PIL 级 round-trip：obfuscate 输出为 RGB、无 EXIF，解混淆还原原图。"""
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width, height = size
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rng = np.random.default_rng(7)
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arr = rng.integers(0, 256, (height, width, 3), dtype=np.uint8)
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img = Image.fromarray(arr, "RGB")
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enc, scaled, extra = obf.obfuscate(img, decrypt=False, max_pixels=10**9)
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assert enc.mode == "RGB"
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assert scaled is False
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assert extra is False
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assert not np.array_equal(np.asarray(enc), arr)
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dec, _, _ = obf.obfuscate(enc, decrypt=True, max_pixels=10**9)
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assert np.array_equal(np.asarray(dec), arr)
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assert not dec.getexif() # 输出统一不带 EXIF
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# JPEG 编码可解码、尺寸不变
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data = obf.to_jpeg_bytes(dec, 95)
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with Image.open(io.BytesIO(data)) as reopened:
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assert reopened.size == (width, height)
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def test_obfuscate_applies_exif_orientation(obf):
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"""带 EXIF 旋转的照片先规范化方向：输出宽高对调（显示方向不突变）。"""
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img = Image.new("RGB", (13, 9), (10, 20, 30))
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exif = Image.Exif()
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exif[0x0112] = 6 # Orientation=6：显示时需顺时针旋转 90°（宽高对调）
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buf = io.BytesIO()
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img.save(buf, format="JPEG", exif=exif)
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buf.seek(0)
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with Image.open(buf) as photo:
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assert photo.size == (13, 9)
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assert photo.getexif().get(0x0112) == 6
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out, _, _ = obf.obfuscate(photo, decrypt=False, max_pixels=10**9)
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assert out.size == (9, 13)
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def test_fit_within(obf):
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"""fit_within：未超限原样返回；超限等比缩小且至少 1 像素。"""
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img = Image.new("RGB", (4000, 3000))
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assert obf.fit_within(img, 12_000_000) is img
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out = obf.fit_within(img, 2_000_000)
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assert out.width * out.height <= 2_000_000
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assert abs(out.width / out.height - 4000 / 3000) < 0.01 # 等比
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tiny = obf.fit_within(img, 1)
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assert tiny.width >= 1 and tiny.height >= 1
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def test_obfuscate_scale_flag_only_for_encrypt(obf):
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"""加密超限缩放并置 scaled；解密绝不缩放（超大由 pipeline 拒绝兜底）。"""
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img = Image.new("RGB", (60, 40))
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enc, scaled, _ = obf.obfuscate(img, decrypt=False, max_pixels=1000)
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assert scaled is True
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assert enc.width * enc.height <= 1000
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dec, scaled2, _ = obf.obfuscate(img, decrypt=True, max_pixels=1000)
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assert scaled2 is False
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assert dec.size == img.size
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def test_obfuscate_extra_frames_flag(obf):
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"""多帧图只取第一帧并置 extra_frames；单帧图不置。"""
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frames = [
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Image.new("RGB", (8, 6), (255, 0, 0)),
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Image.new("RGB", (8, 6), (0, 0, 255)),
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]
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buf = io.BytesIO()
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frames[0].save(buf, format="GIF", save_all=True, append_images=frames[1:])
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buf.seek(0)
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with Image.open(buf) as gif:
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out, _, extra = obf.obfuscate(gif, decrypt=False, max_pixels=10**9)
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assert extra is True
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assert out.size == (8, 6)
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# 首帧是纯红：均匀图重排后仍是纯红（同时证明没拿第二帧的蓝色）
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assert out.getpixel((0, 0)) == (255, 0, 0)
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_, _, extra2 = obf.obfuscate(
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Image.new("RGB", (8, 6)), decrypt=False, max_pixels=10**9
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)
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assert extra2 is False
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