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 —— 纯工具层（无 nonebot 依赖，可单测）。"""
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from .obfuscation import ( # noqa: F401
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decrypt_pixels,
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encrypt_pixels,
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fit_within,
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gilbert_offset,
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obfuscate,
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to_jpeg_bytes,
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)
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"""小番茄图片混淆 —— 纯算法层（无 nonebot / 无配置依赖，可裸加载单测）。
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算法与 singularpoint.cn/hideImg1.html、GitHub ok8634673/Cherry-tomato-image-obfuscation
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一致：Gilbert（广义 Hilbert）空间填充曲线 + 黄金比例偏移做像素重排，混淆/解混淆
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互为逆操作，混淆结果可与网页版互解（关键：曲线与偏移逐像素一致、尺寸不变）。
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性能：朴素实现要逐像素 append 出 total 个坐标（12MP 约 17s）。这里改为「小块模板
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缓存」：曲线递归到 w*h <= _TILE_AREA 的轴对齐子矩形时不再下拆，直接拼接该
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「形状+朝向」的相对坐标模板（模板本身用参考递归生成 + lru_cache，共 8 种朝向），
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每个模板实例只做一条向量化赋值。1920x1080 生成位置数组约 0.03s。
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"""
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from __future__ import annotations
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import functools
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import io
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import math
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import numpy as np
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from PIL import Image, ImageOps
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# 递归到该面积的子矩形改用缓存模板（模板数量与命中率的折中）
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_TILE_AREA = 1024
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# 模板缓存条数：形状+朝向空间有限，512 条足够任何尺寸复用
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_TILE_CACHE_SIZE = 512
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def _sign(x: int) -> int:
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if x > 0:
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return 1
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if x < 0:
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return -1
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return 0
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def _collect(x, y, ax, ay, bx, by, coords) -> None:
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"""参考实现（gilbert2d）的递归本体，逐点收集坐标（仅用于生成模板）。
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与参考实现逐行一致；子问题恒为轴对齐矩形（a、b 各沿一轴），起点平移到
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(0, 0) 后形状不变，故可用「(w,h)+方向」作模板键。
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"""
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w = abs(ax + ay)
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h = abs(bx + by)
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dax = _sign(ax)
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day = _sign(ay)
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dbx = _sign(bx)
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dby = _sign(by)
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if h == 1:
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for _ in range(w):
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coords.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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coords.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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_collect(x, y, ax2, ay2, bx, by, coords)
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_collect(x + ax2, y + ay2, ax - ax2, ay - ay2, bx, by, coords)
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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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_collect(x, y, bx2, by2, ax2, ay2, coords)
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_collect(x + bx2, y + by2, ax, ay, bx - bx2, by - by2, coords)
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_collect(
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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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coords,
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)
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@functools.lru_cache(maxsize=_TILE_CACHE_SIZE)
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def _tile(
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w: int, h: int, dax: int, day: int, dbx: int, dby: int
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) -> tuple[np.ndarray, np.ndarray]:
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"""生成「a 长 w 朝 (dax,day)、b 长 h 朝 (dbx,dby)」子矩形的相对坐标模板。
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返回 (lx, ly) 两个 int32 数组。起点取 (0,0)，与参考递归逐点一致。
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"""
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coords: list[tuple[int, int]] = []
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_collect(0, 0, w * dax, w * day, h * dbx, h * dby, coords)
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lx = np.fromiter((c[0] for c in coords), dtype=np.int32, count=len(coords))
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ly = np.fromiter((c[1] for c in coords), dtype=np.int32, count=len(coords))
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return lx, ly
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def _emit(x, y, ax, ay, bx, by, pos: np.ndarray, k: int, width: int) -> int:
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"""把子矩形 (x,y,ax,ay,bx,by) 的曲线坐标写入 pos[k:]，返回新的写入位置。
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结构与参考递归完全相同，只在叶子处换用模板 + 一条向量化赋值：
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相对坐标 (lx,ly) 平移到 (x+lx, y+ly)，展平下标 = (x+y*width) + lx + ly*width。
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"""
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w = abs(ax + ay)
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h = abs(bx + by)
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if h == 1 or w == 1 or w * h <= _TILE_AREA:
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lx, ly = _tile(w, h, _sign(ax), _sign(ay), _sign(bx), _sign(by))
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n = lx.shape[0]
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pos[k : k + n] = (x + y * width) + lx + ly * width
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return k + n
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dax = _sign(ax)
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day = _sign(ay)
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dbx = _sign(bx)
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dby = _sign(by)
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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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k = _emit(x, y, ax2, ay2, bx, by, pos, k, width)
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k = _emit(x + ax2, y + ay2, ax - ax2, ay - ay2, bx, by, pos, k, width)
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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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k = _emit(x, y, bx2, by2, ax2, ay2, pos, k, width)
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k = _emit(x + bx2, y + by2, ax, ay, bx - bx2, by - by2, pos, k, width)
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k = _emit(
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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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pos,
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k,
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width,
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)
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return k
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def _gilbert_positions(width: int, height: int) -> np.ndarray:
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"""整图 Gilbert 曲线的展平像素下标序列：pos[i] = 曲线第 i 步的 (x+y*width)。
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是 0..total-1 的一个排列；int32 足够（total < 2^31 由调用方上限保证）。
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"""
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total = width * height
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if total >= 2**31:
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raise ValueError(f"图片像素数过大：{width}x{height}")
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pos = np.empty(total, dtype=np.int32)
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if width >= height:
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n = _emit(0, 0, width, 0, 0, height, pos, 0, width)
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else:
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n = _emit(0, 0, 0, height, width, 0, pos, 0, width)
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if n != total: # pragma: no cover - 模板/递归出错时的兜底自检
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raise RuntimeError(f"Gilbert 曲线生成异常：期望 {total} 点，实际 {n}")
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return pos
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def gilbert_offset(total: int) -> int:
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"""黄金比例偏移，与参考实现（JS: Math.round((√5-1)/2*W*H)）一致。
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与 JS Math.round 的差异只存在于不可能精确命中的 .5 边界，忽略。
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"""
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return round((math.sqrt(5) - 1) / 2 * total)
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def encrypt_pixels(src: np.ndarray, width: int, height: int) -> np.ndarray:
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"""混淆：(N,3) RGB 数组重排，dst[curve[i+offset]] = src[curve[i]]。
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以位置数组 pos 数组化：dst[np.roll(pos, -offset)] = src[pos]。
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"""
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pos = _gilbert_positions(width, height)
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dst = np.empty_like(src)
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dst[np.roll(pos, -gilbert_offset(width * height))] = src[pos]
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return dst
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def decrypt_pixels(src: np.ndarray, width: int, height: int) -> np.ndarray:
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"""解混淆：encrypt 的逆操作，dst[curve[i]] = src[curve[i+offset]]。"""
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pos = _gilbert_positions(width, height)
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dst = np.empty_like(src)
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dst[pos] = src[np.roll(pos, -gilbert_offset(width * height))]
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return dst
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def fit_within(img: Image.Image, max_pixels: int) -> Image.Image:
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"""超过 max_pixels 时等比缩小（长宽至少 1 像素）；未超限原样返回。"""
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w, h = img.size
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if w * h <= max_pixels:
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return img
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scale = math.sqrt(max_pixels / (w * h))
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new_size = (max(1, int(w * scale)), max(1, int(h * scale)))
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return img.resize(new_size, Image.Resampling.LANCZOS)
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def obfuscate(
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img: Image.Image, *, decrypt: bool, max_pixels: int
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) -> tuple[Image.Image, bool, bool]:
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"""整图混淆/解混淆。
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- 先 ImageOps.exif_transpose 把手机照片的 EXIF 旋转规范化到显示方向
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（否则输出丢 EXIF 后显示方向会突变），再 convert("RGB")；
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- 多帧图（GIF/动图）只取第一帧，返回 extra_frames=True 供提示；
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- 加密超过 max_pixels 等比缩小（scaled=True）；解密绝不缩放（调用方限硬上限）；
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- 输出是全新 RGB 图，不带 EXIF。
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Returns: (结果图, 是否缩放, 是否丢弃了多余帧)
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"""
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extra_frames = getattr(img, "n_frames", 1) > 1
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# in_place=False 时 exif_transpose 不会返回 None，判空只为过类型检查
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transposed = ImageOps.exif_transpose(img)
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img = (transposed or img).convert("RGB")
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scaled = False
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if not decrypt:
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before = img.width * img.height
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img = fit_within(img, max_pixels)
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scaled = img.width * img.height < before
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width, height = img.size
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src = np.asarray(img, dtype=np.uint8).reshape(-1, 3)
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if decrypt:
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dst = decrypt_pixels(src, width, height)
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else:
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dst = encrypt_pixels(src, width, height)
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out = Image.fromarray(dst.reshape(height, width, 3), "RGB")
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return out, scaled, extra_frames
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def to_jpeg_bytes(img: Image.Image, quality: int) -> bytes:
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"""按给定质量编码为 JPEG（网页版输出也是 JPEG，质量默认最高档）。"""
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buf = io.BytesIO()
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img.save(buf, format="JPEG", quality=quality)
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return buf.getvalue()
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