106 lines
4.0 KiB
Python
106 lines
4.0 KiB
Python
"""只读诊断：同一个会话的档案指纹在多次采样之间是否稳定。
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档案键 = 头像感知哈希(8B) + 名称哈希(32B)，而 store.has_record() 是精确匹配。
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只要指纹漂移，同一个联系人就会被当成“首次遇到”，从而丢弃复制到的聊天文字、
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只靠截图回复。这里量化漂移幅度。
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"""
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import os
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import sys
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import time
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sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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import numpy as np
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import wechat_bot as bot_module
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def hamming(a: bytes, b: bytes) -> int:
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return (int.from_bytes(a, "big") ^ int.from_bytes(b, "big")).bit_count()
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def main():
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rounds = int(sys.argv[1]) if len(sys.argv) > 1 else 8
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bot = bot_module.WeChatBot()
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bot.safe_window_mode = True
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bot.auto_activate_window = True
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if not bot.connect(activate=False, wait_if_missing=False):
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print("[-] 未挂载到企业微信窗口")
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return
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if not bot._ensure_visible():
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print("[-] 企业微信未能切到前台")
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return
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print(
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"容差: 头像 %d 位以内算同一人；名称使用模糊匹配"
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% bot_module.WeChatBot._FP_HAMMING_TOL
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)
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samples = []
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for index in range(rounds):
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img = bot.capture_session_list()
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selected_y = bot.detect_selected_row(img)
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if selected_y < 0:
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print(" 第 %d 次采样：未检测到选中行" % (index + 1))
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time.sleep(0.4)
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continue
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avatar = bot._raw_session_fingerprint(img, selected_y, row_center=True)
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name = bot._session_name_fingerprint(img, selected_y, row_center=True)
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full = bot._session_fingerprint(img, selected_y, row_center=True)
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samples.append((selected_y, avatar, name, full))
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print(
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" 第 %d 次采样：选中行 y=%-4d 头像=%s 名称=%s 完整键=%s"
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% (index + 1, selected_y, avatar.hex(), name.hex()[:16] + "…", full.hex()[:16] + "…")
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)
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time.sleep(0.4)
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if len(samples) < 2:
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print("样本不足，无法比较。")
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return
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print("\n[行中心] y 取值: %s" % sorted({s[0] for s in samples}))
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print("[头像哈希] 去重后 %d 种" % len({s[1] for s in samples}))
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print("[名称哈希] 去重后 %d 种" % len({s[2] for s in samples}))
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print("[完整档案键] 去重后 %d 种 <- 大于 1 就意味着同一会话会被反复当成新会话"
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% len({s[3] for s in samples}))
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base_y, base_avatar, base_name, _ = samples[0]
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worst = 0
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for y, avatar, name, _full in samples[1:]:
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distance = hamming(base_avatar, avatar)
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worst = max(worst, distance)
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if distance:
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print(
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" 头像相对第 1 次漂移 %2d 位（y %d→%d）%s"
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% (distance, base_y, y, " 超出容差！" if distance > bot_module.WeChatBot._FP_HAMMING_TOL else "")
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)
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print("[结论] 头像最大漂移 %d 位，容差 %d 位" % (worst, bot_module.WeChatBot._FP_HAMMING_TOL))
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# 行中心偏移对指纹的影响：模拟 ±1~6 像素的行中心估算误差。
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img = bot.capture_session_list()
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selected_y = bot.detect_selected_row(img)
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if selected_y >= 0:
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anchor = bot._raw_session_fingerprint(img, selected_y, row_center=True)
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print("\n[敏感度] 同一张图，仅把行中心挪动若干像素：")
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for offset in (1, 2, 3, 4, 6, 8):
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for sign in (-1, 1):
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shifted = bot._raw_session_fingerprint(
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img,
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selected_y + sign * offset,
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row_center=True,
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)
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print(
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" 行中心 %+d px -> 头像漂移 %2d 位%s"
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% (
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sign * offset,
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hamming(anchor, shifted),
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" 超出容差！"
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if hamming(anchor, shifted) > bot_module.WeChatBot._FP_HAMMING_TOL
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else "",
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)
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)
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if __name__ == "__main__":
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main()
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