初始提交：识流 AI 助手项目
微信自动回复机器人，基于截图+OCR识别消息，支持关键词规则和 AI（OpenAI/DeepSeek/Dify）自动回复。 技术栈：PySide6 + Flask + Vue3 + RapidOCR + SQLite 注：OCR大模型文件（.onnx / .pdiparams）不纳入版本控制，需单独下载。 🤖 Generated with [Qoder][https://qoder.com]
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figmar
288
app/infrastructure/service/wechat/session_service.py
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288
app/infrastructure/service/wechat/session_service.py
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from __future__ import annotations
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from dataclasses import dataclass
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from io import BytesIO
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import json
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import os
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from typing import Callable
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import cv2
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import numpy as np
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from app.infrastructure.service.logging.log_service import log_event, new_trace_id
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from app.infrastructure.service.wechat.chat_snapshot_analyzer import analyze_pil_image
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from app.infrastructure.service.wechat.unread_session_analyzer import UnreadSessionAnalyzer
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from app.infrastructure.service.wechat.config import (
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BLOCKED_SESSION_KEYWORDS,
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CONTACT_ROW_HEIGHT,
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OCR_SAVE_DIR,
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OCR_SAVE_IMAGES,
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SESSION_NAME_HEIGHT,
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SESSION_NAME_LEFT_OFFSET,
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SESSION_NAME_TOP_OFFSET,
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SESSION_NAME_WIDTH,
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UI_NOISE_KEYWORDS,
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)
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# 会话扫描结果数据类，包含所有会话列表和未读会话列表
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@dataclass
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class SessionScanResult:
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sessions: list[dict]
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unread_sessions: list[dict]
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# 聊天快照分析结果数据类
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@dataclass
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class ChatAnalyzeResult:
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ok: bool
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file_name: str
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latest_text: str
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confidence: str | float
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bubble_side: str
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screenshot_path: str
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# 微信会话服务类，处理会话列表扫描、红点检测和聊天截图分析
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class WechatSessionService:
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def __init__(self, screenshot_service, ocr_service, save_debug_image: Callable | None = None):
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self.screenshot = screenshot_service
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self.ocr = ocr_service
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self.save_debug_image = save_debug_image
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self._session_title_cache = {"value": "", "ts": 0.0}
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self.unread_analyzer = UnreadSessionAnalyzer()
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log_event("INFO", "bot", "bot.session_service.init", new_trace_id("bot"), "init", "ok", "会话服务初始化完成")
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# 根据窗口矩形计算会话列表区域的位置
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def get_contact_list_rect(self, window_rect):
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box = self.screenshot.get_contact_list_box(window_rect)
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return {
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'left': box.left,
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'top': box.top,
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'right': box.right,
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'bottom': box.bottom,
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}
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# 从会话行图片中裁剪出会话名称区域
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def extract_session_name_image(self, row_img):
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return self.screenshot.crop_session_name(row_img)
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# 检测会话列表中的所有红点位置（红色圆点表示未读消息）
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def detect_red_dots(self, window_rect):
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contact_rect = self.get_contact_list_rect(window_rect)
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screenshot = self.screenshot.capture_contact_list_default(window_rect)
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return self.unread_analyzer.detect_red_dots(contact_rect, screenshot)
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# 检测单行会话图片中是否有未读红点标记（严格模式）
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def row_has_red_dot(self, row_img, relaxed=False):
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return self.unread_analyzer.row_has_red_dot(row_img, relaxed=relaxed)
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# 检测单行会话图片中是否有未读红点标记（宽松模式）
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def row_has_red_dot_weak(self, row_img):
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return self.unread_analyzer.row_has_red_dot_weak(row_img)
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# 扫描所有会话行，识别哪些有未读消息标记
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def get_all_sessions_with_unread(self, window_rect, round_count):
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trace_id = new_trace_id("bot")
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contact_rect = self.get_contact_list_rect(window_rect)
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screenshot = self.screenshot.capture_contact_list_default(window_rect)
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sessions, unread_sessions = self.unread_analyzer.get_all_sessions_with_unread(
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contact_rect=contact_rect,
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screenshot=screenshot,
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round_count=round_count,
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save_debug_image=lambda image_obj, filename: self._save_debug_image(image_obj, filename),
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)
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self._save_session_scan_debug(round_count=round_count, sessions=sessions, unread_sessions=unread_sessions, contact_rect=contact_rect)
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log_event("INFO", "bot", "bot.session_scan", trace_id, "scan", "ok", "会话扫描完成", extra={"round": int(round_count), "total": len(sessions), "unread": len(unread_sessions)})
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return SessionScanResult(sessions=sessions, unread_sessions=unread_sessions)
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# 标准化文本用于匹配：去除空格并转为小写
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def normalize_match_text(self, text):
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if not text:
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return ""
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text = str(text).strip().lower()
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return "".join(ch for ch in text if not ch.isspace())
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# 生成会话屏蔽关键字的唯一标识key，用于缓存比对
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def make_block_key(self, text):
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normalized = self.normalize_match_text(text)
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if not normalized:
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return ""
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return f"title:{normalized}"
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# 重置当前会话标题缓存
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def reset_session_title_cache(self):
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self._session_title_cache = {"value": "", "ts": 0.0}
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# 通过OCR识别当前会话窗口的标题文字
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def get_session_title_by_ocr(self, window_rect):
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trace_id = new_trace_id("bot")
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try:
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if not window_rect:
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return ""
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area_name = "main"
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screenshot = self.screenshot.capture_session_title(window_rect)
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img_bytes = BytesIO()
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screenshot.save(img_bytes, format='PNG')
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valid = self.ocr.recognize_session_title(img_bytes.getvalue(), scene=f"session_title_{area_name}")
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if valid:
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title = valid[0]
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log_event("INFO", "bot", "bot.session_title", trace_id, "ocr", "ok", "会话标题识别成功", extra={"title": title})
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return title
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log_event("INFO", "bot", "bot.session_title", trace_id, "ocr", "failed", "会话标题识别为空", reason="empty_result")
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return ""
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except Exception as e:
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log_event("ERROR", "bot", "bot.session_title", trace_id, "ocr", "failed", "会话标题识别异常", reason="ocr_error", extra={"error": str(e)})
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return ""
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# 获取当前会话标题，优先使用缓存避免频繁OCR调用
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def get_current_session_title(self, window_rect):
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try:
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import time
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now_ts = time.time()
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cached_title = (self._session_title_cache.get("value") or "").strip()
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cached_ts = float(self._session_title_cache.get("ts") or 0.0)
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if cached_title and now_ts - cached_ts <= 1.2:
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return cached_title
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title = (self.get_session_title_by_ocr(window_rect) or "").strip()
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if title and title not in UI_NOISE_KEYWORDS:
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self._session_title_cache = {"value": title, "ts": now_ts}
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return title
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except Exception as e:
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return ""
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# 判断当前选中的会话是否应被跳过（点击后标题检查阶段）
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def should_skip_current_session(self, window_rect, session, blocked_row_cache, save_blocked_row_cache: Callable):
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title = self.get_current_session_title(window_rect)
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block_key = self.make_block_key(title)
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if block_key and block_key in blocked_row_cache:
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return True
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normalized_title = self.normalize_match_text(title)
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for keyword in BLOCKED_SESSION_KEYWORDS:
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if self.normalize_match_text(keyword) in normalized_title:
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if block_key:
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blocked_row_cache[block_key] = title or keyword
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save_blocked_row_cache()
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return True
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return False
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# 比较两个会话名称是否匹配（考虑模糊匹配和大小写）
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def is_same_session(self, expected_session, current_session):
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expected = self.normalize_match_text(expected_session)
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current = self.normalize_match_text(current_session)
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if not expected or not current:
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return False
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return expected in current or current in expected
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# 根据OCR识别结果判断会话列表中的会话是否应被跳过
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def should_skip_session_by_ocr(self, session, blocked_row_cache, save_blocked_row_cache: Callable):
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image_obj = session.get('row_img')
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if image_obj is None:
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return False
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try:
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name_img = self.extract_session_name_image(image_obj)
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if name_img is None:
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return False
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crop_box = {
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'left': SESSION_NAME_LEFT_OFFSET,
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'top': SESSION_NAME_TOP_OFFSET,
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'width': SESSION_NAME_WIDTH,
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'height': SESSION_NAME_HEIGHT,
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'row_w': image_obj.size[0],
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'row_h': image_obj.size[1],
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'crop_w': name_img.size[0],
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'crop_h': name_img.size[1],
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}
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if OCR_SAVE_IMAGES:
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file_name = f"row_{int(session.get('row_idx', 0)):03d}_name_raw.png"
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self._save_debug_image(name_img, os.path.join('sessions', 'name_ocr', file_name))
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img_bytes = BytesIO()
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name_img.save(img_bytes, format='PNG')
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lines = self.ocr.recognize_session_name(img_bytes.getvalue(), scene=f"session_row_{session.get('row_idx')}")
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line_text = ' '.join(lines)
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session['list_ocr_title'] = line_text
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normalized_text = self.normalize_match_text(line_text)
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block_key = self.make_block_key(line_text)
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if block_key and block_key in blocked_row_cache:
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return True
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for keyword in BLOCKED_SESSION_KEYWORDS:
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if self.normalize_match_text(keyword) in normalized_text:
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if block_key:
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blocked_row_cache[block_key] = line_text or keyword
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save_blocked_row_cache()
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return True
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return False
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except Exception as e:
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return False
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# 分析点击后的聊天区域截图，提取最新消息文本并返回分析结果
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def analyze_clicked_session(self, window_rect, round_count, row_idx):
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trace_id = new_trace_id("bot")
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chat_box = self.screenshot.get_chat_capture_box(window_rect)
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if not self.screenshot.is_valid_box(chat_box):
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log_event("WARNING", "bot", "bot.chat_analyze", trace_id, "capture", "failed", "聊天区截图区域无效", reason="invalid_box")
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return ChatAnalyzeResult(ok=False, file_name='', latest_text='', confidence='', bubble_side='', screenshot_path='')
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screenshot = self.screenshot.capture_chat_area(window_rect)
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file_name = f"round_{round_count:04d}_row_{row_idx:03d}_chat.png"
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rel_path = os.path.join('sessions', 'clicked', file_name)
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self._save_debug_image(screenshot, rel_path)
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result = analyze_pil_image(screenshot, stem=os.path.splitext(file_name)[0], file_name=file_name)
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latest_text = (getattr(result, 'latest_text', None) or '').strip()
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confidence = getattr(result, 'confidence', '')
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bubble_side = getattr(result, 'bubble_side', '')
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log_event("INFO", "bot", "bot.chat_analyze", trace_id, "analyze", "ok", "聊天截图分析完成", extra={"round": int(round_count), "row_idx": int(row_idx), "has_text": bool(latest_text), "bubble_side": bubble_side or "", "confidence": confidence})
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return ChatAnalyzeResult(
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ok=bool(latest_text),
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file_name=file_name,
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latest_text=latest_text,
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confidence=confidence,
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bubble_side=bubble_side,
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screenshot_path=rel_path,
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)
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# 保存会话扫描调试数据（类似聊天分析输出 result.json）
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def _save_session_scan_debug(self, round_count: int, sessions: list[dict], unread_sessions: list[dict], contact_rect: dict):
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if not OCR_SAVE_IMAGES:
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return
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try:
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debug_dir = os.path.join(OCR_SAVE_DIR, 'sessions', 'scan_debug')
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os.makedirs(debug_dir, exist_ok=True)
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file_name = f"round_{round_count:04d}_scan.json"
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file_path = os.path.join(debug_dir, file_name)
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rows = []
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for session in sessions:
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rows.append({
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'row_idx': session.get('row_idx'),
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'has_red_dot': bool(session.get('has_red_dot')),
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'has_red_by_global': bool(session.get('has_red_by_global')),
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'has_red_by_row': bool(session.get('has_red_by_row')),
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'has_red_by_row_weak': bool(session.get('has_red_by_row_weak')),
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'click_x': session.get('click_x'),
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'click_y': session.get('click_y'),
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'list_ocr_title': session.get('list_ocr_title', ''),
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})
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payload = {
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'round': int(round_count),
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'contact_rect': contact_rect,
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'total_sessions': len(sessions),
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'unread_count': len(unread_sessions),
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'unread_rows': [s.get('row_idx') for s in unread_sessions],
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'rows': rows,
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}
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with open(file_path, 'w', encoding='utf-8') as f:
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json.dump(payload, f, ensure_ascii=False, indent=2)
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except Exception as e:
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pass
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# 保存调试图片的内部方法
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def _save_debug_image(self, image_obj, filename):
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if not self.save_debug_image:
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return
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self.save_debug_image(image_obj, filename)
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