From d2fc0cd7c755f1c92338990b1d9a70f5912ca465 Mon Sep 17 00:00:00 2001 From: HuangHai <10402852@qq.com> Date: Wed, 28 Jan 2026 07:31:28 +0800 Subject: [PATCH] 'commit' --- Apps/TeLaiDian/Config/Setting.py | 2 +- Apps/TeLaiDian/FirstPageKit.py | 18 +- .../__pycache__/FirstPageKit.cpython-310.pyc | Bin 11491 -> 11605 bytes Apps/XinDianTu/Config/Setting.py | 2 +- .../__pycache__/Setting.cpython-310.pyc | Bin 683 -> 683 bytes WeiXin/Output/test_current_screen.jpg | Bin 0 -> 32102 bytes .../Output/test_input_detect_1769473006.jpg | Bin 0 -> 52499 bytes WeiXin/T2_ChatMonitor.py | 225 ++++-------- WeiXin/WxUtil.py | 325 +++++++++++++----- .../T2_ChatMonitor.cpython-310.pyc | Bin 11057 -> 9592 bytes WeiXin/__pycache__/WxUtil.cpython-310.pyc | Bin 27591 -> 31068 bytes clear_sql_cache.py | 35 -- run_log.txt | Bin 7702 -> 0 bytes 13 files changed, 336 insertions(+), 271 deletions(-) create mode 100644 WeiXin/Output/test_current_screen.jpg create mode 100644 WeiXin/Output/test_input_detect_1769473006.jpg delete mode 100644 clear_sql_cache.py delete mode 100644 run_log.txt diff --git a/Apps/TeLaiDian/Config/Setting.py b/Apps/TeLaiDian/Config/Setting.py index c3f44cf..61231eb 100644 --- a/Apps/TeLaiDian/Config/Setting.py +++ b/Apps/TeLaiDian/Config/Setting.py @@ -2,7 +2,7 @@ # 采集配置 SCROLL_DISTANCE_RATIO = 0.3 -MAX_STATIONS_COUNT = 20 +MAX_STATIONS_COUNT = 50 FIRST_RUN_ONLY_ONE_STATION = False REDIS_STATION_EXPIRE = 120 diff --git a/Apps/TeLaiDian/FirstPageKit.py b/Apps/TeLaiDian/FirstPageKit.py index 6f84794..a21c35e 100644 --- a/Apps/TeLaiDian/FirstPageKit.py +++ b/Apps/TeLaiDian/FirstPageKit.py @@ -34,7 +34,6 @@ NON_STATION_KEYWORDS = [ "充电券", "电信积分兑换", "确认", - "广告", "距离/区域", "综合排序", "偏好", @@ -47,6 +46,7 @@ NON_STATION_KEYWORDS = [ "输入", "商城", "推荐", + "新客专享", ] def _load_image(path): @@ -140,11 +140,17 @@ async def run_ocr_rect(image_path, log_path=None): status = "drop" reasons.append("bottom_safe_zone") if status == "keep" and txt: - for kw in NON_STATION_KEYWORDS: - if kw and kw in txt: - status = "drop" - reasons.append("non_station_keyword") - break + # Prevent filter bar items from being treated as stations + if txt.strip() in ["充电站", "快充", "慢充", "超快充", "广告"]: + status = "drop" + reasons.append("exact_filter_keyword") + + if status == "keep": + for kw in NON_STATION_KEYWORDS: + if kw and kw in txt: + status = "drop" + reasons.append("non_station_keyword") + break log_detail( f"OCR[{idx + 1}] text={repr(text)} prob={prob:.3f} " diff --git a/Apps/TeLaiDian/__pycache__/FirstPageKit.cpython-310.pyc b/Apps/TeLaiDian/__pycache__/FirstPageKit.cpython-310.pyc index f9d849561dcdd912d5c026281a41a5d5d6abb284..e5e86e0335eff369b33695ba7ab8817f7643f5a1 100644 GIT binary patch delta 3746 zcmb_fYfN0n6~1%#F1yPsU>*zM&GOpiVKBxv*nk}aegMXfo!DUG^_saR%d&Tu*}E}| z&&?{OmfWbQn@m+T0!xret3?^PQP9=ggT0A5H&xrZ84ekgMSDSJTVk@!_`%zozna^|j(;KI-m&=STP6 zeC^|(@sHnsgAY1>PHg@!M-M6G?b&nCJYHFf)_&DiySi->W}B)c=lDi!cbF~*X?V`ea*N@F 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wEid(10G}ym4>u=)xYrkd?y)ggoL2ea4`qb^@=D+T#^Y8*{;OgQw9xrK0Q1Z;d;kCd literal 0 HcmV?d00001 diff --git a/WeiXin/T2_ChatMonitor.py b/WeiXin/T2_ChatMonitor.py index dc32c34..55559b2 100644 --- a/WeiXin/T2_ChatMonitor.py +++ b/WeiXin/T2_ChatMonitor.py @@ -63,7 +63,8 @@ class ChatMonitorBot: self.input_pos = None self.last_screen_hash = None self.last_processed_msg_hash = None - self.first_run = True # 标记是否为首次运行 + # [User Requested] 移除持久化存储，只在内存中记录，重启即忘 + self.processed_hashes = set() self.check_interval = 3 # 检查频率 (秒) self.persona = ( @@ -76,13 +77,23 @@ class ChatMonitorBot: "3. 仅针对家长明确表达的内容进行回复。\n" "4. 严禁使用列表格式。严禁使用‘首先、其次’等逻辑词。\n" "5. 回复必须简练，字数严格控制在 50 字以内！\n" + "6. 对方问什么就答什么。例如问‘学校叫什么’，就只回答‘少惠林’，不要回复地址和电话！\n" "如果涉及到校区信息，必须且只能使用以下真实数据：\n" - "- 单位：长春市少惠林作文素养培养中心\n" + "- 单位/学校名称：长春市少惠林作文素养培养中心（简称：少惠林）\n" "- 地址：南环城路与临河街交汇，TOUCH12街3楼325号\n" "- 联系人：小张老师（电话：18686619970）\n" "- 每学期开学招收小学三年级至六年级，初中七年级的学生入学，其它年段不招生。\n" ) + def _record_processed_hash(self, msg_hash): + """记录已处理的消息哈希 (仅内存)""" + self.processed_hashes.add(msg_hash) + # 仅保留最近 100 条记录，防止无限增长 + if len(self.processed_hashes) > 100: + # 简单丢弃旧的（转列表切片再转回集合） + temp = list(self.processed_hashes)[-100:] + self.processed_hashes = set(temp) + async def get_reply(self, last_message_text, context_text=""): prompt = ( f"【教师人设】：{self.persona}\n\n" @@ -141,9 +152,30 @@ class ChatMonitorBot: with open(file_path, "rb") as f: return hashlib.md5(f.read()).hexdigest() + def get_stable_message_hash(self, msg): + """ + 计算消息的稳定哈希值（忽略坐标等易变字段） + 仅包含: sender, content, time_display, type + """ + if not msg: + return "" + + stable_data = { + "sender": msg.get("sender", ""), + "content": msg.get("content") or "", # 确保 None 转为空字符串 + "time_display": msg.get("time_display", ""), + "type": msg.get("type", "") + } + + # 序列化并计算哈希 + msg_str = json.dumps(stable_data, sort_keys=True, ensure_ascii=False) + return hashlib.md5(msg_str.encode('utf-8')).hexdigest() + async def run(self): - """主运行循环""" - logger.info("🚀 大张老师自动巡课系统启动 (T2 增强版)...") + """ + 主运行循环 + """ + logger.info("🚀 正在启动 T2_ChatMonitor (Auto-Reply)...") # 定义 JSON 序列化辅助函数 def numpy_serializer(obj): @@ -159,118 +191,11 @@ class ChatMonitorBot: if not self.step_1_prepare_env(): return if not self.step_2_connect_device(): return - # 2. 首次运行：识别所有语音并获取上下文 - logger.info("🔍 [首次运行] 正在进行全量识别，获取对话上下文...") + # [User Requested] 移除首屏概念，直接进入监控循环 + # 以前说过什么都不管了，只关注最后一条 + logger.info("🚀 启动完成，直接进入实时监控阶段...") - # 调用封装好的 get_first_screen - self.dialogue_log, self.input_pos, enter_path, flag_path = await WxUtil.get_first_screen(self.device) - - # 更新 live paths (用于后续监控逻辑的引用) - import shutil - if enter_path and os.path.exists(enter_path): - shutil.copy(enter_path, self.screenshot_path) - - if flag_path and os.path.exists(flag_path): - shutil.copy(flag_path, self.debug_view_path) - logger.info(f"📸 已保存识别标记图: {flag_path}") - - if self.dialogue_log: - logger.info(f"✅ 首次运行识别完成，获取到 {len(self.dialogue_log)} 条消息上下文") - logger.info("\n" + "="*50) - logger.info("【测试模式】最终提取的对话记录：") - for msg in self.dialogue_log: - sender = msg.get('sender', '未知') - content = msg.get('content', '') - time_str = msg.get('time_display', '') - - # 按照用户要求的格式输出: 2026-01-26 10:03 糖豆爸爸 ： 老师您好！ - log_prefix = f"{time_str} " if time_str else "" - log_line = f"{log_prefix}{sender} ： {content}" - logger.info(log_line) - logger.info("="*50 + "\n") - - # --- LLM 总结 --- - logger.info("🤖 正在请求 LLM 生成对话摘要...") - chat_history_text = "" - for msg in self.dialogue_log: - sender = msg.get('sender', '未知') - content = msg.get('content', '') - type_str = "[语音]" if msg.get('type') == 'voice' else "[文字]" - time_str = msg.get('time_display', '') - time_prefix = f"[{time_str}] " if time_str else "" - chat_history_text += f"{time_prefix}{sender}{type_str}: {content}\n" - - prompt = ( - "请根据以下微信对话记录，总结归纳双方交流的主要信息点。\n" - "要求：\n" - "1. 简明扼要，分点列出。\n" - "2. 明确指出双方达成的一致或待解决的问题。\n" - "3. 忽略无关的寒暄。\n\n" - f"对话记录：\n{chat_history_text}" - ) - - try: - full_response = "" - async for chunk in get_llm_response(prompt, stream=True): - full_response += chunk - - logger.info("\n" + "="*20 + " 对话摘要 (LLM) " + "="*20) - logger.info(full_response) - logger.info("="*55 + "\n") - - except Exception as e: - logger.error(f"LLM 摘要生成失败: {e}") - - # 初始化最后处理的消息哈希，避免重复回复第一条 - last_msg = self.dialogue_log[-1] - - # --- 初始回复逻辑 (Added) --- - # 如果最后一条是对方发的消息，说明可能需要回复 - sender = last_msg.get('sender', '') - # 判断逻辑：只要不是"我"，就认为是对方 (可能是 "对方", "糖豆爸爸" 等) - if sender != "我": - logger.info(f"💡 [首屏] 最后一条消息来自 '{sender}'，尝试生成回复...") - - # 构建上下文 - context_text = "\n".join([f"{m.get('time_display', '') + ' ' if m.get('time_display') else ''}{m.get('sender')}: {m.get('content')}" for m in self.dialogue_log[:-1]]) - last_content = last_msg.get('content', '') - - reply = await self.get_reply(last_content, context_text) - if reply: - logger.info(f"🤖 [首屏] LLM 建议回复: {reply}") - - # 检查输入框位置 - if self.input_pos: - logger.info(f"⚡ [首屏] 执行自动回复...") - perform_input_action(self.device, self.input_pos, reply) - - # 发送后更新 hash，避免进入循环后重复回复 - # 发送后，界面会变，但我们需要标记当前这条已经回过了 - msg_str = json.dumps(last_msg, sort_keys=True, ensure_ascii=False, default=numpy_serializer) - self.last_processed_msg_hash = hashlib.md5(msg_str.encode('utf-8')).hexdigest() - - logger.info("✅ [首屏] 回复已发送") - else: - logger.warning("❌ [首屏] 未找到输入框位置，无法发送") - else: - logger.info("⚪ [首屏] LLM 认为无需回复") - else: - logger.info("⚪ [首屏] 最后一条是自己发的，无需回复") - - # 更新 Hash (如果刚才没发回复，也需要记录当前最后一条，防止循环里重复处理) - if not self.last_processed_msg_hash: - msg_str = json.dumps(last_msg, sort_keys=True, ensure_ascii=False, default=numpy_serializer) - self.last_processed_msg_hash = hashlib.md5(msg_str.encode('utf-8')).hexdigest() - - self.last_screen_hash = self.get_image_hash(self.screenshot_path) - else: - logger.warning("⚠️ 首次运行未识别到有效对话") - - # logger.info("🛑 测试结束：已完成所有语音的转换与读取。停止进入监控循环。") - # return # 测试模式：直接退出，不进入监控循环 - # 3. 进入循环阶段 - logger.info("🔄 进入实时监控阶段...") while True: try: # A. 截图并计算哈希 @@ -306,19 +231,22 @@ class ChatMonitorBot: # D. 只关注最后一条消息 last_msg = dialogue_log[-1] - # last_msg 是字典，需要序列化 - msg_str = json.dumps(last_msg, sort_keys=True, ensure_ascii=False, default=numpy_serializer) - current_msg_hash = hashlib.md5(msg_str.encode('utf-8')).hexdigest() + # 计算稳定哈希（忽略坐标变化） + current_msg_hash = self.get_stable_message_hash(last_msg) # E. 判断是否需要回复 (对方发送且非重复消息) sender = last_msg.get('sender', '') - if sender != "我": - if self.first_run: - # 首次运行时，记录最后一条消息的哈希但不回复，防止重启后重复回复历史消息 - logger.info(f"🚦 [启动] 首次扫描，忽略已存在的最后一条消息: {last_msg}") - self.last_processed_msg_hash = current_msg_hash - self.first_run = False - elif current_msg_hash != self.last_processed_msg_hash: + + # Check if hash is already processed (in-memory only) + is_processed = current_msg_hash in self.processed_hashes + + # Log only if it changed from last *in-memory* check to avoid spam + if is_processed and current_msg_hash != self.last_processed_msg_hash: + # logger.info(f"🚫 [监控] 消息哈希已存在于历史记录中，跳过回复 (Hash: {current_msg_hash})") + self.last_processed_msg_hash = current_msg_hash + + if not is_processed and current_msg_hash != self.last_processed_msg_hash: + if sender != "我": event_shot = WxUtil.get_next_debug_path("event_new_msg") self.device.screenshot(event_shot) logger.info(f"💡 [监控] 发现新消息: {last_msg}，保存现场截图: {event_shot}") @@ -345,10 +273,14 @@ class ChatMonitorBot: last_content = last_msg.get('content') or "" logger.info(f"🔄 [重试] 强制转换后内容: {last_content}") - # 重新构建 msg_str 和 hash，确保下次循环不会因为内容变化而再次触发（虽然这里已经处理了） - # 但实际上这里是在处理当前事件，更新 hash 是为了避免重复处理 - msg_str = json.dumps(last_msg, sort_keys=True, ensure_ascii=False, default=numpy_serializer) - current_msg_hash = hashlib.md5(msg_str.encode('utf-8')).hexdigest() + # 重新构建哈希 + current_msg_hash = self.get_stable_message_hash(last_msg) + # 再次检查是否已处理 (因为内容变了，哈希变了) + if current_msg_hash in self.processed_hashes: + logger.info(f"🚫 [重试] 转换后发现该消息已处理，跳过。") + self.last_processed_msg_hash = current_msg_hash + # 跳过本次循环的剩余部分 + continue # 生成回复 reply = await self.get_reply(last_content, context_text) @@ -357,35 +289,35 @@ class ChatMonitorBot: logger.info(f"🤖 [监控] LLM 建议回复: {reply}") if self.input_pos: logger.info(f"⚡ [监控] 执行自动回复...") - perform_input_action(self.device, self.input_pos, reply) + # input_pos 是 ((x,y), box) 格式，取第一个元素坐标点 + target_pos = self.input_pos[0] if isinstance(self.input_pos, (list, tuple)) and len(self.input_pos) == 2 and isinstance(self.input_pos[0], (list, tuple)) else self.input_pos + # 简单兼容处理：如果 input_pos[0] 是 tuple/list 且 input_pos[1] 是 None/box，则取 input_pos[0] + if isinstance(self.input_pos, (list, tuple)) and len(self.input_pos) == 2 and isinstance(self.input_pos[0], (list, tuple)): + target_pos = self.input_pos[0] + + perform_input_action(self.device, target_pos, reply) # 发送后截图留存 reply_sent_shot = WxUtil.get_next_debug_path("event_reply_sent") self.device.screenshot(reply_sent_shot) logger.info(f"✅ [监控] 回复已发送，保存发送后截图: {reply_sent_shot}") + self._record_processed_hash(current_msg_hash) self.last_processed_msg_hash = current_msg_hash else: - logger.warning("❌ [监控] 未找到输入框位置，无法发送") + logger.error("❌ 未找到输入框位置，无法发送回复") else: - logger.warning("⚠️ [监控] LLM 未生成有效回复") + logger.info("⚪ [监控] LLM 认为无需回复") + self._record_processed_hash(current_msg_hash) + self.last_processed_msg_hash = current_msg_hash else: - # 消息已处理过 - pass - else: - # 最后一条是我发送的 - if self.first_run: - logger.info(f"🚦 [启动] 首次扫描，最后一条是自己发的，标记为已处理: {last_msg}") + # 是我发的消息，更新哈希，不再处理 self.last_processed_msg_hash = current_msg_hash - self.first_run = False - elif current_msg_hash != self.last_processed_msg_hash: - logger.info(f"⚪ [监控] 最后一条消息是自己发的，跳过回复: {last_msg}") - self.last_processed_msg_hash = current_msg_hash await asyncio.sleep(self.check_interval) except Exception as e: - logger.error(f"❌ 循环中发生错误: {e}", exc_info=True) + logger.error(f"Error in monitoring loop: {e}", exc_info=True) await asyncio.sleep(self.check_interval) async def run_main(): @@ -398,11 +330,4 @@ async def run_main(): if __name__ == "__main__": # 应用 Win32 补丁 Win32Patch.patch() - - try: - # 运行机器人 - asyncio.run(run_main()) - except KeyboardInterrupt: - logger.info("🛑 用户手动停止程序。") - except Exception as e: - logger.error(f"❌ 程序异常退出: {e}", exc_info=True) + asyncio.run(run_main()) diff --git a/WeiXin/WxUtil.py b/WeiXin/WxUtil.py index 54ef86d..28e25a7 100644 --- a/WeiXin/WxUtil.py +++ b/WeiXin/WxUtil.py @@ -253,6 +253,61 @@ def draw_debug_info(image_path, messages, current_voice_center=None, suffix=""): except Exception as e: logger.warning(f"绘制调试信息失败: {e}") +def _detect_bubble_color(img, bbox): + """ + 检测文本框区域的背景颜色，用于辅助判断发送者。 + :param img: OpenCV 图像 (BGR) + :param bbox: OCR 返回的边界框 4个点 + :return: "green" (我), "white" (对方), or "unknown" + """ + if img is None: return "unknown" + + # 提取 bbox 区域 + h, w = img.shape[:2] + min_x = max(0, int(min(p[0] for p in bbox))) + max_x = min(w, int(max(p[0] for p in bbox))) + min_y = max(0, int(min(p[1] for p in bbox))) + max_y = min(h, int(max(p[1] for p in bbox))) + + if max_x <= min_x or max_y <= min_y: + return "unknown" + + roi = img[min_y:max_y, min_x:max_x] + + # 计算背景颜色 (抗文字干扰) + # 文本是黑色的 (0,0,0)，会拉低平均值/中位数 + # 使用 95% 分位数来获取背景色 (偏亮的部分 - 真正的背景) + try: + # axis=(0,1) 对 h,w 维度操作，保留 c 维度 + # percentile 返回 float，需转 int + bg_color = np.percentile(roi, 95, axis=(0, 1)) + b, g, r = bg_color + except Exception: + # Fallback + mean_color = cv2.mean(roi)[:3] + b, g, r = mean_color + + # 调试日志：打印颜色值 + logger.info(f"Color Debug: B={b:.1f}, G={g:.1f}, R={r:.1f} | bbox={bbox}") + + # 绿色气泡特征 (Light Mode): + # R: 152, G: 225, B: 101 (BGR: 101, 225, 152) + # G 显著大于 R 和 B + # 提高阈值以区分白色/灰色背景的噪声 (White: 255, 255, 255) + if g > r + 30 and g > b + 30 and g > 100: + return "green" + + # 白色气泡特征: + # R, G, B 都很高且接近 + # 考虑黑色文字的影响，如果是中位数，应该很高 (>200) + # 放宽对灰色的容忍度 (Dark Mode 可能偏灰) + if abs(r - g) < 30 and abs(g - b) < 30 and abs(r - b) < 30: + # 且亮度不能太低 (太低可能是黑色背景或深色物体) + if g > 150: + return "white" + + return "unknown" + def _scan_chat_messages(image_path): """ 内部函数：扫描图片中的微信消息（语音、文本、红点） @@ -263,6 +318,7 @@ def _scan_chat_messages(image_path): logger.error(f"无法读取图片: {image_path}") return [], None h, w = img.shape[:2] + logger.info(f"DEBUG: Image size w={w}, h={h}") # 3. 模板匹配寻找语音图标和红点 audio_template = os.path.join(TEMPLATE_DIR, "audio.jpg") @@ -383,6 +439,29 @@ def _scan_chat_messages(image_path): if has_intermediate_audio: continue + # [Fix] 检查中间是否有其他气泡消息阻断 (防止跨消息合并) + # 如果遇到一个明确属于另一方的消息气泡，必须停止关联 + if c_y > ay + 60: # 稍微放宽 Y 轴，避免误判紧贴的转换文本 + bubble_color = _detect_bubble_color(img, bbox) + + if voice_is_left: # 语音在左 (对方) + # 如果遇到绿色气泡 (我)，或者是明显的右对齐文本，视为阻断 + if bubble_color == "green": + logger.info(f"语音({ax},{ay}) 被中间'我'的消息(绿色气泡)阻断: '{text[:10]}...'") + break + if c_x > w * 0.65: # 右侧明显区域 (short message check) + logger.info(f"语音({ax},{ay}) 被中间'我'的消息(右对齐)阻断: '{text[:10]}...'") + break + + else: # 语音在右 (我) + # 如果遇到白色气泡 (对方)，或者是明显的左对齐文本，视为阻断 + if bubble_color == "white": + logger.info(f"语音({ax},{ay}) 被中间'对方'的消息(白色气泡)阻断: '{text[:10]}...'") + break + if c_x < w * 0.35: # 左侧明显区域 + logger.info(f"语音({ax},{ay}) 被中间'对方'的消息(左对齐)阻断: '{text[:10]}...'") + break + clean_text = text.strip() # 判定是否为时间戳 is_timestamp = re.search(r'(\d{1,2}:\d{2})', clean_text) and (len(clean_text) < 15) @@ -456,23 +535,68 @@ def _scan_chat_messages(image_path): if c_y < 150 or c_y > h - 100: continue - # 判定发送者 (增强版几何判定，防止 720p 屏幕下的中心点误判) - # 默认使用中心点判定 - sender = "对方" if c_x < w / 2 else "我" + # 判定发送者 (增强版: 几何 + 颜色) + # 1. 尝试通过背景颜色判定 (最准确) + sender_color = _detect_bubble_color(img, bbox) - # 使用边界特征进行修正 + sender = "unknown" + if sender_color == "green": + sender = "我" + elif sender_color == "white": + sender = "对方" + + # 2. 几何特征强制修正 (Double Check) + # 假设头像+边距约占 15% 宽度 + edge_margin = w * 0.15 min_x = min(p[0] for p in bbox) max_x = max(p[0] for p in bbox) - # 修正阈值：假设头像+边距约占 15% 宽度 - edge_margin = w * 0.15 - - if max_x > w - edge_margin: - # 文本框延伸到了最右侧 -> 肯定是"我" (因为对方的头像在左，文本不会靠右) + # 规则 A: 如果这一行极其靠右 (超过 85% 宽度)，那肯定是"我" + # 即使颜色判成了白色 (比如光照问题)，也得纠正回来 + if max_x > w - edge_margin: + if sender == "对方": + logger.warning(f"Sender detected as '对方' by color but geometry says '我' (max_x={max_x} > {w-edge_margin}). Correcting to '我'.") sender = "我" - elif min_x < edge_margin: - # 文本框延伸到了最左侧 -> 肯定是"对方" (因为我的头像在右，文本不会靠左) - sender = "对方" + + # 规则 B: 如果这一行极其靠左 (小于 35% 宽度)，且不靠右，那肯定是"对方" + # 扩大判定范围，防止因为 OCR 稍微缩进导致判定失效 + # 注意：如果颜色明确为"我"（绿色），则跳过此规则，因为"我"的长消息也可能靠左 + elif min_x < w * 0.35 and max_x < w * 0.85: + if sender == "我": + logger.info(f"Geometry says '对方' (min_x={min_x} < {w*0.35}) but Color is '我' (Green). Trusting Color.") + else: + sender = "对方" + + # 规则 C: 如果颜色是 unknown，且不在极端位置，使用中心点兜底 + if sender == "unknown": + c_x = int((min_x + max_x) / 2) + # 简单中心判断 + if c_x < w / 2: sender = "对方" + else: sender = "我" + + # 规则 D: 强几何中心校验 (Final Geometry Verdict) + # 仅对短消息使用强几何校验 (宽度 < 70% 屏幕宽度) + # 长消息通常铺满屏幕，中心点在中间，容易受字体渲染影响导致误判，应信任颜色检测结果 + box_width = max_x - min_x + if box_width < w * 0.7: + # 如果中心点明显在左半屏 ( < 45% )，判定为"对方" + if c_x < w * 0.45: + # [Fix] 如果颜色明确是绿色，说明是"我"的左对齐文本(长文换行)，不应被几何规则强制改为"对方" + if sender == "我" and sender_color == "green": + logger.info(f"Geometry says '对方' (center={c_x} < {w*0.45}) but Color is 'green'. Keeping '我'.") + else: + if sender == "我": + logger.warning(f"Sender detected as '我' by color but center is left ({c_x} < {w*0.45}). Correcting to '对方'.") + sender = "对方" + # 如果中心点明显在右半屏 ( > 55% )，判定为"我" + elif c_x > w * 0.55: + if sender == "对方": + logger.warning(f"Sender detected as '对方' by color but center is right ({c_x} > {w*0.55}). Correcting to '我'.") + sender = "我" + else: + logger.info(f"Message in middle zone ({w*0.45} < {c_x} < {w*0.55}), trusting color detection: {sender}") + else: + logger.info(f"Wide message (width={box_width} > {w*0.7}), skipping geometry check, trusting color: {sender}") time_pattern = r'(\d{4}年|\d{1,2}月|\d{1,2}日|\d{1,2}:\d{2}|昨天|今天|星期|上午|下午|晚上)' # 优先判断是否为独立的时间戳 (行短且符合时间格式) @@ -775,14 +899,15 @@ async def analyze_chat_image(image_path, output_path, device=None, target_name=" dialogue_log = [] # 使用 debug_img 的尺寸，如果 debug_img 未定义（极端情况），默认 1080x1920 if 'debug_img' in locals() and debug_img is not None: - input_field_coordinates = (debug_img.shape[1] // 2, int(debug_img.shape[0] * 0.9)) + # [User Requested] 几何兜底 Y 轴应为 0.88 (避开底部导航条) + input_field_coordinates = (debug_img.shape[1] // 2, int(debug_img.shape[0] * 0.88)) else: # 尝试读取 current_image_path try: tmp_img = cv2.imread(current_image_path) - input_field_coordinates = (tmp_img.shape[1] // 2, int(tmp_img.shape[0] * 0.9)) + input_field_coordinates = (tmp_img.shape[1] // 2, int(tmp_img.shape[0] * 0.88)) except: - input_field_coordinates = (540, 1728) + input_field_coordinates = (540, 1690) # 1920 * 0.88 # 找出最后一条消息 last_msg = None @@ -827,7 +952,7 @@ async def analyze_chat_image(image_path, output_path, device=None, target_name=" except Exception as e: logger.error(f"分析过程发生异常: {e}", exc_info=True) - return [], None + return [], (540, 1690) def clean_screenshots_dir(): @@ -982,41 +1107,119 @@ def perform_input_action(d, center_point, text, auto_send=True): try: # --- 新增逻辑：确保处于文字输入模式 --- logger.info("正在检查输入模式...") - tmp_check_shot = os.path.join(OUTPUT_DIR, "temp_input_check.jpg") - d.screenshot(tmp_check_shot) - wen_zi_template = os.path.join(TEMPLATE_DIR, "wen_zi_input.jpg") - input_text_template = os.path.join(TEMPLATE_DIR, "input_text.jpg") - - # 1. 检查是否存在 '切换到文字' 图标 (表示当前是语音模式) - # 注意：这里假设 wen_zi_input.jpg 是那个“键盘”图标 - wen_zi_pos = find_template_match(tmp_check_shot, wen_zi_template, threshold=0.8) - - if wen_zi_pos: - logger.info(f"检测到语音模式 (找到切换文字图标: {wen_zi_pos})，点击切换...") - d.click(wen_zi_pos[0], wen_zi_pos[1]) + # 优先使用 uiautomator2 的属性检测（比图像识别更稳） + # 1. 检查是否有 "切换到键盘" 按钮（说明当前是语音模式） + voice_mode_btn = d(description="切换到键盘") + if voice_mode_btn.exists: + logger.info("检测到语音模式 (UI树: '切换到键盘')，点击切换...") + voice_mode_btn.click() time.sleep(1.0) # 等待 UI 切换 - else: - # 2. 如果没找到切换图标，假设是文字模式，尝试点击输入区域标识 - logger.info("未检测到语音模式切换图标，尝试寻找文字输入区域...") - input_text_pos = find_template_match(tmp_check_shot, input_text_template, threshold=0.8) - if input_text_pos: - logger.info(f"找到文字输入区域标识 (input_text.jpg): {input_text_pos}，点击激活...") - d.click(input_text_pos[0], input_text_pos[1]) - time.sleep(0.5) - else: - logger.info("未找到特定的输入区域标识，将使用默认坐标或控件查找。") - # 清理临时文件 - if os.path.exists(tmp_check_shot): - try: - os.remove(tmp_check_shot) - except: - pass + # 2. 检查是否有 "切换到语音" 按钮（说明当前是文字模式） + # 这一步不是必须的，但可以用来确认状态 + # text_mode_btn = d(description="切换到语音") + # if text_mode_btn.exists: + # logger.info("当前已是文字模式 (UI树: '切换到语音')") + + # 3. 如果 UI 树检测失败，尝试图像兜底 + if not voice_mode_btn.exists: + tmp_check_shot = os.path.join(OUTPUT_DIR, "temp_input_check.jpg") + d.screenshot(tmp_check_shot) + + wen_zi_template = os.path.join(TEMPLATE_DIR, "wen_zi_input.jpg") + + # 检查是否存在 '切换到文字' 图标 + wen_zi_pos = find_template_match(tmp_check_shot, wen_zi_template, threshold=0.8) + + if wen_zi_pos: + logger.info(f"检测到语音模式 (图像: 找到切换文字图标: {wen_zi_pos})，点击切换...") + d.click(wen_zi_pos[0], wen_zi_pos[1]) + time.sleep(1.0) + + # 清理临时文件 + if os.path.exists(tmp_check_shot): + try: + os.remove(tmp_check_shot) + except: + pass # --- 新增逻辑结束 --- # 1. 尝试找到原生输入框并输入 + # 增加多种查找方式 edit_text = d(className="android.widget.EditText") + if not edit_text.exists: + # 尝试通过 resourceId 查找 (微信常见ID) + edit_text = d(resourceId="com.tencent.mm:id/b4a") + + # 1.2 [User Request] 尝试使用 input_text.jpg 模板寻找输入框 + if not edit_text.exists: + input_template_path = os.path.join(TEMPLATE_DIR, "input_text.jpg") + if os.path.exists(input_template_path): + # 截图用于匹配 + tmp_input_search = os.path.join(OUTPUT_DIR, "temp_input_search.jpg") + d.screenshot(tmp_input_search) + + logger.info(f"正在尝试使用模板 {input_template_path} 寻找输入框...") + # [User Request] 降低阈值到 0.6 + input_pos = find_template_match(tmp_input_search, input_template_path, threshold=0.6) + + if input_pos: + logger.info(f"✅ [Template] 通过 input_text.jpg 找到输入框: {input_pos}") + + # 绘制调试图 (蓝框) + try: + debug_img = cv2.imread(tmp_input_search) + if debug_img is not None: + # 读取模板获取宽高 + tmpl = cv2.imread(input_template_path) + if tmpl is not None: + th, tw = tmpl.shape[:2] + cx, cy = input_pos + top_left = (cx - tw//2, cy - th//2) + bottom_right = (cx + tw//2, cy + th//2) + + # 蓝色框 BGR=(255, 0, 0) + cv2.rectangle(debug_img, top_left, bottom_right, (255, 0, 0), 3) + cv2.putText(debug_img, "MATCH: input_text.jpg", (top_left[0], top_left[1]-10), + cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 0, 0), 2) + + debug_save_path = os.path.join(OUTPUT_DIR, "debug_input_box_match.jpg") + cv2.imwrite(debug_save_path, debug_img) + logger.info(f"已保存输入框匹配调试图(蓝框): {debug_save_path}") + except Exception as e: + logger.warning(f"绘制输入框调试图失败: {e}") + + # 更新点击坐标 + center_point = input_pos + else: + logger.info(f"❌ [Template] input_text.jpg 未匹配到输入框") + + + # 1.5 如果找不到原生输入框，尝试通过“切换到语音”按钮定位 Y 轴 + # 输入框通常与左侧的“切换到语音”按钮垂直居中对齐 + if not edit_text.exists: + try: + # 确保在文字模式下，左侧会有“切换到语音”按钮 + # 有时候可能是 "切换到键盘" (如果状态判断出错)，都尝试一下作为锚点 + anchor_btn = d(description="切换到语音") + if not anchor_btn.exists: + anchor_btn = d(description="切换到键盘") + + if anchor_btn.exists: + # 获取按钮中心 Y 坐标 + bounds = anchor_btn.info['bounds'] + anchor_y = (bounds['top'] + bounds['bottom']) // 2 + + # 获取屏幕宽度 + w, h = d.window_size() + + # 更新中心点：X居中，Y与按钮对齐 + center_point = (w // 2, anchor_y) + logger.info(f"通过'切换到语音'按钮修正输入框坐标: {center_point}") + except Exception as e: + logger.warning(f"尝试修正坐标失败: {e}") + input_success = False if edit_text.exists: @@ -1126,37 +1329,3 @@ def match_template_center(image_path, template_path, threshold=0.8): -async def get_first_screen(device=None): - """ - 获取刚进入界面的首屏信息： - 1. 截图 - 2. 全量识别 (策略=ALL)，包含语音转文字 Peek-and-Restore - 3. 返回识别结果和相关图片路径 - - Returns: - tuple: (dialogue_log, input_pos, enter_path, flag_path) - """ - logger.info("🔍 [get_first_screen] 正在进行首屏全量识别...") - - if not device: - device = connect_device() - - if not device: - logger.error("设备连接失败，无法获取首屏") - return [], None, None, None - - # 1. 截图 - enter_path = get_next_debug_path("enter") - device.screenshot(enter_path) - logger.info(f"📸 已保存进入截图: {enter_path}") - - # 2. 识别 - flag_path = get_next_debug_path("flag") - dialogue_log, input_pos = await analyze_chat_image( - enter_path, - flag_path, - device=device, - process_strategy="ALL" - ) - - return dialogue_log, input_pos, enter_path, flag_path diff --git a/WeiXin/__pycache__/T2_ChatMonitor.cpython-310.pyc b/WeiXin/__pycache__/T2_ChatMonitor.cpython-310.pyc index 623eeffc4ee8a740bf6447a122ec2bc4c4659a37..ee575b98ce0d945a88b2f08a2ce71499d2d6b738 100644 GIT binary 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